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The Return Period Analysis of Natural Disasters with Statistical Modeling of Bivariate Joint Probability Distribution

Authors

  • Ning Li,

    1. 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. 2 Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing, China. 3 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing 100875, China.
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  • Xueqin Liu,

    Corresponding author
    1. 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. 2 Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing, China. 3 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing 100875, China.
      Xueqin Liu, KeJi Building B#713, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China; tel:(86)-13811593319; fax:(86)-10-58809998; liuxueqin2009@126.com.
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  • Wei Xie,

    1. 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. 2 Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing, China. 3 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing 100875, China.
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  • Jidong Wu,

    1. 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. 2 Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing, China. 3 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing 100875, China.
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  • Peng Zhang

    1. 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. 2 Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing, China. 3 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing 100875, China.
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Xueqin Liu, KeJi Building B#713, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China; tel:(86)-13811593319; fax:(86)-10-58809998; liuxueqin2009@126.com.

Abstract

New features of natural disasters have been observed over the last several years. The factors that influence the disasters’ formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk-based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.

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