考虑局部趋势的非一致性水文频率分析方法

Nonstationary hydrologic frequency analysis method considering local trends

  • 摘要: 变化环境下水文序列常表现出非一致性,导致基于独立同分布假设的频率分析方法受到严峻挑战,故亟需研究适用于非一致性水文序列的频率分析方法。针对具有趋势变异的水文序列,提出了一种考虑局部趋势的非一致性水文频率分析方法:以具有局部与整体趋势识别能力的滑动窗口趋势分析法诊断序列;构造时变概率分布模型组,采用融合了贝叶斯理论的广义极大似然法估计模型参数;优选模型并按照等可靠度原则推求设计值及置信区间。应用该方法对甘肃石羊河流域红崖山水库站年径流量序列开展了实例研究,结果表明从设计值及95%置信区间角度看,所提方法与现行的一致性频率分析方法及考虑整体趋势的时变分布方法相比存在一定的差异,揭示了在准确识别序列局部或整体趋势成分的基础上,开展非一致性水文频率分析有助于合理估计设计值。

     

    Abstract: The phenomenon that hydrological series often present nonstationary under changing environments brings huge challenges for the traditional frequency analysis methods based on the independent and identical distribution hypotheses. Thus, it is urgent to investigate new frequency analysis methods for the nonstationary hydrological series. In this paper, a nonstationary hydrological frequency analysis method considering local trend was proposed. The method consists of three main procedures. Firstly, a moving window trend analysis method with identifying capability both for local and global trend was employed. Secondly, time-varying distribution models were established and their parameters were estimated by generalized maximum likelihood method coupled with Bayesian theory. Finally, selecting the optimal model and then design values and confidence intervals were calculated according to the equal reliability principle. The proposed method was applied to the frequency analysis of the annual runoff series gauged at the Hongyashan Reservoir in the Shiyang River basin, Gansu province. Results show that from the perspective of design values and 95% confidence intervals, there are certain difference between the new method and the conventional stationary frequency analysis method, as well as the time-varying distribution method considering the overall trend. This paper reveals that the nonstationary hydrological frequency analysis on the basis of accurately identifying local or overall trend component of the sequence is conducive to estimating design values reasonably.

     

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