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成都理工大学地质灾害防治与地质环境保护全国重点实验室,四川 成都,610059
Received:15 April 2025,
Published:05 June 2026
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尹道龙,常鸣,陈明,等.四川汉源小沟山洪泥石流侵蚀沉积特征及动力演进过程分析[J].武汉大学学报(信息科学版),2026,51(6):1242-1252.
YIN Daolong,CHANG Ming,CHEN Ming,et al.Analysis of the Erosion-Sedimentation Characteristics and Dynamic Evolution Process of Mountain Flood Debris Flows in Xiaogou, Hanyuan, Sichuan[J].Geomatics and Information Science of Wuhan University,2026,51(6):1242-1252.
尹道龙,常鸣,陈明,等.四川汉源小沟山洪泥石流侵蚀沉积特征及动力演进过程分析[J].武汉大学学报(信息科学版),2026,51(6):1242-1252. DOI:10.13203/j.whugis20250026
YIN Daolong,CHANG Ming,CHEN Ming,et al.Analysis of the Erosion-Sedimentation Characteristics and Dynamic Evolution Process of Mountain Flood Debris Flows in Xiaogou, Hanyuan, Sichuan[J].Geomatics and Information Science of Wuhan University,2026,51(6):1242-1252. DOI:10.13203/j.whugis20250026
中国四川省雅安市汉源县“7·20”暴雨事件引发的山洪泥石流灾害,是典型的暴雨诱发的山洪泥石流灾害。该地区地形复杂、降雨集中且强度大,极易发生山洪泥石流灾害,给当地生态环境和人民生活带来了严重影响。以小沟暴发的山洪泥石流为研究对象,利用小基线集合成孔径雷达干涉测量技术与OpenLISEM数值模型,结合无人机航摄影像和地面观测数据,对小沟流域的山洪泥石流的侵蚀沉积特征及动力演进过程进行分析。结果表明,小沟流域平均侵蚀速率为2 588 m
3
/(km
2
·a),年均侵蚀量为1.63×10
4
m
3
/a;在20年、50年、100年一遇的降雨频率下,小沟山洪泥石流的最大流速分别为18.00 m/s、17.78 m/s、17.64 m/s,最大泥深分别为6.38 m、6.69 m、6.89 m;在50年、100年一遇的降雨频率下,小沟冲出的固体物质较多,对沟口建筑物造成了较大影响,部分物质冲出沟口流入河道,可能抬高河床,造成白岩河半堵的情况,从而对下游工业园区带来较大威胁。该研究成果有助于提升西南山区山洪泥石流的预警能力,也可为灾后应急抢险和防灾减灾提供更科学的决策依据。
Objectives
2
On July 20
2024
an extreme rainstorm struck Hanyuan County
Ya'an City
Sichuan Province
China
delivering a peak hourly rainfall intensity of 53.8 mm and a cumulative precipitation of 107.5 mm within 12 hours. This event triggered a large-scale mountain flood debris flow in the Xiaogou watershed. The disaster filled and broke three check dams. Downstream drainage channels
bridges
houses
and infrastructure were severely damaged. Local residents' lives and property faced major threats.
Methods
2
To reveal the erosion‑sedimentation characteristics and dynamic evolution of this debris flow
the Xiaogou watershed is taken as the research object. The small baseline subset interferometric synthetic aperture radar (SBAS‑InSAR) technique and the OpenLISEM numerical model are used. Unmanned aerial vehicle (UAV) aerial images
high‑resolution remote sensing interpretations
and field survey data are also combined. First
22 Sentinel‑1A ascending images and 24 descending images from 2022 to 2024 are processed. The SBAS‑InSAR technique provides time‑series surface deformation information. Erosion and accumulation areas are identified. A coherence‑point statistical method is used to estimate the erosion rate and annual erosion volume. Second
UAV aerial photography and field surveys identify detailed sediment source distribution. The total volume of loose deposi
ts is calculated as 1.26×10
6
m³. Key sediment parameters are obtained through laboratory grain‑size and mechanical tests. The OpenLISEM model
which couples rainfall
surface runoff
debris flow initiation
erosion
transport
and deposition processes within a physically based framework
is then applied to simulate the entire dynamic evolution. The model's accuracy is validated against the measured deposition footprint and accumulation volume of the “7·20” event
achieving an overlapping accuracy coefficient
Ω
of 1.80
indicative of high reliability. Simulations are subsequently performed for rainfall return periods of 20
50
and 100 a.
Results
2
The average erosion rate in the Xiaogou watershed is 2 588 m³/(km²·a)
with an annual erosion volume of 1.63×10⁴ m³/a. Under rainfall frequencies with return periods of 20
50
and 100 a
the maximum flow velocities of the mountain flood debris flow in Xiaogou are 18.00 m/s
17.78 m/s
and 17.64 m/s
respectively
while the maximum sediment depths are 6.38 m
6.69 m
and 6.89 m
respectively. Under the 50 a and 100 a return periods
a significant amount of solid material is discharged
causing substantial impacts on the structures at the mouth of the gully. Part of the solid material rushes out of the gully into the river channel. It could raise the riverbed and cause semi-blockage of the Baiyan River. Consequently
a significant threat is posed to the downstream industrial park.
Conclusions
2
The integration of SBAS‑InSAR for pre‑disaster erosion‑sedimentation characterization and the OpenLISEM model for multi‑process dynamic simulation provides a robust technical pathway for analyzing debris flow evolution from source to deposition. The results help improve the early warning capability of mountain flood debris flow in the southwestern mountainous regions and offer a sound scientific foundation for post-disaster emergency response and disaster prevention strategies.
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