Environmental Science and Pollution Research

, Volume 24, Issue 26, pp 20934–20948 | Cite as

On the successful use of a simplified model to simulate the succession of toxic cyanobacteria in a hypereutrophic reservoir with a highly fluctuating water level

  • Ali Fadel
  • Bruno J. Lemaire
  • Brigitte Vinçon-Leite
  • Ali Atoui
  • Kamal Slim
  • Bruno Tassin
Research Article


Many freshwater bodies worldwide that suffer from harmful algal blooms would benefit for their management from a simple ecological model that requires few field data, e.g. for early warning systems. Beyond a certain degree, adding processes to ecological models can reduce model predictive capabilities. In this work, we assess whether a simple ecological model without nutrients is able to describe the succession of cyanobacterial blooms of different species in a hypereutrophic reservoir and help understand the factors that determine these blooms. In our study site, Karaoun Reservoir, Lebanon, cyanobacteria Aphanizomenon ovalisporum and Microcystis aeruginosa alternatively bloom. A simple configuration of the model DYRESM-CAEDYM was used; both cyanobacteria were simulated, with constant vertical migration velocity for A. ovalisporum, with vertical migration velocity dependent on light for M. aeruginosa and with growth limited by light and temperature and not by nutrients for both species. The model was calibrated on two successive years with contrasted bloom patterns and high variations in water level. It was able to reproduce the measurements; it showed a good performance for the water level (root-mean-square error (RMSE) lower than 1 m, annual variation of 25 m), water temperature profiles (RMSE of 0.22–1.41 °C, range 13–28 °C) and cyanobacteria biomass (RMSE of 1–57 μg Chl a L−1, range 0–206 μg Chl a L−1). The model also helped understand the succession of blooms in both years. The model results suggest that the higher growth rate of M. aeruginosa during favourable temperature and light conditions allowed it to outgrow A. ovalisporum. Our results show that simple model configurations can be sufficient not only for theoretical works when few major processes can be identified but also for operational applications. This approach could be transposed on other hypereutrophic lakes and reservoirs to describe the competition between dominant phytoplankton species, contribute to early warning systems or be used for management scenarios.


DYRESM-CAEDYM Model simplicity Cyanobacteria succession, water-level variation 


  1. Akomeah E, Chun KP, Lindenschmidt K-E (2015) Dynamic water quality modelling and uncertainty analysis of phytoplankton and nutrient cycles for the Upper South Saskatchewan River. Environ Sci Pollut Res 22(22):18239–18251CrossRefGoogle Scholar
  2. Asaeda T, Pham HS, Nimal Priyantha DG, Manatunge J, Hocking GC (2001) Control of algal blooms in reservoirs with a curtain: a numerical analysis. Ecol Eng 16:395–404CrossRefGoogle Scholar
  3. Atoui A, Hafez H, Slim K (2013) Occurrence of toxic cyanobacterial blooms for the first time in Lake Karaoun, Lebanon. Water Environ J 27:42–49CrossRefGoogle Scholar
  4. Bastien C, Cardin R, Veilleux E, Deblois C, Warren A, Laurion I (2011) Performance evaluation of phycocyanin probes for the monitoring of cyanobacteria. J Environ Monit 13:110–118CrossRefGoogle Scholar
  5. Bruce LC, Hamilton D, Imberger J, Gal G, Gophen M, Zohary T, Hambright KD (2006) A numerical simulation of the role of zooplankton in C, N and P cycling in Lake Kinneret, Israel. Ecol Model 193:412–436CrossRefGoogle Scholar
  6. Burger DF, Hamilton DP, Pilditch CA (2008) Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake. Ecol Model 211:411–423CrossRefGoogle Scholar
  7. Callieri C, Bertoni R, Contesini M, Bertoni F (2014) Lake level fluctuations boost toxic cyanobacterial “oligotrophic blooms”. PLoS One 9:e109526CrossRefGoogle Scholar
  8. Copetti, D., Tartari, G., Morabito, G., A. Oggioni, E.L.a.J.I., 2006. A biogeochemical model of Lake Pusiano (North Italy) and its use in the predictability of phytoplankton blooms: first preliminary results. J Limnol 65, 59–64Google Scholar
  9. Cui Y, Zhu G, Li H, Luo L, Cheng X, Jin Y, Trolle D (2016) Modeling the response of phytoplankton to reduced external nutrient load in a subtropical Chinese reservoir using DYRESM-CAEDYM. Lake and Reservoir Manage 32(2):146–157CrossRefGoogle Scholar
  10. Fadel A (2014) Physico-chemical functioning and development of phytoplankton in Karaoun Reservoir (Lebanon): application of a hydrodynamic-ecological model (Doctoral dissertation, Université Paris-Est)Google Scholar
  11. Fadel A, Atoui A, Lemaire B, Vinçon-Leite B, Slim K (2014a) Dynamics of the toxin cylindrospermopsin and the cyanobacterium Chrysosporum (Aphanizomenon) ovalisporum in a Mediterranean eutrophic reservoir. Toxins 6:3041–3057CrossRefGoogle Scholar
  12. Fadel A, Lemaire BJ, Atoui A, Vinçon-Leite B, Amacha N, Slim K, Tassin B (2014b) First assessment of the ecological status of Karaoun Reservoir, Lebanon. Lakes Reserv Res Manag 19:142–157CrossRefGoogle Scholar
  13. Fadel A, Atoui A, Lemaire BJ, Vinçon-Leite B, Slim K (2015) Environmental factors associated with phytoplankton succession in a Mediterranean reservoir with a highly fluctuating water level. Environ Monit Assess 187(10):1–14CrossRefGoogle Scholar
  14. Fadel A, Faour G, Slim K (2016) Assessment of the trophic state and chlorophyll-a concentrations using Landsat OLI in Karaoun Reservoir, Lebanon. Lebanese Sci J 17(2):130CrossRefGoogle Scholar
  15. Foy RH (1993) The phycocyanin to chlorophyll-a ratio and other cell components as indicators of nutrient limitation in two planktonic cyanobacteria subjected to low-light exposures. J Plankton Res 15:1263–1276CrossRefGoogle Scholar
  16. Gal G, Imberger J, Zohary T, Antenucci J, Anis A, Rosenberg T (2003) Simulating the thermal dynamics of Lake Kinneret. Ecol Model 162:69–86CrossRefGoogle Scholar
  17. Gal G, Hipsey MR, 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. Ecol Model 220:1697–1718CrossRefGoogle Scholar
  18. Gkelis S, Moustaka-Gouni M, Sivonen K, Lanaras T (2005) First report of the cyanobacterium Aphanizomenon ovalisporum Forti in two Greek lakes and cyanotoxin occurrence. J Plankton Res 27:1295–1300CrossRefGoogle Scholar
  19. Hamilton DP, Schladow SG (1997) Prediction of water quality in lakes and reservoirs. Part I. Model description. Ecol Model 96:91–110CrossRefGoogle Scholar
  20. Hipsey MR (2007) Water quality modelling of west Seti hydropower reservoir using DYRESM-CAEDYM. The University of Western Australia, AustraliaGoogle Scholar
  21. Hornung R (2002) Numerical modelling of stratification in Lake Constance with the 1-D hydrodynamic model DYRESM. Master Thesis, University of Stuttgart, GermanyGoogle Scholar
  22. Huisman J, Sharples J, Stroom JM, Visser PM, Kardinaal WEA, Verspagen JMH, Sommeijer B (2004) Changes in turbulent mixing shift competition for light between phytoplankton species. Ecology 85:2960–2970CrossRefGoogle Scholar
  23. Humphries SE, Lyne VD (1988) Cyanophyte blooms: the role of cell buoyancy. Limnol Oceanogr 33:79–91CrossRefGoogle Scholar
  24. Imberger J, Patterson JC (1981) A dynamic reservoir simulation model-DYRESM. In: Fischer HB (ed) Transport models for inland and coastal waters. Academic press, New York, pp 310–361CrossRefGoogle Scholar
  25. Imberger J, Patterson JC, Hebbert B, Loh I (1978) Dynamics of reservoir of medium size. J Hydraul Div ASCE 104:725–743Google Scholar
  26. Istvanovics V (2010) Eutrophication of lakes and reservoirs. In: Likens GE (ed) Lake ecosystem ecology: a global perspective. Elsevier, AmsterdamGoogle Scholar
  27. Janssen AB et al (2015) Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective. Aquat Ecol 49(4):513–548CrossRefGoogle Scholar
  28. Jørgensen SE (2010) A review of recent developments in lake modelling. Ecol Model 221:689–692CrossRefGoogle Scholar
  29. Kara EL, Hanson P, Hamilton D, Hipsey MR, McMahon KD, Read JS, Winslow L, Dedrick J, Rose K, Carey CC, Bertilsson S, da Motta Marques D, Beversdorf L, Miller T, Wu C, Hsieh Y-F, Gaiser E, Kratz T (2012) Time-scale dependence in numerical simulations: assessment of physical, chemical, and biological predictions in a stratified lake at temporal scales of hours to months. Environ Model Softw 35:104–121CrossRefGoogle Scholar
  30. Khemakhem H, Elloumi J, Ayadi H, Aleya L, Moussa M (2013) Modelling the phytoplankton dynamics in a nutrient-rich solar Saltern pond: predicting the impact of restoration and climate change. Environ Sci Pollut Res 20(12):9057–9065CrossRefGoogle Scholar
  31. Kling GW (1988) Comparative transparency, depth of mixing, and stability of stratification in lakes of Cameroon, West Africa. Limnol Oceanogr 33(1):27–40Google Scholar
  32. Komárek J, Anagnostidis K (1999) Cyanoprokaryota 1 Teil: Chroococcales. In: Ettl H, Gärtner G, Heynig GH, Mollenhauer D (eds) Süßwasserflora von Mitteleuropa Band 19/1, Spektrum Akademischer VerlagGoogle Scholar
  33. Komárek J, Anagnostidis K (2005) Cyanoprokaryota 2 Teil: Oscillatoriales. In: Büdel B, Gärtner G, Krienitz L, Schagerl M (eds) Süßwasserflora von Mitteleuropa Band 19/2, Spektrum Akademischer Verlag. ElsevierGoogle Scholar
  34. Kozhevnikova IA, Shveikina VI (2014) Modeling level variations in Lake Kinneret. Water Res 41:627–633CrossRefGoogle Scholar
  35. Lampert W, Sommer U (2007) Limnoecology, the ecology of lakes and streams. Oxford University Press, New YorkGoogle Scholar
  36. Lawson R, Anderson MA (2007) Stratification and mixing in Lake Elsinore, California: An assessment of axial flow pumps for improving water quality in a shallow eutrophic lake. Water Res 41(19):4457–4467Google Scholar
  37. Lurling M, Eshetu F, Faassen EJ, Kosten S, Huszar VL (2013) Comparison of cyanobacterial and green algal growth rates at different temperatures. Freshw Biol 58:552–559CrossRefGoogle Scholar
  38. McDonald CP, Urban NR (2010) Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models. Ecol Model 221:428–432CrossRefGoogle Scholar
  39. Mieleitner J, Reichert P (2008) Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecol Model 211:279–291CrossRefGoogle Scholar
  40. Mooij W, Trolle D, Jeppesen E, Arhonditsis G, Belolipetsky P, Chitamwebwa DR, Degermendzhy A, DeAngelis D, Senerpont Domis L, Downing A, Elliott JA, Fragoso C Jr, Gaedke U, Genova S, Gulati R, Hakanson L, Hamilton D, Hipsey M, 't Hoen J, Hulsmann S, Los FH, Makler-Pick V, Petzoldt T, Prokopkin I, Rinke K, Schep S, Tominaga K, Dam A, Nes E, Wells S, Janse J (2010) Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquat Ecol 44:633–667CrossRefGoogle Scholar
  41. Naselli-Flores L, Barone R (1997) Importance of water-level fluctuation on population dynamics of cladocerans in a hypertrophic reservoir (Lake Arancio, south-west Sicily, Italy). Hydrobiologia 360:223–232CrossRefGoogle Scholar
  42. Patten BC, Egloff DA, Richardson TH (1975) Total ecosystem model for a cove in Lake Texoma. In: Patten BC (ed) System analysis and simulation in ecology. Academic Press, New York, pp 206–423Google Scholar
  43. Paudel R, Jawitz JW (2012) Does increased model complexity improve description of phosphorus dynamics in a large treatment wetland?. Ecol Eng 42:283–294Google Scholar
  44. Pollingher U, Hadas O, Yacobi YZ, Zohary T, Berman T (1998) Aphanizomenon ovalisporum (Forti) in Lake Kinneret, Israel. J Plankton Res 20:1321–1339CrossRefGoogle Scholar
  45. Rigosi A, Rueda FJ (2012) Propagation of uncertainty in ecological models of reservoirs: from physical to population dynamic predictions. Ecol Model 247:199–209CrossRefGoogle Scholar
  46. Rigosi A, Marcé R, Escot C, Rueda FJ (2011) A calibration strategy for dynamic succession models including several phytoplankton groups. Environ Model Softw 26:697–710CrossRefGoogle Scholar
  47. Rigosi A, Hanson P, Hamilton DP, Hipsey M, Rusak JA, Bois J, Sparber K, Chorus I, Watkinson AJ, Qin B, Kim B, Brookes JD (2015) Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems. Ecol Appl 25:186–199CrossRefGoogle Scholar
  48. Rinke K, Yeates P, Rothhaupt K-O (2010) A simulation study of the feedback of phytoplankton on thermal structure via light extinction. Freshw Biol 55:1674–1693Google Scholar
  49. Robson BJ, Hamilton DP (2004) Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia. Ecol Model 174:203–222CrossRefGoogle Scholar
  50. Robson BJ (2014) When do aquatic systems models provide useful predictions, what is changing, and what is next?. Environ Model Softw 61:287–296Google Scholar
  51. Romero JR, Antenucci JP, Imberger J (2004) One-and three-dimensional biogeochemical simulations of two differing reservoirs. Ecol Model 174(1):143–160CrossRefGoogle Scholar
  52. Sarnelle O (2007) Initial conditions mediate the interaction between Daphnia and bloom-forming cyanobacteria. Limnol Oceanogr 52:2120–2127CrossRefGoogle Scholar
  53. Slim K, Fadel A, Atoui A, Lemaire BJ, Vinçon-Leite B, Tassin B (2014) Global warming as a driving factor for cyanobacterial blooms in Lake Karaoun, Lebanon. Desalin Water Treat 52:2094–2101CrossRefGoogle Scholar
  54. Smith VH (2003) Eutrophication of freshwater and coastal marine ecosystems: a global problem. Environ Sci Pollut Res 10(2):126–139CrossRefGoogle Scholar
  55. Smith VH, Bierman VJ, Jones BL, Havens KE (1995) Historical trends in the Lake Okeechobee ecosystem. IV. Nitrogen: phosphorus ratios, cyanobacterial dominance, and nitrogen fixation potential. Archiv fur Hydrobiologie-Supplementband Only 107(1):71–88Google Scholar
  56. Spigel RH, Imberger J, Rayner KN (1986) Modeling the diurnal mixed layer. Limol Oceanogr 31:533–556CrossRefGoogle Scholar
  57. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  58. Takkouk S, Casamitjana X (2015) Application of the DYRESM–CAEDYM model to the Sau Reservoir situated in Catalonia. Desalin Water Treat 57(27):12453–12466CrossRefGoogle Scholar
  59. Tanentzap AJ, Hamilton D, Yan ND (2007) Calibrating the dynamic reservoir simulation model (DYRESM) and filling required data gaps for one-dimensional thermal profile predictions in a boreal lake. Limnol Oceanogr: Methods 5:484–494CrossRefGoogle Scholar
  60. Temsah M, Tarhini K, Fadel A, Slim K (2016) Effect of irrigation with lake water containing cylindrospermopsin toxin on seed germination and seedlings growth of Cucumis sativus and Lycopersicon esculatum. Int J Sci: Basic Appl Res 27(3):108–122Google Scholar
  61. Tennessee Valley Authority (1972) Heat and mass transfer between a water surface and the atmosphere. Water Resources Research Laboratory Report 14, Report No. 0–6803Google Scholar
  62. Tian C, Pei H, Hu W, Hao D, Doblin MA, Ren Y, Wei J, Feng Y (2015) Variation of phytoplankton functional groups modulated by hydraulic controls in Hongze Lake, China. Environ Sci Pollut Res 22(22):18163–18175CrossRefGoogle Scholar
  63. Trolle D, Jørgensen TB, Jeppesen E (2008) Predicting the effects of reduced external nitrogen loading on the nitrogen dynamics and ecological state of deep Lake Ravn, Denmark, using the DYRESM-CAEDYM model. Limnol - Ecol Manag Inland Waters 38:220–232CrossRefGoogle Scholar
  64. Trolle D, Hamilton D, Hipsey M, Bolding K, Bruggeman J, Mooij W, Janse J, Nielsen A, Jeppesen E, Elliott JA, Makler-Pick V, Petzoldt T, Rinke K, Flindt M, Arhonditsis G, Gal G, Bjerring R, Tominaga K, Hoen Jt, Downing A, Marques D, Fragoso C Jr, Sondergaard M, Hanson P (2012) A community-based framework for aquatic ecosystem models. Hydrobiologia 683:25–34CrossRefGoogle Scholar
  65. USAID (2012) Litani River basin managemWatersent support program. Feasibility study for constructed wetlands in the Litani River basin. Washington, DCGoogle Scholar
  66. Valdespino-Castillo P, Merino-Ibarra M, Jiménez-Contreras J, Castillo-Sandoval F, Ramirez-Zierold JA (2014) Community metabolism in a deep (stratified) tropical reservoir during a period of high water-level fluctuations. Environ Monit Assess 186:6505–6520CrossRefGoogle Scholar
  67. Vieira J, Fonseca A, Vilar VJP, Boaventura RAR, Botelho CMS (2013) Water quality modelling of Lis River, Portugal. Environ Sci Pollut Res 20(1):508–524CrossRefGoogle Scholar
  68. Wang W, Liu Y, Yang Z (2010) Combined effects of nitrogen content in media and Ochromonas sp grazing on colony formation of cultured Microcystis aeruginosa. J Limnol 69:193–198CrossRefGoogle Scholar
  69. Wantzen K, Rothhaupt K-O, Mortl M, Cantonati M, G.-Toth L, Fischer P (2008) Ecological effects of water-level fluctuations in lakes: an urgent issue. Hydrobiologia 613:1–4CrossRefGoogle Scholar
  70. Watras CJ, Baker AL (1988) Detection of planktonic cyanobacteria by tandem in vivo fluorometry. Hydrobiologia 169:77–84CrossRefGoogle Scholar
  71. Weinberger S, Vetter M (2012) Using the hydrodynamic model DYRESM based on results of a regional climate model to estimate water temperature changes at Lake Ammersee. Ecol Model 244:38–48CrossRefGoogle Scholar
  72. Yeates PS, Imberger J (2003) Pseudo two-dimensional simulations of internal and boundary fluxes in stratified lakes and reservoirs. Int J River Basin Manage 297-319Google Scholar
  73. Zhang X, Warming TP, Hu H-Y, Christoffersen KS (2009) Life history responses of Daphnia magna feeding on toxic Microcystis aeruginosa alone and mixed with a mixotrophic Poterioochromonas species. Water Res 43:5053–5062CrossRefGoogle Scholar
  74. Zhou J, Qin B, Casenave C, Han X, Yang G, Wu T, Wu P, Ma J (2015) Effects of wind wave turbulence on the phytoplankton community composition in large, shallow Lake Taihu. Environ Sci Pollut Res 22(16):12737–12746CrossRefGoogle Scholar
  75. Zhu X, Wang Q, Lu Z, Liu J, Zhu C, Yang Z (2015) Offspring performance of Daphnia magna after short-term maternal exposure to mixtures of microcystin and ammonia. Environ Sci Pollut Res 22(4):2800–2807CrossRefGoogle Scholar
  76. Zohary T, Ostrovsky I (2011) Ecological impacts of excessive water level fluctuations in stratified freshwater lakes. Inland Waters 1(1):47–59Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.National Center for Remote Sensing, National Council for Scientific ResearchBeirutLebanon
  2. 2.Université Paris-Est, LEESU, UPEC, Ecole des Ponts ParisTech, AgroParisTechMarne-la-ValléeFrance
  3. 3.Laboratory of Microorganisms and Food Irradiation, Lebanese Atomic Energy Commission-CNRSBeirutLebanon

Personalised recommendations