EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Characterisation of sandy beach through morphological indicators and long-term modelling

Alexandre Paris, Julien Chauchat, Éric Barthélemy, and Cyrille Bonamy
Alexandre Paris et al.
  • Univ. Grenoble Alpes, CNRS, Grenoble INP, LEGI, 38000, Grenoble, France (

Coasts are hosting most of the human population worldwide and hosts a large part of the economic activities. Among the various types of coastal environments, sandy beaches represent one third of the global shoreline of which a large proportion is eroding (Luijendijk et al., 2018). This phenomenon is accelerating under the effect of climate change and the understanding and mitigation of the shoreline erosion is a fundamental issue in coastal engineering.

In this contribution we analyse survey data from two well-documented Atlantic beaches: Duck (North Carolina, USA), a microtidal East-exposed beach and Truc Vert (Aquitaine, France) a meso/macrotidal West-exposed beach. A statistical analysis of the waves data over 2 to 3 decades provides useful information to evaluate the various possible morphodynamic beach states following Masselink & Short (1993) classification. This classification is based on the Dean number and the relative tidal range. Using the measured bathymetries, it is possible to verify the Masselink and Short classification. For example, using Duck data, a morphological analysis is performed on the 18 available bathymetries from the year 2019. These data illustrate the up-state and down-state sequences between reflective (summer) and dissipative (winter) states. In particular, the variability of the beach morphology increases significantly during intermediate beach states.

Applied to the two datasets, a modeling approach combining a one-line model, ShoreFor (Splinter et al., 2014), and 2D depth-averaged process-based model, XBeach (Roelvink et al., 2009), is envisaged. ShoreFor is run to predict shoreline and bar location (Splinter et al., 2018) and XBeach simulations are used on specific subsets of the entire computational window for intermediate 2D morphological state predictions.




Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G. and Aarninkhof, S. (2018), The State of the World’s Beaches, Scientific Reports, 8(6641).

Masselink, G. and Short, A. (1993), The Effect of Tide Range on Beach Morphodynamics and Morphology: A Conceptual Beach Model, Journal of Coastal Research, 9(3), 785–800.

Splinter, K.D., Turner, I.L., Davidson, M.A., Barnard, P., Castelle, B. and Oltman-Shay, J. (2014), A generalized equilibrium model for predicting daily to interannual shoreline response, Journal of Geophysical Research: Earth Surface, 119, 1936–1958.

Splinter, K.D., Gonzalez, M.V.G., Oltman-Shay, J., Rutten, J., Holman, R. (2018), Observations and modelling of shoreline and multiple sandbar behaviour on a high-energy meso-tidal beach, Continental Shelf Research, 159, 33—45.

Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., and Lescinski, J. (2009), Modelling storm impacts on beaches, dunes and barrier islands, Coastal Engineering, 56(11–12), 1133–1152.

How to cite: Paris, A., Chauchat, J., Barthélemy, É., and Bonamy, C.: Characterisation of sandy beach through morphological indicators and long-term modelling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8844,, 2023.