Marine renewable energy (MRE) harnesses energy from the ocean and provides a low-carbon sustainable energy source for national grids and remote uses. The international MRE industry is in the early stages of development, focused largely on tidal and riverine turbines, and wave energy converters (WECs), to harness energy from tides, rivers, and waves, respectively. Although MRE supports climate change mitigation, there are concerns that MRE devices and systems could affect portions of the marine and river environments. The greatest concern for tidal and river turbines is the potential for animals to be injured or killed by collision with rotating blades. Other risks associated with MRE device operation include the potential for turbines and WECs to cause disruption from underwater noise emissions, generation of electromagnetic fields, changes in benthic and pelagic habitats, changes in oceanographic processes, and entanglement of large marine animals. The accumulated knowledge of interactions of MRE devices with animals and habitats to date is summarized here, along with a discussion of preferred management methods for encouraging MRE development in an environmentally responsible manner. As there are few devices in the water, understanding is gained largely from examining one to three MRE devices. This information indicates that there will be no significant effects on marine animals and habitats due to underwater noise from MRE devices or emissions of electromagnetic fields from cables, nor changes in benthic and pelagic habitats, or oceanographic systems. Ongoing research to understand potential collision risk of animals with turbine blades still shows significant uncertainty. There has been no significant field research undertaken on entanglement of large animals with mooring lines and cables associated with MRE devices.
The Department of Energy’s (DOE’s) National Energy Technology Laboratory’s (NETL’s) Blowout and Spill Occurrence Model (BLOSOM), and the National Oceanic and Atmospheric Administration’s (NOAA’s) General NOAA Operational Modeling Environment (GNOME) are compared. Increasingly complex simulations are used to assess similarities and differences between the two models’ components. The simulations presented here are forced by ocean currents from a Finite Volume Community Ocean Model (FVCOM) implementation that has excellent skill in representing tidal motion, and with observed wind data that compensates for a coarse vertical ocean model resolution. The comprehensive comparison between GNOME and BLOSOM presented here, should aid modelers in interpreting their results. Beyond many similarities, aspects where both models are distinct are highlighted. Some suggestions for improvement are included, e.g., the inclusion of temporal interpolation of the forcing fields (BLOSOM) or the inclusion of a deflection angle option when parameterizing wind-driven processes (GNOME). Overall, GNOME and BLOSOM perform similarly, and are found to be complementary oil spill models. This paper also sheds light on what drove the historical Point Wells spill, and serves the additional purpose of being a learning resource for those interested in oil spill modeling. The increasingly complex approach used for the comparison is also used, in parallel, to illustrate the approach an oil spill modeler would typically follow when trying to hindcast or forecast an oil spill, including detailed technical information on basic aspects, like choosing a computational time step. We discuss our successful hindcast of the 2003 Point Wells oil spill that, to our knowledge, had remained unexplained. The oil spill models’ solutions are compared to the historical Point Wells’ oil trajectory, in time and space, as determined from overflight information. Our hindcast broadly replicates the correct locations at the correct times, using accurate tide and wind forcing. While the choice of wind coefficient we use is unconventional, a simplified analytic model supported by observations, suggests that it is justified under this study’s circumstances. We highlight some of the key oceanographic findings as they may relate to other oil spills, and to the regional oceanography of the Salish Sea, including recommendations for future studies.
This study examines maritime routes between ports along the Atlantic coast of the US, utilising Automated Identification System (AIS) data for the years 2010 through 2012. The delineation of vessel routes conducted in this study was motivated by development planned for offshore Wind Energy Areas (WEAs) along the Atlantic coast of the US and the need to evaluate the effect of these development areas on commercial shipping. To this end, available AIS data were processed to generate commercial vessel tracks for individual vessels, though cargo vessels are the focus in this study. The individual vessel tracks were sampled at transects placed along the Atlantic coast. The transect samples were analysed and partitioned by voyages between Atlantic ports to facilitate computation of vessel routes between ports. The route boundary analysis utilised a definition from UK guidance in which routes' boundaries encompassed 95% of the vessel traffic between ports. In addition to delineating route boundaries, we found multi-modal transverse distributions of vessels for well-travelled routes, which indicated preference for lanes of travel within the delineated routes.
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.
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