Moving from drought hazard to impact forecasts

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

doi:10.1038/s41467-019-12840-z

Moving from drought hazard to impact forecasts

Samuel J. Sutanto^*, Melati van der Weert, Niko Wanders, Veit Blauhut, Henny A.J. Van Lanen

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database.

Original language	English
Article number	4945
Number of pages	7
Journal	Nature Communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-12840-z
Publication status	Published - 30 Oct 2019

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title = "Moving from drought hazard to impact forecasts",

abstract = "Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database.",

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PY - 2019/10/30

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Moving from drought hazard to impact forecasts

Abstract

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