Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4983-2025
© 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-29-4983-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2025
Research article |
| 08 Oct 2025
| 08 Oct 2025
Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion of satellite and model data
Xuetong Wang
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Raied S. Alharbi
Department of Civil Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
Oscar M. Baez-Villanueva
Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
Amy Green
School of Engineering, Newcastle University, Newcastle upon Tyne, UK
Tyndall Centre for Climate Change Research, Newcastle University, Newcastle upon Tyne, UK
Matthew F. McCabe
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Yoshihide Wada
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Albert I. J. M. Van Dijk
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Muhammad A. Abid
Atmospheric, Oceanic and Planetary Physics (AOPP), Department of Physics, University of Oxford, Oxford, UK
National Centre for Atmospheric Science (NCAS), Leeds, UK
Hylke E. Beck
CORRESPONDING AUTHOR
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
GloH2O LLC, Princeton, NJ, USA
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Earth Syst. Sci. Data, 18, 4345–4371, https://doi.org/10.5194/essd-18-4345-2026, https://doi.org/10.5194/essd-18-4345-2026, 2026
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We present the second version of a large-sample catchment hydrology dataset for Great Britain. The dataset collates (1) climate, river flow and groundwater timeseries at hourly to monthly timescales, (2) catchment attributes characterising topography, climate, streamflow, land cover, soils, hydrogeology and human influences, and (3) catchment boundaries for 671 catchments across Great Britain. The dataset is publicly available to use in a wide range of environmental and modelling analyses.
Francesco Avanzi, Hans Lievens, Michael Matiu, Paolo Filippucci, Oscar M. Baez Villanueva, Simone Gabellani, Fabio Delogu, Lorenzo Alfieri, Andrea Libertino, Pere Quintana-Seguì, Diego G. Miralles, Luca Brocca, Christian Massari, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2851, https://doi.org/10.5194/egusphere-2026-2851, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
Oscar M. Baez-Villanueva, Alfredo Crespo-Otero, Sara Modanesi, Pierre Laluet, Sergio Vicente-Serrano, Jaap Schellekens, Jacopo Dari, Hylke E. Beck, Wouter Dorigo, Christian Massari, Chiara Corbari, Joppe Massant, Kwint Delbare, Olivier Bonte, Aaron Boone, Diego Fernández-Prieto, and Diego G. Miralles
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Pierre Laluet, Chiara Corbari, Oscar Baez-Villanueva, Sophia Walther, Yongqiang Zhang, Joaquín Muñoz-Sabater, Clément Albergel, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 30, 1779–1799, https://doi.org/10.5194/hess-30-1779-2026, https://doi.org/10.5194/hess-30-1779-2026, 2026
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Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
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Robert Reinecke, Tanjila Akhter, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Wim Thiery, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
Geosci. Model Dev., 19, 523–542, https://doi.org/10.5194/gmd-19-523-2026, https://doi.org/10.5194/gmd-19-523-2026, 2026
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Taher Kahil, Safa Baccour, Julian Joseph, Reetik Sahu, Peter Burek, Jia Yi Ng, Samar Asad, Dor Fridman, Jose Albiac, Frank A. Ward, and Yoshihide Wada
Geosci. Model Dev., 18, 7987–8015, https://doi.org/10.5194/gmd-18-7987-2025, https://doi.org/10.5194/gmd-18-7987-2025, 2025
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This study presents the development of the global version of the ECHO hydro-economic model for assessing the economic and environmental performance of water management options. This improved version covers a large number of basins worldwide, includes a detailed representation of irrigated agriculture, and accounts for economic benefits and costs of water use. Results of this study demonstrates the capacity of ECHO-Global to address emerging water-related research and practical questions.
Fan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev., 18, 6195–6217, https://doi.org/10.5194/gmd-18-6195-2025, https://doi.org/10.5194/gmd-18-6195-2025, 2025
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Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.
Mateo Barco Largo, Meshal Alarifi, Sami D. Almalki, Shauna K. Rees, Benjamin P. Y.-H. Lee, Ahmed H. Mohamed, Abdalsamad Aldabaa, Kaoru Kakinuma, and Yoshihide Wada
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 183–188, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-183-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-183-2025, 2025
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Amy C. Green, Chris Kilsby, and András Bárdossy
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Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Diego G. Miralles, Hylke E. Beck, Jonatan F. Siegmund, Camila Alvarez-Garreton, Koen Verbist, René Garreaud, Juan Pablo Boisier, and Mauricio Galleguillos
Hydrol. Earth Syst. Sci., 28, 1415–1439, https://doi.org/10.5194/hess-28-1415-2024, https://doi.org/10.5194/hess-28-1415-2024, 2024
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Various drought indices exist, but there is no consensus on which index to use to assess streamflow droughts. This study addresses meteorological, soil moisture, and snow indices along with their temporal scales to assess streamflow drought across hydrologically diverse catchments. Using data from 100 Chilean catchments, findings suggest that there is not a single drought index that can be used for all catchments and that snow-influenced areas require drought indices with larger temporal scales.
P. Rouault, D. Courault, G. Pouget, F. Flamain, R. Lopez-Lozano, C. Doussan, M. Debolini, and M. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1531–1536, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1531-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1531-2023, 2023
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, and Nguyen Xuan Thinh
Hydrol. Earth Syst. Sci., 25, 5805–5837, https://doi.org/10.5194/hess-25-5805-2021, https://doi.org/10.5194/hess-25-5805-2021, 2021
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Most rivers worldwide are ungauged, which hinders the sustainable management of water resources. Regionalisation methods use information from gauged rivers to estimate streamflow over ungauged ones. Through hydrological modelling, we assessed how the selection of precipitation products affects the performance of three regionalisation methods. We found that a precipitation product that provides the best results in hydrological modelling does not necessarily perform the best for regionalisation.
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Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.
Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product...
