Articles | Volume 24, issue 4
https://doi.org/10.5194/hess-24-2017-2020
© Author(s) 2020. 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-24-2017-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Apr 2020
Research article |
| 23 Apr 2020
| 23 Apr 2020
A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
Lionel Berthet
CORRESPONDING AUTHOR
DREAL Centre-Val de Loire, Loire Cher & Indre Flood Forecasting Service, Orléans, France
François Bourgin
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344
Bouguenais, France
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Charles Perrin
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Julie Viatgé
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Renaud Marty
DREAL Centre-Val de Loire, Loire Cher & Indre Flood Forecasting Service, Orléans, France
Olivier Piotte
Ministry for the Ecological and Inclusive Transition, SCHAPI, Toulouse, France
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Cited
15 citations as recorded by crossref.
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. https://doi.org/10.1175/JHM-D-21-0008.1
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
- Improving the structure of a hydrological model to forecast catchment response to intense rainfall P. Astagneau et al. https://doi.org/10.1080/27678490.2024.2341027
- Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. https://doi.org/10.5194/hess-26-1673-2022
- Reliability of Ensemble Climatological Forecasts Z. Huang et al. https://doi.org/10.1029/2023WR034942
- Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model D. McInerney et al. https://doi.org/10.1029/2020WR029317
- Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin D. Ba et al. https://doi.org/10.2478/johh-2023-0044
- Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction D. De León Pérez et al. https://doi.org/10.3390/w17202932
- A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes D. Biondi et al. https://doi.org/10.2166/nh.2021.045
- Vers la généralisation de la prévision hydrologique probabiliste au sein du réseau Vigicrues : estimation, évaluation et communication A. Belleudy et al. https://doi.org/10.1080/27678490.2024.2374079
- Exploring future water resources and uses considering water demand scenarios and climate change for the French Sèvre Nantaise basin L. Santos et al. https://doi.org/10.5194/hess-30-1915-2026
- RAPID-Hydrology: Pretraining Hydro-Meteorological Representations With Diffusion for Enhanced Causal Prediction X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3732149
- Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling V. Siqueira et al. https://doi.org/10.1016/j.jhydrol.2021.126520
- Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff J. Molina et al. https://doi.org/10.1016/j.jhydrol.2021.126501
- A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2023.129347
15 citations as recorded by crossref.
- On the implementation of post-processing of runoff forecast ensembles J. Skøien et al. https://doi.org/10.1175/JHM-D-21-0008.1
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
- Improving the structure of a hydrological model to forecast catchment response to intense rainfall P. Astagneau et al. https://doi.org/10.1080/27678490.2024.2341027
- Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. https://doi.org/10.5194/hess-26-1673-2022
- Reliability of Ensemble Climatological Forecasts Z. Huang et al. https://doi.org/10.1029/2023WR034942
- Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model D. McInerney et al. https://doi.org/10.1029/2020WR029317
- Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin D. Ba et al. https://doi.org/10.2478/johh-2023-0044
- Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction D. De León Pérez et al. https://doi.org/10.3390/w17202932
- A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes D. Biondi et al. https://doi.org/10.2166/nh.2021.045
- Vers la généralisation de la prévision hydrologique probabiliste au sein du réseau Vigicrues : estimation, évaluation et communication A. Belleudy et al. https://doi.org/10.1080/27678490.2024.2374079
- Exploring future water resources and uses considering water demand scenarios and climate change for the French Sèvre Nantaise basin L. Santos et al. https://doi.org/10.5194/hess-30-1915-2026
- RAPID-Hydrology: Pretraining Hydro-Meteorological Representations With Diffusion for Enhanced Causal Prediction X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3732149
- Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling V. Siqueira et al. https://doi.org/10.1016/j.jhydrol.2021.126520
- Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff J. Molina et al. https://doi.org/10.1016/j.jhydrol.2021.126501
- A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2023.129347
Saved (final revised paper)
Latest update: 13 Nov 2026
Short summary
An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. We present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows and considerable variability among catchments and across lead times.
An increasing number of flood forecasting services assess and communicate the uncertainty...
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