Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling.

Abstract

Groundwater contamination assessment can be useful in taking proper actions during environmental emergency. A three-dimensional deterministic model was taken into consideration to simulate the advective-diffusive transport of non-conservative contaminant in groundwater. Multiple stochastic data assimilation techniques, such as, ensemble Kalman filter (EnKF), local ensemble transform Kalman filter (LETKF), and the global form of the LETKF, denoted as GETKF were applied to the model. The results show that data assimilation improved contaminant concentration prediction. The EnKF method reduced the root-mean-square-error (RMSE) of the contaminant prediction from 12.5 mg/L to 1.31 mg/L, whereas the LETKF reduced that to 0.46 mg/L and GETKF reduced that to 0.38 mg/L. Approximately 89.48%, 96.30% and 96.82% improvement were made by EnKF, LETKF, and GETKF, respectively. The sensitivity analysis suggest that these data assimilation techniques are very sensitive to the observation noise, process noise, and ensemble size.