2016
DOI: 10.5194/hess-20-3289-2016
|View full text |Cite
|
Sign up to set email alerts
|

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

Abstract: Abstract. Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augment… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
38
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
6
1
1

Relationship

4
4

Authors

Journals

citations
Cited by 34 publications
(38 citation statements)
references
References 45 publications
0
38
0
Order By: Relevance
“…A suggested alternative approach is to use a dual EnKF scheme involving two interacting filters: one to update the states, and one to update the parameters. Both heuristic [85] and Bayesian consistent [88] versions of the dual EnKF have been proposed. While the 1D model used in this study is nonlinear, particularly with the inclusion of the sigmoid wall model (4), we have not had such issues estimating the inflow profile parameters using the augmented EnKF-type scheme presented in this work.…”
Section: Discussionmentioning
confidence: 99%
“…A suggested alternative approach is to use a dual EnKF scheme involving two interacting filters: one to update the states, and one to update the parameters. Both heuristic [85] and Bayesian consistent [88] versions of the dual EnKF have been proposed. While the 1D model used in this study is nonlinear, particularly with the inclusion of the sigmoid wall model (4), we have not had such issues estimating the inflow profile parameters using the augmented EnKF-type scheme presented in this work.…”
Section: Discussionmentioning
confidence: 99%
“…Here, we present a summary of the approach and more details can be found in Khaki et al 52 . Ait-El-Fquih et al 45 proposed a new dual EnKF scheme following the one-step-ahead (OSA) smoothing and showed that this could improve data assimilation performance by imposing more information to the system. Their approach comprises two interactive EnKF filters for state-parameter estimation.…”
Section: Methodsmentioning
confidence: 99%
“…The updated parameters and state variables are then integrated with the model to obtain the next state forecast ensemble in the second EnKF, which will be used to acquire the analysis ensemble. Despite the addition of the second EnKF implementation compared to the traditional dual-EnKF due to the OSA smoothing part, it has been shown that this only increases the computational cost minimally while it considerably enhances the performance of the dual approach 44 , 45 , 52 . For the state-parameter estimation problem in a discrete-time dynamical system, one can write, where is the system state vector (with dimension ) and is the observation vector (with dimension ) at time t .…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Although the data assimilation of hydrological observations into the hyperresolution land models has been successfully implemented in the synthetic experiments, it is unclear how and in what case topography-driven surface lateral water flows matter for data assimilation of soil moisture observations. Previous studies on data assimilation with high resolution models mainly focused on assimilating groundwater observations (e.g., Ait-El-Fquih et al 2016;Rasmussen et al 2015;Hendricks-Franssen et al 2008). There are some applications which focused on the observation of soil moisture and pressure head in shallow unsaturated soil layers.…”
Section: Introductionmentioning
confidence: 99%