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

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (17 Aug 2019) by Dimitri Solomatine
AR by Lionel Berthet on behalf of the Authors (26 Sep 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (04 Oct 2019) by Dimitri Solomatine
RR by Kolbjorn Engeland (29 Oct 2019)
RR by Anonymous Referee #1 (21 Nov 2019)
ED: Publish subject to revisions (further review by editor and referees) (03 Dec 2019) by Dimitri Solomatine
AR by Lionel Berthet on behalf of the Authors (13 Jan 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (22 Jan 2020) by Dimitri Solomatine
RR by Kolbjorn Engeland (26 Jan 2020)
RR by Anonymous Referee #1 (24 Feb 2020)
ED: Publish subject to technical corrections (15 Mar 2020) by Dimitri Solomatine
AR by Lionel Berthet on behalf of the Authors (21 Mar 2020)  Author's response   Manuscript 
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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.