Freek Scheel, Wiebe P. de Boer, Rob Brinkman, Arjen P. Luijendijk, Roshanka Ranasinghe


A variety of uncertainty sources are inherent in process-based morphodynamic modelling applications. There is an increasing demand for the quantification of these uncertainties. This contribution introduces a probabilistic-morphodynamic (PM) modelling framework that enables this quantification. The PM modelling framework provides a systematic approach, while also lowering the required effort for inclusion of uncertainty quantification in morphodynamic model studies. Applicability and added value is shown using a pilot application to the Holland coast.


morphodynamics; process-based morphodynamic models; uncertainties; probabilistics; uncertainty quantification; probabilistic-morphodynamic modelling framework; Unibest CL+; Holland coast

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Baart, F. 2013. Confidence in coastal forecasts. PhD thesis Delft University of Technology. Repository Delft University of Technology, the Netherlands.

Callaghan, D.P., Ranasinghe, R. and Roelvink, D. 2013. Probabilistic estimation of storm erosion using analytical, semi-empirical, and process based storm erosion models. Coastal Engineering, 82, 64-75.

Cooke, R.M., and Van Noortwijk, J.M. 1999. Local probabilistic sensitivity measures for comparing

FORM and Monte Carlo calculations illustrated with dike ring reliability cal-culations. Computer

Physics Communications, 117, 86-98.

Deltares. 2011. Unibest CL+ manual – Manual for version 7.1 of the shoreline model Unibest CL+. Delft Chess.

Dong, P. and Chen, H. 1999. A probability method for predicting time-dependent long-term shoreline

erosion. Coastal Engineering. 36, 243-261.

Fortunato, A.B., Bertin, X. and Oliveira, A. 2009. Space and time variability of uncertainty in morphodynamic simulations. Coastal Engineering, 56-8, 886-894.

Huthoff, F., Van Vuren, S., Barneveld, H.J. and Scheel, F. 2010. On the importance of discharge variability in the morphodynamic modelling of rivers. Proceedings of the Fifth International Conference on Fluvial hydraulics. 985-991.

Maskey S. 2004. Modelling uncertainty in flood forecasting systems. PhD thesis UNESCO-IHE.

Balkema Publishers.

Radwan, M., Willems, P. and Berlamont, J. 2002. Sensitivity and uncertainty analysis for river water quality modelling. Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, IWA Publishing, London, 482-487.

Reeve, D.E. 2010. Reliability and Probabilistic Methods in coastal and Hydraulic Engineering. CRC Press.

Reeve, D.E. and Spivack, M. 2004. Evolution of shoreline position moments, Coastal Engineering, 51, 661-673.

Ruggiero, P., List, J., Hanes, D. and Eshleman, J. 2006. Probabilistic shoreline change modeling.

Proceedings of the 30th international conference on coastal engineering. San Diego, California,

USA, 3417-3429.

Van De Graaff, J. 1986. Probabilistic design of dunes; an example from the Netherlands. Coastal Engineering. 9, 479-500.

Van Der Klis, H. 2003. Uncertainty Analysis applied to Numerical Models of River Bed Morphology.

PhD thesis Delft University of Technology. Repository Delft University of Technology, the


Van Der Wegen, M. and Jaffe, B.E. 2013. Towards a probabilistic assessment of process-based,

morphodynamic models. Coastal Engineering, 75, 52-63.

Van Gelder, P.H.A.J.M. 2000. Statistical methods for risk-based design of civil structures. Repository Delft University of Technology, the Netherlands.

Van Vuren, S. 2005. Stochastic modelling of river morphodynamics. PhD thesis Delft University of Technology. Repository Delft University of Technology, the Netherlands.

Vreugdenhil, C.B. 2006. Appropriate models and uncertainties. Coastal Engineering, 53-2-3, 303-310.

Vrijling, J.K. and Meijer, G.J. 1992. Probabilistic coastline position computations. Coastal Engineering, 17, 1-23.

DOI: https://doi.org/10.9753/icce.v34.sediment.88