Regional Carbon and Land Use Carbon Stocks From the Rapid Assessment of Carbon Project.

Regional Carbon and Land Use Carbon Stocks from the Rapid Assessment of Carbon Project The Rapid C Assessment project was undertaken to estimate regional soil carbon stocks across the conterminous US at one point in time. Sample locations were chosen randomly from the NRI (National Resource Inventory) sampling framework and cover all areas in CONUS with SSURGO certified maps as of Dec 2010. Within each of 17 regions, sites were selected by taxa and property-based soil groups and land use/cover class so that more extensive soils groups and land use/covers received more points and less extensive ones fewer point. There were 144,833 samples described from 32,084 pedons at 6, 017 sites. A visible near-infrared (VNIR) spectrophotometer was used to predict organic and inorganic carbon contents on air-dried, crushed samples.  The cluster-nested design (incorporating Region, Group, LULC, and Site) allows for estimation of uncertainty at multiple scales. Monte Carlo simulation was used to assign uncertainty for SOC stocks by pedon, including sample SOC prediction uncertainty and bulk density error distributions. OC stock and error values were summarized by sites, soil groups, and LULC using a mixed model approach.  Regional estimates ranged from 111.4 stderr 4.4 Mg SOC per ha in region 8 (southwestern US) to 758.7 stderr 22.3 Mg SOC per ha in region 15 (southeaster US, including Florida). Wetlands had the highest SOC stocks with an average of 700.3 stderr 17 Mg SOC per ha and rangelands the lowest with 186.2 stderr 4.3 Mg SOC per ha. Other LULC classes do not appear to be significantly different across or within regions. Average SOC stocks for soil group – land use combinations were mapped by linking the values to a raster of SSURGO (Jan 2012) that includes map unit and NLCD classification. Project information and raw data including sample descriptions and VNIR scans, will be made available via web and R based packages. Data summaries, processing scripts, and maps will be made available as analysis and write-ups are completed. Future work will be done to map carbon across landscapes using environmental covariates and produce probabilities of C concentrations and stocks across multiple land use and management scenarios.