Abstract
Interaction uncertainties between tidal energy devices and marine animals have the potential to impede the tidal energy industry as it moves closer towards commercial-scale array installations. Developing standardised environmental impact assessment (EIA) practices would allow for potential impact concerns to the marine environment to be identified and mitigated early during project development. In an effort to help formulate a standardised EIA framework that addresses knowledge gaps in fish-current interactions at tidal energy candidate sites, Scherelis et al. [1] presented a case study for investigating changes in fish aggregations in response to changing environmental conditions including tidal currents at a tidal energy candidate site in Australia prior to turbine installation. Here, we present the dataset utilised for this study titled “Investigating biophysical linkages at tidal energy candidate sites: a case study for combining environmental assessment and resource characterisation” [1]. The dataset includes tidal current information from an Acoustic Doppler Current Profiler (ADCP), volume backscattering measurements from a four-frequency biological echosounder (Acoustic Zooplankton and Fish Profiler – AZFP) as an indicator for fish biomass, and fish aggregation metrics calculated from volume backscatter in post-processing. ADCP and AZFP were installed on a bottom-mounted mooring and engaged in a concurrent sampling plan for ∼2.5 months from December 2018 to February 2019. The mooring was deployed in the Banks Strait, a tidal energy candidate site located in the northeast of Tasmania, Australia, at a location favourable for tidal turbine installations considering current speed, depth, substrate, sediment type and proximity to shore. The ADCP dataset includes current velocity and direction measurements at 1 m vertical and 1-min time intervals. The raw AZFP dataset includes volume backscattering strength collected in 4-s time intervals with a vertical resolution of 0.072 m in raw, and 0.1 m in pre-processed form. Several post-processing steps were implemented to mitigate changes in background noise due to current speed and wind stress, and to isolate acoustic fish returns from remaining scattering sources. Once isolated, volume backscatter containing fish targets underwent post-processing to determine fish aggregation metrics including density, abundance, centre of mass, dispersion,% water column occupied, evenness, and index for aggregation. Each aggregation metric was then binned by minute matched with corresponding environmental conditions for current speed, shear, temperature, diel stage, and tidal stage. Raw and processed datasets for the AZFP and ADCP are provided. Post-processed data includes the derived fish aggregation metrics along with corresponding environmental conditions. The described datasets are freely available on the Australian Ocean Data Network (AODN).
Highlights
Interaction uncertainties between tidal energy devices and marine animals have the potential to impede the tidal energy industry as it moves closer towards commercial-scale array installations
The mooring was deployed in the Banks Strait, a tidal energy candidate site located in the northeast of Tasmania, Australia, at a location favourable for tidal turbine installations considering current speed, depth, substrate, sediment type and proximity to shore
Oceanography Environmental impact assessment and appraisal for tidal energy Fish aggregation metrics derived from volume backscattering (Sv) measurements
Summary
The dataset presented was collected during tidal energy resource assessment of the Banks Strait tidal energy candidate site, Tasmania, Australia. It includes volume backscatter measurements from an echosounder sampling at four frequencies (38–67–125–200 kHz) as well as current speed and direction measurements from an ADCP (500 kHz). Pre-processed – Volume backscattering strength (Sv) following adjustments based on the sonar equation, transducer properties (found in AZFPCalibration.ecs), and resampling into 0.1 cm vertical cells performed in Echoview®. Time for the pre-processed dataset has been converted from UTC to local time (AEST)
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