https://iwrj.sku.ac.ir/ Iranian Water Research Journal en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://iwrj.sku.ac.ir/article_116612.html Introduction: In many watersheds, particularly in semi-arid regions, the lack of long-term hydrometric data and the complexity of hydrological ecosystems pose major challenges for analyzing basin behavior. The difficulties of obtaining accurate and sufficient hydrological data in these regions necessitate the use of conceptual hydrological models that can simulate runoff processes even with limited or incomplete data. Among such models, IHACRES has gained wide recognition for its simple structure, low data requirements, and reliable performance in diverse climatic conditions. Numerous studies worldwide have demonstrated its effectiveness in simulating rainfall-runoff processes at various temporal scales and under climate change scenarios. Despite IHACRES has been successfully applied in humid, semi-arid, and data-scarce catchments, its performance has not yet been evaluated in the Seyedabad watershed, located in a semi-arid region of Kohgiluyeh and Boyer-Ahmad Province, Iran. This study aimed to calibrate and validate the IHACRES model at daily and monthly scales for this understudied basin. By addressing this research gap, the current study contributed to the localization of hydrological modeling approaches in semi-arid environments of Iran.Material and Methods:This study was conducted in the Seyedabad watershed, located in a semi-arid region of Kohgiluyeh and Boyer-Ahmad Province in southwestern Iran. The watershed covers an area of approximately 795 km² and includes the Cham Siah river, a tributary of the Kheirabad river. The basin lies between 30° to 41° N latitude and 43° to 50° E longitude. The Seyedabad watershed is crucial for providing water for drinking, agriculture, and ecological purposes in the region. The semi-arid climate leads to significant variations in rainfall and runoff, making accurate hydrological modeling essential for managing the water resources effectively. Daily and monthly climate data (including precipitation and temperature) were obtained from the Seyedabad meteorological station for the period 2007 to 2017. Streamflow data corresponding to this period were sourced from the Cham Siah hydrometric station. For modeling purposes, the dataset was divided into two segments: calibration and validation. Daily data from 2010 to 2017 and monthly data from March 2008 to August 2013 were used for calibration, while the remaining years were allocated for model validation. To simulate rainfall-runoff processes, the IHACRES hydrological model (version 2.1) was utilized. This model integrates a non-linear module to convert rainfall into effective rainfall and a linear module for streamflow routing. Calibration of model parameters was conducted using a manual trial-and-error approach to optimize performance. Key parameters included the drying rate coefficient (f), temperature modulation coefficient (c), and the catchment drying time constant (τw), along with routing parameters such as τq (quick flow recession constant), τs (slow flow recession constant), and sv (scaling factor). The model’s performance was evaluated using multiple statistical metrics: Nash–Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). These indices were calculated separately for daily and monthly time steps to assess the model’s predictive capabilities in varying temporal resolutions. The approach enabled an in-depth understanding of IHACRES performance in semi-arid, data-scarce environments.Results and Discussion: The IHACRES model was successfully calibrated and validated for the Seyedabad watershed at both daily and monthly time scales. The model's performance indicators revealed acceptable accuracy in simulating streamflow, especially considering the semi-arid nature of the basin and limited data availability. At the daily time scale, the model showed moderate accuracy, with a Nash-Sutcliffe Efficiency (NSE) of 0.51 during the calibration period and 0.47 during the validation period. The Coefficient of Determination (R²) values were 0.55 and 0.49, respectively, indicating a fair fit between observed and simulated streamflow. The Root Mean Square Error (RMSE) values were 1.26 m³/s for calibration and 1.38 m³/s for validation, suggesting relatively low error levels. These results indicated that while the model successfully captured the daily dynamics of streamflow, the accuracy was limited by the complexity of the runoff processes and the natural variability of precipitation and temperature in the semi-arid environment. The performance of the model improved significantly at the monthly time scale. The Nash-Sutcliffe Efficiency (NSE) values reached 0.71 during the calibration phase and 0.65 during validation, reflecting better accuracy in simulating the runoff processes over longer time periods. The corresponding Coefficient of Determination (R²) values were 0.72 and 0.67, and the RMSE values were 0.94 m³/s and 1.09 m³/s, respectively. These improved results at the monthly scale suggested that the IHACRES model was more effective at simulating aggregated flow patterns over extended periods, making it particularly suitable for long-term water resource assessments in semi-arid regions. The improved model performance at the monthly level could be attributed to the smoothing effects of aggregation, which reduces the sensitivity to short-term variability in runoff. Graphical comparisons between observed and simulated flows confirmed the model’s ability to reproduce seasonal variations in streamflow and to predict peak discharge events accurately. However, some discrepancies were observed, particularly during low-flow conditions and high-intensity rainfall periods. Despite these differences, the overall performance of the model in simulating streamflow behavior in the Seyedabad watershed was acceptable. These findings demonstrate that the IHACRES model, with its conceptual structure and low data requirements, can be successfully applied to semi-arid catchments with limited hydrological data.Conclusion: This study evaluated the performance of the conceptual IHACRES model in simulating the streamflow of the Cham Siah river, located in the Kheyrabad watershed of Kohgiluyeh and Boyer-Ahmad Province, Iran. Daily and monthly rainfall, temperature, and discharge data from the Seyedabad station (2007–2017) were used as model inputs. The results of the calibration and validation phases demonstrated that IHACRES could simulate the hydrological behavior of the Seyedabad watershed with reasonable accuracy. The model performed better at the monthly scale, likely due to the natural smoothing of data and reduced sensitivity to short-term variations in streamflow. The model’s performance at the daily scale was also acceptable, with moderate accuracy in simulating the runoff dynamics of the watershed. Given its simplicity, low data requirements, and efficient performance in semi-arid conditions, IHACRES is a suitable tool for simulating and predicting runoff in data-scarce basins. Its ability to simulate long-term water flow patterns makes it particularly useful for water resource management in semi-arid regions like Iran. The model’s application in combination with climate change scenarios can provide valuable insights into the potential impacts of future climatic changes on runoff and water availability. Further research is recommended to integrate IHACRES with other models, such as SWAT or Artificial Neural Networks (ANN), to enhance its predictive capabilities and improve performance under different environmental conditions. https://iwrj.sku.ac.ir/article_116605.html Introduction Drought is one of the most important climate hazards that has widespread impacts on water resources, agriculture and ecosystems. Monitoring and assessing this phenomenon requires the use of valid indicators and reliable data. Preventive measures and planning against drought are of great importance in reducing its effects, which requires the use of sufficient knowledge in drought forecasting. Recently, remote sensing and techniques developed based on satellite images have been able to provide appropriate estimates of drought on a regional scale. Currently, satellite images are regularly obtained from the Earth's surface with high spatial resolution and can provide valuable spatial data. The advantages of using remote sensing over meteorological methods include increased sampling points, wider coverage area, higher temporal resolution and lower cost.Using drought indices based on remote sensing data, it is possible to examine spatial patterns of drought. However, these indices are geographically or temporally specific and their accuracy decreases when used in other regions and times. In this regard, the main objective of this study is to investigate the spatial and temporal distribution patterns of drought and determine the performance of remote sensing indices in the spring and summer seasons in Chaharmahal and Bakhtiari province. Material and MethodsChaharmahal and Bakhtiari Province plays a key role in providing the country's water resources, as it is the headwaters of the Karun and Zayandeh Rood rivers. This makes accurate and timely monitoring of drought in the province essential. This study, along with validating satellite image precipitation data, examines the correlation of each satellite index with the Standardized Precipitation Index (SPI), a global standard index for drought assessment. In this study, a set of indices based on remote sensing data, including the Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Plant Health Index (VHI), was used to assess the drought situation in Chaharmahal and Bakhtiari Province. These indices were calculated using MODIS sensor images on a seasonal scale and for the period 2000 to 2023 for 33 stations. In addition, evaluated IMERG satellite precipitation data were also used. The IMERG data is a satellite precipitation dataset with a spatial resolution of 0.1°, produced by combining data from the TRMM and GPM satellitesResults and DiscussionThe results of the assessment of the accuracy of IMERG precipitation data showed that the correlation of these data is high, between 0.83 and 0.96. These results indicate that IMERG precipitation satellite data can be used as an acceptable source for drought monitoring. The results also showed that, based on the spring VCI index, which is highly sensitive to changes in vegetation cover, the year 2000 was the driest year, with about 78 percent of the province's area affected by drought, while in 2020, about 88 percent of the province was without drought. The TCI index, which mainly reflects temperature conditions and heat stress, showed that in years with adequate precipitation, the index value increased due to a decrease in land surface temperature (such as 2019). In contrast, in dry years such as 2008 and 2021, TCI values decreased, indicating an intensification of the effect of drought through increased heat stress.The quarterly SPI index in spring showed the highest significant correlation with the TCI, VHI and VCI indices (0.85, 0.81 and 0.65, respectively), indicating the effect of temperature and short-term precipitation in determining the health and greenness of vegetation in spring. In summer, the highest correlation was related to the twelve-month and six-month SPI with the VCI index because the effect of precipitation on vegetation appears with a time lag. Therefore, TCI and VHI indices in spring and VCI in summer can provide a more accurate and complete picture of the vegetation response to spring precipitation in the province.ConclusionOne of the important findings of this study is that the northern and eastern regions of the province are more vulnerable to drought. This finding can help provincial decision-makers prioritize areas of the province in terms of allocating financial resources to implement drought adaptation methods or preparing to manage farmers' protests. These areas can also be prioritized for water resource allocation or intra-provincial water transfer projects. In addition, these areas can be prioritized for drought resilience projects, such as planning for appropriate crop timing, changing cropping patterns, selecting drought-resistant species and modifying or replacing irrigation methods. https://iwrj.sku.ac.ir/article_116614.html Extended Abstract Introduction:Integrated Water Resources Management (IWRM) is defined by the Global Water Partnership (GWP) as “a process that promotes the coordinated development and management of water, land, and related resources to maximize economic and social welfare equitably without compromising the sustainability of vital ecosystems and the environment.” Recently, a physical index has been proposed to quantitatively evaluate water resource management by accounting for environmental water requirements. This index, based on the concepts of water accessibility and water supply, allows for a more integrated and comparative assessment of management performance. The Sefidrud watershed, covering an area of 58,452.84 km², is located in northwestern Iran. This study aims to: (a) assess the management levels of surface and groundwater resources across the Sefidrud watershed during the 1995–2017 observation period, and (b) examine the impacts of observed climate changes on water resource management levels during the study period. Materials and Methods:To evaluate and quantify the management levels of surface and groundwater resources in the Sefidrud watershed, SWAT hydrological model was employed. The watershed was delineated into 15 subbasins based on elevation, hydrometric, and dam data, and the model was run with inputs of soil, land-use, slope, and daily dam outflows. The model was implemented over the 1995–2017 period in two stages: a 16-year calibration period (1995–2010) and a 7-year validation period (2011–2017). Level of Management (LOM) for river water was estimated using a physical index that accounts for available water and total water supply. In this context, total water supply is defined as the volume of water withdrawn from the river. To calculate LOM, which denotes the management level of either river or groundwater resources, the available water volume (AW) of the river or groundwater and  the water withdrawn from the river or groundwater for use in sectors (TWS ), were considered. The Environmental Flow Requirement (EFR) is also defined as a percentage of the river’s long-term annual mean flow. In this study, 80% of the river's long-term annual mean flow was allocated to EFR, and the remaining 20% to human needs and Accessible River Water (ARW). EFR for groundwater was determined based on the river’s baseflow contribution. River and groundwater management levels may vary from negative values up to +1, where negative values indicate mismanagement and positive values reflect acceptable management. The management levels of both river and groundwater resources during the observation period were computed under two scenarios: (1) the real scenario (reflecting actual climate and land-use conditions), and (2) the climate scenario (using daily precipitation and temperature data with trends removed, i.e., de-trended), and were subsequently compared. Results and Discussions: The performance evaluation of the SWAT model during the calibration period (1997–2010) indicated an acceptable level of accuracy in simulating monthly river flows at the subbasin scale. The coefficient of determination (R²) and the Nash–Sutcliffe Efficiency (NSE) exceeded 0.7 in most subbasins. To investigate the effect of precipitation changes, the annual mean precipitation derived from the observed time series was compared with the corresponding values from the detrended series. The impact of precipitation changes was estimated as the difference between the mean annual observed precipitation and the detrended values (observed minus detrended). Spatial analysis of the distribution of climate stations showed that decreasing precipitation trends predominantly occurred in the southwestern and eastern parts of the basin, whereas the central and northern regions experienced increases in annual precipitation during the period 1995–2017. Analysis of annual minimum and maximum temperature trends revealed that the entire study area experienced warming during 1995–2017, with the exception of the minimum temperature at the Tabriz station. The effect of climate change on river flow in each subbasin was determined by calculating the difference between simulated river flows under the actual scenario and those under the no-climate-change scenario (flow in actual scenario minus flow in no-climate-change scenario). Climate impact mapping showed that most subbasins experienced reductions in river flow due to decreased precipitation, although a few subbasins exhibited increases in flow associated with increased precipitation during 1995–2017. Because total water withdrawals in both the actual and no-climate-change scenarios remained constant, the observed changes in LOM values were attributable to variations in water availability. Consequently, climate change improved LOM in subbasins that experienced increased precipitation and river flow during the study period, whereas LOM declined in subbasins with decreased precipitation and flow. The impact of climate change on groundwater recharge in each subbasin was assessed by calculating the difference between simulated aquifer recharge under the actual scenario and that under the no-climate-change scenario (recharge in actual scenario minus recharge in no-climate-change scenario). Evaluation of climate change effects revealed that most subbasins experienced reductions in groundwater recharge due to decreased precipitation, whereas some subbasins showed slight increases in recharge rates as a result of higher precipitation during 1995–2017. The groundwater LOM declined in most of the study area because of reduced precipitation, although some areas exhibited increases.  Conclusion:In this study, the impact of climate change on the Level of Management (LOM) of surface and groundwater resources was evaluated in the Sefidrud watershed, Iran, during the period 1995–2017. The watershed has experienced an increase in annual temperature; however, annual precipitation has been shown to increase or decrease depending on geographical location. The assessment of river and groundwater management levels indicated that the river management level in eight subbasins and the groundwater management level in three subbasins had negative values, indicating that water abstraction exceeded the volume of available water resources. Subbasins with negative management levels were recognised as ecological hotspots. Surface and groundwater resources respond to climate change in a coordinated manner. As precipitation is the main source of accessible water for both river and groundwater systems,  long-term climatic trends continuously influence long-term water availability and management levels. In the evaluation of management levels of Sefidrud watershed, ecological hotspots were identified. These results can serve as a model for water resource decision-makers to propose management strategies adapted to local conditions. For example, in sustainable management approaches, special attention should be paid to areas where ecosystems dependent on water resources are stressed. To achieve an acceptable management level, appropriate strategies should be implemented such as reducing abstraction from surface and groundwater resources, improving irrigation efficiency, cultivating crops with lower water requirements, and enhancing agricultural land productivity.  https://iwrj.sku.ac.ir/article_116644.html IntroductionRising temperatures, altered precipitation patterns, and extreme weather events not only affect water resources and food security but can also significantly transform river dynamics, including the transport of suspended sediments. Riverine suspended sediments, as a vital indicator of watershed erosion and aquatic ecosystem health, play a crucial role in water quality, reservoir capacity, and the stability of water infrastructure. Therefore, understanding and accurately predicting future climate changes and their subsequent effects on sediment dynamics are of high importance for optimal water resource management and sustainable development planning. Climate predictions at various scales, including General Circulation Models (GCMs), provide powerful tools for simulating future climatic conditions under different greenhouse gas emission scenarios. After generating future climate scenarios, the next step is to analyze the consequences of these changes on hydrological and sediment processes, which requires more specialized and precise modeling. In summary, considering recent research, General Circulation Models from the sixth report and Artificial Neural Networks are effective tools for estimating climatic parameters and river suspended sediments. In this study, hybrid models of Artificial Neural Network-Wavelet, Artificial Neural Network-Chicken Swarm Optimization, and Artificial Neural Network-Particle Swarm Optimization were used to estimate the suspended sediments of the Keshkan River located in Lorestan Province.Materials and Methods:The methodology of this research was designed and implemented in two main stages. In the first stage, historical meteorological data, including precipitation, minimum temperature, and maximum temperature, as well as hydrological data related to daily discharge and suspended sediment load of the Keshkan River, were collected and refined for a long-term statistical period. Subsequently, using the output of a General Circulation Model under two representative scenarios with different levels of greenhouse gas emissions, future climate data for the study area were downscaled. The performance of the climate model was evaluated during the baseline period using various statistical indices. In the second stage, by combining climate and hydrological data, intelligent hybrid models including Artificial Neural Network-Wavelet Transform, Artificial Neural Network-Particle Swarm Optimization (Correction: the previous text mentioned “Chicken Swarm Optimization”, but in the new text “Particle Swarm Optimization” is mentioned. I will stick to the new information provided), and Artificial Neural Network-Particle Swarm Optimization (Correction: it seems there is a repetition, I will assume it refers to “Artificial Neural Network-Chicken Swarm Optimization” as in the original text, or another swarm-based algorithm if specified) were developed to predict suspended sediment load. The performance of these models was evaluated using error metrics, and the best model was selected for predicting suspended sediment load under climate change conditions. Finally, the trend of suspended sediment load changes under different climate scenarios in the future period was analyzed.Results and Discussion:The statistical analysis of the model’s simulation for rainfall and temperature parameters during the historical period revealed that rainfall simulation error was higher than other parameters. The capability of the LARS-WG model in simulating meteorological parameters was confirmed, but the model showed less accuracy in rainfall simulation. The performance of the Sixth Assessment Report scenarios in forecasting future fluctuations compared to the baseline period indicated that both models successfully reproduced the seasonal rainfall pattern, which includes a maximum in winter and early spring and a minimum in summer. However, there are differences in absolute rainfall values between the models and scenarios. A reduction in rainfall during specific months, such as summer and early autumn, was predicted by some models in the SSP5-8.5 scenario, which could lead to an intensification of seasonal drought during these periods. The temperature graphs clearly showed a general warming trend in Pol-e Dokhtar county in the coming decades, with predicted temperatures in both SSP5-8.5 and SSP1-2.6 scenarios being significantly higher than observed values throughout the year. For modeling sediment load, a Support Vector Artificial Neural Network model was used with Wavelet, Chicken Swarm, and Particle Swarm Optimization algorithms. According to the evaluations, hybrid structures had less error compared to individual structures. Therefore, the results of the model evaluation showed that the Artificial Neural Network-Wavelet hybrid model demonstrated better performance in the validation stage, with a correlation coefficient of 0.965, the lowest root mean square error of 0.067, the lowest mean absolute error of 0.034, and the highest Nash-Sutcliffe efficiency coefficient of 0.970.ConclusionsIn general, the findings of this research indicate that climate change, as an influential factor, will alter the hydrological pattern of the Keshkan River basin in Lorestan Province. The results of temperature changes indicated that during the study period, the county is affected by global warming, with temperature changes showing an increase in temperature from 2020 to 2050 in the SSP126 and SSP585 scenarios, respectively. The results from predicting rainfall and temperature fluctuations showed that the BCC-CSM2-MR model predicts much higher rainfall in the months of June to October, while the SSP585 scenario generally leads to higher temperatures than SSP126. The results from the performance of the hybrid models showed that the examined models in a combined structure, including all input parameters, performed better due to increased memory. The Artificial Neural Network-Wavelet model exhibited greater accuracy and less error compared to the other models investigated. The results from predicting sediment load in the coming years indicate a 23% increase in river sediment. This highlights the necessity for serious attention to comprehensive watershed management and the implementation of erosion control and desilting programs. https://iwrj.sku.ac.ir/article_116606.html Introduction:Climate change and increasing constraints on water resources have emerged as major challenges for agricultural sustainability worldwide. In arid and semi-arid regions such as Iran, these challenges are particularly acute, necessitating strategic interventions to ensure food security and resource efficiency. Among various approaches, improving water productivity, defined as the ratio of crop yield to water consumed, has become a central objective in national and regional agricultural planning. Wheat, as a strategic crop in Iran, plays a vital role in the country’s food security. Irrigated wheat, in particular, is highly sensitive to climatic variations and water availability.Understanding its response to future climate scenarios is therefore essential for developing effective adaptation strategies. Modeling crop yield and water productivity under different climate conditions can provide valuable insights into potential risks and guide decision-making for sustainable agricultural development. This study focuses on Alborz Province, a key agricultural region in Iran, and investigates the quantitative responses of irrigated wheat to projected climate conditions in the 2040 horizon. Using the AquaCrop 7.1 model for crop simulation and the LARS-WG 8 model for climate downscaling, the research evaluates wheat yield and water productivity under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6 (low emissions), SSP2-4.5 (intermediate emissions), and SSP5-8.5 (high emissions). The primary objective is to assess the effects of climate change on wheat water productivity across different irrigation levels and to propose practical solutions for optimizing water use and enhancing agricultural resilience.Materials and Methods:To simulate future climate conditions, the study employed the LARS-WG 8 model, a stochastic weather generator that downscales daily climate data from global circulation models (GCMs) to local station scales. Specifically, climate projections from the HadGEM3-GC31-LL model were used as input. This model is known for its robust representation of atmospheric processes and provides reliable data for scenario-based climate analysis. Climate data for the year 2040 were generated and validated under three SSP scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. These scenarios represent different trajectories of greenhouse gas emissions, socioeconomic development, and mitigation efforts. SSP1-2.6 assumes strong mitigation and low emissions, SSP2-4.5 reflects moderate mitigation and intermediate emissions, and SSP5-8.5 represents a high-emissions pathway with limited climate policy intervention. Following climate data generation, the AquaCrop 7.1 model was used to simulate wheat yield and water productivity. AquaCrop, developed by the Food and Agriculture Organization (FAO), is designed to simulate crop responses to water availability and is particularly suitable for analyzing water productivity under varying irrigation regimes. The model calibration was conducted in multiple stages to ensure accuracy, including the removal of noise treatments and adjustment of parameters. Alborz Province was selected as the study area due to its significant role in irrigated wheat production. The region has a cultivated area of 9587 hectares dedicated to irrigated wheat and an annual production of 48260 tons during the 2022–2023 crop year. Simulations were performed under three irrigation levels—100 percent, 80 percent, and 60 percent of full irrigation—and two irrigation intervals (14 and 7 days). These treatments allowed for a comprehensive analysis of water stress effects.Water productivity was calculated as the ratio of crop yield (kg) to water volume consumed (m³), providing a standardized metric for comparing performance across scenarios. The base year (2023) served as a reference point for evaluating changes in yield and productivity under future climate conditions.Results and Discussion:The simulation results revealed substantial impacts of climate change on both wheat yield and water productivity in Alborz Province. Under full irrigation conditions, water productivity declined from 1.85 kg/m³ in the base year to 1.70 kg/m³ in the SSP2-4.5 scenario, indicating a negative effect of intermediate emissions and moderate warming. In contrast, the SSP5-8.5 scenario, characterized by high CO₂ concentrations and more pronounced warming, resulted in an increase in water productivity to 1.93 kg/m³. This improvement is likely due to the fertilization effect of elevated CO₂, which enhances photosynthesis and biomass accumulation under optimal water conditions. However, the SSP2-4.5 scenario led to a significant yield loss of more than 4600 tons, representing approximately 10 percent of the province’s annual wheat production. At reduced irrigation levels (80 percent and 60 percent), the results were mixed. In the SSP5-8.5 scenario, crop yield increased by 4 percent compared to the base year, suggesting that CO₂ enrichment may partially offset water stress. Conversely, in the SSP2-4.5 scenario, yield decreased by 8 percent, highlighting the compounded effects of warming and reduced water availability. Interestingly, the modeling showed that yield reductions in the SSP2-4.5 scenario were relatively uniform across all irrigation levels. This uniformity implies that climatic factors, especially temperature increases, have a more dominant influence on yield than irrigation rates alone. The findings suggest that beyond a certain threshold, irrigation adjustments may not fully compensate for climate-induced stress. The accuracy of the AquaCrop model was also evaluated under different conditions. The model performed well under full irrigation, with high correlation between simulated and observed yields. However, its accuracy declined significantly under low irrigation levels and longer irrigation intervals. This limitation points to the need for structural improvements in the model, particularly in simulating crop responses to water stress and variable climate inputs.Conclusion:This study demonstrates that future climate scenarios exert differentiated impacts on wheat yield and water productivity in Alborz Province. While the SSP5-8.5 scenario offers potential yield gains due to CO₂ fertilization, the SSP2-4.5 scenario presents a more concerning outlook with notable yield losses and reduced water productivity. These findings have important implications for agricultural planning and water resource management in Iran.To enhance resilience, the study recommends several adaptation strategies, including: • Genetic improvement of wheat varieties to increase tolerance to heat and drought • Optimization of planting dates to align with favorable climate windows • Development of climate-smart policies that integrate crop modeling with water allocation planning • Investment in irrigation technologies that improve efficiency and reduce water loss.  Moreover, the study highlights the value of combining climate and crop models for integrated risk analysis. Such tools can support decision-makers in evaluating trade-offs, prioritizing interventions, and designing adaptive strategies that safeguard food security under changing climate conditions. Finally, the limitations observed in AquaCrop’s performance under water stress conditions suggest the need for further model refinement. Enhancing the model’s sensitivity to drought dynamics and incorporating feedback mechanisms between climate variables and crop physiology could improve its utility in future scenario analysis. https://iwrj.sku.ac.ir/article_116610.html Introduction Earth dams are among the oldest engineering structures built by human civilization. The stability of their slopes, particularly under various conditions, such as rapid drawdown, is important. In this study, one of the most critical parameters in the design of earth dams, slope stability, has been investigated. Rapid drawdown is the main factor contributing to the instability of upstream slopes in earth dams. This research presents an innovative method to improve the safety factor and enhance the slope stability of old and operational earth dams. For this purpose, numerical modeling was carried out using GeoStudio/W software. The Alavian earth dam was modeled numerically in this software. The safety factor of the upstream slope was evaluated before and after the nailing under different conditions. The results indicate a significant increase in slope stability after nailing. The safety factor increased by 18.554% under static conditions after construction, by 17.746% under steady seepage conditions, and by 22.164% under rapid drawdown conditions.Materials and Methods:To obtain realistic results, the cross-section of the Alavian earth dam, located in East Azerbaijan Province, on the slopes of Mount Sahand, approximately 120 kilometers southwest of Tabriz and 3.5 kilometers north of Maragheh, was used. The Alavian dam was selected due to the relatively comprehensive studies previously conducted on it and the availability of its mechanical and strength parameters. The analyses in this study were carried out using the GeoStudio software package. The Seep/W module of this package is used for analyzing soil seepage, while the Slope/W module is employed for slope stability analysis and determining the factor of safety (FOS) of dam slopes. To evaluate the stability of sloped surfaces and calculate the factor of safety in slope design, the Slope/W component of the software was used, applying the limit equilibrium method based on the Morgenstern–Price approach. It is important to note that the finite element method is not used in this part of the analysis. In the modeling conducted in this study, only one parameter was altered at each stage to eliminate the influence of other factors on the results. These are different conditions that were studied in this paper:- Examination of the upstream slope stability under static conditions at the end of construction with and without nailing.- Examination of the upstream slope stability under static conditions after steady seepage with and without nailing.- Examination of the upstream slope stability under static conditions after steady seepage with and without nailing during rapid drawdown.- Examination of the upstream slope stability under dynamic conditions at the end of construction with and without nailing.Results and Discussion: Some of the flow lines are located above the zero-pressure water level, which is because the Seep/W software does not precisely draw the flow net but only provides a path composed of connected vectors. In this study, the output of the Seep/W software was used as the input for the Slope/W software, and the flow lines and seepage discharge were not examined.  Results of the static stability factor of safety for the upstream slope at the end of construction indicate that in the static model after construction, without considering seepage, the dam’s factor of safety is 1.757. By adding nailing to the same model, the factor of safety increases to 2.083. In the presence of seepage, due to seepage forces and reservoir loading, the upstream safety factor generally increases. The results indicate that in the static model with steady seepage, the dam’s factor of safety is 1.775. By adding nailing to the same model, the factor of safety increases to 2.090. The results indicate that in the static model under rapid drawdown conditions, the dam’s factor of safety is 0.961. By adding nailing to the same model, the factor of safety increases to 1.174. Under rapid drawdown conditions, it was observed that the dam’s factor of safety reached a critical level. However, after adding nailing and reevaluating the results, it was found that the factor of safety increased significantly.Conclusion: Results showed that under static conditions after construction and during steady seepage, the safety factors of the dam slopes were automatically calculated using the Morgenstern–Price method. Analysis of the calculated values showed that after the establishment of steady seepage, the downstream safety factor decreased due to seepage pressure, while the upstream safety factor increased as a result of reservoir loading. In addition, the percentage increase in the dam’s stability factor under all three examined conditions was substantial. This improvement was particularly remarkable in the rapid drawdown condition, where the safety factor increased by more than 22%, effectively removing the dam from the critical state. The increase in the safety factor under all three analyzed conditions was considerable, resulting in an 18.554% increase under static conditions after construction, a 17.746% increase under steady seepage conditions, and a 22.164% increase under rapid drawdown conditions. https://iwrj.sku.ac.ir/article_116613.html Introduction: Bridge local scour which is responsible for structural failures in river-crossing bridges, occurs as a result of the interaction between the flow field and the bridge pier, leading to the removal of bed material and the formation of a scour hole around the foundation. Most existing research on the geometry of bridge piers focus on circular or rectangular piers aligned with the flow. However, real-world bridge piers often deviate from the flow direction due to hydraulic constraints or structural alignment. The alignment angle (α), defined as the deviation of the pier’s longitudinal axis from the flow direction, alters the symmetry of the horseshoe vortex and significantly affects the magnitude and position of the maximum scour depth. Moreover, the ratio of pier length (in the direction of flow) to its width (L/B) modifies the distribution of flow velocities and shear stresses around the pier. Despite numerous experimental studies, the simultaneous effect of these two parameters on local scour depth around non-circular piers, particularly rounded-nosed rectangular piers, has received limited attention. The present study aims to investigate the combined influence of L/B and α on the local scour depth under clear-water conditions. Methods: The experiments were conducted in the hydraulic laboratory of Isfahan University of Technology (IUT), Iran. The flume used for this study is a 12-meter-long, 0.6-meter-wide, and 0.5-meter-deep rectangular open channel with a water circulation system. The bed material consisted of uniform sand with a median particle size of d_50=0.78" " mmand a geometric standard deviation σ_g=1.29. Flow depth and discharge were controlled using a tailgate and calibrated pump system. Two pier models were fabricated from transparent Plexiglas to allow clear observation of flow structures. The reference circular pier had a diameter =3" " cm, while the rounded-nosed rectangular pier had a constant width B=3" " cm(equal to the circular pier’s diameter) and variable lengths L=6,9 and 12" " cm, corresponding to aspect ratios L/B=2,3 and 4. The piers were tested at three different alignment angles relative to the flow direction: α=5°,10° and 20°. Thus, a total of 13 experiments were conducted, including the reference circular pier case. The flow velocity was adjusted to achieve clear-water conditions at U/U_c=0.86, where U_c is the critical velocity for the initiation of sediment motion, determined using the Shields criterion. The steady discharge was set at 50 liters per second, corresponding to a flow depth of 18.8 cm. Before each run, the sand bed was leveled and compacted to ensure uniform initial conditions. Each test continued until the scour hole reached equilibrium. After completion, the final bed profile was measured using a digital point gauge with 0.1 mm precision. Longitudinal and transverse profiles were recorded at multiple sections to obtain the maximum local scour depth d_s. All experiments were repeated twice to ensure reproducibility, and the mean values were reported. Flow fluctuations were maintained within ±2% of the set discharge. The experimental data were analyzed using dimensional analysis and regression techniques. Results: The scour process followed the formation of a strong downflow on the upstream face, followed by the development of a horseshoe vortex that transported sediment away from the pier base. The equilibrium scour depth was achieved within 5–6 hours. Effect of Aspect Ratio: At α ≤ 5°, increasing the aspect ratio from 2 to 4 led to a reduction in maximum scour depth which can be attributed to the redistribution of flow momentum along the elongated pier. At α ≥ 10°, the opposite trend was observed. Quantitatively, increasing L/Bfrom 2 to 4 resulted in a 20% decrease in scour depth at α=5°, but a 35% increase at α=20°. Effect of Alignment Angle: Increasing α from 5° to 20° caused the scour hole to deepen and shift laterally toward the upstream corner of the pier. This behavior results from the asymmetric horseshoe vortex system generated under oblique flow conditions. The rounded-nosed rectangular pier produced 15–20% less scour depth at α ≤ 5°, confirming its hydraulic efficiency in near-aligned conditions. However, for α ≥ 10°, the scour depth for rectangular piers exceeded that of the circular pier, indicating the negative impact of larger misalignment angles. Combined Effect of L/B and α: The combined influence of aspect ratio and alignment angle was found to be nonlinear and interactive. For moderate aspect ratios (L/B = 3), the influence of α dominated the scour behavior, while at extreme aspect ratios, both parameters interacted to amplify scour depth. Bed Morphology: Topographic surveys revealed that for α ≤ 5°, the scour hole was nearly symmetrical and shallow, while at α ≥ 10°, the hole became elongated and skewed toward the upstream corner. The deposition zone downstream expanded with increasing α, reflecting stronger wake vortices. Conclusion: The present experimental investigation demonstrated that both pier aspect ratio and alignment angle play crucial and interdependent roles in determining local scour depth around bridge piers. The major conclusions are summarized as follows: For alignment angles below 5°, increasing the pier aspect ratio (L/B) decreases local scour depth by distributing flow energy along the pier face. For α ≥ 10°, larger aspect ratios intensify scour by promoting asymmetric vortex formation and higher local shear stresses. Rounded-nosed rectangular piers perform better than circular piers under small alignment angles (α ≤ 5°), but their performance deteriorates at higher angles. A modified empirical relationship was proposed to estimate the alignment coefficient K_α, which accurately predicts scour depth with R^2=0.94. From a practical perspective, pier alignment angles greater than 10° should be avoided in bridge design to minimize local scour risks. Future work is recommended to examine the effects of surface roughness, pier spacing (group effects), and unsteady flow conditions such as floods using both experimental and computational fluid dynamics (CFD) modeling approaches. https://iwrj.sku.ac.ir/article_116622.html Introduction Piano key weirs (PKWs) are increasingly used as efficient spillway structures due to their ability to significantly increase discharge capacity without requiring an increase in dam crest width. This advantage makes them particularly suitable for dam rehabilitation and for sites where spillway expansion is constrained by topographic or structural limitations. Among different PKW configurations, Type‑A piano key weirs have received considerable attention because of their favorable hydraulic performance and structural simplicity. Despite extensive research on PKWs, the influence of geometric modifications—especially the inclination of side walls and their orientation relative to the flow direction—has not been sufficiently investigated. Side‑wall geometry can affect the flow pattern entering the inlet keys, the formation of separation zones, and the overall hydraulic efficiency of the spillway. Understanding these effects is essential for optimizing PKW performance. Therefore, the present study experimentally investigates the hydraulic behavior of Type‑A piano key weirs with different side‑wall inclinations and orientations. In addition, data‑driven models are employed to predict the discharge coefficient and evaluate the capability of machine‑learning techniques in modeling complex nonlinear hydraulic relationships. Materials and Methods The present study was conducted using a laboratory experimental setup designed to investigate the hydraulic performance of Type A piano key weirs under controlled flow conditions. The experiments were carried out in a rectangular flume with a constant width and adjustable discharge system that allowed accurate control of flow rates. Two geometric configurations of piano key weirs were tested: rectangular and trapezoidal inlet–outlet key shapes. The main objective was to evaluate how the inclination and orientation of the side walls influence the hydraulic efficiency and discharge coefficient of the structure. Side wall inclination angles of 5°, 7.5°, and 10° were selected based on common ranges used in hydraulic structures and previous studies on PKW optimization. For each inclination angle, two orientations were considered: inclination with the flow direction and inclination against the flow direction. These configurations allowed the evaluation of how flow alignment and entrance conditions affect discharge characteristics. During the experiments, the upstream water head was carefully measured using precise point gauges, while the flow discharge was controlled and monitored using calibrated flow measurement devices. The discharge coefficient (C_d) was calculated for each experimental condition to quantify the hydraulic performance of the tested configurations. The collected dataset was then used for developing predictive data driven models. Several machine learning algorithms were employed, including Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Regression (SVR). The input parameters consisted of hydraulic and geometric variables such as upstream head and side wall inclination characteristics, while the output variable was the discharge coefficient. Model performance was evaluated using common statistical indicators including the coefficient of determination (R^2), root mean square error (RMSE), and mean absolute error (MAE). These indicators were used to assess the accuracy and reliability of each model and to determine the most suitable approach for predicting PKW discharge performance Results and Discussion The experimental results clearly demonstrate that the inclination and orientation of side walls significantly influence the hydraulic performance of Type A piano key weirs. Flow observations indicated that the side wall geometry directly affects the way water enters the inlet keys and how the flow is distributed along the crest. When the side walls were inclined against the flow direction, the incoming water was guided more effectively toward the inlet openings. This configuration improved the alignment of streamlines and reduced the formation of recirculation zones near the inlet edges. As a result, flow contraction and energy losses were reduced, leading to an increase in the discharge coefficient. In contrast, when the side walls were inclined with the flow direction, the inflow pattern tended to produce local separation zones near the entrance of the inlet keys. These separation regions reduced the effective flow area and caused additional energy dissipation. Consequently, the discharge coefficient obtained in these cases was generally lower than that of the opposite orientation. The influence of the inclination angle was also evident in the experimental data. Increasing the side wall inclination from 5° to 7.5° and 10° generally improved hydraulic performance, particularly when the inclination was directed against the flow. Among the tested configurations, the best hydraulic behavior was observed for trapezoidal models with side wall inclinations between 7.5° and 10° oriented against the flow direction. These configurations provided the most favorable flow guidance and minimized hydraulic disturbances near the inlet region. A comparison between the two tested geometries showed that trapezoidal PKWs consistently produced higher discharge coefficients than rectangular ones under similar hydraulic conditions. The trapezoidal configuration appears to facilitate smoother flow transitions and better distribution of water across the inlet keys, which enhances the overall discharge efficiency. In some cases, the improvement in discharge coefficient reached approximately 15% compared with the corresponding rectangular configurations. The data driven modeling results further confirmed the experimental findings. Among the applied machine learning techniques, Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) showed the best predictive capability. These models successfully captured the nonlinear relationships between geometric parameters and hydraulic performance. The predictive accuracy of these models exceeded 98%, while the associated prediction errors remained below 2%, indicating excellent agreement with the experimental measurements. The results highlight the effectiveness of combining experimental investigations with modern machine learning techniques to better understand and predict the hydraulic performance of complex spillway structures such as piano key weirs. Conclusions This study investigated the effects of side wall inclination and orientation on the hydraulic performance of Type A piano key weirs using laboratory experiments and data driven modeling. The results showed that inclining the side walls against the flow direction significantly improves flow alignment and reduces energy losses, resulting in higher discharge coefficients. Trapezoidal configurations demonstrated superior performance compared with rectangular geometries, with improvements reaching approximately 15%. Machine learning models, particularly ANN and XGBoost, provided highly accurate predictions of the discharge coefficient. The findings provide useful guidance for optimizing PKW design and demonstrate the value of integrating experimental data with advanced predictive modeling techniques. The study contributes a validated experimental dataset and robust machine learning formulations that can assist designers in optimizing PKW configurations for enhanced hydraulic efficiency and safer spillway design. https://iwrj.sku.ac.ir/article_116623.html 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 employs 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 demonstrate 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 study period covered the water years 2014–2023. 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), 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, indicating robust generalization capability. 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 demonstrates the competitiveness of the proposed approach. The obtained NSE (0.994) substantially exceeds values reported for traditional temperature-index models (NSE~0.80, Pradhananga et al., 2014) and is higher than ANN-based snowmelt runoff predictions (NSE~0.93, Uysal et al., 2016). The results are comparable with other LSTM applications in hydrological modeling (NSE~0.99, Le et al., 2019; Adib et al., 2024). However, this study specifically contributes 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 demonstrates 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. In conclusion, the LSTM model, with optimal input combination of discharge, temperature, and SWE from ERA5-LAND, provides a robust and accurate framework for simulating snowmelt-dominated streamflow in the Bazoft Basin, offering valuable implications for water resources management in similar snow-fed mountainous catchments. Conclusion This study demonstrated that the LSTM model, forced with ERA5-LAND reanalysis data (temperature and snow water equivalent) together with antecedent discharge, accurately simulates 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 confirms 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. https://iwrj.sku.ac.ir/article_116639.html Extended Abstract Introduction: Tannery wastewater is widely recognized as one of the most challenging industrial effluents due to its extremely high organic load, heavy metals (particularly chromium) suspended solids, and substantial salinity. The high salt content originates mainly from the extensive use of sodium chloride during hide preservation and tanning processes. Such salinity poses major environmental threats, including soil salinization, groundwater contamination, and ecological disruption in aquatic systems. Moreover, salinity is a critical inhibitory factor in biological wastewater treatment systems. Elevated salt concentrations impose osmotic stress on microbial cells, leading to reduced metabolic activity, enzyme inhibition, and ultimately lower COD and BOD removal efficiencies. Conventional activated sludge systems function properly at salinity levels below approximately 10 g/L TDS, while real tannery wastewater often ranges between 10 and 40 g/L. This discrepancy highlights the difficulty of biological treatment under real industrial conditions. Although previous studies have examined saline stress in biological systems, comprehensive evaluations based on real tannery wastewater (particularly in hybrid treatment configurations) are still limited. Therefore, this study aims to investigate the performance and salinity tolerance of an integrated chemical-biological-chemical treatment system when exposed to real high-strength tannery wastewater. Materials and Methods: A semi‑industrial pilot plant was developed in Varamin (2025) to evaluate the treatment performance of a hybrid system designed for real tannery wastewater with high salinity. The system consisted of three sequential units. First, a primary chemical pretreatment stage involving coagulation and flocculation was applied to reduce suspended solids, colloids, chromium, and part of the biodegradable organic load. This step was essential for minimizing shock load and ensuring more stable biological activity in the subsequent reactor. The second unit was a Moving Bed Biofilm Reactor (MBBR), selected for its ability to support robust biofilms and maintain microbial stability under fluctuating environmental conditions. The reactor was seeded with halotolerant activated sludge sourced from an industrial facility already treating saline wastewater. The initial MLSS concentration was maintained at approximately 1500 mg/L. Nutrient supplementation followed a COD:N:P ratio of 100:5:1 to ensure appropriate microbial growth. Before continuous operation, an acclimation phase of 3 to 6 days was implemented, during which the microbial community was gradually exposed to real tannery wastewater with salinity levels ranging from 19 to 24 g/L TDS. The third treatment stage provided secondary chemical polishing, aimed at improving effluent clarity and removing remaining suspended solids and residual organic fractions. The pilot operated continuously over three months. Operational parameters such as hydraulic retention time, aeration rate, MLSS content, and dosing of coagulants and flocculants were optimized as needed. Sampling was performed at the influent, after the primary chemical unit, at the MBBR outlet, and at the final effluent point. The main analyzed parameters included COD, TSS, TDS, EC, and pH. The naturally fluctuating salinity levels during the monitoring period enabled a realistic assessment of microbial tolerance and process stability under high‑salinity conditions. Results and Discussions: The hybrid treatment system demonstrated a notable reduction in organic pollution, achieving an overall COD removal efficiency of approximately 70%. The MBBR contributed the largest portion of this removal, accounting for about 35.5%, while the secondary chemical polishing stage added roughly 15.1%, confirming the need for a combined chemical–biological approach for complex saline wastewater. The primary chemical stage effectively removed suspended solids and colloidal material, thereby stabilizing downstream performance. The biological treatment performance was strongly influenced by salinity. The MBBR maintained stable COD removal when TDS levels remained at or below 22 g/L. Under these conditions, the halotolerant microbial community remained active, and biofilm carriers provided structural protection against osmotic stresses. Biofilms generally offer higher resilience than suspended‑growth systems due to slower mass transfer rates and gradual salt exposure within the biofilm matrix. However, when salinity exceeded the threshold of 22 g/L, noticeable decreases in COD removal efficiency occurred. Temporary spikes in salinity led to an immediate decline in metabolic activity, manifested as increased effluent COD concentrations. These observations suggest osmotic shock, which may cause cellular dehydration, membrane dysfunction, and in severe cases, microbial lysis. Although the system maintained partial functionality, the reduced performance reflected a diminished capacity of the biomass to process organic load under fluctuating saline stress. Throughout the entire study, the system exhibited no significant reduction in TDS. Final effluent salinity remained around 22 g/L, indicating that neither biological oxidation nor chemical precipitation effectively removes dissolved salts. This aligns with literature that highlights the inherent limitation of conventional biological and chemical processes for treating saline industrial effluents. Operational observations further emphasized the importance of stable loading conditions, gradual acclimation, and the use of halotolerant sludge. Despite these measures, managing salinity requires more advanced or complementary strategies. Technologies such as reverse osmosis, nanofiltration, or hybrid desalination approaches can significantly reduce TDS but require substantial investment and energy. Source reduction measures within tanning operations, including improved salt‑curing methods, recycling of brine solutions, or adoption of low‑salt tanning technologies, may also reduce the salinity load entering treatment systems. Overall, the results indicate that while the hybrid system effectively addresses organic pollution, salinity remains the dominant limiting factor governing biological performance. Therefore, long‑term wastewater management strategies must integrate both end‑of‑pipe treatment improvements and upstream process optimization. Conclusion: This study showed that the hybrid chemical–biological–chemical system effectively reduced organic pollutants in tannery wastewater, achieving about 70% COD removal. However, biological performance was constrained by salinity, remaining stable only up to 22 g/L TDS, with higher levels causing microbial inhibition and reduced efficiency. Since TDS remained virtually unchanged during treatment, the effluent still exceeded allowable discharge limits. Effective long‑term management therefore requires combining optimized biological treatment with desalination technologies or implementing upstream modifications to reduce salt input at the industrial source. https://iwrj.sku.ac.ir/article_116640.html Extended Abstract Introduction: The limited water resources of Hamedan Province and the decline in groundwater levels will lead to the risk of drying up of the wells that supply water as a source of irrigation for the province's important crops, including garlic and potatoes. This issue, in turn, can seriously threaten the sustainability and durability of their cultivation and work in the suburbs of Hamedan. Therefore, the possibility of using treated urban wastewater in a legal and healthy manner, in addition to providing part of the water needs of these products, will help maintain and sustain the cultivation and cultivation of two important crops, garlic and potatoes in the suburbs of Hamedan. On the other hand, wastewater is considered an unconventional source of water, and its use in agriculture requires careful and necessary management in controlling and purifying it to ensure the health of soil, plants and humans while avoiding environmental hazards (Yargholi and Taran, 2024). Therefore, the present study was conducted to use treated wastewater from Hamedan city to irrigate two crops, garlic and potato, by investigating its effect on soil chemical properties, distribution, and soil contamination with heavy metals such as copper, cadmium, chromium, lead, nickel, and arsenic. Materials and Methods: Initially, before planting the crops, samples were taken from the soil, treated wastewater, and water extracted from a well that was used to irrigate the two crops of garlic and potato. In order to enable the practical application of treated wastewater for the irrigation of garlic and potato cultivation, a suitable land parcel was identified, and a farm with convenient access to the treated wastewater supply was chosen. In the next step, soil samples, treated wastewater samples, and water extracted from the well were transferred to the laboratory and various tests were performed on them. Twenty soil samples from the designated cultivation area for both crops, along with twenty samples each of well water and treated wastewater, were collected randomly and in equal quantities. Irrigation was applied by driple tape irrigation system, using 16 mm diameter tubes with dripper spacing of 33 cm and a discharge rate of 1.6 liters per hour, placed on the ridges. The amount of water consumed was measured using calibrated flowmeters. In these tests, the amount of nitrogen and nitrate, EC value, acidity level, exchangeable sodium percentage (SARadj), saturation index, amount of B-, Cl-, Na+ and So4-2 ions, and levels of heavy metals such as chromium, arsenic, cadmium, copper, nickel, zinc and lead were determined. At the end of the growing season and after harvesting the crops, soil samples were taken from the studied farm and the physical and chemical properties of the soil were also determined. Other chemical properties of the soil, such as salinity levels, pH changes, nitrate nitrogen levels, and heavy metal levels of chromium, cadmium, arsenic, copper, nickel, zinc, and lead, were also measured. Results and Discussions: By evaluating the results of the chemical characteristics of well water and treated wastewater, it was determined that well water and treated wastewater were close in terms of electrical conductivity (EC) and pH, and based on the standards proposed by the Food and Agriculture Organization of the United Nations (FAO, 2004) and the World Health Organization (WHO), there are no restrictions for irrigation and growth of the studied crops. The amount of bicarbonate in well water and treated wastewater was found to be 5.25 and 1.75 milliequivalents per liter, respectively. The much lower bicarbonate content of treated wastewater compared to well water is an important advantage, especially in this regard, in the soil of the area in question, which also has a relatively high pH, it has a positive effect on reducing the fixation of cations such as calcium and magnesium, and there is less inhibition of phosphorus absorption in irrigation with treated wastewater water (Albaji and Asgari, 2017). The amount of sulfate ion in treated wastewater is significantly higher at 3.6 milliequivalents per liter than in well water (average 0.9 milliequivalents per liter). This amount of sulfate is considered an important advantage, especially since it can also be useful in the absorption and transport of elements such as iron, manganese, and zinc by helping to lower the pH of the root environment (Sokolova and Alekseeva, 2008; Chaudhary et al., 2023). The concentrations of calcium and magnesium, as well as the trace elements iron, zinc, and manganese, were significantly lower in well water than in treated wastewater (Tables 1 and 2). The high levels of all these elements in treated wastewater also contribute to its value as a source of essential plant nutrients and can be very useful in providing part of the nutritional needs of crops, especially garlic and potatoes. By examining the results of measuring the amount of heavy metals in treated wastewater, it was determined that all five heavy metals that are considered sources of contamination of wastewater are, fortunately, present in very small amounts in the treated wastewater. There was no significant increase in EC, saturated soil reaction, and bicarbonate in the soil in both garlic and potato crops after harvest in the plot where treated wastewater was used. In contrast, the amount of chlorine, sulfate, and copper ions in the soil, in two stages before planting and after harvest, showed a different situation compared to other nutrients, so that after harvest, their amount in the soil increased relatively. The amount of phosphorus, potassium, and nitrogen in the soil, as well as micronutrients, decreased relatively after harvesting of the both crops. The concentration of heavy metal pollutants in the soil in both plots dedicated to garlic and potato cultivation, especially nickel and arsenic, was higher than copper, chromium, and cadmium. However, in terms of pollution and necessary standards, they were lower than the maximum permissible and harmful levels and were of less concern. Conclusion: The findings of this experiment collectively demonstrate that the treated wastewater effluent from the Hamadan Province treatment plant is of a satisfactory quality. It meets acceptable standards for EC, sodic salinity, bicarbonate, and chloride. Crucially, levels of heavy metal contaminants, specifically arsenic, nickel, chromium, and cadmium, were below the established maximum limits, indicating no adverse impact on soil pollution or the accumulation of harmful residues. Thus, this treated effluent can serve as a viable alternative water source for irrigating both garlic and potato crops, complementing existing groundwater resources. Keywords: Soil pollution, Sewage effluent, Soil analysis, Mineral elements https://iwrj.sku.ac.ir/article_116643.html Extended Abstract Introduction: Surface water reservoirs are among the most critical infrastructures for water supply, particularly in arid and semi-arid regions where water security is increasingly threatened by climate variability, prolonged drought, and intensified human pressure. In such environments, monitoring reservoir storage dynamics is essential for understanding hydrological drought and for supporting sustainable water resources management. The Chah-Nimeh reservoir system, located in the Sistan region of southeastern Iran, is a vital source of drinking and agricultural water. However, this system has experienced substantial storage decline in recent years, raising serious concerns about regional water security and environmental sustainability. Because Chah-Nimeh is strongly dependent on inflow from the transboundary Helmand River, its hydrological regime is influenced not only by local climatic conditions but also by upstream water regulation and cross-border management. Recent developments in remote sensing and cloud-based geospatial analysis have greatly improved the capacity to monitor reservoir surface area and storage variations with high temporal and spatial resolution. Therefore, this study aimed to investigate the temporal dynamics of reservoir storage and hydrological drought in the Chah-Nimeh reservoir during 2017-2025 using Sentinel-2 imagery and multi-source climatic datasets within the Google Earth Engine platform, while also examining the extent to which local climatic indicators can explain observed storage variations. Materials and Methods: This study was designed to evaluate the temporal dynamics of water storage and hydrological drought in the Chah-Nimeh reservoir using an integrated remote sensing and multi-source climate data framework. All analyses were conducted in the Google Earth Engine (GEE) cloud-computing platform, which enabled efficient processing of long-term geospatial datasets. Four main data sources were employed based on their open accessibility, scientific reliability, and appropriate spatial and temporal resolution. Sentinel-2 Level-2A imagery was used as the primary optical dataset for water surface extraction. A total of 345 atmospherically corrected images acquired between March 2017 and December 2025 were processed. Cloud and cloud-shadow contamination were removed using the Scene Classification Layer (SCL), and only images with less than 15% cloud cover were retained. Monthly composites were then generated using the median spectral values, yielding 83 valid monthly observations. To delineate the water body, three spectral water indices, namely NDWI, MNDWI, and AWEI, were calculated for each monthly composite. The use of multiple indices allowed both robust water detection and uncertainty estimation in the derived surface area. Reservoir volume was subsequently estimated from the extracted water surface area using the stage-area-storage relationship of the reservoir. A second-order polynomial regression was fitted between water surface area and storage volume, providing a reliable basis for monthly volume estimation. Water level was also derived through inverse interpolation from the stage-area curve. To assess climatic conditions, CHIRPS daily precipitation data, ERA5-Land monthly temperature and potential evapotranspiration data, and TerraClimate monthly products were incorporated. These datasets were used to compute climatic and hydrological indicators, including SPI, SPEI, PDSI, SRI, SSI, and SWLI. Among them, SWLI, derived directly from reservoir storage anomalies, was considered the principal indicator of hydrological drought, whereas SPI, SPEI, and PDSI were used only for comparative interpretation. The reliability of CHIRPS and ERA5-Land was evaluated against observations from the Zabol meteorological station using correlation coefficient, coefficient of determination, bias, and normalized root mean square error. Trend analysis was performed using the non-parametric Mann-Kendall test and Sen’s slope estimator. Pearson correlation, coefficient of determination, normalized root mean square error, and mean absolute error were applied to examine the relationships between climatic variables and reservoir storage. In addition, lag-time analysis was conducted to identify the temporal response of reservoir storage to precipitation variability. Results and Discussions: The results revealed a pronounced decline in the storage status of the Chah-Nimeh reservoir during the study period, confirming the occurrence of significant hydrological stress. Analysis of the monthly time series showed that both reservoir volume and water surface area followed a statistically significant downward trend. Reservoir storage decreased at an average rate of 5.38 million m³ per year, while water surface area declined by 0.63 km² per year, indicating a persistent reduction in the effective capacity of the reservoir. These findings demonstrate that the reservoir experienced not merely short-term fluctuations, but a sustained long-term depletion trend. The temporal pattern of storage dynamics further showed that the reservoir passed through several distinct phases. A relatively stable and wetter phase was observed during 2019-2020, followed by a gradual decline during 2021-2022, and then a severe depletion phase extending through 2023 to early 2024. The lowest storage value was recorded in February 2024, when reservoir volume dropped to approximately 26.27 million m³, representing the most critical hydrological condition during the study period. In contrast, a sharp recovery occurred in May 2024, when storage increased abruptly from 36.56 to 99.05 million m³ within a single month. Given that local precipitation during this period was negligible, this sudden increase cannot be attributed to local rainfall and is more plausibly associated with changes in upstream inflow conditions or transboundary water release. Hydrological drought analysis provided further evidence of this pattern. Based on the SWLI index, which was adopted as the principal indicator of reservoir hydrological drought, 12 out of 82 analyzed months were classified as drought periods, of which 83% fell into the severe or extreme drought categories. The year 2023 represented the most critical hydrological drought condition, with an average SWLI of -1.35, indicating severe drought. However, a key finding of the study was the clear mismatch between reservoir-based hydrological drought and locally derived climatic drought indicators. During periods when SWLI indicated severe depletion, the meteorological indices SPI and SPEI often remained close to normal. This divergence was especially evident in 2023, when hydrological drought intensified despite the absence of a comparable meteorological drought signal. Correlation analysis supported this interpretation. None of the examined local climatic variables or drought indices showed sufficient explanatory power for reservoir storage variations. Even the relatively better-performing indicators, such as SPEI-6 and SPEI-12, exhibited limited agreement with observed storage dynamics, as confirmed by low R² values and relatively high NRMSE. These results suggest that the Chah-Nimeh reservoir is not governed primarily by local climatic forcing. Rather, its storage behavior is strongly influenced by upstream inflow from the Helmand River and by transboundary and managerial controls. Therefore, the hydrological drought observed in the reservoir should be interpreted as a process driven Conclusion: The findings of this study demonstrated that hydrological drought in the Chah-Nimeh reservoir cannot be adequately interpreted using local meteorological indicators alone. Reservoir storage showed a significant declining trend during 2017-2025, while the SWLI index identified severe hydrological drought conditions, particularly in 2023 and early 2024. In contrast, SPI, SPEI, and PDSI did not show consistent agreement with the observed storage depletion. This mismatch indicates that reservoir behavior is primarily controlled by regulated inflow from the Helmand River and transboundary management factors. The integrated Google Earth Engine-based framework proved effective for continuous monitoring of reservoir dynamics in data-scarce and transboundary dryland systems. Keywords: Remote Sensing, Hydrological Drought, Chah-Nimeh Reservoir, Spectral Water Indice