Articles | Volume 20, issue 7
https://doi.org/10.5194/hess-20-2611-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
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- Final revised paper (published on 04 Jul 2016)
- Supplement to the final revised paper
- Preprint (discussion started on 28 Oct 2015)
Interactive discussion
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
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RC C4892: 'review', Anonymous Referee #1, 17 Nov 2015
- AC C5069: 'Response to anonymous referee #1', Julie Shortridge, 25 Nov 2015
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RC C5080: 'Review', Anonymous Referee #2, 25 Nov 2015
- AC C5644: 'Response to anonymous referee #2', Julie Shortridge, 16 Dec 2015
Peer-review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (Editor review) (06 Feb 2016) by Dominic Mazvimavi
AR by Julie Shortridge on behalf of the Authors (15 Feb 2016)
Author's response
Manuscript
ED: Reconsider after major revisions (24 Feb 2016) by Dominic Mazvimavi
AR by Julie Shortridge on behalf of the Authors (24 Feb 2016)
Author's response
Manuscript
ED: Referee Nomination & Report Request started (07 Mar 2016) by Dominic Mazvimavi
RR by Anonymous Referee #1 (17 Mar 2016)
RR by Anonymous Referee #2 (03 Apr 2016)
ED: Publish subject to minor revisions (Editor review) (10 Apr 2016) by Dominic Mazvimavi
AR by Julie Shortridge on behalf of the Authors (15 Apr 2016)
Author's response
Manuscript
ED: Publish as is (29 May 2016) by Dominic Mazvimavi
AR by Julie Shortridge on behalf of the Authors (07 Jun 2016)