Reviews and syntheses: Eddy covariance-based evapotranspiration partitioning

Abstract. The task of reliably partitioning evapotranspiration (ET) is imperative so that we can better understand how individual components of the terrestrial water flux are contributing to the global hydrological cycle and changing under a warming climate. By constraining how evaporation (E) and transpiration (T) separately adapt to increased global temperatures, we can make more accurate predictions in land surface models, further our understanding of plant water use, and better manage our limited water resources. Eddy covariance (EC) is a globally used technique that measures net biosphere-atmosphere fluxes, including ET, and if reliably partitioned, presents a promising way to constrain E and T values and trends across ecosystems. Several EC-based ET partitioning methods exist, and there is a need for an updated comprehensive guide to the available approaches. This systematic literature review was conducted with the objectives of 1) identifying EC-based ET partitioning methods and categorizing them based on underlying ecosystem assumptions, 2) determining the main advantages and disadvantages of each method dependent on their assumptions and data requirements, and 3) evaluating how broadly these methods have been tested based on geographic location and ecosystem type. The review identified 10 independent partitioning methods tested across 123 studies. Methods using assumptions of underlying water use efficiency (uWUE) and ecosystem conductance all use the relationship between ET and gross primary production with vapor pressure deficit (VPD) to estimate the transpiration ratio (T/ET). Additionally, two machine learning based methods, one method assuming a linear relationship between ET and gross ecosystem photosynthesis, and three methods using high frequency EC data to estimate T/ET were identified. The uWUE methods, while the most frequently used partitioning approach, consistently predicted the lowest T/ET estimates when compared to both other EC and non-EC based partitioning methods. The machine learning methods predicted the highest T/ET values compared to other EC-based methods which agreed well with values estimated with independent methods. Savannas and evergreen broadleaf forests had the highest T/ET of all ecosystem types while deserts and wetlands had the lowest. Leaf area index and soil water content were found to be the most important drivers of T/ET values and trends with VPD and air temperature also displaying significant effects. Of the global studies identified in this review, an average annual T/ET value of 0.58 was found, a value that falls within the range of other studies using isotopic partitioning, remote sensing methods, and global estimates from various ecosystem models. More testing, specifically increased paired analyses of two or more EC-based ET partitioning methods on the same dataset, is needed in order to fully understand the applicability of each method, their differences, and to better constrain global T/ET dynamics.