Articles | Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017

Research article 29 Sep 2017

Research article | 29 Sep 2017

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Hongjuan Zhang et al.

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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 (15 Aug 2016) by Kurt Roth
AR by Hongjuan Zhang on behalf of the Authors (05 Oct 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (26 Oct 2016) by Kurt Roth
RR by Anonymous Referee #1 (23 Nov 2016)
RR by Anonymous Referee #2 (25 Nov 2016)
ED: Publish subject to revisions (further review by Editor and Referees) (27 Nov 2016) by Kurt Roth
AR by Hongjuan Zhang on behalf of the Authors (06 Mar 2017)  Author's response    Manuscript
ED: Publish subject to revisions (further review by Editor and Referees) (03 Apr 2017) by Kurt Roth
ED: Referee Nomination & Report Request started (05 Apr 2017) by Kurt Roth
RR by Anonymous Referee #1 (18 Apr 2017)
RR by Anonymous Referee #2 (01 May 2017)
ED: Reconsider after major revisions (further review by Editor and Referees) (10 May 2017) by Kurt Roth
AR by Hongjuan Zhang on behalf of the Authors (24 May 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (30 May 2017) by Kurt Roth
RR by Anonymous Referee #1 (26 Jun 2017)
ED: Publish subject to technical corrections (19 Jul 2017) by Kurt Roth
AR by Hongjuan Zhang on behalf of the Authors (24 Jul 2017)  Author's response    Manuscript
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Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.