عباس خاشعی سیوکی

Accurate estimation of saffron water demand is essential for sustainable water resource management in saffron-growing regions. This study examines the optimization of the Artificial Neural Network (ANN) model for predicting saffron water demand using the hybrid Coati Optimization Algorithm (COA). The performance of the ANN-COA model was compared with ANN, ANN-GA, ANN-PSO, ANN-MFO, Quadratic Regression (QR), Tree Regression (TR), and Pattern Regression models. Input data included temperature (minimum, maximum, average), wind speed, relative humidity, net radiation, and day of the year. The results showed that under conditions using all climatic parameters, the ANN-COA model achieved an R² of 0.995 and a Mean Squared Error (MSE) of 0.0001 for the Mashhad station, and an R² of 0.973 and MSE of 0.0005 for the Birjand station, indicating acceptable accuracy in predicting saffron water demand. Additionally, under conditions with limited climatic parameters, the ANN-COA model, using maximum temperature, wind speed, and day of the year, exhibited the best performance in predicting saffron water demand. Based on the findings of this research, hybrid neural network models show superior accuracy in estimating saffron water demand with minimal climatic parameters compared to other data mining models.

Water resources in the world are limited, and due to the increase in population and food demand, water consumption for agricultural production will also increase. In our country, due to limited water resources on the one hand and low irrigation efficiency and wastage of a large part of water resources on the other hand, the problem is twofold. Each product in the production chain has a specific temporal and spatial process. This chain has a local to global scope in which water is consumed and disappears. In the meantime, saffron is one of the most important products of the agricultural sector of Iran in arid and semi-arid regions, which is of great importance for earning foreign exchange in order to realize development programs aimed at increasing non-oil exports, and calculating the rial value of its water consumption in the production chain is important.
 
Torbat Heydarieh city is located at 59 degrees 12 minutes east longitude and 34 degrees 17 minutes north latitude at an altitude of 1333 meters above sea level. In this study, the water footprint in saffron production and the comparison of the cost price of saffron based on the amounts of green, blue and gray water in three important production areas, including Rokh Plain of Torbat Heydariyeh County R1, Zaveh Plain R2 and Central Plain of Torbat Heydariyeh R3, were calculated based on field data. The performance of the parameters (saffron yield, saffron water requirement, amount of unconventional water used in field irrigation, amount of water consumed from wells, amount of atmospheric precipitation, evapotranspiration, effective precipitation) that were involved in the research were calculated and collected using statistics from the Agricultural Jihad Organization, meteorological station data, farmers' information, and the country's plant water requirement system. The studied farms had a crop density (9 to 10 tons per hectare), farm age (3 to 4 years), and average farm cultivation area (1 to 10 hectares). Statistical analysis of the data was done using DSTAT software and graphs were drawn using EXCEL software. Also, comparison of means was done using Duncan's test at the level of 5 percent probability.
 
  The saffron yield (dry stigma) in R1, R2 and R3 regions was 5.7, 3.35 and 4.1 kg per hectare respectively, and significant differences were observed between different regions. The results of this study showed that there was no significant effect on the amount of water in the studied regions, but a significant difference was observed in the statistical results of green water and gray water between different regions, such that the highest amount for green water (26%) and the lowest amount for gray water (0%) was observed in R1 region. According to the results of saffron yield in R1, R2 and R3 regions, which are 5.7, 3.35 and 1.4 kg/ha respectively and the amount of water requirement in these regions, which is recorded as 280, 340 and 300 mm/day respectively according to field data, water use efficiency values ​​with significant differences were observed between the regions.
 
Among the Rokh Plain regions, with a value of 5.7 kg/ha, due to more favorable climatic conditions, sufficient water and suitable seasonal rainfall and different planting management, higher yield has been shown than in other research regions. The highest value for green water (26%) and the lowest value for gray water (0%) was observed in R1 region.Water use efficiency values ​​were observed with significant differences between the regions. The highest water use efficiency was observed in the R1 region with 2 gr/m3 according to the results.

Crop growth simulation models are extensively used for various agricultural studies, including optimal crop selection, irrigation management, and assessing climate change impacts. Among these models, the DSSAT (Decision Support System for Agrotechnology Transfer) is particularly prominent for its ability to simulate growth, yield, and other dynamics for 34 different crops. The DSSAT model integrates various components such as soil, weather, crop management, and genetic factors to provide comprehensive insights into crop performance (Jones et al., 2003). Accurate parameter calibration in this model is crucial for reliable simulations. However, the inherent variability and uncertainty in parameter values pose significant challenges. Uncertainty can arise from various sources, including measurement errors, spatial and temporal variability, and model structure. Addressing these uncertainties is essential to enhance the reliability and accuracy of the model predictions. The Generalized Likelihood Uncertainty Estimation (GLUE) algorithm offers a robust framework for quantifying and incorporating parameter uncertainty into model simulations (Beven & Binley, 1992).In this study, we focus on the application of the GLUE algorithm to the DSSAT model for cotton, aiming to improve the model's predictive accuracy by accounting for parameter uncertainty. We utilize observational data from different irrigation treatments to calibrate the model and evaluate the posterior probability distributions of the parameters.

The study used data from a 2009 experiment conducted at the Birjand University research farm. The DSSAT v4.5 model was employed, requiring inputs such as weather, soil properties, and crop management data. Four irrigation treatments (50%, 75%, 100%, and 125% of crop water requirement) were tested to evaluate the GLUE algorithm’s performance in estimating model parameters.

The results demonstrated that the GLUE algorithm effectively estimated the probability distributions of the DSSAT model parameters for cotton. The algorithm’s performance was compared with previous models lacking uncertainty assessments, showing significant improvements in simulation accuracy (Qasemi et al., 2019). The findings highlighted the importance of considering parameter uncertainty for better predictive accuracy and model reliability.

The GLUE algorithm, through Monte Carlo simulations, provides a robust method for assessing and incorporating parameter uncertainty in crop growth models like DSSAT. This approach enhances the model's reliability in predicting crop performance under varying conditions, which is crucial for agricultural planning and management.

If a country wants to move with a caravan of civilization and scientific development, it will have to use its natural resources. One of the most vital resources that life plays in the lives of humans and countries and the continuity of production is water resources. If the use of this vital material, which God has placed at the service of humans for free and unprofitable, would be desirable, many problems of the countries would be solved. One of the water supply methods due to drought problems, especially in the desert areas of the country, is inter-basin water transmission. Water transfer between different basins can be the source of many changes in the areas of origin and destination. In this research, using the ANP method, the criteria used are water quality, environmental factors, social security, sustainable development, utilization, cost of execution, water productivity, and equilibrium in supply and demand, for use in the OWA weight method It was made. Then, using the maximal entropy method in OWA, the weight of each criterion and the weight of the options were determined in two risky and risky situations. Finally, the use of conventional waters of the district (drinking water and sanitation) with a weight of 0.32 in risky and 0.41 in risky and with a relative deviation of risk of 0.11 was chosen as the best option.

Recent advancements in information and communication technology (ICT) and the Internet of Things (IoT) have created new opportunities for real-time monitoring and control of agricultural infrastructures. In this context, smart irrigation water management technology provides data and tools to assist users conserve water. This research focuses on the role of IoT in improving water resource management. Significant advancements in semiconductor technology and the diversity in sensors and smart devices have enabled the precise collection of extensive data to optimize water usage and manage resources effectively. Ultrasonic and water flow sensors, along with accelerometers for leak detection, play a crucial role in these systems. For control and connectivity, open-source software platforms like Arduino and Raspberry Pi are popular options for control units, Additionally, cloud platforms are utilized for efficient data storage and processing. This study demonstrates that IoT as an innovative and intelligent solution, enhances the potential for improved water resource management and efficiency. However, multiple challenges, including cost, energy consumption, data privacy and security, and wireless communications limitations, highlight the need for further optimization and development of these systems. This research serves as a foundational basis for academic and industrial investigations in optimizing water management systems in agriculture through IoT technology

Water scarcity is an increasingly critical global issue, causing a rise in arid lands and highlighting the need to address wasteful water usage in agriculture. Population growth, climate change, industrialization, and human conflicts have exacerbated water shortages, particularly in arid and semi-arid regions. According to the Falcon Mark index and the United Nations, Iran is experiencing water stress and a severe water crisis, threatening food security, economic development, public health, and national security. With over 92% of water consumption attributed to the agricultural sector, efficient water usage and reducing irrigation system losses are paramount. This study focuses on improving furrow irrigation efficiency by investigating surface irrigation efficiency and providing appropriate solutions.

The SIRMOD model, capable of simulating hydraulic surface irrigation, was employed to obtain the cut-off to Advance Time Ratio (CR) indicator. Diagrams based on soil texture, inflow rate, farm length, and the CR indicator were generated to enhance the design and efficiency of furrow irrigation systems. To ensure the accuracy of the simulation and results, the SIRMOD model was validated, and optimal CR indicators were determined for a furrow irrigation system with four different lengths and sandy-loam textured soils. A suitable field under the row irrigation system was selected, and the soil texture was determined using double-cylinder tests. Field operations included establishing forward and backward stations and smoothing the water path to prevent water from exiting the furrow. A pipe at the end of the furrow measured the output runoff using the volumetric method, and standard siphons were chosen to maintain the input flow rate. After turning on the siphons, advance and retreat times were recorded, and instantaneous runoff was estimated during irrigation. This process was repeated for three inflow rates: 0.5, 0.8, and 1.15 l s-1, with three repetitions for each rate l s-1.

The advance times in the first, second, and third furrows were 44.22, 45, and 42.88 min, respectively, while the water recession times were 293.1, 290.73, and 292.7 min, respectively. The relatively high water speed in light-textured soils and the short water regression times indicated the light texture of the farm soil and validated the test results. For an inflow rate of 0.5 l s-1, measurements revealed that half of the water volume entered the furrow along its length, while the other half exited as runoff. Simulation results for an inflow rate of 0.5 l s-1 yielded CR values of 8.37 for a 100 m length, 6.99 for a 120 m length, 5.41 for a 150 m length, and 3.31 for a 200 m length. For an inflow rate of 0.8 l s-1, optimal CR indicators were 8.0, 7.25, 6.19, and 4.63 for lengths of 100, 120, 150, and 200 m, respectively. At an inflow rate of 1.15 l s-1, the optimal CR indicators for lengths of 100, 120, 150, and 200 m were estimated to be 7.42, 6.53, 6.11, and 5.06, respectively.

The study's findings highlight a significant breakthrough in optimizing water usage in agriculture, a sector heavily reliant on water resources. By meticulously experimenting with different inflow rates and furrow lengths, the highest water application efficiency was attained with a specific set of parameters. An inflow rate of 0.5 l s-1, coupled with a furrow length of 200 m, resulted in an impressive water application efficiency of 83%. This efficiency correlates with a cut-off to advance time ratio (CR) value of 3.31, indicating a well-balanced water distribution system. The implications of this discovery are far-reaching, especially in regions facing water scarcity and agricultural challenges. By implementing these optimized settings, farmers can maximize their water usage while minimizing waste. This not only ensures the efficient utilization of a precious resource but also contributes to sustainable agricultural practices. Furthermore, the consistency in results, achieved through the SIRMOD model's validation, underscores the reliability of these findings, providing a solid foundation for future irrigation system designs and improvements. The expanded text emphasizes the significance of the study's findings, highlighting the efficient water usage and its potential impact on sustainable agriculture, especially in water-scarce regions. It also underscores the reliability of the results through the model's validation, providing confidence in the optimized settings for furrow irrigation systems. The best water application efficiency in the farm, 83%, was associated with the inflow rate of 0.5 l s-1 and the length of 200 m, and the CR equaled 3.31.

Due to the toxicity of metals, their side effects may cause serious damage to soil organisms, plants and living organisms. Many conventional methods for cleaning pollution from soil have problems and limitations. The electro kinetic method is a relatively new method for remediating soils contaminated with heavy metals, which has a high efficiency in removing heavy metals from the soil compared to other existing methods.

In this research, the removal of chromium (VI) from nitrate-containing soil has been investigated. At first, the soil was artificially contaminated with potassium dichromate at a concentration of 250 mg/kg, and then in order to investigate the effect of nitrate on the removal of chromium. The soil was also impregnated with calcium nitrate.

By conducting electrokinetic experiments, it was found that with the increase of the electric current introduced into the soil, the electric gradient passing between the electrodes increased, as a result of which the transfer of ions also increased under the influence of the phenomenon of ion migration and electroosmosis. The percentage gives 98.28%. Also, the use of EDTA complexing agent compared to distilled water increases the efficiency of the method from 95.57% to 97.80%. As a result, the use of EDTA electrolyte and increasing the voltage increases the removal efficiency.

Results revealed experiments showed that the use of the electrokinetic method has a negligible effect on the removal of chromium (VI) from soil containing nitrate.

The optimal operation of a multi-purpose reservoir is one of the complex and non-linear problems in multi-objective optimization. The limit of water resources and its non-uniform distribution have made the optimal management and utilization of water resources metaheuristic algorithms are suitable optimization methods, so in this study, water cycle algorithms, The gray wolf algorithms, and a combination of these two algorithms were studied in single-objective and multi-objective and compared to find the optimize operation model of Latian and Dez dams. After examining the effectiveness of the single-objective mode of water cycle algorithms, gray wolf, and combination mode using 8 standard benchmark functions and these algorithms were measured by ZDT functions, they were used to solve the problem of optimal operation in the Latian reservoir system. Also, the performance of the algorithms was evaluated using the performance indicators of the reservoir, and these algorithms were assessed using ZDT functions. The results showed that the hybrid algorithm provides a better answer in almost all functions of both the water cycle algorithm and the gray wolf. According to the results obtained from the operation model of Latian Dam, the hybrid algorithm with the value of the objective function of 0.011, provides 98.57% of the downstream needs, the total amount of deficiencies is 1.43%, and with the stability index of 97% and presents the convergence and release curve. It performed better than the two water cycle algorithms and the gray wolf.

Saffron, is one of the most popular traditional herbs and the most expensive spice in the world. Global production of saffron is estimated at 418 tons per year on 121,338 hectares. This spice is beneficial for human health due to having three main bioactive compounds: crocin, picrocrocin and safranal. In recent years, in addition to numerous researches on various medicinal effects, due to the decrease in its production, researches on factors affecting saffron production have increased. Consequently, conducting a bibliometric analysis of publications has the potential to provide insights into current areas of interest and potential trends for future research. The purpose of this research is to reveal the research topics of the saffron field in the WoS database during the period from 2000 to 2023, which was done with the benefit of scientometric techniques. The current research community includes 3936 articles. Data analysis and drawing of the intellectual structure of knowledge was also done using R Biblioshiny and VOSviewer software. Acta Horticulturae was the most popular journal for saffron research with 216 published articles, while the American Chemical Society's Chemistry journal was the most cited. The scientometric analysis identified the most productive countries and Iran was at the top with 1015 single country publications (SCP), 155 multi-country publications (MCP) and the highest frequency of 0.297. According to the current research, the research topics related to saffron that have recently become more advanced and can be used as a basis for the authors' research include antioxidant, double-blind testing (medical), oxidative stress, inflammation, type 2 diabetes, major depressive disorder, alpha amylase, are fatty acids. This study highlights the link between collaborations and scientific developments and trends in scientific collaboration and serves as a model to show emerging research directions in the field of saffron.

Due to the large volume of municipal and domestic wastewater application in agricultural and agricultural lands on the outskirts of big cities of the country, today, regional research in this field and observing the different effects of irrigation with wastewater is of great importance. In this research, the effect of effluent from the Birjand wastewater treatment plant on soil permeability was investigated. In the first step, soils with almost the same texture and with the characteristics of 1- land that has been irrigated for 8 years with the effluent of Birjand treatment plant and 2- land with the same texture that has not been irrigated with sewage until now, their permeability with potable water and using the method The double cylinder was measured, and to achieve better results, plot number 2 was irrigated several times with wastewater, and the permeability test was performed on it again, and the permeability rate was checked in each case. Then, the permeability of three soil samples has been compared. Irrigation with wastewater has affected the permeability of the land and caused a decrease in the rate of permeability so that the final rate of infiltration in the land that has been irrigated with wastewater for 8 years has decreased by 60% compared to the land with the same characteristics that have not been irrigated with wastewater. Also, the land that has not been irrigated with wastewater until now, after 4 stages of irrigation at intervals of 10 days, has a decrease in permeability by 11%, and the more time the land is irrigated with wastewater, it will decrease the permeability of the soil.

Saffron is a valuable plant that generally faces water stress in its life cycle. Therefore, in order to investigate the effect of corm priming of saffron on physiological and corm characteristics of this product under drought stress conditions, a split plot experiment was carried out in a randomized complete block design with three replications. The experimental treatments included two levels of irrigation based on 70 and 50% of field capacity as a main plot and six corm priming treatments including no priming (control), potassium nitrate, auxin, gibberellin, silicon dioxide nanoparticles, and hydro-priming as sub plot. The results showed that the increase of intensity in drought stress from 70 to 50% of field capacity caused an increase of 38.5%, 59.1% and 57.3% in the amount of chlorophyll a, chlorophyll b and carotenoids, respectively, and a decrease of 32.6% and 20% to the ratio of chlorophyll a/b and the amount of protein respectively. Priming of mother corn with two hormones of auxin and gibberellin, significantly increased the amount of protein and the highest weight of daughter corm was observed at gibberellin hormone treatment at the rate of 3.72 grams per plant. The two treatment levels of gibberellin and auxin hormones, under conditions of medium drought stress, significantly showed the highest number of daughter corms and gibberellin hormone, under medium drought stress conditions, significantly increased the diameter of daughter corm at the rate of 28 mm. In generally, corm priming of saffron with two hormones of gibberellin and auxin, is recommended to improve the physiological traits and tuber characteristics, especially in the conditions of drought stress.

The soil water curve is one of the most critical soil hydraulic characteristics. This characteristic is used to determine soil water in the field capacity point and the permanent wilting point (PWP) beside it has a vital role in the application of soil water models in the study of soil-plant-water relationships. This curve is known as the quality soil index which has an effective role in the explanation of agricultural, ecological, and environmental problems. Impressive and efficient management of soil and water resources, water flow and solute transport survey, soil pollution, and contaminant leakage into water sources are dependent upon the accurate estimation of soil water curve parameters. Moreover, this index has a functional role in applying numerical and hydrological models. On the other hand, to better identify and understand its role, different models were provided to describe this curve mathematically. The efficiency of these models depended on the accuracy of estimated parameters in the model structure that was defined. Soil water curve is known as a non-linear relationship that is used to describe the relation between soil and water content or degree of soil saturation. The soil water curve provides essential information for using irrigation methods and about soil resistance and soil mechanical properties. In this research, the performance trend of two meta-heuristic algorithms, including the differential evolution (DE) and particle swarm optimization (PSO), was studied to estimate hydraulic parameters of soil water curves based on the van Genuchten and the Brooks and Cory models in four soil texture classes; loam, silt loam, sandy loam, and sandy clay loam. Besides, this study evaluated the performance of the meta-heuristic algorithm to RETC software. This software has a non-linear square local algorithm. This study can evaluate the ability of the meta-heuristic algorithms to estimate parameters for exponential relationships and nonlinear models.

At the agricultural farm of the University of Birjand, a study was conducted to analyze soil water content in different texture classes. The research involved the random selection of four soil texture classes and the random sampling of 20 points from each class. The soil water content was measured using a sandbox and pressure plate device, covering a broad suction range of 0-15000 cm. In the first phase, soil water curve parameters were estimated for each soil texture using the van Genuchten model and the Brooks and Cory model in the RETC software. Subsequently, the Matlab desktop environment was utilized to apply meta-heuristic algorithms (DE and PSO) to estimate the soil water curve parameters based on the two models. An objective function was defined to minimize the Root Mean Square Error (RMSE) of the meta-heuristic algorithms' performance. Finally, the study compared the performance of the meta-heuristic algorithms (DE and PSO) with the RETC software in estimating soil water curve parameters based on the van Genuchten and Brooks and Cory models, using statistical indices such as RMSE and R2. The soil texture classes play a crucial role in influencing soil water content and nutrient retention, making them an essential factor in agricultural management and crop suitability. The study's findings can contribute to a better understanding of soil water dynamics and the development of improved agricultural practices.

The obtained results of the statistical indices (RMSE and R2) showed that the least value of RMSE was acquired by the differential evolution algorithm (DE) performance. The values of RMSE during the application of the DE algorithm as an estimated method based on the van Genuchten model were 0.0008, 0.0005,0.0004, and 0.0006 also based on the Brooks and Cory were 0.006, 0.006, 0.005, and 0.0005 in sandy clay loam, sandy loam, loam, and silt loam respectively. Also, the highest value of the R2 index was obtained equal to 0.995, 0.996, 0.994, and 0.994 by the utilization of the DE algorithm based on the van Genuchten model in the sandy clay loam, sandy loam, loam, and silt loam respectively. The values of RMSE by the utilization of the PSO algorithm based on the van Genuchten model were 0.0021, 0.006, 0.0057, and 0.006 in the sandy clay loam, sandy loam, loam, and silt loam classes respectively. The highest and lowest values of the RMSE and R2 indices by the application of RETC software were obtained equal to 0.017 and 0.912 (sandy clay loam), 0.01and 0.963 (sandy loam), 0.085 and 0.972 (loam), and 0.01 and 0.924 (silt loam) based on the van Genuchten model.

It could be concluded that RETC software has poor performance in the estimation of soil water curve parameters in all soil texture classes studied based on the van Genuchten and Brooks and Cory models. This trend represents the weakness of the local algorithms to solve multivariable problems where an exponential relationship exists between the variables and they are influenced by each other. On the other hand, the results show the meta-heuristic algorithms have sufficient ability to estimate parameters in multivariable problems. It could be concluded that the meta-heuristic algorithms have better performance in estimating the parameters of soil hydraulic models. The DE algorithm is the best method to estimate soil hydraulic parameters. The PSO algorithm has the nearest performance to the DE algorithm but the best performance to RETC. Finally, meta-heuristic algorithms are suitable options for estimating soil water curve parameters based on various hydraulic models.

reenhouse conditions, the use of biochar on yield and yield components of cucumber and to determine the effect of water stress and biochar on cucumber yield components in Birjand region. Application of biochar in soil increases the yield of cucumber (Cucumis sativus) of Storm cultivar and the combined effect of water stress and biochar modulates the effect of water stress on reducing crop yield.

Due to the conditions in the greenhouse and the type of cultivation (stacks), this study was selected as a factorial in a randomized complete block design with three dehydration treatments and four biochar treatments. Drought stress has three levels of 100, 75, 50% water requirement. And biochar levels of zero, 2, 4, 6 grams per kilogram of soil are selected The type of cultivation is in the form of a stack or a stack in which a stack is selected and divided into equal plots. Based on the number of treatments and replications selected, 36 plots are required, each plot being 80 cm long and 40 cm wide. After preparing a suitable culture medium, two seeds were planted in each plot with a distance of 2 cm because if the seed did not germinate or was destroyed, another one could be used as an alternative. Then, all plots were irrigated automatically until the plants became four-leafed, and pest control operations were carried out equally and equally among all plots.

The results show that biochar had a significant effect on cucumber height, cucumber diameter, cucumber weight, plant height, number of leaves and stem diameter at 1% level. It can be seen that water stress at the level of 1% had a significant effect on cucumber height, cucumber weight, number of leaves and stem diameter. But its effect on cucumber diameter index and plant height was not significant. In this experiment, the interaction effect of biochar and water stress at 1% level had a significant effect on cucumber height and cucumber weight and at 5% level on cucumber diameter and stem diameter, but its effect on plant height and number of leaves was not significant.

Drought stress was significant on cucumber height, cucumber weight, number of leaves and stem diameter at 1% level and had no effect on cucumber diameter and plant height indices. • The best performance in plant height, cucumber weight, number of leaves and stem diameter at I100 level. Consumption of 6 grams of biochar per kilogram of soil at the level of water stress, 100% water requirement had the highest yield on growth indices (cucumber height, cucumber weight) and at the level of 5% on stem diameter and cucumber diameter indices. The use of biochar on the indices of stem diameter, cucumber diameter, cucumber weight, cucumber height, number of leaves and plant height was positive and significant and the use of 6 g of biochar per kg of soil in all indicators has the highest yield for cucumber plant. The interaction effect of water stress and biochar was significant on cucumber height and cucumber weight indices at 1% level and on cucumber diameter and stem diameter indices at 5% level, but the interaction effect of stress and biochar on plant height and leaf area indices was not significant.

Given its location in the arid belt, South Khorasan Province faces an increasing demand for water due to the simultaneous growth and development of the agricultural and industrial sectors. This necessitates proper management tailored to the region's climatic conditions. Therefore, a quantitative and qualitative study of the region's water is essential for appropriate utilization and optimal management. Various indices exist to assess the quality of groundwater for drinking purposes, including GQI and WQI. In this article, a 10-year qualitative study of groundwater quality using WQI and GQI indices was conducted based on data from 29 well rings, which included parameters such as Na, Mg, Ca, Cl, SO4, TDS, HCO3, NO3, and pH. Raster maps depicting the concentration of these parameters were prepared using the Inverse Distance Weighting (IDW) method in GIS software. The GQI values for the Birjand Plain for the periods 87-90, 90-93, 93-96, and 96-97 ranged between 87-94, 85-93, 85-94, and 86-94, respectively. The WQI value for the period 87-97 ranged from 56 to 84. In conclusion, it can be inferred that the study area ranges from acceptable to suitable in terms of the GQI index, and from poor to very poor in terms of the WQI index.

Nowadays, due to the expansion of cities as well as the increase in the urban population, the lack of drinking water of good quality is always one of the most important issues for the country's water and sewage authorities, especially the people. Delivering water for consumption is one of the important activities of water transmission lines. In this context, optimizing the route of transmission lines is important. In this research, the plan to transfer water from the balance tank near Khoshab's agricultural and industrial lands and Roza Dam in Madhim City to the cities of Tabas Messina, Asadiyeh, and Gazik, has been obtained as the optimal route in terms of technical and engineering, economic and passive defense, and environment.  In this regard, the required information maps, such as topography, land use, rivers and roads, infrastructure, faults, geology, and population centers, were prepared in the form of raster maps in the GIS environment to obtain a cost map. Then, based on the Analytical Hierarchy Process (AHP), these maps were weighted and integrated, and the cost map was obtained. Finally, from the cost map, five routes were determined as the best routes, and to optimize hydraulically, these routes were entered into the EPANET software. The hydraulic design of the routes was done in this software by linking it with the multi-objective particle swarm algorithm. Two technical and economic objective functions of Pareto curves for these routes were obtained in a MATLAB environment. By analyzing these curves, route 2 out of these 5 routes has a more appropriate drop and cost and has been obtained as the optimal route for this water transfer, and this route has a length of 112 km.

The accuracy and efficiency of the analytical and numerical models to describe water flow in soil, in unsaturated environments are affected by input data uncertainty, model structure uncertainty, and hydraulic required parameters by the model. Parameter uncertainty has an impact on the model simulation by displaying uncertainty in the simulation results. Hence, the quantitative assessment of the parameter uncertainty and its influence on the model simulation is important in reducing simulation uncertainty. The Bayesian method is a common method for uncertainty analysis that has widespread application in science and engineering to reconcile the concepts of model structure with data (assimilation of input and model outputs, and inference of the parameters). Therefore, a Markov chain Monte Carlo (MCMC) algorithm based on the Bayesian inference to improve the computational efficiency of the analysis was used. The DREAM algorithm is one of the adaptive methods, the Markov chain sampling method which is known as an effective method in used soil-water models due to searching in vast space and solving complex models with a large number of variables. In addition, one of the main problems in using Bayesian inference for hydrological models is their nonlinear relations and using them in heterogenic conditions, DREAM algorithm has been developed to use Bayesian analysis in soil-water problems. Hence, this study has taken the efficiency of the DREAM algorithm as a global optimization method and convergence parser in sampling chain paths and posterior distribution of parameters. The HYDRUS model is a hydraulic model to study the soil-water processes that include nonlinear equations. In addition, center pivot irrigation is a modern method of water management that need to study using hydraulic models under various conditions. Hence, the main purpose of this article is assessment the role of the management method and environmental prevailing conditions in the uncertainty of hydraulic parameters and model structure in estimating water flow under a center pivot irrigation system in four-year alfalfa cultivation.

The profile was dug at 120 cm depth. The soil profile was divided into three layers and two soil texture classes. The physical-chemical soil properties were studied in each layer. Assessment of soil properties stated that exists a heterogeneous layer in this soil profile. TDR was used to measure soil water content before, after, and during every irrigation period. Soil water content was measured from 10 June to 11 September 2018 consecutively. The van Genuchten-Mualem equation was used to estimate soil hydraulic parameters and describe water flow in the HYDRUS model. The HYDRUS model is coupled with the DREAM algorithm to evaluate parameter uncertainty and the model structure uncertainty based on measured soil water content data using TDR in every three categorized layers. In this article the p-factor, d-factor, and S and T indices were used to evaluate parameter uncertainty, the model structure uncertainty, and model performance.

The qualitative evaluation of soil hydraulic parameters was compiled by the posterior distributions of parameters in every three depths. The parameters had a normal distribution, the model could be recognized the value of parameters, whereas the parameters didn't have a normal distribution and had high uncertainty. The “α” parameter had high uncertainty in every three depths, in other words, in two soil texture classes, this parameter compared to other parameters had high uncertainty. Along heterogeneous soil profiles, the "α", "θs", and "n" parameters were shown high uncertainty to the Hydraulic conductivity parameter of soil saturation. The value of p-factor and d-factor were obtained equal to 83.6 and 0.13 on the soil surface and 10 and 0.14 on the subsurface soil. Reducing the p-factor index in the lower soil layers explained the overlap between measured soil water content points with estimated soil water content. So, along the soil profile could be observed high uncertainty of soil hydraulic parameters under center pivot irrigation. On the other hand, increasing the d-factor index in the sub-surface soil stated increased confidence intervals which indicate the model structure uncertainty and the poor performance of the HYDRUS model in heterogenic conditions. Also, the value of two indices of S and T were obtained 0.3 and 0.76 for the surface layer and 0.88 and 1.4 in the lower soil layers respectively. The values of S and T indices stated the ability of the DREAM algorithm to reduce parameter uncertainty and the model structure uncertainty in soil surface whereas the trend of changes in the two indices explained Asymmetry of the confidence interval with respect to the measured points and the pre-estimation of the model in the lower soil layers. Therefore, the trend of the d-factor, S and T indices showed the influence of the mathematical-physics concepts in the HYDRUS model structure in the heterogenic layer and unsaturated conditions. The research results stated the ability of the HYDRUS model in describing water flow under center pivot irrigation as a novel method of managing water sources, especially in arid and semi-arid areas. Even though, the results of the assessment indices showed decreasing model performance in the lower soil layers.

The results of soil profile indicated the effect of parameter uncertainty and the model structure uncertainty in soil moisture estimation affected by management and environmental conditions. In addition, the results showed the ability of the DREAM algorithm to simultaneously evaluate the uncertainty of the parameters and the model structure in order to increase the accuracy of the HYDRUS model under the applied conditions. Also, in this study, the DREAM algorithm indicated the role of the heterogeneous layer in parameter uncertainty and its effect on the accuracy of the model performance. The DREAM algorithm is a practical and management option to evaluate the HYDRUS model during the application of the center pivot irrigation method at the farm level. So, this is an appropriate option to study the efficiency of the HYDRUS model using modern methods in agricultural practices. Moreover, to survey the efficiency of hydraulic models under the prevailing conditions could be used the ability of the DREAM algorithm based on the Markov chain.

The Qanat is the lifeblood of the villages. This beneficial structure for all, whose invention is attributed to Iranians, has led to the emergence of valuable indigenous knowledge in the water management debate. Iranians, who are famous for their intelligence, hard work and innovation, have left a structure as a memory since long ago, and the stability of this structure has put a seal of approval on all their merits. The values of Qanat as a sustainable and environmentally friendly structure are not hidden from anyone. Preservation of this precious lasting heritage is the least thing that causes the efforts of the past not to be lost. One of these works is the registration of management terms in the distribution of qanat water in different regions.In this research, the management structure of water distribution in Khamsa aqueducts of Gol village, Khosef city, located in South Khorasan province, has been investigated. Information and terms related to Khamsa aqueducts have been collected and presented through interviews with village experts in this field. Words such as: bol, boltaz, dong, tas, finjan, nimroz, taqeh, gunjkhorden are among the local terms used in the studied village in this field. The results obtained in this research showed that the water distribution work of Khamse Gol aqueducts has been done so carefully and thoughtfully that it has an excellent management structure in water distribution.

Identifying the number of wells in groundwater level estimation is an important step in terms of reducing the maintenance cost and saving the cost of harvesting information. Principal Component Analysis (PCA) is one of the techniques that reduces data that plays a significant role in identifying low data. In this research, the average annual groundwater level of 51 wells in Neyshabour plain with a statistical period of 10 years (2010-2019) was studied using statistical analysis of the main components of the wells to determine the level of groundwater level in this plain. Using PCA, the relative importance of each well was calculated between 0 (for completely ineffective well) to 1 (for the very effective wells). The results showed that among the 51 wells in the studied area, 27 wells are considered as wells, and the remaining wells are considered as low-level wells. By eliminating 24 less wells, the estimated ground water level error in the studied area is 26% higher than that used for all wells. Also, to take into account the factor of time, changes in this method were done in two 5-year periods. The results showed that 42 wells were selected as important well during the first 5 years period (2010-2014), which was reduced to 35 wells during the next 5 years (2015-2019).

Qanat is one of the thousands of ancient relics that have been devised by Iranian scientists and engineers and is considered an important indigenous knowledge. The oblivion of Qanat terms and its local dialects in each region is one of the most important and valuable topics. One of the methods of utilizing water from wells is qanat, which eliminates a significant percentage of the water's need in the area, but this legacy is now being forgotten. Also, the oblivion of the terms and words used in this area is significant and high. In this research, an attitude about the local structure and terminology of the village of Chahkand Mood in southern Khorasan province has been studied. It has also been tried to collect and submit Qannat information and terminology related to the same area with interviews from well-known experts and experts. Words such as fenjan, tas, sahm, nimran, taghe, chah tombideh, deilam, dahan, etc. are used in the village in the field. The results of this paper showed that the spread of customs and culture in Iran has led to the creation of a dictionary and dictionary of terms and dialects. These terms differ according to the cultural difference that exists in each city and village.

Considering the limited water resources and the high rate of evaporation, water crisis is one of the most important issues in the country. Due to the importance of Flixweed as a medicinal plant, its water requirement and its single and dual plant coefficients were investigated in the lysimeter laboratory of University of Birjand. For this purpose, six lysimeters (of the water balance type) with a diameter of 60 cm and a height of 100 cm were used. Inside all six lysimeters, Flixweed plants were planted with a density of 20 plants/m2, then, evaporation and transpiration were calculated using the water balance equation. Evaporation and transpiration of grass reference was measured directly by three lysimeters and its average was 322 mm. Then, plant coefficients were calculated for four stages of growth (initial, development, middle, and end). In this research, the average values of evaporation and transpiration, evaporation from the soil surface, and transpiration from the target plant were obtained as 202, 23, and 179 mm, respectively. Finally, the individual plant coefficients for the four initial, development, middle and final stages were 0.52, 0.68, 0.96 and 0.57, respectively, and the basic plant coefficients were 0.38, 0.59, 0.94 and 0.52 mm. In general, considering the climate of South Khorasan Province, Flixweed plant is recommended for cultivation due to its low water requirement.

The aim of this research is to predict the effects of future climate change on cotton yield in Birjand region. In this research, the BCM2 general circulation model under two release scenarios B1 and A1B in three periods (2025 to 2050, 2050 to 2075, and 2075 to 2100) was examined to predict future climate conditions and to generate daily climate parameters of the LARS-WG microscale model. Daily climate data obtained from LARS-WG output were used as inputs for DSSAT model (crop simulation model) to simulate cotton growth under future climate. The selection and preparation of a suitable plot of land for the implementation of the project was done in the beginning of October 2018. The intended experimental design was factorial split plots. The DSSAT model provided acceptable results for cotton yield and phenological stages, and this success was confirmed when the values simulated by the model were compared with the data collected from the field experiments. The maximum NRMSE is related to HW simulation, which is calculated as 9.7%. The value of this index for simulating the phenology stages is much lower and its value is reduced to 1.5%. The results of this research show that the DSSAT model can be a promising tool for predicting yield, leaf area, nitrogen accumulation, phenology and biomass of different cotton cultivars and other crops grown in the region. It seems that this study is useful and appropriate for farmers and their making decisions. The results of the simulations showed that due to future climate change and increase in temperature and carbon dioxide concentration in Birjand city, cotton yield will increase. On average, under all scenarios, the average yield of cotton will increase by 15% in the period of 2025 to 2050, by 15.44% in the period of 2050 to 2075 and by 18.15% in the period of 2075 to 2100. The simulation has shown that climate change increased cotton yield (from 14.73 to 18.53 percent) and reduced the length of the cotton growing season. The main reason for the increase in cotton yield can be attributed to the increase in carbon dioxide concentration.

Climate change phenomenon and warming earth as well as drought, have been reducing water sources in the world wide at the last decades, especially in arid, semi-arid regions. On the other hand, soil qualities factors including organic matter and fertility, in addition to soil salinity are the most critical issues in agricultural management and environmental conservation. Applying chemical fertilizers is one of the management methods in arid and semi-arid regions to amends decreasing water resources and supply required plant nutrients in recent decades. Although using fertilizers cause to increase production and crop yields, but meantime this management way leads to destroying the soil structures. Soil nitrate pollution is the most important challenge in agriculture and environment. The numerical models were used in predicting element concentrations under various climates, management methods, and study conditions (laboratory or field,). Estimated soil hydraulic and solute transport parameters are the most important part of applying numerical models to evaluate trends in soil element concentration changes under management methods. Hence, in this research soil hydraulic and solute transport parameters was predicated under center pivot irrigation using particle swarm optimization algorithm and the obtained results of optimization in soil water and nitrate transport along soil profile was used to survey soil pollution on the farm under four-year- alfalfa cultivation. In this study a farm with 3 hectares under center-pivot irrigation and four- year- alfalfa cultivation was chosen in an arid and semi-arid region. In the first step, a profile on the farm was drilled and sampling were done at the two depths (0-40 cm and 40-100 cm), finally soil physical-chemical properties were measured. In the second step, the particle swarm optimization algorithm linked to the HYDRUS model and parameters of soil water and nitrate transport process was predicated under management conditions. In the final step, the trend of soil water content, ammonium, and nitrate concentration changes were evaluated using the HYDRUS-PSO with the statistical indices (NSE and RMSE). The obtained results of the study soil profile showed that heterogeneous condition dominates the soil profile. Along the soil profile gravity of soil and sand content was changed in the specific. The results of the HYDRUS-PSO model were indicated different values of soil hydraulic and solute transport parameters in both studied two depths. The two indices of NSE and RMSE, were used to evaluate the calibration and validation of the HYDRUS-PSO model. The obtained results show that the HYDEUS-PSO model simulated soil water flow with NSE equal to 0.89 and RMSE equal to 0.001, ammonium transport with NSE equal to 0.98 and RMSE equal to 0.001 and nitrate transport with NSE equal to 0.94 and RMSE equal to 0.035 in the surface soil. Also, the evaluated model in the subsurface soil indicated that the HYDRUS-PSO model reasonably predicated soil water, ammonium and nitrate variation with NSE equal to 0.94, 0.96 and 0.97 respectively. Decreasing accuracy and the effectiveness of the HYDRUS-PSO model along the profile in predicting soil water content, soil ammonium, and nitrate concentrations were indicating the effects of heterogeneous condition on soil water in solute transport processes. Of course, irrigation and fertilizer methods are the other factors that influence the HYDRUS-PSO model performance. In addition, the field studies and uncontrolled conditions on the farm level are other factors to make a heterogeneous environment that causes non-uniform behavior of the model. Decreasing model performance in predicting soil water content could be related to sand content variation and the difference between the saturated hydraulic conductivity of the surface and subsurface soil. The trend changes in soil nitrate concentration in both two studied depths were indicating the amount of soil nitrate did not reach the level of toxicity and did not accumulate nitrates in the soil. Precipitation intensity, irrigation volume, and soil porosity are the most effective factors in the leaching soil nitrate issue. Based on the obtained results it could be mentioned that applied irrigating and fertilizer methods do not lead to accumulating soil nitrate. In addition, the accumulation of root systems and their extension are the other factors in the no accumulation of soil nitrate. Although, these factors had an effective role in the model performance and intensified the soil heterogenic. According to the obtained results it could be mentioned that the HYDRUS-PSO could simulate soil water and solute variations along soil profile under center pivot irrigation. Therefore, the metheuristic algorithms could use in predicting parameters as a powerful and practical tool to increase the efficiency of the numerical models in water and soil studies.

Investigating and identifying the actual amount of water used for different agricultural products is of particular importance, and considering such evaluations, appropriate solutions can be provided to reduce agricultural water consumption, which is of great importance. The water footprint index as a global index shows the actual amount of water consumed by products based on the conditions and climate of each region. On the other hand, the phenomenon of climate change is one of the most important environmental challenges that has a significant impact on water resources. Therefore, it is important and necessary to evaluate this phenomenon in order to predict its impact on water consumption in the agricultural sector. In this research, the simulation of climate parameters using the MIROCES2L model of the 6th Report of the General Oceanic Atmospheric Circulation under three scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 in Birjand plain has been done and then using those results to calculate the footprint prediction The blue water and green water traces of the strategic product of saffron were carried out in the Birjand Plain region. The results of the first part showed that the minimum temperature and maximum temperature in all three scenarios in the future (2050-2022) generally increased and the precipitation parameter increased in autumn and winter and decreased in spring and summer. In the second part, the prediction of saffron crop performance by NIO model showed that under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the future (2022-2038) on average 0.13, 0.21 respectively and 0.05 kg/ha has decreased compared to the observation period (2005-2021) and the results showed that with the increase in water demand in the future period, the water footprint, the green water footprint and the total water footprint of the saffron crop under the influence of climate change In the future period, it has increased by almost 2 times compared to the observation period. Also, the ratio of blue water consumption to green water in this product has increased in the future under all three scenarios compared to the observation period from 1.91 to 2.04. Therefore, despite the phenomenon of climate change, increase in temperature, increase in water demand, and finally increase in the footprint of water consumption in surface and underground water sources in the Birjand plain in the coming years, it is necessary to implement a suitable model of water consumption in the plain and To use appropriate and effective solutions to reduce the water footprint in the study area, the methods of reducing the area under cultivation, less irrigation, changing the cultivation pattern and changing the agricultural calendar should also be proposed and implemented.

Groundwater resources are the second largest freshwater source in the world after polar glaciers. Due to the importance of this resource in arid and semiarid regions, investigating changes in groundwater levels has an important role in planning and sustainable water resources management. The steady decline in groundwater levels has been observed in many parts of the world. For optimal use of available water resources comprehensive planning is necessary to make informed and accurate understanding of this important component of water resources. Neyshabour plain is one of the most plains of Khorasan Razavi province, which has an important role in agricultural production. Due to the reduction of the Groundwater recharge factors and Unallowable discharges of the resources, annually 74 cm decline in groundwater table in Neyshabour plain were observed. Hence the objective current study was forecasting the monthy groundwater changes in Neyshabour aquifer using fuzzy regression model. For this propose, the parameters affecting aquifer level including monthly precipitation and discharge detected and fuzzy linear regression approach is employed for to estimate groundwater level of Neyshabour Aquifer. The results represent that the most accuracy predicts was in august with R2, RMSE and MAE of applied model was 0.93, 6.05 and 6.01 respectively. In conclusion the results obtained from this study, based on R2, MAE and RMSE measures, showed the efficiency of newly developed formulas based on fuzzy regression method in estimate the groundwater table with proper accuracy.