تبخیر و تعرق

Evapotranspiration that includes evaporation from the soil surface and transpiration from vegetation, is one of the most important factors of water loss. Also, it is one of the most effective components of the water balance in a catchment in arid and semi-arid regions of the world. Therefore, it is an important physical parameter for water resource management and determining the plant water requirement in the agricultural sector. so far, many experimental methods have been proposed to calculate evapotranspiration, but, they are only suitable at the local scale and cannot be generalized to large areas due to regional dynamics and changes. whereas the accurate estimation of it is also very difficult and expensive, Therefore, in the present study, calculated the amount of evapotranspiration in the irrigated agricultural sector by using of landsat 8 satellite images and Surface Energy Balance Algorithm (SEBAL) in Nahavand Plain. in SEBAL algorithm by estimating all energy components on the earth's surface, including net radiation flux, soil heat flux, and sensible heat flux and using the energy balance equation, evapotranspiration is calculated. Remote sensing also has the ability to show evapotranspiration spatial distribution in addition to estimating the amount of its, because, it is the only technology that extracts factors such as surface temperature, albedo coefficient and plant index in a way compatible with the environment and is also economically affordable.

in this research, in order to estimate daily actual evapotranspiration of the irrigated agricultural and gardens of Nahavand Plain, extracted irrigated agricultural land use map by using of Sentinel 2 satellite images, Then, by using of Landsat 8 satellite images (13 images, from 13 April to 22 October during the growth period of the irrigated crops) and Surface Energy Balance Algorithm (SEBAL), evapotranspiration maps were obtained during the irrigated crops growth period in 2021. These Landsat 8satellite images are obtained by the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard the satellites and are widely used for water resource applications. The OLI sensor has 9 bands and the TIRS has two bands (10th and 11th are the thermal bands). Landsat images are at intervals of 16-days with a spatial resolution of 30 m. In all images, the imaging time was 7:21. Then, due to FAO- Penman monteith method is one of the most important and reliable reference methods in evapotranspiration calculations, in this research, this method was used as a basis for evaluation and comparison. Finally, in order to evaluate the efficiency of SEBAL method in estimating the actual evapotranspiration of irrigated crops and gardens in Nahavand Plain used RMSE function (Root Mean Squares of Errors).

According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration was related to images 2021.09.04 and 2021.08.19 which fall in the middle of the growing period of irrigated crops. In addition, the surface albedo is noted to be relatively low for these days with the high NDVI values indicating high absorption of radiation by the vegetation during this period. Net solar radiation is directly contingent upon the incoming longwave and shortwave radiations, both of which directly influence the surface temperature. Therefore, areas with higher surface temperatures have higher net solar radiation. The net radiation flux has a direct relationship with NDVI, Greenness, and wetness parameters and is inversely related to albedo, Brightness, and Ts. The vegetative moisture and sensible heat flux are higher on days with high NDVI. Higher NDVI values are an indication of an increase in vegetation greenness, therefore essentially an increase in evapotranspiration is expected to be observed. The lowest mean of actual evapotranspiration is related to the northeast of the case study; due to lack of sufficient surface and ground water resources and consequently the reduction of agricultural lands in this region. Finally, in order to investigate the accuracy of SEBAL method in calculating evapotranspiration, compared the results of SEBAL method with the results of FAO- Penman monteith method. The results of this comparison showed that the SEBAL method with RMSE 0.82 has appropriate efficiency for estimating evapotranspiration.

Due to an increase in population and shortage of water resources, especially in the agricultural sector, researchers are looking for ways to better manage the available water resources. Evapotranspiration rate is one of the most important components of the global hydrologic cycle and has a significant influence on energy balance and climate. the using of indirect methods such as remote sensing can be an important step for estimating the water need of agricultural products, planning and management the country, s water resources. Therefore, according to the position of Nahavand city as the agricultural hub of Hamedan province, in this study, the actual evapotranspiration of the irrigated agricultural land use using of landsat 8 satellite images and SEBAL Algorithm was investigated in this area. According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration in all of the investigated images is related to the southeast and center of the studied area. that the reason of this matter is location of this area in the main branch of the Gamasiab River and focused the irrigated agricultural and gardens in this area. The final results of this research indicated high precision of SEBAL algorithm in estimating evapotranspiration. Thus, the high accuracy and low error indicate that the SEBAL method could be aptly used to estimate evapotranspiration on a regional scale, in the respective time range. Also, the results obtained from the SEBAL method assisted in understanding the spatial and temporal changes in different stages of plant growth.

Evapotranspiration is one of the key components in calculating water balance and determining water consumption in agriculture for planning and management of water resources. Various methods have been proposed for estimating evapotranspiration so far, which depends on different algorithms and techniques governing them, their calculation process and structure are different. The purpose of this study is to compare the performance of the hydrological-vegetation SWAT model and global products of WaPOR and MOD16 based on remote sensing technique for estimating potential and actual evapotranspiration. Hence, Aji Chay watershed, including one of the important sub-basins of Lake Urmia, is selected as case study to investigate the purpose of this study. The Google Earth Engine platform is used to estimate evapotranspiration through global products, which have been developed based on different algorithms. Moreover, the SWAT model according to the streamflows data was set up, calibrated and validated for the watershed and the values of potential and actual evapotranspiration were simulated by the model for the period 2009-2019 with R2 = 0.95 and RMSE = 20 mm. The results of the SWAT model showed that the average simulated actual evapotranspiration has the highest agreement with the corresponding values of the main agricultural products presented in the national water document for the main plains of the watershed. Comparison between the models showed that the estimated values of potential and actual evapotranspiration both on an annual and monthly scales for the WaPOR have the highest agreement and consistency with the SWAT model compared to the MOD16.

amount of reference evapotranspiration using meteorological data, the period of 30-year of Ahvaz meteorological station was used. The reference method for calculating evapotranspiration was the Penman-Monteith method. In this research, four scenarios with different combinations of model input parameters were examined. Finally, the results showed that the best combination of input parameters for the gene expression programming model included parameters of minimum temperature, maximum temperature, relative humidity and sunshine hours. For these input parameters, R2, MAE, and RMSE statistics in the training phase are 0.964, 0.421 mm/d, and 0.507 mm/d, respectively, and in the test phase, they are 0.965, 0.419 mm/d, and 0.506 mm/d, respectively. was obtained Rainfall was the only parameter that showed the least effect on the rate of evaporation and transpiration, so that the increase of this parameter to the input parameters of the model caused a slight change (less than one percent) in the values of the evaluation indices. It seems that the reason for this is the low amount of rainfall in this area, so that in most months, the amount of rainfall is very small. Finally, the results of this research showed that the gene expression programming model can be used as a suitable tool with good accuracy for estimating reference evaporation and transpiration.

Solar radiation is one of the main inputs to crop growth models to estimate growth and yield. This study was conducted with the aim of evaluating the impact of radiation estimation accuracy by different models on the accuracy of simulating wheat evapotranspiration and yield by CSM-CERES-Wheat model in Razavi Khorasan province. The efficiency of simple models including Angstrom-Prescott, Angstrom-Prescott suggested by FAO, Hargreaves-Samani, Hargreaves-Samani suggested by FAO, Kermani, Hunt and Power have been evaluated in this study. The results of the radiation estimation for different models in the calibration stage showed that Kermani, Angstrom-Prescott calibrated, Angstrom-Prescott models with FAO recommended coefficients, Power, Hunt and Hargreaves-Samani gave the closest estimates compared to the measured radiation in Mashhad, respectively. In the validation stage of the calibrated models, it is found that the Power, Kermani, Angstrom-Prescott models had the most accurate estimation of daily radiation compared to the measured radiation in the study areas, respectively. Furthermore, the lowest difference between simulated yield and evapotranspiration of wheat using the observed radiation data and the estimated radiation data is obtained from the models of Power, calibrated Angstrom-Prescott, Angstrom-Prescott with FAO suggested coefficients and finally the Kermani, respectively. Considering the accuracy and extensive spatial coverage of the radiation data in the Power model, it is recommended to use it as the best radiation estimation method for use in the crop models.

Agriculture, especially rainfed agriculture, is one of the branches that is largely affected by the effects of climate change. The previous studies regarding the estimation of the length of the dry season wheat growth period in the country are mainly related to the length of the growth period of the past years or based on the growing degree day method, which is not very realistic. The purpose of this research is to estimate the length of the average growth period of rainfed wheat for the Tabriz Plain for the past 20 years (1993-2017) and the future conditions under the simulation of GCM models, for the next 50 years (1394-1444), using the meteorological parameters of temperature and rainfall. is. In the first step, the state of future conditions was evaluated under the GCM models of the sixth report of the Intergovernmental Panel on Climate Change (IPCC) and under the SSP1-2.6 and SSP5-8.6 scenarios, and then with the proposed method of the ecological zoning guidelines of the Food and Agriculture Organization of the United Nations (FAO), the length of the growth period of each period has been estimated and compared with each other. According to the FAO method, the length of the growth period is equivalent to the period of time when the rainfall is more than 50 percent of the potential evaporation and transpiration. It has been done. The results of comparing the length of the growth period of the base period and the state of future conditions under the simulation of GCM models showed that in the Tabriz Plain, the length of the wheat growth period has increased by an average of 16 days for the next 50 years, and the end of the growth period by an average of It will happen 12 days later.

This study with the aim of determining the effect of deficit irrigation and mulching on watermelon was conducted with a preliminary experiment in the form of split plots and a design of complete randomized blocks with three replications, in the Research and Education Center of Agriculture and Natural Resources in the south of Kerman province (Jiroft), in 2021‒2022. Drip irrigation as the main plot at three levels of 100, 70 and 50% of water requirement and mulching in three levels of crushed date palm leaf, black plastic and no mulch, as the sub-plot, were considered. As results demonstrate, the main and interaction effects of the treatments on stem length, leaf area, yield, water productivity, leaf relative water content, photosynthetic pigments, proline, total phenol, leaf superoxide dismutase enzyme and fruit length and width were significant. The main effects of irrigation and mulching on internode length, membrane stability index and vitamin C, and the main effect of the irrigation on the percentage of fruit dry matter, stem branches, and the main effect of mulching on fruit shape and soluble solids were significant. Fruit pH and total acidity, tasting and lycopene were not significantly affected by the treatments. The highest yield (60.1 ton/ha) was observed in the full irrigation and plastic mulch, and the highest water productivity (15.1 kg/m3) was recorded in the 70% irrigation and plastic mulch, but no significant difference with full irrigation and plastic and date palm leaves mulches. The findings showed that the effectiveness of plastic mulch on the watermelon growth and yield was higher with 100% and 70% irrigation compared to 50% irrigation. The date palm leaf mulch, despite being superior to the control in the full irrigation, had insignificant positive effects in deficit irrigation levels due to its high sodium content (1800 mg/kg).

Deficit irrigation has been mentioned as one of the effective techniques for increasing the water productivity of plants. Deficit irrigation of 50% caused a decrease in yield and water productivity of watermelon in Valencia region of Spain (Abdelkhalik et al., 2019). The role of mulching in compensating the negative effects of deficit irrigation has been confirmed in the literature (Diaz-Perez, 2023). This research aims at investigating the effects of deficit irrigation and mulching on the growth, yield and fruit quality of watermelon and the possibility of replacing plastic mulch with date palm leaf mulch.

Irrigation as the main plot at three levels of 100, 70 and 50% of water requirement and mulching at three levels of crushed date palm leaf, black plastic and no mulch, as the sub-plot, were considered. Crimson B 34 watermelon seeds produced by Seminis company, were planted on January 2021, in plots with the size of 13.5 × 7 m, on furrows and ridges planting system (the width of furrows and ridges were 0.5 and 4 meters, respectively). After planting, bow-shaped wires were put on the planting rows and a transparent plastic was placed as a tunnel on them.

Analysis of variance of the plant growth, yield, water productivity, and some physiological and biological traits displayed that the main and interaction effects of treatments were significant. Some traits such as pH, titratable acidity, fruit taste and shape were not affected by the treatments. The main and interaction effects of the treatments on stem length, leaf area, yield, water productivity, leaf relative water content, photosynthetic pigments, proline, total phenol, leaf superoxide dismutase enzyme and fruit length and width were significant. The main effects of irrigation and mulching on internode length, membrane stability index and vitamin C, and the main effect of the irrigation on the percentage of fruit dry matter, stem branches, and the main effect of mulching on fruit shape and soluble solids were significant. Fruit pH and total acidity, tasting and lycopene were not significantly affected by the treatments. The highest yield (60.1 ton/ha) was observed in the full irrigation and plastic mulch, and the highest water productivity (15.1 kg/m3) was recorded in the 70% irrigation and plastic mulch, but no significant difference with full irrigation and plastic and date palm leaves mulches was obtained. Reducing the irrigation water from 100 to 70 and to 50% of crop evapotranspiration or ETc (estimated by the FAO-Penman-Monteith method) in all three types of the mulch led to a significant decreasing in yield (Y), which was caused by the occurrence of water stress. The first reduction in water use (i.e., from 100 to 70%) increased the water productivity (WP) even though not significantly, but the second reduction in water use (i.e., from 70 to 50%) significantly led to a decrease in WP.

Overall, the 100%ETc treatment with black plastic mulch will probably have the best result in terms of watermelon yield unless water shortage, water price, and irrigation cost are noticeable compared to the crop price. In this case, 70%ETc irrigation with black plastic will be a priority in terms of WP. Of course, the cost of mulching and its possible environmental consequences should be considered.

Evaporation and Transpiration (including soil water evaporation and transpiration in plants) account for about 60% of the annual atmospheric precipitation on the earth. This process is the backbone of the hydrological cycle and a key element in Water Resources Management and the agricultural sector. The Soil and Water Assessment Tool (SWAT) and the Surface Energy Balance Algorithm for Land (SEBAL) are specialized models that address this problem. A SEBAL model is a tool for estimating the spatial distribution of actual evaporation and transpiration using satellite images such as MODIS, and Landsat 8. Although Landsat 8 images have a higher Spatioal resolution than MODIS images (30 m vs. 1000 m), their Tempoural resolution is lower than that of MODIS images (every 16 days vs. every day). On the other hand, daily MODIS images may not always be usable because of problems such as cloudiness. In addition, the interpretation of numerous satellite images is very time-consuming. In the current study, the evaporation and transpiration values in the catchment area of Karun River were obtained in three dry, normal and wet years (2019, 2020, 2021) using the recalibrated SWAT model based on runoff and crop yield and the SEBAL algorithm. Then the results of the SEBAL algorithm and SWAT model were compared with each other based on the water year conditions. In the second stage of the research, the results of the SEBAL algorithm and the range of changes of the main parameters of this algorithm in the plains of Ahvaz-Mlathani and Al-Baji basins were presented according to the ground data and using the MODIS images from the Terra satellite with a suitable time resolution and OLI sensor from Landsat8 satellite, which has a good spatial resolution.

One of the methods for groundwater monitoring is the calculation of the water balance, which provides important information about the status and changes of aquifers. However, estimating the components of the groundwater balance is challenging, which can reduce the accuracy of the results. This study aims to improve the accuracy of evapotranspiration estimation in the groundwater balance using remote sensing, which leads to better estimation of return flow and infiltration from rainfall. Consequently, the groundwater balance is calculated with higher accuracy. For this purpose, data from observation wells, precipitation, withdrawals, rivers, and aquifers in the Qomsheh study area between 1992 and 2016 were used to evaluate the groundwater balance. A conceptual water balance model and the WaPOR remote sensing product were used to estimate ET. Finally, the observed water balance was compared with the calculated water balance. The results showed that during the study period, 40.54 million cubic meters (MCM) of water infiltrated into the aquifer through precipitation, irrigation, and surface flow. In contrast, 262 MCM of water was withdrawn from the aquifer, of which 105.53 MCM returned to the aquifer. Also, 10.21 MCM of water entered as lateral flow and 0.73 MCM of water exited the aquifer. The groundwater balance was estimated to be 120.02 and 119.09 MCM using the computational and observational methods, respectively. Comparison of these two water balances shows that using WaPOR remote sensing products in estimating the components of the water balance reduces uncertainty and increases the accuracy of calculations in the Qomsheh study area.

Salinity is the main problem of the fields under irrigation in many regions of the world which has been caused the farmers encountered with some questions such as determining of the optimum depth of irrigation water by using of saline water, reuse of drained water from the agricultural fields and yield reduction that resulting in reduction revenue by using of saline water. Water shortage and unsuitable quality of irrigation water are two main factors which has restricted the crop production in many regions of Iran. Among environmental stresses, salinity stress is the main factor of crop reduction. Many physiological processes of plants such as germination, seedling growth, flowering and fruit ripening are decrease by increasing in salt concentration in soil and irrigation water. Deficit irrigation and soil or irrigation water salinity decrease the matric and osmotic potential in soil water content respectively, which are resulting in reduction of water absorption by plant roots. Camelina (Camelina sativa L. Crantz) is a plant with oil seed from Brassicaceae family that interest to its planting has increased in recent years. The causes of that are high tolerance of this plant to unfavorable environmental conditions and usage possible of it for many purposes. Appropriate usage of yield- salinity- water crop production functions can be improved agricultural water consumption management in regions with scarce of water resources. Crop production functions such as linear form, Cobb-Douglas, quadratic and transcendental function models were used to simulate relative grain yield of camelina. As the area study in this research namely Kashmar plain, has been forbidden for withdraw of groundwater and many agricultural deep wells in this area have high salinity water, in order to optimum management water consumption in agriculture, determining of the best water-salinity-yield production function for crops such as oil seed plants (canola, seasame and camelina) are essential. For the first time, camelina was planted in Kashmar natural resource and agricultural research center as pilot plan in 2021. As the compounded effects of salinity and water stress on camelina grain yield has been not investigated in this area, the objective of this research was to obtain the optimum crop production function as affected by drought stress and irrigation with saline water. A greenhouse experiment was conducted as a factorial in the form of a completely randomized design with three replications. The experiment treatments were four levels of salinity including drinking water S1=0.7, S2=4, S3=8 and S4= 12 dS m-1 and three levels of irrigation water including W1 (100% provided crop water requirement), W2=0.75W1 and W3=0.5W1 applied in a loamy sand soil texture with bulk density of 1.41 gr cm-3. Linear, Cobb-Douglas, quadratic and transcendental function models were used to simulate relative grain yield of camelina. In order to determine of optimum crop water production function, statistical analysis was done on obtained data and by determining of related statistics the role of each input was characterized quantitatively. For assessing of validation, the obtained functions, the statistics including maximum error (ME), root mean square error (RMSE), determining coefficient (R2), efficiency function (EF) and coefficient of residual mass (CRM) were used. The results showed that quadratic function model was the most appropriate model for estimating of grain yield of camelina under pot planting conditions in greenhouse. Marginal product (MP) and marginal rate of technical substitution (MRTS) indices of grain yield related to quality and quantity of irrigation water showed that with increase a centimeter of irrigation water depth (hypothesized salinity is constant), camelina grain yield increased 56.8 kg ha-1. Also, MP related to irrigation water salinity indicated that with increase a unit of salinity (hypothesized irrigation water depth is constant) camelina grain yield decreased 38.7 kg ha-1. According to findings of this research it could be said that 70 mm depth of irrigation water and EC less than 4 dS m-1 is suitable for achievement to optimum grain yield of camelina under pot planting conditions in greenhouse. It is noteworthy that further consumption of water by plant (almost 300 mm) and longer of growth period duration of plant in field compared to pot planting conditions in greenhouse caused to production function coefficients recommended in this study would not be applicable in field conditions and it is necessary that supplemental studies would be conducted for determining of optimum yield- salinity- water production functions of camelina under field conditions.

Rice is one of the most important agricultural products in the world. Rice cultivation in Iran has great economic and social importance. Mazandaran province is one of the most important rice production centers in Iran, accounting for 44% of the Iran's rice production. Due to the high consumption of water in the agricultural sector, the optimal use of water resources in agriculture is necessary.The water required by rice is directly related to evapotranspiration. The most reliable method of calculating evapotranspiration is using a lysimeter. The purpose of this experiment is to calculate the evapotranspiration of different varieties of rice using mini-lysimeters and to introduce the best variety for cultivation.

The experiment was conducted as randomized complete block design with three replications and eleven treatments, at Rice Research Institute of Iran (Amol) during the 2020. The treatments included different rice varieties in 11 levels T1: AR2, T2: AR6, T3: AHS, T4: DAH, T5: 1117, T6: 952, T7: 956, T8: E104, T9: S715, T10: Tarom, and T11: Shiroodi. Mini lysimeters with open bottom and closed bottom had a diameter of 60 cm and a height of 50 cm. Field soil was sampled from 0 to 30 cm depth and studied in the laboratory. The date of transplanting was similar in all rice cultivars The planting density of seedlings was 20×20 cm and 7 seedlings were placed in lysimeter. The lysimeters were placed 6 cm above the ground in the soil. The seeds were planted in a treasury and the seedlings were placed in the lysimeter after 30-35 days (after 3-4 leaves and height 25-20 cm). Water management in lysimeters was in the form of flooding (5 cm). Finally, variance analysis of the obtained data was done using SAS software and the mean of the treatments were compared through the least significant difference (LSD) test at 5% probability level.

The results showed that different varieties and lines of rice was effective on infiltration, evapotranspiration, yield, water consumption and were statistically significant at 1% level of probablity. The highest and lowest evapotranspiration with averages of 4938.7 and 3747 m3/ha belonged to T9 and T5 treatments, respectively. The highest and lowest yields with averages of 7773.7 and 2938.1 kg/ha belonged to T8 and T6 treatments, respectively. The highest and lowest values of deep percolation were observed with averages of 303.3 and 185.3 mm in T9 and T5 treatments, respectively. The highest and lowest amount of applied water with averages of 9972 and 7600.3 m3/ha belonged to T9 and T5 treatments, respectively. The highest and lowest water productivity with averages of 0.98 and 0.3 kg/m3 were related to T8 and T9 treatments, respectively. The results showed that the length of the plant growth period was different in the tested cultivars and lines and it was effective on the amount of water consumption..

Finally, in the normal conditions of the region, line E104 is introduced as the best treatment due to the production of maximum grain yield, while in water shortage conditions, the line 1117 is recommended for planting due to less water consumption.

Cumin (Cuminum cyminum L.) is an annual and herbaceous plant, with a vertical, round, narrow and branched stem, with a height of approximately 30-60 cm. This plant belongs to the Apiaceae family. This family is known for having plants with aromatic taste. Iran and some countries along the Mediterranean Sea are known as the primary origin for the cumin plant. In addition to Iran, cumin is cultivated in many countries such as Uzbekistan, Tajikistan, Turkey, Morocco, India, Syria, Mexico and Chile. About 300,000 tons of cumin seeds are produced in the world annually, of which China and Asian countries produce 70% and consume 90%. Short growing season (100 to 120 days), low water requirement and the possibility of rained cultivation, non-interference between cultivation and harvesting with other crops and no price fluctuation and proper economic justification are among the factors that interest farmers in cultivating this plant. In different regions, yields of 350 to more than 1000 kg of seeds are obtained from this plant, and 3350 cubic meters of pure water are needed for production.

This research was conducted in 2015 to 2017. The first year of the study included the collection and analysis of long-term climatic data of the region, and the second year included the implementation phase of the research. Analyzing meteorological data on the scale of decades and the cases of temperature, precipitation, wind speed, sunshine hours, relative humidity and evaporation from the pan were considered as criteria and by preparing the gradient equations, the rate of reference evaporation and transpiration was calculated. The required statistical information was obtained from 28 synoptic meteorological and climatology stations in Isfahan and some neighboring provinces. In the studies related to soil, apparent specific gravity and volumetric moisture content (field capacity and wilting point), soil salinity, soil texture and agricultural ability class of land in cultivation areas were considered. Soil-related information was used to calculate the soil evaporation coefficient (Ke), which describes the evaporation component in the trait (ETc). In fact, Ke is the basis for calculating the coefficient of reduction of evaporation from the surface layer (Kr) and the fraction of soil wet and exposed to air (few), and for its calculation, the presence of information related to soil characteristics is necessary. To calculate the soil characteristics, in addition to sampling from the fields in the research, the database of 1600 soil profiles in the soil and water research department of Isfahan province was also used.

The results showed that 18 cities in Isfahan province had cumin cultivation potential, which had a significant difference in terms of pure water requirement per hectare (5% level) and water consumption at different phenological stages (1% statistical level). In terms of water requirement per hectare, the cities of Isfahan province can be divided into three groups. Average water requirement per hectare in the first group (the cities of Golpayegan, Lenjan, Tiran and Karvan, Shahin and Shahr and Mime), the second group (the cities of Isfahan, Khomeini Shahr, Falavarjan, Shahreza, Kashan, Najaf Abad, Natanz), Mobarake, Dehaghan and Borkhar), and the third group (Aran and Bidgol, Ardestan, Khoor and Biabanak and Nain) were equal to 3000, 3240 and 3770 m-3 ha-1, respectively. The water requirement of the growth development stage in the cities of the third group was equal to 2029 m-3 ha-1, which was significantly different from the cities of the first and second groups (p < 1% level). According to the results, cumin might be a suitable plant for crop rotations in Isfahan province due to its low water requirement and tolerance to moisture stress.

The water requirement for cultivating cumin in various regions of the province is notably lower compared to many common crops, such as wheat, barley, and safflower. In 10 out of the 18 cities included in the study, significant water savings of up to 3,240 cubic meters per hectare can be achieved by optimizing water transfer efficiency. For cumin cultivation, this water conservation can even reach 3,000 cubic meters in cities with cooler climates. Surprisingly, in the hot areas of Isfahan province, including Ardestan, Nain, Khoor, Biabanak, Aran, and Bidgol, it is feasible to grow cumin with a water consumption of just 3,770 cubic meters per hectare.

In this study, drought was investigated on water use efficiency (WUE) in different climates and uses of Tehran province. To calculate the efficiency of water consumption, the products of Gross primary production (GPP), Evapotranspiration (ET) and Palmer drought severity index (PDSI) obtained from the MODIS meter are used. Then the trend of changes in the index of primary gross production, evaporation and transpiration, water consumption efficiency and drought in the period of 2001-2021 using linearity and Mann-Kendall and slope tests, and then the response of water consumption efficiency to drought in different climates and consumptions with Use. It was evaluated from correlation analysis. By dividing annual rainfall into potential evapotranspiration (PET), the Aridity index (AI) was calculated and climate classification was done with the help of this index. The results showed that the indices of evapotranspiration, primary gross production and drought increased by 81.15, 86.86, and 99.99% respectively, and the water consumption efficiency index decreased by 76.24% in this 20-year period. Is. These results were confirmed by the slope test. Examining the relationship between the efficiency of water consumption and drought in different climates and applications showed that in dry climates, agricultural land and bushland had a negative ratio of 91.86 and 78.93, respectively, and pasture consumption had a positive ratio of 51.96. Agricultural lands, pastures and bushland related to semi-arid climate have increased by 85.38, 66.22, and 64.27 percent, respectively, and forest use has decreased by 84.87 percent. In semi-humid climate, respectively, 47.53, 60.48, 73.41% of the study area has a positive relationship in the use of agriculture, pasture and shrub land, and 91.30% of the use of forests has a negative relationship. In the climate, the negative impact of drought has been in 66.04, 61.15 and 50.56 percent of agricultural, pasture and forestry lands. In general, according to the mentioned results, drought has had a negative effect on water consumption in Tehran province, and it can be said that the resistance of the ecosystem against drought is determined by our correlation between drought index and water consumption efficiency.

Soil and Water Research Institute (SWRI) has presented NIAZAB system to estimate and determine crops water requirement, water consumption, and irrigation planning at the scale of region, district, and plains in Iran. The current research was conducted in order to use NIAZAB system (including Tafteh, Pasquale and Reas methods) in determining the amount of water used for soybean cv. Williams, based on the inverse solution of the production function. The experimental treatments in this research included no fertilizer and application of of 150 kg N ha-1 and different irrigation treatments including 100%, 80%, 60%, and 40% of water requirement. Experimental design was split plot in the form of randomized complete blocks with three replications, and was conducted in Hajiabad Region, Hormozgan Province, in 2020 and 2021. The values estimated by the system and measured showed that, in the first year, the average relative error (ARE) in eatimation of evapotranspirationin by Tafteh, Pasquale and Reas methods were 7.49%, -0.05%, and 9.14%, respectively. In the second year, these values were 6.47%, -1.29%, and 9.06%, respectively. The ARE in the physical water productivity in the mentioned methods was -8.23%, -0.73%, and -10.08% in the first year, and -7.10%, 0.58%, and -10.07% in the second year, respectively. In Tafteh, Pasquale, and Reas methods, the root mean square error (RMSE) were 43, 35, and 49 mm, respectively, and the normalized root mean square error (RMSEn) were 0.093%, 0.076%, and 0.105%, respectively. Considering the results, NIAZAB system estimated the amount of irrigation water and evapotranspiration with acceptable approximation and it can be used for estimation of water consumption in the studied area.

Determination of reference evapotranspiration is the basis of all calculations of water requirement and is considered as the main foundation in determining water requirement, therefore, proper estimation of reference evapotranspiration is of great importance. Globally, systems have been developed that provide users with this information in a location-based manner. In this study, using the database of the country's water requirement system, the evapotranspiration rates of the reference plant were evaluated using meteorological data at Karaj station as a combination regression function. In this study, using SPSS software, the values ​​of different curves based on the parameters of relative humidity, temperature and sunshine were calibrated. The results showed that there is a significant relationship between transpiration evaporation and parameters of relative humidity, temperature, and sunshine. Therefore, two combined and multivariate regression models were calibrated using 7300 data of Karaj station and for evaluation using 15432 data in Alborz province. The evaluation results showed that the combined regression model with normal error of 29% and root mean square error index of about 0.5 mm, agreement index of 0.98 and efficiency index of model 0.94 had better performance. Therefore, this method can be used to determine the reference evapotranspiration in the stations of IRAN.

The effect of salinity stress on the quantity and quality of crop production highlights the importance of managing and reducing the damage caused by this stress factor in agriculture. Increasing soil salinity and decreasing fertility of arable lands is one of the major problems in saline areas. Cultivation of salt-tolerant crops which can increase soil fertility could be effective in the sustainable production of these lands. Studying photosynthesis and its related factors could provide appropriate physiological views in understanding plant behavior against salinity stress. The present study was conducted to assess the salinity tolerance of chickpea genotypes for cultivation in saline areas.

To evaluate the effects of salinity stress on photosynthetic criteria and yield of chickpeas, an experiment was conducted in 2018 at the research farm of the faculty of agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. The experiment was arranged as a split plot based on a randomized complete block design with three replications. Experimental factors consisted of salinity levels (0.5 and 8 dS.m-1) as the main plot and chickpea genotype (17 kabuli-type genotypes) as the subplot. Seeds were provided from the Mashhad chickpea collection of the Center for Plant Sciences, Ferdowsi University of Mashhad, Mashhad, Iran. Seeds were planted on March 11th and complementary irrigation was done in three growth stages of pre-flowering, flowering, and pod-filling. Sodium chloride was used to prepare saline solutions and the irrigation water rate was measured by water meter. Photosynthetic criteria including photosynthesis rate, evapotranspiration, stomatal conductance, and resistance and concentration of photosynthetic pigments were measured in the 50% flowering stage.

Results indicated that the lowest and highest reduction in the concentration of chlorophyll a was found in MCC65 (6%) and MCC83 (3.3 times increase), respectively. Increasing salinity level increased the concentration of chlorophyll b in MCC65 and MCC139, the ratio of chlorophyll a/b in MCC92, MCC139, and MCC776, carotenoids concentration in MCC77, MCC92, MCC313, and MCC679 and total pigments in MCCMCC77, MCC92, MCC298, and MCC679. Increasing salinity levels led to higher evapotranspiration in 14 genotypes except for MCC65, MCC95, and MCC298 in which 37, 54, and 63% decrease of this parameter was observed. Increasing salinity level increased photosynthesis rate in 7 genotypes of MCC12, MCC65, MCC72, MCC92, MCC95, MCC679 and MCC776 among which MCC95 and MCC679 showed the highest percentage increase (61 and 53%, respectively). The highest increase in sub-stomatal CO2 (51, 49, and 40 ppm) with increasing salinity levels, was found in MCC485, MCC776, and MCC313, respectively. An increase of 28 and 8% in stomatal conductance was found in MCC65 and MCC95. Stomatal resistance was only reduced in MCC77, MCC420, and MCC29. Higher salinity levels also led to 3.4 times, 67, 14, and 13% increase in instantaneous water use efficiency in MCC95, MCC65, MCC92, and MCC298, respectively. Biomass and seed yield declined in all genotypes by salinity. The highest seed yield was observed in MCC65, MCC77, MCC92, and MCC95 with 142, 148, 167, and 166 g.m-2 respectively in saline conditions. There was a negative significant correlation between seed yield and evapotranspiration (r=-0.43**), and stomatal resistance (r=-0.38**), and a significant positive correlation between seed yield and biomass (r=0.61**) and photosynthesis (r=0.24**) and stomatal conductance (0.36**).

In general, the results of this experiment indicated the diversity among chickpea genotypes for salinity tolerance caused by saline irrigation water. Studying some photosynthetic criteria in 17 kabuli-type chickpea genotypes under salinity stress showed high diversity in physiological responses of chickpeas to salinity stress which could be used in the selection and breeding of salt-tolerant cultivars. MCC65, MCC77, MCC92, and MCC95 were superior in most studied criteria in saline conditions and even performed, unlike the declining trend of the other genotypes. It seems that these genotypes could produce reasonable seed yield in salinity levels up to 8dS.m-1.

Water resources are strongly influenced by the hydrological cycle and the estimation of evapotranspiration as the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is nonlinear and very difficult to estimate in the sense that there are many parameters involved in its estimation. There are many methods to estimate evapotranspiration none of which is free from problems. Some of these methods, such as Lysimeter, are costly and time-consuming, and others, such as empirical methods only have local value. Therefore, the application of a method which is able to model this phenomenon based on its entity and with minimum data seems necessary. In recent years, the use of artificial intelligence models to simulate various problems has become very popular. In terms of performance, artificial neural networks are very efficient models whose computational speed is completely independent of the mathematical complexity of the algorithms or the method used. The purpose of this research is to compare artificial neural network models, neural network model optimized with genetic algorithm and experimental models in estimation of reference evaporation and transpiration using meteorological data in Ramhormoz synoptic station.

As mentioned above, the aim of this study was to compare the models of artificial neural network (ANN), artificial neural network optimized with genetic algorithm (ANN + GA) and experimental models (Hargreaves-Samani, Blaney-Criddle and Irmak) in estimating reference evapotranspiration compared to the results obtained from the Penman-Monteith FAO standard model by using Meteorological data in Ramhormoz synoptic station. For this purpose, meteorological parameters of Ramhormoz synoptic station were collected monthly during the years 2011 to 2018. This information includes: minimum temperature, maximum temperature, average temperature, wind speed at 2 meters, minimum relative humidity, maximum relative humidity and was sunny hours.Artificial neural networks are simplified models of the working system of the human brain, which are not comparable to natural systems. These models try to imitate human thought processes.The process of using artificial neural network models includes three stages of training, verification and testing. In the present study, 70% of the data was considered for training, 10% for validation and 20% for testing. Also, the stimulus function considered for the training and test phase is the sigmoid tangent.To extract better results from the artificial neural network model, it is necessary to optimize the parameters used. To determine the most optimal parameters required for the artificial neural network model, such as the number of layers, neurons and the weight of the layers, a lot of time is spent on their calibration using the trial and error method. For this reason, in this research, the combination of artificial neural network model and genetic algorithm (ANN+GA) was used in order to achieve the optimal parameters of the artificial neural network model. Minimizing the amount of simulation error as a function of the objective function and the number of iterations was considered as the stopping condition of the optimization algorithm.

Overall, the results showed that artificial neural network models to empirical models used to model higher correlation with the Penman-Monteith FAO model. In addition, among the neural network models used, the integrated neural network model with the genetic algorithm has a higher correlation with the Penman-Monteith FAO model. So that the value of R2 in Blaney Kridel, Hargreaves Samani, Airmak, ANN and ANN+GA models is 0.65, 0.819, 0.781, 0.969 and 0.973, respectively. The results of using scenarios using meteorological parameters as input for ANN and ANN + GA models showed that the highest accuracy of estimating reference evapotranspiration in both models is related to the scenario with input data such as temperature. The minimum is the maximum temperature, wind speed at a height of 2 meters, minimum relative humidity, maximum relative humidity and sunny hours, and the lowest accuracy of the model was in a scenario with two inputs of maximum temperature and minimum temperature. Among the experimental models, Hargreaves-Samani, Irmak and Blaney-Criddle models had the highest correlation with the standard Penman-Monteith FAO model, respectively.

Evapotranspiration is one of the important factors in the hydrological cycle and among the determining factors of energy equations on the earth's surface and water balance. In this regard, many researchers tried to estimate the amount of evaporation and transpiration with a suitable approximation using a cheap and easier method for different regions. The purpose of this research is to compare artificial neural network (ANN) models, artificial neural network optimized with genetic algorithm (ANN+GA) and experimental models (Blaney-Criddle, Hargreaves Samani and Irmak) in estimating reference evaporation and transpiration compared to the obtained results. It was done from the standard Penman-Monteith-FAO model, using meteorological data at Ramhormoz synoptic station. The general results of this research showed that the artificial neural network models have a higher correlation with the Penman-Manteith-Fau model than the used experimental models. In addition, among the used neural network models, the integrated neural network model with genetic algorithm has a higher correlation with the Penman-Manteith-Fau model than the artificial neural network model. Also, among the experimental models used, respectively, Hargreaves Samani, Irmak, and Blaney-Criddle models have the highest correlation with the standard Penman-Monteith-Fau method. In line with the results of the present research, it is suggested to compare the results of experimental models and artificial neural network with the data obtained from the evaporation pan.

The occurrence of climate change and variability, through changes in the supply and price of crops, overshadows the profitability of production in this sector and the share of food in consumer income. Therefore, social surpluses would fluctuate with physical and economic access to food. Accordingly, to make effective policies to adapt to new climatic conditions, accurate estimates of the welfare changes that will occur as a result of climatic events are needed. According to this approach, in the present study, the potential effects of different climatic scenarios on the cultivation pattern of the Hamadan-Bahar plain were investigated, and the impact of water resources, production, and food security in the agricultural sector of this plain was evaluated. The results showed that with the combined effect of climate change and variability, along with a 44% increase in agricultural consumption from the aquifer of this plain during the 20-year planning period, the present value of consumer and producer surplus and also, total economic welfare in the region's agricultural sector would decrease by 27%, increase by 16% and decrease by 4% compared to the baseline conditions, respectively. Therefore, it will be inevitable to adopt strategies to adapt to this phenomenon to mitigate its negative effects on food security in the region.

The amount of agricultural water consumption and water waste can be reduced by properly adjusting the irrigation schedule of plants based on the precise determination of the water requirement of each plant. This research was carried out to determine water requirement and crop coefficient of Barhee date palm. Three drainage lysimeters were applied to measure date palm evapotranspiration. The amounts of date palm evapotranspiration and reference evapotranspiration were calculated using soil water balance equation and Penman-Monteith method, respectively. The results showed that water requirement of 6, 7, and 8 years old date palm were 1493, 1613 and 1695 mm, respectively, while the amounts of reference evapotranspiration were 2070, 1950, and 1945 mm, respectively. The amount of date palm water requirement increased by 8%, for 6 to 7 years-old  and 7.8% for 7 to 8 years. The crop coefficient of 6 years old date palm varied in the range of 0.54-0.80. The minimum crop coefficient was in November, while the maximum was in June and July. For the 7 and 8 years old date palm, crop coefficient varied in the range of 0.62-0.97 and 0.47-1.01, respectively. In 7 and 8 years old date palm, the minimum crop coefficient was in November and the maximum was in August.

Many of the environmental problems are caused by the changes in the main components of the hydrological cycle. However, water balance modeling can help to better understanding the components of the hydrological cycle in order to develop appropriate management options. The purpose of this study is to calculate three important components of surface water balance using the WetSpass model and evaluate the model in Hamadan-Bahar Watershed located in Hamadan Province on a monthly time scale. The results of the model evaluation in the study showed that the coefficient of determination between the observed and simulated runoff in the calibration and validation period is equal to 0.79 and 0.83, respectively. Groundwater nutrition assessment was also performed according to manual calculations of the variable for 2012-2013. Then, the results of Kramer correlation coefficient between spatial distribution maps of runoff, actual evapotranspiration and groundwater recharge were investigated with input maps of the model. In general, due to the importance of evapotranspiration in water balance calculations, the evaporation and transpiration maps of the model were evaluated separately for different uses. The evaluation results confirmed the capability of the WetSpass model in simulating runoff, evapotranspiration and groundwater feeding with an acceptable accuracy. The results of spatial distribution maps of runoff, actual evapotranspiration and groundwater recharge indicate a high correlation between evapotranspiration component with land use (0.54), soil texture (0.45), evapotranspiration potential (0.42) and temperature (0.31). Also, these results indicate a high correlation between runoff components with land use (0.62) and soil texture (0.58), and average correlations between groundwater recharge component with land use (0.32) and soil texture (0.34). Therefore, land use and soil texture were the first and second factors affecting the distribution of surface balance components, respectively.

The SWAT Model has been accepted as a comprehensive surface water simulation model in estimating water balance components with the ability to study different management scenarios on water resources. In most studies, the SWAT Model is calibrated based on the accuracy of surface runoff estimation, and its comparison with field measurements in hydrometric stations. In a previous study, the performance of the SWAT Model in estimating runoff, evapotranspiration, and yield of wheat, barley, corn, sugar beet, alfalfa, apple, and grape crops in the Mahabad Plain was satisfactory and acceptable. The purpose of the present study was to use the SWAT calibrated model in estimating other components of water balance including deep percolation, lateral flow, return flow from the aquifer to the river in the same area. Assessing the components of water balance in the unsaturated zone, and its effectiveness in aquifer balance is important in managing the utilization of integrated water resources. The results showed that the calibrated model was able to estimate the difference between the deep percolation in the wetted year compared to normal years, and the difference in the infiltration in the wet year compared to the dry year under the influence of increasing rainfall. The results showed that the amount of deep percolation in the wetted year compared to normal and dry years has increased by about 23% and 43%, respectively. The average depth percolation is estimated 157.86 mm (17.1%). At the same time, the model has been able to estimate the difference in the amount of infiltration in different areas of the plain, and in different seasons of the year according to the type of land use and land management patterns. The amount of lateral flow has also increased in the wetted year compared to the dry year, so that the amount of this component has doubled. In this study, the average return flow from the aquifer to the river was estimated to be 20.9% (129.6 mm). Surveys have shown that the presence of shallow groundwater level has a significant role in creating surface return flow from the aquifer to the river. In general, the results showed that recharge due to rainfall and irrigation water from unsaturated zone is one of the most important input components of groundwater models such as MODFLOW in arid and semi-arid regions. The amount of recharge is important in more accurately estimating water level fluctuations. Given that the MODFLOW Model is not well able to estimate the recharge processes and lateral flow in the unsaturated zone. Therefore, the simultaneous use of SWAT and MODFLOW models in estimating the water balance components of the unsaturated zone and combining it with the groundwater model is important in studies and management of operation of integrated water resources.