Forecasting Landslides for Disaster Risk Reduction: Process-Based Approaches and Real-Time Field Monitoring

Abstract

Rainfall-induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the statistical methods are defined in such a way that it does not depend upon any of the in-situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increases and loss in shear strength of soil results in sliding of soil mass, which in turn are termed as landslides. This study explains in detail the potential use of hydrological and process-based models in forecasting the occurrence of landslides in Kalimpong town of West Bengal, India. The town is a part of Darjeeling Himalayas and is highly affected by landslides. The study evaluates the potential use of a hydrological model, called SHETRAN, along with the real-time field monitoring observations, using MEMS tilt sensors installed in the study area. The initiation of landslides is discussed in a geotechnical perspective, for the development of a landslide early warning system (LEWS) for the region. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.