Estimating evapotranspiration using the complementary relationship and the Budyko framework

Abstract

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.

Developing an Integrated Complementary Relationship for Estimating Evapotranspiration

“…[30]. Figure 2 presents the results obtained from Kim [26]. These results are in agreement with Anayah [15], which showed that the modified GG model needs further improvements in dry conditions, and showed the lowest mean RMSE in both dry and wet sites.…”

“…These results are in agreement with Anayah [15], which showed that the modified GG model needs further improvements in dry conditions, and showed the lowest mean RMSE in both dry and wet sites. Overall, these results indicate that, among the groundbased methods [26], model can be used as a powerful methodology to estimate ET. While these findings are good within the realm of complimentary methods (or ground-based methods), some of the more commonly used ET estimation methods now use remote sensing data.…”

“…If the complementary relationship and the corresponding methods, such as the model proposed by Kim [26], are to be accepted as operational models in field conditions, then the results should be compared and validated with remote sensing-based ET estimation methods. Taking into consideration of the improvements made with complementary relationship-based methods, this study examines the work of Kim [26] in comparison with a commonly used remote sensing method and measured ET data from 60 EC flux tower sites located across the USA.…”

“…Comparison of RMSE (mm/month) between different complementary relationship models for 29 dry and 30 wet sites in the US. NGG and GG-NDVI refer to the models of Han [20] and Kim [26], respectively. approach developed by Senay [36].…”

Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015

“…11A, D) fall most closely to the 1:1 line, but the residuals ( fig. 11D) (Reitz and others, 2017;Kim, 2017) have shown that at low ET measurements, the SSEBop method underestimates ET, and at higher ET measurements, the SSEBop method overestimates ET. The estimation bias for the SSEBop method could account for at least some of the skewness in the SWB model results because the errors are in the same direction as they are when comparing actual ET measurements to the SSEBop estimates.…”