Articles | Volume 22, issue 7
© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.
Predicting drought and subsidence risks in France
- Final revised paper (published on 21 Jul 2022)
- Preprint (discussion started on 03 Sep 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
: Report abuse
RC1: 'Comment on nhess-2021-214', Sien Kok, 24 Sep 2021
- AC2: 'Reply on RC1', Arthur Charpentier, 01 Feb 2022
RC2: 'Comment on nhess-2021-214', Anonymous Referee #2, 20 Dec 2021
- AC1: 'Reply on RC2', Arthur Charpentier, 01 Feb 2022
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (11 Apr 2022) by Joaquim G. Pinto
AR by Arthur Charpentier on behalf of the Authors (19 Apr 2022)  Author's response Author's tracked changes Manuscript
ED: Referee Nomination & Report Request started (07 May 2022) by Joaquim G. Pinto
RR by Sien Kok (16 May 2022)
ED: Publish subject to minor revisions (review by editor) (17 May 2022) by Joaquim G. Pinto
AR by Arthur Charpentier on behalf of the Authors (02 Jun 2022)  Author's response Author's tracked changes Manuscript
ED: Publish as is (03 Jul 2022) by Joaquim G. Pinto
ED: Publish as is (03 Jul 2022) by Joaquim G. Pinto(Executive Editor)
The content of the article is very novel and addresses a concrete societal problem: the difficulties in predicting building damage from subsidence, an increasingly costly climate risk for insurers. In some countries, such as France, this risk is insured via household policies. The methodology and results will be useful for the insurance industry in France, and may also be useful in other countries with similar established insurance products, as well as countries where insurance industry and policy makers investigate the possibly for developing insurance products for this risk. Overall the paper is well-structured. It compares various statistical models using a range of indicators to predict subsidence claims, calibrated against a historical dataset from insurance companies.
For detailed comments and suggestions, please see attached word file in zip folder.