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A Probabilistic Model for Hydrokinetic Turbine Collision Risks: Exploring Impacts on Fish
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
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2015 (English)In: PLoS ONE, ISSN 1932-6203, Vol. 10, no 3, e0117756Article in journal (Refereed) Published
Abstract [en]

A variety of hydrokinetic turbines are currently under development for power generation in rivers, tidal straits and ocean currents. Because some of these turbines are large, with rapidly moving rotor blades, the risk of collision with aquatic animals has been brought to attention. The behavior and fate of animals that approach such large hydrokinetic turbines have not yet been monitored at any detail. In this paper, we conduct a synthesis of the current knowledge and understanding of hydrokinetic turbine collision risks. The outcome is a generic fault tree based probabilistic model suitable for estimating population-level ecological risks. New video-based data on fish behavior in strong currents are provided and models describing fish avoidance behaviors are presented. The findings indicate low risk for small-sized fish. However, at large turbines (>= 5 m), bigger fish seem to have high probability of collision, mostly because rotor detection and avoidance is difficult in low visibility. Risks can therefore be substantial for vulnerable populations of large-sized fish, which thrive in strong currents. The suggested collision risk model can be applied to different turbine designs and at a variety of locations as basis for case-specific risk assessments. The structure of the model facilitates successive model validation, refinement and application to other organism groups such as marine mammals.

Place, publisher, year, edition, pages
2015. Vol. 10, no 3, e0117756
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Other Natural Sciences
URN: urn:nbn:se:su:diva-116634DOI: 10.1371/journal.pone.0117756ISI: 000350684900015PubMedID: 25730314OAI: diva2:807603


Available from: 2015-04-24 Created: 2015-04-22 Last updated: 2015-04-24Bibliographically approved

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Gullström, Martin
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