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

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Title: Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests.
Authors: El Ouahabi, Taha-Abderrahman1 (AUTHOR) taha.elouahabi@gmail.com, Bourgin, François1 (AUTHOR) francois.bourgin@inrae.com, Perrin, Charles1 (AUTHOR), Andréassian, Vazken1 (AUTHOR)
Source: Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 11, p3549-3574. 26p.
Subject Terms: *Random forest algorithms, *Streamflow, *Machine learning, *Hydrological forecasting, *Watersheds, *Hydrologic models
Abstract: To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches that enable both enhanced predictive performance and hydrologically informed probabilistic streamflow predictions. Among these, random forests (RF) and their probabilistic extension, quantile random forests (QRF), are increasingly used for their balance between interpretability and performance. However, the application of QRF in regional post-processing settings remains unexplored. In this study, we develop a hydrologically informed QRF post-processor trained in a multi-site setting and compare its performance against a locally (at-site) trained QRF using probabilistic evaluation metrics. The QRF framework leverages simulations and state variables from the GR6J process-based hydrological model, along with readily available catchment descriptors, to predict daily streamflow uncertainty. Our results show that the regional QRF approach is beneficial for hydrological uncertainty estimation, particularly in catchments where local information is insufficient. The findings highlight that multi-site learning enables effective information transfer across hydrologically similar catchments and is especially advantageous for high-flow events. However, the selection of appropriate catchment descriptors is critical to achieving these benefits. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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An: 194731017
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  Data: Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests.
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  Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2026, Vol. 30 Issue 11, p3549-3574. 26p.
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  Data: *<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrological+forecasting%22">Hydrological forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches that enable both enhanced predictive performance and hydrologically informed probabilistic streamflow predictions. Among these, random forests (RF) and their probabilistic extension, quantile random forests (QRF), are increasingly used for their balance between interpretability and performance. However, the application of QRF in regional post-processing settings remains unexplored. In this study, we develop a hydrologically informed QRF post-processor trained in a multi-site setting and compare its performance against a locally (at-site) trained QRF using probabilistic evaluation metrics. The QRF framework leverages simulations and state variables from the GR6J process-based hydrological model, along with readily available catchment descriptors, to predict daily streamflow uncertainty. Our results show that the regional QRF approach is beneficial for hydrological uncertainty estimation, particularly in catchments where local information is insufficient. The findings highlight that multi-site learning enables effective information transfer across hydrologically similar catchments and is especially advantageous for high-flow events. However, the selection of appropriate catchment descriptors is critical to achieving these benefits. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.5194/hess-30-3549-2026
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      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 3549
    Subjects:
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Streamflow
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Hydrological forecasting
        Type: general
      – SubjectFull: Watersheds
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
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      – TitleFull: Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests.
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            NameFull: El Ouahabi, Taha-Abderrahman
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            NameFull: Bourgin, François
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            NameFull: Perrin, Charles
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            NameFull: Andréassian, Vazken
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          Dates:
            – D: 01
              M: 06
              Text: 2026
              Type: published
              Y: 2026
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              Value: 30
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              Value: 11
          Titles:
            – TitleFull: Hydrology & Earth System Sciences
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