The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
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Dissertation for the degree of Doctor of Technology to be presented with due permission of the Department of Engineering Physics and Mathematics for public examination and debate in Auditorium E at Helsinki University of Technology (Espoo, Finland) on the 3rd of December, 2003, at 12 o'clock noon.
Overview in PDF format (ISBN 951-22-6810-8) [558 KB]
Dissertation is also available in print (ISBN 951-22-6773-X)
The 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.
This thesis consists of an overview and of the following 7 publications:
Keywords: dynamic soil model, groundwater sensitivity matrix, artificial neural network, empirical stream water model, acidification, climate change
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© 2003 Helsinki University of Technology