Rapid Hazard Assessment Model for the Extreme Rainfall-induced Regional Clustered Shallow Landslides

The undertaking of stability analysis and impact range prediction of rainfall-induced shallow landslides at the regional scale is of great significance for landslides' early warning and prevention. The existing deterministic physical models for landslides that consider the effect of rainfall rarely consider the kinematics process after landslide destabilization when conducting regional hazard assessments. Thus, the Regional Shallow Landslide Hazards Rapid Assessment Model (RSLHRA) considering dynamic processes is proposed. This model considers the spatiotemporal instantaneous variation characteristics of surface runoff and subsurface wetting front under rainfall conditions, coupled with three-dimensional stability calculation methods to determine unstable units, and predicts the movement characteristics of landslides through dynamic models, achieving rapid assessment of regional-scale landslide hazard. To illustrate, the rainfall-induced regional Clustered shallow landslides that occurred in Guidong County, Hunan Province, China in 2021 were assessed using the RSLHRA model. The results show that soil permeability coefficient, cohesion, and internal friction angle are the most important input parameters of the RSLHRA model; The model can accurately capture the spatiotemporal distribution characteristics of shallow landslides induced by a rainfall process. By comparing the predicted area of the model with the actual occurrence area of the landslide, the accuracy of the model prediction can reach 60-70%. In addition, due to the use of a meshless numerical simulation method suitable for fluid motion analysis under the assumption of depth averaging and incompressibility, the computational efficiency of the model in predicting the kinematics of unstable landslides and debris flows has increased by 20 times compared to other models. The proposed model is expected to provide theoretical and technical support for regional landslide risk prevention and early warning.Keywords: Landslides Hazard, Raid Assessment, Regional Cluster, Landslides Stability, Rainfall Infiltration, Landslides kinematics