IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v306y2015icp216-225.html
   My bibliography  Save this article

Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation

Author

Listed:
  • Zhang, Xiaoqing
  • Recknagel, Friedrich
  • Chen, Qiuwen
  • Cao, Hongqing
  • Li, Ruonan

Abstract

Models are proved to be effective instrument for algae bloom prediction and management. The commonly available prediction models are physically based numerical approach or data-driven approach. However, these models are sometimes restricted by the lack of an explicit representation function or by insufficient data. The present research aimed to develop forecasting models that provide early warning on cyanobacteria outbreaks, as well as understand the ecological thresholds and relationships that determine such events, by means of evolutionary computation. The Lake Taihu, which has been suffering from severe cyanobacteria blooms over the last decades due to eutrophication, was taken as study case. Two modelling approaches were used based on water quality data collected from 31 monitoring sites from 2008 to 2012. First, eight sampling sites representing spatially different environmental conditions across Lake Taihu were selected to develop 2-day ahead forecasting models. The resulting models well-matched the timing and magnitude of the observed cyanobacteria dynamics for all eight sites, which was reflected by coefficients of determination (r2) of 0.62 for eastern site 24 being least favourable to cyanobacteria growth and 0.83 for north-western site 6 being most favourable. The sensitivity analyses revealed inhibitory relationships with nitrate at water temperatures greater than 18°C and excitatory relationships with phosphate at lower water temperatures for most sites, which suggested N-limitation of the lake existed locally in summer and autumn. Second, the aggregated data from all 31 sites were used to develop a generic 2-day ahead forecasting model. When compared with the observed cyanobacteria data of the eight selected sampling sites, the generic model achieved slightly lower coefficients of determination than the site-specific models, with the lowest r2 value for site 24 (0.36) and the highest r2 value for site 6 (0.77). The sensitivity analysis for the generic model revealed a much lower water temperature threshold of 13.01°C, above which N-limitation for cyanobacteria growth was indicated. Overall, both the spatially-explicit models and the generic model were suitable for early warning of cyanobacteria blooms at most sampling sites, and specified understanding on the environmental conditions that favour cyanobacteria growth across Lake Taihu.

Suggested Citation

  • Zhang, Xiaoqing & Recknagel, Friedrich & Chen, Qiuwen & Cao, Hongqing & Li, Ruonan, 2015. "Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation," Ecological Modelling, Elsevier, vol. 306(C), pages 216-225.
  • Handle: RePEc:eee:ecomod:v:306:y:2015:i:c:p:216-225
    DOI: 10.1016/j.ecolmodel.2014.05.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380014002622
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2014.05.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Dong-Kyun & Cao, Hongqing & Jeong, Kwang-Seuk & Recknagel, Friedrich & Joo, Gea-Jae, 2007. "Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms," Ecological Modelling, Elsevier, vol. 203(1), pages 147-156.
    2. Recknagel, Friedrich & Ostrovsky, Ilia & Cao, Hongqing & Zohary, Tamar & Zhang, Xiaoqing, 2013. "Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets," Ecological Modelling, Elsevier, vol. 255(C), pages 70-86.
    3. Cao, Hongqing & Recknagel, Friedrich & Orr, Philip T., 2013. "Enhanced functionality of the redesigned hybrid evolutionary algorithm HEA demonstrated by predictive modelling of algal growth in the Wivenhoe Reservoir, Queensland (Australia)," Ecological Modelling, Elsevier, vol. 252(C), pages 32-43.
    4. Chen, Qiuwen & Zhang, Chengcheng & Recknagel, Friedrich & Guo, Jing & Blanckaert, Koen, 2014. "Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake," Ecological Modelling, Elsevier, vol. 273(C), pages 109-116.
    5. Gal, G. & Hipsey, M.R. & Parparov, A. & Wagner, U. & Makler, V. & Zohary, T., 2009. "Implementation of ecological modeling as an effective management and investigation tool: Lake Kinneret as a case study," Ecological Modelling, Elsevier, vol. 220(13), pages 1697-1718.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chung, S.W. & Imberger, J. & Hipsey, M.R. & Lee, H.S., 2014. "The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir," Ecological Modelling, Elsevier, vol. 289(C), pages 133-149.
    2. Cao, Hongqing & Recknagel, Friedrich & Bartkow, Michael, 2016. "Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms," Ecological Modelling, Elsevier, vol. 342(C), pages 97-112.
    3. A. Jamali & E. Khaleghi & I. Gholaminezhad & N. Nariman-Zadeh & B. Gholaminia & A. Jamal-Omidi, 2017. "Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 149-163, January.
    4. Li, Yu & Waite, Anya M. & Gal, Gideon & Hipsey, Matthew R., 2013. "An analysis of the relationship between phytoplankton internal stoichiometry and water column N:P ratios in a dynamic lake environment," Ecological Modelling, Elsevier, vol. 252(C), pages 196-213.
    5. Weinberger, Stefan & Vetter, Mark, 2012. "Using the hydrodynamic model DYRESM based on results of a regional climate model to estimate water temperature changes at Lake Ammersee," Ecological Modelling, Elsevier, vol. 244(C), pages 38-48.
    6. Cao, Hongqing & Recknagel, Friedrich & Orr, Philip T., 2013. "Enhanced functionality of the redesigned hybrid evolutionary algorithm HEA demonstrated by predictive modelling of algal growth in the Wivenhoe Reservoir, Queensland (Australia)," Ecological Modelling, Elsevier, vol. 252(C), pages 32-43.
    7. Chen, Qiuwen & Zhang, Chengcheng & Recknagel, Friedrich & Guo, Jing & Blanckaert, Koen, 2014. "Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake," Ecological Modelling, Elsevier, vol. 273(C), pages 109-116.
    8. Jeong, Kwang-Seuk & Jang, Ji-Deok & Kim, Dong-Kyun & Joo, Gea-Jae, 2011. "Waterfowls habitat modeling: Simulation of nest site selection for the migratory Little Tern (Sterna albifrons) in the Nakdong estuary," Ecological Modelling, Elsevier, vol. 222(17), pages 3149-3156.
    9. Kerimoglu, Onur & Jacquet, Stéphan & Vinçon-Leite, Brigitte & Lemaire, Bruno J. & Rimet, Frédéric & Soulignac, Frédéric & Trévisan, Dominique & Anneville, Orlane, 2017. "Modelling the plankton groups of the deep, peri-alpine Lake Bourget," Ecological Modelling, Elsevier, vol. 359(C), pages 415-433.
    10. Gilboa, Yael & Friedler, Eran & Gal, Gideon, 2009. "Adapting empirical equations to Lake Kinneret data by using three calibration methods," Ecological Modelling, Elsevier, vol. 220(23), pages 3291-3300.
    11. Farrell, Kaitlin J. & Ward, Nicole K. & Krinos, Arianna I. & Hanson, Paul C. & Daneshmand, Vahid & Figueiredo, Renato J. & Carey, Cayelan C., 2020. "Ecosystem-scale nutrient cycling responses to increasing air temperatures vary with lake trophic state," Ecological Modelling, Elsevier, vol. 430(C).
    12. Kim, MinHyeok & Park, Namyong & (Bob) McKay, R.I. & Shin, Haisoo & Lee, Yun-Geun & Jeong, Kwang-Seuk & Kim, Dong-Kyun, 2014. "Improvement of complex and refractory ecological models: Riverine water quality modelling using evolutionary computation," Ecological Modelling, Elsevier, vol. 291(C), pages 205-217.
    13. Rigosi, Anna & Rueda, Francisco J., 2012. "Propagation of uncertainty in ecological models of reservoirs: From physical to population dynamic predictions," Ecological Modelling, Elsevier, vol. 247(C), pages 199-209.
    14. Shin, Jiyoun & Kim, Kyung-Ho & Lee, Kang-Kun & Kim, Hyoung-Soo, 2010. "Assessing temperature of riverbank filtrate water for geothermal energy utilization," Energy, Elsevier, vol. 35(6), pages 2430-2439.
    15. Ofir, E. & Heymans, J.J. & Shapiro, J. & Goren, M. & Spanier, E. & Gal, G., 2017. "Predicting the impact of Lake Biomanipulation based on food-web modeling—Lake Kinneret as a case study," Ecological Modelling, Elsevier, vol. 348(C), pages 14-24.
    16. Shimoda, Yuko & Arhonditsis, George B., 2016. "Phytoplankton functional type modelling: Running before we can walk? A critical evaluation of the current state of knowledge," Ecological Modelling, Elsevier, vol. 320(C), pages 29-43.
    17. Soares, L.M.V. & Calijuri, M.C., 2021. "Sensitivity and identifiability analyses of parameters for water quality modeling of subtropical reservoirs," Ecological Modelling, Elsevier, vol. 458(C).
    18. Recknagel, Friedrich & Ostrovsky, Ilia & Cao, Hongqing & Zohary, Tamar & Zhang, Xiaoqing, 2013. "Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets," Ecological Modelling, Elsevier, vol. 255(C), pages 70-86.
    19. Wen-Cheng Liu & Hong-Ming Liu & Rita Sau-Wai Yam, 2021. "A Three-Dimensional Coupled Hydrodynamic-Ecological Modeling to Assess the Planktonic Biomass in a Subalpine Lake," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
    20. Snortheim, Craig A. & Hanson, Paul C. & McMahon, Katherine D. & Read, Jordan S. & Carey, Cayelan C. & Dugan, Hilary A., 2017. "Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake," Ecological Modelling, Elsevier, vol. 343(C), pages 39-53.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:306:y:2015:i:c:p:216-225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.