Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7751-2024
© Author(s) 2024. 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-17-7751-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Laura L. Swatridge
CORRESPONDING AUTHOR
Department of Civil Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
Ryan P. Mulligan
Department of Civil Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
Leon Boegman
Department of Civil Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
Shiliang Shan
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
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This preprint is open for discussion and under review for Biogeosciences (BG).
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Fish larvae are carried by ocean currents, but how far they travel also depends on how quickly they grow. Using Japanese anchovy as a case study, we combined ocean-current simulations with temperature-dependent growth. We found that warmer conditions can shorten the time larvae drift enough to offset, or even reverse, the effects of seasonal changes in currents. Accounting for growth may therefore improve predictions of fish dispersal as the climate changes.
Wei Shi, Leon Boegman, Shiliang Shan, Yongjun Tian, Yang Liu, Peng Sun, Zhenjiang Ye, Qinwang Xing, and Jianchao Li
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Japanese anchovy is a highly abundant and important fish, where its larvae travel and settle after spawning were modeled here. Most larvae remain in nearby coastal waters, especially in the Bohai Sea, the northern Yellow Sea, and along the southern Shandong Peninsula. In contrast, larvae from the Changjiang Estuary can travel much farther, reaching distant regions. This highlights its key role in connecting populations and provides useful insight for fisheries management and conservation.
Wei Shi, Leon Boegman, Josef D. Ackerman, Shiliang Shan, and Yingming Zhao
Geosci. Model Dev., 19, 1213–1228, https://doi.org/10.5194/gmd-19-1213-2026, https://doi.org/10.5194/gmd-19-1213-2026, 2026
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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.
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
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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.
Cited articles
Anderson, E. J., Fujisaki-Manome, A., Kessler, J., Land, G.A., Chu, P.Y., Kelley, J. G. W., Chen, Y., and Wang, J.: Ice Forecasting in the Next-Generation Great Lakes Operational Forecast System (GLOFS), J. Mar. Sci. Eng., 6, 123, https://doi.org/10.3390/jmse6040123, 2018.
Asher, T. G., Luettich, R. A., Fleming, J. G., and Blandton, B. O.: Low frequency water level correction in storm surge models using data assimilation, Ocean Model., 144, 101483, https://doi.org/10.1016/j.ocemod.2019.101483, 2019.
Baracchini, T., Wuest, A., and Bouffard, D.: Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics, Water Res., 172.1-12, 115529, https://doi.org/10.1016/j.watres.2020.115529, 2020.
Bender, M. A., Knutson, T. R., Tuleya, R. E., Sirutis, J. J., Vecchi, G. A., Garner, S. T., and Held, I. M.: Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes, Science, 327, 454–458, https://doi.org/10.1126/science.1180568, 2010.
Bilskie, M. V., Asher, T. G., Miller, P. W., Fleming, J. G., Hagen, S. C., and Luettich Jr., R. A.: Real-time simulated storm surge predictions during Hurricane Michael (2018), Weather Forecast., 37, 1085–1102, https://doi.org/10.1175/WAF-D-21-0132.1, 2022.
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L., Heilliette, S., Daneshard, V., Hounshell, A. G., Howard, D. W., Lewis, A. S. L., McClure, R. P., Wander, H. L., Ward, N. K., and Thomas, R. Q.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble-Variational Data-Assimilation at Environment Canada. Part 1: The Global System, Mon. Weather Rev., 143, 2532–2559, https://doi.org/10.1175/MWR-D-14-00354.1, 2015.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1.Model Description and validation, J. Geophys. Res.-Oceans, 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999.
Carey, C. C., Woelmer, W. M., Lofton, M. E., Figueiredo, R. J., Bookout, B. J., Corrigan, R. S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J., and Zadra, A.: Advancing lake and reservoir water quality management with near-term iterative ecological forecasting, Inland Waters, 12, 107–120, https://doi.org/10.1080/20442041.2020.1816421, 2022.
Chisholm, L., Talbot, T., Appleby, W., Tam, B., and Rong, R.: Projected change to air temperature, sea-level rise, and storms for the Gulf of Main region in 2050, Elem Sci Anth. 9, 1–14, https://doi.org/10.1525/elementa.2021.00059, 2021.
Cooper, A. H. and Mulligan, R. P.: Application of a Spectral Wave Model to assess Breakwater Configurations at a small craft harbour on Lake Ontario, J. Mar. Sci. Eng., 2016, 4, 46, https://doi.org/10.3390/jmse4030046, 2016.
Coastlines: Lake Ontario Homepage, https://coastlines.engineering.queensu.ca/lake-ontario/, last access: 11 March 2024.
Danard, M., Munro, A., and Murty, T.: Storm surge hazard in Canada, Nat. Hazards, 28, 407–434, https://doi.org/10.1023/A:1022990310410, 2003.
Deltares: delf3d – Revision 142633, https://svn.oss.deltares.nl/repos/delft3d, last access: 11 March 2024.
Dietrich, J. C., Muhammad, M., Curcic, M., Fathi, A., Dawson, C. N., Chen, S. S., and Luettich Jr., A.: Sensitivity of Storm Surge Prediction to Atmospheric Forcing during Hurricane Isaac, J. Waterw. Port C. Div., 144, 04017035, https://doi.org/10.1061/(ASCE)WW.1943-5460.0000419, 2018.
Elko, N., Dietrich, C., Cialone, M. A., Stockdon, H., Bilksie, M. W., Boyd, B., Charbonneau, B., Cox., D., Desback, K., Elgar, S., Lewis, A., Limber, P., Long, J., Massey, C., Mayo, T., McIntosh, K., Nadal-Caraballo, N.C., Raubenheimer, B., Tomiczek, T., and Wargula, A. E.: Advancing the understanding of storm processes and impacts, Shore & Beach, 87, 37–51, 2019.
Farhadzadeh, A. and Gangai, J.: Numerical Modelling of Coastal Storms for Ice-Free and Ice-Covered Lake Erie, J. Coast. Res., 33, 1383–1396, https://doi.org/10.2112/JCOASTRES-D-16-00101.1, 2017.
FEMA: FEMA Great Lakes Coastal Guidelines, Appendix D.3 Update, FEMA, 2014.
Forbes, C., Luettich, R. A., Mattocks, C. A., and Westerink, J. J.: A retrospective evaluation of the storm surge produced by Hurricane Gustav (2008): Forecast and hindcast results, Weather Forecast., 25, 1577–1602, https://doi.org/10.1175/2010WAF2222416.1, 2010.
Fleming, J. G., Fulcher, C. W., Luettich Jr., R. A., Estade, B. D., Allen, G., and Winer, H. S.: A real time storm surge forecasting system using ADCIRC, Proceedings of the International Conference on Estuarine and Coastal Modeling, Newport, RI, USA, 893–912, https://doi.org/10.1061/40990(324)48, 2008.
Gallagher, G. W., Duncombe, R. K., and Steeves, T. M.: Establishing Climate Change Resilience in the Great Lakes in Response to Flooding, Journal of Science Policy & Governance, 17, https://doi.org/10.38126/JSPG170105, 2020.
Gronewold, A. D. and Rood, R. B.: Recent water level changes across Earth's largest lake system and implications for future variability, J. Great Lakes Res., 45, 1–3, https://doi.org/10.1016/j.jglr.2018.10.012, 2019.
Gronewold, A. D., Fortin, V., Lofgren, B., Clites, A., Stow, C. A., and Quinn, F.: Coasts, water levels, and climate change: A Great Lakes perspective, Clim. Change, 120, 697–711, https://doi.org/10.1007/s10584-013-0840-2, 2013.
Huang, A., Rao, Y. R., Lu, Y., and Zhao, J.: Hydrodynamic modelling of Lake Ontario: An intercomparison of three models, J. Geophys. Res.-Oceans, 115, 1–16, https://doi.org/10.1029/2010JC006269, 2010.
Kelley, J. G. W., Chen, Y., Anderson, E. J., Lang, G. A., and Xu, J.: Upgrade of NOS Lake Erie Operational Forecast System (LEOFS) to FVCOM: Model Development and Hindcast Skill Assessment, NOS CS 40, NOAA, NOAA Technical Memorandum, https://doi.org/10.7289/V5/TM-NOS-CS-40, 2018.
Lacke, M. C., Knox, J. A., Frye, J. D., Stewart, A. E., Durkee, J. D., Fuhrmann, C. M., and Dillingham, S. M.: A climatology of cold-season non convective wind events in the Great Lakes region, J. Climate, 20, 6012–6022, https://doi.org/10.1175/2007JCLI1750.1, 2017.
Lesser, G. R., Roelvink, J. A., and Stelling, G. S.: Development and validation of a three-dimensional morphological model, Coast. Eng., 51, 883–915, https://doi.org/10.1016/j.coastaleng.2004.07.014, 2004.
Lin, S., Boegman, L., Shan, S., and Mulligan, R.: An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie, Geosci. Model Dev., 15, 1331–1353, https://doi.org/10.5194/gmd-15-1331-2022, 2022.
Mao, M. and Xia, M.: Dynamics of wave-current-surge interactions in Lake Michigan: A model comparison, Ocean Model. 110, 1–20, https://doi.org/10.1016/j.ocemod.2016.12.007, 2007.
McCombs, M. P., Mulligan, R. P., Boegman, L., and Rao, Y. R.: Modelling surface waves and wind-driven circulation in eastern Lake Ontario during winter storms, J. Great Lakes Res., 40, 130–142, https://doi.org/10.1016/j.jglr.2014.02.009, 2014a.
McCombs, M. P., Mulligan, R. P., and Boegman, L.: Offshore wind farm impacts on surface waves and circulation in Eastern Lake Ontario, Coast. Eng., 93, 32–39, https://doi.org/10.1016/j.coastaleng.2014.08.001, 2014b.
Milbrandt, J. A., Belair, S., Faucher, M., Vallee, M., Carrera, M. L., and Glazer, A.: The Pan-Canadian High Resolution (2.5 km) Deterministic Prediction System, Weather Forecast., 31, 1791–1816, https://doi.org/10.1175/WAF-D-16-0035.1, 2016.
Oveisy, A., Boegman, L., and Imberger, J.: Three-dimensional simulation of lake and ice dynamics during winter, Limnol. Oceanogr., 57, 43–57, https://https://doi.org/10.4319/lo.2012.57.1.0043, 2012.
Paramygin, V. A., Sheng, Y. P., and Davis, J. R.: Towards the development of an operational forecast system for the Florida coast, J. Mar. Sci. Eng., 5, 8, https://doi.org/10.3390/jmse5010008, 2017.
Paturi, S., Boegman, L., and Rao, Y.R.: Hydrodynamics of eastern Lake Ontario and the upper St. Lawrence River, J. Great Lakes Res., 38, 194–204, https://doi.org/10.1016/j.jglr.2011.09.008, 2012.
Peng, M., Zhang, A., Anderson, E. J., Lang, G. A., Kelley, J. G. W., and Chen, Y.: Implementation of the Lakes Michigan and Huron Operational Forecast System (LMHOFS) and the Nowcast/Forecast Skill Assessment, NOAA technical report NOS CO-OPS, https://doi.org/10.25923/cy7n-ja86, 2019.
Prakash, S., Atkinson, J. F., and Green, M. L.: A semi-Lagrangian study of circulation in Lake Ontario, J. Great Lakes Res., 33, 774–790, https://doi.org/10.3394/0380-1330(2007)33[774:ASSOCA]2.0.CO;2, 2007.
Rey, A. J. M. and Mulligan, R. P.: Influence of hurricane wind field variability on real-time forecast simulations of the coastal environment, J. Geophys. Res.-Oceans, 126, 1–20, https://doi.org/10.1029/2020JC016489, 2021.
Shore, J. A.: Modelling the circulation and exchange of the Kingston Basin and Lake Ontario with FVCOM, Ocean Model., 30, 106–114, https://doi.org/10.1016/j.ocemod.2009.06.007, 2009.
Sogut, D. V., Jensen, R. E., and Farhadzadeh, A.: Characterizing Lake Ontario Marine Renewable Energy Resources, Mar. Technol. Soc. J., 53, 21–38, https://doi.org/10.4031/MTSJ.53.2.3, 2019.
Steinschneider, S.: A hierarchical Bayesian model of storm surge and total water levels across the Great Lakes shoreline – Lake Ontario, J. Great Lakes Res. 47, 829–843, https://doi.org/10.1016/j.jglr.2021.03.007, 2021.
Studholme, J., Fedorov, A. V., Gulev, S. K., Emanuel, K., and Hodges, K.: Poleward expansion of tropical cyclone latitudes in warming climates, Nat. Geosci., 15, 14–18, https://doi.org/10.1038/s41561-021-00859-1, 2022.
Swatridge, L.: Coastlines-LO, Zenodo [data set], https://doi.org/10.5281/zenodo.10407863, 2023.
Swatridge, L. L., Mulligan, R. P., Boegman, L., Shan, S., and Valipour, R.: Coupled modelling of storm surge, circulation, and surface waves in a large, stratified lake, J. Great Lakes Res., 48, 1520–1535, https://doi.org/10.1016/j.jglr.2022.08.023, 2022.
Short summary
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.
We develop an operational forecast system, Coastlines-LO, that can simulate water levels and...