A Simplified Approach for Estimating Soil Carbon and Nitrogen Stocks in Semi-Arid Complex Terrain
NOTICE: This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version has been published in Geoderma, Volume 165, Issue 1, 2011. DOI: 10.1016/j.geoderma.2011.06.011
We investigated soil carbon (C) and nitrogen (N) distribution and developed a model, using readily available geospatial data, to predict that distribution across a mountainous, semi-arid, watershed in southwestern Idaho (USA). Soil core samples were collected and analyzed from 133 locations at 6 depths (n=798), revealing that aspect dramatically influences the distribution of C and N, with north-facing slopes exhibiting up to 5 times more C and N than adjacent southfacing aspects. These differences are superimposed upon an elevation (precipitation) gradient, with soil C and N contents increasing by nearly a factor of 10 from the bottom (1100 m elevation) to the top (1900 m elevation) of the watershed. Among the variables evaluated, vegetation cover, as represented by a Normalized Difference Vegetation Index (NDVI), is the strongest, positively correlated, predictor of C; potential insolation (incoming solar radiation) is a strong, negatively correlated, secondary predictor. Approximately 62% (as R2) of the variance in the C data is explained using NDVI and potential insolation, compared with an R2 of 0.54 for a model using NDVI alone. Soil N is similarly correlated to NDVI and insolation. We hypothesize that the correlations between soil C and N and slope, aspect and elevation reflect, in part, the inhibiting influence of insolation on semi-arid ecosystem productivity via water limitation. Based on these identified relationships, two modeling techniques (multiple linear regression and cokriging) were applied to predict the spatial distribution of soil C and N across the watershed. Both methods produce similar distributions, successfully capturing observed trends with aspect and elevation. This easily applied approach may be applicable to other semi-arid systems at larger scales.
Melvin L. Kunkel, Alejandro N. Flores, Toni J. Smith, James P. McNamara, and Shawn G. Benner. "A Simplified Approach for Estimating Soil Carbon and Nitrogen Stocks in Semi-Arid Complex Terrain" Geoderma 165.1 (2011): 1-11.
Available at: http://works.bepress.com/james_mcnamara/26