Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China

- A landslide inventory was made for Xianshuihe fault (XSF) zone, east Tibetan Plateau.
- The XSF has direct influence on the landslide distribution and susceptibility mapping.
- The FR model performed better than WOE in fault active zone landslide susceptibility.
- Inclusion of more parameters did not necessarily translate into a better predictive capability.
- FR method with few parameters can work well in the landslide mapping.

The Xianshuihe fault (XSF) zone is a sinistral-slip fault system on the eastern margin of the Tibetan Plateau, with high Quaternary activity and frequent historic earthquakes. Large landslides along the fault zone have caused substantial damage and are the second-most important regional hazard after earthquakes. A landslide inventory in a 7339-km2 area along the XSF zone documented 415 landslides and evaluated landslide distribution characteristics using frequency ratio (FR) and weight-of-evidence (WOE) models to map landslide susceptibility. A total of 11 variables were analyzed as input variables: slope angle, slope aspect, altitude, planform curvature, topographic wetness index, distance from active faults, lithology, annual rainfall, distance from rivers, distance from roads, and the NDVI of the study area. Among these factors, the distance from active faults, altitude, slope angle, aspect, lithology, and rainfall were the dominant influencing factors. Assessment of six susceptibility mapping schemes with FR and WOE models using the ROC method showed that, while all six of the model schemes produced good results, on the whole, the FR model performed better than the WOE model. The landslide susceptibility map of the FR model with six variables performed as well as the WOE model with the full 11 variables. Inclusion of more variables did not necessarily translate into a better predictive capability. Several factors typically associated with susceptibility to rainfall-triggered landslides-topographic convergence, rainfall, and topographic wetness index-either did not follow expected patterns (rainfall) or were not good general predictors of landslide locations (topographic convergence and wetness index). In contrast, distance to active faults, slope aspect, and topographic divergence were the best predictors as measured by the FR method. This combination points to earthquakes rather than rainfall as the dominant landslide-triggering mechanism in the study area.