Assessing soil carbon vulnerability in the Western USA by geospatial modeling of pyrogenic and particulate carbon stocks uri icon

abstract

  • To predict how land management practices and climate change will affect soil carbon cycling, improved understanding of factors controlling soil organic carbon fractions at large spatial scales is needed. We analyzed total soil organic (SOC) as well as pyrogenic (PyC), particulate (POC), and other soil organic carbon (OOC) fractions in surface layers from 650 stratified-sampling locations throughout Colorado, Kansas, New Mexico, and Wyoming. PyC varied from 0.29 to 18.0 mg Cg(-1) soil with a mean of 4.05 mg Cg(-1) soil. The mean PyC was 34.6% of the SOC and ranged from 11.8 to 96.6%. Both POC and PyC were highest in forests and canyon bottoms. In the best random forest regression model, normalized vegetation index (NDVI), mean annual precipitation (MAP), mean annual temperature (MAT), and elevation were ranked as the top four important variables determining PyC and POC variability. Random forests regression kriging (RFK) with environmental covariables improved predictions over ordinary kriging by 20 and 7% for PyC and POC, respectively. Based on RFK, 8% of the study area was dominated (>= 50% of SOC) by PyC and less than 1% was dominated by POC. Furthermore, based on spatial analysis of the ratio of POC to PyC, we estimated that about 16% of the study area is medium to highly vulnerable to SOC mineralization in surface soil. These are the first results to characterize PyC and POC stocks geospatially using stratified sampling scheme at the scale of 1,000,000 km(2), and the methods are scalable to other regions.

publication date

  • 2017
  • 2017