A Multi-Hazard Forecast-Driven Early Warning System for Hydrometeorological Hazards. Application to the Upper Garonne River Basin

Early Warning Systems (EWS) for hydro-meteorological hazards rely increasingly on integrated modelling frameworks capable of capturing complex surface and subsurface processes. Here we introduce STORMEW (Spatio-TempOral multi-hazaRd Model for Early Warning), a modular platform for multi-hazard spatio-temporal forecasting, analysis, and monitoring. In this study, STORMEW is configured to integrate SNOW-17, the CREST hydrological model, and a hybrid infinite-slope/Random Forest landslide model. The framework was applied in the Upper Garonne River Basin (Pyrenees, Spain) and forced with bias-corrected GEFS forecasts over a 10-year evaluation period (2010–2020).

GEFS-driven simulations effectively reproduced daily discharge (KGE: 0.53) and correctly identified the landslide initiation areas of the June 2013 event (ACC: 0.73). Building on these performance results, we implemented a consistent multi-hazard framework at the subbasin scale. Landslide hazard was derived from daily probability-of-failure (PoF) maps using the Percentage of Unstable Area (PUA), where cells with PoF > 0.5 were considered unstable and expert-defined PUA thresholds were used to classify four hazard categories. Flood hazard was likewise organised into four levels using subbasin-specific return periods of 2.5, 10, and 50 years computed from simulated daily discharge.

Under these criteria, the resulting classification showed minimal false alarms over the 2010–2020 period, correctly captured the June 2013 warning conditions, and discriminated high-flow events with no associated hazard (e.g., June 2018). Currently, the STORMEW system is implemented as a fully automated workflow that generates real-time flood and landslide warnings. To facilitate the interpretation of these outputs, a prototype web-based dashboard is being developed to visualize hazard dynamics in an operational context. Overall, this study demonstrates the capability of running a forecast-driven, multi-hazard EWS that links snow dynamics, hydrology, floods, and landslides for real-time early warning operations. Future work will explore the application of STORMEW in basins with differing climatic and hydrological conditions.