Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan

Stefano Luigi Gariano (1), Raju Sarkar (2,3), Abhirup Dikshit (2), Kelzang Dorji (2), Maria Teresa Brunetti (1), Silvia Peruccacci (1), Massimo Melillo (1), 2018, Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan, Bulletin of engineering geology and the environment (Internet) (2018): 1–8. doi_10.1007/s10064-018-1415-2,
URL: http://www.cnr.it/prodotto/i/393392

Bhutan is highly prone to landslides, particularly during the monsoon season. Several landslides often occur along the Phuentsholing-Thimphu highway, a very important infrastructure for the country. Worldwide, empirical rainfall thresholds represent a widely used tool to predict the occurrence of rainfall-induced landslides. Nevertheless, no thresholds are currently designed and proposed for any region in Bhutan. In this work, we define empirical cumulated event rainfall-rainfall duration thresholds for the possible initiation of landslides using information on 269 landslides that occurred between 1998 and 2015 along the 90-km highway stretch between the towns of Phuentsholing and Chukha, in southwestern Bhutan, and daily rainfall measurements obtained from three rain gauges. For this purpose, we apply a consolidated frequentist method and use an automatic tool that identifies the rainfall conditions responsible for the failures and calculates thresholds at different exceedance probabilities and the uncertainties associated with them. Analyzing rainfall and landslide data, we exclude from the analysis all the landslides for which the rainfall trigger is not certain, so we reduce the number of landslides from 269 to 43. The calculated thresholds are useful to identify the triggering conditions of rainfall-induced landslides and to predict the occurrence of the failures in the study area, which is, to date, poorly studied. These rainfall thresholds might be implemented in an early warning system, in order to reduce the risk posed by these phenomena to the population living and traveling along the investigated road stretch.

Data from https://intranet.cnr.it/people/