DYNAMIC LANDSLIDE RISK MAPPING FOR INDIA BY INTEGRATING STATIC SUSCEPTIBILITY WITH REAL-TIME RAINFALL DATA

Abstract

Landslide hazards in India cause significant socio-economic losses annually, particularly during monsoon seasons. Traditional static landslide susceptibility maps identify vulnerable terrain but lack the temporal dynamics required for operational early warning systems. This study integrates the India Landslide Susceptibility Map (ILSM) with real-time daily gridded rainfall data from the Indian Meteorological Department (IMD) to create dynamic landslide risk maps for India. The dynamic risk model integrates static terrain susceptibility (100 m resolution) with daily rainfall data (0.25° resolution) through a multiplicative weighting approach, modulating baseline risk based on precipitation intensity. Validation against 769 rainfall-triggered landslide events from NASA’s Cooperative Open Online Landslide Repository (COOLR) spanning 27 test dates (2015–2018) demonstrates substantial performance improvements over static approaches. The dynamic model achieved mean AUC of 0.921 ± 0.095, precision of 0.891 ± 0.084, recall of 0.877 ± 0.083, and F1-score of 0.881 ± 0.082, compared to static ILSM precision of essentially 0.000 despite high AUC of 0.982. This represents approximately 8000-fold improvement in F1-score and demon- strates operational viability. The integrated framework provides a practical, data-driven approach for enhanced landslide early warning and disaster risk reduction in India’s monsoon-affected moun- tainous regions.