Global soil respiration predictions with associated uncertainties from different spatio-temporal data subsets

Abstract

Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.

Keywords

• Soil respiration
• Single global model
• Feature selection
• Random forest algorithm
• Soil carbon cycle