Landslide risk assessment of Kullu valley using frequency ratio methods and its controlling mechanism, Himachal Himalayas, India

Sansar Raj Meena*, Brijendra Kumar Mishra

*Corresponding author for this work

Research output: Contribution to conferenceAbstractAcademic

Abstract

Landslides are amongst the most damaging geologic hazards in mountainous regions like the Himalayas. Globally, every year hundreds of people lose their lives due to landslides; furthermore, there are significant impacts on the local and global economy from these events. However, in the Himalayan orogeny which is tectonically most Active Mountain in the world is highly vulnerable to landslides and associated hazards. The types of landslides rockfalls, rockslides and debris flow are most common natural hazards in Kullu valley, which causes major economic damage and it is the great concern to public administrators as well as geoscientists(Varnes 1978; Larsen and Montgomery 2012). The Kullu valley in Himachal Himalayas has a known history of large-scale landslides and different modes of slope deformation. The valley witnessed a major landslide in 1995 with the causality of 65 persons and substantial devastation in Luggar Bhatti area of Kullu town, which is known for prominent tourist destination(Mishra et al. 2018; Sah and Mazari 1998; Banshtu and Versain 2015). The purpose of this study is to analyse and characterise recent landslide events in the Larji–Kullu Tectonic Window (LKTW), and GIS-based frequency ratio (FR) statistical models for the generation of landslide susceptibility mapping to establish controlling mechanism of the landslides activity within the Kullu valley, Higher Himalayan region. There are nine factors (geological, morphological and topographical) including lithology, geomorphology, terrain slope, slope-aspect, elevation, distance to drainage, distance to fault, distance to lineaments and distance to the road are extracted from Landsat-8, Sentinel-2 images, Geological Survey of India quadrangle maps and Survey of India toposheets. Using multispectral satellite image analysis with selected field investigation, a landslide occurrence database is generated for the period between 1984 and 2015. The relationships between the landslide inventory locations and these nine controlling factors are identified by using GIS-based SMCE statistical models including. The landslide inventory map which has a total of 349 active landslides locations are delineated based on numerous resources such as Landsat-8, Sentinel-2 images and field surveys from 2014 to 2017 (Fig.1). Then, 70% of the landslide inventory is used for training the statistical models, and the remaining 30% is used for validation purpose. The validation results using the Area under the curve (AUC) of the Success rate curve (SRC) is demonstrated that the FR model (accuracy is 75%). To decipher the controlling mechanism of landslides an integrated study is undertaken in the Kullu (also known as Kulu) valley of Beas River basin within the LKTW complex, to analyse the litho-structural and terrain slope interactions using morpho-tectonic parameters such as Topographic/Bedding Plane Interaction Angle (TOBIA) index. A prominent clustering of landslides is observed in the north of Sainj River, contained within the tectonic window. Major sites of landslides are found to be located in the intensely fractured Manikaran Quartzite occurring within the core of the LKTW.
Original languageEnglish
Number of pages3
Publication statusPublished - 4 Dec 2018
Externally publishedYes
Event5th INQUIMUS 2018: Methods and tools to assess multi-hazard risk, vulnerability and resilience - Ca’ Foscari Challenge School, Venice, Italy
Duration: 3 Dec 20185 Dec 2018
Conference number: 5
http://www.inquimus.org/inquimus-2018/

Workshop

Workshop5th INQUIMUS 2018
Abbreviated titleINQUIMUS 2018
CountryItaly
CityVenice
Period3/12/185/12/18
Internet address

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