中文摘要：

洪水频率不确定性分析问题一直是水文领域研究的热点。将大渡河流域典型站点洪水资料作为分析对象,选取广义极值分布作为洪水分布的线型,利用贝叶斯原理和基于Metropolis-Hastings抽样模拟MCMC方法构造一定频率下的洪水设计值,并得到其相应的置信区间,以此定量描述洪水频率分析的不确定性。研究结果表明,贝叶斯MCMC方法可以有效地估计洪水频率线型的参数,拟合效果略优于传统参数估计方法。此外,将贝叶斯MCMC方法得出的置信区间与传统Delta方法进行比较,发现贝叶斯MCMC方法得到的置信区间的宽度相对要小一些,置信上限与估计值之间距离大于置信下限与估计值之间距离,这种不对等性与实际更加接近,更能精准地估计洪水频率的置信区间。

英文摘要：

The uncertainty analysis of flood frequency has been a hotly debated topic in the field of hydrology.In this paper,the unknown parameters of hydrologic frequency distribution linetype are considered as random variables,and Bayesian Markov chain Monte Carlo（MCMC）method based on Metropolis-Hastings algorithm is used to evaluate the posterior distributions of GEV distribution parameters and flood quantities.The application example is given with the flood data in the Dadu River basin.The results show that the Bayesian MCMC method is an effective tool to estimate the parameters of the flood frequency estimation which gets a better fitting effect than the other parameter methods.In addition,confidence intervals obtained by Bayesian MCMC method are compared with those obtained by Delta method,which shows that the width of confidence interval by Bayesian MCMC method is narrower.The distance between upper confidence limit and estimated value is greater than the distance between lower confidence limit and estimated value,which is more close to the actual situation.