Articles | Volume 20, issue 6
https://doi.org/10.5194/nhess-20-1595-2020
https://doi.org/10.5194/nhess-20-1595-2020
Research article
 | 
04 Jun 2020
Research article |  | 04 Jun 2020

Skill of large-scale seasonal drought impact forecasts

Samuel J. Sutanto, Melati van der Weert, Veit Blauhut, and Henny A. J. Van Lanen

Viewed

Total article views: 3,545 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,575 877 93 3,545 345 118 130
  • HTML: 2,575
  • PDF: 877
  • XML: 93
  • Total: 3,545
  • Supplement: 345
  • BibTeX: 118
  • EndNote: 130
Views and downloads (calculated since 09 Mar 2020)
Cumulative views and downloads (calculated since 09 Mar 2020)

Viewed (geographical distribution)

Total article views: 3,545 (including HTML, PDF, and XML) Thereof 3,263 with geography defined and 282 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Oct 2025
Download
Short summary
Present-day drought early warning systems only provide information on drought hazard forecasts. Here, we have developed drought impact functions to forecast drought impacts up to 7 months ahead using machine learning techniques, logistic regression, and random forest. Our results show that random forest produces a higher-impact forecasting skill than logistic regression. For German county levels, drought impacts can be forecasted up to 4 months ahead using random forest.
Share
Altmetrics
Final-revised paper
Preprint