Articles | Volume 23, issue 12
https://doi.org/10.5194/hess-23-5089-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
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- Final revised paper (published on 17 Dec 2019)
- Preprint (discussion started on 02 Aug 2019)
Interactive discussion
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
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RC1: 'review comments for hess-2019-368', Anonymous Referee #1, 28 Aug 2019
- AC1: 'Response to reviewer #1', Frederik Kratzert, 26 Sep 2019
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RC2: 'Review by Hoshin Gupta', Hoshin Gupta, 10 Sep 2019
- AC3: 'Response to Hoshin Gupta', Frederik Kratzert, 15 Oct 2019
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RC3: 'Moderate revision -- Good paper. Some improvements are requested -- hydrologic insights are called for', Anonymous Referee #3, 16 Sep 2019
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RC4: 'I forgot to attach the literature cited in my PDF file so here they are', Anonymous Referee #3, 16 Sep 2019
- AC2: 'Response to reviewer #3', Frederik Kratzert, 10 Oct 2019
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RC4: 'I forgot to attach the literature cited in my PDF file so here they are', Anonymous Referee #3, 16 Sep 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) (17 Oct 2019) by Nadav Peleg
AR by Frederik Kratzert on behalf of the Authors (29 Oct 2019)
Manuscript
ED: Publish as is (06 Nov 2019) by Nadav Peleg
AR by Frederik Kratzert on behalf of the Authors (07 Nov 2019)