A Regional-Scale Early Warning System for Rainfall-Induced Shallow Landslides Based on the Outputs of a Physically Based Model: Application to Cili County, China

This paper presents a new method for a regional-scale rainfall-induced landslide early warning system (LEWS) based on the outputs of the “Fast Shallow Landslide Assessment Model” (FSLAM), a physically based model used to compute slope stability at a regional scale. The LEWS combines landslide susceptibility and rainfall thresholds to depict the areas that are prone to slope failures and issues qualitative warnings over the study area. Both the susceptibility map and the rainfall thresholds were obtained based on the outputs from running FSLAM with 25 different rainfall scenarios. The final output of the LEWS is a slope-unit-based map. The LEWS was implemented for Cili County, Hunan Province, China, and tested for the year 2020. The warning level stayed “Low” during most of the year. High warnings were issued during the summer and were either due to intense rainfall events or abundant long-duration precipitation. The LEWS was able to issue appropriate warnings corresponding to the time and location of three known landslides that occurred in the study area in 2020. Although long-term validation with more landslide data and improved geotechnical data is needed to reduce the LEWS uncertainties, this approach is promising and could support authorities managing landslide risk.