Data-driven modeling of mass movement damage potential across the Alpine Space: A step toward impact-based early warning

European weather services are currently transitioning from traditional weather warnings to impact-based warnings (i.e., from "what the weather will be" to "what it will do"). To inform on what impacts can be expected, meteorological data must be integrated with data on potential hazards and elements at risk.

In this study, we developed three impact models on a daily scale to predict the impact of mass movement across the entire Alpine region (450,000 km²). The models focused on three major process classes (slide-types, flow-types, and fall-types) that impact infrastructure, such as buildings and roads. The study area was first divided into ~18,000 sub-basins, with potential process areas (PPAs) delineated in each basin using the angle of reach principle and random walk routing. PPAs enabled a tailored preparation of data describing environmental drivers (e.g., morphometry, land cover, lithology), dynamic meteorological data (e.g., antecedent precipitation, short-term precipitation, temperature effects), and exposure (e.g., number/density of buildings/roads within the PPA). The impact data consisted of precipitation-induced mass movements in Austria and northern Italy, covering more than 3600 basins. This training area was considered sufficiently representative of diverse Alpine environmental conditions to allow for spatial model transferability. Additional steps involving data sampling and the reclassification of predictor variables further supported the extension of model predictions beyond the training area. For example, lithology and land cover data was reclassified to ensure that each unit within the Alpine Space was adequately represented in the training data.

Generalized additive mixed models (GAMMs) with automated variable selection were used to link binary impact data to driving factors. Rigorous evaluations, including cross-validation and feature importance assessments, showed high predictive performance (e.g., AUROCs > 0.8) and plausible relationships between drivers and impacts. For example, impact probabilities for slide-types were modeled to be highest when intense short-term precipitation followed high antecedent rainfall, particularly in drier regions that are less "adapted" to such events. Further, a higher number/density of buildings or roads within PPAs also increased impact likelihood, while effects related to morphology, temperature, lithology, land cover, and seasonality further supported model plausibility. The applicability of the model is presented from three perspectives: (i) "What-if" scenarios to explore how hypothetical changes in drivers (e.g., precipitation) affect impact probabilities; (ii) hindcasting to validate model predictions for past events and demonstrate potential for impact-based early warning; and (iii) trend analysis, using ~6,000 daily hindcasts (2005–2021) to reveal spatio-temporal trends through the lens of climate change.

The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, “How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change” (X-RISK-CC).