مجله مهندسی آبیاری و آب ایران
پیاپی 48 (تابستان 1401)

Bridges are the most useful structures on rivers which floods cause damage to them every year. One of the known factors in bridges destruction is local scouring around the bridge piers. In this study, to investigate the scour depth around the bridge pier in the river bend, experiments were performed in a laboratory flume with a 90-degree bend with  . By placing a cylindrical pier with a diameter of 45 mm at three locations of 30, 45 and 60 degrees along the bend, for three flow Froude numbers, the scouring around the pier under clear water condition was investigated. Natural sand with an average diameter of 0.85 mm is used for the bed materials. The results showed that the maximum scour depth around the bridge pier varies at a different location along the bend. Besides, the flow discharge increase grows the depth and volume of the scour hole at all positions. Additionally, maximum and minimum depth and volume of scouring hole occurs in the second half of the bend at 60- degree position and in the middle of the bend at 45- degree position, respectively. Finally, it was recorded that the development of the sedimentary hill after the pier and its extent in the first half of the bend was higher than the second half of bend. The result also indicated that the maximum and minimum scour depths relative to the pier diameter are equal to 2.24 and 1.22, respectively.

By the development of technology and the advancement of technology, many intelligent methods have emerged for estimating the discharge coefficient of different hydraulic structures. One of the structures used in power plants is bottom intake structure. The task of this structure is to transfer the flow to the collecting channel. The advantages of these structures are their stability against dynamic and static loads due to their low level alignment. In the present study, four intelligent algorithms capable of extreme learning machine (ELM), general regression neural networks (GRNN), multivariate adaptive regression spline (MARS) and M5 tree model have been evaluated in modeling of discharge coefficient of bottom intake. The modeling results showed that the ELM algorithm is more accurate than the other algorithms in both training (70% of data) and test (30% of data) periods. In addition, R2 coefficient for the mentioned algorithm was up to 3.74% higher than the other algorithms used. Also the DDR criterion and modeling error histogram showed the superiority of the ELM algorithm. Finally, the computational speed of the algorithms used was compared, which ELM algorithm was 2.557 times faster than the other algorithms. Therefore, the ELM algorithm has high potential for modeling the discharge coefficient in overflows due to its good accuracy and high speed.

Study of separation zone is so important in right-angled three-branch or four-branch open channel junctions. Some effective parameters in this case are inlet discharge ratio and flow depth which in this research the effect of inlet discharge ratio and weir height ratios (flow depth) on flow pattern and dimensions of separation zone has been simulated numerically. Investigation of numerical results showed that k-ω model well validated with experimental results and had good agreement, so that simulation error was less than 20%. Dimensions of separation zone in main and side channels were directly proportional with the inlet discharge ratio. Also as increases of height ratio of outlet weirs, flow depth increases and separation zone dimensions decreased. According to the analysis of numerical results, dimensions of separation zone in vertical direction, of the channel bed to water surface increased, so that for discharge ratio 0.6 and height ratio of outlet weirs 0.377, length of separation zone at the channel bed, 0.1 m up the bed and water surface was about 60 cm, 75 cm and 85 cm, respectively. So of the water surface towards the channel bed, length of separation zone decreased about %29.

As for the severe limitation of water resources, costly construction and operation of water supply systems and rapid population growth, the optimal design of these networks is essential. The problem of cost minimization is done by minimizing the diameter of the network pipes, which reduces the pressure in the network. Since providing adequate pressure in the nodes is one of the important design principles, so in this study, the problem of optimization in several sample networks was defined with the objectives of minimizing the cost and lack of pressure in the whole network. EPANET software was used for hydraulic analysis of sample networks and the multi-objective optimization process through coding of PESA-II and SPEA-II algorithms in MATLAB software and their connection to EPANET face Took. The cost function was initially defined only by considering the relationship between cost, diameter, and pipe length. Then, in the next definition, the cost of exceeding the allowable pressure range, where the minimum and maximum allowable pressures are 30 and 60 meters, respectively, was added to this function, and the program again with the number of repetitions that ended in the best answer Was implemented. The results showed that these algorithms have a high ability to find optimal solutions. In these algorithms, considering the cost of exceeding the allowable pressure limits results in the best answer that other researchers have ever obtained for sample networks, which for the two-loop and lansey network, The cost was 419000 and 1069393 $ respectively, and the pressure shortage was zero and with a low number of iterations, in the two-loop network for both algorithms with 20 iterations and in the lansey network for PESA-II and SPEA-II algorithms  with 200 and 140 iterations respectively, to achieve a higher number of optimal answers and the time to achieve convergence is significantly reduced, so that in the two-loop network, the execution time of PESA-II and SPEA-II algorithms are 0.55 and 0.59 minutes respectively, and in the lansey network It was 1/8 and 7.4 minutes respectively.

Flood is a phenomenon that causes a lot of environmental and socio-economic damage. The purpose of this study is to evaluate the efficiency of CART, GLM and GAM machine learning models in identifying flood risk areas in the Kashkan basin. Lorestan province and especially Kashkan basin, including: Selseleh, Delfan, Doreh, Khorramabad, Poldakhtar and Kuhdasht, is flooded and has suffered flood damage many times and in April 2019, experienced the largest flood of the last 200 years. In this regard, various factors including: height, slope direction, land curvature, slope percentage, distance from the river, drainage density, soil, lithology, land use and topographic moisture index were used. The digital map of all the mentioned factors was prepared in ArcGIS10.5 software and in the form of a database. The location of 123 flood events recorded in recent years in this basin was collected and randomly used in two categories of model training (86 cases) and model validation (37 cases) in modeling. Using machine learning models and environmental factors, flood potential prediction maps were prepared and then validated using AUC characteristic curve methods and TSS index. The results of model validation showed that CART machine learning model with AUC = 0.91 and TTS = 0.88 index was the most accurate model in predicting flood risk potential, followed by GAM model with AUC = 0.87 and TSS index = 0.84 and GLM model with AUC = 0.83 and TSS index = 0.88. Accuracy 0.91 CART model indicates the excellent accuracy of this model for the Kashkan basin. This model shows a larger area of ​​the basin under high potential and moderate flood risk conditions, which include most of the western areas as well as the central areas of the basin (Kuhdasht, Khorramabad and Poldakhtar), which are exactly parts The same areas were flooded in the great flood of 2019 and it is necessary to be in the first priority of flood risk planning and management in this basin.

The great importance of modeling the rainfall-runoff process from the perspective of water resources engineering, river engineering, flood control structures and flood storage led researchers to use simulation models as a tool to analyze and evaluate the behavior of the basin. So, in this study WetSpa model was used to simulate rainfall hydrograph flow in Beheshtabad basin. For this purpose, using hydrological and meteorological daily data of during the years 2008-2014, DEM, land use and soil texture were simulated runoff. It is noteworthy that from this statistical period, five years of 2008-2012 have been considered for calibration and the second two years of 2013-2014 have been considered for validation of the model. The simulation results show that the WetSpa model in the watershed is able to predict daily hydrographs with good accuracy and Nash Sutcliffe coefficient of 60.12%. In addition, the results show that the model of hydrological components and factors is accurately estimated and is effective for calculating daily flow rate

Increasing irrigation efficiency and reducing water losses in gardens is necessary due to the severe shortage of water resources in the country. In order to increase the irrigation systems efficiency it is necessary to evaluate them. For this purpose, 6 gardens with basin irrigation system were selected. The amount of irrigation water volume, depth of root development, yield, application efficiency (early, mid and late of the growing season) and water productivity were determined. Also, the water requirement was calculated by the Penman Monteith method using meteorological data (recent ten years) and compared with the values provided in the National Water Document. In apricot orchards, the average application efficiency varied from 43.3 to 58.4 percent (average of 49.7 percent) and in vineyards from 41.7 to 61.9 percent (51.4 percent on average). Deep percolation was the major portion of irrigation water losses in apricot and grape gardens by 49.7 and 51.4%, respectively. The average water productivity of these products was 0.78 and 5.2 kg.m3 respectively. The results showed that in gardens where land leveling and water supply of the trees were well done, irrigation efficiency and water productivity were significantly increased. Comparing the computational water requirement with the volume of irrigation water shows the imposition of deficit irrigation in these gardens. The results also showed that the computational water requirement was much higher than the values mentioned in the National Water Document, which highlights the need to update the National Water Document.

The objective of this study is to evaluate Avan modern irrigation network with an area of 10985 ha. The main district’s water inputs (irrigation, precipitation and canal releases) and outputs (actual evapotranspiration of crops, outflow surface drainage and canal seepage) were measured or estimated during the hydrological years of 2006 to 2009. The district-level irrigation performance was poor (mean value of seasonal irrigation consumptive use coefficient-ICUC in the studied years were equal 37%), due to the low distribution (66%) and on-farm (53%) efficiencies for the 1385-88 irrigation seasons. Thus, despite the high volume of applied irrigation water, the actual district ET was 19% lower than the maximum achievable ET, indicating that the water-stressed crops yielded below their maximums. By using neoclassical approach, it was shown that the values of net (0.83) and effective efficiencies (0.68) were more than classical efficiency (0.53) in surface irrigation systems. The results obtained in this study showed that effective efficiency has suitable expression about irrigation management and method at farm scale, whereas net efficiency only considers the concept of reuse of beneficial losses on spatial scale larger than the field. Potential reductions in water allocation were analyzed for three ICUC values (65, 75, and 85%) and two scenarios of modernization (I and II). In scenario I, where the aim was to achieve maximum ET and crop yields, water allocation could be reduced from 34.8 to 50.2% of the current allocation. In scenario II, where the aim was to achieve the maximum conservation of water under the actual ET and crop yields, reductions in water allocation would be much higher than current allocation (47.4 - 59.8%). Thus, significant volumes of water could be conserved in the rehabilitation of this district by increasing the distribution efficiency and, in particular, the on-farm irrigation efficiency.

Simulation models are suitable tools for predicting the effects of different management scenarios and selecting the most appropriate solutions in agricultural production systems. In this study, after evaluating the efficiency of the DSSAT model, the effect of water table management on rice growth and yield was investigated. The required field experiments were performed under a randomized complete block design with four irrigation treatments and three replications during a rice growing season in a research farm at the Sari Agricultural Sciences and Natural Resources University. Irrigation treatments included conventional or flooding irrigation (control) with water height of 5 cm above the soil surface (I1), water table control at soil level (I2), water table control at 5 cm below soil surface (I3) and water table control at 15 cm below soil surface (I4). During rice growing season and at harvest, leaf area index, shoot weight, plant height, number of tillers, biological yield and grain yield were measured. The data of I1 treatment were used for calibration and the data of other treatments were used for validation of the model. In both calibration and validation processes, the DSSAT model showed a good performance for predicting phenological dates, leaf area index, biological yield and grain yield. In the calibration and validation stages, root mean square error (NRMSE) values were in the range of 0.7-7.6% and 1-7.6%, respectively, and Wilmot agreement index (d) values were in the range of 0.73-0.99 and 0.82-0.99, respectively. Effects of irrigation treatments were significantly different on plant height, number of tillers per hill, leaf area index, grain yield and biological yield. Among different treatments, the highest grain yield was 5584 kg ha-1, related to the control treatment. Grain yield in I2, I3 and I4 treatments was 4.7, 4.6 and 39.2% lower than that in the control treatment, respectively. Water use efficiency in I1, I2, I3 and I4 treatments was 0.48, 0.65, 0.83 and 0.73 kg m-3, respectively. Based on the results, in order to maintain rice production while saving water, it is recommended to control the water table at a depth of 5 cm below the soil surface.

Agricultural sector is the main water consumer in our country. So the appropriate decisions for irrigation scheduling and its optimal allocation is of great importance for an efficient water management. The aim of the present study is to employ some soft computing techniques, such as the particle swarm optimization (PSO) and genetic algorithm (GA), and to determine optimal irrigation scheduling as well as reservoir release for agricultural networks located at downstream of Zayandeh-Rud dam. In this regard, the crop calendar, total amount of available water as well as arable land in agricultural sector, the amount of water available at the beginning of water year and crop water requirements are the most important non-linear constraints of current research. The results showed the integrated PSO modeling with better distribution of water shortages among different crop growth stages could significantly increase the net profit of system while compared to those of traditional irrigation systems. Regarding the time of reaching the convergence as well as total attainable benefit, the PSO has slightly outperformed the GA. Consequently, application of soft computing techniques in irrigation scheduling will provide effective water allocation patterns to save more water in an arid region with limited water resources.

One of the most important criteria for designing groynes is the stability of groynes during floods passing around them. To be able to protect the riverbank, the groynes should maintain their stability over the years. One of the main reasons for groynes instability is the local scouring around them. In this study, to protect and reduce the maximum local scouring depth of the nose of a series of impermeable groynes, a permeable protective groyne (PPG) is installed upstream. For this purpose, the maximum local erosion depth of the nose of series of impermeable groynes was determined under laboratory conditions and the scour depth in two cases without protective groyne and with protective groyne at five different distances i.e. L', 1.5L', 2L', 2.5L', 3L' (L': Protective groyne Length) at the upstream side of the first groyn was determined and evaluated. The results show that the establishment of permeable protective groyne at the upstream of a series of impermeable groynes with equal length at 3L' distance from the first groynes reduces the maximum local scouring depth of the first groyne by 34.3% at 3L' distance from the first groynes.

It’s valuable to predict accurately the discharge coefficient due to its direct role in the determination of the weirs passing capacity. This study was carried out using intelligent GEP and SVM algorithms based on laboratory datasets to simulate the discharge coefficient of the rectangular side weir installed in a rectangular (the first scenario) and a trapezoidal main channel (the second scenario). The most effective parameters were determined as upstream Froud number (Fr1), upstream flow depth (h1 or yo), weir height (P or W), side weir length (L), main canal width (b), sidewall slope (Z). Dimensionless parameters were extracted as (Fr1, , , ) and (Fr1, Z, ) for the first and the second scenarios, respectively. The outputs of the two algorithms were compared with experimental and regression equations using statistical indices as root mean square error (RMSE), deterministic coefficient (R2), relative error (RE), and standardized developed discrepancy ratio (ZDDR). The values of (RMSE, R2, RE, ZDDR) during the test phase for the first scenario for GEP and SVM were calculated as (0.036, 0.962, 7.76, 5.48) and (0.037, 0.952, 9.6, 3.8) and those of the superior regression model were (0.040, 0.912, 4.527, 2.439), respectively. The corresponding values in the second scenario for GEP, SVM and regression model were obtained (0.068, 0.992, 3.1, 1.14), (0.043, 0.934, 10.3, 0.71) and (0.068, 0.818, 11.9, 0.511), respectively. The results showed the superiority of intelligent algorithm over classical regression, and also the GEP to the SVM.

Groundwater monitoring has long been considered as one of the main sources of agricultural water supply. . In this regard, indiscriminate harvesting of this God-given resource, as well as the construction of unauthorized wells and lack of rainfall have caused the water level to drop in many aquifers of the country. The plains of Kermanshah province with its good climate and fertile soil are the agricultural and horticultural hubs of Iran. Therefore, in this study, piezometric well data in the period 1360-1397 and GRACE satellite data with a resolution of one degree in the period 2002 to 2020 with JPL, GFZ and CSR methods were used to study the changes in groundwater levels of aquifers in Kermanshah province. The amount of soil moisture was extracted from Google Earth Engine cloud computing environment using GLDAS model with a resolution of one degree. Zoning maps of all aquifers in the province for the years 1395 and 1397 were drawn by kriging method in ARC GIS software. The results showed that a number of aquifers, including Hassanabad-Shian, Sanjabi and Dinehvar are in critical condition. Long-term monthly and annual water level data of aquifers for different plains according to their effective areas have also been calculated and displayed by Thissen method. The map of changes in soil moisture, actual evapotranspiration, and cumulative precipitation in Kermanshah province was extracted by TERRA satellite with a resolution of 0.04 degrees equivalent to 4 km by 4 km, which shows the changes in these parameters in December 2019. The values ​​of TWS groundwater level changes were plotted by subtracting soil moisture values ​​against the aquifer observational data and the changes in groundwater level decreasing trend in Kermanshah province indicate a decrease of -1.5 cm and -3.8 cm in the period 2002 to 2016 by GRACE satellite and observational values respectively While from 2016 to 2020, groundwater level changes have been increasing.

Rainfall-runoff monthly modelling process plays an important role in dams’ operation. Herein the performances of three data-based models including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN) and K-Nearest Neighbor (KNN) are compared in tandem with IHACRES conceptual model, while they were applied with similar data, and optimal structures. Simulation of monthly inflow to Karkheh reservoir, Iran, was considered as the case study, and 32-year data (1982-2014) of monthly temperature and precipitation belong to the upper sub-basin of the dam, and monthly inflow to the reservoir were used. With respect to the different rainfall-runoff patterns in different months, the models assessed in a general and monthly manners using a rating method based on performance criteria including: Nash-Sutcliff Efficiency (NSE), Root Mean Square Error (RMSE) and Correlation Coefficient(R). Results showed that both model evaluation procedure in validation phase, ANN and KNN models have the highest and lowest efficiency in monthly streamflow forecasting, respectively. Based on the rating general evaluation the performance of ANN (NSE= 0.749, R= 0.868) and IHACRES (NSE= 0.699, R= 0.842) are similar with a score of 8 while the GRNN (NSE= 0.618, R= 0.793) and KNN (NSE= 0.601, R= 0.777) models with similar performance (score 5) were ranked in the second order. However, in accordance with rating monthly assessment of the models, the performance of GRNN was similar to IHACRES with the total score of 38 based on three criteria while they were ranked in the second order after ANN model with score 48.

In this study, using six models of neural network (ANN), ANFIS, support vector machine (SVM), genetic programming (GP), support vector regression (SVR) and multivariate regression (Reg), the mean daily temperature at Kerman and Bam stations, Iran were studied and simulated during the period of 1961-2005. The results showed that the mean daily temperature during the mentioned periods will increase significantly for both stations. The overall results indicate the superiority of the results of the SVR model (Kerman: RMSE = 1.105 oC and R = 0.992) and (Bam: RMSE = 1.01 oC and R = 0.99). The results showed that the SVR model improved the simulation error rate compared to the neural network (ANN), ANFIS, genetic programming (GP) and multivariate regression (Reg) models in Kerman station about 32, 42, 30 and 11 percent respectively and 62, 59, 27 and 27 percent respectively in Bam station. The results of the root mean square error showed that among the six studied models, the support vector regression model and genetic planning for Bam station and the support vector regression model for Kerman station have higher accuracy. The results also showed that estimating the mean temperature of Bam station has more efficiency and accuracy than Kerman station. In this study, although the analysis of the output results of the models did not lead to the same results, but the results of the models indicate an increase in temperature variables in the two stations of Kerman and Bam in future periods.

Using water resources require the determination of components of the hydrological cycle. Understanding of natural systems and physical laws that manage each component of the hydrological cycle is important for the water resources management. One of the crucial components of the hydrological cycle is evapotranspiration. Most methods that have been presented in this study use point measurements to estimate evapotranspiration. Due to dynamic and changing nature of regional evapotranspiration is not generalizable to the basin, the applied methods are only suitable for the local areas. Technology of satellite remote sensing and remote sensing-based methods can be used for the temporal and spatial estimation of actual evapotranspiration in the small and large basins. Remote sensing data derived from satellite imagery calculates the amount of actual evapotranspiration using surface energy balances model. In this study, actual evapotranspiration at the Bakhtegan-Maharloo basin were estimated and evaluated using Landsat 8 satellite images sensor OLI/TIRS and SEBS energy balance models. The values of evapotranspiration derived from energy balance model and FAO-Penman-Monteith method were compared.The results showed that the evapotranspiration values obtained from the energy balance model has a root mean square error (RMSE) and mean absolute difference (MAD) equal to 0.62 and 0.49 mm/d, respectively, indicating its acceptable performance to estimate the evapotranspiration

Reference Evapotranspiration (ETo) has an important role in irrigation management  and scheduling. Evaluating the sensitivity of ET o to different meteorological variables and contribution of each variable in ET o trend is essential for better management of water crisis in Lake Urmia basin. In this study, ETo in 13 synoptic stations in and around Lake Urmia basin for the period 1990-2018 was estimated by the FAO Penman-Monteith method. Trend analysis showed that the average annual ETo of stations has significant increasing trend of 8.0 mm year-2. In addition, the trend analysis of the average annual meteorological variables of the stations reveals significant increasing trends in minimum temperature (Tmin) (0.07 ℃ year-1), maximum temperature (Tmax) (0.09 ℃ year-1), and wind speed (u2) (0.02 m s-1 year-1). Sensitivity analysis of ETo to meteorological variables indicate that on an annual temporal scale, the maximum sensitivity of ETo is to relative humidity (RH) and the minimum sensitivity of ETo is to Tmin. Also, during this period, the sensitivity of ETo to Tmax, u2, and RH increased and the sensitivity of ETo to solar radiation (Rs) decreased. The contribution analysis of meteorological variables to ETo variations indicate that in most stations (with arid and semi-arid climate), u2 has the largest contribution in ETo variations. However, in Sardasht station with a very humid climate, the contribution of other factors in the ETo trend (decrease in RH and increase in Tmin and Tmax) is greater than the effect of decrease in u2. The results of this study highlights the need to pay attention to the different response of ETo to meteorological variables changes in different climates in planning water resources systems and studies that are dependent on climate change scenarios.

The drought phenomenon contains important characteristics, including duration, severity, peak and frequency. In this study, two indices of drought assessment consist of univariate index (Standardized Precipitation Index: SPI) and multivariate copula-based index (JDI: Joint Deficit Index) are compared. In this regard, the drought characteristics, including the severity, duration, and drought frequency have been studied in 39 synoptic stations of Iran during the 1965–2014. The results showed that the longest duration and maximum severity of each station belongs to the JDI. Also, the frequency of drought in different classes shows the JDI estimates the drought frequency with high accurately, but the SPI‐12 provided an unexpected estimation is some stations. In addition, the results of the Mann-Kendall test for the drought characteristics showed that the drought trend with JDI is decreasing while SPI-12 does not show a significant trend in the most stations. Finally, the results showed that JDI provides a complete drought assessment for decision makers and water resource managers.

Evaporation as one of the natural parameters has always been considered by researchers. In this study, the monthly evaporation variable was modeled in two different climates of Iran using artificial neural network, adaptive fuzzy-neural inference system and gene expression programming methods and combining these methods with wavelet theory. For this purpose, meteorological data of precipitation, relative humidity, average temperature, maximum temperature, minimum temperature and wind speed were used during the statistical period of 1384-1397 related to the two catchments of Urmia Lake and Gavkhouni. In this study, the seasonal effect and data noise reduction were applied. The accuracy of the studied methods was evaluated based on statistical correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and nash-sutcliffe efficiency (NSE). The results show that in two different climates, the wavelet-hybrid gene expression programming and the single artificial neural network have the highest and weakest performance, respectively, among other data mining models used in this study. The hybrid wavelet-gene expression programming model with RMSE value of 20.870 and 156.884 had higher performance for Tazehkand station in Urmia Lake catchment area and Kuhpayeh catchment in Gavkhouni catchment area, respectively. Also, the results showed that the effect of seasonal factor utilization and data noise reduction in model performance improvement is significant. Based on the results of the models performance Urmia Lake catchment area with Dsa climate has been better. However, hybrid data mining methods can be introduced as a good alternative to the old methods.

In this study, GIS software was used to locate prone to treated wastewater replacement. For this purpose, parameters of water quality, being close to the wastewater treatment plant, groundwater level, and topographic conditions have been used in different regions. Then, the information layers related to these parameters were weighted through the AHP method. Eventually, by integrating and assembling the layers, a priority map of areas prone to wastewater replacement was drawn. The results of AHP analyzes indicated that according to the water experts, the drop in water level and the quality of groundwater in Mashhad Plain have the highest weighting factor and the distance from the wastewater treatment plant and the height differences in various regions relative to thewastewater treatment plant (topography) were the least weighted coefficient. Also, the areas with the highest drop, the best water quality, the lowest distance and the lower height relative to wastewater treatment plant had the highest weight coefficient. Furthermore, areas with the lowest drop, the worst water quality, the highest distance and height relative to the wastewater treatment plant had the lowest weight coefficient. The areas identified as the first priority indicated the highest water level drop and their water quality was better than other areas of the plain. Therefore, by replacing the agricultural wells of these areas with the treated wastewater and removing these wells from operation circuit, the amount of water withdrawal is reduced and the stored water potential can be used in the future. By direct replacement of the wells with wastewater and excluding these agricultural wells from operation circuit, one hundred percent of replaced water can be stored in the groundwater tables with higher quality and it is possible to use them in the future.

Entering environmental pollutants into water resources have harmful effects on human health and environment. In recent years, adsorption methods using adsorbents to remove contaminants from water resources have been abundant. Solfounate  and Pentaaza. sulfonated polyacrylamide polymer (PAM-SO3) as a novel adsorbent can be effective for removal of chemical pollutants of the aquatic solution. The purpose of this research is removal of Direct Blue using PAM-SO3 as an adsorbent from polluted water in vitro. The effects of variables such as pH, contact time, initial concentration, adsorbent amount were observed to reach best adsorption conditions. Isotherms of Langmuir, Freundlich and Temkin have been fitted with the data of experiment. In addition, kinetics of pseudo- first order, pseudo- second order, intra-particle diffusion and Elovich were also fitted with the [1]experiment data. Also, the results indicated that the best conditions for removal of Direct Blue dye were: pH= 2, removing time= 45 minutes, adsorbent dosage= 0.014 g and initial concentration of dye= 800 mg L-1. For PAM-SO3, Langmuir isotherms showed a good agreement with the experimental data. Using this model to maximize absorption capacity of 5000 (mg g-1) for PAM-SO3. Absorption rates showed a quick responses which was less than one hours. Based on these results, the adsorption kinetics of pseudo- second- order was more consistent with the experimental data (R2=0.99). The results show that PAM-SO3 absorbent is effective in removing Direct Blue contaminants from the aqueous solutions due to its high surface area and rapid kinetics of the reactions. Therefore, PAM-SO3 is recommended as an efficient adsorbent to remove Direct Blue from aqueous solutions.

Pollution of surface water especially rivers has created serious problems for both human health and environment. Finding efficient solutions for controlling river pollution is essential in order to reduce damage for the consumer to protect the environment. In this research, river quality management planning is determined in a way that minimized the rate of pollution damage with using particle swarm optimization. The case study is Gheshlagh River in Iran, Kurdistan province. The timetable for entering contamination is at different hours of the day, and eight source points enter contamination to the Gheshlagh River. Also in all hours of the day, water harvesting from the Gheshlagh River is carried out by the downstream consumers. The 192 of decision variables of this research are corresponding to the amount of contamination entering from these sources at different times. Also, 8 decision variables are corresponding to the optimal location of contamination entering to the river.  The results indicate that in the optimal condition the value of contamination does not exceed from the allowable limited.  However in the unmanaged condition, the contamination value exceeds the allowable limit. The river quality management planning which determines the optimal location and the timetable of pollution discharge, reduced significantly consumer's damages. So that the objective function of this study was reduced by 97.7%.

Storm-inundation models based on hydrology and hydrodynamics require a large amount of input data (detailed terrain, sewer system and land use data). In this paper, in order to determine inundation conditions quickly with only a few usually available input data is proposed an urban storm-inundation simulation method (USISM) based on Geographic Information System (GIS). The USISM is a simplified method of distributed hydrological model based on DEM, in this method depressions in terrain are regarded as the basic inundated area. The amount of water that can be stored in a depression indicates the final inundation distribution. The runoff and maximum storage volume for each depression and the flow direction between these depressions are all considered in the final inundation simulation. The SCS method is used to calculate storm runoff and a water balance equation is used to calculate the water storage in each depression. The result shows that in all four-storm event, the average relative depth errors of depth in all inundation sites are less than 20%, while the average relative errors of area and volume are more than 60% Therefore, the USISM method has a higher ability to simulate the final depth of inundation than the surface and volume of inundation. The result reveals that the USISM method could find the inundation locations in the Damghan Urban Watershed and calculate inundation depth and area quickly and therefore display a significant role in the management of the urban crisis.

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.

Prioritization of sub-watersheds and different areas has an important effect in watershed management. The purpose of prioritization of sub-watersheds is to provide a pattern for control and decrease flood hazards and evaluating the role of each sub-watershed in peak discharge of outlet flood hydrograph. In this study, Talar watershed was selected as study area, because of several floods was occurred in it and then factors affecting floods like rainfall hyetograph for different return periods, flow length, slope percentage, land use, soil hydrological group, amounts of CN and time of concentration were recognized. By combining ArcGIS software and ModClark hydrological model, the potential of flooding in Talar watershed classified into five classes: Very Low, Low, Medium, High and Very High within three return periods of 25, 50 and 100 years. The results showed that the contribution of sub-watersheds in flooding potential will be not only affected by its area but also the location of each sub-watershed and flood routing in main reach have remarkable effect on flooding regime of the watershed. Runoff production potential maps indicate that runoff production potential from downstream to upstream of the watershed is increasing and Chashm sub-watershed in Southeast of the watershed is considered as the most effective area and unit in flooding of the whole watershed because of heavy rainfall, high slope and large curve number (CN).