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 | |
| Publication status | Published - 30 Oct 2019 |
Funding
The research is supported by the ANYWHERE project (Grant Agreement No. 700099), which is funded within EU's Horizon 2020 research and innovation program http://www. anywhere-h2020.eu. The hydro-meteorological output came from the EFAS computational center, which is part of the Copernicus Emergency Management Service (EMS) and Early Warning Systems (EWS) funded by framework contract number 198702 of the European Commission. Finally, we acknowledge the Ministry of Science, Research and the Arts of the State of Baden-Württemberg for financing the DRIeR-project, which maintains EDII. This research is part of the Wageningen Institute for Environment and Climate Research (WIMEK-SENSE) and it supports the work of UNESCO EURO FRIEND-Water program and the IAHS Panta Rhei project Drought in the Anthropocene.