Articles | Volume 21, issue 9
https://doi.org/10.5194/hess-21-4825-2017
https://doi.org/10.5194/hess-21-4825-2017
Research article
 | 
28 Sep 2017
Research article |  | 28 Sep 2017

A hydrological prediction system based on the SVS land-surface scheme: efficient calibration of GEM-Hydro for streamflow simulation over the Lake Ontario basin

Étienne Gaborit, Vincent Fortin, Xiaoyong Xu, Frank Seglenieks, Bryan Tolson, Lauren M. Fry, Tim Hunter, François Anctil, and Andrew D. Gronewold

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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 (further review by Editor and Referees) (14 Feb 2017) by Wouter Buytaert
AR by Etienne Gaborit on behalf of the Authors (03 Mar 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (03 Apr 2017) by Wouter Buytaert
RR by Anonymous Referee #1 (22 Apr 2017)
RR by Anonymous Referee #2 (15 May 2017)
ED: Publish subject to revisions (further review by Editor and Referees) (25 May 2017) by Wouter Buytaert
AR by Etienne Gaborit on behalf of the Authors (04 Jul 2017)  Author's response   Manuscript 
ED: Publish subject to minor revisions (further review by Editor) (14 Jul 2017) by Wouter Buytaert
AR by Etienne Gaborit on behalf of the Authors (24 Jul 2017)  Manuscript 
ED: Publish as is (08 Aug 2017) by Wouter Buytaert
AR by Etienne Gaborit on behalf of the Authors (10 Aug 2017)
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Short summary
The work presents an original methodology for optimizing streamflow simulations with the distributed hydrological model GEM-Hydro. While minimizing the computational time required for automatic calibration, the approach allows us to end up with a spatially coherent and transferable parameter set. The GEM-Hydro model is useful because it allows simulation of all physical components of the hydrological cycle in every part of a domain. It proves to be competitive with other distributed models.