The frequency and intensity of rainfall events have been significantly affected by climate change, leading to an increased occurrence of rainfall-triggered shallow landslides. Although advances have been made in estimating the return periods of such landslides under stationary rainfall assumptions, there remains a lack of methodologies specifically addressing return period estimation under nonstationary rainfall conditions driven by climate change.
This study proposes a novel approach for estimating the return periods of rainfall-triggered shallow landslides while accounting for the effects of climate change. Using historical landslide and rainfall data, critical rainfall thresholds (i.e., intensity–duration and cumulative rainfall) responsible for triggering shallow landslides are identified. These thresholds are then integrated with projected rainfall data derived from global climate models, which are statistically downscaled to achieve higher spatial resolution.
Time-dependent extreme value analysis is applied to characterize rainfall variability, and a probabilistic landslide triggering model is developed to establish the relationship between nonstationary rainfall and landslide occurrence. Finally, the return periods of rainfall-triggered shallow landslides under future climate scenarios are estimated.
The proposed approach is demonstrated through a case study in Lishui City, China. Results indicate that the return periods of rainfall-triggered shallow landslides decrease significantly under projected future climates, particularly for high-intensity rainfall events.
 

Shallow Landslide; Probabilistic assessment; Climate change; Rainfall; Return period