Parsimonious nonstationary flood frequency analysis
Parsimonious nonstationary flood frequency analysis
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DOI：
10.1016/j.advwatres.2017.11.026
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发表时间：
2018-02
影响因子：
4.7
通讯作者：
Jake Serago;R. Vogel
中科院分区：
文献类型：
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作者：
Jake Serago;R. Vogel
现在人们普遍认识到人为影响对极端洪水（和干旱）的影响，因此越来越需要在估计频率分布时考虑这种影响的方法。我们引入了一个简约的方法，非平稳洪水频率分析（NFFA）的基础上，一个二元回归方程，描述了年最大洪水，x，和一个外生变量，可以解释的非平稳行为ofx之间的关系。推导了bothxandy= ln（x）的条件均值、方差和偏度，并结合对数正态分布、广义极值分布和对数Pearson III型模型等多种常见的概率分布，得到了一种非常简单和通用的NFFA方法。我们的方法提供了几个优势，现有的方法，包括：简约，易用性，图形显示，预测区间，和不确定性分析的机会。我们介绍非平稳概率图和文件如何可以使用这些图来评估改进的拟合优度与NFFA。
There is now widespread awareness of the impact of anthropogenic influences on extreme floods (and droughts) and thus an increasing need for methods to account for such influences when estimating a frequency distribution. We introduce a parsimonious approach to nonstationary flood frequency analysis (NFFA) based on a bivariate regression equation which describes the relationship between annual maximum floods,x, and an exogenous variable which may explain the nonstationary behavior ofx. The conditional mean, variance and skewness of bothxandy= ln (x) are derived, and combined with numerous common probability distributions including the lognormal, generalized extreme value and log Pearson type III models, resulting in a very simple and general approach to NFFA. Our approach offers several advantages over existing approaches including: parsimony, ease of use, graphical display, prediction intervals, and opportunities for uncertainty analysis. We introduce nonstationary probability plots and document how such plots can be used to assess the improved goodness of fit associated with a NFFA.