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

This study was conducted to determine the actual evapotranspiration values and water supply status of different cultivation pattern in 341 hectares of agricultural lands of Deryas and Tut-Aghaj of Mahabad plain in West Azarbaijan, with pressurized irrigation systems using remote sensing tools and SEBAL algorithms. To carry out this research, 7 different images of Landsat 8 satellite were downloaded in the period from May to September of 2022 (plant growth season) and the amount of crop water requirement on the basis of the cultivation pattern was estimated in the study area and compared to the amount of water consumed in the irrigation network. On the basis of obtained results, the highest amounts of evapotranspiration in the growth season relates to apple, Peach, alfalfa, cherry, sour cherry, apricot, plum stencil, grape and wheat, respectively. Also, the amount of water consumption during the growing season on the 341 hectares of the study area was estimated to be 2060000 m3, while the water input into the network in the previous agricultural year was more than 2500000 m3, representing an 82% efficiency of the pressurized irrigation network in Daryas. It is mentioned that in most of the study area, unauthorized wells are also used for land irrigation. Based on the obtained results in this study, it is inevitable to control the condition of the irrigation network and the amount and method of water distribution.

Evapotranspiration is considered as the water requirement for plants. Therefore, its measurement is necessary for all agricultural and irrigation projects. Evapotranspiration is one of the main components of the hydrological cycle associated with agricultural systems. Usually, evapotranspiration can be obtained using reference evapotranspiration (ET0). Accurate estimation and prediction of ET0 is essential in managing water resources, planning irrigation, and determining the water requirement of plants. Prediction of the ET0 by providing information about the future state in different time scales can help to make appropriate decisions, plan, and apply water resources management methods. Also, assessing agricultural drought conditions by well-known indices such as the Standardized Precipitation-Evaporation Index (SPEI) and Palmer Drought Severity Index (PDSI) directly requires ET0 of the region. The sharp decrease in the level of Lake Urmia and the threat to the region's ecosystem have also made the need for accurate calculation of ET0 more significant than in the past. One of the solutions to calculate ET0 is to use the FAO-56 Penman-Mantis equation (FAO-56 PM), an acceptable alternative for the scarce lysimeter data. However, the Penman-Mantis equation is highly dependent on the wind speed parameter, so a small error in the wind speed measurement causes a significant error. Therefore, this study aims to provide an innovative and reliable model for estimating ET0 without the need for wind speed parameters in Tabriz and Urmia stations.

In this study, to predict daily ET0 different intelligent models including multi-layer perceptron neural network (ANN-MLP), support vector regression (SVR), and support vector regression combined with firefly algorithm (SVR-FFA) were used in Urmia and Tabriz stations during 2002-2022 period. The input parameters of the models included minimum relative humidity (RHmin), maximum relative humidity (RHmax), average relative humidity (RHavg), sunshine hours (SSH), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg), and average soil temperature (Tsoil) which were obtained from Iran Meteorological Organization (IRIMO). Also, four different scenarios were used to run the models. The selection of different input combinations was based on the correlation coefficient, so the first combination had the lowest correlation and the last combination had the highest correlation concerning ET0. Also, data from 2002-2015 for 14 years were considered for model training and from 2016-2022 for 6 years for model testing. Correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and normalized root mean square error (NRMSE) indices were used to evaluate the used models.

The comparison and evaluation of the models used in Tabriz station showed that the SVR-FFA-4 model was chosen as the best model in this station with the root mean square error of 1.23 mm day-1. Among the SVR models, the SVR-4 model showed a good performance with the root mean square error of 1.95 mm day-1 after the combined model. Finally, the ANN-4 model also obtained an acceptable accuracy compared to other ANN combinations by having the root mean square error of 1.99 mm day-1. Finally, the evaluation of the results used for the Urmia station shows that the SVR-FFA-3 model has made the best predictions compared to other models with a root mean square error of 1.16mm day-1. The SVR-3 and SVR-4 models had a higher accuracy than other SVR combinations with a root mean square error of 1.78 mm day-1, but the third scenario was chosen as the appropriate model in the SVR model due to having less input. Among the ANN combinations, the ANN-3 model has a good performance compared to the other combinations of this model with the root mean square error of 1.81 mm day-1.

The results of this study showed that in both studied stations, the hybrid model showed higher accuracy than the individual models. So, in Tabriz station, the SVR-FFA-4 model had the best performance with an error rate of 1.23 mm day-1. In the Urmia station, the SVR-FFA-3 model showed good accuracy with an error rate of 1.16 mm day-1. Finally, it is suggested to use the hybrid model to predict the daily reference evapotranspiration in the northwest of the country. One of the limitations of this research is the lack of access to the parameters of dew point temperature and solar radiation. Therefore, it is suggested to use these parameters in the subsequent studies.

Due to water resources limitations in the country, future decisions will be based on future status. In the present study, to investigate the effect of climate change on hydrological components (minimum temperature, maximum temperature, precipitation, and sunshine hours), the information of Kamalvand water gauging station from the headwaters of Kashkan River located in Khorramabad City and Khorramabad meteorological stations were used. For this purpose, taking into account the climate information by LARS-WG software, their values were predicted in the future under three scenarios including A1B, A2, and B1 in three time periods of 34 years from 2011 to 2113. Considering the limited water resources, making management decisions will require knowing the future state of water resources. This phenomenon can cause considerable damage in vulnerable areas. Therefore, as water and its related issues are among the main concerns of mankind in the coming periods, it is necessary to evaluate the occurrence of climate change and the extent of its impact on water resources. According to the importance of knowing the amount of river flow in hydrological studies and water resources management, and the lack of information about the changes in the amount of river flow in the coming years, this study was designed and implemented to predict the daily flow of the Kashkan River in Khorramabad City in the coming years. To achieve this purpose, with the application of the atmospheric general circulation model and various intelligent neural models, the prediction of river flow with high accuracy under different climate change scenarios was examined.

This watershed forms an important part of the water-rich tributaries of the Karkhe River and covers about one-third of the land of Lorestan Province. In this study, the data of the Kamalvand River gauge station from the headwaters of the Keshkan River located in Khorram Abad have been used. In this study, relying on the ability of the artificial neural network, the application of this method was evaluated along with two hybrid models including neuro-fuzzy (CANFIS) and neuro-genetics (ANN-GA). The water crisis can be considered one of the challenges facing different regions of the country in the coming years. Managing water resources and dealing with the water shortage crisis requires knowing the state of hydrological components in the coming years. For this purpose, in this study, the status of meteorological parameters and the amount of river flow in the coming years were investigated. To achieve this goal, the capability and application of the LARS-WG model in forecasting meteorological parameters and intelligent neural models were used in river flow forecasting.

Based on the obtained results, the trend of increasing minimum and maximum temperature and evaporation and transpiration was predicted until 2113. Regarding the parameters of precipitation and solar radiation, a decrease in precipitation and an increase in radiation were predicted from 2080 to 2113. Comparing the performance of intelligent neural models in predicting river flow showed the superiority of the neural-fuzzy model over artificial neural and neuro-genetic models. river flow prediction with the neuro-fuzzy model until 2113 under scenarios A1B, A2, and B1 indicated that the lowest amount of river flow will be observed in scenario A1B and the highest amount will be observed in scenario B1. The temporal changes of the river flow during different seasons showed that the river flow will increase in spring, autumn, and winter. In general, according to the changes in meteorological parameters and the observed values ​​of the river flow, the description of the changes in the river flow in scenario A1B was closer to reality. This makes it necessary to properly manage the river flow, especially in the summer season of 2080-2113.

The results indicated that minimum and maximum temperatures and evapotranspiration during the next years will increase. In scenario A2, the precipitation changes trend was decreasing and solar radiation was increasing, however in other scenarios trend of increasing and decreasing. Then the discharge amount under different scenarios was calculated. The forecasting discharge values by intelligent models showed that the CANFIS model had more accuracy than the ANN and ANN-GA. The results of the optimized structure of CANFIS illustrated that the minimum discharge value in the future will occur in scenario A1B and the maximum discharge amount will be recorded in scenario B1. The evaluation of the seasonal trend showed that the flow rate increased in spring, autumn, and winter compared to the base period by 20.60, 17.31, and 9.27%, respectively. The lowest river flow in summer will occur under the A1B scenario during 2080-2113. rivers are one of the most important effective factors in the geomorphological processes of the earth and the hydrological cycle. Effective factors in very diverse hydrological processes and their applications in designed models are very difficult and the existence of high uncertainties and strong nonlinearity of the data complicates the issue. Long-term records of hydrological data show the temporal changes in discharge caused by climate change and vegetation changes.

Cover plants, among the most commonly used plants in the realm of green spaces, are typically characterized by shallow roots. In this research, a simple and flexible algorithm has been introduced for calculating the basal crop coefficient and evaporation coefficient of shallow-rooted plants without the use of (micro-) lysimeters. The presented algorithm requires measurements of moisture at three depths for short-term calculations and only surface layer moisture monitoring for long-term calculations. Using this algorithm, it is possible to calculate the potential evaporation and transpiration of the plant. Furthermore, the presented algorithm is independent of time steps. To evaluate this algorithm, nine experimental plots were utilized, including six Frankenia plots with full coverage and three bare soil plots during the peak water demand period. All experiments were conducted at the educational site of irrigation systems in the vicinity of the meteorological site of Ferdowsi University of Mashhad (FUM). For this purpose, the water content and irrigation of these plots was fulfilled (at most) every 48 hr. The results indicated that the presented algorithm has good capabilities for estimating the basal crop and evaporation coefficients. Additionally, the basal crop coefficient for Frankenia plant was found to be 1, and the evaporation coefficient was 0.58. Therefore, this method can be employed for estimating the water requirements of different plants without using (micro-) lysimeters.

The constant need to increase agricultural production, along with more and more frequent drought events in the country, requires a more accurate assessment of irrigation needs and thus a more accurate estimate of actual evapotranspiration. Prediction of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Irrigated agriculture is expected to produce more crops with less water consumption in the future. Therefore, accurate forecasting of water demand along with sustainable management and more efficient methods to meet the growing demand under scarce water resources is necessary. The models used to predict evapotranspiration should be used in different regions with different climates to evaluate their performance. Therefore, in this research, tree models and Hargreaves were used in Yazd and West Azerbaijan provinces, which have different climates, in order to evaluate the performance of the models used.

In recent years, water management issues have been addressed using models derived from artificial intelligence research. In recent years, water management issues have been addressed using models obtained from multiple types of research. The use of combined models has made significant progress in recent years. combined models are able to perform processing in a short period of time and at the same time with high accuracy. Using these models, the main challenging aspects are represented by the selection of the best possible algorithm, the selection of suitable representative variables and the availability of suitable data sets. Therefore, in this study, the ability of tree models (M5P and RF) with Hargreaves model (Hs) in estimating daily evapotranspiration in Urmia and Yazd stations during the period of 2000-2021. The noteworthy point is that in the combined tree-Hargreaves model, the used tree models were used as input to the Hargreaves model. The combined model has been used for the first time in this research and the use of this model can predict daily evapotranspiration as well as possible.

The results of the model are performed using 5 evaluation criteria of Coefficient of determination, Root mean square error, Nash-Sutcliffe coefficient, and Wilmot’s index of agreement. In all the used models, the best scenario was the model whose input included parameters of minimum temperature, maximum temperature, relative humidity, wind speed, and sunshine hours. Comparison and evaluation of standalone tree models showed that in the Urmia station two models RF-5 and M5P-5 had less error (0.4 and 0.38-mm day-1, respectively) than other standalone models. Similarly, in the Yazd station, RF-5 and M5P-5 models have higher accuracy (0.36 and 0.35 mm day-1(, respectively) than other standalone models. For combined models, the obtained results showed that the fifth scenario of the M5P-Hs model provided the best performance in Urmia and Yazd stations with the lowest error (0.33 and 0.24 mm day-1) respectively. It was also concluded that the fifth scenario of the RF-Hs model in Urmia and Yazd stations had a lower error (0.36 and 0.26 mm day-1) than other models, respectively. Finally, tree models have increased the accuracy of the Hargreaves model in this research.

Finally, the RF, M5P, RF-Hs and M5P-Hs models were able to predict daily evapotranspiration values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. The results of this research showed that the combination of tree models with Hargreaves model is able to predict daily evapotranspiration values with high accuracy compared to individual models. The results of this research showed that the wind speed parameter is one of the most important meteorological parameters needed in estimating daily evapotranspiration, so adding this parameter results in the highest accuracy in all models. Also, due to the important role of wind speed in predicting daily evapotranspiration values and the unavailability of the maximum wind speed parameter in this research, it is recommended to use the maximum wind speed parameter as one of the model inputs for further studies.

Indiscriminate use of water resources and the occurrence of drought in recent years have caused many problems in the country's water resources. The increasing shortage of water resources and high irrigation costs require developing new irrigation methods for optimal water consumption, which can minimize the amount of water used to produce yields. Evapotranspiration is one of the most important parameters needed to estimate the water balance in any ecosystem. Evapotranspiration is an essential parameter in the hydrological cycle process in natural ecosystems, which links the water and energy balance of the earth's surface with the atmosphere. Reference evapotranspiration (ET0) plays an important role in the availability of water resources and stimulating the hydrological effect of climate change. Accurate estimation of ET0 is necessary for forecasting climate changes, predicting and monitoring droughts, assessing the lack of availability of water resources, assessing crop water needs, and planning irrigation. FAO's Penman-Monteith method is known as a standard reference method for estimating ET0. However, this model and, in general, water balance-based assessment methods require accurate and long-term meteorological data, which are not always and everywhere available. Therefore, alternative methods for predicting ET0 at different temporal and spatial scales should be developed, which are easily applied and require fewer input data without compromising the estimation accuracy. Also, due to the high rate of evapotranspiration in the coastal and central stations of the country, so far, few studies have predicted the ET0 parameter. Therefore, this study was carried out to predict daily reference evapotranspiration in Isfahan and Astara stations. The current study is forecasting daily reference evapotranspiration in two stations of Astara and Isfahan using Gaussian Process Regression (GPR), Support Vector Regression (SVR), M5P tree model, and M5Rules linear regression. For this purpose, the daily meteorological data of the stations including average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours during the period of 1990-2021 as inputs to the models was used. Also, to evaluate the effectiveness of the models, the evaluation criteria of determination coefficient (R2), root mean square error (RMSE), Nash-Sutcliffe coefficient (NS), and Wilmott's index of agreement (WI) were used. The evaluation of the results of different scenarios of the GPR model in Astara station showed that the fifth scenario was recognized as the best scenario of this model due to having a lower error value (RMSE=1.52 mm day-1). For the M5Rules model, the fifth scenario has performed better than the other scenarios of the M5Rules model due to having fewer inputs and similar errors compared to the sixth to eighth scenarios (RMSE=1.42 mm day-1). In the M5P model, the fifth scenario has a higher accuracy than the other scenarios due to having a lower error value (RMSE=1.42 mm day-1). For the SVR model, the sixth scenario with the least error (RMSE=1.58 mm day-1) was selected as the best scenario compared to other scenarios of the SVR model. For the Isfahan station, for the GPR model, the fifth scenario has performed better than the other scenarios due to having fewer inputs. The comparison of M5Rules model scenarios also showed that the eighth scenario with RMSE=1.85 (mm day-1), had higher accuracy than other scenarios. The seventh scenario of the M5P model has performed better than other scenarios due to its RMSE=1.86 (mm day-1). Finally, the evaluation of SVR model scenarios showed that the eighth scenario with RMSE=1.88 (mm day-1) had a better performance than other scenarios. The comparison of the models used to predict daily reference evapotranspiration in Astara station showed that the fifth scenario of M5P and M5Rules models having evaluation criteria of R2=0.76, RMSE=1.42 (mm day-1), NS=0.7 and WI=0.89 had the highest accuracy compared to other models and showed the best performance. Also, the evaluation of the results of the models in Isfahan station showed that the eighth scenario of the M5Rules model, having the evaluation criteria of R2=0.8, RMSE=1.85 (mm day-1), NS=0.8 and WI=0.94 had the best performance compared to other models and the M5Rules model was selected as the best model. Also, the seventh scenario of the M5P model had almost the same performance as the eighth scenario of the M5Rules model and showed a good performance. Therefore, M5P and M5Rules models successfully predicted reference evapotranspiration. One of the limitations of the present study is the lack of access to dew point temperature and solar radiation data. Therefore, the use of these parameters is suggested for further studies.

Precipitation is considered one of the most important components of hydrological cycle, and its effective and usable amount for plants is of great importance in the agricultural sector, especially rainfed cultivation. In this research, the effective precipitation (EP) in dry wheat fields of Khomein city was estimated by using RS and SEBAL on 28 available images from Landsat8 in the crop years 2014 to 2022. Penman-Monteith-Fao method was used to evaluate the accuracy of SEBAL. Then, a model of EP estimation was developed with ANN and meteorological data. For this purpose, the correlation between meteorological data and Growin Degree Days (GDD) with EP was investigated by Pearson's correlation method. the meteorological data of three stations from the closest synoptic stations to the study area were used and The meteorological data of the study area were interpolated using the Inverse Distance Weighting method (IDW). According to the results of the correlations, the average temperature parameter with a correlation of 0.92 and the GDD and the maximum relative humidity respectively with a correlation of 0.86 and -0.77 as effective variables in estimating EP. In the next step, the most effective parameters were used for modeling. the networks were trained under different scenarios, and the performance of the networks was evaluated using the RMSE and MBE error criteria. The results showed that by using the BR learning algorithm and having the variables of daily temperature and GDD, it is possible to predict the amount of EP for the target area with very good accuracy. The RMSE value of this model was 0.1899 mm and MBE was estimated as -0.0115 mm. By using the presented model, with simple meteorological variables, the actual evapotranspiration and finally the EP of the desired area can be determined with appropriate accuracy without the need to solve complex algorithms.

The water and soil resources are limited, and the optimal use of water resources in the agriculture needs the most accurate determination of the amount of crop water requirement and crop coefficients in different stages of growth. Quinoa (Chenopodium quinoa Willd.) is one of the plants that has outstanding economic and agronomic characteristics in the production of oil and protein among the sorghums. The present research was conducted to determine the maximum allowable depletion, crop coefficient and water requirement of quinoa under controlled conditions (lysimeter) in two spring and autumn cropping season. The results showed that with a decrease in the moisture level at the beginning of irrigation from 0.4 to 0.2 total available water, the amount of biomass and seed yield had a significant decrease of 24 and 37% in spring cropping and 34 and 47% in autumn cropping, respectively. But the decrease in moisture levels from 0.8 to 0.6 and 0.6 to 0.4 did not cause a significant decrease in seed yield and biomass in both spring and winter cropping. Based on the results, Maximum Allowable Depletion (MAD) was estimated to be 0.6 in both cropping seasons. Overall, the crop coefficient of quinoa (Titicaca variety) in spring cropping was equal to be 0.42 at the beginning of the growing season, 0.95 in the middle of the growing season, and 0.33 at the end of the growing season. In the autumn cropping, the crop coefficient of quinoa was equal to be 0.36 at the beginning of the growing season, 1.09 in the middle of the season, and 0.56 at the end of the growing season.The net water requirement of different treatments varied between 618.1 to 295.5 mm in spring cropping season and between 597.8 to 334.8 mm in autumn cropping season.

One of the first steps for optimal management of water consumption in the agricultural sector is to estimate water needs by determining evapotranspiration. There are several direct and indirect methods for estimating evapotranspiration; each one has advantages and disadvantages. Due to the importance of measuring evapotranspiration in most hydrological studies and estimating the water requirement of plants and due to the limitation of the possibility of direct measurement, there is a serious need for experimental methods to estimate evapotranspiration. In the present study, reference evapotranspiration was initially estimated at selected stations in the east of Lake Urmia. Then, experimental methods of calculating the pan coefficient were used to calculate the reference evapotranspiration using evaporation pan data considering the FAO standard method.

The aim of this study was to evaluate the accuracy of pan coefficient estimation methods to calculate daily evapotranspiration in the east of Lake Urmia basin. There are several direct and indirect methods for estimating evapotranspiration; each one has advantages and disadvantages. The evaporation pan method has been used to estimate evapotranspiration values. For this purpose, data from Tabriz, Sarab, Maragheh, Bostanabad and Herris synoptic stations located in the east of Urmia Lake basin were used. The meteorological data utilized in the current study are minimum, average and maximum temperature, sunny hours, minimum, average and maximum relative humidity, wind speed, and evaporation from the pan. It is worth mentioning that due to the limitation of recording evaporation pan data, the present study was carried out using data for 6 months of the year (May to October) in which continuous data are available. The values of the pan coefficient were estimated using six experimental methods including Konica, Allen and Parvit, Snyder, modified Snyder, Orang and Mohammad et al. To determine the best method for estimating the pan coefficient, the evapotranspiration values obtained from the application of each method were compared with the evapotranspiration values obtained from the standard FAO-Penman-Monteith method. Furthermore, statistical meters of R, RMSE, MAE and box and violin plot diagrams were used to evaluate the obtained results.

In this study, six experimental models were used to estimate the pan coefficient. Based on the obtained results, the highest range of average monthly changes of the pan coefficient is related to the Orang method. Also, considering the average monthly values obtained for the pan coefficient, the Orang method estimates the reference evapotranspiration to a considerable amount. The results showed that in Bostanabad and Harris modified Snyder method, in Sarab and Maragheh method of Mohammad et al. and in Tabriz Allen and Parvit method are the best methods for estimating pan coefficient. Also, in general, in all stations, the Orang method has the highest error in estimating pan coefficient. In order to use experimental models for estimating the pan coefficient to calculate evapotranspiration, it is necessary to determine the appropriate model for each region based on the climatic conditions.

Due to the importance of estimating reference evapotranspiration in most hydrological studies as well as estimating the water requirement of plants, several direct and indirect methods have been developed. In the present study, six models of estimating the pan coefficient were evaluated in order to calculate the daily reference evapotranspiration using evaporation pan data. The obtained results showed that in general, the models for estimating the coefficient of the pan with acceptable accuracy can be used to calculate evapotranspiration. Meanwhile, due to the effect of climatic factors in these models, it is necessary to evaluate the efficiency of each model in different climatic conditions and determine the appropriate model for each region. For example, the results of the present study showed that the Orang method for the study area (east of Lake Urmia) does not provide suitable results and if this model is used for the east of Lake Urmia, it is necessary to calibrate the model. Also, based on the obtained results, the accuracy of other methods is close to each other. In Bostanabad and Herris, the modified Snyder method, in Sarab and Maragheh, the method of Mohammad et al., and in Tabriz, the method of Allen and Parvit, are the best methods in estimating daily reference evapotranspiration.

Most of the water consumed in Iran is allocated to the agricultural sector. The world's population will reach about 9 billion by 2050. The population of our country will exceed 86 million in 1404, in which population growth is faster than many other countries. For the exponential and rapid growth of population of the world and Iran, a great challenge will be created in food production with limited water and land resources. Due to population growth and urbanization, water resources in the world are becoming more and more limited. Therefore, paying attention to the amount of water consumed by plants and agricultural cultivation pattern, to maximize the yield of farms, is one of particular importance. Determining the capacity of canals or water pipes depends on the irrigation hydromodule and this parameter must be carefully extracted for each area. On the one hand, if the irrigation hydromodule of an area is estimated to be less than the actual value, water requirement of the plants, will not be met and the agriculture will suffer a lot from this issue. On the other hand, if the irrigation hydromodule of an area is estimated to be higher than the actual value, farmers will not save water and therefore the efficiency of irrigation systems will decrease. Therefore, it is required that for each area, the cultivation pattern and irrigation hydromodule should be carefully estimated and the combined hydromodule should be extracted and be the basis for designing irrigation projects.

In this research, according to meteorological data, the rate of evapotranspiration and finally the irrigation hydromodule for some stations in the country is determined. Climatic data of Ardabil, Ahvaz, Qazvin, Kerman and Mashhad stations were collected and the evapotranspiration values of grass, as reference plant, were extracted using Cropwat8 software. The climatic data period in different stations was chosen so that the beginning of the period, as far as possible from the establishment of each station (in the absence of incomplete or missing data) and the end of the period ending in 2015. The length of the period in different stations varies depending on the year of establishment and it was tried to consider the maximum length of the period for each station. The cultivation pattern of the desired areas was extracted from the sources. The changes trend in climatic parameters, rainfall and evapotranspiration of the reference plant of each region were determined. Hydromodules, probability of occurrence, return period and Weibull conversion were extracted for each hydromodule (one per year). Firstly, obtained irrigation hydromodules from the Cropwat8 software for all the years were sorted in ascending order and each one was given a number (m). Then the probability percentage (P%) was calculated for each of the hydromodules. The return period (RP) was calculated for the various probabilities. Finally, using the Weibull conversion coefficient, irrigation hydromodules with different return periods were extracted for each region.

The results showed that the average evapotranspiration of the grass, as reference plant, for Ardabil, Ahvaz, Qazvin, Kerman and Mashhad stations were calculated 2.87, 5.75, 3.79, 5.4 and 2.49 mm d-1, respectively. The average irrigation hydromodules for the five mentioned stations were 0.66, 0.99, 0.75, 1.1 and 0.71 lit s-1 ha-1, respectively. Using the linear variation function, in return periods of 2 to 200 years, the irrigation hydromodule values in Ardabil, Ahvaz, Qazvin, Kerman and Mashhad stations varies from 0.673 to 0.768, 1.023 to 1.22, 0.76 to 0.861, 1.095 to 1.248 and 0.7 to 0.759 lit s-1 ha-1, respectively. Using the exponential variation function, in the return periods of 2 to 200 years, the irrigation hydromodule values in Ardabil, Ahvaz, Qazvin, Kerman and Mashhad stations varies from 0.665 to 0.775, 1 to 1.249, 0.754 to 0.867, 1.086 to 1.255 and 0.695 to 0.761 lit s-1 ha-1, respectively. Taking the linear change function, by changing the return period from 2 to 200 years and reducing the probability of occurrence, the amount of irrigation hydromodule in Ardabil, Ahvaz, Qazvin, Kerman and Mashhad stations were increased 0.148, 0.303, 0.156, 0.237 and 0.092 lit s-1 ha-1, respectively that is equivalent to 19.27, 24.84, 18.11, 19 and 12.12 percent, respectively. Also, considering the exponential change function, the amount of irrigation hydromodule in the mentioned stations were increased 0.173, 0.391, 0.177, 0.267 and 0.55 lit s-1 ha-1, respectively that is equivalent to 22.32, 39.1, 23.47, 21.27 and 13.98 percent of the average. In short return periods (including 2, 5 and in some stations 10 years), the linear function estimates the amount of irrigation hydromodule more than the exponential function. At higher return periods (including 25, 50, 100, and 200 years), the exponential function estimates the amount of irrigation hydromodule more than the linear function. In the intermediate return periods (including 10 and 25) the results of the linear and exponential functions are very close to each other.

The lowest amount of hydromodule was obtained in Ardabil station for a return period of 2 years and the highest amount was obtained for Ahvaz station for a return period of 200 years. Taking the linear changes function and considering that with increase in return period (even up to 200 years), the irrigation hydromodule does not change much (about 20% of the average), it is suggested that the construction of water storage, transmission and distribution buildings should be designed and implemented with low probability of occurrence (high return period).

Detailed water resource balance reports are very important in promoting sustainable watershed management programs. The main purpose of this study is to evaluate and use the SWAT+ model as a new version of the SWAT model in simulating hydrological processes and determining the role of effective factors in the changes of water balance components in Tashk Bakhtegan basin. In this regard, after preparing the required information and entering them into the SWAT+ model, the steps of calibration and validation of the model for river flow in 10 hydrometric stations, base flow in three upstream stations, as well as calibration of the underground water level, evapotranspiration and yield of major cultivated crops was done at the basin level during the period (1980-2014). The evaluation of the model calibration results in most of the stations shows the index values of (R2>0.5) and (NSE>0.2) that the results were favorable and acceptable. Comparing the results of the water balance obtained from SWAT+ with the results of other studies shows that the final values of the evaporation-transpiration parameter were higher and the surface currents were lower than the results of other researches, so that only compared to the results of the SWAT model, the three mentioned components increased by 17. 0, 1.13 and showed a decrease of 0.06 billion cubic meters per year. Examining the spatial and temporal changes of the balance components also showed that the changes in precipitation and evapotranspiration decrease from north to south of the basin, and in areas with high rainfall, their fluctuations are more proportionate and congruent. The most important factor in the decrease of water flow (74.7 percent) and the increase of evaporation-transpiration (80.3 percent) of the basin is related to non-climatic and human factors such as land use change, construction of dams, etc. and the effect of climatic factors on water flow changes and evaporation-transpiration is 25.3 percent and 19.7 percent respectively. Evaluation of SWAT+ model calibration results and comparison of its results with other studies conducted in the basin indicate the acceptable performance of this model in simulating and separating the contribution of different climatic and human factors in the hydrological conditions of the studied basin. Therefore, according to the new capabilities of this model and the improvement of the simulation processes of underground water and its exchange with the river compared to the SWAT model, it is recommended to use this model in order to estimate and verify the water balance components of basins.

Different factors are effective in increasing wheat production, one of the most important of which is water. Determining the actual consumed water of wheat in arid and semi-arid regions is of particular importance and the economic use of water is a serious and very important issue for farmers and researchers who cultivate and produce wheat under irrigation. The season of wheat cultivation has a direct effect on its water requirement due to the change in the energy pattern affecting evapotranspiration, and it will definitely have a lower water requirement in winter than in spring and summer. Therefore, the present study was conducted in order to investigate the water requirement system in determining the actual amount of irrigation water and wheat plant yield based on the inverse solution of the production function under water stress conditions for Alvand variety wheat in Qazvin province.

The research was conducted in 2017-2019 crop years in Qazvin province on a land of 600 square meters in Esmailabad research station (49º 52' N, 36º 15' E, 1285 MSL). The experimental design was in the form of split plots and in the form of a randomized complete block design with three replications. So that the main factor of irrigation management includes providing water requirements of 20 (I1), 40 (I2), 60 (I3), 80 (I4) and 100 percent (I5) and secondary treatment includes irrigation until the end of the flowering stage (S1) and The pulping of the seed was (S2). The country's using NIAZAB system was provided by the Soil and Water Research Institute (SWRI). This system is designed to determine the water requirement of farmland and Orchard products, which has the ability to estimate and determine the water requirement, Consumed water and plants irrigation planning at the level of the region, city, catchment and plain. One of the prominent points of this system is its location-based nature, and the user can extract their regional needs by referring to the system and can allocate the water used for the cultivation pattern under different usage options to the beneficiaries of the agricultural water stakeholder with the ability to provide an update.

The results showed that the root mean square error in Tafteh, Pasquale and Raes methods was 122, 83 and 126 mm per day, respectively, and Pasquale method had a better estimation than other methods. In Pasquale's method, the best normalized root mean square error was observed with 0.18%. The index of agreement or consistency in Tafteh, Pasquale and Raes methods was 0.95, 0.98 and 0.95%, respectively, and the Coefficient of Efficiency of the model was 0.77, 0.91 and 0.73, respectively. The results of the statistical analysis showed that the measured and simulated values are close to the 1:1 line and have a good relationship, and the coefficient of determination values in the studied years showed 0.98. The results of estimation the amount of wheat plant evapotranspiration in the using NIAZAB system in the Qazvin plain with the methods of Tafteh et al. (2013), Pasquale et al. (2017) and Raes et al. (R2=0.98) were high and the root mean square error in Tafteh, Pasquale and Raes methods was 120, 83 and 126 mm per day, respectively, in which Pasquale's method had a better estimation than other methods.

In general and according to the statistical results, a good approximation was observed between the real data and the using NIAZAB system in determining the amount of irrigation water under water stress conditions, which indicates the appropriate evaluation of the water requirement system and the ability to simulate the wheat yield function in relation to different treatments. It was irrigation and this system can be used as a suitable tool in estimating water needs to improve water management in wheat.

The fundamental principles of smart irrigation hinges upon precise assessments of soil moisture content within the root zone layer. Various techniques have been developed to ascertain root zone soil moisture content, such as using soil moisture measurement sensors or simulation models. Each one of these methods has its own distinct advantages and disadvantages. Data assimilation encompasses an array of approaches that combine model estimates with the corresponding observed data to derive a more precise estimations of the required data. The purpose of this research is to ascertain the feasibility of reducing the number of depths at which soil moisture measurements were taken and increasing the time interval between two consecutive soil moisture measurements using the Ensemble Kalman filter.

This study was conducted synthetically based on information collected from four farms in Jovein, Khorasan Razavi Province, cultivating sugar beets and corn. Data was collected from four farms during the period of April to November 2020. The numerical solution of the Richards equation with the inclusion of the sink term was used to simulate the soil moisture changes in the root zone layer. To mitigate data assimilation's vulnerability to potential result divergence among members, an identification and correction mechanism, along with handling divergent members, were integrated into the system. This mechanism was found on the sudden model result shift throughout the entire root profile between two consecutive days. Two indicators were used to evaluate the scenarios: a) the sum of covariance matrix diameters at the last simulation time step, and b) the normalized root mean square difference of the soil moisture content within the soil profile, comparing the scenarios with the scenario having the largest number of soil moisture measurement depths and the shortest time interval between two consecutive measurements.

The results indicated that with the application of Ensemble Kalman filter, it is possible to improve the accuracy of the results using a longer time interval between measurements. The Data Assimilation scenarios exhibited a remarkable capability in reducing the diameter of the covariance matrix. This reduction, ranging from 61% to 86%, compared to the open-loop scenario, emphasizes the ability of Ensemble Kalman filter to effectively mitigate uncertainty. The normalized root mean square difference , was notably improved by the Data Assimilation scenarios. The normalized root mean square difference of scenarios ranged from 0.03 to 0.11, while the normalized root mean square difference for the Open Loop was 0.15, highlighting the capacity of Ensemble Kalman filter to minimize discrepancies between simulated and observed soil moisture profiles. Such reductions in normalized root mean square difference values signify the model's improved ability to capture actual soil moisture variations, thus contributing to more reliable predictions and better decision-making in agricultural water management. The application of Ensemble Kalman filter helped to select the proper measurement depths and ultimately to reduce the number of required soil measurement points.

Data Assimilation successfully diminished the uncertainty of the soil moisture content results, even when utilizing the minimum number of soil moisture measurement depths and maximum time intervals between observations. Both of these findings—increasing the time interval between consecutive measurements and reducing the required number of measurement depths—indicate that with the application of data assimilation, it is possible to decrease the cost of the implementation of the smart irrigation.

Accurate estimation of crop water requirement is important especially in arid and semiarid climates. The rate of evapotranspiration can be measured by a lysimeter, however, it is expensive and its measurement is a cumbersome practice and time consuming. Therefore, many researchers use empirical models instead of direct measurements. Evapotranspiration of every crop is related directly to reference crop evapotranspiration (ET0). There are many empirical models for estimation of ET0. Among them the FAO-56 Penman-Monteith (FAO56PM) is known as the standard method in every climate. However, this method needs more climatic parameters which are not available everywhere. Therefore, identification of the model which needs less parameters and easily available climatic parameters will be useful. In this study three candidate models which had such conditions considered. These three models are i) Blaney-Criddle (BC), ii) Doorenbos and Pruitt (DP), and iii) Turk. The suitability of the candidate models compared with the output of the FAO56PM method. The main objective of this study is finding the most suitable model among the three candidate methods in Urmia Lake basin.

The region under study is the Urmia Lake basin located in North West of Iran. Nine stations selected across the basin. Meteorological data gathered from the Islamic Republic of Iran Meteorological Office (IRIMO). The method used here as a bench mark for evaluation of other empirical models is FAO-56 Penman-Monteith (FAO56PM). Three empirical models for ET0 estimation which need less climatic parameters are i) Blaney-Criddle, ii) Doorenbos and Pruit, and iii) Turk. These three models need climatidata that are easily available at least in the region. The recommended form of the FAO56PM method is as follows:ET_0=(0.484×Δ(R_(n-) G)+ϒ 900/(T+273) u_2 (e_s-e_a))/(Δ+ϒ(1+0.34u_2)) (mm day-1) [1] Where Rn is the net solar radiation (MJ/m2/day), G is the ground heat flux (MJ/m2/day), T is mean daily air temperature (℃), u2 is the wind speed at two- meter height (m/s), es is the saturated vapor pressure (kPa), ea actual vapor pressure (kPa). Other parameters explained in Allen et al. (1998).The first empirical candidate model for estimation of ET0 is the Turk model (Shuttleworth 1993) which reads:ET0 =0.31 T/(T+15)(S_n+2.09)(1+(50-RH)/70 ) ( 〖mm day〗^(-1) ) ,if RH<50% [2] Where Sn is the net solar radiation in (MJ/m2/day), RH is the relative humidity (%). If the average RH of a station is great or equal to 50%, then the second parenthesis will be removed from the equation, i.e. its value will be unity. The second empirical candidate model for estimation of ET0 is the Doorenbos and Pruitt (DP) model (Shuttleworth 1993) which reads:ET0 =-0.3+b_dp Δ/(Δ+γ) S_n 〖(mm day〗^(-1)) [3] where bdp calculated from the following relationship:b_dp= 1.066-0.0013 (〖RH〗_mean)+0.045 (U_d)-0.0002 (〖RH〗_mean)(U_d)-0.000315〖(〖RH〗_mean)〗^2-0.0011(〖U_d)〗^2 [4] The third empirical candidate model for estimation of ET0 is the Blaney-Criddle (BC) model (Fooladmand and Ahmadi 2009) which reads:ET0=(a_BC+b_BC f) ) 〖mm day〗^(-1)) [5] Where f=P(0.46T+8.13) [6] and a_BC= 0.0043〖RH〗_min - (n/N)- 1.41 [7] b_BC= 0.82-0.0041 (〖RH〗_min) + 1.07(n/N) + 0.066(U_d) -0.006(〖RH〗_min) (n/N) - 0.0006 (〖RH〗_min) (〖RH〗_min) (U_d [8] In [7] and [8] n is the actual sunshine hours and N is the maximum daily sunshine hours.In order to evaluate the models performances three criteria were used in this study which are i) coefficient of determination (R2), ii) root mean square error (RMSE), and mean absolute error (MAE). The output of models compared with that of the FAO56PM output in each of the selected stations.

Results showed that the median of R2 values of the Turk and BC were great than DP. The median of R2 values of both the Turk and BC were about 0.9. However, this measure for DP was about 0.75. The variance of R2 values in the case of BC was less than DP. The lowest value of RMSE belonged to the BC model which was about 2 mm/day. This value for other two models were more than 2 mm/day. The variance of RMSE obtained for all the nine stations was minimum comparing the three models. Therefore, according to Fig. 3, it can be suggested that when the RMSE was used for selection of the best model then the TURK model was superior than the others. However, when we considered all the three criteria for model selection, then the TURK model was superior than the others. According to Fig. 6 it can be concluded that in summer hot days the DP model overestimate the ET0 comparing the FAo56PM at Tabriz station. However, in the winter cold days DP underestimated the ET0 comparing the FAo56PM at the same station.

In this study three candidate models compared with the base model i.e. FAO56PM. Nine stations around the Urmia lake selected. Results indicated that the TURK model was suitable for ET0 estimation in Urmia Lake basin. This model only need three weather parameters which are i) Tmean, ii) actual sunshine hours (n), and relative humidity (RH). Logical use of fresh water in this basin is recommended.

In recent years, we have witnessed the unsustainable use of water resources, which has led to short-term and long-term water crises. The diversity of water and soil resources, along with climate change, has made the scientific management of agricultural water inevitable. In a such scenario, managing scarce water resources to meet ever-increasing needs is challenging. To make the best use of water resources, it is important to know the amount of water needed for economic production.Determining the water requirement of crops, especially the evapotranspiration potential, indirect ways and in different climates for agricultural and orchard plants is one of the basic strategies of each region. Evapotranspiration (ET) is a process that includes two parts: evaporation (evaporation of water from the surface of soil and vegetation and surface water) and transpiration (evaporation of water from plant organs due to plant physiological activities). The purpose of estimating evapotranspiration is to determine the crop’s water requirement, and irrigation planning, and to evaluate the sensitivity of crops’ performance to water deficiency in different stages of plant growth. Sugar beet is one of agricultural crops that is placed in the cultivation pattern and is cultivated in a wide area of the world due to the need for sugar consumption. Determining the evapotranspiration of this crop and planning its irrigation is of particular importance. Numerous studies showed that the water requirement of sugar beet, based on the variety and climate, differs. Various techniques have been proposed to measure ETc, and each method has advantages and limitations. Some of the widely used methods are lysimetric experiments, eddy covariance, Bowen ratio, energy balance method, and soil water balance method. In recent years, new technologies are also used to estimate evapotranspiration, among them, the Surface Energy Balance Algorithms for Land (SEBAL) can be mentioned. This research aims to compare ET, water requirement, and water productivity of sugar beet in lysimetric data to SEBAL algorithm using Landsat 5 satellite images, from 1996 to 1998.

This experiment was carried out at the Chahar-Takhteh research station (Shahrekord, Iran) at latitude 50 ̊56ʹ and longitude 31̊ 11ʹ, 2066 m above sea level.In the spring of 1996, 1997, and 1998, before planting the crop, the soil inside the lysimeter was irrigated to reach the saturation level. Two days after irrigation and at field capacity, monogram seeds of sugar beet, at the rate of 120,000 crops per hectare, were cropped. The row spacing in the field around the lysimeter was similar. Irrigation was based on the discharge of about 35 to 45 % of the moisture content at field capacity. The required amount of water was calculated by the neutron probe and added to the lysimeter. simultaneously, the surrounding area was also irrigated.Remote sensing data included Landsat 5 satellite images for the years of experiment, path 164, and row 38. The temporal resolution of the satellite was 16 days. Spatial resolution for visible, near, and mid-infrared bands was 30 and 120 m for thermal infrared. The 25 cloud-free images were downloaded (6, 9, and 10 images) for research years. These images were retrieved from the website (https://earthexplorer.usgs.gov) as geometrically and radiometrically corrected and processed in ERDAS Imagine 2022 software. To estimate actual evapotranspiration, the energy balance equation is used, λET=Rn-G0-H. In this equation, Rn is the net incoming radiation flux, H is the sensible heat flux, G0 is the soil heat flux, and λET is the latent heat flux of evaporation (W/m2 ). The statistical indicators include mean absolute error (MAE) which is unsigned, mean bias error (MBE), root mean square error (RMSE), normalized root mean square error (NRMSE), and Coefficient of Determination (R2 ).

Evapotranspiration of sugar beet in lysimeter and in SEBAL on the days of satellite passage in 1996 to 1998 (25 overpasses without clouds) showed that the difference of evapotranspiration in the two methods was -1.20 % and -0.13 mm d -1 which showed high accuracy. The negative sign means that the SEBAL estimates were lower than the corresponding values in the lysimeter. The statistical indices values of RMSE, NRMSE, MAE, and MBE for 25 pairs of evapotranspiration values were 0.7031 mm d -1 , 0.1102, 0.5552, and -0.1312, respectively. The RMSE, NRMSE, MAE and MBE statistical indices for 18 pairs of monthly evapotranspiration were 54.1155 mm month-1 , 0.3225, 40.9462, and - 28.7955, respectively. The total values of evapotranspiration in lysimeter were equal to 1096.6, 1022.6, and 906.3 mm during the growth period (total mean equal to 1040.6 mm) from 1996 to 1998, respectively. The total values of evapotranspiration in the SEBAL were equal to 1004.6, 831.6, and 666.4 mm during the growth period, total mean of 834.2 mm. The mean difference was around 19.8%. The results of mean water productivity were 5.02 kg m-3 in lysimeter and 6.26 kg m-3 in SEBAL. Because of lower evapotranspiration values in SEBAL compared to the lysimeter, the water productivity values were higher.

Determining the water requirement of crops is the basis of planning for the sustainable use of water resources and irrigation of crops. The sugar beet has a great amount of evapotranspiration due to its large green cover. Accurate quantification of crop evapotranspiration (ETc) at local and regional scales can help water policy and decision-making in water resources and their management. The results indicated that the SEBAL algorithm using Landsat 5 satellite images with a coefficient of determination (R2=0.9889) in the daily time period and a coefficient of determination (R2=0.9318) in the monthly time period had a good correlation with lysimetric data. In general, results showed that SEBAL has a special capability as one of the widely used remote sensing algorithms to estimate crop evapotranspiration.

Water is one of the most important restrictive factors in the production of agricultural products worldwide. The aim of this research is to study the effects of different levels of irrigation and organic mulch on yield, water productivity, fresh weight, dry matter percentage, hardness, soluble solid content, titratable acid, and chlorophyll index of Granny Smith apple fruit at harvest. For this purpose, factorial experiment was performed in a randomized complete block design with three replications in 2019. Irrigation treatment were applied at supply levels of 100, 75 and 50 percents of tree water requirement (ETc) and organic mulch to thicknesses of zero, 4, 8 and 12 cm. The results showed that the highest average of fresh weights of fruit were allocated to 75 and 100 percent water supply levels and organic mulch treatment at thicknesses of 8, 4 and 12 cm had a significant positive difference in fresh weight of fruit compared to the control, respectively. The average yield of mulched trees was 11.52 percent more than control trees, the highest yield was obtained in treated trees with 8 cm of mulch. Fruit SSC and the firmness of fruit were improved at 50 percent irrigation. The interaction of 8 cm mulch treatment with 75 percent irrigation had the most amount of fruit acid. Both chlorophyll a, b and total decreased with increasing drought stress and improved with the application of mulch treatments .In addition, interaction of both experimental treatments, increased water productivity and benefit per drop unit area.

Reference evapotranspiration as an important indicator of demand for air evaporation is an important factor for climatic and hydrological studies. Due to the occurrence of climate change and the occurrence of large fluctuations in precipitation and the occurrence of mild to severe droughts, it is important to study the evapotranspiration changes. In this study, to investigate the temporal-spatial changes of trend and change point in the reference evapotranspiration time series for different seasons in Mazandaran province, 40 years data (1981-2020) from satellite networks (with a resolution of about 5 km) were used. Non-parametric Mann-Kendall, Sen's slope, Pettitt's test (Non-Parametric Rank Test) and quantile regression were used to investigate the changes in the trend and to present the range of fracture point changes in the evapotranspiration. The correlation coefficient between network evaporation-transpiration data and ground station data was estimated to be more than 0.9 in most of the stations and the average value of BIAS was 0.24. The results of Pettitt's test showed the time of sudden changes in reference evapotranspiration in spring, autumn and winter seasons in 2013, 2007-2010 and 1999, respectively, in most cases. Very high values of evapotranspiration increased in spring in the eastern half, in summer in the northern half and in winter in the southern and western strips (with a seasonal slope percentage of 45, 75 and 120 percent, respectively), but they decreased in autumn in the north of the province (with a slope of -45 percent). In general, significantly increasing slope rates for high values of evapotranspiration were higher than average. A significant increase in high amounts of evapotranspiration, especially in the dry season, will reduce water resources and disrupt the agricultural sector. Therefore, scientific and practical methods for managing reference evapotranspiration in the province should be considered.

The FAO Penman-Monteith method as a standard method requires a lot of meteorological data. The accurate preparation of these data is not possible in all regions; as a result, alternative methods that require less data are investigated. Prestley-Taylor method require a few meteorological data and its application can be useful in areas where meteorological data is not available. The Sistan region in the southeast of Iran is one of the regions that is unique in Iran due to the 120-day winds and high day-night temperature changes. The purpose of this research is to evaluate Prestley-Taylor method compared to the PMF-56 method and to modify this equation according to the wind conditions for the region of Sistan. For this purpose, 30 years of meteorological data in Sistan region were used. The coefficient of Priestley-Taylor equation (α_PT) is one of the most important parameters which is used for evaluation of the equation. The results showed that the value of the evaporation coefficient in the main equation (1.26) for the Sistan region is too low and should be corrected. Its correction value varied between 1.02 and 6.11. The average value of α_PT was equal to 2.16, which is 71% different from the default value (1.26). Also, a regression relationship between wind speed and α_PT was presented. The results show that the amount of evapotranspiration obtained using the correction factor based on the wind speed (α_(PT-U2)) has the best results.

Considering the importance of water in the agricultural sector, it is necessary to know the usable or effective amount. Therefore, in this research, using remote sensing and implementing the Surface Energy Balance Algorithm (SEBAL) on 28 images from Landsat 8 for the crop years 2014 to 2022 in During the growth period of dry wheat in fields of Khomein city, the rate of evapotranspiration and effective rainfall were estimated. The accuracy of SEBAL has been evaluated with Penman-Monteith and pan evaporation methods, and then the results obtained with experimental methods of effective rainfall estimation have been compared and their relative error (RE) has been estimated. The results showed that the USDA method with a RE of 12.2% had the lowest error and the FAO with a RE of 60% had the highest error compared to the SEBAL.

Conversion of organic matter of municipal waste into compost and its use in agriculture, in addition to reducing pollution and improving the quality of the environment can be effective in improving soil fertility. In this study, the effect of different amounts of this type of compost on yield components, grain yield, oil percentage, nitrogen use efficiency and green water use efficiency of winter canola was investigated under a randomized complete block design with 6 treatments and 3 replications. The required experiments were conductrd during a canola growing season in a research farm in Sari city in Mazandaran province. Experimental treatments were compost application at the rate of 10 and 25 tons per hectare (C10 and C25), chemical fertilizer consumption at the rate of 500 kg per hectare (F500), two combined treatments at 0.5 tons of compost per hectare with 400 kg of chemical fertilizer per hectare (C0 .5F400) and 0.2 tons of compost per hectare with 400 kg of chemical fertilizer per hectare (C0.2F400) and control treatment (Control). At the end of the growing season, number of plants per square meter, number of pods per plant, number of seeds per pod, 1000-seed weight, grain yield, nitrogen uptake by the plant and canola oil content were measured and analyzed by SAS software. Also, the green water use efficiency and nitrogen use efficiency were calculated. Based on the results of analysis of variance, the treatments significantly affected on the number of lateral branches, number of pods, number of plants and grain yield was significant. The highest number of lateral branches per plant and the highest number of plants were obtained in F500 and C25 treatments, respectively. The number of pods and grain yield in F500 treatment were significantly different from its value in control and C10 treatments. The highest amount of seed oil was related to F500 treatment, which was significantly different from the control treatment. The highest green water use efficiency was related to F500 treatment followed by and C25. The use of 25 tons of compost per hectare increased the water use efficiency by 0.49 kg/m3 compared to the control treatment. Based on the results, municipal waste compost can be a good alternative to chemical fertilizers for winter canola cultivation in paddy lands.