Preprints
https://doi.org/10.5194/egusphere-2022-541
https://doi.org/10.5194/egusphere-2022-541
 
13 Jul 2022
13 Jul 2022
Status: this preprint is open for discussion.

Forecasting the cost of drought events in France by super learning

Geoffrey Ecoto1,2, and Antoine Chambaz2, Geoffrey Ecoto and Antoine Chambaz
  • 1Caisse Centrale de Réassurance
  • 2Université Paris Cité, MAP5 (UMR CNRS 8145)
  • These authors contributed equally to this work.

Abstract. Drought events are the second most expensive type of natural disaster within the legal framework of the French natural disasters compensation scheme. In recent years, droughts have been remarkable in their geographical scale and intensity. We develop a new methodology to forecast the cost of a drought event in France. The methodology hinges on super learning and takes into account the complex dependence structure induced in the data by the spatial and temporal nature of drought events.

Geoffrey Ecoto and Antoine Chambaz

Status: open (until 24 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-541', Anonymous Referee #1, 30 Jul 2022 reply
  • RC2: 'Comment on egusphere-2022-541', Anonymous Referee #2, 07 Aug 2022 reply

Geoffrey Ecoto and Antoine Chambaz

Geoffrey Ecoto and Antoine Chambaz

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Short summary
Drought events are the second most expensive type of natural disaster within the legal framework of the French natural disasters compensation scheme. In recent years, droughts have been remarkable in their geographical scale and intensity. We develop a new methodology to forecast the cost of a drought event in France. The methodology hinges on super learning and takes into account the complex dependence structure induced in the data by the spatial and temporal nature of drought events.