SLOPE STABILITY ASSESSMENT IN KALIMPONG REGION OF DARJEELING HIMALAYAS

Abstract

The Darjeeling Himalayas, particularly the Kalimpong region, experience frequent landslides due to a complex interplay of geological, hydrological, seismic, and anthropogenic factors. This research provides a comprehensive slope stability assessment of Kalimpong using a multidisciplinary approach that integrates geotechnical investigation, numerical modeling, and geospatial analysis. The study begins with a detailed review of the region’s geology, geomorphology, and historical landslide activity. Field investigations were conducted to collect soil and rock samples, and extensive laboratory tests were performed to determine key geotechnical parameters such as cohesion, internal friction angle, unit weight, and permeability. Using these inputs, slope stability was evaluated through GeoStudio SLOPE/W software, applying the Morgenstern-Price method under various conditions—static and dynamic, dry and saturated. The results revealed that many natural slopes in Kalimpong are marginally stable under dry conditions but exhibit critical instability when subjected to rainfall infiltration and seismic forces. The Factor of safety (FOS) significantly dropped below 1.0 for several slopes under dynamic-saturated scenarios, indicating a high probability of failure. Furthermore, stabilization strategies such as soil nailing were modelled and validated in SLOPE/W, showing significant improvements in FOS values and thus enhancing slope resilience. To complement the site-specific analyses, the study incorporated GIS-based landslide susceptibility mapping using the Frequency ratio (FR) model. Geospatial layers representing conditioning factors—including slope angle, aspect, elevation, lithology, proximity to roads and faults, and rainfall intensity—were developed using Shuttle Radar Topography Mission (SRTM) Digital Elevation model (DEM) and remote sensing data. The FR model quantified the correlation between historical landslide events and each parameter, producing a susceptibility zonation map that classified the region into low, moderate, and high-risk zones. Approximately 38% of the study area fell into vi moderate-to-high susceptibility classes, aligning with known landslide-prone corridors and anthropogenic ally disturbed slopes. This dual approach—merging deterministic Limit Equilibrium method (LEM) based modelling with probabilistic geospatial assessment—allowed for both micro and macro-scale understanding of slope instability in the region. The study also outlines policy recommendations, emphasizing the integration of slope stability analysis in infrastructure planning, particularly in seismic zones and monsoon-affected terrains. Overall, the thesis delivers a robust framework for landslide hazard mitigation in the Kalimpong region and sets the foundation for future research incorporating machine learning models, real-time monitoring, and climate change projections for improved early warning and slope management strategies.