A probabilistic model for hydrokinetic turbine collision risks: exploring impacts on fish.

Area of Science:

• Marine Biology
• Renewable Energy Technology
• Ecological Risk Assessment

Background:

• Hydrokinetic turbines are being developed for renewable energy generation in aquatic environments.
• Concerns exist regarding potential collisions between aquatic animals and large, fast-rotating turbine blades.
• Limited data exists on animal behavior and fate when approaching these underwater structures.

Purpose of the Study:

• To synthesize current knowledge on hydrokinetic turbine collision risks.
• To develop a probabilistic model for estimating population-level ecological risks.
• To present new data and models on fish behavior and avoidance in strong currents.

Main Methods:

• Literature synthesis on hydrokinetic turbine collision risks.
• Development of a generic fault tree-based probabilistic risk model.
• Collection and analysis of video data on fish behavior in strong currents.
• Modeling of fish avoidance behaviors.

Main Results:

• The study indicates a low collision risk for small fish.
• Large turbines (≥5 m) present a high collision probability for larger fish.
• Low visibility conditions significantly hinder fish detection and avoidance of turbine rotors.
• Vulnerable populations of large fish in strong currents face substantial collision risks.

Conclusions:

• The developed probabilistic model can assess ecological risks for various hydrokinetic turbine designs and locations.
• The model structure allows for validation, refinement, and application to other marine species, including marine mammals.
• Effective risk assessment is crucial for mitigating impacts on aquatic ecosystems and vulnerable fish populations.