An Interactive Shiny Dashboard for Probabilistic Drought Risk Assessment in Europe Using Seasonal Forecast Ensembles of SPI and SSI

Drought events pose significant threats to water resources, agriculture, and socio-economic systems across Europe. Effective early warning systems require the integration of hazard forecasts with vulnerability and exposure information to support risk-based decision-making. Here, we present an interactive web-based dashboard developed using R Shiny that provides a probabilistic drought risk assessment at the NUTS-3 (Nomenclature of Territorial Units for Statistics) regional level across Europe. The dashboard integrates seasonal drought forecasts based on two complementary indicators: the Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSI) for meteorological and hydrological droughts respectively. Operational forecasts are derived from a 51-member ensemble based on the E-HYPE (European Hydrological Predictions for the Environment) hydrological model output, enabling a probabilistic assessment up to seven months ahead. By selecting multiple accumulation periods (SPI and SSI for 1, 3, 6, 9, 12, and 24 months) users can explore drought condition at different lead times and initialization months. A key innovation of our approach is the implementation of a dynamic risk matrix that combines drought severity and probability with exposure indicators. The risk matrix visualizes the intersection of forecast drought probability (categorized as 30%, 50%, and 70% exceedance thresholds) and population density, allowing users to identify regions where drought hazard coincides with high vulnerability. Additionally, the dashboard incorporates land use exposure data derived from CORINE (Coordination of Information on the Environment) Land Cover, providing information on urban, agricultural, and forest areas potentially affected by drought conditions. The interactive map interface allows users to select any NUTS-3 region to instantly visualize region-specific risk assessments, exposure profiles, and forecast statistics. This tool demonstrates the potential of combining ensemble-based seasonal forecasts with geospatial exposure data for operational drought risk management and supports decision-makers in water resource management, agriculture, and civil protection sectors.