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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 2
Hydrol. Earth Syst. Sci., 12, 437–447, 2008
https://doi.org/10.5194/hess-12-437-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Role of climate change in recovery of acidified surface waters...

Hydrol. Earth Syst. Sci., 12, 437–447, 2008
https://doi.org/10.5194/hess-12-437-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  05 Mar 2008

05 Mar 2008

Modelling the effects of climate on long-term patterns of dissolved organic carbon concentrations in the surface waters of a boreal catchment

M. N. Futter1,*, M. Starr2, M. Forsius3, and M. Holmberg3 M. N. Futter et al.
  • 1Watershed Ecosystems Graduate Program, Trent University, Peterborough, K9J 7B8, Canada
  • 2Department of Forest Ecology, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
  • 3Finnish Environment Institute, P.O. Box 140, 00251 Helsinki, Finland
  • *now at: Macaulay Land Use Research Institute, Craigiebuckler, AB15 8QH, UK

Abstract. Dissolved organic carbon concentrations ([DOC]) in surface waters are increasing in many regions of Europe and North America. These increases are likely driven by a combination of changing climate, recovery from acidification and change in severity of winter storms in coastal areas. INCA-C, a process-based model of climate effects on surface water [DOC], was used to explore the mechanisms by which changing climate controls seasonal to inter-annual patterns of [DOC] in the lake and outflow stream of a small Finnish catchment between 1990 and 2003. Both production in the catchment and mineralization in the lake controlled [DOC] in the lake. Concentrations in the catchment outflow were controlled by rates of DOC production in the surrounding organic soils. The INCA-C simulation results were compared to those obtained using artificial neural networks (ANN). In general, "black box" ANN models provide better fits to observed data but process-based models can identify the mechanism responsible for the observed pattern. A statistically significant increase was observed in both INCA-C modelled and measured annual average [DOC] in the lake. This suggests that some of the observed increase in surface water [DOC] is caused by climate-related processes operating in the lake and catchment. However, a full understanding of surface water [DOC] dynamics can only come from catchment-scale process-based models linking the effects of changing climate and deposition on aquatic and terrestrial environments.

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