Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods

Gautam, Pawan Kumar; Kubota, Tetsuya; Sapkota, Lok Mani; Shinohara, Yoshinori

doi:10.1007/s12665-021-09650-2

Cited by 31 publications

(25 citation statements)

References 78 publications

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“…These soils were characterized by a high percentage of rock fragments (>40%) and SL/LS textural classes, which resulted in lower rainfall triggering thresholds compared to finer soils, both considering the thresholds defined considering the soil region and considering the soil texture. Similarly, in a study on rainfall-induced landslides in Central Nepal, Eutric Cambisols showed a strong association with the occurrence of landslides [99].…”

Section: Discussionmentioning

confidence: 81%

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Luino

Graff

Biddoccu³

et al. 2022

Land

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Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study and analysis of shallow landslides and mud-debris flows triggered by rainfall events in Lombardy was carried out for the period of 1911–2010, over an area of 14,019 km2. In this study, intensity–duration rainfall thresholds have been defined using the frequentist approach, considering some pedological characteristics available in regional soil-related databases, such as the soil region, the textural class, and the dominant soil typological units (STU). The soil-based empirical rainfall thresholds obtained considering the soil regions of the study area were significantly different, with a lower threshold for landslide occurrence in the soil region M1 (Alps), where soils developed over siliceous parent material, with respect to the whole study area and the soil region M2 (Prealps), where soils developed over calcareous bedrocks. Furthermore, by considering textural classes, the curves were differentiated, with coarse-textured soils found more likely to triggerlandslides than fine soils. Finally, considering both texture and main soil groups, given the same rainfall duration, the rainfall amount and intensity needed to initiate a landslide increased in the following order: “coarse-skeletal” Cambisols < Umbrisols < Podzols < “fine” Cambisols. The results of this study highlighted the relevant role of pedological conditioning factors in differentiating the activation of rainfall-induced shallow landslides in a definite region. The information on soils can be used to define more precise rainfall–pedological thresholds than empirical thresholds based solely on meteorological conditions, even when they are locally defined. This knowledge is crucial for forecasting and preventing geo-hydrological processes and in developing better warning strategies to mitigate risks and to reduce socio-economic damage.

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Section: Discussionmentioning

confidence: 81%

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Luino

Graff

Biddoccu³

et al. 2022

Land

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“…They were forest land, cultivated land, construction land, bare land, water area and road (Fan et al 2016;Hua et al 2018;Zhou et al 2020). Interactive visual interpretations were carried out by being combined with the field investigation and questionnaire survey in October 2017, and results were with the accuracy of 90.4% and Kappa coefficient of 0.84 (Gautam et al 2021).…”

Section: Data Pre-processingmentioning

confidence: 99%

Ecotourism risk assessment in Yaoluoping Nature Reserve, Anhui, China based on GIS

Tang

et al. 2022

Environ Earth Sci

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As tourism development and human disturbance increase, they have been affecting the functional structure distribution, sustainable utilization of resources, and ecological benefits of nature reserves. At the same time, ecological risk has increased significantly. This study proposes ecotourism development patterns for risk areas at each level, to provide a basis for stabilizing and promoting the ecological sustainable development of nature reserves. Yaoluoping National Nature Reserve in China is used as a study case. Based on GaoFen-2 satellite data within the reserve area in 2017, ENVI, Fragstats and ArcGIS10.2 are used for land use classification as well as evaluation indicators selection and analysis. The ecotourism risk assessment model of the reserve is constructed, which is also imbedded with analytic hierarchy process and Ecological Risk Index. The model consists of landscape ecology, topographic hydrology, land use and human activities factors, and it can produce the distribution map at the ecotourism risk level of the reserve. Results show that the high ecotourism risk areas in the reserve are mainly located around rivers and roads, and they are distributed in strips. Overall, the ecotourism risk level in the northern part of the reserve is higher than that in the southern part. Nearly 90% of the regional ecotourism risk is at the middle or low level, implying the reserve in a healthy level overall. However, except for low-risk areas, the proportion of risk areas at all levels in the core zone is higher than that in the buffer zone.

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“…This study is an integrated approach that assesses landslide susceptibility, exposure, vulnerability, and risk in the Shahpur valley, eastern Hindu Kush, and is a basic part of disaster risk management planning. Worldwide extensive work has been carried out recently on landslide susceptibility assessment and mapping (Myronidis et al, 2016;Stanley and Kirschbaum, 2017;Bui et al, 2020;Wang et al, 2020;Chen and Chen, 2021;Ngo et al, 2021;Youssef and Pourghasemi, 2021) and in the Hindu Kush Himalayan region (Ahmed et al, 2014;Rahman et al, 2017;Rahim et al, 2018;Khan et al, 2019;Gautam et al, 2021), but limited research has been carried out on landslide vulnerability and risk (Ram and Gupta, 2022). It is, therefore, necessary to map the landslide vulnerable and risk areas which will help in minimizing the landslide risk and will provide a base for management.…”

Section: Introductionmentioning

confidence: 99%

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

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Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods

Cited by 31 publications

References 78 publications

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Ecotourism risk assessment in Yaoluoping Nature Reserve, Anhui, China based on GIS

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

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