Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 5981–5999, 2021
https://doi.org/10.5194/hess-25-5981-2021
Hydrol. Earth Syst. Sci., 25, 5981–5999, 2021
https://doi.org/10.5194/hess-25-5981-2021

Research article 22 Nov 2021

Research article | 22 Nov 2021

Design flood estimation for global river networks based on machine learning models

Gang Zhao et al.

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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: Reconsider after major revisions (further review by editor and referees) (26 May 2021) by Luis Samaniego
AR by Gang Zhao on behalf of the Authors (26 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (30 Jun 2021) by Luis Samaniego
RR by Eric Gaume (23 Jul 2021)
RR by Dongyue Li (28 Jul 2021)
ED: Publish as is (28 Sep 2021) by Luis Samaniego
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Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.