Articles | Volume 29, issue 22
https://doi.org/10.5194/hess-29-6419-2025
© 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/hess-29-6419-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2025
Research article |
| 18 Nov 2025
| 18 Nov 2025
A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Jülich, 52428, Germany
College of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China
Heye Reemt Bogena
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Johannes Keller
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Jülich, 52428, Germany
Bagher Bayat
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, 1983969411, Iran
Rahul Raj
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Harrie-Jan Hendricks-Franssen
Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Jülich, 52428, Germany
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Stefan Poll, Paul Rigor, Slavko Brdar, Ha Thi Minh Ho-Hagemann, Carl Hartick, Marco van Hulten, Ana Gonzalez-Nicolas, Johannes Keller, Daniel Caviedes-Voullieme, Harrie-Jan Hendricks-Franssen, Klaus Goergen, and Stefan Kollet
EGUsphere, https://doi.org/10.5194/egusphere-2025-5468, https://doi.org/10.5194/egusphere-2025-5468, 2025
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This paper presents TSMP2, a new version of an regional Earth system model that allows to simulate and analyze the complex interactions within terrestrial ecosystems from groundwater to atmosphere. TSMP2 links an atmospheric, a land surface model and an hydrological model through an external coupler and is fully open-source. We describe the TSMP2 model system, present the impact of coupling approaches, and outline our development strategy along with technical and performance aspects.
Loudi Yap, Jürgen Kusche, Bamidele Oloruntoba, Helena Gerdener, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2025-4600, https://doi.org/10.5194/egusphere-2025-4600, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Rainfall shifts in West Africa strongly affect the agricultural productivity, making it vital to understand how much water is stored in the soil. We investigated soil moisture from 2003 to 2019 using satellite, models and in-situ data. Results show that ESA CCI v0.81 tracks local conditions best, while CLM5.0 and GLWS2.0 capture broader climate patterns. By linking surface signals to deeper layers, we improved insight into root-zone water, helping to guide farming and water planning.
Anke Fluhrer, Martin J. Baur, María Piles, Bagher Bayat, Mehdi Rahmati, David Chaparro, Clémence Dubois, Florian M. Hellwig, Carsten Montzka, Angelika Kübert, Marlin M. Mueller, Isabel Augscheller, Francois Jonard, Konstantin Schellenberg, and Thomas Jagdhuber
Biogeosciences, 22, 3721–3746, https://doi.org/10.5194/bg-22-3721-2025, https://doi.org/10.5194/bg-22-3721-2025, 2025
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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).
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
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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.
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
Revised manuscript under review for ESSD
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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.
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
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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.
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
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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
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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.
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
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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.
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
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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
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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.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
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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.
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
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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
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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.
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
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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
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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.
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
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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
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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.
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
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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.
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
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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.
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
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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
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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
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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
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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
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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.
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
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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.
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
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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.
Cited articles
Ashby, S. F. and Falgout, R. D.: A Parallel Multigrid Preconditioned Conjugate Gradient Algorithm for Groundwater Flow Simulations, Nuclear Science and Engineering, 124, 145–159, https://doi.org/10.13182/nse96-a24230, 1996.
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.
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Monthly Weather Review, 139, 3887–3905, https://doi.org/10.1175/mwr-d-10-05013.1, 2011.
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, International Journal of Applied Earth Observation and Geoinformation, 95, 102240, https://doi.org/10.1016/j.jag.2020.102240, 2021.
Beven, K.: A manifesto for the equifinality thesis, Journal of Hydrology, 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.
Bogena, H. and Ney, P.: Dataset of “COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, Forschungszentrum Jülich, https://doi.org/10.34731/x9s3-kr48, 2021.
Bogena, H. R., Herbst, M., Hake, J. F., Kunkel, R., Montzka,, C., Pütz, T., Vereecken, H., and Wendland, F. MOSYRUR: Water balance analysis in the Rur basin. Forschungszentrum Jülich, Jülich, Reihe Umwelt/Environment 52, 155 pp., https://juser.fz-juelich.de/record/45087 (last access: 1 August 2024), 2005.
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 Journal, 17, 180055, 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.
Bollmeyer, C., Keller, J. D., Ohlwein, C., Bentzien, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S., and Steinke, S.: Towards a high-resolution regional reanalysis for the European CORDEX domain, Quarterly Journal of the Royal Meteorological Society, 141(86), 1–15, https://doi.org/10.1002/qj.2486, 2015.
Borsche, M., Kaiser-Weiss, A. K., and Kaspar, F.: Wind speed variability between 10 and 116 m height from the regional reanalysis COSMO-REA6 compared to wind mast measurements over Northern Germany and the Netherlands, Adv. Sci. Res., 13, 151–161, https://doi.org/10.5194/asr-13-151-2016, 2016.
Botto, A., Belluco, E., and Camporese, M.: Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope, Hydrol. Earth Syst. Sci., 22, 4251–4266, https://doi.org/10.5194/hess-22-4251-2018, 2018.
Brandhorst, N. and Neuweiler, I.: Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model, Hydrol. Earth Syst. Sci., 27, 1301–1323, https://doi.org/10.5194/hess-27-1301-2023, 2023.
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow, Vadose Zone Journal, 8, 837–845, https://doi.org/10.2136/vzj2009.0018, 2009a.
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow, Water Resources Research, 45, 1–14, https://doi.org/10.1029/2008wr007031 2009b.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Climate Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018.
Chander, G., Markham, B. L., and Helder, D. L.: Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, 113, 893–903, https://doi.org/10.1016/j.rse.2009.01.007, 2009.
Chen, X. and Hu, Q.: Groundwater influences on soil moisture and surface evaporation, Journal of Hydrology, 297, 285–300, https://doi.org/10.1016/j.jhydrol.2004.04.019, 2004.
Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Advances in Water Resources, 29, 1107–1122, https://doi.org/10.1016/j.advwatres.2005.09.007, 2006.
Cooper, E., Blyth, E., Cooper, H., Ellis, R., Pinnington, E., and Dadson, S. J.: Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations, Hydrol. Earth Syst. Sci., 25, 2445–2458, https://doi.org/10.5194/hess-25-2445-2021, 2021.
Crow, W. T. and van den Berg, M. J.: An improved approach for estimating observation and model error parameters in soil moisture data assimilation, Water Resources Research, 46, https://doi.org/10.1029/2010WR009402, 2010.
Dan, B., Zheng, X., Wu, G., and Li, T.: Assimilating shallow soil moisture observations into land models with a water budget constraint, Hydrol. Earth Syst. Sci., 24, 5187–5201, https://doi.org/10.5194/hess-24-5187-2020, 2020.
De Lannoy, G. J. M., Reichle, R. H., Houser, P. R., Pauwels, V. R. N., and Verhoest, N. E. C.: Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter, Water Resources Research, 43, https://doi.org/10.1029/2006wr005449, 2007.
Dimitrova-Petrova, K., Rosolem, R., Soulsby, C., Wilkinson, M. E., Lilly, A., and Geris, J.: Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response, Journal of Hydrology, 601, 126659, https://doi.org/10.1016/j.jhydrol.2021.126659, 2021.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote Sensing of Environment, 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, Springer Berlin, Heidelberg, Heidelberg, https://doi.org/10.1007/978-3-642-03711-5, 2009.
Fersch, B., Francke, T., Heistermann, M., Schrön, M., Döpper, V., Jakobi, J., Baroni, G., Blume, T., Bogena, H., Budach, C., Gränzig, T., Förster, M., Güntner, A., Hendricks Franssen, H.-J., Kasner, M., Köhli, M., Kleinschmit, B., Kunstmann, H., Patil, A., Rasche, D., Scheiffele, L., Schmidt, U., Szulc-Seyfried, S., Weimar, J., Zacharias, S., Zreda, M., Heber, B., Kiese, R., Mares, V., Mollenhauer, H., Völksch, I., and Oswald, S.: A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany, Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, 2020.
Franz, T. E., Zreda, M., Rosolem, R., and Ferre, T. P. A.: A universal calibration function for determination of soil moisture with cosmic-ray neutrons, Hydrol. Earth Syst. Sci., 17, 453–460, https://doi.org/10.5194/hess-17-453-2013, 2013.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Quarterly Journal of the Royal Meteorological Society, 723–757, https://doi.org/10.1002/qj.49712555417, 1999.
Gasper, F., Goergen, K., Shrestha, P., Sulis, M., Rihani, J., Geimer, M., and Kollet, S.: Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q), Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, 2014.
Gebler, S., Kurtz, W., Pauwels, V. R. N., Kollet, S. J., Vereecken, H., and Hendricks Franssen, H. J.: Assimilation of High-Resolution Soil Moisture Data Into an Integrated Terrestrial Model for a Small-Scale Head-Water Catchment, Water Resources Research, 55, 10358–10385, https://doi.org/10.1029/2018wr024658, 2019.
Geologischer Dienst NRW: Informationssystem Bodenkarte von Nordrhein-Westfalen 1:50.000 (IS BK 50), Geologischer Dienst NRW [data set], https://gdk.gdi-de.org/geonetwork/srv/api/records/D6775754-266F-4396-A22B-14E1E4096F59 (last access: 1 August 2024), 2009.
Geologischer Dienst NRW: Informationssystem Hydrogeologische Karte von Nordrhein-Westfalen 1:100.000 (IS HK 100 DS), Geologischer Dienst NRW [data set], https://gdk.gdi-de.org/geonetwork/srv/api/records/9FBF9F0A-5D86-4691-8873-D3FC0099BDC6 (last access: 1 August 2024), 2011.
Gleeson, T., Wagener, T., Döll, P., Zipper, S. C., West, C., Wada, Y., Taylor, R., Scanlon, B., Rosolem, R., Rahman, S., Oshinlaja, N., Maxwell, R., Lo, M.-H., Kim, H., Hill, M., Hartmann, A., Fogg, G., Famiglietti, J. S., Ducharne, A., de Graaf, I., Cuthbert, M., Condon, L., Bresciani, E., and Bierkens, M. F. P.: GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models, Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, 2021.
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter, Monthly Weather Review, 2776–2790, 2001.
Han, X., Franssen, H.-J. H., Rosolem, R., Jin, R., Li, X., and Vereecken, H.: Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China, Hydrol. Earth Syst. Sci., 19, 615–629, https://doi.org/10.5194/hess-19-615-2015, 2015.
Hartick, C., Poll, S., Keller, J., Wagner, N., Benke, J., Caviedes-Voullième, D., Hendricks-Franssen, H.-J., and Kollet, S.: TSMP (1.4.0), Zenodo [code], https://doi.org/10.5281/zenodo.8283716, 2023.
Hendricks Franssen, H. J. and Kinzelbach, W.: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem, Water Resources Research, 44, 1–22, https://doi.org/10.1029/2007wr006505, 2008.
Hendricks Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Bauser, G., Stauffer, F., Müller, R., and Kinzelbach, W.: Operational real-time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream-aquifer interaction and parameter updating, Water Resources Research, 47, https://doi.org/10.1029/2010wr009480, 2011.
Herrera, P. A., Marazuela, M. A., and Hofmann, T.: Parameter estimation and uncertainty analysis in hydrological modeling, WIREs Water, 9, e1569, https://doi.org/10.1002/wat2.1569, 2022.
Hostache, R., Rains, D., Mallick, K., Chini, M., Pelich, R., Lievens, H., Fenicia, F., Corato, G., Verhoest, N. E. C., and Matgen, P.: Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case, Hydrol. Earth Syst. Sci., 24, 4793–4812, https://doi.org/10.5194/hess-24-4793-2020, 2020.
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble Kalman Filter Technique, Monthly Weather Review, 796–811, 1998.
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation, Monthly Weather Review, 144, 4489–4532, https://doi.org/10.1175/MWR-D-15-0440.1, 2016.
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 Resources Research, 58, https://doi.org/10.1029/2021wr031549, 2022.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, CGIAR-CSI SRTM 90 m Database, http://srtm.csi.cgiar.org (last access: 1 August 2024), 2008.
Jones, J. E. and Woodward, C. S.: Newton-Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems, Advances in Water Resources, 24, 763–774, https://doi.org/10.1016/S0309-1708(00)00075-0, 2001.
Khaki, M., Schumacher, M., Forootan, E., Kuhn, M., Awange, J. L., and van Dijk, A. I. J. M.: Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization, Advances in Water Resources, 108, 99–112, https://doi.org/10.1016/j.advwatres.2017.07.024, 2017.
Köhli, M., Schron, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S.: Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons, Water Resources Research, 5772–5790, https://doi.org/10.1002/2015WR017169, 2015.
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Advances in Water Resources, 29, 945–958, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006.
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model, Water Resources Research, 44, 1–18, https://doi.org/10.1029/2007wr006004, 2008.
Kollet, S. J., Maxwell, R. M., Woodward, C. S., Smith, S., Vanderborght, J., Vereecken, H., and Simmer, C.: Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources, Water Resources Research, 46, https://doi.org/10.1029/2009wr008730, 2010.
Kollet, S., Gasper, F., Brdar, S., Goergen, K., Hendricks-Franssen, H.-J., Keune, J., Kurtz, W., Küll, V., Pappenberger, F., Poll, S., Trömel, S., Shrestha, P., Simmer, C., and Sulis, M.: Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial Systems Modeling Platform (v1.1.0), Water, 10, 1–17, https://doi.org/10.3390/w10111697, 2018.
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and Hendricks Franssen, H.-J.: TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model, Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, 2016.
Kwon, Y., Jun, S., Kim, E., Seol, K. H., Hong, S., Kwon, I. H., Kang, J. H., and Kim, H.: Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies, Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4871, 2024.
Li, F., Bogena, H. R., Bayat, B., Kurtz, W., and Franssen, a. H.-J. H.: Can a sparse network of cosmic ray neutron sensors improve soil moisture and evapotranspiration estimation at the larger catchment scale?, Water Resources Research, https://doi.org/10.1029/2023WR035056, 2023a.
Li, F., Kurtz, W., Hung, C. P., Vereecken, H., and Hendricks Franssen, H.-J.: Water table depth assimilation in integrated terrestrial system models at the larger catchment scale, Frontiers in Water, 5, https://doi.org/10.3389/frwa.2023.1150999, 2023b.
Lighthill, M. J. and Whitham, G. B.: On kinematic waves I. Flood movement in long rivers, Proceedings of the Royal Society of London. Series A. Mathematical, Physical and Engineering Sciences, 229, 281–316, https://doi.org/10.1098/rspa.1955.0088, 1955.
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012.
Maxwell, R. M.: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling, Advances in Water Resources, 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016.
Maxwell, R. M., Chow, F. K., and Kollet, S. J.: The groundwater–land-surface–atmosphere connection: Soil moisture effects on the atmospheric boundary layer in fully-coupled simulations, Advances in Water Resources, 30, 2447–2466, https://doi.org/10.1016/j.advwatres.2007.05.018, 2007.
McLaughlin, D.: An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering, Advances in Water Resources, 25, 1275–1286, https://doi.org/10.1016/S0309-1708(02)00055-6, 2002.
Montzka, C., Canty, M., Kunkel, R., Menz, G., Vereecken, H., and Wendland, F.: Modelling the water balance of a mesoscale catchment basin using remotely sensed land cover data, Journal of Hydrology, 353, 322–334, https://doi.org/10.1016/j.jhydrol.2008.02.018, 2008.
Montzka, C., Bayat, B., Tewes, A., Mengen, D., and Vereecken, H.: Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany), Frontiers in Forests and Global Change, 4, https://doi.org/10.3389/ffgc.2021.667151, 2021.
Mwangi, S., Zeng, Y., Montzka, C., Yu, L., and Su, Z.: Assimilation of Cosmic-Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau, Journal of Geophysical Research: Atmospheres, 125, https://doi.org/10.1029/2019jd031529, 2020.
Nerger, L., Hiller, W., and Oter, J. S.: PDAF-The Parallel Data Assimilation Framework Experiences with Kalman Filtering, Experiences with Kalman Filtering, 63–83, https://doi.org/10.1142/9789812701831_0006, 2005.
Nicolai-Shaw, N., Hirschi, M., Mittelbach, H., and Seneviratne, S. I.: Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data, Journal of Geophysical Research: Atmospheres, 120, 9955–9964, https://doi.org/10.1002/2015jd023305, 2015.
Oleson, K., Yj, D., Gb, B., Bosilovich, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G.-Y., Thornton, P., Vertenstein, M., Yang, Z.-L., and Xb, Z.: Technical Description of the Community Land Model (CLM), University Corporation for Atmospheric Research, 174 pp., https://doi.org/10.5065/D6N877R0, 2004.
Oleson, K. W., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S., Dai, A., and Qian, T.: Improvements to the Community Land Model and their impact on the hydrological cycle, Journal of Geophysical Research: Biogeosciences, 113, 1–26, https://doi.org/10.1029/2007jg000563, 2008.
Pano, P. The European soil database GEO: Connexion, 5, 32–33, European Commission- DG JRC [data set], https://esdac.jrc.ec.europa.eu/content/european-soil-database-v20-vector-and-attribute-data (last access: 1 August 2024), 2006.
Panzeri, M., Riva, M., Guadagnini, A., and Neuman, S. P.: Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow, Water Resources Research, 49, 1334–1344, https://doi.org/10.1002/wrcr.20113, 2013.
Panzeri, M., Riva, M., Guadagnini, A., and Neuman, S. P.: Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation, Advances in Water Resources, 66, 8–18, https://doi.org/10.1016/j.advwatres.2014.01.007, 2014.
Patil, A., Fersch, B., Hendricks Franssen, H.-J., and Kunstmann, H.: Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model, Frontiers in Water, 3, https://doi.org/10.3389/frwa.2021.729592, 2021.
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y., and Ranagalage, M.: Sentinel-2 Data for Land Cover/Use Mapping: A Review, Remote Sensing, 12, https://doi.org/10.3390/rs12142291, 2020.
Poulter, B., Currey, B., Calle, L., Shiklomanov, A. N., Amaral, C. H., Brookshire, E. N. J., Campbell, P., Chlus, A., Cawse-Nicholson, K., Huemmrich, F., Miller, C. E., Miner, K., Pierrat, Z., Raiho, A. M., Schimel, D., Serbin, S., Smith, W. K., Stavros, N., Stutz, J., Townsend, P., Thompson, D. R., and Zhang, Z.: Simulating Global Dynamic Surface Reflectances for Imaging Spectroscopy Spaceborne Missions: LPJ-PROSAIL, Journal of Geophysical Research: Biogeosciences, 128, https://doi.org/10.1029/2022jg006935, 2023.
Reichle, R. H., McLaughlin, D. B. , and Entekhabi, D.: Hydrologic Data Assimilation With the Ensemble Kalman Filter, Monthly Weather Review, 130, 103–114, https://doi.org/10.1175/1520-0493(2002)130<0103:hdawte>2, 2002.
Reichle, R. H., Kumar, S. V., Mahanama, S. P. P., Koster, R. D., and Liu, Q.: Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models, Journal of Hydrometeorology, 11, 1103–1122, https://doi.org/10.1175/2010jhm1262.1, 2010.
Richards, L.: Capillary Conduction of Liquids Through Porous Mediums, Journal of Applied Physics, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Scanlon, B. R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R. Q., Jobbagy, E., Kebede, S., Kolusu, S. R., Konikow, L. F., Long, D., Mekonnen, M., Schmied, H. M., Mukherjee, A., MacDonald, A., Reedy, R. C., Shamsudduha, M., Simmons, C. T., Sun, A., Taylor, R. G., Villholth, K. G., Vörösmarty, C. J., and Zheng, C.: Global water resources and the role of groundwater in a resilient water future, Nature Reviews Earth & Environment, 4, 87–101, https://doi.org/10.1038/s43017-022-00378-6, 2023.
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.: Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, Journal of Hydrology, 251, 163–176, https://doi.org/10.1016/s0022-1694(01)00466-8, 2001.
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.
Sehgal, V., Mohanty, B. P., and Reichle, R. H.: Rootzone Soil Moisture Dynamics Using Terrestrial Water-Energy Coupling, Geophysical Research Letters, 51, https://doi.org/10.1029/2024gl110342, 2024.
Seo, E., Lee, M.-I., and Reichle, R. H.: Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter, Remote Sensing of Environment, 253, 112222, https://doi.org/10.1016/j.rse.2020.112222, 2021.
Shams Eddin, M. H. and Gall, J.: Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation, Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, 2024.
Shen, W., Lin, Z., Qin, Z., and Li, J.: Development and preliminary validation of a land surface image assimilation system based on the Common Land Model, Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, 2024.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment, Water Resources Research, 50, 706–724, https://doi.org/10.1002/2013wr014070, 2014.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment, Advances in Water Resources, 83, 421–427, https://doi.org/10.1016/j.advwatres.2015.06.009, 2015.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow, Monthly Weather Review, 142, 3466–3483, https://doi.org/10.1175/mwr-d-14-00029.1, 2014.
Shukla, S., Meshesha, T. W., Sen, I. S., Bol, R., Bogena, H., and Wang, J.: Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany, Sustainability, 15, 9811, https://doi.org/10.3390/su15129811, 2023.
Shuttleworth, J., Rosolem, R., Zreda, M., and Franz, T.: The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation, Hydrol. Earth Syst. Sci., 17, 3205–3217, https://doi.org/10.5194/hess-17-3205-2013, 2013.
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.
Tang, Q., Delottier, H., Kurtz, W., Nerger, L., Schilling, O. S., and Brunner, P.: HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model, Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, 2024.
Tangdamrongsub, N., Dong, J., and Shellito, P.: Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses, Water, 14, 621, https://doi.org/10.3390/w14040621, 2022.
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Chang., 5, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019.
Tong, J., Hu, B. X., and Yang, J.: Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field, Stochastic Environmental Research and Risk Assessment, 26, 467–478, https://doi.org/10.1007/s00477-011-0534-0, 2011.
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013.
Verrelst, J., Dethier, S., Rivera, J. P., Munoz-Mari, J., Camps-Valls, G., and Moreno, J.: Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval, IEEE Geoscience and Remote Sensing Letters, 13, 1012–1016, https://doi.org/10.1109/LGRS.2016.2560799, 2016.
Waldhoff, G. and Lussem, U.: Preliminary land use classification of 2015 for the Rur catchment, TR32DB [data set], https://doi.org/10.5880/TR32DB.14, 2015.
Weiss, M. and Baret, F.: S2ToolBox Level 2 products: LAI, FAPAR, FCOVER, Institut National de la Recherche Agronomique (INRA), Avignon, France, https://step.esa.int/docs/extra/ATBD_S2ToolBox_V2.0.pdf (last access: 1 August 2024), 2020.
Xu, T., Valocchi, A. J., Ye, M., Liang, F., and Lin, Y. F.: Bayesian calibration of groundwater models with input data uncertainty, Water Resources Research, 53, 3224–3245, https://doi.org/10.1002/2016wr019512, 2017.
Yamamoto, J. K.: On unbiased backtransform of lognormal kriging estimates, Computational Geosciences, 11, 219–234, https://doi.org/10.1007/s10596-007-9046-x, 2007.
Xue, Y., Houser, P. R., Maggioni, V., Mei, Y., Kumar, S. V., and Yoon, Y.: Evaluation of High Mountain Asia-Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper-Resolution Terrestrial Modeling System, Journal of Geophysical Research: Atmospheres, 126, https://doi.org/10.1029/2020jd034131, 2021.
Zafarmomen, N., Alizadeh, H., Bayat, M., Ehtiat, M., and Moradkhani, H.: Assimilation of Sentinel-Based Leaf Area Index for Modeling Surface-Ground Water Interactions in Irrigation Districts, Water Resources Research, 60, https://doi.org/10.1029/2023wr036080, 2024.
Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., and Refsgaard, J. C.: Multivariate hydrological data assimilation of soil moisture and groundwater head, Hydrol. Earth Syst. Sci., 20, 4341–4357, https://doi.org/10.5194/hess-20-4341-2016, 2016.
Zhang, H., Kurtz, W., Kollet, S., Vereecken, H., and Franssen, H.-J. H.: Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model, Advances in Water Resources, 111, 224–238, https://doi.org/10.1016/j.advwatres.2017.11.003, 2018.
Zhang, Y. and Schaap, M. G.: Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3), Journal of Hydrology, 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017.
Zhou, J., Wu, Z., Crow, W. T., Dong, J., and He, H.: Improving Spatial Patterns Prior to Land Surface Data Assimilation via Model Calibration Using SMAP Surface Soil Moisture Data, Water Resources Research, 56, https://doi.org/10.1029/2020wr027770, 2020.
Zhou, J., Crow, W. T., Wu, Z., Dong, J., He, H., and Feng, H.: Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information, Remote Sensing of Environment, 280, https://doi.org/10.1016/j.rse.2022.113161, 2022.
Zreda, M., Desilets, D., Ferré, T. P. A., and Scott, R. L.: Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons, Geophysical Research Letters, 35, https://doi.org/10.1029/2008gl035655, 2008.
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
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.
We developed a new method to improve hydrological modeling by jointly using soil moisture and...
