Examination of analytical shear stress predictions for coastal dune evolution

Cecil, Orie; Cohn, Nicholas; Farthing, Matthew; Dutta, Sourav; Trautz, Andrew

doi:https://doi.org/10.5194/esurf-13-1-2025

Articles | Volume 13, issue 1

https://doi.org/10.5194/esurf-13-1-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-13-1-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 1

Research article

|

02 Jan 2025

Research article |

| 02 Jan 2025

Examination of analytical shear stress predictions for coastal dune evolution

Orie Cecil, Nicholas Cohn, Matthew Farthing, Sourav Dutta, and Andrew Trautz

Data sets

2D OpenFOAM simulations of flow over several idealistic dune profiles using the RNG k-epsilon turbulence model O. Cecil et al. https://doi.org/10.17603/ds2-4w1m-7998

Model code and software

Interpretable machine learning for science with PySR and SymbolicRegression.jl (https://github.com/MilesCranmer/PySR) M. Cranmer et al. https://doi.org/10.48550/ARXIV.2305.01582

AeoLiS S. de Vries et al. https://doi.org/10.4121/22215562

Scientific colour maps F. Crameri https://doi.org/10.5281/zenodo.1243862

Short summary

Using computational fluid dynamics, we analyze the error trends of an analytical shear stress distribution model used to drive aeolian transport for coastal dunes, which are an important line of defense against storm-related flooding hazards. We find that compared to numerical simulations, the analytical model results in a net overprediction of the landward migration rate. Additionally, two data-driven approaches are proposed for reducing the error while maintaining computational efficiency.