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将集合卡尔曼滤波(EnKF)方法拓展至补给条件下潜水流动的数据同化问题,通过同化水位、水力传导度和降雨补给等测量数据来更新模型状态、反演模型参数,探讨了在不同补给条件下测量数据对水力传导度和降雨入渗补给系数反演的影响,分析了不同类型测量数据在同化中的作用.结果表明:EnKF方法可以通过动态的测量数据改善对地下水模型参数的估计,方法在降雨补给量较大条件下可以取得更好的同化效果,说明在雨季等地下水运动变动剧烈时的测量数据价值更高,有长期水位动态测量数据时,可以通过水位观测值有效地反演出水力传导度和降雨入渗补给系数.
The ensemble Kalman filter (EnKF) method is extended to the data assimilation of subsurface flow under recharge condition. The model state is updated by assimilating measurement data such as water level, hydraulic conductivity and rainfall recharge, and the model parameters are retrieved. The effect of different measurement data on the inversion of hydraulic conductivity and precipitation infiltration was analyzed.The results show that EnKF method can improve the estimation of groundwater model parameters through dynamic measurement data, A better assimilation effect can be obtained under the condition of larger rainfall recharge, which indicates that the measured data are more valuable when the groundwater movement fluctuates drastically in the rainy season. When long-term water level dynamic measurement data is available, the hydraulic force can be effectively retrieved from the observed water level Conductivity and rainfall infiltration recharge coefficient.