为了改进水文建模过程中的不确定性处理,采用一种融合全局优化和数据同化(Simultaneous Optimization and Data Assimilation,SODA)的混合框架,对Hy MOD模型进行了不确定性分析,并与经典SCEM-UA方法进行了比较。SODA方法具有如下特点:(1...为了改进水文建模过程中的不确定性处理,采用一种融合全局优化和数据同化(Simultaneous Optimization and Data Assimilation,SODA)的混合框架,对Hy MOD模型进行了不确定性分析,并与经典SCEM-UA方法进行了比较。SODA方法具有如下特点:(1)具备较高的参数搜索效率和寻优能力;(2)明确考虑包括输入、输出、参数以及模型结构在内的重要不确定性来源。SODA方法在渭河流域的实例应用结果表明:与SCEM-UA方法相比,SODA方法不仅显著提高了预报精度,而且推求出了性质更为优良的预报区间。SODA方法的成功应用,有助于模型概念的改进及对水文系统功能的理解。展开更多
We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilat...We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value （GEV） distribution and Generalized Pareto Distribution （GPD） under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management.展开更多
Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption tha...Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption that the variance is a constant or it changes with the seasons.However,hydrological processes in the real world are often heteroscedastic,which can be tested by McLeod-Li test and Engle Lagrange multiplier test.In such cases,the GARCH model of hydrological processes is established in this article.First,the seasonal factors in the sequence are removed.Second,the traditional ARMA model is established.Then,the GARCH model is used to correct the residual.At last,the daily runoff data in 1949-2001 of Yichang Hydrological Station is taken to be an example.The result shows that compared to the traditional ARMA model,the GARCH model has the ability to predict more accurate confidence intervals under the same confidence level.展开更多
文摘为了改进水文建模过程中的不确定性处理,采用一种融合全局优化和数据同化(Simultaneous Optimization and Data Assimilation,SODA)的混合框架,对Hy MOD模型进行了不确定性分析,并与经典SCEM-UA方法进行了比较。SODA方法具有如下特点:(1)具备较高的参数搜索效率和寻优能力;(2)明确考虑包括输入、输出、参数以及模型结构在内的重要不确定性来源。SODA方法在渭河流域的实例应用结果表明:与SCEM-UA方法相比,SODA方法不仅显著提高了预报精度,而且推求出了性质更为优良的预报区间。SODA方法的成功应用,有助于模型概念的改进及对水文系统功能的理解。
基金grants from the National Science Foundation (NSF) through Coweeta Long Term Ecological Research (LTER)
文摘We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value （GEV） distribution and Generalized Pareto Distribution （GPD） under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management.
基金supported by the National Hi-Tech Research and Development Program of China ("863" Project) (Grant No. 2012BAB02B04)
文摘Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption that the variance is a constant or it changes with the seasons.However,hydrological processes in the real world are often heteroscedastic,which can be tested by McLeod-Li test and Engle Lagrange multiplier test.In such cases,the GARCH model of hydrological processes is established in this article.First,the seasonal factors in the sequence are removed.Second,the traditional ARMA model is established.Then,the GARCH model is used to correct the residual.At last,the daily runoff data in 1949-2001 of Yichang Hydrological Station is taken to be an example.The result shows that compared to the traditional ARMA model,the GARCH model has the ability to predict more accurate confidence intervals under the same confidence level.
