Toward early warning of drought impacts: a framework for predicting drought impacts in the UK

Drought impact forecasting is essential for enhancing preparedness and mitigation strategies. However, identifying key predictors and achieving reliable predictions remains challenging. Previous studies have shown promise in developing indicator-impact relationships and yet these are often region- and impact type-specific. Here, we used data from the European Drought Impact Inventory (EDII, 19702012), and a wide range of meteorological and hydrological predictors, including the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and soil moisture indices (SSMI), to develop a generalized forecasting framework for predicting drought impacts in the UK across multiple lead times. We firstly compared multiple machine learning models for drought impact prediction and identified Random Forest (RF) as the most effective model. Our results show that RF delivers the highest accuracy for up to three months forecasts, with performance declining beyond six months, similar to trends observed in weather prediction models. At longer lead times, the model incorporates a broader set of predictors to maintain accuracy. Key findings highlight the importance of long-accumulation period drought indicators, particularly SPEI24, and deep layer soil moisture (SSMI L4), which were identified as the most influential predictors. A generalized model approach was used, aggregating drought impacts from various regions, and the model was validated using unseen datasets from within the UK, using parts of the EDII UK dataset held back from the training, confirming its robustness. A pilot application to a completely different country (Germany) highlights the potential for extrapolation to new domains. Gridded impact predictions were also developed, and successfully captured the spatial distribution of observed impacts, and a spatially explicit evaluation showed reasonable agreement between predicted and observed drought impacts. Although uncertainties persist, particularly for long lead times, our findings suggest that a generalized approach based on hydrometeorological indices provides an effective framework for operational drought impact forecasting, supporting early warning systems and decision-making in drought risk management.