Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1401-2024
https://doi.org/10.5194/nhess-24-1401-2024
Research article
 | 
24 Apr 2024
Research article |  | 24 Apr 2024

Scoring and ranking probabilistic seismic hazard models: an application based on macroseismic intensity data

Vera D'Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti

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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 nhess-2023-203', Dario Albarello, 08 Dec 2023
    • AC1: 'Reply on RC1', Vera D'Amico, 14 Jan 2024
  • RC2: 'Comment on nhess-2023-203', Anonymous Referee #2, 23 Dec 2023
    • AC2: 'Reply on RC2', Vera D'Amico, 14 Jan 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (09 Feb 2024) by Laurentiu Danciu
AR by Vera D'Amico on behalf of the Authors (19 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (26 Feb 2024) by Laurentiu Danciu
ED: Publish subject to technical corrections (03 Mar 2024) by Paolo Tarolli (Executive editor)
AR by Vera D'Amico on behalf of the Authors (07 Mar 2024)  Manuscript 
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Short summary
We propose a scoring strategy to rank multiple models/branches of a probabilistic seismic hazard analysis (PSHA) model that could be useful to consider specific requests from stakeholders responsible for seismic risk reduction actions. In fact, applications of PSHA often require sampling a few hazard curves from the model. The procedure is introduced through an application aimed to score and rank the branches of a recent Italian PSHA model according to their fit with macroseismic intensity data.
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