[1]赵晨澄,李秀珍*,龚俊豪,等.基于确定性耦合物理模型的滑坡—泥石流链式灾害危险性评价——以青海省贵德县二连沟小流域为例[J].山地学报,2025,(3):408-422.[doi:10.16089/j.cnki.1008-2786.000901]
　ZHAO Chencheng,LI Xiuzhen*,GONG Junhao,et al.Risk Assessment of Landslide-Debris Flow Cascade Based on Deterministic Coupled Physical Models: A Case Study in the Erlian Gully Watershed, Guide County, China[J].Mountain Research,2025,(3):408-422.[doi:10.16089/j.cnki.1008-2786.000901]
点击复制

基于确定性耦合物理模型的滑坡—泥石流链式灾害危险性评价——以青海省贵德县二连沟小流域为例()

《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2025年第3期
页码:
408-422
栏目:
山地灾害
出版日期:
2025-06-20

文章信息/Info

Title:
Risk Assessment of Landslide-Debris Flow Cascade Based on Deterministic Coupled Physical Models: A Case Study in the Erlian Gully Watershed, Guide County, China
文章编号:
1008-2786-(2025)3-408-15
作者:
赵晨澄1; 2; 李秀珍1*; 龚俊豪1; 2; 李泉林1; 2; 张世哲1; 2; 孙建国3
(1. 中国科学院、水利部成都山地灾害与环境研究所,成都 610213; 2. 中国科学院大学,北京 1000493; 3. 中铁长江交通设计集团有限公司,重庆 401121)
Author(s):
ZHAO Chencheng1; 2;  LI Xiuzhen1*;  GONG Junhao1; 2;  LI Quanlin1; 2; 3;  ZHANG Shizhe1; 2;  SUN Jianguo3
(1.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences & Ministry of Water Resources, Chengdu 610213, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3.China Railway Changjiang Transport Design Group Co. Ltd, Chongqing 401121, China)
关键词:
滑坡—泥石流链式灾害;  确定性耦合物理模型;  动态危险性评价;  二连沟;  贵德县
Keywords:
landslide-debris flow cascade;  deterministic coupled physical model;  dynamic risk assessment;  the Erlian gully;  Guide County
分类号:
X43
DOI:
10.16089/j.cnki.1008-2786.000901
文献标志码:
A
摘要:
滑坡—泥石流链式灾害是由极端降雨等触发因素引发,滑坡作为物源补给泥石流,形成前后关联、动态演进的复合地质灾害过程。针对区域岩土参数获取困难及物源动态演化机制不明等瓶颈,链式灾害评价成果多聚焦于已成灾滑坡的物源供给分析,对动态降雨过程中链式灾害过程的模拟仍显不足。黄河上游处于青藏高原与黄土高原的构造过渡带,其复杂的地貌格局与脆弱的生态环境使其成为滑坡—泥石流链式灾害的典型区域。本研究以青海省海南藏族自治州贵德县二连沟泥石流群小流域为典型研究区,通过多源数据整合(历史灾害调查、实地勘测与遥感解译),构建TRIGRS滑坡启动模型与Flow-R泥石流演进模型的确定性耦合框架,实现了降雨诱发链式灾害的全过程动态模拟。主要创新成果包括:(1)构建考虑地质本底条件与滑坡物源补给效应的耦合模型,实现多要素协同的灾害过程动态模拟;(2)突破传统参数获取瓶颈,建立小流域尺度岩土参数(内聚力、内摩擦角等)自动化反演技术;(3)完成链式灾害危险性定量评估,识别出研究区泥石流高危险区占比达6.83%。验证表明,耦合模型AUC值(0.69)显著优于单一模型(0.59),证明其预测效能更优。本研究建立的链式灾害定量评估方法体系,为黄河上游类似地质环境区域的灾害风险防控提供了关键技术支撑与科学决策依据。
Abstract:
Landslide-debris flow cascade represents a type of interconnected geohazard series triggered by extreme rainfall, wherein landslides dynamically feed geo-sources to subsequent debris flows. Previous research efforts on landslide-debris flow cascade concerned about geo-source supply from post-landslides, but with shortage in simulating dynamically evolving cascading processes under ongoing rainfall, particularly given challenges in acquiring local geotechnical parameters and elucidating geo-source dynamics.
The upper reaches of the Yellow River, situated in the tectonic transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, constitutes a global research hotspot for geohazard cascades due to its complex topography and fragile ecosystems prone to landslide-debris flow cascading disaster.
In this study, it took a watershed of the Erliangou gully in Guide County, Hainan Tibetan Autonomous Prefecture, Qinghai Province, China as the typical research area. Through integration of multi-source data(historical disaster records, field surveys, and remote sensing interpretation), a deterministic coupled framework integrating the TRIGRS landslide initiation model and the Flow-R debris flow evolution model was constructed, enabling full-process dynamic simulation of the rainfall-triggered cascading processes. Some positive feedback was achieved to the modle as listed as below.
(1)The development of a coupled model that incorporates geological background conditions and landslide-derived source replenishment effects, realizes dynamic simulation of multi-factor collaborative disaster processes.
(2)It justied the breakthrough of traditional parameter acquisition bottlenecks through an automated inversion technique for geotechnical parameters(e.g., cohesion, internal friction angle)at small watershed scales.
(3)The quantitative risk assessment for landslide-debris flow cascade in the Erliangou gully identified high-risk debris flow areas accounting for 6.83% of the total area of the watershed. Validation confirmed superior predictive performance of the coupled model(AUC=0.69)versus individual models(AUC=0.59).
This quantitative assessment method for landslide-debris flow cascade established in this study provides key technical support and scientific decision-making basis for disaster risk prevention and control in similar geological environment areas in the upper reaches of the Yellow River.

参考文献/References:

[1] ZHAO Bo, SU Lijun, QIU Chenchen, et al. Understanding of landslides induced by 2022 Luding earthquake, China [J]. Journal of Rock Mechanics and Geotechnical Engineering, 2025, 17: 4241-4260. DOI: 10.1016/j.jrmge.2024.07.006
[2] DU Jiantao, LI Zhenhong, SONG Chuang, et al. InSAR-based active landslide detection and characterization along the upper reaches of the Yellow River [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 3819-3830. DOI: 10.1109/JSTARS.2023.3263003
[3] PACK R T, TARBOTON D G, GOODWIN C N. SINMAP user's manual [M]. Salmon Arm: Terratech Consulting Ltd, 2005: 1-68.
[4] MONTGOMERY D R, SULLIVAN K, GREENBERG H M. Regional test of a model for shallow landsliding [J]. Hydrological Processes, 1998, 12(6): 943-945. DOI: 10.1002/(SICI)1099-1085(199805)12:6<943::AID-HYP664>3.0.CO; 2-Z
[5] WU Weinin, SIDLE R C. Application of a distributed shallow landslide analysis model(dSLAM)to managed forested catchments in Oregon, USA [J]. Human Impact on Erosion and Sedimentation, 1997, 245: 213-221.
[6] MONTRASIO L. Stability analysis of soil-slip [J]. Risk Analysis, 2000, 45: 357-366. DOI: 10.2495/RISK000331
[7] BAUM R L, SAVAGE W Z, GODT J W. TRIGRS—A fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0 [R]. Geological Survey Open-File Report, 2008-1159: 1-75.
[8] BEGUERIA S, VAN ASCH T W J, MALET J P, et al. A GIS-based numerical model for simulating the kinematics of mud and debris flows over complex terrain [J]. Natural Hazards and Earth System Sciences, 2009, 9(6): 1897-1909. DOI: 10.5194/nhess-9-1897-2009
[9] OUYANG Chaojun, HE Siming, XU Qiang, et al. A MacCormack-TVD finite difference method to simulate the mass flow in mountainous terrain with variable computational domain [J]. Computers & Geosciences, 2013, 52: 1-10. DOI: 10.1016/j.cageo.2012.08.024
[10] CHRISTEN M, KOWALSKI J, BARTELT P. RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain [J]. Cold Regions Science and Technology, 2010, 63(1): 1-14. DOI: 10.1016/j.coldregions.2010.04.005
[11] HORTON P, JABOYEDOFF M, RUDAZ B, et al. Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale [J]. Natural Hazards and Earth System Sciences, 2013, 13(4): 869-885. DOI: 10.5194/nhess-13-869-2013
[12] O'BRIEN J. FLO-2D users manual [M]. Nutrioso: Arizona, 2007.
[13] 田兴华, 朱厚影, 韩静云, 等. 降雨诱发泥石流危险性动态评价[J]. 科学技术与工程, 2022, 22(13): 5411-5416. [TIAN Xinghua, ZHU Houying, HAN Jingyun, et al. Dynamic evalution of the risk of rainfall induced debris flow [J]. Science Technology and Engineering, 2022, 22(13): 5411-5416] DOI: 10.3969/j.issn.1671-1815.2022.13.041
[14] ZHANG Xianzheng, TANG Chenxiao, TIE Yongbo, et al. Dynamic susceptibility assessment of debris flow hazard after a strong earthquake, Wenchuan county, Sichuan, China [J]. Landslides, 2024, 21(8): 1915-1928. DOI: 10.1007/s10346-024-02246-4
[15] HORTON P, LOMBARDO L, MERGILI M, et al. Regional debris-flow hazard assessments [M]. Cham: Springer, 2024: 383-462.
[16] DIKSHIT A, PRADHAN B, ALAMRI A M. Pathways and challenges of the application of artificial intelligence to geohazards modelling [J]. Gondwana Research, 2021, 100: 290-301. DOI: 10.1016/j.gr.2020.08.007
[17] WU T. Using numerical models for catchment-scale sediment amount variation assessment under extreme rainfall events [C]. American Geophysical Union, Proceedings of the AGU Fall Meeting Abstracts, 2015.
[18] GOMES T R A, GUIMARAES R F, DE CARVALHO JUNIOR O A, et al. Combining spatial models for shallow landslides and debris-flows prediction [J]. Remote Sensing, 2013, 5(5): 2219-2237. DOI: 10.3390/rs5052219
[19] HSU Y C, LIU K F. Combining TRIGRS and DEBRIS-2D models for the simulation of a rainfall infiltration induced shallow landslide and subsequent debris flow [J]. Water, 2019, 11(5): 890. DOI: 10.3390/w11050890
[20] 林文, 周伟, 李婧, 等. 基于Flow-R和FLO-2D的沟谷型泥石流危险性评价[J]. 人民长江, 2022, 53(5): 143-148. [LIN Wen, ZHOU Wei, LI Jing, et al. Gully debris flow hazard assessment based on Flow-R and FLO-2D coupling models [J]. Yangtze River, 2022, 53(5): 143-148] DOI: 10.16232/j.cnki.1001-4179.2022.05.023
[21] 赵无忌. 黄河上游贵德盆地滑坡泥石流扇发育特征及地貌演化过程[D]. 北京: 中国地质大学(北京), 2015: 1-95. [ZHAO Wuji. The formation charcateristics and geomorphical evolution of the landslides and debris flow fans in Guide Basin, the upper reacjes of the Yellow River [D]. Beijing: China University of Geosciences(Beijing), 2015: 1-95].
[22] FAN Junjian, ZHAO Guangju, MU Xingmin, et al. Effects of cascading reservoirs on streamflow and sediment load with machine learning reconstructed time series in the upper Yellow River basin [J]. Catena, 2023, 225: 107008. DOI: 10.1016/j.catena.2023.107008
[23] KAFLE L, XU Wenjie, ZENG Shuyuan, et al. A numerical investigation of slope stability influenced by the combined effects of reservoir water level fluctuations and precipitation: A case study of the Bianjiazhai landslide in China [J]. Engineering Geology, 2022, 297: 106508. DOI: 10.1016/j.enggeo.2021.106508
[24] XIAO Shihao, ZHANG Jie, YE Jiaming, et al. Establishing region-specific N-Vs relationships through hierarchical Bayesian modeling [J]. Engineering Geology, 2021, 287: 106105. DOI: 10.1016/j.enggeo.2021.106105
[25] XIAO Shihao, ZHANG Limin, XIAO Te, et al. Landslide damming threats along the Jinsha River, China [J]. Engineering, 2024, 42: 326-339. DOI: 10.1016/j.eng.2024.07.001
[26] WANG Bo, YANG Sha, CHEN Chen. Landslide dam breaching and outburst floods: A numerical model and its application [J]. Journal of Hydrology, 2022, 609: 127733. DOI: 10.1016/j.jhydrol.2022.127733
[27] 刘玄. 青海省贵德县阿什贡泥石流群风险性评价[D]. 北京: 中国地质大学(北京), 2014: 1-76. [LIU Xuan. Study on risk assesssment of debris flow in Ashigong Guide county, Qinghai province [D]. Beijing: China University of Geosciences(Beijing), 2014: 1-76]
[28] 马吉福, 殷志强, 魏刚, 等. 黄河上游贵德盆地二连村泥流堆积扇发育过程分析[J]. 水文地质工程地质, 2016, 43(4): 78-83. [MA Jifu, YIN Zhiqiang, WEI Gang, et al. An analysis of the development process of the Erlian mud flow fans in the eastern Guide Basin in the upper reaches of the Yellow River [J]. Hydrogeology and Engineering Geology, 2016, 43(4): 78-83] DOI: 10.16030/j.cnki.issn.1000-3665.2016.04.13
[29] 张淼. 黄河李家峡—公伯峡段活动断裂与滑坡灾害相关性研究[D]. 北京: 中国地质大学(北京), 2015: 1-82. [ZHANG Miao. Research on the relevance between active fault and landslide between Lijia Gorge and Gongbo Gorge in the upper region of Yellow River [D]. Beijing: China University of Geosciences(Beijing), 2015: 1-82]
[30] PARK D W, LEE S R, VASU N N, et al. Coupled model for simulation of landslides and debris flows at local scale [J]. Natural Hazards, 2016, 81: 1653-1682. DOI: 10.1007/s11069-016-2150-2

备注/Memo

备注/Memo:
收稿日期(Received date): 2025- 03-31; 改回日期(Accepted date):2025- 06-20
基金项目(Foundation item): 国家重点研发计划(2022YFC3004401); 国家自然科学基金(41772386)。[National Key Research and Development Program of China(2022YFC3004401); National Natural Science Foundation of China(41772386)]
作者简介(Biography): 赵晨澄(2000-),男,甘肃永靖人,硕士研究生,主要研究方向:地质灾害影响及评价。[ZHAO Chencheng(2000-), male, born in Yongjing, Gansu Province, M.Sc. candidate, research on evaluation and prediction of geological hazards] E-mail: zhaochencheng23@mails.ucas.ac.cn
*通讯作者(Corresponding author): 李秀珍(1975-),女,博士,研究员,主要研究方向:地质灾害评价及预测。 [LI Xiuzhen(1975-), female, Ph.D., professor, research on evaluation and prediction of geological hazards] E-mail: lxzljt@imde.ac.cn
更新日期/Last Update: 2025-05-30