Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan

Saponaro, Annamaria; Pilz, Marco; Wieland, Marc; Bindi, Dino; Moldobekov, Bolot; Parolai, Stefano

doi:10.1007/s10064-014-0709-2

Cited by 51 publications

(34 citation statements)

References 31 publications

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“…In Central Asia, tailings ponds and dumps are mostly located in mountain areas, where they are prone to multiple types of natural hazards, for instance, earthquakes, heavy rainfall-and snowmelt-induced flooding, as well as landslides [4][5][6][7]. Among the most troublesome are landslides, which occur on mountain slopes and in narrow canyons [2,6,8]. In the Kyrgyz part of the Fergana basin, the first recorded massive activation of landslides occurred in 1954 and was followed by other events in 1958, 1969, 1979, 1988, 1993-1994, 1998, 2003-2005, and 2017 [2,9].…”

Section: Introductionmentioning

confidence: 99%

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Torgoev

Zaredinov

et al. 2021

Remote Sensing

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Seismically triggered landslides are a major hazard and have caused severe secondary losses. This problem is especially important in the seismic prone Mailuu-Suu catchment in Kyrgyzstan, as it hosts disproportionately sensitive active or legacy uranium sites with deposited radioactive extractive wastes. These sites show a quasi-continuous release of radioactive contamination into surface waters, and especially after natural hazards, a sudden and massive input of pollutants into the surface waters is expected. However, landslides of contaminated sediments into surface waters represent a substantial exposure pathway that has not been properly addressed in the existing river basin management to date. To fill this gap, satellite imagery was massively employed to extract topography and geometric information, and the seismic Scoops3D and the one-dimensional numerical model, Hydrologic Engineering Centre, River Analysis System (HEC-RAS), were chosen to simulate the landslide-induced mass transport of total suspended solids (TSS) and natural radionuclides (Pb-210 as a proxy for modeling purposes) within the Mailuu-Suu river networks under two earthquake and two hydrological scenarios. The results show that the seismically vulnerable areas dominated in the upstream areas, and the mass of landslides increased dramatically with the increase of earthquake levels. After the landslides, the concentrations of radionuclides increased suddenly and dramatically. The peak values decreased along the longitudinal gradient of river networks, with the concentration curves becoming flat and wide in the downstream sections, and the transport speed of radionuclides decreased along the river networks. The conclusions of this study are that landslides commonly release a significant amount of pollutants with a relatively fast transport along river networks. Improved quantitative understanding of waterborne pollution dispersion across national borders will contribute to better co-ordination between governments and regulatory authorities of riparian states and, consequently, to future prevention of transnational political conflicts that have flared up in the last two decades over alleged pollution of transboundary water bodies.

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Section: Introductionmentioning

confidence: 99%

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Torgoev

Zaredinov

et al. 2021

Remote Sensing

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“…Many of these studies have applied probabilistic models such as frequency ratio (FR), weight of evidence (WOE), etc. (Akgun et al 2008;Vijith and Madhu 2008;Oh et al 2009;Yilmaz and Keskin 2009;Youssef et al 2009Youssef et al , 2012Pradhan and Youssef 2010;Pradhan et al 2011;Akgun et al 2012;Neuhäuser et al 2012;Saponaro et al 2014;Sujatha et al 2014). Statistical models such as logistic regression (Akgun 2012;Devkota et al 2013;Grozavu et al 2013;Ozdemir and Altural 2013;Solaimani et al 2013), bivariate (Bednarik et al 2010;Pareek et al 2010;Pradhan and Lee 2010;Pradhan and Youssef 2010;Pourghasemi et al 2013a) and multivariate (Pradhan 2010a, b;Choi et al 2012) have been used in landslide susceptibility mapping.…”

Section: Introductionmentioning

confidence: 99%

GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China

et al. 2015

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The main goal of this study is to produce landslide susceptibility maps for the Qianyang County of Baoji city, China, using both certainty factor (CF) and index of entropy (IOE) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 81 landslide locations were detected. Out of these, 56 (70%) landslides were randomly selected as training data for building landslide susceptibility models and the remaining 25 (30%) were used for the validation purposes. Then, a total number of 15 landslide causative factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, the sediment transport index (STI), the stream power index (SPI), the topographic wetness index (TWI), geomorphology, lithology, and rainfall, were used in the analysis. The susceptibility maps produced using CF and IOE models had five different susceptibility classes such as very low, low, moderate, high, and very high. Finally, the output maps were validated using the validation data (i.e., 30% landslide location data that was not used during the model construction), using the area under the curve (AUC) method. The 'success rate' curve showed that the area under the curve for CF and IOE models were 0.8433 (84.33%) and 0.8227 (82.27%) accuracy, respectively. Similarly, the validation result showed that the susceptibility map using CF model has the higher prediction accuracy of 82.32%, while for IOE model it was 80.88%. The results of this study showed that the two landslide susceptibility maps obtained were successful and can be used for preliminary land use planning and hazard mitigation purpose.

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“…The majority of landslide research in Kyrgyzstan and Tajikistan focused on characterizing landslide susceptibility, i.e., "where" landslides are prone to occur (e.g., Braun et al, 2015;Saponaro et al, 2015;Havenith et al, 2015b). But little attention is paid to the atmospheric triggering conditions, and our knowledge of "when" landslides are likely to occur is limited There are two main approaches to assess rainfall threshold for landslide triggering.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century

Wang¹,

Otto²,

Scherer³

2021

Preprint

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Abstract. Landslide is a major natural hazard in Kyrgyzstan and Tajikistan. Knowledge about atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan is limited, even though this topic has already been investigated thoroughly in other parts of the world. In this study, the newly developed, high-resolution High Asia Refined Analysis version 2 (HAR v2) data set generated by dynamical downscaling was combined with historical landslide inventories to analyze atmospheric conditions that initialized landslides in Kyrgyzstan and Tajikistan. The results indicate the crucial role of snowmelt in landslide triggering processes since it contributes to the initialization of 40 % of landslide events. Objective thresholds for rainfall, snowmelt, as well as the sum of rainfall and snowmelt (rainfall + snowmelt) were defined. Peak intensity (Imax) and accumulated amount (Q) of rainfall + snowmelt events yield the best predictive performance. Mean annual exceedance maps were derived from regional thresholds of Imax = 12.8 mm d−1 and Q = 17.2 mm for rainfall + snowmelt. Mean annual exceedance maps depict climatic disposition and have added value in landslide susceptibility mapping. The results reported in this study highlight the potential of dynamical downscaling products generated by regional climate models in landslide prediction.

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Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan

Cited by 51 publications

References 31 publications

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China

Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century

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