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Volume 67, Issue 2


Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary — Danubian Phytoplankton Growth Model (DPGM)

Csaba Sipkay
  • Hungarian Academy of Sciences, Hungarian Danube Research Institute, Jávorka Sándor út 14, H-2131, Göd, Hungary
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/ Tihamér Kiss-Keve
  • Hungarian Academy of Sciences, Hungarian Danube Research Institute, Jávorka Sándor út 14, H-2131, Göd, Hungary
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/ Csaba Vadadi-Fülöp
  • Department of Systematic Zoology and Ecology, Eötvös Loránd University, Pázmány Péter sétány 1/c, H-1117, Budapest, Hungary
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/ Réka Homoródi / Levente Hufnagel
  • “Adaptation to Climate Change” Research Group of the Hungarian Academy of Sciences, Villányi út 29-43, H-1118, Budapest, Hungary
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Published Online: 2012-02-22 | DOI: https://doi.org/10.2478/s11756-012-0004-2


Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of Göd, Hungary (at 1669 river kilometer - hereafter referred to as “rkm”). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L−1) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979–1990 has a higher level of nutrient excess compared with that of the 1991–2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGM-sB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass.

Keywords: climate change; ecological model; nutrient load; tactical modeling

  • [1] Adrian R., Deneke R., Mischke U., Stellmacher R. & Lederer P. 1995. A long-term study of the Heilingensee (1975–1992). Evidence for effects of climatic change on the dynamics of eutrophied lake ecosystems. Archiv fur Hydrobiol. 133: 315–337. Google Scholar

  • [2] Allan J.D. & Castillo M.M. 2007. Stream Ecology: Structure and Function of Running Waters. Second edition. Springer, The Netherlands Google Scholar

  • [3] Andersen H.E., Kronvang B., Larsen S.E., Hoffmann C.C., Jensen T.S. & Rasmussen E.K. 2006. Climate-change impacts on hydrology and nutrients in a Danish lowland river basin. Science Total Environ. 365: 223–237. http://dx.doi.org/10.1016/j.scitotenv.2006.02.036CrossrefGoogle Scholar

  • [4] Anneville O., Molinero J.C., Souissi S. & Gerdeaux D. 2010. Seasonal and interannual variability of cladoceran communities in two peri-alpin lakes: uncoupled response to the 2003 heat wave. J. Plankton Res. 32(6): 913–925. http://dx.doi.org/10.1093/plankt/fbq004CrossrefGoogle Scholar

  • [5] Barinova S., Medvedeva L. & Nevo E. 2008. Regional influences on algal biodiversity in two polluted rivers (Rudnaya River, Russia, and Quishon River, Israel) by bioindication and canonical correspondence analysis. Applied Ecol. Environ. Res. 6(4): 29–59. Google Scholar

  • [6] Behrendt H., Van Gils J., Schreiber H. & Zessner M. 2005. Point and diffuse nutrient emissions and loads in the transboundary Danube River Basin. — II. Long-term changes. Large Rivers 16/1–2. Arch. Hydrobiol. Suppl. 158(1–2): 221–247. Google Scholar

  • [7] Blenckner T., Omstedt A. & Rummukainen M. 2002. A Swedish case study of contemporary and possible future consequences of climate change on lake function. Aquatic Sci. 64(2): 171–184. http://dx.doi.org/10.1007/s00027-002-8065-xCrossrefGoogle Scholar

  • [8] Csathó P., Sisák I., Radimszky L., Lushaj S., Spiegel H., Nikolova M.T., Nikolov N. Čermák P., Klir J., Astover A., Karklins A., Lazauskas S., Kopiński J., Hera C., Dumitru E., Manojlovic, M., Bogdanović D., Torma S., Leskošek M. & Khristenko A. 2007. Agriculture as a source of phosphorus causing eutrophication in Centra and Eastern Europe. British Society of Soil Science Suppl. 23: 36–56. Google Scholar

  • [9] Déri A. 1991. The role of nitrification the oxygen depletion of the River Danube. Verh. Internat.Verein.Limnol. 24: 1965–1968. Google Scholar

  • [10] Diós N., Szenteleki K., Ferenczy A., Petrányi G. & Hufnagel, L. 2009. A climate profile indicator based comparative analysis of climate change scenarios with regard to maize (Zea mays L) culures. Appl. Ecol. Environ. Res. 7(3): 199–214. CrossrefGoogle Scholar

  • [11] Drégelyi-Kiss Á., Drégelyi-Kiss G. & Hufnagel L. 2008. Ecosystems as climate controllers — biotic feedbacks (a review). Appl. Ecol. Environ. Res. 6(2): 111–135. CrossrefGoogle Scholar

  • [12] Drégelyi-Kiss Á & Hufnagel L. 2009a. Simulations of Theoretical Ecosystem Growth Model (TEGM) during various climate conditions. Appl. Ecol. Environ. Res. 7(1): 71–78. CrossrefGoogle Scholar

  • [13] Drégelyi-Kiss Á. & Hufnagel L. 2009b. Effects of temperature-climate patterns on the production of some competitive species on grounds of modeling. Environ. Model. Assessm. 15(5): 369–380. http://dx.doi.org/10.1007/s10666-009-9216-4CrossrefGoogle Scholar

  • [14] Elliott J.A., Thackeray S.J., Huntingford C. & Jones R.G. 2005. Combining a regional climate model with a phytoplankton community model to predict future changes in phytoplankton in lakes. Freshwater Biol. 50: 1404–1411. http://dx.doi.org/10.1111/j.1365-2427.2005.01409.xCrossrefGoogle Scholar

  • [15] Felföldy L. 1981. A vizek környezettana. általános hidrobiológia. Mezőgazdasági Kiadó, Budapest. Google Scholar

  • [16] Flanagan K.M., McCauley E., Wrona F. & Prowse T. 2003. Climate change: the potential for latitudinal effects on algal biomass in aquatic ecosystems. Can. J. Fish Aquat. Sci. 60: 635–639. http://dx.doi.org/10.1139/f03-062CrossrefGoogle Scholar

  • [17] Gooseff M.N, Strzepek K. & Chapra S.C. 2005. Modeling the potential effects of climate change on water temperature downstream of a shallow reservoir, Lower Madison River, MT. Climatic Change 68(3): 331–353. http://dx.doi.org/10.1007/s10584-005-9076-0CrossrefGoogle Scholar

  • [18] Hammer O., Harper D.A.T. & Ryan P.D. 2001. PAST: Paleontological statistics software package for education and data nalysis. Paleontologia Electronica 4(1): 9. Google Scholar

  • [19] Hartman M.D., Baron J.S. & Ojima D.S. 2006. Application of a coupled ecosystem-chemical equilibrium model, DayCent-Chem, to stream and soil chemistry in a Rocky Mountain watershed. Ecological Modeling 200: 493–510. http://dx.doi.org/10.1016/j.ecolmodel.2006.09.001CrossrefGoogle Scholar

  • [20] Hassen H., Hanaki K. & Matsuo T. 1998. A modeling approach to simulate impact of climate change lake water quality: phytoplankton growth rate assessment. Water Sci. Technol. 37(2): 177–185. http://dx.doi.org/10.1016/S0273-1223(98)00022-5CrossrefGoogle Scholar

  • [21] Hostetler S.W. & Small E.E. 1999. Response of North American freshwater lakes to simulated future climates. J. Amer. Water Resour. Associat. 35(6): 1625–1637. http://dx.doi.org/10.1111/j.1752-1688.1999.tb04241.xCrossrefGoogle Scholar

  • [22] Horváth, L. & Tevanné-Bartalis É. 1999. A vízkémiai viszonyok jellemzése a Duna Rajka-Szob közötti szakaszán. Vízügyi Közlemények 81: 54–85. Google Scholar

  • [23] Hufnagel L. & Gaál M. 2005. Seasonal dynamic pattern analysis service of climate change research. Appl. Ecol. Environ. Res. 3(1): 79–132. CrossrefGoogle Scholar

  • [24] Hufnagel L., Drégelyi-Kiss G. & Drégelyi-Kiss Á. 2010. The effect of the reproductivity’s velocity on the biodiversity of a theoretical ecosystem. Appl. Ecol. Environ. Res. 8(2): 119–130. CrossrefGoogle Scholar

  • [25] ICPDR 2005. The Danube River Basin District. Part A. Basinwide overview. http://www.icpdr.org/pub. Google Scholar

  • [26] Kaur R. 2007. Planning length of long-term field experiments though decision support systems — a case study. Appl. Ecol. Environ. Res. 6(2): 63–78. Google Scholar

  • [27] Kiss K.T. 1994. Trophic level and eutrophication of the River Danube in Hungary. Verh. Internat. Verein. Limnol. 25: 1688–1691. Google Scholar

  • [28] Kiss K.T. 1996. Diurnal change of planktonic diatoms in the River Danube near Budapest (Hungary). Arch. Hydrobiol. Algol. Studies 80: 113–122. Google Scholar

  • [29] Kiss K.T. & Genkal S.I. 1996. Phytoplankton of the Danube’s reservoirs in September 1995 from Germany to Hungary. In: Berczik Á. (ed.), Limnologische Berichte Donau 1996. 1: 143–148. MTA ÖBKI Magyar Dunakutató állomás, Vácrátót/Göd. ISBN 963 8391 20 0. Google Scholar

  • [30] Kiss K.T. & Schmidt A. 1998. Changes of the Chlorophyta species in the phytoplankton of the Hungarian Section of the Danube river during the last decades (1961–1997). Biologia 53: 509–518. Google Scholar

  • [31] Kiss K.T., Ács É. & Szabó K.É. 2007. Algák és anyagforgalmi kapcsolataik, pp. 33–49. In: Nosek J. & Oertel N. (eds), “A Dunának, mely múlt, jelen s jövendő…” 50 éves az MTA Magyar Dunakutató állomása. MTA ÖBKI Magyar Dunakutató állomás, Göd. Google Scholar

  • [32] Kiss Á.K. 2007. A heterotróf egysejtü közösség éves változása és szerepe a Duna planktonjának anyagforgalmában. Hidrológiai Közlöny 87: 159–162. Google Scholar

  • [33] Klapper H. 1991. Control of eutrophication in Inland waters. Ellis Horwood Ltd., West Sussex, UK. Google Scholar

  • [34] Komatsu E., Fukushima T. & Shiraishi H. 2006. Modeling of P-dynamics and algal growth in a stratified reservoirmechanisms of the P-cycle and interactions between water and sediment. Ecol. Model. 197: 331–349. http://dx.doi.org/10.1016/j.ecolmodel.2006.03.023CrossrefGoogle Scholar

  • [35] Krivtsov V., Goldspink C., Sigee D.C. & Bellinger E.G. 2001. Expansion of the model ‘Rostherne’ for fish and zooplankton: Role of top-down effects in modifying the prevailing pattern of ecosystem functioning. Ecol. Model. 138: 153–171. http://dx.doi.org/10.1016/S0304-3800(00)00400-2CrossrefGoogle Scholar

  • [36] Ladányi M. & Horváth L. 2010. A review of the potential climate change impact on insect populations — general and agricultural aspects. Appl. Ecol. Environ. Res. 8(2): 143–152. CrossrefGoogle Scholar

  • [37] Lewandowska A & Sommer U. 2010. Climate change and the spring bloom: a mesocosm study on the influence of light and temperature on phytoplankton and mesozooplankton. Marine Ecology-Progress Ser. 405: 101–111. http://dx.doi.org/10.3354/meps08520CrossrefGoogle Scholar

  • [38] Lofgren B.M. 2002. Global warming influences on water levels, ice, and chemical and biological cycles in lakes: some examples, pp. 15–22. In: McGinn N.A. (ed.), Fisheries in a changing climate. American Fisheries Society, Bethesda, MD. Google Scholar

  • [39] Malmaeus J.M. & Håkanson L. 2004. Development of a lake eutrophication model. Ecol. Model. 171: 35–63. http://dx.doi.org/10.1016/S0304-3800(03)00297-7CrossrefGoogle Scholar

  • [40] Matulla C., Schmutz S., Melcher A. Gerersdorfer T. & Haas P. 2007. Assessing the impact of a downscaled climate change simulation on the fish fauna in an Inner-Alpine River. Int. J. Biometeorol. 52: 127–137. http://dx.doi.org/10.1007/s00484-007-0107-6CrossrefGoogle Scholar

  • [41] Mooij W.M., Janse J.H., Domis L.N., Hülsmann S. & Ibelings B.W. 2007. Predicting the effect of climate change on temperate shallow lakes with the ecosystem model PCLake. Hydrobiologia 584: 443–454. http://dx.doi.org/10.1007/s10750-007-0600-2CrossrefGoogle Scholar

  • [42] Peeters F., Straile D., Lorke A. & Ollinger D. 2007. Turbulent mixing and phytoplankton spring bloom development in a deep lake. Limnol. Oceanogr. 52(1): 286–298. http://dx.doi.org/10.4319/lo.2007.52.1.0286CrossrefGoogle Scholar

  • [43] Porter J., Arzberger P., Braun H.-W., Bryant P., Gage S., Hansen T., Hanson P., Lin C.-C., Lin F.-P., Kratz T., Michener W., Shapiro S. & Williams T. 2005. Wireless sensor networks for ecology. Bioscience 55: 561–572. http://dx.doi.org/10.1641/0006-3568(2005)055[0561:WSNFE]2.0.CO;2CrossrefGoogle Scholar

  • [44] Schreiber H., Behrendt H., Constantinescu L.T., Cvitanic I., Drumea D., Jabucar D., Juran S., Pataki B., Snishko S. & Zessner M. 2005. Nutrient emissions from diffuse and point sources into the River Danube and its main tributaries for the period of 1998–2000 — results and problems. Water Sci. Technol. 51: 283–290. Google Scholar

  • [45] Sipkay Cs., Hufnagel L., Révész A. & Petrányi G. 2008a. Seasonal dynamics of an aquatic macroinvertebrate assembly (Hydrobiological case study of Lake Balaton, L’. 2). Appl. Ecol. Environ. Res. 5(2): 63–78. Google Scholar

  • [46] Sipkay Cs., Horváth L., Nosek J., Oertel N., Vadadi-Fülöp Cs., Farkas E., Drégelyi-Kiss Á. & Hufnagel L. 2008b. Analysis of climate change scenarios based on modeling of the seasonal dynamics of a danubian copepod species. Appl. Ecol. Environ. Res. 6(4): 101–108. CrossrefGoogle Scholar

  • [47] Sipkay Cs., Kiss K. T., Drégelyi-Kiss Á., Farkas E. & Hufnagel L. 2009a. Analysis of climate change scenarios based on the modeling of the seasonal dynamics of phytoplankton in the Danube. Hidrológiai Közlöny 89: 56–59. (In Hungarian) Google Scholar

  • [48] Sipkay Cs., Kiss K.T., Vadadi-Fülöp Cs. & Hufnagel L. 2009b. Trends in research on the possible effects of climate change concerning aquatic ecosystems with special emphasis on the modeling approach. Appl. Ecol. Environ. Res. 7(2): 171–198. CrossrefGoogle Scholar

  • [49] Sipkay Cs., Drégelyi-Kiss Á., Horváth L., Garamvölgyi Á. Kiss K.T. & Hufnagel L. 2010. Community ecological effects of climate change. In: Climate change and vulnerability. Sciyo. ISBN 978-953-7619-X-X (In print) Google Scholar

  • [50] Sommer U. & Lengfellner K. 2008. Climate change and the timing, magnitude, and composition of the phytoplankton spring bloom. Global Change Biology 14(6): 1199–1208. http://dx.doi.org/10.1111/j.1365-2486.2008.01571.xCrossrefGoogle Scholar

  • [51] Tóth B. 2007. Vízkémiai vizsgálatok a Magyar Duna-szakaszon, pp. 33–49. In: Nosek J. & Oertel N. (eds), “A Dunának, mely múlt, jelen s jövendő…” 50 éves az MTA Magyar Dunakutató állomása. MTA ÖBKI Magyar Dunakutató állomás, Göd. Google Scholar

  • [52] Thackeray S.J., Jones I.D. & Maberly S.C. 2008. Long-term change in the phenology of spring phytoplankton: speciesspecific responses to nutrient enrichment and climatic change. J. Ecol. 96: 523–535. http://dx.doi.org/10.1111/j.1365-2745.2008.01355.xCrossrefGoogle Scholar

  • [53] Utermöhl H. 1958. Zur Vervolkommung der quantitativen phytoplankton. Mitt. Int. Verein. Limnol. 9: 1–13. Google Scholar

  • [54] Vadadi-Fülöp Cs., Hufnagel L., Sipkay Cs. & Verasztó Cs. 2008. Evaluation of climate change scenarios based on aquatic food web modeling. Appl. Ecol. Environ. Res. 6(1): 1–28. Google Scholar

  • [55] Vadadi-Fülöp Cs., Türei D., Sipkay Cs., Verasztó Cs., Drégelyi-Kiss Á. & Hufnagel L. 2009. Comparative assessment of climate change scenarios based on aquatic food web modeling. Environ. Model. Asses. 14: 563–576. http://dx.doi.org/10.1007/s10666-008-9158-2CrossrefGoogle Scholar

  • [56] Varga P., Ábrahám M. & Simor J. 1989. A magyar Duna-szakasz vízminősége. Vízügyi Közlemények 71: 582–598. Google Scholar

  • [57] Verasztó Cs., Kiss K.T., Sipkay Cs., Gimesi L., Vadadi-Fulop Cs., Turei D. & Hufnagel L. 2010. Long-term dynamic patterns and diversity of phytoplankton communities in a large eutrophic river (the case of River Danube, Hungary). Appl. Ecol. Environ. Res. 8(4): 329–349. CrossrefGoogle Scholar

  • [58] Vörös L. & Kis N. 1985. A fitoplankton szezonális periodicitása és annak összefüggése az eutrofizálódással. Irodalmi áttekintés és balatoni esettanulmány, pp. 121–134. In: Fekete G. (ed.), A cönológiai szukcesszió kérdései. Akadémiai Kiadó Bp. Google Scholar

About the article

Published Online: 2012-02-22

Published in Print: 2012-04-01

Citation Information: Biologia, Volume 67, Issue 2, Pages 323–337, ISSN (Online) 1336-9563, ISSN (Print) 0006-3088, DOI: https://doi.org/10.2478/s11756-012-0004-2.

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