Modelling in Applied Hydraulics: More Accurate in Decision-Making Than in Science?

  • Erik Mosselman
Conference paper
Part of the Springer Water book series (SPWA)


Marked differences occur between modelling in scientific hydraulic research, in hydraulic engineering and in public decision-making. This study reviews differences in the required accuracy of model results and differences in the choice between physical and numerical modelling. Physical models are used for studying elementary processes and their interactions under controlled conditions in scientific research; for the planning and design of interventions in hydraulic engineering; and for explanation and demonstration in public decision-making. Numerical models are powerful tools in scientific research, but field applications cannot be verified or validated according to rigorous scientific standards. Hydraulic engineers use numerical models for various purposes, some requiring a high accuracy and some not. They are used to uncertainty and deal with this by means of sensitivity analyses or probabilistic approaches. Numerical models are also used for decision-making on interventions that affect stakeholders, sometimes even having the last word in corresponding protocols or legislation. The suggested or perceived accuracy of model results is in this context much higher than the real accuracy. This leads to the paradoxical situation that decision makers and stakeholders put higher demands on accuracy than scientists do.


Physical modelling Numerical modelling Validation Design flood levels 



Thanks are due to Houcine Chbab for tracing back reports on the model uncertainty of WAQUA.

References And Citations

  1. 1.
    Boisson, H.-C., & Crausse, P. (2014). De l’aérodynamique à l’hydraulique; Un siècle d’études sur modèles réduits. Cépaduès-Editions: Toulouse, ISBN 978.2.36493.093.3.Google Scholar
  2. 2.
    Vargas-Luna, A., Crosato, A., Calvani, G., & Uijttewaal, W. S. J. (2016). Representing plants as rigid cylinders in experiments and models. Advances in Water Resources, 93(Part B), 205–222.Google Scholar
  3. 3.
    Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation and confirmation of numerical models in the earth sciences. Science, 263, 641–646.CrossRefGoogle Scholar
  4. 4.
    ASME. (1993). Journal of fluids engineering editorial policy statement on the control of numerical accuracy. Journal of Fluids Engineering-T., American Society of Mechanical Engineers, 115, 339–340.Google Scholar
  5. 5.
    ASME. (2009). Standard for verification and validation in computational fluid dynamics and heat transfer. ASME V & V 20, American Society of Mechanical Engineers: New York, ISBN 9780791832097.Google Scholar
  6. 6.
    Lane, S. N., Hardy, R. J., Ferguson, R. I., & Parsons, D. R. (2005). A framework for model verification and validation of CFD schemes in natural open channel flows. In P. D. Bates, S. N. Lane & R. I. Ferguson. (Eds.), Computational fluid dynamics; applications in environmental hydraulics. Wiley: Chichester, England, ISBN 978-0-470-84359-8, 534.Google Scholar
  7. 7.
    Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 87, 1–3.CrossRefGoogle Scholar
  8. 8.
    Bosboom, J., & Reniers, A. J. H. M. (2014). Displacement-based error metric for morphodynamic models. Advances in Geosciences, 39, 37–43.CrossRefGoogle Scholar
  9. 9.
    Mosselman, E., & Le, T. B. (2016). Five common mistakes in fluvial morphodynamic modeling. Advances in Water Resources, 93, 15–20.CrossRefGoogle Scholar
  10. 10.
    De Vries, M. (1993). Use of models for river problems. Studies and reports in hydrology 51. UNESCO: Paris, ISBN 92-3-102861-8.Google Scholar
  11. 11.
    El Kadi Abderrazzak, K., Die Moran, A., Mosselman, E., Bouchard, J.-P., Habersack, H., & Aelbrecht, D. (2014). A physical, movable-bed model for non-uniform sediment transport, fluvial erosion and bank failure in rivers. Journal of Hydro-environment Research, 8, 95–114. Scholar
  12. 12.
    Makaske, B., Maathuis, B. H. P., Padovani, C. R., Stolker, C., Mosselman, E., & Jongman, R. H. G. (2012). Upstream and downstream controls of recent avulsions on the Taquari megafan, Pantanal, south-western Brazil. Earth Surface Processes and Landforms, BGRG, 37, 1313–1326. Scholar
  13. 13.
    Jakeman, A. J., Letcher, R. A., & Norton, J. P. (2006). Ten iterative steps in development and evaluation of environmental models. Environmental Modelling and Software, 21, 602–614.CrossRefGoogle Scholar
  14. 14.
    Warmink, J. J. (2011), Unraveling uncertainties; the effect of hydraulic roughness on design water levels in river models. PhD thesis, University of Twente, ISBN 978-90-365-3227-3.Google Scholar
  15. 15.
    Thijssen, A., Becker, A., Stuparu, D., & Yossef, M. (2014). Quantification of model uncertainty for WAQUA for the Upper River Area. Deltares, final report 1207807-02, Delft, January 2014.Google Scholar
  16. 16.
    Den Bieman, J. P. (2015). Invloed correlatie modelonzekerheden GRADE en bovenrivieren op waterstand. Deltares, memorandum 1220082-001-HYE-0003, Delft, 31 May 2015.Google Scholar
  17. 17.
    Durant, R. (2008). Personal communication. Orléans, France: Etablissement Public Loire.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Deltares & Delft University of TechnologyDelftThe Netherlands

Personalised recommendations