This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.
综合分析了外界干扰下的塔里木河流域生态水流情势,采用组合回归模型建立了适应生态变化的水流利用关系,修正了水文-生态响应关系和生态水流估算结果,进一步讨论了适应生态过程的水流情势利用策略。结果表明:自20世纪60年代以来,塔里木河源流与干流水流情势变化不一致,源流来水保证率递减,源流区间耗水量呈增加趋势。相比ARMA(auto-regressive and moving average)模型,基于不同利用方式的生态水流组合回归模型模拟效果较好,可作为参考性预报;结合生态水流预测方程,制定了适应生态的水量调度对策。
