State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023, China; School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2015 Dec 15;538:724-32. doi: 10.1016/j.scitotenv.2015.08.093. Epub 2015 Aug 30.
As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters.
作为一种随机事件，自然灾害具有复杂的发生机制。在灾害风险评估中，对多个灾害因素进行综合分析非常重要。为了提高风险分析和预测的准确性，在分析和计算多因素时，应考虑灾害的形成机制。基于对沙尘暴灾害多元分析的重要性和不足之处的考虑，本文选取了 1990 年至 2013 年内蒙古发生的 91 次严重沙尘暴灾害作为研究案例。选取了 500-hPa 大气环流系统、近地表气象系统和下垫面条件等主要灾害因素，对多维联合重现期进行模拟和计算。在将模拟结果与 54 年来的实际沙尘暴事件进行比较后，我们发现二维 Frank Copula 函数在灾害因素的较低尾部表现出更好的拟合结果，而三维 Frank Copula 函数在灾害因素的中上部尾部表现出更好的拟合结果。然而，对于重现期较短的沙尘暴灾害，三维联合重现期模拟并没有明显的优势。如果重现期超过 10 年，在极值拟合方面具有显著优势。因此，我们建议在对地震、海啸等较长重现期的严重灾害进行预测和风险分析时，可以采用多元分析方法。此外，该方法的探索为其他自然灾害的预测和预警奠定了基础。
