Abstract
Yadong County located in the southern Himalayan mountains in Tibet, China, is an import frontier county. It was affected by landslides after the 2011 Sikkim earthquake (Mw = 6.8) and the 2015 Gorkha earthquake (Mw = 7.8). Casualties and property damage were caused by shallow landslides during subsequent rainfall on the earthquake-destabilized slopes. Existing researches have generally examined rainfall- and earthquake-triggered landslides independently, whereas few studies have considered the combined effects of both. Furthermore, there is no previous study reported on landslide hazards in the study area, although the area is strategically applicable for trade as it is close to Bhutan and India. This study developed a new approach that coupled the Newmark method with the hydrological model based on geomorphological, geological, geotechnical, seismological and rainfall data. A rainfall threshold distribution map was generated, indicating that the southeast part of Yadong is prone to rainfall-induced landslides, especially when daily rainfall is higher than 45 mm/day. Permanent displacement predictions were used to identify landslide hazard zones. The regression model used to calculate these permanent displacement values was 71% accurate. Finally, landslide probability distribution maps were generated separately for dry and wet conditions with rainfall of varying intensities. Results can serve as a basis for local governments to manage seismic landslide risks during rainy seasons.
Highlights
Earthquakes are an important triggering factor for the mass movement processes; rainfall is another crucial factor in any slope failures
The Newmark method and assessed landslide deformation based on 189 seismic acceleration digital records from 17 earthquake events in Italy
In the cases of data scarcity, statistical methods are unsuitable for assessing landslide failure probability; combining hydrology and slope stability models has proven to be a good solution to the problem
Summary
Earthquakes are an important triggering factor for the mass movement processes; rainfall is another crucial factor in any slope failures. Researchers have attempted to simultaneously explore both of these effects They have used a range of approaches from developing GIS-based neural networks[35] to using the groundwater depth as an alternative for the slope failure zone[36] and to comparing multiple models in terms of the prediction a ccuracies[19]. Focused efforts are required for the realistic characterisation of slope groundwater conditions and material p roperties[37,38] This recent research has blended databases or compared susceptibility models to assess slope failure probability (due to rainfall and earthquakes). It has not considered the sequential relationship of these two factors, nor has it identified the essential mechanisms by which postearthquake rainfall triggers landslides
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