A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which fu...A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.展开更多
为探讨水文数据非一致性对洪水频率分析的影响,提出基于位置、尺度、形状的广义可加模型(Generalized additive models for location,scale and shape,GAMLSS),从时间和降水两类因素出发,计算单变量、多变量洪水频率,分析经验点据与理...为探讨水文数据非一致性对洪水频率分析的影响,提出基于位置、尺度、形状的广义可加模型(Generalized additive models for location,scale and shape,GAMLSS),从时间和降水两类因素出发,计算单变量、多变量洪水频率,分析经验点据与理论分位曲线拟合效果,选取不同变化条件下洪水适宜理论分布。以汤旺河流域为例,研究结果表明,单变量洪水频率最优分布选取较稳定,而受时间和降水因素影响,多变量洪水频率最优分布选取均不同。与前者相比,引入协变量使原序列参考时间连续性变化和降水极端信息,改进传统洪水频率计算方法。展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 50609005)Chinese Postdoctoral Science Foundation (No. 2009451116)+1 种基金Postdoctoral Foundation of Heilongjiang Province (No. LBH-Z08255)Foundation of Heilongjiang Province Educational Committee (No. 11451022)
文摘A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.
文摘为探讨水文数据非一致性对洪水频率分析的影响,提出基于位置、尺度、形状的广义可加模型(Generalized additive models for location,scale and shape,GAMLSS),从时间和降水两类因素出发,计算单变量、多变量洪水频率,分析经验点据与理论分位曲线拟合效果,选取不同变化条件下洪水适宜理论分布。以汤旺河流域为例,研究结果表明,单变量洪水频率最优分布选取较稳定,而受时间和降水因素影响,多变量洪水频率最优分布选取均不同。与前者相比,引入协变量使原序列参考时间连续性变化和降水极端信息,改进传统洪水频率计算方法。
