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

Dai, Zili; Li, Renjie

doi:10.5194/nhess-20-1321-2020

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…However, it is difficult to obtain this information in a sparsely-populated alpine area, so available secondary factors that can characterize the scale and frequency of debris flows can be adopted. They can be used in combination with primary factors to constitute a multi-factor assessment model (Ji et al, 2020). The spreading area has been calculated by the Flow-R model, from which the following eight factors can be extracted to establish a rapid assessment method.…”

Section: Determination and Quantification Of Assessment Factorsmentioning

confidence: 99%

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: Determination and Quantification Of Assessment Factorsmentioning

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 landslide intensity (simulation volume) (y 1 ), debris flow intensity (peak discharge (y 2 ) and velocity (y 3 ), altitude (y 4 ), relief degree (y 5 ), degree of basin cutting (y 6 ), lithology (y 7 ), distance to the fault (y 8 ), NDVI (y 9 ), FVC (y 10 ), soil erosion (y 11 ), water erosion (y 12 ), and human activity index (y 13 ) were selected as the hazard factors for the debris flow and were divided into five levels according to different thresholds and were assigned values of 0.2, 0.4, 0.6, 0.8, or 1.0. w is the weight of each factor, which was synthesized using the Entropy Method (EM) and the Analytic Hierarchy Process (AHP) which need to refer to the explanation of factor weight in the existing research [61][62][63][64].…”

Section: Hazard Assessment Of Seismic Landslide-generated Debris Flowmentioning

confidence: 99%

“…The results show that the risks posed by landslides in high earthquake intensity areas may be lower than those in low earthquake intensity areas, which may be related to the influence of the regional geological conditions and the errors in the existing earthquake-landslide hazard assessments [41,74]. In view of the uncertainty of regional risk results, factor analysis methods can be further used to explore the sensitivity of risks to topography, environment, and other factors to further clarify the characteristics of disaster chain risks [63,64,75]. In summary, it would be valuable to collect more disaster data, strengthen field investigations, and improve research on mechanism processes and verify the simulation results from multiple aspects [63,64,75].…”

Section: Reliability Of Population Risk For the Disaster Chainmentioning

confidence: 99%

“…In view of the uncertainty of regional risk results, factor analysis methods can be further used to explore the sensitivity of risks to topography, environment, and other factors to further clarify the characteristics of disaster chain risks [63,64,75]. In summary, it would be valuable to collect more disaster data, strengthen field investigations, and improve research on mechanism processes and verify the simulation results from multiple aspects [63,64,75].…”

Section: Reliability Of Population Risk For the Disaster Chainmentioning

confidence: 99%

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Risk Assessment of Population Loss Posed by Earthquake-Landslide-Debris Flow Disaster Chain: A Case Study in Wenchuan, China

Han

Yin

Yu-ming

et al. 2021

IJGI

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Earthquakes often cause secondary disasters in mountainous areas, forming the typical earthquake-landslide-debris flow disaster chain for a long time that results in a series of losses. It is important to improve the risk assessment method from the perspective of cascading effect of such a disaster chain, by strengthening quantitative research on hazards of the debris flows which are affected by landslide volume and rainstorm intensity. Taking Wenchuan County as an example, the risk assessment method for population loss of the disaster chain is established and the risks are evaluated in this paper. The results show that the population loss risk is 2.59–2.71 people/km2 under the scenarios of the Wenchuan Ms8.0 earthquake and four rainstorm intensities. The impacts of landslide and debris flow after the earthquake were long-term and profound. A comparison of risks caused by each element of the chain revealed that the risk associated with the earthquake accounted for the highest proportion, and landslide and debris flow accounted for 38.82–37.18% and 3.42–7.50%, respectively. As the earthquake intensity increases, the total risk posed by the disaster chain increases significantly. The risk caused by the earthquake is the highest in high earthquake intensity zones; while in the lower-intensity zones, landslides and debris flows pose relatively high risks. The risk assessment results were verified through comparison with actual data, indicating that the simulation results are quite consistent with the existing disaster information and that the risk assessment method based on the earthquake-landslide-debris flow cascade process is significant for future risk estimation.

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“…It was concluded that this relation significantly improves the perdition of extremes, with 30 % exhibiting their non-linear relationship with climate mechanisms. Kudryavtseva et al (2021) assessed the non-stationarity of extreme water levels in the eastern Baltic Sea with a particular focus on the Gulf of Riga. The analysis uses tide gauge observations from 1961 to 2018 and the block maxima method to identify extreme events.…”

mentioning

confidence: 99%