Abstract. Recent studies have highlighted the need for improved characterizations of
aerodynamic conductance and temperature (gA and T0) in
thermal remote-sensing-based surface energy balance (SEB) models to reduce
uncertainties in regional-scale evapotranspiration (ET) mapping. By
integrating radiometric surface temperature (TR) into the
Penman–Monteith (PM) equation and finding analytical solutions
of gA and T0, this need was recently addressed by the
Surface Temperature Initiated Closure (STIC) model. However, previous
implementations of STIC were confined to the ecosystem-scale using flux tower
observations of infrared temperature. This study demonstrates the first
regional-scale implementation of the most recent version of the STIC
model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer
(MODIS) derived TR and ancillary land surface variables in
conjunction with NLDAS (North American Land Data Assimilation System)
atmospheric variables into a combined structure of the PM and
Shuttleworth–Wallace (SW) framework for estimating ET at
1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13
core AmeriFlux sites covering a broad spectrum of climates and biomes across
an aridity gradient in the conterminous US suggests that STIC1.2 can provide
spatially explicit ET maps with reliable accuracies from dry to wet extremes.
When observed ET from one wet, one dry, and one normal precipitation year
from all sites were combined, STIC1.2 explained 66 % of the variability in
observed 8-day cumulative ET with a root mean square error (RMSE) of
7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent
bias (PBIAS) of −4 %. These error statistics showed relatively better
accuracies than a widely used but previous version of the SEB-based Surface Energy
Balance System (SEBS) model, which utilized a simple NDVI-based
parameterization of surface roughness (zOM), and the PM-based MOD16
ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate
ET (PBIAS = −26 %).
The performance of STIC1.2 was
better in forest and grassland ecosystems as compared to cropland (20 %
underestimation) and woody savanna (40 % overestimation). Model
inter-comparison suggested that ET differences between the models are
robustly correlated with gA and associated roughness length
estimation uncertainties which are intrinsically connected to
TR uncertainties, vapor pressure deficit (DA), and
vegetation cover. A consistent performance of STIC1.2 in a broad range of
hydrological and biome categories, as well as the capacity to capture
spatio-temporal ET signatures across an aridity gradient, points to the
potential for this simplified analytical model for near-real-time ET mapping
from regional to continental scales.
Surface Energy Balance Based Evapotranspiration Mapping in the Texas High Plains
