Estimating Evapotranspiration Using the Complementary Relationship and the Budyko Framework

Land surface actual evapotranspiration (ET) is an important process in terrestrial water balance and reliable estimates of ET are necessary to improve water resources management. In this regard, there is a growing body of literature that recognizes the importance of an accurate ET model. Among them, the complementary relationship 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

Improving Complementary Methods to Predict Evapotranspiration for Data Deficit Conditions and Global Applications Under Climate Change

A reliable estimate of evapotranspiration (ET) in river basins is important for the purpose of water resources planning and management. ET represents a significant portion of rainfall in the water budget; therefore, the uncertainty in estimating ET can lead to the inaccurate prediction of water resources. While remote sensing techniques are available to estimate ET, such methods are expensive and necessary data may not be readily available. Classical methods of estimating ET require detailed land use/cover information that are not readily available in rural river basins. Complementary methods provide simple and reliable approaches to estimate ET using meteorological data only. However, these methods have not been investigated in detail to assess the overall applicability and the needs for revisions if any. In this work, an improved approach to use the complementary methods using readily available meteorological data is presented. The methodology is validated using 34 global FLUXNET sites with heterogeneous land use/cover, climatic, and physical conditions. The method was compared with classical methods using Ghana as a study area where original pioneering studies of ET have been performed. The work was extended to develop global maps of ET and water surplus (precipitation - ET) for the 20th century followed by climate change-induced 21st century estimates for 2040-2069 and 2070-2099 periods. The emission scenario used was the moderate A1B with the global climate models CGCM3.1 and HADGEM1. The results were assessed at different scales from global to regional such as for potential outcomes of climate change on ET and water surplus

An Advanced Evapotranspiration Method and Application

Estimating evapotranspiration is an important component in the monitoring of agricultural and environmental systems. This chapter will focus on the developing evapotranspiration method using general meteorological data and Normalized Difference Vegetation Index (NDVI). The proposed model in this chapter will be refined by using both the complementary relationship and the Budyko framework. The relative evaporation parameter in the complementary relationship will be derived by using precipitation, potential evapotranspiration, and NDVI based on that the Budyko framework can support the complementary relationship. It is also important to determine whether the proposed model can compete and deliver accuracy similar to remote sending method in the aspect of application. The results in the first phase showed the proposed model could be a powerful methodology to estimate ET among the ground-based method. In the second phase, a nonlinear correction function was proposed to better describe the complementary relationship. We will also demonstrate that the use of ET is a better approach for drought estimations than considering reference ET. More importantly, the advantage of the proposed model is that it can comprehensively consider both effects of precipitation and vegetation information. Taken together, this chapter has extended our knowledge of ET to support water resource management

Improving the Complementary Methods to Estimate Evapotranspiration Under Diverse Climatic and Physical Conditions

Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month−1, 10.55 mm month−1and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration

Evapotranspiration mapping for agricultural water management: An overview

Evapotranspiration (ET) is an essential component of the water balance. Any attempt to improve water use efficiency must be based on reliable estimates of ET, which includes water evaporation from land and water surfaces and transpiration by vegetation. ET varies regionally and seasonally according to weather and wind conditions. Remote sensing based agro-meteorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. The use of remote sensing to estimate ET is presently being developed along two approaches: (a) land surface energy balance (EB) method and (b) Reflectance based crop coefficient and reference ET approach. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. Automated contours are not confined to specific pre-determined geographic areas (as in MLRA), require less time and cost. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (~10 to 200 ha). However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from K1VHRR/Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images