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

  • Mohamad E. Gharamti
  • Boujemaa Ait-El-Fquih
  • Ibrahim Hoteit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Contaminant Concentration Sequential Gaussian Simulation Analysis Ensemble Assimilation Result Forecast Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohamad E. Gharamti
    • 1
  • Boujemaa Ait-El-Fquih
    • 2
  • Ibrahim Hoteit
    • 1
    • 2
  1. 1.Earth Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Applied Mathematics and Computational SciencesKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

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