\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

\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 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

\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\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

\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 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

\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 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

\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 \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

\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 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

\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 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 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

\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 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

\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 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 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

\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 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

\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\n\n\n

\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

\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 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 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

\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 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 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

\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 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 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

\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 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

\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 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

\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 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 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

\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\n\n\n\n\n

\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

\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\n\n\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

\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\n\n\n

\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

\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\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

\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 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

\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 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 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

\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 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

\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 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

\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 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 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

\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 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 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

\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\n\n\n

\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

\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\n\n\n

\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

\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\n\n\n\n\n

\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

\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

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

\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

\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\n\n\n

\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

\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\n\n\n

\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

\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 \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

\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\n\n\n

\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

\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\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

\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 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 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

\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\n\n\n\n\n

\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

\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\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

\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 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

\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\n\n\n

\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

\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

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\n\n\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

\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 \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

\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 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 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

\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 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

\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 \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

\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\n\n\n\n\n

\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

\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\n\n\n

\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

\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 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

\n\n\n

\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

\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\n\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

\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 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

\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\n\n\n

\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

\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 \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

\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 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 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

\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 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 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

\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\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

\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 \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

\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 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 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

\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

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\n\n\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

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@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 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 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

\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 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 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

\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\n\n\n

\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

\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 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

\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

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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

\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\n\n\n

\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

\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

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\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

\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\n\n\n\n\n

\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

\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\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

\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\n\n\n\n\n

\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

\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\n\n\n

\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

\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\n\n\n

\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

\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\n\n\n

\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

\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 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 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

\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

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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

\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\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

\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 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

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@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

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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

\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

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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

\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

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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

\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

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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

\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

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\n\n\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

\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

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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

\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

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\n\n\n\n

\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

\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

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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

\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

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\n\n\n\n

\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

\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

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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

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@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

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

\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

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@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

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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

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@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

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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

\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

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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

\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

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

\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

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@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

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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

\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

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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

\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

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\n\n\n\n

\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

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@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

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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

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@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

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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

\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\n\n\n\n\n

\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

\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

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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

\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

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\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

\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\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

\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 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 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

\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

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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

\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

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

\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

\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

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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

\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

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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

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@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

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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

\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

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

\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

\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 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 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

\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\n\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

\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\n\n\n\n\n

\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

\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 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 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

\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\n\n\n

\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

\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\n\n\n\n\n

\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

\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

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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

\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\n\n\n\n\n

\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

\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

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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

\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

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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

\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\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

\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

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\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

\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 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

\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 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 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

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@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

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

\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

\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

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\n\n\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

\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

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

\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

\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\n\n\n

\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

\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

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

\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

\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\n\n\n

\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

\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 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

\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\n\n\n

\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

\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\n\n\n

\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

\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\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

\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 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

\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\n\n\n

\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

\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\n\n\n

\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

\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\n\n\n\n\n

\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

\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 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

\n\n\n

\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

\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\n\n\n\n\n

\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

\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 \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

\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 \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

\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 \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

\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 \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

\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\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

\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 \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

\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 \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

\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 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

\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 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

\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

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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

\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

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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

\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

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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

\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

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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

\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

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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

\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

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\n\n\n\n

\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

\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

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

\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

\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

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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

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@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

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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

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@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

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\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

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@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

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