FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions

Crop coefficient curves provide simple, reproducible means to estimate crop evapotranspiration (ET) from weather-based reference ET values. The dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United States (FAO) Irrigation and Drainage Paper No. 56 (FAO-56) is intended to improve daily simulation of crop ET by considering separately the contribution of evaporation from soil. The dual method utilizes “basal” crop coefficients representing ET from crops having a dry soil surface and separately predicts evaporation from bare soil based on a water balance of the soil surface layer. Three extensions to the evaporation calculation procedure are described here that are intended to improve accuracy when applications warrant the extra complexity. The first extension uses parallel water balances representing the portion of the soil surface wetted by irrigation and precipitation together and the portion wetted by precipitation alone. The second extension uses three “stages” for surface drying and provides for application to deep cracking soils. The third extension predicts the extraction of the transpiration component from the soil surface layer. Sensitivity and analyses and illustrations indicate moderate sensitivity of daily calculated ET to application of the extensions. The dual Kc procedure, although relatively simple computationally and structurally, estimates daily ET as measured by lysimeter relatively well for periods of bare soil and partial and full vegetation cover.     

Estimating Evaporation from Bare Soil Using Soil Moisture Data

A method is presented that uses continuous soil moisture measurements and hourly reference evapotranspiration data to estimate a soil hydraulic factor (β) for modeling soil evaporation. The β factor is used to assess the end of the energy limited soil evaporation phase (Stage 1) and the evaporation rate during the soil hydraulic limited phase (Stage 2) of a two-stage soil evaporation model. A previously developed and tested method to determine β uses an energy balance approach with sensible heat flux density estimated using the surface renewal method to obtain the continuous soil evaporation. A new method is presented, which uses a hydroprobe soil moisture measuring device to estimate the continuous soil evaporation. The estimation of evaporation with soil moisture sensors was simpler and less expensive when compared to the energy balance technique. The methods, evaluated in two field experiments, showed good agreement with evaporation data. Using the evaporation model and β derived from either method provided a good estimate of measured soil evaporation. Modeled daily soil evaporation, using either energy balance or soil measurements to obtain β, gave a root-mean-square error of 0.6 mm?day?1 when compared with soil evaporation measured using the energy balance method. When daily soil evaporation from soil moisture measurements was compared with soil evaporation estimated from energy balance measurements, the root-mean-square error was 1.3 mm?day?1. Direct soil monitoring method had bigger error, but the method is less costly.     

Measurement of Evaporation on Bare Soil and Estimating Surface Resistance

A new evaporation measuring device incorporating an evaporation chamber has been developed and checked for its accuracy. This device is unique that it uses a chamber that is completely open at one end and thereby minimizes the effect of the chamber on the natural profiles of temperature, humidity, and turbulence. It was used in estimating a newly formulated surface resistance to bare soil evaporation under dry topsoil conditions. A simple energy balance model incorporating the depth of evaporating surface, blended with a new approach for describing surface resistance was developed and successfully tested with a limited set of data obtained from a simple experiment, assuming ideal conditions. In addition to the newly formulated surface resistance, the depth of the dry soil layer was also estimated and was relatively comparable with measured value. The newly formulated surface resistance was found to be insignificant compared to the resistance imposed by the dry soil layer. The total surface resistance was modeled as a power function of soil moisture in the top 0–1 cm of soil, while the newly formulated resistance did not show any relation with the soil moisture.     

Comparison of Measured and FAO-56 Modeled Evaporation from Bare Soil

This paper evaluates how well the FAO-56 style soil water evaporation model simulates measurements of evaporation (E) from bare soil. Seven data sets were identified from the literature and in all but one case, the individuals who took the measurements were contacted and they provided the writers with specific weather and soils data for model input. Missing weather and soils data were obtained from online sources or from the National Climatic Data Center. Simulations for three possible variations of soil data were completed and compared. The measured and the FAO-56 simulated E/ETo and cumulative evaporation trends and values were similar. Specifically, the average evaporation weighted percent difference between the measured and the simulated cumulative evaporation was between ?7.5 and ?0.5%. This evaluation suggests model accuracy of about ±15% with the use of sound weather data and a fairly generalized understanding of soil properties in the location being evaluated.     

Performance Study of SPAW Model with Temperature-Derived ET0 as Input in Place of Pan Evaporation under Wheat Crop in a Semiarid Subtropical Climate

In this study, we have attempted to enhance the utility of soil–plant–atmosphere–water (SPAW) model that has been used successfully by various workers in different countries for soil moisture prediction under different cropping conditions. One of the major climatic inputs for SPAW model is pan evaporation, which in many places is not readily available. To address the above, and to get the benefit of this model in regions characterized by limited weather data availability, this study was undertaken using computed ET0 from air temperature by the 1985 Hargreaves equation, as one of the inputs in place of pan evaporation. For the purpose, actual air temperature collected from experimental farm area, as well as forecast air temperature collected from National Centre for Medium Range Weather Forecasting, Government of India, were used. First, the SPAW model was calibrated and its performance was evaluated under wheat, taking layerwise and profile soil moisture as the variables for comparison between the predicted and observed values. The results showed that the root-mean-square error (RMSE) varied from 0.30?to?0.58?cm for measured values ranging between 2.24 and 4.25?cm. The index of agreement (d) varied from 0.81 to 0.92 and coefficient of determination (r2) from 0.46 to 0.73 for 0–15, 15–30, 30–45, and 45–60?cm soil depths. For the whole 60?cm profile, the RMSE was 1.07?cm with d and r2 values of 0.94 and 0.85 respectively. The RMSE and d varied from 0.36?to?0.63?cm and 0.77 to 0.89 respectively when ET0 computed from actual air temperature was used in place of pan evaporation, where as when ET0 computed from forecast air temperature data was used, the corresponding values were 0.35–0.64?cm and 0.68–0.85 respectively for the four soil layers. There was a tendency of the models to underestimate when the computed ET0 was used as input in place of pan evaporation. In general, performance of the models were better at lower depths.     

Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotranspiration. The estimates of the ANN and ANFIS models were compared with the garlic crop evapotranspiration (ETc) values measured by lysimeter and those of the crop coefficient approach. Based on these comparisons, it can be concluded that the ANN and ANFIS techniques are suitable for simulation of ETc.     

Evaporation Research: Review and Interpretation

Literature regarding evaporation from soil, wet plant surfaces, and sprinkler droplets was examined, normalized, and interpreted. Much of the evaporation literature is difficult to compare and interpret; this paper offers comparisons and discussions of various findings by others as well as by the writers. Techniques of measuring and estimating evaporation from irrigation and rainfall are discussed. The partitioning between increased evaporation and decreased transpiration from a variety of research is quantified. Factors that impact the various forms of evaporation are listed and quantified. This review and summary will provide practitioners and researchers with theoretical and practical guidance on measurement techniques and estimates of evaporation under a wide range of conditions.     

Estimating Soil Moisture Under Low Frequency Surface Irrigation Using Crop Water Stress Index

The present study investigated the relationship between the crop water stress index (CWSI) and soil moisture for surface irrigated cotton (Gossypium hirsutum, Delta Pine 90b) at Maricopa, Arizona during the 1998 season. The CWSI was linked to soil moisture through the water stress coefficient Ks that accounts for reduced crop evapotranspiration when there is a shortage of soil water. A stress recovery coefficient Krec was introduced to account for reduced crop evapotranspiration as the crop recovered from water stress after irrigation events. A soil water stress index (SWSI) was derived in terms of Ks and Krec. The SWSI compared reasonably well to the CWSI, but atmospheric stability correction for the CWSI did not improve comparisons. When the CWSI was substituted into the SWSI formulation, it gave good prediction of soil moisture depletion (fDEP; when to irrigate) and depth of root zone depletion (Dr; how much to irrigate). Disagreement was greatest for fDEP<0.6 because cotton is less sensitive to water stress in this range.     

Evaporation Theory for Deformable Soils

Desiccation of a deformable soil is basically the removal of water by evaporation, which is controlled by evaporativity and evaporability. Surface evaporation improves the trafficability, which is essential for the access of construction equipment in areas reclaimed with soft clay. The existing traditional methods for evaluating evaporation cannot account for the deformation of soils during evaporation. Therefore, a theoretical model for predicting the rate of evaporation from the surface of a deformable material is proposed. The model is based on a system of equations for coupled heat and mass transfer in unsaturated soils. The modified pressure plate extractor test and glass desiccator test were carried out to obtain the soil-water characteristic curve for a deformable soil. A column-drying test was conducted to investigate one-dimensional water flow, heat flow, and evaporation in the surface. A finite difference program was developed to solve the coupled nonlinear partial differential equations, which permits the study of liquid, diffusive vapor, and heat flows in the deformable soil. Comparison between measured and simulated values shows good agreement.     

Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique

This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop. Six RBF networks, each using varied input combinations of climatic variables, have been trained and tested. The model estimates are compared with measured lysimeter ET. The results of the study clearly demonstrate the proficiency of the ANN method in estimating the ET. The analyses suggest that the crop ET could be computed from air temperature using the ANN approach. However, the present study used a single crop data for a limited period, therefore further studies using more crops as well as weather conditions may be required to strengthen these conclusions.     

Simple Equation to Estimate Reference Evapotranspiration from Evaporation Pans Surrounded by Fallow Soil

Reliable estimates of reference evapotranspiration (ET0) are key elements for efficient water resource management, and estimating ET0, based on “Class ‘A’ pan evaporation” data is common in arid climates. A pan coefficient (Kp), which depends on the distance (or fetch) of green vegetation or fallow soil around the pan (F), wind run (U), and relative humidity (RH), is used to convert from pan evaporation to ET0. Several researchers have developed models for estimating Kp values for pans surrounded by green vegetated fetch, but there is only one equation to estimate Kp values for dry fetch conditions. The equation is complex, so the objective of this research was to develop a new simple equation to estimate Kp under fallow soil fetch conditions. The new Kp equation and the more complex equation were compared with tabular values published by the United Nations Food and Agriculture Organization. The new equation performed slightly better at matching the tabular Kp values than the complex equation. The equation derivation and evaluation are presented.     

Prediction Accuracy for Projectwide Evapotranspiration Using Crop Coefficients and Reference Evapotranspiration

The Imperial Irrigation District is a large irrigation project in the western United States having a unique hydrogeologic structure such that only small amounts of deep percolation leave the project directly as subsurface flows. This structure is conducive to relatively accurate application of a surface water balance to the district, enabling the determination of crop evapotranspiration (ETc) as a residual of inflows and outflows. The ability to calculate ETc from discharge measurements provides the opportunity to assess the accuracy and consistency of an independently applied crop coefficient—reference evapotranspiration (Kc?ET0) procedure integrated over the project. The accuracy of the annual crop evapotranspiration via water balance estimates was ±6% at the 95% confidence level. Calculations using Kc and ET0 were based on the FAO-56 dual crop coefficient approach and included separate calculation of evaporation from precipitation and irrigation events. Grass reference ET0 was computed using the CIMIS Penman equation and ETc was computed for over 30 crop types. On average, Kc-based ET computations exceeded ETc determined by water balance (referred to as ETc?WB) by 8% on an annual basis over a 7 year period. The 8% overprediction was concluded to stem primarily from use of Kc that represents potential and ideal growing conditions, whereas crops in the study area were not always in full pristine condition due to various water and agronomic stresses. A 6% reduction to calculated Kc-based ET was applied to all crops, and a further 2% reduction was applied to lower value crops to bring the project-wide ET predicted by Kc-based ET into agreement with ETc?WB. The standard error of estimate (SEE) for annual ETc for the entire project based on Kc, following the reduction adjustment, was 3.4% of total annual ETc, which is considered to be quite good. The SEE for the average monthly ETc was 15% of average monthly ETc. A sensitivity analysis of the computational procedure for Kc showed that relaxation from using the FAO-56 dual Kc method to the more simple mean (i.e., single) Kc curve and relaxation of specificity of planting and harvest dates did not substantially increase the projectwide prediction error The use of the mean Kc curves, where effects of evaporation from wet soil are included as general averages, predicted 5% lower than the dual method for monthly estimates and 8% lower on an annual basis, so that no adjustment was required to match annual ET derived from water balance. About one half of the reduction in estimates when applying the single (or mean) Kc method rather than the dual Kc method was caused by the lack of accounting for evaporation from special irrigations during the off season (i.e., in between crops).     

Using Indicator Kriging Technique for Soil Salinity and Yield Management

This paper presents a practical method to manage soil salinity and yield in order to obtain maximum economic benefits. The method was applied to a study area located in the southeastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The following were the objectives of this study: (1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; (2) compare the expected potential productivity of different crops based on the soil salinity conditions; and (3) assess the expected net revenue of multiple crops under different soil salinity conditions. Four crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a global position system unit. Different scenarios of crops and salinity levels were evaluated. Indicator variograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. Indicator kriging (IK) was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. Expected crop net revenue for each scenario was calculated and all the results were compared to determine the best scenarios. The results of this study show that IK can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated.     

Testing Reference Evapotranspiration Estimation Methods Using Evaporation Pan and Modeling in Maritime Region of Canada

Four methods of estimating daily reference evapotranspiration (ETo) were evaluated with the data collected from 2004 to 2006 in a Maritime weather station, the Potato Research Centre, Fredericton, N.B., Canada. We tested two models [i.e., the FAO-56 Penman–Monteith (PM) and the Priestley–Taylor (PT) equations] and two Class A pan methods (Cuenca and Snyder equations). In order to assess the Evaporation Pan methods, an automatic Class A Pan system was installed in a grassed field surrounded by potato fields and continuously measured from 2004 to 2006. The results from three growing seasons (years 2004–2006) indicated that both evaporation pan methods generated lower estimations of ETo compared to the PM and PT methods. The PT method produced the highest ETo estimation. The Snyder method showed a better agreement with the PM (r2>0.56). However, the agreement varied from year to year with an r2 value range of 0.4–0.7. Kpan coefficients (a factor to convert pan observation to ETo) varied from 0.78 to 0.94. In general, the Cuenca generated lower Kpan values (0.83) than the Snyder method (0.87). Compared to the PM, the PT method overestimated ETo, which may be related to the absence of humidity adjustment in the model. Furthermore, the research suggested that the time step played an important role in the estimation of ETo in this region. The PM method at daily time step was simple but intended to overestimate ETo by 10% compared to the hourly time-step method. In summary, when Class A Pan data are available, the Snyder equation can be used to calculate Kpan with an acceptable accuracy. If the PM method is used to estimate ETo when pan observations are unavailable, a reduction of 10% to the calculated ETo at daily time step could be applied to improve the accuracy of ETo estimation.     

Irrigation Scheduling for Green Bell Peppers Using Capacitance Soil Moisture Sensors

Vegetable production areas are intensively managed with high inputs of fertilizer and irrigation. The objectives of this study were to evaluate the interaction between N-fertilizer rates and irrigation scheduling using soil moisture sensor irrigation controllers (SMS) on yield, irrigation water use efficiency (IWUE) of bell pepper cultivated under plastic mulch and drip irrigation. Treatments included three irrigation scheduling and three N-rates (176, 220, and 330 kg/ha). Irrigation treatments were: SS10, water application controlled by SMS-based irrigation set at 10% volumetric water content (VWC) which was allotted five irrigation windows daily and bypassed events if the soil VWC exceeded the established threshold; SS12, threshold set at 12% VWC; and TIME, control with irrigation being applied once a day similar to grower irrigation management. Marketable yields ranged between 16 and 29 Mg/ha. The SMS treatments reduced the applied irrigation in 7 to 62% compared to TIME treatment without reducing yield. The treatments SS10 and SS12 reduced nitrate leaching by 25 to 73% compared to TIME treatment.     

Actual and Reference Evaporative Losses and Surface Coefficients of a Maize Field during Nongrowing (Dormant) Periods

Effective water resources planning, allocation, management, and use in agroecosystems require accurate quantification of actual evapotranspiration (ETc) during growing and nongrowing (dormant) periods. Prediction of ETc for a variety of vegetation surfaces during the growing season has been researched extensively, but relatively little information exists on evaporative losses during nongrowing periods for different surfaces. The objectives of this research were to evaluate ETc in relation to available energy, precipitation, and grass and alfalfa-reference ET (ETo and ETr) for a maize (Zea mays. L) field and to analyze the dynamics of surface coefficients (Kc) during the nongrowing period (October 15–April 30). The evaporative losses were measured using a Bowen ratio energy balance system (BREBS) on an hourly basis and averaged over 24?h for three consecutive nongrowing periods: 2004–2005 (Season I), 2005–2006 (Season II), and 2006–2007 (Season III). BREBS-measured ETc was approximately 50% of available energy (Rn?G; Rn is net radiation and G is soil heat flux density) during normal and wet seasons (Seasons I and III) and 41% of available energy during a dry season (Season II). Cumulative ETc ranged from 133?mm in Season II to 167?mm in Season III and exceeded precipitation by 21% during the dry season. The ratio of ETc to precipitation was 0.85 in Season I, 1.21 in Season II, and 0.41 in Season III. ETc was approximately 50% of ETo and 36% of ETr in both Seasons I and III, whereas in Season II, ETc was 32% of ETo and 23% of ETr. Overall, measured ETc during the dormant season was generally most strongly correlated with radiation terms, particularly Rn, albedo, incoming shortwave radiation, and outgoing longwave radiation. Average surface coefficients over the three seasons were 0.44 and 0.33 for grass and alfalfa-reference surfaces, respectively. Using geometric mean Kc values to calculate ETc using a KcETref approach over the entire nongrowing season yielded adequate predictions with overall root mean square deviations of 0.64 and 0.67?mm?day?1 for ETo and ETr, respectively. Estimates of ETc using a dual crop coefficient approach were good on a seasonal basis, but performed less well on a daily basis. Regression equations that were developed (accounting for serial autocorrelation in the ETc and ETref time series) yielded good estimates of ETc. Considering nongrowing period evaporative losses in water budget calculations would enable water regulatory agencies to better account for water use in hydrologic balance calculations over the entire year rather than only for the growing season and to better assess the progression and availability of water resources for the next growing season.     

Estimating Dispersion Coefficient and Porosity from Soil-Column Tests

A simple method for simultaneous and explicit estimation of dispersion coefficient and porosity from laboratory data on soil-column tests is presented. It makes use of the peak derivative of the observed breakthrough curve (BTC). An objective procedure for approximate location of the peak is suggested based upon a parabolic fit through three consecutive points around the peak. The computations involved are simple and can be done on a calculator. The method does not require either the rate of flow or the cross-sectional area of the soil-column for estimating the dispersion coefficient. This completely avoids the possible errors involved in the measurement of these quantities. The new method does not require the full BTC to be observed, however, its application does require data slightly beyond the time at which the exit concentration reaches half the input concentration. Application of the method on a number of laboratory data sets shows that it can yield a reliable estimate of the dispersion coefficient and porosity using only a few points near the peak. The method is applicable for high values (>100) of the piclet number.     

Evapotranspiration of Full-, Deficit-Irrigated, and Dryland Cotton on the Northern Texas High Plains

Cotton (Gossypium hirsutum L.) is beginning to be produced on the Northern Texas High Plains as a lower water-requiring crop while producing an acceptable profit. Cotton is a warm season, perennial species produced like an annual yet it requires a delicate balance of water and water deficit controls to most effectively produce high yields in this thermally limited environment. This study measured the water use of cotton in fully irrigated, deficiently irrigated, and dryland regimes in a Northern Texas High Plains environment using precision weighing lysimeters in 2000 and 2001. A lateral-move sprinkler system was used to irrigate the fields. The water use data were used to develop crop coefficient data and compared with the FAO-56 method for estimating crop water use. Cotton yield, water use, and water use efficiency was found to be as good in this region as other more noted cotton regions. FAO-56 evapotranspiration prediction procedures performed better for the more fully irrigated treatments in this environment.     

Methodology for Estimating Specific Yield in Shallow Water Environment Using Continuous Soil Moisture Data

A methodology for estimating specific yield in shallow water environments using continuous soil moisture data is introduced. An accurate estimate of specific yield is critical for management of water resources affecting well yields, water levels, and rates of water level decline. The study area is located in an intensive small-scale hydrologic monitoring field site in Hillsborough County, Florida. Data from four soil moisture monitoring stations were studied. The objectives of this paper are to describe a methodology to obtain precise estimates of the specific yield that can be obtained by a fitting procedure and to determine functional relationships between measured soil parameters and soil moisture storage variability. Estimated specific yield values varied from zero, when the water table was near land surface, to a maximum that was close to drainable porosity consistent with a humid shallow water environment, where sensitive ecosystems and wetlands are dependent on a seasonal hydroperiod of water table fluctuations. The results will be incorporated into regional integrated surface and ground water models that are being applied successfully in West-Central Florida.     

Reference and Crop Evapotranspiration in South Central Nebraska. II: Measurement and Estimation of Actual Evapotranspiration for Corn

In planning, designing, and managing of surface and groundwater supply, it is essential to accurately quantify actual evapotranspiration (ETc) from various vegetation surfaces within the water supply areas to allow water management agencies to manipulate the land use pattern alternatives and scenarios to achieve a desired balance between water supply and demand. However, significant differences among water regulatory agencies and water users exist in terms of methods used to quantify ETc. It is essential to know the potential differences associated with using various empirical equations in quantifying ETc as compared with the measurements of this critical variable. We quantified and analyzed the differences associated with using 15 grass (ETo) and alfalfa-reference (ETr) combination, temperature and radiation-based reference ET (ETref) equations in quantifying grass-reference actual ET (ETco) and alfalfa-reference actual ET (ETcr) as compared with the Bowen ratio energy balance system (BREBS)-measured ETc (ETc-BREBS) for field corn (Zea mays L.). We analyzed the performance of the equations for their full season, irrigation season, peak ET month, and seasonal cumulative ETc estimates on a daily time step for 2005 and 2006. The step-wise Kc values instead of smoothed curves were used in the ETc calculations. The seasonal ETc-BREBS was measured as 572 and 561?mm in 2005 and 2006, respectively. The root-means-quare difference (RMSD) was higher for the full season than the irrigation season and peak ET month estimates for all equations. The standardized ASCE Penman-Monteith (PM) ETco had a RMSD of 1.37?mm?d?1 for the full growing season, 1.05?mm?d?1 for the irrigation season, and 0.76?mm?d?1 for the peak month ET. The ASCE-PM, 1963 and 1948 Penman ETc estimates were closest to the ETc-BREBS. The FAO-24 radiation and the HPRCC Penman ETc estimates also agreed well with the ETc-BREBS. Most combination equations performed best during the peak ET month except the temperature and radiation-based equations. There was an excellent correlation between the ASCE-PM ETco and ETcr with a high r2 of 0.99 and a low RMSD of 0.34?mm?d?1. The difference between the ETcr and ETco was found to be larger at the high ETc range (i.e., >8?mm), but overall, the ETcr and ETco values were within 3%. Significant differences were found between the cumulative ETco-METHOD and ETcr-METHOD versus ETc-BREBS. Most combination equations, including the standardized ASCE-PM ETco and ETcr underestimated ETc-BREBS during the early periods of the growing season where the soil evaporation was the dominant energy flux of the energy balance and in the late season near and after physiological maturity when the transpiration rates were less than the midseason. The underestimations early in the season can be attributed to the lack of ability of the physical structure of the ETref×crop coefficient approach to “fully” account for the soil surface conditions when complete canopy cover is not present. The results of this study can be used as a reference tool by the water resources regulatory agencies and water users and can provide practical information on which method to select based on the data availability for reliable estimates of daily ETc for corn.