Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-635-2017
https://doi.org/10.5194/hess-21-635-2017
Research article
 | 
31 Jan 2017
Research article |  | 31 Jan 2017

Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

Chengcheng Huang, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, and Xiaogu Zheng

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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 (16 Oct 2016) by Ilias Pechlivanidis
AR by Andrew Newman on behalf of the Authors (22 Nov 2016)  Author's response    Manuscript
ED: Publish subject to minor revisions (further review by Editor) (29 Nov 2016) by Ilias Pechlivanidis
AR by Andrew Newman on behalf of the Authors (09 Dec 2016)  Author's response    Manuscript
ED: Publish subject to minor revisions (further review by Editor) (12 Dec 2016) by Ilias Pechlivanidis
AR by Andrew Newman on behalf of the Authors (19 Dec 2016)  Author's response    Manuscript
ED: Publish as is (20 Dec 2016) by Ilias Pechlivanidis
AR by Andrew Newman on behalf of the Authors (30 Dec 2016)  Author's response    Manuscript
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Short summary
This study examined the potential of snow water equivalent data assimilation to improve seasonal streamflow predictions. We examined aspects of the data assimilation system over basins with varying climates across the western US. We found that varying how the data assimilation system is implemented impacts forecast performance, and basins with good initial calibrations see less benefit. This implies that basin-specific configurations and benefits should be expected given this modeling system.