Articles | Volume 19, issue 3
https://doi.org/10.5194/gmd-19-1213-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1213-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Computation of fish larvae self-recruitment in using forward- and backward-in-time particle tracking in a Lagrangian model (SWIM-v2.0) of the simulated circulation of Lake Erie (AEM3D-v1.1.2)
Deep Sea and Polar Fisheries Research Center, and Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, 266100, China
Environmental Fluid Dynamics Laboratory, Department of Civil Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Leon Boegman
Environmental Fluid Dynamics Laboratory, Department of Civil Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Josef D. Ackerman
Physical Ecology Laboratory, Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
Shiliang Shan
Environmental Fluid Dynamics Laboratory, Department of Civil Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Department of Physics and Space Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada
Yingming Zhao
Environmental Fluid Dynamics Laboratory, Department of Civil Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Aquatic Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry Lake Erie Fishery Station, Wheatley, ON N0P 2P0, Canada
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Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
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We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Shuqi Lin, Leon Boegman, Shiliang Shan, and Ryan Mulligan
Geosci. Model Dev., 15, 1331–1353, https://doi.org/10.5194/gmd-15-1331-2022, https://doi.org/10.5194/gmd-15-1331-2022, 2022
Short summary
Short summary
An operational hydrodynamics forecast system, COASTLINES, using the Windows Task Scheduler, Python, and MATLAB scripts, to automate application of a 3-D model (AEM3D) in Lake Erie was developed. The system predicted storm-surge and up-/downwelling events that are important for flood water and drinking water/fishery management. This example of the successful development of an operational forecast system can be adapted to simulate aquatic systems as required for management and public safety.
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Short summary
Self-recruitment of a population at a given larval settlement location is dependent on larval production from each source location, independent of larval recruits at the settlement location. An arbitrary choice of the number of larvae released from each source location in forward tracking is found to cause ambiguous self-recruitment. In contrast, we found that an arbitrary choice of the number of larvae released from the settlement location in backtracking leads to unambiguous self-recruitment.
Self-recruitment of a population at a given larval settlement location is dependent on larval...