PNNL

Abstract

Soil respiration (RS), the soil-to-atmosphere CO2 flux that is a major component of the global carbon cycle, is strongly influenced by soil temperature (Tsoil) and water content (SWC). Because of scant high-quality Tsoil and SWC data at global scales, large-scale RS modeling commonly uses air temperature (Tair) and monthly precipitation (Pm) as surrogate predictors, but their accuracy across sites and potential to introduce bias is unknown. Here, we used data from 878 sites across different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit, and drainage conditions) to determine the suitability of Tair as a surrogate for Tsoil, and data from 506 sites to examine the suitability of Pm as a surrogate for SWC. Using a principal component (PCA) approach and comparing the model statistic evaluation values (i.e., slope, p-value of slope, RMSE, index of agreement, and model efficiency), we found that Tsoil and Tair are highly correlated, explaining similar RS variability. In contrast, Pm is not a good surrogate for SWC, even though Pm explained a similar amount of RS variability as SWC. The results from this study support the use of Tair in macro-to-global scale RS models, but highlight the urgent need for macro-to-global scale SWC datasets for the modeling and evaluation of future soil carbon dynamics under global climate change.