Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Abstract

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors. While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent, integrating spatio-temporal information for dynamic large-area landslide prediction remains a challenge. The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data. Unlike previous studies focusing on space–time landslide modelling, it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results, while ensuring interpretable outcomes. It introduces also other noteworthy innovations, such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.

The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol, Italy (7400 km²) within well-investigated terrain. Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model. Model relationships are then interpreted based on variable importance and partial effect plots, while predictive performance is evaluated through various cross-validation techniques. Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both, the true positive rate (correctly predicted landslides) and the false positive rate (precipitation periods misclassified as landslide-inducing conditions). The resulting dynamic maps directly visualize landslide threshold exceedance. The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge. Notably, the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions. The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context. In the currently evolving field of space–time landslide modelling, we recommend focusing on data error handling, model interpretability, and geomorphic plausibility, rather than allocating excessive resources to algorithm and case study comparisons.