OLIVER BRANCH, CARSTEN MAROHN, THOMAS HILGER, GEORG CADISCH
University of Hohenheim, Dept. of Plant Production and Agroecology in the Tropics and Subtropics, Germany
Carbon stocks form a very important component of a larger set of ecosystem services, provided by South East Asia's watersheds under sustainable land use. Quantifying these carbon stocks under changing land use is one objective of sub-project C4.2 in the SFB 564 Uplands Program. Data on aboveground carbon stocks is relatively easy to obtain and is available for many ecosystems. Few datasets exist however, for soil carbon stocks, particularly for (re)forested parts of the Mae Sa Noi sub"=watershed. Detailed geological and soil information has been gathered as part of the Uplands Program, but this information has been limited to the more easily accessible agricultural lower parts of the subwatershed. This gap in field data has been a major limitation in modelling carbon stocks under different land uses in the area.
Top- and subsoil auger samples will be collected along a spatially representative lay-out for the 2 km2 area and analysed for organic and carbonate carbon, total nitrogen, pH and texture. Soil carbon stocks will be calculated considering horizon thickness and bulk density. Georeferenced carbon values will be entered into a GIS and the major part used to derive regressions of organic C contents to topographic features (elevation, slope, exposition, wetness index, among others), which will be combined with kriging or other interpolation techniques. The remaining samples will be used for model validation.
Expected outcomes of this study are to produce a comprehensive map layer for carbon for the Mae Sa Noi sub-basin by integrating new data with the existing data and by applying weighted interpolation techniques. This will allow to draw conclusions on the impact of different land uses (agriculture, reforestation) on carbon stocks. Map layers for nitrogen, pH and horizonation will be obtained by interpolation. Soil carbon data will serve as inputs and for calibration and validation of a spatially explicit dynamic Land Use Change Impact Assessment model (LUCIA), which includes plant growth and erosion processes.
Keywords: GIS, soil carbon stocks, spatial variability, topography