Partitioning Eddy Covariance Water Flux Components Using Physiological and Micrometeorological Approaches

Abstract

Eddy covariance (EC) provides ecosystem-scale estimates of photosynthesis (P-h) and evapotranspiration (ET; the sum of plant transpiration [T] and evaporation [E-s]). Separating ET into its components is becoming necessary for linking plant-water use strategies to environmental variability. Based on optimality principles, a data-model based approach for partitioning ET was proposed and independently tested. Short-term responses of canopy-scale internal leaf-to-ambient CO2 () were predicted based on a big-leaf representation of the canopy accounting for the influence of boundary-layer conductance. This representation allowed investigating stomatal behavior in accordance with the P-h estimates. With the objective of minimizing the carbon cost of transpiration, a novel optimization approach was implemented to develop solutions for an optimal stomatal conductance model as the basis to derive T. The E-s was then calculated as a residual between the observed ET and modeled T. The proposed method was applied to long-term EC measurements collected above a Mediterranean tree-grass ecosystem. Estimated E-s agreed with independent lysimeter measurements (r=0.69). They also agreed with other partitioning methods derived from similarity theory and conditional sampling applied to turbulence measurements. These similarity schemes appeared to be sensitive to different parameterization. Measured E-s was underestimated by 30% when was assumed constant (=0.8). Diel and seasonal patterns were characterized in response to soil dryness. A surprising result was a large E-s/ET throughout the seasons. The robustness of the results provides a new perspective on EC ET partitioning, which can be utilized across a wide range of climates and biomes.