Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach, optimized for initial water table conditions

Abstract

In landslide-prone areas the magnitude of events is related to recurring rainfall intensity. In a large sector of the Sannio Apennines (Southern Italy), predictive mapping of recurrent shallow landslides was undertaken by combining deterministic and probabilistic predictive approaches. This, with the aim to minimize the negative influence of the uniform distribution of the initial water table depth in steady condition that usually influence the theoretical instability resulting from the application of methods for large-scale estimation. The deterministic approach was performed by means of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability model to obtain triggering maps in multi-temporal transient pore-water pressures. The optimized physical modeling was validated by back-analysis on large-magnitude landslide events which occurred in 2003 by means of the introduction of two cross-mapping correlation indexes. Subsequently, different predictive scenarios were proposed for different probabilistic return periods of the rainstorm events. The output data permitted the definition of a linear log regression curve to estimate the theoretical instability of the study area. This curve is defined as a function of cumulative precipitation, duration and return periods of the possible rainfall events.