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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 2
Hydrol. Earth Syst. Sci., 16, 573–590, 2012
https://doi.org/10.5194/hess-16-573-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 16, 573–590, 2012
https://doi.org/10.5194/hess-16-573-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Feb 2012

Research article | 27 Feb 2012

Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter

L. Li1, H. Zhou1, H. J. Hendricks Franssen2, and J. J. Gómez-Hernández1 L. Li et al.
  • 1Group of Hydrogeology, Universitat Politècnica de València, 46022 Valencia, Spain
  • 2Agrosphere, IBG-3, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

Abstract. The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations.

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