Journal of Irrigation and Water Engineering
Year:2024 | Volume:14 | Issue:55|Papers Count:20

One of the characteristics of concrete as a brittle material is its low tensile strength and brittleness, so it needs to be strengthened to be used as the most basic building material. In fiber-reinforced concrete, thousands of tiny fibers are randomly scattered and distributed throughout the concrete during mixing, this improves the properties of the concrete in all respects. In this research, the aim is to investigate the role of Kevlar fibers in improving the compressive resistance, tensile strength and permeability of Nano-silica concretes. The concrete samples made have 4% of Nano-silica and the ratio of water to cement in this design is equal to 0.32 and the amount of Kevlar fibers used is 0, 0.10, 0.20, 0.30 and 0.40% by volume of concrete. The results of the compressive resistance test show that in these tests, by adding 0.10% of fibers (optimal percentage), the compressive resistance improves by 28, 24, and 23% at the ages of 7, 28, and 90 days, respectively. From the results of the tensile strength tests, it was found that by adding 0.30% of fibers (optimal percentage), the tensile strength of the samples increases by 64%. The permeability test was performed using the Penetration Method, which showed a 30% improvement in water permeability in samples containing 0.20% of fibers (optimal percentage).

Due to limited resources in the field of hydraulic jump in compound channel, this laboratory research was performed by creating a stable and symmetrical jump.The study was implemented on an experimental channel with a length of 10 m, a width of 1 m, and a height of 0.8 m in the Hydraulics Lab at the Gorgan University of Agricultural Sciences and Natural Resources. 6 meters of the total length of the channel has been transformed into a composite channel. Also, the main cross-section and floodplains had adjustable depths and widths. The present study examined the effects of the relative width and initial Froude number. Three relative widths and three initial Froude numbers were studied in a total of nine tests. In each test, the initial Froude number was set to induce a hydraulic jump by changing the opening of the upstream opening height at a given flow rate.The present work experimentally induced hydraulic jumps in compound open channels and quantified the physical properties of hydraulic jumps (e.g., conjugate depths and hydraulic jump length) by point velocity, flow rate, and flow velocity measurements. The results indicate that the physical and hydraulic characteristics of the observed jump are different from the classical type and the use of classical jump equations leads to error.

Watershed management practices by biological, biomechanical and mechanical operations are not only trying to solving watershed problems, but also in line with the sustainable development through improving the economic condition and living standards of stakeholders. Therefore, the present study was conducted to impact evaluation of the soil and water conservation practices on the sediment yield of Kan watershed in Tehran province using the data of the period of 1998-2018. The initial results from the practices simulation using the SWAT model showed that there was the large difference between the simulated and observed values, the model was recalibrated and validated after steps performance of the sensitivity analysis with the 2-SUFI algorithm. Based on the obtained results, the coefficient value of explanation in the calibration and validation stage of model obtained equal to 0.74 and 0.86, respectively, and the Nash-Sutcliffe index value was equal to 0.74 and 0.77, respectively, that these values showed the model had the high efficiency for simulation. Then the simulation of watershed management practices investigated on the watershed level and the results indicated if the management practices implement the sediment values will reduce. In such away that the sediment yield in the watershed reduced using practices simulation of terracing, masonry check dams, gabion check dams, loose stone dams, seeding and planting decreased with rates of 44.65, 26.72, 26.70, 8.03, 4.97 and 9.73 percent, respectively.

This research was conducted with the aim of evaluating the WOFOST for simulating the corn yield and water productivity in two different conditions of water amounts and nitrogen fertilizer supply.In the first research project, the factors of irrigation water amount in four levels and nitrogen fertilizer in four amounts and in the second project, fertilizer in four levels and fertilizer splitting were considered.The results showed that in the first projct, the accuracy of the WOFOST model for simulating water yield and water productivity was in the excellent and medium categories, respectively, while the accuracy of this model in the second project was determined in the excellent category for both aforementioned parameters.The efficiency of the WOFOST model for simulating yield in both project and water productivity in the second one was acceptable, but for simulating water productivity in the first project was in poor conditions.In the first project, changing the nitrogen fertilizer consumption from N1 to N4 caused an increase in the difference between yield in observation and simulation conditions.The amount of irrigation water had no effect on changing the WOFOST accuracy and error.In the second project, increasing the distribution of nitrogen fertilizer from T1 to T2 caused the error of this model to decrease from 6.5 to 3.6 percent.Based on the results, it is necessary to conduct more studies to use the WOFOST model to simulate corn in different amounts of irrigation water, but this model can be used to simulate corn in different amounts and splitting of nitrogen fertilizer.

Currently, about 90 percent of the population of developing countries live in arid and semi-arid areas. Therefore, it is necessary and inevitable to identify the key and effective factors on the growth of agricultural products . The purpose of this research is to evaluate the accuracy and efficiency of the latest version of the AquaCrop model for simulating seed yield, biomass and water productivity efficiency of safflower in the form of a randomized complete block design with one control treatment (Conventional irrigation) and four deficit -irrigation treatments, including (RDI80, RDI60: reduced irrigation adjusted by supplying 80 and 60% of soil moisture deficiency) and (PRD80, PRD60: fixed partial root-zone drying irrigation with supply of 80 and 60% of soil moisture deficiency) and with four repetitions in Khuzestan University of Agriculture and Natural Resources was conducted. In order to evaluate the reliability of the model, some statistical indicator including the average normalized root mean Square error (NRMSE), the Coefficient of Residual Moss (CRM), Coefficient of Determination (CD), maximum error percentage (ME), Wilmot index (d) and the modeling efficiency (EF) was used. The statistical index (NRMSE) was obtained for seed yield (4.14), biological yield (10.16) and water productivity efficiency (4.9) and showed that the accuracy of the model in simulation is excellent. The results of the model validation show the accuracy and efficiency of the model in simulating seed yield, biomass and water productivity efficiency, and therefore, this model can be used to manage safflower irrigation in different states of deficit irrigation.

Surface waters are more exposed to pollution and quality reduction than other sources. In the current research, the seasonal changes of the water transfer channel of the University of Tehran School of Agriculture and Natural Resources have been investigated and analyzed. In this research, water samples were taken from the studied water transmission channel during different seasons of the year from three places along the channel using glass containers, water samples were prepared and transported to the laboratory. The results show that the total amount of soluble substances in water varies from about 27 mg/litr in winter to 375 milligrams per liter in summer. In autumn and winter, canal water is in class C1, which means low salinity, and in spring and summer, it is in class C2, medium salinity. Therefore, it will not create a problem for watering crops. Canal water is evaluated in medium condition in terms of acidity. The amount of oxygen index required for oxidation of canal water ranges from 3.5 mg/100 ml to 5.4 mg/100 ml. The COD value of canal water ranges from 25 mg/100 ml in winter to 47 mg/100 ml . Results of water samples showed that nitrification occurs in the canal. The amount of nitrate varies from 1.08 to 1.6 mg/liter in different seasons.The amount of coliform varies from 770 units per 100 ml in winter to about 1100 units per 100 ml in summer, and this is consistent with the change trend of other quality indicators of canal water.

Investigating the effect of agricultural management methods on the amount of nitrate and urea in the soil has special importance. The purpose of this study is to model and investigate the interrelation of hydraulic, reactivity, and solute absorption variables of different soil depths collected in the year of (2020-2021) from the pilot rice farm located in the Sari Agricultural Sciences and Natural Resources University, Mazandaran province, with an area of 0.6 hectares. In this study, the residual moisture of the soil (θr), nitrification rate (kn), and urea hydrolysis rate (kh) variables were modeled based on four, four, and two defined scenarios, respectively, using Wavelet-Artificial Neural Network (WANN), Wavelet- Artificial Neural-Fuzzy Inference System (WANFIS), and Wavelet-Gene Expression Programming (WGEP) models. The results showed that the performance improvement percentage of WGEP models compared to WANFIS and WANFIS model compared to WANN considering the RMSE evaluation index were obtained (16.96, 41.87), (85.72, 1.00), and (20.37, 3.27) for three variables of θr, kn, and kh, respectively. the volumetric residual moisture in the soil, and the urea hydrolysis rate variable is also highly dependent on the residual moisture in the soil. Also, the results showed that the hydraulic variables, reactivity and absorption of soil solutes can be affected by the climatic conditions of the region. Therefore, providing intelligent applicable models to estimate nitrate and urea variables in soil can help managers and farmers in proper management of water and soil resources and optimal use of nitrogen fertilizer with less time and cost.

The present study evaluates the water shortage indices in the agricultural sector of Jiroft Plain, taking into account the framework of water footprint during the 2008 to 2019. In this study, water stress, agricultural water stress, blue water scarcity, water self-sufficiency, water dependency and water poverty indices were evaluated. The results showed that the annual average of agricultural water resources is 1425 MCM, and the amounts of green and blue water resources are 11 and 1413 MCM, respectively. The annual average agricultural water footprint of Jiroft Plain is 354 MCM. The share of blue, green and gray water footprint is 40, 14 and 26%, respectively. The average water stress index is 37%, which shows that the moderate water stress in Jiroft plain. The average AWSI and BWS is 0.84 and 0.77, respectively, which are located in the class with high stress. The water self-sufficiency and dependency are 89% and 11%, respectively, which indicates high self-sufficiency and low dependency of the study area in terms of importing agricultural products. The high water self-sufficiency index despite the lack of rainfall in Jiroft Plain has caused high water poverty in the region. The most important reason for the high WSS is the high diversity of agricultural products in the food basket of this region. As an example, citrus fruits, dates, wheat, barley, potatoes and onions are cultivated in Jiroft plain.

Today, human society is involved in fundamental threats in the field of production, supply and distribution of water, food and energy. South Khorasan province is located in the arid and semi-arid region of Iran and is highly vulnerable in terms of water resources. Due to the close relationship between the three sources of water, food and energy and their mutual influence on each other, a new concept called the linkage approach has been proposed, which refers to the integrated nature and mutual effects of these three sources. The present research has analyzed the dynamic analysis of sustainable water resource management systems based on the correlation of water, food and energy resources in a 21-year horizon (2011-2031) in Darmian and Sarbisheh cities in South Khorasan province(The scope of implementation of the carbon sequestration project) with the help of Vensim DSS software. This research has been done based on Monte Carlo sensitivity analysis as well as the development of the model under the title of 5 scenarios, and finally, the fifth scenario was selected for the sustainable management of water resources in the research area. In this situation, optimal consumption modeling in different sectors, compliance with limit conditions and culture based on consumption pattern modification are among the key issues in resources crisis management.

Climate changes and human activities are the two main factors affecting the hydrological cycle and stationery/ non-stationary of hydrological systems. The existing indicators for drought monitoring are based on the assumption of stability of atmospheric conditions and environmental factors, and the research results of the last two decades indicate that these indicators do not have the necessary validity in the current non-stationary environment. Therefore, in general, the aim of this article is to provide a comprehensive review of the latest drought indicators in non-stationary conditions and to express the challenges and opportunities of future research. In this regard, the indicators that have been developed for different types of droughts should be reviewed first, since the methodology of these indicators is mainly proposed for stationary weather conditions, then we will review some of these indicators that In a non-stationary state, it is developed in Iran and other regions of the world. Studying and reviewing tools and methods for calculating non-stationary in determining non-stationary drought indicators is one of the other factors that will be discussed in this article. A review of various research showed that indices such as NSPI, NSRI, NSPEI, NRDI, etc. were among the most important non-stationary indices that were used to quantify meteorological/hydrological droughts. From the methodological point of view, all the research used the generalized collective model for location, scale and shape (GAMLSS) to model the non-stationary behavior of indicators.

Improvement of saline soils is very important from the point of view of soil and water resources conservation. Excess Solution salts in the root zone can reduce water uptake by the plant due to reduced soil osmotic potential. Necessary strategy in dealing with this issue is reducing the soil salinity to optimum level by capital leaching. The purpose of this study was to investigate the application of different amounts of leaching water in reducing salinity and evaluating experimental and theoretical models in predicting final salinity. This research was carried out in a part of Mohammadabad lands of Sistan plain as a randomized complete block. This experiment are carried out with five treatments and four iterations using test plots with one-to-one meter. Leaching was performed using 100 cm of water in four intervals of 25 cm. The required physical and chemical analyses before and after leaching and after the application of each irrigation cycle in treatments and at different depths up to 100 cm of soil were performed on the collected data. The results showed that the use of 100 cm of water for leaching could reduce the soil salinity class from S2A2 to S1A2. Experimental and theoretical models were evaluated and the results showed that theoretical models predicted the final salinity better than experimental models. Of all the experimental and theoretical models studied in this study, the theoretical model of a single reservoir with a correlation coefficient of 94% had the best results.

In this research, support vector machine (SVM) and artificial neural network (ANN) were used to simulate the monthly Groundwater level of Nahavand plain located in Hamedan province. Twenty years data (1997-2017) were used by engaging Matlab software. From these data, 14 years were used for training, 3 years for calibration and finally 3 years used for model validation. The statistical comparison of the results was also attempted with the aid of correlation coefficient (r) and standard error (SE). Four observation wells were used along with the variables of Groundwater level, precipitation, evaporation and temperature to simulate the Groundwater level. The highest accuracy among these two models is SVM model, which has SE = 0.11 in the training mode and 0.03 in the test mode. Also, the correlation coefficient in the test mode is 98%. Considering the appropriate accuracy of SVM method in simulating the groundwater level, the results indicate that, the RBF kernel function, with a variance of 6523 and a gamma of 527.23 for the optimal mode. The results of this study showed by using the SVM approach is more realistic in simulation of groundwater level and evaluation of the input parameters. In addition this could help in reducing the number of input parameters as well as can show appropriate accuracy for the simulation.

Estimating the amount of surface runoff and studying the relationship between rainfall and runoff is the most important issue in surface water hydrology studies. Using simple methods to estimate runoff in hydrological applications, especially in ungauged watersheds, is of particular importance. Probably the simplest conceptual method for predicting runoff depth is the use of the curve number rainfall-runoff model, which was originally developed to model the depth of runoff caused by rainstorms in small agricultural and rangeland catchments, but so far it has been applied in applications other than those originally intended. Various studies show that this method is still growing and improving and it needs to improve. In the present study, firstly, a review of the studies that investigated the factors involved in the curve number method to estimate the runoff value was done, and one of the main results of these studies is to suggest changing the value of the standard initial loss ratio (that is, changing the value of λ from 0.2 to 0.05) based on extensive field measurements and also considering rainfall-runoff response classes when using the curve number method. Secondly, some remaining challenges and future perspectives regarding this method are mentioned.

Precipitation is a parameter related to climate and research on it is facing problems due to the lack of continuous data. Accurate planning and management of water resources depends on the availability of continuous and accurate precipitation data at meteorological stations. Hence, estimation of missing rainfall data is important in order to obtain more reliable results. Various imputation methods have been proposed and developed by researchers to estimate missing values in daily and monthly rainfall data. In most situations, spatial interpolation techniques such as normal ratio and inverse distance methods are used for estimating missing rainfall values at a particular target station based on the available rainfall values recorded at the neighboring stations. Moreover, these two methods are found to be very useful in the case where the neighboring stations are very close and highly correlated with the target stations. In this study, several modifications and improvements have been proposed to these methods in order to estimate the missing rainfall values at the target station using the information from the nearby stations. The methods have been tested with different percentages of missing rainfall values and also with a radius range of 75 km to 150 km in the catchment area of South Balochestan. The result indicate that the performance of these modified methods improved the estimation of missing rainfall values at the target station based on the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).

The response of Thymus vulgaris and Zataria multiflora to mycorrhiza fungus inoculation (inoculation and non-inoculation with Funneliformis mosseae) at different irrigation levels (irrigation 100% of field capacity (100% FC, control or no stress); 75% FC (moderate stress); and 50% FC (severe stress); was investigated in greenhouse conditions. The results showed that inoculation with mycorrhiza led to improvement of root volume by 100%, root dry weight by 159%, shoot fresh and dry weight by 77% and 96%, and water use efficiency by 98% under 100% FC and in some cases under 75% FC. The results of evaluating the mycorrhizal inoculation effect in different irrigation conditions, also showed that the effectiveness of mycorrhizal inoculation decreased dramatically with increase in stress intensity. Phosphorus (P) content of the shoot of T. vulgaris and Z. multiflora decreased with drought stress application. In addition, mycorrhizae inoculation increased root P content by 14% in T. vulgaris. In Z. multiflora, inoculation with mycorrhizal improved P content by 63% compared with non-inoculation under severe stress. While in T. vulgaris, root P uptake was improved (170%) by mycorrhizal inoculation under normal and mild stress conditions, mycorrhizal inoculation did not show the efficiency to improve P uptake under severe stress condition. Meanwhile, in Z. multiflora, P uptake was improved by 75% and 215%, respectively in mycorrhizal inoculation under 75% and 50% FC. This can be one of the most important reasons for reducing the shoot to root ratio by mycorrhizal inoculation under severe stress conditions.

Ongoing drought and population growth, the use of treated wastewater for various purposes has become essential. On the other hand, the removal of nitrogen and phosphorus from wastewater is of paramount importancet. In this study, woodchips of Prosopis Juliflora trees, zeolite, and woodchips of Tamarix tress woodchips were used for pollutant removal. Four laboratory-scale reactors were employed to compare the performance of these materials in the removal of nitrates and phosphates from wastewater effluent. The main experimental treatments included a reactor filled with a combination of woodchips of Prosopis Juliflora trees and zeolite (PWZ), a reactor filled with woodchips of Prosopis Juliflora trees (CPW), a reactor filled with woodchips of Tamarix tress (TW), and a reactor filled with a combination of woodchips of Prosopis Juliflora trees, zeolite, and woodchips of Tamarix tress (PWZTW). Each of these treatments was tested at three hydraulic retention times of 12, 18, and 24 hours to investigate the influence of the mentioned filter media on pH, nitrate levels, and orthophosphate levels in the effluent. The average nitrate removal efficiencies in the PWZ, CPW, TW, and PWZTW treatments, across all three retention times, were found to be 82.72%, 72.93%, 76.61%, and 81.12%, respectively. The highest nitrate removal efficiency was achieved in the PWZ treatment , The average orthophosphate removal efficiencies in the mentioned treatments were calculated as 84.04%, 77.46%, 63.94%, and 81.60%, respectively. Overall, the use of a combination of woodchips and zeolite as an amendment improved the removal efficiency of nitrates and phosphates.

The significant wave height is one of the basic parameters for engineering operations of coasts and marine structures. In this study, significant wave height was estimated based on three scenarios including variables 1- waves, 2- meteorological and 3- combination of the first and second scenario, in time steps without time delay, 12 and 24 hours using adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) and combining them with wavelet theory (WANFIS, WGEP). Also, the results indicate that due to de-noising and removal of uncertainty in the data, combined-wavelet models have provided better results than singular models. The performance improvement percentage of WGEP models compared to GEP considering the RMSE criterion was 11%, 35%, and 7% for the first to third scenarios, respectively. Most of the hydrological conditions in the seas depend on temperature changes and the amount of this parameter is an important determining factor in the environmental conditions of each region. Also, changes in temperature and surface wind change the density of sea water. Therefore, the climatic variables of the region can affect different scenarios. The results of this study and the presentation of the governing mathematical relationship for estimating the value of significant wave height by the method of gene expression programming can be very useful in the field of coastal and water resources management and engineering.

Streamflow forecasting models play a crucial role in hydrological issues, such as the determination of reservoir inflows and flood forecasting. In this research, artificial intelligence hybrid models including ANFIS and GA-ANN have been used for short-term (daily) streamflow forecasting. This research aims to predict the outlet of the Latiyan basin, Tehran province, from 2017 to 2018. For this purpose, a snow-covered area (SCA) is obtained from the processing of Sentinel-2 optical satellite images. Then, in order to extract the effective snow, the fusion algorithm is applied for Sentinel-1 and 2 integrations. Finally, the artificial intelligence model with the help of the effective snow parameter along with other daily hydrometric and meteorological data including daily precipitation, temperature, and discharge is applied to forecast the daily outlet of the basin. Also, to improve the model performance, the seasonal index has been used to identify streamflow trends and better model training. The results showed that the prediction model using satellite data has improved its performance by 37%, which shows the direct effect of the snow parameter on the basin runoff. In addition, the trend of changes in the effective snow parameter has a favorable agreement with the flow trend of the basin, especially in the peak flows. Also, using seasonal information as an input parameter can improve the results of the prediction models by approximately 22%. In addition, the AI method based on fuzzy inference (ANFIS) showed better performance than the developed neural network method (GA-ANN) based on statistical indices.

Floods are one of the natural hazards that occur in many parts of the world and cause irreparable financial and human losses. One of the most important aspects of controlling this crisis is the accurate identification of flooded areas. Also, it is important to predict flood-prone areas in order to prevent and reduce losses and casualties related to flooding. In this article, with the help of Sentinel-1 satellite images and a deep learning encoder-decoder network, the flood phenomenon in the images has been identified and segmented. These images belong to the regions of Nebraska, North Alabama, Bangladesh, Red River North, and Florence, and the ground truth map of each image, in which the target and non-target classes are shown as 0 and 1, was provided by NASA in 2021. In this article, flood-affected areas have been identified and segmented using encoder-decoder convolutional neural networks and the aforementioned satellite images. This network consists of encoder and decoder paths each containing convolutional layers which are responsible for extracting features and recovering these features, respectively. Various evaluation criteria were used to evaluate the performance of this method, including accuracy, IoU, F1-Score, and Kappa. This method has shown very good performance in the process of identifying and segmenting flooded areas based on the results obtained. The IoU obtained during the evaluation process was 96.04%, which is higher than the highest IoU obtained in other comparable studies (76.81%).

SWAT hydrological models are one of the methods of estimating runoff obtained from rainfall at points without hydrometric stations. In other words, by simulating the process of converting precipitation into runoff, these models are able to estimate the amount of runoff in watersheds without measuring stations with the least possible time and cost.Based on this, the current study has been conducted with the aim of evaluating the power of the SWAT hydrological model in climatic conditions and simulating the outflow of rivers in the Tajen watershed and comparing it with observations. The most important data used in the research include vegetation, altitude classes, slope, climate data (wind, rain, maximum temperature, minimum temperature), climate changes and climate change conditions, etc.The results of this study show that the error rate of the LARS-WG hydrological model in climate conditions and in the simulation of runoff and average daily discharge was very low, so that the Nash-Sutcliffe coefficient of the simulated daily discharge compared to the observed discharge in the period from 1998 to 2014, during calibration, was more than 0.50, which after re-optimization Soil parameters, this coefficient was obtained in the validation stage (2018-2014) above 0.74 for daily discharge. And this is an indication of the low level of model error in simulating the outflow of the watershed.Keywords: SWAT hydrological model, climatic conditions, Tajen watershed.