TY - JOUR
T1 - Direct partitioning of eddy-covariance water and carbon dioxide fluxes into ground and plant components
AU - Zahn, Einara
AU - Bou-Zeid, Elie
AU - Good, Stephen P.
AU - Katul, Gabriel G.
AU - Thomas, Christoph K.
AU - Ghannam, Khaled
AU - Smith, James A.
AU - Chamecki, Marcelo
AU - Dias, Nelson L.
AU - Fuentes, Jose D.
AU - Alfieri, Joseph G.
AU - Kwon, Hyojung
AU - Caylor, Kelly K.
AU - Gao, Zhiqiu
AU - Soderberg, Keir
AU - Bambach, Nicolas E.
AU - Hipps, Lawrence E.
AU - Prueger, John H.
AU - Kustas, William P.
N1 - Funding Information:
E.Z. and E.B.Z are supported by the Moore Charitable Foundation Science-to-Action Fund from the School of Engineering and Applied Science at Princeton and by the Army Research Office under contract W911NF2010216 (program manager Julia Barzyk). S.P.G. acknowledges NSF grant DEB 1802885. G.K. acknowledges support from the U.S. National Science Foundation (NSF-AGS-1644382, NSF-AGS-2028633, and NSF-IOS-1754893). C.K.T. acknowledges support from the U.S. National Science Foundation (NSF-AGS-0955444) and the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. 724629). N.L.D. acknowledges Brazils National Research Council (CNPq) research scholarship 301420/2017-3. B.K. ackowledges funding and logistical support from E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I) for the vineyard data used in this study as part of the GRAPEX project. In addition, we thank the staff of Viticulture, Chemistry and Enology Division of E. & J. Gallo Winery and the cooperation of the vineyard management staff at the Ripperdan Ranch for supporting the field measurements. USDA is an equal opportunity provider and employer. Z.G. acknowledges the National Natural Science Foundation of China (41875013). We thank the reviewers and the editor for their detailed and thoughtful comments, which helped improve the paper.
Funding Information:
E.Z. and E.B.Z are supported by the Moore Charitable Foundation Science-to-Action Fund from the School of Engineering and Applied Science at Princeton and by the Army Research Office under contract W911NF2010216 (program manager Julia Barzyk). S.P.G. acknowledges NSF grant DEB 1802885. G.K. acknowledges support from the U.S. National Science Foundation (NSF-AGS-1644382, NSF-AGS-2028633, and NSF-IOS-1754893). C.K.T. acknowledges support from the U.S. National Science Foundation (NSF-AGS-0955444) and the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. 724629). N.L.D. acknowledges Brazils National Research Council (CNPq) research scholarship 301420/2017-3. B.K. ackowledges funding and logistical support from E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I) for the vineyard data used in this study as part of the GRAPEX project. In addition, we thank the staff of Viticulture, Chemistry and Enology Division of E. & J. Gallo Winery and the cooperation of the vineyard management staff at the Ripperdan Ranch for supporting the field measurements. USDA is an equal opportunity provider and employer. Z.G. acknowledges the National Natural Science Foundation of China (41875013). We thank the reviewers and the editor for their detailed and thoughtful comments, which helped improve the paper.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/3/15
Y1 - 2022/3/15
N2 - The partitioning of evapotranspiration (ET) into surface evaporation (E) and stomatal-based transpiration (T) is essential for analyzing the water cycle and earth surface energy budget. Similarly, the partitioning of net ecosystem exchange (NEE) of carbon dioxide into respiration (R) and photosynthesis (P) is needed to quantify the controls on its sources and sinks. Promising approaches to obtain these components from field measurements include partitioning models based on analysis of conventional high frequency eddy-covariance data. Here, two such existing approaches, based on similarity between non-stomatal (R and E) and stomatal (P and T) components, are considered: the Modified Relaxed Eddy Accumulation (MREA) and Flux-Variance Similarity (FVS) models. Moreover, a simpler technique is proposed based on a Conditional Eddy-Covariance (CEC) scheme. All approaches were evaluated against independent estimates of transpiration and respiration. The CEC method agreed better with measurements of transpiration over a grass field, with a smaller root mean square error (5.9 W m−2) and higher correlation (0.96). At a forest site, better agreement with soil respiration was found for FVS above the canopy, while CEC and MREA performed better below the canopy. Further application of these methods over a vineyard and a pine forest across different seasons provided insight into the main strengths and weaknesses of each approach. FVS and MREA converge less often when ground flux components dominate, while CEC might result in noisy P and R for small NEE. Finally, in the CEC and MREA framework, the ratio T/ET is shown to be related to the correlation coefficient for carbon dioxide and water vapor concentrations, which can thus be used as a qualitative measure of the importance of stomatal and non-stomatal components. Overall, these results advance the understanding of the skill and agreement of all three methods, and inform future studies where the various approaches can be applied simultaneously and intercompared.
AB - The partitioning of evapotranspiration (ET) into surface evaporation (E) and stomatal-based transpiration (T) is essential for analyzing the water cycle and earth surface energy budget. Similarly, the partitioning of net ecosystem exchange (NEE) of carbon dioxide into respiration (R) and photosynthesis (P) is needed to quantify the controls on its sources and sinks. Promising approaches to obtain these components from field measurements include partitioning models based on analysis of conventional high frequency eddy-covariance data. Here, two such existing approaches, based on similarity between non-stomatal (R and E) and stomatal (P and T) components, are considered: the Modified Relaxed Eddy Accumulation (MREA) and Flux-Variance Similarity (FVS) models. Moreover, a simpler technique is proposed based on a Conditional Eddy-Covariance (CEC) scheme. All approaches were evaluated against independent estimates of transpiration and respiration. The CEC method agreed better with measurements of transpiration over a grass field, with a smaller root mean square error (5.9 W m−2) and higher correlation (0.96). At a forest site, better agreement with soil respiration was found for FVS above the canopy, while CEC and MREA performed better below the canopy. Further application of these methods over a vineyard and a pine forest across different seasons provided insight into the main strengths and weaknesses of each approach. FVS and MREA converge less often when ground flux components dominate, while CEC might result in noisy P and R for small NEE. Finally, in the CEC and MREA framework, the ratio T/ET is shown to be related to the correlation coefficient for carbon dioxide and water vapor concentrations, which can thus be used as a qualitative measure of the importance of stomatal and non-stomatal components. Overall, these results advance the understanding of the skill and agreement of all three methods, and inform future studies where the various approaches can be applied simultaneously and intercompared.
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U2 - 10.1016/j.agrformet.2021.108790
DO - 10.1016/j.agrformet.2021.108790
M3 - Article
AN - SCOPUS:85123603692
SN - 0168-1923
VL - 315
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 108790
ER -