Original Article
Hydraulic
Reza Farzanpour; Salah Kouchakzadeh; Shabnam Moghispour
Abstract
Extended AbstractIntroductionHistorically, the determination of Manning's roughness coefficient, n, has relied on two approaches: (1) field measurements, and (2) laboratory experiments in flumes where quasi-uniform flow is artificially established. While field data are most realistic, they suffer from ...
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Extended AbstractIntroductionHistorically, the determination of Manning's roughness coefficient, n, has relied on two approaches: (1) field measurements, and (2) laboratory experiments in flumes where quasi-uniform flow is artificially established. While field data are most realistic, they suffer from uncontrolled variables and high costs. Laboratory studies offer controlled conditions but face a fundamental challenge: establishing truly uniform flow in a flume requires a long channel length, precise slope adjustment, and a downstream control that balances friction losses—a condition that is rarely achieved in practiceStudies by Tracy and Lester (1961) and Kazemipour and Apelt (1979, 1982, 1999) demonstrated that even in relatively long flumes, achieving true uniform flow is difficult and time-consuming. Moreover, in natural rivers, uniform flow is the exception rather than the rule; gradually varied flow dominates due to changes in cross-section, slope, and roughness along the channel.This study proposes a paradigm shift: instead of forcing uniform flow in the laboratory—which introduces uncertainty and limits repeatability— the GVF was used as the basis for estimating Manning's coefficient. The M2 profile provides a well-defined water-surface profile that can be accurately measured and compared with theoretical GVF solutions. By optimizing n to minimize the discrepancy between observed and computed profiles, we obtain a robust estimate of the roughness coefficient that is both physically meaningful and practically reproducible.The primary objectives of this research were: To develop and validate a GVF-based methodology for estimating Manning's n in a laboratory flume; To investigate the effects of three different bed roughness types on n; to quantify the influence of longitudinal slope and discharge on n; to compare the experimental results with theoretical resistance laws and assess the flow regime (hydraulically smooth, transitional, or rough); to perform a sensitivity analysis regarding the choice of reference depth for roughness-height estimation.MethodologyExperimental SetupExperiments were conducted in a rectangular flume located at the Central Water Research Laboratory of the University of Tehran. The flume has a length of 12 m, a width of 0.8 m, and a depth of 0.6 m. The flume is mounted on an adjustable platform capable of setting longitudinal slopes. Water-surface profiles were measured using data-acquisition system designed for the current research. Three bed materials, 9 longitudinal slopes, and 9 discharges were tested. Experimental Procedure and Data AnalysisFor each bed roughness the desired slope was set, and the flow was established. The downstream gate was fully opened to create a free overfall, generating an M2 gradually varied profile. After steady-state conditions were reached, the water surface elevations along the flume were recorded.Manning's n was determined by solving the standard GVF equation for a prismatic channel. For each experimental profile, the value of n was optimized to find the best match between the observed and the computed profiles. The equivalent roughness height, ks, was then calculated.Results and DiscussionThe optimized Manning's n values for all 81 runs are summarized in Tables 1–3 of the main Contrary to some previous studies (e.g., Merry, 2017; Yilmaz et al., 2023), which reported a decreasing trend of n with increasing discharge, the results did not show a systematic or monotonic relationship. For most roughness–slope combinations, n remained approximately consistent across the discharge range. Small fluctuations (typically ±0.002) were observed but did not follow a consistent pattern. This suggests that within the tested range of relative submergence, the effect of discharge on bulk resistance is secondary to the geometric roughness characteristics. Similarly, slope variations produced only minor changes in n.Computed shear Reynolds numbers ( ) revealed that both bed materials of C2 and C3 operated in the fully rough turbulent regime ( ), confirming that viscous effects were negligible. In contrast, the galvanized bed (C1) fell within the transitional regime.Because the piezometers were connected to the flume floor (i.e., below the installed mesh covers), measured depths included the physical thickness of the roughness elements. To assess whether this introduced systematic bias, the reference depth was artificially reduced by 0.5 cm, 1.0 cm, and 1.5 cm, and the entire optimization was repeated. The resulting changes in ks were consistently less than 1%, and the flow regime classification remained unchanged. This demonstrates that the methodology is robust and insensitive to minor uncertainties in vertical datum selection.ConclusionsThis study validates a novel experimental approach for estimating Manning's n using gradually varied flow (M2 profiles), which offers a practical, repeatable, and lower‑uncertainty alternative to conventional uniform‑flow methods in laboratory flumes. By leveraging gradually varied flow—which prevails in natural channels—this approach bridges the gap between laboratory studies and field applications, offering a physically relevant framework for river engineering and flood modeling.Keywords: Equivalent roughness, Longitudinal slope, Manning's roughness coefficient, Open Channels, Shear Reynolds number Conflict of Interest The authors declare that they have no conflict of interest. All authors have read and approved the final manuscript. Funding The corresponding author conducted this study as an extension of the research project entitled “Experimental Investigation of the Effects of Hydraulic Crossing Structure Geometry on the Trapping of Large Woody Debris and the Aggravation of Flood Hazards,” carried out at the Soil Conservation and Watershed Management Research Institute. The present research was undertaken to develop practical measures for reducing flood risks associated with woody debris accumulation at bridges. No financial support was received from any public, commercial, or non-profit organization for conducting, writing, or publishing this study. Data Availability Statements All relevant data and results supporting the findings of this study are presented within the article. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by R.F. and all authors commented on previous versions of the manuscript (R.F., S.K., and S.M.). The final revisions have been applied by S.K. and Moreover, all authors have read and approved the final manuscript.All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts. AcknowledgementThe vice dean for research affair of the University of Tehran is Acknowledged for providing the research facility of the Central Lab for Water Research
Review paper
Irrigation network management
Nasrin Khodamoradi vatan; Hojat Ahmadi
Abstract
Extended AbstractIntroductionRiver intakes constitute critical hydraulic infrastructure for sustainable water resource management. A primary challenge in the design and operation of these structures is sediment management, as excessive sedimentation significantly impairs storage capacity and operational ...
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Extended AbstractIntroductionRiver intakes constitute critical hydraulic infrastructure for sustainable water resource management. A primary challenge in the design and operation of these structures is sediment management, as excessive sedimentation significantly impairs storage capacity and operational efficiency. The intake angle is a pivotal geometric parameter governing flow patterns and sediment transport mechanisms, exerting a substantial influence on the sedimentation process within the river–intake system. The present study provides a systematic literature review covering an 81-year period (1944–2025). We synthesized findings from 132 peer-reviewed articles sourced from reputable domestic and international scientific databases. Following a comprehensive thematic classification, the literature was analyzed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Conceptual synthesis of the reviewed evidence underscores that the intake angle is a fundamental design variable for effective sediment control. These findings offer practical insights for hydraulic engineers and practitioners, facilitating the optimization of intake configurations and enhancing overall water resource management strategies.Materials and MethodsThis study employs a systematic and documentary review to analyze the hydraulic and sediment-control performance of lateral intakes in open channels. Drawing upon 132 national and international publications retrieved from reputable databases between 1944 and 2025, the research synthesizes findings from both laboratory and numerical investigations. The analysis, structured under the PRISMA framework and complemented by a conceptual review approach, identifies the key hydraulic, geometric, and sedimentary parameters affecting intake efficiency. Results indicate that optimized intake angle and position, along with geometric modifications such as sloped walls, submerged vanes, and deflector structures, substantially reduce vortex formation and sediment entry. Numerical modeling using tools such as FLUENT, FLOW‑3D, and SSIIM demonstrates high agreement with experimental data, validating its use for design optimization. Moreover, hybrid and multi‑component designs integrating vanes, sills, and guide walls achieve sediment reduction rates exceeding 70%. The study concludes that sustainable and efficient water‑diversion systems require an integrated design approach balancing hydraulic performance, sediment dynamics, and site‑specific topographic conditions, supported by advanced numerical modeling and experimental validation. ResultsThe review of eleven studies published between 1944 and 2025 revealed consistent patterns in the hydraulic behavior and sediment‑control performance of lateral intakes. Analysis of laboratory and numerical investigations indicated that variations in intake angle, channel curvature, discharge ratio, and intake geometry have the most significant impact on vortex formation, flow separation, and sediment entry. Experimental results showed that reducing the intake angle generally decreases sediment intrusion, while positioning the intake along the outer bend improves flow distribution and minimizes scour depth. Numerical simulations using CFD models such as FLUENT provided strong agreement with experimental findings, confirming the critical role of geometric configuration and shear stress in controlling flow behavior. After multiple comparative evaluations, seven major parameters were identified as the dominant factors governing intake efficiency: intake angle, bend conditions, bed shear stress, relative curvature‑to‑depth ratio, geometry and dimensions of the intake, discharge ratio, and Froude number. These parameters form a unified analytical framework for optimizing intake design to achieve minimal sediment intrusion and stable hydraulic operation.ConclusionsA comprehensive review of studies conducted between 1944 and 2025 revealed that the hydraulic performance of lateral intakes in open channels is strongly governed by geometric configuration and flow conditions. The intake angle, discharge ratio, and intake position along the channel bend are among the most influential parameters controlling flow patterns, vortex formation, and sediment entry. Findings from previous research indicate that intake angles of 45°–60° effectively minimize flow separation, while positioning the intake on the outer bend (at angles of 115°–135°) enhances flow uniformity and reduces local scour. Both numerical and physical investigations confirm that the geometry of the intake and sidewalls—particularly curved or rounded-corner designs—and regulation of hydrodynamic parameters such as the Froude number play a decisive role in sediment control. The strong agreement between laboratory observations and advanced computational models further supports the reliability of numerical approaches for optimized design.Overall, achieving stable hydraulic performance in lateral intakes requires an integrated approach that simultaneously considers optimal intake geometry and position, improved hydraulic conditions, and the use of auxiliary structures. Despite significant progress in laboratory and numerical studies, research gaps remain—particularly in field-scale investigations, sediment characterization, and interaction analyses of combined structures under complex flow conditions. Future developments in three-dimensional numerical modeling and large-scale field experiments could substantially enhance understanding of these interactions and lead to more sustainable strategies for sediment management and intake efficiency. Conflict of InterestThe authors declare that they have no conflict of interest regarding the preparation and publication of the materials and findings presented in this study. FundingThe authors received no financial support for the research, authorship, or publication of this article. Data Availability Statements The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors’ ContributionsAll authors contributed equally to the conceptualization of the study, preparation of the original draft, and subsequent revisionsAcknowledgementThe authors wish to express their sincere gratitude to the Editor and the two anonymous reviewers for their insightful comments and constructive feedback, which significantly improved the quality of this manuscript. We also thank the Regional Water Company of Qazvin for providing the necessary data for this research.
Original Article
Irrigation network management
Zeynab Rahimi Atani; Mohammad Bijnkhan; Hadi Ramezani Etedali; Hamed Mazandaranizadeh; Sara Fakuri; Sakineh Kohi; Mina Gholizadeh
Abstract
Extended AbstractIntroductionGroundwater resources play a vital role in sustaining agricultural activities in arid and semi-arid regions. In Iran, over 75% of irrigation water is supplied from aquifers (Yazdanpanah et al., 2019), making groundwater the primary source of agricultural production. However, ...
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Extended AbstractIntroductionGroundwater resources play a vital role in sustaining agricultural activities in arid and semi-arid regions. In Iran, over 75% of irrigation water is supplied from aquifers (Yazdanpanah et al., 2019), making groundwater the primary source of agricultural production. However, continuous groundwater level decline due to overexploitation, reduced natural recharge, and climate change has made accurate monitoring of groundwater abstraction an essential requirement forsustainable water resource management. Direct measurement of well discharge faces numerous challenges, including high equipment costs, operational difficulties, spatial distribution of wells, and the presence of unauthorized wells. While smart water meters have been introduced as a solution for abstraction control, their widespread implementation is constrained by high installation and maintenance costs. In contrast, most agricultural wells are already equipped with electricity meters that continuously record power consumption data, presenting a cost-effective alternative for indirect discharge estimation (Wang et al., 2020; Alam et al., 2023). This study investigates the feasibility of estimating agricultural well discharge using electricity meter data and physical well characteristics through an Artificial Neural Network (ANN). The novelty of this research lies in developing a cost-effective approach that eliminates the need for expensive flow measurement equipment, relying solely on accessible electricity consumption data.
Methodology
Study Area and Data Collection
The research was conducted in Qazvin Province, central Iran, characterized by diverse climatic conditions and intensive agricultural activities. Field data were collected from 70 agricultural wells, including geographical coordinates, well depth, pipe diameter, electricity consumption parameters, and measured discharge values. Due to incomplete power records, 35 wells with reliable data were selected for modeling. Discharge measurements obtained using an ultrasonic flowmeter served as reference data.
Artificial Neural Network Modeling
A multilayer perceptron (MLP) neural network was implemented in the MATLAB environment using the Levenberg–Marquardt training algorithm. Eight scenarios were defined to evaluate the sensitivity of model performance to various combinations of input parameters, including power consumption, well depth, pipe diameter, geographical coordinates, and aeration condition. The dataset was randomly divided into training (70%), validation (15%), and testing (15%) subsets. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). Different numbers of neurons (5 to 10) were tested to identify the optimal network architecture.
Results and Discussion
Model Performance and Scenario Selection
The ANN successfully estimated agricultural well discharge using electricity meter data and well characteristics. Among the evaluated scenarios, Scenario 7 comprising power consumption, well depth, pipe diameter, and geographical coordinates—demonstrated the most stable and reliable performance across training, validation, and testing stages. Although Scenario 8, which included aeration condition, produced slightly lower error during training, it required operational parameters difficult to obtain in practice. Consequently, Scenario 7 was selected as the optimal model. The optimal ANN architecture consisted of one hidden layer with 10 neurons, achieving the highest coefficient of determination (R²=0/9204) and lowest prediction error (MAPE=13/96%). The model achieved average errors of approximately 15%, 13%, and 14% during training, validation, and testing phases, respectively.
Analysis of Input Variables
Power consumption was identified as the most influential variable, consistent with the hydraulic power equation (P = ρ·g·Q·H/η), which establishes a direct physical relationship between power and discharge. Well depth affects pumping head (H), while pipe diameter influences friction losses. Geographical coordinates were incorporated into the model to account for spatial heterogeneity in hydrogeological conditions across the study area, as variations in aquifer thickness, sediment texture, hydraulic conductivity, and water table depth are not uniformly distributed throughout the Qazvin Plain.
Comparison with Previous Studies
The results align with findings by Alam et al. (2023), who achieved R²≈0/92 in alluvial aquifers, and Wang et al. (2020), who reported errors below 20% for energy-to-water conversion. The model performance (R²=0/9204) is comparable to Salimi et al. (2023), who obtained R²=0/825 using Random Forest, and Salamat et al. (2023), who achieved 0/945 accuracy using SVM-PSO. Although the dataset (35 wells) is limited compared to previous studies (e.g., 236–359 samples), the model demonstrates competitive performance, confirming the feasibility of the proposed approach.Limitations and Sources of ErrorThe main limitations include: (1) limited sample size (35 wells), increasing the risk of overfitting, mitigated by early stopping and simple network architecture; (2) lack of comparison with other machine learning algorithms (e.g., Random Forest, SVM, XGBoost); (3) absence of hydrogeological variables such as water table level, dynamic head, and pump specifications; (4) limited temporal sampling (August–November 2023), not capturing seasonal variations; and (5) regional specificity, requiring local calibration for application in other areas. Key sources of error include pump efficiency variations, groundwater level fluctuations, voltage instability, aeration, and differences in maintenance conditions.
Conclusions
This study demonstrates that combining electricity meter data with artificial neural networks provides a practical, cost-effective approach for estimating agricultural well discharge. The proposed method eliminates the need for expensive smart water meters while maintaining acceptable accuracy (R²=0/9204, MAPE=13/96%). Power consumption, well depth, pipe diameter, and geographical location were identified as key predictors. The approach can support groundwater monitoring programs, improve water resource management, and assist policymakers in implementing sustainable abstraction strategies. Future research should expand the dataset, incorporate temporal variations, compare multiple algorithms (Random Forest, SVM, XGBoost), and include hydrogeological variables (water table, dynamic head, pump characteristics) to enhance model accuracy and generalizability.
Conflict of InterestThe authors declare that they have no conflict of interest regarding the preparation and publication of the materials and findings presented in this study.
FundingThe authors received no financial support for the research, authorship, or publication of this article.
Data Availability StatementThe datasets generated and analyzed during this study are not publicly available due to applicable restrictions, but are available from the corresponding author upon reasonable request.
Author ContributionsConceptualization: Z. Rahimi Atani, M. Bijankhan, H. Ramazani; Methodology: Z. Rahimi Atani, H. Mazandaranizadeh; Software: Z. Rahimi Atani, H. Mazandaranizadeh; Validation: M. Bijankhan, H. Ramazani; Formal analysis: Z. Rahimi Atani, M. Bijankhan; Investigation: Z. Rahimi Atani, M. Gholizadeh, S. Koohi, Fakouri; Resources: Z. Rahimi Atani; Data curation: M. Bijankhan, H. Ramazani, M. Gholizadeh, S. Koohi, Fakouri; Writing—original draft: Z. Rahimi Atani; Writing—review and editing: M. Bijankhan; Visualization: Z. Rahimi Atani; Supervision: M. Bijankhan, H. Ramazani; Project administration: M. Bijankhan. All authors have read and agreed to the published version of the manuscript.
AcknowledgementThe authors would like to express their sincere gratitude to the Qazvin Regional Water Company for providing access to well data and facilitating field measurements. This research was supported by the Imam Khomeini International University (IKIU), Qazvin, Iran. The authors also acknowledge the cooperation of local farmers and agricultural well owners who participated in this study. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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