Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5669–5683, 2022
https://doi.org/10.5194/hess-26-5669-2022
Hydrol. Earth Syst. Sci., 26, 5669–5683, 2022
https://doi.org/10.5194/hess-26-5669-2022
Research article
10 Nov 2022
Research article | 10 Nov 2022

Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too

David McInerney et al.

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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-2021-589', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', David McInerney, 02 Jun 2022
  • RC2: 'Comment on hess-2021-589', Anonymous Referee #2, 04 May 2022
    • AC2: 'Reply on RC2', David McInerney, 02 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (16 Jun 2022) by Micha Werner
AR by David McInerney on behalf of the Authors (12 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to revisions (further review by editor and referees) (10 Aug 2022) by Micha Werner
AR by David McInerney on behalf of the Authors (15 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to revisions (further review by editor and referees) (18 Aug 2022) by Micha Werner
ED: Referee Nomination & Report Request started (22 Aug 2022) by Micha Werner
RR by Anonymous Referee #1 (23 Sep 2022)
ED: Publish as is (06 Oct 2022) by Micha Werner
AR by David McInerney on behalf of the Authors (13 Oct 2022)  Author's response    Manuscript
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Short summary
Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.