Preprints
https://doi.org/10.5194/egusphere-2022-541
https://doi.org/10.5194/egusphere-2022-541
 
13 Jul 2022
13 Jul 2022

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: final response (author comments only)

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
    • CC1: 'Reply on RC1', Antoine Chambaz, 31 Aug 2022
      • AC1: 'Reply on CC1', Geoffrey Ecoto, 13 Sep 2022
    • AC3: 'Reply on RC1', Geoffrey Ecoto, 13 Sep 2022
  • RC2: 'Comment on egusphere-2022-541', Anonymous Referee #2, 07 Aug 2022
    • CC2: 'Reply on RC2', Antoine Chambaz, 31 Aug 2022
      • AC2: 'Reply on CC2', Geoffrey Ecoto, 13 Sep 2022
      • AC4: 'Reply on CC2', Geoffrey Ecoto, 13 Sep 2022
      • AC5: 'Reply on CC2', Geoffrey Ecoto, 13 Sep 2022
    • AC6: 'Reply on RC2', Geoffrey Ecoto, 13 Sep 2022

Geoffrey Ecoto and Antoine Chambaz

Geoffrey Ecoto and Antoine Chambaz

Viewed

Total article views: 512 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
371 112 29 512 4 4
  • HTML: 371
  • PDF: 112
  • XML: 29
  • Total: 512
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 13 Jul 2022)
Cumulative views and downloads (calculated since 13 Jul 2022)

Viewed (geographical distribution)

Total article views: 447 (including HTML, PDF, and XML) Thereof 447 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Nov 2022
Download
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.