Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-35-2023
© Author(s) 2023. 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-16-35-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jan 2023
Model description paper |
| 03 Jan 2023
| 03 Jan 2023
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Environment and Climate Change Canada, Canada Centre for Inland
Waters, Burlington, L7R 4A6 ON, Canada
Donald C. Pierson
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Jorrit P. Mesman
Erken Laboratory and Limnology Department, Uppsala University,
Uppsala, Sweden
Département F.-A. Forel des sciences de l'environnement et de
l'eau, Université de Genève, Geneva, Switzerland
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Cited articles
Adrian, R., Wilhelm, S., and Gerten, D.: Life-history traits of lake
plankton species may govern their phenological response to climate warming,
Glob. Change Biol., 12, 652–661, https://doi.org/10.1111/j.1365-2486.2006.01125.x, 2006.
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, 2020.
Brookes, J. D. and Carey, C. C.: Resilience to Blooms, Science, 334, 46–47,
https://doi.org/10.1126/science.1207349, 2011.
Bruggeman, J. and Bolding, K.: A general framework for aquatic
biogeochemical models, Environ. Modell. Softw., 61, 249–265,
https://doi.org/10.1016/j.envsoft.2014.04.002, 2014.
Burchard, H., Bolding, K., and Villarreal, M. R.: GOTM, a General Ocean
Turbulence Model: Theory, Implementation and Test Cases, European
Commission, Joint Research Centre, Space Applications Institute, 103,
https://books.google.be/books/about/GOTM_a_General_Ocean_Turbulence_Model.html?id=zsJUHAAACAAJ&redir_esc=y (last access: 19 September 2022), 1999.
Burford, M. A., Carey, C. C., Hamilton, D. P., Huisman, J., Paerl, H. W.,
Wood, S. A., and Wulff, A.: Perspective: Advancing the research agenda for
improving understanding of cyanobacteria in a future of global change,
Harmful Algae, 91, 101601, https://doi.org/10.1016/j.hal.2019.04.004, 2020.
Carey, C. C., Ibelings, B. W., Hoffmann, E. P., Hamilton, D. P., and
Brookes, J. D.: Eco-physiological adaptations that favour freshwater
cyanobacteria in a changing climate, Water Res., 46, 1394–1407,
https://doi.org/10.1016/j.watres.2011.12.016, 2012.
Elliott, J. A.: Is the future blue-green? A review of the current model
predictions of how climate change could affect pelagic freshwater
cyanobacteria, Water Res., 46, 1364–1371, https://doi.org/10.1016/j.watres.2011.12.018,
2012.
Erken Laboratory: Meteorological data from Erken, Malma island, 1988-10-12–2021-12-31, Swedish Infrastructure for Ecosystem Science (SITES) [data set], https://hdl.handle.net/11676.1/qZYc4CMTOyxgvjv_gTAW08SO, last access: 19 September 2022.
Fornarelli, R., Galelli, S., Castelletti, A., Antenucci, J. P., and Marti, C. L.: An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers, Water Resour. Res., 49, 3626–3641, https://doi.org/10.1002/wrcr.20268, 2013.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, 2001.
Hanson, P. C., Stillman, A. B., Jia, X., Karpatne, A., Dugan, H. A., Carey,
C. C., Stachelek, J., Ward, N. K., Zhang, Y., Read, J. S., and Kumar, V.:
Predicting lake surface water phosphorus dynamics using process-guided
machine learning, Ecol. Model., 430, 109136,
https://doi.org/10.1016/j.ecolmodel.2020.109136, 2020.
Harris, T. D.,and Graham, J. L.: Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset, Lake Reserv. Manage., 33, 32–48, https://doi.org/10.1080/10402381.2016.1263694, 2017.
Hense, I. and Beckmann, A.: Towards a model of cyanobacteria life
cycle – effects of growing and resting stages on bloom formation of
N2-fixing species, Ecol. Model., 195, 205–218,
https://doi.org/10.1016/j.ecolmodel.2005.11.018, 2006.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural
Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J. M.
H., and Visser, P. M.: Cyanobacterial blooms, Nat. Rev. Microbiol.,
16, 471–483, https://doi.org/10.1038/s41579-018-0040-1, 2018.
Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J., Steinbach, M., and
Kumar, V.: Physics Guided RNNs for Modeling Dynamical Systems: A Case Study
in Simulating Lake Temperature Profiles, in: Proceedings of the 2019 SIAM
558–566, https://doi.org/10.1137/1.9781611975673.63, 2019.
Jimeno-Sáez, P., Senent-Aparicio, J., Cecilia, J. M., and
Pérez-Sánchez, J.: Using Machine-Learning Algorithms for
Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain),
Int. J. Env. Res. Pub. He., 17, 1189, https://doi.org/10.3390/ijerph17041189,
2020.
Jöhnk, K. D., Brüggemann, R., Rücker, J., Luther, B., Simon, U.,
Nixdorf, B., and Wiedner, C.: Modelling life cycle and population dynamics
of Nostocales (cyanobacteria), Environ. Modell. Softw., 26,
669–677, https://doi.org/10.1016/j.envsoft.2010.11.001, 2011.
Karlsson-Elfgren, I., Rengefors, K., and Gustafsson, S.: Factors regulating
recruitment from the sediment to the water column in the bloom-forming
cyanobacterium Gloeotrichia echinulata, Freshwater Biol., 49, 265–273, https://doi.org/10.1111/j.1365-2427.2004.01182.x, 2004.
Karlsson-Elfgren, I., Hyenstrand, P., and Riydin, E.: Pelagic growth and
colony division of Gloeotrichia echinulata in Lake Erken, J.
Plankton Res., 27, 145–151, https://doi.org/10.1093/plankt/fbh165, 2005.
Lin, S.: Shuqi-Lin/Erken_Algal_Bloom_Machine_Learning_Model: Erken_Algal_Bloom_Machine_Learning_Model (v1.1), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7149563, 2022.
Marcé, R., George, G., Buscarinu, P., Deidda, M., Dunalska, J., de Eyto,
E., Flaim, G., Grossart, H.-P., Istvanovics, V., Lenhardt, M., Moreno-Ostos,
E., Obrador, B., Ostrovsky, I., Pierson, D. C., Potužák, J.,
Poikane, S., Rinke, K., Rodríguez-Mozaz, S., Staehr, P. A.,
Šumberová, K., Waajen, G., Weyhenmeyer, G. A., Weathers, K. C.,
Zion, M., Ibelings, B. W., and Jennings, E.: Automatic High Frequency
Monitoring for Improved Lake and Reservoir Management, Environ. Sci.
Technol., 50, 10780–10794, https://doi.org/10.1021/acs.est.6b01604, 2016.
McHugh, M. L.: Interrater reliability: the kappa statistic, Biochem.
Medica, 22, 276–282, 2012.
Mellios, N., Moe, S. J., and Laspidou, C.: Machine Learning Approaches for
Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes,
Water, 12, 1191, https://doi.org/10.3390/w12041191, 2020.
Mesman, J. P., Ayala, A. I., Goyette, S., Kasparian, J., Marcé, R.,
Markensten, H., Stelzer, J. A. A., Thayne, M. W., Thomas, M. K., Pierson, D.
C., and Ibelings, B. W.: Drivers of phytoplankton responses to summer wind
events in a stratified lake: A modeling study, Limnol. Oceanogr.,
67, 856–873, https://doi.org/10.1002/lno.12040, 2022.
Moras, S., Ayala, A. I., and Pierson, D. C.: Historical modelling of changes in Lake Erken thermal conditions, Hydrol. Earth Syst. Sci., 23, 5001–5016, https://doi.org/10.5194/hess-23-5001-2019, 2019.
Nelson, N. G., Muñoz-Carpena, R., Phlips, E. J., Kaplan, D., Sucsy, P.,
and Hendrickson, J.: Revealing Biotic and Abiotic Controls of Harmful Algal
Blooms in a Shallow Subtropical Lake through Statistical Machine Learning,
Environ. Sci. Technol., 52, 3527–3535,
https://doi.org/10.1021/acs.est.7b05884, 2018.
Paerl, H. W.: Nuisance phytoplankton blooms in coastal, estuarine, and
inland waters, Limnol. Oceanogr., 33, 823–843,
https://doi.org/10.4319/lo.1988.33.4part2.0823, 1988.
Paerl, H. W. and Huisman, J.: Blooms Like It Hot, Science, 320, 57–58,
https://doi.org/10.1126/science.1155398, 2008.
Peretyatko, A., Teissier, S., De Backer, S., and Triest, L.: Classification
trees as a tool for predicting cyanobacterial blooms, Hydrobiologia, 689,
131–146, https://doi.org/10.1007/s10750-011-0803-4, 2012.
Persson, I. and Jones, I. D.: The effect of water colour on lake
hydrodynamics: a modelling study, Freshwater Biol., 53, 2345–2355,
https://doi.org/10.1111/j.1365-2427.2008.02049.x, 2008.
Pettersson, K.: The Availability of Phosphorus and the Species Composition
of the Spring Phytoplankton in Lake Erken, Internationale Revue der gesamten
Hydrobiologie und Hydrographie, 70, 527–546, https://doi.org/10.1002/iroh.19850700407, 1985.
Pettersson, K.: Mechanisms for internal loading of phosphorus in lakes,
Hydrobiologia, 373, 21–25, https://doi.org/10.1023/A:1017011420035, 1998.
Pettersson, K., Grust, K., Weyhenmeyer, G., and Blenckner, T.: Seasonality
of chlorophyll and nutrients in Lake Erken – effects of weather conditions,
Hydrobiologia, 506, 75–81, https://doi.org/10.1023/B:HYDR.0000008582.61851.76, 2003.
Pierson, D. C., Pettersson, K., and Istvanovics, V.: Temporal changes in
biomass specific photosynthesis during the summer: regulation by
environmental factors and the importance of phytoplankton succession,
Hydrobiologia, 243, 119–135, https://doi.org/10.1007/BF00007027, 1992.
Rahmani, F., Lawson, K., Ouyang, W., Appling, A., Oliver, S., and Shen, C.: Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data, Environ. Res. Lett., 16, 024025, https://doi.org/10.1088/1748-9326/abd501, 2021.
Read, J. S., Hamilton, D. P., Jones, I. D., Muraoka, K., Winslow, L. A.,
Kroiss, R., Wu, C. H., and Gaiser, E.: Derivation of lake mixing and
stratification indices from high-resolution lake buoy data, Environ. Modell. Softw., 26, 1325–1336, https://doi.org/10.1016/j.envsoft.2011.05.006, 2011.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S.
K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., Steinbach,
M., and Kumar, V.: Process-Guided Deep Learning Predictions of Lake Water
Temperature, Water Resour. Res., 55, 9173–9190, https://doi.org/10.1029/2019WR024922,
2019.
Rousso, B. Z., Bertone, E., Stewart, R., and Hamilton, D. P.: A systematic
literature review of forecasting and predictive models for cyanobacteria
blooms in freshwater lakes, Water Res., 182, 115959,
https://doi.org/10.1016/j.watres.2020.115959, 2020.
Recknagel, F., Fukushima, T., Hanazato, T., Takamura, N., and Wilson, H.:
Modelling and prediction of phyto- and zooplankton dynamics in Lake
Kasumigaura by artificial neural networks, Lakes & Reservoirs: Research & Management, 3, 123–133,
https://doi.org/10.1111/j.1440-1770.1998.tb00039.x, 1998.
Reichwaldt, E. S. and Ghadouani, A.: Effects of rainfall patterns on toxic
cyanobacterial blooms in a changing climate: Between simplistic scenarios
and complex dynamics, Water Res., 46, 1372–1393,
https://doi.org/10.1016/j.watres.2011.11.052, 2012.
Richardson, J., Miller, C., Maberly, S. C., Taylor, P., Globevnik, L.,
Hunter, P., Jeppesen, E., Mischke, U., Moe, S. J., Pasztaleniec, A.,
Søndergaard, M., and Carvalho, L.: Effects of multiple stressors on
cyanobacteria abundance vary with lake type, Glob. Change Biol., 24,
5044–5055, https://doi.org/10.1111/gcb.14396, 2018.
Stanley, F. K. T., Irvine, J. L., Jacques, W. R., Salgia, S. R., Innes, D.
G., Winquist, B. D., Torr, D., Brenner, D. R., and Goodarzi, A. A.: Radon
exposure is rising steadily within the modern North American residential
environment, and is increasingly uniform across seasons, Scientific Reports,
9, 18472, https://doi.org/10.1038/s41598-019-54891-8, 2019.
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.
Wei, B., Sugiura, N., and Maekawa, T.: Use of artificial neural network in
the prediction of algal blooms, Water Res., 35, 2022–2028,
https://doi.org/10.1016/S0043-1354(00)00464-4, 2001.
Xiao, X., He, J., Huang, H., Miller, T. R., Christakos, G., Reichwaldt, E.
S., Ghadouani, A., Lin, S., Xu, X., and Shi, J.: A novel single-parameter
approach for forecasting algal blooms, Water Res., 108, 222–231,
https://doi.org/10.1016/j.watres.2016.10.076, 2017.
Yang, Y., Stenger-Kovács, C., Padisák, J., and Pettersson, K.:
Effects of winter severity on spring phytoplankton development in a
temperate lake (Lake Erken, Sweden), Hydrobiologia, 780, 47–57,
https://doi.org/10.1007/s10750-016-2777-8, 2016.
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
The risks brought by the proliferation of algal blooms motivate the improvement of bloom...
