Identification of hydrological model parameter variation using ensemble Kalman filter

Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao

doi:https://doi.org/10.5194/hess-20-4949-2016

Articles | Volume 20, issue 12

https://doi.org/10.5194/hess-20-4949-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-4949-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 12

Research article

|

16 Dec 2016

Research article |

| 16 Dec 2016

Identification of hydrological model parameter variation using ensemble Kalman filter

Chao Deng, Pan Liu, Shenglian Guo, Zejun Li, and Dingbao Wang

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Final revised paper (published on 16 Dec 2016)
Preprint (discussion started on 10 Aug 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review on ``Identification of hydrological model parameters variation using ensemble Kalman filter''', Anonymous Referee #1, 07 Sep 2016
- AC1: 'Author's response to anonymous referee #1', Pan Liu, 13 Oct 2016
RC2: 'review', Anonymous Referee #2, 23 Sep 2016
- AC2: 'Author's response to anonymous referee #2', Pan Liu, 13 Oct 2016

Peer-review completion

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

ED: Reconsider after major revisions (further review by Editor and Referees) (20 Oct 2016) by Alberto Guadagnini

AR by Pan Liu on behalf of the Authors (30 Oct 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (08 Nov 2016) by Alberto Guadagnini

RR by Anonymous Referee #1 (22 Nov 2016)

ED: Publish as is (27 Nov 2016) by Alberto Guadagnini

AR by Pan Liu on behalf of the Authors (28 Nov 2016)

Short summary

Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate change and anthropogenic activities. The technique of the ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variations.