Skip to main content
Log in

The Influence of Model Structure Uncertainty on Water Quality Assessment

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Physically-based mathematical water quality models are known as potentially effective tools to simulate the temporal and spatial variations of water quality variables along rivers. Each model relies on specific sets of assumptions and equations to simulate the physico-biochemical processes, which influence on its simulation results. This paper aims to improve the insight in the uncertainties related to state–of–the–art river physico-biochemical water quality modelling. Sensitivity analysis is applied to the processes implemented in three most popular commercial software packages: MIKE11, InfoWorks RS and InfoWorks ICM. This is done for the Molse Neet river case study. Firstly, the physico-biochemical processes are screened to obtain a preliminary assessment on the critical processes and to determine the processes that require more detailed comparison. Then, local sensitivity analysis is carried out to specify the sensitive parameters and processes. Results show that the hydrodynamic results, heat transfer rate and reaeration simulations cause large differences in model simulation outputs for water temperature and dissolved oxygen concentrations. The ignorance of processes related to sediment transport, phytoplankton and bacteria has a significant influence on the higher values of organic matter and lower values of dissolved oxygen concentrations. The three models show consensus on the main pollutant sources explaining organic matter and nitrate concentrations, but disagree on the main factors explaining the DO concentrations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Bowie GL, Mills WB, Camplell CL et al (1985) Rate, constants, and kinetics formulations in surface water quality moelling, 2nd edn. EPA, Georgia

    Google Scholar 

  • Cox B (2003) A review of currently available in-stream water-quality models and their applicability for simulating dissolved oxygen in lowland rivers. Sci Total Environ 314–316(03):335–377

    Article  Google Scholar 

  • Defra (2014) Water framework directive implementation in England and Wales: new and updated standards to protect the water environment. Department of Environment, Food and Rural Affairs, UK

  • DHI (2011) Water quality templates. DHI Water & Environment, Hørsholm

    Google Scholar 

  • Freni G, Mannina G (2010) Uncertainty in water quality modelling: the applicability of Variance Decomposition Approach. J Hydrol 394(3–4):324–333

    Article  Google Scholar 

  • Gaskell PH, Lau AKC (1988) Curvature-compensated convective transport: SMART, a new boundedness- preserving transport algorithm. Int J Numer Methods Fluids 8(6):617–641

    Article  Google Scholar 

  • Han F, Zheng Y (2016) Multiple-response Bayesian calibration of watershed water quality models with significant input and model structure errors. Adv Water Resour 88:109–123

    Article  Google Scholar 

  • Hantush MM, Chaudhary A (2014) Bayesian framework for water quality model uncertainty estimation and risk management. J Hydrol Eng 9(19):04014015

    Article  Google Scholar 

  • Harmel RD, Smith PK (2007) Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling. J Hydrol 337:326–336

    Article  Google Scholar 

  • Holly FM, Preissmann A (1977) Accurate calculation of transport in two dimensions. J Hydraul Div 103(11):1259–1277

    Google Scholar 

  • Innovyze (2012) InfoWorks RS. Innovyze, Wallingford

    Google Scholar 

  • Innovyze (2014) InfoWorks ICM. Innovyze, Wallingford

    Google Scholar 

  • Jørgensen SE (1979) Handbook of environmental data and ecological parameters. Elsevier, New York

    Google Scholar 

  • Kotti ME, Vlessidis AG, Thanasoulias NC, Evmiridis NP (2005) Assessment of river water quality in Northwestern Greece. Water Resour Manag 19(1):77–94

    Article  Google Scholar 

  • Lessels JS, Bishop TFA (2015) A simulation based approach to quantify the difference between event-based and routine water quality monitoring schemes. J Hydrol Reg Stud 4(B):439–451

    Article  Google Scholar 

  • Moore RJ (2007) The PDM rainfall-runoff model. Hydrol Earth Syst Sci 11(1):483–499

    Article  Google Scholar 

  • Nguyen TT, Willems P (2014) In River water quality modelling in InfoWorks RS for the Molse Nete river (Vol. 2). IAHR-APD Congress 2014, 19(2). Hanoi, Vietnam

  • Radwan M, Willems P, Berlamont J (2004) Sensitivity and uncertainty analysis for river water quality modelling. J Hydroinf 06(2):83–99

    Google Scholar 

  • Shaarawi AHE, Kwiatkowski R (1986) Statistical aspects of water quality monitoring. Elsevier Science, New York

    Google Scholar 

  • Tung YK, Yen BC (2006) Hydrosystems engineering uncertainty analysis. McGraw-Hill, New York

    Google Scholar 

  • Uusitalo L, Lehikoinen A, Helle I, Myrberg K (2015) An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ Model Softw 63:24–31

    Article  Google Scholar 

  • Vansteenkiste T, Tavakoli M, Van Steenbergen N, De Smedt F, Batelaan O, Pereira F, Willems P (2014) Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation. J Hydrol 511:335–349

    Article  Google Scholar 

  • VMM (2002) Water quality – discharges to water 2001 (in Dutch). VMM, Erembodegem, p 167

    Google Scholar 

  • Willems P (2000) Probabilistic immission modelling of receiving surface waters. PhD dissertation, Katholieke Universiteit Leuven, Faculty of Engineering, Leuven, Belgium

  • Willems P (2008) Quantification and relative comparison of different types of uncertainties in sewer water quality modeling. Water Res 42:3539–3551

    Article  Google Scholar 

  • Willems P (2009) A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environ Model Softw 24(3):311–321

    Article  Google Scholar 

Download references

Acknowledgments

This study has been made possible by a doctoral scholarship by the KU Leuven Interfaculty council for Development Co-operation (IRO). The Province of Antwerp and the Flemish Environment Agency (VMM) are gratefully acknowledged for providing the initial hydrological and hydrodynamic river models, the measurement data on cross-sections, structures and water quality along the rivers in the Molse Neet catchment. Finally, DHI Water & Environment and Innovyze are responsively acknowledged for the provision of the license for the MIKE11, InfoWorks RS and ICM software packages, respectively.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thanh Thuy Nguyen.

Appendices

Appendix 1

Table 4 Biochemical transformation processes and equations in RS/ICM and MIKE 11

Appendix 2

Table 5 Parameter values in the initial RS, ICM and MIKE 11 simulations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, T.T., Willems, P. The Influence of Model Structure Uncertainty on Water Quality Assessment. Water Resour Manage 30, 3043–3061 (2016). https://doi.org/10.1007/s11269-016-1330-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1330-x

Keywords

Navigation