Articles | Volume 20, issue 6
Nat. Hazards Earth Syst. Sci., 20, 1595–1608, 2020

Special issue: Recent advances in drought and water scarcity monitoring,...

Nat. Hazards Earth Syst. Sci., 20, 1595–1608, 2020

Research article 04 Jun 2020

Research article | 04 Jun 2020

Skill of large-scale seasonal drought impact forecasts

Samuel J. Sutanto et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish as is (11 May 2020) by Athanasios Loukas
AR by Samuel Jonson Sutanto on behalf of the Authors (11 May 2020)  Author's response    Manuscript
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.
Final-revised paper