Articles | Volume 22, issue 14
https://doi.org/10.5194/bg-22-3721-2025
© Author(s) 2025. 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-22-3721-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jul 2025
Research article |
| 31 Jul 2025
| 31 Jul 2025
Assessing evapotranspiration dynamics across central Europe in the context of land–atmosphere drivers
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), Wessling, Germany
Institute of Geography, University of Augsburg, Augsburg, Germany
Martin J. Baur
Department of Geography, Cambridge University, Cambridge, UK
María Piles
Image Processing Laboratory, University of Valencia, Valencia, Spain
Bagher Bayat
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
Mehdi Rahmati
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
David Chaparro
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), Wessling, Germany
Centre for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès, Spain
Clémence Dubois
Department for Earth Observation, Friedrich Schiller University Jena, Jena, Germany
Institute of Data Science (DW), German Aerospace Center (DLR), Jena, Germany
Florian M. Hellwig
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), Wessling, Germany
Department for Earth Observation, Friedrich Schiller University Jena, Jena, Germany
Carsten Montzka
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
Angelika Kübert
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Marlin M. Mueller
Department for Earth Observation, Friedrich Schiller University Jena, Jena, Germany
Institute of Data Science (DW), German Aerospace Center (DLR), Jena, Germany
Isabel Augscheller
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), Wessling, Germany
Francois Jonard
Earth Observation and Ecosystem Modeling Laboratory, University of Liège, Liège, Belgium
Konstantin Schellenberg
Department for Earth Observation, Friedrich Schiller University Jena, Jena, Germany
Department of Biogeochemical Processes, Max Planck Institute for Biogeochemistry, Jena, Germany
Thomas Jagdhuber
Microwaves and Radar Institute (HR), German Aerospace Center (DLR), Wessling, Germany
Institute of Geography, University of Augsburg, Augsburg, Germany
Related authors
Thomas Jagdhuber, François Jonard, Anke Fluhrer, David Chaparro, Martin J. Baur, Thomas Meyer, and María Piles
Biogeosciences, 19, 2273–2294, https://doi.org/10.5194/bg-19-2273-2022, https://doi.org/10.5194/bg-19-2273-2022, 2022
Short summary
Short summary
This is a concept study of water dynamics across winter wheat starting from ground-based L-band radiometry in combination with on-site measurements of soil and atmosphere. We research the feasibility of estimating water potentials and seasonal flux rates of water (water uptake from soil and transpiration rates into the atmosphere) within the soil-plant-atmosphere system (SPAS) of a winter wheat field. The main finding is that L-band radiometry can be integrated into field-based SPAS assessment.
M. A. Stelmaszczuk-Górska, E. Aguilar-Moreno, S. Casteleyn, D. Vandenbroucke, M. Miguel-Lago, C. Dubois, R. Lemmens, G. Vancauwenberghe, M. Olijslagers, S. Lang, F. Albrecht, M. Belgiu, V. Krieger, T. Jagdhuber, A. Fluhrer, M. J. Soja, A. Mouratidis, H. J. Persson, R. Colombo, and G. Masiello
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 15–22, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-15-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-15-2020, 2020
Jordan Bates, Carsten Montzka, Harry Vereecken, and François Jonard
EGUsphere, https://doi.org/10.5194/egusphere-2025-3919, https://doi.org/10.5194/egusphere-2025-3919, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We used unmanned aerial vehicles (UAVs) with advanced cameras and laser scanning to measure crop water use and detect early signs of plant stress. By combining 3D views of crop structure with surface temperature and reflectance data, we improved estimates of water loss, especially in dense crops like wheat. This approach can help farmers use water more efficiently, respond quickly to stress, and support sustainable agriculture in a changing climate.
Maxime Thomas, Thomas Moenaert, Julien Radoux, Baptiste Delhez, Eléonore du Bois d'Aische, Maëlle Villani, Catherine Hirst, Erik Lundin, François Jonard, Sébastien Lambot, Kristof Van Oost, Veerle Vanacker, Matthias B. Siewert, Carl-Magnus Mörth, Michael W. Palace, Ruth K. Varner, Franklin B. Sullivan, Christina Herrick, and Sophie Opfergelt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3788, https://doi.org/10.5194/egusphere-2025-3788, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 5-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), and an increase of ~1.3%/year of GHG emissions.
Fang Li, Heye Reemt Bogena, Johannes Keller, Bagher Bayat, Rahul Raj, and Harrie-Jan Hendricks-Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2124, https://doi.org/10.5194/egusphere-2025-2124, 2025
Short summary
Short summary
We developed a new method to improve hydrological modeling by jointly using soil moisture and groundwater level data from field sensors in a catchment in Germany. By updating the model separately for shallow and deep soil zones, we achieved more accurate predictions of soil water, groundwater depth, and evapotranspiration. Our results show that combining both data types gives more balanced and reliable outcomes than using either alone.
Pauline Walz, Oliver Fritz, Sabrina Marx, Marlin M. Mueller, Christian Thiel, Josefine Lenz, Soraya Kaiser, Roxanne Frappier, Alexander Zipf, and Moritz Langer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1778, https://doi.org/10.5194/egusphere-2025-1778, 2025
Short summary
Short summary
We explored how citizen scientists can help map changes in Arctic landscapes. Using a web tool we created, more than 100 volunteers contributed the approximate center points of particular ground patterns called ice-wedge polygons in aerial images from Alaska and Canada. Our work shows that the data created by volunteers can be used to reconstruct ice-wedge polygon networks and provide valuable insights on the state of frozen ground in the Arctic.
Yanfei Li, Maud Henrion, Angus Moore, Sébastien Lambot, Sophie Opfergelt, Veerle Vanacker, François Jonard, and Kristof Van Oost
EGUsphere, https://doi.org/10.5194/egusphere-2025-1595, https://doi.org/10.5194/egusphere-2025-1595, 2025
Short summary
Short summary
Combining Unmanned Aerial Vehicle (UAV) remote sensing with in-situ monitoring provides high spatial-temporal insights into CO2 fluxes from temperate peatlands. Dynamic factors (soil temperature and moisture) are the primary drivers contributing to 29% of the spatial and 43% of the seasonal variation. UAVs are effective tools for mapping daily soil respiration. CO2 fluxes from hot spots & moments contribute 20% and 30% of total CO2 fluxes, despite representing only 10% of the area and time.
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
Short summary
Short summary
We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Marco M. Lehmann, Josie Geris, Ilja van Meerveld, Daniele Penna, Youri Rothfuss, Matteo Verdone, Pertti Ala-Aho, Matyas Arvai, Alise Babre, Philippe Balandier, Fabian Bernhard, Lukrecija Butorac, Simon Damien Carrière, Natalie C. Ceperley, Zuosinan Chen, Alicia Correa, Haoyu Diao, David Dubbert, Maren Dubbert, Fabio Ercoli, Marius G. Floriancic, Teresa E. Gimeno, Damien Gounelle, Frank Hagedorn, Christophe Hissler, Frédéric Huneau, Alberto Iraheta, Tamara Jakovljević, Nerantzis Kazakis, Zoltan Kern, Karl Knaebel, Johannes Kobler, Jiří Kocum, Charlotte Koeber, Gerbrand Koren, Angelika Kübert, Dawid Kupka, Samuel Le Gall, Aleksi Lehtonen, Thomas Leydier, Philippe Malagoli, Francesca Sofia Manca di Villahermosa, Chiara Marchina, Núria Martínez-Carreras, Nicolas Martin-StPaul, Hannu Marttila, Aline Meyer Oliveira, Gaël Monvoisin, Natalie Orlowski, Kadi Palmik-Das, Aurel Persoiu, Andrei Popa, Egor Prikaziuk, Cécile Quantin, Katja T. Rinne-Garmston, Clara Rohde, Martin Sanda, Matthias Saurer, Daniel Schulz, Michael Paul Stockinger, Christine Stumpp, Jean-Stéphane Venisse, Lukas Vlcek, Stylianos Voudouris, Björn Weeser, Mark E. Wilkinson, Giulia Zuecco, and Katrin Meusburger
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-409, https://doi.org/10.5194/essd-2024-409, 2024
Preprint under review for ESSD
Short summary
Short summary
This study describes a unique large-scale isotope dataset to study water dynamics in European forests. Researchers collected data from 40 beech and spruce forest sites in spring and summer 2023, using a standardized method to ensure consistency. The results show that water sources for trees change between seasons and vary by tree species. This large dataset offers valuable information for understanding plant water use, improving ecohydrological models, and mapping water cycles across Europe.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Laurent Lassabatere, Pierre-Emmanuel Peyneau, Deniz Yilmaz, Joseph Pollacco, Jesús Fernández-Gálvez, Borja Latorre, David Moret-Fernández, Simone Di Prima, Mehdi Rahmati, Ryan D. Stewart, Majdi Abou Najm, Claude Hammecker, and Rafael Angulo-Jaramillo
Hydrol. Earth Syst. Sci., 27, 895–915, https://doi.org/10.5194/hess-27-895-2023, https://doi.org/10.5194/hess-27-895-2023, 2023
Short summary
Short summary
Sorptivity is one of the most important parameters for quantifying water infiltration into soils. In this study, we propose a mixed formulation that avoids numerical issues and allows for the computation of sorptivity for all types of models chosen for describing the soil hydraulic functions and all initial and final conditions. We show the benefits of using the mixed formulation with regard to modeling water infiltration into soils.
François Jonard, Andrew F. Feldman, Daniel J. Short Gianotti, and Dara Entekhabi
Biogeosciences, 19, 5575–5590, https://doi.org/10.5194/bg-19-5575-2022, https://doi.org/10.5194/bg-19-5575-2022, 2022
Short summary
Short summary
We investigate the spatial and temporal patterns of light and water limitation in plant function at the ecosystem scale. Using satellite observations, we characterize the nonlinear relationships between sun-induced chlorophyll fluorescence (SIF) and water and light availability. This study highlights that soil moisture limitations on SIF are found primarily in drier environments, while light limitations are found in intermediately wet regions.
Jordan Bates, Francois Jonard, Rajina Bajracharya, Harry Vereecken, and Carsten Montzka
AGILE GIScience Ser., 3, 23, https://doi.org/10.5194/agile-giss-3-23-2022, https://doi.org/10.5194/agile-giss-3-23-2022, 2022
Thomas Jagdhuber, François Jonard, Anke Fluhrer, David Chaparro, Martin J. Baur, Thomas Meyer, and María Piles
Biogeosciences, 19, 2273–2294, https://doi.org/10.5194/bg-19-2273-2022, https://doi.org/10.5194/bg-19-2273-2022, 2022
Short summary
Short summary
This is a concept study of water dynamics across winter wheat starting from ground-based L-band radiometry in combination with on-site measurements of soil and atmosphere. We research the feasibility of estimating water potentials and seasonal flux rates of water (water uptake from soil and transpiration rates into the atmosphere) within the soil-plant-atmosphere system (SPAS) of a winter wheat field. The main finding is that L-band radiometry can be integrated into field-based SPAS assessment.
Laurent Lassabatere, Pierre-Emmanuel Peyneau, Deniz Yilmaz, Joseph Pollacco, Jesús Fernández-Gálvez, Borja Latorre, David Moret-Fernández, Simone Di Prima, Mehdi Rahmati, Ryan D. Stewart, Majdi Abou Najm, Claude Hammecker, and Rafael Angulo-Jaramillo
Hydrol. Earth Syst. Sci., 25, 5083–5104, https://doi.org/10.5194/hess-25-5083-2021, https://doi.org/10.5194/hess-25-5083-2021, 2021
Short summary
Short summary
Soil sorptivity is a crucial parameter for the modeling of water infiltration into soils. The standard equation used to compute sorptivity from the soil water retention curve, the unsaturated hydraulic conductivity, and initial and final water contents may lead to erroneous estimates due to its complexity. This study proposes a new straightforward scaling procedure for estimations of sorptivity for four famous and commonly used hydraulic models.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
Short summary
Short summary
The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
M. M. Mueller, C. Dubois, T. Jagdhuber, C. Pathe, and C. Schmullius
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 127–134, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-127-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-127-2021, 2021
Yueling Ma, Carsten Montzka, Bagher Bayat, and Stefan Kollet
Hydrol. Earth Syst. Sci., 25, 3555–3575, https://doi.org/10.5194/hess-25-3555-2021, https://doi.org/10.5194/hess-25-3555-2021, 2021
Short summary
Short summary
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) data from integrated hydrologic simulation results over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.
M. M. Salvia, N. Sánchez, M. Piles, A. Gonzalez-Zamora, and J. Martínez-Fernández
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 53–58, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-53-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-53-2020, 2020
Jie Tian, Zhibo Han, Heye Reemt Bogena, Johan Alexander Huisman, Carsten Montzka, Baoqing Zhang, and Chansheng He
Hydrol. Earth Syst. Sci., 24, 4659–4674, https://doi.org/10.5194/hess-24-4659-2020, https://doi.org/10.5194/hess-24-4659-2020, 2020
Short summary
Short summary
Large-scale profile soil moisture (SM) is important for water resource management, but its estimation is a challenge. Thus, based on in situ SM observations in a cold mountain, a strong relationship between the surface SM and subsurface SM is found. Both the subsurface SM of 10–30 cm and the profile SM of 0–70 cm can be estimated from the surface SM of 0–10 cm accurately. By combing with the satellite product, we improve the large-scale profile SM estimation in the cold mountains finally.
M. A. Stelmaszczuk-Górska, E. Aguilar-Moreno, S. Casteleyn, D. Vandenbroucke, M. Miguel-Lago, C. Dubois, R. Lemmens, G. Vancauwenberghe, M. Olijslagers, S. Lang, F. Albrecht, M. Belgiu, V. Krieger, T. Jagdhuber, A. Fluhrer, M. J. Soja, A. Mouratidis, H. J. Persson, R. Colombo, and G. Masiello
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 15–22, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-15-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-15-2020, 2020
Cited articles
Adeluyi, O., Harris, A., Verrelst, J., Foster, T., and Clay, G. D.: Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression, Int. J. Appl. Earth Obs., 102, 102454, https://doi.org/10.1016/j.jag.2021.102454, 2021.
Ahmed, K. R., Paul-Limoges, E., Rascher, U., and Damm, A.: A First Assessment of the 2018 European Drought Impact on Ecosystem Evapotranspiration, Remote Sens., 13, 16, https://doi.org/10.3390/rs13010016, 2020.
Akumaga, U. and Alderman, P. D.: Comparison of Penman–Monteith and Priestley-Taylor Evapotranspiration Methods for Crop Modeling in Oklahoma, Agron. J., 111, 1171–1180, https://doi.org/10.2134/agronj2018.10.0694, 2019.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Fao, Rome, 300, D05109, https://www.avwatermaster.org/filingdocs/195/70653/172618e_5xAGWAx8.pdf (last access: 20 November 2023), 1998.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
Barrios, J. M., Arboleda, A., Dutra, E., Trigo, I., and Gellens-Meulenberghs, F.: Evapotranspiration and surface energy fluxes across Europe, Africa and Eastern South America throughout the operational life of the Meteosat second generation satellite, Geosci. Data J., 11, 589–607, https://doi.org/10.1002/gdj3.235, 2024.
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L., Wigneron, J. P., Weber, U., Reichstein, M., Fu, Z., Anthoni, P., Arneth, A., Haverd, V., Jain, A. K., Joetzjer, E., Knauer, J., Lienert, S., Loughran, T., McGuire, P. C., Tian, H., Viovy, N., and Zaehle, S.: Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity, Sci. Adv., 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724, 2020.
Bayat, B., Camacho, F., Nickeson, J., Cosh, M., Bolten, J., Vereecken, H., and Montzka, C.: Toward operational validation systems for global satellite-based terrestrial essential climate variables, Int. J. Appl. Earth Obs., 95, 102240, https://doi.org/10.1016/j.jag.2020.102240, 2021.
Bayat, B., Montzka, C., Graf, A., Giuliani, G., Santoro, M., and Vereecken, H.: One decade (2011–2020) of European agricultural water stress monitoring by MSG-SEVIRI: workflow implementation on the Virtual Earth Laboratory (VLab) platform, Int. J. Digit., 15, 730–747, https://doi.org/10.1080/17538947.2022.2061617, 2022.
Bayat, B., Raj, R., Graf, A., Vereecken, H., and Montzka, C.: Comprehensive accuracy assessment of long-term geostationary SEVIRI-MSG evapotranspiration estimates across Europe, Remote Sens. Environ., 301, 113875, https://doi.org/10.1016/j.rse.2023.113875, 2024.
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25°, Version 2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Beguería, S., Vicente Serrano, S. M., Reig-Gracia, F., and Latorre Garcés, B.: SPEIbase v.2.8, DIGITAL.CSIC [data set], Version 2.8, https://doi.org/10.20350/DIGITALCSIC/15121, 2023.
Bhattacharya, B. K., Mallick, K., Desai, D., Bhat, G. S., Morrison, R., Clevery, J. R., Woodgate, W., Beringer, J., Cawse-Nicholson, K., Ma, S., Verfaillie, J., and Baldocchi, D.: A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations, Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, 2022.
Bottazzi, M., Bancheri, M., Mobilia, M., Bertoldi, G., Longobardi, A., and Rigon, R.: Comparing Evapotranspiration Estimates from the GEOframe-Prospero Model with Penman–Monteith and Priestley-Taylor Approaches under Different Climate Conditions, Water, 13, 1221, https://doi.org/10.3390/w13091221, 2021.
Brown, S. M., Petrone, R. M., Mendoza, C., and Devito, K. J.: Surface vegetation controls on evapotranspiration from a sub-humid Western Boreal Plain wetland, Hydrol. Process., 24, 1072–1085, https://doi.org/10.1002/hyp.7569, 2010.
Budyko, M. I. and Miller, D. H.: Climate and life, Academic Press, New York, ISBN: 0-12-139450, 1974.
Carminati, A. and Javaux, M.: Soil Rather Than Xylem Vulnerability Controls Stomatal Response to Drought, Trend. Plant Sci., 25, 868–880, https://doi.org/10.1016/j.tplants.2020.04.003, 2020.
Carter, E., Hain, C., Anderson, M., and Steinschneider, S.: A Water Balance–Based, Spatiotemporal Evaluation of Terrestrial Evapotranspiration Products across the Contiguous United States, J. Hydrometeorol., 19, 891–905, https://doi.org/10.1175/JHM-D-17-0186.1, 2018.
Cawse-Nicholson, K. and Anderson, M.: ECOSTRESS Level-3 DisALEXI-JPL Evapotranspiration (ECO3ETALEXI) User Guide, https://lpdaac.usgs.gov/documents/999/ECO3ETALEXI_User_Guide_V1.pdf (last access: 18 November 2023), 2021.
Chen, X., Su, Z., Ma, Y., Trigo, I., and Gentine, P.: Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis Data, JGR Atmos., 126, e2020JD032873, https://doi.org/10.1029/2020JD032873, 2021.
De Santis, D., D'Amato, C., Bartkowiak, P., Azimi, S., Castelli, M., Rigon, R., and Massari, C.: Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy, in: 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2022 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Perugia, Italy, 356–361, https://doi.org/10.1109/MetroAgriFor55389.2022.9964755, 2022.
Díaz, E., Adsuara, J. E., Martínez, Á. M., Piles, M., and Camps-Valls, G.: Inferring causal relations from observational long-term carbon and water fluxes records, Sci. Rep., 12, 1610, https://doi.org/10.1038/s41598-022-05377-7, 2022.
ECMWF: IFS Documentation CY45R1 – Part IV: Physical processes, https://doi.org/10.21957/4WHWO8JW0, 2018.
EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF): Validation Report Evapotranspiration and Turbulent Fluxes v3, SAF/LAND/RMI/VR/ETFv3/1.0, 2024.
European Environment Agency: CORINE Land Cover 2018 (raster 100 m), Europe, 6-yearly – version 2020_20u1, May 2020 (20.01) [data set], https://doi.org/10.2909/960998C1-1870-4E82-8051-6485205EBBAC, 2019.
Feldman, A., Konings, A., Piles, M., and Entekhabi, D.: The Multi-Temporal Dual Channel Algorithm (MT-DCA), Zenodo [data set], https://doi.org/10.5281/ZENODO.5619583, 2021.
Feldman, A., Gianotti, D., Dong, J., Akbar, R., Crow, W., McColl, K., Nippert, J., Tumber-Dávila, S. J., Holbrook, N. M., Rockwell, F., Scott, R., Reichle, R., Chatterjee, A., Joiner, J., Poulter, B., and Entekhabi, D.: Satellites capture soil moisture dynamics deeper than a few centimeters and arerelevant to plant water uptake, ESS Open Archive, https://doi.org/10.1002/essoar.10511280.1, 2022.
Feldman, A. F., Feng, X., Felton, A. J., Konings, A. G., Knapp, A. K., Biederman, J. A., and Poulter, B.: Plant responses to changing rainfall frequency and intensity, Nat. Rev. Earth Environ., 5, 276–294, https://doi.org/10.1038/s43017-024-00534-0, 2024.
Fisher, J. B., Tu, K. P., and Baldocchi, D. D.: Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ., 112, 901–919, https://doi.org/10.1016/j.rse.2007.06.025, 2008.
Fisher, J. B., Lee, B., Purdy, A. J., Halverson, G. H., Dohlen, M. B., Cawse-Nicholson, K., Wang, A., Anderson, R. G., Aragon, B., Arain, M. A., Baldocchi, D. D., Baker, J. M., Barral, H., Bernacchi, C. J., Bernhofer, C., Biraud, S. C., Bohrer, G., Brunsell, N., Cappelaere, B., Castro-Contreras, S., Chun, J., Conrad, B. J., Cremonese, E., Demarty, J., Desai, A. R., De Ligne, A., Foltýnová, L., Goulden, M. L., Griffis, T. J., Grünwald, T., Johnson, M. S., Kang, M., Kelbe, D., Kowalska, N., Lim, J., Maïnassara, I., McCabe, M. F., Missik, J. E. C., Mohanty, B. P., Moore, C. E., Morillas, L., Morrison, R., Munger, J. W., Posse, G., Richardson, A. D., Russell, E. S., Ryu, Y., Sanchez-Azofeifa, A., Schmidt, M., Schwartz, E., Sharp, I., Šigut, L., Tang, Y., Hulley, G., Anderson, M., Hain, C., French, A., Wood, E., and Hook, S.: ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station, Water Resour. Res., 56, e2019WR026058, https://doi.org/10.1029/2019WR026058, 2020.
Fu, Z., Ciais, P., Prentice, I. C., Gentine, P., Makowski, D., Bastos, A., Luo, X., Green, J. K., Stoy, P. C., Yang, H., and Hajima, T.: Atmospheric dryness reduces photosynthesis along a large range of soil water deficits, Nat. Commun., 13, 989, https://doi.org/10.1038/s41467-022-28652-7, 2022.
Ghilain, N., Arboleda, A., and Gellens-Meulenberghs, F.: Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data, Hydrol. Earth Syst. Sci., 15, 771–786, https://doi.org/10.5194/hess-15-771-2011, 2011.
Graf, A., Bogena, H. R., Drüe, C., Hardelauf, H., Pütz, T., Heinemann, G., and Vereecken, H.: Spatiotemporal relations between water budget components and soil water content in a forested tributary catchment, Water Resour. Res., 50, 4837–4857, https://doi.org/10.1002/2013WR014516, 2014.
Gruber, A., Su, C. -H., Crow, W. T., Zwieback, S., Dorigo, W. A., and Wagner, W.: Estimating error cross-correlations in soil moisture data sets using extended collocation analysis, JGR Atmos., 121, 1208–1219, https://doi.org/10.1002/2015JD024027, 2016.
Gupta, A., Rico-Medina, A., and Caño-Delgado, A. I.: The physiology of plant responses to drought, Science, 368, 266–269, https://doi.org/10.1126/science.aaz7614, 2020.
Ha, W., Gowda, P. H., and Howell, T. A.: A review of downscaling methods for remote sensing-based irrigation management: part I, Irrig. Sci., 31, 831–850, https://doi.org/10.1007/s00271-012-0331-7, 2013.
Hao, X., Zhang, S., Li, W., Duan, W., Fang, G., Zhang, Y., and Guo, B.: The Uncertainty of Penman-Monteith Method and the Energy Balance Closure Problem, JGR Atmos., 123, 7433–7443, https://doi.org/10.1029/2018JD028371, 2018.
He, Q.-L., Xiao, J.-L., and Shi, W.-Y.: Responses of Terrestrial Evapotranspiration to Extreme Drought: A Review, Water, 14, 3847, https://doi.org/10.3390/w14233847, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hu, G., Jia, L., and Menenti, M.: Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011, Remote Sens. Environ., 156, 510–526, https://doi.org/10.1016/j.rse.2014.10.017, 2015.
Hu, T., Mallick, K., Hitzelberger, P., Didry, Y., Boulet, G., Szantoi, Z., Koetz, B., Alonso, I., Pascolini-Campbell, M., Halverson, G., Cawse-Nicholson, K., Hulley, G. C., Hook, S., Bhattarai, N., Olioso, A., Roujean, J., Gamet, P., and Su, B.: Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil-Atmospheric Aridity and Biomes Over Europe, Water Resour. Res., 59, e2022WR034132, https://doi.org/10.1029/2022WR034132, 2023.
Jagdhuber, T., Fluhrer, A., Chaparro, D., Dubois, C., Hellwig, F. M., Bayat, B., Montzka, C., Baur, M. J., Ramati, M., Kübert, A., Mueller, M. M., Schellenberg, K., Boehm, M., Jonard, F., Steele-Dunne, S., Piles, M., and Entekhabi, D.: On the Potential of Active and Passive Microwave Remote Sensing for Tracking Seasonal Dynamics of Evapotranspiration, in: IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, 16–21 July 2023, Pasadena, CA, USA, 2610–2613, https://doi.org/10.1109/IGARSS52108.2023.10283234, 2023.
Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M. F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A., Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M., Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of 12 land surface heat flux estimates, J. Geophys. Res., 116, D02102, https://doi.org/10.1029/2010JD014545, 2011.
Jin, Z., Liang, W., Yang, Y., Zhang, W., Yan, J., Chen, X., Li, S., and Mo, X.: Separating Vegetation Greening and Climate Change Controls on Evapotranspiration trend over the Loess Plateau, Sci. Rep., 7, 8191, https://doi.org/10.1038/s41598-017-08477-x, 2017.
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., De Jeu, R., Dolman, A. J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land evapotranspiration trend due to limited moisture supply, Nature, 467, 951–954, https://doi.org/10.1038/nature09396, 2010.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Konings, A., Piles, M., Rötzer, M., McColl, K., Chang, S. K., and Entekhabi, D.: Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations, Elsevier Remote Sensing of Environment, 172, 178–189, https://doi.org/10.1016/j.rse.2015.11.009, 2016.
Li, C., Yang, H., Yang, W., Liu, Z., Jia, Y., Li, S., and Yang, D.: Error characterization of global land evapotranspiration products: Collocation-based approach, J. Hydrol., 612, 128102, https://doi.org/10.1016/j.jhydrol.2022.128102, 2022.
Liu, H., Xin, X., Su, Z., Zeng, Y., Lian, T., Li, L., Yu, S., and Zhang, H.: Intercomparison and evaluation of ten global ET products at site and basin scales, J. Hydrol., 617, 128887, https://doi.org/10.1016/j.jhydrol.2022.128887, 2023.
Liu, L., Gudmundsson, L., Hauser, M., Qin, D., Li, S., and Seneviratne, S. I.: Soil moisture dominates dryness stress on ecosystem production globally, Nat. Commun., 11, 4892, https://doi.org/10.1038/s41467-020-18631-1, 2020.
Liu, N., Oishi, A. C., Miniat, C. F., and Bolstad, P.: An evaluation of ECOSTRESS products of a temperate montane humid forest in a complex terrain environment, Remote Sens. Environ., 265, 112662, https://doi.org/10.1016/j.rse.2021.112662, 2021.
Loustau, D., Chipeaux, C., and ICOS Ecosystem Thematic Centre: Warm winter 2020 ecosystem eddy covariance flux product from Bilos (1.0), https://doi.org/10.18160/MSRT-T1YA, 2022.
LSA SAF and EUMETSAT SAF On Land Surface Analysis: MSG Evapotranspiration Version 3 (Metv3), https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/METv3/ (last access: 21 November 2023), 2019.
Mahour, M., Tolpekin, V., Stein, A., and Sharifi, A.: A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration, ISPRS J. Photogramm. Remote Sens., 126, 56–67, https://doi.org/10.1016/j.isprsjprs.2017.02.004, 2017.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
McColl, K. A.: Practical and Theoretical Benefits of an Alternative to the Penman-Monteith Evapotranspiration Equation, Water Resour. Res., 56, e2020WR027106, https://doi.org/10.1029/2020WR027106, 2020.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and Stoffelen, A.: Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett., 41, 6229–6236, https://doi.org/10.1002/2014GL061322, 2014.
Meng, X., Deng, M., Shu, L., Chen, H., Wang, S., Li, Z., Zhao, L., and Shang, L.: An evaluation of evapotranspiration products over the Tibetan Plateau, J. Hydrometeorol., 25, 1665–1677, https://doi.org/10.1175/JHM-D-23-0223.1, 2024.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Monteith, J. L.: Evaporation and environment, Symposia of the Society for Experimental Biology, 19, 205–234, 1965.
Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I.: Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis, Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, 2013.
Mueller, M. M., Dubois, C., Jagdhuber, T., Hellwig, F. M., Pathe, C., Schmullius, C., and Steele-Dunne, S.: Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests, Remote Sensing, 14, 6384, https://doi.org/10.3390/rs14246384, 2022.
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/CDS.E2161BAC, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes, Nat. Clim. Change, 6, 1023–1027, https://doi.org/10.1038/nclimate3114, 2016.
Pastorello, G., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. de, Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. di, Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., Gitelson, A., 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.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55, 341–366, https://doi.org/10.1002/2016RG000543, 2017.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, Proc. R. Soc. Lond. A, 193, 120–145, https://doi.org/10.1098/rspa.1948.0037, 1948.
Petropoulos, G. P., Ireland, G., Cass, A., and Srivastava, P. K.: Performance Assessment of the SEVIRI Evapotranspiration Operational Product: Results Over Diverse Mediterranean Ecosystems, IEEE Sensors J., 15, 3412–3423, https://doi.org/10.1109/JSEN.2015.2390031, 2015.
Porporato, A., D'Odorico, P., Laio, F., Ridolfi, L., and Rodriguez-Iturbe, I.: Ecohydrology of water-controlled ecosystems, Advances in Water Resources, 25, 1335–1348, https://doi.org/10.1016/S0309-1708(02)00058-1, 2002.
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972.
Rahmati, M., Groh, J., Graf, A., Pütz, T., Vanderborght, J., and Vereecken, H.: On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland, Vadose Zone J., 19, e20029, https://doi.org/10.1002/vzj2.20029, 2020.
Rahmati, M., Graf, A., Poppe Terán, C., Amelung, W., Dorigo, W., Franssen, H.-J. H., Montzka, C., Or, D., Sprenger, M., Vanderborght, J., Verhoest, N. E. C., and Vereecken, H.: Continuous increase in evaporative demand shortened the growing season of European ecosystems in the last decade, Commun. Earth Environ., 4, 236, https://doi.org/10.1038/s43247-023-00890-7, 2023.
Rahmati, M., Amelung, W., Brogi, C., Dari, J., Flammini, A., Bogena, H., Brocca, L., Chen, H., Groh, J., Koster, R. D., McColl, K. A., Montzka, C., Moradi, S., Rahi, A., Sharghi S., F., and Vereecken, H.: Soil Moisture Memory: State-Of-The-Art and the Way Forward, Rev. Geophys., 62, e2023RG000828, https://doi.org/10.1029/2023RG000828, 2024.
Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S., Hanel, M., and Kumar, R.: The 2018–2020 Multi-Year Drought Sets a New Benchmark in Europe, Earth's Future, 10, e2021EF002394, https://doi.org/10.1029/2021EF002394, 2022.
Rebmann, C., Aubinet, M., Schmid, H., Arriga, N., Aurela, M., Burba, G., Clement, R., De Ligne, A., Fratini, G., Gielen, B., Grace, J., Graf, A., Gross, P., Haapanala, S., Herbst, M., Hörtnagl, L., Ibrom, A., Joly, L., Kljun, N., Kolle, O., Kowalski, A., Lindroth, A., Loustau, D., Mammarella, I., Mauder, M., Merbold, L., Metzger, S., Mölder, M., Montagnani, L., Papale, D., Pavelka, M., Peichl, M., Roland, M., Serrano-Ortiz, P., Siebicke, L., Steinbrecher, R., Tuovinen, J.-P., Vesala, T., Wohlfahrt, G., and Franz, D.: ICOS eddy covariance flux-station site setup: a review, Int. Agrophys., 32, 471–494, https://doi.org/10.1515/intag-2017-0044, 2018.
Rui, H. and Beaudoing, H.: README Document for NASA GLDAS Version 2 Data Products, NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), 2022.
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.: Detecting and quantifying causal associations in large nonlinear time series datasets, Sci. Adv., 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996, 2019.
Runge, J., Gerhardus, A., Varando, G., Eyring, V., and Camps-Valls, G.: Causal inference for time series, Nat. Rev. Earth Environ., 4, 487–505, https://doi.org/10.1038/s43017-023-00431-y, 2023.
Running, S., Mu, Q., and Zhao, M.: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006 [data set], https://doi.org/10.5067/MODIS/MOD16A2.006, 2017.
Running, S., Mu, Q., Zhao, M., and Moreno, A.: User's guide MODIS global terrestrial evapotranspiration (ET) product (MOD16A2/A3 and year-end gap-filled MOD16A2GF/A3GF), MODIS Land Team 40, 2019.
Savitzky, Abraham. and Golay, M. J. E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Anal. Chem., 36, 1627–1639, https://doi.org/10.1021/ac60214a047, 1964.
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A., Gharun, M., Grams, T. E. E., Hauck, M., Hajek, P., Hartmann, H., Hiltbrunner, E., Hoch, G., Holloway-Phillips, M., Körner, C., Larysch, E., Lübbe, T., Nelson, D. B., Rammig, A., Rigling, A., Rose, L., Ruehr, N. K., Schumann, K., Weiser, F., Werner, C., Wohlgemuth, T., Zang, C. S., and Kahmen, A.: A first assessment of the impact of the extreme 2018 summer drought on Central European forests, Basic Appl. Ecol., 45, 86–103, https://doi.org/10.1016/j.baae.2020.04.003, 2020.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Sepulcre-Canto, G., Vogt, J., Arboleda, A., and Antofie, T.: Assessment of the EUMETSAT LSA-SAF evapotranspiration product for drought monitoring in Europe, Int. J. Appl. Earth Obs., 30, 190–202, https://doi.org/10.1016/j.jag.2014.01.021, 2014.
Singh, R. P., Paramanik, S., Bhattacharya, B. K., and Behera, M. D.: Modelling of evapotranspiration using land surface energy balance and thermal infrared remote sensing, Trop. Ecol., 61, 42–50, https://doi.org/10.1007/s42965-020-00076-8, 2020.
Stisen, S., Soltani, M., Mendiguren, G., Langkilde, H., Garcia, M., and Koch, J.: Spatial Patterns in Actual Evapotranspiration Climatologies for Europe, Remote Sens., 13, 2410, https://doi.org/10.3390/rs13122410, 2021.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res., 103, 7755–7766, https://doi.org/10.1029/97JC03180, 1998.
Trambauer, P., Dutra, E., Maskey, S., Werner, M., Pappenberger, F., Van Beek, L. P. H., and Uhlenbrook, S.: Comparison of different evaporation estimates over the African continent, Hydrol. Earth Syst. Sci., 18, 193–212, https://doi.org/10.5194/hess-18-193-2014, 2014.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R., Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting eddy-covariance flux underestimates over a grassland, Agr. Forest Meteorol., 103, 279–300, https://doi.org/10.1016/S0168-1923(00)00123-4, 2000.
Vargas Zeppetello, L. R., McColl, K. A., Bernau, J. A., Bowen, B. B., Tang, L. I., Holbrook, N. M., Gentine, P., and Huybers, P.: Apparent surface conductance sensitivity to vapour pressure deficit in the absence of plants, Nat. Water, 1, 941–951, https://doi.org/10.1038/s44221-023-00147-9, 2023.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Clim., 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010.
Wang, K. and Dickinson, R. E.: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability, Rev. Geophys., 50, 2011RG000373, https://doi.org/10.1029/2011RG000373, 2012.
Warm Winter 2020 Team, ICOS Ecosystem Thematic Centre, ICOS Ecosystem Thematic Centre, and Trotta, C.: Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format – release 2022-1, https://doi.org/10.18160/2G60-ZHAK, 2022.
Widmoser, P.: A discussion on and alternative to the Penman–Monteith equation, Agr. Water Manag., 96, 711–721, https://doi.org/10.1016/j.agwat.2008.10.003, 2009.
Wu, J., Feng, Y., Liang, L., He, X., and Zeng, Z.: Assessing evapotranspiration observed from ECOSTRESS using flux measurements in agroecosystems, Agr. Water Manag., 269, 107706, https://doi.org/10.1016/j.agwat.2022.107706, 2022.
Xu, C., Wang, W., Hu, Y., and Liu, Y.: Evaluation of ERA5, ERA5-Land, GLDAS-2.1, and GLEAM potential evapotranspiration data over mainland China, J. Hydrol. Reg. Stud., 51, 101651, https://doi.org/10.1016/j.ejrh.2023.101651, 2024.
Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang, X., and Zhao, C.: Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States, J. Hydrol., 578, 124105, https://doi.org/10.1016/j.jhydrol.2019.124105, 2019.
Yu, X., Qian, L., Wang, W., Hu, X., Dong, J., Pi, Y., and Fan, K.: Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States, Agr. Water Manag., 289, 108555, https://doi.org/10.1016/j.agwat.2023.108555, 2023.
Zhang, J., Guan, K., Peng, B., Pan, M., Zhou, W., Jiang, C., Kimm, H., Franz, T. E., Grant, R. F., Yang, Y., Rudnick, D. R., Heeren, D. M., Suyker, A. E., Bauerle, W. L., and Miner, G. L.: Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand, Nat. Commun., 12, 5549, https://doi.org/10.1038/s41467-021-25254-7, 2021.
Zhang, K., Kimball, J. S., and Running, S. W.: A review of remote sensing based actual evapotranspiration estimation, WIREs Water, 3, 834–853, https://doi.org/10.1002/wat2.1168, 2016.
Zhao, M., A, G., Liu, Y., and Konings, A. G.: Evapotranspiration frequently increases during droughts, Nat. Clim. Change, 12, 1024–1030, https://doi.org/10.1038/s41558-022-01505-3, 2022.
Zhou, S., Yu, B., Zhang, Y., Huang, Y., and Wang, G.: Partitioning evapotranspiration based on the concept of underlying water use efficiency, Water Resour. Res., 52, 1160–1175, https://doi.org/10.1002/2015WR017766, 2016.
Zhou, S., Zhang, Y., Park Williams, A., and Gentine, P.: Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events, Sci. Adv., 5, eaau5740, https://doi.org/10.1126/sciadv.aau5740, 2019.
Short summary
This study compares established evapotranspiration products in central Europe and evaluates their multi-seasonal performance during wet and drought phases in 2017–2020 together with important soil–plant–atmosphere drivers. Results show that SEVIRI, ERA5-land, and GLEAM perform best compared to ICOS (Integrated Carbon Observation System) measurements. During moisture-limited drought years, ET (evapotranspiration) decreases due to decreasing soil moisture and increasing vapor pressure deficit, while in other years ET is mainly controlled by VPD (vapor pressure deficit).
This study compares established evapotranspiration products in central Europe and evaluates...
Altmetrics
Final-revised paper
Preprint