Articles | Volume 26, issue 17
© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.
Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria
- Final revised paper (published on 13 Sep 2022)
- Preprint (discussion started on 10 May 2022)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
: Report abuse
RC1: 'Comment on hess-2022-141', Anonymous Referee #1, 04 Jun 2022
- AC1: 'Reply on RC1', Johannes Laimighofer, 01 Jul 2022
RC2: 'Comment on hess-2022-141', Anonymous Referee #2, 07 Jun 2022
- AC2: 'Reply on RC2', Johannes Laimighofer, 01 Jul 2022
RC3: 'Comment on hess-2022-141', Anonymous Referee #3, 30 Jun 2022
- AC3: 'Reply on RC3', Johannes Laimighofer, 11 Jul 2022
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (28 Jul 2022) by Elena Toth
AR by Johannes Laimighofer on behalf of the Authors (01 Aug 2022)  Author's response Author's tracked changes Manuscript
ED: Publish subject to revisions (further review by editor and referees) (09 Aug 2022) by Elena Toth
ED: Referee Nomination & Report Request started (11 Aug 2022) by Elena Toth
RR by Anonymous Referee #3 (23 Aug 2022)
ED: Publish as is (29 Aug 2022) by Elena Toth
This is a well-prepared manuscript that presents a novel advancement in low-flow estimation using extreme gradient boosting. While the advancement in incremental in that it uses established methods for prediction, it fills an important gap in the literature by addressing low-flow prediction. Overall, I find few technical concerns, and I see no major impediment to eventual publication.
My only concern is with the model fitting. The authors observed middling-to-high performance across a range of methods, and they also note that the methods require something like 20-30 variables for prediction. I would be interested in seeing some discussion on how the large quantity of variables may or may not be indicative of overfitting. It seems, from my arm-chair analysis here, that overfitting could explain the rapid loss in performance for extreme low flows. I’d be interested to hear what the authors have to say.
Overall, the manuscript is extremely thorough while also remaining accessible. The procedures are accurately contextualized in relevant literature, and a good effort has been made to present the results in light of other work. The conclusions are supported by the analysis presented. While some typographical errors may exist, I, again, see no major impediment to eventual publication.