Articles | Volume 18, issue 6
https://doi.org/10.5194/bg-18-1917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-1917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2021
Research article |
| 19 Mar 2021
| 19 Mar 2021
Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: insights from a mechanistic model
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Matthias Zeeman
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany
Paolo Burlando
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Simone Fatichi
Department of Civil and Environmental Engineering, National University of Singapore, Singapore
Related authors
No articles found.
Jianning Ren, Zhaoyang Luo, Xiangzhong Luo, Stefano Galelli, Athanasios Paschalis, Valeriy Ivanov, Shanti Shwarup Mahto, and Simone Fatichi
EGUsphere, https://doi.org/10.5194/egusphere-2025-4570, https://doi.org/10.5194/egusphere-2025-4570, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Southeast Asia’s water and carbon fluxes remain poorly understood due to limited field observations and modelling. Using available data and computer models, we show the region is mostly energy-limited: evapotranspiration is controlled by relative humidity, while plant productivity is driven by solar radiation. In some particular areas, such as the Tibetan Plateau, savannas, and dry deciduous forests, water availability is the main limiting factor.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci., 25, 2271–2286, https://doi.org/10.5194/nhess-25-2271-2025, https://doi.org/10.5194/nhess-25-2271-2025, 2025
Short summary
Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data, 17, 2693–2712, https://doi.org/10.5194/essd-17-2693-2025, https://doi.org/10.5194/essd-17-2693-2025, 2025
Short summary
Short summary
The MSEA-Res database offers an open-access dataset tracking absolute water storage for 186 large reservoirs across Mainland Southeast Asia from 1985 to 2023. It provides valuable insights into how reservoir storage grew by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
William Morrison, Dana Looschelders, Jonnathan Céspedes, Bernie Claxton, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, Martial Haeffelin, Christopher C. Holst, Simone Kotthaus, Valéry Masson, James McGregor, Jeremy Price, Matthias Zeeman, Sue Grimmond, and Andreas Christen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-167, https://doi.org/10.5194/essd-2025-167, 2025
Preprint under review for ESSD
Short summary
Short summary
We conducted research using sophisticated wind sensors to better understand wind patterns in Paris. By installing these sensors across the city, we gathered detailed data on wind speeds and directions from 2022 to 2024. This information helps improve weather and climate models, making them more accurate for city environments. Our findings offer valuable insights for scientists studying urban air and weather, improving predictions and understanding of city-scale atmospheric processes.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Yiran Wang, Naika Meili, and Simone Fatichi
Hydrol. Earth Syst. Sci., 29, 381–396, https://doi.org/10.5194/hess-29-381-2025, https://doi.org/10.5194/hess-29-381-2025, 2025
Short summary
Short summary
In this study, we use climate model simulations and process-based ecohydrological modeling to assess the effects of solar radiation changes on hydrological variables. Results show that direct changes in solar radiation without the land–atmosphere feedback primarily affects sensible heat with limited effects on hydrology and vegetation. However, including land–atmosphere feedbacks exacerbates the effects of radiation changes on evapotranspiration and modifies vegetation productivity.
Matthias Zeeman, Andreas Christen, Sue Grimmond, Daniel Fenner, William Morrison, Gregor Feigel, Markus Sulzer, and Nektarios Chrysoulakis
Geosci. Instrum. Method. Data Syst., 13, 393–424, https://doi.org/10.5194/gi-13-393-2024, https://doi.org/10.5194/gi-13-393-2024, 2024
Short summary
Short summary
This study presents an overview of a data system for documenting, processing, managing, and publishing data streams from research networks of atmospheric and environmental sensors of varying complexity in urban environments. Our solutions aim to deliver resilient, near-time data using freely available software.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Benjamin Schumacher, Marwan Katurji, Jiawei Zhang, Peyman Zawar-Reza, Benjamin Adams, and Matthias Zeeman
Atmos. Meas. Tech., 15, 5681–5700, https://doi.org/10.5194/amt-15-5681-2022, https://doi.org/10.5194/amt-15-5681-2022, 2022
Short summary
Short summary
This investigation presents adaptive thermal image velocimetry (A-TIV), a newly developed algorithm to spatially measure near-surface atmospheric velocities using an infrared camera mounted on uncrewed aerial vehicles. A validation and accuracy assessment of the retrieved velocity fields shows the successful application of the algorithm over short-cut grass and turf surfaces in dry conditions. This provides new opportunities for atmospheric scientists to study surface–atmosphere interactions.
Stefano Manzoni, Simone Fatichi, Xue Feng, Gabriel G. Katul, Danielle Way, and Giulia Vico
Biogeosciences, 19, 4387–4414, https://doi.org/10.5194/bg-19-4387-2022, https://doi.org/10.5194/bg-19-4387-2022, 2022
Short summary
Short summary
Increasing atmospheric carbon dioxide (CO2) causes leaves to close their stomata (through which water evaporates) but also promotes leaf growth. Even if individual leaves save water, how much will be consumed by a whole plant with possibly more leaves? Using different mathematical models, we show that plant stands that are not very dense and can grow more leaves will benefit from higher CO2 by photosynthesizing more while adjusting their stomata to consume similar amounts of water.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
Short summary
Short summary
Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Stefan Fugger, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, Wei Yang, Patrick Wagnon, Walter Immerzeel, Qiao Liu, and Francesca Pellicciotti
The Cryosphere, 16, 1631–1652, https://doi.org/10.5194/tc-16-1631-2022, https://doi.org/10.5194/tc-16-1631-2022, 2022
Short summary
Short summary
The monsoon is important for the shrinking and growing of glaciers in the Himalaya during summer. We calculate the melt of seven glaciers in the region using a complex glacier melt model and weather data. We find that monsoonal weather affects glaciers that are covered with a layer of rocky debris and glaciers without such a layer in different ways. It is important to take so-called turbulent fluxes into account. This knowledge is vital for predicting the future of the Himalayan glaciers.
Matthias Zeeman
Atmos. Meas. Tech., 14, 7475–7493, https://doi.org/10.5194/amt-14-7475-2021, https://doi.org/10.5194/amt-14-7475-2021, 2021
Short summary
Short summary
Understanding turbulence near the surface is important for many applications. In this work, methods for observing and analysing temperature structures in a near-surface volume were explored. Experiments were conducted to identify modes of organised motion. These help explain interactions between the vegetation and the atmosphere that are not currently well understood. Techniques used include fibre-optic sensing, thermal infrared imaging, signal decomposition, and machine learning.
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
Short summary
Short summary
The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Cited articles
Ammann, C.: FLUXNET2015 CH-Oe1 Oensingen grassland, Fluxnet, https://doi.org/10.18140/FLX/1440135, 2020.
Ammann, C., Flechard, C. R., Leifeld, J., Neftel, A., and Fuhrer, J.: The carbon budget
of newly established temperate grassland depends on management intensity, Agric. Ecosyst. Env.,
121, 5–20, https://doi.org/10.1016/j.agee.2006.12.002, 2007.
Ammann, C., Spirig, C., Leifeld, J., and Neftel, A.: Assessment of the nitrogen
and carbon budget of two managed temperate grassland fields, Agric. Ecosyst. Env., 133, 150–162,
https://doi.org/10.1016/j.agee.2009.05.006, 2009.
Ammann, C., Wolff, V., Marx, O., Brümmer, C., and Neftel, A.: Measuring the
biosphere-atmosphere exchange of total reactive nitrogen by eddy covariance, Biogeosciences, 9,
4247–4261, https://doi.org/10.5194/bg-9-4247-2012, 2012.
Amon, B., Kryvoruchko, V., Amon, T., and Zechmeister-Boltenstern, S.: Methane, nitrous
oxide and ammonia emissions during storage and after application of dairy cattle slurry and
influence of slurry treatment, Agric. Ecosyst. Env., 112, 153–162,
https://doi.org/10.1016/j.agee.2005.08.030, 2006.
Aubinet, M., Vesala, T., and Papale, D.: Eddy Covariance: A Practical Guide to
Measurement and Data Analysis, Springer, Dordrecht, 2012.
Bao, C., Li, L., Shi, Y., and Duffy, C.: Understanding watershed hydrogeochemistry:
1. Development of RT-Flux-PIHM, Water Resour. Res., 53, 2328–2345, https://doi.org/10.1002/2016WR018934,
2017.
Behrendt, H., Bach, M., Kunkel, R., Opitz, D., Pagenkopf, W. G., Scholz, G., and
Wendland, F.: Nutrient Emissions into River Basins of Germany on the Basis of a Harmonized
Procedure, available at: http://www.umweltbundesamt.de (last access: July 2020), 2003.
Benettin, P., Queloz, P., Bensimon, M., McDonnell, J. J., and Rinaldo, A.: Velocities,
Residence Times, Tracer Breakthroughs in a Vegetated Lysimeter: A Multitracer Experiment, Water
Resour. Res., 55, 21–33, https://doi.org/10.1029/2018WR023894, 2019
Bergström, L., Johnsson, H., and Torstensson, G.: Simulation of soil nitrogen
dynamics using the SOILN model, Fert. Res., 27, 181–188, https://doi.org/10.1007/BF01051126, 1991.
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., Gate,
P., Devienne-Barret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot,
X., Plenet, D., Cellier, P., Machet, J.-M., Meynard, J. M., and Delécolle, R.: STICS: a
generic model for the simulation of crops and their water and nitrogen balances. I. Theory and
parameterization applied to wheat and corn, Agronomie, 18, 311–346, https://doi.org/10.1051/agro:19980501,
1998.
Brisson, N., Ruget, F., Gate, P., Lorgeou, J., Nicoullaud, B., Tayot, X., Plenet, D.,
Jeuffroy, M.-H., Bouthier, A., Ripoche, D., Mary, B., and Justes, E.: STICS: a generic model for
simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize,
Agronomie, 22, 69–92, https://doi.org/10.1051/agro:2001005, 2002.
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D.,
Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y. M., Cellier, P., Debaeke,
P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B., and Sinoquet, H.: An overview
of the crop model STICS, Eur. J. Agron., 18, 309–332, https://doi.org/10.1016/S1161-0301(02)00110-7, 2003.
Casson, J. P., Olson, B. M., Little, J. L., and Nolan, S. C.: Assessment of
Environmental Sustainability in Alberta's Agricultural Watersheds Project, Volume 4: Nitrogen loss
in surface runoff, Alberta Agriculture and Rural Development, Lethbridge, Alberta, Canada, 71 pp.,
2008.
Chang, J. F., Viovy, N., Vuichard, N., Ciais, P., Wang, T., Cozic, A., Lardy, R.,
Graux, A.-I., Klumpp, K., Martin, R., and Soussana, J.-F.: Incorporating grassland management in
ORCHIDEE: model description and evaluation at 11 eddy-covariance sites in Europe, Geosci. Model
Dev., 6, 2165–2181, https://doi.org/10.5194/gmd-6-2165-2013, 2013.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the
Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather
Rev., 129, 569–585, 2001.
Cremonese, E., Galvagno, M., Morra di Cella, U., and Migliavacca, M.: FLUXNET2015 IT-Tor Torgnon, Dataset, Fluxnet, https://doi.org/10.18140/FLX/1440237, 2020.
Decrem, M., Spiess, E., Richner, W., and Herzog, F.: Impact of Swiss agricultural
policies on nitrate leaching from arable land, Agron. Sustain. Dev., 27, 243–253,
https://doi.org/10.1051/agro:2007012, 2007.
Del Grosso, S. J., Parton, W. J., Mosier, A. R., Ojima, D. S., Kulmala, A. E., and
Phongpan, S.: General model for N2O and N2 gas emissions from soils due to
dentrification, Global Biogeochem. Cy., 14, 1045–1060, https://doi.org/10.1029/1999GB001225, 2000.
Del Grosso, S., Ojima, D., Parton, W., Mosier, A., Peterson, G., and Schimel, D.:
Simulated effects of dryland cropping intensification on soil organic matter and greenhouse gas
exchanges using the DAYCENT ecosystem model, Environ. Pollut., 116, S75–S83,
https://doi.org/10.1016/S0269-7491(01)00260-3, 2002.
Eder, A., Blöschl, G., Feichtinger, F., Herndl, M., Klammler, G., Hösch, J.,
Erhart, E., and Strauss, P.: Indirect nitrogen losses of managed soils contributing to greenhouse
emissions of agricultural areas in Austria: results from lysimeter studies,
Nutr. Cycl. Agroecosyst., 101, 351–364, https://doi.org/10.1007/s10705-015-9682-9, 2015.
EEC: Council Directive 19/676/EEC of 12 December, 1991 concerning the protection of
waters against pollution caused by nitrates from agricultural sources, Official Journal, Brussels, 1991.
EEC: Council Directive 98/83/EC of 3 November 1998 on the quality of water intended for
human consumption, Official Journal, Brussels, 1998.
Fatichi, S.: Tethys-Chloris (T&C) – Terrestrial Biosphere Model – Public release September 2020, Code Ocean, https://doi.org/10.24433/CO.0905087.v1, 2020.
Fatichi, S. and Pappas, C.: Constrained variability of modeled T:ET ratio across
biomes, Geophys. Res. Lett., 44, 6795–6803, https://doi.org/10.1002/2017GL074041, 2017.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological model to
investigate complex interactions in cold and warm water-controlled environments: 1. Theoretical
framework and plot-scale analysis, J. Adv. Model. Earth Syst., 4, M05002,
https://doi.org/10.1029/2011MS000086, 2012a.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological model to
investigate complex interactions in cold and warm water-controlled environments: 2. Spatiotemporal
analyses, J. Adv. Model. Earth Syst., 4, M05003, https://doi.org/10.1029/2011MS000087, 2012b.
Fatichi, S., Zeeman, M. J., Fuhrer, J., and Burlando, P.: Ecohydrological effects of
management on subalpine grasslands: From local to catchment scale, Water Resour. Res., 50,
148–164, https://doi.org/10.1002/2013WR014535, 2014.
Fatichi, S., Katul, G. G., Ivanov, V. Y., Pappas, C., Paschalis, A., Consolo, A., Kim,
J., and Burlando, P.: Abiotic and biotic controls of soil moisture spatiotemporal variability and
the occurrence of hysteresis, Water Resour. Res., 51, 3505–3524, https://doi.org/10.1002/2014WR016102,
2015.
Fatichi, S., Pappas, C., and Ivanov, V. Y.: Modeling plant-water interactions: an
ecohydrological overview from the cell to the global scale, WIRES Water, 3, 327–368,
https://doi.org/10.1002/wat2.1125, 2016.
Fatichi, S., Manzoni, S., Or, D., and Paschalis, A.: A Mechanistic Model of Microbially
Mediated Soil Biogeochemical Processes: A Reality Check, Global Biogeochem. Cy., 33, 2018GB006077,
https://doi.org/10.1029/2018GB006077, 2019.
Feichtinger, F.: STOTRASIM – Ein Modell zur Simulation der Stickstoffdynamik in der
ungesättigten Zone eines Ackerstandortes, Schriftenreihe des Bundesamtes für Wasserwirtschaft, Wien, 1998.
Ferrara, R. M., Trevisiol, P., Acutis, M., Rana, G., Richter, G. M., and Baggaley, N.:
Topographic impacts on wheat yields under climate change: Two contrasted case studies in Europe,
Theor. Appl. Climatol., 99, 53–65, https://doi.org/10.1007/s00704-009-0126-9, 2010.
Filippa, G., Cremonese, E., Galvagno, M., Migliavacca, M., Morra di Cella, U., Petey,
M., and Siniscalco, C.: Five years of phenological monitoring in a mountain grassland:
inter-annual patterns and evaluation of the sampling protocol, Int. J. Biometeorol., 59,
1927–1937, https://doi.org/10.1007/s00484-015-0999-5, 2015.
Finger, R., Gilgen, A. K., Prechsl, U. E., and Buchmann, N.: An economic assessment of
drought effects on three grassland systems in Switzerland, Reg. Environ. Change, 13, 365–374,
https://doi.org/10.1007/s10113-012-0346-x, 2013.
Foken, T.: Die scheinbar ungeschlossene Energiebilanz am Erdboden – eine
Herausforderung an die Experimentelle Meteorologie, Sitzungsberichte der Leibniz-Sozietät, Sitzungsberichte der Leibnitz-Sozietaet, Berlin, 1998.
Foken, T.: The energy balance closure problem: an overview, Ecol. Appl., 18,
1351–1367, https://doi.org/10.1890/06-0922.1, 2008.
Fu, J., Gasche, R., Wang, N., Lu, H., Butterbach-Bahl, K., and Kiese, R.: Impacts of
climate and management on water balance and nitrogen leaching from montane grassland soils of
S-Germany, Environ. Pollut., 229, 119–131, https://doi.org/10.1016/J.ENVPOL.2017.05.071, 2017.
Fu, J., Gasche, R., Wang, N., Lu, H., Butterbach-Bahl, K., and Kiese, R. : Dissolved
organic carbon leaching from montane grasslands under contrasting climate, soil and management
conditions, Biogeochemistry, 145, 47–61, https://doi.org/10.1007/s10533-019-00589-y, 2019.
Gabrielle, B. and Kengni, L.: Analysis and Field-Evaluation of the CERES Models' Soil
Components: Nitrogen Transfer and Transformations, Soil Sci. Soc. Am. J., 60, 142–149,
https://doi.org/10.2136/sssaj1996.03615995006000010023x, 1996.
Gabrielle, B., Menasseri, S., and Houot, S.: Analysis and Field Evaluation of the Ceres
Models Water Balance Component, Soil Sci. Soc. Am. J., 59, 1403–1412,
https://doi.org/10.2136/sssaj1995.03615995005900050029x, 1995.
Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W., Howarth, R. W.,
Seitzinger, S. P., Asner, G. P., Cleveland, C. C., Green, P. A., Holland, E. A., Karl, D. M.,
Michaels, A. F., Porter, J. H., Townsend, A. R., and Vörösmarty, C. J.: Nitrogen cycles:
Past, present, and future, Biogeochemistry, 70, 153–226, https://doi.org/10.1007/s10533-004-0370-0, 2004.
Galvagno, M., Wohlfahrt, G., Cremonese, E., Rossini, M., Colombo, R., Filippa, G.,
Julitta, T., Manca, G., Siniscalco, C., Morra di Cella, U., and Migliavacca, M.: Phenology and
carbon dioxide source/sink strength of a subalpine grassland in response to an exceptionally short
snow season, Environ. Res. Lett., 8, 025008, https://doi.org/10.1088/1748-9326/8/2/025008, 2013.
Gianelle, D., Vescovo, L., Marcolla, B., Manca, G., and Cescatti, A.: Ecosystem carbon
fluxes and canopy spectral reflectance of a mountain meadow, Int. J. Remote Sens., 30, 435–449,
https://doi.org/10.1080/01431160802314855, 2009.
Gianelle, D., Cavagna, M., Zampedri, R., and Marcolla, B.: FLUXNET2015 IT-MBo Monte Bondone, Dataset, Fluxnet, https://doi.org/10.18140/FLX/1440170, 2020.
Gilgen, A. K. and Buchmann, N.: Response of temperate grasslands at different altitudes
to simulated summer drought differed but scaled with annual precipitation, Biogeosciences, 6,
2525–2539, https://doi.org/10.5194/bg-6-2525-2009, 2009.
Gilmanov, T. G., Soussana, J. F., Aires, L., Allard, V., Ammann, C., Balzarolo, M.,
Barcza, Z., Bernhofer, C., Campbell, C. L., Cernusca, A., Cescatti, A., Clifton-Brown, J., Dirks,
B. O. M., Dore, S., Eugster, W., Fuhrer, J., Gimeno, C., Gruenwald, T., Haszpra, L., Hensen, A., Ibrom, A., Jacobs, A. F. G., Jones, M. B.,
Lenigan, G., Laurila, T., Lohila, A., Manca, G., Marcolla, B., Nagy, Z.,
Pilegaard, K., Pinter, K., Pio, C., Raschi, A., Rogiers, N., Sanz, M. J.,
Stefani, P., Sutton, M., Tuba, Z., Valentini, R., Williams, M. L., and
Wohlfahrt, G.: Partitioning European grassland
net ecosystem CO2 exchange into gross primary productivity and ecosystem respiration using
light response function analysis, Agric. Ecosyst. Env., 121, 93–120,
https://doi.org/10.1016/j.agee.2006.12.008, 2007.
Groenendijk, P., Renaud, L. V., and Roelsma, J.: Prediction of nitrogen and phosphorus
leaching to groundwater and surface waters; process descriptions of the animo4.0 model, Alterra, Wageningen, the Netherlands, 2005.
Groenendijk, P., Heinen, M., Klammler, G., Fank, J., Kupfersberger, H., Pisinaras, V.,
Gemitzi, A., Peña-Haro, S., García-Prats, A., Pulido-Velazquez, M., Perego, A., Acutis,
M., and Trevisan, M.: Performance assessment of nitrate leaching models for highly vulnerable
soils used in low-input farming based on lysimeter data, Sci. Total Environ., 499,
463–480, https://doi.org/10.1016/j.scitotenv.2014.07.002, 2014.
Groh, J., Pütz, T., Jülich, F., Vanderborght, J., and Vereecken, H.: Estimation
of evapotranspiration and crop coefficient of an intensively managed grassland ecosystem with
lysimeter measurements, 16. Gumpensteiner Lysimetertagung 2015, 107–112, available at:
https://www.researchgate.net/publication/275533480 (last access: July 2020), 2015.
Hammerle, A., Haslwanter, A., Tappeiner, U., Cernusca, A., and Wohlfahrt, G.: Leaf area
controls on energy partitioning of a temperate mountain grassland, Biogeosciences, 5, 421–431,
https://doi.org/10.5194/bg-5-421-2008, 2008.
Hansen, S.: Equation Section 1 Daisy, a flexible Soil-Plant-Atmosphere system Model, The Royal Veterinary and Agricultural University, Copenhagen, 2002.
Hansen, S., Jensen, H. E., Nielsen, N. E., and Svendsen, H.: DAISY: Soil plant
atmosphere system model, National Agency for Environmental Protection, Copenhagen, 1990.
Heathwaite, L.: Sources of eutrophication: hydrological pathways of catchment nutrient
export, in: Man's Influence on Freshwater Ecosystems and Water Use (Issue 230), Int. Assoc. Hydrol. Sci., 230, 161–176, 1995.
Hénault, C., Bizouard, F., Laville, P., Gabrielle, B., Nicoullaud, B., Germon,
J. C., and Cellier, P.: Predicting in situ soil N2O emission using NOE algorithm and soil
database, Glob. Change Biol., 11, 115–127, https://doi.org/10.1111/j.1365-2486.2004.00879.x, 2005.
Hörtnagl, L., Feigenwinter, I., Fuchs, K., Merbold, L., Buchmann, N., Eugster, W., and Zeeman, M.: FLUXNET2015 CH-Cha Chamau, Dataset, Fluxnet, https://doi.org/10.18140/FLX/1440131, 2020a.
Hörtnagl, L., Feigenwinter, I., Fuchs, K., Merbold, L., Buchmann, N., Eugster, W., Zeeman, M., Käslin, F., Meier, P., Koller, P., Baur, T., and Pluess, P.: FLUXNET2015 CH-Fru Früebüel. Switzerland, Fluxnet, https://doi.org/10.18140/FLX/1440133, 2020b.
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.:
Attribution of N2O sources in a grassland soil with laser spectroscopy based isotopocule
analysis, Biogeosciences, 16, 3247–3266, https://doi.org/10.5194/bg-16-3247-2019, 2019.
IPCC: IPCC – Task Force on National Greenhouse Gas Inventories, available at:
https://www.ipcc-nggip.iges.or.jp/public/gp/english/ (last access: May 2020), 2000.
IPCC: IPCC – Overview 2 2006 IPCC Guidelines for National Greenhouse Gas Inventories,
available at: http://www.ipcc-nggip.iges.or.jp/ (last access: May 2020), 2006.
Ivanov, V. Y., Bras, R. L., and Vivoni, E. R.: Vegetation-hydrology dynamics in complex
terrain of semiarid areas: 1. A mechanistic approach to modeling dynamic feedbacks, Water
Resour. Res., W03429, 44, https://doi.org/10.1029/2006WR005588, 2008.
Jackson, W. A., Asmussen, L. E., Hauser, E. W., and White, A. W.: Nitrate in Surface
and Subsurface Flow from a Small Agricultural Watershed, J. Environ. Qual., 2, 480–482,
https://doi.org/10.2134/jeq1973.00472425000200040017x, 1973.
Jansson, P. E.: CoupModel: Model Use, Calibration, and Validation, T. ASABE, 55,
1337–1346, https://doi.org/10.13031/2013.42245, 2012.
Keeling, R. F., Piper, S. C., Bollenbacher, A. F., and Walker, J. S.: Atmospheric
CO2 records from sites in the sio air sampling network, in trends: A compendium of data on
global change, in: Trends: A Compendium of Data on Global Change. Carbon Dioxide Information
Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, TN, 2009.
Kiese, R., Fersch, B., Baessler, C., Brosy, C., Butterbach-Bahl, K., Chwala, C.,
Dannenmann, M., Fu, J., Gasche, R., Grote, R., Jahn, C., Klatt, J., Kunstmann, H., Mauder, M.,
Rödiger, T., Smiatek, G., Soltani, M., Steinbrecher, R., Völksch, I., Werhahn, J., Wolf,
B., Zeeman, M., and Schmid, H. P.: The TERENO Pre-Alpine Observatory: Integrating Meteorological,
Hydrological, and Biogeochemical Measurements and Modeling, Vadose Zone J., 17, 180060,
https://doi.org/10.2136/vzj2018.03.0060, 2018.
Klammler, G., Kupfersberger, H., Rock, G., and Fank, J.: Modeling coupled unsaturated
and saturated nitrate distribution of the aquifer Westliches Leibnitzer Feld, Austria,
Environ. Earth Sci., 69, 663–678, https://doi.org/10.1007/s12665-013-2302-6, 2013.
Kraus, D., Weller, S., Klatt, S., Haas, E., Wassmann, R., Kiese, R., and
Butterbach-Bahl, K.: A new LandscapeDNDC biogeochemical module to predict CH4 and
N2O emissions from lowland rice and upland cropping systems, Plant Soil, 386,
125–149, https://doi.org/10.1007/s11104-014-2255-x, 2014, 2014.
Kroes, J. G. and van Dam, J. C.: Reference Manual SWAP; version 3.0.3, Alterra-rapport 773, ISSN 1566-7197, 2003.
Kronvang, B., Borgvang, S. A., and Barkved, L. J.: Towards European harmonised
procedures for quantification of nutrient losses from diffuse sources – The EUROHARP project,
J. Environ. Monit., 11, 503–505, https://doi.org/10.1039/b902869m, 2009.
Kuhn, T.: The revision of the German Fertiliser Ordinance in 2017 The revision of the
German Fertiliser Ordinance in 2017 Till Kuhn, Institute for Food and Resource Economics, Discussion Paper 2017, 2, 2017.
Kumar, M., Ou, Y. L., and Lin, J. G.: Co-composting of green waste and food waste at
low C/N ratio, Waste Manage., 30, 602–609, https://doi.org/10.1016/j.wasman.2009.11.023, 2010.
Lamarque, P., Tappeiner, U., Turner, C., Steinbacher, M., Bardgett, R. D., Szukics, U.,
Schermer, M., and Lavorel, S.: Stakeholder perceptions of grassland ecosystem services in relation
to knowledge on soil fertility and biodiversity, Reg. Environ. Change, 11, 791–804,
https://doi.org/10.1007/s10113-011-0214-0, 2011.
Li, C. S.: Modeling trace gas emissions from agricultural ecosystems, in: Methane
Emissions from Major Rice Ecosystems in Asia, 259–276, Springer, Dordrecht,
https://doi.org/10.1007/978-94-010-0898-3_20, 2000.
Li, C., Salas, W., Zhang, R., Krauter, C., Rotz, A., and Mitloehner, F.: Manure-DNDC: A
biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock
manure systems, Nutr. Cycl. Agroecosyst., 93, 163–200, https://doi.org/10.1007/s10705-012-9507-z, 2012.
Liu, S. M., Xu, Z. W., Wang, W. Z., Jia, Z. Z., Zhu, M. J., Bai, J., and Wang, J. M.: A
comparison of eddy-covariance and large aperture scintillometer measurements with respect to the
energy balance closure problem, Hydrol. Earth Syst. Sci., 15, 1291–1306,
https://doi.org/10.5194/hess-15-1291-2011, 2011.
Lü, X. T., Dijkstra, F. A., Kong, D. L., Wang, Z. W., and Han, X. G.: Plant
nitrogen uptake drives responses of productivity to nitrogen and water addition in a grassland,
Sci. Rep., 4, 1–7, https://doi.org/10.1038/srep04817, 2014.
Mahowald, N., Jickells, T. D., Baker, A. R., Artaxo, P., Benitez-Nelson, C. R.,
Bergametti, G., Bond, T. C., Chen, Y., Cohen, D. D., Herut, B., Kubilay, N., Losno, R., Luo, C.,
Maenhaut, W., McGee, K. A., Okin, G. S., Siefert, R. L., and Tsukuda, S.: Global distribution of
atmospheric phosphorus sources, concentrations and deposition rates, and anthropogenic impacts,
Global Biogeochem. Cy., 22, GB4026, https://doi.org/10.1029/2008GB003240, 2008
Manoli, G., Ivanov, V. Y., and Fatichi, S.: Dry-Season Greening and Water Stress in
Amazonia: The Role of Modeling Leaf Phenology, J. Geophys. Res.-Biogeosci., 123, 1909–1926,
https://doi.org/10.1029/2017JG004282, 2018.
Manzoni, S., Moyano, F., Kätterer, T., and Schimel, J.: Modeling coupled enzymatic
and solute transport controls on decomposition in drying soils, Soil Biol. Biochem., 95, 275–287,
https://doi.org/10.1016/j.soilbio.2016.01.006, 2016.
Marcolla, B., Cescatti, A., Manca, G., Zorer, R., Cavagna, M., Fiora, A., Gianelle, D.,
Rodeghiero, M., Sottocornola, M., and Zampedri, R.: Climatic controls and ecosystem responses
drive the inter-annual variability of the net ecosystem exchange of an alpine meadow,
Agric. Forest Meteorol., 151, 1233–1243, https://doi.org/10.1016/j.agrformet.2011.04.015, 2011.
Mastrotheodoros, T., Pappas, C., Molnar, P., Burlando, P., Hadjidoukas, P., and
Fatichi, S.: Ecohydrological dynamics in the Alps: Insights from a modelling analysis of the
spatial variability, Ecohydrology, 12, e2054, https://doi.org/10.1002/eco.2054, 2019.
Mastrotheodoros, T., Pappas, C., Molnar, P., Burlando, P., Manoli, G., Parajka, J.,
Rigon, R., Szeles, B., Bottazzi, M., Hadjidoukas, P., and Fatichi, S.: More green and less blue
water in the Alps during warmer summers, Nat. Clim. Change, 10, 155–161,
https://doi.org/10.1038/s41558-019-0676-5, 2020.
Mauder, M., Liebethal, C., Göckede, M., Leps, J. P., Beyrich, F., and Foken, T.:
Processing and quality control of flux data during LITFASS-2003, Bound.-Layer Meteorol., 121,
67–88, https://doi.org/10.1007/s10546-006-9094-0, 2006.
Mauder, M., Genzel, S., Fu, J., Kiese, R., Soltani, M., Steinbrecher, R., Zeeman, M.,
Banerjee, T., De Roo, F., and Kunstmann, H.: Evaluation of energy balance closure adjustment
methods by independent evapotranspiration estimates from lysimeters and hydrological simulations,
Hydrol. Process., 32, 39–50, https://doi.org/10.1002/hyp.11397, 2018.
Mauder, M., Foken, T., and Cuxart, J.: Surface-Energy-Balance Closure over Land: A
Review, Bound.-Layer Meteorol., 177, 395–426, https://doi.org/10.1007/s10546-020-00529-6, 2020.
Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., and Buchmann, N.:
Greenhouse gas budget (CO2, CH4 and N2O) of intensively managed
grassland following restoration, Glob. Change Biol., 20, 1913–1928, https://doi.org/10.1111/gcb.12518,
2014.
Migliavacca, M., Galvagno, M., Cremonese, E., Rossini, M., Meroni, M., Sonnentag, O.,
Cogliati, S., Manca, G., Diotri, F., Busetto, L., Cescatti, A., Colombo, R., Fava, F., Morra di
Cella, U., Emiliano, P., Consolata, S., and Richardson, A. D.: Using digital repeat photography
and eddy covariance data to model grasslandphenology and photosynthetic CO2 uptake,
Agric. Forest Meteorol., 151, 1325–1337, 2011.
Millar, D. J., Ewers, B. E., Mackay, D. S., Peckham, S., Reed, D. E., and Sekoni, A.:
Improving ecosystem-scale modeling of evapotranspiration using ecological mechanisms that account
for compensatory responses following disturbance, Water Resour. Res., 53, 7853–7868,
https://doi.org/10.1002/2017WR020823, 2017.
Mittelbach, H., Lehner, I., and Seneviratne, S. I.: Comparison of four soil moisture
sensor types under field conditions in Switzerland, J. Hydrol., 430–431, 39–49,
https://doi.org/10.1016/j.jhydrol.2012.01.041, 2012.
Moorhead, D. L., Sinsabaugh, R. L., Linkins, A. E., and Reynolds, J. F.: Decomposition
processes: Modelling approaches and applications, Sci. Total Environ., 183,
137–149. https://doi.org/10.1016/0048-9697(95)04974-6, 1996.
Niklaus, P. A., Wardle, D. A., and Tate, K. R.: Effects of plant species diversity and
composition on nitrogen cycling and the trace gas balance of soils, Plant Soil, 282, 83–98,
https://doi.org/10.1007/s11104-005-5230-8, 2006.
Nyamangara, J., Piha, M. I., and Kirchmann, H.: Interactions of aerobically decomposed
cattle manure and nitrogen fertilizer applied to soil, Nutr. Cycl. Agroecosyst., 54, 183–188, https://doi.org/10.1023/A:1009794416012, 1999.
Oberholzer, S., Prasuhn, V., and Hund, A.: Crop water use under Swiss pedoclimatic
conditions – Evaluation of lysimeter data covering a seven-year period, Field Crops Res., 211,
48–65, https://doi.org/10.1016/j.fcr.2017.06.003, 2017.
Parton, W. J., Hartman, M., Ojima, D., and Schimel, D.: DAYCENT and its land surface
submodel: Description and testing, Global Planet. Change, 19, 35–48,
https://doi.org/10.1016/S0921-8181(98)00040-X, 1998.
Parton, W. J., Schimel, D. S., Ojima, D. S., and Cole, C. V.:
A generalmodel for soil organic matter dynamics, in: Sensitivity to LitterChemistry,
Texture and Management, edited by: Bryant, R. B. and Arnold, R. W., Quantitative modeling of soil
forming processes, Soil Science Society of America Special Publication, 38, 137–167, 1994.
Pastorello, G., Trotta, C., Canfora, E., et al.: The FLUXNET2015 dataset and the ONEFlux processing
pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Perego, A., Giussani, A., Sanna, M., and Fumagalli, M.: The ARMOSA simulation crop
model: Overall features, calibration and validation results Space-time mapping and modelling of
soil properties in Mediterranean and Temperate areas View project, Ital. J. Agrometeorol., 18,
23–38, 2013.
Peukert, S., Griffith, B. A., Murray, P. J., Macleod, C. J. A., and Brazier, R. E.:
Intensive Management in Grasslands Causes Diffuse Water Pollution at the Farm Scale,
J. Environ. Qual., 43, 2009–2023, https://doi.org/10.2134/jeq2014.04.0193, 2014.
Phogat, V., Skewes, M. A., Cox, J. W., Alam, J., Grigson, G., and Šimůnek, J.:
Evaluation of water movement and nitrate dynamics in a lysimeter planted with an orange tree,
Agric. Water Manage., 127, 74–84, https://doi.org/10.1016/j.agwat.2013.05.017, 2013.
Prechsl, U. E., Burri, S., Gilgen, A. K., Kahmen, A., and Buchmann, N.: No shift to a
deeper water uptake depth in response to summer drought of two lowland and sub-alpine
C3-grasslands in Switzerland, Oecologia, 177, 97–111, https://doi.org/10.1007/s00442-014-3092-6, 2015.
Pütz, T., Kiese, R., Wollschläger, U., Groh, J., Rupp, H., Zacharias, S.,
Priesack, E., Gerke, H. H., Gasche, R., Bens, O., Borg, E., Baessler, C., Kaiser, K., Herbrich,
M., Munch, J., Sommer, M., Vogel, H., Vanderborght, J., and Vereecken, H.: TERENO-SOILCan: a lysimeter-network in Germany observing soil
processes and plant diversity influenced by climate change, Environ. Earth Sci., 75, 1242,
https://doi.org/10.1007/s12665-016-6031-5, 2016.
Pütz, T., Fank, J., and Flury, M.: Lysimeters in Vadose Zone Research, Vadose Zone
J., 17, 1–4, https://doi.org/10.2136/vzj2018.02.0035, 2018.
Richter, G. M., Acutis, M., Trevisiol, P., Latiri, K., and Confalonieri, R.:
Sensitivity analysis for a complex crop model applied to Durum wheat in the Mediterranean,
Eur. J. Agron., 32, 127–136, https://doi.org/10.1016/j.eja.2009.09.002, 2010.
Robertson, A. D., Paustian, K., Ogle, S., Wallenstein, M. D., Lugato, E., and Cotrufo,
M. F.: Unifying soil organic matter formation and persistence frameworks: the MEMS model,
Biogeosciences, 16, 1225–1248, https://doi.org/10.5194/bg-16-1225-2019, 2019.
Sala, O. E. and Paruelo, J. M.: Ecosystem services in grasslands, in: Nature's
services: societal dependence on natural ecosystems, edited by: Daily, G. C., Nature's Services: Societal Dependence
on Natural Ecosystems, Island Press, Washington, DC, USA, 237–251, 1997.
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by Texture and
Organic Matter for Hydrologic Solutions, Soil Sci. Soc. Am. J., 70, 1569–1578,
https://doi.org/10.2136/sssaj2005.0117, 2006.
Schirpke, U., Kohler, M., Leitinger, G., Fontana, V., Tasser, E., and Tappeiner, U.:
Future impacts of changing land-use and climate on ecosystem services of mountain grassland and
their resilience, Ecosyst. Serv., 26, 79–94, https://doi.org/10.1016/j.ecoser.2017.06.008, 2017.
Schlingmann, M., Tobler, U., Berauer, B., Garcia-Franco, N., Wilfahrt, P., Wiesmeier,
M., Jentsch, A., Wolf, B., Kiese, R., and Dannenmann, M.: Intensive slurry management and climate
change promote nitrogen mining from organic matter-rich montane grassland soils, Plant Soil, 456,
81–98, https://doi.org/10.1007/s11104-020-04697-9, 2020.
Schoen, R., Gaudet, J. P., and Bariac, T.: Preferential flow and solute transport in a
large lysimeter, under controlled boundary conditions, J. Hydrol., 215, 70–81,
https://doi.org/10.1016/S0022-1694(98)00262-5, 1999.
Shajari, F., Einsiedl, F., and Rein, A.: Characterizing Water Flow in Vegetated
Lysimeters with Stable Water Isotopes and Modeling, Groundwater, 58, 759–770,
https://doi.org/10.1111/gwat.12970, 2019.
Shi, Y., Davis, K. J., Duffy, C. J., and Yu, X.: Development of a Coupled Land Surface
Hydrologic Model and Evaluation at a Critical Zone Observatory, J. Hydrometeorol., 14, 1401–1420,
https://doi.org/10.1175/JHM-D-12-0145.1, 2013.
Siderius C., Groenendijk, P.,
van Gerven, L. P. A., Jeuken, M. H. J. L., and Smit, A. A. M. F. R.: Process
description of NuswaLite; a simplified model for the fate of
nutrients in surface waters, Alterra Report 1226.2, Alterra,
Wageningen, 2008.
Simmelsgaard, S. E. and Djurhuus, J.: An empirical model for estimating nitrate
leaching as affected by crop type and the long-term N fertilizer rate, Soil Use Manage., 14,
37–43, https://doi.org/10.1111/j.1475-2743.1998.tb00608.x, 1998.
Smit, A. A. M. F. R., Siderius, C., and van Gerven, L. P. A.:
Process description of SWQN, A simplified hydraulic
model, Report 1226.1, Alterra, Wageningen, 2009.
Smith, W., Grant, B., Qi, Z., He, W., VanderZaag, A., Drury, C. F., and Helmers, M.:
Development of the DNDC model to improve soil hydrology and incorporate mechanistic tile drainage:
A comparative analysis with RZWQM2, Environ. Model. Softw., 123, 104577,
https://doi.org/10.1016/j.envsoft.2019.104577, 2020.
Sohier, C., Degre, A., and Dautrebande, S.: From root zone modelling to regional
forecasting of nitrate concentration in recharge flows – The case of the Walloon Region
(Belgium), Elsevier, available at:
https://www.sciencedirect.com/science/article/pii/S0022169409001218 (last access: May 2020), 2009.
Sommerfeldt, T. G., Chang, C., and Entz, T.: Long-term Annual Manure Applications
Increase Soil Organic Matter and Nitrogen, and Decrease Carbon to Nitrogen Ratio, Soil
Sci. Soc. Am. J., 52, 1668–1672, https://doi.org/10.2136/sssaj1988.03615995005200060030x, 1988.
Spehn, E. M., Hector, A., Joshi, J., Scherer-Lorenzen, M., Schmid, B., Bazeley-White,
E., Beierkuhnlein, C., Caldeira, M. C., Diemer, M., Dimitrakopoulos, P. G., Finn, J. A., Freitas,
H., Giller, P. S., Good, J., Harris, R., Högberg, P., Huss-Danell, K., Jumpponen, A.,
Koricheva, J., Leadley, P. W., Loreau, M., Minns, A., Mulder, C. P. H.,
O'Donovan, G., Otway, S. J., Palmborg, C., Pereira, J. S.,
Pfisterer, A. B., Prinz, A., Read, D. J., Schulze, E.-D.,
Siamantziouras, A.-S. D., Terry, A. C., Troumbis, A. Y., Woodward, F. I.,
Yachi, S., and Lawton, J. H.: Ecosystem
effects of biodiversity manipulations in european grasslands, Ecol. Monogr., 75, 37–63,
https://doi.org/10.1890/03-4101, 2005.
Stenitzer, E.: Ein numerisches Modell zur
Simulation des Bodenwasserhaushaltes und des
Pflanzenertrages eines Standortes, Mitt. Bundesanstalt
Kulturtech. Bodenwasserhaushalt 31, 201 pp., 1988.
Swiss Federal Council: Verordnung vom 23. Oktober 2013 über die Direktzahlungen an
die Landwirtschaft (Direktzahlungsverordnung, DZV), available at:
https://www.admin.ch/opc/de/classified-compilation/20130216/index.html (last access: May 2020), 1998.
Tafteh, A. and Sepaskhah, A. R.: Application of HYDRUS-1D model for simulating water
and nitrate leaching from continuous and alternate furrow irrigated rapeseed and maize fields,
Agric. Water Manage., 113, 19–29, https://doi.org/10.1016/j.agwat.2012.06.011, 2012.
Tague, C. L., McDowell, N. G., and Allen, C. D.: An Integrated Model of Environmental
Effects on Growth, Carbohydrate Balance, and Mortality of Pinus ponderosa Forests in the Southern
Rocky Mountains, PLoS ONE, 8, e80286, https://doi.org/10.1371/journal.pone.0080286, 2013.
Takruri, M., Rajasegarar, S., Challa, S., Leckie, C., and Palaniswami, M.:
Spatio-temporal modelling-based drift-aware wireless sensor networks, IET Wireless Sens. Syst., 1,
110–122, https://doi.org/10.1049/iet-wss.2010.0091, 2011.
Tilman, D., Wedin, D., and Knops, J.: Productivity and sustainability influenced by
biodiversity in grassland ecosystems, Nature, 379, 718–720, https://doi.org/10.1038/379718a0, 1996.
Van Dam, J. C.: Field-scale water flow and solute transport: SWAP model concepts,
parameter estimation and case studies, Wageningen University, Wageningen, 2000.
Velthof, G. L., Lesschen, J. P., Schils, R. L. M., Smit, A., Elbersen, B. S., Hazeu,
G. W., Mucher, C. A., and Oenema, O.: Grassland areas, production and use. Lot 2. Methodological
studies in the field of Agro-Environmental
Indicators, European Commission, Wageningen, 2014.
Vescovo, L. and Gianelle, D.: Using the MIR bands in vegetation indices for the
estimation of grassland biophysical parameters from satellite remote sensing in the Alps region of
Trentino (Italy), Adv. Space Res., 41, 1764–1772, https://doi.org/10.1016/j.asr.2007.07.043, 2008.
Vet, R., Artz, R. S., Carou, S., Shaw, M., Ro, C. U., Aas, W., Baker, A., Bowersox,
V. C., Dentener, F., Galy-Lacaux, C., Hou, A., Pienaar, J. J., Gillett, R., Forti, M. C., Gromov,
S., Hara, H., Khodzher, T., Mahowald, N. M., Nickovic, S., Rao, P. S. P., and Reid, N. W.: A global assessment of precipitation chemistry and deposition of
sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus,
Atmos. Environ., 93, 3–100, https://doi.org/10.1016/j.atmosenv.2013.10.060, 2014.
Wang, C., Chen, Z., Unteregelsbacher, S., Lu, H., Gschwendtner, S., Gasche, R., Kolar,
A., Schloter, M., Kiese, R., Butterbach-Bahl, K., and Dannenmann, M.: Climate change amplifies
gross nitrogen turnover in montane grasslands of Central Europe in both summer and winter seasons,
Glob, Change Biol., 22, 2963–2978, https://doi.org/10.1111/gcb.13353, 2016.
Widmoser, P. and Wohlfahrt, G.: Attributing the energy imbalance by concurrent
lysimeter and eddy covariance evapotranspiration measurements, Agric. Forest Meteorol., 263,
287–291, https://doi.org/10.1016/j.agrformet.2018.09.003, 2018.
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon projections are
improved by modelling microbial processes, Nat. Clim. Change, 3, 909–912,
https://doi.org/10.1038/nclimate1951, 2013.
Williams, J., Jones, C., and Dyke, P. T.: A modeling approach to determining the
relationship between erosion and soil productivity, T. ASAE, 27, 129–144,
https://doi.org/10.13031/2013.32748, 1984.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P.,
Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B. E., Kowalski,
A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R., and Verma, S.: Energy balance closure at FLUXNET sites, Agric. Forest Meteorol.,
113, 223–243, https://doi.org/10.1016/S0168-1923(02)00109-0, 2002.
Wohlfahrt, G., Anderson-Dunn, M., Bahn, M., Balzarolo, M., Berninger, F., Campbell,
C., Carrara, A., Cescatti, A., Christensen, T., Dore, S., Eugster, W., Friborg, T., Furger, M.,
Gianelle, D., Gimeno, C., Hargreaves, K., Hari, P., Haslwanter, A., Johansson, T.,
Marcolla, B., Milford, C., Nagy, Z., Nemitz, E.,
Rogiers, N., Sanz, M. J., Siegwolf, R. T. W., Susiluoto, S., Sutton, M.,
Tuba, Z., Ugolini, F., Valentini, R., Zorer, R., and Cernusca, A.: Biotic, abiotic, and management
controls on the net ecosystem CO2 exchange of European mountain grassland ecosystems,
Ecosystems, 11, 1338–1351, https://doi.org/10.1007/s10021-008-9196-2, 2008a.
Wohlfahrt, G., Hammerle, A., Haslwanter, A., Bahn, M., Tappeiner, U., and Cernusca,
A.: Seasonal and inter-annual variability of the net ecosystem CO2 exchange of a
temperate mountain grassland: Effects of weather and management, J. Geophys. Res., 113, D8,
https://doi.org/10.1029/2007JD009286, 2008b.
Wohlfahrt, G., Irschick, C., Thalinger, B., Hörtnagl, L., Obojes, N., and
Hammerle, A.: Insights from Independent Evapotranspiration Estimates for Closing the Energy
Balance: A Grassland Case Study, Vadose Zone J., 9, 1025–1033, https://doi.org/10.2136/vzj2009.0158, 2010.
Wohlfahrt, G., Hammerle, A., and Hörtnagl, L.: FLUXNET2015 AT-Neu Neustift, Dataset, Fluxnet, https://doi.org/10.18140/FLX/1440121, 2020.
Wolf, B., Chwala, C., Fersch, B., Garvelmann, J., Junkermann, W., Zeeman, M. J.,
Angerer, A., Adler, B., Beck, C., Brosy, C., Brugggger, P., Emeis, S., Dannenmann, M., De Roo, F.,
Diaz-Pines, E., Haas, E., Hagen, M., Hajnsek, I., Jacobeit, J., Jagdhuber, T., Kalthoff, N.,
Kiese, R., Kunstmann, H., Kosak, O., Krieg, R., Malchow, C., Mauder, M., Merz, R.,
Notarnicola, C., Philipp, A., Reif, W., Reineke, S., Rödiger, T., Ruehr, N., Schäfer, K., Schrön, M., Senatore,
A., Shupe, H., Völksch, I., Wanninger, C., Zacharias, S., and Schmid, H. P.: The scalex campaign: Scale-crossing land surface and boundary
layer processes in the TERENO-prealpine observatory, B. Am. Meteorol. Soc., 98, 1217–1234,
https://doi.org/10.1175/BAMS-D-15-00277.1, 2017.
Yu, L., Fatichi, S., Zeng, Y., and Su, Z.: The role of vadose zone physics in the
ecohydrological response of a Tibetan meadow to freeze–thaw cycles, The Cryosphere, 14, 4653–4673,
https://doi.org/10.5194/tc-14-4653-2020, 2020.
Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T.,
Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O., Borg, E., Brauer, A.,
Dietrich, P., Hajnsek, I., Helle, G., Kiese, R., Kunstmann, H., Klotz, S., and Vereecken, H.: A
Network of Terrestrial Environmental Observatories in Germany, Vadose Zone J., 10, 955–973,
https://doi.org/10.2136/vzj2010.0139, 2011.
Zeeman, M.: Meteorology, environment and surface flux data for grassland sites in Germany, Zenodo, https://doi.org/10.5281/zenodo.4267887, 2020.
Zeeman, M. and Ruehr, N.: Management and plant physiology data for grassland sites in Germany, Zenodo, https://doi.org/10.5281/zenodo.4267810, 2020.
Zeeman, M. J., Hiller, R., Gilgen, A. K., Michna, P., Plüss, P., Buchmann, N., and
Eugster, W.: Management and climate impacts on net CO2 fluxes and carbon budgets of three
grasslands along an elevational gradient in Switzerland, Agric. Forest Meteorol., 150, 519–530,
https://doi.org/10.1016/j.agrformet.2010.01.011, 2010.
Zeeman, M. J., Mauder, M., Steinbrecher, R., Heidbach, K., Eckart, E., and Schmid,
H. P.: Reduced snow cover affects productivity of upland temperate grasslands, Agric. Forest
Meteorol., 232, 514–526, https://doi.org/10.1016/j.agrformet.2016.09.002, 2017.
Zeeman, M. J., Shupe, H., Baessler, C., and Ruehr, N. K.: Productivity and vegetation
structure of three differently managed temperate grasslands, Agric. Ecosyst. Env., 270–271,
129–148, https://doi.org/10.1016/j.agee.2018.10.003, 2019.
Zhu, N.: Effect of low initial C/N ratio on aerobic composting of swine manure with
rice straw, Biores. Technol., 98, 9–13, https://doi.org/10.1016/j.biortech.2005.12.003, 2007.
Altmetrics
Final-revised paper
Preprint