4.4 Article

An empirical-statistical model for predicting debris-flow runout zones in the Wenchuan earthquake area

期刊

QUATERNARY INTERNATIONAL
卷 250, 期 -, 页码 63-73

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.quaint.2010.11.020

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资金

  1. 973 Program [2011CB409903]
  2. State Key Laboratory of Geo-Hazard Prevention [SKLGP2009Z004]
  3. National Foundation for Natural Science of China [40772206]

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Rainstorm-induced debris flows were remobilized from landslide debris triggered by the May 12, 2008 Wenchuan earthquake. The resulting debris flows had very high energy and transported large amounts of loose material. Their peak discharge reached levels several times normal, notably during the rainstorm-induced debris flows of September 24, 2008 in Beichuan County, in the Wenchuan earthquake area. This phenomenon was due to the presence of large amounts of loose material deposited by the earthquake-induced landslides in the debris-flow catchments. Therefore, errors will occur if existing debris-flow runout models are employed to assess the hazardous zones connected with debris flows in earthquake-affected areas. For this reason, it is necessary to establish an appropriate model to predict the possible debris-flow runout zones on the alluvial fans after the Wenchuan earthquake. Debris flows triggered by the September 24 rainstorm were selected and investigated using high-resolution aerial photography and SPOTS imagery to extract a set of landslide deposit volumes in the debris-flow catchment areas and also the dimensions of the deposition fans. Applying multivariate regression analysis, a mathematical model was established to estimate the maximum runout distance and the width of the debris flows. The validation showed that the established model was suitable for the prediction of the debris-flow runout zones in the Wenchuan earthquake area. The results of the study will help authorities select safe sites for rehabilitation and relocation in the future and can also be used as an important basis for debris-flow risk management. (C) 2010 Elsevier Ltd and INQUA. All rights reserved.

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