Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Arsenault, Richard; Côté, Pascal

doi:https://doi.org/10.5194/hess-23-2735-2019

Articles | Volume 23, issue 6

https://doi.org/10.5194/hess-23-2735-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-2735-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 6

Research article

|

28 Jun 2019

Research article |

| 28 Jun 2019

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Richard Arsenault and Pascal Côté

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Final revised paper (published on 28 Jun 2019)
Preprint (discussion started on 03 Jul 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review: Very interesting analysis, but more work needed to corroborate conclusions', Anonymous Referee #1, 16 Aug 2018
- AC1: 'Author repsonse to Reviewer #1', Richard Arsenault, 14 Jan 2019
RC2: 'Reviewer comment for "Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management"', Anonymous Referee #2, 30 Dec 2018
- AC2: 'Author response to Reviewer #2', Richard Arsenault, 14 Jan 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by editor and referees) (26 Jan 2019) by Micha Werner

AR by Richard Arsenault on behalf of the Authors (27 Jan 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (02 Feb 2019) by Micha Werner

RR by Anonymous Referee #3 (11 Jun 2019)

ED: Publish as is (11 Jun 2019) by Micha Werner

Short summary

Hydrological forecasting allows hydropower system operators to make the most efficient use of the available water as possible. Accordingly, hydrologists have been aiming at improving the quality of these forecasts. This work looks at the impacts of improving systematic errors in a forecasting scheme on the hydropower generation using a few decision-aiding tools that are used operationally by hydropower utilities. We find that the impacts differ according to the hydropower system characteristics.