Articles | Volume 20, issue 8
Research article
12 Aug 2016
Research article |  | 12 Aug 2016

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit

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Cited articles

Alcolea, A., Carrera, J., and Medina, A.: Pilot points method incorporating prior information for solving the groundwater flow inverse problem, Adv. Water Resour., 29, 1678–1689, 2006.
Bailey, R. and Baú, D.: Ensemble smoother assimilation of hydraulic head and return flow data to estimate hydraulic conductivity distribution, Water Resour. Res., 46, W12543,, 2010.
Chang, S.-Y., Chowhan, T., and Latif, S.: State and parameter estimation with an SIR particle filter in a three-dimensional groundwater pollutant transport model, J. Environ. Eng., 138, 1114–1121, 2012.
Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107–1122, 2006.
Crestani, E., Camporese, M., Baú, D., and Salandin, P.: Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation, Hydrol. Earth Syst. Sci., 17, 1517–1531,, 2013.
Short summary
We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.