Iranian Water Research Journal

Abstract 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.

Abstract 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 Methods

Chaharmahal 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 satellites

Results and Discussion

The 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.

Conclusion

One 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.

Abstract 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.

Abstract Introduction
Rising 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.

Conclusions
In 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.

Abstract 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.

Abstract 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.

Abstract Introduction:
Circular aquifers, such as those found in karst systems or fault-bounded basins, are common in nature and require precise modeling to understand their response to pumping. While extensive analytical solutions exist for rectangular, triangular, and semi-infinite aquifers, a comprehensive closed-form analytical solution for a finite circular aquifer with an arbitrarily located well has been notably absent. Previous studies have primarily focused on simpler geometries or relied on numerical approximations, leaving a gap in the direct analytical treatment of circular domains under pumping conditions. This study addresses this gap by developing a rigorous mathematical model capable of accurately predicting hydraulic head distribution and flow potential in a confined, homogeneous, and isotropic circular aquifer. The proposed solution eliminates the need for numerical discretization, offering a direct and computationally efficient tool for groundwater management in circular or near-circular aquifer systems.

Materials and Methods:
This study presents a rigorous closed-form analytical solution for modeling groundwater flow to a pumping well in a finite, homogeneous, isotropic, and confined circular aquifer. The aquifer has a fixed radius bb, and the well, with an infinitesimal radius, is positioned arbitrarily at coordinates (r0, θ0) with a constant pumping rate Q. The mathematical formulation begins with the governing partial differential equation in polar coordinates, which is the Poisson equation incorporating a point source term:

where ϕ (r, θ) is the hydraulic head, and the right-hand side uses the Dirac delta function δ to localize the source/sink. The physical constraints are enforced by two boundary conditions: (1) the head remains finite at the origin , and (2) the head is constant (set to zero) at the outer circular boundary ϕ(b,θ)=0.

The solution methodology employs the Green's function approach combined with a Fourier series expansion. First, the unknown potential is expanded into a full trigonometric series:
A similar expansion is applied to the source term. Substituting these series into the governing PDE decomposes the two-dimensional problem into an infinite set of one-dimensional ordinary differential equations for the radial coefficients ϕn(r).
The core of the solution is the construction of the Green's function G(r,θ;ρ,ψ) for the corresponding Dirichlet boundary-value problem on a circle. Due to different fundamental solutions, the cases for n=0 and n≥1 are treated separately. For n=0, the radial component is logarithmic:

For n≥1, the solution involves power laws:
The complete Green's function is then:

The final expression for the hydraulic head is obtained via convolution and simplifies, after applying the properties of the delta function, to a closed-form series solution:

This infinite series can be summed analytically using known identities, resulting in compact algebraic formulas valid in the regions r≤r0 and r≥r0. The analytical results were successfully validated against a finite-element numerical model.


Results and Discussion:
The application of the developed analytical solution to a confined circular aquifer with parameters b=7m, r0=3m, θ0=50∘, Q=−100 m3/d, and T=5 m2/d revealed several key hydraulic behaviors. The primary outcome is the clear asymmetric drawdown distribution resulting from the well’s off-center position. The drawdown cone is deepest immediately around the well location and becomes progressively shallower with increasing radial distance, as visualized in the dimensionless drawdown contour plot (Fig. 2). The non-concentric nature of the equipotential lines is distinctly captured in the hydraulic head contour map (Fig. 4), where contours are elliptical and skewed away from the geometric center of the aquifer, demonstrating that symmetry is broken when the well is not at the center. A detailed analysis of drawdown profiles along various angular directions relative to the well’s azimuth further quantified this asymmetry (Fig. 3). The maximum drawdown occurs precisely along the radial line corresponding to the well’s own azimuth (θ=50∘). As the angular deviation from this direction increases, the drawdown magnitude at a given radial distance systematically decreases. For instance, the drawdown along θ=67.5∘ is less than that at θ=50∘, and the profile along θ=90∘ shows even lower values. This pattern conclusively illustrates how the aquifer’s circular geometry and the well’s specific location jointly control the spatial pattern of the cone of depression. Validation against a numerical model confirmed the high accuracy and reliability of the analytical solution. A finite-element model was constructed for the identical aquifer setup. A comparison of the drawdown versus radial distance profiles along two distinct directions (θ=67.5∘ and θ=90∘) showed nearly perfect overlap between the analytical and numerical results (Fig. 8). The minor discrepancies were within negligible numerical tolerance, verifying that the closed-form solution correctly solves the governing equations and boundary conditions without the approximations required in numerical discretization. This successful validation establishes the solution as a robust and efficient tool for predicting head distributions in finite circular aquifers.

Conclusion:
This study successfully developed a rigorous closed-form analytical solution for predicting hydraulic head distribution and drawdown in a finite, confined, circular aquifer subjected to pumping from a well at an arbitrary location. By employing Green's function and Fourier series techniques, the governing equation was solved analytically, resulting in a direct mathematical formula. The solution efficiently captures the asymmetric hydraulic response caused by an off-center well, eliminating the need for numerical approximations. Validation against a finite element model demonstrated excellent agreement, confirming the solution's accuracy. This analytical tool provides a practical and efficient means for well placement optimization, capture zone delineation, and management of circular or near-circular aquifers, such as karst systems. The methodological framework established here also holds potential for extension to more complex hydrological conditions, including heterogeneous or anisotropic properties.

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.