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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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.