مجله مدیریت آب و آبیاری
سال سیزدهم شماره 4 (زمستان 1402)

The Real-time flood control operation of a reservoir system can greatly reduce human and financial losses. In this study, a model for real-time flood control operations in reservoirs under the occurrence of floods in April 2019 and the uncertainty of hydrographs of inflows is presented. The presented model includes three modules: the flood that occurred in April 2019, Monte Carlo-HEC-ResSim simulation, and uncertainty analysis. The considered uncertainty factor is the hydrograph of the input to the reservoirs and side flows to the river. A Monte Carlo-HEC-ResSim simulation was performed according to the real flood of April 2018. In order to quantify the uncertainty of the HEC-ResSim model in the simulation of the water level of the dam reservoir, two 95 Percent confidence band factors (P-factor) and the band width factor index (d-factor) were used. Based on the results of the Monte Carlo simulation, the uncertainty in the water level of Dez, Karun and Gotvand dams due to the uncertainty of the hydrograph of the inflow was 0.037, 0.107 and 0.034 Percent, respectively. Therefore, the highest and lowest uncertainty in the water level due to the uncertainty of the inflow hydrograph is related to the Karun 1 and Upper Gotvand dams, respectively. In addition, the uncertainty bandwidth of the HEC-ResSim model in simulating the water level in Dez, Karun 1, and Upper Gotvand reservoirs was 0.151, 0.407, and 0.808, respectively. Considering that the maximum allowable amount of the accepted bandwidth factor is equal to one, the uncertainty bandwidth obtained in all the parameters (including the input and output values from the reservoirs, the volume of the reservoirs and the control points downstream of the dams) is accepted. This indicates the low uncertainty of the HEC-ResSim model in reservoir exploitation operations. The 95ppu values of the observational data in the 95 Percent confidence band for the water level of the reservoirs in the three studied dams were 100 Percent. A high 95ppu value indicates that the model has a strong physical and theoretical basis. For other parameters, the 95ppu values were low due to the low uncertainty of the parameters.

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.

The goal of this study, is to evaluate temporal (monthly) and spatial (in both point scale (40 synoptic stations) and region scale (30 river basins and four climate zones) global propitiation products with observational values in Iran as case study. To this end, first observational data (100 synoptic on a daily scale) and global precipitation products including ERA5, MRRRA2, GLDAS and TERRA (with different spatial resolution on a monthly scale) during the period of 1366-1398, were collected and extracted. Then, the stations and river basins were classified based on aridity index. Statistics criteria's such as coefficient of determination (R2), normalized square root mean square error (NRMSE) and mean oblique error (MBE) were used to compare the data products with observational data. The results of the criteria in point scale showed that the TERRA products in all seasons except summer and in the Hyper-arid, Arid and Semi-arid climate, with an average of 70 precent correlation coefficient higher than 0.5 (R) and 90 precent error rate less than 0.5 (NRMASE) showed a better performance than the rest of the products and in the Humid climate zones of the ERA5 product. In region scale, it also showed that TERRA product in Hyper-arid, ERA5, MRRRA2, GLDAS and TERRA products in Arid climate zones, ERA5 and TERRA products in Semi-arid climate and ERA5, TERRA and MERRA2 showed more suitable efficiency in Humid climate with an average of 80% correlation coefficient higher than 0.5 and 70 precent error rate less than 0.5. Consequently, the TERRA and MERRA2 product in point, region and climate has had good performance.

This study was performed to evaluate the AquaCrop model in the crop yield, biomass, crop, and biomass water use efficiency of greenhouse cucumber with two methods of the capillary wick and drip irrigation and three soil textures including Clay-Loam marked with (S1), Sandy-Clay-Loam (S2) and Sandy-Loam (S3) in three replications and two cultivation periods in spring 2018 period (1) and autumn 2018 period (2). Data analysis was done based on compound analysis of variance and mean comparison was done based on Duncan's multi-range test. The data of the first period was used for calibration and sensitivity analysis, and the data of the second period was used to validate the model. The root-mean-square error (nRMSE) and coefficient of determination (R2) were used to validate the model. Based on results in the drip irrigation system, the maximum yield, and water use productivity were observed with 79.64 ton/ha and 46.54 kg/m3 in T2S1 treatment, and in the capillary wick irrigation system, the highest yield, and water use efficiency were observed with 62.05 ton/ha and 44.76 kg/m3 in W2S3. The model investigation results revealed that the highest simulation accuracy was observed in W2S3 treatment so that the nRMSE index in crop yield, biomass yield, crop water use efficiency and biomass water use efficiency were respectively 2.80, 1.70, 1.10, and 1.70 percent were calculated. Due to the optimality of all conditions in the model, most of the estimated parameters were higher than the observational data, still, the increasing and decreasing trend of model simulation in estimating product performance values was completely consistent with the observed data.

Hydropower energy management is essential under climate change conditions. In this research, Slime Mould Optimization Algorithm (SMOA) is used to optimize the system of three hydropower reservoirs, and its results are compared with Genetic Algorithm (GA) to evaluate the performance of SMOA. First of all, SMOA performance is measured for Akley test function, which has been successfully performed. In the next step, the optimization is used on the three-reservoir hydropower system of Sazbon reservoir, Seymareh and Karkheh stream located in the Karkheh basin (Iran). The optimization of the hydropower problem is carried out for the baseline period of 1976-2005 and the future period of 2040-2069 under the RCP8.5 climate change scenario. The objective function is to minimize the lack of hydropower supply. For the optimization of the three-reservoir hydropower problem, the results show that the value of the objective function based on SMOA is close to the absolute optimal value, especially in the period of climate change. In general, the performance of SMOA to achieve the optimal value of the objective function in climate change periods is better than the baseline period and the solutions are more stable. In the comparison between SMOA and GA for the three-reservoir operation mode in the baseline and future period under the RCP8.5 scenario, the performance of SMOA in reaching the desired value of the objective function is much better, the speed of convergence is higher, the run-time is shorter, and the solutions of the objective function are more stable.

In this study, entropy theory and the modified Mann-Kendall test were used to monitoring the quality and quantity of groundwater in the Dez Plain regarding temporal and spatial changes, as well as the excess and deficiency of stations in the aquifer. The values of groundwater level (28 stations), electrical conductivity and chlorine (30 stations) in the period of 1999-2019 were investigated in this study. The results of the analysis of trend changes show major increasing trend in qualitative values (80% of stations) and major decreasing trend in groundwater level values (53 percent of stations). The multivariable regression model has an average error of 0.10 mg/liter in the simulation of chlorine values, 15 μm/cm for the simulation of electrical conductivity values, and 0.49 m in the simulation of groundwater level values. By examining the entropy theory, the values of the information transfer index indicated the average conditions of the groundwater level and relatively excess conditions of qualitative values in terms of information transfer in the region. The transmission of chlorine information in the southwest of the aquifer and the transmission of groundwater level information in the southeast of the aquifer are in a state of deficiency. According to the existing conditions regarding the quantitative monitoring and electrical conductivity monitoring of the groundwater, the Dez aquifer is in a good condition and the distribution of the stations is also suitable, but regarding the optimal monitoring of the aquifer in terms of transmission of chlorine information, the need for a station in the southeast of the aquifer is felt. By ranking the stations using net exchange information, the best stations were introduced in terms of quantitative and qualitative values.

Estimating crop evapotranspiration (ETc) in arid and semi-arid areas can be difficult due to the dynamic nature of this process across both time and space. In addition, obtaining on-site measurements for this variable can be very time-consuming and costly. This study aimed to develop a framework that accurately estimates the sugarcane crop evapotranspiration on a spatio-temporal scale. This was achieved using four machine learning (ML) algorithms (MLR, CART, SVR, and GBRT) combined with remote sensing (RS) data and meteorological variables. Also, to reduce the dependence on several meteorological parameters in conventional ETc equations, the performance of eight different experimental temperature-based methods and four modified Hargreaves & Samani equations was evaluated compared to the standard FAO-Penman-Monteith method. For this purpose, weather data were collected from Hakim Farabi Sugarcane Agro-Industrial meteorological station for three years (2018-2021). Nine combinations of input variables (RS data and meteorological variables) were designed based on the IGR method and then evaluated by the ML algorithms. The results showed that the highest accuracy of ML algorithms based on R2, RMSE, and MAE statistics was obtained in CART (0.99, 0.41, and 0.18) and GBRT algorithms (0.99, 0.65, and 0.26), respectively. Regarding temperature-based methods, Ivanov’s equation had the best performance with an R2 of 0.91, while Baier and Robertson’s equation had the weakest performance with an R2 of 0.78 when estimating ETc. Overall, the combination of RS and ML algorithms effectively produced more precise and reliable ETc values on both temporal and spatial scales.

This research, using the CE-QUAL-W2 hydrodynamic model, investigates and simulates the dissolved oxygen (DO) parameter in the Ekbatan dam reservoir under the influence of thermal stratification and temperature changes. Ekbatan Reservoir plays an important role in providing drinking and agriculture water in Hamedan city, so checking its water quality seems essential. The results show that the CE-QUAL-W2 model simulates well the temperature changes of the reservoir by considering the volume and geometry of the reservoir and meteorological parameters. Due to the fact that dissolved oxygen is affected by thermal stratification, the relationship between these two parameters has been investigated and the possibility of evaluating water quality through thermal stratification of the reservoir has been presented. The results show that thermal stratification in Ekbatan reservoir has periods of thermal stratification change and is complete in summer, while complete mixing occurs in autumn and early winter. These changes also affect the DO stratification in the reservoir. With a 68 percent increase in temperature, the DO concentration decreases by about 37.5 percent, and with a decrease in temperature, the DO concentration increases in the same proportion. In addition, the research results show that changes in air temperature have a significant effect on the concentration of DO in the reservoir. The change in temperature leads to a decrease in the density of water and a decrease in the solubility of oxygen in water, which in turn leads to a decrease in the concentration of DO in the reservoir water, and this issue is widely considered in environmental studies and water resources management.

It is necessary to evaluate irrigation systems in order to determine their optimal performance, modify and manage them. In this research, five drip irrigation systems in Salas Babajani city located in Kermanshah province were selected and evaluated based on the instructions of the American Soil Conservation Service (SCS) and Merriam and Keller methods. The results of investigations showed that the wetted area ranged between 14.89 and 33.49 percent of the total area. The minimum and maximum values of water distribution uniformity in the investigated systems (EUs) were determined as 29.49 and 62.56 percent, respectively. The values of lower quartile application potential efficiency (PELQs) and lower quartile application efficiency (AELQs) of the systems fluctuated between 26.83 to 53.3 and 29.48 to 62.56 percent, respectively, which indicate the poor performance of the systems in all fields. According to Langiller Saturation Index (LSI) values obtained from water sources in the fields under study, in two fields, there was a tendency for calcium carbonate precipitation, but in other fields, there was no risk of precipitation. In general, the poor performance of the systems were due to various reasons, such as: not installing a central control station or a suitable filtration system, insufficient wetted area, inappropriate type and number of droppers, clogging of the droppers, pressure difference in the systems, inappropriate pressure and its uneven distribution, inappropriate distance and duration of irrigation and finally the poor management of exploitation.

This research was conducted in order to investigate the interaction of different levels of irrigation and nitrogen fertilizer on the yield of Forage Corn in Khorramabad. The experiment was conducted as factorial in a randomized complete block design with three replications. The first factor consisted of four irrigation levels including I125 (125 Percent of water requirement), I100 (100 Percent of water requirement), I75 (75 Percent of water requirement), and I50 (50 Percent of water requirement) and the second factor consisted of three levels of nitrogen fertilizer including N400 (400 Kg/ha), N250 (250 Kg/ha), and N100 (100 Kg/ha). Based on the evaporation pan method, the total irrigation requirement for treatments I125, I100, I75 and I50 during the period were 761, 642, 524 and 406 mm, respectively, which were more than the amount of water requirement reported in the national water requirement document of plants.The result showed that the maximum amount of wet and dry matters, plant height, leaf area index which were 80 and 21.2 tons per hectare, 9595 mm2 and 220 cm, respectively, were obtained in I125N400 treatment, which had a significant increase (5 Percent) compared to the control treatment (I100N250). The minimum amount of these traits which were 30 and 10.64 tons per hectare, 7096.133 mm2 and 100 cm, respectively, were obtained in I50N400 treatment, which had a significant decrease (5 Percent) compared to the control treatment (I100N250). It was found from the results of this research that at the levels of I125 and I100, the values of wet matter and leaf area index were increased as the amount of nitrogen fertilizer increased. At irrigation levels of 75 Percent and 50 Percent of water requirement, increasing the amount of fertilizer did not cause significant changes in the measured traits. In this research, the maximum value of water productivity for wet and dry matters was obtained in I100N400 treatment with values of 10.9 and 2.91 kg/m3, respectively.

Flood is one of the most terrible natural disasters in the world, flood damages leave long-term damages in various sectors, especially agriculture. Hazard analysis and vulnerability assessment is one of the primary steps in flood disaster management. Although such studies less done in irrigation networks. In this research, hazard analysis and vulnerability assessment has been done for the irrigation network in the east of Shuaibieh plain. It is an important irrigation project of Khuzestan province, and its flood will cause irreparable damages to the farmers and residents. For this purpose, hydraulic simulation has been done in Hec-Ras-2D and for 25, 50, 100 and 200 years return periods. The extent, inundation depth and flood velocity were extracted from the model. Using the model outputs and with the aim of existing standards, flood hazard maps have been drawn. According to these maps, for each flood with a return period greater than 50 years, more than 50 percent of the Shuaibieh plain is at unsafe level for all people and structures. By using the prepared questionnaires from 42 local farmers and irrigation network operators, various vulnerability indexes (economic, social, environmental, resilience, and flood magnitude) were estimated and by Analytic Hierarchy Process of them, the vulnerability was assessed. Finally, with superposition of each vulnerability index map, the total vulnerability map of the plain against floods was drawn. The highest and lowest levels of vulnerability are related to coping capacity index with 33.7 percent and social index with 9.6 percent, respectively.

Even though dams have many social benefits, it also has caused social, economic, environmental and technical management risks along time. Due to the fact that the dam is considered a great phenomenon in nature, its effects on the environment are extensive. One of the issues that must be investigated during the operation of dams is the negative environmental effects in the dam construction area. The negative environmental effects of dams are including sedimentation in the dam's reservoir and lake, intensification of erosion in the downstream lands, inundation of agricultural lands due to rise of the underground water level. The effects of the mentioned risks for dam construction may be irreparable. The aim of this study is evaluating the effects of social, economic, technical management and environmental risks affected by the construction of dams on the watershed. Decisions related to the management of these dams require recognize the dangers and risks in the dam and prioritize them. Dam construction projects are more risky than other projects due to the high credits and time spent on their construction. Using multi-criteria decision-making methods is a suitable tool for evaluating different risks in these dams. In this study, the combined DEMATEL-ANP method is used to identify the relationships between risk criteria and sub-criteria and prioritize them. So, four criteria and 42 sub-criteria were determined to evaluate different risks in Mamloo, Taleghan and Lar dams and the network relationships between them were determined. The results show that the employment sub-criterion in economic criterion with a weight of 0.0720 has the highest risk and the land acquisition sub-criterion in management technical criterion with a weight of 0.0029 has the lowest risk compared to all sub-criteria.

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

Due to the increasing human need for electrical energy and the reduction of non-renewable resources for its production, the use of non-fossil and renewable resources and resources that have less environmental damage have been taken into consideration. Archimedes screw turbine is used as a means of producing clean energy in waterways, and many theoretical and experimental studies have been done on it. Most of these studies, in laboratory and numerical form, are in the field of optimization of screw constituent components and their effect on its efficiency including length, inner and outer diameter, installation angle, number of blades and screw pitch. In this research, the most important studies conducted in the field of Archimedes screw turbine performance, which have been published in the last two decades, were reviewed. The results showed that the parameters of screw components have a greater effect on efficiency compared to hydraulic parameters.

This study aimed to evaluate the environmental and economic effects of the development of mini-bubbler pressurized irrigation projects as a modern irrigation system in Birjand County, South Khorasan Province, from the perspective of farmers and users, so the quantitative and qualitative processes of these irrigation projects can be evaluated. Data collection was conducted with a survey technique and the results showed that most of the farmers had a favorable assessment of the environmental and economic effects of the plan. The economic factor was found to be the most important factor during the evaluations, which included the variables of reducing the time required for irrigation, increasing the amount of production per hectare, increasing the cultivated area, reducing labor costs, reducing water and irrigation costs, improving the quality of products, and increasing the income from agriculture, which explained ca. 26.2 percent of the total variance. The second factor was the environmental factor, which includes the variables of the more appropriate use of fertilizers and pesticides, reduction of plant diseases, reduction of weeds, and prevention of soil salinization, which explains 21.6 percent of the total variance. Also, the effects of mini-bubbler irrigation on agricultural inputs were also evaluated, which included the variables of less water consumption, less seed consumption per hectare, compliance with the cultivation pattern, and increasing the mechanization factor, and explained 17.8 percent of the total variance. The social factor also includes the variables of gaining social credit, the cooperation of users in maintaining the system, and observing the irrigation schedule, and it explains 11.2 percent of the total variance. Finally, based on the obtained results, suggestions have been made for the success of implementing and using such projects.

In recent years, the use of machine learning algorithms has been a promising way to improve crop yield predictions, especially when using non-linear relationships. This research was conducted with the aim of evaluating the yield estimation of irrigated and rainfed barley as well as the total yield of barley produced in the provincial centers of Iran using remote sensing data and machine learning methods including XGBoost and SVM. The results showed that by using climatic data, drought indices and plant indices of remote sensing and also XGBoost and SVM algorithms, it is possible to reliably estimate barley yield in different regions of the country with different climates. In general, the RMSE error obtained for both models was acceptable (0.41 and 0.77 t/ha). The R2 determination coefficient values for XGBoost and SVR algorithms in modeling rainfed barley cultivation performance were equal to 0.2 and 0.22 respectively, in irrigated barley performance were equal to 0.52 and 0.55 and for total barley were equal to 0.66 and 0.65, indicating that the rainfed yield modeling were not as suitable as irrigated and total barely yield modeling. The RBF kernel was chosen as the best kernel to use for the SVM algorithm. Also, in this research, while examining the effects of change in train and test data dividing, the parameters of precipitation, temperature, and evapotranspiration were determined as the most important parameters affecting the performance of the barley yield for both algorithms in different evaluated conditions.