Evaluation of Meteorological Data-Based Models for Potential and Actual Evapotranspiration Losses Using Flux Measurements

Evapotranspiration is a key process within the hydrological cycle, so it requires an accurate assessment. This work aims at assessing monthly scale performances of six meteorological data-based methods to predict evapotranspiration by comparing model estimates with observations from six flux tower sites differing for land cover and climate. Three of the proposed methodologies use a potential evapotranspiration approach (Penman, Priestley-Taylor and Blaney-Criddle models) while the additional three an actual evapotranspiration approach (the Advection-Aridity, the Granger and Gray and the Antecedent Precipation Index method). The results show that models efficiency varies from site to site, even though land cover and climate features appear to have some influence. It is difficult to comment on a general accuracy, but an overall moderate better performance of the Advection-Aridity model can be reported within a context where model calibration is not accounted for. If model calibration is further taken into consideration, the Granger and Gray model appears the best performing method but, at the same time, it is also the approach which is mostly affected by the calibration process, and therefore less suited to evapotranspiration prediction tools dealing with a data scarcity context.