جستجوی مقالات مرتبط با کلیدواژه « monteith » در نشریات گروه « آب و خاک »

The main objectives of this study was the evaluation of CRU TS4.05 and ERA5 datasets accuracy to monthly precipitation (P), temperature (T) and Potential Evapotranspiration (PET) variables in different climates across Iran. To achieve the research aim, daily measured data from 100 synoptic meteorological stations and monthly P, T and PET products of CRU TS4.05 and ERA5 datasets are used at a spatial resolution of 0.5°×0.5° and 0.25°×0.25° from 1987 to 2019. The mean absolute error values for P, T and PET variables in Hyper-arid (HA), Arid (AR), Semi-arid (SA), Sub-humid (SH) and Humid (HU) climates of CRU TS4.05 datasets are equal to 6, 9, 13, 28 and 81 mm month-1, 1.7, 1.5, 1.8, 2.7, 2.5 oC and 171, 84, 38, 44 and 47 mm month-1, and of ERA5 datasets are equal to 5, 9, 18, 28 and 40 mm month-1, 1.1, 1.6, 1.9, 3.0 and 1.0 oC and 227, 91, 46, 46, 44 and 48 mm month-1, respectively. Results of this study have declared that more accuracy of two datasets (CRU TS4.05 and ERA5) to T, PET and P, respectively. Although, the main results show that accuracy of ERA5 datasets are more than CRU TS4.05 datasets, but it is recommended to use ERA5 P products in SH and HU climates, and CRU TS4.05 PET products in HA and AR climates.

Knowing about plant's water requirement is one of the most important considerations in reforming water productivity in agriculture. So precise estimating of source evapotranspiration is one of the most important factors in improving water management. Penman Mantis FAO is used as a standard formula to estimate the source evapotranspiration. But this detailed and complicated method requires lots of information. Due to the lack of certain required information in all the regions, regional equations for calculating evapotranspiration based on the least of the meteorological data seems necessary. To this end, three experimental equation" Blany Criddle, Hargreaves and Torrent White" was selected and based on Penman Mantis FAO equation for three different climates by the linear regression and nonlinear optimized method was calibrated. A ten year period's data for the three stations of thoroughly humid, semi-arid and dry climate was calculated and divided into two groups: the first 5 years and second one. The data for the first 5 years the equations was calibrated and evaluation of the equations was conducted through F test and RPMSE test, using the second 5 years. Based on the results, using of equations Blany Criddle , and Hargreaves, for the thoroughly humid climate and the Torrent White equation calibrated for semi-arid and dry climate using the regression calibration method is recommended to estimate source evapotranspiration. In the calibrating process of equations, linear regression performed more properly than the optimized non-linear method.

The aim of this study was determination of Pestachio evapotrspitaion in Rafsanjan plain, south of Iran. For this purpose, the results of the six reference evapotranspiration (ETo) estimation equations was compared by ETo grass lysimetric measurements. Among the selected methods, Penman- Monteith FAO was found to be the best one with root mean square error (RMSE) of 1.3 mm.mm-1 and percent absolute error (PAE) equals to 17%, Kimberly-Penman, radiation and Blaney Criddle methods were ranked next to PM equation with acceptable accuracy. Hargreaves and modified Penman method showed poor performance. Pistachio crop evapotranspiration (ETc) was determined using pistachio crop coefficients and 4 years average of lysimeteric values ETo. Based on the results, the mean Pistachio ETc was 9600 cubic meters per hectare during April to November during. Maximum of ETo and ETc occurred in July. More than 50% of ETc was observed in June, July and August which confirms the importance of maintaining sufficient water for irrigation during this period.

Accurate estimation of ET0 in any region is very important. The aim of this study is to compare and calibrate the 20 empirical methods of estimating evapotranspiration (ET0) based on three categories in monthly timescale at the Urmia Lake watershed. These categories are: 1) temperature-based models (Hargreaves (HG), Thornthwaite (TW), Blaney-Criddle (BC), Linacre (Lin)), 2) radiation-based model (the Doorenbos-Pruitt (DP), Priestly-Taylor (PT), Makkink (Mak), Jensen-Haise (JH), Turc (T), Abtew (A), McGuinness-Bordne (MB)) and 3) mass transfer-based model (Meyer (M), Dalton (D), Rohwer (R), Penman (P), Brockamp-Wenner (BW), Mahringer (Ma), Trabert (Tr), WMO and Albrecht (AL)). For this purpose, the information of 10 synoptic meteorological stations during the period of 1986-2010 was used. Results from the above mentioned methods were compared with the output of the FAO Penman-Monteith (PMF-56) method. Performance of the methods evaluated using the R2, RMSE, MBE and MAE statistics. The best and worst methods of each category were determined for the study area. The best methods of each category were calibrated for the area under study. Results indicated that there is a significant difference between the results of selected methods of each category and the PMF-56 method. Performance of the selected methods remarkably increased after calibration. Among the temperature-based group, the HG method having the median R2 value of 0.9597 was recognized as the best method. After calibration the medians of RMSE, MBE, and MAE were 72.09, 3.14 and 10.70 mm/ month, respectively. After HG, the Lin and BC found to be the best second and third methods in the study area. The TW showed Large error, therefore, it was not a suitable method for ET0 estimation in study area. Among the radiation-based group, the DP model was selected as the best method in the study area. Furthermore, the median of R2 values was 0.982. In this method, the medians of RMSE, MBE and MAE after calibration were 7.89. -0.62 and 6.03 mm/month, respectively. Following DP, the PT method was recognized as the 2nd best one. The methods namely M, JH, T, A and MB were put in the 3rd to seventh rank of the radiation category. Finally, among the mass transfer-based group, having R2=0.8945, the Meyer method was selected as the best method of this group for the study area. In the mentioned method (after calibration) the medians of RMSE, MBE, and MAE were 21.8, -8.7 and 17.3 mm per month, respectively. From mass transfer based group, the D method was found as the second best method in the study area. The methods namely R, P, BW, Ma, Tr, WMO and A were ranked 3rd to 7th, respectively. In general, the performance of radiation based methods was superior than others in Urmia Lake basin. Temperature based methods and mass transfer based methods were ranked second and third, respectively. Further examination of the performance resulted in the following rank of accuracy as compared with the PMF-56: DP (Radiation based), HG (Temperature based) and Meyer (Mass transfer). In general, it can be concluded that after calibration the DP method is suitable to estimate reference crop evapotranspiration among 20 selected methods in the Urmia Lake basin.

The present study aimed at investigating four different models to estimate pan evaporation coefficient (Kp) to measure alfalfa-reference evapotranspiration. To this end the values of pan coefficient obtained from Cuenca, Allen and Priutt, Snyder and Orang equations in Kerman with arid climate were calculated. Then, the values of alfalfa-reference evapotranspiration (ET0) were measured using pan evaporation daily data in a period of five months (from 23/07/2013 to 21/12/2013) on experimental farm of Shahid Bahonar University of Kerman. Based on the daily weather data provided by the automatic weather station in this farm ( during the study), ET0 was calculated using ASCE standard Penman-Monteith model and was used as an index to measure alfalfa-reference evapotranspiration obtained from the four stated models. The results indicated that d-index in Allen and Priutt model was closer to 1 (0.823) with fewer errors compared to those of the other models (RMSE=1.607 and MAE=1.412). Accordingly, among the four models under investigation, Allen and Priutt is suggested to estimate evapotranspiration using pan evaporation in such a climate.

From the mathematical point of view, the FAO Penman-Monteith equation shows a homographic function related to the wind speed. The homographic function has horizontal and vertical asymptotes. In this issue horizontal asymptote is applicable; because the vertical asymptote occurs for negative values of wind speed but the negative sign indicates the direction of the wind speed and only the absolute values of the quantities are used. By increasing wind speed from zero, evapotranspiration will increase until it reaches to a limited asymptote value. This value is the horizontal asymptote of the fractional function of FAO Penman Monteith equation. In this research the effect of wind speed variations on the evapotranspiration amounts was analyzed, with considering the mathematical framework of the Penman-Monteith equation. Meteorological data from three weather stations, including Tabriz, Isfahan and Rasht were used in this research. Results showed that in the FAO Penman-Monteith equation, wind speed effect on evapotranspiration is nonlinear and variation in evapotranspiration amounts is more at low wind speeds than high values of wind speeds. As a general result, evapotranspiration has tended to asymptote homographic function and the rate of increasing in evapotranspiration is reduced by wind speed increasing.

Accurate estimation of reference crop evapotranspiration (ETo) plays an important role in water resources management and planning in dry regions. In this study, accuracy and ability of Adaptive Neuro- Fuzzy Inference System (ANFIS) in estimating ETo was evaluated. Daily meteorological data, including air temperature, relative humidity, sunshine hours, vapor pressure deficit, wind speed and solar radiation ofMinab synoptic station, Hormozgan province, during 2006 to 2011 were used for modeling. The evapotranspiration values estimated by the FAO-56 Penman-Monteith equation (PM) were considered as the reference values for calibrating ANFIS model. The performance of the developed model with different input combinations was also compared with the empirical models, namely, Hargreaves-Samani (HS) and Blaney- Criddle (BC). The root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2) were used for comparing the results of ANFIS, HS and BC methods with reference method (FAO-56 Penman- Monteith equation). The results showed that the ANFIS was a more appropriate method for estimating the ETo inMinab and this model with 6 inputs (with 3 membership functions and Gaussian mixture model) had a better performance than the other considered methods with the R2, MAE and RMSE values of 0.99, 0.03 (mm day-1) and 0.04 (mm day-1), respectively. Also, the ANFIS model with 2 inputs (with 3 membership functions and Gaussian mixture model) was the best model for the stations which had only the measured temperature data.

Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA) model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations.
In the present study in all stations (characteristics of the synoptic stations are given in Table 1), the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO) for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data.
Table 1. The geographical location and climate conditions of the synoptic stations
Station Geographical location Altitude (m) Mean air temperature (°C) Mean precipitation (mm) Climate, according to the De Martonne index classification
Longitude (E) Latitude (N) Annual Min. and Max.
Esfahan 51° 40' 32° 37' 1550.4 16.36 9.4-23.3 122 Arid
Semnan 53° 33' 35° 35' 1130.8 18.0 12.4-23.8 140 Arid
Shiraz 52° 36' 29° 32' 1484 18.0 10.2-25.9 324 Semi-arid
Kerman 56° 58' 30° 15' 1753.8 15.6 6.7-24.6 142 Arid
Yazd 54° 17' 31° 54' 1237.2 19.2 11.8-26.0 61 Arid
The monthly meteorological data were used as input for the Ref-ET software and monthly reference evapotranspiration were obtained. The mean values of evapotranspiration in the study period were 4.42, 3.93, 5.05, 5.49, and 5.60 mm day−1 in Esfahan, Semnan, Shiraz, Kerman, and Yazd, respectively. The Augmented Dickey-Fuller (ADF) test was performed to the time series. The results showed that in all stations except Shiraz, time series had unit root and were non-stationary. The non-stationary time series became stationary at 1st difference. Using the EViews 7 software, the seasonal ARIMA models were applied to the evapotranspiration time series and R2 coefficient of determination, Durbin–Watson statistic (DW), Hannan-Quinn (HQ), Schwarz (SC) and Akaike information criteria (AIC) were used to determine, the best models for the stations were selected. The selected models were listed in Table 2. Moreover, information criteria (AIC, SC, and HQ) were used to assess model parsimony. The independence assumption of the model residuals was confirmed by a sensitive diagnostic check. Furthermore, the homoscedasticity and normality assumptions were tested using other diagnostics tests.
Table 2- The selected time series models for the stations
Station Seasonal ARIMA model Information criteria R2 DW
SC HQ AIC
Esfahan ARIMA(1, 1, 1)×(1, 0, 1)12 1.2571 1.2840 1.2396 0.8800 1.9987
Semnan ARIMA(5, 1, 2)×(1, 0, 1)12 1.5665 1.5122 1.4770 0.8543 1.9911
Shiraz ARIMA(2, 0, 3)×(1, 0, 1)12 1.3312 1.2881 1.2601 0.9665 1.9873
Kerman ARIMA(5, 1, 1)×(1, 0, 1)12 1.8097 1.7608 1.8097 0.8557 2.0042
Yazd ARIMA(2, 1, 3)×(1, 1, 1)12 1.7472 1.7032 1.6746 0.5264 1.9943
The seasonal ARIMA models presented in Table 2, were used at the 12 months (2004-2005) forecasting horizon. The results showed that the models produce good out-of-sample forecasts, which in all the stations the lowest correlation coefficient and the highest root mean square error were obtained 0.988 and 0.515 mm day−1, respectively.
In the presented paper, reference evapotranspiration in the five synoptic stations, including Esfahan, Semnan, Shiraz, Kerman, and Yazd, were calculated using the FAO Penman-Monteith method for the 41 years, and the time series were formed. The selected models gave good out-of-sample forecasts of the monthly evapotranspiration for all the stations. The models can be used in the short-term prediction of monthly reference evapotranspiration. Note that, the use of models in long-term forecasting was not recommended. The time series model can be used in lost data. Even though more methods are available for model building, the use of time series models in water resources are advocated in modeling and forecasting. Time series can be used as a tool to find lost data.

In measurements of crop evapotranspiration by lysimeter, climate of cultivated field may be different from lysimeter point, also a lysimeter cannot be utilized for measurements of several crops during a limited time. For measurements of the standard maize evapotranspiration by the water balance method, soil water content was measured in a field experiment under furrow irrigation system during 2003 and 2004. Maize root depth was varied in respect to time and for reducing the error of measured soil moisture and calculating the stored water in soil profile, soil water content was only measured in the root zone until the root reached to its maximum length (1.8 m). In this study, the measured standard evapotranspiration of maize via water balance equation in the field, was compared with the estimated values using the methods of Penman-Monteith, HargreavesSamani and MSM2 model (Maize Simulation Model). The results showed that applying the water balance method for measuring maize standard evapotranspiration was simple and favorable in field conditions. The investigations confirmed that by use of MSM2 model, modified Hargreaves and Penman-Monteith’s equations, the standard maize evapotranspiration values could be estimated, with the errors of -2% , 5% and -25%, respectively. The results showed that with limited data condition, if only, the minimum and maximum air temperature were available, the maize standard evapotranspiration value could be estimated favorably, using the modified Hargreaves-Samani’s equation and crop coefficient.

Introduction Subdaily estimates of reference evapotranspiration (ET¬¬o) are needed in many applications such as dynamic agro-hydrological modeling. The ASCE and FAO56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) has received favorable acceptance and application over much of the world, including the United States, for establishing a reference evapotranspiration (ETo) index as a function of weather parameters. In the past several years various studies have evaluated ASCE-PM and FAO56-PM models for calculating the commonest hourly or 15-min ETo either by comparing them with lysimetric measurements or by comparison with one another (2, 3, 5, 9, 10, 11, 16, 17, 19). In this study, sub-daily ET¬o estimates made by the ASCE-PM and FAO56-PM models at different timescales (1-360 min) were compared through conduction of a computational experiment, using a daily-to-subdaily disaggregation framework developed by Parchami-Araghi et al. (14).
Materials and Methods Daily and subdaily weather data at different timescales (1-360 min) were generated via a daily-to-subdaily weather data disaggregation framework developed by Parchami-Araghi et al. (14), using long-term (59 years) daily weather data obtained from Abadan synoptic weather station. Daily/subdaily net longwave radiation (Rnl) was estimated through 6 different approaches, including using two different criteria for identifying the daytime/nighttime periods : 1) the standard criteria implemented in both ASCE-PM and FAO56-PM models and 2) criterion of actual time of sunset and sunrise in combination with 1) estimation of clear-sky radiation (Rso) based on the standard approach implemented in both ASCE-PM and FAO56-PM models (1st and 2nd Rnl estimation approaches, respectively), 2) integral of the Rso estimates derived via a physically based solar radiation model developed by Yang et al. (25), YNG model, for one-second time-steps (3rd and 4th Rnl estimation approaches, respectively), and 3) integral of the calculated Rnl based on Rso estimates derived via YNG model for one-second time-steps (5th and 6th Rnl estimation approaches, respectively). The capability of the two models for retrieving the daily ETo was evaluated, using root mean square error RMSE (mm), the mean error ME (mm), the mean absolute error ME (mm), Pearson correlation coefficient r (-), and Nash–Sutcliffe model efficiency coefficient EF (-). Different contributions to the overall error were decomposed using a regression-based method (7).
Results Discussion Results showed that during the summer days, 24h sum of subdaily radiation and aerodynamic components of ETo and the estimated ETo derived from both models were in a better agreement with the respective daily values. The reason for this result can be attributed to the nighttime value of cloudiness function (f) and the longer nighttime during the cold seasons. Because the nighttime values for f are equal the f value at the end of the previous daylight period until the next daylight period. The difference between subdailty ETo derived from the ASCE-PM and FAO56-PM models during the day and night was highly dependent on the wind speed. In case of both models, daily aerodynamic component of ETo (ETod,aero) were reproduced more efficiently, compared to radiation component (ETod,rad). Except in the case of 6th Rnl estimation approach, FAO56-PM model (with a mean model efficiency (MEF) of 0.9934 to 0.9972) had better performance in reproducing the daily values of ETo (ETod), compared to ASCE-PM model (with a MEF of 0.9910 to 0.9970). The agreement between 24h sum and daily values of aerodynamic component had a lower sensitivity to the adopted time-scale, compared to the radiation component. Compared to the FAO56-PM model the performance of the ¬ASCE-PM model in reproducing the ETod,rad, ETod,aero and ETod had higher sensitivity to the approach utilized for calculation of Rnl and hence, to the uncertainty of net radiation. Results showed that a smaller time step does not necessarily leads to an improvement in agreement between 24h sum of subdaily and daily values of ETo. Deficiency of the standard daytime/nighttime identification criteria was resulted in a higher daily averaged daytime (1.3831 to 1.6753 h) used in cloudiness function calculations, compared to the respective value used in calculations of the radiation and aerodynamic components. In order to estimate the subdaily ETo under climatic condition of the studied region, the use of ASCE-PM model based on the 6th Rnl estimation approach, (ASCE-PM)6, with a MEF of 0.9970 is preferred, compared to other studied alternatives. Another advantage of the (ASCE-PM)6 and (FAO56-PM)6 models is their computational efficiency in case of their implementation in hydrological models.

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.

Accurate estimation of evapotranspiration plays an important role in quantification of the water balance at regional scale for better planning and managing water resources. Evapotranspiration can be obtained from either estimation of potential ET using data of meteorological stations or، directly، from field measurements. ET is subject to rapid changes in time and space، attributable to the wide spatial variability of precipitation، hydraulic characteristics of soils، and vegetation types and densities. Therefore، it is nearly impossible to determine its spatial and temporal distributions over large areas only from lysimeter and precise measuring instruments. Thus، researchers have used remote sensing data to estimate areal actual ET. In this study، actual evapotranspiration variations of Neyshabour plain was investigated by algorithm of energy balance of the earth since 2000 to 2013 by using MODIS images and meteorological data. Also، the determined evapotranspiration was compared and evaluated with Penman-Monteith and Hargreaves-Samani models. Low amount of coefficients of the error between Penman-Monteith model and SEBAL algorithm showed the accuracy of SEBAL model in the estimation of evapotranspiration and its parameters. The results derived from comparison of evapotranspiration and NDVI vegetation index indicated a good correlation between vegetation and evapotranspiration (R2=0. 908). Also، the variations of NDVI index، land surface temperature، and evapotranspiration in the studied fields showed that evapotranspiration increased by lower land surface temperature and higher densities of vegetation.

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) 12for 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.

The aim of this study is to compare and calibrate nine different mass transfer-based reference crop evapotranspiration () estimation methods in monthly time scale at Urmia Lake basin. The selected methods were Meyer (M)، Dalton (D)، Rohwer (R)، Penman (P)، Brockamp and Wenner (BW)، Mahringer (Ma)، Trabert (T)، WMO and Albrecht (A) methods. For this purpose the information of ten synoptic weather stations in the period 1986-2010 were used. Results of the mentioned methods compared with the output of the FAO-56 Penman-Monteith (PM56) method. Calibration of methods performed in the case of every station and months in the mentioned time period. Performance of methods evaluated using the R2، RMSE، MBE، MAE and PE statistics. Results showed that before calibration larger biases existed for the selected methods comparing the PM56. Calibration of methods considerably improved their performances. The M method was recognized as a best method after calibration at the study watershed. The median of R2 value of this method was 0. 8945. In the mentioned method (after calibration) the median of the RMSE، MBE، MAE and PE was found to be equal to 21. 8، -8. 7، 17. 3 and 21. 5 mm per month. The Dalton method selected as the second best one. Methods namely، R، P، BW، Ma، T، WMO and A ordered as the third till ninth rank methods.

Accurate estimation of crop water requirement based on evapotranspiration calculation has an important role in improvement of field water management. The Penman-Monteith equation is the most common method for estimating reference crop potential evapotranspiration. However, this equation needs full weatherr data, but a few stations with complete weather data exist in Fars province. On the other hand, the Jensen- Haise equation is a more simple method for estimating reference crop potential evapotranspiration. In this study, using the weather data of some synoptic stations in Fars province, the Jensen- Haise equation was calibrated and then evaluated based on Penman-Monteith method for every month of the year. For this purpose, the calibrated coefficients of the Jensen- Haise equation were determined for all months and all stations, separately. Also, the monthly effective temperature (combinatio of minimum and maximum monthly temperature with a constant coefficient) was used instead of monthly mean temperature in calibration stage, and the results indicated the appropriateness of this replacement for estimating evapotranspiration. Furthermore, the estimated sunshine hours (based on minimum and maximum monthly temperature) were used instead of measured sunshine hours in validation stage in Jensen- Haise equation, and the results indicated the appropriateness of this replacement for good validation of this equation in Fars province. Finally, the results indicated that the adjusted Jensen- Haise equation was appropriate in Fars province.

Evapotranspiration is one of the most important determinants of the hydrological cycle and surface energy balance. In fact Evapotranspiration cause the relationship between such as atmosphere and earth important elements. Most methods that have been presented use point measurements to estimate evapotranspiration. These methods are only suitable for the Local areas due to dynamic and changing nature of regional evapotranspiration are not generalizable to the Basin. Remote sensing has the capability to estimate and to assess the amount of evapotranspiration and its spatial distribution. Surface energy balance algorithm for land (SEBAL) is a new algorithm for estimating evapotranspiration in most parts of the world and other heat fluxes at the surface and have a satisfactory results. In this research evapotranspiration results derived with SEBAL algorithm was compared with FAO evapotranspiration standard method. The results indicate that the algorithm SEBAL determine the actual evapotranspiration in a large area without the need for more meteorological data. The results show that the obtained rate of evapotranspiration in SEBAL algorithm has less than the Penman – Monteith about 1-1.5 mm/d.

The aim of this study is to compare and calibrate of four different temperature based estimation methods in monthly time scale at Urmia Lake basin. The selected methods were Hargreaves (HG), Thornthwaite (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 coefficients estimated for each station and every month in a year. Performance of methods evaluated using the , RMSE, MBE and MAE statistics. The effectiveness of method’s calibration evaluated using the. 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 at the study area. The Linacre method was known as the best method of estimation at Urmia Lake basin after calibration (at second state). HG, BC and TW were in the second to fourth rank orders, respectively.

The aim of this study is to compare and calibrate seven different radiation-based reference crop evapotranspiration () estimation methods in monthly time scale at Urmia Lake basin. The selected methods were Doorenbos-Pruitt (DP)، Priestly-Taylor (PT)، Makkink (M)، Jensen-Haise (JH)، Turk (T)، Abtew (A) and McGuinness-Bordne (MB). For this purpose the information of ten synoptic weather stations in the period 1986-2010 wشس used. Results of the mentioned methods were compared with the outputs of the FAO-56 Penman-Monteith (PM56) method. Calibration of the methods were performed for every station and month in the mentioned time period. Performances of the methods were evaluated using the R2، RMSE، MBE، MAE and RE statistics. Results indicated that before calibration، larger biases were shown with the selected methods comparing to the PM56. Calibration of the methods considerably improved their accuracy. The DP method was recognized as the best method after calibration for the studied watershed and R2 value of this method was 0. 982. In the mentioned method (after calibration) the median values of the RMSE، MBE، MAE and RE were equal to 7. 89، -0. 62، 6. 03 and -3. 40 mm per month، respectively. The Priestly-Taylor method was selected as the second best Method. Models of M، JH، T، A and MB were ordered as the third- till seventh-rank، respectively.

Quantitative evaluation of evapotranspiration on a regional scale is necessary for water resources management, crop production and environmental assessments in irrigated lands. In this study, in order to estimate ETo and because of few synoptic stations and also little recorded meteorological data in North Khorasan Province, Iran, with arid and semi-arid climate, 7 stations from neighboring provinces were used. Reference evapotranspiration was calculated using 6 different methods which required a small amount of input data, including Class A pan, Hargreaves-Samani, Priestly-Tailor, Turc, Makkink and the method proposed by Allen et al (1998) to estimate ETo with missing climate data. Besides, the standard FAO-Penman-Monteith was used (because there was no Lysimetric data in the region) to evaluate the applied formulas. Since there was no agreement over the appropriate method to calculate ETo in the selected stations, by using significance test of regression lines, a linear regression equation was computed for each month, in order to convert the best calculating method to FAO-Penman-Monteith formula. Evaluations of these equations showed their acceptable accuracy, in comparison with the previous researches, specifically for cold months (MAE values ranged from 0.3 to 1.4 mm/day).

Evapotranspiration as an important element of hydrologic cycle plays a crucial role in the assessment of watershed balance. To calculate plant water requirements، first reference evapotranspiration (ET0) must be computed، then on this basis this calculation can be applied for any other plants. In the present study the precise assessment of daily reference evapotranspiration was carried out in Bonab station by standard FAO-Penman-Monteith then the combination of daily climatic parameters such as average، minimum and maximum of air temperature، average، minimum and maximum of relative humidity، rainfall، wind speed and sunlight hours were all considered as an input of Multi-Layered-Perceptrons Feed Forward Back Propagation Neural Networks and M5 model tree were both applied to achieve better results. It can be inferred that though the Neural Network approach may render more exact result than M5 model tree، however، M5 model tree provides a more understandable، applicable and simple linear relation in predicting evapotranspiration.