Publications

Abstract : 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 focuses on interlinking the climate, surface, and subsurface flows on the landslide events in the Cauvery River basin, Karnataka, India, using the Soil and Water Assessment Tool (SWAT). Relative humidity, solar irradiance, wind speed, temperature, and precipitation are needed for the model, along with slope, land use, and digital elevation model (DEM). Mann-Kendall trend analysis is carried out to detect trends in rainfall and temperature in the study area, as these parameters can directly influence the surface and subsurface flows. Preliminary results of the study indicate a strong correlation between the surface and subsurface flows on landslides along with the changing climatic conditions. The study region witnessed a series of landslide events in 2018, which, in turn, can be associated with the heavy surface flows observed using the model output for the same period. The derived correlation can further be used to forecast the landslide events with the surface and subsurface flow information. As an outcome, the study also proposes sustainable solutions to mitigate the impact of climate change and land conservation practices to protect the community.