Articles | Volume 26, issue 10
© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.
Performance-based comparison of regionalization methods to improve the at-site estimates of daily precipitation
- Final revised paper (published on 01 Jun 2022)
- Preprint (discussion started on 30 Nov 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
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RC1: 'Comment on hess-2021-546', Anonymous Referee #1, 05 Jan 2022
- AC1: 'Reply on RC1', Abubakar Haruna, 17 Jan 2022
RC2: 'Comment on hess-2021-546', Anonymous Referee #2, 22 Feb 2022
- AC2: 'Reply on RC2', Abubakar Haruna, 09 Mar 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) (17 Mar 2022) by Thomas Kjeldsen
AR by Abubakar Haruna on behalf of the Authors (12 Apr 2022)  Author's response Author's tracked changes Manuscript
ED: Referee Nomination & Report Request started (20 Apr 2022) by Thomas Kjeldsen
RR by Anonymous Referee #1 (05 May 2022)
RR by Anonymous Referee #2 (10 May 2022)
ED: Publish as is (11 May 2022) by Thomas Kjeldsen
This paper presents an interesting comparison of regionalization methods for rainfall frequency analysis. It is based on a large sample of 1176 daily stations in Switzerland and its neighbouring countries. Five models have been compared, based on local data (local EGPD) or regional data (Omega EGPD with an average shape parameter; ROI EGPD Full or Semi with ROI approach; GAM EGPD with parameters depending on covariates). The paper gives confirmation on the interest of regional approach vs local approach, and concludes that the GAM model is better for the upper tail, and the ROI model for the bulk of the distribution.
The paper is clear and well written. I have one requirement on additional simulation and three minor recommendations.
The authors decided to use a EGPD distribution, with the advantage of representing both the bulk of the distribution and the upper tail. As the conclusion is that no model is the best on the whole part of the distribution, I would be interested to see whether a GP-ROI distribution-regional approach performs on the upper tail, compared with a GAM EGPD model.
Line 152. The first time, explain PAM acronym (Partitioning around medoids)
Figure 3 - Histogram. It could be interesting to add (for each class of radius) the mean number of stations belonging to the ROI
Line 420. “The plot of the right of Figure 5”