Blind testing of shoreline evolution models.

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.

Using a Bayesian network to understand the importance of coastal storms and undeveloped landscapes for the creation and maintenance of early successional habitat.

Coastal storms have consequences for human lives and infrastructure but also create important early successional habitats for myriad species. For example, storm-induced overwash creates nesting habitat for shorebirds like piping plovers (Charadrius melodus). We examined how piping plover habitat extent and location changed on barrier islands in New York, New Jersey, and Virginia after Hurricane Sandy made landfall following the 2012 breeding season. We modeled nesting habitat using a nest presence/absence dataset that included characterizations of coastal morphology and vegetation. Using a Bayesian network, we predicted nesting habitat for each study site for the years 2010/2011, 2012, and 2014/2015 based on remotely sensed spatial datasets (e.g., lidar, orthophotos). We found that Hurricane Sandy increased piping plover habitat by 9 to 300% at 4 of 5 study sites but that one site saw a decrease in habitat by 27%. The amount, location, and longevity of new habitat appeared to be influenced by the level of human development at each site. At three of the five sites, the amount of habitat created and the time new habitat persisted were inversely related to the amount of development. Furthermore, the proportion of new habitat created in high-quality overwash was inversely related to the level of development on study areas, from 17% of all new habitat in overwash at one of the most densely developed sites to 80% of all new habitat at an undeveloped site. We also show that piping plovers exploited new habitat after the storm, with 14-57% of all nests located in newly created habitat in the 2013 breeding season. Our results quantify the importance of storms in creating and maintaining coastal habitats for beach-nesting species like piping plovers, and these results suggest a negative correlation between human development and beneficial ecological impacts of these natural disturbances.

Nearshore dynamics of artificial sand and oil agglomerates.

Weathered oil can mix with sediment to form heavier-than-water sand and oil agglomerates (SOAs) that can cause beach re-oiling for years after a spill. Few studies have focused on the physical dynamics of SOAs. In this study, artificial SOAs (aSOAs) were created and deployed in the nearshore, and shear stress-based mobility formulations were assessed to predict SOA response. Prediction sensitivity to uncertainty in hydrodynamic conditions and shear stress parameterizations were explored. Critical stress estimates accounting for large particle exposure in a mixed bed gave the best predictions of mobility under shoaling and breaking waves. In the surf zone, the 10-cm aSOA was immobile and began to bury in the seafloor while smaller size classes dispersed alongshore. aSOAs up to 5 cm in diameter were frequently mobilized in the swash zone. The uncertainty in predicting aSOA dynamics reflects a broader uncertainty in applying mobility and transport formulations to cm-sized particles.

Assessing mobility and redistribution patterns of sand and oil agglomerates in the surf zone.

Heavier-than-water sand and oil agglomerates that formed in the surf zone following the Deepwater Horizon oil spill continued to cause beach re-oiling 3years after initial stranding. To understand this phenomena and inform operational response now and for future spills, a numerical method to assess the mobility and alongshore movement of these "surface residual balls" (SRBs) was developed and applied to the Alabama and western Florida coasts. Alongshore flow and SRB mobility and potential flux were used to identify likely patterns of transport and deposition. Results indicate that under typical calm conditions, cm-size SRBs are unlikely to move alongshore, whereas mobility and transport is likely during storms. The greater mobility of sand compared to SRBs makes burial and exhumation of SRBs likely, and inlets were identified as probable SRB traps. Analysis of field data supports these model results.