Bayesian uncertainty analysis of soil organic carbon stocks and stock changes from croplands in the U.S. Midwest

Quantifying uncertainty in process-based model predictions is essential for evaluating confidence in model predictions and identifying priorities for model improvements. This study applied a Bayesian model analysis framework to quantify and partition multiple sources of uncertainty in DayCent model estimates of soil organic carbon (SOC) stocks and stock changes (0–30 cm) for croplands across the U.S. Midwest from 1990 to 2020. The region gained SOC at an average rate of 10.38 (95% prediction interval (PI) of 4.37–17.83) Tg C year −1 , equivalent to 0.27 (95% PI of 0.11–0.46) t C ha −1 year −1 or a relative increase of 0.46% (95% PI of 0.42%–0.57%). Using Monte Carlo simulation, total predictive uncertainty was decomposed into four components: composite structural uncertainty, parameter uncertainty, input uncertainty associated with management practice adoption, and spatial scaling uncertainty associated with National Resources Inventory (NRI) sample design. At the point level, uncertainties for SOC stocks and stock changes were 29.5 and 11.7 t C ha −1 , respectively, while at the regional scale they were 10.6 and 0.09 t C ha −1 . Uncertainty decomposition showed that the composite structural component was the dominant source of uncertainty for regional SOC stock changes (57.5%), followed by parameters (38.8%), inputs (3.2%), and scaling (0.5%). For SOC stocks, parameter uncertainty dominated at the regional scale (69.8%), followed by composite structural uncertainty (26.0%), inputs (3.7%), and scaling (0.6%). Furthermore, temporal aggregation substantially reduced uncertainty, stabilizing the level of reduction after approximately five years of averaging, whereas spatial uncertainty required aggregation of a relatively large number of sites, about 5,000 to 10,000 sites, to reduce the uncertainty to a stable level. These findings highlight two approaches to reduce parameter and composite structural uncertainties in model-based assessments: advance model development to improve process representation and expand the quantity and quality of SOC observations.