On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (RS), the soil‐to‐atmosphere CO2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (Tsoil) and water content (SWC). Regional to global‐scale RS modelling thus requires this information at local scales, but few high‐quality, wall‐to‐wall (global) Tsoil and SWC data exist. As a result, such modelling efforts commonly use air temperature (Tair) and monthly precipitation (Pm) as surrogate predictors, but their site‐scale accuracy and potential bias are unknown. Here, we used monthly data from 880 sites across a wide variety of 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 507 sites to examine the suitability of Pm as a surrogate for SWC. Site‐specific linear and second‐order exponential non‐linear models were compared using model evaluation metrics (i.e., slope, p‐value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that Tsoil and Tair are highly correlated and explain similar RS variability. In contrast, Pm is not a good surrogate for SWC, even though Pm explains a similar amount of RS variability to SWC. The wide variability in the site‐specific relationships between RS and SWC means that no single relationship can be used for large‐scale modelling. The results from this study support the use of Tair in continental‐to‐global scale RS models, and highlight the urgent need for continental‐to‐global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

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