Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India

Dikshit, Abhirup; Satyam, Neelima; Towhata, Ikuo

doi:10.1007/s11069-018-3417-6

Cited by 48 publications

(28 citation statements)

References 20 publications

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“…The movements are occurring gradually and are observed as cracks in buildings and roads after each monsoon. Since 2017, these slow movements are monitored using micro-electro-mechanical tilt sensors installed at Chibo [33]. During the validation period (years 2016-2017), ground displacements were observed on 7 days at two locations [2].…”

Section: Study Area and Input Datamentioning

confidence: 99%

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

Abraham

Satyam

Kushal

et al. 2020

Water

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Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

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Section: Study Area and Input Datamentioning

confidence: 99%

“…Water 2020, 12, x FOR PEER REVIEW 4 of 13 sensors installed at Chibo [33]. During the validation period (years 2016-2017), ground displacements were observed on 7 days at two locations [2].…”

Section: Monthmentioning

confidence: 99%

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

Abraham

Satyam

Kushal

et al. 2020

Water

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“…Again, the values are highly regional specific and should be defined on a local or regional scale through detailed investigations and should be validated for understanding performance efficiency. For measuring soil moisture in the field, volumetric water content sensors can be installed at site [28]. The drawback of this approach is that the sensor can provide the volumetric moisture content at the point of installation only, not for the whole area considered.…”

Section: Introductionmentioning

confidence: 99%

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

Abraham

Satyam

Pradhan

et al. 2020

Water

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Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.

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“…In situ ground-based observation of slope movement using various instruments (extensometers, inclinometers, and tiltmeters) and installing rain gauges to accumulate local rainfall data. For example, Dikshit et al [54] used tilt sensors for Darjeeling Himalayas, while Falae et al [55] used Electrical resistivity tomography (ERT) for Garhwal Himalayas.…”

mentioning

confidence: 99%

“…Martha et al [57] monitored the landslide dammed lake in Zanskar Himalayas for five months (January-May 2015) using multi-temporal high-resolution satellite images after the landslide event (December 2014) and depicted the variation in the dimension of the impounded lake during the monitoring period. Dikshit et al [54] and Dikshit and Satyam, [22] used tilt sensors at shallow depths to analyze the variation in tilting angle of the instrument, which is related to the lateral displacement of the slope. The study also validated the empirical models thereby encouraging similar studies to be conducted in other Northeast Himalayan regions, which could help in setting up a preliminary early warning system.…”

mentioning

confidence: 99%

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

et al. 2020

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Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area.

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Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India

Cited by 48 publications

References 20 publications

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

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

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

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

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