Articles | Volume 26, issue 10
Research article
01 Jun 2022
Research article |  | 01 Jun 2022

Performance-based comparison of regionalization methods to improve the at-site estimates of daily precipitation

Abubakar Haruna, Juliette Blanchet, and Anne-Catherine Favre


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • 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
Short summary
Reliable prediction of floods depends on the quality of the input data such as precipitation. However, estimation of precipitation from the local measurements is known to be difficult, especially for extremes. Regionalization improves the estimates by increasing the quantity of data available for estimation. Here, we compare three regionalization methods based on their robustness and reliability. We apply the comparison to a dense network of daily stations within and outside Switzerland.