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

doi:10.1007/978-3-319-25138-7_19

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 proceeding › Conference contribution

2 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 language	English (US)
Title of host publication	Dynamic Data-Driven Environmental Systems Science
Publisher	Springer Nature
Pages	207-214
Number of pages	8
ISBN (Print)	9783319251370
DOIs	https://doi.org/10.1007/978-3-319-25138-7_19
State	Published - Nov 27 2015

Access to Document

10.1007/978-3-319-25138-7_19

Cite this

@inproceedings{81950d8044c84446b505ac7cd961a55f,

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

abstract = "The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model{\textquoteright}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.",

author = "{El Gharamti}, Mohamad and Boujemaa Ait-El-Fquih and Ibrahim Hoteit",

note = "KAUST Repository Item: Exported on 2020-10-01",

year = "2015",

month = nov,

day = "27",

doi = "10.1007/978-3-319-25138-7_19",

language = "English (US)",

isbn = "9783319251370",

pages = "207--214",

booktitle = "Dynamic Data-Driven Environmental Systems Science",

publisher = "Springer Nature",

}

TY - GEN

T1 - A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

AU - El Gharamti, Mohamad

AU - Ait-El-Fquih, Boujemaa

AU - Hoteit, Ibrahim

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2015/11/27

Y1 - 2015/11/27

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/622130

UR - http://link.springer.com/10.1007/978-3-319-25138-7_19

UR - http://www.scopus.com/inward/record.url?scp=84951789631&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-25138-7_19

DO - 10.1007/978-3-319-25138-7_19

M3 - Conference contribution

SN - 9783319251370

SP - 207

EP - 214

BT - Dynamic Data-Driven Environmental Systems Science

PB - Springer Nature

ER -

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this