Articles | Volume 27, issue 16
https://doi.org/10.5194/hess-27-3143-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3143-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Heye Reemt Bogena
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Dongryeol Ryu
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Harry Vereecken
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
Andrew Western
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Harrie-Jan Hendricks Franssen
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
Related authors
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
Short summary
Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Shiao Feng, Wenhong Wang, Yonggen Zhang, Zhongwang Wei, Jianzhi Dong, Lutz Weihermüller, and Harry Vereecken
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-410, https://doi.org/10.5194/essd-2025-410, 2025
Preprint under review for ESSD
Short summary
Short summary
Soil moisture is key for weather, farming, and ecosystems, but global datasets have gaps and biases. We compared three products against 1,615 stations with more than 1.9 million measured moisture, finding ERA5-Land highly correlated but biased high, and SMAP-L4 accurate but short-term. Fusing them created an enhanced dataset, improving correlation by 5%, reducing errors by 20%, and enhancing overall fit by 15%. This seamless resource aids drought tracking, water planning, and climate adaptation.
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.
Tobias Frederick Selkirk, Andrew W. Western, and J. Angus Webb
EGUsphere, https://doi.org/10.5194/egusphere-2025-2427, https://doi.org/10.5194/egusphere-2025-2427, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study finds three cycles in yearly rainfall worldwide of approximately 13, 20 and 28 years. The cycles rise and fall together across continents and also appear in the El Niño–Southern Oscillation (ENSO), a major climate driver of rain. However the signal in ENSO is too small to explain the strong local influence, the results point to another, still-unknown force that may shape both ENSO and global rainfall.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3373, https://doi.org/10.5194/egusphere-2025-3373, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study adopts actual evapotranspiration (AET) signatures to diagnose deficiencies in simulation of AET within conceptual rainfall-runoff models. Five models are assessed using flux tower data at 14 Australian sites. Even when AET is included in the calibration, the models struggle to represent aspects of AET dynamics, including interannual variability and timing on seasonal and event scales. The approach shows promise for more insightful critique of model simulations.
Olaleye Babatunde, Meenakshi Arora, Siva Naga Venkat Nara, Danlu Guo, Ian Cartwright, and Andrew W. Western
EGUsphere, https://doi.org/10.5194/egusphere-2025-2456, https://doi.org/10.5194/egusphere-2025-2456, 2025
Short summary
Short summary
Nitrogen inputs can pollute streams and degrade water quality. We estimated fertiliser nitrogen inputs across different land uses and assessed their relationship with stream nitrogen concentrations. Only a small fraction of the applied nitrogen was exported, with most retained within the landscape. Land use, rainfall, and flow patterns strongly influenced nitrogen dynamics and export. These findings support strategies to reduce stream pollution and protect water quality in agricultural areas.
Roland Baatz, Patrick Davies, Paolo Nasta, and Heye Bogena
Hydrol. Earth Syst. Sci., 29, 2583–2597, https://doi.org/10.5194/hess-29-2583-2025, https://doi.org/10.5194/hess-29-2583-2025, 2025
Short summary
Short summary
The data-driven approach enhances soil water content measurements by improving the precision of cosmic ray neutron sensors (CRNSs). This study demonstrates a new method to account for dynamics in air pressure, atmospheric humidity, and incoming neutron intensity. Soil water content measured by CRNSs showed reduced errors compared to reference measurements. The findings highlight the need for precise adjustments to better measure soil moisture for agricultural, water, and climate monitoring.
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.
Heye Reemt Bogena, Frank Herrmann, Andreas Lücke, Thomas Pütz, and Harry Vereecken
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-185, https://doi.org/10.5194/essd-2025-185, 2025
Preprint under review for ESSD
Short summary
Short summary
The Wüstebach catchment in Germany’s TERENO network underwent partial deforestation in 2013 to support natural regrowth in Eifel National Park. This data paper presents 16 years (2010–2024) of estimated hourly stream-water flux data for nine macro- and micronutrients, dissolved ionic aluminum, and dissolved organic carbon, along with measured solute concentrations and discharge rates from two stations—one affected by clear-cutting and one unaffected.
Tobias F. Selkirk, Andrew W. Western, and J. Angus Webb
Hydrol. Earth Syst. Sci., 29, 2167–2184, https://doi.org/10.5194/hess-29-2167-2025, https://doi.org/10.5194/hess-29-2167-2025, 2025
Short summary
Short summary
This study investigated rainfall in eastern Australia to search for patterns that may aid in predicting flood and drought. The current popular consensus is that such cycles do not exist. We analysed 130 years of rainfall using a very modern technique for identifying cycles in complex signals. The results showed strong evidence of three clear cycles of 12.9, 20.4 and 29.1 years with a confidence of 99.99 %. When combined, they showed an 80 % alignment with years of extremely high and low rainfall.
Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, and Lutz Weihermüller
SOIL, 11, 267–285, https://doi.org/10.5194/soil-11-267-2025, https://doi.org/10.5194/soil-11-267-2025, 2025
Short summary
Short summary
To use fertilizers more effectively, non-invasive geophysical methods can be used to understand nutrient distributions in the soil. We utilize, in a long-term field study, geophysical techniques to study soil properties and conditions under different fertilizer treatments. We compared the geophysical response with soil samples and soil sensor data. In particular, electromagnetic induction and electrical resistivity tomography were effective in monitoring changes in nitrate levels over 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.
Salar Saeed Dogar, Cosimo Brogi, Dave O'Leary, Ixchel Hernández-Ochoa, Marco Donat, Harry Vereecken, and Johan Alexander Huisman
EGUsphere, https://doi.org/10.5194/egusphere-2025-827, https://doi.org/10.5194/egusphere-2025-827, 2025
Short summary
Short summary
Farmers need precise information about their fields to use water, fertilizers, and other resources efficiently. This study combines underground soil data and satellite images to create detailed field maps using advanced machine learning. By testing different ways of processing data, we ensured a balanced and accurate approach. The results help farmers manage their land more effectively, leading to better harvests and more sustainable farming practices.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-373, https://doi.org/10.5194/hess-2024-373, 2025
Preprint under review for HESS
Short summary
Short summary
This study is the first to propose actual evapotranspiration (AET) signatures, which can be used to assess multiple aspects of AET dynamics across various temporal scales. As a demonstration, we applied AET signatures to evaluate two remotely sensed (RS) AET products against flux tower AET. The results reveal specific deficiencies in RS AET and provide guidance for selecting appropriate RS AET, including for modelling studies.
Teng Xu, Sinan Xiao, Sebastian Reuschen, Nils Wildt, Harrie-Jan Hendricks Franssen, and Wolfgang Nowak
Hydrol. Earth Syst. Sci., 28, 5375–5400, https://doi.org/10.5194/hess-28-5375-2024, https://doi.org/10.5194/hess-28-5375-2024, 2024
Short summary
Short summary
We provide a set of benchmarking scenarios for geostatistical inversion, and we encourage the scientific community to use these to compare their newly developed methods. To facilitate transparent, appropriate, and uncertainty-aware comparison of novel methods, we provide some accurate reference solutions, a high-end reference algorithm, and a diverse set of benchmarking metrics, all of which are publicly available. With this, we seek to foster more targeted and transparent progress in the field.
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.
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.
Joschka Neumann, Nicolas Brüggemann, Patrick Chaumet, Normen Hermes, Jan Huwer, Peter Kirchner, Werner Lesmeister, Wilhelm August Mertens, Thomas Pütz, Jörg Wolters, Harry Vereecken, and Ghaleb Natour
EGUsphere, https://doi.org/10.5194/egusphere-2024-1598, https://doi.org/10.5194/egusphere-2024-1598, 2024
Short summary
Short summary
Climate change in combination with a steadily growing world population and a simultaneous decrease in agricultural land is one of the greatest global challenges facing mankind. In this context, Forschungszentrum Jülich established an "agricultural simulator" (AgraSim), which enables research into the effects of climate change on agricultural ecosystems and the optimization of agricultural cultivation and management strategies with the aid of combined experimental and numerical simulation.
Lukas Strebel, Heye Bogena, Harry Vereecken, Mie Andreasen, Sergio Aranda-Barranco, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 28, 1001–1026, https://doi.org/10.5194/hess-28-1001-2024, https://doi.org/10.5194/hess-28-1001-2024, 2024
Short summary
Short summary
We present results from using soil water content measurements from 13 European forest sites in a state-of-the-art land surface model. We use data assimilation to perform a combination of observed and modeled soil water content and show the improvements in the representation of soil water content. However, we also look at the impact on evapotranspiration and see no corresponding improvements.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Tanja Denager, Torben O. Sonnenborg, Majken C. Looms, Heye Bogena, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 27, 2827–2845, https://doi.org/10.5194/hess-27-2827-2023, https://doi.org/10.5194/hess-27-2827-2023, 2023
Short summary
Short summary
This study contributes to improvements in the model characterization of water and energy fluxes. The results show that multi-objective autocalibration in combination with mathematical regularization is a powerful tool to improve land surface models. Using the direct measurement of turbulent fluxes as the target variable, parameter optimization matches simulations and observations of latent heat, whereas sensible heat is clearly biased.
Cosimo Brogi, Heye Reemt Bogena, Markus Köhli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, and Olga Dombrowski
Geosci. Instrum. Method. Data Syst., 11, 451–469, https://doi.org/10.5194/gi-11-451-2022, https://doi.org/10.5194/gi-11-451-2022, 2022
Short summary
Short summary
Accurate monitoring of water in soil can improve irrigation efficiency, which is important considering climate change and the growing world population. Cosmic-ray neutrons sensors (CRNSs) are a promising tool in irrigation monitoring due to a larger sensed area and to lower maintenance than other ground-based sensors. Here, we analyse the feasibility of irrigation monitoring with CRNSs and the impact of the irrigated field dimensions, of the variations of water in soil, and of instrument design.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
Short summary
Short summary
Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
Short summary
Short summary
In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
Short summary
Short summary
Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
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
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
Short summary
Short summary
This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, Zongmei Li, and Stephan Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-131, https://doi.org/10.5194/gmd-2022-131, 2022
Publication in GMD not foreseen
Short summary
Short summary
We applied the global sensitivity analysis LH-OAT to the integrated hydrology model ParFlow-CLM to investigate the sensitivity of the 12 parameters for different scenarios. And we found that the general patterns of the parameter sensitivities were consistent, however, for some parameters a significantly larger span of the sensitivities was observed, especially for the higher slope and in subarctic climatic scenarios.
Nicholas Jarvis, Jannis Groh, Elisabet Lewan, Katharina H. E. Meurer, Walter Durka, Cornelia Baessler, Thomas Pütz, Elvin Rufullayev, and Harry Vereecken
Hydrol. Earth Syst. Sci., 26, 2277–2299, https://doi.org/10.5194/hess-26-2277-2022, https://doi.org/10.5194/hess-26-2277-2022, 2022
Short summary
Short summary
We apply an eco-hydrological model to data on soil water balance and grassland growth obtained at two sites with contrasting climates. Our results show that the grassland in the drier climate had adapted by developing deeper roots, which maintained water supply to the plants in the face of severe drought. Our study emphasizes the importance of considering such plastic responses of plant traits to environmental stress in the modelling of soil water balance and plant growth under climate change.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Qichun Yang, Quan J. Wang, Andrew W. Western, Wenyan Wu, Yawen Shao, and Kirsti Hakala
Hydrol. Earth Syst. Sci., 26, 941–954, https://doi.org/10.5194/hess-26-941-2022, https://doi.org/10.5194/hess-26-941-2022, 2022
Short summary
Short summary
Forecasts of evaporative water loss in the future are highly valuable for water resource management. These forecasts are often produced using the outputs of climate models. We developed an innovative method to correct errors in these forecasts, particularly the errors caused by deficiencies of climate models in modeling the changing climate. We apply this method to seasonal forecasts of evaporative water loss across Australia and achieve significant improvements in the forecast quality.
Lukas Strebel, Heye R. Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 15, 395–411, https://doi.org/10.5194/gmd-15-395-2022, https://doi.org/10.5194/gmd-15-395-2022, 2022
Short summary
Short summary
We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.
Veronika Forstner, Jannis Groh, Matevz Vremec, Markus Herndl, Harry Vereecken, Horst H. Gerke, Steffen Birk, and Thomas Pütz
Hydrol. Earth Syst. Sci., 25, 6087–6106, https://doi.org/10.5194/hess-25-6087-2021, https://doi.org/10.5194/hess-25-6087-2021, 2021
Short summary
Short summary
Lysimeter-based manipulative and observational experiments were used to identify responses of water fluxes and aboveground biomass (AGB) to climatic change in permanent grassland. Under energy-limited conditions, elevated temperature actual evapotranspiration (ETa) increased, while seepage, dew, and AGB decreased. Elevated CO2 mitigated the effect on ETa. Under water limitation, elevated temperature resulted in reduced ETa, and AGB was negatively correlated with an increasing aridity.
Yafei Huang, Jonas Weis, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-569, https://doi.org/10.5194/hess-2021-569, 2021
Manuscript not accepted for further review
Short summary
Short summary
Trends in agricultural droughts cannot be easily deduced from measurements. Here trends in agricultural droughts over 31 German and Dutch sites were calculated with model simulations and long-term observed meteorological data as input. We found that agricultural droughts are increasing although precipitation hardly decreases. The increase is driven by increase in evapotranspiration. The year 2018 was for half of the sites the year with the most extreme agricultural drought in the last 55 years.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
Short summary
Short summary
The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Markus Hrachowitz, Michael Stockinger, Miriam Coenders-Gerrits, Ruud van der Ent, Heye Bogena, Andreas Lücke, and Christine Stumpp
Hydrol. Earth Syst. Sci., 25, 4887–4915, https://doi.org/10.5194/hess-25-4887-2021, https://doi.org/10.5194/hess-25-4887-2021, 2021
Short summary
Short summary
Deforestation affects how catchments store and release water. Here we found that deforestation in the study catchment led to a 20 % increase in mean runoff, while reducing the vegetation-accessible water storage from about 258 to 101 mm. As a consequence, fractions of young water in the stream increased by up to 25 % during wet periods. This implies that water and solutes are more rapidly routed to the stream, which can, after contamination, lead to increased contaminant peak concentrations.
Jan Vanderborght, Valentin Couvreur, Felicien Meunier, Andrea Schnepf, Harry Vereecken, Martin Bouda, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 25, 4835–4860, https://doi.org/10.5194/hess-25-4835-2021, https://doi.org/10.5194/hess-25-4835-2021, 2021
Short summary
Short summary
Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implementing root hydraulics in macroscopic soil water flow and land surface models.
Youri Rothfuss, Maria Quade, Nicolas Brüggemann, Alexander Graf, Harry Vereecken, and Maren Dubbert
Biogeosciences, 18, 3701–3732, https://doi.org/10.5194/bg-18-3701-2021, https://doi.org/10.5194/bg-18-3701-2021, 2021
Short summary
Short summary
The partitioning of evapotranspiration into evaporation from soil and transpiration from plants is crucial for a wide range of parties, from farmers to policymakers. In this work, we focus on a particular partitioning method, based on the stable isotopic analysis of water. In particular, we aim at highlighting the challenges that this method is currently facing and, in light of recent methodological developments, propose ways forward for the isotopic-partitioning community.
Shuci Liu, Dongryeol Ryu, J. Angus Webb, Anna Lintern, Danlu Guo, David Waters, and Andrew W. Western
Hydrol. Earth Syst. Sci., 25, 2663–2683, https://doi.org/10.5194/hess-25-2663-2021, https://doi.org/10.5194/hess-25-2663-2021, 2021
Short summary
Short summary
Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation in stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful for identifying important factors determining temporal changes in water quality and, in turn, providing critical information for developing management strategies.
Cosimo Brogi, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken
SOIL, 7, 125–143, https://doi.org/10.5194/soil-7-125-2021, https://doi.org/10.5194/soil-7-125-2021, 2021
Short summary
Short summary
There is a need in agriculture for detailed soil maps that carry quantitative information. Geophysics-based soil maps have the potential to deliver such products, but their added value has not been fully investigated yet. In this study, we compare the use of a geophysics-based soil map with the use of two commonly available maps as input for crop growth simulations. The geophysics-based product results in better simulations, with improvements that depend on precipitation, soil, and crop type.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
Short summary
Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
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.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Cited articles
ABARES – Australian Bureau of Agricultural and Resource Economics and
Sciences: Australian Crop Report, February 2021, Canberra,
https://doi.org/10.25814/xqy3-sx57, 2020.
Ash, A., McIntosh, P., Cullen, B., Carberry, P., and Smith, M. S.: Constraints and opportunities in applying seasonal climate forecasts in
agriculture, Aust. J. Agric. Res., 58, 952–965, https://doi.org/10.1071/AR06188, 2007.
Baatz, R., Hendricks Franssen, H.-J., Han, X., Hoar, T., Bogena, H. R., and
Vereecken, H.: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction, Hydrol. Earth Syst. Sci., 21, 2509–2530, https://doi.org/10.5194/hess-21-2509-2017, 2017.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Bennett, A., Hamman, J., and Nijssen, B.: MetSim: A Python package for
estimation and disaggregation of meteorological data, J. Open Source Softw.,
5, 2042, https://doi.org/10.21105/joss.02042, 2020.
BMEL: Besondere Ernte- und Qualitätsermittlung (BEE) 2019, agricultural yield and quality assessment, https://www.bmel-statistik.de/landwirtschaft/ernte-und-qualitaet/archiv-ernte-und-qualitaet-bee (last access: 15 March 2023), 2020.
BMEL: Besondere Ernte- und Qualitätsermittlung (BEE) 2021, agricultural yield and quality assessment, https://www.bmel-statistik.de/landwirtschaft/ernte-und-qualitaet/archiv-ernte-und-qualitaet-bee (last access: 15 March 2023), 2022.
Boas, T., Bogena, H., Grünwald, T., Heinesch, B., Ryu, D., Schmidt, M.,
Vereecken, H., Western, A., and Hendricks Franssen, H.-J.: Improving the
representation of cropland sites in the Community Land Model (CLM) version 5.0, Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, 2021.
Bogena, H. R., Montzka, C., Huisman, J. A., Graf, A., Schmidt, M., Stockinger, M., von Hebel, C., Hendricks-Franssen, H. J., van der Kruk, J., Tappe, W., Lücke, A., Baatz, R., Bol, R., Groh, J., Pütz, T., Jakobi, J., Kunkel, R., Sorg, J., and Vereecken, H.: The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science, Vadose Zone J., 17, 1–22, https://doi.org/10.2136/vzj2018.03.0055, 2018.
Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., Baatz, R., Boorman, D., Duygu, M. B., Eguibar-Galán, M. A., Fersch, B., Franke, T., Geris, J., González Sanchis, M., Kerr, Y., Korf, T., Mengistu, Z., Mialon, A., Nasta, P., Nitychoruk, J., Pisinaras, V., Rasche, D., Rosolem, R., Said, H., Schattan, P., Zreda, M., Achleitner, S., Albentosa-Hernández, E., Akyürek, Z., Blume, T., del Campo, A.,
Canone, D., Dimitrova-Petrova, K., Evans, J. G., Ferraris, S., Frances, F.,
Gisolo, D., Güntner, A., Herrmann, F., Iwema, J., Jensen, K. H., Kunstmann, H., Lidón, A., Looms, M. C., Oswald, S., Panagopoulos, A.,
Patil, A., Power, D., Rebmann, C., Romano, N., Scheiffele, L., Seneviratne, S., Weltin, G., and Vereecken, H.: COSMOS-Europe: a European network of
cosmic-ray neutron soil moisture sensors, Earth Syst. Sci. Data, 14,
1125–1151, https://doi.org/10.5194/essd-14-1125-2022, 2022.
Bohn, T. J., Livneh, B., Oyler, J. W., Running, S. W., Nijssen, B., and
Lettenmaier, D. P.: Global evaluation of MTCLIM and related algorithms for
forcing of ecological and hydrological models, Agr. Forest Meteorol., 176,
38–49, https://doi.org/10.1016/j.agrformet.2013.03.003, 2013.
BOM: Australian Government: Climate summaries archive, http://www.bom.gov.au/climate/current/statement_archives.shtml (last access: 10 June 2023), 2021.
Calanca, P., Bolius, D., Weigel, A. P., and Liniger, M. A.: Application of
long-range weather forecasts to agricultural decision problems in Europe, J.
Agric. Sci., 149, 15–22, https://doi.org/10.1017/S0021859610000729, 2011.
Cantelaube, P. and Terres, J.-M.: Seasonal weather forecasts for crop yield
modelling in Europe, Tellus A, 57, 476–487, https://doi.org/10.1111/j.1600-0870.2005.00125.x, 2005.
Chang, L.-L., Dwivedi, R., Knowles, J. F., Fang, Y.-H., Niu, G.-Y., Pelletier, J. D., Rasmussen, C., Durcik, M., Barron-Gafford, G. A., and
Meixner, T.: Why Do Large-Scale Land Surface Models Produce a Low Ratio of
Transpiration to Evapotranspiration?, J. Geophys. Res.-Atmos., 123, 9109–9130, https://doi.org/10.1029/2018JD029159, 2018.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger,
J.-C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2
surface reflectance data set, Remote Sens. Environ., 219, 145–161,
https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Coelho, C. A. and Costa, S. M.: Challenges for integrating seasonal climate
forecasts in user applications, Curr. Opin. Environ. Sustain., 2, 317–325,
https://doi.org/10.1016/j.cosust.2010.09.002, 2010.
Collatz, G. J., Ribas-Carbo, M., and Berry, J. A.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/pp9920519, 1992.
Copernicus Climate Change Service, Climate Data Store: Seasonal forecast daily and subdaily data on single levels, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.181d637e, 2018.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A
Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984.
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
DWD – Deutscher Wetter Dienst: German weather archive, https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivtagmonat.html (last access: 15 March 2023), 2021.
Entekhabi, D., Das, N., Njoku, E., Johnson, J., and Shi, J.: SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3, NSIDC, https://doi.org/10.5067/7KKNQ5UURM2W, 2016.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Graf, A., Klosterhalfen, A., Arriga, N., Bernhofer, C., Bogena, H., Bornet,
F., Brüggemann, N., Brümmer, C., Buchmann, N., Chi, J., Chipeaux, C., Cremonese, E., Cuntz, M., Dušek, J., El-Madany, T. S., Fares, S., Fischer, M., Foltýnová, L., Gharun, M., Ghiasi, S., Gielen, B.,
Gottschalk, P., Grünwald, T., Heinemann, G., Heinesch, B., Heliasz, M.,
Holst, J., Hörtnagl, L., Ibrom, A., Ingwersen, J., Jurasinski, G., Klatt, J., Knohl, A., Koebsch, F., Konopka, J., Korkiakoski, M., Kowalska, N., Kremer, P., Kruijt, B., Lafont, S., Léonard, J., De Ligne, A., Longdoz, B., Loustau, D., Magliulo, V., Mammarella, I., Manca, G., Mauder, M., Migliavacca, M., Mölder, M., Neirynck, J., Ney, P., Nilsson, M., Paul-Limoges, E., Peichl, M., Pitacco, A., Poyda, A., Rebmann, C., Roland,
M., Sachs, T., Schmidt, M., Schrader, F., Siebicke, L., Šigut, L., Tuittila, E.-S., Varlagin, A., Vendrame, N., Vincke, C., Völksch, I.,
Weber, S., Wille, C., Wizemann, H.-D., Zeeman, M., and Vereecken, H.: Altered energy partitioning across terrestrial ecosystems in the European drought year 2018, Philos. T. Roy. Soc. B, 375, 20190524, https://doi.org/10.1098/rstb.2019.0524, 2020.
Griffiths, P., Nendel, C., and Hostert, P: National-scale crop- and land-cover map of Germany (2016) based on imagery acquired by Sentinel-2A MSI and Landsat-8 OLI, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.893195, 2018.
Griffiths, P., Nendel, C., and Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land
cover mapping, Remote Sens. Environ., 220, 135–151,
https://doi.org/10.1016/j.rse.2018.10.031, 2019.
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals, IEEE T. Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070, 2017.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.:
Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739,
https://doi.org/10.5194/essd-11-717-2019, 2019.
Gubler, S., Sedlmeier, K., Bhend, J., Avalos, G., Coelho, C. A. S., Escajadillo, Y., Jacques-Coper, M., Martinez, R., Schwierz, C., Skansi, M.
de, and Spirig, C.: Assessment of ECMWF SEAS5 Seasonal Forecast Performance
over South America, Weather Forecast., 35, 561–584, https://doi.org/10.1175/WAF-D-19-0106.1, 2020.
Han, X., Franssen, H.-J. H., Montzka, C., and Vereecken, H.: Soil moisture
and soil properties estimation in the Community Land Model with synthetic
brightness temperature observations, Water Resour. Res., 6081–6105,
https://doi.org/10.1002/2013WR014586@10.1002/(ISSN)1944-7973.SVASYST1, 2018.
Hansen, J. W.: Realizing the potential benefits of climate prediction to
agriculture: issues, approaches, challenges, Agric. Syst., 74, 309–330,
2002.
Hansen, J. W., Challinor, A., Ines, A., Wheeler, T., and Moron, V.: Translating climate forecasts into agricultural terms: advances and challenges, Clim. Res., 33, 27–41, https://doi.org/10.3354/cr033027, 2006.
Harris, I., Jones, P. d., Osborn, T. j., and Lister, D. h.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hawdon, A., McJannet, D., and Wallace, J.: Calibration and correction
procedures for cosmic-ray neutron soil moisture probes located across
Australia, Water Resour. Res., 50, 5029–5043, https://doi.org/10.1002/2013WR015138, 2014.
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda,
M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
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., Chiara, G. D., 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.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hudiburg, T. W., Law, B. E., and Thornton, P. E.: Evaluation and improvement
of the Community Land Model (CLM4) in Oregon forests, Biogeosciences, 10,
453–470, https://doi.org/10.5194/bg-10-453-2013, 2013.
Hung, C. P., Schalge, B., Baroni, G., Vereecken, H., and Hendricks Franssen,
H.-J.: Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface-Subsurface Model for Southwestern Germany, Water
Resour. Res., 58, e2021WR031549, https://doi.org/10.1029/2021WR031549, 2022.
Hungerford, R. D., Nemani, R. R., Running, S. W., and Coughlan, J. C.: MTCLIM: a mountain microclimate simulation model, US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, https://doi.org/10.2737/INT-RP-414, 1989.
ICOS: Integrated Carbon Observation System Carbon Portal, https://www.icos-cp.eu/ (last access: 15 May 2020), 2020.
International Soil Reference and Information Centre (ISRIC) – World Soil Information data hub: SoilGrids, [data set], https://www.isric.org/explore/soilgrids (last access: 10 November 2022), 2023.
IT.NRW: Ernte ausgewählter landwirtschaftlicher Feldfrüchte, yield statistics for certain cash crops, Landesbetrieb ITNRW, Düsseldorf, https://www.it.nrw/statistik/eckdaten/ernte-von-ausgewaehlten-landwirtschaftlichen-feldfruechten-und-gruenland-767 (last access: 5 August 2022), 2019.
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R. and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project, B. Am. Meteorol. Soc., 77, 437–447, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Klemm, T. and McPherson, R. A.: The development of seasonal climate forecasting for agricultural producers, Agr. Forest Meteorol., 232, 384–399, https://doi.org/10.1016/j.agrformet.2016.09.005, 2017.
Kucharik, C. J. and Brye, K. R.: Integrated BIosphere Simulator (IBIS) Yield
and Nitrate Loss Predictions for Wisconsin Maize Receiving Varied Amounts of
Nitrogen Fertilizer, J. Environ. Qual., 32, 247–268, https://doi.org/10.2134/jeq2003.2470, 2003.
Kunkel, R., Sorg, J., Eckardt, R., Kolditz, O., Rink, K., and Vereecken, H.:
TEODOOR: a distributed geodata infrastructure for terrestrial observation
data, Environ. Earth Sci., 69, 507–521, https://doi.org/10.1007/s12665-013-2370-7, 2013.
Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones, A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz, J., Seneviratne, S. I., and Shevliakova, E.: The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design, Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, 2016.
Lawrence, D. M., Fisher, R., Koven, C., Oleson, K., Svenson, S.,
Vertenstein, M., Andre, B., Bonan,
G., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E.,
Knox, R., Lawrence, P., Li, F., Li, H., Lombardozzi, D., Lu, Y.,
Perket, J., Riley,W., Sacks,W., Shi, M.,Wieder,W., Xu, C. (lead
authors), Ali, A., Badger, A., Bisht, G., Broxton, P., Brunke, M.,
Buzan, J., Clark, M., Craig, T., Dahlin, K., Drewniak, B., Emmons,
L., Fisher, J., Flanner, M., Gentine, P., Lenaerts, J., Levis,
S., Leung, L. R., Lipscomb, W., Pelletier, J., Ricciuto, D. M.,
Sanderson, B., Shuman, J., Slater, A., Subin, Z., Tang, J., Tawfik,
A., Thomas, Q., Tilmes, S., Vitt, F., and Zeng, X.: Technical Description
of version 5.0 of the Community Land Model (CLM),
Natl. Cent. Atmospheric Res. (NCAR), http://www.cesm.ucar.edu/models/cesm2/land/CLM50_Tech_Note.pdf (last
access: 1 June 2023), 2018.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L. van, Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M. van den, Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel‐Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Lombardozzi, D. L., Lu, Y., Lawrence, P. J., Lawrence, D. M., Swenson, S.,
Oleson, K. W., Wieder, W. R., and Ainsworth, E. A.: Simulating Agriculture in the Community Land Model Version 5, J. Geophys. Res.-Biogeo., 125, e2019JG005529, https://doi.org/10.1029/2019JG005529, 2020.
Lu, Y., Williams, I. N., Bagley, J. E., Torn, M. S., and Kueppers, L. M.:
Representing winter wheat in the Community Land Model (version 4.5), Geosci.
Model Dev., 10, 1873–1888, https://doi.org/10.5194/gmd-10-1873-2017, 2017.
Marletto, V., Ventura, F., Fontana, G., and Tomei, F.: Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model, Agr. Forest Meteorol., 147, 71–79, https://doi.org/10.1016/j.agrformet.2007.07.003, 2007.
McIntosh, P. C., Pook, M. J., Risbey, J. S., Lisson, S. N., and Rebbeck, M.:
Seasonal climate forecasts for agriculture: Towards better understanding and
value, Field Crops Res., 104, 130–138, https://doi.org/10.1016/j.fcr.2007.03.019, 2007.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.: Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Global Change Biol., 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Meza, F. J., Hansen, J. W., and Osgood, D.: Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and
Recommendations for Future Research, J. Appl. Meteorol. Clim., 47, 1269–1286, https://doi.org/10.1175/2007JAMC1540.1, 2008.
Monhart, S., Spirig, C., Bhend, J., Bogner, K., Schär, C., and Liniger,
M. A.: Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction
and Downscaling Using Surface Observations, J. Geophys. Res.-Atmos., 123, 7999–8016, https://doi.org/10.1029/2017JD027923, 2018.
Morse-McNabb, E., Sheffield, K., Clark, R., Lewis, H., Robson, S., Cherry, D., and Williams, S.: VLUIS, a land use data product for Victoria, Australia, covering 2006 to 2013, Sci. Data, 2, 150070, https://doi.org/10.1038/sdata.2015.70, 2015.
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra Leaf Area
Index/FPAR 8-Day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes
DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006, 2015.
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER
Global Digital Elevation Model V003, https://doi.org/10.5067/ASTER/ASTGTM.003, 2019.
Naz, B. S., Kurtz, W., Montzka, C., Sharples, W., Goergen, K., Keune, J.,
Gao, H., Springer, A., Hendricks Franssen, H.-J., and Kollet, S.: Improving
soil moisture and runoff simulations at 3 km over Europe using land surface
data assimilation, Hydrol. Earth Syst. Sci., 23, 277–301,
https://doi.org/10.5194/hess-23-277-2019, 2019.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
NRW (North Rhine-Westphalia) state government: Preliminary data on cereal grain harvest balance in 2020, https://www.land.nrw/pressemitteilung/nordrhein-westfalen-legt-erntebilanz-2020-vor, (last access: 20 June 2022), 2020.
Parton, K. A., Crean, J., and Hayman, P.: The value of seasonal climate
forecasts for Australian agriculture, Agric. Syst., 174, 1–10,
https://doi.org/10.1016/j.agsy.2019.04.005, 2019.
Potgieter, A. B., Schepen, A., Brider, J., and Hammer, G. L.: Lead time and
skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis, Agr. Forest Meteorol., 324, 109116, https://doi.org/10.1016/j.agrformet.2022.109116, 2022.
Reinermann, S., Gessner, U., Asam, S., Kuenzer, C., and Dech, S.: The Effect
of Droughts on Vegetation Condition in Germany: An Analysis Based on Two
Decades of Satellite Earth Observation Time Series and Crop Yield Statistics, Remote Sens., 11, 1783, https://doi.org/10.3390/rs11151783, 2019.
Running, S., Mu, Q., and Zhao, M.: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006, USGS, https://doi.org/10.5067/MODIS/MOD16A2.006, 2017.
Sacks, B., Kluzek, E., Sobhani, N., mvertens, Levis, S., Swenson, S. C., Cheng, Y., Oleson, K., Andre, B., Hamman, J., Edwards, J., Rothstein, M., Truesdale, J., Lawrence, D., ciceconsortium, van Kampenhout, L., nanr, Koven, C., Andre, B., Fischer, R., djk2120, Wieder, W., Kauffman, B., Dunlap, R., Perket, J., Barlage, M., Serbin, S. P., and Coleman, D.: tboas/CTSM: CLM_WW_CC (reelase_08_2020), Zenodo [code], https://doi.org/10.5281/zenodo.3978092, 2020.
Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv,
L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M.,
Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.:
Improving calibration and validation of cosmic-ray neutron sensors in the
light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009–5030,
https://doi.org/10.5194/hess-21-5009-2017, 2017.
Semenov, M. A. and Doblas-Reyes, F. J.: Utility of dynamical seasonal forecasts in predicting crop yield, Clim. Res., 34, 71–81,
https://doi.org/10.3354/cr034071, 2007.
Sprintsin, M., Karnieli, A., Berliner, P., Rotenberg, E., Yakir, D., Cohen,
S., and Rotenberg, P.: Evaluating the performance of the MODIS Leaf Area Index (LAI) product over a Mediterranean dryland planted forest, Int. J.
Remote Sens., 30, 5061–5069, https://doi.org/10.1080/01431160903032885, 2009.
Strebel, L., Bogena, H. R., Vereecken, H., and Hendricks Franssen, H.-J.:
Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications, Geosci. Model
Dev., 15, 395–411, https://doi.org/10.5194/gmd-15-395-2022, 2022.
TERENO – TERrestrial ENvironment Observatories data portal: http://www.tereno.net/ddp/ (last access: 31 December 2020), 2020.
Thornton, P. E. and Running, S. W.: An improved algorithm for estimating
incident daily solar radiation from measurements of temperature, humidity,
and precipitation, Agr. Forest Meteorol., 93, 211–228, https://doi.org/10.1016/S0168-1923(98)00126-9, 1999.
Thornton, P. E., Hasenauer, H., and White, M. A.: Simultaneous estimation of
daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria, Agr. Forest
Meteorol., 104, 255–271, https://doi.org/10.1016/S0168-1923(00)00170-2, 2000.
Troccoli, A.: Seasonal climate forecasting, Meteorol. Appl., 17, 251–268,
https://doi.org/10.1002/met.184, 2010.
Trugman, A. T., Medvigy, D., Mankin, J. S., and Anderegg, W. R. L.: Soil
Moisture Stress as a Major Driver of Carbon Cycle Uncertainty, Geophys. Res.
Lett., 45, 6495–6503, https://doi.org/10.1029/2018GL078131, 2018.
Victorian Government Data Directory, Agriculture Victoria Research Division in the Department of Economic Development, Jobs, Transport, and Resources, Spatial Sciences Group: Victorian Land Use Information System 2016, Victorian Government Data Directory [data set], https://doi.org/10.4226/92/590abbe6ea3f1, 2018.
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the Community
Land Model, NCAR, https://doi.org/10.5065/PZ8F-F017, 2018.
Wang, B., Feng, P., Waters, C., Cleverly, J., Liu, D. L., and Yu, Q.:
Quantifying the impacts of pre-occurred ENSO signals on wheat yield variation using machine learning in Australia, Agr. Forest Meteorol., 291, 108043, https://doi.org/10.1016/j.agrformet.2020.108043, 2020.
Wang, Q., Tenhunen, J., Dinh, N., Reichstein, M., Otieno, D., Granier, A., and Pilegaard, K.: Evaluation of seasonal variation of MODIS derived leaf
area index at two European deciduous broadleaf forest sites, Remote Sens.
Environ., 96, 475–484, https://doi.org/10.1016/j.rse.2005.04.003, 2005.
Wang, Q. J., Shao, Y., Song, Y., Schepen, A., Robertson, D. E., Ryu, D., and
Pappenberger, F.: An evaluation of ECMWF SEAS5 seasonal climate forecasts
for Australia using a new forecast calibration algorithm, Environ. Model.
Softw., 122, 104550, https://doi.org/10.1016/j.envsoft.2019.104550, 2019.
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., Munch, J. C., Papen, H., Priesack, E., Schmid, H.
P., Steinbrecher, R., Rosenbaum, U., Teutsch, G., 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.
Zhao, H., Montzka, C., Baatz, R., Vereecken, H., and Franssen, H.-J. H.: The
Importance of Subsurface Processes in Land Surface Modeling over a Temperate
Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis, Remote Sens., 13, 3068, https://doi.org/10.3390/rs13163068, 2021.
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the...