Parsimony vs predictive and functional performance of three stomatal optimization principles in a big-leaf framework

Stomatal optimization models can improve estimates of water and carbon fluxes with relatively low complexity, yet there is no consensus on which formulations are most appropriate for ecosystem-scale applications. We implemented three existing analytical equations for stomatal conductance, based on different water penalty functions, in a big-leaf comparison framework, and determined which optimization principles were most consistent with flux tower observations from different biomes. We used information theory to dissect controls of soil water supply and atmospheric demand on evapotranspiration in wet to dry conditions and to quantify missing or inadequate information in model variants. We ranked stomatal optimization principles based on parameter uncertainty, parsimony, predictive accuracy, and functional accuracy of the interactions between soil moisture, vapor pressure deficit, and evapotranspiration. Performance was high for all model variants. Water penalty functions with explicit representation of plant hydraulics did not substantially improve predictive or functional accuracy of ecosystem-scale evapotranspiration estimates, and parameterizations were more uncertain, despite having physiological underpinnings at the plant level. Stomatal optimization based on water use efficiency thus provided more information about ecosystem-scale evapotranspiration compared to those based on xylem vulnerability and proved more useful in improving ecosystem-scale models with less complexity.