IRAN-WATER RESOURCES RESEARCH (IWRR)
Year:2024 | Volume:20 | Issue:2|Papers Count:11

In this research, a numerical prediction infrastructure is developed using the support vector machines and the wavelet transform to predict the changes of water quantity and quality in Anzali lagoon. Due to the necessity of accurate measurement of climate forecasts from the existing quantitative and qualitative statistics of the surface flow, field data and ground level in a 20-year period from 1999 to 2020 were pre-processed in PYTHON software environment and used as the calculation base. The results indicated that the regression extracted with the RBF function had a high match compared to the linear regression. Also, after confirming the experimental method using the SVM model, a wavelet transform model was developed to determine the final parameter of CWT. The results showed very close values for the target function in the radial function and the linear model in the data range of -2 to 2 in the range of -0. 9 to 0. 1. But these numbers were different in the polynomial function, which indicated a high compatibility of the regression extracted with the RBF function compared to the linear regression. Also, the results showed that the SVM prediction model well fitted the RBF function on the data in accordance with the linear regression fitting in the experimental method of discovering the trend and the time series of the user's data. The results in this case indicated CWT with a density of four in the recorded periods in accordance with the images.

Flood is one of the natural disasters that causes the highest number of casualties and economic damage every year in different parts of the world and the country. Khalkhal County is not an exception to this rule and the occurrence of this destructive phenomenon during the past few years has destroyed some communication roads and bridges and damaged some residential area, agricultural lands, and orchards located in this county. Therefore, considering the importance of the subject; the main goal of this research is to produce a flood risk map in Khalkhal County. To this end, firstly the digital layers of the effective parameters, including slope, aspect, DEM, geology, land use, distance from city and village, rainfall, distance from the river, and density of the river were prepared. Then standardization of the maps was done using the fuzzy membership function and the weight of each criterion was determined based on its importance in the occurrence of floods according to the CRITIC method. Finally, using the MARCOS multi-criteria analysis method, a five-story zoning map was prepared. The results of the study showed that the slope, lithology, land use, and precipitation are the most important causes of floods in Khalkhal County with respective weight coefficients of 0.163, 0.155, 0.144, and 0.135. In addition, 25.12% and 14.58% of the range area are in the very high-risk class, respectively, which indicates the potential of this county for floods. In addition, according to the use of the ROC curve method and the sub-curve level (0.79), the accuracy of the MARCOS method is good in identifying and zoning areas prone to flood risk in Khalkhal County. The results of the current research, as a valuable input for decision-makers, can play a key role in the process of identifying flood-producing areas and prioritizing them for watershed measures and resource, and capital management.

Considering that a significant portion of water required for various purposes is supplied through groundwater resources in Iran, understanding the hydrogeochemical conditions and the factors influencing it as well as the concentration levels of dissolved salts and ions is of utmost importance. This study investigated the factors influencing the hydrogeochemical conditions of the Urmia aquifer in the Urmia Lake watershed. The study utilized a 15-year average of monthly hydrochemical analyses of water quality parameters up to the water year 2022-2023. Ion ratios, Gibbs and composite diagrams, and Piper and Chadha plots were employed for the analysis. Results from the ion ratios indicated that sodium ions in the Urmia aquifer have dual origins: direct and reverse ion exchange. It also showed that the Magnesium ions were derived from three sources: dolomite weathering, dolomite-limestone dissolution, and dolomite dissolution. Sulfate ions exhibited an anthropogenic origin, influenced by agricultural fertilizers and wastewater disposal. Additionally, results from ion ratios indicated that Lake Urmia and the associated saltwater intrusion had no significant impact on the groundwater quality of the studied aquifer. These findings were confirmed by Chadha diagrams. The nature of water-rock interaction reactions, as confirmed by Gibbs diagrams, was investigated in terms of direct and reverse ion exchange using the chloride alkalinity index and composite diagrams. Results showed that the reverse ion exchange dominated in the Shahrchay sub-basin, while in the other sub-basins the direct ion exchange prevailed. The presence of the Shahrchay reservoir in the Shahrchay sub-basin appears to have caused the hydrochemical behavior of groundwater to differ from other sub-basins. Geochemical modeling of the aquifer using saturation indices indicated a chemical environment conducive to the dissolution of halite, anhydrite, and gypsum (negative saturation index) and the precipitation of dolomite, calcite, and aragonite (positive saturation index). The use of Piper and Chadha diagrams revealed that calcium and magnesium bicarbonate types were the predominant water types and facies.

Water level estimation is essential for understanding and managing water resources, predicting and mitigating flood effects, and water resource management decisions but it is often hindered by limited resources, capitals and time. This study investigates the performance of a non-contact, image-based water level measurement system using advances in smartphone imaging technology. For this purpose, a smartphone equipped with a camera was used to record the desired images. The images were then processed by image processing to identify and measure the water level. The core of this study involved the development and comparison of two computational models: deep learning (DL) and convolutional neural networks (CNN). These models were used to estimate the water level based on the processed image data. The results showed a different degree of accuracy among the models; the CNN model showed better performance than the DL, which had the lowest root mean square error of 24.36 mm. In contrast, the DL model with the root mean square error of 28.39 mm showed that the relative effectiveness of the convolutional neural network in this study is better than deep learning in terms of accuracy and high reliability. The study showed that it is possible to monitor and control the water level in remote and difficult places completely automatically without the need for relevant personnel.

The development and management of aquifers in coastal belts is a significant contributor to sea saltwater intrusion. Therefore, tempo-spatial monitoring of the qualitative behavior of these aquifers is crucial for understanding the development of salinity, identifying its sources, and implementing measures to control this phenomenon. This study investigates the hydrochemical variations in coastal groundwater resources of the Ghaemshahr-Juybar plain by the Caspian Sea. For this purpose, a combination of statistical methods, principal component analysis, the saline water intrusion groundwater quality index, and hydrochemical facies evolution diagrams was utilized alongside GIS tools to compare, identify, and investigate the tempo-spatial changes of saltwater intrusion in this coastal alluvial aquifer. Based on analysis made on qualitative samples collected from three unconfined, perched, and saline aquifers during four time periods (2011, 2014, 2017, and 2020), saline water intrusion in these aquifers is certain but the sources of their salinity differ. The results indicated that the chemical composition of the groundwater in aquifers of this plain is primarily controlled by three main factors; the saltwater intrusion from the Caspian Sea and the fossil water from the bedrock, the reverse cation exchange process, and the influence of domestic sewage, agricultural activities, and the use of nitrate fertilizers. These factors, whether of natural or anthropogenic origin, have been exacerbated by human activities, including unauthorized and excessive exploitation of agricultural wells in the plain. The findings of the research indicated that the construction and operation of the water supply network from the Alborz Dam in the plain, and the use of surface water for irrigation instead of groundwater wells, significantly reduced the intrusion of saltwater from the sea and fossil water from the bedrock into the coastal aquifers. Therefore, considering the geological and lithological conditions of the studied area, it is essential to restrict water exploitation from the wells to prevent the intrusion of fossil saline water into the existing aquifers, thereby mitigating the saltwater upconing phenomenon. The proposed approach in this study for managing the salinity of coastal aquifers at risk of saltwater intrusion can be highly beneficial for both managers and researchers.

Access to water resources is one of the most challenging issues of this century, which can be central to many future developments in the world. One of the most common causes of disputes between neighboring countries is the disagreement over the exploitation, ownership, or sovereignty of border rivers. The current research was conducted with the aim of analyzing the water dispute between Iran and Afghanistan in the Hirmand River basin and providing a sustainable solution using game theory. Conflict modeling and analysis were done using the GMCR decision support system. After identifying the players and their options, 256 states were created to maintain the balance, of which 40 were identified as possible or feasible states. In the next step, by solving non-cooperative games and prioritizing strategies by decision-makers, 5 potential outcomes were identified. After the final analysis of the model, the strongest and most desirable state among the possible conflict states was identified. This state involves the Iranian government supporting the optimal cultivation of strategic crops in Afghanistan, while also providing engineering services for the development of Afghanistan's water infrastructure. In turn, the Afghan government would cooperate by recognizing the Iranian government's claims and releasing the Iranian water rights.

Water is one of the limiting factors for the development of the country's agricultural economy and one of the main goals of policymakers in the agricultural sector is to allocate this input among different agricultural activities in a correct way in order to improve its efficiency. Improving water efficiency can be a suitable criterion for identifying an appropriate water allocation policy in each region. This study utilized the Positive Mathematical Programming (PMP) technique to investigate the effects of various water-pricing policies in Ajabshir Plain. Data was collected through simple random sampling and face-to-face interviews with farmers during the agricultural year of 1399-1400 (Iranian calendar). Four scenarios were implemented regarding the price and quantity of water consumed for major agricultural products, such as wheat, barley, potato, and onion, as well as major horticultural products, including grape, apple, almond, and walnut. The results indicated that, for all selected products, increasing the water price led to a decrease in cultivation area and an increase in water productivity for the majority of the scenarios. Based on the Net Benefit per Drop (NBPD) index, walnut with 187,910 Rials per cubic meter of water, almond with 180,247 Rials per cubic meter of water, and potato with 83,827 Rials per cubic meter of water had the highest index values. According to this study, increasing the price of water and modifying cultivation patterns while considering the principles of sustainable development are effective strategies to enhance water productivity. Additionally, efficient water demand management can help to prevent waste.

River basin organizations (RBOs) are organizational bodies that set the ground for the Integrated Water Resources Management approach in the scale of a basin. The forms and missions of such organizations are different in different river basins and their performances are affected by different factors. The first and only experience in Iran regarding the establishment of an RBO is the Zayandehrud River Basin Council which was established in 2013. The Council then transformed to the Zayendehrud Restoration Working Group in 2019. This study aimed at assessing the performance of the Zayandehrud Council in the six years of its existence using a performance assessment framework developed in the study. The data needed were gathered through documents, questionnaires, field investigations and interviews. The performance of the Council was assessed in three dimensions of “efficiency”, “effectiveness”, and “learning”. In terms of efficiency, the results showed that the Council suffered from lack of professional human capacity and has not been successful in implementing the river basin management. Collaborative participation was missing in the Council. Furthermore, sectorial thinking and mistrust dominated. There were also lack of binding and accountability mechanisms in the Council and in the associated organizations responsible to implement its approvals. According to the effectiveness dimension, the Council decisions were not affected by the basin hydrological conditions, rather they were mostly influenced by the expected available water to adjust water allocations. The Council was also unsuccessful in resolving social challenges and sometimes even reaching decisions in the Council’s meetings was hindered by such challenges. According to the learning dimension, the Council was not authorized to check the persisting rules, neither was it allowed to modify the overarching taken decisions and policies. Thus, the learning process has ended to destructive results.

Based on the existing studies, earthquakes cause changes in the groundwater levels; however, so far, the mechanism of these changes has not been accurately identified. One of the main objectives of this research is to determine the factors, which directly or indirectly influence the intensity of the groundwater level changes after an earthquake. In this research, some of the factors that caused the increase or decrease in the groundwater level of the Sarpol-e Zahab aquifer after the earthquake of November 12, 2017, have been investigated. Linear regression analysis was used to compare the correlation between these factors and the groundwater level changes. The results showed relatively high correlation between the RT values (resistivity of the saturated zone multiplied by the thickness of the saturated zone) and the increase in groundwater levels with a correlation coefficient of r=0.70. Also, the water table depth (WTD) with r=0.54 and the extensional area caused by strike-slip fault with r=0.48 had the highest correlation with the decrease in groundwater levels.

Access to data with adequate accuracy is one of the most important elements of optimal water resources management. In this context, the use of more accurate methods and reducing the error of measuring devices has always been considered. In the meantime, discharge values in open channels such as rivers are among the most frequently used water data. Despite advances in discharge measurement due to operational constraints, the use of stage-discharge relationships remains the most widely used method for estimating discharge in rivers and drainage channels. These relationships are always subject to various errors and uncertainties and need to be modified and updated regularly. In this study, the Linear Regression and Kalman filter data assimilation technique are evaluated to update these relationships. For this purpose, two series of hypothetical and measured data were used to develop the methodology and prove the efficiency of this method in improving the performance of stage-discharge relationships. The results obtained showed a 22% and 97% reduction in RMSE and MAE assessment statistics respectively by applying the Linear Regression and Kalman filter to hypothetical data. After confirming the effectiveness of the filter, it was also applied to the real data from the Hamidiyeh station in Karkhe basin. The results showed a reduction in the RMSE and MAE by 31 and 42 percent, respectively, when using the Kalman filter. It can be deduced that the use of the Kalman filter to modify and update the coefficients of the relationship between stage and discharge is effective to increase the accuracy of data estimated from these relationships.

Water governance is one of the critical issues in the state of California, significantly impacting the lives of individuals and the economy of the region. Given the climatic conditions and water scarcity challenges in this area, water governance holds immense importance. This article will examine the significance of water governance in California and the necessity of implementing appropriate policies and programs for managing water resources in the region. Additionally, the effects of population growth, climate change, and human activities on the water resources of this area will also be explored. The article will address the role of government and related organizations in managing water resources and improving water quality in California. The importance of water governance in California is vital from economic, social, and environmental perspectives, and further research in this field could enhance water resource management in the region. Therefore, the article aims to explore practical solutions from both financial and engineering viewpoints, stating the relevant institutions and budgets associated with the projects.