Space-time mapping of soil organic carbon stock and its local drivers: Potential for use in carbon accounting

Abstract

The measurement and tracking of changes in soil organic carbon (SOC) stocks and its underpinning drivers are important in greenhouse gas accounting and carbon trading schemes. While repeated direct measurement of a location over time is the ‘gold standard’ for SOC stock change estimation, this is currently unfeasible at scale due to sample collection and analysis costs. Here, we used a data-driven, space–time digital soil mapping modelling approach (ST-DSM) coupled with an interpretive machine learning (IML) technique to both predict and quantify the relative contributions of covariates to annual SOC stocks and their uncertainty at 0–30 cm depth and at a 90 m spatial resolution for Australia between 1990 and 2018. A quantile regression forest model was calibrated using data from a datacube comprising of SOC stock values collected over the prediction years and a suite of dynamic and static environmental covariates. The evaluation of the ST-DSM model on a test set was satisfactory. Long-term annual mean (Mg C/ha), and total (Pg C) topsoil SOC stock were 37.6 (12.3 – 101.1, 90 % PI) and 28.3 (9.25 – 83.1, 90 % PI) respectively. The mean annual rate of SOC stock change over the Australian continent was 0.11 (±0.22) Mg C/ha yr−1 and the total net change was 23.5 Tg C yr−1, indicating Australia’s soils were a net sink of SOC over the 29-year period. However, SOC stocks declined in the montane ecoregion, a great concern given its fragility and high SOC stock. The long-term mean fraction of photosynthetic vegetation was the most important individual local contributor to SOC stock prediction in many parts of Australia. The aggregated effect of covariates based on the soil forming factors indicates vegetation as the principal local driver of SOC stock across most of the Australian continent, followed by climate. The evaluation of our results against independent, temporally paired field data and estimates from a nationally accepted SOC process-based model yielded promising results. Our approach demonstrates the use of readily available and inexpensive covariates to estimate SOC stock changes, as well as the underlying drivers over a larger area but at a spatial resolution suitable for decision making. This approach presents a new monitoring and verification possibility for carbon projects and national carbon accounting especially with the growing number of existing profile observations in soil databases.