The Science

Researchers used a geospatial approach that integrates existing observations with the multivariate spatial heterogeneity of soil-forming factors. The approach was developed to identify the optimal number and spatial distribution of observation sites needed to improve estimates of soil organic carbon (SOC) stocks under current and projected future climatic conditions.

The Impact

The magnitude, vulnerability, and spatial distribution of SOCs are major sources of uncertainty in projected carbon-climate feedbacks attributed to the permafrost region. Study results provide a spatially optimized set of locations designed to guide new field observations for constraining the uncertainties in soil carbon estimates and providing robust spatial benchmarks for Earth system model results.

Summary

Representing land surface spatial heterogeneity is a scientific challenge that is critical for designing observation schemes to reliably estimate soil properties. Researchers led by Argonne National Laboratory developed a geospatial approach to identify an optimum distribution of observation sites for improving the characterization of SOC stocks across Alaska. By using environmental data expected to influence soil formation as proxies for representing the spatial distribution of SOC stocks, the scientists determined that complementing data from existing samples with 484 new observation sites would be needed to characterize average whole-profile SOC stocks across Alaska at a confidence interval of 5 kg C per m². Estimates to depths of 0 m to 1 m and 0 m to 2 m with the same level of confidence would require 309 and 446 new observation sites, respectively. New observation needs are greater for scrub (mostly tundra) than for forest land cover types, and ecoregions in southwestern Alaska are among the most undersampled. The number and locations of required observations are not greatly altered by changes in climatic variables through 2100 as projected by Intergovernmental Panel on Climate Change emission scenarios. Study results serve as a guide for future sampling efforts to reduce existing uncertainty in SOC observations and improve benchmarks for ESM results.

Principal Investigator

Julie Jastrow
Argonne National Laboratory
jdjastrow@anl.gov

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
daniel.stover@science.doe.gov

Funding

This study was supported by the Terrestrial Ecosystem Science program of the Climate and Environmental Science Division of the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science, under Contract No. DE-AC02-06CH11357 to Argonne National Laboratory.

References