变化环境下超定量与年最大值洪水序列频率分析模型对比研究

Abstract

POT模型比AMS模型能更完整灵活地描述洪水及其产生过程，适用于变化情况下的洪水频率分析。本文以武江流域犁市(二)站为例进行超定量(POT)与年最大值(AMS)洪水频率对比分析。应用门限值选取标准确定门限值，根据POT模型分布参数及设计洪水估计结果随门限值变化情况验证所选门限值的合理性；以泊松分布为超定量发生次数分布，用广义Pareto(GP)分布拟合POT样本。对比POT模型和基于P-III分布的AMS模型，探讨大重现期洪水频发是否受频率分析方法不当影响。结果表明：根据门限值选取标准确定的门限值，经验证能够保证POT模型的稳定性以及样本独立性；估计大重现期设计洪峰时，P-Ⅲ分布的AMS模型的估计结果小于GP分布的POT模型，100年一遇洪水估计结果差异度大于6%，可见AMS模型存在低估大重现期设计洪峰的现象，这是近年来计算出大重现期洪水频发的原因之一。 Taking the daily average discharge of Lishi (2) station in the Wujiang river basin for an example, two flood frequency analysis models, respectively based on peaks-over-threshold (POT) and annual maximum series (AMS), are compared. POT model has better flexibility describing floods and the flood generating process than AMS model; thus is suitable for flood frequency analysis under changing environment. Threshold selection approaches are applied to choose the threshold value, which is tested by the relationship of estimated results of parameters of the POT model and designed floods with changing thresholds afterwards. The POT samples are fitted by the generalized Pareto (GP) distribution with a Poisson model for arrival. Compare POT model based on the GP distribution and AMS model based on the P-III distribution and discuss whether the frequent occurrence of large return period flood is influenced by undue frequency analysis method(the large return period flood-prone by the undue influenced of the frequency analysis). The results show that, threshold selected by combined approaches is able to ensure the sample independence and stability of the POT model. When estimating the designed flood peak of large return period, result estimated by the AMS model based on P-III distribution is smaller than the POT model based on GP distribution, the 100-year flood is estimated that the results of differences greater than 6%, visible AMS model underestimate of the phenomenon of large return period designed flood peak, which is one of the reasons of the frequent occurrence of large return period floods in recent years.