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\n \n 2024\n \n \n (8)\n \n \n

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\n \n\n \n \n Siebers, N.; Voggenreiter, E.; Joshi, P.; Rethemeyer, J.; and Wang, L.\n\n\n \n \n \n \n \n Synergistic relationships between the age of soil organic matter, Fe speciation, and aggregate stability in an arable Luvisol.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 187(1): 77–88. February 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Synergistic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siebers_synergistic_2024,\n\ttitle = {Synergistic relationships between the age of soil organic matter, {Fe} speciation, and aggregate stability in an arable {Luvisol}},\n\tvolume = {187},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300020},\n\tdoi = {10.1002/jpln.202300020},\n\tabstract = {Abstract \n             \n              Background \n              Knowledge of soil aggregate formation and stability is essential, as this is important for maintaining soil functions. \n             \n             \n              Aims \n              This study aimed to investigate the influence of organic matter (OM), the content of pedogenic iron (Fe) (oxyhydr)oxides, and aggregate size on the stability of aggregates in arable soil. \n             \n             \n              Methods \n              To this end, the Ap and Bt horizons of a Luvisol were sampled after 14 years of bare fallow, and the results were compared with a control field that had been permanently cropped. \n             \n             \n              Results \n               \n                In the Ap horizon, bare fallow decreased the median diameter of the 53–250 µm size fraction by 26\\%. Simultaneously, the mass of the 20–53 µm size fraction increased by 65\\%, indicating reduced stability—particularly of larger soil microaggregates—due to the lack of input of fresh OM. The range of \n                14 \n                carbon ( \n                14 \n                C) fraction of modern C (F \n                14 \n                C) under bare fallow was between 0.50 and 0.90, and thus lower than the cropped site (F \n                14 \n                C between 0.75 and 1.01), which is particularly pronounced in the smallest size fraction, indicating the presence of older C. This higher stability and the reduced C turnover in {\\textless}20 µm aggregates is probably also due to having the highest content of poorly crystalline Fe (oxy)hydroxides, compared to the other size fractions, which act as a cementing agent. Colloid transport from the Ap to the Bt horizon was observed under bare fallow treatment. \n               \n             \n             \n              Conclusions \n              The lack of input of OM decreased the stability of microaggregates and led to a release of mobile colloids, the transport of which can initiate elemental fluxes with as‐yet unknown environmental consequences.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Siebers, Nina and Voggenreiter, Eva and Joshi, Prachi and Rethemeyer, Janet and Wang, Liming},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {77--88},\n}\n\n\n\n

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\n \n\n \n \n Roosch, S.; Felde, V. J. M. N. L.; Uteau, D.; and Peth, S.\n\n\n \n \n \n \n \n Exploring the mechanisms of diverging mechanical and water stability in macro‐ and microaggregates.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 187(1): 104–117. February 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Exploring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{roosch_exploring_2024,\n\ttitle = {Exploring the mechanisms of diverging mechanical and water stability in macro‐ and microaggregates},\n\tvolume = {187},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300245},\n\tdoi = {10.1002/jpln.202300245},\n\tabstract = {Abstract \n             \n              Background \n              Soil stability is often evaluated using either mechanical or hydraulic stress. The few studies that use both approaches suggest that these two types of stability behave differently. \n             \n             \n              Aims \n              Our aim was to explore the mechanisms of aggregate stability regarding mechanical and water stability at the macro‐ and microscale, among other things, the effect of differing pore structure and soil organic matter content. \n             \n             \n              Methods \n              Samples were taken from two adjacent plots that were expected to differ in stability due to land use, that is, cropped versus bare fallow (BF). The stability of dry‐separated macroaggregates (8–16 mm) and microaggregates (53–250 µm) was determined via wet sieving and unconfined uniaxial compression tests. To explore the mechanisms of stability, 3D pore characteristics were analyzed with microtomography scans. Furthermore, the contents of carbon and exchangeable polyvalent cations as well as contact angles were determined. \n             \n             \n              Results \n              Water stability of macroaggregates was much higher in the cropped plot (geometric mean diameter 0.65–2.37 mm [cropped] vs. 0.31–0.56 mm [BF]), while mechanical stability was very similar (median work 17.3 [cropped] and 17.5 N mm [BF]). The two size fractions behaved similarly regarding both types of stability, with more pronounced differences in macroaggregates. Several soil characteristics, like carbon, exchangeable calcium, and higher connectivity of pores to the aggregate exterior, contributed to water stability. Regarding mechanical stability, the destabilizing effect of lower carbon content and exchangeable calcium in the BF plot was counterbalanced by a lower porosity. \n             \n             \n              Conclusions \n              Mechanical and water stability behaved differently in the two plots due to the different deformation mechanisms.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Roosch, Svenja and Felde, Vincent J. M. N. L. and Uteau, Daniel and Peth, Stephan},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {104--117},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Petrovic, D.; Fersch, B.; and Kunstmann, H.\n\n\n \n \n \n \n \n Heat wave characteristics: evaluation of regional climate model performances for Germany.\n \n \n \n \n\n\n \n\n\n\n Natural Hazards and Earth System Sciences, 24(1): 265–289. January 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Heat$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{petrovic_heat_2024,\n\ttitle = {Heat wave characteristics: evaluation of regional climate model performances for {Germany}},\n\tvolume = {24},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1684-9981},\n\tshorttitle = {Heat wave characteristics},\n\turl = {https://nhess.copernicus.org/articles/24/265/2024/},\n\tdoi = {10.5194/nhess-24-265-2024},\n\tabstract = {Abstract. Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Natural Hazards and Earth System Sciences},\n\tauthor = {Petrovic, Dragan and Fersch, Benjamin and Kunstmann, Harald},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {265--289},\n}\n\n\n\n

\n\n\n

\n Abstract. Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial.\n

\n\n\n

\n \n\n \n \n Nicholson, C. C.; Knapp, J.; Kiljanek, T.; Albrecht, M.; Chauzat, M.; Costa, C.; De La Rúa, P.; Klein, A.; Mänd, M.; Potts, S. G.; Schweiger, O.; Bottero, I.; Cini, E.; De Miranda, J. R.; Di Prisco, G.; Dominik, C.; Hodge, S.; Kaunath, V.; Knauer, A.; Laurent, M.; Martínez-López, V.; Medrzycki, P.; Pereira-Peixoto, M. H.; Raimets, R.; Schwarz, J. M.; Senapathi, D.; Tamburini, G.; Brown, M. J. F.; Stout, J. C.; and Rundlöf, M.\n\n\n \n \n \n \n \n Pesticide use negatively affects bumble bees across European landscapes.\n \n \n \n \n\n\n \n\n\n\n Nature, 628(8007): 355–358. April 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Pesticide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nicholson_pesticide_2024,\n\ttitle = {Pesticide use negatively affects bumble bees across {European} landscapes},\n\tvolume = {628},\n\tissn = {0028-0836, 1476-4687},\n\turl = {https://www.nature.com/articles/s41586-023-06773-3},\n\tdoi = {10.1038/s41586-023-06773-3},\n\tabstract = {Abstract \n             \n              Sustainable agriculture requires balancing crop yields with the effects of pesticides on non-target organisms, such as bees and other crop pollinators. Field studies demonstrated that agricultural use of neonicotinoid insecticides can negatively affect wild bee species \n              1,2 \n              , leading to restrictions on these compounds \n              3 \n              . However, besides neonicotinoids, field-based evidence of the effects of landscape pesticide exposure on wild bees is lacking. Bees encounter many pesticides in agricultural landscapes \n              4–9 \n              and the effects of this landscape exposure on colony growth and development of any bee species remains unknown. Here we show that the many pesticides found in bumble bee-collected pollen are associated with reduced colony performance during crop bloom, especially in simplified landscapes with intensive agricultural practices. Our results from 316 \n              Bombus terrestris \n              colonies at 106 agricultural sites across eight European countries confirm that the regulatory system fails to sufficiently prevent pesticide-related impacts on non-target organisms, even for a eusocial pollinator species in which colony size may buffer against such impacts \n              10,11 \n              . These findings support the need for postapproval monitoring of both pesticide exposure and effects to confirm that the regulatory process is sufficiently protective in limiting the collateral environmental damage of agricultural pesticide use.},\n\tlanguage = {en},\n\tnumber = {8007},\n\turldate = {2024-05-16},\n\tjournal = {Nature},\n\tauthor = {Nicholson, Charlie C. and Knapp, Jessica and Kiljanek, Tomasz and Albrecht, Matthias and Chauzat, Marie-Pierre and Costa, Cecilia and De La Rúa, Pilar and Klein, Alexandra-Maria and Mänd, Marika and Potts, Simon G. and Schweiger, Oliver and Bottero, Irene and Cini, Elena and De Miranda, Joachim R. and Di Prisco, Gennaro and Dominik, Christophe and Hodge, Simon and Kaunath, Vera and Knauer, Anina and Laurent, Marion and Martínez-López, Vicente and Medrzycki, Piotr and Pereira-Peixoto, Maria Helena and Raimets, Risto and Schwarz, Janine M. and Senapathi, Deepa and Tamburini, Giovanni and Brown, Mark J. F. and Stout, Jane C. and Rundlöf, Maj},\n\tmonth = apr,\n\tyear = {2024},\n\tpages = {355--358},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Li, F.; Bogena, H. R.; Bayat, B.; Kurtz, W.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 60(1): e2023WR035056. January 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Can$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_can_2024,\n\ttitle = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},\n\tvolume = {60},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},\n\tdoi = {10.1029/2023WR035056},\n\tabstract = {Abstract \n            Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\\% in 2016 and 16\\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\\% RMSE reduction of monthly ET in the wet year and 9\\% in the dry year. \n          ,  \n            Key Points \n             \n               \n                 \n                  Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale \n                 \n                 \n                  Soil moisture characterization improved more in a wet year than in a very dry year \n                 \n                 \n                  Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {e2023WR035056},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Heistermann, M.; Francke, T.; Schrön, M.; and Oswald, S. E.\n\n\n \n \n \n \n \n Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 28(4): 989–1000. February 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Technical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heistermann_technical_2024,\n\ttitle = {Technical {Note}: {Revisiting} the general calibration of cosmic-ray neutron sensors to estimate soil water content},\n\tvolume = {28},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\tshorttitle = {Technical {Note}},\n\turl = {https://hess.copernicus.org/articles/28/989/2024/},\n\tdoi = {10.5194/hess-28-989-2024},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Heistermann, Maik and Francke, Till and Schrön, Martin and Oswald, Sascha E.},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {989--1000},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Bayat, B.; Raj, R.; Graf, A.; Vereecken, H.; and Montzka, C.\n\n\n \n \n \n \n \n Comprehensive accuracy assessment of long-term geostationary SEVIRI-MSG evapotranspiration estimates across Europe.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 301: 113875. February 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Comprehensive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bayat_comprehensive_2024,\n\ttitle = {Comprehensive accuracy assessment of long-term geostationary {SEVIRI}-{MSG} evapotranspiration estimates across {Europe}},\n\tvolume = {301},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723004261},\n\tdoi = {10.1016/j.rse.2023.113875},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Bayat, Bagher and Raj, Rahul and Graf, Alexander and Vereecken, Harry and Montzka, Carsten},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {113875},\n}\n\n\n\n

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\n \n\n \n \n Amelung, W.; Tang, N.; Siebers, N.; Aehnelt, M.; Eusterhues, K.; Felde, V. J. M. N. L.; Guggenberger, G.; Kaiser, K.; Kögel‐Knabner, I.; Klumpp, E.; Knief, C.; Kruse, J.; Lehndorff, E.; Mikutta, R.; Peth, S.; Ray, N.; Prechtel, A.; Ritschel, T.; Schweizer, S. A.; Woche, S. K.; Wu, B.; and Totsche, K. U.\n\n\n \n \n \n \n \n Architecture of soil microaggregates: Advanced methodologies to explore properties and functions.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 187(1): 17–50. February 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Architecture$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{amelung_architecture_2024,\n\ttitle = {Architecture of soil microaggregates: {Advanced} methodologies to explore properties and functions},\n\tvolume = {187},\n\tissn = {1436-8730, 1522-2624},\n\tshorttitle = {Architecture of soil microaggregates},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300149},\n\tdoi = {10.1002/jpln.202300149},\n\tabstract = {Abstract \n            The functions of soils are intimately linked to their three‐dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates ({\\textless}250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi‐isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting‐edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Amelung, Wulf and Tang, Ni and Siebers, Nina and Aehnelt, Michaela and Eusterhues, Karin and Felde, Vincent J. M. N. L. and Guggenberger, Georg and Kaiser, Klaus and Kögel‐Knabner, Ingrid and Klumpp, Erwin and Knief, Claudia and Kruse, Jens and Lehndorff, Eva and Mikutta, Robert and Peth, Stephan and Ray, Nadja and Prechtel, Alexander and Ritschel, Thomas and Schweizer, Steffen A. and Woche, Susanne K. and Wu, Bei and Totsche, Kai U.},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {17--50},\n}\n\n\n\n

\n\n\n

\n Abstract The functions of soils are intimately linked to their three‐dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates (\\textless250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi‐isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting‐edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.\n

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\n \n 2023\n \n \n (142)\n \n \n

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\n \n\n \n \n Zweifel, R.; Pappas, C.; Peters, R. L.; Babst, F.; Balanzategui, D.; Basler, D.; Bastos, A.; Beloiu, M.; Buchmann, N.; Bose, A. K.; Braun, S.; Damm, A.; D'Odorico, P.; Eitel, J. U.; Etzold, S.; Fonti, P.; Rouholahnejad Freund, E.; Gessler, A.; Haeni, M.; Hoch, G.; Kahmen, A.; Körner, C.; Krejza, J.; Krumm, F.; Leuchner, M.; Leuschner, C.; Lukovic, M.; Martínez-Vilalta, J.; Matula, R.; Meesenburg, H.; Meir, P.; Plichta, R.; Poyatos, R.; Rohner, B.; Ruehr, N.; Salomón, R. L.; Scharnweber, T.; Schaub, M.; Steger, D. N.; Steppe, K.; Still, C.; Stojanović, M.; Trotsiuk, V.; Vitasse, Y.; Von Arx, G.; Wilmking, M.; Zahnd, C.; and Sterck, F.\n\n\n \n \n \n \n \n Networking the forest infrastructure towards near real-time monitoring – A white paper.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 872: 162167. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Networking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zweifel_networking_2023,\n\ttitle = {Networking the forest infrastructure towards near real-time monitoring – {A} white paper},\n\tvolume = {872},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723007830},\n\tdoi = {10.1016/j.scitotenv.2023.162167},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Zweifel, Roman and Pappas, Christoforos and Peters, Richard L. and Babst, Flurin and Balanzategui, Daniel and Basler, David and Bastos, Ana and Beloiu, Mirela and Buchmann, Nina and Bose, Arun K. and Braun, Sabine and Damm, Alexander and D'Odorico, Petra and Eitel, Jan U.H. and Etzold, Sophia and Fonti, Patrick and Rouholahnejad Freund, Elham and Gessler, Arthur and Haeni, Matthias and Hoch, Günter and Kahmen, Ansgar and Körner, Christian and Krejza, Jan and Krumm, Frank and Leuchner, Michael and Leuschner, Christoph and Lukovic, Mirko and Martínez-Vilalta, Jordi and Matula, Radim and Meesenburg, Henning and Meir, Patrick and Plichta, Roman and Poyatos, Rafael and Rohner, Brigitte and Ruehr, Nadine and Salomón, Roberto L. and Scharnweber, Tobias and Schaub, Marcus and Steger, David N. and Steppe, Kathy and Still, Christopher and Stojanović, Marko and Trotsiuk, Volodymyr and Vitasse, Yann and Von Arx, Georg and Wilmking, Martin and Zahnd, Cedric and Sterck, Frank},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {162167},\n}\n\n\n\n

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\n \n\n \n \n Zhou, Y.; Sachs, T.; Li, Z.; Pang, Y.; Xu, J.; Kalhori, A.; Wille, C.; Peng, X.; Fu, X.; Wu, Y.; and Wu, L.\n\n\n \n \n \n \n \n Long-term effects of rewetting and drought on GPP in a temperate peatland based on satellite remote sensing data.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 882: 163395. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhou_long-term_2023,\n\ttitle = {Long-term effects of rewetting and drought on {GPP} in a temperate peatland based on satellite remote sensing data},\n\tvolume = {882},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723020144},\n\tdoi = {10.1016/j.scitotenv.2023.163395},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Zhou, Yinying and Sachs, Torsten and Li, Zhan and Pang, Yuwen and Xu, Junfeng and Kalhori, Aram and Wille, Christian and Peng, Xiaoxue and Fu, Xianhao and Wu, Yanfei and Wu, Lin},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {163395},\n}\n\n\n\n

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\n \n\n \n \n Zhou, X.; Jomaa, S.; Yang, X.; Merz, R.; Wang, Y.; and Rode, M.\n\n\n \n \n \n \n \n Stream restoration can reduce nitrate levels in agricultural landscapes.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 896: 164911. October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Stream$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhou_stream_2023,\n\ttitle = {Stream restoration can reduce nitrate levels in agricultural landscapes},\n\tvolume = {896},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723035349},\n\tdoi = {10.1016/j.scitotenv.2023.164911},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Zhou, Xiangqian and Jomaa, Seifeddine and Yang, Xiaoqiang and Merz, Ralf and Wang, Yanping and Rode, Michael},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {164911},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Zheng, Y.; Coxon, G.; Woods, R.; Power, D.; Rico-Ramirez, M. A.; McJannet, D.; Rosolem, R.; Li, J.; and Feng, P.\n\n\n \n \n \n \n \n Evaluation of reanalysis soil moisture products using Cosmic Ray Neutron Sensor observations across the globe.\n \n \n \n \n\n\n \n\n\n\n October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{zheng_evaluation_2023,\n\ttitle = {Evaluation of reanalysis soil moisture products using {Cosmic} {Ray} {Neutron} {Sensor} observations across the globe},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://hess.copernicus.org/preprints/hess-2023-224/hess-2023-224.pdf},\n\tdoi = {10.5194/hess-2023-224},\n\tabstract = {Abstract. Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale ({\\textasciitilde}250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers.},\n\turldate = {2024-05-16},\n\tauthor = {Zheng, Yanchen and Coxon, Gemma and Woods, Ross and Power, Daniel and Rico-Ramirez, Miguel Angel and McJannet, David and Rosolem, Rafael and Li, Jianzhu and Feng, Ping},\n\tmonth = oct,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers.\n

\n\n\n

\n \n\n \n \n Zhang, X.; Yang, X.; Hensley, R.; Lorke, A.; and Rode, M.\n\n\n \n \n \n \n \n Disentangling In‐Stream Nitrate Uptake Pathways Based on Two‐Station High‐Frequency Monitoring in High‐Order Streams.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 59(3): e2022WR032329. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Disentangling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_disentangling_2023,\n\ttitle = {Disentangling {In}‐{Stream} {Nitrate} {Uptake} {Pathways} {Based} on {Two}‐{Station} {High}‐{Frequency} {Monitoring} in {High}‐{Order} {Streams}},\n\tvolume = {59},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032329},\n\tdoi = {10.1029/2022WR032329},\n\tabstract = {Abstract \n             \n              In‐stream nitrate (NO \n              3 \n              − \n              ) uptake in rivers involves complex autotrophic and heterotrophic pathways, which often vary spatiotemporally due to biotic and abiotic drivers. High‐frequency monitoring of NO \n              3 \n              − \n              mass balance between upstream and downstream measurement sites can quantitatively disentangle multi‐path NO \n              3 \n              − \n              uptake dynamics at the reach scale. However, this approach remains limited to a few river types and has not been fully explored for higher‐order streams with varying hydro‐morphological and biogeochemical conditions. We conducted two‐station 15‐min monitoring in five high‐order stream reaches in central Germany, calculating the NO \n              3 \n              − \n              ‐N mass balance and whole‐stream metabolism based on time series of NO \n              3 \n              − \n              ‐N and dissolved oxygen, respectively. With thorough considerations of lateral inputs, the calculated net NO \n              3 \n              − \n              ‐N uptake rates ( \n               \n              ) differed substantially among campaigns (ranging from −151.1 to 357.6 mg N m \n              2 \n              d \n              −1 \n              , with cases of negative values representing net NO \n              3 \n              − \n              ‐N release), and exhibited higher \n               \n              during the post‐wet season than during the dry season. Subtracting autotrophic assimilation ( \n               \n              , stoichiometrically coupled to stream metabolism) from \n               \n              , \n               \n              represented the net balance of heterotrophic NO \n              3 \n              − \n              ‐N uptake ( \n               \n               {\\textgreater} 0, the dominance of denitrification and heterotrophic assimilation) and NO \n              3 \n              − \n              ‐N release ( \n               \n               {\\textless} 0, the dominance of nitrification/mineralization). This rarely reported uptake pathway contributed substantially to \n               \n              patterns, especially during post‐wet seasons; moreover, it appeared to exhibit various diel patterns, and for \n               \n              {\\textgreater} 0, diel minima occurred during the daytime. These findings advance our understanding of complex reach‐scale N‐retention processes and can help develop future modeling concepts at the river‐network scale. \n             \n          ,  \n            Key Points \n             \n               \n                 \n                  Two‐station monitoring disentangles nitrate uptake pathways and their temporal dynamics in heterogeneous high‐order streams \n                 \n                 \n                  Net nitrate uptake exhibits high variation, seasonally and across reach conditions, with cases of consistent net release \n                 \n                 \n                  Heterotrophic nitrate uptake and release were higher during post‐wet seasons and exhibited various diel patterns},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Zhang, Xiaolin and Yang, Xiaoqiang and Hensley, Robert and Lorke, Andreas and Rode, Michael},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {e2022WR032329},\n}\n\n\n\n

\n\n\n

\n Abstract In‐stream nitrate (NO 3 − ) uptake in rivers involves complex autotrophic and heterotrophic pathways, which often vary spatiotemporally due to biotic and abiotic drivers. High‐frequency monitoring of NO 3 − mass balance between upstream and downstream measurement sites can quantitatively disentangle multi‐path NO 3 − uptake dynamics at the reach scale. However, this approach remains limited to a few river types and has not been fully explored for higher‐order streams with varying hydro‐morphological and biogeochemical conditions. We conducted two‐station 15‐min monitoring in five high‐order stream reaches in central Germany, calculating the NO 3 − ‐N mass balance and whole‐stream metabolism based on time series of NO 3 − ‐N and dissolved oxygen, respectively. With thorough considerations of lateral inputs, the calculated net NO 3 − ‐N uptake rates ( ) differed substantially among campaigns (ranging from −151.1 to 357.6 mg N m 2 d −1 , with cases of negative values representing net NO 3 − ‐N release), and exhibited higher during the post‐wet season than during the dry season. Subtracting autotrophic assimilation ( , stoichiometrically coupled to stream metabolism) from , represented the net balance of heterotrophic NO 3 − ‐N uptake ( \\textgreater 0, the dominance of denitrification and heterotrophic assimilation) and NO 3 − ‐N release ( \\textless 0, the dominance of nitrification/mineralization). This rarely reported uptake pathway contributed substantially to patterns, especially during post‐wet seasons; moreover, it appeared to exhibit various diel patterns, and for \\textgreater 0, diel minima occurred during the daytime. These findings advance our understanding of complex reach‐scale N‐retention processes and can help develop future modeling concepts at the river‐network scale. , Key Points Two‐station monitoring disentangles nitrate uptake pathways and their temporal dynamics in heterogeneous high‐order streams Net nitrate uptake exhibits high variation, seasonally and across reach conditions, with cases of consistent net release Heterotrophic nitrate uptake and release were higher during post‐wet seasons and exhibited various diel patterns\n

\n\n\n

\n \n\n \n \n Zhang, W.; Jung, M.; Migliavacca, M.; Poyatos, R.; Miralles, D. G.; El-Madany, T. S.; Galvagno, M.; Carrara, A.; Arriga, N.; Ibrom, A.; Mammarella, I.; Papale, D.; Cleverly, J. R.; Liddell, M.; Wohlfahrt, G.; Markwitz, C.; Mauder, M.; Paul-Limoges, E.; Schmidt, M.; Wolf, S.; Brümmer, C.; Arain, M. A.; Fares, S.; Kato, T.; Ardö, J.; Oechel, W.; Hanson, C.; Korkiakoski, M.; Biraud, S.; Steinbrecher, R.; Billesbach, D.; Montagnani, L.; Woodgate, W.; Shao, C.; Carvalhais, N.; Reichstein, M.; and Nelson, J. A.\n\n\n \n \n \n \n \n The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 330: 109305. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_effect_2023,\n\ttitle = {The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation},\n\tvolume = {330},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322004919},\n\tdoi = {10.1016/j.agrformet.2022.109305},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Zhang, Weijie and Jung, Martin and Migliavacca, Mirco and Poyatos, Rafael and Miralles, Diego G. and El-Madany, Tarek S. and Galvagno, Marta and Carrara, Arnaud and Arriga, Nicola and Ibrom, Andreas and Mammarella, Ivan and Papale, Dario and Cleverly, Jamie R. and Liddell, Michael and Wohlfahrt, Georg and Markwitz, Christian and Mauder, Matthias and Paul-Limoges, Eugenie and Schmidt, Marius and Wolf, Sebastian and Brümmer, Christian and Arain, M. Altaf and Fares, Silvano and Kato, Tomomichi and Ardö, Jonas and Oechel, Walter and Hanson, Chad and Korkiakoski, Mika and Biraud, Sébastien and Steinbrecher, Rainer and Billesbach, Dave and Montagnani, Leonardo and Woodgate, William and Shao, Changliang and Carvalhais, Nuno and Reichstein, Markus and Nelson, Jacob A.},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {109305},\n}\n\n\n\n

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\n \n\n \n \n Yang, Z.; Huang, J.; and Zhang, Z.\n\n\n \n \n \n \n \n Toward Field Level Drought and Irrigation Monitoring Using Machine Learning Based High-Resolution Soil Moisture (ML-HRSM) Data.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 3570–3573, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{yang_toward_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {Toward {Field} {Level} {Drought} and {Irrigation} {Monitoring} {Using} {Machine} {Learning} {Based} {High}-{Resolution} {Soil} {Moisture} ({ML}-{HRSM}) {Data}},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10283282/},\n\tdoi = {10.1109/IGARSS52108.2023.10283282},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Yang, Zhengwei and Huang, Jingyi and Zhang, Zhou},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {3570--3573},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Yang, X.; Zhang, X.; Graeber, D.; Hensley, R.; Jarvie, H.; Lorke, A.; Borchardt, D.; Li, Q.; and Rode, M.\n\n\n \n \n \n \n \n Large-stream nitrate retention patterns shift during droughts: Seasonal to sub-daily insights from high-frequency data-model fusion.\n \n \n \n \n\n\n \n\n\n\n Water Research, 243: 120347. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-stream$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_large-stream_2023,\n\ttitle = {Large-stream nitrate retention patterns shift during droughts: {Seasonal} to sub-daily insights from high-frequency data-model fusion},\n\tvolume = {243},\n\tissn = {00431354},\n\tshorttitle = {Large-stream nitrate retention patterns shift during droughts},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423007832},\n\tdoi = {10.1016/j.watres.2023.120347},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Water Research},\n\tauthor = {Yang, Xiaoqiang and Zhang, Xiaolin and Graeber, Daniel and Hensley, Robert and Jarvie, Helen and Lorke, Andreas and Borchardt, Dietrich and Li, Qiongfang and Rode, Michael},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {120347},\n}\n\n\n\n

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\n \n\n \n \n Yang, X.; Tetzlaff, D.; Müller, C.; Knöller, K.; Borchardt, D.; and Soulsby, C.\n\n\n \n \n \n \n \n Upscaling Tracer‐Aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 59(3): e2022WR033033. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Upscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_upscaling_2023,\n\ttitle = {Upscaling {Tracer}‐{Aided} {Ecohydrological} {Modeling} to {Larger} {Catchments}: {Implications} for {Process} {Representation} and {Heterogeneity} in {Landscape} {Organization}},\n\tvolume = {59},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Upscaling {Tracer}‐{Aided} {Ecohydrological} {Modeling} to {Larger} {Catchments}},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033033},\n\tdoi = {10.1029/2022WR033033},\n\tabstract = {Abstract \n             \n              Stable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments ({\\textgreater}100 km \n              2 \n              ). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH \n              2 \n              O‐iso model to the Selke catchment (456 km \n              2 \n              , Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes. \n             \n          ,  \n            Key Points \n             \n               \n                 \n                   \n                    Process‐based tracer‐aided ecohydrological modeling is upscaled to {\\textgreater}100 km \n                    2 \n                    catchments using stable water isotopes \n                   \n                 \n                 \n                  Isotopes benefit large‐scale modeling in substantially improving model robustness and reliability of water age estimates \n                 \n                 \n                  Larger‐scale water partitioning and drought responses are controlled by heterogeneity in catchment organization},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Yang, Xiaoqiang and Tetzlaff, Doerthe and Müller, Christin and Knöller, Kay and Borchardt, Dietrich and Soulsby, Chris},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {e2022WR033033},\n}\n\n\n\n

\n\n\n

\n Abstract Stable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments (\\textgreater100 km 2 ). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH 2 O‐iso model to the Selke catchment (456 km 2 , Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes. , Key Points Process‐based tracer‐aided ecohydrological modeling is upscaled to \\textgreater100 km 2 catchments using stable water isotopes Isotopes benefit large‐scale modeling in substantially improving model robustness and reliability of water age estimates Larger‐scale water partitioning and drought responses are controlled by heterogeneity in catchment organization\n

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\n \n\n \n \n Yang, H.; and Wang, Q.\n\n\n \n \n \n \n \n Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 621: 129579. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Reconstruction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_reconstruction_2023,\n\ttitle = {Reconstruction of a spatially seamless, daily {SMAP} ({SSD}\\_SMAP) surface soil moisture dataset from 2015 to 2021},\n\tvolume = {621},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423005218},\n\tdoi = {10.1016/j.jhydrol.2023.129579},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Yang, Haoxuan and Wang, Qunming},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {129579},\n}\n\n\n\n

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\n \n\n \n \n Xie, M.; Ma, X.; Wang, Y.; Li, C.; Shi, H.; Yuan, X.; Hellwich, O.; Chen, C.; Zhang, W.; Zhang, C.; Ling, Q.; Gao, R.; Zhang, Y.; Ochege, F. U.; Frankl, A.; De Maeyer, P.; Buchmann, N.; Feigenwinter, I.; Olesen, J. E.; Juszczak, R.; Jacotot, A.; Korrensalo, A.; Pitacco, A.; Varlagin, A.; Shekhar, A.; Lohila, A.; Carrara, A.; Brut, A.; Kruijt, B.; Loubet, B.; Heinesch, B.; Chojnicki, B.; Helfter, C.; Vincke, C.; Shao, C.; Bernhofer, C.; Brümmer, C.; Wille, C.; Tuittila, E.; Nemitz, E.; Meggio, F.; Dong, G.; Lanigan, G.; Niedrist, G.; Wohlfahrt, G.; Zhou, G.; Goded, I.; Gruenwald, T.; Olejnik, J.; Jansen, J.; Neirynck, J.; Tuovinen, J.; Zhang, J.; Klumpp, K.; Pilegaard, K.; Šigut, L.; Klemedtsson, L.; Tezza, L.; Hörtnagl, L.; Urbaniak, M.; Roland, M.; Schmidt, M.; Sutton, M. A.; Hehn, M.; Saunders, M.; Mauder, M.; Aurela, M.; Korkiakoski, M.; Du, M.; Vendrame, N.; Kowalska, N.; Leahy, P. G.; Alekseychik, P.; Shi, P.; Weslien, P.; Chen, S.; Fares, S.; Friborg, T.; Tallec, T.; Kato, T.; Sachs, T.; Maximov, T.; Di Cella, U. M.; Moderow, U.; Li, Y.; He, Y.; Kosugi, Y.; and Luo, G.\n\n\n \n \n \n \n \n Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 10(1): 587. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xie_monitoring_2023,\n\ttitle = {Monitoring of carbon-water fluxes at {Eurasian} meteorological stations using random forest and remote sensing},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02473-9},\n\tdoi = {10.1038/s41597-023-02473-9},\n\tabstract = {Abstract \n             \n              Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R \n              2 \n              ), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Data},\n\tauthor = {Xie, Mingjuan and Ma, Xiaofei and Wang, Yuangang and Li, Chaofan and Shi, Haiyang and Yuan, Xiuliang and Hellwich, Olaf and Chen, Chunbo and Zhang, Wenqiang and Zhang, Chen and Ling, Qing and Gao, Ruixiang and Zhang, Yu and Ochege, Friday Uchenna and Frankl, Amaury and De Maeyer, Philippe and Buchmann, Nina and Feigenwinter, Iris and Olesen, Jørgen E. and Juszczak, Radoslaw and Jacotot, Adrien and Korrensalo, Aino and Pitacco, Andrea and Varlagin, Andrej and Shekhar, Ankit and Lohila, Annalea and Carrara, Arnaud and Brut, Aurore and Kruijt, Bart and Loubet, Benjamin and Heinesch, Bernard and Chojnicki, Bogdan and Helfter, Carole and Vincke, Caroline and Shao, Changliang and Bernhofer, Christian and Brümmer, Christian and Wille, Christian and Tuittila, Eeva-Stiina and Nemitz, Eiko and Meggio, Franco and Dong, Gang and Lanigan, Gary and Niedrist, Georg and Wohlfahrt, Georg and Zhou, Guoyi and Goded, Ignacio and Gruenwald, Thomas and Olejnik, Janusz and Jansen, Joachim and Neirynck, Johan and Tuovinen, Juha-Pekka and Zhang, Junhui and Klumpp, Katja and Pilegaard, Kim and Šigut, Ladislav and Klemedtsson, Leif and Tezza, Luca and Hörtnagl, Lukas and Urbaniak, Marek and Roland, Marilyn and Schmidt, Marius and Sutton, Mark A. and Hehn, Markus and Saunders, Matthew and Mauder, Matthias and Aurela, Mika and Korkiakoski, Mika and Du, Mingyuan and Vendrame, Nadia and Kowalska, Natalia and Leahy, Paul G. and Alekseychik, Pavel and Shi, Peili and Weslien, Per and Chen, Shiping and Fares, Silvano and Friborg, Thomas and Tallec, Tiphaine and Kato, Tomomichi and Sachs, Torsten and Maximov, Trofim and Di Cella, Umberto Morra and Moderow, Uta and Li, Yingnian and He, Yongtao and Kosugi, Yoshiko and Luo, Geping},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {587},\n}\n\n\n\n

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\n \n\n \n \n Xi, X.; Zhuang, Q.; Kim, S.; and Gentine, P.\n\n\n \n \n \n \n \n Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 59(5): e2022WR034225. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xi_evaluating_2023,\n\ttitle = {Evaluating the {Effects} of {Precipitation} and {Evapotranspiration} on {Soil} {Moisture} {Variability} {Within} {CMIP5} {Using} {SMAP} and {ERA5} {Data}},\n\tvolume = {59},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034225},\n\tdoi = {10.1029/2022WR034225},\n\tabstract = {Abstract \n             \n              The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days \n              −1 \n              , 1/30 ∼ 1/90 days \n              −1 \n              , and 1/90 ∼ 1/365 days \n              −1 \n              ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models. \n             \n          ,  \n            Plain Language Summary \n            Climate is influenced by the interactions between the land surface and atmosphere boundary, and soil moisture is a key component of these physical processes. Precipitation and evapotranspiration, as two major variables involved in these interactions, have been largely regarded as essential processes affecting soil moisture dynamics. However, Earth System Models have large uncertainties in simulating these effects. This study compares the average performance of 14 Earth System Models in capturing the effects of precipitation and evapotranspiration on surface soil moisture variability. We find that (a) soil moisture is mainly affected by precipitation at weekly to seasonal time scales and by evapotranspiration at seasonal to annual time scales; (b) compared to two largely used reference data, the total effects of precipitation and evapotranspiration on soil moisture is smaller at weekly to seasonal time scales and are larger at seasonal to annual time scale; and (c) spatially, models tend to simulate less variability of precipitation or evapotranspiration as a major control on surface soil moisture. \n          ,  \n            Key Points \n             \n               \n                 \n                  The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domain \n                 \n                 \n                  Precipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moisture \n                 \n                 \n                  Earth System Models shall be improved in simulating soil moisture temporal variabilities},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Xi, Xuan and Zhuang, Qianlai and Kim, Seungbum and Gentine, Pierre},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {e2022WR034225},\n}\n\n\n\n

\n\n\n

\n Abstract The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days −1 , 1/30 ∼ 1/90 days −1 , and 1/90 ∼ 1/365 days −1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models. , Plain Language Summary Climate is influenced by the interactions between the land surface and atmosphere boundary, and soil moisture is a key component of these physical processes. Precipitation and evapotranspiration, as two major variables involved in these interactions, have been largely regarded as essential processes affecting soil moisture dynamics. However, Earth System Models have large uncertainties in simulating these effects. This study compares the average performance of 14 Earth System Models in capturing the effects of precipitation and evapotranspiration on surface soil moisture variability. We find that (a) soil moisture is mainly affected by precipitation at weekly to seasonal time scales and by evapotranspiration at seasonal to annual time scales; (b) compared to two largely used reference data, the total effects of precipitation and evapotranspiration on soil moisture is smaller at weekly to seasonal time scales and are larger at seasonal to annual time scale; and (c) spatially, models tend to simulate less variability of precipitation or evapotranspiration as a major control on surface soil moisture. , Key Points The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domain Precipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moisture Earth System Models shall be improved in simulating soil moisture temporal variabilities\n

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\n \n\n \n \n Wu, K.; Ryu, D.; Wagner, W.; and Hu, Z.\n\n\n \n \n \n \n \n A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 285: 113387. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_global-scale_2023,\n\ttitle = {A global-scale intercomparison of {Triple} {Collocation} {Analysis}- and ground-based soil moisture time-variant errors derived from different rescaling techniques},\n\tvolume = {285},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S003442572200493X},\n\tdoi = {10.1016/j.rse.2022.113387},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Wu, Kai and Ryu, Dongryeol and Wagner, Wolfgang and Hu, Zhongmin},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {113387},\n}\n\n\n\n

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\n \n\n \n \n Winter, C.; Nguyen, T. V.; Musolff, A.; Lutz, S. R.; Rode, M.; Kumar, R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Droughts can reduce the nitrogen retention capacity of catchments.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(1): 303–318. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Droughts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{winter_droughts_2023,\n\ttitle = {Droughts can reduce the nitrogen retention capacity of catchments},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/303/2023/},\n\tdoi = {10.5194/hess-27-303-2023},\n\tabstract = {Abstract. In 2018–2019, Central Europe experienced an unprecedented 2-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modeling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration–discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 73 \\% higher compared to the long-term load–discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times ({\\textless}2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream ({\\textgreater}20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Winter, Carolin and Nguyen, Tam V. and Musolff, Andreas and Lutz, Stefanie R. and Rode, Michael and Kumar, Rohini and Fleckenstein, Jan H.},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {303--318},\n}\n\n\n\n

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\n \n\n \n \n Wieser, A.; Güntner, A.; Dietrich, P.; Handwerker, J.; Khordakova, D.; Ködel, U.; Kohler, M.; Mollenhauer, H.; Mühr, B.; Nixdorf, E.; Reich, M.; Rolf, C.; Schrön, M.; Schütze, C.; and Weber, U.\n\n\n \n \n \n \n \n First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 82(17): 406. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"First$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wieser_first_2023,\n\ttitle = {First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding},\n\tvolume = {82},\n\tissn = {1866-6280, 1866-6299},\n\turl = {https://link.springer.com/10.1007/s12665-023-11050-7},\n\tdoi = {10.1007/s12665-023-11050-7},\n\tabstract = {Abstract \n             \n              Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km \n              2 \n              ), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Wieser, Andreas and Güntner, Andreas and Dietrich, Peter and Handwerker, Jan and Khordakova, Dina and Ködel, Uta and Kohler, Martin and Mollenhauer, Hannes and Mühr, Bernhard and Nixdorf, Erik and Reich, Marvin and Rolf, Christian and Schrön, Martin and Schütze, Claudia and Weber, Ute},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {406},\n}\n\n\n\n

\n\n\n

\n Abstract Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km 2 ), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.\n

\n\n\n

\n \n\n \n \n Westermann, S. A.; Hildebrandt, A.; Bousetta, S.; and Thober, S.\n\n\n \n \n \n \n \n Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes? – Insights into dynamic vegetation modules.\n \n \n \n \n\n\n \n\n\n\n October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Does$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{westermann_does_2023,\n\ttitle = {Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes? – {Insights} into dynamic vegetation modules},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tshorttitle = {Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes?},\n\turl = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2101/},\n\tdoi = {10.5194/egusphere-2023-2101},\n\tabstract = {Abstract. Land-surface models represent exchange processes between soil and atmosphere via the surface by coupling water, energy and carbon fluxes. As it strongly mediates the link between these cycles and, vegetation is an important component of land-surface models. In doing so, some of these models include modules for vegetation dynamics which allow adaptation of vegetation biomass, especially leaf area index, to environmental conditions. Here, we conducted a model-data comparison to investigate whether and how vegetation dynamics in the models improves the representation of vegetation processes and related surface fluxes in two specific models ECLand and Noah-MP in contrast to using prescribed values from look-up tables or satellite-based products. We compare model results with stations from the FLUXNET 2015 dataset covering a range in climate and vegetation types, the MODIS leaf area product, and use more detailed information from the TERENO site “Hohes Holz". With the current implementation, switching vegetation dynamics on did not enhance representativeness of e.g. leaf area index and net ecosystem exchange in ECLand, while Noah-MP improved it only for some sites. The representation of energy fluxes and soil moisture was almost unaffected for both models. Interestingly, for both models, the performance regarding vegetation- and hydrology-related variables was unrelated, such that the weak performance regarding e.g. leaf area index did not detoriate the performance regarding e.g. latent heat flux. One reason, we showed here, might be that implemented ecosystem processes diverge from the observations in their seasonal patterns and variability. Noah-MP includes a seasonal hysteresis of the relationship between leaf area index and gross primary production that cannot be found in observations. The same relationship is represented by a strong linear response in ECLand which substantially underestimates the variability seen in observations. For both, water and carbon fluxes, the current implemented modules for vegetation dynamics in these two models yielded no better model performance compared to runs with static vegetation and prescribed leaf-area climatology.},\n\turldate = {2024-05-16},\n\tauthor = {Westermann, Sven Armin and Hildebrandt, Anke and Bousetta, Souhail and Thober, Stephan},\n\tmonth = oct,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. Land-surface models represent exchange processes between soil and atmosphere via the surface by coupling water, energy and carbon fluxes. As it strongly mediates the link between these cycles and, vegetation is an important component of land-surface models. In doing so, some of these models include modules for vegetation dynamics which allow adaptation of vegetation biomass, especially leaf area index, to environmental conditions. Here, we conducted a model-data comparison to investigate whether and how vegetation dynamics in the models improves the representation of vegetation processes and related surface fluxes in two specific models ECLand and Noah-MP in contrast to using prescribed values from look-up tables or satellite-based products. We compare model results with stations from the FLUXNET 2015 dataset covering a range in climate and vegetation types, the MODIS leaf area product, and use more detailed information from the TERENO site “Hohes Holz\". With the current implementation, switching vegetation dynamics on did not enhance representativeness of e.g. leaf area index and net ecosystem exchange in ECLand, while Noah-MP improved it only for some sites. The representation of energy fluxes and soil moisture was almost unaffected for both models. Interestingly, for both models, the performance regarding vegetation- and hydrology-related variables was unrelated, such that the weak performance regarding e.g. leaf area index did not detoriate the performance regarding e.g. latent heat flux. One reason, we showed here, might be that implemented ecosystem processes diverge from the observations in their seasonal patterns and variability. Noah-MP includes a seasonal hysteresis of the relationship between leaf area index and gross primary production that cannot be found in observations. The same relationship is represented by a strong linear response in ECLand which substantially underestimates the variability seen in observations. For both, water and carbon fluxes, the current implemented modules for vegetation dynamics in these two models yielded no better model performance compared to runs with static vegetation and prescribed leaf-area climatology.\n

\n\n\n

\n \n\n \n \n Weilandt, F.; Behling, R.; Goncalves, R.; Madadi, A.; Richter, L.; Sanona, T.; Spengler, D.; and Welsch, J.\n\n\n \n \n \n \n \n Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(3): 799. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Early$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weilandt_early_2023,\n\ttitle = {Early {Crop} {Classification} via {Multi}-{Modal} {Satellite} {Data} {Fusion} and {Temporal} {Attention}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/3/799},\n\tdoi = {10.3390/rs15030799},\n\tabstract = {In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Weilandt, Frank and Behling, Robert and Goncalves, Romulo and Madadi, Arash and Richter, Lorenz and Sanona, Tiago and Spengler, Daniel and Welsch, Jona},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {799},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Wei, J.; Knicker, H.; Zhou, Z.; Eckhardt, K.; Leinweber, P.; Wissel, H.; Yuan, W.; and Brüggemann, N.\n\n\n \n \n \n \n \n Nitrogen immobilization caused by chemical formation of black- and amide-N in soil.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 429: 116274. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Nitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wei_nitrogen_2023,\n\ttitle = {Nitrogen immobilization caused by chemical formation of black- and amide-{N} in soil},\n\tvolume = {429},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S001670612200581X},\n\tdoi = {10.1016/j.geoderma.2022.116274},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Geoderma},\n\tauthor = {Wei, Jing and Knicker, Heike and Zhou, Zheyan and Eckhardt, Kai-Uwe and Leinweber, Peter and Wissel, Holger and Yuan, Wenping and Brüggemann, Nicolas},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {116274},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Gachibu Wangari, E.; Mwangada Mwanake, R.; Houska, T.; Kraus, D.; Gettel, G. M.; Kiese, R.; Breuer, L.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 20(24): 5029–5067. December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gachibu_wangari_identifying_2023,\n\ttitle = {Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data},\n\tvolume = {20},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/20/5029/2023/},\n\tdoi = {10.5194/bg-20-5029-2023},\n\tabstract = {Abstract. Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 \\% to 16 \\%) but accounted for up to 42 \\% of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.},\n\tlanguage = {en},\n\tnumber = {24},\n\turldate = {2024-05-16},\n\tjournal = {Biogeosciences},\n\tauthor = {Gachibu Wangari, Elizabeth and Mwangada Mwanake, Ricky and Houska, Tobias and Kraus, David and Gettel, Gretchen Maria and Kiese, Ralf and Breuer, Lutz and Butterbach-Bahl, Klaus},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {5029--5067},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, Q.; Yang, J.; Heidbüchel, I.; Yu, X.; and Lu, C.\n\n\n \n \n \n \n \n Flow paths and wetness conditions explain spatiotemporal variation of nitrogen retention for a temperate, humid catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 625: 130024. October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Flow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_flow_2023,\n\ttitle = {Flow paths and wetness conditions explain spatiotemporal variation of nitrogen retention for a temperate, humid catchment},\n\tvolume = {625},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423009666},\n\tdoi = {10.1016/j.jhydrol.2023.130024},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Wang, Qiaoyu and Yang, Jie and Heidbüchel, Ingo and Yu, Xuan and Lu, Chunhui},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {130024},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wagner, W.; Lindorfer, R.; Hahn, S.; Kim, H.; Vreugdenhil, M.; Gruber, A.; Fischer, M.; and Trnka, M.\n\n\n \n \n \n \n \n Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals.\n \n \n \n \n\n\n \n\n\n\n August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{wagner_global_2023,\n\ttitle = {Global {Scale} {Mapping} of {Subsurface} {Scattering} {Signals} {Impacting} {ASCAT} {Soil} {Moisture} {Retrievals}},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.24013890.v1},\n\tdoi = {10.36227/techrxiv.24013890.v1},\n\tabstract = {{\\textless}p{\\textgreater}Microwave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.{\\textless}/p{\\textgreater}},\n\turldate = {2024-05-16},\n\tauthor = {Wagner, Wolfgang and Lindorfer, Roland and Hahn, Sebastian and Kim, Hyingglok and Vreugdenhil, Mariette and Gruber, Alexander and Fischer, Milan and Trnka, Miroslav},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Wagner, A.; Chwala, C.; Graf, M.; Polz, J.; Lliso, L.; Lahuerta, J. A.; and Kunstmann, H.\n\n\n \n \n \n \n \n Improved rain event detection in Commercial Microwave Link time series via combination with MSG SEVIRI data.\n \n \n \n \n\n\n \n\n\n\n October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{wagner_improved_2023,\n\ttitle = {Improved rain event detection in {Commercial} {Microwave} {Link} time series via combination with {MSG} {SEVIRI} data},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://amt.copernicus.org/preprints/amt-2023-175/amt-2023-175.pdf},\n\tdoi = {10.5194/amt-2023-175},\n\tabstract = {Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.},\n\turldate = {2024-05-16},\n\tauthor = {Wagner, Andreas and Chwala, Christian and Graf, Maximilian and Polz, Julius and Lliso, Llorenç and Lahuerta, José Alberto and Kunstmann, Harald},\n\tmonth = oct,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.\n

\n\n\n

\n \n\n \n \n Wachholz, A.; Dehaspe, J.; Ebeling, P.; Kumar, R.; Musolff, A.; Saavedra, F.; Winter, C.; Yang, S.; and Graeber, D.\n\n\n \n \n \n \n \n Stoichiometry on the edge—humans induce strong imbalances of reactive C:N:P ratios in streams.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 18(4): 044016. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Stoichiometry$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wachholz_stoichiometry_2023,\n\ttitle = {Stoichiometry on the edge—humans induce strong imbalances of reactive {C}:{N}:{P} ratios in streams},\n\tvolume = {18},\n\tissn = {1748-9326},\n\tshorttitle = {Stoichiometry on the edge—humans induce strong imbalances of reactive {C}},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/acc3b1},\n\tdoi = {10.1088/1748-9326/acc3b1},\n\tabstract = {Abstract \n            Anthropogenic nutrient inputs led to severe degradation of surface water resources, affecting aquatic ecosystem health and functioning. Ecosystem functions such as nutrient cycling and ecosystem metabolism are not only affected by the over-abundance of a single macronutrient but also by the stoichiometry of the reactive molecular forms of dissolved organic carbon (rOC), nitrogen (rN), and phosphorus (rP). So far, studies mainly considered only single macronutrients or used stoichiometric ratios such as N:P or C:N independent from each other. We argue that a mutual assessment of reactive nutrient ratios rOC:rN:rP relative to organismic demands enables us to refine the definition of nutrient depletion versus excess and to understand their linkages to catchment-internal biogeochemical and hydrological processes. Here we show that the majority (94\\%) of the studied 574 German catchments show a depletion or co-depletion in rOC and rP, illustrating the ubiquity of excess N in anthropogenically influenced landscapes. We found an emerging spatial pattern of depletion classes linked to the interplay of agricultural sources and subsurface denitrification for rN and topographic controls of rOC. We classified catchments into stoichio-static and stochio-dynamic catchments based on their degree of intra-annual variability of rOC:rN:rP ratios. Stoichio-static catchments (36\\% of all catchments) tend to have higher rN median concentrations, lower temporal rN variability and generally low rOC medians. Our results demonstrate the severe extent of imbalances in rOC:rN:rP ratios in German rivers due to human activities. This likely affects the inland-water nutrient retention efficiency, their level of eutrophication, and their role in the global carbon cycle. Thus, it calls for a more holistic catchment and aquatic ecosystem management integrating rOC:rN:rP stoichiometry as a fundamental principle.},\n\tnumber = {4},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Wachholz, Alexander and Dehaspe, Joni and Ebeling, Pia and Kumar, Rohini and Musolff, Andreas and Saavedra, Felipe and Winter, Carolin and Yang, Soohyun and Graeber, Daniel},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {044016},\n}\n\n\n\n

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\n \n\n \n \n Vormeier, P.; Schreiner, V. C.; Liebmann, L.; Link, M.; Schäfer, R. B.; Schneeweiss, A.; Weisner, O.; and Liess, M.\n\n\n \n \n \n \n \n Corrigendum to “Temporal scales of pesticide exposure and risks in German small streams” [Sci. Total Environ. (2023) 871/162105].\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 877: 162761. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Corrigendum$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vormeier_corrigendum_2023,\n\ttitle = {Corrigendum to “{Temporal} scales of pesticide exposure and risks in {German} small streams” [{Sci}. {Total} {Environ}. (2023) 871/162105]},\n\tvolume = {877},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723013773},\n\tdoi = {10.1016/j.scitotenv.2023.162761},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Vormeier, Philipp and Schreiner, Verena C. and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Weisner, Oliver and Liess, Matthias},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {162761},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Vormeier, P.; Schreiner, V. C.; Liebmann, L.; Link, M.; Schäfer, R. B.; Schneeweiss, A.; Weisner, O.; and Liess, M.\n\n\n \n \n \n \n \n Temporal scales of pesticide exposure and risks in German small streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 871: 162105. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vormeier_temporal_2023,\n\ttitle = {Temporal scales of pesticide exposure and risks in {German} small streams},\n\tvolume = {871},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723007210},\n\tdoi = {10.1016/j.scitotenv.2023.162105},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Vormeier, Philipp and Schreiner, Verena C. and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Weisner, Oliver and Liess, Matthias},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {162105},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Vormeier, P.; Liebmann, L.; Weisner, O.; and Liess, M.\n\n\n \n \n \n \n \n Width of vegetated buffer strips to protect aquatic life from pesticide effects.\n \n \n \n \n\n\n \n\n\n\n Water Research, 231: 119627. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Width$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vormeier_width_2023,\n\ttitle = {Width of vegetated buffer strips to protect aquatic life from pesticide effects},\n\tvolume = {231},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423000623},\n\tdoi = {10.1016/j.watres.2023.119627},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Water Research},\n\tauthor = {Vormeier, Philipp and Liebmann, Liana and Weisner, Oliver and Liess, Matthias},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {119627},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Nguyen, T. V.; Kumar, R.; Heidbuchel, I.; Borriero, A.; and Fleckenstein, J.\n\n\n \n \n \n \n \n Technical Note: Revisiting the Procedure for Quantification of the Young Water Fraction Based on Seasonal Tracer Cycles.\n \n \n \n \n\n\n \n\n\n\n July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Technical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{nguyen_technical_2023,\n\ttitle = {Technical {Note}: {Revisiting} the {Procedure} for {Quantification} of the {Young} {Water} {Fraction} {Based} on {Seasonal} {Tracer} {Cycles}},\n\tshorttitle = {Technical {Note}},\n\turl = {https://essopenarchive.org/users/420245/articles/651944-technical-note-revisiting-the-procedure-for-quantification-of-the-young-water-fraction-based-on-seasonal-tracer-cycles?commit=8659f13fc8251751907321bdf3d88e803979cb1e},\n\tdoi = {10.22541/essoar.168889846.63034096/v1},\n\tabstract = {The transit time (TT) of streamflow encapsulates information about how \ncatchments store and release water and solutes of different ages. The \nyoung water fraction (Fyw), the fraction of streamflow that is younger \nthan a certain age (normally 2–3 months), has been increasingly used as \nan alternative metric to the commonly used mean TT (mTT). In the \ncommonly used (‘traditional’) procedure presented by Kirchner (2016), \nthe age threshold (τyw) of Fyw separating young from old water is not \npre-defined and differs from catchment to catchment depending on the \nshape of the (gamma) transit time distribution. However, it can be \nargued that it is important to use the same pre-defined τyw for \ninter-catchment comparison of Fyw. In this study, we propose an \nalternative (‘proposed’) procedure for the estimation of Fyw with any \npre-defined τyw. This allows us to also compare the effects of data \nsampling frequencies on the results of Fyw estimation using the same \nτyw. We applied the traditional and proposed procedures using daily \noxygen isotope (δ18O) data in the Alp and Erlenbach catchments, \nSwitzerland. We found that our proposed and the traditional procedure \ncan give very different Fyw values. With the proposed procedure, the \nestimated Fyw significantly increases when the sampling frequency \nchanges from sub-monthly to monthly time steps. Overall, our study \nhighlights the importance of the selection of τyw and the sampling \nfrequency in Fyw estimation, which should be given more attention.},\n\turldate = {2024-05-16},\n\tauthor = {Nguyen, Tam Van and Kumar, Rohini and Heidbuchel, Ingo and Borriero, Arianna and Fleckenstein, Jan},\n\tmonth = jul,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Von Gönner, J.; Bowler, D. E.; Gröning, J.; Klauer, A.; Liess, M.; Neuer, L.; and Bonn, A.\n\n\n \n \n \n \n \n Citizen science for assessing pesticide impacts in agricultural streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 857: 159607. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Citizen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{von_gonner_citizen_2023,\n\ttitle = {Citizen science for assessing pesticide impacts in agricultural streams},\n\tvolume = {857},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722067079},\n\tdoi = {10.1016/j.scitotenv.2022.159607},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Von Gönner, Julia and Bowler, Diana E. and Gröning, Jonas and Klauer, Anna-Katharina and Liess, Matthias and Neuer, Lilian and Bonn, Aletta},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {159607},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Van Der Woude, A. M.; De Kok, R.; Smith, N.; Luijkx, I. T.; Botía, S.; Karstens, U.; Kooijmans, L. M. J.; Koren, G.; Meijer, H. A. J.; Steeneveld, G.; Storm, I.; Super, I.; Scheeren, H. A.; Vermeulen, A.; and Peters, W.\n\n\n \n \n \n \n \n Near-real-time CO $_{\\textrm{2}}$ fluxes from CarbonTracker Europe for high-resolution atmospheric modeling.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 15(2): 579–605. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Near-real-time$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_woude_near-real-time_2023,\n\ttitle = {Near-real-time {CO} $_{\\textrm{2}}$ fluxes from {CarbonTracker} {Europe} for high-resolution atmospheric modeling},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/15/579/2023/},\n\tdoi = {10.5194/essd-15-579-2023},\n\tabstract = {Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 \\% of the simulated CO2 mole fraction differs by {\\textgreater}2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Earth System Science Data},\n\tauthor = {Van Der Woude, Auke M. and De Kok, Remco and Smith, Naomi and Luijkx, Ingrid T. and Botía, Santiago and Karstens, Ute and Kooijmans, Linda M. J. and Koren, Gerbrand and Meijer, Harro A. J. and Steeneveld, Gert-Jan and Storm, Ida and Super, Ingrid and Scheeren, Hubertus A. and Vermeulen, Alex and Peters, Wouter},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {579--605},\n}\n\n\n\n

\n\n\n

\n Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by \\textgreater2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).\n

\n\n\n

\n \n\n \n \n Ueyama, M.; Knox, S. H.; Delwiche, K. B.; Bansal, S.; Riley, W. J.; Baldocchi, D.; Hirano, T.; McNicol, G.; Schafer, K.; Windham‐Myers, L.; Poulter, B.; Jackson, R. B.; Chang, K.; Chen, J.; Chu, H.; Desai, A. R.; Gogo, S.; Iwata, H.; Kang, M.; Mammarella, I.; Peichl, M.; Sonnentag, O.; Tuittila, E.; Ryu, Y.; Euskirchen, E. S.; Göckede, M.; Jacotot, A.; Nilsson, M. B.; and Sachs, T.\n\n\n \n \n \n \n \n Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 29(8): 2313–2334. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ueyama_modeled_2023,\n\ttitle = {Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and {Arctic} regions},\n\tvolume = {29},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16594},\n\tdoi = {10.1111/gcb.16594},\n\tabstract = {Abstract \n             \n              Wetlands are the largest natural source of methane (CH \n              4 \n              ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH \n              4 \n              , but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH \n              4 \n              production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH \n              4 \n              emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH \n              4 \n              production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH \n              4 \n              emissions. Based on a sensitivity analysis, CH \n              4 \n              emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20\\% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH \n              4 \n              transport. Contributions from ebullition and diffusion were relatively high during low LAI ({\\textless}20\\%) periods. The lag time between CH \n              4 \n              production and CH \n              4 \n              emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH \n              4 \n              production, plant‐mediated transport, and diffusion through water explained 77\\% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH \n              4 \n              emissions across biomes. These processes and associated parameters for CH \n              4 \n              emissions among and within the wetlands provide useful insights for interpreting observed net CH \n              4 \n              fluxes, estimating sensitivities to biophysical variables, and modeling global CH \n              4 \n              fluxes.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2024-05-16},\n\tjournal = {Global Change Biology},\n\tauthor = {Ueyama, Masahito and Knox, Sara H. and Delwiche, Kyle B. and Bansal, Sheel and Riley, William J. and Baldocchi, Dennis and Hirano, Takashi and McNicol, Gavin and Schafer, Karina and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B. and Chang, Kuang‐Yu and Chen, Jiquen and Chu, Housen and Desai, Ankur R. and Gogo, Sébastien and Iwata, Hiroki and Kang, Minseok and Mammarella, Ivan and Peichl, Matthias and Sonnentag, Oliver and Tuittila, Eeva‐Stiina and Ryu, Youngryel and Euskirchen, Eugénie S. and Göckede, Mathias and Jacotot, Adrien and Nilsson, Mats B. and Sachs, Torsten},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2313--2334},\n}\n\n\n\n

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\n \n\n \n \n Tumajer, J.; Braun, S.; Burger, A.; Scharnweber, T.; Smiljanic, M.; Walthert, L.; Zweifel, R.; and Wilmking, M.\n\n\n \n \n \n \n \n Dendrometers challenge the ‘moon wood concept’ by elucidating the absence of lunar cycles in tree stem radius oscillation.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 13(1): 19904. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Dendrometers$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tumajer_dendrometers_2023,\n\ttitle = {Dendrometers challenge the ‘moon wood concept’ by elucidating the absence of lunar cycles in tree stem radius oscillation},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-47013-y},\n\tdoi = {10.1038/s41598-023-47013-y},\n\tabstract = {Abstract \n            Wood is a sustainable natural resource and an important global commodity. According to the ‘moon wood theory’, the properties of wood, including its growth and water content, are believed to oscillate with the lunar cycle. Despite contradicting our current understanding of plant functioning, this theory is commonly exploited for marketing wooden products. To examine the moon wood theory, we applied a wavelet power transformation to series of 2,000,000 hourly stem radius records from dendrometers. We separated the influence of 74 consecutive lunar cycles and meteorological conditions on the stem variation of 62 trees and six species. We show that the dynamics of stem radius consist of overlapping oscillations with periods of 1 day, 6 months, and 1 year. These oscillations in stem dimensions were tightly coupled to oscillations in the series of air temperature and vapour pressure deficit. By contrast, we revealed no imprint of the lunar cycle on the stem radius variation of any species. We call for scepticism towards the moon wood theory, at least as far as the stem water content and radial growth are concerned. We foresee that similar studies employing robust scientific approaches will be increasingly needed in the future to cope with misleading concepts.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Reports},\n\tauthor = {Tumajer, Jan and Braun, Sabine and Burger, Andreas and Scharnweber, Tobias and Smiljanic, Marko and Walthert, Lorenz and Zweifel, Roman and Wilmking, Martin},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {19904},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Tiede, J.; Chwala, C.; and Siart, U.\n\n\n \n \n \n \n \n New Insights Into the Dynamics of Wet Antenna Attenuation Based on In Situ Estimations Provided by the Dedicated Field Experiment ATTRRA2.\n \n \n \n \n\n\n \n\n\n\n IEEE Geoscience and Remote Sensing Letters, 20: 1–5. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"New$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tiede_new_2023,\n\ttitle = {New {Insights} {Into} the {Dynamics} of {Wet} {Antenna} {Attenuation} {Based} on {In} {Situ} {Estimations} {Provided} by the {Dedicated} {Field} {Experiment} {ATTRRA2}},\n\tvolume = {20},\n\tcopyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},\n\tissn = {1545-598X, 1558-0571},\n\turl = {https://ieeexplore.ieee.org/document/10268068/},\n\tdoi = {10.1109/LGRS.2023.3320755},\n\turldate = {2024-05-16},\n\tjournal = {IEEE Geoscience and Remote Sensing Letters},\n\tauthor = {Tiede, Jonas and Chwala, Christian and Siart, Uwe},\n\tyear = {2023},\n\tpages = {1--5},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Tang, A. C. I.; Flechard, C. R.; Arriga, N.; Papale, D.; Stoy, P. C.; Buchmann, N.; Cuntz, M.; Douros, J.; Fares, S.; Knohl, A.; Šigut, L.; Simioni, G.; Timmermans, R.; Grünwald, T.; Ibrom, A.; Loubet, B.; Mammarella, I.; Belelli Marchesini, L.; Nilsson, M.; Peichl, M.; Rebmann, C.; Schmidt, M.; Bernhofer, C.; Berveiller, D.; Cremonese, E.; El-Madany, T. S.; Gharun, M.; Gianelle, D.; Hörtnagl, L.; Roland, M.; Varlagin, A.; Fu, Z.; Heinesch, B.; Janssens, I.; Kowalska, N.; Dušek, J.; Gerosa, G.; Mölder, M.; Tuittila, E.; and Loustau, D.\n\n\n \n \n \n \n \n Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 903: 166149. December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tang_detection_2023,\n\ttitle = {Detection and attribution of an anomaly in terrestrial photosynthesis in {Europe} during the {COVID}-19 lockdown},\n\tvolume = {903},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723047745},\n\tdoi = {10.1016/j.scitotenv.2023.166149},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Tang, Angela Che Ing and Flechard, Christophe R. and Arriga, Nicola and Papale, Dario and Stoy, Paul C. and Buchmann, Nina and Cuntz, Matthias and Douros, John and Fares, Silvano and Knohl, Alexander and Šigut, Ladislav and Simioni, Guillaume and Timmermans, Renske and Grünwald, Thomas and Ibrom, Andreas and Loubet, Benjamin and Mammarella, Ivan and Belelli Marchesini, Luca and Nilsson, Mats and Peichl, Matthias and Rebmann, Corinna and Schmidt, Marius and Bernhofer, Christian and Berveiller, Daniel and Cremonese, Edoardo and El-Madany, Tarek S. and Gharun, Mana and Gianelle, Damiano and Hörtnagl, Lukas and Roland, Marilyn and Varlagin, Andrej and Fu, Zheng and Heinesch, Bernard and Janssens, Ivan and Kowalska, Natalia and Dušek, Jiří and Gerosa, Giacomo and Mölder, Meelis and Tuittila, Eeva-Stiina and Loustau, Denis},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {166149},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Steger, D. N.; Peters, R. L.; Blume, T.; Hurley, A. G.; Balanzategui, D.; Balting, D. F.; and Heinrich, I.\n\n\n \n \n \n \n \n Site Matters - Canopy Conductance Regulation in Mature Temperate Trees Diverges at Two Sites with Different Soil Water Availability.\n \n \n \n \n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Site$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{steger_site_2023,\n\ttitle = {Site {Matters} - {Canopy} {Conductance} {Regulation} in {Mature} {Temperate} {Trees} {Diverges} at {Two} {Sites} with {Different} {Soil} {Water} {Availability}},\n\turl = {https://www.ssrn.com/abstract=4555880},\n\tdoi = {10.2139/ssrn.4555880},\n\turldate = {2024-05-16},\n\tauthor = {Steger, David N. and Peters, Richard  L. and Blume, Theresa and Hurley, Alexander  G. and Balanzategui, Daniel and Balting, Daniel  F. and Heinrich, Ingo},\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Späth, F.; Rajtschan, V.; Weber, T. K. D.; Morandage, S.; Lange, D.; Abbas, S. S.; Behrendt, A.; Ingwersen, J.; Streck, T.; and Wulfmeyer, V.\n\n\n \n \n \n \n \n The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Instrumentation, Methods and Data Systems, 12(1): 25–44. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{spath_landatmosphere_2023,\n\ttitle = {The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback},\n\tvolume = {12},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2193-0864},\n\tshorttitle = {The land–atmosphere feedback observatory},\n\turl = {https://gi.copernicus.org/articles/12/25/2023/},\n\tdoi = {10.5194/gi-12-25-2023},\n\tabstract = {Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and\nintensive observation periods (IOPs). Operational measurements aim for long\ntime series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange\nprocesses and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the\natmosphere.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Geoscientific Instrumentation, Methods and Data Systems},\n\tauthor = {Späth, Florian and Rajtschan, Verena and Weber, Tobias K. D. and Morandage, Shehan and Lange, Diego and Abbas, Syed Saqlain and Behrendt, Andreas and Ingwersen, Joachim and Streck, Thilo and Wulfmeyer, Volker},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {25--44},\n}\n\n\n\n

\n\n\n

\n Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.\n

\n\n\n

\n \n\n \n \n Sobaga, A.; Decharme, B.; Habets, F.; Delire, C.; Enjelvin, N.; Redon, P.; Faure-Catteloin, P.; and Le Moigne, P.\n\n\n \n \n \n \n \n Assessment of the interactions between soil–biosphere–atmosphere (ISBA) land surface model soil hydrology, using four closed-form soil water relationships and several lysimeters.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(13): 2437–2461. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sobaga_assessment_2023,\n\ttitle = {Assessment of the interactions between soil–biosphere–atmosphere ({ISBA}) land surface model soil hydrology, using four closed-form soil water relationships and several lysimeters},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/2437/2023/},\n\tdoi = {10.5194/hess-27-2437-2023},\n\tabstract = {Abstract. Soil water drainage is the main source of groundwater recharge and river flow. It is therefore a key process for water resource management. In this study, we evaluate the soil hydrology and the soil water drainage, simulated by the interactions between soil–biosphere–atmosphere (ISBA) land surface model currently used for hydrological applications from the watershed scale to the global scale, where parameters are generally not calibrated. This evaluation is done using seven lysimeters from two long-term model approach sites measuring hourly water dynamics between 2009 and 2019 in northeastern France. These 2 m depth lysimeters are filled with different soil types and are either maintained as bare soil or covered with vegetation. Four closed-form equations describing soil water retention and hydraulic conductivity functions are tested, namely the commonly used equations from Brooks and Corey (1966) and van Genuchten (1980), a combination of the van Genuchten (1980) soil water retention function with the Brooks and Corey (1966) unsaturated hydraulic conductivity function, and, for the very first time in a land surface model (LSM), a modified version of the van Genuchten (1980) equations, with a new hydraulic conductivity curve proposed by Iden et al. (2015). The results indicate good performance by ISBA with the different closure equations in terms of soil volumetric water content and water mass. The drained flow at the bottom of the lysimeter is well simulated, using Brooks and Corey (1966), while some weaknesses appear with van Genuchten (1980) due to the abrupt shape near the saturation of its hydraulic conductivity function. The mixed form or the new van Genuchten (1980) hydraulic conductivity function from Iden et al. (2015) allows the solving of this problem and even improves the simulation of the drainage dynamic, especially for intense drainage events. The study also highlights the importance of the vertical heterogeneity of the soil hydrodynamic parameters to correctly simulate the drainage dynamic, in addition to the primary influence of the parameters characterizing the shape of the soil water retention function.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Sobaga, Antoine and Decharme, Bertrand and Habets, Florence and Delire, Christine and Enjelvin, Noële and Redon, Paul-Olivier and Faure-Catteloin, Pierre and Le Moigne, Patrick},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {2437--2461},\n}\n\n\n\n

\n\n\n

\n Abstract. Soil water drainage is the main source of groundwater recharge and river flow. It is therefore a key process for water resource management. In this study, we evaluate the soil hydrology and the soil water drainage, simulated by the interactions between soil–biosphere–atmosphere (ISBA) land surface model currently used for hydrological applications from the watershed scale to the global scale, where parameters are generally not calibrated. This evaluation is done using seven lysimeters from two long-term model approach sites measuring hourly water dynamics between 2009 and 2019 in northeastern France. These 2 m depth lysimeters are filled with different soil types and are either maintained as bare soil or covered with vegetation. Four closed-form equations describing soil water retention and hydraulic conductivity functions are tested, namely the commonly used equations from Brooks and Corey (1966) and van Genuchten (1980), a combination of the van Genuchten (1980) soil water retention function with the Brooks and Corey (1966) unsaturated hydraulic conductivity function, and, for the very first time in a land surface model (LSM), a modified version of the van Genuchten (1980) equations, with a new hydraulic conductivity curve proposed by Iden et al. (2015). The results indicate good performance by ISBA with the different closure equations in terms of soil volumetric water content and water mass. The drained flow at the bottom of the lysimeter is well simulated, using Brooks and Corey (1966), while some weaknesses appear with van Genuchten (1980) due to the abrupt shape near the saturation of its hydraulic conductivity function. The mixed form or the new van Genuchten (1980) hydraulic conductivity function from Iden et al. (2015) allows the solving of this problem and even improves the simulation of the drainage dynamic, especially for intense drainage events. The study also highlights the importance of the vertical heterogeneity of the soil hydrodynamic parameters to correctly simulate the drainage dynamic, in addition to the primary influence of the parameters characterizing the shape of the soil water retention function.\n

\n\n\n

\n \n\n \n \n Skulovich, O.; and Gentine, P.\n\n\n \n \n \n \n \n A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 10(1): 154. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{skulovich_long-term_2023,\n\ttitle = {A {Long}-term {Consistent} {Artificial} {Intelligence} and {Remote} {Sensing}-based {Soil} {Moisture} {Dataset}},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02053-x},\n\tdoi = {10.1038/s41597-023-02053-x},\n\tabstract = {Abstract \n             \n              The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global \n              in-situ \n              SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Data},\n\tauthor = {Skulovich, Olya and Gentine, Pierre},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {154},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Shukla, S.; Meshesha, T. W.; Sen, I. S.; Bol, R.; Bogena, H.; and Wang, J.\n\n\n \n \n \n \n \n Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany.\n \n \n \n \n\n\n \n\n\n\n Sustainability, 15(12): 9811. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{shukla_assessing_2023,\n\ttitle = {Assessing {Impacts} of {Land} {Use} and {Land} {Cover} ({LULC}) {Change} on {Stream} {Flow} and {Runoff} in {Rur} {Basin}, {Germany}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2071-1050},\n\turl = {https://www.mdpi.com/2071-1050/15/12/9811},\n\tdoi = {10.3390/su15129811},\n\tabstract = {Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43\\%, 14\\%, and 4\\%, respectively.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2024-05-16},\n\tjournal = {Sustainability},\n\tauthor = {Shukla, Saurabh and Meshesha, Tesfa Worku and Sen, Indra S. and Bol, Roland and Bogena, Heye and Wang, Junye},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {9811},\n}\n\n\n\n

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\n \n\n \n \n Schucknecht, A.; Reinermann, S.; and Kiese, R.\n\n\n \n \n \n \n \n Estimating Aboveground Biomass and Nitrogen Concentration in Grasslands with Multispectral and Hyperspectral Satellite Data.\n \n \n \n \n\n\n \n\n\n\n In Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), pages HM1C.2, Munich, 2023. Optica Publishing Group\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{schucknecht_estimating_2023,\n\taddress = {Munich},\n\ttitle = {Estimating {Aboveground} {Biomass} and {Nitrogen} {Concentration} in {Grasslands} with {Multispectral} and {Hyperspectral} {Satellite} {Data}},\n\tisbn = {9781957171241},\n\turl = {https://opg.optica.org/abstract.cfm?URI=HMISE-2023-HM1C.2},\n\tdoi = {10.1364/HMISE.2023.HM1C.2},\n\tabstract = {Spatial information on grassland biomass and nitrogen concentration are important for precision agriculture. We compare machine learning with hybrid models to estimate both parameters with Sentinel-2 data, and test hybrid models with hyperspectral EnMAP data.},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tbooktitle = {Optica {Sensing} {Congress} 2023 ({AIS}, {FTS}, {HISE}, {Sensors}, {ES})},\n\tpublisher = {Optica Publishing Group},\n\tauthor = {Schucknecht, Anne and Reinermann, Sophie and Kiese, Ralf},\n\tyear = {2023},\n\tpages = {HM1C.2},\n}\n\n\n\n

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\n \n\n \n \n Schrön, M.; Köhli, M.; and Zacharias, S.\n\n\n \n \n \n \n \n Signal contribution of distant areas to cosmic-ray neutron sensors – implications for footprint and sensitivity.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(3): 723–738. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Signal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schron_signal_2023,\n\ttitle = {Signal contribution of distant areas to cosmic-ray neutron sensors – implications for footprint and sensitivity},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/723/2023/},\n\tdoi = {10.5194/hess-27-723-2023},\n\tabstract = {Abstract. This paper presents a new theoretical approach to estimate the contribution of distant areas to the measurement signal of cosmic-ray neutron detectors for snow and soil moisture monitoring. The algorithm is based on the local neutron production and the transport mechanism, given by the neutron–moisture relationship and the radial intensity function, respectively. The purely analytical approach has been validated with physics-based neutron transport simulations for heterogeneous soil moisture patterns, exemplary landscape features, and remote fields at a distance. We found that the method provides good approximations of simulated signal contributions in patchy soils with typical deviations of less than 1 \\%. Moreover, implications of this concept have been investigated for the neutron–moisture relationship, where the signal contribution of an area has the potential to explain deviating shapes of this curve that are often reported in the literature. Finally, the method has been used to develop a new practical footprint definition to express whether or not a distant area's soil moisture change is actually detectable in terms of measurement precision. The presented concepts answer long-lasting questions about the influence of distant landscape structures in the integral footprint of the sensor without the need for computationally expensive simulations. The new insights are highly relevant to support signal interpretation, data harmonization, and sensor calibration and will be particularly useful for sensors positioned in complex terrain or on agriculturally managed sites.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Schrön, Martin and Köhli, Markus and Zacharias, Steffen},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {723--738},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Schrön, M.; Rasche, D.; Weimar, J.; Köhli, M. O.; Herbst, K.; Boehrer, B.; Hertle, L.; Kögler, S.; and Zacharias, S.\n\n\n \n \n \n \n \n Buoy-based detection of low-energy cosmic-ray neutrons to monitor the influence of atmospheric, geomagnetic, and heliospheric effects.\n \n \n \n \n\n\n \n\n\n\n December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Buoy-based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{schron_buoy-based_2023,\n\ttitle = {Buoy-based detection of low-energy cosmic-ray neutrons to monitor the influence of atmospheric, geomagnetic, and heliospheric effects},\n\turl = {https://www.authorea.com/users/76472/articles/695074-buoy-based-detection-of-low-energy-cosmic-ray-neutrons-to-monitor-the-influence-of-atmospheric-geomagnetic-and-heliospheric-effects?commit=40122804b18211f8e16402b9c5949d53490b8b5e},\n\tdoi = {10.22541/au.170319441.16528907/v1},\n\tabstract = {Cosmic radiation on Earth responds to heliospheric, geomagnetic, \natmospheric, and lithospheric changes. In order to use its signal for \nsoil hydrological monitoring, the signal of thermal and epithermal \nneutron detectors needs to be corrected for external influencing \nfactors. However, theories about the neutron response to soil water, air \npressure, air humidity, and incoming cosmic radiation are still under \ndebate. To challenge these theories, we isolated the neutron response \nfrom almost any terrestrial changes by operating bare and moderated \nneutron detectors in a buoy on a lake in Germany from July 15 to \nDecember 02, 2014. We found that the count rate over water has been \nbetter predicted by a recent theory compared to the traditional \napproach. We further found strong linear correlation parameters to air \npressure and air humidity for epithermal neutrons, while thermal \nneutrons responded differently. Correction for incoming radiation proved \nto be necessary for both thermal and epithermal neutrons, for which we \ntested different neutron monitors and correction methods. Here, the \nconventional approach worked best with the Jungfraujoch monitor in \nSwitzerland, while the approach from a recent study was able to \nadequately rescale data from more remote neutron monitors. However, no \napproach was able to sufficiently remove the signal from a major Forbush \ndecrease event, to which thermal and epithermal neutrons showed a \ncomparatively strong response. The buoy detector experiment provided a \nunique dataset for empirical testing of traditional and new theories on \nCRNS. It could serve as a local alternative to reference data from \nremote neutron monitors.},\n\turldate = {2024-05-16},\n\tauthor = {Schrön, Martin and Rasche, Daniel and Weimar, Jannis and Köhli, Markus Otto and Herbst, Konstantin and Boehrer, Bertram and Hertle, Lasse and Kögler, Simon and Zacharias, Steffen},\n\tmonth = dec,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Schreiner, V. C.; Liebmann, L.; Feckler, A.; Liess, M.; Link, M.; Schneeweiss, A.; Truchy, A.; Von Tümpling, W.; Vormeier, P.; Weisner, O.; Schäfer, R. B.; and Bundschuh, M.\n\n\n \n \n \n \n \n Standard Versus Natural: Assessing the Impact of Environmental Variables on Organic Matter Decomposition in Streams Using Three Substrates.\n \n \n \n \n\n\n \n\n\n\n Environmental Toxicology and Chemistry, 42(9): 2007–2018. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Standard$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schreiner_standard_2023,\n\ttitle = {Standard {Versus} {Natural}: {Assessing} the {Impact} of {Environmental} {Variables} on {Organic} {Matter} {Decomposition} in {Streams} {Using} {Three} {Substrates}},\n\tvolume = {42},\n\tissn = {0730-7268, 1552-8618},\n\tshorttitle = {Standard {Versus} {Natural}},\n\turl = {https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5577},\n\tdoi = {10.1002/etc.5577},\n\tabstract = {Abstract \n             \n              The decomposition of allochthonous organic matter, such as leaves, is a crucial ecosystem process in low‐order streams. Microbial communities, including fungi and bacteria, colonize allochthonous organic material, break up large molecules, and increase the nutritional value for macroinvertebrates. Environmental variables are known to affect microbial as well as macroinvertebrate communities and alter their ability to decompose organic matter. Studying the relationship between environmental variables and decomposition has mainly been realized using leaves, with the drawbacks of differing substrate composition and consequently between‐study variability. To overcome these drawbacks, artificial substrates have been developed, serving as standardizable surrogates. In the present study, we compared microbial and total decomposition of leaves with the standardized substrates of decotabs and, only for microbial decomposition, of cotton strips, across 70 stream sites in a Germany‐wide study. Furthermore, we identified the most influential environmental variables for the decomposition of each substrate from a range of 26 variables, including pesticide toxicity, concentrations of nutrients, and trace elements, using stability selection. The microbial as well as total decomposition of the standardized substrates (i.e., cotton strips and decotabs) were weak or not associated with that of the natural substrate (i.e., leaves, \n              r \n              ² {\\textless} 0.01 to \n              r \n              ² = 0.04). The decomposition of the two standardized substrates, however, showed a moderate association ( \n              r \n              ² = 0.21), which is probably driven by their similar composition, with both being made of cellulose. Different environmental variables were identified as the most influential for each of the substrates and the directions of these relationships contrasted between the substrates. Our results imply that these standardized substrates are unsuitable surrogates when investigating the decomposition of allochthonous organic matter in streams. \n              Environ Toxicol Chem \n              2023;42:2007–2018. © 2023 The Authors. \n              Environmental Toxicology and Chemistry \n              published by Wiley Periodicals LLC on behalf of SETAC.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Toxicology and Chemistry},\n\tauthor = {Schreiner, Verena C. and Liebmann, Liana and Feckler, Alexander and Liess, Matthias and Link, Moritz and Schneeweiss, Anke and Truchy, Amélie and Von Tümpling, Wolf and Vormeier, Philipp and Weisner, Oliver and Schäfer, Ralf B. and Bundschuh, Mirco},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {2007--2018},\n}\n\n\n\n

\n\n\n

\n Abstract The decomposition of allochthonous organic matter, such as leaves, is a crucial ecosystem process in low‐order streams. Microbial communities, including fungi and bacteria, colonize allochthonous organic material, break up large molecules, and increase the nutritional value for macroinvertebrates. Environmental variables are known to affect microbial as well as macroinvertebrate communities and alter their ability to decompose organic matter. Studying the relationship between environmental variables and decomposition has mainly been realized using leaves, with the drawbacks of differing substrate composition and consequently between‐study variability. To overcome these drawbacks, artificial substrates have been developed, serving as standardizable surrogates. In the present study, we compared microbial and total decomposition of leaves with the standardized substrates of decotabs and, only for microbial decomposition, of cotton strips, across 70 stream sites in a Germany‐wide study. Furthermore, we identified the most influential environmental variables for the decomposition of each substrate from a range of 26 variables, including pesticide toxicity, concentrations of nutrients, and trace elements, using stability selection. The microbial as well as total decomposition of the standardized substrates (i.e., cotton strips and decotabs) were weak or not associated with that of the natural substrate (i.e., leaves, r ² \\textless 0.01 to r ² = 0.04). The decomposition of the two standardized substrates, however, showed a moderate association ( r ² = 0.21), which is probably driven by their similar composition, with both being made of cellulose. Different environmental variables were identified as the most influential for each of the substrates and the directions of these relationships contrasted between the substrates. Our results imply that these standardized substrates are unsuitable surrogates when investigating the decomposition of allochthonous organic matter in streams. Environ Toxicol Chem 2023;42:2007–2018. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.\n

\n\n\n

\n \n\n \n \n Schreiber, M.; Bazaios, E.; Ströbel, B.; Wolf, B.; Ostler, U.; Gasche, R.; Schlingmann, M.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland.\n \n \n \n \n\n\n \n\n\n\n Nutrient Cycling in Agroecosystems, 125(2): 171–186. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Impacts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schreiber_impacts_2023,\n\ttitle = {Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland},\n\tvolume = {125},\n\tissn = {1385-1314, 1573-0867},\n\turl = {https://link.springer.com/10.1007/s10705-022-10239-9},\n\tdoi = {10.1007/s10705-022-10239-9},\n\tabstract = {Abstract \n             \n              Low nitrogen (N) use efficiency of broadcast slurry application leads to nutrient losses, air and water pollution, greenhouse gas emissions and—in particular in a warming climate—to soil N mining. Here we test the alternative slurry acidification and injection techniques for their mitigation potential compared to broadcast spreading in montane grassland. We determined (1) the fate of \n              15 \n              N labelled slurry in the plant-soil-microbe system and soil-atmosphere exchange of greenhouse gases over one fertilization/harvest cycle and (2) assessed the longer-term contribution of fertilizer \n              15 \n              N to soil organic N formation by the end of the growing season. The isotope tracing approach was combined with a space for time climate change experiment. Simulated climate change increased productivity, ecosystem respiration, and net methane uptake irrespective of management, but the generally low N \n              2 \n              O fluxes remained unchanged. Compared to the broadcast spreading, slurry acidification showed lowest N losses, thus increased productivity and fertilizer N use efficiency (38\\% \n              15 \n              N recovery in plant aboveground plant biomass). In contrast, slurry injection showed highest total fertilizer N losses, but increased fertilization-induced soil organic N formation by 9–12 kg N ha \n              −1 \n              season \n              −1 \n              . Slurry management effects on N \n              2 \n              O and CH \n              4 \n              fluxes remained negligible. In sum, our study shows that the tested alternative slurry application techniques can increase N use efficiency and/or promote soil organic N formation from applied fertilizer to a remarkable extent. However, this is still not sufficient to prevent soil N mining mostly resulting from large plant N exports that even exceed total fertilizer N inputs.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Nutrient Cycling in Agroecosystems},\n\tauthor = {Schreiber, Mirella and Bazaios, Elpida and Ströbel, Barbara and Wolf, Benjamin and Ostler, Ulrike and Gasche, Rainer and Schlingmann, Marcus and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {171--186},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Schnepper, T.; Groh, J.; Gerke, H. H.; Reichert, B.; and Pütz, T.\n\n\n \n \n \n \n \n Evaluation of precipitation measurement methods using data from a precision lysimeter network.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(17): 3265–3292. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schnepper_evaluation_2023,\n\ttitle = {Evaluation of precipitation measurement methods using data from a precision lysimeter network},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/3265/2023/},\n\tdoi = {10.5194/hess-27-3265-2023},\n\tabstract = {Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges\nas compared to the lysimeter reference and to analyze the effect of\nprecipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated\nthe precipitation amounts relative to the lysimeter references for long-term\nprecipitation totals with catch ratios (CRs) of between 33 \\%–92 \\%. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 \\% and 9 \\% and by 10 \\% and 11 \\%, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather\nconditions. The results indicate that considerable precipitation measurement\nerrors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads\nto an underestimation of the actual precipitation amounts. The results\nsuggest that the application of relatively simple correction schemes, manual\nor automated data quality checks, instrument calibrations, and/or an adequate\nchoice of observation period can help improve the data quality of\ngauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or\nradar-based precipitation analyses.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Schnepper, Tobias and Groh, Jannis and Gerke, Horst H. and Reichert, Barbara and Pütz, Thomas},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {3265--3292},\n}\n\n\n\n

\n\n\n

\n Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges as compared to the lysimeter reference and to analyze the effect of precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated the precipitation amounts relative to the lysimeter references for long-term precipitation totals with catch ratios (CRs) of between 33 %–92 %. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 % and 9 % and by 10 % and 11 %, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather conditions. The results indicate that considerable precipitation measurement errors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads to an underestimation of the actual precipitation amounts. The results suggest that the application of relatively simple correction schemes, manual or automated data quality checks, instrument calibrations, and/or an adequate choice of observation period can help improve the data quality of gauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or radar-based precipitation analyses.\n

\n\n\n

\n \n\n \n \n Schmidt, T.; Kuester, T.; Smith, T.; and Bochow, M.\n\n\n \n \n \n \n \n Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(8): 2020. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_potential_2023,\n\ttitle = {Potential of {Optical} {Spaceborne} {Sensors} for the {Differentiation} of {Plastics} in the {Environment}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/8/2020},\n\tdoi = {10.3390/rs15082020},\n\tabstract = {Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Schmidt, Toni and Kuester, Theres and Smith, Taylor and Bochow, Mathias},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2020},\n}\n\n\n\n

\n\n\n

\n Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.\n

\n\n\n

\n \n\n \n \n Schmidt, L.; Schäfer, D.; Geller, J.; Lünenschloss, P.; Palm, B.; Rinke, K.; Rebmann, C.; Rode, M.; and Bumberger, J.\n\n\n \n \n \n \n \n System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 169: 105809. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"System$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_system_2023,\n\ttitle = {System for automated {Quality} {Control} ({SaQC}) to enable traceable and reproducible data streams in environmental science},\n\tvolume = {169},\n\tissn = {13648152},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1364815223001950},\n\tdoi = {10.1016/j.envsoft.2023.105809},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Schmidt, Lennart and Schäfer, David and Geller, Juliane and Lünenschloss, Peter and Palm, Bert and Rinke, Karsten and Rebmann, Corinna and Rode, Michael and Bumberger, Jan},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {105809},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Scheiffele, L. M.; Schrön, M.; Köhli, M.; Dimitrova-Petrova, K.; Altdorff, D.; Franz, T.; Rosolem, R.; Evans, J.; Blake, J.; Bogena, H.; McJannet, D.; Baroni, G.; Desilets, D.; and Oswald, S. E.\n\n\n \n \n \n \n \n Comment on ‘Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site’ by Howells, O.D., Petropoulos, G.P., et al., Environ Earth Sci 82, 41 (2023).\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 82(20): 474. October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Comment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{scheiffele_comment_2023,\n\ttitle = {Comment on ‘{Examining} the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a {COSMOS}-{UK} site’ by {Howells}, {O}.{D}., {Petropoulos}, {G}.{P}., et al., {Environ} {Earth} {Sci} 82, 41 (2023)},\n\tvolume = {82},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {Comment on ‘{Examining} the variation of soil moisture from cosmic-ray neutron probes footprint},\n\turl = {https://link.springer.com/10.1007/s12665-023-11186-6},\n\tdoi = {10.1007/s12665-023-11186-6},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Scheiffele, Lena M. and Schrön, Martin and Köhli, Markus and Dimitrova-Petrova, Katya and Altdorff, Daniel and Franz, Trenton and Rosolem, Rafael and Evans, Jonathan and Blake, James and Bogena, Heye and McJannet, David and Baroni, Gabriele and Desilets, Darin and Oswald, Sascha E.},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {474},\n}\n\n\n\n

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\n \n\n \n \n Sánchez-Zapero, J.; Camacho, F.; Martínez-Sánchez, E.; Gorroño, J.; León-Tavares, J.; Benhadj, I.; Toté, C.; Swinnen, E.; and Muñoz-Sabater, J.\n\n\n \n \n \n \n \n Global estimates of surface albedo from Sentinel-3 OLCI and SLSTR data for Copernicus Climate Change Service: Algorithm and preliminary validation.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 287: 113460. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sanchez-zapero_global_2023,\n\ttitle = {Global estimates of surface albedo from {Sentinel}-3 {OLCI} and {SLSTR} data for {Copernicus} {Climate} {Change} {Service}: {Algorithm} and preliminary validation},\n\tvolume = {287},\n\tissn = {00344257},\n\tshorttitle = {Global estimates of surface albedo from {Sentinel}-3 {OLCI} and {SLSTR} data for {Copernicus} {Climate} {Change} {Service}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723000111},\n\tdoi = {10.1016/j.rse.2023.113460},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Sánchez-Zapero, Jorge and Camacho, Fernando and Martínez-Sánchez, Enrique and Gorroño, Javier and León-Tavares, Jonathan and Benhadj, Iskander and Toté, Carolien and Swinnen, Else and Muñoz-Sabater, Joaquín},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {113460},\n}\n\n\n\n

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\n \n\n \n \n Sánchez-Zapero, J.; Martínez-Sánchez, E.; Camacho, F.; Wang, Z.; Carrer, D.; Schaaf, C.; García-Haro, F. J.; Nickeson, J.; and Cosh, M.\n\n\n \n \n \n \n \n Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4—Application to Three Global Albedo Climate Data Records.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(4): 1081. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Surface$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sanchez-zapero_surface_2023,\n\ttitle = {Surface {ALbedo} {VALidation} ({SALVAL}) {Platform}: {Towards} {CEOS} {LPV} {Validation} {Stage} 4—{Application} to {Three} {Global} {Albedo} {Climate} {Data} {Records}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\tshorttitle = {Surface {ALbedo} {VALidation} ({SALVAL}) {Platform}},\n\turl = {https://www.mdpi.com/2072-4292/15/4/1081},\n\tdoi = {10.3390/rs15041081},\n\tabstract = {The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Sánchez-Zapero, Jorge and Martínez-Sánchez, Enrique and Camacho, Fernando and Wang, Zhuosen and Carrer, Dominique and Schaaf, Crystal and García-Haro, Francisco Javier and Nickeson, Jaime and Cosh, Michael},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {1081},\n}\n\n\n\n

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\n \n\n \n \n Rode, M.; Tittel, J.; Reinstorf, F.; Schubert, M.; Knöller, K.; Gilfedder, B.; Merensky-Pöhlein, F.; and Musolff, A.\n\n\n \n \n \n \n \n Seasonal variation and release of soluble reactive phosphorus in an agricultural upland headwater in central Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(6): 1261–1277. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rode_seasonal_2023,\n\ttitle = {Seasonal variation and release of soluble reactive phosphorus in an agricultural upland headwater in central {Germany}},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/1261/2023/},\n\tdoi = {10.5194/hess-27-1261-2023},\n\tabstract = {Abstract. Soluble reactive phosphorus (SRP) concentrations in\nagricultural headwaters can display pronounced seasonal variability at low\nflow, often with the highest concentrations occurring in summer. These SRP\nconcentrations often exceed eutrophication levels, but their main sources,\nspatial distribution, and temporal dynamics are often unknown. The purpose\nof this study is therefore to differentiate between potential SRP losses and\nreleases from soil drainage, anoxic riparian wetlands, and stream sediments\nin an agricultural headwater catchment. To identify the dominant SRP sources,\nwe carried out three longitudinal stream sampling campaigns for SRP\nconcentrations and fluxes. We used salt dilution tests and natural\n222Rn to determine water fluxes in different sections of the stream,\nand we sampled for SRP, Fe, and 14C dissolved organic carbon (DOC) to examine possible redox-mediated\nmobilization from riparian wetlands and stream sediments. The results\nindicate that a single short section in the upper headwater reach was\nresponsible for most of the SRP fluxes to the stream. Analysis of samples\ntaken under summer low-flow conditions revealed that the stream water SRP\nconcentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater\nentering the gaining section. Pore water from the stream sediment showed\nevidence of reductive mobilization of SRP, but the exchange fluxes were\nprobably too small to contribute substantially to SRP stream concentrations.\nWe also found no evidence that shallow flow paths from riparian wetlands\ncontributed to the observed SRP loads in the stream. Combined, the results of\nthis campaign and previous monitoring suggest that groundwater is the main\nlong-term contributor of SRP at low flow, and agricultural phosphorus is\nlargely buffered in the soil zone. We argue that the seasonal variation of\nSRP concentrations was mainly caused by variations in the proportion of\ngroundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important\nfor choosing effective management measures in a given catchment, as it is\nalso possible that observations of seasonal SRP dilution patterns stem from\nincreased mobilization in riparian zones or from point sources.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Rode, Michael and Tittel, Jörg and Reinstorf, Frido and Schubert, Michael and Knöller, Kay and Gilfedder, Benjamin and Merensky-Pöhlein, Florian and Musolff, Andreas},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1261--1277},\n}\n\n\n\n

\n\n\n

\n Abstract. Soluble reactive phosphorus (SRP) concentrations in agricultural headwaters can display pronounced seasonal variability at low flow, often with the highest concentrations occurring in summer. These SRP concentrations often exceed eutrophication levels, but their main sources, spatial distribution, and temporal dynamics are often unknown. The purpose of this study is therefore to differentiate between potential SRP losses and releases from soil drainage, anoxic riparian wetlands, and stream sediments in an agricultural headwater catchment. To identify the dominant SRP sources, we carried out three longitudinal stream sampling campaigns for SRP concentrations and fluxes. We used salt dilution tests and natural 222Rn to determine water fluxes in different sections of the stream, and we sampled for SRP, Fe, and 14C dissolved organic carbon (DOC) to examine possible redox-mediated mobilization from riparian wetlands and stream sediments. The results indicate that a single short section in the upper headwater reach was responsible for most of the SRP fluxes to the stream. Analysis of samples taken under summer low-flow conditions revealed that the stream water SRP concentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater entering the gaining section. Pore water from the stream sediment showed evidence of reductive mobilization of SRP, but the exchange fluxes were probably too small to contribute substantially to SRP stream concentrations. We also found no evidence that shallow flow paths from riparian wetlands contributed to the observed SRP loads in the stream. Combined, the results of this campaign and previous monitoring suggest that groundwater is the main long-term contributor of SRP at low flow, and agricultural phosphorus is largely buffered in the soil zone. We argue that the seasonal variation of SRP concentrations was mainly caused by variations in the proportion of groundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important for choosing effective management measures in a given catchment, as it is also possible that observations of seasonal SRP dilution patterns stem from increased mobilization in riparian zones or from point sources.\n

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\n \n\n \n \n Reitz, O.; Bogena, H.; Neuwirth, B.; Sanchez‐Azofeifa, A.; Graf, A.; Bates, J.; and Leuchner, M.\n\n\n \n \n \n \n \n Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 128(2): e2022JG007197. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reitz_environmental_2023,\n\ttitle = {Environmental {Drivers} of {Gross} {Primary} {Productivity} and {Light} {Use} {Efficiency} of a {Temperate} {Spruce} {Forest}},\n\tvolume = {128},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JG007197},\n\tdoi = {10.1029/2022JG007197},\n\tabstract = {Abstract \n            Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0\\% ± 2.3\\%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale. \n          ,  \n            Plain Language Summary \n            The efficiency by which a forest uses sunlight to perform photosynthesis is an important feature for climate and ecosystem modeling. However, the light that is actually captured by forests and is useable for photosynthesis is difficult to assess. Here, we show a sophisticated approach to estimate the light use efficiency of a spruce forest in Germany and analyze environmental influences on it and on photosynthesis. Our results indicate that about 4\\% of the light useable for photosynthesis was actually used by the forest during the 2021 growing season and that seasonal variations of chlorophyll in the canopy are a good indicator for carbon capture. \n          ,  \n            Key Points \n             \n               \n                 \n                  A seasonal variable such as canopy chlorophyll content was useful to predict gross primary productivity with machine learning models \n                 \n                 \n                  A clockwise hysteretic pattern of sap flow to radiation is a good indicator of water‐related stomatal closure \n                 \n                 \n                  The light use efficiency of green parts of a spruce forest was 4.0\\% with a standard deviation of 2.3\\% during the 2021 growing season},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Reitz, O. and Bogena, H. and Neuwirth, B. and Sanchez‐Azofeifa, A. and Graf, A. and Bates, J. and Leuchner, M.},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {e2022JG007197},\n}\n\n\n\n

\n\n\n

\n Abstract Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0% ± 2.3%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale. , Plain Language Summary The efficiency by which a forest uses sunlight to perform photosynthesis is an important feature for climate and ecosystem modeling. However, the light that is actually captured by forests and is useable for photosynthesis is difficult to assess. Here, we show a sophisticated approach to estimate the light use efficiency of a spruce forest in Germany and analyze environmental influences on it and on photosynthesis. Our results indicate that about 4% of the light useable for photosynthesis was actually used by the forest during the 2021 growing season and that seasonal variations of chlorophyll in the canopy are a good indicator for carbon capture. , Key Points A seasonal variable such as canopy chlorophyll content was useful to predict gross primary productivity with machine learning models A clockwise hysteretic pattern of sap flow to radiation is a good indicator of water‐related stomatal closure The light use efficiency of green parts of a spruce forest was 4.0% with a standard deviation of 2.3% during the 2021 growing season\n

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\n \n\n \n \n Rasche, D.; Weimar, J.; Schrön, M.; Köhli, M.; Morgner, M.; Güntner, A.; and Blume, T.\n\n\n \n \n \n \n \n A change in perspective: downhole cosmic-ray neutron sensing for the estimation of soil moisture.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(16): 3059–3082. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rasche_change_2023,\n\ttitle = {A change in perspective: downhole cosmic-ray neutron sensing for the estimation of soil moisture},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\tshorttitle = {A change in perspective},\n\turl = {https://hess.copernicus.org/articles/27/3059/2023/},\n\tdoi = {10.5194/hess-27-3059-2023},\n\tabstract = {Abstract. Above-ground cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of the field-scale soil moisture content in the upper\ndecimetres of the soil. However, large parts of the deeper vadose zone remain outside of its observational window. Retrieving soil moisture\ninformation from these deeper layers requires extrapolation, modelling or other methods, all of which come with methodological challenges. Against\nthis background, we investigate CRNS for downhole soil moisture measurements in deeper layers of the vadose zone. To render calibration with in situ\nsoil moisture measurements unnecessary, we rescaled neutron intensities observed below the terrain surface with intensities measured above a waterbody. An experimental set-up with a CRNS sensor deployed at different depths of up to 10 m below the surface in a groundwater observation well\ncombined with particle transport simulations revealed the response of downhole thermal neutron intensities to changes in the soil moisture content at\nthe depth of the downhole neutron detector as well as in the layers above it. The simulation results suggest that the sensitive measurement radius\nof several decimetres, which depends on soil moisture and soil bulk density, exceeds that of a standard active neutron probe (which is only about\n30 cm). We derived transfer functions to estimate downhole neutron signals from soil moisture information, and we describe approaches for\nusing these transfer functions in an inverse way to derive soil moisture from the observed neutron signals. The in situ neutron and soil moisture\nobservations confirm the applicability of these functions and prove the concept of passive downhole soil moisture estimation, even at larger depths,\nusing cosmic-ray neutron sensing.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Rasche, Daniel and Weimar, Jannis and Schrön, Martin and Köhli, Markus and Morgner, Markus and Güntner, Andreas and Blume, Theresa},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {3059--3082},\n}\n\n\n\n

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\n \n\n \n \n Ramsauer, T.; and Marzahn, P.\n\n\n \n \n \n \n \n Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index.\n \n \n \n \n\n\n \n\n\n\n International Journal of Remote Sensing, 44(2): 542–566. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ramsauer_global_2023,\n\ttitle = {Global {Soil} {Moisture} {Estimation} based on {GPM} {IMERG} {Data} using a {Site} {Specific} {Adjusted} {Antecedent} {Precipitation} {Index}},\n\tvolume = {44},\n\tissn = {0143-1161, 1366-5901},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/01431161.2022.2162351},\n\tdoi = {10.1080/01431161.2022.2162351},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Ramsauer, Thomas and Marzahn, Philip},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {542--566},\n}\n\n\n\n

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\n \n\n \n \n Rahmati, M.; Or, D.; Amelung, W.; Bauke, S. L.; Bol, R.; Hendricks Franssen, H.; Montzka, C.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Soil is a living archive of the Earth system.\n \n \n \n \n\n\n \n\n\n\n Nature Reviews Earth & Environment, 4(7): 421–423. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahmati_soil_2023,\n\ttitle = {Soil is a living archive of the {Earth} system},\n\tvolume = {4},\n\tissn = {2662-138X},\n\turl = {https://www.nature.com/articles/s43017-023-00454-5},\n\tdoi = {10.1038/s43017-023-00454-5},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2024-05-16},\n\tjournal = {Nature Reviews Earth \\& Environment},\n\tauthor = {Rahmati, Mehdi and Or, Dani and Amelung, Wulf and Bauke, Sara L. and Bol, Roland and Hendricks Franssen, Harrie-Jan and Montzka, Carsten and Vanderborght, Jan and Vereecken, Harry},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {421--423},\n}\n\n\n\n

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\n \n\n \n \n Rahmati, M.; Graf, A.; Poppe Terán, C.; Amelung, W.; Dorigo, W.; Franssen, H. H.; Montzka, C.; Or, D.; Sprenger, M.; Vanderborght, J.; Verhoest, N. E. C.; and Vereecken, H.\n\n\n \n \n \n \n \n Continuous increase in evaporative demand shortened the growing season of European ecosystems in the last decade.\n \n \n \n \n\n\n \n\n\n\n Communications Earth & Environment, 4(1): 236. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Continuous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahmati_continuous_2023,\n\ttitle = {Continuous increase in evaporative demand shortened the growing season of {European} ecosystems in the last decade},\n\tvolume = {4},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-023-00890-7},\n\tdoi = {10.1038/s43247-023-00890-7},\n\tabstract = {Abstract \n            Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Communications Earth \\& Environment},\n\tauthor = {Rahmati, Mehdi and Graf, Alexander and Poppe Terán, Christian and Amelung, Wulf and Dorigo, Wouter and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Or, Dani and Sprenger, Matthias and Vanderborght, Jan and Verhoest, Niko E. C. and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {236},\n}\n\n\n\n

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\n \n\n \n \n Radtke, C. F.; Lutz, S.; Müller, C.; Merz, R.; Kumar, R.; and Knöller, K.\n\n\n \n \n \n \n \n Fractions of Different Young Water Ages are Sensitive to Discharge and Land Use - an Integrated Analysis of Water Age Metrics under Varying Hydrological Conditions for Contrasting Sub-Catchments in Central Germany.\n \n \n \n \n\n\n \n\n\n\n August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Fractions$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{radtke_fractions_2023,\n\ttitle = {Fractions of {Different} {Young} {Water} {Ages} are {Sensitive} to {Discharge} and {Land} {Use} - an {Integrated} {Analysis} of {Water} {Age} {Metrics} under {Varying} {Hydrological} {Conditions} for {Contrasting} {Sub}-{Catchments} in {Central} {Germany}},\n\turl = {https://essopenarchive.org/users/645893/articles/658105-fractions-of-different-young-water-ages-are-sensitive-to-discharge-and-land-use-an-integrated-analysis-of-water-age-metrics-under-varying-hydrological-conditions-for-contrasting-sub-catchments-in-central-germany?commit=0bfd065780aaeaa5510bb12c1bead9ec64b2f58d},\n\tdoi = {10.22541/essoar.169143883.33463468/v1},\n\tabstract = {With ongoing climate change and more frequent high flows and droughts, \nit becomes inevitable to understand potentially altered catchment \nprocesses under changing climatic conditions. Water age metrics such as \nmedian transit times and young water fractions are useful variables to \nunderstand the process dynamics of catchments and the release of solutes \nto the streams. This study, based on extensive high-frequency stable \nisotope data, unravels the changing contribution of different water ages \nto stream water in six heterogeneous catchments, located in the Harz \nmountains and the adjacent northern lowlands in Central Germany. \nFractions of water up to 7 days old (Fyw7), comparable with water from \nrecent precipitation events, and fractions of water up to 60 days old \n(Fyw60) were simulated by the tran-SAS model. As Fyw7 and Fyw60 were \nsensitive to discharge, an integrated analysis of high and low flows was \nconducted. This revealed an increasing contribution of young water for \nincreasing discharge, with larger contributions of young water during \nwet spells compared to dry spells. Considering the seasons, young water \nfractions increased in summer and autumn, which indicates higher \ncontributions of young water after prolonged dry conditions. Moreover, \nthe relationship between catchment characteristics and the water age \nmetrics revealed an increasing amount of young water with increasing \nagricultural area, while the amount of young water decreased with \nincreasing grassland proportion. By combining transit time modelling \nwith high-frequency isotopic signatures in contrasting sub-catchments in \nCentral Germany, our study extends the understanding of hydrological \nprocesses under high and low flow conditions.},\n\turldate = {2024-05-16},\n\tauthor = {Radtke, Christina Franziska and Lutz, Stefanie and Müller, Christin and Merz, Ralf and Kumar, Rohini and Knöller, Kay},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Prikaziuk, E.; Migliavacca, M.; Su, Z. (.; and Van Der Tol, C.\n\n\n \n \n \n \n \n Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 284: 113324. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{prikaziuk_simulation_2023,\n\ttitle = {Simulation of ecosystem fluxes with the {SCOPE} model: {Sensitivity} to parametrization and evaluation with flux tower observations},\n\tvolume = {284},\n\tissn = {00344257},\n\tshorttitle = {Simulation of ecosystem fluxes with the {SCOPE} model},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722004308},\n\tdoi = {10.1016/j.rse.2022.113324},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Prikaziuk, Egor and Migliavacca, Mirco and Su, Zhongbo (Bob) and Van Der Tol, Christiaan},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {113324},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Poppe Terán, C.; Naz, B. S.; Graf, A.; Qu, Y.; Hendricks Franssen, H.; Baatz, R.; Ciais, P.; and Vereecken, H.\n\n\n \n \n \n \n \n Rising water-use efficiency in European grasslands is driven by increased primary production.\n \n \n \n \n\n\n \n\n\n\n Communications Earth & Environment, 4(1): 95. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Rising$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{poppe_teran_rising_2023,\n\ttitle = {Rising water-use efficiency in {European} grasslands is driven by increased primary production},\n\tvolume = {4},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-023-00757-x},\n\tdoi = {10.1038/s43247-023-00757-x},\n\tabstract = {Abstract \n            Water-use efficiency is the amount of carbon assimilated per water used by an ecosystem and a key indicator of ecosystem functioning, but its variability in response to climate change and droughts is not thoroughly understood. Here, we investigated trends, drought response and drivers of three water-use efficiency indices from 1995–2018 in Europe with remote sensing data that considered long-term environmental effects. We show that inherent water-use efficiency decreased by −4.2\\% in Central Europe, exhibiting threatened ecosystem functioning. In European grasslands it increased by +24.2\\%, by regulated transpiration and increased carbon assimilation. Further, we highlight modulation of water-use efficiency drought response by hydro-climate and the importance of adaptive canopy conductance on ecosystem function. Our results imply that decoupling carbon assimilation from canopy conductance and efficient water management strategies could make the difference between threatened and well-coping ecosystems with ongoing climate change, and provide important insights for land surface model development.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Communications Earth \\& Environment},\n\tauthor = {Poppe Terán, Christian and Naz, Bibi S. and Graf, Alexander and Qu, Yuquan and Hendricks Franssen, Harrie-Jan and Baatz, Roland and Ciais, Phillipe and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {95},\n}\n\n\n\n

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\n \n\n \n \n Pohl, F.; Werban, U.; Kumar, R.; Hildebrandt, A.; and Rebmann, C.\n\n\n \n \n \n \n \n Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in Germany.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 13(1): 10863. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Observational$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pohl_observational_2023,\n\ttitle = {Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in {Germany}},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-38087-9},\n\tdoi = {10.1038/s41598-023-38087-9},\n\tabstract = {Abstract \n             \n              Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was \n               \n                 \n                  \\$\\${\\textbackslash}sim 16{\\textbackslash},{\\textbackslash}\\%\\$\\$ \n                   \n                     \n                      ∼ \n                      16 \n                       \n                      \\% \n                     \n                   \n                 \n               \n              higher in 2018 ( \n               \n                 \n                  \\$\\${\\textbackslash}sim 424{\\textbackslash},\\{{\\textbackslash}hbox \\{g\\}\\_\\{{\\textbackslash}text \\{C\\}\\}\\}{\\textbackslash}hbox \\{m\\}{\\textasciicircum}\\{-2\\}\\$\\$ \n                   \n                     \n                      ∼ \n                      424 \n                       \n                       \n                        g \n                        C \n                       \n                       \n                        m \n                         \n                          - \n                          2 \n                         \n                       \n                     \n                   \n                 \n               \n              ) and \n               \n                 \n                  \\$\\${\\textbackslash}sim 25{\\textbackslash},{\\textbackslash}\\%\\$\\$ \n                   \n                     \n                      ∼ \n                      25 \n                       \n                      \\% \n                     \n                   \n                 \n               \n              lower in 2019 ( \n               \n                 \n                  \\$\\${\\textbackslash}sim 274{\\textbackslash},\\{{\\textbackslash}hbox \\{g\\}\\_\\{{\\textbackslash}text \\{C\\}\\}\\}{\\textbackslash}hbox \\{m\\}{\\textasciicircum}\\{-2\\}\\$\\$ \n                   \n                     \n                      ∼ \n                      274 \n                       \n                       \n                        g \n                        C \n                       \n                       \n                        m \n                         \n                          - \n                          2 \n                         \n                       \n                     \n                   \n                 \n               \n              ) compared to pre-drought years ( \n               \n                 \n                  \\$\\${\\textbackslash}sim 367{\\textbackslash},\\{{\\textbackslash}hbox \\{g\\}\\_\\{{\\textbackslash}text \\{C\\}\\}\\}{\\textbackslash}hbox \\{m\\}{\\textasciicircum}\\{-2\\}\\$\\$ \n                   \n                     \n                      ∼ \n                      367 \n                       \n                       \n                        g \n                        C \n                       \n                       \n                        m \n                         \n                          - \n                          2 \n                         \n                       \n                     \n                   \n                 \n               \n              ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly \n               \n                 \n                  \\$\\$50{\\textbackslash},{\\textbackslash}\\%\\$\\$ \n                   \n                     \n                      50 \n                       \n                      \\% \n                     \n                   \n                 \n               \n              . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Reports},\n\tauthor = {Pohl, Felix and Werban, Ulrike and Kumar, Rohini and Hildebrandt, Anke and Rebmann, Corinna},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {10863},\n}\n\n\n\n

\n\n\n

\n Abstract Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was $$\\sim 16\\,\\%$$ ∼ 16 % higher in 2018 ( $$\\sim 424\\,\\\\hbox \\g\\_\\\\text \\C\\\\\\\\hbox \\m\\\\textasciicircum\\-2\\$$ ∼ 424 g C m - 2 ) and $$\\sim 25\\,\\%$$ ∼ 25 % lower in 2019 ( $$\\sim 274\\,\\\\hbox \\g\\_\\\\text \\C\\\\\\\\hbox \\m\\\\textasciicircum\\-2\\$$ ∼ 274 g C m - 2 ) compared to pre-drought years ( $$\\sim 367\\,\\\\hbox \\g\\_\\\\text \\C\\\\\\\\hbox \\m\\\\textasciicircum\\-2\\$$ ∼ 367 g C m - 2 ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly $$50\\,\\%$$ 50 % . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.\n

\n\n\n

\n \n\n \n \n Pohl, F.; Rakovec, O.; Rebmann, C.; Hildebrandt, A.; Boeing, F.; Hermanns, F.; Attinger, S.; Samaniego, L.; and Kumar, R.\n\n\n \n \n \n \n \n Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 10(1): 281. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pohl_long-term_2023,\n\ttitle = {Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for {ICOS} sites},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02192-1},\n\tdoi = {10.1038/s41597-023-02192-1},\n\tabstract = {Abstract \n            Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Data},\n\tauthor = {Pohl, Felix and Rakovec, Oldrich and Rebmann, Corinna and Hildebrandt, Anke and Boeing, Friedrich and Hermanns, Floris and Attinger, Sabine and Samaniego, Luis and Kumar, Rohini},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {281},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Peng, Z.; Zhao, T.; Shi, J.; Kerr, Y. H.; Rodríguez-Fernández, N. J.; Yao, P.; and Che, T.\n\n\n \n \n \n \n \n An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010).\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 10(1): 599. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peng_rfi-suppressed_2023,\n\ttitle = {An {RFI}-suppressed {SMOS} {L}-band multi-angular brightness temperature dataset spanning over a decade (since 2010)},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02499-z},\n\tdoi = {10.1038/s41597-023-02499-z},\n\tabstract = {Abstract \n             \n              The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with \n              in-situ \n              measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Data},\n\tauthor = {Peng, Zhiqing and Zhao, Tianjie and Shi, Jiancheng and Kerr, Yann H. and Rodríguez-Fernández, Nemesio J. and Yao, Panpan and Che, Tao},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {599},\n}\n\n\n\n

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\n \n\n \n \n Paulus, S. J.; Orth, R.; Lee, S.; Hildebrandt, A.; Jung, M.; Nelson, J. A.; El-Madany, T. S.; Carrara, A.; Moreno, G.; Mauder, M.; Groh, J.; Graf, A.; Reichstein, M.; and Migliavacca, M.\n\n\n \n \n \n \n \n Interpretability of negative latent heat fluxes from Eddy Covariance measurements during dry conditions.\n \n \n \n \n\n\n \n\n\n\n November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Interpretability$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{paulus_interpretability_2023,\n\ttitle = {Interpretability of negative latent heat fluxes from {Eddy} {Covariance} measurements during dry conditions},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2556/},\n\tdoi = {10.5194/egusphere-2023-2556},\n\tabstract = {Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor is directly adsorbed by the soil matrix during the night. Soil water vapor adsorption was typically neglected and only recently got attention because of improvements in measurement techniques. One technique rarely explored is eddy covariance (EC). EC nighttime measurements are usually discarded, but soil water vapor adsorption may be detectable as downwards-directed EC latent heat (λE) flux measurements under dry conditions. We propose a classification method to exclude conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwards-directed λE fluxes from EC with measurements from weighable lysimeters for four years in a Mediterranean Savannah ecosystem and three years in a temperate agricultural site. Our aim is to assess if overnight water inputs from soil water vapor adsorption differ between ecosystems and how well they are detectable by EC.  At the Mediterranean site, the lysimeters measured soil water vapor adsorption each summer whereas at the temperate site soil water vapor adsorption was much rarer, and measured predominantly under extreme drought. In 30 \\% of nights in the four-year measurement period at the Mediterranean site, the EC technique detected downward-directed λE fluxes of which 88.8 \\% were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downward-directed λE fluxes were only recorded during 15 \\% of the nights, with only 36.8 \\% of half-hours matching simultaneous lysimeter measurement of soil water vapor adsorption. Although this relationship slightly improved to 60\\% under bare soil conditions and extreme droughts, this underlines that soil water vapor adsorption is likely a much more relevant process in arid ecosystems compared to temperate ones and that the EC method was able to capture this difference. The comparisons of the magnitudes between the two methods revealed a substantial underestimation of soil water vapor adsorption with EC. This underestimation was, however, on par with the underestimation in evaporation. Based on a random forest-based feature selection we found the mismatch between the techniques being dominantly related to the site's inherent spatiotemporal variations in soil conditions, namely soil water status, and soil (surface) temperature.  We further demonstrate that although the water flux is very small with mean values of 0.04 or 0.06 mm per night depending on either EC or lysimeter detection it can be a substantial fraction of the diel soil water balance under dry conditions. Although the two instruments substantially differ with regard to the evaporative fraction with 64\\% and 25\\% for the lysimeter and EC methods, they are in either case substantial. Given the usefulness of EC for detecting soil water vapor adsorption as demonstrated here, there is potential for investigating adsorption in more climate regions at longer timescales thanks to the greater abundance of EC measurements compared to lysimeter observations.},\n\turldate = {2024-05-16},\n\tauthor = {Paulus, Sinikka J. and Orth, Rene and Lee, Sung-Ching and Hildebrandt, Anke and Jung, Martin and Nelson, Jacob A. and El-Madany, Tarek S. and Carrara, Arnaud and Moreno, Gerardo and Mauder, Matthias and Groh, Jannis and Graf, Alexander and Reichstein, Markus and Migliavacca, Mirco},\n\tmonth = nov,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor is directly adsorbed by the soil matrix during the night. Soil water vapor adsorption was typically neglected and only recently got attention because of improvements in measurement techniques. One technique rarely explored is eddy covariance (EC). EC nighttime measurements are usually discarded, but soil water vapor adsorption may be detectable as downwards-directed EC latent heat (λE) flux measurements under dry conditions. We propose a classification method to exclude conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwards-directed λE fluxes from EC with measurements from weighable lysimeters for four years in a Mediterranean Savannah ecosystem and three years in a temperate agricultural site. Our aim is to assess if overnight water inputs from soil water vapor adsorption differ between ecosystems and how well they are detectable by EC. At the Mediterranean site, the lysimeters measured soil water vapor adsorption each summer whereas at the temperate site soil water vapor adsorption was much rarer, and measured predominantly under extreme drought. In 30 % of nights in the four-year measurement period at the Mediterranean site, the EC technique detected downward-directed λE fluxes of which 88.8 % were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downward-directed λE fluxes were only recorded during 15 % of the nights, with only 36.8 % of half-hours matching simultaneous lysimeter measurement of soil water vapor adsorption. Although this relationship slightly improved to 60% under bare soil conditions and extreme droughts, this underlines that soil water vapor adsorption is likely a much more relevant process in arid ecosystems compared to temperate ones and that the EC method was able to capture this difference. The comparisons of the magnitudes between the two methods revealed a substantial underestimation of soil water vapor adsorption with EC. This underestimation was, however, on par with the underestimation in evaporation. Based on a random forest-based feature selection we found the mismatch between the techniques being dominantly related to the site's inherent spatiotemporal variations in soil conditions, namely soil water status, and soil (surface) temperature. We further demonstrate that although the water flux is very small with mean values of 0.04 or 0.06 mm per night depending on either EC or lysimeter detection it can be a substantial fraction of the diel soil water balance under dry conditions. Although the two instruments substantially differ with regard to the evaporative fraction with 64% and 25% for the lysimeter and EC methods, they are in either case substantial. Given the usefulness of EC for detecting soil water vapor adsorption as demonstrated here, there is potential for investigating adsorption in more climate regions at longer timescales thanks to the greater abundance of EC measurements compared to lysimeter observations.\n

\n\n\n

\n \n\n \n \n Pasqualini, J.; Majdi, N.; and Brauns, M.\n\n\n \n \n \n \n \n Effects of incomplete sampling on macroinvertebrate secondary production estimates in a forested headwater stream.\n \n \n \n \n\n\n \n\n\n\n Hydrobiologia, 850(14): 3113–3124. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pasqualini_effects_2023,\n\ttitle = {Effects of incomplete sampling on macroinvertebrate secondary production estimates in a forested headwater stream},\n\tvolume = {850},\n\tissn = {0018-8158, 1573-5117},\n\turl = {https://link.springer.com/10.1007/s10750-023-05238-y},\n\tdoi = {10.1007/s10750-023-05238-y},\n\tabstract = {Abstract \n            Estimates of secondary production depend on the efficiency of sampling methods in capturing abundances and body lengths of the entire macroinvertebrate community. The efficiency of common sampling methods in fulfilling these criteria is poorly understood. We compared the effects of a Surber sampler (250 µm mesh size) and a Freeze corer in capturing abundance, biomass, and secondary production of macroinvertebrates in a forested headwater stream. We then examined how the use of nets with different mesh sizes could affect estimates of secondary production. Macroinvertebrate abundance was three times lower, and biomass was three times higher with the Surber than with the Freeze corer. Neither method captured the entire length distribution, and incomplete sampling of body lengths and abundance resulted in underestimating total secondary production by 48\\% (Surber) and 49\\% (Freeze corer). We estimated that reducing the mesh size from 250 to 100 µm would reduce the underestimation of production from {\\textasciitilde} 48 to {\\textasciitilde} 12\\% due to the inclusion of smaller individuals. Our results improve the efficiency of common sampling methods, allowing a reliable quantification of the role of macroinvertebrates in stream ecosystem functioning.},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2024-05-16},\n\tjournal = {Hydrobiologia},\n\tauthor = {Pasqualini, Julia and Majdi, Nabil and Brauns, Mario},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {3113--3124},\n}\n\n\n\n

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\n \n\n \n \n Pasik, A.; Gruber, A.; Preimesberger, W.; De Santis, D.; and Dorigo, W.\n\n\n \n \n \n \n \n Uncertainty estimation for a new exponential filter-based long-term root-zone soil moisture dataset from C3S surface observations.\n \n \n \n \n\n\n \n\n\n\n March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{pasik_uncertainty_2023,\n\ttitle = {Uncertainty estimation for a new exponential filter-based long-term root-zone soil moisture dataset from {C3S} surface observations},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-47/},\n\tdoi = {10.5194/egusphere-2023-47},\n\tabstract = {Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.},\n\turldate = {2024-05-16},\n\tauthor = {Pasik, Adam and Gruber, Alexander and Preimesberger, Wolfgang and De Santis, Domenico and Dorigo, Wouter},\n\tmonth = mar,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.\n

\n\n\n

\n \n\n \n \n Panwar, A.; Migliavacca, M.; Nelson, J. A.; Cortés, J.; Bastos, A.; Forkel, M.; and Winkler, A. J.\n\n\n \n \n \n \n \n Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 13(1): 13885. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Methodological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{panwar_methodological_2023,\n\ttitle = {Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-41048-x},\n\tdoi = {10.1038/s41598-023-41048-x},\n\tabstract = {Abstract \n            While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series ({\\textgreater} 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Reports},\n\tauthor = {Panwar, Annu and Migliavacca, Mirco and Nelson, Jacob A. and Cortés, José and Bastos, Ana and Forkel, Matthias and Winkler, Alexander J.},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {13885},\n}\n\n\n\n

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\n \n\n \n \n Paasche, H.; and Schröter, I.\n\n\n \n \n \n \n \n Quantification of data‐related uncertainty of spatially dense soil moisture patterns on the small catchment scale estimated using unsupervised multiple regression.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 22(4): e20258. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{paasche_quantification_2023,\n\ttitle = {Quantification of data‐related uncertainty of spatially dense soil moisture patterns on the small catchment scale estimated using unsupervised multiple regression},\n\tvolume = {22},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20258},\n\tdoi = {10.1002/vzj2.20258},\n\tabstract = {Abstract \n            Multiple regression analysis is a valuable method to reduce information gaps in a sparse soil moisture data set by fusing its information content with those of densely mapped data sets. Regression analysis utilizing uncertain data results in an indeterminate regression model and indeterminate soil moisture predictions when applying the regression model. We employ an unsupervised multiple regression approaches, taking optimally located sparse soil moisture measurements directly as coefficients in a linear regression model. We propagate data uncertainties into our probabilistic soil moisture estimation results by embedding the regression in a Monte Carlo approach. The computed uncertainty defines the quantitative limit for information retrieval from the resultant ensemble of soil moisture maps. This raises doubts on the true presence of some prominent channel‐like features of increased soil moisture that are clearly visible in a previously and deterministically derived soil moisture map ignoring the presence of data uncertainty. The approach followed in this work is computationally simple and could be applied routinely to databases of similar size. Insufficient uncertainty communication by the data provider became the biggest obstacle in our efforts and led us to the insight that the geoscientific community may need to revise their standards with regard to uncertainty communication related to measured and processed data. \n          ,  \n            Core Ideas \n             \n               \n                 \n                  Data uncertainty propagation through regression by means of a Monte Carlo approach. \n                 \n                 \n                  Unsupervised nonlinear regression and its dependency on optimal sparse sampling. \n                 \n                 \n                  Uncertainty communication for proper information retrieval.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-05-16},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Paasche, Hendrik and Schröter, Ingmar},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {e20258},\n}\n\n\n\n

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\n \n\n \n \n Orlowski, N.; Rinderer, M.; Dubbert, M.; Ceperley, N.; Hrachowitz, M.; Gessler, A.; Rothfuss, Y.; Sprenger, M.; Heidbüchel, I.; Kübert, A.; Beyer, M.; Zuecco, G.; and McCarter, C.\n\n\n \n \n \n \n \n Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 881: 163510. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Challenges$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{orlowski_challenges_2023,\n\ttitle = {Challenges in studying water fluxes within the soil-plant-atmosphere continuum: {A} tracer-based perspective on pathways to progress},\n\tvolume = {881},\n\tissn = {00489697},\n\tshorttitle = {Challenges in studying water fluxes within the soil-plant-atmosphere continuum},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723021290},\n\tdoi = {10.1016/j.scitotenv.2023.163510},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Orlowski, Natalie and Rinderer, Michael and Dubbert, Maren and Ceperley, Natalie and Hrachowitz, Markus and Gessler, Arthur and Rothfuss, Youri and Sprenger, Matthias and Heidbüchel, Ingo and Kübert, Angelika and Beyer, Matthias and Zuecco, Giulia and McCarter, Colin},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {163510},\n}\n\n\n\n

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\n \n\n \n \n Nwosu, E. C.; Brauer, A.; Monchamp, M.; Pinkerneil, S.; Bartholomäus, A.; Theuerkauf, M.; Schmidt, J.; Stoof-Leichsenring, K. R.; Wietelmann, T.; Kaiser, J.; Wagner, D.; and Liebner, S.\n\n\n \n \n \n \n \n Early human impact on lake cyanobacteria revealed by a Holocene record of sedimentary ancient DNA.\n \n \n \n \n\n\n \n\n\n\n Communications Biology, 6(1): 72. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Early$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nwosu_early_2023,\n\ttitle = {Early human impact on lake cyanobacteria revealed by a {Holocene} record of sedimentary ancient {DNA}},\n\tvolume = {6},\n\tissn = {2399-3642},\n\turl = {https://www.nature.com/articles/s42003-023-04430-z},\n\tdoi = {10.1038/s42003-023-04430-z},\n\tabstract = {Abstract \n             \n              Sedimentary DNA-based studies revealed the effects of human activity on lake cyanobacteria communities over the last centuries, yet we continue to lack information over longer timescales. Here, we apply high-resolution molecular analyses on sedimentary ancient DNA to reconstruct the history of cyanobacteria throughout the Holocene in a lake in north-eastern Germany. We find a substantial increase in cyanobacteria abundance coinciding with deforestation during the early Bronze Age around 4000 years ago, suggesting increased nutrient supply to the lake by local communities settling on the lakeshore. The next substantial human-driven increase in cyanobacteria abundance occurred only about a century ago due to intensified agricultural fertilisation which caused the dominance of potentially toxic taxa (e.g., \n              Aphanizomenon \n              ). Our study provides evidence that humans began to locally impact lake ecology much earlier than previously assumed. Consequently, managing aquatic systems today requires awareness of the legacy of human influence dating back potentially several millennia.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Communications Biology},\n\tauthor = {Nwosu, Ebuka Canisius and Brauer, Achim and Monchamp, Marie-Eve and Pinkerneil, Sylvia and Bartholomäus, Alexander and Theuerkauf, Martin and Schmidt, Jens-Peter and Stoof-Leichsenring, Kathleen R. and Wietelmann, Theresa and Kaiser, Jerome and Wagner, Dirk and Liebner, Susanne},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {72},\n}\n\n\n\n

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\n \n\n \n \n Nieberding, F.; Huisman, J. A.; Huebner, C.; Schilling, B.; Weuthen, A.; and Bogena, H. R.\n\n\n \n \n \n \n \n Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments.\n \n \n \n \n\n\n \n\n\n\n Sensors, 23(14): 6581. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nieberding_evaluation_2023,\n\ttitle = {Evaluation of {Three} {Soil} {Moisture} {Profile} {Sensors} {Using} {Laboratory} and {Field} {Experiments}},\n\tvolume = {23},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1424-8220},\n\turl = {https://www.mdpi.com/1424-8220/23/14/6581},\n\tdoi = {10.3390/s23146581},\n\tabstract = {Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill\\&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill\\&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m−3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m−3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2024-05-16},\n\tjournal = {Sensors},\n\tauthor = {Nieberding, Felix and Huisman, Johan Alexander and Huebner, Christof and Schilling, Bernd and Weuthen, Ansgar and Bogena, Heye Reemt},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {6581},\n}\n\n\n\n

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\n \n\n \n \n Naz, B. S.; Sharples, W.; Ma, Y.; Goergen, K.; and Kollet, S.\n\n\n \n \n \n \n \n Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 16(6): 1617–1639. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Continental-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{naz_continental-scale_2023,\n\ttitle = {Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, {ParFlow}-{CLM} (v3.6.0), over {Europe}},\n\tvolume = {16},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/16/1617/2023/},\n\tdoi = {10.5194/gmd-16-1617-2023},\n\tabstract = {Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R{\\textgreater}0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Naz, Bibi S. and Sharples, Wendy and Ma, Yueling and Goergen, Klaus and Kollet, Stefan},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1617--1639},\n}\n\n\n\n

\n\n\n

\n Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R\\textgreater0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.\n

\n\n\n

\n \n\n \n \n Mwanake, R. M.; Gettel, G. M.; Wangari, E. G.; Butterbach-Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Interactive effects of catchment mean water residence time and agricultural area on water physico-chemical variables and GHG saturations in headwater streams.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 5: 1220544. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Interactive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mwanake_interactive_2023,\n\ttitle = {Interactive effects of catchment mean water residence time and agricultural area on water physico-chemical variables and {GHG} saturations in headwater streams},\n\tvolume = {5},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1220544/full},\n\tdoi = {10.3389/frwa.2023.1220544},\n\tabstract = {Greenhouse gas emissions from headwater streams are linked to multiple sources influenced by terrestrial land use and hydrology, yet partitioning these sources at catchment scales remains highly unexplored. To address this gap, we sampled year-long stable water isotopes (δ \n              18 \n              O and δ \n              2 \n              H) from 17 headwater streams differing in catchment agricultural areas. We calculated mean residence times (MRT) and young water fractions (YWF) based on the seasonality of δ \n              18 \n              O signals and linked these hydrological measures to catchment characteristics, mean annual water physico-chemical variables, and GHG \\% saturations. The MRT and the YWF ranged from 0.25 to 4.77 years and 3 to 53\\%, respectively. The MRT of stream water was significantly negatively correlated with stream slope (r \n              2 \n              = 0.58) but showed no relationship with the catchment area. Streams in agriculture-dominated catchments were annual hotspots of GHG oversaturation, which we attributed to precipitation-driven terrestrial inputs of dissolved GHGs for streams with shorter MRTs and nutrients and GHG inflows from groundwater for streams with longer MRTs. Based on our findings, future research should also consider water mean residence time estimates as indicators of integrated hydrological processes linking discharge and land use effects on annual GHG dynamics in headwater streams.},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Water},\n\tauthor = {Mwanake, Ricky Mwangada and Gettel, Gretchen Maria and Wangari, Elizabeth Gachibu and Butterbach-Bahl, Klaus and Kiese, Ralf},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {1220544},\n}\n\n\n\n

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\n \n\n \n \n Mwanake, R. M.; Gettel, G. M.; Wangari, E. G.; Glaser, C.; Houska, T.; Breuer, L.; Butterbach-Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Anthropogenic activities significantly increase annual greenhouse gas (GHG) fluxes from temperate headwater streams in Germany.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 20(16): 3395–3422. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Anthropogenic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mwanake_anthropogenic_2023,\n\ttitle = {Anthropogenic activities significantly increase annual greenhouse gas ({GHG}) fluxes from temperate headwater streams in {Germany}},\n\tvolume = {20},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/20/3395/2023/},\n\tdoi = {10.5194/bg-20-3395-2023},\n\tabstract = {Abstract. Anthropogenic activities increase the contributions of inland waters to\nglobal greenhouse gas (GHG; CO2, CH4, and N2O) budgets, yet\nthe mechanisms driving these increases are still not well constrained. In\nthis study, we quantified year-long GHG concentrations, fluxes, and water\nphysico-chemical variables from 28 sites contrasted by land use across five\nheadwater catchments in Germany. Based on linear mixed-effects models, we\nshowed that land use was more significant than seasonality in controlling\nthe intra-annual variability of the GHGs. Streams in agriculture-dominated\ncatchments or with wastewater inflows had up to 10 times higher daily\nCO2, CH4, and N2O emissions and were also more temporally\nvariable (CV {\\textgreater} 55 \\%) than forested streams. Our findings also\nsuggested that nutrient, labile carbon, and dissolved GHG inputs from the\nagricultural and settlement areas may have supported these hotspots and\nhot-moments of fluvial GHG emissions. Overall, the annual emission from\nanthropogenic-influenced streams in CO2 equivalents was up to 20 times\nhigher (∼ 71 kg CO2 m−2 yr−1) than from\nnatural streams (∼ 3 kg CO2 m−2 yr−1), with\nCO2 accounting for up to 81 \\% of these annual emissions, while\nN2O and CH4 accounted for up to 18 \\% and 7 \\%, respectively. The\npositive influence of anthropogenic activities on fluvial GHG emissions also\nresulted in a breakdown of the expected declining trends of fluvial GHG\nemissions with stream size. Therefore, future studies should focus on\nanthropogenically perturbed streams, as their GHG emissions are much more\nvariable in space and time and can potentially introduce the largest\nuncertainties to fluvial GHG estimates.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2024-05-16},\n\tjournal = {Biogeosciences},\n\tauthor = {Mwanake, Ricky Mwangada and Gettel, Gretchen Maria and Wangari, Elizabeth Gachibu and Glaser, Clarissa and Houska, Tobias and Breuer, Lutz and Butterbach-Bahl, Klaus and Kiese, Ralf},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {3395--3422},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Donat, M.; Raj, R.; Welter, P.; and Bates, J. S.\n\n\n \n \n \n \n \n Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt.\n \n \n \n \n\n\n \n\n\n\n Drones, 7(2): 121. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Sensitivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_sensitivity_2023,\n\ttitle = {Sensitivity of {LiDAR} {Parameters} to {Aboveground} {Biomass} in {Winter} {Spelt}},\n\tvolume = {7},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2504-446X},\n\turl = {https://www.mdpi.com/2504-446X/7/2/121},\n\tdoi = {10.3390/drones7020121},\n\tabstract = {Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Drones},\n\tauthor = {Montzka, Carsten and Donat, Marco and Raj, Rahul and Welter, Philipp and Bates, Jordan Steven},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {121},\n}\n\n\n\n

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\n \n\n \n \n Mollenhauer, H.; Borg, E.; Pflug, B.; Fichtelmann, B.; Dahms, T.; Lorenz, S.; Mollenhauer, O.; Lausch, A.; Bumberger, J.; and Dietrich, P.\n\n\n \n \n \n \n \n Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(19): 4663. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Ground$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mollenhauer_ground_2023,\n\ttitle = {Ground {Truth} {Validation} of {Sentinel}-2 {Data} {Using} {Mobile} {Wireless} {Ad} {Hoc} {Sensor} {Networks} ({MWSN}) in {Vegetation} {Stands}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/19/4663},\n\tdoi = {10.3390/rs15194663},\n\tabstract = {Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.},\n\tlanguage = {en},\n\tnumber = {19},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Mollenhauer, Hannes and Borg, Erik and Pflug, Bringfried and Fichtelmann, Bernd and Dahms, Thorsten and Lorenz, Sebastian and Mollenhauer, Olaf and Lausch, Angela and Bumberger, Jan and Dietrich, Peter},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {4663},\n}\n\n\n\n

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\n \n\n \n \n Mi, C.; Shatwell, T.; Kong, X.; and Rinke, K.\n\n\n \n \n \n \n \n Cascading climate effects in deep reservoirs: Full assessment of physical and biogeochemical dynamics under ensemble climate projections and ways towards adaptation.\n \n \n \n \n\n\n \n\n\n\n Ambio. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Cascading$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mi_cascading_2023,\n\ttitle = {Cascading climate effects in deep reservoirs: {Full} assessment of physical and biogeochemical dynamics under ensemble climate projections and ways towards adaptation},\n\tissn = {0044-7447, 1654-7209},\n\tshorttitle = {Cascading climate effects in deep reservoirs},\n\turl = {https://link.springer.com/10.1007/s13280-023-01950-0},\n\tdoi = {10.1007/s13280-023-01950-0},\n\tabstract = {Abstract \n             \n              We coupled twenty-first century climate projections with a well-established water quality model to depict future ecological changes of Rappbode Reservoir, Germany. Our results document a chain of climate-driven effects propagating through the aquatic ecosystem and interfering with drinking water supply: intense climate warming (RCP8.5 scenario) will firstly trigger a strong increase in water temperatures, in turn leading to metalimnetic hypoxia, accelerating sediment nutrient release and finally boosting blooms of the cyanobacterium \n              Planktothrix rubescens \n              . Such adverse water quality developments will be suppressed under RCP2.6 and 6.0 indicating that mitigation of climate change is improving water security. Our results also suggested surface withdrawal can be an effective adaptation strategy to make the reservoir ecosystem more resilient to climate warming. The identified consequences from climate warming and adaptation strategies are relevant to many deep waters in the temperate zone, and the conclusion should provide important guidances for stakeholders to confront potential climate changes.},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Ambio},\n\tauthor = {Mi, Chenxi and Shatwell, Tom and Kong, Xiangzhen and Rinke, Karsten},\n\tmonth = nov,\n\tyear = {2023},\n}\n\n\n\n

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\n \n\n \n \n Mi, C.; Rinke, K.; and Shatwell, T.\n\n\n \n \n \n \n \n Optimizing selective withdrawal strategies to mitigate hypoxia under water-level reduction in Germany's largest drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Sciences,S1001074223002760. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Optimizing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mi_optimizing_2023,\n\ttitle = {Optimizing selective withdrawal strategies to mitigate hypoxia under water-level reduction in {Germany}'s largest drinking water reservoir},\n\tissn = {10010742},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1001074223002760},\n\tdoi = {10.1016/j.jes.2023.06.025},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Environmental Sciences},\n\tauthor = {Mi, Chenxi and Rinke, Karsten and Shatwell, Tom},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {S1001074223002760},\n}\n\n\n\n

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\n \n\n \n \n Mengen, D.; Jagdhuber, T.; Balenzano, A.; Mattia, F.; Vereecken, H.; and Montzka, C.\n\n\n \n \n \n \n \n High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(9): 2282. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mengen_high_2023,\n\ttitle = {High {Spatial} and {Temporal} {Soil} {Moisture} {Retrieval} in {Agricultural} {Areas} {Using} {Multi}-{Orbit} and {Vegetation} {Adapted} {Sentinel}-1 {SAR} {Time} {Series}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/9/2282},\n\tdoi = {10.3390/rs15092282},\n\tabstract = {The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Mengen, David and Jagdhuber, Thomas and Balenzano, Anna and Mattia, Francesco and Vereecken, Harry and Montzka, Carsten},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2282},\n}\n\n\n\n

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\n \n\n \n \n Mengen, D.; Balenzano, A.; Jagdhuber, T.; Mattia, F.; Vereecken, H.; and Montzka, C.\n\n\n \n \n \n \n \n Extended Alpha Approximation Method for the Retrieval of Soil Moisture Under Dynamic Vegetation by Multi-Incidence Angle Sentinel-1.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 2649–2652, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Extended$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{mengen_extended_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {Extended {Alpha} {Approximation} {Method} for the {Retrieval} of {Soil} {Moisture} {Under} {Dynamic} {Vegetation} by {Multi}-{Incidence} {Angle} {Sentinel}-1},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10282711/},\n\tdoi = {10.1109/IGARSS52108.2023.10282711},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Mengen, David and Balenzano, Anna and Jagdhuber, Thomas and Mattia, Francesco and Vereecken, Harry and Montzka, Carsten},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {2649--2652},\n}\n\n\n\n

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\n \n\n \n \n McNicol, G.; Fluet‐Chouinard, E.; Ouyang, Z.; Knox, S.; Zhang, Z.; Aalto, T.; Bansal, S.; Chang, K.; Chen, M.; Delwiche, K.; Feron, S.; Goeckede, M.; Liu, J.; Malhotra, A.; Melton, J. R.; Riley, W.; Vargas, R.; Yuan, K.; Ying, Q.; Zhu, Q.; Alekseychik, P.; Aurela, M.; Billesbach, D. P.; Campbell, D. I.; Chen, J.; Chu, H.; Desai, A. R.; Euskirchen, E.; Goodrich, J.; Griffis, T.; Helbig, M.; Hirano, T.; Iwata, H.; Jurasinski, G.; King, J.; Koebsch, F.; Kolka, R.; Krauss, K.; Lohila, A.; Mammarella, I.; Nilson, M.; Noormets, A.; Oechel, W.; Peichl, M.; Sachs, T.; Sakabe, A.; Schulze, C.; Sonnentag, O.; Sullivan, R. C.; Tuittila, E.; Ueyama, M.; Vesala, T.; Ward, E.; Wille, C.; Wong, G. X.; Zona, D.; Windham‐Myers, L.; Poulter, B.; and Jackson, R. B.\n\n\n \n \n \n \n \n Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison.\n \n \n \n \n\n\n \n\n\n\n AGU Advances, 4(5): e2023AV000956. October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Upscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mcnicol_upscaling_2023,\n\ttitle = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0): {Model} {Development}, {Network} {Assessment}, and {Budget} {Comparison}},\n\tvolume = {4},\n\tissn = {2576-604X, 2576-604X},\n\tshorttitle = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0)},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023AV000956},\n\tdoi = {10.1029/2023AV000956},\n\tabstract = {Abstract \n             \n              Wetlands are responsible for 20\\%–31\\% of global methane (CH \n              4 \n              ) emissions and account for a large source of uncertainty in the global CH \n              4 \n              budget. Data‐driven upscaling of CH \n              4 \n              fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH \n              4 \n              emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH \n              4 \n              flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH \n              4 \n              fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH \n              4 \n              emissions of 146 ± 43 TgCH \n              4 \n               y \n              −1 \n              for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH \n              4 \n               y \n              −1 \n              ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH \n              4 \n               y \n              −1 \n              ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH \n              4 \n              fluxes has the potential to produce realistic extra‐tropical wetland CH \n              4 \n              emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( \n              https://doi.org/10.3334/ORNLDAAC/2253 \n              ). \n             \n          ,  \n            Plain Language Summary \n            Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (∼30\\% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties. \n          ,  \n            Key Points \n             \n               \n                 \n                   \n                    Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands ( \n                    R \n                    2 \n                    : 0.59–0.64) \n                   \n                 \n                 \n                   \n                    Globally upscaled annual wetland methane emissions (146 TgCH \n                    4 \n                     y \n                    −1 \n                    ) overlapped with land surface and inversion model ensemble estimates \n                   \n                 \n                 \n                  Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68\\%) and uncertainties (∼78\\%) due to limited FLUXNET‐CH4 site coverage},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2024-05-16},\n\tjournal = {AGU Advances},\n\tauthor = {McNicol, Gavin and Fluet‐Chouinard, Etienne and Ouyang, Zutao and Knox, Sara and Zhang, Zhen and Aalto, Tuula and Bansal, Sheel and Chang, Kuang‐Yu and Chen, Min and Delwiche, Kyle and Feron, Sarah and Goeckede, Mathias and Liu, Jinxun and Malhotra, Avni and Melton, Joe R. and Riley, William and Vargas, Rodrigo and Yuan, Kunxiaojia and Ying, Qing and Zhu, Qing and Alekseychik, Pavel and Aurela, Mika and Billesbach, David P. and Campbell, David I. and Chen, Jiquan and Chu, Housen and Desai, Ankur R. and Euskirchen, Eugenie and Goodrich, Jordan and Griffis, Timothy and Helbig, Manuel and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and King, John and Koebsch, Franziska and Kolka, Randall and Krauss, Ken and Lohila, Annalea and Mammarella, Ivan and Nilson, Mats and Noormets, Asko and Oechel, Walter and Peichl, Matthias and Sachs, Torsten and Sakabe, Ayaka and Schulze, Christopher and Sonnentag, Oliver and Sullivan, Ryan C. and Tuittila, Eeva‐Stiina and Ueyama, Masahito and Vesala, Timo and Ward, Eric and Wille, Christian and Wong, Guan Xhuan and Zona, Donatella and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {e2023AV000956},\n}\n\n\n\n

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\n Abstract Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4 y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4 y −1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( https://doi.org/10.3334/ORNLDAAC/2253 ). , Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (∼30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties. , Key Points Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands ( R 2 : 0.59–0.64) Globally upscaled annual wetland methane emissions (146 TgCH 4 y −1 ) overlapped with land surface and inversion model ensemble estimates Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68%) and uncertainties (∼78%) due to limited FLUXNET‐CH4 site coverage\n

\n\n\n

\n \n\n \n \n Mazzariello, A.; Albano, R.; Lacava, T.; Manfreda, S.; and Sole, A.\n\n\n \n \n \n \n \n Intercomparison of recent microwave satellite soil moisture products on European ecoregions.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 626: 130311. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Intercomparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mazzariello_intercomparison_2023,\n\ttitle = {Intercomparison of recent microwave satellite soil moisture products on {European} ecoregions},\n\tvolume = {626},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423012532},\n\tdoi = {10.1016/j.jhydrol.2023.130311},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Mazzariello, A. and Albano, R. and Lacava, T. and Manfreda, S. and Sole, A.},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {130311},\n}\n\n\n\n

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\n \n\n \n \n Ma, H.; Zeng, J.; Chen, N.; Zhang, X.; Li, X.; and Wigneron, J.\n\n\n \n \n \n \n \n Soil Moisture Retrieval from the Integration of SMAP and ASCAT Using Machine Learning Approach.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 3190–3193, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{ma_soil_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {Soil {Moisture} {Retrieval} from the {Integration} of {SMAP} and {ASCAT} {Using} {Machine} {Learning} {Approach}},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10283186/},\n\tdoi = {10.1109/IGARSS52108.2023.10283186},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Ma, Hongliang and Zeng, Jiangyuan and Chen, Nengcheng and Zhang, Xiang and Li, Xiaojun and Wigneron, Jean-Pierre},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {3190--3193},\n}\n\n\n\n

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\n \n\n \n \n Liu, H.; Liu, J.; Yin, Y.; Walther, S.; Ma, X.; Zhang, Z.; and Chen, Y.\n\n\n \n \n \n \n \n Improved Vegetation Photosynthetic Phenology Monitoring in the Northern Ecosystems Using Total Canopy Solar‐Induced Chlorophyll Fluorescence Derived From TROPOMI.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 128(6): e2022JG007369. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_improved_2023,\n\ttitle = {Improved {Vegetation} {Photosynthetic} {Phenology} {Monitoring} in the {Northern} {Ecosystems} {Using} {Total} {Canopy} {Solar}‐{Induced} {Chlorophyll} {Fluorescence} {Derived} {From} {TROPOMI}},\n\tvolume = {128},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JG007369},\n\tdoi = {10.1029/2022JG007369},\n\tabstract = {Abstract \n            Solar‐Induced chlorophyll Fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) with substantially improved spatiotemporal resolutions provides a new potential to improve satellite‐based phenology monitoring. The performance of TROPOMI SIF for tracking vegetation photosynthetic phenology, and how it compares to conventional vegetation indices (VIs)‐based approaches, however, have not been adequately assessed. Total canopy SIF, as a better proxy of Gross Primary Productivity (GPP) than original directional SIF, is a new SIF to estimate phenology while its performance has not been investigated. This study assesses the capability of TROPOMI SIF before and after canopy correction for phenology monitoring and improves our understanding of these questions. Benchmarked by tower‐based GPP, TROPOMI SIF generally performed better than VIs, especially for capturing the End Of Season (EOS) of vegetation photosynthetic activity at deciduous broadleaf forest (DBF), evergreen forest (ENF), and croplands (CRO) sites, but not for Start Of Season (SOS). This suggested that the advantage of SIF over VIs depended on phenological metrics. The total canopy SIF emission obtained through canopy correction generally performed better than the original SIF retrievals, especially in estimating the EOS of forest sites (DBF, MF, ENF), but soil correction did not further improve the accuracy of phenological monitoring. When comparing SIF‐ and VI‐based phenological metrics over northern terrestrial ecosystems, SIF showed earlier senescence date widely, while the differences in onset date were region dependent. These results indicate the necessity of canopy correction to convert directional SIF to canopy total SIF when using satellite SIF products to estimate phenological metrics. \n          ,  \n            Plain Language Summary \n            Phenology is an important ecological indicator of terrestrial carbon cycle, and satellite‐based remote sensing provides an effective approach to estimating phenological metrics over large scales. However, phenology monitoring using vegetation indices represents canopy “greenness” which is not fully synchronized with photosynthetic activity. Solar‐Induced chlorophyll Fluorescence (SIF) has great potential in phenology monitoring but is limited by the coarse spatiotemporal resolution. The emergence of TROPOspheric Monitoring Instrument (TROPOMI), with spatial resolution up to 7 km × 3.5 km and daily revisit, has brought new opportunities to SIF‐based phenology monitoring. This study demonstrated the advantages of TROPOMI SIF, especially at the total canopy level for phenology monitoring which had potential for improving large‐scale mapping of phenological characterizations. Specifically, total canopy SIF had much better accuracy than the original SIF observations, but soil correction did not have further improvement. This can provide a valuable reference for the application of TROPOMI SIF to monitor vegetation phenology. \n          ,  \n            Key Points \n             \n               \n                 \n                  Total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) more accurately tracked the Gross Primary Productivity (GPP) trajectory than original TROPOMI SIF \n                 \n                 \n                   \n                    SIFtotal from TROPOMI outperformed MODIS EVI and NIR \n                    V \n                    in phenology monitoring using GPP as benchmark \n                   \n                 \n                 \n                  Soil correction did not further improve the performance of SIFtotal in phenology monitoring},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Liu, Haoran and Liu, Junzhi and Yin, Yueqiang and Walther, Sophia and Ma, Xuanlong and Zhang, Zhaoying and Chen, Yuhan},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {e2022JG007369},\n}\n\n\n\n

\n\n\n

\n Abstract Solar‐Induced chlorophyll Fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) with substantially improved spatiotemporal resolutions provides a new potential to improve satellite‐based phenology monitoring. The performance of TROPOMI SIF for tracking vegetation photosynthetic phenology, and how it compares to conventional vegetation indices (VIs)‐based approaches, however, have not been adequately assessed. Total canopy SIF, as a better proxy of Gross Primary Productivity (GPP) than original directional SIF, is a new SIF to estimate phenology while its performance has not been investigated. This study assesses the capability of TROPOMI SIF before and after canopy correction for phenology monitoring and improves our understanding of these questions. Benchmarked by tower‐based GPP, TROPOMI SIF generally performed better than VIs, especially for capturing the End Of Season (EOS) of vegetation photosynthetic activity at deciduous broadleaf forest (DBF), evergreen forest (ENF), and croplands (CRO) sites, but not for Start Of Season (SOS). This suggested that the advantage of SIF over VIs depended on phenological metrics. The total canopy SIF emission obtained through canopy correction generally performed better than the original SIF retrievals, especially in estimating the EOS of forest sites (DBF, MF, ENF), but soil correction did not further improve the accuracy of phenological monitoring. When comparing SIF‐ and VI‐based phenological metrics over northern terrestrial ecosystems, SIF showed earlier senescence date widely, while the differences in onset date were region dependent. These results indicate the necessity of canopy correction to convert directional SIF to canopy total SIF when using satellite SIF products to estimate phenological metrics. , Plain Language Summary Phenology is an important ecological indicator of terrestrial carbon cycle, and satellite‐based remote sensing provides an effective approach to estimating phenological metrics over large scales. However, phenology monitoring using vegetation indices represents canopy “greenness” which is not fully synchronized with photosynthetic activity. Solar‐Induced chlorophyll Fluorescence (SIF) has great potential in phenology monitoring but is limited by the coarse spatiotemporal resolution. The emergence of TROPOspheric Monitoring Instrument (TROPOMI), with spatial resolution up to 7 km × 3.5 km and daily revisit, has brought new opportunities to SIF‐based phenology monitoring. This study demonstrated the advantages of TROPOMI SIF, especially at the total canopy level for phenology monitoring which had potential for improving large‐scale mapping of phenological characterizations. Specifically, total canopy SIF had much better accuracy than the original SIF observations, but soil correction did not have further improvement. This can provide a valuable reference for the application of TROPOMI SIF to monitor vegetation phenology. , Key Points Total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) more accurately tracked the Gross Primary Productivity (GPP) trajectory than original TROPOMI SIF SIFtotal from TROPOMI outperformed MODIS EVI and NIR V in phenology monitoring using GPP as benchmark Soil correction did not further improve the performance of SIFtotal in phenology monitoring\n

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\n \n\n \n \n Li, F.; Kurtz, W.; Hung, C. P.; Vereecken, H.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Water table depth assimilation in integrated terrestrial system models at the larger catchment scale.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 5: 1150999. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_water_2023,\n\ttitle = {Water table depth assimilation in integrated terrestrial system models at the larger catchment scale},\n\tvolume = {5},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full},\n\tdoi = {10.3389/frwa.2023.1150999},\n\tabstract = {As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81\\% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites \\&lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30\\% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Water},\n\tauthor = {Li, Fang and Kurtz, Wolfgang and Hung, Ching Pui and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1150999},\n}\n\n\n\n

\n\n\n

\n As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites <2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).\n

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\n \n\n \n \n Li, C.; Liu, Z.; Yang, W.; Tu, Z.; Han, J.; Li, S.; and Yang, H.\n\n\n \n \n \n \n \n CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data.\n \n \n \n \n\n\n \n\n\n\n July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"CAMELE:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{li_camele_2023,\n\ttitle = {{CAMELE}: {Collocation}-{Analyzed} {Multi}-source {Ensembled} {Land} {Evapotranspiration} {Data}},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tshorttitle = {{CAMELE}},\n\turl = {https://essd.copernicus.org/preprints/essd-2023-226/essd-2023-226.pdf},\n\tdoi = {10.5194/essd-2023-226},\n\tabstract = {Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water-carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land-atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in-situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits excellent performance across various vegetation coverage types, as validated against in-situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square-errors (RMSE) of 0.81 and 0.73 mm/d, unbiased root-mean-square-errors (ubRMSE) of 1.20 and 1.04 mm/d, mean absolute errors (MAE) of 0.81 and 0.73 mm/d, and Kling-Gupta efficiency (KGE) of 0.60 and 0.65 on average over resolutions of 0.1° and 0.25°, respectively.},\n\turldate = {2024-05-16},\n\tauthor = {Li, Changming and Liu, Ziwei and Yang, Wencong and Tu, Zhuoyi and Han, Juntai and Li, Sien and Yang, Hanbo},\n\tmonth = jul,\n\tyear = {2023},\n}\n\n\n\n

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\n \n\n \n \n Leng, P.; and Koschorreck, M.\n\n\n \n \n \n \n \n Metabolism and carbonate buffering drive seasonal dynamics of CO2 emissions from two German reservoirs.\n \n \n \n \n\n\n \n\n\n\n Water Research, 242: 120302. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Metabolism$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{leng_metabolism_2023,\n\ttitle = {Metabolism and carbonate buffering drive seasonal dynamics of {CO2} emissions from two {German} reservoirs},\n\tvolume = {242},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423007388},\n\tdoi = {10.1016/j.watres.2023.120302},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Water Research},\n\tauthor = {Leng, Peifang and Koschorreck, Matthias},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {120302},\n}\n\n\n\n

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\n \n\n \n \n Laux, P.; Weber, E.; Feldmann, D.; and Kunstmann, H.\n\n\n \n \n \n \n \n The Robustness of the Derived Design Life Levels of Heavy Precipitation Events in the Pre-Alpine Oberland Region of Southern Germany.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 14(9): 1384. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{laux_robustness_2023,\n\ttitle = {The {Robustness} of the {Derived} {Design} {Life} {Levels} of {Heavy} {Precipitation} {Events} in the {Pre}-{Alpine} {Oberland} {Region} of {Southern} {Germany}},\n\tvolume = {14},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2073-4433},\n\turl = {https://www.mdpi.com/2073-4433/14/9/1384},\n\tdoi = {10.3390/atmos14091384},\n\tabstract = {Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45\\% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40\\%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Atmosphere},\n\tauthor = {Laux, Patrick and Weber, Elena and Feldmann, David and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {1384},\n}\n\n\n\n

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\n \n\n \n \n Künzel, A.; Mühlbauer, K.; Neelmeijer, J.; and Spengler, D.\n\n\n \n \n \n \n \n WRaINfo: An Open Source Library for Weather Radar INformation for FURUNO Weather Radars Based on Wradlib.\n \n \n \n \n\n\n \n\n\n\n Journal of Open Research Software, 11: 9. October 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"WRaINfo:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunzel_wrainfo_2023,\n\ttitle = {{WRaINfo}: {An} {Open} {Source} {Library} for {Weather} {Radar} {INformation} for {FURUNO} {Weather} {Radars} {Based} on {Wradlib}},\n\tvolume = {11},\n\tissn = {2049-9647},\n\tshorttitle = {{WRaINfo}},\n\turl = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.453/},\n\tdoi = {10.5334/jors.453},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Open Research Software},\n\tauthor = {Künzel, Alice and Mühlbauer, Kai and Neelmeijer, Julia and Spengler, Daniel},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {9},\n}\n\n\n\n

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\n \n\n \n \n Krevh, V.; Groh, J.; Filipović, L.; Gerke, H. H.; Defterdarović, J.; Thompson, S.; Sraka, M.; Bogunović, I.; Kovač, Z.; Robinson, N.; Baumgartl, T.; and Filipović, V.\n\n\n \n \n \n \n \n Soil–Water Dynamics Investigation at Agricultural Hillslope with High-Precision Weighing Lysimeters and Soil–Water Collection Systems.\n \n \n \n \n\n\n \n\n\n\n Water, 15(13): 2398. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil–Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{krevh_soilwater_2023,\n\ttitle = {Soil–{Water} {Dynamics} {Investigation} at {Agricultural} {Hillslope} with {High}-{Precision} {Weighing} {Lysimeters} and {Soil}–{Water} {Collection} {Systems}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/15/13/2398},\n\tdoi = {10.3390/w15132398},\n\tabstract = {A quantitative understanding of actual evapotranspiration (ETa) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ETa rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2024-05-16},\n\tjournal = {Water},\n\tauthor = {Krevh, Vedran and Groh, Jannis and Filipović, Lana and Gerke, Horst H. and Defterdarović, Jasmina and Thompson, Sally and Sraka, Mario and Bogunović, Igor and Kovač, Zoran and Robinson, Nathan and Baumgartl, Thomas and Filipović, Vilim},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {2398},\n}\n\n\n\n

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\n \n\n \n \n Kong, X.; Determann, M.; Andersen, T. K.; Barbosa, C. C.; Dadi, T.; Janssen, A. B.; Paule-Mercado, M. C.; Pujoni, D. G. F.; Schultze, M.; and Rinke, K.\n\n\n \n \n \n \n \n Synergistic Effects of Warming and Internal Nutrient Loading Interfere with the Long-Term Stability of Lake Restoration and Induce Sudden Re-eutrophication.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 57(9): 4003–4013. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Synergistic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kong_synergistic_2023,\n\ttitle = {Synergistic {Effects} of {Warming} and {Internal} {Nutrient} {Loading} {Interfere} with the {Long}-{Term} {Stability} of {Lake} {Restoration} and {Induce} {Sudden} {Re}-eutrophication},\n\tvolume = {57},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.2c07181},\n\tdoi = {10.1021/acs.est.2c07181},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Kong, Xiangzhen and Determann, Maria and Andersen, Tobias Kuhlmann and Barbosa, Carolina Cerqueira and Dadi, Tallent and Janssen, Annette B.G. and Paule-Mercado, Ma. Cristina and Pujoni, Diego Guimarães Florencio and Schultze, Martin and Rinke, Karsten},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {4003--4013},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Xiangzhen Kong; Determann, M.; Andersen, T. K.; Barbosa, C. C.; Tallent Dadi; Janssen, A. B.; Ma. Cristina Paule-Mercado; Pujoni, D. G. F.; Schultze, M.; and Rinke, K.\n\n\n \n \n \n \n \n Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication.\n \n \n \n \n\n\n \n\n\n\n February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Synergistic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@misc{xiangzhen_kong_synergistic_2023,\n\ttitle = {Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication},\n\tcopyright = {Creative Commons Attribution 4.0 International, Open Access},\n\turl = {https://zenodo.org/record/7580961},\n\tdoi = {10.5281/ZENODO.7580961},\n\tabstract = {{\\textless}strong{\\textgreater}This repository contains the dataset linked to the following publication:{\\textless}/strong{\\textgreater} {\\textless}strong{\\textgreater}Article title: {\\textless}/strong{\\textgreater}Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication {\\textless}strong{\\textgreater}Journal: {\\textless}/strong{\\textgreater}{\\textless}em{\\textgreater}Environmental Science \\&amp; Technology{\\textless}/em{\\textgreater} {\\textless}strong{\\textgreater}DOI{\\textless}/strong{\\textgreater}: 10.1021/acs.est.2c07181 {\\textless}strong{\\textgreater}Abstract:{\\textless}/strong{\\textgreater} Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68\\% of the cyanobacterial biomass proliferation, while lake warming contributed to 32\\%, including direct effects via promoting growth (18\\%) and synergistic effects via intensifying internal P loading (14\\%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes. {\\textless}strong{\\textgreater}SYNOPSIS: {\\textless}/strong{\\textgreater}Warming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation. {\\textless}strong{\\textgreater}Data description {\\textless}/strong{\\textgreater}by Xiangzhen Kong (xzkong@niglas.ac.cn), 2023-02-20 ---Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8. File name: BAB\\_BA1\\_TN\\_mgL.obs (total nitrogen concentration) BAB\\_BA1\\_NH4\\_mgL.obs (ammonium nitrogen concentration) BAB\\_BA1\\_NO3\\_mgL.obs (nitrate nitrogen concentration) BAB\\_BA1\\_TP\\_mgL.obs (total phosphorus concentration) BAB\\_BA1\\_SRP\\_mgL.obs (Soluble reactive phosphorus concentration) BAB\\_BA1\\_DP\\_mgL.obs (dissolved P concentration) BAB\\_BA1\\_DOC\\_mgL.obs (Dissolved organic carbon concentration) BAB\\_BA1\\_Si\\_mgL.obs (dissolved silicon concentration) BAB\\_BA1\\_Chla\\_HPLC\\_DIN\\_mgL.obs (Chl-a concentration) ---CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: t\\_prof\\_file\\_barleber\\_ctm644.obs (water temperature) oxy\\_prof\\_file\\_barleber\\_ctm644 (Dissolved oxygen) turb\\_prof\\_file\\_barleber\\_ctm644.obs (Turbidity) chla\\_prof\\_file\\_barleber\\_ctm644.obs (Chl-a concentration) ---BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: totalChla\\_prof\\_file\\_barleber\\_FP2101.obs (Chl-a concentration) bluegreen\\_prof\\_file\\_barleber\\_FP2101.obs (Blue-green algae Chl-a concentration) green\\_prof\\_file\\_barleber\\_FP2101.obs (Green algae Chl-a concentration) diatom\\_prof\\_file\\_barleber\\_FP2101.obs (Diatom Chl-a concentration)},\n\turldate = {2024-05-16},\n\tpublisher = {[object Object]},\n\tauthor = {{Xiangzhen Kong} and Determann, Maria and Andersen, Tobias Kuhlmann and Barbosa, Carolina Cerqueira and {Tallent Dadi} and Janssen, Annette B.G. and {Ma. Cristina Paule-Mercado} and Pujoni, Diego Guimarães Florencio and Schultze, Martin and Rinke, Karsten},\n\tmonth = feb,\n\tyear = {2023},\n\tkeywords = {GOTM-WET, climate change, cyanobacterial blooms, eutrophicatoin, internal loading, phosphorus precipitation, urban lake},\n}\n\n\n\n

\n\n\n

\n \\textlessstrong\\textgreaterThis repository contains the dataset linked to the following publication:\\textless/strong\\textgreater \\textlessstrong\\textgreaterArticle title: \\textless/strong\\textgreaterSynergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication \\textlessstrong\\textgreaterJournal: \\textless/strong\\textgreater\\textlessem\\textgreaterEnvironmental Science & Technology\\textless/em\\textgreater \\textlessstrong\\textgreaterDOI\\textless/strong\\textgreater: 10.1021/acs.est.2c07181 \\textlessstrong\\textgreaterAbstract:\\textless/strong\\textgreater Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes. \\textlessstrong\\textgreaterSYNOPSIS: \\textless/strong\\textgreaterWarming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation. \\textlessstrong\\textgreaterData description \\textless/strong\\textgreaterby Xiangzhen Kong (xzkong@niglas.ac.cn), 2023-02-20 —Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8. File name: BAB_BA1_TN_mgL.obs (total nitrogen concentration) BAB_BA1_NH4_mgL.obs (ammonium nitrogen concentration) BAB_BA1_NO3_mgL.obs (nitrate nitrogen concentration) BAB_BA1_TP_mgL.obs (total phosphorus concentration) BAB_BA1_SRP_mgL.obs (Soluble reactive phosphorus concentration) BAB_BA1_DP_mgL.obs (dissolved P concentration) BAB_BA1_DOC_mgL.obs (Dissolved organic carbon concentration) BAB_BA1_Si_mgL.obs (dissolved silicon concentration) BAB_BA1_Chla_HPLC_DIN_mgL.obs (Chl-a concentration) —CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: t_prof_file_barleber_ctm644.obs (water temperature) oxy_prof_file_barleber_ctm644 (Dissolved oxygen) turb_prof_file_barleber_ctm644.obs (Turbidity) chla_prof_file_barleber_ctm644.obs (Chl-a concentration) —BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: totalChla_prof_file_barleber_FP2101.obs (Chl-a concentration) bluegreen_prof_file_barleber_FP2101.obs (Blue-green algae Chl-a concentration) green_prof_file_barleber_FP2101.obs (Green algae Chl-a concentration) diatom_prof_file_barleber_FP2101.obs (Diatom Chl-a concentration)\n

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\n \n\n \n \n Köhli, M.; Schrön, M.; Zacharias, S.; and Schmidt, U.\n\n\n \n \n \n \n \n URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 16(2): 449–477. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"URANOS$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kohli_uranos_2023,\n\ttitle = {{URANOS} v1.0 – the {Ultra} {Rapid} {Adaptable} {Neutron}-{Only} {Simulation} for {Environmental} {Research}},\n\tvolume = {16},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/16/449/2023/},\n\tdoi = {10.5194/gmd-16-449-2023},\n\tabstract = {Abstract. The understanding of neutron transport by Monte Carlo simulations led to major advancements towards precise interpretation of measurements. URANOS (Ultra Rapid Neutron-Only Simulation) is a free software package which has been developed in the last few years in cooperation with particle physics and environmental sciences, specifically for the purposes of cosmic-ray neutron sensing (CRNS). Its versatile user interface and input/output scheme tailored for CRNS applications offers hydrologists straightforward access to model individual scenarios and to directly perform advanced neutron transport calculations. The geometry can be modeled layer-wise, whereas in each layer a voxel geometry is extruded using a two-dimensional map from pixel images representing predefined materials and allowing for the construction of objects on the basis of pixel graphics without a three-dimensional editor. It furthermore features predefined cosmic-ray neutron spectra and detector configurations and also allows for a replication of important site characteristics of study areas – from a small pond to the catchment scale. The simulation thereby gives precise answers to questions like from which location do neutrons originate? How do they propagate to the sensor? What is the neutron's response to certain environmental changes? In recent years, URANOS has been successfully employed by a number of studies, for example, to calculate the cosmic-ray neutron footprint, signals in complex geometries like mobile applications on roads, urban environments and snow patterns.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Köhli, Markus and Schrön, Martin and Zacharias, Steffen and Schmidt, Ulrich},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {449--477},\n}\n\n\n\n

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\n \n\n \n \n Klesse, S.; Babst, F.; Evans, M. E. K.; Hurley, A.; Pappas, C.; and Peters, R. L.\n\n\n \n \n \n \n \n Legacy effects in radial tree growth are rarely significant after accounting for biological memory.\n \n \n \n \n\n\n \n\n\n\n Journal of Ecology, 111(6): 1188–1202. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Legacy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{klesse_legacy_2023,\n\ttitle = {Legacy effects in radial tree growth are rarely significant after accounting for biological memory},\n\tvolume = {111},\n\tissn = {0022-0477, 1365-2745},\n\turl = {https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14045},\n\tdoi = {10.1111/1365-2745.14045},\n\tabstract = {Abstract \n             \n               \n                 \n                  Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny. \n                 \n                 \n                  We re‐examined legacy effect analyses using the International Tree‐Ring Data Bank (ITRDB) and then produced synthetic tree‐ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto‐correlation of the radial growth time series (phi), (ii) depend on climate‐growth cross‐correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold. \n                 \n                 \n                  Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto‐correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity. \n                 \n                 \n                   \n                    Synthesis \n                    . We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto‐correlated residuals of climate‐growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process‐based representation of vegetation carbon dynamics in mechanistic models.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Ecology},\n\tauthor = {Klesse, Stefan and Babst, Flurin and Evans, Margaret E. K. and Hurley, Alexander and Pappas, Christoforos and Peters, Richard L.},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {1188--1202},\n}\n\n\n\n

\n\n\n

\n Abstract Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny. We re‐examined legacy effect analyses using the International Tree‐Ring Data Bank (ITRDB) and then produced synthetic tree‐ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto‐correlation of the radial growth time series (phi), (ii) depend on climate‐growth cross‐correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold. Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto‐correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity. Synthesis . We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto‐correlated residuals of climate‐growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process‐based representation of vegetation carbon dynamics in mechanistic models.\n

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\n \n\n \n \n Keller, N.; Bol, R.; Herre, M.; Marschner, B.; and Heinze, S.\n\n\n \n \n \n \n \n Catchment scale spatial distribution of soil enzyme activities in a mountainous German coniferous forest.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 177: 108885. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Catchment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{keller_catchment_2023,\n\ttitle = {Catchment scale spatial distribution of soil enzyme activities in a mountainous {German} coniferous forest},\n\tvolume = {177},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S003807172200342X},\n\tdoi = {10.1016/j.soilbio.2022.108885},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Keller, Nora and Bol, Roland and Herre, Michael and Marschner, Bernd and Heinze, Stefanie},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {108885},\n}\n\n\n\n

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\n \n\n \n \n Kalhori, A.; Wille, C.; Gottschalk, P.; Li, Z.; Hashemi, J.; Kemper, K.; and Sachs, T.\n\n\n \n \n \n \n \n Long-term flux measurements suggest dynamic emission factors are needed for rewetted peatlands.\n \n \n \n \n\n\n \n\n\n\n September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{kalhori_long-term_2023,\n\ttitle = {Long-term flux measurements suggest dynamic emission factors are needed for rewetted peatlands},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://www.researchsquare.com/article/rs-3241711/v1},\n\tdoi = {10.21203/rs.3.rs-3241711/v1},\n\tabstract = {Abstract \n          Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas (GHG) emissions. However, rewetting creates novel ecosystems whose emission behavior is not well captured by the currently used emission factors (EFs). These EFs are static and do not capture the temporal dynamics of GHG emissions. Hence, they often do not reflect the true emission reduction potential after rewetting. Here, we provide long-term data showing a mismatch between actual emissions and default EFs and revealing the temporal patterns of annual CO2 and CH4 fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of CO2 and CH4 approach the IPCC default EFs and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual CO2 fluxes with a decreasing trend of -0.36 t CO2-C ha-1 yr-1, and a decrease in annual CH4 emissions of -23.6 kg CH4 ha-1 yr-1. Our results indicate that EFs should represent the temporally dynamic nature of peatlands post rewetting and consider the effects of site characteristics to better estimate associated GHG budgets.},\n\turldate = {2024-05-16},\n\tauthor = {Kalhori, Aram and Wille, Christian and Gottschalk, Pia and Li, Zhan and Hashemi, Joshua and Kemper, Karl and Sachs, Torsten},\n\tmonth = sep,\n\tyear = {2023},\n}\n\n\n\n

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\n \n\n \n \n Jagdhuber, T.; Fluhrer, A.; Chaparro, D.; Dubois, C.; Hellwig, F. M.; Bayat, B.; Montzka, C.; Baur, M. J.; Ramati, M.; Kübert, A.; Mueller, M. M.; Schellenberg, K.; Boehm, M.; Jonard, F.; Steele-Dunne, S.; Piles, M.; and Entekhabi, D.\n\n\n \n \n \n \n \n On the Potential of Active and Passive Microwave Remote Sensing for Tracking Seasonal Dynamics of Evapotranspiration.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 2610–2613, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{jagdhuber_potential_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {On the {Potential} of {Active} and {Passive} {Microwave} {Remote} {Sensing} for {Tracking} {Seasonal} {Dynamics} of {Evapotranspiration}},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10283234/},\n\tdoi = {10.1109/IGARSS52108.2023.10283234},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Jagdhuber, T. and Fluhrer, A. and Chaparro, D. and Dubois, C. and Hellwig, F. M. and Bayat, B. and Montzka, C. and Baur, M. J. and Ramati, M. and Kübert, A. and Mueller, M. M. and Schellenberg, K. and Boehm, M. and Jonard, F. and Steele-Dunne, S. and Piles, M. and Entekhabi, D.},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {2610--2613},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Iannino, A.; Fink, P.; Vosshage, A. T. L.; and Weitere, M.\n\n\n \n \n \n \n \n Resource-dependent foraging behaviour of grazers enhances effects of nutrient enrichment on algal biomass.\n \n \n \n \n\n\n \n\n\n\n Oecologia, 201(2): 479–488. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Resource-dependent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{iannino_resource-dependent_2023,\n\ttitle = {Resource-dependent foraging behaviour of grazers enhances effects of nutrient enrichment on algal biomass},\n\tvolume = {201},\n\tissn = {0029-8549, 1432-1939},\n\turl = {https://link.springer.com/10.1007/s00442-022-05308-3},\n\tdoi = {10.1007/s00442-022-05308-3},\n\tabstract = {Abstract \n             \n              Both the quantity and nutritional quality of food resources can strongly influence the foraging movements of herbivores, which in turn determine the strength of top-down control on primary producer biomass. Nutrient enrichment can alter the biomass and nutritional quality of primary producers, but the consequences for the foraging of herbivores and hence for top-down control are still poorly understood. In this study, we combined a two-factorial experiment (two nutrient levels × grazing by the freshwater gastropod \n              Ancylus fluviatilis \n              ) with video analyses tracking grazers’ movements to investigate nutrient enrichment effects on spatial ranges of grazing activity and algal biomass removal. Natural stream biofilms were grown in phosphorus-enriched (P+) and phosphorus-poor flumes (P−) for two weeks before \n              A. fluviatilis \n              were added to the flumes and allowed to graze on biofilm for an additional 2 weeks. Total periphyton biomass was enhanced by P+ and reduced by grazer presence. However, the total grazer effect depended on the nutrient level: at the end of the experiment, on average 95\\% of algal cover were removed by grazing in the P− flumes versus 26\\% in the P+ flumes. Fast movements of \n              A. fluviatilis \n              were detected significantly more often in the P− treatment, whereas grazers were detected resting more often in the P+ treatment. Our results demonstrate that nutrient enrichment can increase primary producer biomass both directly and indirectly by limiting the foraging ranges of herbivores. The resulting feedback loop between reduced grazing activity and increased plant biomass might in turn exacerbate eutrophication effects on habitat structure.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Oecologia},\n\tauthor = {Iannino, Alessandra and Fink, Patrick and Vosshage, Alexander Tim Ludwig and Weitere, Markus},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {479--488},\n}\n\n\n\n

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\n \n\n \n \n Hu, T.; Mallick, K.; Hitzelberger, P.; Didry, Y.; Boulet, G.; Szantoi, Z.; Koetz, B.; Alonso, I.; Pascolini‐Campbell, M.; Halverson, G.; Cawse‐Nicholson, K.; Hulley, G. C.; Hook, S.; Bhattarai, N.; Olioso, A.; Roujean, J.; Gamet, P.; and Su, B.\n\n\n \n \n \n \n \n Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 59(8): e2022WR034132. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hu_evaluating_2023,\n\ttitle = {Evaluating {European} {ECOSTRESS} {Hub} {Evapotranspiration} {Products} {Across} a {Range} of {Soil}‐{Atmospheric} {Aridity} and {Biomes} {Over} {Europe}},\n\tvolume = {59},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034132},\n\tdoi = {10.1029/2022WR034132},\n\tabstract = {Abstract \n             \n              The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m \n              −2 \n              ). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m \n              −2 \n              . Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m \n              −2 \n              higher) for the PT‐JPL ET estimates. Substantial overestimation ({\\textgreater}80 W m \n              −2 \n              ) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions. \n             \n          ,  \n            Key Points \n             \n               \n                 \n                  Evapotranspiration products over Europe and Africa were generated using three models (Surface Temperature Initiated Closure, Surface Energy Balance System, and Two Source Energy Balance) in the European ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Hub \n                 \n                 \n                  Comparison at 19 eddy covariance sites revealed noteworthy model divergence with increasing aridity and vegetation sparseness \n                 \n                 \n                  A substantial overestimation of the official NASA ECOSTRESS PT‐JPL evapotranspiration product was found under high water limitations},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Hu, Tian and Mallick, Kaniska and Hitzelberger, Patrik and Didry, Yoanne and Boulet, Gilles and Szantoi, Zoltan and Koetz, Benjamin and Alonso, Itziar and Pascolini‐Campbell, Madeleine and Halverson, Gregory and Cawse‐Nicholson, Kerry and Hulley, Glynn C. and Hook, Simon and Bhattarai, Nishan and Olioso, Albert and Roujean, Jean‐Louis and Gamet, Philippe and Su, Bob},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {e2022WR034132},\n}\n\n\n\n

\n\n\n

\n Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m −2 ). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m −2 . Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m −2 higher) for the PT‐JPL ET estimates. Substantial overestimation (\\textgreater80 W m −2 ) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions. , Key Points Evapotranspiration products over Europe and Africa were generated using three models (Surface Temperature Initiated Closure, Surface Energy Balance System, and Two Source Energy Balance) in the European ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Hub Comparison at 19 eddy covariance sites revealed noteworthy model divergence with increasing aridity and vegetation sparseness A substantial overestimation of the official NASA ECOSTRESS PT‐JPL evapotranspiration product was found under high water limitations\n

\n\n\n

\n \n\n \n \n Hu, L.; Zhao, T.; Ju, W.; Peng, Z.; Shi, J.; Rodríguez-Fernández, N. J.; Wigneron, J.; Cosh, M. H.; Yang, K.; Lu, H.; and Yao, P.\n\n\n \n \n \n \n \n A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 292: 113595. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hu_twenty-year_2023,\n\ttitle = {A twenty-year dataset of soil moisture and vegetation optical depth from {AMSR}-{E}/2 measurements using the multi-channel collaborative algorithm},\n\tvolume = {292},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723001463},\n\tdoi = {10.1016/j.rse.2023.113595},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Hu, Lu and Zhao, Tianjie and Ju, Weimin and Peng, Zhiqing and Shi, Jiancheng and Rodríguez-Fernández, Nemesio J. and Wigneron, Jean-Pierre and Cosh, Michael H. and Yang, Kun and Lu, Hui and Yao, Panpan},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {113595},\n}\n\n\n\n

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\n \n\n \n \n Høye, T.; August, T.; Balzan, M. V; Biesmeijer, K.; Bonnet, P.; Breeze, T.; Dominik, C.; Gerard, F.; Joly, A.; Kalkman, V.; Kissling, W. D.; Metodiev, T.; Moeslund, J.; Potts, S.; Roy, D.; Schweiger, O.; Senapathi, D.; Settele, J.; Stoev, P.; and Stowell, D.\n\n\n \n \n \n \n \n Modern Approaches to the Monitoring of Biоdiversity (MAMBO).\n \n \n \n \n\n\n \n\n\n\n Research Ideas and Outcomes, 9: e116951. December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Modern$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hoye_modern_2023,\n\ttitle = {Modern {Approaches} to the {Monitoring} of {Biоdiversity} ({MAMBO})},\n\tvolume = {9},\n\tissn = {2367-7163},\n\turl = {https://riojournal.com/article/116951/},\n\tdoi = {10.3897/rio.9.e116951},\n\tabstract = {EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.},\n\turldate = {2024-05-16},\n\tjournal = {Research Ideas and Outcomes},\n\tauthor = {Høye, Toke and August, Tom and Balzan, Mario V and Biesmeijer, Koos and Bonnet, Pierre and Breeze, Tom and Dominik, Christophe and Gerard, France and Joly, Alexis and Kalkman, Vincent and Kissling, W. Daniel and Metodiev, Teodor and Moeslund, Jesper and Potts, Simon and Roy, David and Schweiger, Oliver and Senapathi, Deepa and Settele, Josef and Stoev, Pavel and Stowell, Dan},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {e116951},\n}\n\n\n\n

\n\n\n

\n EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.\n

\n\n\n

\n \n\n \n \n Heyvaert, Z.; Scherrer, S.; Bechtold, M.; Gruber, A.; Dorigo, W.; Kumar, S.; and De Lannoy, G.\n\n\n \n \n \n \n \n Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 24(7): 1193–1208. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heyvaert_impact_2023,\n\ttitle = {Impact of {Design} {Factors} for {ESA} {CCI} {Satellite} {Soil} {Moisture} {Data} {Assimilation} over {Europe}},\n\tvolume = {24},\n\tcopyright = {http://creativecommons.org/licenses/by/4.0/},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/24/7/JHM-D-22-0141.1.xml},\n\tdoi = {10.1175/JHM-D-22-0141.1},\n\tabstract = {Abstract \n            In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.},\n\tnumber = {7},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Heyvaert, Zdenko and Scherrer, Samuel and Bechtold, Michel and Gruber, Alexander and Dorigo, Wouter and Kumar, Sujay and De Lannoy, Gabriëlle},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {1193--1208},\n}\n\n\n\n

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\n \n\n \n \n Hermans, T.; Goderniaux, P.; Jougnot, D.; Fleckenstein, J. H.; Brunner, P.; Nguyen, F.; Linde, N.; Huisman, J. A.; Bour, O.; Lopez Alvis, J.; Hoffmann, R.; Palacios, A.; Cooke, A.; Pardo-Álvarez, Á.; Blazevic, L.; Pouladi, B.; Haruzi, P.; Fernandez Visentini, A.; Nogueira, G. E. H.; Tirado-Conde, J.; Looms, M. C.; Kenshilikova, M.; Davy, P.; and Le Borgne, T.\n\n\n \n \n \n \n \n Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(1): 255–287. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Advancing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hermans_advancing_2023,\n\ttitle = {Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards {4D} hydrogeology},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\tshorttitle = {Advancing measurements and representations of subsurface heterogeneity and dynamic processes},\n\turl = {https://hess.copernicus.org/articles/27/255/2023/},\n\tdoi = {10.5194/hess-27-255-2023},\n\tabstract = {Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability generally leads to effective behaviors and emerging phenomena that cannot be predicted from conventional approaches based on homogeneous assumptions and models. However, it is not always clear when, why, how, and at what scale the 4D (3D + time) nature of the subsurface needs to be considered in hydrogeological monitoring, modeling, and applications. In this paper, we discuss the interest and potential for the monitoring and characterization of spatial and temporal variability, including 4D imaging, in a series of hydrogeological processes: (1) groundwater fluxes, (2) solute transport and reaction, (3) vadose zone dynamics, and (4) surface–subsurface water interactions. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. We then highlight recent innovations that have led to significant breakthroughs in high-resolution space–time imaging and modeling the characterization, monitoring, and modeling of these spatial and temporal fluctuations. We finally propose a classification of processes and applications at different scales according to their need and potential for high-resolution space–time imaging. We thus advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications. This calls for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Hermans, Thomas and Goderniaux, Pascal and Jougnot, Damien and Fleckenstein, Jan H. and Brunner, Philip and Nguyen, Frédéric and Linde, Niklas and Huisman, Johan Alexander and Bour, Olivier and Lopez Alvis, Jorge and Hoffmann, Richard and Palacios, Andrea and Cooke, Anne-Karin and Pardo-Álvarez, Álvaro and Blazevic, Lara and Pouladi, Behzad and Haruzi, Peleg and Fernandez Visentini, Alejandro and Nogueira, Guilherme E. H. and Tirado-Conde, Joel and Looms, Majken C. and Kenshilikova, Meruyert and Davy, Philippe and Le Borgne, Tanguy},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {255--287},\n}\n\n\n\n

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\n \n\n \n \n Freund, M. B.; Helle, G.; Balting, D. F.; Ballis, N.; Schleser, G. H.; and Cubasch, U.\n\n\n \n \n \n \n \n European tree-ring isotopes indicate unusual recent hydroclimate.\n \n \n \n \n\n\n \n\n\n\n Communications Earth & Environment, 4(1): 26. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"European$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{freund_european_2023,\n\ttitle = {European tree-ring isotopes indicate unusual recent hydroclimate},\n\tvolume = {4},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-022-00648-7},\n\tdoi = {10.1038/s43247-022-00648-7},\n\tabstract = {Abstract \n            In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroclimatic changes across Europe. Here we present a climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to AD 1600. We find three distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age) and pronounced decadal variability superimposed by a long-term drying trend from the mid-20th century. We show that the recent European summer drought (2015–2018) is highly unusual in a multi-century context and unprecedented for large parts of central and western Europe. The reconstruction provides further evidence of European summer droughts potentially being influenced by anthropogenic warming and draws attention to regional differences.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Communications Earth \\& Environment},\n\tauthor = {Freund, Mandy B. and Helle, Gerhard and Balting, Daniel F. and Ballis, Natasha and Schleser, Gerhard H. and Cubasch, Ulrich},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {26},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Helle, G.; Brauer, A.; and Heinrich, I.\n\n\n \n \n \n \n \n Stable oxygen isotope ratios of tree-ring cellulose from oak (Quercus robur) at Lake Tiefer See, Mecklenburg Lake District, Northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@misc{helle_stable_2023,\n\ttitle = {Stable oxygen isotope ratios of tree-ring cellulose from oak ({Quercus} robur) at {Lake} {Tiefer} {See}, {Mecklenburg} {Lake} {District}, {Northeastern} {Germany}},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://dataservices.gfz-potsdam.de/tereno-new/showshort.php?id=6670569a-fa4a-11ed-95b8-f851ad6d1e4b},\n\tdoi = {10.5880/TERENO.TRSI.2023.002},\n\tabstract = {An annually resolved chronologies of oxygen isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ18O records with respect to the international VSMOW standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).},\n\turldate = {2024-05-16},\n\tpublisher = {[object Object]},\n\tauthor = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo},\n\tcollaborator = {Helle, Gerhard and Helle, Gerhard and Helle, Gerhard and Brauer, Achim and Heinrich, Ingo and Heinrich, Ingo and Hilbich, Michelle and Pechipaykoska, Ivana and Schürheck, Lucas and Helle, Gerhard},\n\tyear = {2023},\n\tkeywords = {18O/16O, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS \\&gt; ISOTOPIC ANALYSIS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS \\&gt; ISOTOPIC ANALYSIS \\&gt; CARBON ISOTOPE, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; PALEOCLIMATE RECONSTRUCTIONS, EARTH SCIENCE \\&gt; PALEOCLIMATE \\&gt; LAND RECORDS \\&gt; TREE RINGS, Lake Tiefer See, Mecklenburg lake district, Northeastern Germany, Quercus robur, TERENO, TERENO Nordost, TERENO Northeast, TERrestrial ENvironmental Observatories, cellulose, chronology, d18O, latewood, oak, stable oxygen isotopes, time series, tree rings},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Helle, G.; Brauer, A.; and Heinrich, I.\n\n\n \n \n \n \n \n Stable carbon isotope ratios of tree-ring cellulose from oak (Quercus robur) at Lake Tiefer See, Mecklenburg Lake District, Northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@misc{helle_stable_2023,\n\ttitle = {Stable carbon isotope ratios of tree-ring cellulose from oak ({Quercus} robur) at {Lake} {Tiefer} {See}, {Mecklenburg} {Lake} {District}, {Northeastern} {Germany}},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://dataservices.gfz-potsdam.de/tereno-new/showshort.php?id=7fe28391-fa4a-11ed-95b8-f851ad6d1e4b},\n\tdoi = {10.5880/TERENO.TRSI.2023.001},\n\tabstract = {An annually resolved chronologies of carbon isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ13C records with respect to the international VPDB standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).},\n\turldate = {2024-05-16},\n\tpublisher = {[object Object]},\n\tauthor = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo},\n\tcollaborator = {Helle, Gerhard and Helle, Gerhard and Helle, Gerhard and Brauer, Achim and Heinrich, Ingo and Heinrich, Ingo and Hilbich, Michelle and Pechipaykoska, Ivana and Schürheck, Lucas and Helle, Gerhard},\n\tyear = {2023},\n\tkeywords = {13C/12C, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS \\&gt; ISOTOPIC ANALYSIS, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; BIOLOGICAL RECORDS \\&gt; TREE RINGS \\&gt; ISOTOPIC ANALYSIS \\&gt; CARBON ISOTOPE, EARTH SCIENCE \\&gt; CLIMATE INDICATORS \\&gt; PALEOCLIMATE INDICATORS \\&gt; PALEOCLIMATE RECONSTRUCTIONS, EARTH SCIENCE \\&gt; PALEOCLIMATE \\&gt; LAND RECORDS \\&gt; TREE RINGS, Lake Tiefer See, Mecklenburg lake district, Northeastern Germany, Quercus robur, TERENO, TERENO Nordost, TERENO Northeast, TERrestrial ENvironmental Observatories, Tree rings, cellulose, chronology, d13C, latewood, oak, stable carbon isotopes, time series},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n He, W.; Jiang, F.; Ju, W.; Byrne, B.; Xiao, J.; Nguyen, N. T.; Wu, M.; Wang, S.; Wang, J.; Rödenbeck, C.; Li, X.; Scholze, M.; Monteil, G.; Wang, H.; Zhou, Y.; He, Q.; and Chen, J. M.\n\n\n \n \n \n \n \n Do State‐Of‐The‐Art Atmospheric CO $_{\\textrm{2}}$ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?.\n \n \n \n \n\n\n \n\n\n\n Journal of Advances in Modeling Earth Systems, 15(6): e2022MS003150. June 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Do$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{he_stateart_2023,\n\ttitle = {Do {State}‐{Of}‐{The}‐{Art} {Atmospheric} {CO} $_{\\textrm{2}}$ {Inverse} {Models} {Capture} {Drought} {Impacts} on the {European} {Land} {Carbon} {Uptake}?},\n\tvolume = {15},\n\tissn = {1942-2466, 1942-2466},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022MS003150},\n\tdoi = {10.1029/2022MS003150},\n\tabstract = {Abstract \n             \n              The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO \n              2 \n              inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO \n              2 \n              observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO \n              2 \n              observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO \n              2 \n              or assimilated environmental variables plus surface CO \n              2 \n              observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO \n              2 \n              inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO \n              2 \n              observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO \n              2 \n              and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems. \n             \n          ,  \n            Plain Language Summary \n             \n              Atmospheric CO \n              2 \n              inverse models (AIMs) are useful tools for quantifying the response of large‐scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state‐of‐the‐art AIMs for monitoring drought impacts on the European land carbon uptake over 2001–2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO \n              2 \n              observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO \n              2 \n              observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO \n              2 \n              or assimilated environmental variables plus surface CO \n              2 \n              observations better captured annual and seasonal anomalies caused by droughts. Our study demonstrates that surface CO \n              2 \n              observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, whereby satellite XCO \n              2 \n              and ancillary environmental data can offer observational constraints for improving the estimates. \n             \n          ,  \n            Key Points \n             \n               \n                 \n                   \n                    Global inversions with only limited surface CO \n                    2 \n                    observations give divergent estimates of drought impacts on the European carbon uptake \n                   \n                 \n                 \n                   \n                    Regional inversions assimilating denser CO \n                    2 \n                    observations over Europe demonstrate some improved consistency but are still deficient \n                   \n                 \n                 \n                   \n                    The inversions assimilating satellite XCO \n                    2 \n                    or environmental variables in addition to surface CO \n                    2 \n                    largely improve the estimates},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Advances in Modeling Earth Systems},\n\tauthor = {He, Wei and Jiang, Fei and Ju, Weimin and Byrne, Brendan and Xiao, Jingfeng and Nguyen, Ngoc Tu and Wu, Mousong and Wang, Songhan and Wang, Jun and Rödenbeck, Christian and Li, Xing and Scholze, Marko and Monteil, Guillaume and Wang, Hengmao and Zhou, Yanlian and He, Qiaoning and Chen, Jing M.},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {e2022MS003150},\n}\n\n\n\n

\n\n\n

\n Abstract The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO 2 inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO 2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO 2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO 2 inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO 2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO 2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems. , Plain Language Summary Atmospheric CO 2 inverse models (AIMs) are useful tools for quantifying the response of large‐scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state‐of‐the‐art AIMs for monitoring drought impacts on the European land carbon uptake over 2001–2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO 2 observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO 2 observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured annual and seasonal anomalies caused by droughts. Our study demonstrates that surface CO 2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, whereby satellite XCO 2 and ancillary environmental data can offer observational constraints for improving the estimates. , Key Points Global inversions with only limited surface CO 2 observations give divergent estimates of drought impacts on the European carbon uptake Regional inversions assimilating denser CO 2 observations over Europe demonstrate some improved consistency but are still deficient The inversions assimilating satellite XCO 2 or environmental variables in addition to surface CO 2 largely improve the estimates\n

\n\n\n

\n \n\n \n \n Haubrock, P. J.; Pilotto, F.; Soto, I.; Kühn, I.; Verreycken, H.; Seebens, H.; Cuthbert, R. N.; and Haase, P.\n\n\n \n \n \n \n \n Long-term trends in abundances of non-native species across biomes, realms, and taxonomic groups in Europe.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 884: 163808. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haubrock_long-term_2023,\n\ttitle = {Long-term trends in abundances of non-native species across biomes, realms, and taxonomic groups in {Europe}},\n\tvolume = {884},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723024294},\n\tdoi = {10.1016/j.scitotenv.2023.163808},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Haubrock, Phillip J. and Pilotto, Francesca and Soto, Ismael and Kühn, Ingolf and Verreycken, Hugo and Seebens, Hanno and Cuthbert, Ross N. and Haase, Peter},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {163808},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Haenelt, S.; Richnow, H.; Müller, J. A.; and Musat, N.\n\n\n \n \n \n \n \n Antibiotic resistance indicator genes in biofilm and planktonic microbial communities after wastewater discharge.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Microbiology, 14: 1252870. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Antibiotic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haenelt_antibiotic_2023,\n\ttitle = {Antibiotic resistance indicator genes in biofilm and planktonic microbial communities after wastewater discharge},\n\tvolume = {14},\n\tissn = {1664-302X},\n\turl = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1252870/full},\n\tdoi = {10.3389/fmicb.2023.1252870},\n\tabstract = {The spread of bacteria with antibiotic resistance genes (ARGs) in aquatic ecosystems is of growing concern as this can pose a risk of transmission to humans and animals. While the impact of wastewater treatment plant (WWTP) effluent on ARG abundance in surface waters has been studied extensively, less is known about the fate of ARGs in biofilms. The proximity and dense growth of microorganisms in combination with the accumulation of higher antibiotic concentrations in biofilms might render biofilms a reservoir for ARGs. Seasonal parameters such as water temperature, precipitation, and antibiotic concentrations should be considered as well, as they may further influence the fate of ARGs in aquatic ecosystems. Here we investigated the effect of WWTP effluent on the abundance of the sulfonamide resistance genes \n              sul1 \n              and \n              sul2 \n              , and the integrase gene \n              intI1 \n              in biofilm and surface water compartments of a river in Germany with a gradient of anthropogenic impact using quantitative PCR. Furthermore, we analyzed the bacterial community structure in both compartments via 16S rRNA gene amplicon sequencing, following the river downstream. Additionally, conventional water parameters and sulfonamide concentrations were measured, and seasonal aspects were considered by comparing the fate of ARGs and bacterial community diversity in the surface water compartment between the summer and winter season. Our results show that biofilm compartments near the WWTP had a higher relative abundance of ARGs (up to 4.7\\%) than surface waters (\\&lt;2.8\\%). Sulfonamide resistance genes were more persistent further downstream (\\&gt;10 km) of the WWTP in the hot and dry summer season than in winter. This finding is likely a consequence of the higher proportion of wastewater and thus wastewater-derived microorganisms in the river during summer periods. We observed distinct bacterial communities and ARG abundance between the biofilm and surface water compartment, but even greater variations when considering seasonal and spatiotemporal parameters. This underscores the need to consider seasonal aspects when studying the fate of ARGs in aquatic ecosystems.},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Microbiology},\n\tauthor = {Haenelt, Sarah and Richnow, Hans-Hermann and Müller, Jochen A. and Musat, Niculina},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {1252870},\n}\n\n\n\n

\n\n\n

\n The spread of bacteria with antibiotic resistance genes (ARGs) in aquatic ecosystems is of growing concern as this can pose a risk of transmission to humans and animals. While the impact of wastewater treatment plant (WWTP) effluent on ARG abundance in surface waters has been studied extensively, less is known about the fate of ARGs in biofilms. The proximity and dense growth of microorganisms in combination with the accumulation of higher antibiotic concentrations in biofilms might render biofilms a reservoir for ARGs. Seasonal parameters such as water temperature, precipitation, and antibiotic concentrations should be considered as well, as they may further influence the fate of ARGs in aquatic ecosystems. Here we investigated the effect of WWTP effluent on the abundance of the sulfonamide resistance genes sul1 and sul2 , and the integrase gene intI1 in biofilm and surface water compartments of a river in Germany with a gradient of anthropogenic impact using quantitative PCR. Furthermore, we analyzed the bacterial community structure in both compartments via 16S rRNA gene amplicon sequencing, following the river downstream. Additionally, conventional water parameters and sulfonamide concentrations were measured, and seasonal aspects were considered by comparing the fate of ARGs and bacterial community diversity in the surface water compartment between the summer and winter season. Our results show that biofilm compartments near the WWTP had a higher relative abundance of ARGs (up to 4.7%) than surface waters (<2.8%). Sulfonamide resistance genes were more persistent further downstream (>10 km) of the WWTP in the hot and dry summer season than in winter. This finding is likely a consequence of the higher proportion of wastewater and thus wastewater-derived microorganisms in the river during summer periods. We observed distinct bacterial communities and ARG abundance between the biofilm and surface water compartment, but even greater variations when considering seasonal and spatiotemporal parameters. This underscores the need to consider seasonal aspects when studying the fate of ARGs in aquatic ecosystems.\n

\n\n\n

\n \n\n \n \n Haenelt, S.; Wang, G.; Kasmanas, J. C.; Musat, F.; Richnow, H. H.; Da Rocha, U. N.; Müller, J. A.; and Musat, N.\n\n\n \n \n \n \n \n The fate of sulfonamide resistance genes and anthropogenic pollution marker intI1 after discharge of wastewater into a pristine river stream.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Microbiology, 14: 1058350. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haenelt_fate_2023,\n\ttitle = {The fate of sulfonamide resistance genes and anthropogenic pollution marker {intI1} after discharge of wastewater into a pristine river stream},\n\tvolume = {14},\n\tissn = {1664-302X},\n\turl = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1058350/full},\n\tdoi = {10.3389/fmicb.2023.1058350},\n\tabstract = {Introduction \n               \n                Currently there are sparse regulations regarding the discharge of antibiotics from wastewater treatment plants (WWTP) into river systems, making surface waters a latent reservoir for antibiotics and antibiotic resistance genes (ARGs). To better understand factors that influence the fate of ARGs in the environment and to foster surveillance of antibiotic resistance spreading in such habitats, several indicator genes have been proposed, including the integrase gene \n                intI1 \n                and the sulfonamide resistance genes \n                sul1 \n                and \n                sul2 \n                . \n               \n             \n             \n              Methods \n               \n                Here we used quantitative PCR and long-read nanopore sequencing to monitor the abundance of these indicator genes and ARGs present as class 1 integron gene cassettes in a river system from pristine source to WWTP-impacted water. ARG abundance was compared with the dynamics of the microbial communities determined \n                via \n                16S rRNA gene amplicon sequencing, conventional water parameters and the concentration of sulfamethoxazole (SMX), sulfamethazine (SMZ) and sulfadiazine (SDZ). \n               \n             \n             \n              Results \n               \n                Our results show that WWTP effluent was the principal source of all three sulfonamides with highest concentrations for SMX (median 8.6 ng/l), and of the indicator genes \n                sul1 \n                , \n                sul2 \n                and \n                intI1 \n                with median relative abundance to 16S rRNA gene of 0.55, 0.77 and 0.65\\%, respectively. Downstream from the WWTP, water quality improved constantly, including lower sulfonamide concentrations, decreasing abundances of \n                sul1 \n                and \n                sul2 \n                and lower numbers and diversity of ARGs in the class 1 integron. The riverine microbial community partially recovered after receiving WWTP effluent, which was consolidated by a microbiome recovery model. Surprisingly, the relative abundance of \n                intI1 \n                increased 3-fold over 13 km of the river stretch, suggesting an internal gene multiplication. \n               \n             \n             \n              Discussion \n              We found no evidence that low amounts of sulfonamides in the aquatic environment stimulate the maintenance or even spread of corresponding ARGs. Nevertheless, class 1 integrons carrying various ARGs were still present 13 km downstream from the WWTP. Therefore, limiting the release of ARG-harboring microorganisms may be more crucial for restricting the environmental spread of antimicrobial resistance than attenuating ng/L concentrations of antibiotics.},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Microbiology},\n\tauthor = {Haenelt, Sarah and Wang, Gangan and Kasmanas, Jonas Coelho and Musat, Florin and Richnow, Hans Hermann and Da Rocha, Ulisses Nunes and Müller, Jochen A. and Musat, Niculina},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {1058350},\n}\n\n\n\n

\n\n\n

\n Introduction Currently there are sparse regulations regarding the discharge of antibiotics from wastewater treatment plants (WWTP) into river systems, making surface waters a latent reservoir for antibiotics and antibiotic resistance genes (ARGs). To better understand factors that influence the fate of ARGs in the environment and to foster surveillance of antibiotic resistance spreading in such habitats, several indicator genes have been proposed, including the integrase gene intI1 and the sulfonamide resistance genes sul1 and sul2 . Methods Here we used quantitative PCR and long-read nanopore sequencing to monitor the abundance of these indicator genes and ARGs present as class 1 integron gene cassettes in a river system from pristine source to WWTP-impacted water. ARG abundance was compared with the dynamics of the microbial communities determined via 16S rRNA gene amplicon sequencing, conventional water parameters and the concentration of sulfamethoxazole (SMX), sulfamethazine (SMZ) and sulfadiazine (SDZ). Results Our results show that WWTP effluent was the principal source of all three sulfonamides with highest concentrations for SMX (median 8.6 ng/l), and of the indicator genes sul1 , sul2 and intI1 with median relative abundance to 16S rRNA gene of 0.55, 0.77 and 0.65%, respectively. Downstream from the WWTP, water quality improved constantly, including lower sulfonamide concentrations, decreasing abundances of sul1 and sul2 and lower numbers and diversity of ARGs in the class 1 integron. The riverine microbial community partially recovered after receiving WWTP effluent, which was consolidated by a microbiome recovery model. Surprisingly, the relative abundance of intI1 increased 3-fold over 13 km of the river stretch, suggesting an internal gene multiplication. Discussion We found no evidence that low amounts of sulfonamides in the aquatic environment stimulate the maintenance or even spread of corresponding ARGs. Nevertheless, class 1 integrons carrying various ARGs were still present 13 km downstream from the WWTP. Therefore, limiting the release of ARG-harboring microorganisms may be more crucial for restricting the environmental spread of antimicrobial resistance than attenuating ng/L concentrations of antibiotics.\n

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\n \n\n \n \n Guseva, S.; Armani, F.; Desai, A. R.; Dias, N. L.; Friborg, T.; Iwata, H.; Jansen, J.; Lükő, G.; Mammarella, I.; Repina, I.; Rutgersson, A.; Sachs, T.; Scholz, K.; Spank, U.; Stepanenko, V.; Torma, P.; Vesala, T.; and Lorke, A.\n\n\n \n \n \n \n \n Bulk Transfer Coefficients Estimated From Eddy‐Covariance Measurements Over Lakes and Reservoirs.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 128(2): e2022JD037219. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Bulk$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guseva_bulk_2023,\n\ttitle = {Bulk {Transfer} {Coefficients} {Estimated} {From} {Eddy}‐{Covariance} {Measurements} {Over} {Lakes} and {Reservoirs}},\n\tvolume = {128},\n\tissn = {2169-897X, 2169-8996},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037219},\n\tdoi = {10.1029/2022JD037219},\n\tabstract = {Abstract \n             \n              The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds ({\\textless}3 m s \n              −1 \n              ), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10 \n              −3 \n              , 1.4·10 \n              −3 \n              , 1.0·10 \n              −3 \n              , respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds. \n             \n          ,  \n            Plain Language Summary \n            In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation. \n          ,  \n            Key Points \n             \n               \n                 \n                  Bulk transfer coefficients exhibit a substantial increase at low wind speed \n                 \n                 \n                  The increase is explained by wind gustiness and capillary wave roughness \n                 \n                 \n                  At higher wind speed, drag coefficient and Stanton number decrease with lake surface area},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Guseva, S. and Armani, F. and Desai, A. R. and Dias, N. L. and Friborg, T. and Iwata, H. and Jansen, J. and Lükő, G. and Mammarella, I. and Repina, I. and Rutgersson, A. and Sachs, T. and Scholz, K. and Spank, U. and Stepanenko, V. and Torma, P. and Vesala, T. and Lorke, A.},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {e2022JD037219},\n}\n\n\n\n

\n\n\n

\n Abstract The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (\\textless3 m s −1 ), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10 −3 , 1.4·10 −3 , 1.0·10 −3 , respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds. , Plain Language Summary In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation. , Key Points Bulk transfer coefficients exhibit a substantial increase at low wind speed The increase is explained by wind gustiness and capillary wave roughness At higher wind speed, drag coefficient and Stanton number decrease with lake surface area\n

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\n \n\n \n \n Graf, A.; Wohlfahrt, G.; Aranda-Barranco, S.; Arriga, N.; Brümmer, C.; Ceschia, E.; Ciais, P.; Desai, A. R.; Di Lonardo, S.; Gharun, M.; Grünwald, T.; Hörtnagl, L.; Kasak, K.; Klosterhalfen, A.; Knohl, A.; Kowalska, N.; Leuchner, M.; Lindroth, A.; Mauder, M.; Migliavacca, M.; Morel, A. C.; Pfennig, A.; Poorter, H.; Terán, C. P.; Reitz, O.; Rebmann, C.; Sanchez-Azofeifa, A.; Schmidt, M.; Šigut, L.; Tomelleri, E.; Yu, K.; Varlagin, A.; and Vereecken, H.\n\n\n \n \n \n \n \n Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects.\n \n \n \n \n\n\n \n\n\n\n Communications Earth & Environment, 4(1): 298. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Joint$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_joint_2023,\n\ttitle = {Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects},\n\tvolume = {4},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-023-00958-4},\n\tdoi = {10.1038/s43247-023-00958-4},\n\tabstract = {Abstract \n            Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Communications Earth \\& Environment},\n\tauthor = {Graf, Alexander and Wohlfahrt, Georg and Aranda-Barranco, Sergio and Arriga, Nicola and Brümmer, Christian and Ceschia, Eric and Ciais, Philippe and Desai, Ankur R. and Di Lonardo, Sara and Gharun, Mana and Grünwald, Thomas and Hörtnagl, Lukas and Kasak, Kuno and Klosterhalfen, Anne and Knohl, Alexander and Kowalska, Natalia and Leuchner, Michael and Lindroth, Anders and Mauder, Matthias and Migliavacca, Mirco and Morel, Alexandra C. and Pfennig, Andreas and Poorter, Hendrik and Terán, Christian Poppe and Reitz, Oliver and Rebmann, Corinna and Sanchez-Azofeifa, Arturo and Schmidt, Marius and Šigut, Ladislav and Tomelleri, Enrico and Yu, Ke and Varlagin, Andrej and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {298},\n}\n\n\n\n

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\n \n\n \n \n Golub, M.; Koupaei-Abyazani, N.; Vesala, T.; Mammarella, I.; Ojala, A.; Bohrer, G.; Weyhenmeyer, G. A; Blanken, P. D; Eugster, W.; Koebsch, F.; Chen, J.; Czajkowski, K.; Deshmukh, C.; Guérin, F.; Heiskanen, J.; Humphreys, E.; Jonsson, A.; Karlsson, J.; Kling, G.; Lee, X.; Liu, H.; Lohila, A.; Lundin, E.; Morin, T.; Podgrajsek, E.; Provenzale, M.; Rutgersson, A.; Sachs, T.; Sahlée, E.; Serça, D.; Shao, C.; Spence, C.; Strachan, I. B; Xiao, W.; and Desai, A. R\n\n\n \n \n \n \n \n Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 18(3): 034046. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Diel,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{golub_diel_2023,\n\ttitle = {Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs},\n\tvolume = {18},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/acb834},\n\tdoi = {10.1088/1748-9326/acb834},\n\tabstract = {Abstract \n             \n              Accounting for temporal changes in carbon dioxide (CO \n              2 \n              ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO \n              2 \n              based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO \n              2 \n              flux at all sites. By accounting for diel variation, only 11\\% of site-months were net daily sinks of CO \n              2 \n              . Annual CO \n              2 \n              emissions had an average of 25\\% (range 3\\%–58\\%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO \n              2 \n              flux variability were delineated through mutual information analysis. Sample analysis of CO \n              2 \n              fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO \n              2 \n              fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Golub, Malgorzata and Koupaei-Abyazani, Nikaan and Vesala, Timo and Mammarella, Ivan and Ojala, Anne and Bohrer, Gil and Weyhenmeyer, Gesa A and Blanken, Peter D and Eugster, Werner and Koebsch, Franziska and Chen, Jiquan and Czajkowski, Kevin and Deshmukh, Chandrashekhar and Guérin, Frederic and Heiskanen, Jouni and Humphreys, Elyn and Jonsson, Anders and Karlsson, Jan and Kling, George and Lee, Xuhui and Liu, Heping and Lohila, Annalea and Lundin, Erik and Morin, Tim and Podgrajsek, Eva and Provenzale, Maria and Rutgersson, Anna and Sachs, Torsten and Sahlée, Erik and Serça, Dominique and Shao, Changliang and Spence, Christopher and Strachan, Ian B and Xiao, Wei and Desai, Ankur R},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {034046},\n}\n\n\n\n

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\n \n\n \n \n Giraud, M.; Gall, S. L.; Harings, M.; Javaux, M.; Leitner, D.; Meunier, F.; Rothfuss, Y.; Van Dusschoten, D.; Vanderborght, J.; Vereecken, H.; Lobet, G.; and Schnepf, A.\n\n\n \n \n \n \n \n CPlantBox: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum.\n \n \n \n \n\n\n \n\n\n\n in silico Plants, 5(2): diad009. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"CPlantBox:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{giraud_cplantbox_2023,\n\ttitle = {{CPlantBox}: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum},\n\tvolume = {5},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2517-5025},\n\tshorttitle = {{CPlantBox}},\n\turl = {https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad009/7224987},\n\tdoi = {10.1093/insilicoplants/diad009},\n\tabstract = {Abstract \n            A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu\\$\\{\\}{\\textasciicircum}\\{{\\textbackslash}text\\{x\\}\\}\\$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-05-16},\n\tjournal = {in silico Plants},\n\tauthor = {Giraud, Mona and Gall, Samuel Le and Harings, Moritz and Javaux, Mathieu and Leitner, Daniel and Meunier, Félicien and Rothfuss, Youri and Van Dusschoten, Dagmar and Vanderborght, Jan and Vereecken, Harry and Lobet, Guillaume and Schnepf, Andrea},\n\teditor = {Amy, Marshall-Colon},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {diad009},\n}\n\n\n\n

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\n \n\n \n \n Frindte, K.; Kolb, S.; Sommer, M.; Augustin, J.; and Knief, C.\n\n\n \n \n \n \n \n Spatial patterns of prokaryotic communities in kettle hole soils follow soil horizonation.\n \n \n \n \n\n\n \n\n\n\n Applied Soil Ecology, 185: 104796. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{frindte_spatial_2023,\n\ttitle = {Spatial patterns of prokaryotic communities in kettle hole soils follow soil horizonation},\n\tvolume = {185},\n\tissn = {09291393},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0929139322004127},\n\tdoi = {10.1016/j.apsoil.2022.104796},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Applied Soil Ecology},\n\tauthor = {Frindte, Katharina and Kolb, Steffen and Sommer, Michael and Augustin, Jürgen and Knief, Claudia},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {104796},\n}\n\n\n\n

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\n \n\n \n \n Fluhrer, A.; Jagdhuber, T.; Montzka, C.; Schumacher, M.; Alemohammad, H.; Tabatabaeenejad, A.; Kunstmann, H.; and Entekhabi, D.\n\n\n \n \n \n \n \n Estimating Soil Moisture Profiles by Combining P-Band SAR with Hydrological Modeling.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 2846–2849, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{fluhrer_estimating_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {Estimating {Soil} {Moisture} {Profiles} by {Combining} {P}-{Band} {SAR} with {Hydrological} {Modeling}},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10282246/},\n\tdoi = {10.1109/IGARSS52108.2023.10282246},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Fluhrer, Anke and Jagdhuber, Thomas and Montzka, Carsten and Schumacher, Maike and Alemohammad, Hamed and Tabatabaeenejad, Alireza and Kunstmann, Harald and Entekhabi, Dara},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {2846--2849},\n}\n\n\n\n

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\n \n\n \n \n Fatima, E.; Kumar, R.; Attinger, S.; Kaluza, M.; Rakovec, O.; Rebmann, C.; Rosolem, R.; Oswald, S.; Samaniego, L.; Zacharias, S.; and Schrön, M.\n\n\n \n \n \n \n \n Improved representation of soil moisture simulations through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model.\n \n \n \n \n\n\n \n\n\n\n July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{fatima_improved_2023,\n\ttitle = {Improved representation of soil moisture simulations through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\turl = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1548/},\n\tdoi = {10.5194/egusphere-2023-1548},\n\tabstract = {Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modeling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) have been recognized as a promising tool for soil moisture monitoring due to their hectare-scale footprint and decimeter-scale measurement depth. Different approaches exists that could be the basis for incorporating CRNS data into distributed hydrologic models, but largely still need to be implemented, thoroughly compared, and tested across different soil and vegetation types. This study establishes a framework to accommodate neutron count measurements and assess the accuracy of soil water content simulated by the mesoscale Hydrological Model (mHM) for the first time. It covers CRNS observations across different vegetation types in Germany ranging from agricultural areas to forest. We include two different approaches to estimate CRNS neutron counts in mHM based on the simulated soil moisture: a method based on the Desilets equation and another one based on the Cosmic-ray Soil Moisture Interaction Code (COSMIC). Within the Desilets approach, we further test two different averaging methods for the vertically layered soil moisture, namely uniform vs. non-uniform weighting scheme depending on the CRNS penetrating depth. A Monte Carlos simulation with Latin hypercube sampling approach (with N = 100,000) is employed to explore and constrain the (behavioral) mHM parameterizations against observed CRNS neutron counts. Overall, the three methods perform well with Kling-Gupta efficiency {\\textgreater} 0.8 and percent bias {\\textless} 1 \\% across the majority of investigated sites. We find that the non-uniform weighting scheme in the Desilets method provide the most reliable performance, whereas the more commonly used approach with uniformly weighted average soil moisture overestimates the observed CRNS neutron counts. We then also demonstrate the usefulness of incorporating CRNS measurements into mHM for the simulations of both soil moisture and evapotranspiration and add a broader discussion on the potential and guidelines of incorporating CRNS measurements in large-scale hydrological and land surface models.},\n\turldate = {2024-05-16},\n\tauthor = {Fatima, Eshrat and Kumar, Rohini and Attinger, Sabine and Kaluza, Maren and Rakovec, Oldrich and Rebmann, Corinna and Rosolem, Rafael and Oswald, Sascha and Samaniego, Luis and Zacharias, Steffen and Schrön, Martin},\n\tmonth = jul,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n

\n Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modeling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) have been recognized as a promising tool for soil moisture monitoring due to their hectare-scale footprint and decimeter-scale measurement depth. Different approaches exists that could be the basis for incorporating CRNS data into distributed hydrologic models, but largely still need to be implemented, thoroughly compared, and tested across different soil and vegetation types. This study establishes a framework to accommodate neutron count measurements and assess the accuracy of soil water content simulated by the mesoscale Hydrological Model (mHM) for the first time. It covers CRNS observations across different vegetation types in Germany ranging from agricultural areas to forest. We include two different approaches to estimate CRNS neutron counts in mHM based on the simulated soil moisture: a method based on the Desilets equation and another one based on the Cosmic-ray Soil Moisture Interaction Code (COSMIC). Within the Desilets approach, we further test two different averaging methods for the vertically layered soil moisture, namely uniform vs. non-uniform weighting scheme depending on the CRNS penetrating depth. A Monte Carlos simulation with Latin hypercube sampling approach (with N = 100,000) is employed to explore and constrain the (behavioral) mHM parameterizations against observed CRNS neutron counts. Overall, the three methods perform well with Kling-Gupta efficiency \\textgreater 0.8 and percent bias \\textless 1 % across the majority of investigated sites. We find that the non-uniform weighting scheme in the Desilets method provide the most reliable performance, whereas the more commonly used approach with uniformly weighted average soil moisture overestimates the observed CRNS neutron counts. We then also demonstrate the usefulness of incorporating CRNS measurements into mHM for the simulations of both soil moisture and evapotranspiration and add a broader discussion on the potential and guidelines of incorporating CRNS measurements in large-scale hydrological and land surface models.\n

\n\n\n

\n \n\n \n \n Dombrowski, O.; Brogi, C.; Franssen, H. H.; Pisinaras, V.; Panagopoulos, A.; Swenson, S.; and Bogena, H.\n\n\n \n \n \n \n \n Land surface modeling as a tool to explore sustainable irrigation practices in Mediterranean fruit orchards.\n \n \n \n \n\n\n \n\n\n\n December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Land$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{dombrowski_land_2023,\n\ttitle = {Land surface modeling as a tool to explore sustainable irrigation practices in {Mediterranean} fruit orchards},\n\turl = {https://essopenarchive.org/users/709172/articles/693525-land-surface-modeling-as-a-tool-to-explore-sustainable-irrigation-practices-in-mediterranean-fruit-orchards?commit=aa17a2787bc7795b5c72a83e4c174be511b408d0},\n\tdoi = {10.22541/essoar.170365318.84320452/v1},\n\turldate = {2024-05-16},\n\tauthor = {Dombrowski, Olga and Brogi, Cosimo and Franssen, Harrie-Jan Hendricks and Pisinaras, Vassilios and Panagopoulos, Andreas and Swenson, Sean and Bogena, Heye},\n\tmonth = dec,\n\tyear = {2023},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Deseano Diaz, P. A.; Van Dusschoten, D.; Kübert, A.; Brüggemann, N.; Javaux, M.; Merz, S.; Vanderborght, J.; Vereecken, H.; Dubbert, M.; and Rothfuss, Y.\n\n\n \n \n \n \n \n Response of a grassland species to dry environmental conditions from water stable isotopic monitoring: no evident shift in root water uptake to wetter soil layers.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 482(1-2): 491–512. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Response$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{deseano_diaz_response_2023,\n\ttitle = {Response of a grassland species to dry environmental conditions from water stable isotopic monitoring: no evident shift in root water uptake to wetter soil layers},\n\tvolume = {482},\n\tissn = {0032-079X, 1573-5036},\n\tshorttitle = {Response of a grassland species to dry environmental conditions from water stable isotopic monitoring},\n\turl = {https://link.springer.com/10.1007/s11104-022-05703-y},\n\tdoi = {10.1007/s11104-022-05703-y},\n\tabstract = {Abstract \n             \n              Aims \n               \n                We aimed at assessing the influence of above- and below-ground environmental conditions over the performance of \n                Centaurea jacea \n                L., a drought-resistant grassland forb species. \n               \n             \n             \n              Methods \n               \n                Transpiration rate, CO \n                2 \n                assimilation rate, leaf water potential, instantaneous and intrinsic water use efficiency, temperature, relative humidity, vapor pressure deficit and soil water content in one plant and root length density in four plants, all grown in custom-made columns, were monitored daily for 87 days in the lab. The soil water isotopic composition in eleven depths was recorded daily in a non-destructive manner. The isotopic composition of plant transpiration was inferred from gas chamber measurements. Vertical isotopic gradients in the soil column were created by adding labeled water. Daily root water uptake (RWU) profiles were computed using the multi-source mixing model Stable Isotope Analysis in R (Parnell et al. PLoS ONE 5(3):1–5, 2010). \n               \n             \n             \n              Results \n               \n                RWU occurred mainly in soil layer 0–15 cm, ranging from 79 to 44\\%, even when water was more easily available in deeper layers. In wet soil, the transpiration rate was driven mainly by vapor pressure deficit and light intensity. Once soil water content was less than 0.12 cm \n                3 \n                cm \n                − 3 \n                , the computed canopy conductance declined, which restricted leaf gas exchange. Leaf water potential dropped steeply to around − 3 MPa after soil water content was below 0.10 cm \n                3 \n                cm \n                − 3 \n                . \n               \n             \n             \n              Conclusion \n              Our comprehensive data set contributes to a better understanding of the effects of drought on a grassland species and the limits of its acclimation in dry conditions.},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2024-05-16},\n\tjournal = {Plant and Soil},\n\tauthor = {Deseano Diaz, Paulina Alejandra and Van Dusschoten, Dagmar and Kübert, Angelika and Brüggemann, Nicolas and Javaux, Mathieu and Merz, Steffen and Vanderborght, Jan and Vereecken, Harry and Dubbert, Maren and Rothfuss, Youri},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {491--512},\n}\n\n\n\n

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\n \n\n \n \n Denager, T.; Sonnenborg, T. O.; Looms, M. C.; Bogena, H.; and Jensen, K. H.\n\n\n \n \n \n \n \n Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(14): 2827–2845. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Point-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{denager_point-scale_2023,\n\ttitle = {Point-scale multi-objective calibration of the {Community} {Land} {Model} (version 5.0) using in situ observations of water and energy fluxes and variables},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/2827/2023/},\n\tdoi = {10.5194/hess-27-2827-2023},\n\tabstract = {Abstract. This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly\nobservations of latent heat flux, sensible heat flux, groundwater recharge,\nsoil moisture and soil temperature from an agricultural observatory in\nDenmark. The results show that multi-objective calibration in combination\nwith truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of\nturbulent fluxes as the target variable, parameter optimization is capable\nof matching simulations and observations of latent heat, especially during\nthe summer period, whereas simulated sensible heat is clearly biased. Of the\n30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal\nconductance and root distribution have the highest influence on the\nlocal-scale simulation results. The results from this study contribute to\nimprovements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using\nobservations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Denager, Tanja and Sonnenborg, Torben O. and Looms, Majken C. and Bogena, Heye and Jensen, Karsten H.},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {2827--2845},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Dega, S.; Dietrich, P.; Schrön, M.; and Paasche, H.\n\n\n \n \n \n \n \n Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Environmental Science, 11: 1009191. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Probabilistic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dega_probabilistic_2023,\n\ttitle = {Probabilistic prediction by means of the propagation of response variable uncertainty through a {Monte} {Carlo} approach in regression random forest: {Application} to soil moisture regionalization},\n\tvolume = {11},\n\tissn = {2296-665X},\n\tshorttitle = {Probabilistic prediction by means of the propagation of response variable uncertainty through a {Monte} {Carlo} approach in regression random forest},\n\turl = {https://www.frontiersin.org/articles/10.3389/fenvs.2023.1009191/full},\n\tdoi = {10.3389/fenvs.2023.1009191},\n\tabstract = {Probabilistic predictions aim to produce a prediction interval with probabilities associated with each possible outcome instead of a single value for each outcome. In multiple regression problems, this can be achieved by propagating the known uncertainties in data of the response variables through a Monte Carlo approach. This paper presents an analysis of the impact of the training response variable uncertainty on the prediction uncertainties with the help of a comparison with probabilistic prediction obtained with quantile regression random forest. The result is an uncertainty quantification of the impact on the prediction. The approach is illustrated with the example of the probabilistic regionalization of soil moisture derived from cosmic-ray neutron sensing measurements, providing a regional-scale soil moisture map with data uncertainty quantification covering the Selke river catchment, eastern Germany.},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Environmental Science},\n\tauthor = {Dega, Ségolène and Dietrich, Peter and Schrön, Martin and Paasche, Hendrik},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {1009191},\n}\n\n\n\n

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\n \n\n \n \n De Pue, J.; Wieneke, S.; Bastos, A.; Barrios, J. M.; Liu, L.; Ciais, P.; Arboleda, A.; Hamdi, R.; Maleki, M.; Maignan, F.; Gellens-Meulenberghs, F.; Janssens, I.; and Balzarolo, M.\n\n\n \n \n \n \n \n Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 20(23): 4795–4818. December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_pue_temporal_2023,\n\ttitle = {Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers},\n\tvolume = {20},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/20/4795/2023/},\n\tdoi = {10.5194/bg-20-4795-2023},\n\tabstract = {Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e. short-wave radiation, air temperature, vapour pressure deficit and soil moisture) and the vegetation state (i.e. canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP models to capture this variability. Eleven models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g. dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g. light-use efficiency, LUE, models). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual timescales. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e. short-wave radiation and vapour pressure deficit) and failed to capture the decoupling of photosynthesis and canopy greenness (e.g. in evergreen forests). Conversely, meteo-driven models accurately captured the variability across timescales, despite the challenges in the prognostic simulation of the vegetation state. The largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2024-05-16},\n\tjournal = {Biogeosciences},\n\tauthor = {De Pue, Jan and Wieneke, Sebastian and Bastos, Ana and Barrios, José Miguel and Liu, Liyang and Ciais, Philippe and Arboleda, Alirio and Hamdi, Rafiq and Maleki, Maral and Maignan, Fabienne and Gellens-Meulenberghs, Françoise and Janssens, Ivan and Balzarolo, Manuela},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {4795--4818},\n}\n\n\n\n

\n\n\n

\n Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e. short-wave radiation, air temperature, vapour pressure deficit and soil moisture) and the vegetation state (i.e. canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP models to capture this variability. Eleven models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g. dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g. light-use efficiency, LUE, models). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual timescales. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e. short-wave radiation and vapour pressure deficit) and failed to capture the decoupling of photosynthesis and canopy greenness (e.g. in evergreen forests). Conversely, meteo-driven models accurately captured the variability across timescales, despite the challenges in the prognostic simulation of the vegetation state. The largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.\n

\n\n\n

\n \n\n \n \n Dadi, T.; Friese, K.; Wendt‐Potthoff, K.; Marcé, R.; and Koschorreck, M.\n\n\n \n \n \n \n \n Oxygen Dependent Temperature Regulation of Benthic Fluxes in Reservoirs.\n \n \n \n \n\n\n \n\n\n\n Global Biogeochemical Cycles, 37(4): e2022GB007647. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Oxygen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dadi_oxygen_2023,\n\ttitle = {Oxygen {Dependent} {Temperature} {Regulation} of {Benthic} {Fluxes} in {Reservoirs}},\n\tvolume = {37},\n\tissn = {0886-6236, 1944-9224},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GB007647},\n\tdoi = {10.1029/2022GB007647},\n\tabstract = {Abstract \n             \n              Temperature and dissolved oxygen concentration are critical factors affecting the exchange of solutes between sediment and water; both factors will be affected by warming of lakes and thereby influence water quality. Temperature and oxygen responses of single solute fluxes are well known; however, not much is known about the interaction of temperature and oxygen in regulating the balance of different fluxes in the benthic environment. We analyzed benthic flux (mobilization and immobilization) data of various solutes (dissolved organic carbon (DOC), CH \n              4 \n              , NO \n              3 \n              − \n              ‐N, NH \n              4 \n              + \n              ‐N, SRP, SO \n              4 \n              − \n              , Fe, Mn, and O \n              2 \n              ) collected from laboratory incubations of 142 sediment cores from 5 different reservoirs incubated under varying in situ temperature and oxygen conditions. Oxygen was the primary driver of benthic fluxes, while temperature and total organic content were secondary. Temperature effects on benthic fluxes were stronger under anoxic conditions which imply that warming will substantially increase the benthic fluxes if the sediment surface becomes anoxic. The varying temperature response of processes underlying the studied fluxes will result in a shift of their relative importance in the benthic environment, especially in shallow lakes that are more vulnerable to warming. For example, more anoxic conditions will shift the equilibrium between net sulfate reduction and methane release toward the latter. We also predict that physical effects of warming leading to hypolimnetic oxygen depletion, that is, stronger stratification and longer hypolimnetic confinement will increase the benthic mobilization of phosphorus, DOC, and methane into water and immobilization of sulfate by the sediments even in deep lakes. \n             \n          ,  \n            Plain Language Summary \n            Temperature and dissolved oxygen concentration control the release of undesirable components buried in lake or reservoir sediments, that is, nutrients, metals, and organic matter, which can cause water quality problems. We investigated the effects of rising temperature and levels of oxygen on the release of undesirable components by performing experiments using sediments and water from five different reservoirs. The sediments with a layer of water on top were incubated under different in situ temperature (low and high) and oxygen conditions (with and without). Our results show that the absence of oxygen was the main cause of the release of nutrients and metals. When there was no oxygen in the sediment and water, nutrients and metals were released from the sediment into the water and this effect increased when temperature was high. There is higher possibility that phosphorus, dissolved organic carbon, and methane will be released from sediments in some reservoirs as a result of global warming. \n          ,  \n            Key Points \n             \n               \n                 \n                  Solute fluxes from benthic lake sediments varied in response to temperature, with oxygen fluxes responding most strongly \n                 \n                 \n                  Temperature effects on the magnitude of benthic fluxes were stronger under anoxic than oxic conditions \n                 \n                 \n                  Direct temperature effects on reservoir water quality will be small compared to indirect effects through anoxia facilitation},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-05-16},\n\tjournal = {Global Biogeochemical Cycles},\n\tauthor = {Dadi, Tallent and Friese, Kurt and Wendt‐Potthoff, Katrin and Marcé, Rafael and Koschorreck, Matthias},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {e2022GB007647},\n}\n\n\n\n

\n\n\n

\n Abstract Temperature and dissolved oxygen concentration are critical factors affecting the exchange of solutes between sediment and water; both factors will be affected by warming of lakes and thereby influence water quality. Temperature and oxygen responses of single solute fluxes are well known; however, not much is known about the interaction of temperature and oxygen in regulating the balance of different fluxes in the benthic environment. We analyzed benthic flux (mobilization and immobilization) data of various solutes (dissolved organic carbon (DOC), CH 4 , NO 3 − ‐N, NH 4 + ‐N, SRP, SO 4 − , Fe, Mn, and O 2 ) collected from laboratory incubations of 142 sediment cores from 5 different reservoirs incubated under varying in situ temperature and oxygen conditions. Oxygen was the primary driver of benthic fluxes, while temperature and total organic content were secondary. Temperature effects on benthic fluxes were stronger under anoxic conditions which imply that warming will substantially increase the benthic fluxes if the sediment surface becomes anoxic. The varying temperature response of processes underlying the studied fluxes will result in a shift of their relative importance in the benthic environment, especially in shallow lakes that are more vulnerable to warming. For example, more anoxic conditions will shift the equilibrium between net sulfate reduction and methane release toward the latter. We also predict that physical effects of warming leading to hypolimnetic oxygen depletion, that is, stronger stratification and longer hypolimnetic confinement will increase the benthic mobilization of phosphorus, DOC, and methane into water and immobilization of sulfate by the sediments even in deep lakes. , Plain Language Summary Temperature and dissolved oxygen concentration control the release of undesirable components buried in lake or reservoir sediments, that is, nutrients, metals, and organic matter, which can cause water quality problems. We investigated the effects of rising temperature and levels of oxygen on the release of undesirable components by performing experiments using sediments and water from five different reservoirs. The sediments with a layer of water on top were incubated under different in situ temperature (low and high) and oxygen conditions (with and without). Our results show that the absence of oxygen was the main cause of the release of nutrients and metals. When there was no oxygen in the sediment and water, nutrients and metals were released from the sediment into the water and this effect increased when temperature was high. There is higher possibility that phosphorus, dissolved organic carbon, and methane will be released from sediments in some reservoirs as a result of global warming. , Key Points Solute fluxes from benthic lake sediments varied in response to temperature, with oxygen fluxes responding most strongly Temperature effects on the magnitude of benthic fluxes were stronger under anoxic than oxic conditions Direct temperature effects on reservoir water quality will be small compared to indirect effects through anoxia facilitation\n

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\n \n\n \n \n Conradt, T.; Engelhardt, H.; Menz, C.; Vicente-Serrano, S. M.; Farizo, B. A.; Peña-Angulo, D.; Domínguez-Castro, F.; Eklundh, L.; Jin, H.; Boincean, B.; Murphy, C.; and López-Moreno, J. I.\n\n\n \n \n \n \n \n Cross-sectoral impacts of the 2018–2019 Central European drought and climate resilience in the German part of the Elbe River basin.\n \n \n \n \n\n\n \n\n\n\n Regional Environmental Change, 23(1): 32. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Cross-sectoral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{conradt_cross-sectoral_2023,\n\ttitle = {Cross-sectoral impacts of the 2018–2019 {Central} {European} drought and climate resilience in the {German} part of the {Elbe} {River} basin},\n\tvolume = {23},\n\tissn = {1436-3798, 1436-378X},\n\turl = {https://link.springer.com/10.1007/s10113-023-02032-3},\n\tdoi = {10.1007/s10113-023-02032-3},\n\tabstract = {Abstract \n             \n              The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km \n              2 \n              including the cities of Berlin and Hamburg and contributing about 18\\% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40\\% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Regional Environmental Change},\n\tauthor = {Conradt, Tobias and Engelhardt, Henry and Menz, Christoph and Vicente-Serrano, Sergio M. and Farizo, Begoña Alvarez and Peña-Angulo, Dhais and Domínguez-Castro, Fernando and Eklundh, Lars and Jin, Hongxiao and Boincean, Boris and Murphy, Conor and López-Moreno, J. Ignacio},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {32},\n}\n\n\n\n

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\n \n\n \n \n Chen, J.; Reinoso-Rondinel, R.; Trömel, S.; Simmer, C.; and Ryzhkov, A.\n\n\n \n \n \n \n \n A Radar-Based Quantitative Precipitation Estimation Algorithm to Overcome the Impact of Vertical Gradients of Warm-Rain Precipitation: The Flood in Western Germany on 14 July 2021.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 24(3): 521–536. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chen_radar-based_2023,\n\ttitle = {A {Radar}-{Based} {Quantitative} {Precipitation} {Estimation} {Algorithm} to {Overcome} the {Impact} of {Vertical} {Gradients} of {Warm}-{Rain} {Precipitation}: {The} {Flood} in {Western} {Germany} on 14 {July} 2021},\n\tvolume = {24},\n\tcopyright = {http://www.ametsoc.org/PUBSReuseLicenses},\n\tissn = {1525-755X, 1525-7541},\n\tshorttitle = {A {Radar}-{Based} {Quantitative} {Precipitation} {Estimation} {Algorithm} to {Overcome} the {Impact} of {Vertical} {Gradients} of {Warm}-{Rain} {Precipitation}},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/24/3/JHM-D-22-0111.1.xml},\n\tdoi = {10.1175/JHM-D-22-0111.1},\n\tabstract = {Abstract \n             \n              The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity \n              Z \n              , specific differential phase \n              K \n              DP \n              , and specific attenuation \n              A \n              were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for \n              Z \n              and \n              K \n              DP \n              and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the \n              A \n              -based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23\\% and the normalized mean bias by 20\\%. With the use of gap-filling JuXPol data, the \n              A \n              -based retrieval gives the lowest errors followed by the \n              Z \n              -based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Chen, Ju-Yu and Reinoso-Rondinel, Ricardo and Trömel, Silke and Simmer, Clemens and Ryzhkov, Alexander},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {521--536},\n}\n\n\n\n

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\n \n\n \n \n Chabrillat, S.; Milewski, R.; Ward, K.; Foerster, S.; Guillaso, S.; Loy, C.; Ben-Dor, E.; Tziolas, N.; Schmid, T.; Van Wesemael, B.; and Demattê, J. A. M.\n\n\n \n \n \n \n \n Monitoring Soil Properties Using EnMAP Spaceborne Imaging Spectroscopy Mission.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pages 1130–1133, Pasadena, CA, USA, July 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{chabrillat_monitoring_2023,\n\taddress = {Pasadena, CA, USA},\n\ttitle = {Monitoring {Soil} {Properties} {Using} {EnMAP} {Spaceborne} {Imaging} {Spectroscopy} {Mission}},\n\tcopyright = {https://doi.org/10.15223/policy-029},\n\tisbn = {9798350320107},\n\turl = {https://ieeexplore.ieee.org/document/10282165/},\n\tdoi = {10.1109/IGARSS52108.2023.10282165},\n\turldate = {2024-05-16},\n\tbooktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Chabrillat, Sabine and Milewski, Robert and Ward, Kathrin and Foerster, Saskia and Guillaso, Stephane and Loy, Christopher and Ben-Dor, Eyal and Tziolas, Nikos and Schmid, Thomas and Van Wesemael, Bas and Demattê, José A. M.},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {1130--1133},\n}\n\n\n\n

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\n \n\n \n \n Brogi, C.; Vereecken, H.; Bogena, H. R.; and Brocca, L.\n\n\n \n \n \n \n \n Soil processes in the hydrologic cycle.\n \n \n \n \n\n\n \n\n\n\n In Encyclopedia of Soils in the Environment, pages 469–481. Elsevier, 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{brogi_soil_2023,\n\ttitle = {Soil processes in the hydrologic cycle},\n\tcopyright = {https://www.elsevier.com/tdm/userlicense/1.0/},\n\tisbn = {9780323951333},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000793},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tbooktitle = {Encyclopedia of {Soils} in the {Environment}},\n\tpublisher = {Elsevier},\n\tauthor = {Brogi, Cosimo and Vereecken, Harry and Bogena, Heye Reemt and Brocca, Luca},\n\tyear = {2023},\n\tdoi = {10.1016/B978-0-12-822974-3.00079-3},\n\tpages = {469--481},\n}\n\n\n\n

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\n \n\n \n \n Brogi, C.; Pisinaras, V.; Köhli, M.; Dombrowski, O.; Hendricks Franssen, H.; Babakos, K.; Chatzi, A.; Panagopoulos, A.; and Bogena, H. R.\n\n\n \n \n \n \n \n Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors.\n \n \n \n \n\n\n \n\n\n\n Sensors, 23(5): 2378. February 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brogi_monitoring_2023,\n\ttitle = {Monitoring {Irrigation} in {Small} {Orchards} with {Cosmic}-{Ray} {Neutron} {Sensors}},\n\tvolume = {23},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1424-8220},\n\turl = {https://www.mdpi.com/1424-8220/23/5/2378},\n\tdoi = {10.3390/s23052378},\n\tabstract = {Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of {\\textasciitilde}1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2024-05-16},\n\tjournal = {Sensors},\n\tauthor = {Brogi, Cosimo and Pisinaras, Vassilios and Köhli, Markus and Dombrowski, Olga and Hendricks Franssen, Harrie-Jan and Babakos, Konstantinos and Chatzi, Anna and Panagopoulos, Andreas and Bogena, Heye Reemt},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {2378},\n}\n\n\n\n

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\n \n\n \n \n Bottero, I.; Dominik, C.; Schweiger, O.; Albrecht, M.; Attridge, E.; Brown, M. J. F.; Cini, E.; Costa, C.; De La Rúa, P.; De Miranda, J. R.; Di Prisco, G.; Dzul Uuh, D.; Hodge, S.; Ivarsson, K.; Knauer, A. C.; Klein, A.; Mänd, M.; Martínez-López, V.; Medrzycki, P.; Pereira-Peixoto, H.; Potts, S.; Raimets, R.; Rundlöf, M.; Schwarz, J. M.; Senapathi, D.; Tamburini, G.; Talaván, E. T.; and Stout, J. C.\n\n\n \n \n \n \n \n Impact of landscape configuration and composition on pollinator communities across different European biogeographic regions.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Ecology and Evolution, 11: 1128228. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bottero_impact_2023,\n\ttitle = {Impact of landscape configuration and composition on pollinator communities across different {European} biogeographic regions},\n\tvolume = {11},\n\tissn = {2296-701X},\n\turl = {https://www.frontiersin.org/articles/10.3389/fevo.2023.1128228/full},\n\tdoi = {10.3389/fevo.2023.1128228},\n\tabstract = {Introduction \n              Heterogeneity in composition and spatial configuration of landscape elements support diversity and abundance of flower-visiting insects, but this is likely dependent on taxonomic group, spatial scale, weather and climatic conditions, and is particularly impacted by agricultural intensification. Here, we analyzed the impacts of both aspects of landscape heterogeneity and the role of climatic and weather conditions on pollinating insect communities in two economically important mass-flowering crops across Europe. \n             \n             \n              Methods \n              Using a standardized approach, we collected data on the abundance of five insect groups (honey bees, bumble bees, other bees, hover flies and butterflies) in eight oilseed rape and eight apple orchard sites (in crops and adjacent crop margins), across eight European countries (128 sites in total) encompassing four biogeographic regions, and quantified habitat heterogeneity by calculating relevant landscape metrics for composition (proportion and diversity of land-use types) and configuration (the aggregation and isolation of land-use patches). \n             \n             \n              Results \n              We found that flower-visiting insects responded to landscape and climate parameters in taxon- and crop-specific ways. For example, landscape diversity was positively correlated with honey bee and solitary bee abundance in oilseed rape fields, and hover fly abundance in apple orchards. In apple sites, the total abundance of all pollinators, and particularly bumble bees and solitary bees, decreased with an increasing proportion of orchards in the surrounding landscape. In oilseed rape sites, less-intensively managed habitats (i.e., woodland, grassland, meadows, and hedgerows) positively influenced all pollinators, particularly bumble bees and butterflies. Additionally, our data showed that daily and annual temperature, as well as annual precipitation and precipitation seasonality, affects the abundance of flower-visiting insects, although, again, these impacts appeared to be taxon- or crop-specific. \n             \n             \n              Discussion \n              Thus, in the context of global change, our findings emphasize the importance of understanding the role of taxon-specific responses to both changes in land use and climate, to ensure continued delivery of pollination services to pollinator-dependent crops.},\n\turldate = {2024-05-16},\n\tjournal = {Frontiers in Ecology and Evolution},\n\tauthor = {Bottero, Irene and Dominik, Christophe and Schweiger, Olivier and Albrecht, Matthias and Attridge, Eleanor and Brown, Mark J. F. and Cini, Elena and Costa, Cecilia and De La Rúa, Pilar and De Miranda, Joachim R. and Di Prisco, Gennaro and Dzul Uuh, Daniel and Hodge, Simon and Ivarsson, Kjell and Knauer, Anina C. and Klein, Alexandra-Maria and Mänd, Marika and Martínez-López, Vicente and Medrzycki, Piotr and Pereira-Peixoto, Helena and Potts, Simon and Raimets, Risto and Rundlöf, Maj and Schwarz, Janine M. and Senapathi, Deepa and Tamburini, Giovanni and Talaván, Estefanía Tobajas and Stout, Jane C.},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {1128228},\n}\n\n\n\n

\n\n\n

\n Introduction Heterogeneity in composition and spatial configuration of landscape elements support diversity and abundance of flower-visiting insects, but this is likely dependent on taxonomic group, spatial scale, weather and climatic conditions, and is particularly impacted by agricultural intensification. Here, we analyzed the impacts of both aspects of landscape heterogeneity and the role of climatic and weather conditions on pollinating insect communities in two economically important mass-flowering crops across Europe. Methods Using a standardized approach, we collected data on the abundance of five insect groups (honey bees, bumble bees, other bees, hover flies and butterflies) in eight oilseed rape and eight apple orchard sites (in crops and adjacent crop margins), across eight European countries (128 sites in total) encompassing four biogeographic regions, and quantified habitat heterogeneity by calculating relevant landscape metrics for composition (proportion and diversity of land-use types) and configuration (the aggregation and isolation of land-use patches). Results We found that flower-visiting insects responded to landscape and climate parameters in taxon- and crop-specific ways. For example, landscape diversity was positively correlated with honey bee and solitary bee abundance in oilseed rape fields, and hover fly abundance in apple orchards. In apple sites, the total abundance of all pollinators, and particularly bumble bees and solitary bees, decreased with an increasing proportion of orchards in the surrounding landscape. In oilseed rape sites, less-intensively managed habitats (i.e., woodland, grassland, meadows, and hedgerows) positively influenced all pollinators, particularly bumble bees and butterflies. Additionally, our data showed that daily and annual temperature, as well as annual precipitation and precipitation seasonality, affects the abundance of flower-visiting insects, although, again, these impacts appeared to be taxon- or crop-specific. Discussion Thus, in the context of global change, our findings emphasize the importance of understanding the role of taxon-specific responses to both changes in land use and climate, to ensure continued delivery of pollination services to pollinator-dependent crops.\n

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\n \n\n \n \n Borsdorf, H.; Bentele, M.; Müller, M.; Rebmann, C.; and Mayer, T.\n\n\n \n \n \n \n \n Comparison of Seasonal and Diurnal Concentration Profiles of BVOCs in Coniferous and Deciduous Forests.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 14(9): 1347. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{borsdorf_comparison_2023,\n\ttitle = {Comparison of {Seasonal} and {Diurnal} {Concentration} {Profiles} of {BVOCs} in {Coniferous} and {Deciduous} {Forests}},\n\tvolume = {14},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2073-4433},\n\turl = {https://www.mdpi.com/2073-4433/14/9/1347},\n\tdoi = {10.3390/atmos14091347},\n\tabstract = {Ambient atmospheric concentrations of isoprene and monoterpenes were measured at two forest sites, one deciduous and one coniferous, over the year 2022. Both sites in a regional area were sampled monthly between April and September. The samples were taken using sorbent tubes and analyzed with thermal desorption–gas chromatography–mass spectrometry. The highest concentrations were determined in August at both sites. While isoprene is the most abundant compound at the deciduous forest with an average concentration of 5.59 µg m−3 in August, α-pinene and β-pinene dominate throughout the year at the coniferous forest with the highest concentrations also in August (3.44 µg m−3 and 1.51 µg m−3). Because other monoterpenes (camphene, sabinene, 3-carene, p-cymene and limonene) are also emitted in significant amounts, the total concentration measured in the coniferous forest is higher (7.96 µg m−3 in August) in comparison to the deciduous forest (6.08 µg m−3). Regarding the detected monoterpenes in the deciduous forest, sabinene is the dominant monoterpene in addition to α-pinene and is sometimes present in higher (July) or equal (August) concentrations. The seasonal and diurnal concentrations of all monoterpenes correlate very well with each other at both sites. An exception is sabinene with a diurnal concentration profile similar to isoprene.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Atmosphere},\n\tauthor = {Borsdorf, Helko and Bentele, Maja and Müller, Michael and Rebmann, Corinna and Mayer, Thomas},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {1347},\n}\n\n\n\n

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\n \n\n \n \n Borrmann, P.; Brandt, P.; and Gerighausen, H.\n\n\n \n \n \n \n \n MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(14): 3664. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"MISPEL:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{borrmann_mispel_2023,\n\ttitle = {{MISPEL}: {A} {Multi}-{Crop} {Spectral} {Library} for {Statistical} {Crop} {Trait} {Retrieval} and {Agricultural} {Monitoring}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\tshorttitle = {{MISPEL}},\n\turl = {https://www.mdpi.com/2072-4292/15/14/3664},\n\tdoi = {10.3390/rs15143664},\n\tabstract = {Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Borrmann, Peter and Brandt, Patric and Gerighausen, Heike},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {3664},\n}\n\n\n\n

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\n \n\n \n \n Borriero, A.; Kumar, R.; Nguyen, T. V.; Fleckenstein, J. H.; and Lutz, S. R.\n\n\n \n \n \n \n \n Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(15): 2989–3004. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{borriero_uncertainty_2023,\n\ttitle = {Uncertainty in water transit time estimation with {StorAge} {Selection} functions and tracer data interpolation},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/2989/2023/},\n\tdoi = {10.5194/hess-27-2989-2023},\n\tabstract = {Abstract. Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions.\nHowever, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated.\nThis study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions.\nFor each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE {\\textgreater} 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs.\nWe found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 \\% confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation.\nWe discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies.},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Borriero, Arianna and Kumar, Rohini and Nguyen, Tam V. and Fleckenstein, Jan H. and Lutz, Stefanie R.},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {2989--3004},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Bonet-García, F. J; Pando, F.; Suárez-Muñoz, M.; and Cabello, J.\n\n\n \n \n \n \n \n Environmental research infrastructures are not (yet) ready to address ecosystem conservation challenge.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 18(9): 093002. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bonet-garcia_environmental_2023,\n\ttitle = {Environmental research infrastructures are not (yet) ready to address ecosystem conservation challenge},\n\tvolume = {18},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/acec01},\n\tdoi = {10.1088/1748-9326/acec01},\n\tabstract = {Abstract \n            Research infrastructures (RIs) are tools intended to be a fundamental pillar in producing knowledge regarding the functioning of Earth’s vital systems. However, it is unclear to what extent these instruments can help to deal with global biodiversity challenges. This paper presents the first assessment of the alignment between the services provided by environmental RIs, and the knowledge requested to address three specific Global Challenges concerning biodiversity loss at a global level: threatened species, alien species and ecosystem conservation. We characterized the specific needs and Subchallenges behind each Global Challenge. We also collected the services provided by 44 relevant environmental RIs in a standardized form. Then, we assessed to what extent those services are useful to address the challenges’ needs. Our results show that RIs, as a whole, are better suited to respond to species-related challenges than to challenges involving whole ecosystems. Nevertheless, the overlap among challenges’ needs is quite significant. Nearly half of the identified needs are shared between the ‘threatened species’ and the ‘ecosystem conservation’ challenges. Most of the assessed RIs work with multiple Earth System’s compartments at the same time (e.g. terrestrial + marine, terrestrial + freshwater, etc). Regarding the spatial extent of the studied RIs, most of the ecosystem-based RIs focus on the country scale, while most of the RIs specialized in species-related challenges work at a global scale. Considering the needs required to address the studied challenges, we have found that the RIs assessed in this study do not cover several of them. These gaps comprise complex data combinations that the studied RIs do not provide. Most of these gaps can be attributed to the ‘ecosystem conservation’ challenge. We consider that RIs were generally built to support pure basic research, which hampers their contribution to combat biodiversity loss. Because of the urgency to address global biodiversity challenges, we suggest adding new functionalities to make RIs work as problem-oriented facilities.},\n\tnumber = {9},\n\turldate = {2024-05-16},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Bonet-García, Francisco J and Pando, Francisco and Suárez-Muñoz, María and Cabello, Javier},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {093002},\n}\n\n\n\n

\n\n\n

\n Abstract Research infrastructures (RIs) are tools intended to be a fundamental pillar in producing knowledge regarding the functioning of Earth’s vital systems. However, it is unclear to what extent these instruments can help to deal with global biodiversity challenges. This paper presents the first assessment of the alignment between the services provided by environmental RIs, and the knowledge requested to address three specific Global Challenges concerning biodiversity loss at a global level: threatened species, alien species and ecosystem conservation. We characterized the specific needs and Subchallenges behind each Global Challenge. We also collected the services provided by 44 relevant environmental RIs in a standardized form. Then, we assessed to what extent those services are useful to address the challenges’ needs. Our results show that RIs, as a whole, are better suited to respond to species-related challenges than to challenges involving whole ecosystems. Nevertheless, the overlap among challenges’ needs is quite significant. Nearly half of the identified needs are shared between the ‘threatened species’ and the ‘ecosystem conservation’ challenges. Most of the assessed RIs work with multiple Earth System’s compartments at the same time (e.g. terrestrial + marine, terrestrial + freshwater, etc). Regarding the spatial extent of the studied RIs, most of the ecosystem-based RIs focus on the country scale, while most of the RIs specialized in species-related challenges work at a global scale. Considering the needs required to address the studied challenges, we have found that the RIs assessed in this study do not cover several of them. These gaps comprise complex data combinations that the studied RIs do not provide. Most of these gaps can be attributed to the ‘ecosystem conservation’ challenge. We consider that RIs were generally built to support pure basic research, which hampers their contribution to combat biodiversity loss. Because of the urgency to address global biodiversity challenges, we suggest adding new functionalities to make RIs work as problem-oriented facilities.\n

\n\n\n

\n \n\n \n \n Bollinger, E.; Zubrod, J. P.; Englert, D.; Graf, N.; Weisner, O.; Kolb, S.; Schäfer, R. B.; Entling, M. H.; and Schulz, R.\n\n\n \n \n \n \n \n The influence of season, hunting mode, and habitat specialization on riparian spiders as key predators in the aquatic-terrestrial linkage.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 13(1): 22950. December 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bollinger_influence_2023,\n\ttitle = {The influence of season, hunting mode, and habitat specialization on riparian spiders as key predators in the aquatic-terrestrial linkage},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-50420-w},\n\tdoi = {10.1038/s41598-023-50420-w},\n\tabstract = {Abstract \n            Freshwater ecosystems subsidize riparian zones with high-quality nutrients via the emergence of aquatic insects. Spiders are dominant consumers of these insect subsidies. However, little is known about the variation of aquatic insect consumption across spiders of different hunting modes, habitat specializations, seasons, and systems. To explore this, we assembled a large stable isotope dataset (n {\\textgreater} 1000) of aquatic versus terrestrial sources and six spider species over four points in time adjacent to a lotic and a lentic system. The spiders represent three hunting modes each consisting of a wetland specialist and a habitat generalist. We expected that specialists would feed more on aquatic prey than their generalist counterparts. Mixing models showed that spiders’ diet consisted of 17–99\\% of aquatic sources, with no clear effect of habitat specialization. Averaged over the whole study period, web builders (WB) showed the highest proportions (78\\%) followed by ground hunters (GH, 42\\%) and vegetation hunters (VH, 31\\%). Consumption of aquatic prey was highest in June and August, which is most pronounced in GH and WBs, with the latter feeding almost entirely on aquatic sources during this period. Additionally, the elevated importance of high-quality lipids from aquatic origin during fall is indicated by elemental analyses pointing to an accumulation of lipids in October, which represent critical energy reserves during winter. Consequently, this study underlines the importance of aquatic prey irrespective of the habitat specialization of spiders. Furthermore, it suggests that energy flows vary substantially between spider hunting modes and seasons.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Scientific Reports},\n\tauthor = {Bollinger, Eric and Zubrod, Jochen P. and Englert, Dominic and Graf, Nadin and Weisner, Oliver and Kolb, Sebastian and Schäfer, Ralf B. and Entling, Martin H. and Schulz, Ralf},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {22950},\n}\n\n\n\n

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\n \n\n \n \n Böker, B.; Laux, P.; Olschewski, P.; and Kunstmann, H.\n\n\n \n \n \n \n \n Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain.\n \n \n \n \n\n\n \n\n\n\n International Journal of Climatology, 43(11): 5130–5153. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Added$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{boker_added_2023,\n\ttitle = {Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain},\n\tvolume = {43},\n\tissn = {0899-8418, 1097-0088},\n\turl = {https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.8136},\n\tdoi = {10.1002/joc.8136},\n\tabstract = {Abstract \n            Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2024-05-16},\n\tjournal = {International Journal of Climatology},\n\tauthor = {Böker, Brian and Laux, Patrick and Olschewski, Patrick and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {5130--5153},\n}\n\n\n\n

\n\n\n

\n Abstract Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.\n

\n\n\n

\n \n\n \n \n Boas, T.; Bogena, H. R.; Ryu, D.; Vereecken, H.; Western, A.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 27(16): 3143–3167. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{boas_seasonal_2023,\n\ttitle = {Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system ({SEAS5}) long-range meteorological forecasts in a land surface modelling approach},\n\tvolume = {27},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/27/3143/2023/},\n\tdoi = {10.5194/hess-27-3143-2023},\n\tabstract = {Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 \\% inter-annual differences in recorded yields and up to 17 \\% inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 \\% inter-annual differences in recorded yields and up to 5 \\% in simulated yields). The\nhigh- and low-yield seasons (2020 and 2018) among the 4 simulated years\nwere clearly reproduced in the forecast simulation results. Furthermore,\nsub-seasonal and seasonal simulations reflected the early harvest in the\ndrought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the\nofficial statistics. While general soil moisture trends, such as the\nEuropean drought in 2018, were captured by the seasonal experiments, we\nfound systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the\nrepresentation of these variables in CLM5 to increase the model sensitivity\nto drought stress and other crop stressors.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2024-05-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Boas, Theresa and Bogena, Heye Reemt and Ryu, Dongryeol and Vereecken, Harry and Western, Andrew and Hendricks Franssen, Harrie-Jan},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {3143--3167},\n}\n\n\n\n

\n\n\n

\n Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.\n

\n\n\n

\n \n\n \n \n Blume, T.; Schneider, L.; Güntner, A.; Morgner, M.; and Wummel, J.\n\n\n \n \n \n \n \n Was haben Baumkronen mit dem Grundwasser zu tun?.\n \n \n \n \n\n\n \n\n\n\n ,6 pages, 2 MB. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Was$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blume_was_2023,\n\ttitle = {Was haben {Baumkronen} mit dem {Grundwasser} zu tun?},\n\tcopyright = {Creative Commons Attribution Share Alike 4.0 International},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_5022446},\n\tdoi = {10.48440/GFZ.SYSERDE.13.01.1},\n\tabstract = {Der Kronendurchlass, d. h. der Anteil des Niederschlags, der durch das Kronendach des Waldes dringt, wird stark von der Art des Niederschlags und den Eigenschaften des Waldbestands beeinflusst. Das komplexe Zusammenspiel dieser Faktoren inklusive ihrer jahreszeitlichen Veränderungen zu entschlüsseln, ist eine große wissenschaftliche Herausforderung und nur mit langjährigem Monitoring in verschiedenen Waldbeständen möglich. Das Langzeit-Umweltobservatorium TERENO Nord-Ost zur Erforschung der regionalen Auswirkungen des Globalen Wandels liefert hierfür ideale Voraussetzungen.},\n\tlanguage = {de},\n\turldate = {2024-05-16},\n\tauthor = {Blume, Theresa and Schneider, Lisa and Güntner, Andreas and Morgner, Markus and Wummel, Jörg},\n\tyear = {2023},\n\tpages = {6 pages, 2 MB},\n}\n\n\n\n

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\n \n\n \n \n Bloomfield, K. J.; Van Hoolst, R.; Balzarolo, M.; Janssens, I. A.; Vicca, S.; Ghent, D.; and Prentice, I. C.\n\n\n \n \n \n \n \n Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(6): 1693. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bloomfield_towards_2023,\n\ttitle = {Towards a {General} {Monitoring} {System} for {Terrestrial} {Primary} {Production}: {A} {Test} {Spanning} the {European} {Drought} of 2018},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\tshorttitle = {Towards a {General} {Monitoring} {System} for {Terrestrial} {Primary} {Production}},\n\turl = {https://www.mdpi.com/2072-4292/15/6/1693},\n\tdoi = {10.3390/rs15061693},\n\tabstract = {(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Bloomfield, Keith J. and Van Hoolst, Roel and Balzarolo, Manuela and Janssens, Ivan A. and Vicca, Sara and Ghent, Darren and Prentice, I. Colin},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1693},\n}\n\n\n\n

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\n \n\n \n \n Blettner, N.; Fencl, M.; Bareš, V.; Kunstmann, H.; and Chwala, C.\n\n\n \n \n \n \n \n Transboundary Rainfall Estimation Using Commercial Microwave Links.\n \n \n \n \n\n\n \n\n\n\n Earth and Space Science, 10(8): e2023EA002869. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Transboundary$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blettner_transboundary_2023,\n\ttitle = {Transboundary {Rainfall} {Estimation} {Using} {Commercial} {Microwave} {Links}},\n\tvolume = {10},\n\tissn = {2333-5084, 2333-5084},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EA002869},\n\tdoi = {10.1029/2023EA002869},\n\tabstract = {Abstract \n            Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation. \n          ,  \n            Plain Language Summary \n            Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale. \n          ,  \n            Key Points \n             \n               \n                 \n                  Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time \n                 \n                 \n                  German and Czech data sets of CMLs differ significantly with respect to network characteristics \n                 \n                 \n                  Quality control is important for heterogeneous data of CMLs},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2024-05-16},\n\tjournal = {Earth and Space Science},\n\tauthor = {Blettner, Nico and Fencl, Martin and Bareš, Vojtěch and Kunstmann, Harald and Chwala, Christian},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {e2023EA002869},\n}\n\n\n\n

\n\n\n

\n Abstract Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation. , Plain Language Summary Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale. , Key Points Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time German and Czech data sets of CMLs differ significantly with respect to network characteristics Quality control is important for heterogeneous data of CMLs\n

\n\n\n

\n \n\n \n \n Barouillet, C.; Monchamp, M.; Bertilsson, S.; Brasell, K.; Domaizon, I.; Epp, L. S.; Ibrahim, A.; Mejbel, H.; Nwosu, E. C.; Pearman, J. K.; Picard, M.; Thomson‐Laing, G.; Tsugeki, N.; Von Eggers, J.; Gregory‐Eaves, I.; Pick, F.; Wood, S. A.; and Capo, E.\n\n\n \n \n \n \n \n Investigating the effects of anthropogenic stressors on lake biota using sedimentary \\textlessspan style=\"font-variant:small-caps;\"\\textgreaterDNA\\textless/span\\textgreater.\n \n \n \n \n\n\n \n\n\n\n Freshwater Biology, 68(11): 1799–1817. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{barouillet_investigating_2023,\n\ttitle = {Investigating the effects of anthropogenic stressors on lake biota using sedimentary {\\textless}span style="font-variant:small-caps;"{\\textgreater}{DNA}{\\textless}/span{\\textgreater}},\n\tvolume = {68},\n\tissn = {0046-5070, 1365-2427},\n\tshorttitle = {Investigating the effects of anthropogenic stressors on lake biota using sedimentary {\\textless}span style="font-variant},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/fwb.14027},\n\tdoi = {10.1111/fwb.14027},\n\tabstract = {Abstract \n             \n               \n                 \n                   \n                    Analyses of sedimentary DNA ( \n                    sed \n                    DNA) have increased exponentially over the last decade and hold great potential to study the effects of anthropogenic stressors on lake biota over time. \n                   \n                 \n                 \n                   \n                    Herein, we synthesise the literature that has applied a \n                    sed \n                    DNA approach to track historical changes in lake biodiversity in response to anthropogenic impacts, with an emphasis on the past \n                    c. \n                    200 years. \n                   \n                 \n                 \n                  We identified the following research themes that are of particular relevance: (1) eutrophication and climate change as key drivers of limnetic communities; (2) increasing homogenisation of limnetic communities across large spatial scales; and (3) the dynamics and effects of invasive species as traced in lake sediment archives. \n                 \n                 \n                   \n                    Altogether, this review highlights the potential of \n                    sed \n                    DNA to draw a more comprehensive picture of the response of lake biota to anthropogenic stressors, opening up new avenues in the field of paleoecology by unrevealing a hidden historical biodiversity, building new paleo‐indicators, and reflecting either taxonomic or functional attributes. \n                   \n                 \n                 \n                   \n                    Broadly, \n                    sed \n                    DNA analyses provide new perspectives that can inform ecosystem management, conservation, and restoration by offering an approach to measure ecological integrity and vulnerability, as well as ecosystem functioning.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2024-05-16},\n\tjournal = {Freshwater Biology},\n\tauthor = {Barouillet, Cécilia and Monchamp, Marie‐Eve and Bertilsson, Stefan and Brasell, Katie and Domaizon, Isabelle and Epp, Laura S. and Ibrahim, Anan and Mejbel, Hebah and Nwosu, Ebuka Canisius and Pearman, John K. and Picard, Maïlys and Thomson‐Laing, Georgia and Tsugeki, Narumi and Von Eggers, Jordan and Gregory‐Eaves, Irene and Pick, Frances and Wood, Susanna A. and Capo, Eric},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {1799--1817},\n}\n\n\n\n

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\n \n\n \n \n Asam, S.; Eisfelder, C.; Hirner, A.; Reiners, P.; Holzwarth, S.; and Bachmann, M.\n\n\n \n \n \n \n \n AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series—A TIMELINE Thematic Processor.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 15(6): 1631. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"AVHRR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{asam_avhrr_2023,\n\ttitle = {{AVHRR} {NDVI} {Compositing} {Method} {Comparison} and {Generation} of {Multi}-{Decadal} {Time} {Series}—{A} {TIMELINE} {Thematic} {Processor}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/15/6/1631},\n\tdoi = {10.3390/rs15061631},\n\tabstract = {Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-05-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Asam, Sarah and Eisfelder, Christina and Hirner, Andreas and Reiners, Philipp and Holzwarth, Stefanie and Bachmann, Martin},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1631},\n}\n\n\n\n

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\n \n\n \n \n Arora, B.; Kuppel, S.; Wellen, C.; Oswald, C.; Groh, J.; Payandi-Rolland, D.; Stegen, J.; and Coffinet, S.\n\n\n \n \n \n \n \n Building Cross-Site and Cross-Network collaborations in critical zone science.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 618: 129248. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Building$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{arora_building_2023,\n\ttitle = {Building {Cross}-{Site} and {Cross}-{Network} collaborations in critical zone science},\n\tvolume = {618},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423001907},\n\tdoi = {10.1016/j.jhydrol.2023.129248},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Arora, Bhavna and Kuppel, Sylvain and Wellen, Christopher and Oswald, Claire and Groh, Jannis and Payandi-Rolland, Dahédrey and Stegen, James and Coffinet, Sarah},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {129248},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Andrade-Linares, D. R.; Schwerdtner, U.; Schulz, S.; Dannenmann, M.; Spohn, M.; Baum, C.; Gasche, R.; Wiesmeier, M.; Garcia-Franco, N.; and Schloter, M.\n\n\n \n \n \n \n \n Climate change and management intensity alter spatial distribution and abundance of P mineralizing bacteria and arbuscular mycorrhizal fungi in mountainous grassland soils.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 186: 109175. November 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{andrade-linares_climate_2023,\n\ttitle = {Climate change and management intensity alter spatial distribution and abundance of {P} mineralizing bacteria and arbuscular mycorrhizal fungi in mountainous grassland soils},\n\tvolume = {186},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0038071723002377},\n\tdoi = {10.1016/j.soilbio.2023.109175},\n\tlanguage = {en},\n\turldate = {2024-05-16},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Andrade-Linares, Diana Rocío and Schwerdtner, Ulrike and Schulz, Stefanie and Dannenmann, Michael and Spohn, Marie and Baum, Christel and Gasche, Rainer and Wiesmeier, Martin and Garcia-Franco, Noelia and Schloter, Michael},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {109175},\n}\n\n\n\n

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\n \n\n \n \n Altdorff, D.; Oswald, S. E.; Zacharias, S.; Zengerle, C.; Dietrich, P.; Mollenhauer, H.; Attinger, S.; and Schrön, M.\n\n\n \n \n \n \n \n Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 59(3): e2022WR033514. March 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{altdorff_toward_2023,\n\ttitle = {Toward {Large}‐{Scale} {Soil} {Moisture} {Monitoring} {Using} {Rail}‐{Based} {Cosmic} {Ray} {Neutron} {Sensing}},\n\tvolume = {59},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033514},\n\tdoi = {10.1029/2022WR033514},\n\tabstract = {Abstract \n            Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution. \n          ,  \n            Key Points \n             \n               \n                 \n                  The first rail‐borne Cosmic ray neutron sensing system for automatic and continuous soil water content monitoring at the hectare scale is presented \n                 \n                 \n                  The system provided almost uninterrupted data from September 2021 to July 2022 along a 9 km railway track in the Harz low mountains, Germany \n                 \n                 \n                  Results showed spatial pattern, related to surface features, seasonal change, and individual responses of railway parts to wetting and drying},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-05-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Altdorff, Daniel and Oswald, Sascha E. and Zacharias, Steffen and Zengerle, Carmen and Dietrich, Peter and Mollenhauer, Hannes and Attinger, Sabine and Schrön, Martin},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {e2022WR033514},\n}\n\n\n\n

\n\n\n

\n Abstract Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution. , Key Points The first rail‐borne Cosmic ray neutron sensing system for automatic and continuous soil water content monitoring at the hectare scale is presented The system provided almost uninterrupted data from September 2021 to July 2022 along a 9 km railway track in the Harz low mountains, Germany Results showed spatial pattern, related to surface features, seasonal change, and individual responses of railway parts to wetting and drying\n

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\n \n 2022\n \n \n (148)\n \n \n

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\n \n\n \n \n Płaczkowska, E.; Mostowik, K.; Bogena, H. R.; and Leuchner, M.\n\n\n \n \n \n \n \n The Impact of Partial Deforestation on Solute Fluxes and Stream Water Ionic Composition in a Headwater Catchment.\n \n \n \n \n\n\n \n\n\n\n Water, 15(1): 107. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{placzkowska_impact_2022,\n\ttitle = {The {Impact} of {Partial} {Deforestation} on {Solute} {Fluxes} and {Stream} {Water} {Ionic} {Composition} in a {Headwater} {Catchment}},\n\tvolume = {15},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/15/1/107},\n\tdoi = {10.3390/w15010107},\n\tabstract = {To ensure the good chemical status of surface water across Europe, it is necessary to increase research on the comprehensive impact of land use and land cover changes, i.e., deforestation, on the natural environment. For this reason, we used data from 9-year environmental monitoring in the Wüstebach experimental catchment of the TERENO (Terrestrial Environmental Observatories) network to determine the impact of partial deforestation on solute fluxes and stream water ionic composition. In 2013, a partial deforestation experiment was conducted in the study area using a cut-to-length logging method. To this end, two headwater catchments were compared: one partially deforested (22\\% of the catchment area) and one untreated control catchment. The concentrations of ions in stream water, groundwater, and precipitation were analyzed: Ca2+, Mg2+, Na+, K+, Al3+, Fetot, Mn2+, NO3−, SO4−, and Cl−. Most of the ions (Na+, Ca2+, Mg2+, Cl−, and SO4−) showed decreasing trends in concentrations after deforestation, indicating a dilution effect in stream water due to the reduction of the supply of solutes with precipitation in the open deforested area. The fluxes of these ions decreased by 5–7\\% in the first year after deforestation, although the stream runoff increased by 5\\%. In the second year, the decrease in ion fluxes was greater, from 6\\% to 24\\%. This finding confirms that only limited soil erosion occurred after the deforestation because the soil was well protected during logging works by covering harvester lanes with branches. Only K+ and NO3− ions showed increasing trends in both concentrations and fluxes in the partially deforested catchment in the first two to three years after deforestation. Spruce die-offs, common in Europe, may decrease the concentration and fluxes of base cations in surface water in a nutrient-limited environment. However, the simultaneous planting of young broad-leaved trees with post-harvesting regrowth could create a nutrient sink that protects the catchment area from nutrient depletion.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-05-16},\n\tjournal = {Water},\n\tauthor = {Płaczkowska, Eliza and Mostowik, Karolina and Bogena, Heye Reemt and Leuchner, Michael},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {107},\n}\n\n\n\n

\n\n\n

\n To ensure the good chemical status of surface water across Europe, it is necessary to increase research on the comprehensive impact of land use and land cover changes, i.e., deforestation, on the natural environment. For this reason, we used data from 9-year environmental monitoring in the Wüstebach experimental catchment of the TERENO (Terrestrial Environmental Observatories) network to determine the impact of partial deforestation on solute fluxes and stream water ionic composition. In 2013, a partial deforestation experiment was conducted in the study area using a cut-to-length logging method. To this end, two headwater catchments were compared: one partially deforested (22% of the catchment area) and one untreated control catchment. The concentrations of ions in stream water, groundwater, and precipitation were analyzed: Ca2+, Mg2+, Na+, K+, Al3+, Fetot, Mn2+, NO3−, SO4−, and Cl−. Most of the ions (Na+, Ca2+, Mg2+, Cl−, and SO4−) showed decreasing trends in concentrations after deforestation, indicating a dilution effect in stream water due to the reduction of the supply of solutes with precipitation in the open deforested area. The fluxes of these ions decreased by 5–7% in the first year after deforestation, although the stream runoff increased by 5%. In the second year, the decrease in ion fluxes was greater, from 6% to 24%. This finding confirms that only limited soil erosion occurred after the deforestation because the soil was well protected during logging works by covering harvester lanes with branches. Only K+ and NO3− ions showed increasing trends in both concentrations and fluxes in the partially deforested catchment in the first two to three years after deforestation. Spruce die-offs, common in Europe, may decrease the concentration and fluxes of base cations in surface water in a nutrient-limited environment. However, the simultaneous planting of young broad-leaved trees with post-harvesting regrowth could create a nutrient sink that protects the catchment area from nutrient depletion.\n

\n\n\n

\n \n\n \n \n Zhang, Y.; Liang, S.; Zhu, Z.; Ma, H.; and He, T.\n\n\n \n \n \n \n \n Soil moisture content retrieval from Landsat 8 data using ensemble learning.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 185: 32–47. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_soil_2022,\n\ttitle = {Soil moisture content retrieval from {Landsat} 8 data using ensemble learning},\n\tvolume = {185},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271622000090},\n\tdoi = {10.1016/j.isprsjprs.2022.01.005},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Zhang, Yufang and Liang, Shunlin and Zhu, Zhiliang and Ma, Han and He, Tao},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {32--47},\n}\n\n\n\n

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\n \n\n \n \n Zielhofer, C.; Schmidt, J.; Reiche, N.; Tautenhahn, M.; Ballasus, H.; Burkart, M.; Linstädter, A.; Dietze, E.; Kaiser, K.; and Mehler, N.\n\n\n \n \n \n \n \n The Lower Havel River Region (Brandenburg, Germany): A 230-Year-Long Historical Map Record Indicates a Decrease in Surface Water Areas and Groundwater Levels.\n \n \n \n \n\n\n \n\n\n\n Water, 14(3): 480. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zielhofer_lower_2022,\n\ttitle = {The {Lower} {Havel} {River} {Region} ({Brandenburg}, {Germany}): {A} 230-{Year}-{Long} {Historical} {Map} {Record} {Indicates} a {Decrease} in {Surface} {Water} {Areas} and {Groundwater} {Levels}},\n\tvolume = {14},\n\tissn = {2073-4441},\n\tshorttitle = {The {Lower} {Havel} {River} {Region} ({Brandenburg}, {Germany})},\n\turl = {https://www.mdpi.com/2073-4441/14/3/480},\n\tdoi = {10.3390/w14030480},\n\tabstract = {Instrumental data show that the groundwater and lake levels in Northeast Germany have decreased over the past decades, and this process has accelerated over the past few years. In addition to global warming, the direct influence of humans on the local water balance is suspected to be the cause. Since the instrumental data usually go back only a few decades, little is known about the multidecadal to centennial-scale trend, which also takes long-term climate variation and the long-term influence by humans on the water balance into account. This study aims to quantitatively reconstruct the surface water areas in the Lower Havel Inner Delta and of adjacent Lake Gülpe in Brandenburg. The analysis includes the calculation of surface water areas from historical and modern maps from 1797 to 2020. The major finding is that surface water areas have decreased by approximately 30\\% since the pre-industrial period, with the decline being continuous. Our data show that the comprehensive measures in Lower Havel hydro-engineering correspond with groundwater lowering that started before recent global warming. Further, large-scale melioration measures with increasing water demands in the upstream wetlands beginning from the 1960s to the 1980s may have amplified the decline in downstream surface water areas.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Water},\n\tauthor = {Zielhofer, Christoph and Schmidt, Johannes and Reiche, Niklas and Tautenhahn, Marie and Ballasus, Helen and Burkart, Michael and Linstädter, Anja and Dietze, Elisabeth and Kaiser, Knut and Mehler, Natascha},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {480},\n}\n\n\n\n

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\n \n\n \n \n Zuidema, P. A.; Babst, F.; Groenendijk, P.; Trouet, V.; Abiyu, A.; Acuña-Soto, R.; Adenesky-Filho, E.; Alfaro-Sánchez, R.; Aragão, J. R. V.; Assis-Pereira, G.; Bai, X.; Barbosa, A. C.; Battipaglia, G.; Beeckman, H.; Botosso, P. C.; Bradley, T.; Bräuning, A.; Brienen, R.; Buckley, B. M.; Camarero, J. J.; Carvalho, A.; Ceccantini, G.; Centeno-Erguera, L. R.; Cerano-Paredes, J.; Chávez-Durán, Á. A.; Cintra, B. B. L.; Cleaveland, M. K.; Couralet, C.; D’Arrigo, R.; del Valle, J. I.; Dünisch, O.; Enquist, B. J.; Esemann-Quadros, K.; Eshetu, Z.; Fan, Z.; Ferrero, M. E.; Fichtler, E.; Fontana, C.; Francisco, K. S.; Gebrekirstos, A.; Gloor, E.; Granato-Souza, D.; Haneca, K.; Harley, G. L.; Heinrich, I.; Helle, G.; Inga, J. G.; Islam, M.; Jiang, Y.; Kaib, M.; Khamisi, Z. H.; Koprowski, M.; Kruijt, B.; Layme, E.; Leemans, R.; Leffler, A. J.; Lisi, C. S.; Loader, N. J.; Locosselli, G. M.; Lopez, L.; López-Hernández, M. I.; Lousada, J. L. P. C.; Mendivelso, H. A.; Mokria, M.; Montóia, V. R.; Moors, E.; Nabais, C.; Ngoma, J.; Nogueira Júnior, F. d. C.; Oliveira, J. M.; Olmedo, G. M.; Pagotto, M. A.; Panthi, S.; Pérez-De-Lis, G.; Pucha-Cofrep, D.; Pumijumnong, N.; Rahman, M.; Ramirez, J. A.; Requena-Rojas, E. J.; Ribeiro, A. d. S.; Robertson, I.; Roig, F. A.; Rubio-Camacho, E. A.; Sass-Klaassen, U.; Schöngart, J.; Sheppard, P. R.; Slotta, F.; Speer, J. H.; Therrell, M. D.; Toirambe, B.; Tomazello-Filho, M.; Torbenson, M. C. A.; Touchan, R.; Venegas-González, A.; Villalba, R.; Villanueva-Diaz, J.; Vinya, R.; Vlam, M.; Wils, T.; and Zhou, Z.\n\n\n \n \n \n \n \n Tropical tree growth driven by dry-season climate variability.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 15(4): 269–276. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Tropical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zuidema_tropical_2022,\n\ttitle = {Tropical tree growth driven by dry-season climate variability},\n\tvolume = {15},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/s41561-022-00911-8},\n\tdoi = {10.1038/s41561-022-00911-8},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Nature Geoscience},\n\tauthor = {Zuidema, Pieter A. and Babst, Flurin and Groenendijk, Peter and Trouet, Valerie and Abiyu, Abrham and Acuña-Soto, Rodolfo and Adenesky-Filho, Eduardo and Alfaro-Sánchez, Raquel and Aragão, José Roberto Vieira and Assis-Pereira, Gabriel and Bai, Xue and Barbosa, Ana Carolina and Battipaglia, Giovanna and Beeckman, Hans and Botosso, Paulo Cesar and Bradley, Tim and Bräuning, Achim and Brienen, Roel and Buckley, Brendan M. and Camarero, J. Julio and Carvalho, Ana and Ceccantini, Gregório and Centeno-Erguera, Librado R. and Cerano-Paredes, Julián and Chávez-Durán, Álvaro Agustín and Cintra, Bruno Barçante Ladvocat and Cleaveland, Malcolm K. and Couralet, Camille and D’Arrigo, Rosanne and del Valle, Jorge Ignacio and Dünisch, Oliver and Enquist, Brian J. and Esemann-Quadros, Karin and Eshetu, Zewdu and Fan, Ze-Xin and Ferrero, M. Eugenia and Fichtler, Esther and Fontana, Claudia and Francisco, Kainana S. and Gebrekirstos, Aster and Gloor, Emanuel and Granato-Souza, Daniela and Haneca, Kristof and Harley, Grant Logan and Heinrich, Ingo and Helle, Gerd and Inga, Janet G. and Islam, Mahmuda and Jiang, Yu-mei and Kaib, Mark and Khamisi, Zakia Hassan and Koprowski, Marcin and Kruijt, Bart and Layme, Eva and Leemans, Rik and Leffler, A. Joshua and Lisi, Claudio Sergio and Loader, Neil J. and Locosselli, Giuliano Maselli and Lopez, Lidio and López-Hernández, María I. and Lousada, José Luís Penetra Cerveira and Mendivelso, Hooz A. and Mokria, Mulugeta and Montóia, Valdinez Ribeiro and Moors, Eddy and Nabais, Cristina and Ngoma, Justine and Nogueira Júnior, Francisco de Carvalho and Oliveira, Juliano Morales and Olmedo, Gabriela Morais and Pagotto, Mariana Alves and Panthi, Shankar and Pérez-De-Lis, Gonzalo and Pucha-Cofrep, Darwin and Pumijumnong, Nathsuda and Rahman, Mizanur and Ramirez, Jorge Andres and Requena-Rojas, Edilson Jimmy and Ribeiro, Adauto de Souza and Robertson, Iain and Roig, Fidel Alejandro and Rubio-Camacho, Ernesto Alonso and Sass-Klaassen, Ute and Schöngart, Jochen and Sheppard, Paul R. and Slotta, Franziska and Speer, James H. and Therrell, Matthew D. and Toirambe, Benjamin and Tomazello-Filho, Mario and Torbenson, Max C. A. and Touchan, Ramzi and Venegas-González, Alejandro and Villalba, Ricardo and Villanueva-Diaz, Jose and Vinya, Royd and Vlam, Mart and Wils, Tommy and Zhou, Zhe-Kun},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {269--276},\n}\n\n\n\n

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\n \n\n \n \n Zhuang, L.; Schnepf, A.; Unger, K.; Liang, Z.; and Bol, R.\n\n\n \n \n \n \n \n Home-Field Advantage of Litter Decomposition Faded 8 Years after Spruce Forest Clearcutting in Western Germany.\n \n \n \n \n\n\n \n\n\n\n Soil Systems, 6(1): 26. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Home-Field$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhuang_home-field_2022,\n\ttitle = {Home-{Field} {Advantage} of {Litter} {Decomposition} {Faded} 8 {Years} after {Spruce} {Forest} {Clearcutting} in {Western} {Germany}},\n\tvolume = {6},\n\tissn = {2571-8789},\n\turl = {https://www.mdpi.com/2571-8789/6/1/26},\n\tdoi = {10.3390/soilsystems6010026},\n\tabstract = {Home-field advantage (HFA) encompasses all the processes leading to faster litter decomposition in the ‘home’ environment compared to that of ‘away’ environments. To determine the occurrence of HFA in a forest and adjacent clear-cut, we set up a reciprocal litter decomposition experiment within the forest and clear-cut for two soil types (Cambisols and Gleysols) in temperate Germany. The forest was dominated by Norway spruce (Picea abies), whereas forest regeneration of European Beech (Fagus sylvatica) after clearcutting was encouraged. Our observation that Norway spruce decomposed faster than European beech in 70-yr-old spruce forest was most likely related to specialized litter-soil interaction under existing spruce, leading to an HFA. Elevated soil moisture and temperature, and promoted litter N release, indicated the rapid change of soil-litter affinity of the original spruce forest even after a short-term regeneration following clearcutting, resulting in faster beech decomposition, particularly in moisture- and nutrient-deficient Cambisols. The divergence between forest and clear-cut in the Cambisol of their litter δ15N values beyond nine months implied litter N decomposition was only initially independent of soil and residual C status. We conclude that clearcutting modifies the litter-field affinity and helps promote the establishment or regeneration of European beech in this and similar forest mountain upland areas.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Soil Systems},\n\tauthor = {Zhuang, Liyan and Schnepf, Andrea and Unger, Kirsten and Liang, Ziyi and Bol, Roland},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {26},\n}\n\n\n\n

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\n \n\n \n \n Zhu, S.; Xu, J.; Zhu, H.; Zeng, J.; Wang, Y.; Zeng, Q.; Zhang, D.; Liu, X.; and Yang, S.\n\n\n \n \n \n \n \n Investigating Impacts of Ambient Air Pollution on the Terrestrial Gross Primary Productivity (GPP) From Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n IEEE Geoscience and Remote Sensing Letters, 19: 1–5. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhu_investigating_2022,\n\ttitle = {Investigating {Impacts} of {Ambient} {Air} {Pollution} on the {Terrestrial} {Gross} {Primary} {Productivity} ({GPP}) {From} {Remote} {Sensing}},\n\tvolume = {19},\n\tissn = {1545-598X, 1558-0571},\n\turl = {https://ieeexplore.ieee.org/document/9745487/},\n\tdoi = {10.1109/LGRS.2022.3163775},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Geoscience and Remote Sensing Letters},\n\tauthor = {Zhu, Songyan and Xu, Jian and Zhu, Hao and Zeng, Jingya and Wang, Yapeng and Zeng, Qiaolin and Zhang, Dejun and Liu, Xiaoran and Yang, Shiqi},\n\tyear = {2022},\n\tpages = {1--5},\n}\n\n\n\n

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\n \n\n \n \n Zhou, L.; Zhou, W.; Chen, J.; Xu, X.; Wang, Y.; Zhuang, J.; and Chi, Y.\n\n\n \n \n \n \n \n Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the Northern Hemisphere.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 135: 108579. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Land$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhou_land_2022,\n\ttitle = {Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the {Northern} {Hemisphere}},\n\tvolume = {135},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X22000504},\n\tdoi = {10.1016/j.ecolind.2022.108579},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Ecological Indicators},\n\tauthor = {Zhou, Lei and Zhou, Wen and Chen, Jijing and Xu, Xiyan and Wang, Yonglin and Zhuang, Jie and Chi, Yonggang},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {108579},\n}\n\n\n\n

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\n \n\n \n \n Zhang, Y.; Liu, X.; Lei, L.; and Liu, L.\n\n\n \n \n \n \n \n Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(16): 3899. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_estimating_2022,\n\ttitle = {Estimating {Global} {Anthropogenic} {CO2} {Gridded} {Emissions} {Using} a {Data}-{Driven} {Stacked} {Random} {Forest} {Regression} {Model}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/16/3899},\n\tdoi = {10.3390/rs14163899},\n\tabstract = {The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO2 emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO2 emissions. The driving variables include the annual features of column-averaged CO2 dry-air mole fraction (XCO2) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO2 (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R2 of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R2 of 0.665, and the national total emissions provided a higher R2 at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R2 = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO2 emissions at a grid scale.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Zhang, Yucong and Liu, Xinjie and Lei, Liping and Liu, Liangyun},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {3899},\n}\n\n\n\n

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\n \n\n \n \n Zhang, Y.; and Ye, A.\n\n\n \n \n \n \n \n Improving global gross primary productivity estimation by fusing multi-source data products.\n \n \n \n \n\n\n \n\n\n\n Heliyon, 8(3): e09153. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_improving_2022,\n\ttitle = {Improving global gross primary productivity estimation by fusing multi-source data products},\n\tvolume = {8},\n\tissn = {24058440},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2405844022004418},\n\tdoi = {10.1016/j.heliyon.2022.e09153},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Heliyon},\n\tauthor = {Zhang, Yahai and Ye, Aizhong},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {e09153},\n}\n\n\n\n

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\n \n\n \n \n Zhang, X.; Zhang, Y.; Tian, J.; Ma, N.; and Wang, Y.\n\n\n \n \n \n \n \n CO $_{\\textrm{2}}$ fertilization is spatially distinct from stomatal conductance reduction in controlling ecosystem water-use efficiency increase.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(5): 054048. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CO$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_co_2022,\n\ttitle = {{CO} $_{\\textrm{2}}$ fertilization is spatially distinct from stomatal conductance reduction in controlling ecosystem water-use efficiency increase},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac6c9c},\n\tdoi = {10.1088/1748-9326/ac6c9c},\n\tabstract = {Abstract \n             \n              It is well known that global ecosystem water-use efficiency (EWUE) has noticeably increased over the last several decades. However, it remains unclear how individual environmental drivers contribute to EWUE changes, particularly from CO \n              2 \n              fertilization and stomatal suppression effects. Using a satellite-driven water–carbon coupling model—Penman–Monteith–Leuning version 2 (PML-V2), we quantified individual contributions from the observational drivers (atmospheric CO \n              2 \n              , climate forcing, leaf area index (LAI), albedo and emissivity) across the globe over 1982–2014. The PML-V2 was well-calibrated and showed a good performance for simulating EWUE (with a determination coefficient ( \n              R \n              2 \n              ) of 0.56) compared to observational annual EWUE over 1982–2014 derived from global 95 eddy flux sites from the FLUXNET2015 dataset. Our results showed that global EWUE increasing trend (0.04 ± 0.004 gC mm \n              −1 \n              H \n              2 \n              O decade \n              −1 \n              ) was largely contributed by increasing CO \n              2 \n              (51\\%) and LAI (20\\%), but counteracted by climate forcing (−26\\%). Globally, the CO \n              2 \n              fertilization effect on photosynthesis (23\\%) was similar to the CO \n              2 \n              suppression effect on stomatal conductance (28\\%). Spatially, the fertilization effect dominated EWUE trend over semi-arid regions while the stomatal suppression effect controlled over tropical forests. These findings improve understanding of how environmental factors affect the long-term change of EWUE, and can help policymakers for water use planning and ecosystem management.},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Zhang, Xuanze and Zhang, Yongqiang and Tian, Jing and Ma, Ning and Wang, Ying-Ping},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {054048},\n}\n\n\n\n

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\n \n\n \n \n Zhang, W.; Jung, M.; Migliavacca, M.; Poyatos, R.; Miralles, D.; El-Madany, T. S.; Galvagno, M.; Carrara, A.; Arriga, N.; Ibrom, A.; Mammarella, I.; Papale, D.; Cleverly, J.; Liddell, M. J.; Wohlfahrt, G.; Markwitz, C.; Mauder, M.; Paul-Limoges, E.; Schmidt, M.; Wolf, S.; Brümmer, C.; Arain, M. A.; Fares, S.; Kato, T.; Ardö, J.; Oechel, W.; Hanson, C.; Korkiakoski, M.; Biraud, S.; Steinbrecher, R.; Billesbach, D.; Montagnani, L.; Woodgate, W.; Shao, C.; Carvalhais, N.; Reichstein, M.; and Nelson, J. A.\n\n\n \n \n \n \n \n The Effect of Relative Humidity on Eddy Covariance Latent Heat Flux Measurements and its Implication for Partitioning into Transpiration and Evaporation.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_effect_2022,\n\ttitle = {The {Effect} of {Relative} {Humidity} on {Eddy} {Covariance} {Latent} {Heat} {Flux} {Measurements} and its {Implication} for {Partitioning} into {Transpiration} and {Evaporation}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4106267},\n\tdoi = {10.2139/ssrn.4106267},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Zhang, Weijie and Jung, Martin and Migliavacca, Mirco and Poyatos, Rafael and Miralles, Diego and El-Madany, Tarek S. and Galvagno, Marta and Carrara, Arnaud and Arriga, Nicola and Ibrom, Andreas and Mammarella, Ivan and Papale, Dario and Cleverly, Jamie and Liddell, Michael J. and Wohlfahrt, Georg and Markwitz, Christian and Mauder, Matthias and Paul-Limoges, Eugenie and Schmidt, Marius and Wolf, Sebastian and Brümmer, Christian and Arain, M. Altaf and Fares, Silvano and Kato, Tomomichi and Ardö, Jonas and Oechel, Walter and Hanson, Chad and Korkiakoski, Mika and Biraud, Sébastien and Steinbrecher, Rainer and Billesbach, Dave and Montagnani, Leonardo and Woodgate, William and Shao, Changliang and Carvalhais, Nuno and Reichstein, Markus and Nelson, Jacob A.},\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Yuan, K.; Zhu, Q.; Li, F.; Riley, W. J.; Torn, M.; Chu, H.; McNicol, G.; Chen, M.; Knox, S.; Delwiche, K.; Wu, H.; Baldocchi, D.; Ma, H.; Desai, A. R.; Chen, J.; Sachs, T.; Ueyama, M.; Sonnentag, O.; Helbig, M.; Tuittila, E.; Jurasinski, G.; Koebsch, F.; Campbell, D.; Schmid, H. P.; Lohila, A.; Goeckede, M.; Nilsson, M. B.; Friborg, T.; Jansen, J.; Zona, D.; Euskirchen, E.; Ward, E. J.; Bohrer, G.; Jin, Z.; Liu, L.; Iwata, H.; Goodrich, J.; and Jackson, R.\n\n\n \n \n \n \n \n Causality guided machine learning model on wetland CH4 emissions across global wetlands.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 324: 109115. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Causality$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yuan_causality_2022,\n\ttitle = {Causality guided machine learning model on wetland {CH4} emissions across global wetlands},\n\tvolume = {324},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322003021},\n\tdoi = {10.1016/j.agrformet.2022.109115},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Yuan, Kunxiaojia and Zhu, Qing and Li, Fa and Riley, William J. and Torn, Margaret and Chu, Housen and McNicol, Gavin and Chen, Min and Knox, Sara and Delwiche, Kyle and Wu, Huayi and Baldocchi, Dennis and Ma, Hongxu and Desai, Ankur R. and Chen, Jiquan and Sachs, Torsten and Ueyama, Masahito and Sonnentag, Oliver and Helbig, Manuel and Tuittila, Eeva-Stiina and Jurasinski, Gerald and Koebsch, Franziska and Campbell, David and Schmid, Hans Peter and Lohila, Annalea and Goeckede, Mathias and Nilsson, Mats B. and Friborg, Thomas and Jansen, Joachim and Zona, Donatella and Euskirchen, Eugenie and Ward, Eric J. and Bohrer, Gil and Jin, Zhenong and Liu, Licheng and Iwata, Hiroki and Goodrich, Jordan and Jackson, Robert},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {109115},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Yu, P.; Zhou, T.; Luo, H.; Liu, X.; Shi, P.; Zhao, X.; Xiao, Z.; Zhang, Y.; and Zhou, P.\n\n\n \n \n \n \n \n Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing in Ecology and Conservation, 8(3): 409–425. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Interannual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yu_interannual_2022,\n\ttitle = {Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress},\n\tvolume = {8},\n\tissn = {2056-3485, 2056-3485},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/rse2.252},\n\tdoi = {10.1002/rse2.252},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing in Ecology and Conservation},\n\tauthor = {Yu, Peixin and Zhou, Tao and Luo, Hui and Liu, Xia and Shi, Peijun and Zhao, Xiang and Xiao, Zhiqiang and Zhang, Yajie and Zhou, Peifang},\n\teditor = {Disney, Mat and Zhang, Jian},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {409--425},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Yu, L.; Zhou, S.; Zhao, X.; Gao, X.; Jiang, K.; Zhang, B.; Cheng, L.; Song, X.; and Siddique, K. H. M.\n\n\n \n \n \n \n \n Evapotranspiration Partitioning Based on Leaf and Ecosystem Water Use Efficiency.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(9). September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evapotranspiration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yu_evapotranspiration_2022,\n\ttitle = {Evapotranspiration {Partitioning} {Based} on {Leaf} and {Ecosystem} {Water} {Use} {Efficiency}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030629},\n\tdoi = {10.1029/2021WR030629},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Yu, Liuyang and Zhou, Sha and Zhao, Xining and Gao, Xiaodong and Jiang, Kongtao and Zhang, Baoqing and Cheng, Lei and Song, Xiaolin and Siddique, Kadambot H. M.},\n\tmonth = sep,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Yin, G.; Verger, A.; Descals, A.; Filella, I.; and Peñuelas, J.\n\n\n \n \n \n \n \n A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology.\n \n \n \n \n\n\n \n\n\n\n Journal of Remote Sensing, 2022: 1–10. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yin_broadband_2022,\n\ttitle = {A {Broadband} {Green}-{Red} {Vegetation} {Index} for {Monitoring} {Gross} {Primary} {Production} {Phenology}},\n\tvolume = {2022},\n\tissn = {2694-1589},\n\turl = {https://spj.sciencemag.org/journals/remotesensing/2022/9764982/},\n\tdoi = {10.34133/2022/9764982},\n\tabstract = {The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) ( \n               \n                 \n                   \n                    R \n                   \n                   \n                    2 \n                   \n                 \n                = \n                0.98 \n                , \n                p \n                {\\textless} \n                0.001 \n               \n              ), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 ( \n               \n                 \n                   \n                    R \n                   \n                   \n                    2 \n                   \n                 \n                = \n                0.97 \n                , \n                p \n                {\\textless} \n                0.001 \n               \n              ). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Remote Sensing},\n\tauthor = {Yin, Gaofei and Verger, Aleixandre and Descals, Adrià and Filella, Iolanda and Peñuelas, Josep},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1--10},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Yang, Y.; Bloom, A. A.; Ma, S.; Levine, P.; Norton, A.; Parazoo, N. C.; Reager, J. T.; Worden, J.; Quetin, G. R.; Smallman, T. L.; Williams, M.; Xu, L.; and Saatchi, S.\n\n\n \n \n \n \n \n CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(4): 1789–1802. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CARDAMOM-FluxVal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_cardamom-fluxval_2022,\n\ttitle = {{CARDAMOM}-{FluxVal} version 1.0: a {FLUXNET}-based validation system for {CARDAMOM} carbon and water flux estimates},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{CARDAMOM}-{FluxVal} version 1.0},\n\turl = {https://gmd.copernicus.org/articles/15/1789/2022/},\n\tdoi = {10.5194/gmd-15-1789-2022},\n\tabstract = {Abstract. Land–atmosphere carbon and water exchanges have large uncertainty\nin terrestrial biosphere models (TBMs). Using observations to reduce TBM\nstructural and parametric errors and uncertainty is a critical priority for\nboth understanding and accurately predicting carbon and water fluxes. Recent\nimplementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have\nyielded key insights into ecosystem carbon and water cycling. CARDAMOM\nestimates parameters for an associated TBM of intermediate complexity\n(Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O\nflux observations – have exhibited considerable skill in both representing\nthe variability of assimilated observations and predicting withheld\nobservations. CARDAMOM and DALEC have been continuously developed to\naccommodate new scientific challenges and an expanding variety of\nobservational constraints. However, so far there has been no concerted\neffort to globally and systematically validate CARDAMOM performance across\nindividual model–data fusion configurations. Here we use the FLUXNET 2015\ndataset – an ensemble of 200+ eddy covariance flux tower sites – to\nformulate a concerted benchmarking framework for CARDAMOM carbon\n(photosynthesis and net C exchange) and water (evapotranspiration) flux\nestimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill\nmetrics to evaluate CARDAMOM performance against both assimilated and\nwithheld FLUXNET 2015 photosynthesis, net CO2 exchange, and\nevapotranspiration estimates. We further demonstrate the potential for\ntailored CARDAMOM evaluations by categorizing performance in terms of (i)\nindividual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii)\nlength of assimilation data. The CARDAMOM benchmarking system – along with\nthe CARDAMOM driver files provided – can be readily repeated to support both the\nintercomparison between existing CARDAMOM model configurations and the\nformulation, development, and testing of new CARDAMOM model structures.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Yang, Yan and Bloom, A. Anthony and Ma, Shuang and Levine, Paul and Norton, Alexander and Parazoo, Nicholas C. and Reager, John T. and Worden, John and Quetin, Gregory R. and Smallman, T. Luke and Williams, Mathew and Xu, Liang and Saatchi, Sassan},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1789--1802},\n}\n\n\n\n

\n\n\n

\n Abstract. Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across individual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.\n

\n\n\n

\n \n\n \n \n Yang, X.; Rode, M.; Jomaa, S.; Merbach, I.; Tetzlaff, D.; Soulsby, C.; and Borchardt, D.\n\n\n \n \n \n \n \n Functional Multi‐Scale Integration of Agricultural Nitrogen‐Budgets Into Catchment Water Quality Modeling.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(4). February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Functional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_functional_2022,\n\ttitle = {Functional {Multi}‐{Scale} {Integration} of {Agricultural} {Nitrogen}‐{Budgets} {Into} {Catchment} {Water} {Quality} {Modeling}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021GL096833},\n\tdoi = {10.1029/2021GL096833},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Yang, Xiaoqiang and Rode, Michael and Jomaa, Seifeddine and Merbach, Ines and Tetzlaff, Doerthe and Soulsby, Chris and Borchardt, Dietrich},\n\tmonth = feb,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Yang, H.; Wang, Q.; Zhao, W.; Tong, X.; and Atkinson, P. M.\n\n\n \n \n \n \n \n Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion.\n \n \n \n \n\n\n \n\n\n\n Journal of Remote Sensing, 2022: 1–23. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Reconstruction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_reconstruction_2022,\n\ttitle = {Reconstruction of a {Global} 9 km, 8-{Day} {SMAP} {Surface} {Soil} {Moisture} {Dataset} during 2015–2020 by {Spatiotemporal} {Fusion}},\n\tvolume = {2022},\n\tissn = {2694-1589},\n\turl = {https://spj.sciencemag.org/journals/remotesensing/2022/9871246/},\n\tdoi = {10.34133/2022/9871246},\n\tabstract = {Soil moisture, a crucial property for Earth surface research, has been focused widely in various studies. The Soil Moisture Active Passive (SMAP) global products at 36 km and 9 km (called P36 and AP9 in this research) have been published from April 2015. However, the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015. In this research, the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme (VIPSTF-SW) to simulate the 9 km AP9 data after failure of the active radar. The method makes full use of all the historical AP9 and P36 data available between April and July 2015. As a result, 8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020, by downscaling the corresponding 8-day composited P36 data. The available AP9 data and \n              in situ \n              reference data were used to validate the predicted 9 km data. Generally, the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product. The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology, climatology, ecology, and many other fields at the global scale.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Remote Sensing},\n\tauthor = {Yang, Haoxuan and Wang, Qunming and Zhao, Wei and Tong, Xiaohua and Atkinson, Peter M.},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {1--23},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Xue, Z.; Zhang, Y.; Zhang, L.; and Li, H.\n\n\n \n \n \n \n \n Ensemble Learning Embedded With Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental U.S.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–17. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Ensemble$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xue_ensemble_2022,\n\ttitle = {Ensemble {Learning} {Embedded} {With} {Gaussian} {Process} {Regression} for {Soil} {Moisture} {Estimation}: {A} {Case} {Study} of the {Continental} {U}.{S}.},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\tshorttitle = {Ensemble {Learning} {Embedded} {With} {Gaussian} {Process} {Regression} for {Soil} {Moisture} {Estimation}},\n\turl = {https://ieeexplore.ieee.org/document/9757155/},\n\tdoi = {10.1109/TGRS.2022.3166777},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Xue, Zhaohui and Zhang, Yujuan and Zhang, Ling and Li, Hao},\n\tyear = {2022},\n\tpages = {1--17},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Xu, S.; McVicar, T. R.; Li, L.; Yu, Z.; Jiang, P.; Zhang, Y.; Ban, Z.; Xing, W.; Dong, N.; Zhang, H.; and Zhang, M.\n\n\n \n \n \n \n \n Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale: Patterns and mechanisms.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109085. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Globally$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xu_globally_2022,\n\ttitle = {Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale: {Patterns} and mechanisms},\n\tvolume = {323},\n\tissn = {01681923},\n\tshorttitle = {Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002738},\n\tdoi = {10.1016/j.agrformet.2022.109085},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Xu, Shiqin and McVicar, Tim R. and Li, Lingcheng and Yu, Zhongbo and Jiang, Peng and Zhang, Yuliang and Ban, Zhaoxin and Xing, Wanqiu and Dong, Ningpeng and Zhang, Hua and Zhang, Mingjun},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109085},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Xu, J.; Zhang, X.; Zhang, W.; Hou, N.; Feng, C.; Yang, S.; Jia, K.; Yao, Y.; Xie, X.; Jiang, B.; Cheng, J.; Zhao, X.; and Liang, S.\n\n\n \n \n \n \n \n Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Research, 270: 106056. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xu_assessment_2022,\n\ttitle = {Assessment of surface downward longwave radiation in {CMIP6} with comparison to observations and {CMIP5}},\n\tvolume = {270},\n\tissn = {01698095},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169809522000424},\n\tdoi = {10.1016/j.atmosres.2022.106056},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Atmospheric Research},\n\tauthor = {Xu, Jiawen and Zhang, Xiaotong and Zhang, Weiyu and Hou, Ning and Feng, Chunjie and Yang, Shuyue and Jia, Kun and Yao, Yunjun and Xie, Xianhong and Jiang, Bo and Cheng, Jie and Zhao, Xiang and Liang, Shunlin},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {106056},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Xie, Z.; Yao, Y.; Zhang, X.; Liang, S.; Fisher, J. B.; Chen, J.; Jia, K.; Shang, K.; Yang, J.; Yu, R.; Guo, X.; Liu, L.; Ning, J.; and Zhang, L.\n\n\n \n \n \n \n \n The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 610: 127990. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xie_global_2022,\n\ttitle = {The {Global} {LAnd} {Surface} {Satellite} ({GLASS}) evapotranspiration product {Version} 5.0: {Algorithm} development and preliminary validation},\n\tvolume = {610},\n\tissn = {00221694},\n\tshorttitle = {The {Global} {LAnd} {Surface} {Satellite} ({GLASS}) evapotranspiration product {Version} 5.0},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169422005650},\n\tdoi = {10.1016/j.jhydrol.2022.127990},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Xie, Zijing and Yao, Yunjun and Zhang, Xiaotong and Liang, Shunlin and Fisher, Joshua B. and Chen, Jiquan and Jia, Kun and Shang, Ke and Yang, Junming and Yu, Ruiyang and Guo, Xiaozheng and Liu, Lu and Ning, Jing and Zhang, Lilin},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {127990},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Xi, X.; Gentine, P.; Zhuang, Q.; and Kim, S.\n\n\n \n \n \n \n \n Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 127(5). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xi_evaluating_2022,\n\ttitle = {Evaluating the {Variability} of {Surface} {Soil} {Moisture} {Simulated} {Within} {CMIP5} {Using} {SMAP} {Data}},\n\tvolume = {127},\n\tissn = {2169-897X, 2169-8996},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021JD035363},\n\tdoi = {10.1029/2021JD035363},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Xi, Xuan and Gentine, Pierre and Zhuang, Qianlai and Kim, Seungbum},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wu, S.; Tetzlaff, D.; Yang, X.; and Soulsby, C.\n\n\n \n \n \n \n \n Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(8). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_identifying_2022,\n\ttitle = {Identifying {Dominant} {Processes} in {Time} and {Space}: {Time}‐{Varying} {Spatial} {Sensitivity} {Analysis} for a {Grid}‐{Based} {Nitrate} {Model}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Identifying {Dominant} {Processes} in {Time} and {Space}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR031149},\n\tdoi = {10.1029/2021WR031149},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Wu, Songjun and Tetzlaff, Doerthe and Yang, Xiaoqiang and Soulsby, Chris},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Winter, C.; Nguyen, T. V.; Musolff, A.; Lutz, S. R.; Rode, M.; Kumar, R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Droughts can reduce the nitrogen retention capacity of catchments.\n \n \n \n \n\n\n \n\n\n\n Technical Report Catchment hydrology/Theory development, June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Droughts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{winter_droughts_2022,\n\ttype = {preprint},\n\ttitle = {Droughts can reduce the nitrogen retention capacity of catchments},\n\turl = {https://egusphere.copernicus.org/preprints/2022/egusphere-2022-431/},\n\tabstract = {Abstract. In 2018–2019, Central Europe experienced an unprecedented multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 70 \\% higher, compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times ({\\textless}2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream ({\\textgreater}20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.},\n\turldate = {2022-11-21},\n\tinstitution = {Catchment hydrology/Theory development},\n\tauthor = {Winter, Carolin and Nguyen, Tam V. and Musolff, Andreas and Lutz, Stefanie R. and Rode, Michael and Kumar, Rohini and Fleckenstein, Jan H.},\n\tmonth = jun,\n\tyear = {2022},\n\tdoi = {10.5194/egusphere-2022-431},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Winter, C.; Tarasova, L.; Lutz, S. R.; Musolff, A.; Kumar, R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Explaining the Variability in High‐Frequency Nitrate Export Patterns Using Long‐Term Hydrological Event Classification.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(1). January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Explaining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{winter_explaining_2022,\n\ttitle = {Explaining the {Variability} in {High}‐{Frequency} {Nitrate} {Export} {Patterns} {Using} {Long}‐{Term} {Hydrological} {Event} {Classification}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030938},\n\tdoi = {10.1029/2021WR030938},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Winter, C. and Tarasova, L. and Lutz, S. R. and Musolff, A. and Kumar, R. and Fleckenstein, J. H.},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wild, B.; Teubner, I.; Moesinger, L.; Zotta, R.; Forkel, M.; van der Schalie, R.; Sitch, S.; and Dorigo, W.\n\n\n \n \n \n \n \n VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(3): 1063–1085. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"VODCA2GPP$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wild_vodca2gpp_2022,\n\ttitle = {{VODCA2GPP} – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing},\n\tvolume = {14},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/14/1063/2022/},\n\tdoi = {10.5194/essd-14-1063-2022},\n\tabstract = {Abstract. Long-term global monitoring of terrestrial gross primary\nproduction (GPP) is crucial for assessing ecosystem responses to global\nclimate change. In recent decades, great advances have been made in\nestimating GPP and many global GPP datasets have been published. These\ndatasets are based on observations from optical remote sensing, are\nupscaled from in situ measurements, or rely on process-based models.\nAlthough these approaches are well established within the scientific\ncommunity, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave\nremote sensing estimates of vegetation optical depth (VOD) to estimate GPP\nat the global scale for the period 1988–2020. VODCA2GPP applies a previously\ndeveloped carbon-sink-driven approach (Teubner et al., 2019, 2021) to\nestimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et\nal., 2020; Zotta et al., 2022), which merges VOD observations from\nmultiple sensors into one long-running, coherent data record. VODCA2GPP was\ntrained and evaluated against FLUXNET in situ observations of GPP and\ncompared against largely independent state-of-the-art GPP datasets from\nthe Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust\nperformance of VODCA2GPP with only a small bias and good temporal agreement.\nThe comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP\nexhibits very similar spatial patterns across all biomes but with a\nconsistent positive bias. In terms of temporal dynamics, a high agreement\nwas found for regions outside the humid tropics, with median correlations\naround 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP\ncorrelates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but\nless well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period\n1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale\nbut rather suggests regionally different long-term changes in GPP. For the\nshorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and\nthe TRENDY-v7 ensemble, significant increases in global GPP were found.\nVODCA2GPP can complement existing GPP products and is a valuable dataset for\nthe assessment of large-scale and long-term changes in GPP for global\nvegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al.,\n2021).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Wild, Benjamin and Teubner, Irene and Moesinger, Leander and Zotta, Ruxandra-Maria and Forkel, Matthias and van der Schalie, Robin and Sitch, Stephen and Dorigo, Wouter},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1063--1085},\n}\n\n\n\n

\n\n\n

\n Abstract. Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al., 2021).\n

\n\n\n

\n \n\n \n \n Wieser, A.; Güntner, A.; Dietrich, P.; Handwerker, J.; Khordakova, D.; Ködel, U.; Kohler, M.; Mollenhauer, H.; Mühr, B.; Nixdorf, E.; Reich, M.; Rolf, C.; Schrön, M.; Schütze, C.; and Weber, U.\n\n\n \n \n \n \n \n First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding.\n \n \n \n \n\n\n \n\n\n\n Technical Report Catchment hydrology/Instruments and observation techniques, April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"First$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{wieser_first_2022,\n\ttype = {preprint},\n\ttitle = {First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding},\n\turl = {https://hess.copernicus.org/preprints/hess-2022-131/},\n\tabstract = {Abstract. Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.},\n\turldate = {2022-11-21},\n\tinstitution = {Catchment hydrology/Instruments and observation techniques},\n\tauthor = {Wieser, Andreas and Güntner, Andreas and Dietrich, Peter and Handwerker, Jan and Khordakova, Dina and Ködel, Uta and Kohler, Martin and Mollenhauer, Hannes and Mühr, Bernhard and Nixdorf, Erik and Reich, Marvin and Rolf, Christian and Schrön, Martin and Schütze, Claudia and Weber, Ute},\n\tmonth = apr,\n\tyear = {2022},\n\tdoi = {10.5194/hess-2022-131},\n}\n\n\n\n

\n\n\n

\n Abstract. Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.\n

\n\n\n

\n \n\n \n \n Weisner, O.; Arle, J.; Liebmann, L.; Link, M.; Schäfer, R. B.; Schneeweiss, A.; Schreiner, V. C.; Vormeier, P.; and Liess, M.\n\n\n \n \n \n \n \n Three reasons why the Water Framework Directive (WFD) fails to identify pesticide risks.\n \n \n \n \n\n\n \n\n\n\n Water Research, 208: 117848. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Three$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weisner_three_2022,\n\ttitle = {Three reasons why the {Water} {Framework} {Directive} ({WFD}) fails to identify pesticide risks},\n\tvolume = {208},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135421010423},\n\tdoi = {10.1016/j.watres.2021.117848},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Water Research},\n\tauthor = {Weisner, Oliver and Arle, Jens and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Schreiner, Verena C. and Vormeier, Philipp and Liess, Matthias},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {117848},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wei, J.; Knicker, H.; Zhou, Z.; Eckhardt, K.; Leinweber, P.; Wissel, H.; Yuan, W.; and Brüggemann, N.\n\n\n \n \n \n \n \n Nitrogen Immobilization Caused by Chemical Formation of Black Nitrogen and Amide in Soil.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Nitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wei_nitrogen_2022,\n\ttitle = {Nitrogen {Immobilization} {Caused} by {Chemical} {Formation} of {Black} {Nitrogen} and {Amide} in {Soil}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4108591},\n\tdoi = {10.2139/ssrn.4108591},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Wei, Jing and Knicker, Heike and Zhou, Zheyan and Eckhardt, Kai-Uwe and Leinweber, Peter and Wissel, Holger and Yuan, Wenping and Brüggemann, Nicolas},\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Weber, U.; Attinger, S.; Baschek, B.; Boike, J.; Borchardt, D.; Brix, H.; Brüggemann, N.; Bussmann, I.; Dietrich, P.; Fischer, P.; Greinert, J.; Hajnsek, I.; Kamjunke, N.; Kerschke, D.; Kiendler-Scharr, A.; Körtzinger, A.; Kottmeier, C.; Merz, B.; Merz, R.; Riese, M.; Schloter, M.; Schmid, H.; Schnitzler, J.; Sachs, T.; Schütze, C.; Tillmann, R.; Vereecken, H.; Wieser, A.; and Teutsch, G.\n\n\n \n \n \n \n \n MOSES: A Novel Observation System to Monitor Dynamic Events across Earth Compartments.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 103(2): E339–E348. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"MOSES:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weber_moses_2022,\n\ttitle = {{MOSES}: {A} {Novel} {Observation} {System} to {Monitor} {Dynamic} {Events} across {Earth} {Compartments}},\n\tvolume = {103},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {{MOSES}},\n\turl = {https://journals.ametsoc.org/view/journals/bams/103/2/BAMS-D-20-0158.1.xml},\n\tdoi = {10.1175/BAMS-D-20-0158.1},\n\tabstract = {Abstract \n            Modular Observation Solutions of Earth Systems (MOSES) is a novel observation system that is specifically designed to unravel the impact of distinct, dynamic events on the long-term development of environmental systems. Hydrometeorological extremes such as the recent European droughts or the floods of 2013 caused severe and lasting environmental damage. Modeling studies suggest that abrupt permafrost thaw events accelerate Arctic greenhouse gas emissions. Short-lived ocean eddies seem to comprise a significant share of the marine carbon uptake or release. Although there is increasing evidence that such dynamic events bear the potential for major environmental impacts, our knowledge on the processes they trigger is still very limited. MOSES aims at capturing such events, from their formation to their end, with high spatial and temporal resolution. As such, the observation system extends and complements existing national and international observation networks, which are mostly designed for long-term monitoring. Several German Helmholtz Association centers have developed this research facility as a mobile and modular “system of systems” to record energy, water, greenhouse gas, and nutrient cycles on the land surface, in coastal regions, in the ocean, in polar regions, and in the atmosphere—but especially the interactions between the Earth compartments. During the implementation period (2017–21), the measuring systems were put into operation and test campaigns were performed to establish event-driven campaign routines. With MOSES’s regular operation starting in 2022, the observation system will then be ready for cross-compartment and cross-discipline research on the environmental impacts of dynamic events.},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Weber, Ute and Attinger, Sabine and Baschek, Burkard and Boike, Julia and Borchardt, Dietrich and Brix, Holger and Brüggemann, Nicolas and Bussmann, Ingeborg and Dietrich, Peter and Fischer, Philipp and Greinert, Jens and Hajnsek, Irena and Kamjunke, Norbert and Kerschke, Dorit and Kiendler-Scharr, Astrid and Körtzinger, Arne and Kottmeier, Christoph and Merz, Bruno and Merz, Ralf and Riese, Martin and Schloter, Michael and Schmid, HaPe and Schnitzler, Jörg-Peter and Sachs, Torsten and Schütze, Claudia and Tillmann, Ralf and Vereecken, Harry and Wieser, Andreas and Teutsch, Georg},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {E339--E348},\n}\n\n\n\n

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\n \n\n \n \n Wanner, L.; Calaf, M.; and Mauder, M.\n\n\n \n \n \n \n \n Incorporating the effect of heterogeneous surface heating into a semi-empirical model of the surface energy balance closure.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 17(6): e0268097. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Incorporating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wanner_incorporating_2022,\n\ttitle = {Incorporating the effect of heterogeneous surface heating into a semi-empirical model of the surface energy balance closure},\n\tvolume = {17},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0268097},\n\tdoi = {10.1371/journal.pone.0268097},\n\tabstract = {It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time ({\\textasciitilde}30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {PLOS ONE},\n\tauthor = {Wanner, Luise and Calaf, Marc and Mauder, Matthias},\n\teditor = {Roberti, Débora Regina},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {e0268097},\n}\n\n\n\n

\n\n\n

\n It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time (~30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements.\n

\n\n\n

\n \n\n \n \n Wang, X.; Chen, J. M.; Ju, W.; and Zhang, Y.\n\n\n \n \n \n \n \n Seasonal Variations in Leaf Maximum Photosynthetic Capacity and Its Dependence on Climate Factors Across Global FLUXNET Sites.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 127(5). May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_seasonal_2022,\n\ttitle = {Seasonal {Variations} in {Leaf} {Maximum} {Photosynthetic} {Capacity} and {Its} {Dependence} on {Climate} {Factors} {Across} {Global} {FLUXNET} {Sites}},\n\tvolume = {127},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021JG006709},\n\tdoi = {10.1029/2021JG006709},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Wang, Xiaoping and Chen, Jing M. and Ju, Weimin and Zhang, Yongguang},\n\tmonth = may,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, Q.; Qu, Y.; Robinson, K.; Bogena, H.; Graf, A.; Vereecken, H.; Tietema, A.; and Bol, R.\n\n\n \n \n \n \n \n Deforestation alters dissolved organic carbon and sulfate dynamics in a mountainous headwater catchment—A wavelet analysis.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Forests and Global Change, 5: 1044447. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Deforestation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_deforestation_2022,\n\ttitle = {Deforestation alters dissolved organic carbon and sulfate dynamics in a mountainous headwater catchment—{A} wavelet analysis},\n\tvolume = {5},\n\tissn = {2624-893X},\n\turl = {https://www.frontiersin.org/articles/10.3389/ffgc.2022.1044447/full},\n\tdoi = {10.3389/ffgc.2022.1044447},\n\tabstract = {Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fe \n              tot \n              ), and calcium (Ca \n              2+ \n              ) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31\\%, W14: 25\\%, W17: 3\\%). We found that water temperature and DOC, sulfate, and Fe \n              tot \n              concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L \n              –1 \n              and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L \n              –1 \n              . After clear-cut, DOC significantly increased by 59, 58\\% in the mainstream (W10, W14), but only 26\\% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca \n              2+ \n              , and Fe \n              tot \n              . The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO \n              4 \n              ) was highly correlated with stream water temperature, runoff, and Fe \n              tot \n              in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R \n              2 \n              ) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25\\% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Forests and Global Change},\n\tauthor = {Wang, Qiqi and Qu, Yuquan and Robinson, Kerri-Leigh and Bogena, Heye and Graf, Alexander and Vereecken, Harry and Tietema, Albert and Bol, Roland},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {1044447},\n}\n\n\n\n

\n\n\n

\n Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fe tot ), and calcium (Ca 2+ ) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fe tot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L –1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L –1 . After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca 2+ , and Fe tot . The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO 4 ) was highly correlated with stream water temperature, runoff, and Fe tot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R 2 ) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.\n

\n\n\n

\n \n\n \n \n Wang, R.; Gentine, P.; Li, L.; Chen, J.; Ning, L.; Yuan, L.; and Lü, G.\n\n\n \n \n \n \n \n Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Observational$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_observational_2022,\n\ttitle = {Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-21-0114.1/JHM-D-21-0114.1.xml},\n\tdoi = {10.1175/JHM-D-21-0114.1},\n\tabstract = {Abstract \n            Land-atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land-atmosphere coupling. One of the linkages in land-atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, Northeast Asia, and Southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hotspot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidences for the extreme climate change accelerated by surface energy partitioning.},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Wang, Ren and Gentine, Pierre and Li, Longhui and Chen, Jianyao and Ning, Liang and Yuan, Linwang and Lü, Guonian},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, R.; Li, L.; Gentine, P.; Zhang, Y.; Chen, J.; Chen, X.; Chen, L.; Ning, L.; Yuan, L.; and Lü, G.\n\n\n \n \n \n \n \n Recent increase in the observation-derived land evapotranspiration due to global warming.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(2): 024020. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Recent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_recent_2022,\n\ttitle = {Recent increase in the observation-derived land evapotranspiration due to global warming},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac4291},\n\tdoi = {10.1088/1748-9326/ac4291},\n\tabstract = {Abstract \n             \n              Estimates of change in global land evapotranspiration (ET) are necessary for understanding the terrestrial hydrological cycle under changing environments. However, large uncertainties still exist in our estimates, mostly related to the uncertainties in upscaling \n              in situ \n              observations to large scale under non-stationary surface conditions. Here, we use machine learning models, artificial neural network and random forest informed by ground observations and atmospheric boundary layer theory, to retrieve consistent global long-term latent heat flux (ET in energy units) and sensible heat flux over recent decades. This study demonstrates that recent global land ET has increased significantly and that the main driver for the increased ET is increasing temperature. Moreover, the results suggest that the increasing ET is mostly in humid regions such as the tropics. These observation-driven findings are consistent with the idea that ET would increase with climate warming. Our study has important implications in providing constraints for ET and in understanding terrestrial water cycles in changing environments.},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Wang, Ren and Li, Longhui and Gentine, Pierre and Zhang, Yao and Chen, Jianyao and Chen, Xingwei and Chen, Lijuan and Ning, Liang and Yuan, Linwang and Lü, Guonian},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {024020},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Wang, B.; Yue, X.; Zhou, H.; and Zhu, J.\n\n\n \n \n \n \n \n Impact of diffuse radiation on evapotranspiration and its coupling to carbon fluxes at global FLUXNET sites.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 322: 109006. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_impact_2022,\n\ttitle = {Impact of diffuse radiation on evapotranspiration and its coupling to carbon fluxes at global {FLUXNET} sites},\n\tvolume = {322},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322001964},\n\tdoi = {10.1016/j.agrformet.2022.109006},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Wang, Bin and Yue, Xu and Zhou, Hao and Zhu, Jun},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {109006},\n}\n\n\n\n

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\n \n\n \n \n Wang, B.; Chen, W.; Dai, J.; Li, Z.; Fu, Z.; Sarmah, S.; Luo, Y.; and Niu, S.\n\n\n \n \n \n \n \n Dryness controls temperature-optimized gross primary productivity across vegetation types.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109073. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Dryness$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_dryness_2022,\n\ttitle = {Dryness controls temperature-optimized gross primary productivity across vegetation types},\n\tvolume = {323},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002611},\n\tdoi = {10.1016/j.agrformet.2022.109073},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Wang, Bingxue and Chen, Weinan and Dai, Junhu and Li, Zhaolei and Fu, Zheng and Sarmah, Sangeeta and Luo, Yiqi and Niu, Shuli},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109073},\n}\n\n\n\n

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\n \n\n \n \n Walther, S.; Besnard, S.; Nelson, J. A.; El-Madany, T. S.; Migliavacca, M.; Weber, U.; Carvalhais, N.; Ermida, S. L.; Brümmer, C.; Schrader, F.; Prokushkin, A. S.; Panov, A. V.; and Jung, M.\n\n\n \n \n \n \n \n Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(11): 2805–2840. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Technical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{walther_technical_2022,\n\ttitle = {Technical note: {A} view from space on global flux towers by {MODIS} and {Landsat}: the {FluxnetEO} data set},\n\tvolume = {19},\n\tissn = {1726-4189},\n\tshorttitle = {Technical note},\n\turl = {https://bg.copernicus.org/articles/19/2805/2022/},\n\tdoi = {10.5194/bg-19-2805-2022},\n\tabstract = {Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40∘ off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Walther, Sophia and Besnard, Simon and Nelson, Jacob Allen and El-Madany, Tarek Sebastian and Migliavacca, Mirco and Weber, Ulrich and Carvalhais, Nuno and Ermida, Sofia Lorena and Brümmer, Christian and Schrader, Frederik and Prokushkin, Anatoly Stanislavovich and Panov, Alexey Vasilevich and Jung, Martin},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2805--2840},\n}\n\n\n\n

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\n \n\n \n \n Walker, V. A.; Colliander, A.; and Kimball, J. S.\n\n\n \n \n \n \n \n Satellite Retrievals of Probabilistic Freeze-Thaw Conditions From SMAP and AMSR Brightness Temperatures.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–11. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Satellite$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{walker_satellite_2022,\n\ttitle = {Satellite {Retrievals} of {Probabilistic} {Freeze}-{Thaw} {Conditions} {From} {SMAP} and {AMSR} {Brightness} {Temperatures}},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\turl = {https://ieeexplore.ieee.org/document/9773168/},\n\tdoi = {10.1109/TGRS.2022.3174807},\n\turldate = {2022-11-21},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Walker, Victoria A. and Colliander, Andreas and Kimball, John S.},\n\tyear = {2022},\n\tpages = {1--11},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Vitali, V.; Martínez-Sancho, E.; Treydte, K.; Andreu-Hayles, L.; Dorado-Liñán, I.; Gutierrez, E.; Helle, G.; Leuenberger, M.; Loader, N.; Rinne-Garmston, K.; Schleser, G.; Allen, S.; Waterhouse, J.; Saurer, M.; and Lehmann, M.\n\n\n \n \n \n \n \n The unknown third – Hydrogen isotopes in tree-ring cellulose across Europe.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 813: 152281. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vitali_unknown_2022,\n\ttitle = {The unknown third – {Hydrogen} isotopes in tree-ring cellulose across {Europe}},\n\tvolume = {813},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721073575},\n\tdoi = {10.1016/j.scitotenv.2021.152281},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Vitali, V. and Martínez-Sancho, E. and Treydte, K. and Andreu-Hayles, L. and Dorado-Liñán, I. and Gutierrez, E. and Helle, G. and Leuenberger, M. and Loader, N.J. and Rinne-Garmston, K.T. and Schleser, G.H. and Allen, S. and Waterhouse, J.S. and Saurer, M. and Lehmann, M.M.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {152281},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Vereecken, H.; Amelung, W.; Bauke, S. L.; Bogena, H.; Brüggemann, N.; Montzka, C.; Vanderborght, J.; Bechtold, M.; Blöschl, G.; Carminati, A.; Javaux, M.; Konings, A. G.; Kusche, J.; Neuweiler, I.; Or, D.; Steele-Dunne, S.; Verhoef, A.; Young, M.; and Zhang, Y.\n\n\n \n \n \n \n \n Soil hydrology in the Earth system.\n \n \n \n \n\n\n \n\n\n\n Nature Reviews Earth & Environment, 3(9): 573–587. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vereecken_soil_2022,\n\ttitle = {Soil hydrology in the {Earth} system},\n\tvolume = {3},\n\tissn = {2662-138X},\n\turl = {https://www.nature.com/articles/s43017-022-00324-6},\n\tdoi = {10.1038/s43017-022-00324-6},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Nature Reviews Earth \\& Environment},\n\tauthor = {Vereecken, Harry and Amelung, Wulf and Bauke, Sara L. and Bogena, Heye and Brüggemann, Nicolas and Montzka, Carsten and Vanderborght, Jan and Bechtold, Michel and Blöschl, Günter and Carminati, Andrea and Javaux, Mathieu and Konings, Alexandra G. and Kusche, Jürgen and Neuweiler, Insa and Or, Dani and Steele-Dunne, Susan and Verhoef, Anne and Young, Michael and Zhang, Yonggen},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {573--587},\n}\n\n\n\n

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\n \n\n \n \n Vallentin, C.; Harfenmeister, K.; Itzerott, S.; Kleinschmit, B.; Conrad, C.; and Spengler, D.\n\n\n \n \n \n \n \n Suitability of satellite remote sensing data for yield estimation in northeast Germany.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 23(1): 52–82. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Suitability$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vallentin_suitability_2022,\n\ttitle = {Suitability of satellite remote sensing data for yield estimation in northeast {Germany}},\n\tvolume = {23},\n\tissn = {1385-2256, 1573-1618},\n\turl = {https://link.springer.com/10.1007/s11119-021-09827-6},\n\tdoi = {10.1007/s11119-021-09827-6},\n\tabstract = {Abstract \n            Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Precision Agriculture},\n\tauthor = {Vallentin, Claudia and Harfenmeister, Katharina and Itzerott, Sibylle and Kleinschmit, Birgit and Conrad, Christopher and Spengler, Daniel},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {52--82},\n}\n\n\n\n

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\n \n\n \n \n Ukkola, A. M.; Abramowitz, G.; and De Kauwe, M. G.\n\n\n \n \n \n \n \n A flux tower dataset tailored for land model evaluation.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(2): 449–461. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ukkola_flux_2022,\n\ttitle = {A flux tower dataset tailored for land model evaluation},\n\tvolume = {14},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/14/449/2022/},\n\tdoi = {10.5194/essd-14-449-2022},\n\tabstract = {Abstract. Eddy covariance flux towers measure the exchange of water, energy,\nand carbon fluxes between the land and atmosphere. They have become\ninvaluable for theory development and evaluating land models. However, flux\ntower data as measured (even after site post-processing) are not directly\nsuitable for land surface modelling due to data gaps in model forcing\nvariables, inappropriate gap-filling, formatting, and varying data quality.\nHere we present a quality-control and data-formatting pipeline for tower\ndata from FLUXNET2015, La Thuile, and OzFlux syntheses and the resultant\n170-site globally distributed flux tower dataset specifically designed for\nuse in land modelling. The dataset underpins the second phase of the Protocol for the Analysis of Land Surface\nModels (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER), an international model\nintercomparison project encompassing {\\textgreater}20 land surface and\nbiosphere models. The dataset is provided in the Assistance for Land-surface\nModelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For\nforcing land surface models, the dataset provides fully gap-filled\nmeteorological data that have had periods of low data quality removed.\nAdditional constraints required for land models, such as reference\nmeasurement heights, vegetation types, and satellite-based monthly leaf area\nindex estimates, are also included. For model evaluation, the dataset\nprovides estimates of key water, carbon, and energy variables, with the\nlatent and sensible heat fluxes additionally corrected for energy balance\nclosure. The dataset provides a total of 1040 site years covering the period\n1992–2018, with individual sites spanning from 1 to 21 years. The dataset is\navailable at http://doi.org/10.25914/5fdb0902607e1\n(Ukkola et al., 2021).},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Ukkola, Anna M. and Abramowitz, Gab and De Kauwe, Martin G.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {449--461},\n}\n\n\n\n

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\n \n\n \n \n Tumajer, J.; Scharnweber, T.; Smiljanic, M.; and Wilmking, M.\n\n\n \n \n \n \n \n Limitation by vapour pressure deficit shapes different intra‐annual growth patterns of diffuse‐ and ring‐porous temperate broadleaves.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 233(6): 2429–2441. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Limitation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tumajer_limitation_2022,\n\ttitle = {Limitation by vapour pressure deficit shapes different intra‐annual growth patterns of diffuse‐ and ring‐porous temperate broadleaves},\n\tvolume = {233},\n\tissn = {0028-646X, 1469-8137},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.17952},\n\tdoi = {10.1111/nph.17952},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {New Phytologist},\n\tauthor = {Tumajer, Jan and Scharnweber, Tobias and Smiljanic, Marko and Wilmking, Martin},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2429--2441},\n}\n\n\n\n

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\n \n\n \n \n Travova, S. V.; Stepanenko, V. M.; Medvedev, A. I.; Tolstykh, M. A.; and Bogomolov, V. Y.\n\n\n \n \n \n \n \n Quality of Soil Simulation by the INM RAS–MSU Soil Scheme as a Part of the SL-AV Weather Prediction Model.\n \n \n \n \n\n\n \n\n\n\n Russian Meteorology and Hydrology, 47(3): 159–173. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Quality$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{travova_quality_2022,\n\ttitle = {Quality of {Soil} {Simulation} by the {INM} {RAS}–{MSU} {Soil} {Scheme} as a {Part} of the {SL}-{AV} {Weather} {Prediction} {Model}},\n\tvolume = {47},\n\tissn = {1068-3739, 1934-8096},\n\turl = {https://link.springer.com/10.3103/S1068373922030013},\n\tdoi = {10.3103/S1068373922030013},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Russian Meteorology and Hydrology},\n\tauthor = {Travova, S. V. and Stepanenko, V. M. and Medvedev, A. I. and Tolstykh, M. A. and Bogomolov, V. Yu.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {159--173},\n}\n\n\n\n

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\n \n\n \n \n Theuerkauf, M.; Blume, T.; Brauer, A.; Dräger, N.; Feldens, P.; Kaiser, K.; Kappler, C.; Kästner, F.; Lorenz, S.; Schmidt, J.; and Schult, M.\n\n\n \n \n \n \n \n Holocene lake‐level evolution of Lake Tiefer See, NE Germany, caused by climate and land cover changes.\n \n \n \n \n\n\n \n\n\n\n Boreas, 51(2): 299–316. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Holocene$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{theuerkauf_holocene_2022,\n\ttitle = {Holocene lake‐level evolution of {Lake} {Tiefer} {See}, {NE} {Germany}, caused by climate and land cover changes},\n\tvolume = {51},\n\tissn = {0300-9483, 1502-3885},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12561},\n\tdoi = {10.1111/bor.12561},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Boreas},\n\tauthor = {Theuerkauf, Martin and Blume, Theresa and Brauer, Achim and Dräger, Nadine and Feldens, Peter and Kaiser, Knut and Kappler, Christoph and Kästner, Frederike and Lorenz, Sebastian and Schmidt, Jens‐Peter and Schult, Manuela},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {299--316},\n}\n\n\n\n

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\n \n\n \n \n Teucher, M.; Thürkow, D.; Alb, P.; and Conrad, C.\n\n\n \n \n \n \n \n Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(2): 393. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Digital$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{teucher_digital_2022,\n\ttitle = {Digital {In} {Situ} {Data} {Collection} in {Earth} {Observation}, {Monitoring} and {Agriculture}—{Progress} towards {Digital} {Agriculture}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/2/393},\n\tdoi = {10.3390/rs14020393},\n\tabstract = {Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Teucher, Mike and Thürkow, Detlef and Alb, Philipp and Conrad, Christopher},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {393},\n}\n\n\n\n

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\n \n\n \n \n Terán, C. P.; Naz, B. S.; Graf, A.; Qu, Y.; Hendricks-Franssen, H.; Baatz, R.; Ciais, P.; and Vereecken, H.\n\n\n \n \n \n \n \n Water conductance rather than photosynthesis controls water-use efficiency in Europe.\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{teran_water_2022,\n\ttype = {preprint},\n\ttitle = {Water conductance rather than photosynthesis controls water-use efficiency in {Europe}},\n\turl = {https://www.researchsquare.com/article/rs-1627127/v1},\n\tabstract = {Abstract \n          Water-use efficiency (WUE) is one of the major axes of ecosystem functioning and an indicator for ecosystem health, but its variability due to climate change and droughts has not yet been thoroughly understood. Here, we use remote sensing and reanalysis data to map the trends and responses to droughts of three WUE indices from 1995 – 2018 in Europe. Further, we conduct a causal network discovery analysis to identify drivers of in WUE change. We found an increasing trends of photosynthesis per canopy conductance (IWUE) in forests and grasslands. IWUE also increased during droughts over whole Europe but this was not translated into an increase of photosythesis per water evaporated (i.e. increased EWUE). We highlight that the WUE indices are predominantly explained by ecohydrological variability, which underlines the role of water demand and supply to ecosystem function in Europe.},\n\turldate = {2022-11-21},\n\tinstitution = {In Review},\n\tauthor = {Terán, Christian Poppe and Naz, Bibi S. and Graf, Alexander and Qu, Yuquan and Hendricks-Franssen, Harrie-Jan and Baatz, Roland and Ciais, Philippe and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2022},\n\tdoi = {10.21203/rs.3.rs-1627127/v1},\n}\n\n\n\n

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\n \n\n \n \n Templer, P. H.; Harrison, J. L.; Pilotto, F.; Flores-Díaz, A.; Haase, P.; McDowell, W. H.; Sharif, R.; Shibata, H.; Blankman, D.; Avila, A.; Baatar, U.; Bogena, H. R.; Bourgeois, I.; Campbell, J.; Dirnböck, T.; Dodds, W. K.; Hauken, M.; Kokorite, I.; Lajtha, K.; Lai, I.; Laudon, H.; Lin, T. C.; Lins, S. R. M.; Meesenburg, H.; Pinho, P.; Robison, A.; Rogora, M.; Scheler, B.; Schleppi, P.; Sommaruga, R.; Staszewski, T.; and Taka, M.\n\n\n \n \n \n \n \n Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds: a large-scale data synthesis.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 160(2): 219–241. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Atmospheric$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{templer_atmospheric_2022,\n\ttitle = {Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds: a large-scale data synthesis},\n\tvolume = {160},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Atmospheric deposition and precipitation are important predictors of inorganic nitrogen export to streams from forest and grassland watersheds},\n\turl = {https://link.springer.com/10.1007/s10533-022-00951-7},\n\tdoi = {10.1007/s10533-022-00951-7},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Biogeochemistry},\n\tauthor = {Templer, P. H. and Harrison, J. L. and Pilotto, F. and Flores-Díaz, A. and Haase, P. and McDowell, W. H. and Sharif, R. and Shibata, H. and Blankman, D. and Avila, A. and Baatar, U. and Bogena, H. R. and Bourgeois, I. and Campbell, J. and Dirnböck, T. and Dodds, W. K. and Hauken, M. and Kokorite, I. and Lajtha, K. and Lai, I.-L. and Laudon, H. and Lin, T. C. and Lins, S. R. M. and Meesenburg, H. and Pinho, P. and Robison, A. and Rogora, M. and Scheler, B. and Schleppi, P. and Sommaruga, R. and Staszewski, T. and Taka, M.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {219--241},\n}\n\n\n\n

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\n \n\n \n \n Svenningsen, C. S.; Bowler, D. E.; Hecker, S.; Bladt, J.; Grescho, V.; Dam, N. M.; Dauber, J.; Eichenberg, D.; Ejrnæs, R.; Fløjgaard, C.; Frenzel, M.; Frøslev, T. G.; Hansen, A. J.; Heilmann‐Clausen, J.; Huang, Y.; Larsen, J. C.; Menger, J.; Nayan, N. L. B. M.; Pedersen, L. B.; Richter, A.; Dunn, R. R.; Tøttrup, A. P.; and Bonn, A.\n\n\n \n \n \n \n \n Flying insect biomass is negatively associated with urban cover in surrounding landscapes.\n \n \n \n \n\n\n \n\n\n\n Diversity and Distributions, 28(6): 1242–1254. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Flying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{svenningsen_flying_2022,\n\ttitle = {Flying insect biomass is negatively associated with urban cover in surrounding landscapes},\n\tvolume = {28},\n\tissn = {1366-9516, 1472-4642},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ddi.13532},\n\tdoi = {10.1111/ddi.13532},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Diversity and Distributions},\n\tauthor = {Svenningsen, Cecilie S. and Bowler, Diana E. and Hecker, Susanne and Bladt, Jesper and Grescho, Volker and Dam, Nicole M. and Dauber, Jens and Eichenberg, David and Ejrnæs, Rasmus and Fløjgaard, Camilla and Frenzel, Mark and Frøslev, Tobias G. and Hansen, Anders J. and Heilmann‐Clausen, Jacob and Huang, Yuanyuan and Larsen, Jonas C. and Menger, Juliana and Nayan, Nur L. B. M. and Pedersen, Lene B. and Richter, Anett and Dunn, Robert R. and Tøttrup, Anders P. and Bonn, Aletta},\n\teditor = {Jarvis, Susan},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {1242--1254},\n}\n\n\n\n

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\n \n\n \n \n Sunjidmaa, N.; Mendoza-Lera, C.; Hille, S.; Schmidt, C.; Borchardt, D.; and Graeber, D.\n\n\n \n \n \n \n \n Carbon limitation may override fine-sediment induced alterations of hyporheic nitrogen and phosphorus dynamics.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 837: 155689. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sunjidmaa_carbon_2022,\n\ttitle = {Carbon limitation may override fine-sediment induced alterations of hyporheic nitrogen and phosphorus dynamics},\n\tvolume = {837},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722027851},\n\tdoi = {10.1016/j.scitotenv.2022.155689},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Sunjidmaa, Nergui and Mendoza-Lera, Clara and Hille, Sandra and Schmidt, Christian and Borchardt, Dietrich and Graeber, Daniel},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {155689},\n}\n\n\n\n

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\n \n\n \n \n O, S.; Orth, R.; Weber, U.; and Park, S. K.\n\n\n \n \n \n \n \n High-resolution European daily soil moisture derived with machine learning (2003-2020).\n \n \n \n \n\n\n \n\n\n\n . 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

@article{o_high-resolution_2022,\n\ttitle = {High-resolution {European} daily soil moisture derived with machine learning (2003-2020)},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://arxiv.org/abs/2205.10753},\n\tdoi = {10.48550/ARXIV.2205.10753},\n\tabstract = {Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses. The SoMo.ml-EU is available at figshare.},\n\turldate = {2022-11-21},\n\tauthor = {O, Sungmin and Orth, Rene and Weber, Ulrich and Park, Seon Ki},\n\tyear = {2022},\n\tkeywords = {Atmospheric and Oceanic Physics (physics.ao-ph), FOS: Physical sciences},\n}\n\n\n\n

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\n \n\n \n \n Su, Y.; Yang, X.; Gentine, P.; Maignan, F.; Shang, J.; and Ciais, P.\n\n\n \n \n \n \n \n Observed strong atmospheric water constraints on forest photosynthesis using eddy covariance and satellite-based data across the Northern Hemisphere.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 110: 102808. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Observed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{su_observed_2022,\n\ttitle = {Observed strong atmospheric water constraints on forest photosynthesis using eddy covariance and satellite-based data across the {Northern} {Hemisphere}},\n\tvolume = {110},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1569843222000103},\n\tdoi = {10.1016/j.jag.2022.102808},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Su, Yongxian and Yang, Xueqin and Gentine, Pierre and Maignan, Fabienne and Shang, Jiali and Ciais, Philippe},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {102808},\n}\n\n\n\n

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\n \n\n \n \n Strebel, L.; Bogena, H. R.; Vereecken, H.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(2): 395–411. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Coupling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{strebel_coupling_2022,\n\ttitle = {Coupling the {Community} {Land} {Model} version 5.0 to the parallel data assimilation framework {PDAF}: description and applications},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {Coupling the {Community} {Land} {Model} version 5.0 to the parallel data assimilation framework {PDAF}},\n\turl = {https://gmd.copernicus.org/articles/15/395/2022/},\n\tdoi = {10.5194/gmd-15-395-2022},\n\tabstract = {Abstract. Land surface models are important for improving our understanding\nof the Earth system. They are continuously improving and becoming better in\nrepresenting the different land surface processes, e.g., the Community Land\nModel version 5 (CLM5). Similarly, observational networks and remote sensing\noperations are increasingly providing more data, e.g., from new satellite\nproducts and new in situ measurement sites, with increasingly higher quality\nfor a range of important variables of the Earth system. For the optimal\ncombination of land surface models and observation data, data assimilation\ntechniques have been developed in recent decades that incorporate\nobservations to update modeled states and parameters. The Parallel Data\nAssimilation Framework (PDAF) is a software environment that enables\nensemble data assimilation and simplifies the implementation of data\nassimilation systems in numerical models. In this study, we present the\ndevelopment of the new interface between PDAF and CLM5. This newly\nimplemented coupling integrates the PDAF functionality into CLM5 by\nmodifying the CLM5 ensemble mode to keep changes to the pre-existing\nparallel communication infrastructure to a minimum. Soil water content\nobservations from an extensive in situ measurement network in the\nWüstebach catchment in Germany are used to illustrate the application of\nthe coupled CLM5-PDAF system. The results show overall reductions in root\nmean square error of soil water content from 7 \\% up to 35 \\% compared to\nsimulations without data assimilation. We expect the coupled CLM5-PDAF\nsystem to provide a basis for improved regional to global land surface\nmodeling by enabling the assimilation of globally available observational\ndata.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Strebel, Lukas and Bogena, Heye R. and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {395--411},\n}\n\n\n\n

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\n \n\n \n \n Slabbert, E.; Knight, T.; Wubet, T.; Kautzner, A.; Baessler, C.; Auge, H.; Roscher, C.; and Schweiger, O.\n\n\n \n \n \n \n \n Abiotic factors are more important than land management and biotic interactions in shaping vascular plant and soil fungal communities.\n \n \n \n \n\n\n \n\n\n\n Global Ecology and Conservation, 33: e01960. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Abiotic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{slabbert_abiotic_2022,\n\ttitle = {Abiotic factors are more important than land management and biotic interactions in shaping vascular plant and soil fungal communities},\n\tvolume = {33},\n\tissn = {23519894},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2351989421005102},\n\tdoi = {10.1016/j.gecco.2021.e01960},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Global Ecology and Conservation},\n\tauthor = {Slabbert, E.L. and Knight, T.M. and Wubet, T. and Kautzner, A. and Baessler, C. and Auge, H. and Roscher, C. and Schweiger, O.},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {e01960},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Seo, E.; and Dirmeyer, P. A.\n\n\n \n \n \n \n \n Improving the ESA CCI daily soil moisture time series with physically-based land surface model datasets using a Fourier time-filtering method.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{seo_improving_2022,\n\ttitle = {Improving the {ESA} {CCI} daily soil moisture time series with physically-based land surface model datasets using a {Fourier} time-filtering method},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-21-0120.1/JHM-D-21-0120.1.xml},\n\tdoi = {10.1175/JHM-D-21-0120.1},\n\tabstract = {Abstract \n             \n              Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with \n              in situ \n              observations. \n             \n            When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Seo, Eunkyo and Dirmeyer, Paul A.},\n\tmonth = jan,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Schucknecht, A.; Seo, B.; Krämer, A.; Asam, S.; Atzberger, C.; and Kiese, R.\n\n\n \n \n \n \n \n Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(10): 2699–2727. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schucknecht_estimating_2022,\n\ttitle = {Estimating dry biomass and plant nitrogen concentration in pre-{Alpine} grasslands with low-cost {UAS}-borne multispectral data – a comparison of sensors, algorithms, and predictor sets},\n\tvolume = {19},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/19/2699/2022/},\n\tdoi = {10.5194/bg-19-2699-2022},\n\tabstract = {Abstract. Grasslands are an important part of pre-Alpine and Alpine\nlandscapes. Despite the economic value and the significant role of\ngrasslands in carbon and nitrogen (N) cycling, spatially explicit\ninformation on grassland biomass and quality is rarely available. Remotely\nsensed data from unmanned aircraft systems (UASs) and satellites might be an\noption to overcome this gap. Our study aims to investigate the potential of\nlow-cost UAS-based multispectral sensors for estimating above-ground biomass\n(dry matter, DM) and plant N concentration. In our analysis, we compared two\ndifferent sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three\nstatistical models (linear model; random forests, RFs; gradient-boosting\nmachines, GBMs), and six predictor sets (i.e. different combinations of raw\nreflectance, vegetation indices, and canopy height). Canopy height\ninformation can be derived from UAS sensors but was not available in our\nstudy. Therefore, we tested the added value of this structural information\nwith in situ measured bulk canopy height data. A combined field sampling and\nflight campaign was conducted in April 2018 at different grassland sites in\nsouthern Germany to obtain in situ and the corresponding spectral data. The\nhyper-parameters of the two machine learning (ML) approaches (RF, GBM) were\noptimized, and all model setups were run with a 6-fold cross-validation.\nLinear models were characterized by very low statistical performance\nmeasures, thus were not suitable to estimate DM and plant N concentration\nusing UAS data. The non-linear ML algorithms showed an acceptable regression\nperformance for all sensor–predictor set combinations with average (avg; cross-validated, cv)\nRcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and\nrRMSEcv,avg (relative) of 15.9 \\% for DM and with Rcv,avg2 of\n0.40, RMSEcv,avg of 0.48 wt \\%, and rRMSEcv, avg of\n15.2 \\% for plant N concentration estimation. The optimal combination of\nsensors, ML algorithms, and predictor sets notably improved the model\nperformance. The best model performance for the estimation of DM\n(Rcv2=0.67, RMSEcv=41.9 g m2,\nrRMSEcv=12.6 \\%) was achieved with an RF model that utilizes all\npossible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ\nsensor data (Rcv2=0.47, RMSEcv=0.45 wt \\%,\nrRMSEcv=14.2 \\%). DM models with the spectral input of REM\nperformed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors\nwas most influential on model performance, while the effect of the chosen ML\nalgorithm was generally lower. The addition of canopy height to the spectral\ndata in the predictor set significantly improved the DM models. In our\nstudy, calibrating the ML algorithm improved the model performance\nsubstantially, which shows the importance of this step.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Schucknecht, Anne and Seo, Bumsuk and Krämer, Alexander and Asam, Sarah and Atzberger, Clement and Kiese, Ralf},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2699--2727},\n}\n\n\n\n

\n\n\n

\n Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.\n

\n\n\n

\n \n\n \n \n Schreiber, M.; Bazaios, E.; Ströbel, B.; Wolf, B.; Ostler, U.; Gasche, R.; Schlingmann, M.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland.\n \n \n \n \n\n\n \n\n\n\n Nutrient Cycling in Agroecosystems. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Impacts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schreiber_impacts_2022,\n\ttitle = {Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland},\n\tissn = {1385-1314, 1573-0867},\n\turl = {https://link.springer.com/10.1007/s10705-022-10239-9},\n\tdoi = {10.1007/s10705-022-10239-9},\n\tabstract = {Abstract \n             \n              Low nitrogen (N) use efficiency of broadcast slurry application leads to nutrient losses, air and water pollution, greenhouse gas emissions and—in particular in a warming climate—to soil N mining. Here we test the alternative slurry acidification and injection techniques for their mitigation potential compared to broadcast spreading in montane grassland. We determined (1) the fate of \n              15 \n              N labelled slurry in the plant-soil-microbe system and soil-atmosphere exchange of greenhouse gases over one fertilization/harvest cycle and (2) assessed the longer-term contribution of fertilizer \n              15 \n              N to soil organic N formation by the end of the growing season. The isotope tracing approach was combined with a space for time climate change experiment. Simulated climate change increased productivity, ecosystem respiration, and net methane uptake irrespective of management, but the generally low N \n              2 \n              O fluxes remained unchanged. Compared to the broadcast spreading, slurry acidification showed lowest N losses, thus increased productivity and fertilizer N use efficiency (38\\% \n              15 \n              N recovery in plant aboveground plant biomass). In contrast, slurry injection showed highest total fertilizer N losses, but increased fertilization-induced soil organic N formation by 9–12 kg N ha \n              −1 \n              season \n              −1 \n              . Slurry management effects on N \n              2 \n              O and CH \n              4 \n              fluxes remained negligible. In sum, our study shows that the tested alternative slurry application techniques can increase N use efficiency and/or promote soil organic N formation from applied fertilizer to a remarkable extent. However, this is still not sufficient to prevent soil N mining mostly resulting from large plant N exports that even exceed total fertilizer N inputs.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Nutrient Cycling in Agroecosystems},\n\tauthor = {Schreiber, Mirella and Bazaios, Elpida and Ströbel, Barbara and Wolf, Benjamin and Ostler, Ulrike and Gasche, Rainer and Schlingmann, Marcus and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = oct,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Schneider, C.; Neuwirth, B.; Schneider, S.; Balanzategui, D.; Elsholz, S.; Fenner, D.; Meier, F.; and Heinrich, I.\n\n\n \n \n \n \n \n Using the dendro-climatological signal of urban trees as a measure of urbanization and urban heat island.\n \n \n \n \n\n\n \n\n\n\n Urban Ecosystems, 25(3): 849–865. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schneider_using_2022,\n\ttitle = {Using the dendro-climatological signal of urban trees as a measure of urbanization and urban heat island},\n\tvolume = {25},\n\tissn = {1083-8155, 1573-1642},\n\turl = {https://link.springer.com/10.1007/s11252-021-01196-2},\n\tdoi = {10.1007/s11252-021-01196-2},\n\tabstract = {Abstract \n            Using dendroclimatological techniques this study investigates whether inner city tree-ring width (TRW) chronologies from eight tree species (ash, beech, fir, larch, lime, sessile and pedunculate oak, and pine) are suitable to examine the urban heat island of Berlin, Germany. Climate-growth relationships were analyzed for 18 sites along a gradient of increasing urbanization covering Berlin and surrounding rural areas. As a proxy for defining urban heat island intensities at each site, we applied urbanization parameters such as building fraction, impervious surfaces, and green areas. The response of TRW to monthly and seasonal air temperature, precipitation, aridity, and daily air-temperature ranges were used to identify climate-growth relationships. Trees from urban sites were found to be more sensitive to climate compared to trees in the surrounding hinterland. Ring width of the deciduous species, especially ash, beech, and oak, showed a high sensitivity to summer heat and drought at urban locations (summer signal), whereas conifer species were found suitable for the analysis of the urban heat island in late winter and early spring (winter signal). \n            The summer and winter signals were strongest in tree-ring chronologies when the urban heat island intensities were based on an area of about 200 m to 3000 m centered over the tree locations, and thus reflect the urban climate at the scale of city quarters. For the summer signal, the sensitivity of deciduous tree species to climate increased with urbanity. \n            These results indicate that urban trees can be used for climate response analyses and open new pathways to trace the evolution of urban climate change and more specifically the urban heat island, both in time and space.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Urban Ecosystems},\n\tauthor = {Schneider, Christoph and Neuwirth, Burkhard and Schneider, Sebastian and Balanzategui, Daniel and Elsholz, Stefanie and Fenner, Daniel and Meier, Fred and Heinrich, Ingo},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {849--865},\n}\n\n\n\n

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\n \n\n \n \n Schmitz, M.; Deutschmann, B.; Markert, N.; Backhaus, T.; Brack, W.; Brauns, M.; Brinkmann, M.; Seiler, T.; Fink, P.; Tang, S.; Beitel, S.; Doering, J. A.; Hecker, M.; Shao, Y.; Schulze, T.; Weitere, M.; Wild, R.; Velki, M.; and Hollert, H.\n\n\n \n \n \n \n \n Demonstration of an aggregated biomarker response approach to assess the impact of point and diffuse contaminant sources in feral fish in a small river case study.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 804: 150020. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Demonstration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmitz_demonstration_2022,\n\ttitle = {Demonstration of an aggregated biomarker response approach to assess the impact of point and diffuse contaminant sources in feral fish in a small river case study},\n\tvolume = {804},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050956},\n\tdoi = {10.1016/j.scitotenv.2021.150020},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Schmitz, Markus and Deutschmann, Björn and Markert, Nele and Backhaus, Thomas and Brack, Werner and Brauns, Mario and Brinkmann, Markus and Seiler, Thomas-Benjamin and Fink, Patrick and Tang, Song and Beitel, Shawn and Doering, Jon A. and Hecker, Markus and Shao, Ying and Schulze, Tobias and Weitere, Markus and Wild, Romy and Velki, Mirna and Hollert, Henner},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {150020},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, L.; Schaefer, D.; Geller, J.; Lünenschloss, P.; Palm, B.; Rinke, K.; and Bumberger, J.\n\n\n \n \n \n \n \n System for Automated Quality Control (Saqc) to Enable Traceable and Reproducible Data Streams in Environmental Science.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"System$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_system_2022,\n\ttitle = {System for {Automated} {Quality} {Control} ({Saqc}) to {Enable} {Traceable} and {Reproducible} {Data} {Streams} in {Environmental} {Science}},\n\tissn = {1556-5068},\n\turl = {https://www.ssrn.com/abstract=4173698},\n\tdoi = {10.2139/ssrn.4173698},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Schmidt, Lennart and Schaefer, David and Geller, Juliane and Lünenschloss, Peter and Palm, Bert and Rinke, Karsten and Bumberger, Jan},\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Salomón, R. L.; Peters, R. L.; Zweifel, R.; Sass-Klaassen, U. G. W.; Stegehuis, A. I.; Smiljanic, M.; Poyatos, R.; Babst, F.; Cienciala, E.; Fonti, P.; Lerink, B. J. W.; Lindner, M.; Martinez-Vilalta, J.; Mencuccini, M.; Nabuurs, G.; van der Maaten, E.; von Arx, G.; Bär, A.; Akhmetzyanov, L.; Balanzategui, D.; Bellan, M.; Bendix, J.; Berveiller, D.; Blaženec, M.; Čada, V.; Carraro, V.; Cecchini, S.; Chan, T.; Conedera, M.; Delpierre, N.; Delzon, S.; Ditmarová, Ľ.; Dolezal, J.; Dufrêne, E.; Edvardsson, J.; Ehekircher, S.; Forner, A.; Frouz, J.; Ganthaler, A.; Gryc, V.; Güney, A.; Heinrich, I.; Hentschel, R.; Janda, P.; Ježík, M.; Kahle, H.; Knüsel, S.; Krejza, J.; Kuberski, Ł.; Kučera, J.; Lebourgeois, F.; Mikoláš, M.; Matula, R.; Mayr, S.; Oberhuber, W.; Obojes, N.; Osborne, B.; Paljakka, T.; Plichta, R.; Rabbel, I.; Rathgeber, C. B. K.; Salmon, Y.; Saunders, M.; Scharnweber, T.; Sitková, Z.; Stangler, D. F.; Stereńczak, K.; Stojanović, M.; Střelcová, K.; Světlík, J.; Svoboda, M.; Tobin, B.; Trotsiuk, V.; Urban, J.; Valladares, F.; Vavrčík, H.; Vejpustková, M.; Walthert, L.; Wilmking, M.; Zin, E.; Zou, J.; and Steppe, K.\n\n\n \n \n \n \n \n The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 13(1): 28. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{salomon_2018_2022,\n\ttitle = {The 2018 {European} heatwave led to stem dehydration but not to consistent growth reductions in forests},\n\tvolume = {13},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-021-27579-9},\n\tdoi = {10.1038/s41467-021-27579-9},\n\tabstract = {Abstract \n            Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Nature Communications},\n\tauthor = {Salomón, Roberto L. and Peters, Richard L. and Zweifel, Roman and Sass-Klaassen, Ute G. W. and Stegehuis, Annemiek I. and Smiljanic, Marko and Poyatos, Rafael and Babst, Flurin and Cienciala, Emil and Fonti, Patrick and Lerink, Bas J. W. and Lindner, Marcus and Martinez-Vilalta, Jordi and Mencuccini, Maurizio and Nabuurs, Gert-Jan and van der Maaten, Ernst and von Arx, Georg and Bär, Andreas and Akhmetzyanov, Linar and Balanzategui, Daniel and Bellan, Michal and Bendix, Jörg and Berveiller, Daniel and Blaženec, Miroslav and Čada, Vojtěch and Carraro, Vinicio and Cecchini, Sébastien and Chan, Tommy and Conedera, Marco and Delpierre, Nicolas and Delzon, Sylvain and Ditmarová, Ľubica and Dolezal, Jiri and Dufrêne, Eric and Edvardsson, Johannes and Ehekircher, Stefan and Forner, Alicia and Frouz, Jan and Ganthaler, Andrea and Gryc, Vladimír and Güney, Aylin and Heinrich, Ingo and Hentschel, Rainer and Janda, Pavel and Ježík, Marek and Kahle, Hans-Peter and Knüsel, Simon and Krejza, Jan and Kuberski, Łukasz and Kučera, Jiří and Lebourgeois, François and Mikoláš, Martin and Matula, Radim and Mayr, Stefan and Oberhuber, Walter and Obojes, Nikolaus and Osborne, Bruce and Paljakka, Teemu and Plichta, Roman and Rabbel, Inken and Rathgeber, Cyrille B. K. and Salmon, Yann and Saunders, Matthew and Scharnweber, Tobias and Sitková, Zuzana and Stangler, Dominik Florian and Stereńczak, Krzysztof and Stojanović, Marko and Střelcová, Katarína and Světlík, Jan and Svoboda, Miroslav and Tobin, Brian and Trotsiuk, Volodymyr and Urban, Josef and Valladares, Fernando and Vavrčík, Hanuš and Vejpustková, Monika and Walthert, Lorenz and Wilmking, Martin and Zin, Ewa and Zou, Junliang and Steppe, Kathy},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {28},\n}\n\n\n\n

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\n \n\n \n \n Rummler, T.; Wagner, A.; Arnault, J.; and Kunstmann, H.\n\n\n \n \n \n \n \n Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: Benefits of a 2D groundwater representation.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Lateral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rummler_lateral_2022,\n\ttitle = {Lateral terrestrial water fluxes in the {LSM} of {WRF}‐{Hydro}: {Benefits} of a {2D} groundwater representation},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Lateral terrestrial water fluxes in the {LSM} of {WRF}‐{Hydro}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14510},\n\tdoi = {10.1002/hyp.14510},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Hydrological Processes},\n\tauthor = {Rummler, Thomas and Wagner, Andreas and Arnault, Joël and Kunstmann, Harald},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Robinson, K.; Bogena, H. R.; Wang, Q.; Cammeraat, E.; and Bol, R.\n\n\n \n \n \n \n \n Effects of deforestation on dissolved organic carbon and nitrate in catchment stream water revealed by wavelet analysis.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 4: 1003693. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{robinson_effects_2022,\n\ttitle = {Effects of deforestation on dissolved organic carbon and nitrate in catchment stream water revealed by wavelet analysis},\n\tvolume = {4},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2022.1003693/full},\n\tdoi = {10.3389/frwa.2022.1003693},\n\tabstract = {Deforestation can lead to an increase in the availability of nutrients in the soil and, in turn, have an impact on the quality of water in receiving water bodies. This study assesses the impact of deforestation by evaluating the in-stream concentrations of dissolved organic carbon (DOC) and nitrate, their internal relationship, and those with stream discharge in the Wüstebach headwater catchment (Germany). This catchment has monitored stream water and associated environmental parameters for over a decade as part of the TERENO initiative. Additionally, there is a paired undisturbed forested catchment that serves as a reference stream. Our approach included a more advanced correlation analysis, namely wavelet analysis, that assists in determining changes in the correlation and lag time between the variables of interest over different time scales. This study found that after deforestation, there was an immediate increase in in-stream DOC concentrations, followed by an increase in nitrate {\\textasciitilde}1 year later. Overall, the mean DOC concentration increased, and mean nitrate concentration decreased across the catchment post-deforestation. Elevated stream water nutrient levels peaked around 2 to 3 years after the clear-cutting, and returned to pre-deforestation levels after {\\textasciitilde}5 years. The deforestation had no influence on the anti-correlation between DOC and nitrate. However, the correlation between both compounds and discharge was likely altered due to the increased soil nutrients availability as a result of deforestation. Wavelet coherence analysis revealed the “underlying” changing strengths and directions of the main correlations between DOC, nitrate and discharge on different time scales resulting from severe forest management interventions (here deforestation). This information provides new valuable impact insights for decision making into such forest management interventions.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Water},\n\tauthor = {Robinson, Kerri-Leigh and Bogena, Heye R. and Wang, Qiqi and Cammeraat, Erik and Bol, Roland},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {1003693},\n}\n\n\n\n

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\n \n\n \n \n Rink, K.; Şen, Ö. O.; Hannemann, M.; Ködel, U.; Nixdorf, E.; Weber, U.; Werban, U.; Schrön, M.; Kalbacher, T.; and Kolditz, O.\n\n\n \n \n \n \n \n An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems.\n \n \n \n \n\n\n \n\n\n\n Computers & Graphics, 103: 192–200. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_environmental_2022,\n\ttitle = {An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems},\n\tvolume = {103},\n\tissn = {00978493},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0097849322000309},\n\tdoi = {10.1016/j.cag.2022.02.009},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Computers \\& Graphics},\n\tauthor = {Rink, Karsten and Şen, Özgür Ozan and Hannemann, Marco and Ködel, Uta and Nixdorf, Erik and Weber, Ute and Werban, Ulrike and Schrön, Martin and Kalbacher, Thomas and Kolditz, Olaf},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {192--200},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Reitz, O.; Graf, A.; Schmidt, M.; Ketzler, G.; and Leuchner, M.\n\n\n \n \n \n \n \n Effects of Measurement Height and Low-Pass-Filtering Corrections on Eddy-Covariance Flux Measurements Over a Forest Clearing with Complex Vegetation.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 184(2): 277–299. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reitz_effects_2022,\n\ttitle = {Effects of {Measurement} {Height} and {Low}-{Pass}-{Filtering} {Corrections} on {Eddy}-{Covariance} {Flux} {Measurements} {Over} a {Forest} {Clearing} with {Complex} {Vegetation}},\n\tvolume = {184},\n\tissn = {0006-8314, 1573-1472},\n\turl = {https://link.springer.com/10.1007/s10546-022-00700-1},\n\tdoi = {10.1007/s10546-022-00700-1},\n\tabstract = {Abstract \n             \n              Flux measurements over heterogeneous surfaces with growing vegetation and a limited fetch are a difficult task, as measurement heights that are too high or too low above the canopy adversely affect results. The aim of this study is to assess implications from measurement height in regard to low-pass filtering, footprint representativeness, and energy balance closure for a clear-cut site with regrowing vegetation of varying height. For this, measurements from two open-path eddy-covariance systems at different heights are compared over the course of one growing season. Particular attention is paid to low-pass-filtering corrections, for which five different methods are compared. Results indicate significant differences between fluxes from the upper and lower systems, which likely result from footprint differences and an insufficient spectral correction for the lower system. Different low-pass-filtering corrections add an uncertainty of 3.4\\% (7.0\\%) to CO \n              2 \n              fluxes and 1.4\\% (3.0\\%) to H \n              2 \n              O fluxes for the upper (lower) system, also leading to considerable differences in cumulative fluxes. Despite limitations in the analysis, which include the difficulty of applying a footprint model at this study site and the likely influence of advection on the total exchange, the analysis confirms that information about the choice of spectral correction method and measurement-height changes are critical for interpreting data at complex sites.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Reitz, Oliver and Graf, Alexander and Schmidt, Marius and Ketzler, Gunnar and Leuchner, Michael},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {277--299},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Reinermann, S.; Gessner, U.; Asam, S.; Ullmann, T.; Schucknecht, A.; and Kuenzer, C.\n\n\n \n \n \n \n \n Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(7): 1647. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reinermann_detection_2022,\n\ttitle = {Detection of {Grassland} {Mowing} {Events} for {Germany} by {Combining} {Sentinel}-1 and {Sentinel}-2 {Time} {Series}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/7/1647},\n\tdoi = {10.3390/rs14071647},\n\tabstract = {Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3\\% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84\\%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3\\%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Reinermann, Sophie and Gessner, Ursula and Asam, Sarah and Ullmann, Tobias and Schucknecht, Anne and Kuenzer, Claudia},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1647},\n}\n\n\n\n

\n\n\n

\n Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.\n

\n\n\n

\n \n\n \n \n Reiber, L.; Foit, K.; Liess, M.; Karaoglan, B.; Wogram, J.; and Duquesne, S.\n\n\n \n \n \n \n \n Close to reality? Micro-/mesocosm communities do not represent natural macroinvertebrate communities.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 34(1): 65. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Close$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reiber_close_2022,\n\ttitle = {Close to reality? {Micro}-/mesocosm communities do not represent natural macroinvertebrate communities},\n\tvolume = {34},\n\tissn = {2190-4707, 2190-4715},\n\tshorttitle = {Close to reality?},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-022-00643-x},\n\tdoi = {10.1186/s12302-022-00643-x},\n\tabstract = {Abstract \n             \n              Background \n              The European environmental risk assessment of plant protection products considers aquatic model ecosystem studies (microcosms/mesocosms, M/M) as suitable higher tier approach to assess treatment-related effects and to derive regulatory acceptable concentrations (RAC). However, it is under debate to what extent these artificial test systems reflect the risks of pesticidal substances with potential harmful effects on natural macroinvertebrate communities, and whether the field communities are adequately protected by the results of the M/M studies. We therefore compared the composition, sensitivity and vulnerability of benthic macroinvertebrates established in control (untreated) groups of 47 selected M/M studies with natural stream communities at 26 reference field sites. \n             \n             \n              Results \n               \n                Since 2013 the number of benthic macroinvertebrate taxa present in M/M studies has increased by 39\\% to a mean of 38 families per study. However, there is only an average of 4 families per study that comply with the recommendations provided by EFSA (EFSA J 11:3290, 2013), i.e.: (i) allowing statistical identification of treatment-related effects of at least 70\\% according to the \n                minimum detectable difference \n                (here criteria are slightly modified) and (ii) belonging to insects or crustaceans (potentially sensitive taxa for pesticidal substances). Applying the criterion of physiological sensitivity according to the SPEAR \n                pesticides \n                concept, the number of families decreases from 4 to 2.3 per study. \n               \n             \n             \n              Conclusions \n              Most taxa established in recent M/M studies do not suitably represent natural freshwater communities. First, because their abundances are often not sufficient for statistical detection of treatment-related effects in order to determine an appropriate endpoint and subsequent RAC. Recommendations are given to improve the detectability of such effects and their reliability. Second, the taxa often do not represent especially sensitive or vulnerable taxa in natural communities in terms of their traits. The uncertainties linked to vulnerable taxa in M/M studies are especially high considering their representativity for field assemblages and the comparability of factors determining their recovery time. Thus considering recovery for deriving a RAC (i.e., ERO-RAC) is not recommended. In addition, this paper discusses further concerns regarding M/M studies in a broader regulatory context and recommends the development of alternative assessment tools and a shift towards a new paradigm.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Reiber, Lena and Foit, Kaarina and Liess, Matthias and Karaoglan, Bilgin and Wogram, Joern and Duquesne, Sabine},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {65},\n}\n\n\n\n

\n\n\n

\n Abstract Background The European environmental risk assessment of plant protection products considers aquatic model ecosystem studies (microcosms/mesocosms, M/M) as suitable higher tier approach to assess treatment-related effects and to derive regulatory acceptable concentrations (RAC). However, it is under debate to what extent these artificial test systems reflect the risks of pesticidal substances with potential harmful effects on natural macroinvertebrate communities, and whether the field communities are adequately protected by the results of the M/M studies. We therefore compared the composition, sensitivity and vulnerability of benthic macroinvertebrates established in control (untreated) groups of 47 selected M/M studies with natural stream communities at 26 reference field sites. Results Since 2013 the number of benthic macroinvertebrate taxa present in M/M studies has increased by 39% to a mean of 38 families per study. However, there is only an average of 4 families per study that comply with the recommendations provided by EFSA (EFSA J 11:3290, 2013), i.e.: (i) allowing statistical identification of treatment-related effects of at least 70% according to the minimum detectable difference (here criteria are slightly modified) and (ii) belonging to insects or crustaceans (potentially sensitive taxa for pesticidal substances). Applying the criterion of physiological sensitivity according to the SPEAR pesticides concept, the number of families decreases from 4 to 2.3 per study. Conclusions Most taxa established in recent M/M studies do not suitably represent natural freshwater communities. First, because their abundances are often not sufficient for statistical detection of treatment-related effects in order to determine an appropriate endpoint and subsequent RAC. Recommendations are given to improve the detectability of such effects and their reliability. Second, the taxa often do not represent especially sensitive or vulnerable taxa in natural communities in terms of their traits. The uncertainties linked to vulnerable taxa in M/M studies are especially high considering their representativity for field assemblages and the comparability of factors determining their recovery time. Thus considering recovery for deriving a RAC (i.e., ERO-RAC) is not recommended. In addition, this paper discusses further concerns regarding M/M studies in a broader regulatory context and recommends the development of alternative assessment tools and a shift towards a new paradigm.\n

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\n \n\n \n \n Rinne-Garmston, K. T.; Helle, G.; Lehmann, M. M.; Sahlstedt, E.; Schleucher, J.; and Waterhouse, J. S.\n\n\n \n \n \n \n \n Newer Developments in Tree-Ring Stable Isotope Methods.\n \n \n \n \n\n\n \n\n\n\n In Siegwolf, R. T. W.; Brooks, J. R.; Roden, J.; and Saurer, M., editor(s), Stable Isotopes in Tree Rings, volume 8, pages 215–249. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Newer$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{siegwolf_newer_2022,\n\taddress = {Cham},\n\ttitle = {Newer {Developments} in {Tree}-{Ring} {Stable} {Isotope} {Methods}},\n\tvolume = {8},\n\tisbn = {9783030926977 9783030926984},\n\turl = {https://link.springer.com/10.1007/978-3-030-92698-4_7},\n\tabstract = {Abstract \n            The tree-ring stable C, O and H isotope compositions have proven valuable for examining past changes in the environment and predicting forest responses to environmental change. However, we have not yet recovered the full potential of this archive, partly due to a lack understanding of fractionation processes resulting from methodological constraints. With better understanding of the biochemical and tree physiological processes that lead to differences between the isotopic compositions of primary photosynthates and the isotopic compositions of substrates deposited in stem xylem, more reliable and accurate reconstructions could be obtained. Furthermore, by extending isotopic analysis of tree-ring cellulose to intra-molecular level, more information could be obtained on changing climate, tree metabolism or ecophysiology. This chapter presents newer methods in isotope research that have become available or show high future potential for fully utilising the wealth of information available in tree-rings. These include compound-specific analysis of sugars and cyclitols, high spatial resolution analysis of tree rings with UV-laser, and position-specific isotope analysis of cellulose. The aim is to provide the reader with understanding of the advantages and of the current challenges connected with the use of these methods for stable isotope tree-ring research.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Stable {Isotopes} in {Tree} {Rings}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Rinne-Garmston, Katja T. and Helle, Gerhard and Lehmann, Marco M. and Sahlstedt, Elina and Schleucher, Jürgen and Waterhouse, John S.},\n\teditor = {Siegwolf, Rolf T. W. and Brooks, J. Renée and Roden, John and Saurer, Matthias},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-92698-4_7},\n\tpages = {215--249},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; Koal, P.; and Sánchez-Azofeifa, A.\n\n\n \n \n \n \n \n Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(2): 380. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Fractional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{putzenlechner_fractional_2022,\n\ttitle = {Fractional {Vegetation} {Cover} {Derived} from {UAV} and {Sentinel}-2 {Imagery} as a {Proxy} for {In} {Situ} {FAPAR} in a {Dense} {Mixed}-{Coniferous} {Forest}?},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/2/380},\n\tdoi = {10.3390/rs14020380},\n\tabstract = {The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE {\\textgreater} 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Koal, Philipp and Sánchez-Azofeifa, Arturo},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {380},\n}\n\n\n\n

\n\n\n

\n The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE \\textgreater 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.\n

\n\n\n

\n \n\n \n \n Poorter, H.; Knopf, O.; Wright, I. J.; Temme, A. A.; Hogewoning, S. W.; Graf, A.; Cernusak, L. A.; and Pons, T. L.\n\n\n \n \n \n \n \n A meta‐analysis of responses of C $_{\\textrm{3}}$ plants to atmospheric CO $_{\\textrm{2}}$ : dose–response curves for 85 traits ranging from the molecular to the whole‐plant level.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 233(4): 1560–1596. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{poorter_metaanalysis_2022,\n\ttitle = {A meta‐analysis of responses of {C} $_{\\textrm{3}}$ plants to atmospheric {CO} $_{\\textrm{2}}$ : dose–response curves for 85 traits ranging from the molecular to the whole‐plant level},\n\tvolume = {233},\n\tissn = {0028-646X, 1469-8137},\n\tshorttitle = {A meta‐analysis of responses of {C} $_{\\textrm{3}}$ plants to atmospheric {CO} $_{\\textrm{2}}$},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.17802},\n\tdoi = {10.1111/nph.17802},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {New Phytologist},\n\tauthor = {Poorter, Hendrik and Knopf, Oliver and Wright, Ian J. and Temme, Andries A. and Hogewoning, Sander W. and Graf, Alexander and Cernusak, Lucas A. and Pons, Thijs L.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {1560--1596},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Peng, Z.; Tang, R.; Jiang, Y.; Liu, M.; and Li, Z.\n\n\n \n \n \n \n \n Global estimates of 500 m daily aerodynamic roughness length from MODIS data.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 183: 336–351. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peng_global_2022,\n\ttitle = {Global estimates of 500 m daily aerodynamic roughness length from {MODIS} data},\n\tvolume = {183},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271621003130},\n\tdoi = {10.1016/j.isprsjprs.2021.11.015},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Peng, Zhong and Tang, Ronglin and Jiang, Yazhen and Liu, Meng and Li, Zhao-Liang},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {336--351},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Panwar, A.; and Kleidon, A.\n\n\n \n \n \n \n \n Evaluating the Response of Diurnal Variations in Surface and Air Temperature to Evaporative Conditions across Vegetation Types in FLUXNET and ERA5.\n \n \n \n \n\n\n \n\n\n\n Journal of Climate, 35(19): 2701–2728. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{panwar_evaluating_2022,\n\ttitle = {Evaluating the {Response} of {Diurnal} {Variations} in {Surface} and {Air} {Temperature} to {Evaporative} {Conditions} across {Vegetation} {Types} in {FLUXNET} and {ERA5}},\n\tvolume = {35},\n\tissn = {0894-8755, 1520-0442},\n\turl = {https://journals.ametsoc.org/view/journals/clim/35/19/JCLI-D-21-0345.1.xml},\n\tdoi = {10.1175/JCLI-D-21-0345.1},\n\tabstract = {Abstract \n             \n              The diurnal variations of surface and air temperature are closely related, but their different responses to evaporative conditions can inform us about land–atmosphere interactions. Here, we evaluate the responses of the diurnal ranges in surface (Δ \n               \n                T \n                s \n               \n              ) and air (Δ \n               \n                T \n                a \n               \n              ) temperature to evaporative fraction at 160 FLUXNET sites and in the ERA5 reanalysis. We show that the sensitivity of Δ \n               \n                T \n                s \n               \n              to evaporative fraction depends on vegetation type, whereas Δ \n               \n                T \n                a \n               \n              does not. On days with low evaporative fraction, Δ \n               \n                T \n                s \n               \n              in FLUXNET is enhanced by up to ∼20 K (∼30 K in ERA5) in short vegetation, but only by up to ∼10 K (∼10 K in ERA5) in forests. Note that Δ \n               \n                T \n                a \n               \n              responds rather similarly to evaporative fraction irrespective of vegetation type (∼5 K in FLUXNET, ∼10 K in ERA5). We find a systematic bias in ERA5’s Δ \n              T \n              response to evaporative conditions, showing a stronger sensitivity to evaporative fraction than in FLUXNET. We then demonstrate with a simple atmospheric boundary layer (SABL) model that the weak response of Δ \n               \n                T \n                a \n               \n              to evaporative fraction can be explained by greater boundary layer growth under dry conditions, which increases the heat storage capacity and reduces the response of air temperature to evaporative fraction. Additionally, using a simplified surface energy balance (SSEB) model we show that Δ \n               \n                T \n                s \n               \n              mainly responds to solar radiation, evaporative fraction, and aerodynamic conductance. We conclude that the dominant patterns of diurnal temperature variations can be explained by fundamental physical concepts, which should help us to better understand the main controls of land–atmosphere interactions. \n             \n             \n              Significance Statement \n              Generally, air temperature is used more widely than the surface temperature, and often they are assumed to be equivalent. However, we show that their responses to changes in vegetation type and evaporative conditions are quite different. Using FLUXNET observations, ERA5 reanalysis, and two simple physical models, we found that these responses are much stronger in surface temperature, especially in short vegetation, and relatively weaker in air temperature. Despite being measured just 2 m above the surface, air temperature carries lesser imprints of evaporation and vegetation than the surface temperature because of boundary layer dynamics. These findings suggest the importance of coupled land–atmosphere processes in shaping surface and air temperature differently and provide insights on their distinctive responses to global changes.},\n\tnumber = {19},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Climate},\n\tauthor = {Panwar, Annu and Kleidon, Axel},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {2701--2728},\n}\n\n\n\n

\n\n\n

\n Abstract The diurnal variations of surface and air temperature are closely related, but their different responses to evaporative conditions can inform us about land–atmosphere interactions. Here, we evaluate the responses of the diurnal ranges in surface (Δ T s ) and air (Δ T a ) temperature to evaporative fraction at 160 FLUXNET sites and in the ERA5 reanalysis. We show that the sensitivity of Δ T s to evaporative fraction depends on vegetation type, whereas Δ T a does not. On days with low evaporative fraction, Δ T s in FLUXNET is enhanced by up to ∼20 K (∼30 K in ERA5) in short vegetation, but only by up to ∼10 K (∼10 K in ERA5) in forests. Note that Δ T a responds rather similarly to evaporative fraction irrespective of vegetation type (∼5 K in FLUXNET, ∼10 K in ERA5). We find a systematic bias in ERA5’s Δ T response to evaporative conditions, showing a stronger sensitivity to evaporative fraction than in FLUXNET. We then demonstrate with a simple atmospheric boundary layer (SABL) model that the weak response of Δ T a to evaporative fraction can be explained by greater boundary layer growth under dry conditions, which increases the heat storage capacity and reduces the response of air temperature to evaporative fraction. Additionally, using a simplified surface energy balance (SSEB) model we show that Δ T s mainly responds to solar radiation, evaporative fraction, and aerodynamic conductance. We conclude that the dominant patterns of diurnal temperature variations can be explained by fundamental physical concepts, which should help us to better understand the main controls of land–atmosphere interactions. Significance Statement Generally, air temperature is used more widely than the surface temperature, and often they are assumed to be equivalent. However, we show that their responses to changes in vegetation type and evaporative conditions are quite different. Using FLUXNET observations, ERA5 reanalysis, and two simple physical models, we found that these responses are much stronger in surface temperature, especially in short vegetation, and relatively weaker in air temperature. Despite being measured just 2 m above the surface, air temperature carries lesser imprints of evaporation and vegetation than the surface temperature because of boundary layer dynamics. These findings suggest the importance of coupled land–atmosphere processes in shaping surface and air temperature differently and provide insights on their distinctive responses to global changes.\n

\n\n\n

\n \n\n \n \n Novick, K. A.; Ficklin, D. L.; Baldocchi, D.; Davis, K. J.; Ghezzehei, T. A.; Konings, A. G.; MacBean, N.; Raoult, N.; Scott, R. L.; Shi, Y.; Sulman, B. N.; and Wood, J. D.\n\n\n \n \n \n \n \n Confronting the water potential information gap.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 15(3): 158–164. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Confronting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{novick_confronting_2022,\n\ttitle = {Confronting the water potential information gap},\n\tvolume = {15},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/s41561-022-00909-2},\n\tdoi = {10.1038/s41561-022-00909-2},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Nature Geoscience},\n\tauthor = {Novick, Kimberly A. and Ficklin, Darren L. and Baldocchi, Dennis and Davis, Kenneth J. and Ghezzehei, Teamrat A. and Konings, Alexandra G. and MacBean, Natasha and Raoult, Nina and Scott, Russell L. and Shi, Yuning and Sulman, Benjamin N. and Wood, Jeffrey D.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {158--164},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Nogueira, G. E. H.; Schmidt, C.; Partington, D.; Brunner, P.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Spatiotemporal variations in water sources and mixing spots in a riparian zone.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(7): 1883–1905. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nogueira_spatiotemporal_2022,\n\ttitle = {Spatiotemporal variations in water sources and mixing spots in a riparian zone},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/1883/2022/},\n\tdoi = {10.5194/hess-26-1883-2022},\n\tabstract = {Abstract. Riparian zones are known to modulate water quality in stream corridors. They can act as buffers for groundwater-borne solutes before they enter the stream at harmful, high concentrations or facilitate solute turnover and attenuation in zones where stream water (SW) and groundwater (GW) mix. This natural attenuation capacity is strongly controlled by the dynamic exchange of water and solutes between the stream and the adjoining aquifer, creating potential for mixing-dependent reactions to take place. Here, we couple a previously calibrated transient and fully integrated 3D surface–subsurface numerical flow model with a hydraulic mixing cell (HMC) method to map the source composition of water along a net losing reach (900 m) of the fourth-order Selke stream and track its spatiotemporal evolution. This allows us to define zones in the aquifer with more balanced fractions of the different water sources per aquifer volume (called mixing hot spots), which have a high potential to facilitate mixing-dependent reactions and, in turn, enhance solute turnover. We further evaluated the HMC results against hydrochemical monitoring data. Our results show that, on average, about 50 \\% of the water in the alluvial aquifer consists of infiltrating SW. Within about 200 m around the stream, the aquifer is almost entirely made up of infiltrated SW with practically no significant amounts of other water sources mixed in. On average, about 9 \\% of the model domain could be characterized as mixing hot spots, which were mainly located at the fringe of the geochemical hyporheic zone rather than below or in the immediate vicinity of the streambed. This percentage could rise to values nearly 1.5 times higher following large discharge events. Moreover, event intensity (magnitude of peak flow) was found to be more important for the increase in mixing than event duration. Our modeling results further suggest that discharge events more significantly increase mixing potential at greater distances from the stream. In contrast near and below the stream, the rapid increase in SW influx shifts the ratio between the water fractions to SW, reducing the potential for mixing and the associated reactions. With this easy-to-transfer framework, we seek to show the applicability of the HMC method as a complementary approach for the identification of mixing hot spots in stream corridors, while showing the spatiotemporal controls of the SW–GW mixing process and the implications for riparian biogeochemistry and mixing-dependent turnover processes.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Nogueira, Guilherme E. H. and Schmidt, Christian and Partington, Daniel and Brunner, Philip and Fleckenstein, Jan H.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1883--1905},\n}\n\n\n\n

\n\n\n

\n Abstract. Riparian zones are known to modulate water quality in stream corridors. They can act as buffers for groundwater-borne solutes before they enter the stream at harmful, high concentrations or facilitate solute turnover and attenuation in zones where stream water (SW) and groundwater (GW) mix. This natural attenuation capacity is strongly controlled by the dynamic exchange of water and solutes between the stream and the adjoining aquifer, creating potential for mixing-dependent reactions to take place. Here, we couple a previously calibrated transient and fully integrated 3D surface–subsurface numerical flow model with a hydraulic mixing cell (HMC) method to map the source composition of water along a net losing reach (900 m) of the fourth-order Selke stream and track its spatiotemporal evolution. This allows us to define zones in the aquifer with more balanced fractions of the different water sources per aquifer volume (called mixing hot spots), which have a high potential to facilitate mixing-dependent reactions and, in turn, enhance solute turnover. We further evaluated the HMC results against hydrochemical monitoring data. Our results show that, on average, about 50 % of the water in the alluvial aquifer consists of infiltrating SW. Within about 200 m around the stream, the aquifer is almost entirely made up of infiltrated SW with practically no significant amounts of other water sources mixed in. On average, about 9 % of the model domain could be characterized as mixing hot spots, which were mainly located at the fringe of the geochemical hyporheic zone rather than below or in the immediate vicinity of the streambed. This percentage could rise to values nearly 1.5 times higher following large discharge events. Moreover, event intensity (magnitude of peak flow) was found to be more important for the increase in mixing than event duration. Our modeling results further suggest that discharge events more significantly increase mixing potential at greater distances from the stream. In contrast near and below the stream, the rapid increase in SW influx shifts the ratio between the water fractions to SW, reducing the potential for mixing and the associated reactions. With this easy-to-transfer framework, we seek to show the applicability of the HMC method as a complementary approach for the identification of mixing hot spots in stream corridors, while showing the spatiotemporal controls of the SW–GW mixing process and the implications for riparian biogeochemistry and mixing-dependent turnover processes.\n

\n\n\n

\n \n\n \n \n Nguyen, T. V.; Kumar, R.; Musolff, A.; Lutz, S. R.; Sarrazin, F.; Attinger, S.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Disparate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nguyen_disparate_2022,\n\ttitle = {Disparate {Seasonal} {Nitrate} {Export} {From} {Nested} {Heterogeneous} {Subcatchments} {Revealed} {With} {StorAge} {Selection} {Functions}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030797},\n\tdoi = {10.1029/2021WR030797},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Nguyen, Tam V. and Kumar, Rohini and Musolff, Andreas and Lutz, Stefanie R. and Sarrazin, Fanny and Attinger, Sabine and Fleckenstein, Jan H.},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Nativel, S.; Ayari, E.; Rodriguez-Fernandez, N.; Baghdadi, N.; Madelon, R.; Albergel, C.; and Zribi, M.\n\n\n \n \n \n \n \n Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(10): 2434. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Hybrid$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nativel_hybrid_2022,\n\ttitle = {Hybrid {Methodology} {Using} {Sentinel}-1/{Sentinel}-2 for {Soil} {Moisture} {Estimation}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/10/2434},\n\tdoi = {10.3390/rs14102434},\n\tabstract = {Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index ISSM, radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54\\% and 33\\% with respect to that of the neural network or change detection alone, respectively.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Nativel, Simon and Ayari, Emna and Rodriguez-Fernandez, Nemesio and Baghdadi, Nicolas and Madelon, Remi and Albergel, Clement and Zribi, Mehrez},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2434},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Mwanake, R. M.; Gettel, G. M.; Ishimwe, C.; Wangari, E. G.; Butterbach‐Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Basin‐scale estimates of greenhouse gas emissions from the Mara River, Kenya: Importance of discharge, stream size, and land use/land cover.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 67(8): 1776–1793. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Basin‐scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mwanake_basinscale_2022,\n\ttitle = {Basin‐scale estimates of greenhouse gas emissions from the {Mara} {River}, {Kenya}: {Importance} of discharge, stream size, and land use/land cover},\n\tvolume = {67},\n\tissn = {0024-3590, 1939-5590},\n\tshorttitle = {Basin‐scale estimates of greenhouse gas emissions from the {Mara} {River}, {Kenya}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.12166},\n\tdoi = {10.1002/lno.12166},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Mwanake, Ricky M. and Gettel, Gretchen M. and Ishimwe, Clarisse and Wangari, Elizabeth G. and Butterbach‐Bahl, Klaus and Kiese, Ralf},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {1776--1793},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Moya, M. R.; López‐Ballesteros, A.; Sánchez‐Cañete, E. P.; Serrano‐Ortiz, P.; Oyonarte, C.; Domingo, F.; and Kowalski, A.\n\n\n \n \n \n \n \n Ecosystem CO $_{\\textrm{2}}$ release driven by wind occurs in drylands at global scale.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(17): 5320–5333. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecosystem$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{moya_ecosystem_2022,\n\ttitle = {Ecosystem {CO} $_{\\textrm{2}}$ release driven by wind occurs in drylands at global scale},\n\tvolume = {28},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16277},\n\tdoi = {10.1111/gcb.16277},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-21},\n\tjournal = {Global Change Biology},\n\tauthor = {Moya, María Rosario and López‐Ballesteros, Ana and Sánchez‐Cañete, Enrique P. and Serrano‐Ortiz, Penélope and Oyonarte, Cecilio and Domingo, Francisco and Kowalski, Andrew S.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {5320--5333},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Molnar, P.\n\n\n \n \n \n \n \n Differences between soil and air temperatures: Implications for geological reconstructions of past climate.\n \n \n \n \n\n\n \n\n\n\n Geosphere, 18(2): 800–824. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Differences$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{molnar_differences_2022,\n\ttitle = {Differences between soil and air temperatures: {Implications} for geological reconstructions of past climate},\n\tvolume = {18},\n\tissn = {1553-040X},\n\tshorttitle = {Differences between soil and air temperatures},\n\turl = {https://pubs.geoscienceworld.org/geosphere/article/18/2/800/611659/Differences-between-soil-and-air-temperatures},\n\tdoi = {10.1130/GES02448.1},\n\tabstract = {Abstract \n            Among quantities of interest in paleoclimate, the mean annual air temperature, Ta, directly over the surface looms prominently. Most geologic estimates of past temperatures from continental regions, however, quantify temperatures of the soil or other material below the surface, Ts, and in general Ta \\&lt; Ts. Both theory and data from the FLUXNET2015 data set of surface energy balance indicate systematic dependences of temperature differences ΔT = Ts − Ta and also of Bowen ratios—ratios of sensible to latent heat fluxes from surface to the atmosphere—on the nature of the land-surface cover. In cold regions, with mean annual temperatures ≲5 °C, latent heat flux tends to be small, and values of ΔT can be large, 3–5 °C or larger. Over wet surfaces, latent heat fluxes dominate sensible heat fluxes, and values of both ΔT and Bowen ratios commonly are small. By contrast, over arid surfaces that provide only limited moisture to the overlying atmosphere, the opposite holds. Both theory and observation suggest the following, albeit approximate, mean annual values of ΔT: for wetlands, 1 °C; forests, 1 ± 1 °C; shrublands, 3–4 °C; savannas, 3.5 °C \\&lt; ΔT \\&lt; 5.5 °C; grasslands, 1 °C where wet to 3 °C where arid; and deserts, 4–6 °C. As geological tools for inferring past land-surface conditions improve, these approximate values of ΔT will allow geologic estimates of past mean annual surface temperatures, Ts, to be translated into estimates of past mean annual air temperatures, Ta.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Geosphere},\n\tauthor = {Molnar, Peter},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {800--824},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Min, X.; Shangguan, Y.; Huang, J.; Wang, H.; and Shi, Z.\n\n\n \n \n \n \n \n Relative Strengths Recognition of Nine Mainstream Satellite-Based Soil Moisture Products at the Global Scale.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(12): 2739. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Relative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{min_relative_2022,\n\ttitle = {Relative {Strengths} {Recognition} of {Nine} {Mainstream} {Satellite}-{Based} {Soil} {Moisture} {Products} at the {Global} {Scale}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/12/2739},\n\tdoi = {10.3390/rs14122739},\n\tabstract = {Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Min, Xiaoxiao and Shangguan, Yulin and Huang, Jingyi and Wang, Hongquan and Shi, Zhou},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2739},\n}\n\n\n\n

\n\n\n

\n Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.\n

\n\n\n

\n \n\n \n \n Mi, C.; Hamilton, D. P.; Frassl, M. A.; Shatwell, T.; Kong, X.; Boehrer, B.; Li, Y.; Donner, J.; and Rinke, K.\n\n\n \n \n \n \n \n Controlling blooms of Planktothrix rubescens by optimized metalimnetic water withdrawal: a modelling study on adaptive reservoir operation.\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Controlling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{mi_controlling_2022,\n\ttype = {preprint},\n\ttitle = {Controlling blooms of {Planktothrix} rubescens by optimized metalimnetic water withdrawal: a modelling study on adaptive reservoir operation},\n\tshorttitle = {Controlling blooms of {Planktothrix} rubescens by optimized metalimnetic water withdrawal},\n\turl = {https://www.researchsquare.com/article/rs-1752651/v1},\n\tabstract = {Abstract \n \n Background: \n Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as \n Planktothrix rubescens, \n are involved. Metalimnetic blooms of \n P. rubescens \n have apparently increased in frequency and severity in recent years, so there is a strong need to identify reservoir management options to control it. We hypothesized that \n P. rubescens \n blooms in reservoirs can be suppressed using selective withdrawal to maximize its export from the reservoir. We also expect that altering the light climate can affect the dynamics of this species. We tested our hypothesis in Rappbode Reservoir (the largest drinking water reservoir in Germany) by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2). \n Results: \n The novel withdrawal strategy, in which water is withdrawn from a certain depth below the surface within the metalimnion instead of at a fixed elevation relative to the dam wall, significantly reduced \n P. rubescens \n biomass in the reservoir. According to the simulation results, we defined an optimal withdrawal volume to control \n P. rubescens \n blooms in the reservoir as approximately 10 million m \n 3 \n (10\\% of the reservoir volume) during its bloom phase. The results also illustrated that \n P. rubescens \n growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth, i.e., before the bloom occurs. Additionally, our study showed that \n P. rubescens \n biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m \n -1 \n . \n Conclusion \n : Our study indicates the rise in \n P. rubescens \n biomass can be effectively offset by selective withdrawal strategy and controlling light intensity beneath the water surface. Considering the widespread occurrence of \n P. rubescens \n in stratified lakes and reservoirs worldwide, we believe the results will be helpful for scientists and water managers working on other water bodies to minimize the negative impacts of this harmful algae.},\n\turldate = {2022-11-21},\n\tinstitution = {In Review},\n\tauthor = {Mi, Chenxi and Hamilton, David P. and Frassl, Marieke A. and Shatwell, Tom and Kong, Xiangzhen and Boehrer, Bertram and Li, Yiping and Donner, Jan and Rinke, Karsten},\n\tmonth = jun,\n\tyear = {2022},\n\tdoi = {10.21203/rs.3.rs-1752651/v1},\n}\n\n\n\n

\n\n\n

\n Abstract Background: Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as Planktothrix rubescens, are involved. Metalimnetic blooms of P. rubescens have apparently increased in frequency and severity in recent years, so there is a strong need to identify reservoir management options to control it. We hypothesized that P. rubescens blooms in reservoirs can be suppressed using selective withdrawal to maximize its export from the reservoir. We also expect that altering the light climate can affect the dynamics of this species. We tested our hypothesis in Rappbode Reservoir (the largest drinking water reservoir in Germany) by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2). Results: The novel withdrawal strategy, in which water is withdrawn from a certain depth below the surface within the metalimnion instead of at a fixed elevation relative to the dam wall, significantly reduced P. rubescens biomass in the reservoir. According to the simulation results, we defined an optimal withdrawal volume to control P. rubescens blooms in the reservoir as approximately 10 million m 3 (10% of the reservoir volume) during its bloom phase. The results also illustrated that P. rubescens growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth, i.e., before the bloom occurs. Additionally, our study showed that P. rubescens biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m -1 . Conclusion : Our study indicates the rise in P. rubescens biomass can be effectively offset by selective withdrawal strategy and controlling light intensity beneath the water surface. Considering the widespread occurrence of P. rubescens in stratified lakes and reservoirs worldwide, we believe the results will be helpful for scientists and water managers working on other water bodies to minimize the negative impacts of this harmful algae.\n

\n\n\n

\n \n\n \n \n Mengis, N.; Kalhori, A.; Simon, S.; Harpprecht, C.; Baetcke, L.; Prats‐Salvado, E.; Schmidt‐Hattenberger, C.; Stevenson, A.; Dold, C.; Zohbi, J.; Borchers, M.; Thrän, D.; Korte, K.; Gawel, E.; Dolch, T.; Heß, D.; Yeates, C.; Thoni, T.; Markus, T.; Schill, E.; Xiao, M.; Köhnke, F.; Oschlies, A.; Förster, J.; Görl, K.; Dornheim, M.; Brinkmann, T.; Beck, S.; Bruhn, D.; Li, Z.; Steuri, B.; Herbst, M.; Sachs, T.; Monnerie, N.; Pregger, T.; Jacob, D.; and Dittmeyer, R.\n\n\n \n \n \n \n \n Net‐Zero CO $_{\\textrm{2}}$ Germany—A Retrospect From the Year 2050.\n \n \n \n \n\n\n \n\n\n\n Earth's Future, 10(2). February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Net‐Zero$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mengis_netzero_2022,\n\ttitle = {Net‐{Zero} {CO} $_{\\textrm{2}}$ {Germany}—{A} {Retrospect} {From} the {Year} 2050},\n\tvolume = {10},\n\tissn = {2328-4277, 2328-4277},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021EF002324},\n\tdoi = {10.1029/2021EF002324},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Earth's Future},\n\tauthor = {Mengis, Nadine and Kalhori, Aram and Simon, Sonja and Harpprecht, Carina and Baetcke, Lars and Prats‐Salvado, Enric and Schmidt‐Hattenberger, Cornelia and Stevenson, Angela and Dold, Christian and Zohbi, Juliane and Borchers, Malgorzata and Thrän, Daniela and Korte, Klaas and Gawel, Erik and Dolch, Tobias and Heß, Dominik and Yeates, Christopher and Thoni, Terese and Markus, Till and Schill, Eva and Xiao, Mengzhu and Köhnke, Fiona and Oschlies, Andreas and Förster, Johannes and Görl, Knut and Dornheim, Martin and Brinkmann, Torsten and Beck, Silke and Bruhn, David and Li, Zhan and Steuri, Bettina and Herbst, Michael and Sachs, Torsten and Monnerie, Nathalie and Pregger, Thomas and Jacob, Daniela and Dittmeyer, Roland},\n\tmonth = feb,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Marino, B. D.; and Bautista, N.\n\n\n \n \n \n \n \n Commercial forest carbon protocol over-credit bias delimited by zero-threshold carbon accounting.\n \n \n \n \n\n\n \n\n\n\n Trees, Forests and People, 7: 100171. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Commercial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{marino_commercial_2022,\n\ttitle = {Commercial forest carbon protocol over-credit bias delimited by zero-threshold carbon accounting},\n\tvolume = {7},\n\tissn = {26667193},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2666719321001102},\n\tdoi = {10.1016/j.tfp.2021.100171},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Trees, Forests and People},\n\tauthor = {Marino, Bruno D.V. and Bautista, Nahuel},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {100171},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Mälicke, M.\n\n\n \n \n \n \n \n SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(6): 2505–2532. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"SciKit-GStat$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{malicke_scikit-gstat_2022,\n\ttitle = {{SciKit}-{GStat} 1.0: a {SciPy}-flavored geostatistical variogram estimation toolbox written in {Python}},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{SciKit}-{GStat} 1.0},\n\turl = {https://gmd.copernicus.org/articles/15/2505/2022/},\n\tdoi = {10.5194/gmd-15-2505-2022},\n\tabstract = {Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e.,\nfor interpolation, rescaling, data assimilation or modeling.\nAt its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations.\nThe variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method.\nUnfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram.\nNot least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user.\nThis is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all.\nFurthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied.\nSciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow.\nIts main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python.\nDuring the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat.\nToday, the most important ones can be interfaced by SciKit-GStat.\nAdditionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat.\nCommon data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms.\nCommon approaches to estimate variograms are covered and can be used out of the box.\nAt the same time, the base class is very flexible and can be adjusted to less common problems, as well.\nLast but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases.\nWith broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Mälicke, Mirko},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2505--2532},\n}\n\n\n\n

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\n Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e., for interpolation, rescaling, data assimilation or modeling. At its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations. The variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method. Unfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram. Not least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user. This is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all. Furthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python. During the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat. Today, the most important ones can be interfaced by SciKit-GStat. Additionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat. Common data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well. Last but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases. With broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.\n

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\n \n\n \n \n Luo, P.; Song, Y.; Huang, X.; Ma, H.; Liu, J.; Yao, Y.; and Meng, L.\n\n\n \n \n \n \n \n Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 185: 111–128. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{luo_identifying_2022,\n\ttitle = {Identifying determinants of spatio-temporal disparities in soil moisture of the {Northern} {Hemisphere} using a geographically optimal zones-based heterogeneity model},\n\tvolume = {185},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271622000132},\n\tdoi = {10.1016/j.isprsjprs.2022.01.009},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Luo, Peng and Song, Yongze and Huang, Xin and Ma, Hongliang and Liu, Jin and Yao, Yao and Meng, Liqiu},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {111--128},\n}\n\n\n\n

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\n \n\n \n \n Lopes, F. M.; Dutra, E.; and Trigo, I. F.\n\n\n \n \n \n \n \n Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(7): 1704. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lopes_integrating_2022,\n\ttitle = {Integrating {Reanalysis} and {Satellite} {Cloud} {Information} to {Estimate} {Surface} {Downward} {Long}-{Wave} {Radiation}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/7/1704},\n\tdoi = {10.3390/rs14071704},\n\tabstract = {The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17\\% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Lopes, Francis M. and Dutra, Emanuel and Trigo, Isabel F.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1704},\n}\n\n\n\n

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\n The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.\n

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\n \n\n \n \n Liu, Z.\n\n\n \n \n \n \n \n Estimating land evapotranspiration from potential evapotranspiration constrained by soil water at daily scale.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 834: 155327. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_estimating_2022,\n\ttitle = {Estimating land evapotranspiration from potential evapotranspiration constrained by soil water at daily scale},\n\tvolume = {834},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722024202},\n\tdoi = {10.1016/j.scitotenv.2022.155327},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Liu, Zhaofei},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {155327},\n}\n\n\n\n

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\n \n\n \n \n Liu, Y.; Flournoy, O.; Zhang, Q.; Novick, K. A.; Koster, R. D.; and Konings, A. G.\n\n\n \n \n \n \n \n Canopy Height and Climate Dryness Parsimoniously Explain Spatial Variation of Unstressed Stomatal Conductance.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(15). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Canopy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_canopy_2022,\n\ttitle = {Canopy {Height} and {Climate} {Dryness} {Parsimoniously} {Explain} {Spatial} {Variation} of {Unstressed} {Stomatal} {Conductance}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022GL099339},\n\tdoi = {10.1029/2022GL099339},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Liu, Yanlan and Flournoy, Olivia and Zhang, Quan and Novick, Kimberly A. and Koster, Randal D. and Konings, Alexandra G.},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Liebmann, L.; Vormeier, P.; Weisner, O.; and Liess, M.\n\n\n \n \n \n \n \n Balancing effort and benefit – How taxonomic and quantitative resolution influence the pesticide indicator system SPEARpesticides.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 848: 157642. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Balancing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liebmann_balancing_2022,\n\ttitle = {Balancing effort and benefit – {How} taxonomic and quantitative resolution influence the pesticide indicator system {SPEARpesticides}},\n\tvolume = {848},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722047404},\n\tdoi = {10.1016/j.scitotenv.2022.157642},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Liebmann, Liana and Vormeier, Philipp and Weisner, Oliver and Liess, Matthias},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {157642},\n}\n\n\n\n

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\n \n\n \n \n Li, X.; Wigneron, J.; Fan, L.; Frappart, F.; Yueh, S. H.; Colliander, A.; Ebtehaj, A.; Gao, L.; Fernandez-Moran, R.; Liu, X.; Wang, M.; Ma, H.; Moisy, C.; and Ciais, P.\n\n\n \n \n \n \n \n A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 271: 112921. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_new_2022,\n\ttitle = {A new {SMAP} soil moisture and vegetation optical depth product ({SMAP}-{IB}): {Algorithm}, assessment and inter-comparison},\n\tvolume = {271},\n\tissn = {00344257},\n\tshorttitle = {A new {SMAP} soil moisture and vegetation optical depth product ({SMAP}-{IB})},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000359},\n\tdoi = {10.1016/j.rse.2022.112921},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Li, Xiaojun and Wigneron, Jean-Pierre and Fan, Lei and Frappart, Frédéric and Yueh, Simon H. and Colliander, Andreas and Ebtehaj, Ardeshir and Gao, Lun and Fernandez-Moran, Roberto and Liu, Xiangzhuo and Wang, Mengjia and Ma, Hongliang and Moisy, Christophe and Ciais, Philippe},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {112921},\n}\n\n\n\n

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\n \n\n \n \n Li, L.; Dai, Y.; Shangguan, W.; Wei, Z.; Wei, N.; and Li, Q.\n\n\n \n \n \n \n \n Causality-Structured Deep Learning for Soil Moisture Predictions.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 23(8): 1315–1331. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Causality-Structured$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_causality-structured_2022,\n\ttitle = {Causality-{Structured} {Deep} {Learning} for {Soil} {Moisture} {Predictions}},\n\tvolume = {23},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/23/8/JHM-D-21-0206.1.xml},\n\tdoi = {10.1175/JHM-D-21-0206.1},\n\tabstract = {Abstract \n            The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as the long short-term memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we present a new causality-structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for forecasting SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash–Sutcliffe efficiency (NSE) suggested that more than 67\\% of sites witnessed an improvement of SM simulation larger than 10\\%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables.},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Li, Lu and Dai, Yongjiu and Shangguan, Wei and Wei, Zhongwang and Wei, Nan and Li, Qingliang},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {1315--1331},\n}\n\n\n\n

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\n \n\n \n \n Li, H.; Wei, M.; Dong, L.; Hu, W.; Xiong, J.; Sun, Y.; Sun, Y.; Yao, S.; Gong, H.; Zhang, Y.; Hou, Q.; Wang, X.; Xie, S.; Zhang, L.; Akram, M. A.; Rao, Z.; Degen, A. A.; Niklas, K. J.; Ran, J.; Ye, J.; and Deng, J.\n\n\n \n \n \n \n \n Leaf and ecosystem water use efficiencies differ in their global-scale patterns and drivers.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 319: 108919. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Leaf$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_leaf_2022,\n\ttitle = {Leaf and ecosystem water use efficiencies differ in their global-scale patterns and drivers},\n\tvolume = {319},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322001125},\n\tdoi = {10.1016/j.agrformet.2022.108919},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Li, Hailing and Wei, Maohong and Dong, Longwei and Hu, Weigang and Xiong, Junlan and Sun, Ying and Sun, Yuan and Yao, Shuran and Gong, Haiyang and Zhang, Yahui and Hou, Qingqing and Wang, Xiaoting and Xie, Shubin and Zhang, Liang and Akram, Muhammad Adnan and Rao, Zhiguo and Degen, A. Allan and Niklas, Karl J. and Ran, Jinzhi and Ye, Jian-sheng and Deng, Jianming},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {108919},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Schaepman, M. E.; Skidmore, A. K.; Catana, E.; Bannehr, L.; Bastian, O.; Borg, E.; Bumberger, J.; Dietrich, P.; Glässer, C.; Hacker, J. M.; Höfer, R.; Jagdhuber, T.; Jany, S.; Jung, A.; Karnieli, A.; Klenke, R.; Kirsten, T.; Ködel, U.; Kresse, W.; Mallast, U.; Montzka, C.; Möller, M.; Mollenhauer, H.; Pause, M.; Rahman, M.; Schrodt, F.; Schmullius, C.; Schütze, C.; Selsam, P.; Syrbe, R.; Truckenbrodt, S.; Vohland, M.; Volk, M.; Wellmann, T.; Zacharias, S.; and Baatz, R.\n\n\n \n \n \n \n \n Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(9): 2279. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_remote_2022,\n\ttitle = {Remote {Sensing} of {Geomorphodiversity} {Linked} to {Biodiversity}—{Part} {III}: {Traits}, {Processes} and {Remote} {Sensing} {Characteristics}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\tshorttitle = {Remote {Sensing} of {Geomorphodiversity} {Linked} to {Biodiversity}—{Part} {III}},\n\turl = {https://www.mdpi.com/2072-4292/14/9/2279},\n\tdoi = {10.3390/rs14092279},\n\tabstract = {Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Schaepman, Michael E. and Skidmore, Andrew K. and Catana, Eusebiu and Bannehr, Lutz and Bastian, Olaf and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Glässer, Cornelia and Hacker, Jorg M. and Höfer, Rene and Jagdhuber, Thomas and Jany, Sven and Jung, András and Karnieli, Arnon and Klenke, Reinhard and Kirsten, Toralf and Ködel, Uta and Kresse, Wolfgang and Mallast, Ulf and Montzka, Carsten and Möller, Markus and Mollenhauer, Hannes and Pause, Marion and Rahman, Minhaz and Schrodt, Franziska and Schmullius, Christiane and Schütze, Claudia and Selsam, Peter and Syrbe, Ralf-Uwe and Truckenbrodt, Sina and Vohland, Michael and Volk, Martin and Wellmann, Thilo and Zacharias, Steffen and Baatz, Roland},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2279},\n}\n\n\n\n

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\n Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.\n

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\n \n\n \n \n Lange, M.; Feilhauer, H.; Kühn, I.; and Doktor, D.\n\n\n \n \n \n \n \n Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 277: 112888. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lange_mapping_2022,\n\ttitle = {Mapping land-use intensity of grasslands in {Germany} with machine learning and {Sentinel}-2 time series},\n\tvolume = {277},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000025},\n\tdoi = {10.1016/j.rse.2022.112888},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Lange, Maximilian and Feilhauer, Hannes and Kühn, Ingolf and Doktor, Daniel},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {112888},\n}\n\n\n\n

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\n \n\n \n \n Kwiecien, O.; Braun, T.; Brunello, C. F.; Faulkner, P.; Hausmann, N.; Helle, G.; Hoggarth, J. A.; Ionita, M.; Jazwa, C. S.; Kelmelis, S.; Marwan, N.; Nava-Fernandez, C.; Nehme, C.; Opel, T.; Oster, J. L.; Perşoiu, A.; Petrie, C.; Prufer, K.; Saarni, S. M.; Wolf, A.; and Breitenbach, S. F.\n\n\n \n \n \n \n \n What we talk about when we talk about seasonality – A transdisciplinary review.\n \n \n \n \n\n\n \n\n\n\n Earth-Science Reviews, 225: 103843. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"What$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kwiecien_what_2022,\n\ttitle = {What we talk about when we talk about seasonality – {A} transdisciplinary review},\n\tvolume = {225},\n\tissn = {00128252},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0012825221003445},\n\tdoi = {10.1016/j.earscirev.2021.103843},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Earth-Science Reviews},\n\tauthor = {Kwiecien, Ola and Braun, Tobias and Brunello, Camilla Francesca and Faulkner, Patrick and Hausmann, Niklas and Helle, Gerd and Hoggarth, Julie A. and Ionita, Monica and Jazwa, Christopher S. and Kelmelis, Saige and Marwan, Norbert and Nava-Fernandez, Cinthya and Nehme, Carole and Opel, Thomas and Oster, Jessica L. and Perşoiu, Aurel and Petrie, Cameron and Prufer, Keith and Saarni, Saija M. and Wolf, Annabel and Breitenbach, Sebastian F.M.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {103843},\n}\n\n\n\n

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\n \n\n \n \n Kunz, M.; Abbas, S. S.; Bauckholt, M.; Böhmländer, A.; Feuerle, T.; Gasch, P.; Glaser, C.; Groß, J.; Hajnsek, I.; Handwerker, J.; Hase, F.; Khordakova, D.; Knippertz, P.; Kohler, M.; Lange, D.; Latt, M.; Laube, J.; Martin, L.; Mauder, M.; Möhler, O.; Mohr, S.; Reitter, R. W.; Rettenmeier, A.; Rolf, C.; Saathoff, H.; Schrön, M.; Schütze, C.; Spahr, S.; Späth, F.; Vogel, F.; Völksch, I.; Weber, U.; Wieser, A.; Wilhelm, J.; Zhang, H.; and Dietrich, P.\n\n\n \n \n \n \n \n Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Earth Science, 10: 999593. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Swabian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunz_swabian_2022,\n\ttitle = {Swabian {MOSES} 2021: {An} interdisciplinary field campaign for investigating convective storms and their event chains},\n\tvolume = {10},\n\tissn = {2296-6463},\n\tshorttitle = {Swabian {MOSES} 2021},\n\turl = {https://www.frontiersin.org/articles/10.3389/feart.2022.999593/full},\n\tdoi = {10.3389/feart.2022.999593},\n\tabstract = {The Neckar Valley and the Swabian Jura in southwest Germany comprise a hotspot for severe convective storms, causing tens of millions of euros in damage each year. Possible reasons for the high frequency of thunderstorms and the associated event chain across compartments were investigated in detail during the hydro-meteorological field campaign Swabian MOSES carried out between May and September 2021. Researchers from various disciplines established more than 25 temporary ground-based stations equipped with state-of-the-art \n              in situ \n              and remote sensing observation systems, such as lidars, dual-polarization X- and C-band Doppler weather radars, radiosondes including stratospheric balloons, an aerosol cloud chamber, masts to measure vertical fluxes, autosamplers for water probes in rivers, and networks of disdrometers, soil moisture, and hail sensors. These fixed-site observations were supplemented by mobile observation systems, such as a research aircraft with scanning Doppler lidar, a cosmic ray neutron sensing rover, and a storm chasing team launching swarmsondes in the vicinity of hailstorms. Seven Intensive Observation Periods (IOPs) were conducted on a total of 21 operating days. An exceptionally high number of convective events, including both unorganized and organized thunderstorms such as multicells or supercells, occurred during the study period. This paper gives an overview of the Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign, briefly describes the observation strategy, and presents observational highlights for two IOPs.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Earth Science},\n\tauthor = {Kunz, Michael and Abbas, Syed S. and Bauckholt, Matteo and Böhmländer, Alexander and Feuerle, Thomas and Gasch, Philipp and Glaser, Clarissa and Groß, Jochen and Hajnsek, Irena and Handwerker, Jan and Hase, Frank and Khordakova, Dina and Knippertz, Peter and Kohler, Martin and Lange, Diego and Latt, Melissa and Laube, Johannes and Martin, Lioba and Mauder, Matthias and Möhler, Ottmar and Mohr, Susanna and Reitter, René W. and Rettenmeier, Andreas and Rolf, Christian and Saathoff, Harald and Schrön, Martin and Schütze, Claudia and Spahr, Stephanie and Späth, Florian and Vogel, Franziska and Völksch, Ingo and Weber, Ute and Wieser, Andreas and Wilhelm, Jannik and Zhang, Hengheng and Dietrich, Peter},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {999593},\n}\n\n\n\n

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\n \n\n \n \n Koppa, A.; Rains, D.; Hulsman, P.; Poyatos, R.; and Miralles, D. G.\n\n\n \n \n \n \n \n A deep learning-based hybrid model of global terrestrial evaporation.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 13(1): 1912. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koppa_deep_2022,\n\ttitle = {A deep learning-based hybrid model of global terrestrial evaporation},\n\tvolume = {13},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-022-29543-7},\n\tdoi = {10.1038/s41467-022-29543-7},\n\tabstract = {Abstract \n             \n              Terrestrial evaporation ( \n              E \n              ) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, \n              E \n               \n                t \n               \n              ) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress ( \n              S \n               \n                t \n               \n              ), i.e., the reduction of \n              E \n               \n                t \n               \n              from its theoretical maximum. Then, we embed the new \n              S \n               \n                t \n               \n              formulation within a process-based model of \n              E \n              to yield a global hybrid \n              E \n              model. In this hybrid model, the \n              S \n               \n                t \n               \n              formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate \n              S \n               \n                t \n               \n              and \n              E \n              globally. The proposed framework may be extended to improve the estimation of \n              E \n              in Earth System Models and enhance our understanding of this crucial climatic variable.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Nature Communications},\n\tauthor = {Koppa, Akash and Rains, Dominik and Hulsman, Petra and Poyatos, Rafael and Miralles, Diego G.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {1912},\n}\n\n\n\n

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\n \n\n \n \n Kong, X.; Ghaffar, S.; Determann, M.; Friese, K.; Jomaa, S.; Mi, C.; Shatwell, T.; Rinke, K.; and Rode, M.\n\n\n \n \n \n \n \n Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change.\n \n \n \n \n\n\n \n\n\n\n Water Research, 221: 118721. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Reservoir$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kong_reservoir_2022,\n\ttitle = {Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change},\n\tvolume = {221},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135422006741},\n\tdoi = {10.1016/j.watres.2022.118721},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Water Research},\n\tauthor = {Kong, Xiangzhen and Ghaffar, Salman and Determann, Maria and Friese, Kurt and Jomaa, Seifeddine and Mi, Chenxi and Shatwell, Tom and Rinke, Karsten and Rode, Michael},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {118721},\n}\n\n\n\n

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\n \n\n \n \n Koedel, U.; Dietrich, P.; Fischer, P.; Greinert, J.; Bundke, U.; Burwicz-Galerne, E.; Haas, A.; Herrarte, I.; Haroon, A.; Jegen, M.; Kalbacher, T.; Kennert, M.; Korf, T.; Kunkel, R.; Kwok, C. Y.; Mahnke, C.; Nixdorf, E.; Paasche, H.; González Ávalos, E.; Petzold, A.; Rohs, S.; Wagner, R.; and Walter, A.\n\n\n \n \n \n \n \n The Digital Earth Smart Monitoring Concept and Tools.\n \n \n \n \n\n\n \n\n\n\n In Bouwer, L. M.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; and Greinert, J., editor(s), Integrating Data Science and Earth Science, pages 85–120. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{bouwer_digital_2022,\n\taddress = {Cham},\n\ttitle = {The {Digital} {Earth} {Smart} {Monitoring} {Concept} and {Tools}},\n\tisbn = {9783030995454 9783030995461},\n\turl = {https://link.springer.com/10.1007/978-3-030-99546-1_6},\n\tabstract = {Abstract \n            Reliable data are the base of all scientific analyses, interpretations and conclusions. Evaluating data in a smart way speeds up the process of interpretation and conclusion and highlights where, when and how additionally acquired data in the field will support knowledge gain. An extended SMART monitoring concept is introduced which includes SMART sensors, DataFlows, MetaData and Sampling approaches and tools. In the course of the Digital Earth project, the meaning of SMART monitoring has significantly evolved. It stands for a combination of hard- and software tools enhancing the traditional monitoring approach where a SMART monitoring DataFlow is processed and analyzed sequentially on the way from the sensor to a repository into an integrated analysis approach. The measured values itself, its metadata, and the status of the sensor, and additional auxiliary data can be made available in real time and analyzed to enhance the sensor output concerning accuracy and precision. Although several parts of the four tools are known, technically feasible and sometimes applied in Earth science studies, there is a large discrepancy between knowledge and our derived ambitions and what is feasible and commonly done in the reality and in the field.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Integrating {Data} {Science} and {Earth} {Science}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Koedel, Uta and Dietrich, Peter and Fischer, Philipp and Greinert, Jens and Bundke, Ulrich and Burwicz-Galerne, Ewa and Haas, Antonie and Herrarte, Isabel and Haroon, Amir and Jegen, Marion and Kalbacher, Thomas and Kennert, Marcel and Korf, Tobias and Kunkel, Ralf and Kwok, Ching Yin and Mahnke, Christoph and Nixdorf, Erik and Paasche, Hendrik and González Ávalos, Everardo and Petzold, Andreas and Rohs, Susanne and Wagner, Robert and Walter, Andreas},\n\teditor = {Bouwer, Laurens M. and Dransch, Doris and Ruhnke, Roland and Rechid, Diana and Frickenhaus, Stephan and Greinert, Jens},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-99546-1_6},\n\tpages = {85--120},\n}\n\n\n\n

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\n \n\n \n \n Khordakova, D.; Rolf, C.; Grooß, J.; Müller, R.; Konopka, P.; Wieser, A.; Krämer, M.; and Riese, M.\n\n\n \n \n \n \n \n A case study on the impact of severe convective storms on the water vapor mixing ratio in the lower mid-latitude stratosphere observed in 2019 over Europe.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 22(2): 1059–1079. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{khordakova_case_2022,\n\ttitle = {A case study on the impact of severe convective storms on the water vapor mixing ratio in the lower mid-latitude stratosphere observed in 2019 over {Europe}},\n\tvolume = {22},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/22/1059/2022/},\n\tdoi = {10.5194/acp-22-1059-2022},\n\tabstract = {Abstract. Extreme convective events in the troposphere not only have immediate impacts on the surface, but they can also influence the dynamics and composition of the lower stratosphere (LS). One major impact is the moistening of the LS by overshooting convection. This effect plays a crucial role in climate feedback, as small changes of water vapor in the upper troposphere and lower stratosphere (UTLS) have a large impact on the radiative budget of the atmosphere. In this case study, we investigate water vapor injections into the LS by two consecutive convective events in the European mid-latitudes within the framework of the MOSES (Modular Observation Solutions for Earth Systems) measurement campaign during the early summer of 2019. Using balloon-borne instruments, measurements of convective water vapor injection into the stratosphere were performed. Such measurements with a high vertical resolution are rare. The magnitude of the stratospheric water vapor reached up to 12.1 ppmv (parts per million by volume), with an estimated background value of 5 ppmv. Hence, the water vapor enhancement reported here is of the same order of magnitude as earlier reports of water vapor injection by convective overshooting over North America. However, the overshooting took place in the extratropical stratosphere over Europe and has a stronger impact on long-term water vapor mixing ratios in the stratosphere compared to the monsoon-influenced region in North America. At the altitude of the measured injection, a sharp drop in a local ozone enhancement peak makes the observed composition of air very unique with high ozone up to 650 ppbv (parts per billion by volume) and high water vapor up to 12.1 ppmv. ERA-Interim does not show any signal of the convective overshoot, the water vapor values measured by the Microwave Limb Sounder (MLS) in the LS are lower than the in situ observations, and the ERA5 overestimated water vapor mixing ratios. Backward trajectories of the measured injected air masses reveal that the moistening of the LS took place several hours before the balloon launch. This is in good agreement with the reanalyses, which shows a strong change in the structure of isotherms and a sudden and short-lived increase in potential vorticity at the altitude and location of the trajectory. Similarly, satellite data show low cloud-top brightness temperatures during the overshooting event, which indicates an elevated cloud top height.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {Khordakova, Dina and Rolf, Christian and Grooß, Jens-Uwe and Müller, Rolf and Konopka, Paul and Wieser, Andreas and Krämer, Martina and Riese, Martin},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {1059--1079},\n}\n\n\n\n

\n\n\n

\n Abstract. Extreme convective events in the troposphere not only have immediate impacts on the surface, but they can also influence the dynamics and composition of the lower stratosphere (LS). One major impact is the moistening of the LS by overshooting convection. This effect plays a crucial role in climate feedback, as small changes of water vapor in the upper troposphere and lower stratosphere (UTLS) have a large impact on the radiative budget of the atmosphere. In this case study, we investigate water vapor injections into the LS by two consecutive convective events in the European mid-latitudes within the framework of the MOSES (Modular Observation Solutions for Earth Systems) measurement campaign during the early summer of 2019. Using balloon-borne instruments, measurements of convective water vapor injection into the stratosphere were performed. Such measurements with a high vertical resolution are rare. The magnitude of the stratospheric water vapor reached up to 12.1 ppmv (parts per million by volume), with an estimated background value of 5 ppmv. Hence, the water vapor enhancement reported here is of the same order of magnitude as earlier reports of water vapor injection by convective overshooting over North America. However, the overshooting took place in the extratropical stratosphere over Europe and has a stronger impact on long-term water vapor mixing ratios in the stratosphere compared to the monsoon-influenced region in North America. At the altitude of the measured injection, a sharp drop in a local ozone enhancement peak makes the observed composition of air very unique with high ozone up to 650 ppbv (parts per billion by volume) and high water vapor up to 12.1 ppmv. ERA-Interim does not show any signal of the convective overshoot, the water vapor values measured by the Microwave Limb Sounder (MLS) in the LS are lower than the in situ observations, and the ERA5 overestimated water vapor mixing ratios. Backward trajectories of the measured injected air masses reveal that the moistening of the LS took place several hours before the balloon launch. This is in good agreement with the reanalyses, which shows a strong change in the structure of isotherms and a sudden and short-lived increase in potential vorticity at the altitude and location of the trajectory. Similarly, satellite data show low cloud-top brightness temperatures during the overshooting event, which indicates an elevated cloud top height.\n

\n\n\n

\n \n\n \n \n Kamjunke, N.; Beckers, L.; Herzsprung, P.; von Tümpling, W.; Lechtenfeld, O.; Tittel, J.; Risse-Buhl, U.; Rode, M.; Wachholz, A.; Kallies, R.; Schulze, T.; Krauss, M.; Brack, W.; Comero, S.; Gawlik, B. M.; Skejo, H.; Tavazzi, S.; Mariani, G.; Borchardt, D.; and Weitere, M.\n\n\n \n \n \n \n \n Lagrangian profiles of riverine autotrophy, organic matter transformation, and micropollutants at extreme drought.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 828: 154243. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Lagrangian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_lagrangian_2022,\n\ttitle = {Lagrangian profiles of riverine autotrophy, organic matter transformation, and micropollutants at extreme drought},\n\tvolume = {828},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722013353},\n\tdoi = {10.1016/j.scitotenv.2022.154243},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Kamjunke, Norbert and Beckers, Liza-Marie and Herzsprung, Peter and von Tümpling, Wolf and Lechtenfeld, Oliver and Tittel, Jörg and Risse-Buhl, Ute and Rode, Michael and Wachholz, Alexander and Kallies, Rene and Schulze, Tobias and Krauss, Martin and Brack, Werner and Comero, Sara and Gawlik, Bernd Manfred and Skejo, Hello and Tavazzi, Simona and Mariani, Giulio and Borchardt, Dietrich and Weitere, Markus},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {154243},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kamali, B.; Stella, T.; Berg-Mohnicke, M.; Pickert, J.; Groh, J.; and Nendel, C.\n\n\n \n \n \n \n \n Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics.\n \n \n \n \n\n\n \n\n\n\n European Journal of Agronomy, 134: 126464. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamali_improving_2022,\n\ttitle = {Improving the simulation of permanent grasslands across {Germany} by using multi-objective uncertainty-based calibration of plant-water dynamics},\n\tvolume = {134},\n\tissn = {11610301},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1161030122000120},\n\tdoi = {10.1016/j.eja.2022.126464},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {European Journal of Agronomy},\n\tauthor = {Kamali, Bahareh and Stella, Tommaso and Berg-Mohnicke, Michael and Pickert, Jürgen and Groh, Jannis and Nendel, Claas},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {126464},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jiang, Y.; Tang, R.; and Li, Z.\n\n\n \n \n \n \n \n A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 323: 109087. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_physical_2022,\n\ttitle = {A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration},\n\tvolume = {323},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322002751},\n\tdoi = {10.1016/j.agrformet.2022.109087},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Jiang, Yazhen and Tang, Ronglin and Li, Zhao-Liang},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {109087},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jarvis, N.; Groh, J.; Lewan, E.; Meurer, K. H. E.; Durka, W.; Baessler, C.; Pütz, T.; Rufullayev, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Coupled modelling of hydrological processes and grassland production in two contrasting climates.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(8): 2277–2299. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Coupled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jarvis_coupled_2022,\n\ttitle = {Coupled modelling of hydrological processes and grassland production in two contrasting climates},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/2277/2022/},\n\tdoi = {10.5194/hess-26-2277-2022},\n\tabstract = {Abstract. Projections of global climate models suggest that ongoing human-induced\nclimate change will lead to an increase in the frequency of severe droughts\nin many important agricultural regions of the world. Eco-hydrological models\nthat integrate current understanding of the interacting processes governing\nsoil water balance and plant growth may be useful tools to predict the\nimpacts of climate change on crop production. However, the validation status\nof these models for making predictions under climate change is still\nunclear, since few suitable datasets are available for model testing. One\npromising approach is to test models using data obtained in\n“space-for-time” substitution experiments, in which samples are\ntransferred among locations with contrasting current climates in order to\nmimic future climatic conditions. An important advantage of this approach is\nthat the soil type is the same, so that differences in soil properties are\nnot confounded with the influence of climate on water balance and crop\ngrowth. In this study, we evaluate the capability of a relatively simple\neco-hydrological model to reproduce 6 years (2013–2018) of measurements of\nsoil water contents, water balance components and grass production made in\nweighing lysimeters located at two sites within the TERENO-SoilCan network\nin Germany. Three lysimeters are located at an upland site at Rollesbroich\nwith a cool, wet climate, while three others had been moved from\nRollesbroich to a warmer and drier climate on the lower Rhine valley\nfloodplain at Selhausen. Four of the most sensitive parameters in the model\nwere treated as uncertain within the framework of the GLUE (generalized\nlikelihood uncertainty estimation) methodology, while the remaining\nparameters in the model were set according to site measurements or data in\nthe literature. The model satisfactorily reproduced the measurements at both sites, and some\nsignificant differences in the posterior ranges of the four uncertain\nparameters were found. In particular, the results indicated greater stomatal\nconductance as well an increase in dry-matter allocation below ground and a\nsignificantly larger maximum root depth for the three lysimeters that had\nbeen moved to Selhausen. As a consequence, the apparent water use efficiency\n(above-ground harvest divided by evapotranspiration) was significantly\nsmaller at Selhausen than Rollesbroich. Data on species abundance on the\nlysimeters provide one possible explanation for the differences in the plant\ntraits at the two sites derived from model calibration. These observations\nshowed that the plant community at Selhausen had changed significantly in\nresponse to the drier climate, with a significant decrease in the abundance\nof herbs and an increase in the proportion of grass species. The differences\nin root depth and leaf conductance may also be a consequence of plasticity\nor acclimation at the species level. Regardless of the reason, we may\nconclude that such adaptations introduce significant additional\nuncertainties into model predictions of water balance and plant growth in\nresponse to climate change.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Jarvis, Nicholas and Groh, Jannis and Lewan, Elisabet and Meurer, Katharina H. E. and Durka, Walter and Baessler, Cornelia and Pütz, Thomas and Rufullayev, Elvin and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2277--2299},\n}\n\n\n\n

\n\n\n

\n Abstract. Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (generalized likelihood uncertainty estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model satisfactorily reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry-matter allocation below ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.\n

\n\n\n

\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Fuchs, H.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Potential of Thermal Neutrons to Correct Cosmic‐Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(8). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jakobi_potential_2022,\n\ttitle = {Potential of {Thermal} {Neutrons} to {Correct} {Cosmic}‐{Ray} {Neutron} {Soil} {Moisture} {Content} {Measurements} for {Dynamic} {Biomass} {Effects}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022WR031972},\n\tdoi = {10.1029/2022WR031972},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Jakobi, J. and Huisman, J. A. and Fuchs, H. and Vereecken, H. and Bogena, H. R.},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jähkel, A.; Graeber, D.; Fleckenstein, J. H.; and Schmidt, C.\n\n\n \n \n \n \n \n Hydrologic Turnover Matters — Gross Gains and Losses of Six First‐Order Streams Across Contrasting Landscapes and Flow Regimes.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(7). July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydrologic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jahkel_hydrologic_2022,\n\ttitle = {Hydrologic {Turnover} {Matters} — {Gross} {Gains} and {Losses} of {Six} {First}‐{Order} {Streams} {Across} {Contrasting} {Landscapes} and {Flow} {Regimes}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022WR032129},\n\tdoi = {10.1029/2022WR032129},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Jähkel, A. and Graeber, D. and Fleckenstein, J. H. and Schmidt, C.},\n\tmonth = jul,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jagdhuber, T.; Jonard, F.; Fluhrer, A.; Chaparro, D.; Baur, M. J.; Meyer, T.; and Piles, M.\n\n\n \n \n \n \n \n Toward estimation of seasonal water dynamics of winter wheat from ground-based L-band radiometry: a concept study.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(8): 2273–2294. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jagdhuber_toward_2022,\n\ttitle = {Toward estimation of seasonal water dynamics of winter wheat from ground-based {L}-band radiometry: a concept study},\n\tvolume = {19},\n\tissn = {1726-4189},\n\tshorttitle = {Toward estimation of seasonal water dynamics of winter wheat from ground-based {L}-band radiometry},\n\turl = {https://bg.copernicus.org/articles/19/2273/2022/},\n\tdoi = {10.5194/bg-19-2273-2022},\n\tabstract = {Abstract. The vegetation optical depth (VOD) variable\ncontains information on plant water content and biomass. It can be estimated\nalongside soil moisture from currently operating satellite radiometer\nmissions, such as SMOS (ESA) and SMAP (NASA). The estimation of water\nfluxes, such as plant water uptake (PWU) and transpiration rate (TR),\nfrom these earth system parameters (VOD, soil moisture) requires assessing\nwater potential gradients and flow resistances in the soil, the vegetation\nand the atmosphere. Yet water flux estimation remains an elusive challenge\nespecially on a global scale. In this concept study, we conduct a\nfield-scale experiment to test mechanistic models for the estimation of\nseasonal water fluxes (PWU and TR) of a winter wheat stand using\nmeasurements of soil moisture, VOD, and relative air humidity (RH) in a\ncontrolled environment. We utilize microwave L-band observations from a\ntower-based radiometer to estimate VOD of a wheat stand during the 2017\ngrowing season at the Selhausen test site in Germany. From VOD,\nwe first extract the gravimetric moisture of vegetation and then determine\nthe relative water content (RWC) and vegetation water potential (VWP) of\nthe wheat field. Although the relative water content could be directly\nestimated from VOD, our results indicate this may be challenging for the\nphenological phases, when rapid biomass and plant structure development take\nplace within the wheat canopy. We estimate water uptake from the soil to the\nwheat plants from the difference between the soil and vegetation potentials\ndivided by the flow resistance from soil into wheat plants. The\nTR from the wheat plants into the atmosphere was obtained\nfrom the difference between the vegetation and atmosphere water potentials\ndivided by the flow resistances from plants to the atmosphere. For this, the\nrequired soil matric potential (SMP), the vapor pressure deficit (VPD),\nand the flow resistances were obtained from on-site observations of soil,\nplant, and atmosphere together with simple mechanistic models. This\npathfinder study shows that the L-band microwave radiation contains valuable\ninformation on vegetation water status that enables the estimation of water\ndynamics (up to fluxes) from the soil via wheat plants into the atmosphere,\nwhen combined with additional information of soil and atmosphere water\ncontent. Still, assumptions have to be made when estimating the vegetation\nwater potential from relative water content as well as the water flow\nresistances between soil, wheat plants, and atmosphere. Moreover, direct\nvalidation of water flux estimates for the assessment of their absolute\naccuracy could not be performed due to a lack of in situ PWU and TR\nmeasurements. Nonetheless, our estimates of water status, potentials, and\nfluxes show the expected temporal dynamics, known from the literature, and\nintercompare reasonably well in absolute terms with independent TR\nestimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor\ntype of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics\nnext to traditionally measured stand-scale or plot-scale techniques. They\nmight shed light on future capabilities of monitoring water dynamics in the\nsoil–plant–atmosphere system including wide-area, remote-sensing-based earth\nobservation data.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {Jagdhuber, Thomas and Jonard, François and Fluhrer, Anke and Chaparro, David and Baur, Martin J. and Meyer, Thomas and Piles, María},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {2273--2294},\n}\n\n\n\n

\n\n\n

\n Abstract. The vegetation optical depth (VOD) variable contains information on plant water content and biomass. It can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these earth system parameters (VOD, soil moisture) requires assessing water potential gradients and flow resistances in the soil, the vegetation and the atmosphere. Yet water flux estimation remains an elusive challenge especially on a global scale. In this concept study, we conduct a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWU and TR) of a winter wheat stand using measurements of soil moisture, VOD, and relative air humidity (RH) in a controlled environment. We utilize microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen test site in Germany. From VOD, we first extract the gravimetric moisture of vegetation and then determine the relative water content (RWC) and vegetation water potential (VWP) of the wheat field. Although the relative water content could be directly estimated from VOD, our results indicate this may be challenging for the phenological phases, when rapid biomass and plant structure development take place within the wheat canopy. We estimate water uptake from the soil to the wheat plants from the difference between the soil and vegetation potentials divided by the flow resistance from soil into wheat plants. The TR from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere water potentials divided by the flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit (VPD), and the flow resistances were obtained from on-site observations of soil, plant, and atmosphere together with simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions have to be made when estimating the vegetation water potential from relative water content as well as the water flow resistances between soil, wheat plants, and atmosphere. Moreover, direct validation of water flux estimates for the assessment of their absolute accuracy could not be performed due to a lack of in situ PWU and TR measurements. Nonetheless, our estimates of water status, potentials, and fluxes show the expected temporal dynamics, known from the literature, and intercompare reasonably well in absolute terms with independent TR estimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor type of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. They might shed light on future capabilities of monitoring water dynamics in the soil–plant–atmosphere system including wide-area, remote-sensing-based earth observation data.\n

\n\n\n

\n \n\n \n \n Hurley, A. G.; Peters, R. L.; Pappas, C.; Steger, D. N.; and Heinrich, I.\n\n\n \n \n \n \n \n Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 17(5): e0268426. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Addressing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hurley_addressing_2022,\n\ttitle = {Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr {R} package},\n\tvolume = {17},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0268426},\n\tdoi = {10.1371/journal.pone.0268426},\n\tabstract = {Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {PLOS ONE},\n\tauthor = {Hurley, Alexander G. and Peters, Richard L. and Pappas, Christoforos and Steger, David N. and Heinrich, Ingo},\n\teditor = {Krug, Rainer M.},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {e0268426},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Huber, R.; Le’Clec’h, S.; Buchmann, N.; and Finger, R.\n\n\n \n \n \n \n \n Economic value of three grassland ecosystem services when managed at the regional and farm scale.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 12(1): 4194. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Economic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{huber_economic_2022,\n\ttitle = {Economic value of three grassland ecosystem services when managed at the regional and farm scale},\n\tvolume = {12},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-022-08198-w},\n\tdoi = {10.1038/s41598-022-08198-w},\n\tabstract = {Abstract \n             \n              Grasslands cover a major share of the world’s agricultural land and their management influences ecosystem services. Spatially targeted policy instruments can increase the provision of ecosystem services by exploiting how they respond to spatial differences in environmental characteristics such as altitude, slope, or soil quality. However, most policy instruments focus on individual farms, where spatial differences are small. Here we assess the economic value of three grassland ecosystem services (i.e., forage provision, carbon sequestration, and habitat maintenance) and its variability in a Swiss region of 791 km \n              2 \n              that consists of 19,000 farmland parcels when managed at the regional and farm scale, respectively. Our spatially explicit bio-economic simulation approach combines biophysical information on grassland ecosystem services and their economic values. We find that in our case study region, spatial targeting on a regional scale management increases the economic value of ecosystem services by 45\\% compared to targeting at farm scale. We also find that the heterogeneity of economic values coming from prices and willingness to pay estimates is higher than the economic gains from spatial targeting that make use of the spatial difference in environmental characteristics. This implies that heterogeneity in prices and/or societal demand of these three ecosystem services is more important for grassland management than spatial heterogeneity in our case study region. The here applied framework allows for an ex-ante assessment of economic gains from spatial targeting and thus provides basic information for the implementation of incentive mechanisms addressing the nexus of food production and ecosystem service provision in grasslands.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Reports},\n\tauthor = {Huber, Robert and Le’Clec’h, Solen and Buchmann, Nina and Finger, Robert},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {4194},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Huang, L.; Lin, X.; Jiang, S.; Liu, M.; Jiang, Y.; Li, Z.; and Tang, R.\n\n\n \n \n \n \n \n A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 17(10): 104021. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{huang_two-stage_2022,\n\ttitle = {A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems},\n\tvolume = {17},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ac8b98},\n\tdoi = {10.1088/1748-9326/ac8b98},\n\tabstract = {Abstract \n             \n              Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              , and (b) separately re-parameterizing \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with \n              in-situ \n              measurements and was found to outperform the three \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m \n              −2 \n              d \n              −1 \n              ) by 24.2\\% and 35.4\\% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7\\% and 20\\% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR \n              v \n              ) were used to re-parameterize the \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of \n               \n                 \n                   \n                 \n                 \n                   \n                     \n                      ε \n                       \n                         \n                          max \n                         \n                       \n                     \n                   \n                 \n                 \n               \n              and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Huang, Lingxiao and Lin, Xiaofeng and Jiang, Shouzheng and Liu, Meng and Jiang, Yazhen and Li, Zhao-Liang and Tang, Ronglin},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {104021},\n}\n\n\n\n

\n\n\n

\n Abstract Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( ε max ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of ε max , and (b) separately re-parameterizing ε max in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art ε max –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three ε max –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three ε max –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m −2 d −1 ) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR v ) were used to re-parameterize the ε max , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of ε max and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.\n

\n\n\n

\n \n\n \n \n Huang, F.; Shangguan, W.; Li, Q.; Li, L.; and Zhang, Y.\n\n\n \n \n \n \n \n Beyond Prediction: An Integrated Post–Hoc Approach to Interpret Complex Model in Hydrometeorology.\n \n \n \n \n\n\n \n\n\n\n SSRN Electronic Journal. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Beyond$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{huang_beyond_2022,\n\ttitle = {Beyond {Prediction}: {An} {Integrated} {Post}–{Hoc} {Approach} to {Interpret} {Complex} {Model} in {Hydrometeorology}},\n\tissn = {1556-5068},\n\tshorttitle = {Beyond {Prediction}},\n\turl = {https://www.ssrn.com/abstract=4167751},\n\tdoi = {10.2139/ssrn.4167751},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {SSRN Electronic Journal},\n\tauthor = {Huang, Feini and Shangguan, Wei and Li, Qingliang and Li, Lu and Zhang, Ye},\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Hong, F.; Zhan, W.; Göttsche, F.; Liu, Z.; Dong, P.; Fu, H.; Huang, F.; and Zhang, X.\n\n\n \n \n \n \n \n A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(7): 3091–3113. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hong_global_2022,\n\ttitle = {A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {A global dataset of spatiotemporally seamless daily mean land surface temperatures},\n\turl = {https://essd.copernicus.org/articles/14/3091/2022/},\n\tdoi = {10.5194/essd-14-3091-2022},\n\tabstract = {Abstract. Daily mean land surface temperatures (LSTs) acquired from\npolar orbiters are crucial for various applications such as global and\nregional climate change analysis. However, thermal sensors from\npolar orbiters can only sample the surface effectively with very limited\ntimes per day under cloud-free conditions. These limitations have produced a\nsystematic sampling bias (ΔTsb) on the daily mean LST\n(Tdm) estimated with the traditional method, which uses the averages of\nclear-sky LST observations directly as the Tdm. Several methods have\nbeen proposed for the estimation of the Tdm, yet they are becoming less\ncapable of generating spatiotemporally seamless Tdm across the globe.\nBased on MODIS and reanalysis data, here we propose an improved annual and\ndiurnal temperature cycle-based framework (termed the IADTC framework) to\ngenerate global spatiotemporally seamless Tdm products ranging from 2003\nto 2019 (named the GADTC products). The validations show that the IADTC\nframework reduces the systematic ΔTsb significantly. When\nvalidated only with in situ data, the assessments show that the mean absolute\nerrors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and\nFLUXNET data, respectively, and the mean biases are both close to zero.\nDirect comparisons between the GADTC products and in situ measurements indicate\nthat the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets,\nrespectively, and the mean biases are −1.6 and −1.5 K for these two\ndatasets, respectively. By taking the GADTC products as references, further\nanalysis reveals that the Tdm estimated with the traditional averaging\nmethod yields a positive systematic ΔTsb of greater than 2.0 K\nin low-latitude and midlatitude regions while of a relatively small value in\nhigh-latitude regions. Although the global-mean LST trend (2003 to 2019)\ncalculated with the traditional method and the IADTC framework is relatively\nclose (both between 0.025 to 0.029 K yr−1), regional discrepancies in LST\ntrend do occur – the pixel-based MAE in LST trend between these two\nmethods reaches 0.012 K yr−1. We consider the IADTC framework can guide the\nfurther optimization of Tdm estimation across the globe, and the\ngenerated GADTC products should be valuable in various applications such as\nglobal and regional warming analysis. The GADTC products are freely\navailable at https://doi.org/10.5281/zenodo.6287052 (Hong et\nal., 2022).},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Hong, Falu and Zhan, Wenfeng and Göttsche, Frank-M. and Liu, Zihan and Dong, Pan and Fu, Huyan and Huang, Fan and Zhang, Xiaodong},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {3091--3113},\n}\n\n\n\n

\n\n\n

\n Abstract. Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔTsb) on the daily mean LST (Tdm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the Tdm. Several methods have been proposed for the estimation of the Tdm, yet they are becoming less capable of generating spatiotemporally seamless Tdm across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless Tdm products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔTsb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the Tdm estimated with the traditional averaging method yields a positive systematic ΔTsb of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr−1), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr−1. We consider the IADTC framework can guide the further optimization of Tdm estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).\n

\n\n\n

\n \n\n \n \n Helle, G.; Pauly, M.; Heinrich, I.; Schollän, K.; Balanzategui, D.; and Schürheck, L.\n\n\n \n \n \n \n \n Stable Isotope Signatures of Wood, its Constituents and Methods of Cellulose Extraction.\n \n \n \n \n\n\n \n\n\n\n In Siegwolf, R. T. W.; Brooks, J. R.; Roden, J.; and Saurer, M., editor(s), Stable Isotopes in Tree Rings, volume 8, pages 135–190. Springer International Publishing, Cham, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{siegwolf_stable_2022,\n\taddress = {Cham},\n\ttitle = {Stable {Isotope} {Signatures} of {Wood}, its {Constituents} and {Methods} of {Cellulose} {Extraction}},\n\tvolume = {8},\n\tisbn = {9783030926977 9783030926984},\n\turl = {https://link.springer.com/10.1007/978-3-030-92698-4_5},\n\tabstract = {Abstract \n            In this chapter, we give some basic information on the chemical and isotopic properties of wood constituents and describe their relative contribution to the isotopic signature of wood. Based on these considerations we review studies that have compared stable isotope signals of wood with those of corresponding cellulose. We exemplify how relationships of wood-based tree-ring stable isotope sequences with climate can be affected by varying proportions of wood constituents like cellulose, lignin and extractives. A majority of benchmarking studies suggests that cellulose extraction may not be necessary. However, based upon existing research, a general statement cannot be made on the necessity of cellulose extraction. Changes in wood composition can particularly influence environmental signal strength during periods of low isotope variability. Cellulose extraction removes any effects from changing wood composition. We present the three established chemical approaches of extraction, outline how to test the purity of isolated cellulose and present user-friendly efficient experimental setups allowing to simultaneously process hundreds of samples in one batch. Further, we briefly address the analysis of stable isotopes of lignin methoxyl groups because of easy sample preparation and its potential additional value for studies on fossil wood.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tbooktitle = {Stable {Isotopes} in {Tree} {Rings}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Helle, Gerhard and Pauly, Maren and Heinrich, Ingo and Schollän, Karina and Balanzategui, Daniel and Schürheck, Lucas},\n\teditor = {Siegwolf, Rolf T. W. and Brooks, J. Renée and Roden, John and Saurer, Matthias},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-030-92698-4_5},\n\tpages = {135--190},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Heistermann, M.; Bogena, H.; Francke, T.; Güntner, A.; Jakobi, J.; Rasche, D.; Schrön, M.; Döpper, V.; Fersch, B.; Groh, J.; Patil, A.; Pütz, T.; Reich, M.; Zacharias, S.; Zengerle, C.; and Oswald, S.\n\n\n \n \n \n \n \n Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(5): 2501–2519. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heistermann_soil_2022,\n\ttitle = {Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the {TERENO} site {Wüstebach}},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {Soil moisture observation in a forested headwater catchment},\n\turl = {https://essd.copernicus.org/articles/14/2501/2022/},\n\tdoi = {10.5194/essd-14-2501-2022},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Heistermann, Maik and Bogena, Heye and Francke, Till and Güntner, Andreas and Jakobi, Jannis and Rasche, Daniel and Schrön, Martin and Döpper, Veronika and Fersch, Benjamin and Groh, Jannis and Patil, Amol and Pütz, Thomas and Reich, Marvin and Zacharias, Steffen and Zengerle, Carmen and Oswald, Sascha},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {2501--2519},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n He, M.; Chen, S.; Lian, X.; Wang, X.; Peñuelas, J.; and Piao, S.\n\n\n \n \n \n \n \n Global Spectrum of Vegetation Light‐Use Efficiency.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 49(16). August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{he_global_2022,\n\ttitle = {Global {Spectrum} of {Vegetation} {Light}‐{Use} {Efficiency}},\n\tvolume = {49},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2022GL099550},\n\tdoi = {10.1029/2022GL099550},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-21},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {He, Mingzhu and Chen, Shaoyuan and Lian, Xu and Wang, Xuhui and Peñuelas, Josep and Piao, Shilong},\n\tmonth = aug,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Haruzi, P.; Schmäck, J.; Zhou, Z.; van der Kruk, J.; Vereecken, H.; Vanderborght, J.; and Klotzsche, A.\n\n\n \n \n \n \n \n Detection of Tracer Plumes Using Full‐Waveform Inversion of Time‐Lapse Ground Penetrating Radar Data: A Numerical Study in a High‐Resolution Aquifer Model.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 58(5). May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haruzi_detection_2022,\n\ttitle = {Detection of {Tracer} {Plumes} {Using} {Full}‐{Waveform} {Inversion} of {Time}‐{Lapse} {Ground} {Penetrating} {Radar} {Data}: {A} {Numerical} {Study} in a {High}‐{Resolution} {Aquifer} {Model}},\n\tvolume = {58},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Detection of {Tracer} {Plumes} {Using} {Full}‐{Waveform} {Inversion} of {Time}‐{Lapse} {Ground} {Penetrating} {Radar} {Data}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030110},\n\tdoi = {10.1029/2021WR030110},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Water Resources Research},\n\tauthor = {Haruzi, P. and Schmäck, J. and Zhou, Z. and van der Kruk, J. and Vereecken, H. and Vanderborght, J. and Klotzsche, A.},\n\tmonth = may,\n\tyear = {2022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Hari, M.; and Tyagi, B.\n\n\n \n \n \n \n \n Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems.\n \n \n \n \n\n\n \n\n\n\n Environmental Science: Atmospheres, 2(5): 867–890. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Terrestrial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hari_terrestrial_2022,\n\ttitle = {Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems},\n\tvolume = {2},\n\tissn = {2634-3606},\n\tshorttitle = {Terrestrial carbon cycle},\n\turl = {http://xlink.rsc.org/?DOI=D1EA00102G},\n\tdoi = {10.1039/D1EA00102G},\n\tabstract = {Being a climate change nexus, the study on the carbon cycle depicts the existence of its mechanistic link with the atmospheric and biospheric environment. \n          ,  \n             \n              Owing to its tendency to couple with multiple elements, carbon forms complex molecules, which is the basic chemistry of life. Given that the climate system is inextricably coupled with the biosphere, understanding the terrestrial mechanistic pathway of carbon is critical in the transformation of the augmenting atmospheric carbon dioxide (CO \n              2 \n              ) in future. Although the global terrestrial carbon sink reduces the accumulation of atmospheric CO \n              2 \n              , which is contingent on the climate and ecosystem, the underlying key biophysical function that controls the ecosystem-carbon-climate responses and their feedback is uncertain. Accordingly, numerous unprecedented multi-scale studies have highlighted the dynamics of terrestrial carbon by strategically employing \n              in situ \n              , earth observation and process-based models; however, to date, the driving force for its dynamics remains unclassified. Besides, the significant variability in carbon is related to the large uncertainties from changes in land use, unambiguously increasing the regional carbon source from the seasonal to interannual scale but without long-term positive or negative feedback. Accordingly, in this review, we attempt to present a holistic understanding of the terrestrial carbon cycle by addressing its nature and different key drivers. The heterogenetic data platforms that reliably address the terrestrial carbon sink and its source dynamics are discussed in detail to demonstrate the potential of systematic quantification. Moreover, we summarize the complexity of carbon-climate feedbacks and their associates, extending the pathway for understanding the recent terrestrial carbon allocation, where India's environment is highlighted. This comprehensive review can be valuable to the research community in understanding the importance of the present and future carbon-climate feedback.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Science: Atmospheres},\n\tauthor = {Hari, Manoj and Tyagi, Bhishma},\n\tyear = {2022},\n\tpages = {867--890},\n}\n\n\n\n

\n\n\n

\n Being a climate change nexus, the study on the carbon cycle depicts the existence of its mechanistic link with the atmospheric and biospheric environment. , Owing to its tendency to couple with multiple elements, carbon forms complex molecules, which is the basic chemistry of life. Given that the climate system is inextricably coupled with the biosphere, understanding the terrestrial mechanistic pathway of carbon is critical in the transformation of the augmenting atmospheric carbon dioxide (CO 2 ) in future. Although the global terrestrial carbon sink reduces the accumulation of atmospheric CO 2 , which is contingent on the climate and ecosystem, the underlying key biophysical function that controls the ecosystem-carbon-climate responses and their feedback is uncertain. Accordingly, numerous unprecedented multi-scale studies have highlighted the dynamics of terrestrial carbon by strategically employing in situ , earth observation and process-based models; however, to date, the driving force for its dynamics remains unclassified. Besides, the significant variability in carbon is related to the large uncertainties from changes in land use, unambiguously increasing the regional carbon source from the seasonal to interannual scale but without long-term positive or negative feedback. Accordingly, in this review, we attempt to present a holistic understanding of the terrestrial carbon cycle by addressing its nature and different key drivers. The heterogenetic data platforms that reliably address the terrestrial carbon sink and its source dynamics are discussed in detail to demonstrate the potential of systematic quantification. Moreover, we summarize the complexity of carbon-climate feedbacks and their associates, extending the pathway for understanding the recent terrestrial carbon allocation, where India's environment is highlighted. This comprehensive review can be valuable to the research community in understanding the importance of the present and future carbon-climate feedback.\n

\n\n\n

\n \n\n \n \n Groh, J.; Diamantopoulos, E.; Duan, X.; Ewert, F.; Heinlein, F.; Herbst, M.; Holbak, M.; Kamali, B.; Kersebaum, K.; Kuhnert, M.; Nendel, C.; Priesack, E.; Steidl, J.; Sommer, M.; Pütz, T.; Vanderborght, J.; Vereecken, H.; Wallor, E.; Weber, T. K. D.; Wegehenkel, M.; Weihermüller, L.; and Gerke, H. H.\n\n\n \n \n \n \n \n Same soil, different climate: Crop model intercomparison on translocated lysimeters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 21(4). July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Same$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_same_2022,\n\ttitle = {Same soil, different climate: {Crop} model intercomparison on translocated lysimeters},\n\tvolume = {21},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {Same soil, different climate},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20202},\n\tdoi = {10.1002/vzj2.20202},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Diamantopoulos, Efstathios and Duan, Xiaohong and Ewert, Frank and Heinlein, Florian and Herbst, Michael and Holbak, Maja and Kamali, Bahareh and Kersebaum, Kurt‐Christian and Kuhnert, Matthias and Nendel, Claas and Priesack, Eckart and Steidl, Jörg and Sommer, Michael and Pütz, Thomas and Vanderborght, Jan and Vereecken, Harry and Wallor, Evelyn and Weber, Tobias K. D. and Wegehenkel, Martin and Weihermüller, Lutz and Gerke, Horst H.},\n\tmonth = jul,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Fu, Z.; Ciais, P.; Makowski, D.; Bastos, A.; Stoy, P. C.; Ibrom, A.; Knohl, A.; Migliavacca, M.; Cuntz, M.; Šigut, L.; Peichl, M.; Loustau, D.; El‐Madany, T. S.; Buchmann, N.; Gharun, M.; Janssens, I.; Markwitz, C.; Grünwald, T.; Rebmann, C.; Mölder, M.; Varlagin, A.; Mammarella, I.; Kolari, P.; Bernhofer, C.; Heliasz, M.; Vincke, C.; Pitacco, A.; Cremonese, E.; Foltýnová, L.; and Wigneron, J.\n\n\n \n \n \n \n \n Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(6): 2111–2123. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncovering$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fu_uncovering_2022,\n\ttitle = {Uncovering the critical soil moisture thresholds of plant water stress for {European} ecosystems},\n\tvolume = {28},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16050},\n\tdoi = {10.1111/gcb.16050},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Global Change Biology},\n\tauthor = {Fu, Zheng and Ciais, Philippe and Makowski, David and Bastos, Ana and Stoy, Paul C. and Ibrom, Andreas and Knohl, Alexander and Migliavacca, Mirco and Cuntz, Matthias and Šigut, Ladislav and Peichl, Matthias and Loustau, Denis and El‐Madany, Tarek S. and Buchmann, Nina and Gharun, Mana and Janssens, Ivan and Markwitz, Christian and Grünwald, Thomas and Rebmann, Corinna and Mölder, Meelis and Varlagin, Andrej and Mammarella, Ivan and Kolari, Pasi and Bernhofer, Christian and Heliasz, Michal and Vincke, Caroline and Pitacco, Andrea and Cremonese, Edoardo and Foltýnová, Lenka and Wigneron, Jean‐Pierre},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {2111--2123},\n}\n\n\n\n

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\n \n\n \n \n Francke, T.; Heistermann, M.; Köhli, M.; Budach, C.; Schrön, M.; and Oswald, S. E.\n\n\n \n \n \n \n \n Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Instrumentation, Methods and Data Systems, 11(1): 75–92. February 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{francke_assessing_2022,\n\ttitle = {Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture},\n\tvolume = {11},\n\tissn = {2193-0864},\n\turl = {https://gi.copernicus.org/articles/11/75/2022/},\n\tdoi = {10.5194/gi-11-75-2022},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use. This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution. Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time. The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Instrumentation, Methods and Data Systems},\n\tauthor = {Francke, Till and Heistermann, Maik and Köhli, Markus and Budach, Christian and Schrön, Martin and Oswald, Sascha E.},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {75--92},\n}\n\n\n\n

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\n \n\n \n \n Eingrüber, N.; and Korres, W.\n\n\n \n \n \n \n \n Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model).\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 838: 155775. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{eingruber_climate_2022,\n\ttitle = {Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale {Rur} catchment in western {Germany} until 2099 using {Statistical} {Downscaling} {Model} ({SDSM}) and the {Soil} \\& {Water} {Assessment} {Tool} ({SWAT} model)},\n\tvolume = {838},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722028728},\n\tdoi = {10.1016/j.scitotenv.2022.155775},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Eingrüber, Nils and Korres, Wolfgang},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {155775},\n}\n\n\n\n

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\n \n\n \n \n Dunkl, I.; and Ließ, M.\n\n\n \n \n \n \n \n On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization.\n \n \n \n \n\n\n \n\n\n\n SOIL, 8(2): 541–558. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dunkl_benefits_2022,\n\ttitle = {On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization},\n\tvolume = {8},\n\tissn = {2199-398X},\n\tshorttitle = {On the benefits of clustering approaches in digital soil mapping},\n\turl = {https://soil.copernicus.org/articles/8/541/2022/},\n\tdoi = {10.5194/soil-8-541-2022},\n\tabstract = {Abstract. High-resolution soil maps are urgently needed by land managers and researchers for a variety of applications. Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. In this study, a legacy soil dataset was used to train a machine learning algorithm in order to predict the particle size distribution within the catchment of the Bode River in Saxony-Anhalt (Germany). The random forest ensemble learning method was used to predict soil texture based on environmental covariates originating from a digital elevation model, land cover data and geologic maps. We studied the usefulness of clustering applications in addressing various aspects of the DSM procedure. To improve areal representativity of the legacy soil data in terms of spatial variability, the environmental covariates were used to cluster the landscape of the study area into spatial units for stratified random sampling. Different sampling strategies were used to create balanced training data and were evaluated on their ability to improve model performance. Clustering applications were also involved in feature selection and stratified cross-validation. Under the best-performing sampling strategy, the resulting models achieved an R2 of 0.29 to 0.50 in topsoils and 0.16–0.32 in deeper soil layers. Overall, clustering applications appear to be a versatile tool to be employed at various steps of the DSM procedure. Beyond their successful application, further application fields in DSM were identified. One of them is to find adequate means to include expert knowledge.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-21},\n\tjournal = {SOIL},\n\tauthor = {Dunkl, István and Ließ, Mareike},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {541--558},\n}\n\n\n\n

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\n \n\n \n \n Döpper, V.; Jagdhuber, T.; Holtgrave, A.; Heistermann, M.; Francke, T.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: Exploring the potential of optical and SAR remote sensing.\n \n \n \n \n\n\n \n\n\n\n Science of Remote Sensing, 5: 100056. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Following$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dopper_following_2022,\n\ttitle = {Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: {Exploring} the potential of optical and {SAR} remote sensing},\n\tvolume = {5},\n\tissn = {26660172},\n\tshorttitle = {Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2666017222000189},\n\tdoi = {10.1016/j.srs.2022.100056},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of Remote Sensing},\n\tauthor = {Döpper, Veronika and Jagdhuber, Thomas and Holtgrave, Ann-Kathrin and Heistermann, Maik and Francke, Till and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {100056},\n}\n\n\n\n

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\n \n\n \n \n Döpper, V.; Rocha, A. D.; Berger, K.; Gränzig, T.; Verrelst, J.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 110: 102817. June 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dopper_estimating_2022,\n\ttitle = {Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning},\n\tvolume = {110},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S156984322200019X},\n\tdoi = {10.1016/j.jag.2022.102817},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Döpper, Veronika and Rocha, Alby Duarte and Berger, Katja and Gränzig, Tobias and Verrelst, Jochem and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {102817},\n}\n\n\n\n

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\n \n\n \n \n Dombrowski, O.; Brogi, C.; Hendricks Franssen, H.; Zanotelli, D.; and Bogena, H.\n\n\n \n \n \n \n \n CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5).\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(13): 5167–5193. July 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"CLM5-FruitTree:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dombrowski_clm5-fruittree_2022,\n\ttitle = {{CLM5}-{FruitTree}: a new sub-model for deciduous fruit trees in the {Community} {Land} {Model} ({CLM5})},\n\tvolume = {15},\n\tissn = {1991-9603},\n\tshorttitle = {{CLM5}-{FruitTree}},\n\turl = {https://gmd.copernicus.org/articles/15/5167/2022/},\n\tdoi = {10.5194/gmd-15-5167-2022},\n\tabstract = {Abstract. The inclusion of perennial, woody crops in land surface\nmodels (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these\nagroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land\nModel version 5 (CLM5). The model development included (1) a new perennial\ncrop phenology description, (2) an adapted C and nitrogen allocation scheme,\nconsidering both storage and photosynthetic growth of annual and perennial\nplant organs, (3) typical management practices associated with fruit\norchards, and (4) the parameterization of an apple plant functional type.\nCLM5-FruitTree was tested using extensive field measurements from an apple\norchard in South Tyrol, Italy. Growth and partitioning of biomass to the\nindividual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 \\% of the observed values despite\nlow simulated inter-annual variability compared to observations. The\nsimulated seasonal course of C, energy, and water fluxes was in good\nagreement with the eddy covariance (EC) measurements owing to the accurate\nrepresentation of the prolonged growing season and typical leaf area\ndevelopment of the orchard. We found that gross primary production, net\nradiation, and latent heat flux were highly correlated (r{\\textgreater}0.94)\nwith EC measurements and showed little bias ({\\textless}±5 \\%).\nSimulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in\nthe orchard structure and to the presence of additional management practices\nthat are not yet represented in CLM5-FruitTree. Finally, the results\nsuggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires\nfurther attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land\nsurface processes in fruit orchards at the local, regional, or larger scale.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Dombrowski, Olga and Brogi, Cosimo and Hendricks Franssen, Harrie-Jan and Zanotelli, Damiano and Bogena, Heye},\n\tmonth = jul,\n\tyear = {2022},\n\tpages = {5167--5193},\n}\n\n\n\n

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\n Abstract. The inclusion of perennial, woody crops in land surface models (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these agroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM5-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the eddy covariance (EC) measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r\\textgreater0.94) with EC measurements and showed little bias (\\textless±5 %). Simulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM5-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires further attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional, or larger scale.\n

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\n \n\n \n \n Ding, J.; Zhu, Q.; Li, H.; Zhou, X.; Liu, W.; and Peng, C.\n\n\n \n \n \n \n \n Contribution of Incorporating the Phosphorus Cycle into TRIPLEX-CNP to Improve the Quantification of Land Carbon Cycle.\n \n \n \n \n\n\n \n\n\n\n Land, 11(6): 778. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Contribution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ding_contribution_2022,\n\ttitle = {Contribution of {Incorporating} the {Phosphorus} {Cycle} into {TRIPLEX}-{CNP} to {Improve} the {Quantification} of {Land} {Carbon} {Cycle}},\n\tvolume = {11},\n\tissn = {2073-445X},\n\turl = {https://www.mdpi.com/2073-445X/11/6/778},\n\tdoi = {10.3390/land11060778},\n\tabstract = {Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p {\\textless} 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p {\\textless} 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Land},\n\tauthor = {Ding, Juhua and Zhu, Qiuan and Li, Hanwei and Zhou, Xiaolu and Liu, Weiguo and Peng, Changhui},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {778},\n}\n\n\n\n

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\n \n\n \n \n De Pue, J.; Barrios, J. M.; Liu, L.; Ciais, P.; Arboleda, A.; Hamdi, R.; Balzarolo, M.; Maignan, F.; and Gellens-Meulenberghs, F.\n\n\n \n \n \n \n \n Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 19(17): 4361–4386. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Local-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_pue_local-scale_2022,\n\ttitle = {Local-scale evaluation of the simulated interactions between energy, water and vegetation in {ISBA}, {ORCHIDEE} and a diagnostic model},\n\tvolume = {19},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/19/4361/2022/},\n\tdoi = {10.5194/bg-19-4361-2022},\n\tabstract = {Abstract. The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined.\nTo accurately represent the terrestrial biosphere in land surface models (LSMs), the intrinsic coupling between these processes is required.\nSoil moisture and leaf area index (LAI) are two key variables at the nexus of water, energy and vegetation.\nHere, we evaluated two prognostic LSMs (ISBA and ORCHIDEE) and a diagnostic model (based on the LSA SAF, Satellite Application Facility for Land Surface Analysis, algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently and their interactions through LAI and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote-sensing-based LAI.\nIt was found that the diagnostic model performed consistently well, regardless of land cover, whereas important shortcomings of the prognostic models were revealed for herbaceous and dry sites.\nDespite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses.\nIn both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes.\nInstead, these errors were strongly correlated to errors in LAI.\nThe simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations and failed to capture the observed seasonal variability.\nThe feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSMs.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-21},\n\tjournal = {Biogeosciences},\n\tauthor = {De Pue, Jan and Barrios, José Miguel and Liu, Liyang and Ciais, Philippe and Arboleda, Alirio and Hamdi, Rafiq and Balzarolo, Manuela and Maignan, Fabienne and Gellens-Meulenberghs, Françoise},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {4361--4386},\n}\n\n\n\n

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\n \n\n \n \n De Cannière, S.; Vereecken, H.; Defourny, P.; and Jonard, F.\n\n\n \n \n \n \n \n Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 14(11): 2642. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_canniere_remote_2022,\n\ttitle = {Remote {Sensing} of {Instantaneous} {Drought} {Stress} at {Canopy} {Level} {Using} {Sun}-{Induced} {Chlorophyll} {Fluorescence} and {Canopy} {Reflectance}},\n\tvolume = {14},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/14/11/2642},\n\tdoi = {10.3390/rs14112642},\n\tabstract = {Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing},\n\tauthor = {De Cannière, Simon and Vereecken, Harry and Defourny, Pierre and Jonard, François},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {2642},\n}\n\n\n\n

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\n \n\n \n \n Chen, X.; Huang, Y.; Nie, C.; Zhang, S.; Wang, G.; Chen, S.; and Chen, Z.\n\n\n \n \n \n \n \n A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 9(1): 427. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chen_long-term_2022,\n\ttitle = {A long-term reconstructed {TROPOMI} solar-induced fluorescence dataset using machine learning algorithms},\n\tvolume = {9},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-022-01520-1},\n\tdoi = {10.1038/s41597-022-01520-1},\n\tabstract = {Abstract \n             \n              Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001–2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R \n              2 \n               = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Data},\n\tauthor = {Chen, Xingan and Huang, Yuefei and Nie, Chong and Zhang, Shuo and Wang, Guangqian and Chen, Shiliu and Chen, Zhichao},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {427},\n}\n\n\n\n

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\n \n\n \n \n Chen, H.; Huang, J. J.; Dash, S. S.; Wei, Y.; and Li, H.\n\n\n \n \n \n \n \n A hybrid deep learning framework with physical process description for simulation of evapotranspiration.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 606: 127422. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chen_hybrid_2022,\n\ttitle = {A hybrid deep learning framework with physical process description for simulation of evapotranspiration},\n\tvolume = {606},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421014724},\n\tdoi = {10.1016/j.jhydrol.2021.127422},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Chen, Han and Huang, Jinhui Jeanne and Dash, Sonam Sandeep and Wei, Yizhao and Li, Han},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {127422},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H. R.; Schrön, M.; Jakobi, J.; Ney, P.; Zacharias, S.; Andreasen, M.; Baatz, R.; Boorman, D.; Duygu, M. B.; Eguibar-Galán, M. A.; Fersch, B.; Franke, T.; Geris, J.; González Sanchis, M.; Kerr, Y.; Korf, T.; Mengistu, Z.; Mialon, A.; Nasta, P.; Nitychoruk, J.; Pisinaras, V.; Rasche, D.; Rosolem, R.; Said, H.; Schattan, P.; Zreda, M.; Achleitner, S.; Albentosa-Hernández, E.; Akyürek, Z.; Blume, T.; del Campo, A.; Canone, D.; Dimitrova-Petrova, K.; Evans, J. G.; Ferraris, S.; Frances, F.; Gisolo, D.; Güntner, A.; Herrmann, F.; Iwema, J.; Jensen, K. H.; Kunstmann, H.; Lidón, A.; Looms, M. C.; Oswald, S.; Panagopoulos, A.; Patil, A.; Power, D.; Rebmann, C.; Romano, N.; Scheiffele, L.; Seneviratne, S.; Weltin, G.; and Vereecken, H.\n\n\n \n \n \n \n \n COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 14(3): 1125–1151. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"COSMOS-Europe:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_cosmos-europe_2022,\n\ttitle = {{COSMOS}-{Europe}: a {European} network of cosmic-ray neutron soil moisture sensors},\n\tvolume = {14},\n\tissn = {1866-3516},\n\tshorttitle = {{COSMOS}-{Europe}},\n\turl = {https://essd.copernicus.org/articles/14/1125/2022/},\n\tdoi = {10.5194/essd-14-1125-2022},\n\tabstract = {Abstract. Climate change increases the occurrence and severity of\ndroughts due to increasing temperatures, altered circulation patterns, and\nreduced snow occurrence. While Europe has suffered from drought events in\nthe last decade unlike ever seen since the beginning of weather recordings,\nharmonized long-term datasets across the continent are needed to monitor\nchange and support predictions. Here we present soil moisture data from 66\ncosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short)\ncovering recent drought events. The CRNS sites are distributed across Europe\nand cover all major land use types and climate zones in Europe. The raw\nneutron count data from the CRNS stations were provided by 24 research\ninstitutions and processed using state-of-the-art methods. The harmonized\nprocessing included correction of the raw neutron counts and a harmonized\nmethodology for the conversion into soil moisture based on available in situ\ninformation. In addition, the uncertainty estimate is provided with the\ndataset, information that is particularly useful for remote sensing and\nmodeling applications. This paper presents the current spatiotemporal\ncoverage of CRNS stations in Europe and describes the protocols for data\nprocessing from raw measurements to consistent soil moisture products. The\ndata of the presented COSMOS-Europe network open up a manifold of potential\napplications for environmental research, such as remote sensing data\nvalidation, trend analysis, or model assimilation. The dataset could be of\nparticular importance for the analysis of extreme climatic events at the\ncontinental scale. Due its timely relevance in the scope of climate change\nin the recent years, we demonstrate this potential application with a brief\nanalysis on the spatiotemporal soil moisture variability. The dataset,\nentitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray\nNeutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich:\nhttps://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Earth System Science Data},\n\tauthor = {Bogena, Heye Reemt and Schrön, Martin and Jakobi, Jannis and Ney, Patrizia and Zacharias, Steffen and Andreasen, Mie and Baatz, Roland and Boorman, David and Duygu, Mustafa Berk and Eguibar-Galán, Miguel Angel and Fersch, Benjamin and Franke, Till and Geris, Josie and González Sanchis, María and Kerr, Yann and Korf, Tobias and Mengistu, Zalalem and Mialon, Arnaud and Nasta, Paolo and Nitychoruk, Jerzy and Pisinaras, Vassilios and Rasche, Daniel and Rosolem, Rafael and Said, Hami and Schattan, Paul and Zreda, Marek and Achleitner, Stefan and Albentosa-Hernández, Eduardo and Akyürek, Zuhal and Blume, Theresa and del Campo, Antonio and Canone, Davide and Dimitrova-Petrova, Katya and Evans, John G. and Ferraris, Stefano and Frances, Félix and Gisolo, Davide and Güntner, Andreas and Herrmann, Frank and Iwema, Joost and Jensen, Karsten H. and Kunstmann, Harald and Lidón, Antonio and Looms, Majken Caroline and Oswald, Sascha and Panagopoulos, Andreas and Patil, Amol and Power, Daniel and Rebmann, Corinna and Romano, Nunzio and Scheiffele, Lena and Seneviratne, Sonia and Weltin, Georg and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {1125--1151},\n}\n\n\n\n

\n\n\n

\n Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).\n

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\n \n\n \n \n Boeing, F.; Rakovec, O.; Kumar, R.; Samaniego, L.; Schrön, M.; Hildebrandt, A.; Rebmann, C.; Thober, S.; Müller, S.; Zacharias, S.; Bogena, H.; Schneider, K.; Kiese, R.; Attinger, S.; and Marx, A.\n\n\n \n \n \n \n \n High-resolution drought simulations and comparison to soil moisture observations in Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 26(19): 5137–5161. October 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{boeing_high-resolution_2022,\n\ttitle = {High-resolution drought simulations and comparison to soil moisture observations in {Germany}},\n\tvolume = {26},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/26/5137/2022/},\n\tdoi = {10.5194/hess-26-5137-2022},\n\tabstract = {Abstract. Germany's 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy\nproduction, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new\nsetup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological\nmodel (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor\nnetworks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed\nSM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in\nwinter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but\nsignificant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to\nobserved SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to\nobservations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher\nresolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial\nfor local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of\nthe water cycle and a broad, high-quality, observational soil moisture database.},\n\tlanguage = {en},\n\tnumber = {19},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Boeing, Friedrich and Rakovec, Oldrich and Kumar, Rohini and Samaniego, Luis and Schrön, Martin and Hildebrandt, Anke and Rebmann, Corinna and Thober, Stephan and Müller, Sebastian and Zacharias, Steffen and Bogena, Heye and Schneider, Katrin and Kiese, Ralf and Attinger, Sabine and Marx, Andreas},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {5137--5161},\n}\n\n\n\n

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\n \n\n \n \n Bi, W.; He, W.; Zhou, Y.; Ju, W.; Liu, Y.; Liu, Y.; Zhang, X.; Wei, X.; and Cheng, N.\n\n\n \n \n \n \n \n A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 9(1): 213. December 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bi_global_2022,\n\ttitle = {A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020},\n\tvolume = {9},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-022-01309-2},\n\tdoi = {10.1038/s41597-022-01309-2},\n\tabstract = {Abstract \n             \n              Distinguishing gross primary production of sunlit and shaded leaves (GPP \n              sun \n              and GPP \n              shade \n              ) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP \n              shade \n              and GPP \n              sun \n              over 1992–2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP \n              sun \n              , and GPP \n              shade \n              over 1992–2020 at 125.0 ± 3.8 (mean ± std) Pg C a \n              −1 \n              , 50.5 ± 1.2 Pg C a \n              −1 \n              , and 74.5 ± 2.6 Pg C a \n              −1 \n              , respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP \n              sun \n              and GPP \n              shade \n              ) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP \n              shade \n              overwhelmingly outweighs GPP \n              sun \n              . This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-21},\n\tjournal = {Scientific Data},\n\tauthor = {Bi, Wenjun and He, Wei and Zhou, Yanlian and Ju, Weimin and Liu, Yibo and Liu, Yang and Zhang, Xiaoyu and Wei, Xiaonan and Cheng, Nuo},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {213},\n}\n\n\n\n

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\n \n\n \n \n Beck, H. E.; van Dijk, A. I. J. M.; Larraondo, P. R.; McVicar, T. R.; Pan, M.; Dutra, E.; and Miralles, D. G.\n\n\n \n \n \n \n \n MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 103(3): E710–E732. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"MSWX:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beck_mswx_2022,\n\ttitle = {{MSWX}: {Global} 3-{Hourly} 0.1° {Bias}-{Corrected} {Meteorological} {Data} {Including} {Near}-{Real}-{Time} {Updates} and {Forecast} {Ensembles}},\n\tvolume = {103},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {{MSWX}},\n\turl = {https://journals.ametsoc.org/view/journals/bams/103/3/BAMS-D-21-0145.1.xml},\n\tdoi = {10.1175/BAMS-D-21-0145.1},\n\tabstract = {Abstract \n             \n              We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at \n              www.gloh2o.org/mswx \n              .},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Beck, Hylke E. and van Dijk, Albert I. J. M. and Larraondo, Pablo R. and McVicar, Tim R. and Pan, Ming and Dutra, Emanuel and Miralles, Diego G.},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {E710--E732},\n}\n\n\n\n

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\n \n\n \n \n Bauer, F. M.; Lärm, L.; Morandage, S.; Lobet, G.; Vanderborght, J.; Vereecken, H.; and Schnepf, A.\n\n\n \n \n \n \n \n Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline.\n \n \n \n \n\n\n \n\n\n\n Plant Phenomics, 2022: 1–14. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bauer_development_2022,\n\ttitle = {Development and {Validation} of a {Deep} {Learning} {Based} {Automated} {Minirhizotron} {Image} {Analysis} {Pipeline}},\n\tvolume = {2022},\n\tissn = {2643-6515},\n\turl = {https://spj.sciencemag.org/journals/plantphenomics/2022/9758532/},\n\tdoi = {10.34133/2022/9758532},\n\tabstract = {Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation ( \n               \n                r \n                = \n                0.9 \n               \n              ) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6\\%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Plant Phenomics},\n\tauthor = {Bauer, Felix Maximilian and Lärm, Lena and Morandage, Shehan and Lobet, Guillaume and Vanderborght, Jan and Vereecken, Harry and Schnepf, Andrea},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n\n\n

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\n \n\n \n \n Bao, S.; Ibrom, A.; Wohlfahrt, G.; Koirala, S.; Migliavacca, M.; Zhang, Q.; and Carvalhais, N.\n\n\n \n \n \n \n \n Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 326: 109185. November 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Narrow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bao_narrow_2022,\n\ttitle = {Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level},\n\tvolume = {326},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322003720},\n\tdoi = {10.1016/j.agrformet.2022.109185},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Bao, Shanning and Ibrom, Andreas and Wohlfahrt, Georg and Koirala, Sujan and Migliavacca, Mirco and Zhang, Qian and Carvalhais, Nuno},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {109185},\n}\n\n\n\n

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\n \n\n \n \n Baldocchi, D. D.; Keeney, N.; Rey-Sanchez, C.; and Fisher, J. B.\n\n\n \n \n \n \n \n Atmospheric humidity deficits tell us how soil moisture deficits down-regulate ecosystem evaporation.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 159: 104100. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Atmospheric$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baldocchi_atmospheric_2022,\n\ttitle = {Atmospheric humidity deficits tell us how soil moisture deficits down-regulate ecosystem evaporation},\n\tvolume = {159},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170821002517},\n\tdoi = {10.1016/j.advwatres.2021.104100},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Baldocchi, Dennis D. and Keeney, Nicole and Rey-Sanchez, Camilo and Fisher, Joshua B.},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {104100},\n}\n\n\n\n

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\n \n\n \n \n Bai, Y.; Bhattarai, N.; Mallick, K.; Zhang, S.; Hu, T.; and Zhang, J.\n\n\n \n \n \n \n \n Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 271: 112901. March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Thermally$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bai_thermally_2022,\n\ttitle = {Thermally derived evapotranspiration from the {Surface} {Temperature} {Initiated} {Closure} ({STIC}) model improves cropland {GPP} estimates under dry conditions},\n\tvolume = {271},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722000153},\n\tdoi = {10.1016/j.rse.2022.112901},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Bai, Yun and Bhattarai, Nishan and Mallick, Kaniska and Zhang, Sha and Hu, Tian and Zhang, Jiahua},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {112901},\n}\n\n\n\n

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\n \n\n \n \n Bahrami, B.; Hildebrandt, A.; Thober, S.; Rebmann, C.; Fischer, R.; Samaniego, L.; Rakovec, O.; and Kumar, R.\n\n\n \n \n \n \n \n Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 15(18): 6957–6984. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Developing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bahrami_developing_2022,\n\ttitle = {Developing a parsimonious canopy model ({PCM} v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest},\n\tvolume = {15},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/15/6957/2022/},\n\tdoi = {10.5194/gmd-15-6957-2022},\n\tabstract = {Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 \\%). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2022-11-21},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Bahrami, Bahar and Hildebrandt, Anke and Thober, Stephan and Rebmann, Corinna and Fischer, Rico and Samaniego, Luis and Rakovec, Oldrich and Kumar, Rohini},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {6957--6984},\n}\n\n\n\n

\n\n\n

\n Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.\n

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\n \n\n \n \n Araki, R.; Branger, F.; Wiekenkamp, I.; and McMillan, H.\n\n\n \n \n \n \n \n A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(4). April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{araki_signaturebased_2022,\n\ttitle = {A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14553},\n\tdoi = {10.1002/hyp.14553},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-21},\n\tjournal = {Hydrological Processes},\n\tauthor = {Araki, Ryoko and Branger, Flora and Wiekenkamp, Inge and McMillan, Hilary},\n\tmonth = apr,\n\tyear = {2022},\n}\n\n\n\n

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\n \n\n \n \n Seokhyeon, K.; Sharma, A.; Liu, Y. Y.; and Young, S. I.\n\n\n \n \n \n \n \n Rethinking Satellite Data Merging: From Averaging to SNR Optimization.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1–15. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Rethinking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{seokhyeon_rethinking_2022,\n\ttitle = {Rethinking {Satellite} {Data} {Merging}: {From} {Averaging} to {SNR} {Optimization}},\n\tvolume = {60},\n\tissn = {0196-2892, 1558-0644},\n\tshorttitle = {Rethinking {Satellite} {Data} {Merging}},\n\turl = {https://ieeexplore.ieee.org/document/9531937/},\n\tdoi = {10.1109/TGRS.2021.3107028},\n\turldate = {2022-10-26},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Seokhyeon, Kim and Sharma, Ashish and Liu, Yi Y. and Young, Sean I.},\n\tyear = {2022},\n\tpages = {1--15},\n}\n\n\n\n

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\n \n\n \n \n Tittel, J.; Musolff, A.; Rinke, K.; and Büttner, O.\n\n\n \n \n \n \n \n Anthropogenic Transformation Disconnects a Lowland River From Contemporary Carbon Stores in Its Catchment.\n \n \n \n \n\n\n \n\n\n\n Ecosystems, 25(3): 618–632. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Anthropogenic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tittel_anthropogenic_2022,\n\ttitle = {Anthropogenic {Transformation} {Disconnects} a {Lowland} {River} {From} {Contemporary} {Carbon} {Stores} in {Its} {Catchment}},\n\tvolume = {25},\n\tissn = {1432-9840, 1435-0629},\n\turl = {https://link.springer.com/10.1007/s10021-021-00675-z},\n\tdoi = {10.1007/s10021-021-00675-z},\n\tabstract = {Abstract \n             \n              Rivers transport carbon from continents to oceans. Surprisingly, this carbon has often been found to be centuries old, not originating from contemporary plant biomass. This can be explained by anthropogenic disturbance of soils or discharge of radiocarbon–depleted wastewater. However, land enclosure and channel bypassing transformed many rivers from anabranching networks to single–channel systems with overbank sediment accumulation and lowered floodplain groundwater tables. We hypothesized that human development changed the fluvial carbon towards older sources by changing the morphology of watercourses. We studied radiocarbon in the Elbe, a European, anthropogenically–transformed lowland river at discharges between low flow and record peak flow. We found that the inorganic carbon, dissolved organic carbon (DOC) and particulate organic carbon was aged and up to 1850 years old. The ∆ \n              14 \n              C values remained low and invariant up to median discharges, indicating that the sources of modern carbon (fixed after 1950) were disconnected from the river during half of the time. The total share of modern carbon in DOC export was marginal (0.04\\%), 72\\% of exported DOC was older than 400 years. This was in contrast to undisturbed forested subcatchments, 72\\% of whose exported DOC was modern. Although population density is high, mass balances showed that wastewater did not significantly affect the ∆ \n              14 \n              C-DOC in the Elbe river. We conclude that wetlands and other sources of contemporary carbon were decoupled from the anthropogenically transformed Elbe stream network with incised stream bed relative to overbank sediments, shifting the sources of fluvial carbon in favor of aged stores.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Ecosystems},\n\tauthor = {Tittel, Jörg and Musolff, Andreas and Rinke, Karsten and Büttner, Olaf},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {618--632},\n}\n\n\n\n

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\n \n\n \n \n Wanner, L.; De Roo, F.; Sühring, M.; and Mauder, M.\n\n\n \n \n \n \n \n How Does the Choice of the Lower Boundary Conditions in Large-Eddy Simulations Affect the Development of Dispersive Fluxes Near the Surface?.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 182(1): 1–27. January 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wanner_how_2022,\n\ttitle = {How {Does} the {Choice} of the {Lower} {Boundary} {Conditions} in {Large}-{Eddy} {Simulations} {Affect} the {Development} of {Dispersive} {Fluxes} {Near} the {Surface}?},\n\tvolume = {182},\n\tissn = {0006-8314, 1573-1472},\n\turl = {https://link.springer.com/10.1007/s10546-021-00649-7},\n\tdoi = {10.1007/s10546-021-00649-7},\n\tabstract = {Abstract \n            Large-eddy simulations (LES) are an important tool for investigating the longstanding energy-balance-closure problem, as they provide continuous, spatially-distributed information about turbulent flow at a high temporal resolution. Former LES studies reproduced an energy-balance gap similar to the observations in the field typically amounting to 10–30\\% for heights on the order of 100 m in convective boundary layers even above homogeneous surfaces. The underestimation is caused by dispersive fluxes associated with large-scale turbulent organized structures that are not captured by single-tower measurements. However, the gap typically vanishes near the surface, i.e. at typical eddy-covariance measurement heights below 20 m, contrary to the findings from field measurements. In this study, we aim to find a LES set-up that can represent the correct magnitude of the energy-balance gap close to the surface. Therefore, we use a nested two-way coupled LES, with a fine grid that allows us to resolve fluxes and atmospheric structures at typical eddy-covariance measurement heights of 20 m. Under different stability regimes we compare three different options for lower boundary conditions featuring grassland and forest surfaces, i.e. (1) prescribed surface fluxes, (2) a land-surface model, and (3) a land-surface model in combination with a resolved canopy. We show that the use of prescribed surface fluxes and a land-surface model yields similar dispersive heat fluxes that are very small near the vegetation top for both grassland and forest surfaces. However, with the resolved forest canopy, dispersive heat fluxes are clearly larger, which we explain by a clear impact of the resolved canopy on the relationship between variance and flux–variance similarity functions.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Wanner, Luise and De Roo, Frederik and Sühring, Matthias and Mauder, Matthias},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {1--27},\n}\n\n\n\n

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\n \n\n \n \n Lembrechts, J. J.; van den Hoogen, J.; Aalto, J.; Ashcroft, M. B.; De Frenne, P.; Kemppinen, J.; Kopecký, M.; Luoto, M.; Maclean, I. M. D.; Crowther, T. W.; Bailey, J. J.; Haesen, S.; Klinges, D. H.; Niittynen, P.; Scheffers, B. R.; Van Meerbeek, K.; Aartsma, P.; Abdalaze, O.; Abedi, M.; Aerts, R.; Ahmadian, N.; Ahrends, A.; Alatalo, J. M.; Alexander, J. M.; Allonsius, C. N.; Altman, J.; Ammann, C.; Andres, C.; Andrews, C.; Ardö, J.; Arriga, N.; Arzac, A.; Aschero, V.; Assis, R. L.; Assmann, J. J.; Bader, M. Y.; Bahalkeh, K.; Barančok, P.; Barrio, I. C.; Barros, A.; Barthel, M.; Basham, E. W.; Bauters, M.; Bazzichetto, M.; Marchesini, L. B.; Bell, M. C.; Benavides, J. C.; Benito Alonso, J. L.; Berauer, B. J.; Bjerke, J. W.; Björk, R. G.; Björkman, M. P.; Björnsdóttir, K.; Blonder, B.; Boeckx, P.; Boike, J.; Bokhorst, S.; Brum, B. N. S.; Brůna, J.; Buchmann, N.; Buysse, P.; Camargo, J. L.; Campoe, O. C.; Candan, O.; Canessa, R.; Cannone, N.; Carbognani, M.; Carnicer, J.; Casanova-Katny, A.; Cesarz, S.; Chojnicki, B.; Choler, P.; Chown, S. L.; Cifuentes, E. F.; Čiliak, M.; Contador, T.; Convey, P.; Cooper, E. J.; Cremonese, E.; Curasi, S. R.; Curtis, R.; Cutini, M.; Dahlberg, C. J.; Daskalova, G. N.; de Pablo, M. A.; Della Chiesa, S.; Dengler, J.; Deronde, B.; Descombes, P.; Di Cecco, V.; Di Musciano, M.; Dick, J.; Dimarco, R. D.; Dolezal, J.; Dorrepaal, E.; Dušek, J.; Eisenhauer, N.; Eklundh, L.; Erickson, T. E.; Erschbamer, B.; Eugster, W.; Ewers, R. M.; Exton, D. A.; Fanin, N.; Fazlioglu, F.; Feigenwinter, I.; Fenu, G.; Ferlian, O.; Fernández Calzado, M. R.; Fernández-Pascual, E.; Finckh, M.; Higgens, R. F.; Forte, T. G. W.; Freeman, E. C.; Frei, E. R.; Fuentes-Lillo, E.; García, R. A.; García, M. B.; Géron, C.; Gharun, M.; Ghosn, D.; Gigauri, K.; Gobin, A.; Goded, I.; Goeckede, M.; Gottschall, F.; Goulding, K.; Govaert, S.; Graae, B. J.; Greenwood, S.; Greiser, C.; Grelle, A.; Guénard, B.; Guglielmin, M.; Guillemot, J.; Haase, P.; Haider, S.; Halbritter, A. H.; Hamid, M.; Hammerle, A.; Hampe, A.; Haugum, S. V.; Hederová, L.; Heinesch, B.; Helfter, C.; Hepenstrick, D.; Herberich, M.; Herbst, M.; Hermanutz, L.; Hik, D. S.; Hoffrén, R.; Homeier, J.; Hörtnagl, L.; Høye, T. T.; Hrbacek, F.; Hylander, K.; Iwata, H.; Jackowicz-Korczynski, M. A.; Jactel, H.; Järveoja, J.; Jastrzębowski, S.; Jentsch, A.; Jiménez, J. J.; Jónsdóttir, I. S.; Jucker, T.; Jump, A. S.; Juszczak, R.; Kanka, R.; Kašpar, V.; Kazakis, G.; Kelly, J.; Khuroo, A. A.; Klemedtsson, L.; Klisz, M.; Kljun, N.; Knohl, A.; Kobler, J.; Kollár, J.; Kotowska, M. M.; Kovács, B.; Kreyling, J.; Lamprecht, A.; Lang, S. I.; Larson, C.; Larson, K.; Laska, K.; le Maire, G.; Leihy, R. I.; Lens, L.; Liljebladh, B.; Lohila, A.; Lorite, J.; Loubet, B.; Lynn, J.; Macek, M.; Mackenzie, R.; Magliulo, E.; Maier, R.; Malfasi, F.; Máliš, F.; Man, M.; Manca, G.; Manco, A.; Manise, T.; Manolaki, P.; Marciniak, F.; Matula, R.; Mazzolari, A. C.; Medinets, S.; Medinets, V.; Meeussen, C.; Merinero, S.; Mesquita, R. d. C. G.; Meusburger, K.; Meysman, F. J. R.; Michaletz, S. T.; Milbau, A.; Moiseev, D.; Moiseev, P.; Mondoni, A.; Monfries, R.; Montagnani, L.; Moriana-Armendariz, M.; Morra di Cella, U.; Mörsdorf, M.; Mosedale, J. R.; Muffler, L.; Muñoz-Rojas, M.; Myers, J. A.; Myers-Smith, I. H.; Nagy, L.; Nardino, M.; Naujokaitis-Lewis, I.; Newling, E.; Nicklas, L.; Niedrist, G.; Niessner, A.; Nilsson, M. B.; Normand, S.; Nosetto, M. D.; Nouvellon, Y.; Nuñez, M. A.; Ogaya, R.; Ogée, J.; Okello, J.; Olejnik, J.; Olesen, J. E.; Opedal, Ø. H.; Orsenigo, S.; Palaj, A.; Pampuch, T.; Panov, A. V.; Pärtel, M.; Pastor, A.; Pauchard, A.; Pauli, H.; Pavelka, M.; Pearse, W. D.; Peichl, M.; Pellissier, L.; Penczykowski, R. M.; Penuelas, J.; Petit Bon, M.; Petraglia, A.; Phartyal, S. S.; Phoenix, G. K.; Pio, C.; Pitacco, A.; Pitteloud, C.; Plichta, R.; Porro, F.; Portillo-Estrada, M.; Poulenard, J.; Poyatos, R.; Prokushkin, A. S.; Puchalka, R.; Pușcaș, M.; Radujković, D.; Randall, K.; Ratier Backes, A.; Remmele, S.; Remmers, W.; Renault, D.; Risch, A. C.; Rixen, C.; Robinson, S. A.; Robroek, B. J. M.; Rocha, A. V.; Rossi, C.; Rossi, G.; Roupsard, O.; Rubtsov, A. V.; Saccone, P.; Sagot, C.; Sallo Bravo, J.; Santos, C. C.; Sarneel, J. M.; Scharnweber, T.; Schmeddes, J.; Schmidt, M.; Scholten, T.; Schuchardt, M.; Schwartz, N.; Scott, T.; Seeber, J.; Segalin de Andrade, A. C.; Seipel, T.; Semenchuk, P.; Senior, R. A.; Serra-Diaz, J. M.; Sewerniak, P.; Shekhar, A.; Sidenko, N. V.; Siebicke, L.; Siegwart Collier, L.; Simpson, E.; Siqueira, D. P.; Sitková, Z.; Six, J.; Smiljanic, M.; Smith, S. W.; Smith-Tripp, S.; Somers, B.; Sørensen, M. V.; Souza, J. J. L. L.; Souza, B. I.; Souza Dias, A.; Spasojevic, M. J.; Speed, J. D. M.; Spicher, F.; Stanisci, A.; Steinbauer, K.; Steinbrecher, R.; Steinwandter, M.; Stemkovski, M.; Stephan, J. G.; Stiegler, C.; Stoll, S.; Svátek, M.; Svoboda, M.; Tagesson, T.; Tanentzap, A. J.; Tanneberger, F.; Theurillat, J.; Thomas, H. J. D.; Thomas, A. D.; Tielbörger, K.; Tomaselli, M.; Treier, U. A.; Trouillier, M.; Turtureanu, P. D.; Tutton, R.; Tyystjärvi, V. A.; Ueyama, M.; Ujházy, K.; Ujházyová, M.; Uogintas, D.; Urban, A. V.; Urban, J.; Urbaniak, M.; Ursu, T.; Vaccari, F. P.; Van de Vondel, S.; van den Brink, L.; Van Geel, M.; Vandvik, V.; Vangansbeke, P.; Varlagin, A.; Veen, G. F.; Veenendaal, E.; Venn, S. E.; Verbeeck, H.; Verbrugggen, E.; Verheijen, F. G. A.; Villar, L.; Vitale, L.; Vittoz, P.; Vives-Ingla, M.; von Oppen, J.; Walz, J.; Wang, R.; Wang, Y.; Way, R. G.; Wedegärtner, R. E. M.; Weigel, R.; Wild, J.; Wilkinson, M.; Wilmking, M.; Wingate, L.; Winkler, M.; Wipf, S.; Wohlfahrt, G.; Xenakis, G.; Yang, Y.; Yu, Z.; Yu, K.; Zellweger, F.; Zhang, J.; Zhang, Z.; Zhao, P.; Ziemblińska, K.; Zimmermann, R.; Zong, S.; Zyryanov, V. I.; Nijs, I.; and Lenoir, J.\n\n\n \n \n \n \n \n Global maps of soil temperature.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 28(9): 3110–3144. 2022.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16060\n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{lembrechts_global_2022,\n\ttitle = {Global maps of soil temperature},\n\tvolume = {28},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16060},\n\tdoi = {https://doi.org/10.1111/gcb.16060},\n\tabstract = {Abstract Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.},\n\tnumber = {9},\n\tjournal = {Global Change Biology},\n\tauthor = {Lembrechts, Jonas J. and van den Hoogen, Johan and Aalto, Juha and Ashcroft, Michael B. and De Frenne, Pieter and Kemppinen, Julia and Kopecký, Martin and Luoto, Miska and Maclean, Ilya M. D. and Crowther, Thomas W. and Bailey, Joseph J. and Haesen, Stef and Klinges, David H. and Niittynen, Pekka and Scheffers, Brett R. and Van Meerbeek, Koenraad and Aartsma, Peter and Abdalaze, Otar and Abedi, Mehdi and Aerts, Rien and Ahmadian, Negar and Ahrends, Antje and Alatalo, Juha M. and Alexander, Jake M. and Allonsius, Camille Nina and Altman, Jan and Ammann, Christof and Andres, Christian and Andrews, Christopher and Ardö, Jonas and Arriga, Nicola and Arzac, Alberto and Aschero, Valeria and Assis, Rafael L. and Assmann, Jakob Johann and Bader, Maaike Y. and Bahalkeh, Khadijeh and Barančok, Peter and Barrio, Isabel C. and Barros, Agustina and Barthel, Matti and Basham, Edmund W. and Bauters, Marijn and Bazzichetto, Manuele and Marchesini, Luca Belelli and Bell, Michael C. and Benavides, Juan C. and Benito Alonso, José Luis and Berauer, Bernd J. and Bjerke, Jarle W. and Björk, Robert G. and Björkman, Mats P. and Björnsdóttir, Katrin and Blonder, Benjamin and Boeckx, Pascal and Boike, Julia and Bokhorst, Stef and Brum, Bárbara N. S. and Brůna, Josef and Buchmann, Nina and Buysse, Pauline and Camargo, José Luís and Campoe, Otávio C. and Candan, Onur and Canessa, Rafaella and Cannone, Nicoletta and Carbognani, Michele and Carnicer, Jofre and Casanova-Katny, Angélica and Cesarz, Simone and Chojnicki, Bogdan and Choler, Philippe and Chown, Steven L. and Cifuentes, Edgar F. and Čiliak, Marek and Contador, Tamara and Convey, Peter and Cooper, Elisabeth J. and Cremonese, Edoardo and Curasi, Salvatore R. and Curtis, Robin and Cutini, Maurizio and Dahlberg, C. Johan and Daskalova, Gergana N. and de Pablo, Miguel Angel and Della Chiesa, Stefano and Dengler, Jürgen and Deronde, Bart and Descombes, Patrice and Di Cecco, Valter and Di Musciano, Michele and Dick, Jan and Dimarco, Romina D. and Dolezal, Jiri and Dorrepaal, Ellen and Dušek, Jiří and Eisenhauer, Nico and Eklundh, Lars and Erickson, Todd E. and Erschbamer, Brigitta and Eugster, Werner and Ewers, Robert M. and Exton, Dan A. and Fanin, Nicolas and Fazlioglu, Fatih and Feigenwinter, Iris and Fenu, Giuseppe and Ferlian, Olga and Fernández Calzado, M. Rosa and Fernández-Pascual, Eduardo and Finckh, Manfred and Higgens, Rebecca Finger and Forte, T'ai G. W. and Freeman, Erika C. and Frei, Esther R. and Fuentes-Lillo, Eduardo and García, Rafael A. and García, María B. and Géron, Charly and Gharun, Mana and Ghosn, Dany and Gigauri, Khatuna and Gobin, Anne and Goded, Ignacio and Goeckede, Mathias and Gottschall, Felix and Goulding, Keith and Govaert, Sanne and Graae, Bente Jessen and Greenwood, Sarah and Greiser, Caroline and Grelle, Achim and Guénard, Benoit and Guglielmin, Mauro and Guillemot, Joannès and Haase, Peter and Haider, Sylvia and Halbritter, Aud H. and Hamid, Maroof and Hammerle, Albin and Hampe, Arndt and Haugum, Siri V. and Hederová, Lucia and Heinesch, Bernard and Helfter, Carole and Hepenstrick, Daniel and Herberich, Maximiliane and Herbst, Mathias and Hermanutz, Luise and Hik, David S. and Hoffrén, Raúl and Homeier, Jürgen and Hörtnagl, Lukas and Høye, Toke T. and Hrbacek, Filip and Hylander, Kristoffer and Iwata, Hiroki and Jackowicz-Korczynski, Marcin Antoni and Jactel, Hervé and Järveoja, Järvi and Jastrzębowski, Szymon and Jentsch, Anke and Jiménez, Juan J. and Jónsdóttir, Ingibjörg S. and Jucker, Tommaso and Jump, Alistair S. and Juszczak, Radoslaw and Kanka, Róbert and Kašpar, Vít and Kazakis, George and Kelly, Julia and Khuroo, Anzar A. and Klemedtsson, Leif and Klisz, Marcin and Kljun, Natascha and Knohl, Alexander and Kobler, Johannes and Kollár, Jozef and Kotowska, Martyna M. and Kovács, Bence and Kreyling, Juergen and Lamprecht, Andrea and Lang, Simone I. and Larson, Christian and Larson, Keith and Laska, Kamil and le Maire, Guerric and Leihy, Rachel I. and Lens, Luc and Liljebladh, Bengt and Lohila, Annalea and Lorite, Juan and Loubet, Benjamin and Lynn, Joshua and Macek, Martin and Mackenzie, Roy and Magliulo, Enzo and Maier, Regine and Malfasi, Francesco and Máliš, František and Man, Matěj and Manca, Giovanni and Manco, Antonio and Manise, Tanguy and Manolaki, Paraskevi and Marciniak, Felipe and Matula, Radim and Mazzolari, Ana Clara and Medinets, Sergiy and Medinets, Volodymyr and Meeussen, Camille and Merinero, Sonia and Mesquita, Rita de Cássia Guimarães and Meusburger, Katrin and Meysman, Filip J. R. and Michaletz, Sean T. and Milbau, Ann and Moiseev, Dmitry and Moiseev, Pavel and Mondoni, Andrea and Monfries, Ruth and Montagnani, Leonardo and Moriana-Armendariz, Mikel and Morra di Cella, Umberto and Mörsdorf, Martin and Mosedale, Jonathan R. and Muffler, Lena and Muñoz-Rojas, Miriam and Myers, Jonathan A. and Myers-Smith, Isla H. and Nagy, Laszlo and Nardino, Marianna and Naujokaitis-Lewis, Ilona and Newling, Emily and Nicklas, Lena and Niedrist, Georg and Niessner, Armin and Nilsson, Mats B. and Normand, Signe and Nosetto, Marcelo D. and Nouvellon, Yann and Nuñez, Martin A. and Ogaya, Romà and Ogée, Jérôme and Okello, Joseph and Olejnik, Janusz and Olesen, Jørgen Eivind and Opedal, Øystein H. and Orsenigo, Simone and Palaj, Andrej and Pampuch, Timo and Panov, Alexey V. and Pärtel, Meelis and Pastor, Ada and Pauchard, Aníbal and Pauli, Harald and Pavelka, Marian and Pearse, William D. and Peichl, Matthias and Pellissier, Loïc and Penczykowski, Rachel M. and Penuelas, Josep and Petit Bon, Matteo and Petraglia, Alessandro and Phartyal, Shyam S. and Phoenix, Gareth K. and Pio, Casimiro and Pitacco, Andrea and Pitteloud, Camille and Plichta, Roman and Porro, Francesco and Portillo-Estrada, Miguel and Poulenard, Jérôme and Poyatos, Rafael and Prokushkin, Anatoly S. and Puchalka, Radoslaw and Pușcaș, Mihai and Radujković, Dajana and Randall, Krystal and Ratier Backes, Amanda and Remmele, Sabine and Remmers, Wolfram and Renault, David and Risch, Anita C. and Rixen, Christian and Robinson, Sharon A. and Robroek, Bjorn J. M. and Rocha, Adrian V. and Rossi, Christian and Rossi, Graziano and Roupsard, Olivier and Rubtsov, Alexey V. and Saccone, Patrick and Sagot, Clotilde and Sallo Bravo, Jhonatan and Santos, Cinthya C. and Sarneel, Judith M. and Scharnweber, Tobias and Schmeddes, Jonas and Schmidt, Marius and Scholten, Thomas and Schuchardt, Max and Schwartz, Naomi and Scott, Tony and Seeber, Julia and Segalin de Andrade, Ana Cristina and Seipel, Tim and Semenchuk, Philipp and Senior, Rebecca A. and Serra-Diaz, Josep M. and Sewerniak, Piotr and Shekhar, Ankit and Sidenko, Nikita V. and Siebicke, Lukas and Siegwart Collier, Laura and Simpson, Elizabeth and Siqueira, David P. and Sitková, Zuzana and Six, Johan and Smiljanic, Marko and Smith, Stuart W. and Smith-Tripp, Sarah and Somers, Ben and Sørensen, Mia Vedel and Souza, José João L. L. and Souza, Bartolomeu Israel and Souza Dias, Arildo and Spasojevic, Marko J. and Speed, James D. M. and Spicher, Fabien and Stanisci, Angela and Steinbauer, Klaus and Steinbrecher, Rainer and Steinwandter, Michael and Stemkovski, Michael and Stephan, Jörg G. and Stiegler, Christian and Stoll, Stefan and Svátek, Martin and Svoboda, Miroslav and Tagesson, Torbern and Tanentzap, Andrew J. and Tanneberger, Franziska and Theurillat, Jean-Paul and Thomas, Haydn J. D. and Thomas, Andrew D. and Tielbörger, Katja and Tomaselli, Marcello and Treier, Urs Albert and Trouillier, Mario and Turtureanu, Pavel Dan and Tutton, Rosamond and Tyystjärvi, Vilna A. and Ueyama, Masahito and Ujházy, Karol and Ujházyová, Mariana and Uogintas, Domas and Urban, Anastasiya V. and Urban, Josef and Urbaniak, Marek and Ursu, Tudor-Mihai and Vaccari, Francesco Primo and Van de Vondel, Stijn and van den Brink, Liesbeth and Van Geel, Maarten and Vandvik, Vigdis and Vangansbeke, Pieter and Varlagin, Andrej and Veen, G. F. and Veenendaal, Elmar and Venn, Susanna E. and Verbeeck, Hans and Verbrugggen, Erik and Verheijen, Frank G. A. and Villar, Luis and Vitale, Luca and Vittoz, Pascal and Vives-Ingla, Maria and von Oppen, Jonathan and Walz, Josefine and Wang, Runxi and Wang, Yifeng and Way, Robert G. and Wedegärtner, Ronja E. M. and Weigel, Robert and Wild, Jan and Wilkinson, Matthew and Wilmking, Martin and Wingate, Lisa and Winkler, Manuela and Wipf, Sonja and Wohlfahrt, Georg and Xenakis, Georgios and Yang, Yan and Yu, Zicheng and Yu, Kailiang and Zellweger, Florian and Zhang, Jian and Zhang, Zhaochen and Zhao, Peng and Ziemblińska, Klaudia and Zimmermann, Reiner and Zong, Shengwei and Zyryanov, Viacheslav I. and Nijs, Ivan and Lenoir, Jonathan},\n\tyear = {2022},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16060},\n\tkeywords = {bioclimatic variables, global maps, microclimate, near-surface temperatures, soil temperature, soil-dwelling organisms, temperature offset, weather stations},\n\tpages = {3110--3144},\n}\n\n\n\n

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\n \n\n \n \n Zhan, Q.; Kong, X.; and Rinke, K.\n\n\n \n \n \n \n \n High-frequency monitoring enables operational opportunities to reduce the dissolved organic carbon (DOC) load in Germany’s largest drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Inland Waters, 12(2): 245–260. April 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"High-frequency$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhan_high-frequency_2022,\n\ttitle = {High-frequency monitoring enables operational opportunities to reduce the dissolved organic carbon ({DOC}) load in {Germany}’s largest drinking water reservoir},\n\tvolume = {12},\n\tissn = {2044-2041, 2044-205X},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/20442041.2021.1987796},\n\tdoi = {10.1080/20442041.2021.1987796},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Inland Waters},\n\tauthor = {Zhan, Qing and Kong, Xiangzhen and Rinke, Karsten},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {245--260},\n}\n\n\n\n

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\n \n\n \n \n Blume, T.; Schneider, L.; and Güntner, A.\n\n\n \n \n \n \n \n Comparative analysis of throughfall observations in six different forest stands: Influence of seasons, rainfall‐ and stand characteristics.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 36(3). March 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blume_comparative_2022,\n\ttitle = {Comparative analysis of throughfall observations in six different forest stands: {Influence} of seasons, rainfall‐ and stand characteristics},\n\tvolume = {36},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Comparative analysis of throughfall observations in six different forest stands},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14461},\n\tdoi = {10.1002/hyp.14461},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Hydrological Processes},\n\tauthor = {Blume, Theresa and Schneider, Lisa and Güntner, Andreas},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n\n\n

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\n \n 2021\n \n \n (157)\n \n \n

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\n \n\n \n \n Montzka, C.; Bogena, H. R.; Herbst, M.; Cosh, M. H.; Jagdhuber, T.; and Vereecken, H.\n\n\n \n \n \n \n \n Estimating the Number of Reference Sites Necessary for the Validation of Global Soil Moisture Products.\n \n \n \n \n\n\n \n\n\n\n IEEE Geoscience and Remote Sensing Letters, 18(9): 1530–1534. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_estimating_2021,\n\ttitle = {Estimating the {Number} of {Reference} {Sites} {Necessary} for the {Validation} of {Global} {Soil} {Moisture} {Products}},\n\tvolume = {18},\n\tissn = {1545-598X, 1558-0571},\n\turl = {https://ieeexplore.ieee.org/document/9137351/},\n\tdoi = {10.1109/LGRS.2020.3005730},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {IEEE Geoscience and Remote Sensing Letters},\n\tauthor = {Montzka, Carsten and Bogena, Heye R. and Herbst, Michael and Cosh, Michael H. and Jagdhuber, Thomas and Vereecken, Harry},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {1530--1534},\n}\n\n\n\n

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\n \n\n \n \n Nguyen, T. V.; Kumar, R.; Lutz, S. R.; Musolff, A.; Yang, J.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(2). February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nguyen_modeling_2021,\n\ttitle = {Modeling {Nitrate} {Export} {From} a {Mesoscale} {Catchment} {Using} {StorAge} {Selection} {Functions}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR028490},\n\tdoi = {10.1029/2020WR028490},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Nguyen, Tam V. and Kumar, Rohini and Lutz, Stefanie R. and Musolff, Andreas and Yang, Jie and Fleckenstein, Jan H.},\n\tmonth = feb,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Risse-Buhl, U.; Anlanger, C.; Noss, C.; Lorke, A.; von Schiller, D.; and Weitere, M.\n\n\n \n \n \n \n \n Hydromorphologic Sorting of In-Stream Nitrogen Uptake Across Spatial Scales.\n \n \n \n \n\n\n \n\n\n\n Ecosystems, 24(5): 1184–1202. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydromorphologic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{risse-buhl_hydromorphologic_2021,\n\ttitle = {Hydromorphologic {Sorting} of {In}-{Stream} {Nitrogen} {Uptake} {Across} {Spatial} {Scales}},\n\tvolume = {24},\n\tissn = {1432-9840, 1435-0629},\n\turl = {https://link.springer.com/10.1007/s10021-020-00576-7},\n\tdoi = {10.1007/s10021-020-00576-7},\n\tabstract = {Abstract \n             \n              Nitrogen (N) uptake is a key process in stream ecosystems that is mediated mainly by benthic microorganisms (biofilms on different substrata) and has implications for the biogeochemical fluxes at catchment scale and beyond. Here, we focused on the drivers of assimilatory N uptake, especially the effects of hydromorphology and other environmental constraints, across three spatial scales: micro, meso and reach. In two seasons (summer and spring), we performed whole-reach \n              15 \n              N-labelled ammonium injection experiments in two montane, gravel-bed stream reaches with riffle–pool sequences. N uptake was highest in epilithic biofilms, thallophytes and roots (min–max range 0.2–545.2 mg N m \n              −2 \n              day \n              −1 \n              ) and lowest in leaves, wood and fine benthic organic matter (0.05–209.2 mg N m \n              −2 \n              day \n              −1 \n              ). At the microscale, N uptake of all primary uptake compartments except wood was higher in riffles than in pools. At the mesoscale, hydromorphology determined the distribution of primary uptake compartments, with fast-flowing riffles being dominated by biologically more active compartments and pools being dominated by biologically less active compartments. Despite a lower biomass of primary uptake compartments, mesoscale N uptake was 1.7–3.0 times higher in riffles than in pools. At reach scale, N uptake ranged from 79.6 to 334.1 mg N m \n              −2 \n              day \n              −1 \n              . Highest reach-scale N uptake was caused by a bloom of thallopyhtes, mainly filamentous autotrophs, during stable low discharge and high light conditions. Our results reveal the important role of hydromorphologic sorting of primary uptake compartments at mesoscale as a controlling factor for reach-scale N uptake in streams.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-10-26},\n\tjournal = {Ecosystems},\n\tauthor = {Risse-Buhl, Ute and Anlanger, Christine and Noss, Christian and Lorke, Andreas and von Schiller, Daniel and Weitere, Markus},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {1184--1202},\n}\n\n\n\n

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\n \n\n \n \n Werner, B. J.; Lechtenfeld, O. J.; Musolff, A.; de Rooij, G. H.; Yang, J.; Gründling, R.; Werban, U.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Small-scale topography explains patterns and dynamics of dissolved organic carbon exports from the riparian zone of a temperate, forested catchment.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(12): 6067–6086. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Small-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{werner_small-scale_2021,\n\ttitle = {Small-scale topography explains patterns and dynamics of dissolved organic carbon exports from the riparian zone of a temperate, forested catchment},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/6067/2021/},\n\tdoi = {10.5194/hess-25-6067-2021},\n\tabstract = {Abstract. Export of dissolved organic carbon (DOC) from riparian zones (RZs) is\nan important component of temperate catchment carbon budgets, but\nexport mechanisms are still poorly understood. Here we show that DOC\nexport is predominantly controlled by the microtopography of the RZ\n(lateral variability) and by riparian groundwater level dynamics\n(temporal variability). From February 2017 until July 2019 we studied\ntopography, DOC quality and water fluxes and pathways in the RZ\nof a small forested catchment and the receiving stream in central\nGermany. The chemical classification of the riparian groundwater and\nsurface water samples (n=66) by Fourier transform ion cyclotron\nresonance mass spectrometry revealed a cluster of plant-derived,\naromatic and oxygen-rich DOC with high concentrations\n(DOCI) and a cluster of microbially processed, saturated\nand heteroatom-enriched DOC with lower concentrations\n(DOCII). The two DOC clusters were connected to locations\nwith distinctly different values of the high-resolution topographic\nwetness index (TWIHR; at 1 m resolution) within the study\narea. Numerical water flow modeling using the integrated surface–subsurface model HydroGeoSphere revealed that surface runoff from high-TWIHR zones associated with the DOCI cluster\n(DOCI source zones) dominated overall discharge generation\nand therefore DOC export. Although corresponding to only 15 \\% of the\narea in the studied RZ, the DOCI source zones contributed\n1.5 times the DOC export of the remaining 85 \\% of the area\nassociated with DOCII source zones. Accordingly, DOC quality\nin stream water sampled under five event flow conditions (n=73) was\nclosely reflecting the DOCI quality. Our results suggest\nthat DOC export by surface runoff along dynamically evolving surface\nflow networks can play a dominant role for DOC exports from RZs with\noverall low topographic relief and should consequently be considered\nin catchment-scale DOC export models. We propose that proxies of\nspatial heterogeneity such as the TWIHR can help to\ndelineate the most active source zones and provide a mechanistic basis\nfor improved model conceptualization of DOC exports.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Werner, Benedikt J. and Lechtenfeld, Oliver J. and Musolff, Andreas and de Rooij, Gerrit H. and Yang, Jie and Gründling, Ralf and Werban, Ulrike and Fleckenstein, Jan H.},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {6067--6086},\n}\n\n\n\n

\n\n\n

\n Abstract. Export of dissolved organic carbon (DOC) from riparian zones (RZs) is an important component of temperate catchment carbon budgets, but export mechanisms are still poorly understood. Here we show that DOC export is predominantly controlled by the microtopography of the RZ (lateral variability) and by riparian groundwater level dynamics (temporal variability). From February 2017 until July 2019 we studied topography, DOC quality and water fluxes and pathways in the RZ of a small forested catchment and the receiving stream in central Germany. The chemical classification of the riparian groundwater and surface water samples (n=66) by Fourier transform ion cyclotron resonance mass spectrometry revealed a cluster of plant-derived, aromatic and oxygen-rich DOC with high concentrations (DOCI) and a cluster of microbially processed, saturated and heteroatom-enriched DOC with lower concentrations (DOCII). The two DOC clusters were connected to locations with distinctly different values of the high-resolution topographic wetness index (TWIHR; at 1 m resolution) within the study area. Numerical water flow modeling using the integrated surface–subsurface model HydroGeoSphere revealed that surface runoff from high-TWIHR zones associated with the DOCI cluster (DOCI source zones) dominated overall discharge generation and therefore DOC export. Although corresponding to only 15 % of the area in the studied RZ, the DOCI source zones contributed 1.5 times the DOC export of the remaining 85 % of the area associated with DOCII source zones. Accordingly, DOC quality in stream water sampled under five event flow conditions (n=73) was closely reflecting the DOCI quality. Our results suggest that DOC export by surface runoff along dynamically evolving surface flow networks can play a dominant role for DOC exports from RZs with overall low topographic relief and should consequently be considered in catchment-scale DOC export models. We propose that proxies of spatial heterogeneity such as the TWIHR can help to delineate the most active source zones and provide a mechanistic basis for improved model conceptualization of DOC exports.\n

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\n \n\n \n \n Lischeid, G.; Dannowski, R.; Kaiser, K.; Nützmann, G.; Steidl, J.; and Stüve, P.\n\n\n \n \n \n \n \n Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 596: 126096. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Inconsistent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lischeid_inconsistent_2021,\n\ttitle = {Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers},\n\tvolume = {596},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421001438},\n\tdoi = {10.1016/j.jhydrol.2021.126096},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Lischeid, Gunnar and Dannowski, Ralf and Kaiser, Knut and Nützmann, Gunnar and Steidl, Jörg and Stüve, Peter},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {126096},\n}\n\n\n\n

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\n \n\n \n \n Zhou, Z.; Klotzsche, A.; Schmäck, J.; Vereecken, H.; and van der Kruk, J.\n\n\n \n \n \n \n \n Improvement of ground-penetrating radar full-waveform inversion images using cone penetration test data.\n \n \n \n \n\n\n \n\n\n\n GEOPHYSICS, 86(3): H13–H25. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Improvement$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhou_improvement_2021,\n\ttitle = {Improvement of ground-penetrating radar full-waveform inversion images using cone penetration test data},\n\tvolume = {86},\n\tissn = {0016-8033, 1942-2156},\n\turl = {https://library.seg.org/doi/10.1190/geo2020-0283.1},\n\tdoi = {10.1190/geo2020-0283.1},\n\tabstract = {Detailed characterization of aquifers is critical and challenging due to the existence of heterogeneous small-scale high-contrast layers. For an improved characterization of subsurface hydrologic characteristics, crosshole ground-penetrating radar (GPR) and cone penetration test (CPT) measurements are performed. In comparison to the CPT approach, which can only provide 1D high-resolution data along vertical profiles, crosshole GPR enables measuring 2D cross sections between two boreholes. In general, a standard inversion method for GPR data is the ray-based approach, which considers only a small amount of information and can therefore only provide limited resolution. In the past few decades, full-waveform inversion (FWI) of crosshole GPR data in the time domain has matured, and it provides inversion results with higher resolution by exploiting the full-recorded waveform information. However, FWI results are limited due to complex underground structures and the nonlinear nature of the method. A new approach that uses CPT data in the inversion process is applied to enhance the resolution of the final relative permittivity FWI results by updating the effective source wavelet. The updated effective source wavelet possesses a priori CPT information and a larger bandwidth. Using the same starting models, a synthetic model comparison between the conventional and updated FWI results demonstrates that the updated FWI method provides reliable and more consistent structures. To test the method, five experimental GPR cross section results are analyzed with the standard FWI and the new proposed updated approach. The synthetic and experimental results indicate the potential of improving the reconstruction of subsurface aquifer structures by combining conventional 2D FWI results and 1D CPT data.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-21},\n\tjournal = {GEOPHYSICS},\n\tauthor = {Zhou, Zhen and Klotzsche, Anja and Schmäck, Jessica and Vereecken, Harry and van der Kruk, Jan},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {H13--H25},\n}\n\n\n\n

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\n \n\n \n \n Welti, E. A.; Zajicek, P.; Ayasse, M.; Bornholdt, T.; Buse, J.; Dziock, F.; Engelmann, R. A.; Englmeier, J.; Fellendorf, M.; Förschler, M. I.; Frenzel, M.; Fricke, U.; Ganuza, C.; Hippke, M.; Hoenselaar, G.; Kaus-Thiel, A.; Mandery, K.; Marten, A.; Monaghan, M. T.; Morkel, C.; Müller, J.; Puffpaff, S.; Redlich, S.; Richter, R.; Botero, S. R.; Scharnweber, T.; Scheiffarth, G.; Yáñez, P. S.; Schumann, R.; Seibold, S.; Steffan-Dewenter, I.; Stoll, S.; Tobisch, C.; Twietmeyer, S.; Uhler, J.; Vogt, J.; Weis, D.; Weisser, W. W.; Wilmking, M.; and Haase, P.\n\n\n \n \n \n \n \n Temperature drives variation in flying insect biomass across a German malaise trap network.\n \n \n \n \n\n\n \n\n\n\n Technical Report Ecology, February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Temperature$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{welti_temperature_2021,\n\ttype = {preprint},\n\ttitle = {Temperature drives variation in flying insect biomass across a {German} malaise trap network},\n\turl = {http://biorxiv.org/lookup/doi/10.1101/2021.02.02.429363},\n\tabstract = {ABSTRACT \n           \n             \n               \n                Among the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators and as a food source for many predators. Few insect monitoring programs cover large spatial scales required to provide more generalizable estimates of insect responses to global change drivers. \n               \n               \n                We ask how climate and surrounding habitat affect flying insect biomass and day of peak biomass using data from the first year of a new standardized distributed monitoring network at 84 locations across Germany comprising spatial gradient of land-cover types from protected to urban areas. \n               \n               \n                Flying insect biomass increased linearly with monthly temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages. \n               \n               \n                Land-cover explained little variation in insect biomass, but biomass was lowest in forested sites. Grasslands, pastures and orchards harbored the highest insect biomass. The date of peak biomass was primarily driven by surrounding land-cover type, with grasslands especially having earlier insect biomass phenologies. \n               \n               \n                Standardized, large-scale monitoring is pivotal to uncover underlying processes of insect decline and to develop climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the benefits of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. These results highlighting the importance of local adaptation in climate change-driven impacts on insect communities.},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tinstitution = {Ecology},\n\tauthor = {Welti, Ellen A.R. and Zajicek, Petr and Ayasse, Manfred and Bornholdt, Tim and Buse, Jörn and Dziock, Frank and Engelmann, Rolf A. and Englmeier, Jana and Fellendorf, Martin and Förschler, Marc I. and Frenzel, Mark and Fricke, Ute and Ganuza, Cristina and Hippke, Mathias and Hoenselaar, Günter and Kaus-Thiel, Andrea and Mandery, Klaus and Marten, Andreas and Monaghan, Michael T. and Morkel, Carsten and Müller, Jörg and Puffpaff, Stephanie and Redlich, Sarah and Richter, Ronny and Botero, Sandra Rojas and Scharnweber, Tobias and Scheiffarth, Gregor and Yáñez, Paul Schmidt and Schumann, Rhena and Seibold, Sebastian and Steffan-Dewenter, Ingolf and Stoll, Stefan and Tobisch, Cynthia and Twietmeyer, Sönke and Uhler, Johannes and Vogt, Juliane and Weis, Dirk and Weisser, Wolfgang W. and Wilmking, Martin and Haase, Peter},\n\tmonth = feb,\n\tyear = {2021},\n\tdoi = {10.1101/2021.02.02.429363},\n}\n\n\n\n

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\n \n\n \n \n Thompson, A.; Ștefan, V.; and Knight, T. M.\n\n\n \n \n \n \n \n Oilseed Rape Shares Abundant and Generalized Pollinators with Its Co-Flowering Plant Species.\n \n \n \n \n\n\n \n\n\n\n Insects, 12(12): 1096. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Oilseed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{thompson_oilseed_2021,\n\ttitle = {Oilseed {Rape} {Shares} {Abundant} and {Generalized} {Pollinators} with {Its} {Co}-{Flowering} {Plant} {Species}},\n\tvolume = {12},\n\tissn = {2075-4450},\n\turl = {https://www.mdpi.com/2075-4450/12/12/1096},\n\tdoi = {10.3390/insects12121096},\n\tabstract = {Mass-flowering crops, such as Oilseed Rape (OSR), provide resources for pollinators and benefit from pollination services. Studies that observe the community of interactions between plants and pollinators are critical to understanding the resource needs of pollinators. We observed pollinators on OSR and wild plants in adjacent semi-natural areas in Sachsen-Anhalt, Germany to quantify (1) the co-flowering plants that share pollinators with OSR, (2) the identity and functional traits of plants and pollinators in the network module of OSR, and (3) the identity of the plants and pollinators that act as network connectors and hubs. We found that four common plants share a high percentage of their pollinators with OSR. OSR and these plants all attract abundant pollinators in the community, and the patterns of sharing were not more than would be expected by chance sampling. OSR acts as a module hub, and primarily influences the other plants in its module that have similar functional traits. However, the plants that most influence the pollination of OSR have different functional traits and are part of different modules. Our study demonstrates that supporting the pollination of OSR requires the presence of semi-natural areas with plants that can support a high abundances of generalist pollinators.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-21},\n\tjournal = {Insects},\n\tauthor = {Thompson, Amibeth and Ștefan, Valentin and Knight, Tiffany M.},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {1096},\n}\n\n\n\n

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\n \n\n \n \n Peng, J.; Albergel, C.; Balenzano, A.; Brocca, L.; Cartus, O.; Cosh, M. H.; Crow, W. T.; Dabrowska-Zielinska, K.; Dadson, S.; Davidson, M. W.; de Rosnay, P.; Dorigo, W.; Gruber, A.; Hagemann, S.; Hirschi, M.; Kerr, Y. H.; Lovergine, F.; Mahecha, M. D.; Marzahn, P.; Mattia, F.; Musial, J. P.; Preuschmann, S.; Reichle, R. H.; Satalino, G.; Silgram, M.; van Bodegom, P. M.; Verhoest, N. E.; Wagner, W.; Walker, J. P.; Wegmüller, U.; and Loew, A.\n\n\n \n \n \n \n \n A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 252: 112162. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peng_roadmap_2021,\n\ttitle = {A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements},\n\tvolume = {252},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425720305356},\n\tdoi = {10.1016/j.rse.2020.112162},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Peng, Jian and Albergel, Clement and Balenzano, Anna and Brocca, Luca and Cartus, Oliver and Cosh, Michael H. and Crow, Wade T. and Dabrowska-Zielinska, Katarzyna and Dadson, Simon and Davidson, Malcolm W.J. and de Rosnay, Patricia and Dorigo, Wouter and Gruber, Alexander and Hagemann, Stefan and Hirschi, Martin and Kerr, Yann H. and Lovergine, Francesco and Mahecha, Miguel D. and Marzahn, Philip and Mattia, Francesco and Musial, Jan Pawel and Preuschmann, Swantje and Reichle, Rolf H. and Satalino, Giuseppe and Silgram, Martyn and van Bodegom, Peter M. and Verhoest, Niko E.C. and Wagner, Wolfgang and Walker, Jeffrey P. and Wegmüller, Urs and Loew, Alexander},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {112162},\n}\n\n\n\n

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\n \n\n \n \n Ma, L.; Janz, B.; Kiese, R.; Mwanake, R.; Wangari, E.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n Effect of vole bioturbation on N2O, NO, NH3, CH4 and CO2 fluxes of slurry fertilized and non-fertilized montane grassland soils in Southern Germany.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 800: 149597. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ma_effect_2021,\n\ttitle = {Effect of vole bioturbation on {N2O}, {NO}, {NH3}, {CH4} and {CO2} fluxes of slurry fertilized and non-fertilized montane grassland soils in {Southern} {Germany}},\n\tvolume = {800},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721046726},\n\tdoi = {10.1016/j.scitotenv.2021.149597},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Ma, Lei and Janz, Baldur and Kiese, Ralf and Mwanake, Ricky and Wangari, Elizabeth and Butterbach-Bahl, Klaus},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {149597},\n}\n\n\n\n

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\n \n\n \n \n Kwon, T.; Shibata, H.; Kepfer-Rojas, S.; Schmidt, I. K.; Larsen, K. S.; Beier, C.; Berg, B.; Verheyen, K.; Lamarque, J.; Hagedorn, F.; Eisenhauer, N.; Djukic, I.; and TeaComposition Network\n\n\n \n \n \n \n \n Effects of Climate and Atmospheric Nitrogen Deposition on Early to Mid-Term Stage Litter Decomposition Across Biomes.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Forests and Global Change, 4: 678480. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kwon_effects_2021,\n\ttitle = {Effects of {Climate} and {Atmospheric} {Nitrogen} {Deposition} on {Early} to {Mid}-{Term} {Stage} {Litter} {Decomposition} {Across} {Biomes}},\n\tvolume = {4},\n\tissn = {2624-893X},\n\turl = {https://www.frontiersin.org/articles/10.3389/ffgc.2021.678480/full},\n\tdoi = {10.3389/ffgc.2021.678480},\n\tabstract = {Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73\\% and 62\\% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2\\% and MAT 4\\% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5\\% and 1.1\\% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1–3.5\\% and of the more stable substrates by 3.8–10.6\\%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4–2.2\\% and that of low-quality litter by 0.9–1.5\\% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Forests and Global Change},\n\tauthor = {Kwon, TaeOh and Shibata, Hideaki and Kepfer-Rojas, Sebastian and Schmidt, Inger K. and Larsen, Klaus S. and Beier, Claus and Berg, Björn and Verheyen, Kris and Lamarque, Jean-Francois and Hagedorn, Frank and Eisenhauer, Nico and Djukic, Ika and {TeaComposition Network}},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {678480},\n}\n\n\n\n

\n\n\n

\n Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1–3.5% and of the more stable substrates by 3.8–10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4–2.2% and that of low-quality litter by 0.9–1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.\n

\n\n\n

\n \n\n \n \n Keller, P. S.; Marcé, R.; Obrador, B.; and Koschorreck, M.\n\n\n \n \n \n \n \n Global carbon budget of reservoirs is overturned by the quantification of drawdown areas.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 14(6): 402–408. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{keller_global_2021,\n\ttitle = {Global carbon budget of reservoirs is overturned by the quantification of drawdown areas},\n\tvolume = {14},\n\tissn = {1752-0894, 1752-0908},\n\turl = {http://www.nature.com/articles/s41561-021-00734-z},\n\tdoi = {10.1038/s41561-021-00734-z},\n\tabstract = {Abstract \n             \n              Reservoir drawdown areas—where sediment is exposed to the atmosphere due to water-level fluctuations—are hotspots for carbon dioxide (CO \n              2 \n              ) emissions. However, the global extent of drawdown areas is unknown, precluding an accurate assessment of the carbon budget of reservoirs. Here we show, on the basis of satellite observations of 6,794 reservoirs between 1985 and 2015, that 15\\% of the global reservoir area was dry. Exposure of drawdown areas was most pronounced in reservoirs close to the tropics and shows a complex dependence on climatic (precipitation, temperature) and anthropogenic (water use) drivers. We re-assessed the global carbon emissions from reservoirs by apportioning CO \n              2 \n              and methane emissions to water surfaces and drawdown areas using published areal emission rates. The new estimate assigns 26.2 (15–40) (95\\% confidence interval) TgCO \n              2 \n              -C yr \n              −1 \n              to drawdown areas, and increases current global CO \n              2 \n              emissions from reservoirs by 53\\% (60.3 (43.2–79.5) TgCO \n              2 \n              -C yr \n              −1 \n              ). Taking into account drawdown areas, the ratio between carbon emissions and carbon burial in sediments is 2.02 (1.04–4.26). This suggests that reservoirs emit more carbon than they bury, challenging the current understanding that reservoirs are net carbon sinks. Thus, consideration of drawdown areas overturns our conception of the role of reservoirs in the carbon cycle.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-21},\n\tjournal = {Nature Geoscience},\n\tauthor = {Keller, Philipp S. and Marcé, Rafael and Obrador, Biel and Koschorreck, Matthias},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {402--408},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Heistermann, M.; Francke, T.; Schrön, M.; and Oswald, S. E.\n\n\n \n \n \n \n \n Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(9): 4807–4824. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heistermann_spatio-temporal_2021,\n\ttitle = {Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/4807/2021/},\n\tdoi = {10.5194/hess-25-4807-2021},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Heistermann, Maik and Francke, Till and Schrön, Martin and Oswald, Sascha E.},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {4807--4824},\n}\n\n\n\n

\n\n\n

\n Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.\n

\n\n\n

\n \n\n \n \n Guseva, S.; Casper, P.; Sachs, T.; Spank, U.; and Lorke, A.\n\n\n \n \n \n \n \n Energy Flux Paths in Lakes and Reservoirs.\n \n \n \n \n\n\n \n\n\n\n Water, 13(22): 3270. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Energy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guseva_energy_2021,\n\ttitle = {Energy {Flux} {Paths} in {Lakes} and {Reservoirs}},\n\tvolume = {13},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/13/22/3270},\n\tdoi = {10.3390/w13223270},\n\tabstract = {Mechanical energy in lakes is present in various types of water motion, including turbulent flows, surface and internal waves. The major source of kinetic energy is wind forcing at the water surface. Although a small portion of the vertical wind energy flux in the atmosphere is transferred to water, it is crucial for physical, biogeochemical and ecological processes in lentic ecosystems. To examine energy fluxes and energy content in surface and internal waves, we analyze extensive datasets of air- and water-side measurements collected at two small water bodies ({\\textless}10 km2). For the first time we use directly measured atmospheric momentum fluxes. The estimated energy fluxes and content agree well with results reported for larger lakes, suggesting that the energetics governing water motions in enclosed basins is similar, independent of basin size. The largest fraction of wind energy flux is transferred to surface waves and increases strongly nonlinearly for wind speeds exceeding 3 m s−1. The energy content is largest in basin-scale and high-frequency internal waves but shows seasonal variability and varies among aquatic systems. At one of the study sites, energy dissipation rates varied diurnally, suggesting biogenic turbulence, which appears to be a widespread phenomenon in lakes and reservoirs.},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2022-11-21},\n\tjournal = {Water},\n\tauthor = {Guseva, Sofya and Casper, Peter and Sachs, Torsten and Spank, Uwe and Lorke, Andreas},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {3270},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Ghaffar, S.; Jomaa, S.; Meon, G.; and Rode, M.\n\n\n \n \n \n \n \n Spatial validation of a semi-distributed hydrological nutrient transport model.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 593: 125818. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ghaffar_spatial_2021,\n\ttitle = {Spatial validation of a semi-distributed hydrological nutrient transport model},\n\tvolume = {593},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169420312798},\n\tdoi = {10.1016/j.jhydrol.2020.125818},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Ghaffar, Salman and Jomaa, Seifeddine and Meon, Günter and Rode, Michael},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {125818},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Forstner, V.; Groh, J.; Vremec, M.; Herndl, M.; Vereecken, H.; Gerke, H. H.; Birk, S.; and Pütz, T.\n\n\n \n \n \n \n \n Response of water fluxes and biomass production to climate change in permanent grassland soil ecosystems.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(12): 6087–6106. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Response$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{forstner_response_2021,\n\ttitle = {Response of water fluxes and biomass production to climate change in permanent grassland soil ecosystems},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/6087/2021/},\n\tdoi = {10.5194/hess-25-6087-2021},\n\tabstract = {Abstract. Effects of climate change on the ecosystem productivity and water fluxes\nhave been studied in various types of experiments. However, it is still\nlargely unknown whether and how the experimental approach itself affects the results of such studies. We employed two contrasting experimental approaches, using high-precision weighable monolithic lysimeters, over a period of 4 years to identify and compare the responses of water fluxes and\naboveground biomass to climate change in permanent grassland. The first,\nmanipulative, approach is based on controlled increases of atmospheric\nCO2 concentration and surface temperature. The second, observational,\napproach uses data from a space-for-time substitution along a gradient of\nclimatic conditions. The Budyko framework was used to identify if the soil\necosystem is energy limited or water limited. Elevated temperature reduced the amount of non-rainfall water, particularly\nduring the growing season in both approaches. In energy-limited grassland\necosystems, elevated temperature increased the actual evapotranspiration and decreased aboveground biomass. As a consequence, elevated temperature led to decreasing seepage rates in energy-limited systems. Under water-limited conditions in dry periods, elevated temperature aggravated water stress and, thus, resulted in reduced actual evapotranspiration. The already small seepage rates of the drier soils remained almost unaffected under these conditions compared to soils under wetter conditions. Elevated atmospheric CO2 reduced both actual evapotranspiration and aboveground biomass in the manipulative experiment and, therefore, led to a clear increase and change in seasonality of seepage. As expected, the aboveground biomass productivity and ecosystem efficiency indicators of the water-limited ecosystems were negatively correlated with an increase in aridity, while the trend was unclear for the energy-limited ecosystems. In both experimental approaches, the responses of soil water fluxes and\nbiomass production mainly depend on the ecosystems' status with respect to\nenergy or water limitation. To thoroughly understand the ecosystem response\nto climate change and be able to identify tipping points, experiments need\nto embrace sufficiently extreme boundary conditions and explore\nresponses to individual and multiple drivers, such as temperature, CO2\nconcentration, and precipitation, including non-rainfall water. In this\nregard, manipulative and observational climate change experiments complement one another and, thus, should be combined in the investigation of climate change effects on grassland.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-21},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Forstner, Veronika and Groh, Jannis and Vremec, Matevz and Herndl, Markus and Vereecken, Harry and Gerke, Horst H. and Birk, Steffen and Pütz, Thomas},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {6087--6106},\n}\n\n\n\n

\n\n\n

\n Abstract. Effects of climate change on the ecosystem productivity and water fluxes have been studied in various types of experiments. However, it is still largely unknown whether and how the experimental approach itself affects the results of such studies. We employed two contrasting experimental approaches, using high-precision weighable monolithic lysimeters, over a period of 4 years to identify and compare the responses of water fluxes and aboveground biomass to climate change in permanent grassland. The first, manipulative, approach is based on controlled increases of atmospheric CO2 concentration and surface temperature. The second, observational, approach uses data from a space-for-time substitution along a gradient of climatic conditions. The Budyko framework was used to identify if the soil ecosystem is energy limited or water limited. Elevated temperature reduced the amount of non-rainfall water, particularly during the growing season in both approaches. In energy-limited grassland ecosystems, elevated temperature increased the actual evapotranspiration and decreased aboveground biomass. As a consequence, elevated temperature led to decreasing seepage rates in energy-limited systems. Under water-limited conditions in dry periods, elevated temperature aggravated water stress and, thus, resulted in reduced actual evapotranspiration. The already small seepage rates of the drier soils remained almost unaffected under these conditions compared to soils under wetter conditions. Elevated atmospheric CO2 reduced both actual evapotranspiration and aboveground biomass in the manipulative experiment and, therefore, led to a clear increase and change in seasonality of seepage. As expected, the aboveground biomass productivity and ecosystem efficiency indicators of the water-limited ecosystems were negatively correlated with an increase in aridity, while the trend was unclear for the energy-limited ecosystems. In both experimental approaches, the responses of soil water fluxes and biomass production mainly depend on the ecosystems' status with respect to energy or water limitation. To thoroughly understand the ecosystem response to climate change and be able to identify tipping points, experiments need to embrace sufficiently extreme boundary conditions and explore responses to individual and multiple drivers, such as temperature, CO2 concentration, and precipitation, including non-rainfall water. In this regard, manipulative and observational climate change experiments complement one another and, thus, should be combined in the investigation of climate change effects on grassland.\n

\n\n\n

\n \n\n \n \n De Cannière, S.; Herbst, M.; Vereecken, H.; Defourny, P.; and Jonard, F.\n\n\n \n \n \n \n \n Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 267: 112722. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Constraining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_canniere_constraining_2021,\n\ttitle = {Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence},\n\tvolume = {267},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721004429},\n\tdoi = {10.1016/j.rse.2021.112722},\n\tlanguage = {en},\n\turldate = {2022-11-21},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {De Cannière, S. and Herbst, M. and Vereecken, H. and Defourny, P. and Jonard, F.},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {112722},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Biffi, S.; Traldi, R.; Crezee, B.; Beckmann, M.; Egli, L.; Epp Schmidt, D.; Motzer, N.; Okumah, M.; Seppelt, R.; Louise Slabbert, E.; Tiedeman, K.; Wang, H.; and Ziv, G.\n\n\n \n \n \n \n \n Aligning agri-environmental subsidies and environmental needs: a comparative analysis between the US and EU.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 16(5): 054067. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Aligning$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{biffi_aligning_2021,\n\ttitle = {Aligning agri-environmental subsidies and environmental needs: a comparative analysis between the {US} and {EU}},\n\tvolume = {16},\n\tissn = {1748-9326},\n\tshorttitle = {Aligning agri-environmental subsidies and environmental needs},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/abfa4e},\n\tdoi = {10.1088/1748-9326/abfa4e},\n\tabstract = {Abstract \n            The global recognition of modern agricultural practices’ impact on the environment has fuelled policy responses to ameliorate environmental degradation in agricultural landscapes. In the US and the EU, agri-environmental subsidies (AES) promote widespread adoption of sustainable practices by compensating farmers who voluntarily implement them on working farmland. Previous studies, however, have suggested limitations of their spatial targeting, with funds not allocated towards areas of the greatest environmental need. We analysed AES in the US and EU—specifically through the Environmental Quality Incentives Program (EQIP) and selected measures of the European Agricultural Fund for Rural Development (EAFRD)—to identify if AES are going where they are most needed to achieve environmental goals, using a set of environmental need indicators, socio-economic variables moderating allocation patterns, and contextual variables describing agricultural systems. Using linear mixed models and linear models we explored the associations among AES allocation and these predictors at different scales. We found that higher AES spending was associated with areas of low soil organic carbon and high greenhouse gas emissions both in the US and EU, and nitrogen surplus in the EU. More so than successes, however, clear mismatches of funding and environmental need emerged—AES allocation did not successfully target areas of highest water stress, biodiversity loss, soil erosion, and nutrient runoff. Socio-economic and agricultural context variables may explain some of these mismatches; we show that AES were allocated to areas with higher proportions of female producers in the EU but not in the US, where funds were directed towards areas with less tenant farmers. Moreover, we suggest that the potential for AES to remediate environmental issues may be curtailed by limited participation in intensive agricultural landscapes. These findings can help inform refinements to EQIP and EAFRD allocation mechanisms and identify opportunities for improving future targeting of AES spending.},\n\tnumber = {5},\n\turldate = {2022-11-21},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Biffi, Sofia and Traldi, Rebecca and Crezee, Bart and Beckmann, Michael and Egli, Lukas and Epp Schmidt, Dietrich and Motzer, Nicole and Okumah, Murat and Seppelt, Ralf and Louise Slabbert, Eleonore and Tiedeman, Kate and Wang, Haoluan and Ziv, Guy},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {054067},\n}\n\n\n\n

\n\n\n

\n Abstract The global recognition of modern agricultural practices’ impact on the environment has fuelled policy responses to ameliorate environmental degradation in agricultural landscapes. In the US and the EU, agri-environmental subsidies (AES) promote widespread adoption of sustainable practices by compensating farmers who voluntarily implement them on working farmland. Previous studies, however, have suggested limitations of their spatial targeting, with funds not allocated towards areas of the greatest environmental need. We analysed AES in the US and EU—specifically through the Environmental Quality Incentives Program (EQIP) and selected measures of the European Agricultural Fund for Rural Development (EAFRD)—to identify if AES are going where they are most needed to achieve environmental goals, using a set of environmental need indicators, socio-economic variables moderating allocation patterns, and contextual variables describing agricultural systems. Using linear mixed models and linear models we explored the associations among AES allocation and these predictors at different scales. We found that higher AES spending was associated with areas of low soil organic carbon and high greenhouse gas emissions both in the US and EU, and nitrogen surplus in the EU. More so than successes, however, clear mismatches of funding and environmental need emerged—AES allocation did not successfully target areas of highest water stress, biodiversity loss, soil erosion, and nutrient runoff. Socio-economic and agricultural context variables may explain some of these mismatches; we show that AES were allocated to areas with higher proportions of female producers in the EU but not in the US, where funds were directed towards areas with less tenant farmers. Moreover, we suggest that the potential for AES to remediate environmental issues may be curtailed by limited participation in intensive agricultural landscapes. These findings can help inform refinements to EQIP and EAFRD allocation mechanisms and identify opportunities for improving future targeting of AES spending.\n

\n\n\n

\n \n\n \n \n Balanzategui, D.; Nordhauß, H.; Heinrich, I.; Biondi, F.; Miley, N.; Hurley, A. G.; and Ziaco, E.\n\n\n \n \n \n \n \n Wood Anatomy of Douglas-Fir in Eastern Arizona and Its Relationship With Pacific Basin Climate.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Plant Science, 12: 702442. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Wood$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{balanzategui_wood_2021,\n\ttitle = {Wood {Anatomy} of {Douglas}-{Fir} in {Eastern} {Arizona} and {Its} {Relationship} {With} {Pacific} {Basin} {Climate}},\n\tvolume = {12},\n\tissn = {1664-462X},\n\turl = {https://www.frontiersin.org/articles/10.3389/fpls.2021.702442/full},\n\tdoi = {10.3389/fpls.2021.702442},\n\tabstract = {Dendroclimatic reconstructions, which are a well-known tool for extending records of climatic variability, have recently been expanded by using wood anatomical parameters. However, the relationships between wood cellular structures and large-scale climatic patterns, such as El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), are still not completely understood, hindering the potential for wood anatomy as a paleoclimatic proxy. To better understand the teleconnection between regional and local climate processes in the western United States, our main objective was to assess the value of these emerging tree-ring parameters for reconstructing climate dynamics. Using Confocal Laser Scanning Microscopy, we measured cell lumen diameter and cell wall thickness (CWT) for the period 1966 to 2015 in five Douglas-firs [ \n              Pseudotsuga menziesii \n              (Mirb.) Franco] from two sites in eastern Arizona (United States). Dendroclimatic analysis was performed using chronologies developed for 10 equally distributed sectors of the ring and daily climatic records to identify the strongest climatic signal for each sector. We found that lumen diameter in the first ring sector was sensitive to previous fall–winter temperature (September 25 \n              th \n              to January 23 \n              rd \n              ), while a precipitation signal (October 27 \n              th \n              to February 13 \n              th \n              ) persisted for the entire first half of the ring. The lack of synchronous patterns between trees for CWT prevented conducting meaningful climate-response analysis for that anatomical parameter. Time series of lumen diameter showed an anti-phase relationship with the Southern Oscillation Index (a proxy for ENSO) at 10 to 14year periodicity and particularly in 1980–2005, suggesting that chronologies of wood anatomical parameters respond to multidecadal variability of regional climatic modes. Our findings demonstrate the potential of cell structural characteristics of southwestern United States conifers for reconstructing past climatic variability, while also improving our understanding of how large-scale ocean–atmosphere interactions impact local hydroclimatic patterns.},\n\turldate = {2022-11-21},\n\tjournal = {Frontiers in Plant Science},\n\tauthor = {Balanzategui, Daniel and Nordhauß, Henry and Heinrich, Ingo and Biondi, Franco and Miley, Nicholas and Hurley, Alexander G. and Ziaco, Emanuele},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {702442},\n}\n\n\n\n

\n\n\n

\n Dendroclimatic reconstructions, which are a well-known tool for extending records of climatic variability, have recently been expanded by using wood anatomical parameters. However, the relationships between wood cellular structures and large-scale climatic patterns, such as El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), are still not completely understood, hindering the potential for wood anatomy as a paleoclimatic proxy. To better understand the teleconnection between regional and local climate processes in the western United States, our main objective was to assess the value of these emerging tree-ring parameters for reconstructing climate dynamics. Using Confocal Laser Scanning Microscopy, we measured cell lumen diameter and cell wall thickness (CWT) for the period 1966 to 2015 in five Douglas-firs [ Pseudotsuga menziesii (Mirb.) Franco] from two sites in eastern Arizona (United States). Dendroclimatic analysis was performed using chronologies developed for 10 equally distributed sectors of the ring and daily climatic records to identify the strongest climatic signal for each sector. We found that lumen diameter in the first ring sector was sensitive to previous fall–winter temperature (September 25 th to January 23 rd ), while a precipitation signal (October 27 th to February 13 th ) persisted for the entire first half of the ring. The lack of synchronous patterns between trees for CWT prevented conducting meaningful climate-response analysis for that anatomical parameter. Time series of lumen diameter showed an anti-phase relationship with the Southern Oscillation Index (a proxy for ENSO) at 10 to 14year periodicity and particularly in 1980–2005, suggesting that chronologies of wood anatomical parameters respond to multidecadal variability of regional climatic modes. Our findings demonstrate the potential of cell structural characteristics of southwestern United States conifers for reconstructing past climatic variability, while also improving our understanding of how large-scale ocean–atmosphere interactions impact local hydroclimatic patterns.\n

\n\n\n

\n \n\n \n \n Arnault, J.; Jung, G.; Haese, B.; Fersch, B.; Rummler, T.; Wei, J.; Zhang, Z.; and Kunstmann, H.\n\n\n \n \n \n \n \n A Joint Soil‐Vegetation‐Atmospheric Modeling Procedure of Water Isotopologues: Implementation and Application to Different Climate Zones With WRF‐Hydro‐Iso.\n \n \n \n \n\n\n \n\n\n\n Journal of Advances in Modeling Earth Systems, 13(10). October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{arnault_joint_2021,\n\ttitle = {A {Joint} {Soil}‐{Vegetation}‐{Atmospheric} {Modeling} {Procedure} of {Water} {Isotopologues}: {Implementation} and {Application} to {Different} {Climate} {Zones} {With} {WRF}‐{Hydro}‐{Iso}},\n\tvolume = {13},\n\tissn = {1942-2466, 1942-2466},\n\tshorttitle = {A {Joint} {Soil}‐{Vegetation}‐{Atmospheric} {Modeling} {Procedure} of {Water} {Isotopologues}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021MS002562},\n\tdoi = {10.1029/2021MS002562},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-21},\n\tjournal = {Journal of Advances in Modeling Earth Systems},\n\tauthor = {Arnault, Joël and Jung, Gerlinde and Haese, Barbara and Fersch, Benjamin and Rummler, Thomas and Wei, Jianhui and Zhang, Zhenyu and Kunstmann, Harald},\n\tmonth = oct,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Peters, R. L.; Pappas, C.; Hurley, A. G.; Poyatos, R.; Flo, V.; Zweifel, R.; Goossens, W.; and Steppe, K.\n\n\n \n \n \n \n \n Assimilate, process and analyse thermal dissipation sap flow data using the TREX $_{\\textrm{{R}}}$package.\n \n \n \n \n\n\n \n\n\n\n Methods in Ecology and Evolution, 12(2): 342–350. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Assimilate,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peters_assimilate_2021,\n\ttitle = {Assimilate, process and analyse thermal dissipation sap flow data using the {TREX} $_{\\textrm{{R}}}$package},\n\tvolume = {12},\n\tissn = {2041-210X, 2041-210X},\n\tshorttitle = {Assimilate, process and analyse thermal dissipation sap flow data using the {TREX} $_{\\textrm{{R}}}$},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13524},\n\tdoi = {10.1111/2041-210X.13524},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Methods in Ecology and Evolution},\n\tauthor = {Peters, Richard L. and Pappas, Christoforos and Hurley, Alexander G. and Poyatos, Rafael and Flo, Victor and Zweifel, Roman and Goossens, Willem and Steppe, Kathy},\n\teditor = {Royles, Jessica},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {342--350},\n}\n\n\n\n

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\n \n\n \n \n Vaidya, S.; Schmidt, M.; Rakowski, P.; Bonk, N.; Verch, G.; Augustin, J.; Sommer, M.; and Hoffmann, M.\n\n\n \n \n \n \n \n A novel robotic chamber system allowing to accurately and precisely determining spatio-temporal CO$_{\\textrm{2}}$ flux dynamics of heterogeneous croplands.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 296: 108206. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vaidya_novel_2021,\n\ttitle = {A novel robotic chamber system allowing to accurately and precisely determining spatio-temporal {CO}$_{\\textrm{2}}$ flux dynamics of heterogeneous croplands},\n\tvolume = {296},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192320303087},\n\tdoi = {10.1016/j.agrformet.2020.108206},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Vaidya, Shrijana and Schmidt, Marten and Rakowski, Peter and Bonk, Norbert and Verch, Gernot and Augustin, Jürgen and Sommer, Michael and Hoffmann, Mathias},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {108206},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Beyer, F.; Jansen, F.; Jurasinski, G.; Koch, M.; Schröder, B.; and Koebsch, F.\n\n\n \n \n \n \n \n Drought years in peatland rewetting: rapid vegetation succession can maintain the net CO$_{\\textrm{2}}$ sink function.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 18(3): 917–935. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Drought$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beyer_drought_2021,\n\ttitle = {Drought years in peatland rewetting: rapid vegetation succession can maintain the net {CO}$_{\\textrm{2}}$ sink function},\n\tvolume = {18},\n\tissn = {1726-4189},\n\tshorttitle = {Drought years in peatland rewetting},\n\turl = {https://bg.copernicus.org/articles/18/917/2021/},\n\tdoi = {10.5194/bg-18-917-2021},\n\tabstract = {Abstract. The rewetting of peatlands is regarded as an important nature-based climate solution and intended to reconcile climate protection with the restoration of self-regulating ecosystems that are resistant to climate impacts.\nAlthough the severity and frequency of droughts are predicted to increase as a consequence of climate change, it is not well understood whether such extreme events can jeopardize rewetting measures.\nThe goal of this study was to better understand drought effects on vegetation development and the exchange of the two important greenhouse gases CO2 and CH4, especially in rewetted fens. Based on long-term reference records, we investigated anomalies in vegetation dynamics, CH4 emissions, and net CO2 exchange, including the component ﬂuxes of ecosystem respiration (Reco) and gross ecosystem productivity (GEP), in a rewetted fen during the extreme European summer drought in 2018. Drought-induced vegetation dynamics were derived from remotely sensed data. Since flooding in 2010, the fen was characterized by a patchy mosaic of open-water surfaces and vegetated areas.\nAfter years of stagnant vegetation development, drought acted as a trigger event for pioneer species such as Tephroseris palustris and Ranunculus sceleratus to rapidly close persistent vegetation gaps.\nThe massive spread of vegetation assimilated substantial amounts of CO2.\nIn 2018, the annual GEP budget increased by 20 \\% in comparison to average years (2010–2017).\nReco increased even by 40 \\%, but enhanced photosynthetic CO2 sequestration could compensate for half of the drought-induced increase in respiratory CO2 release. Altogether, the restored fen remained a net CO2 sink in the year of drought, though net CO2 sequestration was lower than in other years.\nCH4 emissions were 20 \\% below average on an annual basis, though stronger reduction effects occurred from August onwards, when daily fluxes were 60 \\% lower than in reference years. Our study reveals an important regulatory mechanism of restored fens to maintain their net CO2 sink function even in extremely dry years.\nIt appears that, in times of more frequent climate extremes, fen restoration can create ecosystems resilient to drought. However, in order to comprehensively assess the mitigation prospects of peatland rewetting as a nature-based climate solution, further research needs to focus on the long-term effects of such extreme events beyond the actual drought period.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Biogeosciences},\n\tauthor = {Beyer, Florian and Jansen, Florian and Jurasinski, Gerald and Koch, Marian and Schröder, Birgit and Koebsch, Franziska},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {917--935},\n}\n\n\n\n

\n\n\n

\n Abstract. The rewetting of peatlands is regarded as an important nature-based climate solution and intended to reconcile climate protection with the restoration of self-regulating ecosystems that are resistant to climate impacts. Although the severity and frequency of droughts are predicted to increase as a consequence of climate change, it is not well understood whether such extreme events can jeopardize rewetting measures. The goal of this study was to better understand drought effects on vegetation development and the exchange of the two important greenhouse gases CO2 and CH4, especially in rewetted fens. Based on long-term reference records, we investigated anomalies in vegetation dynamics, CH4 emissions, and net CO2 exchange, including the component ﬂuxes of ecosystem respiration (Reco) and gross ecosystem productivity (GEP), in a rewetted fen during the extreme European summer drought in 2018. Drought-induced vegetation dynamics were derived from remotely sensed data. Since flooding in 2010, the fen was characterized by a patchy mosaic of open-water surfaces and vegetated areas. After years of stagnant vegetation development, drought acted as a trigger event for pioneer species such as Tephroseris palustris and Ranunculus sceleratus to rapidly close persistent vegetation gaps. The massive spread of vegetation assimilated substantial amounts of CO2. In 2018, the annual GEP budget increased by 20 % in comparison to average years (2010–2017). Reco increased even by 40 %, but enhanced photosynthetic CO2 sequestration could compensate for half of the drought-induced increase in respiratory CO2 release. Altogether, the restored fen remained a net CO2 sink in the year of drought, though net CO2 sequestration was lower than in other years. CH4 emissions were 20 % below average on an annual basis, though stronger reduction effects occurred from August onwards, when daily fluxes were 60 % lower than in reference years. Our study reveals an important regulatory mechanism of restored fens to maintain their net CO2 sink function even in extremely dry years. It appears that, in times of more frequent climate extremes, fen restoration can create ecosystems resilient to drought. However, in order to comprehensively assess the mitigation prospects of peatland rewetting as a nature-based climate solution, further research needs to focus on the long-term effects of such extreme events beyond the actual drought period.\n

\n\n\n

\n \n\n \n \n Arnault, J.; Fersch, B.; Rummler, T.; Zhang, Z.; Quenum, G. M.; Wei, J.; Graf, M.; Laux, P.; and Kunstmann, H.\n\n\n \n \n \n \n \n Lateral terrestrial water flow contribution to summer precipitation at continental scale – A comparison between Europe and West Africa with WRF-Hydro-tag ensembles.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 35(5). May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Lateral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{arnault_lateral_2021,\n\ttitle = {Lateral terrestrial water flow contribution to summer precipitation at continental scale – {A} comparison between {Europe} and {West} {Africa} with {WRF}-{Hydro}-tag ensembles},\n\tvolume = {35},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Lateral terrestrial water flow contribution to summer precipitation at continental scale – {A} comparison between {Europe} and {West} {Africa} with},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14183},\n\tdoi = {10.1002/hyp.14183},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-10-20},\n\tjournal = {Hydrological Processes},\n\tauthor = {Arnault, Joël and Fersch, Benjamin and Rummler, Thomas and Zhang, Zhenyu and Quenum, Gandome Mayeul and Wei, Jianhui and Graf, Maximilian and Laux, Patrick and Kunstmann, Harald},\n\tmonth = may,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Hänsch, R.; Jagdhuber, T.; and Fersch, B.\n\n\n \n \n \n \n \n Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 59(4): 2877–2887. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil-Permittivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hansch_soil-permittivity_2021,\n\ttitle = {Soil-{Permittivity} {Estimation} {Under} {Grassland} {Using} {Machine}-{Learning} and {Polarimetric} {Decomposition} {Techniques}},\n\tvolume = {59},\n\tissn = {0196-2892, 1558-0644},\n\turl = {https://ieeexplore.ieee.org/document/9160965/},\n\tdoi = {10.1109/TGRS.2020.3010104},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Hänsch, Ronny and Jagdhuber, Thomas and Fersch, Benjamin},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {2877--2887},\n}\n\n\n\n

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\n \n\n \n \n Grodtke, M.; Paschke, A.; Harzdorf, J.; Krauss, M.; and Schüürmann, G.\n\n\n \n \n \n \n \n Calibration and field application of the Atlantic HLB Disk containing Chemcatcher® passive sampler – Quantitative monitoring of herbicides, other pesticides, and transformation products in German streams.\n \n \n \n \n\n\n \n\n\n\n Journal of Hazardous Materials, 410: 124538. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Calibration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{grodtke_calibration_2021,\n\ttitle = {Calibration and field application of the {Atlantic} {HLB} {Disk} containing {Chemcatcher}® passive sampler – {Quantitative} monitoring of herbicides, other pesticides, and transformation products in {German} streams},\n\tvolume = {410},\n\tissn = {03043894},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0304389420325280},\n\tdoi = {10.1016/j.jhazmat.2020.124538},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Journal of Hazardous Materials},\n\tauthor = {Grodtke, Mara and Paschke, Albrecht and Harzdorf, Julia and Krauss, Martin and Schüürmann, Gerrit},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {124538},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Bogena, H. R.; Stockinger, M. P.; and Lücke, A.\n\n\n \n \n \n \n \n Long‐term stable water isotope and runoff data for the investigation of deforestation effects on the hydrological system of the Wüstebach catchment, Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 35(1). January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Long‐term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_longterm_2021,\n\ttitle = {Long‐term stable water isotope and runoff data for the investigation of deforestation effects on the hydrological system of the {Wüstebach} catchment, {Germany}},\n\tvolume = {35},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14006},\n\tdoi = {10.1002/hyp.14006},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Hydrological Processes},\n\tauthor = {Bogena, Heye R. and Stockinger, Michael P. and Lücke, Andreas},\n\tmonth = jan,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Bayat, B.; Camacho, F.; Nickeson, J.; Cosh, M.; Bolten, J.; Vereecken, H.; and Montzka, C.\n\n\n \n \n \n \n \n Toward operational validation systems for global satellite-based terrestrial essential climate variables.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 95: 102240. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bayat_toward_2021,\n\ttitle = {Toward operational validation systems for global satellite-based terrestrial essential climate variables},\n\tvolume = {95},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243420308837},\n\tdoi = {10.1016/j.jag.2020.102240},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Bayat, Bagher and Camacho, Fernando and Nickeson, Jaime and Cosh, Michael and Bolten, John and Vereecken, Harry and Montzka, Carsten},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {102240},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Schrön, M.; Oswald, S. E.; Zacharias, S.; Kasner, M.; Dietrich, P.; and Attinger, S.\n\n\n \n \n \n \n \n Neutrons on Rails: Transregional Monitoring of Soil Moisture and Snow Water Equivalent.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 48(24). December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Neutrons$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schron_neutrons_2021,\n\ttitle = {Neutrons on {Rails}: {Transregional} {Monitoring} of {Soil} {Moisture} and {Snow} {Water} {Equivalent}},\n\tvolume = {48},\n\tissn = {0094-8276, 1944-8007},\n\tshorttitle = {Neutrons on {Rails}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021GL093924},\n\tdoi = {10.1029/2021GL093924},\n\tlanguage = {en},\n\tnumber = {24},\n\turldate = {2022-10-26},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Schrön, M. and Oswald, S. E. and Zacharias, S. and Kasner, M. and Dietrich, P. and Attinger, S.},\n\tmonth = dec,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Schönbrodt-Stitt, S.; Ahmadian, N.; Kurtenbach, M.; Conrad, C.; Romano, N.; Bogena, H. R.; Vereecken, H.; and Nasta, P.\n\n\n \n \n \n \n \n Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 3: 655837. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Statistical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schonbrodt-stitt_statistical_2021,\n\ttitle = {Statistical {Exploration} of {SENTINEL}-1 {Data}, {Terrain} {Parameters}, and in-situ {Data} for {Estimating} the {Near}-{Surface} {Soil} {Moisture} in a {Mediterranean} {Agroecosystem}},\n\tvolume = {3},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2021.655837/full},\n\tdoi = {10.3389/frwa.2021.655837},\n\tabstract = {Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with \n              in-situ \n              measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters ( \n               \n                 \n                   \n                     \n                       \n                         \n                          θ \n                         \n                         \n                          Nov \n                         \n                       \n                     \n                     \n                      SAR \n                     \n                   \n                 \n               \n              ) or the combination of 43 SAR and 10 terrain parameters ( \n               \n                 \n                   \n                     \n                       \n                         \n                          θ \n                         \n                         \n                          Nov \n                         \n                       \n                     \n                     \n                      SAR \n                      + \n                      Terrain \n                     \n                   \n                 \n               \n              ). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model ( \n               \n                 \n                   \n                     \n                       \n                         \n                          θ \n                         \n                         \n                          Nov \n                         \n                       \n                     \n                     \n                      SAR \n                      + \n                      Terrain \n                     \n                   \n                 \n               \n              with 0.025 and 0.020 m \n              3 \n              m \n              −3 \n              , and 89\\% compared to \n               \n                 \n                   \n                     \n                       \n                         \n                          θ \n                         \n                         \n                          Nov \n                         \n                       \n                     \n                     \n                      SAR \n                     \n                   \n                 \n               \n              with 0.028 and 0.022 m \n              3 \n              m \n              −3 \n              , and 86\\% in terms of RMSE, MAE, and R \n              2 \n              ). The higher explanatory power for \n               \n                 \n                   \n                     \n                       \n                         \n                          θ \n                         \n                         \n                          Nov \n                         \n                       \n                     \n                     \n                      SAR \n                      + \n                      Terrain \n                     \n                   \n                 \n               \n              is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.},\n\turldate = {2022-10-26},\n\tjournal = {Frontiers in Water},\n\tauthor = {Schönbrodt-Stitt, Sarah and Ahmadian, Nima and Kurtenbach, Markus and Conrad, Christopher and Romano, Nunzio and Bogena, Heye R. and Vereecken, Harry and Nasta, Paolo},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {655837},\n}\n\n\n\n

\n\n\n

\n Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters ( θ Nov SAR ) or the combination of 43 SAR and 10 terrain parameters ( θ Nov SAR + Terrain ). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model ( θ Nov SAR + Terrain with 0.025 and 0.020 m 3 m −3 , and 89% compared to θ Nov SAR with 0.028 and 0.022 m 3 m −3 , and 86% in terms of RMSE, MAE, and R 2 ). The higher explanatory power for θ Nov SAR + Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.\n

\n\n\n

\n \n\n \n \n Schneider, J.; Groh, J.; Pütz, T.; Helmig, R.; Rothfuss, Y.; Vereecken, H.; and Vanderborght, J.\n\n\n \n \n \n \n \n Prediction of soil evaporation measured with weighable lysimeters using the FAO Penman–Monteith method in combination with Richards’ equation.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 20(1). January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Prediction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schneider_prediction_2021,\n\ttitle = {Prediction of soil evaporation measured with weighable lysimeters using the {FAO} {Penman}–{Monteith} method in combination with {Richards}’ equation},\n\tvolume = {20},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20102},\n\tdoi = {10.1002/vzj2.20102},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Schneider, Jana and Groh, Jannis and Pütz, Thomas and Helmig, Rainer and Rothfuss, Youri and Vereecken, Harry and Vanderborght, Jan},\n\tmonth = jan,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Roy, J.; Rineau, F.; De Boeck, H. J.; Nijs, I.; Pütz, T.; Abiven, S.; Arnone, J. A.; Barton, C. V. M.; Beenaerts, N.; Brüggemann, N.; Dainese, M.; Domisch, T.; Eisenhauer, N.; Garré, S.; Gebler, A.; Ghirardo, A.; Jasoni, R. L.; Kowalchuk, G.; Landais, D.; Larsen, S. H.; Leemans, V.; Le Galliard, J.; Longdoz, B.; Massol, F.; Mikkelsen, T. N.; Niedrist, G.; Piel, C.; Ravel, O.; Sauze, J.; Schmidt, A.; Schnitzler, J.; Teixeira, L. H.; Tjoelker, M. G.; Weisser, W. W.; Winkler, B.; and Milcu, A.\n\n\n \n \n \n \n \n Ecotrons: Powerful and versatile ecosystem analysers for ecology, agronomy and environmental science.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 27(7): 1387–1407. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecotrons:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{roy_ecotrons_2021,\n\ttitle = {Ecotrons: {Powerful} and versatile ecosystem analysers for ecology, agronomy and environmental science},\n\tvolume = {27},\n\tissn = {1354-1013, 1365-2486},\n\tshorttitle = {Ecotrons},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15471},\n\tdoi = {10.1111/gcb.15471},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Global Change Biology},\n\tauthor = {Roy, Jacques and Rineau, François and De Boeck, Hans J. and Nijs, Ivan and Pütz, Thomas and Abiven, Samuel and Arnone, John A. and Barton, Craig V. M. and Beenaerts, Natalie and Brüggemann, Nicolas and Dainese, Matteo and Domisch, Timo and Eisenhauer, Nico and Garré, Sarah and Gebler, Alban and Ghirardo, Andrea and Jasoni, Richard L. and Kowalchuk, George and Landais, Damien and Larsen, Stuart H. and Leemans, Vincent and Le Galliard, Jean‐François and Longdoz, Bernard and Massol, Florent and Mikkelsen, Teis N. and Niedrist, Georg and Piel, Clément and Ravel, Olivier and Sauze, Joana and Schmidt, Anja and Schnitzler, Jörg‐Peter and Teixeira, Leonardo H. and Tjoelker, Mark G. and Weisser, Wolfgang W. and Winkler, Barbro and Milcu, Alexandru},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {1387--1407},\n}\n\n\n\n

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\n \n\n \n \n Rothfuss, Y.; Quade, M.; Brüggemann, N.; Graf, A.; Vereecken, H.; and Dubbert, M.\n\n\n \n \n \n \n \n Reviews and syntheses: Gaining insights into evapotranspiration partitioning with novel isotopic monitoring methods.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 18(12): 3701–3732. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Reviews$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rothfuss_reviews_2021,\n\ttitle = {Reviews and syntheses: {Gaining} insights into evapotranspiration partitioning with novel isotopic monitoring methods},\n\tvolume = {18},\n\tissn = {1726-4189},\n\tshorttitle = {Reviews and syntheses},\n\turl = {https://bg.copernicus.org/articles/18/3701/2021/},\n\tdoi = {10.5194/bg-18-3701-2021},\n\tabstract = {Abstract. Disentangling ecosystem evapotranspiration (ET) into evaporation (E) and transpiration (T) is of high relevance for a wide range of\napplications, from land surface modelling to policymaking. Identifying and analysing the determinants of the ratio of T to ET (T/ET) for\nvarious land covers and uses, especially in view of climate change with an increased frequency of extreme events (e.g. heatwaves and floods), is\nprerequisite for forecasting the hydroclimate of the future and tackling present issues, such as agricultural and irrigation practices. One partitioning method consists of determining the water stable isotopic compositions of ET, E, and T (δET,\nδE, and δE, respectively) from the water retrieved from the atmosphere, the soil, and the plant vascular\ntissues. The present work emphasizes the challenges this particular method faces (e.g. the spatial and temporal representativeness of the\nT/ET estimates, the limitations of the models used, and the sensitivities to their driving parameters) and the progress that needs to be\nmade in light of the recent methodological developments. As our review is intended for a broader audience beyond the isotopic ecohydrological and\nmicrometeorological communities, it also attempts to provide a thorough review of the ensemble of techniques used for determining\nδET, δE, and δE and solving the partitioning equation for T/ET. From the current state of research, we conclude that the most promising way forward to ET partitioning and capturing the subdaily dynamics of\nT/ET is by making use of non-destructive online monitoring techniques of the stable isotopic composition of soil and xylem water. Effort\nshould continue towards the application of the eddy covariance technique for high-frequency determination of δET at the field scale\nas well as the concomitant determination of δET, δE, and δE at high vertical resolution with\nfield-deployable lift systems.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Biogeosciences},\n\tauthor = {Rothfuss, Youri and Quade, Maria and Brüggemann, Nicolas and Graf, Alexander and Vereecken, Harry and Dubbert, Maren},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {3701--3732},\n}\n\n\n\n

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\n \n\n \n \n Roeser, P.; Dräger, N.; Brykała, D.; Ott, F.; Pinkerneil, S.; Gierszewski, P.; Lindemann, C.; Plessen, B.; Brademann, B.; Kaszubski, M.; Fojutowski, M.; Schwab, M. J.; Słowiński, M.; Błaszkiewicz, M.; and Brauer, A.\n\n\n \n \n \n \n \n TERENO Monitoring data from Lake Tiefer See and Lake Czechowskie (2012-2017).\n \n \n \n \n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"TERENO$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@misc{roeser_tereno_2021,\n\ttitle = {{TERENO} {Monitoring} data from {Lake} {Tiefer} {See} and {Lake} {Czechowskie} (2012-2017)},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://dataservices.gfz-potsdam.de/panmetaworks/showshort.php?id=be3dfad6-648b-11eb-9603-497c92695674},\n\tdoi = {10.5880/GFZ.4.3.2020.003},\n\tabstract = {This dataset resulted from a parallel monitoring at two lakes, Lake Tiefer See (near Klocksin, TSK; 53° 35.5’ N, 12° 31.8’ E; 62 masl; N Germany) and Lake Czechowskie (Jezioro Czechowskie, JC; 53° 52.4’ N, 18° 14.3’ E; 108 masl; N Poland), and includes four different type of data for both locations: (i) sediment cores microfacies data, (ii) sediment fluxes and composition, (iii) selected water column data, and (iv) selected meteorological information obtained on site. This dual lake monitoring set-up was established in 2012 with the aim to investigate seasonal sedimentation and varve forming processes in detail. The datasets are provided in individual *.csv files, per type of data and per lake. The thin section data from surface sediment cores comprises the thicknesses of the most recent calcite varves’ sub-layers: spring diatom sub-layer, summer calcite sub-layer, and autumn/winter re-suspension sub-layer. The sediment flux data was obtained from sediment traps located in different water depths (epi- and hypolimnion), and the sediment composition is given by the fluxes of total organic carbon (TOC), calcium carbonate (as calculated from total inorganic carbon; TIC), and diatoms \\&amp; inorganic matter. The water column data comprises water temperature from stationary loggers, and dissolved oxygen measured in {\\textasciitilde} 1 meter depth-resolution. The meteorological data includes daily averages of air temperature and mean wind-speed, and summed daily rainfall. Further details about the sampling and analytical methods, data acquisition, and processing are given in Roeser et al. (2021; http://doi.org/10.1111/bor.12506).},\n\turldate = {2022-10-26},\n\tpublisher = {GFZ Data Services},\n\tauthor = {Roeser, Patricia and Dräger, Nadine and Brykała, Dariusz and Ott, Florian and Pinkerneil, Sylvia and Gierszewski, Piotr and Lindemann, Christin and Plessen, Birgit and Brademann, Brian and Kaszubski, Michał and Fojutowski, Michał and Schwab, Markus J. and Słowiński, Michał and Błaszkiewicz, Mirosław and Brauer, Achim},\n\tcollaborator = {Roeser, Patricia and Brykała, Dariusz and Pinkerneil, Sylvia and Brademann, Brian and Kaszubski, Michał and Brauer, Achim and Roeser, Patricia and Brauer, Achim and Brykała, Dariusz},\n\tyear = {2021},\n\tkeywords = {In Situ/Laboratory Instruments \\&gt; Corers \\&gt; SEDIMENT CORERS, In Situ/Laboratory Instruments \\&gt; Samplers \\&gt; Grabbers/Traps/Collectors \\&gt; SEDIMENT TRAPS, geological process \\&gt; sedimentation (geology), hydrosphere \\&gt; water (geographic) \\&gt; surface water \\&gt; freshwater, sediment thin section; sediment fluxes; sediment composition; water column temperature and dissolved oxygen; air temperature; wind speed; precipitation},\n}\n\n\n\n

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\n \n\n \n \n Roeser, P.; Dräger, N.; Brykała, D.; Ott, F.; Pinkerneil, S.; Gierszewski, P.; Lindemann, C.; Plessen, B.; Brademann, B.; Kaszubski, M.; Fojutowski, M.; Schwab, M. J.; Słowiński, M.; Błaszkiewicz, M.; and Brauer, A.\n\n\n \n \n \n \n \n Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern Baltic lowlands.\n \n \n \n \n\n\n \n\n\n\n Boreas, 50(2): 419–440. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Advances$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{roeser_advances_2021,\n\ttitle = {Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern {Baltic} lowlands},\n\tvolume = {50},\n\tissn = {0300-9483, 1502-3885},\n\tshorttitle = {Advances in understanding calcite varve formation},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12506},\n\tdoi = {10.1111/bor.12506},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Boreas},\n\tauthor = {Roeser, Patricia and Dräger, Nadine and Brykała, Dariusz and Ott, Florian and Pinkerneil, Sylvia and Gierszewski, Piotr and Lindemann, Christin and Plessen, Birgit and Brademann, Brian and Kaszubski, Michał and Fojutowski, Michał and Schwab, Markus J. and Słowiński, Michał and Błaszkiewicz, Mirosław and Brauer, Achim},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {419--440},\n}\n\n\n\n

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\n \n\n \n \n Rennie, S.; Goergen, K.; Wohner, C.; Apweiler, S.; Peterseil, J.; and Watkins, J.\n\n\n \n \n \n \n \n A climate service for ecologists: sharing pre-processed EURO-CORDEX regional climate scenario data using the eLTER Information System.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(2): 631–644. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rennie_climate_2021,\n\ttitle = {A climate service for ecologists: sharing pre-processed {EURO}-{CORDEX} regional climate scenario data using the {eLTER} {Information} {System}},\n\tvolume = {13},\n\tissn = {1866-3516},\n\tshorttitle = {A climate service for ecologists},\n\turl = {https://essd.copernicus.org/articles/13/631/2021/},\n\tdoi = {10.5194/essd-13-631-2021},\n\tabstract = {Abstract. eLTER was a “Horizon 2020” project with the aim of\nadvancing the development of long-term ecosystem research infrastructure in\nEurope. This paper describes how eLTER Information System infrastructure has\nbeen expanded by a climate service data product providing access to\nspecifically pre-processed regional climate change scenario data from a\nstate-of-the-art regional climate model ensemble of the Coordinated Regional\nDownscaling Experiment (CORDEX) for 702 registered ecological\nresearch sites across Europe. This tailored, expandable, easily accessible\ndataset follows FAIR principles and allows researchers to describe the\nclimate at these sites, explore future projections for different climate\nchange scenarios and make regional climate change assessments and impact\nstudies. The data for each site are available for download from the EUDAT\ncollaborative data infrastructure B2SHARE service and can be easily accessed\nand visualised through the Dynamic Ecological Information Management System\n– Site and Dataset Registry (DEIMS-SDR), a web-based information management\nsystem which shares detailed information and metadata on ecological research\nsites around the globe. This paper describes these data and how they can be\naccessed by users through the extended eLTER Information System\narchitecture. The data and supporting information are available from B2SHARE. Each\nindividual site (702 sites are available) dataset has its own DOI. To aid\ndata discovery, a persistent B2SHARE lookup table has been created which\nmatches the DOIs of the individual B2SHARE record with each DEIMS site ID.\nThis lookup table is available at https://doi.org/10.23728/b2share.bf41278d91b445bda4505d5b1eaac26c (eLTER\nEURO-CORDEX Climate Service, 2020).},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Rennie, Susannah and Goergen, Klaus and Wohner, Christoph and Apweiler, Sander and Peterseil, Johannes and Watkins, John},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {631--644},\n}\n\n\n\n

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\n \n\n \n \n Poyatos, R.; Granda, V.; Flo, V.; Adams, M. A.; Adorján, B.; Aguadé, D.; Aidar, M. P. M.; Allen, S.; Alvarado-Barrientos, M. S.; Anderson-Teixeira, K. J.; Aparecido, L. M.; Arain, M. A.; Aranda, I.; Asbjornsen, H.; Baxter, R.; Beamesderfer, E.; Berry, Z. C.; Berveiller, D.; Blakely, B.; Boggs, J.; Bohrer, G.; Bolstad, P. V.; Bonal, D.; Bracho, R.; Brito, P.; Brodeur, J.; Casanoves, F.; Chave, J.; Chen, H.; Cisneros, C.; Clark, K.; Cremonese, E.; Dang, H.; David, J. S.; David, T. S.; Delpierre, N.; Desai, A. R.; Do, F. C.; Dohnal, M.; Domec, J.; Dzikiti, S.; Edgar, C.; Eichstaedt, R.; El-Madany, T. S.; Elbers, J.; Eller, C. B.; Euskirchen, E. S.; Ewers, B.; Fonti, P.; Forner, A.; Forrester, D. I.; Freitas, H. C.; Galvagno, M.; Garcia-Tejera, O.; Ghimire, C. P.; Gimeno, T. E.; Grace, J.; Granier, A.; Griebel, A.; Guangyu, Y.; Gush, M. B.; Hanson, P. J.; Hasselquist, N. J.; Heinrich, I.; Hernandez-Santana, V.; Herrmann, V.; Hölttä, T.; Holwerda, F.; Irvine, J.; Isarangkool Na Ayutthaya, S.; Jarvis, P. G.; Jochheim, H.; Joly, C. A.; Kaplick, J.; Kim, H. S.; Klemedtsson, L.; Kropp, H.; Lagergren, F.; Lane, P.; Lang, P.; Lapenas, A.; Lechuga, V.; Lee, M.; Leuschner, C.; Limousin, J.; Linares, J. C.; Linderson, M.; Lindroth, A.; Llorens, P.; López-Bernal, Á.; Loranty, M. M.; Lüttschwager, D.; Macinnis-Ng, C.; Maréchaux, I.; Martin, T. A.; Matheny, A.; McDowell, N.; McMahon, S.; Meir, P.; Mészáros, I.; Migliavacca, M.; Mitchell, P.; Mölder, M.; Montagnani, L.; Moore, G. W.; Nakada, R.; Niu, F.; Nolan, R. H.; Norby, R.; Novick, K.; Oberhuber, W.; Obojes, N.; Oishi, A. C.; Oliveira, R. S.; Oren, R.; Ourcival, J.; Paljakka, T.; Perez-Priego, O.; Peri, P. L.; Peters, R. L.; Pfautsch, S.; Pockman, W. T.; Preisler, Y.; Rascher, K.; Robinson, G.; Rocha, H.; Rocheteau, A.; Röll, A.; Rosado, B. H. P.; Rowland, L.; Rubtsov, A. V.; Sabaté, S.; Salmon, Y.; Salomón, R. L.; Sánchez-Costa, E.; Schäfer, K. V. R.; Schuldt, B.; Shashkin, A.; Stahl, C.; Stojanović, M.; Suárez, J. C.; Sun, G.; Szatniewska, J.; Tatarinov, F.; Tesař, M.; Thomas, F. M.; Tor-ngern, P.; Urban, J.; Valladares, F.; van der Tol, C.; van Meerveld, I.; Varlagin, A.; Voigt, H.; Warren, J.; Werner, C.; Werner, W.; Wieser, G.; Wingate, L.; Wullschleger, S.; Yi, K.; Zweifel, R.; Steppe, K.; Mencuccini, M.; and Martínez-Vilalta, J.\n\n\n \n \n \n \n \n Global transpiration data from sap flow measurements: the SAPFLUXNET database.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(6): 2607–2649. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{poyatos_global_2021,\n\ttitle = {Global transpiration data from sap flow measurements: the {SAPFLUXNET} database},\n\tvolume = {13},\n\tissn = {1866-3516},\n\tshorttitle = {Global transpiration data from sap flow measurements},\n\turl = {https://essd.copernicus.org/articles/13/2607/2021/},\n\tdoi = {10.5194/essd-13-2607-2021},\n\tabstract = {Abstract. Plant transpiration links physiological responses of\nvegetation to water supply and demand with hydrological, energy, and carbon\nbudgets at the land–atmosphere interface. However, despite being the main\nland evaporative flux at the global scale, transpiration and its response to\nenvironmental drivers are currently not well constrained by observations.\nHere we introduce the first global compilation of whole-plant transpiration\ndata from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021).\nWe harmonized and quality-controlled individual datasets supplied by\ncontributors worldwide in a semi-automatic data workflow implemented in the\nR programming language. Datasets include sub-daily time series of sap flow\nand hydrometeorological drivers for one or more growing seasons, as well as\nmetadata on the stand characteristics, plant attributes, and technical\ndetails of the measurements. SAPFLUXNET contains 202 globally distributed\ndatasets with sap flow time series for 2714 plants, mostly trees, of 174\nspecies. SAPFLUXNET has a broad bioclimatic coverage, with\nwoodland/shrubland and temperate forest biomes especially well represented\n(80 \\% of the datasets). The measurements cover a wide variety of stand\nstructural characteristics and plant sizes. The datasets encompass the\nperiod between 1995 and 2018, with 50 \\% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are\navailable for most of the datasets, while on-site soil water content is\navailable for 56 \\% of the datasets. Many datasets contain data for species\nthat make up 90 \\% or more of the total stand basal area, allowing the\nestimation of stand transpiration in diverse ecological settings. SAPFLUXNET\nadds to existing plant trait datasets, ecosystem flux networks, and remote\nsensing products to help increase our understanding of plant water use,\nplant responses to drought, and ecohydrological processes. SAPFLUXNET version\n0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The\n“sapfluxnetr” R package – designed to access, visualize, and process\nSAPFLUXNET data – is available from CRAN.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Poyatos, Rafael and Granda, Víctor and Flo, Víctor and Adams, Mark A. and Adorján, Balázs and Aguadé, David and Aidar, Marcos P. M. and Allen, Scott and Alvarado-Barrientos, M. Susana and Anderson-Teixeira, Kristina J. and Aparecido, Luiza Maria and Arain, M. Altaf and Aranda, Ismael and Asbjornsen, Heidi and Baxter, Robert and Beamesderfer, Eric and Berry, Z. Carter and Berveiller, Daniel and Blakely, Bethany and Boggs, Johnny and Bohrer, Gil and Bolstad, Paul V. and Bonal, Damien and Bracho, Rosvel and Brito, Patricia and Brodeur, Jason and Casanoves, Fernando and Chave, Jérôme and Chen, Hui and Cisneros, Cesar and Clark, Kenneth and Cremonese, Edoardo and Dang, Hongzhong and David, Jorge S. and David, Teresa S. and Delpierre, Nicolas and Desai, Ankur R. and Do, Frederic C. and Dohnal, Michal and Domec, Jean-Christophe and Dzikiti, Sebinasi and Edgar, Colin and Eichstaedt, Rebekka and El-Madany, Tarek S. and Elbers, Jan and Eller, Cleiton B. and Euskirchen, Eugénie S. and Ewers, Brent and Fonti, Patrick and Forner, Alicia and Forrester, David I. and Freitas, Helber C. and Galvagno, Marta and Garcia-Tejera, Omar and Ghimire, Chandra Prasad and Gimeno, Teresa E. and Grace, John and Granier, André and Griebel, Anne and Guangyu, Yan and Gush, Mark B. and Hanson, Paul J. and Hasselquist, Niles J. and Heinrich, Ingo and Hernandez-Santana, Virginia and Herrmann, Valentine and Hölttä, Teemu and Holwerda, Friso and Irvine, James and Isarangkool Na Ayutthaya, Supat and Jarvis, Paul G. and Jochheim, Hubert and Joly, Carlos A. and Kaplick, Julia and Kim, Hyun Seok and Klemedtsson, Leif and Kropp, Heather and Lagergren, Fredrik and Lane, Patrick and Lang, Petra and Lapenas, Andrei and Lechuga, Víctor and Lee, Minsu and Leuschner, Christoph and Limousin, Jean-Marc and Linares, Juan Carlos and Linderson, Maj-Lena and Lindroth, Anders and Llorens, Pilar and López-Bernal, Álvaro and Loranty, Michael M. and Lüttschwager, Dietmar and Macinnis-Ng, Cate and Maréchaux, Isabelle and Martin, Timothy A. and Matheny, Ashley and McDowell, Nate and McMahon, Sean and Meir, Patrick and Mészáros, Ilona and Migliavacca, Mirco and Mitchell, Patrick and Mölder, Meelis and Montagnani, Leonardo and Moore, Georgianne W. and Nakada, Ryogo and Niu, Furong and Nolan, Rachael H. and Norby, Richard and Novick, Kimberly and Oberhuber, Walter and Obojes, Nikolaus and Oishi, A. Christopher and Oliveira, Rafael S. and Oren, Ram and Ourcival, Jean-Marc and Paljakka, Teemu and Perez-Priego, Oscar and Peri, Pablo L. and Peters, Richard L. and Pfautsch, Sebastian and Pockman, William T. and Preisler, Yakir and Rascher, Katherine and Robinson, George and Rocha, Humberto and Rocheteau, Alain and Röll, Alexander and Rosado, Bruno H. P. and Rowland, Lucy and Rubtsov, Alexey V. and Sabaté, Santiago and Salmon, Yann and Salomón, Roberto L. and Sánchez-Costa, Elisenda and Schäfer, Karina V. R. and Schuldt, Bernhard and Shashkin, Alexandr and Stahl, Clément and Stojanović, Marko and Suárez, Juan Carlos and Sun, Ge and Szatniewska, Justyna and Tatarinov, Fyodor and Tesař, Miroslav and Thomas, Frank M. and Tor-ngern, Pantana and Urban, Josef and Valladares, Fernando and van der Tol, Christiaan and van Meerveld, Ilja and Varlagin, Andrej and Voigt, Holm and Warren, Jeffrey and Werner, Christiane and Werner, Willy and Wieser, Gerhard and Wingate, Lisa and Wullschleger, Stan and Yi, Koong and Zweifel, Roman and Steppe, Kathy and Mencuccini, Maurizio and Martínez-Vilalta, Jordi},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {2607--2649},\n}\n\n\n\n

\n\n\n

\n Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.\n

\n\n\n

\n \n\n \n \n Reiber, L.; Knillmann, S.; Kaske, O.; Atencio, L. C.; Bittner, L.; Albrecht, J. E.; Götz, A.; Fahl, A.; Beckers, L.; Krauss, M.; Henkelmann, B.; Schramm, K.; Inostroza, P. A.; Schinkel, L.; Brauns, M.; Weitere, M.; Brack, W.; and Liess, M.\n\n\n \n \n \n \n \n Long-term effects of a catastrophic insecticide spill on stream invertebrates.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 768: 144456. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reiber_long-term_2021,\n\ttitle = {Long-term effects of a catastrophic insecticide spill on stream invertebrates},\n\tvolume = {768},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720379870},\n\tdoi = {10.1016/j.scitotenv.2020.144456},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Reiber, Lena and Knillmann, Saskia and Kaske, Oliver and Atencio, Liseth C. and Bittner, Lisa and Albrecht, Julia E. and Götz, Astrid and Fahl, Ann-Katrin and Beckers, Liza-Marie and Krauss, Martin and Henkelmann, Bernhard and Schramm, Karl-Werner and Inostroza, Pedro A. and Schinkel, Lena and Brauns, Mario and Weitere, Markus and Brack, Werner and Liess, Matthias},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {144456},\n}\n\n\n\n

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\n \n\n \n \n Rasche, D.; Köhli, M.; Schrön, M.; Blume, T.; and Güntner, A.\n\n\n \n \n \n \n \n Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(12): 6547–6566. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rasche_towards_2021,\n\ttitle = {Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/6547/2021/},\n\tdoi = {10.5194/hess-25-6547-2021},\n\tabstract = {Abstract. Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Rasche, Daniel and Köhli, Markus and Schrön, Martin and Blume, Theresa and Güntner, Andreas},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {6547--6566},\n}\n\n\n\n

\n\n\n

\n Abstract. Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.\n

\n\n\n

\n \n\n \n \n Raoult, N.; Ottlé, C.; Peylin, P.; Bastrikov, V.; and Maugis, P.\n\n\n \n \n \n \n \n Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 22(4): 1025–1043. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{raoult_evaluating_2021,\n\ttitle = {Evaluating and {Optimizing} {Surface} {Soil} {Moisture} {Drydowns} in the {ORCHIDEE} {Land} {Surface} {Model} at {In} {Situ} {Locations}},\n\tvolume = {22},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/22/4/JHM-D-20-0115.1.xml},\n\tdoi = {10.1175/JHM-D-20-0115.1},\n\tabstract = {Abstract \n             \n              The rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) “drydowns,” i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale \n              τ \n              , we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider \n              τ \n              calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare \n              τ \n              from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using \n              τ \n              observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based \n              τ \n              estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, \n              τ \n              -calibration parameters were able to improve drydowns for 73\\% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization.},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Raoult, Nina and Ottlé, Catherine and Peylin, Philippe and Bastrikov, Vladislav and Maugis, Pascal},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {1025--1043},\n}\n\n\n\n

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\n \n\n \n \n Ramsauer, T.; Weiß, T.; Löw, A.; and Marzahn, P.\n\n\n \n \n \n \n \n RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(9): 1712. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"RADOLAN_API:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ramsauer_radolan_api_2021,\n\ttitle = {{RADOLAN}\\_API: {An} {Hourly} {Soil} {Moisture} {Data} {Set} {Based} on {Weather} {Radar}, {Soil} {Properties} and {Reanalysis} {Temperature} {Data}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\tshorttitle = {{RADOLAN}\\_API},\n\turl = {https://www.mdpi.com/2072-4292/13/9/1712},\n\tdoi = {10.3390/rs13091712},\n\tabstract = {Soil moisture is a key variable in the terrestrial water and energy system. This study presents an hourly index that provides soil moisture estimates on a high spatial and temporal resolution (1 km × 1 km). The long established Antecedent Precipitation Index (API) is extended with soil characteristic and temperature dependent loss functions. The Soilgrids and ERA5 data sets are used to provide the controlling variables. Precipitation as main driver is provided by the German weather radar data set RADOLAN. Empiric variables in the equations are fitted in a optimization effort using 23 in-situ soil moisture measurement stations from the Terrestial Environmental Observatories (TERENO) and a separately conducted field campaign. The volumetric soil moisture estimation results show error values of 3.45 Vol\\% mean ubRMSD between RADOLAN\\_API and station data with a high temporal accordance especially of soil moisture upsurge. Further potential of the improved API algorithm is shown with a per-station calibration of applied empirical variables. In addition, the RADOLAN\\_API data set was spatially compared to the ESA CCI soil moisture product where it altogether demonstrates good agreement. The resulting data set is provided as open access data.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Ramsauer, Thomas and Weiß, Thomas and Löw, Alexander and Marzahn, Philip},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {1712},\n}\n\n\n\n

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\n \n\n \n \n Simpson, J. E.; Holman, F.; Nieto, H.; Voelksch, I.; Mauder, M.; Klatt, J.; Fiener, P.; and Kaplan, J. O.\n\n\n \n \n \n \n \n High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(7): 1286. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{simpson_high_2021,\n\ttitle = {High {Spatial} and {Temporal} {Resolution} {Energy} {Flux} {Mapping} of {Different} {Land} {Covers} {Using} an {Off}-the-{Shelf} {Unmanned} {Aerial} {System}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/7/1286},\n\tdoi = {10.3390/rs13071286},\n\tabstract = {With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at {\\textless}0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19\\% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Simpson, Jake E. and Holman, Fenner and Nieto, Hector and Voelksch, Ingo and Mauder, Matthias and Klatt, Janina and Fiener, Peter and Kaplan, Jed O.},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {1286},\n}\n\n\n\n

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\n \n\n \n \n Pisek, J.; Erb, A.; Korhonen, L.; Biermann, T.; Carrara, A.; Cremonese, E.; Cuntz, M.; Fares, S.; Gerosa, G.; Grünwald, T.; Hase, N.; Heliasz, M.; Ibrom, A.; Knohl, A.; Kobler, J.; Kruijt, B.; Lange, H.; Leppänen, L.; Limousin, J.; Serrano, F. R. L.; Loustau, D.; Lukeš, P.; Lundin, L.; Marzuoli, R.; Mölder, M.; Montagnani, L.; Neirynck, J.; Peichl, M.; Rebmann, C.; Rubio, E.; Santos-Reis, M.; Schaaf, C.; Schmidt, M.; Simioni, G.; Soudani, K.; and Vincke, C.\n\n\n \n \n \n \n \n Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 18(2): 621–635. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Retrieval$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pisek_retrieval_2021,\n\ttitle = {Retrieval and validation of forest background reflectivity from daily {Moderate} {Resolution} {Imaging} {Spectroradiometer} ({MODIS}) bidirectional reflectance distribution function ({BRDF}) data across {European} forests},\n\tvolume = {18},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/18/621/2021/},\n\tdoi = {10.5194/bg-18-621-2021},\n\tabstract = {Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory)\nwith remote sensing. Separating under- and overstory signals would enable\nmore accurate modeling of forest carbon and energy fluxes. We retrieved\nvalues of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Biogeosciences},\n\tauthor = {Pisek, Jan and Erb, Angela and Korhonen, Lauri and Biermann, Tobias and Carrara, Arnaud and Cremonese, Edoardo and Cuntz, Matthias and Fares, Silvano and Gerosa, Giacomo and Grünwald, Thomas and Hase, Niklas and Heliasz, Michal and Ibrom, Andreas and Knohl, Alexander and Kobler, Johannes and Kruijt, Bart and Lange, Holger and Leppänen, Leena and Limousin, Jean-Marc and Serrano, Francisco Ramon Lopez and Loustau, Denis and Lukeš, Petr and Lundin, Lars and Marzuoli, Riccardo and Mölder, Meelis and Montagnani, Leonardo and Neirynck, Johan and Peichl, Matthias and Rebmann, Corinna and Rubio, Eva and Santos-Reis, Margarida and Schaaf, Crystal and Schmidt, Marius and Simioni, Guillaume and Soudani, Kamel and Vincke, Caroline},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {621--635},\n}\n\n\n\n

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\n \n\n \n \n Petersen, K.; Kraus, D.; Calanca, P.; Semenov, M. A.; Butterbach-Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity.\n \n \n \n \n\n\n \n\n\n\n European Journal of Agronomy, 128: 126306. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{petersen_dynamic_2021,\n\ttitle = {Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity},\n\tvolume = {128},\n\tissn = {11610301},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1161030121000782},\n\tdoi = {10.1016/j.eja.2021.126306},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {European Journal of Agronomy},\n\tauthor = {Petersen, Krischan and Kraus, David and Calanca, Pierluigi and Semenov, Mikhail A. and Butterbach-Bahl, Klaus and Kiese, Ralf},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {126306},\n}\n\n\n\n

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\n \n\n \n \n Patil, A.; Fersch, B.; Hendricks Franssen, H.; and Kunstmann, H.\n\n\n \n \n \n \n \n Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 3: 729592. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Assimilation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{patil_assimilation_2021,\n\ttitle = {Assimilation of {Cosmogenic} {Neutron} {Counts} for {Improved} {Soil} {Moisture} {Prediction} in a {Distributed} {Land} {Surface} {Model}},\n\tvolume = {3},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2021.729592/full},\n\tdoi = {10.3389/frwa.2021.729592},\n\tabstract = {Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km \n              2 \n              pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62\\% for ST and 55–66\\% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm \n              3 \n              cm \n              −3 \n              to –0.012 cm \n              3 \n              cm \n              −3 \n              ) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.},\n\turldate = {2022-10-26},\n\tjournal = {Frontiers in Water},\n\tauthor = {Patil, Amol and Fersch, Benjamin and Hendricks Franssen, Harrie-Jan and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {729592},\n}\n\n\n\n

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\n \n\n \n \n Skoulikidis, N. T.; Nikolaidis, N. P.; Panagopoulos, A.; Fischer-Kowalski, M.; Zogaris, S.; Petridis, P.; Pisinaras, V.; Efstathiou, D.; Petanidou, T.; Maneas, G.; Mihalopoulos, N.; and Mimikou, M.\n\n\n \n \n \n \n \n The LTER-Greece Environmental Observatory Network: Design and Initial Achievements.\n \n \n \n \n\n\n \n\n\n\n Water, 13(21): 2971. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{skoulikidis_lter-greece_2021,\n\ttitle = {The {LTER}-{Greece} {Environmental} {Observatory} {Network}: {Design} and {Initial} {Achievements}},\n\tvolume = {13},\n\tissn = {2073-4441},\n\tshorttitle = {The {LTER}-{Greece} {Environmental} {Observatory} {Network}},\n\turl = {https://www.mdpi.com/2073-4441/13/21/2971},\n\tdoi = {10.3390/w13212971},\n\tabstract = {Five years after its establishment (2016), the LTER-Greece network outlines its vision, aims, objectives and its achievements through a series of case studies. The network consists of eight observatories, focusing on innovative research topics, aiming to be both cooperative and complementary, while currently being in the process of expanding. LTER-Greece acknowledges the complexity of ecosystems and the fact that effective management of natural resources may only be achieved by addressing every sector of a nexus system in order to understand inter-dependencies, thus accounting for solutions that promote resilience. Hence, LTER-Greece focuses on the holistic study of the water-environment-ecosystem-food-energy-society nexus, in order to face environmental and socio-ecological challenges at local and global scales, particularly climate change, biodiversity loss, pollution, natural disasters and unsustainable water and land management. Framed around five research pillars, monitoring and research targets nine research hypotheses related to climate change, environmental management, socio-ecology and economics, biodiversity and environmental process dynamics. As environmental monitoring and related research and conservation in Greece face critical shortcomings, LTER-Greece envisages confronting these gaps and contributing with interdisciplinary solutions to the current and upcoming complex environmental challenges.},\n\tlanguage = {en},\n\tnumber = {21},\n\turldate = {2022-10-26},\n\tjournal = {Water},\n\tauthor = {Skoulikidis, Nikolaos Theodor and Nikolaidis, Nikolaos Pavlos and Panagopoulos, Andreas and Fischer-Kowalski, Marina and Zogaris, Stamatis and Petridis, Panos and Pisinaras, Vassilis and Efstathiou, Dionissis and Petanidou, Theodora and Maneas, Giorgos and Mihalopoulos, Nikolaos and Mimikou, Maria},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {2971},\n}\n\n\n\n

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\n \n\n \n \n Öttl, L. K.; Wilken, F.; Auerswald, K.; Sommer, M.; Wehrhan, M.; and Fiener, P.\n\n\n \n \n \n \n \n Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape.\n \n \n \n \n\n\n \n\n\n\n Land Degradation & Development, 32(10): 3077–3091. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Tillage$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ottl_tillage_2021,\n\ttitle = {Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape},\n\tvolume = {32},\n\tissn = {1085-3278, 1099-145X},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/ldr.3968},\n\tdoi = {10.1002/ldr.3968},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-10-26},\n\tjournal = {Land Degradation \\& Development},\n\tauthor = {Öttl, Lena Katharina and Wilken, Florian and Auerswald, Karl and Sommer, Michael and Wehrhan, Marc and Fiener, Peter},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {3077--3091},\n}\n\n\n\n

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\n \n\n \n \n Nwosu, E. C.; Roeser, P.; Yang, S.; Ganzert, L.; Dellwig, O.; Pinkerneil, S.; Brauer, A.; Dittmann, E.; Wagner, D.; and Liebner, S.\n\n\n \n \n \n \n \n From Water into Sediment—Tracing Freshwater Cyanobacteria via DNA Analyses.\n \n \n \n \n\n\n \n\n\n\n Microorganisms, 9(8): 1778. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nwosu_water_2021,\n\ttitle = {From {Water} into {Sediment}—{Tracing} {Freshwater} {Cyanobacteria} via {DNA} {Analyses}},\n\tvolume = {9},\n\tissn = {2076-2607},\n\turl = {https://www.mdpi.com/2076-2607/9/8/1778},\n\tdoi = {10.3390/microorganisms9081778},\n\tabstract = {Sedimentary ancient DNA-based studies have been used to probe centuries of climate and environmental changes and how they affected cyanobacterial assemblages in temperate lakes. Due to cyanobacteria containing potential bloom-forming and toxin-producing taxa, their approximate reconstruction from sediments is crucial, especially in lakes lacking long-term monitoring data. To extend the resolution of sediment record interpretation, we used high-throughput sequencing, amplicon sequence variant (ASV) analysis, and quantitative PCR to compare pelagic cyanobacterial composition to that in sediment traps (collected monthly) and surface sediments in Lake Tiefer See. Cyanobacterial composition, species richness, and evenness was not significantly different among the pelagic depths, sediment traps and surface sediments (p {\\textgreater} 0.05), indicating that the cyanobacteria in the sediments reflected the cyanobacterial assemblage in the water column. However, total cyanobacterial abundances (qPCR) decreased from the metalimnion down the water column. The aggregate-forming (Aphanizomenon) and colony-forming taxa (Snowella) showed pronounced sedimentation. In contrast, Planktothrix was only very poorly represented in sediment traps (meta- and hypolimnion) and surface sediments, despite its highest relative abundance at the thermocline (10 m water depth) during periods of lake stratification (May–October). We conclude that this skewed representation in taxonomic abundances reflects taphonomic processes, which should be considered in future DNA-based paleolimnological investigations.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-10-26},\n\tjournal = {Microorganisms},\n\tauthor = {Nwosu, Ebuka Canisius and Roeser, Patricia and Yang, Sizhong and Ganzert, Lars and Dellwig, Olaf and Pinkerneil, Sylvia and Brauer, Achim and Dittmann, Elke and Wagner, Dirk and Liebner, Susanne},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {1778},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Nwosu, E. C.; Brauer, A.; Kaiser, J.; Horn, F.; Wagner, D.; and Liebner, S.\n\n\n \n \n \n \n \n Evaluating sedimentary DNA for tracing changes in cyanobacteria dynamics from sediments spanning the last 350 years of Lake Tiefer See, NE Germany.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 66(3): 279–296. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nwosu_evaluating_2021,\n\ttitle = {Evaluating sedimentary {DNA} for tracing changes in cyanobacteria dynamics from sediments spanning the last 350 years of {Lake} {Tiefer} {See}, {NE} {Germany}},\n\tvolume = {66},\n\tissn = {0921-2728, 1573-0417},\n\turl = {https://link.springer.com/10.1007/s10933-021-00206-9},\n\tdoi = {10.1007/s10933-021-00206-9},\n\tabstract = {Abstract \n             \n              Since the beginning of the Anthropocene, lacustrine biodiversity has been influenced by climate change and human activities. These factors advance the spread of harmful cyanobacteria in lakes around the world, which affects water quality and impairs the aquatic food chain. In this study, we assessed changes in cyanobacterial community dynamics via sedimentary DNA (sedaDNA) from well-dated lake sediments of Lake Tiefer See, which is part of the Klocksin Lake Chain spanning the last 350 years. Our diversity and community analysis revealed that cyanobacterial communities form clusters according to the presence or absence of varves. Based on distance-based redundancy and variation partitioning analyses (dbRDA and VPA) we identified that intensified lake circulation inferred from vegetation openness reconstructions, δ \n              13 \n              C data (a proxy for varve preservation) and total nitrogen content were abiotic factors that significantly explained the variation in the reconstructed cyanobacterial community from Lake Tiefer See sediments. Operational taxonomic units (OTUs) assigned to \n              Microcystis \n              sp. and \n              Aphanizomenon \n              sp. were identified as potential eutrophication-driven taxa of growing importance since circa common era (ca. CE) 1920 till present. This result is corroborated by a cyanobacteria lipid biomarker analysis. Furthermore, we suggest that stronger lake circulation as indicated by non-varved sediments favoured the deposition of the non-photosynthetic cyanobacteria sister clade Sericytochromatia, whereas lake bottom anoxia as indicated by subrecent- and recent varves favoured the Melainabacteria in sediments. Our findings highlight the potential of high-resolution amplicon sequencing in investigating the dynamics of past cyanobacterial communities in lake sediments and show that lake circulation, anoxic conditions, and human-induced eutrophication are main factors explaining variations in the cyanobacteria community in Lake Tiefer See during the last 350 years.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Nwosu, Ebuka C. and Brauer, Achim and Kaiser, Jérôme and Horn, Fabian and Wagner, Dirk and Liebner, Susanne},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {279--296},\n}\n\n\n\n

\n\n\n

\n Abstract Since the beginning of the Anthropocene, lacustrine biodiversity has been influenced by climate change and human activities. These factors advance the spread of harmful cyanobacteria in lakes around the world, which affects water quality and impairs the aquatic food chain. In this study, we assessed changes in cyanobacterial community dynamics via sedimentary DNA (sedaDNA) from well-dated lake sediments of Lake Tiefer See, which is part of the Klocksin Lake Chain spanning the last 350 years. Our diversity and community analysis revealed that cyanobacterial communities form clusters according to the presence or absence of varves. Based on distance-based redundancy and variation partitioning analyses (dbRDA and VPA) we identified that intensified lake circulation inferred from vegetation openness reconstructions, δ 13 C data (a proxy for varve preservation) and total nitrogen content were abiotic factors that significantly explained the variation in the reconstructed cyanobacterial community from Lake Tiefer See sediments. Operational taxonomic units (OTUs) assigned to Microcystis sp. and Aphanizomenon sp. were identified as potential eutrophication-driven taxa of growing importance since circa common era (ca. CE) 1920 till present. This result is corroborated by a cyanobacteria lipid biomarker analysis. Furthermore, we suggest that stronger lake circulation as indicated by non-varved sediments favoured the deposition of the non-photosynthetic cyanobacteria sister clade Sericytochromatia, whereas lake bottom anoxia as indicated by subrecent- and recent varves favoured the Melainabacteria in sediments. Our findings highlight the potential of high-resolution amplicon sequencing in investigating the dynamics of past cyanobacterial communities in lake sediments and show that lake circulation, anoxic conditions, and human-induced eutrophication are main factors explaining variations in the cyanobacteria community in Lake Tiefer See during the last 350 years.\n

\n\n\n

\n \n\n \n \n Sungmin, O.; and Orth, R.\n\n\n \n \n \n \n \n Global soil moisture data derived through machine learning trained with in-situ measurements.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 8(1): 170. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sungmin_global_2021,\n\ttitle = {Global soil moisture data derived through machine learning trained with in-situ measurements},\n\tvolume = {8},\n\tissn = {2052-4463},\n\turl = {http://www.nature.com/articles/s41597-021-00964-1},\n\tdoi = {10.1038/s41597-021-00964-1},\n\tabstract = {Abstract \n             \n              While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with \n              in-situ \n              measurements, \n              SoMo.ml \n              . We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on \n              in-situ \n              data collected from more than 1,000 stations across the globe. \n              SoMo.ml \n              provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. \n              SoMo.ml \n              performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. \n              SoMo.ml \n              complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Scientific Data},\n\tauthor = {Sungmin, O. and Orth, Rene},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {170},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Nogueira, G. E. H.; Schmidt, C.; Brunner, P.; Graeber, D.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Transit‐Time and Temperature Control the Spatial Patterns of Aerobic Respiration and Denitrification in the Riparian Zone.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(12). December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Transit‐Time$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nogueira_transittime_2021,\n\ttitle = {Transit‐{Time} and {Temperature} {Control} the {Spatial} {Patterns} of {Aerobic} {Respiration} and {Denitrification} in the {Riparian} {Zone}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR030117},\n\tdoi = {10.1029/2021WR030117},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Nogueira, G. E. H. and Schmidt, C. and Brunner, P. and Graeber, D. and Fleckenstein, J. H.},\n\tmonth = dec,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Švara, V.; Krauss, M.; Michalski, S. G.; Altenburger, R.; Brack, W.; and Luckenbach, T.\n\n\n \n \n \n \n \n Chemical Pollution Levels in a River Explain Site-Specific Sensitivities to Micropollutants within a Genetically Homogeneous Population of Freshwater Amphipods.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 55(9): 6087–6096. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Chemical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{svara_chemical_2021,\n\ttitle = {Chemical {Pollution} {Levels} in a {River} {Explain} {Site}-{Specific} {Sensitivities} to {Micropollutants} within a {Genetically} {Homogeneous} {Population} of {Freshwater} {Amphipods}},\n\tvolume = {55},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.0c07839},\n\tdoi = {10.1021/acs.est.0c07839},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Švara, Vid and Krauss, Martin and Michalski, Stefan G. and Altenburger, Rolf and Brack, Werner and Luckenbach, Till},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {6087--6096},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Nogueira, G. E. H.; Schmidt, C.; Trauth, N.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Seasonal and short‐term controls of riparian oxygen dynamics and the implications for redox processes.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 35(2). February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nogueira_seasonal_2021,\n\ttitle = {Seasonal and short‐term controls of riparian oxygen dynamics and the implications for redox processes},\n\tvolume = {35},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14055},\n\tdoi = {10.1002/hyp.14055},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Hydrological Processes},\n\tauthor = {Nogueira, Guilherme E. H. and Schmidt, Christian and Trauth, Nico and Fleckenstein, Jan H.},\n\tmonth = feb,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Thompson, A.; Frenzel, M.; Schweiger, O.; Musche, M.; Groth, T.; Roberts, S. P.; Kuhlmann, M.; and Knight, T. M.\n\n\n \n \n \n \n \n Pollinator sampling methods influence community patterns assessments by capturing species with different traits and at different abundances.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 132: 108284. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Pollinator$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{thompson_pollinator_2021,\n\ttitle = {Pollinator sampling methods influence community patterns assessments by capturing species with different traits and at different abundances},\n\tvolume = {132},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X21009493},\n\tdoi = {10.1016/j.ecolind.2021.108284},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Ecological Indicators},\n\tauthor = {Thompson, Amibeth and Frenzel, Mark and Schweiger, Oliver and Musche, Martin and Groth, Till and Roberts, Stuart P.M. and Kuhlmann, Michael and Knight, Tiffany M.},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {108284},\n}\n\n\n\n

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\n \n\n \n \n Neuwirth, B.; Rabbel, I.; Bendix, J.; Bogena, H. R.; and Thies, B.\n\n\n \n \n \n \n \n The European Heat Wave 2018: The Dendroecological Response of Oak and Spruce in Western Germany.\n \n \n \n \n\n\n \n\n\n\n Forests, 12(3): 283. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{neuwirth_european_2021,\n\ttitle = {The {European} {Heat} {Wave} 2018: {The} {Dendroecological} {Response} of {Oak} and {Spruce} in {Western} {Germany}},\n\tvolume = {12},\n\tissn = {1999-4907},\n\tshorttitle = {The {European} {Heat} {Wave} 2018},\n\turl = {https://www.mdpi.com/1999-4907/12/3/283},\n\tdoi = {10.3390/f12030283},\n\tabstract = {The European heat wave of 2018 was characterized by extraordinarily dry and hot spring and summer conditions in many central and northern European countries. The average temperatures from June to August 2018 were the second highest since 1881. Accordingly, many plants, especially trees, were pushed to their physiological limits. However, while the drought and heat response of field crops and younger trees have been well investigated in laboratory experiments, little is known regarding the drought and heat response of mature forest trees. In this study, we compared the response of a coniferous and a deciduous tree species, located in western and central–western Germany, to the extreme environmental conditions during the European heat wave of 2018. Combining classic dendroecological techniques (tree–ring analysis) with measurements of the intra–annual stem expansion (dendrometers) and tree water uptake (sap flow sensors), we found contrasting responses of spruce and oak trees. While spruce trees developed a narrow tree ring in 2018 combined with decreasing correlations of daily sap flow and dendrometer parameters to the climatic parameters, oak trees developed a ring with above–average tree–ring width combined with increasing correlations between the daily climatic parameters and the parameters derived from sap flow and the dendrometer sensors. In conclusion, spruce trees reacted to the 2018 heat wave with the early completion of their growth activities, whereas oaks appeared to intensify their activities based on the water content in their tree stems.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Forests},\n\tauthor = {Neuwirth, Burkhard and Rabbel, Inken and Bendix, Jörg and Bogena, Heye R. and Thies, Boris},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {283},\n}\n\n\n\n

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\n \n\n \n \n Nantke, C. K.; Brauer, A.; Frings, P. J.; Czymzik, M.; Hübener, T.; Stadmark, J.; Dellwig, O.; Roeser, P.; and Conley, D. J.\n\n\n \n \n \n \n \n Human influence on the continental Si budget during the last 4300 years: δ30Sidiatom in varved lake sediments (Tiefer See, NE Germany).\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 258: 106869. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Human$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nantke_human_2021,\n\ttitle = {Human influence on the continental {Si} budget during the last 4300 years: δ{30Sidiatom} in varved lake sediments ({Tiefer} {See}, {NE} {Germany})},\n\tvolume = {258},\n\tissn = {02773791},\n\tshorttitle = {Human influence on the continental {Si} budget during the last 4300 years},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379121000767},\n\tdoi = {10.1016/j.quascirev.2021.106869},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Nantke, Carla K.M. and Brauer, Achim and Frings, Patrick J. and Czymzik, Markus and Hübener, Thomas and Stadmark, Johanna and Dellwig, Olaf and Roeser, Patricia and Conley, Daniel J.},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {106869},\n}\n\n\n\n

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\n \n\n \n \n Musolff, A.; Zhan, Q.; Dupas, R.; Minaudo, C.; Fleckenstein, J. H.; Rode, M.; Dehaspe, J.; and Rinke, K.\n\n\n \n \n \n \n \n Spatial and Temporal Variability in Concentration‐Discharge Relationships at the Event Scale.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(10). October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{musolff_spatial_2021,\n\ttitle = {Spatial and {Temporal} {Variability} in {Concentration}‐{Discharge} {Relationships} at the {Event} {Scale}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR029442},\n\tdoi = {10.1029/2020WR029442},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Musolff, A. and Zhan, Q. and Dupas, R. and Minaudo, C. and Fleckenstein, J. H. and Rode, M. and Dehaspe, J. and Rinke, K.},\n\tmonth = oct,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Müller, C.; Hennig, J.; Riedel, F.; and Helle, G.\n\n\n \n \n \n \n \n Quantifying the impact of chemicals on stable carbon and oxygen isotope values of raw pollen.\n \n \n \n \n\n\n \n\n\n\n Journal of Quaternary Science, 36(3): 441–449. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{muller_quantifying_2021,\n\ttitle = {Quantifying the impact of chemicals on stable carbon and oxygen isotope values of raw pollen},\n\tvolume = {36},\n\tissn = {0267-8179, 1099-1417},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jqs.3300},\n\tdoi = {10.1002/jqs.3300},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Quaternary Science},\n\tauthor = {Müller, Carolina and Hennig, Julian and Riedel, Frank and Helle, Gerhard},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {441--449},\n}\n\n\n\n

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\n \n\n \n \n Mueller, L.; Eulenstein, F.; Schindler, U.; Mirschel, W.; Behrendt, U.; Sychev, V. G.; Rukhovich, O. V.; Belichenko, M. V.; Sheudzhen, A. K.; Romanenkov, V. A.; Trofimov, I.; Lukin, S. M.; McKenzie, B. M.; Salnjikov, E.; Gutorova, O.; Onishenko, L.; Saparov, A.; Pachikin, K.; Dannowski, R.; Hennings, V.; Scherber, C.; Römbke, J.; Ivanov, A. I.; and Dronin, N. M.\n\n\n \n \n \n \n \n Exploring Agricultural Landscapes: Recent Progress and Opportunities for Eurasia.\n \n \n \n \n\n\n \n\n\n\n In Mueller, L.; Sychev, V. G.; Dronin, N. M.; and Eulenstein, F., editor(s), Exploring and Optimizing Agricultural Landscapes, pages 55–90. Springer International Publishing, Cham, 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Exploring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{mueller_exploring_2021,\n\taddress = {Cham},\n\ttitle = {Exploring {Agricultural} {Landscapes}: {Recent} {Progress} and {Opportunities} for {Eurasia}},\n\tisbn = {9783030674472 9783030674489},\n\tshorttitle = {Exploring {Agricultural} {Landscapes}},\n\turl = {https://link.springer.com/10.1007/978-3-030-67448-9_2},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tbooktitle = {Exploring and {Optimizing} {Agricultural} {Landscapes}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Mueller, Lothar and Eulenstein, Frank and Schindler, Uwe and Mirschel, Wilfried and Behrendt, Undine and Sychev, Viktor G. and Rukhovich, Olga V. and Belichenko, Maya V. and Sheudzhen, Askhad K. and Romanenkov, Vladimir A. and Trofimov, Ilya and Lukin, Sergey M. and McKenzie, Blair M. and Salnjikov, Elmira and Gutorova, Oksana and Onishenko, Ludmila and Saparov, Abdulla and Pachikin, Konstantin and Dannowski, Ralf and Hennings, Volker and Scherber, Christoph and Römbke, Jörg and Ivanov, Alexey I. and Dronin, Nikolai M.},\n\teditor = {Mueller, Lothar and Sychev, Viktor G. and Dronin, Nikolai M. and Eulenstein, Frank},\n\tyear = {2021},\n\tdoi = {10.1007/978-3-030-67448-9_2},\n\tpages = {55--90},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Bayat, B.; Tewes, A.; Mengen, D.; and Vereecken, H.\n\n\n \n \n \n \n \n Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany).\n \n \n \n \n\n\n \n\n\n\n Frontiers in Forests and Global Change, 4: 667151. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Sentinel-2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_sentinel-2_2021,\n\ttitle = {Sentinel-2 {Analysis} of {Spruce} {Crown} {Transparency} {Levels} and {Their} {Environmental} {Drivers} {After} {Summer} {Drought} in the {Northern} {Eifel} ({Germany})},\n\tvolume = {4},\n\tissn = {2624-893X},\n\turl = {https://www.frontiersin.org/articles/10.3389/ffgc.2021.667151/full},\n\tdoi = {10.3389/ffgc.2021.667151},\n\tabstract = {Droughts in recent years weaken the forest stands in Central Europe, where especially the spruce suffers from an increase in defoliation and mortality. Forest surveys monitor this trend based on sample trees at the local scale, whereas earth observation is able to provide area-wide information. With freely available cloud computing infrastructures such as Google Earth Engine, access to satellite data and high-performance computing resources has become straightforward. In this study, a simple approach for supporting the spruce monitoring by Sentinel-2 satellite data is developed. Based on forest statistics and the spruce NDVI cumulative distribution function of a reference year, a training data set is obtained to classify the satellite data of a target year. This provides insights into the changes in tree crown transparency levels. For the Northern Eifel region, Germany, the evaluation shows an increase in damaged trees from 2018 to 2020, which is in line with the forest inventory of North Rhine-Westphalia. An analysis of tree damages according to precipitation, land surface temperature, elevation, aspect, and slope provides insights into vulnerable spruce habitats of the region and enables to identify locations where the forest management may focus on a transformation from spruce monocultures to mixed forests with higher biodiversity and resilience to further changes in the climate system.},\n\turldate = {2022-10-26},\n\tjournal = {Frontiers in Forests and Global Change},\n\tauthor = {Montzka, Carsten and Bayat, Bagher and Tewes, Andreas and Mengen, David and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {667151},\n}\n\n\n\n

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\n \n\n \n \n Unger, V.; Liebner, S.; Koebsch, F.; Yang, S.; Horn, F.; Sachs, T.; Kallmeyer, J.; Knorr, K.; Rehder, G.; Gottschalk, P.; and Jurasinski, G.\n\n\n \n \n \n \n \n Congruent changes in microbial community dynamics and ecosystem methane fluxes following natural drought in two restored fens.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 160: 108348. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Congruent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{unger_congruent_2021,\n\ttitle = {Congruent changes in microbial community dynamics and ecosystem methane fluxes following natural drought in two restored fens},\n\tvolume = {160},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0038071721002212},\n\tdoi = {10.1016/j.soilbio.2021.108348},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Unger, Viktoria and Liebner, Susanne and Koebsch, Franziska and Yang, Sizhong and Horn, Fabian and Sachs, Torsten and Kallmeyer, Jens and Knorr, Klaus-Holger and Rehder, Gregor and Gottschalk, Pia and Jurasinski, Gerald},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {108348},\n}\n\n\n\n

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\n \n\n \n \n Mobilia, M.; and Longobardi, A.\n\n\n \n \n \n \n \n Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types.\n \n \n \n \n\n\n \n\n\n\n ISPRS International Journal of Geo-Information, 10(3): 192. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Prediction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mobilia_prediction_2021,\n\ttitle = {Prediction of {Potential} and {Actual} {Evapotranspiration} {Fluxes} {Using} {Six} {Meteorological} {Data}-{Based} {Approaches} for a {Range} of {Climate} and {Land} {Cover} {Types}},\n\tvolume = {10},\n\tissn = {2220-9964},\n\turl = {https://www.mdpi.com/2220-9964/10/3/192},\n\tdoi = {10.3390/ijgi10030192},\n\tabstract = {Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200\\% with the consequent loss of model accuracy.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {ISPRS International Journal of Geo-Information},\n\tauthor = {Mobilia, Mirka and Longobardi, Antonia},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {192},\n}\n\n\n\n

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\n \n\n \n \n Li, M.; Wu, P.; Sexton, D. M. H.; and Ma, Z.\n\n\n \n \n \n \n \n Potential shifts in climate zones under a future global warming scenario using soil moisture classification.\n \n \n \n \n\n\n \n\n\n\n Climate Dynamics, 56(7-8): 2071–2092. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_potential_2021,\n\ttitle = {Potential shifts in climate zones under a future global warming scenario using soil moisture classification},\n\tvolume = {56},\n\tissn = {0930-7575, 1432-0894},\n\turl = {https://link.springer.com/10.1007/s00382-020-05576-w},\n\tdoi = {10.1007/s00382-020-05576-w},\n\tabstract = {Abstract \n            Climate zones fundamentally shape the patterns of the terrestrial environment and human habitation. How global warming alters their current distribution is an important question that has yet to be properly addressed. Using root-layer soil moisture as an indicator, this study investigates potential future changes in climate zones with the perturbed parameter ensemble of climate projections by the HadGEM3-GC3.05 model under the CMIP5 RCP8.5 scenario. The total area of global drylands (including arid, semiarid, and subhumid zones) can potentially expand by 10.5\\% (ensemble range is 0.6–19.0\\%) relative to the historical period of 1976–2005 by the end of the 21st century. This global rate of dryland expansion is smaller than the estimate using the ratio between annual precipitation total and potential evapotranspiration (19.2\\%, with an ensemble range of 6.7–33.1\\%). However, regional expansion rates over the mid-high latitudes can be much greater using soil moisture than using atmospheric indicators alone. This result is mainly because of frozen soil thawing and accelerated evapotranspiration with Arctic greening and polar warming, which can be detected in soil moisture but not from atmosphere-only indices. The areal expansion consists of 7.7\\% (–8.3 to 23.6\\%) semiarid zone growth and 9.5\\% (3.1–20.0\\%) subhumid growth at the expense of the 2.3\\% (–10.4 to 7.4\\%) and 12.6\\% (–29.5 to 2.0\\%) contraction of arid and humid zones. Climate risks appear in the peripheries of subtype zones across drylands. Potential alteration of the traditional humid zone, such as those in the mid-high latitudes and the Amazon region, highlights the accompanying vulnerability for local ecosystems.},\n\tlanguage = {en},\n\tnumber = {7-8},\n\turldate = {2022-10-26},\n\tjournal = {Climate Dynamics},\n\tauthor = {Li, Mingxing and Wu, Peili and Sexton, David M. H. and Ma, Zhuguo},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {2071--2092},\n}\n\n\n\n

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\n \n\n \n \n Migliavacca, M.; Musavi, T.; Mahecha, M. D.; Nelson, J. A.; Knauer, J.; Baldocchi, D. D.; Perez-Priego, O.; Christiansen, R.; Peters, J.; Anderson, K.; Bahn, M.; Black, T. A.; Blanken, P. D.; Bonal, D.; Buchmann, N.; Caldararu, S.; Carrara, A.; Carvalhais, N.; Cescatti, A.; Chen, J.; Cleverly, J.; Cremonese, E.; Desai, A. R.; El-Madany, T. S.; Farella, M. M.; Fernández-Martínez, M.; Filippa, G.; Forkel, M.; Galvagno, M.; Gomarasca, U.; Gough, C. M.; Göckede, M.; Ibrom, A.; Ikawa, H.; Janssens, I. A.; Jung, M.; Kattge, J.; Keenan, T. F.; Knohl, A.; Kobayashi, H.; Kraemer, G.; Law, B. E.; Liddell, M. J.; Ma, X.; Mammarella, I.; Martini, D.; Macfarlane, C.; Matteucci, G.; Montagnani, L.; Pabon-Moreno, D. E.; Panigada, C.; Papale, D.; Pendall, E.; Penuelas, J.; Phillips, R. P.; Reich, P. B.; Rossini, M.; Rotenberg, E.; Scott, R. L.; Stahl, C.; Weber, U.; Wohlfahrt, G.; Wolf, S.; Wright, I. J.; Yakir, D.; Zaehle, S.; and Reichstein, M.\n\n\n \n \n \n \n \n The three major axes of terrestrial ecosystem function.\n \n \n \n \n\n\n \n\n\n\n Nature, 598(7881): 468–472. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{migliavacca_three_2021,\n\ttitle = {The three major axes of terrestrial ecosystem function},\n\tvolume = {598},\n\tissn = {0028-0836, 1476-4687},\n\turl = {https://www.nature.com/articles/s41586-021-03939-9},\n\tdoi = {10.1038/s41586-021-03939-9},\n\tabstract = {Abstract \n \n The leaf economics spectrum \n 1,2 \n and the global spectrum of plant forms and functions \n 3 \n revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species \n 2 \n . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities \n 4 \n . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability \n 4,5 \n . Here we derive a set of ecosystem functions \n 6 \n from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8\\%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems \n 7,8 \n .},\n\tlanguage = {en},\n\tnumber = {7881},\n\turldate = {2022-10-26},\n\tjournal = {Nature},\n\tauthor = {Migliavacca, Mirco and Musavi, Talie and Mahecha, Miguel D. and Nelson, Jacob A. and Knauer, Jürgen and Baldocchi, Dennis D. and Perez-Priego, Oscar and Christiansen, Rune and Peters, Jonas and Anderson, Karen and Bahn, Michael and Black, T. Andrew and Blanken, Peter D. and Bonal, Damien and Buchmann, Nina and Caldararu, Silvia and Carrara, Arnaud and Carvalhais, Nuno and Cescatti, Alessandro and Chen, Jiquan and Cleverly, Jamie and Cremonese, Edoardo and Desai, Ankur R. and El-Madany, Tarek S. and Farella, Martha M. and Fernández-Martínez, Marcos and Filippa, Gianluca and Forkel, Matthias and Galvagno, Marta and Gomarasca, Ulisse and Gough, Christopher M. and Göckede, Mathias and Ibrom, Andreas and Ikawa, Hiroki and Janssens, Ivan A. and Jung, Martin and Kattge, Jens and Keenan, Trevor F. and Knohl, Alexander and Kobayashi, Hideki and Kraemer, Guido and Law, Beverly E. and Liddell, Michael J. and Ma, Xuanlong and Mammarella, Ivan and Martini, David and Macfarlane, Craig and Matteucci, Giorgio and Montagnani, Leonardo and Pabon-Moreno, Daniel E. and Panigada, Cinzia and Papale, Dario and Pendall, Elise and Penuelas, Josep and Phillips, Richard P. and Reich, Peter B. and Rossini, Micol and Rotenberg, Eyal and Scott, Russell L. and Stahl, Clement and Weber, Ulrich and Wohlfahrt, Georg and Wolf, Sebastian and Wright, Ian J. and Yakir, Dan and Zaehle, Sönke and Reichstein, Markus},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {468--472},\n}\n\n\n\n

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\n \n\n \n \n van der Schalie, R.; van der Vliet, M.; Rodríguez-Fernández, N.; Dorigo, W. A.; Scanlon, T.; Preimesberger, W.; Madelon, R.; and de Jeu, R. A. M.\n\n\n \n \n \n \n \n L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(13): 2480. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"L-Band$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_schalie_l-band_2021,\n\ttitle = {L-{Band} {Soil} {Moisture} {Retrievals} {Using} {Microwave} {Based} {Temperature} and {Filtering}. {Towards} {Model}-{Independent} {Climate} {Data} {Records}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/13/2480},\n\tdoi = {10.3390/rs13132480},\n\tabstract = {The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days ({\\textasciitilde}15\\% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {van der Schalie, Robin and van der Vliet, Mendy and Rodríguez-Fernández, Nemesio and Dorigo, Wouter A. and Scanlon, Tracy and Preimesberger, Wolfgang and Madelon, Rémi and de Jeu, Richard A. M.},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {2480},\n}\n\n\n\n

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\n \n\n \n \n Mengen, D.; Montzka, C.; Jagdhuber, T.; Fluhrer, A.; Brogi, C.; Baum, S.; Schüttemeyer, D.; Bayat, B.; Bogena, H.; Coccia, A.; Masalias, G.; Trinkel, V.; Jakobi, J.; Jonard, F.; Ma, Y.; Mattia, F.; Palmisano, D.; Rascher, U.; Satalino, G.; Schumacher, M.; Koyama, C.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(4): 825. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mengen_sarsense_2021,\n\ttitle = {The {SARSense} {Campaign}: {Air}- and {Space}-{Borne} {C}- and {L}-{Band} {SAR} for the {Analysis} of {Soil} and {Plant} {Parameters} in {Agriculture}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\tshorttitle = {The {SARSense} {Campaign}},\n\turl = {https://www.mdpi.com/2072-4292/13/4/825},\n\tdoi = {10.3390/rs13040825},\n\tabstract = {With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Mengen, David and Montzka, Carsten and Jagdhuber, Thomas and Fluhrer, Anke and Brogi, Cosimo and Baum, Stephani and Schüttemeyer, Dirk and Bayat, Bagher and Bogena, Heye and Coccia, Alex and Masalias, Gerard and Trinkel, Verena and Jakobi, Jannis and Jonard, François and Ma, Yueling and Mattia, Francesco and Palmisano, Davide and Rascher, Uwe and Satalino, Giuseppe and Schumacher, Maike and Koyama, Christian and Schmidt, Marius and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {825},\n}\n\n\n\n

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\n \n\n \n \n Mauder, M.; Ibrom, A.; Wanner, L.; De Roo, F.; Brugger, P.; Kiese, R.; and Pilegaard, K.\n\n\n \n \n \n \n \n Options to correct local turbulent flux measurements for large-scale fluxes using an approach based on large-eddy simulation.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 14(12): 7835–7850. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Options$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mauder_options_2021,\n\ttitle = {Options to correct local turbulent flux measurements for large-scale fluxes using an approach based on large-eddy simulation},\n\tvolume = {14},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/14/7835/2021/},\n\tdoi = {10.5194/amt-14-7835-2021},\n\tabstract = {Abstract. The eddy-covariance method provides the most direct\nestimates for fluxes between ecosystems and the atmosphere. However,\ndispersive fluxes can occur in the presence of secondary circulations, which\ncan inherently not be captured by such single-tower measurements. In this\nstudy, we present options to correct local flux measurements for such\nlarge-scale transport based on a non-local parametric model that has been\ndeveloped from a set of idealized large-eddy simulations. This method is\ntested for three real-world sites (DK-Sor, DE-Fen, and DE-Gwg), representing\ntypical conditions in the mid-latitudes with different measurement heights,\ndifferent terrain complexities, and different landscape-scale heterogeneities.\nTwo ways to determine the boundary-layer height, which is a necessary input\nvariable for modelling the dispersive fluxes, are applied, which are either based on\noperational radio soundings and local in situ measurements for the flat sites\nor from backscatter-intensity profiles obtained from co-located ceilometers\nfor the two sites in complex terrain. The adjusted total fluxes are\nevaluated by assessing the improvement in energy balance closure and by\ncomparing the resulting latent heat fluxes with evapotranspiration rates\nfrom nearby lysimeters. The results show that not only the accuracy of the\nflux estimates is improved but also the precision, which is indicated by\nRMSE values that are reduced by approximately 50 \\%. Nevertheless, it needs\nto be clear that this method is intended to correct for a bias in\neddy-covariance measurements due to the presence of large-scale dispersive\nfluxes. Other reasons potentially causing a systematic underestimated or\noverestimation, such as low-pass filtering effects and missing storage\nterms, still need to be considered and minimized as much as possible.\nMoreover, additional transport induced by surface heterogeneities is not\nconsidered.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Mauder, Matthias and Ibrom, Andreas and Wanner, Luise and De Roo, Frederik and Brugger, Peter and Kiese, Ralf and Pilegaard, Kim},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {7835--7850},\n}\n\n\n\n

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\n \n\n \n \n Martini, E.; Bauckholt, M.; Kögler, S.; Kreck, M.; Roth, K.; Werban, U.; Wollschläger, U.; and Zacharias, S.\n\n\n \n \n \n \n \n STH-net: A soil monitoring network for process-based hydrological modelling from the pedon to the hillslope scale.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(6): 2529–2539. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"STH-net:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martini_sth-net_2021,\n\ttitle = {{STH}-net: {A} soil monitoring network for process-based hydrological modelling from the pedon to the hillslope scale},\n\tvolume = {13},\n\tissn = {1866-3516},\n\tshorttitle = {\\&lt;i\\&gt;{STH}-net},\n\turl = {https://essd.copernicus.org/articles/13/2529/2021/},\n\tdoi = {10.5194/essd-13-2529-2021},\n\tabstract = {Abstract. The Schäfertal Hillslope site is part of the TERENO Harz/Central German Lowland Observatory, and its soil water dynamics are being\nmonitored intensively as part of an integrated, long-term, multi-scale, and multi-temporal research framework linking hydrological, pedological,\natmospheric, and biodiversity-related research to investigate the influences of climate and land use change on the terrestrial system. Here, a new\nsoil monitoring network, indicated as STH-net, has been recently implemented to provide high-resolution data about the most relevant\nhydrological variables and local soil properties. The monitoring network is spatially optimized, based on previous knowledge from soil mapping and\nsoil moisture monitoring, in order to capture the spatial variability in soil properties and soil water dynamics along a catena across the site as\nwell as in depth. The STH-net comprises eight stations instrumented with time-domain reflectometry (TDR) probes, soil temperature probes,\nand monitoring wells. Furthermore, a weather station provides data about the meteorological variables. A detailed soil characterization exists for\nlocations where the TDR probes are installed. All data have been measured at a 10 min interval since 1 January 2019. The STH-net is intended to\nprovide scientists with data needed for developing and testing modelling approaches in the context of vadose-zone hydrology at spatial scales\nranging from the pedon to the hillslope. The data are available from the EUDAT portal (https://doi.org/10.23728/b2share.82818db7be054f5eb921d386a0bcaa74, Martini et al., 2020).},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Martini, Edoardo and Bauckholt, Matteo and Kögler, Simon and Kreck, Manuel and Roth, Kurt and Werban, Ulrike and Wollschläger, Ute and Zacharias, Steffen},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {2529--2539},\n}\n\n\n\n

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\n \n\n \n \n Wang, N.; Xia, L.; Goodale, C. L.; Butterbach‐Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Climate Change Can Accelerate Depletion of Montane Grassland C Stocks.\n \n \n \n \n\n\n \n\n\n\n Global Biogeochemical Cycles, 35(10). October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_climate_2021,\n\ttitle = {Climate {Change} {Can} {Accelerate} {Depletion} of {Montane} {Grassland} {C} {Stocks}},\n\tvolume = {35},\n\tissn = {0886-6236, 1944-9224},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020GB006792},\n\tdoi = {10.1029/2020GB006792},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-10-26},\n\tjournal = {Global Biogeochemical Cycles},\n\tauthor = {Wang, Na and Xia, Longlong and Goodale, Christine L. and Butterbach‐Bahl, Klaus and Kiese, Ralf},\n\tmonth = oct,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Wang, J.; Bogena, H.; Süß, T.; Graf, A.; Weuthen, A.; and Brüggemann, N.\n\n\n \n \n \n \n \n Investigating the controls on greenhouse gas emission in the riparian zone of a small headwater catchment using an automated monitoring system.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 20(5). September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_investigating_2021,\n\ttitle = {Investigating the controls on greenhouse gas emission in the riparian zone of a small headwater catchment using an automated monitoring system},\n\tvolume = {20},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20149},\n\tdoi = {10.1002/vzj2.20149},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-10-26},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Wang, Jihuan and Bogena, Heye and Süß, Thomas and Graf, Alexander and Weuthen, Ansgar and Brüggemann, Nicolas},\n\tmonth = sep,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Malique, F.; Wangari, E.; Andrade‐Linares, D. R.; Schloter, M.; Wolf, B.; Dannenmann, M.; Schulz, S.; and Butterbach‐Bahl, K.\n\n\n \n \n \n \n \n Effects of slurry acidification on soil N $_{\\textrm{2}}$ O fluxes and denitrification.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 184(6): 696–708. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{malique_effects_2021,\n\ttitle = {Effects of slurry acidification on soil {N} $_{\\textrm{2}}$ {O} fluxes and denitrification},\n\tvolume = {184},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202100095},\n\tdoi = {10.1002/jpln.202100095},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Malique, Francois and Wangari, Elizabeth and Andrade‐Linares, Diana Rocío and Schloter, Michael and Wolf, Benjamin and Dannenmann, Michael and Schulz, Stefanie and Butterbach‐Bahl, Klaus},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {696--708},\n}\n\n\n\n

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\n \n\n \n \n Madelon, R.; Rodriguez-Fernandez, N. J.; Van der Schalie, R.; Kerr, Y.; Albitar, A.; Scanlon, T.; De Jeu, R.; and Dorigo, W.\n\n\n \n \n \n \n \n Towards the Removal of Model Bias from ESA CCI SM by Using an L-Band Scaling Reference.\n \n \n \n \n\n\n \n\n\n\n In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pages 6194–6197, Brussels, Belgium, July 2021. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{madelon_towards_2021,\n\taddress = {Brussels, Belgium},\n\ttitle = {Towards the {Removal} of {Model} {Bias} from {ESA} {CCI} {SM} by {Using} an {L}-{Band} {Scaling} {Reference}},\n\tisbn = {9781665403696},\n\turl = {https://ieeexplore.ieee.org/document/9553024/},\n\tdoi = {10.1109/IGARSS47720.2021.9553024},\n\turldate = {2022-10-26},\n\tbooktitle = {2021 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium} {IGARSS}},\n\tpublisher = {IEEE},\n\tauthor = {Madelon, Remi and Rodriguez-Fernandez, Nemesio J. and Van der Schalie, Robin and Kerr, Y. and Albitar, A. and Scanlon, T. and De Jeu, R. and Dorigo, W.},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {6194--6197},\n}\n\n\n\n

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\n \n\n \n \n Wang, L.; Amelung, W.; and Willbold, S.\n\n\n \n \n \n \n \n 18 O Isotope Labeling Combined with 31 P Nuclear Magnetic Resonance Spectroscopy for Accurate Quantification of Hydrolyzable Phosphorus Species in Environmental Samples.\n \n \n \n \n\n\n \n\n\n\n Analytical Chemistry, 93(4): 2018–2025. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"18$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_18_2021,\n\ttitle = {18 {O} {Isotope} {Labeling} {Combined} with 31 {P} {Nuclear} {Magnetic} {Resonance} {Spectroscopy} for {Accurate} {Quantification} of {Hydrolyzable} {Phosphorus} {Species} in {Environmental} {Samples}},\n\tvolume = {93},\n\tissn = {0003-2700, 1520-6882},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.analchem.0c03379},\n\tdoi = {10.1021/acs.analchem.0c03379},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Analytical Chemistry},\n\tauthor = {Wang, Liming and Amelung, Wulf and Willbold, Sabine},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {2018--2025},\n}\n\n\n\n

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\n \n\n \n \n Löw, J.; Ullmann, T.; and Conrad, C.\n\n\n \n \n \n \n \n The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany).\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(15): 2951. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{low_impact_2021,\n\ttitle = {The {Impact} of {Phenological} {Developments} on {Interferometric} and {Polarimetric} {Crop} {Signatures} {Derived} from {Sentinel}-1: {Examples} from the {DEMMIN} {Study} {Site} ({Germany})},\n\tvolume = {13},\n\tissn = {2072-4292},\n\tshorttitle = {The {Impact} of {Phenological} {Developments} on {Interferometric} and {Polarimetric} {Crop} {Signatures} {Derived} from {Sentinel}-1},\n\turl = {https://www.mdpi.com/2072-4292/13/15/2951},\n\tdoi = {10.3390/rs13152951},\n\tabstract = {This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops.},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Löw, Johannes and Ullmann, Tobias and Conrad, Christopher},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {2951},\n}\n\n\n\n

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\n \n\n \n \n Liess, M.; Liebmann, L.; Vormeier, P.; Weisner, O.; Altenburger, R.; Borchardt, D.; Brack, W.; Chatzinotas, A.; Escher, B.; Foit, K.; Gunold, R.; Henz, S.; Hitzfeld, K. L.; Schmitt-Jansen, M.; Kamjunke, N.; Kaske, O.; Knillmann, S.; Krauss, M.; Küster, E.; Link, M.; Lück, M.; Möder, M.; Müller, A.; Paschke, A.; Schäfer, R. B.; Schneeweiss, A.; Schreiner, V. C.; Schulze, T.; Schüürmann, G.; von Tümpling, W.; Weitere, M.; Wogram, J.; and Reemtsma, T.\n\n\n \n \n \n \n \n Pesticides are the dominant stressors for vulnerable insects in lowland streams.\n \n \n \n \n\n\n \n\n\n\n Water Research, 201: 117262. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Pesticides$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liess_pesticides_2021,\n\ttitle = {Pesticides are the dominant stressors for vulnerable insects in lowland streams},\n\tvolume = {201},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135421004607},\n\tdoi = {10.1016/j.watres.2021.117262},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Water Research},\n\tauthor = {Liess, Matthias and Liebmann, Liana and Vormeier, Philipp and Weisner, Oliver and Altenburger, Rolf and Borchardt, Dietrich and Brack, Werner and Chatzinotas, Antonis and Escher, Beate and Foit, Kaarina and Gunold, Roman and Henz, Sebastian and Hitzfeld, Kristina L. and Schmitt-Jansen, Mechthild and Kamjunke, Norbert and Kaske, Oliver and Knillmann, Saskia and Krauss, Martin and Küster, Eberhard and Link, Moritz and Lück, Maren and Möder, Monika and Müller, Alexandra and Paschke, Albrecht and Schäfer, Ralf B. and Schneeweiss, Anke and Schreiner, Verena C. and Schulze, Tobias and Schüürmann, Gerrit and von Tümpling, Wolf and Weitere, Markus and Wogram, Jörn and Reemtsma, Thorsten},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {117262},\n}\n\n\n\n

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\n \n\n \n \n Li, Z.; Scheffler, D.; Coops, N. C.; Leach, N.; and Sachs, T.\n\n\n \n \n \n \n \n Towards analysis ready data of optical CubeSat images: Demonstrating a hierarchical normalization framework at a wetland site.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 103: 102502. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_towards_2021,\n\ttitle = {Towards analysis ready data of optical {CubeSat} images: {Demonstrating} a hierarchical normalization framework at a wetland site},\n\tvolume = {103},\n\tissn = {15698432},\n\tshorttitle = {Towards analysis ready data of optical {CubeSat} images},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243421002099},\n\tdoi = {10.1016/j.jag.2021.102502},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Li, Zhan and Scheffler, Daniel and Coops, Nicholas C. and Leach, Nicholas and Sachs, Torsten},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {102502},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Leng, P.; Kamjunke, N.; Li, F.; and Koschorreck, M.\n\n\n \n \n \n \n \n Temporal Patterns of Methane Emissions From Two Streams With Different Riparian Connectivity.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 126(8). August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{leng_temporal_2021,\n\ttitle = {Temporal {Patterns} of {Methane} {Emissions} {From} {Two} {Streams} {With} {Different} {Riparian} {Connectivity}},\n\tvolume = {126},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020JG006104},\n\tdoi = {10.1029/2020JG006104},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Leng, Peifang and Kamjunke, Norbert and Li, Fadong and Koschorreck, Matthias},\n\tmonth = aug,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, Y.; Leng, P.; Peng, J.; Marzahn, P.; and Ludwig, R.\n\n\n \n \n \n \n \n Global assessments of two blended microwave soil moisture products CCI and SMOPS with in-situ measurements and reanalysis data.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 94: 102234. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_global_2021,\n\ttitle = {Global assessments of two blended microwave soil moisture products {CCI} and {SMOPS} with in-situ measurements and reanalysis data},\n\tvolume = {94},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243420308771},\n\tdoi = {10.1016/j.jag.2020.102234},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Wang, Yawei and Leng, Pei and Peng, Jian and Marzahn, Philip and Ludwig, Ralf},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {102234},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Weisner, O.; Frische, T.; Liebmann, L.; Reemtsma, T.; Roß-Nickoll, M.; Schäfer, R. B.; Schäffer, A.; Scholz-Starke, B.; Vormeier, P.; Knillmann, S.; and Liess, M.\n\n\n \n \n \n \n \n Risk from pesticide mixtures – The gap between risk assessment and reality.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 796: 149017. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Risk$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weisner_risk_2021,\n\ttitle = {Risk from pesticide mixtures – {The} gap between risk assessment and reality},\n\tvolume = {796},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721040894},\n\tdoi = {10.1016/j.scitotenv.2021.149017},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Weisner, Oliver and Frische, Tobias and Liebmann, Liana and Reemtsma, Thorsten and Roß-Nickoll, Martina and Schäfer, Ralf B. and Schäffer, Andreas and Scholz-Starke, Björn and Vormeier, Philipp and Knillmann, Saskia and Liess, Matthias},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {149017},\n}\n\n\n\n

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\n \n\n \n \n Weitere, M.; Altenburger, R.; Anlanger, C.; Baborowski, M.; Bärlund, I.; Beckers, L.; Borchardt, D.; Brack, W.; Brase, L.; Busch, W.; Chatzinotas, A.; Deutschmann, B.; Eligehausen, J.; Frank, K.; Graeber, D.; Griebler, C.; Hagemann, J.; Herzsprung, P.; Hollert, H.; Inostroza, P. A.; Jäger, C. G.; Kallies, R.; Kamjunke, N.; Karrasch, B.; Kaschuba, S.; Kaus, A.; Klauer, B.; Knöller, K.; Koschorreck, M.; Krauss, M.; Kunz, J. V.; Kurz, M. J.; Liess, M.; Mages, M.; Müller, C.; Muschket, M.; Musolff, A.; Norf, H.; Pöhlein, F.; Reiber, L.; Risse-Buhl, U.; Schramm, K.; Schmitt-Jansen, M.; Schmitz, M.; Strachauer, U.; von Tümpling, W.; Weber, N.; Wild, R.; Wolf, C.; and Brauns, M.\n\n\n \n \n \n \n \n Disentangling multiple chemical and non-chemical stressors in a lotic ecosystem using a longitudinal approach.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 769: 144324. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Disentangling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weitere_disentangling_2021,\n\ttitle = {Disentangling multiple chemical and non-chemical stressors in a lotic ecosystem using a longitudinal approach},\n\tvolume = {769},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720378554},\n\tdoi = {10.1016/j.scitotenv.2020.144324},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Weitere, Markus and Altenburger, Rolf and Anlanger, Christine and Baborowski, Martina and Bärlund, Ilona and Beckers, Liza-Marie and Borchardt, Dietrich and Brack, Werner and Brase, Lisa and Busch, Wibke and Chatzinotas, Antonis and Deutschmann, Björn and Eligehausen, Jens and Frank, Karin and Graeber, Daniel and Griebler, Christian and Hagemann, Jeske and Herzsprung, Peter and Hollert, Henner and Inostroza, Pedro A. and Jäger, Christoph G. and Kallies, René and Kamjunke, Norbert and Karrasch, Bernhard and Kaschuba, Sigrid and Kaus, Andrew and Klauer, Bernd and Knöller, Kay and Koschorreck, Matthias and Krauss, Martin and Kunz, Julia V. and Kurz, Marie J. and Liess, Matthias and Mages, Margarete and Müller, Christin and Muschket, Matthias and Musolff, Andreas and Norf, Helge and Pöhlein, Florian and Reiber, Lena and Risse-Buhl, Ute and Schramm, Karl-Werner and Schmitt-Jansen, Mechthild and Schmitz, Markus and Strachauer, Ulrike and von Tümpling, Wolf and Weber, Nina and Wild, Romy and Wolf, Christine and Brauns, Mario},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {144324},\n}\n\n\n\n

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\n \n\n \n \n Widmoser, P.; and Michel, D.\n\n\n \n \n \n \n \n Partial energy balance closure of eddy covariance evaporation measurements using concurrent lysimeter observations over grassland.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(3): 1151–1163. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Partial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{widmoser_partial_2021,\n\ttitle = {Partial energy balance closure of eddy covariance evaporation measurements using concurrent lysimeter observations over grassland},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/1151/2021/},\n\tdoi = {10.5194/hess-25-1151-2021},\n\tabstract = {Abstract. With respect to ongoing discussions about the causes of energy imbalance and approaches to\nforce energy balance closure, a method has been proposed that allows partial latent heat flux\nclosure (Widmoser and Wohlfahrt, 2018). In the present paper, this method is applied to four\nmeasurement stations over grassland under humid and semiarid climates, where lysimeter\n(LY) and eddy covariance (EC) measurements were taken simultaneously. The results differ significantly from the ones reported in the literature. We distinguish between the resulting\nEC values being weakly and strongly correlated to LY observations as well as\nsystematic and random deviations between the LY and EC values. Overall, an\nexcellent match could be achieved between the LY and EC measurements after applying\nevaporation-linked weights. But there remain large differences between the standard deviations of the\nLY and adjusted EC values. For further studies we recommend data collected at\ntime intervals even below 0.5 h. No correlation could be found between evaporation weights and weather indices. Only for some\ndatasets, a positive correlation between evaporation and the evaporation weight could be\nfound. This effect appears pronounced for cases with high radiation and plant water stress. Without further knowledge of the causes of energy imbalance one might perform full closure using\nequally distributed weights. Full closure, however, is not dealt with in this paper.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Widmoser, Peter and Michel, Dominik},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {1151--1163},\n}\n\n\n\n

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\n \n\n \n \n Wigneron, J.; Li, X.; Frappart, F.; Fan, L.; Al-Yaari, A.; De Lannoy, G.; Liu, X.; Wang, M.; Le Masson, E.; and Moisy, C.\n\n\n \n \n \n \n \n SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 254: 112238. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"SMOS-IC$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wigneron_smos-ic_2021,\n\ttitle = {{SMOS}-{IC} data record of soil moisture and {L}-{VOD}: {Historical} development, applications and perspectives},\n\tvolume = {254},\n\tissn = {00344257},\n\tshorttitle = {{SMOS}-{IC} data record of soil moisture and {L}-{VOD}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425720306118},\n\tdoi = {10.1016/j.rse.2020.112238},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Wigneron, Jean-Pierre and Li, Xiaojun and Frappart, Frédéric and Fan, Lei and Al-Yaari, Amen and De Lannoy, Gabrielle and Liu, Xiangzhuo and Wang, Mengjia and Le Masson, Erwan and Moisy, Christophe},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {112238},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Winter, C.; Lutz, S. R.; Musolff, A.; Kumar, R.; Weber, M.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Disentangling the Impact of Catchment Heterogeneity on Nitrate Export Dynamics From Event to Long‐Term Time Scales.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(1). January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Disentangling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{winter_disentangling_2021,\n\ttitle = {Disentangling the {Impact} of {Catchment} {Heterogeneity} on {Nitrate} {Export} {Dynamics} {From} {Event} to {Long}‐{Term} {Time} {Scales}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR027992},\n\tdoi = {10.1029/2020WR027992},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Winter, Carolin and Lutz, Stefanie R. and Musolff, Andreas and Kumar, Rohini and Weber, Michael and Fleckenstein, Jan H.},\n\tmonth = jan,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wohner, C.; Dirnböck, T.; Peterseil, J.; Pröll, G.; and Geiger, S.\n\n\n \n \n \n \n \n Providing high resolution data for the long-term ecosystem research infrastructure on the national and European scale.\n \n \n \n \n\n\n \n\n\n\n In Freitag, U.; Fuchs-Kittowski, F.; Abecker, A.; and Hosenfeld, F., editor(s), Umweltinformationssysteme – Wie verändert die Digitalisierung unsere Gesellschaft?, pages 53–65. Springer Fachmedien Wiesbaden, Wiesbaden, 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Providing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{freitag_providing_2021,\n\taddress = {Wiesbaden},\n\ttitle = {Providing high resolution data for the long-term ecosystem research infrastructure on the national and {European} scale},\n\tisbn = {9783658308889 9783658308896},\n\turl = {http://link.springer.com/10.1007/978-3-658-30889-6_4},\n\tlanguage = {de},\n\turldate = {2022-10-26},\n\tbooktitle = {Umweltinformationssysteme – {Wie} verändert die {Digitalisierung} unsere {Gesellschaft}?},\n\tpublisher = {Springer Fachmedien Wiesbaden},\n\tauthor = {Wohner, Christoph and Dirnböck, Thomas and Peterseil, Johannes and Pröll, Gisela and Geiger, Sarah},\n\teditor = {Freitag, Ulrike and Fuchs-Kittowski, Frank and Abecker, Andreas and Hosenfeld, Friedhelm},\n\tyear = {2021},\n\tdoi = {10.1007/978-3-658-30889-6_4},\n\tpages = {53--65},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wohner, C.; Ohnemus, T.; Zacharias, S.; Mollenhauer, H.; Ellis, E. C.; Klug, H.; Shibata, H.; and Mirtl, M.\n\n\n \n \n \n \n \n Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 127: 107785. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wohner_assessing_2021,\n\ttitle = {Assessing the biogeographical and socio-ecological representativeness of the {ILTER} site network},\n\tvolume = {127},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X21004507},\n\tdoi = {10.1016/j.ecolind.2021.107785},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Ecological Indicators},\n\tauthor = {Wohner, Christoph and Ohnemus, Thomas and Zacharias, Steffen and Mollenhauer, Hannes and Ellis, Erle C. and Klug, Hermann and Shibata, Hideaki and Mirtl, Michael},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {107785},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wu, K.; Ryu, D.; Nie, L.; and Shu, H.\n\n\n \n \n \n \n \n Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 256: 112324. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Time-variant$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_time-variant_2021,\n\ttitle = {Time-variant error characterization of {SMAP} and {ASCAT} soil moisture using {Triple} {Collocation} {Analysis}},\n\tvolume = {256},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721000420},\n\tdoi = {10.1016/j.rse.2021.112324},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Wu, Kai and Ryu, Dongryeol and Nie, Lei and Shu, Hong},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {112324},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Yang, J.; Heidbüchel, I.; Musolff, A.; Xie, Y.; Lu, C.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Using nitrate as a tracer to constrain age selection preferences in catchments with strong seasonality.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 603: 126889. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_using_2021,\n\ttitle = {Using nitrate as a tracer to constrain age selection preferences in catchments with strong seasonality},\n\tvolume = {603},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421009392},\n\tdoi = {10.1016/j.jhydrol.2021.126889},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Yang, Jie and Heidbüchel, Ingo and Musolff, Andreas and Xie, Yueqing and Lu, Chunhui and Fleckenstein, Jan H.},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {126889},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Yang, X.; Tetzlaff, D.; Soulsby, C.; Smith, A.; and Borchardt, D.\n\n\n \n \n \n \n \n Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(3). March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Catchment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_catchment_2021,\n\ttitle = {Catchment {Functioning} {Under} {Prolonged} {Drought} {Stress}: {Tracer}‐{Aided} {Ecohydrological} {Modeling} in an {Intensively} {Managed} {Agricultural} {Catchment}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Catchment {Functioning} {Under} {Prolonged} {Drought} {Stress}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR029094},\n\tdoi = {10.1029/2020WR029094},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Yang, Xiaoqiang and Tetzlaff, Doerthe and Soulsby, Chris and Smith, Aaron and Borchardt, Dietrich},\n\tmonth = mar,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Yu, Y.; Weihermüller, L.; Klotzsche, A.; Lärm, L.; Vereecken, H.; and Huisman, J. A.\n\n\n \n \n \n \n \n Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 596: 126010. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Sequential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yu_sequential_2021,\n\ttitle = {Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale},\n\tvolume = {596},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421000573},\n\tdoi = {10.1016/j.jhydrol.2021.126010},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Yu, Yi and Weihermüller, Lutz and Klotzsche, Anja and Lärm, Lena and Vereecken, Harry and Huisman, Johan Alexander},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {126010},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Yuan, K.; Zhu, Q.; Zheng, S.; Zhao, L.; Chen, M.; Riley, W. J; Cai, X.; Ma, H.; Li, F.; Wu, H.; and Chen, L.\n\n\n \n \n \n \n \n Deforestation reshapes land-surface energy-flux partitioning.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 16(2): 024014. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Deforestation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yuan_deforestation_2021,\n\ttitle = {Deforestation reshapes land-surface energy-flux partitioning},\n\tvolume = {16},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/abd8f9},\n\tdoi = {10.1088/1748-9326/abd8f9},\n\tabstract = {Abstract \n            Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Yuan, Kunxiaojia and Zhu, Qing and Zheng, Shiyu and Zhao, Lei and Chen, Min and Riley, William J and Cai, Xitian and Ma, Hongxu and Li, Fa and Wu, Huayi and Chen, Liang},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {024014},\n}\n\n\n\n

\n\n\n

\n Abstract Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.\n

\n\n\n

\n \n\n \n \n Zaman, M.; Kleineidam, K.; Bakken, L.; Berendt, J.; Bracken, C.; Butterbach-Bahl, K.; Cai, Z.; Chang, S. X.; Clough, T.; Dawar, K.; Ding, W. X.; Dörsch, P.; dos Reis Martins, M.; Eckhardt, C.; Fiedler, S.; Frosch, T.; Goopy, J.; Görres, C.; Gupta, A.; Henjes, S.; Hofmann, M. E. G.; Horn, M. A.; Jahangir, M. M. R.; Jansen-Willems, A.; Lenhart, K.; Heng, L.; Lewicka-Szczebak, D.; Lucic, G.; Merbold, L.; Mohn, J.; Molstad, L.; Moser, G.; Murphy, P.; Sanz-Cobena, A.; Šimek, M.; Urquiaga, S.; Well, R.; Wrage-Mönnig, N.; Zaman, S.; Zhang, J.; and Müller, C.\n\n\n \n \n \n \n \n Automated Laboratory and Field Techniques to Determine Greenhouse Gas Emissions.\n \n \n \n \n\n\n \n\n\n\n In Zaman, M.; Heng, L.; and Müller, C., editor(s), Measuring Emission of Agricultural Greenhouse Gases and Developing Mitigation Options using Nuclear and Related Techniques, pages 109–139. Springer International Publishing, Cham, 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Automated$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{zaman_automated_2021,\n\taddress = {Cham},\n\ttitle = {Automated {Laboratory} and {Field} {Techniques} to {Determine} {Greenhouse} {Gas} {Emissions}},\n\tisbn = {9783030553951 9783030553968},\n\turl = {http://link.springer.com/10.1007/978-3-030-55396-8_3},\n\tabstract = {Abstract \n             \n              Methods and techniques are described for automated measurements of greenhouse gases (GHGs) in both the laboratory and the field. Robotic systems are currently available to measure the entire range of gases evolved from soils including dinitrogen (N \n              2 \n              ). These systems usually work on an exchange of the atmospheric N \n              2 \n              with helium (He) so that N \n              2 \n              fluxes can be determined. Laboratory systems are often used in microbiology to determine kinetic response reactions via the dynamics of all gaseous N species such as nitric oxide (NO), nitrous oxide (N \n              2 \n              O), and N \n              2 \n              . Latest He incubation techniques also take plants into account, in order to study the effect of plant–soil interactions on GHGsand N \n              2 \n              production. The advantage of automated in-field techniques is that GHG emission rates can be determined at a high temporal resolution. This allows, for instance, to determine diurnal response reactions (e.g. with temperature) and GHG dynamics over longer time periods.},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tbooktitle = {Measuring {Emission} of {Agricultural} {Greenhouse} {Gases} and {Developing} {Mitigation} {Options} using {Nuclear} and {Related} {Techniques}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Zaman, M. and Kleineidam, K. and Bakken, L. and Berendt, J. and Bracken, C. and Butterbach-Bahl, K. and Cai, Z. and Chang, S. X. and Clough, T. and Dawar, K. and Ding, W. X. and Dörsch, P. and dos Reis Martins, M. and Eckhardt, C. and Fiedler, S. and Frosch, T. and Goopy, J. and Görres, C.-M. and Gupta, A. and Henjes, S. and Hofmann, M. E. G. and Horn, M. A. and Jahangir, M. M. R. and Jansen-Willems, A. and Lenhart, K. and Heng, L. and Lewicka-Szczebak, D. and Lucic, G. and Merbold, L. and Mohn, J. and Molstad, L. and Moser, G. and Murphy, P. and Sanz-Cobena, A. and Šimek, M. and Urquiaga, S. and Well, R. and Wrage-Mönnig, N. and Zaman, S. and Zhang, J. and Müller, C.},\n\teditor = {Zaman, Mohammad and Heng, Lee and Müller, Christoph},\n\tyear = {2021},\n\tdoi = {10.1007/978-3-030-55396-8_3},\n\tpages = {109--139},\n}\n\n\n\n

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\n \n\n \n \n Zeeman, M.\n\n\n \n \n \n \n \n Use of thermal signal for the investigation of near-surface turbulence.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 14(12): 7475–7493. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zeeman_use_2021,\n\ttitle = {Use of thermal signal for the investigation of near-surface turbulence},\n\tvolume = {14},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/14/7475/2021/},\n\tdoi = {10.5194/amt-14-7475-2021},\n\tabstract = {Abstract. Organised motion of air in the roughness sublayer of the atmosphere was investigated using novel temperature sensing and data science methods. Despite accuracy drawbacks, current fibre-optic distributed temperature sensing (DTS) and thermal imaging (TIR) instruments offer frequent, moderately precise and highly localised observations of thermal signal in a domain geometry suitable for micrometeorological applications near the surface. The goal of this study was to combine DTS and TIR for the investigation of temperature and wind field statistics. Horizontal and vertical cross-sections allowed a tomographic investigation of the spanwise and streamwise evolution of organised motion, opening avenues for analysis without assumptions on scale relationships. Events in the temperature signal on the order of seconds to minutes could be identified, localised, and classified using signal decomposition and machine learning techniques. However, small-scale turbulence patterns at the surface appeared difficult to resolve due to the heterogeneity of the thermal properties of the vegetation canopy, which are not immediately evident visually. The results highlight a need for physics-aware data science techniques that treat scale and shape of temperature structures in combination, rather than as separate features.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Zeeman, Matthias},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {7475--7493},\n}\n\n\n\n

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\n \n\n \n \n Zhang, L.; and Brutsaert, W.\n\n\n \n \n \n \n \n Blending the Evaporation Precipitation Ratio With the Complementary Principle Function for the Prediction of Evaporation.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(7). July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Blending$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_blending_2021,\n\ttitle = {Blending the {Evaporation} {Precipitation} {Ratio} {With} the {Complementary} {Principle} {Function} for the {Prediction} of {Evaporation}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021WR029729},\n\tdoi = {10.1029/2021WR029729},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Zhang, Lu and Brutsaert, Wilfried},\n\tmonth = jul,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; and Su, Z.\n\n\n \n \n \n \n \n In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(23): 4893. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"In$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_situ_2021,\n\ttitle = {In {Situ} {Observation}-{Constrained} {Global} {Surface} {Soil} {Moisture} {Using} {Random} {Forest} {Model}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/23/4893},\n\tdoi = {10.3390/rs13234893},\n\tabstract = {The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Zhang, Lijie and Zeng, Yijian and Zhuang, Ruodan and Szabó, Brigitta and Manfreda, Salvatore and Han, Qianqian and Su, Zhongbo},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {4893},\n}\n\n\n\n

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\n \n\n \n \n Zhang, Q.; Yuan, Q.; Li, J.; Wang, Y.; Sun, F.; and Zhang, L.\n\n\n \n \n \n \n \n Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(3): 1385–1401. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Generating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_generating_2021,\n\ttitle = {Generating seamless global daily {AMSR2} soil moisture ({SGD}-{SM}) long-term products for the years 2013–2019},\n\tvolume = {13},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/13/1385/2021/},\n\tdoi = {10.5194/essd-13-1385-2021},\n\tabstract = {Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 \\%–80 \\% coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Zhang, Qiang and Yuan, Qiangqiang and Li, Jie and Wang, Yuan and Sun, Fujun and Zhang, Liangpei},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {1385--1401},\n}\n\n\n\n

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\n \n\n \n \n Zhang, Z.; Schmidt, C.; Nixdorf, E.; Kuang, X.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Effects of Heterogeneous Stream‐Groundwater Exchange on the Source Composition of Stream Discharge and Solute Load.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(8). August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_effects_2021,\n\ttitle = {Effects of {Heterogeneous} {Stream}‐{Groundwater} {Exchange} on the {Source} {Composition} of {Stream} {Discharge} and {Solute} {Load}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR029079},\n\tdoi = {10.1029/2020WR029079},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Zhang, Zhi‐Yuan and Schmidt, Christian and Nixdorf, Erik and Kuang, Xingxing and Fleckenstein, Jan H.},\n\tmonth = aug,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Zhao, H.; Montzka, C.; Baatz, R.; Vereecken, H.; and Franssen, H. H.\n\n\n \n \n \n \n \n The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(16): 3068. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhao_importance_2021,\n\ttitle = {The {Importance} of {Subsurface} {Processes} in {Land} {Surface} {Modeling} over a {Temperate} {Region}: {An} {Analysis} with {SMAP}, {Cosmic} {Ray} {Neutron} {Sensing} and {Triple} {Collocation} {Analysis}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\tshorttitle = {The {Importance} of {Subsurface} {Processes} in {Land} {Surface} {Modeling} over a {Temperate} {Region}},\n\turl = {https://www.mdpi.com/2072-4292/13/16/3068},\n\tdoi = {10.3390/rs13163068},\n\tabstract = {Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Zhao, Haojin and Montzka, Carsten and Baatz, Roland and Vereecken, Harry and Franssen, Harrie-Jan Hendricks},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {3068},\n}\n\n\n\n

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\n \n\n \n \n Künzel, A.; Münzel, S.; Böttcher, F.; and Spengler, D.\n\n\n \n \n \n \n \n Analysis of Weather-Related Growth Differences in Winter Wheat in a Three-Year Field Trial in North-East Germany.\n \n \n \n \n\n\n \n\n\n\n Agronomy, 11(9): 1854. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunzel_analysis_2021,\n\ttitle = {Analysis of {Weather}-{Related} {Growth} {Differences} in {Winter} {Wheat} in a {Three}-{Year} {Field} {Trial} in {North}-{East} {Germany}},\n\tvolume = {11},\n\tissn = {2073-4395},\n\turl = {https://www.mdpi.com/2073-4395/11/9/1854},\n\tdoi = {10.3390/agronomy11091854},\n\tabstract = {Winter wheat is the most important crop in Germany, which is why a three-year field trial (2015–2017) investigated the effects of weather on biometric parameters in relation to the phenological growth stage of the winter wheat varieties Opal, Kerubino, Edgar. In Brandenburg, there have been frequent extreme weather events in the growth phases that are relevant to grain yields. Two winter wheat varieties were grown per trial year and parts of the experimental field areas were irrigated. In addition, soil physical, biometric and meteorological data were collected during the growing season (March until end of July). There were five dry periods in 2015, six in 2016, and two in 2017 associated with low soil moisture. Notably, in 2016 the plant height was 5 cm lower and the cover was 15\\% lower than on irrigated plots. The grain yield was increased by 19\\% and 31\\% respectively by irrigation. However, due to irrigation costs, the net grain yield on irrigated plots was lower than on the unirrigated plots. It turned out that in dry years there were hardly any differences between winter wheat varieties. Multiple regression analysis showed a strong correlation between the biometric parameters considered here and the grain yield.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Agronomy},\n\tauthor = {Künzel, Alice and Münzel, Sandra and Böttcher, Falk and Spengler, Daniel},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {1854},\n}\n\n\n\n

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\n \n\n \n \n Kramm, T.; and Hoffmeister, D.\n\n\n \n \n \n \n \n Comprehensive vertical accuracy analysis of freely available DEMs for different landscape types of the Rur catchment, Germany.\n \n \n \n \n\n\n \n\n\n\n Geocarto International,1–26. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Comprehensive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kramm_comprehensive_2021,\n\ttitle = {Comprehensive vertical accuracy analysis of freely available {DEMs} for different landscape types of the {Rur} catchment, {Germany}},\n\tissn = {1010-6049, 1752-0762},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/10106049.2021.1984588},\n\tdoi = {10.1080/10106049.2021.1984588},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Geocarto International},\n\tauthor = {Kramm, Tanja and Hoffmeister, Dirk},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {1--26},\n}\n\n\n\n

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\n \n\n \n \n Kong, X.; Seewald, M.; Dadi, T.; Friese, K.; Mi, C.; Boehrer, B.; Schultze, M.; Rinke, K.; and Shatwell, T.\n\n\n \n \n \n \n \n Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling.\n \n \n \n \n\n\n \n\n\n\n Water Research, 190: 116681. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Unravelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kong_unravelling_2021,\n\ttitle = {Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling},\n\tvolume = {190},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135420312161},\n\tdoi = {10.1016/j.watres.2020.116681},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Water Research},\n\tauthor = {Kong, Xiangzhen and Seewald, Michael and Dadi, Tallent and Friese, Kurt and Mi, Chenxi and Boehrer, Bertram and Schultze, Martin and Rinke, Karsten and Shatwell, Tom},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {116681},\n}\n\n\n\n

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\n \n\n \n \n Köhli, M.; Weimar, J.; Schrön, M.; Baatz, R.; and Schmidt, U.\n\n\n \n \n \n \n \n Soil Moisture and Air Humidity Dependence of the Above-Ground Cosmic-Ray Neutron Intensity.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 544847. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kohli_soil_2021,\n\ttitle = {Soil {Moisture} and {Air} {Humidity} {Dependence} of the {Above}-{Ground} {Cosmic}-{Ray} {Neutron} {Intensity}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.544847/full},\n\tdoi = {10.3389/frwa.2020.544847},\n\tabstract = {Investigations of neutron transport through air and soil by Monte Carlo simulations led to major advancements toward a precise interpretation of measurements; they particularly improved the understanding of the cosmic-ray neutron footprint. Up to now, the conversion of soil moisture to a detectable neutron count rate has relied mainly on the equation presented by Desilets and Zreda in 2010. While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterization needs to be revised for two reasons. Firstly, a rigorous mathematical treatment reveals that the values of the four parameters are ambiguous because their values are not independent. We found a three-parameter equation with unambiguous values of the parameters that is equivalent in any other respect to the four-parameter equation. Secondly, high-resolution Monte-Carlo simulations revealed a systematic deviation of the count rate to soil moisture relation especially for extremely dry conditions as well as very humid conditions. That is a hint that a smaller contribution to the intensity was forgotten or not adequately treated by the conventional approach. Investigating the above-ground neutron flux through a broadly based Monte-Carlo simulation campaign revealed a more detailed understanding of different contributions to this signal, especially targeting air humidity corrections. The packages MCNP and URANOS were used to derive a function able to describe the respective dependencies, including the effect of different hydrogen pools and the detector-specific response function. The new relationship has been tested at two exemplary measurement sites, and its remarkable performance allows for a promising prospect of more comprehensive data quality in the future.},\n\turldate = {2022-10-26},\n\tjournal = {Frontiers in Water},\n\tauthor = {Köhli, Markus and Weimar, Jannis and Schrön, Martin and Baatz, Roland and Schmidt, Ulrich},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {544847},\n}\n\n\n\n

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\n \n\n \n \n Knox, S. H.; Bansal, S.; McNicol, G.; Schafer, K.; Sturtevant, C.; Ueyama, M.; Valach, A. C.; Baldocchi, D.; Delwiche, K.; Desai, A. R.; Euskirchen, E.; Liu, J.; Lohila, A.; Malhotra, A.; Melling, L.; Riley, W.; Runkle, B. R. K.; Turner, J.; Vargas, R.; Zhu, Q.; Alto, T.; Fluet‐Chouinard, E.; Goeckede, M.; Melton, J. R.; Sonnentag, O.; Vesala, T.; Ward, E.; Zhang, Z.; Feron, S.; Ouyang, Z.; Alekseychik, P.; Aurela, M.; Bohrer, G.; Campbell, D. I.; Chen, J.; Chu, H.; Dalmagro, H. J.; Goodrich, J. P.; Gottschalk, P.; Hirano, T.; Iwata, H.; Jurasinski, G.; Kang, M.; Koebsch, F.; Mammarella, I.; Nilsson, M. B.; Ono, K.; Peichl, M.; Peltola, O.; Ryu, Y.; Sachs, T.; Sakabe, A.; Sparks, J. P.; Tuittila, E.; Vourlitis, G. L.; Wong, G. X.; Windham‐Myers, L.; Poulter, B.; and Jackson, R. B.\n\n\n \n \n \n \n \n Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 27(15): 3582–3604. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{knox_identifying_2021,\n\ttitle = {Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales},\n\tvolume = {27},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15661},\n\tdoi = {10.1111/gcb.15661},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-10-26},\n\tjournal = {Global Change Biology},\n\tauthor = {Knox, Sara H. and Bansal, Sheel and McNicol, Gavin and Schafer, Karina and Sturtevant, Cove and Ueyama, Masahito and Valach, Alex C. and Baldocchi, Dennis and Delwiche, Kyle and Desai, Ankur R. and Euskirchen, Eugenie and Liu, Jinxun and Lohila, Annalea and Malhotra, Avni and Melling, Lulie and Riley, William and Runkle, Benjamin R. K. and Turner, Jessica and Vargas, Rodrigo and Zhu, Qing and Alto, Tuula and Fluet‐Chouinard, Etienne and Goeckede, Mathias and Melton, Joe R. and Sonnentag, Oliver and Vesala, Timo and Ward, Eric and Zhang, Zhen and Feron, Sarah and Ouyang, Zutao and Alekseychik, Pavel and Aurela, Mika and Bohrer, Gil and Campbell, David I. and Chen, Jiquan and Chu, Housen and Dalmagro, Higo J. and Goodrich, Jordan P. and Gottschalk, Pia and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and Kang, Minseok and Koebsch, Franziska and Mammarella, Ivan and Nilsson, Mats B. and Ono, Keisuke and Peichl, Matthias and Peltola, Olli and Ryu, Youngryel and Sachs, Torsten and Sakabe, Ayaka and Sparks, Jed P. and Tuittila, Eeva‐Stiina and Vourlitis, George L. and Wong, Guan X. and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {3582--3604},\n}\n\n\n\n

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\n \n\n \n \n Klein, M.; Garvelmann, J.; and Förster, K.\n\n\n \n \n \n \n \n Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 12(6): 710. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Revisiting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{klein_revisiting_2021,\n\ttitle = {Revisiting {Forest} {Effects} on {Winter} {Air} {Temperature} and {Wind} {Speed}—{New} {Open} {Data} and {Transfer} {Functions}},\n\tvolume = {12},\n\tissn = {2073-4433},\n\turl = {https://www.mdpi.com/2073-4433/12/6/710},\n\tdoi = {10.3390/atmos12060710},\n\tabstract = {The diurnal cycle of both air temperature and wind speed is characterized by considerable differences, when comparing open site conditions to forests. In the course of this article, a new two-hourly, open-source dataset, covering a high spatial and temporal variability, is presented and analyzed. It contains air temperature measurements (128 station pairs (open/forest); six winter seasons; six study sites), wind speed measurements (64 station pairs; three winter seasons, four study sites) and related metadata in central Europe. Daily cycles of air temperature and wind speed, as well as further dependencies of the effective Leaf Area Index (effective LAI), the exposure in the context of forest effects, and the distance to the forest edge, are illustrated in this paper. The forest effects on air temperature can be seen particularly with increasing canopy density, in southern exposures, and in the late winter season, while wind speed depends on multiple factors such as effective LAI or the distance to the forest edge. New transfer functions, developed using linear and non-linear regression analysis, in a leave-one-out cross-validation, improve certain efficiency criteria (NSME; r2; RMSE; MAE) compared to existing transfer functions. The dataset enables multiple purposes and capabilities due to its diversity and sample size.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-26},\n\tjournal = {Atmosphere},\n\tauthor = {Klein, Michael and Garvelmann, Jakob and Förster, Kristian},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {710},\n}\n\n\n\n

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\n \n\n \n \n Kang, J.; Jin, R.; Li, X.; and Zhang, Y.\n\n\n \n \n \n \n \n Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(2): 228. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kang_mapping_2021,\n\ttitle = {Mapping {High} {Spatiotemporal}-{Resolution} {Soil} {Moisture} by {Upscaling} {Sparse} {Ground}-{Based} {Observations} {Using} a {Bayesian} {Linear} {Regression} {Method} for {Comparison} with {Microwave} {Remotely} {Sensed} {Soil} {Moisture} {Products}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/2/228},\n\tdoi = {10.3390/rs13020228},\n\tabstract = {In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Kang, Jian and Jin, Rui and Li, Xin and Zhang, Yang},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {228},\n}\n\n\n\n

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\n \n\n \n \n Johnston, A. S. A.; Meade, A.; Ardö, J.; Arriga, N.; Black, A.; Blanken, P. D.; Bonal, D.; Brümmer, C.; Cescatti, A.; Dušek, J.; Graf, A.; Gioli, B.; Goded, I.; Gough, C. M.; Ikawa, H.; Jassal, R.; Kobayashi, H.; Magliulo, V.; Manca, G.; Montagnani, L.; Moyano, F. E.; Olesen, J. E.; Sachs, T.; Shao, C.; Tagesson, T.; Wohlfahrt, G.; Wolf, S.; Woodgate, W.; Varlagin, A.; and Venditti, C.\n\n\n \n \n \n \n \n Temperature thresholds of ecosystem respiration at a global scale.\n \n \n \n \n\n\n \n\n\n\n Nature Ecology & Evolution, 5(4): 487–494. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Temperature$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{johnston_temperature_2021,\n\ttitle = {Temperature thresholds of ecosystem respiration at a global scale},\n\tvolume = {5},\n\tissn = {2397-334X},\n\turl = {http://www.nature.com/articles/s41559-021-01398-z},\n\tdoi = {10.1038/s41559-021-01398-z},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Nature Ecology \\& Evolution},\n\tauthor = {Johnston, Alice S. A. and Meade, Andrew and Ardö, Jonas and Arriga, Nicola and Black, Andy and Blanken, Peter D. and Bonal, Damien and Brümmer, Christian and Cescatti, Alessandro and Dušek, Jiří and Graf, Alexander and Gioli, Beniamino and Goded, Ignacio and Gough, Christopher M. and Ikawa, Hiroki and Jassal, Rachhpal and Kobayashi, Hideki and Magliulo, Vincenzo and Manca, Giovanni and Montagnani, Leonardo and Moyano, Fernando E. and Olesen, Jørgen E. and Sachs, Torsten and Shao, Changliang and Tagesson, Torbern and Wohlfahrt, Georg and Wolf, Sebastian and Woodgate, William and Varlagin, Andrej and Venditti, Chris},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {487--494},\n}\n\n\n\n

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\n \n\n \n \n Jakobi, J.; Huisman, J. A.; and Bogena, H. R.\n\n\n \n \n \n \n \n Comment on Dong and Ochsner (2018): “Soil Texture Often Exerts Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns”.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(1). January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Comment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jakobi_comment_2021,\n\ttitle = {Comment on {Dong} and {Ochsner} (2018): “{Soil} {Texture} {Often} {Exerts} {Stronger} {Influence} {Than} {Precipitation} on {Mesoscale} {Soil} {Moisture} {Patterns}”},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Comment on {Dong} and {Ochsner} (2018)},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR027790},\n\tdoi = {10.1029/2020WR027790},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Jakobi, J. and Huisman, J. A. and Bogena, H. R.},\n\tmonth = jan,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Irvin, J.; Zhou, S.; McNicol, G.; Lu, F.; Liu, V.; Fluet-Chouinard, E.; Ouyang, Z.; Knox, S. H.; Lucas-Moffat, A.; Trotta, C.; Papale, D.; Vitale, D.; Mammarella, I.; Alekseychik, P.; Aurela, M.; Avati, A.; Baldocchi, D.; Bansal, S.; Bohrer, G.; Campbell, D. I; Chen, J.; Chu, H.; Dalmagro, H. J; Delwiche, K. B; Desai, A. R; Euskirchen, E.; Feron, S.; Goeckede, M.; Heimann, M.; Helbig, M.; Helfter, C.; Hemes, K. S; Hirano, T.; Iwata, H.; Jurasinski, G.; Kalhori, A.; Kondrich, A.; Lai, D. Y.; Lohila, A.; Malhotra, A.; Merbold, L.; Mitra, B.; Ng, A.; Nilsson, M. B; Noormets, A.; Peichl, M.; Rey-Sanchez, A. C.; Richardson, A. D; Runkle, B. R.; Schäfer, K. V.; Sonnentag, O.; Stuart-Haëntjens, E.; Sturtevant, C.; Ueyama, M.; Valach, A. C; Vargas, R.; Vourlitis, G. L; Ward, E. J; Wong, G. X.; Zona, D.; Alberto, M. C. R; Billesbach, D. P; Celis, G.; Dolman, H.; Friborg, T.; Fuchs, K.; Gogo, S.; Gondwe, M. J; Goodrich, J. P; Gottschalk, P.; Hörtnagl, L.; Jacotot, A.; Koebsch, F.; Kasak, K.; Maier, R.; Morin, T. H; Nemitz, E.; Oechel, W. C; Oikawa, P. Y; Ono, K.; Sachs, T.; Sakabe, A.; Schuur, E. A; Shortt, R.; Sullivan, R. C; Szutu, D. J; Tuittila, E.; Varlagin, A.; Verfaillie, J. G; Wille, C.; Windham-Myers, L.; Poulter, B.; and Jackson, R. B\n\n\n \n \n \n \n \n Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 308-309: 108528. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Gap-filling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{irvin_gap-filling_2021,\n\ttitle = {Gap-filling eddy covariance methane fluxes: {Comparison} of machine learning model predictions and uncertainties at {FLUXNET}-{CH4} wetlands},\n\tvolume = {308-309},\n\tissn = {01681923},\n\tshorttitle = {Gap-filling eddy covariance methane fluxes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192321002124},\n\tdoi = {10.1016/j.agrformet.2021.108528},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Irvin, Jeremy and Zhou, Sharon and McNicol, Gavin and Lu, Fred and Liu, Vincent and Fluet-Chouinard, Etienne and Ouyang, Zutao and Knox, Sara Helen and Lucas-Moffat, Antje and Trotta, Carlo and Papale, Dario and Vitale, Domenico and Mammarella, Ivan and Alekseychik, Pavel and Aurela, Mika and Avati, Anand and Baldocchi, Dennis and Bansal, Sheel and Bohrer, Gil and Campbell, David I and Chen, Jiquan and Chu, Housen and Dalmagro, Higo J and Delwiche, Kyle B and Desai, Ankur R and Euskirchen, Eugenie and Feron, Sarah and Goeckede, Mathias and Heimann, Martin and Helbig, Manuel and Helfter, Carole and Hemes, Kyle S and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and Kalhori, Aram and Kondrich, Andrew and Lai, Derrick YF and Lohila, Annalea and Malhotra, Avni and Merbold, Lutz and Mitra, Bhaskar and Ng, Andrew and Nilsson, Mats B and Noormets, Asko and Peichl, Matthias and Rey-Sanchez, A. Camilo and Richardson, Andrew D and Runkle, Benjamin RK and Schäfer, Karina VR and Sonnentag, Oliver and Stuart-Haëntjens, Ellen and Sturtevant, Cove and Ueyama, Masahito and Valach, Alex C and Vargas, Rodrigo and Vourlitis, George L and Ward, Eric J and Wong, Guan Xhuan and Zona, Donatella and Alberto, Ma. Carmelita R and Billesbach, David P and Celis, Gerardo and Dolman, Han and Friborg, Thomas and Fuchs, Kathrin and Gogo, Sébastien and Gondwe, Mangaliso J and Goodrich, Jordan P and Gottschalk, Pia and Hörtnagl, Lukas and Jacotot, Adrien and Koebsch, Franziska and Kasak, Kuno and Maier, Regine and Morin, Timothy H and Nemitz, Eiko and Oechel, Walter C and Oikawa, Patricia Y and Ono, Keisuke and Sachs, Torsten and Sakabe, Ayaka and Schuur, Edward A and Shortt, Robert and Sullivan, Ryan C and Szutu, Daphne J and Tuittila, Eeva-Stiina and Varlagin, Andrej and Verfaillie, Joeseph G and Wille, Christian and Windham-Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {108528},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Hrachowitz, M.; Stockinger, M.; Coenders-Gerrits, M.; van der Ent, R.; Bogena, H.; Lücke, A.; and Stumpp, C.\n\n\n \n \n \n \n \n Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(9): 4887–4915. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Reduction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hrachowitz_reduction_2021,\n\ttitle = {Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/4887/2021/},\n\tdoi = {10.5194/hess-25-4887-2021},\n\tabstract = {Abstract. Deforestation can considerably affect transpiration\ndynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from\nterrestrial hydrological systems. However, it has so far remained\nproblematic to directly link reductions in transpiration to changes in the\nphysical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale\nhydrological models. This in turn leads to substantial uncertainties in\npredictions of the hydrological response after deforestation but also to a\npoor understanding of how deforestation affects principal descriptors of\ncatchment-scale transport, such as travel time distributions and young water\nfractions. The objectives of this study in the Wüstebach experimental\ncatchment are therefore to provide a mechanistic explanation of why changes in\nthe partitioning of water fluxes can be observed after deforestation and how\nthis further affects the storage and release dynamics of water. More\nspecifically, we test the hypotheses that (1) post-deforestation changes in\nwater storage dynamics and partitioning of water fluxes are largely a direct\nconsequence of a reduction of the catchment-scale effective\nvegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced\nreduction of SU, max affects the shape of travel time distributions and\nresults in shifts towards higher fractions of young water in the stream.\nSimultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and\npost-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the\neffects of deforestation on catchment travel time distributions and young\nwater fractions Fyw. It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is\nreflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This\nreduction of evaporative fluxes could be linked to a reduction of the\ncatchment-scale water storage volume in the unsaturated soil (SU, max)\nthat is within the reach of active roots and thus accessible for vegetation\ntranspiration from ∼258 mm in the pre-deforestation period to\n∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water\n(Fyw∼0.13) than in the pre-deforestation period\n(Fyw∼0.12). In spite of these limited effects on the\noverall Fyw, changes were found for wet periods, during which\npost-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a\nsignificantly increased sensitivity of young water fractions to discharge\nunder wet conditions from dFyw/dQ=0.25 to 0.36. Overall, this study provides quantitative evidence that deforestation\nresulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning\nbetween drainage and evaporation and thus the fundamental hydrological\nresponse characteristics of the Wüstebach catchment, but also for\nchanges in catchment-scale tracer circulation dynamics. In particular for\nwet conditions, deforestation caused higher proportions of younger water to\nreach the stream, implying faster routing of stable isotopes and plausibly\nalso solutes through the sub-surface.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Hrachowitz, Markus and Stockinger, Michael and Coenders-Gerrits, Miriam and van der Ent, Ruud and Bogena, Heye and Lücke, Andreas and Stumpp, Christine},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {4887--4915},\n}\n\n\n\n

\n\n\n

\n Abstract. Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water fractions. The objectives of this study in the Wüstebach experimental catchment are therefore to provide a mechanistic explanation of why changes in the partitioning of water fluxes can be observed after deforestation and how this further affects the storage and release dynamics of water. More specifically, we test the hypotheses that (1) post-deforestation changes in water storage dynamics and partitioning of water fluxes are largely a direct consequence of a reduction of the catchment-scale effective vegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced reduction of SU, max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. Simultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and post-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the effects of deforestation on catchment travel time distributions and young water fractions Fyw. It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU, max) that is within the reach of active roots and thus accessible for vegetation transpiration from ∼258 mm in the pre-deforestation period to ∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water (Fyw∼0.13) than in the pre-deforestation period (Fyw∼0.12). In spite of these limited effects on the overall Fyw, changes were found for wet periods, during which post-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a significantly increased sensitivity of young water fractions to discharge under wet conditions from dFyw/dQ=0.25 to 0.36. Overall, this study provides quantitative evidence that deforestation resulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning between drainage and evaporation and thus the fundamental hydrological response characteristics of the Wüstebach catchment, but also for changes in catchment-scale tracer circulation dynamics. In particular for wet conditions, deforestation caused higher proportions of younger water to reach the stream, implying faster routing of stable isotopes and plausibly also solutes through the sub-surface.\n

\n\n\n

\n \n\n \n \n Hosseini, M.; McNairn, H.; Mitchell, S.; Robertson, L. D.; Davidson, A.; Ahmadian, N.; Bhattacharya, A.; Borg, E.; Conrad, C.; Dabrowska-Zielinska, K.; de Abelleyra, D.; Gurdak, R.; Kumar, V.; Kussul, N.; Mandal, D.; Rao, Y. S.; Saliendra, N.; Shelestov, A.; Spengler, D.; Verón, S. R.; Homayouni, S.; and Becker-Reshef, I.\n\n\n \n \n \n \n \n A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(7): 1348. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hosseini_comparison_2021,\n\ttitle = {A {Comparison} between {Support} {Vector} {Machine} and {Water} {Cloud} {Model} for {Estimating} {Crop} {Leaf} {Area} {Index}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/7/1348},\n\tdoi = {10.3390/rs13071348},\n\tabstract = {The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Hosseini, Mehdi and McNairn, Heather and Mitchell, Scott and Robertson, Laura Dingle and Davidson, Andrew and Ahmadian, Nima and Bhattacharya, Avik and Borg, Erik and Conrad, Christopher and Dabrowska-Zielinska, Katarzyna and de Abelleyra, Diego and Gurdak, Radoslaw and Kumar, Vineet and Kussul, Nataliia and Mandal, Dipankar and Rao, Y. S. and Saliendra, Nicanor and Shelestov, Andrii and Spengler, Daniel and Verón, Santiago R. and Homayouni, Saeid and Becker-Reshef, Inbal},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {1348},\n}\n\n\n\n

\n\n\n

\n The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.\n

\n\n\n

\n \n\n \n \n Ma, H.; Zeng, J.; Zhang, X.; Fu, P.; Zheng, D.; Wigneron, J.; Chen, N.; and Niyogi, D.\n\n\n \n \n \n \n \n Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 264: 112605. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ma_evaluation_2021,\n\ttitle = {Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations},\n\tvolume = {264},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721003254},\n\tdoi = {10.1016/j.rse.2021.112605},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Ma, Hongliang and Zeng, Jiangyuan and Zhang, Xiang and Fu, Peng and Zheng, Donghai and Wigneron, Jean-Pierre and Chen, Nengcheng and Niyogi, Dev},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {112605},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Holtmann, A.; Huth, A.; Pohl, F.; Rebmann, C.; and Fischer, R.\n\n\n \n \n \n \n \n Carbon Sequestration in Mixed Deciduous Forests: The Influence of Tree Size and Species Composition Derived from Model Experiments.\n \n \n \n \n\n\n \n\n\n\n Forests, 12(6): 726. June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{holtmann_carbon_2021,\n\ttitle = {Carbon {Sequestration} in {Mixed} {Deciduous} {Forests}: {The} {Influence} of {Tree} {Size} and {Species} {Composition} {Derived} from {Model} {Experiments}},\n\tvolume = {12},\n\tissn = {1999-4907},\n\tshorttitle = {Carbon {Sequestration} in {Mixed} {Deciduous} {Forests}},\n\turl = {https://www.mdpi.com/1999-4907/12/6/726},\n\tdoi = {10.3390/f12060726},\n\tabstract = {Forests play an important role in climate regulation due to carbon sequestration. However, a deeper understanding of forest carbon flux dynamics is often missing due to a lack of information about forest structure and species composition, especially for non-even-aged and species-mixed forests. In this study, we integrated field inventory data of a species-mixed deciduous forest in Germany into an individual-based forest model to investigate daily carbon fluxes and to examine the role of tree size and species composition for stand productivity. This approach enables to reproduce daily carbon fluxes derived from eddy covariance measurements (R2 of 0.82 for gross primary productivity and 0.77 for ecosystem respiration). While medium-sized trees (stem diameter 30–60 cm) account for the largest share (66\\%) of total productivity at the study site, small (0–30 cm) and large trees ({\\textgreater}60 cm) contribute less with 8.3\\% and 25.5\\% respectively. Simulation experiments indicate that vertical stand structure and shading influence forest productivity more than species composition. Hence, it is important to incorporate small-scale information about forest stand structure into modelling studies to decrease uncertainties of carbon dynamic predictions.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-26},\n\tjournal = {Forests},\n\tauthor = {Holtmann, Anne and Huth, Andreas and Pohl, Felix and Rebmann, Corinna and Fischer, Rico},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {726},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Hermanns, F.; Pohl, F.; Rebmann, C.; Schulz, G.; Werban, U.; and Lausch, A.\n\n\n \n \n \n \n \n Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(10): 1885. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Inferring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hermanns_inferring_2021,\n\ttitle = {Inferring {Grassland} {Drought} {Stress} with {Unsupervised} {Learning} from {Airborne} {Hyperspectral} {VNIR} {Imagery}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/10/1885},\n\tdoi = {10.3390/rs13101885},\n\tabstract = {The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Hermanns, Floris and Pohl, Felix and Rebmann, Corinna and Schulz, Gundula and Werban, Ulrike and Lausch, Angela},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {1885},\n}\n\n\n\n

\n\n\n

\n The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.\n

\n\n\n

\n \n\n \n \n Herbst, M.; Pohlig, P.; Graf, A.; Weihermüller, L.; Schmidt, M.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 297: 108242. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbst_quantification_2021,\n\ttitle = {Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand},\n\tvolume = {297},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192320303440},\n\tdoi = {10.1016/j.agrformet.2020.108242},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Herbst, M. and Pohlig, P. and Graf, A. and Weihermüller, L. and Schmidt, M. and Vanderborght, J. and Vereecken, H.},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {108242},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Helbig, M.; Gerken, T.; Beamesderfer, E. R.; Baldocchi, D. D.; Banerjee, T.; Biraud, S. C.; Brown, W. O.; Brunsell, N. A.; Burakowski, E. A; Burns, S. P.; Butterworth, B. J.; Chan, W. S.; Davis, K. J.; Desai, A. R.; Fuentes, J. D.; Hollinger, D. Y.; Kljun, N.; Mauder, M.; Novick, K. A.; Perkins, J. M.; Rahn, D. A.; Rey-Sanchez, C.; Santanello, J. A.; Scott, R. L.; Seyednasrollah, B.; Stoy, P. C.; Sullivan, R. C.; de Arellano, J. V.; Wharton, S.; Yi, C.; and Richardson, A. D.\n\n\n \n \n \n \n \n Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 307: 108509. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{helbig_integrating_2021,\n\ttitle = {Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions},\n\tvolume = {307},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192321001933},\n\tdoi = {10.1016/j.agrformet.2021.108509},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Helbig, Manuel and Gerken, Tobias and Beamesderfer, Eric R. and Baldocchi, Dennis D. and Banerjee, Tirtha and Biraud, Sébastien C. and Brown, William O.J. and Brunsell, Nathaniel A. and Burakowski, Elizabeth A and Burns, Sean P. and Butterworth, Brian J. and Chan, W. Stephen and Davis, Kenneth J. and Desai, Ankur R. and Fuentes, Jose D. and Hollinger, David Y. and Kljun, Natascha and Mauder, Matthias and Novick, Kimberly A. and Perkins, John M. and Rahn, David A. and Rey-Sanchez, Camilo and Santanello, Joseph A. and Scott, Russell L. and Seyednasrollah, Bijan and Stoy, Paul C. and Sullivan, Ryan C. and de Arellano, Jordi Vilà-Guerau and Wharton, Sonia and Yi, Chuixiang and Richardson, Andrew D.},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {108509},\n}\n\n\n\n

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\n \n\n \n \n Harfenmeister, K.; Itzerott, S.; Weltzien, C.; and Spengler, D.\n\n\n \n \n \n \n \n Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(24): 5036. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Detecting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{harfenmeister_detecting_2021,\n\ttitle = {Detecting {Phenological} {Development} of {Winter} {Wheat} and {Winter} {Barley} {Using} {Time} {Series} of {Sentinel}-1 and {Sentinel}-2},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/24/5036},\n\tdoi = {10.3390/rs13245036},\n\tabstract = {Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences.},\n\tlanguage = {en},\n\tnumber = {24},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Harfenmeister, Katharina and Itzerott, Sibylle and Weltzien, Cornelia and Spengler, Daniel},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {5036},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Harfenmeister, K.; Itzerott, S.; Weltzien, C.; and Spengler, D.\n\n\n \n \n \n \n \n Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(4): 575. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Agricultural$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{harfenmeister_agricultural_2021,\n\ttitle = {Agricultural {Monitoring} {Using} {Polarimetric} {Decomposition} {Parameters} of {Sentinel}-1 {Data}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/4/575},\n\tdoi = {10.3390/rs13040575},\n\tabstract = {The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10\\% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Harfenmeister, Katharina and Itzerott, Sibylle and Weltzien, Cornelia and Spengler, Daniel},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {575},\n}\n\n\n\n

\n\n\n

\n The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.\n

\n\n\n

\n \n\n \n \n Halbach, K.; Möder, M.; Schrader, S.; Liebmann, L.; Schäfer, R. B.; Schneeweiss, A.; Schreiner, V. C.; Vormeier, P.; Weisner, O.; Liess, M.; and Reemtsma, T.\n\n\n \n \n \n \n \n Small streams–large concentrations? Pesticide monitoring in small agricultural streams in Germany during dry weather and rainfall.\n \n \n \n \n\n\n \n\n\n\n Water Research, 203: 117535. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Small$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{halbach_small_2021,\n\ttitle = {Small streams–large concentrations? {Pesticide} monitoring in small agricultural streams in {Germany} during dry weather and rainfall},\n\tvolume = {203},\n\tissn = {00431354},\n\tshorttitle = {Small streams–large concentrations?},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135421007314},\n\tdoi = {10.1016/j.watres.2021.117535},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Water Research},\n\tauthor = {Halbach, Katharina and Möder, Monika and Schrader, Steffi and Liebmann, Liana and Schäfer, Ralf B. and Schneeweiss, Anke and Schreiner, Verena C. and Vormeier, Philipp and Weisner, Oliver and Liess, Matthias and Reemtsma, Thorsten},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {117535},\n}\n\n\n\n

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\n \n\n \n \n Guglielmo, M.; Tang, F. H. M.; Pasut, C.; and Maggi, F.\n\n\n \n \n \n \n \n SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 8(1): 263. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"SOIL-WATERGRIDS,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guglielmo_soil-watergrids_2021,\n\ttitle = {{SOIL}-{WATERGRIDS}, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014},\n\tvolume = {8},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-021-01032-4},\n\tdoi = {10.1038/s41597-021-01032-4},\n\tabstract = {Abstract \n            We introduce here SOIL-WATERGRIDS, a new dataset of dynamic changes in soil moisture and depth of water table over 45 years from 1970 to 2014 globally resolved at 0.25 × 0.25 degree resolution (about 30 × 30 km at the equator) along a 56 m deep soil profile. SOIL-WATERGRIDS estimates were obtained using the BRTSim model instructed with globally gridded soil physical and hydraulic properties, land cover and use characteristics, and hydrometeorological variables to account for precipitation, ecosystem-specific evapotranspiration, snowmelt, surface runoff, and irrigation. We validate our estimates against independent observations and re-analyses of the soil moisture, water table depth, wetland occurrence, and runoff. SOIL-WATERGRIDS brings into a single product the monthly mean water saturation at three depths in the root zone and the depth of the highest and lowest water tables throughout the reference period, their long-term monthly averages, and data quality. SOIL-WATERGRIDS can therefore be used to analyse trends in water availability for agricultural abstraction, assess the water balance under historical weather patterns, and identify water stress in sensitive managed and unmanaged ecosystems.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Scientific Data},\n\tauthor = {Guglielmo, Magda and Tang, Fiona H. M. and Pasut, Chiara and Maggi, Federico},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {263},\n}\n\n\n\n

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\n \n\n \n \n Guevara, M.; Taufer, M.; and Vargas, R.\n\n\n \n \n \n \n \n Gap-free global annual soil moisture: 15 km grids for 1991–2018.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(4): 1711–1735. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Gap-free$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guevara_gap-free_2021,\n\ttitle = {Gap-free global annual soil moisture: 15 km grids for 1991–2018},\n\tvolume = {13},\n\tissn = {1866-3516},\n\tshorttitle = {Gap-free global annual soil moisture},\n\turl = {https://essd.copernicus.org/articles/13/1711/2021/},\n\tdoi = {10.5194/essd-13-1711-2021},\n\tabstract = {Abstract. Soil moisture is key for understanding\nsoil–plant–atmosphere interactions. We provide a soil moisture pattern\nrecognition framework to increase the spatial resolution and fill gaps of\nthe ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil\nmoisture dataset, which contains {\\textgreater} 40 years of satellite soil\nmoisture global grids with a spatial resolution of ∼ 27 km. We\nuse terrain parameters coupled with bioclimatic and soil type information to\npredict finer-grained (i.e., downscaled) satellite soil moisture. We assess\nthe impact of terrain parameters on the prediction accuracy by\ncross-validating downscaled soil moisture with and without the support of\nbioclimatic and soil type information. The outcome is a dataset of gap-free\nglobal mean annual soil moisture predictions and associated prediction\nvariances for 28 years (1991–2018) across 15 km grids. We use independent in situ\nrecords from the International Soil Moisture Network (ISMN, 987 stations)\nand in situ precipitation records (171 additional stations) only for evaluating the\nnew dataset. Cross-validated correlation between observed and predicted soil\nmoisture values varies from r= 0.69 to r= 0.87 with root mean squared\nerrors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture\npredictions improve (a) the correlation with the ISMN (when compared with\nthe original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to\nr= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= {\\textless} 0.3 up to r= 0.49) or\ntropical areas (from r= {\\textless} 0.3 to r= 0.46) which are currently\npoorly represented in the ISMN. Temporal trends show a decline of global\nannual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] \\%),\n(b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] \\%), (c) associated locations from predictions based on terrain\nparameters (-0.85[-1.01,-0.49] \\%), and (d) associated locations from\npredictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] \\%). We provide a new soil moisture dataset that has no gaps and\nhigher granularity together with validation methods and a modeling approach\nthat can be applied worldwide (Guevara et al., 2020,\nhttps://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Guevara, Mario and Taufer, Michela and Vargas, Rodrigo},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {1711--1735},\n}\n\n\n\n

\n\n\n

\n Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains \\textgreater 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= \\textless 0.3 up to r= 0.49) or tropical areas (from r= \\textless 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).\n

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\n \n\n \n \n Greifeneder, F.; Notarnicola, C.; and Wagner, W.\n\n\n \n \n \n \n \n A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(11): 2099. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{greifeneder_machine_2021,\n\ttitle = {A {Machine} {Learning}-{Based} {Approach} for {Surface} {Soil} {Moisture} {Estimations} with {Google} {Earth} {Engine}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/11/2099},\n\tdoi = {10.3390/rs13112099},\n\tabstract = {Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m3·m−3 and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-10-26},\n\tjournal = {Remote Sensing},\n\tauthor = {Greifeneder, Felix and Notarnicola, Claudia and Wagner, Wolfgang},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {2099},\n}\n\n\n\n

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\n \n\n \n \n Graf, M.; Arnault, J.; Fersch, B.; and Kunstmann, H.\n\n\n \n \n \n \n \n Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils? A comparative study.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 35(9). September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Is$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_is_2021,\n\ttitle = {Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils? {A} comparative study},\n\tvolume = {35},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils?},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14332},\n\tdoi = {10.1002/hyp.14332},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Hydrological Processes},\n\tauthor = {Graf, Maximilian and Arnault, Joël and Fersch, Benjamin and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Graf, M.; El Hachem, A.; Eisele, M.; Seidel, J.; Chwala, C.; Kunstmann, H.; and Bárdossy, A.\n\n\n \n \n \n \n \n Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology: Regional Studies, 37: 100883. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Rainfall$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_rainfall_2021,\n\ttitle = {Rainfall estimates from opportunistic sensors in {Germany} across spatio-temporal scales},\n\tvolume = {37},\n\tissn = {22145818},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2214581821001129},\n\tdoi = {10.1016/j.ejrh.2021.100883},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrology: Regional Studies},\n\tauthor = {Graf, Maximilian and El Hachem, Abbas and Eisele, Micha and Seidel, Jochen and Chwala, Christian and Kunstmann, Harald and Bárdossy, András},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {100883},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Graeber, D.; Tenzin, Y.; Stutter, M.; Weigelhofer, G.; Shatwell, T.; von Tümpling, W.; Tittel, J.; Wachholz, A.; and Borchardt, D.\n\n\n \n \n \n \n \n Bioavailable DOC: reactive nutrient ratios control heterotrophic nutrient assimilation—An experimental proof of the macronutrient-access hypothesis.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 155(1): 1–20. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Bioavailable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graeber_bioavailable_2021,\n\ttitle = {Bioavailable {DOC}: reactive nutrient ratios control heterotrophic nutrient assimilation—{An} experimental proof of the macronutrient-access hypothesis},\n\tvolume = {155},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Bioavailable {DOC}},\n\turl = {https://link.springer.com/10.1007/s10533-021-00809-4},\n\tdoi = {10.1007/s10533-021-00809-4},\n\tabstract = {Abstract \n             \n              We investigate the "macronutrient-access hypothesis", which states that the balance between stoichiometric macronutrient demand and accessible macronutrients controls nutrient assimilation by aquatic heterotrophs. Within this hypothesis, we consider bioavailable dissolved organic carbon (bDOC), reactive nitrogen (N) and reactive phosphorus (P) to be the macronutrients accessible to heterotrophic assimilation. Here, reactive N and P are the sums of dissolved inorganic N (nitrate-N, nitrite-N, ammonium-N), soluble-reactive P (SRP), and bioavailable dissolved organic N (bDON) and P (bDOP). Previous data from various freshwaters suggests this hypothesis, yet clear experimental support is missing. We assessed this hypothesis in a proof-of-concept experiment for waters from four small agricultural streams. We used seven different bDOC:reactive N and bDOC:reactive P ratios, induced by seven levels of alder leaf leachate addition. With these treatments and a stream-water specific bacterial inoculum, we conducted a 3-day experiment with three independent replicates per combination of stream water, treatment, and sampling occasion. Here, we extracted dissolved organic matter (DOM) fluorophores by measuring excitation-emission matrices with subsequent parallel factor decomposition (EEM-PARAFAC). We assessed the true bioavailability of DOC, DON, and the DOM fluorophores as the concentration difference between the beginning and end of each experiment. Subsequently, we calculated the bDOC and bDON concentrations based on the bioavailable EEM-PARAFAC fluorophores, and compared the calculated bDOC and bDON concentrations to their true bioavailability. Due to very low DOP concentrations, the DOP determination uncertainty was high, and we assumed DOP to be a negligible part of the reactive P. For bDOC and bDON, the true bioavailability measurements agreed with the same fractions calculated indirectly from bioavailable EEM-PARAFAC fluorophores (bDOC r \n              2 \n               = 0.96, p {\\textless} 0.001; bDON r \n              2 \n               = 0.77, p {\\textless} 0.001). Hence we could predict bDOC and bDON concentrations based on the EEM-PARAFAC fluorophores. The ratios of bDOC:reactive N (sum of bDON and DIN) and bDOC:reactive P (equal to SRP) exerted a strong, predictable stoichiometric control on reactive N and P uptake (R \n              2 \n               = 0.80 and 0.83). To define zones of C:N:P (co-)limitation of heterotrophic assimilation, we used a novel ternary-plot approach combining our data with literature data on C:N:P ranges of bacterial biomass. Here, we found a zone of maximum reactive N uptake (C:N:P approx. {\\textgreater} 114: {\\textless} 9:1), reactive P uptake (C:N:P approx. {\\textgreater} 170:21: {\\textless} 1) and reactive N and P co-limitation of nutrient uptake (C:N:P approx. {\\textgreater} 204:14:1). The “macronutrient-access hypothesis” links ecological stoichiometry and biogeochemistry, and may be of importance for nutrient uptake in many freshwater ecosystems. However, this experiment is only a starting point and this hypothesis needs to be corroborated by further experiments for more sites, by in-situ studies, and with different DOC sources.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Biogeochemistry},\n\tauthor = {Graeber, Daniel and Tenzin, Youngdoung and Stutter, Marc and Weigelhofer, Gabriele and Shatwell, Tom and von Tümpling, Wolf and Tittel, Jörg and Wachholz, Alexander and Borchardt, Dietrich},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {1--20},\n}\n\n\n\n

\n\n\n

\n Abstract We investigate the \"macronutrient-access hypothesis\", which states that the balance between stoichiometric macronutrient demand and accessible macronutrients controls nutrient assimilation by aquatic heterotrophs. Within this hypothesis, we consider bioavailable dissolved organic carbon (bDOC), reactive nitrogen (N) and reactive phosphorus (P) to be the macronutrients accessible to heterotrophic assimilation. Here, reactive N and P are the sums of dissolved inorganic N (nitrate-N, nitrite-N, ammonium-N), soluble-reactive P (SRP), and bioavailable dissolved organic N (bDON) and P (bDOP). Previous data from various freshwaters suggests this hypothesis, yet clear experimental support is missing. We assessed this hypothesis in a proof-of-concept experiment for waters from four small agricultural streams. We used seven different bDOC:reactive N and bDOC:reactive P ratios, induced by seven levels of alder leaf leachate addition. With these treatments and a stream-water specific bacterial inoculum, we conducted a 3-day experiment with three independent replicates per combination of stream water, treatment, and sampling occasion. Here, we extracted dissolved organic matter (DOM) fluorophores by measuring excitation-emission matrices with subsequent parallel factor decomposition (EEM-PARAFAC). We assessed the true bioavailability of DOC, DON, and the DOM fluorophores as the concentration difference between the beginning and end of each experiment. Subsequently, we calculated the bDOC and bDON concentrations based on the bioavailable EEM-PARAFAC fluorophores, and compared the calculated bDOC and bDON concentrations to their true bioavailability. Due to very low DOP concentrations, the DOP determination uncertainty was high, and we assumed DOP to be a negligible part of the reactive P. For bDOC and bDON, the true bioavailability measurements agreed with the same fractions calculated indirectly from bioavailable EEM-PARAFAC fluorophores (bDOC r 2 = 0.96, p \\textless 0.001; bDON r 2 = 0.77, p \\textless 0.001). Hence we could predict bDOC and bDON concentrations based on the EEM-PARAFAC fluorophores. The ratios of bDOC:reactive N (sum of bDON and DIN) and bDOC:reactive P (equal to SRP) exerted a strong, predictable stoichiometric control on reactive N and P uptake (R 2 = 0.80 and 0.83). To define zones of C:N:P (co-)limitation of heterotrophic assimilation, we used a novel ternary-plot approach combining our data with literature data on C:N:P ranges of bacterial biomass. Here, we found a zone of maximum reactive N uptake (C:N:P approx. \\textgreater 114: \\textless 9:1), reactive P uptake (C:N:P approx. \\textgreater 170:21: \\textless 1) and reactive N and P co-limitation of nutrient uptake (C:N:P approx. \\textgreater 204:14:1). The “macronutrient-access hypothesis” links ecological stoichiometry and biogeochemistry, and may be of importance for nutrient uptake in many freshwater ecosystems. However, this experiment is only a starting point and this hypothesis needs to be corroborated by further experiments for more sites, by in-situ studies, and with different DOC sources.\n

\n\n\n

\n \n\n \n \n Golub, M.; Desai, A. R.; Vesala, T.; Mammarella, I.; Ojala, A.; Bohrer, G.; Weyhenmeyer, G. A; Blanken, P. D.; Eugster, W.; Koebsch, F.; Chen, J.; Czajkowski, K. P.; Deshmukh, C.; Guérin, F.; Heiskanen, J. J.; Humphreys, E. R; Jonsson, A.; Karlsson, J.; Kling, G. W.; Lee, X.; Liu, H.; Lohila, A.; Lundin, E. J.; Morin, T. H.; Podgrajsek, E.; Provenzale, M.; Rutgersson, A.; Sachs, T.; Sahlée, E.; Serça, D.; Shao, C.; Spence, C.; Strachan, I. B.; and Xiao, W.\n\n\n \n \n \n \n \n New insights into diel to interannual variation in carbon dioxide emissions from lakes and reservoirs.\n \n \n \n \n\n\n \n\n\n\n Technical Report Environmental Sciences, June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"New$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{golub_new_2021,\n\ttype = {preprint},\n\ttitle = {New insights into diel to interannual variation in carbon dioxide emissions from lakes and reservoirs},\n\turl = {http://www.essoar.org/doi/10.1002/essoar.10507313.1},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tinstitution = {Environmental Sciences},\n\tauthor = {Golub, Malgorzata and Desai, Ankur Rashmikant and Vesala, Timo and Mammarella, Ivan and Ojala, Anne and Bohrer, Gil and Weyhenmeyer, Gesa A and Blanken, Peter D. and Eugster, Werner and Koebsch, Franziska and Chen, Jiquan and Czajkowski, Kevin P. and Deshmukh, Chandrashekhar and Guérin, Frédéric and Heiskanen, Jouni Juhana and Humphreys, Elyn R and Jonsson, Anders and Karlsson, Jan and Kling, George W. and Lee, Xuhui and Liu, Heping and Lohila, Annalea and Lundin, Erik Johannes and Morin, Timothy Hector and Podgrajsek, Eva and Provenzale, Maria and Rutgersson, Anna and Sachs, Torsten and Sahlée, Erik and Serça, Dominique and Shao, Changliang and Spence, Christopher and Strachan, Ian B. and Xiao, Wei},\n\tmonth = jun,\n\tyear = {2021},\n\tdoi = {10.1002/essoar.10507313.1},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Gholizadeh, A.; Neumann, C.; Chabrillat, S.; van Wesemael, B.; Castaldi, F.; Borůvka, L.; Sanderman, J.; Klement, A.; and Hohmann, C.\n\n\n \n \n \n \n \n Soil organic carbon estimation using VNIR–SWIR spectroscopy: The effect of multiple sensors and scanning conditions.\n \n \n \n \n\n\n \n\n\n\n Soil and Tillage Research, 211: 105017. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gholizadeh_soil_2021,\n\ttitle = {Soil organic carbon estimation using {VNIR}–{SWIR} spectroscopy: {The} effect of multiple sensors and scanning conditions},\n\tvolume = {211},\n\tissn = {01671987},\n\tshorttitle = {Soil organic carbon estimation using {VNIR}–{SWIR} spectroscopy},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167198721000878},\n\tdoi = {10.1016/j.still.2021.105017},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Soil and Tillage Research},\n\tauthor = {Gholizadeh, Asa and Neumann, Carsten and Chabrillat, Sabine and van Wesemael, Bas and Castaldi, Fabio and Borůvka, Luboš and Sanderman, Jonathan and Klement, Aleš and Hohmann, Christian},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {105017},\n}\n\n\n\n

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\n \n\n \n \n Giraud, M.; Groh, J.; Gerke, H.; Brüggemann, N.; Vereecken, H.; and Pütz, T.\n\n\n \n \n \n \n \n Soil Nitrogen Dynamics in a Managed Temperate Grassland Under Changed Climatic Conditions.\n \n \n \n \n\n\n \n\n\n\n Water, 13(7): 931. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{giraud_soil_2021,\n\ttitle = {Soil {Nitrogen} {Dynamics} in a {Managed} {Temperate} {Grassland} {Under} {Changed} {Climatic} {Conditions}},\n\tvolume = {13},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/13/7/931},\n\tdoi = {10.3390/w13070931},\n\tabstract = {Grasslands are one of the most common biomes in the world with a wide range of ecosystem services. Nevertheless, quantitative data on the change in nitrogen dynamics in extensively managed temperate grasslands caused by a shift from energy- to water-limited climatic conditions have not yet been reported. In this study, we experimentally studied this shift by translocating undisturbed soil monoliths from an energy-limited site (Rollesbroich) to a water-limited site (Selhausen). The soil monoliths were contained in weighable lysimeters and monitored for their water and nitrogen balance in the period between 2012 and 2018. At the water-limited site (Selhausen), annual plant nitrogen uptake decreased due to water stress compared to the energy-limited site (Rollesbroich), while nitrogen uptake was higher at the beginning of the growing period. Possibly because of this lower plant uptake, the lysimeters at the water-limited site showed an increased inorganic nitrogen concentration in the soil solution, indicating a higher net mineralization rate. The N2O gas emissions and nitrogen leaching remained low at both sites. Our findings suggest that in the short term, fertilizer should consequently be applied early in the growing period to increase nitrogen uptake and decrease nitrogen losses. Moreover, a shift from energy-limited to water-limited conditions will have a limited effect on gaseous nitrogen emissions and nitrate concentrations in the groundwater in the grassland type of this study because higher nitrogen concentrations are (over-) compensated by lower leaching rates.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Water},\n\tauthor = {Giraud, Mona and Groh, Jannis and Gerke, Horst and Brüggemann, Nicolas and Vereecken, Harry and Pütz, Thomas},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {931},\n}\n\n\n\n

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\n \n\n \n \n George, J.; Yang, W.; Kobayashi, H.; Biermann, T.; Carrara, A.; Cremonese, E.; Cuntz, M.; Fares, S.; Gerosa, G.; Grünwald, T.; Hase, N.; Heliasz, M.; Ibrom, A.; Knohl, A.; Kruijt, B.; Lange, H.; Limousin, J.; Loustau, D.; Lukeš, P.; Marzuoli, R.; Mölder, M.; Montagnani, L.; Neirynck, J.; Peichl, M.; Rebmann, C.; Schmidt, M.; Serrano, F. R. L.; Soudani, K.; Vincke, C.; and Pisek, J.\n\n\n \n \n \n \n \n Method comparison of indirect assessments of understory leaf area index (LAIu): A case study across the extended network of ICOS forest ecosystem sites in Europe.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 128: 107841. September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Method$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{george_method_2021,\n\ttitle = {Method comparison of indirect assessments of understory leaf area index ({LAIu}): {A} case study across the extended network of {ICOS} forest ecosystem sites in {Europe}},\n\tvolume = {128},\n\tissn = {1470160X},\n\tshorttitle = {Method comparison of indirect assessments of understory leaf area index ({LAIu})},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X21005069},\n\tdoi = {10.1016/j.ecolind.2021.107841},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Ecological Indicators},\n\tauthor = {George, Jan-Peter and Yang, Wei and Kobayashi, Hideki and Biermann, Tobias and Carrara, Arnaud and Cremonese, Edoardo and Cuntz, Matthias and Fares, Silvano and Gerosa, Giacomo and Grünwald, Thomas and Hase, Niklas and Heliasz, Michael and Ibrom, Andreas and Knohl, Alexander and Kruijt, Bart and Lange, Holger and Limousin, Jean-Marc and Loustau, Denis and Lukeš, Petr and Marzuoli, Riccardo and Mölder, Meelis and Montagnani, Leonardo and Neirynck, Johan and Peichl, Matthias and Rebmann, Corinna and Schmidt, Marius and Serrano, Francisco Ramon Lopez and Soudani, Kamel and Vincke, Caroline and Pisek, Jan},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {107841},\n}\n\n\n\n

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\n \n\n \n \n Garcia-Franco, N.; Walter, R.; Wiesmeier, M.; Hurtarte, L. C. C.; Berauer, B. J.; Buness, V.; Zistl-Schlingmann, M.; Kiese, R.; Dannenmann, M.; and Kögel-Knabner, I.\n\n\n \n \n \n \n \n Biotic and abiotic controls on carbon storage in aggregates in calcareous alpine and prealpine grassland soils.\n \n \n \n \n\n\n \n\n\n\n Biology and Fertility of Soils, 57(2): 203–218. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Biotic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{garcia-franco_biotic_2021,\n\ttitle = {Biotic and abiotic controls on carbon storage in aggregates in calcareous alpine and prealpine grassland soils},\n\tvolume = {57},\n\tissn = {0178-2762, 1432-0789},\n\turl = {https://link.springer.com/10.1007/s00374-020-01518-0},\n\tdoi = {10.1007/s00374-020-01518-0},\n\tabstract = {Abstract \n             \n              Alpine and prealpine grasslands provide various ecosystem services and are hotspots for the storage of soil organic C (SOC) in Central Europe. Yet, information about aggregate-related SOC storage and its controlling factors in alpine and prealpine grassland soils is limited. In this study, the SOC distribution according to the aggregate size classes large macroaggregates ({\\textgreater} 2000 μm), small macroaggregates (250–2000 μm), microaggregates (63–250 μm), and silt-/clay-sized particles ({\\textless} 63 μm) was studied in grassland soils along an elevation gradient in the Northern Limestone Alps of Germany. This was accompanied by an analysis of earthworm abundance and biomass according to different ecological niches. The SOC and N stocks increased with elevation and were associated with relatively high proportions of water-stable macroaggregates due to high contents of exchangeable Ca \n              2+ \n              and Mg \n              2+ \n              . At lower elevations, earthworms appeared to act as catalyzers for a higher microaggregate formation. Thus, SOC stabilization by aggregate formation in the studied soils is a result of a joined interaction of organic matter and Ca \n              2+ \n              as binding agents for soil aggregates (higher elevations), and the earthworms that act as promoters of aggregate formation through the secretion of biogenic carbonates (low elevation). Our study highlights the importance of aggregate-related factors as potential indices to evaluate the SOC storage potential in other mountainous grassland soils. \n             \n            Graphical abstract},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {Biology and Fertility of Soils},\n\tauthor = {Garcia-Franco, Noelia and Walter, Roswitha and Wiesmeier, Martin and Hurtarte, Luis Carlos Colocho and Berauer, Bernd Josef and Buness, Vincent and Zistl-Schlingmann, Marcus and Kiese, Ralf and Dannenmann, Michael and Kögel-Knabner, Ingrid},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {203--218},\n}\n\n\n\n

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\n \n\n \n \n Eshel, A.; Messer, H.; Kunstmann, H.; Alpert, P.; and Chwala, C.\n\n\n \n \n \n \n \n Quantitative Analysis of the Performance of Spatial Interpolation Methods for Rainfall Estimation Using Commercial Microwave Links.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 22(4): 831–843. April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{eshel_quantitative_2021,\n\ttitle = {Quantitative {Analysis} of the {Performance} of {Spatial} {Interpolation} {Methods} for {Rainfall} {Estimation} {Using} {Commercial} {Microwave} {Links}},\n\tvolume = {22},\n\tissn = {1525-755X, 1525-7541},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/22/4/JHM-D-20-0164.1.xml},\n\tdoi = {10.1175/JHM-D-20-0164.1},\n\tabstract = {Abstract \n             \n              Using signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semisynthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the German Weather Service serves as ground truth for generating semisynthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225 × 245 km \n              2 \n              in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods—inverse distance weighting and ordinary kriging—in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48-h rain event. The results reconfirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Eshel, Adam and Messer, Hagit and Kunstmann, Harald and Alpert, Pinhas and Chwala, Christian},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {831--843},\n}\n\n\n\n

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\n \n\n \n \n Ehrhardt, A.; Groh, J.; and Gerke, H. H.\n\n\n \n \n \n \n \n Wavelet analysis of soil water state variables for identification of lateral subsurface flow: Lysimeter vs. field data.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 20(3). May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Wavelet$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ehrhardt_wavelet_2021,\n\ttitle = {Wavelet analysis of soil water state variables for identification of lateral subsurface flow: {Lysimeter} vs. field data},\n\tvolume = {20},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {Wavelet analysis of soil water state variables for identification of lateral subsurface flow},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20129},\n\tdoi = {10.1002/vzj2.20129},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Ehrhardt, Annelie and Groh, Jannis and Gerke, Horst H.},\n\tmonth = may,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Ebeling, P.; Kumar, R.; Weber, M.; Knoll, L.; Fleckenstein, J. H.; and Musolff, A.\n\n\n \n \n \n \n \n Archetypes and Controls of Riverine Nutrient Export Across German Catchments.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 57(4). April 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Archetypes$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ebeling_archetypes_2021,\n\ttitle = {Archetypes and {Controls} of {Riverine} {Nutrient} {Export} {Across} {German} {Catchments}},\n\tvolume = {57},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR028134},\n\tdoi = {10.1029/2020WR028134},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Ebeling, Pia and Kumar, Rohini and Weber, Michael and Knoll, Lukas and Fleckenstein, Jan H. and Musolff, Andreas},\n\tmonth = apr,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Ebeling, P.; Dupas, R.; Abbott, B.; Kumar, R.; Ehrhardt, S.; Fleckenstein, J. H.; and Musolff, A.\n\n\n \n \n \n \n \n Long‐Term Nitrate Trajectories Vary by Season in Western European Catchments.\n \n \n \n \n\n\n \n\n\n\n Global Biogeochemical Cycles, 35(9). September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Long‐Term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ebeling_longterm_2021,\n\ttitle = {Long‐{Term} {Nitrate} {Trajectories} {Vary} by {Season} in {Western} {European} {Catchments}},\n\tvolume = {35},\n\tissn = {0886-6236, 1944-9224},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2021GB007050},\n\tdoi = {10.1029/2021GB007050},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-10-26},\n\tjournal = {Global Biogeochemical Cycles},\n\tauthor = {Ebeling, Pia and Dupas, Rémi and Abbott, Benjamin and Kumar, Rohini and Ehrhardt, Sophie and Fleckenstein, Jan H. and Musolff, Andreas},\n\tmonth = sep,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Djukic, I.; Kepfer-Rojas, S.; Kappel Schmidt, I.; Steenberg Larsen, K.; Beier, C.; Berg, B.; Verheyen, K.; Trevathan-Tackett, S. M.; Macreadie, P. I.; Bierbaumer, M.; Patoine, G.; Eisenhauer, N.; Guerra, C. A.; Maestre, F. T.; Hagedorn, F.; Oggioni, A.; Bergami, C.; Magagna, B.; Kwon, T.; Shibata, H.; and TeaComposition initiative\n\n\n \n \n \n \n \n The TeaComposition initiative: Unleashing the power of international collaboration to understand litter decomposition.\n \n \n \n \n\n\n \n\n\n\n . 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{djukic_teacomposition_2021,\n\ttitle = {The {TeaComposition} initiative: {Unleashing} the power of international collaboration to understand litter decomposition},\n\turl = {http://soil-organisms.org/index.php/SO/article/view/151},\n\tdoi = {10.25674/SO93ISS1PP73},\n\turldate = {2022-10-26},\n\tauthor = {Djukic, Ika and Kepfer-Rojas, Sebastian and Kappel Schmidt, Inger and Steenberg Larsen, Klaus and Beier, Claus and Berg, Björn and Verheyen, Kris and Trevathan-Tackett, Stacey M. and Macreadie, Peter I. and Bierbaumer, Michael and Patoine, Guillaume and Eisenhauer, Nico and Guerra, Carlos A. and Maestre, Fernando T. and Hagedorn, Frank and Oggioni, Alessandro and Bergami, Caterina and Magagna, Barbara and Kwon, TaeOh and Shibata, Hideaki and {TeaComposition initiative}},\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Dorigo, W.; Himmelbauer, I.; Aberer, D.; Schremmer, L.; Petrakovic, I.; Zappa, L.; Preimesberger, W.; Xaver, A.; Annor, F.; Ardö, J.; Baldocchi, D.; Bitelli, M.; Blöschl, G.; Bogena, H.; Brocca, L.; Calvet, J.; Camarero, J. J.; Capello, G.; Choi, M.; Cosh, M. C.; van de Giesen, N.; Hajdu, I.; Ikonen, J.; Jensen, K. H.; Kanniah, K. D.; de Kat, I.; Kirchengast, G.; Kumar Rai, P.; Kyrouac, J.; Larson, K.; Liu, S.; Loew, A.; Moghaddam, M.; Martínez Fernández, J.; Mattar Bader, C.; Morbidelli, R.; Musial, J. P.; Osenga, E.; Palecki, M. A.; Pellarin, T.; Petropoulos, G. P.; Pfeil, I.; Powers, J.; Robock, A.; Rüdiger, C.; Rummel, U.; Strobel, M.; Su, Z.; Sullivan, R.; Tagesson, T.; Varlagin, A.; Vreugdenhil, M.; Walker, J.; Wen, J.; Wenger, F.; Wigneron, J. P.; Woods, M.; Yang, K.; Zeng, Y.; Zhang, X.; Zreda, M.; Dietrich, S.; Gruber, A.; van Oevelen, P.; Wagner, W.; Scipal, K.; Drusch, M.; and Sabia, R.\n\n\n \n \n \n \n \n The International Soil Moisture Network: serving Earth system science for over a decade.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(11): 5749–5804. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dorigo_international_2021,\n\ttitle = {The {International} {Soil} {Moisture} {Network}: serving {Earth} system science for over a decade},\n\tvolume = {25},\n\tissn = {1607-7938},\n\tshorttitle = {The {International} {Soil} {Moisture} {Network}},\n\turl = {https://hess.copernicus.org/articles/25/5749/2021/},\n\tdoi = {10.5194/hess-25-5749-2021},\n\tabstract = {Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 \\% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-10-26},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Dorigo, Wouter and Himmelbauer, Irene and Aberer, Daniel and Schremmer, Lukas and Petrakovic, Ivana and Zappa, Luca and Preimesberger, Wolfgang and Xaver, Angelika and Annor, Frank and Ardö, Jonas and Baldocchi, Dennis and Bitelli, Marco and Blöschl, Günter and Bogena, Heye and Brocca, Luca and Calvet, Jean-Christophe and Camarero, J. Julio and Capello, Giorgio and Choi, Minha and Cosh, Michael C. and van de Giesen, Nick and Hajdu, Istvan and Ikonen, Jaakko and Jensen, Karsten H. and Kanniah, Kasturi Devi and de Kat, Ileen and Kirchengast, Gottfried and Kumar Rai, Pankaj and Kyrouac, Jenni and Larson, Kristine and Liu, Suxia and Loew, Alexander and Moghaddam, Mahta and Martínez Fernández, José and Mattar Bader, Cristian and Morbidelli, Renato and Musial, Jan P. and Osenga, Elise and Palecki, Michael A. and Pellarin, Thierry and Petropoulos, George P. and Pfeil, Isabella and Powers, Jarrett and Robock, Alan and Rüdiger, Christoph and Rummel, Udo and Strobel, Michael and Su, Zhongbo and Sullivan, Ryan and Tagesson, Torbern and Varlagin, Andrej and Vreugdenhil, Mariette and Walker, Jeffrey and Wen, Jun and Wenger, Fred and Wigneron, Jean Pierre and Woods, Mel and Yang, Kun and Zeng, Yijian and Zhang, Xiang and Zreda, Marek and Dietrich, Stephan and Gruber, Alexander and van Oevelen, Peter and Wagner, Wolfgang and Scipal, Klaus and Drusch, Matthias and Sabia, Roberto},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {5749--5804},\n}\n\n\n\n

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\n \n\n \n \n Döpper, V.; Duarte Rocha, A.; Gränzig, T. .; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data.\n \n \n \n \n\n\n \n\n\n\n In Neale, C. M.; and Maltese, A., editor(s), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, pages 41, Online Only, Spain, September 2021. SPIE\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{dopper_using_2021,\n\taddress = {Online Only, Spain},\n\ttitle = {Using radiative transfer models for mapping soil moisture content under grassland with {UAS}-borne hyperspectral data},\n\tisbn = {9781510645561 9781510645578},\n\turl = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11856/2600296/Using-radiative-transfer-models-for-mapping-soil-moisture-content-under/10.1117/12.2600296.full},\n\tdoi = {10.1117/12.2600296},\n\turldate = {2022-10-26},\n\tbooktitle = {Remote {Sensing} for {Agriculture}, {Ecosystems}, and {Hydrology} {XXIII}},\n\tpublisher = {SPIE},\n\tauthor = {Döpper, Veronika and Duarte Rocha, Alby and Gränzig, Tobias \t. and Kleinschmit, Birgit and Förster, Michael},\n\teditor = {Neale, Christopher M. and Maltese, Antonino},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {41},\n}\n\n\n\n

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\n \n\n \n \n Dombrowski, O.; Hendricks Franssen, H.; Brogi, C.; and Bogena, H. R.\n\n\n \n \n \n \n \n Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring.\n \n \n \n \n\n\n \n\n\n\n Sensors, 21(3): 741. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Performance$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dombrowski_performance_2021,\n\ttitle = {Performance of the {ATMOS41} {All}-in-{One} {Weather} {Station} for {Weather} {Monitoring}},\n\tvolume = {21},\n\tissn = {1424-8220},\n\turl = {https://www.mdpi.com/1424-8220/21/3/741},\n\tdoi = {10.3390/s21030741},\n\tabstract = {Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties {\\textgreater}0.6 hPa and solar radiation was underestimated by 3\\%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5\\% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-26},\n\tjournal = {Sensors},\n\tauthor = {Dombrowski, Olga and Hendricks Franssen, Harrie-Jan and Brogi, Cosimo and Bogena, Heye Reemt},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {741},\n}\n\n\n\n

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\n \n\n \n \n Dirmeyer, P. A.; Balsamo, G.; Blyth, E. M.; Morrison, R.; and Cooper, H. M.\n\n\n \n \n \n \n \n Land‐Atmosphere Interactions Exacerbated the Drought and Heatwave Over Northern Europe During Summer 2018.\n \n \n \n \n\n\n \n\n\n\n AGU Advances, 2(2). June 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Land‐Atmosphere$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dirmeyer_landatmosphere_2021,\n\ttitle = {Land‐{Atmosphere} {Interactions} {Exacerbated} the {Drought} and {Heatwave} {Over} {Northern} {Europe} {During} {Summer} 2018},\n\tvolume = {2},\n\tissn = {2576-604X, 2576-604X},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020AV000283},\n\tdoi = {10.1029/2020AV000283},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-10-26},\n\tjournal = {AGU Advances},\n\tauthor = {Dirmeyer, Paul A. and Balsamo, Gianpaolo and Blyth, Eleanor M. and Morrison, Ross and Cooper, Hollie M.},\n\tmonth = jun,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Delwiche, K. B.; Knox, S. H.; Malhotra, A.; Fluet-Chouinard, E.; McNicol, G.; Feron, S.; Ouyang, Z.; Papale, D.; Trotta, C.; Canfora, E.; Cheah, Y.; Christianson, D.; Alberto, M. C. R.; Alekseychik, P.; Aurela, M.; Baldocchi, D.; Bansal, S.; Billesbach, D. P.; Bohrer, G.; Bracho, R.; Buchmann, N.; Campbell, D. I.; Celis, G.; Chen, J.; Chen, W.; Chu, H.; Dalmagro, H. J.; Dengel, S.; Desai, A. R.; Detto, M.; Dolman, H.; Eichelmann, E.; Euskirchen, E.; Famulari, D.; Fuchs, K.; Goeckede, M.; Gogo, S.; Gondwe, M. J.; Goodrich, J. P.; Gottschalk, P.; Graham, S. L.; Heimann, M.; Helbig, M.; Helfter, C.; Hemes, K. S.; Hirano, T.; Hollinger, D.; Hörtnagl, L.; Iwata, H.; Jacotot, A.; Jurasinski, G.; Kang, M.; Kasak, K.; King, J.; Klatt, J.; Koebsch, F.; Krauss, K. W.; Lai, D. Y. F.; Lohila, A.; Mammarella, I.; Belelli Marchesini, L.; Manca, G.; Matthes, J. H.; Maximov, T.; Merbold, L.; Mitra, B.; Morin, T. H.; Nemitz, E.; Nilsson, M. B.; Niu, S.; Oechel, W. C.; Oikawa, P. Y.; Ono, K.; Peichl, M.; Peltola, O.; Reba, M. L.; Richardson, A. D.; Riley, W.; Runkle, B. R. K.; Ryu, Y.; Sachs, T.; Sakabe, A.; Sanchez, C. R.; Schuur, E. A.; Schäfer, K. V. R.; Sonnentag, O.; Sparks, J. P.; Stuart-Haëntjens, E.; Sturtevant, C.; Sullivan, R. C.; Szutu, D. J.; Thom, J. E.; Torn, M. S.; Tuittila, E.; Turner, J.; Ueyama, M.; Valach, A. C.; Vargas, R.; Varlagin, A.; Vazquez-Lule, A.; Verfaillie, J. G.; Vesala, T.; Vourlitis, G. L.; Ward, E. J.; Wille, C.; Wohlfahrt, G.; Wong, G. X.; Zhang, Z.; Zona, D.; Windham-Myers, L.; Poulter, B.; and Jackson, R. B.\n\n\n \n \n \n \n \n FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(7): 3607–3689. July 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"FLUXNET-CH4:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{delwiche_fluxnet-ch4_2021,\n\ttitle = {{FLUXNET}-{CH4}: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands},\n\tvolume = {13},\n\tissn = {1866-3516},\n\tshorttitle = {{FLUXNET}-{CH}\\&lt;sub\\&gt;4\\&lt;/sub\\&gt;},\n\turl = {https://essd.copernicus.org/articles/13/3607/2021/},\n\tdoi = {10.5194/essd-13-3607-2021},\n\tabstract = {Abstract. Methane (CH4) emissions from natural landscapes constitute\nroughly half of global CH4 contributions to the atmosphere, yet large\nuncertainties remain in the absolute magnitude and the seasonality of\nemission quantities and drivers. Eddy covariance (EC) measurements of\nCH4 flux are ideal for constraining ecosystem-scale CH4\nemissions due to quasi-continuous and high-temporal-resolution CH4\nflux measurements, coincident carbon dioxide, water, and energy flux\nmeasurements, lack of ecosystem disturbance, and increased availability of\ndatasets over the last decade. Here, we (1) describe the newly published\ndataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of\nCH4 EC measurements (available at\nhttps://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4\nincludes half-hourly and daily gap-filled and non-gap-filled aggregated\nCH4 fluxes and meteorological data from 79 sites globally: 42\nfreshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained\necosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage\nglobally because the majority of sites in FLUXNET-CH4 Version 1.0 are\nfreshwater wetlands which are a substantial source of total atmospheric\nCH4 emissions; and (3) we provide the first global estimates of the\nseasonal variability and seasonality predictors of freshwater wetland\nCH4 fluxes. Our representativeness analysis suggests that the\nfreshwater wetland sites in the dataset cover global wetland bioclimatic\nattributes (encompassing energy, moisture, and vegetation-related\nparameters) in arctic, boreal, and temperate regions but only sparsely\ncover humid tropical regions. Seasonality metrics of wetland CH4\nemissions vary considerably across latitudinal bands. In freshwater wetlands\n(except those between 20∘ S to 20∘ N) the spring onset\nof elevated CH4 emissions starts 3 d earlier, and the CH4\nemission season lasts 4 d longer, for each degree Celsius increase in mean\nannual air temperature. On average, the spring onset of increasing CH4\nemissions lags behind soil warming by 1 month, with very few sites experiencing\nincreased CH4 emissions prior to the onset of soil warming. In\ncontrast, roughly half of these sites experience the spring onset of rising\nCH4 emissions prior to the spring increase in gross primary\nproductivity (GPP). The timing of peak summer CH4 emissions does not\ncorrelate with the timing for either peak summer temperature or peak GPP.\nOur results provide seasonality parameters for CH4 modeling and\nhighlight seasonality metrics that cannot be predicted by temperature or GPP\n(i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource\nfor diagnosing and understanding the role of terrestrial ecosystems and\nclimate drivers in the global CH4 cycle, and future additions of sites\nin tropical ecosystems and site years of data collection will provide added\nvalue to this database. All seasonality parameters are available at\nhttps://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021).\nAdditionally, raw FLUXNET-CH4 data used to extract seasonality parameters\ncan be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete\nlist of the 79 individual site data DOIs is provided in Table 2 of this paper.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Delwiche, Kyle B. and Knox, Sara Helen and Malhotra, Avni and Fluet-Chouinard, Etienne and McNicol, Gavin and Feron, Sarah and Ouyang, Zutao and Papale, Dario and Trotta, Carlo and Canfora, Eleonora and Cheah, You-Wei and Christianson, Danielle and Alberto, Ma. Carmelita R. and Alekseychik, Pavel and Aurela, Mika and Baldocchi, Dennis and Bansal, Sheel and Billesbach, David P. and Bohrer, Gil and Bracho, Rosvel and Buchmann, Nina and Campbell, David I. and Celis, Gerardo and Chen, Jiquan and Chen, Weinan and Chu, Housen and Dalmagro, Higo J. and Dengel, Sigrid and Desai, Ankur R. and Detto, Matteo and Dolman, Han and Eichelmann, Elke and Euskirchen, Eugenie and Famulari, Daniela and Fuchs, Kathrin and Goeckede, Mathias and Gogo, Sébastien and Gondwe, Mangaliso J. and Goodrich, Jordan P. and Gottschalk, Pia and Graham, Scott L. and Heimann, Martin and Helbig, Manuel and Helfter, Carole and Hemes, Kyle S. and Hirano, Takashi and Hollinger, David and Hörtnagl, Lukas and Iwata, Hiroki and Jacotot, Adrien and Jurasinski, Gerald and Kang, Minseok and Kasak, Kuno and King, John and Klatt, Janina and Koebsch, Franziska and Krauss, Ken W. and Lai, Derrick Y. F. and Lohila, Annalea and Mammarella, Ivan and Belelli Marchesini, Luca and Manca, Giovanni and Matthes, Jaclyn Hatala and Maximov, Trofim and Merbold, Lutz and Mitra, Bhaskar and Morin, Timothy H. and Nemitz, Eiko and Nilsson, Mats B. and Niu, Shuli and Oechel, Walter C. and Oikawa, Patricia Y. and Ono, Keisuke and Peichl, Matthias and Peltola, Olli and Reba, Michele L. and Richardson, Andrew D. and Riley, William and Runkle, Benjamin R. K. and Ryu, Youngryel and Sachs, Torsten and Sakabe, Ayaka and Sanchez, Camilo Rey and Schuur, Edward A. and Schäfer, Karina V. R. and Sonnentag, Oliver and Sparks, Jed P. and Stuart-Haëntjens, Ellen and Sturtevant, Cove and Sullivan, Ryan C. and Szutu, Daphne J. and Thom, Jonathan E. and Torn, Margaret S. and Tuittila, Eeva-Stiina and Turner, Jessica and Ueyama, Masahito and Valach, Alex C. and Vargas, Rodrigo and Varlagin, Andrej and Vazquez-Lule, Alma and Verfaillie, Joseph G. and Vesala, Timo and Vourlitis, George L. and Ward, Eric J. and Wille, Christian and Wohlfahrt, Georg and Wong, Guan Xhuan and Zhang, Zhen and Zona, Donatella and Windham-Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},\n\tmonth = jul,\n\tyear = {2021},\n\tpages = {3607--3689},\n}\n\n\n\n

\n\n\n

\n Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.\n

\n\n\n

\n \n\n \n \n Dehaspe, J.; Sarrazin, F.; Kumar, R.; Fleckenstein, J. H.; and Musolff, A.\n\n\n \n \n \n \n \n Bending of the concentration discharge relationship can inform about in-stream nitrate removal.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(12): 6437–6463. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Bending$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dehaspe_bending_2021,\n\ttitle = {Bending of the concentration discharge relationship can inform about in-stream nitrate removal},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/6437/2021/},\n\tdoi = {10.5194/hess-25-6437-2021},\n\tabstract = {Abstract. Nitrate (NO3-) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO3- uptake is a crucial element of the catchment-scale nitrogen balance and can be measured at small spatiotemporal scales, while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low-frequency NO3- concentration and streamflow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C–Q) relationships. Previous studies suggest that steeper positive log (C)–log (Q) slopes under low flow conditions (than under high flows) are linked to biological NO3- uptake, creating a bent rather than linear log (C)–log (Q) relationship. Here we explore if network-scale NO3- uptake creates bent log (C)–log (Q)\nrelationships and when in turn uptake can be quantified from observed low-frequency C–Q data. To this end we apply a parsimonious mass-balance-based river network uptake model in 13 mesoscale German catchments (21–1450 km2) and explore the linkages between log (C)–log (Q) bending and different model parameter combinations. The modeling results show that uptake and transport in the river network can create bent log (C)–log (Q) relationships at the basin outlet from log–log linear C–Q relationships describing the NO3- land-to-stream transfer. We find that within the chosen parameter range the\nbending is mainly shaped by geomorphological parameters that control the\nchannel reactive surface area rather than by the biological uptake velocity\nitself. Further we show that in this exploratory modeling environment,\nbending is positively correlated to percentage of NO3- load removed in the network (Lr.perc) but that network-wide flow velocities should be taken into account when interpreting log (C)–log (Q) bending. Classification trees, finally, can successfully predict classes of low (∼4 \\%), intermediate (∼32 \\%) and high (∼68 \\%) Lr.perc using information on water velocity and log (C)–log (Q) bending. These results can help to identify stream networks that efficiently attenuate NO3- loads based on low-frequency NO3- and Q observations and\ngenerally show the importance of the channel geomorphology on the emerging\nlog (C)–log (Q) bending at network scales.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-26},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Dehaspe, Joni and Sarrazin, Fanny and Kumar, Rohini and Fleckenstein, Jan H. and Musolff, Andreas},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {6437--6463},\n}\n\n\n\n

\n\n\n

\n Abstract. Nitrate (NO3-) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO3- uptake is a crucial element of the catchment-scale nitrogen balance and can be measured at small spatiotemporal scales, while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low-frequency NO3- concentration and streamflow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C–Q) relationships. Previous studies suggest that steeper positive log (C)–log (Q) slopes under low flow conditions (than under high flows) are linked to biological NO3- uptake, creating a bent rather than linear log (C)–log (Q) relationship. Here we explore if network-scale NO3- uptake creates bent log (C)–log (Q) relationships and when in turn uptake can be quantified from observed low-frequency C–Q data. To this end we apply a parsimonious mass-balance-based river network uptake model in 13 mesoscale German catchments (21–1450 km2) and explore the linkages between log (C)–log (Q) bending and different model parameter combinations. The modeling results show that uptake and transport in the river network can create bent log (C)–log (Q) relationships at the basin outlet from log–log linear C–Q relationships describing the NO3- land-to-stream transfer. We find that within the chosen parameter range the bending is mainly shaped by geomorphological parameters that control the channel reactive surface area rather than by the biological uptake velocity itself. Further we show that in this exploratory modeling environment, bending is positively correlated to percentage of NO3- load removed in the network (Lr.perc) but that network-wide flow velocities should be taken into account when interpreting log (C)–log (Q) bending. Classification trees, finally, can successfully predict classes of low (∼4 %), intermediate (∼32 %) and high (∼68 %) Lr.perc using information on water velocity and log (C)–log (Q) bending. These results can help to identify stream networks that efficiently attenuate NO3- loads based on low-frequency NO3- and Q observations and generally show the importance of the channel geomorphology on the emerging log (C)–log (Q) bending at network scales.\n

\n\n\n

\n \n\n \n \n Czymzik, M.; Dellwig, O.; Muscheler, R.; Roeser, P.; Brauer, A.; Kaiser, J.; Christl, M.; and Arz, H. W.\n\n\n \n \n \n \n \n RETRACTED: Delayed Western Gotland Basin (Baltic Sea) ventilation in response to the onset of a Mid-Holocene climate oscillation.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 273: 107253. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"RETRACTED:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{czymzik_retracted_2021,\n\ttitle = {{RETRACTED}: {Delayed} {Western} {Gotland} {Basin} ({Baltic} {Sea}) ventilation in response to the onset of a {Mid}-{Holocene} climate oscillation},\n\tvolume = {273},\n\tissn = {02773791},\n\tshorttitle = {{RETRACTED}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379121004601},\n\tdoi = {10.1016/j.quascirev.2021.107253},\n\tlanguage = {en},\n\turldate = {2022-10-26},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Czymzik, Markus and Dellwig, Olaf and Muscheler, Raimund and Roeser, Patricia and Brauer, Achim and Kaiser, Jérôme and Christl, Marcus and Arz, Helge W.},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {107253},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Chen, Y.; Feng, X.; and Fu, B.\n\n\n \n \n \n \n \n An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 13(1): 1–31. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chen_improved_2021,\n\ttitle = {An improved global remote-sensing-based surface soil moisture ({RSSSM}) dataset covering 2003–2018},\n\tvolume = {13},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/13/1/2021/},\n\tdoi = {10.5194/essd-13-1-2021},\n\tabstract = {Abstract. Soil moisture is an important variable linking the\natmosphere and terrestrial ecosystems. However, long-term satellite\nmonitoring of surface soil moisture at the global scale needs improvement.\nIn this study, we conducted data calibration and data fusion of 11\nwell-acknowledged microwave remote-sensing soil moisture products since 2003\nthrough a neural network approach, with Soil Moisture Active Passive (SMAP)\nsoil moisture data applied as the primary training target. The training\nefficiency was high (R2=0.95) due to the selection of nine quality\nimpact factors of microwave soil moisture products and the complicated\norganizational structure of multiple neural networks (five rounds of iterative\nsimulations, eight substeps, 67 independent neural networks, and more than 1\nmillion localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering\n2003–2018 at 0.1∘ resolution. The temporal\nresolution is approximately 10 d, meaning that three data records are\nobtained within a month, for days 1–10, 11–20,\nand from the 21st to the last day of that month. RSSSM is proven comparable to the\nin situ surface soil moisture measurements of the International Soil\nMoisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing\npopular similar products are usually within the ranges of\n0.31–0.41 and 0.095–0.142 m3 m−3),\nrespectively. RSSSM generally presents advantages over other products in\narid and relatively cold areas, which is probably because of the difficulty\nin simulating the impacts of thawing and transient precipitation on soil\nmoisture, and during the growing seasons. Moreover, the persistent high\nquality during 2003–2018 as well as the complete spatial\ncoverage ensure the applicability of RSSSM to studies on both the spatial\nand temporal patterns (e.g. long-term trend). RSSSM data suggest an\nincrease in the global mean surface soil moisture. Moreover, without\nconsidering the deserts and rainforests, the surface soil moisture loss on\nconsecutive rainless days is highest in summer over the low latitudes\n(30∘ S–30∘ N) but mostly in winter over\nthe mid-latitudes (30–60∘ N,\n30–60∘ S). Notably, the error\npropagation is well controlled with the extension of the simulation period\nto the past, indicating that the data fusion algorithm proposed here will be\nmore meaningful in the future when more advanced microwave sensors become\noperational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Earth System Science Data},\n\tauthor = {Chen, Yongzhe and Feng, Xiaoming and Fu, Bojie},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {1--31},\n}\n\n\n\n

\n\n\n

\n Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).\n

\n\n\n

\n \n\n \n \n Chang, K.; Riley, W. J.; Knox, S. H.; Jackson, R. B.; McNicol, G.; Poulter, B.; Aurela, M.; Baldocchi, D.; Bansal, S.; Bohrer, G.; Campbell, D. I.; Cescatti, A.; Chu, H.; Delwiche, K. B.; Desai, A. R.; Euskirchen, E.; Friborg, T.; Goeckede, M.; Helbig, M.; Hemes, K. S.; Hirano, T.; Iwata, H.; Kang, M.; Keenan, T.; Krauss, K. W.; Lohila, A.; Mammarella, I.; Mitra, B.; Miyata, A.; Nilsson, M. B.; Noormets, A.; Oechel, W. C.; Papale, D.; Peichl, M.; Reba, M. L.; Rinne, J.; Runkle, B. R. K.; Ryu, Y.; Sachs, T.; Schäfer, K. V. R.; Schmid, H. P.; Shurpali, N.; Sonnentag, O.; Tang, A. C. I.; Torn, M. S.; Trotta, C.; Tuittila, E.; Ueyama, M.; Vargas, R.; Vesala, T.; Windham-Myers, L.; Zhang, Z.; and Zona, D.\n\n\n \n \n \n \n \n Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 12(1): 2266. December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Substantial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chang_substantial_2021,\n\ttitle = {Substantial hysteresis in emergent temperature sensitivity of global wetland {CH4} emissions},\n\tvolume = {12},\n\tissn = {2041-1723},\n\turl = {http://www.nature.com/articles/s41467-021-22452-1},\n\tdoi = {10.1038/s41467-021-22452-1},\n\tabstract = {Abstract \n             \n              Wetland methane (CH \n              4 \n              ) emissions ( \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              ) are important in global carbon budgets and climate change assessments. Currently, \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              are often controlled by factors beyond temperature. Here, we evaluate the relationship between \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              and temperature using observations from the FLUXNET-CH \n              4 \n              database. Measurements collected across the globe show substantial seasonal hysteresis between \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              and temperature, suggesting larger \n               \n                 \n                  \\$\\$\\{F\\}\\_\\{\\{\\{CH\\}\\}\\_\\{4\\}\\}\\$\\$ \n                   \n                     \n                       \n                        F \n                       \n                       \n                         \n                           \n                            C \n                            H \n                           \n                           \n                            4 \n                           \n                         \n                       \n                     \n                   \n                 \n               \n              sensitivity to temperature later in the frost-free season (about 77\\% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH \n              4 \n              production are thus needed to improve global CH \n              4 \n              budget assessments.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-26},\n\tjournal = {Nature Communications},\n\tauthor = {Chang, Kuang-Yu and Riley, William J. and Knox, Sara H. and Jackson, Robert B. and McNicol, Gavin and Poulter, Benjamin and Aurela, Mika and Baldocchi, Dennis and Bansal, Sheel and Bohrer, Gil and Campbell, David I. and Cescatti, Alessandro and Chu, Housen and Delwiche, Kyle B. and Desai, Ankur R. and Euskirchen, Eugenie and Friborg, Thomas and Goeckede, Mathias and Helbig, Manuel and Hemes, Kyle S. and Hirano, Takashi and Iwata, Hiroki and Kang, Minseok and Keenan, Trevor and Krauss, Ken W. and Lohila, Annalea and Mammarella, Ivan and Mitra, Bhaskar and Miyata, Akira and Nilsson, Mats B. and Noormets, Asko and Oechel, Walter C. and Papale, Dario and Peichl, Matthias and Reba, Michele L. and Rinne, Janne and Runkle, Benjamin R. K. and Ryu, Youngryel and Sachs, Torsten and Schäfer, Karina V. R. and Schmid, Hans Peter and Shurpali, Narasinha and Sonnentag, Oliver and Tang, Angela C. I. and Torn, Margaret S. and Trotta, Carlo and Tuittila, Eeva-Stiina and Ueyama, Masahito and Vargas, Rodrigo and Vesala, Timo and Windham-Myers, Lisamarie and Zhang, Zhen and Zona, Donatella},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {2266},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Brauer, A.; and Tiedemann, R.\n\n\n \n \n \n \n \n GEOfokus: See- und Ozeansedimente in der Paläoklimaforschung.\n \n \n \n \n\n\n \n\n\n\n Geowissenschaftliche Mitteilungen, 83: 7–22. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"GEOfokus:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brauer_a_tiedemann_r_geofokus_2021,\n\ttitle = {{GEOfokus}: {See}- und {Ozeansedimente} in der {Paläoklimaforschung}.},\n\tvolume = {83},\n\turl = {https://e-docs.geo-leo.de/handle/11858/8335},\n\tdoi = {10.23689/fidgeo-3995},\n\tabstract = {Die Ausgabe der Geowissenschaftlichen Mitteilungen vom März 2021 enthält die Themenblöcke: GEOfokus: (See- und Ozeansedimente in der Paläoklimaforschung ), GEOaktiv (Wirtschaft, Beruf, Forschung und Lehre), GEOlobby (Gesellschaften, Verbände, Institutionen), GEOreport (Geowissenschaftliche Öffentlichkeitsarbeit, Tagungsberichte, Ausstellungen, Exkursionen, Publikationen), GEOszene (Personalia, Nachrufe).},\n\tlanguage = {de},\n\turldate = {2022-10-26},\n\tjournal = {Geowissenschaftliche Mitteilungen},\n\tauthor = {{Brauer, A., Tiedemann, R.}},\n\tyear = {2021},\n\tpages = {7--22},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Bujak, I.; Müller, C.; Merz, R.; and Knöller, K.\n\n\n \n \n \n \n \n High monitoring to investigate nitrate export and its drivers in a mesoscale river catchment along an anthropogenic gradient.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 35(12). December 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bujak_high_2021,\n\ttitle = {High monitoring to investigate nitrate export and its drivers in a mesoscale river catchment along an anthropogenic gradient},\n\tvolume = {35},\n\tissn = {0885-6087, 1099-1085},\n\tshorttitle = {High {\\textless}span style="font-variant},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14361},\n\tdoi = {10.1002/hyp.14361},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-10-25},\n\tjournal = {Hydrological Processes},\n\tauthor = {Bujak, Izabela and Müller, Christin and Merz, Ralf and Knöller, Kay},\n\tmonth = dec,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Burger, D. J.; Vogel, J.; Kooijman, A. M.; Bol, R.; de Rijke, E.; Schoorl, J.; Lücke, A.; and Gottselig, N.\n\n\n \n \n \n \n \n Colloidal catchment response to snowmelt and precipitation events differs in a forested headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 20(3). May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Colloidal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{burger_colloidal_2021,\n\ttitle = {Colloidal catchment response to snowmelt and precipitation events differs in a forested headwater catchment},\n\tvolume = {20},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20126},\n\tdoi = {10.1002/vzj2.20126},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Burger, Dymphie J. and Vogel, Johnny and Kooijman, Annemieke M. and Bol, Roland and de Rijke, Eva and Schoorl, Jorien and Lücke, Andreas and Gottselig, Nina},\n\tmonth = may,\n\tyear = {2021},\n}\n\n\n\n

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\n \n\n \n \n Brogi, C.; Huisman, J. A.; Weihermüller, L.; Herbst, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Added value of geophysics-based soil mapping in agro-ecosystem simulations.\n \n \n \n \n\n\n \n\n\n\n SOIL, 7(1): 125–143. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Added$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brogi_added_2021,\n\ttitle = {Added value of geophysics-based soil mapping in agro-ecosystem simulations},\n\tvolume = {7},\n\tissn = {2199-398X},\n\turl = {https://soil.copernicus.org/articles/7/125/2021/},\n\tdoi = {10.5194/soil-7-125-2021},\n\tabstract = {Abstract. There is an increased demand for quantitative\nhigh-resolution soil maps that enable within-field management. Commonly\navailable soil maps are generally not suited for this purpose, but digital\nsoil mapping and geophysical methods in particular allow soil\ninformation to be obtained with an unprecedented level of detail. However, it is often\ndifficult to quantify the added value of such high-resolution soil\ninformation for agricultural management and agro-ecosystem modelling. In\nthis study, a detailed geophysics-based soil map was compared to two\ncommonly available general-purpose soil maps. In particular, the three maps\nwere used as input for crop growth models to simulate leaf area index (LAI)\nof five crops for an area of ∼ 1 km2. The simulated\ndevelopment of LAI for the five crops was evaluated using LAI obtained from\nmultispectral satellite images. Overall, it was found that the\ngeophysics-based soil map provided better LAI predictions than the two\ngeneral-purpose soil maps in terms of correlation coefficient R2, model\nefficiency (ME), and root mean square error (RMSE). Improved performance was\nmost apparent in the case of prolonged periods of drought and was strongly\nrelated to the combination of soil characteristics and crop type.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-25},\n\tjournal = {SOIL},\n\tauthor = {Brogi, Cosimo and Huisman, Johan A. and Weihermüller, Lutz and Herbst, Michael and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {125--143},\n}\n\n\n\n

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\n \n\n \n \n Botter, M.; Zeeman, M.; Burlando, P.; and Fatichi, S.\n\n\n \n \n \n \n \n Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: insights from a mechanistic model.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 18(6): 1917–1939. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Impacts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{botter_impacts_2021,\n\ttitle = {Impacts of fertilization on grassland productivity and water quality across the {European} {Alps} under current and warming climate: insights from a mechanistic model},\n\tvolume = {18},\n\tissn = {1726-4189},\n\tshorttitle = {Impacts of fertilization on grassland productivity and water quality across the {European} {Alps} under current and warming climate},\n\turl = {https://bg.copernicus.org/articles/18/1917/2021/},\n\tdoi = {10.5194/bg-18-1917-2021},\n\tabstract = {Abstract. Alpine grasslands sustain local economy by providing fodder for livestock. Intensive fertilization is\ncommon to enhance their yields, thus creating negative externalities on water quality that are\ndifficult to evaluate without reliable estimates of nutrient fluxes. We apply a mechanistic\necosystem model, seamlessly integrating land-surface energy balance, soil hydrology, vegetation\ndynamics, and soil biogeochemistry, aiming at assessing the grassland response to fertilization. We\nsimulate the major water, carbon, nutrient, and energy fluxes of nine grassland plots across the\nbroad European Alpine region. We provide an interdisciplinary model evaluation by confirming its\nperformance against observed variables from different datasets. Subsequently, we apply the model\nto test the influence of fertilization practices on grassland yields and nitrate\n(NO3-) losses through leaching under both current and modified climate scenarios. Despite the generally low NO3- concentration in groundwater recharge, the variability\nacross sites is remarkable, which is mostly (but not exclusively) dictated by elevation. In high-Alpine\nsites, short growing seasons lead to less efficient nitrogen (N) uptake for biomass production.\nThis combined with lower evapotranspiration rates results in higher amounts of drainage and\nNO3- leaching to groundwater. Scenarios with increased temperature lead to a longer\ngrowing season characterized by higher biomass production and, consequently, to a reduction of\nwater leakage and N leaching. While the intersite variability is maintained, climate change\nimpacts are stronger on sites at higher elevations. The local soil hydrology has a crucial role in driving the NO3- use efficiency. The\ncommonly applied fixed threshold limit on fertilizer N input is suboptimal. We suggest that major\nhydrological and soil property differences across sites should be considered in the delineation of\nbest practices or regulations for management. Using distributed maps informed with key soil and\nclimatic attributes or systematically implementing integrated ecosystem models as shown here can\ncontribute to achieving more sustainable practices.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-10-25},\n\tjournal = {Biogeosciences},\n\tauthor = {Botter, Martina and Zeeman, Matthias and Burlando, Paolo and Fatichi, Simone},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {1917--1939},\n}\n\n\n\n

\n\n\n

\n Abstract. Alpine grasslands sustain local economy by providing fodder for livestock. Intensive fertilization is common to enhance their yields, thus creating negative externalities on water quality that are difficult to evaluate without reliable estimates of nutrient fluxes. We apply a mechanistic ecosystem model, seamlessly integrating land-surface energy balance, soil hydrology, vegetation dynamics, and soil biogeochemistry, aiming at assessing the grassland response to fertilization. We simulate the major water, carbon, nutrient, and energy fluxes of nine grassland plots across the broad European Alpine region. We provide an interdisciplinary model evaluation by confirming its performance against observed variables from different datasets. Subsequently, we apply the model to test the influence of fertilization practices on grassland yields and nitrate (NO3-) losses through leaching under both current and modified climate scenarios. Despite the generally low NO3- concentration in groundwater recharge, the variability across sites is remarkable, which is mostly (but not exclusively) dictated by elevation. In high-Alpine sites, short growing seasons lead to less efficient nitrogen (N) uptake for biomass production. This combined with lower evapotranspiration rates results in higher amounts of drainage and NO3- leaching to groundwater. Scenarios with increased temperature lead to a longer growing season characterized by higher biomass production and, consequently, to a reduction of water leakage and N leaching. While the intersite variability is maintained, climate change impacts are stronger on sites at higher elevations. The local soil hydrology has a crucial role in driving the NO3- use efficiency. The commonly applied fixed threshold limit on fertilizer N input is suboptimal. We suggest that major hydrological and soil property differences across sites should be considered in the delineation of best practices or regulations for management. Using distributed maps informed with key soil and climatic attributes or systematically implementing integrated ecosystem models as shown here can contribute to achieving more sustainable practices.\n

\n\n\n

\n \n\n \n \n Bogena, H. R.; Strati, V.; Güntner, A.; Chew, C. C.; and Schrön, M.\n\n\n \n \n \n \n \n Editorial: Innovative Methods for Non-invasive Monitoring of Hydrological Processes From Field to Catchment Scale.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 3: 641458. March 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Editorial:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_editorial_2021,\n\ttitle = {Editorial: {Innovative} {Methods} for {Non}-invasive {Monitoring} of {Hydrological} {Processes} {From} {Field} to {Catchment} {Scale}},\n\tvolume = {3},\n\tissn = {2624-9375},\n\tshorttitle = {Editorial},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2021.641458/full},\n\tdoi = {10.3389/frwa.2021.641458},\n\turldate = {2022-10-25},\n\tjournal = {Frontiers in Water},\n\tauthor = {Bogena, Heye R. and Strati, Virginia and Güntner, Andreas and Chew, Clara C. and Schrön, Martin},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {641458},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kleinert, F.; Leufen, L. H.; and Schultz, M. G.\n\n\n \n \n \n \n \n IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 14(1): 1–25. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"IntelliO3-ts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kleinert_intellio3-ts_2021,\n\ttitle = {{IntelliO3}-ts v1.0: a neural network approach to predict near-surface ozone concentrations in {Germany}},\n\tvolume = {14},\n\tissn = {1991-9603},\n\tshorttitle = {{IntelliO3}-ts v1.0},\n\turl = {https://gmd.copernicus.org/articles/14/1/2021/},\n\tdoi = {10.5194/gmd-14-1-2021},\n\tabstract = {Abstract. The prediction of near-surface ozone concentrations is important for supporting regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named “IntelliO3-ts”, which consists of multiple convolutional neural network (CNN) layers, grouped together as inception blocks. The model is trained with measured multi-year ozone and nitrogen oxide concentrations of more than 300 German measurement stations in rural environments and six meteorological variables from the meteorological COSMO reanalysis. This is by far the most extensive dataset used for time series predictions based on neural networks so far. IntelliO3-ts allows the prediction of daily maximum 8 h average (dma8eu) ozone concentrations for a lead time of up to 4 d, and we show that the model outperforms standard reference models like persistence models.\nMoreover, we demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d, while it does not add any genuine value for longer lead times. We attribute this to the limited deterministic information that is contained in the single-station time series training data. We applied a bootstrapping technique to analyse the influence of different input variables and found that the previous-day ozone concentrations are of major importance, followed by 2 m temperature. As we did not use any geographic information to train IntelliO3-ts in its current version and included no relation between stations, the influence of the horizontal wind components on the model performance is minimal. We expect that the inclusion of advection–diffusion terms in the model could improve results in future versions of our model.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-25},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Kleinert, Felix and Leufen, Lukas H. and Schultz, Martin G.},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {1--25},\n}\n\n\n\n

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\n \n\n \n \n Beck, H. E.; Pan, M.; Miralles, D. G.; Reichle, R. H.; Dorigo, W. A.; Hahn, S.; Sheffield, J.; Karthikeyan, L.; Balsamo, G.; Parinussa, R. M.; van Dijk, A. I. J. M.; Du, J.; Kimball, J. S.; Vergopolan, N.; and Wood, E. F.\n\n\n \n \n \n \n \n Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 25(1): 17–40. January 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beck_evaluation_2021,\n\ttitle = {Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors},\n\tvolume = {25},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/25/17/2021/},\n\tdoi = {10.5194/hess-25-17-2021},\n\tabstract = {Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 \\% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-10-25},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Beck, Hylke E. and Pan, Ming and Miralles, Diego G. and Reichle, Rolf H. and Dorigo, Wouter A. and Hahn, Sebastian and Sheffield, Justin and Karthikeyan, Lanka and Balsamo, Gianpaolo and Parinussa, Robert M. and van Dijk, Albert I. J. M. and Du, Jinyang and Kimball, John S. and Vergopolan, Noemi and Wood, Eric F.},\n\tmonth = jan,\n\tyear = {2021},\n\tpages = {17--40},\n}\n\n\n\n

\n\n\n

\n Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.\n

\n\n\n

\n \n\n \n \n Beamish, A. L.; Anbuhl, L.; Behling, R.; Goncalves, R.; Lingenfelser, A.; Neelmeijer, J.; Rabe, D.; Scheffler, D.; Thiele, M.; and Spengler, D.\n\n\n \n \n \n \n \n FERN.Lab: Bridging the gap between remote sensing academic research and society.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing Applications: Society and Environment, 24: 100641. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"FERN.Lab:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beamish_fernlab_2021,\n\ttitle = {{FERN}.{Lab}: {Bridging} the gap between remote sensing academic research and society},\n\tvolume = {24},\n\tissn = {23529385},\n\tshorttitle = {{FERN}.{Lab}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2352938521001774},\n\tdoi = {10.1016/j.rsase.2021.100641},\n\tlanguage = {en},\n\turldate = {2022-10-25},\n\tjournal = {Remote Sensing Applications: Society and Environment},\n\tauthor = {Beamish, Alison L. and Anbuhl, Lasse and Behling, Robert and Goncalves, Romulo and Lingenfelser, André and Neelmeijer, Julia and Rabe, Daniela and Scheffler, Daniel and Thiele, Maria and Spengler, Daniel},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {100641},\n}\n\n\n\n

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\n \n\n \n \n Bates, J. S.; Montzka, C.; Schmidt, M.; and Jonard, F.\n\n\n \n \n \n \n \n Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(4): 710. February 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bates_estimating_2021,\n\ttitle = {Estimating {Canopy} {Density} {Parameters} {Time}-{Series} for {Winter} {Wheat} {Using} {UAS} {Mounted} {LiDAR}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/4/710},\n\tdoi = {10.3390/rs13040710},\n\tabstract = {Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-10-25},\n\tjournal = {Remote Sensing},\n\tauthor = {Bates, Jordan Steven and Montzka, Carsten and Schmidt, Marius and Jonard, François},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {710},\n}\n\n\n\n

\n\n\n

\n Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.\n

\n\n\n

\n \n\n \n \n Balting, D. F.; Ionita, M.; Wegmann, M.; Helle, G.; Schleser, G. H.; Rimbu, N.; Freund, M. B.; Heinrich, I.; Caldarescu, D.; and Lohmann, G.\n\n\n \n \n \n \n \n Large-scale climate signals of a European oxygen isotope network from tree rings.\n \n \n \n \n\n\n \n\n\n\n Climate of the Past, 17(3): 1005–1023. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{balting_large-scale_2021,\n\ttitle = {Large-scale climate signals of a {European} oxygen isotope network from tree rings},\n\tvolume = {17},\n\tissn = {1814-9332},\n\turl = {https://cp.copernicus.org/articles/17/1005/2021/},\n\tdoi = {10.5194/cp-17-1005-2021},\n\tabstract = {Abstract. We investigate the climate signature of δ18O tree-ring records from sites distributed all over Europe covering the last 400\nyears. An empirical orthogonal function (EOF) analysis reveals two distinct\nmodes of variability on the basis of the existing δ18O tree-ring records. The first mode is associated with anomaly patterns projecting\nonto the El Niño–Southern Oscillation (ENSO) and reflects a\nmulti-seasonal climatic signal. The ENSO link is pronounced for the last 130 years, but it is found to be weak over the period from 1600 to 1850, suggesting that the relationship between ENSO and the European climate may not be stable over time. The second mode of δ18O variability, which captures a north–south dipole in the European δ18O tree-ring records, is related to a regional summer atmospheric circulation pattern, revealing a pronounced centre over the North Sea. Locally, the δ18O anomalies associated with this mode show the same (opposite) sign with temperature (precipitation). Based on the oxygen isotopic signature derived from tree rings, we argue that the prevailing large-scale atmospheric circulation patterns and the related teleconnections can be analysed beyond instrumental records.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Climate of the Past},\n\tauthor = {Balting, Daniel F. and Ionita, Monica and Wegmann, Martin and Helle, Gerhard and Schleser, Gerhard H. and Rimbu, Norel and Freund, Mandy B. and Heinrich, Ingo and Caldarescu, Diana and Lohmann, Gerrit},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {1005--1023},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Baatz, R.; Hendricks Franssen, H. J.; Euskirchen, E.; Sihi, D.; Dietze, M.; Ciavatta, S.; Fennel, K.; Beck, H.; De Lannoy, G.; Pauwels, V. R. N.; Raiho, A.; Montzka, C.; Williams, M.; Mishra, U.; Poppe, C.; Zacharias, S.; Lausch, A.; Samaniego, L.; Van Looy, K.; Bogena, H.; Adamescu, M.; Mirtl, M.; Fox, A.; Goergen, K.; Naz, B. S.; Zeng, Y.; and Vereecken, H.\n\n\n \n \n \n \n \n Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis.\n \n \n \n \n\n\n \n\n\n\n Reviews of Geophysics, 59(3). September 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Reanalysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baatz_reanalysis_2021,\n\ttitle = {Reanalysis in {Earth} {System} {Science}: {Toward} {Terrestrial} {Ecosystem} {Reanalysis}},\n\tvolume = {59},\n\tissn = {8755-1209, 1944-9208},\n\tshorttitle = {Reanalysis in {Earth} {System} {Science}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020RG000715},\n\tdoi = {10.1029/2020RG000715},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-10-25},\n\tjournal = {Reviews of Geophysics},\n\tauthor = {Baatz, R. and Hendricks Franssen, H. J. and Euskirchen, E. and Sihi, D. and Dietze, M. and Ciavatta, S. and Fennel, K. and Beck, H. and De Lannoy, G. and Pauwels, V. R. N. and Raiho, A. and Montzka, C. and Williams, M. and Mishra, U. and Poppe, C. and Zacharias, S. and Lausch, A. and Samaniego, L. and Van Looy, K. and Bogena, H. and Adamescu, M. and Mirtl, M. and Fox, A. and Goergen, K. and Naz, B. S. and Zeng, Y. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2021},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Anlanger, C.; Risse‐Buhl, U.; Schiller, D.; Noss, C.; Weitere, M.; and Lorke, A.\n\n\n \n \n \n \n \n Hydraulic and biological controls of biofilm nitrogen uptake in gravel‐bed streams.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 66(11): 3887–3900. November 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydraulic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{anlanger_hydraulic_2021,\n\ttitle = {Hydraulic and biological controls of biofilm nitrogen uptake in gravel‐bed streams},\n\tvolume = {66},\n\tissn = {0024-3590, 1939-5590},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.11927},\n\tdoi = {10.1002/lno.11927},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-10-25},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Anlanger, Christine and Risse‐Buhl, Ute and Schiller, Daniel and Noss, Christian and Weitere, Markus and Lorke, Andreas},\n\tmonth = nov,\n\tyear = {2021},\n\tpages = {3887--3900},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Ahmad, U.; Alvino, A.; and Marino, S.\n\n\n \n \n \n \n \n A Review of Crop Water Stress Assessment Using Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 13(20): 4155. October 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ahmad_review_2021,\n\ttitle = {A {Review} of {Crop} {Water} {Stress} {Assessment} {Using} {Remote} {Sensing}},\n\tvolume = {13},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/13/20/4155},\n\tdoi = {10.3390/rs13204155},\n\tabstract = {Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2022-10-25},\n\tjournal = {Remote Sensing},\n\tauthor = {Ahmad, Uzair and Alvino, Arturo and Marino, Stefano},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {4155},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Andrade-Linares, D. R.; Zistl-Schlingmann, M.; Foesel, B.; Dannenmann, M.; Schulz, S.; and Schloter, M.\n\n\n \n \n \n \n \n Short term effects of climate change and intensification of management on the abundance of microbes driving nitrogen turnover in montane grassland soils.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 780: 146672. August 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Short$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{andrade-linares_short_2021,\n\ttitle = {Short term effects of climate change and intensification of management on the abundance of microbes driving nitrogen turnover in montane grassland soils},\n\tvolume = {780},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S004896972101740X},\n\tdoi = {10.1016/j.scitotenv.2021.146672},\n\tlanguage = {en},\n\turldate = {2022-10-20},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Andrade-Linares, Diana R. and Zistl-Schlingmann, Marcus and Foesel, Baerbel and Dannenmann, Michael and Schulz, Stefanie and Schloter, Michael},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {146672},\n}\n

\n\n\n\n

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\n \n 2020\n \n \n (103)\n \n \n

\n \n \n

\n \n\n \n \n Scharnweber, T.; Smiljanic, M.; Cruz-García, R.; Manthey, M.; and Wilmking, M.\n\n\n \n \n \n \n \n Tree growth at the end of the 21st century - the extreme years 2018/19 as template for future growth conditions.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(7): 074022. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Tree$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{scharnweber_tree_2020,\n\ttitle = {Tree growth at the end of the 21st century - the extreme years 2018/19 as template for future growth conditions},\n\tvolume = {15},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ab865d},\n\tdoi = {10.1088/1748-9326/ab865d},\n\tnumber = {7},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Scharnweber, Tobias and Smiljanic, Marko and Cruz-García, Roberto and Manthey, Michael and Wilmking, Martin},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {074022},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Lausch, A.; Schaepman, M. E.; Skidmore, A. K.; Truckenbrodt, S. C.; Hacker, J. M.; Baade, J.; Bannehr, L.; Borg, E.; Bumberger, J.; Dietrich, P.; Gläßer, C.; Haase, D.; Heurich, M.; Jagdhuber, T.; Jany, S.; Krönert, R.; Möller, M.; Mollenhauer, H.; Montzka, C.; Pause, M.; Rogass, C.; Salepci, N.; Schmullius, C.; Schrodt, F.; Schütze, C.; Schweitzer, C.; Selsam, P.; Spengler, D.; Vohland, M.; Volk, M.; Weber, U.; Wellmann, T.; Werban, U.; Zacharias, S.; and Thiel, C.\n\n\n \n \n \n \n \n Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(22): 3690. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_linking_2020,\n\ttitle = {Linking the {Remote} {Sensing} of {Geodiversity} and {Traits} {Relevant} to {Biodiversity}—{Part} {II}: {Geomorphology}, {Terrain} and {Surfaces}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Linking the {Remote} {Sensing} of {Geodiversity} and {Traits} {Relevant} to {Biodiversity}—{Part} {II}},\n\turl = {https://www.mdpi.com/2072-4292/12/22/3690},\n\tdoi = {10.3390/rs12223690},\n\tabstract = {The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Schaepman, Michael E. and Skidmore, Andrew K. and Truckenbrodt, Sina C. and Hacker, Jörg M. and Baade, Jussi and Bannehr, Lutz and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Gläßer, Cornelia and Haase, Dagmar and Heurich, Marco and Jagdhuber, Thomas and Jany, Sven and Krönert, Rudolf and Möller, Markus and Mollenhauer, Hannes and Montzka, Carsten and Pause, Marion and Rogass, Christian and Salepci, Nesrin and Schmullius, Christiane and Schrodt, Franziska and Schütze, Claudia and Schweitzer, Christian and Selsam, Peter and Spengler, Daniel and Vohland, Michael and Volk, Martin and Weber, Ute and Wellmann, Thilo and Werban, Ulrike and Zacharias, Steffen and Thiel, Christian},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {3690},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Pisinaras, V.; Brogi, C.; Bogena, H.; Hendricks-Franssen, H.; Dombrowski, O.; and Panagopoulos, A.\n\n\n \n \n \n \n \n Development of irrigation management services based on integration of innovative soil moisture monitoring and hydrological modelling.\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{pisinaras_development_2020,\n\ttype = {other},\n\ttitle = {Development of irrigation management services based on integration of innovative soil moisture monitoring and hydrological modelling},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-4093.html},\n\tabstract = {\\&lt;p\\&gt;The H2020 ATLAS project (www.atlas-h2020.eu/) aims to develop an open, flexible and distributed platform that will provide services for the agricultural sector based on the seamless interconnection of sensors and machines. Two interconnected services that will be included in the platform are the soil moisture monitoring and the irrigation management services. The soil moisture monitoring service will integrate both invasive (wireless sensor network (SoilNet)) and non-invasive soil moisture monitoring methods (cosmic-ray neutron sensors (CRNS)). Ultimately, a model will be developed that combines SoilNet and CRNS measurements to predict soil moisture time series. Soil water potential sensors will be incorporated as well.\\&lt;/p\\&gt;\\&lt;p\\&gt;Data provided by the above described service will be incorporated in an irrigation management service which will be based on hydrological modelling. The fully distributed, deterministic Community Land Model (CLM, version 5) will be applied which incorporates physically-based simulation of soil water balance and crop growth. Two different levels of application will be considered, namely the farm and watershed scale, which will be combined to weather forecast in order to provide irrigation scheduling advice. The farm scale application will take advantage of soil moisture monitoring data and provide farm specific irrigation scheduling, while the watershed scale application will provide a more generic irrigation advice based on the average cultivation practices. Furthermore, the CLM model will be coupled to a groundwater flow model in order to connect irrigation to groundwater availability. By doing so, it will be possible to support the efficient and sustainable groundwater management as well as competent water uses in an area that suffers from water scarcity.\\&lt;/p\\&gt;\\&lt;p\\&gt;These services will be implemented in the area of Pinios Hydrologic Observatory, located in central Greece. Three pilot orchards will be established introducing different soil moisture monitoring setups, while the boundaries of the Observatory will be used for the pilot implementation of irrigation management service on the watershed scale. Furthermore, two pilot vineyards located in northern Greece will be established in order to further test the services functionality on the farm scale.\\&lt;/p\\&gt;},\n\turldate = {2022-11-21},\n\tinstitution = {oral},\n\tauthor = {Pisinaras, Vassilios and Brogi, Cosimo and Bogena, Heye and Hendricks-Franssen, Harrie-Jan and Dombrowski, Olga and Panagopoulos, Andreas},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-4093},\n}\n\n\n\n

\n\n\n

\n The H2020 ATLAS project (www.atlas-h2020.eu/) aims to develop an open, flexible and distributed platform that will provide services for the agricultural sector based on the seamless interconnection of sensors and machines. Two interconnected services that will be included in the platform are the soil moisture monitoring and the irrigation management services. The soil moisture monitoring service will integrate both invasive (wireless sensor network (SoilNet)) and non-invasive soil moisture monitoring methods (cosmic-ray neutron sensors (CRNS)). Ultimately, a model will be developed that combines SoilNet and CRNS measurements to predict soil moisture time series. Soil water potential sensors will be incorporated as well.Data provided by the above described service will be incorporated in an irrigation management service which will be based on hydrological modelling. The fully distributed, deterministic Community Land Model (CLM, version 5) will be applied which incorporates physically-based simulation of soil water balance and crop growth. Two different levels of application will be considered, namely the farm and watershed scale, which will be combined to weather forecast in order to provide irrigation scheduling advice. The farm scale application will take advantage of soil moisture monitoring data and provide farm specific irrigation scheduling, while the watershed scale application will provide a more generic irrigation advice based on the average cultivation practices. Furthermore, the CLM model will be coupled to a groundwater flow model in order to connect irrigation to groundwater availability. By doing so, it will be possible to support the efficient and sustainable groundwater management as well as competent water uses in an area that suffers from water scarcity.These services will be implemented in the area of Pinios Hydrologic Observatory, located in central Greece. Three pilot orchards will be established introducing different soil moisture monitoring setups, while the boundaries of the Observatory will be used for the pilot implementation of irrigation management service on the watershed scale. Furthermore, two pilot vineyards located in northern Greece will be established in order to further test the services functionality on the farm scale.\n

\n\n\n

\n \n\n \n \n Nguyen, T. H.; Langensiepen, M.; Vanderborght, J.; Hüging, H.; Mboh, C. M.; and Ewert, F.\n\n\n \n \n \n \n \n Comparison of root water uptake models in simulating CO$_{\\textrm{2}}$ and H$_{\\textrm{2}}$O fluxes and growth of wheat.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(10): 4943–4969. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nguyen_comparison_2020,\n\ttitle = {Comparison of root water uptake models in simulating {CO}$_{\\textrm{2}}$ and {H}$_{\\textrm{2}}${O} fluxes and growth of wheat},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/4943/2020/},\n\tdoi = {10.5194/hess-24-4943-2020},\n\tabstract = {Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models\nestimate the effect of soil water stress on stomatal conductance directly\nfrom soil water content or soil hydraulic potential without explicit\nrepresentation of hydraulic signals between the soil and stomata. In order\nto explicitly represent stomatal regulation by soil water status as a\nfunction of the hydraulic signal and its relation to the whole plant\nhydraulic conductance, we coupled the crop model LINTULCC2 and the root\ngrowth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of\nconsidering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model).\nSimulations were compared with gas flux measurements and crop growth data\nfrom a wheat crop grown under three water supply regimes (sheltered,\nrainfed, and irrigated) and two soil types (stony and silty) in western\nGermany in 2016. The two models showed a relatively similar performance in\nthe simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate,\nand soil water content. The Feddes model predicts more stress and less\ngrowth in the silty soil than in the stony soil, which is opposite to the\nobserved growth. The Couvreur model better represents the difference in\ngrowth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance\nover the growing season. This demonstrates the importance of two-way\nfeedbacks between growth and root water uptake for predicting the crop\nresponse to different soil water conditions in different soils. Our results\nsuggest that a better representation of the effects of soil characteristics\non root growth is needed for reliable estimations of root hydraulic\nconductance and gas fluxes, particularly in heterogeneous fields. The newly\ncoupled soil–plant model marks a promising approach but requires further\ntesting for other scenarios regarding crops, soil, and climate.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Nguyen, Thuy Huu and Langensiepen, Matthias and Vanderborght, Jan and Hüging, Hubert and Mboh, Cho Miltin and Ewert, Frank},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {4943--4969},\n}\n\n\n\n

\n\n\n

\n Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate.\n

\n\n\n

\n \n\n \n \n Kaufmann, M. S.; Hebel, C.; Weihermüller, L.; Baumecker, M.; Döring, T.; Schweitzer, K.; Hobley, E.; Bauke, S. L.; Amelung, W.; Vereecken, H.; and Kruk, J.\n\n\n \n \n \n \n \n Effect of fertilizers and irrigation on multi‐configuration electromagnetic induction measurements.\n \n \n \n \n\n\n \n\n\n\n Soil Use and Management, 36(1): 104–116. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaufmann_effect_2020,\n\ttitle = {Effect of fertilizers and irrigation on multi‐configuration electromagnetic induction measurements},\n\tvolume = {36},\n\tissn = {0266-0032, 1475-2743},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/sum.12530},\n\tdoi = {10.1111/sum.12530},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Soil Use and Management},\n\tauthor = {Kaufmann, Manuela S. and Hebel, Christian and Weihermüller, Lutz and Baumecker, Michael and Döring, Thomas and Schweitzer, Kathlin and Hobley, Eleanor and Bauke, Sara L. and Amelung, Wulf and Vereecken, Harry and Kruk, Jan},\n\teditor = {Triantafilis, John},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {104--116},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Zistl-Schlingmann, M.; Kwatcho Kengdo, S.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Management Intensity Controls Nitrogen-Use-Efficiency and Flows in Grasslands—A 15N Tracing Experiment.\n \n \n \n \n\n\n \n\n\n\n Agronomy, 10(4): 606. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Management$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zistl-schlingmann_management_2020,\n\ttitle = {Management {Intensity} {Controls} {Nitrogen}-{Use}-{Efficiency} and {Flows} in {Grasslands}—{A} {15N} {Tracing} {Experiment}},\n\tvolume = {10},\n\tissn = {2073-4395},\n\turl = {https://www.mdpi.com/2073-4395/10/4/606},\n\tdoi = {10.3390/agronomy10040606},\n\tabstract = {The consequences of land use intensification and climate warming on productivity, fates of fertilizer nitrogen (N) and the overall soil N balance of montane grasslands remain poorly understood. Here, we report findings of a 15N slurry-tracing experiment on large grassland plant–soil lysimeters exposed to different management intensities (extensive vs. intensive) and climates (control; translocation: +2 °C, reduced precipitation). Surface-applied cattle slurry was enriched with both 15NH4+ and 15N-urea in order to trace its fate in the plant–soil system. Recovery of 15N tracer in plants was low (7–17\\%), while it was considerably higher in the soil N pool (32–42\\%), indicating N stabilization in soil organic nitrogen (SON). Total 15N recovery was only 49\\% ± 7\\% indicating substantial fertilizer N losses to the environment. With harvest N exports exceeding N fertilization rates, the N balance was negative for all climate and management treatments. Intensive management had an increased deficit relative to extensive management. In contrast, simulated climate change had no significant effects on the grassland N balance. These results suggest a risk of soil N mining in montane grasslands under land use intensification based on broadcast liquid slurry application.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Agronomy},\n\tauthor = {Zistl-Schlingmann, Marcus and Kwatcho Kengdo, Steve and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {606},\n}\n\n\n\n

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\n \n\n \n \n Zhang, X.; Yang, X.; Jomaa, S.; and Rode, M.\n\n\n \n \n \n \n \n Analyzing impacts of seasonality and landscape gradient on event scale nitrate-discharge dynamics based on nested high-frequency monitoring.\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Analyzing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{zhang_analyzing_2020,\n\ttype = {other},\n\ttitle = {Analyzing impacts of seasonality and landscape gradient on event scale nitrate-discharge dynamics based on nested high-frequency monitoring},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-3188.html},\n\tabstract = {\\&lt;p\\&gt;Strom event-scale analysis provides insights into nitrate transport dynamics at catchment scale. Investigating different hysteretic relationships between nitrate and discharge can disentangle catchment nitrate functioning both spatially and temporally. In this study, we explored seasonality and landscape gradiemt effects on nitrate concentration-discharge (C-Q) hysteresis patterns based on six-year (2012-2017), high-frequency (15 min) data in the Selke catchment (central Germany). The Selke catchment exhibits heterogeneous combinations of meteorological, hydrogeological, and anthropogenic conditions. Three nested gauging stations were built along the main Selke River, capturing discharge and nitrate concentration from the dominant uppermost mixed forest and arable land, middle catchment pure steep forest and lowland arable and urban land areas, respectively. Amongst the 227 storm events that have been detected, anticlockwise and accretion of C-Q relationships accounted for 76.6\\% and 75.3\\%, respectively, while the proportions decreased with the increasing areal share of arable land during summer season. Accretion pattern predominated forest areas (e.g., the middle catchment) throughout the whole year suggesting higher nitrate concentration in dominating interflow than baseflow. In contrast, dilution pattern was almost exclusively observed in lowland areas (dominated by arable and urban areas) in dry periods, indicating lower nitrate concentration in quick runoff components like surface runoff. We further investigated the consistency and variability of hysteresis patterns from upstream to downstream based on shared events. Results indicated hysteresis patterns seemed to be consistent at the three stations when discharge was high enough. Moreover, we found that nitrate load contributions from the upper and lower areas changed seasonally, albeit the dominant share of runoff volume from the upper area throughout the whole year. Such a comprehensive analysis (i.e., clockwise vs. anticlockwise, accretion vs. dilution) enables in-deep discussion of the plausible mechanisms of nitrate dynamics under different landscape conditions. We are also aware of limitations of such statistical data analysis, which can likely be tackled by mechanistic modelling at higher temporal resolutions.\\&lt;/p\\&gt;},\n\turldate = {2022-11-02},\n\tinstitution = {oral},\n\tauthor = {Zhang, Xiaolin and Yang, Xiaoqiang and Jomaa, Seifeddine and Rode, Michael},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-3188},\n}\n\n\n\n

\n\n\n

\n Strom event-scale analysis provides insights into nitrate transport dynamics at catchment scale. Investigating different hysteretic relationships between nitrate and discharge can disentangle catchment nitrate functioning both spatially and temporally. In this study, we explored seasonality and landscape gradiemt effects on nitrate concentration-discharge (C-Q) hysteresis patterns based on six-year (2012-2017), high-frequency (15 min) data in the Selke catchment (central Germany). The Selke catchment exhibits heterogeneous combinations of meteorological, hydrogeological, and anthropogenic conditions. Three nested gauging stations were built along the main Selke River, capturing discharge and nitrate concentration from the dominant uppermost mixed forest and arable land, middle catchment pure steep forest and lowland arable and urban land areas, respectively. Amongst the 227 storm events that have been detected, anticlockwise and accretion of C-Q relationships accounted for 76.6% and 75.3%, respectively, while the proportions decreased with the increasing areal share of arable land during summer season. Accretion pattern predominated forest areas (e.g., the middle catchment) throughout the whole year suggesting higher nitrate concentration in dominating interflow than baseflow. In contrast, dilution pattern was almost exclusively observed in lowland areas (dominated by arable and urban areas) in dry periods, indicating lower nitrate concentration in quick runoff components like surface runoff. We further investigated the consistency and variability of hysteresis patterns from upstream to downstream based on shared events. Results indicated hysteresis patterns seemed to be consistent at the three stations when discharge was high enough. Moreover, we found that nitrate load contributions from the upper and lower areas changed seasonally, albeit the dominant share of runoff volume from the upper area throughout the whole year. Such a comprehensive analysis (i.e., clockwise vs. anticlockwise, accretion vs. dilution) enables in-deep discussion of the plausible mechanisms of nitrate dynamics under different landscape conditions. We are also aware of limitations of such statistical data analysis, which can likely be tackled by mechanistic modelling at higher temporal resolutions.\n

\n\n\n

\n \n\n \n \n Yu, Y.; Klotzsche, A.; Weihermüller, L.; Huisman, J. A.; Vanderborght, J.; Vereecken, H.; and der Kruk, J.\n\n\n \n \n \n \n \n Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data.\n \n \n \n \n\n\n \n\n\n\n Near Surface Geophysics, 18(3): 275–294. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Measuring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yu_measuring_2020,\n\ttitle = {Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data},\n\tvolume = {18},\n\tissn = {1569-4445, 1873-0604},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/nsg.12099},\n\tdoi = {10.1002/nsg.12099},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Near Surface Geophysics},\n\tauthor = {Yu, Yi and Klotzsche, Anja and Weihermüller, Lutz and Huisman, Johan Alexander and Vanderborght, Jan and Vereecken, Harry and der Kruk, Jan},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {275--294},\n}\n\n\n\n

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\n \n\n \n \n Wulf, M.; Kaiser, K.; Mrotzek, A.; Geiges-Erzgräber, L.; Schulz, L.; Stockmann, I.; Schneider, T.; Kappler, C.; and Bens, O.\n\n\n \n \n \n \n \n A Multisource Approach to Verify a Forest as a Reference of Natural Conditions in an Intensively Used Rural Landscape (Uckermark, Ne Germany).\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{wulf_multisource_2020,\n\ttype = {preprint},\n\ttitle = {A {Multisource} {Approach} to {Verify} a {Forest} as a {Reference} of {Natural} {Conditions} in an {Intensively} {Used} {Rural} {Landscape} ({Uckermark}, {Ne} {Germany})},\n\turl = {https://www.researchsquare.com/article/rs-113844/v1},\n\tabstract = {Abstract \n          BackgroundThe sharp decline in near-natural areas worldwide is undisputed, but the consequences of this decline, apart from the loss of biodiversity, cannot be fully assessed. Biotic components of a landscape are usually more easily assessed than the abiotic components, since biotic components are often more easily detectable. A forest of (semi)natural stocking was selected in the northeastern part of Brandenburg (northeast Germany) to check whether it can serve as reference site for near-natural conditions or not. Analyses of archival sources and historic maps as well as field investigations were combined to reconstruct the dynamics of vegetation and soil as far back in time as possible.ResultsPalynological data from nearby sites provide evidence that the investigated area has been forested for several thousands of years and has hardly been structurally influenced by humans in the last 450 years. This evidence together with historical maps of tree species composition allows us to infer that the specific forest has been very close to a natural state for at least 250 years. Soil investigations support this conclusion, since a soil inventory, field studies on two catenas and corings at selected depressions rarely show signs of anthropogenic erosion and related colluviation. Parts of the area were cleared in prehistory, but near-natural soils have been preserved in other parts. ConclusionsThe area with these undisturbed parts is regarded as an ideal reference site. With this study, we show that using a multi-source approach it is possible to find potential reference sites and that such an approach is applicable in other regions.},\n\turldate = {2022-11-02},\n\tinstitution = {In Review},\n\tauthor = {Wulf, Monika and Kaiser, Knut and Mrotzek, Almut and Geiges-Erzgräber, Lina and Schulz, Lars and Stockmann, Irina and Schneider, Thomas and Kappler, Christoph and Bens, Oliver},\n\tmonth = dec,\n\tyear = {2020},\n\tdoi = {10.21203/rs.3.rs-113844/v1},\n}\n\n\n\n

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\n \n\n \n \n Wu, X.; Chen, Z.; Kiese, R.; Fu, J.; Gschwendter, S.; Schloter, M.; Liu, C.; Butterbach-Bahl, K.; Wolf, B.; and Dannenmann, M.\n\n\n \n \n \n \n \n Dinitrogen (N2) pulse emissions during freeze-thaw cycles from montane grassland soil.\n \n \n \n \n\n\n \n\n\n\n Biology and Fertility of Soils, 56(7): 959–972. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Dinitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_dinitrogen_2020,\n\ttitle = {Dinitrogen ({N2}) pulse emissions during freeze-thaw cycles from montane grassland soil},\n\tvolume = {56},\n\tissn = {0178-2762, 1432-0789},\n\turl = {https://link.springer.com/10.1007/s00374-020-01476-7},\n\tdoi = {10.1007/s00374-020-01476-7},\n\tabstract = {Abstract \n             \n              Short-lived pulses of soil nitrous oxide (N \n              2 \n              O) emissions during freeze-thaw periods can dominate annual cumulative N \n              2 \n              O fluxes from temperate managed and natural soils. However, the effects of freeze thaw cycles (FTCs) on dinitrogen (N \n              2 \n              ) emissions, i.e., the dominant terminal product of the denitrification process, and ratios of N \n              2 \n              /N \n              2 \n              O emissions have remained largely unknown because methodological difficulties were so far hampering detailed studies. Here, we quantified both N \n              2 \n              and N \n              2 \n              O emissions of montane grassland soils exposed to three subsequent FTCs under two different soil moisture levels (40 and 80\\% WFPS) and under manure addition at 80\\% WFPS. In addition, we also quantified abundance and expression of functional genes involved in nitrification and denitrification to better understand microbial drivers of gaseous N losses. Our study shows that each freeze thaw cycle was associated with pulse emissions of both N \n              2 \n              O and N \n              2 \n              , with soil N \n              2 \n              emissions exceeding N \n              2 \n              O emissions by a factor of 5–30. Increasing soil moisture from 40 to 80\\% WFPS and addition of cow slurry increased the cumulative FTC N \n              2 \n              emissions by 102\\% and 77\\%, respectively. For N \n              2 \n              O, increasing soil moisture from 40 to 80\\% WFPS and addition of slurry increased the cumulative emissions by 147\\% and 42\\%, respectively. Denitrification gene \n              cnorB \n              and \n              nosZ \n              clade I transcript levels showed high explanatory power for N \n              2 \n              O and N \n              2 \n              emissions, thereby reflecting both N gas flux dynamics due to FTC and effects of different water availability and fertilizer addition. In agreement with several other studies for various ecosystems, we show here for mountainous grassland soils that pulse emissions of N \n              2 \n              O were observed during freeze-thaw. More importantly, this study shows that the freeze-thaw N \n              2 \n              pulse emissions strongly exceeded those of N \n              2 \n              O in magnitude, which indicates that N \n              2 \n              emissions during FTCs could represent an important N loss pathway within the grassland N mass balances. However, their actual significance needs to be assessed under field conditions using intact plant-soil systems.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-02},\n\tjournal = {Biology and Fertility of Soils},\n\tauthor = {Wu, Xing and Chen, Zhe and Kiese, Ralf and Fu, Jin and Gschwendter, Silvia and Schloter, Michael and Liu, Chunyan and Butterbach-Bahl, Klaus and Wolf, Benjamin and Dannenmann, Michael},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {959--972},\n}\n\n\n\n

\n\n\n

\n Abstract Short-lived pulses of soil nitrous oxide (N 2 O) emissions during freeze-thaw periods can dominate annual cumulative N 2 O fluxes from temperate managed and natural soils. However, the effects of freeze thaw cycles (FTCs) on dinitrogen (N 2 ) emissions, i.e., the dominant terminal product of the denitrification process, and ratios of N 2 /N 2 O emissions have remained largely unknown because methodological difficulties were so far hampering detailed studies. Here, we quantified both N 2 and N 2 O emissions of montane grassland soils exposed to three subsequent FTCs under two different soil moisture levels (40 and 80% WFPS) and under manure addition at 80% WFPS. In addition, we also quantified abundance and expression of functional genes involved in nitrification and denitrification to better understand microbial drivers of gaseous N losses. Our study shows that each freeze thaw cycle was associated with pulse emissions of both N 2 O and N 2 , with soil N 2 emissions exceeding N 2 O emissions by a factor of 5–30. Increasing soil moisture from 40 to 80% WFPS and addition of cow slurry increased the cumulative FTC N 2 emissions by 102% and 77%, respectively. For N 2 O, increasing soil moisture from 40 to 80% WFPS and addition of slurry increased the cumulative emissions by 147% and 42%, respectively. Denitrification gene cnorB and nosZ clade I transcript levels showed high explanatory power for N 2 O and N 2 emissions, thereby reflecting both N gas flux dynamics due to FTC and effects of different water availability and fertilizer addition. In agreement with several other studies for various ecosystems, we show here for mountainous grassland soils that pulse emissions of N 2 O were observed during freeze-thaw. More importantly, this study shows that the freeze-thaw N 2 pulse emissions strongly exceeded those of N 2 O in magnitude, which indicates that N 2 emissions during FTCs could represent an important N loss pathway within the grassland N mass balances. However, their actual significance needs to be assessed under field conditions using intact plant-soil systems.\n

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\n \n\n \n \n Westphal, K.; Musolff, A.; Graeber, D.; and Borchardt, D.\n\n\n \n \n \n \n \n Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar N:P ratios.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(10): 104009. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Controls$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{westphal_controls_2020,\n\ttitle = {Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar {N}:{P} ratios},\n\tvolume = {15},\n\tissn = {1748-9326},\n\tshorttitle = {Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar {N}},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ab98b6},\n\tdoi = {10.1088/1748-9326/ab98b6},\n\tabstract = {Abstract \n            The input of nitrogen (N) and phosphorus (P) into rivers has been reduced in recent decades in many regions of the world to mitigate adverse eutrophication effects. However, legislation focused first on the reduction of nutrient loads from point sources and only later on diffuse sources. These reduction strategies have implications on riverine N:P stoichiometry, which potentially alter patterns of algal nutrient limitation and the functions or community structure of aquatic ecosystems. Here, we use a dataset spanning four decades of water quality for the Ruhr River (Germany) to show that the asynchronous implementation of point and diffuse source mitigation measures combined with time lags of catchment transport processes caused a temporally asynchronous reduction in dissolved inorganic nitrogen and total phosphorus concentrations. This asynchronous reduction increased the molar N:P ratios from around 30 to 100 in the river sections dominated by point sources, reducing the probability of N limitation of algae in favor of P limitation. \n            The Ruhr River catchment and the environmental policies implemented here illustrate the unintended effects of nutrient control strategies on the ecological stoichiometry at the catchment scale. We urge to assess systematically, whether unintentionally warped macronutrient ratios are observable in other managed river systems and to evaluate their environmental impacts.},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Westphal, Katja and Musolff, Andreas and Graeber, Daniel and Borchardt, Dietrich},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {104009},\n}\n\n\n\n

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\n \n\n \n \n Wentzky, V. C.; Tittel, J.; Jäger, C. G.; Bruggeman, J.; and Rinke, K.\n\n\n \n \n \n \n \n Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state.\n \n \n \n \n\n\n \n\n\n\n Journal of Ecology, 108(4): 1649–1663. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wentzky_seasonal_2020,\n\ttitle = {Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state},\n\tvolume = {108},\n\tissn = {0022-0477, 1365-2745},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2745.13395},\n\tdoi = {10.1111/1365-2745.13395},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Journal of Ecology},\n\tauthor = {Wentzky, Valerie Carolin and Tittel, Jörg and Jäger, Christoph Gerald and Bruggeman, Jorn and Rinke, Karsten},\n\teditor = {Nilsson, Christer},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {1649--1663},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Weitere, M.; Brauns, M.; Rinke, K.; Borchardt, D.; and Wentzky, V.\n\n\n \n \n \n \n \n Wasserqualität und Biodiversität. Eine enge wechselseitige Beziehung.\n \n \n \n \n\n\n \n\n\n\n ESKP-Themenspezial: Biodiversität,576 KB, p. 54–57. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Wasserqualität$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weitere_wasserqualitat_2020,\n\ttitle = {Wasserqualität und {Biodiversität}. {Eine} enge wechselseitige {Beziehung}},\n\tcopyright = {CC-BY 4.0},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000867},\n\tdoi = {10.2312/ESKP.2020.1.2.4},\n\tabstract = {Nur knapp 10 Prozent unserer Gewässer sind in einem guten ökologischen Zustand. Die Ursachen sind vielfältig: Abflussregulierung, Gewässerverbauung, Nährstoffe, Bodenerosion und Pestizide aus der Landwirtschaft sowie Rückstände aus städtischen Kläranlagen. Was aber heißt der Verlust von Biodiversität für die Gewässerqualität? Führt der Verlust von Artenvielfalt zu einer Verschlechterung der Gewässerqualität? Die Methode der stabilen Isotope beantwortet diese Fragen.},\n\tlanguage = {de},\n\turldate = {2022-11-02},\n\tjournal = {ESKP-Themenspezial: Biodiversität},\n\tauthor = {Weitere, Markus and Brauns, Mario and Rinke, Karsten and Borchardt, Dietrich and Wentzky, Valerie},\n\tyear = {2020},\n\tpages = {576 KB, p. 54--57},\n}\n\n\n\n

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\n \n\n \n \n Wellmann, T.; Lausch, A.; Andersson, E.; Knapp, S.; Cortinovis, C.; Jache, J.; Scheuer, S.; Kremer, P.; Mascarenhas, A.; Kraemer, R.; Haase, A.; Schug, F.; and Haase, D.\n\n\n \n \n \n \n \n Remote sensing in urban planning: Contributions towards ecologically sound policies?.\n \n \n \n \n\n\n \n\n\n\n Landscape and Urban Planning, 204: 103921. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wellmann_remote_2020,\n\ttitle = {Remote sensing in urban planning: {Contributions} towards ecologically sound policies?},\n\tvolume = {204},\n\tissn = {01692046},\n\tshorttitle = {Remote sensing in urban planning},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169204620308860},\n\tdoi = {10.1016/j.landurbplan.2020.103921},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Landscape and Urban Planning},\n\tauthor = {Wellmann, Thilo and Lausch, Angela and Andersson, Erik and Knapp, Sonja and Cortinovis, Chiara and Jache, Jessica and Scheuer, Sebastian and Kremer, Peleg and Mascarenhas, André and Kraemer, Roland and Haase, Annegret and Schug, Franz and Haase, Dagmar},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {103921},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Ward, K. J.; Chabrillat, S.; Brell, M.; Castaldi, F.; Spengler, D.; and Foerster, S.\n\n\n \n \n \n \n \n Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(20): 3451. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ward_mapping_2020,\n\ttitle = {Mapping {Soil} {Organic} {Carbon} for {Airborne} and {Simulated} {EnMAP} {Imagery} {Using} the {LUCAS} {Soil} {Database} and a {Local} {PLSR}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/20/3451},\n\tdoi = {10.3390/rs12203451},\n\tabstract = {Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Ward, Kathrin J. and Chabrillat, Sabine and Brell, Maximilian and Castaldi, Fabio and Spengler, Daniel and Foerster, Saskia},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {3451},\n}\n\n\n\n

\n\n\n

\n Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.\n

\n\n\n

\n \n\n \n \n Wang, N.; Wang, C.; Dannenmann, M.; Butterbach-Bahl, K.; and Huang, J.\n\n\n \n \n \n \n \n Response of microbial community and net nitrogen turnover to modify climate change in Alpine meadow.\n \n \n \n \n\n\n \n\n\n\n Applied Soil Ecology, 152: 103553. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Response$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_response_2020,\n\ttitle = {Response of microbial community and net nitrogen turnover to modify climate change in {Alpine} meadow},\n\tvolume = {152},\n\tissn = {09291393},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0929139319314350},\n\tdoi = {10.1016/j.apsoil.2020.103553},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Applied Soil Ecology},\n\tauthor = {Wang, Nannan and Wang, Changhui and Dannenmann, Michael and Butterbach-Bahl, Klaus and Huang, Jianhui},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {103553},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, J.; Bogena, H. R.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n Stable-Isotope-Aided Investigation of the Effect of Redox Potential on Nitrous Oxide Emissions as Affected by Water Status and N Fertilization.\n \n \n \n \n\n\n \n\n\n\n Water, 12(10): 2918. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable-Isotope-Aided$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_stable-isotope-aided_2020,\n\ttitle = {Stable-{Isotope}-{Aided} {Investigation} of the {Effect} of {Redox} {Potential} on {Nitrous} {Oxide} {Emissions} as {Affected} by {Water} {Status} and {N} {Fertilization}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/10/2918},\n\tdoi = {10.3390/w12102918},\n\tabstract = {Soils are the dominant source of atmospheric nitrous oxide (N2O), especially agricultural soils that experience both waterlogging and intensive nitrogen fertilization. However, soil heterogeneity and the irregular occurrence of hydrological events hamper the prediction of the temporal and spatial dynamics of N2O production and transport in soils. Because soil moisture influences soil redox potential, and as soil N cycling processes are redox-sensitive, redox potential measurements could help us to better understand and predict soil N cycling and N2O emissions. Despite its importance, only a few studies have investigated the control of redox potential on N2Oemission from soils in detail. This study aimed to partition the different microbial processes involved in N2O production (nitrification and denitrification) by using redox measurements combined with isotope analysis at natural abundance and 15N-enriched. To this end, we performed long-term laboratory lysimeter experiments to mimic common agricultural irrigation and fertilization procedures. In addition, we used isotope analysis to characterize the distribution and partitioning of N2O sources and explored the 15N-N2O site preference to further constrain N2O microbial processes. We found that irrigation, saturation, and drainage induced changes in soil redox potential, which were closely related to changes in N2O emission from the soil as well as to changes in the vertical concentration profiles of dissolved N2O, nitrate (NO3−) and ammonium (NH4+). The results showed that the redox potential could be used as an indicator for NH4+, NO3−, and N2O production and consumption processes along the soil profile. For example, after a longer saturation period of unfertilized soil, the NO3− concentration was linearly correlated with the average redox values at the different depths (R2 = 0.81). During the transition from saturation to drainage, but before fertilization, the soil showed an increase in N2O emissions, which originated mainly from nitrification as indicated by the isotopic signatures of N2O (δ15N bulk, δ18O and 15N-N2O site preference). After fertilization, N2O still mainly originated from nitrification at the beginning, also indicated by high redox potential and the increase of dissolved NO3−. Denitrification mainly occurred during the last saturation period, deduced from the simultaneous 15N isotope analysis of NO3− and N2O. Our findings suggest that redox potential measurements provide suitable information for improving the prediction of soil N2O emissions and the distribution of mineral N species along the soil profile under different hydrological and fertilization regimes.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Wang, Jihuan and Bogena, Heye R. and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2918},\n}\n\n\n\n

\n\n\n

\n Soils are the dominant source of atmospheric nitrous oxide (N2O), especially agricultural soils that experience both waterlogging and intensive nitrogen fertilization. However, soil heterogeneity and the irregular occurrence of hydrological events hamper the prediction of the temporal and spatial dynamics of N2O production and transport in soils. Because soil moisture influences soil redox potential, and as soil N cycling processes are redox-sensitive, redox potential measurements could help us to better understand and predict soil N cycling and N2O emissions. Despite its importance, only a few studies have investigated the control of redox potential on N2Oemission from soils in detail. This study aimed to partition the different microbial processes involved in N2O production (nitrification and denitrification) by using redox measurements combined with isotope analysis at natural abundance and 15N-enriched. To this end, we performed long-term laboratory lysimeter experiments to mimic common agricultural irrigation and fertilization procedures. In addition, we used isotope analysis to characterize the distribution and partitioning of N2O sources and explored the 15N-N2O site preference to further constrain N2O microbial processes. We found that irrigation, saturation, and drainage induced changes in soil redox potential, which were closely related to changes in N2O emission from the soil as well as to changes in the vertical concentration profiles of dissolved N2O, nitrate (NO3−) and ammonium (NH4+). The results showed that the redox potential could be used as an indicator for NH4+, NO3−, and N2O production and consumption processes along the soil profile. For example, after a longer saturation period of unfertilized soil, the NO3− concentration was linearly correlated with the average redox values at the different depths (R2 = 0.81). During the transition from saturation to drainage, but before fertilization, the soil showed an increase in N2O emissions, which originated mainly from nitrification as indicated by the isotopic signatures of N2O (δ15N bulk, δ18O and 15N-N2O site preference). After fertilization, N2O still mainly originated from nitrification at the beginning, also indicated by high redox potential and the increase of dissolved NO3−. Denitrification mainly occurred during the last saturation period, deduced from the simultaneous 15N isotope analysis of NO3− and N2O. Our findings suggest that redox potential measurements provide suitable information for improving the prediction of soil N2O emissions and the distribution of mineral N species along the soil profile under different hydrological and fertilization regimes.\n

\n\n\n

\n \n\n \n \n Vitale, D.; Fratini, G.; Bilancia, M.; Nicolini, G.; Sabbatini, S.; and Papale, D.\n\n\n \n \n \n \n \n A robust data cleaning procedure for eddy covariance flux measurements.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(6): 1367–1391. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vitale_robust_2020,\n\ttitle = {A robust data cleaning procedure for eddy covariance flux measurements},\n\tvolume = {17},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/17/1367/2020/},\n\tdoi = {10.5194/bg-17-1367-2020},\n\tabstract = {Abstract. The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning.\nThe proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure.\nResults evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality.\nWe expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Vitale, Domenico and Fratini, Gerardo and Bilancia, Massimo and Nicolini, Giacomo and Sabbatini, Simone and Papale, Dario},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {1367--1391},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Vilà-Guerau de Arellano, J.; Ney, P.; Hartogensis, O.; de Boer, H.; van Diepen, K.; Emin, D.; de Groot, G.; Klosterhalfen, A.; Langensiepen, M.; Matveeva, M.; Miranda-García, G.; Moene, A. F.; Rascher, U.; Röckmann, T.; Adnew, G.; Brüggemann, N.; Rothfuss, Y.; and Graf, A.\n\n\n \n \n \n \n \n CloudRoots: integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land–atmosphere interactions.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(17): 4375–4404. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"CloudRoots:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vila-guerau_de_arellano_cloudroots_2020,\n\ttitle = {{CloudRoots}: integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land–atmosphere interactions},\n\tvolume = {17},\n\tissn = {1726-4189},\n\tshorttitle = {{CloudRoots}},\n\turl = {https://bg.copernicus.org/articles/17/4375/2020/},\n\tdoi = {10.5194/bg-17-4375-2020},\n\tabstract = {Abstract. The CloudRoots field experiment was designed to obtain a\ncomprehensive observational dataset that includes soil, plant, and\natmospheric variables to investigate the interaction between a heterogeneous\nland surface and its overlying atmospheric boundary layer at the sub-hourly\nand sub-kilometre scale. Our findings demonstrate the need to include\nmeasurements at leaf level to better understand the relations between\nstomatal aperture and evapotranspiration (ET) during the growing season at\nthe diurnal scale. Based on these observations, we obtain accurate\nparameters for the mechanistic representation of photosynthesis and stomatal\naperture. Once the new parameters are implemented, the model reproduces the\nstomatal leaf conductance and the leaf-level photosynthesis satisfactorily.\nAt the canopy scale, we find a consistent diurnal pattern on the\ncontributions of plant transpiration and soil evaporation using different\nmeasurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a\nprofile of the CO2 assimilation in the canopy with non-linear\nvariations with height. Observations taken with a laser scintillometer allow\nus to quantify the non-steadiness of the surface turbulent fluxes during the\nrapid changes driven by perturbation of photosynthetically active radiation\nby cloud flecks. More specifically, we find 2 min delays between the\ncloud radiation perturbation and ET. To study the relevance of advection and\nsurface heterogeneity for the land–atmosphere interaction, we employ a\ncoupled surface–atmospheric conceptual model that integrates the surface and\nupper-air observations made at different scales from leaf to the landscape.\nAt the landscape scale, we calculate a composite sensible heat flux by\nweighting measured fluxes with two different land use categories, which is\nconsistent with the diurnal evolution of the boundary layer depth. Using\nsun-induced fluorescence measurements, we also quantify the spatial\nvariability of ET and find large variations at the sub-kilometre scale\naround the CloudRoots site. Our study shows that throughout the entire\ngrowing season, the wide variations in stomatal opening and photosynthesis\nlead to large diurnal variations of plant transpiration at the leaf, plant,\ncanopy, and landscape scales. Integrating different advanced instrumental\ntechniques with modelling also enables us to determine variations of ET that\ndepend on the scale where the measurement were taken and on the plant\ngrowing stage.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Vilà-Guerau de Arellano, Jordi and Ney, Patrizia and Hartogensis, Oscar and de Boer, Hugo and van Diepen, Kevin and Emin, Dzhaner and de Groot, Geiske and Klosterhalfen, Anne and Langensiepen, Matthias and Matveeva, Maria and Miranda-García, Gabriela and Moene, Arnold F. and Rascher, Uwe and Röckmann, Thomas and Adnew, Getachew and Brüggemann, Nicolas and Rothfuss, Youri and Graf, Alexander},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {4375--4404},\n}\n\n\n\n

\n\n\n

\n Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational dataset that includes soil, plant, and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level to better understand the relations between stomatal aperture and evapotranspiration (ET) during the growing season at the diurnal scale. Based on these observations, we obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the model reproduces the stomatal leaf conductance and the leaf-level photosynthesis satisfactorily. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a profile of the CO2 assimilation in the canopy with non-linear variations with height. Observations taken with a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of photosynthetically active radiation by cloud flecks. More specifically, we find 2 min delays between the cloud radiation perturbation and ET. To study the relevance of advection and surface heterogeneity for the land–atmosphere interaction, we employ a coupled surface–atmospheric conceptual model that integrates the surface and upper-air observations made at different scales from leaf to the landscape. At the landscape scale, we calculate a composite sensible heat flux by weighting measured fluxes with two different land use categories, which is consistent with the diurnal evolution of the boundary layer depth. Using sun-induced fluorescence measurements, we also quantify the spatial variability of ET and find large variations at the sub-kilometre scale around the CloudRoots site. Our study shows that throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large diurnal variations of plant transpiration at the leaf, plant, canopy, and landscape scales. Integrating different advanced instrumental techniques with modelling also enables us to determine variations of ET that depend on the scale where the measurement were taken and on the plant growing stage.\n

\n\n\n

\n \n\n \n \n Vidal, A.; Schucknecht, A.; Toechterle, P.; Linares, D. R. A.; Garcia-Franco, N.; von Heßberg, A.; Krämer, A.; Sierts, A.; Fischer, A.; Willibald, G.; Fuetterer, S.; Ewald, J.; Baumert, V.; Weiss, M.; Schulz, S.; Schloter, M.; Bogacki, W.; Wiesmeier, M.; Mueller, C. W.; and Dannenmann, M.\n\n\n \n \n \n \n \n High resistance of soils to short-term re-grazing in a long-term abandoned alpine pasture.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 300: 107008. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vidal_high_2020,\n\ttitle = {High resistance of soils to short-term re-grazing in a long-term abandoned alpine pasture},\n\tvolume = {300},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880920301936},\n\tdoi = {10.1016/j.agee.2020.107008},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Vidal, Alix and Schucknecht, Anne and Toechterle, Paul and Linares, Diana Rocio Andrade and Garcia-Franco, Noelia and von Heßberg, Andreas and Krämer, Alexander and Sierts, Andrea and Fischer, Alfred and Willibald, Georg and Fuetterer, Sarah and Ewald, Jörg and Baumert, Vera and Weiss, Michael and Schulz, Stefanie and Schloter, Michael and Bogacki, Wolfgang and Wiesmeier, Martin and Mueller, Carsten W. and Dannenmann, Michael},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {107008},\n}\n\n\n\n

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\n \n\n \n \n Vallentin, C.; Dobers, E. S.; Itzerott, S.; Kleinschmit, B.; and Spengler, D.\n\n\n \n \n \n \n \n Delineation of management zones with spatial data fusion and belief theory.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 21(4): 802–830. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Delineation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vallentin_delineation_2020,\n\ttitle = {Delineation of management zones with spatial data fusion and belief theory},\n\tvolume = {21},\n\tissn = {1385-2256, 1573-1618},\n\turl = {http://link.springer.com/10.1007/s11119-019-09696-0},\n\tdoi = {10.1007/s11119-019-09696-0},\n\tabstract = {Abstract \n            Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Precision Agriculture},\n\tauthor = {Vallentin, Claudia and Dobers, Eike Stefan and Itzerott, Sibylle and Kleinschmit, Birgit and Spengler, Daniel},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {802--830},\n}\n\n\n\n

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\n \n\n \n \n Utom, A. U.; Werban, U.; Leven, C.; Müller, C.; Knöller, K.; Vogt, C.; and Dietrich, P.\n\n\n \n \n \n \n \n Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively: an assessment of dual-nitrate isotopic and chemical evidence in a stratified alluvial aquifer.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 147(2): 211–223. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Groundwater$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{utom_groundwater_2020,\n\ttitle = {Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively: an assessment of dual-nitrate isotopic and chemical evidence in a stratified alluvial aquifer},\n\tvolume = {147},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively},\n\turl = {http://link.springer.com/10.1007/s10533-020-00637-y},\n\tdoi = {10.1007/s10533-020-00637-y},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-02},\n\tjournal = {Biogeochemistry},\n\tauthor = {Utom, Ahamefula U. and Werban, Ulrike and Leven, Carsten and Müller, Christin and Knöller, Kay and Vogt, Carsten and Dietrich, Peter},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {211--223},\n}\n\n\n\n

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\n \n\n \n \n Tveit, A. T.; Kiss, A.; Winkel, M.; Horn, F.; Hájek, T.; Svenning, M. M.; Wagner, D.; and Liebner, S.\n\n\n \n \n \n \n \n Environmental patterns of brown moss- and Sphagnum-associated microbial communities.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 10(1): 22412. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tveit_environmental_2020,\n\ttitle = {Environmental patterns of brown moss- and {Sphagnum}-associated microbial communities},\n\tvolume = {10},\n\tissn = {2045-2322},\n\turl = {http://www.nature.com/articles/s41598-020-79773-2},\n\tdoi = {10.1038/s41598-020-79773-2},\n\tabstract = {Abstract \n             \n              Northern peatlands typically develop through succession from fens dominated by the moss family Amblystegiaceae to bogs dominated by the moss genus \n              Sphagnum \n              . How the different plants and abiotic environmental conditions provided in Amblystegiaceae and \n              Sphagnum \n              peat shape the respective moss associated microbial communities is unknown. Through a large-scale molecular and biogeochemical study spanning Arctic, sub-Arctic and temperate regions we assessed how the endo- and epiphytic microbial communities of natural northern peatland mosses relate to peatland type ( \n              Sphagnum \n              and Amblystegiaceae), location, moss taxa and abiotic environmental variables. Microbial diversity and community structure were distinctly different between Amblystegiaceae and \n              Sphagnum \n              peatlands, and within each of these two peatland types moss taxon explained the largest part of microbial community variation. \n              Sphagnum \n              and Amblystegiaceae shared few ({\\textless} 1\\% of all operational taxonomic units (OTUs)) but strikingly abundant (up to 65\\% of relative abundance) OTUs. This core community overlapped by one third with the \n              Sphagnum \n              -specific core-community. Thus, the most abundant microorganisms in \n              Sphagnum \n              that are also found in all the \n              Sphagnum \n              plants studied, are the same OTUs as those few shared with Amblystegiaceae. Finally, we could confirm that these highly abundant OTUs were endophytes in \n              Sphagnum \n              , but epiphytes on Amblystegiaceae. We conclude that moss taxa and abiotic environmental variables associate with particular microbial communities. While moss taxon was the most influential parameter, hydrology, pH and temperature also had significant effects on the microbial communities. A small though highly abundant core community is shared between \n              Sphagnum \n              and Amblystegiaceae.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Reports},\n\tauthor = {Tveit, Alexander Tøsdal and Kiss, Andrea and Winkel, Matthias and Horn, Fabian and Hájek, Tomáš and Svenning, Mette Marianne and Wagner, Dirk and Liebner, Susanne},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {22412},\n}\n\n\n\n

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\n \n\n \n \n Tarasova, L.; Basso, S.; and Merz, R.\n\n\n \n \n \n \n \n Transformation of Generation Processes From Small Runoff Events to Large Floods.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 47(22). November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Transformation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tarasova_transformation_2020,\n\ttitle = {Transformation of {Generation} {Processes} {From} {Small} {Runoff} {Events} to {Large} {Floods}},\n\tvolume = {47},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020GL090547},\n\tdoi = {10.1029/2020GL090547},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2022-11-02},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Tarasova, L. and Basso, S. and Merz, R.},\n\tmonth = nov,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Tarasova, L.; Basso, S.; Wendi, D.; Viglione, A.; Kumar, R.; and Merz, R.\n\n\n \n \n \n \n \n A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(5). May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tarasova_processbased_2020,\n\ttitle = {A {Process}‐{Based} {Framework} to {Characterize} and {Classify} {Runoff} {Events}: {The} {Event} {Typology} of {Germany}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {A {Process}‐{Based} {Framework} to {Characterize} and {Classify} {Runoff} {Events}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026951},\n\tdoi = {10.1029/2019WR026951},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Tarasova, L. and Basso, S. and Wendi, D. and Viglione, A. and Kumar, R. and Merz, R.},\n\tmonth = may,\n\tyear = {2020},\n}\n\n\n\n

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\n \n\n \n \n Bayat, A. T.; Schonbrodt-Stitt, S.; Nasta, P.; Ahmadian, N.; Conrad, C.; Bogena, H. R.; Vereecken, H.; Jakobi, J.; Baatz, R.; and Romano, N.\n\n\n \n \n \n \n \n Mapping near-surface soil moisture in a Mediterranean agroforestry ecosystem using Cosmic-Ray Neutron Probe and Sentinel-1 Data.\n \n \n \n \n\n\n \n\n\n\n In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pages 201–206, Trento, Italy, November 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{bayat_mapping_2020,\n\taddress = {Trento, Italy},\n\ttitle = {Mapping near-surface soil moisture in a {Mediterranean} agroforestry ecosystem using {Cosmic}-{Ray} {Neutron} {Probe} and {Sentinel}-1 {Data}},\n\tisbn = {9781728187839},\n\turl = {https://ieeexplore.ieee.org/document/9277557/},\n\tdoi = {10.1109/MetroAgriFor50201.2020.9277557},\n\turldate = {2022-11-02},\n\tbooktitle = {2020 {IEEE} {International} {Workshop} on {Metrology} for {Agriculture} and {Forestry} ({MetroAgriFor})},\n\tpublisher = {IEEE},\n\tauthor = {Bayat, Aida Taghavi and Schonbrodt-Stitt, Sarah and Nasta, Paolo and Ahmadian, Nima and Conrad, Christopher and Bogena, Heye R. and Vereecken, Harry and Jakobi, Jannis and Baatz, Roland and Romano, Nunzio},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {201--206},\n}\n\n\n\n

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\n \n\n \n \n Sun, Y.; Wu, B.; Wiekenkamp, I.; Kooijman, A. M.; and Bol, R.\n\n\n \n \n \n \n \n Uranium Vertical and Lateral Distribution in a German Forested Catchment.\n \n \n \n \n\n\n \n\n\n\n Forests, 11(12): 1351. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Uranium$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sun_uranium_2020,\n\ttitle = {Uranium {Vertical} and {Lateral} {Distribution} in a {German} {Forested} {Catchment}},\n\tvolume = {11},\n\tissn = {1999-4907},\n\turl = {https://www.mdpi.com/1999-4907/11/12/1351},\n\tdoi = {10.3390/f11121351},\n\tabstract = {The natural measurements of uranium (U) are important for establishing natural baseline levels of U in soil. The relations between U and other elements are important to determine the extent of geological origin of soil U. The present study was aimed at providing a three-dimensional view of soil U distribution in a forested catchment (ca. 38.5 ha) in western Germany. The evaluated data, containing 155 sampled points, each with four major soil horizons (L/Of, Oh, A, and B), were collected from two existing datasets. The vertical U distribution, the lateral pattern of U in the catchment, and the occurrence of correlations between U and three groups of elements (nutrient elements, heavy metals, and rare earth elements) were examined. The results showed the median U concentration increased sevenfold from the top horizon L/Of (0.14 mg kg−1) to the B horizon (1.01 mg kg−1), suggesting a geogenic origin of soil U. Overall, soil U concentration was found to be negatively correlated with some plant macronutrients (C, N, K, S, Ca) but positively with others (P, Mg, Cu, Zn, Fe, Mn, Mo). The negative correlations between U and some macronutrients indicated a limited accumulation of plant-derived U in soil, possibly due to low phytoavailability of U. Positive correlations were also found between U concentration and heavy metals (Cr, Co, Ni, Ga, As, Cd, Hg, Pb) or rare earth elements, which further pointed to a geogenic origin of soil U in this forested catchment.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-02},\n\tjournal = {Forests},\n\tauthor = {Sun, Yajie and Wu, Bei and Wiekenkamp, Inge and Kooijman, Annemieke M. and Bol, Roland},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {1351},\n}\n\n\n\n

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\n \n\n \n \n Spank, U.; Hehn, M.; Keller, P.; Koschorreck, M.; and Bernhofer, C.\n\n\n \n \n \n \n \n A Season of Eddy-Covariance Fluxes Above an Extensive Water Body Based on Observations from a Floating Platform.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 174(3): 433–464. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{spank_season_2020,\n\ttitle = {A {Season} of {Eddy}-{Covariance} {Fluxes} {Above} an {Extensive} {Water} {Body} {Based} on {Observations} from a {Floating} {Platform}},\n\tvolume = {174},\n\tissn = {0006-8314, 1573-1472},\n\turl = {http://link.springer.com/10.1007/s10546-019-00490-z},\n\tdoi = {10.1007/s10546-019-00490-z},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Spank, Uwe and Hehn, Markus and Keller, Philipp and Koschorreck, Matthias and Bernhofer, Christian},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {433--464},\n}\n\n\n\n

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\n \n\n \n \n Siddique, A.; Liess, M.; Shahid, N.; and Becker, J. M.\n\n\n \n \n \n \n \n Insecticides in agricultural streams exert pressure for adaptation but impair performance in Gammarus pulex at regulatory acceptable concentrations.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 722: 137750. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Insecticides$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siddique_insecticides_2020,\n\ttitle = {Insecticides in agricultural streams exert pressure for adaptation but impair performance in {Gammarus} pulex at regulatory acceptable concentrations},\n\tvolume = {722},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720312614},\n\tdoi = {10.1016/j.scitotenv.2020.137750},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Siddique, Ayesha and Liess, Matthias and Shahid, Naeem and Becker, Jeremias Martin},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {137750},\n}\n\n\n\n

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\n \n\n \n \n Schucknecht, A.; Krämer, A.; Asam, S.; Mejia-Aguilar, A.; Garcia-Franco, N.; Schuchardt, M. A.; Jentsch, A.; and Kiese, R.\n\n\n \n \n \n \n \n Vegetation traits of pre-Alpine grasslands in southern Germany.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 316. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Vegetation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schucknecht_vegetation_2020,\n\ttitle = {Vegetation traits of pre-{Alpine} grasslands in southern {Germany}},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-020-00651-7},\n\tdoi = {10.1038/s41597-020-00651-7},\n\tabstract = {Abstract \n             \n              The data set contains information on aboveground vegetation traits of {\\textgreater} 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project (“Sustainable use of alpine and pre-alpine grassland soils in a changing climate”; \n              https://www.susalps.de/ \n              ) to provide \n              in-situ \n              data for the calibration and validation of remote sensing based models to estimate grassland traits.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Data},\n\tauthor = {Schucknecht, Anne and Krämer, Alexander and Asam, Sarah and Mejia-Aguilar, Abraham and Garcia-Franco, Noelia and Schuchardt, Max A. and Jentsch, Anke and Kiese, Ralf},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {316},\n}\n\n\n\n

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\n \n\n \n \n Schubert, M.; Knoeller, K.; Mueller, C.; and Gilfedder, B.\n\n\n \n \n \n \n \n Investigating River Water/Groundwater Interaction along a Rivulet Section by 222Rn Mass Balancing.\n \n \n \n \n\n\n \n\n\n\n Water, 12(11): 3027. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schubert_investigating_2020,\n\ttitle = {Investigating {River} {Water}/{Groundwater} {Interaction} along a {Rivulet} {Section} by {222Rn} {Mass} {Balancing}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/11/3027},\n\tdoi = {10.3390/w12113027},\n\tabstract = {Investigation of river water/groundwater interaction aims generally at: (i) localizing water migration pathways; and (ii) quantifying water and associated matter exchange between the two natural water resources. Related numerical models generally rely on model-specific parameters that represent the physical conditions of the catchment and suitable aqueous tracer data. A generally applicable approach for this purpose is based on the finite element model FINIFLUX that is using the radioactive noble gas radon-222 as naturally occurring tracer. During the study discussed in this paper, radon and physical stream data were used with the aim to localize and quantify groundwater discharge into a well-defined section of a small headwater stream. Besides site-specific results of two sampling campaigns, the outcomes of the study reveal: (i) the general difficulties of conducting river water/groundwater interaction studies in small and heterogeneous headwater catchments; and (ii) the particular challenge of defining well constrained site- and campaign-specific values for both the groundwater radon endmember and the radon degassing coefficient. It was revealed that determination of both parameters should be based on as many data sources as possible and include a critical assessment of the reasonability of the gathered and used datasets. The results of our study exposed potential limitations of the approach if executed in small and turbulent headwater streams. Hence, we want to emphasize that the project was not only executed as a case study at a distinct site but rather aimed at evaluating the applicability of the chosen approach for conducting river water/groundwater interaction studies in heterogeneous headwater catchments.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Schubert, Michael and Knoeller, Kay and Mueller, Christin and Gilfedder, Benjamin},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {3027},\n}\n\n\n\n

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\n \n\n \n \n Schlingmann, M.; Tobler, U.; Berauer, B.; Garcia-Franco, N.; Wilfahrt, P.; Wiesmeier, M.; Jentsch, A.; Wolf, B.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Intensive slurry management and climate change promote nitrogen mining from organic matter-rich montane grassland soils.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 456(1-2): 81–98. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Intensive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schlingmann_intensive_2020,\n\ttitle = {Intensive slurry management and climate change promote nitrogen mining from organic matter-rich montane grassland soils},\n\tvolume = {456},\n\tissn = {0032-079X, 1573-5036},\n\turl = {https://link.springer.com/10.1007/s11104-020-04697-9},\n\tdoi = {10.1007/s11104-020-04697-9},\n\tabstract = {Abstract \n             \n              Aims \n              Consequences of climate change and land use intensification on the nitrogen (N) cycle of organic-matter rich grassland soils in the alpine region remain poorly understood. We aimed to identify fates of fertilizer N and to determine the overall N balance of an organic-matter rich grassland in the European alpine region as influenced by intensified management and warming. \n             \n             \n              Methods \n               \n                We combined \n                15 \n                N cattle slurry labelling with a space for time climate change experiment, which was based on translocation of intact plant-soil mesocosms down an elevational gradient to induce warming of +1 °C and + 3 °C. Mesocosms were subject to either extensive or intensive management. The fate of slurry-N was traced in the plant-soil system. \n               \n             \n             \n              Results \n               \n                Grassland productivity was very high (8.2 t - 19.4 t dm ha \n                −1 \n                 yr \n                −1 \n                ), recovery of slurry \n                15 \n                N in mowed plant biomass was, however, low (9.6–14.7\\%), illustrating low fertilizer N use efficiency and high supply of plant available N via mineralization of soil organic matter (SOM). Higher \n                15 \n                N recovery rates (20.2–31.8\\%) were found in the soil N pool, dominated by recovery in unextractable N. Total \n                15 \n                N recovery was approximately half of the applied tracer, indicating substantial loss to the environment. Overall, high N export by harvest (107–360 kg N ha \n                −1 \n                 yr \n                −1 \n                ) markedly exceeded N inputs, leading to a negative grassland N balance. \n               \n             \n             \n              Conclusions \n              Here provided results suggests a risk of soil N mining in montane grasslands, which increases both under climate change and land use intensification.},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-02},\n\tjournal = {Plant and Soil},\n\tauthor = {Schlingmann, Marcus and Tobler, Ursina and Berauer, Bernd and Garcia-Franco, Noelia and Wilfahrt, Peter and Wiesmeier, Martin and Jentsch, Anke and Wolf, Benjamin and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {81--98},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Scheiffele, L. M.; Baroni, G.; Franz, T. E.; Jakobi, J.; and Oswald, S. E.\n\n\n \n \n \n \n \n A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{scheiffele_profile_2020,\n\ttitle = {A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20083},\n\tdoi = {10.1002/vzj2.20083},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Scheiffele, Lena M. and Baroni, Gabriele and Franz, Trenton E. and Jakobi, Jannis and Oswald, Sascha E.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Risse‐Buhl, U.; Anlanger, C.; Chatzinotas, A.; Noss, C.; Lorke, A.; and Weitere, M.\n\n\n \n \n \n \n \n Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 65(10): 2261–2277. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Near$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rissebuhl_near_2020,\n\ttitle = {Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms},\n\tvolume = {65},\n\tissn = {0024-3590, 1939-5590},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.11451},\n\tdoi = {10.1002/lno.11451},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Risse‐Buhl, Ute and Anlanger, Christine and Chatzinotas, Antonis and Noss, Christian and Lorke, Andreas and Weitere, Markus},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2261--2277},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Reichenau, T. G.; Korres, W.; Schmidt, M.; Graf, A.; Welp, G.; Meyer, N.; Stadler, A.; Brogi, C.; and Schneider, K.\n\n\n \n \n \n \n \n A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 12(4): 2333–2364. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reichenau_comprehensive_2020,\n\ttitle = {A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western {Germany}},\n\tvolume = {12},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/12/2333/2020/},\n\tdoi = {10.5194/essd-12-2333-2020},\n\tabstract = {Abstract. The development and validation of hydroecological\nland-surface models to simulate agricultural areas require extensive data\non weather, soil properties, agricultural management, and vegetation states\nand fluxes. However, these comprehensive data are rarely available since\nmeasurement, quality control, documentation, and compilation of the different\ndata types are costly in terms of time and money. Here, we present a\ncomprehensive dataset, which was collected at four agricultural sites within\nthe Rur catchment in western Germany in the framework of the Transregional\nCollaborative Research Centre 32 (TR32) “Patterns in\nSoil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data\nAssimilation”. Vegetation-related data comprise fresh and dry\nbiomass (green and brown, predominantly per organ), plant height, green and\nbrown leaf area index, phenological development state, nitrogen and carbon\ncontent (overall {\\textgreater} 17 000 entries), and masses of harvest residues\nand regrowth of vegetation after harvest or before planting of the main crop\n({\\textgreater} 250 entries). Vegetation data including LAI were collected in\nfrequencies of 1 to 3 weeks in the years 2015 until 2017, mostly\nduring overflights of the Sentinel 1 and Radarsat 2 satellites. In addition,\nfluxes of carbon, energy, and water ({\\textgreater} 180 000 half-hourly\nrecords) measured using the eddy covariance technique are included. Three\nflux time series have simultaneous data from two different heights. Data on\nagricultural management include sowing and harvest dates as well as information\non cultivation, fertilization, and agrochemicals (27 management periods). The\ndataset also includes gap-filled weather data ({\\textgreater} 200 000 hourly\nrecords) and soil parameters (particle size distributions, carbon and\nnitrogen content; {\\textgreater} 800 records). These data can also be useful\nfor development and validation of remote-sensing products. The dataset\nis hosted at the TR32 database\n(https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI\nhttps://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Earth System Science Data},\n\tauthor = {Reichenau, Tim G. and Korres, Wolfgang and Schmidt, Marius and Graf, Alexander and Welp, Gerhard and Meyer, Nele and Stadler, Anja and Brogi, Cosimo and Schneider, Karl},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2333--2364},\n}\n\n\n\n

\n\n\n

\n Abstract. The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall \\textgreater 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (\\textgreater 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (\\textgreater 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (\\textgreater 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; \\textgreater 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).\n

\n\n\n

\n \n\n \n \n Reiber, L.; Knillmann, S.; Foit, K.; and Liess, M.\n\n\n \n \n \n \n \n Species occurrence relates to pesticide gradient in streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 735: 138807. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Species$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reiber_species_2020,\n\ttitle = {Species occurrence relates to pesticide gradient in streams},\n\tvolume = {735},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S004896972032324X},\n\tdoi = {10.1016/j.scitotenv.2020.138807},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Reiber, Lena and Knillmann, Saskia and Foit, Kaarina and Liess, Matthias},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {138807},\n}\n\n\n\n

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\n \n\n \n \n Rahmati, M.; Groh, J.; Graf, A.; Pütz, T.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahmati_impact_2020,\n\ttitle = {On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20029},\n\tdoi = {10.1002/vzj2.20029},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Rahmati, Mehdi and Groh, Jannis and Graf, Alexander and Pütz, Thomas and Vanderborght, Jan and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; and Sanchez-Azofeifa, A.\n\n\n \n \n \n \n \n Accuracy assessment on the number of flux terms needed to estimate in situ fAPAR.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 88: 102061. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Accuracy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{putzenlechner_accuracy_2020,\n\ttitle = {Accuracy assessment on the number of flux terms needed to estimate in situ {fAPAR}},\n\tvolume = {88},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243419310955},\n\tdoi = {10.1016/j.jag.2020.102061},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Sanchez-Azofeifa, Arturo},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {102061},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Preidl, S.; Lange, M.; and Doktor, D.\n\n\n \n \n \n \n \n Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 240: 111673. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Introducing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{preidl_introducing_2020,\n\ttitle = {Introducing {APiC} for regionalised land cover mapping on the national scale using {Sentinel}-{2A} imagery},\n\tvolume = {240},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425720300420},\n\tdoi = {10.1016/j.rse.2020.111673},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Preidl, Sebastian and Lange, Maximilian and Doktor, Daniel},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {111673},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Pauly, M.; Helle, G.; Büntgen, U.; Wacker, L.; Treydte, K.; Reinig, F.; Turney, C.; Nievergelt, D.; Kromer, B.; Friedrich, M.; Sookdeo, A.; Heinrich, I.; Riedel, F.; Balting, D.; and Brauer, A.\n\n\n \n \n \n \n \n An annual-resolution stable isotope record from Swiss subfossil pine trees growing in the late Glacial.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 247: 106550. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pauly_annual-resolution_2020,\n\ttitle = {An annual-resolution stable isotope record from {Swiss} subfossil pine trees growing in the late {Glacial}},\n\tvolume = {247},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379120305126},\n\tdoi = {10.1016/j.quascirev.2020.106550},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Pauly, Maren and Helle, Gerhard and Büntgen, Ulf and Wacker, Lukas and Treydte, Kerstin and Reinig, Frederick and Turney, Chris and Nievergelt, Daniel and Kromer, Bernd and Friedrich, Michael and Sookdeo, Adam and Heinrich, Ingo and Riedel, Frank and Balting, Daniel and Brauer, Achim},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {106550},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Pastorello, G.; Trotta, C.; Canfora, E.; Chu, H.; Christianson, D.; Cheah, Y.; Poindexter, C.; Chen, J.; Elbashandy, A.; Humphrey, M.; Isaac, P.; Polidori, D.; Reichstein, M.; Ribeca, A.; van Ingen, C.; Vuichard, N.; Zhang, L.; Amiro, B.; Ammann, C.; Arain, M. A.; Ardö, J.; Arkebauer, T.; Arndt, S. K.; Arriga, N.; Aubinet, M.; Aurela, M.; Baldocchi, D.; Barr, A.; Beamesderfer, E.; Marchesini, L. B.; Bergeron, O.; Beringer, J.; Bernhofer, C.; Berveiller, D.; Billesbach, D.; Black, T. A.; Blanken, P. D.; Bohrer, G.; Boike, J.; Bolstad, P. V.; Bonal, D.; Bonnefond, J.; Bowling, D. R.; Bracho, R.; Brodeur, J.; Brümmer, C.; Buchmann, N.; Burban, B.; Burns, S. P.; Buysse, P.; Cale, P.; Cavagna, M.; Cellier, P.; Chen, S.; Chini, I.; Christensen, T. R.; Cleverly, J.; Collalti, A.; Consalvo, C.; Cook, B. D.; Cook, D.; Coursolle, C.; Cremonese, E.; Curtis, P. S.; D’Andrea, E.; da Rocha, H.; Dai, X.; Davis, K. J.; Cinti, B. D.; Grandcourt, A. d.; Ligne, A. D.; De Oliveira, R. C.; Delpierre, N.; Desai, A. R.; Di Bella, C. M.; Tommasi, P. d.; Dolman, H.; Domingo, F.; Dong, G.; Dore, S.; Duce, P.; Dufrêne, E.; Dunn, A.; Dušek, J.; Eamus, D.; Eichelmann, U.; ElKhidir, H. A. M.; Eugster, W.; Ewenz, C. M.; Ewers, B.; Famulari, D.; Fares, S.; Feigenwinter, I.; Feitz, A.; Fensholt, R.; Filippa, G.; Fischer, M.; Frank, J.; Galvagno, M.; Gharun, M.; Gianelle, D.; Gielen, B.; Gioli, B.; Gitelson, A.; Goded, I.; Goeckede, M.; Goldstein, A. H.; Gough, C. M.; Goulden, M. L.; Graf, A.; Griebel, A.; Gruening, C.; Grünwald, T.; Hammerle, A.; Han, S.; Han, X.; Hansen, B. U.; Hanson, C.; Hatakka, J.; He, Y.; Hehn, M.; Heinesch, B.; Hinko-Najera, N.; Hörtnagl, L.; Hutley, L.; Ibrom, A.; Ikawa, H.; Jackowicz-Korczynski, M.; Janouš, D.; Jans, W.; Jassal, R.; Jiang, S.; Kato, T.; Khomik, M.; Klatt, J.; Knohl, A.; Knox, S.; Kobayashi, H.; Koerber, G.; Kolle, O.; Kosugi, Y.; Kotani, A.; Kowalski, A.; Kruijt, B.; Kurbatova, J.; Kutsch, W. L.; Kwon, H.; Launiainen, S.; Laurila, T.; Law, B.; Leuning, R.; Li, Y.; Liddell, M.; Limousin, J.; Lion, M.; Liska, A. J.; Lohila, A.; López-Ballesteros, A.; López-Blanco, E.; Loubet, B.; Loustau, D.; Lucas-Moffat, A.; Lüers, J.; Ma, S.; Macfarlane, C.; Magliulo, V.; Maier, R.; Mammarella, I.; Manca, G.; Marcolla, B.; Margolis, H. A.; Marras, S.; Massman, W.; Mastepanov, M.; Matamala, R.; Matthes, J. H.; Mazzenga, F.; McCaughey, H.; McHugh, I.; McMillan, A. M. S.; Merbold, L.; Meyer, W.; Meyers, T.; Miller, S. D.; Minerbi, S.; Moderow, U.; Monson, R. K.; Montagnani, L.; Moore, C. E.; Moors, E.; Moreaux, V.; Moureaux, C.; Munger, J. W.; Nakai, T.; Neirynck, J.; Nesic, Z.; Nicolini, G.; Noormets, A.; Northwood, M.; Nosetto, M.; Nouvellon, Y.; Novick, K.; Oechel, W.; Olesen, J. E.; Ourcival, J.; Papuga, S. A.; Parmentier, F.; Paul-Limoges, E.; Pavelka, M.; Peichl, M.; Pendall, E.; Phillips, R. P.; Pilegaard, K.; Pirk, N.; Posse, G.; Powell, T.; Prasse, H.; Prober, S. M.; Rambal, S.; Rannik, Ü.; Raz-Yaseef, N.; Rebmann, C.; Reed, D.; Dios, V. R. d.; Restrepo-Coupe, N.; Reverter, B. R.; Roland, M.; Sabbatini, S.; Sachs, T.; Saleska, S. R.; Sánchez-Cañete, E. P.; Sanchez-Mejia, Z. M.; Schmid, H. P.; Schmidt, M.; Schneider, K.; Schrader, F.; Schroder, I.; Scott, R. L.; Sedlák, P.; Serrano-Ortíz, P.; Shao, C.; Shi, P.; Shironya, I.; Siebicke, L.; Šigut, L.; Silberstein, R.; Sirca, C.; Spano, D.; Steinbrecher, R.; Stevens, R. M.; Sturtevant, C.; Suyker, A.; Tagesson, T.; Takanashi, S.; Tang, Y.; Tapper, N.; Thom, J.; Tomassucci, M.; Tuovinen, J.; Urbanski, S.; Valentini, R.; van der Molen, M.; van Gorsel, E.; van Huissteden, K.; Varlagin, A.; Verfaillie, J.; Vesala, T.; Vincke, C.; Vitale, D.; Vygodskaya, N.; Walker, J. P.; Walter-Shea, E.; Wang, H.; Weber, R.; Westermann, S.; Wille, C.; Wofsy, S.; Wohlfahrt, G.; Wolf, S.; Woodgate, W.; Li, Y.; Zampedri, R.; Zhang, J.; Zhou, G.; Zona, D.; Agarwal, D.; Biraud, S.; Torn, M.; and Papale, D.\n\n\n \n \n \n \n \n The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 225. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pastorello_fluxnet2015_2020,\n\ttitle = {The {FLUXNET2015} dataset and the {ONEFlux} processing pipeline for eddy covariance data},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {https://doi.org/10.1038/s41597-020-0534-3},\n\tdoi = {10.1038/s41597-020-0534-3},\n\tabstract = {The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.},\n\tnumber = {1},\n\tjournal = {Scientific Data},\n\tauthor = {Pastorello, Gilberto and Trotta, Carlo and Canfora, Eleonora and Chu, Housen and Christianson, Danielle and Cheah, You-Wei and Poindexter, Cristina and Chen, Jiquan and Elbashandy, Abdelrahman and Humphrey, Marty and Isaac, Peter and Polidori, Diego and Reichstein, Markus and Ribeca, Alessio and van Ingen, Catharine and Vuichard, Nicolas and Zhang, Leiming and Amiro, Brian and Ammann, Christof and Arain, M. Altaf and Ardö, Jonas and Arkebauer, Timothy and Arndt, Stefan K. and Arriga, Nicola and Aubinet, Marc and Aurela, Mika and Baldocchi, Dennis and Barr, Alan and Beamesderfer, Eric and Marchesini, Luca Belelli and Bergeron, Onil and Beringer, Jason and Bernhofer, Christian and Berveiller, Daniel and Billesbach, Dave and Black, Thomas Andrew and Blanken, Peter D. and Bohrer, Gil and Boike, Julia and Bolstad, Paul V. and Bonal, Damien and Bonnefond, Jean-Marc and Bowling, David R. and Bracho, Rosvel and Brodeur, Jason and Brümmer, Christian and Buchmann, Nina and Burban, Benoit and Burns, Sean P. and Buysse, Pauline and Cale, Peter and Cavagna, Mauro and Cellier, Pierre and Chen, Shiping and Chini, Isaac and Christensen, Torben R. and Cleverly, James and Collalti, Alessio and Consalvo, Claudia and Cook, Bruce D. and Cook, David and Coursolle, Carole and Cremonese, Edoardo and Curtis, Peter S. and D’Andrea, Ettore and da Rocha, Humberto and Dai, Xiaoqin and Davis, Kenneth J. and Cinti, Bruno De and Grandcourt, Agnes de and Ligne, Anne De and De Oliveira, Raimundo C. and Delpierre, Nicolas and Desai, Ankur R. and Di Bella, Carlos Marcelo and Tommasi, Paul di and Dolman, Han and Domingo, Francisco and Dong, Gang and Dore, Sabina and Duce, Pierpaolo and Dufrêne, Eric and Dunn, Allison and Dušek, Jiří and Eamus, Derek and Eichelmann, Uwe and ElKhidir, Hatim Abdalla M. and Eugster, Werner and Ewenz, Cacilia M. and Ewers, Brent and Famulari, Daniela and Fares, Silvano and Feigenwinter, Iris and Feitz, Andrew and Fensholt, Rasmus and Filippa, Gianluca and Fischer, Marc and Frank, John and Galvagno, Marta and Gharun, Mana and Gianelle, Damiano and Gielen, Bert and Gioli, Beniamino and Gitelson, Anatoly and Goded, Ignacio and Goeckede, Mathias and Goldstein, Allen H. and Gough, Christopher M. and Goulden, Michael L. and Graf, Alexander and Griebel, Anne and Gruening, Carsten and Grünwald, Thomas and Hammerle, Albin and Han, Shijie and Han, Xingguo and Hansen, Birger Ulf and Hanson, Chad and Hatakka, Juha and He, Yongtao and Hehn, Markus and Heinesch, Bernard and Hinko-Najera, Nina and Hörtnagl, Lukas and Hutley, Lindsay and Ibrom, Andreas and Ikawa, Hiroki and Jackowicz-Korczynski, Marcin and Janouš, Dalibor and Jans, Wilma and Jassal, Rachhpal and Jiang, Shicheng and Kato, Tomomichi and Khomik, Myroslava and Klatt, Janina and Knohl, Alexander and Knox, Sara and Kobayashi, Hideki and Koerber, Georgia and Kolle, Olaf and Kosugi, Yoshiko and Kotani, Ayumi and Kowalski, Andrew and Kruijt, Bart and Kurbatova, Julia and Kutsch, Werner L. and Kwon, Hyojung and Launiainen, Samuli and Laurila, Tuomas and Law, Bev and Leuning, Ray and Li, Yingnian and Liddell, Michael and Limousin, Jean-Marc and Lion, Marryanna and Liska, Adam J. and Lohila, Annalea and López-Ballesteros, Ana and López-Blanco, Efrén and Loubet, Benjamin and Loustau, Denis and Lucas-Moffat, Antje and Lüers, Johannes and Ma, Siyan and Macfarlane, Craig and Magliulo, Vincenzo and Maier, Regine and Mammarella, Ivan and Manca, Giovanni and Marcolla, Barbara and Margolis, Hank A. and Marras, Serena and Massman, William and Mastepanov, Mikhail and Matamala, Roser and Matthes, Jaclyn Hatala and Mazzenga, Francesco and McCaughey, Harry and McHugh, Ian and McMillan, Andrew M. S. and Merbold, Lutz and Meyer, Wayne and Meyers, Tilden and Miller, Scott D. and Minerbi, Stefano and Moderow, Uta and Monson, Russell K. and Montagnani, Leonardo and Moore, Caitlin E. and Moors, Eddy and Moreaux, Virginie and Moureaux, Christine and Munger, J. William and Nakai, Taro and Neirynck, Johan and Nesic, Zoran and Nicolini, Giacomo and Noormets, Asko and Northwood, Matthew and Nosetto, Marcelo and Nouvellon, Yann and Novick, Kimberly and Oechel, Walter and Olesen, Jørgen Eivind and Ourcival, Jean-Marc and Papuga, Shirley A. and Parmentier, Frans-Jan and Paul-Limoges, Eugenie and Pavelka, Marian and Peichl, Matthias and Pendall, Elise and Phillips, Richard P. and Pilegaard, Kim and Pirk, Norbert and Posse, Gabriela and Powell, Thomas and Prasse, Heiko and Prober, Suzanne M. and Rambal, Serge and Rannik, Üllar and Raz-Yaseef, Naama and Rebmann, Corinna and Reed, David and Dios, Victor Resco de and Restrepo-Coupe, Natalia and Reverter, Borja R. and Roland, Marilyn and Sabbatini, Simone and Sachs, Torsten and Saleska, Scott R. and Sánchez-Cañete, Enrique P. and Sanchez-Mejia, Zulia M. and Schmid, Hans Peter and Schmidt, Marius and Schneider, Karl and Schrader, Frederik and Schroder, Ivan and Scott, Russell L. and Sedlák, Pavel and Serrano-Ortíz, Penélope and Shao, Changliang and Shi, Peili and Shironya, Ivan and Siebicke, Lukas and Šigut, Ladislav and Silberstein, Richard and Sirca, Costantino and Spano, Donatella and Steinbrecher, Rainer and Stevens, Robert M. and Sturtevant, Cove and Suyker, Andy and Tagesson, Torbern and Takanashi, Satoru and Tang, Yanhong and Tapper, Nigel and Thom, Jonathan and Tomassucci, Michele and Tuovinen, Juha-Pekka and Urbanski, Shawn and Valentini, Riccardo and van der Molen, Michiel and van Gorsel, Eva and van Huissteden, Ko and Varlagin, Andrej and Verfaillie, Joseph and Vesala, Timo and Vincke, Caroline and Vitale, Domenico and Vygodskaya, Natalia and Walker, Jeffrey P. and Walter-Shea, Elizabeth and Wang, Huimin and Weber, Robin and Westermann, Sebastian and Wille, Christian and Wofsy, Steven and Wohlfahrt, Georg and Wolf, Sebastian and Woodgate, William and Li, Yuelin and Zampedri, Roberto and Zhang, Junhui and Zhou, Guoyi and Zona, Donatella and Agarwal, Deb and Biraud, Sebastien and Torn, Margaret and Papale, Dario},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {225},\n}\n\n\n\n

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\n \n\n \n \n Park, C.; Jagdhuber, T.; Colliander, A.; Lee, J.; Berg, A.; Cosh, M.; Kim, S.; Kim, Y.; and Wulfmeyer, V.\n\n\n \n \n \n \n \n Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(18): 2939. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameterization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{park_parameterization_2020,\n\ttitle = {Parameterization of {Vegetation} {Scattering} {Albedo} in the {Tau}-{Omega} {Model} for {Soil} {Moisture} {Retrieval} on {Croplands}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/18/2939},\n\tdoi = {10.3390/rs12182939},\n\tabstract = {An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16\\% for SCA and 15\\% for DCA. The reduction for positive and negative biases was 45\\% and 5\\% for SCA and 26\\% and 12\\% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Park, Chang-Hwan and Jagdhuber, Thomas and Colliander, Andreas and Lee, Johan and Berg, Aaron and Cosh, Michael and Kim, Seung-Bum and Kim, Yoonjae and Wulfmeyer, Volker},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2939},\n}\n\n\n\n

\n\n\n

\n An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.\n

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\n \n\n \n \n Pampel, H.; and Bertelmann, R.\n\n\n \n \n \n \n Helmholtz Open Science Office - Shaping the PID Landscape in Germany and Beyond.\n \n \n \n\n\n \n\n\n\n 2020.\n Publication Title: Poster Type: Konferenzabstract\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@misc{pampel_helmholtz_2020,\n\ttitle = {Helmholtz {Open} {Science} {Office} - {Shaping} the {PID} {Landscape} in {Germany} and {Beyond}},\n\tauthor = {Pampel, Heinz and Bertelmann, Roland},\n\tyear = {2020},\n\tnote = {Publication Title: Poster\nType: Konferenzabstract},\n}\n\n\n\n

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\n \n\n \n \n Naz, B. S.; Kollet, S.; Franssen, H. H.; Montzka, C.; and Kurtz, W.\n\n\n \n \n \n \n \n A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 111. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{naz_3_2020,\n\ttitle = {A 3 km spatially and temporally consistent {European} daily soil moisture reanalysis from 2000 to 2015},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {http://www.nature.com/articles/s41597-020-0450-6},\n\tdoi = {10.1038/s41597-020-0450-6},\n\tabstract = {Abstract \n             \n              High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 \n              in-situ \n              soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m \n              3 \n              m \n              −3 \n              and correlation values above 0.5 over 70\\% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Data},\n\tauthor = {Naz, Bibi S. and Kollet, Stefan and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Kurtz, Wolfgang},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {111},\n}\n\n\n\n

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\n \n\n \n \n Nasta, P.; Bogena, H. R.; Sica, B.; Weuthen, A.; Vereecken, H.; and Romano, N.\n\n\n \n \n \n \n \n Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 26. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nasta_integrating_2020,\n\ttitle = {Integrating {Invasive} and {Non}-invasive {Monitoring} {Sensors} to {Detect} {Field}-{Scale} {Soil} {Hydrological} {Behavior}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/article/10.3389/frwa.2020.00026/full},\n\tdoi = {10.3389/frwa.2020.00026},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Nasta, Paolo and Bogena, Heye R. and Sica, Benedetto and Weuthen, Ansgar and Vereecken, Harry and Romano, Nunzio},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {26},\n}\n\n\n\n

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\n \n\n \n \n Nanusha, M. Y.; Krauss, M.; Schönsee, C. D.; Günthardt, B. F.; Bucheli, T. D.; and Brack, W.\n\n\n \n \n \n \n \n Target screening of plant secondary metabolites in river waters by liquid chromatography coupled to high-resolution mass spectrometry (LC–HRMS).\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 32(1): 142. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Target$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nanusha_target_2020,\n\ttitle = {Target screening of plant secondary metabolites in river waters by liquid chromatography coupled to high-resolution mass spectrometry ({LC}–{HRMS})},\n\tvolume = {32},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-020-00399-2},\n\tdoi = {10.1186/s12302-020-00399-2},\n\tabstract = {Abstract \n             \n              Background \n              Substantial efforts have been made to monitor potentially hazardous anthropogenic contaminants in surface waters while for plant secondary metabolites (PSMs) almost no data on occurrence in the water cycle are available. These metabolites enter river waters through various pathways such as leaching, surface run-off and rain sewers or input of litter from vegetation and might add to the biological activity of the chemical mixture. To reduce this data gap, we conducted a LC–HRMS target screening in river waters from two different catchments for 150 plant metabolites which were selected from a larger database considering their expected abundance in the vegetation, their potential mobility, persistence and toxicity in the water cycle and commercial availability of standards. \n             \n             \n              Results \n              The screening revealed the presence of 12 out of 150 possibly toxic PSMs including coumarins (bergapten, scopoletin, fraxidin, esculetin and psoralen), a flavonoid (formononetin) and alkaloids (lycorine and narciclasine). The compounds narciclasine and lycorine were detected at concentrations up to 3 µg/L while esculetin and fraxidin occurred at concentrations above 1 µg/L. Nine compounds occurred at concentrations above 0.1 µg/L, the Threshold for Toxicological Concern (TTC) for non-genotoxic and non-endocrine disrupting chemicals in drinking water. \n             \n             \n              Conclusions \n              Our study provides an overview of potentially biologically active PSMs in surface waters and recommends their consideration in monitoring and risk assessment of water resources. This is currently hampered by a lack of effect data including toxicity to aquatic organisms, endocrine disruption and genotoxicity and demands for involvement of these compounds in biotesting.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Nanusha, Mulatu Yohannes and Krauss, Martin and Schönsee, Carina D. and Günthardt, Barbara F. and Bucheli, Thomas D. and Brack, Werner},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {142},\n}\n\n\n\n

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\n \n\n \n \n Nanusha, M. Y.; Krauss, M.; and Brack, W.\n\n\n \n \n \n \n \n Non-target screening for detecting the occurrence of plant metabolites in river waters.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 32(1): 130. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Non-target$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nanusha_non-target_2020,\n\ttitle = {Non-target screening for detecting the occurrence of plant metabolites in river waters},\n\tvolume = {32},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-020-00415-5},\n\tdoi = {10.1186/s12302-020-00415-5},\n\tabstract = {Abstract \n             \n              Background \n              In surface waters, using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS), typically large numbers of chemical signals often with high peak intensity remain unidentified. These chemical signals may represent natural compounds released from plants, animals and microorganisms, which may contribute to the cumulative toxic risk. Thus, attempts were made to identify natural compounds in significant concentrations in surface waters by identifying overlapping LC-HRMS peaks between extracts of plants abundant in the catchment and river waters using a non-target screening (NTS) work flow. \n             \n             \n              Results \n              The result revealed the presence of several thousands of overlapping peaks between water—and plants from local vegetation. Taking this overlap as a basis, 12 SPMs from different compound classes were identified to occur in river waters with flavonoids as a dominant group. The concentrations of the identified compounds ranged from 0.02 to 5 µg/L with apiin, hyperoside and guanosine with highest concentrations. Most of the identified compounds exceeded the threshold for toxicological concern (TTC) (0.1 µg/L) for non-genotoxic and non-endocrine disrupting chemicals in drinking water often by more than one order of magnitude. \n             \n             \n              Conclusion \n              Our results revealed the contribution of chemicals eluted from the vegetation in the catchment to the chemical load in surface waters and help to reduce the number of unknowns among NTS high-intensity peaks detected in rivers. Since secondary plant metabolites (SPMs) are often produced for defence against other organisms and since concentrations ranges are clearly above TTC a contribution to toxic risks on aquatic organisms and impacts on drinking water safety cannot be excluded. This demands for including these compounds into monitoring and assessment of water quality.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Nanusha, Mulatu Yohannes and Krauss, Martin and Brack, Werner},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {130},\n}\n\n\n\n

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\n \n\n \n \n Mozaffari, A.; Klotzsche, A.; Warren, C.; He, G.; Giannopoulos, A.; Vereecken, H.; and van der Kruk, J.\n\n\n \n \n \n \n \n 2.5D crosshole GPR full-waveform inversion with synthetic and measured data.\n \n \n \n \n\n\n \n\n\n\n GEOPHYSICS, 85(4): H71–H82. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"2.5D$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mozaffari_25d_2020,\n\ttitle = {2.{5D} crosshole {GPR} full-waveform inversion with synthetic and measured data},\n\tvolume = {85},\n\tissn = {0016-8033, 1942-2156},\n\turl = {https://library.seg.org/doi/10.1190/geo2019-0600.1},\n\tdoi = {10.1190/geo2019-0600.1},\n\tabstract = {Full-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30\\% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2\\% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20\\%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {GEOPHYSICS},\n\tauthor = {Mozaffari, Amirpasha and Klotzsche, Anja and Warren, Craig and He, Guowei and Giannopoulos, Antonios and Vereecken, Harry and van der Kruk, Jan},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {H71--H82},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Cosh, M. H.; Bayat, B.; Al Bitar, A.; Berg, A.; Bindlish, R.; Bogena, H. R.; Bolton, J. D.; Cabot, F.; Caldwell, T.; Chan, S.; Colliander, A.; Wade Crow; Das, N.; De Lannoy, G.; Dorigo, W.; Evett, S. R.; Gruber, A.; Hahn, S.; Jagdhuber, T.; Jones, S. F.; Kerr, Y.; Kim, S.; Koyama, C.; Kurum, M.; Lopez-Baeza, E.; Mattia, F.; McColl, K. A.; Mecklenburg, S.; Mohanty, B.; O´Neill, P.; Or, D.; Pellarin, T.; Petropoulos, G. P.; Piles, M.; Reichle, R. H.; Rodriguez-Fernandez, N.; Rüdiger, C.; Scanlon, T.; Schwartz, R. C.; Spengler, D.; Srivastava, P. K.; Suman, S.; van der Schalie, R.; Wagner, W.; Wegmüller, U.; Wigneron, J.; Camacho, F.; and Nickeson, J.\n\n\n \n \n \n \n \n Soil moisture product validation good practices protocol, version 1.0.\n \n \n \n \n\n\n \n\n\n\n Technical Report 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{montzka_soil_2020,\n\ttype = {Report},\n\ttitle = {Soil moisture product validation good practices protocol, version 1.0},\n\turl = {http://pubs.er.usgs.gov/publication/70216425},\n\tlanguage = {English},\n\tauthor = {Montzka, Carsten and Cosh, Michael H. and Bayat, Bagher and Al Bitar, Ahmad and Berg, Aaron and Bindlish, Rajat and Bogena, Heye Reemt and Bolton, John D. and Cabot, Francois and Caldwell, Todd and Chan, Steven and Colliander, Andreas and {Wade Crow} and Das, Narendra and De Lannoy, Gabrielle and Dorigo, Wouter and Evett, Steven R. and Gruber, Alexander and Hahn, Sebastian and Jagdhuber, Thomas and Jones, Scott F. and Kerr, Yann and Kim, Seung-bum and Koyama, Christian and Kurum, Mehmed and Lopez-Baeza, Ernesto and Mattia, Francesco and McColl, Kaighin A. and Mecklenburg, Susanne and Mohanty, Binayak and O´Neill, Peggy and Or, Dani and Pellarin, Thierry and Petropoulos, George P. and Piles, Maria and Reichle, Rolf H. and Rodriguez-Fernandez, Nemesio and Rüdiger, Christoph and Scanlon, Tracy and Schwartz, Robert C. and Spengler, Daniel and Srivastava, Prashant K. and Suman, Swati and van der Schalie, Robin and Wagner, Wolfgang and Wegmüller, Urs and Wigneron, Jean-Pierre and Camacho, Fernando and Nickeson, Jaime},\n\teditor = {Montzka, Carsten and Cosh, Michael H. and Camacho, Fernando and Nickeson, Jaime},\n\tyear = {2020},\n\tdoi = {10.5067/doc/ceoswgcv/lpv/sm.001},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Montzka, C.; Brogi, C.; Mengen, D.; Matveeva, M.; Baum, S.; Schuttemeyer, D.; Bayat, B.; Bogena, H.; Coccia, A.; Masalias, G.; Graf, V.; Jakobi, J.; Jonard, F.; Ma, Y.; Mattia, F.; Palmisano, D.; Rascher, U.; Satalino, G.; Jagdhuber, T.; Fluhrer, A.; Schumacher, M.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Sarsense: A C- and L-Band SAR Rehearsal Campaign in Germany in Preparation for ROSE-L.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pages 2137–2140, Waikoloa, HI, USA, September 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Sarsense:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{montzka_sarsense_2020,\n\taddress = {Waikoloa, HI, USA},\n\ttitle = {Sarsense: {A} {C}- and {L}-{Band} {SAR} {Rehearsal} {Campaign} in {Germany} in {Preparation} for {ROSE}-{L}},\n\tisbn = {9781728163741},\n\tshorttitle = {Sarsense},\n\turl = {https://ieeexplore.ieee.org/document/9324090/},\n\tdoi = {10.1109/IGARSS39084.2020.9324090},\n\turldate = {2022-11-02},\n\tbooktitle = {{IGARSS} 2020 - 2020 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Montzka, Carsten and Brogi, Cosimo and Mengen, David and Matveeva, Maria and Baum, Stephani and Schuttemeyer, Dirk and Bayat, Bagher and Bogena, Heye and Coccia, Alex and Masalias, Gerard and Graf, Verena and Jakobi, Jannis and Jonard, Francois and Ma, Yueling and Mattia, Francesco and Palmisano, Davide and Rascher, Uwe and Satalino, Guiseppe and Jagdhuber, Thomas and Fluhrer, Anke and Schumacher, Maike and Schmidt, Marius and Vereecken, Harry},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2137--2140},\n}\n\n\n\n

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\n \n\n \n \n Mobilia, M.; Schmidt, M.; and Longobardi, A.\n\n\n \n \n \n \n \n Modelling Actual Evapotranspiration Seasonal Variability by Meteorological Data-Based Models.\n \n \n \n \n\n\n \n\n\n\n Hydrology, 7(3): 50. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mobilia_modelling_2020,\n\ttitle = {Modelling {Actual} {Evapotranspiration} {Seasonal} {Variability} by {Meteorological} {Data}-{Based} {Models}},\n\tvolume = {7},\n\tissn = {2306-5338},\n\turl = {https://www.mdpi.com/2306-5338/7/3/50},\n\tdoi = {10.3390/hydrology7030050},\n\tabstract = {This study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80\\% in the case of the Sister site, of about 30\\% in the case of Rollesbroich, and close to 60\\% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology},\n\tauthor = {Mobilia, Mirka and Schmidt, Marius and Longobardi, Antonia},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {50},\n}\n\n\n\n

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\n \n\n \n \n Mi, C.; Shatwell, T.; Ma, J.; Xu, Y.; Su, F.; and Rinke, K.\n\n\n \n \n \n \n \n Ensemble warming projections in Germany's largest drinking water reservoir and potential adaptation strategies.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 748: 141366. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Ensemble$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mi_ensemble_2020,\n\ttitle = {Ensemble warming projections in {Germany}'s largest drinking water reservoir and potential adaptation strategies},\n\tvolume = {748},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720348956},\n\tdoi = {10.1016/j.scitotenv.2020.141366},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Mi, Chenxi and Shatwell, Tom and Ma, Jun and Xu, Yaqian and Su, Fangli and Rinke, Karsten},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {141366},\n}\n\n\n\n

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\n \n\n \n \n Merz, R.; Tarasova, L.; and Basso, S.\n\n\n \n \n \n \n \n The flood cooking book: ingredients and regional flavors of floods across Germany.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(11): 114024. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{merz_flood_2020,\n\ttitle = {The flood cooking book: ingredients and regional flavors of floods across {Germany}},\n\tvolume = {15},\n\tissn = {1748-9326},\n\tshorttitle = {The flood cooking book},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/abb9dd},\n\tdoi = {10.1088/1748-9326/abb9dd},\n\tabstract = {Abstract \n            River flooding is a major natural hazard worldwide, whose prediction is impaired by limited understanding of the interplay of processes triggering floods within large regions. In this study we use machine learning techniques such as decision trees and random forests to pinpoint spatio-temporal features of precipitation and catchment wetness states which led to floods among 177 267 rainfall-runoff events observed in 373 German river basins. In mountainous catchments with high annual precipitation rates and shallow soils, event rainfall characteristics primarily control flood occurrence, while wetness conditions and the spatial interplay between rainfall and catchment soil moisture drive flood occurrence even more than event rainfall volume in drier basins. The existence of a snow cover also enhances flood occurrence. The identified ingredients and regional flavors shed new light on the spatial dynamics of hydro-meteorological processes leading to floods and foster regional adaptation of flood management strategies and early warning systems.},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Merz, Ralf and Tarasova, Larisa and Basso, Stefano},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {114024},\n}\n\n\n\n

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\n \n\n \n \n McKenna, A.; Schultz, A.; Borg, E.; Neumann, M.; and Mund, J.\n\n\n \n \n \n \n \n Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO).\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{mckenna_remote_2020,\n\ttype = {other},\n\ttitle = {Remote sensing and {GIS} based ecological modelling of potential red deer habitats in the test site region {DEMMIN} ({TERENO})},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-19953.html},\n\tabstract = {\\&lt;p\\&gt;\\&lt;strong\\&gt;Introduction:\\&lt;/strong\\&gt; The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Objective:\\&lt;/strong\\&gt; This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Methods:\\&lt;/strong\\&gt; Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of \\&gt; 60\\% and in the best case 71,4\\%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach.\\&amp;\\#160; Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Conclusion:\\&lt;/strong\\&gt; \\&amp;\\#160;Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species \\&amp;\\#8211;habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.\\&lt;/p\\&gt;},\n\turldate = {2022-11-02},\n\tinstitution = {oral},\n\tauthor = {McKenna, Amelie and Schultz, Alfred and Borg, Erik and Neumann, Matthias and Mund, Jan-Peter},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-19953},\n}\n\n\n\n

\n\n\n

\n Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach.  Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion:  Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species –habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.\n

\n\n\n

\n \n\n \n \n Marzahn, P.; and Meyer, S.\n\n\n \n \n \n \n \n Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(16): 2660. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Utilization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{marzahn_utilization_2020,\n\ttitle = {Utilization of {Multi}-{Temporal} {Microwave} {Remote} {Sensing} {Data} within a {Geostatistical} {Regionalization} {Approach} for the {Derivation} of {Soil} {Texture}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/16/2660},\n\tdoi = {10.3390/rs12162660},\n\tabstract = {Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-\\%) could be achieved outperforming classical deterministic and geostatistical approaches.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Marzahn, Philip and Meyer, Swen},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {2660},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Lutz, S. R.; Trauth, N.; Musolff, A.; Van Breukelen, B. M.; Knöller, K.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n How Important is Denitrification in Riparian Zones? Combining End‐Member Mixing and Isotope Modeling to Quantify Nitrate Removal from Riparian Groundwater.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lutz_how_2020,\n\ttitle = {How {Important} is {Denitrification} in {Riparian} {Zones}? {Combining} {End}‐{Member} {Mixing} and {Isotope} {Modeling} to {Quantify} {Nitrate} {Removal} from {Riparian} {Groundwater}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {How {Important} is {Denitrification} in {Riparian} {Zones}?},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR025528},\n\tdoi = {10.1029/2019WR025528},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Lutz, Stefanie R. and Trauth, Nico and Musolff, Andreas and Van Breukelen, Boris M. and Knöller, Kay and Fleckenstein, Jan H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Liu, X.; Hilfert, L.; Barth, J. A. C.; van Geldem, R.; and Friese, K.\n\n\n \n \n \n \n \n Isotope alteration caused by changes in biochemical composition of sedimentary organic matter.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 147(3): 277–292. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Isotope$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_isotope_2020,\n\ttitle = {Isotope alteration caused by changes in biochemical composition of sedimentary organic matter},\n\tvolume = {147},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-020-00640-3},\n\tdoi = {10.1007/s10533-020-00640-3},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Biogeochemistry},\n\tauthor = {Liu, Xiaoqing and Hilfert, Liane and Barth, Johannes A. C. and van Geldem, Robert and Friese, Kurt},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {277--292},\n}\n\n\n\n

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\n \n\n \n \n Lampert, A.; Pätzold, F.; Asmussen, M. O.; Lobitz, L.; Krüger, T.; Rausch, T.; Sachs, T.; Wille, C.; Sotomayor Zakharov, D.; Gaus, D.; Bansmer, S.; and Damm, E.\n\n\n \n \n \n \n \n Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 13(4): 1937–1952. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Studying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lampert_studying_2020,\n\ttitle = {Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system},\n\tvolume = {13},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/13/1937/2020/},\n\tdoi = {10.5194/amt-13-1937-2020},\n\tabstract = {Abstract. The combination of two well-established methods, of quadrocopter-borne air sampling and methane isotopic analyses, is applied to determine the\nsource process of methane at different altitudes and to study mixing processes. A proof-of-concept study was performed to demonstrate the\ncapabilities of quadrocopter air sampling for subsequently analysing the methane isotopic composition δ13C in the laboratory. The\nadvantage of the system compared to classical sampling on the ground and at tall towers is the flexibility concerning sampling location, and in\nparticular the flexible choice of sampling altitude, allowing the study of the layering and mixing of air masses with potentially different spatial origin\nof air masses and methane. Boundary layer mixing processes and the methane isotopic composition were studied at Polder Zarnekow in\nMecklenburg–West Pomerania in the north-east of Germany, which has become a strong source of biogenically produced methane after rewetting the\ndrained and degraded peatland. Methane fluxes are measured continuously at the site. They show high emissions from May to September, and a strong\ndiurnal variability. For two case studies on 23 May and 5 September 2018, vertical profiles of temperature and humidity were recorded up to an\naltitude of 650 and 1000 m, respectively, during the morning transition. Air samples were taken at different altitudes and analysed in the\nlaboratory for methane isotopic composition. The values showed a different isotopic composition in the vertical distribution during stable\nconditions in the morning (delta values of −51.5 ‰ below the temperature inversion at an altitude of 150 m on 23 May 2018 and at an\naltitude of 50 m on 5 September 2018, delta values of −50.1 ‰ above). After the onset of turbulent mixing, the isotopic composition was\nthe same throughout the vertical column with a mean delta value of −49.9 ± 0.45 ‰. The systematically more negative delta values\noccurred only as long as the nocturnal temperature inversion was present. During the September study, water samples were analysed as well for\nmethane concentration and isotopic composition in order to provide a link between surface and atmosphere. The water samples reveal high variability\non horizontal scales of a few tens of metres for this particular case. The airborne sampling system and consecutive analysis chain were shown to provide\nreliable and reproducible results for two samples obtained simultaneously. The method presents a powerful tool for distinguishing the source process\nof methane at different altitudes. The isotopic composition showed clearly depleted delta values directly above a biological methane source when\nvertical mixing was hampered by a temperature inversion, and different delta values above, where the air masses originate from a different footprint\narea. The vertical distribution of methane isotopic composition can serve as tracer for mixing processes of methane within the atmospheric boundary\nlayer.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Lampert, Astrid and Pätzold, Falk and Asmussen, Magnus O. and Lobitz, Lennart and Krüger, Thomas and Rausch, Thomas and Sachs, Torsten and Wille, Christian and Sotomayor Zakharov, Denis and Gaus, Dominik and Bansmer, Stephan and Damm, Ellen},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1937--1952},\n}\n\n\n\n

\n\n\n

\n Abstract. The combination of two well-established methods, of quadrocopter-borne air sampling and methane isotopic analyses, is applied to determine the source process of methane at different altitudes and to study mixing processes. A proof-of-concept study was performed to demonstrate the capabilities of quadrocopter air sampling for subsequently analysing the methane isotopic composition δ13C in the laboratory. The advantage of the system compared to classical sampling on the ground and at tall towers is the flexibility concerning sampling location, and in particular the flexible choice of sampling altitude, allowing the study of the layering and mixing of air masses with potentially different spatial origin of air masses and methane. Boundary layer mixing processes and the methane isotopic composition were studied at Polder Zarnekow in Mecklenburg–West Pomerania in the north-east of Germany, which has become a strong source of biogenically produced methane after rewetting the drained and degraded peatland. Methane fluxes are measured continuously at the site. They show high emissions from May to September, and a strong diurnal variability. For two case studies on 23 May and 5 September 2018, vertical profiles of temperature and humidity were recorded up to an altitude of 650 and 1000 m, respectively, during the morning transition. Air samples were taken at different altitudes and analysed in the laboratory for methane isotopic composition. The values showed a different isotopic composition in the vertical distribution during stable conditions in the morning (delta values of −51.5 ‰ below the temperature inversion at an altitude of 150 m on 23 May 2018 and at an altitude of 50 m on 5 September 2018, delta values of −50.1 ‰ above). After the onset of turbulent mixing, the isotopic composition was the same throughout the vertical column with a mean delta value of −49.9 ± 0.45 ‰. The systematically more negative delta values occurred only as long as the nocturnal temperature inversion was present. During the September study, water samples were analysed as well for methane concentration and isotopic composition in order to provide a link between surface and atmosphere. The water samples reveal high variability on horizontal scales of a few tens of metres for this particular case. The airborne sampling system and consecutive analysis chain were shown to provide reliable and reproducible results for two samples obtained simultaneously. The method presents a powerful tool for distinguishing the source process of methane at different altitudes. The isotopic composition showed clearly depleted delta values directly above a biological methane source when vertical mixing was hampered by a temperature inversion, and different delta values above, where the air masses originate from a different footprint area. The vertical distribution of methane isotopic composition can serve as tracer for mixing processes of methane within the atmospheric boundary layer.\n

\n\n\n

\n \n\n \n \n Kunz, M.; Lavric, J. V.; Gasche, R.; Gerbig, C.; Grant, R. H.; Koch, F.; Schumacher, M.; Wolf, B.; and Zeeman, M.\n\n\n \n \n \n \n \n Surface flux estimates derived from UAS-based mole fraction measurements by means of a nocturnal boundary layer budget approach.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 13(4): 1671–1692. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Surface$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunz_surface_2020,\n\ttitle = {Surface flux estimates derived from {UAS}-based mole fraction measurements by means of a nocturnal boundary layer budget approach},\n\tvolume = {13},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/13/1671/2020/},\n\tdoi = {10.5194/amt-13-1671-2020},\n\tabstract = {Abstract. The carbon exchange between ecosystems and the atmosphere has a large\ninfluence on the Earth system and specifically on the climate. This\nexchange is therefore being studied intensively, often using the eddy\ncovariance (EC) technique. EC measurements provide reliable results\nunder turbulent atmospheric conditions, but under calm and stable\nconditions – as they often occur at night – these measurements\nare known to misrepresent exchange fluxes. Nocturnal boundary layer\n(NBL) budgets can provide independent flux estimates under stable\nconditions, but their application so far has been limited by rather\nhigh cost and practical difficulties. Unmanned aircraft systems (UASs)\nequipped with trace gas analysers have the potential to make this\nmethod more accessible. We present the methodology and results of\na proof-of-concept study carried out during the ScaleX 2016 campaign.\nSuccessive vertical profiles of carbon dioxide dry-air mole fraction\nin the NBL were taken with a compact analyser carried by a UAS. We\nestimate an average carbon dioxide flux of 12 µmolm-2s-1,\nwhich is plausible for nocturnal respiration in this region in summer.\nTransport modelling suggests that the NBL budgets represent an area\non the order of 100 km2.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Kunz, Martin and Lavric, Jost V. and Gasche, Rainer and Gerbig, Christoph and Grant, Richard H. and Koch, Frank-Thomas and Schumacher, Marcus and Wolf, Benjamin and Zeeman, Matthias},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1671--1692},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Kreuzburg, M.; Rezanezhad, F.; Milojevic, T.; Voss, M.; Gosch, L.; Liebner, S.; Van Cappellen, P.; and Rehder, G.\n\n\n \n \n \n \n \n Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge: a column experiment study.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 65(5): 1116–1135. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kreuzburg_carbon_2020,\n\ttitle = {Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge: a column experiment study},\n\tvolume = {65},\n\tissn = {0024-3590, 1939-5590},\n\tshorttitle = {Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.11438},\n\tdoi = {10.1002/lno.11438},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Kreuzburg, Matthias and Rezanezhad, Fereidoun and Milojevic, Tatjana and Voss, Maren and Gosch, Lennart and Liebner, Susanne and Van Cappellen, Philippe and Rehder, Gregor},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {1116--1135},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Koebsch, F.; Gottschalk, P.; Beyer, F.; Wille, C.; Jurasinski, G.; and Sachs, T.\n\n\n \n \n \n \n \n The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens.\n \n \n \n \n\n\n \n\n\n\n Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1810): 20190685. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koebsch_impact_2020,\n\ttitle = {The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens},\n\tvolume = {375},\n\tissn = {0962-8436, 1471-2970},\n\turl = {https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0685},\n\tdoi = {10.1098/rstb.2019.0685},\n\tabstract = {Peatland rewetting aims at stopping the emissions of carbon dioxide (CO \n              2 \n              ) and establishing net carbon sinks. However, in times of global warming, restoration projects must increasingly deal with extreme events such as drought periods. Here, we evaluate the effect of the European summer drought 2018 on vegetation development and the exchange of methane (CH \n              4 \n              ) and CO \n              2 \n              in two rewetted minerotrophic fens (Hütelmoor—Hte and Zarnekow—Zrk) including potential carry-over effects in the post-drought year. Drought was a major stress factor for the established vegetation but also promoted the rapid spread of new vegetation, which will likely gain a lasting foothold in Zrk. Accordingly, drought increased not only respiratory CO \n              2 \n              losses but also photosynthetic CO \n              2 \n              uptake. Altogether, the drought reduced the net CO \n              2 \n              sink in Hte, while it stopped the persistent net CO \n              2 \n              emissions of Zrk. In addition, the drought reduced CH \n              4 \n              emissions in both fens, though this became most apparent in the post-drought year and suggests a lasting shift towards non-methanogenic organic matter decomposition. Occasional droughts can be beneficial for the restoration of the peatland carbon sink function if the newly grown vegetation increases CO \n              2 \n              sequestration in the long term. Nonetheless, care must be taken to prevent extensive peat decay. \n             \n            This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.},\n\tlanguage = {en},\n\tnumber = {1810},\n\turldate = {2022-11-02},\n\tjournal = {Philosophical Transactions of the Royal Society B: Biological Sciences},\n\tauthor = {Koebsch, Franziska and Gottschalk, Pia and Beyer, Florian and Wille, Christian and Jurasinski, Gerald and Sachs, Torsten},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {20190685},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Keller, P. S.; Catalán, N.; von Schiller, D.; Grossart, H.; Koschorreck, M.; Obrador, B.; Frassl, M. A.; Karakaya, N.; Barros, N.; Howitt, J. A.; Mendoza-Lera, C.; Pastor, A.; Flaim, G.; Aben, R.; Riis, T.; Arce, M. I.; Onandia, G.; Paranaíba, J. R.; Linkhorst, A.; del Campo, R.; Amado, A. M.; Cauvy-Fraunié, S.; Brothers, S.; Condon, J.; Mendonça, R. F.; Reverey, F.; Rõõm, E.; Datry, T.; Roland, F.; Laas, A.; Obertegger, U.; Park, J.; Wang, H.; Kosten, S.; Gómez, R.; Feijoó, C.; Elosegi, A.; Sánchez-Montoya, M. M.; Finlayson, C. M.; Melita, M.; Oliveira Junior, E. S.; Muniz, C. C.; Gómez-Gener, L.; Leigh, C.; Zhang, Q.; and Marcé, R.\n\n\n \n \n \n \n \n Global CO2 emissions from dry inland waters share common drivers across ecosystems.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 11(1): 2126. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{keller_global_2020,\n\ttitle = {Global {CO2} emissions from dry inland waters share common drivers across ecosystems},\n\tvolume = {11},\n\tissn = {2041-1723},\n\turl = {http://www.nature.com/articles/s41467-020-15929-y},\n\tdoi = {10.1038/s41467-020-15929-y},\n\tabstract = {Abstract \n             \n              Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO \n              2 \n              ) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO \n              2 \n              emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO \n              2 \n              emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6\\% ({\\textasciitilde}0.12 Pg C y \n              −1 \n              ). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Nature Communications},\n\tauthor = {Keller, P. S. and Catalán, N. and von Schiller, D. and Grossart, H.-P. and Koschorreck, M. and Obrador, B. and Frassl, M. A. and Karakaya, N. and Barros, N. and Howitt, J. A. and Mendoza-Lera, C. and Pastor, A. and Flaim, G. and Aben, R. and Riis, T. and Arce, M. I. and Onandia, G. and Paranaíba, J. R. and Linkhorst, A. and del Campo, R. and Amado, A. M. and Cauvy-Fraunié, S. and Brothers, S. and Condon, J. and Mendonça, R. F. and Reverey, F. and Rõõm, E.-I. and Datry, T. and Roland, F. and Laas, A. and Obertegger, U. and Park, J.-H. and Wang, H. and Kosten, S. and Gómez, R. and Feijoó, C. and Elosegi, A. and Sánchez-Montoya, M. M. and Finlayson, C. M. and Melita, M. and Oliveira Junior, E. S. and Muniz, C. C. and Gómez-Gener, L. and Leigh, C. and Zhang, Q. and Marcé, R.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {2126},\n}\n\n\n\n

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\n \n\n \n \n Katata, G.; Grote, R.; Mauder, M.; Zeeman, M. J.; and Ota, M.\n\n\n \n \n \n \n \n Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(4): 1071–1085. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Wintertime$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{katata_wintertime_2020,\n\ttitle = {Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis},\n\tvolume = {17},\n\tissn = {1726-4189},\n\tshorttitle = {Wintertime grassland dynamics may influence belowground biomass under climate change},\n\turl = {https://bg.copernicus.org/articles/17/1071/2020/},\n\tdoi = {10.5194/bg-17-1071-2020},\n\tabstract = {Abstract. Rising temperatures and changes in snow cover, as can be expected under a warmer global climate, may have large impacts on mountain grassland productivity limited by cold and long winters. Here, we combined two existing models, the multi-layer atmosphere-SOiL-VEGetation model (SOLVEG) and the BASic GRAssland model (BASGRA), which accounts for snow, freeze–thaw events, grass growth, and soil carbon balance. The model was applied to simulate the responses of managed grasslands to anomalously warm winter conditions. The grass growth module considered key ecological processes under a cold environment, such as leaf formation, elongation and death, tillering, carbon allocation, and cold acclimation, in terms of photosynthetic activity. Input parameters were derived for two pre-Alpine grassland sites in Germany, for which the model was run using 3 years of data that included a winter with an exceptionally small amount of snow. The model reproduced the temporal variability of observed daily mean heat fluxes, soil temperatures, and snow depth throughout the study period. High physiological activity levels during the extremely warm winter led to a simulated CO2 uptake of 100 gC m−2, which was mainly allocated into the belowground biomass and only to a minor extent used for additional plant growth during early spring. If these temporary dynamics are representative of long-term changes, this process, which is so far largely unaccounted for in scenario analysis using global terrestrial biosphere models, may lead to carbon accumulation in the soil and/or carbon loss from the soil as a response to global warming.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Katata, Genki and Grote, Rüdiger and Mauder, Matthias and Zeeman, Matthias J. and Ota, Masakazu},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {1071--1085},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Kaufmann, M. S.; Klotzsche, A.; Vereecken, H.; and der Kruk, J.\n\n\n \n \n \n \n \n Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaufmann_simultaneous_2020,\n\ttitle = {Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20017},\n\tdoi = {10.1002/vzj2.20017},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Kaufmann, Manuela Sarah and Klotzsche, Anja and Vereecken, Harry and der Kruk, Jan},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kaiser, K.; Schneider, T.; Küster, M.; Dietze, E.; Fülling, A.; Heinrich, S.; Kappler, C.; Nelle, O.; Schult, M.; Theuerkauf, M.; Vogel, S.; de Boer, A. M.; Börner, A.; Preusser, F.; Schwabe, M.; Ulrich, J.; Wirner, M.; and Bens, O.\n\n\n \n \n \n \n \n Palaeosols and their cover sediments of a glacial landscape in northern central Europe: Spatial distribution, pedostratigraphy and evidence on landscape evolution.\n \n \n \n \n\n\n \n\n\n\n CATENA, 193: 104647. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Palaeosols$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_palaeosols_2020,\n\ttitle = {Palaeosols and their cover sediments of a glacial landscape in northern central {Europe}: {Spatial} distribution, pedostratigraphy and evidence on landscape evolution},\n\tvolume = {193},\n\tissn = {03418162},\n\tshorttitle = {Palaeosols and their cover sediments of a glacial landscape in northern central {Europe}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0341816220301971},\n\tdoi = {10.1016/j.catena.2020.104647},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {CATENA},\n\tauthor = {Kaiser, Knut and Schneider, Thomas and Küster, Mathias and Dietze, Elisabeth and Fülling, Alexander and Heinrich, Susann and Kappler, Christoph and Nelle, Oliver and Schult, Manuela and Theuerkauf, Martin and Vogel, Sebastian and de Boer, Anna Maartje and Börner, Andreas and Preusser, Frank and Schwabe, Matthias and Ulrich, Jens and Wirner, Michael and Bens, Oliver},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {104647},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kaiser, K.; Hrubý, P.; Tolksdorf, J. F.; Alper, G.; Herbig, C.; Kočár, P.; Petr, L.; Schulz, L.; and Heinrich, I.\n\n\n \n \n \n \n \n Cut and covered: Subfossil trees in buried soils reflect medieval forest composition and exploitation of the central European uplands.\n \n \n \n \n\n\n \n\n\n\n Geoarchaeology, 35(1): 42–62. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Cut$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_cut_2020,\n\ttitle = {Cut and covered: {Subfossil} trees in buried soils reflect medieval forest composition and exploitation of the central {European} uplands},\n\tvolume = {35},\n\tissn = {0883-6353, 1520-6548},\n\tshorttitle = {Cut and covered},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/gea.21756},\n\tdoi = {10.1002/gea.21756},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Geoarchaeology},\n\tauthor = {Kaiser, Knut and Hrubý, Petr and Tolksdorf, Johann Friedrich and Alper, Götz and Herbig, Christoph and Kočár, Petr and Petr, Libor and Schulz, Lars and Heinrich, Ingo},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {42--62},\n}\n\n\n\n

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\n \n\n \n \n Kaden, U. S.; Fuchs, E.; Hecht, C.; Hein, T.; Rupp, H.; Scholz, M.; and Schulz-Zunkel, C.\n\n\n \n \n \n \n \n Advancement of the Acetylene Inhibition Technique Using Time Series Analysis on Air-Dried Floodplain Soils to Quantify Denitrification Potential.\n \n \n \n \n\n\n \n\n\n\n Geosciences, 10(11): 431. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Advancement$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaden_advancement_2020,\n\ttitle = {Advancement of the {Acetylene} {Inhibition} {Technique} {Using} {Time} {Series} {Analysis} on {Air}-{Dried} {Floodplain} {Soils} to {Quantify} {Denitrification} {Potential}},\n\tvolume = {10},\n\tissn = {2076-3263},\n\turl = {https://www.mdpi.com/2076-3263/10/11/431},\n\tdoi = {10.3390/geosciences10110431},\n\tabstract = {Denitrification in floodplain soils is one key process that determines the buffering capacity of riparian zones in terms of diffuse nitrate pollution. One widely used approach to measure the denitrification potential is the acetylene inhibition technique that requires fresh soil samples. We conducted experiments with air-dried soils using a time series analysis to determine the optimal rewetting period. Thus, air-dried soil samples from six different floodplain areas in Germany were rewetted for 1 to 13days to 100\\% water-filled pore space. We analyzed nitrogen accumulated as N2O in the top of anaerobic flasks with and without acetylene by gas chromatography after four hours of incubation. We observed an overall optimal rewetting of at least seven days for complete denitrification. We also saw the strong influence of pH and field capacity on the denitrification product ratio; in soils with pH {\\textless} 7, we hardly assumed complete denitrification, whereas the treatments with pH {\\textgreater} 7 achieved stable values after seven days of rewetting. This advanced method provides the opportunity to carry out campaigns with large soil sample sizes on the landscape scale, as samples can be stored dry until measurements are taken.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Geosciences},\n\tauthor = {Kaden, Ute Susanne and Fuchs, Elmar and Hecht, Christian and Hein, Thomas and Rupp, Holger and Scholz, Mathias and Schulz-Zunkel, Christiane},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {431},\n}\n\n\n\n

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\n \n\n \n \n Li, M.; Wu, P.; and Ma, Z.\n\n\n \n \n \n \n \n A comprehensive evaluation of soil moisture and soil temperature from third‐generation atmospheric and land reanalysis data sets.\n \n \n \n \n\n\n \n\n\n\n International Journal of Climatology, 40(13): 5744–5766. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_comprehensive_2020,\n\ttitle = {A comprehensive evaluation of soil moisture and soil temperature from third‐generation atmospheric and land reanalysis data sets},\n\tvolume = {40},\n\tissn = {0899-8418, 1097-0088},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/joc.6549},\n\tdoi = {10.1002/joc.6549},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-02},\n\tjournal = {International Journal of Climatology},\n\tauthor = {Li, Mingxing and Wu, Peili and Ma, Zhuguo},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {5744--5766},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Heurich, M.; Magdon, P.; Rocchini, D.; Schulz, K.; Bumberger, J.; and King, D. J.\n\n\n \n \n \n \n \n A Range of Earth Observation Techniques for Assessing Plant Diversity.\n \n \n \n \n\n\n \n\n\n\n In Cavender-Bares, J.; Gamon, J. A.; and Townsend, P. A., editor(s), Remote Sensing of Plant Biodiversity, pages 309–348. Springer International Publishing, Cham, 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{cavender-bares_range_2020,\n\taddress = {Cham},\n\ttitle = {A {Range} of {Earth} {Observation} {Techniques} for {Assessing} {Plant} {Diversity}},\n\tisbn = {9783030331566 9783030331573},\n\turl = {http://link.springer.com/10.1007/978-3-030-33157-3_13},\n\tabstract = {Abstract \n            Vegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tbooktitle = {Remote {Sensing} of {Plant} {Biodiversity}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Lausch, Angela and Heurich, Marco and Magdon, Paul and Rocchini, Duccio and Schulz, Karsten and Bumberger, Jan and King, Doug J.},\n\teditor = {Cavender-Bares, Jeannine and Gamon, John A. and Townsend, Philip A.},\n\tyear = {2020},\n\tdoi = {10.1007/978-3-030-33157-3_13},\n\tpages = {309--348},\n}\n\n\n\n

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\n \n\n \n \n Jonard, F.; De Cannière, S.; Brüggemann, N.; Gentine, P.; Short Gianotti, D.; Lobet, G.; Miralles, D.; Montzka, C.; Pagán, B.; Rascher, U.; and Vereecken, H.\n\n\n \n \n \n \n \n Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 291: 108088. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Value$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jonard_value_2020,\n\ttitle = {Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: {Current} status and challenges},\n\tvolume = {291},\n\tissn = {01681923},\n\tshorttitle = {Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192320301908},\n\tdoi = {10.1016/j.agrformet.2020.108088},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Jonard, F. and De Cannière, S. and Brüggemann, N. and Gentine, P. and Short Gianotti, D.J. and Lobet, G. and Miralles, D.G. and Montzka, C. and Pagán, B.R. and Rascher, U. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {108088},\n}\n\n\n\n

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\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Schrön, M.; Fiedler, J.; Brogi, C.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Corrigendum: Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 604482. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Corrigendum:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jakobi_corrigendum_2020,\n\ttitle = {Corrigendum: {Error} {Estimation} for {Soil} {Moisture} {Measurements} {With} {Cosmic} {Ray} {Neutron} {Sensing} and {Implications} for {Rover} {Surveys}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Corrigendum},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.604482/full},\n\tdoi = {10.3389/frwa.2020.604482},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Jakobi, Jannis and Huisman, Johan A. and Schrön, Martin and Fiedler, Justus and Brogi, Cosimo and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {604482},\n}\n\n\n\n

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\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Schrön, M.; Fiedler, J.; Brogi, C.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 10. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Error$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jakobi_error_2020,\n\ttitle = {Error {Estimation} for {Soil} {Moisture} {Measurements} {With} {Cosmic} {Ray} {Neutron} {Sensing} and {Implications} for {Rover} {Surveys}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.00010/full},\n\tdoi = {10.3389/frwa.2020.00010},\n\tabstract = {Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3 \n              rd \n              order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Jakobi, Jannis and Huisman, Johan A. and Schrön, Martin and Fiedler, Justus and Brogi, Cosimo and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {10},\n}\n\n\n\n

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\n \n\n \n \n Huang, Y.; Hendricks Franssen, H.; Herbst, M.; Hirschi, M.; Michel, D.; Seneviratne, S. I.; Teuling, A. J.; Vogt, R.; Detlef, S.; Pütz, T.; and Vereecken, H.\n\n\n \n \n \n \n \n Evaluation of different methods for gap filling of long‐term actual evapotranspiration time series measured by lysimeters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{huang_evaluation_2020,\n\ttitle = {Evaluation of different methods for gap filling of long‐term actual evapotranspiration time series measured by lysimeters},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20020},\n\tdoi = {10.1002/vzj2.20020},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Huang, Yafei and Hendricks Franssen, Harrie‐Jan and Herbst, Michael and Hirschi, Martin and Michel, Dominik and Seneviratne, Sonia I. and Teuling, Adriaan J. and Vogt, Roland and Detlef, Schumacher and Pütz, Thomas and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

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\n \n\n \n \n Huang, J.; Desai, A. R.; Zhu, J.; Hartemink, A. E.; Stoy, P. C.; Loheide, S. P.; Bogena, H. R.; Zhang, Y.; Zhang, Z.; and Arriaga, F.\n\n\n \n \n \n \n \n Retrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 578367. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Retrieving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{huang_retrieving_2020,\n\ttitle = {Retrieving {Heterogeneous} {Surface} {Soil} {Moisture} at 100 m {Across} the {Globe} via {Fusion} of {Remote} {Sensing} and {Land} {Surface} {Parameters}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.578367/full},\n\tdoi = {10.3389/frwa.2020.578367},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Huang, Jingyi and Desai, Ankur R. and Zhu, Jun and Hartemink, Alfred E. and Stoy, Paul C. and Loheide, Steven P. and Bogena, Heye R. and Zhang, Yakun and Zhang, Zhou and Arriaga, Francisco},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {578367},\n}\n\n\n\n

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\n \n\n \n \n Herzsprung, P.; Wentzky, V.; Kamjunke, N.; von Tümpling, W.; Wilske, C.; Friese, K.; Boehrer, B.; Reemtsma, T.; Rinke, K.; and Lechtenfeld, O. J.\n\n\n \n \n \n \n \n Improved Understanding of Dissolved Organic Matter Processing in Freshwater Using Complementary Experimental and Machine Learning Approaches.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 54(21): 13556–13565. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herzsprung_improved_2020,\n\ttitle = {Improved {Understanding} of {Dissolved} {Organic} {Matter} {Processing} in {Freshwater} {Using} {Complementary} {Experimental} and {Machine} {Learning} {Approaches}},\n\tvolume = {54},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.0c02383},\n\tdoi = {10.1021/acs.est.0c02383},\n\tlanguage = {en},\n\tnumber = {21},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Herzsprung, Peter and Wentzky, Valerie and Kamjunke, Norbert and von Tümpling, Wolf and Wilske, Christin and Friese, Kurt and Boehrer, Bertram and Reemtsma, Thorsten and Rinke, Karsten and Lechtenfeld, Oliver J.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {13556--13565},\n}\n\n\n\n

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\n \n\n \n \n Heidbüchel, I.; Yang, J.; Musolff, A.; Troch, P.; Ferré, T.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n On the shape of forward transit time distributions in low-order catchments.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(6): 2895–2920. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heidbuchel_shape_2020,\n\ttitle = {On the shape of forward transit time distributions in low-order catchments},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/2895/2020/},\n\tdoi = {10.5194/hess-24-2895-2020},\n\tabstract = {Abstract. Transit time distributions (TTDs) integrate information\non timing, amount, storage, mixing and flow paths of water and thus\ncharacterize hydrologic and hydrochemical catchment response unlike any\nother descriptor. Here, we simulate the shape of TTDs in an idealized\nlow-order catchment and investigate whether it changes systematically with\ncertain catchment and climate properties. To this end, we used a physically\nbased, spatially explicit 3-D model, injected tracer with a precipitation\nevent and recorded the resulting forward TTDs at the outlet of a small\n(∼6000 m2) catchment for different scenarios. We found\nthat the TTDs can be subdivided into four parts: (1) early part – controlled\nby soil hydraulic conductivity and antecedent soil moisture content, (2) middle part – a transition zone with no clear pattern or control, (3) later\npart – influenced by soil hydraulic conductivity and subsequent\nprecipitation amount, and (4) very late tail of the breakthrough curve –\ngoverned by bedrock hydraulic conductivity. The modeled TTD shapes can be\npredicted using a dimensionless number: higher initial peaks are observed if\nthe inflow of water to a catchment is not equal to its capacity to discharge\nwater via subsurface flow paths, and lower initial peaks are connected to\nincreasing available storage. In most cases the modeled TTDs were humped\nwith nonzero initial values and varying weights of the tails. Therefore,\nnone of the best-fit theoretical probability functions could describe the\nentire TTD shape exactly. Still, we found that generally gamma and\nlog-normal distributions work better for scenarios of low and high soil\nhydraulic conductivity, respectively.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Heidbüchel, Ingo and Yang, Jie and Musolff, Andreas and Troch, Peter and Ferré, Ty and Fleckenstein, Jan H.},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {2895--2920},\n}\n\n\n\n

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\n \n\n \n \n Heck, K.; Coltman, E.; Schneider, J.; and Helmig, R.\n\n\n \n \n \n \n \n Influence of Radiation on Evaporation Rates: A Numerical Analysis.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(10). October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Influence$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heck_influence_2020,\n\ttitle = {Influence of {Radiation} on {Evaporation} {Rates}: {A} {Numerical} {Analysis}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Influence of {Radiation} on {Evaporation} {Rates}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR027332},\n\tdoi = {10.1029/2020WR027332},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Heck, K. and Coltman, E. and Schneider, J. and Helmig, R.},\n\tmonth = oct,\n\tyear = {2020},\n}\n\n\n\n

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\n \n\n \n \n Hartmann, E.; Schulz, J.; Seibert, R.; Schmidt, M.; Zhang, M.; Luterbacher, J.; and Tölle, M. H.\n\n\n \n \n \n \n \n Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 11(12): 1364. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hartmann_impact_2020,\n\ttitle = {Impact of {Environmental} {Conditions} on {Grass} {Phenology} in the {Regional} {Climate} {Model} {COSMO}-{CLM}},\n\tvolume = {11},\n\tissn = {2073-4433},\n\turl = {https://www.mdpi.com/2073-4433/11/12/1364},\n\tdoi = {10.3390/atmos11121364},\n\tabstract = {Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional types.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-02},\n\tjournal = {Atmosphere},\n\tauthor = {Hartmann, Eva and Schulz, Jan-Peter and Seibert, Ruben and Schmidt, Marius and Zhang, Mingyue and Luterbacher, Jürg and Tölle, Merja H.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {1364},\n}\n\n\n\n

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\n \n\n \n \n Harris, R. M. B.; Loeffler, F.; Rumm, A.; Fischer, C.; Horchler, P.; Scholz, M.; Foeckler, F.; and Henle, K.\n\n\n \n \n \n \n \n Biological responses to extreme weather events are detectable but difficult to formally attribute to anthropogenic climate change.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 10(1): 14067. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Biological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{harris_biological_2020,\n\ttitle = {Biological responses to extreme weather events are detectable but difficult to formally attribute to anthropogenic climate change},\n\tvolume = {10},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-020-70901-6},\n\tdoi = {10.1038/s41598-020-70901-6},\n\tabstract = {Abstract \n            As the frequency and intensity of extreme events such as droughts, heatwaves and floods have increased over recent decades, more extreme biological responses are being reported, and there is widespread interest in attributing such responses to anthropogenic climate change. However, the formal detection and attribution of biological responses to climate change is associated with many challenges. We illustrate these challenges with data from the Elbe River floodplain, Germany. Using community turnover and stability indices, we show that responses in plant, carabid and mollusc communities are detectable following extreme events. Community composition and species dominance changed following the extreme flood and summer heatwave of 2002/2003 (all taxa); the 2006 flood and heatwave (molluscs); and after the recurring floods and heatwave of 2010 and the 2013 flood (plants). Nevertheless, our ability to attribute these responses to anthropogenic climate change is limited by high natural variability in climate and biological data; lack of long-term data and replication, and the effects of multiple events. Without better understanding of the mechanisms behind change and the interactions, feedbacks and potentially lagged responses, multiple-driver attribution is unlikely. We discuss whether formal detection and/or attribution is necessary and suggest ways in which understanding of biological responses to extreme events could progress.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Reports},\n\tauthor = {Harris, R. M. B. and Loeffler, F. and Rumm, A. and Fischer, C. and Horchler, P. and Scholz, M. and Foeckler, F. and Henle, K.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {14067},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Hagenau, J.; Kaufmann, V.; and Borg, H.\n\n\n \n \n \n \n \n Monitoring water content changes in a soil profile with TDR-probes at just three depths - How well does it work?.\n \n \n \n \n\n\n \n\n\n\n RBRH, 25: e8. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hagenau_monitoring_2020,\n\ttitle = {Monitoring water content changes in a soil profile with {TDR}-probes at just three depths - {How} well does it work?},\n\tvolume = {25},\n\tissn = {2318-0331, 1414-381X},\n\turl = {http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312020000100213&tlng=en},\n\tdoi = {10.1590/2318-0331.252020190113},\n\tabstract = {ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies. \n          ,  \n            RESUMO As sondas TDR são usadas para monitorar alterações no conteúdo de água no perfil do solo (DW). Freqüentemente, as sondas são colocadas em três profundidades. Isso levanta a questão de como essa configuração pode mostrar os verdadeiros DW´s. Para respondê-lo, usamos um lisímetro de pesagem de alta precisão de 2 m, no qual as sondas TDR são instaladas horizontalmente a 20, 60 e 120 cm de profundidade (uma por profundidade). Os dados DW coletados pela pesagem do lisímetro foram comparados com os valores reais dos dados DW determinados com as sondas TDR. Obtivemos os seguintes resultados: Há um atraso na resposta das sondas TDR à precipitação, evaporação, transpiração ou drenagem, porque uma frente de umedecimento ou secagem deve primeiro alcançá-las. Além disso, os dados do TDR são medições pontuais que são extrapoladas para um volume de solo maior. Isso freqüentemente leva a erros. Por esses motivos, as sondas TDR em apenas três profundidades não podem fornecer dados confiáveis sobre alterações de curto prazo (por exemplo, diárias) no conteúdo de água do solo devido aos processos apresentados aqui. Por períodos mais longos (por exemplo, uma semana), os dados são melhores, mas ainda não são precisos o suficiente para estudos científicos confiáveis.},\n\turldate = {2022-11-02},\n\tjournal = {RBRH},\n\tauthor = {Hagenau, Jens and Kaufmann, Vander and Borg, Heinz},\n\tyear = {2020},\n\tpages = {e8},\n}\n\n\n\n

\n\n\n

\n ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies. , RESUMO As sondas TDR são usadas para monitorar alterações no conteúdo de água no perfil do solo (DW). Freqüentemente, as sondas são colocadas em três profundidades. Isso levanta a questão de como essa configuração pode mostrar os verdadeiros DW´s. Para respondê-lo, usamos um lisímetro de pesagem de alta precisão de 2 m, no qual as sondas TDR são instaladas horizontalmente a 20, 60 e 120 cm de profundidade (uma por profundidade). Os dados DW coletados pela pesagem do lisímetro foram comparados com os valores reais dos dados DW determinados com as sondas TDR. Obtivemos os seguintes resultados: Há um atraso na resposta das sondas TDR à precipitação, evaporação, transpiração ou drenagem, porque uma frente de umedecimento ou secagem deve primeiro alcançá-las. Além disso, os dados do TDR são medições pontuais que são extrapoladas para um volume de solo maior. Isso freqüentemente leva a erros. Por esses motivos, as sondas TDR em apenas três profundidades não podem fornecer dados confiáveis sobre alterações de curto prazo (por exemplo, diárias) no conteúdo de água do solo devido aos processos apresentados aqui. Por períodos mais longos (por exemplo, uma semana), os dados são melhores, mas ainda não são precisos o suficiente para estudos científicos confiáveis.\n

\n\n\n

\n \n\n \n \n Grosse, M.; Hierold, W.; Ahlborn, M. C.; Piepho, H.; and Helming, K.\n\n\n \n \n \n \n \n Long-term field experiments in Germany: classification and spatial representation.\n \n \n \n \n\n\n \n\n\n\n SOIL, 6(2): 579–596. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{grosse_long-term_2020,\n\ttitle = {Long-term field experiments in {Germany}: classification and spatial representation},\n\tvolume = {6},\n\tissn = {2199-398X},\n\tshorttitle = {Long-term field experiments in {Germany}},\n\turl = {https://soil.copernicus.org/articles/6/579/2020/},\n\tdoi = {10.5194/soil-6-579-2020},\n\tabstract = {Abstract. The collective analysis of long-term field experiments (LTFEs), here defined as agricultural\nexperiments with a minimum duration of 20 years and research in the context of sustainable\nsoil use and yield, can be used for detecting changes in soil properties and yield such as those induced\nby climate change. However, information about existing LTFEs is scattered, and the research data\nare not easily accessible. In this study, meta-information on LTFEs in Germany is compiled and\ntheir spatial representation is analyzed. The study is conducted within the framework of the\nBonaRes project, which, inter alia, has established a central access point for LTFE information\nand research data. A total of 205 LTFEs which fit to the definition above are identified. Of these,\n140 LTFEs are ongoing. The land use in 168 LTFEs is arable field crops, in 34 trials grassland, in\n2 trials vegetables and in 1 trial pomiculture. Field crop LTFEs are categorized into\nfertilization (n=158), tillage (n=38) and crop rotation (n=32; multiple nominations\npossible) experiments, while all grassland experiments (n=34) deal with fertilization. The\nspatial representation is analyzed according to the climatic water balance of the growing season\n(1 May to 31 October) (CWBg), the Müncheberg Soil Quality Rating (MSQR) and clay content. The\nresults show that, in general, the LTFEs well represent the area shares of both the CWBg and the\nMSQR classes. Eighty-nine percent of the arable land and 65 \\% of the grassland in Germany are covered by\nthe three driest CWBg classes, hosting 89 \\% and 71 \\% of the arable and grassland LTFEs,\nrespectively. LTFEs cover all six MSQR classes but with a bias towards the high and very high\nsoil quality classes. LTFEs on arable land are present in all clay content classes according to\nthe European Soil Data Centre (ESDAC) but with a bias towards the clay content class 4. Grassland LTFEs show a bias towards\nthe clay content classes 5, 6 and 7, while well representing the other clay content classes,\nexcept clay content class 3, where grassland LTFEs are completely missing. The results confirm\nthe very high potential of LTFE data for spatially differentiated analyses and modeling.\nHowever, reuse is restricted by the difficult access to LTFE research data. The common database\nis an important step in overcoming this restriction.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-02},\n\tjournal = {SOIL},\n\tauthor = {Grosse, Meike and Hierold, Wilfried and Ahlborn, Marlen C. and Piepho, Hans-Peter and Helming, Katharina},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {579--596},\n}\n\n\n\n

\n\n\n

\n Abstract. The collective analysis of long-term field experiments (LTFEs), here defined as agricultural experiments with a minimum duration of 20 years and research in the context of sustainable soil use and yield, can be used for detecting changes in soil properties and yield such as those induced by climate change. However, information about existing LTFEs is scattered, and the research data are not easily accessible. In this study, meta-information on LTFEs in Germany is compiled and their spatial representation is analyzed. The study is conducted within the framework of the BonaRes project, which, inter alia, has established a central access point for LTFE information and research data. A total of 205 LTFEs which fit to the definition above are identified. Of these, 140 LTFEs are ongoing. The land use in 168 LTFEs is arable field crops, in 34 trials grassland, in 2 trials vegetables and in 1 trial pomiculture. Field crop LTFEs are categorized into fertilization (n=158), tillage (n=38) and crop rotation (n=32; multiple nominations possible) experiments, while all grassland experiments (n=34) deal with fertilization. The spatial representation is analyzed according to the climatic water balance of the growing season (1 May to 31 October) (CWBg), the Müncheberg Soil Quality Rating (MSQR) and clay content. The results show that, in general, the LTFEs well represent the area shares of both the CWBg and the MSQR classes. Eighty-nine percent of the arable land and 65 % of the grassland in Germany are covered by the three driest CWBg classes, hosting 89 % and 71 % of the arable and grassland LTFEs, respectively. LTFEs cover all six MSQR classes but with a bias towards the high and very high soil quality classes. LTFEs on arable land are present in all clay content classes according to the European Soil Data Centre (ESDAC) but with a bias towards the clay content class 4. Grassland LTFEs show a bias towards the clay content classes 5, 6 and 7, while well representing the other clay content classes, except clay content class 3, where grassland LTFEs are completely missing. The results confirm the very high potential of LTFE data for spatially differentiated analyses and modeling. However, reuse is restricted by the difficult access to LTFE research data. The common database is an important step in overcoming this restriction.\n

\n\n\n

\n \n\n \n \n Groh, J.; Vanderborght, J.; Pütz, T.; Vogel, H.; Gründling, R.; Rupp, H.; Rahmati, M.; Sommer, M.; Vereecken, H.; and Gerke, H. H.\n\n\n \n \n \n \n \n Responses of soil water storage and crop water use efficiency to changing climatic conditions: a lysimeter-based space-for-time approach.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(3): 1211–1225. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Responses$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_responses_2020,\n\ttitle = {Responses of soil water storage and crop water use efficiency to changing climatic conditions: a lysimeter-based space-for-time approach},\n\tvolume = {24},\n\tissn = {1607-7938},\n\tshorttitle = {Responses of soil water storage and crop water use efficiency to changing climatic conditions},\n\turl = {https://hess.copernicus.org/articles/24/1211/2020/},\n\tdoi = {10.5194/hess-24-1211-2020},\n\tabstract = {Abstract. Future crop production will be affected by climatic\nchanges. In several regions, the projected changes in total rainfall and\nseasonal rainfall patterns will lead to lower soil water storage (SWS), which\nin turn affects crop water uptake, crop yield, water use efficiency (WUE), grain\nquality and groundwater recharge. Effects of climate change on those\nvariables depend on the soil properties and were often estimated based on\nmodel simulations. The objective of this study was to investigate the\nresponse of key variables in four different soils and for two different\nclimates in Germany with a different aridity index (AI): 1.09 for the wetter\n(range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done\nby using high-precision weighable lysimeters. According to a\n“space-for-time” (SFT) concept, intact soil monoliths that were moved to sites\nwith contrasting climatic conditions have been monitored from April 2011\nuntil December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier\nclimate, whereas crop yield was significantly higher, without affecting grain\nquality. Especially “non-productive” water losses (evapotranspiration out of\nthe main growing period) were lower, which led to a more efficient crop water\nuse in the drier climate. A characteristic decrease of the SWS for soils\nwith a finer texture was observed after a longer drought period under a\ndrier climate. The reduced SWS after the drought remained until the end of\nthe observation period which demonstrates carry-over of drought from one\ngrowing season to another and the overall long-term effects of single\ndrought events. In the relatively drier climate, water flow at the soil\nprofile bottom showed a small net upward flux over the entire monitoring\nperiod as compared to downward fluxes (groundwater recharge) or drainage in\nthe relatively wetter climate and larger recharge rates in the coarser- as\ncompared to finer-textured soils. The large variability of recharge from\nyear to year and the long-lasting effects of drought periods on the SWS imply\nthat long-term monitoring of soil water balance components is necessary to\nobtain representative estimates. Results confirmed a more efficient crop\nwater use under less-plant-available soil moisture conditions. Long-term effects of\nchanging climatic conditions on the SWS and ecosystem productivity should be\nconsidered when trying to develop adaptation strategies in the agricultural\nsector.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Groh, Jannis and Vanderborght, Jan and Pütz, Thomas and Vogel, Hans-Jörg and Gründling, Ralf and Rupp, Holger and Rahmati, Mehdi and Sommer, Michael and Vereecken, Harry and Gerke, Horst H.},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {1211--1225},\n}\n\n\n\n

\n\n\n

\n Abstract. Future crop production will be affected by climatic changes. In several regions, the projected changes in total rainfall and seasonal rainfall patterns will lead to lower soil water storage (SWS), which in turn affects crop water uptake, crop yield, water use efficiency (WUE), grain quality and groundwater recharge. Effects of climate change on those variables depend on the soil properties and were often estimated based on model simulations. The objective of this study was to investigate the response of key variables in four different soils and for two different climates in Germany with a different aridity index (AI): 1.09 for the wetter (range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done by using high-precision weighable lysimeters. According to a “space-for-time” (SFT) concept, intact soil monoliths that were moved to sites with contrasting climatic conditions have been monitored from April 2011 until December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier climate, whereas crop yield was significantly higher, without affecting grain quality. Especially “non-productive” water losses (evapotranspiration out of the main growing period) were lower, which led to a more efficient crop water use in the drier climate. A characteristic decrease of the SWS for soils with a finer texture was observed after a longer drought period under a drier climate. The reduced SWS after the drought remained until the end of the observation period which demonstrates carry-over of drought from one growing season to another and the overall long-term effects of single drought events. In the relatively drier climate, water flow at the soil profile bottom showed a small net upward flux over the entire monitoring period as compared to downward fluxes (groundwater recharge) or drainage in the relatively wetter climate and larger recharge rates in the coarser- as compared to finer-textured soils. The large variability of recharge from year to year and the long-lasting effects of drought periods on the SWS imply that long-term monitoring of soil water balance components is necessary to obtain representative estimates. Results confirmed a more efficient crop water use under less-plant-available soil moisture conditions. Long-term effects of changing climatic conditions on the SWS and ecosystem productivity should be considered when trying to develop adaptation strategies in the agricultural sector.\n

\n\n\n

\n \n\n \n \n Groh, J.; Diamantopoulos, E.; Duan, X.; Ewert, F.; Herbst, M.; Holbak, M.; Kamali, B.; Kersebaum, K.; Kuhnert, M.; Lischeid, G.; Nendel, C.; Priesack, E.; Steidl, J.; Sommer, M.; Pütz, T.; Vereecken, H.; Wallor, E.; Weber, T. K.; Wegehenkel, M.; Weihermüller, L.; and Gerke, H. H.\n\n\n \n \n \n \n \n Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Crop$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_crop_2020,\n\ttitle = {Crop growth and soil water fluxes at erosion‐affected arable sites: {Using} weighing lysimeter data for model intercomparison},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {Crop growth and soil water fluxes at erosion‐affected arable sites},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20058},\n\tdoi = {10.1002/vzj2.20058},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Diamantopoulos, Efstathios and Duan, Xiaohong and Ewert, Frank and Herbst, Michael and Holbak, Maja and Kamali, Bahareh and Kersebaum, Kurt‐Christian and Kuhnert, Matthias and Lischeid, Gunnar and Nendel, Claas and Priesack, Eckart and Steidl, Jörg and Sommer, Michael and Pütz, Thomas and Vereecken, Harry and Wallor, Evelyn and Weber, Tobias K.D. and Wegehenkel, Martin and Weihermüller, Lutz and Gerke, Horst H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Graf, A.; Klosterhalfen, A.; Arriga, N.; Bernhofer, C.; Bogena, H.; Bornet, F.; Brüggemann, N.; Brümmer, C.; Buchmann, N.; Chi, J.; Chipeaux, C.; Cremonese, E.; Cuntz, M.; Dušek, J.; El-Madany, T. S.; Fares, S.; Fischer, M.; Foltýnová, L.; Gharun, M.; Ghiasi, S.; Gielen, B.; Gottschalk, P.; Grünwald, T.; Heinemann, G.; Heinesch, B.; Heliasz, M.; Holst, J.; Hörtnagl, L.; Ibrom, A.; Ingwersen, J.; Jurasinski, G.; Klatt, J.; Knohl, A.; Koebsch, F.; Konopka, J.; Korkiakoski, M.; Kowalska, N.; Kremer, P.; Kruijt, B.; Lafont, S.; Léonard, J.; De Ligne, A.; Longdoz, B.; Loustau, D.; Magliulo, V.; Mammarella, I.; Manca, G.; Mauder, M.; Migliavacca, M.; Mölder, M.; Neirynck, J.; Ney, P.; Nilsson, M.; Paul-Limoges, E.; Peichl, M.; Pitacco, A.; Poyda, A.; Rebmann, C.; Roland, M.; Sachs, T.; Schmidt, M.; Schrader, F.; Siebicke, L.; Šigut, L.; Tuittila, E.; Varlagin, A.; Vendrame, N.; Vincke, C.; Völksch, I.; Weber, S.; Wille, C.; Wizemann, H.; Zeeman, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Altered energy partitioning across terrestrial ecosystems in the European drought year 2018.\n \n \n \n \n\n\n \n\n\n\n Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1810): 20190524. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Altered$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_altered_2020,\n\ttitle = {Altered energy partitioning across terrestrial ecosystems in the {European} drought year 2018},\n\tvolume = {375},\n\tissn = {0962-8436, 1471-2970},\n\turl = {https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0524},\n\tdoi = {10.1098/rstb.2019.0524},\n\tabstract = {Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO \n              2 \n              exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9\\% more solar radiation and released 32\\% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO \n              2 \n              uptake by 17.8\\%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. \n             \n            This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.},\n\tlanguage = {en},\n\tnumber = {1810},\n\turldate = {2022-11-02},\n\tjournal = {Philosophical Transactions of the Royal Society B: Biological Sciences},\n\tauthor = {Graf, Alexander and Klosterhalfen, Anne and Arriga, Nicola and Bernhofer, Christian and Bogena, Heye and Bornet, Frédéric and Brüggemann, Nicolas and Brümmer, Christian and Buchmann, Nina and Chi, Jinshu and Chipeaux, Christophe and Cremonese, Edoardo and Cuntz, Matthias and Dušek, Jiří and El-Madany, Tarek S. and Fares, Silvano and Fischer, Milan and Foltýnová, Lenka and Gharun, Mana and Ghiasi, Shiva and Gielen, Bert and Gottschalk, Pia and Grünwald, Thomas and Heinemann, Günther and Heinesch, Bernard and Heliasz, Michal and Holst, Jutta and Hörtnagl, Lukas and Ibrom, Andreas and Ingwersen, Joachim and Jurasinski, Gerald and Klatt, Janina and Knohl, Alexander and Koebsch, Franziska and Konopka, Jan and Korkiakoski, Mika and Kowalska, Natalia and Kremer, Pascal and Kruijt, Bart and Lafont, Sebastien and Léonard, Joël and De Ligne, Anne and Longdoz, Bernard and Loustau, Denis and Magliulo, Vincenzo and Mammarella, Ivan and Manca, Giovanni and Mauder, Matthias and Migliavacca, Mirco and Mölder, Meelis and Neirynck, Johan and Ney, Patrizia and Nilsson, Mats and Paul-Limoges, Eugénie and Peichl, Matthias and Pitacco, Andrea and Poyda, Arne and Rebmann, Corinna and Roland, Marilyn and Sachs, Torsten and Schmidt, Marius and Schrader, Frederik and Siebicke, Lukas and Šigut, Ladislav and Tuittila, Eeva-Stiina and Varlagin, Andrej and Vendrame, Nadia and Vincke, Caroline and Völksch, Ingo and Weber, Stephan and Wille, Christian and Wizemann, Hans-Dieter and Zeeman, Matthias and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {20190524},\n}\n\n\n\n

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\n \n\n \n \n González-Sanchis, M.; García-Soro, J. M.; Molina, A. J.; Lidón, A. L.; Bautista, I.; Rouzic, E.; Bogena, H. R.; Hendricks Franssen, H.; and del Campo, A. D.\n\n\n \n \n \n \n \n Comparison of Soil Water Estimates From Cosmic-Ray Neutron and Capacity Sensors in a Semi-arid Pine Forest: Which Is Able to Better Assess the Role of Environmental Conditions and Thinning?.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 552508. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gonzalez-sanchis_comparison_2020,\n\ttitle = {Comparison of {Soil} {Water} {Estimates} {From} {Cosmic}-{Ray} {Neutron} and {Capacity} {Sensors} in a {Semi}-arid {Pine} {Forest}: {Which} {Is} {Able} to {Better} {Assess} the {Role} of {Environmental} {Conditions} and {Thinning}?},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Comparison of {Soil} {Water} {Estimates} {From} {Cosmic}-{Ray} {Neutron} and {Capacity} {Sensors} in a {Semi}-arid {Pine} {Forest}},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.552508/full},\n\tdoi = {10.3389/frwa.2020.552508},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {González-Sanchis, María and García-Soro, Juan M. and Molina, Antonio J. and Lidón, Antonio L. and Bautista, Inmaculada and Rouzic, Elie and Bogena, Heye R. and Hendricks Franssen, Harrie-JanHarrie and del Campo, Antonio D.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {552508},\n}\n\n\n\n

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\n \n\n \n \n Fürst, J.; Nachtnebel, H. P.; Gasch, J.; Nolz, R.; Stockinger, M. P.; Stumpp, C.; and Schulz, K.\n\n\n \n \n \n \n \n Rosalia: an experimental research site to study hydrological processes in a forest catchment.\n \n \n \n \n\n\n \n\n\n\n Technical Report Hydrology and Soil Science – Hydrology, November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Rosalia:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{furst_rosalia_2020,\n\ttype = {preprint},\n\ttitle = {Rosalia: an experimental research site to study hydrological processes in a forest catchment},\n\tshorttitle = {Rosalia},\n\turl = {https://essd.copernicus.org/preprints/essd-2020-254/essd-2020-254.pdf},\n\tabstract = {Abstract. Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19th and early 20th century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called Rosalia with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the Rosalia instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum. This article describes the characteristics of the site, the recently installed monitoring network and its instrumentation, as well as the datasets. The network includes 4 discharge gauging stations, 7 rain-gauges, together with observation of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community on a publicly accessible repository. The datasets are available at https://doi.org/10.5281/zenodo.3997141 (Fürst et al., 2020).},\n\turldate = {2022-11-02},\n\tinstitution = {Hydrology and Soil Science – Hydrology},\n\tauthor = {Fürst, Josef and Nachtnebel, Hans Peter and Gasch, Josef and Nolz, Reinhard and Stockinger, Michael Paul and Stumpp, Christine and Schulz, Karsten},\n\tmonth = nov,\n\tyear = {2020},\n\tdoi = {10.5194/essd-2020-254},\n}\n\n\n\n

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\n \n\n \n \n Fisher, J. B.; Lee, B.; Purdy, A. J.; Halverson, G. H.; Dohlen, M. B.; Cawse‐Nicholson, K.; Wang, A.; Anderson, R. G.; Aragon, B.; Arain, M. A.; Baldocchi, D. D.; Baker, J. M.; Barral, H.; Bernacchi, C. J.; Bernhofer, C.; Biraud, S. C.; Bohrer, G.; Brunsell, N.; Cappelaere, B.; Castro‐Contreras, S.; Chun, J.; Conrad, B. J.; Cremonese, E.; Demarty, J.; Desai, A. R.; De Ligne, A.; Foltýnová, L.; Goulden, M. L.; Griffis, T. J.; Grünwald, T.; Johnson, M. S.; Kang, M.; Kelbe, D.; Kowalska, N.; Lim, J.; Maïnassara, I.; McCabe, M. F.; Missik, J. E.; Mohanty, B. P.; Moore, C. E.; Morillas, L.; Morrison, R.; Munger, J. W.; Posse, G.; Richardson, A. D.; Russell, E. S.; Ryu, Y.; Sanchez‐Azofeifa, A.; Schmidt, M.; Schwartz, E.; Sharp, I.; Šigut, L.; Tang, Y.; Hulley, G.; Anderson, M.; Hain, C.; French, A.; Wood, E.; and Hook, S.\n\n\n \n \n \n \n \n ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(4). April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"ECOSTRESS:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fisher_ecostress_2020,\n\ttitle = {{ECOSTRESS}: {NASA}'s {Next} {Generation} {Mission} to {Measure} {Evapotranspiration} {From} the {International} {Space} {Station}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {{ECOSTRESS}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026058},\n\tdoi = {10.1029/2019WR026058},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Fisher, Joshua B. and Lee, Brian and Purdy, Adam J. and Halverson, Gregory H. and Dohlen, Matthew B. and Cawse‐Nicholson, Kerry and Wang, Audrey and Anderson, Ray G. and Aragon, Bruno and Arain, M. Altaf and Baldocchi, Dennis D. and Baker, John M. and Barral, Hélène and Bernacchi, Carl J. and Bernhofer, Christian and Biraud, Sébastien C. and Bohrer, Gil and Brunsell, Nathaniel and Cappelaere, Bernard and Castro‐Contreras, Saulo and Chun, Junghwa and Conrad, Bryan J. and Cremonese, Edoardo and Demarty, Jérôme and Desai, Ankur R. and De Ligne, Anne and Foltýnová, Lenka and Goulden, Michael L. and Griffis, Timothy J. and Grünwald, Thomas and Johnson, Mark S. and Kang, Minseok and Kelbe, Dave and Kowalska, Natalia and Lim, Jong‐Hwan and Maïnassara, Ibrahim and McCabe, Matthew F. and Missik, Justine E.C. and Mohanty, Binayak P. and Moore, Caitlin E. and Morillas, Laura and Morrison, Ross and Munger, J. William and Posse, Gabriela and Richardson, Andrew D. and Russell, Eric S. and Ryu, Youngryel and Sanchez‐Azofeifa, Arturo and Schmidt, Marius and Schwartz, Efrat and Sharp, Iain and Šigut, Ladislav and Tang, Yao and Hulley, Glynn and Anderson, Martha and Hain, Christopher and French, Andrew and Wood, Eric and Hook, Simon},\n\tmonth = apr,\n\tyear = {2020},\n}\n\n\n\n

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\n \n\n \n \n Fink, P.; Norf, H.; Anlanger, C.; Brauns, M.; Kamjunke, N.; Risse‐Buhl, U.; Schmitt‐Jansen, M.; Weitere, M.; and Borchardt, D.\n\n\n \n \n \n \n \n Streamside mobile mesocosms (MOBICOS): A new modular research infrastructure for hydro‐ecological process studies across catchment‐scale gradients.\n \n \n \n \n\n\n \n\n\n\n International Review of Hydrobiology, 105(3-4): 63–73. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Streamside$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fink_streamside_2020,\n\ttitle = {Streamside mobile mesocosms ({MOBICOS}): {A} new modular research infrastructure for hydro‐ecological process studies across catchment‐scale gradients},\n\tvolume = {105},\n\tissn = {1434-2944, 1522-2632},\n\tshorttitle = {Streamside mobile mesocosms ({MOBICOS})},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/iroh.201902009},\n\tdoi = {10.1002/iroh.201902009},\n\tlanguage = {en},\n\tnumber = {3-4},\n\turldate = {2022-11-02},\n\tjournal = {International Review of Hydrobiology},\n\tauthor = {Fink, Patrick and Norf, Helge and Anlanger, Christine and Brauns, Mario and Kamjunke, Norbert and Risse‐Buhl, Ute and Schmitt‐Jansen, Mechthild and Weitere, Markus and Borchardt, Dietrich},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {63--73},\n}\n\n\n\n

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\n \n\n \n \n Fersch, B.; Francke, T.; Heistermann, M.; Schrön, M.; Döpper, V.; Jakobi, J.; Baroni, G.; Blume, T.; Bogena, H.; Budach, C.; Gränzig, T.; Förster, M.; Güntner, A.; Hendricks Franssen, H.; Kasner, M.; Köhli, M.; Kleinschmit, B.; Kunstmann, H.; Patil, A.; Rasche, D.; Scheiffele, L.; Schmidt, U.; Szulc-Seyfried, S.; Weimar, J.; Zacharias, S.; Zreda, M.; Heber, B.; Kiese, R.; Mares, V.; Mollenhauer, H.; Völksch, I.; and Oswald, S.\n\n\n \n \n \n \n \n A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 12(3): 2289–2309. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fersch_dense_2020,\n\ttitle = {A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-{Alpine} headwater catchment in {Germany}},\n\tvolume = {12},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/12/2289/2020/},\n\tdoi = {10.5194/essd-12-2289-2020},\n\tabstract = {Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Earth System Science Data},\n\tauthor = {Fersch, Benjamin and Francke, Till and Heistermann, Maik and Schrön, Martin and Döpper, Veronika and Jakobi, Jannis and Baroni, Gabriele and Blume, Theresa and Bogena, Heye and Budach, Christian and Gränzig, Tobias and Förster, Michael and Güntner, Andreas and Hendricks Franssen, Harrie-Jan and Kasner, Mandy and Köhli, Markus and Kleinschmit, Birgit and Kunstmann, Harald and Patil, Amol and Rasche, Daniel and Scheiffele, Lena and Schmidt, Ulrich and Szulc-Seyfried, Sandra and Weimar, Jannis and Zacharias, Steffen and Zreda, Marek and Heber, Bernd and Kiese, Ralf and Mares, Vladimir and Mollenhauer, Hannes and Völksch, Ingo and Oswald, Sascha},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2289--2309},\n}\n\n\n\n

\n\n\n

\n Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).\n

\n\n\n

\n \n\n \n \n Döpper, V.; Gränzig, T.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(10): 1552. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Challenges$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dopper_challenges_2020,\n\ttitle = {Challenges in {UAS}-{Based} {TIR} {Imagery} {Processing}: {Image} {Alignment} and {Uncertainty} {Quantification}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Challenges in {UAS}-{Based} {TIR} {Imagery} {Processing}},\n\turl = {https://www.mdpi.com/2072-4292/12/10/1552},\n\tdoi = {10.3390/rs12101552},\n\tabstract = {Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Döpper, Veronika and Gränzig, Tobias and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {1552},\n}\n\n\n\n

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\n \n\n \n \n Dong, F.; Mi, C.; Hupfer, M.; Lindenschmidt, K.; Peng, W.; Liu, X.; and Rinke, K.\n\n\n \n \n \n \n \n Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 34(5): 1131–1143. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dong_assessing_2020,\n\ttitle = {Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model},\n\tvolume = {34},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.13653},\n\tdoi = {10.1002/hyp.13653},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Hydrological Processes},\n\tauthor = {Dong, Fei and Mi, Chenxi and Hupfer, Michael and Lindenschmidt, Karl‐Erich and Peng, Wenqi and Liu, Xiaobo and Rinke, Karsten},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {1131--1143},\n}\n\n\n\n

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\n \n\n \n \n Dhillon, M. S.; Dahms, T.; Kuebert-Flock, C.; Borg, E.; Conrad, C.; and Ullmann, T.\n\n\n \n \n \n \n \n Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(11): 1819. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dhillon_modelling_2020,\n\ttitle = {Modelling {Crop} {Biomass} from {Synthetic} {Remote} {Sensing} {Time} {Series}: {Example} for the {DEMMIN} {Test} {Site}, {Germany}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Modelling {Crop} {Biomass} from {Synthetic} {Remote} {Sensing} {Time} {Series}},\n\turl = {https://www.mdpi.com/2072-4292/12/11/1819},\n\tdoi = {10.3390/rs12111819},\n\tabstract = {This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 ({\\textgreater}0.82), low RMSE ({\\textless}600 g/m2) and significant p-value ({\\textless}0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 ({\\textless}0.68) and high RMSE ({\\textgreater}600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {1819},\n}\n\n\n\n

\n\n\n

\n This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (\\textgreater0.82), low RMSE (\\textless600 g/m2) and significant p-value (\\textless0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (\\textless0.68) and high RMSE (\\textgreater600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).\n

\n\n\n

\n \n\n \n \n Vila-Guerau de Arellano, J.; Ney, P.; Hartogensis, O.; de Boer, H.; van Diepen, K.; Emin, D.; de Groot, G.; Klosterhalfen, A.; Langensiepen, M.; Matveeva, M.; Miranda, G.; Moene, A.; Rascher, U.; Röckmann, T.; Adnew, G.; and Graf, A.\n\n\n \n \n \n \n \n CloudRoots: Integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land-atmosphere interactions.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Air - Land Exchange, May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"CloudRoots:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{vila-guerau_de_arellano_cloudroots_2020,\n\ttype = {preprint},\n\ttitle = {{CloudRoots}: {Integration} of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land-atmosphere interactions},\n\tshorttitle = {{CloudRoots}},\n\turl = {https://bg.copernicus.org/preprints/bg-2020-132/bg-2020-132.pdf},\n\tabstract = {Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational data set that includes soil, plant and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub–kilometre scale. Our findings demonstrate the need to include measurements at leaf level in order to obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the mechanistic model reproduces satisfactorily the stomatal leaf conductance and the leaf-level photosynthesis. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From the high frequency and vertical resolution state variables and CO2 measurements, we infer a profile of the plant assimilation that shows a strong non-linear behaviour. Observations taken by a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of the photosynthetically active radiation (PAR) by clouds, the so-called cloud flecks. More specifically, we find two-minute delays between the cloud radiation perturbation and ET. The impact of surface heterogeneity was further studied using ET estimates infer from the sun-induced fluorescence data and show small variation of ET in spite of the plant functional type differences. To study the relevance of advection and surface heterogeneity on the land-atmosphere interaction, we employ a coupled surface-atmospheric conceptual model that integrates the surface and upper-air observations taken at different scales: from the leaf-level to the landscape. At the landscape scale, we obtain the representative sensible heat flux that is consistent with the evolution of the boundary-layer depth evolution. Finally, throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large variations of plant transpiration at the leaf and canopy scales. The use of different instrumental techniques enables us to compare the total ET at various growing stages, from booting to senescence. There is satisfactory agreement between evapotranspiration of total ET, but the values remain sensitive to the scale at which ET is measured or modelled.},\n\turldate = {2022-11-02},\n\tinstitution = {Biogeochemistry: Air - Land Exchange},\n\tauthor = {Vila-Guerau de Arellano, Jordi and Ney, Patrizia and Hartogensis, Oscar and de Boer, Hugo and van Diepen, Kevien and Emin, Dzhaner and de Groot, Gesike and Klosterhalfen, Anne and Langensiepen, Matthias and Matveeva, Maria and Miranda, Gabriela and Moene, Arnold and Rascher, Uwe and Röckmann, Thomas and Adnew, Getachew and Graf, Alexander},\n\tmonth = may,\n\tyear = {2020},\n\tdoi = {10.5194/bg-2020-132},\n}\n\n\n\n

\n\n\n

\n Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational data set that includes soil, plant and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub–kilometre scale. Our findings demonstrate the need to include measurements at leaf level in order to obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the mechanistic model reproduces satisfactorily the stomatal leaf conductance and the leaf-level photosynthesis. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From the high frequency and vertical resolution state variables and CO2 measurements, we infer a profile of the plant assimilation that shows a strong non-linear behaviour. Observations taken by a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of the photosynthetically active radiation (PAR) by clouds, the so-called cloud flecks. More specifically, we find two-minute delays between the cloud radiation perturbation and ET. The impact of surface heterogeneity was further studied using ET estimates infer from the sun-induced fluorescence data and show small variation of ET in spite of the plant functional type differences. To study the relevance of advection and surface heterogeneity on the land-atmosphere interaction, we employ a coupled surface-atmospheric conceptual model that integrates the surface and upper-air observations taken at different scales: from the leaf-level to the landscape. At the landscape scale, we obtain the representative sensible heat flux that is consistent with the evolution of the boundary-layer depth evolution. Finally, throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large variations of plant transpiration at the leaf and canopy scales. The use of different instrumental techniques enables us to compare the total ET at various growing stages, from booting to senescence. There is satisfactory agreement between evapotranspiration of total ET, but the values remain sensitive to the scale at which ET is measured or modelled.\n

\n\n\n

\n \n\n \n \n Dadi, T.; Rinke, K.; and Friese, K.\n\n\n \n \n \n \n \n Trajectories of Sediment-Water Interactions in Reservoirs as a Result of Temperature and Oxygen Conditions.\n \n \n \n \n\n\n \n\n\n\n Water, 12(4): 1065. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Trajectories$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dadi_trajectories_2020,\n\ttitle = {Trajectories of {Sediment}-{Water} {Interactions} in {Reservoirs} as a {Result} of {Temperature} and {Oxygen} {Conditions}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/4/1065},\n\tdoi = {10.3390/w12041065},\n\tabstract = {Temperate lakes/reservoirs are warming; this can influence the benthic release of nutrients. They undergo seasonal changes resulting in an array of temperature and oxygen conditions; oxic-low, oxic-high, anoxic-low, and anoxic-high temperature. We sought to understand the interaction of temperature and oxygen conditions on benthic solutes exchange through a two-factorial sediment core incubation experiment by varying either temperature or oxygen conditions of sediment cores from an oligotrophic and eutrophic reservoir. Temperature and oxygen conditions are both important for nutrient release; however, they influence solutes differently; differences in the fluxes of the treatments were explained more by temperature for P, DOC and N, while for Fe, Mn and SO42−, differences were explained more by oxygen conditions. The combination of strongly reducing conditions (due to anoxia) and high temperature (20 °C) led to a significant increase in nutrients concentrations in the overlying water. Under these conditions, SRP flux was 0.04 and 0.5 mmol m−2 d−1; ammonium was 0.9 and 5.6 mmol m−2 d−1 for the oligotrophic and eutrophic reservoir, respectively. We observed a synergistic interaction between temperature and oxygen conditions which resulted in release of solutes from sediments. An increase in nutrients release under increasing temperatures is more likely and so are algal blooms.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Dadi, Tallent and Rinke, Karsten and Friese, Kurt},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1065},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Chukalla, A. D.; Reidsma, P.; van Vliet, M. T.; Silva, J. V.; van Ittersum, M. K.; Jomaa, S.; Rode, M.; Merbach, I.; and van Oel, P. R.\n\n\n \n \n \n \n \n Balancing indicators for sustainable intensification of crop production at field and river basin levels.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 705: 135925. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Balancing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chukalla_balancing_2020,\n\ttitle = {Balancing indicators for sustainable intensification of crop production at field and river basin levels},\n\tvolume = {705},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969719359200},\n\tdoi = {10.1016/j.scitotenv.2019.135925},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Chukalla, Abebe Demissie and Reidsma, Pytrik and van Vliet, Michelle T.H. and Silva, João Vasco and van Ittersum, Martin K. and Jomaa, Seifeddine and Rode, Michael and Merbach, Ines and van Oel, Pieter R.},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {135925},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Carolus, J. F.; Bartosova, A.; Olsen, S. B.; Jomaa, S.; Veinbergs, A.; Zīlāns, A.; Pedersen, S. M.; Schwarz, G.; Rode, M.; and Tonderski, K.\n\n\n \n \n \n \n \n Nutrient mitigation under the impact of climate and land-use changes: A hydro-economic approach to participatory catchment management.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Management, 271: 110976. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Nutrient$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{carolus_nutrient_2020,\n\ttitle = {Nutrient mitigation under the impact of climate and land-use changes: {A} hydro-economic approach to participatory catchment management},\n\tvolume = {271},\n\tissn = {03014797},\n\tshorttitle = {Nutrient mitigation under the impact of climate and land-use changes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S030147972030904X},\n\tdoi = {10.1016/j.jenvman.2020.110976},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Journal of Environmental Management},\n\tauthor = {Carolus, Johannes Friedrich and Bartosova, Alena and Olsen, Søren Bøye and Jomaa, Seifeddine and Veinbergs, Artūrs and Zīlāns, Andis and Pedersen, Søren Marcus and Schwarz, Gerald and Rode, Michael and Tonderski, Karin},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {110976},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Botter, M.; Li, L.; Hartmann, J.; Burlando, P.; and Fatichi, S.\n\n\n \n \n \n \n \n Depth of Solute Generation Is a Dominant Control on Concentration‐Discharge Relations.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(8). August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Depth$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{botter_depth_2020,\n\ttitle = {Depth of {Solute} {Generation} {Is} a {Dominant} {Control} on {Concentration}‐{Discharge} {Relations}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026695},\n\tdoi = {10.1029/2019WR026695},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Botter, M. and Li, L. and Hartmann, J. and Burlando, P. and Fatichi, S.},\n\tmonth = aug,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Brogi, C.; Huisman, J. A.; Herbst, M.; Weihermüller, L.; Klosterhalfen, A.; Montzka, C.; Reichenau, T. G.; and Vereecken, H.\n\n\n \n \n \n \n \n Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brogi_simulation_2020,\n\ttitle = {Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20009},\n\tdoi = {10.1002/vzj2.20009},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Brogi, C. and Huisman, J. A. and Herbst, M. and Weihermüller, L. and Klosterhalfen, A. and Montzka, C. and Reichenau, T. G. and Vereecken, H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Bogena, H. R.; Herrmann, F.; Jakobi, J.; Brogi, C.; Ilias, A.; Huisman, J. A.; Panagopoulos, A.; and Pisinaras, V.\n\n\n \n \n \n \n \n Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 19. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_monitoring_2020,\n\ttitle = {Monitoring of {Snowpack} {Dynamics} {With} {Cosmic}-{Ray} {Neutron} {Probes}: {A} {Comparison} of {Four} {Conversion} {Methods}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Monitoring of {Snowpack} {Dynamics} {With} {Cosmic}-{Ray} {Neutron} {Probes}},\n\turl = {https://www.frontiersin.org/article/10.3389/frwa.2020.00019/full},\n\tdoi = {10.3389/frwa.2020.00019},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Bogena, Heye R. and Herrmann, Frank and Jakobi, Jannis and Brogi, Cosimo and Ilias, Andreas and Huisman, Johan Alexander and Panagopoulos, Andreas and Pisinaras, Vassilios},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {19},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Berauer, B. J.; Wilfahrt, P. A.; Reu, B.; Schuchardt, M. A.; Garcia-Franco, N.; Zistl-Schlingmann, M.; Dannenmann, M.; Kiese, R.; Kühnel, A.; and Jentsch, A.\n\n\n \n \n \n \n \n Predicting forage quality of species-rich pasture grasslands using vis-NIRS to reveal effects of management intensity and climate change.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 296: 106929. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Predicting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{berauer_predicting_2020,\n\ttitle = {Predicting forage quality of species-rich pasture grasslands using vis-{NIRS} to reveal effects of management intensity and climate change},\n\tvolume = {296},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880920301146},\n\tdoi = {10.1016/j.agee.2020.106929},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Berauer, Bernd J. and Wilfahrt, Peter A. and Reu, Björn and Schuchardt, Max A. and Garcia-Franco, Noelia and Zistl-Schlingmann, Marcus and Dannenmann, Michael and Kiese, Ralf and Kühnel, Anna and Jentsch, Anke},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {106929},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Beegum, S.; Vanderborght, J.; Šimůnek, J.; Herbst, M.; Sudheer, K. P.; and Nambi, I. M\n\n\n \n \n \n \n \n Investigating Atrazine Concentrations in the Zwischenscholle Aquifer Using MODFLOW with the HYDRUS-1D Package and MT3DMS.\n \n \n \n \n\n\n \n\n\n\n Water, 12(4): 1019. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beegum_investigating_2020,\n\ttitle = {Investigating {Atrazine} {Concentrations} in the {Zwischenscholle} {Aquifer} {Using} {MODFLOW} with the {HYDRUS}-{1D} {Package} and {MT3DMS}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/4/1019},\n\tdoi = {10.3390/w12041019},\n\tabstract = {Simulation models that describe the flow and transport processes of pesticides in soil and groundwater are important tools to analyze how surface pesticide applications influence groundwater quality. The aim of this study is to investigate whether the slow decline and the stable spatial pattern of atrazine concentrations after its ban, which were observed in a long-term monitoring study of pesticide concentrations in the Zwischenscholle aquifer (Germany), could be explained by such model simulations. Model simulations were carried out using MODFLOW model coupled with the HYDRUS-1D package and MT3DMS. The results indicate that the spatial variability in the atrazine application rate and the volume of water entering and leaving the aquifer through lateral boundaries produced variations in the spatial distribution of atrazine in the aquifer. The simulated and observed water table levels and the average annual atrazine concentrations were found to be comparable. The long-term analysis of the simulated impact of atrazine applications in the study area shows that atrazine persisted in groundwater even 20 years after its ban at an average atrazine concentration of 0.035 µg/L. These results corroborate the findings of the previous monitoring studies.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Beegum, Sahila and Vanderborght, Jan and Šimůnek, Jiří and Herbst, Michael and Sudheer, K. P. and Nambi, Indumathi M},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1019},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Beckers, L.; Brack, W.; Dann, J. P.; Krauss, M.; Müller, E.; and Schulze, T.\n\n\n \n \n \n \n \n Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 727: 138388. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Unraveling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beckers_unraveling_2020,\n\ttitle = {Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis},\n\tvolume = {727},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S004896972031901X},\n\tdoi = {10.1016/j.scitotenv.2020.138388},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Beckers, Liza-Marie and Brack, Werner and Dann, Janek Paul and Krauss, Martin and Müller, Erik and Schulze, Tobias},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {138388},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Albergel, C.; Zheng, Y.; Bonan, B.; Dutra, E.; Rodríguez-Fernández, N.; Munier, S.; Draper, C.; de Rosnay, P.; Muñoz-Sabater, J.; Balsamo, G.; Fairbairn, D.; Meurey, C.; and Calvet, J.\n\n\n \n \n \n \n \n Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(9): 4291–4316. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Data$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{albergel_data_2020,\n\ttitle = {Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/4291/2020/},\n\tdoi = {10.5194/hess-24-4291-2020},\n\tabstract = {Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of\nsurface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model\n(LSM). This study demonstrates that LDAS-Monde is able to detect, monitor\nand forecast the impact of extreme weather on land surface states. Firstly,\nLDAS-Monde is run globally at 0.25∘ spatial resolution over\n2010–2018. It is forced by the state-of-the-art ERA5 reanalysis\n(LDAS\\_ERA5) from the European Centre for Medium Range Weather\nForecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets\nof evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration\nand river discharge are employed for the validation. Secondly, the global\nanalysis is used to (i) detect regions exposed to extreme weather such as\ndroughts and heatwave events and (ii) address specific monitoring and\nforecasting requirements of LSVs for those regions. This is performed by\ncomputing anomalies of the land surface states. They display strong negative\nvalues for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS\\_HRES, 0.10∘\nspatial resolution) over 2017–2018. Monitoring capacities are studied by\ncomparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS\\_HRES forecasts of the LSVs with the\nLDAS\\_HRES assimilation run compared to the open-loop\nexperiment. The positive impact of initialization from an analysis in\nforecast mode is particularly visible for LAI that evolves at a slower pace\nthan SSM and is more sensitive to initial conditions than to atmospheric\nforcing, even at an 8 d lead time. This highlights the impact of initial\nconditions on LSV forecasts and the value of jointly analysing soil moisture\nand vegetation states.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Albergel, Clément and Zheng, Yongjun and Bonan, Bertrand and Dutra, Emanuel and Rodríguez-Fernández, Nemesio and Munier, Simon and Draper, Clara and de Rosnay, Patricia and Muñoz-Sabater, Joaquin and Balsamo, Gianpaolo and Fairbairn, David and Meurey, Catherine and Calvet, Jean-Christophe},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {4291--4316},\n}\n\n\n\n

\n\n\n

\n Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.\n

\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2019\n \n \n (68)\n \n \n

\n \n \n

\n \n\n \n \n Rudolph, S.; Marchant, B. P.; Weihermüller, L.; and Vereecken, H.\n\n\n \n \n \n \n \n Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 20(1): 19–39. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rudolph_assessment_2019,\n\ttitle = {Assessment of the position accuracy of a single-frequency {GPS} receiver designed for electromagnetic induction surveys},\n\tvolume = {20},\n\tissn = {1385-2256, 1573-1618},\n\turl = {http://link.springer.com/10.1007/s11119-018-9578-1},\n\tdoi = {10.1007/s11119-018-9578-1},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Precision Agriculture},\n\tauthor = {Rudolph, Sebastian and Marchant, Ben Paul and Weihermüller, Lutz and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {19--39},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Liu, S.; Schloter, M.; Hu, R.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n Hydroxylamine Contributes More to Abiotic N$_{\\textrm{2}}$O Production in Soils Than Nitrite.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Environmental Science, 7: 47. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydroxylamine$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_hydroxylamine_2019,\n\ttitle = {Hydroxylamine {Contributes} {More} to {Abiotic} {N}$_{\\textrm{2}}${O} {Production} in {Soils} {Than} {Nitrite}},\n\tvolume = {7},\n\tissn = {2296-665X},\n\turl = {https://www.frontiersin.org/article/10.3389/fenvs.2019.00047/full},\n\tdoi = {10.3389/fenvs.2019.00047},\n\turldate = {2022-11-17},\n\tjournal = {Frontiers in Environmental Science},\n\tauthor = {Liu, Shurong and Schloter, Michael and Hu, Ronggui and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {47},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Ibraim, E.; Wolf, B.; Harris, E.; Gasche, R.; Wei, J.; Yu, L.; Kiese, R.; Eggleston, S.; Butterbach-Bahl, K.; Zeeman, M.; Tuzson, B.; Emmenegger, L.; Six, J.; Henne, S.; and Mohn, J.\n\n\n \n \n \n \n \n Attribution of N$_{\\textrm{2}}$O sources in a grassland soil with laser spectroscopy based isotopocule analysis.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(16): 3247–3266. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Attribution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ibraim_attribution_2019,\n\ttitle = {Attribution of {N}$_{\\textrm{2}}${O} sources in a grassland soil with laser spectroscopy based isotopocule analysis},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/3247/2019/},\n\tdoi = {10.5194/bg-16-3247-2019},\n\tabstract = {Abstract. Nitrous oxide (N2O) is the primary atmospheric constituent involved in\nstratospheric ozone depletion and contributes strongly to changes in the\nclimate system through a positive radiative forcing mechanism. The\natmospheric abundance of N2O has increased from 270 ppb (parts per billion, 10−9 mole mole−1) during the\npre-industrial era to approx. 330 ppb in 2018. Even though it is well known\nthat microbial processes in agricultural and natural soils are the major\nN2O source, the contribution of specific soil processes is still\nuncertain. The relative abundance of N2O isotopocules\n(14N14N16N, 14N15N16O,\n15N14N16O, and 14N14N18O) carries\nprocess-specific information and thus can be used to trace production and\nconsumption pathways. While isotope ratio mass spectroscopy (IRMS) was\ntraditionally used for high-precision measurement of the isotopic\ncomposition of N2O, quantum cascade laser absorption spectroscopy\n(QCLAS) has been put forward as a complementary technique with the potential\nfor on-site analysis. In recent years, pre-concentration combined with QCLAS\nhas been presented as a technique to resolve subtle changes in ambient\nN2O isotopic composition. From the end of May until the beginning of August 2016, we investigated\nN2O emissions from an intensively managed grassland at the study site\nFendt in southern Germany. In total, 612 measurements of ambient\nN2O were taken by combining pre-concentration with QCLAS analyses,\nyielding δ15Nα, δ15Nβ,\nδ18O, and N2O concentration with a temporal resolution of\napproximately 1 h and precisions of 0.46 ‰, 0.36 ‰, 0.59 ‰, and 1.24 ppb,\nrespectively. Soil δ15N-NO3- values and\nconcentrations of NO3- and NH4+ were measured to further\nconstrain possible N2O-emitting source processes. Furthermore, the\nconcentration footprint area of measured N2O was determined with a\nLagrangian particle dispersion model (FLEXPART-COSMO) using local wind and\nturbulence observations. These simulations indicated that night-time\nconcentration observations were largely sensitive to local fluxes. While\nbacterial denitrification and nitrifier denitrification were identified as\nthe primary N2O-emitting processes, N2O reduction to N2\nlargely dictated the isotopic composition of measured N2O. Fungal\ndenitrification and nitrification-derived N2O accounted for 34 \\%–42 \\% of total N2O emissions and had a clear effect on the measured\nisotopic source signatures. This study presents the suitability of on-site\nN2O isotopocule analysis for disentangling source and sink processes\nin situ and found that at the Fendt site bacterial denitrification or nitrifier denitrification is the major source for N2O, while N2O\nreduction acted as a major sink for soil-produced N2O.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Ibraim, Erkan and Wolf, Benjamin and Harris, Eliza and Gasche, Rainer and Wei, Jing and Yu, Longfei and Kiese, Ralf and Eggleston, Sarah and Butterbach-Bahl, Klaus and Zeeman, Matthias and Tuzson, Béla and Emmenegger, Lukas and Six, Johan and Henne, Stephan and Mohn, Joachim},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {3247--3266},\n}\n\n\n\n

\n\n\n

\n Abstract. Nitrous oxide (N2O) is the primary atmospheric constituent involved in stratospheric ozone depletion and contributes strongly to changes in the climate system through a positive radiative forcing mechanism. The atmospheric abundance of N2O has increased from 270 ppb (parts per billion, 10−9 mole mole−1) during the pre-industrial era to approx. 330 ppb in 2018. Even though it is well known that microbial processes in agricultural and natural soils are the major N2O source, the contribution of specific soil processes is still uncertain. The relative abundance of N2O isotopocules (14N14N16N, 14N15N16O, 15N14N16O, and 14N14N18O) carries process-specific information and thus can be used to trace production and consumption pathways. While isotope ratio mass spectroscopy (IRMS) was traditionally used for high-precision measurement of the isotopic composition of N2O, quantum cascade laser absorption spectroscopy (QCLAS) has been put forward as a complementary technique with the potential for on-site analysis. In recent years, pre-concentration combined with QCLAS has been presented as a technique to resolve subtle changes in ambient N2O isotopic composition. From the end of May until the beginning of August 2016, we investigated N2O emissions from an intensively managed grassland at the study site Fendt in southern Germany. In total, 612 measurements of ambient N2O were taken by combining pre-concentration with QCLAS analyses, yielding δ15Nα, δ15Nβ, δ18O, and N2O concentration with a temporal resolution of approximately 1 h and precisions of 0.46 ‰, 0.36 ‰, 0.59 ‰, and 1.24 ppb, respectively. Soil δ15N-NO3- values and concentrations of NO3- and NH4+ were measured to further constrain possible N2O-emitting source processes. Furthermore, the concentration footprint area of measured N2O was determined with a Lagrangian particle dispersion model (FLEXPART-COSMO) using local wind and turbulence observations. These simulations indicated that night-time concentration observations were largely sensitive to local fluxes. While bacterial denitrification and nitrifier denitrification were identified as the primary N2O-emitting processes, N2O reduction to N2 largely dictated the isotopic composition of measured N2O. Fungal denitrification and nitrification-derived N2O accounted for 34 %–42 % of total N2O emissions and had a clear effect on the measured isotopic source signatures. This study presents the suitability of on-site N2O isotopocule analysis for disentangling source and sink processes in situ and found that at the Fendt site bacterial denitrification or nitrifier denitrification is the major source for N2O, while N2O reduction acted as a major sink for soil-produced N2O.\n

\n\n\n

\n \n\n \n \n Klosterhalfen, A.; Graf, A.; Brüggemann, N.; Drüe, C.; Esser, O.; González-Dugo, M. P.; Heinemann, G.; Jacobs, C. M. J.; Mauder, M.; Moene, A. F.; Ney, P.; Pütz, T.; Rebmann, C.; Ramos Rodríguez, M.; Scanlon, T. M.; Schmidt, M.; Steinbrecher, R.; Thomas, C. K.; Valler, V.; Zeeman, M. J.; and Vereecken, H.\n\n\n \n \n \n \n \n Source partitioning of H$_{\\textrm{2}}$O and CO$_{\\textrm{2}}$ fluxes based on high-frequency eddy covariance data: a comparison between study sites.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(6): 1111–1132. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Source$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{klosterhalfen_source_2019,\n\ttitle = {Source partitioning of {H}$_{\\textrm{2}}${O} and {CO}$_{\\textrm{2}}$ fluxes based on high-frequency eddy covariance data: a comparison between study sites},\n\tvolume = {16},\n\tissn = {1726-4189},\n\tshorttitle = {Source partitioning of {H}\\&lt;sub\\&gt;2\\&lt;/sub\\&gt;{O} and {CO}\\&lt;sub\\&gt;2\\&lt;/sub\\&gt; fluxes based on high-frequency eddy covariance data},\n\turl = {https://bg.copernicus.org/articles/16/1111/2019/},\n\tdoi = {10.5194/bg-16-1111-2019},\n\tabstract = {Abstract. For an assessment of the roles of soil and vegetation in the\nclimate system, a further understanding of the flux components of\nH2O and CO2 (e.g., transpiration, soil respiration) and\ntheir interaction with physical conditions and physiological functioning of\nplants and ecosystems is necessary. To obtain magnitudes of these flux\ncomponents, we applied source partitioning approaches after Scanlon and\nKustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency\neddy covariance measurements of 12 study sites covering different\necosystems (croplands, grasslands, and forests) in different climatic\nregions. Both partitioning methods are based on higher-order statistics of\nthe H2O and CO2 fluctuations, but proceed differently to\nestimate transpiration, evaporation, net primary production, and soil\nrespiration. We compared and evaluated the partitioning results obtained with\nSK10 and TH08, including slight modifications of both approaches. Further, we\nanalyzed the interrelations among the performance of the partitioning\nmethods, turbulence characteristics, and site characteristics (such as plant\ncover type, canopy height, canopy density, and measurement height). We were\nable to identify characteristics of a data set that are prerequisites for\nadequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux\ncomponents. For both methods, the partitioning of CO2 fluxes was\nless robust than for H2O fluxes. Results derived with SK10 showed\nrelatively large dependencies on estimated water use efficiency (WUE) at the\nleaf level, which is a required input. Measurements of outgoing longwave\nradiation used for the estimation of foliage temperature (used in WUE) could\nslightly increase the quality of the partitioning results. A modification of\nthe TH08 approach, by applying a cluster analysis for the conditional\nsampling of respiration–evaporation events, performed satisfactorily, but did\nnot result in significant advantages compared to the original method versions\ndeveloped by Thomas et al. (2008). The performance of each partitioning\napproach was dependent on meteorological conditions, plant development,\ncanopy height, canopy density, and measurement height. Foremost, the\nperformance of SK10 correlated negatively with the ratio between measurement\nheight and canopy height. The performance of TH08 was more dependent on\ncanopy height and leaf area index. In general, all site characteristics that\nincrease dissimilarities between scalars appeared to enhance partitioning\nperformance for SK10 and TH08.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Klosterhalfen, Anne and Graf, Alexander and Brüggemann, Nicolas and Drüe, Clemens and Esser, Odilia and González-Dugo, María P. and Heinemann, Günther and Jacobs, Cor M. J. and Mauder, Matthias and Moene, Arnold F. and Ney, Patrizia and Pütz, Thomas and Rebmann, Corinna and Ramos Rodríguez, Mario and Scanlon, Todd M. and Schmidt, Marius and Steinbrecher, Rainer and Thomas, Christoph K. and Valler, Veronika and Zeeman, Matthias J. and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {1111--1132},\n}\n\n\n\n

\n\n\n

\n Abstract. For an assessment of the roles of soil and vegetation in the climate system, a further understanding of the flux components of H2O and CO2 (e.g., transpiration, soil respiration) and their interaction with physical conditions and physiological functioning of plants and ecosystems is necessary. To obtain magnitudes of these flux components, we applied source partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency eddy covariance measurements of 12 study sites covering different ecosystems (croplands, grasslands, and forests) in different climatic regions. Both partitioning methods are based on higher-order statistics of the H2O and CO2 fluctuations, but proceed differently to estimate transpiration, evaporation, net primary production, and soil respiration. We compared and evaluated the partitioning results obtained with SK10 and TH08, including slight modifications of both approaches. Further, we analyzed the interrelations among the performance of the partitioning methods, turbulence characteristics, and site characteristics (such as plant cover type, canopy height, canopy density, and measurement height). We were able to identify characteristics of a data set that are prerequisites for adequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux components. For both methods, the partitioning of CO2 fluxes was less robust than for H2O fluxes. Results derived with SK10 showed relatively large dependencies on estimated water use efficiency (WUE) at the leaf level, which is a required input. Measurements of outgoing longwave radiation used for the estimation of foliage temperature (used in WUE) could slightly increase the quality of the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditional sampling of respiration–evaporation events, performed satisfactorily, but did not result in significant advantages compared to the original method versions developed by Thomas et al. (2008). The performance of each partitioning approach was dependent on meteorological conditions, plant development, canopy height, canopy density, and measurement height. Foremost, the performance of SK10 correlated negatively with the ratio between measurement height and canopy height. The performance of TH08 was more dependent on canopy height and leaf area index. In general, all site characteristics that increase dissimilarities between scalars appeared to enhance partitioning performance for SK10 and TH08.\n

\n\n\n

\n \n\n \n \n Klosterhalfen, A.; Moene, A.; Schmidt, M.; Scanlon, T.; Vereecken, H.; and Graf, A.\n\n\n \n \n \n \n \n Sensitivity analysis of a source partitioning method for H$_{\\textrm{2}}$O and CO$_{\\textrm{2}}$ fluxes based on high frequency eddy covariance data: Findings from field data and large eddy simulations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 265: 152–170. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sensitivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{klosterhalfen_sensitivity_2019,\n\ttitle = {Sensitivity analysis of a source partitioning method for {H}$_{\\textrm{2}}${O} and {CO}$_{\\textrm{2}}$ fluxes based on high frequency eddy covariance data: {Findings} from field data and large eddy simulations},\n\tvolume = {265},\n\tissn = {01681923},\n\tshorttitle = {Sensitivity analysis of a source partitioning method for {H2O} and {CO2} fluxes based on high frequency eddy covariance data},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303496},\n\tdoi = {10.1016/j.agrformet.2018.11.003},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Klosterhalfen, A. and Moene, A.F. and Schmidt, M. and Scanlon, T.M. and Vereecken, H. and Graf, A.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {152--170},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Zistl-Schlingmann, M.; Feng, J.; Kiese, R.; Stephan, R.; Zuazo, P.; Willibald, G.; Wang, C.; Butterbach-Bahl, K.; and Dannenmann, M.\n\n\n \n \n \n \n \n Dinitrogen emissions: an overlooked key component of the N balance of montane grasslands.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 143(1): 15–30. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Dinitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zistl-schlingmann_dinitrogen_2019,\n\ttitle = {Dinitrogen emissions: an overlooked key component of the {N} balance of montane grasslands},\n\tvolume = {143},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Dinitrogen emissions},\n\turl = {http://link.springer.com/10.1007/s10533-019-00547-8},\n\tdoi = {10.1007/s10533-019-00547-8},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Biogeochemistry},\n\tauthor = {Zistl-Schlingmann, Marcus and Feng, Jinchao and Kiese, Ralf and Stephan, Ruth and Zuazo, Pablo and Willibald, Georg and Wang, Changhui and Butterbach-Bahl, Klaus and Dannenmann, Michael},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {15--30},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Zeeman, M. J.; Shupe, H.; Baessler, C.; and Ruehr, N. K.\n\n\n \n \n \n \n \n Productivity and vegetation structure of three differently managed temperate grasslands.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 270-271: 129–148. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Productivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zeeman_productivity_2019,\n\ttitle = {Productivity and vegetation structure of three differently managed temperate grasslands},\n\tvolume = {270-271},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880918304262},\n\tdoi = {10.1016/j.agee.2018.10.003},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Zeeman, Matthias J. and Shupe, Heather and Baessler, Cornelia and Ruehr, Nadine K.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {129--148},\n}\n\n\n\n

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\n \n\n \n \n Yang, X.; Jomaa, S.; Büttner, O.; and Rode, M.\n\n\n \n \n \n \n \n Autotrophic nitrate uptake in river networks: A modeling approach using continuous high-frequency data.\n \n \n \n \n\n\n \n\n\n\n Water Research, 157: 258–268. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Autotrophic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_autotrophic_2019,\n\ttitle = {Autotrophic nitrate uptake in river networks: {A} modeling approach using continuous high-frequency data},\n\tvolume = {157},\n\tissn = {00431354},\n\tshorttitle = {Autotrophic nitrate uptake in river networks},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135419302751},\n\tdoi = {10.1016/j.watres.2019.02.059},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Yang, Xiaoqiang and Jomaa, Seifeddine and Büttner, Olaf and Rode, Michael},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {258--268},\n}\n\n\n\n

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\n \n\n \n \n Wohner, C.; Peterseil, J.; Poursanidis, D.; Kliment, T.; Wilson, M.; Mirtl, M.; and Chrysoulakis, N.\n\n\n \n \n \n \n \n DEIMS-SDR – A web portal to document research sites and their associated data.\n \n \n \n \n\n\n \n\n\n\n Ecological Informatics, 51: 15–24. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"DEIMS-SDR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wohner_deims-sdr_2019,\n\ttitle = {{DEIMS}-{SDR} – {A} web portal to document research sites and their associated data},\n\tvolume = {51},\n\tissn = {15749541},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118302528},\n\tdoi = {10.1016/j.ecoinf.2019.01.005},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Informatics},\n\tauthor = {Wohner, Christoph and Peterseil, Johannes and Poursanidis, Dimitris and Kliment, Tomáš and Wilson, Mike and Mirtl, Michael and Chrysoulakis, Nektarios},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {15--24},\n}\n\n\n\n

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\n \n\n \n \n Wild, R.; Gücker, B.; and Brauns, M.\n\n\n \n \n \n \n \n Agricultural land use alters temporal dynamics and the composition of organic matter in temperate headwater streams.\n \n \n \n \n\n\n \n\n\n\n Freshwater Science, 38(3): 566–581. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Agricultural$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wild_agricultural_2019,\n\ttitle = {Agricultural land use alters temporal dynamics and the composition of organic matter in temperate headwater streams},\n\tvolume = {38},\n\tissn = {2161-9549, 2161-9565},\n\turl = {https://www.journals.uchicago.edu/doi/10.1086/704828},\n\tdoi = {10.1086/704828},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Freshwater Science},\n\tauthor = {Wild, Romy and Gücker, Björn and Brauns, Mario},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {566--581},\n}\n\n\n\n

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\n \n\n \n \n Weyhenmeyer, G. A.; Hartmann, J.; Hessen, D. O.; Kopáček, J.; Hejzlar, J.; Jacquet, S.; Hamilton, S. K.; Verburg, P.; Leach, T. H.; Schmid, M.; Flaim, G.; Nõges, T.; Nõges, P.; Wentzky, V. C.; Rogora, M.; Rusak, J. A.; Kosten, S.; Paterson, A. M.; Teubner, K.; Higgins, S. N.; Lawrence, G.; Kangur, K.; Kokorite, I.; Cerasino, L.; Funk, C.; Harvey, R.; Moatar, F.; de Wit, H. A.; and Zechmeister, T.\n\n\n \n \n \n \n \n Widespread diminishing anthropogenic effects on calcium in freshwaters.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 9(1): 10450. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Widespread$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weyhenmeyer_widespread_2019,\n\ttitle = {Widespread diminishing anthropogenic effects on calcium in freshwaters},\n\tvolume = {9},\n\tissn = {2045-2322},\n\turl = {http://www.nature.com/articles/s41598-019-46838-w},\n\tdoi = {10.1038/s41598-019-46838-w},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Scientific Reports},\n\tauthor = {Weyhenmeyer, Gesa A. and Hartmann, Jens and Hessen, Dag O. and Kopáček, Jiří and Hejzlar, Josef and Jacquet, Stéphan and Hamilton, Stephen K. and Verburg, Piet and Leach, Taylor H. and Schmid, Martin and Flaim, Giovanna and Nõges, Tiina and Nõges, Peeter and Wentzky, Valerie C. and Rogora, Michela and Rusak, James A. and Kosten, Sarian and Paterson, Andrew M. and Teubner, Katrin and Higgins, Scott N. and Lawrence, Gregory and Kangur, Külli and Kokorite, Ilga and Cerasino, Leonardo and Funk, Clara and Harvey, Rebecca and Moatar, Florentina and de Wit, Heleen A. and Zechmeister, Thomas},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {10450},\n}\n\n\n\n

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\n \n\n \n \n Werner, B. J.; Musolff, A.; Lechtenfeld, O. J.; de Rooij, G. H.; Oosterwoud, M. R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n High-frequency measurements of dissolved organic carbon quantity and quality in a headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Rivers & Streams, May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"High-frequency$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{werner_high-frequency_2019,\n\ttype = {preprint},\n\ttitle = {High-frequency measurements of dissolved organic carbon quantity and quality in a headwater catchment},\n\turl = {https://bg.copernicus.org/preprints/bg-2019-188/bg-2019-188.pdf},\n\tabstract = {Abstract. Increasing dissolved organic carbon (DOC) exports from headwater catchments impact the quality of downstream waters and pose challenges to water supply. The importance of riparian zones for DOC export from catchments in humid, temperate climates has generally been acknowledged, but the hydrological controls and biogeochemical factors that govern mobilization of DOC from riparian zones remain elusive. A one-year high-frequency (15 minutes) dataset from a headwater catchment in the Harz Mountains (Germany) was analyzed for dominant patterns in DOC concentration (CDOC) and optical DOC quality parameters SUVA254 and S275-295 (spectral slope between 275 nm and 295 nm) on event and seasonal scale. Quality parameters and CDOC systematically changed with increasing fractions of high-frequency quick flow (Qhf) and antecedent hydroclimatic conditions, defined by the following metrics: Aridity Index (AI60) of the preceding 60 days, mean temperature (T30) and discharge (Q30) of the preceding 30 days and the quotient T30/Q30 which we refer to as discharge-normalized temperature (DNT30). Selected statistical regression models for the complete time series (R² = 0.72, 0.64 and 0.65 for CDOC, SUVA254 and S275-295, resp.) captured DOC dynamics based on event (Qhf and baseflow) and seasonal-scale predictors (AI60, DNT30). The relative importance of seasonal-scale predictors allowed for the separation of three hydroclimatic states (warm \\&amp; dry, cold \\&amp; wet and intermediate). The specific DOC quality for each state indicates a shift in the activated source zones and highlights the importance of antecedent conditions and its impact on DOC accumulation and mobilization in the riparian zone. The warm \\&amp; dry state results in high DOC concentrations during events and low concentrations between events and thus can be seen as mobilization limited, whereas the cold \\&amp; wet state results in low concentration between and during events due to limited DOC accumulation in the riparian zone. We conclude that the high concentration variability of DOC in the stream can be explained by only a few controlling variables. These variables can be linked to DOC source activation by discharge events and the more seasonal control of DOC production in riparian soils.},\n\turldate = {2022-11-17},\n\tinstitution = {Biogeochemistry: Rivers \\&amp; Streams},\n\tauthor = {Werner, Benedikt J. and Musolff, Andreas and Lechtenfeld, Oliver J. and de Rooij, Gerrit H. and Oosterwoud, Marieke R. and Fleckenstein, Jan H.},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {10.5194/bg-2019-188},\n}\n\n\n\n

\n\n\n

\n Abstract. Increasing dissolved organic carbon (DOC) exports from headwater catchments impact the quality of downstream waters and pose challenges to water supply. The importance of riparian zones for DOC export from catchments in humid, temperate climates has generally been acknowledged, but the hydrological controls and biogeochemical factors that govern mobilization of DOC from riparian zones remain elusive. A one-year high-frequency (15 minutes) dataset from a headwater catchment in the Harz Mountains (Germany) was analyzed for dominant patterns in DOC concentration (CDOC) and optical DOC quality parameters SUVA254 and S275-295 (spectral slope between 275 nm and 295 nm) on event and seasonal scale. Quality parameters and CDOC systematically changed with increasing fractions of high-frequency quick flow (Qhf) and antecedent hydroclimatic conditions, defined by the following metrics: Aridity Index (AI60) of the preceding 60 days, mean temperature (T30) and discharge (Q30) of the preceding 30 days and the quotient T30/Q30 which we refer to as discharge-normalized temperature (DNT30). Selected statistical regression models for the complete time series (R² = 0.72, 0.64 and 0.65 for CDOC, SUVA254 and S275-295, resp.) captured DOC dynamics based on event (Qhf and baseflow) and seasonal-scale predictors (AI60, DNT30). The relative importance of seasonal-scale predictors allowed for the separation of three hydroclimatic states (warm & dry, cold & wet and intermediate). The specific DOC quality for each state indicates a shift in the activated source zones and highlights the importance of antecedent conditions and its impact on DOC accumulation and mobilization in the riparian zone. The warm & dry state results in high DOC concentrations during events and low concentrations between events and thus can be seen as mobilization limited, whereas the cold & wet state results in low concentration between and during events due to limited DOC accumulation in the riparian zone. We conclude that the high concentration variability of DOC in the stream can be explained by only a few controlling variables. These variables can be linked to DOC source activation by discharge events and the more seasonal control of DOC production in riparian soils.\n

\n\n\n

\n \n\n \n \n Wentzky, V. C.; Frassl, M. A.; Rinke, K.; and Boehrer, B.\n\n\n \n \n \n \n \n Metalimnetic oxygen minimum and the presence of Planktothrix rubescens in a low-nutrient drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Water Research, 148: 208–218. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Metalimnetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wentzky_metalimnetic_2019,\n\ttitle = {Metalimnetic oxygen minimum and the presence of {Planktothrix} rubescens in a low-nutrient drinking water reservoir},\n\tvolume = {148},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135418308443},\n\tdoi = {10.1016/j.watres.2018.10.047},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Wentzky, Valerie C. and Frassl, Marieke A. and Rinke, Karsten and Boehrer, Bertram},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {208--218},\n}\n\n\n\n

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\n \n\n \n \n Wang, N.; Quesada, B.; Xia, L.; Butterbach‐Bahl, K.; Goodale, C. L.; and Kiese, R.\n\n\n \n \n \n \n \n Effects of climate warming on carbon fluxes in grasslands— A global meta‐analysis.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 25(5): 1839–1851. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_effects_2019,\n\ttitle = {Effects of climate warming on carbon fluxes in grasslands— {A} global meta‐analysis},\n\tvolume = {25},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.14603},\n\tdoi = {10.1111/gcb.14603},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Global Change Biology},\n\tauthor = {Wang, Na and Quesada, Benjamin and Xia, Longlong and Butterbach‐Bahl, Klaus and Goodale, Christine L. and Kiese, Ralf},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {1839--1851},\n}\n\n\n\n

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\n \n\n \n \n Wang, H.; Wellmann, F.; Zhang, T.; Schaaf, A.; Kanig, R. M.; Verweij, E.; Hebel, C.; and Kruk, J.\n\n\n \n \n \n \n \n Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 124(6): 1524–1544. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Pattern$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_pattern_2019,\n\ttitle = {Pattern {Extraction} of {Topsoil} and {Subsoil} {Heterogeneity} and {Soil}‐{Crop} {Interaction} {Using} {Unsupervised} {Bayesian} {Machine} {Learning}: {An} {Application} to {Satellite}‐{Derived} {NDVI} {Time} {Series} and {Electromagnetic} {Induction} {Measurements}},\n\tvolume = {124},\n\tissn = {2169-8953, 2169-8961},\n\tshorttitle = {Pattern {Extraction} of {Topsoil} and {Subsoil} {Heterogeneity} and {Soil}‐{Crop} {Interaction} {Using} {Unsupervised} {Bayesian} {Machine} {Learning}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005046},\n\tdoi = {10.1029/2019JG005046},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Wang, Hui and Wellmann, Florian and Zhang, Tianqi and Schaaf, Alexander and Kanig, Robin Maximilian and Verweij, Elizabeth and Hebel, Christian and Kruk, Jan},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {1524--1544},\n}\n\n\n\n

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\n \n\n \n \n Vereecken, H.; Pachepsky, Y.; Bogena, H.; and Montzka, C.\n\n\n \n \n \n \n \n Upscaling Issues in Ecohydrological Observations.\n \n \n \n \n\n\n \n\n\n\n In Li, X.; and Vereecken, H., editor(s), Observation and Measurement of Ecohydrological Processes, volume 2, pages 435–454. Springer Berlin Heidelberg, Berlin, Heidelberg, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Upscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{li_upscaling_2019,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Upscaling {Issues} in {Ecohydrological} {Observations}},\n\tvolume = {2},\n\tisbn = {9783662482964 9783662482971},\n\turl = {http://link.springer.com/10.1007/978-3-662-48297-1_14},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Observation and {Measurement} of {Ecohydrological} {Processes}},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Vereecken, Harry and Pachepsky, Yakov and Bogena, Heye and Montzka, Carsten},\n\teditor = {Li, Xin and Vereecken, Harry},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-662-48297-1_14},\n\tpages = {435--454},\n}\n\n\n\n

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\n \n\n \n \n Tarasova, L.; Merz, R.; Kiss, A.; Basso, S.; Blöschl, G.; Merz, B.; Viglione, A.; Plötner, S.; Guse, B.; Schumann, A.; Fischer, S.; Ahrens, B.; Anwar, F.; Bárdossy, A.; Bühler, P.; Haberlandt, U.; Kreibich, H.; Krug, A.; Lun, D.; Müller‐Thomy, H.; Pidoto, R.; Primo, C.; Seidel, J.; Vorogushyn, S.; and Wietzke, L.\n\n\n \n \n \n \n \n Causative classification of river flood events.\n \n \n \n \n\n\n \n\n\n\n WIREs Water, 6(4). July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Causative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tarasova_causative_2019,\n\ttitle = {Causative classification of river flood events},\n\tvolume = {6},\n\tissn = {2049-1948, 2049-1948},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/wat2.1353},\n\tdoi = {10.1002/wat2.1353},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {WIREs Water},\n\tauthor = {Tarasova, Larisa and Merz, Ralf and Kiss, Andrea and Basso, Stefano and Blöschl, Günter and Merz, Bruno and Viglione, Alberto and Plötner, Stefan and Guse, Björn and Schumann, Andreas and Fischer, Svenja and Ahrens, Bodo and Anwar, Faizan and Bárdossy, András and Bühler, Philipp and Haberlandt, Uwe and Kreibich, Heidi and Krug, Amelie and Lun, David and Müller‐Thomy, Hannes and Pidoto, Ross and Primo, Cristina and Seidel, Jochen and Vorogushyn, Sergiy and Wietzke, Luzie},\n\tmonth = jul,\n\tyear = {2019},\n}\n\n\n\n

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\n \n\n \n \n Tan, X.; Mester, A.; von Hebel, C.; Zimmermann, E.; Vereecken, H.; van Waasen, S.; and van der Kruk, J.\n\n\n \n \n \n \n \n Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations.\n \n \n \n \n\n\n \n\n\n\n GEOPHYSICS, 84(1): EN1–EN14. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tan_simultaneous_2019,\n\ttitle = {Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations},\n\tvolume = {84},\n\tissn = {0016-8033, 1942-2156},\n\turl = {https://library.seg.org/doi/10.1190/geo2018-0264.1},\n\tdoi = {10.1190/geo2018-0264.1},\n\tabstract = {Electromagnetic induction (EMI) is a contactless and fast geophysical measurement technique. Frequency-domain EMI systems are available as portable rigid booms with fixed separations up to approximately 4 m between the transmitter and the receivers. These EMI systems are often used for high-resolution characterization of the upper subsurface meters (up to depths of approximately 1.5 times the maximum coil separation). The availability of multiconfiguration EMI systems, which measure multiple apparent electrical conductivity ([Formula: see text]) values of different but overlapping soil volumes, enables EMI data inversions to estimate electrical conductivity ([Formula: see text]) changes with depth. However, most EMI systems currently do not provide absolute [Formula: see text] values, but erroneous shifts occur due to calibration problems, which hinder a reliable inversion of the data. Instead of using physical soil data or additional methods to calibrate the EMI data, we have used an efficient and accurate simultaneous calibration and inversion approach to avoid a possible bias of other methods while reducing the acquisition time for the calibration. By measuring at multiple elevations above the ground surface using a multiconfiguration EMI system, we simultaneously obtain multiplicative and additive calibration factors for each coil configuration plus an inverted layered subsurface electrical conductivity model at the measuring location. Using synthetic data, we verify our approach. Experimental data from five different calibration positions along a transect line showed similar calibration results as the data obtained by more elaborate vertical electrical sounding reference measurements. The synthetic and experimental results demonstrate that the multielevation calibration and inversion approach is a promising tool for quantitative electrical conductivity analyses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {GEOPHYSICS},\n\tauthor = {Tan, Xihe and Mester, Achim and von Hebel, Christian and Zimmermann, Egon and Vereecken, Harry and van Waasen, Stefan and van der Kruk, Jan},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {EN1--EN14},\n}\n\n\n\n

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\n \n\n \n \n Sulis, M.; Couvreur, V.; Keune, J.; Cai, G.; Trebs, I.; Junk, J.; Shrestha, P.; Simmer, C.; Kollet, S. J.; Vereecken, H.; and Vanderborght, J.\n\n\n \n \n \n \n \n Incorporating a root water uptake model based on the hydraulic architecture approach in terrestrial systems simulations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 269-270: 28–45. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Incorporating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sulis_incorporating_2019,\n\ttitle = {Incorporating a root water uptake model based on the hydraulic architecture approach in terrestrial systems simulations},\n\tvolume = {269-270},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192319300437},\n\tdoi = {10.1016/j.agrformet.2019.01.034},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Sulis, Mauro and Couvreur, Valentin and Keune, Jessica and Cai, Gaochao and Trebs, Ivonne and Junk, Juergen and Shrestha, Prabhakar and Simmer, Clemens and Kollet, Stefan J. and Vereecken, Harry and Vanderborght, Jan},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {28--45},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Stockinger, M. P.; Bogena, H. R.; Lücke, A.; Stumpp, C.; and Vereecken, H.\n\n\n \n \n \n \n \n Time variability and uncertainty in the fraction of young water in a small headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 23(10): 4333–4347. October 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Time$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stockinger_time_2019,\n\ttitle = {Time variability and uncertainty in the fraction of young water in a small headwater catchment},\n\tvolume = {23},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/23/4333/2019/},\n\tdoi = {10.5194/hess-23-4333-2019},\n\tabstract = {Abstract. The time precipitation needs to travel through a\ncatchment to its outlet is an important descriptor of a catchment's\nsusceptibility to pollutant contamination, nutrient loss, and hydrological\nfunctioning. The fast component of total water flow can be estimated by the\nfraction of young water (Fyw), which is the percentage of streamflow younger\nthan 3 months. Fyw is calculated by comparing the amplitudes of sine\nwaves fitted to seasonal precipitation and streamflow tracer signals. This\nis usually done for the complete tracer time series available, neglecting\nannual differences in the amplitudes of longer time series. Considering\ninter-annual amplitude differences, we employed a moving time window of\n1 year in weekly time steps over a 4.5-year δ18O\ntracer time series to calculate 189 Fyw estimates and their uncertainty.\nThey were then tested against the following null hypotheses: (1) at least\n90 \\% of Fyw results do not deviate more than ±0.04 (4 \\%) from the\nmean of all Fyw results, indicating long-term invariance. Larger deviations\nwould indicate changes in the relative contribution of different flow paths;\n(2) for any 4-week window, Fyw does not change more than ±0.04,\nindicating short-term invariance. Larger deviations would indicate a high\nsensitivity of Fyw to a 1-week to 4-week shift in the start of a 1-year sampling\ncampaign; (3) the Fyw results of 1-year sampling campaigns started in a\ngiven calendar month do not change more than ±0.04, indicating\nseasonal invariance. In our study, all three null hypotheses were rejected.\nThus, the Fyw results were time-variable, showed variability in the chosen\nsampling time, and had no pronounced seasonality. We furthermore found\nevidence that the 2015 European heat wave and including two winters into a\n1-year sampling campaign increased the uncertainty of Fyw. Based on an\nincrease in Fyw uncertainty when the mean adjusted R2 was\nbelow 0.2, we recommend further investigations into the dependence of Fyw and\nits uncertainty to goodness-of-fit measures. Furthermore, while investigated\nindividual meteorological factors did not sufficiently explain variations of\nFyw, the runoff coefficient showed a moderate negative correlation of r=-0.50 with Fyw. The results of this study suggest that care must be taken\nwhen comparing Fyw of catchments that were based on different calculation\nperiods and that the influence of extreme events and snow must be\nconsidered.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Stockinger, Michael Paul and Bogena, Heye Reemt and Lücke, Andreas and Stumpp, Christine and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2019},\n\tpages = {4333--4347},\n}\n\n\n\n

\n\n\n

\n Abstract. The time precipitation needs to travel through a catchment to its outlet is an important descriptor of a catchment's susceptibility to pollutant contamination, nutrient loss, and hydrological functioning. The fast component of total water flow can be estimated by the fraction of young water (Fyw), which is the percentage of streamflow younger than 3 months. Fyw is calculated by comparing the amplitudes of sine waves fitted to seasonal precipitation and streamflow tracer signals. This is usually done for the complete tracer time series available, neglecting annual differences in the amplitudes of longer time series. Considering inter-annual amplitude differences, we employed a moving time window of 1 year in weekly time steps over a 4.5-year δ18O tracer time series to calculate 189 Fyw estimates and their uncertainty. They were then tested against the following null hypotheses: (1) at least 90 % of Fyw results do not deviate more than ±0.04 (4 %) from the mean of all Fyw results, indicating long-term invariance. Larger deviations would indicate changes in the relative contribution of different flow paths; (2) for any 4-week window, Fyw does not change more than ±0.04, indicating short-term invariance. Larger deviations would indicate a high sensitivity of Fyw to a 1-week to 4-week shift in the start of a 1-year sampling campaign; (3) the Fyw results of 1-year sampling campaigns started in a given calendar month do not change more than ±0.04, indicating seasonal invariance. In our study, all three null hypotheses were rejected. Thus, the Fyw results were time-variable, showed variability in the chosen sampling time, and had no pronounced seasonality. We furthermore found evidence that the 2015 European heat wave and including two winters into a 1-year sampling campaign increased the uncertainty of Fyw. Based on an increase in Fyw uncertainty when the mean adjusted R2 was below 0.2, we recommend further investigations into the dependence of Fyw and its uncertainty to goodness-of-fit measures. Furthermore, while investigated individual meteorological factors did not sufficiently explain variations of Fyw, the runoff coefficient showed a moderate negative correlation of r=-0.50 with Fyw. The results of this study suggest that care must be taken when comparing Fyw of catchments that were based on different calculation periods and that the influence of extreme events and snow must be considered.\n

\n\n\n

\n \n\n \n \n Soltani, M.; Laux, P.; Mauder, M.; and Kunstmann, H.\n\n\n \n \n \n \n \n Inverse distributed modelling of streamflow and turbulent fluxes: A sensitivity and uncertainty analysis coupled with automatic optimization.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 571: 856–872. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Inverse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{soltani_inverse_2019,\n\ttitle = {Inverse distributed modelling of streamflow and turbulent fluxes: {A} sensitivity and uncertainty analysis coupled with automatic optimization},\n\tvolume = {571},\n\tissn = {00221694},\n\tshorttitle = {Inverse distributed modelling of streamflow and turbulent fluxes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169419301908},\n\tdoi = {10.1016/j.jhydrol.2019.02.033},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Soltani, Mohsen and Laux, Patrick and Mauder, Matthias and Kunstmann, Harald},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {856--872},\n}\n\n\n\n

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\n \n\n \n \n Smiatek, G.; and Kunstmann, H.\n\n\n \n \n \n \n \n Simulating Future Runoff in a Complex Terrain Alpine Catchment with EURO-CORDEX Data.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 20(9): 1925–1940. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{smiatek_simulating_2019,\n\ttitle = {Simulating {Future} {Runoff} in a {Complex} {Terrain} {Alpine} {Catchment} with {EURO}-{CORDEX} {Data}},\n\tvolume = {20},\n\tissn = {1525-755X, 1525-7541},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-18-0214.1},\n\tdoi = {10.1175/JHM-D-18-0214.1},\n\tabstract = {Abstract \n            With large elevation gradients and high hydrometeorological variability, Alpine catchments pose special challenges to hydrological climate change impact assessment. Data from seven regional climate models run within the Coordinated Regional Climate Downscaling Experiments (CORDEX), each driven with a different boundary forcing, are used to exemplarily evaluate the reproduction of observed flow duration curves and access the future discharge of the Ammer River located in Alpine southern Germany applying the hydrological simulation model called the Water Flow and Balance Simulation Model (WaSiM). The results show that WaSiM reasonably reproduces the observed runoff for the entire catchment when driven with observed precipitation. When applied with CORDEX evaluation data (1989–2008) forced by ERA-Interim, the simulations underestimate the extreme runoff and reproduce the high percentile values with errors in the range from −37\\% to 55\\% with an ensemble mean of around 15\\%. Runs with historical data 1975–2005 reveal larger errors, up to 120\\%, with an ensemble mean of around 50\\% overestimation. Also, the results show a large spread between the simulations, primarily resulting from deficiencies in the precipitation data. Results indicate future changes for 2071–2100 in the 99.5th percentile runoff value of up to 9\\% compared to 1975–2005. An increase in high flows is also supported by flow return periods obtained from a larger sample of highest flows over 50 years, which reveals for 2051–2100 lower return periods for high runoff values compared to 1956–2005. Obtained results are associated with substantial uncertainties leading to the conclusion that CORDEX data at 0.11° resolution are likely inadequate for driving hydrologic analyses in mesoscale catchments that require a high standard of fidelity for hydrologic simulation performance.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Smiatek, Gerhard and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {1925--1940},\n}\n\n\n\n

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\n \n\n \n \n Schweiger, O.; Franzén, M.; Frenzel, M.; Galpern, P.; Kerr, J.; Papanikolaou, A.; and Rasmont, P.\n\n\n \n \n \n \n \n Minimising Risks of Global Change by Enhancing Resilience of Pollinators in Agricultural Systems.\n \n \n \n \n\n\n \n\n\n\n In Schröter, M.; Bonn, A.; Klotz, S.; Seppelt, R.; and Baessler, C., editor(s), Atlas of Ecosystem Services, pages 105–111. Springer International Publishing, Cham, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Minimising$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{schroter_minimising_2019,\n\taddress = {Cham},\n\ttitle = {Minimising {Risks} of {Global} {Change} by {Enhancing} {Resilience} of {Pollinators} in {Agricultural} {Systems}},\n\tisbn = {9783319962283 9783319962290},\n\turl = {http://link.springer.com/10.1007/978-3-319-96229-0_17},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Atlas of {Ecosystem} {Services}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Schweiger, Oliver and Franzén, Markus and Frenzel, Mark and Galpern, Paul and Kerr, Jeremy and Papanikolaou, Alexandra and Rasmont, Pierre},\n\teditor = {Schröter, Matthias and Bonn, Aletta and Klotz, Stefan and Seppelt, Ralf and Baessler, Cornelia},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-319-96229-0_17},\n\tpages = {105--111},\n}\n\n\n\n

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\n \n\n \n \n Rummler, T.; Arnault, J.; Gochis, D.; and Kunstmann, H.\n\n\n \n \n \n \n \n Role of Lateral Terrestrial Water Flow on the Regional Water Cycle in a Complex Terrain Region: Investigation With a Fully Coupled Model System.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 124(2): 507–529. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Role$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rummler_role_2019,\n\ttitle = {Role of {Lateral} {Terrestrial} {Water} {Flow} on the {Regional} {Water} {Cycle} in a {Complex} {Terrain} {Region}: {Investigation} {With} a {Fully} {Coupled} {Model} {System}},\n\tvolume = {124},\n\tissn = {2169-897X, 2169-8996},\n\tshorttitle = {Role of {Lateral} {Terrestrial} {Water} {Flow} on the {Regional} {Water} {Cycle} in a {Complex} {Terrain} {Region}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2018JD029004},\n\tdoi = {10.1029/2018JD029004},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Rummler, Thomas and Arnault, Joel and Gochis, David and Kunstmann, Harald},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {507--529},\n}\n\n\n\n

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\n \n\n \n \n Rodionov, A.; Lehndorff, E.; Stremtan, C. C.; Brand, W. A.; Königshoven, H.; and Amelung, W.\n\n\n \n \n \n \n \n Spatial Microanalysis of Natural $^{\\textrm{13}}$ C/ $^{\\textrm{12}}$ C Abundance in Environmental Samples Using Laser Ablation-Isotope Ratio Mass Spectrometry.\n \n \n \n \n\n\n \n\n\n\n Analytical Chemistry, 91(9): 6225–6232. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rodionov_spatial_2019,\n\ttitle = {Spatial {Microanalysis} of {Natural} $^{\\textrm{13}}$ {C}/ $^{\\textrm{12}}$ {C} {Abundance} in {Environmental} {Samples} {Using} {Laser} {Ablation}-{Isotope} {Ratio} {Mass} {Spectrometry}},\n\tvolume = {91},\n\tissn = {0003-2700, 1520-6882},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.analchem.9b00892},\n\tdoi = {10.1021/acs.analchem.9b00892},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Analytical Chemistry},\n\tauthor = {Rodionov, Andrei and Lehndorff, Eva and Stremtan, Ciprian C. and Brand, Willi A. and Königshoven, Heinz-Peter and Amelung, Wulf},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {6225--6232},\n}\n\n\n\n

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\n \n\n \n \n Quade, M.; Klosterhalfen, A.; Graf, A.; Brüggemann, N.; Hermes, N.; Vereecken, H.; and Rothfuss, Y.\n\n\n \n \n \n \n \n In-situ monitoring of soil water isotopic composition for partitioning of evapotranspiration during one growing season of sugar beet (Beta vulgaris).\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 266-267: 53–64. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"In-situ$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{quade_-situ_2019,\n\ttitle = {In-situ monitoring of soil water isotopic composition for partitioning of evapotranspiration during one growing season of sugar beet ({Beta} vulgaris)},\n\tvolume = {266-267},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303952},\n\tdoi = {10.1016/j.agrformet.2018.12.002},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Quade, Maria and Klosterhalfen, Anne and Graf, Alexander and Brüggemann, Nicolas and Hermes, Normen and Vereecken, Harry and Rothfuss, Youri},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {53--64},\n}\n\n\n\n

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\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; Kiese, R.; Ludwig, R.; and Sanchez-Azofeifa, A.\n\n\n \n \n \n \n \n Assessing the variability and uncertainty of two-flux FAPAR measurements in a conifer-dominated forest.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 264: 149–163. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{putzenlechner_assessing_2019,\n\ttitle = {Assessing the variability and uncertainty of two-flux {FAPAR} measurements in a conifer-dominated forest},\n\tvolume = {264},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303319},\n\tdoi = {10.1016/j.agrformet.2018.10.007},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Kiese, Ralf and Ludwig, Ralf and Sanchez-Azofeifa, Arturo},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {149--163},\n}\n\n\n\n

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\n \n\n \n \n Ney, P.; Graf, A.; Bogena, H.; Diekkrüger, B.; Drüe, C.; Esser, O.; Heinemann, G.; Klosterhalfen, A.; Pick, K.; Pütz, T.; Schmidt, M.; Valler, V.; and Vereecken, H.\n\n\n \n \n \n \n \n CO2 fluxes before and after partial deforestation of a Central European spruce forest.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 274: 61–74. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"CO2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ney_co2_2019,\n\ttitle = {{CO2} fluxes before and after partial deforestation of a {Central} {European} spruce forest},\n\tvolume = {274},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192319301492},\n\tdoi = {10.1016/j.agrformet.2019.04.009},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Ney, Patrizia and Graf, Alexander and Bogena, Heye and Diekkrüger, Bernd and Drüe, Clemens and Esser, Odilia and Heinemann, Günther and Klosterhalfen, Anne and Pick, Katharina and Pütz, Thomas and Schmidt, Marius and Valler, Veronika and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {61--74},\n}\n\n\n\n

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\n \n\n \n \n Morandage, S.; Schnepf, A.; Leitner, D.; Javaux, M.; Vereecken, H.; and Vanderborght, J.\n\n\n \n \n \n \n \n Parameter sensitivity analysis of a root system architecture model based on virtual field sampling.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 438(1-2): 101–126. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameter$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{morandage_parameter_2019,\n\ttitle = {Parameter sensitivity analysis of a root system architecture model based on virtual field sampling},\n\tvolume = {438},\n\tissn = {0032-079X, 1573-5036},\n\turl = {https://link.springer.com/10.1007/s11104-019-03993-3},\n\tdoi = {10.1007/s11104-019-03993-3},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-17},\n\tjournal = {Plant and Soil},\n\tauthor = {Morandage, Shehan and Schnepf, Andrea and Leitner, Daniel and Javaux, Mathieu and Vereecken, Harry and Vanderborght, Jan},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {101--126},\n}\n\n\n\n

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\n \n\n \n \n Mi, C.; Sadeghian, A.; Lindenschmidt, K.; and Rinke, K.\n\n\n \n \n \n \n \n Variable withdrawal elevations as a management tool to counter the effects of climate warming in Germany’s largest drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 19. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Variable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mi_variable_2019,\n\ttitle = {Variable withdrawal elevations as a management tool to counter the effects of climate warming in {Germany}’s largest drinking water reservoir},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0202-4},\n\tdoi = {10.1186/s12302-019-0202-4},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Mi, Chenxi and Sadeghian, Amir and Lindenschmidt, Karl-Erich and Rinke, Karsten},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {19},\n}\n\n\n\n

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\n \n\n \n \n Marcé, R.; Obrador, B.; Gómez-Gener, L.; Catalán, N.; Koschorreck, M.; Arce, M. I.; Singer, G.; and von Schiller, D.\n\n\n \n \n \n \n \n Emissions from dry inland waters are a blind spot in the global carbon cycle.\n \n \n \n \n\n\n \n\n\n\n Earth-Science Reviews, 188: 240–248. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Emissions$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{marce_emissions_2019,\n\ttitle = {Emissions from dry inland waters are a blind spot in the global carbon cycle},\n\tvolume = {188},\n\tissn = {00128252},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0012825218301971},\n\tdoi = {10.1016/j.earscirev.2018.11.012},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Earth-Science Reviews},\n\tauthor = {Marcé, Rafael and Obrador, Biel and Gómez-Gener, Lluís and Catalán, Núria and Koschorreck, Matthias and Arce, María Isabel and Singer, Gabriel and von Schiller, Daniel},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {240--248},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Ma, H.; Zeng, J.; Chen, N.; Zhang, X.; Cosh, M. H.; and Wang, W.\n\n\n \n \n \n \n \n Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 231: 111215. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Satellite$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ma_satellite_2019,\n\ttitle = {Satellite surface soil moisture from {SMAP}, {SMOS}, {AMSR2} and {ESA} {CCI}: {A} comprehensive assessment using global ground-based observations},\n\tvolume = {231},\n\tissn = {00344257},\n\tshorttitle = {Satellite surface soil moisture from {SMAP}, {SMOS}, {AMSR2} and {ESA} {CCI}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425719302287},\n\tdoi = {10.1016/j.rse.2019.111215},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Ma, Hongliang and Zeng, Jiangyuan and Chen, Nengcheng and Zhang, Xiang and Cosh, Michael H. and Wang, Wei},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {111215},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Li, X.; Vereecken, H.; and Ma, C.\n\n\n \n \n \n \n \n Observing Ecohydrological Processes: Challenges and Perspectives.\n \n \n \n \n\n\n \n\n\n\n In Li, X.; and Vereecken, H., editor(s), Observation and Measurement of Ecohydrological Processes, volume 2, pages 1–27. Springer Berlin Heidelberg, Berlin, Heidelberg, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Observing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{li_observing_2019,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Observing {Ecohydrological} {Processes}: {Challenges} and {Perspectives}},\n\tvolume = {2},\n\tisbn = {9783662482964 9783662482971},\n\tshorttitle = {Observing {Ecohydrological} {Processes}},\n\turl = {http://link.springer.com/10.1007/978-3-662-48297-1_1},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Observation and {Measurement} of {Ecohydrological} {Processes}},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Li, Xin and Vereecken, Harry and Ma, Chunfeng},\n\teditor = {Li, Xin and Vereecken, Harry},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-662-48297-1_1},\n\tpages = {1--27},\n}\n\n\n\n

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\n \n\n \n \n Li, J.; Ju, W.; He, W.; Wang, H.; Zhou, Y.; and Xu, M.\n\n\n \n \n \n \n \n An Algorithm Differentiating Sunlit and Shaded Leaves for Improving Canopy Conductance and Vapotranspiration Estimates.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 124(4): 807–824. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{li_algorithm_2019,\n\ttitle = {An {Algorithm} {Differentiating} {Sunlit} and {Shaded} {Leaves} for {Improving} {Canopy} {Conductance} and {Vapotranspiration} {Estimates}},\n\tvolume = {124},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018JG004675},\n\tdoi = {10.1029/2018JG004675},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Li, Jing and Ju, Weimin and He, Wei and Wang, Hengmao and Zhou, Yanlian and Xu, Mingzhu},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {807--824},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Kühnel, A.; Garcia-Franco, N.; Wiesmeier, M.; Burmeister, J.; Hobley, E.; Kiese, R.; Dannenmann, M.; and Kögel-Knabner, I.\n\n\n \n \n \n \n \n Controlling factors of carbon dynamics in grassland soils of Bavaria between 1989 and 2016.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 280: 118–128. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Controlling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kuhnel_controlling_2019,\n\ttitle = {Controlling factors of carbon dynamics in grassland soils of {Bavaria} between 1989 and 2016},\n\tvolume = {280},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880919301239},\n\tdoi = {10.1016/j.agee.2019.04.036},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Kühnel, Anna and Garcia-Franco, Noelia and Wiesmeier, Martin and Burmeister, Johannes and Hobley, Eleanor and Kiese, Ralf and Dannenmann, Michael and Kögel-Knabner, Ingrid},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {118--128},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Krauss, M.; Hug, C.; Bloch, R.; Schulze, T.; and Brack, W.\n\n\n \n \n \n \n \n Prioritising site-specific micropollutants in surface water from LC-HRMS non-target screening data using a rarity score.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 45. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Prioritising$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{krauss_prioritising_2019,\n\ttitle = {Prioritising site-specific micropollutants in surface water from {LC}-{HRMS} non-target screening data using a rarity score},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0231-z},\n\tdoi = {10.1186/s12302-019-0231-z},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Krauss, Martin and Hug, Christine and Bloch, Robert and Schulze, Tobias and Brack, Werner},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {45},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Koebsch, F.; Winkel, M.; Liebner, S.; Liu, B.; Westphal, J.; Schmiedinger, I.; Spitzy, A.; Gehre, M.; Jurasinski, G.; Köhler, S.; Unger, V.; Koch, M.; Sachs, T.; and Böttcher, M. E.\n\n\n \n \n \n \n \n Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(9): 1937–1953. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sulfate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koebsch_sulfate_2019,\n\ttitle = {Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/1937/2019/},\n\tdoi = {10.5194/bg-16-1937-2019},\n\tabstract = {Abstract. In natural coastal wetlands, high supplies of marine\nsulfate suppress methanogenesis. Coastal wetlands are, however, often\nsubject to disturbance by diking and drainage for agricultural use and can\nturn to potent methane sources when rewetted for remediation. This suggests\nthat preceding land use measures can suspend the sulfate-related methane\nsuppressing mechanisms. Here, we unravel the hydrological relocation and\nbiogeochemical S and C transformation processes that induced high methane\nemissions in a disturbed and rewetted peatland despite former brackish\nimpact. The underlying processes were investigated along a transect of\nincreasing distance to the coastline using a combination of concentration\npatterns, stable isotope partitioning, and analysis of the microbial\ncommunity structure. We found that diking and freshwater rewetting caused a\ndistinct freshening and an efficient depletion of the brackish sulfate\nreservoir by dissimilatory sulfate reduction (DSR). Despite some legacy\neffects of brackish impact expressed as high amounts of sedimentary S and\nelevated electrical conductivities, contemporary metabolic processes\noperated mainly under sulfate-limited conditions. This opened up favorable\nconditions for the establishment of a prospering methanogenic community in\nthe top 30–40 cm of peat, the structure and physiology of which resemble\nthose of terrestrial organic-rich environments. Locally, high amounts of\nsulfate persisted in deeper peat layers through the inhibition of DSR,\nprobably by competitive electron acceptors of terrestrial origin, for\nexample Fe(III). However, as sulfate occurred only in peat layers below\n30–40 cm, it did not interfere with high methane emissions on an ecosystem\nscale. Our results indicate that the climate effect of disturbed and\nremediated coastal wetlands cannot simply be derived by analogy with their\nnatural counterparts. From a greenhouse gas perspective, the re-exposure of\ndiked wetlands to natural coastal dynamics would literally open up the\nfloodgates for a replenishment of the marine sulfate pool and therefore\nconstitute an efficient measure to reduce methane emissions.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Koebsch, Franziska and Winkel, Matthias and Liebner, Susanne and Liu, Bo and Westphal, Julia and Schmiedinger, Iris and Spitzy, Alejandro and Gehre, Matthias and Jurasinski, Gerald and Köhler, Stefan and Unger, Viktoria and Koch, Marian and Sachs, Torsten and Böttcher, Michael E.},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {1937--1953},\n}\n\n\n\n

\n\n\n

\n Abstract. In natural coastal wetlands, high supplies of marine sulfate suppress methanogenesis. Coastal wetlands are, however, often subject to disturbance by diking and drainage for agricultural use and can turn to potent methane sources when rewetted for remediation. This suggests that preceding land use measures can suspend the sulfate-related methane suppressing mechanisms. Here, we unravel the hydrological relocation and biogeochemical S and C transformation processes that induced high methane emissions in a disturbed and rewetted peatland despite former brackish impact. The underlying processes were investigated along a transect of increasing distance to the coastline using a combination of concentration patterns, stable isotope partitioning, and analysis of the microbial community structure. We found that diking and freshwater rewetting caused a distinct freshening and an efficient depletion of the brackish sulfate reservoir by dissimilatory sulfate reduction (DSR). Despite some legacy effects of brackish impact expressed as high amounts of sedimentary S and elevated electrical conductivities, contemporary metabolic processes operated mainly under sulfate-limited conditions. This opened up favorable conditions for the establishment of a prospering methanogenic community in the top 30–40 cm of peat, the structure and physiology of which resemble those of terrestrial organic-rich environments. Locally, high amounts of sulfate persisted in deeper peat layers through the inhibition of DSR, probably by competitive electron acceptors of terrestrial origin, for example Fe(III). However, as sulfate occurred only in peat layers below 30–40 cm, it did not interfere with high methane emissions on an ecosystem scale. Our results indicate that the climate effect of disturbed and remediated coastal wetlands cannot simply be derived by analogy with their natural counterparts. From a greenhouse gas perspective, the re-exposure of diked wetlands to natural coastal dynamics would literally open up the floodgates for a replenishment of the marine sulfate pool and therefore constitute an efficient measure to reduce methane emissions.\n

\n\n\n

\n \n\n \n \n Kiesel, J.; Gericke, A.; Rathjens, H.; Wetzig, A.; Kakouei, K.; Jähnig, S. C.; and Fohrer, N.\n\n\n \n \n \n \n \n Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions.\n \n \n \n \n\n\n \n\n\n\n Ecological Engineering, 127: 404–416. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kiesel_climate_2019,\n\ttitle = {Climate change impacts on ecologically relevant hydrological indicators in three catchments in three {European} ecoregions},\n\tvolume = {127},\n\tissn = {09258574},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S092585741830466X},\n\tdoi = {10.1016/j.ecoleng.2018.12.019},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Engineering},\n\tauthor = {Kiesel, Jens and Gericke, Andreas and Rathjens, Hendrik and Wetzig, Annett and Kakouei, Karan and Jähnig, Sonja C. and Fohrer, Nicola},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {404--416},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Karrasch, B.; Horovitz, O.; Norf, H.; Hillel, N.; Hadas, O.; Beeri-Shlevin, Y.; and Laronne, J. B.\n\n\n \n \n \n \n \n Quantitative ecotoxicological impacts of sewage treatment plant effluents on plankton productivity and assimilative capacity of rivers.\n \n \n \n \n\n\n \n\n\n\n Environmental Science and Pollution Research, 26(23): 24034–24049. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{karrasch_quantitative_2019,\n\ttitle = {Quantitative ecotoxicological impacts of sewage treatment plant effluents on plankton productivity and assimilative capacity of rivers},\n\tvolume = {26},\n\tissn = {0944-1344, 1614-7499},\n\turl = {http://link.springer.com/10.1007/s11356-019-04940-6},\n\tdoi = {10.1007/s11356-019-04940-6},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Science and Pollution Research},\n\tauthor = {Karrasch, Bernhard and Horovitz, Omer and Norf, Helge and Hillel, Noa and Hadas, Ora and Beeri-Shlevin, Yaron and Laronne, Jonathan B.},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {24034--24049},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kappler, C.; Kaiser, K.; Küster, M.; Nicolay, A.; Fülling, A.; Bens, O.; and Raab, T.\n\n\n \n \n \n \n \n Late Pleistocene and Holocene terrestrial geomorphodynamics and soil formation in northeastern Germany: a review of geochronological data.\n \n \n \n \n\n\n \n\n\n\n Physical Geography, 40(5): 405–432. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Late$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kappler_late_2019,\n\ttitle = {Late {Pleistocene} and {Holocene} terrestrial geomorphodynamics and soil formation in northeastern {Germany}: a review of geochronological data},\n\tvolume = {40},\n\tissn = {0272-3646, 1930-0557},\n\tshorttitle = {Late {Pleistocene} and {Holocene} terrestrial geomorphodynamics and soil formation in northeastern {Germany}},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02723646.2019.1573621},\n\tdoi = {10.1080/02723646.2019.1573621},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Physical Geography},\n\tauthor = {Kappler, Christoph and Kaiser, Knut and Küster, Mathias and Nicolay, Alexander and Fülling, Alexander and Bens, Oliver and Raab, Thomas},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {405--432},\n}\n\n\n\n

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\n \n\n \n \n Kamjunke, N.; Hertkorn, N.; Harir, M.; Schmitt-Kopplin, P.; Griebler, C.; Brauns, M.; von Tümpling, W.; Weitere, M.; and Herzsprung, P.\n\n\n \n \n \n \n \n Molecular change of dissolved organic matter and patterns of bacterial activity in a stream along a land-use gradient.\n \n \n \n \n\n\n \n\n\n\n Water Research, 164: 114919. November 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Molecular$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_molecular_2019,\n\ttitle = {Molecular change of dissolved organic matter and patterns of bacterial activity in a stream along a land-use gradient},\n\tvolume = {164},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135419306931},\n\tdoi = {10.1016/j.watres.2019.114919},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Kamjunke, Norbert and Hertkorn, Norbert and Harir, Mourad and Schmitt-Kopplin, Philippe and Griebler, Christian and Brauns, Mario and von Tümpling, Wolf and Weitere, Markus and Herzsprung, Peter},\n\tmonth = nov,\n\tyear = {2019},\n\tpages = {114919},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Kabisch, N.; Selsam, P.; Kirsten, T.; Lausch, A.; and Bumberger, J.\n\n\n \n \n \n \n \n A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 99: 273–282. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kabisch_multi-sensor_2019,\n\ttitle = {A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes},\n\tvolume = {99},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X1830966X},\n\tdoi = {10.1016/j.ecolind.2018.12.033},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Indicators},\n\tauthor = {Kabisch, Nadja and Selsam, Peter and Kirsten, Toralf and Lausch, Angela and Bumberger, Jan},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {273--282},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Jiang, S.; Zhang, Q.; Werner, A.; Wellen, C.; Jomaa, S.; Zhu, Q.; Büttner, O.; Meon, G.; and Rode, M.\n\n\n \n \n \n \n \n Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 569: 22–36. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_effects_2019,\n\ttitle = {Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty},\n\tvolume = {569},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418309041},\n\tdoi = {10.1016/j.jhydrol.2018.11.049},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Jiang, S.Y. and Zhang, Q. and Werner, A.D. and Wellen, C. and Jomaa, S. and Zhu, Q.D. and Büttner, O. and Meon, G. and Rode, M.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {22--36},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Hongtao, J.; Huanfeng, S.; Xinghua, L.; Chao, Z.; Huiqin, L.; and Fangni, L.\n\n\n \n \n \n \n \n Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 231: 111224. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Extending$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hongtao_extending_2019,\n\ttitle = {Extending the {SMAP} 9-km soil moisture product using a spatio-temporal fusion model},\n\tvolume = {231},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425719302433},\n\tdoi = {10.1016/j.rse.2019.111224},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Hongtao, Jiang and Huanfeng, Shen and Xinghua, Li and Chao, Zeng and Huiqin, Liu and Fangni, Lei},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {111224},\n}\n\n\n\n

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\n \n\n \n \n Hobley, E. U.; and Prater, I.\n\n\n \n \n \n \n \n Estimating soil texture from vis-NIR spectra: Estimating soil texture from vis-NIR spectra.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Science, 70(1): 83–95. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hobley_estimating_2019,\n\ttitle = {Estimating soil texture from vis-{NIR} spectra: {Estimating} soil texture from vis-{NIR} spectra},\n\tvolume = {70},\n\tissn = {13510754},\n\tshorttitle = {Estimating soil texture from vis-{NIR} spectra},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ejss.12733},\n\tdoi = {10.1111/ejss.12733},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {European Journal of Soil Science},\n\tauthor = {Hobley, E. U. and Prater, I.},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {83--95},\n}\n\n\n\n

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\n \n\n \n \n Hering, J. G.\n\n\n \n \n \n \n \n From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Engineering, 145(8): 02519001. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hering_slide_2019,\n\ttitle = {From {Slide} {Rule} to {Big} {Data}: {How} {Data} {Science} is {Changing} {Water} {Science} and {Engineering}},\n\tvolume = {145},\n\tissn = {0733-9372, 1943-7870},\n\tshorttitle = {From {Slide} {Rule} to {Big} {Data}},\n\turl = {https://ascelibrary.org/doi/10.1061/%28ASCE%29EE.1943-7870.0001578},\n\tdoi = {10.1061/(ASCE)EE.1943-7870.0001578},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Environmental Engineering},\n\tauthor = {Hering, Janet G.},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {02519001},\n}\n\n\n\n

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\n \n\n \n \n Heinrich, I.; Balanzategui, D.; Bens, O.; Blume, T.; Brauer, A.; Dietze, E.; Gottschalk, P.; Güntner, A.; Harfenmeister, K.; Helle, G.; Hohmann, C.; Itzerott, S.; Kaiser, K.; Liebner, S.; Merz, B.; Pinkerneil, S.; Plessen, B.; Sachs, T.; Schwab, M. J.; Spengler, D.; Vallentin, C.; and Wille, C.\n\n\n \n \n \n \n \n Regionale Auswirkungen des Globalen Wandels: Der Extremsommer 2018 in Nordostdeutschland.\n \n \n \n \n\n\n \n\n\n\n System Erde; 9,4 MB. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Regionale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heinrich_regionale_2019,\n\ttitle = {Regionale {Auswirkungen} des {Globalen} {Wandels}: {Der} {Extremsommer} 2018 in {Nordostdeutschland}},\n\tcopyright = {CC-BY-SA 4.0},\n\tshorttitle = {Regionale {Auswirkungen} des {Globalen} {Wandels}},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_4296898},\n\tdoi = {10.2312/GFZ.SYSERDE.09.01.6},\n\tabstract = {The main focus of the TERENO Northeastern German Lowland Observatory (TERENO-Northeast) is the regional impact of Global Change. Since 2011, the observatory has recorded changes in the geo-, hydro-, bio- and atmosphere at six main study sites. The year 2018, particularly in northeast Germany, was record-breaking in regard to dryness and heat. The mean temperature in Mecklenburg-Vorpommern was 2 °C above the long-term average and precipitation was very low at 440 mm (normally around 600 mm). The extreme summer of 2018 was a special opportunity for TERENO-Northeast to measure the regional effects of climate change. One of the consequences was the large number of forest fires, with one major fire destroying around 400 hectares. Other extreme reactions of the ecosystems were shown in TERENO-Northeast. For example, for the first time since its rewetting, Polder Zarnekov fell dry, with unpredictable consequences for the greenhouse gas exchanges. The forest ecosystems of Müritz National Park, on the other hand, survived the extreme summer surprisingly well, partly because the months before the drought were relatively damp. The research activities of TERENO-Northeast form an important basis to develop realistic options for improved adaptation strategies to the ongoing global change with its particular region-specific effects and challenges.},\n\tlanguage = {de},\n\turldate = {2022-11-17},\n\tjournal = {System Erde; 9},\n\tauthor = {Heinrich, Ingo and Balanzategui, Daniel and Bens, Oliver and Blume, Theresa and Brauer, Achim and Dietze, Elisabeth and Gottschalk, Pia and Güntner, Andreas and Harfenmeister, Katharina and Helle, Gerhard and Hohmann, Christian and Itzerott, Sibylle and Kaiser, Knut and Liebner, Susanne and Merz, Bruno and Pinkerneil, Sylvia and Plessen, Birgit and Sachs, Torsten and Schwab, Markus J. and Spengler, Daniel and Vallentin, Claudia and Wille, Christian},\n\tyear = {2019},\n\tpages = {4 MB},\n}\n\n\n\n

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\n \n\n \n \n Harfenmeister, K.; Spengler, D.; and Weltzien, C.\n\n\n \n \n \n \n \n Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 11(13): 1569. July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Analyzing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{harfenmeister_analyzing_2019,\n\ttitle = {Analyzing {Temporal} and {Spatial} {Characteristics} of {Crop} {Parameters} {Using} {Sentinel}-1 {Backscatter} {Data}},\n\tvolume = {11},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/11/13/1569},\n\tdoi = {10.3390/rs11131569},\n\tabstract = {The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R     2    ) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R     2     values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R     2     values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing},\n\tauthor = {Harfenmeister, Katharina and Spengler, Daniel and Weltzien, Cornelia},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {1569},\n}\n\n\n\n

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\n \n\n \n \n Hardy, R.; Quinton, J.; James, M.; Fiener, P.; and Pates, J.\n\n\n \n \n \n \n \n High precision tracing of soil and sediment movement using fluorescent tracers at hillslope scale.\n \n \n \n \n\n\n \n\n\n\n Earth Surface Processes and Landforms, 44(5): 1091–1099. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hardy_high_2019,\n\ttitle = {High precision tracing of soil and sediment movement using fluorescent tracers at hillslope scale},\n\tvolume = {44},\n\tissn = {0197-9337, 1096-9837},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/esp.4557},\n\tdoi = {10.1002/esp.4557},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Earth Surface Processes and Landforms},\n\tauthor = {Hardy, R.A. and Quinton, J.N. and James, M.R. and Fiener, P. and Pates, J.M.},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {1091--1099},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Groh, J.; Pütz, T.; Gerke, H. H.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Quantification and Prediction of Nighttime Evapotranspiration for Two Distinct Grassland Ecosystems.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 55(4): 2961–2975. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_quantification_2019,\n\ttitle = {Quantification and {Prediction} of {Nighttime} {Evapotranspiration} for {Two} {Distinct} {Grassland} {Ecosystems}},\n\tvolume = {55},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018WR024072},\n\tdoi = {10.1029/2018WR024072},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Groh, J. and Pütz, T. and Gerke, H. H. and Vanderborght, J. and Vereecken, H.},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {2961--2975},\n}\n\n\n\n

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\n \n\n \n \n Graeber, D.; Gücker, B.; Wild, R.; Wells, N. S.; Anlanger, C.; Kamjunke, N.; Norf, H.; Schmidt, C.; and Brauns, M.\n\n\n \n \n \n \n \n Biofilm-specific uptake does not explain differences in whole-stream DOC tracer uptake between a forest and an agricultural stream.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 144(1): 85–101. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Biofilm-specific$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graeber_biofilm-specific_2019,\n\ttitle = {Biofilm-specific uptake does not explain differences in whole-stream {DOC} tracer uptake between a forest and an agricultural stream},\n\tvolume = {144},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-019-00573-6},\n\tdoi = {10.1007/s10533-019-00573-6},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Biogeochemistry},\n\tauthor = {Graeber, D. and Gücker, B. and Wild, R. and Wells, N. S. and Anlanger, C. and Kamjunke, N. and Norf, H. and Schmidt, C. and Brauns, M.},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {85--101},\n}\n\n\n\n

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\n \n\n \n \n Eshonkulov, R.; Poyda, A.; Ingwersen, J.; Wizemann, H.; Weber, T. K. D.; Kremer, P.; Högy, P.; Pulatov, A.; and Streck, T.\n\n\n \n \n \n \n \n Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern Germany.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(2): 521–540. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{eshonkulov_evaluating_2019,\n\ttitle = {Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern {Germany}},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/521/2019/},\n\tdoi = {10.5194/bg-16-521-2019},\n\tabstract = {Abstract. The energy balance of eddy-covariance (EC) measurements is\ntypically not closed, resulting in one of the main challenges in evaluating\nand interpreting EC flux data. Energy balance closure (EBC) is crucial for\nvalidating and improving regional and global climate models. To investigate\nthe nature of the gap in EBC for agroecosystems, we analyzed EC measurements\nfrom two climatically contrasting regions (Kraichgau – KR – and Swabian Jura – SJ) in southwestern Germany. Data were taken at six fully equipped EC sites\nfrom 2010 to 2017. The gap in EBC was quantified by ordinary linear\nregression, relating the energy balance ratio (EBR), calculated as the\nquotient of turbulent fluxes and available energy, to the residual energy\nterm. In order to examine potential reasons for differences in EBC, we\ncompared the EBC under varying environmental conditions and investigated a\nwide range of possible controls. Overall, the variation in EBC was found to\nbe higher during winter than summer. Moreover, we determined that the site had a\nstatistically significant effect on EBC but no significant effect on either crop or region (KR\nvs SJ). The time-variable footprints of all EC stations were estimated based\non data measured in 2015, complimented by micro-topographic analyses along\nthe prevailing wind direction. The smallest mean annual energy balance gap\nwas 17 \\% in KR and 13 \\% in SJ. Highest EBRs were mostly found for winds\nfrom the prevailing wind direction. The spread of EBRs distinctly narrowed\nunder unstable atmospheric conditions, strong buoyancy, and high friction\nvelocities. Smaller footprint areas led to better EBC due to increasing\nhomogeneity. Flow distortions caused by the back head of the anemometer\nnegatively affected EBC during corresponding wind conditions.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Eshonkulov, Ravshan and Poyda, Arne and Ingwersen, Joachim and Wizemann, Hans-Dieter and Weber, Tobias K. D. and Kremer, Pascal and Högy, Petra and Pulatov, Alim and Streck, Thilo},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {521--540},\n}\n\n\n\n

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\n \n\n \n \n Dräger, N.; Plessen, B.; Kienel, U.; Słowiński, M.; Ramisch, A.; Tjallingii, R.; Pinkerneil, S.; and Brauer, A.\n\n\n \n \n \n \n \n Hypolimnetic oxygen conditions influence varve preservation and δ13C of sediment organic matter in Lake Tiefer See, NE Germany.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 62(2): 181–194. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Hypolimnetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{drager_hypolimnetic_2019,\n\ttitle = {Hypolimnetic oxygen conditions influence varve preservation and δ{13C} of sediment organic matter in {Lake} {Tiefer} {See}, {NE} {Germany}},\n\tvolume = {62},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-019-00084-2},\n\tdoi = {10.1007/s10933-019-00084-2},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Dräger, Nadine and Plessen, Birgit and Kienel, Ulrike and Słowiński, Michał and Ramisch, Arne and Tjallingii, Rik and Pinkerneil, Sylvia and Brauer, Achim},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {181--194},\n}\n\n\n\n

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\n \n\n \n \n Dias Neto, J.; Kneifel, S.; Ori, D.; Trömel, S.; Handwerker, J.; Bohn, B.; Hermes, N.; Mühlbauer, K.; Lenefer, M.; and Simmer, C.\n\n\n \n \n \n \n \n The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 11(2): 845–863. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dias_neto_triple-frequency_2019,\n\ttitle = {The {TRIple}-frequency and {Polarimetric} radar {Experiment} for improving process observations of winter precipitation},\n\tvolume = {11},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/11/845/2019/},\n\tdoi = {10.5194/essd-11-845-2019},\n\tabstract = {Abstract. This paper describes a 2-month dataset of ground-based triple-frequency (X,\nKa, and W band) Doppler radar observations during the winter season obtained\nat the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF),\nGermany. All relevant post-processing steps, such as re-gridding and offset and\nattenuation correction, as well as quality flagging, are described. The\ndataset contains all necessary information required to recover data at\nintermediate processing steps for user-specific applications and corrections\n(https://doi.org/10.5281/zenodo.1341389; Dias Neto et al., 2019). The large number of ice clouds included in the dataset\nallows for a first statistical analysis of their multifrequency radar\nsignatures. The reflectivity differences quantified by dual-wavelength ratios\n(DWRs) reveal temperature regimes where aggregation seems to be triggered.\nOverall, the aggregation signatures found in the triple-frequency space agree\nwith and corroborate conclusions from previous studies. The combination of\nDWRs with mean Doppler velocity and linear depolarization ratio enables us to\ndistinguish signatures of rimed particles and melting snowflakes. The riming\nsignatures in the DWRs agree well with results found in previous\ntriple-frequency studies. Close to the melting layer, however, we find very\nlarge DWRs (up to 20 dB), which have not been reported before. A combined\nanalysis of these extreme DWR with mean Doppler velocity and a linear\ndepolarization ratio allows this signature to be separated, which is most likely\nrelated to strong aggregation, from the triple-frequency characteristics of\nmelting particles.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Earth System Science Data},\n\tauthor = {Dias Neto, José and Kneifel, Stefan and Ori, Davide and Trömel, Silke and Handwerker, Jan and Bohn, Birger and Hermes, Normen and Mühlbauer, Kai and Lenefer, Martin and Simmer, Clemens},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {845--863},\n}\n\n\n\n

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\n \n\n \n \n Curdt, C.\n\n\n \n \n \n \n \n Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services.\n \n \n \n \n\n\n \n\n\n\n Data Science Journal, 18: 5. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Supporting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{curdt_supporting_2019,\n\ttitle = {Supporting the {Interdisciplinary}, {Long}-{Term} {Research} {Project} ‘{Patterns} in {Soil}-{Vegetation}-{Atmosphere}-{Systems}’ by {Data} {Management} {Services}},\n\tvolume = {18},\n\tissn = {1683-1470},\n\turl = {http://datascience.codata.org/articles/10.5334/dsj-2019-005/},\n\tdoi = {10.5334/dsj-2019-005},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Data Science Journal},\n\tauthor = {Curdt, Constanze},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {5},\n}\n\n\n\n

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\n \n\n \n \n Chen, C.; Montzka, C.; Huth, J.; Kuenzer, C.; Kunstmann, H.; Yue, T.; and Kolditz, O.\n\n\n \n \n \n \n \n Research Centre for Environmental Information Science (RCEIS).\n \n \n \n \n\n\n \n\n\n\n In Yue, T.; Nixdorf, E.; Zhou, C.; Xu, B.; Zhao, N.; Fan, Z.; Huang, X.; Chen, C.; and Kolditz, O., editor(s), Chinese Water Systems, pages 311–334. Springer International Publishing, Cham, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Research$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{yue_research_2019,\n\taddress = {Cham},\n\ttitle = {Research {Centre} for {Environmental} {Information} {Science} ({RCEIS})},\n\tisbn = {9783319977249 9783319977256},\n\turl = {http://link.springer.com/10.1007/978-3-319-97725-6_19},\n\turldate = {2022-11-17},\n\tbooktitle = {Chinese {Water} {Systems}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Chen, Cui and Montzka, Carsten and Huth, Juliane and Kuenzer, Claudia and Kunstmann, Harald and Yue, TianXiang and Kolditz, Olaf},\n\teditor = {Yue, TianXiang and Nixdorf, Erik and Zhou, Chengzi and Xu, Bing and Zhao, Na and Fan, Zhewen and Huang, Xiaolan and Chen, Cui and Kolditz, Olaf},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-319-97725-6_19},\n\tpages = {311--334},\n}\n\n\n\n

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\n \n\n \n \n Castaldi, F.; Chabrillat, S.; and van Wesemael, B.\n\n\n \n \n \n \n \n Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 11(3): 309. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sampling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{castaldi_sampling_2019,\n\ttitle = {Sampling {Strategies} for {Soil} {Property} {Mapping} {Using} {Multispectral} {Sentinel}-2 and {Hyperspectral} {EnMAP} {Satellite} {Data}},\n\tvolume = {11},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/11/3/309},\n\tdoi = {10.3390/rs11030309},\n\tabstract = {Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7\\%), and Kennard-Stones (9.2\\%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing},\n\tauthor = {Castaldi, Fabio and Chabrillat, Sabine and van Wesemael, Bas},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {309},\n}\n\n\n\n

\n\n\n

\n Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.\n

\n\n\n

\n \n\n \n \n Castaldi, F.; Hueni, A.; Chabrillat, S.; Ward, K.; Buttafuoco, G.; Bomans, B.; Vreys, K.; Brell, M.; and van Wesemael, B.\n\n\n \n \n \n \n \n Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 147: 267–282. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{castaldi_evaluating_2019,\n\ttitle = {Evaluating the capability of the {Sentinel} 2 data for soil organic carbon prediction in croplands},\n\tvolume = {147},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271618303289},\n\tdoi = {10.1016/j.isprsjprs.2018.11.026},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Castaldi, Fabio and Hueni, Andreas and Chabrillat, Sabine and Ward, Kathrin and Buttafuoco, Gabriele and Bomans, Bart and Vreys, Kristin and Brell, Maximilian and van Wesemael, Bas},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {267--282},\n}\n\n\n\n

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\n \n\n \n \n Brunetti, G.; Šimůnek, J.; Bogena, H.; Baatz, R.; Huisman, J. A.; Dahlke, H.; and Vereecken, H.\n\n\n \n \n \n \n \n On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 18(1): 1–24. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brunetti_information_2019,\n\ttitle = {On the {Information} {Content} of {Cosmic}‐{Ray} {Neutron} {Data} in the {Inverse} {Estimation} of {Soil} {Hydraulic} {Properties}},\n\tvolume = {18},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2018.06.0123},\n\tdoi = {10.2136/vzj2018.06.0123},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Brunetti, Giuseppe and Šimůnek, Jiří and Bogena, Heye and Baatz, Roland and Huisman, Johan Alexander and Dahlke, Helen and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {1--24},\n}\n\n\n\n

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\n \n\n \n \n Brogi, C.; Huisman, J.; Pätzold, S.; von Hebel, C.; Weihermüller, L.; Kaufmann, M.; van der Kruk, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Large-scale soil mapping using multi-configuration EMI and supervised image classification.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 335: 133–148. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brogi_large-scale_2019,\n\ttitle = {Large-scale soil mapping using multi-configuration {EMI} and supervised image classification},\n\tvolume = {335},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117315641},\n\tdoi = {10.1016/j.geoderma.2018.08.001},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Geoderma},\n\tauthor = {Brogi, C. and Huisman, J.A. and Pätzold, S. and von Hebel, C. and Weihermüller, L. and Kaufmann, M.S. and van der Kruk, J. and Vereecken, H.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {133--148},\n}\n\n\n\n

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\n \n\n \n \n Brack, W.; Aissa, S. A.; Backhaus, T.; Dulio, V.; Escher, B. I.; Faust, M.; Hilscherova, K.; Hollender, J.; Hollert, H.; Müller, C.; Munthe, J.; Posthuma, L.; Seiler, T.; Slobodnik, J.; Teodorovic, I.; Tindall, A. J.; de Aragão Umbuzeiro, G.; Zhang, X.; and Altenburger, R.\n\n\n \n \n \n \n \n Effect-based methods are key. The European Collaborative Project SOLUTIONS recommends integrating effect-based methods for diagnosis and monitoring of water quality.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 10. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect-based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brack_effect-based_2019,\n\ttitle = {Effect-based methods are key. {The} {European} {Collaborative} {Project} {SOLUTIONS} recommends integrating effect-based methods for diagnosis and monitoring of water quality},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0192-2},\n\tdoi = {10.1186/s12302-019-0192-2},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Brack, Werner and Aissa, Selim Ait and Backhaus, Thomas and Dulio, Valeria and Escher, Beate I. and Faust, Michael and Hilscherova, Klara and Hollender, Juliane and Hollert, Henner and Müller, Christin and Munthe, John and Posthuma, Leo and Seiler, Thomas-Benjamin and Slobodnik, Jaroslav and Teodorovic, Ivana and Tindall, Andrew J. and de Aragão Umbuzeiro, Gisela and Zhang, Xiaowei and Altenburger, Rolf},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {10},\n}\n\n\n\n

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\n \n\n \n \n Born, J.; and Michalski, S. G.\n\n\n \n \n \n \n \n Trait expression and signatures of adaptation in response to nitrogen addition in the common wetland plant Juncus effusus.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 14(1): e0209886. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Trait$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{born_trait_2019,\n\ttitle = {Trait expression and signatures of adaptation in response to nitrogen addition in the common wetland plant {Juncus} effusus},\n\tvolume = {14},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0209886},\n\tdoi = {10.1371/journal.pone.0209886},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {PLOS ONE},\n\tauthor = {Born, Jennifer and Michalski, Stefan G.},\n\teditor = {Gomory, Dusan},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {e0209886},\n}\n\n\n\n

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\n \n\n \n \n Blöschl, G.; Bierkens, M. F.; Chambel, A.; Cudennec, C.; Destouni, G.; Fiori, A.; Kirchner, J. W.; McDonnell, J. J.; Savenije, H. H.; Sivapalan, M.; Stumpp, C.; Toth, E.; Volpi, E.; Carr, G.; Lupton, C.; Salinas, J.; Széles, B.; Viglione, A.; Aksoy, H.; Allen, S. T.; Amin, A.; Andréassian, V.; Arheimer, B.; Aryal, S. K.; Baker, V.; Bardsley, E.; Barendrecht, M. H.; Bartosova, A.; Batelaan, O.; Berghuijs, W. R.; Beven, K.; Blume, T.; Bogaard, T.; Borges de Amorim, P.; Böttcher, M. E.; Boulet, G.; Breinl, K.; Brilly, M.; Brocca, L.; Buytaert, W.; Castellarin, A.; Castelletti, A.; Chen, X.; Chen, Y.; Chen, Y.; Chifflard, P.; Claps, P.; Clark, M. P.; Collins, A. L.; Croke, B.; Dathe, A.; David, P. C.; de Barros, F. P. J.; de Rooij, G.; Di Baldassarre, G.; Driscoll, J. M.; Duethmann, D.; Dwivedi, R.; Eris, E.; Farmer, W. H.; Feiccabrino, J.; Ferguson, G.; Ferrari, E.; Ferraris, S.; Fersch, B.; Finger, D.; Foglia, L.; Fowler, K.; Gartsman, B.; Gascoin, S.; Gaume, E.; Gelfan, A.; Geris, J.; Gharari, S.; Gleeson, T.; Glendell, M.; Gonzalez Bevacqua, A.; González-Dugo, M. P.; Grimaldi, S.; Gupta, A. B.; Guse, B.; Han, D.; Hannah, D.; Harpold, A.; Haun, S.; Heal, K.; Helfricht, K.; Herrnegger, M.; Hipsey, M.; Hlaváčiková, H.; Hohmann, C.; Holko, L.; Hopkinson, C.; Hrachowitz, M.; Illangasekare, T. H.; Inam, A.; Innocente, C.; Istanbulluoglu, E.; Jarihani, B.; Kalantari, Z.; Kalvans, A.; Khanal, S.; Khatami, S.; Kiesel, J.; Kirkby, M.; Knoben, W.; Kochanek, K.; Kohnová, S.; Kolechkina, A.; Krause, S.; Kreamer, D.; Kreibich, H.; Kunstmann, H.; Lange, H.; Liberato, M. L. R.; Lindquist, E.; Link, T.; Liu, J.; Loucks, D. P.; Luce, C.; Mahé, G.; Makarieva, O.; Malard, J.; Mashtayeva, S.; Maskey, S.; Mas-Pla, J.; Mavrova-Guirguinova, M.; Mazzoleni, M.; Mernild, S.; Misstear, B. D.; Montanari, A.; Müller-Thomy, H.; Nabizadeh, A.; Nardi, F.; Neale, C.; Nesterova, N.; Nurtaev, B.; Odongo, V. O.; Panda, S.; Pande, S.; Pang, Z.; Papacharalampous, G.; Perrin, C.; Pfister, L.; Pimentel, R.; Polo, M. J.; Post, D.; Prieto Sierra, C.; Ramos, M.; Renner, M.; Reynolds, J. E.; Ridolfi, E.; Rigon, R.; Riva, M.; Robertson, D. E.; Rosso, R.; Roy, T.; Sá, J. H.; Salvadori, G.; Sandells, M.; Schaefli, B.; Schumann, A.; Scolobig, A.; Seibert, J.; Servat, E.; Shafiei, M.; Sharma, A.; Sidibe, M.; Sidle, R. C.; Skaugen, T.; Smith, H.; Spiessl, S. M.; Stein, L.; Steinsland, I.; Strasser, U.; Su, B.; Szolgay, J.; Tarboton, D.; Tauro, F.; Thirel, G.; Tian, F.; Tong, R.; Tussupova, K.; Tyralis, H.; Uijlenhoet, R.; van Beek, R.; van der Ent, R. J.; van der Ploeg, M.; Van Loon, A. F.; van Meerveld, I.; van Nooijen, R.; van Oel, P. R.; Vidal, J.; von Freyberg, J.; Vorogushyn, S.; Wachniew, P.; Wade, A. J.; Ward, P.; Westerberg, I. K.; White, C.; Wood, E. F.; Woods, R.; Xu, Z.; Yilmaz, K. K.; and Zhang, Y.\n\n\n \n \n \n \n \n Twenty-three unsolved problems in hydrology (UPH) – a community perspective.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 64(10): 1141–1158. July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Twenty-three$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bloschl_twenty-three_2019,\n\ttitle = {Twenty-three unsolved problems in hydrology ({UPH}) – a community perspective},\n\tvolume = {64},\n\tissn = {0262-6667, 2150-3435},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1620507},\n\tdoi = {10.1080/02626667.2019.1620507},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-17},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Blöschl, Günter and Bierkens, Marc F.P. and Chambel, Antonio and Cudennec, Christophe and Destouni, Georgia and Fiori, Aldo and Kirchner, James W. and McDonnell, Jeffrey J. and Savenije, Hubert H.G. and Sivapalan, Murugesu and Stumpp, Christine and Toth, Elena and Volpi, Elena and Carr, Gemma and Lupton, Claire and Salinas, Josè and Széles, Borbála and Viglione, Alberto and Aksoy, Hafzullah and Allen, Scott T. and Amin, Anam and Andréassian, Vazken and Arheimer, Berit and Aryal, Santosh K. and Baker, Victor and Bardsley, Earl and Barendrecht, Marlies H. and Bartosova, Alena and Batelaan, Okke and Berghuijs, Wouter R. and Beven, Keith and Blume, Theresa and Bogaard, Thom and Borges de Amorim, Pablo and Böttcher, Michael E. and Boulet, Gilles and Breinl, Korbinian and Brilly, Mitja and Brocca, Luca and Buytaert, Wouter and Castellarin, Attilio and Castelletti, Andrea and Chen, Xiaohong and Chen, Yangbo and Chen, Yuanfang and Chifflard, Peter and Claps, Pierluigi and Clark, Martyn P. and Collins, Adrian L. and Croke, Barry and Dathe, Annette and David, Paula C. and de Barros, Felipe P. J. and de Rooij, Gerrit and Di Baldassarre, Giuliano and Driscoll, Jessica M. and Duethmann, Doris and Dwivedi, Ravindra and Eris, Ebru and Farmer, William H. and Feiccabrino, James and Ferguson, Grant and Ferrari, Ennio and Ferraris, Stefano and Fersch, Benjamin and Finger, David and Foglia, Laura and Fowler, Keirnan and Gartsman, Boris and Gascoin, Simon and Gaume, Eric and Gelfan, Alexander and Geris, Josie and Gharari, Shervan and Gleeson, Tom and Glendell, Miriam and Gonzalez Bevacqua, Alena and González-Dugo, María P. and Grimaldi, Salvatore and Gupta, A. B. and Guse, Björn and Han, Dawei and Hannah, David and Harpold, Adrian and Haun, Stefan and Heal, Kate and Helfricht, Kay and Herrnegger, Mathew and Hipsey, Matthew and Hlaváčiková, Hana and Hohmann, Clara and Holko, Ladislav and Hopkinson, Christopher and Hrachowitz, Markus and Illangasekare, Tissa H. and Inam, Azhar and Innocente, Camyla and Istanbulluoglu, Erkan and Jarihani, Ben and Kalantari, Zahra and Kalvans, Andis and Khanal, Sonu and Khatami, Sina and Kiesel, Jens and Kirkby, Mike and Knoben, Wouter and Kochanek, Krzysztof and Kohnová, Silvia and Kolechkina, Alla and Krause, Stefan and Kreamer, David and Kreibich, Heidi and Kunstmann, Harald and Lange, Holger and Liberato, Margarida L. R. and Lindquist, Eric and Link, Timothy and Liu, Junguo and Loucks, Daniel Peter and Luce, Charles and Mahé, Gil and Makarieva, Olga and Malard, Julien and Mashtayeva, Shamshagul and Maskey, Shreedhar and Mas-Pla, Josep and Mavrova-Guirguinova, Maria and Mazzoleni, Maurizio and Mernild, Sebastian and Misstear, Bruce Dudley and Montanari, Alberto and Müller-Thomy, Hannes and Nabizadeh, Alireza and Nardi, Fernando and Neale, Christopher and Nesterova, Nataliia and Nurtaev, Bakhram and Odongo, Vincent O. and Panda, Subhabrata and Pande, Saket and Pang, Zhonghe and Papacharalampous, Georgia and Perrin, Charles and Pfister, Laurent and Pimentel, Rafael and Polo, María J. and Post, David and Prieto Sierra, Cristina and Ramos, Maria-Helena and Renner, Maik and Reynolds, José Eduardo and Ridolfi, Elena and Rigon, Riccardo and Riva, Monica and Robertson, David E. and Rosso, Renzo and Roy, Tirthankar and Sá, João H.M. and Salvadori, Gianfausto and Sandells, Mel and Schaefli, Bettina and Schumann, Andreas and Scolobig, Anna and Seibert, Jan and Servat, Eric and Shafiei, Mojtaba and Sharma, Ashish and Sidibe, Moussa and Sidle, Roy C. and Skaugen, Thomas and Smith, Hugh and Spiessl, Sabine M. and Stein, Lina and Steinsland, Ingelin and Strasser, Ulrich and Su, Bob and Szolgay, Jan and Tarboton, David and Tauro, Flavia and Thirel, Guillaume and Tian, Fuqiang and Tong, Rui and Tussupova, Kamshat and Tyralis, Hristos and Uijlenhoet, Remko and van Beek, Rens and van der Ent, Ruud J. and van der Ploeg, Martine and Van Loon, Anne F. and van Meerveld, Ilja and van Nooijen, Ronald and van Oel, Pieter R. and Vidal, Jean-Philippe and von Freyberg, Jana and Vorogushyn, Sergiy and Wachniew, Przemyslaw and Wade, Andrew J. and Ward, Philip and Westerberg, Ida K. and White, Christopher and Wood, Eric F. and Woods, Ross and Xu, Zongxue and Yilmaz, Koray K. and Zhang, Yongqiang},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {1141--1158},\n}\n\n\n\n

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\n \n\n \n \n Babaeian, E.; Sadeghi, M.; Jones, S. B.; Montzka, C.; Vereecken, H.; and Tuller, M.\n\n\n \n \n \n \n \n Ground, Proximal, and Satellite Remote Sensing of Soil Moisture.\n \n \n \n \n\n\n \n\n\n\n Reviews of Geophysics, 57(2): 530–616. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Ground,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{babaeian_ground_2019,\n\ttitle = {Ground, {Proximal}, and {Satellite} {Remote} {Sensing} of {Soil} {Moisture}},\n\tvolume = {57},\n\tissn = {8755-1209, 1944-9208},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2018RG000618},\n\tdoi = {10.1029/2018RG000618},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Reviews of Geophysics},\n\tauthor = {Babaeian, Ebrahim and Sadeghi, Morteza and Jones, Scott B. and Montzka, Carsten and Vereecken, Harry and Tuller, Markus},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {530--616},\n}\n\n\n\n

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\n \n\n \n \n Altdorff, D.; Botschek, J.; Honds, M.; van der Kruk, J.; and Vereecken, H.\n\n\n \n \n \n \n \n In Situ Detection of Tree Root Systems under Heterogeneous Anthropogenic Soil Conditions Using Ground Penetrating Radar.\n \n \n \n \n\n\n \n\n\n\n Journal of Infrastructure Systems, 25(3): 05019008. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"In$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{altdorff_situ_2019,\n\ttitle = {In {Situ} {Detection} of {Tree} {Root} {Systems} under {Heterogeneous} {Anthropogenic} {Soil} {Conditions} {Using} {Ground} {Penetrating} {Radar}},\n\tvolume = {25},\n\tissn = {1076-0342, 1943-555X},\n\turl = {https://ascelibrary.org/doi/10.1061/%28ASCE%29IS.1943-555X.0000501},\n\tdoi = {10.1061/(ASCE)IS.1943-555X.0000501},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Infrastructure Systems},\n\tauthor = {Altdorff, D. and Botschek, J. and Honds, M. and van der Kruk, J. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {05019008},\n}\n\n\n\n

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\n \n\n \n \n Ehrhardt, S.; Kumar, R.; Fleckenstein, J. H.; Attinger, S.; and Musolff, A.\n\n\n \n \n \n \n \n Trajectories of nitrate input and output in three nested catchments along a land use gradient.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 23(9): 3503–3524. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Trajectories$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ehrhardt_trajectories_2019,\n\ttitle = {Trajectories of nitrate input and output in three nested catchments along a land use gradient},\n\tvolume = {23},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/23/3503/2019/},\n\tdoi = {10.5194/hess-23-3503-2019},\n\tabstract = {Abstract. Increased anthropogenic inputs of nitrogen (N) to the\nbiosphere during the last few decades have resulted in increased groundwater and\nsurface water concentrations of N (primarily as nitrate), posing a global\nproblem. Although measures have been implemented to reduce N inputs, they\nhave not always led to decreasing riverine nitrate concentrations and loads.\nThis limited response to the measures can either be caused by the\naccumulation of organic N in the soils (biogeochemical legacy) – or by long\ntravel times (TTs) of inorganic N to the streams (hydrological legacy).\nHere, we compare atmospheric and agricultural N inputs with long-term\nobservations (1970–2016) of riverine nitrate concentrations and loads in a\ncentral German mesoscale catchment with three nested subcatchments\nof increasing agricultural land use. Based on a data-driven\napproach, we assess jointly the N budget and the effective TTs of N through\nthe soil and groundwater compartments. In combination with long-term\ntrajectories of the C–Q relationships, we evaluate the potential for and\nthe characteristics of an N legacy. We show that in the 40-year-long observation period, the catchment (270 km2) with 60 \\% agricultural area received an N input of\n53 437 t, while it exported 6592 t, indicating an overall retention of\n88 \\%. Removal of N by denitrification could not sufficiently explain this\nimbalance. Log-normal travel time distributions (TTDs) that link the N input\nhistory to the riverine export differed seasonally, with modes spanning\n7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the\nC–Q relationships were noticed over time that could be attributed to strong\nchanges in N inputs resulting from agricultural intensification before 1989,\nthe break-down of East German agriculture after 1989 and the\nseasonal differences in TTs. A chemostatic export regime of nitrate was only\nfound after several years of stabilized N inputs. The changes in C–Q\nrelationships suggest a dominance of the hydrological N legacy over the\nbiogeochemical N fixation in the soils, as we expected to observe a stronger\nand even increasing dampening of the riverine N concentrations after\nsustained high N inputs. Our analyses reveal an imbalance between N input\nand output, long time-lags and a lack of significant denitrification in the\ncatchment. All these suggest that catchment management needs to address\nboth a longer-term reduction of N inputs and shorter-term mitigation of\ntoday's high N loads. The latter may be covered by interventions triggering\ndenitrification, such as hedgerows around agricultural fields, riparian\nbuffers zones or constructed wetlands. Further joint analyses of N budgets\nand TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Ehrhardt, Sophie and Kumar, Rohini and Fleckenstein, Jan H. and Attinger, Sabine and Musolff, Andreas},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {3503--3524},\n}\n\n\n\n

\n\n\n

\n Abstract. Increased anthropogenic inputs of nitrogen (N) to the biosphere during the last few decades have resulted in increased groundwater and surface water concentrations of N (primarily as nitrate), posing a global problem. Although measures have been implemented to reduce N inputs, they have not always led to decreasing riverine nitrate concentrations and loads. This limited response to the measures can either be caused by the accumulation of organic N in the soils (biogeochemical legacy) – or by long travel times (TTs) of inorganic N to the streams (hydrological legacy). Here, we compare atmospheric and agricultural N inputs with long-term observations (1970–2016) of riverine nitrate concentrations and loads in a central German mesoscale catchment with three nested subcatchments of increasing agricultural land use. Based on a data-driven approach, we assess jointly the N budget and the effective TTs of N through the soil and groundwater compartments. In combination with long-term trajectories of the C–Q relationships, we evaluate the potential for and the characteristics of an N legacy. We show that in the 40-year-long observation period, the catchment (270 km2) with 60 % agricultural area received an N input of 53 437 t, while it exported 6592 t, indicating an overall retention of 88 %. Removal of N by denitrification could not sufficiently explain this imbalance. Log-normal travel time distributions (TTDs) that link the N input history to the riverine export differed seasonally, with modes spanning 7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the C–Q relationships were noticed over time that could be attributed to strong changes in N inputs resulting from agricultural intensification before 1989, the break-down of East German agriculture after 1989 and the seasonal differences in TTs. A chemostatic export regime of nitrate was only found after several years of stabilized N inputs. The changes in C–Q relationships suggest a dominance of the hydrological N legacy over the biogeochemical N fixation in the soils, as we expected to observe a stronger and even increasing dampening of the riverine N concentrations after sustained high N inputs. Our analyses reveal an imbalance between N input and output, long time-lags and a lack of significant denitrification in the catchment. All these suggest that catchment management needs to address both a longer-term reduction of N inputs and shorter-term mitigation of today's high N loads. The latter may be covered by interventions triggering denitrification, such as hedgerows around agricultural fields, riparian buffers zones or constructed wetlands. Further joint analyses of N budgets and TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.\n

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\n \n\n \n \n Wiekenkamp, I.; Huisman, J. A.; Bogena, H. R.; and Vereecken, H.\n\n\n \n \n \n \n \n Effects of Deforestation on Water Flow in the Vadose Zone.\n \n \n \n \n\n\n \n\n\n\n Water, 12(1): 35. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wiekenkamp_effects_2019,\n\ttitle = {Effects of {Deforestation} on {Water} {Flow} in the {Vadose} {Zone}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/1/35},\n\tdoi = {10.3390/w12010035},\n\tabstract = {The effects of land use change on the occurrence and frequency of preferential flow (fast water flow through a small fraction of the pore space) and piston flow (slower water flow through a large fraction of the pore space) are still not fully understood. In this study, we used a five year high resolution soil moisture monitoring dataset in combination with a response time analysis to identify factors that control preferential and piston flow before and after partial deforestation in a small headwater catchment. The sensor response times at 5, 20 and 50 cm depths were classified into one of four classes: (1) non-sequential preferential flow, (2) velocity based preferential flow, (3) sequential (piston) flow, and (4) no response. The results of this analysis showed that partial deforestation increased sequential flow occurrence and decreased the occurrence of no flow in the deforested area. Similar precipitation conditions (total precipitation) after deforestation caused more sequential flow in the deforested area, which was attributed to higher antecedent moisture conditions and the lack of interception. At the same time, an increase in preferential flow occurrence was also observed for events with identical total precipitation. However, as the events in the treatment period (after deforestation) generally had lower total, maximum, and mean precipitation, this effect was not observed in the overall occurrence of preferential flow. The results of this analysis demonstrate that the combination of a sensor response time analysis and a soil moisture dataset that includes pre- and post-deforestation conditions can offer new insights in preferential and sequential flow conditions after land use change.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Wiekenkamp, Inge and Huisman, Johan Alexander and Bogena, Heye Reemt and Vereecken, Harry},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {35},\n}\n\n\n\n

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\n \n\n \n \n Shrestha, P.; and Simmer, C.\n\n\n \n \n \n \n \n Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models.\n \n \n \n \n\n\n \n\n\n\n Water, 12(1): 46. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{shrestha_modeled_2019,\n\ttitle = {Modeled {Land} {Atmosphere} {Coupling} {Response} to {Soil} {Moisture} {Changes} with {Different} {Generations} of {Land} {Surface} {Models}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/1/46},\n\tdoi = {10.3390/w12010046},\n\tabstract = {An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Shrestha, Prabhakar and Simmer, Clemens},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {46},\n}\n\n\n\n

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\n \n 2018\n \n \n (142)\n \n \n

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\n \n\n \n \n Soltani, M.; Mauder, M.; Laux, P.; and Kunstmann, H.\n\n\n \n \n \n \n \n Turbulent flux variability and energy balance closure in the TERENO prealpine observatory: a hydrometeorological data analysis.\n \n \n \n \n\n\n \n\n\n\n Theoretical and Applied Climatology, 133(3-4): 937–956. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Turbulent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{soltani_turbulent_2018,\n\ttitle = {Turbulent flux variability and energy balance closure in the {TERENO} prealpine observatory: a hydrometeorological data analysis},\n\tvolume = {133},\n\tissn = {0177-798X, 1434-4483},\n\tshorttitle = {Turbulent flux variability and energy balance closure in the {TERENO} prealpine observatory},\n\turl = {http://link.springer.com/10.1007/s00704-017-2235-1},\n\tdoi = {10.1007/s00704-017-2235-1},\n\tlanguage = {en},\n\tnumber = {3-4},\n\turldate = {2022-11-16},\n\tjournal = {Theoretical and Applied Climatology},\n\tauthor = {Soltani, Mohsen and Mauder, Matthias and Laux, Patrick and Kunstmann, Harald},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {937--956},\n}\n\n\n\n

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\n \n\n \n \n Rink, K.; Chen, C.; Bilke, L.; Liao, Z.; Rinke, K.; Frassl, M.; Yue, T.; and Kolditz, O.\n\n\n \n \n \n \n \n Virtual geographic environments for water pollution control.\n \n \n \n \n\n\n \n\n\n\n International Journal of Digital Earth, 11(4): 397–407. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Virtual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_virtual_2018,\n\ttitle = {Virtual geographic environments for water pollution control},\n\tvolume = {11},\n\tissn = {1753-8947, 1753-8955},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/17538947.2016.1265016},\n\tdoi = {10.1080/17538947.2016.1265016},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {International Journal of Digital Earth},\n\tauthor = {Rink, Karsten and Chen, Cui and Bilke, Lars and Liao, Zhenliang and Rinke, Karsten and Frassl, Marieke and Yue, Tianxiang and Kolditz, Olaf},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {397--407},\n}\n\n\n\n

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\n \n\n \n \n Balanzategui, D.; Knorr, A.; Heussner, K.; Wazny, T.; Beck, W.; Słowiński, M.; Helle, G.; Buras, A.; Wilmking, M.; Van Der Maaten, E.; Scharnweber, T.; Dorado-Liñán, I.; and Heinrich, I.\n\n\n \n \n \n \n \n An 810-year history of cold season temperature variability for northern Poland.\n \n \n \n \n\n\n \n\n\n\n Boreas, 47(2): 443–453. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{balanzategui_810-year_2018,\n\ttitle = {An 810-year history of cold season temperature variability for northern {Poland}},\n\tvolume = {47},\n\tissn = {03009483},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12274},\n\tdoi = {10.1111/bor.12274},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Boreas},\n\tauthor = {Balanzategui, Daniel and Knorr, Antje and Heussner, Karl-Uwe and Wazny, Tomasz and Beck, Wolfgang and Słowiński, Michał and Helle, Gerhard and Buras, Allan and Wilmking, Martin and Van Der Maaten, Ernst and Scharnweber, Tobias and Dorado-Liñán, Isabel and Heinrich, Ingo},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {443--453},\n}\n\n\n\n

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\n \n\n \n \n Sabbatini, S.; Mammarella, I.; Arriga, N.; Fratini, G.; Graf, A.; Hörtnagl, L.; Ibrom, A.; Longdoz, B.; Mauder, M.; Merbold, L.; Metzger, S.; Montagnani, L.; Pitacco, A.; Rebmann, C.; Sedlák, P.; Šigut, L.; Vitale, D.; and Papale, D.\n\n\n \n \n \n \n \n Eddy covariance raw data processing for CO$_{\\textrm{2}}$ and energy fluxes calculation at ICOS ecosystem stations.\n \n \n \n \n\n\n \n\n\n\n International Agrophysics, 32(4): 495–515. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Eddy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sabbatini_eddy_2018,\n\ttitle = {Eddy covariance raw data processing for {CO}$_{\\textrm{2}}$ and energy fluxes calculation at {ICOS} ecosystem stations},\n\tvolume = {32},\n\tissn = {2300-8725},\n\turl = {http://archive.sciendo.com/INTAG/intag.2017.32.issue-4/intag-2017-0043/intag-2017-0043.pdf},\n\tdoi = {10.1515/intag-2017-0043},\n\tabstract = {Abstract \n             \n              The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO \n              2 \n              , water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in \n              e.g \n              . ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO \n              2 \n              and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods.},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {International Agrophysics},\n\tauthor = {Sabbatini, Simone and Mammarella, Ivan and Arriga, Nicola and Fratini, Gerardo and Graf, Alexander and Hörtnagl, Lukas and Ibrom, Andreas and Longdoz, Bernard and Mauder, Matthias and Merbold, Lutz and Metzger, Stefan and Montagnani, Leonardo and Pitacco, Andrea and Rebmann, Corinna and Sedlák, Pavel and Šigut, Ladislav and Vitale, Domenico and Papale, Dario},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {495--515},\n}\n\n\n\n

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\n \n\n \n \n Qiu, C.; Zhu, D.; Ciais, P.; Guenet, B.; Krinner, G.; Peng, S.; Aurela, M.; Bernhofer, C.; Brümmer, C.; Bret-Harte, S.; Chu, H.; Chen, J.; Desai, A. R.; Dušek, J.; Euskirchen, E. S.; Fortuniak, K.; Flanagan, L. B.; Friborg, T.; Grygoruk, M.; Gogo, S.; Grünwald, T.; Hansen, B. U.; Holl, D.; Humphreys, E.; Hurkuck, M.; Kiely, G.; Klatt, J.; Kutzbach, L.; Largeron, C.; Laggoun-Défarge, F.; Lund, M.; Lafleur, P. M.; Li, X.; Mammarella, I.; Merbold, L.; Nilsson, M. B.; Olejnik, J.; Ottosson-Löfvenius, M.; Oechel, W.; Parmentier, F. W.; Peichl, M.; Pirk, N.; Peltola, O.; Pawlak, W.; Rasse, D.; Rinne, J.; Shaver, G.; Schmid, H. P.; Sottocornola, M.; Steinbrecher, R.; Sachs, T.; Urbaniak, M.; Zona, D.; and Ziemblinska, K.\n\n\n \n \n \n \n \n ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO$_{\\textrm{2}}$, water, and energy fluxes on daily to annual scales.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 11(2): 497–519. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"ORCHIDEE-PEAT$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{qiu_orchidee-peat_2018,\n\ttitle = {{ORCHIDEE}-{PEAT} (revision 4596), a model for northern peatland {CO}$_{\\textrm{2}}$, water, and energy fluxes on daily to annual scales},\n\tvolume = {11},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/11/497/2018/},\n\tdoi = {10.5194/gmd-11-497-2018},\n\tabstract = {Abstract. Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r2 =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r2 =  0.42, MEF  =  0.14) and and net ecosystem CO2 exchange (NEE, r2 =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2 {\\textless} 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized Vcmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average Vcmax value.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Qiu, Chunjing and Zhu, Dan and Ciais, Philippe and Guenet, Bertrand and Krinner, Gerhard and Peng, Shushi and Aurela, Mika and Bernhofer, Christian and Brümmer, Christian and Bret-Harte, Syndonia and Chu, Housen and Chen, Jiquan and Desai, Ankur R. and Dušek, Jiří and Euskirchen, Eugénie S. and Fortuniak, Krzysztof and Flanagan, Lawrence B. and Friborg, Thomas and Grygoruk, Mateusz and Gogo, Sébastien and Grünwald, Thomas and Hansen, Birger U. and Holl, David and Humphreys, Elyn and Hurkuck, Miriam and Kiely, Gerard and Klatt, Janina and Kutzbach, Lars and Largeron, Chloé and Laggoun-Défarge, Fatima and Lund, Magnus and Lafleur, Peter M. and Li, Xuefei and Mammarella, Ivan and Merbold, Lutz and Nilsson, Mats B. and Olejnik, Janusz and Ottosson-Löfvenius, Mikaell and Oechel, Walter and Parmentier, Frans-Jan W. and Peichl, Matthias and Pirk, Norbert and Peltola, Olli and Pawlak, Włodzimierz and Rasse, Daniel and Rinne, Janne and Shaver, Gaius and Schmid, Hans Peter and Sottocornola, Matteo and Steinbrecher, Rainer and Sachs, Torsten and Urbaniak, Marek and Zona, Donatella and Ziemblinska, Klaudia},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {497--519},\n}\n\n\n\n

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\n Abstract. Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r2 =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r2 =  0.42, MEF  =  0.14) and and net ecosystem CO2 exchange (NEE, r2 =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2 \\textless 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized Vcmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average Vcmax value.\n

\n\n\n

\n \n\n \n \n Ehrhardt, S.; Kumar, R.; Fleckenstein, J. H.; Attinger, S.; and Musolff, A.\n\n\n \n \n \n \n \n Decadal trajectories of nitrate input and output in three nested catchments along a land use gradient.\n \n \n \n \n\n\n \n\n\n\n Technical Report Catchment hydrology/Theory development, October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Decadal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{ehrhardt_decadal_2018,\n\ttype = {preprint},\n\ttitle = {Decadal trajectories of nitrate input and output in three nested catchments along a land use gradient},\n\turl = {https://hess.copernicus.org/preprints/hess-2018-475/hess-2018-475.pdf},\n\tabstract = {Abstract. Increased anthropogenic inputs of nitrogen (N) to the biosphere during the last decades have resulted in increased groundwater and surface water concentrations of N (primarily as nitrate) posing a global problem. Although measures have been implemented to reduce N-inputs especially from agricultural sources, they have not always led to decreasing riverine nitrate concentrations and loads. The limited response to the measures can either be caused by the accumulation of slowly mineralized organic N in the soils acting as a biogeochemical legacy or by long travel times (TTs) of inorganic N to the streams forming a hydrological legacy. Both types of legacy are hard to distinguish from the TTs and N budgets alone. Here we jointly analyze atmospheric and agricultural N inputs with long-term observations of nitrate concentrations and discharge in a mesoscale catchment in Central Germany. For three nested sub-catchments with increasing agricultural land use, we assess the catchment scale N budget, the effective TT of N. In combination with long-term trajectories of C-Q relationships we finally evaluate the potential for and the characteristics of an N-legacy. We show that in the 42-year-long observation period, the catchment received an N-input of 42 758 t, of which 97 \\% derived from agricultural sources. The riverine N-export sums up to 6 592 t indicating that the catchment retained 85 \\% of the N-input. Removal of N by denitrification could not fully explain this imbalance. Log-normal travel time distributions (TTD) for N that link the input history to the riverine export differed seasonally, with modes spanning 8–17 years. Under low-flow conditions, TTs were found to be systematically longer than during high discharges. Systematic shifts in the C-Q relationships could be attributed to significant changes in N-inputs resulting from agricultural intensification and the break-down of the East German agriculture after 1989 and to the longer travel times of nitrate during low flows compared to high flows. A chemostatic export regime of nitrate was only found after several years of stabilized N-inputs. We explain these observations by the vertical migration of the N-input and the seasonally changing contribution of subsurface flow paths with differing ages and thus differing N-loads. The changes in C-Q relationships suggest a dominance of hydrological N-legacy rather than a biogeochemical N-fixation in the soils, which should result in a stronger and even increasing dampening of riverine N-concentrations after sustained high N-inputs. Despite the strong N-legacy, a chemostatic nitrate export regime is not necessarily a persistent endpoint of intense agricultural land use, but rather depends on a steady replenishment of the mass of N propagating through the catchments subsurface. The input-output imbalance, the long time-lags and the lack of significant denitrification in the catchment let us conclude that catchment management needs to address both, a longer-term reduction of N-inputs and shorter-term mitigation of today’s high N-loads.},\n\turldate = {2022-11-17},\n\tinstitution = {Catchment hydrology/Theory development},\n\tauthor = {Ehrhardt, Sophie and Kumar, Rohini and Fleckenstein, Jan H. and Attinger, Sabine and Musolff, Andreas},\n\tmonth = oct,\n\tyear = {2018},\n\tdoi = {10.5194/hess-2018-475},\n}\n\n\n\n

\n\n\n

\n Abstract. Increased anthropogenic inputs of nitrogen (N) to the biosphere during the last decades have resulted in increased groundwater and surface water concentrations of N (primarily as nitrate) posing a global problem. Although measures have been implemented to reduce N-inputs especially from agricultural sources, they have not always led to decreasing riverine nitrate concentrations and loads. The limited response to the measures can either be caused by the accumulation of slowly mineralized organic N in the soils acting as a biogeochemical legacy or by long travel times (TTs) of inorganic N to the streams forming a hydrological legacy. Both types of legacy are hard to distinguish from the TTs and N budgets alone. Here we jointly analyze atmospheric and agricultural N inputs with long-term observations of nitrate concentrations and discharge in a mesoscale catchment in Central Germany. For three nested sub-catchments with increasing agricultural land use, we assess the catchment scale N budget, the effective TT of N. In combination with long-term trajectories of C-Q relationships we finally evaluate the potential for and the characteristics of an N-legacy. We show that in the 42-year-long observation period, the catchment received an N-input of 42 758 t, of which 97 % derived from agricultural sources. The riverine N-export sums up to 6 592 t indicating that the catchment retained 85 % of the N-input. Removal of N by denitrification could not fully explain this imbalance. Log-normal travel time distributions (TTD) for N that link the input history to the riverine export differed seasonally, with modes spanning 8–17 years. Under low-flow conditions, TTs were found to be systematically longer than during high discharges. Systematic shifts in the C-Q relationships could be attributed to significant changes in N-inputs resulting from agricultural intensification and the break-down of the East German agriculture after 1989 and to the longer travel times of nitrate during low flows compared to high flows. A chemostatic export regime of nitrate was only found after several years of stabilized N-inputs. We explain these observations by the vertical migration of the N-input and the seasonally changing contribution of subsurface flow paths with differing ages and thus differing N-loads. The changes in C-Q relationships suggest a dominance of hydrological N-legacy rather than a biogeochemical N-fixation in the soils, which should result in a stronger and even increasing dampening of riverine N-concentrations after sustained high N-inputs. Despite the strong N-legacy, a chemostatic nitrate export regime is not necessarily a persistent endpoint of intense agricultural land use, but rather depends on a steady replenishment of the mass of N propagating through the catchments subsurface. The input-output imbalance, the long time-lags and the lack of significant denitrification in the catchment let us conclude that catchment management needs to address both, a longer-term reduction of N-inputs and shorter-term mitigation of today’s high N-loads.\n

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\n \n\n \n \n Zhang, Y.; Xiao, X.; Zhang, Y.; Wolf, S.; Zhou, S.; Joiner, J.; Guanter, L.; Verma, M.; Sun, Y.; Yang, X.; Paul-Limoges, E.; Gough, C. M.; Wohlfahrt, G.; Gioli, B.; van der Tol, C.; Yann, N.; Lund, M.; and de Grandcourt, A.\n\n\n \n \n \n \n \n On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 205: 276–289. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_relationship_2018,\n\ttitle = {On the relationship between sub-daily instantaneous and daily total gross primary production: {Implications} for interpreting satellite-based {SIF} retrievals},\n\tvolume = {205},\n\tissn = {00344257},\n\tshorttitle = {On the relationship between sub-daily instantaneous and daily total gross primary production},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425717305801},\n\tdoi = {10.1016/j.rse.2017.12.009},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Zhang, Yao and Xiao, Xiangming and Zhang, Yongguang and Wolf, Sebastian and Zhou, Sha and Joiner, Joanna and Guanter, Luis and Verma, Manish and Sun, Ying and Yang, Xi and Paul-Limoges, Eugénie and Gough, Christopher M. and Wohlfahrt, Georg and Gioli, Beniamino and van der Tol, Christiaan and Yann, Nouvellon and Lund, Magnus and de Grandcourt, Agnès},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {276--289},\n}\n\n\n\n

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\n \n\n \n \n Zhang, Y.; Xiao, X.; Wolf, S.; Wu, J.; Wu, X.; Gioli, B.; Wohlfahrt, G.; Cescatti, A.; van der Tol, C.; Zhou, S.; Gough, C. M.; Gentine, P.; Zhang, Y.; Steinbrecher, R.; and Ardö, J.\n\n\n \n \n \n \n \n Spatio‐Temporal Convergence of Maximum Daily Light‐Use Efficiency Based on Radiation Absorption by Canopy Chlorophyll.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 45(8): 3508–3519. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio‐Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_spatiotemporal_2018,\n\ttitle = {Spatio‐{Temporal} {Convergence} of {Maximum} {Daily} {Light}‐{Use} {Efficiency} {Based} on {Radiation} {Absorption} by {Canopy} {Chlorophyll}},\n\tvolume = {45},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2017GL076354},\n\tdoi = {10.1029/2017GL076354},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-16},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Zhang, Yao and Xiao, Xiangming and Wolf, Sebastian and Wu, Jin and Wu, Xiaocui and Gioli, Beniamino and Wohlfahrt, Georg and Cescatti, Alessandro and van der Tol, Christiaan and Zhou, Sha and Gough, Christopher M. and Gentine, Pierre and Zhang, Yongguang and Steinbrecher, Rainer and Ardö, Jonas},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {3508--3519},\n}\n\n\n\n

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\n \n\n \n \n Zhang, H.; Kurtz, W.; Kollet, S.; Vereecken, H.; and Franssen, H. H.\n\n\n \n \n \n \n \n Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 111: 224–238. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zhang_comparison_2018,\n\ttitle = {Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model},\n\tvolume = {111},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170817304888},\n\tdoi = {10.1016/j.advwatres.2017.11.003},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Zhang, Hongjuan and Kurtz, Wolfgang and Kollet, Stefan and Vereecken, Harry and Franssen, Harrie-Jan Hendricks},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {224--238},\n}\n\n\n\n

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\n \n\n \n \n Yang, X.; Jomaa, S.; Zink, M.; Fleckenstein, J. H.; Borchardt, D.; and Rode, M.\n\n\n \n \n \n \n \n A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(8): 5856–5877. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_new_2018,\n\ttitle = {A {New} {Fully} {Distributed} {Model} of {Nitrate} {Transport} and {Removal} at {Catchment} {Scale}},\n\tvolume = {54},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2017WR022380},\n\tdoi = {10.1029/2017WR022380},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Yang, Xiaoqiang and Jomaa, Seifeddine and Zink, Matthias and Fleckenstein, Jan H. and Borchardt, Dietrich and Rode, Michael},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {5856--5877},\n}\n\n\n\n

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\n \n\n \n \n Yang, J.; Heidbüchel, I.; Musolff, A.; Reinstorf, F.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Exploring the Dynamics of Transit Times and Subsurface Mixing in a Small Agricultural Catchment.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(3): 2317–2335. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Exploring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{yang_exploring_2018,\n\ttitle = {Exploring the {Dynamics} of {Transit} {Times} and {Subsurface} {Mixing} in a {Small} {Agricultural} {Catchment}},\n\tvolume = {54},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/2017WR021896},\n\tdoi = {10.1002/2017WR021896},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Yang, Jie and Heidbüchel, Ingo and Musolff, Andreas and Reinstorf, Frido and Fleckenstein, Jan H.},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {2317--2335},\n}\n\n\n\n

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\n \n\n \n \n Wu, X.; Xiao, X.; Zhang, Y.; He, W.; Wolf, S.; Chen, J.; He, M.; Gough, C. M.; Qin, Y.; Zhou, Y.; Doughty, R.; and Blanken, P. D.\n\n\n \n \n \n \n \n Spatiotemporal Consistency of Four Gross Primary Production Products and Solar‐Induced Chlorophyll Fluorescence in Response to Climate Extremes Across CONUS in 2012.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 123(10): 3140–3161. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_spatiotemporal_2018,\n\ttitle = {Spatiotemporal {Consistency} of {Four} {Gross} {Primary} {Production} {Products} and {Solar}‐{Induced} {Chlorophyll} {Fluorescence} in {Response} to {Climate} {Extremes} {Across} {CONUS} in 2012},\n\tvolume = {123},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2018JG004484},\n\tdoi = {10.1029/2018JG004484},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-16},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Wu, Xiaocui and Xiao, Xiangming and Zhang, Yao and He, Wei and Wolf, Sebastian and Chen, Jiquan and He, Mingzhu and Gough, Christopher M. and Qin, Yuanwei and Zhou, Yanlian and Doughty, Russell and Blanken, Peter D.},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {3140--3161},\n}\n\n\n\n

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\n \n\n \n \n Wilken, F.; Baur, M.; Sommer, M.; Deumlich, D.; Bens, O.; and Fiener, P.\n\n\n \n \n \n \n \n Uncertainties in rainfall kinetic energy-intensity relations for soil erosion modelling.\n \n \n \n \n\n\n \n\n\n\n CATENA, 171: 234–244. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainties$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wilken_uncertainties_2018,\n\ttitle = {Uncertainties in rainfall kinetic energy-intensity relations for soil erosion modelling},\n\tvolume = {171},\n\tissn = {03418162},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S034181621830273X},\n\tdoi = {10.1016/j.catena.2018.07.002},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {CATENA},\n\tauthor = {Wilken, Florian and Baur, Martin and Sommer, Michael and Deumlich, Detlef and Bens, Oliver and Fiener, Peter},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {234--244},\n}\n\n\n\n

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\n \n\n \n \n Wieneke, S.; Burkart, A.; Cendrero-Mateo, M.; Julitta, T.; Rossini, M.; Schickling, A.; Schmidt, M.; and Rascher, U.\n\n\n \n \n \n \n \n Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 219: 247–258. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wieneke_linking_2018,\n\ttitle = {Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales},\n\tvolume = {219},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425718304759},\n\tdoi = {10.1016/j.rse.2018.10.019},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Wieneke, S. and Burkart, A. and Cendrero-Mateo, M.P. and Julitta, T. and Rossini, M. and Schickling, A. and Schmidt, M. and Rascher, U.},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {247--258},\n}\n\n\n\n

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\n \n\n \n \n Wentzky, V. C.; Tittel, J.; Jäger, C. G.; and Rinke, K.\n\n\n \n \n \n \n \n Mechanisms preventing a decrease in phytoplankton biomass after phosphorus reductions in a German drinking water reservoir-results from more than 50 years of observation.\n \n \n \n \n\n\n \n\n\n\n Freshwater Biology, 63(9): 1063–1076. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Mechanisms$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wentzky_mechanisms_2018,\n\ttitle = {Mechanisms preventing a decrease in phytoplankton biomass after phosphorus reductions in a {German} drinking water reservoir-results from more than 50 years of observation},\n\tvolume = {63},\n\tissn = {00465070},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/fwb.13116},\n\tdoi = {10.1111/fwb.13116},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-16},\n\tjournal = {Freshwater Biology},\n\tauthor = {Wentzky, Valerie Carolin and Tittel, Jörg and Jäger, Christoph Gerald and Rinke, Karsten},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {1063--1076},\n}\n\n\n\n

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\n \n\n \n \n Wen, X.; Unger, V.; Jurasinski, G.; Koebsch, F.; Horn, F.; Rehder, G.; Sachs, T.; Zak, D.; Lischeid, G.; Knorr, K.; Böttcher, M.; Winkel, M.; and Liebner, S.\n\n\n \n \n \n \n \n Predominance of methanogens over methanotrophs contributes to high methane emissions in rewetted fens.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Environmental Microbiology, April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Predominance$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{wen_predominance_2018,\n\ttype = {preprint},\n\ttitle = {Predominance of methanogens over methanotrophs contributes to high methane emissions in rewetted fens},\n\turl = {https://bg.copernicus.org/preprints/bg-2018-184/bg-2018-184.pdf},\n\tabstract = {Abstract. The rewetting of drained peatlands alters peat geochemistry and often leads to sustained elevated methane emission. Although this methane is produced entirely by microbial activity, the distribution and abundance of methane-cycling microbes in rewetted peatlands, especially in fens, is rarely described. In this study, we compare the community composition and abundance of methane-cycling microbes in relation to peat porewater geochemistry in two rewetted fens in northeastern Germany, a coastal brackish fen and a freshwater riparian fen, with known high methane fluxes. We utilized 16S rDNA high-throughput sequencing and quantitative polymerase chain reaction on 16S rDNA, mcrA, and pmoA genes to determine microbial community composition and the abundance of total bacteria, methanogens, and methanotrophs. Electrical conductivity was more than three times higher in the coastal fen than in the riparian fen, averaging 5.3 and 1.5 mS cm−1, respectively. Porewater concentrations of terminal electron acceptors varied within and among the fens. This was also reflected in similarly high intra- and inter-site variations of microbial community composition. Despite these differences in environmental conditions and electron acceptor availability, we found a low abundance of methanotrophs and a high abundance of methanogens, represented in particular by Methanosaetaceae, in both fens. This suggests that rapid re/establishment of methanogens and slow re/establishment of methanotrophs contributes to prolonged increased methane emissions following rewetting.},\n\turldate = {2022-11-16},\n\tinstitution = {Biogeochemistry: Environmental Microbiology},\n\tauthor = {Wen, Xi and Unger, Viktoria and Jurasinski, Gerald and Koebsch, Franziska and Horn, Fabian and Rehder, Gregor and Sachs, Torsten and Zak, Dominik and Lischeid, Gunnar and Knorr, Klaus-Holger and Böttcher, Michael and Winkel, Matthias and Liebner, Susanne},\n\tmonth = apr,\n\tyear = {2018},\n\tdoi = {10.5194/bg-2018-184},\n}\n\n\n\n

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\n \n\n \n \n Wang, J.; Bogena, H. R.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n Characterizing Redox Potential Effects on Greenhouse Gas Emissions Induced by Water‐Level Changes.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 1–13. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Characterizing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_characterizing_2018,\n\ttitle = {Characterizing {Redox} {Potential} {Effects} on {Greenhouse} {Gas} {Emissions} {Induced} by {Water}‐{Level} {Changes}},\n\tvolume = {17},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2017.08.0152},\n\tdoi = {10.2136/vzj2017.08.0152},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Wang, Jihuan and Bogena, Heye R. and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {1--13},\n}\n\n\n\n

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\n \n\n \n \n Wagner, K.; Oswald, S. E.; and Frick, A.\n\n\n \n \n \n \n \n Multitemporal soil moisture monitoring by use of optical remote sensing data in a dike relocation area.\n \n \n \n \n\n\n \n\n\n\n In Neale, C. M.; and Maltese, A., editor(s), Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, pages 71, Berlin, Germany, October 2018. SPIE\n \n\n\n\n
\n\n\n\n \n \n $\"Multitemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{wagner_multitemporal_2018,\n\taddress = {Berlin, Germany},\n\ttitle = {Multitemporal soil moisture monitoring by use of optical remote sensing data in a dike relocation area},\n\tisbn = {9781510621497 9781510621503},\n\turl = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10783/2325319/Multitemporal-soil-moisture-monitoring-by-use-of-optical-remote-sensing/10.1117/12.2325319.full},\n\tdoi = {10.1117/12.2325319},\n\turldate = {2022-11-16},\n\tbooktitle = {Remote {Sensing} for {Agriculture}, {Ecosystems}, and {Hydrology} {XX}},\n\tpublisher = {SPIE},\n\tauthor = {Wagner, Kathrin and Oswald, Sascha E. and Frick, Annett},\n\teditor = {Neale, Christopher M. and Maltese, Antonino},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {71},\n}\n\n\n\n

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\n \n\n \n \n von Hebel, C.; Matveeva, M.; Verweij, E.; Rademske, P.; Kaufmann, M. S.; Brogi, C.; Vereecken, H.; Rascher, U.; and van der Kruk, J.\n\n\n \n \n \n \n \n Understanding Soil and Plant Interaction by Combining Ground-Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 45(15): 7571–7579. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{von_hebel_understanding_2018,\n\ttitle = {Understanding {Soil} and {Plant} {Interaction} by {Combining} {Ground}-{Based} {Quantitative} {Electromagnetic} {Induction} and {Airborne} {Hyperspectral} {Data}},\n\tvolume = {45},\n\tissn = {00948276},\n\turl = {http://doi.wiley.com/10.1029/2018GL078658},\n\tdoi = {10.1029/2018GL078658},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-11-16},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {von Hebel, Christian and Matveeva, Maria and Verweij, Elizabeth and Rademske, Patrick and Kaufmann, Manuela Sarah and Brogi, Cosimo and Vereecken, Harry and Rascher, Uwe and van der Kruk, Jan},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {7571--7579},\n}\n\n\n\n

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\n \n\n \n \n van der Maaten, E.; Pape, J.; van der Maaten-Theunissen, M.; Scharnweber, T.; Smiljanić, M.; Cruz-García, R.; and Wilmking, M.\n\n\n \n \n \n \n \n Distinct growth phenology but similar daily stem dynamics in three co-occurring broadleaved tree species.\n \n \n \n \n\n\n \n\n\n\n Tree Physiology, 38(12): 1820–1828. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Distinct$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_maaten_distinct_2018,\n\ttitle = {Distinct growth phenology but similar daily stem dynamics in three co-occurring broadleaved tree species},\n\tvolume = {38},\n\tissn = {1758-4469},\n\turl = {https://academic.oup.com/treephys/article/38/12/1820/4987942},\n\tdoi = {10.1093/treephys/tpy042},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-16},\n\tjournal = {Tree Physiology},\n\tauthor = {van der Maaten, Ernst and Pape, Jonas and van der Maaten-Theunissen, Marieke and Scharnweber, Tobias and Smiljanić, Marko and Cruz-García, Roberto and Wilmking, Martin},\n\teditor = {Mäkelä, Annikki},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {1820--1828},\n}\n\n\n\n

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\n \n\n \n \n van der Kruk, J.; Liu, T.; Mozaffari, A.; Gueting, N.; Klotzsche, A.; Vereecken, H.; Warren, C.; and Giannopoulos, A.\n\n\n \n \n \n \n \n GPR full-waveform inversion, recent developments, and future opportunities.\n \n \n \n \n\n\n \n\n\n\n In 2018 17th International Conference on Ground Penetrating Radar (GPR), pages 1–6, Rapperswil, Switzerland, June 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"GPR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{van_der_kruk_gpr_2018,\n\taddress = {Rapperswil, Switzerland},\n\ttitle = {{GPR} full-waveform inversion, recent developments, and future opportunities},\n\tisbn = {9781538657775},\n\turl = {https://ieeexplore.ieee.org/document/8441667/},\n\tdoi = {10.1109/ICGPR.2018.8441667},\n\turldate = {2022-11-16},\n\tbooktitle = {2018 17th {International} {Conference} on {Ground} {Penetrating} {Radar} ({GPR})},\n\tpublisher = {IEEE},\n\tauthor = {van der Kruk, J. and Liu, T. and Mozaffari, A. and Gueting, N. and Klotzsche, A. and Vereecken, H. and Warren, C. and Giannopoulos, A.},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {1--6},\n}\n\n\n\n

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\n \n\n \n \n Georgi, C.; Spengler, D.; Itzerott, S.; and Kleinschmit, B.\n\n\n \n \n \n \n \n Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 19(4): 684–707. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Automatic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{georgi_automatic_2018,\n\ttitle = {Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data},\n\tvolume = {19},\n\tissn = {1385-2256, 1573-1618},\n\turl = {http://link.springer.com/10.1007/s11119-017-9549-y},\n\tdoi = {10.1007/s11119-017-9549-y},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {Precision Agriculture},\n\tauthor = {Georgi, Claudia and Spengler, Daniel and Itzerott, Sibylle and Kleinschmit, Birgit},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {684--707},\n}\n\n\n\n

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\n \n\n \n \n Unger, A.; Drager, N.; Sips, M.; and Lehmann, D. J.\n\n\n \n \n \n \n \n Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Visualization and Computer Graphics, 24(1): 66–76. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{unger_understanding_2018,\n\ttitle = {Understanding a {Sequence} of {Sequences}: {Visual} {Exploration} of {Categorical} {States} in {Lake} {Sediment} {Cores}},\n\tvolume = {24},\n\tissn = {1077-2626},\n\tshorttitle = {Understanding a {Sequence} of {Sequences}},\n\turl = {http://ieeexplore.ieee.org/document/8022969/},\n\tdoi = {10.1109/TVCG.2017.2744686},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {IEEE Transactions on Visualization and Computer Graphics},\n\tauthor = {Unger, Andrea and Drager, Nadine and Sips, Mike and Lehmann, Dirk J.},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {66--76},\n}\n\n\n\n

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\n \n\n \n \n Trigo, I. F.; de Bruin, H.; Beyrich, F.; Bosveld, F. C.; Gavilán, P.; Groh, J.; and López-Urrea, R.\n\n\n \n \n \n \n \n Validation of reference evapotranspiration from Meteosat Second Generation (MSG) observations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 259: 271–285. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Validation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trigo_validation_2018,\n\ttitle = {Validation of reference evapotranspiration from {Meteosat} {Second} {Generation} ({MSG}) observations},\n\tvolume = {259},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318301606},\n\tdoi = {10.1016/j.agrformet.2018.05.008},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Trigo, Isabel F. and de Bruin, Henk and Beyrich, Frank and Bosveld, Fred C. and Gavilán, Pedro and Groh, Jannis and López-Urrea, Ramón},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {271--285},\n}\n\n\n\n

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\n \n\n \n \n Trauth, N.; Musolff, A.; Knöller, K.; Kaden, U. S.; Keller, T.; Werban, U.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n River water infiltration enhances denitrification efficiency in riparian groundwater.\n \n \n \n \n\n\n \n\n\n\n Water Research, 130: 185–199. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"River$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trauth_river_2018,\n\ttitle = {River water infiltration enhances denitrification efficiency in riparian groundwater},\n\tvolume = {130},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135417309867},\n\tdoi = {10.1016/j.watres.2017.11.058},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Water Research},\n\tauthor = {Trauth, Nico and Musolff, Andreas and Knöller, Kay and Kaden, Ute S. and Keller, Toralf and Werban, Ulrike and Fleckenstein, Jan H.},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {185--199},\n}\n\n\n\n

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\n \n\n \n \n Thomas, F. M; Rzepecki, A.; Lücke, A.; Wiekenkamp, I.; Rabbel, I.; Pütz, T.; and Neuwirth, B.\n\n\n \n \n \n \n \n Growth and wood isotopic signature of Norway spruce ( Picea abies ) along a small-scale gradient of soil moisture.\n \n \n \n \n\n\n \n\n\n\n Tree Physiology, 38(12): 1855–1870. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Growth$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{thomas_growth_2018,\n\ttitle = {Growth and wood isotopic signature of {Norway} spruce ( \\textit{{Picea} abies} ) along a small-scale gradient of soil moisture},\n\tvolume = {38},\n\tissn = {1758-4469},\n\turl = {https://academic.oup.com/treephys/article/38/12/1855/5108531},\n\tdoi = {10.1093/treephys/tpy100},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-16},\n\tjournal = {Tree Physiology},\n\tauthor = {Thomas, Frank M and Rzepecki, Andreas and Lücke, Andreas and Wiekenkamp, Inge and Rabbel, Inken and Pütz, Thomas and Neuwirth, Burkhard},\n\teditor = {Cernusak, Lucas},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {1855--1870},\n}\n\n\n\n

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\n \n\n \n \n Tauro, F.; Selker, J.; van de Giesen, N.; Abrate, T.; Uijlenhoet, R.; Porfiri, M.; Manfreda, S.; Caylor, K.; Moramarco, T.; Benveniste, J.; Ciraolo, G.; Estes, L.; Domeneghetti, A.; Perks, M. T.; Corbari, C.; Rabiei, E.; Ravazzani, G.; Bogena, H.; Harfouche, A.; Brocca, L.; Maltese, A.; Wickert, A.; Tarpanelli, A.; Good, S.; Lopez Alcala, J. M.; Petroselli, A.; Cudennec, C.; Blume, T.; Hut, R.; and Grimaldi, S.\n\n\n \n \n \n \n \n Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 63(2): 169–196. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Measurements$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tauro_measurements_2018,\n\ttitle = {Measurements and {Observations} in the {XXI} century ({MOXXI}): innovation and multi-disciplinarity to sense the hydrological cycle},\n\tvolume = {63},\n\tissn = {0262-6667, 2150-3435},\n\tshorttitle = {Measurements and {Observations} in the {XXI} century ({MOXXI})},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2017.1420191},\n\tdoi = {10.1080/02626667.2017.1420191},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Tauro, Flavia and Selker, John and van de Giesen, Nick and Abrate, Tommaso and Uijlenhoet, Remko and Porfiri, Maurizio and Manfreda, Salvatore and Caylor, Kelly and Moramarco, Tommaso and Benveniste, Jerome and Ciraolo, Giuseppe and Estes, Lyndon and Domeneghetti, Alessio and Perks, Matthew T. and Corbari, Chiara and Rabiei, Ehsan and Ravazzani, Giovanni and Bogena, Heye and Harfouche, Antoine and Brocca, Luca and Maltese, Antonino and Wickert, Andy and Tarpanelli, Angelica and Good, Stephen and Lopez Alcala, Jose Manuel and Petroselli, Andrea and Cudennec, Christophe and Blume, Theresa and Hut, Rolf and Grimaldi, Salvatore},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {169--196},\n}\n\n\n\n

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\n \n\n \n \n Sun, H.; Koal, P.; Gerl, G.; Schroll, R.; Gattinger, A.; Joergensen, R. G.; and Munch, J. C.\n\n\n \n \n \n \n \n Microbial communities and residues in robinia- and poplar-based alley-cropping systems under organic and integrated management.\n \n \n \n \n\n\n \n\n\n\n Agroforestry Systems, 92(1): 35–46. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Microbial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sun_microbial_2018,\n\ttitle = {Microbial communities and residues in robinia- and poplar-based alley-cropping systems under organic and integrated management},\n\tvolume = {92},\n\tissn = {0167-4366, 1572-9680},\n\turl = {http://link.springer.com/10.1007/s10457-016-0009-x},\n\tdoi = {10.1007/s10457-016-0009-x},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Agroforestry Systems},\n\tauthor = {Sun, Hanyin and Koal, Philipp and Gerl, Georg and Schroll, Reiner and Gattinger, Andreas and Joergensen, Rainer Georg and Munch, Jean Charles},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {35--46},\n}\n\n\n\n

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\n \n\n \n \n Sulis, M.; Keune, J.; Shrestha, P.; Simmer, C.; and Kollet, S. J.\n\n\n \n \n \n \n \n Quantifying the Impact of Subsurface-Land Surface Physical Processes on the Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 123(17): 9131–9151. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sulis_quantifying_2018,\n\ttitle = {Quantifying the {Impact} of {Subsurface}-{Land} {Surface} {Physical} {Processes} on the {Predictive} {Skill} of {Subseasonal} {Mesoscale} {Atmospheric} {Simulations}},\n\tvolume = {123},\n\tissn = {2169897X},\n\turl = {http://doi.wiley.com/10.1029/2017JD028187},\n\tdoi = {10.1029/2017JD028187},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-16},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Sulis, M. and Keune, J. and Shrestha, P. and Simmer, C. and Kollet, S. J.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {9131--9151},\n}\n\n\n\n

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\n \n\n \n \n Spengler, D.; Förster, M.; and Borg, E.\n\n\n \n \n \n \n \n Editorial.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2): 49–51. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Editorial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{spengler_editorial_2018,\n\ttitle = {Editorial},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0052-5},\n\tdoi = {10.1007/s41064-018-0052-5},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Spengler, Daniel and Förster, Michael and Borg, Erik},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {49--51},\n}\n\n\n\n

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\n \n\n \n \n Soltani, M.; Laux, P.; Mauder, M.; and Kunstmann, H.\n\n\n \n \n \n \n \n Spatiotemporal variability and empirical Copula-based dependence structure of modeled and observed coupled water and energy fluxes.\n \n \n \n \n\n\n \n\n\n\n Hydrology Research, 49(5): 1396–1416. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{soltani_spatiotemporal_2018,\n\ttitle = {Spatiotemporal variability and empirical {Copula}-based dependence structure of modeled and observed coupled water and energy fluxes},\n\tvolume = {49},\n\tissn = {0029-1277, 2224-7955},\n\turl = {https://iwaponline.com/hr/article/49/5/1396/38883/Spatiotemporal-variability-and-empirical},\n\tdoi = {10.2166/nh.2018.163},\n\tabstract = {Abstract \n            The spatial variations of water and energy budgets are highly influenced by the heterogeneity of land-surface characteristics. We investigate the spatiotemporal variability and dependence structure patterns of water and energy fluxes along an elevation gradient. Our analysis is based on the application of the GEOtop model and empirical Copulas. It is performed for the Rott (∼55 km2) and Upper-Ammer (∼300 km2) catchments in the TERrestrial ENvironmental Observatories prealpine region over two recent summer episodes, as a test case. We found that GEOtop is capable of quantifying the spatiotemporal variability of the water and energy budgets with consideration for the elevation-gradient effect of this heterogeneous landscape, which is confirmed by the linear statistical metrics. Furthermore, the empirical Copula-based function reveals that the dependence structures between the measured and simulated hydrometeorological variables are similar either at upper or lower density maxima. This suggests a reasonable performance of the model, as the interaction of variables is described properly; however, the model shows poorer performance in the middle ranks of the data. It is concluded that the presented Copula-based model performance analysis is a valuable complement to traditional global performance model analyses.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-16},\n\tjournal = {Hydrology Research},\n\tauthor = {Soltani, Mohsen and Laux, Patrick and Mauder, Matthias and Kunstmann, Harald},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {1396--1416},\n}\n\n\n\n

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\n \n\n \n \n Jagdhuber, T.; Link, M.; Meyer, T.; Soebjaerg, S.; Dill, S.; and Peichl, M.\n\n\n \n \n \n \n Fully Polarimetric Radiometer Signatures of Azimuthal Permittivity Patterns: Ground-Based L-band Brightness Temperature Observations and Model Simulations.\n \n \n \n\n\n \n\n\n\n In March 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{jagdhuber_fully_2018,\n\ttitle = {Fully {Polarimetric} {Radiometer} {Signatures} of {Azimuthal} {Permittivity} {Patterns}: {Ground}-{Based} {L}-band {Brightness} {Temperature} {Observations} and {Model} {Simulations}},\n\tauthor = {Jagdhuber, Thomas and Link, Moritz and Meyer, Thomas and Soebjaerg, Sten and Dill, Stephan and Peichl, Markus},\n\tmonth = mar,\n\tyear = {2018},\n}\n\n\n\n

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\n \n\n \n \n Siebers, N.; Bauke, S. L.; Tamburini, F.; and Amelung, W.\n\n\n \n \n \n \n \n Short-term impacts of forest clear-cut on P accessibility in soil microaggregates: An oxygen isotope study.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 315: 59–64. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Short-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siebers_short-term_2018,\n\ttitle = {Short-term impacts of forest clear-cut on {P} accessibility in soil microaggregates: {An} oxygen isotope study},\n\tvolume = {315},\n\tissn = {00167061},\n\tshorttitle = {Short-term impacts of forest clear-cut on {P} accessibility in soil microaggregates},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117312879},\n\tdoi = {10.1016/j.geoderma.2017.11.024},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Geoderma},\n\tauthor = {Siebers, Nina and Bauke, Sara L. and Tamburini, Federica and Amelung, Wulf},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {59--64},\n}\n\n\n\n

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\n \n\n \n \n Siebers, N.; Abdelrahman, H.; Krause, L.; and Amelung, W.\n\n\n \n \n \n \n \n Bias in aggregate geometry and properties after disintegration and drying procedures.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 313: 163–171. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Bias$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siebers_bias_2018,\n\ttitle = {Bias in aggregate geometry and properties after disintegration and drying procedures},\n\tvolume = {313},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117308455},\n\tdoi = {10.1016/j.geoderma.2017.10.028},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Geoderma},\n\tauthor = {Siebers, Nina and Abdelrahman, Hamada and Krause, Lars and Amelung, Wulf},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {163--171},\n}\n\n\n\n

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\n \n\n \n \n Shrestha, P.; Sulis, M.; Simmer, C.; and Kollet, S.\n\n\n \n \n \n \n \n Effects of horizontal grid resolution on evapotranspiration partitioning using TerrSysMP.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 557: 910–915. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{shrestha_effects_2018,\n\ttitle = {Effects of horizontal grid resolution on evapotranspiration partitioning using {TerrSysMP}},\n\tvolume = {557},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418300246},\n\tdoi = {10.1016/j.jhydrol.2018.01.024},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Shrestha, P. and Sulis, M. and Simmer, C. and Kollet, S.},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {910--915},\n}\n\n\n\n

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\n \n\n \n \n Shahid, N.; Becker, J. M.; Krauss, M.; Brack, W.; and Liess, M.\n\n\n \n \n \n \n \n Pesticide Body Burden of the Crustacean Gammarus pulex as a Measure of Toxic Pressure in Agricultural Streams.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 52(14): 7823–7832. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Pesticide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{shahid_pesticide_2018,\n\ttitle = {Pesticide {Body} {Burden} of the {Crustacean} {Gammarus} pulex as a {Measure} of {Toxic} {Pressure} in {Agricultural} {Streams}},\n\tvolume = {52},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.8b01751},\n\tdoi = {10.1021/acs.est.8b01751},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2022-11-16},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Shahid, Naeem and Becker, Jeremias Martin and Krauss, Martin and Brack, Werner and Liess, Matthias},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {7823--7832},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Shahid, N.; Becker, J. M.; Krauss, M.; Brack, W.; and Liess, M.\n\n\n \n \n \n \n \n Adaptation of Gammarus pulex to agricultural insecticide contamination in streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 621: 479–485. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Adaptation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{shahid_adaptation_2018,\n\ttitle = {Adaptation of {Gammarus} pulex to agricultural insecticide contamination in streams},\n\tvolume = {621},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717332783},\n\tdoi = {10.1016/j.scitotenv.2017.11.220},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Shahid, Naeem and Becker, Jeremias Martin and Krauss, Martin and Brack, Werner and Liess, Matthias},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {479--485},\n}\n\n\n\n

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\n \n\n \n \n Schwank, M.; and Naderpour, R.\n\n\n \n \n \n \n \n Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(3): 354. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Snow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schwank_snow_2018,\n\ttitle = {Snow {Density} and {Ground} {Permittivity} {Retrieved} from {L}-{Band} {Radiometry}: {Melting} {Effects}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\tshorttitle = {Snow {Density} and {Ground} {Permittivity} {Retrieved} from {L}-{Band} {Radiometry}},\n\turl = {http://www.mdpi.com/2072-4292/10/2/354},\n\tdoi = {10.3390/rs10020354},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Schwank, Mike and Naderpour, Reza},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {354},\n}\n\n\n\n

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\n \n\n \n \n Schrön, M.; Zacharias, S.; Womack, G.; Köhli, M.; Desilets, D.; Oswald, S. E.; Bumberger, J.; Mollenhauer, H.; Kögler, S.; Remmler, P.; Kasner, M.; Denk, A.; and Dietrich, P.\n\n\n \n \n \n \n \n Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Instrumentation, Methods and Data Systems, 7(1): 83–99. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Intercomparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schron_intercomparison_2018,\n\ttitle = {Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment},\n\tvolume = {7},\n\tissn = {2193-0864},\n\turl = {https://gi.copernicus.org/articles/7/83/2018/},\n\tdoi = {10.5194/gi-7-83-2018},\n\tabstract = {Abstract. Sensor-to-sensor variability is a source of error common to all\ngeoscientific instruments that needs to be assessed before comparative and\napplied research can be performed with multiple sensors. Consistency among\nsensor systems is especially critical when subtle features of the surrounding\nterrain are to be identified. Cosmic-ray neutron sensors (CRNSs) are a recent\ntechnology used to monitor hectometre-scale environmental water\nstorages, for which a rigorous comparison study of numerous co-located\nsensors has not yet been performed. In this work, nine stationary CRNS probes\nof type “CRS1000” were installed in relative proximity on a grass patch\nsurrounded by trees, buildings, and sealed areas. While the dynamics of the\nneutron count rates were found to be similar, offsets of a few percent from\nthe absolute average neutron count rates were found. Technical adjustments of\nthe individual detection parameters brought all instruments into good\nagreement. Furthermore, we found a critical integration time of 6 h above\nwhich all sensors showed consistent dynamics in the data and their RMSE fell\nbelow 1 \\% of gravimetric water content. The residual differences between\nthe nine signals indicated local effects of the complex urban terrain on the\nscale of several metres. Mobile CRNS measurements and spatial simulations\nwith the URANOS neutron transport code in the surrounding area (25 ha)\nhave revealed substantial sub-footprint heterogeneity to which CRNS detectors\nare sensitive despite their large averaging volume. The sealed and constantly\ndry structures in the footprint furthermore damped the dynamics of the CRNS-derived soil moisture. We developed strategies to correct for the sealed-area\neffect based on theoretical insights about the spatial sensitivity of the\nsensor. This procedure not only led to reliable soil moisture estimation\nduring dry-out periods, it further revealed a strong signal of intercepted\nwater that emerged over the sealed surfaces during rain events. The presented\narrangement offered a unique opportunity to demonstrate the CRNS performance\nin complex terrain, and the results indicated great potential for further\napplications in urban climate research.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Geoscientific Instrumentation, Methods and Data Systems},\n\tauthor = {Schrön, Martin and Zacharias, Steffen and Womack, Gary and Köhli, Markus and Desilets, Darin and Oswald, Sascha E. and Bumberger, Jan and Mollenhauer, Hannes and Kögler, Simon and Remmler, Paul and Kasner, Mandy and Denk, Astrid and Dietrich, Peter},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {83--99},\n}\n\n\n\n

\n\n\n

\n Abstract. Sensor-to-sensor variability is a source of error common to all geoscientific instruments that needs to be assessed before comparative and applied research can be performed with multiple sensors. Consistency among sensor systems is especially critical when subtle features of the surrounding terrain are to be identified. Cosmic-ray neutron sensors (CRNSs) are a recent technology used to monitor hectometre-scale environmental water storages, for which a rigorous comparison study of numerous co-located sensors has not yet been performed. In this work, nine stationary CRNS probes of type “CRS1000” were installed in relative proximity on a grass patch surrounded by trees, buildings, and sealed areas. While the dynamics of the neutron count rates were found to be similar, offsets of a few percent from the absolute average neutron count rates were found. Technical adjustments of the individual detection parameters brought all instruments into good agreement. Furthermore, we found a critical integration time of 6 h above which all sensors showed consistent dynamics in the data and their RMSE fell below 1 % of gravimetric water content. The residual differences between the nine signals indicated local effects of the complex urban terrain on the scale of several metres. Mobile CRNS measurements and spatial simulations with the URANOS neutron transport code in the surrounding area (25 ha) have revealed substantial sub-footprint heterogeneity to which CRNS detectors are sensitive despite their large averaging volume. The sealed and constantly dry structures in the footprint furthermore damped the dynamics of the CRNS-derived soil moisture. We developed strategies to correct for the sealed-area effect based on theoretical insights about the spatial sensitivity of the sensor. This procedure not only led to reliable soil moisture estimation during dry-out periods, it further revealed a strong signal of intercepted water that emerged over the sealed surfaces during rain events. The presented arrangement offered a unique opportunity to demonstrate the CRNS performance in complex terrain, and the results indicated great potential for further applications in urban climate research.\n

\n\n\n

\n \n\n \n \n Schrön, M.; Rosolem, R.; Köhli, M.; Piussi, L.; Schröter, I.; Iwema, J.; Kögler, S.; Oswald, S. E.; Wollschläger, U.; Samaniego, L.; Dietrich, P.; and Zacharias, S.\n\n\n \n \n \n \n \n Cosmic-ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(9): 6441–6459. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Cosmic-ray$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schron_cosmic-ray_2018,\n\ttitle = {Cosmic-ray {Neutron} {Rover} {Surveys} of {Field} {Soil} {Moisture} and the {Influence} of {Roads}},\n\tvolume = {54},\n\tissn = {00431397},\n\turl = {http://doi.wiley.com/10.1029/2017WR021719},\n\tdoi = {10.1029/2017WR021719},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Schrön, M. and Rosolem, R. and Köhli, M. and Piussi, L. and Schröter, I. and Iwema, J. and Kögler, S. and Oswald, S. E. and Wollschläger, U. and Samaniego, L. and Dietrich, P. and Zacharias, S.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {6441--6459},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, J.; and Hauck, J.\n\n\n \n \n \n \n \n Implementing green infrastructure policy in agricultural landscapes—scenarios for Saxony-Anhalt, Germany.\n \n \n \n \n\n\n \n\n\n\n Regional Environmental Change, 18(3): 899–911. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Implementing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_implementing_2018,\n\ttitle = {Implementing green infrastructure policy in agricultural landscapes—scenarios for {Saxony}-{Anhalt}, {Germany}},\n\tvolume = {18},\n\tissn = {1436-3798, 1436-378X},\n\turl = {http://link.springer.com/10.1007/s10113-017-1241-2},\n\tdoi = {10.1007/s10113-017-1241-2},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Regional Environmental Change},\n\tauthor = {Schmidt, Jenny and Hauck, Jennifer},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {899--911},\n}\n\n\n\n

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\n \n\n \n \n Russo, R.; Becker, J. M.; and Liess, M.\n\n\n \n \n \n \n \n Sequential exposure to low levels of pesticides and temperature stress increase toxicological sensitivity of crustaceans.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 610-611: 563–569. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Sequential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{russo_sequential_2018,\n\ttitle = {Sequential exposure to low levels of pesticides and temperature stress increase toxicological sensitivity of crustaceans},\n\tvolume = {610-611},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717320715},\n\tdoi = {10.1016/j.scitotenv.2017.08.073},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Russo, Renato and Becker, Jeremias Martin and Liess, Matthias},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {563--569},\n}\n\n\n\n

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\n \n\n \n \n Romero-Ruiz, A.; Linde, N.; Keller, T.; and Or, D.\n\n\n \n \n \n \n \n A Review of Geophysical Methods for Soil Structure Characterization: GEOPHYSICS AND SOIL STRUCTURE.\n \n \n \n \n\n\n \n\n\n\n Reviews of Geophysics, 56(4): 672–697. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{romero-ruiz_review_2018,\n\ttitle = {A {Review} of {Geophysical} {Methods} for {Soil} {Structure} {Characterization}: {GEOPHYSICS} {AND} {SOIL} {STRUCTURE}},\n\tvolume = {56},\n\tissn = {87551209},\n\tshorttitle = {A {Review} of {Geophysical} {Methods} for {Soil} {Structure} {Characterization}},\n\turl = {http://doi.wiley.com/10.1029/2018RG000611},\n\tdoi = {10.1029/2018RG000611},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {Reviews of Geophysics},\n\tauthor = {Romero-Ruiz, Alejandro and Linde, Niklas and Keller, Thomas and Or, Dani},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {672--697},\n}\n\n\n\n

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\n \n\n \n \n Robinet, J.; von Hebel, C.; Govers, G.; van der Kruk, J.; Minella, J. P.; Schlesner, A.; Ameijeiras-Mariño, Y.; and Vanderborght, J.\n\n\n \n \n \n \n \n Spatial variability of soil water content and soil electrical conductivity across scales derived from Electromagnetic Induction and Time Domain Reflectometry.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 314: 160–174. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{robinet_spatial_2018,\n\ttitle = {Spatial variability of soil water content and soil electrical conductivity across scales derived from {Electromagnetic} {Induction} and {Time} {Domain} {Reflectometry}},\n\tvolume = {314},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117307875},\n\tdoi = {10.1016/j.geoderma.2017.10.045},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Geoderma},\n\tauthor = {Robinet, Jérémy and von Hebel, Christian and Govers, Gerard and van der Kruk, Jan and Minella, Jean P.G. and Schlesner, Alexandre and Ameijeiras-Mariño, Yolanda and Vanderborght, Jan},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {160--174},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Rebmann, C.; Aubinet, M.; Schmid, H.; Arriga, N.; Aurela, M.; Burba, G.; Clement, R.; De Ligne, A.; Fratini, G.; Gielen, B.; Grace, J.; Graf, A.; Gross, P.; Haapanala, S.; Herbst, M.; Hörtnagl, L.; Ibrom, A.; Joly, L.; Kljun, N.; Kolle, O.; Kowalski, A.; Lindroth, A.; Loustau, D.; Mammarella, I.; Mauder, M.; Merbold, L.; Metzger, S.; Mölder, M.; Montagnani, L.; Papale, D.; Pavelka, M.; Peichl, M.; Roland, M.; Serrano-Ortiz, P.; Siebicke, L.; Steinbrecher, R.; Tuovinen, J.; Vesala, T.; Wohlfahrt, G.; and Franz, D.\n\n\n \n \n \n \n \n ICOS eddy covariance flux-station site setup: a review.\n \n \n \n \n\n\n \n\n\n\n International Agrophysics, 32(4): 471–494. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"ICOS$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rebmann_icos_2018,\n\ttitle = {{ICOS} eddy covariance flux-station site setup: a review},\n\tvolume = {32},\n\tissn = {2300-8725},\n\tshorttitle = {{ICOS} eddy covariance flux-station site setup},\n\turl = {http://archive.sciendo.com/INTAG/intag.2017.32.issue-4/intag-2017-0044/intag-2017-0044.pdf},\n\tdoi = {10.1515/intag-2017-0044},\n\tabstract = {Abstract \n            The Integrated Carbon Observation System Research Infrastructure aims to provide long-term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features.},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {International Agrophysics},\n\tauthor = {Rebmann, Corinna and Aubinet, Marc and Schmid, HaPe and Arriga, Nicola and Aurela, Mika and Burba, George and Clement, Robert and De Ligne, Anne and Fratini, Gerardo and Gielen, Bert and Grace, John and Graf, Alexander and Gross, Patrick and Haapanala, Sami and Herbst, Mathias and Hörtnagl, Lukas and Ibrom, Andreas and Joly, Lilian and Kljun, Natascha and Kolle, Olaf and Kowalski, Andrew and Lindroth, Anders and Loustau, Denis and Mammarella, Ivan and Mauder, Matthias and Merbold, Lutz and Metzger, Stefan and Mölder, Meelis and Montagnani, Leonardo and Papale, Dario and Pavelka, Marian and Peichl, Matthias and Roland, Marilyn and Serrano-Ortiz, Penélope and Siebicke, Lukas and Steinbrecher, Rainer and Tuovinen, Juha-Pekka and Vesala, Timo and Wohlfahrt, Georg and Franz, Daniela},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {471--494},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Rahmati, M.; Weihermüller, L.; Vanderborght, J.; Pachepsky, Y. A.; Mao, L.; Sadeghi, S. H.; Moosavi, N.; Kheirfam, H.; Montzka, C.; Van Looy, K.; Toth, B.; Hazbavi, Z.; Al Yamani, W.; Albalasmeh, A. A.; Alghzawi, M. Z.; Angulo-Jaramillo, R.; Antonino, A. C. D.; Arampatzis, G.; Armindo, R. A.; Asadi, H.; Bamutaze, Y.; Batlle-Aguilar, J.; Béchet, B.; Becker, F.; Blöschl, G.; Bohne, K.; Braud, I.; Castellano, C.; Cerdà, A.; Chalhoub, M.; Cichota, R.; Císlerová, M.; Clothier, B.; Coquet, Y.; Cornelis, W.; Corradini, C.; Coutinho, A. P.; de Oliveira, M. B.; de Macedo, J. R.; Durães, M. F.; Emami, H.; Eskandari, I.; Farajnia, A.; Flammini, A.; Fodor, N.; Gharaibeh, M.; Ghavimipanah, M. H.; Ghezzehei, T. A.; Giertz, S.; Hatzigiannakis, E. G.; Horn, R.; Jiménez, J. J.; Jacques, D.; Keesstra, S. D.; Kelishadi, H.; Kiani-Harchegani, M.; Kouselou, M.; Kumar Jha, M.; Lassabatere, L.; Li, X.; Liebig, M. A.; Lichner, L.; López, M. V.; Machiwal, D.; Mallants, D.; Mallmann, M. S.; de Oliveira Marques, J. D.; Marshall, M. R.; Mertens, J.; Meunier, F.; Mohammadi, M. H.; Mohanty, B. P.; Pulido-Moncada, M.; Montenegro, S.; Morbidelli, R.; Moret-Fernández, D.; Moosavi, A. A.; Mosaddeghi, M. R.; Mousavi, S. B.; Mozaffari, H.; Nabiollahi, K.; Neyshabouri, M. R.; Ottoni, M. V.; Ottoni Filho, T. B.; Pahlavan-Rad, M. R.; Panagopoulos, A.; Peth, S.; Peyneau, P.; Picciafuoco, T.; Poesen, J.; Pulido, M.; Reinert, D. J.; Reinsch, S.; Rezaei, M.; Roberts, F. P.; Robinson, D.; Rodrigo-Comino, J.; Rotunno Filho, O. C.; Saito, T.; Suganuma, H.; Saltalippi, C.; Sándor, R.; Schütt, B.; Seeger, M.; Sepehrnia, N.; Sharifi Moghaddam, E.; Shukla, M.; Shutaro, S.; Sorando, R.; Stanley, A. A.; Strauss, P.; Su, Z.; Taghizadeh-Mehrjardi, R.; Taguas, E.; Teixeira, W. G.; Vaezi, A. R.; Vafakhah, M.; Vogel, T.; Vogeler, I.; Votrubova, J.; Werner, S.; Winarski, T.; Yilmaz, D.; Young, M. H.; Zacharias, S.; Zeng, Y.; Zhao, Y.; Zhao, H.; and Vereecken, H.\n\n\n \n \n \n \n \n Development and analysis of the Soil Water Infiltration Global database.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 10(3): 1237–1263. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahmati_development_2018,\n\ttitle = {Development and analysis of the {Soil} {Water} {Infiltration} {Global} database},\n\tvolume = {10},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/10/1237/2018/},\n\tdoi = {10.5194/essd-10-1237-2018},\n\tabstract = {Abstract. In this paper, we present and analyze a novel global database of\nsoil infiltration measurements, the Soil Water Infiltration Global (SWIG)\ndatabase. In total, 5023 infiltration curves were collected across all\ncontinents in the SWIG database. These data were either provided and quality\nchecked by the scientists who performed the experiments or they were\ndigitized from published articles. Data from 54 different countries were\nincluded in the database with major contributions from Iran, China, and the USA.\nIn addition to its extensive geographical coverage, the collected\ninfiltration curves cover research from 1976 to late 2017. Basic information\non measurement location and method, soil properties, and land use was\ngathered along with the infiltration data, making the database valuable for\nthe development of pedotransfer functions (PTFs) for estimating soil hydraulic\nproperties, for the evaluation of infiltration measurement methods, and for\ndeveloping and validating infiltration models. Soil textural information\n(clay, silt, and sand content) is available for 3842 out of 5023 infiltration\nmeasurements (∼ 76\\%) covering nearly all soil USDA textural classes\nexcept for the sandy clay and silt classes. Information on land use is\navailable for 76 \\% of the experimental sites with agricultural land use as\nthe dominant type (∼ 40\\%). We are convinced that the SWIG database\nwill allow for a better parameterization of the infiltration process in land\nsurface models and for testing infiltration models. All collected data and\nrelated soil characteristics are provided online in\n*.xlsx and *.csv formats for reference, and we add a disclaimer that the\ndatabase is for public domain use only and can be copied freely by\nreferencing it. Supplementary data are available at\nhttps://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data\nquality assessment is strongly advised prior to any use of this database.\nFinally, we would like to encourage scientists to extend and update the SWIG database\nby uploading new data to it.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Earth System Science Data},\n\tauthor = {Rahmati, Mehdi and Weihermüller, Lutz and Vanderborght, Jan and Pachepsky, Yakov A. and Mao, Lili and Sadeghi, Seyed Hamidreza and Moosavi, Niloofar and Kheirfam, Hossein and Montzka, Carsten and Van Looy, Kris and Toth, Brigitta and Hazbavi, Zeinab and Al Yamani, Wafa and Albalasmeh, Ammar A. and Alghzawi, Ma'in Z. and Angulo-Jaramillo, Rafael and Antonino, Antônio Celso Dantas and Arampatzis, George and Armindo, Robson André and Asadi, Hossein and Bamutaze, Yazidhi and Batlle-Aguilar, Jordi and Béchet, Béatrice and Becker, Fabian and Blöschl, Günter and Bohne, Klaus and Braud, Isabelle and Castellano, Clara and Cerdà, Artemi and Chalhoub, Maha and Cichota, Rogerio and Císlerová, Milena and Clothier, Brent and Coquet, Yves and Cornelis, Wim and Corradini, Corrado and Coutinho, Artur Paiva and de Oliveira, Muriel Bastista and de Macedo, José Ronaldo and Durães, Matheus Fonseca and Emami, Hojat and Eskandari, Iraj and Farajnia, Asghar and Flammini, Alessia and Fodor, Nándor and Gharaibeh, Mamoun and Ghavimipanah, Mohamad Hossein and Ghezzehei, Teamrat A. and Giertz, Simone and Hatzigiannakis, Evangelos G. and Horn, Rainer and Jiménez, Juan José and Jacques, Diederik and Keesstra, Saskia Deborah and Kelishadi, Hamid and Kiani-Harchegani, Mahboobeh and Kouselou, Mehdi and Kumar Jha, Madan and Lassabatere, Laurent and Li, Xiaoyan and Liebig, Mark A. and Lichner, Lubomír and López, María Victoria and Machiwal, Deepesh and Mallants, Dirk and Mallmann, Micael Stolben and de Oliveira Marques, Jean Dalmo and Marshall, Miles R. and Mertens, Jan and Meunier, Félicien and Mohammadi, Mohammad Hossein and Mohanty, Binayak P. and Pulido-Moncada, Mansonia and Montenegro, Suzana and Morbidelli, Renato and Moret-Fernández, David and Moosavi, Ali Akbar and Mosaddeghi, Mohammad Reza and Mousavi, Seyed Bahman and Mozaffari, Hasan and Nabiollahi, Kamal and Neyshabouri, Mohammad Reza and Ottoni, Marta Vasconcelos and Ottoni Filho, Theophilo Benedicto and Pahlavan-Rad, Mohammad Reza and Panagopoulos, Andreas and Peth, Stephan and Peyneau, Pierre-Emmanuel and Picciafuoco, Tommaso and Poesen, Jean and Pulido, Manuel and Reinert, Dalvan José and Reinsch, Sabine and Rezaei, Meisam and Roberts, Francis Parry and Robinson, David and Rodrigo-Comino, Jesús and Rotunno Filho, Otto Corrêa and Saito, Tadaomi and Suganuma, Hideki and Saltalippi, Carla and Sándor, Renáta and Schütt, Brigitta and Seeger, Manuel and Sepehrnia, Nasrollah and Sharifi Moghaddam, Ehsan and Shukla, Manoj and Shutaro, Shiraki and Sorando, Ricardo and Stanley, Ajayi Asishana and Strauss, Peter and Su, Zhongbo and Taghizadeh-Mehrjardi, Ruhollah and Taguas, Encarnación and Teixeira, Wenceslau Geraldes and Vaezi, Ali Reza and Vafakhah, Mehdi and Vogel, Tomas and Vogeler, Iris and Votrubova, Jana and Werner, Steffen and Winarski, Thierry and Yilmaz, Deniz and Young, Michael H. and Zacharias, Steffen and Zeng, Yijian and Zhao, Ying and Zhao, Hong and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {1237--1263},\n}\n\n\n\n

\n\n\n

\n Abstract. In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.\n

\n\n\n

\n \n\n \n \n Rabbel, I.; Bogena, H.; Neuwirth, B.; and Diekkrüger, B.\n\n\n \n \n \n \n \n Using Sap Flow Data to Parameterize the Feddes Water Stress Model for Norway Spruce.\n \n \n \n \n\n\n \n\n\n\n Water, 10(3): 279. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rabbel_using_2018,\n\ttitle = {Using {Sap} {Flow} {Data} to {Parameterize} the {Feddes} {Water} {Stress} {Model} for {Norway} {Spruce}},\n\tvolume = {10},\n\tissn = {2073-4441},\n\turl = {http://www.mdpi.com/2073-4441/10/3/279},\n\tdoi = {10.3390/w10030279},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Water},\n\tauthor = {Rabbel, Inken and Bogena, Heye and Neuwirth, Burkhard and Diekkrüger, Bernd},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {279},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Rabbel, I.; Neuwirth, B.; Bogena, H.; and Diekkrüger, B.\n\n\n \n \n \n \n \n Exploring the growth response of Norway spruce (Picea abies) along a small-scale gradient of soil water supply.\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 52: 123–130. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Exploring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rabbel_exploring_2018,\n\ttitle = {Exploring the growth response of {Norway} spruce ({Picea} abies) along a small-scale gradient of soil water supply},\n\tvolume = {52},\n\tissn = {11257865},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786518301425},\n\tdoi = {10.1016/j.dendro.2018.10.007},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Dendrochronologia},\n\tauthor = {Rabbel, Inken and Neuwirth, Burkhard and Bogena, Heye and Diekkrüger, Bernd},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {123--130},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Pütz, T.; Fank, J.; and Flury, M.\n\n\n \n \n \n \n \n Lysimeters in Vadose Zone Research.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 1–4. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Lysimeters$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{putz_lysimeters_2018,\n\ttitle = {Lysimeters in {Vadose} {Zone} {Research}},\n\tvolume = {17},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2018.02.0035},\n\tdoi = {10.2136/vzj2018.02.0035},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Pütz, Thomas and Fank, Johann and Flury, Markus},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {1--4},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Post, H.; Hendricks Franssen, H.; Han, X.; Baatz, R.; Montzka, C.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 15(1): 187–208. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{post_evaluation_2018,\n\ttitle = {Evaluation and uncertainty analysis of regional-scale {CLM4}.5 net carbon flux estimates},\n\tvolume = {15},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/15/187/2018/},\n\tdoi = {10.5194/bg-15-187-2018},\n\tabstract = {Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from  ∼ 2 to 3 g C m−2 yr−1 (with uncertain parameters) to  ∼ 45 g C m−2 yr−1 (C3 grass) and  ∼ 75 g C m−2 yr−1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ∼ 4.0–13.5 g C m−2 yr−1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Biogeosciences},\n\tauthor = {Post, Hanna and Hendricks Franssen, Harrie-Jan and Han, Xujun and Baatz, Roland and Montzka, Carsten and Schmidt, Marius and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {187--208},\n}\n\n\n\n

\n\n\n

\n Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ∼ 2 to 3 g C m−2 yr−1 (with uncertain parameters) to ∼ 45 g C m−2 yr−1 (C3 grass) and ∼ 75 g C m−2 yr−1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ∼ 4.0–13.5 g C m−2 yr−1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.\n

\n\n\n

\n \n\n \n \n Polst, B. H.; Anlanger, C.; Risse-Buhl, U.; Larras, F.; Hein, T.; Weitere, M.; and Schmitt-Jansen, M.\n\n\n \n \n \n \n \n Hydrodynamics Alter the Tolerance of Autotrophic Biofilm Communities Toward Herbicides.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Microbiology, 9: 2884. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydrodynamics$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{polst_hydrodynamics_2018,\n\ttitle = {Hydrodynamics {Alter} the {Tolerance} of {Autotrophic} {Biofilm} {Communities} {Toward} {Herbicides}},\n\tvolume = {9},\n\tissn = {1664-302X},\n\turl = {https://www.frontiersin.org/article/10.3389/fmicb.2018.02884/full},\n\tdoi = {10.3389/fmicb.2018.02884},\n\turldate = {2022-11-16},\n\tjournal = {Frontiers in Microbiology},\n\tauthor = {Polst, Bastian H. and Anlanger, Christine and Risse-Buhl, Ute and Larras, Floriane and Hein, Thomas and Weitere, Markus and Schmitt-Jansen, Mechthild},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {2884},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Peters, R. L.; Fonti, P.; Frank, D. C.; Poyatos, R.; Pappas, C.; Kahmen, A.; Carraro, V.; Prendin, A. L.; Schneider, L.; Baltzer, J. L.; Baron‐Gafford, G. A.; Dietrich, L.; Heinrich, I.; Minor, R. L.; Sonnentag, O.; Matheny, A. M.; Wightman, M. G.; and Steppe, K.\n\n\n \n \n \n \n \n Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 219(4): 1283–1299. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peters_quantification_2018,\n\ttitle = {Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method},\n\tvolume = {219},\n\tissn = {0028-646X, 1469-8137},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.15241},\n\tdoi = {10.1111/nph.15241},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {New Phytologist},\n\tauthor = {Peters, Richard L. and Fonti, Patrick and Frank, David C. and Poyatos, Rafael and Pappas, Christoforos and Kahmen, Ansgar and Carraro, Vinicio and Prendin, Angela Luisa and Schneider, Loïc and Baltzer, Jennifer L. and Baron‐Gafford, Greg A. and Dietrich, Lars and Heinrich, Ingo and Minor, Rebecca L. and Sonnentag, Oliver and Matheny, Ashley M. and Wightman, Maxwell G. and Steppe, Kathy},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {1283--1299},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Peters, R. L.; Balanzategui, D.; Hurley, A. G.; von Arx, G.; Prendin, A. L.; Cuny, H. E.; Björklund, J.; Frank, D. C.; and Fonti, P.\n\n\n \n \n \n \n \n RAPTOR: Row and position tracheid organizer in R.\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 47: 10–16. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"RAPTOR:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peters_raptor_2018,\n\ttitle = {{RAPTOR}: {Row} and position tracheid organizer in {R}},\n\tvolume = {47},\n\tissn = {11257865},\n\tshorttitle = {{RAPTOR}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786517301236},\n\tdoi = {10.1016/j.dendro.2017.10.003},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Dendrochronologia},\n\tauthor = {Peters, Richard L. and Balanzategui, Daniel and Hurley, Alexander G. and von Arx, Georg and Prendin, Angela Luisa and Cuny, Henri E. and Björklund, Jesper and Frank, David C. and Fonti, Patrick},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {10--16},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Pavelka, M.; Acosta, M.; Kiese, R.; Altimir, N.; Brümmer, C.; Crill, P.; Darenova, E.; Fuß, R.; Gielen, B.; Graf, A.; Klemedtsson, L.; Lohila, A.; Longdoz, B.; Lindroth, A.; Nilsson, M.; Jiménez, S. M.; Merbold, L.; Montagnani, L.; Peichl, M.; Pihlatie, M.; Pumpanen, J.; Ortiz, P. S.; Silvennoinen, H.; Skiba, U.; Vestin, P.; Weslien, P.; Janous, D.; and Kutsch, W.\n\n\n \n \n \n \n \n Standardisation of chamber technique for CO2, N2O and CH4 fluxes measurements from terrestrial ecosystems.\n \n \n \n \n\n\n \n\n\n\n International Agrophysics, 32(4): 569–587. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Standardisation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pavelka_standardisation_2018,\n\ttitle = {Standardisation of chamber technique for {CO2}, {N2O} and {CH4} fluxes measurements from terrestrial ecosystems},\n\tvolume = {32},\n\tissn = {2300-8725},\n\turl = {http://archive.sciendo.com/INTAG/intag.2017.32.issue-4/intag-2017-0045/intag-2017-0045.pdf},\n\tdoi = {10.1515/intag-2017-0045},\n\tabstract = {Abstract \n             \n              Chamber measurements of trace gas fluxes between the land surface and the atmosphere have been conducted for almost a century. Different chamber techniques, including static and dynamic, have been used with varying degrees of success in estimating greenhouse gases (CO \n              2 \n              , CH \n              4 \n              , N \n              2 \n              O) fluxes. However, all of these have certain disadvantages which have either prevented them from providing an adequate estimate of greenhouse gas exchange or restricted them to be used under limited conditions. Generally, chamber methods are relatively low in cost and simple to operate. In combination with the appropriate sample allocations, chamber methods are adaptable for a wide variety of studies from local to global spatial scales, and they are particularly well suited for in situ and laboratory-based studies. Consequently, chamber measurements will play an important role in the portfolio of the Pan-European long-term research infrastructure Integrated Carbon Observation System. The respective working group of the Integrated Carbon Observation System Ecosystem Monitoring Station Assembly has decided to ascertain standards and quality checks for automated and manual chamber systems instead of defining one or several standard systems provided by commercial manufacturers in order to define minimum requirements for chamber measurements. The defined requirements and recommendations related to chamber measurements are described here.},\n\tnumber = {4},\n\turldate = {2022-11-16},\n\tjournal = {International Agrophysics},\n\tauthor = {Pavelka, Marian and Acosta, Manuel and Kiese, Ralf and Altimir, Núria and Brümmer, Christian and Crill, Patrick and Darenova, Eva and Fuß, Roland and Gielen, Bert and Graf, Alexander and Klemedtsson, Leif and Lohila, Annalea and Longdoz, Bernhard and Lindroth, Anders and Nilsson, Mats and Jiménez, Sara Maraňón and Merbold, Lutz and Montagnani, Leonardo and Peichl, Matthias and Pihlatie, Mari and Pumpanen, Jukka and Ortiz, Penelope Serrano and Silvennoinen, Hanna and Skiba, Ute and Vestin, Patrik and Weslien, Per and Janous, Dalibor and Kutsch, Werner},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {569--587},\n}\n\n\n\n

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\n \n\n \n \n Pauly, M.; Helle, G.; Miramont, C.; Büntgen, U.; Treydte, K.; Reinig, F.; Guibal, F.; Sivan, O.; Heinrich, I.; Riedel, F.; Kromer, B.; Balanzategui, D.; Wacker, L.; Sookdeo, A.; and Brauer, A.\n\n\n \n \n \n \n \n Subfossil trees suggest enhanced Mediterranean hydroclimate variability at the onset of the Younger Dryas.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 8(1): 13980. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Subfossil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pauly_subfossil_2018,\n\ttitle = {Subfossil trees suggest enhanced {Mediterranean} hydroclimate variability at the onset of the {Younger} {Dryas}},\n\tvolume = {8},\n\tissn = {2045-2322},\n\turl = {http://www.nature.com/articles/s41598-018-32251-2},\n\tdoi = {10.1038/s41598-018-32251-2},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Scientific Reports},\n\tauthor = {Pauly, Maren and Helle, Gerhard and Miramont, Cécile and Büntgen, Ulf and Treydte, Kerstin and Reinig, Frederick and Guibal, Frédéric and Sivan, Olivier and Heinrich, Ingo and Riedel, Frank and Kromer, Bernd and Balanzategui, Daniel and Wacker, Lukas and Sookdeo, Adam and Brauer, Achim},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {13980},\n}\n\n\n\n

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\n \n\n \n \n Nixdorf, E.; and Trauth, N.\n\n\n \n \n \n \n \n Evaluating the reliability of time series analysis to estimate variable riparian travel times by numerical groundwater modelling.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 32(3): 408–420. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{nixdorf_evaluating_2018,\n\ttitle = {Evaluating the reliability of time series analysis to estimate variable riparian travel times by numerical groundwater modelling},\n\tvolume = {32},\n\tissn = {08856087},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.11428},\n\tdoi = {10.1002/hyp.11428},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Hydrological Processes},\n\tauthor = {Nixdorf, Erik and Trauth, Nico},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {408--420},\n}\n\n\n\n

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\n \n\n \n \n Ney, P.; and Graf, A.\n\n\n \n \n \n \n \n High-Resolution Vertical Profile Measurements for Carbon Dioxide and Water Vapour Concentrations Within and Above Crop Canopies.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 166(3): 449–473. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"High-Resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ney_high-resolution_2018,\n\ttitle = {High-{Resolution} {Vertical} {Profile} {Measurements} for {Carbon} {Dioxide} and {Water} {Vapour} {Concentrations} {Within} and {Above} {Crop} {Canopies}},\n\tvolume = {166},\n\tissn = {0006-8314, 1573-1472},\n\turl = {http://link.springer.com/10.1007/s10546-017-0316-4},\n\tdoi = {10.1007/s10546-017-0316-4},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Ney, Patrizia and Graf, Alexander},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {449--473},\n}\n\n\n\n

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\n \n\n \n \n Naderpour, R.; and Schwank, M.\n\n\n \n \n \n \n \n Snow Wetness Retrieved from L-Band Radiometry.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(3): 359. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Snow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{naderpour_snow_2018,\n\ttitle = {Snow {Wetness} {Retrieved} from {L}-{Band} {Radiometry}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/10/3/359},\n\tdoi = {10.3390/rs10030359},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Naderpour, Reza and Schwank, Mike},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {359},\n}\n\n\n\n

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\n \n\n \n \n Musolff, A.; Fleckenstein, J.; Opitz, M.; Büttner, O.; Kumar, R.; and Tittel, J.\n\n\n \n \n \n \n \n Spatio-temporal controls of dissolved organic carbon stream water concentrations.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 566: 205–215. November 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{musolff_spatio-temporal_2018,\n\ttitle = {Spatio-temporal controls of dissolved organic carbon stream water concentrations},\n\tvolume = {566},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418306978},\n\tdoi = {10.1016/j.jhydrol.2018.09.011},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Musolff, A. and Fleckenstein, J.H. and Opitz, M. and Büttner, O. and Kumar, R. and Tittel, J.},\n\tmonth = nov,\n\tyear = {2018},\n\tpages = {205--215},\n}\n\n\n\n

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\n \n\n \n \n Müller, C.; Musolff, A.; Strachauer, U.; Brauns, M.; Tarasova, L.; Merz, R.; and Knöller, K.\n\n\n \n \n \n \n \n Tomography of anthropogenic nitrate contribution along a mesoscale river.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 615: 773–783. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Tomography$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{muller_tomography_2018,\n\ttitle = {Tomography of anthropogenic nitrate contribution along a mesoscale river},\n\tvolume = {615},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717326487},\n\tdoi = {10.1016/j.scitotenv.2017.09.297},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Müller, Christin and Musolff, Andreas and Strachauer, Ulrike and Brauns, Mario and Tarasova, Larisa and Merz, Ralf and Knöller, Kay},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {773--783},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Rötzer, K.; Bogena, H.; Sanchez, N.; and Vereecken, H.\n\n\n \n \n \n \n \n A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(3): 427. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_new_2018,\n\ttitle = {A {New} {Soil} {Moisture} {Downscaling} {Approach} for {SMAP}, {SMOS}, and {ASCAT} by {Predicting} {Sub}-{Grid} {Variability}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/10/3/427},\n\tdoi = {10.3390/rs10030427},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Montzka, Carsten and Rötzer, Kathrina and Bogena, Heye and Sanchez, Nilda and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {427},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Mollenhauer, H.; Kasner, M.; Haase, P.; Peterseil, J.; Wohner, C.; Frenzel, M.; Mirtl, M.; Schima, R.; Bumberger, J.; and Zacharias, S.\n\n\n \n \n \n \n \n Long-term environmental monitoring infrastructures in Europe: observations, measurements, scales, and socio-ecological representativeness.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 624: 968–978. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mollenhauer_long-term_2018,\n\ttitle = {Long-term environmental monitoring infrastructures in {Europe}: observations, measurements, scales, and socio-ecological representativeness},\n\tvolume = {624},\n\tissn = {00489697},\n\tshorttitle = {Long-term environmental monitoring infrastructures in {Europe}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717335210},\n\tdoi = {10.1016/j.scitotenv.2017.12.095},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Mollenhauer, Hannes and Kasner, Max and Haase, Peter and Peterseil, Johannes and Wohner, Christoph and Frenzel, Mark and Mirtl, Michael and Schima, Robert and Bumberger, Jan and Zacharias, Steffen},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {968--978},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Missong, A.; Bol, R.; Nischwitz, V.; Krüger, J.; Lang, F.; Siemens, J.; and Klumpp, E.\n\n\n \n \n \n \n \n Phosphorus in water dispersible-colloids of forest soil profiles.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 427(1-2): 71–86. June 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Phosphorus$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{missong_phosphorus_2018,\n\ttitle = {Phosphorus in water dispersible-colloids of forest soil profiles},\n\tvolume = {427},\n\tissn = {0032-079X, 1573-5036},\n\turl = {http://link.springer.com/10.1007/s11104-017-3430-7},\n\tdoi = {10.1007/s11104-017-3430-7},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-16},\n\tjournal = {Plant and Soil},\n\tauthor = {Missong, Anna and Bol, Roland and Nischwitz, Volker and Krüger, Jaane and Lang, Friederike and Siemens, Jan and Klumpp, Erwin},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {71--86},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Missong, A.; Holzmann, S.; Bol, R.; Nischwitz, V.; Puhlmann, H.; v. Wilpert, K.; Siemens, J.; and Klumpp, E.\n\n\n \n \n \n \n \n Leaching of natural colloids from forest topsoils and their relevance for phosphorus mobility.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 634: 305–315. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Leaching$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{missong_leaching_2018,\n\ttitle = {Leaching of natural colloids from forest topsoils and their relevance for phosphorus mobility},\n\tvolume = {634},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969718310246},\n\tdoi = {10.1016/j.scitotenv.2018.03.265},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Missong, Anna and Holzmann, Stefan and Bol, Roland and Nischwitz, Volker and Puhlmann, Heike and v. Wilpert, Klaus and Siemens, Jan and Klumpp, Erwin},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {305--315},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Mi, C.; Frassl, M. A.; Boehrer, B.; and Rinke, K.\n\n\n \n \n \n \n \n Episodic wind events induce persistent shifts in the thermal stratification of a reservoir (Rappbode Reservoir, Germany).\n \n \n \n \n\n\n \n\n\n\n International Review of Hydrobiology, 103(3-4): 71–82. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Episodic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mi_episodic_2018,\n\ttitle = {Episodic wind events induce persistent shifts in the thermal stratification of a reservoir ({Rappbode} {Reservoir}, {Germany})},\n\tvolume = {103},\n\tissn = {14342944},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/iroh.201701916},\n\tdoi = {10.1002/iroh.201701916},\n\tlanguage = {en},\n\tnumber = {3-4},\n\turldate = {2022-11-16},\n\tjournal = {International Review of Hydrobiology},\n\tauthor = {Mi, Chenxi and Frassl, Marieke A. and Boehrer, Bertram and Rinke, Karsten},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {71--82},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Meyer, T.; Weihermüller, L.; Vereecken, H.; and Jonard, F.\n\n\n \n \n \n \n \n Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(10): 1637. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Vegetation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{meyer_vegetation_2018,\n\ttitle = {Vegetation {Optical} {Depth} and {Soil} {Moisture} {Retrieved} from {L}-{Band} {Radiometry} over the {Growth} {Cycle} of a {Winter} {Wheat}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/10/10/1637},\n\tdoi = {10.3390/rs10101637},\n\tabstract = {L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Meyer, Thomas and Weihermüller, Lutz and Vereecken, Harry and Jonard, François},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {1637},\n}\n\n\n\n

\n\n\n

\n L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.\n

\n\n\n

\n \n\n \n \n Meyer, N.; Meyer, H.; Welp, G.; and Amelung, W.\n\n\n \n \n \n \n \n Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 323: 31–40. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{meyer_soil_2018,\n\ttitle = {Soil respiration and its temperature sensitivity ({Q10}): {Rapid} acquisition using mid-infrared spectroscopy},\n\tvolume = {323},\n\tissn = {00167061},\n\tshorttitle = {Soil respiration and its temperature sensitivity ({Q10})},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117311242},\n\tdoi = {10.1016/j.geoderma.2018.02.031},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Geoderma},\n\tauthor = {Meyer, Nele and Meyer, Hanna and Welp, Gerhard and Amelung, Wulf},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {31--40},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Meyer, M.; Krabel, D.; Kniesel, B.; and Helle, G.\n\n\n \n \n \n \n \n Inter-annual variation of tree-ring width, δ13C and δ18O in juvenile trees of five plantation poplar cultivars (Populus spp.).\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 51: 32–39. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Inter-annual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{meyer_inter-annual_2018,\n\ttitle = {Inter-annual variation of tree-ring width, δ{13C} and δ{18O} in juvenile trees of five plantation poplar cultivars ({Populus} spp.)},\n\tvolume = {51},\n\tissn = {11257865},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786517301832},\n\tdoi = {10.1016/j.dendro.2018.07.002},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Dendrochronologia},\n\tauthor = {Meyer, Matthias and Krabel, Doris and Kniesel, Britt and Helle, Gerhard},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {32--39},\n}\n\n\n\n

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\n \n\n \n \n Mauder, M.; Genzel, S.; Fu, J.; Kiese, R.; Soltani, M.; Steinbrecher, R.; Zeeman, M.; Banerjee, T.; De Roo, F.; and Kunstmann, H.\n\n\n \n \n \n \n \n Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 32(1): 39–50. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mauder_evaluation_2018,\n\ttitle = {Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations},\n\tvolume = {32},\n\tissn = {08856087},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.11397},\n\tdoi = {10.1002/hyp.11397},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Hydrological Processes},\n\tauthor = {Mauder, Matthias and Genzel, Sandra and Fu, Jin and Kiese, Ralf and Soltani, Mohsen and Steinbrecher, Rainer and Zeeman, Matthias and Banerjee, Tirtha and De Roo, Frederik and Kunstmann, Harald},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {39--50},\n}\n\n\n\n

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\n \n\n \n \n Mauder, M.; and Zeeman, M. J.\n\n\n \n \n \n \n \n Field intercomparison of prevailing sonic anemometers.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 11(1): 249–263. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Field$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mauder_field_2018,\n\ttitle = {Field intercomparison of prevailing sonic anemometers},\n\tvolume = {11},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/11/249/2018/},\n\tdoi = {10.5194/amt-11-249-2018},\n\tabstract = {Abstract. Three-dimensional sonic anemometers are the core component of eddy covariance systems, which are widely used for micrometeorological and ecological research. In order to characterize the measurement uncertainty of these instruments we present and analyse the results from a field intercomparison experiment of six commonly used sonic anemometer models from four major manufacturers. These models include Campbell CSAT3, Gill HS-50 and R3, METEK uSonic-3 Omni, R. M. Young 81000 and 81000RE. The experiment was conducted over a meadow at the TERENO/ICOS site DE-Fen in southern Germany over a period of 16 days in June of 2016 as part of the ScaleX campaign. The measurement height was 3 m for all sensors, which were separated by 9 m from each other, each on its own tripod, in order to limit contamination of the turbulence measurements by adjacent structures as much as possible. Moreover, the high-frequency data from all instruments were treated with the same post-processing algorithm. In this study, we compare the results for various turbulence statistics, which include mean horizontal wind speed, standard deviations of vertical wind velocity and sonic temperature, friction velocity, and the buoyancy flux. Quantitative measures of uncertainty, such as bias and comparability, are derived from these results. We find that biases are generally very small for all sensors and all computed variables, except for the sonic temperature measurements of the two Gill sonic anemometers (HS and R3), confirming a known transducer-temperature dependence of the sonic temperature measurement. The best overall agreement between the different instruments was found for the mean wind speed and the buoyancy flux.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Mauder, Matthias and Zeeman, Matthias J.},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {249--263},\n}\n\n\n\n

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\n \n\n \n \n Massei, R.; Byers, H.; Beckers, L.; Prothmann, J.; Brack, W.; Schulze, T.; and Krauss, M.\n\n\n \n \n \n \n \n A sediment extraction and cleanup method for wide-scope multitarget screening by liquid chromatography–high-resolution mass spectrometry.\n \n \n \n \n\n\n \n\n\n\n Analytical and Bioanalytical Chemistry, 410(1): 177–188. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{massei_sediment_2018,\n\ttitle = {A sediment extraction and cleanup method for wide-scope multitarget screening by liquid chromatography–high-resolution mass spectrometry},\n\tvolume = {410},\n\tissn = {1618-2642, 1618-2650},\n\turl = {http://link.springer.com/10.1007/s00216-017-0708-9},\n\tdoi = {10.1007/s00216-017-0708-9},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Analytical and Bioanalytical Chemistry},\n\tauthor = {Massei, Riccardo and Byers, Harry and Beckers, Liza-Marie and Prothmann, Jens and Brack, Werner and Schulze, Tobias and Krauss, Martin},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {177--188},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Martínez, B.; Sanchez-Ruiz, S.; Gilabert, M.; Moreno, A.; Campos-Taberner, M.; García-Haro, F.; Trigo, I.; Aurela, M.; Brümmer, C.; Carrara, A.; De Ligne, A.; Gianelle, D.; Grünwald, T.; Limousin, J.; Lohila, A.; Mammarella, I.; Sottocornola, M.; Steinbrecher, R.; and Tagesson, T.\n\n\n \n \n \n \n \n Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 65: 124–136. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Retrieval$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martinez_retrieval_2018,\n\ttitle = {Retrieval of daily gross primary production over {Europe} and {Africa} from an ensemble of {SEVIRI}/{MSG} products},\n\tvolume = {65},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243417302301},\n\tdoi = {10.1016/j.jag.2017.10.011},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Martínez, B. and Sanchez-Ruiz, S. and Gilabert, M.A. and Moreno, A. and Campos-Taberner, M. and García-Haro, F.J. and Trigo, I.F. and Aurela, M. and Brümmer, C. and Carrara, A. and De Ligne, A. and Gianelle, D. and Grünwald, T. and Limousin, J.M. and Lohila, A. and Mammarella, I. and Sottocornola, M. and Steinbrecher, R. and Tagesson, T.},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {124--136},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Martens, B.; de Jeu, R.; Verhoest, N.; Schuurmans, H.; Kleijer, J.; and Miralles, D.\n\n\n \n \n \n \n \n Towards Estimating Land Evaporation at Field Scales Using GLEAM.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(11): 1720. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martens_towards_2018,\n\ttitle = {Towards {Estimating} {Land} {Evaporation} at {Field} {Scales} {Using} {GLEAM}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/10/11/1720},\n\tdoi = {10.3390/rs10111720},\n\tabstract = {The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Martens, Brecht and de Jeu, Richard and Verhoest, Niko and Schuurmans, Hanneke and Kleijer, Jonne and Miralles, Diego},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {1720},\n}\n\n\n\n

\n\n\n

\n The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.\n

\n\n\n

\n \n\n \n \n Marke, T.; Crewell, S.; Schemann, V.; Schween, J. H.; and Tuononen, M.\n\n\n \n \n \n \n \n Long-Term Observations and High-Resolution Modeling of Midlatitude Nocturnal Boundary Layer Processes Connected to Low-Level Jets.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Meteorology and Climatology, 57(5): 1155–1170. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-Term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{marke_long-term_2018,\n\ttitle = {Long-{Term} {Observations} and {High}-{Resolution} {Modeling} of {Midlatitude} {Nocturnal} {Boundary} {Layer} {Processes} {Connected} to {Low}-{Level} {Jets}},\n\tvolume = {57},\n\tissn = {1558-8424, 1558-8432},\n\turl = {https://journals.ametsoc.org/view/journals/apme/57/5/jamc-d-17-0341.1.xml},\n\tdoi = {10.1175/JAMC-D-17-0341.1},\n\tabstract = {Abstract \n             \n              Low-level-jet (LLJ) periods are investigated by exploiting a long-term record of ground-based remote sensing Doppler wind lidar measurements supported by tower observations and surface flux measurements at the Jülich Observatory for Cloud Evolution (JOYCE), a midlatitude site in western Germany. LLJs were found 13\\% of the time during continuous observations over more than 4 yr. The climatological behavior of the LLJs shows a prevailing nighttime appearance of the jets, with a median height of 375 m and a median wind speed of 8.8 m s \n              −1 \n              at the jet nose. Significant turbulence below the jet nose only occurs for high bulk wind shear, which is an important parameter for describing the turbulent characteristics of the jets. The numerous LLJs (16\\% of all jets) in the range of wind-turbine rotor heights below 200 m demonstrate the importance of LLJs and the associated intermittent turbulence for wind-energy applications. Also, a decrease in surface fluxes and an accumulation of carbon dioxide are observed if LLJs are present. A comprehensive analysis of an LLJ case shows the influence of the surrounding topography, dominated by an open pit mine and a 200-m-high hill, on the wind observed at JOYCE. High-resolution large-eddy simulations that complement the observations show that the spatial distribution of the wind field exhibits variations connected with the orographic flow depending on the wind direction, causing high variability in the long-term measurements of the vertical velocity.},\n\tnumber = {5},\n\turldate = {2022-11-16},\n\tjournal = {Journal of Applied Meteorology and Climatology},\n\tauthor = {Marke, Tobias and Crewell, Susanne and Schemann, Vera and Schween, Jan H. and Tuononen, Minttu},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {1155--1170},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Makselon, J.; Siebers, N.; Meier, F.; Vereecken, H.; and Klumpp, E.\n\n\n \n \n \n \n \n Role of rain intensity and soil colloids in the retention of surfactant-stabilized silver nanoparticles in soil.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 238: 1027–1034. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Role$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{makselon_role_2018,\n\ttitle = {Role of rain intensity and soil colloids in the retention of surfactant-stabilized silver nanoparticles in soil},\n\tvolume = {238},\n\tissn = {02697491},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749117346201},\n\tdoi = {10.1016/j.envpol.2018.02.025},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Environmental Pollution},\n\tauthor = {Makselon, Joanna and Siebers, Nina and Meier, Florian and Vereecken, Harry and Klumpp, Erwin},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {1027--1034},\n}\n\n\n\n

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\n \n\n \n \n Mader, M.; Roberts, A. M.; Porst, D.; Schmidt, C.; Trauth, N.; van Geldern, R.; and Barth, J. A.\n\n\n \n \n \n \n \n River recharge versus O2 supply from the unsaturated zone in shallow riparian groundwater: A case study from the Selke River (Germany).\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 634: 374–381. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"River$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mader_river_2018,\n\ttitle = {River recharge versus {O2} supply from the unsaturated zone in shallow riparian groundwater: {A} case study from the {Selke} {River} ({Germany})},\n\tvolume = {634},\n\tissn = {00489697},\n\tshorttitle = {River recharge versus {O2} supply from the unsaturated zone in shallow riparian groundwater},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969718309896},\n\tdoi = {10.1016/j.scitotenv.2018.03.230},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Mader, Michael and Roberts, André M. and Porst, David and Schmidt, Christian and Trauth, Nico and van Geldern, Robert and Barth, Johannes A.C.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {374--381},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Lutz, S. R.; Krieg, R.; Müller, C.; Zink, M.; Knöller, K.; Samaniego, L.; and Merz, R.\n\n\n \n \n \n \n \n Spatial Patterns of Water Age: Using Young Water Fractions to Improve the Characterization of Transit Times in Contrasting Catchments.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(7): 4767–4784. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lutz_spatial_2018,\n\ttitle = {Spatial {Patterns} of {Water} {Age}: {Using} {Young} {Water} {Fractions} to {Improve} the {Characterization} of {Transit} {Times} in {Contrasting} {Catchments}},\n\tvolume = {54},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Spatial {Patterns} of {Water} {Age}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2017WR022216},\n\tdoi = {10.1029/2017WR022216},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Lutz, S. R. and Krieg, R. and Müller, C. and Zink, M. and Knöller, K. and Samaniego, L. and Merz, R.},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {4767--4784},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Liu, S.; Schloter, M.; and Brüggemann, N.\n\n\n \n \n \n \n \n Accumulation of NO $_{\\textrm{2}}$ $^{\\textrm{−}}$ during periods of drying stimulates soil N $_{\\textrm{2}}$ O emissions during subsequent rewetting: Nitrite stimulates N $_{\\textrm{2}}$ O emissions during rewetting.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Science, 69(5): 936–946. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Accumulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_accumulation_2018,\n\ttitle = {Accumulation of {NO} $_{\\textrm{2}}$ $^{\\textrm{−}}$ during periods of drying stimulates soil {N} $_{\\textrm{2}}$ {O} emissions during subsequent rewetting: {Nitrite} stimulates {N} $_{\\textrm{2}}$ {O} emissions during rewetting},\n\tvolume = {69},\n\tissn = {13510754},\n\tshorttitle = {Accumulation of {NO} $_{\\textrm{2}}$ $^{\\textrm{−}}$ during periods of drying stimulates soil {N} $_{\\textrm{2}}$ {O} emissions during subsequent rewetting},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ejss.12683},\n\tdoi = {10.1111/ejss.12683},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-16},\n\tjournal = {European Journal of Soil Science},\n\tauthor = {Liu, S. and Schloter, M. and Brüggemann, N.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {936--946},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Lischeid, G.; Kalettka, T.; Holländer, M.; Steidl, J.; Merz, C.; Dannowski, R.; Hohenbrink, T.; Lehr, C.; Onandia, G.; Reverey, F.; and Pätzig, M.\n\n\n \n \n \n \n \n Natural ponds in an agricultural landscape: External drivers, internal processes, and the role of the terrestrial-aquatic interface.\n \n \n \n \n\n\n \n\n\n\n Limnologica, 68: 5–16. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Natural$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lischeid_natural_2018,\n\ttitle = {Natural ponds in an agricultural landscape: {External} drivers, internal processes, and the role of the terrestrial-aquatic interface},\n\tvolume = {68},\n\tissn = {00759511},\n\tshorttitle = {Natural ponds in an agricultural landscape},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0075951116300524},\n\tdoi = {10.1016/j.limno.2017.01.003},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Limnologica},\n\tauthor = {Lischeid, Gunnar and Kalettka, Thomas and Holländer, Matthias and Steidl, Jörg and Merz, Christoph and Dannowski, Ralf and Hohenbrink, Tobias and Lehr, Christian and Onandia, Gabriela and Reverey, Florian and Pätzig, Marlene},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {5--16},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Lehr, C.; Dannowski, R.; Kalettka, T.; Merz, C.; Schröder, B.; Steidl, J.; and Lischeid, G.\n\n\n \n \n \n \n \n Detecting dominant changes in irregularly sampled multivariate water quality data sets.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 22(8): 4401–4424. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Detecting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lehr_detecting_2018,\n\ttitle = {Detecting dominant changes in irregularly sampled multivariate water quality data sets},\n\tvolume = {22},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/22/4401/2018/},\n\tdoi = {10.5194/hess-22-4401-2018},\n\tabstract = {Abstract. Time series of groundwater and stream water quality often exhibit substantial\ntemporal and spatial variability, whereas typical existing monitoring data\nsets, e.g. from environmental agencies, are usually characterized by\nrelatively low sampling frequency and irregular sampling in space and/or\ntime. This complicates the differentiation between anthropogenic influence\nand natural variability as well as the detection of changes in water quality\nwhich indicate changes in single drivers. We suggest the new term “dominant\nchanges” for changes in multivariate water quality data which concern\n(1) multiple variables, (2) multiple sites and (3) long-term patterns and\npresent an exploratory framework for the detection of such dominant changes\nin data sets with irregular sampling in space and time. Firstly, a non-linear\ndimension-reduction technique was used to summarize the dominant\nspatiotemporal dynamics in the multivariate water quality data set in a few\ncomponents. Those were used to derive hypotheses on the dominant drivers\ninfluencing water quality. Secondly, different sampling sites were compared\nwith respect to median component values. Thirdly, time series of the\ncomponents at single sites were analysed for long-term patterns. We tested\nthe approach with a joint stream water and groundwater data set quality\nconsisting of 1572 samples, each comprising sixteen variables, sampled with a\nspatially and temporally irregular sampling scheme at 29 sites in northeast\nGermany from 1998 to 2009. The first four components were interpreted as\n(1) an agriculturally induced enhancement of the natural background level of\nsolute concentration, (2) a redox sequence from reducing conditions in deep\ngroundwater to post-oxic conditions in shallow groundwater and oxic\nconditions in stream water, (3) a mixing ratio of deep and shallow\ngroundwater to the streamflow and (4) sporadic events of slurry application\nin the agricultural practice. Dominant changes were observed for the first\ntwo components. The changing intensity of the first component was interpreted\nas response to the temporal variability of the thickness of the unsaturated\nzone. A steady increase in the second component at most stream water sites\npointed towards progressing depletion of the denitrification capacity of the\ndeep aquifer.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-16},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Lehr, Christian and Dannowski, Ralf and Kalettka, Thomas and Merz, Christoph and Schröder, Boris and Steidl, Jörg and Lischeid, Gunnar},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {4401--4424},\n}\n\n\n\n

\n\n\n

\n Abstract. Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term “dominant changes” for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.\n

\n\n\n

\n \n\n \n \n Lausch, A.; Bastian, O.; Klotz, S.; Leitão, P. J.; Jung, A.; Rocchini, D.; Schaepman, M. E.; Skidmore, A. K.; Tischendorf, L.; and Knapp, S.\n\n\n \n \n \n \n \n Understanding and assessing vegetation health by in situ species and remote‐sensing approaches.\n \n \n \n \n\n\n \n\n\n\n Methods in Ecology and Evolution, 9(8): 1799–1809. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_understanding_2018,\n\ttitle = {Understanding and assessing vegetation health by in situ species and remote‐sensing approaches},\n\tvolume = {9},\n\tissn = {2041-210X, 2041-210X},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13025},\n\tdoi = {10.1111/2041-210X.13025},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-16},\n\tjournal = {Methods in Ecology and Evolution},\n\tauthor = {Lausch, Angela and Bastian, Olaf and Klotz, Stefan and Leitão, Pedro J. and Jung, András and Rocchini, Duccio and Schaepman, Michael E. and Skidmore, Andrew K. and Tischendorf, Lutz and Knapp, Sonja},\n\teditor = {Vihervaara, Petteri},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {1799--1809},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Lausch, A.; Borg, E.; Bumberger, J.; Dietrich, P.; Heurich, M.; Huth, A.; Jung, A.; Klenke, R.; Knapp, S.; Mollenhauer, H.; Paasche, H.; Paulheim, H.; Pause, M.; Schweitzer, C.; Schmulius, C.; Settele, J.; Skidmore, A.; Wegmann, M.; Zacharias, S.; Kirsten, T.; and Schaepman, M.\n\n\n \n \n \n \n \n Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(7): 1120. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_understanding_2018,\n\ttitle = {Understanding {Forest} {Health} with {Remote} {Sensing}, {Part} {III}: {Requirements} for a {Scalable} {Multi}-{Source} {Forest} {Health} {Monitoring} {Network} {Based} on {Data} {Science} {Approaches}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\tshorttitle = {Understanding {Forest} {Health} with {Remote} {Sensing}, {Part} {III}},\n\turl = {http://www.mdpi.com/2072-4292/10/7/1120},\n\tdoi = {10.3390/rs10071120},\n\tabstract = {Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, András and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {1120},\n}\n\n\n\n

\n\n\n

\n Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.\n

\n\n\n

\n \n\n \n \n Kröniger, K.; Banerjee, T.; De Roo, F.; and Mauder, M.\n\n\n \n \n \n \n \n Flow adjustment inside homogeneous canopies after a leading edge – An analytical approach backed by LES.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 255: 17–30. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Flow$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kroniger_flow_2018,\n\ttitle = {Flow adjustment inside homogeneous canopies after a leading edge – {An} analytical approach backed by {LES}},\n\tvolume = {255},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192317303143},\n\tdoi = {10.1016/j.agrformet.2017.09.019},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Kröniger, Konstantin and Banerjee, Tirtha and De Roo, Frederik and Mauder, Matthias},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {17--30},\n}\n\n\n\n

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\n \n\n \n \n Kollet, S.; Gasper, F.; Brdar, S.; Goergen, K.; Hendricks-Franssen, H.; Keune, J.; Kurtz, W.; Küll, V.; Pappenberger, F.; Poll, S.; Trömel, S.; Shrestha, P.; Simmer, C.; and Sulis, M.\n\n\n \n \n \n \n \n Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0).\n \n \n \n \n\n\n \n\n\n\n Technical Report EARTH SCIENCES, October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Introduction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{kollet_introduction_2018,\n\ttype = {preprint},\n\ttitle = {Introduction of an {Experimental} {Terrestrial} {Forecasting}/{Monitoring} {System} at {Regional} to {Continental} {Scales} {Based} on the {Terrestrial} {System} {Modeling} {Platform} (v1.1.0)},\n\turl = {https://www.preprints.org/manuscript/201810.0625/v1},\n\tabstract = {Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.},\n\turldate = {2022-11-16},\n\tinstitution = {EARTH SCIENCES},\n\tauthor = {Kollet, Stefan and Gasper, Fabian and Brdar, Slavko and Goergen, Klaus and Hendricks-Franssen, Harrie-Jan and Keune, Jessica and Kurtz, Wolfgang and Küll, Volker and Pappenberger, Florian and Poll, Stefan and Trömel, Silke and Shrestha, Prabhakar and Simmer, Clemens and Sulis, Mauro},\n\tmonth = oct,\n\tyear = {2018},\n\tdoi = {10.20944/preprints201810.0625.v1},\n}\n\n\n\n

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\n \n\n \n \n Köhli, M.; Schrön, M.; and Schmidt, U.\n\n\n \n \n \n \n \n Response functions for detectors in cosmic ray neutron sensing.\n \n \n \n \n\n\n \n\n\n\n Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 902: 184–189. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Response$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kohli_response_2018,\n\ttitle = {Response functions for detectors in cosmic ray neutron sensing},\n\tvolume = {902},\n\tissn = {01689002},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168900218307745},\n\tdoi = {10.1016/j.nima.2018.06.052},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},\n\tauthor = {Köhli, M. and Schrön, M. and Schmidt, U.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {184--189},\n}\n\n\n\n

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\n \n\n \n \n Knillmann, S.; Orlinskiy, P.; Kaske, O.; Foit, K.; and Liess, M.\n\n\n \n \n \n \n \n Indication of pesticide effects and recolonization in streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 630: 1619–1627. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Indication$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{knillmann_indication_2018,\n\ttitle = {Indication of pesticide effects and recolonization in streams},\n\tvolume = {630},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969718304443},\n\tdoi = {10.1016/j.scitotenv.2018.02.056},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Knillmann, Saskia and Orlinskiy, Polina and Kaske, Oliver and Foit, Kaarina and Liess, Matthias},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {1619--1627},\n}\n\n\n\n

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\n \n\n \n \n Klotzsche, A.; Lärm, L.; Weihermüller, L.; Vanderborght, J.; Vereecken, H.; and van der Kruk, J.\n\n\n \n \n \n \n \n Time-lapse horizontal borehole GPR measurements to investigate spatial and temporal soil-water content changes.\n \n \n \n \n\n\n \n\n\n\n In SEG Technical Program Expanded Abstracts 2018, pages 4904–4908, Anaheim, California, August 2018. Society of Exploration Geophysicists\n \n\n\n\n
\n\n\n\n \n \n $\"Time-lapse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{klotzsche_time-lapse_2018,\n\taddress = {Anaheim, California},\n\ttitle = {Time-lapse horizontal borehole {GPR} measurements to investigate spatial and temporal soil-water content changes},\n\turl = {https://library.seg.org/doi/10.1190/segam2018-2995843.1},\n\tdoi = {10.1190/segam2018-2995843.1},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tbooktitle = {{SEG} {Technical} {Program} {Expanded} {Abstracts} 2018},\n\tpublisher = {Society of Exploration Geophysicists},\n\tauthor = {Klotzsche, Anja and Lärm, Lena and Weihermüller, Lutz and Vanderborght, Jan and Vereecken, Harry and van der Kruk, Jan},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {4904--4908},\n}\n\n\n\n

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\n \n\n \n \n Klinke, R.; Kuechly, H.; Frick, A.; Förster, M.; Schmidt, T.; Holtgrave, A.; Kleinschmit, B.; Spengler, D.; and Neumann, C.\n\n\n \n \n \n \n \n Indicator-Based Soil Moisture Monitoring of Wetlands by Utilizing Sentinel and Landsat Remote Sensing Data.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2): 71–84. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Indicator-Based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{klinke_indicator-based_2018,\n\ttitle = {Indicator-{Based} {Soil} {Moisture} {Monitoring} of {Wetlands} by {Utilizing} {Sentinel} and {Landsat} {Remote} {Sensing} {Data}},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0044-5},\n\tdoi = {10.1007/s41064-018-0044-5},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Klinke, Randolf and Kuechly, Helga and Frick, Annett and Förster, Michael and Schmidt, Tobias and Holtgrave, Ann-Kathrin and Kleinschmit, Birgit and Spengler, Daniel and Neumann, Carsten},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {71--84},\n}\n\n\n\n

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\n \n\n \n \n Kiese, R.; Fersch, B.; Baessler, C.; Brosy, C.; Butterbach-Bahl, K.; Chwala, C.; Dannenmann, M.; Fu, J.; Gasche, R.; Grote, R.; Jahn, C.; Klatt, J.; Kunstmann, H.; Mauder, M.; Rödiger, T.; Smiatek, G.; Soltani, M.; Steinbrecher, R.; Völksch, I.; Werhahn, J.; Wolf, B.; Zeeman, M.; and Schmid, H.\n\n\n \n \n \n \n \n The TERENO Pre‐Alpine Observatory: Integrating Meteorological, Hydrological, and Biogeochemical Measurements and Modeling.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 1–17. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kiese_tereno_2018,\n\ttitle = {The {TERENO} {Pre}‐{Alpine} {Observatory}: {Integrating} {Meteorological}, {Hydrological}, and {Biogeochemical} {Measurements} and {Modeling}},\n\tvolume = {17},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {The {TERENO} {Pre}‐{Alpine} {Observatory}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2018.03.0060},\n\tdoi = {10.2136/vzj2018.03.0060},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Kiese, R. and Fersch, B. and Baessler, C. and Brosy, C. and Butterbach-Bahl, K. and Chwala, C. and Dannenmann, M. and Fu, J. and Gasche, R. and Grote, R. and Jahn, C. and Klatt, J. and Kunstmann, H. and Mauder, M. and Rödiger, T. and Smiatek, G. and Soltani, M. and Steinbrecher, R. and Völksch, I. and Werhahn, J. and Wolf, B. and Zeeman, M. and Schmid, H.P.},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {1--17},\n}\n\n\n\n

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\n \n\n \n \n Kaufmann, M. S.; Klotzsche, A.; Vereecken, H; and van der Kruk, J.\n\n\n \n \n \n \n \n Simultaneous multi-channel GPR measurements for soil characterization.\n \n \n \n \n\n\n \n\n\n\n In 2018 17th International Conference on Ground Penetrating Radar (GPR), pages 1–4, Rapperswil, June 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{kaufmann_simultaneous_2018,\n\taddress = {Rapperswil},\n\ttitle = {Simultaneous multi-channel {GPR} measurements for soil characterization},\n\tisbn = {9781538657775},\n\turl = {https://ieeexplore.ieee.org/document/8441602/},\n\tdoi = {10.1109/ICGPR.2018.8441602},\n\turldate = {2022-11-16},\n\tbooktitle = {2018 17th {International} {Conference} on {Ground} {Penetrating} {Radar} ({GPR})},\n\tpublisher = {IEEE},\n\tauthor = {Kaufmann, M. S. and Klotzsche, A. and Vereecken, H and van der Kruk, J.},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {1--4},\n}\n\n\n\n

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\n \n\n \n \n Kappler, C.; Kaiser, K.; Tanski, P.; Klos, F.; Fülling, A.; Mrotzek, A.; Sommer, M.; and Bens, O.\n\n\n \n \n \n \n \n Stratigraphy and age of colluvial deposits indicating Late Holocene soil erosion in northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n CATENA, 170: 224–245. November 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Stratigraphy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kappler_stratigraphy_2018,\n\ttitle = {Stratigraphy and age of colluvial deposits indicating {Late} {Holocene} soil erosion in northeastern {Germany}},\n\tvolume = {170},\n\tissn = {03418162},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S034181621830242X},\n\tdoi = {10.1016/j.catena.2018.06.010},\n\tlanguage = {en},\n\turldate = {2022-11-16},\n\tjournal = {CATENA},\n\tauthor = {Kappler, Christoph and Kaiser, Knut and Tanski, Phillipp and Klos, Friederike and Fülling, Alexander and Mrotzek, Almut and Sommer, Michael and Bens, Oliver},\n\tmonth = nov,\n\tyear = {2018},\n\tpages = {224--245},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Oldorff, S.; Breitbach, C.; Kappler, C.; Theuerkauf, M.; Scharnweber, T.; Schult, M.; Küster, M.; Engelhardt, C.; Heinrich, I.; Hupfer, M.; Schwalbe, G.; Kirschey, T.; and Bens, O.\n\n\n \n \n \n \n \n A submerged pine forest from the early Holocene in the Mecklenburg Lake District, northern Germany.\n \n \n \n \n\n\n \n\n\n\n Boreas, 47(3): 910–925. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_submerged_2018,\n\ttitle = {A submerged pine forest from the early {Holocene} in the {Mecklenburg} {Lake} {District}, northern {Germany}},\n\tvolume = {47},\n\tissn = {03009483},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12314},\n\tdoi = {10.1111/bor.12314},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-16},\n\tjournal = {Boreas},\n\tauthor = {Kaiser, Knut and Oldorff, Silke and Breitbach, Carsten and Kappler, Christoph and Theuerkauf, Martin and Scharnweber, Tobias and Schult, Manuela and Küster, Mathias and Engelhardt, Christof and Heinrich, Ingo and Hupfer, Michael and Schwalbe, Grit and Kirschey, Tom and Bens, Oliver},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {910--925},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Keller, N.; Brande, A.; Dalitz, S.; Hensel, N.; Heußner, K.; Kappler, C.; Michas, U.; Müller, J.; Schwalbe, G.; Weiße, R.; and Bens, O.\n\n\n \n \n \n \n \n A large-scale medieval dam-lake cascade in central Europe: Water level dynamics of the Havel River, Berlin-Brandenburg region, Germany.\n \n \n \n \n\n\n \n\n\n\n Geoarchaeology, 33(2): 237–259. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_large-scale_2018,\n\ttitle = {A large-scale medieval dam-lake cascade in central {Europe}: {Water} level dynamics of the {Havel} {River}, {Berlin}-{Brandenburg} region, {Germany}},\n\tvolume = {33},\n\tissn = {08836353},\n\tshorttitle = {A large-scale medieval dam-lake cascade in central {Europe}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/gea.21649},\n\tdoi = {10.1002/gea.21649},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {Geoarchaeology},\n\tauthor = {Kaiser, Knut and Keller, Nora and Brande, Arthur and Dalitz, Stefan and Hensel, Nicola and Heußner, Karl-Uwe and Kappler, Christoph and Michas, Uwe and Müller, Joachim and Schwalbe, Grit and Weiße, Roland and Bens, Oliver},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {237--259},\n}\n\n\n\n

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\n \n\n \n \n Jonard, F.; Bircher, S.; Demontoux, F.; Weihermüller, L.; Razafindratsima, S.; Wigneron, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 10(2): 304. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Passive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jonard_passive_2018,\n\ttitle = {Passive {L}-{Band} {Microwave} {Remote} {Sensing} of {Organic} {Soil} {Surface} {Layers}: {A} {Tower}-{Based} {Experiment}},\n\tvolume = {10},\n\tissn = {2072-4292},\n\tshorttitle = {Passive {L}-{Band} {Microwave} {Remote} {Sensing} of {Organic} {Soil} {Surface} {Layers}},\n\turl = {http://www.mdpi.com/2072-4292/10/2/304},\n\tdoi = {10.3390/rs10020304},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-16},\n\tjournal = {Remote Sensing},\n\tauthor = {Jonard, François and Bircher, Simone and Demontoux, François and Weihermüller, Lutz and Razafindratsima, Stephen and Wigneron, Jean-Pierre and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {304},\n}\n\n\n\n

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\n \n\n \n \n Jonard, F.; Bogena, H.; Caterina, D.; Garré, S.; Klotzsche, A.; Monerris, A.; Schwank, M.; and von Hebel, C.\n\n\n \n \n \n \n \n Ground-Based Soil Moisture Determination.\n \n \n \n \n\n\n \n\n\n\n In Li, X.; and Vereecken, H., editor(s), Observation and Measurement, pages 1–42. Springer Berlin Heidelberg, Berlin, Heidelberg, 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Ground-Based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{li_ground-based_2018,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Ground-{Based} {Soil} {Moisture} {Determination}},\n\tisbn = {9783662478714},\n\turl = {http://link.springer.com/10.1007/978-3-662-47871-4_2-1},\n\turldate = {2022-11-16},\n\tbooktitle = {Observation and {Measurement}},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Jonard, François and Bogena, Heye and Caterina, David and Garré, Sarah and Klotzsche, Anja and Monerris, Alessandra and Schwank, Mike and von Hebel, Christian},\n\teditor = {Li, Xin and Vereecken, Harry},\n\tyear = {2018},\n\tdoi = {10.1007/978-3-662-47871-4_2-1},\n\tpages = {1--42},\n}\n\n\n\n

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\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Vereecken, H.; Diekkrüger, B.; and Bogena, H. R.\n\n\n \n \n \n \n \n Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Biomass Quantification in Drought Conditions.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(10): 7383–7402. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Cosmic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jakobi_cosmic_2018,\n\ttitle = {Cosmic {Ray} {Neutron} {Sensing} for {Simultaneous} {Soil} {Water} {Content} and {Biomass} {Quantification} in {Drought} {Conditions}},\n\tvolume = {54},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018WR022692},\n\tdoi = {10.1029/2018WR022692},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-16},\n\tjournal = {Water Resources Research},\n\tauthor = {Jakobi, J. and Huisman, J. A. and Vereecken, H. and Diekkrüger, B. and Bogena, H. R.},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {7383--7402},\n}\n\n\n\n

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\n \n\n \n \n De Roo, F.; Zhang, S.; Huq, S.; and Mauder, M.\n\n\n \n \n \n \n \n A semi-empirical model of the energy balance closure in the surface layer.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 13(12): e0209022. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_roo_semi-empirical_2018,\n\ttitle = {A semi-empirical model of the energy balance closure in the surface layer},\n\tvolume = {13},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0209022},\n\tdoi = {10.1371/journal.pone.0209022},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-04},\n\tjournal = {PLOS ONE},\n\tauthor = {De Roo, Frederik and Zhang, Sha and Huq, Sadiq and Mauder, Matthias},\n\teditor = {Sihi, Debjani},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {e0209022},\n}\n\n\n\n

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\n \n\n \n \n Jagdhuber, T.; Fersch, B.; Schron, M.; Jager, M.; Voormansik, K.; and Lopez-Martinez, C.\n\n\n \n \n \n \n \n Field-Scale Assessment of Multi-Sensor Soil Moisture Retrieval Under Grassland.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 6111–6114, Valencia, July 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Field-Scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{jagdhuber_field-scale_2018,\n\taddress = {Valencia},\n\ttitle = {Field-{Scale} {Assessment} of {Multi}-{Sensor} {Soil} {Moisture} {Retrieval} {Under} {Grassland}},\n\tisbn = {9781538671504},\n\turl = {https://ieeexplore.ieee.org/document/8517560/},\n\tdoi = {10.1109/IGARSS.2018.8517560},\n\turldate = {2022-11-04},\n\tbooktitle = {{IGARSS} 2018 - 2018 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Jagdhuber, T. and Fersch, B. and Schron, M. and Jager, M. and Voormansik, K. and Lopez-Martinez, C.},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {6111--6114},\n}\n\n\n\n

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\n \n\n \n \n Inostroza, P. A.; Vera-Escalona, I.; Wild, R.; Norf, H.; and Brauns, M.\n\n\n \n \n \n \n \n Tandem Action of Natural and Chemical Stressors in Stream Ecosystems: Insights from a Population Genetic Perspective.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 52(14): 7962–7971. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Tandem$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{inostroza_tandem_2018,\n\ttitle = {Tandem {Action} of {Natural} and {Chemical} {Stressors} in {Stream} {Ecosystems}: {Insights} from a {Population} {Genetic} {Perspective}},\n\tvolume = {52},\n\tissn = {0013-936X, 1520-5851},\n\tshorttitle = {Tandem {Action} of {Natural} and {Chemical} {Stressors} in {Stream} {Ecosystems}},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.8b01259},\n\tdoi = {10.1021/acs.est.8b01259},\n\tlanguage = {en},\n\tnumber = {14},\n\turldate = {2022-11-04},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Inostroza, Pedro A. and Vera-Escalona, Iván and Wild, Romy and Norf, Helge and Brauns, Mario},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {7962--7971},\n}\n\n\n\n

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\n \n\n \n \n Holtgrave, A.; Förster, M.; Greifeneder, F.; Notarnicola, C.; and Kleinschmit, B.\n\n\n \n \n \n \n \n Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2): 85–101. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{holtgrave_estimation_2018,\n\ttitle = {Estimation of {Soil} {Moisture} in {Vegetation}-{Covered} {Floodplains} with {Sentinel}-1 {SAR} {Data} {Using} {Support} {Vector} {Regression}},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0045-4},\n\tdoi = {10.1007/s41064-018-0045-4},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Holtgrave, Ann-Kathrin and Förster, Michael and Greifeneder, Felix and Notarnicola, Claudia and Kleinschmit, Birgit},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {85--101},\n}\n\n\n\n

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\n \n\n \n \n Hörtnagl, L.; Barthel, M.; Buchmann, N.; Eugster, W.; Butterbach-Bahl, K.; Díaz-Pinés, E.; Zeeman, M.; Klumpp, K.; Kiese, R.; Bahn, M.; Hammerle, A.; Lu, H.; Ladreiter-Knauss, T.; Burri, S.; and Merbold, L.\n\n\n \n \n \n \n \n Greenhouse gas fluxes over managed grasslands in Central Europe.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 24(5): 1843–1872. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Greenhouse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hortnagl_greenhouse_2018,\n\ttitle = {Greenhouse gas fluxes over managed grasslands in {Central} {Europe}},\n\tvolume = {24},\n\tissn = {13541013},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.14079},\n\tdoi = {10.1111/gcb.14079},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-04},\n\tjournal = {Global Change Biology},\n\tauthor = {Hörtnagl, Lukas and Barthel, Matti and Buchmann, Nina and Eugster, Werner and Butterbach-Bahl, Klaus and Díaz-Pinés, Eugenio and Zeeman, Matthias and Klumpp, Katja and Kiese, Ralf and Bahn, Michael and Hammerle, Albin and Lu, Haiyan and Ladreiter-Knauss, Thomas and Burri, Susanne and Merbold, Lutz},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {1843--1872},\n}\n\n\n\n

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\n \n\n \n \n Heupel, K.; Spengler, D.; and Itzerott, S.\n\n\n \n \n \n \n \n A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2): 53–69. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heupel_progressive_2018,\n\ttitle = {A {Progressive} {Crop}-{Type} {Classification} {Using} {Multitemporal} {Remote} {Sensing} {Data} and {Phenological} {Information}},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0050-7},\n\tdoi = {10.1007/s41064-018-0050-7},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Heupel, Katharina and Spengler, Daniel and Itzerott, Sibylle},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {53--69},\n}\n\n\n\n

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\n \n\n \n \n Herbrich, M.; Gerke, H. H.; and Sommer, M.\n\n\n \n \n \n \n \n Root development of winter wheat in erosion‐affected soils depending on the position in a hummocky ground moraine soil landscape.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 181(2): 147–157. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Root$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbrich_root_2018,\n\ttitle = {Root development of winter wheat in erosion‐affected soils depending on the position in a hummocky ground moraine soil landscape},\n\tvolume = {181},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.201600536},\n\tdoi = {10.1002/jpln.201600536},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Herbrich, Marcus and Gerke, Horst H. and Sommer, Michael},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {147--157},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Heinrich, I.; Balanzategui, D.; Bens, O.; Blasch, G.; Blume, T.; Böttcher, F.; Borg, E.; Brademann, B.; Brauer, A.; Conrad, C.; Dietze, E.; Dräger, N.; Fiener, P.; Gerke, H. H.; Güntner, A.; Heine, I.; Helle, G.; Herbrich, M.; Harfenmeister, K.; Heußner, K.; Hohmann, C.; Itzerott, S.; Jurasinski, G.; Kaiser, K.; Kappler, C.; Koebsch, F.; Liebner, S.; Lischeid, G.; Merz, B.; Missling, K. D.; Morgner, M.; Pinkerneil, S.; Plessen, B.; Raab, T.; Ruhtz, T.; Sachs, T.; Sommer, M.; Spengler, D.; Stender, V.; Stüve, P.; and Wilken, F.\n\n\n \n \n \n \n \n Interdisciplinary Geo-ecological Research across Time Scales in the Northeast German Lowland Observatory (TERENO-NE).\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 180116. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Interdisciplinary$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heinrich_interdisciplinary_2018,\n\ttitle = {Interdisciplinary {Geo}-ecological {Research} across {Time} {Scales} in the {Northeast} {German} {Lowland} {Observatory} ({TERENO}-{NE})},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2018.06.0116},\n\tdoi = {10.2136/vzj2018.06.0116},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Heinrich, Ingo and Balanzategui, Daniel and Bens, Oliver and Blasch, Gerald and Blume, Theresa and Böttcher, Falk and Borg, Erik and Brademann, Brian and Brauer, Achim and Conrad, Christopher and Dietze, Elisabeth and Dräger, Nadine and Fiener, Peter and Gerke, Horst H. and Güntner, Andreas and Heine, Iris and Helle, Gerhard and Herbrich, Marcus and Harfenmeister, Katharina and Heußner, Karl-Uwe and Hohmann, Christian and Itzerott, Sibylle and Jurasinski, Gerald and Kaiser, Knut and Kappler, Christoph and Koebsch, Franziska and Liebner, Susanne and Lischeid, Gunnar and Merz, Bruno and Missling, Klaus Dieter and Morgner, Markus and Pinkerneil, Sylvia and Plessen, Birgit and Raab, Thomas and Ruhtz, Thomas and Sachs, Torsten and Sommer, Michael and Spengler, Daniel and Stender, Vivien and Stüve, Peter and Wilken, Florian},\n\tyear = {2018},\n\tpages = {180116},\n}\n\n\n\n

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\n \n\n \n \n Hassler, S. K.; Weiler, M.; and Blume, T.\n\n\n \n \n \n \n \n Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 22(1): 13–30. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Tree-,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hassler_tree-_2018,\n\ttitle = {Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration},\n\tvolume = {22},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/22/13/2018/},\n\tdoi = {10.5194/hess-22-13-2018},\n\tabstract = {Abstract. Transpiration is a key process in the hydrological cycle,\nand a sound understanding and quantification of transpiration and its\nspatial variability is essential for management decisions as well as for\nimproving the parameterisation and evaluation of hydrological and\nsoil–vegetation–atmosphere transfer models. For individual trees,\ntranspiration is commonly estimated by measuring sap flow. Besides\nevaporative demand and water availability, tree-specific characteristics\nsuch as species, size or social status control sap flow amounts of\nindividual trees. Within forest stands, properties such as species\ncomposition, basal area or stand density additionally affect sap flow, for\nexample via competition mechanisms. Finally, sap flow patterns might also be\ninfluenced by landscape-scale characteristics such as geology and soils,\nslope position or aspect because they affect water and energy availability;\nhowever, little is known about the dynamic interplay of these controls. We studied the relative importance of various tree-, stand- and\nsite-specific characteristics with multiple linear regression models to\nexplain the variability of sap velocity measurements in 61 beech and oak\ntrees, located at 24 sites across a 290 km2 catchment in\nLuxembourg. For each of 132 consecutive days of the growing season of 2014\nwe modelled the daily sap velocity and derived sap flow patterns of these 61\ntrees, and we determined the importance of the different controls. Results indicate that a combination of mainly tree- and site-specific\nfactors controls sap velocity patterns in the landscape, namely tree\nspecies, tree diameter, geology and aspect. For sap flow we included only\nthe stand- and site-specific predictors in the models to ensure variable\nindependence. Of those, geology and aspect were most important. Compared to\nthese predictors, spatial variability of atmospheric demand and soil\nmoisture explains only a small fraction of the variability in the daily\ndatasets. However, the temporal dynamics of the explanatory power of the\ntree-specific characteristics, especially species, are correlated to the\ntemporal dynamics of potential evaporation. We conclude that transpiration\nestimates on the landscape scale would benefit from not only consideration of\nhydro-meteorological drivers, but also tree, stand and site characteristics\nin order to improve the spatial and temporal representation of transpiration\nfor hydrological and soil–vegetation–atmosphere transfer models.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Hassler, Sibylle Kathrin and Weiler, Markus and Blume, Theresa},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {13--30},\n}\n\n\n\n

\n\n\n

\n Abstract. Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil–vegetation–atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls. We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290 km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls. Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal dynamics of the explanatory power of the tree-specific characteristics, especially species, are correlated to the temporal dynamics of potential evaporation. We conclude that transpiration estimates on the landscape scale would benefit from not only consideration of hydro-meteorological drivers, but also tree, stand and site characteristics in order to improve the spatial and temporal representation of transpiration for hydrological and soil–vegetation–atmosphere transfer models.\n

\n\n\n

\n \n\n \n \n Haase, P.; Tonkin, J. D.; Stoll, S.; Burkhard, B.; Frenzel, M.; Geijzendorffer, I. R.; Häuser, C.; Klotz, S.; Kühn, I.; McDowell, W. H.; Mirtl, M.; Müller, F.; Musche, M.; Penner, J.; Zacharias, S.; and Schmeller, D. S.\n\n\n \n \n \n \n \n The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 613-614: 1376–1384. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haase_next_2018,\n\ttitle = {The next generation of site-based long-term ecological monitoring: {Linking} essential biodiversity variables and ecosystem integrity},\n\tvolume = {613-614},\n\tissn = {00489697},\n\tshorttitle = {The next generation of site-based long-term ecological monitoring},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717321095},\n\tdoi = {10.1016/j.scitotenv.2017.08.111},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Haase, Peter and Tonkin, Jonathan D. and Stoll, Stefan and Burkhard, Benjamin and Frenzel, Mark and Geijzendorffer, Ilse R. and Häuser, Christoph and Klotz, Stefan and Kühn, Ingolf and McDowell, William H. and Mirtl, Michael and Müller, Felix and Musche, Martin and Penner, Johannes and Zacharias, Steffen and Schmeller, Dirk S.},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {1376--1384},\n}\n\n\n\n

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\n \n\n \n \n Gueting, N.; Caers, J.; Comunian, A.; Vanderborght, J.; and Englert, A.\n\n\n \n \n \n \n \n Reconstruction of Three-Dimensional Aquifer Heterogeneity from Two-Dimensional Geophysical Data.\n \n \n \n \n\n\n \n\n\n\n Mathematical Geosciences, 50(1): 53–75. January 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Reconstruction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gueting_reconstruction_2018,\n\ttitle = {Reconstruction of {Three}-{Dimensional} {Aquifer} {Heterogeneity} from {Two}-{Dimensional} {Geophysical} {Data}},\n\tvolume = {50},\n\tissn = {1874-8961, 1874-8953},\n\turl = {http://link.springer.com/10.1007/s11004-017-9694-x},\n\tdoi = {10.1007/s11004-017-9694-x},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Mathematical Geosciences},\n\tauthor = {Gueting, Nils and Caers, Jef and Comunian, Alessandro and Vanderborght, Jan and Englert, Andreas},\n\tmonth = jan,\n\tyear = {2018},\n\tpages = {53--75},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Groh, J.; Slawitsch, V.; Herndl, M.; Graf, A.; Vereecken, H.; and Pütz, T.\n\n\n \n \n \n \n \n Determining dew and hoar frost formation for a low mountain range and alpine grassland site by weighable lysimeter.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 563: 372–381. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Determining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_determining_2018,\n\ttitle = {Determining dew and hoar frost formation for a low mountain range and alpine grassland site by weighable lysimeter},\n\tvolume = {563},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418304153},\n\tdoi = {10.1016/j.jhydrol.2018.06.009},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Groh, Jannis and Slawitsch, Veronika and Herndl, Markus and Graf, Alexander and Vereecken, Harry and Pütz, Thomas},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {372--381},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Groh, J.; Stumpp, C.; Lücke, A.; Pütz, T.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Inverse Estimation of Soil Hydraulic and Transport Parameters of Layered Soils from Water Stable Isotope and Lysimeter Data.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 170168. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Inverse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_inverse_2018,\n\ttitle = {Inverse {Estimation} of {Soil} {Hydraulic} and {Transport} {Parameters} of {Layered} {Soils} from {Water} {Stable} {Isotope} and {Lysimeter} {Data}},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.09.0168},\n\tdoi = {10.2136/vzj2017.09.0168},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Stumpp, Christine and Lücke, Andreas and Pütz, Thomas and Vanderborght, Jan and Vereecken, Harry},\n\tyear = {2018},\n\tpages = {170168},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Gerstmann, H.; Gläßer, C.; Thürkow, D.; and Möller, M.\n\n\n \n \n \n \n \n Detection of Phenology-Defined Data Acquisition Time Frames For Crop Type Mapping.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(1): 15–27. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gerstmann_detection_2018,\n\ttitle = {Detection of {Phenology}-{Defined} {Data} {Acquisition} {Time} {Frames} {For} {Crop} {Type} {Mapping}},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0043-6},\n\tdoi = {10.1007/s41064-018-0043-6},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Gerstmann, Henning and Gläßer, Cornelia and Thürkow, Detlef and Möller, Markus},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {15--27},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Frenck, G.; Leitinger, G.; Obojes, N.; Hofmann, M.; Newesely, C.; Deutschmann, M.; Tappeiner, U.; and Tasser, E.\n\n\n \n \n \n \n \n Community-specific hydraulic conductance potential of soil water decomposed for two Alpine grasslands by small-scale lysimetry.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 15(4): 1065–1078. February 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Community-specific$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{frenck_community-specific_2018,\n\ttitle = {Community-specific hydraulic conductance potential of soil water decomposed for two {Alpine} grasslands by small-scale lysimetry},\n\tvolume = {15},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/15/1065/2018/},\n\tdoi = {10.5194/bg-15-1065-2018},\n\tabstract = {Abstract. For central Europe in addition to rising temperatures an increasing\nvariability in precipitation is predicted. This will increase the probability\nof drought periods in the Alps, where water supply has been sufficient in\nmost areas so far. For Alpine grasslands, community-specific imprints on\ndrought responses are poorly analyzed so far due to the sufficient natural\nwater supply. In a replicated mesocosm experiment we compared\nevapotranspiration (ET) and biomass productivity of two differently\ndrought-adapted Alpine grassland communities during two artificial drought\nperiods divided by extreme precipitation events using high-precision small\nlysimeters. The drought-adapted vegetation type showed a high potential to\nutilize even scarce water resources. This is combined with a low potential to\ntranslate atmospheric deficits into higher water conductance and a lower\nbiomass production as those measured for the non-drought-adapted type. The\nnon-drought-adapted type, in contrast, showed high water conductance\npotential and a strong increase in ET rates when environmental conditions\nbecame less constraining. With high rates even at dry conditions, this\ncommunity appears not to be optimized to save water and might experience\ndrought effects earlier and probably more strongly. As a result, the water\nuse efficiency of the drought-adapted plant community is with\n2.6 gDW kg−1 of water much higher than that of the\nnon-drought-adapted plant community (0.16 gDW kg−1). In\nsummary, the vegetation's reaction to two covarying gradients of potential\nevapotranspiration and soil water content revealed a clear difference in\nvegetation development and between water-saving and water-spending strategies\nregarding evapotranspiration.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-04},\n\tjournal = {Biogeosciences},\n\tauthor = {Frenck, Georg and Leitinger, Georg and Obojes, Nikolaus and Hofmann, Magdalena and Newesely, Christian and Deutschmann, Mario and Tappeiner, Ulrike and Tasser, Erich},\n\tmonth = feb,\n\tyear = {2018},\n\tpages = {1065--1078},\n}\n\n\n\n

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\n \n\n \n \n Franz, D.; Acosta, M.; Altimir, N.; Arriga, N.; Arrouays, D.; Aubinet, M.; Aurela, M.; Ayres, E.; López-Ballesteros, A.; Barbaste, M.; Berveiller, D.; Biraud, S.; Boukir, H.; Brown, T.; Brümmer, C.; Buchmann, N.; Burba, G.; Carrara, A.; Cescatti, A.; Ceschia, E.; Clement, R.; Cremonese, E.; Crill, P.; Darenova, E.; Dengel, S.; D’Odorico, P.; Filippa, G.; Fleck, S.; Fratini, G.; Fuß, R.; Gielen, B.; Gogo, S.; Grace, J.; Graf, A.; Grelle, A.; Gross, P.; Grünwald, T.; Haapanala, S.; Hehn, M.; Heinesch, B.; Heiskanen, J.; Herbst, M.; Herschlein, C.; Hörtnagl, L.; Hufkens, K.; Ibrom, A.; Jolivet, C.; Joly, L.; Jones, M.; Kiese, R.; Klemedtsson, L.; Kljun, N.; Klumpp, K.; Kolari, P.; Kolle, O.; Kowalski, A.; Kutsch, W.; Laurila, T.; de Ligne, A.; Linder, S.; Lindroth, A.; Lohila, A.; Longdoz, B.; Mammarella, I.; Manise, T.; Jiménez, S. M.; Matteucci, G.; Mauder, M.; Meier, P.; Merbold, L.; Mereu, S.; Metzger, S.; Migliavacca, M.; Mölder, M.; Montagnani, L.; Moureaux, C.; Nelson, D.; Nemitz, E.; Nicolini, G.; Nilsson, M. B.; de Beeck, M. O.; Osborne, B.; Löfvenius, M. O.; Pavelka, M.; Peichl, M.; Peltola, O.; Pihlatie, M.; Pitacco, A.; Pokorný, R.; Pumpanen, J.; Ratié, C.; Rebmann, C.; Roland, M.; Sabbatini, S.; Saby, N. P.; Saunders, M.; Schmid, H. P.; Schrumpf, M.; Sedlák, P.; Ortiz, P. S.; Siebicke, L.; Šigut, L.; Silvennoinen, H.; Simioni, G.; Skiba, U.; Sonnentag, O.; Soudani, K.; Soulé, P.; Steinbrecher, R.; Tallec, T.; Thimonier, A.; Tuittila, E.; Tuovinen, J.; Vestin, P.; Vincent, G.; Vincke, C.; Vitale, D.; Waldner, P.; Weslien, P.; Wingate, L.; Wohlfahrt, G.; Zahniser, M.; and Vesala, T.\n\n\n \n \n \n \n \n Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review.\n \n \n \n \n\n\n \n\n\n\n International Agrophysics, 32(4): 439–455. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{franz_towards_2018,\n\ttitle = {Towards long-term standardised carbon and greenhouse gas observations for monitoring {Europe}’s terrestrial ecosystems: a review},\n\tvolume = {32},\n\tissn = {2300-8725},\n\tshorttitle = {Towards long-term standardised carbon and greenhouse gas observations for monitoring {Europe}’s terrestrial ecosystems},\n\turl = {http://archive.sciendo.com/INTAG/intag.2017.32.issue-4/intag-2017-0039/intag-2017-0039.pdf},\n\tdoi = {10.1515/intag-2017-0039},\n\tabstract = {Abstract \n             \n              Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO \n              2 \n              , CH \n              4 \n              , N \n              2 \n              O, H \n              2 \n              O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.},\n\tnumber = {4},\n\turldate = {2022-11-04},\n\tjournal = {International Agrophysics},\n\tauthor = {Franz, Daniela and Acosta, Manuel and Altimir, Núria and Arriga, Nicola and Arrouays, Dominique and Aubinet, Marc and Aurela, Mika and Ayres, Edward and López-Ballesteros, Ana and Barbaste, Mireille and Berveiller, Daniel and Biraud, Sébastien and Boukir, Hakima and Brown, Timothy and Brümmer, Christian and Buchmann, Nina and Burba, George and Carrara, Arnaud and Cescatti, Allessandro and Ceschia, Eric and Clement, Robert and Cremonese, Edoardo and Crill, Patrick and Darenova, Eva and Dengel, Sigrid and D’Odorico, Petra and Filippa, Gianluca and Fleck, Stefan and Fratini, Gerardo and Fuß, Roland and Gielen, Bert and Gogo, Sébastien and Grace, John and Graf, Alexander and Grelle, Achim and Gross, Patrick and Grünwald, Thomas and Haapanala, Sami and Hehn, Markus and Heinesch, Bernard and Heiskanen, Jouni and Herbst, Mathias and Herschlein, Christine and Hörtnagl, Lukas and Hufkens, Koen and Ibrom, Andreas and Jolivet, Claudy and Joly, Lilian and Jones, Michael and Kiese, Ralf and Klemedtsson, Leif and Kljun, Natascha and Klumpp, Katja and Kolari, Pasi and Kolle, Olaf and Kowalski, Andrew and Kutsch, Werner and Laurila, Tuomas and de Ligne, Anne and Linder, Sune and Lindroth, Anders and Lohila, Annalea and Longdoz, Bernhard and Mammarella, Ivan and Manise, Tanguy and Jiménez, Sara Maraňón and Matteucci, Giorgio and Mauder, Matthias and Meier, Philip and Merbold, Lutz and Mereu, Simone and Metzger, Stefan and Migliavacca, Mirco and Mölder, Meelis and Montagnani, Leonardo and Moureaux, Christine and Nelson, David and Nemitz, Eiko and Nicolini, Giacomo and Nilsson, Mats B. and de Beeck, Maarten Op and Osborne, Bruce and Löfvenius, Mikaell Ottosson and Pavelka, Marian and Peichl, Matthias and Peltola, Olli and Pihlatie, Mari and Pitacco, Andrea and Pokorný, Radek and Pumpanen, Jukka and Ratié, Céline and Rebmann, Corinna and Roland, Marilyn and Sabbatini, Simone and Saby, Nicolas P.A. and Saunders, Matthew and Schmid, Hans Peter and Schrumpf, Marion and Sedlák, Pavel and Ortiz, Penelope Serrano and Siebicke, Lukas and Šigut, Ladislav and Silvennoinen, Hanna and Simioni, Guillaume and Skiba, Ute and Sonnentag, Oliver and Soudani, Kamel and Soulé, Patrice and Steinbrecher, Rainer and Tallec, Tiphaine and Thimonier, Anne and Tuittila, Eeva-Stiina and Tuovinen, Juha-Pekka and Vestin, Patrik and Vincent, Gaëlle and Vincke, Caroline and Vitale, Domenico and Waldner, Peter and Weslien, Per and Wingate, Lisa and Wohlfahrt, Georg and Zahniser, Mark and Vesala, Timo},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {439--455},\n}\n\n\n\n

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\n \n\n \n \n Filipović, V.; Gerke, H. H.; Filipović, L.; and Sommer, M.\n\n\n \n \n \n \n \n Quantifying Subsurface Lateral Flow along Sloping Horizon Boundaries in Soil Profiles of a Hummocky Ground Moraine.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 170106. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{filipovic_quantifying_2018,\n\ttitle = {Quantifying {Subsurface} {Lateral} {Flow} along {Sloping} {Horizon} {Boundaries} in {Soil} {Profiles} of a {Hummocky} {Ground} {Moraine}},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.05.0106},\n\tdoi = {10.2136/vzj2017.05.0106},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Filipović, Vilim and Gerke, Horst H. and Filipović, Lana and Sommer, Michael},\n\tyear = {2018},\n\tpages = {170106},\n}\n\n\n\n

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\n \n\n \n \n Fiener, P.; Wilken, F.; Aldana-Jague, E.; Deumlich, D.; Gómez, J.; Guzmán, G.; Hardy, R.; Quinton, J.; Sommer, M.; Van Oost, K.; and Wexler, R.\n\n\n \n \n \n \n \n Uncertainties in assessing tillage erosion – How appropriate are our measuring techniques?.\n \n \n \n \n\n\n \n\n\n\n Geomorphology, 304: 214–225. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainties$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fiener_uncertainties_2018,\n\ttitle = {Uncertainties in assessing tillage erosion – {How} appropriate are our measuring techniques?},\n\tvolume = {304},\n\tissn = {0169555X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169555X17305391},\n\tdoi = {10.1016/j.geomorph.2017.12.031},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Geomorphology},\n\tauthor = {Fiener, P. and Wilken, F. and Aldana-Jague, E. and Deumlich, D. and Gómez, J.A. and Guzmán, G. and Hardy, R.A. and Quinton, J.N. and Sommer, M. and Van Oost, K. and Wexler, R.},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {214--225},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Fersch, B.; Jagdhuber, T.; Schrön, M.; Völksch, I.; and Jäger, M.\n\n\n \n \n \n \n \n Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 54(11): 9364–9383. November 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Synergies$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fersch_synergies_2018,\n\ttitle = {Synergies for {Soil} {Moisture} {Retrieval} {Across} {Scales} {From} {Airborne} {Polarimetric} {SAR}, {Cosmic} {Ray} {Neutron} {Roving}, and an {In} {Situ} {Sensor} {Network}},\n\tvolume = {54},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023337},\n\tdoi = {10.1029/2018WR023337},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-04},\n\tjournal = {Water Resources Research},\n\tauthor = {Fersch, B. and Jagdhuber, T. and Schrön, M. and Völksch, I. and Jäger, M.},\n\tmonth = nov,\n\tyear = {2018},\n\tpages = {9364--9383},\n}\n\n\n\n

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\n \n\n \n \n Emeis, S.; Kalthoff, N.; Adler, B.; Pardyjak, E.; Paci, A.; and Junkermann, W.\n\n\n \n \n \n \n \n High-Resolution Observations of Transport and Exchange Processes in Mountainous Terrain.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 9(12): 457. November 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"High-Resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{emeis_high-resolution_2018,\n\ttitle = {High-{Resolution} {Observations} of {Transport} and {Exchange} {Processes} in {Mountainous} {Terrain}},\n\tvolume = {9},\n\tissn = {2073-4433},\n\turl = {http://www.mdpi.com/2073-4433/9/12/457},\n\tdoi = {10.3390/atmos9120457},\n\tabstract = {Mountainous areas require appropriate measurement strategies to cover the full spectrum of details concerning the energy exchange at the Earth’s surface and to capture the spatiotemporal distribution of atmospheric dynamic and thermodynamic fields over them. This includes the range from turbulence to mesoscale processes and its interaction. The surface energy balance needs appropriate measurement strategies as well. In this paper, we present an overview of important experiments performed over mountainous terrain and summarize the available techniques for flow and energy measurements in complex terrain. The description includes ground-based and airborne in situ observations as well as ground-based and airborne remote sensing (passive and active) observations. Emphasis is placed on systems which retrieve spatiotemporal information on mesoscale and smaller scales, fitting mountainous terrain research needs. Finally, we conclude with a short list summarizing challenges and gaps one faces when dealing with measurements over complex terrain.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-04},\n\tjournal = {Atmosphere},\n\tauthor = {Emeis, Stefan and Kalthoff, Norbert and Adler, Bianca and Pardyjak, Eric and Paci, Alexandre and Junkermann, Wolfgang},\n\tmonth = nov,\n\tyear = {2018},\n\tpages = {457},\n}\n\n\n\n

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\n \n\n \n \n Ebrahimi-Khusfi, M.; Alavipanah, S. K.; Hamzeh, S.; Amiraslani, F.; Neysani Samany, N.; and Wigneron, J.\n\n\n \n \n \n \n \n Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 67: 148–160. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ebrahimi-khusfi_comparison_2018,\n\ttitle = {Comparison of soil moisture retrieval algorithms based on the synergy between {SMAP} and {SMOS}-{IC}},\n\tvolume = {67},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243417302945},\n\tdoi = {10.1016/j.jag.2017.12.005},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Ebrahimi-Khusfi, Mohsen and Alavipanah, Seyed Kazem and Hamzeh, Saeid and Amiraslani, Farshad and Neysani Samany, Najmeh and Wigneron, Jean-Pierre},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {148--160},\n}\n\n\n\n

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\n \n\n \n \n Dupas, R.; Tittel, J.; Jordan, P.; Musolff, A.; and Rode, M.\n\n\n \n \n \n \n \n Non-domestic phosphorus release in rivers during low-flow: Mechanisms and implications for sources identification.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 560: 141–149. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Non-domestic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dupas_non-domestic_2018,\n\ttitle = {Non-domestic phosphorus release in rivers during low-flow: {Mechanisms} and implications for sources identification},\n\tvolume = {560},\n\tissn = {00221694},\n\tshorttitle = {Non-domestic phosphorus release in rivers during low-flow},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418301896},\n\tdoi = {10.1016/j.jhydrol.2018.03.023},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Dupas, Rémi and Tittel, Jörg and Jordan, Phil and Musolff, Andreas and Rode, Michael},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {141--149},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Dörnhöfer, K.; Scholze, J.; Stelzer, K.; and Oppelt, N.\n\n\n \n \n \n \n \n Water Colour Analysis of Lake Kummerow Using Time Series of Remote Sensing and In Situ Data.\n \n \n \n \n\n\n \n\n\n\n PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2): 103–120. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dornhofer_water_2018,\n\ttitle = {Water {Colour} {Analysis} of {Lake} {Kummerow} {Using} {Time} {Series} of {Remote} {Sensing} and {In} {Situ} {Data}},\n\tvolume = {86},\n\tissn = {2512-2789, 2512-2819},\n\turl = {http://link.springer.com/10.1007/s41064-018-0046-3},\n\tdoi = {10.1007/s41064-018-0046-3},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n\tauthor = {Dörnhöfer, K. and Scholze, J. and Stelzer, K. and Oppelt, N.},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {103--120},\n}\n\n\n\n

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\n \n\n \n \n Djukic, I.; Kepfer-Rojas, S.; Schmidt, I. K.; Larsen, K. S.; Beier, C.; Berg, B.; Verheyen, K.; Caliman, A.; Paquette, A.; Gutiérrez-Girón, A.; Humber, A.; Valdecantos, A.; Petraglia, A.; Alexander, H.; Augustaitis, A.; Saillard, A.; Fernández, A. C. R.; Sousa, A. I.; Lillebø, A. I.; da Rocha Gripp, A.; Francez, A.; Fischer, A.; Bohner, A.; Malyshev, A.; Andrić, A.; Smith, A.; Stanisci, A.; Seres, A.; Schmidt, A.; Avila, A.; Probst, A.; Ouin, A.; Khuroo, A. A.; Verstraeten, A.; Palabral-Aguilera, A. N.; Stefanski, A.; Gaxiola, A.; Muys, B.; Bosman, B.; Ahrends, B.; Parker, B.; Sattler, B.; Yang, B.; Juráni, B.; Erschbamer, B.; Ortiz, C. E. R.; Christiansen, C. T.; Carol Adair, E.; Meredieu, C.; Mony, C.; Nock, C. A.; Chen, C.; Wang, C.; Baum, C.; Rixen, C.; Delire, C.; Piscart, C.; Andrews, C.; Rebmann, C.; Branquinho, C.; Polyanskaya, D.; Delgado, D. F.; Wundram, D.; Radeideh, D.; Ordóñez-Regil, E.; Crawford, E.; Preda, E.; Tropina, E.; Groner, E.; Lucot, E.; Hornung, E.; Gacia, E.; Lévesque, E.; Benedito, E.; Davydov, E. A.; Ampoorter, E.; Bolzan, F. P.; Varela, F.; Kristöfel, F.; Maestre, F. T.; Maunoury-Danger, F.; Hofhansl, F.; Kitz, F.; Sutter, F.; Cuesta, F.; de Almeida Lobo, F.; de Souza, F. L.; Berninger, F.; Zehetner, F.; Wohlfahrt, G.; Vourlitis, G.; Carreño-Rocabado, G.; Arena, G.; Pinha, G. D.; González, G.; Canut, G.; Lee, H.; Verbeeck, H.; Auge, H.; Pauli, H.; Nacro, H. B.; Bahamonde, H. A.; Feldhaar, H.; Jäger, H.; Serrano, H. C.; Verheyden, H.; Bruelheide, H.; Meesenburg, H.; Jungkunst, H.; Jactel, H.; Shibata, H.; Kurokawa, H.; Rosas, H. L.; Rojas Villalobos, H. L.; Yesilonis, I.; Melece, I.; Van Halder, I.; Quirós, I. G.; Makelele, I.; Senou, I.; Fekete, I.; Mihal, I.; Ostonen, I.; Borovská, J.; Roales, J.; Shoqeir, J.; Lata, J.; Theurillat, J.; Probst, J.; Zimmerman, J.; Vijayanathan, J.; Tang, J.; Thompson, J.; Doležal, J.; Sanchez-Cabeza, J.; Merlet, J.; Henschel, J.; Neirynck, J.; Knops, J.; Loehr, J.; von Oppen, J.; Þorláksdóttir, J. S.; Löffler, J.; Cardoso-Mohedano, J.; Benito-Alonso, J.; Torezan, J. M.; Morina, J. C.; Jiménez, J. J.; Quinde, J. D.; Alatalo, J.; Seeber, J.; Stadler, J.; Kriiska, K.; Coulibaly, K.; Fukuzawa, K.; Szlavecz, K.; Gerhátová, K.; Lajtha, K.; Käppeler, K.; Jennings, K. A.; Tielbörger, K.; Hoshizaki, K.; Green, K.; Yé, L.; Pazianoto, L. H. R.; Dienstbach, L.; Williams, L.; Yahdjian, L.; Brigham, L. M.; van den Brink, L.; Rustad, L.; Zhang, L.; Morillas, L.; Xiankai, L.; Carneiro, L. S.; Di Martino, L.; Villar, L.; Bader, M. Y.; Morley, M.; Lebouvier, M.; Tomaselli, M.; Sternberg, M.; Schaub, M.; Santos-Reis, M.; Glushkova, M.; Torres, M. G. A.; Giroux, M.; de Graaff, M.; Pons, M.; Bauters, M.; Mazón, M.; Frenzel, M.; Didion, M.; Wagner, M.; Hamid, M.; Lopes, M. L.; Apple, M.; Schädler, M.; Weih, M.; Gualmini, M.; Vadeboncoeur, M. A.; Bierbaumer, M.; Danger, M.; Liddell, M.; Mirtl, M.; Scherer-Lorenzen, M.; Růžek, M.; Carbognani, M.; Di Musciano, M.; Matsushita, M.; Zhiyanski, M.; Pușcaș, M.; Barna, M.; Ataka, M.; Jiangming, M.; Alsafran, M.; Carnol, M.; Barsoum, N.; Tokuchi, N.; Eisenhauer, N.; Lecomte, N.; Filippova, N.; Hölzel, N.; Ferlian, O.; Romero, O.; Pinto, O. B.; Peri, P.; Weber, P.; Vittoz, P.; Turtureanu, P. D.; Fleischer, P.; Macreadie, P.; Haase, P.; Reich, P.; Petřík, P.; Choler, P.; Marmonier, P.; Muriel, P.; Ponette, Q.; Guariento, R. D.; Canessa, R.; Kiese, R.; Hewitt, R.; Rønn, R.; Adrian, R.; Kanka, R.; Weigel, R.; Gatti, R. C.; Martins, R. L.; Georges, R.; Meneses, R. I.; Gavilán, R. G.; Dasgupta, S.; Wittlinger, S.; Puijalon, S.; Freda, S.; Suzuki, S.; Charles, S.; Gogo, S.; Drollinger, S.; Mereu, S.; Wipf, S.; Trevathan-Tackett, S.; Löfgren, S.; Stoll, S.; Trogisch, S.; Hoeber, S.; Seitz, S.; Glatzel, S.; Milton, S. J.; Dousset, S.; Mori, T.; Sato, T.; Ise, T.; Hishi, T.; Kenta, T.; Nakaji, T.; Michelan, T. S.; Camboulive, T.; Mozdzer, T. J.; Scholten, T.; Spiegelberger, T.; Zechmeister, T.; Kleinebecker, T.; Hiura, T.; Enoki, T.; Ursu, T.; di Cella, U. M.; Hamer, U.; Klaus, V. H.; Rêgo, V. M.; Di Cecco, V.; Busch, V.; Fontana, V.; Piscová, V.; Carbonell, V.; Ochoa, V.; Bretagnolle, V.; Maire, V.; Farjalla, V.; Zhou, W.; Luo, W.; McDowell, W. H.; Hu, Y.; Utsumi, Y.; Kominami, Y.; Zaika, Y.; Rozhkov, Y.; Kotroczó, Z.; and Tóth, Z.\n\n\n \n \n \n \n \n Early stage litter decomposition across biomes.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 628-629: 1369–1394. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Early$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{djukic_early_2018,\n\ttitle = {Early stage litter decomposition across biomes},\n\tvolume = {628-629},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969718300123},\n\tdoi = {10.1016/j.scitotenv.2018.01.012},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Djukic, Ika and Kepfer-Rojas, Sebastian and Schmidt, Inger Kappel and Larsen, Klaus Steenberg and Beier, Claus and Berg, Björn and Verheyen, Kris and Caliman, Adriano and Paquette, Alain and Gutiérrez-Girón, Alba and Humber, Alberto and Valdecantos, Alejandro and Petraglia, Alessandro and Alexander, Heather and Augustaitis, Algirdas and Saillard, Amélie and Fernández, Ana Carolina Ruiz and Sousa, Ana I. and Lillebø, Ana I. and da Rocha Gripp, Anderson and Francez, André-Jean and Fischer, Andrea and Bohner, Andreas and Malyshev, Andrey and Andrić, Andrijana and Smith, Andy and Stanisci, Angela and Seres, Anikó and Schmidt, Anja and Avila, Anna and Probst, Anne and Ouin, Annie and Khuroo, Anzar A. and Verstraeten, Arne and Palabral-Aguilera, Arely N. and Stefanski, Artur and Gaxiola, Aurora and Muys, Bart and Bosman, Bernard and Ahrends, Bernd and Parker, Bill and Sattler, Birgit and Yang, Bo and Juráni, Bohdan and Erschbamer, Brigitta and Ortiz, Carmen Eugenia Rodriguez and Christiansen, Casper T. and Carol Adair, E. and Meredieu, Céline and Mony, Cendrine and Nock, Charles A. and Chen, Chi-Ling and Wang, Chiao-Ping and Baum, Christel and Rixen, Christian and Delire, Christine and Piscart, Christophe and Andrews, Christopher and Rebmann, Corinna and Branquinho, Cristina and Polyanskaya, Dana and Delgado, David Fuentes and Wundram, Dirk and Radeideh, Diyaa and Ordóñez-Regil, Eduardo and Crawford, Edward and Preda, Elena and Tropina, Elena and Groner, Elli and Lucot, Eric and Hornung, Erzsébet and Gacia, Esperança and Lévesque, Esther and Benedito, Evanilde and Davydov, Evgeny A. and Ampoorter, Evy and Bolzan, Fabio Padilha and Varela, Felipe and Kristöfel, Ferdinand and Maestre, Fernando T. and Maunoury-Danger, Florence and Hofhansl, Florian and Kitz, Florian and Sutter, Flurin and Cuesta, Francisco and de Almeida Lobo, Francisco and de Souza, Franco Leandro and Berninger, Frank and Zehetner, Franz and Wohlfahrt, Georg and Vourlitis, George and Carreño-Rocabado, Geovana and Arena, Gina and Pinha, Gisele Daiane and González, Grizelle and Canut, Guylaine and Lee, Hanna and Verbeeck, Hans and Auge, Harald and Pauli, Harald and Nacro, Hassan Bismarck and Bahamonde, Héctor A. and Feldhaar, Heike and Jäger, Heinke and Serrano, Helena C. and Verheyden, Hélène and Bruelheide, Helge and Meesenburg, Henning and Jungkunst, Hermann and Jactel, Hervé and Shibata, Hideaki and Kurokawa, Hiroko and Rosas, Hugo López and Rojas Villalobos, Hugo L. and Yesilonis, Ian and Melece, Inara and Van Halder, Inge and Quirós, Inmaculada García and Makelele, Isaac and Senou, Issaka and Fekete, István and Mihal, Ivan and Ostonen, Ivika and Borovská, Jana and Roales, Javier and Shoqeir, Jawad and Lata, Jean-Christophe and Theurillat, Jean-Paul and Probst, Jean-Luc and Zimmerman, Jess and Vijayanathan, Jeyanny and Tang, Jianwu and Thompson, Jill and Doležal, Jiří and Sanchez-Cabeza, Joan-Albert and Merlet, Joël and Henschel, Joh and Neirynck, Johan and Knops, Johannes and Loehr, John and von Oppen, Jonathan and Þorláksdóttir, Jónína Sigríður and Löffler, Jörg and Cardoso-Mohedano, José-Gilberto and Benito-Alonso, José-Luis and Torezan, Jose Marcelo and Morina, Joseph C. and Jiménez, Juan J. and Quinde, Juan Dario and Alatalo, Juha and Seeber, Julia and Stadler, Jutta and Kriiska, Kaie and Coulibaly, Kalifa and Fukuzawa, Karibu and Szlavecz, Katalin and Gerhátová, Katarína and Lajtha, Kate and Käppeler, Kathrin and Jennings, Katie A. and Tielbörger, Katja and Hoshizaki, Kazuhiko and Green, Ken and Yé, Lambiénou and Pazianoto, Laryssa Helena Ribeiro and Dienstbach, Laura and Williams, Laura and Yahdjian, Laura and Brigham, Laurel M. and van den Brink, Liesbeth and Rustad, Lindsey and Zhang, Lipeng and Morillas, Lourdes and Xiankai, Lu and Carneiro, Luciana Silva and Di Martino, Luciano and Villar, Luis and Bader, Maaike Y. and Morley, Madison and Lebouvier, Marc and Tomaselli, Marcello and Sternberg, Marcelo and Schaub, Marcus and Santos-Reis, Margarida and Glushkova, Maria and Torres, María Guadalupe Almazán and Giroux, Marie-Andrée and de Graaff, Marie-Anne and Pons, Marie-Noëlle and Bauters, Marijn and Mazón, Marina and Frenzel, Mark and Didion, Markus and Wagner, Markus and Hamid, Maroof and Lopes, Marta L. and Apple, Martha and Schädler, Martin and Weih, Martin and Gualmini, Matteo and Vadeboncoeur, Matthew A. and Bierbaumer, Michael and Danger, Michael and Liddell, Michael and Mirtl, Michael and Scherer-Lorenzen, Michael and Růžek, Michal and Carbognani, Michele and Di Musciano, Michele and Matsushita, Michinari and Zhiyanski, Miglena and Pușcaș, Mihai and Barna, Milan and Ataka, Mioko and Jiangming, Mo and Alsafran, Mohammed and Carnol, Monique and Barsoum, Nadia and Tokuchi, Naoko and Eisenhauer, Nico and Lecomte, Nicolas and Filippova, Nina and Hölzel, Norbert and Ferlian, Olga and Romero, Oscar and Pinto, Osvaldo B. and Peri, Pablo and Weber, Paige and Vittoz, Pascal and Turtureanu, Pavel Dan and Fleischer, Peter and Macreadie, Peter and Haase, Peter and Reich, Peter and Petřík, Petr and Choler, Philippe and Marmonier, Pierre and Muriel, Priscilla and Ponette, Quentin and Guariento, Rafael Dettogni and Canessa, Rafaella and Kiese, Ralf and Hewitt, Rebecca and Rønn, Regin and Adrian, Rita and Kanka, Róbert and Weigel, Robert and Gatti, Roberto Cazzolla and Martins, Rodrigo Lemes and Georges, Romain and Meneses, Rosa Isela and Gavilán, Rosario G. and Dasgupta, Sabyasachi and Wittlinger, Sally and Puijalon, Sara and Freda, Sarah and Suzuki, Satoshi and Charles, Sean and Gogo, Sébastien and Drollinger, Simon and Mereu, Simone and Wipf, Sonja and Trevathan-Tackett, Stacey and Löfgren, Stefan and Stoll, Stefan and Trogisch, Stefan and Hoeber, Stefanie and Seitz, Steffen and Glatzel, Stephan and Milton, Sue J. and Dousset, Sylvie and Mori, Taiki and Sato, Takanori and Ise, Takeshi and Hishi, Takuo and Kenta, Tanaka and Nakaji, Tatsuro and Michelan, Thaisa Sala and Camboulive, Thierry and Mozdzer, Thomas J. and Scholten, Thomas and Spiegelberger, Thomas and Zechmeister, Thomas and Kleinebecker, Till and Hiura, Tsutom and Enoki, Tsutomu and Ursu, Tudor-Mihai and di Cella, Umberto Morra and Hamer, Ute and Klaus, Valentin H. and Rêgo, Vanessa Mendes and Di Cecco, Valter and Busch, Verena and Fontana, Veronika and Piscová, Veronika and Carbonell, Victoria and Ochoa, Victoria and Bretagnolle, Vincent and Maire, Vincent and Farjalla, Vinicius and Zhou, Wenjun and Luo, Wentao and McDowell, William H. and Hu, Yalin and Utsumi, Yasuhiro and Kominami, Yuji and Zaika, Yulia and Rozhkov, Yury and Kotroczó, Zsolt and Tóth, Zsolt},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {1369--1394},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n De Roo, F.; and Mauder, M.\n\n\n \n \n \n \n \n The influence of idealized surface heterogeneity on virtual turbulent flux measurements.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 18(7): 5059–5074. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{de_roo_influence_2018,\n\ttitle = {The influence of idealized surface heterogeneity on virtual turbulent flux measurements},\n\tvolume = {18},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/18/5059/2018/},\n\tdoi = {10.5194/acp-18-5059-2018},\n\tabstract = {Abstract. The imbalance of the surface energy budget in eddy-covariance measurements is still an unsolved problem. A possible cause is the presence of land surface heterogeneity, which affects the boundary-layer turbulence. To investigate the impact of surface variables on the partitioning of the energy budget of flux measurements in the surface layer under convective conditions, we set up a systematic parameter study by means of large-eddy simulation. For the study we use a virtual control volume approach, which allows the determination of advection by the mean flow, flux-divergence and storage terms of the energy budget at the virtual measurement site, in addition to the standard turbulent flux. We focus on the heterogeneity of the surface fluxes and keep the topography flat. The surface fluxes vary locally in intensity and these patches have different length scales. Intensity and length scales can vary for the two horizontal dimensions but follow an idealized chessboard pattern. Our main focus lies on surface heterogeneity of the kilometer scale, and one order of magnitude smaller. For these two length scales, we investigate the average response of the fluxes at a number of virtual towers, when varying the heterogeneity length within the length scale and when varying the contrast between the different patches. For each simulation, virtual measurement towers were positioned at functionally different positions (e.g., downdraft region, updraft region, at border between domains, etc.). As the storage term is always small, the non-closure is given by the sum of the advection by the mean flow and the flux-divergence. Remarkably, the missing flux can be described by either the advection by the mean flow or the flux-divergence separately, because the latter two have a high correlation with each other. For kilometer scale heterogeneity, we notice a clear dependence of the updrafts and downdrafts on the surface heterogeneity and likewise we also see a dependence of the energy partitioning on the tower location. For the hectometer scale, we do not notice such a clear dependence. Finally, we seek correlators for the energy balance ratio in the simulations. The correlation with the friction velocity is less pronounced than previously found, but this is likely due to our concentration on effectively strongly to freely convective conditions.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-04},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {De Roo, Frederik and Mauder, Matthias},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {5059--5074},\n}\n\n\n\n

\n\n\n

\n Abstract. The imbalance of the surface energy budget in eddy-covariance measurements is still an unsolved problem. A possible cause is the presence of land surface heterogeneity, which affects the boundary-layer turbulence. To investigate the impact of surface variables on the partitioning of the energy budget of flux measurements in the surface layer under convective conditions, we set up a systematic parameter study by means of large-eddy simulation. For the study we use a virtual control volume approach, which allows the determination of advection by the mean flow, flux-divergence and storage terms of the energy budget at the virtual measurement site, in addition to the standard turbulent flux. We focus on the heterogeneity of the surface fluxes and keep the topography flat. The surface fluxes vary locally in intensity and these patches have different length scales. Intensity and length scales can vary for the two horizontal dimensions but follow an idealized chessboard pattern. Our main focus lies on surface heterogeneity of the kilometer scale, and one order of magnitude smaller. For these two length scales, we investigate the average response of the fluxes at a number of virtual towers, when varying the heterogeneity length within the length scale and when varying the contrast between the different patches. For each simulation, virtual measurement towers were positioned at functionally different positions (e.g., downdraft region, updraft region, at border between domains, etc.). As the storage term is always small, the non-closure is given by the sum of the advection by the mean flow and the flux-divergence. Remarkably, the missing flux can be described by either the advection by the mean flow or the flux-divergence separately, because the latter two have a high correlation with each other. For kilometer scale heterogeneity, we notice a clear dependence of the updrafts and downdrafts on the surface heterogeneity and likewise we also see a dependence of the energy partitioning on the tower location. For the hectometer scale, we do not notice such a clear dependence. Finally, we seek correlators for the energy balance ratio in the simulations. The correlation with the friction velocity is less pronounced than previously found, but this is likely due to our concentration on effectively strongly to freely convective conditions.\n

\n\n\n

\n \n\n \n \n Czymzik, M.; Muscheler, R.; Adolphi, F.; Mekhaldi, F.; Dräger, N.; Ott, F.; Słowinski, M.; Błaszkiewicz, M.; Aldahan, A.; Possnert, G.; and Brauer, A.\n\n\n \n \n \n \n \n Synchronizing 10Be in two varved lake sediment records to IntCal13 14C during three grand solar minima.\n \n \n \n \n\n\n \n\n\n\n Climate of the Past, 14(5): 687–696. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Synchronizing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{czymzik_synchronizing_2018,\n\ttitle = {Synchronizing {10Be} in two varved lake sediment records to {IntCal13} {14C} during three grand solar minima},\n\tvolume = {14},\n\tissn = {1814-9332},\n\turl = {https://cp.copernicus.org/articles/14/687/2018/},\n\tdoi = {10.5194/cp-14-687-2018},\n\tabstract = {Abstract. Timescale uncertainties between paleoclimate reconstructions often inhibit studying the exact timing, spatial expression and driving mechanisms of climate variations. Detecting and aligning the globally common cosmogenic radionuclide production signal via a curve fitting method provides a tool for the quasi-continuous synchronization of paleoclimate archives. In this study, we apply this approach to synchronize 10Be records from varved sediments of Tiefer See and Lake Czechowskie covering the Maunder, Homeric and 5500 a BP grand solar minima with 14C production rates inferred from the IntCal13 calibration curve. Our analyses indicate best fits with 14C production rates when the 10Be records from Tiefer See were shifted for 8 (−12∕ + 4) (Maunder Minimum), 31 (−16∕ + 12) (Homeric Minimum) and 86 (−22∕ + 18) years (5500 a BP grand solar minimum) towards the past. The best fit between the Lake Czechowskie 10Be record for the 5500 a BP grand solar minimum and 14C production was obtained when the 10Be time series was shifted 29 (−8∕ + 7) years towards present. No significant fits were detected between the Lake Czechowskie 10Be records for the Maunder and Homeric minima and 14C production, likely due to intensified in-lake sediment resuspension since about 2800 a BP, transporting old 10Be to the coring location. Our results provide a proof of concept for facilitating 10Be in varved lake sediments as a novel synchronization tool required for investigating leads and lags of proxy responses to climate variability. However, they also point to some limitations of 10Be in these archives, mainly connected to in-lake sediment resuspension processes.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-04},\n\tjournal = {Climate of the Past},\n\tauthor = {Czymzik, Markus and Muscheler, Raimund and Adolphi, Florian and Mekhaldi, Florian and Dräger, Nadine and Ott, Florian and Słowinski, Michał and Błaszkiewicz, Mirosław and Aldahan, Ala and Possnert, Göran and Brauer, Achim},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {687--696},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Carrara, A.; Kolari, P.; de Beeck, M. O.; Arriga, N.; Berveiller, D.; Dengel, S.; Ibrom, A.; Merbold, L.; Rebmann, C.; Sabbatini, S.; Serrano-Ortíz, P.; and Biraud, S. C.\n\n\n \n \n \n \n \n Radiation measurements at ICOS ecosystem stations.\n \n \n \n \n\n\n \n\n\n\n International Agrophysics, 32(4): 589–605. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Radiation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{carrara_radiation_2018,\n\ttitle = {Radiation measurements at {ICOS} ecosystem stations},\n\tvolume = {32},\n\tissn = {2300-8725},\n\turl = {http://archive.sciendo.com/INTAG/intag.2017.32.issue-4/intag-2017-0049/intag-2017-0049.pdf},\n\tdoi = {10.1515/intag-2017-0049},\n\tabstract = {Abstract \n            Solar radiation is a key driver of energy and carbon fluxes in natural ecosystems. Radiation measurements are essential for interpreting ecosystem scale greenhouse gases and energy fluxes as well as many other observations performed at ecosystem stations of the Integrated Carbon Observation System (ICOS). We describe and explain the relevance of the radiation variables that are monitored continuously at ICOS ecosystem stations and define recommendations to perform these measurements with consistent and comparable accuracy. The measurement methodology and instruments are described including detailed technical specifications. Guidelines for instrumental set up as well as for operation, maintenance and data collection are defined considering both ICOS scientific objectives and practical operational constraints. For measurements of short-wave (solar) and long wave (infrared) radiation components, requirements for the ICOS network are based on available well-defined state-of-the art standards (World Meteorological Organization, International Organization for Standardization). For photosynthetically active radiation measurements, some basic instrumental requirements are based on the performance of commercially available sensors. Since site specific conditions and practical constraints at individual ICOS ecosystem stations may hamper the applicability of standard requirements, we recommend that ICOS develops mid-term coordinated actions to assess the effective level of uncertainties in radiation measurements at the network scale.},\n\tnumber = {4},\n\turldate = {2022-11-04},\n\tjournal = {International Agrophysics},\n\tauthor = {Carrara, Arnaud and Kolari, Pasi and de Beeck, Maarten Op and Arriga, Nicola and Berveiller, Daniel and Dengel, Sigrid and Ibrom, Andreas and Merbold, Lutz and Rebmann, Corinna and Sabbatini, Simone and Serrano-Ortíz, Penelope and Biraud, Sébastien C.},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {589--605},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Cai, G.; Vanderborght, J.; Couvreur, V.; Mboh, C. M.; and Vereecken, H.\n\n\n \n \n \n \n \n Parameterization of Root Water Uptake Models Considering Dynamic Root Distributions and Water Uptake Compensation.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 160125. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameterization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cai_parameterization_2018,\n\ttitle = {Parameterization of {Root} {Water} {Uptake} {Models} {Considering} {Dynamic} {Root} {Distributions} and {Water} {Uptake} {Compensation}},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.12.0125},\n\tdoi = {10.2136/vzj2016.12.0125},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Cai, Gaochao and Vanderborght, Jan and Couvreur, Valentin and Mboh, Cho Miltin and Vereecken, Harry},\n\tyear = {2018},\n\tpages = {160125},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Cai, G.; Vanderborght, J.; Langensiepen, M.; Schnepf, A.; Hüging, H.; and Vereecken, H.\n\n\n \n \n \n \n \n Root growth, water uptake, and sap flow of winter wheat in response to different soil water conditions.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 22(4): 2449–2470. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Root$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cai_root_2018,\n\ttitle = {Root growth, water uptake, and sap flow of winter wheat in response to different soil water conditions},\n\tvolume = {22},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/22/2449/2018/},\n\tdoi = {10.5194/hess-22-2449-2018},\n\tabstract = {Abstract. How much water can be taken up by roots and how this depends on the root and water distributions in the root zone are\nimportant questions that need to be answered to describe water fluxes in the soil–plant–atmosphere\nsystem. Physically based root water uptake (RWU) models that relate RWU to transpiration, root density, and water\npotential distributions have been developed but used or tested far less. This study aims at evaluating the simulated RWU\nof winter wheat using the empirical Feddes–Jarvis (FJ) model and the physically based Couvreur (C) model for different soil\nwater conditions and soil textures compared to sap flow measurements. Soil water content (SWC), water potential, and root\ndevelopment were monitored noninvasively at six soil depths in two rhizotron facilities that were constructed in two soil\ntextures: stony vs. silty, with each of three water treatments: sheltered, rainfed, and irrigated. Soil and root parameters\nof the two models were derived from inverse modeling and simulated RWU was compared with sap flow measurements for\nvalidation. The different soil types and water treatments resulted in different crop biomass, root densities, and root\ndistributions with depth. The two models simulated the lowest RWU in the sheltered plot of the stony soil where RWU was\nalso lower than the potential RWU. In the silty soil, simulated RWU was equal to the potential uptake for all\ntreatments. The variation of simulated RWU among the different plots agreed well with measured sap flow but the C model\npredicted the ratios of the transpiration fluxes in the two soil types slightly better than the FJ model. The root\nhydraulic parameters of the C model could be constrained by the field data but not the water stress parameters of the FJ\nmodel. This was attributed to differences in root densities between the different soils and treatments which are accounted\nfor by the C model, whereas the FJ model only considers normalized root densities. The impact of differences in root\ndensity on RWU could be accounted for directly by the physically based RWU model but not by empirical models that use\nnormalized root density functions.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Cai, Gaochao and Vanderborght, Jan and Langensiepen, Matthias and Schnepf, Andrea and Hüging, Hubert and Vereecken, Harry},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {2449--2470},\n}\n\n\n\n

\n\n\n

\n Abstract. How much water can be taken up by roots and how this depends on the root and water distributions in the root zone are important questions that need to be answered to describe water fluxes in the soil–plant–atmosphere system. Physically based root water uptake (RWU) models that relate RWU to transpiration, root density, and water potential distributions have been developed but used or tested far less. This study aims at evaluating the simulated RWU of winter wheat using the empirical Feddes–Jarvis (FJ) model and the physically based Couvreur (C) model for different soil water conditions and soil textures compared to sap flow measurements. Soil water content (SWC), water potential, and root development were monitored noninvasively at six soil depths in two rhizotron facilities that were constructed in two soil textures: stony vs. silty, with each of three water treatments: sheltered, rainfed, and irrigated. Soil and root parameters of the two models were derived from inverse modeling and simulated RWU was compared with sap flow measurements for validation. The different soil types and water treatments resulted in different crop biomass, root densities, and root distributions with depth. The two models simulated the lowest RWU in the sheltered plot of the stony soil where RWU was also lower than the potential RWU. In the silty soil, simulated RWU was equal to the potential uptake for all treatments. The variation of simulated RWU among the different plots agreed well with measured sap flow but the C model predicted the ratios of the transpiration fluxes in the two soil types slightly better than the FJ model. The root hydraulic parameters of the C model could be constrained by the field data but not the water stress parameters of the FJ model. This was attributed to differences in root densities between the different soils and treatments which are accounted for by the C model, whereas the FJ model only considers normalized root densities. The impact of differences in root density on RWU could be accounted for directly by the physically based RWU model but not by empirical models that use normalized root density functions.\n

\n\n\n

\n \n\n \n \n Brugger, P.; Katul, G. G.; De Roo, F.; Kröniger, K.; Rotenberg, E.; Rohatyn, S.; and Mauder, M.\n\n\n \n \n \n \n \n Scalewise invariant analysis of the anisotropic Reynolds stress tensor for atmospheric surface layer and canopy sublayer turbulent flows.\n \n \n \n \n\n\n \n\n\n\n Physical Review Fluids, 3(5): 054608. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Scalewise$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brugger_scalewise_2018,\n\ttitle = {Scalewise invariant analysis of the anisotropic {Reynolds} stress tensor for atmospheric surface layer and canopy sublayer turbulent flows},\n\tvolume = {3},\n\tissn = {2469-990X},\n\turl = {https://link.aps.org/doi/10.1103/PhysRevFluids.3.054608},\n\tdoi = {10.1103/PhysRevFluids.3.054608},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-04},\n\tjournal = {Physical Review Fluids},\n\tauthor = {Brugger, Peter and Katul, Gabriel G. and De Roo, Frederik and Kröniger, Konstantin and Rotenberg, Eyal and Rohatyn, Shani and Mauder, Matthias},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {054608},\n}\n\n\n\n

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\n \n\n \n \n Bruce, L. C.; Frassl, M. A.; Arhonditsis, G. B.; Gal, G.; Hamilton, D. P.; Hanson, P. C.; Hetherington, A. L.; Melack, J. M.; Read, J. S.; Rinke, K.; Rigosi, A.; Trolle, D.; Winslow, L.; Adrian, R.; Ayala, A. I.; Bocaniov, S. A.; Boehrer, B.; Boon, C.; Brookes, J. D.; Bueche, T.; Busch, B. D.; Copetti, D.; Cortés, A.; de Eyto, E.; Elliott, J. A.; Gallina, N.; Gilboa, Y.; Guyennon, N.; Huang, L.; Kerimoglu, O.; Lenters, J. D.; MacIntyre, S.; Makler-Pick, V.; McBride, C. G.; Moreira, S.; Özkundakci, D.; Pilotti, M.; Rueda, F. J.; Rusak, J. A.; Samal, N. R.; Schmid, M.; Shatwell, T.; Snorthheim, C.; Soulignac, F.; Valerio, G.; van der Linden, L.; Vetter, M.; Vinçon-Leite, B.; Wang, J.; Weber, M.; Wickramaratne, C.; Woolway, R. I.; Yao, H.; and Hipsey, M. R.\n\n\n \n \n \n \n \n A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 102: 274–291. April 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bruce_multi-lake_2018,\n\ttitle = {A multi-lake comparative analysis of the {General} {Lake} {Model} ({GLM}): {Stress}-testing across a global observatory network},\n\tvolume = {102},\n\tissn = {13648152},\n\tshorttitle = {A multi-lake comparative analysis of the {General} {Lake} {Model} ({GLM})},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1364815216311562},\n\tdoi = {10.1016/j.envsoft.2017.11.016},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Bruce, Louise C. and Frassl, Marieke A. and Arhonditsis, George B. and Gal, Gideon and Hamilton, David P. and Hanson, Paul C. and Hetherington, Amy L. and Melack, John M. and Read, Jordan S. and Rinke, Karsten and Rigosi, Anna and Trolle, Dennis and Winslow, Luke and Adrian, Rita and Ayala, Ana I. and Bocaniov, Serghei A. and Boehrer, Bertram and Boon, Casper and Brookes, Justin D. and Bueche, Thomas and Busch, Brendan D. and Copetti, Diego and Cortés, Alicia and de Eyto, Elvira and Elliott, J. Alex and Gallina, Nicole and Gilboa, Yael and Guyennon, Nicolas and Huang, Lei and Kerimoglu, Onur and Lenters, John D. and MacIntyre, Sally and Makler-Pick, Vardit and McBride, Chris G. and Moreira, Santiago and Özkundakci, Deniz and Pilotti, Marco and Rueda, Francisco J. and Rusak, James A. and Samal, Nihar R. and Schmid, Martin and Shatwell, Tom and Snorthheim, Craig and Soulignac, Frédéric and Valerio, Giulia and van der Linden, Leon and Vetter, Mark and Vinçon-Leite, Brigitte and Wang, Junbo and Weber, Michael and Wickramaratne, Chaturangi and Woolway, R. Iestyn and Yao, Huaxia and Hipsey, Matthew R.},\n\tmonth = apr,\n\tyear = {2018},\n\tpages = {274--291},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Brenner, C.; Zeeman, M.; Bernhardt, M.; and Schulz, K.\n\n\n \n \n \n \n \n Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems.\n \n \n \n \n\n\n \n\n\n\n International Journal of Remote Sensing, 39(15-16): 5141–5174. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brenner_estimation_2018,\n\ttitle = {Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems},\n\tvolume = {39},\n\tissn = {0143-1161, 1366-5901},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1471550},\n\tdoi = {10.1080/01431161.2018.1471550},\n\tlanguage = {en},\n\tnumber = {15-16},\n\turldate = {2022-11-04},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Brenner, Claire and Zeeman, Matthias and Bernhardt, Matthias and Schulz, Karsten},\n\tmonth = aug,\n\tyear = {2018},\n\tpages = {5141--5174},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Brase, L.; Sanders, T.; and Dähnke, K.\n\n\n \n \n \n \n \n Anthropogenic changes of nitrogen loads in a small river: external nutrient sources vs. internal turnover processes.\n \n \n \n \n\n\n \n\n\n\n Isotopes in Environmental and Health Studies, 54(2): 168–184. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Anthropogenic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brase_anthropogenic_2018,\n\ttitle = {Anthropogenic changes of nitrogen loads in a small river: external nutrient sources vs. internal turnover processes},\n\tvolume = {54},\n\tissn = {1025-6016, 1477-2639},\n\tshorttitle = {Anthropogenic changes of nitrogen loads in a small river},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/10256016.2018.1428580},\n\tdoi = {10.1080/10256016.2018.1428580},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {Isotopes in Environmental and Health Studies},\n\tauthor = {Brase, Lisa and Sanders, Tina and Dähnke, Kirstin},\n\tmonth = mar,\n\tyear = {2018},\n\tpages = {168--184},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H.; Montzka, C.; Huisman, J.; Graf, A.; Schmidt, M.; Stockinger, M.; von Hebel, C.; Hendricks-Franssen, H.; van der Kruk, J.; Tappe, W.; Lücke, A.; Baatz, R.; Bol, R.; Groh, J.; Pütz, T.; Jakobi, J.; Kunkel, R.; Sorg, J.; and Vereecken, H.\n\n\n \n \n \n \n \n The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 180055. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_tereno-rur_2018,\n\ttitle = {The {TERENO}-{Rur} {Hydrological} {Observatory}: {A} {Multiscale} {Multi}-{Compartment} {Research} {Platform} for the {Advancement} of {Hydrological} {Science}},\n\tvolume = {17},\n\tissn = {15391663},\n\tshorttitle = {The {TERENO}-{Rur} {Hydrological} {Observatory}},\n\turl = {http://doi.wiley.com/10.2136/vzj2018.03.0055},\n\tdoi = {10.2136/vzj2018.03.0055},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Bogena, H.R. and Montzka, C. and Huisman, J.A. and Graf, A. and Schmidt, M. and Stockinger, M. and von Hebel, C. and Hendricks-Franssen, H.J. and van der Kruk, J. and Tappe, W. and Lücke, A. and Baatz, R. and Bol, R. and Groh, J. and Pütz, T. and Jakobi, J. and Kunkel, R. and Sorg, J. and Vereecken, H.},\n\tyear = {2018},\n\tpages = {180055},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Bogena, H.; White, T.; Bour, O.; Li, X.; and Jensen, K.\n\n\n \n \n \n \n \n Toward Better Understanding of Terrestrial Processes through Long-Term Hydrological Observatories.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 180194. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_toward_2018,\n\ttitle = {Toward {Better} {Understanding} of {Terrestrial} {Processes} through {Long}-{Term} {Hydrological} {Observatories}},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2018.10.0194},\n\tdoi = {10.2136/vzj2018.10.0194},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Bogena, H.R. and White, T. and Bour, O. and Li, X. and Jensen, K.H.},\n\tyear = {2018},\n\tpages = {180194},\n}\n\n\n\n

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\n \n\n \n \n Blume, T.; van Meerveld, I.; and Weiler, M.\n\n\n \n \n \n \n \n Why and when it is useful to publish and share inconclusive results and failures: reply to “Reporting negative results to stimulate experimental hydrology”.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 63(8): 1273–1274. June 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Why$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blume_why_2018,\n\ttitle = {Why and when it is useful to publish and share inconclusive results and failures: reply to “{Reporting} negative results to stimulate experimental hydrology”},\n\tvolume = {63},\n\tissn = {0262-6667, 2150-3435},\n\tshorttitle = {Why and when it is useful to publish and share inconclusive results and failures},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2018.1493204},\n\tdoi = {10.1080/02626667.2018.1493204},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-04},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Blume, Theresa and van Meerveld, Ilja and Weiler, Markus},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {1273--1274},\n}\n\n\n\n

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\n \n\n \n \n Blume, T.; van Meerveld, I.; and Weiler, M.\n\n\n \n \n \n \n \n Incentives for field hydrology and data sharing: collaboration and compensation: reply to “A need for incentivizing field hydrology, especially in an era of open data”*.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 63(8): 1266–1268. June 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Incentives$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blume_incentives_2018,\n\ttitle = {Incentives for field hydrology and data sharing: collaboration and compensation: reply to “{A} need for incentivizing field hydrology, especially in an era of open data”*},\n\tvolume = {63},\n\tissn = {0262-6667, 2150-3435},\n\tshorttitle = {Incentives for field hydrology and data sharing},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2018.1495839},\n\tdoi = {10.1080/02626667.2018.1495839},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-04},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Blume, Theresa and van Meerveld, Ilja and Weiler, Markus},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {1266--1268},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Beylich, M.; Pöhlein, F.; and Reinstorf, F.\n\n\n \n \n \n \n Das hydrologische Versuchsgebiet Schäferbach - Referenzgebiet für Klima-Simulationen.\n \n \n \n\n\n \n\n\n\n Wasserwirtschaft, 108(1): S.30–34. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beylich_marcus_hydrologische_2018,\n\ttitle = {Das hydrologische {Versuchsgebiet} {Schäferbach} - {Referenzgebiet} für {Klima}-{Simulationen}},\n\tvolume = {108},\n\tissn = {0043-0978},\n\tnumber = {1},\n\tjournal = {Wasserwirtschaft},\n\tauthor = {Beylich, Marcus and Pöhlein, Florian and Reinstorf, Frido},\n\tyear = {2018},\n\tpages = {S.30--34},\n}\n\n\n\n

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\n \n\n \n \n Berns, A. E.; Flath, A.; Mehmood, K.; Hofmann, D.; Jacques, D.; Sauter, M.; Vereecken, H.; and Engelhardt, I.\n\n\n \n \n \n \n \n Numerical and Experimental Investigations of Cesium and Strontium Sorption and Transport in Agricultural Soils.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 17(1): 170126. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Numerical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{berns_numerical_2018,\n\ttitle = {Numerical and {Experimental} {Investigations} of {Cesium} and {Strontium} {Sorption} and {Transport} in {Agricultural} {Soils}},\n\tvolume = {17},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.06.0126},\n\tdoi = {10.2136/vzj2017.06.0126},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-04},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Berns, Anne E. and Flath, Alexander and Mehmood, Khalid and Hofmann, Diana and Jacques, Diederik and Sauter, Martin and Vereecken, Harry and Engelhardt, Irina},\n\tyear = {2018},\n\tpages = {170126},\n}\n\n\n\n

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\n \n\n \n \n Beriozkin, A.; and Mualem, Y.\n\n\n \n \n \n \n \n Comparative analysis of the apparent saturation hysteresis approach and the domain theory of hysteresis in respect of prediction of scanning curves and air entrapment.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 115: 253–263. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beriozkin_comparative_2018,\n\ttitle = {Comparative analysis of the apparent saturation hysteresis approach and the domain theory of hysteresis in respect of prediction of scanning curves and air entrapment},\n\tvolume = {115},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170817301586},\n\tdoi = {10.1016/j.advwatres.2018.01.016},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Beriozkin, A. and Mualem, Y.},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {253--263},\n}\n\n\n\n

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\n \n\n \n \n Beckers, L.; Busch, W.; Krauss, M.; Schulze, T.; and Brack, W.\n\n\n \n \n \n \n \n Characterization and risk assessment of seasonal and weather dynamics in organic pollutant mixtures from discharge of a separate sewer system.\n \n \n \n \n\n\n \n\n\n\n Water Research, 135: 122–133. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Characterization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beckers_characterization_2018,\n\ttitle = {Characterization and risk assessment of seasonal and weather dynamics in organic pollutant mixtures from discharge of a separate sewer system},\n\tvolume = {135},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135418301015},\n\tdoi = {10.1016/j.watres.2018.02.002},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Water Research},\n\tauthor = {Beckers, Liza-Marie and Busch, Wibke and Krauss, Martin and Schulze, Tobias and Brack, Werner},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {122--133},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Barrios, J. M.; Ghilain, N.; Arboleda, A.; Sachs, T.; and Gellens-Meulenberghs, F.\n\n\n \n \n \n \n \n Daily evapotranspiration at sub-kilometre spatial resolution by combining observations from geostationary and polar-orbit satellites.\n \n \n \n \n\n\n \n\n\n\n International Journal of Remote Sensing, 39(23): 8984–9003. December 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Daily$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{barrios_daily_2018,\n\ttitle = {Daily evapotranspiration at sub-kilometre spatial resolution by combining observations from geostationary and polar-orbit satellites},\n\tvolume = {39},\n\tissn = {0143-1161, 1366-5901},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1504340},\n\tdoi = {10.1080/01431161.2018.1504340},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2022-11-04},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Barrios, J. M. and Ghilain, N. and Arboleda, A. and Sachs, T. and Gellens-Meulenberghs, F.},\n\tmonth = dec,\n\tyear = {2018},\n\tpages = {8984--9003},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Baroni, G.; Scheiffele, L.; Schrön, M.; Ingwersen, J.; and Oswald, S.\n\n\n \n \n \n \n \n Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 564: 873–887. September 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baroni_uncertainty_2018,\n\ttitle = {Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing},\n\tvolume = {564},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418305675},\n\tdoi = {10.1016/j.jhydrol.2018.07.053},\n\tlanguage = {en},\n\turldate = {2022-11-04},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Baroni, G. and Scheiffele, L.M. and Schrön, M. and Ingwersen, J. and Oswald, S.E.},\n\tmonth = sep,\n\tyear = {2018},\n\tpages = {873--887},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Banerjee, T.; Brugger, P.; De Roo, F.; Kröniger, K.; Yakir, D.; Rotenberg, E.; and Mauder, M.\n\n\n \n \n \n \n \n Turbulent transport of energy across a forest and a semiarid shrubland.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 18(13): 10025–10038. July 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Turbulent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{banerjee_turbulent_2018,\n\ttitle = {Turbulent transport of energy across a forest and a semiarid shrubland},\n\tvolume = {18},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/18/10025/2018/},\n\tdoi = {10.5194/acp-18-10025-2018},\n\tabstract = {Abstract. The role of secondary circulations has recently been studied in the context of well-defined surface heterogeneity in a semiarid ecosystem where it was found that energy balance closure over a desert–forest system and the structure of the boundary layer was impacted by advection and flux divergence. As a part of the CliFF (“Climate feedbacks and benefits of semi-arid forests”, a collaboration between KIT, Germany, and the Weizmann Institute, Israel) campaign, we studied the boundary layer dynamics and turbulent transport of energy corresponding to this effect in Yatir Forest situated in the Negev Desert in Israel. The forest surrounded by small shrubs presents a distinct feature of surface heterogeneity, allowing us to study the differences between their interactions with the atmosphere above by conducting measurements with two eddy covariance (EC) stations and two Doppler lidars. As expected, the turbulence intensity and vertical fluxes of momentum and sensible heat are found to be higher above the forest compared to the shrubland. Turbulent statistics indicative of nonlocal motions are also found to differ over the forest and shrubland and also display a strong diurnal cycle. The production of turbulent kinetic energy (TKE) over the forest is strongly mechanical, while buoyancy effects generate most of the TKE over the shrubland. Overall TKE production is much higher above the forest compared to the shrubland. The forest is also found to be more efficient in dissipating TKE. The TKE budget appears to be balanced on average both for the forest and shrubland, although the imbalance of the TKE budget, which includes the role of TKE transport, is found to be quite different in terms of diurnal cycles for the forest and shrubland. The difference in turbulent quantities and the relationships between the components of TKE budget are used to infer the characteristics of the turbulent transport of energy between the desert and the forest.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-04},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {Banerjee, Tirtha and Brugger, Peter and De Roo, Frederik and Kröniger, Konstantin and Yakir, Dan and Rotenberg, Eyal and Mauder, Matthias},\n\tmonth = jul,\n\tyear = {2018},\n\tpages = {10025--10038},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Baatz, R.; Sullivan, P. L.; Li, L.; Weintraub, S. R.; Loescher, H. W.; Mirtl, M.; Groffman, P. M.; Wall, D. H.; Young, M.; White, T.; Wen, H.; Zacharias, S.; Kühn, I.; Tang, J.; Gaillardet, J.; Braud, I.; Flores, A. N.; Kumar, P.; Lin, H.; Ghezzehei, T.; Jones, J.; Gholz, H. L.; Vereecken, H.; and Van Looy, K.\n\n\n \n \n \n \n \n Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling.\n \n \n \n \n\n\n \n\n\n\n Earth System Dynamics, 9(2): 593–609. May 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Steering$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baatz_steering_2018,\n\ttitle = {Steering operational synergies in terrestrial observation networks: opportunity for advancing {Earth} system dynamics modelling},\n\tvolume = {9},\n\tissn = {2190-4987},\n\tshorttitle = {Steering operational synergies in terrestrial observation networks},\n\turl = {https://esd.copernicus.org/articles/9/593/2018/},\n\tdoi = {10.5194/esd-9-593-2018},\n\tabstract = {Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on the\ndevelopment of models and other analytical tools that apply physical,\nbiological, and chemical data. This ambition to increase understanding and\ndevelop models of ESD based on site observations was the stimulus for\ncreating the networks of Long-Term Ecological Research (LTER), Critical Zone\nObservatories (CZOs), and others. We organized a survey, the results of which\nidentified pressing gaps in data availability from these networks, in\nparticular for the future development and evaluation of models that represent\nESD processes, and provide insights for improvement in both data collection\nand model integration. From this survey overview of data applications in the context of LTER and\nCZO research, we identified three challenges: (1) widen application of\nterrestrial observation network data in Earth system modelling,\n(2) develop integrated Earth system models that incorporate process\nrepresentation and data of multiple disciplines, and (3) identify\ncomplementarity in measured variables and spatial extent, and promoting\nsynergies in the existing observational networks. These challenges lead to\nperspectives and recommendations for an improved dialogue between the\nobservation networks and the ESD modelling community, including co-location\nof sites in the existing networks and further formalizing these\nrecommendations among these communities. Developing these synergies will\nenable cross-site and cross-network comparison and synthesis studies, which\nwill help produce insights around organizing principles, classifications,\nand general rules of coupling processes with environmental conditions.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-04},\n\tjournal = {Earth System Dynamics},\n\tauthor = {Baatz, Roland and Sullivan, Pamela L. and Li, Li and Weintraub, Samantha R. and Loescher, Henry W. and Mirtl, Michael and Groffman, Peter M. and Wall, Diana H. and Young, Michael and White, Tim and Wen, Hang and Zacharias, Steffen and Kühn, Ingolf and Tang, Jianwu and Gaillardet, Jérôme and Braud, Isabelle and Flores, Alejandro N. and Kumar, Praveen and Lin, Henry and Ghezzehei, Teamrat and Jones, Julia and Gholz, Henry L. and Vereecken, Harry and Van Looy, Kris},\n\tmonth = may,\n\tyear = {2018},\n\tpages = {593--609},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n\n \n \n Alemohammad, S. H.; Kolassa, J.; Prigent, C.; Aires, F.; and Gentine, P.\n\n\n \n \n \n \n \n Global downscaling of remotely sensed soil moisture using neural networks.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 22(10): 5341–5356. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{alemohammad_global_2018,\n\ttitle = {Global downscaling of remotely sensed soil moisture using neural networks},\n\tvolume = {22},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/22/5341/2018/},\n\tdoi = {10.5194/hess-22-5341-2018},\n\tabstract = {Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e.,\nof the order of 1 km) is necessary in order to quantify its role in regional\nfeedbacks between the land surface and the atmospheric boundary layer.\nMoreover, several applications such as agricultural management can benefit\nfrom soil moisture information at fine spatial scales. Soil moisture\nestimates from current satellite missions have a reasonably good temporal\nrevisit over the globe (2–3-day repeat time); however, their finest spatial\nresolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has\nestimated soil moisture at two different spatial scales of 36 and 9 km since\nApril 2015. In this study, we develop a neural-network-based downscaling\nalgorithm using SMAP observations and disaggregate soil moisture to 2.25 km\nspatial resolution. Our approach uses the mean monthly Normalized Differenced\nVegetation Index (NDVI) as ancillary data to quantify the subpixel\nheterogeneity of soil moisture. Evaluation of the downscaled soil moisture\nestimates against in situ observations shows that their accuracy is better\nthan or equal to the SMAP 9 km soil moisture estimates.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Alemohammad, Seyed Hamed and Kolassa, Jana and Prigent, Catherine and Aires, Filipe and Gentine, Pierre},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {5341--5356},\n}\n\n\n\n

\n\n\n\n\n\n

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\n \n 2017\n \n \n (82)\n \n \n

\n \n \n

\n \n\n \n \n Wilken, F.; Fiener, P.; and Van Oost, K.\n\n\n \n \n \n \n \n Modelling a century of soil redistribution processes and carbon delivery from small watersheds using a multi-class sediment transport model.\n \n \n \n \n\n\n \n\n\n\n Earth Surface Dynamics, 5(1): 113–124. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wilken_modelling_2017,\n\ttitle = {Modelling a century of soil redistribution processes and carbon delivery from small watersheds using a multi-class sediment transport model},\n\tvolume = {5},\n\tissn = {2196-632X},\n\turl = {https://esurf.copernicus.org/articles/5/113/2017/},\n\tdoi = {10.5194/esurf-5-113-2017},\n\tabstract = {Abstract. Over the last few decades, soil erosion and carbon redistribution modelling has received a lot of attention due to large uncertainties and conflicting results. For a physically based representation of event dynamics, coupled soil and carbon erosion models have been developed. However, there is a lack of research utilizing models which physically represent preferential erosion and transport of different carbon fractions (i.e. mineral bound carbon, carbon encapsulated by aggregates and particulate organic carbon). Furthermore, most of the models that have a high temporal resolution are applied to relatively short time series ({\\textless} 10 yr−1), which might not cover the episodic nature of soil erosion. We applied the event-based multi-class sediment transport (MCST) model to a 100-year time series of rainfall observation. The study area was a small agricultural catchment (3 ha) located in the Belgium loess belt about 15 km southwest of Leuven, with a rolling topography of slopes up to 14 \\%. Our modelling analysis indicates (i) that interrill erosion is a selective process which entrains primary particles, while (ii) rill erosion is non-selective and entrains aggregates, (iii) that particulate organic matter is predominantly encapsulated in aggregates, and (iv) that the export enrichment in carbon is highest during events dominated by interrill erosion and decreases with event size.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Earth Surface Dynamics},\n\tauthor = {Wilken, Florian and Fiener, Peter and Van Oost, Kristof},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {113--124},\n}\n\n\n\n

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\n \n\n \n \n Zeeman, M.; Mauder, M.; Steinbrecher, R.; Heidbach, K.; Eckart, E.; and Schmid, H.\n\n\n \n \n \n \n \n Reduced snow cover affects productivity of upland temperate grasslands.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 232: 514–526. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Reduced$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zeeman_reduced_2017,\n\ttitle = {Reduced snow cover affects productivity of upland temperate grasslands},\n\tvolume = {232},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192316303811},\n\tdoi = {10.1016/j.agrformet.2016.09.002},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Zeeman, M.J. and Mauder, M. and Steinbrecher, R. and Heidbach, K. and Eckart, E. and Schmid, H.P.},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {514--526},\n}\n\n\n\n

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\n \n\n \n \n Wu, B.; Wiekenkamp, I.; Sun, Y.; Fisher, A. S.; Clough, R.; Gottselig, N.; Bogena, H.; Pütz, T.; Brüggemann, N.; Vereecken, H.; and Bol, R.\n\n\n \n \n \n \n \n A Dataset for Three-Dimensional Distribution of 39 Elements Including Plant Nutrients and Other Metals and Metalloids in the Soils of a Forested Headwater Catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Quality, 46(6): 1510–1518. November 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wu_dataset_2017,\n\ttitle = {A {Dataset} for {Three}-{Dimensional} {Distribution} of 39 {Elements} {Including} {Plant} {Nutrients} and {Other} {Metals} and {Metalloids} in the {Soils} of a {Forested} {Headwater} {Catchment}},\n\tvolume = {46},\n\tissn = {00472425},\n\turl = {http://doi.wiley.com/10.2134/jeq2017.05.0193},\n\tdoi = {10.2134/jeq2017.05.0193},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Environmental Quality},\n\tauthor = {Wu, B. and Wiekenkamp, I. and Sun, Y. and Fisher, A. S. and Clough, R. and Gottselig, N. and Bogena, H. and Pütz, T. and Brüggemann, N. and Vereecken, H. and Bol, R.},\n\tmonth = nov,\n\tyear = {2017},\n\tpages = {1510--1518},\n}\n\n\n\n

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\n \n\n \n \n Wollschläger, U.; Attinger, S.; Borchardt, D.; Brauns, M.; Cuntz, M.; Dietrich, P.; Fleckenstein, J. H.; Friese, K.; Friesen, J.; Harpke, A.; Hildebrandt, A.; Jäckel, G.; Kamjunke, N.; Knöller, K.; Kögler, S.; Kolditz, O.; Krieg, R.; Kumar, R.; Lausch, A.; Liess, M.; Marx, A.; Merz, R.; Mueller, C.; Musolff, A.; Norf, H.; Oswald, S. E.; Rebmann, C.; Reinstorf, F.; Rode, M.; Rink, K.; Rinke, K.; Samaniego, L.; Vieweg, M.; Vogel, H.; Weitere, M.; Werban, U.; Zink, M.; and Zacharias, S.\n\n\n \n \n \n \n \n The Bode hydrological observatory: a platform for integrated, interdisciplinary hydro-ecological research within the TERENO Harz/Central German Lowland Observatory.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 76(1): 29. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wollschlager_bode_2017,\n\ttitle = {The {Bode} hydrological observatory: a platform for integrated, interdisciplinary hydro-ecological research within the {TERENO} {Harz}/{Central} {German} {Lowland} {Observatory}},\n\tvolume = {76},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {The {Bode} hydrological observatory},\n\turl = {http://link.springer.com/10.1007/s12665-016-6327-5},\n\tdoi = {10.1007/s12665-016-6327-5},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Wollschläger, Ute and Attinger, Sabine and Borchardt, Dietrich and Brauns, Mario and Cuntz, Matthias and Dietrich, Peter and Fleckenstein, Jan H. and Friese, Kurt and Friesen, Jan and Harpke, Alexander and Hildebrandt, Anke and Jäckel, Greta and Kamjunke, Norbert and Knöller, Kay and Kögler, Simon and Kolditz, Olaf and Krieg, Ronald and Kumar, Rohini and Lausch, Angela and Liess, Matthias and Marx, Andreas and Merz, Ralf and Mueller, Christin and Musolff, Andreas and Norf, Helge and Oswald, Sascha E. and Rebmann, Corinna and Reinstorf, Frido and Rode, Michael and Rink, Karsten and Rinke, Karsten and Samaniego, Luis and Vieweg, Michael and Vogel, Hans-Jörg and Weitere, Markus and Werban, Ulrike and Zink, Matthias and Zacharias, Steffen},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {29},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Wolff, M.; Haase, A.; Haase, D.; and Kabisch, N.\n\n\n \n \n \n \n \n The impact of urban regrowth on the built environment.\n \n \n \n \n\n\n \n\n\n\n Urban Studies, 54(12): 2683–2700. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wolff_impact_2017,\n\ttitle = {The impact of urban regrowth on the built environment},\n\tvolume = {54},\n\tissn = {0042-0980, 1360-063X},\n\turl = {http://journals.sagepub.com/doi/10.1177/0042098016658231},\n\tdoi = {10.1177/0042098016658231},\n\tabstract = {After several decades, an increasing number of European cities have been experiencing population growth after a longer phase of decline. This new growth represents not just a quantitative phenomenon but also has qualitative implications for the urban space and the built environment. A juxtaposition of re- and de-densification, as well as changes in land use, in the form of a small-scale spatial mosaic, can be observed. A crucial factor for estimating the relationship between the built environment and demand for it is population density. Increasing population densities may put pressure on sustaining a certain quality of life and on ecological recovery spaces. In this vein, an indicator concept for re- and de-densification will be applied to the city of Leipzig, one of the most illustrative examples of a regrowing city, in order to shed light on the complex relationship between changing human housing demands and their impact on land use. The concept involves measuring population density. Our study has demonstrated that, although similar density changes can be observed in different periods in different parts of the city, they are dominated by different drivers, leading to the formation of different spatial patterns. The results of our study emphasise that regrowth should be understood as a distinctive process because it is distributed very heterogeneously within the city area, with a variety of spatial effects and impacts. The concept allows us to draw conclusions about processes that mitigate, drive or reinforce regrowth, and therefore contributes to a better understanding of this phenomenon and its implications for land use.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-18},\n\tjournal = {Urban Studies},\n\tauthor = {Wolff, Manuel and Haase, Annegret and Haase, Dagmar and Kabisch, Nadja},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {2683--2700},\n}\n\n\n\n

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\n \n\n \n \n Wolf, B.; Chwala, C.; Fersch, B.; Garvelmann, J.; Junkermann, W.; Zeeman, M. J.; Angerer, A.; Adler, B.; Beck, C.; Brosy, C.; Brugger, P.; Emeis, S.; Dannenmann, M.; De Roo, F.; Diaz-Pines, E.; Haas, E.; Hagen, M.; Hajnsek, I.; Jacobeit, J.; Jagdhuber, T.; Kalthoff, N.; Kiese, R.; Kunstmann, H.; Kosak, O.; Krieg, R.; Malchow, C.; Mauder, M.; Merz, R.; Notarnicola, C.; Philipp, A.; Reif, W.; Reineke, S.; Rödiger, T.; Ruehr, N.; Schäfer, K.; Schrön, M.; Senatore, A.; Shupe, H.; Völksch, I.; Wanninger, C.; Zacharias, S.; and Schmid, H. P.\n\n\n \n \n \n \n \n The SCALEX Campaign: Scale-Crossing Land Surface and Boundary Layer Processes in the TERENO-preAlpine Observatory.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 98(6): 1217–1234. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wolf_scalex_2017,\n\ttitle = {The {SCALEX} {Campaign}: {Scale}-{Crossing} {Land} {Surface} and {Boundary} {Layer} {Processes} in the {TERENO}-{preAlpine} {Observatory}},\n\tvolume = {98},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {The {SCALEX} {Campaign}},\n\turl = {https://journals.ametsoc.org/doi/10.1175/BAMS-D-15-00277.1},\n\tdoi = {10.1175/BAMS-D-15-00277.1},\n\tabstract = {Abstract \n            ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-18},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Wolf, B. and Chwala, C. and Fersch, B. and Garvelmann, J. and Junkermann, W. and Zeeman, M. J. and Angerer, A. and Adler, B. and Beck, C. and Brosy, C. and Brugger, P. and Emeis, S. and Dannenmann, M. and De Roo, F. and Diaz-Pines, E. and Haas, E. and Hagen, M. and Hajnsek, I. and Jacobeit, J. and Jagdhuber, T. and Kalthoff, N. and Kiese, R. and Kunstmann, H. and Kosak, O. and Krieg, R. and Malchow, C. and Mauder, M. and Merz, R. and Notarnicola, C. and Philipp, A. and Reif, W. and Reineke, S. and Rödiger, T. and Ruehr, N. and Schäfer, K. and Schrön, M. and Senatore, A. and Shupe, H. and Völksch, I. and Wanninger, C. and Zacharias, S. and Schmid, H. P.},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {1217--1234},\n}\n\n\n\n

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\n \n\n \n \n Wilken, F.; Sommer, M.; Van Oost, K.; Bens, O.; and Fiener, P.\n\n\n \n \n \n \n \n Process-oriented modelling to identify main drivers of erosion-induced carbon fluxes.\n \n \n \n \n\n\n \n\n\n\n SOIL, 3(2): 83–94. May 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Process-oriented$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wilken_process-oriented_2017,\n\ttitle = {Process-oriented modelling to identify main drivers of erosion-induced carbon fluxes},\n\tvolume = {3},\n\tissn = {2199-398X},\n\turl = {https://soil.copernicus.org/articles/3/83/2017/},\n\tdoi = {10.5194/soil-3-83-2017},\n\tabstract = {Abstract. Coupled modelling of soil erosion, carbon redistribution, and turnover has received great attention over the last decades due to large uncertainties regarding erosion-induced carbon fluxes. For a process-oriented representation of event dynamics, coupled soil–carbon erosion models have been developed. However, there are currently few models that represent tillage erosion, preferential water erosion, and transport of different carbon fractions (e.g. mineral bound carbon, carbon encapsulated by soil aggregates). We couple a process-oriented multi-class sediment transport model with a carbon turnover model (MCST-C) to identify relevant redistribution processes for carbon dynamics. The model is applied for two arable catchments (3.7 and 7.8 ha) located in the Tertiary Hills about 40 km north of Munich, Germany. Our findings indicate the following: (i) redistribution by tillage has a large effect on erosion-induced vertical carbon fluxes and has a large carbon sequestration potential; (ii) water erosion has a minor effect on vertical fluxes, but episodic soil organic carbon (SOC) delivery controls the long-term erosion-induced carbon balance; (iii) delivered sediments are highly enriched in SOC compared to the parent soil, and sediment delivery is driven by event size and catchment connectivity; and (iv) soil aggregation enhances SOC deposition due to the transformation of highly mobile carbon-rich fine primary particles into rather immobile soil aggregates.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {SOIL},\n\tauthor = {Wilken, Florian and Sommer, Michael and Van Oost, Kristof and Bens, Oliver and Fiener, Peter},\n\tmonth = may,\n\tyear = {2017},\n\tpages = {83--94},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Weigand, M.; and Kemna, A.\n\n\n \n \n \n \n \n Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 14(4): 921–939. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-frequency$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weigand_multi-frequency_2017,\n\ttitle = {Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems},\n\tvolume = {14},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/14/921/2017/},\n\tdoi = {10.5194/bg-14-921-2017},\n\tabstract = {Abstract. A better understanding of root–soil interactions and associated processes is essential in achieving progress in crop breeding and management, prompting the need for high-resolution and non-destructive characterization methods. To date, such methods are still lacking or restricted by technical constraints, in particular the charactization and monitoring of root growth and function in the field. A promising technique in this respect is electrical impedance tomography (EIT), which utilizes low-frequency ({\\textless} 1 kHz)- electrical conduction- and polarization properties in an imaging framework. It is well established that cells and cell clusters exhibit an electrical polarization response in alternating electric-current fields due to electrical double layers which form at cell membranes. This double layer is directly related to the electrical surface properties of the membrane, which in turn are influenced by nutrient dynamics (fluxes and concentrations on both sides of the membranes). Therefore, it can be assumed that the electrical polarization properties of roots are inherently related to ion uptake and translocation processes in the root systems. We hereby propose broadband (mHz to hundreds of Hz) multi-frequency EIT as a non-invasive methodological approach for the monitoring and physiological, i.e., functional, characterization of crop root systems. The approach combines the spatial-resolution capability of an imaging method with the diagnostic potential of electrical-impedance spectroscopy. The capability of multi-frequency EIT to characterize and monitor crop root systems was investigated in a rhizotron laboratory experiment, in which the root system of oilseed plants was monitored in a water–filled rhizotron, that is, in a nutrient-deprived environment. We found a low-frequency polarization response of the root system, which enabled the successful delineation of its spatial extension. The magnitude of the overall polarization response decreased along with the physiological decay of the root system due to the stress situation. Spectral polarization parameters, as derived from a pixel-based Debye decomposition analysis of the multi-frequency imaging results, reveal systematic changes in the spatial and spectral electrical response of the root system. In particular, quantified mean relaxation times (of the order of 10 ms) indicate changes in the length scales on which the polarization processes took place in the root system, as a response to the prolonged induced stress situation. Our results demonstrate that broadband EIT is a capable, non-invasive method to image root system extension as well as to monitor changes associated with the root physiological processes. Given its applicability on both laboratory and field scales, our results suggest an enormous potential of the method for the structural and functional imaging of root systems for various applications. This particularly holds for the field scale, where corresponding methods are highly desired but to date are lacking.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-18},\n\tjournal = {Biogeosciences},\n\tauthor = {Weigand, Maximilian and Kemna, Andreas},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {921--939},\n}\n\n\n\n

\n\n\n

\n Abstract. A better understanding of root–soil interactions and associated processes is essential in achieving progress in crop breeding and management, prompting the need for high-resolution and non-destructive characterization methods. To date, such methods are still lacking or restricted by technical constraints, in particular the charactization and monitoring of root growth and function in the field. A promising technique in this respect is electrical impedance tomography (EIT), which utilizes low-frequency (\\textless 1 kHz)- electrical conduction- and polarization properties in an imaging framework. It is well established that cells and cell clusters exhibit an electrical polarization response in alternating electric-current fields due to electrical double layers which form at cell membranes. This double layer is directly related to the electrical surface properties of the membrane, which in turn are influenced by nutrient dynamics (fluxes and concentrations on both sides of the membranes). Therefore, it can be assumed that the electrical polarization properties of roots are inherently related to ion uptake and translocation processes in the root systems. We hereby propose broadband (mHz to hundreds of Hz) multi-frequency EIT as a non-invasive methodological approach for the monitoring and physiological, i.e., functional, characterization of crop root systems. The approach combines the spatial-resolution capability of an imaging method with the diagnostic potential of electrical-impedance spectroscopy. The capability of multi-frequency EIT to characterize and monitor crop root systems was investigated in a rhizotron laboratory experiment, in which the root system of oilseed plants was monitored in a water–filled rhizotron, that is, in a nutrient-deprived environment. We found a low-frequency polarization response of the root system, which enabled the successful delineation of its spatial extension. The magnitude of the overall polarization response decreased along with the physiological decay of the root system due to the stress situation. Spectral polarization parameters, as derived from a pixel-based Debye decomposition analysis of the multi-frequency imaging results, reveal systematic changes in the spatial and spectral electrical response of the root system. In particular, quantified mean relaxation times (of the order of 10 ms) indicate changes in the length scales on which the polarization processes took place in the root system, as a response to the prolonged induced stress situation. Our results demonstrate that broadband EIT is a capable, non-invasive method to image root system extension as well as to monitor changes associated with the root physiological processes. Given its applicability on both laboratory and field scales, our results suggest an enormous potential of the method for the structural and functional imaging of root systems for various applications. This particularly holds for the field scale, where corresponding methods are highly desired but to date are lacking.\n

\n\n\n

\n \n\n \n \n Weigand, S.; Bol, R.; Reichert, B.; Graf, A.; Wiekenkamp, I.; Stockinger, M.; Luecke, A.; Tappe, W.; Bogena, H.; Puetz, T.; Amelung, W.; and Vereecken, H.\n\n\n \n \n \n \n \n Spatiotemporal Analysis of Dissolved Organic Carbon and Nitrate in Waters of a Forested Catchment Using Wavelet Analysis.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(3): vzj2016.09.0077. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weigand_spatiotemporal_2017,\n\ttitle = {Spatiotemporal {Analysis} of {Dissolved} {Organic} {Carbon} and {Nitrate} in {Waters} of a {Forested} {Catchment} {Using} {Wavelet} {Analysis}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.09.0077},\n\tdoi = {10.2136/vzj2016.09.0077},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Weigand, Susanne and Bol, Roland and Reichert, Barbara and Graf, Alexander and Wiekenkamp, Inge and Stockinger, Michael and Luecke, Andreas and Tappe, Wolfgang and Bogena, Heye and Puetz, Thomas and Amelung, Wulf and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {vzj2016.09.0077},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wei, J.; Amelung, W.; Lehndorff, E.; Schloter, M.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n N$_{\\textrm{2}}$O and NO$_{\\textrm{{X}}}$ emissions by reactions of nitrite with soil organic matter of a Norway spruce forest.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 132(3): 325–342. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"N$_{\\textrm{2}}$O$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wei_n2o_2017,\n\ttitle = {N$_{\\textrm{2}}${O} and {NO}$_{\\textrm{{X}}}$ emissions by reactions of nitrite with soil organic matter of a {Norway} spruce forest},\n\tvolume = {132},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-017-0306-0},\n\tdoi = {10.1007/s10533-017-0306-0},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Biogeochemistry},\n\tauthor = {Wei, Jing and Amelung, Wulf and Lehndorff, Eva and Schloter, Michael and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {325--342},\n}\n\n\n\n

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\n \n\n \n \n Jingxia Wang; and Banzhaf, E.\n\n\n \n \n \n \n \n Derive an understanding of Green Infrastructure for the quality of life in cities by means of integrated RS mapping tools.\n \n \n \n \n\n\n \n\n\n\n In 2017 Joint Urban Remote Sensing Event (JURSE), pages 1–4, Dubai, United Arab Emirates, March 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Derive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{jingxia_wang_derive_2017,\n\taddress = {Dubai, United Arab Emirates},\n\ttitle = {Derive an understanding of {Green} {Infrastructure} for the quality of life in cities by means of integrated {RS} mapping tools},\n\tisbn = {9781509058082},\n\turl = {http://ieeexplore.ieee.org/document/7924585/},\n\tdoi = {10.1109/JURSE.2017.7924585},\n\turldate = {2022-11-18},\n\tbooktitle = {2017 {Joint} {Urban} {Remote} {Sensing} {Event} ({JURSE})},\n\tpublisher = {IEEE},\n\tauthor = {{Jingxia Wang} and Banzhaf, Ellen},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {1--4},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Waldhoff, G.; Lussem, U.; and Bareth, G.\n\n\n \n \n \n \n \n Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 61: 55–69. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-Data$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{waldhoff_multi-data_2017,\n\ttitle = {Multi-{Data} {Approach} for remote sensing-based regional crop rotation mapping: {A} case study for the {Rur} catchment, {Germany}},\n\tvolume = {61},\n\tissn = {15698432},\n\tshorttitle = {Multi-{Data} {Approach} for remote sensing-based regional crop rotation mapping},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243417300934},\n\tdoi = {10.1016/j.jag.2017.04.009},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Waldhoff, Guido and Lussem, Ulrike and Bareth, Georg},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {55--69},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Uebel, M.; Herbst, M.; and Bott, A.\n\n\n \n \n \n \n \n Mesoscale simulations of atmospheric CO $_{\\textrm{2}}$ variations using a high-resolution model system with process-based CO $_{\\textrm{2}}$ fluxes: Mesoscale Simulations of Atmospheric CO $_{\\textrm{2}}$ Variations.\n \n \n \n \n\n\n \n\n\n\n Quarterly Journal of the Royal Meteorological Society, 143(705): 1860–1876. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Mesoscale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{uebel_mesoscale_2017,\n\ttitle = {Mesoscale simulations of atmospheric {CO} $_{\\textrm{2}}$ variations using a high-resolution model system with process-based {CO} $_{\\textrm{2}}$ fluxes: {Mesoscale} {Simulations} of {Atmospheric} {CO} $_{\\textrm{2}}$ {Variations}},\n\tvolume = {143},\n\tissn = {00359009},\n\tshorttitle = {Mesoscale simulations of atmospheric {CO} $_{\\textrm{2}}$ variations using a high-resolution model system with process-based {CO} $_{\\textrm{2}}$ fluxes},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/qj.3047},\n\tdoi = {10.1002/qj.3047},\n\tlanguage = {en},\n\tnumber = {705},\n\turldate = {2022-11-18},\n\tjournal = {Quarterly Journal of the Royal Meteorological Society},\n\tauthor = {Uebel, M. and Herbst, M. and Bott, A.},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {1860--1876},\n}\n\n\n\n

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\n \n\n \n \n Trauth, N.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Single discharge events increase reactive efficiency of the hyporheic zone: DISCHARGE EVENTS INCREASE REACTIVITY.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 53(1): 779–798. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Single$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trauth_single_2017,\n\ttitle = {Single discharge events increase reactive efficiency of the hyporheic zone: {DISCHARGE} {EVENTS} {INCREASE} {REACTIVITY}},\n\tvolume = {53},\n\tissn = {00431397},\n\tshorttitle = {Single discharge events increase reactive efficiency of the hyporheic zone},\n\turl = {http://doi.wiley.com/10.1002/2016WR019488},\n\tdoi = {10.1002/2016WR019488},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Water Resources Research},\n\tauthor = {Trauth, Nico and Fleckenstein, Jan H.},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {779--798},\n}\n\n\n\n

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\n \n\n \n \n Tecklenburg, C.; and Blume, T.\n\n\n \n \n \n \n \n Identifying, characterizing and predicting spatial patterns of lacustrine groundwater discharge.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 21(10): 5043–5063. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tecklenburg_identifying_2017,\n\ttitle = {Identifying, characterizing and predicting spatial patterns of lacustrine groundwater discharge},\n\tvolume = {21},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/21/5043/2017/},\n\tdoi = {10.5194/hess-21-5043-2017},\n\tabstract = {Abstract. Lacustrine groundwater discharge (LGD) can significantly affect lake water balances and lake water quality. However, quantifying LGD and its spatial patterns is challenging because of the large spatial extent of the aquifer–lake interface and pronounced spatial variability. This is the first experimental study to specifically study these larger-scale patterns with sufficient spatial resolution to systematically investigate how landscape and local characteristics affect the spatial variability in LGD. We measured vertical temperature profiles around a 0.49 km2 lake in northeastern Germany with a needle thermistor, which has the advantage of allowing for rapid (manual) measurements and thus, when used in a survey, high spatial coverage and resolution. Groundwater inflow rates were then estimated using the heat transport equation. These near-shore temperature profiles were complemented with sediment temperature measurements with a fibre-optic cable along six transects from shoreline to shoreline and radon measurements of lake water samples to qualitatively identify LGD patterns in the offshore part of the lake. As the hydrogeology of the catchment is sufficiently homogeneous (sandy sediments of a glacial outwash plain; no bedrock control) to avoid patterns being dominated by geological discontinuities, we were able to test the common assumptions that spatial patterns of LGD are mainly controlled by sediment characteristics and the groundwater flow field. We also tested the assumption that topographic gradients can be used as a proxy for gradients of the groundwater flow field. Thanks to the extensive data set, these tests could be carried out in a nested design, considering both small- and large-scale variability in LGD. We found that LGD was concentrated in the near-shore area, but alongshore variability was high, with specific regions of higher rates and higher spatial variability. Median inflow rates were 44 L m−2 d−1 with maximum rates in certain locations going up to 169 L m−2 d−1. Offshore LGD was negligible except for two local hotspots on steep steps in the lake bed topography. Large-scale groundwater inflow patterns were correlated with topography and the groundwater flow field, whereas small-scale patterns correlated with grain size distributions of the lake sediment. These findings confirm results and assumptions of theoretical and modelling studies more systematically than was previously possible with coarser sampling designs. However, we also found that a significant fraction of the variance in LGD could not be explained by these controls alone and that additional processes need to be considered. While regression models using these controls as explanatory variables had limited power to predict LGD rates, the results nevertheless encourage the use of topographic indices and sediment heterogeneity as an aid for targeted campaigns in future studies of groundwater discharge to lakes.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-18},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Tecklenburg, Christina and Blume, Theresa},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {5043--5063},\n}\n\n\n\n

\n\n\n

\n Abstract. Lacustrine groundwater discharge (LGD) can significantly affect lake water balances and lake water quality. However, quantifying LGD and its spatial patterns is challenging because of the large spatial extent of the aquifer–lake interface and pronounced spatial variability. This is the first experimental study to specifically study these larger-scale patterns with sufficient spatial resolution to systematically investigate how landscape and local characteristics affect the spatial variability in LGD. We measured vertical temperature profiles around a 0.49 km2 lake in northeastern Germany with a needle thermistor, which has the advantage of allowing for rapid (manual) measurements and thus, when used in a survey, high spatial coverage and resolution. Groundwater inflow rates were then estimated using the heat transport equation. These near-shore temperature profiles were complemented with sediment temperature measurements with a fibre-optic cable along six transects from shoreline to shoreline and radon measurements of lake water samples to qualitatively identify LGD patterns in the offshore part of the lake. As the hydrogeology of the catchment is sufficiently homogeneous (sandy sediments of a glacial outwash plain; no bedrock control) to avoid patterns being dominated by geological discontinuities, we were able to test the common assumptions that spatial patterns of LGD are mainly controlled by sediment characteristics and the groundwater flow field. We also tested the assumption that topographic gradients can be used as a proxy for gradients of the groundwater flow field. Thanks to the extensive data set, these tests could be carried out in a nested design, considering both small- and large-scale variability in LGD. We found that LGD was concentrated in the near-shore area, but alongshore variability was high, with specific regions of higher rates and higher spatial variability. Median inflow rates were 44 L m−2 d−1 with maximum rates in certain locations going up to 169 L m−2 d−1. Offshore LGD was negligible except for two local hotspots on steep steps in the lake bed topography. Large-scale groundwater inflow patterns were correlated with topography and the groundwater flow field, whereas small-scale patterns correlated with grain size distributions of the lake sediment. These findings confirm results and assumptions of theoretical and modelling studies more systematically than was previously possible with coarser sampling designs. However, we also found that a significant fraction of the variance in LGD could not be explained by these controls alone and that additional processes need to be considered. While regression models using these controls as explanatory variables had limited power to predict LGD rates, the results nevertheless encourage the use of topographic indices and sediment heterogeneity as an aid for targeted campaigns in future studies of groundwater discharge to lakes.\n

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\n \n\n \n \n Stockinger, M. P.; Lücke, A.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Accounting for seasonal isotopic patterns of forest canopy intercepted precipitation in streamflow modeling.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 555: 31–40. December 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Accounting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stockinger_accounting_2017,\n\ttitle = {Accounting for seasonal isotopic patterns of forest canopy intercepted precipitation in streamflow modeling},\n\tvolume = {555},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169417306674},\n\tdoi = {10.1016/j.jhydrol.2017.10.003},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Stockinger, Michael P. and Lücke, Andreas and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = dec,\n\tyear = {2017},\n\tpages = {31--40},\n}\n\n\n\n

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\n \n\n \n \n Stadler, J.; Klotz, S.; Brandl, R.; and Knapp, S.\n\n\n \n \n \n \n \n Species richness and phylogenetic structure in plant communities: 20 years of succession.\n \n \n \n \n\n\n \n\n\n\n Web Ecology, 17(2): 37–46. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Species$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stadler_species_2017,\n\ttitle = {Species richness and phylogenetic structure in plant communities: 20 years of succession},\n\tvolume = {17},\n\tissn = {1399-1183},\n\tshorttitle = {Species richness and phylogenetic structure in plant communities},\n\turl = {https://we.copernicus.org/articles/17/37/2017/},\n\tdoi = {10.5194/we-17-37-2017},\n\tabstract = {Abstract. Secondary succession on arable fields is a popular system for studying processes influencing community assembly of plants. During early succession, the arrival and establishment of those propagules that can pass the environmental filters operating at a given site should be among the dominant processes leading to an initial increase in species richness. With ongoing succession, environmental filtering should decrease in relative importance compared to competitive interactions, which then should decrease species richness. Thereby, the phylogenetic structure of communities should change from random or clustered patterns during early succession to overdispersion. Disturbance is supposed to act as an additional filter, causing communities to be phylogenetically clustered. By analysing the species richness and phylogenetic structure of secondary succession in two different regions in Germany with three different disturbance levels each, we tested this general model. Although in one of the regions (Gimritz) we found the expected trajectory of species richness, phylogenetic structure did not follow the expected trend from random or clustered towards overdispersed communities. In the other region (Bayreuth), species richness did not follow the expected trajectory and phylogenetic structure remained clustered over the course of succession. A preliminary analysis of autecological characteristics of the species involved (Ellenberg indicator values) nevertheless showed clear contrasting trends. The idiosyncrasies of successional trajectories across sites might be due to the environmental context, the regional species pool as well as the legacy of former land use reflected in the seed bank.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Web Ecology},\n\tauthor = {Stadler, Jutta and Klotz, Stefan and Brandl, Roland and Knapp, Sonja},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {37--46},\n}\n\n\n\n

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\n \n\n \n \n Schröter, I.; Paasche, H.; Doktor, D.; Xu, X.; Dietrich, P.; and Wollschläger, U.\n\n\n \n \n \n \n \n Estimating Soil Moisture Patterns with Remote Sensing and Terrain Data at the Small Catchment Scale.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(10): vzj2017.01.0012. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schroter_estimating_2017,\n\ttitle = {Estimating {Soil} {Moisture} {Patterns} with {Remote} {Sensing} and {Terrain} {Data} at the {Small} {Catchment} {Scale}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.01.0012},\n\tdoi = {10.2136/vzj2017.01.0012},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Schröter, Ingmar and Paasche, Hendrik and Doktor, Daniel and Xu, Xingmei and Dietrich, Peter and Wollschläger, Ute},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {vzj2017.01.0012},\n}\n\n\n\n

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\n \n\n \n \n Słowiński, M.; Zawiska, I.; Ott, F.; Noryśkiewicz, A. M.; Plessen, B.; Apolinarska, K.; Rzodkiewicz, M.; Michczyńska, D. J.; Wulf, S.; Skubała, P.; Kordowski, J.; Błaszkiewicz, M.; and Brauer, A.\n\n\n \n \n \n \n \n Differential proxy responses to late Allerød and early Younger Dryas climatic change recorded in varved sediments of the Trzechowskie palaeolake in Northern Poland.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 158: 94–106. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Differential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{slowinski_differential_2017,\n\ttitle = {Differential proxy responses to late {Allerød} and early {Younger} {Dryas} climatic change recorded in varved sediments of the {Trzechowskie} palaeolake in {Northern} {Poland}},\n\tvolume = {158},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379117300161},\n\tdoi = {10.1016/j.quascirev.2017.01.005},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Słowiński, Michał and Zawiska, Izabela and Ott, Florian and Noryśkiewicz, Agnieszka M. and Plessen, Birgit and Apolinarska, Karina and Rzodkiewicz, Monika and Michczyńska, Danuta J. and Wulf, Sabine and Skubała, Piotr and Kordowski, Jarosław and Błaszkiewicz, Mirosław and Brauer, Achim},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {94--106},\n}\n\n\n\n

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\n \n\n \n \n Schollaen, K.; Baschek, H.; Heinrich, I.; Slotta, F.; Pauly, M.; and Helle, G.\n\n\n \n \n \n \n \n A guideline for sample preparation in modern tree-ring stable isotope research.\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 44: 133–145. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schollaen_guideline_2017,\n\ttitle = {A guideline for sample preparation in modern tree-ring stable isotope research},\n\tvolume = {44},\n\tissn = {11257865},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786516301606},\n\tdoi = {10.1016/j.dendro.2017.05.002},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Dendrochronologia},\n\tauthor = {Schollaen, Karina and Baschek, Heiko and Heinrich, Ingo and Slotta, Franziska and Pauly, Maren and Helle, Gerhard},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {133--145},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, J.; Fassnacht, F. E.; Lausch, A.; and Schmidtlein, S.\n\n\n \n \n \n \n \n Assessing the functional signature of heathland landscapes via hyperspectral remote sensing.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 73: 505–512. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_assessing_2017,\n\ttitle = {Assessing the functional signature of heathland landscapes via hyperspectral remote sensing},\n\tvolume = {73},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X16306033},\n\tdoi = {10.1016/j.ecolind.2016.10.017},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Ecological Indicators},\n\tauthor = {Schmidt, Johannes and Fassnacht, Fabian Ewald and Lausch, Angela and Schmidtlein, Sebastian},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {505--512},\n}\n\n\n\n

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\n \n\n \n \n Schiedung, H.; Bornemann, L.; and Welp, G.\n\n\n \n \n \n \n \n Seasonal Variability of Soil Organic Carbon Fractions Under Arable Land.\n \n \n \n \n\n\n \n\n\n\n Pedosphere, 27(2): 380–386. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schiedung_seasonal_2017,\n\ttitle = {Seasonal {Variability} of {Soil} {Organic} {Carbon} {Fractions} {Under} {Arable} {Land}},\n\tvolume = {27},\n\tissn = {10020160},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1002016017603266},\n\tdoi = {10.1016/S1002-0160(17)60326-6},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Pedosphere},\n\tauthor = {Schiedung, Henning and Bornemann, Ludger and Welp, Gerhard},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {380--386},\n}\n\n\n\n

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\n \n\n \n \n Schiedung, H.; Tilly, N.; Hütt, C.; Welp, G.; Brüggemann, N.; and Amelung, W.\n\n\n \n \n \n \n \n Spatial controls of topsoil and subsoil organic carbon turnover under C3–C4 vegetation change.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 303: 44–51. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schiedung_spatial_2017,\n\ttitle = {Spatial controls of topsoil and subsoil organic carbon turnover under {C3}–{C4} vegetation change},\n\tvolume = {303},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706116308886},\n\tdoi = {10.1016/j.geoderma.2017.05.006},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Geoderma},\n\tauthor = {Schiedung, H. and Tilly, N. and Hütt, C. and Welp, G. and Brüggemann, N. and Amelung, W.},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {44--51},\n}\n\n\n\n

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\n \n\n \n \n Rach, O.; Engels, S.; Kahmen, A.; Brauer, A.; Martín-Puertas, C.; van Geel, B.; and Sachse, D.\n\n\n \n \n \n \n \n Hydrological and ecological changes in western Europe between 3200 and 2000 years BP derived from lipid biomarker δD values in lake Meerfelder Maar sediments.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 172: 44–54. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydrological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rach_hydrological_2017,\n\ttitle = {Hydrological and ecological changes in western {Europe} between 3200 and 2000 years {BP} derived from lipid biomarker δ{D} values in lake {Meerfelder} {Maar} sediments},\n\tvolume = {172},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379117302305},\n\tdoi = {10.1016/j.quascirev.2017.07.019},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Rach, O. and Engels, S. and Kahmen, A. and Brauer, A. and Martín-Puertas, C. and van Geel, B. and Sachse, D.},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {44--54},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Rach, O.; Kahmen, A.; Brauer, A.; and Sachse, D.\n\n\n \n \n \n \n \n A dual-biomarker approach for quantification of changes in relative humidity from sedimentary lipid D∕H ratios.\n \n \n \n \n\n\n \n\n\n\n Climate of the Past, 13(7): 741–757. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rach_dual-biomarker_2017,\n\ttitle = {A dual-biomarker approach for quantification of changes in relative humidity from sedimentary lipid \\&lt;i\\&gt;{D}\\&lt;/i\\&gt;∕\\&lt;i\\&gt;{H}\\&lt;/i\\&gt; ratios},\n\tvolume = {13},\n\tissn = {1814-9332},\n\turl = {https://cp.copernicus.org/articles/13/741/2017/},\n\tdoi = {10.5194/cp-13-741-2017},\n\tabstract = {Abstract. Past climatic change can be reconstructed from sedimentary archives by a number of proxies. However, few methods exist to directly estimate hydrological changes and even fewer result in quantitative data, impeding our understanding of the timing, magnitude and mechanisms of hydrological changes. Here we present a novel approach based on δ2H values of sedimentary lipid biomarkers in combination with plant physiological modeling to extract quantitative information on past changes in relative humidity. Our initial application to an annually laminated lacustrine sediment sequence from western Europe deposited during the Younger Dryas cold period revealed relative humidity changes of up to 15 \\% over sub-centennial timescales, leading to major ecosystem changes, in agreement with palynological data from the region. We show that by combining organic geochemical methods and mechanistic plant physiological models on well characterized lacustrine archives it is possible to extract quantitative ecohydrological parameters from sedimentary lipid biomarker δ2H data.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-18},\n\tjournal = {Climate of the Past},\n\tauthor = {Rach, Oliver and Kahmen, Ansgar and Brauer, Achim and Sachse, Dirk},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {741--757},\n}\n\n\n\n

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\n \n\n \n \n Post, H.; Vrugt, J. A.; Fox, A.; Vereecken, H.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites: Estimation of CLM Parameters.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 122(3): 661–689. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{post_estimation_2017,\n\ttitle = {Estimation of {Community} {Land} {Model} parameters for an improved assessment of net carbon fluxes at {European} sites: {Estimation} of {CLM} {Parameters}},\n\tvolume = {122},\n\tissn = {21698953},\n\tshorttitle = {Estimation of {Community} {Land} {Model} parameters for an improved assessment of net carbon fluxes at {European} sites},\n\turl = {http://doi.wiley.com/10.1002/2015JG003297},\n\tdoi = {10.1002/2015JG003297},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Post, Hanna and Vrugt, Jasper A. and Fox, Andrew and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {661--689},\n}\n\n\n\n

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\n \n\n \n \n Peters, A.; Groh, J.; Schrader, F.; Durner, W.; Vereecken, H.; and Pütz, T.\n\n\n \n \n \n \n \n Towards an unbiased filter routine to determine precipitation and evapotranspiration from high precision lysimeter measurements.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 549: 731–740. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{peters_towards_2017,\n\ttitle = {Towards an unbiased filter routine to determine precipitation and evapotranspiration from high precision lysimeter measurements},\n\tvolume = {549},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169417302330},\n\tdoi = {10.1016/j.jhydrol.2017.04.015},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Peters, Andre and Groh, Jannis and Schrader, Frederik and Durner, Wolfgang and Vereecken, Harry and Pütz, Thomas},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {731--740},\n}\n\n\n\n

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\n \n\n \n \n Papanikolaou, A. D.; Kühn, I.; Frenzel, M.; and Schweiger, O.\n\n\n \n \n \n \n \n Semi-natural habitats mitigate the effects of temperature rise on wild bees.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Ecology, 54(2): 527–536. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Semi-natural$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{papanikolaou_semi-natural_2017,\n\ttitle = {Semi-natural habitats mitigate the effects of temperature rise on wild bees},\n\tvolume = {54},\n\tissn = {00218901},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12763},\n\tdoi = {10.1111/1365-2664.12763},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Applied Ecology},\n\tauthor = {Papanikolaou, Alexandra D. and Kühn, Ingolf and Frenzel, Mark and Schweiger, Oliver},\n\teditor = {Kleijn, David},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {527--536},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Münze, R.; Hannemann, C.; Orlinskiy, P.; Gunold, R.; Paschke, A.; Foit, K.; Becker, J.; Kaske, O.; Paulsson, E.; Peterson, M.; Jernstedt, H.; Kreuger, J.; Schüürmann, G.; and Liess, M.\n\n\n \n \n \n \n \n Pesticides from wastewater treatment plant effluents affect invertebrate communities.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 599-600: 387–399. December 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Pesticides$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{munze_pesticides_2017,\n\ttitle = {Pesticides from wastewater treatment plant effluents affect invertebrate communities},\n\tvolume = {599-600},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969717305132},\n\tdoi = {10.1016/j.scitotenv.2017.03.008},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Münze, Ronald and Hannemann, Christin and Orlinskiy, Polina and Gunold, Roman and Paschke, Albrecht and Foit, Kaarina and Becker, Jeremias and Kaske, Oliver and Paulsson, Elin and Peterson, Märit and Jernstedt, Henrik and Kreuger, Jenny and Schüürmann, Gerrit and Liess, Matthias},\n\tmonth = dec,\n\tyear = {2017},\n\tpages = {387--399},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Munz, M.; and Schmidt, C.\n\n\n \n \n \n \n \n Estimation of vertical water fluxes from temperature time series by the inverse numerical computer program FLUX-BOT.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 31(15): 2713–2724. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{munz_estimation_2017,\n\ttitle = {Estimation of vertical water fluxes from temperature time series by the inverse numerical computer program {FLUX}-{BOT}},\n\tvolume = {31},\n\tissn = {08856087},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.11198},\n\tdoi = {10.1002/hyp.11198},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2022-11-18},\n\tjournal = {Hydrological Processes},\n\tauthor = {Munz, Matthias and Schmidt, Christian},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {2713--2724},\n}\n\n\n\n

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\n \n\n \n \n Morling, K.; Raeke, J.; Kamjunke, N.; Reemtsma, T.; and Tittel, J.\n\n\n \n \n \n \n \n Tracing Aquatic Priming Effect During Microbial Decomposition of Terrestrial Dissolved Organic Carbon in Chemostat Experiments.\n \n \n \n \n\n\n \n\n\n\n Microbial Ecology, 74(3): 534–549. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Tracing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{morling_tracing_2017,\n\ttitle = {Tracing {Aquatic} {Priming} {Effect} {During} {Microbial} {Decomposition} of {Terrestrial} {Dissolved} {Organic} {Carbon} in {Chemostat} {Experiments}},\n\tvolume = {74},\n\tissn = {0095-3628, 1432-184X},\n\turl = {http://link.springer.com/10.1007/s00248-017-0976-0},\n\tdoi = {10.1007/s00248-017-0976-0},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Microbial Ecology},\n\tauthor = {Morling, Karoline and Raeke, Julia and Kamjunke, Norbert and Reemtsma, Thorsten and Tittel, Jörg},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {534--549},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Morling, K.; Herzsprung, P.; and Kamjunke, N.\n\n\n \n \n \n \n \n Discharge determines production of, decomposition of and quality changes in dissolved organic carbon in pre-dams of drinking water reservoirs.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 577: 329–339. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Discharge$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{morling_discharge_2017,\n\ttitle = {Discharge determines production of, decomposition of and quality changes in dissolved organic carbon in pre-dams of drinking water reservoirs},\n\tvolume = {577},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969716323804},\n\tdoi = {10.1016/j.scitotenv.2016.10.192},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Morling, Karoline and Herzsprung, Peter and Kamjunke, Norbert},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {329--339},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Bogena, H.; Zreda, M.; Monerris, A.; Morrison, R.; Muddu, S.; and Vereecken, H.\n\n\n \n \n \n \n \n Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 9(2): 103. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Validation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_validation_2017,\n\ttitle = {Validation of {Spaceborne} and {Modelled} {Surface} {Soil} {Moisture} {Products} with {Cosmic}-{Ray} {Neutron} {Probes}},\n\tvolume = {9},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/9/2/103},\n\tdoi = {10.3390/rs9020103},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Remote Sensing},\n\tauthor = {Montzka, Carsten and Bogena, Heye and Zreda, Marek and Monerris, Alessandra and Morrison, Ross and Muddu, Sekhar and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {103},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Meyer, N.; Bornemann, L.; Welp, G.; Schiedung, H.; Herbst, M.; and Amelung, W.\n\n\n \n \n \n \n \n Carbon saturation drives spatial patterns of soil organic matter losses under long-term bare fallow.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 306: 89–98. November 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{meyer_carbon_2017,\n\ttitle = {Carbon saturation drives spatial patterns of soil organic matter losses under long-term bare fallow},\n\tvolume = {306},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117305864},\n\tdoi = {10.1016/j.geoderma.2017.07.004},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Geoderma},\n\tauthor = {Meyer, N. and Bornemann, L. and Welp, G. and Schiedung, H. and Herbst, M. and Amelung, W.},\n\tmonth = nov,\n\tyear = {2017},\n\tpages = {89--98},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Meyer, N.; Welp, G.; Bornemann, L.; and Amelung, W.\n\n\n \n \n \n \n \n Microbial nitrogen mining affects spatio-temporal patterns of substrate-induced respiration during seven years of bare fallow.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 104: 175–184. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Microbial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{meyer_microbial_2017,\n\ttitle = {Microbial nitrogen mining affects spatio-temporal patterns of substrate-induced respiration during seven years of bare fallow},\n\tvolume = {104},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0038071716302887},\n\tdoi = {10.1016/j.soilbio.2016.10.019},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Meyer, Nele and Welp, Gerhard and Bornemann, Ludger and Amelung, Wulf},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {175--184},\n}\n\n\n\n

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\n \n\n \n \n Martini, E.; Wollschläger, U.; Musolff, A.; Werban, U.; and Zacharias, S.\n\n\n \n \n \n \n \n Principal Component Analysis of the Spatiotemporal Pattern of Soil Moisture and Apparent Electrical Conductivity.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(10): vzj2016.12.0129. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Principal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martini_principal_2017,\n\ttitle = {Principal {Component} {Analysis} of the {Spatiotemporal} {Pattern} of {Soil} {Moisture} and {Apparent} {Electrical} {Conductivity}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.12.0129},\n\tdoi = {10.2136/vzj2016.12.0129},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Martini, Edoardo and Wollschläger, Ute and Musolff, Andreas and Werban, Ulrike and Zacharias, Steffen},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {vzj2016.12.0129},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Martini, E.; Werban, U.; Zacharias, S.; Pohle, M.; Dietrich, P.; and Wollschläger, U.\n\n\n \n \n \n \n \n Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 21(1): 495–513. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Repeated$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martini_repeated_2017,\n\ttitle = {Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network},\n\tvolume = {21},\n\tissn = {1607-7938},\n\tshorttitle = {Repeated electromagnetic induction measurements for mapping soil moisture at the field scale},\n\turl = {https://hess.copernicus.org/articles/21/495/2017/},\n\tdoi = {10.5194/hess-21-495-2017},\n\tabstract = {Abstract. Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ, with particular focus on the temporal variability of the spatial patterns of ECa and θ. To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ, and (iii) the ECa–θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ, and the use of EMI-based ECa data with respect to hydrological applications.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Martini, Edoardo and Werban, Ulrike and Zacharias, Steffen and Pohle, Marco and Dietrich, Peter and Wollschläger, Ute},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {495--513},\n}\n\n\n\n

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\n \n\n \n \n Martin-Puertas, C.; Tjallingii, R.; Bloemsma, M.; and Brauer, A.\n\n\n \n \n \n \n \n Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data: VARVED SEDIMENT RESPONSES IN LAKE MEERFELDER MAAR, GERMANY.\n \n \n \n \n\n\n \n\n\n\n Journal of Quaternary Science, 32(3): 427–436. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Varved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martin-puertas_varved_2017,\n\ttitle = {Varved sediment responses to early {Holocene} climate and environmental changes in {Lake} {Meerfelder} {Maar} ({Germany}) obtained from multivariate analyses of micro {X}-ray fluorescence core scanning data: {VARVED} {SEDIMENT} {RESPONSES} {IN} {LAKE} {MEERFELDER} {MAAR}, {GERMANY}},\n\tvolume = {32},\n\tissn = {02678179},\n\tshorttitle = {Varved sediment responses to early {Holocene} climate and environmental changes in {Lake} {Meerfelder} {Maar} ({Germany}) obtained from multivariate analyses of micro {X}-ray fluorescence core scanning data},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jqs.2935},\n\tdoi = {10.1002/jqs.2935},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Quaternary Science},\n\tauthor = {Martin-Puertas, Celia and Tjallingii, Rik and Bloemsma, Menno and Brauer, Achim},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {427--436},\n}\n\n\n\n

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\n \n\n \n \n Liu, S.; Shao, Y.; Kunoth, A.; and Simmer, C.\n\n\n \n \n \n \n \n Impact of surface-heterogeneity on atmosphere and land-surface interactions.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 88: 35–47. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_impact_2017,\n\ttitle = {Impact of surface-heterogeneity on atmosphere and land-surface interactions},\n\tvolume = {88},\n\tissn = {13648152},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1364815216309173},\n\tdoi = {10.1016/j.envsoft.2016.11.006},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Liu, Shaofeng and Shao, Yaping and Kunoth, Angela and Simmer, Clemens},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {35--47},\n}\n\n\n\n

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\n \n\n \n \n Lindauer, M.; Schmid, H. P.; Grote, R.; Steinbrecher, R.; Mauder, M.; and Wolpert, B.\n\n\n \n \n \n \n \n A Simple New Model for Incoming Solar Radiation Dependent Only on Screen-Level Relative Humidity.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Meteorology and Climatology, 56(7): 1817–1825. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lindauer_simple_2017,\n\ttitle = {A {Simple} {New} {Model} for {Incoming} {Solar} {Radiation} {Dependent} {Only} on {Screen}-{Level} {Relative} {Humidity}},\n\tvolume = {56},\n\tissn = {1558-8424, 1558-8432},\n\turl = {https://journals.ametsoc.org/view/journals/apme/56/7/jamc-d-16-0085.1.xml},\n\tdoi = {10.1175/JAMC-D-16-0085.1},\n\tabstract = {Abstract \n             \n              Global incoming shortwave radiation (Rg) is the energy source for the majority of biogeochemical processes on Earth as well as for photovoltaic power production. Existing simple site-specific models to estimate Rg commonly use the daily range of air temperature as input variables. Here, the authors present a simple model for incoming shortwave radiation, requiring only screen-level relative humidity data (and site-specific astronomical information). The model was developed and parameterized using high-quality global radiation data covering a broad range of climate conditions. It was evaluated at independent sites, which were not involved in the process of model development and parameterization. The mean 1:1 slope was 1.02 with an average \n              r \n              2 \n              of 0.98. Normalized root-mean-square error (NRMSE) averaged at 43\\%. Despite its simplicity, the new model clearly outperforms conventional approaches, and it comes close to more labor- and data-intensive alternative models.},\n\tnumber = {7},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Applied Meteorology and Climatology},\n\tauthor = {Lindauer, M. and Schmid, H. P. and Grote, R. and Steinbrecher, R. and Mauder, M. and Wolpert, B.},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {1817--1825},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Erasmi, S.; King, D.; Magdon, P.; and Heurich, M.\n\n\n \n \n \n \n \n Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 9(2): 129. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_understanding_2017,\n\ttitle = {Understanding {Forest} {Health} with {Remote} {Sensing}-{Part} {II}—{A} {Review} of {Approaches} and {Data} {Models}},\n\tvolume = {9},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/9/2/129},\n\tdoi = {10.3390/rs9020129},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Erasmi, Stefan and King, Douglas and Magdon, Paul and Heurich, Marco},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {129},\n}\n\n\n\n

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\n \n\n \n \n Lange, M.; Dechant, B.; Rebmann, C.; Vohland, M.; Cuntz, M.; and Doktor, D.\n\n\n \n \n \n \n \n Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors.\n \n \n \n \n\n\n \n\n\n\n Sensors, 17(8): 1855. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Validating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lange_validating_2017,\n\ttitle = {Validating {MODIS} and {Sentinel}-2 {NDVI} {Products} at a {Temperate} {Deciduous} {Forest} {Site} {Using} {Two} {Independent} {Ground}-{Based} {Sensors}},\n\tvolume = {17},\n\tissn = {1424-8220},\n\turl = {http://www.mdpi.com/1424-8220/17/8/1855},\n\tdoi = {10.3390/s17081855},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-18},\n\tjournal = {Sensors},\n\tauthor = {Lange, Maximilian and Dechant, Benjamin and Rebmann, Corinna and Vohland, Michael and Cuntz, Matthias and Doktor, Daniel},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {1855},\n}\n\n\n\n

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\n \n\n \n \n Landl, M.; Huber, K.; Schnepf, A.; Vanderborght, J.; Javaux, M.; Glyn Bengough, A.; and Vereecken, H.\n\n\n \n \n \n \n \n A new model for root growth in soil with macropores.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 415(1-2): 99–116. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{landl_new_2017,\n\ttitle = {A new model for root growth in soil with macropores},\n\tvolume = {415},\n\tissn = {0032-079X, 1573-5036},\n\turl = {http://link.springer.com/10.1007/s11104-016-3144-2},\n\tdoi = {10.1007/s11104-016-3144-2},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-18},\n\tjournal = {Plant and Soil},\n\tauthor = {Landl, Magdalena and Huber, Katrin and Schnepf, Andrea and Vanderborght, Jan and Javaux, Mathieu and Glyn Bengough, A. and Vereecken, Harry},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {99--116},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Kurtz, W.; Lapin, A.; Schilling, O. S.; Tang, Q.; Schiller, E.; Braun, T.; Hunkeler, D.; Vereecken, H.; Sudicky, E.; Kropf, P.; Hendricks Franssen, H.; and Brunner, P.\n\n\n \n \n \n \n \n Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 93: 418–435. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kurtz_integrating_2017,\n\ttitle = {Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources},\n\tvolume = {93},\n\tissn = {13648152},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S136481521630977X},\n\tdoi = {10.1016/j.envsoft.2017.03.011},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Kurtz, Wolfgang and Lapin, Andrei and Schilling, Oliver S. and Tang, Qi and Schiller, Eryk and Braun, Torsten and Hunkeler, Daniel and Vereecken, Harry and Sudicky, Edward and Kropf, Peter and Hendricks Franssen, Harrie-Jan and Brunner, Philip},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {418--435},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kunz, J. V.; Annable, M. D.; Cho, J.; von Tümpling, W.; Hatfield, K.; Rao, S.; Borchardt, D.; and Rode, M.\n\n\n \n \n \n \n \n Quantifying nutrient fluxes with a new hyporheic passive flux meter (HPFM).\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 14(3): 631–649. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunz_quantifying_2017,\n\ttitle = {Quantifying nutrient fluxes with a new hyporheic passive flux meter ({HPFM})},\n\tvolume = {14},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/14/631/2017/},\n\tdoi = {10.5194/bg-14-631-2017},\n\tabstract = {Abstract. The hyporheic zone is a hotspot of biogeochemical turnover and nutrient removal in running waters. However, nutrient fluxes through the hyporheic zone are highly variable in time and locally heterogeneous. Resulting from the lack of adequate methodologies to obtain representative long-term measurements, our quantitative knowledge on transport and turnover in this important transition zone is still limited.In groundwater systems passive flux meters, devices which simultaneously detect horizontal water and solute flow through a screen well in the subsurface, are valuable tools for measuring fluxes of target solutes and water through those ecosystems. Their functioning is based on accumulation of target substances on a sorbent and concurrent displacement of a resident tracer which is previously loaded on the sorbent.Here we evaluate the applicability of this methodology for investigating water and nutrient fluxes in hyporheic zones. Based on laboratory experiments we developed hyporheic passive flux meters (HPFMs) with a length of 50 cm which were separated in 5–7 segments allowing for vertical resolution of horizontal nutrient and water transport. The HPFMs were tested in a 7 day field campaign including simultaneous measurements of oxygen and temperature profiles and manual sampling of pore water. The results highlighted the advantages of the novel method: with HPFMs, cumulative values for the average N and P flux during the complete deployment time could be captured. Thereby the two major deficits of existing methods are overcome: first, flux rates are measured within one device instead of being calculated from separate measurements of water flow and pore-water concentrations; second, time-integrated measurements are insensitive to short-term fluctuations and therefore deliver more representable values for overall hyporheic nutrient fluxes at the sampling site than snapshots from grab sampling. A remaining limitation to the HPFM is the potential susceptibility to biofilm growth on the resin, an issue which was not considered in previous passive flux meter applications. Potential techniques to inhibit biofouling are discussed based on the results of the presented work. Finally, we exemplarily demonstrate how HPFM measurements can be used to explore hyporheic nutrient dynamics, specifically nitrate uptake rates, based on the measurements from our field test. Being low in costs and labour effective, many flux meters can be installed in order to capture larger areas of river beds. This novel technique has therefore the potential to deliver quantitative data which are required to answer unsolved questions about transport and turnover of nutrients in hyporheic zones.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Biogeosciences},\n\tauthor = {Kunz, Julia Vanessa and Annable, Michael D. and Cho, Jaehyun and von Tümpling, Wolf and Hatfield, Kirk and Rao, Suresh and Borchardt, Dietrich and Rode, Michael},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {631--649},\n}\n\n\n\n

\n\n\n

\n Abstract. The hyporheic zone is a hotspot of biogeochemical turnover and nutrient removal in running waters. However, nutrient fluxes through the hyporheic zone are highly variable in time and locally heterogeneous. Resulting from the lack of adequate methodologies to obtain representative long-term measurements, our quantitative knowledge on transport and turnover in this important transition zone is still limited.In groundwater systems passive flux meters, devices which simultaneously detect horizontal water and solute flow through a screen well in the subsurface, are valuable tools for measuring fluxes of target solutes and water through those ecosystems. Their functioning is based on accumulation of target substances on a sorbent and concurrent displacement of a resident tracer which is previously loaded on the sorbent.Here we evaluate the applicability of this methodology for investigating water and nutrient fluxes in hyporheic zones. Based on laboratory experiments we developed hyporheic passive flux meters (HPFMs) with a length of 50 cm which were separated in 5–7 segments allowing for vertical resolution of horizontal nutrient and water transport. The HPFMs were tested in a 7 day field campaign including simultaneous measurements of oxygen and temperature profiles and manual sampling of pore water. The results highlighted the advantages of the novel method: with HPFMs, cumulative values for the average N and P flux during the complete deployment time could be captured. Thereby the two major deficits of existing methods are overcome: first, flux rates are measured within one device instead of being calculated from separate measurements of water flow and pore-water concentrations; second, time-integrated measurements are insensitive to short-term fluctuations and therefore deliver more representable values for overall hyporheic nutrient fluxes at the sampling site than snapshots from grab sampling. A remaining limitation to the HPFM is the potential susceptibility to biofilm growth on the resin, an issue which was not considered in previous passive flux meter applications. Potential techniques to inhibit biofouling are discussed based on the results of the presented work. Finally, we exemplarily demonstrate how HPFM measurements can be used to explore hyporheic nutrient dynamics, specifically nitrate uptake rates, based on the measurements from our field test. Being low in costs and labour effective, many flux meters can be installed in order to capture larger areas of river beds. This novel technique has therefore the potential to deliver quantitative data which are required to answer unsolved questions about transport and turnover of nutrients in hyporheic zones.\n

\n\n\n

\n \n\n \n \n Knapp, S.; Winter, M.; and Klotz, S.\n\n\n \n \n \n \n \n Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Ecology, 54(4): 1152–1160. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Increasing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{knapp_increasing_2017,\n\ttitle = {Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization},\n\tvolume = {54},\n\tissn = {00218901},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12826},\n\tdoi = {10.1111/1365-2664.12826},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Applied Ecology},\n\tauthor = {Knapp, Sonja and Winter, Marten and Klotz, Stefan},\n\teditor = {Bennett, Joseph},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {1152--1160},\n}\n\n\n\n

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\n \n\n \n \n Kienel, U.; Kirillin, G.; Brademann, B.; Plessen, B.; Lampe, R.; and Brauer, A.\n\n\n \n \n \n \n \n Effects of spring warming and mixing duration on diatom deposition in deep Tiefer See, NE Germany.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 57(1): 37–49. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kienel_effects_2017,\n\ttitle = {Effects of spring warming and mixing duration on diatom deposition in deep {Tiefer} {See}, {NE} {Germany}},\n\tvolume = {57},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-016-9925-z},\n\tdoi = {10.1007/s10933-016-9925-z},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Kienel, Ulrike and Kirillin, Georgiy and Brademann, Brian and Plessen, Birgit and Lampe, Reinhard and Brauer, Achim},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {37--49},\n}\n\n\n\n

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\n \n\n \n \n Karthe, D.; Lin, P.; and Westphal, K.\n\n\n \n \n \n \n \n Instream coliform gradients in the Holtemme, a small headwater stream in the Elbe River Basin, Northern Germany.\n \n \n \n \n\n\n \n\n\n\n Frontiers of Earth Science, 11(3): 544–553. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Instream$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{karthe_instream_2017,\n\ttitle = {Instream coliform gradients in the {Holtemme}, a small headwater stream in the {Elbe} {River} {Basin}, {Northern} {Germany}},\n\tvolume = {11},\n\tissn = {2095-0195, 2095-0209},\n\turl = {http://link.springer.com/10.1007/s11707-017-0648-x},\n\tdoi = {10.1007/s11707-017-0648-x},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Frontiers of Earth Science},\n\tauthor = {Karthe, Daniel and Lin, Pei-Ying and Westphal, Katja},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {544--553},\n}\n\n\n\n

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\n \n\n \n \n Karthe, D.; Chifflard, P.; Cyffka, B.; Menzel, L.; Nacken, H.; Raeder, U.; Sommerhäuser, M.; and Weiler, M.\n\n\n \n \n \n \n \n Water research in Germany: from the reconstruction of the Roman Rhine to a risk assessment for aquatic neophytes.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 76(16): 549, s12665–017–6863–7. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{karthe_water_2017,\n\ttitle = {Water research in {Germany}: from the reconstruction of the {Roman} {Rhine} to a risk assessment for aquatic neophytes},\n\tvolume = {76},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {Water research in {Germany}},\n\turl = {http://link.springer.com/10.1007/s12665-017-6863-7},\n\tdoi = {10.1007/s12665-017-6863-7},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Karthe, Daniel and Chifflard, Peter and Cyffka, Bernd and Menzel, Lucas and Nacken, Heribert and Raeder, Uta and Sommerhäuser, Mario and Weiler, Markus},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {549, s12665--017--6863--7},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Jiang, X.; Bol, R.; Cade-Menun, B. J.; Nischwitz, V.; Willbold, S.; Bauke, S. L.; Vereecken, H.; Amelung, W.; and Klumpp, E.\n\n\n \n \n \n \n \n Colloid-bound and dissolved phosphorus species in topsoil water extracts along a grassland transect from Cambisol to Stagnosol.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 14(5): 1153–1164. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Colloid-bound$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_colloid-bound_2017,\n\ttitle = {Colloid-bound and dissolved phosphorus species in topsoil water extracts along a grassland transect from {Cambisol} to {Stagnosol}},\n\tvolume = {14},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/14/1153/2017/},\n\tdoi = {10.5194/bg-14-1153-2017},\n\tabstract = {Abstract. Phosphorus (P) species in colloidal and dissolved soil fractions may have different distributions. To understand which P species are potentially involved, we obtained water extracts from the surface soils of a gradient from Cambisol, Stagnic Cambisol to Stagnosol from temperate grassland in Germany. These were filtered to  {\\textless}  450 nm, and divided into three procedurally defined fractions: small-sized colloids (20–450 nm), nano-sized colloids (1–20 nm), and dissolved P ({\\textless}  1 nm), using asymmetric flow field-flow fractionation (AF4), as well as filtration for solution 31P-nuclear magnetic resonance (NMR) spectroscopy. The total P of soil water extracts increased in the order Cambisol  {\\textless}  Stagnic Cambisol  {\\textless}  Stagnosol due to increasing contributions from the dissolved P fraction. Associations of C–Fe/Al–PO43−/pyrophosphate were absent in nano-sized (1–20 nm) colloids from the Cambisol but not in the Stagnosol. The 31P-NMR results indicated that this was accompanied by elevated portions of organic P in the order Cambisol  {\\textgreater}  Stagnic Cambisol  {\\textgreater}  Stagnosol. Across all soil types, elevated proportions of inositol hexakisphosphate (IHP) species (e.g., myo-, scyllo- and D-chiro-IHP) were associated with soil mineral particles (i.e., bulk soil and small-sized soil colloids), whereas other orthophosphate monoesters and phosphonates were found in the dissolved P fraction. We conclude that P species composition varies among colloidal and dissolved soil fractions after characterization using advanced techniques, i.e., AF4 and NMR. Furthermore, stagnic properties affect P speciation and availability by potentially releasing dissolved inorganic and ester-bound P forms as well as nano-sized organic matter–Fe/Al–P colloids.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-18},\n\tjournal = {Biogeosciences},\n\tauthor = {Jiang, Xiaoqian and Bol, Roland and Cade-Menun, Barbara J. and Nischwitz, Volker and Willbold, Sabine and Bauke, Sara L. and Vereecken, Harry and Amelung, Wulf and Klumpp, Erwin},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {1153--1164},\n}\n\n\n\n

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\n \n\n \n \n Jiang, C.; Séquaris, J.; Vereecken, H.; and Klumpp, E.\n\n\n \n \n \n \n \n Effects of temperature and associated organic carbon on the fractionation of water-dispersible colloids from three silt loam topsoils under different land use.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 299: 43–53. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_effects_2017,\n\ttitle = {Effects of temperature and associated organic carbon on the fractionation of water-dispersible colloids from three silt loam topsoils under different land use},\n\tvolume = {299},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117303919},\n\tdoi = {10.1016/j.geoderma.2017.03.009},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Geoderma},\n\tauthor = {Jiang, Canlan and Séquaris, Jean-Marie and Vereecken, Harry and Klumpp, Erwin},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {43--53},\n}\n\n\n\n

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\n \n\n \n \n Jäger, C. G.; Hagemann, J.; and Borchardt, D.\n\n\n \n \n \n \n \n Can nutrient pathways and biotic interactions control eutrophication in riverine ecosystems? Evidence from a model driven mesocosm experiment.\n \n \n \n \n\n\n \n\n\n\n Water Research, 115: 162–171. May 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Can$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jager_can_2017,\n\ttitle = {Can nutrient pathways and biotic interactions control eutrophication in riverine ecosystems? {Evidence} from a model driven mesocosm experiment},\n\tvolume = {115},\n\tissn = {00431354},\n\tshorttitle = {Can nutrient pathways and biotic interactions control eutrophication in riverine ecosystems?},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135417301616},\n\tdoi = {10.1016/j.watres.2017.02.062},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Water Research},\n\tauthor = {Jäger, Christoph G. and Hagemann, Jeske and Borchardt, Dietrich},\n\tmonth = may,\n\tyear = {2017},\n\tpages = {162--171},\n}\n\n\n\n

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\n \n\n \n \n Inostroza, P. A.; Massei, R.; Wild, R.; Krauss, M.; and Brack, W.\n\n\n \n \n \n \n \n Chemical activity and distribution of emerging pollutants: Insights from a multi-compartment analysis of a freshwater system.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 231: 339–347. December 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Chemical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{inostroza_chemical_2017,\n\ttitle = {Chemical activity and distribution of emerging pollutants: {Insights} from a multi-compartment analysis of a freshwater system},\n\tvolume = {231},\n\tissn = {02697491},\n\tshorttitle = {Chemical activity and distribution of emerging pollutants},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749117307315},\n\tdoi = {10.1016/j.envpol.2017.08.015},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Pollution},\n\tauthor = {Inostroza, Pedro A. and Massei, Riccardo and Wild, Romy and Krauss, Martin and Brack, Werner},\n\tmonth = dec,\n\tyear = {2017},\n\tpages = {339--347},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Hoffmann, M.; Schulz-Hanke, M.; Garcia Alba, J.; Jurisch, N.; Hagemann, U.; Sachs, T.; Sommer, M.; and Augustin, J.\n\n\n \n \n \n \n \n A simple calculation algorithm to separate high-resolution CH4 flux measurements into ebullition- and diffusion-derived components.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 10(1): 109–118. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hoffmann_simple_2017,\n\ttitle = {A simple calculation algorithm to separate high-resolution {CH}\\&lt;sub\\&gt;4\\&lt;/sub\\&gt; flux measurements into ebullition- and diffusion-derived components},\n\tvolume = {10},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/10/109/2017/},\n\tdoi = {10.5194/amt-10-109-2017},\n\tabstract = {Abstract. Processes driving the production, transformation and transport of methane (CH4) in wetland ecosystems are highly complex. We present a simple calculation algorithm to separate open-water CH4 fluxes measured with automatic chambers into diffusion- and ebullition-derived components. This helps to reveal underlying dynamics, to identify potential environmental drivers and, thus, to calculate reliable CH4 emission estimates. The flux separation is based on identification of ebullition-related sudden concentration changes during single measurements. Therefore, a variable ebullition filter is applied, using the lower and upper quartile and the interquartile range (IQR). Automation of data processing is achieved by using an established R script, adjusted for the purpose of CH4 flux calculation. The algorithm was validated by performing a laboratory experiment and tested using flux measurement data (July to September 2013) from a former fen grassland site, which converted into a shallow lake as a result of rewetting. Ebullition and diffusion contributed equally (46 and 55 \\%) to total CH4 emissions, which is comparable to ratios given in the literature. Moreover, the separation algorithm revealed a concealed shift in the diurnal trend of diffusive fluxes throughout the measurement period. The water temperature gradient was identified as one of the major drivers of diffusive CH4 emissions, whereas no significant driver was found in the case of erratic CH4 ebullition events.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Hoffmann, Mathias and Schulz-Hanke, Maximilian and Garcia Alba, Juana and Jurisch, Nicole and Hagemann, Ulrike and Sachs, Torsten and Sommer, Michael and Augustin, Jürgen},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {109--118},\n}\n\n\n\n

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\n \n\n \n \n Herbrich, M.; and Gerke, H. H.\n\n\n \n \n \n \n \n Scales of Water Retention Dynamics Observed in Eroded Luvisols from an Arable Postglacial Soil Landscape.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(10): vzj2017.01.0003. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Scales$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbrich_scales_2017,\n\ttitle = {Scales of {Water} {Retention} {Dynamics} {Observed} in {Eroded} {Luvisols} from an {Arable} {Postglacial} {Soil} {Landscape}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.01.0003},\n\tdoi = {10.2136/vzj2017.01.0003},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Herbrich, Marcus and Gerke, Horst H.},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {vzj2017.01.0003},\n}\n\n\n\n

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\n \n\n \n \n Herbrich, M.; Gerke, H. H.; Bens, O.; and Sommer, M.\n\n\n \n \n \n \n \n Water balance and leaching of dissolved organic and inorganic carbon of eroded Luvisols using high precision weighing lysimeters.\n \n \n \n \n\n\n \n\n\n\n Soil and Tillage Research, 165: 144–160. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Water$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbrich_water_2017,\n\ttitle = {Water balance and leaching of dissolved organic and inorganic carbon of eroded {Luvisols} using high precision weighing lysimeters},\n\tvolume = {165},\n\tissn = {01671987},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167198716301520},\n\tdoi = {10.1016/j.still.2016.08.003},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Soil and Tillage Research},\n\tauthor = {Herbrich, Marcus and Gerke, Horst H. and Bens, Oliver and Sommer, Michael},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {144--160},\n}\n\n\n\n

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\n \n\n \n \n Heinlein, F.; Biernath, C.; Klein, C.; Thieme, C.; and Priesack, E.\n\n\n \n \n \n \n \n Evaluation of Simulated Transpiration from Maize Plants on Lysimeters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(1): vzj2016.05.0042. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heinlein_evaluation_2017,\n\ttitle = {Evaluation of {Simulated} {Transpiration} from {Maize} {Plants} on {Lysimeters}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.05.0042},\n\tdoi = {10.2136/vzj2016.05.0042},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Heinlein, Florian and Biernath, Christian and Klein, Christian and Thieme, Christoph and Priesack, Eckart},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {vzj2016.05.0042},\n}\n\n\n\n

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\n \n\n \n \n Heine, I.; Brauer, A.; Heim, B.; Itzerott, S.; Kasprzak, P.; Kienel, U.; and Kleinschmit, B.\n\n\n \n \n \n \n \n Monitoring of Calcite Precipitation in Hardwater Lakes with Multi-Spectral Remote Sensing Archives.\n \n \n \n \n\n\n \n\n\n\n Water, 9(1): 15. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heine_monitoring_2017,\n\ttitle = {Monitoring of {Calcite} {Precipitation} in {Hardwater} {Lakes} with {Multi}-{Spectral} {Remote} {Sensing} {Archives}},\n\tvolume = {9},\n\tissn = {2073-4441},\n\turl = {http://www.mdpi.com/2073-4441/9/1/15},\n\tdoi = {10.3390/w9010015},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Water},\n\tauthor = {Heine, Iris and Brauer, Achim and Heim, Birgit and Itzerott, Sibylle and Kasprzak, Peter and Kienel, Ulrike and Kleinschmit, Birgit},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {15},\n}\n\n\n\n

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\n \n\n \n \n Heidbach, K.; Schmid, H. P.; and Mauder, M.\n\n\n \n \n \n \n \n Experimental evaluation of flux footprint models.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 246: 142–153. November 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Experimental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heidbach_experimental_2017,\n\ttitle = {Experimental evaluation of flux footprint models},\n\tvolume = {246},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192317302071},\n\tdoi = {10.1016/j.agrformet.2017.06.008},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Heidbach, Katja and Schmid, Hans Peter and Mauder, Matthias},\n\tmonth = nov,\n\tyear = {2017},\n\tpages = {142--153},\n}\n\n\n\n

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\n \n\n \n \n Grossmann, K.; Kabisch, N.; and Kabisch, S.\n\n\n \n \n \n \n \n Understanding the social development of a post-socialist large housing estate: The case of Leipzig-Grünau in eastern Germany in long-term perspective.\n \n \n \n \n\n\n \n\n\n\n European Urban and Regional Studies, 24(2): 142–161. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{grossmann_understanding_2017,\n\ttitle = {Understanding the social development of a post-socialist large housing estate: {The} case of {Leipzig}-{Grünau} in eastern {Germany} in long-term perspective},\n\tvolume = {24},\n\tissn = {0969-7764, 1461-7145},\n\tshorttitle = {Understanding the social development of a post-socialist large housing estate},\n\turl = {http://journals.sagepub.com/doi/10.1177/0969776415606492},\n\tdoi = {10.1177/0969776415606492},\n\tabstract = {For decades, public and scholarly debates on large, post-war housing estates in western Europe have been concerned with social decline. After 1989/1990, the point in time of fundamental societal change in eastern Europe, this concern was transferred to estates in post-socialist cities. However, empirical evidence for a general negative trend has not emerged. Recent publications confirm the persistence of social mix and highlight the differentiated trajectories of estates. This paper aims to contribute to an approach of how to conceptually make sense of these differentiated trajectories. Using data from a unique longitudinal survey in East Germany, starting in 1979, we investigate the state of social mix, drivers of social change and the inner differentiation in the housing estate Leipzig-Grünau. We found no proof for a dramatic social decline, rather there is evidence for a slow and multi-faceted change in the social and demographic structure of the residents contributing to a gradual social fragmentation of the estate. This is a result of path dependencies, strategic planning effects and ownership structures. We discuss these drivers of large housing estate trajectories and their related impacts by adapting a framework of multiple, overlapping institutional, social and urban post-socialist transformations. We suggest embedding the framework in a wider and a local context in which transformations need to be seen. In conclusion, we argue for a theoretical debate that makes sense of contextual differences within such transformations.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-18},\n\tjournal = {European Urban and Regional Studies},\n\tauthor = {Grossmann, Katrin and Kabisch, Nadja and Kabisch, Sigrun},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {142--161},\n}\n\n\n\n

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\n \n\n \n \n Gueting, N.; Vienken, T.; Klotzsche, A.; van der Kruk, J.; Vanderborght, J.; Caers, J.; Vereecken, H.; and Englert, A.\n\n\n \n \n \n \n \n High resolution aquifer characterization using crosshole GPR full-waveform tomography: Comparison with direct-push and tracer test data: HIGH RESOLUTION AQUIFER CHARACTERIZATION.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 53(1): 49–72. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gueting_high_2017,\n\ttitle = {High resolution aquifer characterization using crosshole {GPR} full-waveform tomography: {Comparison} with direct-push and tracer test data: {HIGH} {RESOLUTION} {AQUIFER} {CHARACTERIZATION}},\n\tvolume = {53},\n\tissn = {00431397},\n\tshorttitle = {High resolution aquifer characterization using crosshole {GPR} full-waveform tomography},\n\turl = {http://doi.wiley.com/10.1002/2016WR019498},\n\tdoi = {10.1002/2016WR019498},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Water Resources Research},\n\tauthor = {Gueting, Nils and Vienken, Thomas and Klotzsche, Anja and van der Kruk, Jan and Vanderborght, Jan and Caers, Jef and Vereecken, Harry and Englert, Andreas},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {49--72},\n}\n\n\n\n

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\n \n\n \n \n Güntner, A.; Reich, M.; Mikolaj, M.; Creutzfeldt, B.; Schroeder, S.; and Wziontek, H.\n\n\n \n \n \n \n \n Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 21(6): 3167–3182. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Landscape-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{guntner_landscape-scale_2017,\n\ttitle = {Landscape-scale water balance monitoring with an {iGrav} superconducting gravimeter in a field enclosure},\n\tvolume = {21},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/21/3167/2017/},\n\tdoi = {10.5194/hess-21-3167-2017},\n\tabstract = {Abstract. In spite of the fundamental role of the landscape water balance for the Earth's water and energy cycles, monitoring the water balance and its components beyond the point scale is notoriously difficult due to the multitude of flow and storage processes and their spatial heterogeneity. Here, we present the first field deployment of an iGrav superconducting gravimeter (SG) in a minimized enclosure for long-term integrative monitoring of water storage changes. Results of the field SG on a grassland site under wet–temperate climate conditions were compared to data provided by a nearby SG located in the controlled environment of an observatory building. The field system proves to provide gravity time series that are similarly precise as those of the observatory SG. At the same time, the field SG is more sensitive to hydrological variations than the observatory SG. We demonstrate that the gravity variations observed by the field setup are almost independent of the depth below the terrain surface where water storage changes occur (contrary to SGs in buildings), and thus the field SG system directly observes the total water storage change, i.e., the water balance, in its surroundings in an integrative way. We provide a framework to single out the water balance components actual evapotranspiration and lateral subsurface discharge from the gravity time series on annual to daily timescales. With about 99 and 85 \\% of the gravity signal due to local water storage changes originating within a radius of 4000 and 200 m around the instrument, respectively, this setup paves the road towards gravimetry as a continuous hydrological field-monitoring technique at the landscape scale.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-18},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Güntner, Andreas and Reich, Marvin and Mikolaj, Michal and Creutzfeldt, Benjamin and Schroeder, Stephan and Wziontek, Hartmut},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {3167--3182},\n}\n\n\n\n

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\n \n\n \n \n Gottselig, N.; Wiekenkamp, I.; Weihermüller, L.; Brüggemann, N.; Berns, A. E.; Bogena, H. R.; Borchard, N.; Klumpp, E.; Lücke, A.; Missong, A.; Pütz, T.; Vereecken, H.; Huisman, J. A.; and Bol, R.\n\n\n \n \n \n \n \n A Three-Dimensional View on Soil Biogeochemistry: A Dataset for a Forested Headwater Catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Quality, 46(1): 210–218. 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gottselig_three-dimensional_2017,\n\ttitle = {A {Three}-{Dimensional} {View} on {Soil} {Biogeochemistry}: {A} {Dataset} for a {Forested} {Headwater} {Catchment}},\n\tvolume = {46},\n\tissn = {00472425},\n\tshorttitle = {A {Three}-{Dimensional} {View} on {Soil} {Biogeochemistry}},\n\turl = {http://doi.wiley.com/10.2134/jeq2016.07.0276},\n\tdoi = {10.2134/jeq2016.07.0276},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Environmental Quality},\n\tauthor = {Gottselig, N. and Wiekenkamp, I. and Weihermüller, L. and Brüggemann, N. and Berns, A. E. and Bogena, H. R. and Borchard, N. and Klumpp, E. and Lücke, A. and Missong, A. and Pütz, T. and Vereecken, H. and Huisman, J. A. and Bol, R.},\n\tyear = {2017},\n\tpages = {210--218},\n}\n\n\n\n

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\n \n\n \n \n Gottselig, N.; Nischwitz, V.; Meyn, T.; Amelung, W.; Bol, R.; Halle, C.; Vereecken, H.; Siemens, J.; and Klumpp, E.\n\n\n \n \n \n \n \n Phosphorus Binding to Nanoparticles and Colloids in Forest Stream Waters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(3): vzj2016.07.0064. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Phosphorus$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gottselig_phosphorus_2017,\n\ttitle = {Phosphorus {Binding} to {Nanoparticles} and {Colloids} in {Forest} {Stream} {Waters}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.07.0064},\n\tdoi = {10.2136/vzj2016.07.0064},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Gottselig, Nina and Nischwitz, Volker and Meyn, Thomas and Amelung, Wulf and Bol, Roland and Halle, Cynthia and Vereecken, Harry and Siemens, Jan and Klumpp, Erwin},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {vzj2016.07.0064},\n}\n\n\n\n

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\n \n\n \n \n Giling, D. P.; Staehr, P. A.; Grossart, H. P.; Andersen, M. R.; Boehrer, B.; Escot, C.; Evrendilek, F.; Gómez-Gener, L.; Honti, M.; Jones, I. D.; Karakaya, N.; Laas, A.; Moreno-Ostos, E.; Rinke, K.; Scharfenberger, U.; Schmidt, S. R.; Weber, M.; Woolway, R. I.; Zwart, J. A.; and Obrador, B.\n\n\n \n \n \n \n \n Delving deeper: Metabolic processes in the metalimnion of stratified lakes: Metalimnetic metabolism in lakes.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 62(3): 1288–1306. May 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Delving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{giling_delving_2017,\n\ttitle = {Delving deeper: {Metabolic} processes in the metalimnion of stratified lakes: {Metalimnetic} metabolism in lakes},\n\tvolume = {62},\n\tissn = {00243590},\n\tshorttitle = {Delving deeper},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.10504},\n\tdoi = {10.1002/lno.10504},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Giling, Darren P. and Staehr, Peter A. and Grossart, Hans Peter and Andersen, Mikkel René and Boehrer, Bertram and Escot, Carmelo and Evrendilek, Fatih and Gómez-Gener, Lluís and Honti, Mark and Jones, Ian D. and Karakaya, Nusret and Laas, Alo and Moreno-Ostos, Enrique and Rinke, Karsten and Scharfenberger, Ulrike and Schmidt, Silke R. and Weber, Michael and Woolway, R. Iestyn and Zwart, Jacob A. and Obrador, Biel},\n\tmonth = may,\n\tyear = {2017},\n\tpages = {1288--1306},\n}\n\n\n\n

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\n \n\n \n \n Gebler, S.; Hendricks Franssen, H.; Kollet, S.; Qu, W.; and Vereecken, H.\n\n\n \n \n \n \n \n High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 547: 309–331. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gebler_high_2017,\n\ttitle = {High resolution modelling of soil moisture patterns with {TerrSysMP}: {A} comparison with sensor network data},\n\tvolume = {547},\n\tissn = {00221694},\n\tshorttitle = {High resolution modelling of soil moisture patterns with {TerrSysMP}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169417300586},\n\tdoi = {10.1016/j.jhydrol.2017.01.048},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Gebler, S. and Hendricks Franssen, H.-J. and Kollet, S.J. and Qu, W. and Vereecken, H.},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {309--331},\n}\n\n\n\n

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\n \n\n \n \n Fu, J.; Gasche, R.; Wang, N.; Lu, H.; Butterbach-Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Impacts of climate and management on water balance and nitrogen leaching from montane grassland soils of S-Germany.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 229: 119–131. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Impacts$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fu_impacts_2017,\n\ttitle = {Impacts of climate and management on water balance and nitrogen leaching from montane grassland soils of {S}-{Germany}},\n\tvolume = {229},\n\tissn = {02697491},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749117303974},\n\tdoi = {10.1016/j.envpol.2017.05.071},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Pollution},\n\tauthor = {Fu, Jin and Gasche, Rainer and Wang, Na and Lu, Haiyan and Butterbach-Bahl, Klaus and Kiese, Ralf},\n\tmonth = oct,\n\tyear = {2017},\n\tpages = {119--131},\n}\n\n\n\n

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\n \n\n \n \n Dräger, N.; Theuerkauf, M.; Szeroczyńska, K.; Wulf, S.; Tjallingii, R.; Plessen, B.; Kienel, U.; and Brauer, A.\n\n\n \n \n \n \n \n Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at Lake Tiefer See (NE Germany).\n \n \n \n \n\n\n \n\n\n\n The Holocene, 27(3): 450–464. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Varve$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{drager_varve_2017,\n\ttitle = {Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at {Lake} {Tiefer} {See} ({NE} {Germany})},\n\tvolume = {27},\n\tissn = {0959-6836, 1477-0911},\n\turl = {http://journals.sagepub.com/doi/10.1177/0959683616660173},\n\tdoi = {10.1177/0959683616660173},\n\tabstract = {The Holocene sediment record of Lake Tiefer See exhibits striking alternations between well-varved and non-varved intervals. Here, we present a high-resolution multi-proxy record for the past {\\textasciitilde}6000 years and discuss possible causes for the observed sediment variability. This approach comprises microfacies, geochemical and microfossil analyses and a multiple dating concept including varve counting, tephrochronology and radiocarbon dating. Four periods of predominantly well-varved sediment were identified at 6000–3950, 3100–2850 and 2100–750 cal. a BP and AD 1924–present. Except of sub-recent varve formation, these periods are considered to reflect reduced lake circulation and consequently, stronger anoxic bottom water conditions. In contrast, intercalated intervals of poor varve preservation or even extensively mixed non-varved sediments indicate strengthened lake circulation. Sub-recent varve formation since AD 1924 is, in addition to natural forcing, influenced by enhanced lake productivity due to modern anthropogenic eutrophication. The general increase in periods of intensified lake circulation in Lake Tiefer See since {\\textasciitilde}4000 cal. a BP presumably is caused by gradual changes in the northern hemisphere orbital forcing, leading to cooler and windier conditions in Central Europe. Superimposed decadal- to centennial-scale variability of the lake circulation regime is likely the result of additional human-induced changes of the catchment vegetation. The coincidence of major non-varved periods at Lake Tiefer See and intervals of bioturbated sediments in the Baltic Sea implies a broader regional significance of our findings.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {The Holocene},\n\tauthor = {Dräger, Nadine and Theuerkauf, Martin and Szeroczyńska, Krystyna and Wulf, Sabine and Tjallingii, Rik and Plessen, Birgit and Kienel, Ulrike and Brauer, Achim},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {450--464},\n}\n\n\n\n

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\n \n\n \n \n Dick, D. D.; Dormann, C. F.; and Henle, K.\n\n\n \n \n \n \n \n Environmental determinants and temporal variation of amphibian habitat use in a temperate floodplain.\n \n \n \n \n\n\n \n\n\n\n The Herpetological Journal, 27(2): 161–171. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dick_environmental_2017,\n\ttitle = {Environmental determinants and temporal variation of amphibian habitat use in a temperate floodplain},\n\tvolume = {27},\n\tissn = {0268-0130},\n\turl = {https://www.thebhs.org/publications/the-herpetological-journal/volume-27-number-2-april-2017/1009-05-environmental-determinants-and-temporal-variation-of-amphibian-habitat-use-in-a-temperate-floodplain},\n\tnumber = {2},\n\tjournal = {The Herpetological Journal},\n\tauthor = {Dick, Daniela D.C and Dormann, Carsten F. and Henle, Klaus},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {161--171},\n}\n\n\n\n

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\n \n\n \n \n Dadi, T.; Wendt-Potthoff, K.; and Koschorreck, M.\n\n\n \n \n \n \n \n Sediment resuspension effects on dissolved organic carbon fluxes and microbial metabolic potentials in reservoirs.\n \n \n \n \n\n\n \n\n\n\n Aquatic Sciences, 79(3): 749–764. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Sediment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dadi_sediment_2017,\n\ttitle = {Sediment resuspension effects on dissolved organic carbon fluxes and microbial metabolic potentials in reservoirs},\n\tvolume = {79},\n\tissn = {1015-1621, 1420-9055},\n\turl = {http://link.springer.com/10.1007/s00027-017-0533-4},\n\tdoi = {10.1007/s00027-017-0533-4},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Aquatic Sciences},\n\tauthor = {Dadi, Tallent and Wendt-Potthoff, Katrin and Koschorreck, Matthias},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {749--764},\n}\n\n\n\n

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\n \n\n \n \n Colliander, A.; Jackson, T.; Bindlish, R.; Chan, S.; Das, N.; Kim, S.; Cosh, M.; Dunbar, R.; Dang, L.; Pashaian, L.; Asanuma, J.; Aida, K.; Berg, A.; Rowlandson, T.; Bosch, D.; Caldwell, T.; Caylor, K.; Goodrich, D.; al Jassar, H.; Lopez-Baeza, E.; Martínez-Fernández, J.; González-Zamora, A.; Livingston, S.; McNairn, H.; Pacheco, A.; Moghaddam, M.; Montzka, C.; Notarnicola, C.; Niedrist, G.; Pellarin, T.; Prueger, J.; Pulliainen, J.; Rautiainen, K.; Ramos, J.; Seyfried, M.; Starks, P.; Su, Z.; Zeng, Y.; van der Velde, R.; Thibeault, M.; Dorigo, W.; Vreugdenhil, M.; Walker, J.; Wu, X.; Monerris, A.; O'Neill, P.; Entekhabi, D.; Njoku, E.; and Yueh, S.\n\n\n \n \n \n \n \n Validation of SMAP surface soil moisture products with core validation sites.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 191: 215–231. March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Validation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{colliander_validation_2017,\n\ttitle = {Validation of {SMAP} surface soil moisture products with core validation sites},\n\tvolume = {191},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425717300329},\n\tdoi = {10.1016/j.rse.2017.01.021},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Colliander, A. and Jackson, T.J. and Bindlish, R. and Chan, S. and Das, N. and Kim, S.B. and Cosh, M.H. and Dunbar, R.S. and Dang, L. and Pashaian, L. and Asanuma, J. and Aida, K. and Berg, A. and Rowlandson, T. and Bosch, D. and Caldwell, T. and Caylor, K. and Goodrich, D. and al Jassar, H. and Lopez-Baeza, E. and Martínez-Fernández, J. and González-Zamora, A. and Livingston, S. and McNairn, H. and Pacheco, A. and Moghaddam, M. and Montzka, C. and Notarnicola, C. and Niedrist, G. and Pellarin, T. and Prueger, J. and Pulliainen, J. and Rautiainen, K. and Ramos, J. and Seyfried, M. and Starks, P. and Su, Z. and Zeng, Y. and van der Velde, R. and Thibeault, M. and Dorigo, W. and Vreugdenhil, M. and Walker, J.P. and Wu, X. and Monerris, A. and O'Neill, P.E. and Entekhabi, D. and Njoku, E.G. and Yueh, S.},\n\tmonth = mar,\n\tyear = {2017},\n\tpages = {215--231},\n}\n\n\n\n

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\n \n\n \n \n Cabral, I.; Keim, J.; Engelmann, R.; Kraemer, R.; Siebert, J.; and Bonn, A.\n\n\n \n \n \n \n \n Ecosystem services of allotment and community gardens: A Leipzig, Germany case study.\n \n \n \n \n\n\n \n\n\n\n Urban Forestry & Urban Greening, 23: 44–53. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecosystem$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cabral_ecosystem_2017,\n\ttitle = {Ecosystem services of allotment and community gardens: {A} {Leipzig}, {Germany} case study},\n\tvolume = {23},\n\tissn = {16188667},\n\tshorttitle = {Ecosystem services of allotment and community gardens},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1618866716302540},\n\tdoi = {10.1016/j.ufug.2017.02.008},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Urban Forestry \\& Urban Greening},\n\tauthor = {Cabral, Ines and Keim, Jessica and Engelmann, Rolf and Kraemer, Roland and Siebert, Julia and Bonn, Aletta},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {44--53},\n}\n\n\n\n

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\n \n\n \n \n Brosy, C.; Krampf, K.; Zeeman, M.; Wolf, B.; Junkermann, W.; Schäfer, K.; Emeis, S.; and Kunstmann, H.\n\n\n \n \n \n \n \n Simultaneous multicopter-based air sampling and sensing of meteorological variables.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 10(8): 2773–2784. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brosy_simultaneous_2017,\n\ttitle = {Simultaneous multicopter-based air sampling and sensing of meteorological variables},\n\tvolume = {10},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/10/2773/2017/},\n\tdoi = {10.5194/amt-10-2773-2017},\n\tabstract = {Abstract. The state and composition of the lowest part of the planetary boundary layer (PBL), i.e., the atmospheric surface layer (SL), reflects the interactions of external forcing, land surface, vegetation, human influence and the atmosphere. Vertical profiles of atmospheric variables in the SL at high spatial (meters) and temporal (1 Hz and better) resolution increase our understanding of these interactions but are still challenging to measure appropriately. Traditional ground-based observations include towers that often cover only a few measurement heights at a fixed location. At the same time, most remote sensing techniques and aircraft measurements have limitations to achieve sufficient detail close to the ground (up to 50 m). Vertical and horizontal transects of the PBL can be complemented by unmanned aerial vehicles (UAV). Our aim in this case study is to assess the use of a multicopter-type UAV for the spatial sampling of air and simultaneously the sensing of meteorological variables for the study of the surface exchange processes. To this end, a UAV was equipped with onboard air temperature and humidity sensors, while wind conditions were determined from the UAV's flight control sensors. Further, the UAV was used to systematically change the location of a sample inlet connected to a sample tube, allowing the observation of methane abundance using a ground-based analyzer. Vertical methane gradients of about 0.3 ppm were found during stable atmospheric conditions. Our results showed that both methane and meteorological conditions were in agreement with other observations at the site during the ScaleX-2015 campaign. The multicopter-type UAV was capable of simultaneous in situ sensing of meteorological state variables and sampling of air up to 50 m above the surface, which extended the vertical profile height of existing tower-based infrastructure by a factor of 5.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-18},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Brosy, Caroline and Krampf, Karina and Zeeman, Matthias and Wolf, Benjamin and Junkermann, Wolfgang and Schäfer, Klaus and Emeis, Stefan and Kunstmann, Harald},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {2773--2784},\n}\n\n\n\n

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\n \n\n \n \n Brandt, T.; Vieweg, M.; Laube, G.; Schima, R.; Goblirsch, T.; Fleckenstein, J. H.; and Schmidt, C.\n\n\n \n \n \n \n \n Automated in Situ Oxygen Profiling at Aquatic–Terrestrial Interfaces.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 51(17): 9970–9978. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Automated$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brandt_automated_2017,\n\ttitle = {Automated in {Situ} {Oxygen} {Profiling} at {Aquatic}–{Terrestrial} {Interfaces}},\n\tvolume = {51},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.7b01482},\n\tdoi = {10.1021/acs.est.7b01482},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Brandt, Tanja and Vieweg, Michael and Laube, Gerrit and Schima, Robert and Goblirsch, Tobias and Fleckenstein, Jan H. and Schmidt, Christian},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {9970--9978},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H.; Wiekenkamp, I.; Huisman, J.; Pütz, T.; Graf, A.; Drüe, C.; and Vereecken, H.\n\n\n \n \n \n \n Integrierte Untersuchung der Effekte eines Kahlschlags auf das hydrologische Systemverhalten eines Kleineinzugsgebiets.\n \n \n \n\n\n \n\n\n\n In Den Wandel messen - Wie gehen wir mit Nichtstationarität in der Hydrologie um? - Beiträge zum Tag der Hydrologie 23./24. März 2017. Forum für Hydrologie und Wasserbewirtschaftung, volume 38.17, pages 39–50, Trier, 2017. M. Casper, O. Gronz, R. Ley and T. Schütz (eds.)\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{bogena_integrierte_2017,\n\taddress = {Trier},\n\ttitle = {Integrierte {Untersuchung} der {Effekte} eines {Kahlschlags} auf das hydrologische {Systemverhalten} eines {Kleineinzugsgebiets}.},\n\tvolume = {38.17},\n\tbooktitle = {Den {Wandel} messen - {Wie} gehen wir mit {Nichtstationarität} in der {Hydrologie} um? - {Beiträge} zum {Tag} der {Hydrologie} 23./24. {März} 2017. {Forum} für {Hydrologie} und {Wasserbewirtschaftung}},\n\tpublisher = {M. Casper, O. Gronz, R. Ley and T. Schütz (eds.)},\n\tauthor = {Bogena, H.R. and Wiekenkamp, I. and Huisman, J.A. and Pütz, T. and Graf, A. and Drüe, C. and Vereecken, H.},\n\tyear = {2017},\n\tpages = {39--50},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H.; Huisman, J.; Schilling, B.; Weuthen, A.; and Vereecken, H.\n\n\n \n \n \n \n \n Effective Calibration of Low-Cost Soil Water Content Sensors.\n \n \n \n \n\n\n \n\n\n\n Sensors, 17(12): 208. January 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Effective$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_effective_2017,\n\ttitle = {Effective {Calibration} of {Low}-{Cost} {Soil} {Water} {Content} {Sensors}},\n\tvolume = {17},\n\tissn = {1424-8220},\n\turl = {http://www.mdpi.com/1424-8220/17/1/208},\n\tdoi = {10.3390/s17010208},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-18},\n\tjournal = {Sensors},\n\tauthor = {Bogena, Heye and Huisman, Johan and Schilling, Bernd and Weuthen, Ansgar and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {208},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H.; Franssen, H. H.; Montzka, C.; and Vereecken, H.\n\n\n \n \n \n \n \n A Blueprint for a Distributed Terrestrial Ecosystem Research Infrastructure.\n \n \n \n \n\n\n \n\n\n\n In Chabbi, A.; and Loescher, H. W., editor(s), Terrestrial Ecosystem Research Infrastructures, pages 279–303. CRC Press, Boca Raton, FL : CRC Press, 2017., 1 edition, March 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{chabbi_blueprint_2017,\n\taddress = {Boca Raton, FL : CRC Press, 2017.},\n\tedition = {1},\n\ttitle = {A {Blueprint} for a {Distributed} {Terrestrial} {Ecosystem} {Research} {Infrastructure}},\n\tisbn = {9781315368252},\n\turl = {https://www.taylorfrancis.com/books/9781498751339/chapters/10.1201/9781315368252-14},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tbooktitle = {Terrestrial {Ecosystem} {Research} {Infrastructures}},\n\tpublisher = {CRC Press},\n\tauthor = {Bogena, Heye and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Vereecken, Harry},\n\teditor = {Chabbi, Abad and Loescher, Henry W.},\n\tmonth = mar,\n\tyear = {2017},\n\tdoi = {10.1201/9781315368252-14},\n\tpages = {279--303},\n}\n\n\n\n

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\n \n\n \n \n Barth, J. A. C.; Mader, M.; Nenning, F.; van Geldern, R.; and Friese, K.\n\n\n \n \n \n \n \n Stable isotope mass balances versus concentration differences of dissolved inorganic carbon – implications for tracing carbon turnover in reservoirs.\n \n \n \n \n\n\n \n\n\n\n Isotopes in Environmental and Health Studies, 53(4): 413–426. July 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{barth_stable_2017,\n\ttitle = {Stable isotope mass balances versus concentration differences of dissolved inorganic carbon – implications for tracing carbon turnover in reservoirs},\n\tvolume = {53},\n\tissn = {1025-6016, 1477-2639},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/10256016.2017.1282478},\n\tdoi = {10.1080/10256016.2017.1282478},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-18},\n\tjournal = {Isotopes in Environmental and Health Studies},\n\tauthor = {Barth, Johannes A. C. and Mader, Michael and Nenning, Franziska and van Geldern, Robert and Friese, Kurt},\n\tmonth = jul,\n\tyear = {2017},\n\tpages = {413--426},\n}\n\n\n\n

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\n \n\n \n \n Baatz, R.; Hendricks Franssen, H.; Han, X.; Hoar, T.; Bogena, H. R.; and Vereecken, H.\n\n\n \n \n \n \n \n Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 21(5): 2509–2530. May 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baatz_evaluation_2017,\n\ttitle = {Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction},\n\tvolume = {21},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/21/2509/2017/},\n\tdoi = {10.5194/hess-21-2509-2017},\n\tabstract = {Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-18},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Baatz, Roland and Hendricks Franssen, Harrie-Jan and Han, Xujun and Hoar, Tim and Bogena, Heye Reemt and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2017},\n\tpages = {2509--2530},\n}\n\n\n\n

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\n \n\n \n \n Andreasen, M.; Jensen, K. H.; Desilets, D.; Franz, T. E.; Zreda, M.; Bogena, H. R.; and Looms, M. C.\n\n\n \n \n \n \n \n Status and Perspectives on the Cosmic-Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 16(8): vzj2017.04.0086. August 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Status$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{andreasen_status_2017,\n\ttitle = {Status and {Perspectives} on the {Cosmic}-{Ray} {Neutron} {Method} for {Soil} {Moisture} {Estimation} and {Other} {Environmental} {Science} {Applications}},\n\tvolume = {16},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2017.04.0086},\n\tdoi = {10.2136/vzj2017.04.0086},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-18},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Andreasen, Mie and Jensen, Karsten H. and Desilets, Darin and Franz, Trenton E. and Zreda, Marek and Bogena, Heye R. and Looms, Majken C.},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {vzj2017.04.0086},\n}\n\n\n\n

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\n \n\n \n \n Andreasen, M.; Jensen, K. H.; Desilets, D.; Zreda, M.; Bogena, H. R.; and Looms, M. C.\n\n\n \n \n \n \n \n Cosmic-ray neutron transport at a forest field site: the sensitivity to various environmental conditions with focus on biomass and canopy interception.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 21(4): 1875–1894. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Cosmic-ray$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{andreasen_cosmic-ray_2017,\n\ttitle = {Cosmic-ray neutron transport at a forest field site: the sensitivity to various environmental conditions with focus on biomass and canopy interception},\n\tvolume = {21},\n\tissn = {1607-7938},\n\tshorttitle = {Cosmic-ray neutron transport at a forest field site},\n\turl = {https://hess.copernicus.org/articles/21/1875/2017/},\n\tdoi = {10.5194/hess-21-1875-2017},\n\tabstract = {Abstract. Cosmic-ray neutron intensity is inversely correlated to all hydrogen present in the upper decimeters of the subsurface and the first few hectometers of the atmosphere above the ground surface. This correlation forms the base of the cosmic-ray neutron soil moisture estimation method. The method is, however, complicated by the fact that several hydrogen pools other than soil moisture affect the neutron intensity. In order to improve the cosmic-ray neutron soil moisture estimation method and explore the potential for additional applications, knowledge about the environmental effect on cosmic-ray neutron intensity is essential (e.g., the effect of vegetation, litter layer and soil type). In this study the environmental effect is examined by performing a sensitivity analysis using neutron transport modeling. We use a neutron transport model with various representations of the forest and different parameters describing the subsurface to match measured height profiles and time series of thermal and epithermal neutron intensities at a field site in Denmark. Overall, modeled thermal and epithermal neutron intensities are in satisfactory agreement with measurements; however, the choice of forest canopy conceptualization is found to be significant. Modeling results show that the effect of canopy interception, soil chemistry and dry bulk density of litter and mineral soil on neutron intensity is small. On the other hand, the neutron intensity decreases significantly with added litter-layer thickness, especially for epithermal neutron energies. Forest biomass also has a significant influence on the neutron intensity height profiles at the examined field site, altering both the shape of the profiles and the ground-level thermal-to-epithermal neutron ratio. This ratio increases with increasing amounts of biomass, and was confirmed by measurements from three sites representing agricultural, heathland and forest land cover. A much smaller effect of canopy interception on the ground-level thermal-to-epithermal neutron ratio was modeled. Overall, the results suggest a potential to use ground-level thermal-to-epithermal neutron ratios to discriminate the effect of different hydrogen contributions on the neutron signal.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-18},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Andreasen, Mie and Jensen, Karsten H. and Desilets, Darin and Zreda, Marek and Bogena, Heye R. and Looms, Majken C.},\n\tmonth = apr,\n\tyear = {2017},\n\tpages = {1875--1894},\n}\n\n\n\n

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\n Abstract. Cosmic-ray neutron intensity is inversely correlated to all hydrogen present in the upper decimeters of the subsurface and the first few hectometers of the atmosphere above the ground surface. This correlation forms the base of the cosmic-ray neutron soil moisture estimation method. The method is, however, complicated by the fact that several hydrogen pools other than soil moisture affect the neutron intensity. In order to improve the cosmic-ray neutron soil moisture estimation method and explore the potential for additional applications, knowledge about the environmental effect on cosmic-ray neutron intensity is essential (e.g., the effect of vegetation, litter layer and soil type). In this study the environmental effect is examined by performing a sensitivity analysis using neutron transport modeling. We use a neutron transport model with various representations of the forest and different parameters describing the subsurface to match measured height profiles and time series of thermal and epithermal neutron intensities at a field site in Denmark. Overall, modeled thermal and epithermal neutron intensities are in satisfactory agreement with measurements; however, the choice of forest canopy conceptualization is found to be significant. Modeling results show that the effect of canopy interception, soil chemistry and dry bulk density of litter and mineral soil on neutron intensity is small. On the other hand, the neutron intensity decreases significantly with added litter-layer thickness, especially for epithermal neutron energies. Forest biomass also has a significant influence on the neutron intensity height profiles at the examined field site, altering both the shape of the profiles and the ground-level thermal-to-epithermal neutron ratio. This ratio increases with increasing amounts of biomass, and was confirmed by measurements from three sites representing agricultural, heathland and forest land cover. A much smaller effect of canopy interception on the ground-level thermal-to-epithermal neutron ratio was modeled. Overall, the results suggest a potential to use ground-level thermal-to-epithermal neutron ratios to discriminate the effect of different hydrogen contributions on the neutron signal.\n

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\n \n\n \n \n Altdorff, D.; von Hebel, C.; Borchard, N.; van der Kruk, J.; Bogena, H. R.; Vereecken, H.; and Huisman, J. A.\n\n\n \n \n \n \n \n Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 76(3): 111. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{altdorff_potential_2017,\n\ttitle = {Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem},\n\tvolume = {76},\n\tissn = {1866-6280, 1866-6299},\n\turl = {http://link.springer.com/10.1007/s12665-016-6361-3},\n\tdoi = {10.1007/s12665-016-6361-3},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-18},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Altdorff, Daniel and von Hebel, Christian and Borchard, Nils and van der Kruk, Jan and Bogena, Heye Reemt and Vereecken, Harry and Huisman, Johan Alexander},\n\tmonth = feb,\n\tyear = {2017},\n\tpages = {111},\n}\n\n\n\n

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\n \n 2016\n \n \n (98)\n \n \n

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\n \n\n \n \n Van Stan, J. T.; Lewis, E. S.; Hildebrandt, A.; Rebmann, C.; and Friesen, J.\n\n\n \n \n \n \n \n Impact of interacting bark structure and rainfall conditions on stemflow variability in a temperate beech-oak forest, central Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 61(11): 2071–2083. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_stan_impact_2016,\n\ttitle = {Impact of interacting bark structure and rainfall conditions on stemflow variability in a temperate beech-oak forest, central {Germany}},\n\tvolume = {61},\n\tissn = {0262-6667, 2150-3435},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2015.1083104},\n\tdoi = {10.1080/02626667.2015.1083104},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-06-19},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Van Stan, John T. and Lewis, Elliott S. and Hildebrandt, Anke and Rebmann, Corinna and Friesen, Jan},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {2071--2083},\n}\n\n\n\n\n\n\n\n

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\n \n\n \n \n Verheyen, K.; Vanhellemont, M.; Auge, H.; Baeten, L.; Baraloto, C.; Barsoum, N.; Bilodeau-Gauthier, S.; Bruelheide, H.; Castagneyrol, B.; Godbold, D.; Haase, J.; Hector, A.; Jactel, H.; Koricheva, J.; Loreau, M.; Mereu, S.; Messier, C.; Muys, B.; Nolet, P.; Paquette, A.; Parker, J.; Perring, M.; Ponette, Q.; Potvin, C.; Reich, P.; Smith, A.; Weih, M.; and Scherer-Lorenzen, M.\n\n\n \n \n \n \n \n Contributions of a global network of tree diversity experiments to sustainable forest plantations.\n \n \n \n \n\n\n \n\n\n\n Ambio, 45(1): 29–41. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Contributions$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{verheyen_contributions_2016,\n\ttitle = {Contributions of a global network of tree diversity experiments to sustainable forest plantations},\n\tvolume = {45},\n\tissn = {0044-7447, 1654-7209},\n\turl = {https://link.springer.com/10.1007/s13280-015-0685-1},\n\tdoi = {10.1007/s13280-015-0685-1},\n\tabstract = {Abstract \n            The area of forest plantations is increasing worldwide helping to meet timber demand and protect natural forests. However, with global change, monospecific plantations are increasingly vulnerable to abiotic and biotic disturbances. As an adaption measure we need to move to plantations that are more diverse in genotypes, species, and structure, with a design underpinned by science. TreeDivNet, a global network of tree diversity experiments, responds to this need by assessing the advantages and disadvantages of mixed species plantations. The network currently consists of 18 experiments, distributed over 36 sites and five ecoregions. With plantations 1–15 years old, TreeDivNet can already provide relevant data for forest policy and management. In this paper, we highlight some early results on the carbon sequestration and pest resistance potential of more diverse plantations. Finally, suggestions are made for new, innovative experiments in understudied regions to complement the existing network.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-02-23},\n\tjournal = {Ambio},\n\tauthor = {Verheyen, Kris and Vanhellemont, Margot and Auge, Harald and Baeten, Lander and Baraloto, Christopher and Barsoum, Nadia and Bilodeau-Gauthier, Simon and Bruelheide, Helge and Castagneyrol, Bastien and Godbold, Douglas and Haase, Josephine and Hector, Andy and Jactel, Hervé and Koricheva, Julia and Loreau, Michel and Mereu, Simone and Messier, Christian and Muys, Bart and Nolet, Philippe and Paquette, Alain and Parker, John and Perring, Mike and Ponette, Quentin and Potvin, Catherine and Reich, Peter and Smith, Andy and Weih, Martin and Scherer-Lorenzen, Michael},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {29--41},\n}\n\n\n\n

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\n \n\n \n \n Mueller, C.; Krieg, R.; Merz, R.; and Knöller, K.\n\n\n \n \n \n \n \n Regional nitrogen dynamics in the TERENO Bode River catchment, Germany, as constrained by stable isotope patterns.\n \n \n \n \n\n\n \n\n\n\n Isotopes in Environmental and Health Studies, 52(1-2): 61–74. March 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Regional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mueller_regional_2016,\n\ttitle = {Regional nitrogen dynamics in the {TERENO} {Bode} {River} catchment, {Germany}, as constrained by stable isotope patterns},\n\tvolume = {52},\n\tissn = {1025-6016, 1477-2639},\n\turl = {http://www.tandfonline.com/doi/full/10.1080/10256016.2015.1019489},\n\tdoi = {10.1080/10256016.2015.1019489},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2023-02-23},\n\tjournal = {Isotopes in Environmental and Health Studies},\n\tauthor = {Mueller, Christin and Krieg, Ronald and Merz, Ralf and Knöller, Kay},\n\tmonth = mar,\n\tyear = {2016},\n\tpages = {61--74},\n}\n\n\n\n

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\n \n\n \n \n Maurer, V.; Kalthoff, N.; Wieser, A.; Kohler, M.; Mauder, M.; and Gantner, L.\n\n\n \n \n \n \n \n Observed spatiotemporal variability of boundary-layer turbulence over flat, heterogeneous terrain.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 16(3): 1377–1400. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Observed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{maurer_observed_2016,\n\ttitle = {Observed spatiotemporal variability of boundary-layer turbulence over flat, heterogeneous terrain},\n\tvolume = {16},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/16/1377/2016/},\n\tdoi = {10.5194/acp-16-1377-2016},\n\tabstract = {Abstract. In the spring of 2013, extensive measurements with multiple Doppler lidar systems were performed. The instruments were arranged in a triangle with edge lengths of about 3 km in a moderately flat, agriculturally used terrain in northwestern Germany. For 6 mostly cloud-free convective days, vertical velocity variance profiles were calculated. Weighted-averaged surface fluxes proved to be more appropriate than data from individual sites for scaling the variance profiles; but even then, the scatter of profiles was mostly larger than the statistical error. The scatter could not be explained by mean wind speed or stability, whereas time periods with significantly increased variance contained broader thermals. Periods with an elevated maximum of the variance profiles could also be related to broad thermals. Moreover, statistically significant spatial differences of variance were found. They were not influenced by the existing surface heterogeneity. Instead, thermals were preserved between two sites when the travel time was shorter than the large-eddy turnover time. At the same time, no thermals passed for more than 2 h at a third site that was located perpendicular to the mean wind direction in relation to the first two sites. Organized structures of turbulence with subsidence prevailing in the surroundings of thermals can thus partly explain significant spatial variance differences existing for several hours. Therefore, the representativeness of individual variance profiles derived from measurements at a single site cannot be assumed.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {Maurer, V. and Kalthoff, N. and Wieser, A. and Kohler, M. and Mauder, M. and Gantner, L.},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {1377--1400},\n}\n\n\n\n

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\n \n\n \n \n Koch, J.; Cornelissen, T.; Fang, Z.; Bogena, H.; Diekkrüger, B.; Kollet, S.; and Stisen, S.\n\n\n \n \n \n \n \n Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 533: 234–249. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Inter-comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koch_inter-comparison_2016,\n\ttitle = {Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment},\n\tvolume = {533},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415009415},\n\tdoi = {10.1016/j.jhydrol.2015.12.002},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Koch, Julian and Cornelissen, Thomas and Fang, Zhufeng and Bogena, Heye and Diekkrüger, Bernd and Kollet, Stefan and Stisen, Simon},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {234--249},\n}\n\n\n\n

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\n \n\n \n \n Díaz-Pinés, E.; Heras, P.; Gasche, R.; Rubio, A.; Rennenberg, H.; Butterbach-Bahl, K.; and Kiese, R.\n\n\n \n \n \n \n \n Nitrous oxide emissions from stems of ash (Fraxinus angustifolia Vahl) and European beech (Fagus sylvatica L.).\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 398(1-2): 35–45. January 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Nitrous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{diaz-pines_nitrous_2016,\n\ttitle = {Nitrous oxide emissions from stems of ash ({Fraxinus} angustifolia {Vahl}) and {European} beech ({Fagus} sylvatica {L}.)},\n\tvolume = {398},\n\tissn = {0032-079X, 1573-5036},\n\turl = {http://link.springer.com/10.1007/s11104-015-2629-8},\n\tdoi = {10.1007/s11104-015-2629-8},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2023-02-23},\n\tjournal = {Plant and Soil},\n\tauthor = {Díaz-Pinés, Eugenio and Heras, Paloma and Gasche, Rainer and Rubio, Agustín and Rennenberg, Heinz and Butterbach-Bahl, Klaus and Kiese, Ralf},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {35--45},\n}\n\n\n\n

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\n \n\n \n \n Liu, S.; Herbst, M.; Bol, R.; Gottselig, N.; Pütz, T.; Weymann, D.; Wiekenkamp, I.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n The contribution of hydroxylamine content to spatial variability of N$_{\\textrm{2}}$O formation in soil of a Norway spruce forest.\n \n \n \n \n\n\n \n\n\n\n Geochimica et Cosmochimica Acta, 178: 76–86. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_contribution_2016,\n\ttitle = {The contribution of hydroxylamine content to spatial variability of {N}$_{\\textrm{2}}${O} formation in soil of a {Norway} spruce forest},\n\tvolume = {178},\n\tissn = {00167037},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016703716300102},\n\tdoi = {10.1016/j.gca.2016.01.026},\n\tlanguage = {en},\n\turldate = {2022-11-18},\n\tjournal = {Geochimica et Cosmochimica Acta},\n\tauthor = {Liu, Shurong and Herbst, Michael and Bol, Roland and Gottselig, Nina and Pütz, Thomas and Weymann, Daniel and Wiekenkamp, Inge and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {76--86},\n}\n\n\n\n

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\n \n\n \n \n Zhang, H.; Hendricks Franssen, H.; Han, X.; Vrugt, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Joint State and Parameter Estimation of Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter.\n \n \n \n \n\n\n \n\n\n\n Technical Report Vadose Zone Hydrology/Stochastic approaches, February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Joint$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{zhang_joint_2016,\n\ttype = {preprint},\n\ttitle = {Joint {State} and {Parameter} {Estimation} of {Two} {Land} {Surface} {Models} {Using} the {Ensemble} {Kalman} {Filter} and {Particle} {Filter}},\n\turl = {https://hess.copernicus.org/preprints/hess-2016-42/hess-2016-42.pdf},\n\tabstract = {Abstract. Land surface models (LSMs) contain a suite of different parameters and state variables to resolve the water and energy balance at the soil-atmosphere interface. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. In this paper, we use the Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM) to simulate temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany. Four different data assimilation (DA) methods are used to jointly estimate the spatially distributed water content values, and hydraulic and/or thermal properties of the resolved soil domain. This includes the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month data period, and subsequently evaluated for another five month period. Our results show that all the different DA methods improve the fit of the VIC and CLM model to the observed water content data, particularly if the maximum baseflow velocity (VIC), soil hydraulic (VIC) properties and/or soil texture (CLM) are jointly estimated along with the model states. In the evaluation period, the augmentation and dual estimation method performed slightly better than RRPF and MCMCPF. The differences in simulated soil moisture values between the CLM and VIC model were larger than variations among the data assimilation algorithms. The best performance for the Rollesbroich site was observed for the CLM model. The strong underestimation of the soil moisture values of the third VIC-layer are likely explained by an inadequate parameterization of groundwater drainage.},\n\turldate = {2023-01-23},\n\tinstitution = {Vadose Zone Hydrology/Stochastic approaches},\n\tauthor = {Zhang, Hongjuan and Hendricks Franssen, Harrie-Jan and Han, Xujun and Vrugt, Jasper and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2016},\n\tdoi = {10.5194/hess-2016-42},\n}\n\n\n\n

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\n \n\n \n \n Zerenner, T.; Venema, V.; Friederichs, P.; and Simmer, C.\n\n\n \n \n \n \n \n Downscaling near-surface atmospheric fields with multi-objective Genetic Programming.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 84: 85–98. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Downscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zerenner_downscaling_2016,\n\ttitle = {Downscaling near-surface atmospheric fields with multi-objective {Genetic} {Programming}},\n\tvolume = {84},\n\tissn = {13648152},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1364815216302122},\n\tdoi = {10.1016/j.envsoft.2016.06.009},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Zerenner, Tanja and Venema, Victor and Friederichs, Petra and Simmer, Clemens},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {85--98},\n}\n\n\n\n

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\n \n\n \n \n Xie, X.; Evaristo, R.; Simmer, C.; Handwerker, J.; and Trömel, S.\n\n\n \n \n \n \n \n Precipitation and microphysical processes observed by three polarimetric X-band radars and ground-based instrumentation during HOPE.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Chemistry and Physics, 16(11): 7105–7116. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Precipitation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{xie_precipitation_2016,\n\ttitle = {Precipitation and microphysical processes observed by three polarimetric {X}-band radars and ground-based instrumentation during {HOPE}},\n\tvolume = {16},\n\tissn = {1680-7324},\n\turl = {https://acp.copernicus.org/articles/16/7105/2016/},\n\tdoi = {10.5194/acp-16-7105-2016},\n\tabstract = {Abstract. This study presents a first analysis of precipitation and related microphysical processes observed by three polarimetric X-band Doppler radars (BoXPol, JuXPol and KiXPol) in conjunction with a ground-based network of disdrometers, rain gauges and vertically pointing micro rain radars (MRRs) during the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) during April and May 2013 in Germany. While JuXPol and KiXPol were continuously observing the central HOPE area near Forschungszentrum Jülich at a close distance, BoXPol observed the area from a distance of about 48.5 km. MRRs were deployed in the central HOPE area and one MRR close to BoXPol in Bonn, Germany. Seven disdrometers and three rain gauges providing point precipitation observations were deployed at five locations within a 5 km  ×  5 km region, while three other disdrometers were collocated with the MRR in Bonn. The daily rainfall accumulation at each rain gauge/disdrometer location estimated from the three X-band polarimetric radar observations showed very good agreement. Accompanying microphysical processes during the evolution of precipitation systems were well captured by the polarimetric X-band radars and corroborated by independent observations from the other ground-based instruments.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-01-23},\n\tjournal = {Atmospheric Chemistry and Physics},\n\tauthor = {Xie, Xinxin and Evaristo, Raquel and Simmer, Clemens and Handwerker, Jan and Trömel, Silke},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {7105--7116},\n}\n\n\n\n

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\n \n\n \n \n Wulf, S.; Dräger, N.; Ott, F.; Serb, J.; Appelt, O.; Guðmundsdóttir, E.; van den Bogaard, C.; Słowiński, M.; Błaszkiewicz, M.; and Brauer, A.\n\n\n \n \n \n \n \n Holocene tephrostratigraphy of varved sediment records from Lakes Tiefer See (NE Germany) and Czechowskie (N Poland).\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 132: 1–14. January 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Holocene$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wulf_holocene_2016,\n\ttitle = {Holocene tephrostratigraphy of varved sediment records from {Lakes} {Tiefer} {See} ({NE} {Germany}) and {Czechowskie} ({N} {Poland})},\n\tvolume = {132},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379115301736},\n\tdoi = {10.1016/j.quascirev.2015.11.007},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Wulf, Sabine and Dräger, Nadine and Ott, Florian and Serb, Johanna and Appelt, Oona and Guðmundsdóttir, Esther and van den Bogaard, Christel and Słowiński, Michał and Błaszkiewicz, Mirosław and Brauer, Achim},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {1--14},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wissenbach, D. K.; Winkler, B.; Otto, W.; Kohajda, T.; Roeder, S.; Müller, A.; Hoeke, H.; Matysik, S.; Schlink, U.; Borte, M.; Herbarth, O.; Lehmann, I.; and von-Bergen , M.\n\n\n \n \n \n \n \n Long-term indoor VOC concentrations assessment a trend analysis of distribution, disposition, and personal exposure in cohort study samples.\n \n \n \n \n\n\n \n\n\n\n Air Quality, Atmosphere & Health, 9(8): 941–950. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wissenbach_long-term_2016,\n\ttitle = {Long-term indoor {VOC} concentrations assessment a trend analysis of distribution, disposition, and personal exposure in cohort study samples},\n\tvolume = {9},\n\tissn = {1873-9318, 1873-9326},\n\turl = {http://link.springer.com/10.1007/s11869-016-0396-1},\n\tdoi = {10.1007/s11869-016-0396-1},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-01-23},\n\tjournal = {Air Quality, Atmosphere \\& Health},\n\tauthor = {Wissenbach, D. K. and Winkler, B. and Otto, W. and Kohajda, T. and Roeder, S. and Müller, A. and Hoeke, H. and Matysik, S. and Schlink, U. and Borte, M. and Herbarth, O. and Lehmann, I. and von-Bergen, M.},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {941--950},\n}\n\n\n\n

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\n \n\n \n \n Wieneke, S.; Ahrends, H.; Damm, A.; Pinto, F.; Stadler, A.; Rossini, M.; and Rascher, U.\n\n\n \n \n \n \n \n Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 184: 654–667. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Airborne$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wieneke_airborne_2016,\n\ttitle = {Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: {Implications} for improved estimates of gross primary productivity},\n\tvolume = {184},\n\tissn = {00344257},\n\tshorttitle = {Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425716302826},\n\tdoi = {10.1016/j.rse.2016.07.025},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Wieneke, S. and Ahrends, H. and Damm, A. and Pinto, F. and Stadler, A. and Rossini, M. and Rascher, U.},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {654--667},\n}\n\n\n\n

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\n \n\n \n \n Wiekenkamp, I.; Huisman, J.; Bogena, H.; Lin, H.; and Vereecken, H.\n\n\n \n \n \n \n \n Spatial and temporal occurrence of preferential flow in a forested headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 534: 139–149. March 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wiekenkamp_spatial_2016,\n\ttitle = {Spatial and temporal occurrence of preferential flow in a forested headwater catchment},\n\tvolume = {534},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415009981},\n\tdoi = {10.1016/j.jhydrol.2015.12.050},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Wiekenkamp, I. and Huisman, J.A. and Bogena, H.R. and Lin, H.S. and Vereecken, H.},\n\tmonth = mar,\n\tyear = {2016},\n\tpages = {139--149},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wiekenkamp, I.; Huisman, J.; Bogena, H.; Graf, A.; Lin, H.; Drüe, C.; and Vereecken, H.\n\n\n \n \n \n \n \n Changes in measured spatiotemporal patterns of hydrological response after partial deforestation in a headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 542: 648–661. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Changes$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wiekenkamp_changes_2016,\n\ttitle = {Changes in measured spatiotemporal patterns of hydrological response after partial deforestation in a headwater catchment},\n\tvolume = {542},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416305911},\n\tdoi = {10.1016/j.jhydrol.2016.09.037},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Wiekenkamp, I. and Huisman, J.A. and Bogena, H.R. and Graf, A. and Lin, H.S. and Drüe, C. and Vereecken, H.},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {648--661},\n}\n\n\n\n

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\n \n\n \n \n Wiedemann, A.; Marañón-Jiménez, S.; Rebmann, C.; Herbst, M.; and Cuntz, M.\n\n\n \n \n \n \n \n An empirical study of the wound effect on sap flux density measured with thermal dissipation probes.\n \n \n \n \n\n\n \n\n\n\n Tree Physiology, 36(12): 1471–1484. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wiedemann_empirical_2016,\n\ttitle = {An empirical study of the wound effect on sap flux density measured with thermal dissipation probes},\n\tvolume = {36},\n\tissn = {0829-318X, 1758-4469},\n\turl = {https://academic.oup.com/treephys/article-lookup/doi/10.1093/treephys/tpw071},\n\tdoi = {10.1093/treephys/tpw071},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-01-23},\n\tjournal = {Tree Physiology},\n\tauthor = {Wiedemann, Andreas and Marañón-Jiménez, Sara and Rebmann, Corinna and Herbst, Mathias and Cuntz, Matthias},\n\teditor = {Oren, Ram},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {1471--1484},\n}\n\n\n\n

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\n \n\n \n \n Wen, Y.; Chen, Z.; Dannenmann, M.; Carminati, A.; Willibald, G.; Kiese, R.; Wolf, B.; Veldkamp, E.; Butterbach-Bahl, K.; and Corre, M. D.\n\n\n \n \n \n \n \n Disentangling gross N2O production and consumption in soil.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 6(1): 36517. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Disentangling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wen_disentangling_2016,\n\ttitle = {Disentangling gross {N2O} production and consumption in soil},\n\tvolume = {6},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/srep36517},\n\tdoi = {10.1038/srep36517},\n\tabstract = {Abstract \n             \n              The difficulty of measuring gross N \n              2 \n              O production and consumption in soil impedes our ability to predict N \n              2 \n              O dynamics across the soil-atmosphere interface. Our study aimed to disentangle these processes by comparing measurements from gas-flow soil core (GFSC) and \n              15 \n              N \n              2 \n              O pool dilution ( \n              15 \n              N \n              2 \n              OPD) methods. GFSC directly measures soil N \n              2 \n              O and N \n              2 \n              fluxes, with their sum as the gross N \n              2 \n              O production, whereas \n              15 \n              N \n              2 \n              OPD involves addition of \n              15 \n              N \n              2 \n              O into a chamber headspace and measuring its isotopic dilution over time. Measurements were conducted on intact soil cores from grassland, cropland, beech and pine forests. Across sites, gross N \n              2 \n              O production and consumption measured by \n              15 \n              N \n              2 \n              OPD were only 10\\% and 6\\%, respectively, of those measured by GFSC. However, \n              15 \n              N \n              2 \n              OPD remains the only method that can be used under field conditions to measure atmospheric N \n              2 \n              O uptake in soil. We propose to use different terminologies for the gross N \n              2 \n              O fluxes that these two methods quantified. For \n              15 \n              N \n              2 \n              OPD, we suggest using ‘gross N \n              2 \n              O emission and uptake’, which encompass gas exchange within the \n              15 \n              N \n              2 \n              O-labelled, soil air-filled pores. For GFSC, ‘gross N \n              2 \n              O production and consumption’ can be used, which includes both N \n              2 \n              O emitted into the soil air-filled pores and N \n              2 \n              O directly consumed, forming N \n              2 \n              , in soil anaerobic microsites.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {Scientific Reports},\n\tauthor = {Wen, Yuan and Chen, Zhe and Dannenmann, Michael and Carminati, Andrea and Willibald, Georg and Kiese, Ralf and Wolf, Benjamin and Veldkamp, Edzo and Butterbach-Bahl, Klaus and Corre, Marife D.},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {36517},\n}\n\n\n\n

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\n \n\n \n \n Weidauer, C.; Davis, C.; Raeke, J.; Seiwert, B.; and Reemtsma, T.\n\n\n \n \n \n \n \n Sunlight photolysis of benzotriazoles – Identification of transformation products and pathways.\n \n \n \n \n\n\n \n\n\n\n Chemosphere, 154: 416–424. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Sunlight$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weidauer_sunlight_2016,\n\ttitle = {Sunlight photolysis of benzotriazoles – {Identification} of transformation products and pathways},\n\tvolume = {154},\n\tissn = {00456535},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0045653516304088},\n\tdoi = {10.1016/j.chemosphere.2016.03.090},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Chemosphere},\n\tauthor = {Weidauer, Cindy and Davis, Caroline and Raeke, Julia and Seiwert, Bettina and Reemtsma, Thorsten},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {416--424},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, S.; Seiwert, B.; Kästner, M.; Miltner, A.; Schäffer, A.; Reemtsma, T.; Yang, Q.; and Nowak, K. M.\n\n\n \n \n \n \n \n (Bio)degradation of glyphosate in water-sediment microcosms – A stable isotope co-labeling approach.\n \n \n \n \n\n\n \n\n\n\n Water Research, 99: 91–100. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"(Bio)degradation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_biodegradation_2016,\n\ttitle = {({Bio})degradation of glyphosate in water-sediment microcosms – {A} stable isotope co-labeling approach},\n\tvolume = {99},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135416302391},\n\tdoi = {10.1016/j.watres.2016.04.041},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Water Research},\n\tauthor = {Wang, Shizong and Seiwert, Bettina and Kästner, Matthias and Miltner, Anja and Schäffer, Andreas and Reemtsma, Thorsten and Yang, Qi and Nowak, Karolina M.},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {91--100},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wang, C.; Chen, Z.; Unteregelsbacher, S.; Lu, H.; Gschwendtner, S.; Gasche, R.; Kolar, A.; Schloter, M.; Kiese, R.; Butterbach-Bahl, K.; and Dannenmann, M.\n\n\n \n \n \n \n \n Climate change amplifies gross nitrogen turnover in montane grasslands of Central Europe in both summer and winter seasons.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 22(9): 2963–2978. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_climate_2016,\n\ttitle = {Climate change amplifies gross nitrogen turnover in montane grasslands of {Central} {Europe} in both summer and winter seasons},\n\tvolume = {22},\n\tissn = {13541013},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.13353},\n\tdoi = {10.1111/gcb.13353},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-01-23},\n\tjournal = {Global Change Biology},\n\tauthor = {Wang, Changhui and Chen, Zhe and Unteregelsbacher, Sebastian and Lu, Haiyan and Gschwendtner, Silvia and Gasche, Rainer and Kolar, Allison and Schloter, Michael and Kiese, Ralf and Butterbach-Bahl, Klaus and Dannenmann, Michael},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {2963--2978},\n}\n\n\n\n

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\n \n\n \n \n Vieweg, M.; Kurz, M. J.; Trauth, N.; Fleckenstein, J. H.; Musolff, A.; and Schmidt, C.\n\n\n \n \n \n \n \n Estimating time-variable aerobic respiration in the streambed by combining electrical conductivity and dissolved oxygen time series: Variable Respiration in the Streambed.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 121(8): 2199–2215. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vieweg_estimating_2016,\n\ttitle = {Estimating time-variable aerobic respiration in the streambed by combining electrical conductivity and dissolved oxygen time series: {Variable} {Respiration} in the {Streambed}},\n\tvolume = {121},\n\tissn = {21698953},\n\tshorttitle = {Estimating time-variable aerobic respiration in the streambed by combining electrical conductivity and dissolved oxygen time series},\n\turl = {http://doi.wiley.com/10.1002/2016JG003345},\n\tdoi = {10.1002/2016JG003345},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Vieweg, Michael and Kurz, Marie J. and Trauth, Nico and Fleckenstein, Jan H. and Musolff, Andreas and Schmidt, Christian},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {2199--2215},\n}\n\n\n\n

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\n \n\n \n \n Vereecken, H.; Schnepf, A.; Hopmans, J.; Javaux, M.; Or, D.; Roose, T.; Vanderborght, J.; Young, M.; Amelung, W.; Aitkenhead, M.; Allison, S.; Assouline, S.; Baveye, P.; Berli, M.; Brüggemann, N.; Finke, P.; Flury, M.; Gaiser, T.; Govers, G.; Ghezzehei, T.; Hallett, P.; Hendricks Franssen, H.; Heppell, J.; Horn, R.; Huisman, J.; Jacques, D.; Jonard, F.; Kollet, S.; Lafolie, F.; Lamorski, K.; Leitner, D.; McBratney, A.; Minasny, B.; Montzka, C.; Nowak, W.; Pachepsky, Y.; Padarian, J.; Romano, N.; Roth, K.; Rothfuss, Y.; Rowe, E.; Schwen, A.; Šimůnek, J.; Tiktak, A.; Van Dam, J.; van der Zee, S.; Vogel, H.; Vrugt, J.; Wöhling, T.; and Young, I.\n\n\n \n \n \n \n \n Modeling Soil Processes: Review, Key Challenges, and New Perspectives.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 15(5): vzj2015.09.0131. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vereecken_modeling_2016,\n\ttitle = {Modeling {Soil} {Processes}: {Review}, {Key} {Challenges}, and {New} {Perspectives}},\n\tvolume = {15},\n\tissn = {15391663},\n\tshorttitle = {Modeling {Soil} {Processes}},\n\turl = {http://doi.wiley.com/10.2136/vzj2015.09.0131},\n\tdoi = {10.2136/vzj2015.09.0131},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-01-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Vereecken, H. and Schnepf, A. and Hopmans, J.W. and Javaux, M. and Or, D. and Roose, T. and Vanderborght, J. and Young, M.H. and Amelung, W. and Aitkenhead, M. and Allison, S.D. and Assouline, S. and Baveye, P. and Berli, M. and Brüggemann, N. and Finke, P. and Flury, M. and Gaiser, T. and Govers, G. and Ghezzehei, T. and Hallett, P. and Hendricks Franssen, H.J. and Heppell, J. and Horn, R. and Huisman, J.A. and Jacques, D. and Jonard, F. and Kollet, S. and Lafolie, F. and Lamorski, K. and Leitner, D. and McBratney, A. and Minasny, B. and Montzka, C. and Nowak, W. and Pachepsky, Y. and Padarian, J. and Romano, N. and Roth, K. and Rothfuss, Y. and Rowe, E.C. and Schwen, A. and Šimůnek, J. and Tiktak, A. and Van Dam, J. and van der Zee, S.E.A.T.M. and Vogel, H.J. and Vrugt, J.A. and Wöhling, T. and Young, I.M.},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {vzj2015.09.0131},\n}\n\n\n\n

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\n \n\n \n \n Vereecken, H.; Pachepsky, Y.; Simmer, C.; Rihani, J.; Kunoth, A.; Korres, W.; Graf, A.; Franssen, H.; Thiele-Eich, I.; and Shao, Y.\n\n\n \n \n \n \n \n On the role of patterns in understanding the functioning of soil-vegetation-atmosphere systems.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 542: 63–86. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vereecken_role_2016,\n\ttitle = {On the role of patterns in understanding the functioning of soil-vegetation-atmosphere systems},\n\tvolume = {542},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416305431},\n\tdoi = {10.1016/j.jhydrol.2016.08.053},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Vereecken, H. and Pachepsky, Y. and Simmer, C. and Rihani, J. and Kunoth, A. and Korres, W. and Graf, A. and Franssen, H.J.-Hendricks and Thiele-Eich, Insa and Shao, Y.},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {63--86},\n}\n\n\n\n

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\n \n\n \n \n van der Tol, C.; Rossini, M.; Cogliati, S.; Verhoef, W.; Colombo, R.; Rascher, U.; and Mohammed, G.\n\n\n \n \n \n \n \n A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 186: 663–677. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_tol_model_2016,\n\ttitle = {A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops},\n\tvolume = {186},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425716303649},\n\tdoi = {10.1016/j.rse.2016.09.021},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {van der Tol, Christiaan and Rossini, Micol and Cogliati, Sergio and Verhoef, Wouter and Colombo, Roberto and Rascher, Uwe and Mohammed, Gina},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {663--677},\n}\n\n\n\n

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\n \n\n \n \n Ueberham, M.; Kabisch, S.; and Kuhlicke, C.\n\n\n \n \n \n \n Resilience, risk communication and responsibility in the context of flood prevention - the relation between public protection and private mitigation in flood-prone settlements.\n \n \n \n\n\n \n\n\n\n Hydrologie und Wasserbewirtschaftung, 60(2): 135–145. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ueberham_resilience_2016,\n\ttitle = {Resilience, risk communication and responsibility in the context of flood prevention - the relation between public protection and private mitigation in flood-prone settlements},\n\tvolume = {60},\n\tnumber = {2},\n\tjournal = {Hydrologie und Wasserbewirtschaftung},\n\tauthor = {Ueberham, M. and Kabisch, S. and Kuhlicke, C.},\n\tyear = {2016},\n\tpages = {135--145},\n}\n\n\n\n

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\n \n\n \n \n Stockinger, M. P.; Bogena, H. R.; Lücke, A.; Diekkrüger, B.; Cornelissen, T.; and Vereecken, H.\n\n\n \n \n \n \n \n Tracer sampling frequency influences estimates of young water fraction and streamwater transit time distribution.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 541: 952–964. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Tracer$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stockinger_tracer_2016,\n\ttitle = {Tracer sampling frequency influences estimates of young water fraction and streamwater transit time distribution},\n\tvolume = {541},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416304863},\n\tdoi = {10.1016/j.jhydrol.2016.08.007},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Stockinger, Michael P. and Bogena, Heye R. and Lücke, Andreas and Diekkrüger, Bernd and Cornelissen, Thomas and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {952--964},\n}\n\n\n\n

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\n \n\n \n \n Siegmund, J. F.; Sanders, T. G. M.; Heinrich, I.; van der Maaten, E.; Simard, S.; Helle, G.; and Donner, R. V.\n\n\n \n \n \n \n \n Meteorological Drivers of Extremes in Daily Stem Radius Variations of Beech, Oak, and Pine in Northeastern Germany: An Event Coincidence Analysis.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Plant Science, 7. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Meteorological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siegmund_meteorological_2016,\n\ttitle = {Meteorological {Drivers} of {Extremes} in {Daily} {Stem} {Radius} {Variations} of {Beech}, {Oak}, and {Pine} in {Northeastern} {Germany}: {An} {Event} {Coincidence} {Analysis}},\n\tvolume = {7},\n\tissn = {1664-462X},\n\tshorttitle = {Meteorological {Drivers} of {Extremes} in {Daily} {Stem} {Radius} {Variations} of {Beech}, {Oak}, and {Pine} in {Northeastern} {Germany}},\n\turl = {http://journal.frontiersin.org/Article/10.3389/fpls.2016.00733/abstract},\n\tdoi = {10.3389/fpls.2016.00733},\n\turldate = {2023-01-23},\n\tjournal = {Frontiers in Plant Science},\n\tauthor = {Siegmund, Jonatan F. and Sanders, Tanja G. M. and Heinrich, Ingo and van der Maaten, Ernst and Simard, Sonia and Helle, Gerhard and Donner, Reik V.},\n\tmonth = jun,\n\tyear = {2016},\n}\n\n\n\n

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\n \n\n \n \n Schneidewind, U.; van Berkel, M.; Anibas, C.; Vandersteen, G.; Schmidt, C.; Joris, I.; Seuntjens, P.; Batelaan, O.; and Zwart, H. J.\n\n\n \n \n \n \n \n LPMLE3: A novel 1-D approach to study water flow in streambeds using heat as a tracer: LPMLE3 METHOD.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 52(8): 6596–6610. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"LPMLE3:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schneidewind_lpmle3_2016,\n\ttitle = {{LPMLE3}: {A} novel 1-{D} approach to study water flow in streambeds using heat as a tracer: {LPMLE3} {METHOD}},\n\tvolume = {52},\n\tissn = {00431397},\n\tshorttitle = {{LPMLE3}},\n\turl = {http://doi.wiley.com/10.1002/2015WR017453},\n\tdoi = {10.1002/2015WR017453},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-01-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Schneidewind, U. and van Berkel, M. and Anibas, C. and Vandersteen, G. and Schmidt, C. and Joris, I. and Seuntjens, P. and Batelaan, O. and Zwart, H. J.},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {6596--6610},\n}\n\n\n\n

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\n \n\n \n \n Schmadel, N. M.; Ward, A. S.; Kurz, M. J.; Fleckenstein, J. H.; Zarnetske, J. P.; Hannah, D. M.; Blume, T.; Vieweg, M.; Blaen, P. J.; Schmidt, C.; Knapp, J. L.; Klaar, M. J.; Romeijn, P.; Datry, T.; Keller, T.; Folegot, S.; Arricibita, A. I. M.; and Krause, S.\n\n\n \n \n \n \n \n Stream solute tracer timescales changing with discharge and reach length confound process interpretation: SOLUTE TRACER TIMESCALES CONFOUND PROCESS INTERPRETATION.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 52(4): 3227–3245. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Stream$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmadel_stream_2016,\n\ttitle = {Stream solute tracer timescales changing with discharge and reach length confound process interpretation: {SOLUTE} {TRACER} {TIMESCALES} {CONFOUND} {PROCESS} {INTERPRETATION}},\n\tvolume = {52},\n\tissn = {00431397},\n\tshorttitle = {Stream solute tracer timescales changing with discharge and reach length confound process interpretation},\n\turl = {http://doi.wiley.com/10.1002/2015WR018062},\n\tdoi = {10.1002/2015WR018062},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Schmadel, Noah M. and Ward, Adam S. and Kurz, Marie J. and Fleckenstein, Jan H. and Zarnetske, Jay P. and Hannah, David M. and Blume, Theresa and Vieweg, Michael and Blaen, Phillip J. and Schmidt, Christian and Knapp, Julia L.A. and Klaar, Megan J. and Romeijn, Paul and Datry, Thibault and Keller, Toralf and Folegot, Silvia and Arricibita, Amaia I. Marruedo and Krause, Stefan},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {3227--3245},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Schickling, A.; Matveeva, M.; Damm, A.; Schween, J.; Wahner, A.; Graf, A.; Crewell, S.; and Rascher, U.\n\n\n \n \n \n \n \n Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 8(7): 574. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Combining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schickling_combining_2016,\n\ttitle = {Combining {Sun}-{Induced} {Chlorophyll} {Fluorescence} and {Photochemical} {Reflectance} {Index} {Improves} {Diurnal} {Modeling} of {Gross} {Primary} {Productivity}},\n\tvolume = {8},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/8/7/574},\n\tdoi = {10.3390/rs8070574},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Schickling, Anke and Matveeva, Maria and Damm, Alexander and Schween, Jan and Wahner, Andreas and Graf, Alexander and Crewell, Susanne and Rascher, Uwe},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {574},\n}\n\n\n\n

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\n \n\n \n \n Scheer, C.; Meier, R.; Brüggemann, N.; Grace, P. R.; and Dannenmann, M.\n\n\n \n \n \n \n \n An improved $^{\\textrm{15}}$ N tracer approach to study denitrification and nitrogen turnover in soil incubations: Improved $^{\\textrm{15}}$ N tracer approach to study soil nitrogen turnover.\n \n \n \n \n\n\n \n\n\n\n Rapid Communications in Mass Spectrometry, 30(18): 2017–2026. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{scheer_improved_2016,\n\ttitle = {An improved $^{\\textrm{15}}$ {N} tracer approach to study denitrification and nitrogen turnover in soil incubations: {Improved} $^{\\textrm{15}}$ {N} tracer approach to study soil nitrogen turnover},\n\tvolume = {30},\n\tissn = {09514198},\n\tshorttitle = {An improved $^{\\textrm{15}}$ {N} tracer approach to study denitrification and nitrogen turnover in soil incubations},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/rcm.7689},\n\tdoi = {10.1002/rcm.7689},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2023-01-23},\n\tjournal = {Rapid Communications in Mass Spectrometry},\n\tauthor = {Scheer, Clemens and Meier, Rudolf and Brüggemann, Nicolas and Grace, Peter R. and Dannenmann, Michael},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {2017--2026},\n}\n\n\n\n

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\n \n\n \n \n Sanders, T.; Heinrich, I.; Günther, B.; and Beck, W.\n\n\n \n \n \n \n \n Increasing Water Use Efficiency Comes at a Cost for Norway Spruce.\n \n \n \n \n\n\n \n\n\n\n Forests, 7(12): 296. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Increasing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sanders_increasing_2016,\n\ttitle = {Increasing {Water} {Use} {Efficiency} {Comes} at a {Cost} for {Norway} {Spruce}},\n\tvolume = {7},\n\tissn = {1999-4907},\n\turl = {http://www.mdpi.com/1999-4907/7/12/296},\n\tdoi = {10.3390/f7120296},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-01-23},\n\tjournal = {Forests},\n\tauthor = {Sanders, Tanja and Heinrich, Ingo and Günther, Björn and Beck, Wolfgang},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {296},\n}\n\n\n\n

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\n \n\n \n \n Rumm, A.; Foeckler, F.; Deichner, O.; Scholz, M.; and Gerisch, M.\n\n\n \n \n \n \n \n Dyke-slotting initiated rapid recovery of habitat specialists in floodplain mollusc assemblages of the Elbe River, Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrobiologia, 771(1): 151–163. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Dyke-slotting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rumm_dyke-slotting_2016,\n\ttitle = {Dyke-slotting initiated rapid recovery of habitat specialists in floodplain mollusc assemblages of the {Elbe} {River}, {Germany}},\n\tvolume = {771},\n\tissn = {0018-8158, 1573-5117},\n\turl = {http://link.springer.com/10.1007/s10750-015-2627-0},\n\tdoi = {10.1007/s10750-015-2627-0},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {Hydrobiologia},\n\tauthor = {Rumm, Andrea and Foeckler, Francis and Deichner, Oskar and Scholz, Mathias and Gerisch, Michael},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {151--163},\n}\n\n\n\n

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\n \n\n \n \n Rode, M.; Halbedel née Angelstein, S.; Anis, M. R.; Borchardt, D.; and Weitere, M.\n\n\n \n \n \n \n \n Continuous In-Stream Assimilatory Nitrate Uptake from High-Frequency Sensor Measurements.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 50(11): 5685–5694. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Continuous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rode_continuous_2016,\n\ttitle = {Continuous {In}-{Stream} {Assimilatory} {Nitrate} {Uptake} from {High}-{Frequency} {Sensor} {Measurements}},\n\tvolume = {50},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.6b00943},\n\tdoi = {10.1021/acs.est.6b00943},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Rode, Michael and Halbedel née Angelstein, Susanne and Anis, Muhammad Rehan and Borchardt, Dietrich and Weitere, Markus},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {5685--5694},\n}\n\n\n\n

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\n \n\n \n \n Rink, D.; and Arndt, T.\n\n\n \n \n \n \n \n Investigating perception of green structure configuration for afforestation in urban brownfield development by visual methods—A case study in Leipzig, Germany.\n \n \n \n \n\n\n \n\n\n\n Urban Forestry & Urban Greening, 15: 65–74. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_investigating_2016,\n\ttitle = {Investigating perception of green structure configuration for afforestation in urban brownfield development by visual methods—{A} case study in {Leipzig}, {Germany}},\n\tvolume = {15},\n\tissn = {16188667},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1618866715001752},\n\tdoi = {10.1016/j.ufug.2015.11.010},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Urban Forestry \\& Urban Greening},\n\tauthor = {Rink, Dieter and Arndt, Thomas},\n\tyear = {2016},\n\tpages = {65--74},\n}\n\n\n\n

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\n \n\n \n \n Richter, R.; Reu, B.; Wirth, C.; Doktor, D.; and Vohland, M.\n\n\n \n \n \n \n \n The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 52: 464–474. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{richter_use_2016,\n\ttitle = {The use of airborne hyperspectral data for tree species classification in a species-rich {Central} {European} forest area},\n\tvolume = {52},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243416301258},\n\tdoi = {10.1016/j.jag.2016.07.018},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Richter, Ronny and Reu, Björn and Wirth, Christian and Doktor, Daniel and Vohland, Michael},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {464--474},\n}\n\n\n\n

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\n \n\n \n \n Reichenau, T. G.; Korres, W.; Montzka, C.; Fiener, P.; Wilken, F.; Stadler, A.; Waldhoff, G.; and Schneider, K.\n\n\n \n \n \n \n \n Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 11(7): e0158451. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{reichenau_spatial_2016,\n\ttitle = {Spatial {Heterogeneity} of {Leaf} {Area} {Index} ({LAI}) and {Its} {Temporal} {Course} on {Arable} {Land}: {Combining} {Field} {Measurements}, {Remote} {Sensing} and {Simulation} in a {Comprehensive} {Data} {Analysis} {Approach} ({CDAA})},\n\tvolume = {11},\n\tissn = {1932-6203},\n\tshorttitle = {Spatial {Heterogeneity} of {Leaf} {Area} {Index} ({LAI}) and {Its} {Temporal} {Course} on {Arable} {Land}},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0158451},\n\tdoi = {10.1371/journal.pone.0158451},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {PLOS ONE},\n\tauthor = {Reichenau, Tim G. and Korres, Wolfgang and Montzka, Carsten and Fiener, Peter and Wilken, Florian and Stadler, Anja and Waldhoff, Guido and Schneider, Karl},\n\teditor = {Hui, Dafeng},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {e0158451},\n}\n\n\n\n

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\n \n\n \n \n Rakovec, O.; Kumar, R.; Mai, J.; Cuntz, M.; Thober, S.; Zink, M.; Attinger, S.; Schäfer, D.; Schrön, M.; and Samaniego, L.\n\n\n \n \n \n \n \n Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 17(1): 287–307. January 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiscale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rakovec_multiscale_2016,\n\ttitle = {Multiscale and {Multivariate} {Evaluation} of {Water} {Fluxes} and {States} over {European} {River} {Basins}},\n\tvolume = {17},\n\tissn = {1525-755X, 1525-7541},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-15-0054.1},\n\tdoi = {10.1175/JHM-D-15-0054.1},\n\tabstract = {Abstract \n            Accurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical characteristics. The model yields generally better results (with lower spread of model statistics) in basins with higher rain gauge density. Model performance for other fluxes and states is strongly driven by the degree of seasonality that each variable exhibits, with the best match being observed for evapotranspiration, followed by total water storage anomaly, and the least for soil moisture. Results show that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables. The study emphasizes the need to account for other complementary datasets besides streamflow during parameter estimation to improve model skill with respect to “hidden” variables.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Rakovec, Oldrich and Kumar, Rohini and Mai, Juliane and Cuntz, Matthias and Thober, Stephan and Zink, Matthias and Attinger, Sabine and Schäfer, David and Schrön, Martin and Samaniego, Luis},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {287--307},\n}\n\n\n\n

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\n \n\n \n \n Rahman, M.; Sulis, M.; and Kollet, S. J.\n\n\n \n \n \n \n \n Evaluating the dual-boundary forcing concept in subsurface-land surface interactions of the hydrological cycle: Evaluating the Dual-Boundary Forcing Concept.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 30(10): 1563–1573. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahman_evaluating_2016,\n\ttitle = {Evaluating the dual-boundary forcing concept in subsurface-land surface interactions of the hydrological cycle: {Evaluating} the {Dual}-{Boundary} {Forcing} {Concept}},\n\tvolume = {30},\n\tissn = {08856087},\n\tshorttitle = {Evaluating the dual-boundary forcing concept in subsurface-land surface interactions of the hydrological cycle},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.10702},\n\tdoi = {10.1002/hyp.10702},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-01-23},\n\tjournal = {Hydrological Processes},\n\tauthor = {Rahman, M. and Sulis, M. and Kollet, S. J.},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {1563--1573},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Raeke, J.; Lechtenfeld, O. J.; Wagner, M.; Herzsprung, P.; and Reemtsma, T.\n\n\n \n \n \n \n \n Selectivity of solid phase extraction of freshwater dissolved organic matter and its effect on ultrahigh resolution mass spectra.\n \n \n \n \n\n\n \n\n\n\n Environmental Science: Processes & Impacts, 18(7): 918–927. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Selectivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{raeke_selectivity_2016,\n\ttitle = {Selectivity of solid phase extraction of freshwater dissolved organic matter and its effect on ultrahigh resolution mass spectra},\n\tvolume = {18},\n\tissn = {2050-7887, 2050-7895},\n\turl = {http://xlink.rsc.org/?DOI=C6EM00200E},\n\tdoi = {10.1039/C6EM00200E},\n\tabstract = {Solid phase extracts of freshwater dissolved organic matter are compared to the original sample with use of complementary techniques. \n          ,  \n            Solid phase extraction (SPE) is often used for enrichment and clean-up prior to analysis of dissolved organic matter (DOM) by electrospray ionization (ESI) coupled to ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). It is generally accepted that extraction by SPE is not quantitative with respect to carbon concentration. However, little information is available on the selectivity of different SPE sorbents and the resulting effect for the acquired DOM mass spectra. Freshwater samples were extracted by the widely used PPL, HLB and C18 sorbents and the molecular composition and size distribution of the DOM in the extracts and in the permeates was compared to the original sample. Dissolved organic carbon (DOC) recoveries ranged between 20\\% and 65\\% for the three tested SPE sorbents. Size-exclusion chromatography coupled to organic carbon detection (SEC-OCD) revealed that limited recovery by PPL and HLB was primarily due to incomplete elution of a fraction of apparent high molecular weight from the solid phase. In contrast, incomplete retention on the solid phase, mainly observed for the C18 cartridge, was attributed to a fraction of low molecular weight. The FT-ICR mass spectra of the original sample and the SPE extracts did not differ significantly in their molecular weight distribution, but they showed sorbent specific differences in the degree of oxygenation and saturation. We concluded that the selective enrichment of freshwater DOM by SPE is less critical for subsequent FT-ICR MS analysis, because those fractions that are not sufficiently recovered have comparatively small effects on the mass spectra. This was confirmed by the extraction of model compounds, showing that very polar and small molecules are poorly extracted, but also have a low response in ESI-MS. Of the three tested SPE cartridges the PPL material offered the best properties for DOM enrichment for subsequent FT-ICR MS analysis as it minimizes too strong and too weak DOM–sorbent interactions.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Science: Processes \\& Impacts},\n\tauthor = {Raeke, Julia and Lechtenfeld, Oliver J. and Wagner, Martin and Herzsprung, Peter and Reemtsma, Thorsten},\n\tyear = {2016},\n\tpages = {918--927},\n}\n\n\n\n

\n\n\n

\n Solid phase extracts of freshwater dissolved organic matter are compared to the original sample with use of complementary techniques. , Solid phase extraction (SPE) is often used for enrichment and clean-up prior to analysis of dissolved organic matter (DOM) by electrospray ionization (ESI) coupled to ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). It is generally accepted that extraction by SPE is not quantitative with respect to carbon concentration. However, little information is available on the selectivity of different SPE sorbents and the resulting effect for the acquired DOM mass spectra. Freshwater samples were extracted by the widely used PPL, HLB and C18 sorbents and the molecular composition and size distribution of the DOM in the extracts and in the permeates was compared to the original sample. Dissolved organic carbon (DOC) recoveries ranged between 20% and 65% for the three tested SPE sorbents. Size-exclusion chromatography coupled to organic carbon detection (SEC-OCD) revealed that limited recovery by PPL and HLB was primarily due to incomplete elution of a fraction of apparent high molecular weight from the solid phase. In contrast, incomplete retention on the solid phase, mainly observed for the C18 cartridge, was attributed to a fraction of low molecular weight. The FT-ICR mass spectra of the original sample and the SPE extracts did not differ significantly in their molecular weight distribution, but they showed sorbent specific differences in the degree of oxygenation and saturation. We concluded that the selective enrichment of freshwater DOM by SPE is less critical for subsequent FT-ICR MS analysis, because those fractions that are not sufficiently recovered have comparatively small effects on the mass spectra. This was confirmed by the extraction of model compounds, showing that very polar and small molecules are poorly extracted, but also have a low response in ESI-MS. Of the three tested SPE cartridges the PPL material offered the best properties for DOM enrichment for subsequent FT-ICR MS analysis as it minimizes too strong and too weak DOM–sorbent interactions.\n

\n\n\n

\n \n\n \n \n Rabbel, I.; Diekkrüger, B.; Voigt, H.; and Neuwirth, B.\n\n\n \n \n \n \n \n Comparing ∆Tmax Determination Approaches for Granier-Based Sapflow Estimations.\n \n \n \n \n\n\n \n\n\n\n Sensors, 16(12): 2042. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rabbel_comparing_2016,\n\ttitle = {Comparing ∆{Tmax} {Determination} {Approaches} for {Granier}-{Based} {Sapflow} {Estimations}},\n\tvolume = {16},\n\tissn = {1424-8220},\n\turl = {http://www.mdpi.com/1424-8220/16/12/2042},\n\tdoi = {10.3390/s16122042},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-01-23},\n\tjournal = {Sensors},\n\tauthor = {Rabbel, Inken and Diekkrüger, Bernd and Voigt, Holm and Neuwirth, Burkhard},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {2042},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Qu, W.; Bogena, H. R.; Huisman, J. A.; Schmidt, M.; Kunkel, R.; Weuthen, A.; Schiedung, H.; Schilling, B.; Sorg, J.; and Vereecken, H.\n\n\n \n \n \n \n \n The integrated water balance and soil data set of the Rollesbroich hydrological observatory.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 8(2): 517–529. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{qu_integrated_2016,\n\ttitle = {The integrated water balance and soil data set of the {Rollesbroich} hydrological observatory},\n\tvolume = {8},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/8/517/2016/},\n\tdoi = {10.5194/essd-8-517-2016},\n\tabstract = {Abstract. The Rollesbroich headwater catchment located in western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive data set that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e., precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatiotemporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models, to improve the understanding of spatial temporal dynamics of soil water content, to optimize data assimilation and inverse techniques for hydrological models, and to develop upscaling and downscaling procedures of soil water content information. The complete data set is freely available online (http://www.tereno.net, doi:10.5880/TERENO.2016.001, doi:10.5880/TERENO.2016.004, doi:10.5880/TERENO.2016.003) and additionally referenced by three persistent identifiers securing the long-term data and metadata availability.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Earth System Science Data},\n\tauthor = {Qu, Wei and Bogena, Heye R. and Huisman, Johan A. and Schmidt, Marius and Kunkel, Ralf and Weuthen, Ansgar and Schiedung, Henning and Schilling, Bernd and Sorg, Jürgen and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {517--529},\n}\n\n\n\n

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\n \n\n \n \n Pütz, T.; Kiese, R.; Wollschläger, U.; Groh, J.; Rupp, H.; Zacharias, S.; Priesack, E.; Gerke, H. H.; Gasche, R.; Bens, O.; Borg, E.; Baessler, C.; Kaiser, K.; Herbrich, M.; Munch, J.; Sommer, M.; Vogel, H.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n TERENO-SOILCan: a lysimeter-network in Germany observing soil processes and plant diversity influenced by climate change.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 75(18): 1242. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"TERENO-SOILCan:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{putz_tereno-soilcan_2016,\n\ttitle = {{TERENO}-{SOILCan}: a lysimeter-network in {Germany} observing soil processes and plant diversity influenced by climate change},\n\tvolume = {75},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {{TERENO}-{SOILCan}},\n\turl = {http://link.springer.com/10.1007/s12665-016-6031-5},\n\tdoi = {10.1007/s12665-016-6031-5},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Pütz, Th. and Kiese, R. and Wollschläger, U. and Groh, J. and Rupp, H. and Zacharias, S. and Priesack, E. and Gerke, H. H. and Gasche, R. and Bens, O. and Borg, E. and Baessler, C. and Kaiser, K. and Herbrich, M. and Munch, J.-C. and Sommer, M. and Vogel, H.-J. and Vanderborght, J. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {1242},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Pritzkow, C.; Wazny, T.; Heußner, K.; Słowiński, M.; Bieber, A.; Liñán, I. D.; Helle, G.; and Heinrich, I.\n\n\n \n \n \n \n \n Minimum winter temperature reconstruction from average earlywood vessel area of European oak (Quercus robur) in N-Poland.\n \n \n \n \n\n\n \n\n\n\n Palaeogeography, Palaeoclimatology, Palaeoecology, 449: 520–530. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Minimum$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pritzkow_minimum_2016,\n\ttitle = {Minimum winter temperature reconstruction from average earlywood vessel area of {European} oak ({Quercus} robur) in {N}-{Poland}},\n\tvolume = {449},\n\tissn = {00310182},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0031018216001486},\n\tdoi = {10.1016/j.palaeo.2016.02.046},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Palaeogeography, Palaeoclimatology, Palaeoecology},\n\tauthor = {Pritzkow, C. and Wazny, T. and Heußner, K.U. and Słowiński, M. and Bieber, A. and Liñán, I. Dorado and Helle, G. and Heinrich, I.},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {520--530},\n}\n\n\n\n

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\n \n\n \n \n Pause, M.; Schweitzer, C.; Rosenthal, M.; Keuck, V.; Bumberger, J.; Dietrich, P.; Heurich, M.; Jung, A.; and Lausch, A.\n\n\n \n \n \n \n \n In Situ/Remote Sensing Integration to Assess Forest Health—A Review.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 8(6): 471. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"In$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pause_situremote_2016,\n\ttitle = {In {Situ}/{Remote} {Sensing} {Integration} to {Assess} {Forest} {Health}—{A} {Review}},\n\tvolume = {8},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/8/6/471},\n\tdoi = {10.3390/rs8060471},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Pause, Marion and Schweitzer, Christian and Rosenthal, Michael and Keuck, Vanessa and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Jung, András and Lausch, Angela},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {471},\n}\n\n\n\n

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\n \n\n \n \n Musolff, A.; Schmidt, C.; Rode, M.; Lischeid, G.; Weise, S. M.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Groundwater head controls nitrate export from an agricultural lowland catchment.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 96: 95–107. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Groundwater$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{musolff_groundwater_2016,\n\ttitle = {Groundwater head controls nitrate export from an agricultural lowland catchment},\n\tvolume = {96},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170816302172},\n\tdoi = {10.1016/j.advwatres.2016.07.003},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Musolff, Andreas and Schmidt, Christian and Rode, Michael and Lischeid, Gunnar and Weise, Stephan M. and Fleckenstein, Jan H.},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {95--107},\n}\n\n\n\n

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\n \n\n \n \n Munz, M.; Oswald, S. E.; and Schmidt, C.\n\n\n \n \n \n \n \n Analysis of riverbed temperatures to determine the geometry of subsurface water flow around in-stream geomorphological structures.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 539: 74–87. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{munz_analysis_2016,\n\ttitle = {Analysis of riverbed temperatures to determine the geometry of subsurface water flow around in-stream geomorphological structures},\n\tvolume = {539},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416302797},\n\tdoi = {10.1016/j.jhydrol.2016.05.012},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Munz, Matthias and Oswald, Sascha E. and Schmidt, Christian},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {74--87},\n}\n\n\n\n

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\n \n\n \n \n Mueller, C.; Zink, M.; Samaniego, L.; Krieg, R.; Merz, R.; Rode, M.; and Knöller, K.\n\n\n \n \n \n \n \n Discharge Driven Nitrogen Dynamics in a Mesoscale River Basin As Constrained by Stable Isotope Patterns.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 50(17): 9187–9196. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Discharge$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mueller_discharge_2016,\n\ttitle = {Discharge {Driven} {Nitrogen} {Dynamics} in a {Mesoscale} {River} {Basin} {As} {Constrained} by {Stable} {Isotope} {Patterns}},\n\tvolume = {50},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.6b01057},\n\tdoi = {10.1021/acs.est.6b01057},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Mueller, Christin and Zink, Matthias and Samaniego, Luis and Krieg, Ronald and Merz, Ralf and Rode, Michael and Knöller, Kay},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {9187--9196},\n}\n\n\n\n

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\n \n\n \n \n Mozaffari, A.; Klotzsche, A.; He, G.; Vereecken, H.; van der Kruk, J.; Warren, C.; and Giannopoulos, A.\n\n\n \n \n \n \n \n Towards 3D full-waveform inversion of crosshole GPR data.\n \n \n \n \n\n\n \n\n\n\n In 2016 16th International Conference on Ground Penetrating Radar (GPR), pages 1–4, Hong Kong, Hong Kong, June 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{mozaffari_towards_2016,\n\taddress = {Hong Kong, Hong Kong},\n\ttitle = {Towards {3D} full-waveform inversion of crosshole {GPR} data},\n\tisbn = {9781509051816},\n\turl = {http://ieeexplore.ieee.org/document/7572687/},\n\tdoi = {10.1109/ICGPR.2016.7572687},\n\turldate = {2023-01-23},\n\tbooktitle = {2016 16th {International} {Conference} on {Ground} {Penetrating} {Radar} ({GPR})},\n\tpublisher = {IEEE},\n\tauthor = {Mozaffari, A. and Klotzsche, A. and He, G. and Vereecken, H. and van der Kruk, J. and Warren, C. and Giannopoulos, A.},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {1--4},\n}\n\n\n\n

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\n \n\n \n \n Morling, K.; Kamjunke, N.; and Tittel, J.\n\n\n \n \n \n \n \n A simplified method of recovering CO2 from bacterioplankton respiration for isotopic analysis.\n \n \n \n \n\n\n \n\n\n\n Journal of Microbiological Methods, 121: 8–10. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{morling_simplified_2016,\n\ttitle = {A simplified method of recovering {CO2} from bacterioplankton respiration for isotopic analysis},\n\tvolume = {121},\n\tissn = {01677012},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167701215301305},\n\tdoi = {10.1016/j.mimet.2015.12.008},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Microbiological Methods},\n\tauthor = {Morling, Karoline and Kamjunke, Norbert and Tittel, Jörg},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {8--10},\n}\n\n\n\n

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\n \n\n \n \n Moreira, S.; Schultze, M.; Rahn, K.; and Boehrer, B.\n\n\n \n \n \n \n \n A practical approach to lake water density from electrical conductivity and temperature.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 20(7): 2975–2986. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{moreira_practical_2016,\n\ttitle = {A practical approach to lake water density from electrical conductivity and temperature},\n\tvolume = {20},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/20/2975/2016/},\n\tdoi = {10.5194/hess-20-2975-2016},\n\tabstract = {Abstract. Density calculations are essential to study stratification, circulation patterns, internal wave formation and other aspects of hydrodynamics in lakes and reservoirs. Currently, the most common procedure is the use of CTD (conductivity, temperature and depth) profilers and the conversion of measurements of temperature and electrical conductivity into density. In limnic waters, such approaches are of limited accuracy if they do not consider lake-specific composition of solutes, as we show. A new approach is presented to correlate density and electrical conductivity, using only two specific coefficients based on the composition of solutes. First, it is necessary to evaluate the lake-specific coefficients connecting electrical conductivity with density. Once these coefficients have been obtained, density can easily be calculated based on CTD data. The new method has been tested against measured values and the most common equations used in the calculation of density in limnic and ocean conditions. The results show that our new approach can reproduce the density contribution of solutes with a relative error of less than 10 \\% in lake waters from very low to very high concentrations as well as in lakes of very particular water chemistry, which is better than all commonly implemented density calculations in lakes. Finally, a web link is provided for downloading the corresponding density calculator.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Moreira, Santiago and Schultze, Martin and Rahn, Karsten and Boehrer, Bertram},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {2975--2986},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Montzka, C.; Jagdhuber, T.; Horn, R.; Bogena, H. R.; Hajnsek, I.; Reigber, A.; and Vereecken, H.\n\n\n \n \n \n \n \n Investigation of SMAP Fusion Algorithms With Airborne Active and Passive L-Band Microwave Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 54(7): 3878–3889. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_investigation_2016,\n\ttitle = {Investigation of {SMAP} {Fusion} {Algorithms} {With} {Airborne} {Active} and {Passive} {L}-{Band} {Microwave} {Remote} {Sensing}},\n\tvolume = {54},\n\tissn = {0196-2892, 1558-0644},\n\turl = {http://ieeexplore.ieee.org/document/7426813/},\n\tdoi = {10.1109/TGRS.2016.2529659},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Montzka, Carsten and Jagdhuber, Thomas and Horn, Ralf and Bogena, Heye R. and Hajnsek, Irena and Reigber, Andreas and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {3878--3889},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Missong, A.; Bol, R.; Willbold, S.; Siemens, J.; and Klumpp, E.\n\n\n \n \n \n \n \n Phosphorus forms in forest soil colloids as revealed by liquid‐state $^{\\textrm{31}}$ P‐NMR.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 179(2): 159–167. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Phosphorus$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{missong_phosphorus_2016,\n\ttitle = {Phosphorus forms in forest soil colloids as revealed by liquid‐state $^{\\textrm{31}}$ {P}‐{NMR}},\n\tvolume = {179},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.201500119},\n\tdoi = {10.1002/jpln.201500119},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Missong, Anna and Bol, Roland and Willbold, Sabine and Siemens, Jan and Klumpp, Erwin},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {159--167},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Medinets, S; Gasche, R; Skiba, U; Schindlbacher, A; Kiese, R; and Butterbach-Bahl, K\n\n\n \n \n \n \n \n Cold season soil NO fluxes from a temperate forest: drivers and contribution to annual budgets.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 11(11): 114012. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Cold$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{medinets_cold_2016,\n\ttitle = {Cold season soil {NO} fluxes from a temperate forest: drivers and contribution to annual budgets},\n\tvolume = {11},\n\tissn = {1748-9326},\n\tshorttitle = {Cold season soil {NO} fluxes from a temperate forest},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/11/11/114012},\n\tdoi = {10.1088/1748-9326/11/11/114012},\n\tnumber = {11},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Medinets, S and Gasche, R and Skiba, U and Schindlbacher, A and Kiese, R and Butterbach-Bahl, K},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {114012},\n}\n\n\n\n

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\n \n\n \n \n Merz, S.; Pohlmeier, A.; Balcom, B. J.; Enjilela, R.; and Vereecken, H.\n\n\n \n \n \n \n \n Drying of a Natural Soil Under Evaporative Conditions: A Comparison of Different Magnetic Resonance Methods.\n \n \n \n \n\n\n \n\n\n\n Applied Magnetic Resonance, 47(2): 121–138. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Drying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{merz_drying_2016,\n\ttitle = {Drying of a {Natural} {Soil} {Under} {Evaporative} {Conditions}: {A} {Comparison} of {Different} {Magnetic} {Resonance} {Methods}},\n\tvolume = {47},\n\tissn = {0937-9347, 1613-7507},\n\tshorttitle = {Drying of a {Natural} {Soil} {Under} {Evaporative} {Conditions}},\n\turl = {http://link.springer.com/10.1007/s00723-015-0736-6},\n\tdoi = {10.1007/s00723-015-0736-6},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Applied Magnetic Resonance},\n\tauthor = {Merz, Steffen and Pohlmeier, Andreas and Balcom, Bruce J. and Enjilela, Razieh and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {121--138},\n}\n\n\n\n

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\n \n\n \n \n Marcé, R.; George, G.; Buscarinu, P.; Deidda, M.; Dunalska, J.; de Eyto, E.; Flaim, G.; Grossart, H.; Istvanovics, V.; Lenhardt, M.; Moreno-Ostos, E.; Obrador, B.; Ostrovsky, I.; Pierson, D. C.; Potužák, J.; Poikane, S.; Rinke, K.; Rodríguez-Mozaz, S.; Staehr, P. A.; Šumberová, K.; Waajen, G.; Weyhenmeyer, G. A.; Weathers, K. C.; Zion, M.; Ibelings, B. W.; and Jennings, E.\n\n\n \n \n \n \n \n Automatic High Frequency Monitoring for Improved Lake and Reservoir Management.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 50(20): 10780–10794. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Automatic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{marce_automatic_2016,\n\ttitle = {Automatic {High} {Frequency} {Monitoring} for {Improved} {Lake} and {Reservoir} {Management}},\n\tvolume = {50},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.6b01604},\n\tdoi = {10.1021/acs.est.6b01604},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Marcé, Rafael and George, Glen and Buscarinu, Paola and Deidda, Melania and Dunalska, Julita and de Eyto, Elvira and Flaim, Giovanna and Grossart, Hans-Peter and Istvanovics, Vera and Lenhardt, Mirjana and Moreno-Ostos, Enrique and Obrador, Biel and Ostrovsky, Ilia and Pierson, Donald C. and Potužák, Jan and Poikane, Sandra and Rinke, Karsten and Rodríguez-Mozaz, Sara and Staehr, Peter A. and Šumberová, Kateřina and Waajen, Guido and Weyhenmeyer, Gesa A. and Weathers, Kathleen C. and Zion, Mark and Ibelings, Bas W. and Jennings, Eleanor},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {10780--10794},\n}\n\n\n\n

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\n \n\n \n \n Luft, L.; Neumann, C.; Itzerott, S.; Lausch, A.; Doktor, D.; Freude, M.; Blaum, N.; and Jeltsch, F.\n\n\n \n \n \n \n \n Digital and real-habitat modeling of Hipparchia statilinus based on hyper spectral remote sensing data.\n \n \n \n \n\n\n \n\n\n\n International Journal of Environmental Science and Technology, 13(1): 187–200. January 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Digital$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{luft_digital_2016,\n\ttitle = {Digital and real-habitat modeling of {Hipparchia} statilinus based on hyper spectral remote sensing data},\n\tvolume = {13},\n\tissn = {1735-1472, 1735-2630},\n\turl = {http://link.springer.com/10.1007/s13762-015-0859-1},\n\tdoi = {10.1007/s13762-015-0859-1},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {International Journal of Environmental Science and Technology},\n\tauthor = {Luft, L. and Neumann, C. and Itzerott, S. and Lausch, A. and Doktor, D. and Freude, M. and Blaum, N. and Jeltsch, F.},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {187--200},\n}\n\n\n\n

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\n \n\n \n \n Liu, S.; Hintz, M.; and Li, X.\n\n\n \n \n \n \n \n Evaluation of atmosphere–land interactions in an LES from the perspective of heterogeneity propagation.\n \n \n \n \n\n\n \n\n\n\n Advances in Atmospheric Sciences, 33(5): 571–578. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_evaluation_2016,\n\ttitle = {Evaluation of atmosphere–land interactions in an {LES} from the perspective of heterogeneity propagation},\n\tvolume = {33},\n\tissn = {0256-1530, 1861-9533},\n\turl = {http://link.springer.com/10.1007/s00376-015-5212-6},\n\tdoi = {10.1007/s00376-015-5212-6},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-01-23},\n\tjournal = {Advances in Atmospheric Sciences},\n\tauthor = {Liu, Shaofeng and Hintz, Michael and Li, Xiaolong},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {571--578},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Lausch, A.; Erasmi, S.; King, D.; Magdon, P.; and Heurich, M.\n\n\n \n \n \n \n \n Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 8(12): 1029. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_understanding_2016,\n\ttitle = {Understanding {Forest} {Health} with {Remote} {Sensing} -{Part} {I}—{A} {Review} of {Spectral} {Traits}, {Processes} and {Remote}-{Sensing} {Characteristics}},\n\tvolume = {8},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/8/12/1029},\n\tdoi = {10.3390/rs8121029},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Erasmi, Stefan and King, Douglas and Magdon, Paul and Heurich, Marco},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {1029},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Lausch, A.; Bannehr, L.; Beckmann, M.; Boehm, C.; Feilhauer, H.; Hacker, J.; Heurich, M.; Jung, A.; Klenke, R.; Neumann, C.; Pause, M.; Rocchini, D.; Schaepman, M.; Schmidtlein, S.; Schulz, K.; Selsam, P.; Settele, J.; Skidmore, A.; and Cord, A.\n\n\n \n \n \n \n \n Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 70: 317–339. November 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_linking_2016,\n\ttitle = {Linking {Earth} {Observation} and taxonomic, structural and functional biodiversity: {Local} to ecosystem perspectives},\n\tvolume = {70},\n\tissn = {1470160X},\n\tshorttitle = {Linking {Earth} {Observation} and taxonomic, structural and functional biodiversity},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X16303223},\n\tdoi = {10.1016/j.ecolind.2016.06.022},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Ecological Indicators},\n\tauthor = {Lausch, A. and Bannehr, L. and Beckmann, M. and Boehm, C. and Feilhauer, H. and Hacker, J.M. and Heurich, M. and Jung, A. and Klenke, R. and Neumann, C. and Pause, M. and Rocchini, D. and Schaepman, M.E. and Schmidtlein, S. and Schulz, K. and Selsam, P. and Settele, J. and Skidmore, A.K. and Cord, A.F.},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {317--339},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kurtz, W.; He, G.; Kollet, S. J.; Maxwell, R. M.; Vereecken, H.; and Hendricks Franssen, H.\n\n\n \n \n \n \n \n TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Model Development, 9(4): 1341–1360. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"TerrSysMP–PDAF$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kurtz_terrsysmppdaf_2016,\n\ttitle = {{TerrSysMP}–{PDAF} (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model},\n\tvolume = {9},\n\tissn = {1991-9603},\n\tshorttitle = {{TerrSysMP}–{PDAF} (version 1.0)},\n\turl = {https://gmd.copernicus.org/articles/9/1341/2016/},\n\tdoi = {10.5194/gmd-9-1341-2016},\n\tabstract = {Abstract. Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for  {\\textgreater} 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Kurtz, Wolfgang and He, Guowei and Kollet, Stefan J. and Maxwell, Reed M. and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {1341--1360},\n}\n\n\n\n

\n\n\n

\n Abstract. Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for \\textgreater 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.\n

\n\n\n

\n \n\n \n \n Knapp, S.; Stadler, J.; Harpke, A.; and Klotz, S.\n\n\n \n \n \n \n \n Dispersal traits as indicators of vegetation dynamics in long-term old-field succession.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 65: 44–54. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Dispersal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{knapp_dispersal_2016,\n\ttitle = {Dispersal traits as indicators of vegetation dynamics in long-term old-field succession},\n\tvolume = {65},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X15005385},\n\tdoi = {10.1016/j.ecolind.2015.10.003},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Ecological Indicators},\n\tauthor = {Knapp, Sonja and Stadler, Jutta and Harpke, Alexander and Klotz, Stefan},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {44--54},\n}\n\n\n\n

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\n \n\n \n \n Kamjunke, N.; Oosterwoud, M. R.; Herzsprung, P.; and Tittel, J.\n\n\n \n \n \n \n \n Bacterial production and their role in the removal of dissolved organic matter from tributaries of drinking water reservoirs.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 548-549: 51–59. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Bacterial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_bacterial_2016,\n\ttitle = {Bacterial production and their role in the removal of dissolved organic matter from tributaries of drinking water reservoirs},\n\tvolume = {548-549},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969716300183},\n\tdoi = {10.1016/j.scitotenv.2016.01.017},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Kamjunke, Norbert and Oosterwoud, Marieke R. and Herzsprung, Peter and Tittel, Jörg},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {51--59},\n}\n\n\n\n

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\n \n\n \n \n Klotzsche, A.; van der Kruk, J.; He, G.; and Vereecken, H.\n\n\n \n \n \n \n \n GPR full-waveform inversion of horizontal ZOP borehole data using GprMax.\n \n \n \n \n\n\n \n\n\n\n In 2016 16th International Conference on Ground Penetrating Radar (GPR), pages 1–5, Hong Kong, Hong Kong, June 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"GPR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{klotzsche_gpr_2016,\n\taddress = {Hong Kong, Hong Kong},\n\ttitle = {{GPR} full-waveform inversion of horizontal {ZOP} borehole data using {GprMax}},\n\tisbn = {9781509051816},\n\turl = {http://ieeexplore.ieee.org/document/7572695/},\n\tdoi = {10.1109/ICGPR.2016.7572695},\n\turldate = {2023-01-23},\n\tbooktitle = {2016 16th {International} {Conference} on {Ground} {Penetrating} {Radar} ({GPR})},\n\tpublisher = {IEEE},\n\tauthor = {Klotzsche, A. and van der Kruk, J. and He, G. and Vereecken, H.},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {1--5},\n}\n\n\n\n

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\n \n\n \n \n Jagdhuber, T.\n\n\n \n \n \n \n \n An Approach to Extended Fresnel Scattering for Modeling of Depolarizing Soil-Trunk Double-Bounce Scattering.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 8(10): 818. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jagdhuber_approach_2016,\n\ttitle = {An {Approach} to {Extended} {Fresnel} {Scattering} for {Modeling} of {Depolarizing} {Soil}-{Trunk} {Double}-{Bounce} {Scattering}},\n\tvolume = {8},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/8/10/818},\n\tdoi = {10.3390/rs8100818},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Jagdhuber, Thomas},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {818},\n}\n\n\n\n

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\n \n\n \n \n Jaganmohan, M.; Knapp, S.; Buchmann, C. M.; and Schwarz, N.\n\n\n \n \n \n \n \n The Bigger, the Better? The Influence of Urban Green Space Design on Cooling Effects for Residential Areas.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Quality, 45(1): 134–145. January 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jaganmohan_bigger_2016,\n\ttitle = {The {Bigger}, the {Better}? {The} {Influence} of {Urban} {Green} {Space} {Design} on {Cooling} {Effects} for {Residential} {Areas}},\n\tvolume = {45},\n\tissn = {00472425},\n\tshorttitle = {The {Bigger}, the {Better}?},\n\turl = {http://doi.wiley.com/10.2134/jeq2015.01.0062},\n\tdoi = {10.2134/jeq2015.01.0062},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Environmental Quality},\n\tauthor = {Jaganmohan, Madhumitha and Knapp, Sonja and Buchmann, Carsten M. and Schwarz, Nina},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {134--145},\n}\n\n\n\n

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\n \n\n \n \n Inostroza, P. A.; Vera-Escalona, I.; Wicht, A.; Krauss, M.; Brack, W.; and Norf, H.\n\n\n \n \n \n \n \n Anthropogenic Stressors Shape Genetic Structure: Insights from a Model Freshwater Population along a Land Use Gradient.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 50(20): 11346–11356. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Anthropogenic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{inostroza_anthropogenic_2016,\n\ttitle = {Anthropogenic {Stressors} {Shape} {Genetic} {Structure}: {Insights} from a {Model} {Freshwater} {Population} along a {Land} {Use} {Gradient}},\n\tvolume = {50},\n\tissn = {0013-936X, 1520-5851},\n\tshorttitle = {Anthropogenic {Stressors} {Shape} {Genetic} {Structure}},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.6b04629},\n\tdoi = {10.1021/acs.est.6b04629},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Inostroza, Pedro A. and Vera-Escalona, Iván and Wicht, Anna-Jorina and Krauss, Martin and Brack, Werner and Norf, Helge},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {11346--11356},\n}\n\n\n\n

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\n \n\n \n \n Hingerl, L.; Kunstmann, H.; Wagner, S.; Mauder, M.; Bliefernicht, J.; and Rigon, R.\n\n\n \n \n \n \n \n Spatio‐temporal variability of water and energy fluxes – a case study for a mesoscale catchment in pre‐alpine environment.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 30(21): 3804–3823. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio‐temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hingerl_spatiotemporal_2016,\n\ttitle = {Spatio‐temporal variability of water and energy fluxes – a case study for a mesoscale catchment in pre‐alpine environment},\n\tvolume = {30},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.10893},\n\tdoi = {10.1002/hyp.10893},\n\tlanguage = {en},\n\tnumber = {21},\n\turldate = {2023-01-23},\n\tjournal = {Hydrological Processes},\n\tauthor = {Hingerl, Luitpold and Kunstmann, Harald and Wagner, Sven and Mauder, Matthias and Bliefernicht, Jan and Rigon, Riccardo},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {3804--3823},\n}\n\n\n\n

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\n \n\n \n \n Herbst, M.; Tappe, W.; Kummer, S.; and Vereecken, H.\n\n\n \n \n \n \n \n The impact of sieving on heterotrophic respiration response to water content in loamy and sandy topsoils.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 272: 73–82. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbst_impact_2016,\n\ttitle = {The impact of sieving on heterotrophic respiration response to water content in loamy and sandy topsoils},\n\tvolume = {272},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706116301008},\n\tdoi = {10.1016/j.geoderma.2016.03.002},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Geoderma},\n\tauthor = {Herbst, M. and Tappe, W. and Kummer, S. and Vereecken, H.},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {73--82},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Herbrich, M.; and Gerke, H. H.\n\n\n \n \n \n \n \n Autocorrelation analysis of high resolution weighing lysimeter time series as a basis for determination of precipitation.\n \n \n \n \n\n\n \n\n\n\n Journal of Plant Nutrition and Soil Science, 179(6): 784–798. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Autocorrelation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herbrich_autocorrelation_2016,\n\ttitle = {Autocorrelation analysis of high resolution weighing lysimeter time series as a basis for determination of precipitation},\n\tvolume = {179},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.201600169},\n\tdoi = {10.1002/jpln.201600169},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Herbrich, Marcus and Gerke, Horst H.},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {784--798},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Heine, I.; Jagdhuber, T.; and Itzerott, S.\n\n\n \n \n \n \n \n Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 8(7): 552. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Classification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heine_classification_2016,\n\ttitle = {Classification and {Monitoring} of {Reed} {Belts} {Using} {Dual}-{Polarimetric} {TerraSAR}-{X} {Time} {Series}},\n\tvolume = {8},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/8/7/552},\n\tdoi = {10.3390/rs8070552},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Heine, Iris and Jagdhuber, Thomas and Itzerott, Sibylle},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {552},\n}\n\n\n\n

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\n \n\n \n \n Heidbüchel, I.; Güntner, A.; and Blume, T.\n\n\n \n \n \n \n \n Use of cosmic-ray neutron sensors for soil moisture monitoring in forests.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 20(3): 1269–1288. March 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heidbuchel_use_2016,\n\ttitle = {Use of cosmic-ray neutron sensors for soil moisture monitoring in forests},\n\tvolume = {20},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/20/1269/2016/},\n\tdoi = {10.5194/hess-20-1269-2016},\n\tabstract = {Abstract. Measuring soil moisture with cosmic-ray neutrons is a promising technique for intermediate spatial scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). The calibration is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a cosmic-ray neutron sensor (CRS) in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.1 m3 m−3 (24 \\% of the total range) for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a modified calibration function with a different shape that can vary from one location to another. A two-point calibration was found to effectively define the shape of the modified calibration function if the calibration points were taken during both dry and wet conditions spanning at least half of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and nonlinearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. As a final point, we provide a calibration guide for a CRS in forested environments.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-01-23},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Heidbüchel, Ingo and Güntner, Andreas and Blume, Theresa},\n\tmonth = mar,\n\tyear = {2016},\n\tpages = {1269--1288},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Hannes, M.; Wollschläger, U.; Wöhling, T.; and Vogel, H.\n\n\n \n \n \n \n \n Revisiting hydraulic hysteresis based on long-term monitoring of hydraulic states in lysimeters: REVISITING HYDRAULIC HYSTERESIS.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 52(5): 3847–3865. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Revisiting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hannes_revisiting_2016,\n\ttitle = {Revisiting hydraulic hysteresis based on long-term monitoring of hydraulic states in lysimeters: {REVISITING} {HYDRAULIC} {HYSTERESIS}},\n\tvolume = {52},\n\tissn = {00431397},\n\tshorttitle = {Revisiting hydraulic hysteresis based on long-term monitoring of hydraulic states in lysimeters},\n\turl = {http://doi.wiley.com/10.1002/2015WR018319},\n\tdoi = {10.1002/2015WR018319},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-01-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Hannes, M. and Wollschläger, U. and Wöhling, T. and Vogel, H.-J.},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {3847--3865},\n}\n\n\n\n

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\n \n\n \n \n Han, X.; Hendricks Franssen, H.; Jiménez Bello, M. Á.; Rosolem, R.; Bogena, H.; Alzamora, F. M.; Chanzy, A.; and Vereecken, H.\n\n\n \n \n \n \n \n Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 539: 611–624. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{han_simultaneous_2016,\n\ttitle = {Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity},\n\tvolume = {539},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416303171},\n\tdoi = {10.1016/j.jhydrol.2016.05.050},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Han, Xujun and Hendricks Franssen, Harrie-Jan and Jiménez Bello, Miguel Ángel and Rosolem, Rafael and Bogena, Heye and Alzamora, Fernando Martínez and Chanzy, André and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {611--624},\n}\n\n\n\n

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\n \n\n \n \n Haase, P.; Frenzel, M.; Klotz, S.; Musche, M.; and Stoll, S.\n\n\n \n \n \n \n \n The long-term ecological research (LTER) network: Relevance, current status, future perspective and examples from marine, freshwater and terrestrial long-term observation.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 65: 1–3. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haase_long-term_2016,\n\ttitle = {The long-term ecological research ({LTER}) network: {Relevance}, current status, future perspective and examples from marine, freshwater and terrestrial long-term observation},\n\tvolume = {65},\n\tissn = {1470160X},\n\tshorttitle = {The long-term ecological research ({LTER}) network},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X16000546},\n\tdoi = {10.1016/j.ecolind.2016.01.040},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Ecological Indicators},\n\tauthor = {Haase, Peter and Frenzel, Mark and Klotz, Stefan and Musche, Martin and Stoll, Stefan},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {1--3},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Groh, J.; Vanderborght, J.; Pütz, T.; and Vereecken, H.\n\n\n \n \n \n \n \n How to Control the Lysimeter Bottom Boundary to Investigate the Effect of Climate Change on Soil Processes?.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 15(7): vzj2015.08.0113. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{groh_how_2016,\n\ttitle = {How to {Control} the {Lysimeter} {Bottom} {Boundary} to {Investigate} the {Effect} of {Climate} {Change} on {Soil} {Processes}?},\n\tvolume = {15},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2015.08.0113},\n\tdoi = {10.2136/vzj2015.08.0113},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Vanderborght, Jan and Pütz, Thomas and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {vzj2015.08.0113},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Frenzel, M.; Everaars, J.; and Schweiger, O.\n\n\n \n \n \n \n \n Bird communities in agricultural landscapes: What are the current drivers of temporal trends?.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 65: 113–121. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Bird$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{frenzel_bird_2016,\n\ttitle = {Bird communities in agricultural landscapes: {What} are the current drivers of temporal trends?},\n\tvolume = {65},\n\tissn = {1470160X},\n\tshorttitle = {Bird communities in agricultural landscapes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X15006524},\n\tdoi = {10.1016/j.ecolind.2015.11.020},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Ecological Indicators},\n\tauthor = {Frenzel, Mark and Everaars, Jeroen and Schweiger, Oliver},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {113--121},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Franz, D.; Koebsch, F.; Larmanou, E.; Augustin, J.; and Sachs, T.\n\n\n \n \n \n \n \n High net $_{\\textrm{2}}$ and CH$_{\\textrm{4}}$ release at a eutrophic shallow lake on a formerly drained fen.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 13(10): 3051–3070. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{franz_high_2016,\n\ttitle = {High net $_{\\textrm{2}}$ and {CH}$_{\\textrm{4}}$ release at a eutrophic shallow lake on a formerly drained fen},\n\tvolume = {13},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/13/3051/2016/},\n\tdoi = {10.5194/bg-13-3051-2016},\n\tabstract = {Abstract. Drained peatlands often act as carbon dioxide (CO2) hotspots. Raising the groundwater table is expected to reduce their CO2 contribution to the atmosphere and revitalise their function as carbon (C) sink in the long term. Without strict water management rewetting often results in partial flooding and the formation of spatially heterogeneous, nutrient-rich shallow lakes. Uncertainties remain as to when the intended effect of rewetting is achieved, as this specific ecosystem type has hardly been investigated in terms of greenhouse gas (GHG) exchange. In most cases of rewetting, methane (CH4) emissions increase under anoxic conditions due to a higher water table and in terms of global warming potential (GWP) outperform the shift towards CO2 uptake, at least in the short term.Based on eddy covariance measurements we studied the ecosystem–atmosphere exchange of CH4 and CO2 at a shallow lake situated on a former fen grassland in northeastern Germany. The lake evolved shortly after flooding, 9 years previous to our investigation period. The ecosystem consists of two main surface types: open water (inhabited by submerged and floating vegetation) and emergent vegetation (particularly including the eulittoral zone of the lake, dominated by Typha latifolia). To determine the individual contribution of the two main surface types to the net CO2 and CH4 exchange of the whole lake ecosystem, we combined footprint analysis with CH4 modelling and net ecosystem exchange partitioning.The CH4 and CO2 dynamics were strikingly different between open water and emergent vegetation. Net CH4 emissions from the open water area were around 4-fold higher than from emergent vegetation stands, accounting for 53 and 13 g CH4 m−2 a−1 respectively. In addition, both surface types were net CO2 sources with 158 and 750 g CO2 m−2 a−1 respectively. Unusual meteorological conditions in terms of a warm and dry summer and a mild winter might have facilitated high respiration rates. In sum, even after 9 years of rewetting the lake ecosystem exhibited a considerable C loss and global warming impact, the latter mainly driven by high CH4 emissions. We assume the eutrophic conditions in combination with permanent high inundation as major reasons for the unfavourable GHG balance.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-01-23},\n\tjournal = {Biogeosciences},\n\tauthor = {Franz, Daniela and Koebsch, Franziska and Larmanou, Eric and Augustin, Jürgen and Sachs, Torsten},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {3051--3070},\n}\n\n\n\n

\n\n\n

\n Abstract. Drained peatlands often act as carbon dioxide (CO2) hotspots. Raising the groundwater table is expected to reduce their CO2 contribution to the atmosphere and revitalise their function as carbon (C) sink in the long term. Without strict water management rewetting often results in partial flooding and the formation of spatially heterogeneous, nutrient-rich shallow lakes. Uncertainties remain as to when the intended effect of rewetting is achieved, as this specific ecosystem type has hardly been investigated in terms of greenhouse gas (GHG) exchange. In most cases of rewetting, methane (CH4) emissions increase under anoxic conditions due to a higher water table and in terms of global warming potential (GWP) outperform the shift towards CO2 uptake, at least in the short term.Based on eddy covariance measurements we studied the ecosystem–atmosphere exchange of CH4 and CO2 at a shallow lake situated on a former fen grassland in northeastern Germany. The lake evolved shortly after flooding, 9 years previous to our investigation period. The ecosystem consists of two main surface types: open water (inhabited by submerged and floating vegetation) and emergent vegetation (particularly including the eulittoral zone of the lake, dominated by Typha latifolia). To determine the individual contribution of the two main surface types to the net CO2 and CH4 exchange of the whole lake ecosystem, we combined footprint analysis with CH4 modelling and net ecosystem exchange partitioning.The CH4 and CO2 dynamics were strikingly different between open water and emergent vegetation. Net CH4 emissions from the open water area were around 4-fold higher than from emergent vegetation stands, accounting for 53 and 13 g CH4 m−2 a−1 respectively. In addition, both surface types were net CO2 sources with 158 and 750 g CO2 m−2 a−1 respectively. Unusual meteorological conditions in terms of a warm and dry summer and a mild winter might have facilitated high respiration rates. In sum, even after 9 years of rewetting the lake ecosystem exhibited a considerable C loss and global warming impact, the latter mainly driven by high CH4 emissions. We assume the eutrophic conditions in combination with permanent high inundation as major reasons for the unfavourable GHG balance.\n

\n\n\n

\n \n\n \n \n Fóti, S.; Balogh, J.; Herbst, M.; Papp, M.; Koncz, P.; Bartha, S.; Zimmermann, Z.; Komoly, C.; Szabó, G.; Margóczi, K.; Acosta, M.; and Nagy, Z.\n\n\n \n \n \n \n \n Meta-analysis of field scale spatial variability of grassland soil $_{\\textrm{2}}$ efflux: Interaction of biotic and abiotic drivers.\n \n \n \n \n\n\n \n\n\n\n CATENA, 143: 78–89. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Meta-analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{foti_meta-analysis_2016,\n\ttitle = {Meta-analysis of field scale spatial variability of grassland soil $_{\\textrm{2}}$ efflux: {Interaction} of biotic and abiotic drivers},\n\tvolume = {143},\n\tissn = {03418162},\n\tshorttitle = {Meta-analysis of field scale spatial variability of grassland soil {CO2} efflux},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0341816216301205},\n\tdoi = {10.1016/j.catena.2016.03.034},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {CATENA},\n\tauthor = {Fóti, Szilvia and Balogh, János and Herbst, Michael and Papp, Marianna and Koncz, Péter and Bartha, Sándor and Zimmermann, Zita and Komoly, Cecília and Szabó, Gábor and Margóczi, Katalin and Acosta, Manuel and Nagy, Zoltán},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {78--89},\n}\n\n\n\n

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\n \n\n \n \n Feilhauer, H.; Doktor, D.; Schmidtlein, S.; and Skidmore, A. K.\n\n\n \n \n \n \n \n Mapping pollination types with remote sensing.\n \n \n \n \n\n\n \n\n\n\n Journal of Vegetation Science, 27(5): 999–1011. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{feilhauer_mapping_2016,\n\ttitle = {Mapping pollination types with remote sensing},\n\tvolume = {27},\n\tissn = {11009233},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/jvs.12421},\n\tdoi = {10.1111/jvs.12421},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Vegetation Science},\n\tauthor = {Feilhauer, Hannes and Doktor, Daniel and Schmidtlein, Sebastian and Skidmore, Andrew K.},\n\teditor = {Prinzing, Andreas},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {999--1011},\n}\n\n\n\n

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\n \n\n \n \n Fang, Z.; Bogena, H.; Kollet, S.; and Vereecken, H.\n\n\n \n \n \n \n \n Scale dependent parameterization of soil hydraulic conductivity in 3D simulation of hydrological processes in a forested headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 536: 365–375. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fang_scale_2016,\n\ttitle = {Scale dependent parameterization of soil hydraulic conductivity in {3D} simulation of hydrological processes in a forested headwater catchment},\n\tvolume = {536},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169416301251},\n\tdoi = {10.1016/j.jhydrol.2016.03.020},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Fang, Zhufeng and Bogena, Heye and Kollet, Stefan and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {365--375},\n}\n\n\n\n

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\n \n\n \n \n Englert, A.; Kemna, A.; Zhu, J.; Vanderborght, J.; Vereecken, H.; and Yeh, T. J.\n\n\n \n \n \n \n \n Comparison of smoothness-constrained and geostatistically based cross-borehole electrical resistivity tomography for characterization of solute tracer plumes.\n \n \n \n \n\n\n \n\n\n\n Water Science and Engineering, 9(4): 274–286. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{englert_comparison_2016,\n\ttitle = {Comparison of smoothness-constrained and geostatistically based cross-borehole electrical resistivity tomography for characterization of solute tracer plumes},\n\tvolume = {9},\n\tissn = {16742370},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1674237017300029},\n\tdoi = {10.1016/j.wse.2017.01.002},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {Water Science and Engineering},\n\tauthor = {Englert, Andreas and Kemna, Andreas and Zhu, Jun-feng and Vanderborght, Jan and Vereecken, Harry and Yeh, Tian-Chyi J.},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {274--286},\n}\n\n\n\n

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\n \n\n \n \n Dressler, G.; Müller, B.; Frank, K.; and Kuhlicke, C.\n\n\n \n \n \n \n \n Towards thresholds of disaster management performance under demographic change: exploring functional relationships using agent-based modeling.\n \n \n \n \n\n\n \n\n\n\n Natural Hazards and Earth System Sciences, 16(10): 2287–2301. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dressler_towards_2016,\n\ttitle = {Towards thresholds of disaster management performance under demographic change: exploring functional relationships using agent-based modeling},\n\tvolume = {16},\n\tissn = {1684-9981},\n\tshorttitle = {Towards thresholds of disaster management performance under demographic change},\n\turl = {https://nhess.copernicus.org/articles/16/2287/2016/},\n\tdoi = {10.5194/nhess-16-2287-2016},\n\tabstract = {Abstract. Effective disaster management is a core feature for the protection of communities against natural disasters such as floods. Disaster management organizations (DMOs) are expected to contribute to ensuring this protection. However, what happens when their resources to cope with a flood are at stake or the intensity and frequency of the event exceeds their capacities? Many cities in the Free State of Saxony, Germany, were strongly hit by several floods in the last years and are additionally challenged by demographic change, with an ageing society and out-migration leading to population shrinkage in many parts of Saxony. Disaster management, which is mostly volunteer-based in Germany, is particularly affected by this change, leading to a loss of members. We propose an agent-based simulation model that acts as a "virtual lab" to explore the impact of various changes on disaster management performance. Using different scenarios we examine the impact of changes in personal resources of DMOs, their access to operation relevant information, flood characteristics as well as differences between geographic regions. A loss of DMOs and associated manpower caused by demographic change has the most profound impact on the performance. Especially in rural, upstream regions population decline in combination with very short lead times can put disaster management performance at risk.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-01-23},\n\tjournal = {Natural Hazards and Earth System Sciences},\n\tauthor = {Dressler, Gunnar and Müller, Birgit and Frank, Karin and Kuhlicke, Christian},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {2287--2301},\n}\n\n\n\n

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\n \n\n \n \n Dräger, N.; Brauer, A.; Brademann, B.; Tjallingii, R.; Słowiński, M.; Błaszkiewicz, M.; and Schlaak, N.\n\n\n \n \n \n \n \n Spontaneous self-combustion of organic-rich lateglacial lake sediments after freeze-drying.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 55(2): 185–194. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Spontaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{drager_spontaneous_2016,\n\ttitle = {Spontaneous self-combustion of organic-rich lateglacial lake sediments after freeze-drying},\n\tvolume = {55},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-015-9875-x},\n\tdoi = {10.1007/s10933-015-9875-x},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Dräger, Nadine and Brauer, Achim and Brademann, Brian and Tjallingii, Rik and Słowiński, Michał and Błaszkiewicz, Mirosław and Schlaak, Norbert},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {185--194},\n}\n\n\n\n

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\n \n\n \n \n Dietze, E.; Słowiński, M.; Zawiska, I.; Veh, G.; and Brauer, A.\n\n\n \n \n \n \n \n Multiple drivers of Holocene lake level changes at a lowland lake in northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n Boreas, 45(4): 828–845. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiple$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dietze_multiple_2016,\n\ttitle = {Multiple drivers of {Holocene} lake level changes at a lowland lake in northeastern {Germany}},\n\tvolume = {45},\n\tissn = {03009483},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/bor.12190},\n\tdoi = {10.1111/bor.12190},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {Boreas},\n\tauthor = {Dietze, Elisabeth and Słowiński, Michał and Zawiska, Izabela and Veh, Georg and Brauer, Achim},\n\tmonth = oct,\n\tyear = {2016},\n\tpages = {828--845},\n}\n\n\n\n

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\n \n\n \n \n Desai, A R; Wohlfahrt, G; Zeeman, M J; Katata, G; Eugster, W; Montagnani, L; Gianelle, D; Mauder, M; and Schmid, H.\n\n\n \n \n \n \n \n Montane ecosystem productivity responds more to global circulation patterns than climatic trends.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 11(2): 024013. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Montane$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{desai_montane_2016,\n\ttitle = {Montane ecosystem productivity responds more to global circulation patterns than climatic trends},\n\tvolume = {11},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/11/2/024013},\n\tdoi = {10.1088/1748-9326/11/2/024013},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Desai, A R and Wohlfahrt, G and Zeeman, M J and Katata, G and Eugster, W and Montagnani, L and Gianelle, D and Mauder, M and Schmid, H-P},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {024013},\n}\n\n\n\n

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\n \n\n \n \n Dadi, T.; Friese, K.; Wendt-Potthoff, K.; and Koschorreck, M.\n\n\n \n \n \n \n \n Benthic dissolved organic carbon fluxes in a drinking water reservoir: Benthic DOC flux.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 61(2): 445–459. March 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Benthic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dadi_benthic_2016,\n\ttitle = {Benthic dissolved organic carbon fluxes in a drinking water reservoir: {Benthic} {DOC} flux},\n\tvolume = {61},\n\tissn = {00243590},\n\tshorttitle = {Benthic dissolved organic carbon fluxes in a drinking water reservoir},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.10224},\n\tdoi = {10.1002/lno.10224},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-01-23},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Dadi, Tallent and Friese, Kurt and Wendt-Potthoff, Katrin and Koschorreck, Matthias},\n\tmonth = mar,\n\tyear = {2016},\n\tpages = {445--459},\n}\n\n\n\n

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\n \n\n \n \n Dahms, T.; Seissiger, S.; Conrad, C.; and Borg, E.\n\n\n \n \n \n \n \n MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES.\n \n \n \n \n\n\n \n\n\n\n The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B2: 171–175. June 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"MODELLING$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dahms_modelling_2016,\n\ttitle = {{MODELLING} {BIOPHYSICAL} {PARAMETERS} {OF} {MAIZE} {USING} {LANDSAT} 8 {TIME} {SERIES}},\n\tvolume = {XLI-B2},\n\tissn = {2194-9034},\n\turl = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/171/2016/},\n\tdoi = {10.5194/isprs-archives-XLI-B2-171-2016},\n\tabstract = {Abstract. Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution.  In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015.  The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement.  Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9.  Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},\n\tauthor = {Dahms, Thorsten and Seissiger, Sylvia and Conrad, Christopher and Borg, Erik},\n\tmonth = jun,\n\tyear = {2016},\n\tpages = {171--175},\n}\n\n\n\n

\n\n\n

\n Abstract. Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.\n

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\n \n\n \n \n Creutzburg, F.; and Frenzel, M.\n\n\n \n \n \n \n Langzeit-Untersuchung von Wildbienen in Agrarlandschaften in Sachsen-Anhalt im TERENO-Projekt (Hymenoptera: Apoidea).\n \n \n \n\n\n \n\n\n\n Entomologische Zeitschrift, 126: 225–240. December 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{creutzburg_langzeit-untersuchung_2016,\n\ttitle = {Langzeit-{Untersuchung} von {Wildbienen} in {Agrarlandschaften} in {Sachsen}-{Anhalt} im {TERENO}-{Projekt} ({Hymenoptera}: {Apoidea})},\n\tvolume = {126},\n\tjournal = {Entomologische Zeitschrift},\n\tauthor = {Creutzburg, Frank and Frenzel, Mark},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {225--240},\n}\n\n\n\n

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\n \n\n \n \n Cornelissen, T.; Diekkrüger, B.; and Bogena, H.\n\n\n \n \n \n \n \n Using High-Resolution Data to Test Parameter Sensitivity of the Distributed Hydrological Model HydroGeoSphere.\n \n \n \n \n\n\n \n\n\n\n Water, 8(5): 202. May 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cornelissen_using_2016,\n\ttitle = {Using {High}-{Resolution} {Data} to {Test} {Parameter} {Sensitivity} of the {Distributed} {Hydrological} {Model} {HydroGeoSphere}},\n\tvolume = {8},\n\tissn = {2073-4441},\n\turl = {http://www.mdpi.com/2073-4441/8/5/202},\n\tdoi = {10.3390/w8050202},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-01-23},\n\tjournal = {Water},\n\tauthor = {Cornelissen, Thomas and Diekkrüger, Bernd and Bogena, Heye},\n\tmonth = may,\n\tyear = {2016},\n\tpages = {202},\n}\n\n\n\n

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\n \n\n \n \n Chwala, C.; Keis, F.; and Kunstmann, H.\n\n\n \n \n \n \n \n Real-time data acquisition of commercial microwave link networks for hydrometeorological applications.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 9(3): 991–999. March 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Real-time$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chwala_real-time_2016,\n\ttitle = {Real-time data acquisition of commercial microwave link networks for hydrometeorological applications},\n\tvolume = {9},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/9/991/2016/},\n\tdoi = {10.5194/amt-9-991-2016},\n\tabstract = {Abstract. The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open-source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to 1 s. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real-time transfer to our database. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine ski resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of 1 s. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-01-23},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Chwala, Christian and Keis, Felix and Kunstmann, Harald},\n\tmonth = mar,\n\tyear = {2016},\n\tpages = {991--999},\n}\n\n\n\n

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\n \n\n \n \n Cai, G.; Vanderborght, J.; Klotzsche, A.; van der Kruk, J.; Neumann, J.; Hermes, N.; and Vereecken, H.\n\n\n \n \n \n \n \n Construction of Minirhizotron Facilities for Investigating Root Zone Processes.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 15(9): vzj2016.05.0043. September 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Construction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cai_construction_2016,\n\ttitle = {Construction of {Minirhizotron} {Facilities} for {Investigating} {Root} {Zone} {Processes}},\n\tvolume = {15},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2016.05.0043},\n\tdoi = {10.2136/vzj2016.05.0043},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-01-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Cai, Gaochao and Vanderborght, Jan and Klotzsche, Anja and van der Kruk, Jan and Neumann, Joschka and Hermes, Normen and Vereecken, Harry},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {vzj2016.05.0043},\n}\n\n\n\n

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\n \n\n \n \n Buras, A.; van der Maaten-Theunissen, M.; van der Maaten, E.; Ahlgrimm, S.; Hermann, P.; Simard, S.; Heinrich, I.; Helle, G.; Unterseher, M.; Schnittler, M.; Eusemann, P.; and Wilmking, M.\n\n\n \n \n \n \n \n Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 11(7): e0158346. July 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Tuning$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{buras_tuning_2016,\n\ttitle = {Tuning the {Voices} of a {Choir}: {Detecting} {Ecological} {Gradients} in {Time}-{Series} {Populations}},\n\tvolume = {11},\n\tissn = {1932-6203},\n\tshorttitle = {Tuning the {Voices} of a {Choir}},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0158346},\n\tdoi = {10.1371/journal.pone.0158346},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-01-23},\n\tjournal = {PLOS ONE},\n\tauthor = {Buras, Allan and van der Maaten-Theunissen, Marieke and van der Maaten, Ernst and Ahlgrimm, Svenja and Hermann, Philipp and Simard, Sonia and Heinrich, Ingo and Helle, Gerd and Unterseher, Martin and Schnittler, Martin and Eusemann, Pascal and Wilmking, Martin},\n\teditor = {Guralnick, Robert},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {e0158346},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Bogena, H. R.\n\n\n \n \n \n \n \n TERENO: German network of terrestrial environmental observatories.\n \n \n \n \n\n\n \n\n\n\n Journal of large-scale research facilities JLSRF, 2: A52. February 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"TERENO:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_tereno_2016,\n\ttitle = {{TERENO}: {German} network of terrestrial environmental observatories},\n\tvolume = {2},\n\tissn = {2364-091X},\n\tshorttitle = {{TERENO}},\n\turl = {http://jlsrf.org/index.php/lsf/article/view/98},\n\tdoi = {10.17815/jlsrf-2-98},\n\tabstract = {Central elements of the TERENO network are “terrestrial observatories” at the catchment scale which were selected in climate sensitive regions of Germany for the regional analyses of climate change impacts. Within these observatories small scale research facilities and test areas are placed in order to accomplish energy, water, carbon and nutrient process studies across the different compartments of the terrestrial environment. Following a hierarchical scaling approach (point-plot-field) these detailed information and the gained knowledge will be transferred to the regional scale using integrated modelling approaches. Furthermore, existing research stations are enhanced and embedded within the observatories. In addition, mobile measurement platforms enable monitoring of dynamic processes at the local scale up to the determination of spatial pattern at the regional scale are applied within TERENO.},\n\turldate = {2023-01-23},\n\tjournal = {Journal of large-scale research facilities JLSRF},\n\tauthor = {Bogena, Heye Reemt},\n\tmonth = feb,\n\tyear = {2016},\n\tpages = {A52},\n}\n\n\n\n

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\n \n\n \n \n Bircher, S.; Andreasen, M.; Vuollet, J.; Vehviläinen, J.; Rautiainen, K.; Jonard, F.; Weihermüller, L.; Zakharova, E.; Wigneron, J.; and Kerr, Y. H.\n\n\n \n \n \n \n \n Soil moisture sensor calibration for organic soil surface layers.\n \n \n \n \n\n\n \n\n\n\n Geoscientific Instrumentation, Methods and Data Systems, 5(1): 109–125. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bircher_soil_2016,\n\ttitle = {Soil moisture sensor calibration for organic soil surface layers},\n\tvolume = {5},\n\tissn = {2193-0864},\n\turl = {https://gi.copernicus.org/articles/5/109/2016/},\n\tdoi = {10.5194/gi-5-109-2016},\n\tabstract = {Abstract. This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here-proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but prove to be of value for the acquisition of instantaneous large-scale soil moisture estimates.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-01-23},\n\tjournal = {Geoscientific Instrumentation, Methods and Data Systems},\n\tauthor = {Bircher, Simone and Andreasen, Mie and Vuollet, Johanna and Vehviläinen, Juho and Rautiainen, Kimmo and Jonard, François and Weihermüller, Lutz and Zakharova, Elena and Wigneron, Jean-Pierre and Kerr, Yann H.},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {109--125},\n}\n\n\n\n

\n\n\n

\n Abstract. This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here-proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but prove to be of value for the acquisition of instantaneous large-scale soil moisture estimates.\n

\n\n\n

\n \n\n \n \n Andreasen, M.; Jensen, K. H.; Zreda, M.; Desilets, D.; Bogena, H.; and Looms, M. C.\n\n\n \n \n \n \n \n Modeling cosmic ray neutron field measurements: MODELING COSMIC RAY NEUTRON FIELD MEASUREMENTS.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 52(8): 6451–6471. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{andreasen_modeling_2016,\n\ttitle = {Modeling cosmic ray neutron field measurements: {MODELING} {COSMIC} {RAY} {NEUTRON} {FIELD} {MEASUREMENTS}},\n\tvolume = {52},\n\tissn = {00431397},\n\tshorttitle = {Modeling cosmic ray neutron field measurements},\n\turl = {http://doi.wiley.com/10.1002/2015WR018236},\n\tdoi = {10.1002/2015WR018236},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-01-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Andreasen, Mie and Jensen, Karsten H. and Zreda, Marek and Desilets, Darin and Bogena, Heye and Looms, Majken C.},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {6451--6471},\n}\n\n\n\n

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\n \n\n \n \n Altenkirch, N.; Zlatanovic, S.; Woodward, K. B.; Trauth, N.; Mutz, M.; and Mokenthin, F.\n\n\n \n \n \n \n \n “Untangling Hyporheic Residence time Distributions and Whole Stream” “Metabolism Using a Hydrological Process Model”.\n \n \n \n \n\n\n \n\n\n\n Procedia Engineering, 154: 1071–1078. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"“Untangling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{altenkirch_untangling_2016,\n\ttitle = {“{Untangling} {Hyporheic} {Residence} time {Distributions} and {Whole} {Stream}” “{Metabolism} {Using} a {Hydrological} {Process} {Model}”},\n\tvolume = {154},\n\tissn = {18777058},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1877705816319877},\n\tdoi = {10.1016/j.proeng.2016.07.598},\n\tlanguage = {en},\n\turldate = {2023-01-23},\n\tjournal = {Procedia Engineering},\n\tauthor = {Altenkirch, Nora and Zlatanovic, Sanja and Woodward, K. Benjamin and Trauth, Nico and Mutz, Michael and Mokenthin, Frank},\n\tyear = {2016},\n\tpages = {1071--1078},\n}\n\n\n\n

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\n \n\n \n \n Al-Hazaimay, S.; Huisman, J. A.; Zimmermann, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Using electrical anisotropy for structural characterization of sediments: an experimental validation study.\n \n \n \n \n\n\n \n\n\n\n Near Surface Geophysics, 14(4): 357–369. August 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{al-hazaimay_using_2016,\n\ttitle = {Using electrical anisotropy for structural characterization of sediments: an experimental validation study},\n\tvolume = {14},\n\tissn = {15694445, 18730604},\n\tshorttitle = {Using electrical anisotropy for structural characterization of sediments},\n\turl = {http://doi.wiley.com/10.3997/1873-0604.2016026},\n\tdoi = {10.3997/1873-0604.2016026},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-01-23},\n\tjournal = {Near Surface Geophysics},\n\tauthor = {Al-Hazaimay, Sadam and Huisman, Johan A. and Zimmermann, Egon and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2016},\n\tpages = {357--369},\n}\n\n\n\n

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\n\n

\n \n 2015\n \n \n (80)\n \n \n

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\n \n\n \n \n Jagdhuber, T.; Hajnsek, I.; and Papathanassiou, K. P.\n\n\n \n \n \n \n \n An Iterative Generalized Hybrid Decomposition for Soil Moisture Retrieval Under Vegetation Cover Using Fully Polarimetric SAR.\n \n \n \n \n\n\n \n\n\n\n IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8): 3911–3922. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jagdhuber_iterative_2015,\n\ttitle = {An {Iterative} {Generalized} {Hybrid} {Decomposition} for {Soil} {Moisture} {Retrieval} {Under} {Vegetation} {Cover} {Using} {Fully} {Polarimetric} {SAR}},\n\tvolume = {8},\n\tissn = {1939-1404, 2151-1535},\n\turl = {https://ieeexplore.ieee.org/document/6977883/},\n\tdoi = {10.1109/JSTARS.2014.2371468},\n\tnumber = {8},\n\turldate = {2023-06-19},\n\tjournal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},\n\tauthor = {Jagdhuber, Thomas and Hajnsek, Irena and Papathanassiou, Konstantinos P.},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {3911--3922},\n}\n\n\n\n

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\n \n\n \n \n Großmann, K.; Arndt, T.; Haase, A.; Rink, D.; and Steinführer, A.\n\n\n \n \n \n \n \n The influence of housing oversupply on residential segregation: exploring the post-socialist city of Leipzig $^{\\textrm{†}}$.\n \n \n \n \n\n\n \n\n\n\n Urban Geography, 36(4): 550–577. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{grosmann_influence_2015,\n\ttitle = {The influence of housing oversupply on residential segregation: exploring the post-socialist city of {Leipzig} $^{\\textrm{†}}$},\n\tvolume = {36},\n\tissn = {0272-3638, 1938-2847},\n\tshorttitle = {The influence of housing oversupply on residential segregation},\n\turl = {http://www.tandfonline.com/doi/full/10.1080/02723638.2015.1014672},\n\tdoi = {10.1080/02723638.2015.1014672},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-06-19},\n\tjournal = {Urban Geography},\n\tauthor = {Großmann, Katrin and Arndt, T. and Haase, A. and Rink, D. and Steinführer, A.},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {550--577},\n}\n\n\n\n

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\n \n\n \n \n Wurst, S.; Kaiser, N.; Nitzsche, S.; Haase, J.; Auge, H.; Rillig, M. C.; and Powell, J. R.\n\n\n \n \n \n \n \n Tree diversity modifies distance-dependent effects on seedling emergence but not plant–soil feedbacks of temperate trees.\n \n \n \n \n\n\n \n\n\n\n Ecology, 96(6): 1529–1539. June 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Tree$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wurst_tree_2015,\n\ttitle = {Tree diversity modifies distance-dependent effects on seedling emergence but not plant–soil feedbacks of temperate trees},\n\tvolume = {96},\n\tissn = {0012-9658},\n\turl = {http://doi.wiley.com/10.1890/14-1166.1},\n\tdoi = {10.1890/14-1166.1},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-02-23},\n\tjournal = {Ecology},\n\tauthor = {Wurst, Susanne and Kaiser, Nina and Nitzsche, Susann and Haase, Josephine and Auge, Harald and Rillig, Matthias C. and Powell, Jeff R.},\n\tmonth = jun,\n\tyear = {2015},\n\tpages = {1529--1539},\n}\n\n\n\n

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\n \n\n \n \n Vereecken, H.; Huisman, J. A.; Hendricks Franssen, H. J.; Brüggemann, N.; Bogena, H. R.; Kollet, S.; Javaux, M.; Van Der Kruk, J.; and Vanderborght, J.\n\n\n \n \n \n \n \n Soil hydrology: Recent methodological advances, challenges, and perspectives.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 51(4): 2616–2633. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vereecken_soil_2015,\n\ttitle = {Soil hydrology: {Recent} methodological advances, challenges, and perspectives},\n\tvolume = {51},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Soil hydrology},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2014WR016852},\n\tdoi = {10.1002/2014WR016852},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Vereecken, H. and Huisman, J. A. and Hendricks Franssen, H. J. and Brüggemann, N. and Bogena, H. R. and Kollet, S. and Javaux, M. and Van Der Kruk, J. and Vanderborght, J.},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {2616--2633},\n}\n\n\n\n

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\n \n\n \n \n Hoesel, A.; Hoek, W. Z.; Pennock, G. M.; Kaiser, K.; Plümper, O.; Jankowski, M.; Hamers, M. F.; Schlaak, N.; Küster, M.; Andronikov, A. V.; and Drury, M. R.\n\n\n \n \n \n \n \n A search for shocked quartz grains in the Allerød‐Younger Dryas boundary layer.\n \n \n \n \n\n\n \n\n\n\n Meteoritics & Planetary Science, 50(3): 483–498. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hoesel_search_2015,\n\ttitle = {A search for shocked quartz grains in the {Allerød}‐{Younger} {Dryas} boundary layer},\n\tvolume = {50},\n\tissn = {1086-9379, 1945-5100},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/maps.12435},\n\tdoi = {10.1111/maps.12435},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {Meteoritics \\& Planetary Science},\n\tauthor = {Hoesel, Annelies and Hoek, Wim Z. and Pennock, Gillian M. and Kaiser, Knut and Plümper, Oliver and Jankowski, Michal and Hamers, Maartje F. and Schlaak, Norbert and Küster, Mathias and Andronikov, Alexander V. and Drury, Martyn R.},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {483--498},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Van Der Kruk, J.; Gueting, N.; Klotzsche, A.; He, G.; Rudolph, S.; Von Hebel, C.; Yang, X.; Weihermüller, L.; Mester, A.; and Vereecken, H.\n\n\n \n \n \n \n \n Quantitative multi-layer electromagnetic induction inversion and full-waveform inversion of crosshole ground penetrating radar data.\n \n \n \n \n\n\n \n\n\n\n Journal of Earth Science, 26(6): 844–850. December 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_kruk_quantitative_2015,\n\ttitle = {Quantitative multi-layer electromagnetic induction inversion and full-waveform inversion of crosshole ground penetrating radar data},\n\tvolume = {26},\n\tissn = {1674-487X, 1867-111X},\n\turl = {http://link.springer.com/10.1007/s12583-015-0610-3},\n\tdoi = {10.1007/s12583-015-0610-3},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Earth Science},\n\tauthor = {Van Der Kruk, Jan and Gueting, Nils and Klotzsche, Anja and He, Guowei and Rudolph, Sebastian and Von Hebel, Christian and Yang, Xi and Weihermüller, Lutz and Mester, Achim and Vereecken, Harry},\n\tmonth = dec,\n\tyear = {2015},\n\tpages = {844--850},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Van Der Maaten, E.; Van Der Maaten-Theunissen, M.; Buras, A.; Scharnweber, T.; Simard, S.; Kaiser, K.; Lorenz, S.; and Wilmking, M.\n\n\n \n \n \n \n \n Can We Use Tree Rings of Black Alder to Reconstruct Lake Levels? A Case Study for the Mecklenburg Lake District, Northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 10(8): e0137054. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Can$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_der_maaten_can_2015,\n\ttitle = {Can {We} {Use} {Tree} {Rings} of {Black} {Alder} to {Reconstruct} {Lake} {Levels}? {A} {Case} {Study} for the {Mecklenburg} {Lake} {District}, {Northeastern} {Germany}},\n\tvolume = {10},\n\tissn = {1932-6203},\n\tshorttitle = {Can {We} {Use} {Tree} {Rings} of {Black} {Alder} to {Reconstruct} {Lake} {Levels}?},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0137054},\n\tdoi = {10.1371/journal.pone.0137054},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-02-23},\n\tjournal = {PLOS ONE},\n\tauthor = {Van Der Maaten, Ernst and Van Der Maaten-Theunissen, Marieke and Buras, Allan and Scharnweber, Tobias and Simard, Sonia and Kaiser, Knut and Lorenz, Sebastian and Wilmking, Martin},\n\teditor = {Zhu, Liping},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {e0137054},\n}\n\n\n\n

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\n \n\n \n \n Trauth, N.; Schmidt, C.; Vieweg, M.; Oswald, S. E.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Hydraulic controls of in‐stream gravel bar hyporheic exchange and reactions.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 51(4): 2243–2263. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydraulic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trauth_hydraulic_2015,\n\ttitle = {Hydraulic controls of in‐stream gravel bar hyporheic exchange and reactions},\n\tvolume = {51},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2014WR015857},\n\tdoi = {10.1002/2014WR015857},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Trauth, Nico and Schmidt, Christian and Vieweg, Michael and Oswald, Sascha E. and Fleckenstein, Jan H.},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {2243--2263},\n}\n\n\n\n

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\n \n\n \n \n Theuerkauf, M.; Dräger, N.; Kienel, U.; Kuparinen, A.; and Brauer, A.\n\n\n \n \n \n \n \n Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments.\n \n \n \n \n\n\n \n\n\n\n The Holocene, 25(5): 733–744. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{theuerkauf_effects_2015,\n\ttitle = {Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments},\n\tvolume = {25},\n\tissn = {0959-6836, 1477-0911},\n\turl = {http://journals.sagepub.com/doi/10.1177/0959683614567881},\n\tdoi = {10.1177/0959683614567881},\n\tabstract = {Pollen productivity is a key parameter to quantify past plant abundances and vegetation openness. In this study we explore how changes in land management influence pollen productivity. We study pollen deposition in largely annually laminated sediments from Lake Tiefer See in the Northeastern German lowlands deposited between AD 1880 and 2010. During this period, land use intensity has increased predominantly through the widespread introduction of artificial fertilizers, herbicides and heavy machinery mainly since the 1950s. Although land use statistics show that overall vegetation openness remained largely constant, non-arboreal pollen deposition (from herbs and grasses) sharply declined over the study period. This decline can be partly explained by a shift towards crops that emit little pollen such as wheat and oilseed rape. Furthermore, intensified grassland management, including drainage, also contributed to lower pollen deposition because of the decline of Plantago lanceolata and Rumex from grassland communities. However, the most important effect is a decline in pollen productivity of grasses of about 60\\%, which most likely is a response to earlier and more frequent mowing, although changes in grass species composition may also have played a role. Our results show that the type and intensity of land use have a strong effect on pollen productivity of grasses (and smaller effects on further crops). Since grass pollen deposition is a main proxy for vegetation openness and grasses are the common reference taxon in most PPE studies, variations in the pollen productivity of grasses introduce so far neglected errors in the reconstruction of the past vegetation cover. Our study provides a first estimate about the magnitude of this effect.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-02-23},\n\tjournal = {The Holocene},\n\tauthor = {Theuerkauf, Martin and Dräger, Nadine and Kienel, Ulrike and Kuparinen, Anna and Brauer, Achim},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {733--744},\n}\n\n\n\n

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\n \n\n \n \n Sulis, M.; Langensiepen, M.; Shrestha, P.; Schickling, A.; Simmer, C.; and Kollet, S. J.\n\n\n \n \n \n \n \n Evaluating the Influence of Plant-Specific Physiological Parameterizations on the Partitioning of Land Surface Energy Fluxes.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 16(2): 517–533. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sulis_evaluating_2015,\n\ttitle = {Evaluating the {Influence} of {Plant}-{Specific} {Physiological} {Parameterizations} on the {Partitioning} of {Land} {Surface} {Energy} {Fluxes}},\n\tvolume = {16},\n\tissn = {1525-755X, 1525-7541},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-14-0153.1},\n\tdoi = {10.1175/JHM-D-14-0153.1},\n\tabstract = {Abstract \n            Plant physiological properties have a significant influence on the partitioning of radiative forcing, the spatial and temporal variability of soil water and soil temperature dynamics, and the rate of carbon fixation. Because of the direct impact on latent heat fluxes, these properties may also influence weather-generating processes, such as the evolution of the atmospheric boundary layer (ABL). In this work, crop-specific physiological characteristics, retrieved from detailed field measurements, are included in the biophysical parameterization of the Terrestrial Systems Modeling Platform (TerrSysMP). The physiological parameters for two typical European midlatitudinal crops (sugar beet and winter wheat) are validated using eddy covariance fluxes over multiple years from three measurement sites located in the North Rhine–Westphalia region of Germany. Comparison with observations and a simulation utilizing the generic crop type shows clear improvements when using the crop-specific physiological characteristics of the plant. In particular, the increase of latent heat fluxes in conjunction with decreased sensible heat fluxes as simulated by the two crops leads to an improved quantification of the diurnal energy partitioning. An independent analysis carried out using estimates of gross primary production reveals that the better agreement between observed and simulated latent heat adopting the plant-specific physiological properties largely stems from an improved simulation of the photosynthesis process. Finally, to evaluate the effects of the crop-specific parameterizations on the ABL dynamics, a series of semi-idealized land–atmosphere coupled simulations is performed by hypothesizing three cropland configurations. These numerical experiments reveal different heat and moisture budgets of the ABL using the crop-specific physiological properties, which clearly impacts the evolution of the boundary layer.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Sulis, Mauro and Langensiepen, Matthias and Shrestha, Prabhakar and Schickling, Anke and Simmer, Clemens and Kollet, Stefan J.},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {517--533},\n}\n\n\n\n

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\n \n\n \n \n Stockinger, M. P.; Lücke, A.; McDonnell, J. J.; Diekkrüger, B.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Interception effects on stable isotope driven streamwater transit time estimates: INTERCEPTION AFFECTS TTD.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 42(13): 5299–5308. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Interception$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stockinger_interception_2015,\n\ttitle = {Interception effects on stable isotope driven streamwater transit time estimates: {INTERCEPTION} {AFFECTS} {TTD}},\n\tvolume = {42},\n\tissn = {00948276},\n\tshorttitle = {Interception effects on stable isotope driven streamwater transit time estimates},\n\turl = {http://doi.wiley.com/10.1002/2015GL064622},\n\tdoi = {10.1002/2015GL064622},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2023-02-23},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Stockinger, Michael P. and Lücke, Andreas and McDonnell, Jeffrey J. and Diekkrüger, Bernd and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {5299--5308},\n}\n\n\n\n

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\n \n\n \n \n Stadler, A.; Rudolph, S.; Kupisch, M.; Langensiepen, M.; Van Der Kruk, J.; and Ewert, F.\n\n\n \n \n \n \n \n Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements.\n \n \n \n \n\n\n \n\n\n\n European Journal of Agronomy, 64: 8–20. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stadler_quantifying_2015,\n\ttitle = {Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements},\n\tvolume = {64},\n\tissn = {11610301},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1161030114001452},\n\tdoi = {10.1016/j.eja.2014.12.004},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {European Journal of Agronomy},\n\tauthor = {Stadler, Anja and Rudolph, Sebastian and Kupisch, Moritz and Langensiepen, Matthias and Van Der Kruk, Jan and Ewert, Frank},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {8--20},\n}\n\n\n\n

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\n \n\n \n \n Sorg, J.; and Kunkel, R.\n\n\n \n \n \n \n \n Conception and Implementation of an OGC-Compliant Sensor Observation Service for a Standardized Access to Raster Data.\n \n \n \n \n\n\n \n\n\n\n ISPRS International Journal of Geo-Information, 4(3): 1076–1096. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Conception$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sorg_conception_2015,\n\ttitle = {Conception and {Implementation} of an {OGC}-{Compliant} {Sensor} {Observation} {Service} for a {Standardized} {Access} to {Raster} {Data}},\n\tvolume = {4},\n\tissn = {2220-9964},\n\turl = {http://www.mdpi.com/2220-9964/4/3/1076},\n\tdoi = {10.3390/ijgi4031076},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {ISPRS International Journal of Geo-Information},\n\tauthor = {Sorg, Juergen and Kunkel, Ralf},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {1076--1096},\n}\n\n\n\n

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\n \n\n \n \n Słowiński, M.; Błaszkiewicz, M.; Brauer, A.; Noryśkiewicz, B.; Ott, F.; and Tyszkowski, S.\n\n\n \n \n \n \n \n The role of melting dead ice on landscape transformation in the early Holocene in Tuchola Pinewoods, North Poland.\n \n \n \n \n\n\n \n\n\n\n Quaternary International, 388: 64–75. November 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{slowinski_role_2015,\n\ttitle = {The role of melting dead ice on landscape transformation in the early {Holocene} in {Tuchola} {Pinewoods}, {North} {Poland}},\n\tvolume = {388},\n\tissn = {10406182},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S104061821400384X},\n\tdoi = {10.1016/j.quaint.2014.06.018},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Quaternary International},\n\tauthor = {Słowiński, Michał and Błaszkiewicz, Mirosław and Brauer, Achim and Noryśkiewicz, Bożena and Ott, Florian and Tyszkowski, Sebastian},\n\tmonth = nov,\n\tyear = {2015},\n\tpages = {64--75},\n}\n\n\n\n

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\n \n\n \n \n Simmer, C.; Thiele-Eich, I.; Masbou, M.; Amelung, W.; Bogena, H.; Crewell, S.; Diekkrüger, B.; Ewert, F.; Hendricks Franssen, H.; Huisman, J. A.; Kemna, A.; Klitzsch, N.; Kollet, S.; Langensiepen, M.; Löhnert, U.; Rahman, A. S. M. M.; Rascher, U.; Schneider, K.; Schween, J.; Shao, Y.; Shrestha, P.; Stiebler, M.; Sulis, M.; Vanderborght, J.; Vereecken, H.; Van Der Kruk, J.; Waldhoff, G.; and Zerenner, T.\n\n\n \n \n \n \n \n Monitoring and Modeling the Terrestrial System from Pores to Catchments: The Transregional Collaborative Research Center on Patterns in the Soil–Vegetation–Atmosphere System.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 96(10): 1765–1787. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{simmer_monitoring_2015,\n\ttitle = {Monitoring and {Modeling} the {Terrestrial} {System} from {Pores} to {Catchments}: {The} {Transregional} {Collaborative} {Research} {Center} on {Patterns} in the {Soil}–{Vegetation}–{Atmosphere} {System}},\n\tvolume = {96},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {Monitoring and {Modeling} the {Terrestrial} {System} from {Pores} to {Catchments}},\n\turl = {https://journals.ametsoc.org/doi/10.1175/BAMS-D-13-00134.1},\n\tdoi = {10.1175/BAMS-D-13-00134.1},\n\tabstract = {Abstract \n            Most activities of humankind take place in the transition zone between four compartments of the terrestrial system: the unconfined aquifer, including the unsaturated zone; surface water; vegetation; and atmosphere. The mass, momentum, and heat energy fluxes between these compartments drive their mutual state evolution. Improved understanding of the processes that drive these fluxes is important for climate projections, weather prediction, flood forecasting, water and soil resources management, agriculture, and water quality control. The different transport mechanisms and flow rates within the compartments result in complex patterns on different temporal and spatial scales that make predictions of the terrestrial system challenging for scientists and policy makers. The Transregional Collaborative Research Centre 32 (TR32) was formed in 2007 to integrate monitoring with modeling and data assimilation in order to develop a holistic view of the terrestrial system. TR32 is a long-term research program funded by the German national science foundation Deutsche Forschungsgemeinschaft (DFG), in order to focus and integrate research activities of several universities on an emerging scientific topic of high societal relevance. Aiming to bridge the gap between microscale soil pores and catchment-scale atmospheric variables, TR32 unites research groups from the German universities of Aachen, Bonn, and Cologne, and from the environmental and geoscience departments of Forschungszentrum Jülich GmbH. Here, we report about recent achievements in monitoring and modeling of the terrestrial system, including the development of new observation techniques for the subsurface, the establishment of cross-scale, multicompartment modeling platforms from the pore to the catchment scale, and their use to investigate the propagation of patterns in the state and structure of the subsurface to the atmospheric boundary layer.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-02-23},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Simmer, Clemens and Thiele-Eich, Insa and Masbou, Matthieu and Amelung, Wulf and Bogena, Heye and Crewell, Susanne and Diekkrüger, Bernd and Ewert, Frank and Hendricks Franssen, Harrie-Jan and Huisman, Johan Alexander and Kemna, Andreas and Klitzsch, Norbert and Kollet, Stefan and Langensiepen, Matthias and Löhnert, Ulrich and Rahman, A. S. M. Mostaquimur and Rascher, Uwe and Schneider, Karl and Schween, Jan and Shao, Yaping and Shrestha, Prabhakar and Stiebler, Maik and Sulis, Mauro and Vanderborght, Jan and Vereecken, Harry and Van Der Kruk, Jan and Waldhoff, Guido and Zerenner, Tanja},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {1765--1787},\n}\n\n\n\n

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\n \n\n \n \n Siebenkäs, A.; Schumacher, J.; and Roscher, C.\n\n\n \n \n \n \n \n Phenotypic plasticity to light and nutrient availability alters functional trait ranking across eight perennial grassland species.\n \n \n \n \n\n\n \n\n\n\n AoB PLANTS, 7. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Phenotypic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{siebenkas_phenotypic_2015,\n\ttitle = {Phenotypic plasticity to light and nutrient availability alters functional trait ranking across eight perennial grassland species},\n\tvolume = {7},\n\tissn = {2041-2851},\n\turl = {https://academic.oup.com/aobpla/article/doi/10.1093/aobpla/plv029/200562},\n\tdoi = {10.1093/aobpla/plv029},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {AoB PLANTS},\n\tauthor = {Siebenkäs, Alrun and Schumacher, Jens and Roscher, Christiane},\n\tmonth = jan,\n\tyear = {2015},\n}\n\n\n\n

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\n \n\n \n \n Schwarz, B.; Dietrich, C.; Cesarz, S.; Scherer-Lorenzen, M.; Auge, H.; Schulz, E.; and Eisenhauer, N.\n\n\n \n \n \n \n \n Non-significant tree diversity but significant identity effects on earthworm communities in three tree diversity experiments.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Biology, 67: 17–26. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Non-significant$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schwarz_non-significant_2015,\n\ttitle = {Non-significant tree diversity but significant identity effects on earthworm communities in three tree diversity experiments},\n\tvolume = {67},\n\tissn = {11645563},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1164556315000023},\n\tdoi = {10.1016/j.ejsobi.2015.01.001},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {European Journal of Soil Biology},\n\tauthor = {Schwarz, Benjamin and Dietrich, Christoph and Cesarz, Simone and Scherer-Lorenzen, Michael and Auge, Harald and Schulz, Elke and Eisenhauer, Nico},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {17--26},\n}\n\n\n\n

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\n \n\n \n \n Schröter, I.; Paasche, H.; Dietrich, P.; and Wollschläger, U.\n\n\n \n \n \n \n \n Estimation of Catchment-Scale Soil Moisture Patterns Based on Terrain Data and Sparse TDR Measurements Using a Fuzzy C-Means Clustering Approach.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 14(11): vzj2015.01.0008. November 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schroter_estimation_2015,\n\ttitle = {Estimation of {Catchment}-{Scale} {Soil} {Moisture} {Patterns} {Based} on {Terrain} {Data} and {Sparse} {TDR} {Measurements} {Using} a {Fuzzy} {C}-{Means} {Clustering} {Approach}},\n\tvolume = {14},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2015.01.0008},\n\tdoi = {10.2136/vzj2015.01.0008},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Schröter, Ingmar and Paasche, Hendrik and Dietrich, Peter and Wollschläger, Ute},\n\tmonth = nov,\n\tyear = {2015},\n\tpages = {vzj2015.01.0008},\n}\n\n\n\n

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\n \n\n \n \n Schröder, B.; Steiner, N.; Merbach, I.; Schädler, M.; and Filser, J.\n\n\n \n \n \n \n \n Collembolan reproduction in soils from a long-term fertilisation experiment opposes the Growth Rate Hypothesis.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Biology, 68: 56–60. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Collembolan$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schroder_collembolan_2015,\n\ttitle = {Collembolan reproduction in soils from a long-term fertilisation experiment opposes the {Growth} {Rate} {Hypothesis}},\n\tvolume = {68},\n\tissn = {11645563},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1164556315000333},\n\tdoi = {10.1016/j.ejsobi.2015.03.007},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {European Journal of Soil Biology},\n\tauthor = {Schröder, Birthe and Steiner, Natalie and Merbach, Ines and Schädler, Martin and Filser, Juliane},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {56--60},\n}\n\n\n\n

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\n \n\n \n \n Schollaen, K.; Baschek, H.; Heinrich, I.; and Helle, G.\n\n\n \n \n \n \n \n Technical Note: An improved guideline for rapid and precise sample preparation of tree-ring stable isotope analysis.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Stable Isotopes & Other Tracers, July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Technical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{schollaen_technical_2015,\n\ttype = {preprint},\n\ttitle = {Technical {Note}: {An} improved guideline for rapid and precise sample preparation of tree-ring stable isotope analysis},\n\tshorttitle = {Technical {Note}},\n\turl = {https://bg.copernicus.org/preprints/12/11587/2015/},\n\tabstract = {Abstract. The procedure of wood sample preparation, including tree-ring dissection, cellulose extraction, homogenization and finally weighing and packing for stable isotope analysis is labour intensive and time consuming.  We present an elaborated methodical guideline from pre-analyses considerations, wood sample preparation through semi-automated chemical extraction of cellulose directly from tree-ring cross-sections to tree-ring dissection for high-precision isotope ratio mass spectrometry. This guideline reduces time and maximizes the tree-ring stable isotope data throughput significantly.  The method was applied to ten different tree species (coniferous and angiosperm wood) with different wood growth rates and differently shaped tree-ring boundaries. The tree-ring structures of the cellulose cross-sections largely remained well identifiable. FTIR (Fourier transform infrared) spectrometry and the comparison of stable isotope values with classical method confirm chemical purity of the resultant cellulose. Sample homogenization is no longer necessary.  Cellulose extraction is now faster, cheaper and more user friendly allowing (i) the simultaneous treatment of wood cross-sections of a total length of 180 cm (equivalent to 6 increment cores of 30 cm length) and thickness of 0.5 to 2 mm, and (ii) precise tree-ring separation at annual to high-resolution scale utilizing manual devices or UV-laser microdissection microscopes.},\n\turldate = {2023-02-23},\n\tinstitution = {Biogeochemistry: Stable Isotopes \\&amp; Other Tracers},\n\tauthor = {Schollaen, K. and Baschek, H. and Heinrich, I. and Helle, G.},\n\tmonth = jul,\n\tyear = {2015},\n\tdoi = {10.5194/bgd-12-11587-2015},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Scharnweber, T.; Couwenberg, J.; Heinrich, I.; and Wilmking, M.\n\n\n \n \n \n \n \n New insights for the interpretation of ancient bog oak chronologies? Reactions of oak (Quercus robur L.) to a sudden peatland rewetting.\n \n \n \n \n\n\n \n\n\n\n Palaeogeography, Palaeoclimatology, Palaeoecology, 417: 534–543. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"New$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{scharnweber_new_2015,\n\ttitle = {New insights for the interpretation of ancient bog oak chronologies? {Reactions} of oak ({Quercus} robur {L}.) to a sudden peatland rewetting},\n\tvolume = {417},\n\tissn = {00310182},\n\tshorttitle = {New insights for the interpretation of ancient bog oak chronologies?},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0031018214005215},\n\tdoi = {10.1016/j.palaeo.2014.10.017},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Palaeogeography, Palaeoclimatology, Palaeoecology},\n\tauthor = {Scharnweber, Tobias and Couwenberg, John and Heinrich, Ingo and Wilmking, Martin},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {534--543},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Sachs, T.; Koebsch, F.; Franz, D.; Larmanou, E.; Serafimovich, A.; Kohnert, K.; Jurasinski, G.; and Augustin, J.\n\n\n \n \n \n \n \n Mehr Moor? Zur Treibhausgasdynamik wiedervernässter Feuchtgebiete.\n \n \n \n \n\n\n \n\n\n\n System Erde; 5. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Mehr$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sachs_mehr_2015,\n\ttitle = {Mehr {Moor}? {Zur} {Treibhausgasdynamik} wiedervernässter {Feuchtgebiete}},\n\tshorttitle = {Mehr {Moor}?},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_1199648},\n\tdoi = {10.2312/GFZ.SYSERDE.05.01.4},\n\tlanguage = {de},\n\turldate = {2023-02-23},\n\tjournal = {System Erde; 5},\n\tauthor = {Sachs, Torsten and Koebsch, Franziska and Franz, Daniela and Larmanou, Eric and Serafimovich, Andrei and Kohnert, Katrin and Jurasinski, Gerald and Augustin, Jürgen},\n\tyear = {2015},\n}\n\n\n\n

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\n \n\n \n \n Rudolph, S.; Van Der Kruk, J.; Von Hebel, C.; Ali, M.; Herbst, M.; Montzka, C.; Pätzold, S.; Robinson, D.; Vereecken, H.; and Weihermüller, L.\n\n\n \n \n \n \n \n Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 241-242: 262–271. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rudolph_linking_2015,\n\ttitle = {Linking satellite derived {LAI} patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements},\n\tvolume = {241-242},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706114004133},\n\tdoi = {10.1016/j.geoderma.2014.11.015},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Geoderma},\n\tauthor = {Rudolph, S. and Van Der Kruk, J. and Von Hebel, C. and Ali, M. and Herbst, M. and Montzka, C. and Pätzold, S. and Robinson, D.A. and Vereecken, H. and Weihermüller, L.},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {262--271},\n}\n\n\n\n

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\n \n\n \n \n Rahman, M.; Sulis, M.; and Kollet, S.\n\n\n \n \n \n \n \n The subsurface–land surface–atmosphere connection under convective conditions.\n \n \n \n \n\n\n \n\n\n\n Advances in Water Resources, 83: 240–249. September 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahman_subsurfaceland_2015,\n\ttitle = {The subsurface–land surface–atmosphere connection under convective conditions},\n\tvolume = {83},\n\tissn = {03091708},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0309170815001281},\n\tdoi = {10.1016/j.advwatres.2015.06.003},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Advances in Water Resources},\n\tauthor = {Rahman, M. and Sulis, M. and Kollet, S.J.},\n\tmonth = sep,\n\tyear = {2015},\n\tpages = {240--249},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Rennenberg, H.; and Dannenmann, M.\n\n\n \n \n \n \n \n Nitrogen Nutrition of Trees in Temperate Forests—The Significance of Nitrogen Availability in the Pedosphere and Atmosphere.\n \n \n \n \n\n\n \n\n\n\n Forests, 6(12): 2820–2835. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Nitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rennenberg_nitrogen_2015,\n\ttitle = {Nitrogen {Nutrition} of {Trees} in {Temperate} {Forests}—{The} {Significance} of {Nitrogen} {Availability} in the {Pedosphere} and {Atmosphere}},\n\tvolume = {6},\n\tissn = {1999-4907},\n\turl = {http://www.mdpi.com/1999-4907/6/8/2820},\n\tdoi = {10.3390/f6082820},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-02-23},\n\tjournal = {Forests},\n\tauthor = {Rennenberg, Heinz and Dannenmann, Michael},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {2820--2835},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Qu, W.; Bogena, H. R.; Huisman, J. A.; Vanderborght, J.; Schuh, M.; Priesack, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Predicting subgrid variability of soil water content from basic soil information.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 42(3): 789–796. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Predicting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{qu_predicting_2015,\n\ttitle = {Predicting subgrid variability of soil water content from basic soil information},\n\tvolume = {42},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/2014GL062496},\n\tdoi = {10.1002/2014GL062496},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Qu, W. and Bogena, H. R. and Huisman, J. A. and Vanderborght, J. and Schuh, M. and Priesack, E. and Vereecken, H.},\n\tmonth = feb,\n\tyear = {2015},\n\tpages = {789--796},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Post, H.; Hendricks Franssen, H. J.; Graf, A.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Uncertainty analysis of eddy covariance CO2 flux measurements for different EC tower distances using an extended two-tower approach.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 12(4): 1205–1221. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{post_uncertainty_2015,\n\ttitle = {Uncertainty analysis of eddy covariance {CO}\\&lt;sub\\&gt;2\\&lt;/sub\\&gt; flux measurements for different {EC} tower distances using an extended two-tower approach},\n\tvolume = {12},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/12/1205/2015/},\n\tdoi = {10.5194/bg-12-1205-2015},\n\tabstract = {Abstract. The use of eddy covariance (EC) CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the (classical) two-tower approach has yielded robust uncertainty estimates, but care must be taken to meet the often competing requirements of statistical independence (non-overlapping footprints) and ecosystem homogeneity when choosing an appropriate tower distance. The role of the tower distance was investigated with help of a roving station separated between 8 m and 34 km from a permanent EC grassland station. Random uncertainty was estimated for five separation distances with the classical two-tower approach and an extended approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a data set where (i) only similar weather conditions at the two sites were included, and (ii) an unfiltered one. The extended approach, applied to weather-filtered data for separation distances of 95 and 173 m gave uncertainty estimates in best correspondence with an independent reference method. The introduced correction for systematic flux differences considerably reduced the overestimation of the two-tower based uncertainty of net CO2 flux measurements and decreased the sensitivity of results to tower distance. We therefore conclude that corrections for systematic flux differences (e.g., caused by different environmental conditions at both EC towers) can help to apply the two-tower approach to more site pairs with less ideal conditions.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Biogeosciences},\n\tauthor = {Post, H. and Hendricks Franssen, H. J. and Graf, A. and Schmidt, M. and Vereecken, H.},\n\tmonth = feb,\n\tyear = {2015},\n\tpages = {1205--1221},\n}\n\n\n\n

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\n \n\n \n \n Petrescu, A. M. R.; Lohila, A.; Tuovinen, J.; Baldocchi, D. D.; Desai, A. R.; Roulet, N. T.; Vesala, T.; Dolman, A. J.; Oechel, W. C.; Marcolla, B.; Friborg, T.; Rinne, J.; Matthes, J. H.; Merbold, L.; Meijide, A.; Kiely, G.; Sottocornola, M.; Sachs, T.; Zona, D.; Varlagin, A.; Lai, D. Y. F.; Veenendaal, E.; Parmentier, F. W.; Skiba, U.; Lund, M.; Hensen, A.; Van Huissteden, J.; Flanagan, L. B.; Shurpali, N. J.; Grünwald, T.; Humphreys, E. R.; Jackowicz-Korczyński, M.; Aurela, M. A.; Laurila, T.; Grüning, C.; Corradi, C. A. R.; Schrier-Uijl, A. P.; Christensen, T. R.; Tamstorf, M. P.; Mastepanov, M.; Martikainen, P. J.; Verma, S. B.; Bernhofer, C.; and Cescatti, A.\n\n\n \n \n \n \n \n The uncertain climate footprint of wetlands under human pressure.\n \n \n \n \n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 112(15): 4594–4599. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{petrescu_uncertain_2015,\n\ttitle = {The uncertain climate footprint of wetlands under human pressure},\n\tvolume = {112},\n\tissn = {0027-8424, 1091-6490},\n\turl = {https://pnas.org/doi/full/10.1073/pnas.1416267112},\n\tdoi = {10.1073/pnas.1416267112},\n\tabstract = {Significance \n Wetlands are unique ecosystems because they are in general sinks for carbon dioxide and sources of methane. Their climate footprint therefore depends on the relative sign and magnitude of the land–atmosphere exchange of these two major greenhouse gases. This work presents a synthesis of simultaneous measurements of carbon dioxide and methane fluxes to assess the radiative forcing of natural wetlands converted to agricultural or forested land. The net climate impact of wetlands is strongly dependent on whether they are natural or managed. Here we show that the conversion of natural wetlands produces a significant increase of the atmospheric radiative forcing. The findings suggest that management plans for these complex ecosystems should carefully account for the potential biogeochemical effects on climate. \n , \n \n Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO \n 2 \n and CH \n 4 \n fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH \n 4 \n emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO \n 2 \n uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH \n 4 \n emissions for the benefit of net carbon sequestration. With a sustained pulse–response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH \n 4 \n emissions and cumulative CO \n 2 \n exchange.},\n\tlanguage = {en},\n\tnumber = {15},\n\turldate = {2023-02-23},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Petrescu, Ana Maria Roxana and Lohila, Annalea and Tuovinen, Juha-Pekka and Baldocchi, Dennis D. and Desai, Ankur R. and Roulet, Nigel T. and Vesala, Timo and Dolman, Albertus Johannes and Oechel, Walter C. and Marcolla, Barbara and Friborg, Thomas and Rinne, Janne and Matthes, Jaclyn Hatala and Merbold, Lutz and Meijide, Ana and Kiely, Gerard and Sottocornola, Matteo and Sachs, Torsten and Zona, Donatella and Varlagin, Andrej and Lai, Derrick Y. F. and Veenendaal, Elmar and Parmentier, Frans-Jan W. and Skiba, Ute and Lund, Magnus and Hensen, Arjan and Van Huissteden, Jacobus and Flanagan, Lawrence B. and Shurpali, Narasinha J. and Grünwald, Thomas and Humphreys, Elyn R. and Jackowicz-Korczyński, Marcin and Aurela, Mika A. and Laurila, Tuomas and Grüning, Carsten and Corradi, Chiara A. R. and Schrier-Uijl, Arina P. and Christensen, Torben R. and Tamstorf, Mikkel P. and Mastepanov, Mikhail and Martikainen, Pertti J. and Verma, Shashi B. and Bernhofer, Christian and Cescatti, Alessandro},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {4594--4599},\n}\n\n\n\n

\n\n\n

\n Significance Wetlands are unique ecosystems because they are in general sinks for carbon dioxide and sources of methane. Their climate footprint therefore depends on the relative sign and magnitude of the land–atmosphere exchange of these two major greenhouse gases. This work presents a synthesis of simultaneous measurements of carbon dioxide and methane fluxes to assess the radiative forcing of natural wetlands converted to agricultural or forested land. The net climate impact of wetlands is strongly dependent on whether they are natural or managed. Here we show that the conversion of natural wetlands produces a significant increase of the atmospheric radiative forcing. The findings suggest that management plans for these complex ecosystems should carefully account for the potential biogeochemical effects on climate. , Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO 2 and CH 4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH 4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO 2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH 4 emissions for the benefit of net carbon sequestration. With a sustained pulse–response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH 4 emissions and cumulative CO 2 exchange.\n

\n\n\n

\n \n\n \n \n Paine, C. E. T.; Amissah, L.; Auge, H.; Baraloto, C.; Baruffol, M.; Bourland, N.; Bruelheide, H.; Daïnou, K.; De Gouvenain, R. C.; Doucet, J.; Doust, S.; Fine, P. V. A.; Fortunel, C.; Haase, J.; Holl, K. D.; Jactel, H.; Li, X.; Kitajima, K.; Koricheva, J.; Martínez-Garza, C.; Messier, C.; Paquette, A.; Philipson, C.; Piotto, D.; Poorter, L.; Posada, J. M.; Potvin, C.; Rainio, K.; Russo, S. E.; Ruiz-Jaen, M.; Scherer-Lorenzen, M.; Webb, C. O.; Wright, S. J.; Zahawi, R. A.; and Hector, A.\n\n\n \n \n \n \n \n Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why.\n \n \n \n \n\n\n \n\n\n\n Journal of Ecology, 103(4): 978–989. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Globally,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{paine_globally_2015,\n\ttitle = {Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why},\n\tvolume = {103},\n\tissn = {00220477},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12401},\n\tdoi = {10.1111/1365-2745.12401},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Ecology},\n\tauthor = {Paine, C. E. Timothy and Amissah, Lucy and Auge, Harald and Baraloto, Christopher and Baruffol, Martin and Bourland, Nils and Bruelheide, Helge and Daïnou, Kasso and De Gouvenain, Roland C. and Doucet, Jean-Louis and Doust, Susan and Fine, Paul V. A. and Fortunel, Claire and Haase, Josephine and Holl, Karen D. and Jactel, Hervé and Li, Xuefei and Kitajima, Kaoru and Koricheva, Julia and Martínez-Garza, Cristina and Messier, Christian and Paquette, Alain and Philipson, Christopher and Piotto, Daniel and Poorter, Lourens and Posada, Juan M. and Potvin, Catherine and Rainio, Kalle and Russo, Sabrina E. and Ruiz-Jaen, Mariacarmen and Scherer-Lorenzen, Michael and Webb, Campbell O. and Wright, S. Joseph and Zahawi, Rakan A. and Hector, Andy},\n\teditor = {Gibson, David},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {978--989},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Orlinskiy, P.; Münze, R.; Beketov, M.; Gunold, R.; Paschke, A.; Knillmann, S.; and Liess, M.\n\n\n \n \n \n \n \n Forested headwaters mitigate pesticide effects on macroinvertebrate communities in streams: Mechanisms and quantification.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 524-525: 115–123. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Forested$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{orlinskiy_forested_2015,\n\ttitle = {Forested headwaters mitigate pesticide effects on macroinvertebrate communities in streams: {Mechanisms} and quantification},\n\tvolume = {524-525},\n\tissn = {00489697},\n\tshorttitle = {Forested headwaters mitigate pesticide effects on macroinvertebrate communities in streams},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969715004271},\n\tdoi = {10.1016/j.scitotenv.2015.03.143},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Orlinskiy, Polina and Münze, Ronald and Beketov, Mikhail and Gunold, Roman and Paschke, Albrecht and Knillmann, Saskia and Liess, Matthias},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {115--123},\n}\n\n\n\n

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\n \n\n \n \n Molina-Herrera, S.; Grote, R.; Santabárbara-Ruiz, I.; Kraus, D.; Klatt, S.; Haas, E.; Kiese, R.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n Simulation of CO2 Fluxes in European Forest Ecosystems with the Coupled Soil-Vegetation Process Model “LandscapeDNDC”.\n \n \n \n \n\n\n \n\n\n\n Forests, 6(12): 1779–1809. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{molina-herrera_simulation_2015,\n\ttitle = {Simulation of {CO2} {Fluxes} in {European} {Forest} {Ecosystems} with the {Coupled} {Soil}-{Vegetation} {Process} {Model} “{LandscapeDNDC}”},\n\tvolume = {6},\n\tissn = {1999-4907},\n\turl = {http://www.mdpi.com/1999-4907/6/6/1779},\n\tdoi = {10.3390/f6061779},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-02-23},\n\tjournal = {Forests},\n\tauthor = {Molina-Herrera, Saúl and Grote, Rüdiger and Santabárbara-Ruiz, Ignacio and Kraus, David and Klatt, Steffen and Haas, Edwin and Kiese, Ralf and Butterbach-Bahl, Klaus},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {1779--1809},\n}\n\n\n\n

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\n \n\n \n \n Martini, E.; Wollschläger, U.; Kögler, S.; Behrens, T.; Dietrich, P.; Reinstorf, F.; Schmidt, K.; Weiler, M.; Werban, U.; and Zacharias, S.\n\n\n \n \n \n \n \n Spatial and Temporal Dynamics of Hillslope-Scale Soil Moisture Patterns: Characteristic States and Transition Mechanisms.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 14(4): vzj2014.10.0150. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martini_spatial_2015,\n\ttitle = {Spatial and {Temporal} {Dynamics} of {Hillslope}-{Scale} {Soil} {Moisture} {Patterns}: {Characteristic} {States} and {Transition} {Mechanisms}},\n\tvolume = {14},\n\tissn = {15391663},\n\tshorttitle = {Spatial and {Temporal} {Dynamics} of {Hillslope}-{Scale} {Soil} {Moisture} {Patterns}},\n\turl = {http://doi.wiley.com/10.2136/vzj2014.10.0150},\n\tdoi = {10.2136/vzj2014.10.0150},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Martini, Edoardo and Wollschläger, Ute and Kögler, Simon and Behrens, Thorsten and Dietrich, Peter and Reinstorf, Frido and Schmidt, Karsten and Weiler, Markus and Werban, Ulrike and Zacharias, Steffen},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {vzj2014.10.0150},\n}\n\n\n\n

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\n \n\n \n \n Löhnert, U.; Schween, J. H.; Acquistapace, C.; Ebell, K.; Maahn, M.; Barrera-Verdejo, M.; Hirsikko, A.; Bohn, B.; Knaps, A.; O’Connor, E.; Simmer, C.; Wahner, A.; and Crewell, S.\n\n\n \n \n \n \n \n JOYCE: Jülich Observatory for Cloud Evolution.\n \n \n \n \n\n\n \n\n\n\n Bulletin of the American Meteorological Society, 96(7): 1157–1174. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"JOYCE:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lohnert_joyce_2015,\n\ttitle = {{JOYCE}: {Jülich} {Observatory} for {Cloud} {Evolution}},\n\tvolume = {96},\n\tissn = {0003-0007, 1520-0477},\n\tshorttitle = {{JOYCE}},\n\turl = {https://journals.ametsoc.org/doi/10.1175/BAMS-D-14-00105.1},\n\tdoi = {10.1175/BAMS-D-14-00105.1},\n\tabstract = {Abstract \n            The Jülich Observatory for Cloud Evolution (JOYCE), located at Forschungszentrum Jülich in the most western part of Germany, is a recently established platform for cloud research. The main objective of JOYCE is to provide observations, which improve our understanding of the cloudy boundary layer in a midlatitude environment. Continuous and temporally highly resolved measurements that are specifically suited to characterize the diurnal cycle of water vapor, stability, and turbulence in the lower troposphere are performed with a special focus on atmosphere–surface interaction. In addition, instruments are set up to measure the micro- and macrophysical properties of clouds in detail and how they interact with different boundary layer processes and the large-scale synoptic situation. For this, JOYCE is equipped with an array of state-of-the-art active and passive remote sensing and in situ instruments, which are briefly described in this scientific overview. As an example, a 24-h time series of the evolution of a typical cumulus cloud-topped boundary layer is analyzed with respect to stability, turbulence, and cloud properties. Additionally, we present longer-term statistics, which can be used to elucidate the diurnal cycle of water vapor, drizzle formation through autoconversion, and warm versus cold rain precipitation formation. Both case studies and long-term observations are important for improving the representation of clouds in climate and numerical weather prediction models.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-02-23},\n\tjournal = {Bulletin of the American Meteorological Society},\n\tauthor = {Löhnert, U. and Schween, J. H. and Acquistapace, C. and Ebell, K. and Maahn, M. and Barrera-Verdejo, M. and Hirsikko, A. and Bohn, B. and Knaps, A. and O’Connor, E. and Simmer, C. and Wahner, A. and Crewell, S.},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {1157--1174},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Salbach, C.; Schmidt, A.; Doktor, D.; Merbach, I.; and Pause, M.\n\n\n \n \n \n \n \n Deriving phenology of barley with imaging hyperspectral remote sensing.\n \n \n \n \n\n\n \n\n\n\n Ecological Modelling, 295: 123–135. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Deriving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_deriving_2015,\n\ttitle = {Deriving phenology of barley with imaging hyperspectral remote sensing},\n\tvolume = {295},\n\tissn = {03043800},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0304380014004670},\n\tdoi = {10.1016/j.ecolmodel.2014.10.001},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Ecological Modelling},\n\tauthor = {Lausch, Angela and Salbach, Christoph and Schmidt, Andreas and Doktor, Daniel and Merbach, Ines and Pause, Marion},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {123--135},\n}\n\n\n\n

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\n \n\n \n \n Lane, C.; Brauer, A; Martín-Puertas, C; Blockley, S.; Smith, V.; and Tomlinson, E.\n\n\n \n \n \n \n \n The Late Quaternary tephrostratigraphy of annually laminated sediments from Meerfelder Maar, Germany.\n \n \n \n \n\n\n \n\n\n\n . August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lane_late_2015,\n\ttitle = {The {Late} {Quaternary} tephrostratigraphy of annually laminated sediments from {Meerfelder} {Maar}, {Germany}},\n\tcopyright = {Attribution-NonCommercial-NoDerivatives 4.0 International, Attribution-NonCommercial-NoDerivatives 4.0 International, Attribution-NonCommercial-NoDerivatives 4.0 International, Attribution-NonCommercial-NoDerivatives 4.0 International, Attribution-NonCommercial-NoDerivatives 4.0 International, Creative Commons Attribution Non Commercial No Derivatives 4.0 International, Creative Commons Attribution Non Commercial No Derivatives 4.0 International, Creative Commons Attribution Non Commercial No Derivatives 4.0 International, Creative Commons Attribution Non Commercial No Derivatives 4.0 International, Creative Commons Attribution Non Commercial No Derivatives 4.0 International},\n\turl = {https://www.repository.cam.ac.uk/handle/1810/261308},\n\tdoi = {10.17863/CAM.6482},\n\tabstract = {© 2015 Elsevier Ltd.The record of Late Quaternary environmental change within the sediments of Meerfelder Maar in the Eifel region of Germany is renowned for its high precision chronology, which is annually laminated throughout the Last Glacial to Interglacial transition (LGIT) and most of the Holocene. Two visible tephra layers are prominent within the floating varve chronology of Meerfelder Maar. An Early Holocene tephra layer, the Ulmener Maar Tephra ({\\textasciitilde}11,000 varve years BP), provides a tie-line of the Meerfelder Maar record to the varved Holocene record of nearby Lake Holzmaar. The Laacher See Tephra provides another prominent time marker for the late Allerød, {\\textasciitilde}200 varve years before the transition into the Younger Dryas at 12,680 varve years BP. Further investigation has now shown that there are also 15 cryptotephra layers within the Meerfelder Maar LGIT-Holocene stratigraphy and these layers hold the potential to make direct comparisons between the Meerfelder Maar record and other palaeoenvironmental archives from across Europe and the North Atlantic. Most notable is the presence of the Vedde Ash, the most widespread Icelandic eruption known from the Late Quaternary, which occurred midway through the Younger Dryas. The Vedde Ash has also been found in the Greenland ice cores and can be used as an isochron around which the GICC05 and Meerfelder Maar annual chronologies can be compared. Near the base of the annual laminations in Meerfelder Maar a cryptotephra is found that correlates to the Neapolitan Yellow Tuff, erupted from Campi Flegrei in southern Italy, 1200km away. This is the furthest north that the Neapolitan Yellow Tuff has been found, highlighting its importance in the construction of a European-wide tephrostratigraphic framework. The co-location of cryptotephra layers from Italian, Icelandic and Eifel volcanic sources, within such a precise chronological record, makes Meerfelder Maar one of the most important tephrostratotype records for continental Europe during the Last Glacial to Interglacial transition.},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tauthor = {Lane, CS and Brauer, A and Martín-Puertas, C and Blockley, SPE and Smith, VC and Tomlinson, EL},\n\tcollaborator = {{Apollo-University Of Cambridge Repository} and {Apollo-University Of Cambridge Repository}},\n\tmonth = aug,\n\tyear = {2015},\n}\n\n\n\n

\n\n\n

\n © 2015 Elsevier Ltd.The record of Late Quaternary environmental change within the sediments of Meerfelder Maar in the Eifel region of Germany is renowned for its high precision chronology, which is annually laminated throughout the Last Glacial to Interglacial transition (LGIT) and most of the Holocene. Two visible tephra layers are prominent within the floating varve chronology of Meerfelder Maar. An Early Holocene tephra layer, the Ulmener Maar Tephra (~11,000 varve years BP), provides a tie-line of the Meerfelder Maar record to the varved Holocene record of nearby Lake Holzmaar. The Laacher See Tephra provides another prominent time marker for the late Allerød, ~200 varve years before the transition into the Younger Dryas at 12,680 varve years BP. Further investigation has now shown that there are also 15 cryptotephra layers within the Meerfelder Maar LGIT-Holocene stratigraphy and these layers hold the potential to make direct comparisons between the Meerfelder Maar record and other palaeoenvironmental archives from across Europe and the North Atlantic. Most notable is the presence of the Vedde Ash, the most widespread Icelandic eruption known from the Late Quaternary, which occurred midway through the Younger Dryas. The Vedde Ash has also been found in the Greenland ice cores and can be used as an isochron around which the GICC05 and Meerfelder Maar annual chronologies can be compared. Near the base of the annual laminations in Meerfelder Maar a cryptotephra is found that correlates to the Neapolitan Yellow Tuff, erupted from Campi Flegrei in southern Italy, 1200km away. This is the furthest north that the Neapolitan Yellow Tuff has been found, highlighting its importance in the construction of a European-wide tephrostratigraphic framework. The co-location of cryptotephra layers from Italian, Icelandic and Eifel volcanic sources, within such a precise chronological record, makes Meerfelder Maar one of the most important tephrostratotype records for continental Europe during the Last Glacial to Interglacial transition.\n

\n\n\n

\n \n\n \n \n Kraus, D.; Weller, S.; Klatt, S.; Haas, E.; Wassmann, R.; Kiese, R.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n A new LandscapeDNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 386(1-2): 125–149. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kraus_new_2015,\n\ttitle = {A new {LandscapeDNDC} biogeochemical module to predict {CH4} and {N2O} emissions from lowland rice and upland cropping systems},\n\tvolume = {386},\n\tissn = {0032-079X, 1573-5036},\n\turl = {http://link.springer.com/10.1007/s11104-014-2255-x},\n\tdoi = {10.1007/s11104-014-2255-x},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2023-02-23},\n\tjournal = {Plant and Soil},\n\tauthor = {Kraus, David and Weller, Sebastian and Klatt, Steffen and Haas, Edwin and Wassmann, Reiner and Kiese, Ralf and Butterbach-Bahl, Klaus},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {125--149},\n}\n\n\n\n

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\n \n\n \n \n Korres, W.; Reichenau, T.; Fiener, P.; Koyama, C.; Bogena, H.; Cornelissen, T.; Baatz, R.; Herbst, M.; Diekkrüger, B.; Vereecken, H.; and Schneider, K.\n\n\n \n \n \n \n \n Spatio-temporal soil moisture patterns – A meta-analysis using plot to catchment scale data.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 520: 326–341. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{korres_spatio-temporal_2015,\n\ttitle = {Spatio-temporal soil moisture patterns – {A} meta-analysis using plot to catchment scale data},\n\tvolume = {520},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169414009627},\n\tdoi = {10.1016/j.jhydrol.2014.11.042},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Korres, W. and Reichenau, T.G. and Fiener, P. and Koyama, C.N. and Bogena, H.R. and Cornelissen, T. and Baatz, R. and Herbst, M. and Diekkrüger, B. and Vereecken, H. and Schneider, K.},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {326--341},\n}\n\n\n\n

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\n \n\n \n \n Köhli, M.; Schrön, M.; Zreda, M.; Schmidt, U.; Dietrich, P.; and Zacharias, S.\n\n\n \n \n \n \n \n Footprint characteristics revised for field‐scale soil moisture monitoring with cosmic‐ray neutrons.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 51(7): 5772–5790. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Footprint$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kohli_footprint_2015,\n\ttitle = {Footprint characteristics revised for field‐scale soil moisture monitoring with cosmic‐ray neutrons},\n\tvolume = {51},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2015WR017169},\n\tdoi = {10.1002/2015WR017169},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-02-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Köhli, M. and Schrön, M. and Zreda, M. and Schmidt, U. and Dietrich, P. and Zacharias, S.},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {5772--5790},\n}\n\n\n\n

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\n \n\n \n \n Koebsch, F.; Jurasinski, G.; Koch, M.; Hofmann, J.; and Glatzel, S.\n\n\n \n \n \n \n \n Controls for multi-scale temporal variation in ecosystem methane exchange during the growing season of a permanently inundated fen.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 204: 94–105. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Controls$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koebsch_controls_2015,\n\ttitle = {Controls for multi-scale temporal variation in ecosystem methane exchange during the growing season of a permanently inundated fen},\n\tvolume = {204},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192315000301},\n\tdoi = {10.1016/j.agrformet.2015.02.002},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Koebsch, Franziska and Jurasinski, Gerald and Koch, Marian and Hofmann, Joachim and Glatzel, Stephan},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {94--105},\n}\n\n\n\n

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\n \n\n \n \n Kamjunke, N.; Herzsprung, P.; and Neu, T. R.\n\n\n \n \n \n \n \n Quality of dissolved organic matter affects planktonic but not biofilm bacterial production in streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 506-507: 353–360. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Quality$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_quality_2015,\n\ttitle = {Quality of dissolved organic matter affects planktonic but not biofilm bacterial production in streams},\n\tvolume = {506-507},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969714016258},\n\tdoi = {10.1016/j.scitotenv.2014.11.043},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Kamjunke, Norbert and Herzsprung, Peter and Neu, Thomas R.},\n\tmonth = feb,\n\tyear = {2015},\n\tpages = {353--360},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Kamjunke, N.; Mages, M.; Büttner, O.; Marcus, H.; and Weitere, M.\n\n\n \n \n \n \n \n Relationship between the elemental composition of stream biofilms and water chemistry—a catchment approach.\n \n \n \n \n\n\n \n\n\n\n Environmental Monitoring and Assessment, 187(7): 432. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Relationship$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_relationship_2015,\n\ttitle = {Relationship between the elemental composition of stream biofilms and water chemistry—a catchment approach},\n\tvolume = {187},\n\tissn = {0167-6369, 1573-2959},\n\turl = {http://link.springer.com/10.1007/s10661-015-4664-6},\n\tdoi = {10.1007/s10661-015-4664-6},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-02-23},\n\tjournal = {Environmental Monitoring and Assessment},\n\tauthor = {Kamjunke, Norbert and Mages, Margarete and Büttner, Olaf and Marcus, Hanna and Weitere, Markus},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {432},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Kobel, J.; Korzetz, A.; Lehmann, T.; and Schwabe, M.\n\n\n \n \n \n \n Bibliographie wissenschaftlicher und populärwissenschaftlicher Arbeiten mit Bezug zum Teilgebiet Serrahn des Müritz-Nationalparks und Umgebung – Titel 1986-2015.\n \n \n \n\n\n \n\n\n\n In pages 259–276. August 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{kaiser_bibliographie_2015,\n\ttitle = {Bibliographie wissenschaftlicher und populärwissenschaftlicher {Arbeiten} mit {Bezug} zum {Teilgebiet} {Serrahn} des {Müritz}-{Nationalparks} und {Umgebung} – {Titel} 1986-2015},\n\tauthor = {Kaiser, Knut and Kobel, Joachim and Korzetz, Alf and Lehmann, Tobias and Schwabe, Matthias},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {259--276},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Dreibrodt, J.; Küster, M.; and Stüve, P.\n\n\n \n \n \n \n Die hydrologische Entwicklung des Großen Fürstenseer Sees (Müritz-Nationalpark) im letzten Jahrtausend – Ein Überblick.\n \n \n \n\n\n \n\n\n\n In pages 61–81. September 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{kaiser_hydrologische_2015,\n\ttitle = {Die hydrologische {Entwicklung} des {Großen} {Fürstenseer} {Sees} ({Müritz}-{Nationalpark}) im letzten {Jahrtausend} – {Ein} Überblick},\n\tauthor = {Kaiser, Knut and Dreibrodt, Janek and Küster, Mathias and Stüve, Peter},\n\tmonth = sep,\n\tyear = {2015},\n\tpages = {61--81},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Kobel, J.; Küster, M.; and Schwabe, M.\n\n\n \n \n \n \n Neue Beiträge zum Naturraum und zur Landschaftsgeschichte im Teilgebiet Serrahn des Müritz-Nationalparks [New contributions to the environment and landscape history in the Serrahn sub-area of the Müritz National Park, NE Germany].\n \n \n \n\n\n \n\n\n\n September 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@book{kaiser_neue_2015,\n\ttitle = {Neue {Beiträge} zum {Naturraum} und zur {Landschaftsgeschichte} im {Teilgebiet} {Serrahn} des {Müritz}-{Nationalparks} [{New} contributions to the environment and landscape history in the {Serrahn} sub-area of the {Müritz} {National} {Park}, {NE} {Germany}]},\n\tauthor = {Kaiser, Knut and Kobel, Joachim and Küster, Mathias and Schwabe, Matthias},\n\tmonth = sep,\n\tyear = {2015},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kaiser, K.; Heinrich, I.; Heine, I.; Natkhin, M.; Dannowski, R.; Lischeid, G.; Schneider, T.; Henkel, J.; Küster, M.; Heussner, K.; Bens, O.; and Chmieleski, J.\n\n\n \n \n \n \n \n Multi-decadal lake-level dynamics in north-eastern Germany as derived by a combination of gauging, proxy-data and modelling.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 529: 584–599. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-decadal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_multi-decadal_2015,\n\ttitle = {Multi-decadal lake-level dynamics in north-eastern {Germany} as derived by a combination of gauging, proxy-data and modelling},\n\tvolume = {529},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415000025},\n\tdoi = {10.1016/j.jhydrol.2014.12.057},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Kaiser, Knut and Heinrich, Ingo and Heine, Iris and Natkhin, Marco and Dannowski, Ralf and Lischeid, Gunnar and Schneider, Thomas and Henkel, Johanna and Küster, Mathias and Heussner, Karl-Uwe and Bens, Oliver and Chmieleski, Jana},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {584--599},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jonard, F.; Weihermuller, L.; Schwank, M.; Jadoon, K. Z.; Vereecken, H.; and Lambot, S.\n\n\n \n \n \n \n \n Estimation of Hydraulic Properties of a Sandy Soil Using Ground-Based Active and Passive Microwave Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 53(6): 3095–3109. June 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jonard_estimation_2015,\n\ttitle = {Estimation of {Hydraulic} {Properties} of a {Sandy} {Soil} {Using} {Ground}-{Based} {Active} and {Passive} {Microwave} {Remote} {Sensing}},\n\tvolume = {53},\n\tissn = {0196-2892, 1558-0644},\n\turl = {http://ieeexplore.ieee.org/document/7027207/},\n\tdoi = {10.1109/TGRS.2014.2368831},\n\tnumber = {6},\n\turldate = {2023-02-23},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Jonard, Francois and Weihermuller, Lutz and Schwank, Mike and Jadoon, Khan Zaib and Vereecken, Harry and Lambot, Sebastien},\n\tmonth = jun,\n\tyear = {2015},\n\tpages = {3095--3109},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Jiang, S.; Jomaa, S.; Büttner, O.; Meon, G.; and Rode, M.\n\n\n \n \n \n \n \n Multi-site identification of a distributed hydrological nitrogen model using Bayesian uncertainty analysis.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 529: 940–950. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-site$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_multi-site_2015,\n\ttitle = {Multi-site identification of a distributed hydrological nitrogen model using {Bayesian} uncertainty analysis},\n\tvolume = {529},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415006927},\n\tdoi = {10.1016/j.jhydrol.2015.09.009},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Jiang, Sanyuan and Jomaa, Seifeddine and Büttner, Olaf and Meon, Günter and Rode, Michael},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {940--950},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Iwema, J.; Rosolem, R.; Baatz, R.; Wagener, T.; and Bogena, H. R.\n\n\n \n \n \n \n \n Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 19(7): 3203–3216. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{iwema_investigating_2015,\n\ttitle = {Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods},\n\tvolume = {19},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/19/3203/2015/},\n\tdoi = {10.5194/hess-19-3203-2015},\n\tabstract = {Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-02-23},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Iwema, J. and Rosolem, R. and Baatz, R. and Wagener, T. and Bogena, H. R.},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {3203--3216},\n}\n\n\n\n

\n\n\n

\n Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.\n

\n\n\n

\n \n\n \n \n Ippolito, A.; Kattwinkel, M.; Rasmussen, J. J.; Schäfer, R. B.; Fornaroli, R.; and Liess, M.\n\n\n \n \n \n \n \n Modeling global distribution of agricultural insecticides in surface waters.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 198: 54–60. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ippolito_modeling_2015,\n\ttitle = {Modeling global distribution of agricultural insecticides in surface waters},\n\tvolume = {198},\n\tissn = {02697491},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749114005168},\n\tdoi = {10.1016/j.envpol.2014.12.016},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Environmental Pollution},\n\tauthor = {Ippolito, Alessio and Kattwinkel, Mira and Rasmussen, Jes J. and Schäfer, Ralf B. and Fornaroli, Riccardo and Liess, Matthias},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {54--60},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Hug, C.; Zhang, X.; Guan, M.; Krauss, M.; Bloch, R.; Schulze, T.; Reinecke, T.; Hollert, H.; and Brack, W.\n\n\n \n \n \n \n \n Microbial reporter gene assay as a diagnostic and early warning tool for the detection and characterization of toxic pollution in surface waters: Microbial reporter gene assay as a diagnostic tool.\n \n \n \n \n\n\n \n\n\n\n Environmental Toxicology and Chemistry, 34(11): 2523–2532. November 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Microbial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hug_microbial_2015,\n\ttitle = {Microbial reporter gene assay as a diagnostic and early warning tool for the detection and characterization of toxic pollution in surface waters: {Microbial} reporter gene assay as a diagnostic tool},\n\tvolume = {34},\n\tissn = {07307268},\n\tshorttitle = {Microbial reporter gene assay as a diagnostic and early warning tool for the detection and characterization of toxic pollution in surface waters},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/etc.3083},\n\tdoi = {10.1002/etc.3083},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-23},\n\tjournal = {Environmental Toxicology and Chemistry},\n\tauthor = {Hug, Christine and Zhang, Xiaowei and Guan, Miao and Krauss, Martin and Bloch, Robert and Schulze, Tobias and Reinecke, Tim and Hollert, Henner and Brack, Werner},\n\tmonth = nov,\n\tyear = {2015},\n\tpages = {2523--2532},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Herrmann, F.; Keller, L.; Kunkel, R.; Vereecken, H.; and Wendland, F.\n\n\n \n \n \n \n \n Determination of spatially differentiated water balance components including groundwater recharge on the Federal State level – A case study using the mGROWA model in North Rhine-Westphalia (Germany).\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology: Regional Studies, 4: 294–312. September 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Determination$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{herrmann_determination_2015,\n\ttitle = {Determination of spatially differentiated water balance components including groundwater recharge on the {Federal} {State} level – {A} case study using the {mGROWA} model in {North} {Rhine}-{Westphalia} ({Germany})},\n\tvolume = {4},\n\tissn = {22145818},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2214581815000804},\n\tdoi = {10.1016/j.ejrh.2015.06.018},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology: Regional Studies},\n\tauthor = {Herrmann, Frank and Keller, Luise and Kunkel, Ralf and Vereecken, Harry and Wendland, Frank},\n\tmonth = sep,\n\tyear = {2015},\n\tpages = {294--312},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Heine, I.; Stüve, P.; Kleinschmit, B.; and Itzerott, S.\n\n\n \n \n \n \n \n Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series.\n \n \n \n \n\n\n \n\n\n\n Water, 7(12): 4175–4199. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Reconstruction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{heine_reconstruction_2015,\n\ttitle = {Reconstruction of {Lake} {Level} {Changes} of {Groundwater}-{Fed} {Lakes} in {Northeastern} {Germany} {Using} {RapidEye} {Time} {Series}},\n\tvolume = {7},\n\tissn = {2073-4441},\n\turl = {http://www.mdpi.com/2073-4441/7/8/4175},\n\tdoi = {10.3390/w7084175},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-02-23},\n\tjournal = {Water},\n\tauthor = {Heine, Iris and Stüve, Peter and Kleinschmit, Birgit and Itzerott, Sibylle},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {4175--4199},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Hannes, M.; Wollschläger, U.; Schrader, F.; Durner, W.; Gebler, S.; Pütz, T.; Fank, J.; Von Unold, G.; and Vogel, H.\n\n\n \n \n \n \n \n A comprehensive filtering scheme for high-resolution estimation of the water balance components from high-precision lysimeters.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 19(8): 3405–3418. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hannes_comprehensive_2015,\n\ttitle = {A comprehensive filtering scheme for high-resolution estimation of the water balance components from high-precision lysimeters},\n\tvolume = {19},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/19/3405/2015/},\n\tdoi = {10.5194/hess-19-3405-2015},\n\tabstract = {Abstract. Large weighing lysimeters are currently the most precise method to directly measure all components of the terrestrial water balance in parallel via the built-in weighing system. As lysimeters are exposed to several external forces such as management practices or wind influencing the weighing data, the calculated fluxes of precipitation and evapotranspiration can be altered considerably without having applied appropriate corrections to the raw data. Therefore, adequate filtering schemes for obtaining most accurate estimates of the water balance components are required. In this study, we use data from the TERENO (TERrestrial ENvironmental Observatories) SoilCan research site in Bad Lauchstädt to develop a comprehensive filtering procedure for high-precision lysimeter data, which is designed to deal with various kinds of possible errors starting from the elimination of large disturbances in the raw data resulting e.g., from management practices all the way to the reduction of noise caused e.g., by moderate wind. Furthermore, we analyze the influence of averaging times and thresholds required by some of the filtering steps on the calculated water balance and investigate the ability of two adaptive filtering methods (the adaptive window and adaptive threshold filter (AWAT filter; Peters et al., 2014), and a new synchro filter applicable to the data from a set of several lysimeters) to further reduce the filtering error. Finally, we take advantage of the data sets of all 18 lysimeters running in parallel at the Bad Lauchstädt site to evaluate the performance and accuracy of the proposed filtering scheme. For the tested time interval of 2 months, we show that the estimation of the water balance with high temporal resolution and good accuracy is possible. The filtering code can be downloaded from the journal website as Supplement to this publication.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-02-23},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Hannes, M. and Wollschläger, U. and Schrader, F. and Durner, W. and Gebler, S. and Pütz, T. and Fank, J. and Von Unold, G. and Vogel, H.-J.},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {3405--3418},\n}\n\n\n\n

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\n \n\n \n \n Haase, A.; and Rink, D.\n\n\n \n \n \n \n \n Inner-city transformation between reurbanization and gentrification: Leipzig, eastern Germany.\n \n \n \n \n\n\n \n\n\n\n Geografie, 120(2): 226–250. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Inner-city$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haase_inner-city_2015,\n\ttitle = {Inner-city transformation between reurbanization and gentrification: {Leipzig}, eastern {Germany}},\n\tvolume = {120},\n\tissn = {1212-0014, 2571-421X},\n\tshorttitle = {Inner-city transformation between reurbanization and gentrification},\n\turl = {https://geografie.cz/120/2/0226/},\n\tdoi = {10.37040/geografie2015120020226},\n\tabstract = {After the beginning of the post-socialist transformation, the eastern German city of Leipzig underwent various changes within a short time span. These changes have been especially dynamic in its inner city. Whereas it was hit by the loss of large parts of its population and increasing housing vacancies in the 1990s, the 2000s brought about a revitalization and new attractiveness of many inner-city districts. Since then, reurbanization and – in some places – gentrification have become the predominant trends in a rising number of inner-city districts. This development has also reshaped patterns of socio-spatial differentiation in the city as a whole and its inner parts. Set against this background, the paper describes the development of Leipzig’s inner city after 1990. The focus of the paper is it to show how various concepts – reurbanization and gentrification – help to explain this development. Of particular interest thereby is the impact of Leipzig’s specific housing market situation that is characterized by long-term experiences of supply surplus and shrinkage.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-02-23},\n\tjournal = {Geografie},\n\tauthor = {Haase, Annegret and Rink, Dieter},\n\tyear = {2015},\n\tpages = {226--250},\n}\n\n\n\n

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\n \n\n \n \n Gebler, S.; Hendricks Franssen, H.; Pütz, T.; Post, H.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 19(5): 2145–2161. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Actual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gebler_actual_2015,\n\ttitle = {Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket},\n\tvolume = {19},\n\tissn = {1607-7938},\n\tshorttitle = {Actual evapotranspiration and precipitation measured by lysimeters},\n\turl = {https://hess.copernicus.org/articles/19/2145/2015/},\n\tdoi = {10.5194/hess-19-2145-2015},\n\tabstract = {Abstract. This study compares actual evapotranspiration (ETa) measurements by a set of six weighable lysimeters, ETa estimates obtained with the eddy covariance (EC) method, and evapotranspiration calculated with the full-form Penman–Monteith equation (ETPM) for the Rollesbroich site in the Eifel (western Germany). The comparison of ETa measured by EC (including correction of the energy balance deficit) and by lysimeters is rarely reported in the literature and allows more insight into the performance of both methods. An evaluation of ETa for the two methods for the year 2012 shows a good agreement with a total difference of 3.8\\% (19 mm) between the ETa estimates. The highest agreement and smallest relative differences ({\\textless} 8\\%) on a monthly basis between both methods are found in summer. ETa was close to ETPM, indicating that ET was energy limited and not limited by water availability. ETa differences between lysimeter and EC were mainly related to differences in grass height caused by harvest and the EC footprint. The lysimeter data were also used to estimate precipitation amounts in combination with a filter algorithm for the high-precision lysimeters recently introduced by Peters et al. (2014). The estimated precipitation amounts from the lysimeter data differ significantly from precipitation amounts recorded with a standard rain gauge at the Rollesbroich test site. For the complete year 2012 the lysimeter records show a 16 \\% higher precipitation amount than the tipping bucket. After a correction of the tipping bucket measurements by the method of Richter (1995) this amount was reduced to 3\\%. With the help of an on-site camera the precipitation measurements of the lysimeters were analyzed in more detail. It was found that the lysimeters record more precipitation than the tipping bucket, in part related to the detection of rime and dew, which contribute 17\\% to the yearly difference between both methods. In addition, fog and drizzle explain an additional 5.5\\% of the total difference. Larger differences are also recorded for snow and sleet situations. During snowfall, the tipping bucket device underestimated precipitation severely, and these situations contributed also 7.9\\% to the total difference. However, 36\\% of the total yearly difference was associated with snow cover without apparent snowfall, and under these conditions snow bridges and snow drift seem to explain the strong overestimation of precipitation by the lysimeter. The remaining precipitation difference (about 33\\%) could not be explained and did not show a clear relation to wind speed. The variation of the individual lysimeters devices compared to the lysimeter mean are small, showing variations up to 3\\% for precipitation and 8\\% for evapotranspiration.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-02-23},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Gebler, S. and Hendricks Franssen, H.-J. and Pütz, T. and Post, H. and Schmidt, M. and Vereecken, H.},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {2145--2161},\n}\n\n\n\n

\n\n\n

\n Abstract. This study compares actual evapotranspiration (ETa) measurements by a set of six weighable lysimeters, ETa estimates obtained with the eddy covariance (EC) method, and evapotranspiration calculated with the full-form Penman–Monteith equation (ETPM) for the Rollesbroich site in the Eifel (western Germany). The comparison of ETa measured by EC (including correction of the energy balance deficit) and by lysimeters is rarely reported in the literature and allows more insight into the performance of both methods. An evaluation of ETa for the two methods for the year 2012 shows a good agreement with a total difference of 3.8% (19 mm) between the ETa estimates. The highest agreement and smallest relative differences (\\textless 8%) on a monthly basis between both methods are found in summer. ETa was close to ETPM, indicating that ET was energy limited and not limited by water availability. ETa differences between lysimeter and EC were mainly related to differences in grass height caused by harvest and the EC footprint. The lysimeter data were also used to estimate precipitation amounts in combination with a filter algorithm for the high-precision lysimeters recently introduced by Peters et al. (2014). The estimated precipitation amounts from the lysimeter data differ significantly from precipitation amounts recorded with a standard rain gauge at the Rollesbroich test site. For the complete year 2012 the lysimeter records show a 16 % higher precipitation amount than the tipping bucket. After a correction of the tipping bucket measurements by the method of Richter (1995) this amount was reduced to 3%. With the help of an on-site camera the precipitation measurements of the lysimeters were analyzed in more detail. It was found that the lysimeters record more precipitation than the tipping bucket, in part related to the detection of rime and dew, which contribute 17% to the yearly difference between both methods. In addition, fog and drizzle explain an additional 5.5% of the total difference. Larger differences are also recorded for snow and sleet situations. During snowfall, the tipping bucket device underestimated precipitation severely, and these situations contributed also 7.9% to the total difference. However, 36% of the total yearly difference was associated with snow cover without apparent snowfall, and under these conditions snow bridges and snow drift seem to explain the strong overestimation of precipitation by the lysimeter. The remaining precipitation difference (about 33%) could not be explained and did not show a clear relation to wind speed. The variation of the individual lysimeters devices compared to the lysimeter mean are small, showing variations up to 3% for precipitation and 8% for evapotranspiration.\n

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\n \n\n \n \n Friesen, J.; Lundquist, J.; and Van Stan, J. T.\n\n\n \n \n \n \n \n Evolution of forest precipitation water storage measurement methods: EVOLUTION OF FOREST PRECIPITATION WATER STORAGE MEASUREMENT METHODS.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 29(11): 2504–2520. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Evolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{friesen_evolution_2015,\n\ttitle = {Evolution of forest precipitation water storage measurement methods: {EVOLUTION} {OF} {FOREST} {PRECIPITATION} {WATER} {STORAGE} {MEASUREMENT} {METHODS}},\n\tvolume = {29},\n\tissn = {08856087},\n\tshorttitle = {Evolution of forest precipitation water storage measurement methods},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.10376},\n\tdoi = {10.1002/hyp.10376},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-23},\n\tjournal = {Hydrological Processes},\n\tauthor = {Friesen, Jan and Lundquist, Jessica and Van Stan, John T.},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {2504--2520},\n}\n\n\n\n

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\n \n\n \n \n Fang, Z.; Bogena, H.; Kollet, S.; Koch, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 529: 1754–1767. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fang_spatio-temporal_2015,\n\ttitle = {Spatio-temporal validation of long-term {3D} hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis},\n\tvolume = {529},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415005703},\n\tdoi = {10.1016/j.jhydrol.2015.08.011},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Fang, Zhufeng and Bogena, Heye and Kollet, Stefan and Koch, Julian and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {1754--1767},\n}\n\n\n\n

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\n \n\n \n \n Eder, F.; Schmidt, M.; Damian, T.; Träumner, K.; and Mauder, M.\n\n\n \n \n \n \n \n Mesoscale Eddies Affect Near-Surface Turbulent Exchange: Evidence from Lidar and Tower Measurements.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Meteorology and Climatology, 54(1): 189–206. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Mesoscale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{eder_mesoscale_2015,\n\ttitle = {Mesoscale {Eddies} {Affect} {Near}-{Surface} {Turbulent} {Exchange}: {Evidence} from {Lidar} and {Tower} {Measurements}},\n\tvolume = {54},\n\tissn = {1558-8424, 1558-8432},\n\tshorttitle = {Mesoscale {Eddies} {Affect} {Near}-{Surface} {Turbulent} {Exchange}},\n\turl = {https://journals.ametsoc.org/view/journals/apme/54/1/jamc-d-14-0140.1.xml},\n\tdoi = {10.1175/JAMC-D-14-0140.1},\n\tabstract = {Abstract \n            The eddy-covariance technique tends to underestimate turbulent heat fluxes, which results in nonclosure of the surface energy balance. This study shows experimental evidence that mesoscale turbulent organized structures, which are inherently not captured by the standard eddy-covariance technique, can affect near-surface turbulent exchange. By using a combined setup of three Doppler wind lidars above a cropland-dominated area in Germany, low-frequency turbulent structures were detected in the surface layer down to a few meters above ground. In addition, data from two micrometeorological stations in the study area were analyzed with respect to energy balance closure. In accordance with several previous studies, the data confirm a strong friction velocity dependence of the energy balance residual. At both stations, the energy balance residual was found to be positively correlated with the vertical moisture gradient in the lower atmospheric boundary layer, but at only one station was it correlated with the temperature gradient. This result indicates that mesoscale transport probably contributes more to the latent heat flux than to the sensible heat flux, but this conclusion depends largely on the measurement site. Moreover, flow distortion due to tower mountings and measurement devices affects the energy balance closure considerably for certain wind directions.},\n\tnumber = {1},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Applied Meteorology and Climatology},\n\tauthor = {Eder, Fabian and Schmidt, Marius and Damian, Thomas and Träumner, Katja and Mauder, Matthias},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {189--206},\n}\n\n\n\n

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\n \n\n \n \n Diederich, M.; Ryzhkov, A.; Simmer, C.; Zhang, P.; and Trömel, S.\n\n\n \n \n \n \n \n Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths. Part I: Radar Calibration and Partial Beam Blockage Estimation.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 16(2): 487–502. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{diederich_use_2015,\n\ttitle = {Use of {Specific} {Attenuation} for {Rainfall} {Measurement} at {X}-{Band} {Radar} {Wavelengths}. {Part} {I}: {Radar} {Calibration} and {Partial} {Beam} {Blockage} {Estimation}},\n\tvolume = {16},\n\tissn = {1525-755X, 1525-7541},\n\tshorttitle = {Use of {Specific} {Attenuation} for {Rainfall} {Measurement} at {X}-{Band} {Radar} {Wavelengths}. {Part} {I}},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-14-0066.1},\n\tdoi = {10.1175/JHM-D-14-0066.1},\n\tabstract = {Abstract \n            In a two-part paper, radar rain-rate retrievals using specific attenuation A suggested by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two twin polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of rain-rate retrieval, including miscalibration correction, mitigation of ground clutter contamination and partial beam blockage (PBB), sensitivity to precipitation characteristics, and the temperature assumptions of the R(A) technique. In this paper, the relations inherent to the R(A) method are used to estimate radar reflectivity Z from A and compare it to the measured Z in order to estimate PBB and calibration offsets for both radars. The fields of Z estimated from A for both radars are consistent, and the differences between Z(A) and measured Z are in good agreement with the ones calculated using either consistency relations between reflectivity at horizontal polarization ZH, differential reflectivity ZDR, and specific differential phase KDP in rain or a digital elevation model in the presence of PBB. In the analysis, the dependence of A on temperature appears to have minimal effects on the overall performance of the method. As expected, the difference between Z(A) and attenuation-corrected measured Z observations varies with rain type and exhibits a weak systematic dependency on rainfall intensity; thus, averaging over several rain events is required to obtain reliable estimates of the Z biases caused by radar miscalibration and PBB.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Diederich, Malte and Ryzhkov, Alexander and Simmer, Clemens and Zhang, Pengfei and Trömel, Silke},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {487--502},\n}\n\n\n\n

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\n \n\n \n \n Diederich, M.; Ryzhkov, A.; Simmer, C.; Zhang, P.; and Trömel, S.\n\n\n \n \n \n \n \n Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths. Part II: Rainfall Estimates and Comparison with Rain Gauges.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 16(2): 503–516. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{diederich_use_2015,\n\ttitle = {Use of {Specific} {Attenuation} for {Rainfall} {Measurement} at {X}-{Band} {Radar} {Wavelengths}. {Part} {II}: {Rainfall} {Estimates} and {Comparison} with {Rain} {Gauges}},\n\tvolume = {16},\n\tissn = {1525-755X, 1525-7541},\n\tshorttitle = {Use of {Specific} {Attenuation} for {Rainfall} {Measurement} at {X}-{Band} {Radar} {Wavelengths}. {Part} {II}},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-14-0067.1},\n\tdoi = {10.1175/JHM-D-14-0067.1},\n\tabstract = {Abstract \n            In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R(A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R(A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R(A) rain estimates are compared to R(Z) and R(KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R(Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R(KDP) is used and weaker impact on R(A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R(KDP) or R(A) estimates with R(Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R(A) complemented by R(Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R(KDP) substituted with R(Z) at rain rates below 8 mm h−1.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Diederich, Malte and Ryzhkov, Alexander and Simmer, Clemens and Zhang, Pengfei and Trömel, Silke},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {503--516},\n}\n\n\n\n

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\n \n\n \n \n Devaraju, A.; Jirka, S.; Kunkel, R.; and Sorg, J.\n\n\n \n \n \n \n \n Q-SOS—A Sensor Observation Service for Accessing Quality Descriptions of Environmental Data.\n \n \n \n \n\n\n \n\n\n\n ISPRS International Journal of Geo-Information, 4(3): 1346–1365. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Q-SOS—A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{devaraju_q-sossensor_2015,\n\ttitle = {Q-{SOS}—{A} {Sensor} {Observation} {Service} for {Accessing} {Quality} {Descriptions} of {Environmental} {Data}},\n\tvolume = {4},\n\tissn = {2220-9964},\n\turl = {http://www.mdpi.com/2220-9964/4/3/1346},\n\tdoi = {10.3390/ijgi4031346},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {ISPRS International Journal of Geo-Information},\n\tauthor = {Devaraju, Anusuriya and Jirka, Simon and Kunkel, Ralf and Sorg, Juergen},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {1346--1365},\n}\n\n\n\n

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\n \n\n \n \n Devaraju, A.; Kuhn, W.; and Renschler, C. S.\n\n\n \n \n \n \n \n A formal model to infer geographic events from sensor observations.\n \n \n \n \n\n\n \n\n\n\n International Journal of Geographical Information Science, 29(1): 1–27. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{devaraju_formal_2015,\n\ttitle = {A formal model to infer geographic events from sensor observations},\n\tvolume = {29},\n\tissn = {1365-8816, 1362-3087},\n\turl = {http://www.tandfonline.com/doi/abs/10.1080/13658816.2014.933480},\n\tdoi = {10.1080/13658816.2014.933480},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-02-23},\n\tjournal = {International Journal of Geographical Information Science},\n\tauthor = {Devaraju, Anusuriya and Kuhn, Werner and Renschler, Chris S.},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {1--27},\n}\n\n\n\n

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\n \n\n \n \n Czymzik, M.; Muscheler, R.; Brauer, A.; Adolphi, F.; Ott, F.; Kienel, U.; Dräger, N.; Słowiński, M.; Aldahan, A.; and Possnert, G.\n\n\n \n \n \n \n \n Solar cycles and depositional processes in annual 10 Be from two varved lake sediment records.\n \n \n \n \n\n\n \n\n\n\n Earth and Planetary Science Letters, 428: 44–51. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Solar$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{czymzik_solar_2015,\n\ttitle = {Solar cycles and depositional processes in annual 10 {Be} from two varved lake sediment records},\n\tvolume = {428},\n\tissn = {0012821X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0012821X15004720},\n\tdoi = {10.1016/j.epsl.2015.07.037},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Earth and Planetary Science Letters},\n\tauthor = {Czymzik, Markus and Muscheler, Raimund and Brauer, Achim and Adolphi, Florian and Ott, Florian and Kienel, Ulrike and Dräger, Nadine and Słowiński, Michał and Aldahan, Ala and Possnert, Göran},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {44--51},\n}\n\n\n\n

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\n \n\n \n \n Creutzfeldt, B.; Heinrich, I.; and Merz, B.\n\n\n \n \n \n \n \n Total water storage dynamics derived from tree-ring records and terrestrial gravity observations.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 529: 640–649. October 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Total$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{creutzfeldt_total_2015,\n\ttitle = {Total water storage dynamics derived from tree-ring records and terrestrial gravity observations},\n\tvolume = {529},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415002553},\n\tdoi = {10.1016/j.jhydrol.2015.04.006},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Creutzfeldt, Benjamin and Heinrich, Ingo and Merz, Bruno},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {640--649},\n}\n\n\n\n

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\n \n\n \n \n Clasen, A.; Somers, B.; Pipkins, K.; Tits, L.; Segl, K.; Brell, M.; Kleinschmit, B.; Spengler, D.; Lausch, A.; and Förster, M.\n\n\n \n \n \n \n \n Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 7(11): 15361–15387. November 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spectral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{clasen_spectral_2015,\n\ttitle = {Spectral {Unmixing} of {Forest} {Crown} {Components} at {Close} {Range}, {Airborne} and {Simulated} {Sentinel}-2 and {EnMAP} {Spectral} {Imaging} {Scale}},\n\tvolume = {7},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/7/11/15361},\n\tdoi = {10.3390/rs71115361},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Clasen, Anne and Somers, Ben and Pipkins, Kyle and Tits, Laurent and Segl, Karl and Brell, Max and Kleinschmit, Birgit and Spengler, Daniel and Lausch, Angela and Förster, Michael},\n\tmonth = nov,\n\tyear = {2015},\n\tpages = {15361--15387},\n}\n\n\n\n

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\n \n\n \n \n Chen, Z.; Wang, C.; Gschwendtner, S.; Willibald, G.; Unteregelsbacher, S.; Lu, H.; Kolar, A.; Schloter, M.; Butterbach-Bahl, K.; and Dannenmann, M.\n\n\n \n \n \n \n \n Relationships between denitrification gene expression, dissimilatory nitrate reduction to ammonium and nitrous oxide and dinitrogen production in montane grassland soils.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 87: 67–77. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Relationships$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chen_relationships_2015,\n\ttitle = {Relationships between denitrification gene expression, dissimilatory nitrate reduction to ammonium and nitrous oxide and dinitrogen production in montane grassland soils},\n\tvolume = {87},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S003807171500142X},\n\tdoi = {10.1016/j.soilbio.2015.03.030},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Chen, Zhe and Wang, Changhui and Gschwendtner, Silvia and Willibald, Georg and Unteregelsbacher, Sebastian and Lu, Haiyan and Kolar, Allison and Schloter, Michael and Butterbach-Bahl, Klaus and Dannenmann, Michael},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {67--77},\n}\n\n\n\n

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\n \n\n \n \n Caravaca, F.; Maboreke, H.; Kurth, F.; Herrmann, S.; Tarkka, M. T.; and Ruess, L.\n\n\n \n \n \n \n \n Synergists and antagonists in the rhizosphere modulate microbial communities and growth of Quercus robur L.\n \n \n \n \n\n\n \n\n\n\n Soil Biology and Biochemistry, 82: 65–73. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Synergists$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{caravaca_synergists_2015,\n\ttitle = {Synergists and antagonists in the rhizosphere modulate microbial communities and growth of {Quercus} robur {L}.},\n\tvolume = {82},\n\tissn = {00380717},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0038071714004180},\n\tdoi = {10.1016/j.soilbio.2014.12.004},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Soil Biology and Biochemistry},\n\tauthor = {Caravaca, Fuensanta and Maboreke, Hazel and Kurth, Florence and Herrmann, Sylvie and Tarkka, Mika T. and Ruess, Liliane},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {65--73},\n}\n\n\n\n

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\n \n\n \n \n Buras, A.; Scharnweber, T.; Simard, S.; van der Maaten, E.; Tober, A.; Heinrich, I.; Kaiser, K.; and Wilmking, M.\n\n\n \n \n \n \n Aktuelle dendroökologische Fragestellungen im Teilgebiet Serrahn des Müritz-Nationalparks.\n \n \n \n\n\n \n\n\n\n In . September 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{buras_aktuelle_2015,\n\ttitle = {Aktuelle dendroökologische {Fragestellungen} im {Teilgebiet} {Serrahn} des {Müritz}-{Nationalparks}},\n\tauthor = {Buras, Allan and Scharnweber, Tobias and Simard, Sonia and van der Maaten, Ernst and Tober, Anne and Heinrich, Ingo and Kaiser, Knut and Wilmking, Martin},\n\tmonth = sep,\n\tyear = {2015},\n}\n\n\n\n

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\n \n\n \n \n Bowler, D.; Haase, P.; Kröncke, I.; Tackenberg, O.; Bauer, H.; Brendel, C.; Brooker, R.; Gerisch, M.; Henle, K.; Hickler, T.; Hof, C.; Klotz, S.; Kühn, I.; Matesanz, S.; O‘Hara, R.; Russell, D.; Schweiger, O.; Valladares, F.; Welk, E.; Wiemers, M.; and Böhning-Gaese, K.\n\n\n \n \n \n \n \n A cross-taxon analysis of the impact of climate change on abundance trends in central Europe.\n \n \n \n \n\n\n \n\n\n\n Biological Conservation, 187: 41–50. July 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bowler_cross-taxon_2015,\n\ttitle = {A cross-taxon analysis of the impact of climate change on abundance trends in central {Europe}},\n\tvolume = {187},\n\tissn = {00063207},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0006320715001457},\n\tdoi = {10.1016/j.biocon.2015.03.034},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Biological Conservation},\n\tauthor = {Bowler, D.E. and Haase, P. and Kröncke, I. and Tackenberg, O. and Bauer, H.G. and Brendel, C. and Brooker, R.W. and Gerisch, M. and Henle, K. and Hickler, T. and Hof, C. and Klotz, S. and Kühn, I. and Matesanz, S. and O‘Hara, R. and Russell, D. and Schweiger, O. and Valladares, F. and Welk, E. and Wiemers, M. and Böhning-Gaese, K.},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {41--50},\n}\n\n\n\n

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\n \n\n \n \n Borchard, N.; Schirrmann, M.; Hebel, C. V.; Schmidt, M.; Baatz, R.; Firbank, L.; Vereecken, H.; and Herbst, M.\n\n\n \n \n \n \n \n Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in Germany.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 211: 84–93. December 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{borchard_spatio-temporal_2015,\n\ttitle = {Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in {Germany}},\n\tvolume = {211},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880915001905},\n\tdoi = {10.1016/j.agee.2015.05.008},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Borchard, Nils and Schirrmann, Michael and Hebel, Christian Von and Schmidt, Marius and Baatz, Roland and Firbank, Les and Vereecken, Harry and Herbst, Michael},\n\tmonth = dec,\n\tyear = {2015},\n\tpages = {84--93},\n}\n\n\n\n

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\n \n\n \n \n Bol, R.; Lücke, A.; Tappe, W.; Kummer, S.; Krause, M.; Weigand, S.; Pütz, T.; and Vereecken, H.\n\n\n \n \n \n \n \n Spatio-temporal Variations of Dissolved Organic Matter in a German Forested Mountainous Headwater Catchment.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 14(4): vzj2015.01.0005. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatio-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bol_spatio-temporal_2015,\n\ttitle = {Spatio-temporal {Variations} of {Dissolved} {Organic} {Matter} in a {German} {Forested} {Mountainous} {Headwater} {Catchment}},\n\tvolume = {14},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2015.01.0005},\n\tdoi = {10.2136/vzj2015.01.0005},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Bol, Roland and Lücke, Andreas and Tappe, Wolfgang and Kummer, Sirgit and Krause, Martina and Weigand, Susanne and Pütz, Thomas and Vereecken, Harry},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {vzj2015.01.0005},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H. R.; Bol, R.; Borchard, N.; Brüggemann, N.; Diekkrüger, B.; Drüe, C.; Groh, J.; Gottselig, N.; Huisman, J. A.; Lücke, A.; Missong, A.; Neuwirth, B.; Pütz, T.; Schmidt, M.; Stockinger, M.; Tappe, W.; Weihermüller, L.; Wiekenkamp, I.; and Vereecken, H.\n\n\n \n \n \n \n \n A terrestrial observatory approach to the integrated investigation of the effects of deforestation on water, energy, and matter fluxes.\n \n \n \n \n\n\n \n\n\n\n Science China Earth Sciences, 58(1): 61–75. January 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_terrestrial_2015,\n\ttitle = {A terrestrial observatory approach to the integrated investigation of the effects of deforestation on water, energy, and matter fluxes},\n\tvolume = {58},\n\tissn = {1674-7313, 1869-1897},\n\turl = {http://link.springer.com/10.1007/s11430-014-4911-7},\n\tdoi = {10.1007/s11430-014-4911-7},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-02-23},\n\tjournal = {Science China Earth Sciences},\n\tauthor = {Bogena, H. R. and Bol, R. and Borchard, N. and Brüggemann, N. and Diekkrüger, B. and Drüe, C. and Groh, J. and Gottselig, N. and Huisman, J. A. and Lücke, A. and Missong, A. and Neuwirth, B. and Pütz, T. and Schmidt, M. and Stockinger, M. and Tappe, W. and Weihermüller, L. and Wiekenkamp, I. and Vereecken, H.},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {61--75},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H. R.; Huisman, J. A.; Güntner, A.; Hübner, C.; Kusche, J.; Jonard, F.; Vey, S.; and Vereecken, H.\n\n\n \n \n \n \n \n Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale: a review.\n \n \n \n \n\n\n \n\n\n\n WIREs Water, 2(6): 635–647. November 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Emerging$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_emerging_2015,\n\ttitle = {Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale: a review},\n\tvolume = {2},\n\tissn = {2049-1948, 2049-1948},\n\tshorttitle = {Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/wat2.1097},\n\tdoi = {10.1002/wat2.1097},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-02-23},\n\tjournal = {WIREs Water},\n\tauthor = {Bogena, Heye R. and Huisman, Johan A. and Güntner, Andreas and Hübner, Christof and Kusche, Jürgen and Jonard, François and Vey, Sibylle and Vereecken, Harry},\n\tmonth = nov,\n\tyear = {2015},\n\tpages = {635--647},\n}\n\n\n\n

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\n \n\n \n \n Błaszkiewicz, M.; Piotrowski, J.; Brauer, A.; Gierszewski, P.; Kordowski, J.; Kramkowski, M.; Lamparski, P.; Lorenz, S.; Noryśkiewicz, A.; Ott, F.; Słowiński, M.; and Tyszkowski, S.\n\n\n \n \n \n \n \n Climatic and morphological controls on diachronous postglacial lake and river valley evolution in the area of Last Glaciation, northern Poland.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 109: 13–27. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Climatic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blaszkiewicz_climatic_2015,\n\ttitle = {Climatic and morphological controls on diachronous postglacial lake and river valley evolution in the area of {Last} {Glaciation}, northern {Poland}},\n\tvolume = {109},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379114004831},\n\tdoi = {10.1016/j.quascirev.2014.11.023},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Błaszkiewicz, M. and Piotrowski, J.A. and Brauer, A. and Gierszewski, P. and Kordowski, J. and Kramkowski, M. and Lamparski, P. and Lorenz, S. and Noryśkiewicz, A.M. and Ott, F. and Słowiński, M. and Tyszkowski, S.},\n\tmonth = feb,\n\tyear = {2015},\n\tpages = {13--27},\n}\n\n\n\n

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\n \n\n \n \n Blasch, G.; Spengler, D.; Hohmann, C.; Neumann, C.; Itzerott, S.; and Kaufmann, H.\n\n\n \n \n \n \n \n Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale.\n \n \n \n \n\n\n \n\n\n\n Computers and Electronics in Agriculture, 113: 1–13. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Multitemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blasch_multitemporal_2015,\n\ttitle = {Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale},\n\tvolume = {113},\n\tissn = {01681699},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168169915000277},\n\tdoi = {10.1016/j.compag.2015.01.012},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Computers and Electronics in Agriculture},\n\tauthor = {Blasch, Gerald and Spengler, Daniel and Hohmann, Christian and Neumann, Carsten and Itzerott, Sibylle and Kaufmann, Herrmann},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {1--13},\n}\n\n\n\n

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\n \n\n \n \n Baroni, G.; and Oswald, S.\n\n\n \n \n \n \n \n A scaling approach for the assessment of biomass changes and rainfall interception using cosmic-ray neutron sensing.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 525: 264–276. June 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baroni_scaling_2015,\n\ttitle = {A scaling approach for the assessment of biomass changes and rainfall interception using cosmic-ray neutron sensing},\n\tvolume = {525},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169415002243},\n\tdoi = {10.1016/j.jhydrol.2015.03.053},\n\tlanguage = {en},\n\turldate = {2023-02-23},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Baroni, G. and Oswald, S.E.},\n\tmonth = jun,\n\tyear = {2015},\n\tpages = {264--276},\n}\n\n\n\n

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\n \n\n \n \n Blasch, G.; Spengler, D.; Itzerott, S.; and Wessolek, G.\n\n\n \n \n \n \n \n Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 7(9): 11125–11150. August 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Organic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blasch_organic_2015,\n\ttitle = {Organic {Matter} {Modeling} at the {Landscape} {Scale} {Based} on {Multitemporal} {Soil} {Pattern} {Analysis} {Using} {RapidEye} {Data}},\n\tvolume = {7},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/7/9/11125},\n\tdoi = {10.3390/rs70911125},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-02-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Blasch, Gerald and Spengler, Daniel and Itzerott, Sibylle and Wessolek, Gerd},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {11125--11150},\n}\n\n\n\n

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\n \n\n \n \n Baatz, R.; Bogena, H. R.; Hendricks Franssen, H.; Huisman, J. A.; Montzka, C.; and Vereecken, H.\n\n\n \n \n \n \n \n An empirical vegetation correction for soil water content quantification using cosmic ray probes.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 51(4): 2030–2046. April 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baatz_empirical_2015,\n\ttitle = {An empirical vegetation correction for soil water content quantification using cosmic ray probes},\n\tvolume = {51},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2014WR016443},\n\tdoi = {10.1002/2014WR016443},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-02-23},\n\tjournal = {Water Resources Research},\n\tauthor = {Baatz, R. and Bogena, H. R. and Hendricks Franssen, H.‐J. and Huisman, J. A. and Montzka, C. and Vereecken, H.},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {2030--2046},\n}\n\n\n\n

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\n \n\n \n \n Anis, M. R.; and Rode, M.\n\n\n \n \n \n \n \n Effect of climate change on overland flow generation: a case study in central Germany: EFFECT OF CLIMATE CHANGE ON OVERLAND FLOW GENERATION.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 29(11): 2478–2490. May 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{anis_effect_2015,\n\ttitle = {Effect of climate change on overland flow generation: a case study in central {Germany}: {EFFECT} {OF} {CLIMATE} {CHANGE} {ON} {OVERLAND} {FLOW} {GENERATION}},\n\tvolume = {29},\n\tissn = {08856087},\n\tshorttitle = {Effect of climate change on overland flow generation},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.10381},\n\tdoi = {10.1002/hyp.10381},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-23},\n\tjournal = {Hydrological Processes},\n\tauthor = {Anis, Muhammad Rehan and Rode, Michael},\n\tmonth = may,\n\tyear = {2015},\n\tpages = {2478--2490},\n}\n\n\n\n

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\n \n\n \n \n Ali, M.; Montzka, C.; Stadler, A.; Menz, G.; Thonfeld, F.; and Vereecken, H.\n\n\n \n \n \n \n \n Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany).\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 7(3): 2808–2831. March 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ali_estimation_2015,\n\ttitle = {Estimation and {Validation} of {RapidEye}-{Based} {Time}-{Series} of {Leaf} {Area} {Index} for {Winter} {Wheat} in the {Rur} {Catchment} ({Germany})},\n\tvolume = {7},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/7/3/2808},\n\tdoi = {10.3390/rs70302808},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-23},\n\tjournal = {Remote Sensing},\n\tauthor = {Ali, Muhammad and Montzka, Carsten and Stadler, Anja and Menz, Gunter and Thonfeld, Frank and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2015},\n\tpages = {2808--2831},\n}\n\n\n\n

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\n \n 2014\n \n \n (64)\n \n \n

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\n \n\n \n \n Paasche, H.; Eberle, D.; Das, S.; Cooper, A.; Debba, P.; Dietrich, P.; Dudeni-Thlone, N.; Gläßer, C.; Kijko, A.; Knobloch, A.; Lausch, A.; Meyer, U.; Smit, A.; Stettler, E.; and Werban, U.\n\n\n \n \n \n \n \n Are Earth Sciences lagging behind in data integration methodologies?.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 71(4): 1997–2003. February 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Are$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{paasche_are_2014,\n\ttitle = {Are {Earth} {Sciences} lagging behind in data integration methodologies?},\n\tvolume = {71},\n\tissn = {1866-6280, 1866-6299},\n\turl = {http://link.springer.com/10.1007/s12665-013-2931-9},\n\tdoi = {10.1007/s12665-013-2931-9},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Paasche, Hendrik and Eberle, Detlef and Das, Sonali and Cooper, Antony and Debba, Pravesh and Dietrich, Peter and Dudeni-Thlone, Nontembeko and Gläßer, Cornelia and Kijko, Andrzej and Knobloch, Andreas and Lausch, Angela and Meyer, Uwe and Smit, Ansie and Stettler, Edgar and Werban, Ulrike},\n\tmonth = feb,\n\tyear = {2014},\n\tpages = {1997--2003},\n}\n\n\n\n

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\n \n\n \n \n Brosinsky, A.; Lausch, A.; Doktor, D.; Salbach, C.; Merbach, I.; Gwillym-Margianto, S.; and Pause, M.\n\n\n \n \n \n \n \n Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Remote Sensing.\n \n \n \n \n\n\n \n\n\n\n Journal of the Indian Society of Remote Sensing, 42(2): 311–324. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brosinsky_analysis_2014,\n\ttitle = {Analysis of {Spectral} {Vegetation} {Signal} {Characteristics} as a {Function} of {Soil} {Moisture} {Conditions} {Using} {Hyperspectral} {Remote} {Sensing}},\n\tvolume = {42},\n\tissn = {0255-660X, 0974-3006},\n\turl = {http://link.springer.com/10.1007/s12524-013-0298-8},\n\tdoi = {10.1007/s12524-013-0298-8},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Journal of the Indian Society of Remote Sensing},\n\tauthor = {Brosinsky, A. and Lausch, A. and Doktor, D. and Salbach, C. and Merbach, I. and Gwillym-Margianto, S. and Pause, M.},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {311--324},\n}\n\n\n\n

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\n \n\n \n \n Förster, M.; Bruzual-Alfonzo, H.; Clasen, A.; and Kleinschmit, B.\n\n\n \n \n \n \n \n The utilization of GEOBIA for a hyperspectral mixture analysis of tree crown components.\n \n \n \n \n\n\n \n\n\n\n South-Eastern European Journal of Earth Observation and Geomatics, 3: 363–367. May 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{forster_utilization_2014,\n\ttitle = {The utilization of {GEOBIA} for a hyperspectral mixture analysis of tree crown components},\n\tvolume = {3},\n\turl = {https://ejournals.lib.auth.gr/seejeog/article/view/4236},\n\tjournal = {South-Eastern European Journal of Earth Observation and Geomatics},\n\tauthor = {Förster, Michael and Bruzual-Alfonzo, Helene and Clasen, Anne and Kleinschmit, Birgit},\n\tmonth = may,\n\tyear = {2014},\n\tpages = {363--367},\n}\n\n\n\n

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\n \n\n \n \n Schollaen, K.; Heinrich, I.; and Helle, G.\n\n\n \n \n \n \n \n UV‐laser‐based microscopic dissection of tree rings – a novel sampling tool for δ$^{\\textrm{13}}$C and δ$^{\\textrm{18}}$O studies.\n \n \n \n \n\n\n \n\n\n\n New Phytologist, 201(3): 1045–1055. February 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"UV‐laser‐based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schollaen_uvlaserbased_2014,\n\ttitle = {{UV}‐laser‐based microscopic dissection of tree rings – a novel sampling tool for δ$^{\\textrm{13}}${C} and δ$^{\\textrm{18}}${O} studies},\n\tvolume = {201},\n\tissn = {0028-646X, 1469-8137},\n\tshorttitle = {{\\textless}span style="font-variant},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/nph.12587},\n\tdoi = {10.1111/nph.12587},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {New Phytologist},\n\tauthor = {Schollaen, Karina and Heinrich, Ingo and Helle, Gerhard},\n\tmonth = feb,\n\tyear = {2014},\n\tpages = {1045--1055},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Rink, D.; Haase, A.; and Schneider, A.\n\n\n \n \n \n \n \n Vom Leerstand zum Bauboom? Zur Entwicklung des Leipziger Wohnungsmarkts.\n \n \n \n \n\n\n \n\n\n\n Statistischer Quartalsbericht, Stadt Leipzig, Amt für Statistik und Wahlen, 1: 25–28. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Vom$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_vom_2014,\n\ttitle = {Vom {Leerstand} zum {Bauboom}? {Zur} {Entwicklung} des {Leipziger} {Wohnungsmarkts}},\n\tvolume = {1},\n\turl = {https://www.leipzig.de/fileadmin/mediendatenbank/leipzig-de/Stadt/02.1_Dez1_Allgemeine_Verwaltung/12_Statistik_und_Wahlen/Statistik/Statistischer_Quartalsbericht_Leipzig_2014_1.pdf},\n\tjournal = {Statistischer Quartalsbericht, Stadt Leipzig, Amt für Statistik und Wahlen},\n\tauthor = {Rink, Dieter and Haase, Annegret and Schneider, Andreas},\n\tyear = {2014},\n\tpages = {25--28},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Rink, D.; Schneider, A.; and Haase, A.\n\n\n \n \n \n \n \n Das gehobene Wohnsegment in Leipzig.\n \n \n \n \n\n\n \n\n\n\n Statistischer Quartalsbericht II/2014,25–30. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Das$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_gehobene_2014,\n\ttitle = {Das gehobene {Wohnsegment} in {Leipzig}},\n\turl = {https://www.leipzig.de/fileadmin/mediendatenbank/leipzig-de/Stadt/02.1_Dez1_Allgemeine_Verwaltung/12_Statistik_und_Wahlen/Statistik/Statistischer_Quartalsbericht_Leipzig_2014_2.pdf},\n\tjournal = {Statistischer Quartalsbericht II/2014},\n\tauthor = {Rink, Dieter and Schneider, Andreas and Haase, Annegret},\n\tyear = {2014},\n\tpages = {25--30},\n}\n\n\n\n

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\n \n\n \n \n Brauer, A.\n\n\n \n \n \n \n Jahresgeschichtete Seesedimente als Paläoklimaarchive.\n \n \n \n\n\n \n\n\n\n Geographische Rundschau, 66(7/8): 25–30. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{brauer_jahresgeschichtete_2014,\n\ttitle = {Jahresgeschichtete {Seesedimente} als {Paläoklimaarchive}},\n\tvolume = {66},\n\tissn = {0016-7460},\n\tnumber = {7/8},\n\tjournal = {Geographische Rundschau},\n\tauthor = {Brauer, Achim},\n\tyear = {2014},\n\tpages = {25--30},\n}\n\n\n\n

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\n \n\n \n \n Devaraju, A.; Kunkel, R.; Sorg, J.; Bogena, H.; and Vereecken, H.\n\n\n \n \n \n \n Enabling quality control of sensor web observations.\n \n \n \n\n\n \n\n\n\n SENSORNETS 2014 - Proceedings of the 3rd International Conference on Sensor Networks,17–27. January 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{devaraju_enabling_2014,\n\ttitle = {Enabling quality control of sensor web observations},\n\tjournal = {SENSORNETS 2014 - Proceedings of the 3rd International Conference on Sensor Networks},\n\tauthor = {Devaraju, Anusuriya and Kunkel, Ralf and Sorg, Jürgen and Bogena, Heye and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {17--27},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wendt-Potthoff, K.; Kloß, C.; Schultze, M.; and Koschorreck, M.\n\n\n \n \n \n \n \n Anaerobic metabolism of two hydro-morphological similar pre-dams under contrasting nutrient loading (Rappbode Reservoir System, Germany): Anaerobic metabolism in two hydro-morphologically similar pre-dams.\n \n \n \n \n\n\n \n\n\n\n International Review of Hydrobiology, 99(5): 350–362. October 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Anaerobic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wendt-potthoff_anaerobic_2014,\n\ttitle = {Anaerobic metabolism of two hydro-morphological similar pre-dams under contrasting nutrient loading ({Rappbode} {Reservoir} {System}, {Germany}): {Anaerobic} metabolism in two hydro-morphologically similar pre-dams},\n\tvolume = {99},\n\tissn = {14342944},\n\tshorttitle = {Anaerobic metabolism of two hydro-morphological similar pre-dams under contrasting nutrient loading ({Rappbode} {Reservoir} {System}, {Germany})},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/iroh.201301673},\n\tdoi = {10.1002/iroh.201301673},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-06-19},\n\tjournal = {International Review of Hydrobiology},\n\tauthor = {Wendt-Potthoff, Katrin and Kloß, Christin and Schultze, Martin and Koschorreck, Matthias},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {350--362},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wang, C.; Dannenmann, M.; Meier, R.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n Inhibitory and side effects of acetylene (C2H2) and sodium chlorate (NaClO3) on gross nitrification, gross ammonification and soil-atmosphere exchange of N2O and CH4 in acidic to neutral montane grassland soil.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Biology, 65: 7–14. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Inhibitory$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wang_inhibitory_2014,\n\ttitle = {Inhibitory and side effects of acetylene ({C2H2}) and sodium chlorate ({NaClO3}) on gross nitrification, gross ammonification and soil-atmosphere exchange of {N2O} and {CH4} in acidic to neutral montane grassland soil},\n\tvolume = {65},\n\tissn = {11645563},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1164556314000909},\n\tdoi = {10.1016/j.ejsobi.2014.08.006},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {European Journal of Soil Biology},\n\tauthor = {Wang, Changhui and Dannenmann, Michael and Meier, Rudi and Butterbach-Bahl, Klaus},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {7--14},\n}\n\n\n\n

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\n \n\n \n \n Von Hebel, C.; Rudolph, S.; Mester, A.; Huisman, J. A.; Kumbhar, P.; Vereecken, H.; and Van Der Kruk, J.\n\n\n \n \n \n \n \n Three-dimensional imaging of subsurface structural patterns using quantitative large-scale multiconfiguration electromagnetic induction data.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 50(3): 2732–2748. March 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Three-dimensional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{von_hebel_three-dimensional_2014,\n\ttitle = {Three-dimensional imaging of subsurface structural patterns using quantitative large-scale multiconfiguration electromagnetic induction data},\n\tvolume = {50},\n\tissn = {00431397},\n\turl = {http://doi.wiley.com/10.1002/2013WR014864},\n\tdoi = {10.1002/2013WR014864},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {Water Resources Research},\n\tauthor = {Von Hebel, Christian and Rudolph, Sebastian and Mester, Achim and Huisman, Johan A. and Kumbhar, Pramod and Vereecken, Harry and Van Der Kruk, Jan},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {2732--2748},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Vonberg, D.; Vanderborght, J.; Cremer, N.; Pütz, T.; Herbst, M.; and Vereecken, H.\n\n\n \n \n \n \n \n 20 years of long-term atrazine monitoring in a shallow aquifer in western Germany.\n \n \n \n \n\n\n \n\n\n\n Water Research, 50: 294–306. March 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"20$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vonberg_20_2014,\n\ttitle = {20 years of long-term atrazine monitoring in a shallow aquifer in western {Germany}},\n\tvolume = {50},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135413008130},\n\tdoi = {10.1016/j.watres.2013.10.032},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Water Research},\n\tauthor = {Vonberg, David and Vanderborght, Jan and Cremer, Nils and Pütz, Thomas and Herbst, Michael and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {294--306},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Vonberg, D.; Hofmann, D.; Vanderborght, J.; Lelickens, A.; Köppchen, S.; Pütz, T.; Burauel, P.; and Vereecken, H.\n\n\n \n \n \n \n \n Atrazine Soil Core Residue Analysis from an Agricultural Field 21 Years after Its Ban.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Quality, 43(4): 1450–1459. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Atrazine$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vonberg_atrazine_2014,\n\ttitle = {Atrazine {Soil} {Core} {Residue} {Analysis} from an {Agricultural} {Field} 21 {Years} after {Its} {Ban}},\n\tvolume = {43},\n\tissn = {00472425},\n\turl = {http://doi.wiley.com/10.2134/jeq2013.12.0497},\n\tdoi = {10.2134/jeq2013.12.0497},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Environmental Quality},\n\tauthor = {Vonberg, David and Hofmann, Diana and Vanderborght, Jan and Lelickens, Anna and Köppchen, Stephan and Pütz, Thomas and Burauel, Peter and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {1450--1459},\n}\n\n\n\n

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\n \n\n \n \n Vereecken, H.; Huisman, J.; Pachepsky, Y.; Montzka, C.; Van Der Kruk, J.; Bogena, H.; Weihermüller, L.; Herbst, M.; Martinez, G.; and Vanderborght, J.\n\n\n \n \n \n \n \n On the spatio-temporal dynamics of soil moisture at the field scale.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 516: 76–96. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vereecken_spatio-temporal_2014,\n\ttitle = {On the spatio-temporal dynamics of soil moisture at the field scale},\n\tvolume = {516},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169413008858},\n\tdoi = {10.1016/j.jhydrol.2013.11.061},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Vereecken, H. and Huisman, J.A. and Pachepsky, Y. and Montzka, C. and Van Der Kruk, J. and Bogena, H. and Weihermüller, L. and Herbst, M. and Martinez, G. and Vanderborght, J.},\n\tmonth = aug,\n\tyear = {2014},\n\tpages = {76--96},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Trauth, N.; Schmidt, C.; Vieweg, M.; Maier, U.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Hyporheic transport and biogeochemical reactions in pool-riffle systems under varying ambient groundwater flow conditions.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 119(5): 910–928. May 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Hyporheic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trauth_hyporheic_2014,\n\ttitle = {Hyporheic transport and biogeochemical reactions in pool-riffle systems under varying ambient groundwater flow conditions},\n\tvolume = {119},\n\tissn = {21698953},\n\turl = {http://doi.wiley.com/10.1002/2013JG002586},\n\tdoi = {10.1002/2013JG002586},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Trauth, Nico and Schmidt, Christian and Vieweg, Michael and Maier, Uli and Fleckenstein, Jan H.},\n\tmonth = may,\n\tyear = {2014},\n\tpages = {910--928},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Tolksdorf, J. F.; Turner, F.; Kaiser, K.; Eckmeier, E.; Bittmann, F.; and Veil, S.\n\n\n \n \n \n \n \n Potential of palaeosols, sediments and archaeological features to reconstruct Late Glacial fire regimes in northern Central Europe - case study Grabow site and overview.\n \n \n \n \n\n\n \n\n\n\n Zeitschrift für Geomorphologie, Supplementary Issues, 58(1): 211–232. February 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Potential$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tolksdorf_potential_2014,\n\ttitle = {Potential of palaeosols, sediments and archaeological features to reconstruct {Late} {Glacial} fire regimes in northern {Central} {Europe} - case study {Grabow} site and overview},\n\tvolume = {58},\n\tissn = {1864-1687},\n\turl = {http://www.schweizerbart.de/papers/zfg_suppl/detail/58/82000/Potential_of_palaeosols_sediments_and_archaeologic?af=crossref},\n\tdoi = {10.1127/0372-8854/2013/S-00155},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-06-19},\n\tjournal = {Zeitschrift für Geomorphologie, Supplementary Issues},\n\tauthor = {Tolksdorf, Johann Friedrich and Turner, Falko and Kaiser, Knut and Eckmeier, Eileen and Bittmann, Felix and Veil, Stephan},\n\tmonth = feb,\n\tyear = {2014},\n\tpages = {211--232},\n}\n\n\n\n

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\n \n\n \n \n Trömel, S.; Ziegert, M.; Ryzhkov, A. V.; Chwala, C.; and Simmer, C.\n\n\n \n \n \n \n \n Using Microwave Backhaul Links to Optimize the Performance of Algorithms for Rainfall Estimation and Attenuation Correction.\n \n \n \n \n\n\n \n\n\n\n Journal of Atmospheric and Oceanic Technology, 31(8): 1748–1760. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tromel_using_2014,\n\ttitle = {Using {Microwave} {Backhaul} {Links} to {Optimize} the {Performance} of {Algorithms} for {Rainfall} {Estimation} and {Attenuation} {Correction}},\n\tvolume = {31},\n\tissn = {0739-0572, 1520-0426},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JTECH-D-14-00016.1},\n\tdoi = {10.1175/JTECH-D-14-00016.1},\n\tabstract = {Abstract \n            The variability in raindrop size distributions and attenuation effects are the two major sources of uncertainty in radar-based quantitative precipitation estimation (QPE) even when dual-polarization radars are used. New methods are introduced to exploit the measurements by commercial microwave radio links to reduce the uncertainties in both attenuation correction and rainfall estimation. The ratio α of specific attenuation A and specific differential phase KDP is the key parameter used in attenuation correction schemes and the recently introduced R(A) algorithm. It is demonstrated that the factor α can be optimized using microwave links at Ku band oriented along radar radials with an accuracy of about 20\\%–30\\%. The microwave links with arbitrary orientation can be utilized to optimize the intercepts in the R(KDP) and R(A) relations with an accuracy of about 25\\%. The performance of the suggested methods is tested using the polarimetric C-band radar operated by the German Weather Service on Mount Hohenpeissenberg in southern Germany and two radially oriented Ku-band microwave links from Ericsson GmbH.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Atmospheric and Oceanic Technology},\n\tauthor = {Trömel, Silke and Ziegert, Michael and Ryzhkov, Alexander V. and Chwala, Christian and Simmer, Clemens},\n\tmonth = aug,\n\tyear = {2014},\n\tpages = {1748--1760},\n}\n\n\n\n

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\n \n\n \n \n Tillack, A.; Clasen, A.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 141: 52–63. February 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tillack_estimation_2014,\n\ttitle = {Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices},\n\tvolume = {141},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425713003866},\n\tdoi = {10.1016/j.rse.2013.10.018},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Tillack, Adina and Clasen, Anne and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = feb,\n\tyear = {2014},\n\tpages = {52--63},\n}\n\n\n\n

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\n \n\n \n \n Stockinger, M. P.; Bogena, H. R.; Lücke, A.; Diekkrüger, B.; Weiler, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Seasonal soil moisture patterns: Controlling transit time distributions in a forested headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 50(6): 5270–5289. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stockinger_seasonal_2014,\n\ttitle = {Seasonal soil moisture patterns: {Controlling} transit time distributions in a forested headwater catchment},\n\tvolume = {50},\n\tissn = {00431397},\n\tshorttitle = {Seasonal soil moisture patterns},\n\turl = {http://doi.wiley.com/10.1002/2013WR014815},\n\tdoi = {10.1002/2013WR014815},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-06-19},\n\tjournal = {Water Resources Research},\n\tauthor = {Stockinger, Michael Paul and Bogena, Heye Reemt and Lücke, Andreas and Diekkrüger, Bernd and Weiler, Markus and Vereecken, Harry},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {5270--5289},\n}\n\n\n\n

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\n \n\n \n \n Stampfli, N. C.; Knillmann, S.; Noskov, Y. A.; Schäfer, R. B.; Liess, M.; and Beketov, M. A.\n\n\n \n \n \n \n \n Environmental stressors can enhance the development of community tolerance to a toxicant.\n \n \n \n \n\n\n \n\n\n\n Ecotoxicology, 23(9): 1690–1700. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{stampfli_environmental_2014,\n\ttitle = {Environmental stressors can enhance the development of community tolerance to a toxicant},\n\tvolume = {23},\n\tissn = {0963-9292, 1573-3017},\n\turl = {http://link.springer.com/10.1007/s10646-014-1308-5},\n\tdoi = {10.1007/s10646-014-1308-5},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-06-19},\n\tjournal = {Ecotoxicology},\n\tauthor = {Stampfli, Nathalie C. and Knillmann, Saskia and Noskov, Yury A. and Schäfer, Ralf B. and Liess, Matthias and Beketov, Mikhail A.},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {1690--1700},\n}\n\n\n\n

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\n \n\n \n \n Smetanová, S.; Bláha, L.; Liess, M.; Schäfer, R.; and Beketov, M.\n\n\n \n \n \n \n \n Do predictions from Species Sensitivity Distributions match with field data?.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 189: 126–133. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Do$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{smetanova_predictions_2014,\n\ttitle = {Do predictions from {Species} {Sensitivity} {Distributions} match with field data?},\n\tvolume = {189},\n\tissn = {02697491},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749114000876},\n\tdoi = {10.1016/j.envpol.2014.03.002},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Environmental Pollution},\n\tauthor = {Smetanová, S. and Bláha, L. and Liess, M. and Schäfer, R.B. and Beketov, M.A.},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {126--133},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, K.; Behrens, T.; Daumann, J.; Ramirez-Lopez, L.; Werban, U.; Dietrich, P.; and Scholten, T.\n\n\n \n \n \n \n \n A comparison of calibration sampling schemes at the field scale.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 232-234: 243–256. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_comparison_2014,\n\ttitle = {A comparison of calibration sampling schemes at the field scale},\n\tvolume = {232-234},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706114002183},\n\tdoi = {10.1016/j.geoderma.2014.05.013},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Geoderma},\n\tauthor = {Schmidt, K. and Behrens, T. and Daumann, J. and Ramirez-Lopez, L. and Werban, U. and Dietrich, P. and Scholten, T.},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {243--256},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, C.; Büttner, O.; Musolff, A.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n A method for automated, daily, temperature-based vertical streambed water-fluxes.\n \n \n \n \n\n\n \n\n\n\n Fundamental and Applied Limnology, 184(3): 173–181. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_method_2014,\n\ttitle = {A method for automated, daily, temperature-based vertical streambed water-fluxes},\n\tvolume = {184},\n\tissn = {1863-9135},\n\turl = {http://www.schweizerbart.de/papers/fal/detail/184/83070/A_method_for_automated_daily_temperature_based_ver?af=crossref},\n\tdoi = {10.1127/1863-9135/2014/0548},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {Fundamental and Applied Limnology},\n\tauthor = {Schmidt, Christian and Büttner, Olaf and Musolff, Andreas and Fleckenstein, Jan H.},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {173--181},\n}\n\n\n\n

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\n \n\n \n \n Schlink, U.; Kindler, A.; Großmann, K.; Schwarz, N.; and Franck, U.\n\n\n \n \n \n \n \n The temperature recorded by simulated mobile receptors is an indicator for the thermal exposure of the urban inhabitants.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 36: 607–616. January 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schlink_temperature_2014,\n\ttitle = {The temperature recorded by simulated mobile receptors is an indicator for the thermal exposure of the urban inhabitants},\n\tvolume = {36},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X1300349X},\n\tdoi = {10.1016/j.ecolind.2013.09.017},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Ecological Indicators},\n\tauthor = {Schlink, Uwe and Kindler, Annegret and Großmann, Katrin and Schwarz, Nina and Franck, Ulrich},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {607--616},\n}\n\n\n\n

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\n \n\n \n \n Rötzer, K.; Montzka, C.; Bogena, H.; Wagner, W.; Kerr, Y.; Kidd, R.; and Vereecken, H.\n\n\n \n \n \n \n \n Catchment scale validation of SMOS and ASCAT soil moisture products using hydrological modeling and temporal stability analysis.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 519: 934–946. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Catchment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rotzer_catchment_2014,\n\ttitle = {Catchment scale validation of {SMOS} and {ASCAT} soil moisture products using hydrological modeling and temporal stability analysis},\n\tvolume = {519},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169414006192},\n\tdoi = {10.1016/j.jhydrol.2014.07.065},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Rötzer, K. and Montzka, C. and Bogena, H. and Wagner, W. and Kerr, Y.H. and Kidd, R. and Vereecken, H.},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {934--946},\n}\n\n\n\n

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\n \n\n \n \n Rink, K.; Bilke, L.; and Kolditz, O.\n\n\n \n \n \n \n \n Visualisation strategies for environmental modelling data.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 72(10): 3857–3868. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Visualisation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_visualisation_2014,\n\ttitle = {Visualisation strategies for environmental modelling data},\n\tvolume = {72},\n\tissn = {1866-6280, 1866-6299},\n\turl = {http://link.springer.com/10.1007/s12665-013-2970-2},\n\tdoi = {10.1007/s12665-013-2970-2},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-06-19},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Rink, Karsten and Bilke, Lars and Kolditz, Olaf},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {3857--3868},\n}\n\n\n\n

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\n \n\n \n \n Rahman, M.; Sulis, M.; and Kollet, S. J.\n\n\n \n \n \n \n \n The concept of dual-boundary forcing in land surface-subsurface interactions of the terrestrial hydrologic and energy cycles.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 50(11): 8531–8548. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rahman_concept_2014,\n\ttitle = {The concept of dual-boundary forcing in land surface-subsurface interactions of the terrestrial hydrologic and energy cycles},\n\tvolume = {50},\n\tissn = {00431397},\n\turl = {http://doi.wiley.com/10.1002/2014WR015738},\n\tdoi = {10.1002/2014WR015738},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-06-19},\n\tjournal = {Water Resources Research},\n\tauthor = {Rahman, M. and Sulis, M. and Kollet, S. J.},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {8531--8548},\n}\n\n\n\n

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\n \n\n \n \n Rach, O.; Brauer, A.; Wilkes, H.; and Sachse, D.\n\n\n \n \n \n \n \n Delayed hydrological response to Greenland cooling at the onset of the Younger Dryas in western Europe.\n \n \n \n \n\n\n \n\n\n\n Nature Geoscience, 7(2): 109–112. February 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Delayed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rach_delayed_2014,\n\ttitle = {Delayed hydrological response to {Greenland} cooling at the onset of the {Younger} {Dryas} in western {Europe}},\n\tvolume = {7},\n\tissn = {1752-0894, 1752-0908},\n\turl = {https://www.nature.com/articles/ngeo2053},\n\tdoi = {10.1038/ngeo2053},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-06-19},\n\tjournal = {Nature Geoscience},\n\tauthor = {Rach, O. and Brauer, A. and Wilkes, H. and Sachse, D.},\n\tmonth = feb,\n\tyear = {2014},\n\tpages = {109--112},\n}\n\n\n\n

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\n \n\n \n \n Pritzkow, C.; Heinrich, I.; Grudd, H.; and Helle, G.\n\n\n \n \n \n \n \n Relationship between wood anatomy, tree-ring widths and wood density of Pinus sylvestris L. and climate at high latitudes in northern Sweden.\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 32(4): 295–302. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Relationship$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pritzkow_relationship_2014,\n\ttitle = {Relationship between wood anatomy, tree-ring widths and wood density of {Pinus} sylvestris {L}. and climate at high latitudes in northern {Sweden}},\n\tvolume = {32},\n\tissn = {11257865},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786514000575},\n\tdoi = {10.1016/j.dendro.2014.07.003},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-06-19},\n\tjournal = {Dendrochronologia},\n\tauthor = {Pritzkow, C. and Heinrich, I. and Grudd, H. and Helle, G.},\n\tyear = {2014},\n\tpages = {295--302},\n}\n\n\n\n

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\n \n\n \n \n Prolingheuer, N.; Scharnagl, B.; Graf, A.; Vereecken, H.; and Herbst, M.\n\n\n \n \n \n \n \n On the spatial variation of soil rhizospheric and heterotrophic respiration in a winter wheat stand.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 195-196: 24–31. September 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{prolingheuer_spatial_2014,\n\ttitle = {On the spatial variation of soil rhizospheric and heterotrophic respiration in a winter wheat stand},\n\tvolume = {195-196},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192314001166},\n\tdoi = {10.1016/j.agrformet.2014.04.016},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Prolingheuer, N. and Scharnagl, B. and Graf, A. and Vereecken, H. and Herbst, M.},\n\tmonth = sep,\n\tyear = {2014},\n\tpages = {24--31},\n}\n\n\n\n

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\n \n\n \n \n Pieper, H.; Heinrich, I.; Heußner, K.; and Helle, G.\n\n\n \n \n \n \n \n The influence of volcanic eruptions on growth of central European lowland trees in NE-Germany during the last millennium.\n \n \n \n \n\n\n \n\n\n\n Palaeogeography, Palaeoclimatology, Palaeoecology, 411: 155–166. October 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pieper_influence_2014,\n\ttitle = {The influence of volcanic eruptions on growth of central {European} lowland trees in {NE}-{Germany} during the last millennium},\n\tvolume = {411},\n\tissn = {00310182},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0031018214003241},\n\tdoi = {10.1016/j.palaeo.2014.06.012},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Palaeogeography, Palaeoclimatology, Palaeoecology},\n\tauthor = {Pieper, H. and Heinrich, I. and Heußner, K.U. and Helle, G.},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {155--166},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Bogena, H.; Jagdhuber, T.; Hajnsek, I.; Horn, R.; Reigber, A.; Hasan, S.; Rudiger, C.; Jaeger, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Active and passive L-band microwave remote sensing for soil moisture 2014; A test-bed for SMAP fusion algorithms.\n \n \n \n \n\n\n \n\n\n\n In 2014 IEEE Geoscience and Remote Sensing Symposium, pages 2427–2430, Quebec City, QC, July 2014. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Active$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{montzka_active_2014,\n\taddress = {Quebec City, QC},\n\ttitle = {Active and passive {L}-band microwave remote sensing for soil moisture 2014; {A} test-bed for {SMAP} fusion algorithms},\n\tisbn = {9781479957750},\n\turl = {http://ieeexplore.ieee.org/document/6946962/},\n\tdoi = {10.1109/IGARSS.2014.6946962},\n\turldate = {2023-06-19},\n\tbooktitle = {2014 {IEEE} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Montzka, C. and Bogena, H. and Jagdhuber, T. and Hajnsek, I. and Horn, R. and Reigber, A. and Hasan, S. and Rudiger, C. and Jaeger, M. and Vereecken, H.},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {2427--2430},\n}\n\n\n\n

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\n \n\n \n \n Pause, M.; Lausch, A.; Bernhardt, M.; Hacker, J.; and Schulz, K.\n\n\n \n \n \n \n \n Improving Soil Moisture Data Retrieval From Airborne L-Band Radiometer Data by Considering Spatially Varying Roughness.\n \n \n \n \n\n\n \n\n\n\n Canadian Journal of Remote Sensing, 40(1): 15–25. January 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Improving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{pause_improving_2014,\n\ttitle = {Improving {Soil} {Moisture} {Data} {Retrieval} {From} {Airborne} {L}-{Band} {Radiometer} {Data} by {Considering} {Spatially} {Varying} {Roughness}},\n\tvolume = {40},\n\tissn = {0703-8992, 1712-7971},\n\turl = {http://www.tandfonline.com/doi/abs/10.1080/07038992.2014.907522},\n\tdoi = {10.1080/07038992.2014.907522},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-06-19},\n\tjournal = {Canadian Journal of Remote Sensing},\n\tauthor = {Pause, Marion and Lausch, Angela and Bernhardt, Matthias and Hacker, Jorg and Schulz, Karsten},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {15--25},\n}\n\n\n\n

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\n \n\n \n \n Liu, S.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n A highly sensitive method for the determination of hydroxylamine in soils.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 232-234: 117–122. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liu_highly_2014,\n\ttitle = {A highly sensitive method for the determination of hydroxylamine in soils},\n\tvolume = {232-234},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706114002031},\n\tdoi = {10.1016/j.geoderma.2014.05.006},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Geoderma},\n\tauthor = {Liu, Shurong and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {117--122},\n}\n\n\n\n

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\n \n\n \n \n Lindauer, M.; Schmid, H.; Grote, R.; Mauder, M.; Steinbrecher, R.; and Wolpert, B.\n\n\n \n \n \n \n \n Net ecosystem exchange over a non-cleared wind-throw-disturbed upland spruce forest—Measurements and simulations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 197: 219–234. October 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Net$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lindauer_net_2014,\n\ttitle = {Net ecosystem exchange over a non-cleared wind-throw-disturbed upland spruce forest—{Measurements} and simulations},\n\tvolume = {197},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192314001725},\n\tdoi = {10.1016/j.agrformet.2014.07.005},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Lindauer, M. and Schmid, H.P. and Grote, R. and Mauder, M. and Steinbrecher, R. and Wolpert, B.},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {219--234},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Küster, M.; Fülling, A.; Kaiser, K.; and Ulrich, J.\n\n\n \n \n \n \n \n Aeolian sands and buried soils in the Mecklenburg Lake District, NE Germany: Holocene land-use history and pedo-geomorphic response.\n \n \n \n \n\n\n \n\n\n\n Geomorphology, 211: 64–76. April 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Aeolian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kuster_aeolian_2014,\n\ttitle = {Aeolian sands and buried soils in the {Mecklenburg} {Lake} {District}, {NE} {Germany}: {Holocene} land-use history and pedo-geomorphic response},\n\tvolume = {211},\n\tissn = {0169555X},\n\tshorttitle = {Aeolian sands and buried soils in the {Mecklenburg} {Lake} {District}, {NE} {Germany}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169555X13006387},\n\tdoi = {10.1016/j.geomorph.2013.12.030},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Geomorphology},\n\tauthor = {Küster, Mathias and Fülling, Alexander and Kaiser, Knut and Ulrich, Jens},\n\tmonth = apr,\n\tyear = {2014},\n\tpages = {64--76},\n}\n\n\n\n

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\n \n\n \n \n Kroeger, I.; Liess, M.; and Duquesne, S.\n\n\n \n \n \n \n \n Temporal and spatial habitat preferences and biotic interactions between mosquito larvae and antagonistic crustaceans in the field.\n \n \n \n \n\n\n \n\n\n\n Journal of Vector Ecology, 39(1): 103–111. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kroeger_temporal_2014,\n\ttitle = {Temporal and spatial habitat preferences and biotic interactions between mosquito larvae and antagonistic crustaceans in the field},\n\tvolume = {39},\n\tissn = {10811710},\n\turl = {http://doi.wiley.com/10.1111/j.1948-7134.2014.12076.x},\n\tdoi = {10.1111/j.1948-7134.2014.12076.x},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-06-19},\n\tjournal = {Journal of Vector Ecology},\n\tauthor = {Kroeger, Iris and Liess, Matthias and Duquesne, Sabine},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {103--111},\n}\n\n\n\n

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\n \n\n \n \n Koch, S.; Jurasinski, G.; Koebsch, F.; Koch, M.; and Glatzel, S.\n\n\n \n \n \n \n \n Spatial Variability of Annual Estimates of Methane Emissions in a Phragmites Australis (Cav.) Trin. ex Steud. Dominated Restored Coastal Brackish Fen.\n \n \n \n \n\n\n \n\n\n\n Wetlands, 34(3): 593–602. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{koch_spatial_2014,\n\ttitle = {Spatial {Variability} of {Annual} {Estimates} of {Methane} {Emissions} in a {Phragmites} {Australis} ({Cav}.) {Trin}. ex {Steud}. {Dominated} {Restored} {Coastal} {Brackish} {Fen}},\n\tvolume = {34},\n\tissn = {0277-5212, 1943-6246},\n\turl = {https://link.springer.com/10.1007/s13157-014-0528-z},\n\tdoi = {10.1007/s13157-014-0528-z},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {Wetlands},\n\tauthor = {Koch, Stefan and Jurasinski, Gerald and Koebsch, Franziska and Koch, Marian and Glatzel, Stephan},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {593--602},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Koch, P. J.; Mauersberger, R.; Stüve, P.; Dreibrodt, J.; and Bens, O.\n\n\n \n \n \n \n \n Detection and attribution of lake-level dynamics in north-eastern central Europe in recent decades.\n \n \n \n \n\n\n \n\n\n\n Regional Environmental Change, 14(4): 1587–1600. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_detection_2014,\n\ttitle = {Detection and attribution of lake-level dynamics in north-eastern central {Europe} in recent decades},\n\tvolume = {14},\n\tissn = {1436-3798, 1436-378X},\n\turl = {http://link.springer.com/10.1007/s10113-014-0600-5},\n\tdoi = {10.1007/s10113-014-0600-5},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-06-19},\n\tjournal = {Regional Environmental Change},\n\tauthor = {Kaiser, Knut and Koch, Paul Jörg and Mauersberger, Rüdiger and Stüve, Peter and Dreibrodt, Janek and Bens, Oliver},\n\tmonth = aug,\n\tyear = {2014},\n\tpages = {1587--1600},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Küster, M.; Fülling, A.; Theuerkauf, M.; Dietze, E.; Graventein, H.; Koch, P. J.; Bens, O.; and Brauer, A.\n\n\n \n \n \n \n \n Littoral landforms and pedosedimentary sequences indicating late Holocene lake-level changes in northern central Europe — A case study from northeastern Germany.\n \n \n \n \n\n\n \n\n\n\n Geomorphology, 216: 58–78. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Littoral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_littoral_2014,\n\ttitle = {Littoral landforms and pedosedimentary sequences indicating late {Holocene} lake-level changes in northern central {Europe} — {A} case study from northeastern {Germany}},\n\tvolume = {216},\n\tissn = {0169555X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169555X14001597},\n\tdoi = {10.1016/j.geomorph.2014.03.025},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Geomorphology},\n\tauthor = {Kaiser, Knut and Küster, Mathias and Fülling, Alexander and Theuerkauf, Martin and Dietze, Elisabeth and Graventein, Hagen and Koch, Paul Jörg and Bens, Oliver and Brauer, Achim},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {58--78},\n}\n\n\n\n

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\n \n\n \n \n Jiang, C.; Séquaris, J.; Wacha, A.; Bóta, A.; Vereecken, H.; and Klumpp, E.\n\n\n \n \n \n \n \n Effect of metal oxide on surface area and pore size of water-dispersible colloids from three German silt loam topsoils.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 235-236: 260–270. December 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_effect_2014,\n\ttitle = {Effect of metal oxide on surface area and pore size of water-dispersible colloids from three {German} silt loam topsoils},\n\tvolume = {235-236},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706114002882},\n\tdoi = {10.1016/j.geoderma.2014.07.017},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Geoderma},\n\tauthor = {Jiang, Canlan and Séquaris, Jean-Marie and Wacha, András and Bóta, Attila and Vereecken, Harry and Klumpp, Erwin},\n\tmonth = dec,\n\tyear = {2014},\n\tpages = {260--270},\n}\n\n\n\n

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\n \n\n \n \n Jiang, S.; Jomaa, S.; and Rode, M.\n\n\n \n \n \n \n \n Modelling inorganic nitrogen leaching in nested mesoscale catchments in central Germany: MODELLING INORGANIC NITROGEN LEACHING IN CENTRAL GERMANY.\n \n \n \n \n\n\n \n\n\n\n Ecohydrology,n/a–n/a. January 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jiang_modelling_2014,\n\ttitle = {Modelling inorganic nitrogen leaching in nested mesoscale catchments in central {Germany}: {MODELLING} {INORGANIC} {NITROGEN} {LEACHING} {IN} {CENTRAL} {GERMANY}},\n\tissn = {19360584},\n\tshorttitle = {Modelling inorganic nitrogen leaching in nested mesoscale catchments in central {Germany}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/eco.1462},\n\tdoi = {10.1002/eco.1462},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Ecohydrology},\n\tauthor = {Jiang, Sanyuan and Jomaa, Seifeddine and Rode, Michael},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {n/a--n/a},\n}\n\n\n\n

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\n \n\n \n \n Jagdhuber, T.; Stockamp, J.; Hajnsek, I.; and Ludwig, R.\n\n\n \n \n \n \n \n Identification of Soil Freezing and Thawing States Using SAR Polarimetry at C-Band.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 6(3): 2008–2023. March 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Identification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jagdhuber_identification_2014,\n\ttitle = {Identification of {Soil} {Freezing} and {Thawing} {States} {Using} {SAR} {Polarimetry} at {C}-{Band}},\n\tvolume = {6},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/6/3/2008},\n\tdoi = {10.3390/rs6032008},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {Remote Sensing},\n\tauthor = {Jagdhuber, Thomas and Stockamp, Julia and Hajnsek, Irena and Ludwig, Ralf},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {2008--2023},\n}\n\n\n\n

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\n \n\n \n \n Hommeltenberg, J.; Mauder, M.; Drösler, M.; Heidbach, K.; Werle, P.; and Schmid, H. P.\n\n\n \n \n \n \n \n Ecosystem scale methane fluxes in a natural temperate bog-pine forest in southern Germany.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 198-199: 273–284. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecosystem$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hommeltenberg_ecosystem_2014,\n\ttitle = {Ecosystem scale methane fluxes in a natural temperate bog-pine forest in southern {Germany}},\n\tvolume = {198-199},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192314002081},\n\tdoi = {10.1016/j.agrformet.2014.08.017},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Hommeltenberg, Janina and Mauder, Matthias and Drösler, Matthias and Heidbach, Katja and Werle, Peter and Schmid, Hans Peter},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {273--284},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Hommeltenberg, J.; Schmid, H. P.; Drösler, M.; and Werle, P.\n\n\n \n \n \n \n \n Can a bog drained for forestry be a stronger carbon sink than a natural bog forest?.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 11(13): 3477–3493. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Can$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hommeltenberg_can_2014,\n\ttitle = {Can a bog drained for forestry be a stronger carbon sink than a natural bog forest?},\n\tvolume = {11},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/11/3477/2014/},\n\tdoi = {10.5194/bg-11-3477-2014},\n\tabstract = {Abstract. This study compares the CO2 exchange of a natural bog forest, and of a bog drained for forestry in the pre-Alpine region of southern Germany. The sites are separated by only 10 km, they share the same soil formation history and are exposed to the same climate and weather conditions. In contrast, they differ in land use history: at the Schechenfilz site a natural bog-pine forest (Pinus mugo ssp. rotundata) grows on an undisturbed, about 5 m thick peat layer; at Mooseurach a planted spruce forest (Picea abies) grows on drained and degraded peat (3.4 m). The net ecosystem exchange of CO2 (NEE) at both sites has been investigated for 2 years (July 2010–June 2012), using the eddy covariance technique. Our results indicate that the drained, forested bog at Mooseurach is a much stronger carbon dioxide sink (−130 ± 31 and −300 ± 66 g C m−2 a−1 in the first and second year, respectively) than the natural bog forest at Schechenfilz (−53 ± 28 and −73 ± 38 g C m−2 a−1). The strong net CO2 uptake can be explained by the high gross primary productivity of the 44-year old spruces that over-compensates the two-times stronger ecosystem respiration at the drained site. The larger productivity of the spruces can be clearly attributed to the larger plant area index (PAI) of the spruce site. However, even though current flux measurements indicate strong CO2 uptake of the drained spruce forest, the site is a strong net CO2 source when the whole life-cycle since forest planting is considered. It is important to access this result in terms of the long-term biome balance. To do so, we used historical data to estimate the difference between carbon fixation by the spruces and the carbon loss from the peat due to drainage since forest planting. This rough estimate indicates a strong carbon release of +134 t C ha−1 within the last 44 years. Thus, the spruces would need to grow for another 100 years at about the current rate, to compensate the potential peat loss of the former years. In contrast, the natural bog-pine ecosystem has likely been a small but stable carbon sink for decades, which our results suggest is very robust regarding short-term changes of environmental factors.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2023-06-19},\n\tjournal = {Biogeosciences},\n\tauthor = {Hommeltenberg, J. and Schmid, H. P. and Drösler, M. and Werle, P.},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {3477--3493},\n}\n\n\n\n

\n\n\n

\n Abstract. This study compares the CO2 exchange of a natural bog forest, and of a bog drained for forestry in the pre-Alpine region of southern Germany. The sites are separated by only 10 km, they share the same soil formation history and are exposed to the same climate and weather conditions. In contrast, they differ in land use history: at the Schechenfilz site a natural bog-pine forest (Pinus mugo ssp. rotundata) grows on an undisturbed, about 5 m thick peat layer; at Mooseurach a planted spruce forest (Picea abies) grows on drained and degraded peat (3.4 m). The net ecosystem exchange of CO2 (NEE) at both sites has been investigated for 2 years (July 2010–June 2012), using the eddy covariance technique. Our results indicate that the drained, forested bog at Mooseurach is a much stronger carbon dioxide sink (−130 ± 31 and −300 ± 66 g C m−2 a−1 in the first and second year, respectively) than the natural bog forest at Schechenfilz (−53 ± 28 and −73 ± 38 g C m−2 a−1). The strong net CO2 uptake can be explained by the high gross primary productivity of the 44-year old spruces that over-compensates the two-times stronger ecosystem respiration at the drained site. The larger productivity of the spruces can be clearly attributed to the larger plant area index (PAI) of the spruce site. However, even though current flux measurements indicate strong CO2 uptake of the drained spruce forest, the site is a strong net CO2 source when the whole life-cycle since forest planting is considered. It is important to access this result in terms of the long-term biome balance. To do so, we used historical data to estimate the difference between carbon fixation by the spruces and the carbon loss from the peat due to drainage since forest planting. This rough estimate indicates a strong carbon release of +134 t C ha−1 within the last 44 years. Thus, the spruces would need to grow for another 100 years at about the current rate, to compensate the potential peat loss of the former years. In contrast, the natural bog-pine ecosystem has likely been a small but stable carbon sink for decades, which our results suggest is very robust regarding short-term changes of environmental factors.\n

\n\n\n

\n \n\n \n \n Hasan, S.; Montzka, C.; Rüdiger, C.; Ali, M.; R. Bogena, H.; and Vereecken, H.\n\n\n \n \n \n \n \n Soil moisture retrieval from airborne L-band passive microwave using high resolution multispectral data.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 91: 59–71. May 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hasan_soil_2014,\n\ttitle = {Soil moisture retrieval from airborne {L}-band passive microwave using high resolution multispectral data},\n\tvolume = {91},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271614000422},\n\tdoi = {10.1016/j.isprsjprs.2014.02.005},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Hasan, Sayeh and Montzka, Carsten and Rüdiger, Christoph and Ali, Muhammad and R. Bogena, Heye and Vereecken, Harry},\n\tmonth = may,\n\tyear = {2014},\n\tpages = {59--71},\n}\n\n\n\n

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\n \n\n \n \n Haase, D.; Haase, A.; and Rink, D.\n\n\n \n \n \n \n \n Conceptualizing the nexus between urban shrinkage and ecosystem services.\n \n \n \n \n\n\n \n\n\n\n Landscape and Urban Planning, 132: 159–169. December 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Conceptualizing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{haase_conceptualizing_2014,\n\ttitle = {Conceptualizing the nexus between urban shrinkage and ecosystem services},\n\tvolume = {132},\n\tissn = {01692046},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169204614002138},\n\tdoi = {10.1016/j.landurbplan.2014.09.003},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Landscape and Urban Planning},\n\tauthor = {Haase, Dagmar and Haase, Annegret and Rink, Dieter},\n\tmonth = dec,\n\tyear = {2014},\n\tpages = {159--169},\n}\n\n\n\n

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\n \n\n \n \n Graf, A.; Bogena, H. R.; Drüe, C.; Hardelauf, H.; Pütz, T.; Heinemann, G.; and Vereecken, H.\n\n\n \n \n \n \n \n Spatiotemporal relations between water budget components and soil water content in a forested tributary catchment.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 50(6): 4837–4857. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_spatiotemporal_2014,\n\ttitle = {Spatiotemporal relations between water budget components and soil water content in a forested tributary catchment},\n\tvolume = {50},\n\tissn = {00431397},\n\turl = {http://doi.wiley.com/10.1002/2013WR014516},\n\tdoi = {10.1002/2013WR014516},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-06-19},\n\tjournal = {Water Resources Research},\n\tauthor = {Graf, Alexander and Bogena, Heye R. and Drüe, Clemens and Hardelauf, Horst and Pütz, Thomas and Heinemann, Günther and Vereecken, Harry},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {4837--4857},\n}\n\n\n\n

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\n \n\n \n \n Gottselig, N.; Bol, R.; Nischwitz, V.; Vereecken, H.; Amelung, W.; and Klumpp, E.\n\n\n \n \n \n \n \n Distribution of Phosphorus-Containing Fine Colloids and Nanoparticles in Stream Water of a Forest Catchment.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 13(7): vzj2014.01.0005. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Distribution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{gottselig_distribution_2014,\n\ttitle = {Distribution of {Phosphorus}-{Containing} {Fine} {Colloids} and {Nanoparticles} in {Stream} {Water} of a {Forest} {Catchment}},\n\tvolume = {13},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2014.01.0005},\n\tdoi = {10.2136/vzj2014.01.0005},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-06-19},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Gottselig, Nina and Bol, Roland and Nischwitz, Volker and Vereecken, Harry and Amelung, Wulf and Klumpp, Erwin},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {vzj2014.01.0005},\n}\n\n\n\n

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\n \n\n \n \n Friese, K.; Schultze, M.; Boehrer, B.; Büttner, O.; Herzsprung, P.; Koschorreck, M.; Kuehn, B.; Rönicke, H.; Tittel, J.; Wendt-Potthoff, K.; Wollschläger, U.; Dietze, M.; and Rinke, K.\n\n\n \n \n \n \n \n Ecological response of two hydro-morphological similar pre-dams to contrasting land-use in the Rappbode reservoir system (Germany): Ecological response of two pre-dams to contrasting land-use.\n \n \n \n \n\n\n \n\n\n\n International Review of Hydrobiology, 99(5): 335–349. October 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{friese_ecological_2014,\n\ttitle = {Ecological response of two hydro-morphological similar pre-dams to contrasting land-use in the {Rappbode} reservoir system ({Germany}): {Ecological} response of two pre-dams to contrasting land-use},\n\tvolume = {99},\n\tissn = {14342944},\n\tshorttitle = {Ecological response of two hydro-morphological similar pre-dams to contrasting land-use in the {Rappbode} reservoir system ({Germany})},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/iroh.201301672},\n\tdoi = {10.1002/iroh.201301672},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-06-19},\n\tjournal = {International Review of Hydrobiology},\n\tauthor = {Friese, Kurt and Schultze, Martin and Boehrer, Bertram and Büttner, Olaf and Herzsprung, Peter and Koschorreck, Matthias and Kuehn, Burkhard and Rönicke, Helmut and Tittel, Jörg and Wendt-Potthoff, Katrin and Wollschläger, Ute and Dietze, Maren and Rinke, Karsten},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {335--349},\n}\n\n\n\n

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\n \n\n \n \n Fratini, G.; and Mauder, M.\n\n\n \n \n \n \n \n Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 7(7): 2273–2281. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fratini_towards_2014,\n\ttitle = {Towards a consistent eddy-covariance processing: an intercomparison of {EddyPro} and {TK3}},\n\tvolume = {7},\n\tissn = {1867-8548},\n\tshorttitle = {Towards a consistent eddy-covariance processing},\n\turl = {https://amt.copernicus.org/articles/7/2273/2014/},\n\tdoi = {10.5194/amt-7-2273-2014},\n\tabstract = {Abstract. A comparison of two popular eddy-covariance software packages is presented, namely, EddyPro and TK3. Two approximately 1-month long test data sets were processed, representing typical instrumental setups (i.e., CSAT3/LI-7500 above grassland and Solent R3/LI-6262 above a forest). The resulting fluxes and quality flags were compared. Achieving a satisfying agreement and understanding residual discrepancies required several iterations and interventions of different nature, spanning from simple software reconfiguration to actual code manipulations. In this paper, we document our comparison exercise and show that the two software packages can provide utterly satisfying agreement when properly configured. Our main aim, however, is to stress the complexity of performing a rigorous comparison of eddy-covariance software. We show that discriminating actual discrepancies in the results from inconsistencies in the software configuration requires deep knowledge of both software packages and of the eddy-covariance method. In some instances, it may be even beyond the possibility of the investigator who does not have access to and full knowledge of the source code. Being the developers of EddyPro and TK3, we could discuss the comparison at all levels of details and this proved necessary to achieve a full understanding. As a result, we suggest that researchers are more likely to get comparable results when using EddyPro (v5.1.1) and TK3 (v3.11) – at least with the setting presented in this paper – than they are when using any other pair of EC software which did not undergo a similar cross-validation.  As a further consequence, we also suggest that, to the aim of assuring consistency and comparability of centralized flux databases, and for a confident use of eddy fluxes in synthesis studies on the regional, continental and global scale, researchers only rely on software that have been extensively validated in documented intercomparisons.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-06-19},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Fratini, G. and Mauder, M.},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {2273--2281},\n}\n\n\n\n

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\n \n\n \n \n Fassnacht, F. E.; Neumann, C.; Forster, M.; Buddenbaum, H.; Ghosh, A.; Clasen, A.; Joshi, P. K.; and Koch, B.\n\n\n \n \n \n \n \n Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites.\n \n \n \n \n\n\n \n\n\n\n IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2547–2561. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fassnacht_comparison_2014,\n\ttitle = {Comparison of {Feature} {Reduction} {Algorithms} for {Classifying} {Tree} {Species} {With} {Hyperspectral} {Data} on {Three} {Central} {European} {Test} {Sites}},\n\tvolume = {7},\n\tissn = {1939-1404, 2151-1535},\n\turl = {https://ieeexplore.ieee.org/document/6851112/},\n\tdoi = {10.1109/JSTARS.2014.2329390},\n\tnumber = {6},\n\turldate = {2023-06-19},\n\tjournal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},\n\tauthor = {Fassnacht, Fabian E. and Neumann, Carsten and Forster, Michael and Buddenbaum, Henning and Ghosh, Aniruddha and Clasen, Anne and Joshi, Pawan Kumar and Koch, Barbara},\n\tmonth = jun,\n\tyear = {2014},\n\tpages = {2547--2561},\n}\n\n\n\n

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\n \n\n \n \n Eder, F.; De Roo, F.; Kohnert, K.; Desjardins, R. L.; Schmid, H. P.; and Mauder, M.\n\n\n \n \n \n \n \n Evaluation of Two Energy Balance Closure Parametrizations.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 151(2): 195–219. May 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{eder_evaluation_2014,\n\ttitle = {Evaluation of {Two} {Energy} {Balance} {Closure} {Parametrizations}},\n\tvolume = {151},\n\tissn = {0006-8314, 1573-1472},\n\turl = {http://link.springer.com/10.1007/s10546-013-9904-0},\n\tdoi = {10.1007/s10546-013-9904-0},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-06-19},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Eder, Fabian and De Roo, Frederik and Kohnert, Katrin and Desjardins, Raymond L. and Schmid, Hans Peter and Mauder, Matthias},\n\tmonth = may,\n\tyear = {2014},\n\tpages = {195--219},\n}\n\n\n\n

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\n \n\n \n \n Dimitrov, M.; Vanderborght, J.; Kostov, K. G.; Jadoon, K. Z.; Weihermüller, L.; Jackson, T. J.; Bindlish, R.; Pachepsky, Y.; Schwank, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Soil Hydraulic Parameters and Surface Soil Moisture of a Tilled Bare Soil Plot Inversely Derived from L-Band Brightness Temperatures.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 13(1): vzj2013.04.0075. January 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{dimitrov_soil_2014,\n\ttitle = {Soil {Hydraulic} {Parameters} and {Surface} {Soil} {Moisture} of a {Tilled} {Bare} {Soil} {Plot} {Inversely} {Derived} from {L}-{Band} {Brightness} {Temperatures}},\n\tvolume = {13},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2013.04.0075},\n\tdoi = {10.2136/vzj2013.04.0075},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-06-19},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Dimitrov, M. and Vanderborght, J. and Kostov, K. G. and Jadoon, K. Z. and Weihermüller, L. and Jackson, T. J. and Bindlish, R. and Pachepsky, Y. and Schwank, M. and Vereecken, H.},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {vzj2013.04.0075},\n}\n\n\n\n

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\n \n\n \n \n Cornelissen, T.; Diekkrüger, B.; and Bogena, H. R.\n\n\n \n \n \n \n \n Significance of scale and lower boundary condition in the 3D simulation of hydrological processes and soil moisture variability in a forested headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 516: 140–153. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Significance$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{cornelissen_significance_2014,\n\ttitle = {Significance of scale and lower boundary condition in the {3D} simulation of hydrological processes and soil moisture variability in a forested headwater catchment},\n\tvolume = {516},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169414000821},\n\tdoi = {10.1016/j.jhydrol.2014.01.060},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Cornelissen, Thomas and Diekkrüger, Bernd and Bogena, Heye R.},\n\tmonth = aug,\n\tyear = {2014},\n\tpages = {140--153},\n}\n\n\n\n

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\n \n\n \n \n Chwala, C.; Kunstmann, H.; Hipp, S.; and Siart, U.\n\n\n \n \n \n \n \n A monostatic microwave transmission experiment for line integrated precipitation and humidity remote sensing.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Research, 144: 57–72. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chwala_monostatic_2014,\n\ttitle = {A monostatic microwave transmission experiment for line integrated precipitation and humidity remote sensing},\n\tvolume = {144},\n\tissn = {01698095},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169809513001531},\n\tdoi = {10.1016/j.atmosres.2013.05.014},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Atmospheric Research},\n\tauthor = {Chwala, Christian and Kunstmann, Harald and Hipp, Susanne and Siart, Uwe},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {57--72},\n}\n\n\n\n

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\n \n\n \n \n Busch, S.; Van Der Kruk, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Improved Characterization of Fine-Texture Soils Using On-Ground GPR Full-Waveform Inversion.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 52(7): 3947–3958. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{busch_improved_2014,\n\ttitle = {Improved {Characterization} of {Fine}-{Texture} {Soils} {Using} {On}-{Ground} {GPR} {Full}-{Waveform} {Inversion}},\n\tvolume = {52},\n\tissn = {0196-2892, 1558-0644},\n\turl = {http://ieeexplore.ieee.org/document/6648422/},\n\tdoi = {10.1109/TGRS.2013.2278297},\n\tnumber = {7},\n\turldate = {2023-06-16},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Busch, Sebastian and Van Der Kruk, Jan and Vereecken, Harry},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {3947--3958},\n}\n\n\n\n

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\n \n\n \n \n Bunzel, K.; Liess, M.; and Kattwinkel, M.\n\n\n \n \n \n \n \n Landscape parameters driving aquatic pesticide exposure and effects.\n \n \n \n \n\n\n \n\n\n\n Environmental Pollution, 186: 90–97. March 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Landscape$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bunzel_landscape_2014,\n\ttitle = {Landscape parameters driving aquatic pesticide exposure and effects},\n\tvolume = {186},\n\tissn = {02697491},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0269749113006106},\n\tdoi = {10.1016/j.envpol.2013.11.021},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Environmental Pollution},\n\tauthor = {Bunzel, Katja and Liess, Matthias and Kattwinkel, Mira},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {90--97},\n}\n\n\n\n

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\n \n\n \n \n Borg, E.; Schiller, C.; Daedelow, H.; Fichtelmann, B.; Jahncke, D.; Renke, F.; Tamm, H.; and Asche, H.\n\n\n \n \n \n \n \n Automated Generation of Value-Added Products for the Validation of Remote Sensing Information Based on In-Situ Data.\n \n \n \n \n\n\n \n\n\n\n In Hutchison, D.; Kanade, T.; Kittler, J.; Kleinberg, J. M.; Kobsa, A.; Mattern, F.; Mitchell, J. C.; Naor, M.; Nierstrasz, O.; Pandu Rangan, C.; Steffen, B.; Terzopoulos, D.; Tygar, D.; Weikum, G.; Murgante, B.; Misra, S.; Rocha, A. M. A. C.; Torre, C.; Rocha, J. G.; Falcão, M. I.; Taniar, D.; Apduhan, B. O.; and Gervasi, O., editor(s), Computational Science and Its Applications – ICCSA 2014, volume 8579, pages 393–407. Springer International Publishing, Cham, 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Automated$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{hutchison_automated_2014,\n\taddress = {Cham},\n\ttitle = {Automated {Generation} of {Value}-{Added} {Products} for the {Validation} of {Remote} {Sensing} {Information} {Based} on {In}-{Situ} {Data}},\n\tvolume = {8579},\n\tisbn = {9783319091433 9783319091440},\n\turl = {http://link.springer.com/10.1007/978-3-319-09144-0_27},\n\turldate = {2023-06-16},\n\tbooktitle = {Computational {Science} and {Its} {Applications} – {ICCSA} 2014},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Borg, Erik and Schiller, Chris and Daedelow, Holger and Fichtelmann, Bernd and Jahncke, Dirk and Renke, Frank and Tamm, Hans-Peter and Asche, Hartmut},\n\teditor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Kobsa, Alfred and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Terzopoulos, Demetri and Tygar, Doug and Weikum, Gerhard and Murgante, Beniamino and Misra, Sanjay and Rocha, Ana Maria A. C. and Torre, Carmelo and Rocha, Jorge Gustavo and Falcão, Maria Irene and Taniar, David and Apduhan, Bernady O. and Gervasi, Osvaldo},\n\tyear = {2014},\n\tdoi = {10.1007/978-3-319-09144-0_27},\n\tpages = {393--407},\n}\n\n\n\n

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\n \n\n \n \n Bocaniov, S. A.; Ullmann, C.; Rinke, K.; Lamb, K. G.; and Boehrer, B.\n\n\n \n \n \n \n \n Internal waves and mixing in a stratified reservoir: Insights from three-dimensional modeling.\n \n \n \n \n\n\n \n\n\n\n Limnologica, 49: 52–67. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Internal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bocaniov_internal_2014,\n\ttitle = {Internal waves and mixing in a stratified reservoir: {Insights} from three-dimensional modeling},\n\tvolume = {49},\n\tissn = {00759511},\n\tshorttitle = {Internal waves and mixing in a stratified reservoir},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0075951114000565},\n\tdoi = {10.1016/j.limno.2014.08.004},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Limnologica},\n\tauthor = {Bocaniov, Serghei A. and Ullmann, Christian and Rinke, Karsten and Lamb, Kevin G. and Boehrer, Bertram},\n\tmonth = nov,\n\tyear = {2014},\n\tpages = {52--67},\n}\n\n\n\n

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\n \n\n \n \n Blagodatskaya, Е.; Zheng, X.; Blagodatsky, S.; Wiegl, R.; Dannenmann, M.; and Butterbach-Bahl, K.\n\n\n \n \n \n \n \n Oxygen and substrate availability interactively control the temperature sensitivity of CO2 and N2O emission from soil.\n \n \n \n \n\n\n \n\n\n\n Biology and Fertility of Soils, 50(5): 775–783. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Oxygen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blagodatskaya_oxygen_2014,\n\ttitle = {Oxygen and substrate availability interactively control the temperature sensitivity of {CO2} and {N2O} emission from soil},\n\tvolume = {50},\n\tissn = {0178-2762, 1432-0789},\n\turl = {http://link.springer.com/10.1007/s00374-014-0899-6},\n\tdoi = {10.1007/s00374-014-0899-6},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-06-16},\n\tjournal = {Biology and Fertility of Soils},\n\tauthor = {Blagodatskaya, Е. and Zheng, X. and Blagodatsky, S. and Wiegl, R. and Dannenmann, M. and Butterbach-Bahl, K.},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {775--783},\n}\n\n\n\n

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\n \n\n \n \n Bassu, S.; Brisson, N.; Durand, J.; Boote, K.; Lizaso, J.; Jones, J. W.; Rosenzweig, C.; Ruane, A. C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.; Hoek, S.; Izaurralde, C.; Jongschaap, R.; Kemanian, A. R.; Kersebaum, K. C.; Kim, S.; Kumar, N. S.; Makowski, D.; Müller, C.; Nendel, C.; Priesack, E.; Pravia, M. V.; Sau, F.; Shcherbak, I.; Tao, F.; Teixeira, E.; Timlin, D.; and Waha, K.\n\n\n \n \n \n \n \n How do various maize crop models vary in their responses to climate change factors?.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 20(7): 2301–2320. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bassu_how_2014,\n\ttitle = {How do various maize crop models vary in their responses to climate change factors?},\n\tvolume = {20},\n\tissn = {13541013},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.12520},\n\tdoi = {10.1111/gcb.12520},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-06-16},\n\tjournal = {Global Change Biology},\n\tauthor = {Bassu, Simona and Brisson, Nadine and Durand, Jean-Louis and Boote, Kenneth and Lizaso, Jon and Jones, James W. and Rosenzweig, Cynthia and Ruane, Alex C. and Adam, Myriam and Baron, Christian and Basso, Bruno and Biernath, Christian and Boogaard, Hendrik and Conijn, Sjaak and Corbeels, Marc and Deryng, Delphine and De Sanctis, Giacomo and Gayler, Sebastian and Grassini, Patricio and Hatfield, Jerry and Hoek, Steven and Izaurralde, Cesar and Jongschaap, Raymond and Kemanian, Armen R. and Kersebaum, K. Christian and Kim, Soo-Hyung and Kumar, Naresh S. and Makowski, David and Müller, Christoph and Nendel, Claas and Priesack, Eckart and Pravia, Maria Virginia and Sau, Federico and Shcherbak, Iurii and Tao, Fulu and Teixeira, Edmar and Timlin, Dennis and Waha, Katharina},\n\tmonth = jul,\n\tyear = {2014},\n\tpages = {2301--2320},\n}\n\n\n\n

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\n \n\n \n \n Banzhaf, E.; De La Barrera, F.; Kindler, A.; Reyes-Paecke, S.; Schlink, U.; Welz, J.; and Kabisch, S.\n\n\n \n \n \n \n \n A conceptual framework for integrated analysis of environmental quality and quality of life.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 45: 664–668. October 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{banzhaf_conceptual_2014,\n\ttitle = {A conceptual framework for integrated analysis of environmental quality and quality of life},\n\tvolume = {45},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X14002532},\n\tdoi = {10.1016/j.ecolind.2014.06.002},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Ecological Indicators},\n\tauthor = {Banzhaf, Ellen and De La Barrera, Francisco and Kindler, Annegret and Reyes-Paecke, Sonia and Schlink, Uwe and Welz, Juliane and Kabisch, Sigrun},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {664--668},\n}\n\n\n\n

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\n \n\n \n \n Baatz, R.; Bogena, H.; Hendricks Franssen, H.; Huisman, J.; Qu, W.; Montzka, C.; and Vereecken, H.\n\n\n \n \n \n \n \n Calibration of a catchment scale cosmic-ray probe network: A comparison of three parameterization methods.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 516: 231–244. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Calibration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{baatz_calibration_2014,\n\ttitle = {Calibration of a catchment scale cosmic-ray probe network: {A} comparison of three parameterization methods},\n\tvolume = {516},\n\tissn = {00221694},\n\tshorttitle = {Calibration of a catchment scale cosmic-ray probe network},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169414001346},\n\tdoi = {10.1016/j.jhydrol.2014.02.026},\n\tlanguage = {en},\n\turldate = {2023-06-16},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Baatz, R. and Bogena, H.R. and Hendricks Franssen, H.-J. and Huisman, J.A. and Qu, W. and Montzka, C. and Vereecken, H.},\n\tmonth = aug,\n\tyear = {2014},\n\tpages = {231--244},\n}\n\n\n\n

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\n \n 2013\n \n \n (54)\n \n \n

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\n \n\n \n \n Fregien, K.; Heinrich, I.; Helle, G.; and Neuwirth, B.\n\n\n \n \n \n \n Growth response of sessile oak to regional climatic variability in West and Northeast Germany.\n \n \n \n\n\n \n\n\n\n In TRACE Tree Rings in Archaeology, Climatology and Ecology - Proceedings of the DENDROSYMPOSIUM 2012, May 8th - 12th, 2012 in Potsdam and Eberswalde, Germany Scientific Technical Report STR 13/05, volume 11, pages 31–42, Potsdam, January 2013. Deutsches GeoForschungsZentrum\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{fregien_growth_2013,\n\taddress = {Potsdam},\n\ttitle = {Growth response of sessile oak to regional climatic variability in {West} and {Northeast} {Germany}},\n\tvolume = {11},\n\tbooktitle = {{TRACE} {Tree} {Rings} in {Archaeology}, {Climatology} and {Ecology} - {Proceedings} of the {DENDROSYMPOSIUM} 2012, {May} 8th - 12th, 2012 in {Potsdam} and {Eberswalde}, {Germany} {Scientific} {Technical} {Report} {STR} 13/05},\n\tpublisher = {Deutsches GeoForschungsZentrum},\n\tauthor = {Fregien, K. and Heinrich, Ingo and Helle, Gerhard and Neuwirth, Burkhard},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {31--42},\n}\n\n\n\n

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\n \n\n \n \n Trömel, S.; Kumjian, M. R.; Ryzhkov, A. V.; Simmer, C.; and Diederich, M.\n\n\n \n \n \n \n \n Backscatter Differential Phase—Estimation and Variability.\n \n \n \n \n\n\n \n\n\n\n Journal of Applied Meteorology and Climatology, 52(11): 2529–2548. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Backscatter$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tromel_backscatter_2013,\n\ttitle = {Backscatter {Differential} {Phase}—{Estimation} and {Variability}},\n\tvolume = {52},\n\tissn = {1558-8424, 1558-8432},\n\turl = {https://journals.ametsoc.org/view/journals/apme/52/11/jamc-d-13-0124.1.xml},\n\tdoi = {10.1175/JAMC-D-13-0124.1},\n\tabstract = {Abstract \n             \n              On the basis of simulations and observations made with polarimetric radars operating at X, C, and S bands, the backscatter differential phase \n              δ \n              has been explored; \n              δ \n              has been identified as an important polarimetric variable that should not be ignored in precipitation estimations that are based on specific differential phase \n              K \n              DP \n              , especially at shorter radar wavelengths. Moreover, \n              δ \n              bears important information about the dominant size of raindrops and wet snowflakes in the melting layer. New methods for estimating \n              δ \n              in rain and in the melting layer are suggested. The method for estimating \n              δ \n              in rain is based on a modified version of the “ZPHI” algorithm and provides reasonably robust estimates of \n              δ \n              and \n              K \n              DP \n              in pure rain except in regions where the total measured differential phase Φ \n              DP \n              behaves erratically, such as areas affected by nonuniform beam filling or low signal-to-noise ratio. The method for estimating \n              δ \n              in the melting layer results in reliable estimates of \n              δ \n              in stratiform precipitation and requires azimuthal averaging of radial profiles of Φ \n              DP \n              at high antenna elevations. Comparisons with large disdrometer datasets collected in Oklahoma and Germany confirm a strong interdependence between \n              δ \n              and differential reflectivity \n              Z \n              DR \n              . Because \n              δ \n              is immune to attenuation, partial beam blockage, and radar miscalibration, the strong correlation between \n              Z \n              DR \n              and \n              δ \n              is of interest for quantitative precipitation estimation: \n              δ \n              and \n              Z \n              DR \n              are differently affected by the particle size distribution (PSD) and thus may complement each other for PSD moment estimation. Furthermore, the magnitude of \n              δ \n              can be utilized as an important calibration parameter for improving microphysical models of the melting layer.},\n\tnumber = {11},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Applied Meteorology and Climatology},\n\tauthor = {Trömel, Silke and Kumjian, Matthew R. and Ryzhkov, Alexander V. and Simmer, Clemens and Diederich, Malte},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {2529--2548},\n}\n\n\n\n

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\n \n\n \n \n Séquaris, J.; Klumpp, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Colloidal properties and potential release of water-dispersible colloids in an agricultural soil depth profile.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 193-194: 94–101. February 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Colloidal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{sequaris_colloidal_2013,\n\ttitle = {Colloidal properties and potential release of water-dispersible colloids in an agricultural soil depth profile},\n\tvolume = {193-194},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706112003679},\n\tdoi = {10.1016/j.geoderma.2012.10.014},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Geoderma},\n\tauthor = {Séquaris, Jean-Marie and Klumpp, Erwin and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2013},\n\tpages = {94--101},\n}\n\n\n\n

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\n \n\n \n \n Kunstmann, H.; and Strasser, U.\n\n\n \n \n \n \n Tackling complexity in modelling mountain hydrology: where do we stand, where do we go?.\n \n \n \n\n\n \n\n\n\n of IAHS publicationJanuary 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@book{kunstmann_tackling_2013,\n\tseries = {{IAHS} publication},\n\ttitle = {Tackling complexity in modelling mountain hydrology: where do we stand, where do we go?},\n\tisbn = {978-1-907161-38-4},\n\tshorttitle = {Cold and {Mountain} {Region} {Hydrological} {Systems} {Under} {Climate} {Change}: {Towards} {Improved} {Projections}},\n\tnumber = {360},\n\tauthor = {Kunstmann, Harald and Strasser, Ulrich},\n\tmonth = jan,\n\tyear = {2013},\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n Fersch, B.; Wagner, S.; Rummler, T.; Gochis, D.; and Kunstmann, H.\n\n\n \n \n \n \n Impact of groundwater dynamics and soil-type on modelling coupled water exchange processes between land and atmosphere.\n \n \n \n\n\n \n\n\n\n IAHS-AISH Proceedings and Reports, 359: 140–145. January 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{fersch_impact_2013,\n\ttitle = {Impact of groundwater dynamics and soil-type on modelling coupled water exchange processes between land and atmosphere},\n\tvolume = {359},\n\tissn = {0144-7815},\n\tjournal = {IAHS-AISH Proceedings and Reports},\n\tauthor = {Fersch, Benjamin and Wagner, Sven and Rummler, T. and Gochis, D. and Kunstmann, H.},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {140--145},\n}\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n Kunstmann, H.; Hingerl, L.; Mauder, M.; Wagner, S.; and Rigon, R.\n\n\n \n \n \n \n A combined water and energy flux observation and modelling study at the TERENO-preAlpine observatory.\n \n \n \n\n\n \n\n\n\n IAHS-AISH Proceedings and Reports, 359: 221–225. January 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunstmann_combined_2013,\n\ttitle = {A combined water and energy flux observation and modelling study at the {TERENO}-{preAlpine} observatory},\n\tvolume = {359},\n\tissn = {0144-7815},\n\tjournal = {IAHS-AISH Proceedings and Reports},\n\tauthor = {Kunstmann, H. and Hingerl, Luitpold and Mauder, Matthias and Wagner, Sven and Rigon, Riccardo},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {221--225},\n}\n\n\n\n

\n\n\n\n

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\n \n\n \n \n Wulf, S.; Ott, F.; Słowiński, M.; Noryśkiewicz, A. M.; Dräger, N.; Martin-Puertas, C.; Czymzik, M.; Neugebauer, I.; Dulski, P.; Bourne, A. J.; Błaszkiewicz, M.; and Brauer, A.\n\n\n \n \n \n \n \n Tracing the Laacher See Tephra in the varved sediment record of the Trzechowskie palaeolake in central Northern Poland.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 76: 129–139. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Tracing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wulf_tracing_2013,\n\ttitle = {Tracing the {Laacher} {See} {Tephra} in the varved sediment record of the {Trzechowskie} palaeolake in central {Northern} {Poland}},\n\tvolume = {76},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379113002680},\n\tdoi = {10.1016/j.quascirev.2013.07.010},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Wulf, Sabine and Ott, Florian and Słowiński, Michał and Noryśkiewicz, Agnieszka M. and Dräger, Nadine and Martin-Puertas, Celia and Czymzik, Markus and Neugebauer, Ina and Dulski, Peter and Bourne, Anna J. and Błaszkiewicz, Mirosław and Brauer, Achim},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {129--139},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wöhling, T.; Gayler, S.; Priesack, E.; Ingwersen, J.; Wizemann, H.; Högy, P.; Cuntz, M.; Attinger, S.; Wulfmeyer, V.; and Streck, T.\n\n\n \n \n \n \n \n Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5: MULTIOBJECTIVE CALIBRATION AS DIAGNOSTIC TOOL.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 49(12): 8200–8221. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiresponse,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wohling_multiresponse_2013,\n\ttitle = {Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and {CLM3}.5: {MULTIOBJECTIVE} {CALIBRATION} {AS} {DIAGNOSTIC} {TOOL}},\n\tvolume = {49},\n\tissn = {00431397},\n\tshorttitle = {Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and {CLM3}.5},\n\turl = {http://doi.wiley.com/10.1002/2013WR014536},\n\tdoi = {10.1002/2013WR014536},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Wöhling, Thomas and Gayler, Sebastian and Priesack, Eckart and Ingwersen, Joachim and Wizemann, Hans-Dieter and Högy, Petra and Cuntz, Matthias and Attinger, Sabine and Wulfmeyer, Volker and Streck, Thilo},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {8200--8221},\n}\n\n\n\n

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\n \n\n \n \n Weihermüller, L.; Huisman, J.; Hermes, N.; Pickel, S.; and Vereecken, H.\n\n\n \n \n \n \n \n A New TDR Multiplexing System for Reliable Electrical Conductivity and Soil Water Content Measurements.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 12(2): vzj2012.0194. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{weihermuller_new_2013,\n\ttitle = {A {New} {TDR} {Multiplexing} {System} for {Reliable} {Electrical} {Conductivity} and {Soil} {Water} {Content} {Measurements}},\n\tvolume = {12},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2012.0194},\n\tdoi = {10.2136/vzj2012.0194},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Weihermüller, L. and Huisman, J.A. and Hermes, N. and Pickel, S. and Vereecken, H.},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {vzj2012.0194},\n}\n\n\n\n

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\n \n\n \n \n Vihervaara, P.; D’Amato, D.; Forsius, M.; Angelstam, P.; Baessler, C.; Balvanera, P.; Boldgiv, B.; Bourgeron, P.; Dick, J.; Kanka, R.; Klotz, S.; Maass, M.; Melecis, V.; Petřík, P.; Shibata, H.; Tang, J.; Thompson, J.; and Zacharias, S.\n\n\n \n \n \n \n \n Using long-term ecosystem service and biodiversity data to study the impacts and adaptation options in response to climate change: insights from the global ILTER sites network.\n \n \n \n \n\n\n \n\n\n\n Current Opinion in Environmental Sustainability, 5(1): 53–66. March 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vihervaara_using_2013,\n\ttitle = {Using long-term ecosystem service and biodiversity data to study the impacts and adaptation options in response to climate change: insights from the global {ILTER} sites network},\n\tvolume = {5},\n\tissn = {18773435},\n\tshorttitle = {Using long-term ecosystem service and biodiversity data to study the impacts and adaptation options in response to climate change},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S187734351200187X},\n\tdoi = {10.1016/j.cosust.2012.11.002},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-07-17},\n\tjournal = {Current Opinion in Environmental Sustainability},\n\tauthor = {Vihervaara, Petteri and D’Amato, Dalia and Forsius, Martin and Angelstam, Per and Baessler, Cornelia and Balvanera, Patricia and Boldgiv, Bazartseren and Bourgeron, Patrick and Dick, Jan and Kanka, Robert and Klotz, Stefan and Maass, Manuel and Melecis, Viesturs and Petřík, Petr and Shibata, Hideaki and Tang, Jianwu and Thompson, Jill and Zacharias, Steffen},\n\tmonth = mar,\n\tyear = {2013},\n\tpages = {53--66},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Vieweg, M.; Trauth, N.; Fleckenstein, J. H.; and Schmidt, C.\n\n\n \n \n \n \n \n Robust Optode-Based Method for Measuring in Situ Oxygen Profiles in Gravelly Streambeds.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 47(17): 9858–9865. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Robust$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vieweg_robust_2013,\n\ttitle = {Robust {Optode}-{Based} {Method} for {Measuring} in {Situ} {Oxygen} {Profiles} in {Gravelly} {Streambeds}},\n\tvolume = {47},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/es401040w},\n\tdoi = {10.1021/es401040w},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Vieweg, Michael and Trauth, Nico and Fleckenstein, Jan H. and Schmidt, Christian},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {9858--9865},\n}\n\n\n\n

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\n \n\n \n \n Van Stan, J. T.; Martin, K.; Friesen, J.; Jarvis, M. T.; Lundquist, J. D.; and Levia, D. F.\n\n\n \n \n \n \n \n Evaluation of an instrumental method to reduce error in canopy water storage estimates via mechanical displacement: EVALUATION OF DIRECT CANOPY WATER STORAGE MONITORING.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 49(1): 54–63. January 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{van_stan_evaluation_2013,\n\ttitle = {Evaluation of an instrumental method to reduce error in canopy water storage estimates via mechanical displacement: {EVALUATION} {OF} {DIRECT} {CANOPY} {WATER} {STORAGE} {MONITORING}},\n\tvolume = {49},\n\tissn = {00431397},\n\tshorttitle = {Evaluation of an instrumental method to reduce error in canopy water storage estimates via mechanical displacement},\n\turl = {http://doi.wiley.com/10.1029/2012WR012666},\n\tdoi = {10.1029/2012WR012666},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Van Stan, John T. and Martin, Kael and Friesen, Jan and Jarvis, Matthew T. and Lundquist, Jessica D. and Levia, Delphis F.},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {54--63},\n}\n\n\n\n

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\n \n\n \n \n Unteregelsbacher, S.; Gasche, R.; Lipp, L.; Sun, W.; Kreyling, O.; Geitlinger, H.; Kögel-Knabner, I.; Papen, H.; Kiese, R.; Schmid, H.; and Dannenmann, M.\n\n\n \n \n \n \n \n Increased methane uptake but unchanged nitrous oxide flux in montane grasslands under simulated climate change conditions: Methane uptake under climate change conditions.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Science, 64(5): 586–596. October 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Increased$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{unteregelsbacher_increased_2013,\n\ttitle = {Increased methane uptake but unchanged nitrous oxide flux in montane grasslands under simulated climate change conditions: {Methane} uptake under climate change conditions},\n\tvolume = {64},\n\tissn = {13510754},\n\tshorttitle = {Increased methane uptake but unchanged nitrous oxide flux in montane grasslands under simulated climate change conditions},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ejss.12092},\n\tdoi = {10.1111/ejss.12092},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-07-17},\n\tjournal = {European Journal of Soil Science},\n\tauthor = {Unteregelsbacher, S. and Gasche, R. and Lipp, L. and Sun, W. and Kreyling, O. and Geitlinger, H. and Kögel-Knabner, I. and Papen, H. and Kiese, R. and Schmid, H.-P. and Dannenmann, M.},\n\tmonth = oct,\n\tyear = {2013},\n\tpages = {586--596},\n}\n\n\n\n

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\n \n\n \n \n Turner, F.; Tolksdorf, J. F.; Viehberg, F.; Schwalb, A.; Kaiser, K.; Bittmann, F.; Von Bramann, U.; Pott, R.; Staesche, U.; Breest, K.; and Veil, S.\n\n\n \n \n \n \n \n Lateglacial/early Holocene fluvial reactions of the Jeetzel river (Elbe valley, northern Germany) to abrupt climatic and environmental changes.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 60: 91–109. January 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Lateglacial/early$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{turner_lateglacialearly_2013,\n\ttitle = {Lateglacial/early {Holocene} fluvial reactions of the {Jeetzel} river ({Elbe} valley, northern {Germany}) to abrupt climatic and environmental changes},\n\tvolume = {60},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379112004416},\n\tdoi = {10.1016/j.quascirev.2012.10.037},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Turner, Falko and Tolksdorf, Johann Friedrich and Viehberg, Finn and Schwalb, Antje and Kaiser, Knut and Bittmann, Felix and Von Bramann, Ullrich and Pott, Richard and Staesche, Ulrich and Breest, Klaus and Veil, Stephan},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {91--109},\n}\n\n\n\n

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\n \n\n \n \n Trauth, N.; Schmidt, C.; Maier, U.; Vieweg, M.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Coupled 3-D stream flow and hyporheic flow model under varying stream and ambient groundwater flow conditions in a pool-riffle system: Coupled 3-D Stream Flow and Hyporheic Flow Model.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 49(9): 5834–5850. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Coupled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{trauth_coupled_2013,\n\ttitle = {Coupled 3-{D} stream flow and hyporheic flow model under varying stream and ambient groundwater flow conditions in a pool-riffle system: {Coupled} 3-{D} {Stream} {Flow} and {Hyporheic} {Flow} {Model}},\n\tvolume = {49},\n\tissn = {00431397},\n\tshorttitle = {Coupled 3-{D} stream flow and hyporheic flow model under varying stream and ambient groundwater flow conditions in a pool-riffle system},\n\turl = {http://doi.wiley.com/10.1002/wrcr.20442},\n\tdoi = {10.1002/wrcr.20442},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Trauth, Nico and Schmidt, Christian and Maier, Uli and Vieweg, Michael and Fleckenstein, Jan H.},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {5834--5850},\n}\n\n\n\n

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\n \n\n \n \n Tolksdorf, J. F.; Turner, F.; Kaiser, K.; Eckmeier, E.; Stahlschmidt, M.; Housley, R. A.; Breest, K.; and Veil, S.\n\n\n \n \n \n \n \n Multiproxy Analyses of Stratigraphy and Palaeoenvironment of the Late Palaeolithic Grabow Floodplain Site, Northern Germany: MULTIPROXY ANALYSES OF A LATE PALAEOLITHIC FLOODPLAIN SITE, GERMANY.\n \n \n \n \n\n\n \n\n\n\n Geoarchaeology, 28(1): 50–65. January 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiproxy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tolksdorf_multiproxy_2013,\n\ttitle = {Multiproxy {Analyses} of {Stratigraphy} and {Palaeoenvironment} of the {Late} {Palaeolithic} {Grabow} {Floodplain} {Site}, {Northern} {Germany}: {MULTIPROXY} {ANALYSES} {OF} {A} {LATE} {PALAEOLITHIC} {FLOODPLAIN} {SITE}, {GERMANY}},\n\tvolume = {28},\n\tissn = {08836353},\n\tshorttitle = {Multiproxy {Analyses} of {Stratigraphy} and {Palaeoenvironment} of the {Late} {Palaeolithic} {Grabow} {Floodplain} {Site}, {Northern} {Germany}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/gea.21429},\n\tdoi = {10.1002/gea.21429},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-07-17},\n\tjournal = {Geoarchaeology},\n\tauthor = {Tolksdorf, Johann Friedrich and Turner, Falko and Kaiser, Knut and Eckmeier, Eileen and Stahlschmidt, Mareike and Housley, Rupert A. and Breest, Klaus and Veil, Stephan},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {50--65},\n}\n\n\n\n

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\n \n\n \n \n Schwab, M. J.; Lamparski, P.; Brauer, A.; and Blaszkiewicz, M.\n\n\n \n \n \n \n \n 2nd Annual ICLEA Workshop 2013 : Dynamics of climate and landscape evolution of cultural landscapes in the Northern Central European Lowlands since the last ice age ; Abstract volume and Excursion guide.\n \n \n \n \n\n\n \n\n\n\n Technical Report Deutsches GeoForschungsZentrum GFZ, 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"2nd$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{schwab_2nd_2013,\n\ttitle = {2nd {Annual} {ICLEA} {Workshop} 2013 : {Dynamics} of climate and landscape evolution of cultural landscapes in the {Northern} {Central} {European} {Lowlands} since the last ice age ; {Abstract} volume and {Excursion} guide},\n\tshorttitle = {2nd {Annual} {ICLEA} {Workshop} 2013},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_117032},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tinstitution = {Deutsches GeoForschungsZentrum GFZ},\n\tauthor = {Schwab, Markus J. and Lamparski, Piotr and Brauer, Achim and Blaszkiewicz, Miroslaw},\n\tyear = {2013},\n\tdoi = {10.2312/GFZ.B103-13047},\n}\n\n\n\n

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\n \n\n \n \n Schroeder, M.; Stender, V.; Klump, J.; Wachter, J.; and Kunkel, R.\n\n\n \n \n \n \n \n The design of monitoring and data infrastructures — Applying a forward-thinking reference architecture.\n \n \n \n \n\n\n \n\n\n\n In 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), pages 216–220, Evry, April 2013. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{schroeder_design_2013,\n\taddress = {Evry},\n\ttitle = {The design of monitoring and data infrastructures \\&\\#x2014; {Applying} a forward-thinking reference architecture},\n\tisbn = {9781467352000 9781467351980 9781467351997},\n\turl = {http://ieeexplore.ieee.org/document/6548739/},\n\tdoi = {10.1109/ICNSC.2013.6548739},\n\turldate = {2023-07-17},\n\tbooktitle = {2013 10th {IEEE} {INTERNATIONAL} {CONFERENCE} {ON} {NETWORKING}, {SENSING} {AND} {CONTROL} ({ICNSC})},\n\tpublisher = {IEEE},\n\tauthor = {Schroeder, M. and Stender, V. and Klump, J. and Wachter, J. and Kunkel, R.},\n\tmonth = apr,\n\tyear = {2013},\n\tpages = {216--220},\n}\n\n\n\n

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\n \n\n \n \n Schrader, F.; Durner, W.; Fank, J.; Gebler, S.; Pütz, T.; Hannes, M.; and Wollschläger, U.\n\n\n \n \n \n \n \n Estimating Precipitation and Actual Evapotranspiration from Precision Lysimeter Measurements.\n \n \n \n \n\n\n \n\n\n\n Procedia Environmental Sciences, 19: 543–552. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schrader_estimating_2013,\n\ttitle = {Estimating {Precipitation} and {Actual} {Evapotranspiration} from {Precision} {Lysimeter} {Measurements}},\n\tvolume = {19},\n\tissn = {18780296},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1878029613003319},\n\tdoi = {10.1016/j.proenv.2013.06.061},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Procedia Environmental Sciences},\n\tauthor = {Schrader, Frederik and Durner, Wolfgang and Fank, Johann and Gebler, Sebastian and Pütz, Thomas and Hannes, Matthias and Wollschläger, Ute},\n\tyear = {2013},\n\tpages = {543--552},\n}\n\n\n\n

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\n \n\n \n \n Rinke, K.; Kuehn, B.; Bocaniov, S.; Wendt-Potthoff, K.; Büttner, O.; Tittel, J.; Schultze, M.; Herzsprung, P.; Rönicke, H.; Rink, K.; Rinke, K.; Dietze, M.; Matthes, M.; Paul, L.; and Friese, K.\n\n\n \n \n \n \n \n Reservoirs as sentinels of catchments: the Rappbode Reservoir Observatory (Harz Mountains, Germany).\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 69(2): 523–536. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Reservoirs$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rinke_reservoirs_2013,\n\ttitle = {Reservoirs as sentinels of catchments: the {Rappbode} {Reservoir} {Observatory} ({Harz} {Mountains}, {Germany})},\n\tvolume = {69},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {Reservoirs as sentinels of catchments},\n\turl = {http://link.springer.com/10.1007/s12665-013-2464-2},\n\tdoi = {10.1007/s12665-013-2464-2},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Rinke, Karsten and Kuehn, Burkhard and Bocaniov, Serghei and Wendt-Potthoff, Katrin and Büttner, Olaf and Tittel, Jörg and Schultze, Martin and Herzsprung, Peter and Rönicke, Helmut and Rink, Karsten and Rinke, Kristine and Dietze, Maren and Matthes, Marco and Paul, Lothar and Friese, Kurt},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {523--536},\n}\n\n\n\n

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\n \n\n \n \n Qu, W.; Bogena, H.; Huisman, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Calibration of a Novel Low-Cost Soil Water Content Sensor Based on a Ring Oscillator.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 12(2): vzj2012.0139. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Calibration$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{qu_calibration_2013,\n\ttitle = {Calibration of a {Novel} {Low}-{Cost} {Soil} {Water} {Content} {Sensor} {Based} on a {Ring} {Oscillator}},\n\tvolume = {12},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2012.0139},\n\tdoi = {10.2136/vzj2012.0139},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Qu, W. and Bogena, H.R. and Huisman, J.A. and Vereecken, H.},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {vzj2012.0139},\n}\n\n\n\n

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\n \n\n \n \n Ott, I.; Duethmann, D.; Liebert, J.; Berg, P.; Feldmann, H.; Ihringer, J.; Kunstmann, H.; Merz, B.; Schaedler, G.; and Wagner, S.\n\n\n \n \n \n \n \n High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 14(4): 1175–1193. August 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"High-Resolution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{ott_high-resolution_2013,\n\ttitle = {High-{Resolution} {Climate} {Change} {Impact} {Analysis} on {Medium}-{Sized} {River} {Catchments} in {Germany}: {An} {Ensemble} {Assessment}},\n\tvolume = {14},\n\tissn = {1525-755X, 1525-7541},\n\tshorttitle = {High-{Resolution} {Climate} {Change} {Impact} {Analysis} on {Medium}-{Sized} {River} {Catchments} in {Germany}},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-12-091.1},\n\tdoi = {10.1175/JHM-D-12-091.1},\n\tabstract = {Abstract \n            The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Ott, Irena and Duethmann, Doris and Liebert, Joachim and Berg, Peter and Feldmann, Hendrik and Ihringer, Juergen and Kunstmann, Harald and Merz, Bruno and Schaedler, Gerd and Wagner, Sven},\n\tmonth = aug,\n\tyear = {2013},\n\tpages = {1175--1193},\n}\n\n\n\n

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\n \n\n \n \n Oberröhrmann, M.; Klotzsche, A.; Vereecken, H.; and Van Der Krak, J.\n\n\n \n \n \n \n \n Optimization of acquisition setup for cross-hole: GPR full-waveform inversion using checkerboard analysis.\n \n \n \n \n\n\n \n\n\n\n Near Surface Geophysics, 11(2): 197–209. April 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Optimization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{oberrohrmann_optimization_2013,\n\ttitle = {Optimization of acquisition setup for cross-hole: {GPR} full-waveform inversion using checkerboard analysis},\n\tvolume = {11},\n\tissn = {15694445, 18730604},\n\tshorttitle = {Optimization of acquisition setup for cross-hole},\n\turl = {http://doi.wiley.com/10.3997/1873-0604.2012045},\n\tdoi = {10.3997/1873-0604.2012045},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Near Surface Geophysics},\n\tauthor = {Oberröhrmann, Max and Klotzsche, Anja and Vereecken, Harry and Van Der Krak, Jan},\n\tmonth = apr,\n\tyear = {2013},\n\tpages = {197--209},\n}\n\n\n\n

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\n \n\n \n \n Montzka, C.; Bogena, H. R.; Weihermuller, L.; Jonard, F.; Bouzinac, C.; Kainulainen, J.; Balling, J. E.; Loew, A.; dall'Amico , J. T.; Rouhe, E.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Brightness Temperature and Soil Moisture Validation at Different Scales During the SMOS Validation Campaign in the Rur and Erft Catchments, Germany.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1728–1743. March 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Brightness$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{montzka_brightness_2013,\n\ttitle = {Brightness {Temperature} and {Soil} {Moisture} {Validation} at {Different} {Scales} {During} the {SMOS} {Validation} {Campaign} in the {Rur} and {Erft} {Catchments}, {Germany}},\n\tvolume = {51},\n\tissn = {0196-2892, 1558-0644},\n\turl = {http://ieeexplore.ieee.org/document/6261546/},\n\tdoi = {10.1109/TGRS.2012.2206031},\n\tnumber = {3},\n\turldate = {2023-07-17},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Montzka, Carsten and Bogena, Heye R. and Weihermuller, Lutz and Jonard, François and Bouzinac, Catherine and Kainulainen, Juha and Balling, Jan E. and Loew, Alexander and dall'Amico, Johanna T. and Rouhe, Erkka and Vanderborght, Jan and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2013},\n\tpages = {1728--1743},\n}\n\n\n\n

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\n \n\n \n \n Mauder, M.; Cuntz, M.; Drüe, C.; Graf, A.; Rebmann, C.; Schmid, H. P.; Schmidt, M.; and Steinbrecher, R.\n\n\n \n \n \n \n \n A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 169: 122–135. February 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mauder_strategy_2013,\n\ttitle = {A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements},\n\tvolume = {169},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192312002808},\n\tdoi = {10.1016/j.agrformet.2012.09.006},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Mauder, Matthias and Cuntz, Matthias and Drüe, Clemens and Graf, Alexander and Rebmann, Corinna and Schmid, Hans Peter and Schmidt, Marius and Steinbrecher, Rainer},\n\tmonth = feb,\n\tyear = {2013},\n\tpages = {122--135},\n}\n\n\n\n

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\n \n\n \n \n Liang, W.; Heinrich, I.; Simard, S.; Helle, G.; Linan, I. D.; and Heinken, T.\n\n\n \n \n \n \n \n Climate signals derived from cell anatomy of Scots pine in NE Germany.\n \n \n \n \n\n\n \n\n\n\n Tree Physiology, 33(8): 833–844. August 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liang_climate_2013,\n\ttitle = {Climate signals derived from cell anatomy of {Scots} pine in {NE} {Germany}},\n\tvolume = {33},\n\tissn = {0829-318X, 1758-4469},\n\turl = {https://academic.oup.com/treephys/article-lookup/doi/10.1093/treephys/tpt059},\n\tdoi = {10.1093/treephys/tpt059},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-07-17},\n\tjournal = {Tree Physiology},\n\tauthor = {Liang, W. and Heinrich, I. and Simard, S. and Helle, G. and Linan, I. D. and Heinken, T.},\n\tmonth = aug,\n\tyear = {2013},\n\tpages = {833--844},\n}\n\n\n\n

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\n \n\n \n \n Liang, W.; Heinrich, I.; Helle, G.; Liñán, I. D.; and Heinken, T.\n\n\n \n \n \n \n \n Applying CLSM to increment core surfaces for histometric analyses: A novel advance in quantitative wood anatomy.\n \n \n \n \n\n\n \n\n\n\n Dendrochronologia, 31(2): 140–145. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Applying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{liang_applying_2013,\n\ttitle = {Applying {CLSM} to increment core surfaces for histometric analyses: {A} novel advance in quantitative wood anatomy},\n\tvolume = {31},\n\tissn = {11257865},\n\tshorttitle = {Applying {CLSM} to increment core surfaces for histometric analyses},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1125786512000823},\n\tdoi = {10.1016/j.dendro.2012.09.002},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Dendrochronologia},\n\tauthor = {Liang, Wei and Heinrich, Ingo and Helle, Gerhard and Liñán, Isabel Dorado and Heinken, Thilo},\n\tyear = {2013},\n\tpages = {140--145},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Zacharias, S.; Dierke, C.; Pause, M.; Kühn, I.; Doktor, D.; Dietrich, P.; and Werban, U.\n\n\n \n \n \n \n \n Analysis of Vegetation and Soil Patterns using Hyperspectral Remote Sensing, EMI, and Gamma-Ray Measurements.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 12(4): vzj2012.0217. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_analysis_2013,\n\ttitle = {Analysis of {Vegetation} and {Soil} {Patterns} using {Hyperspectral} {Remote} {Sensing}, {EMI}, and {Gamma}-{Ray} {Measurements}},\n\tvolume = {12},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2012.0217},\n\tdoi = {10.2136/vzj2012.0217},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Lausch, Angela and Zacharias, Steffen and Dierke, Claudia and Pause, Marion and Kühn, Ingolf and Doktor, Daniel and Dietrich, Peter and Werban, Ulrike},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {vzj2012.0217},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Pause, M.; Doktor, D.; Preidl, S.; and Schulz, K.\n\n\n \n \n \n \n \n Monitoring and assessing of landscape heterogeneity at different scales.\n \n \n \n \n\n\n \n\n\n\n Environmental Monitoring and Assessment, 185(11): 9419–9434. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_monitoring_2013,\n\ttitle = {Monitoring and assessing of landscape heterogeneity at different scales},\n\tvolume = {185},\n\tissn = {0167-6369, 1573-2959},\n\turl = {http://link.springer.com/10.1007/s10661-013-3262-8},\n\tdoi = {10.1007/s10661-013-3262-8},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Monitoring and Assessment},\n\tauthor = {Lausch, Angela and Pause, Marion and Doktor, Daniel and Preidl, Sebastian and Schulz, Karsten},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {9419--9434},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Pause, M.; Merbach, I.; Zacharias, S.; Doktor, D.; Volk, M.; and Seppelt, R.\n\n\n \n \n \n \n \n A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape.\n \n \n \n \n\n\n \n\n\n\n Environmental Monitoring and Assessment, 185(2): 1215–1235. February 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_new_2013,\n\ttitle = {A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape},\n\tvolume = {185},\n\tissn = {0167-6369, 1573-2959},\n\turl = {http://link.springer.com/10.1007/s10661-012-2627-8},\n\tdoi = {10.1007/s10661-012-2627-8},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Monitoring and Assessment},\n\tauthor = {Lausch, Angela and Pause, Marion and Merbach, Ines and Zacharias, Steffen and Doktor, Daniel and Volk, Martin and Seppelt, Ralf},\n\tmonth = feb,\n\tyear = {2013},\n\tpages = {1215--1235},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Pause, M.; Schmidt, A.; Salbach, C.; Gwillym-Margianto, S.; and Merbach, I.\n\n\n \n \n \n \n \n Temporal hyperspectral monitoring of chlorophyll, LAI, and water content of barley during a growing season.\n \n \n \n \n\n\n \n\n\n\n Canadian Journal of Remote Sensing, 39(3): 191–207. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_temporal_2013,\n\ttitle = {Temporal hyperspectral monitoring of chlorophyll, {LAI}, and water content of barley during a growing season},\n\tvolume = {39},\n\tissn = {0703-8992, 1712-7971},\n\turl = {http://www.tandfonline.com/doi/abs/10.5589/m13-028},\n\tdoi = {10.5589/m13-028},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-07-17},\n\tjournal = {Canadian Journal of Remote Sensing},\n\tauthor = {Lausch, Angela and Pause, Marion and Schmidt, Andreas and Salbach, Christoph and Gwillym-Margianto, Sarah and Merbach, Ines},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {191--207},\n}\n\n\n\n

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\n \n\n \n \n Kunkel, R.; Sorg, J.; Klump, J.; Kolditz, O.; Rink, K.; Gasche, R.; and Neidl, F.\n\n\n \n \n \n \n \n TEODOOR - A Spatial Data Infrastructure for terrestrial observation data.\n \n \n \n \n\n\n \n\n\n\n In 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), pages 242–245, Evry, April 2013. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"TEODOOR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{kunkel_teodoor_2013,\n\taddress = {Evry},\n\ttitle = {{TEODOOR} - {A} {Spatial} {Data} {Infrastructure} for terrestrial observation data},\n\tisbn = {9781467352000 9781467351980 9781467351997},\n\turl = {http://ieeexplore.ieee.org/document/6548744/},\n\tdoi = {10.1109/ICNSC.2013.6548744},\n\turldate = {2023-07-17},\n\tbooktitle = {2013 10th {IEEE} {INTERNATIONAL} {CONFERENCE} {ON} {NETWORKING}, {SENSING} {AND} {CONTROL} ({ICNSC})},\n\tpublisher = {IEEE},\n\tauthor = {Kunkel, R. and Sorg, J. and Klump, J. and Kolditz, Olaf and Rink, Karsten and Gasche, Rainer and Neidl, Frank},\n\tmonth = apr,\n\tyear = {2013},\n\tpages = {242--245},\n}\n\n\n\n

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\n \n\n \n \n Kroeger, I.; Duquesne, S.; and Liess, M.\n\n\n \n \n \n \n \n Crustacean biodiversity as an important factor for mosquito larval control.\n \n \n \n \n\n\n \n\n\n\n Journal of Vector Ecology, 38(2): 390–400. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Crustacean$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kroeger_crustacean_2013,\n\ttitle = {Crustacean biodiversity as an important factor for mosquito larval control},\n\tvolume = {38},\n\tissn = {10811710},\n\turl = {http://doi.wiley.com/10.1111/j.1948-7134.2013.12055.x},\n\tdoi = {10.1111/j.1948-7134.2013.12055.x},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Vector Ecology},\n\tauthor = {Kroeger, Iris and Duquesne, Sabine and Liess, Matthias},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {390--400},\n}\n\n\n\n

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\n \n\n \n \n Kolditz, O.; Rügner, H.; Grathwohl, P.; Dietrich, P.; and Streck, T.\n\n\n \n \n \n \n \n WESS: an interdisciplinary approach to catchment research.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 69(2): 313–315. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"WESS:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kolditz_wess_2013,\n\ttitle = {{WESS}: an interdisciplinary approach to catchment research},\n\tvolume = {69},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {{WESS}},\n\turl = {http://link.springer.com/10.1007/s12665-013-2466-0},\n\tdoi = {10.1007/s12665-013-2466-0},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Kolditz, Olaf and Rügner, Hermann and Grathwohl, Peter and Dietrich, Peter and Streck, Thilo},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {313--315},\n}\n\n\n\n

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\n \n\n \n \n Kistner, I.; Ollesch, G.; Meissner, R.; and Rode, M.\n\n\n \n \n \n \n \n Spatial-temporal dynamics of water soluble phosphorus in the topsoil of a low mountain range catchment.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 176: 24–38. August 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial-temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kistner_spatial-temporal_2013,\n\ttitle = {Spatial-temporal dynamics of water soluble phosphorus in the topsoil of a low mountain range catchment},\n\tvolume = {176},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880913001771},\n\tdoi = {10.1016/j.agee.2013.05.016},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Kistner, Irina and Ollesch, Gregor and Meissner, Ralph and Rode, Michael},\n\tmonth = aug,\n\tyear = {2013},\n\tpages = {24--38},\n}\n\n\n\n

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\n \n\n \n \n Kienel, U.; Dulski, P.; Ott, F.; Lorenz, S.; and Brauer, A.\n\n\n \n \n \n \n \n Recently induced anoxia leading to the preservation of seasonal laminae in two NE-German lakes.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 50(4): 535–544. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Recently$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kienel_recently_2013,\n\ttitle = {Recently induced anoxia leading to the preservation of seasonal laminae in two {NE}-{German} lakes},\n\tvolume = {50},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-013-9745-3},\n\tdoi = {10.1007/s10933-013-9745-3},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Kienel, Ulrike and Dulski, Peter and Ott, Florian and Lorenz, Sebastian and Brauer, Achim},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {535--544},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kienel, U.; Vos, H.; Dulski, P.; Lücke, A.; Moschen, R.; Nowaczyk, N. R.; and Schwab, M. J.\n\n\n \n \n \n \n \n Modification of climate signals by human activities recorded in varved sediments (AD 1608–1942) of Lake Holzmaar (Germany).\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 50(4): 561–575. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Modification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kienel_modification_2013,\n\ttitle = {Modification of climate signals by human activities recorded in varved sediments ({AD} 1608–1942) of {Lake} {Holzmaar} ({Germany})},\n\tvolume = {50},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-013-9749-z},\n\tdoi = {10.1007/s10933-013-9749-z},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Kienel, Ulrike and Vos, Heinz and Dulski, Peter and Lücke, Andreas and Moschen, Robert and Nowaczyk, Norbert R. and Schwab, Markus J.},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {561--575},\n}\n\n\n\n

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\n \n\n \n \n Kamjunke, N.; Büttner, O.; Jäger, C. G.; Marcus, H.; Von Tümpling, W.; Halbedel, S.; Norf, H.; Brauns, M.; Baborowski, M.; Wild, R.; Borchardt, D.; and Weitere, M.\n\n\n \n \n \n \n \n Biogeochemical patterns in a river network along a land use gradient.\n \n \n \n \n\n\n \n\n\n\n Environmental Monitoring and Assessment, 185(11): 9221–9236. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Biogeochemical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kamjunke_biogeochemical_2013,\n\ttitle = {Biogeochemical patterns in a river network along a land use gradient},\n\tvolume = {185},\n\tissn = {0167-6369, 1573-2959},\n\turl = {http://link.springer.com/10.1007/s10661-013-3247-7},\n\tdoi = {10.1007/s10661-013-3247-7},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Monitoring and Assessment},\n\tauthor = {Kamjunke, Norbert and Büttner, Olaf and Jäger, Christoph G. and Marcus, Hanna and Von Tümpling, Wolf and Halbedel, Susanne and Norf, Helge and Brauns, Mario and Baborowski, Martina and Wild, Romy and Borchardt, Dietrich and Weitere, Markus},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {9221--9236},\n}\n\n\n\n

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\n \n\n \n \n Hentschel, R.; Bittner, S.; Janott, M.; Biernath, C.; Holst, J.; Ferrio, J. P.; Gessler, A.; and Priesack, E.\n\n\n \n \n \n \n \n Simulation of stand transpiration based on a xylem water flow model for individual trees.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 182-183: 31–42. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{hentschel_simulation_2013,\n\ttitle = {Simulation of stand transpiration based on a xylem water flow model for individual trees},\n\tvolume = {182-183},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192313002098},\n\tdoi = {10.1016/j.agrformet.2013.08.002},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Hentschel, Rainer and Bittner, Sebastian and Janott, Michael and Biernath, Christian and Holst, Jutta and Ferrio, Juan Pedro and Gessler, Arthur and Priesack, Eckart},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {31--42},\n}\n\n\n\n

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\n \n\n \n \n Helle, G.; Gaertner, H.; Beck, W.; Heinrich, I.; Heussner, K.; Mueller, A.; and Sanders, T.\n\n\n \n \n \n \n \n Proceedings of the DENDROSYMPOSIUM 2012 : May 8th - 12th, 2012 in Potsdam and Eberswalde, Germany.\n \n \n \n \n\n\n \n\n\n\n Scientific Technical Report; 13/05. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Proceedings$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{helle_proceedings_2013,\n\ttitle = {Proceedings of the {DENDROSYMPOSIUM} 2012 : {May} 8th - 12th, 2012 in {Potsdam} and {Eberswalde}, {Germany}},\n\tshorttitle = {Proceedings of the {DENDROSYMPOSIUM} 2012},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_147613},\n\tdoi = {10.2312/GFZ.B103-13058},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Scientific Technical Report; 13/05},\n\tauthor = {Helle, Gerhard and Gaertner, Holger and Beck, Wolfgang and Heinrich, Ingo and Heussner, Karl-Uwe and Mueller, Alexander and Sanders, Tanja},\n\tyear = {2013},\n}\n\n\n\n

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\n \n\n \n \n Halbedel, S.; Büttner, O.; and Weitere, M.\n\n\n \n \n \n \n \n Linkage between the temporal and spatial variability of dissolved organic matter and whole-stream metabolism.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 10(8): 5555–5569. August 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Linkage$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{halbedel_linkage_2013,\n\ttitle = {Linkage between the temporal and spatial variability of dissolved organic matter and whole-stream metabolism},\n\tvolume = {10},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/10/5555/2013/},\n\tdoi = {10.5194/bg-10-5555-2013},\n\tabstract = {Abstract. Dissolved organic matter (DOM) is an important resource for microbes, thus affecting whole-stream metabolism. However, the factors influencing its chemical composition and thereby also its bio-availability are complex and not thoroughly understood. It was hypothesized that whole-stream metabolism is linked to DOM composition and that the coupling of both is influenced by seasonality and different land-use types. We tested this hypothesis in a comparative study on two pristine forestry streams and two non-forestry streams. The investigated streams were located in the Harz Mountains (central Europe, Germany). The metabolic rate was measured with a classical two-station oxygen change technique and the variability of DOM with fluorescence spectroscopy. All streams were clearly net heterotrophic, whereby non-forestry streams showed a higher primary production, which was correlated to irradiance and phosphorus concentration. We detected three CDOM components (C1, C2, C3) using parallel factor (PARAFAC) analysis. We compared the excitation and emission maxima of these components with the literature and correlated the PARAFAC components with each other and with fluorescence indices. The correlations suggest that two PARAFAC components are derived from allochthonous sources (C1, C3) and one is derived autochthonously (C2). The chromophoric DOM matrix was dominated by signals of humic-like substances with a highly complex structure, followed by humic-like, fulfic acids, low-molecular-weight substances, and with minor amounts of amino acids and proteins. The ratios of these PARAFAC components (C1 : C2, C1 : C3, C3 : C2) differed with respect to stream types (forestry versus non-forestry). We demonstrated a significant correlation between gross primary production (GPP) and signals of autochthonously derived, low-molecular-weight humic-like substances. A positive correlation between P / R (i.e. GPP/daily community respiration) and the fluorescence index FI suggests that the amount of autochthonously produced DOM increased overall with increasing GPP. In accordance with the coupling between DOM and the metabolism, our data also indicate that the composition of DOM is subject to seasonal fluctuations. We concluded that temporal and spatial differences in DOM composition are driven by whole-stream metabolism, in addition to pronounced effects coming from allochthonous sources.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-07-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Halbedel, S. and Büttner, O. and Weitere, M.},\n\tmonth = aug,\n\tyear = {2013},\n\tpages = {5555--5569},\n}\n\n\n\n

\n\n\n

\n Abstract. Dissolved organic matter (DOM) is an important resource for microbes, thus affecting whole-stream metabolism. However, the factors influencing its chemical composition and thereby also its bio-availability are complex and not thoroughly understood. It was hypothesized that whole-stream metabolism is linked to DOM composition and that the coupling of both is influenced by seasonality and different land-use types. We tested this hypothesis in a comparative study on two pristine forestry streams and two non-forestry streams. The investigated streams were located in the Harz Mountains (central Europe, Germany). The metabolic rate was measured with a classical two-station oxygen change technique and the variability of DOM with fluorescence spectroscopy. All streams were clearly net heterotrophic, whereby non-forestry streams showed a higher primary production, which was correlated to irradiance and phosphorus concentration. We detected three CDOM components (C1, C2, C3) using parallel factor (PARAFAC) analysis. We compared the excitation and emission maxima of these components with the literature and correlated the PARAFAC components with each other and with fluorescence indices. The correlations suggest that two PARAFAC components are derived from allochthonous sources (C1, C3) and one is derived autochthonously (C2). The chromophoric DOM matrix was dominated by signals of humic-like substances with a highly complex structure, followed by humic-like, fulfic acids, low-molecular-weight substances, and with minor amounts of amino acids and proteins. The ratios of these PARAFAC components (C1 : C2, C1 : C3, C3 : C2) differed with respect to stream types (forestry versus non-forestry). We demonstrated a significant correlation between gross primary production (GPP) and signals of autochthonously derived, low-molecular-weight humic-like substances. A positive correlation between P / R (i.e. GPP/daily community respiration) and the fluorescence index FI suggests that the amount of autochthonously produced DOM increased overall with increasing GPP. In accordance with the coupling between DOM and the metabolism, our data also indicate that the composition of DOM is subject to seasonal fluctuations. We concluded that temporal and spatial differences in DOM composition are driven by whole-stream metabolism, in addition to pronounced effects coming from allochthonous sources.\n

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\n \n\n \n \n Halbedel, S.; and Koschorreck, M.\n\n\n \n \n \n \n \n Regulation of CO2 emissions from temperate streams and reservoirs.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 10(11): 7539–7551. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Regulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{halbedel_regulation_2013,\n\ttitle = {Regulation of {CO2} emissions from temperate streams and reservoirs},\n\tvolume = {10},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/10/7539/2013/},\n\tdoi = {10.5194/bg-10-7539-2013},\n\tabstract = {Abstract. It has become more and more evident that CO2 emission (FCO2) from freshwater systems is an important part of the global carbon cycle. To date, only a few studies have addressed the different mechanisms that regulate FCO2 in lotic and lentic systems. In a comparative study we investigated how different biogeochemical and physical factors can affect FCO2 values in streams and reservoirs. We examined the seasonal variability in CO2 concentrations and emissions from four streams and two pre-dams of a large drinking water reservoir located in the same catchment, and compared them with environmental factors that were measured concurrently. All the streams were generally supersaturated with CO2 throughout the year, while both reservoirs functioned to a small degree as CO2 sinks during summer stratification and CO2 sources after circulation had set in. FCO2 from streams ranged from 23 to 355 mmol m−2 d−1 and exceeded the fluxes recorded for the reservoirs (−8.9 to 161.1 mmol m−2 d−1). Both the generally high piston velocity (k) and the CO2 oversaturation contributed to the higher FCO2 from streams in comparison to lakes. In both streams and reservoirs FCO2 was mainly governed by the CO2 concentration (r = 0.92, p {\\textless} 0.001 for dams; r = 0.90, p {\\textless} 0.001 for streams), which was in turn affected by metabolic processes and nutrients in both systems and also by lateral inflow in the streams. Besides CO2 concentration, physical factors also influence FCO2 in lakes and streams. During stratification, FCO2 in both pre-dams was regulated by primary production in the epilimnion, which led to a decrease of FCO2. During circulation, when CO2 from the hypolimnion was mixed with the epilimnion, FCO2 increased on account of the CO2 input from the hypolimnion. The CO2 from the hypolimnion originates from the mineralisation of organic matter. FCO2 from streams was mainly influenced by geomorphological and hydrological factors affecting k, which is less relevant in low-wind lakes. Under high-wind conditions, however, k regulates FCO2 from lotic systems as well. We developed a theoretical framework describing the role of the different regulation mechanisms for FCO2 from streams and lakes.  In summary, the dominant factor affecting FCO2 is the concentration of CO2 in the surface water. Lake stratification has a very important regulatory effect on FCO2 from lakes on account of its influence on CO2 concentrations and metabolic processes. Nevertheless, FCO2 values in heterotrophic streams are generally higher. The higher k values are responsible for the comparatively high degree of FCO2. On a Central European scale, CO2 emission from streams is probably of greater importance than the CO2 flux from standing waters.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-07-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Halbedel, S. and Koschorreck, M.},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {7539--7551},\n}\n\n\n\n

\n\n\n

\n Abstract. It has become more and more evident that CO2 emission (FCO2) from freshwater systems is an important part of the global carbon cycle. To date, only a few studies have addressed the different mechanisms that regulate FCO2 in lotic and lentic systems. In a comparative study we investigated how different biogeochemical and physical factors can affect FCO2 values in streams and reservoirs. We examined the seasonal variability in CO2 concentrations and emissions from four streams and two pre-dams of a large drinking water reservoir located in the same catchment, and compared them with environmental factors that were measured concurrently. All the streams were generally supersaturated with CO2 throughout the year, while both reservoirs functioned to a small degree as CO2 sinks during summer stratification and CO2 sources after circulation had set in. FCO2 from streams ranged from 23 to 355 mmol m−2 d−1 and exceeded the fluxes recorded for the reservoirs (−8.9 to 161.1 mmol m−2 d−1). Both the generally high piston velocity (k) and the CO2 oversaturation contributed to the higher FCO2 from streams in comparison to lakes. In both streams and reservoirs FCO2 was mainly governed by the CO2 concentration (r = 0.92, p \\textless 0.001 for dams; r = 0.90, p \\textless 0.001 for streams), which was in turn affected by metabolic processes and nutrients in both systems and also by lateral inflow in the streams. Besides CO2 concentration, physical factors also influence FCO2 in lakes and streams. During stratification, FCO2 in both pre-dams was regulated by primary production in the epilimnion, which led to a decrease of FCO2. During circulation, when CO2 from the hypolimnion was mixed with the epilimnion, FCO2 increased on account of the CO2 input from the hypolimnion. The CO2 from the hypolimnion originates from the mineralisation of organic matter. FCO2 from streams was mainly influenced by geomorphological and hydrological factors affecting k, which is less relevant in low-wind lakes. Under high-wind conditions, however, k regulates FCO2 from lotic systems as well. We developed a theoretical framework describing the role of the different regulation mechanisms for FCO2 from streams and lakes. In summary, the dominant factor affecting FCO2 is the concentration of CO2 in the surface water. Lake stratification has a very important regulatory effect on FCO2 from lakes on account of its influence on CO2 concentrations and metabolic processes. Nevertheless, FCO2 values in heterotrophic streams are generally higher. The higher k values are responsible for the comparatively high degree of FCO2. On a Central European scale, CO2 emission from streams is probably of greater importance than the CO2 flux from standing waters.\n

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\n \n\n \n \n Grathwohl, P.; Rügner, H.; Wöhling, T.; Osenbrück, K.; Schwientek, M.; Gayler, S.; Wollschläger, U.; Selle, B.; Pause, M.; Delfs, J.; Grzeschik, M.; Weller, U.; Ivanov, M.; Cirpka, O. A.; Maier, U.; Kuch, B.; Nowak, W.; Wulfmeyer, V.; Warrach-Sagi, K.; Streck, T.; Attinger, S.; Bilke, L.; Dietrich, P.; Fleckenstein, J. H.; Kalbacher, T.; Kolditz, O.; Rink, K.; Samaniego, L.; Vogel, H.; Werban, U.; and Teutsch, G.\n\n\n \n \n \n \n \n Catchments as reactors: a comprehensive approach for water fluxes and solute turnover.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 69(2): 317–333. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Catchments$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{grathwohl_catchments_2013,\n\ttitle = {Catchments as reactors: a comprehensive approach for water fluxes and solute turnover},\n\tvolume = {69},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {Catchments as reactors},\n\turl = {http://link.springer.com/10.1007/s12665-013-2281-7},\n\tdoi = {10.1007/s12665-013-2281-7},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Grathwohl, Peter and Rügner, Hermann and Wöhling, Thomas and Osenbrück, Karsten and Schwientek, Marc and Gayler, Sebastian and Wollschläger, Ute and Selle, Benny and Pause, Marion and Delfs, Jens-Olaf and Grzeschik, Matthias and Weller, Ulrich and Ivanov, Martin and Cirpka, Olaf A. and Maier, Ulrich and Kuch, Bertram and Nowak, Wolfgang and Wulfmeyer, Volker and Warrach-Sagi, Kirsten and Streck, Thilo and Attinger, Sabine and Bilke, Lars and Dietrich, Peter and Fleckenstein, Jan H. and Kalbacher, Thomas and Kolditz, Olaf and Rink, Karsten and Samaniego, Luis and Vogel, Hans-Jörg and Werban, Ulrike and Teutsch, Georg},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {317--333},\n}\n\n\n\n

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\n \n\n \n \n David, T.; Borchardt, D.; Von Tümpling, W.; and Krebs, P.\n\n\n \n \n \n \n \n Combined sewer overflows, sediment accumulation and element patterns of river bed sediments: a quantitative study based on mixing models of composite fingerprints.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 69(2): 479–489. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Combined$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{david_combined_2013,\n\ttitle = {Combined sewer overflows, sediment accumulation and element patterns of river bed sediments: a quantitative study based on mixing models of composite fingerprints},\n\tvolume = {69},\n\tissn = {1866-6280, 1866-6299},\n\tshorttitle = {Combined sewer overflows, sediment accumulation and element patterns of river bed sediments},\n\turl = {http://link.springer.com/10.1007/s12665-013-2447-3},\n\tdoi = {10.1007/s12665-013-2447-3},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {David, Telse and Borchardt, Dietrich and Von Tümpling, Wolf and Krebs, Peter},\n\tmonth = may,\n\tyear = {2013},\n\tpages = {479--489},\n}\n\n\n\n

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\n \n\n \n \n Czymzik, M.; Brauer, A.; Dulski, P.; Plessen, B.; Naumann, R.; Von Grafenstein, U.; and Scheffler, R.\n\n\n \n \n \n \n \n Orbital and solar forcing of shifts in Mid- to Late Holocene flood intensity from varved sediments of pre-alpine Lake Ammersee (southern Germany).\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 61: 96–110. February 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Orbital$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{czymzik_orbital_2013,\n\ttitle = {Orbital and solar forcing of shifts in {Mid}- to {Late} {Holocene} flood intensity from varved sediments of pre-alpine {Lake} {Ammersee} (southern {Germany})},\n\tvolume = {61},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379112004817},\n\tdoi = {10.1016/j.quascirev.2012.11.010},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Czymzik, Markus and Brauer, Achim and Dulski, Peter and Plessen, Birgit and Naumann, Rudolf and Von Grafenstein, Ulrich and Scheffler, Raphael},\n\tmonth = feb,\n\tyear = {2013},\n\tpages = {96--110},\n}\n\n\n\n

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\n \n\n \n \n Bunzel, K.; Kattwinkel, M.; and Liess, M.\n\n\n \n \n \n \n \n Effects of organic pollutants from wastewater treatment plants on aquatic invertebrate communities.\n \n \n \n \n\n\n \n\n\n\n Water Research, 47(2): 597–606. February 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bunzel_effects_2013,\n\ttitle = {Effects of organic pollutants from wastewater treatment plants on aquatic invertebrate communities},\n\tvolume = {47},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135412007610},\n\tdoi = {10.1016/j.watres.2012.10.031},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Water Research},\n\tauthor = {Bunzel, Katja and Kattwinkel, Mira and Liess, Matthias},\n\tmonth = feb,\n\tyear = {2013},\n\tpages = {597--606},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H. R.; Huisman, J. A.; Baatz, R.; Hendricks Franssen, H.; and Vereecken, H.\n\n\n \n \n \n \n \n Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems: The worst case scenario: Cosmic-Ray Probe in Humid Forested Ecosystems.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 49(9): 5778–5791. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Accuracy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_accuracy_2013,\n\ttitle = {Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems: {The} worst case scenario: {Cosmic}-{Ray} {Probe} in {Humid} {Forested} {Ecosystems}},\n\tvolume = {49},\n\tissn = {00431397},\n\tshorttitle = {Accuracy of the cosmic-ray soil water content probe in humid forest ecosystems},\n\turl = {http://doi.wiley.com/10.1002/wrcr.20463},\n\tdoi = {10.1002/wrcr.20463},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Bogena, H. R. and Huisman, J. A. and Baatz, R. and Hendricks Franssen, H.-J. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {5778--5791},\n}\n\n\n\n

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\n \n\n \n \n Blume, T.; Krause, S.; Meinikmann, K.; and Lewandowski, J.\n\n\n \n \n \n \n \n Upscaling lacustrine groundwater discharge rates by fiber-optic distributed temperature sensing: Upscaling Lacustrine Groundwater Discharge Rates.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 49(12): 7929–7944. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Upscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{blume_upscaling_2013,\n\ttitle = {Upscaling lacustrine groundwater discharge rates by fiber-optic distributed temperature sensing: {Upscaling} {Lacustrine} {Groundwater} {Discharge} {Rates}},\n\tvolume = {49},\n\tissn = {00431397},\n\tshorttitle = {Upscaling lacustrine groundwater discharge rates by fiber-optic distributed temperature sensing},\n\turl = {http://doi.wiley.com/10.1002/2012WR013215},\n\tdoi = {10.1002/2012WR013215},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Blume, Theresa and Krause, Stefan and Meinikmann, Karin and Lewandowski, Jörg},\n\tmonth = dec,\n\tyear = {2013},\n\tpages = {7929--7944},\n}\n\n\n\n

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\n \n\n \n \n Biernath, C.; Bittner, S.; Klein, C.; Gayler, S.; Hentschel, R.; Hoffmann, P.; Högy, P.; Fangmeier, A.; and Priesack, E.\n\n\n \n \n \n \n \n Modeling acclimation of leaf photosynthesis to atmospheric CO2 enrichment.\n \n \n \n \n\n\n \n\n\n\n European Journal of Agronomy, 48: 74–87. July 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{biernath_modeling_2013,\n\ttitle = {Modeling acclimation of leaf photosynthesis to atmospheric {CO2} enrichment},\n\tvolume = {48},\n\tissn = {11610301},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1161030113000269},\n\tdoi = {10.1016/j.eja.2013.02.008},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {European Journal of Agronomy},\n\tauthor = {Biernath, Christian and Bittner, Sebastian and Klein, Christian and Gayler, Sebastian and Hentschel, Rainer and Hoffmann, Peter and Högy, Petra and Fangmeier, Andreas and Priesack, Eckart},\n\tmonth = jul,\n\tyear = {2013},\n\tpages = {74--87},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Beketov, M. A.; Kefford, B. J.; Schäfer, R. B.; and Liess, M.\n\n\n \n \n \n \n \n Pesticides reduce regional biodiversity of stream invertebrates.\n \n \n \n \n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 110(27): 11039–11043. July 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Pesticides$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beketov_pesticides_2013,\n\ttitle = {Pesticides reduce regional biodiversity of stream invertebrates},\n\tvolume = {110},\n\tissn = {0027-8424, 1091-6490},\n\turl = {https://pnas.org/doi/full/10.1073/pnas.1305618110},\n\tdoi = {10.1073/pnas.1305618110},\n\tabstract = {The biodiversity crisis is one of the greatest challenges facing humanity, but our understanding of the drivers remains limited. Thus, after decades of studies and regulation efforts, it remains unknown whether to what degree and at what concentrations modern agricultural pesticides cause regional-scale species losses. We analyzed the effects of pesticides on the regional taxa richness of stream invertebrates in Europe (Germany and France) and Australia (southern Victoria). Pesticides caused statistically significant effects on both the species and family richness in both regions, with losses in taxa up to 42\\% of the recorded taxonomic pools. Furthermore, the effects in Europe were detected at concentrations that current legislation considers environmentally protective. Thus, the current ecological risk assessment of pesticides falls short of protecting biodiversity, and new approaches linking ecology and ecotoxicology are needed.},\n\tlanguage = {en},\n\tnumber = {27},\n\turldate = {2023-07-17},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Beketov, Mikhail A. and Kefford, Ben J. and Schäfer, Ralf B. and Liess, Matthias},\n\tmonth = jul,\n\tyear = {2013},\n\tpages = {11039--11043},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Beketov, M. A.; Cedergreen, N.; Wick, L. Y.; Kattwinkel, M.; Duquesne, S.; and Liess, M.\n\n\n \n \n \n \n \n Sediment Toxicity Testing for Prospective Risk Assessment—A New Framework and How to Establish It.\n \n \n \n \n\n\n \n\n\n\n Human and Ecological Risk Assessment: An International Journal, 19(1): 98–117. January 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Sediment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{beketov_sediment_2013,\n\ttitle = {Sediment {Toxicity} {Testing} for {Prospective} {Risk} {Assessment}—{A} {New} {Framework} and {How} to {Establish} {It}},\n\tvolume = {19},\n\tissn = {1080-7039, 1549-7860},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/10807039.2012.683741},\n\tdoi = {10.1080/10807039.2012.683741},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-07-17},\n\tjournal = {Human and Ecological Risk Assessment: An International Journal},\n\tauthor = {Beketov, M. A. and Cedergreen, N. and Wick, L. Y. and Kattwinkel, M. and Duquesne, S. and Liess, M.},\n\tmonth = jan,\n\tyear = {2013},\n\tpages = {98--117},\n}\n\n\n\n

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\n \n\n \n \n Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; and Wolf, J.\n\n\n \n \n \n \n \n Uncertainty in simulating wheat yields under climate change.\n \n \n \n \n\n\n \n\n\n\n Nature Climate Change, 3(9): 827–832. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{asseng_uncertainty_2013,\n\ttitle = {Uncertainty in simulating wheat yields under climate change},\n\tvolume = {3},\n\tissn = {1758-678X, 1758-6798},\n\turl = {https://www.nature.com/articles/nclimate1916},\n\tdoi = {10.1038/nclimate1916},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-07-17},\n\tjournal = {Nature Climate Change},\n\tauthor = {Asseng, S. and Ewert, F. and Rosenzweig, C. and Jones, J. W. and Hatfield, J. L. and Ruane, A. C. and Boote, K. J. and Thorburn, P. J. and Rötter, R. P. and Cammarano, D. and Brisson, N. and Basso, B. and Martre, P. and Aggarwal, P. K. and Angulo, C. and Bertuzzi, P. and Biernath, C. and Challinor, A. J. and Doltra, J. and Gayler, S. and Goldberg, R. and Grant, R. and Heng, L. and Hooker, J. and Hunt, L. A. and Ingwersen, J. and Izaurralde, R. C. and Kersebaum, K. C. and Müller, C. and Naresh Kumar, S. and Nendel, C. and O’Leary, G. and Olesen, J. E. and Osborne, T. M. and Palosuo, T. and Priesack, E. and Ripoche, D. and Semenov, M. A. and Shcherbak, I. and Steduto, P. and Stöckle, C. and Stratonovitch, P. and Streck, T. and Supit, I. and Tao, F. and Travasso, M. and Waha, K. and Wallach, D. and White, J. W. and Williams, J. R. and Wolf, J.},\n\tmonth = sep,\n\tyear = {2013},\n\tpages = {827--832},\n}\n\n\n\n

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\n \n\n \n \n Voormansik, K.; Jagdhuber, T.; Olesk, A.; Hajnsek, I.; and Papathanassiou, K. P.\n\n\n \n \n \n \n \n Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data.\n \n \n \n \n\n\n \n\n\n\n International Journal of Remote Sensing, 34(22): 8081–8103. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{voormansik_towards_2013,\n\ttitle = {Towards a detection of grassland cutting practices with dual polarimetric {TerraSAR}-{X} data},\n\tvolume = {34},\n\tissn = {0143-1161, 1366-5901},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/01431161.2013.829593},\n\tdoi = {10.1080/01431161.2013.829593},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2023-06-19},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Voormansik, Kaupo and Jagdhuber, Thomas and Olesk, Aire and Hajnsek, Irena and Papathanassiou, Konstantinos P.},\n\tmonth = nov,\n\tyear = {2013},\n\tpages = {8081--8103},\n}\n\n\n\n

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\n \n\n \n \n Koch, M.; Koebsch, F.; Hahn, J.; and Jurasinski, G.\n\n\n \n \n \n \n From Meadow to Shallow Lake: Short-term Vegetation Dynamics After Rewetting of a Coastal Brackish Fen Studied Using RGB Aerial Photographs.\n \n \n \n\n\n \n\n\n\n In January 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{koch_meadow_2013,\n\ttitle = {From {Meadow} to {Shallow} {Lake}: {Short}-term {Vegetation} {Dynamics} {After} {Rewetting} of a {Coastal} {Brackish} {Fen} {Studied} {Using} {RGB} {Aerial} {Photographs}},\n\tauthor = {Koch, Marian and Koebsch, Franziska and Hahn, Juliane and Jurasinski, Gerald},\n\tmonth = jan,\n\tyear = {2013},\n}\n\n\n\n

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\n \n 2012\n \n \n (21)\n \n \n

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\n \n\n \n \n Kaiser, K.; Friedrich; Oldorff; Germer, S.; Mauersberger; Natkhin; Hupfer, M.; Pingel; Schönfel-der; Spicher; Stüve, P.; Vedder; Bens, O.; Mietz; and Hüttl, R.\n\n\n \n \n \n \n Aktuelle hydrologische Veränderungen von Seen in Nordostdeutschland: Wasserspiegeltrends, ökologische Konsequenzen, Handlungsmöglichkeiten [Current hydrological changes of lakes in Northeast Germany: water-level trends, ecological consequences, management options].\n \n \n \n\n\n \n\n\n\n In Wasserbezogene Anpassungsmaßnahmen an den Landschafts- Und Klimawandel, pages 148–170. January 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@incollection{kaiser_aktuelle_2012,\n\ttitle = {Aktuelle hydrologische {Veränderungen} von {Seen} in {Nordostdeutschland}: {Wasserspiegeltrends}, ökologische {Konsequenzen}, {Handlungsmöglichkeiten} [{Current} hydrological changes of lakes in {Northeast} {Germany}: water-level trends, ecological consequences, management options]},\n\tisbn = {978-3-510-65274-7},\n\tbooktitle = {Wasserbezogene {Anpassungsmaßnahmen} an den {Landschafts}- {Und} {Klimawandel}},\n\tauthor = {Kaiser, Knut and Friedrich and Oldorff and Germer, Sonja and Mauersberger and Natkhin and Hupfer, Michael and Pingel and Schönfel-der and Spicher and Stüve, Peter and Vedder and Bens, Oliver and Mietz and Hüttl, Reinhard},\n\tmonth = jan,\n\tyear = {2012},\n\tpages = {148--170},\n}\n\n\n\n

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\n \n\n \n \n Kunkel, R.; Sorg, J.; Gasche, R.; Klump, J.; Kolditz, O.; Frenzel, M.; and Neidl, F.\n\n\n \n \n \n \n TEODOOR : Geodateninfrastruktur zur Verwaltung und Veröffentlichung von terrestrischen Beobachtungsdaten der HGF Infrastrukturmaßnahme TERENO aus verteilten Quellen.\n \n \n \n\n\n \n\n\n\n In Vernetztes Wissen - Daten, Menschen, Systeme : 6. Konferenz der Zentralbibliothek Forschungszentrum Jülich; 5. - 7. November 2012; Proceedingsband; [WissKom2012], volume 21, of Schriften des Forschungszentrums Jülich. Reihe Umwelt/Environment, pages 75–92, January 2012. Mittermaier, B.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{kunkel_teodoor_2012,\n\tseries = {Schriften des {Forschungszentrums} {Jülich}. {Reihe} {Umwelt}/{Environment}},\n\ttitle = {{TEODOOR} : {Geodateninfrastruktur} zur {Verwaltung} und {Veröffentlichung} von terrestrischen {Beobachtungsdaten} der {HGF} {Infrastrukturmaßnahme} {TERENO} aus verteilten {Quellen}},\n\tvolume = {21},\n\tlanguage = {deutsch},\n\tbooktitle = {Vernetztes {Wissen} - {Daten}, {Menschen}, {Systeme} : 6. {Konferenz} der {Zentralbibliothek} {Forschungszentrum} {Jülich}; 5. - 7. {November} 2012; {Proceedingsband}; [{WissKom2012}]},\n\tpublisher = {Mittermaier, B.},\n\tauthor = {Kunkel, Ralf and Sorg, Jürgen and Gasche, Rainer and Klump, Jens and Kolditz, Olaf and Frenzel, Mark and Neidl, Frank},\n\tmonth = jan,\n\tyear = {2012},\n\tpages = {75--92},\n}\n\n\n\n

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\n \n\n \n \n Bogena, H.; Kunkel, R.; Pütz, T.; Vereecken, H.; Kruger, E.; Zacharias, S.; Dietrich, P.; Wollschläger, U.; Kunstmann, H.; Papen, H.; Schmid, H.; Munch, J.; Priesack, E.; Schwank, M.; Bens, O.; Brauer, A.; Borg, E.; and Hajnsek, I.\n\n\n \n \n \n \n TERENO - Long-term monitoring network for terrestrial environmental research.\n \n \n \n\n\n \n\n\n\n Hydrologie und Wasserbewirtschaftung, 56: 138–143. June 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_tereno_2012,\n\ttitle = {{TERENO} - {Long}-term monitoring network for terrestrial environmental research},\n\tvolume = {56},\n\tjournal = {Hydrologie und Wasserbewirtschaftung},\n\tauthor = {Bogena, Heye and Kunkel, Ralf and Pütz, Thomas and Vereecken, Harry and Kruger, E. and Zacharias, Steffen and Dietrich, Peter and Wollschläger, Ute and Kunstmann, Harald and Papen, Hans and Schmid, Hans and Munch, Jean and Priesack, Eckart and Schwank, Mike and Bens, Oliver and Brauer, Achim and Borg, Erik and Hajnsek, Irena},\n\tmonth = jun,\n\tyear = {2012},\n\tpages = {138--143},\n}\n\n\n\n

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\n \n\n \n \n Bauer, J.; Weihermüller, L.; Huisman, J. A.; Herbst, M.; Graf, A.; Séquaris, J. M.; and Vereecken, H.\n\n\n \n \n \n \n \n Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 108(1-3): 119–134. April 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Inverse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bauer_inverse_2012,\n\ttitle = {Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions},\n\tvolume = {108},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-011-9583-1},\n\tdoi = {10.1007/s10533-011-9583-1},\n\tlanguage = {en},\n\tnumber = {1-3},\n\turldate = {2023-07-17},\n\tjournal = {Biogeochemistry},\n\tauthor = {Bauer, J. and Weihermüller, L. and Huisman, J. A. and Herbst, M. and Graf, A. and Séquaris, J. M. and Vereecken, H.},\n\tmonth = apr,\n\tyear = {2012},\n\tpages = {119--134},\n}\n\n\n\n

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\n \n\n \n \n Vogl, S.; Laux, P.; Qiu, W.; Mao, G.; and Kunstmann, H.\n\n\n \n \n \n \n \n Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 16(7): 2311–2328. July 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Copula-based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{vogl_copula-based_2012,\n\ttitle = {Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields},\n\tvolume = {16},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/16/2311/2012/},\n\tdoi = {10.5194/hess-16-2311-2012},\n\tabstract = {Abstract. This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values.  In this study copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. After appropriate time series transformation to generate "iid" variates, only the positive pairs (radar {\\textgreater}0, gauge {\\textgreater}0) of the residuals are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches, namely Maximum Theta and Multiple Theta, are presented. They finally allow for generating precipitation fields that mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar fields, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (ordinary kriging) and are validated with available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN). The copula-based approach performs similarly well as indicated by different validation measures and successfully corrects for errors in the radar precipitation.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-07-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Vogl, S. and Laux, P. and Qiu, W. and Mao, G. and Kunstmann, H.},\n\tmonth = jul,\n\tyear = {2012},\n\tpages = {2311--2328},\n}\n\n\n\n

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\n \n\n \n \n Tum, M.; and Borg, E.\n\n\n \n \n \n \n \n Comparing results of a remote sensing driven interception-infiltration model for regional to global applications with ECMWF data.\n \n \n \n \n\n\n \n\n\n\n In Neale, C. M. U.; and Maltese, A., editor(s), pages 853102, Edinburgh, United Kingdom, October 2012. \n \n\n\n\n
\n\n\n\n \n \n $\"Comparing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@inproceedings{tum_comparing_2012,\n\taddress = {Edinburgh, United Kingdom},\n\ttitle = {Comparing results of a remote sensing driven interception-infiltration model for regional to global applications with {ECMWF} data},\n\turl = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.974553},\n\tdoi = {10.1117/12.974553},\n\turldate = {2023-07-17},\n\tauthor = {Tum, M. and Borg, E.},\n\teditor = {Neale, Christopher M. U. and Maltese, Antonino},\n\tmonth = oct,\n\tyear = {2012},\n\tpages = {853102},\n}\n\n\n\n

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\n \n\n \n \n Tolksdorf, J. F.; and Kaiser, K.\n\n\n \n \n \n \n \n Holocene aeolian dynamics in the European sand-belt as indicated by geochronological data: Holocene aeolian dynamics in the European sand-belt.\n \n \n \n \n\n\n \n\n\n\n Boreas, 41(3): 408–421. July 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Holocene$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{tolksdorf_holocene_2012,\n\ttitle = {Holocene aeolian dynamics in the {European} sand-belt as indicated by geochronological data: {Holocene} aeolian dynamics in the {European} sand-belt},\n\tvolume = {41},\n\tissn = {03009483},\n\tshorttitle = {Holocene aeolian dynamics in the {European} sand-belt as indicated by geochronological data},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/j.1502-3885.2012.00247.x},\n\tdoi = {10.1111/j.1502-3885.2012.00247.x},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-07-17},\n\tjournal = {Boreas},\n\tauthor = {Tolksdorf, Johann Friedrich and Kaiser, Knut},\n\tmonth = jul,\n\tyear = {2012},\n\tpages = {408--421},\n}\n\n\n\n

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\n \n\n \n \n Smiatek, G.; Kunstmann, H.; and Werhahn, J.\n\n\n \n \n \n \n \n Implementation and performance analysis of a high resolution coupled numerical weather and river runoff prediction model system for an Alpine catchment.\n \n \n \n \n\n\n \n\n\n\n Environmental Modelling & Software, 38: 231–243. December 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Implementation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{smiatek_implementation_2012,\n\ttitle = {Implementation and performance analysis of a high resolution coupled numerical weather and river runoff prediction model system for an {Alpine} catchment},\n\tvolume = {38},\n\tissn = {13648152},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1364815212001818},\n\tdoi = {10.1016/j.envsoft.2012.06.001},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Modelling \\& Software},\n\tauthor = {Smiatek, Gerhard and Kunstmann, Harald and Werhahn, Johannes},\n\tmonth = dec,\n\tyear = {2012},\n\tpages = {231--243},\n}\n\n\n\n

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\n \n\n \n \n Schmidt, C.; Musolff, A.; Trauth, N.; Vieweg, M.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n Transient analysis of fluctuations of electrical conductivity as tracer in the stream bed.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 16(10): 3689–3697. October 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Transient$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{schmidt_transient_2012,\n\ttitle = {Transient analysis of fluctuations of electrical conductivity as tracer in the stream bed},\n\tvolume = {16},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/16/3689/2012/},\n\tdoi = {10.5194/hess-16-3689-2012},\n\tabstract = {Abstract. Spatial patterns of water flux in the stream bed are controlled by the distribution of hydraulic conductivity, bedform-induced head gradients and the connectivity to the adjoining groundwater system. The water fluxes vary over time driven by short-term flood events or seasonal variations in stream flow and groundwater level. Variations of electrical conductivity (EC) are used as a natural tracer to detect transient travel times and flow velocities in an in-stream gravel bar. We present a method to estimate travel times between the stream and measuring locations in the gravel bar by non-linearly matching the EC signals in the time domain. The amount of temporal distortion required to obtain the optimal matching is related to the travel time of the signal. Our analysis revealed that the travel times increase at higher stream flows because lateral head gradients across the gravel bar become significantly smaller at the time.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-07-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Schmidt, C. and Musolff, A. and Trauth, N. and Vieweg, M. and Fleckenstein, J. H.},\n\tmonth = oct,\n\tyear = {2012},\n\tpages = {3689--3697},\n}\n\n\n\n

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\n \n\n \n \n Rosenbaum, U.; Bogena, H. R.; Herbst, M.; Huisman, J. A.; Peterson, T. J.; Weuthen, A.; Western, A. W.; and Vereecken, H.\n\n\n \n \n \n \n \n Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 48(10): 2011WR011518. October 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rosenbaum_seasonal_2012,\n\ttitle = {Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale},\n\tvolume = {48},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2011WR011518},\n\tdoi = {10.1029/2011WR011518},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2023-07-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Rosenbaum, U. and Bogena, H. R. and Herbst, M. and Huisman, J. A. and Peterson, T. J. and Weuthen, A. and Western, A. W. and Vereecken, H.},\n\tmonth = oct,\n\tyear = {2012},\n\tpages = {2011WR011518},\n}\n\n\n\n

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\n \n\n \n \n Rink, K.; Kalbacher, T.; and Kolditz, O.\n\n\n \n \n \n \n \n Visual data exploration for hydrological analysis.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 65(5): 1395–1403. March 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Visual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rink_visual_2012,\n\ttitle = {Visual data exploration for hydrological analysis},\n\tvolume = {65},\n\tissn = {1866-6280, 1866-6299},\n\turl = {http://link.springer.com/10.1007/s12665-011-1230-6},\n\tdoi = {10.1007/s12665-011-1230-6},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Rink, Karsten and Kalbacher, Thomas and Kolditz, Olaf},\n\tmonth = mar,\n\tyear = {2012},\n\tpages = {1395--1403},\n}\n\n\n\n

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\n \n\n \n \n Neugebauer, I.; Brauer, A.; Dräger, N.; Dulski, P.; Wulf, S.; Plessen, B.; Mingram, J.; Herzschuh, U.; and Brande, A.\n\n\n \n \n \n \n \n A Younger Dryas varve chronology from the Rehwiese palaeolake record in NE-Germany.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 36: 91–102. March 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{neugebauer_younger_2012,\n\ttitle = {A {Younger} {Dryas} varve chronology from the {Rehwiese} palaeolake record in {NE}-{Germany}},\n\tvolume = {36},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379111004033},\n\tdoi = {10.1016/j.quascirev.2011.12.010},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Neugebauer, Ina and Brauer, Achim and Dräger, Nadine and Dulski, Peter and Wulf, Sabine and Plessen, Birgit and Mingram, Jens and Herzschuh, Ulrike and Brande, Arthur},\n\tmonth = mar,\n\tyear = {2012},\n\tpages = {91--102},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Merz, B.\n\n\n \n \n \n \n \n Wie gut können wir vergangene und zukünftige Veränderungen des Wasserhaushalts quantifizieren?.\n \n \n \n \n\n\n \n\n\n\n Hydrologie und Wasserbewirtschaftung / BfG – Jahrgang: 56.2012,5ISSN 1439. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Wie$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{merz_wie_2012,\n\ttitle = {Wie gut können wir vergangene und zukünftige {Veränderungen} des {Wasserhaushalts} quantifizieren?},\n\turl = {http://doi.bafg.de/HyWa/2012/HyWa_2012,5_1.pdf},\n\tdoi = {10.5675/HYWA_2012,5_1},\n\turldate = {2023-07-17},\n\tjournal = {Hydrologie und Wasserbewirtschaftung / BfG – Jahrgang: 56.2012},\n\tauthor = {Merz, Bruno},\n\tyear = {2012},\n\tkeywords = {Water resources research},\n\tpages = {5ISSN 1439},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Martin-Puertas, C.; Brauer, A.; Dulski, P.; and Brademann, B.\n\n\n \n \n \n \n \n Testing climate–proxy stationarity throughout the Holocene: an example from the varved sediments of Lake Meerfelder Maar (Germany).\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 58: 56–65. December 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Testing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{martin-puertas_testing_2012,\n\ttitle = {Testing climate–proxy stationarity throughout the {Holocene}: an example from the varved sediments of {Lake} {Meerfelder} {Maar} ({Germany})},\n\tvolume = {58},\n\tissn = {02773791},\n\tshorttitle = {Testing climate–proxy stationarity throughout the {Holocene}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379112004192},\n\tdoi = {10.1016/j.quascirev.2012.10.023},\n\tlanguage = {en},\n\turldate = {2023-07-17},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Martin-Puertas, Celia and Brauer, Achim and Dulski, Peter and Brademann, Brian},\n\tmonth = dec,\n\tyear = {2012},\n\tpages = {56--65},\n}\n\n\n\n

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\n \n\n \n \n Lausch, A.; Pause, M.; Merbach, I.; Gwillym-Margianto, S.; Schulz, K.; Zacharias, S.; and Seppelt, R.\n\n\n \n \n \n \n \n Scale-specific Hyperspectral Remote Sensing Approach in Environmental Research.\n \n \n \n \n\n\n \n\n\n\n Photogrammetrie - Fernerkundung - Geoinformation, 2012(5): 589–601. October 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Scale-specific$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{lausch_scale-specific_2012,\n\ttitle = {Scale-specific {Hyperspectral} {Remote} {Sensing} {Approach} in {Environmental} {Research}},\n\tvolume = {2012},\n\tissn = {1432-8364},\n\turl = {http://www.schweizerbart.de/papers/pfg/detail/2012/78559/Scale_specific_Hyperspectral_Remote_Sensing_Approa?af=crossref},\n\tdoi = {10.1127/1432-8364/2012/0141},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-07-17},\n\tjournal = {Photogrammetrie - Fernerkundung - Geoinformation},\n\tauthor = {Lausch, Angela and Pause, Marion and Merbach, Ines and Gwillym-Margianto, Sarah and Schulz, Karsten and Zacharias, Steffen and Seppelt, Ralf},\n\tmonth = oct,\n\tyear = {2012},\n\tpages = {589--601},\n}\n\n\n\n

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\n \n\n \n \n Kolditz, O.; Rink, K.; Shao, H.; Kalbacher, T.; Zacharias, S.; and Dietrich, P.\n\n\n \n \n \n \n \n International viewpoint and news.\n \n \n \n \n\n\n \n\n\n\n Environmental Earth Sciences, 66(4): 1279–1284. June 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"International$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kolditz_international_2012,\n\ttitle = {International viewpoint and news},\n\tvolume = {66},\n\tissn = {1866-6280, 1866-6299},\n\turl = {http://link.springer.com/10.1007/s12665-012-1661-8},\n\tdoi = {10.1007/s12665-012-1661-8},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Environmental Earth Sciences},\n\tauthor = {Kolditz, Olaf and Rink, Karsten and Shao, Haibing and Kalbacher, Thomas and Zacharias, Steffen and Dietrich, Peter},\n\tmonth = jun,\n\tyear = {2012},\n\tpages = {1279--1284},\n}\n\n\n\n

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\n \n\n \n \n Kaiser, K.; Germer, S.; Kuester, M.; Lorenz, S.; Stueve, P.; and Bens, O.\n\n\n \n \n \n \n \n Seespiegelschwankungen in Nordost-Deutschland : Beobachtung und Rekonstruktion.\n \n \n \n \n\n\n \n\n\n\n System Erde; Vol. 2,Issue 1; ISSN 21918589. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Seespiegelschwankungen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_seespiegelschwankungen_2012,\n\ttitle = {Seespiegelschwankungen in {Nordost}-{Deutschland} : {Beobachtung} und {Rekonstruktion}},\n\tshorttitle = {Seespiegelschwankungen in {Nordost}-{Deutschland}},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_65132},\n\tdoi = {10.2312/GFZ.SYSERDE.02.01.12},\n\tlanguage = {de},\n\turldate = {2023-07-17},\n\tjournal = {System Erde; Vol. 2},\n\tauthor = {Kaiser, Knut and Germer, Sonja and Kuester, Mathias and Lorenz, Sebastian and Stueve, Peter and Bens, Oliver},\n\tyear = {2012},\n\tpages = {Issue 1; ISSN 21918589},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Kaiser, K.; Lorenz, S.; Germer, S.; Juschus, O.; Küster, M.; Libra, J.; Bens, O.; and Hüttl, R. F.\n\n\n \n \n \n \n \n Late Quaternary evolution of rivers, lakes and peatlands in northeast Germany reflecting past climatic and human impact – an overview.\n \n \n \n \n\n\n \n\n\n\n E&G Quaternary Science Journal, 61(2): 103–132. July 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Late$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kaiser_late_2012,\n\ttitle = {Late {Quaternary} evolution of rivers, lakes and peatlands in northeast {Germany} reflecting past climatic and human impact – an overview},\n\tvolume = {61},\n\tissn = {2199-9090},\n\turl = {https://egqsj.copernicus.org/articles/61/103/2012/},\n\tdoi = {10.3285/eg.61.2.01},\n\tabstract = {Abstract. Die Kenntnis der regionalen Paläohydrologie ist eine wesentliche Grundlage für das Verständnis aktueller Umweltfragen, wie zum Beispiel nach den Gründen von hydrologischen Veränderungen, dem Einfluss von Landnutzungsstrategien und der Wirksamkeit von Renaturierungsvorhaben in Feuchtgebieten. Auch die Interpretation von Modellierungsergebnissen zu den künftigen Einflüssen des Klima- und Landnutzungswandels auf das Gewässersystem kann durch die Einbeziehung (prä-) historischer Analogien verbessert werden. Für das glazial geprägte nordostdeutsche Tiefland wurde eine Übersicht der vorliegenden paläohydrologischen Befunde für den Zeitraum der letzten etwa 20.000 Jahre erarbeitet. Die Entwicklung der Flüsse wurde mit Blick auf die Tal-/Auengenese und das Ablagerungsmilieu, die Veränderung des Tal- und Gerinneverlaufs sowie den Paläoabfluss bzw. das Paläohochwasser betrachtet. Wesentliche genetische Unterschiede bestehen zwischen Alt- (Elster- und Saalekaltzeit) und Jungmoränengebieten (Weichselkaltzeit) sowie zwischen hoch und tief gelegenen Tälern. Letztere sind stark durch Wasserspiegelveränderungen in der Nord- und Ostsee beeinflusst worden. Die Entwicklung der Seen wurde hinsichtlich der Seebildung, die überwiegend eine Folge der spätpleistozänen bis frühholozänen Toteistieftau-Dynamik ist, und der Veränderungen im Ablagerungsmilieu analysiert. Weiterhin standen Seespiegelveränderungen im Fokus, wobei sich hoch variable lokale Befunde mit einigen Übereinstimmungen zeigten. Der Überblick zur Moorentwicklung konzentrierte sich auf hydrogenetische Moorentwicklungsphasen und auf die langfristige Entwicklung des Grundwasserspiegels. Enge Beziehungen zwischen der Entwicklung der Flüsse, Seen und Moore bestanden insbesondere im Spätholozän durch komplexe Vermoorungsprozesse in den großen Flusstälern. Bis in das Spätholozän wurde die regionale Hydrologie überwiegend durch klimatische, geomorphologische und nicht-anthropogene biologische Faktoren gesteuert. Seit dem Spätmittelalter wurde in der Region das Gewässernetz und der Wasserkreislauf im starken Maß durch anthropogene Interventionen beeinflusst (z.B. Aufstau von Flüssen und Seen, Bau von Kanälen und Deichen, Moorkultivierung). In den letzten etwa 50 Jahren haben dann sogar die kurzfristigen anthropogenen Eingriffe, z.B. in Form von Abflussregulierung, Hydromelioration und künstlicher Seebildung, die Wirksamkeit langfristiger klimatischer und geomorphologischer Prozesse übertroffen.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {E\\&amp;G Quaternary Science Journal},\n\tauthor = {Kaiser, Knut and Lorenz, Sebastian and Germer, Sonja and Juschus, Olaf and Küster, Mathias and Libra, Judy and Bens, Oliver and Hüttl, Reinhard F.},\n\tmonth = jul,\n\tyear = {2012},\n\tpages = {103--132},\n}\n\n\n\n

\n\n\n

\n Abstract. Die Kenntnis der regionalen Paläohydrologie ist eine wesentliche Grundlage für das Verständnis aktueller Umweltfragen, wie zum Beispiel nach den Gründen von hydrologischen Veränderungen, dem Einfluss von Landnutzungsstrategien und der Wirksamkeit von Renaturierungsvorhaben in Feuchtgebieten. Auch die Interpretation von Modellierungsergebnissen zu den künftigen Einflüssen des Klima- und Landnutzungswandels auf das Gewässersystem kann durch die Einbeziehung (prä-) historischer Analogien verbessert werden. Für das glazial geprägte nordostdeutsche Tiefland wurde eine Übersicht der vorliegenden paläohydrologischen Befunde für den Zeitraum der letzten etwa 20.000 Jahre erarbeitet. Die Entwicklung der Flüsse wurde mit Blick auf die Tal-/Auengenese und das Ablagerungsmilieu, die Veränderung des Tal- und Gerinneverlaufs sowie den Paläoabfluss bzw. das Paläohochwasser betrachtet. Wesentliche genetische Unterschiede bestehen zwischen Alt- (Elster- und Saalekaltzeit) und Jungmoränengebieten (Weichselkaltzeit) sowie zwischen hoch und tief gelegenen Tälern. Letztere sind stark durch Wasserspiegelveränderungen in der Nord- und Ostsee beeinflusst worden. Die Entwicklung der Seen wurde hinsichtlich der Seebildung, die überwiegend eine Folge der spätpleistozänen bis frühholozänen Toteistieftau-Dynamik ist, und der Veränderungen im Ablagerungsmilieu analysiert. Weiterhin standen Seespiegelveränderungen im Fokus, wobei sich hoch variable lokale Befunde mit einigen Übereinstimmungen zeigten. Der Überblick zur Moorentwicklung konzentrierte sich auf hydrogenetische Moorentwicklungsphasen und auf die langfristige Entwicklung des Grundwasserspiegels. Enge Beziehungen zwischen der Entwicklung der Flüsse, Seen und Moore bestanden insbesondere im Spätholozän durch komplexe Vermoorungsprozesse in den großen Flusstälern. Bis in das Spätholozän wurde die regionale Hydrologie überwiegend durch klimatische, geomorphologische und nicht-anthropogene biologische Faktoren gesteuert. Seit dem Spätmittelalter wurde in der Region das Gewässernetz und der Wasserkreislauf im starken Maß durch anthropogene Interventionen beeinflusst (z.B. Aufstau von Flüssen und Seen, Bau von Kanälen und Deichen, Moorkultivierung). In den letzten etwa 50 Jahren haben dann sogar die kurzfristigen anthropogenen Eingriffe, z.B. in Form von Abflussregulierung, Hydromelioration und künstlicher Seebildung, die Wirksamkeit langfristiger klimatischer und geomorphologischer Prozesse übertroffen.\n

\n\n\n

\n \n\n \n \n Bittner, S.; Legner, N.; Beese, F.; and Priesack, E.\n\n\n \n \n \n \n \n Individual tree branch-level simulation of light attenuation and water flow of three F. sylvatica L. trees: LIGHT REGIME AND TREE WATER FLOW MODEL.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 117(G1). March 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Individual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bittner_individual_2012,\n\ttitle = {Individual tree branch-level simulation of light attenuation and water flow of three {F}. sylvatica {L}. trees: {LIGHT} {REGIME} {AND} {TREE} {WATER} {FLOW} {MODEL}},\n\tvolume = {117},\n\tissn = {01480227},\n\tshorttitle = {Individual tree branch-level simulation of light attenuation and water flow of three \\textit{{F}. sylvatica} {L}. trees},\n\turl = {http://doi.wiley.com/10.1029/2011JG001780},\n\tdoi = {10.1029/2011JG001780},\n\tlanguage = {en},\n\tnumber = {G1},\n\turldate = {2023-07-17},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Bittner, S. and Legner, N. and Beese, F. and Priesack, E.},\n\tmonth = mar,\n\tyear = {2012},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Bens, O.; Schwank, M.; Blume, T.; Brauer, A.; Guentner, A.; Heinrich, I.; Helle, G.; Itzerott, S.; Kaiser, K.; Sachs, T.; and Huettl, R. F.\n\n\n \n \n \n \n \n TERENO - eine Monitoring- und Forschungsplattform zur Erfassung langfristiger Auswirkungen des globalen Wandels auf regionaler Ebene.\n \n \n \n \n\n\n \n\n\n\n System Erde; Vol. 2,Issue 1; ISSN 21918589. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"TERENO$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bens_tereno_2012,\n\ttitle = {{TERENO} - eine {Monitoring}- und {Forschungsplattform} zur {Erfassung} langfristiger {Auswirkungen} des globalen {Wandels} auf regionaler {Ebene}},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_65133},\n\tdoi = {10.2312/GFZ.SYSERDE.02.01.13},\n\tlanguage = {de},\n\turldate = {2023-07-17},\n\tjournal = {System Erde; Vol. 2},\n\tauthor = {Bens, Oliver and Schwank, Mike and Blume, Theresa and Brauer, Achim and Guentner, Andreas and Heinrich, Ingo and Helle, Gerd and Itzerott, Sybille and Kaiser, Knut and Sachs, Torsten and Huettl, Reinhard F.},\n\tyear = {2012},\n\tpages = {Issue 1; ISSN 21918589},\n}\n\n\n\n

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\n \n\n \n \n Chwala, C.; Gmeiner, A.; Qiu, W.; Hipp, S.; Nienaber, D.; Siart, U.; Eibert, T.; Pohl, M.; Seltmann, J.; Fritz, J.; and Kunstmann, H.\n\n\n \n \n \n \n \n Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 16(8): 2647–2661. August 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Precipitation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{chwala_precipitation_2012,\n\ttitle = {Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of {Southern} {Germany}},\n\tvolume = {16},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/16/2647/2012/},\n\tdoi = {10.5194/hess-16-2647-2012},\n\tabstract = {Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties, because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data are recorded minutely with small data loggers at the towers and then sent to a database server via GSM (Global System for Mobile Communications). Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. Its performance and limitations are presented, showing that the mean detection error rates of wet and dry periods can be reduced to 10\\% for all five links. After, the wet/dry classification rain rates are derived from the RSL and compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R2 = 0.81 for the link-gauge comparison and R2 = 0.85 for the link-radar comparison.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-06-19},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Chwala, C. and Gmeiner, A. and Qiu, W. and Hipp, S. and Nienaber, D. and Siart, U. and Eibert, T. and Pohl, M. and Seltmann, J. and Fritz, J. and Kunstmann, H.},\n\tmonth = aug,\n\tyear = {2012},\n\tpages = {2647--2661},\n}\n\n\n\n

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\n \n 2011\n \n \n (11)\n \n \n

\n \n \n

\n \n\n \n \n Germer, S.; Kaiser, K.; Bens, O.; and Hüttl, R.\n\n\n \n \n \n \n Water Balance Changes and Responses of Ecosystems and Society in the Berlin-Brandenburg Region - a Review.\n \n \n \n\n\n \n\n\n\n Die Erde, 142: 65–95. January 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{germer_water_2011,\n\ttitle = {Water {Balance} {Changes} and {Responses} of {Ecosystems} and {Society} in the {Berlin}-{Brandenburg} {Region} - a {Review}},\n\tvolume = {142},\n\tjournal = {Die Erde},\n\tauthor = {Germer, Sonja and Kaiser, Knut and Bens, Oliver and Hüttl, Reinhard},\n\tmonth = jan,\n\tyear = {2011},\n\tpages = {65--95},\n}\n\n\n\n

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\n \n\n \n \n Zacharias, S.; Bogena, H.; Samaniego, L.; Mauder, M.; Fuß, R.; Pütz, T.; Frenzel, M.; Schwank, M.; Baessler, C.; Butterbach-Bahl, K.; Bens, O.; Borg, E.; Brauer, A.; Dietrich, P.; Hajnsek, I.; Helle, G.; Kiese, R.; Kunstmann, H.; Klotz, S.; Munch, J. C.; Papen, H.; Priesack, E.; Schmid, H. P.; Steinbrecher, R.; Rosenbaum, U.; Teutsch, G.; and Vereecken, H.\n\n\n \n \n \n \n \n A Network of Terrestrial Environmental Observatories in Germany.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 10(3): 955–973. August 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{zacharias_network_2011,\n\ttitle = {A {Network} of {Terrestrial} {Environmental} {Observatories} in {Germany}},\n\tvolume = {10},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2010.0139},\n\tdoi = {10.2136/vzj2010.0139},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Zacharias, Steffen and Bogena, Heye and Samaniego, Luis and Mauder, Matthias and Fuß, Roland and Pütz, Thomas and Frenzel, Mark and Schwank, Mike and Baessler, Cornelia and Butterbach-Bahl, Klaus and Bens, Oliver and Borg, Erik and Brauer, Achim and Dietrich, Peter and Hajnsek, Irena and Helle, Gerhard and Kiese, Ralf and Kunstmann, Harald and Klotz, Stefan and Munch, Jean Charles and Papen, Hans and Priesack, Eckart and Schmid, Hans Peter and Steinbrecher, Rainer and Rosenbaum, Ulrike and Teutsch, Georg and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2011},\n\tpages = {955--973},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Wloczyk, C.; Borg, E.; Richter, R.; and Miegel, K.\n\n\n \n \n \n \n \n Estimation of instantaneous air temperature above vegetation and soil surfaces from Landsat 7 ETM+ data in northern Germany.\n \n \n \n \n\n\n \n\n\n\n International Journal of Remote Sensing, 32(24): 9119–9136. December 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{wloczyk_estimation_2011,\n\ttitle = {Estimation of instantaneous air temperature above vegetation and soil surfaces from {Landsat} 7 {ETM}+ data in northern {Germany}},\n\tvolume = {32},\n\tissn = {0143-1161, 1366-5901},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/01431161.2010.550332},\n\tdoi = {10.1080/01431161.2010.550332},\n\tlanguage = {en},\n\tnumber = {24},\n\turldate = {2023-07-17},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Wloczyk, Carolin and Borg, Erik and Richter, Rudolf and Miegel, Konrad},\n\tmonth = dec,\n\tyear = {2011},\n\tpages = {9119--9136},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Scharnagl, B.; Vrugt, J. A.; Vereecken, H.; and Herbst, M.\n\n\n \n \n \n \n \n Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties.\n \n \n \n \n\n\n \n\n\n\n Technical Report Vadose Zone Hydrology/Modelling approaches, February 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@techreport{scharnagl_bayesian_2011,\n\ttype = {preprint},\n\ttitle = {Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties},\n\tshorttitle = {Bayesian inverse modelling of in situ soil water dynamics},\n\turl = {https://hess.copernicus.org/preprints/8/2019/2011/hessd-8-2019-2011.pdf},\n\tabstract = {Abstract. In situ observations of soil water state variables under natural boundary conditions are often used to estimate field-scale soil hydraulic properties. However, many contributions to the soil hydrological literature have demonstrated that the information content of such data is insufficient to reliably estimate all the soil hydraulic parameters. In this case study, we tested whether prior information about the soil hydraulic properties could help improve the identifiability of the van Genuchten-Mualem (VGM) parameters. Three different prior distributions with increasing complexity were formulated using the ROSETTA pedotransfer function (PTF) with input data that constitutes basic soil information and is readily available in most vadose zone studies. The inverse problem was posed in a formal Bayesian framework and solved using Markov chain Monte Carlo (MCMC) simulation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Synthetic and real-world soil water content data were used to illustrate our approach. The results of this study corroborate and explicate findings previously reported in the literature. Indeed, soil water content data alone contained insufficient information to reasonably constrain all VGM parameters. The identifiability of these soil hydraulic parameters was substantially improved when an informative prior distribution was used with detailed knowledge of the correlation structure among the respective VGM parameters. A biased prior did not distort the results, which inspires confidence in the robustness and effectiveness of the presented method. The Bayesian framework presented in this study can be applied to a wide range of vadose zone studies and provides a blueprint for the use of prior information in inverse modelling of soil hydraulic properties at various spatial scales.},\n\turldate = {2023-07-17},\n\tinstitution = {Vadose Zone Hydrology/Modelling approaches},\n\tauthor = {Scharnagl, B. and Vrugt, J. A. and Vereecken, H. and Herbst, M.},\n\tmonth = feb,\n\tyear = {2011},\n\tdoi = {10.5194/hessd-8-2019-2011},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Rosenbaum, U.; Huisman, J. A.; Vrba, J.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Correction of Temperature and Electrical Conductivity Effects on Dielectric Permittivity Measurements with ECH $_{\\textrm{2}}$ O Sensors.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 10(2): 582–593. May 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Correction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rosenbaum_correction_2011,\n\ttitle = {Correction of {Temperature} and {Electrical} {Conductivity} {Effects} on {Dielectric} {Permittivity} {Measurements} with {ECH} $_{\\textrm{2}}$ {O} {Sensors}},\n\tvolume = {10},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2010.0083},\n\tdoi = {10.2136/vzj2010.0083},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Rosenbaum, U. and Huisman, J. A. and Vrba, J. and Vereecken, H. and Bogena, H. R.},\n\tmonth = may,\n\tyear = {2011},\n\tpages = {582--593},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Rivera Villarreyes, C. A.; Baroni, G.; and Oswald, S. E.\n\n\n \n \n \n \n \n Integral quantification of seasonal soil moisture changes in farmland by cosmic-ray neutrons.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 15(12): 3843–3859. December 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Integral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{rivera_villarreyes_integral_2011,\n\ttitle = {Integral quantification of seasonal soil moisture changes in farmland by cosmic-ray neutrons},\n\tvolume = {15},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/15/3843/2011/},\n\tdoi = {10.5194/hess-15-3843-2011},\n\tabstract = {Abstract. Soil moisture at the plot or hill-slope scale is an important link between local vadose zone hydrology and catchment hydrology. However, so far only a few methods are on the way to close this gap between point measurements and remote sensing. One new measurement methodology that could determine integral soil moisture at this scale is the aboveground sensing of cosmic-ray neutrons, more precisely of ground albedo neutrons. The present study performed ground albedo neutron sensing (GANS) at an agricultural field in northern Germany. To test the method it was accompanied by other soil moisture measurements for a summer period with corn crops growing on the field and a later autumn-winter period without crops and a longer period of snow cover. Additionally, meteorological data and aboveground crop biomass were included in the evaluation. Hourly values of ground albedo neutron sensing showed a high statistical variability. Six-hourly values corresponded well with classical soil moisture measurements, after calibration based on one reference dry period and three wet periods of a few days each. Crop biomass seemed to influence the measurements only to minor degree, opposed to snow cover which has a more substantial impact on the measurements. The latter could be quantitatively related to a newly introduced field neutron ratio estimated from neutron counting rates of two energy ranges. Overall, our study outlines a procedure to apply the ground albedo neutron sensing method based on devices now commercially available, without the need for accompanying numerical simulations and suited for longer monitoring periods after initial calibration.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-07-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Rivera Villarreyes, C. A. and Baroni, G. and Oswald, S. E.},\n\tmonth = dec,\n\tyear = {2011},\n\tpages = {3843--3859},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Mester, A.; Kruk, J.; Zimmermann, E.; and Vereecken, H.\n\n\n \n \n \n \n \n Quantitative Two‐Layer Conductivity Inversion of Multi‐Configuration Electromagnetic Induction Measurements.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 10(4): 1319–1330. November 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{mester_quantitative_2011,\n\ttitle = {Quantitative {Two}‐{Layer} {Conductivity} {Inversion} of {Multi}‐{Configuration} {Electromagnetic} {Induction} {Measurements}},\n\tvolume = {10},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2011.0035},\n\tdoi = {10.2136/vzj2011.0035},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Mester, Achim and Kruk, Jan and Zimmermann, Egon and Vereecken, Harry},\n\tmonth = nov,\n\tyear = {2011},\n\tpages = {1319--1330},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Laux, P.; Vogl, S.; Qiu, W.; Knoche, H. R.; and Kunstmann, H.\n\n\n \n \n \n \n \n Copula-based statistical refinement of precipitation in RCM simulations over complex terrain.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 15(7): 2401–2419. July 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Copula-based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{laux_copula-based_2011,\n\ttitle = {Copula-based statistical refinement of precipitation in {RCM} simulations over complex terrain},\n\tvolume = {15},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/15/2401/2011/},\n\tdoi = {10.5194/hess-15-2401-2011},\n\tabstract = {Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-07-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Laux, P. and Vogl, S. and Qiu, W. and Knoche, H. R. and Kunstmann, H.},\n\tmonth = jul,\n\tyear = {2011},\n\tpages = {2401--2419},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n Jonard, F.; Weihermuller, L.; Jadoon, K. Z.; Schwank, M.; Vereecken, H.; and Lambot, S.\n\n\n \n \n \n \n \n Mapping Field-Scale Soil Moisture With L-Band Radiometer and Ground-Penetrating Radar Over Bare Soil.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 49(8): 2863–2875. August 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{jonard_mapping_2011,\n\ttitle = {Mapping {Field}-{Scale} {Soil} {Moisture} {With} {L}-{Band} {Radiometer} and {Ground}-{Penetrating} {Radar} {Over} {Bare} {Soil}},\n\tvolume = {49},\n\tissn = {0196-2892, 1558-0644},\n\turl = {http://ieeexplore.ieee.org/document/5751671/},\n\tdoi = {10.1109/TGRS.2011.2114890},\n\tnumber = {8},\n\turldate = {2023-07-17},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Jonard, François and Weihermuller, Lutz and Jadoon, Khan Zaib and Schwank, Mike and Vereecken, Harry and Lambot, Sébastien},\n\tmonth = aug,\n\tyear = {2011},\n\tpages = {2863--2875},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n Graf, A.; Prolingheuer, N.; Schickling, A.; Schmidt, M.; Schneider, K.; Schüttemeyer, D.; Herbst, M.; Huisman, J. A.; Weihermüller, L.; Scharnagl, B.; Steenpass, C.; Harms, R.; and Vereecken, H.\n\n\n \n \n \n \n \n Temporal Downscaling of Soil Carbon Dioxide Efflux Measurements Based on Time-Stable Spatial Patterns.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 10(1): 239–251. February 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Temporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_temporal_2011,\n\ttitle = {Temporal {Downscaling} of {Soil} {Carbon} {Dioxide} {Efflux} {Measurements} {Based} on {Time}-{Stable} {Spatial} {Patterns}},\n\tvolume = {10},\n\tissn = {15391663},\n\turl = {http://doi.wiley.com/10.2136/vzj2009.0152},\n\tdoi = {10.2136/vzj2009.0152},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-07-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Graf, Alexander and Prolingheuer, Nils and Schickling, Anke and Schmidt, Marius and Schneider, Karl and Schüttemeyer, Dirk and Herbst, Michael and Huisman, Johan A. and Weihermüller, Lutz and Scharnagl, Benedikt and Steenpass, Christian and Harms, Rainer and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2011},\n\tpages = {239--251},\n}\n\n\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n David, T.; Krebs, P.; Borchardt, D.; and Von Tümpling, W.\n\n\n \n \n \n \n \n Element patterns for particulate matter in stormwater effluent.\n \n \n \n \n\n\n \n\n\n\n Water Science and Technology, 63(12): 3013–3019. June 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Element$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{david_element_2011,\n\ttitle = {Element patterns for particulate matter in stormwater effluent},\n\tvolume = {63},\n\tissn = {0273-1223, 1996-9732},\n\turl = {https://iwaponline.com/wst/article/63/12/3013/14589/Element-patterns-for-particulate-matter-in},\n\tdoi = {10.2166/wst.2011.606},\n\tabstract = {Particulate matter in stormwater deteriorates the quality of receiving water and sediment. Characterization of stormwater particulate matter by means of its particle-associated element pattern provides an aid to determining its impact on receiving surface waters. During a 6 month measurement campaign, we determined particle-associated concentrations of major pollutants and rare earths for three combined water/stormwater outlets in the town of Staßfurt. We differentiated the particle-associated constituents on the basis of a hierarchical cluster analysis. Repeating the cluster analysis on random subsets, we gained information about the variability of the element patterns between and within the sites. In general, constituents associated with sewage and sewer sediment behave differently compared with constituents associated with runoff. The degree to which associations can be established for element patterns from site to site is limited by the variability encountered within sample sets taken from individual sites. The latter variability depends on the complexity of the catchment.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-07-17},\n\tjournal = {Water Science and Technology},\n\tauthor = {David, T. and Krebs, P. and Borchardt, D. and Von Tümpling, W.},\n\tmonth = jun,\n\tyear = {2011},\n\tpages = {3013--3019},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2010\n \n \n (2)\n \n \n

\n \n \n

\n \n\n \n \n Graf, A.; Schüttemeyer, D.; Geiß, H.; Knaps, A.; Möllmann-Coers, M.; Schween, J. H.; Kollet, S.; Neininger, B.; Herbst, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Boundedness of Turbulent Temperature Probability Distributions, and their Relation to the Vertical Profile in the Convective Boundary Layer.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 134(3): 459–486. March 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Boundedness$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{graf_boundedness_2010,\n\ttitle = {Boundedness of {Turbulent} {Temperature} {Probability} {Distributions}, and their {Relation} to the {Vertical} {Profile} in the {Convective} {Boundary} {Layer}},\n\tvolume = {134},\n\tissn = {0006-8314, 1573-1472},\n\turl = {http://link.springer.com/10.1007/s10546-009-9444-9},\n\tdoi = {10.1007/s10546-009-9444-9},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-06-19},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Graf, Alexander and Schüttemeyer, Dirk and Geiß, Heiner and Knaps, Axel and Möllmann-Coers, Michael and Schween, Jan H. and Kollet, Stefan and Neininger, Bruno and Herbst, Michael and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2010},\n\tpages = {459--486},\n}\n\n\n\n

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\n\n\n

\n \n\n \n \n Czymzik, M.; Dulski, P.; Plessen, B.; Von Grafenstein, U.; Naumann, R.; and Brauer, A.\n\n\n \n \n \n \n \n A 450 year record of spring-summer flood layers in annually laminated sediments from Lake Ammersee (southern Germany): A 450 YEAR FLOOD LAYER RECORD.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 46(11). November 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{czymzik_450_2010,\n\ttitle = {A 450 year record of spring-summer flood layers in annually laminated sediments from {Lake} {Ammersee} (southern {Germany}): {A} 450 {YEAR} {FLOOD} {LAYER} {RECORD}},\n\tvolume = {46},\n\tissn = {00431397},\n\tshorttitle = {A 450 year record of spring-summer flood layers in annually laminated sediments from {Lake} {Ammersee} (southern {Germany})},\n\turl = {http://doi.wiley.com/10.1029/2009WR008360},\n\tdoi = {10.1029/2009WR008360},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-06-19},\n\tjournal = {Water Resources Research},\n\tauthor = {Czymzik, Markus and Dulski, Peter and Plessen, Birgit and Von Grafenstein, Ulrich and Naumann, Rudolf and Brauer, Achim},\n\tmonth = nov,\n\tyear = {2010},\n}\n\n\n\n

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\n\n

\n \n 2006\n \n \n (1)\n \n \n

\n \n \n

\n \n\n \n \n Bogena, H.; Schulz, K.; and Vereecken, H.\n\n\n \n \n \n \n \n Towards a network of observatories in terrestrial environmental research.\n \n \n \n \n\n\n \n\n\n\n Advances in Geosciences, 9: 109–114. September 2006.\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{bogena_towards_2006,\n\ttitle = {Towards a network of observatories in terrestrial environmental research},\n\tvolume = {9},\n\tissn = {1680-7359},\n\turl = {https://adgeo.copernicus.org/articles/9/109/2006/},\n\tdoi = {10.5194/adgeo-9-109-2006},\n\tabstract = {Abstract. In order to address the challenges of global change, interdisciplinary research in terrestrial environmental science is of great importance. Several environmental research networks have already been established in order to monitor, analyse and predict the impact of global change on different compartments and/or matter cycles of the environment. Typically these environmental research networks have focused on specific research questions, and compartments, such as CarboEurope, FLUXNET and ILTER. In this paper, we propose the establishment of a network of terrestrial observatories, defined as a system consisting of the subsurface environment, the land surface including the biosphere, the lower atmosphere and the anthroposphere. Hydrological units will be used as the basic scaling units in a hierarchy of evolving scales and structures ranging from the local scale to the regional scale for multi-disciplinary process studies. Although terrestrial systems are extremely complex, the terrestrial component in most process-based climate and biosphere models is typically represented in a very conceptual and often rudimentary way. Remedying this deficiency is therefore one of the most important challenges in environmental and terrestrial research, and we suggest that terrestrial observatories could be an important step towards a new quality in environmental and terrestrial research.},\n\tlanguage = {en},\n\turldate = {2023-06-19},\n\tjournal = {Advances in Geosciences},\n\tauthor = {Bogena, H. and Schulz, K. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2006},\n\tpages = {109--114},\n}\n\n\n\n

\n\n\n\n\n\n

\n\n

\n \n undefined\n \n \n (1)\n \n \n

\n \n \n

\n \n\n \n \n Künzel, A.\n\n\n \n \n \n \n Furuno-Wetterradar: Niederschlagsmengen genauer lokalisieren.\n \n \n \n\n\n \n\n\n\n Bauernzeitung: Wochenblatt für die ostdeutsche Landwirtschaft. .\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{kunzel_furuno-wetterradar_nodate,\n\ttitle = {Furuno-{Wetterradar}: {Niederschlagsmengen} genauer lokalisieren.},\n\tjournal = {Bauernzeitung: Wochenblatt für die ostdeutsche Landwirtschaft},\n\tauthor = {Künzel, A.},\n}\n\n\n\n

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