Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5759–5779, 2018
https://doi.org/10.5194/hess-22-5759-2018
Hydrol. Earth Syst. Sci., 22, 5759–5779, 2018
https://doi.org/10.5194/hess-22-5759-2018

Research article 09 Nov 2018

Research article | 09 Nov 2018

Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting

Felipe Hernández and Xu Liang

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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) (22 Jan 2018) by Dimitri Solomatine
AR by Felipe Hernández on behalf of the Authors (12 Mar 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 Mar 2018) by Dimitri Solomatine
RR by Maurizio Mazzoleni (16 Apr 2018)
RR by Anonymous Referee #2 (23 Apr 2018)
ED: Publish subject to revisions (further review by editor and referees) (12 May 2018) by Dimitri Solomatine
AR by Felipe Hernández on behalf of the Authors (23 Jun 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (28 Jun 2018) by Dimitri Solomatine
RR by Anonymous Referee #2 (03 Aug 2018)
ED: Publish subject to minor revisions (review by editor) (23 Aug 2018) by Dimitri Solomatine
AR by Felipe Hernández on behalf of the Authors (29 Aug 2018)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (03 Sep 2018) by Dimitri Solomatine
AR by Felipe Hernández on behalf of the Authors (05 Sep 2018)  Author's response    Manuscript
ED: Publish as is (06 Sep 2018) by Dimitri Solomatine
AR by Felipe Hernández on behalf of the Authors (16 Sep 2018)  Author's response    Manuscript
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Short summary
Predicting floods requires first knowing the amount of water in the valleys, which is complicated because we cannot know for sure how much water there is in the soil. We created a unique system that combines the best methods to estimate these conditions accurately based on the observed water flow in the rivers and on detailed simulations of the valleys. Comparisons with popular methods show that our system can produce realistic predictions efficiently, even for very detailed river networks.