Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: Application to Tunisia

The Food and Agriculture Organization of the United Nations had improved the version of the Penman–Monteith method (FAO-56 PM) which has recently been proposed as the standard for estimating reference evapotranspiration (ET_o). Unfortunately, some weather variables, especially solar radiation, relative humidity and wind speed, are often missing which could impede the estimation of ET_o with the FAO-56 PM method. To overcome the problem of the availability of climatic parameters, procedures to estimate ET_o with missing climate data are proposed as part of the FAO methodology. Therefore, assessing the accuracy of these procedures for different Tunisian locations is important. The comparison of ET_o estimates using limited data to those computed with full data set revealed that the difference between ET_o obtained from full and limited data set is small considering the 8 locations studied. Both the Mean Bias Error (MBE) and the Root Mean Square Error (RMSE) of the comparison were less than 0.6 and 0.8 with a minimum of −0.4 and 0.2 mm day⁻¹, respectively, leading to small errors in the ET_o estimates. The higher deviations occur when the only available information is minimum and maximum air temperature. These deviations were significantly higher when using the Hargreaves equation to calculate ET_o.     

Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment

The Penman–Monteith equation (PM) is widely recommended because of its detailed theoretical base. This method is recommended by FAO as the sole method to calculate reference evapotranspiration (ET_o) and for evaluating other methods. However, the detailed climatological data required by the Penman–Monteith equation are not often available especially in developing nations. Hargreaves equation (HG) has been successfully used in some locations for estimating ET_o where sufficient data were not available to use PM method. The HG equation requires only maximum and minimum air temperature data that are usually available at most weather stations worldwide. Another method used to estimate ET_o is the artificial neural network (ANN). Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to compare HG and ANN methods for estimating ET_o only on the basis of the temperature data. The 12 weather stations selected for this study are located in Khuzestan plain (southwest of Iran). The HG method mostly underestimated or overestimated ET_o obtained by the PM method. The ANN method predicted ET_o better than HG method at all sites.     

Evaluation of estimated weather data for calculating Penman-Monteith reference crop evapotranspiration

Utilizing the weather generator ClimGen, daily solar radiation (R_s) and vapor pressure deficit (VPD) were estimated from temperature data and used to calculate evapotranspiration at five locations, representing tropical, temperate, semi-arid, and arid climates. ClimGen was calibrated for each location using the most recent 2 or 5 years of complete daily weather records. Actual and estimated values were compared on a daily and weekly (7-day running average) basis. Error indices were defined to indicate excellent to poor performance of the estimation methods. Overall in all locations, the ClimGen estimates for both daily R_s and VPD were poor to acceptable. The weekly analyses showed significant improvement in performance for both R_s and VPD estimations in arid and semi-arid locations. Daily reference crop evapotranspiration values using the FAO Penman-Monteith equation (PM ET_o) were calculated using complete daily weather records. These values were compared with (1) ET_o calculated with the PM model, actual temperature data, and ClimGen estimates of daily R_s, VPD, and generated wind speed (PM_Est ET_o), and (2) ET_o calculated solely from actual daily temperature data using a calibrated version of the Hargreaves method (HG_Adj ET_o). The daily PM_Est ET_o results were poor to acceptable in all locations, but analyses for weekly periods showed improved performance to acceptable and good levels for arid and semi-arid locations. The performance of the HG_Adj ET_o method was also poor to acceptable for daily ET estimates in all locations, while weekly analyses showed improvement. A non-calibrated version of the Hargreaves method did not work for either daily or weekly periods. The PM_Est ET_o and HG_adj ET_o methods appeared suitable for weekly periods in arid and semi-arid locations provided that at least 2 years of complete weather records were available to calibrate the parameters required. There was no advantage in using 5 years of weather records for calibration.Communicated by E. Fereres     

Water requirements of maize in the middle Heihe River basin, China

As part of an intercomparison study on crop evapotranspiration (ET_c), six methods for estimating ET_c have been applied to maize field in the middle Heihe River basin, China. The ET_c was estimated by the soil water balance and Bowen ratio-energy balance methods while the Priestley-Taylor, Penman, Penman-Monteith and Hargreaves methods were used for estimating the reference evapotranspiration (ET₀). The results showed that the trend of ET_c was very similar, while the differences were significant among the different methods. The variations of ET_c were closely related to the LAI as well as to the meteorological features. The ET_c for the Bowen ratio-energy balance, Penman, Penman-Monteith, soil water balance, Priestley-Taylor and Hargreaves methods totaled 777.75, 693.13, 618.34, 615.67, 560.31 and 552.07 mm, respectively, with the daily mean values for 5.26, 4.68, 4.18, 4.16, 3.79 and 3.73 mm day⁻¹. The Penman-Monteith method provided fairly good estimation of ET_o as compared with the Priestley-Taylor, Penman, Hargreaves methods. By contrast with the Penman-Monteith method, the Bowen ratio-energy balance and Penman methods were 25.8% and 12.0% higher, while the Priestley-Taylor and Hargreaves methods were 9.4% and 10.7% lower, respectively. Therefore, the Hargreaves and Priestley-Taylor methods were the alternative ET_c methods in arid regions of Northwest China.     

An evaluation of two hourly reference evapotranspiration equations for semiarid conditions

The methodology proposed by the Food and Agriculture Organization (FAO) (Doorenbos, J., Pruitt, W.O., 1977. Crop water requirements. FAO irrigation and drainage. Paper No. 24. FAO, Rome) and updated by Allen et al. (Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO irrigation and drainage. Paper No. 56. FAO, Rome) for calculating crop water requirements is the most extended and accepted method worldwide. This method requires the prior calculation of reference evapotranspiration (ET_o). This study evaluates the FAO-56 and American Society of Civil Engineers (ASCE) Penman–Monteith (PM) equations for estimation of hourly ET_o under the semiarid conditions of the province of Albacete (Spain). The FAO-56 and ASCE equations (hourly time step) were compared against measured lysimeter ET_o values at Albacete for 13 days during the period of April–October 2002 and 16 days during April–October 2003. The average of estimated FAO-56 Penman–Monteith ET_o values was equal to the average of measured values. However, the average of estimated ASCE Penman–Monteith values was 4% higher than the average of measured lysimeter ET_o values. This method overestimated measured lysimeter ET_o values by 0.45 mm h⁻¹.Simple linear regression and error analysis statistics suggest that agreement between both estimation methods and the lysimeter was quite good for the province of Albacete.In this paper, the FAO-56 Penman–Monteith equation for calculating hourly ET_o values was more accurate than the ASCE Penman–Monteith method under semiarid weather conditions in Albacete.     

Measurement and estimation of plastic greenhouse reference evapotranspiration in a Mediterranean climate

The standard FAO methodology for the determination of crop water requirements uses the product of reference evapotranspiration (ET_o) and crop coefficient values. This methodology can be also applied to soil-grown plastic greenhouse crops, which occupy extended areas in the Mediterranean basin, but there are few data assessing methodologies for estimating ET_o in plastic greenhouses. Free-drainage lysimeters were used between 1993 and 2004 to measure ET_o inside a plastic greenhouse with a perennial grass in Almería, south-eastern Spain. Mean daily measured greenhouse ET_o ranged from values slightly less than 1 mm day⁻¹ during winter to values of approximately 4 mm day⁻¹ during summer in July. When the greenhouse surface was whitened from March to September (a common practice to control temperature), measured ET_o was reduced by an average of 21.4%. Different methodologies to calculate ET_o were checked against the measurements in the greenhouse without and with whitening. The methods that performed best in terms of accuracy and statistics were: FAO56 Penman–Monteith with a fixed aerodynamic resistance of 150 s m⁻¹, FAO24 Pan Evaporation with a constant Kp of 0.79, a locally-calibrated radiation method and Hargreaves. Given the data requirements of the different methods, the Hargreaves and the radiation methods are recommended for the calculation of greenhouse ET_o because of their simplicity.     

Comparative analysis of 31 reference evapotranspiration methods under humid conditions

Evaluation of simple reference evapotranspiration (ET_o) methods has received considerable attention in developing countries where the weather data needed to estimate ET_o by the Penman–Monteith FAO 56 (PMF-56) model are often incomplete and/or not available. In this study, eight pan evaporation-based, seven temperature-based, four radiation-based and ten mass transfer-based methods were evaluated against the PMF-56 model in the humid climate of Iran, and the best and worst methods were selected from each group. In addition, two radiation-based methods for estimating ET_o were derived using air temperature and solar radiation data based on the PMF-56 model as a reference. Among pan evaporation-based and temperature-based methods, the Snyder and Blaney–Criddle methods yielded the best ET_o estimates. The ET_o values obtained from the radiation-based equations developed here were better than those estimated by existing radiation-based methods. The Romanenko equation was the best model in estimating ET_o among the mass transfer-based methods. Cross-comparison of the 31 tested methods showed that the five best methods as compared with the PMF-56 model were: the two radiation-based equations developed here, the temperature-based Blaney–Criddle and Hargreves-M4 equations and the Snyder pan evaporation-based equation.     

A wind-based qualitative calibration of the Hargreaves ET0 estimation equation in semiarid regions

This study evaluates the Hargreaves (HARG) equation for estimation of monthly ET₀ under the semiarid conditions of the middle Ebro River Valley (NE Spain). First, the Hargreaves equation was compared against measured lysimeter ET₀ values at Zaragoza for the period May 1997–October 2000. The average of estimated values was only 5.6% above the average of measured values. Later, the Hargreaves equation was compared against the FAO Penman–Monteith equation for monthly ET₀ estimation at nine locations. These locations can be grouped as non-windy (Alcañiz, Daroca and Tamarite) and windy (Almudévar, Ejea, Gallocanta, Monflorite, Sariñena and Zaragoza). Simple linear regression and error analysis statistics suggest that agreement between the two estimation methods was quite good for the windy locations. Average errors ranged between 2 and 5% for Almudévar, Ejea, Sariñena and Zaragoza, and between 7 and 10% for Gallocanta and Monflorite where some underestimation was observed. However, the agreement between the Hargreaves and FAO Penman–Monteith equations was lower for the non-windy locations. In this case, the Hargreaves equation overestimated ET₀ and average errors varied between 14 and 20%. According to these results, it is proposed that, under the semiarid conditions of this study, no local calibration would be required for windy locations (those where monthly average windspeeds above 2.0 m s⁻¹ are frequent), while a value of 0.0020 instead of the original 0.0023 should be used in the Hargreaves equation for non-windy locations. Further research should be undertaken to evaluate whether these results can be extended to other semiarid regions of the world.     

Prediction of annual reference evapotranspiration using climatic data

It is important to determine how well ET_o can be estimated from easily observed E_pan (free water evaporation measured by a pan) measurements and the other climatic data. Our objectives are to predict annual ET_o with E_pan data (with a calibrated k_p (=ET_o/E_pan)) and with a 4-variable regression function method. The significance of the trends of E_pan, ET_o and k_p series were detected. The whole data series (ET_o, E_pan, mean temperature, sunlight hours, relative humidity and wind speed) were divided into the early (L-5) years for calibrating k_p and coefficients of a 4-variable function and the last 5 years for predicting ET_o. From the results, significance of series trends decreased when using the modified Mann-Kendall (MMK) test compared to the Mann-Kendall (MK) method. For ET_o, five out of six sites showed significant trends according to the MK statistic Z, and two sites were significant in trend combining with the MMK statistic Z*(j). For E_pan, two sites were significant in trends according to Z, and zero sites were significant in trends combining with Z*(j). For k_p, two sites were significant in trends according to Z, and no sites were significant in trends combining with Z*(j). Thus the calibrated k_p can be treated as a constant when using the E_pan method. The predicted annual ET_o using the E_pan and the multi-variable methods showed generally good agreements with the estimated annual ET_o (based on monthly PM equation) with low relative errors (RE). Mean ET_o values were well predicted by both methods. When using E_pan method, RE ranged from −14.7 to −3.3% for Urumqi, from 17.6 to 21.7% for Xning, from 1.8 to 10.7% for Lanzhou, from 4.7 to 17.0% for Huhehaote, from −7.4 to 9.1% for Beijing, and from −8.6 to 2.3% for Changchun. RE of predicting annual ET_o with 4-variable regression function were even lower compared to E_pan method. The main error source of the predictions came from the deviation between calibrated k_p and the actual k_p of the predicted years when using E_pan method and from random fluctuations of climatic data when using the 4-varible regression function. In conclusion, the MMK test was a robust method for trend detection because it considered serial time dependence. Insignificant trend of the k_p series supports the choice of a mean value as the calibrated k_p and for ET_o predictions. The E_pan method is recommended for prediction of annual ET_o.     

Forecasting daily potential evapotranspiration using machine learning and limited climatic data

Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET_o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET_o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET_o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET_o using historically computed ET_o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET_o calculation based on historical data and then computing ET_o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET_o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.     

Least squares support vector machine for modeling daily reference evapotranspiration

The accuracy of a least square support vector machine (LSSVM) in modeling of reference evapotranspiration (ET₀) was examined in this study. The daily weather data, solar radiation, air temperature, relative humidity and wind speed of two stations, Glendale and Oxnard, in southern district of California, were used as inputs to the LSSVM models to estimate ET₀ obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, LSSVM estimates were compared with those of the following empirical models: Priestley–Taylor, Hargreaves and Ritchie methods. The comparison results indicated that the LSSVM performed better than the empirical models. In the second part of the study, the LSSVM results were compared with those of the conventional feed-forward artificial neural networks (ANN). It was found that the LSSVM models were superior to the ANN in modeling ET₀ process.     

Evapotranspiration and responses to irrigation of broccoli

In cold, semi-arid areas, the options for crop diversification are limited by climate and by the water supply available. Growing irrigated crops outside the main season is not easy, because of climatic and market constraints. We carried out an experiment in Albacete, Central Spain, to measure the water use (evapotranspiration, ET) of broccoli (Brassica oleracea L. var. italica Plenck) planted in late summer and harvested at the end of fall. A weighing lysimeter was used to measure the seasonal ET under sprinkler irrigation. Consumptive use reached 359 mm for a period of 109 days after transplanting. The crop coefficient (K_c) for broccoli was obtained and compared to the standard recommendations for normal planting dates. Dual crop coefficient computations of the lysimeter ET data indicated that evaporation represented 31% of seasonal ET. An analysis of the variation in daily K_c values at a time of full cover suggested that the use of a grass lysimeter as a reference ET (ET_o) was superior to using the ASCE Penman-Monteith (ASCE PM) equation at hourly time steps, which in turn caused less variability in K_c than when using the FAO-56 Penman-Monteith (FAO-56 PM) equation at daily time steps for the ET_o calculation. An additional experiment aimed at evaluating the yield response to applied irrigation water by the drip method (seven treatments, from 59 to 108% of ET_c) generated a production function that gave maximum yields of near 12 t ha⁻¹ at an irrigation level of 345 mm, and a water use efficiency of 3.37 kg m⁻³. It is concluded that growing broccoli in the fall season is a viable alternative for crop diversification, as the lower yields obtained here may be more than compensated for by the higher produce prices in autumn, at a time of the year where irrigation water demand for other crops is very low.     

Spatially distributed monthly reference evapotranspiration derived from the calibration of Thornthwaite equation: a case study, South of Iran

The Penman-Monteith equation is the most common method for estimating reference crop evapotranspiration (ET_o). Using this method reqiures many different meteorological data, yet few stations with adequate meteorological data may exist in a region. Setting up a station that records the required data for Penman-Monteith equation is expensive. Alternatively, the Thornthwaite equation is a simpler method for estimating ET_o since it is a temperature-based method. In this study, the Thornthwaite equation was spatially calibrated based on the Penman-Monteith method (as the standard and reference method to compute ET_o) for every month of the year, using the meteorologica data of seven synoptic weather stations in Fars province, and seven synoptic stations outside the Fars province. The Thornthwaite equation using effective temperature that has been introduced recently in other studies was used (Camargo et al. in Revista Brasileira de Agrometeorologica 7:251–257, 1999). For this purpose a calibration coefficient k must be determined. The results of the spatial and temporal calibration of the new approach using the Thornthwaite equation showed that for each station different k values should be used monthly. Generally, the k values fluctuated between 0.55 and 1.12, and the mean RMSE for all stations was less than 1 mm day⁻¹, which showed good and reliable agreement between the ET_o estimations obtained from the Penman-Monteith and calibrated Thornthwaite equations. Depending on the geographical location of each station, spatial distribution maps of monthly k values were created for the study area using the inverse distance weighting (IDW) interpolation method. It is therefore possible to estimate monthly ET_o using the appropriate k map and the Thornthwaite equation for different regions of study area instead of using the Penman-Monteith method. This case study showed that the same analysis might be used for the other parts of the country or any part of the world and would result in efficient scheduling of water resources for agriculture.     

Agricultural water management in a humid region: sensitivity to climate,soil and crop parameters

A sensitivity analysis of irrigation water requirements at the regional scale was conducted for the humid southeastern United States. The GIS-based water resources and agricultural permitting and planning system (GWRAPPS), a regional scale, GIS-based, crop water requirement model, was used to simulate the effect of climate, soil, and crop parameters on crop irrigation requirements. The effects of reference evapotranspiration (ET_o) methods, available soil water holding capacities (ASWHC), crop coefficients (K_c), and crop root zone depths (z) were quantified for 203 ferneries and 152 potato farms. The irrigation demand exhibited a positive relationship with K_c and z, a negative relationship with ASWHC, and seasonal variations depending on the choice of ET_o methods. The average irrigation demand was most sensitive to the choice of K_c with a 10% shift in K_c values resulting in approximately 15% change in irrigation requirements. Most ET_o methods performed reasonably well in estimating annual irrigation requirements as compared to the FAO-56 PM method. However, large differences in monthly irrigation estimates were observed due to the effect of the seasonal variability exhibited by the methods. Our results suggested that the selection of ET_o method is more critical when modeling irrigation requirements at a shorter temporal scale (daily or monthly) as necessary for many applications, such as daily irrigation scheduling, than at a longer temporal scale (seasonal or annual). The irrigation requirements were more sensitive to z when the resultant timing of irrigation coincided with rainfall events. When compared with the overall average of the irrigation requirements differences, the site-to-site variability was low for K_c values and high for the other variables. In particular, soil properties had considerable average regional differences and variability among sites. Thus, the extrapolation of site-specific sensitivity studies may not be appropriate for the determination of regional responses crop water demand.     

Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration

The study investigates the ability of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm in daily reference evapotranspiration (ET₀) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN–ABC model so as to estimate ET₀ obtained using the FAO-56 Penman–Monteith (PM) equation. In the first part of the study, the accuracy of ANN–ABC models is compared with those of the ANN models trained with Levenberg–Marquardt (LM) and standard back-propagation (SBP) algorithms and those of the following empirical models: The California Irrigation Management System (CIMIS) Penman, Hargreaves, and Ritchie methods. The mean square error (MSE), mean absolute error (MAE) and determination coefficient (R²) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the ANN–ABC and ANN–LM models are found to be superior alternative to the ANN–SBP models. In the second part of the study, the potential of the ANN–ABC, ANN–LM, and ANN–SBP models in estimation ET₀ using nearby station data is investigated.     

Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain

Accurate estimation of actual evapotranspiration (ET_a) is essential for effective local or regional water management. At a local scale, ET estimates can be made accurately considering a soil-plant-atmospheric system, if adequate meteorological-ground data or ET measurements are available. However, at a regional scale, ET_a values cannot be measured directly and the estimates are frequently subject to errors. Although it is possible to extrapolate to the regional scale from local (point) data of meteorological stations, the relative sparse coverage of ground estimate can make this problematic without some spatial analysis to demonstrate the similarity of the climate in the area. The introduction of remote sensing data and techniques offers alternatives both to estimate variables (i.e. radiation) and parameters (i.e. ET) with few spatial restrictions, thus, it provides potential advantages to the regional ET_a computation. In particular, the use of remote sensing procedures like the surface energy balance-based algorithms (SEB) have been successfully applied in different climates, enabling the estimation of ET_a at local and regional scales. A proper variation of the Surface Energy Balance Algorithm for Land (SEBAL) was applied to 4 years of data for the Flumen District in the Ebro Basin at the N.E. of Spain. Results obtained show that the remote sensing algorithm can provide accurate daily ET_a estimations as compared with lysimeter measurements of daily ET values for two crop plots: one with a reference grass and other with maize or wheat as function of the season. Also a comparison between ET_a and the reference and crop ET values applying the Penman-Monteith method was carried out. The comparison analysis consider an accepted error difference of 1.0 mm d⁻¹ (20% of error) for local scale, the ET_a values for the grass show a bias of 0.30 mm d⁻¹ against the ET_grass and a bias of 0.36 mm d⁻¹ against ET_o. Differences between ET_maize or ET_wheat and ET_a against their average showed an acceptable agreement for the field with s_diff ± 0.6 mm d⁻¹. For the crop fields at regional scale external causes associated to atmospheric and surface variations (i.e. land preparation) rather to the remote sensing algorithm made difficult to determine a percentage of error. Finally, ET_a values were obtained at regional scale and it was demonstrated that using the remote sensing improve significantly the crop ET estimations computed in the area using traditional methods.     

Temporal and spatial variations of evapotranspiration for spring wheat in the Shiyang river basin in northwest China

The methods for estimating temporal and spatial variation of crop evapotranspiration are useful tools for irrigation scheduling and regional water allocation. The purpose of this study was to develop a method for mapping spatial distribution of crop evapotranspiration and analyze the temporal and spatial variation of spring wheat evapotranspiration in the Shiyang river basin in Northwest China in the last 50 years. DEM-based methods were employed to estimate the spatial distribution of spring wheat evapotranspiration (ET_c). Reference crop evapotranspiration (ET₀) was calculated with the Penman–Monteith equation using meteorological data measured from eight stations in the basin. Crop coefficient (K_c) was determined from measured evapotranspiration in spring wheat season in the region. The results showed that ET_c gradually increased in the upper reaches of the basin in the last 50 years, while the middle reaches showed a significant decreasing trend, and in other regions, no significant trend was found. These changes can be attributed to expansion of irrigation areas and climate change. The multiple regression analysis between ET_c and altitude, latitude, and aspect were carried out for eight weather stations and the relationships were used to map ET_c for the basin. The spatial variations of ET_c were analyzed for three typical growing seasons based their precipitation. Results showed that long-term average ET_c over cultivated land was increasing from 270 mm in southwest mountainous area to 591 mm in northeast oasis of the basin, and the relative error between the estimated ET_c in spring wheat growing season by reference evapotranspiration (ET₀) and crop coefficient (K_c), and the interpolated ET_c was within 11.1%.     

Impacts of climate variability on reference evapotranspiration over 58 years in the Haihe river basin of north China

Physically, evaporative demand is driven by net radiation (R_n), vapour pressure (e_a), wind speed (u₂), and air temperature (T_a), each of which changes over time. By analyzing temporal variations in reference evapotranspiration (ET₀), improved understanding of the impacts of climate change on hydrological processes can be obtained. In this study, variations in ET₀ over 58 years (1950-2007) at 34 stations in the Haihe river basin of China were analyzed. ET₀ was calculated by the FAO Penman-Monteith formula. Calculation of Kendall rank coefficient was done by analyzing the annual and seasonal trends in ET₀ derived from its dependent climate variables. Inverse distance weighting (IDW) was used to analyze the spatial variation in annual and seasonal ET₀, and in each climate variable. An attribution analysis was performed to quantify the contribution of each input variable to ET₀ variation. The results showed that ET₀ gradually decreased in the whole basin over the 58 years at a rate of −1.0 mm yr⁻², at the same time, R_n, u₂ and precipitation also decreased. Changes in ET₀ were attributed to the variations in net radiation (−0.9 mm yr⁻²), vapour pressure (−0.5 mm yr⁻²), wind speed (−1.3 mm yr⁻²) and air temperature (1.7 mm yr⁻²). Looking at all data on a month by month basis, we found that T_a had a positive effect on dET₀/dt (the derivative of reference evapotranspiration to time) and R_n and u₂ had negative effects on dET₀/dt. While changes in air temperature were found to produce a large increase in dET₀/dt, changes in other key variables each reduced rates, resulting in an overall negative trend in dET₀/dt.     

Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for converting pan evaporation data (E _p) to estimate reference evapotranspiration (ET₀) as a function of the maximum and minimum air temperature. The conventional method that uses Pan coefficient (K _p) as a factor to convert E _p to ET₀, is also considered for the comparison. The ANN has been evaluated under semi-arid conditions in Safiabad Agricultural Research Center (SARC) in the southwest of Iran, comparing daily estimates against those from the FAO-56 Penman–Monteith equation (PM), which was used as standard. The comparison shows that, the conventional method underestimated ET₀ obtained by the PM method. The ANN method gave better estimates than the conventional method that requires wind speed and humidity data.     

Net irrigation requirements for Florida turfgrasses

A long-term (30 year) historical analysis of turfgrass monthly net irrigation requirements for southeast USA is analyzed and discussed in this paper. The process involved gathering weather data for ten locations in Florida plus one in Alabama, from 1980 through 2009, and data quality. Available weather data included maximum and minimum temperature, maximum and minimum relative humidity, wind speed, and rainfall. Solar radiation was estimated using the Hargreaves–Samani equation, and coefficients were calibrated for every location. Reference evapotranspiration (ET_os) was calculated using the ASCE-EWRI standardized reference evapotranspiration equation. Net irrigation was estimated using a daily soil–water balance. Variability in soil types and root depth was taken into account during the simulations, and three sets of monthly K _c values from the literature were applied from north through south Florida. Results showed that the calibrated Hargreaves–Samani adjustment coefficients varied from 0.14 in Tallahassee to 0.24 in Key West, with an inland average value of 0.15, and a coastal average value of 0.18. The calculated ET_os ranged from 1,296 mm year^?1 in Tallahassee to 1,658 mm year^?1 in Miami. The estimated net irrigation ranged from 423 mm year^?1 in Mobile, AL, to 1,063 mm year^?1 in Key West, FL. The number of irrigation events per year varied from 25 in Mobile to 161 in Key West. May and December were the months with the highest and lowest net irrigation requirements, respectively.