AI Article Synopsis
- Groundwater contamination assessment is important for effective actions during environmental emergencies and involves using a three-dimensional deterministic model to simulate contaminant transport.
- Multiple stochastic data assimilation techniques, including EnKF, LETKF, and GETKF, were applied to improve predictions of contaminant concentration.
- The study found significant improvements in prediction accuracy, with RMSE reductions showing nearly 89.48%, 96.30%, and 96.82% improvements respectively, while also noting that the effectiveness of these methods is influenced by observation noise, process noise, and ensemble size.
Article 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.
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| http://dx.doi.org/10.2175/106143017X15051465918930 | DOI Listing |
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