Monthly land cover-specific evapotranspiration models derived from global eddy flux measurements and remote sensing data

Authors

Yuan Fang, Department of Forestry and Environmental Resources, North Carolina State University
Ge Sun, Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest ServiceFollow
Peter Caldwell, Coweeta Hydrologic Laboratory, Southern Research Station, USDA Forest Service
Steven G. McNulty, Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service
Asko Noormets, Department of Forestry and Environmental Resources, North Carolina State University
Jean-Christophe Domec, Department of Forestry and Environmental Resources, North Carolina State University; University of Bordeaux, Bordeaux Sciences Agro UMR INRA-ISPA 1391
John King, Department of Forestry and Environmental Resources, North Carolina State University
Zhiqiang Zhang, College of Soil and Water Conservation, Beijing Forestry University
Xudong Zhang, Institute of Forest Research, Chinese Academy of Forestry
Guanghui Lin, Center for Earth System Science, Tsinghua University
Guangsheng Zhou, Institute of Botany, Chinese Academy of Sciences
Jingfeng Xiao, Earth Systems Research Center, University of New Hampshire
Jiquan Chen, Center for Global Change and Earth Observations (CGCEO), and Department of Geography, Michigan State UniversityFollow

Document Type

Article

Date of this Version

2015

Citation

Ecohydrology, 2015

Comments

U.S. Government work

Abstract

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 temperaturebased 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.