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a Center for Development Research (ZEF), Univ. of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany
b Dep. of Geography, Univ. of Durham, Science Laboratories, South Road, Durham, DH1 3LE, UK
* Corresponding author (spark{at}uni-bonn.de)
This study aims to identify process-response relationships between pedogeomorphological processes and the spatial distribution of soil properties on a hillslope. Thirty-two physical and chemical attributes of 502 soil samples collected from 64 soil profiles on a hillslope in Somerset, UK, were analyzed using an ordination technique. Five factors explaining 80% of the total variation were first identified. Each factor was then regressed with terrain attributes to examine the influence of hillslope geomorphometry on soil distributions. Two-way ANOVA was used to separate the total variance of individual soil properties into vertical and lateral components, and the results were compared with the results of the ordination. The five factors were interpreted as demonstrating the effects of podzolization, nutrient dynamics, Mn leaching, erosion weathering, and soil acidification. The ability to predict soil properties over the landscape using terrain information varies widely among soil properties measured. In general, topsoil properties are better correlated with terrain attributes than subsoil properties. Contemporary pedological and geomorphological processes are most active in the surface horizon, which results in clear lateral differentiation of soil properties. For subsurface soils, only those few soil properties that showed a clear spatial pattern could be successfully modeled using terrain attributes. This research demonstrates that clear differences in the spatial distribution of individual soil properties are mainly determined by differential involvement in in situ pedogenesis and lateral slope processes. Future soil-landscape modeling attempts should pay more attention to the selection of soil properties and the interpretation of statistical results in relation to process-response dynamics.
Abbreviations: DEM, digital elevation models ECEC, effective cation-exchange capacity LOI, loss-on-ignition PC1, Principal Component 1 PC2, Principal Component 2 PC3, Principal Component 3 PC4, Principal Component 4 PC5, Principal Component 5 PCA, principal component analysis SS, sum of the squares TEB, total exchangeable bases TOd, extractable total oxides
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