Projecting the effect of climate change on multiple Geomorphological hazard using machine learning data driven approaches

This study provides crucial insights into the interplay between climate change and geomorphological hazards, specifically LS and CP, in Khorasan-Razavi province, Iran. By implementing an ensemble forecasting approach, the authors have successfully pinpointed areas vulnerable to these hazards under both current and projected future climate scenarios.

The findings reveal that key factors influencing susceptibility include slope and clay content for CP, and distance from faults, and precipitation seasonality for LS. Notably, while 57.16% of the region currently remains safe from both hazards, a concerning 6.16% faces dual risks, with over 35% exposed to at least one type of hazard. Projections for the future indicate a dramatic rise in susceptibility, especially under high-emission scenarios (SSP 5-8.5), suggesting that by 2070, nearly half of the region may be at risk for LS, particularly in southern hotspots.