Landslide Susceptibility Assessment using Open-Source Data in the Far Western Nepal Himalaya: Case Studies from Selected Local Level Units

Abstract

This paper explores openly available geo-spatial and earth observatory data to understand landslide risk in data scarce rural areas of Nepal. It attempts to explore the application of open-source data and analytical models to inform future landslide research. The first step of this procedure starts from the review of global open datasets, literatures and case studies relevant to landslide research. The second step is followed by the case study in one of the mountainous municipalities of Nepal where we tested the identified open-source data and models to produce landslide susceptibility maps. Past studies and experiences show that the major potential sites of landslide in Nepal are highly concentrated in a geologically weak area such as the active fault regions, shear zones, axis of folds and unfavorable setting of lithology. Triggering factors like concentrated precipitation, frequent earthquake phenomenon and haphazard infrastructural development activities in the marginally stable mountain slopes have posed serious issues of landslides mostly through the geologically weak regions. In this context, openly available geo-spatial datasets can provide baseline information for exploring the landslide hazard scenario in the data scarce areas of Nepal. This research has used the available open-source data to produce a landslide susceptibility map of the Bithadchir Rural Municipality in Bajhang District and Budiganga Municipality in Bajura District of the Sudurpaschim Province of Nepal. We used qualitative analysis to evaluate the parameters and assess the susceptibility of landslide; the result was classified into five susceptibility zones: Very High, High, Moderate, Low, and Very Low. Slope and Aspect were identified to be the major determinants for the assessment. This approach is applicable, specifically, for the preliminary investigation in the data scarce region using open data sources. Furthermore, the result can be used to plan and prioritize effective disaster risk reduction strategies.