Estimation of REFERENCE CROP potential EVAPOTRANSPIRATION (ETo) is essential in many irrigation projects. Also, concerning the deficit of water reresources in Iran, ETo prediction through using time series has paramount role in future programming. Based on this, monthly ETo values were calculated using monthly weather data up to year 1388 of synoptic stations in Fars province containing Abadeh, Eghlid, Darab, Zarghan, Dorodzan Dam, Shiraz, Fasa and Lar. Then, in each station it was assumed that about 20 percent of last ETo values were not available, and consequently these values were predicted using time series model of SARIMA, and then the predicted and calculated values of ETo in each station were compared with line one: one, separately. The results showed that the same time series model were not appropriate for all stations. Moreover, the results indicated that there were not significant diferences between the predicted and calculated values of ETo in each stations. Therefore, ETo values were predicted up to year 1403 for each station, separately. The results generally indicated an increasing trend in ETo prediction in Fars province.

EVAPOTRANSPIRATION is the main component of hydrologic cycle and has an important role in CROP water requirement estimations, water balances studies, and water resource management. There are a lot of direct and indirect methods to estimate REFERENCE CROP EVAPOTRANSPIRATION, but each has some limitations. For example, limitations that can be mentioned for direct measuring are the insufficient precision in measuring devices and the scale problems. An indirect method like Penman-Monteith on the other hand needs a lot of daily climatic parameters. This research tried to use selforganizing maps as an unsupervised artificial neural network method to predict EVAPOTRANSPIRATION by minimum meteorological data input. Based on fuzzy clustering indices, EVAPOTRANSPIRATION values in the study area, Mashhad plain, are divided into two clusters with low and high ETo coincided with the climate of the area. Also, in order to validate the model, statistical indices containing root mean square error, determination coefficient, and Nash–Sutcliffe model efficiency coefficient are used and the results are compared with the experimental models output. The results showed that even the simplest SOM model which employs mean temperature and maximum sunshine duration as input have less errors compared to the experimental equations.

The main aim of this study is the trend analysis of the REFERENCE CROP EVAPOTRANSPIRATION (ET0) at Hamadan (Nodjeh) station. For this purpose the information of this station was used in the period of 1951-2005. The amount of ET0 was estimated using the Penman-Monteith (PM) approach. For trend analysis two methods which are a) fitting a straight line on time series and calculating the slope of trend line using the least square method, and 2) Mann-Kendall test and calculating the slope of trend line by Sen's estimator method were used.  Stepwise linear multiple regression analysis was also used for the purpose of sensitivity analysis of ET0 to meteorological parameters. Results show that lowest and highest (7.2 mm/day) ET0 occurred on January and July, respectively. The slope of trend line was positive for all months (except January and February). In the warm six months of the year slope of trend line was statistically significant at 1% level. The largest positive slope of ET0 trend line observed at July, which is statistically significant at 1% level. Results of sensitivity analysis showed that in the hot months of the year the wind speed and maximum air temperature are two main important factors which influence the ET0.

EVAPOTRANSPIRATION is the most important component of the irrigation requirement. In this study, the fuzzy inference systems and artificial neural network was used to accurate estimate of the REFERENCE CROP EVAPOTRANSPIRATION (ET0) due to the effect of uncertain factors on this parameter. It is needed some meteorological data for calculating the ET0 in Penman-Monteith method. The EVAPOTRANSPIRATION modeling based on existent data is required, as regards to restriction of meteorological data in temporal and spatial scales. In this study site (Kor and Sivand stream watershed, Fars Province), the temperature data is the only meteorological parameter that was measured in a good temporal and spatial distributions. Therefore, at the first, the monthly ET0 was calculated using Penman-Monteith method for a 30 years period in two meteorological stations of study district. Then, a simulation model was derived between calculated ET0 from Penman-Monteith method and the monthly mean temperature, for one of the stations, based on an adaptive neuro fuzzy system (ANFIS) simulation method. Using this model, the ET0 was calculated for other station due to the monthly mean temperature. Also, these calculations and ANFIS model derivation were conducted with adding of the class A pan evaporation data to temperature data. The results demonstrated the ability of ANFIS model for monthly ET0 estimation in meteorological data restriction conditions.

In this study, the performance of two different artificial neural network software's named neuro solution (NS) and neural works professional II (NW) in estimation of CROP REFERENCE EVAPOTRANSPIRATION (ET0) were evaluated. For models evaluation, some statistical parameters such as root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were calculated for different arrays, learning rules and transfer functions. For the NS software the best fitted array characterizing with lowest values of RMSE, MAE and highest R2 were found to be 0.08, 0.07 (mm day-1) and 0.87, respectively. Results showed that the NS software with the best fitted network array of: learning rule of conjugate gradient and transfer function of sigmoid type, which required shorter computational time and less iteration loops, can perform better prediction. The results indicated that using two hidden layers did not improve the accuracy of ET0 predictions, in comparison with the results obtained by one hidden layer layout. The sensitivity analysis of neural network model revealed that ET0 is very sensitive to maximum air temperature (Tmax). In contrast, the estimated daily ET0 showed the lowest sensitivity to minimum relative humidity (RHmin).

One of the most important factors in calculation of greenhouse CROP water requirement is the accurate estimation of REFERENCE CROP EVAPOTRANSPIRATION. Numerous parameters affect the EVAPOTRANSPIRATION rate of the REFERENCE CROP,with the knowledge of the most important factor the simulation error will be reduced. Because of the differences among mathematical structure and underlying assumption of indirect REFERENCE CROP EVAPOTRANSPIRATION estimation methods, behavior of the response variable to changes in climatic parameters may become inconsistent. Therefore, it is mandatory to analyze the sensitivity as well as determining the influence of meteorological factors on REFERENCE CROP EVAPOTRANSPIRATION. In this study, the sensitivity of Penman-Monteith, Irmak, Copais and Valiantzas methods to changes in meteorological factors including maximum and minimum daily temperature, maximum and minimum relative humidity and short-wave radiation was analyzed in the research greenhouse of the College of Agriculture and Natural Resources of the University of Tehran, located in Karaj, Iran. Sensitivity coefficients were calculated by changing meteorological factors in the range of [20,-20] percent with 5% intervals. Results showed that in all methods, the internal radiation was the most influential variable in determining the REFERENCE CROP EVAPOTRANSPIRATION under greenhouse conditions. The obtained sensitivity coefficient for this variable in Penman-Monteith, Irmak, Copais and Valiantzas methods was equal to 0. 78, 0. 71, 0. 68 and 0. 63, respectively. In contrast, the least important climatic variable was the minimum humidity in Penman-Monteith, Copais and Valiantzas methods and the minimum temperature in Irmak method. The results also showed that Penman-Monteith is the most sensitive method to changes in meteorological factors. As a result, in case of using of Penman-Monteith method for REFERENCE CROP EVAPOTRANSPIRATION estimation in greenhouse conditions, ensuring the correct operation of measuring instruments and their accuracy is essential.

The aim of this study is to compare and calibrate of four different temperature based ETo estimation methods in monthly time scale at Urmia Lake basin. The selected methods were Hargreaves (HG), Thornth waite (TW), Blaney- Criddle (BC) and Linacre (Lin). For this purpose the information of weather parameters in the period 1986-2010 were used. Results of mentioned methods compared with the output of the FAO Penman- Monteith (PM56). Methods calibrated using the two distinct approaches. At first state only one calibration coefficient estimated along every year. At second state calibration coefficientsestimated for each station and every month in a year. Performance of methods evaluated using the R2, RMSE, MBE and MAE statistics. The effectiveness of method’s calibration evaluated using the RaRMSE.Results showed that before calibration larger biases existed for selected methods comparing PM56.Calibration of methods considerably improved their performances. Results indicated that calibration of methods in the case of second state was very effective comparing the first state. The HG method was recognized as a best method either before or after calibration (in the first state) at the Urmia lake watershed. Linacre and Blaney- Criddle methods selected as the best methods following the Linacre method (after calibration at first state). Thornthwaite method had large error and therefore was not suitable method for estimation of E To at the study area. The Linacre method was known as the best method of E To estimation at Urmia Lake basin after calibration (at second state). HG, BC and TW were in the second to fourth rank orders, respectively.

To evaluate seven equations for computing EVAPOTRANSPIRATION over a grass REFERENCE CROP (ETo)., this experiment was carried out in a greenhouse at the a the Plant Pathology Research Institute located in Tehran, Iran (latitude 35 41 N, altitude 51 19 E; 1190.8 meter above sea level). Meteorological parameters (solar radiation, air temperature, air humidity and wind speed) were recorded manually on a daily basis in order to calculate EVAPOTRANSPIRATION. Additionally, one microlysimeter were was installed inside the greenhouse to measure grass REFERENCE EVAPOTRANSPIRATION. Results indicated that there was a reasonable correlation between ETo values measured by microlysimeter and that estimated by FAO-Penman-Monteith equation with a root mean square error and coefficient of determination of 1.39 mm.d-1 and 0.69, respectively. In comparison, the relationship between ETo values measured by Microlysimeter and that estimated by Belaney-Ceriddle equation was the weakest, with a root mean square error and coefficient of determination of 1.54 mm.d-1 and 0.49, respectively. In addition, by using a small evaporation pan in during the study, pan coefficient was found to be 0.4562, with a coefficient of determination was of 0.82.

EVAPOTRANSPIRATION (ET) plays a key role in agriculture planning and water resources management, hence the simulation and pattern studying of this parameter has been a major field of interest for researchers. Considering the data repeatability of EVAPOTRANSPIRATION, The Box-Jenkins method is an appropriate method for ET modeling. In this study, the mean monthly values of EVAPOTRANSPIRATION for five stations of Kermanshah province, Iran was calculated by FAO 56 Penman-Monteith equation using a 21 years period meteorological dataset. Then, a time series analysis was carried out to explore the variation of the EVAPOTRANSPIRATION series. Finally, using SARIMA stochastic model, the corresponding parameters of normal condition, stochastic, white noise, whiteness of the residual, and the final EVAPOTRANSPIRATION patterns for the each station were obtained. The final EVAPOTRANSPIRATION patterns were obtained as SARIMA (2, 1, 0)×(1, 1, 1)12, for Kermanshah station, SARIMA (3, 1, 1) × (1, 0, 1)12 for Kangavar, SARIMA (2, 1, 2)×(1, 1, 1)12 for Sar pol zahab station, Ravansar station SARIMA (3, 1, 1)×(1.0.1)12 and Eslam Abad Gharb station SARIMA (1, 1, 1)×(0, 1, 1)12. Besides the selected models were used to predict the EVAPOTRANSPIRATION of 12 months which had not been used in model development stage (Training) and the results were compared with the actual values. The corresponding values of RMSE and r indices for year 2009 were 0.38 mm and 0.99 in Kangavar station, 0.37 mm and 0.99 in Sarpol-Zahab and 0.40 mm and 0.99 in Kangavar respectively, which indicated a better performance in these three stations comparing to Ravansar and Eslam Abad stations with less satisfactorily results.

Increasing CROP production depends on supplying CROP water demands, thus, accurate estimation of CROP water requirement helps not only to CROP production, but also is effective in the management of water resources. Based on this, the purpose of the present research was to investigate the estimates of REFERENCE EVAPOTRANSPIRATION from SEBAL and METRIC models using satellite imageries of Landsat 7 and 8 and Terra in the Qazvin plain. In the first step, the estimates of METRIC and SEBAL models with a total of 10 images obtained from MODIS sensor, Terra satellite, and ETM+ sensor, Landsat 7 satellite, were evaluated using lysimeter data for grass REFERENCE CROP in 2001. Estimates of MODIS sensor with r=0.88, RMSE=1.91, and SE=0.85 mm/day and Landsat ETM + with r=1.00, RMSE =0.91, and SE=0.09 in METRIC model were closer to the lysimeter data compared with the SEBAL model. In the next step, the results of the METRIC and SEBAL models obtained from OLI & TIRS sensor images of Landsat 8 satellite were evaluated with the results of METRIC model on ETM+ due to lack of lysimeter data at the time of checking. Evaluation of the results indicate that the METRIC model with r=0.96, RMSE=0.28 and SE=0.29 mm/day may be recommended as a superior model compared with SEBAL model, for estimating REFERENCE CROP EVAPOTRANSPIRATION in the Qazvin plain.