javad behmanesh

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

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

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

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

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

Zeolites have been developed to utilize in agricultural sector due to their high water holding capacity and cation exchange. In this research, the effect of zeolite on evaporation from the soil surface and volume of drainage water in sandy loam soil was investigated. For this purpose, the HYDRUS-2D model was used to simulate evaporation and drainage and the required data was collected by conducting pot experiments. The studied treatments included four levels including zero, 5, 10, and 15 grams of zeolite gr kg-1 of soil. Four irrigation frequency were done on bases of readily soil moisture and the soil moisture values before and after irrigation events were measured using the Wet sensor in the depth and radial directions and recorded for 45 days. The initial amounts of evaporation and the volume of drainage water were determined by using the water balance equation and measuring the drainage water output, respectably from the pot at each stage. The results showed that increasing in the amount of zeolite cause to decries the amount of evaporation from the soil surface and the volume of drainage water in the treatment of 15 grams of zeolite by 17.11% and 20.6%, respectively. The coefficient of determination (R2) for HYDRUS-2D model simulation varied between 0.90 and 0.93. The value of the efficiency factor (EF) of the model, which shows the quality and the fitting of the observed and estimated data, is between 0.59 and 0.98 for evaporation and between 0.86 and 0.92 for the drainage water volume. These results show the high efficiency of the model in simulating the mentioned parameters.

Given that conventional ground-based methods require significant costs, time, and manpower, there is an increasing inclination towards using indirect methods such as remote sensing. Therefore, the aim of this research was to calculate dry biomass, harvest index, and grain yield of irrigated wheat using remote sensing in the Urmia Lake Basin. For this purpose, the Urmia Lake Basin was divided into six sub-basins, and the Radiation Use Efficiency (RUE) model was utilized. In this study, 1115 irrigated wheat fields were used. Monitoring results of the fields and the NDVI index indicated that irrigated wheat germination in the basin began on average from November 11th, and harvesting of this crop continued until July 21th. The values of correlation coefficient (R), Nash-Sutcliffe coefficient (NSE), normalized root mean square error (nRMSE), and coefficient of residual mass (CRM) in the calibration step were 0.90, 0.76, 15%, and 6.38%, respectively, and in the validation step, they were 0.92, 0.72, 14%, and 7.21%, respectively. Examination of the harvest index (HI) indicated that the average of this index in different sub-basins ranged from 0.40% to 0.45%, with an overall average of 0.42% for the Urmia Lake Basin. The results of biomass and grain yield assessment revealed significant differences in their values among different sub-basins. The highest biomass and grain yield were observed in the Zarrineh-Simineh sub-basin with averages of 12098 and 5261 kilograms per hectare, respectively, while the lowest values were observed in the West-Lake sub-basin with averages of 8651 and 3723 kilograms per hectare, respectively.

Due to population growth and Iran's location in arid and semi-arid regions of the world, the need for water and food has increased and as a result, the pressure on water and soil resources will be more than before. On the other hand, the risk of drying up Lake Urmia, which causes environmental problems in the region, requires macro-water planning for the region and the use of optimal cultivation pattern to deal with water scarcity. Therefore, optimal use of water preserves water resources and increases the quality of products. Today more than ever, increasing the production of strategic crops such as wheat requires the proper use of water resources. The main source of food for the Iranian people is wheat and related products, and any action that increases the yield of wheat due to limited soil resources, especially water resources, is important and necessary at the same time. In recent years, significant advances have been made in modeling product growth and development using mechanical models. Plant growth models are increasingly used in the analysis of agricultural systems and simulate the plant's response to growth factors using mathematical equations. The AquaCrop model is one of the dynamic and user-friendly models developed by the FAO. The AquaCrop model receives information about farm, plant, soil, irrigation and climate, and ultimately predicts important parameters such as crop. Wheat yield simulation allows efficient management and better planning under various environmental inputs such as soil and water. To achieve higher accuracy and less model error, field parameters must be properly calibrated by the model to achieve proper performance. Also, calibration of the model, if not done correctly, causes a high error prediction by the model, which leads to incorrect management, water loss, plant drought and other cases. Therefore, using a model that has accurate and close prediction to the AquaCrop model and requires fewer input parameters is essential, which saves time, reduces costs and eliminates calibration errors. However, this model requires relatively large input parameters and is a time-consuming model in the presence of multiple scenarios.3

In recent years, smart models have been able to show high accuracy and become reliable models. Therefore, in the present study, to solve this problem and develop a model with less input data, using the ANN, SVR and SVR-FFA intelligent models and creating 440 scenarios in 2 farms, the performance of the AquaCrop model was compared.99WestW2 farm is located in Miandoab city and has a yield of 6.588 (ton ha-1) and WestW10 farm is located in Mahabad city and has a yield of 5.05 (ton ha-1).

The results of the model are performed using 5 evaluation criteria of Correlation coefficient, Root mean square error, Nash-Sutcliffe coefficient, Wilmot’s index of agreement and, Mean absolute percentage error. The results of this study showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model could have the lowest error rate, so that for the yield index, the RMSE value for the mentioned farms was 0.033 and 0.069 (ton ha-1), respectively. The use of three models SVR, SVR-FFA, and ANN and their comparison with the AquaCrop model to predict wheat yield has been done for the first time in this study. The SVR model was able to show the highest accuracy after the SVR-FFA model. For 99WestW2 farm, it can reduce the error rate to 0.043 (ton ha-1) and for WestW10 farm to 0.077 (ton ha-1) and show good performance. The ANN model, after the SVR model, was able to show acceptable accuracy. The ANN model for 99WestW2 farm was able to reduce the error rate to 0.123 (ton ha-1) and for WestW10 farm to 0.094 (ton ha-1). Finally, the ANN model had a relatively higher error than the SVR-FFA and SVR models, respectively, and showed a relatively lower performance than the two models.

Finally, the intelligent SVR-FFA, SVR and ANN models, despite having the least number of inputs, were able to predict yield values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. For further studies, it is suggested that the ANN model be combined using the firefly algorithm (MLP-FFA) to increase the accuracy of the ANN model and make more accurate predictions of wheat yield.

Biochar as a porous material is rich in biological carbon in a stable form. Its structure is capable to store water and nutrients. Therefore, it is used for retention of such materials, especially in sandy soils. Biochar can improve water productivity in the agricultural sector in drought stress condition to achieve food security in the world. In order to study the effect of deficit irrigation and biochar on yield of coriander in a coarse-grained soil under water stress, this research was conducted using a factorial manner and a completely randomized design in the research greenhouse of Urmia University. The experimental treatments included three levels of irrigation: 100%, 75%, and 50% of the plant water requirement, and three levels of biochar application: 0, 2.5%, and 5% of the weight of each pot, which totally included 27 treatments. Irrigation was applied on the basis of plant requirement and soil water content below field capacity and the applied water was measured during the growing season. For each pot, quantitative parameters including weights of dry and wet plant, stem, leaf, and root were accurately measured. The results showed that the effects of irrigation levels and biochar on some measured parameters were significant at the probability level of 1% and 5%. The results demonstrate that by applying deficit irrigation, the measured quantitative parameters and plant yield decreased. The highest value of the parameters was obtained from the treatment of 100% water requirement and use of biochar up to the level of 2.5% of the soil. Therefore, applying suitable amount of the biochar as soil amendment improves coriander plant's growth and yield.

One of the new ways to save water and reduce irrigation costs is the use of superabsorbents. Since the zeolite nanoparticle has a larger specific surface area, it can absorb water and nutrients and release them when the plant needs them. A two-year greenhouse experiment was conducted to investigate the effect of different levels of irrigation and nanozeolite on growth indicators, relative water content and ion leakage in basil plants. The experiment was done as chopped plots in the form of randomized complete blocks with three replications. The experimental treatments included irrigation levels at three levels (I1 = 0.75 FC or underwatering, FC = I2 or normal irrigation and I3 = 1.25 FC or overwatering) and nanozeolite superabsorbent levels in four levels (Z0 without superabsorbent, Z5 five grams of superabsorbent per kilogram of soil, Z10, 10 grams of superabsorbent per kilogram of soil and Z15 was 15 grams of superabsorbent per kilogram of soil). The results showed that with the increase in drought severity, diameter and height growth and other traits such as the number of leaves, leaf surface, weight of dry matter (biomass) of basil, the irrigation level decreased significantly by 75Percent, leading to a decrease in vegetative traits, a decrease in the relative water content of the leaves. and increased ionic leakage, also the results showed that the use of nanozeolite has a significant effect at the level of 1Percent on the investigated parameters, so that the use of this superabsorbent in the amount of 5 and 10 grams per kilogram of soil in all irrigation treatments (I1, I2, I3) ) by increasing the height of the plant stem by 22 and 30Percent in two consecutive years, as well as increasing the diameter of the basil stem by 33 and 29Percent respectively, increasing the number of lateral branches of basil in two years by 90 and 89Percent, respectively. The increase in the number of basil leaves was 49 and 76Percent, respectively, and the growth of the leaf surface was also increased by 25 and 19Percent, respectively. The dry weight of the plant increased by 40Percent and 30Percent, respectively, and the relative water content of the leaves increased by 14Percent and 7Percent in two consecutive years. In electrolyte leakage, contrary to the previous parameters, the application of superabsorbent reduced the ionic leakage of basil, but with the application of low irrigation, we saw an increase in this parameter. This study showed that there is a statistically significant difference (P>0.5) in the yield of the crop among treatments Z5, There is no Z10.

In this study, the groundwater resources of Urmia Plain were evaluated for agricultural uses using a combined method that includes vulnerability, potential, and groundwater quality models. The vulnerability was evaluated by the DRASTIC method and the final map was classified into five classes with very high to very low vulnerability. The layers of NDVI, digital elevation model, drainage density, precipitation, and well density were considered as inputs of the potential model, and Analytical Hierarchy Process (AHP) was used to determine the weight of each layer. The potential map was also classified into five classes with very high to very low potential. The groundwater quality map was produced with three input layers including electrical conductivity (EC), sodium absorption ratio (SAR), and residual sodium carbonate (RSC) and weighting method. The combined map was produced by the maximum likelihood classification (MLC) method with five classes. The first and second zones with 40.31% of the area are suitable for agricultural uses, which include the northern, northwestern, and central semi-northern regions. The third zone with 22.90% of the area is in moderate condition and includes the central areas of the northern half and the southern half. The fourth and fifth zones with 36.79% of the area are in unsuitable conditions for agricultural use.

The aim of this research is evaluating the ability of the QUAL2Kw model in simulating the water quality parameters of Qarasu-River. The Qarasu-River is the main water supplier of the Sabalan reservoir dam which will be used for the drinking water consumption of Meshginshahr city in the near future. Therefore, it is very important to closely monitor the quality of the mentioned river for formulating policies and management plans.A section of 71.5 km was chosen as the study route. The field measurements were carried out in six stations in November 5, 2021 and 2022, for calibration and validation of the model. The value of the root mean square error (RMSE) for the parameters of flow rate, electrical conductivity, biological oxygen demand, nitrate, pH, dissolved oxygen, alkalinity and water temperature in the calibration stage were obtained 1.17, 505.8, 9.2, 0.22, 0.35, 109.85, 2.54 and in the validation stage were obtained 0.51, 212.88, 86.11, 10.79, 0.89, 5.11, 35/54 ,4/8 respectively. The results showed that the trend of changes in quality parameters along the studied route due to the discharge of wastewater treatment plant of Ardabil Industrial Town, discharge of wastewater from the Ardabil sewage treatment plant, the discharge of the effluent from the Samian Poultry Slaughterhouse, and the discharge of the effluent from the Ardabil Industrial Slaughterhouse into the Qarasu River were accompanied with extreme fluctuations. Nevertheless, due to the high power of self-purification of the river, the quality parameters of the river improved significantly in the downstream sections of the studied route. Also, two scenarios were defined with the conditions close to the reality. The results of the scenarios showed that the performance of the model is reasonable and the quality of the river water decreases with increasing the temperature of the point pollutant sources.

Water shortage is the most important factor in reducing the yield of agricultural products all over the world and this factor is more effective in arid and semi-arid regions. Iran is a country whose average rainfall is about one-third of the global average, and in terms of water resources, it is in dire straits. Therefore, it is necessary to help preserve limited water resources by optimal use, saving and increasing irrigation efficiency. This research was defined according to the consumption of the strategic product of corn and the importance of managing the country's limited renewable water resources, on top of that, the use of pressurized irrigation systems in the region and in the climatic conditions of Urmia.

In order to investigate different levels of irrigation on performance indicators and plant traits of sweet corn, under the subsurface drip irrigation system in the form of a complete randomized block design with four treatments of 20% over-irrigation, equal to the water requirement, 20 and 40% under-irrigation in four replicates in the research farm of the Engineering Department. water of Urmia University. The plant index values of stem diameter, leaf area, grain yield, number of leaves per stem, biological yield (wet), plant dry matter and cob yield during the growing season on specific dates and also on the day of harvest. was taken and evaluated.

The results showed that in general, the effects of under-irrigation and over-irrigation on biological yield, dry matter, uncoated cob yield, 300 seed weight, number of leaves and stem diameter were significant. Also, based on the results, it can be stated that with the reduction of water consumption, the cob yield decreases linearly. The highest and lowest cob yields were obtained in overwatering and 60% water requirement treatments with an average of 1.83 and 1.13 kg/m2, respectively.

In general, under-irrigation and over-irrigation have a significant effect on biological yield, dry matter, uncoated bale yield, three hundred weight, number of leaves and stem diameter. Also, based on the results, it can be stated that with the reduction of water consumption, the quantity of cobs decreases linearly. The highest and lowest cob yields were obtained in over-irrigation and 60% water requirement treatments with an average of 1.83 and 1.13 weight per square meter, respectively. According to the results of the research, with a 20% reduction in water consumption compared to full irrigation, only 5.02% of the biomass weight is reduced, apart from significance. Control (equal to water requirement) and 20% less irrigation in dry matter traits, uncoated cob yield, 300 seed weight, number of leaves and stem diameter, no significant difference was observed. The process of changes in the accumulation rate of biological yield of sweet corn initially started from 21 days after planting and after 6-9 days it entered the linear growth stage and then continued steadily. The highest weight of biological yield of sweet corn in different treatments was observed in 87 days after planting.

Due to the heterogeneous distribution of precipitation, predicting its occurrence is one of the primary and basic solutions to prevent possible disasters and damages caused by them. Considering the high amount of precipitation in Sardasht County, the people of this city turning to agriculture in recent years and not using classification models in the studied station, it is necessary to predict the daily precipitation parameter as accurately as possible. On the other hand, although the optimal performance of lazy algorithms and tree models has increased their use for predicting various hydrological phenomena, these algorithms have not been used in Sardasht County. Therefore, in this research, four models Kstar, M5P, learning algorithm with local weighting, and random forest are used to predict the daily precipitation of Sardasht Station. In this study, seven input parameters of average temperature, maximum temperature, average relative humidity, maximum relative humidity, average wind speed, maximum wind speed, and sunshine hours which were the same time as daily rainfall were used for the models. The comparison and evaluation between the input parameters showed that the sunshine hours was one of the most important input parameters, which played a significant role in the prediction accuracy of the used models. The obtained results showed that the M5P tree model had the best performance in the seventh scenario with the highest correlation coefficient (0.734 mm/day) compared to other models. In addition, the seventh scenario showed a high performance compared to the rest of the scenarios. Therefore, it can be said that increasing the input of the models has a direct relationship with their accuracy. In general, it can be said that the M5P tree model is suitable for modeling and forecasting daily rainfall in Sardasht City and it is recommended for future use.

The correct and accurate estimation of the river flow using different models is a significant issue in water resources research. In this research, two hydrometric stations of Sari-Qomish and Nizam-Abad located in West Azarbaijan province were used to accurately estimate the daily flow of Zarineh-Rood River. To reach this aim, Empirical Mode Decomposition (EMD) preprocessing algorithm was used to deal with the complexity and instability of time series data. EMD is a data analysis method for extracting signals in data generation through non-linear and non-stationary operations. In this research, the method of gene expression programming model and artificial neural network model were used. The results of the research showed that the performance of the gene expression programming model was equal and sometimes less than the performance of the artificial neural network. However, the combination of the two mentioned models with the technique (EMD) increased the accuracy of the model and reduced the error in simulating the river flow in Sari-Qomish and Nizam-Abad stations.

Planning for the best use of water resources in agriculture is inevitable. In this regard, the method of subsurface drip irrigation can, in addition to reducing the amount of irrigation water, increase crop yield. This study was conducted to investigate the different levels of under-irrigation and over-irrigation on yield indices and plant traits of sweet corn cultivar CHASE, under subsurface drip irrigation system in a randomized complete block design with four treatments of 20 Percent over-irrigation, equal to water requirement, 20 and 40 Percent under-irrigation in four Repeated. The results showed that in general, the effects of under-irrigation and over-irrigation on cob yield, 1000-seed weight, uncovered cob diameter, pod-free cob length, number of cob rows and number of seeds were significant. Also, based on the results, it can be stated that by reducing the amount of water consumption, the yield of the product decreases linearly. The highest and lowest cob yields were obtained in over-irrigation treatments and 60 Percent of water requirement with an average of 30.4 and 19.9 tons per hectare, respectively. Accordingly, with a 20 Percent reduction in water consumption compared to full irrigation, only 4.72 Percent of the crop is significantly reduced. This is while with a 20 Percent increase in irrigation water, the yield of cob increases by 10.54 Percent. 1000-seed weight in full irrigation and 20 Percent low irrigation treatments was 375.7 and 372.6 g, respectively. There was no significant difference between over-irrigation and 20 Percent under-irrigation treatment. There was a significant difference between the means of ear yield indices, length and number of seeds per ear row in all irrigation treatments.

Water transmission systems consist of storage reservoirs, pumping stations and pipelines. Pump station power failure, opening and closing of control valves and pipe rupture because transient conditions in pipelines. Transient flow principal equations such as momentum and continuity equations are mainly analyzed by the method of characteristics using engineering software and under boundary conditions. In this study, friction head losses were calculated by Bentley Hammer software under steady, quasi steady, unsteady and unsteady-Vitkovsky states and the results were compared. In Piranshahr water transmission system, the pressure changes were measured using a rapid and sensitive pressure indicator at the pumping station and under critical conditions of electricity power failure. Field data were compared to the Bentley Hammer output data using statistical methods. A comparison between the two, demonstrated the correspondence of time periods. Under unsteady-Vitkovsky method, the mean absolute errors were 5.13 and 5.45 mH2o for the initial stages and full period of data logging, respectively. For the maximum pressures values, the maximum difference was observed in the unsteady method (-14.62 mH2o) and the least values were associated with the steady and quasi steady method (-4.58 and -4.56m H2o respectively). As for the minimum pressures, the steady method yielded the highest (-5.55 mH2o) and the unsteady method the lowest difference (-1.52 mH2o). Increasing and decreasing the wave speed directly affected the maximum pressures while having an inverse impact on the minimum values. Based on the results of this study, the use of unsteady-Vitkovsky method in. Hydraulic modeling is recommnded

Dew point temperature is the temperature to which under constant pressure, air becomes saturated with water vapor. The goal of the present research is to evaluate the capability of Artificial Neural Networks (ANN (and Multivariate Adaptive Regression Splines (MARS) for estimating the dew point temperature using meteorological parameters in Khoy synoptic station located in northwest of Iran. Used meteorological data were including maximum air temperature (Tmax), minimum air temperature (Tmin), mean air temperature (T), relative humidity (RH), maximum relative humidity (RHmax), minimum relative humidity (RHmin), solar radiation (S), wind speed (W), station atmospheric pressure (Pa), actual vapor pressure (ea) and saturate vapor pressure (es). The mentioned parameters were entered to the used models with various combinations as inputs. To assess the models outputs results, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were employed. On the basis of the obtained results, the actual vapor pressure (ea) and minimum temperature (Tmin) were the most effective parameters in estimating dew point temperature. Also, the results showed that two used models have adequate accurate to estimate dew point temperature using meteorological parameters. However, the MARS had better performance than ANN in estimating dew point temperature. In general, among the used models and parameters, the MARS with single input of the actual vapor pressure and RMSE= 0.343ºcMAE= 0.480 ºcو R2 =0.991, results the best estimation for of dew point temperature in the test state.

Optimization of cropping pattern can be the best approach to reduce water consumption with maintaining maintain or increasing agricultural productions and consequently increase farmers' incomes. In this research, the cropping pattern of Mahabad irrigation and drainage network (with an approximation area of 12,000 ha) was determined using the PSO optimization algorithm with the objective of increasing the net benefit of the network based on the specified amount of delivered water and increasing water productivity. Optimization was done in 6 treatments of full and deficient irrigation and two strategies of deficient irrigation implementation (H1: in delivered water to the network and farm and H2: in delivered water to the network). Finally, the amount of water consumption, net benefit and economic productivity in the optimal cropping pattern were compared with the present condition. The results showed that in H1 strategy, with water consumption equal to the present condition, the amount of net benefit and economic productivity in the optimum cropping pattern increased about 16.7% compared to the current situation. In H2 strategy, productivity increased compared to the present situation in all treatments. Even by a reduction of about 40% of delivered water to the network, economic productivity increased about 37% compared to the present condition.

To determine the flow rate with different return periods, it is necessary to use the skewness coefficient with acceptable precision. Estimation of population skewness in different regions is improved by different methods such as weighted skewness and generalized skewness. The main objective of the present research is to determine the coefficients of skewness using different methods and present the best method for determining the mentioned coefficients in East Azerbaijan, Iran. East Azerbaijan has three different hydrologic regions. The north of the province is a part of Aras catchment area, the center of the province is a part of Urmia lake watershed and a part of the south of the province is a part of Sefidrood catchment area. In this research, different generalized skewness coefficients methods were used. In the present study, the generalized skewness coefficients were estimated using four methods including generalized skewness coefficient without considering hydrological regions, generalized skewness coefficient with accounting hydrological regions, unbiased skewness coefficient and weighted skewness coefficient for three hydrological regions and 62 observed stations during studied period. Based on the obtained results and on the basis of the analysis of RMSE values for different methods, the method of weighting skewness coefficient can be suggested as the selected method in East Azerbaijan with an average value of 0.104. Also, the results of calculating the Nash-Shutcliffe and the square of the mean squares of relative error coefficients showed that the weighting method is the best method for calculating the skewness coefficient.

The study of behavior of water level fluctuations is important for water balance due to the unbalanced distribution in the hydrological cycle. In present study, the groundwater level fluctuations of Urmia and Miandoab plains in two models SVM and Wavelet from the group of soft calculations have been investigated. For this purpose, information about the period years of 1381-1386 for both plains of Urmia and Miandoab by considering four scenarios of groundwater level -groundwater level with a delay of one month, groundwater level -groundwater level with a delay of two months, Groundwater level -precipitation and groundwater level -temperature. The simulation results were compared and the best model was introduced. The results of regression analysis showed that R2=0.9 and MSE=0.4 belong to the second piezometer well of Urmia plain and the third piezometer well of Miandoab plain in both SVM and Wavelet models for 3 scenarios of groundwater level -groundwater level with a delay of one month, Groundwater level was precipitation and groundwater level was temperature. Data on climatic variables of temperature, precipitation and groundwater level with a delay of one month as an independent variable and groundwater level data as a dependent variable formed the mentioned scenarios with 80% of educational data and 20% of test data. The results showed that the climatic variable of precipitation has a significant effect on groundwater level fluctuations. In general, the prediction results of Wavelet model were better than SVM model in investigating groundwater level fluctuations in Urmia and Miandoab plains. In the present study, an independent study of the mentioned models in order to simulate groundwater level fluctuations and compare each of the two models in their application.

Solar radiation is one of the most important climate parameters that has direct relationship with many hydrological and meteorological processes. Due to the lack of real time records of this parameter in meteorological stations, solar radiation estimation models based on meteorological parameters are usually applied in practical issues. The Angstrom-Prescott equation that works on the basis of the sunshine hours has been widely applied for calculating solar radiation values due to its simplicity and acceptability. So far, many studies have been conducted about calibrating coefficients of this equation based on the meteorological parameters. The purpose of this study was to evaluate the atmospheric air polutions effects on received solar radiation using the Angstrom-Prescott model. Calibration of the Angstrom-Prescott equation coefficients was performed using daily radiation data of Urmia and Tabriz stations during 3 years period (2014-2017) with taking into account the Air Pollution Index (API). Also,in order to compare the results of modified models with general equation of Angstrom-Prescott,different linear,exponential and logarithmic models were developed. In order to evaluate the models precision, RMSE, MABE, NSE and R2 indices were employed. The results of statistical analysis of the models indicated that the calibrated models of Urmia and Tabriz stations on the basis of the API and the logarithmic structure had the best perfermace to general equation of Angstrom-Prescott. The average values of the RMSE of the modified logarithmic model for Urmia and Tabriz were obtained 0.1263 and 0.1050 jcm-2day-1, respectively.

The importance of roots and the pattern of water uptake from soil is one of the effective factors in the growth and development of crops. The distribution of roots in the soil is different under various agronomic conditions and the application of appropriate management will lead to increased water uptake by the roots and increase water use efficiency and ultimately crop yield. For this purpose, a study was conducted to investigate the effect of subsurface drip irrigation (Sb), surface drip irrigation (S) and surface drip irrigation with plastic mulch (Sm) which has been considered in limited studies, on water use efficiency, yield and simulation of water movement and root water uptake by HYDRUS-2D model and its characteristics was conducted as a randomized complete block design with three replicated at Research Field of Agriculture Faculty of Urmia University in 2018-2019 growing season. Results showed that the treatment (Sm) and (S) had 36.6 tons per hectare and 2.1 kg per cubic meter, respectively. Also, the highest volume and weight of roots was obtained from treatment (S), respectively, 325.6 gr and 251.8 ml. Based on the simulations, it was found that changes in soil moisture in treatment (S) occurred more at the soil surface, but in treatment (Sb) these changes in soil depth and in treatment (Sm) at a certain depth that a small distance It can be seen from the surface of the soil.

The concern of the present research was to do a comparative study between the GEP, ANN and ANFIS models to estimate monthly global solar radiation. For this purpose, long-term (24-years) monthly data of global solar radiation (RS, MJ m−2), sunshine hours and air temperature (°C), from Tabriz synoptic station were used. To perform the artificial intelligence models, a new combination of inputs including monthly mean clearness index (KT), monthly temperature range (ΔT), relative sunshine hours (n/N) and extraterrestrial global solar radiation (Ra) were employed. Since the lowest values of MBE and RMSE (0. 13 and 1.97 MJ m−2 respectively) and the highest value of R2 (0.92) were obtained for ANN model, and therefore, the ANN model was selected as the best model to estimate the monthly global solar radiation. Using quarter-quarter (Q-Q) plots revealed that although the ANN model generally presents the best fit for monthly global solar radiation data, this model is found to be not successful in estimating the higher values of monthly global solar radiation data. Therefore, the application of ANN model is recommended for regions with lower solar radiation values. The performance of the ANFIS model was better than other models in covering the highest and lowest values (the first and fourth quarter). Therefore, it can be concluded that the ANFIS model gives more accurate results in the areas with the higher values of solar radiation. The findings also show that unlike previous researches which were carried out in daily scale, the performance of GEP technique for modeling monthly global solar radiation is satisfactory especially in the ranges of 250 to 800 MJ m−2. Thus, it can be inferred that GEP can be more powerful in modeling the phenomena which have low fluctuations or a limited range.

The correct and accurate estimation of river flow can play an important role in reducing the effects of flood damage. In this research, Gene Expression Programming (GEP) model and Bayesian Network (BN) were used to predict daily flow of Mahabad River in Urmia Lake Basin. Accordingly, four input models with a delay of one to four days used to estimate daily flow at time t+1 over a 23-years period and 75% of data was used to train the models and 25% of the remaining data was used for the test stage. Results showed that the best model in both methods was the input pattern with three-time lags. Also, based on the correlation coefficient (R), Root Mean Square Error (RMSE) and Nash-Sutcliffe (E) coefficient in the test stage of the GEP method with R=0.902, RMSE=2.71(m3s-1) and E=0.812 compared to the BN method with R=0.905, RMSE=2.679(m3s-1( and E=0.817 is more accurate. In general, both methods have acceptable accuracy and are they relatively similar, but because of the simpler modeling, Bayesian Network method can be used as an efficient method for predicting river flow.

In the present research, the climate change effect on variation of surface runoff of Zarrinehrud located in the Miandoab plain was investigated. In this direction, the scenarios including A1B, A2 and B1 via LARS-WG downscaling model and with applying the HadCM3 general circulation model and artificial neural network model in two different periods (2046-2065, 2080 -2099) were studied. For this purpose, the best combination of input parameters of the MLP artificial neural network model was selected to estimate the runoff among various meteorological parameters with time delay of zero and one day and runoff parameter with one-day delay. Then, the meteorological data predicted by the LARS-WG in the future were used as inputs for the selected neural network model and consequently the runoff was predicted. The comparison of results between observed and simulated data by LARS-WG model using different statistical and error measurement indices indicates that there is no significant difference between simulated and observed values. Performance analysis of the artificial neural network model indicates that the mentioned model has good and suitable accuracy to simulate the runoff variations in the studied area. The results showed that the average annual runoff in the period of 2046-2065 will increase about 4.62 CMS than base period and it will decrease about 14.7 CMS during the period 2080-2099 compared to the base period.

Nitrogen is one the most important nutrients in achieving optimal yield of crops. The objective of this study was to investigate the effect of clinoptilolite zeolite on nitrate ion adsorbstion and growth indices and cucumber parameters including, number, weight, length and diameter of the cucumber. This research was conducted at greenhouse of the Faculty of Agriculture of Urmia University and in completely randomized design experiment with four levels including 0, 5, 10 and 15 gr zeolite per kg of sandy loam soil in three replications with drip irrigation system.The results show that zeolite has positive effect of on reducing nitrate leaching at p < 0.01 level.Comparing the averages, it showed that there was a statistically significant difference between the control treatment and 15 g zeolite per kg of soil. Also, results showed that the application of zeolite on crop yield (Multiplied by fruit number in fruit weight), number of fruits, leaf area and plant diameter was significant at p < 0.01 level and also on fruit weight, fruit diameter, fruit length and plant height at p < 0.05 probability but had significant effect on leaf number. Increasing amount of zeolite cause to increase yield by 2.6 time greater than control treatment.

So far, the linear time series parameters are estimated, generally based on graphical and approximate methods. Therefore, the use of a new approach to increase the speed and ease of access to the best time series model can play an important role in using this method for predicting hydrological events. In this research, an optimization approach based on genetic algorithm has been used to estimate the ARMA time series parameters. A hybrid of Genetic Algorithm-ARMA method was used to drought prediction at three selected stations in the Urmia Lake basin, including Tabriz, Saqhez and Urmia, based on the SPEI drought index. The results showed that according to the BDS test, the model had the ability to predict the drought in all three stations and in all time scales. The Ljung-Box statistic was also used to evaluate the reliability of the prediction model. Its p-value at all stations and time-scales were greater than 0.05 which indicated the residuals of models were random and reliable. Also, the best time series model was calculated at different time scales and based on this, the SPEI index was predicted. The results of the prediction section showed that ARMA-GA hybrid method had a high accuracy at all long-term time scales of SPEI index at all the stations, but its performance was not suitable for short-term time scales.