Setting Priorities for Environmental Effects of Tidal and Wave Development

Harvesting power from the oceans has been the dream for entrepreneurs, scientists, and energy system developers for decades.  Today this dream is closer to reality as technologies and the means to deploy them in estuaries and nearshore ocean areas are emerging from early stage development into viable industrial uses.  However, in order for the marine and hydrokinetic (MHK) industry to become a viable contributor to renewable energy portfolios, responsible deployment and testing is needed to determine potentially adverse environmental effects, and to devise appropriate mitigation strategies to avoid those effects. 

The regulatory system in developed countries, lead by the stringent environmental regulatory framework in the U.S., requires substantial information on potential interactions among marine animals (marine mammals, fish, birds, sea turtles, and invertebrates) and the many different technologies for generating power from tides and waves.  Little is known about these interactions, requiring research and monitoring studies to parse and evaluate the risk of individual stressors (elements of MHK systems including tidal rotors, wave buoys, anchors, foundations, mooring lines, and power cables) and marine receptors (animals, habitats and ecosystem processes that may be affected).

Pacific Northwest National Laboratory and partner organizations, under direction of the U.S. Department of Energy, are engaged in evaluating stressor/receptor interactions for tidal and wave development, using a risk-informed approach, known as ERES (Environmental Risk Evaluation System).  Accurate risk forecasts of stressor/receptor interactions cannot be completed until there are more devices and arrays in the water, laboratory research programs provide exposure response curves for stressors such as EMF and acoustic outputs, and post-installation monitoring programs provide further data.  The ERES approach is setting the stage for a robust and accessible system of data sharing and analysis.  Preliminary results of two case studies will be discussed to demonstrate the direction of this broad-based effort.