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Keywords:

  • geostatistical regression;
  • scaling;
  • eddy-covariance

[1] The relationships between terrestrial carbon dioxide flux and its primary environmental drivers are uncertain because the processes controlling CO2 cycling, especially at ecosystem scales, are not well understood. This uncertainty is compounded by the fact that the importance of controlling processes, and therefore environmental drivers, may differ across temporal scales. This paper presents and applies a geostatistical regression (GR) approach that can be used with eddy-covariance data to investigate the relationships between carbon flux and environmental variables at multiple time scales, ranging from monthly to daily. The approach uses an adaptation of the Bayes Information Criterion to identify an optimal set of environmental variables that are able to explain the observed variability in carbon flux. In addition, GR quantifies the temporal correlation in the portion of the flux signal that cannot be explained by the selected variables and directly accounts for this correlation in the analysis. This GR approach was applied to data from the University of Michigan Biological Station (UMBS) AmeriFlux site to (i) identify the dominant explanatory variables for Net Ecosystem Exchange (NEE), Gross Ecosystem Exchange (GEE), and heterotrophic and autotrophic respiration (Rh+a) at different temporal scales, (ii) evaluate whether environmental variables can be used to isolate the GEE and Rh+a signals from the NEE measurements, and (iii) determine the impact of temporal scale on the inferred relationships between environmental variables and CO2 flux. The results confirm the strong correlation between respiration and temperature and the influence of solar radiation on carbon uptake during the growing season. In addition, results highlight the influence of variables such as precipitation, vapor pressure deficit, and the fraction of photosynthetically active radiation (fPAR) in carbon cycling at UMBS. Many relationships between flux and auxiliary variables are found to be scale-dependent. Site-specific and remote-sensing leaf area index and fPAR data are not found to be interchangeable at finer temporal scales. Results also show that a linear GR model is able to capture what may initially appear to be nonlinear relationships between flux and environmental variables, because this apparent nonlinearity is found to be explained by the covariability among key auxiliary variables. Finally, results indicate that GR can be used to identify variables that partially isolate GEE and Rh+a from the NEE signal at finer temporal scales at UMBS.