Articles | Volume 24, issue 4
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020
https://doi.org/10.5194/hess-24-2061-2020
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020
https://doi.org/10.5194/hess-24-2061-2020

Research article 23 Apr 2020

Research article | 23 Apr 2020

A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

Xudong Zhou et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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 Aug 2019) by Dimitri Solomatine
AR by Xudong Zhou on behalf of the Authors (14 Sep 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Oct 2019) by Dimitri Solomatine
RR by Anonymous Referee #1 (26 Oct 2019)
RR by Anonymous Referee #2 (27 Oct 2019)
ED: Publish subject to revisions (further review by editor and referees) (03 Dec 2019) by Dimitri Solomatine
AR by Xudong Zhou on behalf of the Authors (13 Jan 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (22 Jan 2020) by Dimitri Solomatine
RR by Anonymous Referee #2 (12 Feb 2020)
ED: Publish subject to minor revisions (review by editor) (15 Feb 2020) by Dimitri Solomatine
AR by Xudong Zhou on behalf of the Authors (25 Feb 2020)  Author's response    Manuscript
ED: Publish as is (15 Mar 2020) by Dimitri Solomatine
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Short summary
This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.