The July – September 2023 monsoon season in Himachal Pradesh, India, triggered a cascade of hydro-meteorological hazards – primarily floods and landslides – resulting in 428 fatalities (over 40% due to slope failures), displacement of more than 50,000 people, and economic losses exceeding INR 5,000 crore. Kangra, Kullu, and Chamba districts recorded rainfall over 1,800 mm – more than 150% above long-term averages. This study adopts a mixed-methods design, integrating high-resolution satellite imagery (Sentinel-2, Landsat-8), geospatial modelling (Kriging, Inverse Distance Weighting, Kernel Density Estimation, Getis-Ord Gi*), and terrain analysis with advanced statistical techniques, including Mann-Kendall trend tests, Pearson's correlation, multiple and geographically weighted regressions, and spatial autocorrelation (Moran's I). A flood severity model explained 85% of observed variance, identifying vegetation loss ( > 40%), steep slopes, and extreme precipitation as primary disaster predictors. Stratified household surveys (n = 240) revealed systemic deficiencies in early warning dissemination, evacuation logistics, and post-disaster recovery – particularly in remote, high-altitude, and rain-fed communities. These findings inform a spatially targeted, evidence-based disaster risk reduction framework. The study recommends district-specific strategies – such as lope stabilisation, floodplain zoning, and green infrastructure – that align with key Sustainable Development Goals and offer a scalable model for climate resilience in mountainous regions.
Ajitesh Singh Chandel (Thu,) studied this question.