Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-3881-2026
https://doi.org/10.5194/nhess-26-3881-2026
Research article
 | 
18 Aug 2026
Research article |  | 18 Aug 2026

Projecting changes in rainfall-induced landslide susceptibility across inhabited areas of China under climate change

Jinqi Wang, Hao Fang, Kai Liu, Yi Yue, Ming Wang, Bohao Li, and Xiaoyi Miao

Related authors

Spatial accessibility of emergency medical services under inclement weather: a case study in Beijing, China
Yuting Zhang, Kai Liu, Xiaoyong Ni, Ming Wang, Jianchun Zheng, Mengting Liu, and Dapeng Yu
Nat. Hazards Earth Syst. Sci., 24, 63–77, https://doi.org/10.5194/nhess-24-63-2024,https://doi.org/10.5194/nhess-24-63-2024, 2024
Short summary
An assessment of short–medium-term interventions using CAESAR-Lisflood in a post-earthquake mountainous area
Di Wang, Ming Wang, Kai Liu, and Jun Xie
Nat. Hazards Earth Syst. Sci., 23, 1409–1423, https://doi.org/10.5194/nhess-23-1409-2023,https://doi.org/10.5194/nhess-23-1409-2023, 2023
Short summary
Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data
Jianxin Zhang, Kai Liu, and Ming Wang
Earth Syst. Sci. Data, 15, 521–540, https://doi.org/10.5194/essd-15-521-2023,https://doi.org/10.5194/essd-15-521-2023, 2023
Short summary
GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022,https://doi.org/10.5194/essd-14-3273-2022, 2022
Short summary
System vulnerability to flood events and risk assessment of railway systems based on national and river basin scales in China
Weihua Zhu, Kai Liu, Ming Wang, Philip J. Ward, and Elco E. Koks
Nat. Hazards Earth Syst. Sci., 22, 1519–1540, https://doi.org/10.5194/nhess-22-1519-2022,https://doi.org/10.5194/nhess-22-1519-2022, 2022
Short summary

Cited articles

Alvioli, M., Melillo, M., Guzzetti, F., Rossi, M., Palazzi, E., Von Hardenberg, J., Brunetti, M. T., and Peruccacci, S.: Implications of climate change on landslide hazard in Central Italy, Sci. Total Environ., 630, 1528–1543, https://doi.org/10.1016/j.scitotenv.2018.02.315, 2018 
Avand, M., Janizadeh, S., Naghibi, S. A., Pourghasemi, H. R., Khosrobeigi Bozchaloei, S., and Blaschke, T.: A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping, Water, 11, 2076, https://doi.org/10.3390/w11102076, 2019. 
Bondarenko, M., Priyatikanto, R., Tejedor-Garavito, N., Zhang, W., McKeen, T., Cunningham, A., Woods, T., Hilton, J., Cihan, D., Nosatiuk, B., Brinkhoff, T., Tatem, A., and Sorichetta, A.: The spatial distribution of population in 2015–2030 at a resolution of 30 arc (approximately 1 km at the Equator), R2025A version v1, WorldPop, School of Geography and Environmental Science, University of Southampton, https://doi.org/10.5258/SOTON/WP00845, 2025. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Bureau, A., Dupuis, J., Hayward, B., Falls, K., and Van Eerdewegh, P.: Mapping complex traits using Random Forests, BMC Genet, 4, S64, https://doi.org/10.1186/1471-2156-4-S1-S64, 2003. 
Download
Short summary
This study assesses how rainfall-induced landslide susceptibility may change across inhabited areas of China under future climate conditions using national landslide records and high-resolution precipitation projections. The results indicate an overall increase in susceptibility, with stronger changes in several regional hotspots. These findings can support landslide risk management and climate adaptation planning in inhabited areas.
Share
Altmetrics
Final-revised paper
Preprint