Soil carbon storage prediction in temperate hydromorphic soils using a morphologic index and digital elevation model

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 soil s. In the Armorican Massif (Western France) where these soils represent 20% of the total surface area, the spatial characterization of OC pools is dif ficult to assess due to methodological problems such as high spatial variab ility. Soil color indexes, which combine various characteristics of soil ho rizons or profiles, are an alternative approach for quantifying the differe nces in OC storage. In addition, terrain attributes derived from Digital El evation Models (DEM) may be useful in characterizing the distribution of so il 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 organi c carbon (OC) content of soil areas. To accomplish this, extensive examinat ion of soil morphology combined with selected terrain attributes measured i n the field and calculated from a digital elevation model (DEM) were used. Soil samples were collected in Western France from a 2-ha agricultural parc el that forms the major part of a hillslope. The results indicate that OC s tocks of the entire profile were correlated highly to a soil hydromorphic i ndex (HI) (r(2) = 0.80). HI is a function of the percent of the total soil profile depth constituted by horizons with some degree of hydromorphic feat ure development and the moist color of the surface A horizon. Using a stepw ise regression technique, we constructed a prediction model of HI distribut ion by using the relations between HI and (i) the elevation above the strea m bank (ES) (r(2) = 0.80); (ii) the downslope gradient (DG) (r(2) = 0.55); and (iii) the upslope contributing area (AMU) (r(2) = 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 morpho logic indexes could significantly improved the construction and the validat ion of soil-landscape models because it would minimize laboratory measureme nts of OC reservoirs.