Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 5
Articles | Volume 26, issue 5
https://doi.org/10.5194/hess-26-1203-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-26-1203-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 5
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

Viewed

Total article views: 2,632 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,839 741 52 2,632 48 51
  • HTML: 1,839
  • PDF: 741
  • XML: 52
  • Total: 2,632
  • BibTeX: 48
  • EndNote: 51
Views and downloads (calculated since 12 Nov 2020)
Cumulative views and downloads (calculated since 12 Nov 2020)

Viewed (geographical distribution)

Total article views: 2,632 (including HTML, PDF, and XML) Thereof 2,469 with geography defined and 163 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
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.
Read more
Special issue
Frontiers in the application of Bayesian approaches in water...