Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area

• Published in: Water resources research Vol. 49; no. 10; pp. 7064 - 7076
• Main Authors: Chen, Xingyuan, Hammond, Glenn E., Murray, Chris J., Rockhold, Mark L., Vermeul, Vince R., Zachara, John M.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01-10-2013; John Wiley & Sons, Inc
• Subjects: Aquifers; Boundary conditions; Complex systems; Data assimilation; Data collection; ensemble Kalman filter; inverse modeling; Parameter estimation; USA, Washington, Hanford
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2012WR013285

Subsurface aquifer characterization often involves high parameter dimensionality and requires tremendous computational resources if employing a full Bayesian approach. Ensemble‐based data assimilation techniques, including filtering and smoothing, are computationally efficient alternatives. Despite the increasing use of ensemble‐based methods in assimilating flow and transport related data for subsurface aquifer characterization, most applications have been limited to synthetic studies or two‐dimensional problems. In this study, we applied ensemble‐based techniques adapted for parameter estimation, including the p‐space ensemble Kalman filter and ensemble smoother, for assimilating field tracer experimental data obtained from the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area. The forward problem was simulated using the massively parallel three‐dimensional flow and transport code PFLOTRAN to effectively deal with the highly transient flow boundary conditions at the site and to meet the computational demands of ensemble‐based methods. This study demonstrates the effectiveness of ensemble‐based methods for characterizing a heterogeneous aquifer by assimilating experimental tracer data, with refined prior information obtained from assimilating other types of data available at the site. It is demonstrated that high‐performance computing enables the use of increasingly mechanistic nonlinear forward simulations for a complex system within the data assimilation framework with reasonable turnaround time.
Key Points
p‐space EnKF was effective for characterizing a heterogeneous aquifer.
Iterative approaches are necessary to reduce the nonlinearity of a problem.
HPC is necessary for ensemble‐based data assimilation in complex systems .