Flux Partitioning Methods for Water Vapor and Carbon Dioxide: Observation and numerical investigations

Yearly budgets of water vapor and CO₂ exchange between ecosystems and the atmosphere can now be measured through a growing worldwide network of eddy-covariance towers continuously measuring data across seasons. On the other hand, the quantification of water vapor and CO₂ exchanges from specific sources and sinks, namely from the soil and from the plant canopy, remains an elusive yet critical research objective. For instance, understanding the connection between environmental variables and soil evaporation and respiration or plant transpiration and photosynthesis is essential to characterize the influence of climate change and variability on ecosystem functioning. To this end, methods capable of reliably partitioning the total water vapor and CO₂ fluxes into their respective soil and plant components are highly valuable. In this work, we investigate partitioning methods that are based on analysis of conventional high frequency eddy-covariance (EC) data: the flux variance similarity method and the conditional eddy covariance method. Based on their framework, we also propose and test two modified approaches. The physical validity of the assumptions of all four methods, as well as their performance, are tested with the aid of large eddy simulations, which are used to replicate eddy-covariance field experiments. Our findings elucidate how the methods’ performance depends on the flux magnitude of the different components and how it is modulated by the turbulent mixing directly caused by different canopy sparseness and arrangements. Overall, our findings enable the development of best practices for partitioning ecosystems fluxes for each method, as well as the joint application of these methods to obtain reliable partitioning under a wide range of conditions and in various ecosystems. Following this line of applications, we present a new public data base that applies these methods across all sites of National Ecological Observatory Network (NEON) and report the partitioned fluxes.