Scaling Properties of Rainfall-Induced Landslides Predicted by a Physically Based Model

TL;DR

This study demonstrates that a physically-based numerical model, TRIGRS, can effectively predict the scaling properties of rainfall-induced landslides, matching empirical size distributions and rainfall thresholds in a regional case study.

Contribution

The paper shows that TRIGRS can reproduce landslide size distributions and rainfall conditions, advancing understanding of landslide scaling mechanisms.

Findings

TRIGRS predicts landslide size frequency matching empirical data.

The model reproduces rainfall intensity-duration thresholds for landslides.

Results support the physical basis of landslide scaling properties.

Abstract

Natural landslides exhibit scaling properties revealed by power law relationships. These relationships include the frequency of the size (e.g., area, volume) of the landslides, and the rainfall conditions responsible for slope failures in a region. Reasons for the scaling behavior of landslides are poorly known. We investigate the possibility of using the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability analysis code (TRIGRS), a consolidated, physically-based, numerical model that describes the stability/instability conditions of natural slopes forced by rainfall, to determine the frequency statistics of the area of the unstable slopes and the rainfall intensity (I) - duration (D) conditions that result in landslides in a region. We apply TRIGRS in a portion of the Upper Tiber River Basin, Central Italy. The spatially distributed model predicts the stability/instability conditions of individual grid cells, given the local terrain and rainfall conditions. We run TRIGRS using multiple, synthetic rainfall histories, and we compare the modeling results with empirical evidences of the area of landslides and of the rainfall conditions that have caused landslides in the study area. Our findings revealed that TRIGRS is capable of reproducing the frequency of the size of the patches of terrain predicted as unstable by the model, which match the frequency size statistics of landslides in the study area, and the mean rainfall D, I conditions that result in unstable slopes in the study area, which match rainfall I-D thresholds for possible landslide occurrence. Our results are a step towards understanding the mechanisms that give rise to landslide scaling properties.