Articles | Volume 22, issue 8
Research article
03 Aug 2022
Research article |  | 03 Aug 2022

Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding

Mariana C. A. Clare, Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-74', Anonymous Referee #1, 18 Apr 2022
    • AC1: 'Reply on RC1', Mariana Clare, 23 Jun 2022
  • RC2: 'Comment on nhess-2022-74', Anonymous Referee #2, 01 Jun 2022
    • AC2: 'Reply on RC2', Mariana Clare, 23 Jun 2022

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) (24 Jun 2022) by Animesh Gain
AR by Mariana Clare on behalf of the Authors (24 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (03 Jul 2022) by Animesh Gain
RR by Anonymous Referee #1 (04 Jul 2022)
ED: Publish as is (11 Jul 2022) by Animesh Gain
AR by Mariana Clare on behalf of the Authors (13 Jul 2022)  Author's response   Manuscript 
Short summary
Assessing uncertainty is computationally expensive because it requires multiple runs of expensive models. We take the novel approach of assessing uncertainty from coastal flooding using a multilevel multifidelity (MLMF) method which combines the efficiency of less accurate models with the accuracy of more expensive models at different resolutions. This significantly reduces the computational cost but maintains accuracy, making previously unfeasible real-world studies possible.
Final-revised paper