A multivariate statistical method for susceptibility analysis of debris flow in southwestern China
<p>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 sign...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-05-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/20/1321/2020/nhess-20-1321-2020.pdf |
| Summary: | <p>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 %.</p> |
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| ISSN: | 1561-8633 1684-9981 |