Volume 31 Issue 5
Oct.  2025
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XU C,DAI K B,XUE Z W,et al.,2025. Probabilistic study of rainfall-induced landslides at a monthly scale in China[J]. Journal of Geomechanics,31(5):960−971 doi: 10.12090/j.issn.1006-6616.2025134
Citation: XU C,DAI K B,XUE Z W,et al.,2025. Probabilistic study of rainfall-induced landslides at a monthly scale in China[J]. Journal of Geomechanics,31(5):960−971 doi: 10.12090/j.issn.1006-6616.2025134

Probabilistic study of rainfall-induced landslides at a monthly scale in China

doi: 10.12090/j.issn.1006-6616.2025134
Funds:  This research is financially supported by Research Fund of the National Institute of Natural Hazards, Ministry of Emergency Management of China (Grant No. ZDJ2025-54), the Science and Technology Project of the State Grid Corporation of China (SGCC) headquarters (Grant No. 5500-202455159A-1-1-ZN), the Project of the Chongqing Water Resources Bureau, China (Grant No. CQS24C00836), the Science and Technology Projects of the Research Institute of China Southern Power Grid Co., Ltd. (Grant Nos. 1500002024030103SJ00003 and 1500002024030103SJ00009), and the National Natural Science Foundation of China (Grant No. 42407275).
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  • Author Bio:

    许冲,九三学社社员,应急管理部国家自然灾害防治研究院研究员,博士生导师;地质灾害研究中心主任,复合链生自然灾害动力学应急管理部重点实验室主任,中国地震学会地震灾害链专业委员会首任主任,地质灾害机理与评价学科带头人。入选国家级青年人才、新疆维吾尔自治区“天池英才”特聘教授、爱思唯尔“中国高被引学者”;获AOGS SE Distinguished Lecture奖、国际地质灾害减灾协会“杰出青年科学家奖”、应急管理部直属机关优秀青年干部标兵、中国地质学会银锤奖。在滑坡识别与大数据建设、机理与规律探索、危险性与风险评价等领域取得了系统性成果。现为Springer Nature集团“npj Natural Hazards”期刊主编,“Natural Hazards Research”等9个期刊的副主编,《地质力学学报》等20余个期刊的编委。发表论文480余篇,第一/通讯作者论文300余篇,总被引用18000多次。获批国家发明专利和软件著作权等70余项,出版英文专著和科普图书等8本

  • Received: 2025-09-15
  • Revised: 2025-10-19
  • Accepted: 2025-10-21
  • Available Online: 2025-10-22
  • Published: 2025-10-28
  •   Objective  Rainfall is a major trigger for landslides in mountainous regions of China, exhibiting distinct seasonal and spatial aggregation characteristics. To achieve quantitative identification of medium- to long-term geological disaster risks, this study aims to develop a nationwide monthly-scale probabilistic framework for rainfall-induced landslides, integrating rainfall forecasting and data-driven modeling to reveal the spatiotemporal response between rainfall variability and landslide occurrence.   Methods  A 1 km resolution monthly rainfall forecasting model was established using a Patch-based Long Short-Term Memory (Patch-LSTM) network trained on multi-source precipitation data spanning 1901–2023. The model’s spatial continuity and predictive performance were validated using records from independent meteorological stations. A database of rainfall-induced landslides was compiled from four typical heavy rainfall events in Yunnan Province (Dehong 2020, Daguan 2021, Gongshan 2020 and 2022), containing 8503 mapped landslides with associated topographic, geological, hydrological, and climatic factors. These datasets were used to train a probabilistic rainfall–landslide occurrence model combining logistic regression and Gradient Boosting Tree (GBT) algorithms. The model outputs were spatially coupled with monthly rainfall forecasts to generate nationwide monthly probability maps of rainfall-induced landslides. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), the precision–recall AUC (PR-AUC), and the Brier score.   Results  The Patch-LSTM model achieved an average absolute error (MAE) of 14.6 mm, a root mean square error (RMSE) of 35.1 mm, and a coefficient of determination (R2) of 0.51, indicating reliable capability in reproducing monthly precipitation patterns. The probabilistic landslide model showed robust predictive performance, with AUC = 0.83, PR-AUC = 0.78, and Brier score = 0.17. The spatial-temporal patterns revealed distinct “low–high–low” annual variations, with markedly elevated probabilities between June and August corresponding to the main monsoon season. Spatially, high-risk zones were concentrated in (1) southwestern China—southeastern Tibet, central Sichuan, and southern Yunnan—where steep terrain and fractured lithology amplify rainfall effects; (2) southern China—northeastern Guangxi and central Guangdong—influenced by prolonged monsoon rains and typhoon events; and (3) eastern coastal areas—southern Zhejiang and northern Fujian—where intense rainfall interacts with deeply weathered granite and volcanic formations. Across these regions, the probability of landslide occurrence exhibited a significant positive correlation with monthly rainfall intensity, confirming rainfall as the dominant trigger, while topographic relief and geological structure exerted secondary amplifying influences.  Conclusions  The proposed probabilistic framework effectively bridges the gap between event-scale case studies and national-scale monthly prediction, enabling a continuous spatial assessment of rainfall-induced landslide hazards. [Significance] The results provide scientific evidence for identifying seasonal risk hotspots, optimizing disaster prevention and mitigation planning, and supporting mid-term early warning and resource allocation during the flood season. This study establishes a methodological foundation for integrating monthly-scale hazard prediction into China’s geological disaster risk management system, offering a scalable approach applicable to other regions and hazard types worldwide.

     

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