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

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.

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.

One of the most important factors limiting peanut production is water stress and shortage in plant growth stages. Therefore, crop simulation models can be useful for predicting yield and assessing the effect of water stress on plant growth and development. In this study, the WOFOST model was used to simulate biomass yield, seed yield and water use productivity in peanut. As the peanut modeling research has not been well-addressed in Iran, thus, the purpose of this study is to evaluate the ability of WOFOST model to simulate the yield of two peanut cultivars under irrigation conditions at different growth stages in Astaneh Ashrafiyeh region.

This experiment was performed in the form of split plots in a randomized complete block design with three iterations. The main treatment included irrigation until the beginning of flowering time (I1), irrigation until the beginning of pod time (I2) and full irrigation (I3), and the sub-treatment included two cultivars of Jonobi peanut (C1) and Guil (C2). The WOFOST version 7.1.3 model was employed. In this model, crop growth simulation is based on carbon cycle, which models crop growth in three situations: absence of limiting factor, water limitation, and nutrient limitation.

Model evaluation showed that the root mean square between the observed and simulated values in estimating biomass yield for Jonobi and Guil cultivars in 2017 were 0.554 and 0.501%, in 2018 were 0.872 and 0.897%, and in 2019 were 0.449 and 0.466%, respectively. The root of the mean squares between the observed and simulated values in estimating seed yield for Jonobi and Guil cultivars in 2017 were 0.052 and 0.065%, in 2018 were 0.132 and 0.131%, and in 2019 were 0.101 and 0.096%, respectively. The mean values of relative error between the observed and simulated values in 2017, 2018 and 2019 for biomass yield were 10.2, 18.1, and 7.7%, and for seed yield were 4.35, 6.4 and, -4.5%, respectively; also the efficiency coefficient index ranged from 0.442 to 0.960.

Evaluation of simulated and observed values on biomass yield and seed yield demonstrate that RMSE, RMSEn and other statistical indexes were acceptable and the WOFOST model simulated two peanut cultivars accurately in different irrigation treatments.

Drought stress is the most important environmental factor limiting growth and development of plants worldwide. Growth reduction due to drought stress has been reported more than other environmental stresses. So far, many studies have been conducted on the relationship and correlation between important agronomic traits in rapeseed, which have introduced 1000-grain weight, number of seeds per pod and number of pods per plant as the most important traits with high correlation in yield. The results showed that the application of drought stress had an effect on the yield components of sesame and the cultivars that were more sensitive to drought stress had a greater decrease in their yield. The aims of this study were to investigate (1) the effect of consumed water volume as the independent variable on other variables of the study, and (2) the effect of total independent variables (yield components and other independent factors) on yield and water productivity (dependent variables). Finally, the most important independent variables affecting water productivity and the most sensitive variables to the amount of consumed water were determined.

In order to achieve aforementioned objectives of this study, an experiment was conducted during two growing season of 2011-2011 and 2010-2011 in Behbahan Agricultural Research Station. The experiment was conducted as randomized complete block design with 4 replications. The applied amount of water in drip irrigation was composed of four levels of 50, 75, 100 and 125% water requirement in main plots and two canola varieties Hyola 401 and RGS003 in sub plots were placed.

The results of the analysis of variance of the regression model showed that the higher absolute value of beta coefficients and t-statistic of each independent variable caused that variable to be introduced as the most sensitive independent variable affecting the dependent variable. Therefore, the independent variable of water volume, with beta coefficient of 0.860 and t-statistic of 13.246 had the greatest effect on plant height variable. In terms of yield, the studied variables (the number of pods per plant, the number of seeds per pod, and 1000-seed weight, consumed water volume, flowering period, growth period and plant height) showed 74.1% of variation (R2 = 0.741) of dependent variable (Yield of canola). The consumed water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on yield at the level of 1%. Among the dependent variables, the consumed water volume with the highest absolute value of beta -1.013 and t-statistic at -12.415 had the most significant effect on water productivity at the level of 1%. consumed of water volume with the highest absolute value of beta coefficient of 0.563 and t-statistic with 2.967 had the most significant effect on performance at the level of 1%. The results of Pearson correlation coefficient showed that the highest correlation between the number of pods per plant and seed per pod with both plant height were calculated to be 0.763 and 0.849, respectively, indicating that increasing plant height was effective in increasing the number of pods per plant and seed per pod.

The results of analysis of variance of regression model showed the effect on volume of consumed water as an dependent variable through other variables (number of pods per plant, number of seeds per pod, yield, water productivity, 1000-seed weight, flowering period, growth period and plant height). Results showed a significant effect of all variables at the level of 1%, except for the variable of flowering period which had a significant effect but just at 5%. The volume of consumed water by r= 66.2% on grain yield variation in the pods, had the most significant effect on yield components. Therefore, seed number in the pods received the most negative effect from reducing water consumption due to drought stress. With increasing the growth period of canola, water productivity showed a significant decrease at 1%. The results of Pearson correlation coefficient showed that grain water productivity had a negative and significant correlation at the level of 1% with all variables. The highest correlation between water productivity (r = -0.939) was calculated with volume of consumed water, which indicates the importance of reducing water consumption in increasing canola water productivity.

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.

Vineyard irrigation scheduling according to actual crop water requirements in the arid and semiarid regions is crucial to improve water productivity and maintain sustainable production. This experiment was carried out to evaluate water consumption (), crop coefficient () and water productivity of grapevine in the Malekan region located in the eastern Azerbaijan province. Two different field experiments in the form of complete randomized design with three replications were carried out during the 2020 growing season in two vineyards with trellis and creeping trained systems under subsurface drip irrigation (SDI). The grape's  was determined by measuring the components of the water balance equation including variations of soil moisture at different levels of the root zone. In order to prevent water stress, the soil moisture content was kept to the range of the readily available water during the whole growing period. The grape's  was 451, 394 mm, respectively, in the trellis and creeping system. Accordingly, water productivity was 5.83 and 6.1 kg per cubic meter of . Maximal daily  was 0.8–0.9, respectively, in the trellis and creeping system, implying that the maximum daily actual water consumption was lower than the calculated corresponding reference evapotranspiration.

Iran has a large area under cultivation, and reliable and timely crop yield forecasts are critical for making timely food supply decisions. As a result, several methods for estimating crop yield based on remote sensing observations have been published in recent years. One of the first and most critical steps in this study is to estimate real evapotranspiration using the SEBAL algorithm and high spatial and temporal resolution Landsat8 satellite images. The evapotranspiration obtained from satellite images was compared to evaporation data from the Zanjan synoptic meteorological station's evaporation pan, with statistical indicators (MAE=0/36, RMSE=0/45, R2 =0/94) indicating satisfactory results. The AquaCrop model and its four execution steps were used to simulate the product yield, and the necessary coefficients and data from the model were applied in satellite imagery calculations for each of these steps. For comparing CC data determined by satellite imagery and AquaCrop model, the RMSE, MAE, and R2 indices were respectively 11/06, 9/2 percent, and 0/94, and 0.633 and 0/359 mm/day and 0/95 for transpiration. The average yield was calculated to be 1/189 ton/ha in field pixels and its comparison with agricultural statistics and field studies (1-1/2 ton/ha) showed acceptable estimates for wheat yield in the area of this study.