

Polhamus, A; Fisher, JB; Tu, KP (2013). What controls the error structure in evapotranspiration models?. AGRICULTURAL AND FOREST METEOROLOGY, 169, 1224. Abstract Evapotranspiration models allow climate modelers to describe surfaceatmosphere interactions, ecologists to understand the impact that global temperature change and increased radiation budgets will have on ecosystems, and farmers to decide how much irrigation to give their crops. Physically based algorithms for estimating evapotranspiration must manage a tradeoff between physical realism and the difficulty of parameterizing key inputs, namely resistance factors associated with water vapor transport through the canopy and turbulent transport of water vapor from the canopy to ambient air. In this study we calculate predicted evapotranspiration at 42 AmeriFlux sites using two types of dedicated evapotranspiration modelsone using physical resistances from the PenmanMonteith equation (Monteith, 1965) (Mu et al., 2007, 2011) and another based on the PriestleyTaylor (1972) equation, substituting functional constraints for resistances (Fisher et al., 2008). We analyze the structure of the residual series with respect to various meteorological and biophysical inputs, specifically Jarvis and McNaughton's (1986) decoupling coefficient, Omega, which is designed to represent the degree of control that plant stomata versus atmospheric demand and net radiation exercise over transpiration. We find that vegetation indices, magnitude of daytime fluxes, and bulk canopy resistance (r(c))which largely drives Omegaare strong predictors of patterns in model bias for all flux products. Though our analysis suggests a consistently negative relationship between Omega and mean predicted error for all evapotranspiration models, we found that vegetation indices and flux magnitudes were the most significant drivers of model error. Before addressing error associated with canopy resistance and Omega, refinements to existing models should focus on correcting biases with respect to flux magnitudes and canopy indices. We suggest a dualmodel approach for backsolving r(c) (rather than estimating it from lookup tables and canopy indices), and increased attention to water availability, which largely drives stomatal opening and closure. (C) 2012 Elsevier B.V. All rights reserved. DOI:
ISSN: 01681923 