Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Harilal, Geethu Thottungal; Madhu, Dhanya; Ramesh, Maneesha Vinodini; Pullarkatt, Divya

doi:10.1007/s10346-019-01244-1

Cited by 69 publications

(37 citation statements)

References 69 publications

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“…The challenge associated with the first approach is that the landslide can be detected but not predicted. The second approach can be appropriate, but the threshold for each influencing factor (such as hydrological, land use, lithological, and soil characteristics) should be determined [65]. In the current study, the rainfall intensity inducing shallow landslides is estimated considering the soil-forming factors and environmental covariates.…”

Section: Methodsmentioning

confidence: 99%

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

et al. 2021

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Shallow landslides are among the natural threats causing death and damage. They are mostly triggered by rainfall in mountainous areas where precipitation used to be abundant. The amount of rainfall inducing this natural threat differs from one site to another based on the geographical characteristics of that area. In addition to the rainfall depth, the determination of soil water content in a specific zone has a major contribution to the landslide prediction and early warning systems. Rwanda being a country with hilly terrains, some areas are susceptible to both rainfall and soil water content inducing landslides. But an analytical study of the physical threshold determination of both rainfall and soil water content inducing landslides is lacking. Therefore, this experimental study is conducted to determine the rainfall and soil water content threshold that can be fed in to the landslide early warning system (LEWS) for alert messages using the Internet of Things (IoT) technology. Various experiments have been conducted for the real-time monitoring of slope failure using the toolset composed of a rain gauge, soil moisture sensors, and a rainfall simulating tool. The results obtained show that the threshold for landslide occurrence does not solely correlate with the total rainfall amount (or intensity) or soil moisture, but also influenced by internal (geological, morphological) and environmental factors. Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. The experiments revealed that for a specific site, the minimum duration to induce slope failure was 8 hours, 41 minutes with the rainfall intensity of 8 mm/hour, and the soil moisture was above 90% for deeper sensors. These values are used as thresholds for LEWS for that specific site to improve predictions.

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Section: Methodsmentioning

confidence: 99%

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

et al. 2021

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“…The use of probabilistic [22,46] and semi-automated algorithmic approach [47] has only been attempted for Kalimpong region. Harilal et al [48] developed both regional (Sikkim) and local (Gangtok, Sikkim) ID thresholds using statistical approach. The main concerns with these studies are the coarse spatial distribution of rain gauges, all studies being performed using a single rain gauge, and the availability of only daily rainfall data.…”

Section: Landslide Forecastingmentioning

confidence: 99%

“…Landslide monitoring is a very significant aspect of landslide assessment, especially in the Indian Himalayan region where the slopes are generally creepy in nature, which could immediately fail during an abrupt rainfall event or seismic occurrence. Landslide monitoring is generally categorized into three types [ Gangtok, [48] Sikkim, [48] Global, [52] Kalimpong, [47]…”

Section: Landslide Monitoring and Investigationmentioning

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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“…India is the country with the highest number of non-seismically triggered disastrous landslides as per the global dataset published by Froude and Petley (2018), with more than 600 records and 16 % of catalogued rainfall-induced landslides. According to the EM-DAT disaster database (Guha-Sapir et al, 2021), a total of 51 fatal landslide events have been recorded since 1950, with 4671 deaths and more than 400 000 people affected. In recent years, regional and local early warning systems were adopted in various countries to forecast rainfall-induced landslides and to mitigate the associated risk (Guzzetti et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Satellite rainfall products outperform ground observations for landslide prediction in India

Brunetti

Melillo

Gariano

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

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Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Cited by 69 publications

References 69 publications

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

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

Satellite rainfall products outperform ground observations for landslide prediction in India

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