基于 EnKF 的新安江模型参数和变量同步估计方法

数据同化方法可提高数值预报的时效性和准确性, 且该方法已在水文领域得到应用, 并得到快速发展。为了提高新安江模型径流模拟预报精度, 采用集合卡尔曼滤波方法同化径流数据, 对参数和状态变量进行同步校正估计。通过对三水源新安江模型进行理想条件下的数值实验, 在同时考虑模型自身、模型参数以及观测数据的不确定性的情况下, 分析了参数均值和方差改变、集合大小、同化参数的敏感性以及相关性分析对同化过程的影响。结果表明: 集合卡尔曼滤波算法具有可行性, 且参数均值越接近真值、方差适当增加, 集合大小适中, 同化参数敏感性较低以及参数与变量间相互独立时, 能在一定程度上增加径流同化精度。该研究可为同类型参数同化估计提供一定参考依据。 The data assimilation method can improve the timeliness and accuracy of numerical fo recasting , and has been applied and developing rapidly in the field of hydrology . In order to improve the accuracy of runoff fo recast of Xinanjiang model, we adopted the ensemble Kalman filter method for synchronous correction of the model parameters and state variables. We designed anumerical experiment of the three-component Xinanjiang model under ideal conditions, and analyzed the effects of the mean and variance of parameters, the ensemble size, and the sensitivity and correlation of parameters on the data assimilation with consideration to the uncertainty of the model itself, model parameters, and the observation data. Results showed that the ensemble Kalman filter algorithm is feasible. Moreover, the accur cy of data assimilation can be improved when the mean value of the parameter is closer to the true value, the variance is increased appropriately , the ensemble size is proper, the sensitivity of parameters is lower, and the correlation between parameters and variables is small. This study can provide a reference for similar research of data assimilation. “十三五”国家重点研发计划项目( 2016YFC0402204) ; 国家自然科学基金项目( 51209187) ; 中央高校基本科研业务费资助项目 (2652015116)