Identifying Minimum Detectable Change in US Forest Soil Carbon under the Forest Inventory Analysis (FIA) Sampling Strategy

Abstract:

Estimates of carbon stocks and stock changes in the U.S. Department of Agriculture Forest Service's Forest Inventory and Analysis (FIA) Program are reported as the official United States submission to the UN Framework Convention on Climate Change. Soil, as a critical component of the forest carbon stocks, has been sampled in about 10-year intervals in FIA with the re-measurement underway. However, the magnitude of detectable change in soil organic carbon (SOC) with the current sampling scheme is unknown. We aim to identify SOC variability and to best determine minimum detectable changes in SOC under the current sampling scheme. The project seeks to: identify statistical relationships between SOC and environmental covariates; normalize SOC data for main forest-type groups (FTGs) using identified covariates; and determine the minimum detectable change in the normalized SOC using power analysis. We investigated SOC variability for 8 FTGs: Oak-Hickory, Maple-Beech-Birch, Pinyon-Juniper, Loblolly-Shortleaf Pine, Aspen-Birch, Douglas-Fir, Fir-Spruce-Mountain Hemlock and Woodland Hardwoods. Relationships between SOC and environmental covariates (biomass/soil properties in FIA, PRISM climate data, and DEM-derived terrain attributes) are determined by multiple linear regression and are used to normalize SOC variability. The results showed that terrain attributes were not significant in explaining SOC in the FIA dataset and climate data were only significant in certain FTGs locations. Except for Oak-Hickory, Maple-Beech-Birch and Pinyon-Juniper groups, sample numbers are insufficient to detect a change in SOC less than 10 percent (%) of the mean. To guide future sampling efforts, we will continue our study on detecting minimal change in SOC and to explore sample number and sampling frequency scenarios to inform future soil sampling protocols.