Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-3919-2026
https://doi.org/10.5194/nhess-26-3919-2026
Research article
 | 
19 Aug 2026
Research article |  | 19 Aug 2026

Investigating metamodeling capability to predict sea levels and marine flooding maps for early-warning systems: application on the Arcachon Lagoon (France)

Sophie Lecacheux, Jeremy Rohmer, Eva Membrado, Rodrigo Pedreros, Andrea Filippini, Deborah Idier, Servane Gueben-Vénière, Denis Paradis, Alice Dalphinet, and David Ayache

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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-2024-3615', Anonymous Referee #1, 12 Nov 2025
    • AC1: 'Reply on RC1', Sophie Lecacheux, 17 Mar 2026
  • RC2: 'Comment on egusphere-2024-3615', Anonymous Referee #2, 19 Feb 2026
    • AC2: 'Reply on RC2', Sophie Lecacheux, 17 Mar 2026

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) (21 Mar 2026) by Dung Tran
AR by Sophie Lecacheux on behalf of the Authors (04 May 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (07 May 2026) by Dung Tran
RR by Anonymous Referee #1 (11 May 2026)
RR by Antonis Chatzipavlis (27 Jun 2026)
ED: Publish subject to technical corrections (02 Jul 2026) by Dung Tran
AR by Sophie Lecacheux on behalf of the Authors (03 Jul 2026)  Manuscript 
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Short summary
This study comparer three data-driven methodologies to overcome the computational burden of numerical simulations for early warning purpose. They are all based on the statistical analysis of pre-calculated databases, to downscale total sea levels and predict marine flooding maps from offshore metocean forecasts. Conclusions highlight the relevance of metamodel-based approaches for fast prediction and the added value of precalculated databases during the prepardness phase.
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