Moving from drought hazard to impact forecasts
Abstract
Present-day drought early warning systems only provide the end-users information on the ongoing and forecasted drought hazard (e.g. precipitation and river flow deficit). However, information on the forecasted drought impacts, which is 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 taken from the European Drought Impact Inventory (EDII). Gridded meteorological observations of precipitation and evaporation were used to simulate runoff with a state-of-the-art hydrological model LISFLOOD. These observations and simulations then were used to define the historical drought hazard using the drought indices, such as the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), and the standardized runoff index (SRI). Results show that the skill of drought impact forecasts resembles the skill of drought hazard forecasts, which is in general up to 3-4 months in advance. Models, which were built with sufficient amount of reported drought impacts in a certain sector, are able to forecast drought impacts a few months ahead. This study also highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend institutions that provide operational drought early warnings to move from forecasted hazard only to forecasted drought impacts.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H12F..03S
- Keywords:
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- 1812 Drought;
- HYDROLOGY;
- 1817 Extreme events;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY;
- 1880 Water management;
- HYDROLOGY