Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 11
Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3549-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3549-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 11
Research article
 | 
12 Jun 2026
Research article |  | 12 Jun 2026

Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests

Taha-Abderrahman El Ouahabi, François Bourgin, Charles Perrin, and Vazken Andréassian

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Latest update: 12 Jun 2026
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Short summary
To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches. Among these, quantile random forests (QRF) are increasingly used for their balance between interpretability and performance. We develop a hydrologically informed QRF trained in a multi-site setting. Our results show that the regional QRF approach is beneficial, particularly in catchments where local information is insufficient.
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