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.     

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.     

Integrated Agro-Economic Approach to Deficit Irrigation on Lettuce Crops in Sicily (Italy)

The effect of four different irrigation levels on the marketable yield and economic return of summer-growth lettuce was evaluated during 2005 and 2006 in Eastern Sicily, Italy. The viability of deficit irrigation was evaluated by estimating optimum applied water levels. Actual evapotranspiration (ETa) was estimated by combining pan evaporation measures and the Penman–Monteith approach (ET0-PM). The highest marketable yield of lettuce was recorded for plots receiving 100% ET0-PM. For deficit irrigated plots, reductions in crop production were ascribed to a decrease in lettuce weight. Crop coefficients equal to 1 determined maximum crop production values. Crop water use efficiency was maximum at a 100% ET0-PM level of water applied, corresponding to yield of 0.3?t?ha?1?mm?1. Irrigation water use efficiency reached its maximum at a 40% ET0-PM level, with values of 0.54 and 0.44?t?ha?1?mm?1 during 2005 and 2006, respectively. Water applied and marketable yield of lettuce showed a significant quadratic relationship. Cost functions had a quadratic form during 2005 and a linear form during 2006. In the land-limiting condition the optimal economic levels fit the agronomic ones well. In the water-limiting condition, ranges of water deficit of 15–44% and 74–94% were as profitable as full irrigation, thus contributing to appreciable water savings.     

Evaporation Estimation for Lake Okeechobee in South Florida

Lake Okeechobee, located in subtropical South Florida, is the second largest completely contained freshwater lake in the United States. The average, annual evaporation is 132 cm, reported following five years of meteorology data analysis (1993–1997). Simple models, developed from an open-water lysimeter evaporation study, are recommended to estimate daily lake evaporation from solar radiation or solar radiation and maximum air temperature. Seven evaporation estimation methods were evaluated to compare their applicability in providing daily, lake evaporation estimation for water management purposes. The analysis used five-year meteorology data, measured inside the lake. Monthly pan coefficients and annual average pan coefficients were produced for seven pan evaporation stations in the vicinity of Lake Okeechobee. Using the recommended simple models and the remote meteorology data collection, Lake Okeechobee daily evaporation can be reported at the end of each day for water management decision-making.     

Effects of Various Irrigation Regimes on Soil Water Balance, Yield, and Fruit Quality of Drip-Irrigated Peach Trees

The aim of this research was to determine the amount of irrigation water, irrigation interval, and water consumption that gave the greatest yield, to determine the effect of irrigation on fruit quality characteristics, and to investigate variations in soil moisture in Redhaven peaches irrigated by drip irrigation in the Aegean region of Turkey. The study was performed in 2003 and 2004 on split plots in randomized blocks with three replications. Main treatments were 3 and 6?days between irrigations, and subtreatments comprised four different pan coefficients (Kp1.25, Kp1.00, Kp0.75, and Kp0.50). According to the 2-year averages of peach yields, the effect on yield of the amount of irrigation water was found to be significant (p<0.01), but the effect of the irrigation interval was found not to be significant. Total yield varied between 5,966 and 16,340??kg?ha-1, and marketable yield between 5,349 and 14,164??kg?ha-1, according to irrigation treatments. A maximum average yield of 14,101??kg?ha-1 was obtained from treatment Kp1.00. Average irrigation water amount for this treatment was 482?mm, average water consumption was 705?mm, and the Kpc value was 0.785. Maximum water-use efficiency (WUE) of 2.02??kg?m-3 was obtained from Kp1.00. The yield response factor (ky) was found to be 1.2. Weight of individual fruit varied between 203 and 253?g, height varied from 6.3–6.6?cm, diameter from 7.2–7.7?cm, soluble dry matter from 10.8–14.5%, and juice pH from 4.14–4.37. In the years of the study, the declining trend of soil moisture was greater in the treatments that received little irrigation water than in those that received more. After irrigation was ended, soil moisture decreased rapidly and eventually reached the wilting point. To conclude, when setting up a drip-irrigation program for fully grown peach trees in the Aegean region, the irrigation interval may be 3 or 6?days. The amount of water to be applied at each irrigation can be determined by correcting the total evaporation from a Class-A evaporation pan over the chosen irrigation interval by a coefficient of 0.785.     

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.     

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.     

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.     

Pan Evaporation to Reference Evapotranspiration Conversion Methods

Reference evapotranspiration (ET0) is often estimated from evaporation pan data as they are widely available and of longer duration than more recently available micrometeorologically based ET0 estimates. Evaporation pan estimation of ET0 ( = KpEpan) relies on determination of the pan coefficient (Kp), which depends on upwind fetch distance, wind run, and relative humidity at the pan site. The Kp estimation equations have been developed using regression techniques applied either to the table presented in FAO-24 or to the original data upon which this table was based (from lysimeter studies in Davis, Calif.). Here, the relative performances of the FAO-24 table and six different Kp equations are evaluated with respect to reproducing the original data table using the FAO-24 table as a standard. Evaporation pan- and CIMIS-based estimates of ET0 are also compared for stations having ranges of mean humidities (48–66%) and mean wind runs (156–193 km/day) located in the Sacramento and San Joaquin valleys, and for a coastal station (Point Heuneme) near Ventura, Calif., having a greater mean humidity (71%). In comparing the means, standard deviations, root-mean-square errors, and linear regression coefficients, five of the six equations reproduced the original data table with approximately the same accuracy as the FAO-24 table. Use of either Kp table slightly underestimated measured ET0 at the coastal site, while the Cuenca, Allen-Pruitt, and Snyder Kp equations most closely approximated the average measured ET0 at all seven sites.     

Data Mining Process for Integrated Evaporation Model

The purpose of this study is to develop an integrating evaporation estimation model using a data mining process for the Lakes District in the southern part of Turkey. Lakes E?irdir, Kovada, and Karaca?ren Dam are located in the Lakes District. The proposed data mining process is applied on these lakes for evaporation estimation. The daily pan evaporation data used in the data mining process are taken from State Hydraulic Works in southern Turkey. These data cover an 8-year period between 1998 and 2005 inclusively for daily pan evaporation of Lakes E?irdir, Kovada, and Karaca?ren Dam. It is known that a developed integrated daily pan evaporation model is necessary for these lakes, which are so important to the Lakes District. Therefore, a data mining model having two inputs and one output is developed. Input parameters used in the developed models for Lakes E?irdir, Kovada, and Karaca?ren Dam were daily pan evaporation values of Lakes Kovada + Karaca?ren Dam, Lakes E?irdir + Karaca?ren Dam, and Lakes E?irdir Kovada, respectively. As a result, in comparing the developed models with measured daily pan evaporation values, the REP tree model has better agreement with measured daily pan evaporation than other models. The results show the developed model was more accurate.     

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.     

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.     

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.     

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.     

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.     

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).     

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.     

Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (R), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data.     

Evaluation of Water Retention Functions and Computer Program “Rosetta” in Predicting Soil Water Characteristics of Seasonally Impounded Shrink–Swell Soils

Soil water retention is a critical factor influencing irrigation decisions and hence agricultural crop yields. However, information on soil water retention characteristics (SWRC) is seldom available for irrigation planning, crop yield modeling, or hydrological simulations, especially for problematic soils, such as seasonally impounded shrink-swell soils. As large scale direct measurement of SWRC is not viable due to a number of reasons, researchers have developed pedotransfer functions (PTFs) to estimate SWRC from easily measured soil properties, such as texture, organic matter content, bulk density, etc. However, PTF applicability in locations other than those of data collection has been rarely reported. One of the most recent PTFs that has shown overall reasonable predictions in evaluation studies is Rosetta, a numerical code for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Relatively, the development of large databases makes it one of the widely used PTFs. If validated for spatial application, it has immense use potential in countries like India, where data on soil hydraulic properties are seldom available, a deficiency that hampers better simulations in processes, like partitioning runoff and infiltration, assessing evapotranspiration, irrigation scheduling, etc. Rosetta is also relatively flexible allowing estimation of hydraulic properties from easily available minimum input of textural fractions. This study was conducted to evaluate (1) an applicability of four widely used soil water retention functions to describe SWRC; and (2) the computer program Rosetta for its validity. Statistical indices, i.e., root mean square error (RMSE), mean absolute error, maximum absolute error, and degree of agreement (d) were computed to evaluate “goodness-of-fit” of the four functions to the measured SWRC data. These indices were also used to compare measured SWRC with estimates of SWRC by Rosetta. For soil samples collected from 41 profiles, 175 SWRC were measured in the laboratory. The van Genuchten function fitted relatively better (RMSE = 0.052?m3?m?3) to SWRC of clay soils, whereas the Brooks–Corey (BC) function was better in expressing SWRC of clay loam and sandy clay loam soils with RMSE = 0.06 and 0.07?m3?m?3, respectively. Campbell and Cass–Hutson (CH) functions were of intermediate value. Worst performing functions were BC (clay soils), Campbell (clay loam), and CH (sandy clay loam) with corresponding RMSE = 0.059, 0.065, and 0.077?m3?m?3. Estimates of two important points on the SWRC curve, i.e., field capacity and permanent wilting point were predicted with relatively better accuracy for clay and sandy clay loam soils by all the four functions. RMSE and d ranged from 0.027?to?0.043?m3?m?3 and from 0.73 to 0.88 for clay soils. Corresponding values for sandy clay loam soils were 0.008?–0.019?m3?m?3, and 0.92–0.98. However, in clay loam soils, only two functions were found suitable. Estimates of SWRC obtained by applying hierarchical rules in Rosetta were reliable (RMSE<0.05?m3?m?3). Magnitude of average RMSE increased progressively in clay loam, clay and sandy clay loam soils (0.028<0.035<0.042?m3?m?3). The study established that SWRC of the “Haveli” soils could be estimated using generic PTF and thus information that is prerequisite in simulating hydrological processes occurring in seasonally impounded soils could be acquired.     

Assessment of Soil Moisture Dynamics of the Nebraska Sandhills Using Long-Term Measurements and a Hydrology Model

Soil moisture, evapotranspiration, and other major water balance components were investigated for six Nebraska Sandhills locations during a 6 year period (1998–2004) using a hydrological model. Annual precipitation in the study period ranged from 330 to 580?mm. Soil moisture was measured continuously at 10, 25, 50, and 100?cm depth at each site. Model estimates of surface (0–30?cm), subsurface (30–91?cm), and root zone (0–122?cm) soil moisture were generally well correlated with observed soil moisture. The correlations were poorest for the surface layer, where soil moisture values fluctuated sharply, and best for the root zone as a whole. Modeled annual estimates of evapotranspiration and drainage beneath the rooting zone showed large differences between sites and between years. Despite the Sandhills’ relatively homogeneous vegetation and soils, the high spatiotemporal variability of major water balance components suggest an active interaction among various hydrological processes in response to precipitation in this semiarid region.