Evaluating the Impact of Daily Net Radiation Models on Grass and Alfalfa-Reference Evapotranspiration Using the Penman-Monteith Equation in a Subhumid and Semiarid Climate

Net radiation (Rn) is the main driving force of evapotranspiration (ET) and is a key input variable to the Penman-type combination and energy balance equations. However, Rn is not commonly measured. This paper analyzes the impact of 19 net radiation models that differ in model structure and intricacy on estimated grass and alfalfa-reference ET (ETo and ETr, respectively) and investigates how climate, season and cloud cover influence the impact of the Rn models on ETo and ETr. Datasets from two locations (Clay Center, Nebraska, subhumid; and Davis, California, a Mediterranean-type semiarid climate) were used. Rn values computed from the 19 models were used in the standardized ASCE-EWRI Penman-Monteith equation to estimate ETo and ETr on a daily time step. The influence of seasons on the estimation of Rn and on estimated ETo and ETr was investigated in winter (November–March) and summer (May–September) months. To analyze the influence of clouds on the impact of Rn models, relative shortwave radiation (Rrs) was used as a means to express the cloudiness of the days as: 0 ≤ Rrs ≤ 0.35 for completely cloudy days; 0.35相似文献   

Assessing Reference Evapotranspiration by the Hargreaves Method in Southern Spain

The Hargreaves method enables reference crop evapotranspiration (ET0) estimation in areas where meteorological information is scarce, as, for example, southern Spain. However, this method is known to produce considerable bias in this region, especially during the dry, hot summer months. An evaluation of the method is made by comparing daily estimates with those made by the more commonly recommended Penman–Monteith method at 16 meteorological stations. Computed ET0 values at the coastal stations are, on average, 0.69 mm?d?1 smaller than the Penman–Monteith estimates whereas at inland stations a small average overestimation of 0.13 mm?d?1 is shown. The adjusted Hargreaves coefficient (AHC), obtained through regression analysis, increases at the coastal stations, on average, to 0.0029, and decreases at the inland stations to 0.0022. Adjustment with the Samani method does generally not produce more accurate estimates in this region. Finally a linear relationship between the AHC and the rate of the average temperature to the average daily temperature range is proposed for the regional adjustment of the Hargreaves coefficient.     

Regional Estimation of Reference Evapotranspiration in Arid and Semiarid Regions

Evapotranspiration is critical to many applications including water resource management, irrigation scheduling, and environmental studies. Many models based on meteorological data have already been developed to estimate reference evapotranspiration (ET0) in various climatic and geographical conditions. The main purpose of this study was to evaluate the performances of the Makkink, Priestley-Taylor, and Hargreaves models versus the Penman-Monteith FAO-56 (PMF-56) method in arid and semiarid regions of Iran during 1993–2005 and to identify the alternative ET0 model that presents results closest to the PMF-56 method. Additionally, a regional estimation of monthly ET0 with the best-performed model is presented by using the spatially distributed physical parameters and geographical information system. The results indicated that the Hargreaves model was the best model to estimate ET0 in eastern arid and semiarid regions of Iran. The spatial distribution maps of ET0 showed that ET0 values increased from north to south as the aridity increased in the study area. The estimated total monthly ET0 revealed a significant variation during the growing seasons (April–September) so that the study region experienced the highest and lowest ET0 values of 250 and 80 mm in July and April, respectively.     

Modeling Reference Evapotranspiration Using Evolutionary Neural Networks

The ability of evolutionary neural networks (ENN) to model reference evapotranspiration (ET0) was investigated in this study. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed of three stations in central California, Windsor, Oakville, and Santa Rosa, were used as inputs to the ENN models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. In the first part of the study, a comparison was made between the estimates provided by the ENN and those of the following empirical models: the California Irrigation Management System, Penman, Hargreaves, modified Hargreaves, and Ritchie methods. Root-mean-squared error, coefficient of efficiency, and correlation coefficient statistics were used as comparing criteria for the evaluation of the models’ accuracies. The ENN performed better than the empirical models. In the second part of the study, the ENN results were compared with those of the conventional artificial neural networks (ANN). The comparison results revealed that the ENN models were superior to ANN in modeling the ET0 process.     

Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation

The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ET0) is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM ET0 values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the ET0 process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation ET0 using nearby station data are investigated.     

Using a Surface Energy Balance Model to Calculate Spatially Distributed Actual Evapotranspiration

Remote sensing algorithms are currently being used to estimate regional surface energy fluxes [e.g., latent heat flux or evapotranspiration (ET)]. Many of these surface energy balance models use information derived from satellite imagery such as Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimate ET provides advantages over traditional methods. One of the most important advantages is that it can provide regional estimates of actual ET at low cost. Most conventional methods are based on point measurements (e.g., soil water sensors, lysimeters, and weather station data), limiting their ability to capture the spatial variability of ET. Another advantage of remote sensing/surface energy balance ET models is that they are able to estimate the actual crop ET as a residual of the energy balance without the need of using reference crop ET and tabulated crop coefficients. This paper focuses on the application of the energy balance-based model “Remote Sensing of ET” (ReSET) that uses a procedure to deal with the spatial and temporal variability of ET. The model was used to estimate actual ET for multiple dates in the Arkansas River Basin in Colorado, South Platte River Basin in Colorado, and Palo Verde Irrigation District in California along with a 1-day ET estimate for the Southern High Plains (Texas). Comparisons between ReSET results and ET values from more conventional ET methods [e.g., 2005 ASCE-EWRI Standardized Reference Evapotranspiration (Penman-Monteith) Equation] are also presented.     

Comparison of Some Reference Evapotranspiration Equations for California

Four reference evapotranspiration (ETo) equations are compared using weather data from 37 agricultural weather stations across the state of California. The equations compared include the California Irrigation Management Information System (CIMIS) Penman equation, the Penman–Monteith equation standardized by the Food and Agriculture Organization (FAO), the Penman–Monteith equation standardized by the American Society of Civil Engineers, and the Hargreaves equation. Hourly and daily comparisons of ETo and net radiation (Rn) are made using graphics and simple linear regressions. ETo values estimated by the CIMIS Penman equation correlated very well with the corresponding values estimated by the standardized Penman–Monteith equations on both hourly and daily time steps. However, there are greater differences between the Rn values estimated by the two procedures. Although there are exceptions, the Hargreaves equation compared well to the FAO Penman–Monteith method. Spatial variability of the resulting correlations between the different equations is also assessed. Despite the wide variability of microclimates in the state, there are no visible spatial trends in correlations between the different ETo and/or Rn estimates.     

Evaluation of Dust Palliatives for Unpaved Roads in Arid Climates

An evaluation of commercial and experimental dust palliatives was conducted to determine their effectiveness for mitigating fugitive dust on roads in arid climates. Several types of chemicals were tested including polymer emulsions, lignosulfonates, chloride salts, synthetic fluids, an asphalt emulsion, a polysaccharide solution, a polyacrylamide, and a guar gum. Each product was placed in an individual test section at a rate of 3.8?L/m2 using an admix construction method (grade/spray/till/compact/spray). Fourteen test sections were constructed and observed at 30-day intervals to monitor product performance. Data from both stationary and mobile particle collectors were analyzed to determine the ability of each product to suppress dust for extended periods. Several products are recommended for use on roads in arid climates as a result of this evaluation.     

Predicting Incoming Solar Radiation and Its Application to Radiation-Based Equation for Estimating Reference Evapotranspiration

This paper presents an inverse square weighted interpolation for predicting the incoming solar radiation (Rs) from nearby weather stations. The predicted Rs is applied to the well-known Priestley-Taylor equation for estimating reference evapotranspiration (ETo). This cross-validation estimated bias and error in the final model predictions of the Rs and ETo at the 21 meteorological weather stations in Korea Peninsula. The coefficient of determination and the root-mean-square error (RMSE) for monthly estimates of Rs was in the range of 0.83–0.95 and 17.90–76.34?MJ?m?2?day?1, respectively. The RMSE for monthly estimate of ETo values at inland and coastal areas was 11.08 and 15.01 mm respectively. The estimates of ETo using thus predicted Rs to provide reasonable accuracy. The study can provide further useful guidelines for crop production, water resources conservation, irrigation scheduling, and environmental assessment.     

Performance of Penman-Monteith FAO56 in a Semiarid Highland Environment

Reliable estimates of evapotranspiration are essential for irrigation and water resources planning and management. Although several methods are available for computing reference evapotranspiration (ETo), the provision of complete and accurate climate data is often a problem. Therefore, weighing lysimeter data from a semiarid highland environment were used to evaluate the performance of six commonly used reference evapotranspiration estimation methods with different data requirements (Penman-Monteith-FAO56, Priestley-Taylor, Radiation-FAO24, Hargreaves, Blaney-Criddle, Class A pan). The lysimeter experiments were conducted at Ankara Research Institute of Rural Services in Turkey, during the April–October cropping seasons of the years 2000–2002. The average ETo for the three seasons, computed from the lysimeter data, was 964 mm. The Penman-Monteith-FAO56 method was also evaluated for cases where relative humidity, wind speed, solar radiation, or all three parameters would be missing. This resulted in a total of 10 different methods. The RMS errors (RMSE) and index of agreement for the daily data and the monthly averages as well as the mean absolute error (MAE) for the seasonal totals were computed to compare these methods. The methods were ranked based on the sum of the ranks for all five evaluation criteria. The Penman-Montheith-FAO56 method with the full data set, with replacement of wind speed, and with replacement of relative humidity took the top three spots, with MAEs for the seasonal totals ranging between 40 and 70 mm. The Hargreaves method came in fourth (MAE 54 mm), followed by the Penman-Montheith-FAO56 method with replacement of all three parameters (MAE 57 mm). The RMSE for the monthly average ETo was 0.43 and 0.50?mm?days?1 for the Penman-Monteith-FAO56 without and with replacement of all three parameters and 0.48?mm?days?1 for Hargreaves. Thus, if only temperature data would be available, the much easier to use Hargreaves method would be preferred above the Penman-Montheith-FAO56 equation with replacement of humidity, radiation, and wind speed data, for this semiarid highland environment.     

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.     

Crop Coefficients for Microsprinkler-Irrigated, Clean-Cultivated, Mature Citrus in an Arid Climate

Crop evapotranspiration (ETc) was measured over a clean-cultivated, mature navel orange orchard with microsprinkler irrigation located near Lindsay, California. Hourly mean latent heat flux density was determined as the residual of the energy balance equation with measured net radiation, soil heat flux density and sensible heat flux density estimated using the surface renewal method. The ETc was compared with ETo calculated using hourly weather data and the ASCE-EWRI Penman-Monteith equation. Following pruning and topping of the trees in the spring of 2001, the Kco values slowly increased as the canopy developed in the following season. An average Kco = 0.82 was observed. In the following year, the mean summertime value increased to about Kco = 0.95, and in 2003 and 2004, the summertime value averaged near Kco = 1.00, which is somewhat higher than observed for drip irrigated trees in southwestern Arizona and considerably higher than reported in the widely used Food and Agricultural Organization of the United Nations publications that were based on infrequent surface irrigation.     

Evaluation of Class A Pan Coefficients for Estimating Reference Evapotranspiration in Humid Location

Evaporation pans [Class A pan, U.S. Weather Bureau (USWB)] are used extensively throughout the world to measure free-water evaporation and to estimate reference evapotranspiration (ET0). However, reliable estimation of ET0 using pan evaporation (Epan) depends on the accurate determination of pan coefficients (Kpan). Two equations developed by Frevert et al. in 1983 and Snyder in 1992 to estimate daily Kpan values were evaluated using a 23-year climate dataset in a humid location (Gainesville, Florida). The ET0 data, calculated using daily Kpan values from these equations, were compared to the Food and Agricultural Organization (FAO)-Penman-Monteith (FAO56-PM) method. The two equations resulted in significantly different daily Kpan values that produced different daily, monthly, and annual total ET0 estimates. The ET0 values calculated using Frevert et al.’s 1983 Kpan coefficients were in very good agreement with the FAO56-PM method with daily, monthly, and annual mean percent errors (PE) of 5.8, 5.5, and 5.7%, respectively. The daily and annual mean-root-mean-square error (RMSE) of the estimates using this method were as low as 0.33 and 7.3 mm, respectively. Snyder’s 1992 equation overestimated FAO56-PM ET0 with daily, monthly, and annual mean PEs of 16.3, 13.8, and 13.2%, respectively. The daily and annual mean RMSEs for this method were higher (0.6 and 18 mm) than those obtained with Frevert et al.’s 1983 coefficients. The overestimations with Snyder’s 1992 method were highest in the peak ET0 month of May and in summer months. The performances of the Kpan equations were also evaluated using randomly selected individual years (1979, 1988, 1990, and 1994) of climate data that had different climate characteristics than the 23-year average dataset. Frevert et al.’s 1983 coefficients resulted in good ET0 estimates with lower annual mean PEs of 7.0, 0.1, 15.7, and 1.3% for 1979, 1988, 1990, and 1994, respectively, compared to Snyder’s 1992 equation, which resulted in considerably higher PEs of 17.6, 9.1, 26.2, and 14.3% in 1979, 1988, 1990, and 1994, respectively. It was concluded that using Frevert et al.’s 1983 equation to calculate daily Kpan provided more accurate ET0 estimates, relative to the FAO56-PM method, from Epan data compared to Snyder’s 1992 equation under the humid-region climatic conditions in this study. The method is very useful in computer calculations of ET0 since it does not require “table lookup” for Kpan values.     

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.     

Simplified Estimation of Reference Evapotranspiration from Pan Evaporation Data in California

Evaporation pan (Ep) data are often used to estimate reference evapotranspiration (ET0) for use in water resource planning and irrigation scheduling. This paper reviews equations to estimate ET0 from Ep and provides a simpler method to make this conversion for arid climatic conditions like in California. The new method accounts for fetch differences by first adjusting the Ep rates to values expected for 100?m of grass fetch. Then it relies on an empirical relationship between ET0 and the adjusted Ep to determine Kp values; thus, eliminating the need for relative humidity and wind speed data that are often unavailable. The method is conceptually simpler, easier to code into computer applications, and within California, it gave better results than methods based on relative humidity and wind speed. However, the method might require calibration in more humid or windier climates.     

Daily Grass and Alfalfa-Reference Evapotranspiration Estimates and Alfalfa-to-Grass Evapotranspiration Ratios in Florida

Efficient use of natural water resources in agriculture is becoming an important issue in Florida because of the rapid depletion of freshwater resources due to the increasing trend of industrial development and population. Reliable and consistent estimates of evapotranspiration (ET) are a key element of managing water resources efficiently. Since the 1940s numerous grass- and alfalfa-reference evapotranspiration (ETo and ETr, respectively) equations have been developed and used by researchers and decision makers, resulting in confusion as to which equation to select as the most accurate reference ET estimates. Twenty-one ETo and ETr methods were evaluated based on their daily performance in a humid climate. The Food and Agriculture Organization Penman-Monteith (FAO56-PM) equation was used as the basis for comparison for the other methods. Measured and carefully screened daily climate data during a 23-year period (1978–2000) were used for method performance analyses, in which the methods were ranked based on the standard error of estimate (SEE) on a daily basis. In addition, the performance of the four alfalfa-based ET (ETr) equations and the ratio of alfalfa ET to grass ET (Kr values) were evaluated, which have not been studied before in Florida’s humid climatic conditions. The peak month ETo estimates by each method were also evaluated. All methods produced significantly different ETo estimates than the FAO56-PM method. The 1948 Penman method estimates were closest to the FAO56-PM method on a daily basis throughout the year, with the daily SEE averaging 0.11 mm?d?1; thus this method was ranked the second best overall. Although 1963 Penman (with the original wind function) slightly overestimated ET, especially at high ETo rates, it provided remarkably good estimates as well and ranked as the third best method, with a daily average SEE value of 0.14 mm?d?1. Both methods produced peak month ETo estimates closest to the FAO56-PM method among all methods evaluated, with daily peak month SEEs averaging 0.07 and 0.09 mm?d?1, respectively. Significant variations were observed in terms of the performance of the various forms of Penman’s equations. For example, the original Penman-Monteith method produced the poorest ETo estimates among the combination equations, with a daily SEE for all months and peak month averaging 0.50 and 0.35 mm?d?1, respectively and ranked 11th. An average value of 1.18 was used to convert ETr estimates to ETo values for alfalfa-reference methods. The Kr value of 1.18 resulted in reasonable estimates of ETo throughout the year by the Kimberley forms of the Penman equations. Another ETr-based equation, Jensen-Haise, gave consistently poor estimates. The Stephens-Stewart radiation method was the highest-ranked (10th) noncombination method overall. The temperature-based McCloud method (ranked 19th) produced the poorest ETo estimates among all methods with a daily SEE for all months and for the peak month averaging 1.93 and 1.22 mm?d?1, respectively. In general, the results obtained from the temperature methods suggest that all of the temperature methods, with the possible exception of the Turc method, can only be applicable for these climatic conditions after they are calibrated or modified locally or regionally. The FAO and Christiansen pan evaporation methods (ranked 17th and 18th, respectively) produced poor ETo estimates and had the largest amount of point scatter in daily ETo estimates relative to the FAO56-PM ETo. Both methods resulted in the highest daily SEE of 1.18 and 1.19 mm?d?1 for all months, after the McCloud method (1.93 mm?d?1), and with the highest SEE of 1.30 and 1.24 mm?d?1 for the peak month of all methods evaluated. The FAO56-PM method uses solar radiation, wind speed, relative humidity, and minimum and maximum air temperature to estimate ETo. It has been recommended that the FAO56-PM be used for estimating ETo when all the necessary input parameters are available. However, all these input variables may not be available, or some of them may not be reliable for a given location if the FAO56-PM equation is used, and one may need to choose other temperature, radiation, or pan evaporation methods based on the availability of data for estimating ETo. The results of this study can be used as a reference tool to provide practical information on which method to select based on the availability of data for reliable and consistent estimates of daily ETo relative to the FAO56-PM method in a humid climate.     

Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates

Reference crop evapotranspiration (ET0) is a key variable in procedures established for estimation of evapotranspiration rates of agricultural crops. In recent years, there is growing evidence to show that the more physically based FAO-56 Penman–Monteith (PM) combination method yields consistently more accurate ET0 estimates across a wide range of climates and is being proposed as the sole method for ET0 computations. However, other methods continue to remain popular among Indian practitioners either because of traditional usage or because of their simpler input data requirements. In this study, we evaluated the performances of several ET0 methods in the major climate regimes of India with a view to quantify differences in ET0 estimates as influenced by climatic conditions and also to identify methods that yield results closest to the FAO-56 PM method. Performances of seven ET0 methods, representing temperature-based, radiation-based, pan evaporation-based, and combination-type equations, were compared with the FAO-56 PM method using historical climate data from four stations located one each in arid (Jodhpur), semiarid (Hyderabad), subhumid (Bangalore), and humid (Pattambi) climates of India. For each location, ET0 estimates by all the methods for assumed hypothetical grass reference crop were statistically compared using daily climate records extending over periods of 3–4 years. Comparisons were performed for daily and monthly computational time steps. Overall results while providing information on variations in FAO-56 PM ET0 values across climates also indicated climate-specific differences in ET0 estimates obtained by the various methods. Among the ET0 methods evaluated, the FAO-56 Hargreaves (temperature-based) method yielded ET0 estimates closest to the FAO-56 PM method both for daily and monthly time steps, in all climates except the humid one where the Turc (radiation-based) was best. Considering daily comparisons, the associated minimum standard errors of estimate (SEE) were 1.35, 0.78, 0.67, and 0.31 mm/day, for the arid, semiarid, subhumid, and humid locations, respectively. For monthly comparisons, minimum SEE values were smaller at 0.95, 0.59, 0.38, and 0.20 mm/day for arid, semiarid, subhumid, and humid locations, respectively. These results indicate that the choice of an alternative simpler equation in a particular climate on the basis of SEE is dictated by the time step adopted and also it appears that the simpler equations yield much smaller errors when monthly computations are made. In order to provide simple ET0 estimation tools for practitioners, linear regression equations for preferred FAO-56 PM ET0 estimates in terms of ET0 estimates by the simpler methods were developed and validated for each climate. A novel attempt was made to investigate the reasons for the climate-dependent success of the simpler alternative ET0 equations using multivariate factor analysis techniques. For each climate, datasets comprising FAO-56 PM ET0 estimates and the climatic variables were subject to factor analysis and the resulting rotated factor loadings were used to interpret the relative importance of climatic variables in explaining the observed variabilities in ET0 estimates. Results of factor analysis more or less conformed the results of the statistical comparisons and provided a statistical justification for the ranking of alternative methods based on performance indices. Factor analysis also indicated that windspeed appears to be an important variable in the arid climate, whereas sunshine hours appear to be more dominant in subhumid and humid climates. Temperature related variables appear to be the most crucial inputs required to obtain ET0 estimates comparable to those from the FAO-56 PM method across all the climates considered.     

Reference and Crop Evapotranspiration in South Central Nebraska. I: Comparison and Analysis of Grass and Alfalfa-Reference Evapotranspiration

In Nebraska, historically, there have been differences among the water regulatory agencies in terms of the methods used to compute reference evapotranspiration (ETref) to determine actual crop water requirements and hydrologic balances of watersheds. Because simplified and/or empirical temperature or radiation-based methods lack some of the major weather parameters that can significantly affect grass and alfalfa-reference ET (ETo and ETr) the performance of these methods needs to be investigated to help decision makers to determine the potential differences associated with using various ETref equations relative to the standardized ASCE Penman–Monteith (ASCE-PM) equations. The performance of 12 ETo and five ETr equations were analyzed on a daily basis for south central Nebraska from 1983 to 2004. The standardized ASCE-PM ETo and ETr values were used as the basis for comparisons. The maximum ASCE-PM ETo value was estimated as 12.6?mm?d?1, and the highest ETr value was estimated as 19?mm?d?1 on June 21, 1988. On this day, the atmospheric demand for evaporation was extremely high and the vapor pressure deficit (VPD) reached a remarkably high value of 4.05?kPa. The combination-based equations exhibited significant differences in performance. The 1963 Penman method resulted in the lowest RMSD of 0.30?mm?d?1 (r2 = 0.98) and its estimates were within 2% of the ASCE-PM ETo estimates. The 1948 Penman estimates were similar to the 1963 Penman (r2 = 0.98, RMSD = 0.39?mm?d?1). Kimberly forms of alfalfa-reference Penman equations performed well with RMSD of 0.48?mm?d?1 for the 1972 Kimberly–Penman and 0.67?mm?d?1 for the 1982 Kimberly–Penman. The locally-calibrated High Plains Regional Climate Center (HPRCC) Penman method, ranked 6th, performed well and underestimated the ASCE-PM ET by 5% (RMSD = 0.56?mm?d?1). Most of the underestimations occurred at the high ET range (>11?mm) and this was attributed to the upper limits applied by the HPRCC on VPD, (2.3?kPa) and wind speed (5.1?m?s?1). The lack of ability of the radiation methods in accounting for the wind speed and relative humidity hindered the performance of these methods in the windy and rapidly changing VPD conditions of south central Nebraska. The 1977 FAO24 Blaney–Criddle method was the highest ranked (seventh) noncombination method (RMSD = 0.64?mm?d?1, r2 = 0.94). The FAO24 Penman estimates were within 4% of the ASCE-PM ETo. Overall, there were large differences between the ASCE-PM ETo and ETr versus other ETref equations that need to be considered when other forms of the combination or radiation and temperature-based equations are used to compute ETref. We recommend that the ASCE-PM ETo or ETr equations be used for estimating ETref when necessary weather variables are available and have good quality. The results of this study can be used as a reference tool to provide practical information, for Nebraska and similar climates, on the potential differences between the ASCE-PM ETo and ETr and other ETref equations. Results can aid in selection of the alternative method(s) for reasonable ETref estimations when all the necessary weather inputs are not available to solve the ASCE-PM equation.