Comparing measured and modelled foredune change at the English coast using AeoLiS.

With rising sea levels and changes to storm regimes, tidal inundation and coastal erosion pose a significant threat to communities near the coast (Jones et al., 2008). Foredunes have been proposed as a nature-based solution to such threats due to their ability to protect coastal communities from overwash, recover from storm damage, and provide a range of ecosystem services (van Puijenbroek et al., 2017; Strypsteen et al., 2024). However, coastal dune change depends on complex feedback between sediment deposition, marine erosion, and vegetation growth (Maun, 2009), making dune change difficult to predict and limiting our understanding of coastal dunes as management options. The development of predictive, accurate, numerical models may permit land managers to reliably use coastal dunes as a nature-based solution to many threats facing coastal communities (van Westen et al., 2024). One such potential model is AeoLiS.

AeoLiS is a process-based model for simulating sediment transport and morphological evolution in supply-limited environments (Hoonhout & de Vries, 2016).  The model has been validated against the sand engine nourishment project in the Netherlands (Hoonhout & de Vries, 2019), and a range of dune forms including barchan, parabolic and coastal embryo dunes (van Westen et al., 2024). The purpose of this research was to assess how accurately the AeoLiS model could replicate coastal dune change at a range of sites along the English coast, ranging from embryo dunes to established foredunes and varying in the degree of management. The results show that the AeoLiS model is capable of replicating coastal dune changes along two-dimensional transects at the English coast, accurately simulating both embryo and established foredune changes as well as dune changes following sand fence installation. The fact that this model is capable of replicating dune growth accurately suggests that it may be a powerful tool for land managers to predict future dune changes.

 

 

 

References:

Hoonhout, B. M., & de Vries, S. (2016). Aprocess-based modelforaeolian sediment transport andspatiotemporal varying sediment availability. Journal of Geophysical Research: Earth Surface, 121, https://doi.org/10.1002/2015JF003692.

Hoonhout, B., & de Vries, S. (2019). Simulating spatiotemporal aeolian sediment supply at a mega nourishment. Coastal Engineering, 145, 21-35. https://doi.org/10.1016/j.coastaleng.2018.12.007

Jones, M. L. M., Sowerby, A., Williams, D. L., & Jones, R. E. (2008). Factors controlling soil development in sand dunes: evidence from a coastal dune soil chronosequence. Plant and Soil, 307, 219-234. https://doi.org/10.1007/s11104-008-9601-9

Maun, M. A. (2009). The Biology of Coastal Sand Dunes. Oxford University Press. 10.1093/oso/9780198570356.001.0001

Strypsteen, G., Bonte, D., Taelman, C., Derijckere, J., & Rauwoens, P. (2024). Three years of morphological dune development after planting marram grass on a beach. Earth Surface Processes and Landforms, , https://doi.org/10.1002/esp.5870

van Puijenbroek , M. E. B., Limpens, J., de Groot, A. V., Riksen, M. J. P. M., Gleichman, M., van Dobben, H. F., & Berendse, F. (2017). Embryo dune development drivers: beach morphology, growing season precipitation, and storms. Earth Surface Processes and Landforms, 42, 1733-1744. https://doi.org/10.1002/esp.4144

van Westen, B., de Vries, S., Cohn, N., van Ijzendoorn, C., Strypsteen, G., & Hallin, C. (2024). AeoLiS: Numerical modelling of coastal dunes and aeolian landform development for real-world applications. Environmental Modelling and Software, 179(106093), https://doi.org/10.1016/j.envsoft.2024.106093