Modelling studies on soil-mediated response to acid deposition and climate variability
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Doctoral thesis (article-based)
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Research reports / Helsinki University of Technology, Systems Analysis Laboratory. A, 87
AbstractThe impact of acidifying atmospheric precipitation and climate variability on forest soil was studied using three approaches: (i) dynamic process-oriented modelling, (ii) static vulnerability assessment, and (iii) non-linear response pattern identification. The dynamic soil acidification model MIDAS is presented, with applications at the plot and catchment scale in Norway, Sweden and Finland, which show that growing vegetation contributes to soil acidification and which illustrate that the recovery phase is not symmetrical to the acidification phase. I report the results of an analysis using the SMART acidification model and the DEPUPT nutrient uptake model for selected deposition and forest growth scenarios at the Integrated Monitoring site of Hietajärvi, eastern Finland. The results show the importance of how the present day deposition is estimated. A combinatory matrix approach is described that uses regional data together with expert judgement to provide an assessment of groundwater sensitivity to acidification in Europe, without the need for detailed mathematical process formulations. I also demonstrate the variability that is introduced in the Finnish critical loads of sulphur for forest soils by using alternative criteria and by extending the critical loads model to include organic complexation of aluminium and the leaching of organic anions. The impact of climate variability on runoff water quality is illustrated with an empirical stream water model that builds on artificial neural networks for reproducing patterns in the observations of TOC, Ntot and Ptot at Hietajärvi, and also at Valkea-Kotinen, an Integrated Monitoring site in southern Finland. The stream water model is used to predict changes in element fluxes from these forested catchments in response to climate change.
dynamic soil model, groundwater sensitivity matrix, artificial neural network, empirical stream water model, acidification, climate change
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