基于GLUE方法的新安江模型参数不确定性研究
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摘要
由于流域水文模型本身参数众多且相互制约,参数优选是一项十分复杂的工作。针对水文模型参数初始优化范围广、优化耗时长、精度不够且优选过程中易陷入局部优化的问题,采用多目标GLUE方法分析新安江模型中较敏感参数的不确定性,确定了其后验分布范围。在此基础上,以龙湾流域12场洪水为例,相同条件下分别在模型参数先验分布和后验分布范围内对参数进行优化率定。结果表明,取后验分布范围作为参数优选范围提高了参数率定效率,提升了模型预报整体性能和预报精度,可为缺资料地区参数识别和参数移植提供参考。
The parameter optimization is a very complicated task because of many parameters and their mutual constraints of the hydrological model of the basin.In order to solve the problem of large initial optimization range of hydrological model parameters,long optimization time,lack of precision and prone to local extremum in optimization process,a multi-objective GLUE method was used to analyze the uncertainty of the sensitive parameters of Xin'Anjiang model and determine the posterior distribution range of the sensitive parameters.Taking the 12-field flood in Longwan River basin as the research object,the parameters were optimized in the prior distribution and the posterior distribution of the model parameters under the same conditions.It shows that the result of using the posterior distribution range is better than that of the parameter selection range,which improves efficiency,and the overall performance and prediction precision of the model prediction.Thus,the research lays the foundation for parameter identification and parameter transplant in the missing data area.
The parameter optimization is a very complicated task because of many parameters and their mutual constraints of the hydrological model of the basin.In order to solve the problem of large initial optimization range of hydrological model parameters,long optimization time,lack of precision and prone to local extremum in optimization process,a multi-objective GLUE method was used to analyze the uncertainty of the sensitive parameters of Xin'Anjiang model and determine the posterior distribution range of the sensitive parameters.Taking the 12-field flood in Longwan River basin as the research object,the parameters were optimized in the prior distribution and the posterior distribution of the model parameters under the same conditions.It shows that the result of using the posterior distribution range is better than that of the parameter selection range,which improves efficiency,and the overall performance and prediction precision of the model prediction.Thus,the research lays the foundation for parameter identification and parameter transplant in the missing data area.
引文
[1]任政,盛东.基于多目标GLUE算法的新安江模型参数不确定性研究[J].水电能源科学.2016,34(3):15-18.
[2]Brazier R E,Beven K J,Freer J,et al.Equifinality and Uncertainty in Physically Based Soil Erosion Models:Application of the GLUE Methodology to WEPP-the Water Erosion Prediction Project-for Sites in the UK and USA[J].Earth Surface Processes and Landforms,2015,25(8):825-845.
[3]GB/T 22482-2008,水文情报预报规范[S].北京:中国标准出版社,2009.
[4]张波,马德莲,包为民,等.新安江模型在富水水库流域洪水预报中的应用[J].水电能源科学,2010,28(4):49-51,21.
[5]田向荣,赵英林.遗传算法和Rosenbrock法在水文预报汇流参数优选中的比较研究[J].水电能源科学,2002,20(1):23-25.
[2]Brazier R E,Beven K J,Freer J,et al.Equifinality and Uncertainty in Physically Based Soil Erosion Models:Application of the GLUE Methodology to WEPP-the Water Erosion Prediction Project-for Sites in the UK and USA[J].Earth Surface Processes and Landforms,2015,25(8):825-845.
[3]GB/T 22482-2008,水文情报预报规范[S].北京:中国标准出版社,2009.
[4]张波,马德莲,包为民,等.新安江模型在富水水库流域洪水预报中的应用[J].水电能源科学,2010,28(4):49-51,21.
[5]田向荣,赵英林.遗传算法和Rosenbrock法在水文预报汇流参数优选中的比较研究[J].水电能源科学,2002,20(1):23-25.
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