Integrating geoinformatics and numerical modelling for landslide back-analysis and forecasting: a proactive mitigation study of the Shiv Bawri landslide

In the monsoon season of 2023, Himachal Pradesh witnessed the catastrophic Shiv Bawri landslide, underscoring the vulnerability of mountainous regions to natural disasters. This study employs advanced geoinformatics and numerical modelling to provide a comprehensive back-analysis and forecasting of landslide dynamics. A detailed methodology encompassing field investigations, drone surveys, and data compilation for rainfall and satellite imagery forms the basis of the analysis. A multi-phase mass flow model and the TRIGRS-derived factors of safety for pre-event and post-event analysis, considering vegetation’s influence through root reinforcement models, are employed. The findings reveal a high correspondence between modelled and actual landslide events, with the models effectively predicting the landslide’s volume, flow height, and velocity. The multi-phase mass flow calculations yield a volume estimate of 4.12 \(\times\) $\times$ 10⁴ m³ (post-event) and 2.92 \(\times\) $\times$ 10⁴ m³ (pre-event), with respective validation success rates of 88.99% and 93.9%. The analysis indicates maximum flow height and velocity of 14.2 m and 16.2 m/s for post-event and 12.1 m and 12.6 m/s for pre-event analysis. The study emphasises the necessity of integrating detailed terrain analysis and numerical modelling for effective landslide risk mitigation and preparedness. By providing insights into the complex interplay of natural factors leading to landslides, this research advances the proactive management of landslide risks in susceptible mountainous regions.