Hazard Assessment of Shallow Loess Landslides Under Different Rainfall Intensities Based on the SINMAP Model: A Case Study of Yuzhong County

The Loess Plateau is one of the most landslide-prone regions in China, where rainfall-induced shallow loess landslides severely constrain regional economic and social development. Therefore, investigating the stability of shallow loess slopes under rainfall conditions is of great significance. Taking Yuzhong County in Gansu Province as an example, this study uses the SINMAP model (Version 2.0) to assess slope stability. The areas of unstable zones under different rainfall intensities were identified, and the spatial distribution of hazard sites was analyzed to evaluate the applicability of this deterministic physical model in the study area. Furthermore, a Personnel Risk Level (PRL) determined by combining population density with the Stability Index (SI, defined as the probability that the factor of safety exceeds 1: SI = Prob (FS > 1)) was proposed and applied to assess the potential impact of landslides on local residents. The novelty of this study lies in three aspects: (1) targeting Yuzhong County (a loess region with scarce comprehensive landslide risk assessments) to fill the regional research gap, (2) quantifying PRL through a modified hazard index (HI = population density × (1/SI)) to achieve spatialized risk mapping for vulnerable populations, and (3) systematically analyzing the dynamic response of slope stability to five gradient rainfall intensities (from light rain to severe rainstorm) and verifying model sensitivity to key parameters. The results show that as rainfall intensity increases, stable areas gradually decrease while unstable areas expand, with stable zones progressively transforming into unstable ones. Greater rainfall intensity also leads to an increase in the number of landslides within unstable zones. The proposed PRL helps delineate the severity of hazards in different townships, providing new references for mitigating casualties and property losses caused by landslides.