基于EnKF综合水头和浓度观测数据推估地下水流模型参数

地下水反应运移模型具有参数个数众多，观测数据类型多样的特点。为了探究不同类型观测数据在反应运移模拟数据同化中的数据价值，构建了三氯乙烯降解反应运移模型的理想算例，基于水头和浓度两种类型观测数据，采用集合卡尔曼滤波方法推估渗透系数和贮水系数的非均质空间分布，讨论了影响同化结果的因素。结果表明:与仅同化水头数据的结果相比，联合同化水头和浓度观测数据推估渗透系数场和贮水系数场时具有更高的精度，在观测数据拟合和模型预测方面也有更好的表现。与目前溶质运移模型、非饱和流模型等地下水模型中的研究结果相似，数据同化结果受样本数量，观测井的数量和位置的影响，合理优化布置监测井和选择样本数量可有效改善数据同化效果并提高计算效率。

Joint assimilation of heads and concentrations for estimating parameters of groundwater flow models using the Ensemble Kalman Filter

Reactive transport models are characterized by a large number of parameters and a variety of observations. Taking the TCE degradation model as an example, this article used the Ensemble Kalman Filter method to estimate the heterogeneous distribution of hydraulic conductivity field and storage coefficient field in order to compare the data values of different types of observations (i.e., only groundwater head, head and concentration) during the data assimilation. The results show that compared with only assimilating head data, jointly using head and concentration can improve the accuracy of parameter estimation and has a better performance in terms of data match and model prediction; the ensemble size and the number and location of observation wells will affect the results of the data assimilation, which is similar to the conclusions in other groundwater models such as the solute transport model and the unsaturated flow model. Better results and higher computational efficiency can be obtained by reasonably setting the layout of observation wells and choosing the ensemble size.