Articles | Volume 16, issue 6
https://doi.org/10.5194/gmd-16-1601-2023
https://doi.org/10.5194/gmd-16-1601-2023
Review and perspective paper
 | 
21 Mar 2023
Review and perspective paper |  | 21 Mar 2023

Addressing challenges in uncertainty quantification: the case of geohazard assessments

Ibsen Chivata Cardenas, Terje Aven, and Roger Flage

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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 gmd-2022-210', Anonymous Referee #1, 26 Nov 2022
  • RC2: 'Comment on gmd-2022-210', Anonymous Referee #2, 11 Dec 2022
  • AC1: 'Comment on gmd-2022-210', Ibsen Chivata Cardenas, 24 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ibsen Chivata Cardenas on behalf of the Authors (24 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (24 Jan 2023) by Dan Lu
RR by Anthony Gruber (25 Jan 2023)
ED: Publish subject to technical corrections (02 Feb 2023) by Dan Lu
ED: Publish subject to technical corrections (06 Mar 2023) by David Ham (Executive editor)
AR by Ibsen Chivata Cardenas on behalf of the Authors (06 Mar 2023)  Author's response   Manuscript 
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Executive editor
This paper provides a review of uncertainty quantification, with particular application to geohazard modelling. It provides a review of the state of this field along with a large set of references to current and established literature in this area.
Short summary
We discuss challenges in uncertainty quantification for geohazard assessments. The challenges arise from limited data and the one-off nature of geohazard features. The challenges include the credibility of predictions, input uncertainty, and assumptions’ impact. Considerations to increase credibility of the quantification are provided. Crucial tasks in the quantification are the exhaustive scrutiny of the background knowledge coupled with the assessment of deviations of assumptions made.