Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

Abstract

Soil respiration (Rs), the efflux of CO₂ from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year^-1 , and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year^-1 , whereas parameterization using SRDBv5 yielded 96.5 Pg C year^-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year^-1 . Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C year^-1 , respectively. SRDBv5's inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 ± 21.4 Pg C year^-1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.

Keywords: carbon cycle; heterotrophic respiration; machine learning; network design; network representativeness; soil CO2 efflux.