Abstract
Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system.
Highlights
Landslides are one of the most destructive natural disasters, causing massive loss of human lives and property [1]
The reconstruction of rainfall thresholds is performed by means of a Calculation of Thresholds for Rainfall-Induced Landslides-Tool (CTRL-T) proposed in [15,24]
The tool works in three different steps: (i) The input is received as continuous rainfall series and distinct rainfall events are reconstructed determining the duration (D) in hours and cumulated event rainfall (E) in mm. (ii) The reconstructed rainfall events depend on the selection of a rain gauge to minimize the effect of spatial variability of precipitation distribution (this is achieved by reconstructing single or multiple rainfall conditions (MRC) most likely to result in failures and assigning a weight to them); (iii) the tool reconstructs the MRC within the selected rainfall event and assigns a weight (w) proportional to the cumulated rainfall and the mean intensity of the MRC and to the inverse square distance between the rain gauge and the landslide
Summary
Landslides are one of the most destructive natural disasters, causing massive loss of human lives and property [1]. One of the most affected regions in this area is the Darjeeling-Sikkim Himalayas, which contributes to 40% of the land area in India susceptible to landsliding [3]. Landslides in this region cause havoc during the monsoon season, incurring substantial human and financial losses and disrupting the livelihood of local people. One of the most affected areas in this region is Kalimpong, where a high number of landslides are initiated by monsoonal rainfall (Geological Survey of India Report, 2016)
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