Vegetation controls on the maximum size of coastal dunes

Abstract

Coastal dunes, in particular foredunes, support a resilient ecosystem and reduce coastal vulnerability to storms. In contrast to dry desert dunes, coastal dunes arise from interactions between biological and physical processes. Ecologists have traditionally addressed coastal ecosystems by assuming that they adapt to preexisting dune topography, whereas geomorphologists have studied the properties of foredunes primarily in connection to physical, not biological, factors. Here, we study foredune development using an ecomorphodynamic model that resolves the coevolution of topography and vegetation in response to both physical and ecological factors. We find that foredune growth is eventually limited by a negative feedback between wind flow and topography. As a consequence, steady-state foredunes are scale invariant, which allows us to derive scaling relations for maximum foredune height and formation time. These relations suggest that plant zonation (in particular for strand "dune-building" species) is the primary factor controlling the maximum size of foredunes and therefore the amount of sand stored in a coastal dune system. We also find that aeolian sand supply to the dunes determines the timescale of foredune formation. These results offer a potential explanation for the empirical relation between beach type and foredune size, in which large (small) foredunes are found on dissipative (reflective) beaches. Higher waves associated with dissipative beaches increase the disturbance of strand species, which shifts foredune formation landward and thus leads to larger foredunes. In this scenario, plants play a much more active role in modifying their habitat and altering coastal vulnerability than previously thought.ecomorphodynamic modeling | dune stabilization | sediment budget

Lateral vegetation growth rates exert control on coastal foredune hummockiness and coalescing time

“…Because in reality conditions sufficient for transport do not occur all the time, Durán and Moore (2013) suggest that model time can be converted to real time by multiplying model time by a factor (r t ) that varies from 0 to 1 and represents the fraction of time there is no transport. Therefore, reduction in the flux of sand from beach to dune, because of low wind speeds, large grain sizes or narrow beaches, can be encapsulated through variation in r t and has an effect similar to decreasing β and increasing H v .…”

“…This model is based on the coastal dune model of Durán and Moore (2013), itself based on previous models used to study a variety of dunes (e.g., Parteli et al, 2009;Durán and Herrmann, 2006;Durán et al, 2010). We briefly summarize the model and the vegetation formulation below.…”

“…We use a simplified version of the vegetation formulation presented in Moore et al (2016), which is itself a modification of earlier models (Durán and Moore, 2013;Duránt Vinent and Moore, 2015;Durán and Herrmann, 2006). We now present the simplified vegetation model and then discuss the physical interpretation for the two key sensitivity parameters.…”

“…There is a gap in this seeding located near the center of the model domain. The seeded "line" represents the development of vegetation around a drift line of wrack and is set at the seaward vegetation limit of plant growth (e.g., Durán and Moore, 2013;Kuriyama et al, 2005). As a consequence, vegetation does not propagate seaward in model experiments.…”

Satellite and Unmanned Aerial Vehicle Data for the Classification of Sand Dune Vegetation

“…This study fits in the context of the development and application of innovative methods to analyse the morphological dynamics of coastal systems. High spectral and spatial resolution satellite data, already widely used in the bibliography (Durán & Moore, 2013), were compared with orthophotos obtained from multispectral frames acquired by an unmanned aerial vehicle (UAV). Several studies report the use of aerial or satellite images and data provided by UAV platforms in order to: compare their elaboration results (Matese et al, 2015), integrate the different information extracted (Solazzo, 2016), combine two kinds of data for a multiscale analysis (Duro et al, 2015) and to classify the natural or agricultural coverage (Gini et al, 2017).…”