تبخیر-تعرق مرجع

Iran is one of the world's semi-arid and arid regions, it has serious water resource limitations. Two important independent processes of the hydrological cycle are the combined processes of subsoil evaporation and transpiration from plant. It is critical to estimate evapotranspiration accurately since a significant amount of water is lost through this process. The FAO Penman-Monteith method is among the mostly-used indirect methods to calculate evapotranspiration. In this method, the water demands of the plant are calculated using the plant coefficients after estimating the reference evapotranspiration (Allen et al., 1998). Researchers typically use several kinds of models for predicting evapotranspiration in an effort to select a model with the highest accuracy, lowest error, and fewest input variables. This is because the FAO Penman-Monteith method requires a larger number of data points despite having acceptable accuracy. These models can be utilized for prediction since they do not have the problems and expenses of direct methods. This category of models involves machine learning models, that have excellent modeling potential that is not only in the area of evapotranspiration predicts but also in other areas of hydrology (Malekahmadi et al., 2022). The purpose of this research is to compare the performance of machine learning methods including multiple linear regression (MLR), gene expression programming (GEP) and random forest (RF) in modeling daily reference evapotranspiration and its spatial distribution in Zanjan province.

This study investigates the effect of drought stress on water requirement and morphological characteristics of Eldarica pine and Chinaberry species at different vegetative ages in Robat-Karim region of Tehran province. This study was conducted as a factorial design with three factors: species type, age, and drought stress with a completely random basic design in 10 replications. The factors included the plant component i.e. Tehran pine and bitter olive species and age in three levels (one, three and five years) and drought stress in three levels: mild, medium and high (0.3, 0.5 and 0.7). A total of 18 treatments (9 treatments for each species) and considering the number of 10 repetitions, a total of 180 microlysimeters were obtained. This research was defined according to its objectives, i.e. determining the water requirement, determining the plant factor and examining the effect of drought stress on the water requirement and plant factor. In the phases of determining irrigation planning, there was moisture monitoring in drought stress levels and the measurement of water requirements of species, the phase of calculating reference evaporation and transpiration and vegetation coefficient, and the phase of evaluating the effects of different drought stresses on water requirement and vegetation coefficient. The amount of water consumption in Eldarica pine seedlings during the plant growth period and in different treatments applied including drought stress treatments of 0.3, 0.5, 0.7 at age 1, drought 0.3, 0.5 and 0.7 at age 3, drought 0.3, drought 0.5 and 0.7 at age 5, including 239.3, 258.3, 307.13,

Reference evapotranspiration (ET0) is an important parameter in the interactions among soil, vegetation, atmosphere, surface energy and water. Direct measurement of evapotranspiration values is costly and time consuming. On the other hand, modeling this complex process in which many variables interact with each other is not feasible without considering multiple assumptions. In this regard, the FAO Penman-Monteith method is used in a wide range of climatic and environmental conditions. One of the weaknesses of FAO Penman-Monteith method is its dependence on various meteorological parameters. Therefore, it is necessary to implement methods with lower meteorological variables that can estimate ET0 with suitable accuracy. Thus, in the present study, an attempt was made to estimate ET0 with acceptable accuracy using machine learning models.

In the present study, daily meteorological parameters in the time period of 2000-2020 including maximum and minimum air temperature (Tmax, Tmin), mean temperature (T), wind speed (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin) and sunshine hours (n) were obtained on a daily basis in three stations of East Azerbaijan province (Tabriz, Sarab, and Maragheh). Moreover, six scenarios were defined as input combinations. Then, using random forest (RF) method in two cases: Single random forest and using the genetic algorithm (GA) to optimize its effective parameters with considering the FAO Penman-Monteith model as a basis, the machine learning models were calibrated and validated for estimating ET0 values at studied stations. Furthermore, the performance of empirical equations in three groups based on temperature (Hargreaves, Blaney-Criddle and Romanenko), radiation (Irmak) and mass transfer (Meyer) were also investigated. It should be noted that 75% of the data were considered for calibration and 25% for the validation of machine learning methods. Finally, using the statistical criteria of correlation coefficient (CC), scattered index (SI) and Willmott’s Index of agreement (WI), a suitable machine learning method was introduced to estimate the reference evapotranspiration. Also, the most suitable combination of meteorological parameters for ET0 estimation was suggested.

The obtained results showed that in all studied stations, scenario 6 has the best performance, either in the case of single random forest (RF) or in the case of random forest optimized by genetic algorithm (GA-RF). Meteorological parameters of this scenario include minimum and maximum air temperature, minimum and maximum relative humidity, sunshine hours and wind speed. By optimizing the RF-6 parameters with the genetic algorithm at Tabriz station, the statistical criteria were improved (CC from 0.990 to 0.991, SI from 0.103 to 0.098). At Sarab station, the CC was increased from 0.980 to 0.982, the SI was decreased from 0.140 to 0.132 and the WI was increased from 0.989 to 0.990. At Maragheh station, CC was increased from 0.990 to 0.991, SI was decreased from 0.103 to 0.098 and WI remained unchanged at 0.995. In general, the decreasing trend of the scattered index for RF method from scenarios 1 to 6 can be understood by increasing the input parameters of the random forest method. Among the three groups of empirical methods based on air temperature, radiation and mass transfer for estimating ET0, the best performance was seen for the Blaney-Criddle method based on air temperature. In all studied stations, the GA-RF model showed better performance than the empirical methods. Also, GA-RF-5 with similar meteorological parameters with Blaney-Criddle method provided accurate ET0 estimations.

Determining the amount of daily evapotranspiration and consequently accurate estimation of water requirement of plants provide the basis for proper designing of irrigation systems by reducing installation costs and providing a suitable program for the use of water resources in the agriculture sector. So, in the present study, meteorological data from Tabriz, Sarab and Maragheh stations were used to evaluate the ability of machine learning methods including RF and GA-RF to estimate the values of reference evapotranspiration. The results showed the high accuracies of RF-6 and GA-RF-6 for all studied stations and Belany-criddel among the empirical models. In a more detailed look, the genetic algorithm had positive effects on increasing the model accuracies by reducing scattered index of GA-RF scenarios 1, 4, 5 and 6 in Tabriz and Maragheh stations as well as scenarios 1, 5 and 6 at Sarab station. Finally, it can be concluded that both RF and GA-RF models provided the most accurate estimates of daily reference evapotranspiration in the East Azerbaijan province.

Evapotranspiration is the combination of two separate processes, soil moisture evaporation, and plant transpiration, which amount depends on various meteorological elements. Therefore, identifying the effective factors and the amount of their impact on reference evapotranspiration (ET0) is important. This component plays an important role in various agricultural studies, including the design of irrigation and drainage systems, reservoir design, and irrigation planning (Ahmadyan et al., 2023). Accurate estimates of evaporation and transpiration play an important role in studies such as global climate change, and environmental evolution, and in various scientific fields such as hydrology, agriculture, forest and pasture management, and water resources management (Kazemi, 2020).

The research was conducted in Iran, and the data analyzed encompass various meteorological parameters, including maximum, average, and minimum temperatures, average relative humidity, wind speed, and sunshine hours. These data were collected on a daily basis from 40 synoptic stations across the country. The dataset spans from 1976 to 2020 and was sourced from the Meteorological Organization of the country (IMO, 2022).The research employed the FAO Penman-Monteith method, specifically the 56th version, to estimate seasonal ET0 (evapotranspiration) values.In this research, for statistical evaluations of ET0 and revealing the trend of time series on a seasonal scale, the non-parametric Mann-Kendall (M-K) test; (Kendall, 1948; Mann, 1945) was used. To identify the changing trend of the ET0 time series, the ITA method was used on a seasonal scale. Four meteorological stations and the 45-year time scale (1976-2020) used in the current research, it had a better performance than other interpolation methods, which was used as the superior method. To understand the possible changes of one or more meteorological variables in ET0, the sensitivity of Reference Evapotranspiration to six meteorological variables (relative humidity, hours of sunshine, average temperature, maximum temperature, minimum temperature, and wind speed) was estimated. For this purpose, Sobol's method (Sobol, 1993). Sensitivity analysis was used.

According to the ET0 survey results, the highest amount of ET0 was observed in the spring season in the south and south-eastern parts, and the highest average value was 1050 mm/year in Zabul station. The increase of ET0 in these areas can be due to the sun's radiation and more warming of the earth's surface in the southern latitudes of the country. In summer due to the length of the day and higher temperature, we saw an increase in ET0, especially in the southern and southeastern regions of the country. In autumn, due to the decrease in the length of the day and the decrease in temperature, the amount of ET0 has also decreased significantly in the northern parts of the country. In winter, with a decrease in temperature and an increase in relative humidity, which is more noticeable in northern than southern regions.In the summer season, all stations generally showed an increasing trend in ET0. In most of the stations, the significance level was 5% and it did not follow a specific pattern. In the autumn season, an increasing trend of ET0 was observed at a significant level of 5% in Khoy and Saqez stations, and a significant decreasing trend was observed in Qazvin and Shiraz stations. In the winter season, in the western and northwestern regions, all study stations showed an increasing trend of ET0. Finally, the overall results indicate that there is a significant increasing trend of ET0 during the summer in Iran. The graphical results of the ET0 trend by the four seasons on a scale of 44 showed that, in general, there was an increasing trend in ET0 in both high and low areas in all seasons. The values of meteorological variables have been changed by the Sobol method in the range of 40% to investigate the effect of meteorological elements on ET0 in different seasons of the year. The ranking of the sensitivity coefficient of the most effective meteorological parameter on the increase of the seasonal ET0 using Sobol's method showed that, in general, in the spring season, the minimum temperature had the greatest effect on the reference evaporation and transpiration rate. Also, the ratings obtained in the summer season indicate that wind speed has the greatest effect on the ET0 amount. In the autumn season, wind speed is still the first rank in affecting the rate of evaporation and transpiration. Finally, in the winter, the maximum temperature is the most important influencing factor among the other meteorological parameters. 

According to the results, the amount of ET0 was increasing and it has been noteworthy in the eastern half of Iran in recent years. The trend of changes in ET0 showed that most stations had a positive value. The ET0 seasonal time series analysis with the ITA method indicated that in Kerman station; ET0 increased in all seasons and these results were at Bandar Anzali station. It was also observed that the seasonal trend of ET0 was increasing. The results of the sensitivity analysis graphs showed that relative humidity generally had a negative effect, and the other parameters indicates a positive effect in increasing the ET0. Also, the results explained that in spring, summer, autumn, and winter, meteorological variables of minimum temperature, wind speed, and maximum temperature played a greater role in increasing ET0. The findings of the present research and the results of the ranking of the sensitivity of factors affecting the ET0 rate showed that in each period, different conditions prevail in terms of the influence of meteorological elements on the ET0 rate.

Evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and efficient factors for accurate irrigation planning and management. Direct measurement of evapotranspiration values is time consuming and costly. On the other hand, modeling such a complex process in which many parameters interact with each other is so difficult that it is not possible to simplify the issue without multiple assumptions. Therefore, accurate estimation of this parameter has always been considered by the researchers. In the other point of view, the FAO-56 method was used as the accurate and accepted method for calculating reference evapotranspiration. One of the weaknesses of this model is its dependence on various meteorological variables. Therefore, it is necessary to use methods which need low number of meteorological variables and estimate the reference evapotranspiration with high accuracy. Additionally, due to the use of many meteorological variables and the complexity of the calculations, it is difficult to use FAO-56 method in all regions. Therefore, in the recent years, many researchers implemented machine learning methods to estimate reference evapotranspiration. Most studies in the field of reference evapotranspiration estimation use experimental models that require all the effective reference evapotranspiration parameters to provide an acceptable estimate. Hence, the aim of the current study was to present a superior model from three machine learning models, including random forest (RF), gradient boosted tree (GBT) and generalized linear model (GLM) for estimating reference evapotranspiration in three synoptic stations located at arid, semi-arid and wet climates of Iran. To the best of our knowledge, the proposed GBT and GLM methods have not been used for estimating reference evapotranspiration in the mentioned stations.

In this research, the FAO-56 method was used to estimate the reference evapotranspiration. Also, three machine learning methods including GBT, GLM and RF were implemented to estimate the amount of reference evapotranspiration. Daily parameters of some fundamental and effective meteorological variables on evapotranspiration during 21-years statistical period (2000-2020) were collected in three stations located at different climates including Yazd station (arid), Birjand station (semi-arid) and Sari station (wet). In order to investigate the possibility of using different combinations of meteorological parameters to estimate the reference evapotranspiration as accurately as possible, seven different combinations of meteorological parameters were defined. The accuracy of the utilized methods was evaluated using three criteria such as correlation coefficient, scattering index and Nash-Sutcliffe coefficient. Additionally, Taylor diagrams were implemented for evaluating the accuracy of the used methods. It should be noted that the Taylor diagram shows the three parameters of root mean square error, correlation coefficient and standard deviation simultaneously in one figure. Also, the most suitable combination of meteorological parameters that had good accuracy for estimating reference evapotranspiration, was suggested.

The results showed that in the best model at Birjand Station, and Yazd stations scenario number three by two meteorological variables of temperature and wind speed and in Sari station the scenario number two with temperature and relative humidity, the gradient boosted tree model was reinforced with Nash-Sutcliffe coefficient of 0.804, 0.826 and 0.733, with correlation coefficient of 0.997, 0.997 and 919 and scatter index of 0.249, 0.218 and 0.361 and the generalized linear model with Nash-Sutcliffe coefficient of 0.892, 0.931 and 0.869 correlation coefficient of 0.952, 0.966 and 0.933 and scatter index of 0.185, 0.137 and 0.252, respectively. Finally, the RF method with Nash-Sutcliffe coefficient of 0.954, 0.956 and 0.929, correlation coefficient of 0.978, 0.978 and 0.965 and scatter index of 0.121, 0.110 and 0.186 had good performance for estimating the reference evapotranspiration. On the other hand, in all methods, the scenario number seven using the meteorological parameters of temperature, relative humidity of sunny hours and wind speed in all three stations, presented the most accurate performance. Therefore, all three methods may be proposed as models with high degree of accuracy for estimating reference evapotranspiration.

Reference evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and influential factors for accurate irrigation planning. Therefore, accurate estimation of this parameter has a significant role on reducing excessive water consumption. In this study, three data-driven models of RF, GBT and GLM were used in three stations of Yazd, Birjand and Sari stations. The obtained results indicated that the seventh scenario using all four meteorological parameters in all stations with the highest correlation coefficient, the lowest scatter index and the highest Nash-Sutcliffe coefficient provided most accurate estimates of the reference evapotranspiration and may be recommended for proper estimation of reference evapotranspiration.

The gradual increase in the world’s population requires continues increase in agricultural production. Climate change is one of the challenges of our society and frequent droughts affect large areas of the world, which requires more accurate management of water resources, both globally and in local catchments. Accurate estimation of components of the hydrological cycle is essential for proper irrigation scheduling. Most of the precipitation received by the earth is returned to the earth’s atmosphere by the process of evapotranspiration. On the other hand, because every process that takes place in the plant is dependent on water and one of the most common uses of water in the plant is evapotranspiration, so reducing amount of the water will have adverse effects on photosynthesis, crop production, product quality, etc. The complex and nonlinear relationship between the factors affecting the process of evapotranspiration, has caused researchers today to use new methods to accurately identify and predict this parameter. Reference evapotranspiration is a concept that uses the crop coefficient to obtain the actual water requirement. According to the FAO proposal, the FAO- Penman- Monteith equation was introduced as a benchmark method for calculating reference evapotranspiration values when measurements of this parameter are not available and there is no access to lysimetric data. One of the major advantages of this model is its physical basis and global validity, but this equation needs a large number of meteorological parameters that are often not available, instead empirical equations with low meteorological variables or modern methods such as artificial intelligence and machine learning methods can be used.

In this study, meteorological data related to two stations of Astara located in the humid region and Sirjan located in the arid region of Iran in the period of 2000-2020 were studied to predict the crop evapotranspiration values. As mentioned, the FAO- Penman- Monteith method has used as a standard method for calibration and evaluation of the other functional equations and machine learning methods. In this study, four types of empirical equations including Hargreaves –Samani, Makkink, Turk and Dalton were evaluated against the FAO- Penman- Monteith model. Also, modelling was performed using Support Vector Regression, Random forest and M5P Tree model. In this study, 70% of data were considered for training and 30% for testing. Finally, statistical parameters including root mean squared error (RMSE), correlation coefficient (R), scatter index (SI), Nash-Sutcliffe coefficient (NS) and Wilmot index (WI) were used to determine the performance of each mentioned methods in estimating reference evapotranspiration values.

Using different meteorological parameters in accurate prediction of evapotranspiration using 4 combined scenarios, calibration calculations were performed on 70% of data and validation calculations were performed on 30% of testing data implementing Weka software. The obtained results showed that the SVR3 and M5P3 models in Astara station with all meteorological parameters and having R= 0.993, RMSE= 0.201 and also, the SVR3 model in Sirjan station with R= 0.982, RMSE= 0.410 compared to the studied empirical methods provided better results in estimating the reference evapotranspiration and scenario 3 with all meteorological parameters was introduced as the top scenario. Among the empirical methods, Hargreaves- Samani was superior to some models only in Astara station. At Sirjan station, none of the empirical models performed better than the machine methods.

Accurate estimation of reference evapotranspiration in water resource management is essential. In this study, meteorological data from Astara and Sirjan stations were used to evaluate the ability of machine learning methods including SVR, RF and M5P to estimate the values of reference evapotranspiration and compared the results with empirical methods. The results showed that the high accuracy of the SVR3 model in both stations and in the next position M5P3 model for humid area. Empirical methods except Hargreaves- Samani had poor performance compared to data- driven models. Finally, the use of SVR and M5P methods in irrigation scheduling is recommended.

Estimating water requirement and determining the actual amount of water is one of the most important factors in the management of agricultural water resources and one of the requirements of any irrigation project. The present study was conducted to investigate the water requirement system in determining the actual amount of peanut irrigation water based on the inverse solution of yield function under water stress in two crop years 2017 and 2018 in Guilan province. The experimental design was performed in the form of split plots in a randomized complete block design with three replications. The main factor of irrigation management included rainfed and 40, 60, 80, 100% Water requirement supply and sub treatment included two cultivars of peanut Guil and Jonobi. The results showed that the highest evapotranspiration in supplying 100% of water requirement in 2017 and 2018 was 446.7 and 524.1 mm in the Jonobi cultivar and 438.5 and 521.9 mm in the Guil cultivar, respectively. in the measured and estimated values of irrigation need by the water requirement system, the values of the coefficient of determination in the Jonobi and Guil cultivars are 0.65 and 0.64 respectively, and the root mean square error for Jonobi and Guil cultivars were 52.60 and 52.24, respectively, and the mean error deviation was 15.53 and 15.03, respectively. The agreement Wilmot index and efficiency coefficient of the model in 2017 and 2018 were 0.95 and 0.75 for the Jonobi cultivar and 0.95 and 0.76 for the Guil cultivar, respectively. According to the available results and statistical indicators, the water demand system provided a reasonable estimate of the actual amount of irrigation water in two peanut cultivars.

Reference Evapotranspiration (ETo) has an important role in irrigation management  and scheduling. Evaluating the sensitivity of ET o to different meteorological variables and contribution of each variable in ET o trend is essential for better management of water crisis in Lake Urmia basin. In this study, ETo in 13 synoptic stations in and around Lake Urmia basin for the period 1990-2018 was estimated by the FAO Penman-Monteith method. Trend analysis showed that the average annual ETo of stations has significant increasing trend of 8.0 mm year-2. In addition, the trend analysis of the average annual meteorological variables of the stations reveals significant increasing trends in minimum temperature (Tmin) (0.07 ℃ year-1), maximum temperature (Tmax) (0.09 ℃ year-1), and wind speed (u2) (0.02 m s-1 year-1). Sensitivity analysis of ETo to meteorological variables indicate that on an annual temporal scale, the maximum sensitivity of ETo is to relative humidity (RH) and the minimum sensitivity of ETo is to Tmin. Also, during this period, the sensitivity of ETo to Tmax, u2, and RH increased and the sensitivity of ETo to solar radiation (Rs) decreased. The contribution analysis of meteorological variables to ETo variations indicate that in most stations (with arid and semi-arid climate), u2 has the largest contribution in ETo variations. However, in Sardasht station with a very humid climate, the contribution of other factors in the ETo trend (decrease in RH and increase in Tmin and Tmax) is greater than the effect of decrease in u2. The results of this study highlights the need to pay attention to the different response of ETo to meteorological variables changes in different climates in planning water resources systems and studies that are dependent on climate change scenarios.

The purpose of this study was to investigate the effects of irrigation management on water requirement and crop coefficient of Hashemi and Kuhsar cultivars in the first crop. This experiment was carried out in Mahmoudabad District of Mazandaran Province in Iran in the form of factorial in a randomized complete block design, in two growing seasons of 2016-2018. The water requirements for the fields (from transplanting to harvest) and the crop coefficient of these two cultivars were studied in two systems of traditional flooding and system of rice intensification (SRI).  The results showed that, in the first crop, the two-year average water requirements of Kohsar and Hashemi in SRI were 534 and 556 mm and for the traditional flooding system 623 and 632 mm, respectively. The water required by the farm in traditional flooding decreased by 13.1% in the SRI. Based on reference evapotranspiration using pan evaporation method for the three stages of vegetative, reproductive, and maturing, crop coefficient values for the flooding system for Kuhsar were, respectively, 1.14, 1.29, and 0.92; and for Hashemi, they were 1.18, 1.32, and 0.92. According to the obtained results, SRI causes significant savings in paddy water consumption, therefore, it is suggested that, due to water shortage in the country, this method be used as one of the adaptation strategies in rice fields.

The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with ‎lower input data and appropriate accuracy is necessary. ‎ The purpose of this study is to investigate the capability of the experimental ‎models, gene expression programming, stepwise regression, and Bayesian network in estimating ‎reference evapotranspiration.‎ In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. ‎Based on the correlation between input and output parameters, six input patterns were ‎determined for modeling. The results showed that the Kimberly-Penman model has the ‎best performance among the experimental models.‎ Gene expression programming with fourth pattern ‎‎and Default Model Operators (R2 = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R2=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R2 = 0.91 and RMSE = 0.9 in the ‎training stage.‎ Comparison of the performance of the three models showed that the gene expression ‎programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94.‎ The results showed that gene expression programming had an acceptable ability to estimate ‎reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a ‎suitable model.‎

Accurate estimation of reference evapotranspiration (ETo) is an important issue in agricultural engineering, irrigation scheduling, water resources management, etc. Nowadays, the physics-based Penman-FAO-Monteith model is a standard model of ETo determining as well as calibrating other ETo equations. However, the need for large amount of hydro climatologic data has limited its application, especially in regions without sufficient necessary weather data. The present study aimed at assessing the performance accuracy of empirical temperature/radiation- based ETo models in humid regions and comparing the results with random forest (RF)-based models that used the same input variables of the applied empirical equations. The obtained results showed that the Random forest models gave the most accurate results when compared to the commonly used original and calibrated empirical equations (with similar input variables) with global MAE and SI values of 0.4 and 0.008. Nonetheless, the calibration procedure could not improve the performance accuracy of the equations in some cases.

The reference evapotranspiration (ETo) is one of the most important effective factors in hydrological cycle and has been used in the calculation of many drought and aridity indices and the studies related to the projection of climate change impacts. The investigation of climate change impact on ETo is necessary for longterm scheduling and management of water resources. In this study, the downscaled projections of two climatic models of EC-EARTH and GFDL-ESM2M (based on RCP8.5 scenario) under CORDEX project were used and using the modified method of FAO Penman-Monteith and by considering the effects of the atmospheric CO2 concentration on plants stomatal resistance, the ETo was estimated for the historical period (1976-2005) and future period (2006-2100) in lake Urmia basin. The results of investigation of ETo changes in three near-future (2011-2040), mid-future (2041-2070) and far-future (2071-2100) periods in comparison to the historical period revealed an increasing trend of ETo especially for far-future. The contribution analysis of the meteorological variables in ETo changes showed that the maximum temperature (with the average contribution of 91.54 %), the minimum temperature (with the average contribution of 37.68 %) and the atmospheric CO2 concentration (with the average contribution of 41.40 %) will have the most contribution in ETo changes. However, because of increasing trend of the agricultural development, the effects of irrigation on the climate and ignoring the irrigation effects in climatic models, projection of ETo changes based on climatic models may be along with some uncertainties.

Measuring the actual evapotranspiration rate of rice is too much important, in terms of appropriate and optimal water management of the Northern provinces in Iran. The present study aims to measure the actual evapotranspiration rate for two Shiroudi & Hashemi cultivars of rice, in Sari Agricultural and Natural Resources University’s paddy fields. For this propose, six drainage lysimeters were designed and constructed. After cultivating rice inside the designed lysimeters, the amount of precipitation, water content and deep percolation, were measured during 5-days periods. Investigation of the lysimeter results, shows difference between water requirement and also deep percolation of the two studied cultivars, in growth period (seeding to harvest steps); so that in these phases, water requirement rates was 350.81 & 396.67 millimeters and the deep percolation was 47.81 & 97.11, for the cultivars Shiroudi and Hashemi, respectively. Also the crop coefficient was obtained from the adaption of the lysimeter’s actual evapotranspiration and 14 indirect methods of reference evapotranspiration estimation, for the both cultivars. Considering the cultivar and estimating method, the crop coefficients are reported as the ranges 0.73-1.12 for the initial stage, 0.83-1.41 for the middle stage and 0.64-1.15 for the final stage. Also, the average estimation Error Percentage (PE) of the resulted crop coefficients compared to the recommended coefficients of FAO-Penmann56 method, is 11, 11 & 8 for Hashemi cultivar and 15, 23 & 16 for Shiroudi cultivar, in initial, middle and final stages respectively; which shows the necessity of extracting crop coefficients, for different cultivars under different local conditions. The obtained coefficients of the current study can be useful to calculate the water requirements of Hashemi and Shiroudi cultivars based on indirect estimation methods, for designing water projects, water delivery planning and also the irrigation and drainage networks in the research area.

One of the strategies to reduce water losses in agriculture is proper irrigation scheduling, which is based on accurate estimation of crop water requirement. In order to determine the evapotranspiration rate of three rose varieties, a one-year study was conducted in a hydroponic greenhouse equipped with drip irrigation system in Arak Plain using drainage type lysimeters. Reference evapotranspiration was also determined using a drained micro-lysimeter with grass. The results showed that the total amount of reference evapotranspiration in the greenhouse was 1608 mm. Also, the total evapotranspiration value of Utopia rose during this growth period was 1423 mm, Dolcevita rose 1480 mm and White rose 1313 mm. The annual average of daily evapotranspiration of rose varieties was 3.8 mm/day. Due to rose bushes being perennial, the crop coefficients did not have four growth stages and, therefore, the average coefficients varied between 0.7 and 0.96 in 10-day intervals. The results also indicated that the average crop coefficient of rose varieties was 0.81 over a year and the average amount of applied irrigation water was 20573 m3.ha-1.year-1 for approximately 75000 rose plant /ha.

Evapotranspiration (ET) is one of the essential components of the hydrological cycle, which plays a crucial role in the study of a watershed water balance. In calculating the water requirement of plants, it is essential to calculate the reference evapotranspiration, and then, the crop evapotranspiration is estimated using the calculated value. In the present research, for accurate determining of daily reference evapotranspiration of Lake Urmia watershed, three stations of the watershed, including Urmia, Mahabad, and Khoy, were selected and daily reference evapotranspiration values were calculated based on the standard FAO-Penman-Monteith method. The best input parameters for modeling reference evapotranspiration were selected based on Malo’s coefficient. The MT model, which is used in the current study, is one of the inference-classification algorithms. To deal with the complexity and instability of time series data, the empirical mode decomposition (EMD) preprocessing algorithm was used. The results of the methods were compared with the empirical relationships of Romanenko and Schendel. The results of this study show that although the tree modeling method performs relatively equal and sometimes weaker than the ANN method, the combination with EMD technique increases the accuracy of the model and reduces the error in daily ET0 prediction. According to the results, the EMD-MT method in correlation coefficient index for Urmia, Mahabad, and Khoy stations increased 1.02%, 4.39%, and 2.04%, respectively. Also, among the empirical relations, the Romanenko relation is more accurate than the Schendel equation, and it is a reliable empirical model.

Evapotranspiration (ET), a major component of the hydrologic cycle, is important in water resources management and irrigation scheduling. Nowadays, due to the lack of the lysimetric data in weather stations, the ET values calculated by the standard FAO Penman-Monteith model ( ) are used as benchmark values of grass reference crop. Also, the Penman-Kimberly model is widely applied for computing the alfalfa-reference crop ET ( ). In the present study, the meteorological data from 6 weather stations located in the Sistan-Va-Baluchestan Province covering a period of 10 years were used to calculate the and values. Then, the to  ratios were computed for all six stations during the studied period. The Penman-Kimberly model at Mirjavah station had the worst result compared to other stations. The NS coefficient values for this station are the lowest (0.07) and the SI and RMSE values for this station are 0.43 and 2.48, respectively, which is the highest value among the study stations. Finally, the contributions of the energy balance and aerodynamic components on the final ET values were determined using the Penman-Kimberly model, which showed the important influence of both components on the ET process. Consequently, the use of radiation-based models e.g. Priestly-Taylor model in these stations should be carried out by special care.

An accurate estimation of reference evapotranspiration is crucial for optimal irrigation scheduling and management. Also, achieving accurate medium range forecasts of effective parameters in estimating reference evapotranspiration is a key component for dynamic irrigation scheduling. This study was aimed to investigate the effect of using Weather Research and Forecasting Model (WRF) regional forecasts to increase the accuracy of reference evapotranspiration estimation. Consequently, the precision and accuracy of the model, and the outcome of forecasts performance at 24, 48, 72, 96, and 120-hours were evaluated to estimate the reference evapotranspiration. For this purpose, the output of the model for four stations including Qazvin, Esmaeil-Abad, Karaj and Hashtgerd were extracted for a period of three months (May-July, 2018) with a 10-days average of the base period. The weather data of 2018 at these stations with the corresponding ones were compared afterwards. The results indicated that the 10-days reference evapotranspiration (average of all stations) in the study period, according to the base period were -20.9, -8.12 and 7.83 percent, respectively. These variations reflects the deviation of the reference evapotranspiration value in the study period in comparison with the base period, indicating the need for using medium-range forecasting in order to correct reference evapotranspiration estimates. The range of determination coefficient (R2) of model output was obtained to be between 0.813 and 0.921. Due to the statistics, the model output for all stations and the lead-time forecasting periods of 24, 48, 72, 96 and 120 hours can be evaluated with high accuracy and its application would enhance the accuracy of reference evapotranspiration estimates. According to the results, not only in terms of time coordination, but also in terms of quantity, there was a high similarity between the estimated values of evapotranspiration derived from its post-statistical output of the (WRF) with calculated values.

This study was carried out in order to investigate and determine the water requirement and irrigation scheduling in the Vardij district of Tehran province. In order to calculate the reference evapotranspiration, the FAO Penman-Monteith equation was utilized and WUCOLS III method was recruited to vegetation coefficient. The climate data of Chitgar Station (from 2008 to 2017) was used to calculate the reference evapotranspiration and effective rainfall. The results showed that effective rainfall in the studied area was not sufficient to provide the required water for the plant and there is a need for additional irrigation during the growing season. The highest amount (i.e. 41.57 mm) of evapotranspiration of Elaeagnus angustifolia L. was observed in July and the lowest amount of evapotranspiration in January (i.e. 6.65 mm) was measured. The total amount of evapotranspiration was 266.31 mm. The irrigation with a amount of 12.28 mm begins from May 22 and ends on Octobre 7 with a value of 10.23 mm. The maximum amount of required irrigation (i.e. 20.23 m) was applied from July 7 to july 22 and the lowest amount of required irrigation (i.e. 10.23 mm) applied from Octoble 7 to Octoble 22.

The precise estimation of reference crop evapotranspiration (ETo) has a significant role in improving water management in field and irrigation management. Different methods are available for ETo estimating. The FAO-PM equation is the most reliable method for ETo estimating, but this method requires large input data that is not fully measured in most meteorological stations. Also, the measured data lacks enough precision, especially in developing countries. Therefore, the researchers have used the empirical equations with minimum input data. In this study, the daily values of ETowere calculated at the synoptic station of Mashhad using Priestley-Taylor, Hargreaves, the FAO-PM proposed method by the lack of data and two forms of the Valiantzas equation. Then, the calculated values were compared with FAO Penman Monteith (PM) method. In this paper, new models for estimating ETo were presented by lack of wind speed and relative humidity data by modifying two forms of the Valiantzas equations. The results showed that by the lack of wind speed and relative humidity data, the modified Valiantzas model had the highest accuracy compared with the other methods. It was showed that the R2, RMSE and MAE indices were 0.85, 1 and 1.26 mm per day, respectively. Also, the results showed that the modified Hargreaves method had the highest accuracy when only temperature data is available.

Evapotranspiration is one of the most important factors in hydrological cycle. Potential evapotranspiration is used to design in irrigation and drainage networks and hydrological studies (Davis & Dukes, 2010). The conducted studies in different regions of the world have shown that the FAO-Penman-Monteith model can be presented as the most accurate method under various climatic conditions (Irmak et al, 2003, ASCE-EWRI, 2005, Jabloun & Sahli, 2008, Martinez & Thepadia, 2010 and Azhar & Perera, 2011). The calibration of the mentioned equation using lysimetric data in a wide range of climatic conditions and its applicability without the requirement for local calibration in different climatic conditions are the benefits of the FAO-Penman-Monteith equation (Landeras et al, 2008). On the other hand, the Valiantzas model is one of the newest methods for estimating ETo. Advantages of using the Valiantzas equations in the estimation of reference evapotranspiration include simple application for spatial calibration, easy application for temporal distribution of reference evapotranspiration values, easy use for routine hydrological applications and simplicity of equations for other hydrological applications (a, b, c, Valiantzas, 2013). The purpose of this study was to evaluate and compare the accuracy of five different Valiantzas models for estimating reference evapotranspiration at the studied stations located in the northwest of Iran (Urmia Lake basin) including Urmia, Salmas, Mahabad, Takab, Tabriz, Sarab and Maragheh and providing the best version of the Valiantzas as the results of which are the highest concurrence with the FAO-Penman-Monteith method.