Evapotranspiration is the single most important mechanism of mass and energy exchange between atmosphere, biosphere, and hydrosphere. Among the common approaches to estimating evapotranspiration, the complementary relationship has been the subject of many recent studies given its simplicity and the use of meteorological data only. Recently, a modified version of the complementary relationship, Modified GG, was developed using meteorological data only and had been successfully applied at 34 diverse global sites to provide more accurate information of evapotranspiration. However, the complementary relationship including Modified GG showed weak performance under dry conditions. This dissertation addressed this limitation of the complementary relationship using the Budyko hypothesis and extended its application to drought monitoring. between ET and potential ET (ETP) has been the subject of many studies because it uses only meteorological data as inputs. However, there is an increasing concern that some complementary relationship models perform poorly under dry conditions. To overcome this limitation, this dissertation was designed to extend the latest complementary relationship model, Modified GG, using both meteorological data and vegetation information, NDVI, which is readily available from remote sensing data. The proposed model, Adjusted GG-NDVI, was validated by comparing to other ET models and measured ET data. With Adjusted GG-NDVI, this dissertation addressed the applicability of using ET as a proxy for drought monitoring. As a result, the drought patterns from the proposed drought index, EWDI, were consistent with commonly used USDM in the United States. More importantly, this study described drought conditions by comprehensively considering both precipitation and vegetation conditions. Taken together, these findings have significant implications for the understanding of how ET can assist in water resources management.

Abstract. The Granger and Gray (GG) model, which uses 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 modified GG 10 model using the vegetation index 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 using 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

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 proposed a 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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