水文多变量趋势分析的应用研究

水文频率分析需要进行一系列的假设,包括一致性、同分布性和独立性。由于水文事件通常都包含多个水文变量,例如洪水过程包含洪峰和洪量,低径流过程包括年最小月径流和年最小3个月径流等,因此近年来多变量水文分析与计算方法在水文领域中得到广泛应用。然而,多变量频率分析并未对上述假设进行检验,而是直接对频率分布进行函数拟合,不能保证得到的频率分布函数符合实际情况。引入水质研究领域内广泛使用的多变量MK检验方法,分别对长江上游干支流主要控制站点的年最大洪峰、年最大7d洪量和年最低月径流、年最低3个月径流的单变量和联合变量进行变化趋势分析。结果表明,单变量趋势分析仅仅能够检验出单个变量的变化趋势,而多变量趋势分析则能够综合检验出联合变量是否存在变化趋势。因此,为保证水文频率分析结果的准确,有必要在进行多变量水文频率分析前对单变量和联合变量进行趋势分析。

Application of Hydrological Multivariate Trend Analysis

H ydrological frequency analysis relies on a serie s of assumptions, especially nonstationarity, homogeneity and independence. As hydrological events should be described through several dependent hydrological variables, e.g. peak, volume of flood; annual minimum monthly runoff, annual minimum 3 monthly runoff for low runoff process, multivariate hydrological frequency analysis methods have gained popularity during recent years. However, multivariate hydrological frequency analysis focused directly on fitting the frequency distribution without testing the assumption procedure. Neglecting the testing procedure may get the incorrect frequency distribution. This paper introduced multivariate MK testing, which was widely used in the field of water quality, to test both univariate and multivariate trends of annual peak and annual max 7 day volume, annual minimum morthly runoff and annual minimum 3 monthly runoff at the control stations of the upper reaches of the Yangtze River Basin. The results indicate that multivariate tests can detect the integrated trends of joint variables, whereas univariate tests can only detect the univariate trends. Therefor, it is recommended to jointly apply univariate and multivariate trend tests in order to get a precise result.