Using the Topographic Wetness Index (TWI) to Identify Landslide Risks to Rural Water Supplies and Sanitation in the Indian Himalayan

Abstract

Rural water supply and sanitation facilities across the Himalayan region are frequently destroyed or contaminated by landslides. Despite this, WASH (Water, Sanitation, and Hygiene) infrastructure is rarely considered in hazard assessments. This study demonstrates that the Topographic Wetness Index (TWI) can effectively identify landslide-vulnerable water sources in data-scarce mountain environments. Using landslide inventory from the Bhukosh portal for Solan district in Himachal Pradesh, India, logistic regression models and quadratic discriminant functions were employed to calculate TWI, rainfall and Stream Power Index (SPI). Results indicated that TWI explained 75.7% of the variation in landslide incidence, which is greater than yearly rainfall (R² = 0.6598) and far greater than the SPI model (R² = 0.5094). The high predictive value of TWI was confirmed by logistic regression (AUC = 0.872) with an average AUC (based on five-fold cross-validation) of 0.865 (±0.021). Overlay mapping indicates that high-TWI (TWI > 6.2) areas have the greatest susceptibility to landslides; 78% of the springs and 71% of the shallow wells are in this zone. The integrated software of the full research was under $500 (approximately ₹43,000) and this satellite-based data is free and can be used by district WASH departments to replicate the software. The data deposited in Zenodo show that after a significant landslide in August 2023, the turbidity in the impacted springs increased by 359%, and the coliform counts jumped from less than 10 to more than 500 MPN/100 mL. Simple, low-cost interventions identified to be effective in reducing risk were cleaning of drains around springs, diversion of run-off, moving latrines away from high TWI areas, and implementing post landslide water quality emergency procedures. TWI mapping is an approach that can be implemented realistically and reproducibly for climate-resilient water, sanitation, and hygiene (WASH) planning in the Himalayan regions.