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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationDynamic Data-Driven Environmental Systems Science
PublisherSpringer Nature
Pages207-214
Number of pages8
ISBN (Print)9783319251370
DOIs
StatePublished - Nov 27 2015

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