Description of periodic variation in parameters of hydrologic time series
Description of periodic variation in parameters of hydrologic time series
复制标题
水文时间序列参数周期性变化的描述
DOI：
10.1029/wr025i003p00421
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
N. Harmancioglu
中科院分区：
文献类型：
--
作者：
V. Yevjevich;N. Harmancioglu
边际分布的多变量方法及其序列依赖模型常用于描述周期性随机水文时间序列。这是一种描述基本参数周期性变化的非泛函（或非参数）方法。通常，它需要大量的估计参数。这通常表示缺乏对模型参数总数节俭的可靠统计原则的尊重。当这些模型生成新样本时，这种方法会产生两种扭曲。首先，在生成的样本中，一些估计值的变化比应有的要小。第二，在历史极端时期出现新的极端的趋势是存在的，其中一些比历史极端时期更为明显。拟合的傅里叶函数对基本参数的周期变化具有有限数量的显著谐波，作为时间序列周期性的函数（或参数）描述，需要的参数总数要少得多。它们避免或最小化生成样本中的扭曲。
The multivariate approach of marginal distributions and their sequential dependence models is often used for description of periodic-stochastic hydrologic time series. This is a nonfunctional (or nonparametric) method of description of periodic variation in basic parameters. Usually, it requires a large number of estimated parameters. This often represents a lack of respect for the sound statistical principle of parsimony in the total number of model parameters. Two distortions result from this approach when new samples are generated by these models. First, some estimates vary less in generated samples than they should. Second, a tendency exists to generate new extremes at the times of historic extremes, with some of them more pronounced than the historic extremes. The fitted Fourier functions with a limited number of significant harmonics to the periodic variation of basic parameters, as the functional (or parametric) description of periodicity in time series, require a much smaller total number of parameters. They either avoid or minimize the distortions in generated samples.