Abstract
One of the most effective strategies to reduce the impacts of drought is by issuing a timely and targeted warning from month to seasons ahead to end users. Yet to accurately forecast the drought hazard on a sub-seasonal to seasonal time scale remains a challenge, and usually, meteorological drought is forecasted instead of hydrological drought, although the latter is more relevant for several impacted sectors. Therefore, we evaluate the hydro-meteorological drought forecast skill for the pan-European region using categorical drought classification method. The results show that the hydrological drought forecasts outperform the meteorological drought forecasts. Hydrological drought forecasts even show predictive power (area with perfect prediction > 50%) beyond two months ahead. Our study also concludes that dynamical forecasts, derived from seasonal forecasts, have higher predictability than ensemble streamflow predictions. The results suggest that further development of seasonal hydrological drought forecasting systems are beneficial, particularly important in the context of global warming, where drought hazard will become more frequent and severe in multiple regions in the world.
Highlights
Drought is one of the most severe weather-related natural hazards, which causes damage and losses comparable to other destructive hazards, such as floods, landslides, and earthquakes [1]
The results suggest that further development of seasonal hydrological drought forecasting systems are beneficial, important in the context of global warming, where drought hazard will become more frequent and severe in multiple regions in the world
This research shows the strengths of hydrological drought forecasts to predict drought in runoff and groundwater from one month up to several months ahead, which outperforms meteorological drought forecast, and complements conventional hydrological forecasts
Summary
Drought is one of the most severe weather-related natural hazards, which causes damage and losses comparable to other destructive hazards, such as floods, landslides, and earthquakes [1]. To reduce impacts of weather-related drought hazards, the development of drought Early Warning System (EWS) is of utmost important. Weather forecasts as the main component of the warning system have in the past lacked the skill to produce reliable forecasts for longer periods than days and weeks [2, 3]. Dynamical seasonal forecast systems based on numerical prediction models have become more skillful. These models generally have shown greater skill than statistical models [4]. Drought EWS modules, including a seasonal forecasting component, have been developed in some regions, such as the US, Europe, and Africa [5–9]
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