Measurements and Modelling of Small Scale Processes of Vegetation Preventing Dune Erosion

Mendoza, E. et al. | Journal of Coastal Research | 2017 | Peer Reviewed | Original research | https://doi.org/10.2112/SI77-003.1

Abstract

Traditionally, actions taken to reduce vulnerability to beach erosion have been based on protecting economic resources, recreational activities and human lives. Hard infrastructure for coastal protection has proven effective, but the side effects have been called into question, given that making the coastal system more rigid alters the natural dynamics, degrades environmental services and damages the landscape. Ecosystem based coastal defence strategies are now seen as a more environmentally friendly alternative which can maintain and even increase the resilience and resistance of coastal zones. This work aims to improve the understanding of the behaviour of nature-based coastal defences by analysing the morphodynamic response of a dune-beach system with vegetation to storms. Small scale tests were performed in which beach profiles with natural dune vegetation were exposed to high energy waves. Free surface elevation and velocity profiles were recorded during the tests and the profile evolution was measured at the end of each experiment. Erosion regimes of collision and overwash were observed in the dune profiles with a berm, whereas swash and overwash regimes were observed when no berm was present. Retarding erosion time seems to be the most relevant morphological effect of the dune vegetation, which gives a slight, but relevant, contribution to the resilience and resistance of the beach profile. In turn, the wave breaking point is displaced seawards and bed velocities close to the shoreline are lower when vegetation is present, both of which explain the protective role of vegetation on the beach profile. To develop a numerical tool capable of reproducing the morphological evolution of the beach profiles tested, the CSHORE model was calibrated and validated for the laboratory data finding good correlation.