Numerical Modelling of Debris Flows for Simulation-Based Decision Support: An Indian Perspective

被引:5
作者
Abraham, Minu Treesa [1 ,2 ]
Satyam, Neelima [3 ]
Kowalski, Julia [1 ]
机构
[1] Rhein Westfal TH Aachen, Methods Model based Dev Computat Engn, Aachen, Germany
[2] Norwegian Geotech Inst, Instrumentat & Monitoring Sect, Oslo, Norway
[3] Indian Inst Technol Indore, Dept Civil Engn, Indore, Madhya Pradesh, India
关键词
Debris flows; Numerical models; Landslides; Risk reduction; Model-based decision support; LANDSLIDE SUSCEPTIBILITY; ENTRAINMENT; MASS; HAZARD; CLASSIFICATION; SENSITIVITY; GIS;
D O I
10.1007/s40098-024-00988-5
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Debris flows are catastrophic landslides owing to their very high velocities and impact. The number of such flows is likely to increase due to an increase of extreme weather events in a changing climate. At the same time, risk reduction and mitigation plans call for a quantitative assessment of the hazard. Numerical models are powerful tools in quantifying debris flows in terms of flow height and velocity with respect to both space and time, and to derive mitigation-relevant diagnostics such as impacted area. However, the current modelling practices possess critical challenges that limit their application in a forward-directed analysis to predict the debris flow's impact. This work provides an overview of the past and current practices in debris flow modelling, their potential use in simulation-based decision support and the challenges and future research scope in computational debris flow modelling, based on the recent literature.
引用
收藏
页码:2680 / 2692
页数:13
相关论文
共 72 条
[1]   Two methodologies to calibrate landslide runout models [J].
Aaron, Jordan ;
McDougall, Scott ;
Nolde, Natalia .
LANDSLIDES, 2019, 16 (05) :907-920
[2]   Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring [J].
Abraham, Minu T. ;
Satyam, Neelima ;
Pradhan, Biswajeet ;
Segoni, Samuele ;
Alamri, Abdullah .
GEOSCIENCES JOURNAL, 2022, 26 (02) :289-301
[3]   A novel approach for quantifying similarities between different debris flow sites using field investigations and numerical modelling [J].
Abraham, Minu Treesa ;
Satyam, Neelima ;
Pradhan, Biswajeet .
TERRA NOVA, 2024, 36 (02) :138-147
[4]   Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India [J].
Abraham, Minu Treesa ;
Vaddapally, Manjunath ;
Satyam, Neelima ;
Pradhan, Biswajeet .
CATENA, 2023, 223
[5]   Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms [J].
Abraham, Minu Treesa ;
Satyam, Neelima ;
Jain, Prashita ;
Pradhan, Biswajeet ;
Alamri, Abdullah .
GEOMATICS NATURAL HAZARDS & RISK, 2021, 12 (01) :3381-3408
[6]   Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting [J].
Abraham, Minu Treesa ;
Satyam, Neelima ;
Lokesh, Revuri ;
Pradhan, Biswajeet ;
Alamri, Abdullah .
LAND, 2021, 10 (09)
[7]   Runout modeling and calibration of friction parameters of Kurichermala debris flow, India [J].
Abraham, Minu Treesa ;
Satyam, Neelima ;
Reddy, Sai Kumar Peddholla ;
Pradhan, Biswajeet .
LANDSLIDES, 2021, 18 (02) :737-754
[8]   IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas [J].
Abraham, Minu Treesa ;
Satyam, Neelima ;
Pradhan, Biswajeet ;
Alamri, Abdullah M. .
SENSORS, 2020, 20 (09)
[9]   Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters [J].
Abrahama, Minu Treesa ;
Satyama, Neelima ;
Pradhan, Biswajeet ;
Tian, Hongling .
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2022, 14 (06) :1747-1760
[10]  
[Anonymous], 2009, TERM DIS RISK RED