Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-611-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-611-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2026
Research article |
| 29 Jan 2026
| 29 Jan 2026
From typhoon rainfall to slope failure: optimizing susceptibility models and dynamic thresholds for landslide warnings in Zixing City, China
Weifeng Xiao
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Guangchong Yao
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Zhenghui Xiao
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Ge Liu
CORRESPONDING AUTHOR
Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
Luguang Luo
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Yunjiang Cao
School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Wei Yin
Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha 410004, China
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To improve typhoon-induced landslide early warning systems, we used data from Typhoon Gaemi (705 landslides in Zixing City, China, 2024) to study how slope steepness, rock type, fault distance and vegetation affect landslides; we found steeper slopes and fault-adjacent areas need less rain, larger landslides need more prolonged heavy rain, and combining total and peak rainfall improved accuracy, helping build better systems to protect lives and property in typhoon-prone mountains.
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To improve typhoon-induced landslide early warning systems, we used data from Typhoon Gaemi (705 landslides in Zixing City, China, 2024) to study how slope steepness, rock type, fault distance and vegetation affect landslides; we found steeper slopes and fault-adjacent areas need less rain, larger landslides need more prolonged heavy rain, and combining total and peak rainfall improved accuracy, helping build better systems to protect lives and property in typhoon-prone mountains.
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Short summary
In China’s Zixing City, typhoon landslides are rising with climate change. This study used machine learning on Typhoon Gaemi (2024) data, identifying 86.4 % of high-risk landslides. A rainfall model (24 h+7-day) achieved 71.8 % accuracy, guiding a warning system matching 71.4 % of historical events.
In China’s Zixing City, typhoon landslides are rising with climate change. This study used...
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