Iranian Water Research Journal

Abstract Introduction:
Drought is a natural phenomenon that significantly impacts human life and ecosystems. Lack of rainfall can cause various problems, including damage to crops and a shortage of drinking water for local communities. These effects can lead to devastating economic and social disasters, such as famine, forced migration away from drought-affected areas, and conflicts over remaining resources. Predicting drought indices, such as the Standardized Precipitation Index (SPI), is crucial for assessing this phenomenon in water resource management. The SPI is a tool for evaluating and measuring the intensity and duration of drought, allowing for drought analysis on both short-term and long-term time scales, and providing accurate information on drought severity to water resource managers and policymakers. However, predicting the SPI can be challenging due to the complex interactions of influencing factors.
While deep learning models, especially Long Short-Term Memory (LSTM) networks, have shown promise in predicting SPI, they also have key limitations. First, the LSTM model has parameters that need fine-tuning. Second, selecting the best input combination for the LSTM model is complex and challenging. The inputs required for drought prediction are usually associated with various time lags. Choosing the best input combination from a large number of inputs is important, as it affects the performance of the LSTM model. The third challenge is that the LSTM model lacks connections that directly transfer information from the model's memory to its computation gates, which can lead to the loss of important information.
Materials and Methods:
This study investigates the challenges and aims to improve the performance of the Long Short-Term Memory (LSTM) model in predicting the drought index (SPI) at 3-, 6-, and 9-month time scales (SPI-3, SPI-6, and SPI-9) in the northern region of Isfahan Province. To achieve this, a novel "Collaborative LSTM" model was proposed and a binary genetic algorithm was developed. Various large-scale climate indices were utilized as input data for the prediction models. To optimize model performance, the binary genetic algorithm was employed to select the optimal input scenario and fine-tune the model parameters. In the first step, the input data, including time series, were divided into training and test datasets. Eighty percent of the input data were selected for training, and twenty percent were used for testing to minimize prediction error. The algorithm then selected the best input parameters from large-scale climate signals with different delays. Each chromosome comprised two parts: the first part contained the model inputs, and the second part contained the initial parameter values. Binary chromosomes were encoded based on 0 and 1 values, indicating the absence or presence of a factor in the modeling process, respectively. Each binary value in the second part determined how the initial value of a particular parameter was chosen. A new version of the LSTM model was then built with three additional weighted connections compared to the original LSTM model. The Collaborative LSTM incorporates three novel weight connections that facilitate the flow of crucial information from the memory cell to the input, forget, and output gates, enhancing information retention and improving prediction accuracy. The performance of the Collaborative LSTM was compared against traditional LSTM, Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR) models. Multiple error metrics were used to assess model accuracy. The modeling process involved a multi-step approach: (1) optimal input scenario selection using the binary genetic algorithm, (2) model parameter optimization using the genetic algorithm, and (3) development and evaluation of the Collaborative LSTM model for SPI prediction.
Result and Discussion:
The results demonstrated the effectiveness of the binary genetic algorithm in optimizing both input selection and model parameters. The Collaborative LSTM consistently outperformed other models, achieving a 2.1-6.1% improvement in the Willmott Index (WI) for SPI-3, 1.1-5.1% for SPI-6, and 1.1-2.2% for SPI-9. Statistical analysis revealed that the predicted SPI values from the Collaborative LSTM closely resembled the observed SPI data. The superior performance of the Collaborative LSTM can be attributed to its enhanced information flow architecture, which enables more effective utilization of past information stored in the memory cell. Boxplots further visualized the improved accuracy of the Collaborative LSTM in predicting SPI values across different scenarios. The findings of this study demonstrate the potential of the Collaborative LSTM model as a reliable tool for accurate drought index forecasting. By addressing the limitations of traditional LSTMs and incorporating optimized input selection and parameter tuning, this novel approach offers significant advancements in drought prediction capabilities.

Abstract Introduction:
Land use around rivers is one of the key factors contributing to their pollution. Burying waste and garbage in the soil, agricultural activities, and the use of various fertilizers and pesticides ultimately lead to river pollution. Spatial and temporal changes in the chemical quality of river water depend on various factors. Due to the importance of quantitative parameters in determining the expected response of the river, it is essential to measure these parameters using models. Measuring these parameters in large volumes is time-consuming, costly, and requires high accuracy; so, the need for some indirect methods to estimate these parameters is already apparent. Principal component analysis (PCA) is one of the methods used to overcome this problem. So that the most accurate answer is achieved with less cost and with the least information. Artificial neural networks (ANNs) are suitable for water quality assessment due to their ability to solve nonlinear problems compared to conventional techniques. The aim of this research is to investigate the efficiency of parameter reduction using PCA and the effect of land use and precipitation on some water quality factors of the Zayandeh-Rood River using ANN.
Materials and Methods:
To evaluate the effect of land use and precipitation on some water quality factors, data were collected from the Water and Environment Organization, the General Directorate of Natural Resources and Watershed Management, and the Provincial Research Center. These data were collected daily from 12 hydrological stations in the two provinces of Chaharmahal and Bakhtiari and Isfahan for the Zayandeh-Rood River over a 10-year period. According to the hydrological stations monitoring the water quality of the river from upstream to downstream, the Zayandeh-Rood River basin was divided into 12 sub-basins. The area of each sub-basin was calculated using partitioning software. PCA was applied to some water quality parameters, including total dissolved solids (TDS), electrical conductivity (EC), water reaction (pH), carbonate (CO3), bicarbonate (HCO3), chlorine (Cl), sulfate (SO4), calcium

(Ca), magnesium (Mg), sodium (Na), potassium (K), and sodium absorption ratio (SAR), as well as discharge. This was done to reduce the main data arrays affecting the water quality of the Zayandeh-Rood River and to identify pollution sources using XLSTAT 2018 software. To check the suitability of the dataset for PCA, the Kaiser-Meyer-Olkin test and the Bartlett sphericity test were used. After extracting land use and rainfall data for the Zayandeh-Rood basin, modeling was performed using MATLAB software, with 60% of the data randomly selected for network training and the remaining 40% for validation and entered into the network. The effect of these two factors on some river water properties was analyzedusing the backpropagation algorithm, and by changing the number of neurons and layers, the mean square error, coefficient of determination, and Mean Absolute Error (MAE) were calculated for each factor.
Result and Discussion:
According to the results, PCA can clarify the order of the data in detail by eliminating unimportant data as the Kaiser Meyer Olkin (KMO) sample adequacy index was 0.618 and Bartlett's Sphericity test was 0.001, indicating that the number of samples for the test was sufficient. The results of PCA analysis showed that the first six components accounted for 94.8% of the total variance. This indicates that PCA is effective in reducing the slope of change among variables. Because the sum of the variances of the first six components is close to one. This implies a linear correlation among variables, because PCA uses a linear transformation to transform variables into principal components. The ANN results showed that the total coefficient of determination was 0.79 and the root mean square error was 0.20, and the coefficient of determination for SAR, TDS, EC, and Na were 0.88, 0.84, 0.83, and 0.83, respectively, indicating the high accuracy of the network predictions. The lowest coefficient of determination for the three factors pH, CO3, and discharge (0.16, 0.10, and 0.43, respectively), were all below0.50, and for other water quality factors, the coefficient of determination was above 0.50. The results indicated that the coefficient of determination for land use and rainfall on the three factors of discharge, pH and CO3 is very weak and the model provides very little explanation of the variance of the dependent variable. The results of the Nash-Sutcliffe coefficient showed that it is appropriate for all measured factors (except pH, CO₃ and discharge) and is above 0.6 and this value indicates that the model has been able to explain the observed changes well and provide relatively accurate predictions. The outcomes also revealed that the reduced values in PCA are the same factors predicted with the highest coefficient of determination in the ANN model.

Abstract Introduction:
By growing the population and expanding industries, the need for sustainable and efficient wastewater treatment methods has become increasingly urgent. Among the different technologies, advanced photocatalytic oxidation is a promising technology, gaining attention for its ability to disinfect water and wastewater with minimal formation of harmful by-products. This study explored the use of titanium dioxide (TiO₂) nanoparticles for photocatalytic disinfection of water contaminated with Escherichia coli (E. coli), focusing on using different stainless-steel substrates to enhance disinfection performance. The primary objective was to identify optimal conditions for achieving maximum bacterial removal efficiency while ensuring the stability and reusability of the coated substrates.
Materials and Methods:
In this research, TiO₂ nanoparticles were deposited on various stainless-steel substrates, including the inner surface of stainless-steel pipes, brushed stainless-steel sheets, and stainless-steel meshes. The TiO₂ nanoparticles were coated at different dosages (5, 10, 15, and 35 mg/cm²) on surfaces, and their performance was evaluated under UV light (6 W) for a residence time of 90 minutes. The brushed stainless-steel sheet was coated using physical vapor deposition (PVD), providing a durable and uniform TiO₂ layer. In contrast, stainless-steel meshes and the inner surfaces of stainless-steel pipes were coated using solution-based methods by brushing, followed by heat treatment to stabilize the coating. To assess the effectiveness of these coatings, laboratory experiments were conducted using ultrapure water contaminated with E. coli at an initial concentration of approximately 10⁹ CFU/ml. The photocatalytic disinfection process was carried out in a reactor equipped with a UV light source (6 W). Samples were collected at intervals (2, 5, 10, 20, 30, 60, and 90 minutes), and bacterial concentrations were determined using standard agar plate culture methods. Disinfection efficiency was calculated based on the bacterial log reduction and removal percentages of E. coli. The pseudo-first-order kinetic model was applied to analyze the disinfection data, and reaction rate constants (k) were determined to assess the effectiveness of different coatings. In addition, statistical analyses, including the Mann-Whitney U test and Kruskal-Wallis test, were employed to evaluate differences in performance between various coating conditions.

Result and Discussion:
The study revealed that TiO2 coatings on the inner surfaces of stainless-steel pipes achieved the highest bacterial removal efficiencies, reaching 99.99% disinfection within 20 minutes at dosages of 35 mg/cm² and 5 mg/cm². However, the coatings on stainless-steel pipes exhibited stability issues, as TiO₂ particles gradually shedd over time, which could reduce their long-term effectiveness. In contrast, stainless-steel mesh coated with TiO₂ nanoparticles at a dosage of 10 mg/cm², combined with UV irradiation, achieved a removal efficiency of 99.96% within 30 minutes. This configuration demonstrated a balance between high efficiency and stability, making it a reliable choice for sustained applications. Furthermore, the stainless-steel mesh showed better resistance to particle shedding compared with the inner surfaces of the pipes. The disinfection process was effectively described by the pseudo-first-order kinetic model, as indicated by high correlation coefficients (R²). The highest reaction rate constants (k) were observed for coatings on stainless-steel pipes (35 mg/cm²) and stainless-steel meshes (10 mg/cm²). Statistical analyses revealed no significant differences in disinfection efficiency between these two configurations. This result suggested that stainless-steel mesh coated with 10 mg/cm² dosages of TiO₂ offers an equally effective and more stable alternative. The findings established the critical role of nanoparticle dosage in determining the performance of photocatalytic systems. The findings from this research provided valuable insights into the design and optimization of sustainable water treatment technologies that align with global efforts to address water scarcity and pollution challenges. Currently, numerous water treatment plants worldwide are equipped with UV lamps that use ultraviolet light for water disinfection. However, studies have shown that relying solely on UV light can lead to the production of smaller-than-average colony phenotypes in surviving bacteria, which can pose a serious health risk. Therefore, the industrial-scale application of TiO₂ nanoparticles as a sustainable and effective disinfection method in water treatment should be considered. However, challenges such as costs, nanoparticle stability, and the need for advanced technologies to separate and recover photocatalysts must still be addressed. Therefore, it is recommended to investigate the durability and reusability of TiO₂ coatings under continuous operation, to evaluate the effectiveness of alternative light sources, including solar or energy-efficient LEDs, to reduce operational costs, to extend the findings to pilot-scale studies to assess the feasibility of implementing TiO₂-based photocatalytic systems in industrial water treatment facilities, and to examine the potential environmental risks associated with TiO₂ nanoparticle shedding and develop strategies for nanoparticle recovery and reuse.

Abstract Introduction:
Porous media, as systems with special properties, consist of porous structures that play an important role in many industrial and natural processes such as filtration, material separation, and fluid transport. Due to their specific physical properties, such as porosity and permeability, these media can exhibit interesting and unpredictable behaviors. Therefore, a detailed understanding of the fluid flow behavior in these media not only helps to improve existing processes, but can also lead to the optimal design of new and more efficient systems. For this reason, accurate simulation and analysis of the flow behavior in porous-walled tubes is of great importance. The main objective of this research was to investigate the effect of various factors on the flow of fluid through a porous-walled tube. This study sought to clarify the flow-related processes in these systems by analyzing the data and results obtained from numerical simulations and to reach reliable results regarding the optimization of tube design and its applications. Also, this research attempts to provide solutions to optimize the performance of filtration and fluid transfer systems.
Methods:
In this study, the governing equations for flow in porous media, including the continuity equations, Navier-Stokes, and Darcy's law, were introduced. The continuity and momentum equations were thoroughly defined mathematically, along with a detailed examination of boundary conditions and the properties of the fluid and porous media, such as geometry and technical parameters. Fluid flow in a tube with a porous wall was simulated numerically using ANSYS Fluent 2021 software. A tube with a diameter of 2.4 cm and a length of 120 cm was selected, with boundary conditions set for an axial Reynolds number of 500, indicating a transitional flow regime. Air was modeled as the fluid, characterized by a density of 1.29 kg/m³ and a dynamic viscosity of 1.87 × 10⁻⁵ Pascal seconds. The porous medium in the tube wall was designed to be 2 mm thick, with a permeability of 1.85 × 10^⁻17 m², based on findings from previous research. A comprehensive mesh convergence analysis was conducted to ensure the accuracy of the simulation results, leading to a selection of 203150 elements that provided an optimal balance between the results accuracy and the computational time required. The study compared flow velocity parameters at two different Reynolds numbers, investigating the effect of wall seepage on flow by varying the porous medium Reynolds number (R_ew) from0 to 0.1. The axial Reynolds number (R_e) was also used as a key criterion to describe flow behavior within the tube. The results were analyzed to understand the influence of porosity, thickness, and tube length on flow velocity. Furthermore, the simulation outcomes were compared with data from previous studies to validate the findings and enhance understanding of flow behavior in porous media.

Results:
The results obtained revealed that the Reynolds number had a significant effect on the flow performance in the porous tube. For example, when the Reynolds number associated with fluid seepage through the porous wall was equal to zero, the velocity profile of the flow through the tube showed good agreement with the data in the study of Belhouideg (2017). Also, at a Reynolds number of 0.1, the difference between the simulation results and the reference data was only 0.03%, which indicated the high accuracy of the simulation and proper validation of the results. The study of the effects of different tube wall thicknesses, porosity, and tube length on the velocity profile showed that the wall thickness and the application of different porosities from 0.2 to 0.8 had no significant effect on the fluid flow behavior. In contrast, with the lengthening in tube from 0.6 to 2.4 m, the difference in flow velocity between the regions close to the wall and the tube axis decreased as well as the overall flow velocity. The changes in the axial velocity of the flow were analyses for different Reynolds numbers in the radial direction. In the impermeable state, the velocity at the center of the tube was equal to the initial velocity, whereas in the presence of a porous medium, the flow penetration led to the creation of a compensatory force that prevented a sharp drop in velocity. This research also showed that in tu especially its length bes with porous walls, the flow seepage from the wall reduced in the velocity drop, which is clearly evident in comparison with impermeable tubes. The obtained results also emphasized the effect of wall thickness and porosity on the velocity profile and showed that these parameters did not have a significant effect under certain conditions. In addition, the results of the research indicated that the effect of the Reynolds number and the physical characteristics of the tube, especially its length, were factors affecting the behavior of the fluid flow in tubes with porous walls. So that an increase in the tube length leads to a decrease in the flow penetration velocity in the porous wall.
This research clearly shows that the Reynolds number and the physical properties of the tube, especially its length, are factors affecting the fluid flow behavior in porous-walled tubes. The results of this research can help optimize filtration systems and design optimal tubes in industrial and engineering applications. These results can be used as a basis for future studies in the field of design and optimization of fluid transport and filtration systems in porous media. Finally, the importance of this research in improving processes related to material separation and designing more efficient systems is demonstrated. Also, it can be concluded that improving simulation methods and further investigating flow behavior under different conditions can lead to a deeper understanding of flow processes in porous-walled tubes and ultimately help to promote science and technology in this field.

Abstract Introduction:
There are some well-known large-scale patterns in the earth atmosphere which could affect the faraways locations. Studying these patterns that influence regional climate has led to identify the factors affecting the weather and climate and helps improve prediction of atmospheric phenomena such as wet weather and drought. This research examines the impact of the El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the frequency and intensity of Atmospheric Rivers (ARs), which affect the precipitation of Iran. The Atmospheric Rivers (ARs), the phenomena which were first identified in 1992 time, are long and narrow filamentary pathways that transport about 90% of the water vapor in the atmosphere to the Earth's middle latitudes and transport the moist air of faraway sources around the Earth. They play a significant role in the world's weather and regional hydrology. Previous studies covered the effect of large-scale phenomena on precipitation and temperature, or the detection of the frequency, path, and strength of ARs, individually. Here, the relationship between various phases of the NAO and ENSO and the frequency and strength of ARs are examined.
Materials and Methods:
The six-hour ERA5 reanalysis data, available from 1940, were used from November to May between 1979 and 2020, for computing Integrated water Vapor Transport (IVT) to identify Atmospheric Rivers (ARs). The study area covered from 20^◦W to 80^◦E longitude and 10^◦S to 50^◦N latitude. The first condition to identify the ARs is recognizing an area where the IVT exceed a certain value of IVT, which is noted as the threshold value. In this study, the IVT threshold was calculated as the 90th percentile of all IVT value computed during the period under study at each point of a 101*61 grid. The geometrical characteristics such as the length of more than 2000 km, the length-to-width ratio of more than 2, and the angle of the main axis of more than 10 relative to the orbits, were applied to identify ARs’ regions that affect Iran. The IVT average of ARs' regions over a limited area around Iran (40^◦E to 64^◦E longitude and 15^◦N to 40^◦N latitude) was used to calculate the intensity of each AR. Afterward, the frequency of ARs, the Odd Ratio (OR), Non-occurrence Ratio (NR), and Occurrence Ratio (OR) were examined in different phases of the ENSO and NAO (neutral and critical phases; negative and positive phases) by a 2*2 cross tables. Also, the intensity of ARs analyzed for different phases of surveyed phenomena. The monthly ENSO index (SOI) were classified in critical positive (negative) mode when its monthly index was more (less) than 7 (-7). In the same way, the +1 (-1) value were used for critical positive (negative) phases monthly NAO index. Neutral phases of ENSO and NAO were defined when monthly index was between the critical value.

Result and Discussion:
The results showed that the annual frequency of ARs affecting Iran varies between 8 and 25. The monthly frequency range is between zero and 6. December and February 1980 received the maximum number of the events (6) of ARs during the studied period. Regarding the results, the frequency percentage of ARs was 55.81 and 67.11 in the neutral phase of the SO and NAO indexes, respectively. In addition, the frequency of this phenomenon was about 26 percent in the negative phase of ENSO which is more than in positive phases. For the positive phase of NAO, the frequency of atmospheric rivers was much higher (4 times) than the occurrence in the negative phase of NAO. The previous studies on the effect of SOI and NAO indexes on the precipitation of Iran showed the negative phase of SOI (or El Nino) and the positive phase of NAO occurred more in wet years. The high occurrence ratio of ARs in the negative phase (positive) of ENSO (NAO) revealed that the results confirmed the former study on the impact of large-scale atmospheric patterns like ENSO and NAO. The intensity of ARs indicated the measure of water vapor transferred by this phenomenon, and this study showed that the monthly average intensity of atmospheric rivers was higher in the negative phases of ENSO and positive phases of NAO. The monthly average of ARs’ intensity in the negative phase of SOI was 263.46 kg/ms^-1 which was more than that in the neutral and positive phases. In comparison, the monthly average intensity of AR in the positive phase of NAO was 263.04 kg/ms^-1 which means that the ARs occurred in negative and neutral phases of NAO experienced lower intensity and transferred less moisture to Iran.

Abstract Introduction:
Groundwater is a vital resource, particularly in arid and semi-arid regions, where surface water availability is limited. Sustainable groundwater management is crucial for balancing agricultural, industrial, and domestic consumption. However, excessive groundwater extraction, rapid urbanization, and climate variability have led to significant declines in groundwater levels worldwide. In Iran, one of the most concerning cases of groundwater depletion is observed in the Mashhad-Chenaran aquifer, which has experienced a persistent decrease in groundwater levels over the past decades. Understanding the factors influencing groundwater depletion is critical for developing appropriate mitigation strategies. Various studies have investigated the role of climate variability and human activities in groundwater level fluctuations. While precipitation deficits and prolonged droughts can contribute to declining groundwater recharge, human-induced factors such as land use changes, agricultural expansion, and unregulated groundwater extraction often play a more dominant role. Despite the importance of this issue, a comprehensive assessment combining groundwater trend analysis, drought evaluation, and land use change impacts remains limited in the Mashhad-Chenaran aquifer. Therefore, this study aims to analyze the long-term trend of groundwater level changes and groundwater drought conditions, with a particular emphasis on the impact of land use changes and climatic indices. By integrating hydrogeological, meteorological, and remote sensing data, this study provides a multifaceted approach to understanding groundwater depletion and its driving factors.
Materials and Methods:
In this study, the trend of groundwater depth changes in Mashhad-Chenaran aquifer was analyzed using time-series data from 36 piezometers between 1989 and 2020. Modified Mann-Kendall and Sen's slope tests, along with the Pettitt test, were employed to examine groundwater depth trends and identify time-series breakpoints. The Mann-Kendall test is a non-parametric method widely used to identify monotonic trends in hydrological and environmental time series, while Sen’s slope provides an estimate of the rate of change over time. To further investigate the groundwater changes over the study period and evaluate groundwater drought conditions, the Standardized Groundwater Index (SGI) was calculated for each well. This index is particularly useful in distinguishing normal, moderately dry, severely dry, and extremely dry periods. By analyzing SGI over the 32-year study period, this research identifies critical drought periods and their spatial distribution across the aquifer. In addition, to assess the relationship between anthropogenic and meteorological droughts, Palmer Drought Index (PDSI) values at the location of each well were obtained from the TerraClimate product, and correlations between SGI and PDSI were calculated. To investigate the anthropogenic effects on groundwater depth changes, GLAD land cover maps from 2000 to 2020 were used. These maps, derived from Landsat satellite imagery, provide high-resolution information on urban expansion, agricultural growth, and natural land cover transformations. By comparing land use classifications at different time intervals, the study quantifies the extent of urbanization and agricultural intensification.

Result and Discussion:
The trend analysis revealed that the most piezometers exhibit an increasing trend in groundwater depth, with one-quarter of them experiencing an increase in depth with a exceeding rate of one meter per year. Furthermore, the transfer of water resources to the city of Mashhad has led to some piezometers showing a decrease in depth and water table rise. The SGI index revealed that groundwater drought in a significant number of piezometers began between 2001 and 2006 and continues to intensify. Over 20 years, since 2000, urban land use within the aquifer region has doubled, reaching approximately 745 square kilometers, which has noticeably impacted some piezometers. Additionally, agricultural land use, the dominant land use type in this area, has increased by about 167 square kilometers. To further investigate the influence of land use and land cover types, the changes in urban and agricultural class were investigated over 5-year periods. While urban land use has been steadily increasing over the whole period, agricultural land use experienced significant growth between 1999 and 2004. This could be one of the reasons for the onset of the drought period and the presence of a breakpoint between 2001 and 2006 in many piezometers. To evaluate the impact of meteorological drought on groundwater drought, the correlation between the Standardized Groundwater Index (SGI) and the Palmer Drought Severity Index (PDSI) was calculated for each piezometer. The lack of correlation between these two indices in most piezometers suggests a significant role of human activities in groundwater level decline in the region.

Abstract Introduction:
Labyrinth weirs are used in river sections that have limited width, to increase the flow capacity. Due to the turbulence of the flow and the increase in velocity, scour is one of the important issues downstream of the weirs. Moreover, the type of materials used downstream of the structure usually has a great impact on the amount of scour downstream of the weirs. For this reason, it is essential to evaluate the effect of material type on scour. Given that weirs are exposed to numerous floods, the most important challenge is to ensure the stability of the structures against erosion and scour, and the amount of scour must be estimated. In this research, the erosion downstream of labyrinth weirs is investigated, focusing on the effect of sediment bed compaction and non-uniformity of gradation on scour.
Materials and Methods:
Experiments were conducted in a glass flume with a length of 12 meters and a width of 80 cm. Also, three discharges of 5, 10 and 15 liter/s were considered. A triangular labyrinth weir with L/W equal to two, height of 35 cm and thickness of 4 mm was constructed. Downstream of the weir, the examined sediments were placed in the channel with a height of 20 cm. Two types of sediment were used: uniform sediment (A) and non-uniform sediment (B), both with a mean diameter of 2 mm. Sediment A had uniform gradation with the particles diameter varied between 0.7 and 3 mm, and the uniformity coefficient of 2.4. Sediment B had non-uniform gradation, with particles diameter of 0.07 to 6 mm, and a uniformity coefficient of 5.96. The uniformity coefficient, is defined as ratio d₆₀/d₁₀, indicates uniformity when less than 5 and non-uniformity when greater than 5. The important point was that d₅₀ of both sediments was the same and equal to 2, meaning that both sediments had the same mean diameter, but different in uniformity. Therefore, the effect of uniformity and non-uniformity of sediments could be investigated. The tailwater depth in the experiments was 11 cm, and the equilibrium time was 6 hours. After each test, the scour depth was measured on a 2x2 cm grid and the scour profile was plotted in Sigma Plot software.
Result and Discussion:
Investigations revealed that the maximum scouring depth occurred near the junction of the weir and the channel wall. With increase in discharge, the scour rate increased by up to 68 percent in sediment A with 80 percent compaction. Also, the scouring profile of sediment B for a discharge of 15 Lit/s and 80 percent compaction showed a 40 percent increase compared to a discharge of 5 Lit/s. Therefore, higher discharges significantly increased the scour rate. Increasing the compaction from 80 percent to 90 percent, slightly reduced the scour depth. For sediment A, at a discharge of 5 Lit/s, increasing compaction resulted in over than 50 percent reduction in scour depth, but at a discharge of 15 Lit/s, only 5 percent change occurred. For sediment B, at a discharge of 5 Lit/s, a 35 percent reduction was obtained in scour depth with an increase in compaction from 80 percent to 90 percent; while at a discharge of 15 Lit/s, 2 percent change occurred in scour with an increase in compaction. This indicated that in high discharge, compaction had not significant effect. In comparison to sediment A, the sediment B had a 20 percent reduction in scour on the sides at a discharge of 15 liters per second and 90 percent compaction. Regarding the results, the non-uniformity of sediment B caused less erosion than sediment A. The sediment A was almost uniform and the sediment particles had less overlap and interlocking.

Studies confirmed that the maximum scour depth occurs near the junction of the labyrinth weir with the channel wall and this location remains consistent with increasing discharge. The second maximum scour occurs near the labyrinth weir apex. Therefore, to prevent erosion downstream of the labyrinth weir, necessary considerations should be taken regarding the maximum erosion locations. Increasing the compaction from 80 percent to 90 percent, the scour depth decreases to some extent, but at high discharge, compaction is less effective. However, the non-uniformity of the sediment affects the scour rate, because it enhances sediment particles contact and resistance to erosion.

Abstract Introduction:
Bridge stability under hydraulic forces is a vital area of study, especially during extreme flow events such as floods. Among the various bridge components, piers play a critical role as they directly interact with river flows. This interaction often results in scouring, a process in which sediment around bridge piers is eroded by flowing water. Scouring poses a significant threat to the structural integrity of bridges, making it essential to understand its mechanisms under different hydraulic conditions. Under high river discharge, flows may become pressurized as they pass beneath the bridge deck, leading to unique scouring patterns that differ significantly from those observed under free-surface flow conditions. Factors such as flow velocity, turbulence, and the arrangement of piers relative to the flow direction strongly influence the extent and characteristics of scour. This research investigates contraction scour around double bridge piers during flood scenarios, focusing on the effects of the presence or absence of a bridge deck. Additionally, the study explores the impact of pier arrangements whether perpendicular or parallel to the flow direction on flow dynamics and scour behavior. Through comprehensive numerical modeling, this study aims to address existing knowledge gaps regarding scouring under complex hydraulic conditions and contribute to safer, more resilient bridge-design strategies.
Materials and Methods:
Advanced numerical modeling was used to simulate and analyze the complex interactions between flow and scour around bridge piers. FLOW-3D, a computational fluid dynamics (CFD) software, was utilized along with the RNG turbulence model, well-suited for capturing turbulent flow characteristics. The experimental flume had a channel length, width and water depth of 9 meters, 0.6 meters, and 0.19 meters, respectively. The piers were modeled as cylindrical structures with a diameter of 0.06 meters and were arranged either perpendicular or parallel to the flow direction. The bridge deck was positioned 0.133 meters above the bed. To simulate sediment behavior, uniform sediment particles with a mean diameter of 0.74 mm and a geometric standard deviation of 1.4 were used. The key parameters included scour depth, turbulent kinetic energy (TKE), and bed shear stress, provided a comprehensive understanding of scour dynamics. Simulations were ran for 600 seconds to capture the development and stabilization of scour patterns under different configurations. The results were validated against experimental data, to confirm the credibility of the numerical approach. This methodology facilitated a detailed assessment of the effects of bridge deck presence, pier arrangement, and flow conditions on scour and associated hydraulic parameters.

Result and Discussion:
The numerical simulations revealed significant differences in flow and scour patterns between scenarios with and without a bridge deck. When a deck was present, flow accelerated beneath the structure, exhibiting jet-like behavior that intensified scour around the piers.For perpendicular pier arrangements, the maximum scour depth in the decked scenario was approximately 2.35 times greater than in the deckless case. For parallel configurations, scour depths around the upstream and downstream piers increased by 2.7 and 3.85 times, respectively, when a deck was present. The study also highlighted the role of turbulence in influencing scour dynamics. The presence of the bridge deck significantly amplified turbulent kinetic energy (TKE), particularly around the piers and beneath the deck. In perpendicular arrangements, TKE values were 2.1 times higher in the decked scenario. In parallel configurations, the highest TKE values occurred between the two piers, with values approximately 2.8 times greater than in the deckless case. Velocity distributions also varied notably between the two conditions. Horizontal flow velocity increased substantially in the presence of a bridge deck, with maximum velocities observed beneath the deck near the piers. For instance, in the perpendicular arrangement, the flow velocity between the two piers was 1.51 times higher in the decked condition than in the deckless one. These findings underscore the bridge deck's impact on increasing flow velocities and turbulent interactions, leading to more severe scour patterns around piers.
This study highlighted the significant influence of bridge decks on hydraulic conditions and scour patterns around double bridge piers. Numerical simulations demonstrated that the presence of a deck not only altered flow structures but also substantially increased scour depth, turbulence, and bed shear stress. These effects were particularly pronounced under pressurized flow conditions, where the interaction between the deck and underlying flow intensified hydraulic forces. For perpendicular pier arrangements, scour depths were approximately 2.35 times greater with a deck. In parallel arrangements, scour depths increased by 2.7 and 3.85 times for upstream and downstream piers, respectively. Turbulence parameters, such as Turbulent Kinetic Energy (TKE), also showed significant increases in the decked scenarios, further emphasizing the deck’s role in modifying scour dynamics. The findings provided valuable insights into the mechanisms of scour under complex hydraulic conditions and stressed the importance of accounting for bridge deck effects in design and maintenance practices. These insights contribute to the development of more resilient and safer bridge structures, particularly in flood-prone regions.