Abstract. Early warning systems play a crucial role in mitigating the impacts of severe weather events and related hazards. Traditional systems typically focus on meteorological forecasts and often do not account for the potential consequences that may follow, unlike impact-based approaches. In densely populated mountainous regions, such as the Alps, heavy precipitation frequently causes damaging mass movements. Since mass movement impacts ultimately result from a complex interplay of meteorological, geo-environmental, and socio-economic factors, warnings based solely on precipitation may have limited effectiveness. This study introduces a dynamic, spatially explicit modelling framework for impact-based early warning of precipitation-induced mass movement processes, tailored to three movement types: slides, flows, and falls. The framework integrates predisposing, preparatory, and triggering conditions, combining geo-environmental, meteorological, and exposure data to estimate daily impact potential across the Alpine region (450,000 km²). Using Generalized Additive Mixed Models (GAMMs), the approach captures non-linear relationships between impacts and predictors, ensuring interpretability and operational relevance. Beyond accounting for meteorological, geo-environmental, and exposure information, further key elements of the approach include incorporation of potential runout paths while maintaining a basin-based landscape representation, focusing model training on relevant terrain and time-periods to avoid trivial predictions, generating interpretable outputs, and demonstrating applicability through time-series predictive maps derived from hindcasting and "what-if" scenarios. Results highlight the strong operational potential of slide- and flow-type models, while the fall-type model exhibits limited usability for early warning, due to its low sensitivity to short-term weather conditions. Beyond early warning, the framework demonstrates broad applicability for analysing spatio-temporal patterns, conducting trend analyses, and assessing climate change impacts. This research advances the fields of landslide prediction and impact-based warning by providing a transferable and generalizable approach, offering actionable insights for disaster risk reduction and climate adaptation strategies.
Steger et al. (Wed,) studied this question.