The frequency of landslides has increased recently due to uneven patterns of weather distribution and climate change. Major landslide predictions are based on rainfall models, and only a few studies consist of hydrological models, leading to low accuracy. On the other hand, using hydrological parameters has considerably increased the accuracy of landslide predictions. This study focused on interlinking the climate, surface, and subsurface flows (lateral flow) on the landslide events in the Cauvery River basin, Karnataka, India, using the Soil and Water Assessment Tool (SWAT). Preliminary results of the study indicate an increasing trend in the climatic variables. The subsurface flow generated using the model indicated a strong connection between the subsurface flow and landslides along with the changing climatic conditions. However, no such link is observed in the case of surface flow with landslide events. The study region witnessed a series of landslide events in 2018, which, in turn, is associated with the heavy subsurface flow observed using the model output for the same period. This interconnection between the subsurface flow and landslide events can further be used to enhance the accuracy of the existing landslide warning system and also to predict the potential landslide-prone areas in the future due to the changing climatic conditions.
Amrita Team Members : Ms. Surya Harilal, MTech Student, WNA
Name of the International Collaborators : Dr. Cibin Raj, Associate Professor, Pennsylvania State University, USA
