Partitioning carbon dioxide and water vapor fluxes using correlation analysis

Abstract

Partitioning of eddy covariance flux measurements is routinely done to quantify the contributions of separate processes to the overall fluxes. Measurements of carbon dioxide fluxes represent the difference between gross ecosystem photosynthesis and total respiration, while measurements of water vapor fluxes represent the sum of transpiration and direct evaporation. Existing flux partitioning procedures typically require additional instrumentation and/or invoke scaling assumptions that may or may not be appropriate. Here, we present a novel flux partitioning procedure that relies upon the simple assumption that contributions to the measured high-frequency time series of carbon dioxide and water vapor concentrations derived from stomatal processes (i.e., photosynthesis and transpiration) and non-stomatal processes (i.e., respiration and direct evaporation) separately conform to flux-variance similarity. Vegetation water use efficiency is the only parameter needed to perform the partitioning. We apply this technique to eddy covariance data collected over the course of a growing season above a maize field. Results yielded by the correlation-based partitioning approach are consistent with expected trends throughout the growing season, as photosynthesis and transpiration fluxes increase in parallel with observed increases in maize leaf area. Magnitudes of the derived fluxes compare well with literature-based values, and short-term, transient features are also detected as both respiration and direct evaporation fluxes are found to respond to wetting events. These results support the validity of the theory-based partitioning approach, which has the benefit of being simultaneously applied to both carbon dioxide and water vapor fluxes, while relying solely upon standard eddy covariance instrumentation.