SOIL CARBON STORAGE PREDICTION IN TEMPERATE HYDROMORPHIC SOILS USING A MORPHOLOGIC INDEX AND DIGITAL ELEVATION MODEL

Abstract

Because soils are both a source and a sink for atmospheric CO2, there is an increasing need to characterize the spatial distribution of soil C pools. Large amounts of organic carbon (OC) accumulate in hydric bottom-lands soils. In the Armorican Massif (Western France) where these soils represent 20% of the total surface area, the spatial characterization of OC pools is difficult to assess due to methodological problems such as high spatial variability. Soil color indexes, which combine various characteristics of soil horizons or profiles, are an alternative approach for quantifying the differences in OC storage. In addition, terrain attributes derived from Digital Elevation Models (DEM) may be useful in characterizing the distribution of soil color indexes over large areas. Thus, the overall goal of this work was the development and application of a model for use in predicting the organic carbon (OC) content of soil areas. To accomplish this, extensive examination of soil morphology combined with selected terrain attributes measured in the field and calculated from a digital elevation model (DEM) were used. Soil samples were collected in Western France from a 2-ha agricultural parcel that forms the major part of a hillslope. The results indicate that OC stocks of the entire profile were correlated highly to a soil hydromorphic index (HI) (r2 = 0.80). HI is a function of the percent of the total soil profile depth constituted by horizons with some degree of hydromorphic feature development and the moist color of the surface A horizon. Using a stepwise regression technique, we constructed a prediction model of HI distribution by using the relations between HI and (i) the elevation above the stream bank (ES) (r2 = 0.80); (ii) the downslope gradient (DG) (r2 = 0.55); and (iii) the upslope contributing area (AMU) (r2 = 0.60). Validation of this model on a second site showed that topographical attributes explained up to 75% of the profile OC stock variability. These results confirmed that the integration of a soil index and topographical information is a useful tool for prediction of OC distribution. In addition, the use of soil morphologic indexes could significantly improved the construction and the validation of soil-landscape models because it would minimize laboratory measurements of OC reservoirs.