Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2531-2024
https://doi.org/10.5194/hess-28-2531-2024
Research article
 | 
14 Jun 2024
Research article |  | 14 Jun 2024

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

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) (03 Apr 2024) by Silvia De Angeli
AR by David F. Muñoz on behalf of the Authors (03 Apr 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (11 Apr 2024) by Silvia De Angeli
RR by Anonymous Referee #2 (06 May 2024)
ED: Publish subject to technical corrections (07 May 2024) by Silvia De Angeli
ED: Publish subject to technical corrections (07 May 2024) by Thom Bogaard (Executive editor)
AR by David F. Muñoz on behalf of the Authors (07 May 2024)  Author's response   Manuscript 
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Short summary
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking their evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analyses.