The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)

Abstract

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

Uncertainty analysis of a rainfall threshold estimate for stony debris flow based on the backward dynamical approach

“…Indeed, CV is very sensitive to the extremes (e.g. Chau et al, 2005;Arachchige et al, 2020), mainly if they are located in the right-hand tail of the distribution (Bendel et al, 1989). For instance, the effect of the extremes on the CV D is evident in event 58: the D distribution of this event has an extreme much greater than the mean (Fig.…”

Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India

“…Depending upon the location of rain gauges, the district is divided into for different subzones, called reference areas, and the rainfall is assumed to be uniform within the subzones. The division was done using a proximity analysis by using the Thiessen polygon concept [32,34,35] and was improved further using the administrative boundaries of the district (Figure 3). This division was done due to the variation in meteorological conditions throughout the district.…”

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

“…These thresholds can be used to determine the temporal probability of the occurrence of landslides as a part of a landslide early warning system (LEWS). The thresholds can be estimated using empirical methods [9][10][11][12], probabilistic methods [8,13,14], and/or physical methods [15,16], and we can use other tools like geographic information system (GIS) [17] and global positioning system (GPS) [18,19] and new technologies in order to make the calculation processes automatic [20,21]. When long-term monitoring data is not available for the study area, it is often difficult to identify the triggering rainfall corresponding to landslides [22].…”