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.

Data sets

National Real-Time Water Quality website U.S. Geological Survey https://nrtwq.usgs.gov/explore/dyplot?site_no=04087030&pcode=00530&period=2009_all&timestep=dv&modelhistory=

Download
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.