Articles | Volume 26, issue 5
Hydrol. Earth Syst. Sci., 26, 1203–1221, 2022

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

Hydrol. Earth Syst. Sci., 26, 1203–1221, 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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Cited articles

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Bonhomme, C. and Petrucci, G.: Should we trust build-up/wash-off water quality models at the scale of urban catchments?, Water Res., 108, 422–431,, 2017. 
Chaudhary, A. and Hantush, M. M.: Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model, Water Res., 108, 301–311,, 2017. 
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.