Enhancing Landslide Hazard Assessment Using Monte Carlo Simulations and Improved Soil Thickness Mapping in Uttarakhand, India
Bibliographic Data
| ID | 21738255 |
|---|---|
| Authors | Kunal Gupta (0000-0002-4166-2743, Indian Institute of Technology Indore), Neelima Satyam (0000-0002-5434-0671, Indian Institute of Technology Indore) |
| Year | 2026 |
| Volume | 27 |
| Issue | 1 |
| Publication date | 2026-02-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Natural Hazards Review (JOURNAL) |
| Journal identifiers | ISSN: 1527-6988 • E-ISSN: 1527-6996 |
| Publisher | American Society of Civil Engineers (ASCE) (PUBLISHER • US) |
| DOI | 10.1061/nhrefo.nheng-2449 |
| OpenAlex | W4415046746 |
| Language | EN |
| References cited | 52 |
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
Hazard · Hazard analysis · Landslide · Monte Carlo method · Natural hazard · Terrain · Geotechnical Engineering and Analysis · Landslides and related hazards · Soil and Unsaturated Flow
A review of statistically-based landslide susceptibility models
Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region
Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region
| Highly cited | No |
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