RESUMO
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.
Assuntos
Respiração , Solo , África , Ásia , Viés , Carbono/análise , América do SulRESUMO
Hydraulic redistribution (HR) of water from moist to drier soils, through plant roots, occurs world-wide in seasonally dry ecosystems. Although the influence of HR on landscape hydrology and plant water use has been amply demonstrated, HR's effects on microbe-controlled processes sensitive to soil moisture, including carbon and nutrient cycling at ecosystem scales, remain difficult to observe in the field and have not been integrated into a predictive framework. We incorporated a representation of HR into the Community Land Model (CLM4.5) and found the new model improved predictions of water, energy, and system-scale carbon fluxes observed by eddy covariance at four seasonally dry yet ecologically diverse temperate and tropical AmeriFlux sites. Modeled plant productivity and microbial activities were differentially stimulated by upward HR, resulting at times in increased plant demand outstripping increased nutrient supply. Modeled plant productivity and microbial activities were diminished by downward HR. Overall, inclusion of HR tended to increase modeled annual ecosystem uptake of CO2 (or reduce annual CO2 release to the atmosphere). Moreover, engagement of CLM4.5's ground-truthed fire module indicated that though HR increased modeled fuel load at all four sites, upward HR also moistened surface soil and hydrated vegetation sufficiently to limit the modeled spread of dry season fire and concomitant very large CO2 emissions to the atmosphere. Historically, fire has been a dominant ecological force in many seasonally dry ecosystems, and intensification of soil drought and altered precipitation regimes are expected for seasonally dry ecosystems in the future. HR may play an increasingly important role mitigating development of extreme soil water potential gradients and associated limitations on plant and soil microbial activities, and may inhibit the spread of fire in seasonally dry ecosystems.