The complementary relationship for estimating
evapotranspiration (ET) is a simple approach requiring only commonly available
meteorological data; however, most complementary relationship models decrease
in predictive power with increasing aridity. In this study, a previously
developed Granger and Gray (GG) model by using Budyko framework is further
improved to estimate ET under a variety of climatic conditions. This updated GG
model, GG-NDVI, includes Normalized Difference Vegetation Index (NDVI), precipitation,
and potential evapotranspiration based on the Budyko framework. The Budyko
framework is consistent with the complementary relationship and performs well
under dry conditions. We validated the GG-NDVI model under operational
conditions with the commonly used remote sensing-based Operational Simplified
Surface Energy Balance (SSEBop) model at 60 Eddy Covariance AmeriFlux sites
located in the USA. Results showed that the Root Mean Square Error (RMSE) for
GG-NDVI ranged between 15 and 20 mm/month, which is lower than for SSEBop every
year. Although the magnitude of agreement seems to vary from site to site and
from season to season, the occurrences of RMSE less than 20 mm/month with the
proposed model are more frequent than with SSEBop in both dry and wet sites.
Another finding is that the assumption of symmetric complementary relationship
is a deficiency in GG-NDVI that may introduce an inherent limitation under
certain conditions. We proposeda nonlinear correction
function that was incorporated into GG-NDVI to overcome this limitation. As a
result, the proposed model produced much lower RMSE values, along with lower
RMSE across more sites, as compared to SSEBop.
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