Articles | Volume 25, issue 8
Research article
30 Aug 2021
Research article |  | 30 Aug 2021

Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models

Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil


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: Reconsider after major revisions (further review by editor and referees) (02 Apr 2021) by Fabrizio Fenicia
AR by Étienne Guilpart on behalf of the Authors (14 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (18 May 2021) by Fabrizio Fenicia
RR by Anonymous Referee #1 (08 Jun 2021)
ED: Publish as is (16 Jun 2021) by Fabrizio Fenicia
Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.