Iranian Water Research Journal

Abstract Extended Abstract
Introduction
Mountain snow reserves are a crucial component of the hydrological cycle in high-altitude basins, supplying a substantial portion of river flow during the warm season. Forecasting discharge during the snowmelt-dominated period is therefore essential for water resource management and flood mitigation, a challenge intensified by climate change impacts on melt timing. While physically-based and conceptual temperature-index models are common, they face limitations due to data scarcity or an inability to capture complex, non-linear melt-runoff processes. Consequently, this study employed a data-driven approach using a Long Short-Term Memory (LSTM) deep learning network to model the non-linear temporal dynamics of snowmelt. Specifically, an LSTM model was developed and evaluated to forecast daily discharge during the snowmelt period in the data-scarce, snow-fed Bazoft Basin, Iran. To address the lack of in-situ data, meteorological time series from the ERA5 reanalysis product were utilized as model inputs. The results demonstrated the LSTM model's effectiveness in learning the long-term dependencies between snowmelt processes and streamflow, offering a robust framework for seasonal runoff prediction in snow-dominated regions to support sustainable water management.

Materials and Methods
This study was conducted in the snow-covered Bazoft Basin, located in the northern Karun River catchment, Iran (31.6°–32.65° N, 49.56°–50.46° E). The basin experiences snow accumulation from December to March, with the snowmelt-dominated period occurring from mid-February to April, during which snowmelt is the primary contributor to streamflow. Daily streamflow data from the Landi hydrometric station were used as the target variable. Due to the scarcity of ground-based meteorological stations in this mountainous region, this study utilized the ERA5-LAND reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5-LAND provides improved spatial resolution (9 km) compared to ERA5 (31 km) and has been extensively validated in previous studies over Iran, demonstrating high correlation with ground observations, particularly for temperature (R² > 0.95). The following daily variables were extracted: air temperature (T), precipitation (P), and snow water equivalent (SWE). The Gamma Test (GT), a non-parametric method for identifying the optimal input combination, was employed to select the most relevant input variables. Four different input combinations were evaluated: M1 (Q and SWE), M2 (Q, T, and SWE), M3 (Q, P, and SWE), and M4 (Q, P, T, and SWE). The combination with the minimum Gamma value and V-ratio was selected as the optimal input set. A Long Short-Term Memory (LSTM) neural network was developed to model the non-linear, long-term temporal dependencies inherent in the snowmelt-runoff process. The LSTM architecture includes forget, input, and output gates that regulate information flow, enabling the network to retain relevant information over extended time steps. The model was implemented using the Keras framework. The dataset was randomly divided into training (80%) and testing (20%) subsets. Various hyperparameters were optimized, including the number of hidden layers (1–3), the number of hidden units (10–40), and seven optimization algorithms (Adam, Adamax, SGD, RMSprop, Nadam, Adagrad, and Adadelta). Model performance was evaluated using the Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²).
Results and Discussion
The Gamma Test results identified the optimal input combination for LSTM modeling. Among the four evaluated combinations, M2 (discharge, temperature, and snow water equivalent) exhibited the minimum Gamma value (0.00151) and V-ratio (0.00412), indicating the smoothest input-output relationship with the lowest irreducible noise. Consequently, M2 was selected as the most appropriate input set for subsequent LSTM modeling. Evaluation of LSTM hyperparameters revealed that a single hidden layer architecture outperformed deeper configurations (2-3 layers), achieving the lowest RMSE (0.220) and MAE (0.125). Regarding hidden units, 20 neurons yielded optimal performance. Among seven optimization algorithms compared, Adamax demonstrated superior results with RMSE and MAE values of 0.37 and 0.18, respectively, and was therefore selected as the optimizer for the final model. The LSTM model with M2 inputs (Q, T, SWE) demonstrated exceptional performance in simulating daily streamflow during the snowmelt-dominated period. During the training phase, the model achieved NSE=0.994, R²=0.991, and RMSE=0.08. In the testing phase, performance remained excellent with NSE=0.994, R²=0.991, and RMSE=0.174. Visual comparison of observed and simulated hydrographs for the 2021-2022 and 2022-2023 water years showed that the LSTM accurately captured both low-flow conditions and peak discharges during snowmelt events. Scatter plots further confirmed this accuracy, with points closely clustered around the 1:1 line and R² values of 0.997, demonstrating that the model effectively reproduced the variance in observed streamflow across the entire flow regime. The superior performance of the LSTM model can be attributed to its inherent capability to capture long-term temporal dependencies through memory cells and gating mechanisms, which is particularly advantageous for snowmelt-runoff processes characterized by lagged responses and memory effects. The inclusion of SWE as an input variable proved crucial, as it directly represents the accumulated snowpack available for melt, while temperature controls the melt energy. Notably, the M2 combination (Q, T, SWE) outperformed combinations including precipitation (M3 and M4), suggesting that during the snowmelt-dominated period, the hydrological signal is primarily governed by snowmelt dynamics rather than concurrent rainfall. Comparison with previous studies demonstrated the competitiveness of the proposed approach. The obtained NSE (0.994) substantially exceeded values reported for traditional temperature-index models (NSE~0.80) and was higher than ANN-based snowmelt runoff predictions (NSE~0.93). The results were comparable with other LSTM applications in hydrological modeling (NSE~0.99). However, this study specifically contributed to the limited literature on LSTM application for snowmelt-dominated period streamflow modeling in data-scarce mountainous regions. The successful integration of ERA5-LAND reanalysis data addresses the critical challenge of data scarcity in mountainous basins. The high accuracy achieved demonstrated that reanalysis products can effectively substitute for ground-based meteorological observations when the latter are unavailable, provided they are appropriately validated for the study region.

Conclusion
This study demonstrated that the LSTM model, forced with ERA5-LAND reanalysis data (temperature and snow water equivalent) together with antecedent discharge, accurately simulated daily streamflow during the snowmelt-dominated period in the data-scarce Bazoft Basin (NSE=0.994, RMSE=0.174). The optimal performance was achieved with a single hidden layer, 20 hidden units, and the Adamax optimizer. The superior performance of the temperature-SWE-discharge combination over precipitation-inclusive inputs confirmed the dominance of snowmelt processes during the study period. This framework offers an effective solution for seasonal runoff prediction in snow-dominated mountainous regions lacking dense ground-based observations, supporting informed water resource management and planning.

Abstract
Introduction
Bridge pier scour is recognized as one of the most significant hydraulic challenges in the design, op-eration, and maintenance of bridge structures. It arises from the complex interplay between flow dy-namics, alluvial bed characteristics, and pier geometry. Scour occurs when alterations in flow patterns and increased bed shear stress lead to sediment erosion, ultimately compromising structural stability. In recent decades, the growing body of research on localized pier scour underscores its critical im-portance in mitigating both structural and environmental risks. Despite considerable advancements in numerical, experimental, and analytical modeling, significant uncertainties persist in predicting scour depth and extent. This study aims to provide a comprehensive review of the physical mechanisms governing scour, identify key influencing factors, and evaluate both structural and non-structural miti-gation strategies. The current research establishes a scientific framework for scour prediction, man-agement, and mitigation. By examining flow behavior around piers, sediment characteristics, and the application of numerical and artificial intelligence models, this framework enhances the safety and longevity of bridge infrastructure under variable hydrological conditions.
Materials and Methods
This article employs a systematic review and analytical approach to examine bridge pier scour from the perspectives of physical mechanisms, influencing parameters, and mitigation strategies. Initially, the theoretical foundations and classifications of scour types (including general, local, and combined scour) were synthesized from peer-reviewed literature. Subsequently, flow patterns around piers were analyzed using experimental data and numerical simulations (CFD and two-phase models) to elucidate the formation of vortices, wake currents, and flow separation zones. The influence of hydraulic pa-rameters (e.g., flow velocity, water depth, Froude number) and sediment properties (e.g., density, grain size, cohesion) was evaluated through a comprehensive review and categorization of empirical data from prior studies. In the second phase, scour mitigation techniques were compared across two prima-ry categories: “bed armoring” (e.g., riprap, gabions, and articulated concrete block mats) and “flow alteration measures” (e.g., collars, sacrificial piles, and submerged vanes). These approaches were as-sessed based on hydraulic efficiency, implementation cost, and long-term durability. Scour prediction methodologies—including empirical equations, single-phase and two-phase CFD models, Smoothed Particle Hydrodynamics (SPH), and machine learning algorithms—were comparatively evaluated to determine their accuracy, advantages, and limitations. Additionally, global case studies were reviewed to assess climatic and geomorphological impacts on scour behavior. The review follows a structured methodological framework comprising the following stages: (1) Literature collection and screening: Relevant studies were retrieved from reputable scientific databases using targeted keywords and fil-tered according to predefined inclusion criteria. (2) Thematic classification: Extracted literature was categorized into core themes (flow dynamics, influencing factors, mitigation strategies, predictive modeling, and future research directions). (3) Synthesis and critical analysis: Findings from diverse studies were analyzed to identify consensus, discrepancies, and overarching trends, which were then integrated into the article’s conceptual framework. (4) Comparative evaluation: Mitigation and model-ing techniques were benchmarked against key parameters, including efficiency, cost-effectiveness, structural stability, and environmental impact. (5) Identification of research gaps: The final section outlines critical knowledge gaps and future research needs, providing a forward-looking perspective on the field.
Results and Discussions
Comprehensive analysis of bridge pier scour mitigation techniques revealed that no single measure can entirely eliminate scour. However, a strategically selected combination of methods, tailored to site-specific hydraulic conditions, sediment characteristics, and economic constraints, can substantially reduce maximum scour depth. Integrating flow alteration techniques with bed armoring proves to be the most effective strategy. Among bed armoring solutions, riprap (particularly when installed in sloped configurations) can achieve scour reductions exceeding 80%. Articulated concrete blocks and gabions also serve as viable alternatives, albeit with certain design and maintenance limitations. Acoording to the analysis in the category of flow alteration measures, submerged vanes demonstrated scour depth reductions approaching 88%, while slots achieved reductions of 30–40%, depending on their geometric configuration. Sacrificial piles, despite requiring periodic replacement, reduce scour depth by up to 50%. The efficacy of collars is highly geometry-dependent: conventional collars exhib-ited limited performance, whereas modified designs incorporating fins, hooks, or polygonal shapes yielded reductions of 50–73%. Advanced configurations, such as lenticular or airfoil collars, showed highly variable effectiveness contingent upon specific design parameters. Pervious collars, which guide flow through controlled permeability, achieved approximately 78% scour reduction. Further-more, bed regulation structures like sills and curved plates dissipate incoming flow energy, resulted in 30–55% reductions and enhanced cross-sectional stability. Ultimately, the synergistic integration of flow alteration and bed armoring techniques represented the optimal mitigation approach. Optimal de-sign must account for local hydraulic conditions, sediment properties, flow intensity, and maintenance requirements to ensure both scour control and long-term structural and economic viability. In terms of predictive modeling, comparative analysis indicated that two-phase CFD models, particularly those utilizing an Eulerian–Lagrangian framework, offered superior accuracy in simulating scour initiation and evolution, despite higher computational demands. The SPH method has also demonstrated en-hanced capability in capturing complex vortex dynamics compared to conventional grid-based ap-proaches. Meanwhile, machine learning models (such as artificial neural networks and random forest algorithms) trained on extensive experimental datasets, exhibit strong potential for rapid, real-time scour depth estimation in practical engineering applications.
Conclusion
Bridge pier scour is a complex, multi-factorial phenomenon driven by the dynamic interaction be-tween flow hydrodynamics, pier geometry, and bed sediment properties. The integration of advanced numerical modeling, data-driven predictive tools, and site-specific mitigation strategies enables effec-tive scour management in bridge engineering. Developing a systematic framework for selecting coun-termeasures—based on hydraulic performance, long-term stability, and cost-effectiveness—represents a critical step toward enhancing the safety, durability, and environmental resilience of bridge infra-structure. Future research should prioritize investigating bridge vulnerability under climate change sce-narios and extreme flood events to improve adaptive design practices.

Abstract Introduction
Bridges are critical structures in river engineering and require robust protection against scour, which is a significant hydraulic concern. Scour around bridge piers, exacerbated by flood events of varying intensity and duration, poses a substantial risk of structural failure. Furthermore, the accumulation of floating debris around piers can significantly alter local flow dynamics and scour patterns. Understanding these phenomena under diverse conditions is paramount for accurate scour depth estimation and the development of effective bridge protection strategies. Accumulation of floating debris around bridges, particularly near the piers, ranks among the primary factors contributing to scour. This debris reduces the flow cross-sectional area, thereby increasing the flow velocity to the levels beyond design thresholds. Consequently, the accumulation of floating debris causes a compounded increase in flow velocity, leading to severe scour. This ouccures especially in regions characterized by high-velocity water flows such as mountainous and forested areas. Thus, it is imperative to proactively identify bridges susceptible to debris accumulation and implement appropriate mitigation measures, such as installing debris traps upstream of the bridge piers, to prevent excessive debris buildup. This research investigated laboratory-scale scour around bridge pier groups under live-bed conditions. Also, it examined the influence of pier arrangement and the presence of accumulated floating debris.

Materials and Methods
The study analyzed pier groups consisting of three, five, and six piers, with a relative pier width (pier diameter to channel width ratio) of 1/10. A total of 18 experiments were conducted across two scenarios: with floating debris and without floating debris (control). To eliminate the influence of channel walls on scour depth, the pier diameter should not exceed 10 percent of the channel width. Therefore, piers with a diameter of 6 cm (exactly 10 percent of the channel width) were utilized in these experiments. The related tests were performed in a mobile-bed flume to evaluate how varying discharge rates (24, 39, and 54 L/s) affect scour dynamics. The experimental design aimed to demonstrate that increases in the relative flow velocity lead to greater scour depth and volume, and to quantify the significant exacerbation of scour caused by floating debris accumulation. To prevent the influence of bed particle size on scour depth, the ratio of pier diameter to the mean sediment particle diameter was maintained at 50, in accordance with previous related studies. Furthermore, to eliminate the effect of sediment non-uniformity on scour reduction, the geometric standard deviation of the sediment was kept below 1.3. Consequently, a mean particle diameter of 0.96 mm was selected for this study. Prior to initiating each experiment, the sediment bed surface was leveled using a trowel, and the area around the piers was precisely leveled using a laser level and a spirit level. The tailgate at the downstream end of the flume was fully raised to prevent initial scour and the formation of bed forms. Subsequently, the pump was started at a low discharge rate. After a few minutes, once the sediment was fully saturated, the discharge was gradually increased to the target flow rate. The previously closed tailgate was then slowly adjusted to establish the required water depth. After allowing several hours for the scour around the piers to reach an equilibrium state, the pump was shut down, and the water in the flume was slowly drained. Following the cessation of flow, the topography around the pier group was measured using a non-contact bed profiler to ensure accurate assessment.

Results and Discussions
In the absence of floating debris, the maximum scour depths for the three-, five-, and six-pier groups were recorded as 11.5 cm, 12.1 cm, and 14.8 cm, respectively. Increasing the number of piers significantly amplifies the maximum scour depth; specifically, the six-pier configuration exhibited a 28.70% increase compared to the three-pier group, which is primarily attributed to the complex interference and overlap of horseshoe and updraft vortices. Furthermore, the accumulation of floating debris acts as a critical aggravating factor by reducing the effective flow cross-section and intensifying local flow velocities. In the densest pier arrangement, this debris accumulation exacerbated the maximum scour depth by 35.14% relative to the no-debris condition. The analysis of the blockage ratio revealed that denser pier configurations, particularly at shallower flow depths, severely restrict the effective flow area, thereby forcing intense flow channelization between the piers and exacerbating localized bed erosion. Under supra-threshold live-bed conditions, a direct and substantial correlation was observed between the relative flow velocity and the expansion of the scour hole, where a 125% increase in discharge led to a remarkable 94.17% surge in scour depth when debris was present. Three-dimensional bed topography measurements further demonstrated that the presence of debris not only deepens the scour hole but also significantly increases its lateral extent and morphological complexity, particularly around the front piers where the primary downflow originates. Finally, based on dimensional analysis of the experimental data, a robust empirical predictive model was successfully formulated and statistically validated with a high coefficient of determination (R² = 0.85), providing a reliable mathematical tool for estimating maximum scour depth under these specific hydraulic conditions.

Conclusion
Based on the conducted experiments and the obtained results, an empirical relationship for predicting scour depth was derived using dimensional analysis for the three investigated groups of cylindrical piers. Statistical validation, performed with the aid of SPSS software, confirmed the high accuracy of this relationship in predicting scour depth.

Abstract Introduction:
Given the increasing population, the decreasing of annual precipitation, and the scarticy of water supply resources, optimal use of existing water resources is one of the best options for achieving sustainable agriculture. Maximizing irrigation water productivity has been a centeral policy of managers and planners of the agricultural sector in recent years. Among the appropriate techniques for improving productivity and efficiency of water use are modern irrigation systems and the application of management scenarios such as deficit irrigation. In the field of modern irrigation, the invention of the subsurface drip irrigation system represents a major advance in the drip irrigation technology. Cotton is one of the most important agricultural products that, in addition to providing raw materials for the textile and oil industries, plays a significant role in jobs creation across the agricultural, industrial, and commercial sectors. Regarding continuous and severe droughts in Kerman province, especially in the Orzoueyeh agricultural region, and the lack of comprehensive studies on this issue—which challenges the economic justification for implementing modern irrigation systems—it is necessary to evaluate both surface and subsurface drip irrigation systems from technical and economic perspectives, particularly for cotton crops in Orzoueyeh County.

Materials and Methods:
The present study was conducted at the Orzoueyeh Research Farm, affiliated with the Kerman Agricultural and Natural Resources Research and Education Center, Orzoueyeh County, Kerman, Iran, during 2024-2025. To evaluate surface and subsurface drip irrigation systems on cotton yield, an experiment was conducted using split strip plots in a randomized complete block design with three replications. This experiment consisted of 12 treatments, three irrigation levels (I₁=125, I₂=100, and I₃=75 percent of water requirement), and two irrigation systems including surface drip irrigation (S₁) and subsurface drip irrigation (S₂), as well as two lateral placement patterns (all rows=L₁ and alternate-row =L₂). The factors I and S were applied in strip and cross arrangements, respectively. In each replication, factor I was arranged vertically, and factor S horizontally. Factor L was assigned to the plots resulting from the intersection of the two factors I and S. To measure and calculate the studied traits after removing the margins, two middle lines of each plot were used. Finally, the yield of the plant per hectare, the number of bolls per plant, and the water use efficiency (WUE) were measured and calculated as the amount of product per volume of water used. The studied traits were subjected to a combined analysis of variance for two years using SAS 9.2 software, and the least significant difference (LSD) test was applied at a probability level of 5% to compare the means. After collecting field data to economically select the best treatment, the results were examined using the partial budgeting method and investment priority analysis. Since determining the investment priority among the treatments under study was essential, an analysis of the final rate of return on investment was conducted.

Results and Discussions:
Based on the results, the crop yield at the irrigation level of 100 percent of water requirement was obtained as 2292 and 2555 kg/ha, and the lateral placement pattern in all rows and alternate rows at this irrigation level yielded 2565 and 2282 kg/ha, in surface and subsurface drip irrigation systems, respectively. The study of various indicators of crop yield, water consumption, water use efficiency, number of bolls per plant, boll weight, and the results of economic analysis of the treatments indicated the superiority of the subsurface drip irrigation over the surface irrigation system, and the superiority of the lateral placement pattern in all rows over the alternate-row, during cotton cultivation for the following reasons. The cotton crop yield in the subsurface drip irrigation system treatment was 234 kg/ha higher than that in the surface drip irrigation system. The number of bolls per plant and boll weight in the subsurface drip irrigation system were 16 and 3 percent higher, respectively, compare with the surface irrigation system. The cotton yield in the all-row irrigation pattern was 254 kg/ha higher than in the alternate-row lateral pattern. Despite the same amount of water consumption, the water use efficiency in the all-row irrigation pattern was about 8 percent higher than in the alternate-row lateral pattern. The number of bolls per plant and boll weight in the all-row irrigation pattern were 12 and 3 percent higher, respectively, than in alternate-row lateral pattern. From an economic point of view, investing in the I₂S₂L₁ treatment (subsurface drip irrigation at 100 percent of water requirement and with planting pattern in all rows) was preferable to investing in other treatments.

Conclusion:
Investment in the I₂S₂L₁ treatment (subsurface drip irrigation at 100 percent water requirement and in the in all rows lateral palcement) was preferable to investment in other treatments. The results of the technical analysis of the project also showed that the I₂S₂L₁ treatment had the best statistical position. Considering that cotton is a water-intensive crop and the amount of irrigation has a great impact on yield, all the studied traits showed better results in the 100 percent water requirement treatment compared with the other two treatments. Therefore, it can be conducted that this treatment is more economically justifiable. In addition, the full irrigation treatment was made based on economic evaluation and profitability for the farmer. Although deficit irrigation is beneficial for water conservation in the field, it is seldom adopted by farmers who primarily prioritize maximizing financial returns.