AMBIO

, Volume 43, Issue 3, pp 337–351 | Cite as

Future Nutrient Load Scenarios for the Baltic Sea Due to Climate and Lifestyle Changes

  • Hanna Eriksson Hägg
  • Steve W. Lyon
  • Teresia Wällstedt
  • Carl-Magnus Mörth
  • Björn Claremar
  • Christoph Humborg
Report

Abstract

Dynamic model simulations of the future climate and projections of future lifestyles within the Baltic Sea Drainage Basin (BSDB) were considered in this study to estimate potential trends in future nutrient loads to the Baltic Sea. Total nitrogen and total phosphorus loads were estimated using a simple proxy based only on human population (to account for nutrient sources) and stream discharges (to account for nutrient transport). This population-discharge proxy provided a good estimate for nutrient loads across the seven sub-basins of the BSDB considered. All climate scenarios considered here produced increased nutrient loads to the Baltic Sea over the next 100 years. There was variation between the climate scenarios such that sub-basin and regional differences were seen in future nutrient runoff depending on the climate model and scenario considered. Regardless, the results of this study indicate that changes in lifestyle brought about through shifts in consumption and population potentially overshadow the climate effects on future nutrient runoff for the entire BSDB. Regionally, however, lifestyle changes appear relatively more important in the southern regions of the BSDB while climatic changes appear more important in the northern regions with regards to future increases in nutrient loads. From a whole-ecosystem management perspective of the BSDB, this implies that implementation of improved and targeted management practices can still bring about improved conditions in the Baltic Sea in the face of a warmer and wetter future climate.

Keywords

Baltic Sea Drainage Basin Nutrient transport Population growth Climate change Eutrophication Baltic Nest Institute 

Notes

Acknowledgments

This study was supported by funding from the Baltic Nest Institute, the EU BONUS RECOCA and EU BONUS Baltic-C programs (http://www.bonusportal.org). Additional funding for this study comes from Stockholm University’s Strategic Marine Environmental Research Funds through the BEAM Program.

References

  1. Arheimer, B., J. Dahné, and C. Donnelly. 2012. Climate change impact on riverine nutrient load and land-based remedial measures of the Baltic Sea action plan. AMBIO 41: 600–612. doi: 10.1007/s13280-012-0323-0.CrossRefGoogle Scholar
  2. Basu, N.B., G. Destouni, J.W. Jawitz, S.E. Thompson, N.V. Loukinova, A. Darracq, S. Zanardo, M. Yaeger, et al. 2010. Nutrient loads exported from managed catchments reveal emergent biogeochemical stationarity. Geophysical Research Letters 37: L23404. doi: 10.1029/2010GL045168.CrossRefGoogle Scholar
  3. Beven, K.J. 2001. Rainfall-runoff modeling: The primer, 360 pp. West Sussex: Wiley.Google Scholar
  4. Boé, J., A. Hall, and X. Qu. 2009. Current GCMs’ unrealistic negative feedback in the Arctic. Journal of Climate 22: 4682–4695.CrossRefGoogle Scholar
  5. Conley, D.J., S. Björck, E. Bonsdorff, J. Carstensen, G. Destouni, B.G. Gustafsson, S. Hietanen, M. Kortekaas, et al. 2009. Hypoxia-related processes in the Baltic Sea. Environmental Science and Technology 43: 3412–3420.CrossRefGoogle Scholar
  6. Destouni, G., G. Lindgren, and I.M. Gren. 2006. Effects of inland nitrogen transport and attenuation modeling on coastal nitrogen load abatement. Environmental Science and Technology 40: 6208–6214.CrossRefGoogle Scholar
  7. Espenshade, T.J., J.C. Guzman, and C.F. Westoff. 2003. The surprising global variation in replacement fertility. Population Research and Policy Review 22: 575–583.CrossRefGoogle Scholar
  8. Food and Agriculture Organization Statistic Database (FAOSTAT). 2011. Population Time Series. In FAOSTAT FAAOOTUN, Rome, Italy.Google Scholar
  9. Gordon, C., C. Cooper, C.A. Senior, H. Banks, J.M. Gregory, T.C. Johns, J.F.B. Mitchell, and R.A. Wood. 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: 147–168.CrossRefGoogle Scholar
  10. Graham, L.P. 2004. Climate change effect on river flow to the Baltic Sea. AMBIO 33: 235–241.Google Scholar
  11. Graham, L.P., and S. Bergström. 2001. Water balance modelling in the Baltic Sea drainage basin—Analysis of meteorological and hydrological approaches. Meteorology and Atmospheric Physics 77: 45–60.CrossRefGoogle Scholar
  12. Gren, I.M., and G. Destouni. 2012. Does divergence of nutrient load measurements matter for successful mitigation of marine eutrophication? AMBIO 41: 151–160.CrossRefGoogle Scholar
  13. Hägg, H.E., C. Humborg, C.M. Mörth, M.R. Medina, and F. Wulff. 2010. Scenario analysis on protein consumption and climate change effects on Riverine N export to the Baltic Sea. Environmental Science and Technology 44: 2379–2385.CrossRefGoogle Scholar
  14. Haith, D.A., and L.L. Shoemaker. 1987. Generalized watershed loading functions for stream-flow nutrients. Water Resources Bulletin 23: 471–478.CrossRefGoogle Scholar
  15. Hannerz, F., and G. Destouni. 2006. Spatial characterization of the Baltic Sea drainage basin and its unmonitored catchments. AMBIO 35: 214–219.CrossRefGoogle Scholar
  16. Haylock, M.R., N. Hofstra, A. Tank, E.J. Klok, P.D. Jones, and M. New. 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research-Atmospheres 113: D20. doi: 10.1029/2008JD010201.CrossRefGoogle Scholar
  17. HELCOM. 2007. Baltic Sea Action Plan. HELCOM ministerial meeting. Krakow, Poland, 15 Nov 2007.Google Scholar
  18. Hewitt, C.D., and D.J. Griggs. 2004. Ensemble-based predictions of climate changes and their impacts. EOS, Transactions AGU 85: 566. doi: 10.1029/2004EO520005.CrossRefGoogle Scholar
  19. Hipel, K.W., and A.I. McLeod. 2005. Time series modelling of water resources and environmental systems. Amsterdam: Elsevier.Google Scholar
  20. Hong, B., D.P. Swaney, C.M. Mörth, E. Smedberg, H.E. Hägg, C. Humborg, R.W. Howarth, and F. Bouraoui. 2012. Evaluating regional variation of net anthropogenic nitrogen and phosphorus inputs (NANI/NAPI), major drivers, nutrient retention pattern and management implications in the multinational areas of Baltic Sea basin. Ecological Modelling 227: 117–135. doi: 10.1016/j.ecolmodel.2011.12.002.CrossRefGoogle Scholar
  21. Jaramillo, F., C. Prieto, S.W. Lyon, and G. Destouni. 2013. Multimethod assessment of evapotranspiration shifts due to non-irrigated agricultural development in Sweden. Journal of Hydrology 484: 55–62.CrossRefGoogle Scholar
  22. Jungclaus, J.H., N. Keenlyside, M. Botzet, H. Haak, J.J. Luo, M. Latif, J. Marotzke, U. Mikolajewicz, et al. 2006. Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. Journal of Climate 19: 3952–3972.CrossRefGoogle Scholar
  23. Kjellström, E., Bärring, L., Gollvik, S., Hansson, U., Jones, C., Samuelsson, P., Rummukainen, C., Ullerstig, A., et al. 2005. A 140-year simulation of European climate with the new version of the Rossby Centre regional atmospheric climate model (RCA3). Reports Meteorology and Climatology. SMHI, Norrköping, Sweden, 54 pp.Google Scholar
  24. Klein Goldewijk, K., A. Beusen, G. van Drecht, and M. de Vos. 2011. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Global Ecology and Biogeography 20: 73–86.CrossRefGoogle Scholar
  25. Kronvang, B., H.E. Andersen, C. Borgesen, T. Dalgaard, S.E. Larsen, J. Bogestrand, and G. Blicher-Mathiasen. 2008. Effects of policy measures implemented in Denmark on nitrogen pollution of the aquatic environment. Environmental Science & Policy 11: 144–152.CrossRefGoogle Scholar
  26. Lyon, S.W., M.R. McHale, M.T. Walter, and T.S. Steenhuis. 2006. The impact of runoff-generation mechanisms on the location of critical source areas. Journal of the American Water Resources Association 42: 793–804.CrossRefGoogle Scholar
  27. Lyon, S.W., F. Dominguez, D.J. Gochis, N.A. Brunsell, C.L. Castro, F.K. Chow, Y. Fan, D. Fuka, et al. 2008. Coupling terrestrial and atmospheric water dynamics to improve prediction in a changing environment. Bulletin of the American Meteorological Society 89: 1275–1279.CrossRefGoogle Scholar
  28. Lyon, S.W., H. Laudon, J. Seibert, M. Mörth, D. Tetzlaff, and K. Bishop. 2010. Controls on snowmelt water mean transit times in northern boreal catchments. Hydrological Processes 24: 1672–1684. doi: 10.1002/hyp.7577.CrossRefGoogle Scholar
  29. Mann, H.B. 1945. Nonparametric tests against trend. Econometrica 13: 245–259.CrossRefGoogle Scholar
  30. Meidani, R. 2012. Parameter sensitivity and optimization of a catchment-scale hydrologic model across Sweden. MSc Thesis. Stockholm, Sweden: Stockholm University.Google Scholar
  31. Mörth, C.M., C. Humborg, H. Eriksson, A. Danielsson, M.R. Medina, S. Löfgren, D.P. Swaney, and L. Rahm. 2007. Modeling riverine nutrient transport to the Baltic Sea: A large-scale approach. AMBIO 36: 124–133.CrossRefGoogle Scholar
  32. Nakicenovic, N., et al. 2000. Emission scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, 599 pp.Google Scholar
  33. Rabbinge, R., and C.A. van Diepen. 2000. Changes in agriculture and land use in Europe. European Journal of Agronomy 13: 85–99. doi: 10.1016/S1161-0301(00)00067-8.CrossRefGoogle Scholar
  34. Reckermann, M., J. Langner, A. Omstedt, H. von Storch, S. Keevallik, B. Schneider, B. Arheimer, H.E.M. Meier, et al. 2011. BALTEX—An interdisciplinary research network for the Baltic Sea region. Environmental Research Letters 6: 045205. doi: 10.1088/1748-9326/6/4/045205.CrossRefGoogle Scholar
  35. Roeckner, E., R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, L. Kornblueh, E. Manzini, U. Schlese, et al. 2006. Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. Journal of Climate 19: 3771–3791.CrossRefGoogle Scholar
  36. Smith, B., I.C. Prentice, and M.T. Sykes. 2001. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: Comparing two contrasting approaches within European climate space. Global Ecology and Biogeology 10: 621–637. doi: 10.1046/j.1466-822X.2001.t01-1-00256.x.CrossRefGoogle Scholar
  37. Smith, S.V., et al. 2003. Humans, hydrology, and the distribution of inorganic nutrient loading to the ocean. BioScience 53: 235–245.CrossRefGoogle Scholar
  38. Smith, S.V., D.P. Swaney, R.W. Buddemeier, M.R. Scarsbrook, M.A. Weatherhead, C. Humborg, H. Eriksson, and F. Hannerz. 2005. River nutrient loads and catchment size. Biogeochemistry 75: 83–107.CrossRefGoogle Scholar
  39. Teutschbein, C., and J. Seibert. 2010. Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. Geography Compass 4: 834–860. doi: 10.1111/j.1749-8198.2010.00357.x.CrossRefGoogle Scholar
  40. United Nations (UN). 2004. World Population to 2300. New York: Department of Economic and Social Affairs, Population Division.Google Scholar
  41. Van der Velde, Y., S.W. Lyon, and G. Destouni. 2013. Data-driven regionalization of river discharges and emergent land cover-evapotranspiration relationships across Sweden. Journal of Geophysical Research Atmospheres 118: 2576–2587. doi: 10.1002/jgrd.50224.CrossRefGoogle Scholar
  42. Vertenstein, M., and Kauffman, B. 2004. The CCSM Climatological Data Ocean Model (docn6), Version 6.0. Combined User’s Guide, Source Code Reference and Scientific Description, National Centre for Atmospheric Research, Boulder, Colorado, USA.Google Scholar
  43. Voss, M., J.W. Dippner, C. Humborg, J. Hurdler, F. Korth, T. Neumann, G. Schernewski, and M. Venohr. 2011. History and scenarios of future development of Baltic Sea eutrophication. Estuarine, Coastal and Shelf Science 92: 307–322.CrossRefGoogle Scholar
  44. Walter, M.T., M.F. Walter, E.S. Brooks, T.S. Steenhuis, J. Boll, and K. Weiler. 2000. Hydrologically sensitive areas: Variable source area hydrology implications for water quality risk assessment. Journal of Soil and Water Conservation 55: 277–284.Google Scholar
  45. Wulff, F., O. Savchuk, A. Sokolov, C. Humborg, and C.M. Mört. 2007. Management options and effects on a marine ecosystem: Assessing the future of the Baltic. AMBIO 36: 243–249.CrossRefGoogle Scholar

Copyright information

© Royal Swedish Academy of Sciences 2013

Authors and Affiliations

  • Hanna Eriksson Hägg
    • 1
  • Steve W. Lyon
    • 1
    • 2
  • Teresia Wällstedt
    • 3
  • Carl-Magnus Mörth
    • 1
    • 3
  • Björn Claremar
    • 4
  • Christoph Humborg
    • 1
    • 5
  1. 1.Baltic Nest Institute, Baltic Sea CentreStockholm UniversityStockholmSweden
  2. 2.Department of Physical Geography and Quaternary GeologyStockholm UniversityStockholmSweden
  3. 3.Department of Geological SciencesStockholm UniversityStockholmSweden
  4. 4.Department of Earth SciencesUppsala UniversityUppsalaSweden
  5. 5.Applied Environmental ScienceStockholm UniversityStockholmSweden

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