Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach

TLDR

Traditional landslide prediction methods rely on deterministic rainfall thresholds, but because slope stability depends on multiple factors, a probabilistic approach is more appropriate. This study proposes a Bayesian method to evaluate rainfall thresholds for landslides. The Bayesian model assigns a landslide probability (0–1) to each rainfall variable combination and was applied to the Emilia‑Romagna region using a historical archive of over 4,000 daily‑dated events. Results reveal that landsliding is strongly linked to rainfall duration, intensity, and total rainfall, with a sharp probability increase at specific duration‑intensity combinations indicating a real physical threshold, while antecedent rainfall is less influential.

Abstract

Various methods have been proposed in the literature to predict the rainfall conditions that are likely to trigger landslides in a given area. Most of these methods, however, only consider the rainfall events that resulted in landslides and provide deterministic thresholds with a single possible output (landslide or no‐landslide) for a given input (rainfall conditions). Such a deterministic view is not always suited to landslides. Slope stability, in fact, is not ruled by rainfall alone and failure conditions are commonly achieved with a combination of numerous relevant factors. When different outputs (landslide or no‐landslide) can be obtained for the same input a probabilistic approach is preferable. In this work we propose a new method for evaluating rainfall thresholds based on Bayesian probability. The method is simple, statistically rigorous, and returns a value of landslide probability (from 0 to 1) for each combination of the selected rainfall variables. The proposed approach was applied to the Emilia‐Romagna Region of Italy taking advantage of the historical landslide archive, which includes more than 4000 events for which the date of occurrence is known with daily accuracy. The results show that landsliding in the study area is strongly related to rainfall event parameters (duration, intensity, total rainfall) while antecedent rainfall seems to be less important. The distribution of landslide probability in the rainfall duration‐intensity shows an abrupt increase at certain duration‐intensity values which indicates a radical change of state of the system and suggests the existence of a real physical threshold.