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Characteristics and dynamic analysis of the February 2021 long-runout disaster chain triggered by massive rock and ice avalanche at Chamoli, Indian Himalaya

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第2期15卷 296-308页

作者： Tiantian Zhang Yueping Yin Bin Li Xiaojie Liu Meng Wang Yang Gao Jiawei Wan Kaushal Raj Gnyawali Institute of Geomechanics Chinese Academy of Geological SciencesBeijing100081China China Institute of Geological Environment Monitoring China Geological SurveyBeijing100081China College of Geology Engineering and Geomatics Chang’an UniversityXi’an710100China Sichuan Geological Survey Institute Chengdu610081China Natural Hazards Section Himalayan Risk Research Institute(HRI)Bhaktapur44800Nepal

A massive rock and ice avalanche occurred on the western slope of the Ronti Gad valley in the northern part of Chamoli,Indian Himalaya,on 7 February 7,*** avalanche on the high mountain slope at an elevation of 5600 m above sea level triggered a long runout disaster chain,including rock mass avalanche,debris avalanche,and *** disaster chain had a horizontal travel distance of larger than 17,600 m and an elevation difference of 4300 *** this study,the disaster characteristics and dynamic process were analyzed by multitemporal satellite *** results show that the massive rock and ice avalanche was caused by four large expanding discontinuity *** disaster chain was divided into five zones by satellite images and field observation,including source zone,transition zone,dynamic entrainment zone,flow deposition zone,and flood *** entrainment effect and melting water were recognized as the main causes of the long-runout *** on the seismic wave records and field videos,the time progress of the disaster was analyzed and the velocity of frontal debris at different stages was *** total analyzed disaster duration was 1247 s,and the frontal debris velocity colliding with the second hydropower station was approximately 23 m/*** study also carried out the numerical simulation of the disaster by rapid mass movement simulation(RAMMS).The numerical results reproduced the dynamic process of the debris avalanche,and the mechanism of long-runout avalanche was further verified by parametric ***,this study discussed the potential causes of disaster and flood and the roles of satellite images and seismic networks in the monitoring and early-warning.

关键词： Rock and ice avalanche Disaster chain long-runout Entrainment Flood

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An AI-Based Method for Estimating the Potential runout Distance of Post-Seismic Debris Flows

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INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE 2024年第4期15卷 608-621页

作者： Qiu, Chenchen Su, Lijun Bian, Congchao Zhao, Bo Geng, Xueyu Univ Warwick Sch Engn Coventry CV4 7AL England Chinese Acad Sci Inst Mt Hazards & Environm IMHE Key Lab Mt Hazards & Earth Surface Proc Chengdu 610041 Peoples R China Hohai Univ Sch Comp & Informat Nanjing 211100 Peoples R China

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method-Dual Attention Network (DAN)-was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) Th

关键词： Deep learning GPM downscaling Post-seismic debris flows runout distance prediction 2022 Luding earthquake WENCHUAN EARTHQUAKE long-runout RAINFALL HAZARD LANDSLIDE PRECIPITATION VARIABILITY REGRESSION THRESHOLD SATELLITE

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