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.     

Estimating Reference Crop Evapotranspiration with ETgages

Three years of daily reference evapotranspiration measured by atmometers (ETg) were compared to the values computed from the ASCE standardized Penman–Monteith equation (ETr) using co-located meteorological measurements at 19 locations across North Carolina. The atmometers underestimated daily ETr by an average of 21% across the study area. Linear regression models between ETg and ETr had intercepts significantly different from zero and slopes different from one, but would generally yield a gauge error within 1?mm?day?1. The ETg-ETr relationship was found to be highly sensitive to precipitation and wind speed, but rather insensitive to humidity, radiation, and temperature. Although wind speed is generally low in the study area, the insensitivity of ETgages to wind caused severe underestimation in those periods when wind speed was high. Mean absolute error increased from 17% when wind speed was below 1?m?s?1 to 64% when wind speed was greater than 5?m?s?1. Precipitation can temporarily disrupt ETgage evaporation and cause underestimation of ETr. The linear relationship between ETg and ETr in rainy days was significantly different than that of the clear days. Analysis of the local relationships suggested that they are sensitive to their major surrounding physiographic environment and to the strictly local surface conditions, but not to the intermediate mesoscale surface environment. As a result, different linear regression equations were developed to adjust ETg to ETr in three land regions and in dry or wet conditions.     

Estimating Reference Evapotranspiration with Minimum Data in Florida

Reference evapotranspiration estimation methods that require minimal data are necessary when climatic data sets are incomplete, inaccurate, or unavailable. This study was conducted to evaluate temperature-based reference evapotranspiration methods in Florida. Using reference evapotranspiration estimates using satellite-derived radiation as the standard for comparison, the “reduced-set” Penman-Monteith, Hargreaves, and Turc equations were evaluated using monthly temperature data from 72 weather stations in Florida. The reduced-set Penman-Monteith equation requires maximum and minimum temperature only and uses recommended methods to estimate radiation, humidity, and wind speed. The reduced-set Penman-Monteith and Hargreaves equations were found to overestimate reference evapotranspiration while the Turc equation neither overestimated nor underestimated. The reduced-set Penman-Monteith equation showed greatest error in coastal stations while the Hargreaves equation showed greatest error at inland and island locations. In the absence of regionally calibrated methods the Turc equation is recommended for estimating reference evapotranspiration using measured maximum and minimum temperature and estimated radiation in Florida.     

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.     

Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data

The objective of this study was to test an artificial neural network (ANN) for estimating the reference evapotranspiration (ETo) as a function of the maximum and minimum air temperatures in the Campos dos Goytacazes county, State of Rio de Janeiro. The data used in the network training were obtained from a historical series (September 1996 to August 2002) of daily climatic data collected in Campos dos Goytacazes county. When testing the artificial neural network, two historical series were used (September 2002 to August 2003) relative to Campos dos Goytacazes, and Vi?osa, State of Minas Gerais. The ANNs (multilayer perceptron type) were trained to estimate ETo as a function of the maximum and minimum air temperatures, extraterrestrial radiation, and the daylight hours; and the last two were previously calculated as a function of either the local latitude or the Julian date. According to the results obtained in this ANN testing phase, it is concluded that when taking into account just the maximum and minimum air temperatures, it is possible to estimate ETo in Campos dos Goytacazes.     

Surface Energy Balance-Based Model for Estimating Evapotranspiration Taking into Account Spatial Variability in Weather

Reliable estimates of evapotranspiration (ET) from vegetation are needed for many types of water-resource investigations. How well models can estimate ET from vegetation varies, depending on the capabilities of the model as well as the nature of the targeted vegetation. Model accuracy also depends heavily on the quality and quantity of the data used. Several ET models have been developed that use an energy balance approach in which the data used by the models are derived from satellite imagery. This research introduces an enhanced surface energy balance-based model, the remote sensing of evapotranspiration or ReSET model, for estimating ET. ReSET is an ET estimation model that takes into consideration the spatial variability in weather parameters, which makes it particularly applicable for calculating regional scale ET. ReSET also has the capability of interpolating between the available weather stations in time and space. The model’s accuracy at daily and seasonal time scales is evaluated in several case studies.     

Temperature Adjustment for Reference Evapotranspiration Calculation in Central Arizona

A key component in the calculation of reference crop evapotranspiration (ETr) is the weather data. If the weather data have been collected from a station under nonreference conditions, the data itself may contain errors, which will in turn yield inaccurate ETr estimates. It was proposed by Allen in 1996 that data used for evapotranspiration be scrutinized by comparing daily minimum temperature (Tmin) and the daily average dew point temperature (Tdew). If the difference between Tmin and Tdew is greater than 3°C, then the site is considered to be arid (nonreference) and adjustments are recommended for temperature and dew point data. In Arizona, normal weather conditions often occur where Tmin and Tdew do not approach each other. This study examined the appropriateness of applying the conditions set forth by Allen to temperature data collected in central Arizona. Two weather stations were set up in a 35.5?ha alfalfa field in central Arizona to measure dry bulb and wet bulb temperatures. Additionally, plant temperature data were collected to verify field conditions. Daily data were taken for 1.5 years at the University of Arizona’s Maricopa Agricultural Center. Of the 611 days of data collected, the difference between Tmin and Tdew was greater than 3°C on 329 days, indicating that these data were not taken under reference conditions. Among these data, 178 days were verified as nonreference but 151 were verified as actually being under reference conditions. Making adjustments for these days (151 days) resulted in a 47?mm decrease in ETr estimation, which mostly occurred during the summer.     

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.     

Estimating Regional Evapotranspiration Using Remote Sensing: Application to Sone Low Level Canal System, India

Remote sensing-derived spectral data have been used in the past to partition net radiation, soil heat, and sensible heat fluxes for estimating latent heat flux as a residual of surface energy balance, and thus regional evapotranspiration. Attempts to provide a simplified procedure for estimating sensible heat flux at a regional scale have not been successful because of the relatively strong dependence of the heat transfer coefficient on the land–atmosphere boundary condition. This paper presents a remote sensing-based procedure to estimate the sensible heat flux incorporating the local meteorological conditions, and in turn to determine the regional evapotranspiration. The model utilizes satellite-derived surface albedo, surface temperature, and leaf area index along with a very few agrometeorological data as inputs. The proposed procedure has been tested on a part of the Western Yamuna Canal system, India, and is found to be computationally simple as well as stable. For a well-watered wheat crop, the average evapotranspiration by the proposed model is estimated to be 2.05?mm?d?1 on January 30, 1996, whereas it is estimated to be 1.89?mm?d?1 using the Penman-Monteith equation, indicating a difference of less than 10%. The model is subjected to sensitivity analysis for uncertainties in the observed wind velocity and the computed leaf area index (by ±20%) to estimate sensible heat flux. Results reveal that the percentage change in mean sensible heat flux for the image is less than 5% in all cases, thus indicating the acceptability of the model against the uncertainties. Further, the model has been applied to three sets of Landsat-TM data covering the Sone Low Level Canal system, India, to demonstrate its usefulness in evaluating water delivery performance.     

Infilling Missing Daily Evapotranspiration Data Using Neural Networks

This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination (R2) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN and readily available weather data where the ET observations exist before and after the gap is a reliable and convenient method. It could be used to obtain continuous ET data for modeling and water management practices.     

Comparison of Simplified Pan-Based Equations for Estimating Reference Evapotranspiration

Accurate estimation of reference evapotranspiration (ET0) is essential for irrigation practice. Conversion from pan evaporation data to reference evapotranspiration is commonly practiced. The objective of this study was to evaluate the reliability of simplified pan-based approaches for estimating ET0 directly that do not require the data of relative humidity and wind speed. In this study, three pan-based (FAO-24 pan, Snyder ET0, and Ghare ET0) equations were compared against lysimeter measurements of grass evapotranspiration using daily data from Policoro, Italy. Based on summary statistics, the Snyder ET0 equation ranked first with the lowest RMSE value (0.449?mm?day?1). The pan-based equations were additional tested using mean daily data collected in Novi Sad, Serbia. The Snyder ET0 equation best matched ET0 estimates by Penman-Monteith equation at Novi Sad with lowest root mean square error value of 0.288?mm?day?1. The obtained results demonstrate that simplified pan-based equations can be successful alternative to FAO-56 Penman-Monteith equation for estimating reference evapotranspiration. The overall results recommended Snyder ET0 equation for pan evaporation to evapotranspiration conversions. The Snyder ET0 equation consistently provides better results compared to FAO-24 pan equation, although required measurements of only one weather parameter pan evaporation.     

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.     

Solar and Net Radiation-Based Equations to Estimate Reference Evapotranspiration in Humid Climates

Two equations for estimating grass reference evapotranspiration (ET0) were derived using the Food and Agriculture Organization Penman–Monteith (FAO56-PM) method as an index. The first equation, solar radiation (Rs) based, estimates ET0 from incoming Rs and maximum and minimum air temperature, and the second equation, net radiation (Rn) based, uses Rn and maximum and minimum air temperature. The equations were derived using 15 years (1980–1994) of daily ET0 values estimated from the FAO56-PM method using the measured and carefully screened weather data from near Gainesville, Florida. The performance of the derived equations was evaluated for 6 validation years (1995–2000), including dry and wet years, for the same site and for other humid locations in the Southeast United States. Comparisons of the performance of the derived equations with the other commonly used methods indicated that they estimate ET0 as good or better than those other ET0 methods. The Rs- and Rn-based equations resulted in the lowest 6 year average standard error of estimate (SEE) of daily ET0 (0.44 and 0.41 mm?day?1, respectively). Both equations performed quite well for estimating peak month ET0 and had the lowest 6 year average daily SEE for the peak month ET0 (0.24 mm?day?1 for both equations). Estimates for annual total ET0 were very close to those obtained from the FAO56-PM method. The 6 year average ratio of ET0?method to ET0?FAO56-PM were 1.05 and 1.03 for the Rs- and Rn-based equations, respectively. The derived equations were further evaluated in other humid locations in the Southeast United States, including two locations in coastal regions in Florida, one location in Georgia, and another location in Alabama. The comparisons showed that both equations are likely to provide good estimates of ET0 in humid locations of the Southeast United States. When the required input variables are considered, the Priestley–Taylor (PT) method was the closest method to the second derived equation (Rn based). Therefore, it was necessary to evaluate how the PT method would perform compared to the Rn-based equation relative to the FAO56-PM method after it is calibrated locally. Although the performance of the PT method improved slightly after the calibration, its performances for estimating daily and peak month ET0 remained poorer than the Rn-based equation in all cases. Considering the limitations associated with the availability and reliability of the climatological data, especially in developing countries, the derived equations presented in this study are suggested as practical methods for estimating ET0 if the standard FAO56-PM equation cannot be used because of the above-mentioned limitations. These equations are recommended over the other commonly used simplified temperature and radiation-based methods evaluated in this study for humid climates in the Southeast United States.     

Using Modified Bellani Plate Evapotranspiration Gauges to Estimate Short Canopy Reference Evapotranspiration

Modified Bellani plate atmometer has been offered as an alternative and simpler technique to combination-based equations to estimate evapotranspiration (ET) rate from green grass surface. However, there is a lack of information on its’ accuracy in humid climates. The evaporation rate (EA) from one type of atmometer marketed under the brand name ETgage? (or ETG) with a Number 30 green canvas cover that simulates the ET rate from a green grass surface was tested against the reference ET of a short green grass canopy (ETo) computed using the Food and Agriculture Organization of the United Nations Paper No. 56 Penman–Monteith (FAO56-PM) equation in two sites in north-central Florida. The ETG underestimated the ETo as much as 27%. The root mean square error (RMSE) of daily estimates of EA ranged from 1.03 to 1.15?mm. Data analyses indicated that the most of the poor performances and underestimations of the ETG occurred on rainy days. Using only the nonrainy day EA versus ETo relationship, the daily RMSE was as low as 0.47?mm and r2 was as high as 0.89, and the underestimations were within 3% of the ETo. Averaging daily ETG readings over 3 and 7 day periods considerably improved (lower RMSE and percent error, %E, and higher r2) ETo estimates. The ETG performed quite well on nonrainy days. The adjustment factors were developed and tabulated as a function of rainfall amount to adjust the EA values on rainy days. Results showed that an average adjustment factor of 0.84?(EA/0.84 = ETo) can be used as a practical number if rainfall observations are not available. The underestimations of the ETG on rainy days were attributed, in part, to the wetting of the green canvas cover due to the rainwater accumulations on it and to the lower diffusivity (higher resistance) value of the canvas cover (112–294?s?m?1) compared to the diffusivity of a green grass surface used in the ETo definition (70?s?m?1). Although it is found that the ETG is feasible and practical device, the EA values measured on rainy days require careful interpretation in humid and rainy climates such as Florida. The rainy day EA values should be used cautiously with the proper regression equation and adjustment factors to estimate ETo for irrigation scheduling if the input variables are not available to use the FAO56-PM equation for ETo estimates.     

Comparison of Standardized Reference Evapotranspiration Equations in Southern Spain

ASCE and FAO-56 standardized reference evapotranspiration (ET0) equations were compared using data from 31 meteorological stations in Andalusia, Southern Spain. Comparisons were made between daily ET0 obtained by summing hourly standardized ASCE–Penman–Monteith estimations and calculated from the addition of hourly FAO56–Penman–Monteith estimations, daily ET0 estimated on a daily basis, and calculated by the Hargreaves equation. On an hourly basis, the FAO-56 version estimated lower than the ASCE version as 6% in some locations, with a difference of 4% on the average, mainly due to the higher surface resistance (70?s?m?1) used in the FAO-56 version during daytime periods, as opposed to the 50?s?m?1 rs value used by the ASCE version. Differences between both estimates were higher when evaporative demand increases. The level of agreement improved when the two computational time steps were compared, because differences were lower (2% on the average) and did not depend on the wind speed or ET0 values. The Hargreaves equation showed a higher spatial variability. At coastal areas, the equation generally underpredicted ASCE Penman–Monteith ET0 and provided good estimations for inland locations. Accuracy of the equation was affected by annual averages of evaporative demand and wind speed.     

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).     

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.     

Evaluation of Reference Evapotranspiration Estimation Methods under Southeast Australian Conditions

Reference evapotranspiration (ETo) estimates are often required for use in water resources planning and irrigation scheduling. Ten ETo estimation methods ranging from simple temperature-based to data-extensive combination methods, including Hargreaves (HAR), improved Hargreaves (IHA), FAO-24 Radiation (RAD), Ritchi-type (RIT), FAO-24 Class-A Pan with pan coefficients of Doorenbos and Pruitt (PEV) and empirical regression coefficient (SEV), combination methods McIlroy (McI), FAO-Penman with wind functions of Watts and Hancock (W_H) and Meyer (M_PY), and the Penman-Monteith (P_M) were evaluated at three sites, namely, Aspendale, Griffith, and Tatura in the Goulburn-Murray Irrigation Area (GMIA) of southeastern Australia. At Aspendale, 4 out of 10 ETo methods (McI, M_PY, SEV, and RAD) overestimated the ETo estimates; at Griffith no method overestimated them, whereas at Tatura only the RAD method overestimated ETo. The overestimations were at Griffith, McI (1%), M_PY (10%), and SEV (4%); at Tatura, RAD (2%). At the Griffith and Tatura sites, almost all methods showed a strong tendency to underestimate daily ETo estimates throughout the entire range of evaporative demand. Overall, the underestimation ranges observed were McI (12–27%), W_H (7–22%), RIT (6–25%), PEV (19–31%), HAR (18–31%), and IHA (8–11%). The underestimation of daily ETo estimates by the P_M method ranged from 21 to 29%, raising caution about its use as a base method (without calibration against measured data under local conditions) to evaluate other ETo methods, as has been advocated in recent literature. The use of the McI method as the top-ranked method at Aspendale and Tatura, and the W_H method at Griffith, indicated that no single daily ETo estimation method using meteorological data was satisfactory for all three sites. Generally, the combination methods proved to be the most accurate ETo estimates. At Tatura, the fact that the RAD method was ahead of the W_H and M_P combination methods indicates how a less data-intensive ETo method, if calibrated, can perform even better than a physically based combination method. All ETo estimation methods required local calibration against measured lysimeter ETo data for better performance.     

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.     

Predicting Daily Net Radiation Using Minimum Climatological Data

Net radiation (Rn) is a key variable for computing reference evapotranspiration and is a driving force in many other physical and biological processes. The procedures outlined in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 [FAO56 (reported by Allen et al. in 1998)] for predicting daily Rn have been widely used. However, when the paucity of detailed climatological data in the United States and around the world is considered, it appears that there is a need for methods that can predict daily Rn with fewer input and computation. The objective of this study was to develop two alternative equations to reduce the input and computation intensity of the FAO56-Rn procedures to predict daily Rn and evaluate the performance of these equations in the humid regions of the southeast and two arid regions in the United States. Two equations were developed. The first equation [measured-Rs-based (Rs-M)] requires measured maximum and minimum air temperatures (Tmax and Tmin), measured solar radiation (Rs), and inverse relative distance from Earth to sun (dr). The second equation [predicted-Rs-based (Rs-P)] requires Tmax, Tmin, mean relative humidity (RHmean), and predicted Rs. The performance of both equations was evaluated in different locations including humid and arid, and coastal and inland regions (Gainesville, Fla.; Miami, Fla.; Tampa, Fla.; Tifton, Ga.; Watkinsville, Ga.; Mobile, Ala.; Logan, Utah; and Bushland, Tex.) in the United States. The daily Rn values predicted by the Rs-M equation were in close agreement with those obtained from the FAO56-Rn in all locations and for all years evaluated. In general, the standard error of daily Rn predictions (SEP) were relatively small, ranging from 0.35 to 0.73 MJ?m?2?d?1 with coastal regions having lower SEP values. The coefficients of determination were high, ranging from 0.96 for Gainesville to 0.99 for Miami and Tampa. Similar results, with approximately 30% lower SEP values, were obtained when daily predictions were averaged over a three-day period. Comparisons of Rs-M equation and FAO56-Rn predictions with the measured Rn values showed that the Rs-M equations’ predictions were as good or better than the FAO56-Rn in most cases. The performance of the Rs-P equation was quite good when compared with the measured Rn in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily Rn predictions than the FAO56-Rn procedures. The Rs-P equation was able to explain at least 79% of the variability in Rn predictions using only Tmax, Tmin, and RH data for all locations. It was concluded that both proposed equations are simple, reliable, and practical to predict daily Rn. The significant advantage of the Rs-P equation is that it can be used to predict daily Rn with a reasonable precision when measured Rs is not available. This is a significant improvement and contribution for engineers, agronomists, climatologists, and others when working with National Weather Service climatological datasets that only record Tmax and Tmin on a regular basis.