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Deployment of wave energy converters (WECs) relies on consistent and accurate wave resource characterization, which is typically achieved through numerical modeling using deterministic wave models. The accurate predictions of large-wave events are critical to the success of wave resource characterization because of the risk on WEC installation, maintenance, and damage caused by extreme sea states. Because wind forcing is the primary driver of wave models, the quality of wind data plays an important role in the accuracy of wave predictions. This study evaluates the sensitivity of large-wave prediction to different wind-forcing products, and identifies a feasible approach to improve wave model results through improved wind forcing. Using a multi-level nested-grid modeling approach, we perform a series of sensitivity tests at four representative National Data Buoy Center buoy locations on the U.S. East and West Coasts. The selected wind-forcing products include the Climate Forecast System Reanalysis global wind product and North American Regional Reanalysis regional wind product as well as the observed wind at the buoys. Sensitivity test results indicate a consistent improvement in model predictions for the large-wave events (e.g., >90th percentile of significant wave height) at all buoys when observed-wind data were used to drive the wave model simulations.

As tidal turbine deployments continue at test sites and in commercial areas, the potential risk for injury or death of marine mammals from colliding with rotating turbine blades continues to confound efficient consenting (permitting) of devices. Direct observation of collisions is technically very challenging and costly. Estimates of collision risk to date have been derived from complex collision risk models that depend on estimates of the number of marine mammals found in the area. Using a simple collision model, the risk of collision was examined at three real-world sites, each of which featured an indigenous marine mammal. Two different turbine designs were examined at each site to extend the range of the estimates. The results of the model runs allow for comparison of risk at a range of tidal sites for a variety of the marine mammals thought to be at potential risk.

Offshore wind energy development is planned for areas off the Atlantic coast. Many of the planned wind development areas fall within traditional commercial vessel routes. In order to mitigate possible hazards to ships and to wind turbines, it is important to understand the potential for increased risk to commercial shipping from the presence of wind farms. Risk is identified as the likelihood that an occurrence will happen, and the consequences of that occurrence, should it occur. This paper deals with the likelihood of commercial vessel accidents, because of the development of offshore wind energy along the US Atlantic coast. Using Automatic Identification System (AIS) data, historical shipping routes between ports in the Atlantic were identified, from Maine to the Florida Straits. The AIS data were also used as inputs to a numerical model that can simulate cargo, tanker and tug/towing vessel movement along typical routes. The model was used to recreate present day vessel movement, as well as to simulate future routing that may be required to avoid wind farms. By comparing the present and future routing of vessels, an analysis of potential maritime accidents was used to determine the increased marginal risk of vessel collisions, groundings and allisions with stationary objects, because of the presence of wind farms. The outcome of the analysis showed little increase in vessel collisions or allisions, and a decrease in groundings as more vessels were forced seaward by the wind farms.