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 empirical 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 increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.
Highlights
Rainfall induced landslides are among the major natural disasters in hilly terrains, with an alarmingly high frequency of occurrence [1]
Moisture conditions are simulated for the soil above phreatic line, in a shallow depth which is often susceptible to shallow landslides
The study proves Système Hydrologique Européen Transport (SHETRAN) model can be used as an effective tool to predict moisture content of the region when direct field-based observations are not available
Summary
Rainfall induced landslides are among the major natural disasters in hilly terrains, with an alarmingly high frequency of occurrence [1]. Such incidents are becoming common in Indian. The need for forecasting landslides induced by rainfall is vital for minimizing losses and managing the hazard. Forecasting landslides can reduce the causalities by providing a warning to the officials and public in general. For an effective warning system to work, it is crucial to identify the rainfall threshold conditions, which can initialize a landslide event. The threshold can be set on process or empirical bases [2]. Process/physically based thresholds require detailed field investigations and continuous monitoring of physical parameters. The hydrological, Water 2020, 12, 804; doi:10.3390/w12030804 www.mdpi.com/journal/water
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