Abstract
Landslides have been and are prominent and devastating natural disasters in Bhutan due to its orography and intense monsoonal rainfall. The damage caused by landslides is huge, causing significant loss of lives, damage to infrastructure and loss of agricultural land. Several methods have been developed to understand the relationship between rainfall and landslide incidences. The most common method to understand the relationship is by defining thresholds using empirical methods which are expressed in either intensity-duration or event rainfall-duration terms. However, such thresholds determine the results in a binary form which may not be useful for landslide cases. Apart from defining thresholds, it is significant to validate the results. The article attempts to address both these issues by adopting a probabilistic approach and validating the results. The region of interest is the Chukha region located along the Phuentsholing-Thimphu Highway, which is a significant trade route between neighbouring countries and the national capital Thimphu. In the present study, probabilities are determined by Bayes’ theorem considering rainfall and landslide data from 2004 to 2014. Singular (rainfall intensity, rainfall duration and event rainfall) along with a combination (rainfall intensity and rainfall duration) of precipitation parameters were considered to determine the probabilities for landslide events. A sensitivity analysis was performed to verify the determined probabilities. The results depict that a combination of rainfall parameters is a better indicator to forecast landslides as compared to single rainfall parameter. Finally, the probabilities are validated using landslide records for 2015 using a threat score. The validation signifies that the probabilities can be used as the first line of action for an operational landslide warning system.
Funder
Natural Environment Research Council
Subject
Earth-Surface Processes,Waste Management and Disposal,Water Science and Technology,Oceanography
Cited by
27 articles.
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