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International Conference on Dynamic Data-Driven Environmental Systems Science

DyDESS 2014: Dynamic Data-Driven Environmental Systems Science pp 207–214Cite as

A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8964))

Abstract

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

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Authors and Affiliations

  1. Earth Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

    Mohamad E. Gharamti & Ibrahim Hoteit

  2. Applied Mathematics and Computational Sciences, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

    Boujemaa Ait-El-Fquih & Ibrahim Hoteit

Authors
  1. Mohamad E. Gharamti
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  2. Boujemaa Ait-El-Fquih
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  3. Ibrahim Hoteit
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Corresponding author

Correspondence to Ibrahim Hoteit .

Editor information

Editors and Affiliations

  1. Massachusetts Inst. of Technology, Cambridge, Massachusetts, USA

    Sai Ravela

  2. VA Polytechnic Institute and State Univ., Blacksburg, Virginia, USA

    Adrian Sandu

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© 2015 Springer International Publishing Switzerland

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Gharamti, M.E., Ait-El-Fquih, B., Hoteit, I. (2015). A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-25138-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25137-0

  • Online ISBN: 978-3-319-25138-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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