Articles | Volume 26, issue 23
https://doi.org/10.5194/hess-26-6121-2022
https://doi.org/10.5194/hess-26-6121-2022
Research article
 | 
06 Dec 2022
Research article |  | 06 Dec 2022

Evaluation of a new observationally based channel parameterization for the National Water Model

Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, and Toby Minear

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-552', Anonymous Referee #1, 03 Jan 2022
    • AC1: 'Reply on RC1', Aaron Heldmyer, 27 May 2022
  • RC2: 'Comment on hess-2021-552', Anonymous Referee #2, 04 May 2022
    • AC2: 'Reply on RC2', Aaron Heldmyer, 27 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (06 Jun 2022) by Pieter van der Zaag
AR by Aaron Heldmyer on behalf of the Authors (30 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (04 Sep 2022) by Pieter van der Zaag
AR by Aaron Heldmyer on behalf of the Authors (09 Sep 2022)
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Short summary
Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.