基于临界降雨阈值和易发性的区域滑坡预警研究

Research on regional landslide early warning based on critical rainfall threshold and susceptibility

摘要

本文选取兰州市城关区为研究对象，应用Python、最小二乘法、证据权、信息量和频率比等方法探讨不同条件的临界降雨阈值，计算区域滑坡易发性，建立临界降雨阈值和易发性相融合的滑坡预警模型。研究结果表明，区域历史滑坡的降雨历时范围是3~216h，其降雨强度为0.07~23.60mm/h。极高、高、中、低和极低易发区面积比例均值分别为2.42%、5.45%、11.20%、32.11%和48.82%，其不同等级易发区滑坡数量比例均值分别为20.71%、22.62%、25.48%、21.19%和10.00%。融合后的预警等级分布更加符合当地实际情况，其中红－橙等级主要位于沿黄河北岸沟道和黄河沿线南北山区，黄－蓝等级主要位于黄河沿线沟道和区内高山地区。

Abstract

This paper takes the Chengguan District of Lanzhou City as an example, and uses Python, least squares method, evidence weight, information content, and frequency ratio to explore critical rainfall thresholds under different conditions, calculate regional landslide susceptibility, and establish a landslide early warning model that integrates critical rainfall thresholds and landslide susceptibility. The results show that the rainfall duration of historical landslides in the region ranges from 3 to 216 hours, and the rainfall intensity ranges from 0.07 to 23.60 mm/h. The average proportions of area in the extremely high, high, moderate, low, and extremely low susceptibility zones are 2.42%, 5.45%, 11.20%, 32.11%, and 48.82%, respectively. The average proportions of landslide numbers in these zones were 20.71%, 22.62%, 25.48%, 21.19%, and 10.00%. The integrated early warning level distribution closely aligns with the actual conditions in the region. Red-orange warning levels are primarily located along the north bank of the Yellow River and in the mountainous areas north and south of the river, while yellow-blue levels are mainly located along the Yellow River’s channels and in the high mountain areas within the district.

关键词

滑坡预警 / 降雨阈值 / 易发性 / 二元统计 / 城关区

Key words

landslide early warning / rainfall threshold / susceptibility / binary statistics / Chengguan District

参考文献

[1] SASSA K, TSUCHIYA S, FUKUOKA H, et al. Landslides: Review of achievements in the second 5-year period (2009-2013)[J]. Landslides, 2015, 12(2): 213-223.

[2] SHU H P, HE J L, ZHANG F Y, et al. Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the loess plateau: A case of Lanzhou City, China[J]. Journal of Hydrology, 2024, 645: 132148.

[3] 杨永鹏, 郭雅, 陈浩, 等 . 青藏高原西部滑坡易发性评价对比研究[J]. 自然灾害学报, 2025, 34(1): 95-104.

[4] YANG Yongpeng, GUO Ya, CHEN Hao, et al. Comparative study on evaluation of landslide susceptibility in western Qinghai-Xizang Plateau[J]. Journal of Natural Disasters, 2025, 34(1): 95-104. (in Chinese)

[5] 肖波, 赵良军, 郑莉萍, 等 . 融合时序InSAR与多因子耦合模型的长宁县滑坡灾害易发性评价[J]. 自然灾害学报, 2025, 34(1): 117-126.

[6] XIAO Bo, ZHAO Liangjun, ZHENG Liping, et al. Landslide hazard susceptibility assessment in Changning County through fusion of time-series InSAR and multi-factors coupling model[J]. Journal of Natural Disasters, 2025, 34(1): 117-126. (in Chinese)

[7] 张云, 资锋, 曹运江, 等 . 基于MaxEnt模型的沅陵县滑坡易发性评价[J]. 自然灾害学报, 2025, 34(1): 105-116.

[8] ZHANG Yun, ZI Feng, CAO Yunjiang, et al. Evaluation of landslide susceptibility in Yuanling County based on MaxEnt model[J]. Journal of Natural Disasters, 2025, 34(1): 105-116. (in Chinese)

[9] 吴益平, 张秋霞, 唐辉明, 等 . 基于有效降雨强度的滑坡灾害危险性预警[J]. 地球科学, 2014, 39(7): 889-895.

[10] WU Yiping, ZHANG Qiuxia, TANG Huiming, et al. Landslide hazard warning based on effective rainfall intensity[J]. Earth Science, 2014, 39(7): 889-895. (in Chinese)

[11] 彭建兵, 吴迪, 段钊, 等 . 典型人类工程活动诱发黄土滑坡灾害特征与致灾机理[J]. 西南交通大学学报, 2016, 51(5): 971-980.

[12] PENG Jianbing, WU Di, DUAN Zhao, et al. Disaster characteristics and destructive mechanism of typical loess landslide cases triggered by human engineering activities[J]. Journal of Southwest Jiaotong University, 2016, 51(5): 971-980. (in Chinese)

[13] 彭建兵, 兰恒星, 钱会, 等 . 宜居黄河科学构想[J]. 工程地质学报, 2020, 28(2): 189-201.

[14] PENG Jianbing, LAN Hengxing, QIAN Hui, et al. Scientific research framework of livable Yellow River[J]. Journal of Engineering Geology, 2020, 28(2): 189-201. (in Chinese)

[15] 舒和平. 融合降雨阈值和易发性的黄土滑坡预警研究[R]. 兰州: 兰州大学, 2022.

[16] SHU Heping. Study on early warning of loess landslide by fusing rainfall threshold and landslide susceptibility[R]. Lanzhou: Lanzhou University, 2022. (in Chinese)

[17] HÜRLIMANN M, RICKENMANN D, GRAF C. Field and monitoring data of debris-flow events in the Swiss Alps[J]. Canadian Geotechnical Journal, 2003, 40(1): 161-175.

[18] TAKAHASHI T. A review of Japanese debris flow research[J]. International Journal of Erosion Control Engineering, 2009, 2(1): 1-14.

[19] 舒和平. 黄土丘陵地区小流域泥石流形成与运动特征模拟研究[D]. 兰州: 兰州大学, 2019.

[20] SHU Heping. Study on the formation and motion characteristics of debris flow in small watershed in hilly region of loess area[D]. Lanzhou: Lanzhou University, 2019. (in Chinese)

[21] 彭启园. 甘肃滑坡与泥石流监测体系评价与数据分析研究[D]. 兰州: 兰州大学, 2020.

[22] PENG Qiyuan. Evaluation and data analysis of Gansu landslide and debris flow monitoring system[D]. Lanzhou: Lanzhou University, 2020. (in Chinese)

[23] GUZZETTI F, ARDIZZONE F, CARDINALI M, et al. Distribution of landslides in the Upper Tiber River basin, central Italy[J]. Geomorphology, 2008, 96(1/2): 105-122.

[24] GUZZETTI F, PERUCCACCI S, ROSSI M, et al. The rainfall intensity-duration control of shallow landslides and debris flows: An update[J]. Landslides, 2008, 5(1): 3-17.

[25] PRADHAN A M S, LEE S R, KIM Y T. A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea[J]. Landslides, 2019, 16(3): 647-659.

[26] SEGONI S, LAGOMARSINO D, FANTI R, et al. Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system[J]. Landslides, 2015, 12(4): 773-785.

[27] 黎志恒, 张永军, 梁收运. 兰州城市地质灾害与人类工程活动[J]. 兰州大学学报(自然科学版), 2014, 50(5): 588-593.

[28] LI Zhiheng, ZHANG Yongjun, LIANG Shouyun. Urban geological hazards and human engineering activities in Lanzhou City[J]. Journal of Lanzhou University (Natural Sciences), 2014, 50(5): 588-593. (in Chinese)

[29] GUZZETTI F, PERUCCACCI S, ROSSI M, et al. Rainfall thresholds for the initiation of landslides in central and southern Europe[J]. Meteorology and Atmospheric Physics, 2007, 98(3): 239-267.

[30] BRUNETTI M T, PERUCCACCI S, ROSSI M, et al. Rainfall thresholds for the possible occurrence of landslides in Italy[J]. Natural Hazards and Earth System Sciences, 2010, 10(3): 447-458.

[31] PERUCCACCI S, BRUNETTI M T, LUCIANI S, et al. Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy[J]. Geomorphology, 2012, 139: 79-90.

[32] MELILLO M, BRUNETTI M T, PERUCCACCI S, et al. Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events[J]. Landslides, 2016, 13(1): 165-172.

[33] MELILLO M, BRUNETTI M T, PERUCCACCI S, et al. A tool for the automatic calculation of rainfall thresholds for landslide occurrence[J]. Environmental Modelling & Software, 2018, 105: 230-243.

[34] CAINE N. The rainfall intensity: Duration control of shallow landslides and debris flows[J]. Geografiska Annaler Series A, Physical Geography, 1980, 62(1/2): 23-27.

[35] 翟进乾. 克里金(kriging)插值方法在煤层分布检测中的应用研究[D]. 太原: 太原理工大学, 2008.

[36] ZHAI Jinqian. Applied research of Kriging interpolation method in distributed detection of coal seam[D]. Taiyuan: Taiyuan University of Technology, 2008. (in Chinese)

[37] MERGHADI A, YUNUS A P, DOU J, et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance[J]. Earth-Science Reviews, 2020, 207: 103225.

[38] REICHENBACH P, ROSSI M, MALAMUD B D, et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews, 2018, 180: 60-91.

[39] SHU H P, GUO Z Z, QI S, et al. Integrating landslide typology with weighted frequency ratio model for landslide susceptibility mapping: A case study from Lanzhou City of northwestern China[J]. Remote Sensing, 2021, 13(18): 3623.

[40] REGMI N R, GIARDINO J R, VITEK J D. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA[J]. Geomorphology, 2010, 115(1/2): 172-187.

[41] 古腾飞, 段平, 王伟鹏, 等 . 滑坡易发性评价方法综述[J]. 自然灾害学报, 2024, 33(6): 1-16.

[42] GU Tengfei, DUAN Ping, WANG Weipeng, et al. Review of methods for landslide susceptibility assessment[J]. Journal of Natural Disasters, 2024, 33(6): 1-16. (in Chinese)

[43] LI W B, FAN X M, HUANG F M, et al. Uncertainties analysis of collapse susceptibility prediction based on remote sensing and GIS: Influences of different data-based models and connections between collapses and environmental factors[J]. Remote Sensing, 2020, 12(24): 4134.

[44] 王喜乐. 天水盆地多灾种危险性评价研究[D]. 兰州: 兰州大学, 2021.

[45] WANG Xile. Research on hazard assessment of multigeohazards in Tianshui Basin[D]. Lanzhou: Lanzhou University, 2021. (in Chinese)

[46] ABEDIGHESHLAGHI H, FEIZIZADEH B. GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping[J]. Natural Hazards, 2021, 107(2): 1981-2014.

[47] XU W B, YU W J, JING S C, et al. Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China)[J]. Natural Hazards, 2013, 65(3): 1379-1392.

[48] HUANG F M, YE Z, JIANG S H, et al. Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models[J]. Catena, 2021, 202: 105250.

[49] TRIGILA A, IADANZA C, ESPOSITO C, et al. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)[J]. Geomorphology, 2015, 249: 119-136.

[50] HUANG F M, CAO Z S, GUO J F, et al. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping[J]. Catena, 2020, 191: 104580.

[51] BEGUERÍA S. Validation and evaluation of predictive models in hazard assessment and risk management[J]. Natural Hazards, 2006, 37(3): 315-329.

[52] SHU H P, HÜRLIMANN M, MOLOWNY-HORAS R, et al. Relation between land cover and landslide susceptibility in Val d’Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction[J]. Science of the Total Environment, 2019, 693: 133557.

[53] VAKHSHOORI V, ZARE M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods[J]. Geomatics, Natural Hazards and Risk, 2016, 7(5): 1731-1752.

[54] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.

[55] 杨桑杰. 人口迁移驱动地质灾害风险转移研究[D]. 兰州: 兰州大学, 2023.

[56] YANG Sangjie. Human migration driven geohazard risk shifts[D]. Lanzhou: Lanzhou University, 2023. (in Chinese)

[57] 林沛文, 何书, 鲜木斯艳·阿布迪克依木, 等 . 基于逻辑回归-模糊层次分析模型的公路沿线滑坡易发性评价[J]. 自然灾害学报, 2024, 33(6): 27-36.

[58] LIN Peiwen, HE Shu, ABUDIKEIMU Xmsy, et al. Landslide susceptibility assessment along highways based on LR-FAHP model[J]. Journal of Natural Disasters, 2024, 33(6): 27-36. (in Chinese)

[59] FELL R, COROMINAS J, BONNARD C, et al. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning[J]. Engineering Geology, 2008, 102(3/4): 85-98.

[60] SEGONI S, PICIULLO L, GARIANO S L. A review of the recent literature on rainfall thresholds for landslide occurrence[J]. Landslides, 2018, 15(8): 1483-1501.

基金资助

甘肃省地质灾害监测预警技术创新中心开放基金项目(2024PT03-10)

甘肃省自然科学基金青年基金项目(24JRRA661)