An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling.

Groundwater reactive transport models that consider the coupling of hydraulic and biochemical processes are vital tools for predicting the fate of groundwater contaminants and effective groundwater management. The models involve a large number of parameters whose specification greatly affects the model performance. Thus model parameters calibration is crucial to its successful application. The Bayesian inference framework implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate the model parameters. However, its application is hampered by the large computational requirements caused by repeated evaluations of the model in MCMC sampling. This study develops an adaptive Kriging-based MCMC method to overcome the bottleneck of Bayesian inference by replacing the simulation model with a computationally inexpensive Kriging surrogate model. In the adaptive Kriging-based MCMC method, instead of constructing a globally accurate surrogate of the simulation model, we sequentially build a locally accurate surrogate with an iterative refinement to the high probability regions. The performance of the proposed method is demonstrated using a synthetic groundwater reactive transport model for describing sequential Kinetic degradation of Tetrachloroethene (PCE), whose hydraulic and biochemical parameters are jointly estimated. The results suggest that the adaptive Kriging-based MCMC method is able to achieve an accurate Bayesian inference with a hundredfold reduction in the computational cost compared to the conventional MCMC method.