Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-1331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Feb 2022
Model description paper |
| 16 Feb 2022
| 16 Feb 2022
An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie
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 Physics and Space Science, Royal Military College of
Canada, Kingston, ON, K7K 7B4, Canada
Ryan Mulligan
Department of Civil Engineering, Queen's University, Kingston, ON,
K7L 3N6, Canada
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Cited articles
Anderson, E. J., Fujisaki-Manome, A., Kessler, J., Lang, G. A., Chu, P. Y.,
Kelly, 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.
Antenucci, J., and Imerito, A.: The CWR dynamic reservoir simulation model DYRESM: Science Manual, Center for Water Research: The University of Western Australia, Perth, Australia, 2000.
Baracchini, T., Hummel, S., Verlaan, M., Cimatoribus, A., Wüest, A., and
Bouffard, D.: An automated calibration framework and open source tools for
3D lake hydrodynamic models, Environ. Modell. Softw., 134,
104787, https://doi.org/10.1016/j.envsoft.2020.104787, 2020a.
Baracchini, T., Wüest, A., and Bouffard, D.: Meteolakes: An operational
online three-dimensional forecasting platform for lake hydrodynamics, Water
Res., 172, 115529, https://doi.org/10.1016/j.watres.2020.115529, 2020b.
Beletsky, D., Hawley, N., Rao, Y. R., Vanderploeg, H. A., Beletsky, R.,
Schwab, D. J., and Ruberg, S. A.: Summer thermal structure and anticyclonic
circulation of Lake Erie, Geophys. Res. Lett., 39, L06605, https://doi.org/10.1029/2012GL051002,
2012.
Bocaniov, S. A. and Scavia, D.: Temporal and spatial dynamics of large lake
hypoxia: Integrating statistical and three-dimensional dynamic models to
enhance lake management criteria, Water Resour. Res., 52, 4247–4263,
https://doi.org/10.1002/2015WR018170, 2016.
Bocaniov, S. A., Lamb, K. G., Liu, W., Rao, Y. R., and Smith, R. E.: High
sensitivity of lake hypoxia to air temperatures, winds, and nutrient
loading: Insights from a 3-D lake model, Water Resour. Res., 56,
e2019WR027040, https://doi.org/10.1029/2019WR027040, 2020.
Boegman, L., Loewen, M. R., Hamblin, P., and Culver, D.: Application of a
two-dimensional hydrodynamic reservoir model to Lake Erie, Can. J.
Fish. Aquat. Sci., 58, 858–869, 2001.
Boegman, L., Loewen, M. R., Hamblin, P. F., and Culver, D. A.: Vertical mixing and weak stratification over zebra mussel colonies in western Lake Erie, Limnol. Oceanogr., 53, 1093–1110, https://doi.org/10.4319/lo.2008.53.3.1093, 2008.
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.
Bouffard, D. and Lemmin, U.: Kelvin waves in Lake Geneva, J. Great Lakes
Res., 39, 637–645, https://doi.org/10.1016/j.jglr.2013.09.005, 2013.
Bouffard, D., Boegman, L., and Rao, Y. R.: Poincaré wave–induced mixing
in a large lake, Limnol. Oceanogr., 57, 1201–1216, 2012.
Bouffard, D., Ackerman, J. D., and Boegman, L.: Factors affecting the
development and dynamics of hypoxia in a large shallow stratified lake:
hourly to seasonal patterns, Water Resour. Res., 49, 2380–2394, 2013.
Bouffard, D., Boegman, L., Ackerman, J. D., Valipour, R., and Rao, Y. R.:
Near-inertial wave driven dissolved oxygen transfer through the thermocline
of a large lake, J. Great Lakes Res., 40, 300–307,
https://doi.org/10.1016/j.jglr.2014.03.014, 2014.
Brookes, J. D. and Carey, C. C.: Resilence to blooms, Science, 334, 46–47, https://doi.org/10.1126/science.1207349, 2011.
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L.,
Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J.,
and Zadra, A.: Implementation of deterministic weather forecasting systems
based on ensemble–variational data assimilation at Environment Canada. Part
I: the global system, Mon. Weather Rev., 143, 2532–2559, https://doi.org/10.1175/MWR-D-14-00354.1, 2015.
Caramatti, I., Peeters, F., Hamilton, D., and Hofmann, H.: Modelling
inter-annual and spatial variability of ice cover in a temperate lake with
complex morphology, Hydrol. Process., 34, 691–704, https://doi.org/10.1002/hyp.13618, 2019.
Casulli, V. and Cheng, R.: Semi-implicit finite difference methods for
three-dimensional shallow water flow, Int. J. Numer. Meth. Fl., 15,
629–648, https://doi.org/10.1002/fld.1650150602 1992.
Chen, C., Beardsley, R. C., Cowles, G., Qi, J., Lai, Z., Gao, G., Stuebe,
D., Xu, Q., Xue, P., Ge, J., Ji, R., Tian, R., Huang, H., Wu, L., and Lin,
H.: An unstructured grid, finite-volume community ocean model FVCOM user
manual, SMAST/UMASSD Tech. Rep. 11-1101, Dartmouth, Mass., Sea Grant College Program, Massachusetts Institute of Technology Cambridge, 373 pp., available at: http://fvcom.smast.umassd.edu/wp-content/uploads/2013/11/MITSG_12-25.pdf (last access: December 2021), 2012.
Chu, P. Y., Kelley, J. G. W., Mott, G. V., Zhang, A., and Lang, G. A.:
Development, implementation, and skill assessment of the NOAA/NOS Great
Lakes Operational Forecast System, Ocean Dynam., 61, 1305–1316,
https://doi.org/10.1007/s10236-011-0424-5, 2011.
Gaudard, A., Schwefel, R., Vinnå, L. R., Schmid, M., Wüest, A., and Bouffard, D.: Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3, Geosci. Model Dev., 10, 3411–3423, https://doi.org/10.5194/gmd-10-3411-2017, 2017.
Gaudard, A., Råman Vinnå, L., Bärenbold, F., Schmid, M., and Bouffard, D.: Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners – the case of Swiss lakes using Simstrat v2.1, Geosci. Model Dev., 12, 3955–3974, https://doi.org/10.5194/gmd-12-3955-2019, 2019.
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.
Gu, H., Jin, J., Wu, Y., Ek, M. B., and Subin, Z. M.: Calibration and
validation of lake surface temperature simulations with the coupled WRF-lake
model, Climatic Change, 129, 471–483, 2015.
Hamblin, P. F.: Great Lakes storm surge of April 6, 1979, J. Great Lakes
Res., 5, 312–315, https://doi.org/10.1016/S0380-1330(79)72157-5, 1979.
Hamblin, P. F.: Meteorological forecing and water level fluctuations on Lake
Erie, J. Great Lakes Res., 13, 436–453, https://doi.org/10.1016/S0380-1330(87)71665-7, 1987.
Hecky, R. E., Smith, R. E. H., Barton, D. R., Guildford, S. J., Taylor, W.
D., Charlton, M. N., and Howell, T.: The nearshore phosphorus shunt: a
consequence of ecosystem engineering by dreissenids in the Laurentian Great
Lakes, Can. J. Fish. Aquat. Sci., 61, 1285–1293, https://doi.org/10.1139/F04-065, 2004.
Higgins, S. N., Hecky, R. E., and Guildford, S. J.: Environmental controls
of cladophora growth dynamics in eastern Lake Erie: Application of the
Cladophora Growth Model (CGM), J. Great Lakes Res., 32, 629–644,
https://doi.org/10.3394/0380-1330(2006)32[629:ECOCGD]2.0.CO;2, 2006.
Hipsey, M. R., Bruce, L. C., and Hamilton, D. P.: GLM – General Lake Model.
Model overview and user information, Technical Manual, The University of
Western Australia, Perth, Australia, available at: https://aed.see.uwa.edu.au/research/models/GLM/downloads/GLM_Tutorial_2014.pdf (last access: December 2021), 2014.
Hodges, B. R., Imberger, J., Saggio, A., and Winters, K. B.: Modeling
basin-scale internal waves in a stratified lake, Limnol. Oceanogr.,
45, 1603–1620, https://doi.org/10.4319/lo.2000.45.7.1603, 2000.
HydroNumerics: Homepage, available at: https://www.hydronumerics.com.au/ (last access: December 2021), 2022.
Jabbari, A., Ackerman, J. D., Boegman, L., and Zhao, Y.: Episodic hypoxia in
the western basin of Lake Erie, Limnol. Oceanogr., 64, 2220–2236,
2019.
Jabbari, A., Ackerman, J. D., Boegman, L., and Zhao, Y.: Increases in Great
Lake winds and extreme events facilitate interbasin coupling and reduce
water quality in Lake Erie, Scientific Reports, 11, 5733,
https://doi.org/10.1038/s41598-021-84961-9, 2021.
Kelley, J. G. W., Hobgood, J. S., Bedford, K. W., and Schwab, D. J.:
Generation of Three-Dimensional Lake Model Forecasts for Lake Erie, Weather
Forecast., 13, 659–687, https://doi.org/10.1175/1520-0434(1998)013<0659:GOTDLM>2.0.CO;2, 1998.
Laval, B., Imberger, J., Hodges, B. R., and Stocker, R.: Modeling
circulation in lakes: Spatial and temporal variations, Limnol.
Oceanogr., 48, 983–994, 2003.
León, L. F., Imberger, J., Smith, R. E. H., Hecky, R. E., Lam, D. C. L.,
and Schertzer, W. M.: Modeling as a tool for nutrient management in Lake
Erie: a hydrodynamics study, J. Great Lakes Res., 31, 309–318,
https://doi.org/10.1016/S0380-1330(05)70323-3, 2005.
León, L. F., Smith, R. E. H., Hipsey, M. R., Bocaniov, S. A., Higgins,
S. N., Hecky, R. E., Antenucci, J. P., Imberger, J. A., and Guildford, S.
J.: Application of a 3D hydrodynamic-biological model for seasonal and
spatial dynamics of water quality and phytoplankton in Lake Erie, J. Great
Lakes Res., 37, 41–53, https://doi.org/10.1016/j.jglr.2010.12.007, 2011.
Leonard, B. P.: The ULTIMATE conservative difference scheme applied to
unsteady one-dimensional advection, Comp. Methods Appl. Mech. Eng., 88,
17–74, 1991.
Lesser, G. R., Roelvink, J. V., Van Kester, J. A. T. M., 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 model for Lake Erie, V1, Scholars Portal Dataverse [data set], https://doi.org/10.5683/SP2/VTN7WC, 2021.
Liu, W., Bocaniov, S. A., Lamb, K. G., and Smith, R. E. H.: Three
dimensional modeling of the effects of changes in meteorological forcing on
the thermal structure of Lake Erie, J. Great Lakes Res., 40, 827–840,
https://doi.org/10.1016/j.jglr.2014.08.002, 2014.
Loewen, M., Ackerman, J. D., and Hamblin, P. F.: Environmental implications
of stratification and turbulent mixing in a shallow lake basin, Can. J.
Fish. Aquat. Sci., 64, 43–57, https://doi.org/10.1139/F06-165, 2007.
Lv, Z., Zhang, S., Jin, J., Wu, Y., and Ek, M. B.: Coupling of a physically
based lake model into the climate forecast system to improve winter climate
forecasts for the Great Lakes region, Clim. Dynam., 53, 6503–6517,
https://doi.org/10.1007/s00382-019-04939-2, 2019.
Madani, M., Seth, R., León, L. F., Valipour, R., and McCrimmon, C.:
Three dimensional modelling to assess contributions of major tributaries to
fecal microbial pollution of lake St. Clair and Sandpoint Beach, J. Great
Lakes Res., 46, 159–179, https://doi.org/10.1016/j.jglr.2019.12.005, 2020.
Meyers, T. and Dale, R.: Predicting daily insolation with hourly cloud
height and coverage, J. Appl. Meteorol. Clim., 22,
537–545, 1983.
Michalak, A. a. M., Anderson, E. J., Beletsky, D., Boland, S., Bosch, N. S.,
Bridgeman, T. B., Chaffin, J. D., Cho, K., Confesor, R., Daloğlu, I.,
DePinto, J. V., Evans, M. A., Fahnenstiel, G. L., He, L., Ho, J. C.,
Jenkins, L., Johengen, T. H., Kuo, K. C., LaPorte, E., Liu, X., McWilliams,
M. R., Moore, M. R., Posselt, D. J., Richards, R. P., Scavia, D., Steiner,
A. L., Verhamme, E., Wright, D. M., and Zagorski, M. A.: Record-setting
algal bloom in Lake Erie caused by agricultural and meteorological trends
consistent with expected future conditions, P. Natl.
Acad. Sci., 110, 6448–6452, https://doi.org/10.1073/pnas.1216006110, 2013.
Mortimer, C. H.: Fifty Years of Physical Investigations and Related
Limnological Studies on Lake Erie, 1928–1977, J. Great Lakes
Res., 13, 407–435, https://doi.org/10.1016/S0380-1330(87)71664-5, 1987.
Nakhaei, N., Boegman, L., Mehdizadeh, M., and Loewen, M.: Hydrodynamic
modeling of Edmonton storm-water ponds, Environ. Fluid Mech., 19, 305–327,
https://doi.org/10.1007/s10652-018-9625-5, 2019.
Nakhaei, N., Ackerman, J. D., Bouffard, D., Rao, Y. R., and Boegman, L.:
Empirical modeling of hypolimnion and sediment oxygen demand in temperate
Canadian lakes, Inland Waters, 11, 351–367, https://doi.org/10.1080/20442041.2021.1880244, 2021.
O'Connor, W. P., Schwab, D. J., and Lang, G. A.: Forecast verification for
Eta Model winds using Lake Erie storm surge water levels, Weather
Forecast., 14, 119–133, 1999.
O'Neil, J. M., Davis, T. W., Burford, M. A., and Gobler, C. J.: The rise of
harmful cyanobacteria blooms: The potential roles of eutrophication and
climate change, Harmful Algae, 14, 313–334, https://doi.org/10.1016/j.scitotenv.2011.02.001,
2012.
The Ontario Ministry of Environment (MOE): Lake Erie fish kill
incident on September 1, 2012, Summary Report, Ontario Ministry of the Environment Southwestern Region 14, available at: https://www.ontario.ca/page/water-quality-ontario-2014-report (last access: December 2021), 2014.
O'Reilly, C. M., Sharma, S., Gray, D. K., Hampton, S. E., Read, J. S., Rowley, R. J., Schneider, P., Lenters, J. D., McIntyre, P. B., Kraemer, B. M., Weyhenmeyer, G. A., Straile, D., Dong, B., Adrian, R., Allan, M. G., Anneville, O., Arvola, L., Austin, J., Bailey, J. L., Baron, J. S., Brookes, J. D., de Eyto, E., Dokulil, M. T., Hamilton, D. P., Havens, K., Hetherington, A. L., Higgins, S. N., Hook, S., Izmest'eva, L. R., Joehnk, K. D., Kangur, K., Kasprzak, P., Kumagai, M., Kuusisto, E., Leshkevich, G., Livingstone, D. M., MacIntyre, S., May, L., Melack, J. M., Mueller-Navarra, D. C., Naumenko, M., Noges, P., Noges, T., North, R. P., Plisnier, P.-D., Rigosi, A., Rimmer, A., Rogora, M., Rudstam, L. G., Rusak, J. A., Salmaso, N., Samal, N. R., Schindler, D. E., Schladow, S. G., Schmid, M., Schmidt, S. R., Silow, E., Soylu, M. E., Teubner, K., Verburg, P., Voutilainen, A., Watkinson, A., Williamson, C. E., and Zhang, G.: Rapid and highly variable warming of lake surface waters around the globe, Geophys. Res. Lett., 42, 10773–10781, https://doi.org/10.1002/2015GL066235, 2015.
Oveisy, A., Boegman, L., and Imberger, J.: Three-dimensional simulation of
lake and ice dynamics during winter, Limnol. Oceanogr., 57, 42–57,
https://doi.org/10.4319/lo.2012.57.1.0043, 2012.
Paerl, H. W. and Paul, V. J.: Climate change: Links to global expansion of
harmful cyanobacteria, Water Res., 46, 1349–1363,
https://doi.org/10.1016/j.watres.2011.08.002, 2012.
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.
Rao, Y. R. and Murthy, C. R.: Coastal boundary layer characteristics during
summer stratification in Lake Ontario, J. Phys. Oceanogr., 31, 1088–1104,
https://doi.org/10.1175/1520-0485(2001)031<1088:CBLCDS>2.0.CO;2, 2001.
Rao, Y. R., Hawley, N., Charlton, M. N., and Schertzer, W. M.: Physical
processes and hypoxia in the central basin of Lake Erie, Limnol. Oceanogr.,
53, 2007–2020, https://doi.org/10.4319/lo.2008.53.5.2007, 2008.
Rao, Y. R., Howell, T., Watson, S. B., and Abernethy, S.: On hypoxia and
fish kills along the north shore of Lake Erie, J. Great Lakes Res., 40,
187–191, https://doi.org/10.1016/j.jglr.2013.11.007, 2014.
Rey, A. and Mulligan, R. P.: Influence of Hurricane Wind Field Variability
on RealTime Forecast Simulations of the Coastal Environment, J. Geophys.
Res.-Oceans, 126, e2020JC016489, https://doi.org/10.1029/2020JC016489, 2021.
Rowe, M. D., Anderson, E. J., Beletsky, D., Stow, C. A., Moegling, S. D., Chaffin, J. D., May, J. C., Collingsworth, P. D., Jabbari, A., and Ackerman, J. D.: Coastal Upwelling Influences Hypoxia Spatial Patterns and Nearshore Dynamics in Lake Erie, J. Geophys. Res.-Oceans, 124, 6154–6175, https://doi.org/10.1029/2019JC015192, 2019.
Ruberg, S. A., Guasp, E., Hawley, N., Muzzi, R. W., Brandt, S. B., and Vanderploeg, H. A., Lane, J. C., Miller, T., and Constant, S. A.: Societal benefits of the Real‐time Coastal Observation Network (ReCON): Implications for municipal drinking water quality, Mar. Technol. Soc. J., 42, 103–109, https://doi.org/10.4031/002533208786842471, 2008.
Saber, A., James, D. E., and Hannoun, I. A.: Effects of lake water level
fluctuation due to drought and extreme winter precipitation on mixing and
water quality of an alpine lake, Case Study: Lake Arrowhead, California,
Sci. Total Environ., 714, 136762, https://doi.org/10.1016/j.scitotenv.2020.136762, 2020.
Scavia, D., David Allan, J., Arend, K. K., Bartell, S., Beletsky, D., Bosch, N. S., Brandt, S. B., Briland, R. D., Daloğlu, I., DePinto, J. V., Dolan, D. M., Evans, M. A., Farmer, T. M., Goto, D., Han, H., Höök, T. O., Knight, R., Ludsin, S. A., Mason, D., Michalak, A. M., Peter Richards, R., Roberts, J. J., Rucinski, D. K., Rutherford, E., Schwab, D. J., Sesterhenn, T. M., Zhang, H., and Zhou, Y.: Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia, J. Great Lakes Res., 40, 226–246, https://doi.org/10.1016/j.jglr.2014.02.004, 2014.
Scavia, D., DePinto, J. V., and Bertani, I.: A multi-model approach to
evaluating target phosphorus loads for Lake Erie, J. Great Lakes Res., 42,
1139–1150, https://doi.org/10.1016/j.jglr.2016.09.007, 2016.
Schertzer, W. M., Saylor, J. H., Boyce, F. M., Robertson, D. G., and Rosa,
F.: Seasonal Thermal Cycle of Lake Erie, J. Great Lakes Res.,
13, 468–486, https://doi.org/10.1016/S0380-1330(87)71667-0, 1987.
Schwab, D. J. and Beletsky, D.: Propagation of kelvin waves along irregular
coastlines in finite-difference models, Adv. Water Resour., 22, 239–245,
https://doi.org/10.1016/S0309-1708(98)00015-3, 1998.
Schwab, D. J., Leshkevich, G. A., and Muhr, G. C.: Automated Mapping of
Surface Water Temperature in the Great Lakes, J. Great Lakes Res., 25,
468–481, https://doi.org/10.1016/S0380-1330(99)70755-0, 1999.
Trebitz, A. S.: Characterizing seiche and tide-driven daily water level
fluctuations affecting coastal ecosystems of the Great Lakes, J. Great Lakes
Res., 32, 102–116, https://doi.org/10.3394/0380-1330(2006)32[102:CSATDW]2.0.CO;2, 2006.
Valipour, R., Bouffard, D., Boegman, L., and Rao, Y. R.: Near-inertial waves
in Lake Erie, Limnol. Oceanogr., 60, 1522–1535, https://doi.org/10.1021/es301422r, 2015.
Valipour, R., Rao, Y. R., León, L. F., and Depew, D.: Nearshore-offshore
exchanges in multi-basin coastal waters: Observations and three-dimensional
modeling in Lake Erie, J. Great Lakes Res., 45, 50–60,
https://doi.org/10.1016/j.jglr.2018.10.005, 2019.
Wang, Q., and Boegman, L.: Multi-Year Simulation of Western Lake Erie Hydrodynamics and Biogeochemistry to Evaluate Nutrient Management Scenarios, Sustainability, 13, 7516, https://doi.org/10.3390/su13147516, 2021.
Watson, S. B., Miller, C., Arhonditsis, G., Boyer, G. L., Carmichael, W., Charlton, M. N., Confesor, R., Depew, D. C., Höök, T. O., Ludsin, S. A., Matisoff, G., McElmurry, S. P., Murray, M. W., Peter Richards, R., Rao, Y. R., Steffen, M. M., and Wilhelm, S. W.: The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia, Harmful Algae, 56, 44–66, https://doi.org/10.1016/j.hal.2016.04.010, 2016.
Woolway, R. I. and Merchant, C. J.: Worldwide alteration of lake mixing
regimes in response to climate change, Nat. Geosci., 12, 271–276,
https://doi.org/10.1038/s41561-019-0322-x, 2019.
Woolway, R. I., Kraemer, B. M., Lenters, J. D., Merchant, C. J., O’Reilly, C. M., and Sharma, S.: Global lake responses to climate change, Nat. Rev. Earth Environ., 1, 388–403, https://doi.org/10.1038/s43017-020-0067-5, 2020.
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.
An operational hydrodynamics forecast system, COASTLINES, using the Windows Task Scheduler,...
