Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1539-2024
https://doi.org/10.5194/hess-28-1539-2024
Research article
 | 
04 Apr 2024
Research article |  | 04 Apr 2024

Multi-model approach in a variable spatial framework for streamflow simulation

Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-569', Wouter Knoben, 02 May 2023
    • AC1: 'Reply on RC1', Cyril Thébault, 02 Jun 2023
  • RC2: 'Comment on egusphere-2023-569', Trine Jahr Hegdahl, 12 May 2023
    • AC2: 'Reply on RC2', Cyril Thébault, 02 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (17 Jun 2023) by Hilary McMillan
AR by Cyril Thébault on behalf of the Authors (13 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Nov 2023) by Hilary McMillan
RR by Wouter Knoben (06 Dec 2023)
RR by Trine Jahr Hegdahl (09 Dec 2023)
ED: Publish subject to minor revisions (review by editor) (21 Dec 2023) by Hilary McMillan
AR by Cyril Thébault on behalf of the Authors (20 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (15 Feb 2024) by Hilary McMillan
AR by Cyril Thébault on behalf of the Authors (18 Feb 2024)  Manuscript 
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Short summary
Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.