Coupling hydrological, geotechnical and machine learning models to enhance landslide prediction for an early warning system: application to Upper Garonne River Basin, Pyrenees, Spain

Abstract Landslide Early Warning Systems (LEWS) aim to anticipate slope failures by identifying critical hydrometeorological conditions, but their accuracy is often limited by simplified approaches that do not fully capture subsurface hydrological processes. To strengthen planning and management strategies for landslide hazard-prone areas in alpine regions, we developed the SCLAM model, which integrates the SNOW-17 snowmelt model, the coupled routing and excess storage (CREST) hydrological model, and a landslide model that combines the infinite slope method with a random forest approach. The SCLAM model was validated in the Upper Garonne River Basin (Pyrenees, Spain), a region that experienced multiple-occurrence landslide events on 18 June 2013 due to intense rainfall and snowmelt. The SCLAM model showed fast performance, with an average accuracy of 75% in identifying landslide initiation areas at a 30-m pixel scale. Furthermore, a key innovation of this study is the use of the baseflow excess variable from the CREST model as an indicator of subsurface hydrological conditions. As a result, we proposed a prototype LEWS capable of representing landslide-prone areas both spatially and temporally. The baseflow excess variable proved to be a reliable temporal indicator of slope instability, enabling the system to identify potential landslide triggers and reduce the number of false alerts. Spatial warnings were generated by aggregating landslide probabilities of failure within subbasins, using the percentage of unstable area to classify landslide warnings into four levels. Together, these components provide a practical foundation for an operational early warning system capable of supporting real-time decision-making.