\n \n\n \n \n Altdorff, D.; Botschek, J.; Honds, M.; van der Kruk, J.; and Vereecken, H.\n\n\n \n \n \n \n \n In Situ Detection of Tree Root Systems under Heterogeneous Anthropogenic Soil Conditions Using Ground Penetrating Radar.\n \n \n \n \n\n\n \n\n\n\n Journal of Infrastructure Systems, 25(3): 05019008. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"In$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{altdorff_situ_2019,\n\ttitle = {In {Situ} {Detection} of {Tree} {Root} {Systems} under {Heterogeneous} {Anthropogenic} {Soil} {Conditions} {Using} {Ground} {Penetrating} {Radar}},\n\tvolume = {25},\n\tissn = {1076-0342, 1943-555X},\n\turl = {https://ascelibrary.org/doi/10.1061/%28ASCE%29IS.1943-555X.0000501},\n\tdoi = {10.1061/(ASCE)IS.1943-555X.0000501},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Infrastructure Systems},\n\tauthor = {Altdorff, D. and Botschek, J. and Honds, M. and van der Kruk, J. and Vereecken, H.},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {05019008},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Babaeian, E.; Sadeghi, M.; Jones, S. B.; Montzka, C.; Vereecken, H.; and Tuller, M.\n\n\n \n \n \n \n \n Ground, Proximal, and Satellite Remote Sensing of Soil Moisture.\n \n \n \n \n\n\n \n\n\n\n Reviews of Geophysics, 57(2): 530–616. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Ground,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{babaeian_ground_2019,\n\ttitle = {Ground, {Proximal}, and {Satellite} {Remote} {Sensing} of {Soil} {Moisture}},\n\tvolume = {57},\n\tissn = {8755-1209, 1944-9208},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2018RG000618},\n\tdoi = {10.1029/2018RG000618},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Reviews of Geophysics},\n\tauthor = {Babaeian, Ebrahim and Sadeghi, Morteza and Jones, Scott B. and Montzka, Carsten and Vereecken, Harry and Tuller, Markus},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {530--616},\n}\n\n

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\n \n\n \n \n Blöschl, G.; Bierkens, M. F.; Chambel, A.; Cudennec, C.; Destouni, G.; Fiori, A.; Kirchner, J. W.; McDonnell, J. J.; Savenije, H. H.; Sivapalan, M.; Stumpp, C.; Toth, E.; Volpi, E.; Carr, G.; Lupton, C.; Salinas, J.; Széles, B.; Viglione, A.; Aksoy, H.; Allen, S. T.; Amin, A.; Andréassian, V.; Arheimer, B.; Aryal, S. K.; Baker, V.; Bardsley, E.; Barendrecht, M. H.; Bartosova, A.; Batelaan, O.; Berghuijs, W. R.; Beven, K.; Blume, T.; Bogaard, T.; Borges de Amorim, P.; Böttcher, M. E.; Boulet, G.; Breinl, K.; Brilly, M.; Brocca, L.; Buytaert, W.; Castellarin, A.; Castelletti, A.; Chen, X.; Chen, Y.; Chen, Y.; Chifflard, P.; Claps, P.; Clark, M. P.; Collins, A. L.; Croke, B.; Dathe, A.; David, P. C.; de Barros, F. P. J.; de Rooij, G.; Di Baldassarre, G.; Driscoll, J. M.; Duethmann, D.; Dwivedi, R.; Eris, E.; Farmer, W. H.; Feiccabrino, J.; Ferguson, G.; Ferrari, E.; Ferraris, S.; Fersch, B.; Finger, D.; Foglia, L.; Fowler, K.; Gartsman, B.; Gascoin, S.; Gaume, E.; Gelfan, A.; Geris, J.; Gharari, S.; Gleeson, T.; Glendell, M.; Gonzalez Bevacqua, A.; González-Dugo, M. P.; Grimaldi, S.; Gupta, A. B.; Guse, B.; Han, D.; Hannah, D.; Harpold, A.; Haun, S.; Heal, K.; Helfricht, K.; Herrnegger, M.; Hipsey, M.; Hlaváčiková, H.; Hohmann, C.; Holko, L.; Hopkinson, C.; Hrachowitz, M.; Illangasekare, T. H.; Inam, A.; Innocente, C.; Istanbulluoglu, E.; Jarihani, B.; Kalantari, Z.; Kalvans, A.; Khanal, S.; Khatami, S.; Kiesel, J.; Kirkby, M.; Knoben, W.; Kochanek, K.; Kohnová, S.; Kolechkina, A.; Krause, S.; Kreamer, D.; Kreibich, H.; Kunstmann, H.; Lange, H.; Liberato, M. L. R.; Lindquist, E.; Link, T.; Liu, J.; Loucks, D. P.; Luce, C.; Mahé, G.; Makarieva, O.; Malard, J.; Mashtayeva, S.; Maskey, S.; Mas-Pla, J.; Mavrova-Guirguinova, M.; Mazzoleni, M.; Mernild, S.; Misstear, B. D.; Montanari, A.; Müller-Thomy, H.; Nabizadeh, A.; Nardi, F.; Neale, C.; Nesterova, N.; Nurtaev, B.; Odongo, V. O.; Panda, S.; Pande, S.; Pang, Z.; Papacharalampous, G.; Perrin, C.; Pfister, L.; Pimentel, R.; Polo, M. J.; Post, D.; Prieto Sierra, C.; Ramos, M.; Renner, M.; Reynolds, J. E.; Ridolfi, E.; Rigon, R.; Riva, M.; Robertson, D. E.; Rosso, R.; Roy, T.; Sá, J. H.; Salvadori, G.; Sandells, M.; Schaefli, B.; Schumann, A.; Scolobig, A.; Seibert, J.; Servat, E.; Shafiei, M.; Sharma, A.; Sidibe, M.; Sidle, R. C.; Skaugen, T.; Smith, H.; Spiessl, S. M.; Stein, L.; Steinsland, I.; Strasser, U.; Su, B.; Szolgay, J.; Tarboton, D.; Tauro, F.; Thirel, G.; Tian, F.; Tong, R.; Tussupova, K.; Tyralis, H.; Uijlenhoet, R.; van Beek, R.; van der Ent, R. J.; van der Ploeg, M.; Van Loon, A. F.; van Meerveld, I.; van Nooijen, R.; van Oel, P. R.; Vidal, J.; von Freyberg, J.; Vorogushyn, S.; Wachniew, P.; Wade, A. J.; Ward, P.; Westerberg, I. K.; White, C.; Wood, E. F.; Woods, R.; Xu, Z.; Yilmaz, K. K.; and Zhang, Y.\n\n\n \n \n \n \n \n Twenty-three unsolved problems in hydrology (UPH) – a community perspective.\n \n \n \n \n\n\n \n\n\n\n Hydrological Sciences Journal, 64(10): 1141–1158. July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Twenty-three$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{bloschl_twenty-three_2019,\n\ttitle = {Twenty-three unsolved problems in hydrology ({UPH}) – a community perspective},\n\tvolume = {64},\n\tissn = {0262-6667, 2150-3435},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1620507},\n\tdoi = {10.1080/02626667.2019.1620507},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-17},\n\tjournal = {Hydrological Sciences Journal},\n\tauthor = {Blöschl, Günter and Bierkens, Marc F.P. and Chambel, Antonio and Cudennec, Christophe and Destouni, Georgia and Fiori, Aldo and Kirchner, James W. and McDonnell, Jeffrey J. and Savenije, Hubert H.G. and Sivapalan, Murugesu and Stumpp, Christine and Toth, Elena and Volpi, Elena and Carr, Gemma and Lupton, Claire and Salinas, Josè and Széles, Borbála and Viglione, Alberto and Aksoy, Hafzullah and Allen, Scott T. and Amin, Anam and Andréassian, Vazken and Arheimer, Berit and Aryal, Santosh K. and Baker, Victor and Bardsley, Earl and Barendrecht, Marlies H. and Bartosova, Alena and Batelaan, Okke and Berghuijs, Wouter R. and Beven, Keith and Blume, Theresa and Bogaard, Thom and Borges de Amorim, Pablo and Böttcher, Michael E. and Boulet, Gilles and Breinl, Korbinian and Brilly, Mitja and Brocca, Luca and Buytaert, Wouter and Castellarin, Attilio and Castelletti, Andrea and Chen, Xiaohong and Chen, Yangbo and Chen, Yuanfang and Chifflard, Peter and Claps, Pierluigi and Clark, Martyn P. and Collins, Adrian L. and Croke, Barry and Dathe, Annette and David, Paula C. and de Barros, Felipe P. J. and de Rooij, Gerrit and Di Baldassarre, Giuliano and Driscoll, Jessica M. and Duethmann, Doris and Dwivedi, Ravindra and Eris, Ebru and Farmer, William H. and Feiccabrino, James and Ferguson, Grant and Ferrari, Ennio and Ferraris, Stefano and Fersch, Benjamin and Finger, David and Foglia, Laura and Fowler, Keirnan and Gartsman, Boris and Gascoin, Simon and Gaume, Eric and Gelfan, Alexander and Geris, Josie and Gharari, Shervan and Gleeson, Tom and Glendell, Miriam and Gonzalez Bevacqua, Alena and González-Dugo, María P. and Grimaldi, Salvatore and Gupta, A. B. and Guse, Björn and Han, Dawei and Hannah, David and Harpold, Adrian and Haun, Stefan and Heal, Kate and Helfricht, Kay and Herrnegger, Mathew and Hipsey, Matthew and Hlaváčiková, Hana and Hohmann, Clara and Holko, Ladislav and Hopkinson, Christopher and Hrachowitz, Markus and Illangasekare, Tissa H. and Inam, Azhar and Innocente, Camyla and Istanbulluoglu, Erkan and Jarihani, Ben and Kalantari, Zahra and Kalvans, Andis and Khanal, Sonu and Khatami, Sina and Kiesel, Jens and Kirkby, Mike and Knoben, Wouter and Kochanek, Krzysztof and Kohnová, Silvia and Kolechkina, Alla and Krause, Stefan and Kreamer, David and Kreibich, Heidi and Kunstmann, Harald and Lange, Holger and Liberato, Margarida L. R. and Lindquist, Eric and Link, Timothy and Liu, Junguo and Loucks, Daniel Peter and Luce, Charles and Mahé, Gil and Makarieva, Olga and Malard, Julien and Mashtayeva, Shamshagul and Maskey, Shreedhar and Mas-Pla, Josep and Mavrova-Guirguinova, Maria and Mazzoleni, Maurizio and Mernild, Sebastian and Misstear, Bruce Dudley and Montanari, Alberto and Müller-Thomy, Hannes and Nabizadeh, Alireza and Nardi, Fernando and Neale, Christopher and Nesterova, Nataliia and Nurtaev, Bakhram and Odongo, Vincent O. and Panda, Subhabrata and Pande, Saket and Pang, Zhonghe and Papacharalampous, Georgia and Perrin, Charles and Pfister, Laurent and Pimentel, Rafael and Polo, María J. and Post, David and Prieto Sierra, Cristina and Ramos, Maria-Helena and Renner, Maik and Reynolds, José Eduardo and Ridolfi, Elena and Rigon, Riccardo and Riva, Monica and Robertson, David E. and Rosso, Renzo and Roy, Tirthankar and Sá, João H.M. and Salvadori, Gianfausto and Sandells, Mel and Schaefli, Bettina and Schumann, Andreas and Scolobig, Anna and Seibert, Jan and Servat, Eric and Shafiei, Mojtaba and Sharma, Ashish and Sidibe, Moussa and Sidle, Roy C. and Skaugen, Thomas and Smith, Hugh and Spiessl, Sabine M. and Stein, Lina and Steinsland, Ingelin and Strasser, Ulrich and Su, Bob and Szolgay, Jan and Tarboton, David and Tauro, Flavia and Thirel, Guillaume and Tian, Fuqiang and Tong, Rui and Tussupova, Kamshat and Tyralis, Hristos and Uijlenhoet, Remko and van Beek, Rens and van der Ent, Ruud J. and van der Ploeg, Martine and Van Loon, Anne F. and van Meerveld, Ilja and van Nooijen, Ronald and van Oel, Pieter R. and Vidal, Jean-Philippe and von Freyberg, Jana and Vorogushyn, Sergiy and Wachniew, Przemyslaw and Wade, Andrew J. and Ward, Philip and Westerberg, Ida K. and White, Christopher and Wood, Eric F. and Woods, Ross and Xu, Zongxue and Yilmaz, Koray K. and Zhang, Yongqiang},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {1141--1158},\n}\n\n

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\n \n\n \n \n Born, J.; and Michalski, S. G.\n\n\n \n \n \n \n \n Trait expression and signatures of adaptation in response to nitrogen addition in the common wetland plant Juncus effusus.\n \n \n \n \n\n\n \n\n\n\n PLOS ONE, 14(1): e0209886. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Trait$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{born_trait_2019,\n\ttitle = {Trait expression and signatures of adaptation in response to nitrogen addition in the common wetland plant {Juncus} effusus},\n\tvolume = {14},\n\tissn = {1932-6203},\n\turl = {https://dx.plos.org/10.1371/journal.pone.0209886},\n\tdoi = {10.1371/journal.pone.0209886},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {PLOS ONE},\n\tauthor = {Born, Jennifer and Michalski, Stefan G.},\n\teditor = {Gomory, Dusan},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {e0209886},\n}\n\n

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\n \n\n \n \n Brack, W.; Aissa, S. A.; Backhaus, T.; Dulio, V.; Escher, B. I.; Faust, M.; Hilscherova, K.; Hollender, J.; Hollert, H.; Müller, C.; Munthe, J.; Posthuma, L.; Seiler, T.; Slobodnik, J.; Teodorovic, I.; Tindall, A. J.; de Aragão Umbuzeiro, G.; Zhang, X.; and Altenburger, R.\n\n\n \n \n \n \n \n Effect-based methods are key. The European Collaborative Project SOLUTIONS recommends integrating effect-based methods for diagnosis and monitoring of water quality.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 10. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect-based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{brack_effect-based_2019,\n\ttitle = {Effect-based methods are key. {The} {European} {Collaborative} {Project} {SOLUTIONS} recommends integrating effect-based methods for diagnosis and monitoring of water quality},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0192-2},\n\tdoi = {10.1186/s12302-019-0192-2},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Brack, Werner and Aissa, Selim Ait and Backhaus, Thomas and Dulio, Valeria and Escher, Beate I. and Faust, Michael and Hilscherova, Klara and Hollender, Juliane and Hollert, Henner and Müller, Christin and Munthe, John and Posthuma, Leo and Seiler, Thomas-Benjamin and Slobodnik, Jaroslav and Teodorovic, Ivana and Tindall, Andrew J. and de Aragão Umbuzeiro, Gisela and Zhang, Xiaowei and Altenburger, Rolf},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {10},\n}\n\n

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\n \n\n \n \n Brogi, C.; Huisman, J.; Pätzold, S.; von Hebel, C.; Weihermüller, L.; Kaufmann, M.; van der Kruk, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Large-scale soil mapping using multi-configuration EMI and supervised image classification.\n \n \n \n \n\n\n \n\n\n\n Geoderma, 335: 133–148. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{brogi_large-scale_2019,\n\ttitle = {Large-scale soil mapping using multi-configuration {EMI} and supervised image classification},\n\tvolume = {335},\n\tissn = {00167061},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0016706117315641},\n\tdoi = {10.1016/j.geoderma.2018.08.001},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Geoderma},\n\tauthor = {Brogi, C. and Huisman, J.A. and Pätzold, S. and von Hebel, C. and Weihermüller, L. and Kaufmann, M.S. and van der Kruk, J. and Vereecken, H.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {133--148},\n}\n\n

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\n \n\n \n \n Brunetti, G.; Šimůnek, J.; Bogena, H.; Baatz, R.; Huisman, J. A.; Dahlke, H.; and Vereecken, H.\n\n\n \n \n \n \n \n On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties.\n \n \n \n \n\n\n \n\n\n\n Vadose Zone Journal, 18(1): 1–24. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{brunetti_information_2019,\n\ttitle = {On the {Information} {Content} of {Cosmic}‐{Ray} {Neutron} {Data} in the {Inverse} {Estimation} of {Soil} {Hydraulic} {Properties}},\n\tvolume = {18},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://onlinelibrary.wiley.com/doi/10.2136/vzj2018.06.0123},\n\tdoi = {10.2136/vzj2018.06.0123},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Brunetti, Giuseppe and Šimůnek, Jiří and Bogena, Heye and Baatz, Roland and Huisman, Johan Alexander and Dahlke, Helen and Vereecken, Harry},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {1--24},\n}\n\n

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\n \n\n \n \n Castaldi, F.; Chabrillat, S.; and van Wesemael, B.\n\n\n \n \n \n \n \n Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 11(3): 309. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sampling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{castaldi_sampling_2019,\n\ttitle = {Sampling {Strategies} for {Soil} {Property} {Mapping} {Using} {Multispectral} {Sentinel}-2 and {Hyperspectral} {EnMAP} {Satellite} {Data}},\n\tvolume = {11},\n\tissn = {2072-4292},\n\turl = {http://www.mdpi.com/2072-4292/11/3/309},\n\tdoi = {10.3390/rs11030309},\n\tabstract = {Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7\\%), and Kennard-Stones (9.2\\%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing},\n\tauthor = {Castaldi, Fabio and Chabrillat, Sabine and van Wesemael, Bas},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {309},\n}\n\n

\n

\n\n\n

\n Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale.\n

\n\n\n

\n \n\n \n \n Castaldi, F.; Hueni, A.; Chabrillat, S.; Ward, K.; Buttafuoco, G.; Bomans, B.; Vreys, K.; Brell, M.; and van Wesemael, B.\n\n\n \n \n \n \n \n Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands.\n \n \n \n \n\n\n \n\n\n\n ISPRS Journal of Photogrammetry and Remote Sensing, 147: 267–282. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{castaldi_evaluating_2019,\n\ttitle = {Evaluating the capability of the {Sentinel} 2 data for soil organic carbon prediction in croplands},\n\tvolume = {147},\n\tissn = {09242716},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0924271618303289},\n\tdoi = {10.1016/j.isprsjprs.2018.11.026},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\n\tauthor = {Castaldi, Fabio and Hueni, Andreas and Chabrillat, Sabine and Ward, Kathrin and Buttafuoco, Gabriele and Bomans, Bart and Vreys, Kristin and Brell, Maximilian and van Wesemael, Bas},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {267--282},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Chen, C.; Montzka, C.; Huth, J.; Kuenzer, C.; Kunstmann, H.; Yue, T.; and Kolditz, O.\n\n\n \n \n \n \n \n Research Centre for Environmental Information Science (RCEIS).\n \n \n \n \n\n\n \n\n\n\n In Yue, T.; Nixdorf, E.; Zhou, C.; Xu, B.; Zhao, N.; Fan, Z.; Huang, X.; Chen, C.; and Kolditz, O., editor(s), Chinese Water Systems, pages 311–334. Springer International Publishing, Cham, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Research$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{yue_research_2019,\n\taddress = {Cham},\n\ttitle = {Research {Centre} for {Environmental} {Information} {Science} ({RCEIS})},\n\tisbn = {9783319977249 9783319977256},\n\turl = {http://link.springer.com/10.1007/978-3-319-97725-6_19},\n\turldate = {2022-11-17},\n\tbooktitle = {Chinese {Water} {Systems}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Chen, Cui and Montzka, Carsten and Huth, Juliane and Kuenzer, Claudia and Kunstmann, Harald and Yue, TianXiang and Kolditz, Olaf},\n\teditor = {Yue, TianXiang and Nixdorf, Erik and Zhou, Chengzi and Xu, Bing and Zhao, Na and Fan, Zhewen and Huang, Xiaolan and Chen, Cui and Kolditz, Olaf},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-319-97725-6_19},\n\tpages = {311--334},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Curdt, C.\n\n\n \n \n \n \n \n Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services.\n \n \n \n \n\n\n \n\n\n\n Data Science Journal, 18: 5. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Supporting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{curdt_supporting_2019,\n\ttitle = {Supporting the {Interdisciplinary}, {Long}-{Term} {Research} {Project} ‘{Patterns} in {Soil}-{Vegetation}-{Atmosphere}-{Systems}’ by {Data} {Management} {Services}},\n\tvolume = {18},\n\tissn = {1683-1470},\n\turl = {http://datascience.codata.org/articles/10.5334/dsj-2019-005/},\n\tdoi = {10.5334/dsj-2019-005},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Data Science Journal},\n\tauthor = {Curdt, Constanze},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {5},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Dias Neto, J.; Kneifel, S.; Ori, D.; Trömel, S.; Handwerker, J.; Bohn, B.; Hermes, N.; Mühlbauer, K.; Lenefer, M.; and Simmer, C.\n\n\n \n \n \n \n \n The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation.\n \n \n \n \n\n\n \n\n\n\n Earth System Science Data, 11(2): 845–863. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{dias_neto_triple-frequency_2019,\n\ttitle = {The {TRIple}-frequency and {Polarimetric} radar {Experiment} for improving process observations of winter precipitation},\n\tvolume = {11},\n\tissn = {1866-3516},\n\turl = {https://essd.copernicus.org/articles/11/845/2019/},\n\tdoi = {10.5194/essd-11-845-2019},\n\tabstract = {Abstract. This paper describes a 2-month dataset of ground-based triple-frequency (X,\nKa, and W band) Doppler radar observations during the winter season obtained\nat the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF),\nGermany. All relevant post-processing steps, such as re-gridding and offset and\nattenuation correction, as well as quality flagging, are described. The\ndataset contains all necessary information required to recover data at\nintermediate processing steps for user-specific applications and corrections\n(https://doi.org/10.5281/zenodo.1341389; Dias Neto et al., 2019). The large number of ice clouds included in the dataset\nallows for a first statistical analysis of their multifrequency radar\nsignatures. The reflectivity differences quantified by dual-wavelength ratios\n(DWRs) reveal temperature regimes where aggregation seems to be triggered.\nOverall, the aggregation signatures found in the triple-frequency space agree\nwith and corroborate conclusions from previous studies. The combination of\nDWRs with mean Doppler velocity and linear depolarization ratio enables us to\ndistinguish signatures of rimed particles and melting snowflakes. The riming\nsignatures in the DWRs agree well with results found in previous\ntriple-frequency studies. Close to the melting layer, however, we find very\nlarge DWRs (up to 20 dB), which have not been reported before. A combined\nanalysis of these extreme DWR with mean Doppler velocity and a linear\ndepolarization ratio allows this signature to be separated, which is most likely\nrelated to strong aggregation, from the triple-frequency characteristics of\nmelting particles.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Earth System Science Data},\n\tauthor = {Dias Neto, José and Kneifel, Stefan and Ori, Davide and Trömel, Silke and Handwerker, Jan and Bohn, Birger and Hermes, Normen and Mühlbauer, Kai and Lenefer, Martin and Simmer, Clemens},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {845--863},\n}\n\n

\n

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

\n \n\n \n \n Dräger, N.; Plessen, B.; Kienel, U.; Słowiński, M.; Ramisch, A.; Tjallingii, R.; Pinkerneil, S.; and Brauer, A.\n\n\n \n \n \n \n \n Hypolimnetic oxygen conditions influence varve preservation and δ13C of sediment organic matter in Lake Tiefer See, NE Germany.\n \n \n \n \n\n\n \n\n\n\n Journal of Paleolimnology, 62(2): 181–194. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Hypolimnetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{drager_hypolimnetic_2019,\n\ttitle = {Hypolimnetic oxygen conditions influence varve preservation and δ{13C} of sediment organic matter in {Lake} {Tiefer} {See}, {NE} {Germany}},\n\tvolume = {62},\n\tissn = {0921-2728, 1573-0417},\n\turl = {http://link.springer.com/10.1007/s10933-019-00084-2},\n\tdoi = {10.1007/s10933-019-00084-2},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Paleolimnology},\n\tauthor = {Dräger, Nadine and Plessen, Birgit and Kienel, Ulrike and Słowiński, Michał and Ramisch, Arne and Tjallingii, Rik and Pinkerneil, Sylvia and Brauer, Achim},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {181--194},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Ehrhardt, S.; Kumar, R.; Fleckenstein, J. H.; Attinger, S.; and Musolff, A.\n\n\n \n \n \n \n \n Trajectories of nitrate input and output in three nested catchments along a land use gradient.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 23(9): 3503–3524. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Trajectories$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{ehrhardt_trajectories_2019,\n\ttitle = {Trajectories of nitrate input and output in three nested catchments along a land use gradient},\n\tvolume = {23},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/23/3503/2019/},\n\tdoi = {10.5194/hess-23-3503-2019},\n\tabstract = {Abstract. Increased anthropogenic inputs of nitrogen (N) to the\nbiosphere during the last few decades have resulted in increased groundwater and\nsurface water concentrations of N (primarily as nitrate), posing a global\nproblem. Although measures have been implemented to reduce N inputs, they\nhave not always led to decreasing riverine nitrate concentrations and loads.\nThis limited response to the measures can either be caused by the\naccumulation of organic N in the soils (biogeochemical legacy) – or by long\ntravel times (TTs) of inorganic N to the streams (hydrological legacy).\nHere, we compare atmospheric and agricultural N inputs with long-term\nobservations (1970–2016) of riverine nitrate concentrations and loads in a\ncentral German mesoscale catchment with three nested subcatchments\nof increasing agricultural land use. Based on a data-driven\napproach, we assess jointly the N budget and the effective TTs of N through\nthe soil and groundwater compartments. In combination with long-term\ntrajectories of the C–Q relationships, we evaluate the potential for and\nthe characteristics of an N legacy. We show that in the 40-year-long observation period, the catchment (270 km2) with 60 \\% agricultural area received an N input of\n53 437 t, while it exported 6592 t, indicating an overall retention of\n88 \\%. Removal of N by denitrification could not sufficiently explain this\nimbalance. Log-normal travel time distributions (TTDs) that link the N input\nhistory to the riverine export differed seasonally, with modes spanning\n7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the\nC–Q relationships were noticed over time that could be attributed to strong\nchanges in N inputs resulting from agricultural intensification before 1989,\nthe break-down of East German agriculture after 1989 and the\nseasonal differences in TTs. A chemostatic export regime of nitrate was only\nfound after several years of stabilized N inputs. The changes in C–Q\nrelationships suggest a dominance of the hydrological N legacy over the\nbiogeochemical N fixation in the soils, as we expected to observe a stronger\nand even increasing dampening of the riverine N concentrations after\nsustained high N inputs. Our analyses reveal an imbalance between N input\nand output, long time-lags and a lack of significant denitrification in the\ncatchment. All these suggest that catchment management needs to address\nboth a longer-term reduction of N inputs and shorter-term mitigation of\ntoday's high N loads. The latter may be covered by interventions triggering\ndenitrification, such as hedgerows around agricultural fields, riparian\nbuffers zones or constructed wetlands. Further joint analyses of N budgets\nand TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Ehrhardt, Sophie and Kumar, Rohini and Fleckenstein, Jan H. and Attinger, Sabine and Musolff, Andreas},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {3503--3524},\n}\n\n

\n

\n\n\n

\n Abstract. Increased anthropogenic inputs of nitrogen (N) to the biosphere during the last few decades have resulted in increased groundwater and surface water concentrations of N (primarily as nitrate), posing a global problem. Although measures have been implemented to reduce N inputs, they have not always led to decreasing riverine nitrate concentrations and loads. This limited response to the measures can either be caused by the accumulation of organic N in the soils (biogeochemical legacy) – or by long travel times (TTs) of inorganic N to the streams (hydrological legacy). Here, we compare atmospheric and agricultural N inputs with long-term observations (1970–2016) of riverine nitrate concentrations and loads in a central German mesoscale catchment with three nested subcatchments of increasing agricultural land use. Based on a data-driven approach, we assess jointly the N budget and the effective TTs of N through the soil and groundwater compartments. In combination with long-term trajectories of the C–Q relationships, we evaluate the potential for and the characteristics of an N legacy. We show that in the 40-year-long observation period, the catchment (270 km2) with 60 % agricultural area received an N input of 53 437 t, while it exported 6592 t, indicating an overall retention of 88 %. Removal of N by denitrification could not sufficiently explain this imbalance. Log-normal travel time distributions (TTDs) that link the N input history to the riverine export differed seasonally, with modes spanning 7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the C–Q relationships were noticed over time that could be attributed to strong changes in N inputs resulting from agricultural intensification before 1989, the break-down of East German agriculture after 1989 and the seasonal differences in TTs. A chemostatic export regime of nitrate was only found after several years of stabilized N inputs. The changes in C–Q relationships suggest a dominance of the hydrological N legacy over the biogeochemical N fixation in the soils, as we expected to observe a stronger and even increasing dampening of the riverine N concentrations after sustained high N inputs. Our analyses reveal an imbalance between N input and output, long time-lags and a lack of significant denitrification in the catchment. All these suggest that catchment management needs to address both a longer-term reduction of N inputs and shorter-term mitigation of today's high N loads. The latter may be covered by interventions triggering denitrification, such as hedgerows around agricultural fields, riparian buffers zones or constructed wetlands. Further joint analyses of N budgets and TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.\n

\n\n\n

\n \n\n \n \n Eshonkulov, R.; Poyda, A.; Ingwersen, J.; Wizemann, H.; Weber, T. K. D.; Kremer, P.; Högy, P.; Pulatov, A.; and Streck, T.\n\n\n \n \n \n \n \n Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern Germany.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(2): 521–540. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{eshonkulov_evaluating_2019,\n\ttitle = {Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern {Germany}},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/521/2019/},\n\tdoi = {10.5194/bg-16-521-2019},\n\tabstract = {Abstract. The energy balance of eddy-covariance (EC) measurements is\ntypically not closed, resulting in one of the main challenges in evaluating\nand interpreting EC flux data. Energy balance closure (EBC) is crucial for\nvalidating and improving regional and global climate models. To investigate\nthe nature of the gap in EBC for agroecosystems, we analyzed EC measurements\nfrom two climatically contrasting regions (Kraichgau – KR – and Swabian Jura – SJ) in southwestern Germany. Data were taken at six fully equipped EC sites\nfrom 2010 to 2017. The gap in EBC was quantified by ordinary linear\nregression, relating the energy balance ratio (EBR), calculated as the\nquotient of turbulent fluxes and available energy, to the residual energy\nterm. In order to examine potential reasons for differences in EBC, we\ncompared the EBC under varying environmental conditions and investigated a\nwide range of possible controls. Overall, the variation in EBC was found to\nbe higher during winter than summer. Moreover, we determined that the site had a\nstatistically significant effect on EBC but no significant effect on either crop or region (KR\nvs SJ). The time-variable footprints of all EC stations were estimated based\non data measured in 2015, complimented by micro-topographic analyses along\nthe prevailing wind direction. The smallest mean annual energy balance gap\nwas 17 \\% in KR and 13 \\% in SJ. Highest EBRs were mostly found for winds\nfrom the prevailing wind direction. The spread of EBRs distinctly narrowed\nunder unstable atmospheric conditions, strong buoyancy, and high friction\nvelocities. Smaller footprint areas led to better EBC due to increasing\nhomogeneity. Flow distortions caused by the back head of the anemometer\nnegatively affected EBC during corresponding wind conditions.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Eshonkulov, Ravshan and Poyda, Arne and Ingwersen, Joachim and Wizemann, Hans-Dieter and Weber, Tobias K. D. and Kremer, Pascal and Högy, Petra and Pulatov, Alim and Streck, Thilo},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {521--540},\n}\n\n

\n

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

\n \n\n \n \n Graeber, D.; Gücker, B.; Wild, R.; Wells, N. S.; Anlanger, C.; Kamjunke, N.; Norf, H.; Schmidt, C.; and Brauns, M.\n\n\n \n \n \n \n \n Biofilm-specific uptake does not explain differences in whole-stream DOC tracer uptake between a forest and an agricultural stream.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 144(1): 85–101. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Biofilm-specific$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{graeber_biofilm-specific_2019,\n\ttitle = {Biofilm-specific uptake does not explain differences in whole-stream {DOC} tracer uptake between a forest and an agricultural stream},\n\tvolume = {144},\n\tissn = {0168-2563, 1573-515X},\n\turl = {http://link.springer.com/10.1007/s10533-019-00573-6},\n\tdoi = {10.1007/s10533-019-00573-6},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Biogeochemistry},\n\tauthor = {Graeber, D. and Gücker, B. and Wild, R. and Wells, N. S. and Anlanger, C. and Kamjunke, N. and Norf, H. and Schmidt, C. and Brauns, M.},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {85--101},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Groh, J.; Pütz, T.; Gerke, H. H.; Vanderborght, J.; and Vereecken, H.\n\n\n \n \n \n \n \n Quantification and Prediction of Nighttime Evapotranspiration for Two Distinct Grassland Ecosystems.\n \n \n \n \n\n\n \n\n\n\n Water Resources Research, 55(4): 2961–2975. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantification$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{groh_quantification_2019,\n\ttitle = {Quantification and {Prediction} of {Nighttime} {Evapotranspiration} for {Two} {Distinct} {Grassland} {Ecosystems}},\n\tvolume = {55},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018WR024072},\n\tdoi = {10.1029/2018WR024072},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {Water Resources Research},\n\tauthor = {Groh, J. and Pütz, T. and Gerke, H. H. and Vanderborght, J. and Vereecken, H.},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {2961--2975},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Hardy, R.; Quinton, J.; James, M.; Fiener, P.; and Pates, J.\n\n\n \n \n \n \n \n High precision tracing of soil and sediment movement using fluorescent tracers at hillslope scale.\n \n \n \n \n\n\n \n\n\n\n Earth Surface Processes and Landforms, 44(5): 1091–1099. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"High$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hardy_high_2019,\n\ttitle = {High precision tracing of soil and sediment movement using fluorescent tracers at hillslope scale},\n\tvolume = {44},\n\tissn = {0197-9337, 1096-9837},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/esp.4557},\n\tdoi = {10.1002/esp.4557},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Earth Surface Processes and Landforms},\n\tauthor = {Hardy, R.A. and Quinton, J.N. and James, M.R. and Fiener, P. and Pates, J.M.},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {1091--1099},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Harfenmeister, K.; Spengler, D.; and Weltzien, C.\n\n\n \n \n \n \n \n Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing, 11(13): 1569. July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Analyzing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{harfenmeister_analyzing_2019,\n\ttitle = {Analyzing {Temporal} and {Spatial} {Characteristics} of {Crop} {Parameters} {Using} {Sentinel}-1 {Backscatter} {Data}},\n\tvolume = {11},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/11/13/1569},\n\tdoi = {10.3390/rs11131569},\n\tabstract = {The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R     2    ) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R     2     values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R     2     values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing},\n\tauthor = {Harfenmeister, Katharina and Spengler, Daniel and Weltzien, Cornelia},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {1569},\n}\n\n

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

\n \n\n \n \n Heinrich, I.; Balanzategui, D.; Bens, O.; Blume, T.; Brauer, A.; Dietze, E.; Gottschalk, P.; Güntner, A.; Harfenmeister, K.; Helle, G.; Hohmann, C.; Itzerott, S.; Kaiser, K.; Liebner, S.; Merz, B.; Pinkerneil, S.; Plessen, B.; Sachs, T.; Schwab, M. J.; Spengler, D.; Vallentin, C.; and Wille, C.\n\n\n \n \n \n \n \n Regionale Auswirkungen des Globalen Wandels: Der Extremsommer 2018 in Nordostdeutschland.\n \n \n \n \n\n\n \n\n\n\n System Erde; 9,4 MB. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Regionale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{heinrich_regionale_2019,\n\ttitle = {Regionale {Auswirkungen} des {Globalen} {Wandels}: {Der} {Extremsommer} 2018 in {Nordostdeutschland}},\n\tcopyright = {CC-BY-SA 4.0},\n\tshorttitle = {Regionale {Auswirkungen} des {Globalen} {Wandels}},\n\turl = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_4296898},\n\tdoi = {10.2312/GFZ.SYSERDE.09.01.6},\n\tabstract = {The main focus of the TERENO Northeastern German Lowland Observatory (TERENO-Northeast) is the regional impact of Global Change. Since 2011, the observatory has recorded changes in the geo-, hydro-, bio- and atmosphere at six main study sites. The year 2018, particularly in northeast Germany, was record-breaking in regard to dryness and heat. The mean temperature in Mecklenburg-Vorpommern was 2 °C above the long-term average and precipitation was very low at 440 mm (normally around 600 mm). The extreme summer of 2018 was a special opportunity for TERENO-Northeast to measure the regional effects of climate change. One of the consequences was the large number of forest fires, with one major fire destroying around 400 hectares. Other extreme reactions of the ecosystems were shown in TERENO-Northeast. For example, for the first time since its rewetting, Polder Zarnekov fell dry, with unpredictable consequences for the greenhouse gas exchanges. The forest ecosystems of Müritz National Park, on the other hand, survived the extreme summer surprisingly well, partly because the months before the drought were relatively damp. The research activities of TERENO-Northeast form an important basis to develop realistic options for improved adaptation strategies to the ongoing global change with its particular region-specific effects and challenges.},\n\tlanguage = {de},\n\turldate = {2022-11-17},\n\tjournal = {System Erde; 9},\n\tauthor = {Heinrich, Ingo and Balanzategui, Daniel and Bens, Oliver and Blume, Theresa and Brauer, Achim and Dietze, Elisabeth and Gottschalk, Pia and Güntner, Andreas and Harfenmeister, Katharina and Helle, Gerhard and Hohmann, Christian and Itzerott, Sibylle and Kaiser, Knut and Liebner, Susanne and Merz, Bruno and Pinkerneil, Sylvia and Plessen, Birgit and Sachs, Torsten and Schwab, Markus J. and Spengler, Daniel and Vallentin, Claudia and Wille, Christian},\n\tyear = {2019},\n\tpages = {4 MB},\n}\n\n

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\n \n\n \n \n Hering, J. G.\n\n\n \n \n \n \n \n From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering.\n \n \n \n \n\n\n \n\n\n\n Journal of Environmental Engineering, 145(8): 02519001. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hering_slide_2019,\n\ttitle = {From {Slide} {Rule} to {Big} {Data}: {How} {Data} {Science} is {Changing} {Water} {Science} and {Engineering}},\n\tvolume = {145},\n\tissn = {0733-9372, 1943-7870},\n\tshorttitle = {From {Slide} {Rule} to {Big} {Data}},\n\turl = {https://ascelibrary.org/doi/10.1061/%28ASCE%29EE.1943-7870.0001578},\n\tdoi = {10.1061/(ASCE)EE.1943-7870.0001578},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Environmental Engineering},\n\tauthor = {Hering, Janet G.},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {02519001},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Hobley, E. U.; and Prater, I.\n\n\n \n \n \n \n \n Estimating soil texture from vis-NIR spectra: Estimating soil texture from vis-NIR spectra.\n \n \n \n \n\n\n \n\n\n\n European Journal of Soil Science, 70(1): 83–95. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hobley_estimating_2019,\n\ttitle = {Estimating soil texture from vis-{NIR} spectra: {Estimating} soil texture from vis-{NIR} spectra},\n\tvolume = {70},\n\tissn = {13510754},\n\tshorttitle = {Estimating soil texture from vis-{NIR} spectra},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/ejss.12733},\n\tdoi = {10.1111/ejss.12733},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {European Journal of Soil Science},\n\tauthor = {Hobley, E. U. and Prater, I.},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {83--95},\n}\n\n

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\n \n\n \n \n Hongtao, J.; Huanfeng, S.; Xinghua, L.; Chao, Z.; Huiqin, L.; and Fangni, L.\n\n\n \n \n \n \n \n Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 231: 111224. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Extending$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{hongtao_extending_2019,\n\ttitle = {Extending the {SMAP} 9-km soil moisture product using a spatio-temporal fusion model},\n\tvolume = {231},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425719302433},\n\tdoi = {10.1016/j.rse.2019.111224},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Hongtao, Jiang and Huanfeng, Shen and Xinghua, Li and Chao, Zeng and Huiqin, Liu and Fangni, Lei},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {111224},\n}\n\n

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\n \n\n \n \n Ibraim, E.; Wolf, B.; Harris, E.; Gasche, R.; Wei, J.; Yu, L.; Kiese, R.; Eggleston, S.; Butterbach-Bahl, K.; Zeeman, M.; Tuzson, B.; Emmenegger, L.; Six, J.; Henne, S.; and Mohn, J.\n\n\n \n \n \n \n \n Attribution of N$_{\\textrm{2}}$O sources in a grassland soil with laser spectroscopy based isotopocule analysis.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(16): 3247–3266. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Attribution$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{ibraim_attribution_2019,\n\ttitle = {Attribution of {N}$_{\\textrm{2}}${O} sources in a grassland soil with laser spectroscopy based isotopocule analysis},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/3247/2019/},\n\tdoi = {10.5194/bg-16-3247-2019},\n\tabstract = {Abstract. Nitrous oxide (N2O) is the primary atmospheric constituent involved in\nstratospheric ozone depletion and contributes strongly to changes in the\nclimate system through a positive radiative forcing mechanism. The\natmospheric abundance of N2O has increased from 270 ppb (parts per billion, 10−9 mole mole−1) during the\npre-industrial era to approx. 330 ppb in 2018. Even though it is well known\nthat microbial processes in agricultural and natural soils are the major\nN2O source, the contribution of specific soil processes is still\nuncertain. The relative abundance of N2O isotopocules\n(14N14N16N, 14N15N16O,\n15N14N16O, and 14N14N18O) carries\nprocess-specific information and thus can be used to trace production and\nconsumption pathways. While isotope ratio mass spectroscopy (IRMS) was\ntraditionally used for high-precision measurement of the isotopic\ncomposition of N2O, quantum cascade laser absorption spectroscopy\n(QCLAS) has been put forward as a complementary technique with the potential\nfor on-site analysis. In recent years, pre-concentration combined with QCLAS\nhas been presented as a technique to resolve subtle changes in ambient\nN2O isotopic composition. From the end of May until the beginning of August 2016, we investigated\nN2O emissions from an intensively managed grassland at the study site\nFendt in southern Germany. In total, 612 measurements of ambient\nN2O were taken by combining pre-concentration with QCLAS analyses,\nyielding δ15Nα, δ15Nβ,\nδ18O, and N2O concentration with a temporal resolution of\napproximately 1 h and precisions of 0.46 ‰, 0.36 ‰, 0.59 ‰, and 1.24 ppb,\nrespectively. Soil δ15N-NO3- values and\nconcentrations of NO3- and NH4+ were measured to further\nconstrain possible N2O-emitting source processes. Furthermore, the\nconcentration footprint area of measured N2O was determined with a\nLagrangian particle dispersion model (FLEXPART-COSMO) using local wind and\nturbulence observations. These simulations indicated that night-time\nconcentration observations were largely sensitive to local fluxes. While\nbacterial denitrification and nitrifier denitrification were identified as\nthe primary N2O-emitting processes, N2O reduction to N2\nlargely dictated the isotopic composition of measured N2O. Fungal\ndenitrification and nitrification-derived N2O accounted for 34 \\%–42 \\% of total N2O emissions and had a clear effect on the measured\nisotopic source signatures. This study presents the suitability of on-site\nN2O isotopocule analysis for disentangling source and sink processes\nin situ and found that at the Fendt site bacterial denitrification or nitrifier denitrification is the major source for N2O, while N2O\nreduction acted as a major sink for soil-produced N2O.},\n\tlanguage = {en},\n\tnumber = {16},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Ibraim, Erkan and Wolf, Benjamin and Harris, Eliza and Gasche, Rainer and Wei, Jing and Yu, Longfei and Kiese, Ralf and Eggleston, Sarah and Butterbach-Bahl, Klaus and Zeeman, Matthias and Tuzson, Béla and Emmenegger, Lukas and Six, Johan and Henne, Stephan and Mohn, Joachim},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {3247--3266},\n}\n\n

\n

\n\n\n

\n Abstract. Nitrous oxide (N2O) is the primary atmospheric constituent involved in stratospheric ozone depletion and contributes strongly to changes in the climate system through a positive radiative forcing mechanism. The atmospheric abundance of N2O has increased from 270 ppb (parts per billion, 10−9 mole mole−1) during the pre-industrial era to approx. 330 ppb in 2018. Even though it is well known that microbial processes in agricultural and natural soils are the major N2O source, the contribution of specific soil processes is still uncertain. The relative abundance of N2O isotopocules (14N14N16N, 14N15N16O, 15N14N16O, and 14N14N18O) carries process-specific information and thus can be used to trace production and consumption pathways. While isotope ratio mass spectroscopy (IRMS) was traditionally used for high-precision measurement of the isotopic composition of N2O, quantum cascade laser absorption spectroscopy (QCLAS) has been put forward as a complementary technique with the potential for on-site analysis. In recent years, pre-concentration combined with QCLAS has been presented as a technique to resolve subtle changes in ambient N2O isotopic composition. From the end of May until the beginning of August 2016, we investigated N2O emissions from an intensively managed grassland at the study site Fendt in southern Germany. In total, 612 measurements of ambient N2O were taken by combining pre-concentration with QCLAS analyses, yielding δ15Nα, δ15Nβ, δ18O, and N2O concentration with a temporal resolution of approximately 1 h and precisions of 0.46 ‰, 0.36 ‰, 0.59 ‰, and 1.24 ppb, respectively. Soil δ15N-NO3- values and concentrations of NO3- and NH4+ were measured to further constrain possible N2O-emitting source processes. Furthermore, the concentration footprint area of measured N2O was determined with a Lagrangian particle dispersion model (FLEXPART-COSMO) using local wind and turbulence observations. These simulations indicated that night-time concentration observations were largely sensitive to local fluxes. While bacterial denitrification and nitrifier denitrification were identified as the primary N2O-emitting processes, N2O reduction to N2 largely dictated the isotopic composition of measured N2O. Fungal denitrification and nitrification-derived N2O accounted for 34 %–42 % of total N2O emissions and had a clear effect on the measured isotopic source signatures. This study presents the suitability of on-site N2O isotopocule analysis for disentangling source and sink processes in situ and found that at the Fendt site bacterial denitrification or nitrifier denitrification is the major source for N2O, while N2O reduction acted as a major sink for soil-produced N2O.\n

\n\n\n

\n \n\n \n \n Jiang, S.; Zhang, Q.; Werner, A.; Wellen, C.; Jomaa, S.; Zhu, Q.; Büttner, O.; Meon, G.; and Rode, M.\n\n\n \n \n \n \n \n Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 569: 22–36. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{jiang_effects_2019,\n\ttitle = {Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty},\n\tvolume = {569},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169418309041},\n\tdoi = {10.1016/j.jhydrol.2018.11.049},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Jiang, S.Y. and Zhang, Q. and Werner, A.D. and Wellen, C. and Jomaa, S. and Zhu, Q.D. and Büttner, O. and Meon, G. and Rode, M.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {22--36},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kabisch, N.; Selsam, P.; Kirsten, T.; Lausch, A.; and Bumberger, J.\n\n\n \n \n \n \n \n A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes.\n \n \n \n \n\n\n \n\n\n\n Ecological Indicators, 99: 273–282. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kabisch_multi-sensor_2019,\n\ttitle = {A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes},\n\tvolume = {99},\n\tissn = {1470160X},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X1830966X},\n\tdoi = {10.1016/j.ecolind.2018.12.033},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Indicators},\n\tauthor = {Kabisch, Nadja and Selsam, Peter and Kirsten, Toralf and Lausch, Angela and Bumberger, Jan},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {273--282},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kamjunke, N.; Hertkorn, N.; Harir, M.; Schmitt-Kopplin, P.; Griebler, C.; Brauns, M.; von Tümpling, W.; Weitere, M.; and Herzsprung, P.\n\n\n \n \n \n \n \n Molecular change of dissolved organic matter and patterns of bacterial activity in a stream along a land-use gradient.\n \n \n \n \n\n\n \n\n\n\n Water Research, 164: 114919. November 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Molecular$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kamjunke_molecular_2019,\n\ttitle = {Molecular change of dissolved organic matter and patterns of bacterial activity in a stream along a land-use gradient},\n\tvolume = {164},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135419306931},\n\tdoi = {10.1016/j.watres.2019.114919},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Kamjunke, Norbert and Hertkorn, Norbert and Harir, Mourad and Schmitt-Kopplin, Philippe and Griebler, Christian and Brauns, Mario and von Tümpling, Wolf and Weitere, Markus and Herzsprung, Peter},\n\tmonth = nov,\n\tyear = {2019},\n\tpages = {114919},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kappler, C.; Kaiser, K.; Küster, M.; Nicolay, A.; Fülling, A.; Bens, O.; and Raab, T.\n\n\n \n \n \n \n \n Late Pleistocene and Holocene terrestrial geomorphodynamics and soil formation in northeastern Germany: a review of geochronological data.\n \n \n \n \n\n\n \n\n\n\n Physical Geography, 40(5): 405–432. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Late$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kappler_late_2019,\n\ttitle = {Late {Pleistocene} and {Holocene} terrestrial geomorphodynamics and soil formation in northeastern {Germany}: a review of geochronological data},\n\tvolume = {40},\n\tissn = {0272-3646, 1930-0557},\n\tshorttitle = {Late {Pleistocene} and {Holocene} terrestrial geomorphodynamics and soil formation in northeastern {Germany}},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/02723646.2019.1573621},\n\tdoi = {10.1080/02723646.2019.1573621},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Physical Geography},\n\tauthor = {Kappler, Christoph and Kaiser, Knut and Küster, Mathias and Nicolay, Alexander and Fülling, Alexander and Bens, Oliver and Raab, Thomas},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {405--432},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Karrasch, B.; Horovitz, O.; Norf, H.; Hillel, N.; Hadas, O.; Beeri-Shlevin, Y.; and Laronne, J. B.\n\n\n \n \n \n \n \n Quantitative ecotoxicological impacts of sewage treatment plant effluents on plankton productivity and assimilative capacity of rivers.\n \n \n \n \n\n\n \n\n\n\n Environmental Science and Pollution Research, 26(23): 24034–24049. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{karrasch_quantitative_2019,\n\ttitle = {Quantitative ecotoxicological impacts of sewage treatment plant effluents on plankton productivity and assimilative capacity of rivers},\n\tvolume = {26},\n\tissn = {0944-1344, 1614-7499},\n\turl = {http://link.springer.com/10.1007/s11356-019-04940-6},\n\tdoi = {10.1007/s11356-019-04940-6},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Science and Pollution Research},\n\tauthor = {Karrasch, Bernhard and Horovitz, Omer and Norf, Helge and Hillel, Noa and Hadas, Ora and Beeri-Shlevin, Yaron and Laronne, Jonathan B.},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {24034--24049},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kiesel, J.; Gericke, A.; Rathjens, H.; Wetzig, A.; Kakouei, K.; Jähnig, S. C.; and Fohrer, N.\n\n\n \n \n \n \n \n Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions.\n \n \n \n \n\n\n \n\n\n\n Ecological Engineering, 127: 404–416. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Climate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kiesel_climate_2019,\n\ttitle = {Climate change impacts on ecologically relevant hydrological indicators in three catchments in three {European} ecoregions},\n\tvolume = {127},\n\tissn = {09258574},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S092585741830466X},\n\tdoi = {10.1016/j.ecoleng.2018.12.019},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Engineering},\n\tauthor = {Kiesel, Jens and Gericke, Andreas and Rathjens, Hendrik and Wetzig, Annett and Kakouei, Karan and Jähnig, Sonja C. and Fohrer, Nicola},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {404--416},\n}\n\n

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\n \n\n \n \n Klosterhalfen, A.; Graf, A.; Brüggemann, N.; Drüe, C.; Esser, O.; González-Dugo, M. P.; Heinemann, G.; Jacobs, C. M. J.; Mauder, M.; Moene, A. F.; Ney, P.; Pütz, T.; Rebmann, C.; Ramos Rodríguez, M.; Scanlon, T. M.; Schmidt, M.; Steinbrecher, R.; Thomas, C. K.; Valler, V.; Zeeman, M. J.; and Vereecken, H.\n\n\n \n \n \n \n \n Source partitioning of H$_{\\textrm{2}}$O and CO$_{\\textrm{2}}$ fluxes based on high-frequency eddy covariance data: a comparison between study sites.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(6): 1111–1132. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Source$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{klosterhalfen_source_2019,\n\ttitle = {Source partitioning of {H}$_{\\textrm{2}}${O} and {CO}$_{\\textrm{2}}$ fluxes based on high-frequency eddy covariance data: a comparison between study sites},\n\tvolume = {16},\n\tissn = {1726-4189},\n\tshorttitle = {Source partitioning of {H}\\&lt;sub\\&gt;2\\&lt;/sub\\&gt;{O} and {CO}\\&lt;sub\\&gt;2\\&lt;/sub\\&gt; fluxes based on high-frequency eddy covariance data},\n\turl = {https://bg.copernicus.org/articles/16/1111/2019/},\n\tdoi = {10.5194/bg-16-1111-2019},\n\tabstract = {Abstract. For an assessment of the roles of soil and vegetation in the\nclimate system, a further understanding of the flux components of\nH2O and CO2 (e.g., transpiration, soil respiration) and\ntheir interaction with physical conditions and physiological functioning of\nplants and ecosystems is necessary. To obtain magnitudes of these flux\ncomponents, we applied source partitioning approaches after Scanlon and\nKustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency\neddy covariance measurements of 12 study sites covering different\necosystems (croplands, grasslands, and forests) in different climatic\nregions. Both partitioning methods are based on higher-order statistics of\nthe H2O and CO2 fluctuations, but proceed differently to\nestimate transpiration, evaporation, net primary production, and soil\nrespiration. We compared and evaluated the partitioning results obtained with\nSK10 and TH08, including slight modifications of both approaches. Further, we\nanalyzed the interrelations among the performance of the partitioning\nmethods, turbulence characteristics, and site characteristics (such as plant\ncover type, canopy height, canopy density, and measurement height). We were\nable to identify characteristics of a data set that are prerequisites for\nadequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux\ncomponents. For both methods, the partitioning of CO2 fluxes was\nless robust than for H2O fluxes. Results derived with SK10 showed\nrelatively large dependencies on estimated water use efficiency (WUE) at the\nleaf level, which is a required input. Measurements of outgoing longwave\nradiation used for the estimation of foliage temperature (used in WUE) could\nslightly increase the quality of the partitioning results. A modification of\nthe TH08 approach, by applying a cluster analysis for the conditional\nsampling of respiration–evaporation events, performed satisfactorily, but did\nnot result in significant advantages compared to the original method versions\ndeveloped by Thomas et al. (2008). The performance of each partitioning\napproach was dependent on meteorological conditions, plant development,\ncanopy height, canopy density, and measurement height. Foremost, the\nperformance of SK10 correlated negatively with the ratio between measurement\nheight and canopy height. The performance of TH08 was more dependent on\ncanopy height and leaf area index. In general, all site characteristics that\nincrease dissimilarities between scalars appeared to enhance partitioning\nperformance for SK10 and TH08.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Klosterhalfen, Anne and Graf, Alexander and Brüggemann, Nicolas and Drüe, Clemens and Esser, Odilia and González-Dugo, María P. and Heinemann, Günther and Jacobs, Cor M. J. and Mauder, Matthias and Moene, Arnold F. and Ney, Patrizia and Pütz, Thomas and Rebmann, Corinna and Ramos Rodríguez, Mario and Scanlon, Todd M. and Schmidt, Marius and Steinbrecher, Rainer and Thomas, Christoph K. and Valler, Veronika and Zeeman, Matthias J. and Vereecken, Harry},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {1111--1132},\n}\n\n

\n

\n\n\n

\n Abstract. For an assessment of the roles of soil and vegetation in the climate system, a further understanding of the flux components of H2O and CO2 (e.g., transpiration, soil respiration) and their interaction with physical conditions and physiological functioning of plants and ecosystems is necessary. To obtain magnitudes of these flux components, we applied source partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency eddy covariance measurements of 12 study sites covering different ecosystems (croplands, grasslands, and forests) in different climatic regions. Both partitioning methods are based on higher-order statistics of the H2O and CO2 fluctuations, but proceed differently to estimate transpiration, evaporation, net primary production, and soil respiration. We compared and evaluated the partitioning results obtained with SK10 and TH08, including slight modifications of both approaches. Further, we analyzed the interrelations among the performance of the partitioning methods, turbulence characteristics, and site characteristics (such as plant cover type, canopy height, canopy density, and measurement height). We were able to identify characteristics of a data set that are prerequisites for adequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux components. For both methods, the partitioning of CO2 fluxes was less robust than for H2O fluxes. Results derived with SK10 showed relatively large dependencies on estimated water use efficiency (WUE) at the leaf level, which is a required input. Measurements of outgoing longwave radiation used for the estimation of foliage temperature (used in WUE) could slightly increase the quality of the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditional sampling of respiration–evaporation events, performed satisfactorily, but did not result in significant advantages compared to the original method versions developed by Thomas et al. (2008). The performance of each partitioning approach was dependent on meteorological conditions, plant development, canopy height, canopy density, and measurement height. Foremost, the performance of SK10 correlated negatively with the ratio between measurement height and canopy height. The performance of TH08 was more dependent on canopy height and leaf area index. In general, all site characteristics that increase dissimilarities between scalars appeared to enhance partitioning performance for SK10 and TH08.\n

\n\n\n

\n \n\n \n \n Klosterhalfen, A.; Moene, A.; Schmidt, M.; Scanlon, T.; Vereecken, H.; and Graf, A.\n\n\n \n \n \n \n \n Sensitivity analysis of a source partitioning method for H$_{\\textrm{2}}$O and CO$_{\\textrm{2}}$ fluxes based on high frequency eddy covariance data: Findings from field data and large eddy simulations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 265: 152–170. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sensitivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{klosterhalfen_sensitivity_2019,\n\ttitle = {Sensitivity analysis of a source partitioning method for {H}$_{\\textrm{2}}${O} and {CO}$_{\\textrm{2}}$ fluxes based on high frequency eddy covariance data: {Findings} from field data and large eddy simulations},\n\tvolume = {265},\n\tissn = {01681923},\n\tshorttitle = {Sensitivity analysis of a source partitioning method for {H2O} and {CO2} fluxes based on high frequency eddy covariance data},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303496},\n\tdoi = {10.1016/j.agrformet.2018.11.003},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Klosterhalfen, A. and Moene, A.F. and Schmidt, M. and Scanlon, T.M. and Vereecken, H. and Graf, A.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {152--170},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Koebsch, F.; Winkel, M.; Liebner, S.; Liu, B.; Westphal, J.; Schmiedinger, I.; Spitzy, A.; Gehre, M.; Jurasinski, G.; Köhler, S.; Unger, V.; Koch, M.; Sachs, T.; and Böttcher, M. E.\n\n\n \n \n \n \n \n Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland.\n \n \n \n \n\n\n \n\n\n\n Biogeosciences, 16(9): 1937–1953. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Sulfate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{koebsch_sulfate_2019,\n\ttitle = {Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland},\n\tvolume = {16},\n\tissn = {1726-4189},\n\turl = {https://bg.copernicus.org/articles/16/1937/2019/},\n\tdoi = {10.5194/bg-16-1937-2019},\n\tabstract = {Abstract. In natural coastal wetlands, high supplies of marine\nsulfate suppress methanogenesis. Coastal wetlands are, however, often\nsubject to disturbance by diking and drainage for agricultural use and can\nturn to potent methane sources when rewetted for remediation. This suggests\nthat preceding land use measures can suspend the sulfate-related methane\nsuppressing mechanisms. Here, we unravel the hydrological relocation and\nbiogeochemical S and C transformation processes that induced high methane\nemissions in a disturbed and rewetted peatland despite former brackish\nimpact. The underlying processes were investigated along a transect of\nincreasing distance to the coastline using a combination of concentration\npatterns, stable isotope partitioning, and analysis of the microbial\ncommunity structure. We found that diking and freshwater rewetting caused a\ndistinct freshening and an efficient depletion of the brackish sulfate\nreservoir by dissimilatory sulfate reduction (DSR). Despite some legacy\neffects of brackish impact expressed as high amounts of sedimentary S and\nelevated electrical conductivities, contemporary metabolic processes\noperated mainly under sulfate-limited conditions. This opened up favorable\nconditions for the establishment of a prospering methanogenic community in\nthe top 30–40 cm of peat, the structure and physiology of which resemble\nthose of terrestrial organic-rich environments. Locally, high amounts of\nsulfate persisted in deeper peat layers through the inhibition of DSR,\nprobably by competitive electron acceptors of terrestrial origin, for\nexample Fe(III). However, as sulfate occurred only in peat layers below\n30–40 cm, it did not interfere with high methane emissions on an ecosystem\nscale. Our results indicate that the climate effect of disturbed and\nremediated coastal wetlands cannot simply be derived by analogy with their\nnatural counterparts. From a greenhouse gas perspective, the re-exposure of\ndiked wetlands to natural coastal dynamics would literally open up the\nfloodgates for a replenishment of the marine sulfate pool and therefore\nconstitute an efficient measure to reduce methane emissions.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Biogeosciences},\n\tauthor = {Koebsch, Franziska and Winkel, Matthias and Liebner, Susanne and Liu, Bo and Westphal, Julia and Schmiedinger, Iris and Spitzy, Alejandro and Gehre, Matthias and Jurasinski, Gerald and Köhler, Stefan and Unger, Viktoria and Koch, Marian and Sachs, Torsten and Böttcher, Michael E.},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {1937--1953},\n}\n\n

\n

\n\n\n

\n Abstract. In natural coastal wetlands, high supplies of marine sulfate suppress methanogenesis. Coastal wetlands are, however, often subject to disturbance by diking and drainage for agricultural use and can turn to potent methane sources when rewetted for remediation. This suggests that preceding land use measures can suspend the sulfate-related methane suppressing mechanisms. Here, we unravel the hydrological relocation and biogeochemical S and C transformation processes that induced high methane emissions in a disturbed and rewetted peatland despite former brackish impact. The underlying processes were investigated along a transect of increasing distance to the coastline using a combination of concentration patterns, stable isotope partitioning, and analysis of the microbial community structure. We found that diking and freshwater rewetting caused a distinct freshening and an efficient depletion of the brackish sulfate reservoir by dissimilatory sulfate reduction (DSR). Despite some legacy effects of brackish impact expressed as high amounts of sedimentary S and elevated electrical conductivities, contemporary metabolic processes operated mainly under sulfate-limited conditions. This opened up favorable conditions for the establishment of a prospering methanogenic community in the top 30–40 cm of peat, the structure and physiology of which resemble those of terrestrial organic-rich environments. Locally, high amounts of sulfate persisted in deeper peat layers through the inhibition of DSR, probably by competitive electron acceptors of terrestrial origin, for example Fe(III). However, as sulfate occurred only in peat layers below 30–40 cm, it did not interfere with high methane emissions on an ecosystem scale. Our results indicate that the climate effect of disturbed and remediated coastal wetlands cannot simply be derived by analogy with their natural counterparts. From a greenhouse gas perspective, the re-exposure of diked wetlands to natural coastal dynamics would literally open up the floodgates for a replenishment of the marine sulfate pool and therefore constitute an efficient measure to reduce methane emissions.\n

\n\n\n

\n \n\n \n \n Krauss, M.; Hug, C.; Bloch, R.; Schulze, T.; and Brack, W.\n\n\n \n \n \n \n \n Prioritising site-specific micropollutants in surface water from LC-HRMS non-target screening data using a rarity score.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 45. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Prioritising$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{krauss_prioritising_2019,\n\ttitle = {Prioritising site-specific micropollutants in surface water from {LC}-{HRMS} non-target screening data using a rarity score},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0231-z},\n\tdoi = {10.1186/s12302-019-0231-z},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Krauss, Martin and Hug, Christine and Bloch, Robert and Schulze, Tobias and Brack, Werner},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {45},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Kühnel, A.; Garcia-Franco, N.; Wiesmeier, M.; Burmeister, J.; Hobley, E.; Kiese, R.; Dannenmann, M.; and Kögel-Knabner, I.\n\n\n \n \n \n \n \n Controlling factors of carbon dynamics in grassland soils of Bavaria between 1989 and 2016.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 280: 118–128. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Controlling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{kuhnel_controlling_2019,\n\ttitle = {Controlling factors of carbon dynamics in grassland soils of {Bavaria} between 1989 and 2016},\n\tvolume = {280},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880919301239},\n\tdoi = {10.1016/j.agee.2019.04.036},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Kühnel, Anna and Garcia-Franco, Noelia and Wiesmeier, Martin and Burmeister, Johannes and Hobley, Eleanor and Kiese, Ralf and Dannenmann, Michael and Kögel-Knabner, Ingrid},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {118--128},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Li, J.; Ju, W.; He, W.; Wang, H.; Zhou, Y.; and Xu, M.\n\n\n \n \n \n \n \n An Algorithm Differentiating Sunlit and Shaded Leaves for Improving Canopy Conductance and Vapotranspiration Estimates.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 124(4): 807–824. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{li_algorithm_2019,\n\ttitle = {An {Algorithm} {Differentiating} {Sunlit} and {Shaded} {Leaves} for {Improving} {Canopy} {Conductance} and {Vapotranspiration} {Estimates}},\n\tvolume = {124},\n\tissn = {2169-8953, 2169-8961},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018JG004675},\n\tdoi = {10.1029/2018JG004675},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Li, Jing and Ju, Weimin and He, Wei and Wang, Hengmao and Zhou, Yanlian and Xu, Mingzhu},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {807--824},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Li, X.; Vereecken, H.; and Ma, C.\n\n\n \n \n \n \n \n Observing Ecohydrological Processes: Challenges and Perspectives.\n \n \n \n \n\n\n \n\n\n\n In Li, X.; and Vereecken, H., editor(s), Observation and Measurement of Ecohydrological Processes, volume 2, pages 1–27. Springer Berlin Heidelberg, Berlin, Heidelberg, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Observing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{li_observing_2019,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Observing {Ecohydrological} {Processes}: {Challenges} and {Perspectives}},\n\tvolume = {2},\n\tisbn = {9783662482964 9783662482971},\n\tshorttitle = {Observing {Ecohydrological} {Processes}},\n\turl = {http://link.springer.com/10.1007/978-3-662-48297-1_1},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Observation and {Measurement} of {Ecohydrological} {Processes}},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Li, Xin and Vereecken, Harry and Ma, Chunfeng},\n\teditor = {Li, Xin and Vereecken, Harry},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-662-48297-1_1},\n\tpages = {1--27},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Liu, S.; Schloter, M.; Hu, R.; Vereecken, H.; and Brüggemann, N.\n\n\n \n \n \n \n \n Hydroxylamine Contributes More to Abiotic N$_{\\textrm{2}}$O Production in Soils Than Nitrite.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Environmental Science, 7: 47. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Hydroxylamine$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{liu_hydroxylamine_2019,\n\ttitle = {Hydroxylamine {Contributes} {More} to {Abiotic} {N}$_{\\textrm{2}}${O} {Production} in {Soils} {Than} {Nitrite}},\n\tvolume = {7},\n\tissn = {2296-665X},\n\turl = {https://www.frontiersin.org/article/10.3389/fenvs.2019.00047/full},\n\tdoi = {10.3389/fenvs.2019.00047},\n\turldate = {2022-11-17},\n\tjournal = {Frontiers in Environmental Science},\n\tauthor = {Liu, Shurong and Schloter, Michael and Hu, Ronggui and Vereecken, Harry and Brüggemann, Nicolas},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {47},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Ma, H.; Zeng, J.; Chen, N.; Zhang, X.; Cosh, M. H.; and Wang, W.\n\n\n \n \n \n \n \n Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations.\n \n \n \n \n\n\n \n\n\n\n Remote Sensing of Environment, 231: 111215. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Satellite$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{ma_satellite_2019,\n\ttitle = {Satellite surface soil moisture from {SMAP}, {SMOS}, {AMSR2} and {ESA} {CCI}: {A} comprehensive assessment using global ground-based observations},\n\tvolume = {231},\n\tissn = {00344257},\n\tshorttitle = {Satellite surface soil moisture from {SMAP}, {SMOS}, {AMSR2} and {ESA} {CCI}},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425719302287},\n\tdoi = {10.1016/j.rse.2019.111215},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Remote Sensing of Environment},\n\tauthor = {Ma, Hongliang and Zeng, Jiangyuan and Chen, Nengcheng and Zhang, Xiang and Cosh, Michael H. and Wang, Wei},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {111215},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Marcé, R.; Obrador, B.; Gómez-Gener, L.; Catalán, N.; Koschorreck, M.; Arce, M. I.; Singer, G.; and von Schiller, D.\n\n\n \n \n \n \n \n Emissions from dry inland waters are a blind spot in the global carbon cycle.\n \n \n \n \n\n\n \n\n\n\n Earth-Science Reviews, 188: 240–248. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Emissions$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{marce_emissions_2019,\n\ttitle = {Emissions from dry inland waters are a blind spot in the global carbon cycle},\n\tvolume = {188},\n\tissn = {00128252},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0012825218301971},\n\tdoi = {10.1016/j.earscirev.2018.11.012},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Earth-Science Reviews},\n\tauthor = {Marcé, Rafael and Obrador, Biel and Gómez-Gener, Lluís and Catalán, Núria and Koschorreck, Matthias and Arce, María Isabel and Singer, Gabriel and von Schiller, Daniel},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {240--248},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Mi, C.; Sadeghian, A.; Lindenschmidt, K.; and Rinke, K.\n\n\n \n \n \n \n \n Variable withdrawal elevations as a management tool to counter the effects of climate warming in Germany’s largest drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Environmental Sciences Europe, 31(1): 19. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Variable$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{mi_variable_2019,\n\ttitle = {Variable withdrawal elevations as a management tool to counter the effects of climate warming in {Germany}’s largest drinking water reservoir},\n\tvolume = {31},\n\tissn = {2190-4707, 2190-4715},\n\turl = {https://enveurope.springeropen.com/articles/10.1186/s12302-019-0202-4},\n\tdoi = {10.1186/s12302-019-0202-4},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Environmental Sciences Europe},\n\tauthor = {Mi, Chenxi and Sadeghian, Amir and Lindenschmidt, Karl-Erich and Rinke, Karsten},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {19},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Morandage, S.; Schnepf, A.; Leitner, D.; Javaux, M.; Vereecken, H.; and Vanderborght, J.\n\n\n \n \n \n \n \n Parameter sensitivity analysis of a root system architecture model based on virtual field sampling.\n \n \n \n \n\n\n \n\n\n\n Plant and Soil, 438(1-2): 101–126. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameter$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{morandage_parameter_2019,\n\ttitle = {Parameter sensitivity analysis of a root system architecture model based on virtual field sampling},\n\tvolume = {438},\n\tissn = {0032-079X, 1573-5036},\n\turl = {https://link.springer.com/10.1007/s11104-019-03993-3},\n\tdoi = {10.1007/s11104-019-03993-3},\n\tlanguage = {en},\n\tnumber = {1-2},\n\turldate = {2022-11-17},\n\tjournal = {Plant and Soil},\n\tauthor = {Morandage, Shehan and Schnepf, Andrea and Leitner, Daniel and Javaux, Mathieu and Vereecken, Harry and Vanderborght, Jan},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {101--126},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Ney, P.; Graf, A.; Bogena, H.; Diekkrüger, B.; Drüe, C.; Esser, O.; Heinemann, G.; Klosterhalfen, A.; Pick, K.; Pütz, T.; Schmidt, M.; Valler, V.; and Vereecken, H.\n\n\n \n \n \n \n \n CO2 fluxes before and after partial deforestation of a Central European spruce forest.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 274: 61–74. August 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"CO2$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{ney_co2_2019,\n\ttitle = {{CO2} fluxes before and after partial deforestation of a {Central} {European} spruce forest},\n\tvolume = {274},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192319301492},\n\tdoi = {10.1016/j.agrformet.2019.04.009},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Ney, Patrizia and Graf, Alexander and Bogena, Heye and Diekkrüger, Bernd and Drüe, Clemens and Esser, Odilia and Heinemann, Günther and Klosterhalfen, Anne and Pick, Katharina and Pütz, Thomas and Schmidt, Marius and Valler, Veronika and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {61--74},\n}\n\n

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\n \n\n \n \n Putzenlechner, B.; Marzahn, P.; Kiese, R.; Ludwig, R.; and Sanchez-Azofeifa, A.\n\n\n \n \n \n \n \n Assessing the variability and uncertainty of two-flux FAPAR measurements in a conifer-dominated forest.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 264: 149–163. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{putzenlechner_assessing_2019,\n\ttitle = {Assessing the variability and uncertainty of two-flux {FAPAR} measurements in a conifer-dominated forest},\n\tvolume = {264},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303319},\n\tdoi = {10.1016/j.agrformet.2018.10.007},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Putzenlechner, Birgitta and Marzahn, Philip and Kiese, Ralf and Ludwig, Ralf and Sanchez-Azofeifa, Arturo},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {149--163},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Quade, M.; Klosterhalfen, A.; Graf, A.; Brüggemann, N.; Hermes, N.; Vereecken, H.; and Rothfuss, Y.\n\n\n \n \n \n \n \n In-situ monitoring of soil water isotopic composition for partitioning of evapotranspiration during one growing season of sugar beet (Beta vulgaris).\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 266-267: 53–64. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"In-situ$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{quade_-situ_2019,\n\ttitle = {In-situ monitoring of soil water isotopic composition for partitioning of evapotranspiration during one growing season of sugar beet ({Beta} vulgaris)},\n\tvolume = {266-267},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192318303952},\n\tdoi = {10.1016/j.agrformet.2018.12.002},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Quade, Maria and Klosterhalfen, Anne and Graf, Alexander and Brüggemann, Nicolas and Hermes, Normen and Vereecken, Harry and Rothfuss, Youri},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {53--64},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Rodionov, A.; Lehndorff, E.; Stremtan, C. C.; Brand, W. A.; Königshoven, H.; and Amelung, W.\n\n\n \n \n \n \n \n Spatial Microanalysis of Natural $^{\\textrm{13}}$ C/ $^{\\textrm{12}}$ C Abundance in Environmental Samples Using Laser Ablation-Isotope Ratio Mass Spectrometry.\n \n \n \n \n\n\n \n\n\n\n Analytical Chemistry, 91(9): 6225–6232. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatial$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rodionov_spatial_2019,\n\ttitle = {Spatial {Microanalysis} of {Natural} $^{\\textrm{13}}$ {C}/ $^{\\textrm{12}}$ {C} {Abundance} in {Environmental} {Samples} {Using} {Laser} {Ablation}-{Isotope} {Ratio} {Mass} {Spectrometry}},\n\tvolume = {91},\n\tissn = {0003-2700, 1520-6882},\n\turl = {https://pubs.acs.org/doi/10.1021/acs.analchem.9b00892},\n\tdoi = {10.1021/acs.analchem.9b00892},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Analytical Chemistry},\n\tauthor = {Rodionov, Andrei and Lehndorff, Eva and Stremtan, Ciprian C. and Brand, Willi A. and Königshoven, Heinz-Peter and Amelung, Wulf},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {6225--6232},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Rudolph, S.; Marchant, B. P.; Weihermüller, L.; and Vereecken, H.\n\n\n \n \n \n \n \n Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys.\n \n \n \n \n\n\n \n\n\n\n Precision Agriculture, 20(1): 19–39. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rudolph_assessment_2019,\n\ttitle = {Assessment of the position accuracy of a single-frequency {GPS} receiver designed for electromagnetic induction surveys},\n\tvolume = {20},\n\tissn = {1385-2256, 1573-1618},\n\turl = {http://link.springer.com/10.1007/s11119-018-9578-1},\n\tdoi = {10.1007/s11119-018-9578-1},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-16},\n\tjournal = {Precision Agriculture},\n\tauthor = {Rudolph, Sebastian and Marchant, Ben Paul and Weihermüller, Lutz and Vereecken, Harry},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {19--39},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Rummler, T.; Arnault, J.; Gochis, D.; and Kunstmann, H.\n\n\n \n \n \n \n \n Role of Lateral Terrestrial Water Flow on the Regional Water Cycle in a Complex Terrain Region: Investigation With a Fully Coupled Model System.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Atmospheres, 124(2): 507–529. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Role$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{rummler_role_2019,\n\ttitle = {Role of {Lateral} {Terrestrial} {Water} {Flow} on the {Regional} {Water} {Cycle} in a {Complex} {Terrain} {Region}: {Investigation} {With} a {Fully} {Coupled} {Model} {System}},\n\tvolume = {124},\n\tissn = {2169-897X, 2169-8996},\n\tshorttitle = {Role of {Lateral} {Terrestrial} {Water} {Flow} on the {Regional} {Water} {Cycle} in a {Complex} {Terrain} {Region}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1029/2018JD029004},\n\tdoi = {10.1029/2018JD029004},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Rummler, Thomas and Arnault, Joel and Gochis, David and Kunstmann, Harald},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {507--529},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Schweiger, O.; Franzén, M.; Frenzel, M.; Galpern, P.; Kerr, J.; Papanikolaou, A.; and Rasmont, P.\n\n\n \n \n \n \n \n Minimising Risks of Global Change by Enhancing Resilience of Pollinators in Agricultural Systems.\n \n \n \n \n\n\n \n\n\n\n In Schröter, M.; Bonn, A.; Klotz, S.; Seppelt, R.; and Baessler, C., editor(s), Atlas of Ecosystem Services, pages 105–111. Springer International Publishing, Cham, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Minimising$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{schroter_minimising_2019,\n\taddress = {Cham},\n\ttitle = {Minimising {Risks} of {Global} {Change} by {Enhancing} {Resilience} of {Pollinators} in {Agricultural} {Systems}},\n\tisbn = {9783319962283 9783319962290},\n\turl = {http://link.springer.com/10.1007/978-3-319-96229-0_17},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Atlas of {Ecosystem} {Services}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Schweiger, Oliver and Franzén, Markus and Frenzel, Mark and Galpern, Paul and Kerr, Jeremy and Papanikolaou, Alexandra and Rasmont, Pierre},\n\teditor = {Schröter, Matthias and Bonn, Aletta and Klotz, Stefan and Seppelt, Ralf and Baessler, Cornelia},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-319-96229-0_17},\n\tpages = {105--111},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Shrestha, P.; and Simmer, C.\n\n\n \n \n \n \n \n Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models.\n \n \n \n \n\n\n \n\n\n\n Water, 12(1): 46. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeled$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{shrestha_modeled_2019,\n\ttitle = {Modeled {Land} {Atmosphere} {Coupling} {Response} to {Soil} {Moisture} {Changes} with {Different} {Generations} of {Land} {Surface} {Models}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/1/46},\n\tdoi = {10.3390/w12010046},\n\tabstract = {An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Shrestha, Prabhakar and Simmer, Clemens},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {46},\n}\n\n

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

\n \n\n \n \n Smiatek, G.; and Kunstmann, H.\n\n\n \n \n \n \n \n Simulating Future Runoff in a Complex Terrain Alpine Catchment with EURO-CORDEX Data.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrometeorology, 20(9): 1925–1940. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{smiatek_simulating_2019,\n\ttitle = {Simulating {Future} {Runoff} in a {Complex} {Terrain} {Alpine} {Catchment} with {EURO}-{CORDEX} {Data}},\n\tvolume = {20},\n\tissn = {1525-755X, 1525-7541},\n\turl = {http://journals.ametsoc.org/doi/10.1175/JHM-D-18-0214.1},\n\tdoi = {10.1175/JHM-D-18-0214.1},\n\tabstract = {Abstract \n            With large elevation gradients and high hydrometeorological variability, Alpine catchments pose special challenges to hydrological climate change impact assessment. Data from seven regional climate models run within the Coordinated Regional Climate Downscaling Experiments (CORDEX), each driven with a different boundary forcing, are used to exemplarily evaluate the reproduction of observed flow duration curves and access the future discharge of the Ammer River located in Alpine southern Germany applying the hydrological simulation model called the Water Flow and Balance Simulation Model (WaSiM). The results show that WaSiM reasonably reproduces the observed runoff for the entire catchment when driven with observed precipitation. When applied with CORDEX evaluation data (1989–2008) forced by ERA-Interim, the simulations underestimate the extreme runoff and reproduce the high percentile values with errors in the range from −37\\% to 55\\% with an ensemble mean of around 15\\%. Runs with historical data 1975–2005 reveal larger errors, up to 120\\%, with an ensemble mean of around 50\\% overestimation. Also, the results show a large spread between the simulations, primarily resulting from deficiencies in the precipitation data. Results indicate future changes for 2071–2100 in the 99.5th percentile runoff value of up to 9\\% compared to 1975–2005. An increase in high flows is also supported by flow return periods obtained from a larger sample of highest flows over 50 years, which reveals for 2051–2100 lower return periods for high runoff values compared to 1956–2005. Obtained results are associated with substantial uncertainties leading to the conclusion that CORDEX data at 0.11° resolution are likely inadequate for driving hydrologic analyses in mesoscale catchments that require a high standard of fidelity for hydrologic simulation performance.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Smiatek, Gerhard and Kunstmann, Harald},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {1925--1940},\n}\n\n

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\n \n\n \n \n Soltani, M.; Laux, P.; Mauder, M.; and Kunstmann, H.\n\n\n \n \n \n \n \n Inverse distributed modelling of streamflow and turbulent fluxes: A sensitivity and uncertainty analysis coupled with automatic optimization.\n \n \n \n \n\n\n \n\n\n\n Journal of Hydrology, 571: 856–872. April 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Inverse$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{soltani_inverse_2019,\n\ttitle = {Inverse distributed modelling of streamflow and turbulent fluxes: {A} sensitivity and uncertainty analysis coupled with automatic optimization},\n\tvolume = {571},\n\tissn = {00221694},\n\tshorttitle = {Inverse distributed modelling of streamflow and turbulent fluxes},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169419301908},\n\tdoi = {10.1016/j.jhydrol.2019.02.033},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Soltani, Mohsen and Laux, Patrick and Mauder, Matthias and Kunstmann, Harald},\n\tmonth = apr,\n\tyear = {2019},\n\tpages = {856--872},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Stockinger, M. P.; Bogena, H. R.; Lücke, A.; Stumpp, C.; and Vereecken, H.\n\n\n \n \n \n \n \n Time variability and uncertainty in the fraction of young water in a small headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Hydrology and Earth System Sciences, 23(10): 4333–4347. October 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Time$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{stockinger_time_2019,\n\ttitle = {Time variability and uncertainty in the fraction of young water in a small headwater catchment},\n\tvolume = {23},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/23/4333/2019/},\n\tdoi = {10.5194/hess-23-4333-2019},\n\tabstract = {Abstract. The time precipitation needs to travel through a\ncatchment to its outlet is an important descriptor of a catchment's\nsusceptibility to pollutant contamination, nutrient loss, and hydrological\nfunctioning. The fast component of total water flow can be estimated by the\nfraction of young water (Fyw), which is the percentage of streamflow younger\nthan 3 months. Fyw is calculated by comparing the amplitudes of sine\nwaves fitted to seasonal precipitation and streamflow tracer signals. This\nis usually done for the complete tracer time series available, neglecting\nannual differences in the amplitudes of longer time series. Considering\ninter-annual amplitude differences, we employed a moving time window of\n1 year in weekly time steps over a 4.5-year δ18O\ntracer time series to calculate 189 Fyw estimates and their uncertainty.\nThey were then tested against the following null hypotheses: (1) at least\n90 \\% of Fyw results do not deviate more than ±0.04 (4 \\%) from the\nmean of all Fyw results, indicating long-term invariance. Larger deviations\nwould indicate changes in the relative contribution of different flow paths;\n(2) for any 4-week window, Fyw does not change more than ±0.04,\nindicating short-term invariance. Larger deviations would indicate a high\nsensitivity of Fyw to a 1-week to 4-week shift in the start of a 1-year sampling\ncampaign; (3) the Fyw results of 1-year sampling campaigns started in a\ngiven calendar month do not change more than ±0.04, indicating\nseasonal invariance. In our study, all three null hypotheses were rejected.\nThus, the Fyw results were time-variable, showed variability in the chosen\nsampling time, and had no pronounced seasonality. We furthermore found\nevidence that the 2015 European heat wave and including two winters into a\n1-year sampling campaign increased the uncertainty of Fyw. Based on an\nincrease in Fyw uncertainty when the mean adjusted R2 was\nbelow 0.2, we recommend further investigations into the dependence of Fyw and\nits uncertainty to goodness-of-fit measures. Furthermore, while investigated\nindividual meteorological factors did not sufficiently explain variations of\nFyw, the runoff coefficient showed a moderate negative correlation of r=-0.50 with Fyw. The results of this study suggest that care must be taken\nwhen comparing Fyw of catchments that were based on different calculation\nperiods and that the influence of extreme events and snow must be\nconsidered.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-17},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Stockinger, Michael Paul and Bogena, Heye Reemt and Lücke, Andreas and Stumpp, Christine and Vereecken, Harry},\n\tmonth = oct,\n\tyear = {2019},\n\tpages = {4333--4347},\n}\n\n

\n

\n\n\n

\n Abstract. The time precipitation needs to travel through a catchment to its outlet is an important descriptor of a catchment's susceptibility to pollutant contamination, nutrient loss, and hydrological functioning. The fast component of total water flow can be estimated by the fraction of young water (Fyw), which is the percentage of streamflow younger than 3 months. Fyw is calculated by comparing the amplitudes of sine waves fitted to seasonal precipitation and streamflow tracer signals. This is usually done for the complete tracer time series available, neglecting annual differences in the amplitudes of longer time series. Considering inter-annual amplitude differences, we employed a moving time window of 1 year in weekly time steps over a 4.5-year δ18O tracer time series to calculate 189 Fyw estimates and their uncertainty. They were then tested against the following null hypotheses: (1) at least 90 % of Fyw results do not deviate more than ±0.04 (4 %) from the mean of all Fyw results, indicating long-term invariance. Larger deviations would indicate changes in the relative contribution of different flow paths; (2) for any 4-week window, Fyw does not change more than ±0.04, indicating short-term invariance. Larger deviations would indicate a high sensitivity of Fyw to a 1-week to 4-week shift in the start of a 1-year sampling campaign; (3) the Fyw results of 1-year sampling campaigns started in a given calendar month do not change more than ±0.04, indicating seasonal invariance. In our study, all three null hypotheses were rejected. Thus, the Fyw results were time-variable, showed variability in the chosen sampling time, and had no pronounced seasonality. We furthermore found evidence that the 2015 European heat wave and including two winters into a 1-year sampling campaign increased the uncertainty of Fyw. Based on an increase in Fyw uncertainty when the mean adjusted R2 was below 0.2, we recommend further investigations into the dependence of Fyw and its uncertainty to goodness-of-fit measures. Furthermore, while investigated individual meteorological factors did not sufficiently explain variations of Fyw, the runoff coefficient showed a moderate negative correlation of r=-0.50 with Fyw. The results of this study suggest that care must be taken when comparing Fyw of catchments that were based on different calculation periods and that the influence of extreme events and snow must be considered.\n

\n\n\n

\n \n\n \n \n Sulis, M.; Couvreur, V.; Keune, J.; Cai, G.; Trebs, I.; Junk, J.; Shrestha, P.; Simmer, C.; Kollet, S. J.; Vereecken, H.; and Vanderborght, J.\n\n\n \n \n \n \n \n Incorporating a root water uptake model based on the hydraulic architecture approach in terrestrial systems simulations.\n \n \n \n \n\n\n \n\n\n\n Agricultural and Forest Meteorology, 269-270: 28–45. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Incorporating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{sulis_incorporating_2019,\n\ttitle = {Incorporating a root water uptake model based on the hydraulic architecture approach in terrestrial systems simulations},\n\tvolume = {269-270},\n\tissn = {01681923},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0168192319300437},\n\tdoi = {10.1016/j.agrformet.2019.01.034},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agricultural and Forest Meteorology},\n\tauthor = {Sulis, Mauro and Couvreur, Valentin and Keune, Jessica and Cai, Gaochao and Trebs, Ivonne and Junk, Juergen and Shrestha, Prabhakar and Simmer, Clemens and Kollet, Stefan J. and Vereecken, Harry and Vanderborght, Jan},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {28--45},\n}\n\n

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\n \n\n \n \n Tan, X.; Mester, A.; von Hebel, C.; Zimmermann, E.; Vereecken, H.; van Waasen, S.; and van der Kruk, J.\n\n\n \n \n \n \n \n Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations.\n \n \n \n \n\n\n \n\n\n\n GEOPHYSICS, 84(1): EN1–EN14. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Simultaneous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{tan_simultaneous_2019,\n\ttitle = {Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations},\n\tvolume = {84},\n\tissn = {0016-8033, 1942-2156},\n\turl = {https://library.seg.org/doi/10.1190/geo2018-0264.1},\n\tdoi = {10.1190/geo2018-0264.1},\n\tabstract = {Electromagnetic induction (EMI) is a contactless and fast geophysical measurement technique. Frequency-domain EMI systems are available as portable rigid booms with fixed separations up to approximately 4 m between the transmitter and the receivers. These EMI systems are often used for high-resolution characterization of the upper subsurface meters (up to depths of approximately 1.5 times the maximum coil separation). The availability of multiconfiguration EMI systems, which measure multiple apparent electrical conductivity ([Formula: see text]) values of different but overlapping soil volumes, enables EMI data inversions to estimate electrical conductivity ([Formula: see text]) changes with depth. However, most EMI systems currently do not provide absolute [Formula: see text] values, but erroneous shifts occur due to calibration problems, which hinder a reliable inversion of the data. Instead of using physical soil data or additional methods to calibrate the EMI data, we have used an efficient and accurate simultaneous calibration and inversion approach to avoid a possible bias of other methods while reducing the acquisition time for the calibration. By measuring at multiple elevations above the ground surface using a multiconfiguration EMI system, we simultaneously obtain multiplicative and additive calibration factors for each coil configuration plus an inverted layered subsurface electrical conductivity model at the measuring location. Using synthetic data, we verify our approach. Experimental data from five different calibration positions along a transect line showed similar calibration results as the data obtained by more elaborate vertical electrical sounding reference measurements. The synthetic and experimental results demonstrate that the multielevation calibration and inversion approach is a promising tool for quantitative electrical conductivity analyses.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {GEOPHYSICS},\n\tauthor = {Tan, Xihe and Mester, Achim and von Hebel, Christian and Zimmermann, Egon and Vereecken, Harry and van Waasen, Stefan and van der Kruk, Jan},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {EN1--EN14},\n}\n\n

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\n \n\n \n \n Tarasova, L.; Merz, R.; Kiss, A.; Basso, S.; Blöschl, G.; Merz, B.; Viglione, A.; Plötner, S.; Guse, B.; Schumann, A.; Fischer, S.; Ahrens, B.; Anwar, F.; Bárdossy, A.; Bühler, P.; Haberlandt, U.; Kreibich, H.; Krug, A.; Lun, D.; Müller‐Thomy, H.; Pidoto, R.; Primo, C.; Seidel, J.; Vorogushyn, S.; and Wietzke, L.\n\n\n \n \n \n \n \n Causative classification of river flood events.\n \n \n \n \n\n\n \n\n\n\n WIREs Water, 6(4). July 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Causative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{tarasova_causative_2019,\n\ttitle = {Causative classification of river flood events},\n\tvolume = {6},\n\tissn = {2049-1948, 2049-1948},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/wat2.1353},\n\tdoi = {10.1002/wat2.1353},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-11-17},\n\tjournal = {WIREs Water},\n\tauthor = {Tarasova, Larisa and Merz, Ralf and Kiss, Andrea and Basso, Stefano and Blöschl, Günter and Merz, Bruno and Viglione, Alberto and Plötner, Stefan and Guse, Björn and Schumann, Andreas and Fischer, Svenja and Ahrens, Bodo and Anwar, Faizan and Bárdossy, András and Bühler, Philipp and Haberlandt, Uwe and Kreibich, Heidi and Krug, Amelie and Lun, David and Müller‐Thomy, Hannes and Pidoto, Ross and Primo, Cristina and Seidel, Jochen and Vorogushyn, Sergiy and Wietzke, Luzie},\n\tmonth = jul,\n\tyear = {2019},\n}\n\n

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\n \n\n \n \n Vereecken, H.; Pachepsky, Y.; Bogena, H.; and Montzka, C.\n\n\n \n \n \n \n \n Upscaling Issues in Ecohydrological Observations.\n \n \n \n \n\n\n \n\n\n\n In Li, X.; and Vereecken, H., editor(s), Observation and Measurement of Ecohydrological Processes, volume 2, pages 435–454. Springer Berlin Heidelberg, Berlin, Heidelberg, 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Upscaling$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@incollection{li_upscaling_2019,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Upscaling {Issues} in {Ecohydrological} {Observations}},\n\tvolume = {2},\n\tisbn = {9783662482964 9783662482971},\n\turl = {http://link.springer.com/10.1007/978-3-662-48297-1_14},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tbooktitle = {Observation and {Measurement} of {Ecohydrological} {Processes}},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Vereecken, Harry and Pachepsky, Yakov and Bogena, Heye and Montzka, Carsten},\n\teditor = {Li, Xin and Vereecken, Harry},\n\tyear = {2019},\n\tdoi = {10.1007/978-3-662-48297-1_14},\n\tpages = {435--454},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Wang, H.; Wellmann, F.; Zhang, T.; Schaaf, A.; Kanig, R. M.; Verweij, E.; Hebel, C.; and Kruk, J.\n\n\n \n \n \n \n \n Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements.\n \n \n \n \n\n\n \n\n\n\n Journal of Geophysical Research: Biogeosciences, 124(6): 1524–1544. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Pattern$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_pattern_2019,\n\ttitle = {Pattern {Extraction} of {Topsoil} and {Subsoil} {Heterogeneity} and {Soil}‐{Crop} {Interaction} {Using} {Unsupervised} {Bayesian} {Machine} {Learning}: {An} {Application} to {Satellite}‐{Derived} {NDVI} {Time} {Series} and {Electromagnetic} {Induction} {Measurements}},\n\tvolume = {124},\n\tissn = {2169-8953, 2169-8961},\n\tshorttitle = {Pattern {Extraction} of {Topsoil} and {Subsoil} {Heterogeneity} and {Soil}‐{Crop} {Interaction} {Using} {Unsupervised} {Bayesian} {Machine} {Learning}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005046},\n\tdoi = {10.1029/2019JG005046},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2022-11-17},\n\tjournal = {Journal of Geophysical Research: Biogeosciences},\n\tauthor = {Wang, Hui and Wellmann, Florian and Zhang, Tianqi and Schaaf, Alexander and Kanig, Robin Maximilian and Verweij, Elizabeth and Hebel, Christian and Kruk, Jan},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {1524--1544},\n}\n\n

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\n \n\n \n \n Wang, N.; Quesada, B.; Xia, L.; Butterbach‐Bahl, K.; Goodale, C. L.; and Kiese, R.\n\n\n \n \n \n \n \n Effects of climate warming on carbon fluxes in grasslands— A global meta‐analysis.\n \n \n \n \n\n\n \n\n\n\n Global Change Biology, 25(5): 1839–1851. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wang_effects_2019,\n\ttitle = {Effects of climate warming on carbon fluxes in grasslands— {A} global meta‐analysis},\n\tvolume = {25},\n\tissn = {1354-1013, 1365-2486},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.14603},\n\tdoi = {10.1111/gcb.14603},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-11-17},\n\tjournal = {Global Change Biology},\n\tauthor = {Wang, Na and Quesada, Benjamin and Xia, Longlong and Butterbach‐Bahl, Klaus and Goodale, Christine L. and Kiese, Ralf},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {1839--1851},\n}\n\n

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\n \n\n \n \n Wentzky, V. C.; Frassl, M. A.; Rinke, K.; and Boehrer, B.\n\n\n \n \n \n \n \n Metalimnetic oxygen minimum and the presence of Planktothrix rubescens in a low-nutrient drinking water reservoir.\n \n \n \n \n\n\n \n\n\n\n Water Research, 148: 208–218. January 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Metalimnetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wentzky_metalimnetic_2019,\n\ttitle = {Metalimnetic oxygen minimum and the presence of {Planktothrix} rubescens in a low-nutrient drinking water reservoir},\n\tvolume = {148},\n\tissn = {00431354},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135418308443},\n\tdoi = {10.1016/j.watres.2018.10.047},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Wentzky, Valerie C. and Frassl, Marieke A. and Rinke, Karsten and Boehrer, Bertram},\n\tmonth = jan,\n\tyear = {2019},\n\tpages = {208--218},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Werner, B. J.; Musolff, A.; Lechtenfeld, O. J.; de Rooij, G. H.; Oosterwoud, M. R.; and Fleckenstein, J. H.\n\n\n \n \n \n \n \n High-frequency measurements of dissolved organic carbon quantity and quality in a headwater catchment.\n \n \n \n \n\n\n \n\n\n\n Technical Report Biogeochemistry: Rivers & Streams, May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"High-frequency$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@techreport{werner_high-frequency_2019,\n\ttype = {preprint},\n\ttitle = {High-frequency measurements of dissolved organic carbon quantity and quality in a headwater catchment},\n\turl = {https://bg.copernicus.org/preprints/bg-2019-188/bg-2019-188.pdf},\n\tabstract = {Abstract. Increasing dissolved organic carbon (DOC) exports from headwater catchments impact the quality of downstream waters and pose challenges to water supply. The importance of riparian zones for DOC export from catchments in humid, temperate climates has generally been acknowledged, but the hydrological controls and biogeochemical factors that govern mobilization of DOC from riparian zones remain elusive. A one-year high-frequency (15 minutes) dataset from a headwater catchment in the Harz Mountains (Germany) was analyzed for dominant patterns in DOC concentration (CDOC) and optical DOC quality parameters SUVA254 and S275-295 (spectral slope between 275 nm and 295 nm) on event and seasonal scale. Quality parameters and CDOC systematically changed with increasing fractions of high-frequency quick flow (Qhf) and antecedent hydroclimatic conditions, defined by the following metrics: Aridity Index (AI60) of the preceding 60 days, mean temperature (T30) and discharge (Q30) of the preceding 30 days and the quotient T30/Q30 which we refer to as discharge-normalized temperature (DNT30). Selected statistical regression models for the complete time series (R² = 0.72, 0.64 and 0.65 for CDOC, SUVA254 and S275-295, resp.) captured DOC dynamics based on event (Qhf and baseflow) and seasonal-scale predictors (AI60, DNT30). The relative importance of seasonal-scale predictors allowed for the separation of three hydroclimatic states (warm \\&amp; dry, cold \\&amp; wet and intermediate). The specific DOC quality for each state indicates a shift in the activated source zones and highlights the importance of antecedent conditions and its impact on DOC accumulation and mobilization in the riparian zone. The warm \\&amp; dry state results in high DOC concentrations during events and low concentrations between events and thus can be seen as mobilization limited, whereas the cold \\&amp; wet state results in low concentration between and during events due to limited DOC accumulation in the riparian zone. We conclude that the high concentration variability of DOC in the stream can be explained by only a few controlling variables. These variables can be linked to DOC source activation by discharge events and the more seasonal control of DOC production in riparian soils.},\n\turldate = {2022-11-17},\n\tinstitution = {Biogeochemistry: Rivers \\&amp; Streams},\n\tauthor = {Werner, Benedikt J. and Musolff, Andreas and Lechtenfeld, Oliver J. and de Rooij, Gerrit H. and Oosterwoud, Marieke R. and Fleckenstein, Jan H.},\n\tmonth = may,\n\tyear = {2019},\n\tdoi = {10.5194/bg-2019-188},\n}\n\n

\n

\n\n\n

\n Abstract. Increasing dissolved organic carbon (DOC) exports from headwater catchments impact the quality of downstream waters and pose challenges to water supply. The importance of riparian zones for DOC export from catchments in humid, temperate climates has generally been acknowledged, but the hydrological controls and biogeochemical factors that govern mobilization of DOC from riparian zones remain elusive. A one-year high-frequency (15 minutes) dataset from a headwater catchment in the Harz Mountains (Germany) was analyzed for dominant patterns in DOC concentration (CDOC) and optical DOC quality parameters SUVA254 and S275-295 (spectral slope between 275 nm and 295 nm) on event and seasonal scale. Quality parameters and CDOC systematically changed with increasing fractions of high-frequency quick flow (Qhf) and antecedent hydroclimatic conditions, defined by the following metrics: Aridity Index (AI60) of the preceding 60 days, mean temperature (T30) and discharge (Q30) of the preceding 30 days and the quotient T30/Q30 which we refer to as discharge-normalized temperature (DNT30). Selected statistical regression models for the complete time series (R² = 0.72, 0.64 and 0.65 for CDOC, SUVA254 and S275-295, resp.) captured DOC dynamics based on event (Qhf and baseflow) and seasonal-scale predictors (AI60, DNT30). The relative importance of seasonal-scale predictors allowed for the separation of three hydroclimatic states (warm & dry, cold & wet and intermediate). The specific DOC quality for each state indicates a shift in the activated source zones and highlights the importance of antecedent conditions and its impact on DOC accumulation and mobilization in the riparian zone. The warm & dry state results in high DOC concentrations during events and low concentrations between events and thus can be seen as mobilization limited, whereas the cold & wet state results in low concentration between and during events due to limited DOC accumulation in the riparian zone. We conclude that the high concentration variability of DOC in the stream can be explained by only a few controlling variables. These variables can be linked to DOC source activation by discharge events and the more seasonal control of DOC production in riparian soils.\n

\n\n\n

\n \n\n \n \n Weyhenmeyer, G. A.; Hartmann, J.; Hessen, D. O.; Kopáček, J.; Hejzlar, J.; Jacquet, S.; Hamilton, S. K.; Verburg, P.; Leach, T. H.; Schmid, M.; Flaim, G.; Nõges, T.; Nõges, P.; Wentzky, V. C.; Rogora, M.; Rusak, J. A.; Kosten, S.; Paterson, A. M.; Teubner, K.; Higgins, S. N.; Lawrence, G.; Kangur, K.; Kokorite, I.; Cerasino, L.; Funk, C.; Harvey, R.; Moatar, F.; de Wit, H. A.; and Zechmeister, T.\n\n\n \n \n \n \n \n Widespread diminishing anthropogenic effects on calcium in freshwaters.\n \n \n \n \n\n\n \n\n\n\n Scientific Reports, 9(1): 10450. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Widespread$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{weyhenmeyer_widespread_2019,\n\ttitle = {Widespread diminishing anthropogenic effects on calcium in freshwaters},\n\tvolume = {9},\n\tissn = {2045-2322},\n\turl = {http://www.nature.com/articles/s41598-019-46838-w},\n\tdoi = {10.1038/s41598-019-46838-w},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Scientific Reports},\n\tauthor = {Weyhenmeyer, Gesa A. and Hartmann, Jens and Hessen, Dag O. and Kopáček, Jiří and Hejzlar, Josef and Jacquet, Stéphan and Hamilton, Stephen K. and Verburg, Piet and Leach, Taylor H. and Schmid, Martin and Flaim, Giovanna and Nõges, Tiina and Nõges, Peeter and Wentzky, Valerie C. and Rogora, Michela and Rusak, James A. and Kosten, Sarian and Paterson, Andrew M. and Teubner, Katrin and Higgins, Scott N. and Lawrence, Gregory and Kangur, Külli and Kokorite, Ilga and Cerasino, Leonardo and Funk, Clara and Harvey, Rebecca and Moatar, Florentina and de Wit, Heleen A. and Zechmeister, Thomas},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {10450},\n}\n\n

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\n \n\n \n \n Wiekenkamp, I.; Huisman, J. A.; Bogena, H. R.; and Vereecken, H.\n\n\n \n \n \n \n \n Effects of Deforestation on Water Flow in the Vadose Zone.\n \n \n \n \n\n\n \n\n\n\n Water, 12(1): 35. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wiekenkamp_effects_2019,\n\ttitle = {Effects of {Deforestation} on {Water} {Flow} in the {Vadose} {Zone}},\n\tvolume = {12},\n\tissn = {2073-4441},\n\turl = {https://www.mdpi.com/2073-4441/12/1/35},\n\tdoi = {10.3390/w12010035},\n\tabstract = {The effects of land use change on the occurrence and frequency of preferential flow (fast water flow through a small fraction of the pore space) and piston flow (slower water flow through a large fraction of the pore space) are still not fully understood. In this study, we used a five year high resolution soil moisture monitoring dataset in combination with a response time analysis to identify factors that control preferential and piston flow before and after partial deforestation in a small headwater catchment. The sensor response times at 5, 20 and 50 cm depths were classified into one of four classes: (1) non-sequential preferential flow, (2) velocity based preferential flow, (3) sequential (piston) flow, and (4) no response. The results of this analysis showed that partial deforestation increased sequential flow occurrence and decreased the occurrence of no flow in the deforested area. Similar precipitation conditions (total precipitation) after deforestation caused more sequential flow in the deforested area, which was attributed to higher antecedent moisture conditions and the lack of interception. At the same time, an increase in preferential flow occurrence was also observed for events with identical total precipitation. However, as the events in the treatment period (after deforestation) generally had lower total, maximum, and mean precipitation, this effect was not observed in the overall occurrence of preferential flow. The results of this analysis demonstrate that the combination of a sensor response time analysis and a soil moisture dataset that includes pre- and post-deforestation conditions can offer new insights in preferential and sequential flow conditions after land use change.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-02},\n\tjournal = {Water},\n\tauthor = {Wiekenkamp, Inge and Huisman, Johan Alexander and Bogena, Heye Reemt and Vereecken, Harry},\n\tmonth = dec,\n\tyear = {2019},\n\tpages = {35},\n}\n\n

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\n \n\n \n \n Wild, R.; Gücker, B.; and Brauns, M.\n\n\n \n \n \n \n \n Agricultural land use alters temporal dynamics and the composition of organic matter in temperate headwater streams.\n \n \n \n \n\n\n \n\n\n\n Freshwater Science, 38(3): 566–581. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Agricultural$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wild_agricultural_2019,\n\ttitle = {Agricultural land use alters temporal dynamics and the composition of organic matter in temperate headwater streams},\n\tvolume = {38},\n\tissn = {2161-9549, 2161-9565},\n\turl = {https://www.journals.uchicago.edu/doi/10.1086/704828},\n\tdoi = {10.1086/704828},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-11-17},\n\tjournal = {Freshwater Science},\n\tauthor = {Wild, Romy and Gücker, Björn and Brauns, Mario},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {566--581},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Wohner, C.; Peterseil, J.; Poursanidis, D.; Kliment, T.; Wilson, M.; Mirtl, M.; and Chrysoulakis, N.\n\n\n \n \n \n \n \n DEIMS-SDR – A web portal to document research sites and their associated data.\n \n \n \n \n\n\n \n\n\n\n Ecological Informatics, 51: 15–24. May 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"DEIMS-SDR$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{wohner_deims-sdr_2019,\n\ttitle = {{DEIMS}-{SDR} – {A} web portal to document research sites and their associated data},\n\tvolume = {51},\n\tissn = {15749541},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118302528},\n\tdoi = {10.1016/j.ecoinf.2019.01.005},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Ecological Informatics},\n\tauthor = {Wohner, Christoph and Peterseil, Johannes and Poursanidis, Dimitris and Kliment, Tomáš and Wilson, Mike and Mirtl, Michael and Chrysoulakis, Nektarios},\n\tmonth = may,\n\tyear = {2019},\n\tpages = {15--24},\n}\n\n

\n

\n\n\n\n

\n \n\n \n \n Yang, X.; Jomaa, S.; Büttner, O.; and Rode, M.\n\n\n \n \n \n \n \n Autotrophic nitrate uptake in river networks: A modeling approach using continuous high-frequency data.\n \n \n \n \n\n\n \n\n\n\n Water Research, 157: 258–268. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Autotrophic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{yang_autotrophic_2019,\n\ttitle = {Autotrophic nitrate uptake in river networks: {A} modeling approach using continuous high-frequency data},\n\tvolume = {157},\n\tissn = {00431354},\n\tshorttitle = {Autotrophic nitrate uptake in river networks},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0043135419302751},\n\tdoi = {10.1016/j.watres.2019.02.059},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Water Research},\n\tauthor = {Yang, Xiaoqiang and Jomaa, Seifeddine and Büttner, Olaf and Rode, Michael},\n\tmonth = jun,\n\tyear = {2019},\n\tpages = {258--268},\n}\n\n

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\n\n\n\n

\n \n\n \n \n Zeeman, M. J.; Shupe, H.; Baessler, C.; and Ruehr, N. K.\n\n\n \n \n \n \n \n Productivity and vegetation structure of three differently managed temperate grasslands.\n \n \n \n \n\n\n \n\n\n\n Agriculture, Ecosystems & Environment, 270-271: 129–148. February 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Productivity$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zeeman_productivity_2019,\n\ttitle = {Productivity and vegetation structure of three differently managed temperate grasslands},\n\tvolume = {270-271},\n\tissn = {01678809},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0167880918304262},\n\tdoi = {10.1016/j.agee.2018.10.003},\n\tlanguage = {en},\n\turldate = {2022-11-17},\n\tjournal = {Agriculture, Ecosystems \\& Environment},\n\tauthor = {Zeeman, Matthias J. and Shupe, Heather and Baessler, Cornelia and Ruehr, Nadine K.},\n\tmonth = feb,\n\tyear = {2019},\n\tpages = {129--148},\n}\n\n

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\n \n\n \n \n Zistl-Schlingmann, M.; Feng, J.; Kiese, R.; Stephan, R.; Zuazo, P.; Willibald, G.; Wang, C.; Butterbach-Bahl, K.; and Dannenmann, M.\n\n\n \n \n \n \n \n Dinitrogen emissions: an overlooked key component of the N balance of montane grasslands.\n \n \n \n \n\n\n \n\n\n\n Biogeochemistry, 143(1): 15–30. March 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Dinitrogen$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zistl-schlingmann_dinitrogen_2019,\n\ttitle = {Dinitrogen emissions: an overlooked key component of the {N} balance of montane grasslands},\n\tvolume = {143},\n\tissn = {0168-2563, 1573-515X},\n\tshorttitle = {Dinitrogen emissions},\n\turl = {http://link.springer.com/10.1007/s10533-019-00547-8},\n\tdoi = {10.1007/s10533-019-00547-8},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-11-17},\n\tjournal = {Biogeochemistry},\n\tauthor = {Zistl-Schlingmann, Marcus and Feng, Jinchao and Kiese, Ralf and Stephan, Ruth and Zuazo, Pablo and Willibald, Georg and Wang, Changhui and Butterbach-Bahl, Klaus and Dannenmann, Michael},\n\tmonth = mar,\n\tyear = {2019},\n\tpages = {15--30},\n}\n\n

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