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 flexible, dynamic, and spatially explicit modelling framework for supporting impact-based early warning of precipitation-induced mass movement processes. The framework is tailored to three movement types: slides, flows, and falls. It integrates predisposing, preparatory, and triggering conditions, combining geo-environmental, meteorological, and exposure data to estimate daily impact potential for an Alpine core study area covering more than 91 000 km2 (Austria and South Tyrol), and subsequently explores the transferability of these relationships to the wider Alpine region, subject to important limitations. Using Generalized Additive Mixed Models (GAMMs), the approach captures non-linear relationships between impacts and predictors, ensuring interpretability and operational relevance. Further key elements 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 illustrating potential applicability through time-series predictive maps derived from hindcasting and “what-if” scenarios. Results indicate promising performance of slide- and flow-type models for the core study area, suggesting potential for operational application, while the fall-type model shows limited applicability for early warning, likely due to its lower sensitivity to short-term weather conditions. The primary contribution of this research lies in demonstrating a transferable and generalizable modelling framework, rather than delivering an operational regional warning system. Beyond early warning, the framework shows 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.