Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-5013-2021
© Author(s) 2021. 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-25-5013-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
17 Sep 2021
Technical note |
| 17 Sep 2021
| 17 Sep 2021
Technical note: RAT – a robustness assessment test for calibrated and uncalibrated hydrological models
Pierre Nicolle
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
now at: Laboratoire Eau & Environnement, Université Gustave Eiffel, Nantes, France
Vazken Andréassian
CORRESPONDING AUTHOR
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
Paul Royer-Gaspard
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
Charles Perrin
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
Guillaume Thirel
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
Laurent Coron
EDF, DTG, Toulouse, France
Léonard Santos
Université Paris-Saclay, INRAE, UR HYCAR, Antony, France
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Cited
14 citations as recorded by crossref.
- Streamflow forecasting using Kolmogorov-Arnold network V. Varma & J. Patel https://doi.org/10.1088/2631-8695/addd66
- On the (im)possible validation of hydrogeological models V. Andréassian https://doi.org/10.5802/crgeos.142
- Robustness of hydrological models for simulating impacts of climate change on high and low streamflow A. Ten Berge et al. https://doi.org/10.1016/j.jhydrol.2025.133734
- Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
- The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. https://doi.org/10.1016/j.jhydrol.2023.129666
- Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning A. Husic et al. https://doi.org/10.5194/hess-29-4457-2025
- Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers L. Santos et al. https://doi.org/10.5194/hess-29-683-2025
- 140-year daily ensemble streamflow reconstructions over 661 catchments in France A. Devers et al. https://doi.org/10.5194/hess-28-3457-2024
- Improving Distributed Hydrological Model Robustness Through Multi‐Variable Calibration M. Gibbs et al. https://doi.org/10.1002/hyp.70525
- OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
- The added value of considering evapotranspiration fluxes in the calibration of a semi-distributed hydrological model J. Collignan et al. https://doi.org/10.1080/15715124.2026.2711679
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- A large transient multi-scenario multi-model ensemble of future streamflow and groundwater projections in France E. Sauquet et al. https://doi.org/10.5194/hess-30-2277-2026
- How to assess water quality change in temperate headwater catchments of western Europe under climate change: examples and perspectives C. Gascuel-Odoux et al. https://doi.org/10.5802/crgeos.147
14 citations as recorded by crossref.
- Streamflow forecasting using Kolmogorov-Arnold network V. Varma & J. Patel https://doi.org/10.1088/2631-8695/addd66
- On the (im)possible validation of hydrogeological models V. Andréassian https://doi.org/10.5802/crgeos.142
- Robustness of hydrological models for simulating impacts of climate change on high and low streamflow A. Ten Berge et al. https://doi.org/10.1016/j.jhydrol.2025.133734
- Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
- The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. https://doi.org/10.1016/j.jhydrol.2023.129666
- Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning A. Husic et al. https://doi.org/10.5194/hess-29-4457-2025
- Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers L. Santos et al. https://doi.org/10.5194/hess-29-683-2025
- 140-year daily ensemble streamflow reconstructions over 661 catchments in France A. Devers et al. https://doi.org/10.5194/hess-28-3457-2024
- Improving Distributed Hydrological Model Robustness Through Multi‐Variable Calibration M. Gibbs et al. https://doi.org/10.1002/hyp.70525
- OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
- The added value of considering evapotranspiration fluxes in the calibration of a semi-distributed hydrological model J. Collignan et al. https://doi.org/10.1080/15715124.2026.2711679
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- A large transient multi-scenario multi-model ensemble of future streamflow and groundwater projections in France E. Sauquet et al. https://doi.org/10.5194/hess-30-2277-2026
- How to assess water quality change in temperate headwater catchments of western Europe under climate change: examples and perspectives C. Gascuel-Odoux et al. https://doi.org/10.5802/crgeos.147
Saved (final revised paper)
Latest update: 25 Sep 2026
Short summary
In this note, a new method (RAT) is proposed to assess the robustness of hydrological models. The RAT method is particularly interesting because it does not require multiple calibrations (it is therefore applicable to uncalibrated models), and it can be used to determine whether a hydrological model may be safely used for climate change impact studies. Success at the robustness assessment test is a necessary (but not sufficient) condition of model robustness.
In this note, a new method (RAT) is proposed to assess the robustness of hydrological models....
