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Abstract This study aimed to forecast dam inflows and subsequently predict its capability in producing HEPP using machine learning and evolutionary optimization techniques. Mahabad Dam, located in the northwest of Iran and recognized as one of the nation’s key dams, served as a case study. First, artificial neural networks (ANN) and support vector regression (SVR) were employed to predict dam inflows, with optimization of parameters achieved through Harris hawks optimization (HHO), a robust optimization technique. The data of temperature, precipitation, and dam inflow over a 24-year period on a monthly basis, incorporating various lag times, were used to train these machines. Then, HEPP from the dam was predicted using temperature, precipitation, dam inflow, and dam evaporation as input variables. The models were applied to data covering the years 2000 to 2020. The results of the first part indicated both hybrid models (HHO-ANFIS and HHO-SVR) improved the prediction performance compared to the single models. Based on the results of Taylor’s diagram and the error evaluation criteria, the HHO-ANFIS hybrid model (RMSE, MAE, and NSE of 3.90, 2.41, and 0.86, respectively) exerted better performance than HHO-SVR (RMSE, MAE, and NSE of 4.39, 2.70, and 0.86, respectively). The results of the second part showed that using the HHO algorithm to optimize single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively) predicted HEPP better than single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively). The results of Taylor’s diagram also showed that the HHO-ANFIS model exerted better performance. The findings of this study indicated the promising performance of machine learning models optimized by metaheuristic algorithms in the simultaneous prediction of dam inflows and HEPP in multi-purpose dams for better management and allocation of surface water resources.

Abstract For managing water resources and operating reservoirs in dynamic contexts, accurate hydrological forecasting is essential. However, it is difficult to track complex hydrological time series with highly non-linear and non-stationary characteristics. The intricacy of the issue is further increased by the risk and uncertainty that are brought about by the dependence of several factors on the hydrological system’s output. To hydrologically model river outflows, a hybrid GARCH time series model technique has been applied in this study. To improve the precision of the proposed model estimation, this hybrid model employs a controllable fuzzy logic system to explore the impact of various input variables and an Archimedean detail function to account for the uncertainty in the dependence of the variables. The prediction error in this model is minimized by utilizing weighting factors and problem analysis parameters that are calculated using the water cycle algorithm. It was found that the minimum root-mean-square error values for the training and testing modeling stages are RMSE = 1.89 m and 1.92 m, respectively, by looking at the hydrological modeling results for a watershed of the Karaj dam. For extended lead (i.e., a 6-month rainfall lag), the weakest forecasting capacity was found. The modeling of the copula function using a higher percentage of answers in the confidence band and a smaller bandwidth resulted in less uncertainty for the estimation of the suggested model, according to the uncertainty analysis.

Introduction and ObjectiveGiven the application of hydrological response in a watershed, various methods have been used to determine this response and the results have shown a high degree of accuracy and accuracy variability depending on the data used. By reviewing and summarizing the results of research carried out in the modeling of rainfall-runoff, particularly the time-area method, it was found that in most of these studies, the concept of watershed time concentration was used, which in most of the formulas used, the physical properties of the watershed were used and the dependence of time concentration on rainfall conditions was not studied. Therefore, this study was conducted to evaluate these methods using the kinematic wave method in the GIS environment, the HEC-1 method, and optimization methods using genetic algorithms in a V-shaped experimental watershed.Materials and Methods Observational data available in the V-shaped experimental watershed of the University of Illinois was used for rainfall-runoff modeling. The studied watershed had an impermeable aluminum surface and two uniform side sheets with a one-sided slope towards the channel with a constant value of 1%. In addition, a central channel with a one-sided slope towards the outlet of the watershed with a constant value of 1% was present. The roughness coefficient in this watershed was determined based on trial and error at 0.014.Results and DiscussionAfter preparing the time-area histogram of the watershed using each of the mentioned methods, the corresponding outflow hydrographs of the watershed were determined. Then, the results were compared with observational data, and various components of the computational hydrographs were also examined. The results showed that the performance of the genetic algorithm in determining the peak time of the hydrograph with a 15% relative error was better than the performance of the kinematic wave and HEC-1 models. Additionally, the genetic algorithm model had the highest correlation coefficient with observational hydrographs with an average Nash-Sutcliffe Index of 0.968 and an average correlation coefficient of 0.983. Furthermore, by fitting the curve to the modeling results, an equation was obtained to determine the equilibrium time of the watershed relative to rainfall intensity, with a determination coefficient of 0.999. This equation expresses the inverse relationship between equilibrium time and rainfall intensity (with a power of 0.33), i.e., doubling the rainfall intensity reduces the equilibrium time by 20%. Finally, the coefficient of the equation determining the equilibrium time for this watershed was found to be 495.2, and for each rainfall intensity, its corresponding equilibrium time can be calculated with high accuracy.Conclusion and Suggestions In this study, under experimental watershed conditions, rainfall-runoff modeling was performed with three categories of events and three categories of different rainfall durations, each event category having four different rainfall intensity sizes. Corresponding hydrographs were obtained for each event by examining the computational hydrographs and comparing them with observational hydrographs, and it was found that the three models had one to two percent relative error in determining the maximum runoff, but in determining the time to reach the hydrograph peak, the kinematic wave and HEC-1 models had an average error of 44%. Finally, using the correlation and Nash-Sutcliffe coefficients, it was determined that the computational hydrographs produced by the genetic algorithm method were closer to the observational hydrographs and had a higher degree of correlation. It is worth mentioning that in this experimental watershed, the dependence of equilibrium time on rainfall intensity in impermeable conditions was confirmed. It is recommended that the effect of infiltration or changes in surface roughness and the determination of the dependence size be investigated.

In the context of sustainable development and considering water distribution networks (WDNs) as vital infrastructure systems, designing a resilient and efficient network to deliver water demand to consumption nodes while adhering to engineering standards is of utmost importance. This study specifically focused on the complex structure of the north-west Tehran's WDN, encompassing 1124 pipes totaling 92552 m in length, along with four gravity reservoirs and 988 nodes. Genetic Algorithm (GA) and Nonlinear Programming (NLP) were employed as optimization techniques to enhance the WDN by minimizing leakage and improving its resilience. The study involved determining leakage coefficients for nodes using measured data and GA. Subsequently, the WDN was optimized by defining an objective function, constraints, and decision variables using both GA and NLP. The results demonstrated the superiority of GA in terms of pressure reduction, achieving a significant decrease of 23.7%. Additionally, GA outperformed NLP in enhancing the resiliency index, underscoring its effectiveness in optimizing the network's performance and ensuring its robustness against potential disruptions.

Direct soil water retention curve (SWRC) measurement is laborious, time-consuming, and expensive. To estimate its parameters, mathematical models and optimization approaches are applied. In this study, an improved alternative method to solve the optimization problem was introduced and developed. To evaluate the efficiency of this method, 12 soil samples with 6 different textures from 9 different regions of the world were used. The optimization equations were solved using a genetic algorithm (GA) and standard mathematical models, and the best model was chosen based on Taylor diagrams, R2, and computing time. The Bimodal versions of Fredlund-Xing (FX-b) and Brooks-Corney (BC) mathematical models represented the best and poorest findings of this stage, with R2 values of 0.913 and 0.825, respectively. Then, the optimization problem is solved with differential evolution (DE), Salp Swarm Algorithm (SSA), Jaya, and improved Jaya (I-Jaya) method, and the mean values of R2 were obtained 0.919, 0.931, 0.921, and 0.958, respectively. The results indicated a 16% improvement in the average R2, by selecting the suitable mathematical model and also using the I-Jaya.

Abstract Introduction: Water-crop production functions indicate an increase or a decrease in harvest for the plant’s consumed water (evaporation-respiration). To assess production functions of wheat and barley products, linear and second-order methods are used in numerous researches. On the other hand, since the mentioned relationships for some products may not have an acceptable correlation, third-order, logarithmic, and polynomial functions are used in various deficit irrigation treatments of 50, 60, 70, 80, and 90 percent. Methods: Farm data were evaluated and validated with resulted data from discussed equations. In order to study the proposed functions, statistical indices such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), Wilmott index (d), and F-test were used via measurement data in every scenario. Findings: All statistic values of both winter wheat and barley were in permissible range, indicating properness and acceptance of the equations. All functions were acceptable at a significance level of 5 percent for both products. According to the results of studying irrigation depth in terms of winter wheat and barley yield, it can be argued that third-order functions provide more accurate prediction in comparison with logarithmic and polynomial functions in both winter wheat and barley crops, and therefore, third-order function is prioritized over other functions

Nowadays, machine learning models are able to make good predictions based on pattern extraction between data. In this study, a neural-fuzzy network (ANFIS) was used to predict the inflow to the reservoirs of a dam namely, the Mahabad dam located in the northwestern part of Iran. A new Harris Hawk (HHO) optimization algorithm was also used to improve the ANFIS (HHO-ANFIS) structure. Monthly precipitation and temperature and inlet flow data to the reservoir one to three months ago were used as input parameters as 6 different input patterns. About 70% of the data was used for training and 30% to test the models. The results showed that the ANFIS model has good accuracy in training data although, for test data, its accuracy was greatly reduced. The development of the HHO-ANFIS model improved the accuracy of the prediction. The patterns with all input parameters had the highest prediction accuracy. In this pattern, values ​​of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash Sutcliffe Efficiency coefficient (NSE) for test data were 3.9 MCM, 2.41 MCM, and 0.86, respectively. Due to the good performance of the model used, it can be recommended for time series predictions.

Abstract An important issue in the operation of the Gotvand reservoir is the presence of salt formation in this reservoir. The present study has sought to identify whether it is possible to modify the operation curve of the Gotvand Dam to reduce the destructive effects of the Anbal salt dome and increase the operation of this reservoir. To achieve this goal, the performance of the Gotvand Dam reservoir was modeled from the time it was used (from August 2011 to March 2017), as well as the behavior of active salt karstic activity inside the reservoir with the dynamic system tools. The results showed that during the simulation period, the average amount of corrosion of the salt dome in contact with the reservoir water was 20.1 cm per day. Besides, 43.72 million tons of salt has been accumulated in the reservoir. Analysis of the results showed that 39% of the reservoir salinity is due to the dissolution of the salt formation. The rest of the salinity enters the reservoir with the upstream runoff. Based on the results of sensitivity analysis, changes in water level in the reservoir is the most important factor determining the salinity distribution parameters in the reservoir and the best remaining solution to reduce the effects of the salt dome inside the Gotvand Reservoir is to implement the curve of the optimal use of the reservoir for water released through the Gotvand Dam and the upstream basin of this dam. Keywords: Reservoir Operation, Quality management, Water withdrawal from different layers of the reservoir, Vensim. Introduction Most of the karst areas in Iran are in the Zagros Mountains, which is one of the rainy areas and due to the favorable weather conditions, large karst areas have been formed in these mountains. The Gotvand Dam is one of the embankment dams with clay core, which was built in the Zagros zone, folded with evaporative rocks. The outcrops of Gachsaran Formation in Gotvand dam reservoir, which is located five to nine km upstream of the dam axis and on the left bank of the river, have a large volume of thick and soluble salt strata with many cavities and caves and its surface is in direct contact with groundwater, surface water and dam lake water. Assessment of environmental effects resulting from the construction of Gotvand Dam has shown that the construction and operation of the Gotvand Dam have caused salt drainage into the water, increased salinity of groundwater in the region, dissolution of soluble substances, production of toxic substances and increased pollution of organic matter. The present study sought an answer to the question of whether it is possible to optimize the operation of the Gotvand Dam by modifying the command curve to reduce the destructive effects of salt karst. Materials & Method In this research, simulation of water quality conditions and interaction of dynamic phenomena in the reservoir and how to dissolve the active salt karst in it was done according to the curve of the reservoir operation by the dynamic system method and using Vesim software, in the first 68 months of the reservoir operation period. On the other hand, considering the changes in the volume of reservoir water due to different inlet flow rates upstream and also changes in water velocity in the reservoir, the behavior of the model was compared with the reality using the collected field information of the Gotvand reservoir. As the quality of reservoir water is affected by the type, location and how to operate the reservoir water, a solution to increase the optimal operation of the Gotvand Dam was presented. Discussion of Results & Conclusions As the results show, 43.72 million tons of salt were accumulated in the reservoir during the simulation period. The karst dissolution coefficient was also observed to change from 35.46 to 599.86 kg/s. Besides, the average rate of the salt karst corrosion in the reservoir water was 22.1 cm/day, while the mean discharge rate of the salt (Qsalt) flowing into the dam was calculated to be 3923.94 m3/day. In addition, the best level of water release from the lower gates of the dam to obtain quality water from the reservoir, instead of releasing water from the lower gate level, was estimated to be 147 m.Based on our research findings, the parameters of salt dissolution coefficient and flow rate of water entering the dam reservoir were the most important factors affecting the behavior of salinity distribution in the reservoir. Through the quality management and flexible operation of the dam reservoir and by changing the command curve of the Gotvand dam reservoir (in the form of releasing water from the different levels of the reservoir), the operation rate of the dam reservoir increase by 57% on average, compared to the typical case of releasing water from the lower gate valves level). The results showed that larger variations in the reservoir’s water surface level in a short period of time further increased the salt concentration due to in the increase in the hydraulic gradient of the reservoir, water circulation in karstic pores and cavities, and hence the dissolution of the salt karst. Accordingly, a quite linear relationship exists between the water surface level in the reservoir and the hydraulic gradient. It should be noted that in a real reservoir, in addition to surficial dissolution, other factors such as water penetration into cavities, regional fractures, and slip of salt layers into the reservoir are involved in the introduction of salt into the reservoir water, which are not considered in simulation models. Therefore, the actual value of the salt dissolution coefficient is greater than the dissolution coefficient of the simulation model. However, the main source of salt entering the reservoir is the surficial dissolution due to the contact of the salt layers with the volume of the reservoir formation, which has been observed to occur slowly, continuously and dynamically. Finally, from the SD model results analysis of the Gotvand Dam, it can be understood that as the volume of the reservoir water increases, a greater amount of salt karst is dissolved. In brief, by controlling the amount of water flowing into the Gotvand Dam’s reservoir via controlling the amount of water flowing out of the Dez Dam, which is situated on the upstream side of the Gotvand Dam, and by controlling the amount of water flowing out of the Gotvand Dam’s reservoir, and to put it differently, by implementing the optimum rule curves for the Gotvand and Dez dams, the salinity of the water flowing out of the Gotvand Dam’s reservoir can be considerably reduced.

Scouring depends on several factors, including the water flow of artificial obstacles, sections, piers, and foundations, the disturbance of bed materials, and soil permeability. The other factors are the non-parallelism between piers and the water flow, the type of river activity (static or dynamic), and the existence of a waterfall or an obstacle that forms a waterfall in natural bed materials, causing the underlying bed materials to be washed away. This study fully investigated how the movement of a tree trunk affects a river’s flow by considering different flow conditions using the artificial neural network (ANN) model. A feedforward optimal network with the error back-propagation training algorithm and sigmoid transfer functions was used for four models. To determine the number of neurons in the hidden layer, one and ten neurons were selected in the hidden layer according to verification indicators. In addition, a physical model was utilized to measure data. To verify and test the models, our data were gathered in a laboratory using the physical model. Considering the network structure of one neuron in the hidden layer, a comparison was made between dimensional and dimensionless parameter models that are effective in terms of the dimensions of the scour hole. The comparison between the results of the ANN and the measured data using nonlinear regression models demonstrated that the ANN was more accurate and capable of simulating phenomena. Additionally, R and RMSE values were between 0.93 and 0.98, as well as 0.18 and 0.013, respectively. Finally, the results related to the width, height, length, and depth of the scour revealed that the modified DOT model had the best agreement with Mahdavizadeh’s measured data.

Ilam province is located in the southwest of Iran, and the primary motivation for this research is to study different dimensions of rainfall fluctuations in the Ilam province. This study is of great importance for the management of the environment in terms of the application of rainfall distribution in different areas. After collecting the data, first, the average number of rainfall months for each of the studied stations for a period was obtained. Then the data were arranged numerically in the order of Gregorian months. Ultra-innovative artificial intelligence methods were used to perform spatial–temporal analysis. The results show that in autumn and winter all three factors were influential on rainfall in the region. The equation method of regression line trend was used to express the changes in rainfall in the study period, and it was concluded that during this period the rainfall trend in March, June, and December in all stations was decreasing. In May, all stations had an upward trend except for Harsin station. In other months, there are decreasing and increasing trends among the stations. The general trend for rainfall during the study period is also one of decreasing. Regarding the results, the standard deviation for the simulation is equal to 10.22 for autumn and 12.35% for winter. This value is about 17.97% and 7.19%, respectively, for the observed rainfall. The results show there are no significant differences between the model and measured data, so the artificial network is applicable for the simulated monthly precipitation.

Combined triangular crested weir-rectangular gate is one of the most important structures which controlling the level of water surface, measuring discharge and avoiding the sedimentation behind the weir. In this study, 4 models with different geometric conditions were simulated via Flow-3D software. Then, dimensional analysis was done to evaluate the effect of involved dimensionless parameters on the discharge coefficient. These parameters are h/b, h/d and h/y, where h is the water head over the weir, b is the gate width, d is the gate height and y is the distance between the top of the gate to the bottom of weir. Moreover, four different intelligent system models prepared and the accuracy of these models for estimation of discharge were evaluated and compared with each other. Results show that Flow-3D is very accurate in 3D simulation of this structure. Besides, Statistical indices are good for simulation the water head and discharge coefficient, in this study (RMSE, MAE and MRAE for discharge coefficient are 0.0673, 0.221 and 0.295, respectively). Moreover, results show that in all models the dimensionless parameters of h/b, h/d and h/y has inverse proportion with discharge coefficient. Discharge coefficient of combined weir-gate of this study is in the range of 0.3-0.9. On the other hand, the results of four intelligent system models of MLP, M5P, RBF and GRNN show that the MLP model is the superior model among others and respectively M5P, RBF and GRNN are in the next grade.

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in two future middle and far period downscaled and studied using 15 CMIP3 climatic models, under emission scenarios A2, B1and A1B, from the database of the LARS WG5.5 model. The difference in the amount of variations in temperature and rainfall in the periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, an ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15- models file scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next two periods were produced using the FEEDFORWARD neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, results showed a reduction in the average annual rainfall. The maximum amount of reduction was in far future far period. Besides, a reduction occurred in the average runoff of the catchment in the periods, values in the most years.

The AquaCrop model is one of the most recent models that can simulate the growth rates and yields of various crops based on water consumption levels. To determine the optimal irrigation level, data measured in two crop years (2018–2019) in different irrigation scenarios (full irrigation or 100% water requirement and 90, 80, 70, 60, and 50% irrigation) were calibrated and validated for arid and semiarid climatic conditions using the AquaCrop model. The model was calibrated to simulate the grain yields of winter wheat and barley using R2, RMSE, d, EF, and NRMSE statistical indicators. The obtained values of these indices were, respectively, 0.97, 3, 0.98, 0.94, and 4 for winter wheat and 0.98, 4, 0.92, 0.89, and 7 for barley. The model efficiency was also validated using crop harvest data in the crop year 2019. For grain yield simulation, the calculated values of R2, RMSE, d, EF, and NRMSE statistical indicators were, respectively, 0.99, 4, 0.97, 0.93, and 4.4 for winter wheat and 0.97, 7, 0.94, 0.91, and 9 for barley. The data of field and modeled samples were analyzed by analysis of variance (ANOVA) using the F-test, and significant results were obtained for both crops in all applied scenarios at the 95% level.

This research investigated various drainage parameters for unsteady conditions, including depth of installation, reflection coefficient, and depth of water table. For this purpose, Bouwer & Van Schilfgarrd, Dumm, Glover, Hemmad, and Bouwer equations were used. For the distance of computed drainage compared with measured data in central Iran, the results showed that the Bouwer & Van Schilfgaarde equation is better than others. Additionally, the installed depth was obtained 130 cm below the exiting underground, and this depth was applicable more than other depths; 1, 3, and 5-day precipitation were used to determine water table changes. The results illustrated that a 5-day duration had a better effect, which appeared in the existing condition drainage area. The reflection coefficient for the superior equation was also obtained as 0.65, which was very close to the measured data in the area. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard deviation (σ) were used to evaluate the results. MAE, RMSE, and σ were computed as 1.78, 2.02, and 0.02, for the superior equation respectively, and the appropriate distance between the two drains was determined as 51.26 m. The obtained results have a close agreement with other researchers in this regard.

In recent decades, groundwater overexploitation has caused an important aquifer level decline in arid zones each year. In addition to this issue, large volumes of effluent are produced each year in metropolitan areas of these regions. In this situation, an aquifer storage and recovery system (ASR) using the reclaimed domestic wastewater can be a local solution to these two challenges. In this research, a post-treatment of reclaimed municipal wastewater has been investigated through unsaturated–saturated porous media. A large-scale, L-shaped experimental model was set up near the second-stage wastewater treatment plant (WWTP) in the west of greater Tehran. The water, soil, and treated wastewater of the experimental model were supplied from the aquifer, site, and WWTP, respectively. The 13 physicochemical parameters, temperature and fecal coliform were analyzed every 10 days in seven points for a period of four months (two active periods of 40 days with a 12-h on–off rate (wet cycles) and a rest period of 40 days (dry cycle) between the two wet cycles). The results showed that the effects of the saturated zone were twice as great as those of the unsaturated zone and two-thirds of the total treatment efficiency. Furthermore, a discontinuous wet–dry–wet cycle had a significant effect on effluent treatment efficiency and contaminants’ reduction. In conclusion, an aquifer storage and recovery system using treated wastewater through the unsaturated–saturated zones is a sustainable water resource that can be used for agriculture, environmental and non-potable water demands.

The paper demonstrates a simulation optimization framework for enhancing the real-time flood control with gated spillways at places where no flood forecasting data are available. A multiobjective modeling scheme is presented for the flood management in a gated spillway in which the operator may specify the priorities on floods based on their different return periods. Two different operation strategies were devised. Both operating strategies employ ten-stage policies, which rely on the reservoir water level as the input data. The second strategy benefits from both the observed reservoir water level and the flood peak. The optimal values of the models’ parameters were obtained using a genetic algorithm. This is a novel approach because none of its policies needs flood forecasting data, thus, making them adaptable to any flood with any return period. To evaluate the performances of the proposed models, the flood control through a gated spillway of the Karkheh reservoir was considered, where flood hydrographs with different return periods were routed through the reservoir.

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in three periods including 2011–2030, 2046–2065, and 2080–2099 were downscaled and studied using 15 Coupled Model Intercomparison Project, Phase 3 (CMIP3) climatic models, under three scenarios of greenhouse gas emissions A2, B1, and A1B, from the database of the LARS-WG model. The difference in the amount of changes in temperature and rainfall in these three periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15-model scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next three periods, under the second approach and three emission scenarios, were produced using the feedforwad neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature in all three periods under the three scenarios. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, the average annual rainfall in all these three periods under the climatic ensemble model, and three emission scenarios showed a reduction in the average annual rainfall in the three periods. The maximum amount of reduction was in 2080–2099 (101 mm) under the scenario B1. Besides, a reduction occurred in the average runoff of the catchment under three ensemble models and the emission scenario in all three periods, as compared to the average of the long-term observational values in most years.

Transient flow control in Water Transmission Systems (WTS) is one of the requirements of designing these systems. Hence, among control equipment, air chambers offer the best solution to control transient flow effects, i.e. both prevents water column separation and absorbs pressure increase. It is essential to carry out an accurate and optimized design of air chambers, not only due to high costs of their manufacturing but also their important protective role. Accordingly, hydraulic design parameters comprise tank volume, diameter of nozzle and coefficients of inflow and outflow of nozzle. In this paper, it is intended to optimize these parameters in order to minimize manufacturing costs. On the other hand, maximum and minimum pressures in main pipeline are considered as constraints which shall fall in allowed range. Therefore, a model has been developed which is a combination of a hydraulic simulation model of WTS and an optimization model based on genetic algorithm. This model is first applied to WTS of Dehgolan-Ghorveh plain as a case study. Results of this research demonstrate that based on suggested model, negative wave creation and pressure increase in pipeline is prevented as well as decrease in manufacturing costs of air chamber.

This study focuses on the reliable parametrization of the full Soil Water Retention Curve (SWRC) from saturation to oven-dryness using high resolution but limited range measured water retention data by the Hydraulic Property Analyzer (HYPROP) system. We studied the performance of five unimodal water retention models including the Brooks and Corey model (BC model), the Fredlund and Xing model (FX model), the Kosugi model (K model), the van Genuchten constrained model with four free parameters (VG model), and the van Genuchten unconstrained model with five free parameters (VGm model). In addition, eleven alternative expressions including Peters−Durner−Iden (PDI), bimodal, and bimodal-PDI variants of the original models were evaluated. We used a data set consisting of 94 soil samples from Turkey and the United States with high-resolution measured data (a total of 9264 measured water retention data pairs) mainly via the HYPROP system and supplemented for some samples with measured dry-end data using the WP4C instrument. Among unimodal expressions, the FX and the K models with the Mean Absolute Error (MAE) values equal to 0.005 cm3 cm−3 and 0.015 cm3 cm−3 have the highest and the lowest accuracy, respectively. Overall, the alternative variants provided a better fit than the unimodal expressions. The unimodal models, except for the FX model, fail to provide reliable dry-end estimations using HYPROP data (average MAE: 0.041 cm3 cm−3, average r: 0.52). Our results suggested that only models that account for the zero water content at the oven dryness and properly shift from the middle range to dry-end (i.e., the FX model and PDI variants) can adequately represent the full SWRC using typical data obtained via the HYPROP system.