Preprints
https://doi.org/10.5194/hess-2020-563
https://doi.org/10.5194/hess-2020-563

  12 Nov 2020

12 Nov 2020

Review status: this preprint is currently under review for the journal HESS.

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

Xia Wu1,2, Lucy Marshall2, and Ashish Sharma2 Xia Wu et al.
  • 1College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
  • 2School of Civil and Environmental Engineering, University of New South Wales, Sydney, 2052, Australia

Abstract. Uncertainty in inputs can significantly impair parameter estimation in water quality modeling, necessitating accurate quantification of input errors. However, decomposing input error from model residual error is still challenging. This study develops a new algorithm, referred to as Bayesian error analysis with reshuffling (BEAR), to address this problem. The basic approach requires sampling errors from a pre-estimated error distribution and then reshuffling them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully isolates the input error and parameter error. The results of a real case study demonstrate that even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness is limited by the assumption that the input uncertainty should be dominant and that the prior information of the input error model can be estimated. The application of the BEAR method in TSS simulation is effective for understanding a range of water quality conditions and the further developed algorithm can be extended to other water quality predictions.

Xia Wu et al.

 
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Xia Wu et al.

Xia Wu et al.

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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, Bayesian error analysis with reshuffling (BEAR), for improved error estimation. In the context of total suspended solids (TSS) simulation, a synthetic study and a real study demonstrates that the BEAR method is effective and robust to improve water quality model calibration.