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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Forage and Livestock Production Research » Research » Publications at this Location » Publication #370074

Research Project: Integrated Agroecosystem Research to Enhance Forage and Food Production in the Southern Great Plains

Location: Forage and Livestock Production Research

Title: Flux variance similarity-based partitioning of evapotranspiration over a rainfed alfalfa field using high frequency eddy covariance data

Author
item Wagle, Pradeep
item Skaggs, Todd
item Gowda, Prasanna
item Northup, Brian
item Neel, James

Submitted to: Grazinglands Research Laboratory Miscellaneous Publication
Publication Type: Abstract Only
Publication Acceptance Date: 2/10/2020
Publication Date: 10/1/2020
Citation: Wagle, P., Skaggs, T.H., Gowda, P.H., Northup, B.K., Neel, J.P. 2020. Flux variance similarity-based partitioning of evapotranspiration over a rainfed alfalfa field using high frequency eddy covariance data. NAPA Conference 2020 Second Biennial Conference. September 25-28, 2020. Absract No. 104. Available: https://www.napaamericas.org/proceedings.php.

Interpretive Summary: Abstract Only

Technical Abstract: Although the eddy covariance (EC) technique provide direct and continuous measurements of evapotranspiration (ET), separate measurement of individual components of ET: evaporation (E, wasted water use) and transpiration (T, productive water use) at the ecosystem level is not possible. For partitioning ET into E and T, high frequency (10 Hz) time series EC observations collected from April 2016 to May 2018 over a rainfed alfalfa (Medicago sativa L.) field in central Oklahoma, USA were analyzed using a recently developed open source software, Fluxpart. The Fluxpart partitions ET by examining the correlation (Rqc) between water vapor (q) and CO2 (c) based on Flux Variance Similarity (FVS). Patterns of partitioned E and T, and Rqc were consistent with expected trends associated with the vegetation dynamics. The Rqc increased with increase in alfalfa leaf area and exhibited a strong anti-correlation (Rqc close to -1) during peak growth when T and photosynthesis (P) were dominants and co-regulated by the leaf stomata. Decorrelation of q and c or dominance of non-photosynthetic (e.g., E and respiration, R) fluxes resulted in less negative or positive Rqc values during hay harvest, rainy, and nighttime periods. Consequently, Rqc showed pronounced diurnal cycles and temporal variations. The diurnal cycle of Rqc also varied during different periods. The partitioned results tracked short-term transient features such as hay harvesting and rainfall events as well. These results validate the performance of the FVS-based ET partitioning technique using high frequency eddy covariance observations.