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Assessing a model of Pacific Northwest harmful algal bloom transport as a decision-support tool.

  • Stone, Hally B1
  • Banas, Neil S2
  • MacCready, Parker3
  • Trainer, Vera L4
  • Ayres, Daniel L5
  • Hunter, Matthew V6
  • 1 School of Oceanography, University of Washington, 1503 NE Boat St., Box 357940, Seattle, WA 98195, USA. Electronic address: [email protected].
  • 2 Department of Mathematics & Statistics, University of Strathclyde, 26 Richmond St., Glasgow, G1 1XH, UK.
  • 3 School of Oceanography, University of Washington, 1503 NE Boat St., Box 357940, Seattle, WA 98195, USA.
  • 4 Environmental and Fisheries Science Division, National Marine Fisheries Service, Northwest Fisheries Science Center, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd. E., Seattle, WA 98112, USA.
  • 5 Washington Department of Fish & Wildlife, 48 Devonshire Rd., Montesano, WA 98563, USA.
  • 6 Marine Resources Program, Oregon Department of Fish & Wildlife, 2001 Marine Dr. Suite 120, Astoria, OR 97013, USA.
Published Article
Harmful algae
Publication Date
Nov 01, 2022
DOI: 10.1016/j.hal.2022.102334
PMID: 36344195


In the Pacific Northwest, blooms of the diatom Pseudo-nitzschia (PN) sometimes produce domoic acid, a neurotoxin that causes amnesic shellfish poisoning, leading to a Harmful Algal Bloom (HAB) event. The Pacific Northwest (PNW) HAB Bulletin project, a partnership between academic, government, and tribal stakeholders, uses a combination of beach and offshore monitoring data and ocean forecast modeling to better understand the formation, evolution, and transport of HABs in this region. This project produces periodic Bulletins to inform local stakeholders of current and forecasted conditions. The goal of this study was to help improve how the forecast model is used in the Bulletin's preparation through a retrospective particle-tracking experiment. Using past observations of beach PN cell counts, events were identified that likely originated in the Juan de Fuca eddy, a known PN hotspot, and then particle tracks were used in the model to simulate these events. A variety of "beaching definitions" were tested, based on both water depth and distance offshore, to define when a particle in the model was close enough to the coast that it was likely to correspond to cells appearing in the intertidal zone and in shellfish diets, as well as a variety of observed PN cell thresholds to determine what cell count should be used to describe an event that would warrant further action. The skill of these criteria was assessed by determining the fraction of true positives, true negatives, false positives, and false negatives within the model in comparison with observations, as well as a variety of derived model performance metrics. This analysis suggested that for our stakeholders' purposes, the most useful beaching definition is the 30 m isobath and the most useful PN cell threshold for coincident field-based sample PN density estimates is 10,000 PN cells/L. Lastly, the performance of a medium-resolution (1.5 km horizontal resolution) version of the model was compared with that of a high-resolution (0.5 km horizontal resolution) version, the latter currently used in forecasting for the PNW HAB Bulletin project. This analysis includes a direct comparison of the two model resolutions for one overlapping year (2017). These results suggested that a narrower, more realistic beaching definition is most useful in a high-resolution model, while a wider beaching definition is more appropriate in a lower resolution model like the medium-resolution version used in this analysis. Overall, this analysis demonstrated the importance of incorporating stakeholder needs into the statistical approach in order to generate the most effective decision-support information from oceanographic modeling. Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.

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