مجله مهندسی آبیاری و آب ایران
پیاپی 52 (تابستان 1402)

The subject of dam break has always been one of the most important and favorite issues of researchers over the past years. Due to the release of a large volume of water and its flow to the downstream areas, predicting how the flood wave due to partial or complete dam break will be transmitted, from the point of view of financial damage to downstream facilities and casualties due to it is very important. Several one-dimensional, two-dimensional and three-dimensional numerical methods and computer models have been proposed to solve the hydraulic equations of flow due to dam failure in open channels. One of these computer models is the two-dimensional SRH-2D model, which is based on solving shallow water equations, and its latest version 3.3.1 was developed recently. This study was conducted to investigate the ability of this model to simulate hydraulic flow due to sudden dam break.For this purpose, the available laboratory data as well as the results of other 2D and 3D models were used. After implementing the model, the results were compared with the analytical solution of sudden one-dimensional dam break on the frictionless bed, partial and asymmetric dam break with the presence of a triangular obstruction in the downstream flow path and also comparing the results of two-dimensional failure of water cylindrical column. In all cases, the present model accurately simulated the flow due to dam failure.

Accurate determination of the discharge coefficients plays an important role in estimating the discharge of the weirs. As a result, it is significant to estimate the discharge coefficients correctly. The purpose of this study is simulation and estimation the discharge coefficients of the broad-crested weirs. Hence, numerical simulation of hydraulic characteristics of these weirs were performed by ANSYS FLUENT software and results were obtained. Then, three intelligent models of ANN (MLP), GEP and RF were used to determine the discharge coefficients and the results of these models were compared. Assessment of the results were carried out using the statistical indices as R2 , RMSE, KGE, and graphical diagrams. The amounts of R2 , RMSE and KGE for ANN (MLP) were 0.906, 0.016, 0.927, for GEP 0.790, 0.025, and 0.780, and for RF 0.898, 0.013, and 0.841 were obtained, respectively. Due to the statistical criteria results and the range of RE% (∓5%), the ANN (MLP) was selected as the superior model. Violin plot illustrates a great and close agreement between the probability distribution of estimated data with ANN (MLP) and the obtained data from the numerical simulation using the finite volume method. Furthermore, the probability density function plot of residuals in ANN (MLP) model was closer to the normal distribution function, compared with the other models in this study.

In this study, the calculation of hydraulic flow and sediment (suspended load and total load) of Zaremrood river located in Mazandaran province has been investigated using Shiono and Knight quasi-two-dimensional model. This model is based on the Navier-Stokes continuity and momentum equations and is simplified by depth averaged concept. For this purpose, using the finite element method, this model was solved numerically and the lateral velocity distribution were calibrated at the Garmrood hydrometric station. Comparison of obtained results by Shiono and Knight model in different flow discharges against measured data indicates the high accuracy of the model for lateral velocity distribution. Afterward, by using the computed lateral velocity distribution, the distribution of suspended load and total load across the river was simulated. The results showed that among the 5 empirical suspended load equations selected in this study, the Einstein formula (1950) has the best accuracy in both one and quasi-two-dimensional modeling cases. This relationship in one-dimensional state with standard deviation of discrepancy ratio of 0.56% has a higher accuracy than the Rouse (1937) and Brooks (1963) sedimentary relations with a standard deviation of 0.52% and 2.47%, respectively. Finally, among the 5 experimental relationships of the total load studied, Meyer-Peter-Muller (1973), Ackers-White (1973) and Engelund-Hansen (1967) sedimentary relationships with discrepancy ratios of 66.67, 41.67 and 16.67, respectively presented the best results in one-dimensional mode, and the accuracy of all three methods was better in two-dimensional mode than in one-dimensional mode. Finally, it was found that the accuracy of suspended load and total load modeling in quasi-two-dimensional mode is more than one-dimensional, which shows the high impact of Shiono and Knight model in simulation of hydraulic flow and sediment.

In order to evaluate the efficiency of six different macroscopic water uptake functions, consisted of additive (M1), multiplicative (M2, M3 and M4) and conceptual (M5 and M6) models using field data of Ghods cultivar of wheat under salinity and drought stress, a study was conducted in the research field of university of Birjand during 2005-06 in split plots design based on randomized complete blocks (RCBD) with three replications. In this study, the main plots consisted of different levels of salinity (1.4, 4.5 and 9.6 ds m-1) and sub plots consisted of four levels of irrigation (50, 75, 100 and 120 percent of crop water requirement). The results showed that the additive model (M1) estimated yield less than actual amount. In other word, the effect of combined stresses on wheat yield was less than the one as compared to summation effects of salinity and water stress. The effect of drought stress on reduction of yield was more than salinity stress. The results also revealed that reduction functions of M6 and M5 models were better fitness to measured data than the other functions. The results revealed that M6 model estimated the reduction of relative yield of wheat under simultaneous stresses conditions of salinity and drought better than other models ( RMSD=0.17), although the M5 model also had acceptable accuracy (RMSD = 0.22).

One of the most important goals of irrigation planning and management methods, considering the water crisis in recent years, is to increase water productivity for various plants, including rice. To investigate the effects of deficit irrigation methods on water productivity of rice (Binam Variety), an experiment was carried out with eight treatments and three replications in Babolsar city of Mazandaran province in 2020. The treatments were regulated deficit irrigation and partial root zone drying in soil drought stresses of 10, 30 and 60 kPa (RDI10, RDI30, RDI60, PRD10, PRD30 and PRD60) and full irrigation with drip tape and flood irrigation systems (control). Soil water deficit was measured by a tensiometer. In this experiment, traits including yield, water consumption, and plant shoot dry weight, root weight density, root length, physical water productivity, net profit efficiency per unit volume of water, irrigation and rainfall water efficiency and evapotranspiration efficiency were investigated. According to the results, the highest and lowest physical water productivity was corresponded for PRD60 and flood irrigation treatments, respectively (1.39 and 0.59 kg / m3). PRD treatments at similar stresses compared to RDI treatments had higher rough rice yield and higher physical and economic productivity despite of lower water consumption. PRD60 treatment increased the physical productivity of water by 135.6% and decreased the water consumption by 76.3%..

Determination of reference evapotranspiration is the basis of all calculations of water requirement and is considered as the main foundation in determining water requirement, therefore, proper estimation of reference evapotranspiration is of great importance. Globally, systems have been developed that provide users with this information in a location-based manner. In this study, using the database of the country's water requirement system, the evapotranspiration rates of the reference plant were evaluated using meteorological data at Karaj station as a combination regression function. In this study, using SPSS software, the values ​​of different curves based on the parameters of relative humidity, temperature and sunshine were calibrated. The results showed that there is a significant relationship between transpiration evaporation and parameters of relative humidity, temperature, and sunshine. Therefore, two combined and multivariate regression models were calibrated using 7300 data of Karaj station and for evaluation using 15432 data in Alborz province. The evaluation results showed that the combined regression model with normal error of 29% and root mean square error index of about 0.5 mm, agreement index of 0.98 and efficiency index of model 0.94 had better performance. Therefore, this method can be used to determine the reference evapotranspiration in the stations of IRAN.

 In order to simulate the yield and water productivity of cucumber plant (Cucumis sativus L.), an experiment was conducted in the form of a completely randomized block design with three irrigation levels of 100, 85 and 75% of the water requirement in two growing seasons during 2017 and 2018 and using perceptron neural networks (MLP) and support vector machine (SVM) methods were used and finally, to select the appropriate and optimal model, the indices of explanatory coefficient, mean squared error and normalized mean squared error were used. The amount of irrigation water, number of leaves on the plant, temperature, evaporation rate and relative humidity were selected as input data and 60%, 20% and 20% of the total data were allocated for training, validation and testing of the model, respectively. The results showed that the MLP neural network with the inputs of irrigation water and number of leaves was more accurate in simulating fruit yield and water productivity in cucumber plants with an explanation coefficient of 0.92 and 0.86, respectively. The results of the sensitivity analysis indicated that the irrigation water input parameters are the most important effective parameters on the water consumption efficiency model and cucumber fruit yield with sensitivity coefficients of 0.9 and 0.86, respectively.

Water-centric thinking in water resources management and recognizing water as a purely economic commodity can create many challenges in a country's policies. But a holistic view, such as the Water-Energy-Food Nexus, sheds light on the multicentric nature of water resources management, and that consider each as a security commodity, not a purely economic one. The nexus approach pays equal attention to the security of all three parts and their interaction. Therefore, in this study, using the global database and global projects conducted to estimate the Nexus index, the results of Iran's output and position among 170 registered countries were analyzed. Also, the criteria are ranked by a Water-centric perspective using Analytical Hierarchy process (AHP) and compared with project weights. Iran ranks 84th in the global nexus index, with the energy sector ranking 49th and the highest among other water and food sectors. According to studies, Iran is in the middle position, but some neighboring countries such as Turkey and the UAE are in the 54th and 52nd positions. Weight prioritization also showed that the water-centric perspective can underestimate the effect of important indicators.

Today, leak detection is an important issue in water distribution networks, because leakage increases costs and causes a waste of water resources. In this study, a new method was used to identify the leak area in the water distribution network at part of Mahan city. First, the network was divided into a number of areas by the K-means clustering algorithm. Then, training samples related to each area were made using random leakage simulation in the network hydraulic model. The number of each area was used as the classification label of the multi-class support vector machine and along with the training samples, the classification model was taught. Finally, the trained model was used as a leak area identification model and was applied to determine possible leak areas with the observed field samples. The results show that out of 10 structures used to build the model, only three structures include; "Three areas and radial basis kernel function", "Three areas and linear kernel function" and "Five areas and linear kernel function", respectively, make an acceptable rate of 99.27%, 99.18% and 98.99% for the "Accuracy" evaluation index. On the other hand, among these three structures, dividing the network into five leak areas and using the linear kernel function divides the network into more areas and makes leak detection easier in restricted areas. As a result, this structure was used to build the final model.

Optimal use of dam’s water resources is a serious challenge in water resources management and engineering, due to continuous droughts and water shortages. Therefore, in order to comprehensively management in the used amount of dam’s water resources, it is essential to establish a control mechanism in the release of water resources. In this study, an improved evolutionary algorithm entitled "Adaptive Differential Evolution by Choosing of Dynamic incremental intervals (ADECDII)" was proposed to optimize the single-reservoir water system. Efficiency of the ADECDII approach was to use the dynamic incremental interval selection scheme for parameters adjustment of the classical differential evolution (DE). Problem modeling was defined as a problem of minimization with the objective function of error values between real demand and released water, with one-month time periods between 2008-2018 years, on Baft Dam in Kerman province. ADECDII performance evaluation was compared with six advanced algorithms. Based on the statistical results, lowest average of the dam water shortages with mean error of zero ( ) MCM (Million Cubic Meters) was recorded for ADECDII, which proved the efficiency of the proposed approach in the optimal allocation of released water values. This is while, other comparable algorithms could not report a real shortage of less than 0.37 MCM. The average of runtime for ADECDII was 7.15 sec, which was three seconds longer than DE runtime. Also, ADECDII in the tests of reliability index, vulnerability index, resilience index, sustainability index, difference absolute error values between released water values and total demand in an annual average, and convergence rate clearly showed a higher performance in compared to other comparable algorithms.

Calculation and estimation of evapotranspiration is one of the most important parameters of water management in agricultural engineering projects. The aim of this study was to evaluate the models of gene expression programming (GEP), group method of data handling (GMDH), and Multivariate adaptive regression spline (MARS) to estimating daily reference evapotranspiration. For this purpose, daily data recorded during the last 25-year period (1993-2017) of Aligudarz region (located in the east of Lorestan province) were used. 80% of the data were used for training and the remaining 20% for testing the models. The modeling results showed that only with the maximum temperature and average wind speed can evapotranspiration be estimated with very good accuracy. The error indices of GEP model in testing stage are , the error indices of MARS and GMDH models are .  Comparing the performance of the models showed that the March model performed better than the other models.

The use of appropriate methods for estimating sediment load has long been considered by experts in river problems. In this study, The suspended sediment load of the river was estimated using two artificial intelligence methods, including genetic programming (GP) based on graph and radial base functions (RBF) method. The implementations use data from 12-year statistics and information from four hydrometric stations of Gourab, Sarkmar, Dehloran, and Bayat roads on Meymeh River in Ilam province. In this study, the parameters of moon number and river flow were used as inlet parameters and river sediment load as outlet parameters. In the equations obtained from genetic programming method (GP), the highest correlation In the equations obtained from genetic programming (GP) method, the highest correlation obtained was related to Gorab hydrometric station with 99.18% and the lowest correlation was obtained related to the aggregated data of four hydrometric stations with 92.17%. In the radial baseline functions (RBF) method, the maximum correlation between educational and experimental data related to Bayat station was obtained with 100% and 94.20%, respectively, and the results. The results showed that radial basis functions (RBF) had better performance than genetic programming (GP) in estimating the suspended sediment load of Meymeh River

Of major constraints for crop production in arid and semi-arid regions of Iran are the lack of water resources and drought, and due to uncontrolled groundwater abstraction, the quality of agricultural water is declining gradually. Water pricing is one of the tools for water resources planning and water demand management. In this regard, the present study aims to determine the economic value of water in agriculture with respect to its different qualities in conditions of uncertainty in Rafsanjan and Anar provinces. Using interval mathematical programming, the shadow price range for different qualities of water was obtained. The price elasticity of demand was estimated in order to evaluate the effectiveness of pricing, the standard water demand function and to use it more efficiently. The findings show that the shadow price of water varies within different groups, and fresh water, moderately saline water and highly saline water were of the highest economic value, respectively. Comparing the efficiency of the plan in the current and equilibrium modes reveals that although water consumption has decreased by 50%, but the efficiency of activity has decreased by only 13.44%, and this implies that the amount of water consumption can be reduced in the virtue of redistributing water in pistachio orchards with minimal reduction in activity efficiency. This is feasible via eliminating low yield and profit, yet preserving higher yield pistachio orchards, may lead to reducing water consumption and extraction, and it also ensures high economic benefits for farmers.

Licorice (Glycyrrhiza glabra L.) is an important medicinal plant as it accumulates active ingredients, glycyrrhizin and liquiritin, in its roots. In the present study, mycorrhizal fungus inoculation (no inoculation and inoculation with Funneliformis mosseae) and gibberellic acid (GA) priming (no priming and priming with 1000 mg L-1) effects on growth, photosynthesis, and different characteristics of licorice root under different water regimes (100% of field capacity (FC), 65% FC, and 30% FC) were investigated. The results showed that among different irrigation levels, only the application of 30% FC led to a significant decrease in the shoot morphological characteristics, leaf chlorophyll content (SPAD), shoot dry weight, transpiration rate, net photosynthesis rate, and root length. While the application of 65% FC not only showed no significant decrease in most of the mentioned characteristics compared with 100% FC but also improved the photosynthesis parameters. Priming with GA at the 30% FC led to an improvement in photosynthetic water use efficiency compared to no pre-treatment. Also, GA increased plant height and root length, especially in combination with mycorrhizal inoculation. Inoculation with mycorrhiza improved chlorophyll content, shoot dry weight, and photosynthesis rate. Also, inoculation with mycorrhiza under 100% FC and 30% FC improved photosynthesis and photosynthetic water use efficiency. Inoculation with mycorrhiza also under 100% irrigation, and the integrated application of mycorrhiza inoculation and GA priming in 60% FC caused a significant increase in leaf area, root volume, root yield, and agricultural water use efficiency. Such results can be used in developing and promoting licorice cultivation based on water availability.

Because groundwater quality monitoring is very expensive and time consuming, it is necessary to find a method for estimating the hydrochemical variables with the minimum sampling. The aim of this research is to determine the optimal number of sampling wells in Khanmirza plain in Chaharmahal and Bakhtiari. For this purpose, the results of chemical sampling of 19 wells and seven water quality variables including SAR, EC, pH, TH, TDS and the sum of anions and cations for the period 1991 to 2011 were used. At the first, the best geostatistical method for drawing zones for each variable is selected. Then by means of semi-variogram curve the radius of influence of each well is calculated for each variable. According to the amount of radius overlap the minimum number of wells were determined to estimate the average of each variable in the plain. The results showed that to estimate the pH variable one sample well, for the SAR variable 3 sample wells, for the sum of anions and total cations 4 sample wells and for the variables EC, TDS and TH only 5 sampling wells are enough. The results of two-way t-test to validate the results also showed the ability of the proposed wells to properly estimate the studied variables in the plain. Therefore, it can be concluded that in special cases that require necessary and immediate information on groundwater quality in Khanmiza plain, instead of sampling from 19 wells, sampling can be done from a limited number of wells

The locals' health in mining sites can significantly benefit from the protection of water and soil resources. These heavy metal sources can negatively impact human health and the amount and quality of goods produced in the area. For this reason, the concentration of two elements, cadmium, and arsenic, in the Savad Kouh mining area of the province was calculated using three indices: pollution factor, degree of pollution or modified pollution factor, and accumulation of land. Fifty-three samples from the area, including 45 samples of agricultural topsoil and eight samples of riverside sediment, were collected to analyze the site. The samples prepared in a dry environment were brought into the lab and subjected to ICP-MS analysis using a 75-micron sample size. According to the pollution factor, cadmium and arsenic levels in agricultural soil have low pollution, which has a CF value of 1. Cadmium has a low pollution status, and arsenic with a CF = 6 has a low to medium status, according to the concentrations of elements in the riverside sediment. According to the modified pollution degree index (mCd), the region's agricultural soil and sediment have extremely low or low pollution levels. In other words, the mCd values for the two elements cadmium and arsenic are in the very low degree of pollution (0≤mCd≤1.5). According to the findings of the soil accumulation index for two elements, agricultural soil has a non-polluted or low-pollution status. In other words, the index value is between 0 and 1. The area falls into the low to medium pollution category in terms of two elements, according to the principal component analysis (PCA) results.