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A study was conducted in southern Iraq. The study area was 16485.4418 km 2 with the administrative boundaries of Basrah (North region) and Dhi-Qar (south region) Governorates. The aim of this study to design a digital model for estimating evapotranspiration values (mm) using remote sensing data and geographic information systems and comparing the results with the actual values calculated from four climatic stations distributed within the study area. As well as to study January and July from each year for a four-years chronology. The results showed that there is a highly significant correlation (r = 0.923**) between the calculated and actual values, and this proves the accuracy of the proposed model in estimating the values of evapotranspiration. The results also showed that there is a rise in the values of evapotranspiration in the month of July compared to the month of January of each year due to the high temperatures and the angle of incidence of the sun’s rays, which is almost vertical in the month of July. The results also showed that the study area varies in the values of evapotranspiration according to the different vegetation cover, as the values decrease with the increase of vegetation cover and Vice versa. As well as the effect of the values of evapotranspiration by rising for limited areas in the areas of electric power plants and oil fields, and this gives evidence of large quantities of thermal and radioactive emissions in the areas. The results also showed that there is a inconsistency between the values of evapotranspiration and the areas covered, which were divided into four categories for each scene, and that the values of these categories differ according to the types of land cover. [ABSTRACT FROM AUTHOR]

Although the evapotranspiration (ET) process has historically received limited attention, it is an important factor for assessing the health and behavior of urban green spaces, including green roofs. In this study, common potential evapotranspiration (PET) models, which assume non-water-limited substrate moisture conditions, and actual evapotranspiration (AET) models, which account for water-limited substrate moisture conditions, are used to predict ET from local climate conditions at two extensive Sedum green roof sites in New York City (NYC); one a vegetated mat system (termed W118) and the other a built in place system (termed USPS). Results from the predictions are compared to 12,000 h of on-site ET measurements obtained using a dynamic chamber system. Among the Hargreaves, Priestley–Taylor, Penman, and American Society of Civil Engineers Penman–Monteith PET models, results from the Priestley–Taylor model, which was developed to predict ET from a wet vegetated surface with minimal advection, best correlate with the dynamic chamber measurements ( r -squared = 0.96 for W118, 0.82 for USPS). Nonetheless, a systematic error is seen whereby the Priestley–Taylor model overestimates the low ET fluxes observed during the winter months and underestimates the high ET fluxes observed during the summer months. This error is only exaggerated by the inclusion of an advective ET term. To estimate green roof ET under water-limited conditions, a storage model, antecedent precipitation index (API), and advection-aridity model are applied to the Priestley–Taylor formulation to calculate AET. Results indicate that only the API model, which is based on precipitation history alone, can improve upon the Priestley–Taylor PET predictions ( r -squared = 0.96 on W118, 0.85 on USPS). Use of a more-physically based, substrate moisture storage greatly over-estimates ET reduction during dry periods. The work provides insight into which common ET models best capture the behavior of full-scale, extensive green roofs, and points to the need for better estimates of green roof ET under climate conditions typical of NYC's winter and summer months. [ABSTRACT FROM AUTHOR]

Quantification of actual crop evapotranspiration (ETa) over large areas is a critical issue to manage water resources, particularly in semi-arid regions. In this study, four models driven by high resolution remote sensing data were intercompared and evaluated over an heterogeneous and complex traditional irrigated area located in the piedmont of the High Atlas mountain, Morocco, during the 2017 and 2018 seasons: (1) SAtellite Monitoring of IRrigation (SAMIR) which is a software-based on the FAO-56 dual crop coefficient water balance model fed with Sentinel-2 high-resolution Normalized Difference Vegetation Index (NDVI) to derive the basal crop coefficient (K cb); (2) Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) which is a surface energy balance model fed with land surface temperature (LST) derived from thermal data provided from Landsat 7 and 8; (3) a modified version of the Shuttleworth–Wallace (SW) model which uses the LST to compute surface resistances and (4) METRIC-GEE which is a version of METRIC model ("Mapping Evapotranspiration at high Resolution with Internalized Calibration") that operates on the Google Earth Engine platform, also driven by LST. Actual evapotranspiration (ETa) measurements from two Eddy-Covariance (EC) systems and a Large Aperture Scintillometer (LAS) were used to evaluate the four models. One EC was used to calibrate SAMIR and SPARSE (EC1) which were validated using the second one (EC2), providing a Root Mean Square Error (RMSE) and a determination coefficient (R) of 0.53 mm/day (R=0.82) and 0.66 mm/day (R=0.74), respectively. SW and METRIC-GEE simulations were obtained respectively from a previous study and Google Earth Engine (GEE), therefore no calibration was performed in this study. The four models predict well the seasonal course of ETa during two successive growing seasons (2017 and 2018). However, their performances were contrasted and varied depending on the seasons, the water stress conditions and the vegetation development. By comparing the statistical results between the simulation and the measurements of ETa it has been shown that SAMIR and METRIC-GEE are the less scattered and the better in agreement with the LAS measurements (RMSE equal to 0.73 and 0.68 mm/day and R equal to 0.74 and 0.82, respectively). On the other hand, SPARSE is less scattered (RMSE = 0.90 mm/day, R = 0.54) than SW which is slightly better correlated (RMSE = 0.98 mm/day, R = 0.60) with the observations. This study contributes to explore the complementarities between these approaches in order to improve the evapotranspiration mapping monitored with high-resolution remote sensing data • Four crop evapotranspiration estimates approaches based on remote sensing were compared. • Eddy-Covariance and Scintillometer measurements were used for models' assessment. • The four models estimate correctly the seasonal variations of actual ET. • FAO-56 and METRIC GEE show the best results over a mixed irrigated area. [ABSTRACT FROM AUTHOR]

An accurate and simple Reference Evapotranspiration (ETo) numerical model eases to use for supporting irrigation planning and its effective management is highly desired in Sahelian regions. This paper investigates the performance ability of the Gene-expression Programming (GEP) for modeling ETo using decadal climatic data from a Sahelian country; Burkina Faso. For the study; important data are collected from six synoptic meteorological stations located in different regions; Gaoua, Pô, Boromo, Ouahigouya, Bogandé and Dori. The climatic data combinations are used as inputs to develop the GEP models at regional-specific data basis for estimating ETo. GEP performances are evaluated with the root mean square error (RMSE), and coefficient of correlation (R) between estimated and targeted Penman-Monteith FAO56 set as the true reference values. Obviously; from the statistical viewpoint; GEP computing technique has showed a good ability for providing numerical models on a regional data basis. The performances of GEP based on temperatures data are quite good able to substitute empirical equations at regional level to some extent. It is found that the models with wind velocity yield high accuracies by causing radical improve of the performances with R (0.925-0.961) and RMSE (0.131-0.272 mm day); while relative humidity may cause only (R = 0.801-0.933 and RMSE = 0.370-0.578 mm day). Statistically; GEP is an effectual modeling tool for computing successfully evapotranspiration in Sahel. [ABSTRACT FROM AUTHOR]

Abstract: Reliable estimation of the surface energy balance from local to regional scales is crucial for many applications including weather forecasting, hydrologic modeling, irrigation scheduling, water resource management, and climate change research. Numerous models have been developed using remote sensing, which permits spatially distributed mapping of the surface energy balance over large areas. This study compares flux maps over a relatively simple agricultural landscape in central Iowa, comprised of soybean and corn fields, generated with three different remote sensing-based surface energy balance models: the Two-Source Energy Balance (TSEB) model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). The three models have different levels of complexity and input requirements, but all have operational capabilities. METRIC and TIM make use of the remotely sensed surface temperature–vegetation cover relation to define key model variables linked to wet and dry hydrologic extremes, while TSEB uses these remotely sensed inputs to define component soil and canopy temperatures, aerodynamic resistances, and fluxes. The models were run using Landsat imagery collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX) in 2002 and model results were compared with observations from a network of flux towers deployed within the study area. While TSEB and METRIC yielded similar and reasonable agreement with measured heat fluxes, with root-mean-square errors (RMSE) of ∼50–75W/m2, errors for TIM exceeded 100W/m2. Despite the good agreement between TSEB and METRIC at discrete locations sampled by the flux towers, a spatial intercomparison of gridded model output (i.e., comparing output on a pixel-by-pixel basis) revealed significant discrepancies in modeled turbulent heat flux patterns that were largely correlated with vegetation density. Generally, the largest discrepancies, primarily a bias in H, between these two models occurred in areas with partial vegetation cover and a leaf area index (LAI)<2.0. Adjustment of the minimum LE assumed for the hot/dry hydrologic extreme condition in METRIC reduced the bias in H between METRIC and TSEB, but caused a significant increase in bias in LE between the models. Spatial intercomparison of modeled flux patterns over a variety of landscapes will be required to better assess uncertainties in remote sensing surface energy balance models, and to work toward an improved hybrid modeling system. [Copyright &y& Elsevier]

Root zone water quality model (RZWQM) has proven to be useful in evaluating the agricultural systems. However, it has previously been recognized that its evapotranspiration estimates somewhat depart from the measured values indicating the potential for some improvement in this module. Estimation procedure is based on the dual surface approach of Shuttleworth and Wallace, that although having a solid theoretical basis, it is greatly empirical in its application due to the lack of accurate quantitative knowledge of the resistance terms that control the heat fluxes in the canopy. Analysis of the formulation used in RZWQM allowed to detect some weakness in the calculation of bulk surface resistance (rsc), which is based on stomatal resistance averaged by effective leaf area index (LAIeff). In this work, an alteration to the definition of LAIeff is proposed, which leads to an improvement of the estimates. It is also discussed that a greater improvement could be obtained by introducing a model of stomatal resistance response to environmental conditions, which would increase the complexity of the model and be difficult to apply given the lack of adequate knowledge of the quantitative behavior of stomata. Alternatively, the simple crop coefficient approach could be incorporated to define an upper limit to evapotranspiration fluxes and avoid some otherwise unrealistically high estimates of the current version of the model. [Copyright &y& Elsevier]