How to apply the dependence structure analysis to extreme temperature and precipitation for disaster risk assessment
作者： Jieling Feng , Ning Li , Zhengtao Zhang , Xi Chen
DOI: 10.1007/S00704-017-2187-5
关键词: Risk assessment 、 Climate change 、 Statistics 、 Probability distribution 、 Extreme temperature 、 Mathematics 、 Structure analysis 、 Gumbel distribution 、 Copula (probability theory) 、 Bivariate analysis
摘要: IPCC reports that a changing climate can affect the frequency and intensity of extreme events. However, extremes appear in tail probability distribution. In order to know relationship between events temperature precipitation, an important but previously unobserved dependence structure is analyzed this paper. Here, we examine by building bivariate joint Gumbel copula model for precipitation using monthly average (T) (P) data from Beijing station China covering period 1951–2015 find be divided into two sections, they are middle part upper tail. We show T P have strong positive correlation high section (T > 25.85 °C P > 171.1 mm) (=0.66, p < 0.01) while do not demonstrate same relation other section, which suggests identification influence on needs help analysis. also every 1 °C increase associated with 73.45 mm P. Our results suggested fluctuations changes will allow included disaster risk assessment under future change scenarios. Copula jointed distribution useful
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