Horne, John KPeraza, Jezella Ileana2024-10-162024-10-162024-10-162024Peraza_washington_0250O_27269.pdfhttps://hdl.handle.net/1773/52519Thesis (Master's)--University of Washington, 2024Marine Renewable Energy has potential to become a valuable and predictable energy source in regions with strong tidal and ocean currents. Tidal energy is a prominent sector within the industry but concerns regarding its potential impact on marine life hinder its development and deployment. Concerns include animal-turbine encounters, collisions with turbine structures, blade strikes, and risk of injury or mortality. Statistical and simulation models are employed to assess encounter and interaction risks between aquatic animals and tidal turbines, yet there is a need for a comprehensive model incorporating animal trajectories and behaviors. This study aims to develop an encounter-impact probability model, using acoustic and hydrodynamic data from Admiralty Inlet, Washington, USA, and insights from published literature. Our encounter-impact model calculates conditional probabilities of fish-turbine interactions in sequential steps by incorporating empirical data and considering factors such as avoidance behavior and turbine dimensions. The model evaluates collision and blade strike risks, employing published values and empirical measurements, to assess overall impact probabilities. The statistical, encounter-impact model assesses probabilities of fish-turbine interactions influenced by turbine type, time of day, and avoidance behavior. As an agent-based simulation, the probability model assesses fish-turbine interactions considering factors of animal behaviors and tidal flow. Fish locomotion and aggregation behaviors are simulated, incorporating active and passive avoidance of turbines. Interactions between fish and turbines (i.e., collision and/or blade strike) are simulated. Experimental factors like fish abundance, aggregation, and tidal speeds are explored to understand their effect on fish avoidance and potential interactions. Lastly, results from the statistical and agent-based model are compared. For the statistical model, probabilities of fish presence vary between turbine type and avoidance scenarios, with collision, blade strike, and combined impact probabilities spanning several orders of magnitude. Light cycles slightly influence probabilities, with higher estimates observed at night. Turbine size also influences interaction probabilities, with larger turbines posing higher risks. Results from the agent-based model found that probabilities depend on aggregation behavior and tidal speed for both axial and cross-flow turbines. As expected, zone of influence and entrainment probabilities decrease with increasing tidal flow, while asocial fish are unaffected by changes in current speed. Overall impact probabilities increase with tidal speed for both turbine types and are primarily observed when fish are within social groups. Comparison between the simulation and statistical model reveals differences in mean probabilities for each model component. While both models rely on empirical data and literature values, there remain knowledge gaps in estimating potential impacts and turbine avoidance. Future research should focus on validating encounter-impact models with real-world data to enhance mitigation efforts and conservation strategies.application/pdfen-USnonecollisionencountermarine renewable energytidal energytidal turbinesAquatic sciencesStatisticsEnvironmental scienceFisheriesThesis