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A Comparison of Bulk Estuarine Turnover Timescales to Particle Tracking Timescales Using a Model of the Yaquina Bay Estuary

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https://ir.library.oregonstate.edu/concern/articles/p5547t04c

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  • The ability to determine a bulk estuarine turnover timescale that is well defined under realistic conditions is in high demand for estuarine research and management. We compare how turnover timescales vary with tidal and river forcing from idealized forcing scenarios using a three-dimensional circulation model of the Yaquina Bay estuary in order to understand the limitations and benefits of different timescale methods for future application. Using model results, we compare bulk formula approaches—the tidal prism method, freshwater fraction method, and a relatively new estuarine timescale calculation method based on the total exchange flow (TEF)—to directly calculated timescales from particle tracking in order to assess the utility of the bulk formula timescales. All of the timescales calculated had similar magnitudes during high river discharge but varied significantly at low discharge and had different dependences on tidal amplitude. Even in the application of a single estuary-averaged timescale, we did not find that any of the bulk timescales described the estuary over a realistic range of tidal and river discharge forcing. During high discharge, the Yaquina Bay timescale is on the order of 2–5 tidal cycles based on the particle tracking analysis, but during low discharge, the turnover time varies across methods and spatial considerations appear to be more important
  • Keywords: Turnover time, Particle tracking, Residence time, Total exchange flow
  • Keywords: Turnover time, Particle tracking, Residence time, Total exchange flow
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  • Lemagie, E. P., & Lerczak, J. A. (2015). A comparison of bulk estuarine turnover timescales to particle tracking timescales using a model of the Yaquina Bay Estuary. Estuaries and Coasts, 38(5), 1797-1814. doi:10.1007/s12237-014-9915-1
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  • 38
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  • 5
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  • This research was supported by Oregon Sea Grant grant number NA10OAR4170059 and National Science Foundation grant number 1260394.
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