Developing an Integrated Complementary Relationship for Estimating Evapotranspiration

Abstract

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.

A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

“…We found that such NAMEs will be normally associated with much higher MAPEs by a factor of 1.7, yielding equivalent MAPEs of 27 to 107% with an average of 55%. Due to probable lack of reliability of the conventional CR models, they were later improved by adding NDVI and precipitation (Kim & Kaluarachchi, 2017, 2018), embedding surface temperature for near‐instantaneous ET simulations (Crago & Crowley, 2005), or by algorithm enhancement (Crago & Qualls, 2018). The enhancements of the algorithm, though, are not well‐established yet (Crago et al, 2020; Szilagyi & Crago, 2019).…”

Urban evaporative consumptive use for water‐scarce cities in the United States and Mexico

“…Although multiple methods have been used to successfully estimate ET for agricultural areas, the heterogeneity of urban land use and cover makes it particularly difficult to estimate urban ECU using traditional ET measurement methods (Anderson & Vivoni, 2016;Grimmond & Oke, 1999;Litvak et al, 2017a;Qiu, Tan, Wang, Yu, & Yan, 2017). Given the uncertainty inherent in ET estimation methods, particularly when applying these methods to urban landscapes, using a combination of methods to estimate urban ECU can provide a more accurate overall representation of urban ECU and the identification of separate components of urban ECU (Allen, Pereira, Howell, & Jensen, 2011;Kim & Kaluarachchi, 2018;Reitz, Senay, & Sanford, 2017).…”