جستجوی مقالات مرتبط با کلیدواژه « فائو-پنمن-مانتیث » در نشریات گروه « آب و خاک »

The purpose of this research was to investigate the sensitivity of reference evapotranspiration to meteorological variables and to introduce the most accurate database in providing these variables in the Urmia Lake basin. For this purpose, 24 synoptic stations were selected and their meteorological data was prepared on a daily basis between 2010 and 2019. Then, the reference evapotranspiration was calculated using the FAO-Penman-Monteith equation, and the effect of changes in meteorological variables on ET0 was investigated individually in the range of ±20%. In the next step, the accuracy of ERA5, CFSv2 and MERRA2 meteorological data was evaluated and the most accurate one was introduced. The ten-year average of reference evapotranspiration of the meteorological stations was obtained 3.1 mm d-1, and the results showed that the maximum temperature is the most influential meteorological variable on reference evapotranspiration changes. After that, wind speed and minimum temperature had the greatest effect, respectively. The value of sensitivity coefficient for maximum temperature, wind speed and minimum temperature was obtained 0.4, 0.2 and 0.1 respectively. In the review of meteorological data of ERA5, CFSv2 and MERRA2, statistical indicators showed that the ERA5 dataset has the most accuracy. Based on the results, the 10-year average of reference evapotranspiration of the ERA5 was obtained 2.86 mm d-1 and according to the CRM index, this value was 8% underestimated compared to the value obtained from meteorological stations. Finally, the values of EF indices equal to 0.92 and nRMSE equal to 0.17 put the reference evapotranspiration of the ERA5 in a suitable and reliable rank.

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.

According to the effects of climate change on evapotranspiration and using of water resources, climate change prediction is vital due to water resources management improvement and decreasing damages of drought. The first rank of mango production in Iran belonged to Hormozgan province and the most amount of mango produced in Minab plain. In the present study, the amount of evapotranspiration of mango plants was calculated with FAO Penman-Monteith from 1985 to 2020 using meteorological data at Minab station. The evapotranspiration values of the plant were estimated from 2021 to 2100 with two optimistic and pessimistic scenarios using the last version of CMIP (CMIP6), atmospheric-ocean general circulation models, and performing statistical deviation corrections by the Python software. The results showed that the values of annual evapotranspiration will increase by 0.31 and 1.23 mm on average in the optimistic and pessimistic scenario, respectively in the future due to the increase in annual temperature.

Irrigation performance assessments under actual operation conditions are required as a first step toward improving agricultural water management. In this study, the seasonal irrigation performance of peaches and nectarines was evaluated under actual operation conditions by monitoring 24 orchards, in Ardabil Province (Parsabad and Meshginshahr counties), Iran, during the growing season 2018-2019. The total sum of seasonal applied water and effective precipitation (I + Pe) and the fruit yield ranged from 280-1675 mm and 1.00-32.43 ton ha-1 (with a weighted average of 582 mm and 14.61 ton ha-1), respectively. The mean Relative Irrigation Supply index (RIS) for the drip irrigation method (1.25) was significantly (P < 0.05) lower than the surface irrigation method (1.66). The water productivity indicators were significantly (P < 0.05) affected by the orchardist's skill level, interplantion of trees, tree spacing, irrigation method, and disease/frost (DF) damage. Post-harvest period accounted for a mean proportion of 75, 25, and 15% of the seasonal applied water in orchards with early-season, late-season, and mixed early- and late-season cultivars, respectively. DF damage accounted for an 87 and 85% reduction in fruit yield and water productivity (WPI+Pe), respectively, compared to the orchards without severe DF damage. Under the current technological and economic constraints, most of the study orchards experienced a rational (but yet with low productivity) irrigation water management. Improving irrigation efficiency and fruit yield, controlling DF damage, and implementing regulated deficit irrigation during pre- and post-harvest stages are the most effective approaches to improve water productivity indicators in the study area.

Reliable estimates of the seasonal applied water and irrigation performance assessment indicators under current irrigation and farm management are perquisites for improving water resources management. In this study, seasonal applied water and irrigation performance assessment indicators of winter barley were studied by monitoring 25 farms under actual conditions, in Ardabil Province (Ardabil, Namin, Nir and Kosar counties), Iran, during the growing season 2020-2021. The seasonal estimates of the net irrigation requirement during the growing season 2020-2021 and its 10-year average ranged from 476 to 652 mm and from 403 to 535 mm (with a mean of 530 and 449 mm), respectively, over the study farms. The total sum of seasonal applied water and effective precipitation (I + Pe) and the barley grain yield ranged from 266 to 716 mm and from 0.14 to 4.07 ton ha-1 (with a weighted average, WA, of 475 mm and 2.33 ton ha-1), respectively. As a result of limited irrigation water availability, 3-91% (with a WA of 52%) of the intended yield of irrigated barley in the study area (4.5 ton ha-1) was achieved. Physical and economic water productivity indicators were significantly (P < 0.05) affected by farmer's skill level, crop rotation, type of irrigation water source, and irrigation method. The results indicated that farms with surface water supply showed the highest vulnerability to drought periods. Improving the on-farm water management flexibility and mitigating the negative impacts of hot and windy days during the grain filling stage can improve water productivity indicators in the study area.

Evapotranspiration calculations by the FAO Penman-Monteith method require many parameters, the lack of some of which makes the calculations difficult. On the other hand, it is necessary to use methods that are easier to calculate and are easily understood by farmers. One of the indirect methods of estimating reference evapotranspiration is the pan evaporation method, which can be considered as a suitable index to estimate the evapotranspiration of the reference plant and finally the main plant. In this method, in order to calculate the potential evapotranspiration, the results of pan evaporation must be multiplied by the pan coefficient. Accurate calculations related to the estimation of the pan coefficient are very important because it can be used for irrigation planning when there is no lysimeter equipment. In this study, by using of daily, hourly meteorological data, and data from class pan A evaporation from meteorological stations of Kermanshah province in the long term, potential evapotranspiration values of the FAO Penman-Monteith and then pan coefficients were calculated. The index coefficients as a measure of estimation methods for Cuenca, Allan, Snyder, Modified Snyder, and Orang were used. The results showed that the, Orang and modified Snyder method had a higher level of accuracy compared to the other methods. The R2 with Orang method for different cities of Islamabad Gharb, Qasr-e Shirin, Kermanshah, Sararood, Sonqor, Gilan-e Gharb, Sarpol-e zahab, Kangavar were determined as: 0.84, 0.77, 0.72, 0.76, 0.85, 0.79, 0.77, and 0.73, respectively. Finally, the values of pan coefficients calculated by all methods in study stations in different months were calculated and presented. It is hoped that the results of this study will be used by managers and farmers in the region.

Due to limitation of available water resources, improving agricultural water productivity has become an inevitable necessity. Therefore, it is important to have reliable estimates of the seasonal applied water and water productivity under current irrigation and farm management. In this paper, the seasonal applied water and physical and economic water productivity of soybean were studied through monitoring 29 farms under actual conditions located at the tail end region of Moghan irrigation and drainage network, Ardabil Province, Iran, during the growing season 2020-2021. The net water requirement estimates of soybean during the growing season 2020-2021 and its 10-year average ranged from 417-719 mm and 457-797 mm with a mean of 539 and 581 mm, respectively, over the studied farms. The total applied water (irrigation + effective precipitation, I + Pe) and the grain yield ranged from 3859-7105 m3 ha-1 and 1.30-2.80 ton ha-1, with a mean of 5664 m3 ha-1 and 2.35 ton ha-1, respectively. The lack of flexibility in water allocations led irrigation schedule to be not adapted with the crop water requirement. The limiting factors of soybean production in the study area caused the observed maximum grain yield to be significantly lower than the potential level of soybean yield in Moghan plain (4.00 ton ha-1). The soybean grain yield exhibited a quadratic correlation with I + Pe. Total water productivity (WPI+Pe) and economic water productivity (WP$) ranged from 0.33 to 0.47 kg m-3 and 21.18 ´ 103 to 48.29 ´ 103 Rials m-3 with a mean of 0.42 kg m-3 and 39.89 ´ 103 Rials m-3, respectively. The mean Israelsen's application efficiency (AE) over initial, development, and mid-season plant growth stages in the study fields were obtained 19, 95, and 100%, respectively.

Accurate estimation of water requirements of plants is a key factor in controlling several hydrological processes including: planning and management of water resources, especially in arid and semi-arid regions (Laaboudi et al., 2012; Wen et al., 2015) water pricing and water requirement for Irrigation (Yassin et al., 2016). In this study, multivariate regression methods and gene expression planning were evaluated to estimate reference evapotranspiration. For model input data, the Khorramabad Synoptic Station information including: maximum and minimum temperatures, maximum and minimum relative humidity, sunny hours and monthly wind speeds in the range of 1983-2017(420 months) were used. Based on the relationship between input and output parameters, six input patterns were determined for modeling.70% of the data were used for training and 30% were used for model validation.The results of multivariate regression showed that the proposed model had acceptable accuracy with R2 = 0.952.The analysis of model coefficients showed the greatest effect of maximum temperature with a coefficient of 0.604 on reference evapotranspiration. Gene expression planning results showed that the fifth pattern with four main operators was R2 = 0.958 and RMSE = 0.704 in the training phase and R2=0.977 and RMSE = 0.615 in the test phase had better performance.

The purpose of this study is to compare and evaluate five different methods of estimating the evapotranspiration of the reference plant on a nine-year daily data scale in Yazd province. Selected methods included Hargreaves-Samani (HS) , Priestley-Taylor (PT) , Turk (Turc) , Makkink (MK) and Dalton (D). For this purpose, data from ten synoptic meteorological stations were used covering a period of 9 years. The results of the mentioned methods were evaluated by FPM-56 method. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. RMSE, NS, SI, MAE, R^2 statistical criteria were used to evaluate the results. The results showed that before calibration the results of different methods are very different from FPM-56. The only acceptable model before calibration was the HS model. After calibration, the results of the models improved and model D, which was the worst model before calibration, was recognized as the best model for estimating evapotranspiration in Yazd province among the five selected methods. Mean values of Model D before calibration MAE = 3.83, R^2= 0.84, NS = -1.99, SI = 0.83, RMSE = 4.21 and after calibration MAE=0.83, R^2=0.86, NS=0.72, SI = 0.22, RMSE=1.02 was obtained. After calibration, Turc, PT and MK models were in the next categories of the best models.

In this work, the seasonal applied water and physical water productivity of rapeseed (Brassica napus L.) were evaluated through monitoring 26 farmer fields (including 18, five, and three farmer fields with furrow, center-pivot, solid-set irrigation systems, respectively) over Moghan Plain, Ardabil Province, Iran, during the growing season 2019-2020. The rapeseed seasonal water use (irrigation + effective precipitation) and the grain yield value ranged from 2921-6762 m3 ha-1 and 1.00-3.70 ton ha-1, respectively (with a mean of 4798 m3 ha-1 and 2.64 ton ha-1, respectively). The net rapeseed water requirement during the growing season 2019-2020 and its 10-year mean value ranged from 285-399 mm and 282-354 mm, respectively (with a mean of 325 and 304 mm, respectively). The mean seasonal applied water at farmer fields with furrow irrigation (4328 m3 ha-1) was significantly (p < 0.01) higher compared to the farmer fields with center pivot and solid set sprinkler irrigation (2154 and 2633 m3 ha-1, respectively). Total water productivity (WPI+Pe) and irrigation water productivity (WPI) ranged from 0.28 to 0.95 kg m-3 and 0.41 to 1.45 kg m-3, respectively (with a mean of 0.58 and 0.77 kg m-3, respectively). Based on the results of multivariate linear regression analysis, the factors, including irrigation water salinity, sowing date, seeding rate, date of the first irrigation, growth period length, strong winds near harvest time, the latitude of farmer fields, flowrate of delivered water, mean irrigation interval, and rapeseed grain moisture content and impurity rate were recognized as the most important factors affecting the grain yield and the physical water productivity indices.

Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a deficiency of meteorological stations. Therefore, today numerical models such as WRF are a powerful tool for generating and predicting meteorological quantities (wind speed, humidity, etc.) that are needed to estimate evapotranspiration. So far, no research has been conducted to investigate the effect of different schemes of the WRF model on the estimate of rice evapotranspiration. The purpose of this study is to evaluate the efficiency of the WRF model and obtain the result for estimating evaporation for rice plant in the central plain of Guilan.

Evapotranspiration rates vary from 2.7 to 8.5 mm per day. The average ET during three different periods of plant growth, including the initial, middle, and final periods, is estimated to be 4.63, 5.97, and 5.98 mm per day, respectively. The three configurations 1, 2, and 4 are mainly overestimated in predicting evapotranspiration of rice plants, and the computational values are estimated to be higher than the values measured by the lysimeter. The results show that the highest amount of RMSE occurred in configuration No. 4 at 8.47 and the lowest rate occurred in configuration No. 3 at 1.26. Summary of results shows that configuration No. 3 in all four criteria mentioned has performed better than other configurations to predict daily evapotranspiration of rice. The results showed that the non-local schema used in the model, simulates better than the local schemas for the daily evapotranspiration of the rice plant. Findings show that in the local YSU schema, the accuracy of predictions is significantly increased and is only 0.64 mm on average less than the estimated lysimetric data.

The results showed that using appropriate schemas in the surface layer and boundary layer of the WRF model, affects on accuracy of evapotranspiration predictions. The results of this study showed that, this model by using the YSU non-local boundary layer scheme can accurately predict the evapotranspiration rates of the rice plant for the next day and this is due to the higher ability of this schema in predicting the parameters affecting evapotranspiration (including temperature and wind). Therefore, the WRF model can be implemented by using GFS forecast data for the next few days and by applying the FAO-Penman-Monteith equations to the model outputs, the values of potential evapotranspiration for different regions of the country can be calculated. Since evapotranspiration is directly related to atmospheric thermodynamic processes, so using other different atmospheric physics schemas (not considered in this study) can produce different results.

The Crop coefficient (Kc) is one of the most important parameters in irrigation scheduling. The purpose of this study is to determine silage maize Kc under different irrigation levels using the soil water balance method and presenting the equations for estimating silage maize crop coefficients in terms of the days after sowing during the summer growing season in the Varamin region. The Kc was calculated from the division of actual evapotranspiration (ETa) to reference evapotranspiration (ETo). Soil water balance equation was used to determine silage maize ETa during the growing season. An experiment in the form of split-strip plots based on a randomized complete block design with three replications was conducted in 2019. The main factor included three levels of irrigation, supplying 100, 80, and 60% of maize water requirement (I1, I2, and I3, respectively) and the sub-main factor included two types of irrigation management: pulsed (P) and continuous (C). The value of ETa calculated from the soil water balance method for different treatments ranged from 319.6 to 242.5 mm. Also, the value of irrigation depth during the growing season for full and deficit irrigation treatments ranged from 325 to 195 mm. The mean values of silage maize crop coefficient for initial, middle and final growing stages of full irrigation treatments were 0.27, 1.04, and 0.89, respectively. The water stress coefficients were calculated for deficit irrigation treatments during the growing season. Statistical analysis between ETc calculated from Kc values obtained from the proposed equation in this study and Ks values and Eta obtained from the water balance method indicated that the proposed Kc equation and Ks values in this study have good accuracy in the research area. Therefore, the extracted Ks and proposed Kc equations can be used to estimate silage maize evapotranspiration in the studied region with good accuracy.

Due to the scarcity of water around the world especially in arid and semi-arid regions, accurate determination of crop water requirement is essential for proper irrigation planning and management. One of the common methods for estimating crop evapotranspiration is the use of reference evapotranspiration and crop coefficient (Kc) (or the FAO-56 Kc-ETo approach). Different climatic conditions, plant variety, and differences in crops, soils and irrigation management practices result in variations in crop coefficient for the same crop between the locations, therefore locally developed Kc values are necessary for more accurate estimation of crop evapotranspiration. The aims of this research were to estimate silage maize crop coefficient using water balance method under pulsed drip irrigation system during two growing seasons (spring and summer) and to develop an equation to calculate silage maize crop coefficient based on growing-degree-days in Varamin. Silage maize actual evapotranspiration based on water balance method was 465 and 373 mm for spring and summer growing seasons respectively. Silage maize crop coefficient for initial, mid and late growth stages of spring and summer growing seasons were calculated 0.24-0.27, 1.28-1.3 and 0.8-0.88 respectively. The results showed that using silage maize crop coefficient proposed by FAO-56 caused 8% underestimation in crop evapotranspiration. Significant difference ( ) was found between  and , while using the equation presented in this study estimates silage maize evapotranspiration reasonably well, with the mean absolute error (MAE) of 0.53 mm/day, the root mean square error (RMSE) of 0.7 mm/day and the agreement index (d) of 0.98. Therefore, using developed regionally based and growth-stage-specific Kc helps in irrigation management and provides precise water applications for this region.

Reference evapotranspiration (ETo) is one of the most critical parameters in proper design of irrigation systems. Accurate estimation of ETo leads to reduction of water losses. Due to the ability of Artificial Neural Networks (ANNs) in computational analysis of complex processes, the main objective of this study was to investigate the sensitivity of the ETo trends to key climatic factors in Tehran province using the artificial neural networks, and compare it with the ETo-calculator software results. The ETo was calculated using meteorological data (10-year data of 12 meteorological stations in Tehran province) using the ETo-calculator software. In order to model ETo, a set of inputs to artificial neural networks including the minimum and maximum air temperature (Tmax and Tmin), the minimum and maximum relative humidity (RHmin and RHmax), sunshine hours (n), and wind speed (U2) were considered. After data tagging, by optimizing the number of hidden layers and network algorithms, output values were estimated. The results indicated that artificial neural network is a suitable technique for ETo analysis(R^2≅98% ). The best model for estimation of ETo is feed-forward Multi-Layer Perceptron (MLP) with two hidden layers in its structure (6-11-14-1), Levenberg–Marquardt training algorithm for both hidden and output layers and Linear Tanh and Tanh transfer functions for hidden and output layers, respectively. The sensitivity analysis of the model for input parameters showed that the optimal artificial neural network model and ETo calculator software have the same trend and the Tmax and n are the most effective and least effective parameters in ETo estimation, respectively. Also, based on PCA analysis results the scenario of using of four parameters (Tmax, Tmin, RHmax and U2) as the only inputs to the selected artificial neural network, can estimate ETo with an acceptable accuracy〖(R〗^2≅94% ).

Plant water requirement or evapotranspiration (ET) is one of the main components of water balance and a key factor in irrigation planning to improve water use efficiency of agricultural lands. Different methods have been proposed to determine evapotranspiration directly using lysimeter and indirectly using computational methods. Evaporation pan is one of the indirect, simple and suitable methods for estimating the evapotranspiration of the reference plant and the main plant, which shows the combined effects of atmospheric parameters such as air temperature, air humidity, radiation and wind. In this study, using 20-year meteorological data (1999-2018) of the all synoptic stations in Kurdistan province, the value of pan coefficient was estimated using the methods Cuenca (1989), Raghuwanshi & wallender (1998), Modified Snyder, Mohamed et al (2008), Allen and Pruitt (1991), Snyder (1992), Orang (1998), Pereira (1995), Christiansen (1990) and FAO-24 (1997). In order to evaluate the accuracy of estimating evapotranspiration obtained from pan evaporation mehod, the FAO Penman-Mantith 56 method was used. To evaluate the accuracy of the models and to select the best one, four indicators; (RMSE), (MAE), (MBE) and t-test were used. The final results showed that on a daily, monthly and seasonal basis, the methods of FAO-24 (1997) and Christiansen (1990) had the best performance and the methods of Raghuwanshi & Wallender (1998) and Allen and Perot (1991) had the worst performance.

Agriculture is directly affected by climate conditions and changes; therefore, it is essential to understand the effects of climate change on agricultural water resources in order to adapt negative effects on sustainable crop production. In this study using three scenarios; RCP2.6, RCP4.5 and RCP8.5 of the HadGEM2-ES climate model, the future climate data (precipitation, minimum and maximum temperatures) for two periods (2020-2039 and 2040-2059) were forecasted under climate change and based on a 20-years basic period (1985-2005) climate data using LARS-WG software. The base-period evapotranspiration was estimated using Hargreaves-Samani method and they were converted to Fao-Penman-Monteith method by Coefficient (G) in order to improve accyracy. According to this, irrigation requirement of rice (most important and major crop in Gilan province) and evapotranspiration were compared between the past and future. Results declared that the average annual maximum and minimum temperatures for the periods of (2020-2039) and (2040-2059) in comparison to the base period, will increase 2.2 and 2.1, 3.2 and 2.8 ˚C respectively. Also, the average monthly rainfall under all three scenarios will increase 11.8 and 13.6 millimeters for the proposed future periods, respectively. In addition, the net irrigation changes of rice for the both proposed periods will increase 0-31% and 0-45%, respectively. The trend of evapotranspiration changes is ascending and the range of changes during the two periods will be 0.4%-13.6% and 6.1%-27.6%, respectively. This method demonstrated the outlook of climate change impacts on irrigation to the farmers. The results of this study suggest that it is necessary to understand climate change impacts on agriculture, for improved agricultural management planning. The results of this study highlight the need to pay more attention to the effects of climate change on reliable agricultural management and planning.

The aim of this study was to estimate the ET0 in a moderately cold semi-humid climate in a 22-year statistical period by applying a wavelet-neuro-fuzzy model with a minimum number of input parameters.The results were compared with the ANN and ANFIS models to evaluate the performance of the wavelet-neuro-fuzzy model, The sensitivity analysis of the input parameters was done in three ways: Hill method, coefficient of determination, and StatSoft. Sensitivity analysis showed that temperature (T), Rs, Ra, mean daily wind speed at 2 meters (U2) and Rn were an effective parameter. Based on the results of the sensitivity analysis, six combinations with these parameters were selected.The results indicate that the wavelet-neural-fuzzy model has a better performance than the artificial neural network model. The results also showed that the estimated ET0 value with three inputs parameters of maximum and minimum temperature and solar radiation using fuzzy-neural-wavelet model was more accurate than the neural network. Based on the coefficient of determination and the amount of calculated error for the artificial neural network and the Anfis, use of the combination of 7 input parameters (Ra, Rn, Rs, U2, Tmean, Tmin and Tmax) and four meteorological input parameters (Ra, U2, Tmean and Tmax) lead to more accurate estimates of ET0 in comparison to the FAO Penman-Monteith method. The results also showed that the highest amount of explanatory factor and the lowest error value among the different wavelets used in the fuzzy-neuro-wavelet model were for the 7 and three input parameters (Tmax, Tmin, Rs), respectively.

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.

Due to the impact of climate change on the plant water demand and the availability of water, especially in drylands, it is vital to estimate the evapotranspiration rates accurately. In this study, the vegetation status in the marginal desert areas of Varamin Plain was studied, and the actual evapotranspiration and water demand of intercropped farms were assessed. This study also evaluated the potential relationship between the evapotranspiration of different agricultural lands and their vegetation index using remote sensing techniques. A collection of satellite images from Landsat 7 in consecutive seasons was used to determine the greenness rate of marginal desert areas during 2013 and 2014. ENVI software was used for the image processing, which included geometric corrections and atmospheric corrections, to develop NDVI maps. Also, weather data and crop properties of Varamin Plain were collected, and the actual evapotranspiration rate of plant cover was estimated using CropWat. The correlation between NDVI extracted from satellite images and the evaluated evapotranspiration rate was assessed. The results showed a strong relationship between evapotranspiration of heterogeneous agricultural lands and NDVI. This confirmed that the NDVI derived by remote sensing approach could be a useful index to evaluate vegetation status and water demand of farmlands in the desert borders.

Evapotranspiration as an important component of the hydrological cycle plays a very important role in the planning and management of water resources in dry and hyper dry areas. Accurate estimation of evapotranspiration requires a costly tool which can not be used anywhere. Hence, researchers are always looking for applied relationships and practices that are low-cost and accurate for the correct estimation of the value of this parameter. Several methods have been developed for the accurate estimation of evapotranspiration throughout the world. Among of these methods can be pointed out empirical equations, Artificial Neural Network, support vector machin, and tree models. Therefore, the purpose of this study was to investigate the accuracy and the capability of linear supporting vector machine models, the decision tree of the type of chiad and the random forest model in the estimation of reference evapotranspiration.The data used in this study include maximum temperature, minimum temperature, average temperature, maximum moisture content, minimum humidity, average humidity, precipitation, sunny hours, wind speed, and a shift from the meteorological station of Sistan Plain between2009-2018. In this study, using meteorological data and the FAO Penman-Monteith model, the values of evapotranspiration were calculated and then by providing different combination scenarios of the meteorological parameters as inputs of the studied models on a daily basis, an attempt was made to find a more accurate estimate of the refrence evapotranspiration as the output of the models. In this research, correlation coefficient (R) and Mean Absolute amount of Error (MAE) were used to compare different model. The results showed among the support model carriers, the random knife, the random forest model with M7 patterns has the highest accuracy with the correlation coefficient (R = 0.983) and the lowest mean error magnitude (MAE = 0.798). Therefore, this research recommends a random forest model for estimating evapotranspiration in the area of Sistan plain.