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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Partitioning Carbon Dioxide and Water Vapor Fluxes Using Correlation Analysis

Authors
item Scanlon, Todd -
item Kustas, William

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 28, 2009
Publication Date: March 10, 2009
Citation: Scanlon, T.M., Kustas, W.P. 2009. Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agricultural and Forest Meteorology. 150:89-99.

Interpretive Summary: Eddy covariance is a widely-used technique for quantifying land-atmosphere exchange of carbon dioxide and water vapor or evapotranspiration (ET). The implementation of such monitoring networks throughout the world has led to significant advances in defining how these natural and agro ecosystem-level fluxes respond to variability in plant conditions and climate forcing . A key step in the use and interpretation of eddy covariance data centers on partitioning measurements into component fluxes associated with soil and plant processes. Existing flux partitioning procedures typically require additional instrumentation and/or invoke scaling assumptions that are difficult to validate and may or may not be appropriate. Here we present a novel flux partitioning approach that requires only standard eddy covariance instrumentation and relies upon a limited number of assumptions for its theoretical development. The method is applied to eddy covariance data collected over a maize crop in Beltsville MD. The results support the validity of the theory-based partitioning approach for soil and plant components, which has the benefit of being simultaneously applied to both carbon dioxide and ET fluxes, while relying solely upon standard eddy covariance instrumentation. This technique can be very useful for validating crop water use, yield and carbon sequestration models using eddy covariance monitoring networks over agricultural lands already deployed globally.

Technical 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.

Last Modified: 10/20/2014
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