Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3289-2016
https://doi.org/10.5194/hess-20-3289-2016
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, https://doi.org/10.1029/2010WR009147, 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.
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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.