Combined numerical investigation of the Gangda paleolandslide runout and associated dam breach flood propagation in the upper Jinsha River, SE Tibetan Plateau

A large paleolandslide occurred opposite the Gangda village in the upper Jinsha River, SE Tibetan Plateau. Field geological investigations and remote sensing indicated that the Gangda paleolandslide once blocked the Jinsha River. Evidence of river blocking, including landslide dam relics, upstream lacustrine sediments, and downstream outburst sediments, has been well preserved. To understand the river-blocking event including landslide, dam breach, and associated outburst flooding, optically stimulated luminescence (OSL) dating and numerical simulations were performed in this study. OSL dating results showed that the paleolandslide dam was formed at 5.4 +/- 0.5 ka BP and breached at 3.4 +/- 0.3 ka BP, indicating that the dam lasted approximately 2000 years. The discrete element method was used to simulate the dynamics of the Gangda rock landslide based on the restored topography, while a fluid-solid coupling model was performed to simulate the landslide dam breaching and flooding. The fluid-solid coupling model can simultaneously reflect the process of landslide-dam collapse and the propagation of outburst flood. The simulated results indicate that the whole landslide process lasted about 60 s with a peak velocity of 38 m/s. It is significant that the simulated morphology of the residual landslide dam and downstream outburst sediments is consistent with the field observations. The combined numerical investigation in this paper provided new insights into the research of landscape evolution and helped to understand the chain disaster of landslide, dam breach, and flooding.

Automated summary

This study investigates a large paleolandslide and its impacts on river blocking and flooding in the upper Jinsha River, SE Tibetan Plateau, through optically stimulated luminescence dating and numerical simulations. The simulated results are consistent with field observations, providing new insights into landscape evolution.