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Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas
Open AccessArticle

Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Discipline of Civil Engineering, Indian Institute of Technology Indore, Madhya Pradesh 452020, India
Author to whom correspondence should be addressed.
Water 2019, 11(10), 2113; (registering DOI)
Received: 29 August 2019 / Revised: 7 October 2019 / Accepted: 9 October 2019 / Published: 11 October 2019
Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D-0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.
Keywords: rainfall thresholds; landslides; Idukki; early warning system rainfall thresholds; landslides; Idukki; early warning system
MDPI and ACS Style

Abraham, M.T.; Pothuraju, D.; Satyam, N. Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach. Water 2019, 11, 2113.

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