جستجوی مقالات مرتبط با کلیدواژه "تبخیر و تعرق مرجع" در نشریات گروه "آب و خاک"

Climate change is a phenomenon that affects many natural processes, including the hydrological cycle. Evapotranspiration is also an important part of the hydrological cycle, which is crucial in water resource management and agricultural planning. Since the estimation of evapotranspiration is always associated with uncertainties, this study examines the effects of climate change on the evapotranspiration process at the Crumbed station in Preston province. The study uses the SAP1-2.6, SAP2-4.5, SAP3-7.0, and SAP5-8.5 scenarios according to the Sixth Assessment Report (AR6) in three future time periods: near future (2023-2048), mid future (2049-2074), and far future (2075-2100). The reference evapotranspiration for the base period and future periods is calculated using the Hargreaves method. The results show that the maximum temperature at the Crumbed station will increase by an average of 0.26 to 6.3 degrees Celsius by the year 2100, compared to the base period (1988-2014). The minimum temperature will also increase by an average of 0.32 to 4.9 degrees Celsius during the same period. Additionally, the average evaporation-transpiration in all periods will increase compared to the base period. The average evaporation-transpiration in the near future will range from 4.69 to 4.82, in the mid-term future from 4.7 to 4.94, and in the far future, from 4.72 to 5.04

Water resources management, especially irrigation practices, is heavily reliant on reference evapotranspiration (ET0). ET0 is the rate of evaporation and transpiration from a standard reference surface with a presumed surface resistance of 70 s.m-1, the height of 0.12 m and an albedo of 0.23. Penman-Monteith FAO-56 (P-M FAO-56) approach is the most commonly used method for calculating ET0. In spite of the fact that FAO-PM is achievable, its implementation remains inconvenient because it requires a large amount of meteorological data, which is derived from standard meteorological observation stations. In the absence of complete climate data, it is highly desirable to have a model with fewer input climatic dates. Therefore, remote sensing methods have been used and improved over time to estimate ET0 at various spatial scales. Alternatively, it has been observed that the research community has become increasingly interested in obtaining data from metaheuristic algorithms that are based on artificial intelligence (AI).

In this research, it has been attempted to estimate the amount of daily reference evapotranspiration (ET0) using two data-driven models, using a combination of inputs from meteorological station data and satellite imagery data from MODIS sensor, by considering different inputs from these sources. The models include the random forest (RF) and hybridized RF with genetic algorithm optimization (GA-RF). Moreover, the correlation of input variables with ET0 is evaluated and the possibility of training a simple and accurate machine learning model in the conditions of lack or absence of meteorological data using satellite image data is investigated. So, this study aimed to determine ET0 in the time period of 2003-2021 using land surface temperature (LST) data and leaf area index (LAI) acquired from MODIS sensor and Tabriz meteorological station data including maximum and minimum air temperatures (Tmax, Tmin), average temperatures (T), wind speeds (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin), and sunny hours (n). For the study area, daily LST were extracted from the Terra (MOD11A1) and Aqua (MYD11A1) satellites. Moreover, the LST of Terra and Aqua satellites were combined, since the LST values had missing data due to the presence of clouds. Furthermore, MODIS MCD15A3H version 6.1 using four-day data from Terra and Aqua satellites was used to determine the leaf area index (LAI). The standard P-M FAO-56 method for calculating daily reference evapotranspiration was considered as the base method. The set of input parameters was considered based on the cross-correlation of the parameters with reference evapotranspiration obtained from the FAO-Penman-Monteith equation.

The results of two data-driven models including standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) to estimate ET0 values were compared with calculated ET0 by P-M FAO-56 equation. The results indicated that all of the studied input variables are highly correlated with the target variable. Based on the P-M FAO-56 method, the average air temperature with the highest value (R2=0.903) and the wind speed with the lowest value (R2=0.282) has a high and low correlation with reference evapotranspiration. Also, by comparing LAI and LST MODIS parameters, LST has the highest correlation coefficient with ET0 with R2=0.865. A total of twelve scenarios for estimating ET0 are evaluated, each with a different set of input parameters. Based on the correlation between the parameters and ET0, the first ten scenarios are categorized. Additionally, the eleventh scenario is based only on satellite images, and the twelfth scenario is based solely on weather station data. Based on the results, the GA-RF-10 (R2=0.976, RMSE=0.200, MAPE=11.373, and MBE=0.028), which includes all input parameters, outperforms the other models. There was a greater degree of accuracy with the RF-10 (R2=0.949, RMSE=0.293, MAPE=16.442, and MBE=0.017) when compared with the other random forest models. Based on the comparison of scenario 11 (satellite image data) and scenario 12 (meteorological station data), it appears that scenario 12 is more accurate for both RF (R2=0.922, RMSE=0.357, MAPE=20.712, and MBE=0.009) and GA-RF (R2=0.944, RMSE=0.306, MAPE=17.037, and MBE=0.013) models. Despite the fact that only satellite image parameters did not provide accurate estimation of ET0 compared to independent meteorological parameters, the inclusion of these parameters in the ET0 estimation resulted in more acceptable results, demonstrating the importance of satellite image parameters. Thus, satellite data may be useful and recommended for estimating ET0, particularly in areas without meteorological stations.

Evaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors on the performance and efficiency of plant water consumption by spending less time and money. Since the most important agricultural product in Iran is wheat, so proper management of wheat fields has an important role in food security and sustainable agriculture in the country. The main source of food for the people in Iran is wheat and its products, and any action to increase the yield of wheat is necessary due to limited water and soil resources. Evapotranspiration is a complex and non-linear process and depends on various climatic factors such as temperature, humidity, wind speed, radiation, type and stage of plant growth. Therefore, in the present study, by using daily meteorological data of Urmia, Rasht, Qazvin, Mashhad and Yazd stations, the average daily evapotranspiration values based on the results of the FAO-Penman-Monteith method are modeled and the accuracy of the two methods temperature method (Hargreaves-Samani and Blaney-Criddle) and three radiation methods (Priestley-Taylor, Turc and Makkink) were compared with FAO-56 for wheat.

The present study was conducted to evaluate the accuracy and efficiency of the AquaCrop model in simulation of evapotranspiration and biomass, using different methods for estimation reference evapotranspiration in five stations (Urmia, Qazvin, Rasht, Yazd and Mashhad). Four different climates (arid, semi-arid, humid and semi-humid) were considered in Iran for wheat production. The equations used to estimate the reference evapotranspiration in this study are: Hargreaves-Samani (H.S), Blaney-Criddle (B.C), Priestley-Taylor (P.T), Turc (T) and Makkink (Mak). Then, the results were compared with the data of the mentioned stations for wheat by error statistical criteria including: explanation coefficient (R2), normal root mean square error (NRMSE) and Nash-Sutcliffe index (N.S).

The value of the explanation coefficient (R2) of simulation ET and biomass in the Blaney-Criddle method is close to one, which shows a good correlation between the data. The NRMSE and Nash-Sutcliffe values for both parameters and the five stations are in the range of 0-20 and close to one, respectively, which indicates the AquaCrop model's ability to simulate ET and biomass. On the other hand, the value of R2 in the Hargreaves-Samani method for biomass close to one, NRMSE in the range of 0-10 and Nash-Sutcliffe index is more than 0.5, which indicates a good simulation. The NRMSE index in the evaluation of ET and biomass wheat is excellent for the Blaney-Criddle method and about Hargreaves-Samani for ET is poor and for the biomass is excellent.The Turc method with NRMSE in the range of 0-30, explanation coefficient close to or equal to one and a Nash-Sutcliffe index of one or close to one can be used to simulate ET and biomass at all five stations. Also, for biomass simulation, Priestley-Taylor and Makkink methods have acceptable statistical values in all five stations.Based on the value of explanation coefficient (R2) of estimation ET and biomass wheat for radiation methods, the correlation between the data in all three radiation methods is high. Percentage of NRMSE index of Makkink method for wheat in ET evaluation in Qazvin station is poor category and in Urmia and Rasht is good and in Mashhad and Yazd is moderate and about biomass in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd) is excellent category, the error percentage of Priestley-Taylor method for wheat in ET evaluation in Yazd station is good and the rest of the stations is poor, about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd). The error rate of Turc method for wheat in ET evaluation in Urmia, Rasht and Mashhad stations is good and in Qazvin and Yazd is poor and about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd).

According to the results obtained using Blaney-Criddle method with R2 value close to one, NRMSE in the range of 0-20% (excellent to good) and Nash-Sutcliffe index close to one and Turc method with R2 value close to one, NRMSE in the range of 0-10% (excellent) and Nash-Sutcliffe index close to one was showed a good accuracy of AquaCrop model in simulation of evapotranspiration and biomass with these methods of estimation of evapotranspiration compared to other methods.

Changes in climatic variables that affecting access to water can play a key role in the sustainable development of agriculture and the environment. Since ET0 is the most important variable affecting access to water resources after rainfall. Therefore, the present study examines temporal and spatial variations of ET0, temporal analysis of trend, sensitivity and relative contribution rate of climatic variables including Sun Duration (SD), Relative Humidity (RH), Wind Speed (WS), air pressure (P), maximum Temperature (Tmax) and minimum Temperature (Tmin) for a period of 30 years (1988-2017) at 7 synoptic stations on the southern shore of the Caspian Sea at different time scales. The spatial distribution of ET0 in the Caspian Sea basin showed that ET0 increases from west to east. The results showed that on the annual and monthly time scale including March, May, June, July, August and September, all stations had upward trend at 5% probability level of Significance. While the results of sensitivity analysis showed that ET0 has the highest sensitivity to variables of RH, Tmax and WS respectively, but results of relative contribution rate showed that variables of RH, WS and Tmin are the main actors on ET0, in the southern shore of the Caspian Sea during the last 30 years, respectively.

Estimation of evapotranspiration of the reference plant is one of the important components in the design of irrigation systems and water management for irrigation of plants. In this study, four coefficients of evaporation pan Schneider (1992), Aurang (1998), Allen and Perot (1991) and Konica (1989) to estimate the reference evapotranspiration compared to the FAO-Penman-Monteith model using indices RMSE, MAE, AARE, MR and Spearman coefficient were evaluated. Information required for different coefficients for estimation (ETo) was collected from Khorramabad synoptic station. The results of statistical evaluation showed that in the daily period, Orang (1998) and Schneider (1992) pan coefficients with RMSE equal to 1.004 and 1.13 (mm.day-1), respectively, as the most appropriate and Allen and Prot coefficients ( 1991) and Konica (1989) were selected as the most unsuitable coefficients with RMSE of 1.83 and 1.47 (mm.day-1), respectively. On the other hand, the results of statistical evaluation for the 10-day period of Konica (1989) and Schneider (1992) pan coefficients with RMSE of 6.01 and 8.25 (mm.day-1), respectively, as the most appropriate coefficients and coefficients of Allen and Perot (1991) and Aurang (1998) were selected as the most unsuitable coefficients with RMSE of 13.90 and 9.43 (mm.day-1). Spearman correlation test also showed that the results of all coefficients have an acceptable correlation with the reference method.

The main purpose of this study was to calculate the net irrigation requirement of agricultural products in the Qaleh Chay catchment in the current climatic conditions and evaluate it with the data of the National Water Document (NETWAT). For this purpose, the reference evapotranspiration was calculated by the Penman-Monteith-FAO method for the years 2013 to 2019. Subsequently, the plant coefficients were modified according to the guidelines of FAO 56. Then the effective rainfall and net irrigation requirement were calculated. Finally, the net irrigation requirements of the region were compared with the data of the National Water Document. The results showed that the values obtained in this research were in all cases higher than the values mentioned in the National Water Document. This difference was due to the fact that the information used for the calculations in this study was from the year of establishment of the meteorological station of the catchment (2012), while the calculations in the national water document were related to meteorological stations in neighboring cities and years before 1378. Also, increasing temperature and decreasing rainfall in recent years have increased the irrigation requirement compared to previous years. Finally, it is suggested that the estimation of irrigation requirements be done according to meteorological data of recent years to be closer to reality.

In this research, a greenhouse connected to a solar distiller device was constructed for the desalination of saline water and irrigation at Shahid Chamran University of Ahvaz. Sand, sand-bagasse-perlite, and bagasse-perlite beds were used in the greenhouse as a condenser of hot and humid air of the distiller tank. By measuring air temperature, relative humidity, air flow velocity in the distiller tank, temperature around the pipes and moisture in the beds, daily production of freshwater from the distiller was calculated. The FAO Penman-Montieth method was used to estimate greenhouse reference evapotranspiration. The purpose of this research was to estimate the percentage of supplied evapotranspiration in the greenhouse by produced freshwater from the condensation method and to select the proper condenser bed in terms of moisture and temperature distribution. Daily and periodic measurements of freshwater produced by each bed were compared to daily and periodic evapotranspiration of the greenhouse. The average amount of freshwater produced per square meter by the solar distiller was calculated to be 1.23, 1.97, 2, and 1.97 kg/day in May, June, July, and August, respectively, with the average of 1.75 kg/day during the whole period. The average evapotranspiration amount in the greenhouse was estimated to be 4.08 millimeter per day. Production water in sand, sand-bagasse-perlite and bagasse-perlite beds was calculated to be 0.94, 0.79, and 0.82 times of the evapotranspiration rate within the greenhouse, respectively. Due to the lower temperature of the sand bed and the higher production of water, the sand bed is a better condenser than the other two beds. Evapotranspiration from April 21 to July 29, 2018, was estimated to be 408.2 millimeters, which %85 of it was supplied by a distiller with a production of 2799.5 kilograms for an area of 8.1 square meters of bed (without considering its type).

AquaCrop model simulates biomass yield under deficit irrigation management. This model developed by FAO and requires less input data than other similar models. One of the input data of this model is the normalized water productivity (wp*) that should be known for each plant. This parameter for the radish plant, which is part of the C3 plant, has not yet been determined. Therefore, the first objective of this study is to determine it for Pakdasht area and its second objective is to analyze the sensitivity of the Aquacrop model to the input data of reference evapotranspiration (ETo), Wp*, initial canopy cover (CCo) and maximum canopy cover(ccx). This research was carried out in the research center of Abourayhan Campus, University of Tehran in Pakdasht District during 2018 crop year. According to the results of this study, normalized water productivity of radish was determined to be 11.3 g / m2. The results of sensitivity analysis showed that the AquaCrop model has the most sensitivity to the normalized water productivity parameter for full irrigation conditions, in which the sensitivity coefficient was estimated to be 0.88. Under deficit condition, the model is most sensitive to the ETo parameter, and the less irrigation is more severe, the sensitivity coefficient increases. The sensitivity coefficient for 60% deficit irrigation was -10.99.

In recent years, a lot of research has been done on the Aquacrop model, the results show that this model simulates the product performance for deficit irrigation conditions. But this model, like other models, is sensitive to values of independent variables (model inputs). In this research, the sensitivity of the Aquacrop model was analyzed for 4 input parameters of reference evapotranspiration, normalized water productivity, initial canopy cover percentage and maximum canopy cover for barley. Irrigation treatments included full irrigation and two deficit irrigation treatments of 80% and 60%, the experiment was done in 2014-15 growing season in the field of Abourihan College. The values of measured biomass were used as the base values for treatments. The Beven’s method (Beven et al., 1979) was used for sensitivity analysis of Aquacrop model. The results showed that the model is most sensitive to the reference crop evapotranspiration, So the sensitivity coefficient for this parameter for full irrigation treatments, 80% full irrigation and 60% full irrigation were -1.1, -1.2 and -2.3 respectively. The negative sign indicates that if the value of reference evapotranspiration input is exceeded the actual value into the model, Yield performance is simulated less than actual value. In the meantime, the higher the degree of deficit irrigation, the greater the sensitivity of the model.

Several indicators have been proposed by researchers for the study of drought, each of which having certain strengths and drawbacks depending on the regional conditions and parameters affecting drought. In recent years, RDI has received greater attention as it accounts for precipitation and evapotranspiration, and is sometimes used as the basis for comparison of drought severity and the assessment of its potential damage at the national level. The present research examines RDI index along with benefits, drawbacks and problems of zoning and generalization of findings at the national level to provinces. For this purpose, the statistics derived from synoptic stations of Yazd province was utilized and after calculating RDI for each station, a comparison was drawn with the results presented in the national RDI map for the same time period. The results of this study provide technical points and considerations for calculating and zoning RDI at the national scale and generalizing findings to provincial levels, with special emphasis on Yazd province. It also offers relevant executive suggestions for improving the precision of drought zonation studies and methods across the country, which have been discussed in details

Changes of ET0 and its atmospheric control were investigated in six stations located in West Iran during the period of 1966-2100. The outputs related to HadCM3 under B2 emission scenario were downscaled through SDSM. The results revealed that ET0, as averaged across all stations, would increase by 5.12, 7.33 and 11.01% respectively over the early, middle and late 21st century relative to the baseline period (1966-2010). The results obtained through Mann-Kendall test revealed that there was an insignificant positive trend in ET0 at the level of 95% over 1966-2010 in most of the surveyed sites. This increasing trend could be explained by an upward trend in temperature and solar radiation vs. a negative trend of relative humidity within the study area. The increasing trend in ET0 will likely be significant during 2011-2040 and 2071-2100 while insignificant during 2041-2070. The occurrence of ET0 upward trend in the future is most likely due to temperature rise.

Evapotranspiration has a main role in water budget assessment and management. In plant water requirements and evapotranspiration volume calculation, firstly reference evapotranspiration (ET0) have to be computed and then plant water requirements can be estimated using different methods. In this research, firstly the reference evapotranspiration factor was calculated by standard FAO-Penman-Monteith formula via climatic data of Tabriz station, East Azerbaijan province. Climatic parameters include mean, minimum and maximum of air temperature also mean, minimum and maximum of relative humidity, rainfall, wind speed and sunshine hours were considered as an input of genetic programming and M5 tree models to estimation of monthly reference evapotranspiration as an output. Results showed that both of two approaches present exact results (determination coefficient for M5 tree model equal to 0.99 and for GP equal to 0.96) in estimating monthly reference evapotranspiration in Tabriz region, but M5 model tree, provides understandable, applicable and simple linear relations to estimate reference evapotranspiration.

Reference evapotranspiration is one of the most effective components of agricultural water use and management of water resources. Determination of the water requirements of various plants during the growing season is necessary in order to prevent water waste and proper planning (7). In recent years the use of artificial intelligence technics and hybrid model in forecasting of hydrological parameters has become very popular (12). The objective of this study is to evaluate GMDH neural network and wavelet-GMDH hybrid models in forecasting of daily reference evapotranspiration at Ahvaz synoptic station.
For this purpose, 10-year period (2000 to 2009), 7 years (2000-2006) for training and 3 years (2007-2009) to test different models were considered. Reference crop evapotranspiration time series generated using standard penman-monteith equation. Different combinations of inputs (different delays) and various mother wavelets were examined. To test different models were considered different combinations of inputs (9 different delays); and different mother wavelets (13 mother wavelets). A total of 126 models 117 of them related to hybrid models wavelet- GMDH and 9 for GMDH neural network were carried out. To choose the best model, statistical criteria such as coefficient of determination (R2), root mean square error (RMSE) and the mean percentage error (MARE) was used.
The results showed that wavelet-GMDH hybrid model (RMSE = 0.31 mm / day) compared with GMDH neural network (RMSE = 1.22 mm / day) has higher accuracy in forecasting daily reference evapotranspiration. The results showed that use of delays longer than four days have little effect on the accuracy of models and in some cases can result in reduced accuracy. The results of similar studies that have used wavelet transform to preprocessing data are in correspondence with our findings (1, 4, 5 and 12). Results of GMDH neural network showed that number of delays did not affect the accuracy of model. This study evaluates the accuracy of the wavelet-artificial neural network hybrid model for different mother wavelets. Results showed that Meyer mother wavelet due to its complexity and its shape improved the accuracy of models. These findings correspond with the findings of Rajaee and Ebrahimi(2014), Shoaib et al(2015) and Toofani et al (2012) (13, 15 and 17).
Results of this study showed that, at the forecasting of one day ahead reference evapotranspiration Wavelet-GMDH model (MAPE=5.53%) has significant superiority to GMDH model (MAPE=22.11%). The results of this study can be used in the planning of irrigation water in this area. At the end it is recommended that proposed models has been evaluated in different climate conditions of Iran.

The water resources are severely affected by hydrological cycle.Estimation of evapotranspiration which is the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is non-linear and many factors affect on that and its estimation is very difficult. Various methods have been employed to estimate evapotranspiration although they have some limitations or problems. Some of these methods are costly and time-consuming such as lysimeters, and other empirical methods have local authority. Accordingly, applying a method that can be able to model the evapotranspiration regard to the nature of the gathered data and usage of minimum climate parameters is necessary. Nowadays, Artificial Neural Network (ANN) as a novel intelligent method are used in various sciences. In this study, the daily data of two climatological stations, namely Farokhshahr and Shahrekord airport in the interval of 2004-2013 including minimum temperature, maximum temperature, average relative humidity, sunshine, and wind speed at the height of two meters under different scenarios were utilized. Initially, empirical methods of reference evapotranspiration were approximated.The used empirical methods in this research have been Hargreaves, Blany Criddle, Priestley Taylor, and Jensen Hayes. The ANN model has been designed based on different scenarios of input data through MATLAB (R2012 b) Software. In this step, different ANN architectures were evaluated based on sensitivity and accuracy So, threshold functions such as tangent sigmoid and log sigmoid in hidden layers, linear function output layer were tested in topology where as Levenberg Marquardt employed as learning function.To evaluate the models, Penman Monteis FAO 56 model was employed.The statistical indexes, namely RMSE, MAE and R were calculated. Ten scenarios have been examined, and the results demonstrated that Scenario one with five parameters had the lowest error in comparision to FAO 56 technique. Furthermore, the perposed model show superior performance than empirical methods. However, between the empirical methods, Priestley Taylor and Hargreaves had better performance. On the other hand, the sensitivity analysis illustrated that the maximum temperature and wind speed had the greatest influence on reference evapotranspiration in these regions.

Correct estimation of reference evapotranspiration (ETo) needs to estimate accurate solar radiation values. There are many models to estimate solar radiation so that one of these relationships is Angstrom-Prescott (A-P) equation. The accuracy of Angstrom-Prescott equation in estimating solar radiation value depends on the estimation accuracy of a and b coefficients of the mentioned equation. The purpose of the present research was to investigate the accuracy of models for estimating coefficients in A-P equation and their effects in the solar radiation and reference evapotranspiration values. To do this purpose, the meteorological data of four synoptic stations including Urmia, Takab, Mahabad and Salmas in the Urmia lake watershed in the daily scale from 1998 to 2008 were used. The models which used for the determination of Angstrom-Prescott equation coefficients were included models based on the annual average of relative sunshine hours , elevation and , elevation and latitude and , elevation and latitude, annual average of air temperature, elevation. The obtained coefficients were used to compute the solar radiation values in Angstrom-Prescott equation and then reference evapotranspiration was calculated by FAO-Penman-Monteith equation. To evaluate the models accuracy, statistical indices including RMSE, MBE, MPE and R2 were used. The evaluation results of the models showed that the model based on annual average of relative sunshine hours (Rietveld model) had the best performance in determining the coefficients of AngstromPrescott equation. The average values of RMSE, MBE, MPE and R2 indices for actual reference evapotranspiration and reference evapotranspiration obtained from coefficients of mentioned model were found 0.33 and 0.25 mm/day, 6.27% and 0.9977, respectively. For AngstromPrescott equation coefficients from selected model, a=0.26 and b=0.43 were suggested in the studied stations in the Urmia lake watershed.

Evapotranspiration is one of the important factors in water resources consumption in the agriculture part. Therefore, presenting a method which gives suitable and accurate prediction of reference evapotranspiration can help to take optimum decision for water resource programing. In this research, time series and artificial neural networks methods were compared each other in order to predict the monthly reference evapotranspiration in Urmia synoptic station. To achieve this goal, at the first step, the best time series model between AR and ARMA models and the best artificial neural networks model between radial basis function (RBF) and multilayer perceptron (MLP) neural networks were selected. In the second step, the two models chosen were compared each other. In the mentioned artificial neural networks, the deferent monthly lags of reference evapotranspiration were used as network input. In this process, the monthly reference evapotranspirations were computed from 1971 to 2010 using FAO Penman-Monteith method. The mentioned dates from 1971 to 2005 were used to select the best time series model and the best structure of networks and the dates from 2006 to 2010 were utilized to compare the methods used. The results showed that the AR(11) model has the best performance among other time series models and the RBF model has the lower error than the MLP model. The comparison of the best time series model (AR(11) model) with the best artificial neural networks model (RBF model) showed that the RBF model could predict the reference evapotranspiration by the lowest error from 1971 to 2010 period. The root mean square error in AR(11) and RBF models was obtained 1.85 and 0.999 mm/month respectively.

Different equations have been introduced for estimating grass reference evapotranspiration (ETo) that are in the range of easy to very complex equation of energy balance. Empirical ETo equations have remained in popular use because of simplicity and the smaller number of input parameters needed for computation. The Hargreaves−Samani (1985) equation is one of the empirical equations which widely used by researchers for estimating the ETo. The most important parameters in estimating ETo are temperature and solar radiation (Rs). The RS has a key role in energy balance in the ground-atmosphere system and is a key parameter in calculating ETo. Hargreaves and Samani (1982) recommended a simple equation to estimate solar radiation (Rs): R_S=(KT)(R_a) 〖(TD)〗^0.5 (1) Combining equation 1 with the original Hargreaves equation (Hansen et al, 1979) resulted in a simplified equation which requires only temperature and latitude (Hargreaves and Samani, 1982, 1985). The simplified equation is as:ET_o=0.0135 (KT)(R_a) (TD)^0.5 (T+17.8) (2)In the above equations, TD is the difference between maximum and minimum daily air temperature (Tmax-Tmin) (oC) for weekly or monthly periods; Ra is extraterrestrial radiation (mm/day); KT is empirical coefficient and T is the average daily air temperature (oC). The equation 2 and its empirical coefficient (KT) were investigated in various studies and its application in ETo proper estimation was validated. Ever now it was not done any study about the effect of solar radiation estimation methods on the results of Hargreaves-Samani ETo equation in Shahrekord plain. In the present study, the effect of 8 radiation estimation models on estimating ETo by Hargreaves-Samani model based on lysimeter measurements was investigated. This study was carried out in the agricultural station of Shahrekord in Charmahal-va-Bakhtiari province in Karoon basin, Iran. The experimental site is characterized with cold semi humid climate, altitude 2070 m above sea level, mean annual air temperature equals to 12.02 oC and average annual precipitation of 321.5 mm (Mahdavi et al, 2011). The present study was done by using a volumetric lysimeter planted with alfalfa in the Agricultural Research Center of Charmahal-va-Bakhtiari province in 2011. The date of the beginning and end of the measurement was on 21 April and 22 October 2011, respectively. In the next of the lysimeter, a drainage water measuring hole was constructed. The time of irrigation was based on 50% of maximum allowable depletion (MAD) of soil moisture and this amount was applied based on measuring the soil moisture in depth of 180 centimeters of the lysimeter. Meteorological data used in this study were collected from the weather station of Agricultural Research Center of Shahrekord in 2011. The used meteorological variables were maximum, minimum and average daily air temperature, solar radiation (RS), precipitation, wind speed, soil temperature, dew point temperature, sunshine hours, relative humidity, cloudiness ratio and evaporation from pan. The amount of Rs was calculated from 8 estimation models including Hargreaves-Samani (HS), Doorenbos- Pruitt, Annandale et al, Allen, Ertekin-Yaldiz, Samani, Goodin et al and Mahmood-Hubbard. Then ETo calculated from HS equation based on the Rs resulted from the radiation models was compared with ETo measured by lysimeter. For evaluating model accuracy, some indices include mean bias error (MBE), root mean square error (RMSE), relative error (RE), coefficient of determination (R2), index of agreement (d) and model efficiency (ME) were used.The results showed that the Hargreaves-Samani equation had underestimation based on more Rs models. With respect to differences related to lysimeter data, the Goodin et al and Ertekin-Yaldiz models had the poorest results. Also ETo calculated from Mahmood-Hubbard and Annandale et al models had the best results with 8% and 10% underestimation, respectively. With respect to all the evaluation criteria, it can be concluded that the Mahmood-Hubbard is the best solar radiation model with respect to effecting on calculating ETo in the climate of the present study and the Annandale et al model is in the next order. Also the Goodin et al and Samani are the poorest models in the solar radiation estimation. The Mahmood-Hubbard model generally has more physically base and obtained under various conditions and 9 years data. For this reason, probably the model had the best results in calculating ETo. Also the Annandale et al model takes into account the effect of elevation and reduced thickness of the atmosphere on the primary Hargreaves-Samani model and because the area of study (Shahrekord plain) is a region of high elevation, therefore the Annandale et al model has been a proper model in second order in estimating ETo. The poor result of the Goodin et al model was probably because of obtaining the coefficients of this model under limited conditions.

However, several methods exist for calculation of reference crop evapotranspiration (ETo) but the FAO- 56 Penman- Monteith (FAO- 56 PM) method has been recommended by the Food and Agriculture Organization of the United Nations (FAO) as the standard equation. This method is difficult to use because it requires several weather parameters and complex calculations. On the other, over the last decades Artificial Neural Network (ANNs) have shown a good ability for modeling complex and nonlinear systems. The present study was carried out to investigate the sensitivity of the reference crop evapotranspiration to climate parameters using ANNs in Lorestan province. For this purpose in period 10 years (2001 – 2010) daily ETo were calculated using FAO-56 PM method based on weather data daily in the eight weather stations in Lorestan province. Then an Artificial Neural Network was designed with 18 scenarios. Combinations of six weather parameters (maximum and minimum air temperature, maximum and minimum relative humidity, wind speed and daily sunshine hours) which are required to calculate ETo with using FAO-56 PM method were considered as inputs and calculated ETo as output of the ANN in various scenarios. The results of this study showed that increasing the number of data in the input layers will not necessarily lead to improved outcomes of intelligence models. In case of weather data limitation, scenario 13 which was used maximum temperature and wind speed as input layer showed reliable results.

Climate change via variations in temperature pattern، precipitation and other climate variables patterns affects all the hydrologic processes. One of the main consequences of the climate change is its impacts on agricultural water use، which can cause serious challenges for water resources management. The purpose of this research was to investigate the global warming impacts on reference evapotranspiration، precipitation deficit (PD) and vapor pressure deficit (VPD) in Urmia synoptic station. To achieve this goal، using the HadCM3 global circulation model and LARS-WG5 weather generator، the minimum and maximum temperature and precipitation values for 2011-2030، 2046-2065 and 2080-2099 periods based on A1B، A2 and B1 scenarios were simulated. Then، using the forecasted parameters، the reference evapotranspiration، PD and VPD were calculated and compared with the corresponding base data (1971-2010). The results show that in each of the three periods the precipitation in the spring will be decreased but the autumn precipitation will be increased in comparison with the base period. Meanwhile، the maximum event of the precipitation will be shifted from the spring season in the base period to the autumn season in 2090. Moreover، similar to air temperature، the reference evapotranspiration، PD and VPD will be increased in 2090 and such increment will be more in the warm months than the cold months. The amounts of these parameters’ increments in the A2 scenario are more than those in the A1B scenario and also in the A1B scenario are more than those in the B1 scenario.

Evapotranspiration is one of the important components of hydrological cycle whose accurate estimate is needed for design and management of irrigation systems، simulation of crops products، and programming water resources management. In this research، to predict monthly reference evapotranspiration، ANFIS and GP models were employed and 38 years (1973-2010) of data were collected from six synoptic weather stations located in the northwest of Iran. At first، monthly reference evapotranspiration was estimated by FAO-Penman-Montieth method for the selected stations and was considered as the output of GP and ANFIS models. Then، a regression equation between effective meteorological parameters and evapotranspiration was fitted and different input patterns for the models were determined. Relative humidity as the less effective parameter was deleted from input of the models. Also، in this study، to investigate effect of “memory” on prediction of evapotranspiration، one، two، three and four months lags were used as the input of the models. Results showed that both models estimated monthly evapotranspiration with high accuracy، but ANFIS model was better than GP model.