Articles | Volume 26, issue 5
https://doi.org/10.5194/hess-26-1203-2022
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, Lucy Marshall, and Ashish Sharma

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Latest update: 19 Jun 2024
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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.