Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Accelerated sea level rise and the potential for an increase in frequency of the most intense hurricanes due to climate change threaten the vitality and habitability of barrier islands by lowering their relative elevation and altering frequency of overwash. High-density development may further increase island vulnerability by restricting delivery of overwash to the subaerial island. We analyzed pre-Hurricane Sandy and post-Hurricane Sandy (2012) lidar surveys of the New Jersey coast to assess human influence on barrier overwash, comparing natural environments to two developed environments (commercial and residential) using shore-perpendicular topographic profiles. The volumes of overwash delivered to residential and commercial environments are reduced by 40% and 90%, respectively, of that delivered to natural environments. We use this analysis and an exploratory barrier island evolution model to assess long-term impacts of anthropogenic structures. Simulations suggest that natural barrier islands may persist under a range of likely future sea level rise scenarios (7–13 mm/yr), whereas developed barrier islands will have a long-term tendency toward drowning.

Coastal dunes arise from feedbacks between vegetation and sediment supply. Species-specific differences in plant functional morphology affect sand capture and dune shape. In this study, we build on research showing a relationship between dune grass species and dune geomorphology on the US central Atlantic Coast. This study seeks to determine the ways in which four co-occurring dune grass species (Ammophila breviligulata, Panicum amarum, Spartina patens, Uniola paniculata) differ in their functional morphology and sand accretion. We surveyed the biogeography, functional morphology, and associated change in sand elevation of the four dune grass species along a 320-kilometer distance across the Outer Banks. We found that A. breviligulata had dense and clumped shoots, which correlated with the greatest sand accretion. Coupled with fast lateral spread, it tends to build tall and wide foredunes. Uniola paniculata had fewer but taller shoots and was associated with ~42% lower sand accretion. Coupled with slow lateral spread, it tends to build steeper and narrower dunes. Panicum amarum had similar shoot densities and associated sand accretion to U. paniculata despite its shorter shoots, suggesting that shoot density is more important than morphology. Finally, we hypothesize, given the distributions of the grass species, that foredunes may be taller and wider and have better coastal protection properties in the north where A. breviligulata is dominant. If under a warming climate A. breviligulata experiences a range shift to the north, as appears to be occurring with U. paniculata, changes in grass dominance and foredune morphology could make for more vulnerable coastlines.

Coastal landscape change represents aggregated sediment transport gradients from spatially and temporally variable marine and aeolian forces. Numerous tools exist that independently simulate subaqueous and subaerial coastal profile change in response to these physical forces on a range of time scales. In this capacity, coastal foredunes have been treated primarily as wind-driven features. However, there are several marine controls on coastal foredune growth, such as sediment supply and moisture effects on aeolian processes. To improve understanding of interactions across the land-sea interface, here the development of the new Windsurf-coupled numerical modeling framework is presented. Windsurf couples standalone subaqueous and subaerial coastal change models to simulate the co-evolution of the coastal zone in response to both marine and aeolian processes. Windsurf is applied to a progradational, dissipative coastal system in Washington, USA, demonstrating the ability of the model framework to simulate sediment exchanges between the nearshore, beach, and dune for a one-year period. Windsurf simulations generally reproduce observed cycles of seasonal beach progradation and retreat, as well as dune growth, with reasonable skill. Exploratory model simulations are used to further explore the implications of environmental forcing variability on annual-scale coastal profile evolution. The findings of this work support the hypothesis that there are both direct and indirect oceanographic and meteorological controls on coastal foredune progradation, with this new modeling tool providing a new means of exploring complex morphodynamic feedback mechanisms.

We use a machine learning approach based on genetic programming to predict noncohesive particle settling velocity. The genetic programming routine is coupled to a novel selection algorithm that determines training data from a collected database of published experiments (985 measurements). While varying the training data set size and retaining an invariant validation set we perform multiple iterations of genetic programming to determine the least data needed to train the algorithm. This method retains a maximum quantity of data for testing against published predictors. The machine learning predictor for settling velocity performs better than two common predictors in the literature and indicates that particle settling velocity is a nonlinear function of all the provided independent variables: nominal diameter of the settling particle, kinematic viscosity of the fluid, and submerged specific gravity of the particle.