A simplified approach for estimating soil carbon and nitrogen stocks in semi-arid complex terrain

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 south-facing 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.