Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya

Mountainous regions are highly hazardous, and these hazards often lead to loss of human life. The Hindu Kush Himalaya (HKH), like many mountainous regions, is the site multiple overlapping natural hazards, but distribution multi-hazard risk populations exposed it poorly understood. Here, we present high-resolution transboundary models describing susceptibility floods, landslides, wildfires understand population exposure across HKH. These created from historical remotely sensed data hazard catalogs by maximum entropy (Maxent) machine learning technique. Our results show that settlements in HKH disproportionately concentrated areas high risk. In contrast, low-hazard unpopulated. Nearly half region lives susceptible more than one hazard. Warm low-altitude foothill with perennially moist soils were identified as hazards. This area comprises only 31% study region, home 49% its population. also major corridors current migration urban expansion, suggesting rates patterns urbanization will continue put people at establishes suggests movement increase multi-hazards