关键词: 数据同化, 渗透系数场估计, 水头观测, 溶质运移数据, 局域化EnKF

Abstract: Localized Ensemble Kalman Filter (EnKF) is used to overcome the defect of EnKF that the accuracy of parameter estimation
is low when a small ensemble is used. There are numerous study on using EnKF to estimate heterogeneous hydraulic conductivity by
assimilating hydraulic head observations. In practice, solute concentration data are easy to collect. Cui (2013) used localized EnKF to
estimate heterogeneous hydraulic conductivity field in a 2-D confined aquifer by assimilating the hydraulic head and solute concentration
observations simultaneously, but the result was not satisfying for some unknown reason. In this study we use two solute injection wells
(instead of one solute injection well) so that more effective solute concentration data can be collected during the assimilation process.
In addition, we compare the results with the fields estimated based only on head and solute transport data, respectively. It is found that:
(1) Localized EnKF yields more accurate hydraulic conductivity estimates than EnKF; (2) assimilating hydraulic head observations
and solute concentration observations together results in better estimates than assimilating any single type of observations. In addition,
hydraulic conductivity estimates in the reach of plumes can be well estimated by using solute concentration or head observations.

Key words: data assimilation, hydraulic conductivity estimation, hydraulic head, solute concentration observations, localized EnKF