Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system

Abstract

The Himalayan region has been severely affected by landslides especially during the monsoons. In particular, Kalimpong region in Darjeeling Himalayas has recorded several landslides and has caused significant loss of life, property and agricultural land. The study region, Chibo has experienced several landslides in the past which were mainly debris and earth slide. Globally, several types of rainfall thresholds have been used to determine rainfall-induced landslide incidents. In this paper, probabilistic thresholds have been defined as it would provide a better understanding compared to deterministic thresholds which provide binary results, i.e., either landslide or no landslide for a particular rainfall event. Not much research has been carried out towards validation of rainfall thresholds using an effective and robust monitoring system. The thresholds are then validated using a reliable system utilizing Microelectromechanical Systems (MEMS) tilt sensor and volumetric water content sensor installed in the region. The system measures the tilt of the instrument which is installed at shallow depths and is ideal for an early warning system for shallow landslides. The change in observed tilt angles due to rainfall would give an understanding of the applicability of the probabilistic model. The probabilities determined using Bayes’ theorem have been calculated using the rainfall parameters and landslide data in 2010–2016. The rainfall values were collected from an automatic rain gauge setup near the Chibo region. The probabilities were validated using the MEMS based monitoring system setup in Chibo for the monsoon season of 2017. This is the first attempt to determine probabilities and validate it with a robust and effective monitoring system in Darjeeling Himalayas. This study would help in developing an early warning system for regions where the installation of monitoring systems may not be feasible.