DEVELOPING A DYNAMIC LANDSLIDE SUSCEPTIBILITY MODEL FOR THE WESTERN HIMALAYAN REGION

Abstract

In the western Himalayas of India, landslides pose a constant threat to the region's ecosystems, communities, and development. Rainfall is a key trigger and as it intensifies it further exacerbates the landslide risk. This study focuses on the western Himalayas in Uttarakhand, India, where landslide occurrence is exacerbated by increased rainfall which causes substantial damages annually. The dynamic nature of landslide susceptibility refers to the fact that, the susceptibility of a landscape to landslides can change over time due to various factors and processes. This study recognizes that the conditions influencing landslide occurrence are not static but are subject to continual modification and evolution. Environmental controls such as slope, drainange and fault proximities, rainfall, and lithology contribute to susceptibility of the landscape to landslides. Rainfall is a predominant trigger, when water infiltrates the ground it increases the soil moisture content in-turn reducing shear strength and elevating pore water pressure hence results in inducing slope instability. The effects of rainfall and ever evolving geological and environmental conditions on landslide susceptibility are dynamic and necessitate assessment before and after extreme events. This study addresses this gap with two main objectives. First, we developed strategies for organizing the landslide inventory and relevant geospatial data like slope, lithology, rainfall etc, to effectively capture temporal variations. Second, we conducted an extensive evaluation of how temporal variations impact landslide susceptibility model performance. This includes testing models across different configurations to see how performance changes, and formulating and testing hypotheses about extreme events. By unravelling the intricate relationship between landslide susceptibility and the impact of extreme events, this research contributes valuable insights for landslide risk assessment and management in the vulnerable western Himalayas. This study underscores the critical need for dynamic susceptibility models that can account for the ever-evolving nature of the Himalayan landscape.