Triton’s expertise in collision risk and predictive modeling is coming together to advance predictions of collision risk with current energy converters, including tidal or riverine energy devices. Turbines only occupy part of the water column. Knowing where the moving components are relative to fish distribution and how fish behave in that habitat informs how likely they are to be at risk for collision. For example, if most fish move at the top of the water column and the turbine is close to the bottom, this might indicate that interactions are unlikely—and have a lower probability—than if the turbine were closer to the surface.
Close-range outcomes—like collision, near-miss, or evasion behavior—of interactions when fish encounter turbines are challenging to observe (see Staines’s Triton Story on collision risk here). Determining the probability of fish encountering a turbine through models like the Probability of Encounter Model (PoEM) can inform whether field observations are needed to detect direct interactions. This information helps determine what monitoring is needed to estimate the risk of current energy converters to animals and the environment. Improved understanding of these risks helps reduce barriers to installing and testing turbines and support future collision mitigation studies.
In collaboration with partners at Aquacoustics, LLC, the project developed a prototype PoEM that estimates the likelihood of fish encountering turbines during major movement or migration periods. During this first phase of the project, the team demonstrated the prototype PoEM for current energy converter turbines and salmon smolt during downstream migration to the ocean. To inform this model, the team processed and analyzed sonar data of salmon smolt approaching a deployed riverine turbine in the Kvichak River, Alaska to calculate the probability that smolt would enter the volume of water located directly upstream of the turbine. These probabilities were then broken down to assess how conditions like turbine operation (on/off) and time of day affected this probability.
As part of this project, Triton led a workshop with several sonar subject matter experts at the University of New Hampshire (UNH) Jere A. Chase Ocean Engineering Laboratory. The goal of the workshop was to inform one of the monitoring challenges identified during the development of the prototype PoEM to help improve the model’s performance. The model uses data collected from side looking sonar, an active acoustic method for estimating the biomass of salmon smolt approaching the turbine. Schools of fish—like the out-migrating sockeye salmon smolt in the Kvichak River, are easy to detect acoustically but since the return sonar signal shows a combination of many fish rather than individuals localizing them in space is challenging. The PoEM team is investigating if the depth location of the fish schools detected in the sonar beam are precise enough to inform the PoEM and help regulators make scientifically supported decisions about fish interactions with turbines in the river.
