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

Abstract

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.

Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas

“…The cell conditions are defined based on the six threshold classifications and five soil wetness classifications. Thresholds are classified as less than T min , T min -T 5 , T 5 -T 10 The values indicate that for one day duration (24 hours) the chances of occurrence of landslide when the rainfall is less than 11.83 mm is nil, 18.78 mm is 5%, 26.11 mm is 10%, 32.19 mm is 20%, and 47.58 mm is 50%.…”

“…The physically based thresholds work on integrated hydrological model and slope stability analysis while empirical thresholds are developed based on rainfall and occurrence of landslide in the past only [6]. Hence empirical thresholds are often followed by practitioners [2,[7][8][9][10], as it bypasses the difficulties in understanding and evaluating the physical processes involved in the initiation of landslides [6,[11][12][13]. The threshold defines a critical condition of rainfall, if crossed can initiate failure of slopes in the region.…”

“…The variables generally used to define empirical threshold conditions are intensity-duration (ID), event rainfall-duration (ED) and antecedent rainfall. ID threshold was first proposed by Caine in 1980 [7] and has been used extensively by researchers [2,8,10,17]. In most cases, for long observation periods, the term intensity denotes the average rainfall over the duration, ignoring the peak values, which makes the ID threshold models complicated.…”

“…One predicts the minimum levels to initiate slides (minimum thresholds) and the other balances the number of true and false warnings while forecasting landslides (warning thresholds). Establishing minimum thresholds for different locations has been in practice across the world [7,8,10,14]. These are very conservative in nature and may lead to false alarms.…”

Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

“…The only input data used for the threshold definition are a dataset of rainfall recordings and a catalogue of landslides for which the time and location of occurrence are known with sufficient approximation. This approach completely bypasses the physical mechanism of triggering, thus simplifying the modeling effort, the computational resources required, and the amount of data needed for the analysis [5].During the last few decades, many attempts were made across the world to define critical rainfall thresholds based on a number of different rainfall parameter, but the most common are intensity and duration (ID thresholds) [6][7][8][9][10][11][12][13], total event rainfall and duration (ED thresholds) [14-18] and antecedent rainfall [19][20][21][22]. The selection of the optimal rainfall parameters which are used for defining the threshold depends mainly on the landslide typology and physical characteristics of the region.…”