\n \n\n \n \n Albergel, C.; Zheng, Y.; Bonan, B.; Dutra, E.; Rodríguez-Fernández, N.; Munier, S.; Draper, C.; de Rosnay, P.; Muñoz-Sabater, J.; Balsamo, G.; Fairbairn, D.; Meurey, C.; and Calvet, J.\n\n\n \n \n \n \n \n Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(9): 4291–4316. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Data$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{albergel_data_2020,\n\ttitle = {Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/4291/2020/},\n\tdoi = {10.5194/hess-24-4291-2020},\n\tabstract = {Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of\nsurface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model\n(LSM). This study demonstrates that LDAS-Monde is able to detect, monitor\nand forecast the impact of extreme weather on land surface states. Firstly,\nLDAS-Monde is run globally at 0.25∘ spatial resolution over\n2010–2018. It is forced by the state-of-the-art ERA5 reanalysis\n(LDAS\\_ERA5) from the European Centre for Medium Range Weather\nForecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets\nof evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration\nand river discharge are employed for the validation. Secondly, the global\nanalysis is used to (i) detect regions exposed to extreme weather such as\ndroughts and heatwave events and (ii) address specific monitoring and\nforecasting requirements of LSVs for those regions. This is performed by\ncomputing anomalies of the land surface states. They display strong negative\nvalues for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS\\_HRES, 0.10∘\nspatial resolution) over 2017–2018. Monitoring capacities are studied by\ncomparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS\\_HRES forecasts of the LSVs with the\nLDAS\\_HRES assimilation run compared to the open-loop\nexperiment. The positive impact of initialization from an analysis in\nforecast mode is particularly visible for LAI that evolves at a slower pace\nthan SSM and is more sensitive to initial conditions than to atmospheric\nforcing, even at an 8 d lead time. This highlights the impact of initial\nconditions on LSV forecasts and the value of jointly analysing soil moisture\nand vegetation states.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Albergel, Clément and Zheng, Yongjun and Bonan, Bertrand and Dutra, Emanuel and Rodríguez-Fernández, Nemesio and Munier, Simon and Draper, Clara and de Rosnay, Patricia and Muñoz-Sabater, Joaquin and Balsamo, Gianpaolo and Fairbairn, David and Meurey, Catherine and Calvet, Jean-Christophe},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {4291--4316},\n}\n\n

\n

\n\n\n

\n Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.\n

\n\n\n

\n \n\n \n \n Bayat, A. T.; Schonbrodt-Stitt, S.; Nasta, P.; Ahmadian, N.; Conrad, C.; Bogena, H. R.; Vereecken, H.; Jakobi, J.; Baatz, R.; and Romano, N.\n\n\n \n \n \n \n \n Mapping near-surface soil moisture in a Mediterranean agroforestry ecosystem using Cosmic-Ray Neutron Probe and Sentinel-1 Data.\n \n \n \n \n\n\n \n\n\n\n In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pages 201–206, Trento, Italy, November 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@inproceedings{bayat_mapping_2020,\n\taddress = {Trento, Italy},\n\ttitle = {Mapping near-surface soil moisture in a {Mediterranean} agroforestry ecosystem using {Cosmic}-{Ray} {Neutron} {Probe} and {Sentinel}-1 {Data}},\n\tisbn = {9781728187839},\n\turl = {https://ieeexplore.ieee.org/document/9277557/},\n\tdoi = {10.1109/MetroAgriFor50201.2020.9277557},\n\turldate = {2022-11-02},\n\tbooktitle = {2020 {IEEE} {International} {Workshop} on {Metrology} for {Agriculture} and {Forestry} ({MetroAgriFor})},\n\tpublisher = {IEEE},\n\tauthor = {Bayat, Aida Taghavi and Schonbrodt-Stitt, Sarah and Nasta, Paolo and Ahmadian, Nima and Conrad, Christopher and Bogena, Heye R. and Vereecken, Harry and Jakobi, Jannis and Baatz, Roland and Romano, Nunzio},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {201--206},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Beckers, L.; Brack, W.; Dann, J. P.; Krauss, M.; Müller, E.; and Schulze, T.\n\n\n \n \n \n \n \n Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 727: 138388. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Unraveling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{beckers_unraveling_2020,\n\ttitle = {Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis},\n\tvolume = {727},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S004896972031901X},\n\tdoi = {10.1016/j.scitotenv.2020.138388},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Beckers, Liza-Marie and Brack, Werner and Dann, Janek Paul and Krauss, Martin and Müller, Erik and Schulze, Tobias},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {138388},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Beegum, S.; Vanderborght, J.; Šimůnek, J.; Herbst, M.; Sudheer, K. P.; and Nambi, I. M\n\n\n \n \n \n \n \n Investigating Atrazine Concentrations in the Zwischenscholle Aquifer Using MODFLOW with the HYDRUS-1D Package and MT3DMS.\n \n \n \n \n\n\n \n\n\n\n Water, 12(4): 1019. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{beegum_investigating_2020,\n\ttitle = {Investigating {Atrazine} {Concentrations} in the {Zwischenscholle} {Aquifer} {Using} {MODFLOW} with the {HYDRUS}-{1D} {Package} and {MT3DMS}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/4/1019},\n\tdoi = {10.3390/w12041019},\n\tabstract = {Simulation models that describe the flow and transport processes of pesticides in soil and groundwater are important tools to analyze how surface pesticide applications influence groundwater quality. The aim of this study is to investigate whether the slow decline and the stable spatial pattern of atrazine concentrations after its ban, which were observed in a long-term monitoring study of pesticide concentrations in the Zwischenscholle aquifer (Germany), could be explained by such model simulations. Model simulations were carried out using MODFLOW model coupled with the HYDRUS-1D package and MT3DMS. The results indicate that the spatial variability in the atrazine application rate and the volume of water entering and leaving the aquifer through lateral boundaries produced variations in the spatial distribution of atrazine in the aquifer. The simulated and observed water table levels and the average annual atrazine concentrations were found to be comparable. The long-term analysis of the simulated impact of atrazine applications in the study area shows that atrazine persisted in groundwater even 20 years after its ban at an average atrazine concentration of 0.035 µg/L. These results corroborate the findings of the previous monitoring studies.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Beegum, Sahila and Vanderborght, Jan and Šimůnek, Jiří and Herbst, Michael and Sudheer, K. P. and Nambi, Indumathi M},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1019},\n}\n\n

\n

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

\n \n\n \n \n Berauer, B. J.; Wilfahrt, P. A.; Reu, B.; Schuchardt, M. A.; Garcia-Franco, N.; Zistl-Schlingmann, M.; Dannenmann, M.; Kiese, R.; Kühnel, A.; and Jentsch, A.\n\n\n \n \n \n \n \n Predicting forage quality of species-rich pasture grasslands using vis-NIRS to reveal effects of management intensity and climate change.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 296: 106929. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Predicting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{berauer_predicting_2020,\n\ttitle = {Predicting forage quality of species-rich pasture grasslands using vis-{NIRS} to reveal effects of management intensity and climate change},\n\tvolume = {296},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880920301146},\n\tdoi = {10.1016/j.agee.2020.106929},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Berauer, Bernd J. and Wilfahrt, Peter A. and Reu, Björn and Schuchardt, Max A. and Garcia-Franco, Noelia and Zistl-Schlingmann, Marcus and Dannenmann, Michael and Kiese, Ralf and Kühnel, Anna and Jentsch, Anke},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {106929},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Bogena, H. R.; Herrmann, F.; Jakobi, J.; Brogi, C.; Ilias, A.; Huisman, J. A.; Panagopoulos, A.; and Pisinaras, V.\n\n\n \n \n \n \n \n Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 19. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{bogena_monitoring_2020,\n\ttitle = {Monitoring of {Snowpack} {Dynamics} {With} {Cosmic}-{Ray} {Neutron} {Probes}: {A} {Comparison} of {Four} {Conversion} {Methods}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Monitoring of {Snowpack} {Dynamics} {With} {Cosmic}-{Ray} {Neutron} {Probes}},\n\turl = {https://www.frontiersin.org/article/10.3389/frwa.2020.00019/full},\n\tdoi = {10.3389/frwa.2020.00019},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Bogena, Heye R. and Herrmann, Frank and Jakobi, Jannis and Brogi, Cosimo and Ilias, Andreas and Huisman, Johan Alexander and Panagopoulos, Andreas and Pisinaras, Vassilios},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {19},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Botter, M.; Li, L.; Hartmann, J.; Burlando, P.; and Fatichi, S.\n\n\n \n \n \n \n \n Depth of Solute Generation Is a Dominant Control on Concentration‐Discharge Relations.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(8). August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Depth$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{botter_depth_2020,\n\ttitle = {Depth of {Solute} {Generation} {Is} a {Dominant} {Control} on {Concentration}‐{Discharge} {Relations}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026695},\n\tdoi = {10.1029/2019WR026695},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Botter, M. and Li, L. and Hartmann, J. and Burlando, P. and Fatichi, S.},\n\tmonth = aug,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Brogi, C.; Huisman, J. A.; Herbst, M.; Weihermüller, L.; Klosterhalfen, A.; Montzka, C.; Reichenau, T. G.; and Vereecken, H.\n\n\n \n \n \n \n \n Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{brogi_simulation_2020,\n\ttitle = {Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20009},\n\tdoi = {10.1002/vzj2.20009},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Brogi, C. and Huisman, J. A. and Herbst, M. and Weihermüller, L. and Klosterhalfen, A. and Montzka, C. and Reichenau, T. G. and Vereecken, H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Carolus, J. F.; Bartosova, A.; Olsen, S. B.; Jomaa, S.; Veinbergs, A.; Zīlāns, A.; Pedersen, S. M.; Schwarz, G.; Rode, M.; and Tonderski, K.\n\n\n \n \n \n \n \n Nutrient mitigation under the impact of climate and land-use changes: A hydro-economic approach to participatory catchment management.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Management, 271: 110976. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Nutrient$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{carolus_nutrient_2020,\n\ttitle = {Nutrient mitigation under the impact of climate and land-use changes: {A} hydro-economic approach to participatory catchment management},\n\tvolume = {271},\n\tissn = {03014797},\n\tshorttitle = {Nutrient mitigation under the impact of climate and land-use changes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S030147972030904X},\n\tdoi = {10.1016/j.jenvman.2020.110976},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Journal of Environmental Management},\n\tauthor = {Carolus, Johannes Friedrich and Bartosova, Alena and Olsen, Søren Bøye and Jomaa, Seifeddine and Veinbergs, Artūrs and Zīlāns, Andis and Pedersen, Søren Marcus and Schwarz, Gerald and Rode, Michael and Tonderski, Karin},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {110976},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Chukalla, A. D.; Reidsma, P.; van Vliet, M. T.; Silva, J. V.; van Ittersum, M. K.; Jomaa, S.; Rode, M.; Merbach, I.; and van Oel, P. R.\n\n\n \n \n \n \n \n Balancing indicators for sustainable intensification of crop production at field and river basin levels.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 705: 135925. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Balancing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{chukalla_balancing_2020,\n\ttitle = {Balancing indicators for sustainable intensification of crop production at field and river basin levels},\n\tvolume = {705},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969719359200},\n\tdoi = {10.1016/j.scitotenv.2019.135925},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Chukalla, Abebe Demissie and Reidsma, Pytrik and van Vliet, Michelle T.H. and Silva, João Vasco and van Ittersum, Martin K. and Jomaa, Seifeddine and Rode, Michael and Merbach, Ines and van Oel, Pieter R.},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {135925},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Dadi, T.; Rinke, K.; and Friese, K.\n\n\n \n \n \n \n \n Trajectories of Sediment-Water Interactions in Reservoirs as a Result of Temperature and Oxygen Conditions.\n \n \n \n \n\n\n \n\n\n\n Water, 12(4): 1065. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Trajectories$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dadi_trajectories_2020,\n\ttitle = {Trajectories of {Sediment}-{Water} {Interactions} in {Reservoirs} as a {Result} of {Temperature} and {Oxygen} {Conditions}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/4/1065},\n\tdoi = {10.3390/w12041065},\n\tabstract = {Temperate lakes/reservoirs are warming; this can influence the benthic release of nutrients. They undergo seasonal changes resulting in an array of temperature and oxygen conditions; oxic-low, oxic-high, anoxic-low, and anoxic-high temperature. We sought to understand the interaction of temperature and oxygen conditions on benthic solutes exchange through a two-factorial sediment core incubation experiment by varying either temperature or oxygen conditions of sediment cores from an oligotrophic and eutrophic reservoir. Temperature and oxygen conditions are both important for nutrient release; however, they influence solutes differently; differences in the fluxes of the treatments were explained more by temperature for P, DOC and N, while for Fe, Mn and SO42−, differences were explained more by oxygen conditions. The combination of strongly reducing conditions (due to anoxia) and high temperature (20 °C) led to a significant increase in nutrients concentrations in the overlying water. Under these conditions, SRP flux was 0.04 and 0.5 mmol m−2 d−1; ammonium was 0.9 and 5.6 mmol m−2 d−1 for the oligotrophic and eutrophic reservoir, respectively. We observed a synergistic interaction between temperature and oxygen conditions which resulted in release of solutes from sediments. An increase in nutrients release under increasing temperatures is more likely and so are algal blooms.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Dadi, Tallent and Rinke, Karsten and Friese, Kurt},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1065},\n}\n\n

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

\n \n\n \n \n Dhillon, M. S.; Dahms, T.; Kuebert-Flock, C.; Borg, E.; Conrad, C.; and Ullmann, T.\n\n\n \n \n \n \n \n Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(11): 1819. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dhillon_modelling_2020,\n\ttitle = {Modelling {Crop} {Biomass} from {Synthetic} {Remote} {Sensing} {Time} {Series}: {Example} for the {DEMMIN} {Test} {Site}, {Germany}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Modelling {Crop} {Biomass} from {Synthetic} {Remote} {Sensing} {Time} {Series}},\n\turl = {https://www.mdpi.com/2072-4292/12/11/1819},\n\tdoi = {10.3390/rs12111819},\n\tabstract = {This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 ({\\textgreater}0.82), low RMSE ({\\textless}600 g/m2) and significant p-value ({\\textless}0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 ({\\textless}0.68) and high RMSE ({\\textgreater}600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {1819},\n}\n\n

\n

\n\n\n

\n This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (\\textgreater0.82), low RMSE (\\textless600 g/m2) and significant p-value (\\textless0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (\\textless0.68) and high RMSE (\\textgreater600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).\n

\n\n\n

\n \n\n \n \n Dong, F.; Mi, C.; Hupfer, M.; Lindenschmidt, K.; Peng, W.; Liu, X.; and Rinke, K.\n\n\n \n \n \n \n \n Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model.\n \n \n \n \n\n\n \n\n\n\n Hydrological Processes, 34(5): 1131–1143. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dong_assessing_2020,\n\ttitle = {Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model},\n\tvolume = {34},\n\tissn = {0885-6087, 1099-1085},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.13653},\n\tdoi = {10.1002/hyp.13653},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Hydrological Processes},\n\tauthor = {Dong, Fei and Mi, Chenxi and Hupfer, Michael and Lindenschmidt, Karl‐Erich and Peng, Wenqi and Liu, Xiaobo and Rinke, Karsten},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {1131--1143},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Döpper, V.; Gränzig, T.; Kleinschmit, B.; and Förster, M.\n\n\n \n \n \n \n \n Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(10): 1552. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Challenges$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dopper_challenges_2020,\n\ttitle = {Challenges in {UAS}-{Based} {TIR} {Imagery} {Processing}: {Image} {Alignment} and {Uncertainty} {Quantification}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Challenges in {UAS}-{Based} {TIR} {Imagery} {Processing}},\n\turl = {https://www.mdpi.com/2072-4292/12/10/1552},\n\tdoi = {10.3390/rs12101552},\n\tabstract = {Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Döpper, Veronika and Gränzig, Tobias and Kleinschmit, Birgit and Förster, Michael},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {1552},\n}\n\n

\n

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

\n \n\n \n \n Fersch, B.; Francke, T.; Heistermann, M.; Schrön, M.; Döpper, V.; Jakobi, J.; Baroni, G.; Blume, T.; Bogena, H.; Budach, C.; Gränzig, T.; Förster, M.; Güntner, A.; Hendricks Franssen, H.; Kasner, M.; Köhli, M.; Kleinschmit, B.; Kunstmann, H.; Patil, A.; Rasche, D.; Scheiffele, L.; Schmidt, U.; Szulc-Seyfried, S.; Weimar, J.; Zacharias, S.; Zreda, M.; Heber, B.; Kiese, R.; Mares, V.; Mollenhauer, H.; Völksch, I.; and Oswald, S.\n\n\n \n \n \n \n \n A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 12(3): 2289–2309. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{fersch_dense_2020,\n\ttitle = {A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-{Alpine} headwater catchment in {Germany}},\n\tvolume = {12},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/12/2289/2020/},\n\tdoi = {10.5194/essd-12-2289-2020},\n\tabstract = {Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Earth System Science Data},\n\tauthor = {Fersch, Benjamin and Francke, Till and Heistermann, Maik and Schrön, Martin and Döpper, Veronika and Jakobi, Jannis and Baroni, Gabriele and Blume, Theresa and Bogena, Heye and Budach, Christian and Gränzig, Tobias and Förster, Michael and Güntner, Andreas and Hendricks Franssen, Harrie-Jan and Kasner, Mandy and Köhli, Markus and Kleinschmit, Birgit and Kunstmann, Harald and Patil, Amol and Rasche, Daniel and Scheiffele, Lena and Schmidt, Ulrich and Szulc-Seyfried, Sandra and Weimar, Jannis and Zacharias, Steffen and Zreda, Marek and Heber, Bernd and Kiese, Ralf and Mares, Vladimir and Mollenhauer, Hannes and Völksch, Ingo and Oswald, Sascha},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2289--2309},\n}\n\n

\n

\n\n\n

\n Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).\n

\n\n\n

\n \n\n \n \n Fink, P.; Norf, H.; Anlanger, C.; Brauns, M.; Kamjunke, N.; Risse‐Buhl, U.; Schmitt‐Jansen, M.; Weitere, M.; and Borchardt, D.\n\n\n \n \n \n \n \n Streamside mobile mesocosms (MOBICOS): A new modular research infrastructure for hydro‐ecological process studies across catchment‐scale gradients.\n \n \n \n \n\n\n \n\n\n\n International Review of Hydrobiology, 105(3-4): 63–73. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Streamside$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{fink_streamside_2020,\n\ttitle = {Streamside mobile mesocosms ({MOBICOS}): {A} new modular research infrastructure for hydro‐ecological process studies across catchment‐scale gradients},\n\tvolume = {105},\n\tissn = {1434-2944, 1522-2632},\n\tshorttitle = {Streamside mobile mesocosms ({MOBICOS})},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/iroh.201902009},\n\tdoi = {10.1002/iroh.201902009},\n\tlanguage = {en},\n\tnumber = {3-4},\n\turldate = {2022-11-02},\n\tjournal = {International Review of Hydrobiology},\n\tauthor = {Fink, Patrick and Norf, Helge and Anlanger, Christine and Brauns, Mario and Kamjunke, Norbert and Risse‐Buhl, Ute and Schmitt‐Jansen, Mechthild and Weitere, Markus and Borchardt, Dietrich},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {63--73},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Fisher, J. B.; Lee, B.; Purdy, A. J.; Halverson, G. H.; Dohlen, M. B.; Cawse‐Nicholson, K.; Wang, A.; Anderson, R. G.; Aragon, B.; Arain, M. A.; Baldocchi, D. D.; Baker, J. M.; Barral, H.; Bernacchi, C. J.; Bernhofer, C.; Biraud, S. C.; Bohrer, G.; Brunsell, N.; Cappelaere, B.; Castro‐Contreras, S.; Chun, J.; Conrad, B. J.; Cremonese, E.; Demarty, J.; Desai, A. R.; De Ligne, A.; Foltýnová, L.; Goulden, M. L.; Griffis, T. J.; Grünwald, T.; Johnson, M. S.; Kang, M.; Kelbe, D.; Kowalska, N.; Lim, J.; Maïnassara, I.; McCabe, M. F.; Missik, J. E.; Mohanty, B. P.; Moore, C. E.; Morillas, L.; Morrison, R.; Munger, J. W.; Posse, G.; Richardson, A. D.; Russell, E. S.; Ryu, Y.; Sanchez‐Azofeifa, A.; Schmidt, M.; Schwartz, E.; Sharp, I.; Šigut, L.; Tang, Y.; Hulley, G.; Anderson, M.; Hain, C.; French, A.; Wood, E.; and Hook, S.\n\n\n \n \n \n \n \n ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(4). April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"ECOSTRESS:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fisher_ecostress_2020,\n\ttitle = {{ECOSTRESS}: {NASA}'s {Next} {Generation} {Mission} to {Measure} {Evapotranspiration} {From} the {International} {Space} {Station}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {{ECOSTRESS}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026058},\n\tdoi = {10.1029/2019WR026058},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Fisher, Joshua B. and Lee, Brian and Purdy, Adam J. and Halverson, Gregory H. and Dohlen, Matthew B. and Cawse‐Nicholson, Kerry and Wang, Audrey and Anderson, Ray G. and Aragon, Bruno and Arain, M. Altaf and Baldocchi, Dennis D. and Baker, John M. and Barral, Hélène and Bernacchi, Carl J. and Bernhofer, Christian and Biraud, Sébastien C. and Bohrer, Gil and Brunsell, Nathaniel and Cappelaere, Bernard and Castro‐Contreras, Saulo and Chun, Junghwa and Conrad, Bryan J. and Cremonese, Edoardo and Demarty, Jérôme and Desai, Ankur R. and De Ligne, Anne and Foltýnová, Lenka and Goulden, Michael L. and Griffis, Timothy J. and Grünwald, Thomas and Johnson, Mark S. and Kang, Minseok and Kelbe, Dave and Kowalska, Natalia and Lim, Jong‐Hwan and Maïnassara, Ibrahim and McCabe, Matthew F. and Missik, Justine E.C. and Mohanty, Binayak P. and Moore, Caitlin E. and Morillas, Laura and Morrison, Ross and Munger, J. William and Posse, Gabriela and Richardson, Andrew D. and Russell, Eric S. and Ryu, Youngryel and Sanchez‐Azofeifa, Arturo and Schmidt, Marius and Schwartz, Efrat and Sharp, Iain and Šigut, Ladislav and Tang, Yao and Hulley, Glynn and Anderson, Martha and Hain, Christopher and French, Andrew and Wood, Eric and Hook, Simon},\n\tmonth = apr,\n\tyear = {2020},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Fürst, J.; Nachtnebel, H. P.; Gasch, J.; Nolz, R.; Stockinger, M. P.; Stumpp, C.; and Schulz, K.\n\n\n \n \n \n \n \n Rosalia: an experimental research site to study hydrological processes in a forest catchment.\n \n \n \n \n\n\n \n\n\n\n Technical Report Hydrology and Soil Science – Hydrology, November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Rosalia:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{furst_rosalia_2020,\n\ttype = {preprint},\n\ttitle = {Rosalia: an experimental research site to study hydrological processes in a forest catchment},\n\tshorttitle = {Rosalia},\n\turl = {https://essd.copernicus.org/preprints/essd-2020-254/essd-2020-254.pdf},\n\tabstract = {Abstract. Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19th and early 20th century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called Rosalia with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the Rosalia instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum. This article describes the characteristics of the site, the recently installed monitoring network and its instrumentation, as well as the datasets. The network includes 4 discharge gauging stations, 7 rain-gauges, together with observation of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community on a publicly accessible repository. The datasets are available at https://doi.org/10.5281/zenodo.3997141 (Fürst et al., 2020).},\n\turldate = {2022-11-02},\n\tinstitution = {Hydrology and Soil Science – Hydrology},\n\tauthor = {Fürst, Josef and Nachtnebel, Hans Peter and Gasch, Josef and Nolz, Reinhard and Stockinger, Michael Paul and Stumpp, Christine and Schulz, Karsten},\n\tmonth = nov,\n\tyear = {2020},\n\tdoi = {10.5194/essd-2020-254},\n}\n\n

\n

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

\n \n\n \n \n González-Sanchis, M.; García-Soro, J. M.; Molina, A. J.; Lidón, A. L.; Bautista, I.; Rouzic, E.; Bogena, H. R.; Hendricks Franssen, H.; and del Campo, A. D.\n\n\n \n \n \n \n \n Comparison of Soil Water Estimates From Cosmic-Ray Neutron and Capacity Sensors in a Semi-arid Pine Forest: Which Is Able to Better Assess the Role of Environmental Conditions and Thinning?.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 552508. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{gonzalez-sanchis_comparison_2020,\n\ttitle = {Comparison of {Soil} {Water} {Estimates} {From} {Cosmic}-{Ray} {Neutron} and {Capacity} {Sensors} in a {Semi}-arid {Pine} {Forest}: {Which} {Is} {Able} to {Better} {Assess} the {Role} of {Environmental} {Conditions} and {Thinning}?},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Comparison of {Soil} {Water} {Estimates} {From} {Cosmic}-{Ray} {Neutron} and {Capacity} {Sensors} in a {Semi}-arid {Pine} {Forest}},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.552508/full},\n\tdoi = {10.3389/frwa.2020.552508},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {González-Sanchis, María and García-Soro, Juan M. and Molina, Antonio J. and Lidón, Antonio L. and Bautista, Inmaculada and Rouzic, Elie and Bogena, Heye R. and Hendricks Franssen, Harrie-JanHarrie and del Campo, Antonio D.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {552508},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Graf, A.; Klosterhalfen, A.; Arriga, N.; Bernhofer, C.; Bogena, H.; Bornet, F.; Brüggemann, N.; Brümmer, C.; Buchmann, N.; Chi, J.; Chipeaux, C.; Cremonese, E.; Cuntz, M.; Dušek, J.; El-Madany, T. S.; Fares, S.; Fischer, M.; Foltýnová, L.; Gharun, M.; Ghiasi, S.; Gielen, B.; Gottschalk, P.; Grünwald, T.; Heinemann, G.; Heinesch, B.; Heliasz, M.; Holst, J.; Hörtnagl, L.; Ibrom, A.; Ingwersen, J.; Jurasinski, G.; Klatt, J.; Knohl, A.; Koebsch, F.; Konopka, J.; Korkiakoski, M.; Kowalska, N.; Kremer, P.; Kruijt, B.; Lafont, S.; Léonard, J.; De Ligne, A.; Longdoz, B.; Loustau, D.; Magliulo, V.; Mammarella, I.; Manca, G.; Mauder, M.; Migliavacca, M.; Mölder, M.; Neirynck, J.; Ney, P.; Nilsson, M.; Paul-Limoges, E.; Peichl, M.; Pitacco, A.; Poyda, A.; Rebmann, C.; Roland, M.; Sachs, T.; Schmidt, M.; Schrader, F.; Siebicke, L.; Šigut, L.; Tuittila, E.; Varlagin, A.; Vendrame, N.; Vincke, C.; Völksch, I.; Weber, S.; Wille, C.; Wizemann, H.; Zeeman, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Altered energy partitioning across terrestrial ecosystems in the European drought year 2018.\n \n \n \n \n\n\n \n\n\n\n Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1810): 20190524. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Altered$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{graf_altered_2020,\n\ttitle = {Altered energy partitioning across terrestrial ecosystems in the {European} drought year 2018},\n\tvolume = {375},\n\tissn = {0962-8436, 1471-2970},\n\turl = {https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0524},\n\tdoi = {10.1098/rstb.2019.0524},\n\tabstract = {Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO \n              2 \n              exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9\\% more solar radiation and released 32\\% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO \n              2 \n              uptake by 17.8\\%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. \n             \n            This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.},\n\tlanguage = {en},\n\tnumber = {1810},\n\turldate = {2022-11-02},\n\tjournal = {Philosophical Transactions of the Royal Society B: Biological Sciences},\n\tauthor = {Graf, Alexander and Klosterhalfen, Anne and Arriga, Nicola and Bernhofer, Christian and Bogena, Heye and Bornet, Frédéric and Brüggemann, Nicolas and Brümmer, Christian and Buchmann, Nina and Chi, Jinshu and Chipeaux, Christophe and Cremonese, Edoardo and Cuntz, Matthias and Dušek, Jiří and El-Madany, Tarek S. and Fares, Silvano and Fischer, Milan and Foltýnová, Lenka and Gharun, Mana and Ghiasi, Shiva and Gielen, Bert and Gottschalk, Pia and Grünwald, Thomas and Heinemann, Günther and Heinesch, Bernard and Heliasz, Michal and Holst, Jutta and Hörtnagl, Lukas and Ibrom, Andreas and Ingwersen, Joachim and Jurasinski, Gerald and Klatt, Janina and Knohl, Alexander and Koebsch, Franziska and Konopka, Jan and Korkiakoski, Mika and Kowalska, Natalia and Kremer, Pascal and Kruijt, Bart and Lafont, Sebastien and Léonard, Joël and De Ligne, Anne and Longdoz, Bernard and Loustau, Denis and Magliulo, Vincenzo and Mammarella, Ivan and Manca, Giovanni and Mauder, Matthias and Migliavacca, Mirco and Mölder, Meelis and Neirynck, Johan and Ney, Patrizia and Nilsson, Mats and Paul-Limoges, Eugénie and Peichl, Matthias and Pitacco, Andrea and Poyda, Arne and Rebmann, Corinna and Roland, Marilyn and Sachs, Torsten and Schmidt, Marius and Schrader, Frederik and Siebicke, Lukas and Šigut, Ladislav and Tuittila, Eeva-Stiina and Varlagin, Andrej and Vendrame, Nadia and Vincke, Caroline and Völksch, Ingo and Weber, Stephan and Wille, Christian and Wizemann, Hans-Dieter and Zeeman, Matthias and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {20190524},\n}\n\n

\n

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

\n \n\n \n \n Groh, J.; Diamantopoulos, E.; Duan, X.; Ewert, F.; Herbst, M.; Holbak, M.; Kamali, B.; Kersebaum, K.; Kuhnert, M.; Lischeid, G.; Nendel, C.; Priesack, E.; Steidl, J.; Sommer, M.; Pütz, T.; Vereecken, H.; Wallor, E.; Weber, T. K.; Wegehenkel, M.; Weihermüller, L.; and Gerke, H. H.\n\n\n \n \n \n \n \n Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Crop$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{groh_crop_2020,\n\ttitle = {Crop growth and soil water fluxes at erosion‐affected arable sites: {Using} weighing lysimeter data for model intercomparison},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\tshorttitle = {Crop growth and soil water fluxes at erosion‐affected arable sites},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20058},\n\tdoi = {10.1002/vzj2.20058},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Groh, Jannis and Diamantopoulos, Efstathios and Duan, Xiaohong and Ewert, Frank and Herbst, Michael and Holbak, Maja and Kamali, Bahareh and Kersebaum, Kurt‐Christian and Kuhnert, Matthias and Lischeid, Gunnar and Nendel, Claas and Priesack, Eckart and Steidl, Jörg and Sommer, Michael and Pütz, Thomas and Vereecken, Harry and Wallor, Evelyn and Weber, Tobias K.D. and Wegehenkel, Martin and Weihermüller, Lutz and Gerke, Horst H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Groh, J.; Vanderborght, J.; Pütz, T.; Vogel, H.; Gründling, R.; Rupp, H.; Rahmati, M.; Sommer, M.; Vereecken, H.; and Gerke, H. H.\n\n\n \n \n \n \n \n Responses of soil water storage and crop water use efficiency to changing climatic conditions: a lysimeter-based space-for-time approach.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(3): 1211–1225. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Responses$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{groh_responses_2020,\n\ttitle = {Responses of soil water storage and crop water use efficiency to changing climatic conditions: a lysimeter-based space-for-time approach},\n\tvolume = {24},\n\tissn = {1607-7938},\n\tshorttitle = {Responses of soil water storage and crop water use efficiency to changing climatic conditions},\n\turl = {https://hess.copernicus.org/articles/24/1211/2020/},\n\tdoi = {10.5194/hess-24-1211-2020},\n\tabstract = {Abstract. Future crop production will be affected by climatic\nchanges. In several regions, the projected changes in total rainfall and\nseasonal rainfall patterns will lead to lower soil water storage (SWS), which\nin turn affects crop water uptake, crop yield, water use efficiency (WUE), grain\nquality and groundwater recharge. Effects of climate change on those\nvariables depend on the soil properties and were often estimated based on\nmodel simulations. The objective of this study was to investigate the\nresponse of key variables in four different soils and for two different\nclimates in Germany with a different aridity index (AI): 1.09 for the wetter\n(range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done\nby using high-precision weighable lysimeters. According to a\n“space-for-time” (SFT) concept, intact soil monoliths that were moved to sites\nwith contrasting climatic conditions have been monitored from April 2011\nuntil December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier\nclimate, whereas crop yield was significantly higher, without affecting grain\nquality. Especially “non-productive” water losses (evapotranspiration out of\nthe main growing period) were lower, which led to a more efficient crop water\nuse in the drier climate. A characteristic decrease of the SWS for soils\nwith a finer texture was observed after a longer drought period under a\ndrier climate. The reduced SWS after the drought remained until the end of\nthe observation period which demonstrates carry-over of drought from one\ngrowing season to another and the overall long-term effects of single\ndrought events. In the relatively drier climate, water flow at the soil\nprofile bottom showed a small net upward flux over the entire monitoring\nperiod as compared to downward fluxes (groundwater recharge) or drainage in\nthe relatively wetter climate and larger recharge rates in the coarser- as\ncompared to finer-textured soils. The large variability of recharge from\nyear to year and the long-lasting effects of drought periods on the SWS imply\nthat long-term monitoring of soil water balance components is necessary to\nobtain representative estimates. Results confirmed a more efficient crop\nwater use under less-plant-available soil moisture conditions. Long-term effects of\nchanging climatic conditions on the SWS and ecosystem productivity should be\nconsidered when trying to develop adaptation strategies in the agricultural\nsector.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Groh, Jannis and Vanderborght, Jan and Pütz, Thomas and Vogel, Hans-Jörg and Gründling, Ralf and Rupp, Holger and Rahmati, Mehdi and Sommer, Michael and Vereecken, Harry and Gerke, Horst H.},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {1211--1225},\n}\n\n

\n

\n\n\n

\n Abstract. Future crop production will be affected by climatic changes. In several regions, the projected changes in total rainfall and seasonal rainfall patterns will lead to lower soil water storage (SWS), which in turn affects crop water uptake, crop yield, water use efficiency (WUE), grain quality and groundwater recharge. Effects of climate change on those variables depend on the soil properties and were often estimated based on model simulations. The objective of this study was to investigate the response of key variables in four different soils and for two different climates in Germany with a different aridity index (AI): 1.09 for the wetter (range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done by using high-precision weighable lysimeters. According to a “space-for-time” (SFT) concept, intact soil monoliths that were moved to sites with contrasting climatic conditions have been monitored from April 2011 until December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier climate, whereas crop yield was significantly higher, without affecting grain quality. Especially “non-productive” water losses (evapotranspiration out of the main growing period) were lower, which led to a more efficient crop water use in the drier climate. A characteristic decrease of the SWS for soils with a finer texture was observed after a longer drought period under a drier climate. The reduced SWS after the drought remained until the end of the observation period which demonstrates carry-over of drought from one growing season to another and the overall long-term effects of single drought events. In the relatively drier climate, water flow at the soil profile bottom showed a small net upward flux over the entire monitoring period as compared to downward fluxes (groundwater recharge) or drainage in the relatively wetter climate and larger recharge rates in the coarser- as compared to finer-textured soils. The large variability of recharge from year to year and the long-lasting effects of drought periods on the SWS imply that long-term monitoring of soil water balance components is necessary to obtain representative estimates. Results confirmed a more efficient crop water use under less-plant-available soil moisture conditions. Long-term effects of changing climatic conditions on the SWS and ecosystem productivity should be considered when trying to develop adaptation strategies in the agricultural sector.\n

\n\n\n

\n \n\n \n \n Grosse, M.; Hierold, W.; Ahlborn, M. C.; Piepho, H.; and Helming, K.\n\n\n \n \n \n \n \n Long-term field experiments in Germany: classification and spatial representation.\n \n \n \n \n\n\n \n\n\n\n SOIL, 6(2): 579–596. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Long-term$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{grosse_long-term_2020,\n\ttitle = {Long-term field experiments in {Germany}: classification and spatial representation},\n\tvolume = {6},\n\tissn = {2199-398X},\n\tshorttitle = {Long-term field experiments in {Germany}},\n\turl = {https://soil.copernicus.org/articles/6/579/2020/},\n\tdoi = {10.5194/soil-6-579-2020},\n\tabstract = {Abstract. The collective analysis of long-term field experiments (LTFEs), here defined as agricultural\nexperiments with a minimum duration of 20 years and research in the context of sustainable\nsoil use and yield, can be used for detecting changes in soil properties and yield such as those induced\nby climate change. However, information about existing LTFEs is scattered, and the research data\nare not easily accessible. In this study, meta-information on LTFEs in Germany is compiled and\ntheir spatial representation is analyzed. The study is conducted within the framework of the\nBonaRes project, which, inter alia, has established a central access point for LTFE information\nand research data. A total of 205 LTFEs which fit to the definition above are identified. Of these,\n140 LTFEs are ongoing. The land use in 168 LTFEs is arable field crops, in 34 trials grassland, in\n2 trials vegetables and in 1 trial pomiculture. Field crop LTFEs are categorized into\nfertilization (n=158), tillage (n=38) and crop rotation (n=32; multiple nominations\npossible) experiments, while all grassland experiments (n=34) deal with fertilization. The\nspatial representation is analyzed according to the climatic water balance of the growing season\n(1 May to 31 October) (CWBg), the Müncheberg Soil Quality Rating (MSQR) and clay content. The\nresults show that, in general, the LTFEs well represent the area shares of both the CWBg and the\nMSQR classes. Eighty-nine percent of the arable land and 65 \\% of the grassland in Germany are covered by\nthe three driest CWBg classes, hosting 89 \\% and 71 \\% of the arable and grassland LTFEs,\nrespectively. LTFEs cover all six MSQR classes but with a bias towards the high and very high\nsoil quality classes. LTFEs on arable land are present in all clay content classes according to\nthe European Soil Data Centre (ESDAC) but with a bias towards the clay content class 4. Grassland LTFEs show a bias towards\nthe clay content classes 5, 6 and 7, while well representing the other clay content classes,\nexcept clay content class 3, where grassland LTFEs are completely missing. The results confirm\nthe very high potential of LTFE data for spatially differentiated analyses and modeling.\nHowever, reuse is restricted by the difficult access to LTFE research data. The common database\nis an important step in overcoming this restriction.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-02},\n\tjournal = {SOIL},\n\tauthor = {Grosse, Meike and Hierold, Wilfried and Ahlborn, Marlen C. and Piepho, Hans-Peter and Helming, Katharina},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {579--596},\n}\n\n

\n

\n\n\n

\n Abstract. The collective analysis of long-term field experiments (LTFEs), here defined as agricultural experiments with a minimum duration of 20 years and research in the context of sustainable soil use and yield, can be used for detecting changes in soil properties and yield such as those induced by climate change. However, information about existing LTFEs is scattered, and the research data are not easily accessible. In this study, meta-information on LTFEs in Germany is compiled and their spatial representation is analyzed. The study is conducted within the framework of the BonaRes project, which, inter alia, has established a central access point for LTFE information and research data. A total of 205 LTFEs which fit to the definition above are identified. Of these, 140 LTFEs are ongoing. The land use in 168 LTFEs is arable field crops, in 34 trials grassland, in 2 trials vegetables and in 1 trial pomiculture. Field crop LTFEs are categorized into fertilization (n=158), tillage (n=38) and crop rotation (n=32; multiple nominations possible) experiments, while all grassland experiments (n=34) deal with fertilization. The spatial representation is analyzed according to the climatic water balance of the growing season (1 May to 31 October) (CWBg), the Müncheberg Soil Quality Rating (MSQR) and clay content. The results show that, in general, the LTFEs well represent the area shares of both the CWBg and the MSQR classes. Eighty-nine percent of the arable land and 65 % of the grassland in Germany are covered by the three driest CWBg classes, hosting 89 % and 71 % of the arable and grassland LTFEs, respectively. LTFEs cover all six MSQR classes but with a bias towards the high and very high soil quality classes. LTFEs on arable land are present in all clay content classes according to the European Soil Data Centre (ESDAC) but with a bias towards the clay content class 4. Grassland LTFEs show a bias towards the clay content classes 5, 6 and 7, while well representing the other clay content classes, except clay content class 3, where grassland LTFEs are completely missing. The results confirm the very high potential of LTFE data for spatially differentiated analyses and modeling. However, reuse is restricted by the difficult access to LTFE research data. The common database is an important step in overcoming this restriction.\n

\n\n\n

\n \n\n \n \n Hagenau, J.; Kaufmann, V.; and Borg, H.\n\n\n \n \n \n \n \n Monitoring water content changes in a soil profile with TDR-probes at just three depths - How well does it work?.\n \n \n \n \n\n\n \n\n\n\n RBRH, 25: e8. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hagenau_monitoring_2020,\n\ttitle = {Monitoring water content changes in a soil profile with {TDR}-probes at just three depths - {How} well does it work?},\n\tvolume = {25},\n\tissn = {2318-0331, 1414-381X},\n\turl = {http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312020000100213&tlng=en},\n\tdoi = {10.1590/2318-0331.252020190113},\n\tabstract = {ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies. \n          ,  \n            RESUMO As sondas TDR são usadas para monitorar alterações no conteúdo de água no perfil do solo (DW). Freqüentemente, as sondas são colocadas em três profundidades. Isso levanta a questão de como essa configuração pode mostrar os verdadeiros DW´s. Para respondê-lo, usamos um lisímetro de pesagem de alta precisão de 2 m, no qual as sondas TDR são instaladas horizontalmente a 20, 60 e 120 cm de profundidade (uma por profundidade). Os dados DW coletados pela pesagem do lisímetro foram comparados com os valores reais dos dados DW determinados com as sondas TDR. Obtivemos os seguintes resultados: Há um atraso na resposta das sondas TDR à precipitação, evaporação, transpiração ou drenagem, porque uma frente de umedecimento ou secagem deve primeiro alcançá-las. Além disso, os dados do TDR são medições pontuais que são extrapoladas para um volume de solo maior. Isso freqüentemente leva a erros. Por esses motivos, as sondas TDR em apenas três profundidades não podem fornecer dados confiáveis sobre alterações de curto prazo (por exemplo, diárias) no conteúdo de água do solo devido aos processos apresentados aqui. Por períodos mais longos (por exemplo, uma semana), os dados são melhores, mas ainda não são precisos o suficiente para estudos científicos confiáveis.},\n\turldate = {2022-11-02},\n\tjournal = {RBRH},\n\tauthor = {Hagenau, Jens and Kaufmann, Vander and Borg, Heinz},\n\tyear = {2020},\n\tpages = {e8},\n}\n\n

\n

\n\n\n

\n ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies. , RESUMO As sondas TDR são usadas para monitorar alterações no conteúdo de água no perfil do solo (DW). Freqüentemente, as sondas são colocadas em três profundidades. Isso levanta a questão de como essa configuração pode mostrar os verdadeiros DW´s. Para respondê-lo, usamos um lisímetro de pesagem de alta precisão de 2 m, no qual as sondas TDR são instaladas horizontalmente a 20, 60 e 120 cm de profundidade (uma por profundidade). Os dados DW coletados pela pesagem do lisímetro foram comparados com os valores reais dos dados DW determinados com as sondas TDR. Obtivemos os seguintes resultados: Há um atraso na resposta das sondas TDR à precipitação, evaporação, transpiração ou drenagem, porque uma frente de umedecimento ou secagem deve primeiro alcançá-las. Além disso, os dados do TDR são medições pontuais que são extrapoladas para um volume de solo maior. Isso freqüentemente leva a erros. Por esses motivos, as sondas TDR em apenas três profundidades não podem fornecer dados confiáveis sobre alterações de curto prazo (por exemplo, diárias) no conteúdo de água do solo devido aos processos apresentados aqui. Por períodos mais longos (por exemplo, uma semana), os dados são melhores, mas ainda não são precisos o suficiente para estudos científicos confiáveis.\n

\n\n\n

\n \n\n \n \n Harris, R. M. B.; Loeffler, F.; Rumm, A.; Fischer, C.; Horchler, P.; Scholz, M.; Foeckler, F.; and Henle, K.\n\n\n \n \n \n \n \n Biological responses to extreme weather events are detectable but difficult to formally attribute to anthropogenic climate change.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 10(1): 14067. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Biological$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{harris_biological_2020,\n\ttitle = {Biological responses to extreme weather events are detectable but difficult to formally attribute to anthropogenic climate change},\n\tvolume = {10},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-020-70901-6},\n\tdoi = {10.1038/s41598-020-70901-6},\n\tabstract = {Abstract \n            As the frequency and intensity of extreme events such as droughts, heatwaves and floods have increased over recent decades, more extreme biological responses are being reported, and there is widespread interest in attributing such responses to anthropogenic climate change. However, the formal detection and attribution of biological responses to climate change is associated with many challenges. We illustrate these challenges with data from the Elbe River floodplain, Germany. Using community turnover and stability indices, we show that responses in plant, carabid and mollusc communities are detectable following extreme events. Community composition and species dominance changed following the extreme flood and summer heatwave of 2002/2003 (all taxa); the 2006 flood and heatwave (molluscs); and after the recurring floods and heatwave of 2010 and the 2013 flood (plants). Nevertheless, our ability to attribute these responses to anthropogenic climate change is limited by high natural variability in climate and biological data; lack of long-term data and replication, and the effects of multiple events. Without better understanding of the mechanisms behind change and the interactions, feedbacks and potentially lagged responses, multiple-driver attribution is unlikely. We discuss whether formal detection and/or attribution is necessary and suggest ways in which understanding of biological responses to extreme events could progress.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Reports},\n\tauthor = {Harris, R. M. B. and Loeffler, F. and Rumm, A. and Fischer, C. and Horchler, P. and Scholz, M. and Foeckler, F. and Henle, K.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {14067},\n}\n\n

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

\n \n\n \n \n Hartmann, E.; Schulz, J.; Seibert, R.; Schmidt, M.; Zhang, M.; Luterbacher, J.; and Tölle, M. H.\n\n\n \n \n \n \n \n Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM.\n \n \n \n \n\n\n \n\n\n\n Atmosphere, 11(12): 1364. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Impact$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hartmann_impact_2020,\n\ttitle = {Impact of {Environmental} {Conditions} on {Grass} {Phenology} in the {Regional} {Climate} {Model} {COSMO}-{CLM}},\n\tvolume = {11},\n\tissn = {2073-4433},\n\turl = {https://www.mdpi.com/2073-4433/11/12/1364},\n\tdoi = {10.3390/atmos11121364},\n\tabstract = {Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional types.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-02},\n\tjournal = {Atmosphere},\n\tauthor = {Hartmann, Eva and Schulz, Jan-Peter and Seibert, Ruben and Schmidt, Marius and Zhang, Mingyue and Luterbacher, Jürg and Tölle, Merja H.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {1364},\n}\n\n

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

\n \n\n \n \n Heck, K.; Coltman, E.; Schneider, J.; and Helmig, R.\n\n\n \n \n \n \n \n Influence of Radiation on Evaporation Rates: A Numerical Analysis.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(10). October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Influence$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{heck_influence_2020,\n\ttitle = {Influence of {Radiation} on {Evaporation} {Rates}: {A} {Numerical} {Analysis}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Influence of {Radiation} on {Evaporation} {Rates}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR027332},\n\tdoi = {10.1029/2020WR027332},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Heck, K. and Coltman, E. and Schneider, J. and Helmig, R.},\n\tmonth = oct,\n\tyear = {2020},\n}\n\n

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\n \n\n \n \n Heidbüchel, I.; Yang, J.; Musolff, A.; Troch, P.; Ferré, T.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n On the shape of forward transit time distributions in low-order catchments.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(6): 2895–2920. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{heidbuchel_shape_2020,\n\ttitle = {On the shape of forward transit time distributions in low-order catchments},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/2895/2020/},\n\tdoi = {10.5194/hess-24-2895-2020},\n\tabstract = {Abstract. Transit time distributions (TTDs) integrate information\non timing, amount, storage, mixing and flow paths of water and thus\ncharacterize hydrologic and hydrochemical catchment response unlike any\nother descriptor. Here, we simulate the shape of TTDs in an idealized\nlow-order catchment and investigate whether it changes systematically with\ncertain catchment and climate properties. To this end, we used a physically\nbased, spatially explicit 3-D model, injected tracer with a precipitation\nevent and recorded the resulting forward TTDs at the outlet of a small\n(∼6000 m2) catchment for different scenarios. We found\nthat the TTDs can be subdivided into four parts: (1) early part – controlled\nby soil hydraulic conductivity and antecedent soil moisture content, (2) middle part – a transition zone with no clear pattern or control, (3) later\npart – influenced by soil hydraulic conductivity and subsequent\nprecipitation amount, and (4) very late tail of the breakthrough curve –\ngoverned by bedrock hydraulic conductivity. The modeled TTD shapes can be\npredicted using a dimensionless number: higher initial peaks are observed if\nthe inflow of water to a catchment is not equal to its capacity to discharge\nwater via subsurface flow paths, and lower initial peaks are connected to\nincreasing available storage. In most cases the modeled TTDs were humped\nwith nonzero initial values and varying weights of the tails. Therefore,\nnone of the best-fit theoretical probability functions could describe the\nentire TTD shape exactly. Still, we found that generally gamma and\nlog-normal distributions work better for scenarios of low and high soil\nhydraulic conductivity, respectively.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Heidbüchel, Ingo and Yang, Jie and Musolff, Andreas and Troch, Peter and Ferré, Ty and Fleckenstein, Jan H.},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {2895--2920},\n}\n\n

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

\n \n\n \n \n Herzsprung, P.; Wentzky, V.; Kamjunke, N.; von Tümpling, W.; Wilske, C.; Friese, K.; Boehrer, B.; Reemtsma, T.; Rinke, K.; and Lechtenfeld, O. J.\n\n\n \n \n \n \n \n Improved Understanding of Dissolved Organic Matter Processing in Freshwater Using Complementary Experimental and Machine Learning Approaches.\n \n \n \n \n\n\n \n\n\n\n Environmental Science & Technology, 54(21): 13556–13565. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{herzsprung_improved_2020,\n\ttitle = {Improved {Understanding} of {Dissolved} {Organic} {Matter} {Processing} in {Freshwater} {Using} {Complementary} {Experimental} and {Machine} {Learning} {Approaches}},\n\tvolume = {54},\n\tissn = {0013-936X, 1520-5851},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.est.0c02383},\n\tdoi = {10.1021/acs.est.0c02383},\n\tlanguage = {en},\n\tnumber = {21},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Science \\& Technology},\n\tauthor = {Herzsprung, Peter and Wentzky, Valerie and Kamjunke, Norbert and von Tümpling, Wolf and Wilske, Christin and Friese, Kurt and Boehrer, Bertram and Reemtsma, Thorsten and Rinke, Karsten and Lechtenfeld, Oliver J.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {13556--13565},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Huang, J.; Desai, A. R.; Zhu, J.; Hartemink, A. E.; Stoy, P. C.; Loheide, S. P.; Bogena, H. R.; Zhang, Y.; Zhang, Z.; and Arriaga, F.\n\n\n \n \n \n \n \n Retrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 578367. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Retrieving$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{huang_retrieving_2020,\n\ttitle = {Retrieving {Heterogeneous} {Surface} {Soil} {Moisture} at 100 m {Across} the {Globe} via {Fusion} of {Remote} {Sensing} and {Land} {Surface} {Parameters}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.578367/full},\n\tdoi = {10.3389/frwa.2020.578367},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Huang, Jingyi and Desai, Ankur R. and Zhu, Jun and Hartemink, Alfred E. and Stoy, Paul C. and Loheide, Steven P. and Bogena, Heye R. and Zhang, Yakun and Zhang, Zhou and Arriaga, Francisco},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {578367},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Huang, Y.; Hendricks Franssen, H.; Herbst, M.; Hirschi, M.; Michel, D.; Seneviratne, S. I.; Teuling, A. J.; Vogt, R.; Detlef, S.; Pütz, T.; and Vereecken, H.\n\n\n \n \n \n \n \n Evaluation of different methods for gap filling of long‐term actual evapotranspiration time series measured by lysimeters.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{huang_evaluation_2020,\n\ttitle = {Evaluation of different methods for gap filling of long‐term actual evapotranspiration time series measured by lysimeters},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20020},\n\tdoi = {10.1002/vzj2.20020},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Huang, Yafei and Hendricks Franssen, Harrie‐Jan and Herbst, Michael and Hirschi, Martin and Michel, Dominik and Seneviratne, Sonia I. and Teuling, Adriaan J. and Vogt, Roland and Detlef, Schumacher and Pütz, Thomas and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Schrön, M.; Fiedler, J.; Brogi, C.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Corrigendum: Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 604482. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Corrigendum:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jakobi_corrigendum_2020,\n\ttitle = {Corrigendum: {Error} {Estimation} for {Soil} {Moisture} {Measurements} {With} {Cosmic} {Ray} {Neutron} {Sensing} and {Implications} for {Rover} {Surveys}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\tshorttitle = {Corrigendum},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.604482/full},\n\tdoi = {10.3389/frwa.2020.604482},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Jakobi, Jannis and Huisman, Johan A. and Schrön, Martin and Fiedler, Justus and Brogi, Cosimo and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {604482},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Jakobi, J.; Huisman, J. A.; Schrön, M.; Fiedler, J.; Brogi, C.; Vereecken, H.; and Bogena, H. R.\n\n\n \n \n \n \n \n Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 10. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Error$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jakobi_error_2020,\n\ttitle = {Error {Estimation} for {Soil} {Moisture} {Measurements} {With} {Cosmic} {Ray} {Neutron} {Sensing} and {Implications} for {Rover} {Surveys}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2020.00010/full},\n\tdoi = {10.3389/frwa.2020.00010},\n\tabstract = {Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3 \n              rd \n              order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Jakobi, Jannis and Huisman, Johan A. and Schrön, Martin and Fiedler, Justus and Brogi, Cosimo and Vereecken, Harry and Bogena, Heye R.},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {10},\n}\n\n

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

\n \n\n \n \n Jonard, F.; De Cannière, S.; Brüggemann, N.; Gentine, P.; Short Gianotti, D.; Lobet, G.; Miralles, D.; Montzka, C.; Pagán, B.; Rascher, U.; and Vereecken, H.\n\n\n \n \n \n \n \n Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 291: 108088. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Value$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jonard_value_2020,\n\ttitle = {Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: {Current} status and challenges},\n\tvolume = {291},\n\tissn = {01681923},\n\tshorttitle = {Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192320301908},\n\tdoi = {10.1016/j.agrformet.2020.108088},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Jonard, F. and De Cannière, S. and Brüggemann, N. and Gentine, P. and Short Gianotti, D.J. and Lobet, G. and Miralles, D.G. and Montzka, C. and Pagán, B.R. and Rascher, U. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {108088},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Kaden, U. S.; Fuchs, E.; Hecht, C.; Hein, T.; Rupp, H.; Scholz, M.; and Schulz-Zunkel, C.\n\n\n \n \n \n \n \n Advancement of the Acetylene Inhibition Technique Using Time Series Analysis on Air-Dried Floodplain Soils to Quantify Denitrification Potential.\n \n \n \n \n\n\n \n\n\n\n Geosciences, 10(11): 431. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Advancement$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kaden_advancement_2020,\n\ttitle = {Advancement of the {Acetylene} {Inhibition} {Technique} {Using} {Time} {Series} {Analysis} on {Air}-{Dried} {Floodplain} {Soils} to {Quantify} {Denitrification} {Potential}},\n\tvolume = {10},\n\tissn = {2076-3263},\n\turl = {https://www.mdpi.com/2076-3263/10/11/431},\n\tdoi = {10.3390/geosciences10110431},\n\tabstract = {Denitrification in floodplain soils is one key process that determines the buffering capacity of riparian zones in terms of diffuse nitrate pollution. One widely used approach to measure the denitrification potential is the acetylene inhibition technique that requires fresh soil samples. We conducted experiments with air-dried soils using a time series analysis to determine the optimal rewetting period. Thus, air-dried soil samples from six different floodplain areas in Germany were rewetted for 1 to 13days to 100\\% water-filled pore space. We analyzed nitrogen accumulated as N2O in the top of anaerobic flasks with and without acetylene by gas chromatography after four hours of incubation. We observed an overall optimal rewetting of at least seven days for complete denitrification. We also saw the strong influence of pH and field capacity on the denitrification product ratio; in soils with pH {\\textless} 7, we hardly assumed complete denitrification, whereas the treatments with pH {\\textgreater} 7 achieved stable values after seven days of rewetting. This advanced method provides the opportunity to carry out campaigns with large soil sample sizes on the landscape scale, as samples can be stored dry until measurements are taken.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Geosciences},\n\tauthor = {Kaden, Ute Susanne and Fuchs, Elmar and Hecht, Christian and Hein, Thomas and Rupp, Holger and Scholz, Mathias and Schulz-Zunkel, Christiane},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {431},\n}\n\n

\n

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

\n \n\n \n \n Kaiser, K.; Hrubý, P.; Tolksdorf, J. F.; Alper, G.; Herbig, C.; Kočár, P.; Petr, L.; Schulz, L.; and Heinrich, I.\n\n\n \n \n \n \n \n Cut and covered: Subfossil trees in buried soils reflect medieval forest composition and exploitation of the central European uplands.\n \n \n \n \n\n\n \n\n\n\n Geoarchaeology, 35(1): 42–62. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Cut$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kaiser_cut_2020,\n\ttitle = {Cut and covered: {Subfossil} trees in buried soils reflect medieval forest composition and exploitation of the central {European} uplands},\n\tvolume = {35},\n\tissn = {0883-6353, 1520-6548},\n\tshorttitle = {Cut and covered},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/gea.21756},\n\tdoi = {10.1002/gea.21756},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Geoarchaeology},\n\tauthor = {Kaiser, Knut and Hrubý, Petr and Tolksdorf, Johann Friedrich and Alper, Götz and Herbig, Christoph and Kočár, Petr and Petr, Libor and Schulz, Lars and Heinrich, Ingo},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {42--62},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kaiser, K.; Schneider, T.; Küster, M.; Dietze, E.; Fülling, A.; Heinrich, S.; Kappler, C.; Nelle, O.; Schult, M.; Theuerkauf, M.; Vogel, S.; de Boer, A. M.; Börner, A.; Preusser, F.; Schwabe, M.; Ulrich, J.; Wirner, M.; and Bens, O.\n\n\n \n \n \n \n \n Palaeosols and their cover sediments of a glacial landscape in northern central Europe: Spatial distribution, pedostratigraphy and evidence on landscape evolution.\n \n \n \n \n\n\n \n\n\n\n CATENA, 193: 104647. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Palaeosols$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kaiser_palaeosols_2020,\n\ttitle = {Palaeosols and their cover sediments of a glacial landscape in northern central {Europe}: {Spatial} distribution, pedostratigraphy and evidence on landscape evolution},\n\tvolume = {193},\n\tissn = {03418162},\n\tshorttitle = {Palaeosols and their cover sediments of a glacial landscape in northern central {Europe}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0341816220301971},\n\tdoi = {10.1016/j.catena.2020.104647},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {CATENA},\n\tauthor = {Kaiser, Knut and Schneider, Thomas and Küster, Mathias and Dietze, Elisabeth and Fülling, Alexander and Heinrich, Susann and Kappler, Christoph and Nelle, Oliver and Schult, Manuela and Theuerkauf, Martin and Vogel, Sebastian and de Boer, Anna Maartje and Börner, Andreas and Preusser, Frank and Schwabe, Matthias and Ulrich, Jens and Wirner, Michael and Bens, Oliver},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {104647},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Katata, G.; Grote, R.; Mauder, M.; Zeeman, M. J.; and Ota, M.\n\n\n \n \n \n \n \n Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(4): 1071–1085. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Wintertime$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{katata_wintertime_2020,\n\ttitle = {Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis},\n\tvolume = {17},\n\tissn = {1726-4189},\n\tshorttitle = {Wintertime grassland dynamics may influence belowground biomass under climate change},\n\turl = {https://bg.copernicus.org/articles/17/1071/2020/},\n\tdoi = {10.5194/bg-17-1071-2020},\n\tabstract = {Abstract. Rising temperatures and changes in snow cover, as can be expected under a warmer global climate, may have large impacts on mountain grassland productivity limited by cold and long winters. Here, we combined two existing models, the multi-layer atmosphere-SOiL-VEGetation model (SOLVEG) and the BASic GRAssland model (BASGRA), which accounts for snow, freeze–thaw events, grass growth, and soil carbon balance. The model was applied to simulate the responses of managed grasslands to anomalously warm winter conditions. The grass growth module considered key ecological processes under a cold environment, such as leaf formation, elongation and death, tillering, carbon allocation, and cold acclimation, in terms of photosynthetic activity. Input parameters were derived for two pre-Alpine grassland sites in Germany, for which the model was run using 3 years of data that included a winter with an exceptionally small amount of snow. The model reproduced the temporal variability of observed daily mean heat fluxes, soil temperatures, and snow depth throughout the study period. High physiological activity levels during the extremely warm winter led to a simulated CO2 uptake of 100 gC m−2, which was mainly allocated into the belowground biomass and only to a minor extent used for additional plant growth during early spring. If these temporary dynamics are representative of long-term changes, this process, which is so far largely unaccounted for in scenario analysis using global terrestrial biosphere models, may lead to carbon accumulation in the soil and/or carbon loss from the soil as a response to global warming.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Katata, Genki and Grote, Rüdiger and Mauder, Matthias and Zeeman, Matthias J. and Ota, Masakazu},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {1071--1085},\n}\n\n

\n

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

\n \n\n \n \n Kaufmann, M. S.; Hebel, C.; Weihermüller, L.; Baumecker, M.; Döring, T.; Schweitzer, K.; Hobley, E.; Bauke, S. L.; Amelung, W.; Vereecken, H.; and Kruk, J.\n\n\n \n \n \n \n \n Effect of fertilizers and irrigation on multi‐configuration electromagnetic induction measurements.\n \n \n \n \n\n\n \n\n\n\n Soil Use and Management, 36(1): 104–116. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kaufmann_effect_2020,\n\ttitle = {Effect of fertilizers and irrigation on multi‐configuration electromagnetic induction measurements},\n\tvolume = {36},\n\tissn = {0266-0032, 1475-2743},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/sum.12530},\n\tdoi = {10.1111/sum.12530},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Soil Use and Management},\n\tauthor = {Kaufmann, Manuela S. and Hebel, Christian and Weihermüller, Lutz and Baumecker, Michael and Döring, Thomas and Schweitzer, Kathlin and Hobley, Eleanor and Bauke, Sara L. and Amelung, Wulf and Vereecken, Harry and Kruk, Jan},\n\teditor = {Triantafilis, John},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {104--116},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kaufmann, M. S.; Klotzsche, A.; Vereecken, H.; and der Kruk, J.\n\n\n \n \n \n \n \n Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kaufmann_simultaneous_2020,\n\ttitle = {Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20017},\n\tdoi = {10.1002/vzj2.20017},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Kaufmann, Manuela Sarah and Klotzsche, Anja and Vereecken, Harry and der Kruk, Jan},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Keller, P. S.; Catalán, N.; von Schiller, D.; Grossart, H.; Koschorreck, M.; Obrador, B.; Frassl, M. A.; Karakaya, N.; Barros, N.; Howitt, J. A.; Mendoza-Lera, C.; Pastor, A.; Flaim, G.; Aben, R.; Riis, T.; Arce, M. I.; Onandia, G.; Paranaíba, J. R.; Linkhorst, A.; del Campo, R.; Amado, A. M.; Cauvy-Fraunié, S.; Brothers, S.; Condon, J.; Mendonça, R. F.; Reverey, F.; Rõõm, E.; Datry, T.; Roland, F.; Laas, A.; Obertegger, U.; Park, J.; Wang, H.; Kosten, S.; Gómez, R.; Feijoó, C.; Elosegi, A.; Sánchez-Montoya, M. M.; Finlayson, C. M.; Melita, M.; Oliveira Junior, E. S.; Muniz, C. C.; Gómez-Gener, L.; Leigh, C.; Zhang, Q.; and Marcé, R.\n\n\n \n \n \n \n \n Global CO2 emissions from dry inland waters share common drivers across ecosystems.\n \n \n \n \n\n\n \n\n\n\n Nature Communications, 11(1): 2126. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Global$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{keller_global_2020,\n\ttitle = {Global {CO2} emissions from dry inland waters share common drivers across ecosystems},\n\tvolume = {11},\n\tissn = {2041-1723},\n\turl = {http://www.nature.com/articles/s41467-020-15929-y},\n\tdoi = {10.1038/s41467-020-15929-y},\n\tabstract = {Abstract \n             \n              Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO \n              2 \n              ) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO \n              2 \n              emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO \n              2 \n              emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6\\% ({\\textasciitilde}0.12 Pg C y \n              −1 \n              ). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Nature Communications},\n\tauthor = {Keller, P. S. and Catalán, N. and von Schiller, D. and Grossart, H.-P. and Koschorreck, M. and Obrador, B. and Frassl, M. A. and Karakaya, N. and Barros, N. and Howitt, J. A. and Mendoza-Lera, C. and Pastor, A. and Flaim, G. and Aben, R. and Riis, T. and Arce, M. I. and Onandia, G. and Paranaíba, J. R. and Linkhorst, A. and del Campo, R. and Amado, A. M. and Cauvy-Fraunié, S. and Brothers, S. and Condon, J. and Mendonça, R. F. and Reverey, F. and Rõõm, E.-I. and Datry, T. and Roland, F. and Laas, A. and Obertegger, U. and Park, J.-H. and Wang, H. and Kosten, S. and Gómez, R. and Feijoó, C. and Elosegi, A. and Sánchez-Montoya, M. M. and Finlayson, C. M. and Melita, M. and Oliveira Junior, E. S. and Muniz, C. C. and Gómez-Gener, L. and Leigh, C. and Zhang, Q. and Marcé, R.},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {2126},\n}\n\n

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

\n \n\n \n \n Koebsch, F.; Gottschalk, P.; Beyer, F.; Wille, C.; Jurasinski, G.; and Sachs, T.\n\n\n \n \n \n \n \n The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens.\n \n \n \n \n\n\n \n\n\n\n Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1810): 20190685. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{koebsch_impact_2020,\n\ttitle = {The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens},\n\tvolume = {375},\n\tissn = {0962-8436, 1471-2970},\n\turl = {https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0685},\n\tdoi = {10.1098/rstb.2019.0685},\n\tabstract = {Peatland rewetting aims at stopping the emissions of carbon dioxide (CO \n              2 \n              ) and establishing net carbon sinks. However, in times of global warming, restoration projects must increasingly deal with extreme events such as drought periods. Here, we evaluate the effect of the European summer drought 2018 on vegetation development and the exchange of methane (CH \n              4 \n              ) and CO \n              2 \n              in two rewetted minerotrophic fens (Hütelmoor—Hte and Zarnekow—Zrk) including potential carry-over effects in the post-drought year. Drought was a major stress factor for the established vegetation but also promoted the rapid spread of new vegetation, which will likely gain a lasting foothold in Zrk. Accordingly, drought increased not only respiratory CO \n              2 \n              losses but also photosynthetic CO \n              2 \n              uptake. Altogether, the drought reduced the net CO \n              2 \n              sink in Hte, while it stopped the persistent net CO \n              2 \n              emissions of Zrk. In addition, the drought reduced CH \n              4 \n              emissions in both fens, though this became most apparent in the post-drought year and suggests a lasting shift towards non-methanogenic organic matter decomposition. Occasional droughts can be beneficial for the restoration of the peatland carbon sink function if the newly grown vegetation increases CO \n              2 \n              sequestration in the long term. Nonetheless, care must be taken to prevent extensive peat decay. \n             \n            This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.},\n\tlanguage = {en},\n\tnumber = {1810},\n\turldate = {2022-11-02},\n\tjournal = {Philosophical Transactions of the Royal Society B: Biological Sciences},\n\tauthor = {Koebsch, Franziska and Gottschalk, Pia and Beyer, Florian and Wille, Christian and Jurasinski, Gerald and Sachs, Torsten},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {20190685},\n}\n\n

\n

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

\n \n\n \n \n Kreuzburg, M.; Rezanezhad, F.; Milojevic, T.; Voss, M.; Gosch, L.; Liebner, S.; Van Cappellen, P.; and Rehder, G.\n\n\n \n \n \n \n \n Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge: a column experiment study.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 65(5): 1116–1135. May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Carbon$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kreuzburg_carbon_2020,\n\ttitle = {Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge: a column experiment study},\n\tvolume = {65},\n\tissn = {0024-3590, 1939-5590},\n\tshorttitle = {Carbon release and transformation from coastal peat deposits controlled by submarine groundwater discharge},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.11438},\n\tdoi = {10.1002/lno.11438},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Kreuzburg, Matthias and Rezanezhad, Fereidoun and Milojevic, Tatjana and Voss, Maren and Gosch, Lennart and Liebner, Susanne and Van Cappellen, Philippe and Rehder, Gregor},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {1116--1135},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kunz, M.; Lavric, J. V.; Gasche, R.; Gerbig, C.; Grant, R. H.; Koch, F.; Schumacher, M.; Wolf, B.; and Zeeman, M.\n\n\n \n \n \n \n \n Surface flux estimates derived from UAS-based mole fraction measurements by means of a nocturnal boundary layer budget approach.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 13(4): 1671–1692. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Surface$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kunz_surface_2020,\n\ttitle = {Surface flux estimates derived from {UAS}-based mole fraction measurements by means of a nocturnal boundary layer budget approach},\n\tvolume = {13},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/13/1671/2020/},\n\tdoi = {10.5194/amt-13-1671-2020},\n\tabstract = {Abstract. The carbon exchange between ecosystems and the atmosphere has a large\ninfluence on the Earth system and specifically on the climate. This\nexchange is therefore being studied intensively, often using the eddy\ncovariance (EC) technique. EC measurements provide reliable results\nunder turbulent atmospheric conditions, but under calm and stable\nconditions – as they often occur at night – these measurements\nare known to misrepresent exchange fluxes. Nocturnal boundary layer\n(NBL) budgets can provide independent flux estimates under stable\nconditions, but their application so far has been limited by rather\nhigh cost and practical difficulties. Unmanned aircraft systems (UASs)\nequipped with trace gas analysers have the potential to make this\nmethod more accessible. We present the methodology and results of\na proof-of-concept study carried out during the ScaleX 2016 campaign.\nSuccessive vertical profiles of carbon dioxide dry-air mole fraction\nin the NBL were taken with a compact analyser carried by a UAS. We\nestimate an average carbon dioxide flux of 12 µmolm-2s-1,\nwhich is plausible for nocturnal respiration in this region in summer.\nTransport modelling suggests that the NBL budgets represent an area\non the order of 100 km2.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Kunz, Martin and Lavric, Jost V. and Gasche, Rainer and Gerbig, Christoph and Grant, Richard H. and Koch, Frank-Thomas and Schumacher, Marcus and Wolf, Benjamin and Zeeman, Matthias},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1671--1692},\n}\n\n

\n

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

\n \n\n \n \n Lampert, A.; Pätzold, F.; Asmussen, M. O.; Lobitz, L.; Krüger, T.; Rausch, T.; Sachs, T.; Wille, C.; Sotomayor Zakharov, D.; Gaus, D.; Bansmer, S.; and Damm, E.\n\n\n \n \n \n \n \n Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system.\n \n \n \n \n\n\n \n\n\n\n Atmospheric Measurement Techniques, 13(4): 1937–1952. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Studying$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{lampert_studying_2020,\n\ttitle = {Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system},\n\tvolume = {13},\n\tissn = {1867-8548},\n\turl = {https://amt.copernicus.org/articles/13/1937/2020/},\n\tdoi = {10.5194/amt-13-1937-2020},\n\tabstract = {Abstract. The combination of two well-established methods, of quadrocopter-borne air sampling and methane isotopic analyses, is applied to determine the\nsource process of methane at different altitudes and to study mixing processes. A proof-of-concept study was performed to demonstrate the\ncapabilities of quadrocopter air sampling for subsequently analysing the methane isotopic composition δ13C in the laboratory. The\nadvantage of the system compared to classical sampling on the ground and at tall towers is the flexibility concerning sampling location, and in\nparticular the flexible choice of sampling altitude, allowing the study of the layering and mixing of air masses with potentially different spatial origin\nof air masses and methane. Boundary layer mixing processes and the methane isotopic composition were studied at Polder Zarnekow in\nMecklenburg–West Pomerania in the north-east of Germany, which has become a strong source of biogenically produced methane after rewetting the\ndrained and degraded peatland. Methane fluxes are measured continuously at the site. They show high emissions from May to September, and a strong\ndiurnal variability. For two case studies on 23 May and 5 September 2018, vertical profiles of temperature and humidity were recorded up to an\naltitude of 650 and 1000 m, respectively, during the morning transition. Air samples were taken at different altitudes and analysed in the\nlaboratory for methane isotopic composition. The values showed a different isotopic composition in the vertical distribution during stable\nconditions in the morning (delta values of −51.5 ‰ below the temperature inversion at an altitude of 150 m on 23 May 2018 and at an\naltitude of 50 m on 5 September 2018, delta values of −50.1 ‰ above). After the onset of turbulent mixing, the isotopic composition was\nthe same throughout the vertical column with a mean delta value of −49.9 ± 0.45 ‰. The systematically more negative delta values\noccurred only as long as the nocturnal temperature inversion was present. During the September study, water samples were analysed as well for\nmethane concentration and isotopic composition in order to provide a link between surface and atmosphere. The water samples reveal high variability\non horizontal scales of a few tens of metres for this particular case. The airborne sampling system and consecutive analysis chain were shown to provide\nreliable and reproducible results for two samples obtained simultaneously. The method presents a powerful tool for distinguishing the source process\nof methane at different altitudes. The isotopic composition showed clearly depleted delta values directly above a biological methane source when\nvertical mixing was hampered by a temperature inversion, and different delta values above, where the air masses originate from a different footprint\narea. The vertical distribution of methane isotopic composition can serve as tracer for mixing processes of methane within the atmospheric boundary\nlayer.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Atmospheric Measurement Techniques},\n\tauthor = {Lampert, Astrid and Pätzold, Falk and Asmussen, Magnus O. and Lobitz, Lennart and Krüger, Thomas and Rausch, Thomas and Sachs, Torsten and Wille, Christian and Sotomayor Zakharov, Denis and Gaus, Dominik and Bansmer, Stephan and Damm, Ellen},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {1937--1952},\n}\n\n

\n

\n\n\n

\n Abstract. The combination of two well-established methods, of quadrocopter-borne air sampling and methane isotopic analyses, is applied to determine the source process of methane at different altitudes and to study mixing processes. A proof-of-concept study was performed to demonstrate the capabilities of quadrocopter air sampling for subsequently analysing the methane isotopic composition δ13C in the laboratory. The advantage of the system compared to classical sampling on the ground and at tall towers is the flexibility concerning sampling location, and in particular the flexible choice of sampling altitude, allowing the study of the layering and mixing of air masses with potentially different spatial origin of air masses and methane. Boundary layer mixing processes and the methane isotopic composition were studied at Polder Zarnekow in Mecklenburg–West Pomerania in the north-east of Germany, which has become a strong source of biogenically produced methane after rewetting the drained and degraded peatland. Methane fluxes are measured continuously at the site. They show high emissions from May to September, and a strong diurnal variability. For two case studies on 23 May and 5 September 2018, vertical profiles of temperature and humidity were recorded up to an altitude of 650 and 1000 m, respectively, during the morning transition. Air samples were taken at different altitudes and analysed in the laboratory for methane isotopic composition. The values showed a different isotopic composition in the vertical distribution during stable conditions in the morning (delta values of −51.5 ‰ below the temperature inversion at an altitude of 150 m on 23 May 2018 and at an altitude of 50 m on 5 September 2018, delta values of −50.1 ‰ above). After the onset of turbulent mixing, the isotopic composition was the same throughout the vertical column with a mean delta value of −49.9 ± 0.45 ‰. The systematically more negative delta values occurred only as long as the nocturnal temperature inversion was present. During the September study, water samples were analysed as well for methane concentration and isotopic composition in order to provide a link between surface and atmosphere. The water samples reveal high variability on horizontal scales of a few tens of metres for this particular case. The airborne sampling system and consecutive analysis chain were shown to provide reliable and reproducible results for two samples obtained simultaneously. The method presents a powerful tool for distinguishing the source process of methane at different altitudes. The isotopic composition showed clearly depleted delta values directly above a biological methane source when vertical mixing was hampered by a temperature inversion, and different delta values above, where the air masses originate from a different footprint area. The vertical distribution of methane isotopic composition can serve as tracer for mixing processes of methane within the atmospheric boundary layer.\n

\n\n\n

\n \n\n \n \n Lausch, A.; Heurich, M.; Magdon, P.; Rocchini, D.; Schulz, K.; Bumberger, J.; and King, D. J.\n\n\n \n \n \n \n \n A Range of Earth Observation Techniques for Assessing Plant Diversity.\n \n \n \n \n\n\n \n\n\n\n In Cavender-Bares, J.; Gamon, J. A.; and Townsend, P. A., editor(s), Remote Sensing of Plant Biodiversity, pages 309–348. Springer International Publishing, Cham, 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{cavender-bares_range_2020,\n\taddress = {Cham},\n\ttitle = {A {Range} of {Earth} {Observation} {Techniques} for {Assessing} {Plant} {Diversity}},\n\tisbn = {9783030331566 9783030331573},\n\turl = {http://link.springer.com/10.1007/978-3-030-33157-3_13},\n\tabstract = {Abstract \n            Vegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tbooktitle = {Remote {Sensing} of {Plant} {Biodiversity}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Lausch, Angela and Heurich, Marco and Magdon, Paul and Rocchini, Duccio and Schulz, Karsten and Bumberger, Jan and King, Doug J.},\n\teditor = {Cavender-Bares, Jeannine and Gamon, John A. and Townsend, Philip A.},\n\tyear = {2020},\n\tdoi = {10.1007/978-3-030-33157-3_13},\n\tpages = {309--348},\n}\n\n

\n

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

\n \n\n \n \n Lausch, A.; Schaepman, M. E.; Skidmore, A. K.; Truckenbrodt, S. C.; Hacker, J. M.; Baade, J.; Bannehr, L.; Borg, E.; Bumberger, J.; Dietrich, P.; Gläßer, C.; Haase, D.; Heurich, M.; Jagdhuber, T.; Jany, S.; Krönert, R.; Möller, M.; Mollenhauer, H.; Montzka, C.; Pause, M.; Rogass, C.; Salepci, N.; Schmullius, C.; Schrodt, F.; Schütze, C.; Schweitzer, C.; Selsam, P.; Spengler, D.; Vohland, M.; Volk, M.; Weber, U.; Wellmann, T.; Werban, U.; Zacharias, S.; and Thiel, C.\n\n\n \n \n \n \n \n Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(22): 3690. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{lausch_linking_2020,\n\ttitle = {Linking the {Remote} {Sensing} of {Geodiversity} and {Traits} {Relevant} to {Biodiversity}—{Part} {II}: {Geomorphology}, {Terrain} and {Surfaces}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\tshorttitle = {Linking the {Remote} {Sensing} of {Geodiversity} and {Traits} {Relevant} to {Biodiversity}—{Part} {II}},\n\turl = {https://www.mdpi.com/2072-4292/12/22/3690},\n\tdoi = {10.3390/rs12223690},\n\tabstract = {The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Lausch, Angela and Schaepman, Michael E. and Skidmore, Andrew K. and Truckenbrodt, Sina C. and Hacker, Jörg M. and Baade, Jussi and Bannehr, Lutz and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Gläßer, Cornelia and Haase, Dagmar and Heurich, Marco and Jagdhuber, Thomas and Jany, Sven and Krönert, Rudolf and Möller, Markus and Mollenhauer, Hannes and Montzka, Carsten and Pause, Marion and Rogass, Christian and Salepci, Nesrin and Schmullius, Christiane and Schrodt, Franziska and Schütze, Claudia and Schweitzer, Christian and Selsam, Peter and Spengler, Daniel and Vohland, Michael and Volk, Martin and Weber, Ute and Wellmann, Thilo and Werban, Ulrike and Zacharias, Steffen and Thiel, Christian},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {3690},\n}\n\n

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\n \n\n \n \n Li, M.; Wu, P.; and Ma, Z.\n\n\n \n \n \n \n \n A comprehensive evaluation of soil moisture and soil temperature from third‐generation atmospheric and land reanalysis data sets.\n \n \n \n \n\n\n \n\n\n\n International Journal of Climatology, 40(13): 5744–5766. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{li_comprehensive_2020,\n\ttitle = {A comprehensive evaluation of soil moisture and soil temperature from third‐generation atmospheric and land reanalysis data sets},\n\tvolume = {40},\n\tissn = {0899-8418, 1097-0088},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/joc.6549},\n\tdoi = {10.1002/joc.6549},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-02},\n\tjournal = {International Journal of Climatology},\n\tauthor = {Li, Mingxing and Wu, Peili and Ma, Zhuguo},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {5744--5766},\n}\n\n

\n

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\n \n\n \n \n Liu, X.; Hilfert, L.; Barth, J. A. C.; van Geldem, R.; and Friese, K.\n\n\n \n \n \n \n \n Isotope alteration caused by changes in biochemical composition of sedimentary organic matter.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 147(3): 277–292. February 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Isotope$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{liu_isotope_2020,\n\ttitle = {Isotope alteration caused by changes in biochemical composition of sedimentary organic matter},\n\tvolume = {147},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-020-00640-3},\n\tdoi = {10.1007/s10533-020-00640-3},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Biogeochemistry},\n\tauthor = {Liu, Xiaoqing and Hilfert, Liane and Barth, Johannes A. C. and van Geldem, Robert and Friese, Kurt},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {277--292},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Lutz, S. R.; Trauth, N.; Musolff, A.; Van Breukelen, B. M.; Knöller, K.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n How Important is Denitrification in Riparian Zones? Combining End‐Member Mixing and Isotope Modeling to Quantify Nitrate Removal from Riparian Groundwater.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{lutz_how_2020,\n\ttitle = {How {Important} is {Denitrification} in {Riparian} {Zones}? {Combining} {End}‐{Member} {Mixing} and {Isotope} {Modeling} to {Quantify} {Nitrate} {Removal} from {Riparian} {Groundwater}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {How {Important} is {Denitrification} in {Riparian} {Zones}?},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR025528},\n\tdoi = {10.1029/2019WR025528},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Lutz, Stefanie R. and Trauth, Nico and Musolff, Andreas and Van Breukelen, Boris M. and Knöller, Kay and Fleckenstein, Jan H.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Marzahn, P.; and Meyer, S.\n\n\n \n \n \n \n \n Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(16): 2660. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Utilization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{marzahn_utilization_2020,\n\ttitle = {Utilization of {Multi}-{Temporal} {Microwave} {Remote} {Sensing} {Data} within a {Geostatistical} {Regionalization} {Approach} for the {Derivation} of {Soil} {Texture}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/16/2660},\n\tdoi = {10.3390/rs12162660},\n\tabstract = {Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-\\%) could be achieved outperforming classical deterministic and geostatistical approaches.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Marzahn, Philip and Meyer, Swen},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {2660},\n}\n\n

\n

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

\n \n\n \n \n McKenna, A.; Schultz, A.; Borg, E.; Neumann, M.; and Mund, J.\n\n\n \n \n \n \n \n Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO).\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{mckenna_remote_2020,\n\ttype = {other},\n\ttitle = {Remote sensing and {GIS} based ecological modelling of potential red deer habitats in the test site region {DEMMIN} ({TERENO})},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-19953.html},\n\tabstract = {\\&lt;p\\&gt;\\&lt;strong\\&gt;Introduction:\\&lt;/strong\\&gt; The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Objective:\\&lt;/strong\\&gt; This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Methods:\\&lt;/strong\\&gt; Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of \\&gt; 60\\% and in the best case 71,4\\%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach.\\&amp;\\#160; Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.\\&lt;/p\\&gt;\\&lt;p\\&gt;\\&lt;strong\\&gt;Conclusion:\\&lt;/strong\\&gt; \\&amp;\\#160;Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species \\&amp;\\#8211;habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.\\&lt;/p\\&gt;},\n\turldate = {2022-11-02},\n\tinstitution = {oral},\n\tauthor = {McKenna, Amelie and Schultz, Alfred and Borg, Erik and Neumann, Matthias and Mund, Jan-Peter},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-19953},\n}\n\n

\n

\n\n\n

\n Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach.  Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion:  Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species –habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.\n

\n\n\n

\n \n\n \n \n Merz, R.; Tarasova, L.; and Basso, S.\n\n\n \n \n \n \n \n The flood cooking book: ingredients and regional flavors of floods across Germany.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(11): 114024. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{merz_flood_2020,\n\ttitle = {The flood cooking book: ingredients and regional flavors of floods across {Germany}},\n\tvolume = {15},\n\tissn = {1748-9326},\n\tshorttitle = {The flood cooking book},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/abb9dd},\n\tdoi = {10.1088/1748-9326/abb9dd},\n\tabstract = {Abstract \n            River flooding is a major natural hazard worldwide, whose prediction is impaired by limited understanding of the interplay of processes triggering floods within large regions. In this study we use machine learning techniques such as decision trees and random forests to pinpoint spatio-temporal features of precipitation and catchment wetness states which led to floods among 177 267 rainfall-runoff events observed in 373 German river basins. In mountainous catchments with high annual precipitation rates and shallow soils, event rainfall characteristics primarily control flood occurrence, while wetness conditions and the spatial interplay between rainfall and catchment soil moisture drive flood occurrence even more than event rainfall volume in drier basins. The existence of a snow cover also enhances flood occurrence. The identified ingredients and regional flavors shed new light on the spatial dynamics of hydro-meteorological processes leading to floods and foster regional adaptation of flood management strategies and early warning systems.},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Merz, Ralf and Tarasova, Larisa and Basso, Stefano},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {114024},\n}\n\n

\n

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

\n \n\n \n \n Mi, C.; Shatwell, T.; Ma, J.; Xu, Y.; Su, F.; and Rinke, K.\n\n\n \n \n \n \n \n Ensemble warming projections in Germany's largest drinking water reservoir and potential adaptation strategies.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 748: 141366. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Ensemble$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{mi_ensemble_2020,\n\ttitle = {Ensemble warming projections in {Germany}'s largest drinking water reservoir and potential adaptation strategies},\n\tvolume = {748},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720348956},\n\tdoi = {10.1016/j.scitotenv.2020.141366},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Mi, Chenxi and Shatwell, Tom and Ma, Jun and Xu, Yaqian and Su, Fangli and Rinke, Karsten},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {141366},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Mobilia, M.; Schmidt, M.; and Longobardi, A.\n\n\n \n \n \n \n \n Modelling Actual Evapotranspiration Seasonal Variability by Meteorological Data-Based Models.\n \n \n \n \n\n\n \n\n\n\n Hydrology, 7(3): 50. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{mobilia_modelling_2020,\n\ttitle = {Modelling {Actual} {Evapotranspiration} {Seasonal} {Variability} by {Meteorological} {Data}-{Based} {Models}},\n\tvolume = {7},\n\tissn = {2306-5338},\n\turl = {https://www.mdpi.com/2306-5338/7/3/50},\n\tdoi = {10.3390/hydrology7030050},\n\tabstract = {This study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80\\% in the case of the Sister site, of about 30\\% in the case of Rollesbroich, and close to 60\\% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology},\n\tauthor = {Mobilia, Mirka and Schmidt, Marius and Longobardi, Antonia},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {50},\n}\n\n

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\n \n\n \n \n Montzka, C.; Brogi, C.; Mengen, D.; Matveeva, M.; Baum, S.; Schuttemeyer, D.; Bayat, B.; Bogena, H.; Coccia, A.; Masalias, G.; Graf, V.; Jakobi, J.; Jonard, F.; Ma, Y.; Mattia, F.; Palmisano, D.; Rascher, U.; Satalino, G.; Jagdhuber, T.; Fluhrer, A.; Schumacher, M.; Schmidt, M.; and Vereecken, H.\n\n\n \n \n \n \n \n Sarsense: A C- and L-Band SAR Rehearsal Campaign in Germany in Preparation for ROSE-L.\n \n \n \n \n\n\n \n\n\n\n In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pages 2137–2140, Waikoloa, HI, USA, September 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Sarsense:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{montzka_sarsense_2020,\n\taddress = {Waikoloa, HI, USA},\n\ttitle = {Sarsense: {A} {C}- and {L}-{Band} {SAR} {Rehearsal} {Campaign} in {Germany} in {Preparation} for {ROSE}-{L}},\n\tisbn = {9781728163741},\n\tshorttitle = {Sarsense},\n\turl = {https://ieeexplore.ieee.org/document/9324090/},\n\tdoi = {10.1109/IGARSS39084.2020.9324090},\n\turldate = {2022-11-02},\n\tbooktitle = {{IGARSS} 2020 - 2020 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tpublisher = {IEEE},\n\tauthor = {Montzka, Carsten and Brogi, Cosimo and Mengen, David and Matveeva, Maria and Baum, Stephani and Schuttemeyer, Dirk and Bayat, Bagher and Bogena, Heye and Coccia, Alex and Masalias, Gerard and Graf, Verena and Jakobi, Jannis and Jonard, Francois and Ma, Yueling and Mattia, Francesco and Palmisano, Davide and Rascher, Uwe and Satalino, Guiseppe and Jagdhuber, Thomas and Fluhrer, Anke and Schumacher, Maike and Schmidt, Marius and Vereecken, Harry},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2137--2140},\n}\n\n

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\n \n\n \n \n Montzka, C.; Cosh, M. H.; Bayat, B.; Al Bitar, A.; Berg, A.; Bindlish, R.; Bogena, H. R.; Bolton, J. D.; Cabot, F.; Caldwell, T.; Chan, S.; Colliander, A.; Wade Crow; Das, N.; De Lannoy, G.; Dorigo, W.; Evett, S. R.; Gruber, A.; Hahn, S.; Jagdhuber, T.; Jones, S. F.; Kerr, Y.; Kim, S.; Koyama, C.; Kurum, M.; Lopez-Baeza, E.; Mattia, F.; McColl, K. A.; Mecklenburg, S.; Mohanty, B.; O´Neill, P.; Or, D.; Pellarin, T.; Petropoulos, G. P.; Piles, M.; Reichle, R. H.; Rodriguez-Fernandez, N.; Rüdiger, C.; Scanlon, T.; Schwartz, R. C.; Spengler, D.; Srivastava, P. K.; Suman, S.; van der Schalie, R.; Wagner, W.; Wegmüller, U.; Wigneron, J.; Camacho, F.; and Nickeson, J.\n\n\n \n \n \n \n \n Soil moisture product validation good practices protocol, version 1.0.\n \n \n \n \n\n\n \n\n\n\n Technical Report 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Soil$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{montzka_soil_2020,\n\ttype = {Report},\n\ttitle = {Soil moisture product validation good practices protocol, version 1.0},\n\turl = {http://pubs.er.usgs.gov/publication/70216425},\n\tlanguage = {English},\n\tauthor = {Montzka, Carsten and Cosh, Michael H. and Bayat, Bagher and Al Bitar, Ahmad and Berg, Aaron and Bindlish, Rajat and Bogena, Heye Reemt and Bolton, John D. and Cabot, Francois and Caldwell, Todd and Chan, Steven and Colliander, Andreas and {Wade Crow} and Das, Narendra and De Lannoy, Gabrielle and Dorigo, Wouter and Evett, Steven R. and Gruber, Alexander and Hahn, Sebastian and Jagdhuber, Thomas and Jones, Scott F. and Kerr, Yann and Kim, Seung-bum and Koyama, Christian and Kurum, Mehmed and Lopez-Baeza, Ernesto and Mattia, Francesco and McColl, Kaighin A. and Mecklenburg, Susanne and Mohanty, Binayak and O´Neill, Peggy and Or, Dani and Pellarin, Thierry and Petropoulos, George P. and Piles, Maria and Reichle, Rolf H. and Rodriguez-Fernandez, Nemesio and Rüdiger, Christoph and Scanlon, Tracy and Schwartz, Robert C. and Spengler, Daniel and Srivastava, Prashant K. and Suman, Swati and van der Schalie, Robin and Wagner, Wolfgang and Wegmüller, Urs and Wigneron, Jean-Pierre and Camacho, Fernando and Nickeson, Jaime},\n\teditor = {Montzka, Carsten and Cosh, Michael H. and Camacho, Fernando and Nickeson, Jaime},\n\tyear = {2020},\n\tdoi = {10.5067/doc/ceoswgcv/lpv/sm.001},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Mozaffari, A.; Klotzsche, A.; Warren, C.; He, G.; Giannopoulos, A.; Vereecken, H.; and van der Kruk, J.\n\n\n \n \n \n \n \n 2.5D crosshole GPR full-waveform inversion with synthetic and measured data.\n \n \n \n \n\n\n \n\n\n\n GEOPHYSICS, 85(4): H71–H82. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"2.5D$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mozaffari_25d_2020,\n\ttitle = {2.{5D} crosshole {GPR} full-waveform inversion with synthetic and measured data},\n\tvolume = {85},\n\tissn = {0016-8033, 1942-2156},\n\turl = {https://library.seg.org/doi/10.1190/geo2019-0600.1},\n\tdoi = {10.1190/geo2019-0600.1},\n\tabstract = {Full-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30\\% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2\\% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20\\%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {GEOPHYSICS},\n\tauthor = {Mozaffari, Amirpasha and Klotzsche, Anja and Warren, Craig and He, Guowei and Giannopoulos, Antonios and Vereecken, Harry and van der Kruk, Jan},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {H71--H82},\n}\n\n

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\n \n\n \n \n Nanusha, M. Y.; Krauss, M.; and Brack, W.\n\n\n \n \n \n \n \n Non-target screening for detecting the occurrence of plant metabolites in river waters.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 32(1): 130. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Non-target$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{nanusha_non-target_2020,\n\ttitle = {Non-target screening for detecting the occurrence of plant metabolites in river waters},\n\tvolume = {32},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-020-00415-5},\n\tdoi = {10.1186/s12302-020-00415-5},\n\tabstract = {Abstract \n             \n              Background \n              In surface waters, using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS), typically large numbers of chemical signals often with high peak intensity remain unidentified. These chemical signals may represent natural compounds released from plants, animals and microorganisms, which may contribute to the cumulative toxic risk. Thus, attempts were made to identify natural compounds in significant concentrations in surface waters by identifying overlapping LC-HRMS peaks between extracts of plants abundant in the catchment and river waters using a non-target screening (NTS) work flow. \n             \n             \n              Results \n              The result revealed the presence of several thousands of overlapping peaks between water—and plants from local vegetation. Taking this overlap as a basis, 12 SPMs from different compound classes were identified to occur in river waters with flavonoids as a dominant group. The concentrations of the identified compounds ranged from 0.02 to 5 µg/L with apiin, hyperoside and guanosine with highest concentrations. Most of the identified compounds exceeded the threshold for toxicological concern (TTC) (0.1 µg/L) for non-genotoxic and non-endocrine disrupting chemicals in drinking water often by more than one order of magnitude. \n             \n             \n              Conclusion \n              Our results revealed the contribution of chemicals eluted from the vegetation in the catchment to the chemical load in surface waters and help to reduce the number of unknowns among NTS high-intensity peaks detected in rivers. Since secondary plant metabolites (SPMs) are often produced for defence against other organisms and since concentrations ranges are clearly above TTC a contribution to toxic risks on aquatic organisms and impacts on drinking water safety cannot be excluded. This demands for including these compounds into monitoring and assessment of water quality.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Nanusha, Mulatu Yohannes and Krauss, Martin and Brack, Werner},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {130},\n}\n\n

\n

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

\n \n\n \n \n Nanusha, M. Y.; Krauss, M.; Schönsee, C. D.; Günthardt, B. F.; Bucheli, T. D.; and Brack, W.\n\n\n \n \n \n \n \n Target screening of plant secondary metabolites in river waters by liquid chromatography coupled to high-resolution mass spectrometry (LC–HRMS).\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 32(1): 142. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Target$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{nanusha_target_2020,\n\ttitle = {Target screening of plant secondary metabolites in river waters by liquid chromatography coupled to high-resolution mass spectrometry ({LC}–{HRMS})},\n\tvolume = {32},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-020-00399-2},\n\tdoi = {10.1186/s12302-020-00399-2},\n\tabstract = {Abstract \n             \n              Background \n              Substantial efforts have been made to monitor potentially hazardous anthropogenic contaminants in surface waters while for plant secondary metabolites (PSMs) almost no data on occurrence in the water cycle are available. These metabolites enter river waters through various pathways such as leaching, surface run-off and rain sewers or input of litter from vegetation and might add to the biological activity of the chemical mixture. To reduce this data gap, we conducted a LC–HRMS target screening in river waters from two different catchments for 150 plant metabolites which were selected from a larger database considering their expected abundance in the vegetation, their potential mobility, persistence and toxicity in the water cycle and commercial availability of standards. \n             \n             \n              Results \n              The screening revealed the presence of 12 out of 150 possibly toxic PSMs including coumarins (bergapten, scopoletin, fraxidin, esculetin and psoralen), a flavonoid (formononetin) and alkaloids (lycorine and narciclasine). The compounds narciclasine and lycorine were detected at concentrations up to 3 µg/L while esculetin and fraxidin occurred at concentrations above 1 µg/L. Nine compounds occurred at concentrations above 0.1 µg/L, the Threshold for Toxicological Concern (TTC) for non-genotoxic and non-endocrine disrupting chemicals in drinking water. \n             \n             \n              Conclusions \n              Our study provides an overview of potentially biologically active PSMs in surface waters and recommends their consideration in monitoring and risk assessment of water resources. This is currently hampered by a lack of effect data including toxicity to aquatic organisms, endocrine disruption and genotoxicity and demands for involvement of these compounds in biotesting.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Nanusha, Mulatu Yohannes and Krauss, Martin and Schönsee, Carina D. and Günthardt, Barbara F. and Bucheli, Thomas D. and Brack, Werner},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {142},\n}\n\n

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\n \n\n \n \n Nasta, P.; Bogena, H. R.; Sica, B.; Weuthen, A.; Vereecken, H.; and Romano, N.\n\n\n \n \n \n \n \n Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Water, 2: 26. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{nasta_integrating_2020,\n\ttitle = {Integrating {Invasive} and {Non}-invasive {Monitoring} {Sensors} to {Detect} {Field}-{Scale} {Soil} {Hydrological} {Behavior}},\n\tvolume = {2},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/article/10.3389/frwa.2020.00026/full},\n\tdoi = {10.3389/frwa.2020.00026},\n\turldate = {2022-11-02},\n\tjournal = {Frontiers in Water},\n\tauthor = {Nasta, Paolo and Bogena, Heye R. and Sica, Benedetto and Weuthen, Ansgar and Vereecken, Harry and Romano, Nunzio},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {26},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Naz, B. S.; Kollet, S.; Franssen, H. H.; Montzka, C.; and Kurtz, W.\n\n\n \n \n \n \n \n A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 111. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{naz_3_2020,\n\ttitle = {A 3 km spatially and temporally consistent {European} daily soil moisture reanalysis from 2000 to 2015},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {http://www.nature.com/articles/s41597-020-0450-6},\n\tdoi = {10.1038/s41597-020-0450-6},\n\tabstract = {Abstract \n             \n              High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 \n              in-situ \n              soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m \n              3 \n              m \n              −3 \n              and correlation values above 0.5 over 70\\% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Data},\n\tauthor = {Naz, Bibi S. and Kollet, Stefan and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Kurtz, Wolfgang},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {111},\n}\n\n

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

\n \n\n \n \n Nguyen, T. H.; Langensiepen, M.; Vanderborght, J.; Hüging, H.; Mboh, C. M.; and Ewert, F.\n\n\n \n \n \n \n \n Comparison of root water uptake models in simulating CO$_{\\textrm{2}}$ and H$_{\\textrm{2}}$O fluxes and growth of wheat.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 24(10): 4943–4969. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{nguyen_comparison_2020,\n\ttitle = {Comparison of root water uptake models in simulating {CO}$_{\\textrm{2}}$ and {H}$_{\\textrm{2}}${O} fluxes and growth of wheat},\n\tvolume = {24},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/24/4943/2020/},\n\tdoi = {10.5194/hess-24-4943-2020},\n\tabstract = {Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models\nestimate the effect of soil water stress on stomatal conductance directly\nfrom soil water content or soil hydraulic potential without explicit\nrepresentation of hydraulic signals between the soil and stomata. In order\nto explicitly represent stomatal regulation by soil water status as a\nfunction of the hydraulic signal and its relation to the whole plant\nhydraulic conductance, we coupled the crop model LINTULCC2 and the root\ngrowth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of\nconsidering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model).\nSimulations were compared with gas flux measurements and crop growth data\nfrom a wheat crop grown under three water supply regimes (sheltered,\nrainfed, and irrigated) and two soil types (stony and silty) in western\nGermany in 2016. The two models showed a relatively similar performance in\nthe simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate,\nand soil water content. The Feddes model predicts more stress and less\ngrowth in the silty soil than in the stony soil, which is opposite to the\nobserved growth. The Couvreur model better represents the difference in\ngrowth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance\nover the growing season. This demonstrates the importance of two-way\nfeedbacks between growth and root water uptake for predicting the crop\nresponse to different soil water conditions in different soils. Our results\nsuggest that a better representation of the effects of soil characteristics\non root growth is needed for reliable estimations of root hydraulic\nconductance and gas fluxes, particularly in heterogeneous fields. The newly\ncoupled soil–plant model marks a promising approach but requires further\ntesting for other scenarios regarding crops, soil, and climate.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Nguyen, Thuy Huu and Langensiepen, Matthias and Vanderborght, Jan and Hüging, Hubert and Mboh, Cho Miltin and Ewert, Frank},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {4943--4969},\n}\n\n

\n

\n\n\n

\n Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate.\n

\n\n\n

\n \n\n \n \n Pampel, H.; and Bertelmann, R.\n\n\n \n \n \n \n Helmholtz Open Science Office - Shaping the PID Landscape in Germany and Beyond.\n \n \n \n\n\n \n\n\n\n 2020.\n Publication Title: Poster Type: Konferenzabstract\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@misc{pampel_helmholtz_2020,\n\ttitle = {Helmholtz {Open} {Science} {Office} - {Shaping} the {PID} {Landscape} in {Germany} and {Beyond}},\n\tauthor = {Pampel, Heinz and Bertelmann, Roland},\n\tyear = {2020},\n\tnote = {Publication Title: Poster\nType: Konferenzabstract},\n}\n\n

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\n \n\n \n \n Park, C.; Jagdhuber, T.; Colliander, A.; Lee, J.; Berg, A.; Cosh, M.; Kim, S.; Kim, Y.; and Wulfmeyer, V.\n\n\n \n \n \n \n \n Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(18): 2939. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameterization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{park_parameterization_2020,\n\ttitle = {Parameterization of {Vegetation} {Scattering} {Albedo} in the {Tau}-{Omega} {Model} for {Soil} {Moisture} {Retrieval} on {Croplands}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/18/2939},\n\tdoi = {10.3390/rs12182939},\n\tabstract = {An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16\\% for SCA and 15\\% for DCA. The reduction for positive and negative biases was 45\\% and 5\\% for SCA and 26\\% and 12\\% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.},\n\tlanguage = {en},\n\tnumber = {18},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Park, Chang-Hwan and Jagdhuber, Thomas and Colliander, Andreas and Lee, Johan and Berg, Aaron and Cosh, Michael and Kim, Seung-Bum and Kim, Yoonjae and Wulfmeyer, Volker},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {2939},\n}\n\n

\n

\n\n\n

\n An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.\n

\n\n\n

\n \n\n \n \n Pastorello, G.; Trotta, C.; Canfora, E.; Chu, H.; Christianson, D.; Cheah, Y.; Poindexter, C.; Chen, J.; Elbashandy, A.; Humphrey, M.; Isaac, P.; Polidori, D.; Reichstein, M.; Ribeca, A.; van Ingen, C.; Vuichard, N.; Zhang, L.; Amiro, B.; Ammann, C.; Arain, M. A.; Ardö, J.; Arkebauer, T.; Arndt, S. K.; Arriga, N.; Aubinet, M.; Aurela, M.; Baldocchi, D.; Barr, A.; Beamesderfer, E.; Marchesini, L. B.; Bergeron, O.; Beringer, J.; Bernhofer, C.; Berveiller, D.; Billesbach, D.; Black, T. A.; Blanken, P. D.; Bohrer, G.; Boike, J.; Bolstad, P. V.; Bonal, D.; Bonnefond, J.; Bowling, D. R.; Bracho, R.; Brodeur, J.; Brümmer, C.; Buchmann, N.; Burban, B.; Burns, S. P.; Buysse, P.; Cale, P.; Cavagna, M.; Cellier, P.; Chen, S.; Chini, I.; Christensen, T. R.; Cleverly, J.; Collalti, A.; Consalvo, C.; Cook, B. D.; Cook, D.; Coursolle, C.; Cremonese, E.; Curtis, P. S.; D’Andrea, E.; da Rocha, H.; Dai, X.; Davis, K. J.; Cinti, B. D.; Grandcourt, A. d.; Ligne, A. D.; De Oliveira, R. C.; Delpierre, N.; Desai, A. R.; Di Bella, C. M.; Tommasi, P. d.; Dolman, H.; Domingo, F.; Dong, G.; Dore, S.; Duce, P.; Dufrêne, E.; Dunn, A.; Dušek, J.; Eamus, D.; Eichelmann, U.; ElKhidir, H. A. M.; Eugster, W.; Ewenz, C. M.; Ewers, B.; Famulari, D.; Fares, S.; Feigenwinter, I.; Feitz, A.; Fensholt, R.; Filippa, G.; Fischer, M.; Frank, J.; Galvagno, M.; Gharun, M.; Gianelle, D.; Gielen, B.; Gioli, B.; Gitelson, A.; Goded, I.; Goeckede, M.; Goldstein, A. H.; Gough, C. M.; Goulden, M. L.; Graf, A.; Griebel, A.; Gruening, C.; Grünwald, T.; Hammerle, A.; Han, S.; Han, X.; Hansen, B. U.; Hanson, C.; Hatakka, J.; He, Y.; Hehn, M.; Heinesch, B.; Hinko-Najera, N.; Hörtnagl, L.; Hutley, L.; Ibrom, A.; Ikawa, H.; Jackowicz-Korczynski, M.; Janouš, D.; Jans, W.; Jassal, R.; Jiang, S.; Kato, T.; Khomik, M.; Klatt, J.; Knohl, A.; Knox, S.; Kobayashi, H.; Koerber, G.; Kolle, O.; Kosugi, Y.; Kotani, A.; Kowalski, A.; Kruijt, B.; Kurbatova, J.; Kutsch, W. L.; Kwon, H.; Launiainen, S.; Laurila, T.; Law, B.; Leuning, R.; Li, Y.; Liddell, M.; Limousin, J.; Lion, M.; Liska, A. J.; Lohila, A.; López-Ballesteros, A.; López-Blanco, E.; Loubet, B.; Loustau, D.; Lucas-Moffat, A.; Lüers, J.; Ma, S.; Macfarlane, C.; Magliulo, V.; Maier, R.; Mammarella, I.; Manca, G.; Marcolla, B.; Margolis, H. A.; Marras, S.; Massman, W.; Mastepanov, M.; Matamala, R.; Matthes, J. H.; Mazzenga, F.; McCaughey, H.; McHugh, I.; McMillan, A. M. S.; Merbold, L.; Meyer, W.; Meyers, T.; Miller, S. D.; Minerbi, S.; Moderow, U.; Monson, R. K.; Montagnani, L.; Moore, C. E.; Moors, E.; Moreaux, V.; Moureaux, C.; Munger, J. W.; Nakai, T.; Neirynck, J.; Nesic, Z.; Nicolini, G.; Noormets, A.; Northwood, M.; Nosetto, M.; Nouvellon, Y.; Novick, K.; Oechel, W.; Olesen, J. E.; Ourcival, J.; Papuga, S. A.; Parmentier, F.; Paul-Limoges, E.; Pavelka, M.; Peichl, M.; Pendall, E.; Phillips, R. P.; Pilegaard, K.; Pirk, N.; Posse, G.; Powell, T.; Prasse, H.; Prober, S. M.; Rambal, S.; Rannik, Ü.; Raz-Yaseef, N.; Rebmann, C.; Reed, D.; Dios, V. R. d.; Restrepo-Coupe, N.; Reverter, B. R.; Roland, M.; Sabbatini, S.; Sachs, T.; Saleska, S. R.; Sánchez-Cañete, E. P.; Sanchez-Mejia, Z. M.; Schmid, H. P.; Schmidt, M.; Schneider, K.; Schrader, F.; Schroder, I.; Scott, R. L.; Sedlák, P.; Serrano-Ortíz, P.; Shao, C.; Shi, P.; Shironya, I.; Siebicke, L.; Šigut, L.; Silberstein, R.; Sirca, C.; Spano, D.; Steinbrecher, R.; Stevens, R. M.; Sturtevant, C.; Suyker, A.; Tagesson, T.; Takanashi, S.; Tang, Y.; Tapper, N.; Thom, J.; Tomassucci, M.; Tuovinen, J.; Urbanski, S.; Valentini, R.; van der Molen, M.; van Gorsel, E.; van Huissteden, K.; Varlagin, A.; Verfaillie, J.; Vesala, T.; Vincke, C.; Vitale, D.; Vygodskaya, N.; Walker, J. P.; Walter-Shea, E.; Wang, H.; Weber, R.; Westermann, S.; Wille, C.; Wofsy, S.; Wohlfahrt, G.; Wolf, S.; Woodgate, W.; Li, Y.; Zampedri, R.; Zhang, J.; Zhou, G.; Zona, D.; Agarwal, D.; Biraud, S.; Torn, M.; and Papale, D.\n\n\n \n \n \n \n \n The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 225. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{pastorello_fluxnet2015_2020,\n\ttitle = {The {FLUXNET2015} dataset and the {ONEFlux} processing pipeline for eddy covariance data},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {https://doi.org/10.1038/s41597-020-0534-3},\n\tdoi = {10.1038/s41597-020-0534-3},\n\tabstract = {The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.},\n\tnumber = {1},\n\tjournal = {Scientific Data},\n\tauthor = {Pastorello, Gilberto and Trotta, Carlo and Canfora, Eleonora and Chu, Housen and Christianson, Danielle and Cheah, You-Wei and Poindexter, Cristina and Chen, Jiquan and Elbashandy, Abdelrahman and Humphrey, Marty and Isaac, Peter and Polidori, Diego and Reichstein, Markus and Ribeca, Alessio and van Ingen, Catharine and Vuichard, Nicolas and Zhang, Leiming and Amiro, Brian and Ammann, Christof and Arain, M. Altaf and Ardö, Jonas and Arkebauer, Timothy and Arndt, Stefan K. and Arriga, Nicola and Aubinet, Marc and Aurela, Mika and Baldocchi, Dennis and Barr, Alan and Beamesderfer, Eric and Marchesini, Luca Belelli and Bergeron, Onil and Beringer, Jason and Bernhofer, Christian and Berveiller, Daniel and Billesbach, Dave and Black, Thomas Andrew and Blanken, Peter D. and Bohrer, Gil and Boike, Julia and Bolstad, Paul V. and Bonal, Damien and Bonnefond, Jean-Marc and Bowling, David R. and Bracho, Rosvel and Brodeur, Jason and Brümmer, Christian and Buchmann, Nina and Burban, Benoit and Burns, Sean P. and Buysse, Pauline and Cale, Peter and Cavagna, Mauro and Cellier, Pierre and Chen, Shiping and Chini, Isaac and Christensen, Torben R. and Cleverly, James and Collalti, Alessio and Consalvo, Claudia and Cook, Bruce D. and Cook, David and Coursolle, Carole and Cremonese, Edoardo and Curtis, Peter S. and D’Andrea, Ettore and da Rocha, Humberto and Dai, Xiaoqin and Davis, Kenneth J. and Cinti, Bruno De and Grandcourt, Agnes de and Ligne, Anne De and De Oliveira, Raimundo C. and Delpierre, Nicolas and Desai, Ankur R. and Di Bella, Carlos Marcelo and Tommasi, Paul di and Dolman, Han and Domingo, Francisco and Dong, Gang and Dore, Sabina and Duce, Pierpaolo and Dufrêne, Eric and Dunn, Allison and Dušek, Jiří and Eamus, Derek and Eichelmann, Uwe and ElKhidir, Hatim Abdalla M. and Eugster, Werner and Ewenz, Cacilia M. and Ewers, Brent and Famulari, Daniela and Fares, Silvano and Feigenwinter, Iris and Feitz, Andrew and Fensholt, Rasmus and Filippa, Gianluca and Fischer, Marc and Frank, John and Galvagno, Marta and Gharun, Mana and Gianelle, Damiano and Gielen, Bert and Gioli, Beniamino and Gitelson, Anatoly and Goded, Ignacio and Goeckede, Mathias and Goldstein, Allen H. and Gough, Christopher M. and Goulden, Michael L. and Graf, Alexander and Griebel, Anne and Gruening, Carsten and Grünwald, Thomas and Hammerle, Albin and Han, Shijie and Han, Xingguo and Hansen, Birger Ulf and Hanson, Chad and Hatakka, Juha and He, Yongtao and Hehn, Markus and Heinesch, Bernard and Hinko-Najera, Nina and Hörtnagl, Lukas and Hutley, Lindsay and Ibrom, Andreas and Ikawa, Hiroki and Jackowicz-Korczynski, Marcin and Janouš, Dalibor and Jans, Wilma and Jassal, Rachhpal and Jiang, Shicheng and Kato, Tomomichi and Khomik, Myroslava and Klatt, Janina and Knohl, Alexander and Knox, Sara and Kobayashi, Hideki and Koerber, Georgia and Kolle, Olaf and Kosugi, Yoshiko and Kotani, Ayumi and Kowalski, Andrew and Kruijt, Bart and Kurbatova, Julia and Kutsch, Werner L. and Kwon, Hyojung and Launiainen, Samuli and Laurila, Tuomas and Law, Bev and Leuning, Ray and Li, Yingnian and Liddell, Michael and Limousin, Jean-Marc and Lion, Marryanna and Liska, Adam J. and Lohila, Annalea and López-Ballesteros, Ana and López-Blanco, Efrén and Loubet, Benjamin and Loustau, Denis and Lucas-Moffat, Antje and Lüers, Johannes and Ma, Siyan and Macfarlane, Craig and Magliulo, Vincenzo and Maier, Regine and Mammarella, Ivan and Manca, Giovanni and Marcolla, Barbara and Margolis, Hank A. and Marras, Serena and Massman, William and Mastepanov, Mikhail and Matamala, Roser and Matthes, Jaclyn Hatala and Mazzenga, Francesco and McCaughey, Harry and McHugh, Ian and McMillan, Andrew M. S. and Merbold, Lutz and Meyer, Wayne and Meyers, Tilden and Miller, Scott D. and Minerbi, Stefano and Moderow, Uta and Monson, Russell K. and Montagnani, Leonardo and Moore, Caitlin E. and Moors, Eddy and Moreaux, Virginie and Moureaux, Christine and Munger, J. William and Nakai, Taro and Neirynck, Johan and Nesic, Zoran and Nicolini, Giacomo and Noormets, Asko and Northwood, Matthew and Nosetto, Marcelo and Nouvellon, Yann and Novick, Kimberly and Oechel, Walter and Olesen, Jørgen Eivind and Ourcival, Jean-Marc and Papuga, Shirley A. and Parmentier, Frans-Jan and Paul-Limoges, Eugenie and Pavelka, Marian and Peichl, Matthias and Pendall, Elise and Phillips, Richard P. and Pilegaard, Kim and Pirk, Norbert and Posse, Gabriela and Powell, Thomas and Prasse, Heiko and Prober, Suzanne M. and Rambal, Serge and Rannik, Üllar and Raz-Yaseef, Naama and Rebmann, Corinna and Reed, David and Dios, Victor Resco de and Restrepo-Coupe, Natalia and Reverter, Borja R. and Roland, Marilyn and Sabbatini, Simone and Sachs, Torsten and Saleska, Scott R. and Sánchez-Cañete, Enrique P. and Sanchez-Mejia, Zulia M. and Schmid, Hans Peter and Schmidt, Marius and Schneider, Karl and Schrader, Frederik and Schroder, Ivan and Scott, Russell L. and Sedlák, Pavel and Serrano-Ortíz, Penélope and Shao, Changliang and Shi, Peili and Shironya, Ivan and Siebicke, Lukas and Šigut, Ladislav and Silberstein, Richard and Sirca, Costantino and Spano, Donatella and Steinbrecher, Rainer and Stevens, Robert M. and Sturtevant, Cove and Suyker, Andy and Tagesson, Torbern and Takanashi, Satoru and Tang, Yanhong and Tapper, Nigel and Thom, Jonathan and Tomassucci, Michele and Tuovinen, Juha-Pekka and Urbanski, Shawn and Valentini, Riccardo and van der Molen, Michiel and van Gorsel, Eva and van Huissteden, Ko and Varlagin, Andrej and Verfaillie, Joseph and Vesala, Timo and Vincke, Caroline and Vitale, Domenico and Vygodskaya, Natalia and Walker, Jeffrey P. and Walter-Shea, Elizabeth and Wang, Huimin and Weber, Robin and Westermann, Sebastian and Wille, Christian and Wofsy, Steven and Wohlfahrt, Georg and Wolf, Sebastian and Woodgate, William and Li, Yuelin and Zampedri, Roberto and Zhang, Junhui and Zhou, Guoyi and Zona, Donatella and Agarwal, Deb and Biraud, Sebastien and Torn, Margaret and Papale, Dario},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {225},\n}\n\n

\n

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

\n \n\n \n \n Pauly, M.; Helle, G.; Büntgen, U.; Wacker, L.; Treydte, K.; Reinig, F.; Turney, C.; Nievergelt, D.; Kromer, B.; Friedrich, M.; Sookdeo, A.; Heinrich, I.; Riedel, F.; Balting, D.; and Brauer, A.\n\n\n \n \n \n \n \n An annual-resolution stable isotope record from Swiss subfossil pine trees growing in the late Glacial.\n \n \n \n \n\n\n \n\n\n\n Quaternary Science Reviews, 247: 106550. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{pauly_annual-resolution_2020,\n\ttitle = {An annual-resolution stable isotope record from {Swiss} subfossil pine trees growing in the late {Glacial}},\n\tvolume = {247},\n\tissn = {02773791},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0277379120305126},\n\tdoi = {10.1016/j.quascirev.2020.106550},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Quaternary Science Reviews},\n\tauthor = {Pauly, Maren and Helle, Gerhard and Büntgen, Ulf and Wacker, Lukas and Treydte, Kerstin and Reinig, Frederick and Turney, Chris and Nievergelt, Daniel and Kromer, Bernd and Friedrich, Michael and Sookdeo, Adam and Heinrich, Ingo and Riedel, Frank and Balting, Daniel and Brauer, Achim},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {106550},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Pisinaras, V.; Brogi, C.; Bogena, H.; Hendricks-Franssen, H.; Dombrowski, O.; and Panagopoulos, A.\n\n\n \n \n \n \n \n Development of irrigation management services based on integration of innovative soil moisture monitoring and hydrological modelling.\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{pisinaras_development_2020,\n\ttype = {other},\n\ttitle = {Development of irrigation management services based on integration of innovative soil moisture monitoring and hydrological modelling},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-4093.html},\n\tabstract = {\\&lt;p\\&gt;The H2020 ATLAS project (www.atlas-h2020.eu/) aims to develop an open, flexible and distributed platform that will provide services for the agricultural sector based on the seamless interconnection of sensors and machines. Two interconnected services that will be included in the platform are the soil moisture monitoring and the irrigation management services. The soil moisture monitoring service will integrate both invasive (wireless sensor network (SoilNet)) and non-invasive soil moisture monitoring methods (cosmic-ray neutron sensors (CRNS)). Ultimately, a model will be developed that combines SoilNet and CRNS measurements to predict soil moisture time series. Soil water potential sensors will be incorporated as well.\\&lt;/p\\&gt;\\&lt;p\\&gt;Data provided by the above described service will be incorporated in an irrigation management service which will be based on hydrological modelling. The fully distributed, deterministic Community Land Model (CLM, version 5) will be applied which incorporates physically-based simulation of soil water balance and crop growth. Two different levels of application will be considered, namely the farm and watershed scale, which will be combined to weather forecast in order to provide irrigation scheduling advice. The farm scale application will take advantage of soil moisture monitoring data and provide farm specific irrigation scheduling, while the watershed scale application will provide a more generic irrigation advice based on the average cultivation practices. Furthermore, the CLM model will be coupled to a groundwater flow model in order to connect irrigation to groundwater availability. By doing so, it will be possible to support the efficient and sustainable groundwater management as well as competent water uses in an area that suffers from water scarcity.\\&lt;/p\\&gt;\\&lt;p\\&gt;These services will be implemented in the area of Pinios Hydrologic Observatory, located in central Greece. Three pilot orchards will be established introducing different soil moisture monitoring setups, while the boundaries of the Observatory will be used for the pilot implementation of irrigation management service on the watershed scale. Furthermore, two pilot vineyards located in northern Greece will be established in order to further test the services functionality on the farm scale.\\&lt;/p\\&gt;},\n\turldate = {2022-11-21},\n\tinstitution = {oral},\n\tauthor = {Pisinaras, Vassilios and Brogi, Cosimo and Bogena, Heye and Hendricks-Franssen, Harrie-Jan and Dombrowski, Olga and Panagopoulos, Andreas},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-4093},\n}\n\n

\n

\n\n\n

\n The H2020 ATLAS project (www.atlas-h2020.eu/) aims to develop an open, flexible and distributed platform that will provide services for the agricultural sector based on the seamless interconnection of sensors and machines. Two interconnected services that will be included in the platform are the soil moisture monitoring and the irrigation management services. The soil moisture monitoring service will integrate both invasive (wireless sensor network (SoilNet)) and non-invasive soil moisture monitoring methods (cosmic-ray neutron sensors (CRNS)). Ultimately, a model will be developed that combines SoilNet and CRNS measurements to predict soil moisture time series. Soil water potential sensors will be incorporated as well.Data provided by the above described service will be incorporated in an irrigation management service which will be based on hydrological modelling. The fully distributed, deterministic Community Land Model (CLM, version 5) will be applied which incorporates physically-based simulation of soil water balance and crop growth. Two different levels of application will be considered, namely the farm and watershed scale, which will be combined to weather forecast in order to provide irrigation scheduling advice. The farm scale application will take advantage of soil moisture monitoring data and provide farm specific irrigation scheduling, while the watershed scale application will provide a more generic irrigation advice based on the average cultivation practices. Furthermore, the CLM model will be coupled to a groundwater flow model in order to connect irrigation to groundwater availability. By doing so, it will be possible to support the efficient and sustainable groundwater management as well as competent water uses in an area that suffers from water scarcity.These services will be implemented in the area of Pinios Hydrologic Observatory, located in central Greece. Three pilot orchards will be established introducing different soil moisture monitoring setups, while the boundaries of the Observatory will be used for the pilot implementation of irrigation management service on the watershed scale. Furthermore, two pilot vineyards located in northern Greece will be established in order to further test the services functionality on the farm scale.\n

\n\n\n

\n \n\n \n \n Preidl, S.; Lange, M.; and Doktor, D.\n\n\n \n \n \n \n \n Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 240: 111673. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Introducing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{preidl_introducing_2020,\n\ttitle = {Introducing {APiC} for regionalised land cover mapping on the national scale using {Sentinel}-{2A} imagery},\n\tvolume = {240},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425720300420},\n\tdoi = {10.1016/j.rse.2020.111673},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Preidl, Sebastian and Lange, Maximilian and Doktor, Daniel},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {111673},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; and Sanchez-Azofeifa, A.\n\n\n \n \n \n \n \n Accuracy assessment on the number of flux terms needed to estimate in situ fAPAR.\n \n \n \n \n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 88: 102061. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Accuracy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{putzenlechner_accuracy_2020,\n\ttitle = {Accuracy assessment on the number of flux terms needed to estimate in situ {fAPAR}},\n\tvolume = {88},\n\tissn = {15698432},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0303243419310955},\n\tdoi = {10.1016/j.jag.2020.102061},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {International Journal of Applied Earth Observation and Geoinformation},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Sanchez-Azofeifa, Arturo},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {102061},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Rahmati, M.; Groh, J.; Graf, A.; Pütz, T.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rahmati_impact_2020,\n\ttitle = {On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20029},\n\tdoi = {10.1002/vzj2.20029},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Rahmati, Mehdi and Groh, Jannis and Graf, Alexander and Pütz, Thomas and Vanderborght, Jan and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Reiber, L.; Knillmann, S.; Foit, K.; and Liess, M.\n\n\n \n \n \n \n \n Species occurrence relates to pesticide gradient in streams.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 735: 138807. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Species$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{reiber_species_2020,\n\ttitle = {Species occurrence relates to pesticide gradient in streams},\n\tvolume = {735},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S004896972032324X},\n\tdoi = {10.1016/j.scitotenv.2020.138807},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Reiber, Lena and Knillmann, Saskia and Foit, Kaarina and Liess, Matthias},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {138807},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Reichenau, T. G.; Korres, W.; Schmidt, M.; Graf, A.; Welp, G.; Meyer, N.; Stadler, A.; Brogi, C.; and Schneider, K.\n\n\n \n \n \n \n \n A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 12(4): 2333–2364. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{reichenau_comprehensive_2020,\n\ttitle = {A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western {Germany}},\n\tvolume = {12},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/12/2333/2020/},\n\tdoi = {10.5194/essd-12-2333-2020},\n\tabstract = {Abstract. The development and validation of hydroecological\nland-surface models to simulate agricultural areas require extensive data\non weather, soil properties, agricultural management, and vegetation states\nand fluxes. However, these comprehensive data are rarely available since\nmeasurement, quality control, documentation, and compilation of the different\ndata types are costly in terms of time and money. Here, we present a\ncomprehensive dataset, which was collected at four agricultural sites within\nthe Rur catchment in western Germany in the framework of the Transregional\nCollaborative Research Centre 32 (TR32) “Patterns in\nSoil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data\nAssimilation”. Vegetation-related data comprise fresh and dry\nbiomass (green and brown, predominantly per organ), plant height, green and\nbrown leaf area index, phenological development state, nitrogen and carbon\ncontent (overall {\\textgreater} 17 000 entries), and masses of harvest residues\nand regrowth of vegetation after harvest or before planting of the main crop\n({\\textgreater} 250 entries). Vegetation data including LAI were collected in\nfrequencies of 1 to 3 weeks in the years 2015 until 2017, mostly\nduring overflights of the Sentinel 1 and Radarsat 2 satellites. In addition,\nfluxes of carbon, energy, and water ({\\textgreater} 180 000 half-hourly\nrecords) measured using the eddy covariance technique are included. Three\nflux time series have simultaneous data from two different heights. Data on\nagricultural management include sowing and harvest dates as well as information\non cultivation, fertilization, and agrochemicals (27 management periods). The\ndataset also includes gap-filled weather data ({\\textgreater} 200 000 hourly\nrecords) and soil parameters (particle size distributions, carbon and\nnitrogen content; {\\textgreater} 800 records). These data can also be useful\nfor development and validation of remote-sensing products. The dataset\nis hosted at the TR32 database\n(https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI\nhttps://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Earth System Science Data},\n\tauthor = {Reichenau, Tim G. and Korres, Wolfgang and Schmidt, Marius and Graf, Alexander and Welp, Gerhard and Meyer, Nele and Stadler, Anja and Brogi, Cosimo and Schneider, Karl},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2333--2364},\n}\n\n

\n

\n\n\n

\n Abstract. The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall \\textgreater 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (\\textgreater 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (\\textgreater 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (\\textgreater 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; \\textgreater 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).\n

\n\n\n

\n \n\n \n \n Risse‐Buhl, U.; Anlanger, C.; Chatzinotas, A.; Noss, C.; Lorke, A.; and Weitere, M.\n\n\n \n \n \n \n \n Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms.\n \n \n \n \n\n\n \n\n\n\n Limnology and Oceanography, 65(10): 2261–2277. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Near$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rissebuhl_near_2020,\n\ttitle = {Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms},\n\tvolume = {65},\n\tissn = {0024-3590, 1939-5590},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/lno.11451},\n\tdoi = {10.1002/lno.11451},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Limnology and Oceanography},\n\tauthor = {Risse‐Buhl, Ute and Anlanger, Christine and Chatzinotas, Antonis and Noss, Christian and Lorke, Andreas and Weitere, Markus},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2261--2277},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Scharnweber, T.; Smiljanic, M.; Cruz-García, R.; Manthey, M.; and Wilmking, M.\n\n\n \n \n \n \n \n Tree growth at the end of the 21st century - the extreme years 2018/19 as template for future growth conditions.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(7): 074022. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Tree$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{scharnweber_tree_2020,\n\ttitle = {Tree growth at the end of the 21st century - the extreme years 2018/19 as template for future growth conditions},\n\tvolume = {15},\n\tissn = {1748-9326},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ab865d},\n\tdoi = {10.1088/1748-9326/ab865d},\n\tnumber = {7},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Scharnweber, Tobias and Smiljanic, Marko and Cruz-García, Roberto and Manthey, Michael and Wilmking, Martin},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {074022},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Scheiffele, L. M.; Baroni, G.; Franz, T. E.; Jakobi, J.; and Oswald, S. E.\n\n\n \n \n \n \n \n A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 19(1). January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{scheiffele_profile_2020,\n\ttitle = {A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture},\n\tvolume = {19},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/vzj2.20083},\n\tdoi = {10.1002/vzj2.20083},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Scheiffele, Lena M. and Baroni, Gabriele and Franz, Trenton E. and Jakobi, Jannis and Oswald, Sascha E.},\n\tmonth = jan,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Schlingmann, M.; Tobler, U.; Berauer, B.; Garcia-Franco, N.; Wilfahrt, P.; Wiesmeier, M.; Jentsch, A.; Wolf, B.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Intensive slurry management and climate change promote nitrogen mining from organic matter-rich montane grassland soils.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 456(1-2): 81–98. November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Intensive$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schlingmann_intensive_2020,\n\ttitle = {Intensive slurry management and climate change promote nitrogen mining from organic matter-rich montane grassland soils},\n\tvolume = {456},\n\tissn = {0032-079X, 1573-5036},\n\turl = {https://link.springer.com/10.1007/s11104-020-04697-9},\n\tdoi = {10.1007/s11104-020-04697-9},\n\tabstract = {Abstract \n             \n              Aims \n              Consequences of climate change and land use intensification on the nitrogen (N) cycle of organic-matter rich grassland soils in the alpine region remain poorly understood. We aimed to identify fates of fertilizer N and to determine the overall N balance of an organic-matter rich grassland in the European alpine region as influenced by intensified management and warming. \n             \n             \n              Methods \n               \n                We combined \n                15 \n                N cattle slurry labelling with a space for time climate change experiment, which was based on translocation of intact plant-soil mesocosms down an elevational gradient to induce warming of +1 °C and + 3 °C. Mesocosms were subject to either extensive or intensive management. The fate of slurry-N was traced in the plant-soil system. \n               \n             \n             \n              Results \n               \n                Grassland productivity was very high (8.2 t - 19.4 t dm ha \n                −1 \n                 yr \n                −1 \n                ), recovery of slurry \n                15 \n                N in mowed plant biomass was, however, low (9.6–14.7\\%), illustrating low fertilizer N use efficiency and high supply of plant available N via mineralization of soil organic matter (SOM). Higher \n                15 \n                N recovery rates (20.2–31.8\\%) were found in the soil N pool, dominated by recovery in unextractable N. Total \n                15 \n                N recovery was approximately half of the applied tracer, indicating substantial loss to the environment. Overall, high N export by harvest (107–360 kg N ha \n                −1 \n                 yr \n                −1 \n                ) markedly exceeded N inputs, leading to a negative grassland N balance. \n               \n             \n             \n              Conclusions \n              Here provided results suggests a risk of soil N mining in montane grasslands, which increases both under climate change and land use intensification.},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-02},\n\tjournal = {Plant and Soil},\n\tauthor = {Schlingmann, Marcus and Tobler, Ursina and Berauer, Bernd and Garcia-Franco, Noelia and Wilfahrt, Peter and Wiesmeier, Martin and Jentsch, Anke and Wolf, Benjamin and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = nov,\n\tyear = {2020},\n\tpages = {81--98},\n}\n\n

\n

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

\n \n\n \n \n Schubert, M.; Knoeller, K.; Mueller, C.; and Gilfedder, B.\n\n\n \n \n \n \n \n Investigating River Water/Groundwater Interaction along a Rivulet Section by 222Rn Mass Balancing.\n \n \n \n \n\n\n \n\n\n\n Water, 12(11): 3027. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schubert_investigating_2020,\n\ttitle = {Investigating {River} {Water}/{Groundwater} {Interaction} along a {Rivulet} {Section} by {222Rn} {Mass} {Balancing}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/11/3027},\n\tdoi = {10.3390/w12113027},\n\tabstract = {Investigation of river water/groundwater interaction aims generally at: (i) localizing water migration pathways; and (ii) quantifying water and associated matter exchange between the two natural water resources. Related numerical models generally rely on model-specific parameters that represent the physical conditions of the catchment and suitable aqueous tracer data. A generally applicable approach for this purpose is based on the finite element model FINIFLUX that is using the radioactive noble gas radon-222 as naturally occurring tracer. During the study discussed in this paper, radon and physical stream data were used with the aim to localize and quantify groundwater discharge into a well-defined section of a small headwater stream. Besides site-specific results of two sampling campaigns, the outcomes of the study reveal: (i) the general difficulties of conducting river water/groundwater interaction studies in small and heterogeneous headwater catchments; and (ii) the particular challenge of defining well constrained site- and campaign-specific values for both the groundwater radon endmember and the radon degassing coefficient. It was revealed that determination of both parameters should be based on as many data sources as possible and include a critical assessment of the reasonability of the gathered and used datasets. The results of our study exposed potential limitations of the approach if executed in small and turbulent headwater streams. Hence, we want to emphasize that the project was not only executed as a case study at a distinct site but rather aimed at evaluating the applicability of the chosen approach for conducting river water/groundwater interaction studies in heterogeneous headwater catchments.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Schubert, Michael and Knoeller, Kay and Mueller, Christin and Gilfedder, Benjamin},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {3027},\n}\n\n

\n

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

\n \n\n \n \n Schucknecht, A.; Krämer, A.; Asam, S.; Mejia-Aguilar, A.; Garcia-Franco, N.; Schuchardt, M. A.; Jentsch, A.; and Kiese, R.\n\n\n \n \n \n \n \n Vegetation traits of pre-Alpine grasslands in southern Germany.\n \n \n \n \n\n\n \n\n\n\n Scientific Data, 7(1): 316. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Vegetation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{schucknecht_vegetation_2020,\n\ttitle = {Vegetation traits of pre-{Alpine} grasslands in southern {Germany}},\n\tvolume = {7},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-020-00651-7},\n\tdoi = {10.1038/s41597-020-00651-7},\n\tabstract = {Abstract \n             \n              The data set contains information on aboveground vegetation traits of {\\textgreater} 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project (“Sustainable use of alpine and pre-alpine grassland soils in a changing climate”; \n              https://www.susalps.de/ \n              ) to provide \n              in-situ \n              data for the calibration and validation of remote sensing based models to estimate grassland traits.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Data},\n\tauthor = {Schucknecht, Anne and Krämer, Alexander and Asam, Sarah and Mejia-Aguilar, Abraham and Garcia-Franco, Noelia and Schuchardt, Max A. and Jentsch, Anke and Kiese, Ralf},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {316},\n}\n\n

\n

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

\n \n\n \n \n Siddique, A.; Liess, M.; Shahid, N.; and Becker, J. M.\n\n\n \n \n \n \n \n Insecticides in agricultural streams exert pressure for adaptation but impair performance in Gammarus pulex at regulatory acceptable concentrations.\n \n \n \n \n\n\n \n\n\n\n Science of The Total Environment, 722: 137750. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Insecticides$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{siddique_insecticides_2020,\n\ttitle = {Insecticides in agricultural streams exert pressure for adaptation but impair performance in {Gammarus} pulex at regulatory acceptable concentrations},\n\tvolume = {722},\n\tissn = {00489697},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0048969720312614},\n\tdoi = {10.1016/j.scitotenv.2020.137750},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Siddique, Ayesha and Liess, Matthias and Shahid, Naeem and Becker, Jeremias Martin},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {137750},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Spank, U.; Hehn, M.; Keller, P.; Koschorreck, M.; and Bernhofer, C.\n\n\n \n \n \n \n \n A Season of Eddy-Covariance Fluxes Above an Extensive Water Body Based on Observations from a Floating Platform.\n \n \n \n \n\n\n \n\n\n\n Boundary-Layer Meteorology, 174(3): 433–464. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{spank_season_2020,\n\ttitle = {A {Season} of {Eddy}-{Covariance} {Fluxes} {Above} an {Extensive} {Water} {Body} {Based} on {Observations} from a {Floating} {Platform}},\n\tvolume = {174},\n\tissn = {0006-8314, 1573-1472},\n\turl = {http://link.springer.com/10.1007/s10546-019-00490-z},\n\tdoi = {10.1007/s10546-019-00490-z},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Boundary-Layer Meteorology},\n\tauthor = {Spank, Uwe and Hehn, Markus and Keller, Philipp and Koschorreck, Matthias and Bernhofer, Christian},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {433--464},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Sun, Y.; Wu, B.; Wiekenkamp, I.; Kooijman, A. M.; and Bol, R.\n\n\n \n \n \n \n \n Uranium Vertical and Lateral Distribution in a German Forested Catchment.\n \n \n \n \n\n\n \n\n\n\n Forests, 11(12): 1351. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Uranium$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{sun_uranium_2020,\n\ttitle = {Uranium {Vertical} and {Lateral} {Distribution} in a {German} {Forested} {Catchment}},\n\tvolume = {11},\n\tissn = {1999-4907},\n\turl = {https://www.mdpi.com/1999-4907/11/12/1351},\n\tdoi = {10.3390/f11121351},\n\tabstract = {The natural measurements of uranium (U) are important for establishing natural baseline levels of U in soil. The relations between U and other elements are important to determine the extent of geological origin of soil U. The present study was aimed at providing a three-dimensional view of soil U distribution in a forested catchment (ca. 38.5 ha) in western Germany. The evaluated data, containing 155 sampled points, each with four major soil horizons (L/Of, Oh, A, and B), were collected from two existing datasets. The vertical U distribution, the lateral pattern of U in the catchment, and the occurrence of correlations between U and three groups of elements (nutrient elements, heavy metals, and rare earth elements) were examined. The results showed the median U concentration increased sevenfold from the top horizon L/Of (0.14 mg kg−1) to the B horizon (1.01 mg kg−1), suggesting a geogenic origin of soil U. Overall, soil U concentration was found to be negatively correlated with some plant macronutrients (C, N, K, S, Ca) but positively with others (P, Mg, Cu, Zn, Fe, Mn, Mo). The negative correlations between U and some macronutrients indicated a limited accumulation of plant-derived U in soil, possibly due to low phytoavailability of U. Positive correlations were also found between U concentration and heavy metals (Cr, Co, Ni, Ga, As, Cd, Hg, Pb) or rare earth elements, which further pointed to a geogenic origin of soil U in this forested catchment.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2022-11-02},\n\tjournal = {Forests},\n\tauthor = {Sun, Yajie and Wu, Bei and Wiekenkamp, Inge and Kooijman, Annemieke M. and Bol, Roland},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {1351},\n}\n\n

\n

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

\n \n\n \n \n Tarasova, L.; Basso, S.; and Merz, R.\n\n\n \n \n \n \n \n Transformation of Generation Processes From Small Runoff Events to Large Floods.\n \n \n \n \n\n\n \n\n\n\n Geophysical Research Letters, 47(22). November 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Transformation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{tarasova_transformation_2020,\n\ttitle = {Transformation of {Generation} {Processes} {From} {Small} {Runoff} {Events} to {Large} {Floods}},\n\tvolume = {47},\n\tissn = {0094-8276, 1944-8007},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2020GL090547},\n\tdoi = {10.1029/2020GL090547},\n\tlanguage = {en},\n\tnumber = {22},\n\turldate = {2022-11-02},\n\tjournal = {Geophysical Research Letters},\n\tauthor = {Tarasova, L. and Basso, S. and Merz, R.},\n\tmonth = nov,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Tarasova, L.; Basso, S.; Wendi, D.; Viglione, A.; Kumar, R.; and Merz, R.\n\n\n \n \n \n \n \n A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 56(5). May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{tarasova_processbased_2020,\n\ttitle = {A {Process}‐{Based} {Framework} to {Characterize} and {Classify} {Runoff} {Events}: {The} {Event} {Typology} of {Germany}},\n\tvolume = {56},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {A {Process}‐{Based} {Framework} to {Characterize} and {Classify} {Runoff} {Events}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR026951},\n\tdoi = {10.1029/2019WR026951},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-02},\n\tjournal = {Water Resources Research},\n\tauthor = {Tarasova, L. and Basso, S. and Wendi, D. and Viglione, A. and Kumar, R. and Merz, R.},\n\tmonth = may,\n\tyear = {2020},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Tveit, A. T.; Kiss, A.; Winkel, M.; Horn, F.; Hájek, T.; Svenning, M. M.; Wagner, D.; and Liebner, S.\n\n\n \n \n \n \n \n Environmental patterns of brown moss- and Sphagnum-associated microbial communities.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 10(1): 22412. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Environmental$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{tveit_environmental_2020,\n\ttitle = {Environmental patterns of brown moss- and {Sphagnum}-associated microbial communities},\n\tvolume = {10},\n\tissn = {2045-2322},\n\turl = {http://www.nature.com/articles/s41598-020-79773-2},\n\tdoi = {10.1038/s41598-020-79773-2},\n\tabstract = {Abstract \n             \n              Northern peatlands typically develop through succession from fens dominated by the moss family Amblystegiaceae to bogs dominated by the moss genus \n              Sphagnum \n              . How the different plants and abiotic environmental conditions provided in Amblystegiaceae and \n              Sphagnum \n              peat shape the respective moss associated microbial communities is unknown. Through a large-scale molecular and biogeochemical study spanning Arctic, sub-Arctic and temperate regions we assessed how the endo- and epiphytic microbial communities of natural northern peatland mosses relate to peatland type ( \n              Sphagnum \n              and Amblystegiaceae), location, moss taxa and abiotic environmental variables. Microbial diversity and community structure were distinctly different between Amblystegiaceae and \n              Sphagnum \n              peatlands, and within each of these two peatland types moss taxon explained the largest part of microbial community variation. \n              Sphagnum \n              and Amblystegiaceae shared few ({\\textless} 1\\% of all operational taxonomic units (OTUs)) but strikingly abundant (up to 65\\% of relative abundance) OTUs. This core community overlapped by one third with the \n              Sphagnum \n              -specific core-community. Thus, the most abundant microorganisms in \n              Sphagnum \n              that are also found in all the \n              Sphagnum \n              plants studied, are the same OTUs as those few shared with Amblystegiaceae. Finally, we could confirm that these highly abundant OTUs were endophytes in \n              Sphagnum \n              , but epiphytes on Amblystegiaceae. We conclude that moss taxa and abiotic environmental variables associate with particular microbial communities. While moss taxon was the most influential parameter, hydrology, pH and temperature also had significant effects on the microbial communities. A small though highly abundant core community is shared between \n              Sphagnum \n              and Amblystegiaceae.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Scientific Reports},\n\tauthor = {Tveit, Alexander Tøsdal and Kiss, Andrea and Winkel, Matthias and Horn, Fabian and Hájek, Tomáš and Svenning, Mette Marianne and Wagner, Dirk and Liebner, Susanne},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {22412},\n}\n\n

\n

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

\n \n\n \n \n Utom, A. U.; Werban, U.; Leven, C.; Müller, C.; Knöller, K.; Vogt, C.; and Dietrich, P.\n\n\n \n \n \n \n \n Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively: an assessment of dual-nitrate isotopic and chemical evidence in a stratified alluvial aquifer.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 147(2): 211–223. January 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Groundwater$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{utom_groundwater_2020,\n\ttitle = {Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively: an assessment of dual-nitrate isotopic and chemical evidence in a stratified alluvial aquifer},\n\tvolume = {147},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Groundwater nitrification and denitrification are not always strictly aerobic and anaerobic processes, respectively},\n\turl = {http://link.springer.com/10.1007/s10533-020-00637-y},\n\tdoi = {10.1007/s10533-020-00637-y},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-02},\n\tjournal = {Biogeochemistry},\n\tauthor = {Utom, Ahamefula U. and Werban, Ulrike and Leven, Carsten and Müller, Christin and Knöller, Kay and Vogt, Carsten and Dietrich, Peter},\n\tmonth = jan,\n\tyear = {2020},\n\tpages = {211--223},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Vallentin, C.; Dobers, E. S.; Itzerott, S.; Kleinschmit, B.; and Spengler, D.\n\n\n \n \n \n \n \n Delineation of management zones with spatial data fusion and belief theory.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 21(4): 802–830. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Delineation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{vallentin_delineation_2020,\n\ttitle = {Delineation of management zones with spatial data fusion and belief theory},\n\tvolume = {21},\n\tissn = {1385-2256, 1573-1618},\n\turl = {http://link.springer.com/10.1007/s11119-019-09696-0},\n\tdoi = {10.1007/s11119-019-09696-0},\n\tabstract = {Abstract \n            Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Precision Agriculture},\n\tauthor = {Vallentin, Claudia and Dobers, Eike Stefan and Itzerott, Sibylle and Kleinschmit, Birgit and Spengler, Daniel},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {802--830},\n}\n\n

\n

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

\n \n\n \n \n Vidal, A.; Schucknecht, A.; Toechterle, P.; Linares, D. R. A.; Garcia-Franco, N.; von Heßberg, A.; Krämer, A.; Sierts, A.; Fischer, A.; Willibald, G.; Fuetterer, S.; Ewald, J.; Baumert, V.; Weiss, M.; Schulz, S.; Schloter, M.; Bogacki, W.; Wiesmeier, M.; Mueller, C. W.; and Dannenmann, M.\n\n\n \n \n \n \n \n High resistance of soils to short-term re-grazing in a long-term abandoned alpine pasture.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 300: 107008. September 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{vidal_high_2020,\n\ttitle = {High resistance of soils to short-term re-grazing in a long-term abandoned alpine pasture},\n\tvolume = {300},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880920301936},\n\tdoi = {10.1016/j.agee.2020.107008},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Vidal, Alix and Schucknecht, Anne and Toechterle, Paul and Linares, Diana Rocio Andrade and Garcia-Franco, Noelia and von Heßberg, Andreas and Krämer, Alexander and Sierts, Andrea and Fischer, Alfred and Willibald, Georg and Fuetterer, Sarah and Ewald, Jörg and Baumert, Vera and Weiss, Michael and Schulz, Stefanie and Schloter, Michael and Bogacki, Wolfgang and Wiesmeier, Martin and Mueller, Carsten W. and Dannenmann, Michael},\n\tmonth = sep,\n\tyear = {2020},\n\tpages = {107008},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Vila-Guerau de Arellano, J.; Ney, P.; Hartogensis, O.; de Boer, H.; van Diepen, K.; Emin, D.; de Groot, G.; Klosterhalfen, A.; Langensiepen, M.; Matveeva, M.; Miranda, G.; Moene, A.; Rascher, U.; Röckmann, T.; Adnew, G.; and Graf, A.\n\n\n \n \n \n \n \n CloudRoots: Integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land-atmosphere interactions.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Air - Land Exchange, May 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"CloudRoots:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{vila-guerau_de_arellano_cloudroots_2020-1,\n\ttype = {preprint},\n\ttitle = {{CloudRoots}: {Integration} of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land-atmosphere interactions},\n\tshorttitle = {{CloudRoots}},\n\turl = {https://bg.copernicus.org/preprints/bg-2020-132/bg-2020-132.pdf},\n\tabstract = {Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational data set that includes soil, plant and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub–kilometre scale. Our findings demonstrate the need to include measurements at leaf level in order to obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the mechanistic model reproduces satisfactorily the stomatal leaf conductance and the leaf-level photosynthesis. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From the high frequency and vertical resolution state variables and CO2 measurements, we infer a profile of the plant assimilation that shows a strong non-linear behaviour. Observations taken by a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of the photosynthetically active radiation (PAR) by clouds, the so-called cloud flecks. More specifically, we find two-minute delays between the cloud radiation perturbation and ET. The impact of surface heterogeneity was further studied using ET estimates infer from the sun-induced fluorescence data and show small variation of ET in spite of the plant functional type differences. To study the relevance of advection and surface heterogeneity on the land-atmosphere interaction, we employ a coupled surface-atmospheric conceptual model that integrates the surface and upper-air observations taken at different scales: from the leaf-level to the landscape. At the landscape scale, we obtain the representative sensible heat flux that is consistent with the evolution of the boundary-layer depth evolution. Finally, throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large variations of plant transpiration at the leaf and canopy scales. The use of different instrumental techniques enables us to compare the total ET at various growing stages, from booting to senescence. There is satisfactory agreement between evapotranspiration of total ET, but the values remain sensitive to the scale at which ET is measured or modelled.},\n\turldate = {2022-11-02},\n\tinstitution = {Biogeochemistry: Air - Land Exchange},\n\tauthor = {Vila-Guerau de Arellano, Jordi and Ney, Patrizia and Hartogensis, Oscar and de Boer, Hugo and van Diepen, Kevien and Emin, Dzhaner and de Groot, Gesike and Klosterhalfen, Anne and Langensiepen, Matthias and Matveeva, Maria and Miranda, Gabriela and Moene, Arnold and Rascher, Uwe and Röckmann, Thomas and Adnew, Getachew and Graf, Alexander},\n\tmonth = may,\n\tyear = {2020},\n\tdoi = {10.5194/bg-2020-132},\n}\n\n

\n

\n\n\n

\n Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational data set that includes soil, plant and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub–kilometre scale. Our findings demonstrate the need to include measurements at leaf level in order to obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the mechanistic model reproduces satisfactorily the stomatal leaf conductance and the leaf-level photosynthesis. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From the high frequency and vertical resolution state variables and CO2 measurements, we infer a profile of the plant assimilation that shows a strong non-linear behaviour. Observations taken by a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of the photosynthetically active radiation (PAR) by clouds, the so-called cloud flecks. More specifically, we find two-minute delays between the cloud radiation perturbation and ET. The impact of surface heterogeneity was further studied using ET estimates infer from the sun-induced fluorescence data and show small variation of ET in spite of the plant functional type differences. To study the relevance of advection and surface heterogeneity on the land-atmosphere interaction, we employ a coupled surface-atmospheric conceptual model that integrates the surface and upper-air observations taken at different scales: from the leaf-level to the landscape. At the landscape scale, we obtain the representative sensible heat flux that is consistent with the evolution of the boundary-layer depth evolution. Finally, throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large variations of plant transpiration at the leaf and canopy scales. The use of different instrumental techniques enables us to compare the total ET at various growing stages, from booting to senescence. There is satisfactory agreement between evapotranspiration of total ET, but the values remain sensitive to the scale at which ET is measured or modelled.\n

\n\n\n

\n \n\n \n \n Vilà-Guerau de Arellano, J.; Ney, P.; Hartogensis, O.; de Boer, H.; van Diepen, K.; Emin, D.; de Groot, G.; Klosterhalfen, A.; Langensiepen, M.; Matveeva, M.; Miranda-García, G.; Moene, A. F.; Rascher, U.; Röckmann, T.; Adnew, G.; Brüggemann, N.; Rothfuss, Y.; and Graf, A.\n\n\n \n \n \n \n \n CloudRoots: integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land–atmosphere interactions.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(17): 4375–4404. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"CloudRoots:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{vila-guerau_de_arellano_cloudroots_2020,\n\ttitle = {{CloudRoots}: integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land–atmosphere interactions},\n\tvolume = {17},\n\tissn = {1726-4189},\n\tshorttitle = {{CloudRoots}},\n\turl = {https://bg.copernicus.org/articles/17/4375/2020/},\n\tdoi = {10.5194/bg-17-4375-2020},\n\tabstract = {Abstract. The CloudRoots field experiment was designed to obtain a\ncomprehensive observational dataset that includes soil, plant, and\natmospheric variables to investigate the interaction between a heterogeneous\nland surface and its overlying atmospheric boundary layer at the sub-hourly\nand sub-kilometre scale. Our findings demonstrate the need to include\nmeasurements at leaf level to better understand the relations between\nstomatal aperture and evapotranspiration (ET) during the growing season at\nthe diurnal scale. Based on these observations, we obtain accurate\nparameters for the mechanistic representation of photosynthesis and stomatal\naperture. Once the new parameters are implemented, the model reproduces the\nstomatal leaf conductance and the leaf-level photosynthesis satisfactorily.\nAt the canopy scale, we find a consistent diurnal pattern on the\ncontributions of plant transpiration and soil evaporation using different\nmeasurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a\nprofile of the CO2 assimilation in the canopy with non-linear\nvariations with height. Observations taken with a laser scintillometer allow\nus to quantify the non-steadiness of the surface turbulent fluxes during the\nrapid changes driven by perturbation of photosynthetically active radiation\nby cloud flecks. More specifically, we find 2 min delays between the\ncloud radiation perturbation and ET. To study the relevance of advection and\nsurface heterogeneity for the land–atmosphere interaction, we employ a\ncoupled surface–atmospheric conceptual model that integrates the surface and\nupper-air observations made at different scales from leaf to the landscape.\nAt the landscape scale, we calculate a composite sensible heat flux by\nweighting measured fluxes with two different land use categories, which is\nconsistent with the diurnal evolution of the boundary layer depth. Using\nsun-induced fluorescence measurements, we also quantify the spatial\nvariability of ET and find large variations at the sub-kilometre scale\naround the CloudRoots site. Our study shows that throughout the entire\ngrowing season, the wide variations in stomatal opening and photosynthesis\nlead to large diurnal variations of plant transpiration at the leaf, plant,\ncanopy, and landscape scales. Integrating different advanced instrumental\ntechniques with modelling also enables us to determine variations of ET that\ndepend on the scale where the measurement were taken and on the plant\ngrowing stage.},\n\tlanguage = {en},\n\tnumber = {17},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Vilà-Guerau de Arellano, Jordi and Ney, Patrizia and Hartogensis, Oscar and de Boer, Hugo and van Diepen, Kevin and Emin, Dzhaner and de Groot, Geiske and Klosterhalfen, Anne and Langensiepen, Matthias and Matveeva, Maria and Miranda-García, Gabriela and Moene, Arnold F. and Rascher, Uwe and Röckmann, Thomas and Adnew, Getachew and Brüggemann, Nicolas and Rothfuss, Youri and Graf, Alexander},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {4375--4404},\n}\n\n

\n

\n\n\n

\n Abstract. The CloudRoots field experiment was designed to obtain a comprehensive observational dataset that includes soil, plant, and atmospheric variables to investigate the interaction between a heterogeneous land surface and its overlying atmospheric boundary layer at the sub-hourly and sub-kilometre scale. Our findings demonstrate the need to include measurements at leaf level to better understand the relations between stomatal aperture and evapotranspiration (ET) during the growing season at the diurnal scale. Based on these observations, we obtain accurate parameters for the mechanistic representation of photosynthesis and stomatal aperture. Once the new parameters are implemented, the model reproduces the stomatal leaf conductance and the leaf-level photosynthesis satisfactorily. At the canopy scale, we find a consistent diurnal pattern on the contributions of plant transpiration and soil evaporation using different measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (CO2) and other state variables, we infer a profile of the CO2 assimilation in the canopy with non-linear variations with height. Observations taken with a laser scintillometer allow us to quantify the non-steadiness of the surface turbulent fluxes during the rapid changes driven by perturbation of photosynthetically active radiation by cloud flecks. More specifically, we find 2 min delays between the cloud radiation perturbation and ET. To study the relevance of advection and surface heterogeneity for the land–atmosphere interaction, we employ a coupled surface–atmospheric conceptual model that integrates the surface and upper-air observations made at different scales from leaf to the landscape. At the landscape scale, we calculate a composite sensible heat flux by weighting measured fluxes with two different land use categories, which is consistent with the diurnal evolution of the boundary layer depth. Using sun-induced fluorescence measurements, we also quantify the spatial variability of ET and find large variations at the sub-kilometre scale around the CloudRoots site. Our study shows that throughout the entire growing season, the wide variations in stomatal opening and photosynthesis lead to large diurnal variations of plant transpiration at the leaf, plant, canopy, and landscape scales. Integrating different advanced instrumental techniques with modelling also enables us to determine variations of ET that depend on the scale where the measurement were taken and on the plant growing stage.\n

\n\n\n

\n \n\n \n \n Vitale, D.; Fratini, G.; Bilancia, M.; Nicolini, G.; Sabbatini, S.; and Papale, D.\n\n\n \n \n \n \n \n A robust data cleaning procedure for eddy covariance flux measurements.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 17(6): 1367–1391. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{vitale_robust_2020,\n\ttitle = {A robust data cleaning procedure for eddy covariance flux measurements},\n\tvolume = {17},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/17/1367/2020/},\n\tdoi = {10.5194/bg-17-1367-2020},\n\tabstract = {Abstract. The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning.\nThe proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure.\nResults evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality.\nWe expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-02},\n\tjournal = {Biogeosciences},\n\tauthor = {Vitale, Domenico and Fratini, Gerardo and Bilancia, Massimo and Nicolini, Giacomo and Sabbatini, Simone and Papale, Dario},\n\tmonth = mar,\n\tyear = {2020},\n\tpages = {1367--1391},\n}\n\n

\n

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

\n \n\n \n \n Wang, J.; Bogena, H. R.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n Stable-Isotope-Aided Investigation of the Effect of Redox Potential on Nitrous Oxide Emissions as Affected by Water Status and N Fertilization.\n \n \n \n \n\n\n \n\n\n\n Water, 12(10): 2918. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Stable-Isotope-Aided$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_stable-isotope-aided_2020,\n\ttitle = {Stable-{Isotope}-{Aided} {Investigation} of the {Effect} of {Redox} {Potential} on {Nitrous} {Oxide} {Emissions} as {Affected} by {Water} {Status} and {N} {Fertilization}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/10/2918},\n\tdoi = {10.3390/w12102918},\n\tabstract = {Soils are the dominant source of atmospheric nitrous oxide (N2O), especially agricultural soils that experience both waterlogging and intensive nitrogen fertilization. However, soil heterogeneity and the irregular occurrence of hydrological events hamper the prediction of the temporal and spatial dynamics of N2O production and transport in soils. Because soil moisture influences soil redox potential, and as soil N cycling processes are redox-sensitive, redox potential measurements could help us to better understand and predict soil N cycling and N2O emissions. Despite its importance, only a few studies have investigated the control of redox potential on N2Oemission from soils in detail. This study aimed to partition the different microbial processes involved in N2O production (nitrification and denitrification) by using redox measurements combined with isotope analysis at natural abundance and 15N-enriched. To this end, we performed long-term laboratory lysimeter experiments to mimic common agricultural irrigation and fertilization procedures. In addition, we used isotope analysis to characterize the distribution and partitioning of N2O sources and explored the 15N-N2O site preference to further constrain N2O microbial processes. We found that irrigation, saturation, and drainage induced changes in soil redox potential, which were closely related to changes in N2O emission from the soil as well as to changes in the vertical concentration profiles of dissolved N2O, nitrate (NO3−) and ammonium (NH4+). The results showed that the redox potential could be used as an indicator for NH4+, NO3−, and N2O production and consumption processes along the soil profile. For example, after a longer saturation period of unfertilized soil, the NO3− concentration was linearly correlated with the average redox values at the different depths (R2 = 0.81). During the transition from saturation to drainage, but before fertilization, the soil showed an increase in N2O emissions, which originated mainly from nitrification as indicated by the isotopic signatures of N2O (δ15N bulk, δ18O and 15N-N2O site preference). After fertilization, N2O still mainly originated from nitrification at the beginning, also indicated by high redox potential and the increase of dissolved NO3−. Denitrification mainly occurred during the last saturation period, deduced from the simultaneous 15N isotope analysis of NO3− and N2O. Our findings suggest that redox potential measurements provide suitable information for improving the prediction of soil N2O emissions and the distribution of mineral N species along the soil profile under different hydrological and fertilization regimes.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Wang, Jihuan and Bogena, Heye R. and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {2918},\n}\n\n

\n

\n\n\n

\n Soils are the dominant source of atmospheric nitrous oxide (N2O), especially agricultural soils that experience both waterlogging and intensive nitrogen fertilization. However, soil heterogeneity and the irregular occurrence of hydrological events hamper the prediction of the temporal and spatial dynamics of N2O production and transport in soils. Because soil moisture influences soil redox potential, and as soil N cycling processes are redox-sensitive, redox potential measurements could help us to better understand and predict soil N cycling and N2O emissions. Despite its importance, only a few studies have investigated the control of redox potential on N2Oemission from soils in detail. This study aimed to partition the different microbial processes involved in N2O production (nitrification and denitrification) by using redox measurements combined with isotope analysis at natural abundance and 15N-enriched. To this end, we performed long-term laboratory lysimeter experiments to mimic common agricultural irrigation and fertilization procedures. In addition, we used isotope analysis to characterize the distribution and partitioning of N2O sources and explored the 15N-N2O site preference to further constrain N2O microbial processes. We found that irrigation, saturation, and drainage induced changes in soil redox potential, which were closely related to changes in N2O emission from the soil as well as to changes in the vertical concentration profiles of dissolved N2O, nitrate (NO3−) and ammonium (NH4+). The results showed that the redox potential could be used as an indicator for NH4+, NO3−, and N2O production and consumption processes along the soil profile. For example, after a longer saturation period of unfertilized soil, the NO3− concentration was linearly correlated with the average redox values at the different depths (R2 = 0.81). During the transition from saturation to drainage, but before fertilization, the soil showed an increase in N2O emissions, which originated mainly from nitrification as indicated by the isotopic signatures of N2O (δ15N bulk, δ18O and 15N-N2O site preference). After fertilization, N2O still mainly originated from nitrification at the beginning, also indicated by high redox potential and the increase of dissolved NO3−. Denitrification mainly occurred during the last saturation period, deduced from the simultaneous 15N isotope analysis of NO3− and N2O. Our findings suggest that redox potential measurements provide suitable information for improving the prediction of soil N2O emissions and the distribution of mineral N species along the soil profile under different hydrological and fertilization regimes.\n

\n\n\n

\n \n\n \n \n Wang, N.; Wang, C.; Dannenmann, M.; Butterbach-Bahl, K.; and Huang, J.\n\n\n \n \n \n \n \n Response of microbial community and net nitrogen turnover to modify climate change in Alpine meadow.\n \n \n \n \n\n\n \n\n\n\n Applied Soil Ecology, 152: 103553. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Response$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_response_2020,\n\ttitle = {Response of microbial community and net nitrogen turnover to modify climate change in {Alpine} meadow},\n\tvolume = {152},\n\tissn = {09291393},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0929139319314350},\n\tdoi = {10.1016/j.apsoil.2020.103553},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Applied Soil Ecology},\n\tauthor = {Wang, Nannan and Wang, Changhui and Dannenmann, Michael and Butterbach-Bahl, Klaus and Huang, Jianhui},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {103553},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Ward, K. J.; Chabrillat, S.; Brell, M.; Castaldi, F.; Spengler, D.; and Foerster, S.\n\n\n \n \n \n \n \n Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 12(20): 3451. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{ward_mapping_2020,\n\ttitle = {Mapping {Soil} {Organic} {Carbon} for {Airborne} and {Simulated} {EnMAP} {Imagery} {Using} the {LUCAS} {Soil} {Database} and a {Local} {PLSR}},\n\tvolume = {12},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/12/20/3451},\n\tdoi = {10.3390/rs12203451},\n\tabstract = {Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.},\n\tlanguage = {en},\n\tnumber = {20},\n\turldate = {2022-11-02},\n\tjournal = {Remote Sensing},\n\tauthor = {Ward, Kathrin J. and Chabrillat, Sabine and Brell, Maximilian and Castaldi, Fabio and Spengler, Daniel and Foerster, Saskia},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {3451},\n}\n\n

\n

\n\n\n

\n Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.\n

\n\n\n

\n \n\n \n \n Weitere, M.; Brauns, M.; Rinke, K.; Borchardt, D.; and Wentzky, V.\n\n\n \n \n \n \n \n Wasserqualität und Biodiversität. Eine enge wechselseitige Beziehung.\n \n \n \n \n\n\n \n\n\n\n ESKP-Themenspezial: Biodiversität,576 KB, p. 54–57. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Wasserqualität$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{weitere_wasserqualitat_2020,\n\ttitle = {Wasserqualität und {Biodiversität}. {Eine} enge wechselseitige {Beziehung}},\n\tcopyright = {CC-BY 4.0},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000867},\n\tdoi = {10.2312/ESKP.2020.1.2.4},\n\tabstract = {Nur knapp 10 Prozent unserer Gewässer sind in einem guten ökologischen Zustand. Die Ursachen sind vielfältig: Abflussregulierung, Gewässerverbauung, Nährstoffe, Bodenerosion und Pestizide aus der Landwirtschaft sowie Rückstände aus städtischen Kläranlagen. Was aber heißt der Verlust von Biodiversität für die Gewässerqualität? Führt der Verlust von Artenvielfalt zu einer Verschlechterung der Gewässerqualität? Die Methode der stabilen Isotope beantwortet diese Fragen.},\n\tlanguage = {de},\n\turldate = {2022-11-02},\n\tjournal = {ESKP-Themenspezial: Biodiversität},\n\tauthor = {Weitere, Markus and Brauns, Mario and Rinke, Karsten and Borchardt, Dietrich and Wentzky, Valerie},\n\tyear = {2020},\n\tpages = {576 KB, p. 54--57},\n}\n\n

\n

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

\n \n\n \n \n Wellmann, T.; Lausch, A.; Andersson, E.; Knapp, S.; Cortinovis, C.; Jache, J.; Scheuer, S.; Kremer, P.; Mascarenhas, A.; Kraemer, R.; Haase, A.; Schug, F.; and Haase, D.\n\n\n \n \n \n \n \n Remote sensing in urban planning: Contributions towards ecologically sound policies?.\n \n \n \n \n\n\n \n\n\n\n Landscape and Urban Planning, 204: 103921. December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Remote$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wellmann_remote_2020,\n\ttitle = {Remote sensing in urban planning: {Contributions} towards ecologically sound policies?},\n\tvolume = {204},\n\tissn = {01692046},\n\tshorttitle = {Remote sensing in urban planning},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0169204620308860},\n\tdoi = {10.1016/j.landurbplan.2020.103921},\n\tlanguage = {en},\n\turldate = {2022-11-02},\n\tjournal = {Landscape and Urban Planning},\n\tauthor = {Wellmann, Thilo and Lausch, Angela and Andersson, Erik and Knapp, Sonja and Cortinovis, Chiara and Jache, Jessica and Scheuer, Sebastian and Kremer, Peleg and Mascarenhas, André and Kraemer, Roland and Haase, Annegret and Schug, Franz and Haase, Dagmar},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {103921},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Wentzky, V. C.; Tittel, J.; Jäger, C. G.; Bruggeman, J.; and Rinke, K.\n\n\n \n \n \n \n \n Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state.\n \n \n \n \n\n\n \n\n\n\n Journal of Ecology, 108(4): 1649–1663. July 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Seasonal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wentzky_seasonal_2020,\n\ttitle = {Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state},\n\tvolume = {108},\n\tissn = {0022-0477, 1365-2745},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2745.13395},\n\tdoi = {10.1111/1365-2745.13395},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Journal of Ecology},\n\tauthor = {Wentzky, Valerie Carolin and Tittel, Jörg and Jäger, Christoph Gerald and Bruggeman, Jorn and Rinke, Karsten},\n\teditor = {Nilsson, Christer},\n\tmonth = jul,\n\tyear = {2020},\n\tpages = {1649--1663},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Westphal, K.; Musolff, A.; Graeber, D.; and Borchardt, D.\n\n\n \n \n \n \n \n Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar N:P ratios.\n \n \n \n \n\n\n \n\n\n\n Environmental Research Letters, 15(10): 104009. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Controls$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{westphal_controls_2020,\n\ttitle = {Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar {N}:{P} ratios},\n\tvolume = {15},\n\tissn = {1748-9326},\n\tshorttitle = {Controls of point and diffuse sources lowered riverine nutrient concentrations asynchronously, thereby warping molar {N}},\n\turl = {https://iopscience.iop.org/article/10.1088/1748-9326/ab98b6},\n\tdoi = {10.1088/1748-9326/ab98b6},\n\tabstract = {Abstract \n            The input of nitrogen (N) and phosphorus (P) into rivers has been reduced in recent decades in many regions of the world to mitigate adverse eutrophication effects. However, legislation focused first on the reduction of nutrient loads from point sources and only later on diffuse sources. These reduction strategies have implications on riverine N:P stoichiometry, which potentially alter patterns of algal nutrient limitation and the functions or community structure of aquatic ecosystems. Here, we use a dataset spanning four decades of water quality for the Ruhr River (Germany) to show that the asynchronous implementation of point and diffuse source mitigation measures combined with time lags of catchment transport processes caused a temporally asynchronous reduction in dissolved inorganic nitrogen and total phosphorus concentrations. This asynchronous reduction increased the molar N:P ratios from around 30 to 100 in the river sections dominated by point sources, reducing the probability of N limitation of algae in favor of P limitation. \n            The Ruhr River catchment and the environmental policies implemented here illustrate the unintended effects of nutrient control strategies on the ecological stoichiometry at the catchment scale. We urge to assess systematically, whether unintentionally warped macronutrient ratios are observable in other managed river systems and to evaluate their environmental impacts.},\n\tnumber = {10},\n\turldate = {2022-11-02},\n\tjournal = {Environmental Research Letters},\n\tauthor = {Westphal, Katja and Musolff, Andreas and Graeber, Daniel and Borchardt, Dietrich},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {104009},\n}\n\n

\n

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

\n \n\n \n \n Wu, X.; Chen, Z.; Kiese, R.; Fu, J.; Gschwendter, S.; Schloter, M.; Liu, C.; Butterbach-Bahl, K.; Wolf, B.; and Dannenmann, M.\n\n\n \n \n \n \n \n Dinitrogen (N2) pulse emissions during freeze-thaw cycles from montane grassland soil.\n \n \n \n \n\n\n \n\n\n\n Biology and Fertility of Soils, 56(7): 959–972. October 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Dinitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wu_dinitrogen_2020,\n\ttitle = {Dinitrogen ({N2}) pulse emissions during freeze-thaw cycles from montane grassland soil},\n\tvolume = {56},\n\tissn = {0178-2762, 1432-0789},\n\turl = {https://link.springer.com/10.1007/s00374-020-01476-7},\n\tdoi = {10.1007/s00374-020-01476-7},\n\tabstract = {Abstract \n             \n              Short-lived pulses of soil nitrous oxide (N \n              2 \n              O) emissions during freeze-thaw periods can dominate annual cumulative N \n              2 \n              O fluxes from temperate managed and natural soils. However, the effects of freeze thaw cycles (FTCs) on dinitrogen (N \n              2 \n              ) emissions, i.e., the dominant terminal product of the denitrification process, and ratios of N \n              2 \n              /N \n              2 \n              O emissions have remained largely unknown because methodological difficulties were so far hampering detailed studies. Here, we quantified both N \n              2 \n              and N \n              2 \n              O emissions of montane grassland soils exposed to three subsequent FTCs under two different soil moisture levels (40 and 80\\% WFPS) and under manure addition at 80\\% WFPS. In addition, we also quantified abundance and expression of functional genes involved in nitrification and denitrification to better understand microbial drivers of gaseous N losses. Our study shows that each freeze thaw cycle was associated with pulse emissions of both N \n              2 \n              O and N \n              2 \n              , with soil N \n              2 \n              emissions exceeding N \n              2 \n              O emissions by a factor of 5–30. Increasing soil moisture from 40 to 80\\% WFPS and addition of cow slurry increased the cumulative FTC N \n              2 \n              emissions by 102\\% and 77\\%, respectively. For N \n              2 \n              O, increasing soil moisture from 40 to 80\\% WFPS and addition of slurry increased the cumulative emissions by 147\\% and 42\\%, respectively. Denitrification gene \n              cnorB \n              and \n              nosZ \n              clade I transcript levels showed high explanatory power for N \n              2 \n              O and N \n              2 \n              emissions, thereby reflecting both N gas flux dynamics due to FTC and effects of different water availability and fertilizer addition. In agreement with several other studies for various ecosystems, we show here for mountainous grassland soils that pulse emissions of N \n              2 \n              O were observed during freeze-thaw. More importantly, this study shows that the freeze-thaw N \n              2 \n              pulse emissions strongly exceeded those of N \n              2 \n              O in magnitude, which indicates that N \n              2 \n              emissions during FTCs could represent an important N loss pathway within the grassland N mass balances. However, their actual significance needs to be assessed under field conditions using intact plant-soil systems.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2022-11-02},\n\tjournal = {Biology and Fertility of Soils},\n\tauthor = {Wu, Xing and Chen, Zhe and Kiese, Ralf and Fu, Jin and Gschwendter, Silvia and Schloter, Michael and Liu, Chunyan and Butterbach-Bahl, Klaus and Wolf, Benjamin and Dannenmann, Michael},\n\tmonth = oct,\n\tyear = {2020},\n\tpages = {959--972},\n}\n\n

\n

\n\n\n

\n Abstract Short-lived pulses of soil nitrous oxide (N 2 O) emissions during freeze-thaw periods can dominate annual cumulative N 2 O fluxes from temperate managed and natural soils. However, the effects of freeze thaw cycles (FTCs) on dinitrogen (N 2 ) emissions, i.e., the dominant terminal product of the denitrification process, and ratios of N 2 /N 2 O emissions have remained largely unknown because methodological difficulties were so far hampering detailed studies. Here, we quantified both N 2 and N 2 O emissions of montane grassland soils exposed to three subsequent FTCs under two different soil moisture levels (40 and 80% WFPS) and under manure addition at 80% WFPS. In addition, we also quantified abundance and expression of functional genes involved in nitrification and denitrification to better understand microbial drivers of gaseous N losses. Our study shows that each freeze thaw cycle was associated with pulse emissions of both N 2 O and N 2 , with soil N 2 emissions exceeding N 2 O emissions by a factor of 5–30. Increasing soil moisture from 40 to 80% WFPS and addition of cow slurry increased the cumulative FTC N 2 emissions by 102% and 77%, respectively. For N 2 O, increasing soil moisture from 40 to 80% WFPS and addition of slurry increased the cumulative emissions by 147% and 42%, respectively. Denitrification gene cnorB and nosZ clade I transcript levels showed high explanatory power for N 2 O and N 2 emissions, thereby reflecting both N gas flux dynamics due to FTC and effects of different water availability and fertilizer addition. In agreement with several other studies for various ecosystems, we show here for mountainous grassland soils that pulse emissions of N 2 O were observed during freeze-thaw. More importantly, this study shows that the freeze-thaw N 2 pulse emissions strongly exceeded those of N 2 O in magnitude, which indicates that N 2 emissions during FTCs could represent an important N loss pathway within the grassland N mass balances. However, their actual significance needs to be assessed under field conditions using intact plant-soil systems.\n

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\n \n\n \n \n Wulf, M.; Kaiser, K.; Mrotzek, A.; Geiges-Erzgräber, L.; Schulz, L.; Stockmann, I.; Schneider, T.; Kappler, C.; and Bens, O.\n\n\n \n \n \n \n \n A Multisource Approach to Verify a Forest as a Reference of Natural Conditions in an Intensively Used Rural Landscape (Uckermark, Ne Germany).\n \n \n \n \n\n\n \n\n\n\n Technical Report In Review, December 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{wulf_multisource_2020,\n\ttype = {preprint},\n\ttitle = {A {Multisource} {Approach} to {Verify} a {Forest} as a {Reference} of {Natural} {Conditions} in an {Intensively} {Used} {Rural} {Landscape} ({Uckermark}, {Ne} {Germany})},\n\turl = {https://www.researchsquare.com/article/rs-113844/v1},\n\tabstract = {Abstract \n          BackgroundThe sharp decline in near-natural areas worldwide is undisputed, but the consequences of this decline, apart from the loss of biodiversity, cannot be fully assessed. Biotic components of a landscape are usually more easily assessed than the abiotic components, since biotic components are often more easily detectable. A forest of (semi)natural stocking was selected in the northeastern part of Brandenburg (northeast Germany) to check whether it can serve as reference site for near-natural conditions or not. Analyses of archival sources and historic maps as well as field investigations were combined to reconstruct the dynamics of vegetation and soil as far back in time as possible.ResultsPalynological data from nearby sites provide evidence that the investigated area has been forested for several thousands of years and has hardly been structurally influenced by humans in the last 450 years. This evidence together with historical maps of tree species composition allows us to infer that the specific forest has been very close to a natural state for at least 250 years. Soil investigations support this conclusion, since a soil inventory, field studies on two catenas and corings at selected depressions rarely show signs of anthropogenic erosion and related colluviation. Parts of the area were cleared in prehistory, but near-natural soils have been preserved in other parts. ConclusionsThe area with these undisturbed parts is regarded as an ideal reference site. With this study, we show that using a multi-source approach it is possible to find potential reference sites and that such an approach is applicable in other regions.},\n\turldate = {2022-11-02},\n\tinstitution = {In Review},\n\tauthor = {Wulf, Monika and Kaiser, Knut and Mrotzek, Almut and Geiges-Erzgräber, Lina and Schulz, Lars and Stockmann, Irina and Schneider, Thomas and Kappler, Christoph and Bens, Oliver},\n\tmonth = dec,\n\tyear = {2020},\n\tdoi = {10.21203/rs.3.rs-113844/v1},\n}\n\n

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\n \n\n \n \n Yu, Y.; Klotzsche, A.; Weihermüller, L.; Huisman, J. A.; Vanderborght, J.; Vereecken, H.; and der Kruk, J.\n\n\n \n \n \n \n \n Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data.\n \n \n \n \n\n\n \n\n\n\n Near Surface Geophysics, 18(3): 275–294. June 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Measuring$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{yu_measuring_2020,\n\ttitle = {Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data},\n\tvolume = {18},\n\tissn = {1569-4445, 1873-0604},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/nsg.12099},\n\tdoi = {10.1002/nsg.12099},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-02},\n\tjournal = {Near Surface Geophysics},\n\tauthor = {Yu, Yi and Klotzsche, Anja and Weihermüller, Lutz and Huisman, Johan Alexander and Vanderborght, Jan and Vereecken, Harry and der Kruk, Jan},\n\tmonth = jun,\n\tyear = {2020},\n\tpages = {275--294},\n}\n\n

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\n \n\n \n \n Zhang, X.; Yang, X.; Jomaa, S.; and Rode, M.\n\n\n \n \n \n \n \n Analyzing impacts of seasonality and landscape gradient on event scale nitrate-discharge dynamics based on nested high-frequency monitoring.\n \n \n \n \n\n\n \n\n\n\n Technical Report oral, March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Analyzing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{zhang_analyzing_2020,\n\ttype = {other},\n\ttitle = {Analyzing impacts of seasonality and landscape gradient on event scale nitrate-discharge dynamics based on nested high-frequency monitoring},\n\turl = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-3188.html},\n\tabstract = {\\&lt;p\\&gt;Strom event-scale analysis provides insights into nitrate transport dynamics at catchment scale. Investigating different hysteretic relationships between nitrate and discharge can disentangle catchment nitrate functioning both spatially and temporally. In this study, we explored seasonality and landscape gradiemt effects on nitrate concentration-discharge (C-Q) hysteresis patterns based on six-year (2012-2017), high-frequency (15 min) data in the Selke catchment (central Germany). The Selke catchment exhibits heterogeneous combinations of meteorological, hydrogeological, and anthropogenic conditions. Three nested gauging stations were built along the main Selke River, capturing discharge and nitrate concentration from the dominant uppermost mixed forest and arable land, middle catchment pure steep forest and lowland arable and urban land areas, respectively. Amongst the 227 storm events that have been detected, anticlockwise and accretion of C-Q relationships accounted for 76.6\\% and 75.3\\%, respectively, while the proportions decreased with the increasing areal share of arable land during summer season. Accretion pattern predominated forest areas (e.g., the middle catchment) throughout the whole year suggesting higher nitrate concentration in dominating interflow than baseflow. In contrast, dilution pattern was almost exclusively observed in lowland areas (dominated by arable and urban areas) in dry periods, indicating lower nitrate concentration in quick runoff components like surface runoff. We further investigated the consistency and variability of hysteresis patterns from upstream to downstream based on shared events. Results indicated hysteresis patterns seemed to be consistent at the three stations when discharge was high enough. Moreover, we found that nitrate load contributions from the upper and lower areas changed seasonally, albeit the dominant share of runoff volume from the upper area throughout the whole year. Such a comprehensive analysis (i.e., clockwise vs. anticlockwise, accretion vs. dilution) enables in-deep discussion of the plausible mechanisms of nitrate dynamics under different landscape conditions. We are also aware of limitations of such statistical data analysis, which can likely be tackled by mechanistic modelling at higher temporal resolutions.\\&lt;/p\\&gt;},\n\turldate = {2022-11-02},\n\tinstitution = {oral},\n\tauthor = {Zhang, Xiaolin and Yang, Xiaoqiang and Jomaa, Seifeddine and Rode, Michael},\n\tmonth = mar,\n\tyear = {2020},\n\tdoi = {10.5194/egusphere-egu2020-3188},\n}\n\n

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\n Strom event-scale analysis provides insights into nitrate transport dynamics at catchment scale. Investigating different hysteretic relationships between nitrate and discharge can disentangle catchment nitrate functioning both spatially and temporally. In this study, we explored seasonality and landscape gradiemt effects on nitrate concentration-discharge (C-Q) hysteresis patterns based on six-year (2012-2017), high-frequency (15 min) data in the Selke catchment (central Germany). The Selke catchment exhibits heterogeneous combinations of meteorological, hydrogeological, and anthropogenic conditions. Three nested gauging stations were built along the main Selke River, capturing discharge and nitrate concentration from the dominant uppermost mixed forest and arable land, middle catchment pure steep forest and lowland arable and urban land areas, respectively. Amongst the 227 storm events that have been detected, anticlockwise and accretion of C-Q relationships accounted for 76.6% and 75.3%, respectively, while the proportions decreased with the increasing areal share of arable land during summer season. Accretion pattern predominated forest areas (e.g., the middle catchment) throughout the whole year suggesting higher nitrate concentration in dominating interflow than baseflow. In contrast, dilution pattern was almost exclusively observed in lowland areas (dominated by arable and urban areas) in dry periods, indicating lower nitrate concentration in quick runoff components like surface runoff. We further investigated the consistency and variability of hysteresis patterns from upstream to downstream based on shared events. Results indicated hysteresis patterns seemed to be consistent at the three stations when discharge was high enough. Moreover, we found that nitrate load contributions from the upper and lower areas changed seasonally, albeit the dominant share of runoff volume from the upper area throughout the whole year. Such a comprehensive analysis (i.e., clockwise vs. anticlockwise, accretion vs. dilution) enables in-deep discussion of the plausible mechanisms of nitrate dynamics under different landscape conditions. We are also aware of limitations of such statistical data analysis, which can likely be tackled by mechanistic modelling at higher temporal resolutions.\n

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\n \n\n \n \n Zistl-Schlingmann, M.; Kwatcho Kengdo, S.; Kiese, R.; and Dannenmann, M.\n\n\n \n \n \n \n \n Management Intensity Controls Nitrogen-Use-Efficiency and Flows in Grasslands—A 15N Tracing Experiment.\n \n \n \n \n\n\n \n\n\n\n Agronomy, 10(4): 606. April 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Management$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zistl-schlingmann_management_2020,\n\ttitle = {Management {Intensity} {Controls} {Nitrogen}-{Use}-{Efficiency} and {Flows} in {Grasslands}—{A} {15N} {Tracing} {Experiment}},\n\tvolume = {10},\n\tissn = {2073-4395},\n\turl = {https://www.mdpi.com/2073-4395/10/4/606},\n\tdoi = {10.3390/agronomy10040606},\n\tabstract = {The consequences of land use intensification and climate warming on productivity, fates of fertilizer nitrogen (N) and the overall soil N balance of montane grasslands remain poorly understood. Here, we report findings of a 15N slurry-tracing experiment on large grassland plant–soil lysimeters exposed to different management intensities (extensive vs. intensive) and climates (control; translocation: +2 °C, reduced precipitation). Surface-applied cattle slurry was enriched with both 15NH4+ and 15N-urea in order to trace its fate in the plant–soil system. Recovery of 15N tracer in plants was low (7–17\\%), while it was considerably higher in the soil N pool (32–42\\%), indicating N stabilization in soil organic nitrogen (SON). Total 15N recovery was only 49\\% ± 7\\% indicating substantial fertilizer N losses to the environment. With harvest N exports exceeding N fertilization rates, the N balance was negative for all climate and management treatments. Intensive management had an increased deficit relative to extensive management. In contrast, simulated climate change had no significant effects on the grassland N balance. These results suggest a risk of soil N mining in montane grasslands under land use intensification based on broadcast liquid slurry application.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-02},\n\tjournal = {Agronomy},\n\tauthor = {Zistl-Schlingmann, Marcus and Kwatcho Kengdo, Steve and Kiese, Ralf and Dannenmann, Michael},\n\tmonth = apr,\n\tyear = {2020},\n\tpages = {606},\n}\n\n

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