Biological Systems Engineering


Date of this Version



Remote Sensing of Environment 124 (2012) 581–595; doi:10.1016/j.rse.2012.06.004


Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and environmental constraints on E. It uses remotely sensed leaf area index (Lai) and surface meteorological data to estimate E by: 1) introducing a simple biophysical model for canopy conductance (Gc), defined as a constant maximum stomatal conductance gsmax of 12.2mm s−1multiplied by air relative humidity (Rh) and Lai (Gc = gs max×Rh × Lai); 2) calculating canopy transpiration with the Gc-based Penman–Monteith (PM) E model; 3) calculating soil evaporation from an air relative- humidity-based model of evapotranspiration (Yan & Shugart, 2010); 4) calculating total E (E0) as the sum of the canopy transpiration and soil evaporation, assuming the absence of soil water stress; and 5) correcting E0 for soil water stress using a soil water balance model.

This physiological ARTS E model requires no calibration. Evaluation against eddy covariance measurements at 19 flux sites, representing a wide variety of climate and vegetation types, indicates that daily estimated E had a root mean square error = 0.77 mm d−1, bias=−0.14 mm d−1, and coefficient of determination, R2=0.69. Global, monthly, 0.5°-gridded ARTS E simulations from1984 to 1998,whichwere forced using Advanced Very High Resolution Radiometer Lai data, Climate Research Unit climate data, and surface radiation budget data, predicted a mean annual land E of 58.4×103 km3. This falls within the range (58×103–85×103 km3) estimated by the Second Global Soil Wetness Project (GSWP-2; Dirmeyer et al., 2006). The ARTS E spatial pattern agrees well with that of the global E estimated by GSWP-2. The global annual ARTS E increased by 15.5mm per decade from 1984 to 1998, comparable to an increase of 9.9 mm per decade from the model tree ensemble approach (Jung et al., 2010). These comparisons confirm the effectivity of the ARTS E model to simulate the spatial pattern and climate response of global E. This model is the first of its kind among remote-sensing-based PM E models to provide global land E estimation with consideration of the soil water balance.