Evapotranspiration (ET) is arguably the most uncertain ecohydrologic variable for quantifying watershed water budgets. Although numerous ET and hydrological models exist, accurately predicting the effects of global change on water use and availability remains challenging because of model deficiency and/or a lack of input parameters. The objective of this study was to create a new set of monthly ET models that can better quantify landscape-level ET with readily available meteorological and biophysical information. We integrated eddy covariance flux measurements from over 200 sites, multiple year remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and statistical modelling. Through examining the key biophysical controls on ET by land cover type (i.e. shrubland, cropland, deciduous forest, evergreen forest, mixed forest, grassland, and savannas), we created unique ET regression models for each land cover type using different combinations of biophysical independent factors. Leaf area index and net radiation explained most of the variability of observed ET for shrubland, cropland, grassland, savannas, and evergreen forest ecosystems. In contrast, potential ET (PET) as estimated by the temperature-based Hamon method was most useful for estimating monthly ET for deciduous and mixed forests. The more data-demanding PET method, FAO reference ET model, had similar power as the simpler Hamon PET method for estimating actual ET. We developed three sets of monthly ET models by land cover type for different practical applications with different data availability. Our models may be used to improve water balance estimates for large basins or regions with mixed land cover types.
eddy covariance flux
Fang, Yuan; Sun, Ge; Caldwell, Peter; McNulty, Steven G.; Noormets, Asko; Domec, Jean-Christophe; King, John; Zhang, Zhiqiang; Zhang, Xudong; Lin, Guanghui; Zhou, Guangsheng; Xiao, Jingfeng; Chen, Jiquan. 2015. Monthly land cover-specific evapotranspiration models derived from global eddy flux measurements and remote sensing data. Ecohydrology: (2015) 19 p.