Spatial predictions and associated uncertainty of annual soil respiration at the global scale
Soil respiration (Rs), the soil-to-atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates, however. We predicted annual Rs and associated uncertainty across the world at 1-km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database (SRDB). This model yielded a global annual Rs estimate of 87.9 Pg C y-1 with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C y-1 over the period 1961-2011. The estimated annual heterotrophic respiration (Rh), derived from an empirical relationship with Rs, was 49.8 Pg C y-1 over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in open shrublands, grasslands, and permanent wetlands, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs at unprecedentedly high spatial resolution across the globe that could help to constrain local-to-global process-based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.
Revised: May 27, 2020 |
Published: December 31, 2019