Enhancing Landslide Hazard Assessment Using Monte Carlo Simulations and Improved Soil Thickness Mapping in Uttarakhand, India

Abstract

Landslides are a major threat to communities and infrastructure in mountainous regions, highlighting the need for precise landslide hazard assessments to enhance risk management strategies. This study aimed to enhance landslide hazard assessment methodologies by incorporating Monte Carlo simulation (MCS) and refining soil thickness mapping techniques. The study area, a crucial road section situated in the Joshimath region of Uttarakhand, India, is characterized by steep terrain and high rainfall intensity, making it prone to landslides triggered by transient rainfall infiltration. Previous studies have identified limitations in existing methodologies, particularly regarding the characterization of uncertainties in geotechnical and hydrological factors and the accuracy of soil thickness mapping. To address these limitations, the present study applied MCS separately for each layer within the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, allowing for a detailed understanding of uncertainties associated with key factors such as cohesion, soil unit weight, and internal friction angle. Additionally, the study developed the geomorphologically indexed soil thickness (GIST)-MCS model for soil thickness mapping, leveraging its higher accuracy compared to traditional models such as the Z-model. The findings of the study indicate that the MCS-based approach offers a pragmatic improvement in the accuracy and reliability of landslide hazard assessments by enabling better estimations of factor of safety values. Furthermore, the GIST-MCS model presents an alternative modeling approach that enhances both the accuracy of soil thickness estimation and computational efficiency. This improvement contributes to the generation of more reliable landslide hazard maps. The results highlight the value of adopting advanced and efficient modeling techniques to strengthen landslide risk assessment and support more effective mitigation strategies in the Joshimath region and other landslide-prone areas worldwide. © 2025 Elsevier B.V., All rights reserved.