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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-181
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-181
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 May 2019

Research article | 07 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

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

Lionel Berthet1, François Bourgin2, Charles Perrin3, Julie Viatgé3, Renaud Marty1, and Olivier Piotte4 Lionel Berthet et al.
  • 1DREAL Centre-Val de Loire, Loire Cher & Indre Flood Forecasting Service, Orléans, France
  • 2IFSTTAR, GERS, EE, Bouguenais, France
  • 3IRSTEA, HYCAR Research Unit, Antony, France
  • 4Ministry for the Ecological and Inclusive Transition, SCHAPI, Toulouse, France

Abstract. An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment setup is based on (i) a large set of catchments in France, (ii) the GRP rainfall-runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they used (log, Box–Cox and log–sinh) to account for heteroscedasticity. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality, were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation with a parameter between 0.1 and 0.3 can be a reasonable choice for flood forecasting.

Lionel Berthet et al.
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Lionel Berthet et al.
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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.
An increasing number of flood forecasting services assess and communicate the uncertainty...
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