03 Sep 2021
03 Sep 2021
Status: a revised version of this preprint is currently under review for the journal NHESS.

Predicting Drought and Subsidence Risks in France

Arthur Charpentier1, Molly Rose James2, and Hani Ali3 Arthur Charpentier et al.
  • 1UQAM, Université du Québec à Montréal (UQAM), Montréal (Québec), Canada
  • 2EURo Institut d’Actuariat (EURIA), Université de Brest, France
  • 3Willis Re, Paris, France

Abstract. The economic consequences of drought episodes are increasingly important, although they are often difficult to apprehend in part because of the complexity of the underlying mechanisms. In this article, we will study one of the consequences of drought, namely the risk of subsidence (or more specifically clay shrinkage induced subsidence), for which insurance has been mandatory in France for several decades. Using data obtained from several insurers, representing about a quarter of the household insurance market, over the past twenty years, we propose some statistical models to predict the frequency but also the intensity of these droughts, for insurers, showing that climate change will have probably major economic consequences on this risk. But even if we use more advanced models than standard regression-type models (here random forests to capture non linearity and cross effects), it is still difficult to predict the economic cost of subsidence claims, even if all geophysical and climatic information is available.

Arthur Charpentier et al.

Status: final response (author comments only)

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

Arthur Charpentier et al.

Arthur Charpentier et al.


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Short summary
Predicting consequences of drought episodes is complex, all the more when focusing on subsidence. We use twenty years of insurers data to derive a model to predict both intensity and severity of such events, using geophysical and climatic information, located in space and time.