Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

Abstract

The upper reaches of the Jinsha River, with their complex terrain and active tectonic movements, are vulnerable to landslide-induced hazard chain events, which endanger the safety of residents and infrastructure construction. Based on the analysis of the development background of the hazard chain in the upstream area of the Jinsha River, five factors, including the lithology, distance to faults, distance to rivers, peak ground acceleration, and slope degree, were selected to identify the critical landslide-prone areas. Principal component and grey correlation analyses were then conducted to determine the contributions of these different factors. Based on ArcGIS, the study zone was categorized into five classes of landslide susceptibility: very high, high, moderate, low, and very low. The identification of the critical target areas for landslide hazard chain formation showed satisfactory accuracy. The very high- and high-susceptibility areas are concentrated along the Jinsha River. The dynamic process of a typical landslide in a very high-susceptibility area was numerically simulated using OpenLISEM. The high-precision Baige landslide data of the study area were used to calibrate the practicality of the input mass parameters, including cohesion, internal friction angle, D50, and D90. The movement and accumulation processes of a typical landslide were then numerically simulated with the verified data. The entire landslide accumulation covers an area of 0.45 (Formula presented.), with a length of 1,600 m and a width of 270 m. Thus, the OpenLISEM model, which combines mass, topography, and landcover parameters, is feasible for the numerical simulation of landslide dynamic processes. The prediction of the dynamic processes and accumulation morphology of landslides can provide a reference for the formation processes and mechanisms of the landslide-induced hazard chain in the upper Jinsha River.