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Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region.

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]

The latent heat flux, one of the key components of the surface energy balance, can be inferred from remotely sensed thermal infrared data. However, discrepancies between modeled and observed evapotranspiration are large. Thermal cameras might provide a suitable tool for model evaluation under variable atmospheric conditions. Here, we evaluate the results from the Penman-Monteith, surface energy balance and Bowen ratio approaches, which estimate the diurnal course of latent heat fluxes at a ripe winter wheat stand using measured and modeled temperatures. Under overcast conditions, the models perform similarly, and radiometric image temperatures are linearly correlated with the inverted aerodynamic temperature. During clear sky conditions, the temperature of the wheat ear layer could be used to predict daytime turbulent fluxes (root mean squared error and mean absolute error: 20–35 W∙m−2, r2: 0.76–0.88), whereas spatially-averaged temperatures caused underestimation of pre-noon and overestimation of afternoon fluxes. Errors are dependent on the models’ ability to simulate diurnal hysteresis effects and are largest during intermittent clouds, due to the discrepancy between the timing of image capture and the time needed for the leaf-air-temperature gradient to adapt to changes in solar radiation. During such periods, we suggest using modeled surface temperatures for temporal upscaling and the validation of image data.

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]

Traditionally, oasis farmers in hyperarid northern Oman have adapted to the interannual variation of irrigation water supply by dedicating an often sizable proportion of the agricultural area to the production of annual crops and leaving this area uncultivated in drought years. We hypothesized that increases in the share of perennial crops may put long-term hydrological sustainability at risk. To test this hypothesis, we compared agricultural water demand patterns of five oases in the mountain region of Al Jabal al Akhdar for 1978 and 2005. We analyzed land use changes by classifying aerial photographs taken in 2005 and 1978 into five land use types. Water demand in 2005 was estimated based on two GIS-based detailed crop inventories during the hot and cool season and on measurements of temperature, solar radiation and wind speed, from which crop evapotranspiration was calculated using the Penman–Monteith equation. Radiation and wind speed measurements were improved by topographic modeling. Water demand per area was summarized for each land use type and the results used to estimate water demand in 1978. Water supply estimates were based on spring flow measurements. Between 1978 and 2005, agricultural water demand in the study area rose from 218,800 to 256,377 m3 a−1. The most prominent land use changes were the disappearance of non-palm orchards at Masayrat ar Ruwajah and expansions of the area under perennial crops at Al ‘Aqr, Ash Sharayjah and Qasha’ at the expense of field crops. As a consequence, the summer peak of crop water demand decreased at Masayrat ar Ruwajah, where all perennial crops could easily be irrigated with the minimum water supply observed during our study, but this summer peak increased at the other oases. Meeting the water demand of all perennial crops at Qasha’ required average spring flow conditions in 2005, indicating severe water deficiency in drought years. In addition to increased water demand, the oases’ water balance might also be under pressure from reductions in water supply caused by increased extraction of water from the same aquifer by a rapidly growing new town in the area. [ABSTRACT FROM AUTHOR]

In 1988, the Florida Institute of Phosphate Research (FIPR) funded a project to develop an advanced hydrologic model for shallow water table systems. The FIPR hydrologic model (FHM) was developed to provide an improved predictive capability of the interactions of surface water and ground water using its component models, HSPF and MODFLOW. The Integrated Surface and Ground Water (ISGW) model was developed from an early version of FHM and the two models were developed relatively independently in the late 1990s. Hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, and base flow are explicitly accounted for in both models. Considerable review of FHM and ISGW and their applications occurred through a series of projects. One model evolved, known as the Integrated Hydrological Model IHM. This model more appropriately describes hydrologic processes, including evapotranspiration fluxes within small distributed land-based discretization. There is a significant departure of many IHM algorithms from FHM and ISGW, especially for soil water and evapotranspiration (ET). In this paper, the ET concepts in FHM, ISGW, and IHM will be presented. The paper also identifies the advantages and data costs of the improved methods. In FHM and IHM, ground water ET algorithms of the MODFLOW ET package replace those of HSPF (ISGW used a different model for ground water ET). However, IHM builds on an improved understanding and characterization of ET partitioning between surface storages, vadose zone storage, and saturated ground water storage. The IHM considers evaporative flux from surface sources, proximity of the water table to land surface, relative moisture condition of the unsaturated zone, thickness of the capillary zone, thickness of the root zone, and relative plant cover density. The improvements provide a smooth transition to satisfy ET demand between the vadose zone and deeper saturated ground water. While the IHM approach provides a more sound representation of the actual soil profile than FHM, and has shown promise at reproducing soil moisture and water table fluctuations as well as field measured ET rates, more rigorous testing is necessary to understand the robustness and/or limitations of this methodology. [ABSTRACT FROM AUTHOR]

Abstract: In countries facing water scarcity, governmental water agencies try to transfer this constraint to farmers, e.g. by encouraging them to shift from traditional to localized irrigation methods to save water. However, water shortage is often much less a problem for farmers than soil limitations, their objective being mostly to maximize their income per cultivated area (US$ per hectare rather than per cubic meter of water). This discrepancy can only be solved if governments find ways to ‘transfer’ water scarcity, e.g. through economic incentives such as water pricing and/or subsidies. The aim of this study was to address the question of how to match the interest of both water managers and farmers. We aimed particularly at evaluating whether shifting to drip irrigation is a relevant way to save water and increase farmer''s income. Our analysis was based on the interactive impacts among economic, environmental, technical and methodological parameters on the net productivity of two crops. We focused on the case study of Turkey considering two crops with contrasted gross productivity, tomato and cotton, characterized by partial vegetation cover during a large part of crop cycle. A 3D crop energy balance model was applied showing that crop transpiration is increased by up to 10% when shifting from furrow to drip irrigation. These results were used to correct the maximal evapotranspiration (ETm), estimated with the simple “crop coefficient” (K c) method, and then used to enhance net productivity estimation both for furrow and drip irrigation. The results suggest that water managers and farmers share a common interest in adopting drip irrigation of tomato. Inversely, interests divergence may increase with low/medium value crops as cotton; the combination between water pricing and subsidies could be a way of agreement, but it would require subsidies for irrigation equipment of at least 40%, for low water tariffs, to 60%, for high water tariffs, to make the transfer from furrow to drip irrigation acceptable. This approach appeared generic enough to be applied for other economic, technical or environmental conditions, to modernize irrigation by harmonizing constraints faced by water managers and farmers. [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]

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity ( R 2 < 0.4); however, they correlated better with modeled ET in 2018 ( R 2 = 0. 69, VRI-Full; R 2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year ( F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient., (Copyright © 2019 Barker, Bhatti, Heeren, Neale and Rudnick.)