Groundwater Contaminant Transport Modeling Using Multiple Adaptive Data Assimilation Techniques.

摘要

In modeling the behavior of contaminants in a subsurface environment using data assimilation schemes, accurate assignment of model and observation errors are significant for the successful application of the techniques. In this study, a three-dimensional numerical model was used to simulate the advection and dispersion transport of contaminant in the subsurface. Stochastic data assimilation schemes were coupled with the subsurface contaminant transport model to predict the state of the contaminant. Four data assimilation techniques namely the Ensemble Kalman Filter, Mollified Ensemble Kalman Filter, Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter were adopted to improve the prediction of the contaminant fate and transport in the groundwater. The Ensemble Kalman Filter applies a Monte Carlo approach to the filtering problem. The adaptive filtering technique employs the diagnostic approach to fine tune the model and observation covariance matrix. The hybrid technique uses combination of the forecast covariance matrix and the invariant background covariance matrix to explore the Ensemble Kalman filter.;The impact of the filters on the numerical model is examined by using the Normalized Root Mean Squared Error (NRMSE), Average Absolute Bias (AAB) metric, and Maximum Absolute Deviation (MAD) techniques. The AAB evaluation of Mollified Ensemble Kalman Filter, Hybrid Adaptive Ensemble Kalman Filter and Adaptive Ensemble Kalman Filter, on the average shows error reduction of 81%, 87% and 89%, respectively, while the MAD assessment recorded 88%, 90% and 92% improvement respectively, relative to the numerical model. The results of simulations show that the prediction accuracy of the filters is better than numerical model. The proposed Mollified Ensemble Kalman Filter, Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter, takes advantage of the mollified technique, adaptive factor and the weighting factor, respectively to improve the prediction efficiency of the Ensemble Kalman filter. Sensitivity analysis performed on Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter using NRMSE indicates that the adaptive factor selected to initialize the filtering process does not affect the prediction accuracy of the former whereas the weighting factor has influence on the latter.