Abstract
Assessment of landslide risk is crucial given the substantial related economic losses and infrastructure damage in mountain areas every year. Particularly, the Sagarmatha National Park (SNP), a key destination for Himalayan glacier tourism, remains relatively understudied in this context. Existing studies primarily focus on regional inventories or simply inventory landslides and lack tourism-specific hazard assessment. This study evaluates landslide distribution, its controlling factors, and the exposure of infrastructure to varying degrees of landslide susceptibility in SNP. A blind inventory of 680 landslides and twelve conditioning factors, including six topographic and six non-topographic variables, were analyzed using Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) models. In addition, spatial overlay analysis was employed to assess the degree of infrastructure exposure. Results indicate that Land Surface Temperature (LST) is the most dominant factor influencing landslides occurrence, followed by rainfall, elevation, and slope, along with specific aspects like south and west and, land cover class like Barren land and Alpine meadows. Random Forest achieved the highest predictive accuracy (91%), outperforming both Logistic Regression (87%) and Frequency Ratio (84%). Exposure assessment of key tourism infrastructure indicates that trekking routes, helipads, buildings, campsites, and bridges are subject to varying levels of landslide risk. Although only 2.73 km (0.52%) of trekking routes intersect active landslide scars, 147 km (28%) lie within high-exposure zones. Consequently, both typical and paraglacial landslides threaten access to glacier tourism destinations, highlighting significant implications for Nepal’s tourism.