Skill of large-scale seasonal drought impact forecasts

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

doi:https://doi.org/10.5194/nhess-20-1595-2020

Articles | Volume 20, issue 6

https://doi.org/10.5194/nhess-20-1595-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Recent advances in drought and water scarcity monitoring,...

https://doi.org/10.5194/nhess-20-1595-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 6

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

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Final revised paper (published on 04 Jun 2020)
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Preprint (discussion started on 09 Mar 2020)
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Skill of large-scale seasonal drought impact forecasts', Anonymous Referee #1, 04 Apr 2020
- AC1: 'Authors response R1', Samuel Jonson Sutanto, 11 May 2020
RC2: 'Skill of large-scale seasonal drought impact forecasts', Anonymous Referee #2, 07 Apr 2020
- AC2: 'Authors response R2', Samuel Jonson Sutanto, 11 May 2020

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

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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.