Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Shallow landslides represent potentially damaging processes in mountain areas worldwide.These geomorphic processes are usually caused by a combination of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7,400 km²). The Slope Unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques and tested potential applications. The analysis revealed that the best-performing model combines static ground conditions and two precipitation time windows: short-term cumulative precipitation prior to the landslide event and medium-term cumulative precipitation. We tested the model's predictive capabilities by predicting the dynamic landslide probabilities over hypothetical non-spatially explicit precipitation scenarios and historical precipitation associated with a heavy precipitation event on August 5th, 2016. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.