Data assimilation with multiple types of observation  boreholes via the ensemble Kalman filter embedded  within stochastic moment equations

Xia, Chuan-An; Luo, Xiaodong; Hu, Bill X.; Riva, Monica; Guadagnini, Alberto

doi:https://doi.org/10.5194/hess-25-1689-2021

Articles | Volume 25, issue 4

https://doi.org/10.5194/hess-25-1689-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-1689-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 4

Research article

|

06 Apr 2021

Research article |

| 06 Apr 2021

Data assimilation with multiple types of observation boreholes via the ensemble Kalman filter embedded within stochastic moment equations

Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, and Alberto Guadagnini

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Final revised paper (published on 06 Apr 2021)
Preprint (discussion started on 04 Dec 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Data assimilation with multiple types of observation boreholes via ensemble Kalman filter embedded within stochastic moment equations', Anonymous Referee #1, 20 Jan 2021
- AC1: 'Reply on RC1', chuanan Xia, 24 Feb 2021
RC2: 'Review of Data assimilation with multiple types of observation boreholes via ensemble Kalman filter embedded within stochastic moment equations', Anonymous Referee #2, 04 Feb 2021
- AC2: 'Reply on RC2', chuanan Xia, 24 Feb 2021
RC3: 'Comment on "Data assimilation with multiple types of observation boreholes via ensemble Kalman filter embedded within stochastic moment equations"', Anonymous Referee #3, 18 Feb 2021
- AC3: 'Reply on RC3', chuanan Xia, 24 Feb 2021

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (further review by editor) (24 Feb 2021) by Brian Berkowitz

AR by chuanan Xia on behalf of the Authors (24 Feb 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (24 Feb 2021) by Brian Berkowitz

AR by chuanan Xia on behalf of the Authors (25 Feb 2021)

Short summary

Our study shows that (i) monitoring wells installed with packers provide the (overall) best conductivity estimates; (ii) conductivity estimates anchored on information from partially and fully screened wells are of similar quality; (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when a simplified flow model is adopted; and (iv) when compared to the MC-based EnKF, the MEs-based EnKF can efficiently and accurately estimate conductivity and head fields.