A New Approach to Extract Forest Water Use Efficiency from Eddy Covariance Data

Determination of forest water use efficiency (WUE) from eddy covariance data typically involves the following steps: (a) estimating gross primary productivity (GPP) from direct measurements of net ecosystem exchange (NEE) by extrapolating nighttime ecosystem respiration (ER) to daytime conditions, and (b) assuming direct evaporation (E) is minimal several days after rainfall, meaning that direct measurements of evapotranspiration (ET) are identical to transpiration (T). Both of these steps could lead to errors in the estimation of forest WUE. Here, we present a theoretical approach for estimating WUE through the analysis of standard eddy covariance data, which circumvents these steps. Only five statistics are needed from the high-frequency time series to extract WUE: CO2 flux, water vapor flux, standard deviation in CO2 concentration, standard deviation in water vapor concentration, and the correlation coefficient between CO2 and water vapor concentration for each half-hour period. The approach is based on the assumption that stomatal fluxes (i.e. photosynthesis and transpiration) lead to perfectly negative correlations and non-stomatal fluxes (i.e. ecosystem respiration and direct evaporation) lead to perfectly positive correlations within the CO2 and water vapor high frequency time series measured above forest canopies. A mathematical framework is presented, followed by a proof of concept using eddy covariance data and leaf-level measurements of WUE.