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Peer-reviewedCC BYNatural hazards and earth system sciences

Constructing physical-based rainfall landslides prediction model: insights from rainfall threshold curves database of slope units

  • Kai Wang1
  • Linmao Xie1
  • Shuailong Xie1
  • Shaojie Zhang2
  • Yongyang Jiang3
  • Ji Zhang4
  • Lin Zhu1
  • Zhiliu Wang1
  • Fuzhou Qi1
4 affiliations
  1. 1School of architecture and civil engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China
  2. 2Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China
  3. 3Zhejiang Zhongnan Construction Group Steel Structure Co., Ltd, Hangzhou, 311400, China
  4. 4Sichuan Institution of Geological Engineering Investigation Group Co.LTD, Chengdu, 610041, China
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Abstract

The commonly used rainfall threshold warning method relies heavily on historical rainfall and landslide inventory data, which limits its applicability in regions that lack these data. While physical methods do not rely on landslide inventories to establish warning criteria, the calculation of the safety factor typically requires considerable time. To address these issues, this study integrates physical methods, rainfall threshold warning methods, and slope units to develop a rapid forecasting model for rainfall landslides at a regional scale. A hydrological analysis technique for slope units based on grid cells was developed to calculate the instability probability of slope units. Then, each slope unit was analyzed under 20 levels of antecedent effective precipitation and nearly 200 combinations of rainfall intensity (I) and duration (D) to derive the key fitting parameters α and β of the I-D curves under various rainfall scenarios. The application results from Fengjie County indicate that the model runs in less than 12 min, with missing alarm and false alarm rates of 11.8 % and 21.1 %, respectively, highlighting its excellent potential for practical application. This study is expected to provide insights for the rapid forecasting of rainfall landslides in the impoverished mountainous regions of developing countries.

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Cite

BibTeXRIS
BibTeX
@article{wang2026constructing,
  title = {{Constructing physical-based rainfall landslides prediction model: insights from rainfall threshold curves database of slope units}},
  author = {Kai Wang and Linmao Xie and Shuailong Xie and Shaojie Zhang and Yongyang Jiang and Ji Zhang and Lin Zhu and Zhiliu Wang and Fuzhou Qi},
  journal = {Natural hazards and earth system sciences},
  year = {2026},
  month = may,
  doi = {10.5194/nhess-26-2367-2026},
  url = {https://doi.org/10.5194/nhess-26-2367-2026},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Constructing physical-based rainfall landslides prediction model: insights from rainfall threshold curves database of slope units
AU  - Kai Wang
AU  - Linmao Xie
AU  - Shuailong Xie
AU  - Shaojie Zhang
AU  - Yongyang Jiang
AU  - Ji Zhang
AU  - Lin Zhu
AU  - Zhiliu Wang
AU  - Fuzhou Qi
PY  - 2026
DA  - 2026/05/26
JO  - Natural hazards and earth system sciences
DO  - 10.5194/nhess-26-2367-2026
UR  - https://doi.org/10.5194/nhess-26-2367-2026
AB  - The commonly used rainfall threshold warning method relies heavily on historical rainfall and landslide inventory data, which limits its applicability in regions that lack these data. While physical methods do not rely on landslide inventories to establish warning criteria, the calculation of the safety factor typically requires considerable time. To address these issues, this study integrates physical methods, rainfall threshold warning methods, and slope units to develop a rapid forecasting model for rainfall landslides at a regional scale. A hydrological analysis technique for slope units based on grid cells was developed to calculate the instability probability of slope units. Then, each slope unit was analyzed under 20 levels of antecedent effective precipitation and nearly 200 combinations of rainfall intensity (I) and duration (D) to derive the key fitting parameters α and β of the I-D curves under various rainfall scenarios. The application results from Fengjie County indicate that the model runs in less than 12 min, with missing alarm and false alarm rates of 11.8 % and 21.1 %, respectively, highlighting its excellent potential for practical application. This study is expected to provide insights for the rapid forecasting of rainfall landslides in the impoverished mountainous regions of developing countries.
SN  - 1561-8633
SN  - 1684-9981
ER  -