Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-21-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-21-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2026
Research article |
| 06 Jan 2026
| 06 Jan 2026
Evaluation of microphysics and boundary layer schemes for simulating extreme rainfall events over Saudi Arabia using WRF-ARW
Rajesh Kumar Sahu
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Hamza Kunhu Bangalath
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Suleiman Mostamandi
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Jason Evans
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Paul A. Kucera
COMET Program, University Corporation for Atmospheric Research, Boulder, Colorado, USA
Hylke E. Beck
CORRESPONDING AUTHOR
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, https://doi.org/10.5194/hess-30-3399-2026, 2026
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Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Moetasim Ashfaq, Erika Coppola, Chris Lennard, Claas Teichmann, Deeksha Rastogi, Elias Massoud, Erasmo Buonomo, Eun-Soon Im, George Zittis, Jason P. Evans, Jesus Fernandez, Katherine J. Evans, Maria Leidinice da Silva, Marianna Adinolfi, Melissa Bukovsky, Rosmeri Porfirio da Rocha, Shabeh ul Hasson, Silvina A. Solman, Stefan Sobolowski, Sushant Das, Swen Brands, Tereza Cavazos, Thanh Ngo-Duc, and Xuejie Gao
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Producing reliable regional climate projections requires carefully selecting which global climate models drive them. We developed a transparent and reproducible framework to identify a balanced, representative subset of models for the Coordinated Regional Climate Downscaling Experiment, a major international effort to generate high-resolution climate projections worldwide. While designed for this initiative, the framework is broadly applicable to other models sub-selection efforts.
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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EGUsphere, https://doi.org/10.5194/egusphere-2025-6411, https://doi.org/10.5194/egusphere-2025-6411, 2026
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Hydrol. Earth Syst. Sci., 29, 4983–5003, https://doi.org/10.5194/hess-29-4983-2025, https://doi.org/10.5194/hess-29-4983-2025, 2025
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Anjana Devanand, Jason P. Evans, Andy J. Pitman, Sujan Pal, David Gochis, and Kevin Sampson
Hydrol. Earth Syst. Sci., 29, 4491–4513, https://doi.org/10.5194/hess-29-4491-2025, https://doi.org/10.5194/hess-29-4491-2025, 2025
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Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
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Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, Cinzia Mazzetti, and Luc Feyen
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Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
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We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
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Short summary
This study tests 36 combinations of microphysics and boundary layer schemes in the Weather Research and Forecasting model for extreme rainfall over Saudi Arabia. Using the Kling–Gupta Efficiency, the Yonsei University boundary layer with the Thompson microphysics performs best; the Morrison microphysics with the Mellor–Yamada–Nakanishi–Niino boundary layer ranks lowest. Mean temporal efficiency is 0.37, spatial efficiency is 0.26, revealing spatial prediction challenges in arid regions.
This study tests 36 combinations of microphysics and boundary layer schemes in the Weather...
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