Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1147-2024
https://doi.org/10.5194/hess-28-1147-2024
Research article
 | 
07 Mar 2024
Research article |  | 07 Mar 2024

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Mohammed Abdallah, Ke Zhang, Lijun Chao, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, and Omar M. Nour

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-179', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', Ke Zhang, 22 Oct 2023
  • RC2: 'Comment on hess-2023-179', Anonymous Referee #2, 20 Sep 2023
    • AC2: 'Reply on RC2', Ke Zhang, 22 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (24 Oct 2023) by Xing Yuan
AR by Ke Zhang on behalf of the Authors (04 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Dec 2023) by Xing Yuan
RR by Anonymous Referee #1 (13 Dec 2023)
ED: Publish as is (12 Jan 2024) by Xing Yuan
AR by Ke Zhang on behalf of the Authors (20 Jan 2024)  Author's response   Manuscript 
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Short summary
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation products. The performance of the DVQR model is compared with the simple model average and one-outlier-removed average methods. The nonlinear DVQR model outperforms the quantile-regression-based multivariate linear and Bayesian model averaging methods.