山东省近50年来降水事件变化特征

基于1951—2017年淮河流域及其周边68个气象站的年暴雨日数和年最大日降水量数据,采用经验模态分解方法分析暴雨频率和强度的平均趋势和周期,采用Copulas函数分析暴雨的综合危险度及重现期,探讨淮河流域暴雨危险度的时空变化特征。结果表明：(1) 流域暴雨频次呈现先降后升趋势,转折点在1980年左右;暴雨强度则一直呈显著的线性上升趋势。暴雨频率和强度的主周期均为准2 a。(2) 全流域90 % 以上的暴雨频次在3 d及以内,且暴雨强度小于100 mm。暴雨频率和强度超过该阈值后,增加显著变缓。暴雨重现期值随两单指标值的增加而增大,且增大速率逐步变快。(3) 暴雨频次和强度的平均变化趋势的高值在空间格局上基本一致,且两指标升降趋势的面积比相当。(4) 流域暴雨危险度联合概率的均值和最小值空间格局基本一致,最大值分布则比较分散。流域西北部的部分区域虽然暴雨频次和强度呈现下降趋势,但联合概率估计的暴雨危险度均值和最小值却很高。

     

Environmental contours using copulas

A procedure is proposed for constructing environmental contours using copula theory. Copulas are functions that define the multivariate probability distribution of a random vector or a set of random variables, and, thus, also determine their dependence structure. Constructing environmental contours requires knowledge of the joint probability distribution of the environmental variables. In many practical applications, the available statistical data is used to estimate the marginal distributions and the linear correlation matrix, and then the Nataf distribution model is employed to obtain the multivariate probability distribution. It turns out that such an approach implies a particular model of dependence structure defined by a Gaussian copula, which might not always be the appropriate one. In this work, some classes of bivariate copulas are considered for modeling the dependence structure of the environmental variables. We examine measures of association, rank-based methods for estimation of copulas, goodness of fit tests for copulas, and copula selection criteria, and apply them to metocean data from hindcasts of tropical storms and extra-tropical events in the Gulf of Mexico. A formulation is proposed for expressing the variates that define the environmental contours as functions of copulas. It is then applied for computing environmental contours of significant wave height, peak spectral period and wind velocity using the estimated copula models.