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.
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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- Improving the structure of a hydrological model to forecast catchment response to intense rainfall P. Astagneau et al. 10.1080/27678490.2024.2341027
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- A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data Z. Huang et al. 10.1016/j.jhydrol.2023.129347
- Reliability of Ensemble Climatological Forecasts Z. Huang et al. 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. 10.1029/2020WR029317
- Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin D. Ba et al. 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. 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. 10.2166/nh.2021.045
Latest update: 21 Oct 2025
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...