Direct partitioning of eddy covariance water and carbon dioxide fluxes into ground and plant components

Authors: ZAHN, E. - Princeton University; BOU-ZEID, E. - Princeton University; GOOD, S. - Oregon State University; KATUL, G. - Duke University; KHALED, G. - Princeton University; SNITH, J. - Princeton University; CHAMECKI, M. - University Of California (UCLA); DIAS, N. - Federal University Of Paraná; FUENTES, J. - Pennsylvania State University; Alfieri, Joseph; CAYLOR, K. - University Of California; SODERBERG, K. - Collaborator; GOA, Z. - Nanjing University Of Information Science And Technology (NUIST); BAMBACH, N. - University Of California, Davis; HIPPS, L. - Utah State University; Prueger, John; Kustas, William - Bill

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/20/2021
Publication Date: 1/28/2022
Citation: Zahn, E., Bou-Zeid, E., Good, S., Katul, G., Khaled, G., Snith, J., Chamecki, M., Dias, N., Fuentes, J., Alfieri, J.G., Caylor, K., Soderberg, K., Goa, Z., Bambach, N., Hipps, L.E., Prueger, J.H., Kustas, W.P. 2022. Direct partitioning of eddy covariance water and carbon dioxide fluxes into ground and plant components. Agricultural and Forest Meteorology. 315:10879. https://doi.org/10.1016/j.agrformet.2021.108790.
DOI: https://doi.org/10.1016/j.agrformet.2021.108790

Interpretive Summary: Evapotranspiration (ET) represents the total water loss due to soil evaporation (E) and plant transpiration (T). Knowing the source of the moisture is important for many applications including irrigation scheduling and crop yield prediction. Measuring E and T independently can be costly, labor intensive, and prone to error, particularly at larger spatial scales, so improved methods for partitioning ET are needed. Three different methods that take advantage of the high-frequency data collected via eddy covariance were evaluated over grasslands, forests, and vineyards. The results show that the conditional eddy covariance (CEC) method produced reliable estimates of E and T that were typically more accurate than the other two methods, suggesting that CEC can be a valuable tool for determining the contribution of the soil and vegetation to the total ET.

Technical Abstract: The partitioning of total evapotranspiration (ET) into surface evaporation (E) and stomatal-based transpiration (T) is necessary for water and energy budgets. Similarly, the partitioning of net ecosystem carbon dioxide (CO2) exchange (NEE) into respiration (R) and photosynthesis (P) is needed to quantify the controls on carbon dioxide sinks and sources. Promising approaches to obtain the four components from field measurements are partitioning models based on analysis of conventional high frequency water vapor (H2O) and CO2 eddy-covariance data. Here, two such existing approaches that are based on similarity between non-stomatal (R and E) and stomatal (P and T) components are considered: the modified relaxed-eddy accumulation and flux-variance similarity models. In addition, a simpler technique is proposed based on a conditional eddy covariance (CEC) scheme. These approaches are evaluated against independent estimates of transpiration measured over an irrigated grass field. Using leaf-level measurements as the \true" transpiration, the CEC estimates result in a root mean square error of 5.4 W m-2, a smaller error than that produced by the other techniques (9.0-15.8 W m-2). The three methods are then inter-compared using data collected over a vineyard and a forest. The CEC yields reliable results over the investigated surfaces as long as E and R are both non-negligible, whereas the two other techniques do not always converge to the physically-expected flux trend or valid solutions. In addition, the ratio T/(E + T) is shown to be a function of the correlation coefficient for CO2 and H2O components, which can be used as a qualitative measure of the importance of stomatal and non-stomatal components of the fluxes. Overall, the newly proposed CEC is a robust method to partitioning fluxes across a wide range of surface cover.