Machine learning modeling demonstrates increased rainfall-induced landslide risks across inhabited areas of China under climate change, indicating an urgent need for proactive mitigation.
Landslides pose a significant threat to human lives and property. Evaluating dynamic changes in landslide susceptibility under climate change can provide decision-making support for future landslide risk reduction and mitigation. Using multiple landslide event inventories (2008–2023) and a random forest algorithm, this study developed an annual-scale landslide susceptibility model to assess spatiotemporal patterns of landslide susceptibility across inhabited areas of China under different simulated scenarios. The results showed that the model achieved excellent performance (AUC = 0.96), with annual precipitation being the most influential factor (16 % contribution). Compared to the baseline (1950–2014), landslide susceptibility within inhabited areas of China is projected to increase under future climate conditions. By the late 21st century (2076–2100), national mean annual precipitation is expected to rise by 59–111 mm, corresponding to an expansion of 4.9 × 104–1.2 × 105 km2 in areas with median to very high susceptibility within inhabited areas across SSP scenarios. Spatially, increased susceptibility is expected mainly in the border region between the South China bedrock hills region (SC) and the Southwest karst mountain region (SW), the inhabited parts of the eastern permafrost region of the Qinghai–Tibet Plateau (Tibet), and the Northwest Loess Plateau region (Loess) near the Taihang Mountains, with SSP5-8.5 further amplifying susceptibility toward the end of the century. These findings underscore the necessity of proactive landslide risk management in these identified hotspots to mitigate escalating landslide threats.
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Wang et al. (2026) studied this question.
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