Regional risk assessment of debris flows in China—An HRU-based approach

Qiang, Zhimin; Cui, Peng; He, Jing; Lei, Yu; Li, Shusong

doi:10.1016/j.geomorph.2019.04.027

Cited by 53 publications

(36 citation statements)

References 53 publications

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“…At present, depending on the types of the assessment cells, the methods can be classified as follows: (1-a) Grid cell: grid cells are obtained by superposing multiple factor layers related to debris flow occurrence, using the statistical method. Regular grids of the same size are used to express assessment results; this method is mostly adopted for the regional scale (Zou et al, 2013;Zou et al, 2019). (1-b) Catchment cell: catchment cells are extracted through hydrological analysis, and the hazard levels are determined by analyzing catchment features.…”

Section: Introductionmentioning

confidence: 99%

“…(1-b) Catchment cell: catchment cells are extracted through hydrological analysis, and the hazard levels are determined by analyzing catchment features. This method is mostly adopted for mediumscale (1:10,000-1:50,000) (Zou et al, 2019). (1-c) Single-valley cell: single-valley cells are used to calculate the motion process of debris flows; this is achieved using physical models.…”

Section: Introductionmentioning

confidence: 99%

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Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway

Jiang

et al. 2021

Front. Earth Sci.

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Highways frequently run through the flow and accumulation areas of debris flow gullies and thus are susceptible to debris flow hazards. Assessing debris flows along highways can provide references for highway planners and debris flow control, emergency management. However, the existing assessment methods mostly neglect the essential information of the flow paths and spreading areas of debris flows at the regional scale. Taking the Gaizi Village-Bulunkou Township Section (hereinafter referred to as “the Gaizi-Bulunkou Section”) of the Karakoram highway as the study area, this research introduces a simple empirical model (the Flow-R model) and establishes a method for assessing the debris flow hazard level. The main processes include data collection, inventory of former events, calculating source areas and spreading probability, verification of the model, extraction of hazard assessment factors, and calculation of debris flow hazard levels. The results show that: 1) the accuracy, sensitivity, and positive predictive power of the Flow-R model in simulating the debris flow spreading probability of the study area were 81.87, 70.80 and 72.70%, respectively. The errors mainly occurred in the debris flow fans. 2) The calculation results make it possible to divide debris flow hazard levels into four levels. N5, N19, and N28 gullies had the highest hazard level during the study period. 3) In the Gaizi-Bulunkou Section of the Karakoram highway, during the study period, the highways with very high, high, medium, and low hazards were 4.33, 0.62, 1.41, and 1.68 km in length, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway

Jiang

et al. 2021

Front. Earth Sci.

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“…The initiation of landslides is the result of multiple factors, including intrinsic factors that determine soil structure and slope stability (like topography, geology and soil regolith) and extrinsic factors that can change soil shear strength and lead to slope failure ultimately like rainfall [44,62]. The rainfall variables (intensity-duration, cumulated event rainfall and rainfall duration) were common to study the relationship between rainfall and landslide occurrences.…”

Section: Landslide Causal Factors and Deformation Mechanismmentioning

confidence: 99%

“…However, the slope stability depended on the intrinsic factors and the slope unstable depended on the extrinsic factors, like weathering, tectonic events, or human impact on the slope. While the slope finally failed determination by triggering factors such as rainfall, snowmelt or earthquakes [44,62]. Therefore, the relative deformation cannot veritably reflect the movement process in different environments.…”

Section: Quantifying Landslide Activity and Insar Signal Separationmentioning

confidence: 99%

Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model

Hao

et al. 2019

Remote Sensing

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Landslides are one of the major geohazards in the Qinghai-Tibet Plateau, and have recently increased in both frequency and size. SAR interferometry (InSAR) has been widely applied in landslide research, but studies on monitoring small-scale landslides are rare. In this study, we investigated the performance of Small Baseline Subsets method (SBAS) in monitoring small-scale landslide and further developed a new deformation model to obtain the absolute deformation time series. The results showed that SBAS could well capture the small-scale landslide characteristics including spatiotemporal abnormal displacement and progressive failure processes. The newly developed absolute deformation model further detected the process of landslide details, such as instances of noticeable creeps induced by rainfall and snowmelt. Finally, a conceptual model of the kinematics-based failure mechanism for small-scale landslide was proposed. This study extended the monitoring capability of InSAR and improved our knowledge on the deformation in the frozen ground regions.

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“…Debris flow susceptibility expresses the occurrence possibility of debris flow in an area with respect to its geomorphologic characteristics (Kappes et al, 2011;Bertrand et al, 2013). Therefore, susceptibility analysis is an essential step for conducting risk assessments of debris flow hazards (Di et al, 2019;Zou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A multivariate statistical method for susceptibility analysis of debris flow in southwestern China

Dai

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Southwestern China is characterized by many steep mountains and deep valleys due to the uplift activity of the Tibetan Plateau. The 2008 Wenchuan earthquake left large amounts of loose materials in this area, making it a severe disaster zone in terms of debris flow. Susceptibility is a significant factor of debris flows for evaluating their formation and impact. Therefore, there is an urgent need to analyze the susceptibility to debris flows of this area. To quantitatively predict the susceptibility of the area to debris flows, this study evaluates 70 typical debris flow gullies, which are distributed along the Brahmaputra River, Nujiang River, Yalong River, Dadu River, and Ming River, as statistical samples. Nine indexes are chosen to construct a factor index system and then to evaluate the susceptibility to debris flow. They are the catchment area, longitudinal gradient, average gradient of the slope on both sides of the gully, catchment morphology, valley orientation, loose material reserves, location of the main loose material, antecedent precipitation, and rainfall intensity. Following this, an empirical model based on the Type I quantification theory is established for susceptibility prediction for debris flows in southwestern China. Finally, 10 debris flow gullies upstream of the Dadu River are analyzed to verify the reliability of the proposed model. The results show that the accuracy of the statistical model is 90 %.

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Regional risk assessment of debris flows in China—An HRU-based approach

Cited by 53 publications

References 53 publications

Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway

Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway

Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model

A multivariate statistical method for susceptibility analysis of debris flow in southwestern China

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