Giant river-blocking landslide dams with multiple failure sources in the Nu River and the impact on transient landscape evolution in southeastern Tibet

Zhao, Siyuan; He, Zhiliang; Dai, Fuchu; Li, Hua; Deng, Jiewei; Gao, Yunjian; Chen, Fei

doi:10.1016/j.geomorph.2022.108357

Cited by 12 publications

(3 citation statements)

References 74 publications

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“…In addition, the shapes of SUs were also sensitive to various topographic features. The shape index (R index) was calculated using the equation from Hang et al [18,45], which was the ratio of the square of the perimeter to the corresponding area. For more narrow-flat or more strip-shaped polygons, the R index increased, such as the triangle is 20.78 and the rectangle that the length to width of 6:1 is 32.67.…”

Section: Discussionmentioning

confidence: 99%

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

et al. 2022

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Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus’ groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU’s area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual.

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Section: Discussionmentioning

confidence: 99%

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

et al. 2022

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“…At the same time, the water system in this area is being developed. The Nu River, the Lancang River, and the Jinsha River start from the Qinghai-Tibet Plateau and flow southward through Yunnan Province [27]. Additionally, the strong erosion of tectonic movement and the downcutting action of the river lead to severe rock and soil fragmentation along both sides of the valley [28][29][30][31], which provide excellent geological conditions for the occurrence of landslide dam disasters.…”

Section: Introductionmentioning

confidence: 99%

Research on Development Characteristics and Landslide Dam Hazard Prediction of Zhuangfang Landslide in the Upper Reaches of the Nu River

Di,

Wei,

Tan

et al. 2023

Sustainability

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The upper reaches of the Nu River have strong tectonic activities and broken rock mass structures, often causing landslide disasters. The Zhuangfang landslide has apparent signs of surface deformation, and there is a risk of further sliding and blocking of the river. Taking the Zhuangfang landslide as an example, this paper analyzes the development characteristics and stability through geological field surveys, a drone aerial survey, field drilling, and GEO5 geotechnical engineering software. Then through the indoor tests and RAMMS numerical simulation software, the parameters of the landslide are determined, and the risk of a landslide dam is analyzed. Our results demonstrated that the Zhuangfang landslide is a large-scale landslide with a volume of about 4.5 × 106 m3. The front edge of the landslide is seriously deformed and is in an under-stable state, with risks of sliding and river blockage. The numerical simulation results showed that the total movement time of the landslide was 130 s, and the landslide entered the Nu River at 55 s. However, the landslide does not completely block the river and cannot form a landslide dam. The study proposed a parameter inversion method to determine the landslide mass parameters based on RAMMS numerical simulation software. The related results of this study can provide a reference for the sustainable development of the ecological environment in the Nu River Basin.

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“…Landslides commonly occur in mountainous terrains and pose a serious threat to the safety of local residents and ecological environments (Hungr et al, 2014;Huang et al, 2020). The southeastern and eastern margins of the Tibetan Plateau are characterized by intense tectonic activity and strong landscape evolution, where landslides frequently occur (Ouimet et al, 2007;Zhao et al, 2019;Ling et al, 2021;Cui et al, 2022;Zhao et al, 2022). In particular, the upstream area of the Minjiang River in the eastern plateau is a transition zone between the Sichuan Basin and Tibetan Plateau, and an increasing number of landslides have been induced in recent years (Dai et al, 2011;Fan et al, 2012;Hong et al, 2017;Fan et al, 2018;Zhao et al, 2018;Ling and Chigira, 2020;Cui et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China

et al. 2022

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Landslides have frequently occurred in deeply incised valleys in the upper reaches of the Minjiang River. Long-term interactions between rock uplift and river undercutting developed widely distributed landslides in this catchment, which recorded the typical tectonic geomorphology in the eastern margin of the Tibetan Plateau. In this study, we examined the landslides in the Minjiang catchment and aimed to compare the prediction ability of the statistical and machine learning (ML) models in landslide susceptibility assessment. We adopted the statistical models of the frequency ratio (FR) and information value (IV) models, and the ML models represented by a logistic model tree (LMT) and radial basis function classifier (RBFC) for landslide prediction. An inventory map of 668 landslides was compiled, and the landslides were randomly divided into training (80%) and validation (20%) datasets. Furthermore, 11 control factors of landslides based on topography, geology, hydrology, and other environments were applied for the analysis. The comprehensive performance of the four models was validated and compared using accuracy and area under the receiver operating characteristic curve (AUC). The results indicated that both sides of the valley along the Mingjiang and Heishuihe Rivers are in the high and very high susceptibility zones; in particular, the river segment from Wenchuan to Maoxian County has the highest susceptibility. The AUC values of the FR, IV, LMT, and RBFC models with the training data were 0.842, 0.862, 0.898, and 0.894, respectively, while the validation dataset illustrated the highest AUC value of 0.879 in the LMT model, followed by the RBFC (0.871), IV (0.869), and FR (0.839) models. Moreover, the LMT and RBFC models had higher accuracy values than the FR and IV models. This suggests that the ML models are superior to the statistical models in generating adequate landslide susceptibility maps, and the LMT model is the most efficient one for landslide prediction in the study region. This study provides a typical case in a landslide-prone region in the plateau margin to advance the understanding of landslide susceptibility assessment.

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Giant river-blocking landslide dams with multiple failure sources in the Nu River and the impact on transient landscape evolution in southeastern Tibet

Cited by 12 publications

References 74 publications

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

Research on Development Characteristics and Landslide Dam Hazard Prediction of Zhuangfang Landslide in the Upper Reaches of the Nu River

Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China

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