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    Empirical model for assessing dynamic susceptibility of post-earthquake debris flows

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    Author
    Hu, Kaiheng
    Wang, Zhang
    Chen, Cheng
    Li, Xiuzhen
    Date issued
    2019
    Keywords
    debris flow
    susceptibility
    earthquake
    rainfall
    dynamic assessment
    
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    URI
    https://hdl.handle.net/11124/173119; http://dx.doi.org/10.25676/11124/173119
    Abstract
    Debris-flow susceptibility is controlled not only by static effective factors such as topography and lithology, but also by dynamic effective factors such as earthquake, rainfall and human activity. In this paper, a simple model of calculating the dynamic susceptibility is developed based on the assumption of linear relationship between the static and dynamic susceptibilities. The influence of earthquake and rainfall events is represent by two coefficients. The earthquake coefficient is considered as an exponential function of intensity, and a negative power function of elapsed time. The rainfall coefficient is proportional to the occurrence days of heavy rainfall. This model is applied to assess the debris-flow susceptibility of Hengduan mountainous area from 2000 to 2015. Four static effective factors including relative relief, slope, lithology and fault density are used to calculate the static susceptibility by ARCGIS grid toolbox. There are six earthquake events since 1995 whose intensity zones of >= VI are intersected with the Hengduan area. The earthquake coefficient is calculated with the intensity zoning data of each of the six events and then is accumulated to get the final earthquake coefficient in each year. TRMM satellite rainfall data from 2000 to 2015 are collected to extract the occurrence days of heavy rainfall which is used to calculate the rainfall coefficient. The dynamic susceptibilities from 2000 to 2015 are obtained by multiplying the static susceptibility with the earthquake and rainfall coefficients in respective year. The 2015 susceptibility map shows a qualitative agreement with the distribution map of disasters in 2015.
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