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Climatic Change

, Volume 141, Issue 2, pp 347–361 | Cite as

Is the future of large shallow lakes blue-green? Comparing the response of a catchment-lake model chain to climate predictions

  • Fabien Cremona
  • Sirje Vilbaste
  • Raoul-Marie Couture
  • Peeter Nõges
  • Tiina Nõges
Article

Abstract

We constructed a model chain into which regional climate-related variables (air temperature, precipitation) and a lake’s main tributary hydrological indicators (river flow, dissolved inorganic carbon) were employed for predicting the evolution of planktonic blue-green algae (cyanobacteria) and zooplankton (rotifer) biomass in that lake for the mid-21st century. Simulations were based on the future climate predicted under both the Representative Concentration Pathways 4.5 and 8.5 scenarios which, combined with three realistic policy-making and basin land-use evolution lead to six scenarios for future water quality. Model outputs revealed that mean annual river flow is expected to decline between 3 and 20%, depending on the scenario. Concentration of river dissolved inorganic carbon is predicted to follow the opposite trend and might soar up to twice the 2005–2014 average concentration. Lake planktonic primary producers will display quantitative changes in the future decades whereas zooplankters will not. A 2 to 10% increase in mean cyanobacteria biomass is accompanied by a stagnation (−3 to +2%) of rotifer biomass. Changes in cyanobacteria and rotifer phenology are expected: a surge of cyanobacteria biomass in winter and a shortening of the rotifer biomass spring peak. The expected quantitative changes on the biota were magnified in those scenarios where forested area conversions to cropland and water abstraction were the greatest.

Keywords

Phytoplankton Dissolve Inorganic Carbon Shallow Lake Water Abstraction Geophysical Fluid Dynamics Laboratory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors are grateful to Katri Rankinen (Finnish Environment Institute) for providing copies of the INCA-C and PERSiST executable, Alo Laas (Estonian University of Life Sciences) and Ivo Saaremäe (Estonian Environment Agency) for assistance in data collection. Cayetano Gutierrez (Cardiff University) was very helpful with the empirical modelling process. RMC acknowledges funding from the Norwegian Research Council project “Lakes in Transition” (244558). This research was supported by Start-Up Personal Research Grant PUT 777 to FC and IUT 21–2 of the Estonian Ministry of Education and Research, and by MARS project (managing aquatic ecosystems and water resources under multiple Stress) funded under the 7th EU Framework Programme, Theme 6 (Environment including Climate Change), Contract No.: 603378 (http://www.mars-project.eu).

Supplementary material

10584_2016_1894_MOESM1_ESM.docx (118 kb)
ESM 1 (DOCX 118 kb)

References

  1. Adrian R, Walz N, Hintze T, Hoeg S, Rusche R (1999) Effects of ice duration on plankton succession during spring in a shallow polymictic lake. Freshwat Biol 41:621–634CrossRefGoogle Scholar
  2. Barnes RT, Raymond PA (2009) The contribution of agricultural and urban activities to inorganic carbon fluxes within temperate watersheds. Chem Geol 266:318–327CrossRefGoogle Scholar
  3. Cheung MY, Liang S, Lee J (2013) Toxin-producing cyanobacteria in freshwater: a review of the problems, impact on drinking water safety, and efforts for protecting public health. J Microbiol 51:1–10CrossRefGoogle Scholar
  4. Core Team R (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/. ISBN 3-900051-07-0Google Scholar
  5. Couture R-M, Tominaga K, Starrfelt J, Moe SJ, Kaste O, Wright RF (2014) Modelling phosphorus loading and algal blooms in a Nordic agricultural catchment-lake system under changing land-use and climate. Env Sci Process Impact 16:1588–1599CrossRefGoogle Scholar
  6. Cremona F, Kõiv T, Kisand V, Laas A, Zingel P, Agasild H, Feldmann T, Järvalt A, Nõges P, Nõges T (2014a) From Bacteria to Piscivorous Fish: Estimates of Whole-Lake and Component-Specific Metabolism with an Ecosystem Approach. PLoS One 9, e101845Google Scholar
  7. Cremona F, Kõiv T, Nõges P, Pall P, Rõõm E-I, Feldmann T, Viik M, Nõges T (2014b) Dynamic carbon budget of a large shallow lake assessed by a mass balance approach. Hydrobiologia 731:109–123Google Scholar
  8. Cremona F, Laas A, Arvola L, Pierson D, Nõges P, Nõges T (2016) Numerical exploration of the planktonic to benthic primary production ratios in lakes of the Baltic Sea catchment. Ecosystems. doi: 10.1007/s10021-016-0006-y Google Scholar
  9. de Wit HA, Ledesma JLJ, Futter MN (2016) Aquatic DOC export from subarctic Atlantic blanket bog in Norway is controlled by seasalt deposition, temperature and precipitation. Biogeochemistry 127:305–321CrossRefGoogle Scholar
  10. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813CrossRefGoogle Scholar
  11. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578CrossRefGoogle Scholar
  12. Futter MN, de Wit HA (2008) Testing seasonal and long-term controls of streamwater DOC using empirical and process-based models. Sci Total Environ 407:698–707CrossRefGoogle Scholar
  13. Futter MN, Butterfield D, Cosby BJ, Dillon PJ, Wade AJ, Whitehead PG (2007) Modeling the mechanisms that control in-stream dissolved organic carbon dynamics in upland and forested catchments. Water Resour Res 43:1–16CrossRefGoogle Scholar
  14. Futter MN, Erlandsson MA, Butterfield D, Whitehead PG, Oni SK, Wade AJ (2014) PERSiST: the precipitation, evapotranspiration and runoff simulator for solute transport. Hydrol Earth Syst Sci 18:855–873CrossRefGoogle Scholar
  15. Gyllström M, Hansson LA, Jeppesen E, Criado FG, Gross E, Irvine K, Kairesalo T, Kornijow R, Miracle MR, Nykänen M, Nõges T, Romo S, Stephen D, Van Donk E, Moss B (2005) The role of climate in shaping zooplankton communities of shallow lakes. Limnol Oceanogr 50:2008–2021CrossRefGoogle Scholar
  16. Haberman J, Virro T (2004) Zooplankton. In: Haberman J, Pihu E, Raukas A (eds) Lake Võrtsjärv. Estonian Encyclopedia Publishers, TallinnGoogle Scholar
  17. Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F (2013) A trend-preserving bias correction - the ISI-MIP approach. Earth Syst Dynam 4:219–236CrossRefGoogle Scholar
  18. Hering D et al (2015) Managing aquatic ecosystems and water resources under multiple stress-an introduction to the MARS project. Sci Total Environ 503–504:10–21CrossRefGoogle Scholar
  19. IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  20. Jeppesen E, Meerhoff M, Davidson TA, Trolle D, Søndergaard M, Lauridsen TL, Beklioglu M, Brucet S, Volta P, Gonzalez-Bergonzoni I, Nielsen A (2014) Climate change impacts on lakes: an integrated ecological perspective based on a multi-faceted approach, with special focus on shallow lakes. J Limnol 73:88–111CrossRefGoogle Scholar
  21. Jeppesen E, Brucet S, Naselli-Flores L, Papastergiadou E, Stefanidis K, Nõges T, Nõges P, Attayde JL, Zohary T, Coppens J, Bucak T, Rosemberg FM, Sousa Freitas FR, Kernan M, Søndergaard M, Beklioglu M (2015) Ecological impacts of global warming and water abstraction on lakes and reservoirs due to changes in water level and related changes in salinity. Hydrobiologia 750:201–227CrossRefGoogle Scholar
  22. Kaste Ø, Wright RF, Barkved LJ, Bjerkeng B, Engen-Skaugen T, Magnusson J, Sælthun NR (2006) Linked models to assess the impacts of climate change on nitrogen in a Norwegian river basin and fjord system. Sci Total Environ 365:200–222CrossRefGoogle Scholar
  23. Kosten S, Huszar VL, Bécares E, Costa LS, Donk E, Hansson LA, Jeppesen E, Kruk C, Lacerot G, Mazzeo N, De Meester L, Moss B, Lürling M, Nõges T, Romo S, Scheffer M (2012) Warmer climates boost cyanobacterial dominance in shallow lakes. Glob Change Biol 18:118–126CrossRefGoogle Scholar
  24. Kriegler E, O’Neill BC, Hallegatte S, Kram T, Lempert R, Moss R, Wilbanks T (2012) The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socioeconomic pathways. Glob Environ Change 22:807–822CrossRefGoogle Scholar
  25. Ledesma JLJ, Köhler SJ, Futter MN (2012) Long-term dynamics of dissolved organic carbon: implications for drinking water supply. Sci Total Environ 432:1–11CrossRefGoogle Scholar
  26. Malmaeus JM, Blenckner T, Markensten H, Persson I (2006) Lake phosphorus dynamics and climate warming: a mechanistic model approach. Ecol Model 190:1–14CrossRefGoogle Scholar
  27. MARS project (2015) Report task 2.6: definition of future scenarios., p 77Google Scholar
  28. Moe SJ, Haande S, Couture R-M (2016) Climate change, cyanobacteria blooms and ecological status of lakes: a Bayesian network approach. Ecol Model 337:330–347. doi: 10.1016/j.ecolmodel.2016.07.004 CrossRefGoogle Scholar
  29. Mooij WM, Hülsmann S, De Senerpont Domis LN, Nolet BA, Bodelier PLE, Boers PCM, Dionisio Pires LM, Gons HJ, Ibelings BW, Noordhuis R, Portielje R, Wolfstein K, Lammens EHRR (2005) The impact of climate change on lakes in the Netherlands: a review. Aquat Ecol 39:381–400CrossRefGoogle Scholar
  30. Nõges T, Nõges P, Laugaste R (2003) Water level as the mediator between climate change and phytoplankton composition in a large shallow temperate lake. Hydrobiologia 506:257–263CrossRefGoogle Scholar
  31. Nõges P, Nõges T, Laas A (2010) Climate-related changes of phytoplankton seasonality in large shallow Lake Võrtsjärv, Estonia. Aquat Ecosyst Health 13:154–163CrossRefGoogle Scholar
  32. Nõges P, Argillier C, Borja Á, Garmendia JM, Hanganu J, Kodeš V, Pletterbauer F, Sagouis A, Birk S (2016a) Quantified biotic and abiotic responses to multiple stress in freshwater, marine and ground waters. Sci Total Environ 540:43–52Google Scholar
  33. Nõges T, Järvalt A, Haberman J, Zingel P, Nõges P (2016b) Is fish able to regulate filamentous blue-green dominated phytoplankton? Hydrobiologia, 1–11. DOI: 10.1007/s10750-016-2849-9Google Scholar
  34. O’Reilly CM et al (2015) Rapid and highly variable warming of lake surface waters around the globe. Geophys Res Lett 42. doi:10.1002/2015GL066235Google Scholar
  35. Pall P, Vilbaste S, Kõiv T, Kõrs A, Käiro K, Laas A, Nõges P, Nõges T, Piirsoo K, Toomsalu L, Viik M (2011) Fluxes of carbon and nutrients through the inflows and outflow of Lake Võrtsjärv, Estonia. Est J Ecol 60:39–53CrossRefGoogle Scholar
  36. Shrestha M (2015) Data analysis relied on Linear Scaling bias correction (V.1.0) Microsoft Excel fileGoogle Scholar
  37. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Change 109:5–31CrossRefGoogle Scholar
  38. van Vuuren DP, Kriegler E, O’Neill BC, Ebi KL, Riahi K, Carter TR, Edmonds J, Hallegatte S, Kram T, Mathur M, Winkler (2014) A new scenario framework for climate change research: scenario matrix architecture. Clim Change 122:373–386. doi: 10.1007/s10584-013-0906-1 CrossRefGoogle Scholar
  39. Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J (2014) The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc Natl Acad Sci U S A 111:3228–3232CrossRefGoogle Scholar
  40. Weyhenmeyer GA, Meili M, Livingstone DM (2004) Nonlinear temperature response of lake ice breakup. Geophys Res Lett 31:ᅟ. doi: 10.1029/2004GL019530 CrossRefGoogle Scholar
  41. Whitehead PG, Futter MN, Wilby R (2006) Impacts of climate change on hydrology, nitrogen and carbon in upland and lowland streams: assessment of adaptation strategies to meet Water Framework Directive Objectives. BHS 9th National Hydrology Symposium, DurhamGoogle Scholar
  42. Zingel P, Haberman J (2008) A comparison of zooplankton densities and biomass in Lakes Peipsi and Võrtsjärv (Estonia): rotifers and crustaceans versus ciliates. Hydrobiologia 599:153–159CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Fabien Cremona
    • 1
  • Sirje Vilbaste
    • 1
  • Raoul-Marie Couture
    • 2
    • 3
  • Peeter Nõges
    • 1
  • Tiina Nõges
    • 1
  1. 1.Centre for Limnology, Institute of Agricultural and Environmental SciencesEstonian University of Life SciencesTartuEstonia
  2. 2.Norwegian Institute for Water ResearchOsloNorway
  3. 3.Ecohydrology Research Group, Water Institute and Department of Earth and Environmental SciencesUniversity of WaterlooWaterlooCanada

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