Developing Best Practices for Detecting Change at Marine Renewable Energy Sites

In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.