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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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.
Gemma Coxon, Yanchen Zheng, Rafael Barbedo, Hollie Cooper, Felipe Fileni, Hayley J. Fowler, Matt Fry, Amy Green, Tom Gribbin, Helen Harfoot, Elizabeth Lewis, Germano Gondim Ribeiro Neto, Xiaobin Qiu, Saskia Salwey, and Doris E. Wendt
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-608, https://doi.org/10.5194/essd-2025-608, 2025
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Pierre Laluet, Chiara Corbari, Oscar Baez-Villanueva, Sophia Walther, Yongqiang Zhang, Joaquín Muñoz-Sabater, Gabriel B. Senay, Clément Albergel, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2025-5716, https://doi.org/10.5194/egusphere-2025-5716, 2025
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We examined how well global datasets estimate evapotranspiration, the water moving from land to the air, in irrigated areas. We compared six widely used products with irrigation maps, satellite estimates, and field measurements across different climate regions. Some datasets better capture irrigation effects, especially those using temperature and vegetation satellite data. This work helps guide dataset selection and supports better inclusion of irrigation in hydrological and climate models.
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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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
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Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
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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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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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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
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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...
