Preprints
https://doi.org/10.5194/gmd-2022-210
https://doi.org/10.5194/gmd-2022-210
Submitted as: review and perspective paper
17 Oct 2022
Submitted as: review and perspective paper | 17 Oct 2022
Status: this preprint is currently under review for the journal GMD.

Addressing challenges in uncertainty quantification. The case of geohazard assessments

Ibsen Chivata Cardenas, Terje Aven, and Roger Flage Ibsen Chivata Cardenas et al.
  • Department of Safety, Economics and Planning, University of Stavanger, Stavanger, 4021, Norway

Abstract. By describing critical tasks in quantifying uncertainty using geohazard models, we analyse some of the challenges involved. Under the often-seen condition of very limited data and despite the availability of recently developed sophistications to parameterise models, a major challenge that remains is the constraining of the many model parameters involved. However, challenges also lie in the credibility of predictions required in the assessments, the uncertainty of input quantities, and the conditional nature of the quantification on the choices and assumptions made by analysts. Addressing these challenges calls for more insightful approaches that are yet to be developed; however, clarifications and reinterpretations of some fundamental concepts together with practical simplifications may be required first, and these are discussed in this paper. The research aims at strengthening both the foundation of geohazard risk assessments and its practice.

Ibsen Chivata Cardenas et al.

Status: open (until 29 Dec 2022)

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 reply

Ibsen Chivata Cardenas et al.

Ibsen Chivata Cardenas et al.

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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, assumptions’ impact. Considerations to increase credibility of the quantifications are provided. Crucial tasks in the quantifications are the exhaustive scrutiny of the background knowledge coupled to the assessment of deviations of assumptions made.