Kalimpong Resting on the Himalayan fear

Studying rain-triggered landslides in Kalimpong, Darjeeling regions     


The Himalayas are truly one of the most amazing ecosystems on our planet. But danger lurks in the form of landslides that routinely wreak havoc in the hilly terrains. Landslides affect 15% of India’s land area which is around 0.49 million sq km. It is estimated that 42% of all landslide-prone areas in the country fall in north-east Himalayas, especially Darjeeling and Sikkim regions. Every disaster leads to massive loss of life and property, especially agricultural land.

Studies on the hilly regions have turned international spotlight on the hysteria of urbanization and its increasing impact on socio-economic activities. This calls for better understanding of landslides, estimation of their occurrence potential (or modeling), and formulation of strategies to minimize the impact.

Some of the recent studies on Darjeeling-Sikkim Himalayas have largely looked at severity zonation, identifying rainfall thresholds and other related aspects. However, need for a comprehensive study of the entire region of Kalimpong has been largely left unattended.

75% cases triggered by rainfall

Landslides are either shallow or deep-seated failure of the soil mass. Majority of the landslides in Kalimpong are categorized as shallow landslides. Such landslides are caused or reactivated or induced primarily by rainfall.

A recent report by Geological Survey of India (GSI, 2016) has identified 75% of the landslide occurrences in the region during 2006-2013 as those triggered by rainfall. Therefore, it is imperative to understand the relationship between landslide incidences and rainfall conditions, primarily in the context of the Himalayan region in Kalimpong.

Due to the extraordinarily broad spectrum of landslide phenomena, no single method exists to identify and map landslides, to ascertain landslide hazards, and to evaluate the associated risks. The GSI study contributed to reducing this shortcoming by providing the scientific rationale, and a set of validated tools for the optimal use of landslide prediction models for Kalimpong region in Darjeeling Himalayas.

The work conducted is broadly categorized in three ways:

1)     Determination of minimum rainfall conditions using empirical, probabilistic approach, algorithm-based and mathematical model.

2)    Installation of a robust monitoring system using Microelectromechanical Systems (MEMS) tilt sensor and volumetric water content sensors and validating the thresholds.

3)    Use of physical approach for a specific landslide event and understanding its applicability for future landslide events.

To understand the relationship between rainfall and landslide occurrences in the Darjeeling and Sikkim regions two types of methods are used: empirical and physical methods.

Assessing rainfall intensities

Empirical methods study the landslides that are caused by rainfall events – the downpour that triggers instantaneous landslides and the low but continuous antecedent rain that destabilizes the slope. Though this approach is based on a single parameter, precipitation rates, it is significant to note that rainwater is the cause of many changes in soil properties, pressure variations etc, and hence can be approximated to the changes in rainfall. This work makes use of an empirical approach for assessing the landslides in the Kalimpong region by considering the daily rainfall intensities.

As such methods produce only binary results ie, either landslide occurs or do not occur, we have also adopted and evaluated probabilistic methods for the region. The study also focused on the use of algorithm-based model CTRL-T, (Calculation of Thresholds for Rainfall-Induced Landslides-Tool). The tool uses an algorithm to automatically extract rainfall events from daily rainfall series, reconstruct triggering rainfall conditions responsible for landslide occurrences, and calculates rainfall thresholds at various exceedance probabilities.

The input parameters for the tool require the locations and the dates of occurrence of the landslides, coordinates of the rain gauge and hourly rainfall series. The results depict that  rainfall conditions necessary for landslides cover a range of 288 h (24 ≤ D ≤ 288 h), which is the range of validity for the thresholds with the range of cumulative rainfall being 10 ≤ E ≤ 226 mm. In addition, the study further explores the relationship between rainfall and landslide occurrences, by using a mathematical model to simulate the potential triggering conditions in Chibo, one of the most active landslide regions in Kalimpong.

Also, it was found that antecedent rainfall of 20 days or more is one of the major causes of rainfall-induced landslides in this region. The results also signify that a rainfall intensity of 60-70 mm/day has the highest probability of landslide occurrence for the Kalimpong region.

Physical process models are based on numerical models which study the relationship between rainfall, pore water pressure, soil type, and volumetric water content that can lead to slope instability. A physical method (TRIGRS) was applied to understand the variation in the factor of safety of every cell for July 1, 2015 landslide event and back analysis for various historical landslide events were also conducted.

The quantification between the actual and predicted landslides was carried out and evaluated using receiver operating characteristics (ROC) technique. The results indicate that the model is capable of predicting landslides from a temporal perspective and correctly predicted 54% of the pixels.

Sensor-based field observations

To validate and assess the empirical model, an IoT sensor-based field observations were carried out. The installed sensors are Microelectromechanical Systems (MEMS) tilt sensor and volumetric water content sensors in the Chibo area. While the former measures the tilting angle of the instrument at shallow depths and hence the lateral displacement at the slope surface, the latter measures the soil moisture levels.

These were used to assess the model performance. After the initial investigation and identification of unstable slopes, the sensor unit was installed on top of a steel rod and tilt sensor was placed at shallow depths on another steel rod placed adjacently. The sensor unit has a wireless communication module and is powered by four-size C alkaline batteries which can be functional for over a year. It also has a temperature sensor for rectifying any fluctuations on the tilt sensor.

The tilt sensors have dual axis orthogonal to each other of which one is placed along the slope angle. The sensor detects the small changes in the angle and transmits the data wirelessly through radio communication. The data logger gathers the data from all the sensors and is then transferred to a data server via the internet. To power the data logger unit in case of battery drainage scenarios, a solar panel is also installed.

In addition to the sensors, a rain gauge is also installed beside the data logger to acquire the local precipitation data which would help in better understanding of the movement. The cost of one sensor unit is roughly $1000, including installation work.

Of the four approaches tested in this study area, the probabilistic model is closer to reality. The work shows that early warning systems can hence be designed based on these rainfall thresholds as the first line of action.

Apart from the threshold determination and validation, landslide zonation map was developed using weighted overlay analysis. Six landslides affecting maps (Hydrogeological, Land use/Landcover, Elevation, Slope, Proximity to river and Proximity to road map) were used. Thereafter, the above-mentioned maps were converted to raster form and weightage was given to all the parameters.

Eventually, weighted overlay analysis was conducted by providing rank to each class of different parameters based on field study and judgement. The maximum weightage was given to elevation with 40%, followed by proximity to major rivers of 25%, proximity to major roads of 20% and 15% to land use/landcover. The ranking of each triggering factor was based on a common scale of 1-5 with 5 being the lowest and 1 being the highest. The results depict that more than one-third of the area falls under high landslide zone with 43% in moderate zone.

The results signify that an increase in anthropogenic activities may lead to major landslide events especially at plantation region situated at an elevation of 1000-1500m.


Figure 1: Results of threshold and validation analysis

Figure 2: Landslide hazard micro zonation map of Kalimpong town using weighted overlay analysis


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