Multi-Scale Rainfall Thresholds for Landslide Prediction: Advancing Early Warning Systems in Diverse Landscapes of Nepal

In Nepal, efforts to establish landslide-triggering rainfall thresholds across multiple scales are well underway, aiming to enhance the effectiveness of landslide early warning systems (LEWS). These thresholds are being developed at regional, provincial, municipal, and single-slope levels, supporting landslide prediction across diverse geographic and administrative contexts.

In the vast and landslide-prone Himalayan region, establishing a regional rainfall threshold is crucial. Analysis of historical data from 1951–2006 covering 677 landslides identified a threshold relationship between rainfall intensity and duration, revealing that daily precipitation exceeding 144 mm significantly increases landslide risk. This regional threshold, developed by Dahal and Hasegawa (2008), serves as a valuable basis for early warnings across the Nepal Himalaya, providing essential risk management information for large-scale events.

Landslides in Nepal are frequently triggered by high-intensity rainfall, seismic activity, and hillside modification. A study using satellite rainfall data and landslide records from 2011 to 2022 developed a provincial-level threshold for Bagmati Province. The threshold equation at a 5% non-exceedance probability indicates that even low rainfall levels can trigger landslides due to geological weaknesses, particularly following the 2015 Gorkha Earthquake. This provincial threshold has been validated through real-time analysis, establishing a robust foundation for LEWS of Bagmati Province.

At the municipal level, rainfall thresholds have been developed for Helambu and Panchpokhari Thangpal municipalities. Using inventory data, intensity-duration threshold equations were established with high accuracy, showing strong predictive capability for landslides in these areas. The correlation between landslide susceptibility and terrain features underscores the importance of localized LEWS and community awareness initiatives to improve response during intense rainfall events.

On a single-slope scale, a physically based model was used to establish a landslide threshold for a slope on the Narayangadh-Mugling road. Here, variations in pore water pressure were analyzed under different rainfall return periods, revealing that increased topographic hollow size amplifies pore water pressure, which elevates landslide risk. The slope at Nau Kilo, with an extremely low safety factor, is highly susceptible to collapse during heavy rainfall, underscoring the need for targeted monitoring and stabilization at high-risk sites.

Developing these multilevel rainfall thresholds, tailored to Nepal’s diverse landscapes, provides essential tools for advancing LEWS and reducing landslide impacts on vulnerable communities. Enhancing rain gauge density, ensuring consistent landslide data management, and refining thresholds continuously will further improve prediction accuracy, offering valuable insights for disaster preparedness and community risk reduction across landslide-prone areas of Nepal.