Insight from a Physical-Based Model for the Triggering Mechanism of Loess Landslides Induced by the 2013 Tianshui Heavy Rainfall Event

Abstract

Rainfall-induced landslides pose a significant threat to human life, destroy highways and railways, and cause farmland degradation in the Loess Plateau. From 19 June 2013 to 26 July 2013, continuous and heavy rainfall events occurred in the Tianshui area, Gansu Province. This strong rainfall process included four short-term serious rainfall events and long-term intermittent rainfall, triggering many shallow loess landslides. To improve our understanding of this rainfall process as the triggering mechanism of the loess landslides, we conducted the physical-based spatiotemporal prediction of rainfall-induced landslides. By utilizing precipitation data recorded every 12 h from the rain gauge stations and 51 soil samples from within a 50 km radius of the study area, we predicted 1000 physical-based model-calculated pictures of potential landslides, and the slope failure probability (Pf) of the study area was obtained by Monte Carlo simulations. The model was validated by the actual landslide data of the 2013 heavy rainfall event, and the effects of the precipitation process and the trigger mechanism on the landslides were discussed. The results showed that the fourth rainfall event had the best prediction ability, while the third event had the second-best prediction ability. There was a solid linear link between the antecedent precipitation (Pa) and the predicted landslide area (Pls) based on the fitting relationship, indicating that antecedent rainfall may play a significant role in the occurrence of landslides in the region. By comparing the distribution of the predicted results of the four heavy rainfall events with the actual landslide, we observed that the first two rainfall processes may not have been the main reason for slope failure, contributing only to prepare for the landslides in the later period. The superposition of the fourth and third rainfall events finally determined the spatial distribution characteristics of the landslide induced by the 2013 heavy rainfall event.