Epsilon Archive for Student Projects

Abstract

Soil organic carbon (SOC) is an important pool in the carbon cycle that is dependent on a wide array of environmental variables. These are known as the soil forming factors: Parent material, climate, biota, topography and time. Which of those are best suited to predict SOC for a given area depends on the environmental conditions in that area (e.g. arid or humid, vegetation type), but is also understood to be dependent on spatial scale. The aim of this thesis was to investigate this scale dependency of the relations between SOC and environmental variables, using the example of Swedish forest soils. I hypothesized that climate variables would be most important at the national scale, texture would not depend on scale and topographic and chemical variables are the most important at smaller scales.
Data on soil properties, soil chemistry and vegetation from the Swedish forest soil inventory was combined with topographic and climatic data to train random forest models of soil organic carbon concentrations in different depths and soil organic carbon stocks in the organic layer. These models were created for the whole of Sweden and for parts of Sweden in the same way, enabling the analysis of how the spatial scale influenced variable importances. The nature of the relationships between SOC and environmental variables was investigated with linear regressions.
The main finding was that carbon concentrations are in general best predicted by chemical parameters and that the exact parameters depend more on sample depth than scale. Climate variables were more important at national scale as hypothesized, while the hypotheses regarding chemistry, topography and texture could not be confirmed. For carbon stocks, chemical variables were in total slightly less important and lead by Mn. The lower importance of chemical variables in comparison to carbon concentration models is mainly balanced by the higher importance of topographic variables. Overall, scale dependencies of relationships were observable only for climate, and overall, the variable importances were dominated by chemical variables.