Articles | Volume 26, issue 5
Hydrol. Earth Syst. Sci., 26, 1203–1221, 2022
https://doi.org/10.5194/hess-26-1203-2022

Special issue: Frontiers in the application of Bayesian approaches in water...

Hydrol. Earth Syst. Sci., 26, 1203–1221, 2022
https://doi.org/10.5194/hess-26-1203-2022

Research article 04 Mar 2022

Research article | 04 Mar 2022

Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method

Xia Wu 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: Reconsider after major revisions (further review by editor and referees) (12 Apr 2021) by Lorenz Ammann
AR by Lucy Marshall on behalf of the Authors (05 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (23 Aug 2021) by Lorenz Ammann
RR by Anonymous Referee #3 (26 Aug 2021)
RR by Anonymous Referee #1 (13 Sep 2021)
ED: Publish subject to revisions (further review by editor and referees) (20 Sep 2021) by Lorenz Ammann
AR by Lucy Marshall on behalf of the Authors (01 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (18 Nov 2021) by Lorenz Ammann
RR by Anonymous Referee #1 (03 Dec 2021)
ED: Publish subject to minor revisions (review by editor) (04 Dec 2021) by Lorenz Ammann
AR by Lucy Marshall on behalf of the Authors (14 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (17 Dec 2021) by Lorenz Ammann
AR by Lucy Marshall on behalf of the Authors (20 Dec 2021)  Author's response    Manuscript
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Short summary
Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, i.e., Bayesian error analysis with reordering (BEAR). In the context of a total suspended solids simulation, two synthetic studies and a real study demonstrate that the BEAR method is effective for improving the input error estimation and water quality model calibration.