中国月尺度降雨型滑坡发生概率研究

许冲; 代克滨; 薛智文; 黄远东; 谢晨晨; 李涛; 张志强; 朱登杰; 赵斌滨; 刘毅; 孔小昂; 高会然; 邵霄怡

doi:10.12090/j.issn.1006-6616.2025134

中国月尺度降雨型滑坡发生概率研究

doi: 10.12090/j.issn.1006-6616.2025134

许冲^{1, 2, 3,},
代克滨^{2, 4,},
薛智文^5,,
黄远东^{2, 4,},
谢晨晨^{2, 6,},
李涛^{2, 4,},
张志强^7,,
朱登杰^7,,
赵斌滨^8,,
刘毅^8,,
孔小昂^8,,
高会然^{2, 3,},
邵霄怡^{2, 3,}

1.
新疆大学地质与矿业工程学院，新疆乌鲁木齐 830047
2.
应急管理部国家自然灾害防治研究院，北京 100029
3.
复合链生自然灾害动力学应急管理部重点实验室，北京 100085
4.
中国科学院大学应急管理科学与工程学院，北京 100049
5.
邢台学院资源与环境学院，河北邢台 054001
6.
中国地质大学（北京）地球科学与资源学院，北京 100083
7.
南方电网科学研究院有限责任公司，广东广州 510663
8.
国网电力工程研究院有限公司，北京 100071

基金项目: 应急管理部国家自然灾害防治研究院基本科研业务专项（ZDJ2025-54）；国家电网公司总部科技项目（5500-202455159A-1-1-ZN）；重庆市水利局项目（CQS24C00836）；南方电网科学研究院有限责任公司科技项目（1500002024030103SJ00003，1500002024030103SJ00009）；国家自然科学基金项目（42407275）

详细信息

作者简介:
许冲（1982—），男，研究员，研究方向为地震与降雨触发地质灾害机理及风险减轻应用。Email：xc11111111@126.com

中图分类号: P642.22
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- 文章访问数: 116
- HTML全文浏览量: 37
- PDF下载量: 25
- 被引次数: 0
出版历程
- 收稿日期: 2025-09-15
- 修回日期: 2025-10-19
- 录用日期: 2025-10-21
- 预出版日期: 2025-10-22
- 刊出日期: 2025-10-28

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

XU Chong^{1, 2, 3
,},
DAI Kebin^{2, 4
,},
XUE Zhiwen^5
,,
HUANG Yuandong^{2, 4
,},
XIE Chenchen^{2, 6
,},
LI Tao^{2, 4
,},
ZHANG Zhiqiang^7
,,
ZHU Dengjie^7
,,
ZHAO Binbin^8
,,
LIU Yi^8
,,
KONG Xiaoang^8
,,
GAO Huiran^{2, 3
,},
SHAO Xiaoyi^{2, 3
,}

1.
School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, Xinjiang, China
2.
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100029, China
3.
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
4.
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
5.
College of Resources and Environment, Xingtai University, Xingtai 054001, Hebei, China
6.
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
7.
Electric Power Research Institute, China Southern Power Grid Company Limited. Guangzhou 510663, Guangdong, China
8.
State Grid Electric Power Engineering Research Institute Co., Ltd., Beijing 100071, China

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).

More Information

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

摘要

摘要: 降雨是中国山区滑坡的主要触发因子，滑坡在时间上具有显著的季节性集中特征。为实现对中长期地质灾害风险的定量识别，构建适用于全国尺度的月尺度降雨型滑坡发生概率评估框架，揭示降雨与滑坡发生的时空响应规律。基于全国1 km分辨率月度降雨数据，采用Patch分块的长短时记忆网络（LSTM）构建月降雨预测模型，验证其在月尺度的时空连续性与精度表现。以云南省4次典型强降雨事件（德宏州2020年、大关县2021年、贡山县2020年与2022年）为样本，建立包含8503条滑坡数据的降雨触发滑坡数据库，融合地形、地质、水文与气候等多源因子，采用逻辑回归与梯度提升树模型构建降雨–滑坡发生概率模型。利用全国月度降雨预测结果进行概率映射，形成全国月尺度滑坡发生概率分布图，并通过AUC、PR-AUC与Brier分数等指标开展精度评估。全国月度降雨预测结果与台站观测数据的平均绝对误差为14.6 mm，均方根误差为35.1 mm，决定系数R²为0.51，表明Patch-LSTM模型在月尺度预测中表现良好。概率模型的AUC值达到0.83，PR-AUC为0.78，Brier分数为0.17，显示预测稳定性较高。结果表明，滑坡发生概率呈现“前后低、中间高”的年变化趋势，6—8月为高发期；空间上高风险区集中于西南（藏东南、四川中部、云南南部）、华南（桂东北、粤中）及华东（浙南、闽北）等地。滑坡概率与月降雨量呈显著正相关，说明降雨是主控驱动力，地形起伏与地层破碎度对其具有放大作用。建立的月尺度降雨型滑坡概率评估框架实现了从典型事件到全国尺度的可推广应用，揭示了降雨驱动下滑坡发生的时空规律。研究成果可为地质灾害风险普查、汛期防灾准备与中期风险预警提供科学依据，并为构建中国国家地质灾害中长期风险预测体系提供技术支撑。
- 月尺度 /
- 降雨型滑坡 /
- 发生概率 /
- 数据驱动建模 /
- 空间分布特征 /
- 地质灾害危险性评估
Abstract: 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.
- monthly scale /
- rainfall-induced landslides /
- occurrence probability /
- data-driven modeling /
- spatial distribution characteristics /
- geological hazard risk assessment

HTML全文

图 1 技术路线图

Figure 1. Technical roadmap

下载: 全尺寸图片幻灯片

图 2 月度降雨预测图

a— 一月；b—四月；c—七月；d—十月

Figure 2. Monthly rainfall prediction maps

(a) January; (b) April; (c) July; (d) October

下载: 全尺寸图片幻灯片

图 3 导致群发滑坡的4次降雨事件所在区域位置示意图

Figure 3. Schematic location map of the four rainfall events that triggered widespread landslides

下载: 全尺寸图片幻灯片

图 4 4次降雨滑坡空间分布及滑坡点密度图，

a— 德宏州局部（2020年7月）；b—大关县局部及邻区（2021年9月）；c—贡山县（2022年4月）；d—贡山县局部（2020年5月）

Figure 4. Spatial distribution of landslides and landslide point density maps for the four rainfall events

(a) A part of Dehong Prefecture (July 2020); (b) A part of Daguan County and adjacent regions (September 2021); (c) Gongshan County (April 2022); (d) A part of Gongshan County (May 2020)

下载: 全尺寸图片幻灯片

图 5 全国月度降雨滑坡发生概率图

a— 一月；b—四月；c—七月；d—十月

Figure 5. National monthly probability maps of rainfall-induced landslides

(a) January; (b) April; (c) July; (d) October

下载: 全尺寸图片幻灯片

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