Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models

摘要

The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models' performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y = 24.57x(0.)(18) and y = 11.59x(0.33), respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction.