Articles | Volume 24, issue 4
https://doi.org/10.5194/hess-24-2017-2020
https://doi.org/10.5194/hess-24-2017-2020
Research article
 | 
23 Apr 2020
Research article |  | 23 Apr 2020

A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

Lionel Berthet, François Bourgin, Charles Perrin, Julie Viatgé, Renaud Marty, and Olivier Piotte

Data sets

Summary sheets of watershed-scale hydroclimatic observed data for France P. Brigode, B. Génot, F. Lobligeois, and O. Delaigue https://doi.org/10.15454/UV01P1

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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.