https://jhwe.shahroodut.ac.ir/ Journal of Hydraulic and Water Engineering en daily 1 Wed, 01 Jan 2025 00:00:00 +0330 Wed, 01 Jan 2025 00:00:00 +0330 https://jhwe.shahroodut.ac.ir/article_3454.html Soil water infiltration is a critical process that affects soil water availability, crop growth, and environmental sustainability. This study aimed to model soil infiltration of soil water amended with cow dung and poultry litters’ organic matter using Design Expert 13.05.0 software. The organic matter mixing ratios percentages for the cow dung and the poultry litters are respectively, 100/0 for T2, 0/100 for T3, 75/25 for T4, 50/50 for T5, 25/75 for T6, and T1 for the bare soil, to make up a total of six strips. A double ring infiltrometer was used for the infiltration fieldwork experiment during the dry season and the raining season in Samaru, Zaria, Nigeria. The experimental fieldwork data was imported into Design Expert 13.05.0 software to analyze and model the soil water infiltration on the amended soil. The results of the study showed that 2FI (two factor interaction) model source described the interactions between the variables (time and depth) and the response (infiltration rate) during the dry season while the linear model source described the interaction during the raining season. Likewise, the optimal combination of cow dung and poultry litters that resulted in the highest R2 value for dry season as 0.8786 and for raining season as 0.8901 was 75% cow dung and 25% poultry litters for T4. Furthermore, the results of this study can be used to develop sustainable soil management practices that enhance soil infiltration and reduce soil erosion. https://jhwe.shahroodut.ac.ir/article_3455.html One principal cause of river bank collapse is the removal of more sediment from stream banks than the system can replenish. This study aims to assess river bank erosion rate and erosion monitoring assessment for about 3 km of the Shelie River. The primary data (more than 20 River Bank and 20 bed materials, 104 river cross-sections were collected using standard surveying equipment. Sieve and hydrometer analysis were implemented. Secondary (metrological) data were collected from an Ethiopian national metrological agency. HEC-RAS model was used to compute the water surface profile and discharge-stage analysis. Additionally, BSTEM model was used to depict river bank migration rate and its stability analysis. BSTEM model result showed that left river banks are retreating laterally on average 0.012 m/hr for considered reach, and overall safety factor is greater than unity. Further, steady state flow simulation results confirmed that conveyance capacity of the considered reach is not enough to carry 50-year return period design discharge and flood inundation raises a maximum of 1.2 m from normal flood level and extends 20 m laterally. https://jhwe.shahroodut.ac.ir/article_3536.html The study assesses the pollution of the Lukaya River and its tributaries by identifying factors that degrade water quality in order to formulate recommendations for sustainable management of this resource. The analysis uses Principal Component Analysis (PCA), a statistical method that efficiently handles complex water quality data. PCA reduces the dimensionality of the data while preserving variance, facilitating the identification of relationships between key physicochemical parameters such as pH, conductivity, nitrates, and phosphates. The results show that two principal components explain more than 70.23% of the variation in the river data. The sampling sites reveal significant pollution, especially by nutrients, affecting biodiversity and the health of aquatic ecosystems. Agricultural and livestock activities are the main sources of pollution. PCA helps identify these sources and provides a detailed visual analysis. Continuous monitoring and targeted actions are essential to maintain water quality and protect aquatic ecosystems, ensuring their viability for future generations. https://jhwe.shahroodut.ac.ir/article_3537.html This study introduces a framework for assessing climate change and flow conditions by integrating the latest climate simulations from the CMIP6 project (HadGEM3-GC31-LL model) with the Soil and Water Assessment Tool (SWAT), while also evaluating the influence of different climate model resolutions. A total of 66 hydrological and environmental flow indicators from the Indicators of Hydrologic Alteration (IHA) were calculated to assess future extreme flows in the Sarbaz River Basin, located in Sistan Province, which is particularly vulnerable to flooding. Results indicate that by the 2030–2050 period, compared to the baseline period of 1990–2019, annual precipitation, streamflow, and maximum and minimum temperatures are projected to increase by 6.9%, 9.9%, 0.8°C, and 0.9°C, respectively. Monthly precipitation and streamflow are expected to rise especially during the monsoon season (June–September) and early wet periods (December). The magnitude of minimum 1-, 3-, 7-, 30-, and 90-day flows is projected to increase by 7.2% to 8.2%, while peak flows could rise by 10.4% to 28.4%. Finally, significant differences were observed between high- and low-resolution climate models, with high-resolution models predicting an 11.8% increase in average monthly flows during November–January, compared to just 3.2% in low-resolution models. https://jhwe.shahroodut.ac.ir/article_3540.html Accurate prediction of evaporation is critical for effective water resource management, particularly in arid and semi-arid regions. This research evaluates the performance of five machine learning algorithms Decision Tree, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Artificial Neural Network in estimating monthly evaporation rates using meteorological data collected at Shahrood Synoptic Station from 1992 to April 2025. The dataset includes key climatic parameters such as average temperature, wind speed, precipitation, and relative humidity. Model performance was assessed through four metrics: Mean Absolute Error, Coefficient of Determination, Kling-Gupta Efficiency, and Average Absolute Relative Deviation. Results indicate that the Random Forest model outperformed all others, achieving the lowest MAE of 19.94 mm, highest KGE of 0.973, and lowest AARD of 0.521, reflecting superior accuracy and stability. The Artificial Neural Network model also demonstrated strong predictive capability, closely followed by Support Vector Regression, while simpler models like Decision Tree and K-Nearest Neighbors showed comparatively weaker performance due to their limited ability to capture complex evaporation dynamics. Temporal analysis revealed that all models effectively captured seasonal evaporation patterns, with Random Forest and Artificial Neural Network most accurately tracing peak and trough fluctuations. The results demonstrate that machine learning models possess strong predictive accuracy for evaporation estimation and offer a reliable approach for assessing evaporation and water loss. https://jhwe.shahroodut.ac.ir/article_3556.html Water is a fundamental component of life and one of the most vital natural resources, playing an indispensable role in sustaining human health, supporting agricultural productivity, enabling industrial development, and preserving ecological balance. As global populations expand and urbanization accelerates, ensuring access to safe and clean drinking water has become a significant public health priority. Throughout history, civilizations have recognized the necessity of monitoring water quality to prevent the spread of waterborne diseases and protect community well-being. In modern times, the evaluation of key physicochemical parameters such as Total Hardness (TH), Potential of Hydrogen (pH), and Total Dissolved Solids (TDS) has become essential for assessing the suitability of drinking water. These indicators are directly linked to various health outcomes and are commonly used by national and international agencies to set water quality standards. This study focuses on assessing the physical and chemical characteristics of drinking water in Shahrood County, aiming to determine potential health risks and ensure compliance with regulatory guidelines. Over nine years (2016–2024), water samples were systematically collected from local drinking water wells and analyzed using standard laboratory protocols. The average values recorded during this time were 7.7 for pH, 239.2 mg/L for TH, and 394.1 mg/L for TDS. When compared with benchmarks set by the Iranian National Standards Organization, the World Health Organization (WHO), and the United States Environmental Protection Agency (EPA), the results confirm that all measured parameters fall within acceptable limits. Thus, it can be concluded that the drinking water in Shahrood County meets health safety standards and poses no significant risks to consumers. https://jhwe.shahroodut.ac.ir/article_3567.html This study presents a comprehensive stability assessment of the Daroongar earth dam in Iran's semi-arid region through a three-year monitoring program (2019-2022) combining precision instrumentation and finite element modeling (Plaxis 8.6). Field data from 19 embankment piezometers, 10 electric piezometers, 28 standpipe piezometers, and 13 total pressure cells installed in critical sections were systematically analyzed. Comparative analysis of key parameters revealed significant discrepancies between field measurements and numerical simulations: total stress showed 22% average deviation, pore pressure in the dam body exhibited 37.9% mismatch, while foundation pore pressure demonstrated a 35% discrepancy (Δ = 304.3 kN/m², p<0.05), primarily attributed to instrument blockages. Arching effects analysis indicated minor 0.032 unit variations (95% CI: -0.3-0.37), within acceptable safety limits. The research highlights the importance of implementing shorter monitoring intervals and incorporating thermometric methods for enhanced seepage detection. Statistical validation via SPSS emphasized the need for constitutive model recalibration, particularly for soil-specific gravity and shear strength parameters, to reduce simulation-field measurement gaps. Practical recommendations include proactive maintenance protocols addressing instrument blockages and optimized drainage system designs. These findings provide actionable insights for improving safety and longevity of earth dams in semi-arid climates, demonstrating the critical synergy between advanced numerical modeling and robust field instrumentation systems. The study contributes to better understanding of earth dam behavior under operational conditions while proposing concrete measures for enhanced monitoring accuracy. https://jhwe.shahroodut.ac.ir/article_3571.html This study examines river dynamics and flooding in the town of Boma, Democratic Republic of Congo, where vulnerability to flooding is increased by climate change and anthropogenic pressures. This study aims to address gaps in flood-related fatality prediction by developing a predictive model incorporating the interaction between the Congo River water level and the Kalamu River discharge. The objectives include the use of a generalized linear model (GLM) with a Poisson distribution, combined with optimization algorithms such as particle swarm optimization (PSO) and genetic algorithms (GA). The methodology relies on the collection of historical data on water levels, discharges, rainfall, and fatalities, followed by rigorous data analysis using preprocessing and optimization techniques. The results show that PSO outperforms GA in terms of convergence speed and efficiency, achieving a better fitness value. Fitness values reveal an RMSE of 8.37, an MAE of 6.42, and an R² of -4.04, indicating significant inaccuracies in the forecasts. Simulations reveal a direct relationship between water level, discharge, and deaths, highlighting the importance of these interactions for risk management. These results provide valuable tools for infrastructure planning and raising awareness of the impact of floods on vulnerable populations, thus contributing to more effective prevention strategies. https://jhwe.shahroodut.ac.ir/article_3596.html The Hamun Wetlands, a vital transboundary ecosystem located between Iran and Afghanistan, vividly illustrate the complex interplay of water management, environmental sustainability, and international diplomacy in arid regions. These wetlands are predominantly sustained by the Hirmand (Helmand) River, but in recent decades, they have suffered significant degradation. This decline is primarily due to prolonged droughts, reduced river inflow, and ongoing disputes over water allocation, which are deeply rooted in historical agreements-most notably the 1973 Iran-Afghanistan water treaty. This article provides a comprehensive analysis of the historical background, current conflicts, environmental and human rights consequences, as well as diplomatic initiatives related to the Hamun Wetlands. It examines major challenges, including Afghanistan’s inconsistent adherence to water-sharing commitments, the impact of the Taliban’s return to power, and broader regional geopolitical dynamics. The article also explores opportunities for cooperation through international frameworks such as the Ramsar Convention, ultimately emphasizing the urgent need for sustainable, cooperative diplomacy to prevent ecological collapse and secure equitable water access for affected communities, while offering policy recommendations for future bilateral and multilateral engagement. https://jhwe.shahroodut.ac.ir/article_3608.html Wave reflection is an important hydrodynamic parameter in the design procedure of berm breakwaters. The existing formulae for predicting the wave reflection from berm breakwater are mainly based on regression model of available experimental data. Recently, applications of soft computing approaches and data mining techniques in tackling coastal engineering related problems received considerable attention. In this paper, accuracy of existing berm reflection formulae was evaluated by use of statistical measures and M5′ model tree algorithm is employed to predict the wave reflection with high precision. M5′ model trees are trained and tested with the available experimental data. Both hydrodynamic and structural factors including wave steepness, berm permeability and structure slope have been considered in developing the prediction models. Performance of developed models is tested against the experimental data by using statistical error parameters. The results show that the proposed formulae by M5′ model tree algorithm yields in more accurate prediction of wave reflection from berm breakwaters than existing formulae. https://jhwe.shahroodut.ac.ir/article_3622.html Over the past two decades, improving water transfer efficiency has become a major priority, especially given the significant drawbacks of traditional open concrete channels- such as high water loss due to seepage and evaporation, flow inefficiencies, and frequent maintenance needs. To address these challenges, many irrigation and drainage systems have shifted from conventional channels to pressurized or gravity-fed pipe systems. Among the most popular alternatives are Glass Reinforced Plastic (GRP) pipes, which have gained widespread acceptance in recent years. GRP pipes offer several advantages over concrete channels, both technically and economically. Not only do they provide superior strength, durability, and resistance to corrosion, but they also significantly reduce water loss during transfer. From a financial standpoint, GRP systems often prove more cost-effective—not just in initial installation, but also over the long term, thanks to lower maintenance requirements, reduced need for dredging, and shorter project completion times. When factoring in additional costs such as land acquisition and losses from seepage and evaporation, the overall life-cycle cost of GRP pipelines is considerably lower than that of traditional concrete channels. Moreover, the reliability of GRP pipes has been reinforced through rigorous short-term and long-term performance testing, conducted in accordance with internationally recognized ASTM and ISO standards. These assessments have boosted confidence among engineers, contractors, and project owners, making GRP an increasingly trusted choice for modern water conveyance systems. This study thoroughly examined and compared the technical and economic aspects of using concrete channels versus GRP pipes in irrigation and drainage networks. The findings demonstrate that GRP pipe systems offer a more efficient, sustainable, and economically viable solution, making them a wise choice for the future of water infrastructure. https://jhwe.shahroodut.ac.ir/article_3623.html The electroperoxone (EPO) process, integrating ozonation and electrochemical hydrogen peroxide generation, has gained attention as an efficient advanced oxidation technology for treating recalcitrant pollutants. This study investigates the application of EPO for the removal of organic dye from synthetic wastewater using a two-stage analytical framework. In the first stage, a series of systematic batch experiments were conducted to explore the effects of key operational parameters, including initial pH, applied current, ozone dosage, and reaction time, on decolorization efficiency. In the second stage, predictive models were developed using machine learning algorithms—Support Vector Regression (SVR) and Random Forest (RF)—to capture the complex nonlinear behavior of the process. The Random Forest model outperformed others, achieving an R² value above 0.823 and demonstrating superior accuracy in predicting removal efficiency. Sensitivity analysis revealed ozone dosage and applied current as the most influential factors. These results highlight the potential of combining experimental optimization with robust data-driven modeling to enhance the design and scalability of advanced oxidation processes in wastewater treatment. https://jhwe.shahroodut.ac.ir/article_3641.html Groundwater serves as a vital resource for agricultural, domestic, and industrial purposes, particularly in arid and semi-arid regions. The increasing pressure on groundwater resources, driven by population growth, climate change, and overexploitation, has rendered them increasingly susceptible to contamination from anthropogenic activities. Excessive use of chemical fertilizers in agriculture, improper disposal of urban and industrial wastewater, and leakage of pollutants from various sources are among the primary contributors to groundwater pollution. Assessing the vulnerability of these resources is essential for ensuring their quality and sustainability. Using the DRASTIC model, this study evaluates the groundwater vulnerability of the Gonabad Plain, situated in Khorasan Razavi Province, Iran. This model incorporates seven hydrogeological parameters: depth to the water table, net recharge, aquifer media, soil media, topography, unsaturated zone, and hydraulic conductivity. The DRASTIC index calculated for the study area ranges from 63 to 193. According to the results, the pollution risk levels in the region were classified as follows: 0.65% of the area had no pollution risk, 2.06% was categorized as very low risk, 21.67% as low risk, 34.62% as low to moderate risk, 33.52% as moderate to high risk, 6.66% as high risk, 0.8% as very high risk, and 0.006% was identified as thoroughly contaminated areas. The increased vulnerability index observed in the northern parts of the plain can be attributed to factors such as a shallower water table, gentle slopes (less than 2%), and coarse-grained soils in both the aquifer and the unsaturated zone. To validate the vulnerability assessment, nitrate concentration data from wells in the region, collected in 2021, were analyzed. The findings confirmed the accuracy of the DRASTIC model in identifying areas vulnerable to landslides. This study underscores the importance of localized assessments of groundwater vulnerability. It emphasizes the need for targeted management and monitoring strategies in high-risk regions to mitigate pollution and ensure the long-term sustainability of groundwater resources. https://jhwe.shahroodut.ac.ir/article_3684.html Debris flows are among the destructive natural phenomena in mountainous regions, and effective design of control structures for their management requires accurate analysis of the hydrodynamic behavior of the flow. In this study, using the OpenFOAM environment, debris flow is numerically simulated with the Eulerian–Eulerian multiphase model to investigate the effect of opening dimensions on the performance of slit check dams.Three different geometries with openings of 0.25, 0.5, and 0.75 times the flume width, along with a reference scenario without a check dam, were evaluated. The developed numerical model was validated against reliable experimental data and demonstrated appropriate accuracy in reproducing characteristics such as front arrival time, peak amplitude, and local flow fluctuations. The findings indicate that the opening width plays a decisive role in controlling the hydrodynamic behavior of the flow. The 0.5-flume-width opening offered the best balance between controlled passage, reduced fluctuations, and hydraulic flow stability, and is introduced as the optimal option. Although the 0.25-flume-width opening resulted in the greatest delay in peak detection at downstream sensors, it was accompanied by unstable behavior and severe fluctuations. Moreover, the 0.75-flume-width opening performed similarly to the no-dam scenario and showed limited effectiveness in restraining flow transfer. The results of this study can serve as engineering guidelines for the effective design of check dams to control debris flows in high-risk areas.