Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system

Dikshit, Abhirup; Satyam, Neelima

doi:10.1007/s11629-018-5189-6

Cited by 37 publications

(24 citation statements)

References 28 publications

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“…Among the eight events, six were correctly predicted by the SIGMA model. Ground displacements were reported at two locations in the Chibo-Pashyor area during seven days in 2017: on 28th-29th July 2017 and 13th-17th August 2017 [2]. In all seven days, ordinary criticality was well-predicted in the present analysis.…”

Section: Resultssupporting

confidence: 58%

“…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%

“…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]. The annual cumulative rainfall for these years is plotted in Figure 2a and the temporal distribution of landslides along with the average rainfall is shown in Figure 2b.…”

Section: Monthmentioning

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: Resultssupporting

confidence: 58%

Section: Study Area and Input Datamentioning

confidence: 99%

Section: Monthmentioning

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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“…The Himalayan region experiences very high rainfall and contributes over 70% of global fatal landslides [2]. Landslides in the Himalayas occur mainly due to monsoonal precipitation leading to huge financial loss and often disrupting the only livelihood of people [3,4]. The global fatal landslide database developed by [2] shows a total of 64 deaths in Bhutan due to nonseismic landslide events from 2004 to 2017, but the actual number of deaths is expected to be very high.…”

Section: Introductionmentioning

confidence: 99%

“…The studies on rainfall-induced landslides are quite varied and focus on several aspects like threshold modeling [5,6], monitoring systems [4,7], and computational techniques [8], along with GIS approach. Apart from landslide forecasting, landslide mitigation is important to understand the landslide hazard and the associated risk.…”

Section: Introductionmentioning

confidence: 99%

Spatial Landslide Risk Assessment at Phuentsholing, Bhutan

et al. 2020

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Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigated area. The tool can serve as an indicator for the civil authorities to analyze the risk posed by landslides due to the rapid infrastructure development in the region.

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Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India

Gariano

Melillo

Brunetti

et al. 2023

Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022

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In India, rainfall-induced landslides cause a high toll in terms of fatalities and damages. Therefore, the adoption of tools to predict the occurrence of such phenomena is urgent. For the purpose, the LANDSLIP project aimed at developing a landslide early warning system (LEWS) to forecast the occurrence of rainfall-induced landslides in two Indian pilot areas: Darjeeling and Nilgiris. Rainfall thresholds are a widely used tool to define critical probability levels for the possible occurrence of landslides in large areas, and are particularly suitable to be implemented in LEWSs.In this work, we exploited two catalogues of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively. Adopting a frequentist statistical method and using an automatic tool, we determined rainfall thresholds at different non-exceedance probabilities for the two pilot areas. Despite the daily temporal resolution of rainfall data and the spatial and temporal distribution of the documented landslides, the thresholds calculated for the two areas have acceptable uncertainties and were implemented in the LANDSLIP LEWS prototype. We expect that the new thresholds and the whole system will contribute to mitigate the landslide risk in the study areas.

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Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system

Cited by 37 publications

References 28 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

Spatial Landslide Risk Assessment at Phuentsholing, Bhutan

Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India

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