https://jwim.ut.ac.ir/ Water and Irrigation Management en daily 1 Mon, 22 Jun 2026 00:00:00 +0330 Mon, 22 Jun 2026 00:00:00 +0330 https://jwim.ut.ac.ir/article_104813.html In this study, precipitation, minimum temperature, maximum temperature, and evapotranspiration data from the CNRM-CM6-1, GFDL-ESM4, ACCESS-CM2, and CanESM5 climate models were compared with Qazvin synoptic data for the base period 1986-2014 individually and ensemble. The results showed that evapotranspiration, minimum and maximum temperatures in the group model (combination of the aforementioned climate models using the weighted linear averaging method of the models) are associated with reasonable and appropriate estimates with coefficient of determination values of 0.95 and low RMSE values. The results also showed that running models in groups reduces errors. Using an ensemble model, precipitation data, minimum temperature, maximum temperature, and evapotranspiration were simulated under two scenarios, SSP2_4.5 and SSP5_8.5, for future periods, and the results showed that temperature and evapotranspiration will increase and precipitation will decrease in future periods. The maximum and minimum temperature changes compared to the base period in the period 2026-2050 for the SSP2_4.5 and SSP5_8.5 scenarios will be 1.9, 2.49, 2.98, and 3.31 degrees Celsius, respectively, and the precipitation changes for the SSP2_4.5 and SSP5_8.5 scenarios will be -37.82 and -11.24 mm, respectively. Using climatic parameters, wheat yield was evaluated using random forest, neural network, and ensemble model methods in the baseline period, and the results showed that the ensemble model reduced the error. Therefore, the ensemble model was used to simulate wheat yield in future periods, and the results showed that wheat yield would decrease in future periods. The yield changes in the period 2076-2100 will be -7.22 and -10.81 percent in the SSP2_4.5 and SSP5_8.5 scenarios, respectively. https://jwim.ut.ac.ir/article_105751.html The primary goal of an Environmental Impact Assessment (EIA) is to establish baseline environmental conditions, evaluate the potential impacts of a project’s activities, apply corrective measures to address deficiencies, and reassess conditions after these measures are implemented. This environmental auditing cycle—assessment, corrective action, verification, and re‑auditing—must be executed with accuracy and logical coherence to ensure effective mitigation strategies. The Varamin Irrigation and Drainage Project was examined from multiple perspectives using three assessment tools: the conventional ICOLD matrix, the modified ICOLD matrix, and the Leopold matrix. Findings indicate that each approach offers distinct methodological advantages, and their integration yields a more comprehensive evaluation. The conventional ICOLD matrix delivers a rapid, general overview of environmental impacts, particularly during the construction phase, serving as an initial decision‑support framework. The modified ICOLD matrix, adapted for greater precision and regional relevance, identifies more detailed and cumulative impacts, enabling both qualitative and quantitative analysis. The Leopold matrix provides a multidimensional framework that systematically links project activities such as canal and drain construction or operational stages to environmental components including water resources, soil quality, ecosystems, and local communities. Through its structured cause‑and‑effect analysis, it facilitates prioritization of key environmental concerns. By combining these three methodologies, decision‑makers gain a holistic understanding of environmental trade‑offs, making it possible to minimize adverse outcomes while enhancing project benefits. This integrated approach ensures that environmental considerations are embedded in planning and management processes, leading to more sustainable project outcomes in the Varamin region. https://jwim.ut.ac.ir/article_105777.html Climate change, along with extensive water development in upstream basins, has altered the balance of water allocation and intensified water scarcity and conflicts among stakeholders. Considering the close interlinkage of water resources with socio-economic and environmental systems, inadequate integrated water management can lead to significant implications across these sectors. This study investigates the impacts of climate change on surface water resources and demands in the Sefidrood basin and Anzali Wetland watershed, and subsequently examines potential changes in inflow discharge to Anzali Wetland using a system dynamics modeling approach. Precipitation projections from the CMIP6 framework under SSP2.6, SSP4.5, and SSP8.5 scenarios for the period 2017–2040 were utilized, and the subsystems of the Sefidrood basin, Anzali Wetland catchment, and part of the irrigation–drainage network were simulated in VensimPLE. Results indicate that despite projected increases in mean precipitation, the rising upstream water withdrawals and future agricultural water demand may reduce the annual average inflow to Anzali Wetland by approximately 3.1% to 16.3%. Moreover, given the critical importance of the second half of the water year for irrigation supply, average river inflow during this period is expected to decline by 16.3% to 32.9% across scenarios. https://jwim.ut.ac.ir/article_103129.html Erosion and sedimentation significantly alter watershed morphology and river dynamics. While natural, excessive bed erosion can destabilize banks and increase soil loss, harming aquatic ecosystems and infrastructure. This study models and optimizes erosion control strategies in the Fomanat region (Talesh-Anzali Wetland basin) using vegetative filter strips (VFS) for sediment reduction. The SWAT model simulated the watershed, with runoff and sediment variables calibrated/validated via SUFI2 in SWAT-CUP. The Differential Evolution (DE) algorithm was then linked to SWAT for optimization, focusing on maximizing economic benefits from sediment reduction. Twenty-six decision variables including HRU-to-VFS area ratio and VFS permeability (90% flow passage) were analyzed. Results showed that VFS implementation significantly reduced sediment load while generating 82,673 million Tomans in economic benefits. Land-use changes also influenced total sediment load. Sediment removal costs (based on 2020 watershed pricing) highlighted the need for precise management, considering both financial and ecological impacts on the Anzali Wetland. Climate change effects were also integrated into the model. https://jwim.ut.ac.ir/article_106648.html Accurate discharge measurement under submerged flow conditions remains a major challenge in irrigation and drainage networks. Portable SMBF flumes, despite their widespread application, experience reduced accuracy under submergence conditions. In this study, the hydraulic performance of an SMBF flume under submerged conditions was evaluated through an integrated experimental and numerical approach using the Flow-3D simulator. Experiments were conducted in a rectangular flume with contraction ratios of (r=0.342) and (r=0.561). In the numerical modeling framework, the RNG turbulence model and the Volume of Fluid (VOF) method were employed to accurately capture the free-surface dynamics. Model performance was assessed using RMSE, NSE, the Kling–Gupta Efficiency (KGE) index, and a systematic mesh sensitivity analysis. The results indicated that the contraction ratio (r=0.342) showed the best agreement with experimental data, while increasing the contraction ratio to (r=0.561) led to a reduction in model accuracy. Furthermore, an optimal grid configuration with a cell size of approximately one hundredth of a meter and a total number of cells between 105× 7/5 and 106×1/1 provided a suitable balance between numerical accuracy and computational cost. The main novelty of this study lies in presenting an integrated framework combining three-dimensional numerical modeling, mesh sensitivity analysis, and the application of the KGE index for multi-dimensional performance evaluation under submerged flow conditions. This approach enables simultaneous assessment of accuracy, stability, and variability of flow. The findings can be directly applied to the calibration of SMBF flumes and the improvement of discharge measurement accuracy in irrigation networks. https://jwim.ut.ac.ir/article_105783.html In this study, with the aim of identifying the optimal growth stage of quinoa for sustainable water resource management under the semi-arid conditions of Karaj, three key indicators biomass, crop evapotranspiration (ETc), and crop coefficient (Kc) were measured across four growth stages (initial, development, mid-season, and late season) and evaluated using a multi-criteria decision-making approach. The data were analyzed based on the results of two years of lysimeter experiments and reference evapotranspiration calculated using the Penman–Monteith method. The results showed that the mid-season stage, characterized by the highest crop coefficient, the greatest water requirement, and a significant contribution to biomass accumulation, achieved the highest rank under equal weighting of the criteria. Sensitivity analysis indicated that the ranking of quinoa growth stages was robust to changes in criterion weights. Across all managerial weighting combinations, as well as in single-variable sensitivity analysis (±20%) and multivariate sensitivity analysis using the Monte Carlo method, the mid-season stage consistently maintained the top rank and was identified as the most stable option in the decision-making process. In contrast, the initial and late growth stages exhibited poor performance across all scenarios. These results suggest that water resource management should focus on the mid-season stage to maximize water productivity and crop yield. The findings of this study provide a practical framework for irrigation planning and optimal allocation of water resources in quinoa cultivation. https://jwim.ut.ac.ir/article_107426.html In many regions, snow cover in mountainous areas are the main source of surface and ground water supply. Thus, it’s very important to estimate the snow melt runoff in these areas. To achieve this objective, it’s necessary to estimate the basin snow cover area and its variations for a complete water year. Considering that measuring of ground snow can’t be done daily, Satellite images give a suitable information. In this study, the snow cover area as the most important hydrological variable can be extracted using TERRA- MODIS satellite images with a spatial resolution of 500 meters on daily basis. Thus, to calculate watershed snow melt runoff, the Snowmelt Runoff Model (SRM) was used. This model produces snow melt runoff using the data pertaining to meteorological parameters, hydrology and watershed characteristics. In this study, the necessary data for model application were extracted on daily basis for the years 2002-2003 and 2003-2004. Snowmelt runoff model has been calibrated using the data during 2002-2003 year. The results show that the square of correlation coefficient (R2) between the simulated and observed daily runoff is 0.93 and the difference between the simulated and observed annual runoff volume is 4.74 percent. The SRM has been performed using the 2003-2004 data. The results show that the difference between the simulated and observed annual runoff volume is 2.15 percent and the square of correlation coefficient (R2) between the simulated and observed daily runoff is 0.84. The above values show accuracy of used images in the estimation of snow melt runoff which is the sign of ability and quality of this model and MODIS images to use for other watershed regions. Estimating runoff from snowmelt in mountains is of great importance. To achieve this goal, it is necessary to accurately and reliably estimate the snow cover of the basin and its changes throughout the year. Given that ground-based snow measurements are not performed daily in our country, satellite images provide the necessary information with good accuracy. In this study, the level of snow cover in the basin, as the most important hydrological variable, was obtained daily using TERRA_ MODIS satellite images with a spatial resolution of 500 meters. Then, the snowmelt model (SRM) was used to calculate snow runoff in the basin. This model calculates snowmelt runoff using meteorological, hydrological, and basin characteristics parameters and presents it graphically and numerically along with observed runoff. In the present study, the necessary data for running the model daily and for the water years 2001-2002 and 2003-2004 were extracted. Data from 2001-02 were used to calibrate the SRM model. The model simulated annual runoff volume with a 4.47% difference and daily flow rate with a coefficient of determination of 0.93. Data from 2002-03 were used to evaluate the model. The model estimated annual runoff volume with a 2.15% difference and daily flow rate with a coefficient of determination of 0.84. The above values ​​indicate the high accuracy of the images used and the model in estimating snowmelt runoff for the aforementioned basin, which indicates the capability and ability of the model and MODIS images to be applied to other basins in the region. https://jwim.ut.ac.ir/article_105846.html One of the most important pollutants of surface and underground water resources are nitrogen compounds. These compounds enter the environment, particularly surface water resources, through various means, including agriculture and the use of chemical fertilizers, aquaculture, food industries, and refineries, and cause numerous problems directly or indirectly. Therefore, it is necessary to find a solution to remove or reduce these compounds. The use of inorganic and organic adsorbents can be an easy, effective, and low-cost method. To investigate the efficiency of organic and inorganic adsorbents in removing nitrogenous compounds, some available organic adsorbents (including 7 treatments: rice straw and husk, biochar-rice straw and husk prepared at two temperatures of 300 and 600 °C, and Leonardite) and inorganic adsorbents (including 3 treatments: bentonite, pumice, and zeolite) were used to remove nitrogenous compounds (nitrate, urea and ammonium). To increase efficiency and comparison, the adsorbents were used in simple form, modified with acid, and with iron at two different acidity levels (pH=2 and pH=6). The results showed that biochar prepared from rice straw at a temperature of 600 °C and modified with iron at pH=2, with an absorption of about 79% of nitrate from water, was the best adsorbent for removing nitrate from water among all organic and inorganic adsorbents studied in this study. Rice straw biochar prepared at 600°C and modified with iron at pH=6 removed the highest amount of urea, and acid-modified zeolite showed the best performance with 92% ammonium absorption. Overall, this study indicates the effective and efficient removal of nitrogen compounds by these adsorbents, and modification with acid and iron improved the removal capability of these adsorbents. As a result, they can be used as a cheap and accessible method for removing pollutants from water sources. https://jwim.ut.ac.ir/article_107141.html Climate fluctuations are a fundamental and unavoidable challenge in the present era, with widespread impacts on all aspects of the environment; aquifers, as vital resources, are affected by these changes. This study examines the impact of climate fluctuations on the Chadegan Plain aquifer of Iran. For this purpose, the groundwater level was examined as the main criterion of the GRI index. The years 2002-2023 were selected as the time period due to complete data and information, and the necessary calculations and evaluations were performed on the data. The drought status of two wells in western Chadegan and three wells in Se Rahe Ghorghor were investigated from the 12 piezometric wells in the region, and the highest rate of groundwater level drop occurred in these two wells during the time periods 2015-2018 with nine meters and 2006-2011 with 18 meters, respectively. Evaluation of the wells in the study area with the GRI index shows the good performance of this index in the Chadegan plain. The results showed a decreasing trend in the GRI index values during the time periods 2006-2011 and 2015-2018, and the highest degree of GRI drought in the wes of Chadegan well and Se Rahe Ghorghor well was recorded with values of -2.47 in 2018 and -2.54 in 2011, respectively. Also, the Spearman nonparametric test was used to evaluate the correlation between the meteorological drought index SPI and the hydrogeological drought index GRI. The results indicated no significant trend, and the main reason for the drop in groundwater level can be attributed to human factors. https://jwim.ut.ac.ir/article_105975.html Missing values in hydrology studies are a common challenge for hydrologists, especially in statistical analyses that require complete datasets. This research evaluates the performance of the Multiple Imputation by Chained Equations (MICE) method in predicting and reconstructing daily river flow values. The study area is the Kajo River basin in southeastern Iran, and the statistical period covers the hydrological years from 1972-1973 to 2021-2022. To investigate and validate the effectiveness of the MICE approach in managing missing flow data, complete historical daily flow records from the hydrological years 2011–2012 to 2021–2022 were used. Subsequently, the MICE method along with Multiple Linear Regression (MLR) was applied to reconstruct all missing daily flow values. The best-performing estimation methods were evaluated using criteria such as the adjusted coefficient of determination (Adj R2), residual standard error (RSE), and mean absolute percentage error (MAPE). The findings indicated that the Classification and Regression Trees (CART) method combined with MLR outperformed other tested methods, achieving the highest  value and the lowest RSE and MAPE values. The RSE and MAPE values for the CART-MLR method at the Pirsehrab station are 0.472 and 0.583, respectively, and at the Chandokan station are 0.475 and 0.588, respectively. https://jwim.ut.ac.ir/article_106806.html Water resources management in semi-arid regions is challenged by climate variability, supply–demand imbalances, and environmental constraints. In this study, the Multi-Objective Horse Optimization (MOHO) algorithm was developed to optimize the operation of the Garanqo Reservoir in northwestern Iran. Two main objectives were considered: (1) minimizing vulnerability caused by water shortages and (2) maximizing reliability in meeting downstream water demands. The performance of MOHO was compared with the reference Multi-Objective Genetic Algorithm (MOGA) using the Fonseca–Fleming test function. Results showed that MOHO achieved a well-distributed Pareto front and maintained solution diversity effectively. Analysis of the base period (1971–2000) revealed that vulnerability ranged from 15% to 36%, while reliability ranged from 29% to 70% across the set of Pareto-optimal solutions. The compromise zone was identified within vulnerability levels of 31–33% and reliability levels of 40–55%, highlighting the trade-off between the two conflicting objectives. Reservoir performance analysis also showed a significant temporal mismatch between natural inflows and human water demand, emphasizing the need for accurate release planning and storage management. Finally, several management strategies were proposed to reduce shortages, prevent spill losses, and enhance system resilience under variable hydrological conditions. https://jwim.ut.ac.ir/article_107504.html This study introduces a practical, data-driven framework for assessing surface water delivery failure risk in irrigation districts exposed to inflow fluctuations at diversion dams. The framework evaluates the vulnerability of manual standard operating procedures using a dynamic hydraulic-operational simulation model developed in MATLAB based on the Integrator–Delay model and manual SOP rules. Risk probability is estimated from historical diversion-flow records through frequency analysis. Vulnerability is quantified using a demand–delivery indicator that compares delivered water with allocated water rights. Risk consequence is represented by an integrated PCA-based index derived from adequacy, dependability, and efficiency indicators. The framework was applied to the Mahyar–Jarghooyeh Irrigation District in arid central Iran, including 659 irrigated units. Results indicate that under normal conditions, more than 90% of the district remains below a risk value of 0.4%. Under low fluctuation conditions, 30–35% of units show risk values between 1.5% and 3%, while 15–20% exceed 3%. Under moderate fluctuations, 60–80% of units fall within the 4–10% risk range. Under severe fluctuations, risk values exceed 60% across 50–60% of the district, and approximately 85% of units fall within the 40–100% range. The spatial results reveal a transition from localized risk under low-stress conditions to widespread systemic vulnerability under severe inflow fluctuations. The proposed framework can support revision of manual operating rules, targeted monitoring, and spatially explicit risk management in surface-water-dependent irrigation districts. https://jwim.ut.ac.ir/article_105799.html Rivers serve as a fundamental component of the hydrological cycle, exhibiting continuous and stable flow. However, river flow modeling is inherently associated with uncertainties in input data and geometric parameters, which make sensitivity analysis and the assessment of error sources a complex and challenging task. Among the most significant sources of uncertainty are measurement errors and inaccuracies in defining river cross-sections, which can directly influence the outcomes of hydrodynamic models. In this study, a Monte Carlo simulation framework was employed to investigate the propagation of uncertainty in hydraulic flow modeling. The uncertainties were analyzed through three case studies, including two real rivers and one hypothetical river. Simulation scenarios were constructed based on normal and uniform probability distributions, incorporating random errors of 10% and 20%, as well as systematic errors of 0% and ±3% in the cross-sectional data. The results demonstrated that random errors following a uniform distribution introduced the greatest variability in flow predictions, and an increase in the magnitude of geometric data errors directly led to higher variance in the model outputs. In contrast, the influence of systematic errors on the results was comparatively smaller, indicating the model’s greater sensitivity to stochastic variations in geometric input data. The findings of this study can contribute to enhancing the accuracy of hydrodynamic models and improving the interpretation of their results. Moreover, the outcomes provide practical implications for water resources management, hydraulic structure design, and risk-informed decision-making in the field of river engineering. https://jwim.ut.ac.ir/article_105347.html The increasing scarcity of water and its associated challenges have heightened the importance of making appropriate decisions regarding the reuse of wastewater. This review aims to provide a comprehensive overview of the considerations surrounding water reuse by addressing its multidimensional perspectives. Furthermore, this study seeks to identify existing research gaps in the country and propose directions for future investigations. According to the findings, ensuring the economic sustainability of reuse projects requires establishing a strong linkage between wastewater management and other economic sectors to mitigate investment risks. Water pricing reform plays a key role in the success of such programs. Public awareness and education have a significant impact on alleviating concerns regarding the consequences of wastewater reuse and on strengthening public support for reuse initiatives. In general, wastewater does not constitute a new source of water, and its reuse will not alter the overall hydrological balance of the basin. Therefore, reuse projects must carefully assess the reduction in return flows and its impacts on local aquifers, environmental flows, and downstream water-dependent users. Developing an integrated framework for assessing the overall sustainability and long-term effectiveness of wastewater reuse strategies is essential for effective water management in the country. https://jwim.ut.ac.ir/article_104814.html Shadegan Wetland, designated under the Ramsar Convention and recognized for its international importance, is located in the southwest of Iran, north of the Persian Gulf. With its high species diversity and extensive ecosystem services, the wetland plays a key role in biodiversity conservation, climate regulation, and supporting local livelihoods. The present study aims to conduct a comprehensive economic valuation of the wetland and to analyze changes in its value across different functional levels.The methodology involved classifying wetland services into three levels: the minimum environmental flow requirement (basic ecological functions), the normal hydrological condition (optimal vegetation cover and primary ecological services), and the optimal inundation level (maximizing all services including fishing and recreation). The economic value of each level was estimated using willingness-to-pay surveys, market-based approaches, and production-based valuation methods.Results indicate that the economic value of the wetland is approximately IRR 86 billion under the first level, increasing to IRR 2,568 billion when all services and the presence of rare bird species are considered. This significant difference highlights the necessity of restoring and maintaining the wetland’s full functionality. Given the threats posed by climate change and human pressures, integrated water resource management across the Jarrahi–Zohreh Basin and the adoption of strong conservation policies are essential. https://jwim.ut.ac.ir/article_105692.html In this study, the performance of the Nekouabad Irrigation Network was evaluated under two operating systems: the conventional manual operation and an automated operation based on a Proportional–Integral (PI) controller, in response to consecutive inflow shortage scenarios. Using a hydraulic simulation model and performance indicators of adequacy and sustainability, the network behavior was analyzed across seven levels of hydrological stress, ranging from normal conditions to inflow deficits exceeding 40%. The results revealed that the PI-based system significantly enhanced the level of water service, reduced temporal and spatial fluctuations, and improved the equity of water distribution. Compared with the manual operation, the PI controller increased mean adequacy by up to 40% and reduced mean variability (instability) by 39%, indicating a higher degree of stability and resilience across the network. Statistical, distributional, and spatial analyses confirmed that the PI control system prevented functional collapse under critical conditions and transformed the network into a more coordinated, predictable, and equitable system. The reduced coefficient of variation, synchronized regulator responses, and spatial uniformity of performance demonstrated the controller’s capacity to absorb disturbances and prevent the propagation of fluctuations throughout the network. These features facilitate a transition from reactive management toward predictive regulation, enabling more accurate planning, reduced dependence on groundwater resources, and enhanced resilience to climate variability. The findings support the applicability of comprehensive assessment frameworks—such as sustainability evaluation frameworks based on the Water–Food–Energy Nexus and risk-based system failure assessment—for future studies. https://jwim.ut.ac.ir/article_106647.html In this study, the accuracy of the ERA5-Land reanalysis dataset in estimating climatic parameters (precipitation, temperature, wind speed, and evapotranspiration) over the Varamin Plain was evaluated by comparison with data from the Varamin synoptic station during the common period of 2007–2023. The validation results indicated that ERA5-Land shows satisfactory performance in estimating daily maximum, minimum, and mean temperatures (R² > 0.95), daily evapotranspiration (R² > 0.85), and monthly precipitation (R² > 0.70) in the study area. However, the dataset demonstrated lower accuracy in estimating daily precipitation and wind speed. Given the acceptable performance of ERA5-Land, the drought indices SPI-1 and SPEI-2 were calculated at 3-, 6-, 12-, and 24-month time scales for the long-term period of 1990–2023 (33 years) using data from this dataset. The drought monitoring results indicate that the Varamin Plain has entered a regime of severe and persistent drought since approximately 2014. Furthermore, the findings suggest that in warm and semi-arid regions of Iran, the SPEI index—due to its consideration of warming effects and increased evapotranspiration—provides a more accurate and reliable tool than SPI for drought monitoring and water resources management. https://jwim.ut.ac.ir/article_106664.html Accurate Manitoring of date-palm plantations and reliable estimation of cultivated area are essential for water-resource management, agricultural planning and regional policy making in arid and semi-arid regions, specially in south-west of Iran. This research utilized multi-temporal, Sentinel-2, and radar, Sentinel-1 imageries and supervised classification based on random forest, to present accurate and applicable map of date-palms plantations in Maroon-Jarahi sub-basin. To address this, Sentinel-2 optical and Sentinel-1 radar time series were derieved and processed, then together with Spectral, Radar, thermal indices and collected field reference samples during the 2022-2024 agricultural seasons were applied to training process of random forest model. Classification was run based on 36 inputs including, main bands, Spectral, Radar, and thermal indices. Accuracy assessment of the Model according to independent sample test displayed that The Random Forest classifier yielded very high performance due to, overall accuracy= 95%, kappa coefficient = 0.94, and precision, recall and F1-score were approximately 0.99. furthermore, mapped date-palm area closely matched reference inventories, the actual area was 26197.55 ha while the mapped area was 21764.55 ha for the Random Forest result, thus approximately +1% over-estimation was derieved. The results showed that, The integration of multi-temporal and thermal indicators within a Random Forest classification framework provides an efficient, reliable and scalable method to map date-palm plantations in arid and semi-arid basins.Practically, the proposed workflow can be a validated approach for water resource management, Orchards’ health monitoring and agricultural planning in reginal and national scales. https://jwim.ut.ac.ir/article_106665.html The aim of the present study is to investigate the effect of the Tang-Sorkh Reservoir Dam on controlling the inflow of the city of Yasuj. The Tang-Sorkh Reservoir-Regulatory Dam is one of the important dams under construction between Fars and Kohgiluyeh and Boyer-Ahmad provinces, which, in addition to providing drinking water to the cities of Shiraz and Yasuj, is also responsible for controlling seasonal floods. In this study, the effect of the Tang-Sorkh Reservoir Dam on reducing peak discharge and flood volume in the Bashar watershed has been investigated using the HEC-HMS 4.9 model and the HecRas-2D model. To calculate the precipitation for different return periods for each station, the randomness of the 24-hour maximum precipitation data was first examined by SPSS software with the Run Test test. The results showed that the data of all stations had a significance level greater than 0.05, which indicates the randomness of the data. Then, based on the available statistical distributions such as the Weibull 3-parameter, normal, lognormal 3-parameter, Pearson 3-log, loggamma, generalized extreme values (GEV) and gamma 3-parameter) the best fitted distribution function for the data was selected in the EasyFit software. The results showed that this dam can reduce the peak discharge on average in different scenarios in the half-full and full reservoir for the return periods of 2 to 100 years, respectively, compared to the situation without a reservoir, by 58.3 and 45.5 percent. Also, the flood volume will be reduced by 66 and 2.6 percent in the three scenarios for empty, half-full and full reservoirs, respectively. In addition, with the increase in the peak discharge and flood volume in different return periods, the effect of the reservoir on reducing the peak discharge and flood volume decreases. https://jwim.ut.ac.ir/article_107159.html Vertical hydraulic conductivity of ephemeral streambeds is one of the key parameters in surface‑subsurface flow exchange and water resource management in arid and semi‑arid regions. However, its direct measurement is associated with high cost, time, and uncertainty. The aim of this study is to develop and evaluate a machine‑learning‑based framework for predicting vertical hydraulic conductivity of the bed, emphasizing the role of morphological heterogeneities such as the main channel and bars, to understand the behavior of ephemeral rivers. To this end, reinforcement‑based models were developed using grain‑size data, bed structural indices, and hydraulic parameters. The models performance was evaluated with three validation methods. Results show that the selected model achieved a coefficient of determination above 0.85 on the test data. Uncertainty analysis indicated that the 95 % confidence intervals of the predictions were narrow, and the model skill rate exceeded 85 % in most validation scenarios. Comparing model performance across different morphological units revealed that predicting hydraulic conductivity in bars was more stable than in the main channel, with the standard deviation of test results ranging from 10 % to 50 % lower depending on the validation method. Sensitivity analysis results indicate that the parameters mp and Ar, along with the 10th percentile grain size (d10), are the most important controlling factors for vertical hydraulic conductivity. These findings suggest that integrating machine learning with morphology‑based analysis can provide an efficient approach for estimating hydraulic conductivity of ephemeral streambeds and reducing uncertainty in hydrological studies. https://jwim.ut.ac.ir/article_107335.html Drought prediction is of great importance in scientific planning and optimal management of water resources in each region. In this study, modeling and forecasting of drought index (SPEI) time series were carried out for three stations located in Ardabil province using ARMA and SARIMA models. For this purpose, meteorological data from Ardabil, Khalkhal and Parsabad stations in the statistical period (1992-2021) were used. The SPEI index was calculated at each station at different time scales, and the first 25 years of data were used for calibration and the last 5 years for validation. The most appropriate model for each station was selected. The results showed that at all three stations, the SPEI1, SPEI3 and SPEI6 time series had a strong seasonal trend, with a 12-month periodicity. The importance of the seasonal trend decreased with increasing time scale. So, no seasonal trend was observed in the SPEI12 series. At Ardabil station, the most accurate prediction among all time scales belonged to SPEI3, which was evaluated with the SARIMA(0,0,2)(3,1,0)12 model and the criteria RMSE=0.25, NS=0.94 and AIC=-144.94. At Khalkhal station, the most accurate prediction related to the SPEI3 series was evaluated with the SARIMA(1,0,2)(3,1,0)12 model and the criteria RMSE=0.3, NS=0.91 and AIC=-185.52. At Parsabad station, the best prediction related to the SPEI6 series was obtained with the SARIMA(3,0,0)(0,1,1)12 model and the criteria RMSE=0.33, NS=0.89 and ACI=-251.5. The results of this study can be useful in the management of water resources of Ardabil province. https://jwim.ut.ac.ir/article_107475.html In this study, the impact of climate change on the design discharge and sediment loading in the Kan River was studied using data from the Soulghan hydrometric station. The effective discharge, the full-flow discharge, and the discharges with return periods of 1.58 and 2.33 years were calculated for current and future conditions under three climate scenarios SSP126, SSP245, and SSP585. The results showed that the effective discharge under current conditions is about 5.05 m3 / s, which transports more than 69.4 percent of the sediment, but in the future, with the decrease in the amplitude of the flows, the effective discharge will decrease to about 2.76 to 2.86 m3 / s, carrying more than 91 percent of the accumulated sediment. The sensitivity index of sediment transport to discharge also decreased from 1.32 in the past to about 0.45 to 0.49 in the future, indicating a decrease in the sharp jump in sediment with an increase in discharge. The LARS-WG model performed well in generating future climate data (r=0.97), (RMSE=2.57), and the LSTM and SVM machine learning models performed well with a correlation coefficient of about 0.90 and an NSE efficiency coefficient higher than 0.79 were able to predict discharge and sediment with acceptable accuracy. The results indicate increased flow fluctuations, increased frequency of extreme events, and redistribution of sediment load towards flows with lower amplitude due to climate change, which requires a review of the hydraulic design and management of urban waterways. https://jwim.ut.ac.ir/article_107601.html Sustainable groundwater management in arid and semi‑arid regions, particularly in major agricultural hubs such as the Qazvin Plain, faces significant challenges arising from the synergistic impacts of excessive groundwater extraction and hydrochemical degradation. Conventional vulnerability assessment models, which are largely based on compensatory operators (e.g., weighted linear combination), tend to mask critical zones because unfavorable conditions in one variable are averaged with favorable conditions in another. This study introduces a novel non‑compensatory analytical framework to evaluate the cumulative risk of the Qazvin Plain aquifer over a 21‑year period (2001–2022). Within this approach, long‑term piezometric data and hydrochemical parameters were integrated using non‑compensatory spatial operators, particularly the spatial product operator, ensuring that areas simultaneously affected by severe quantitative decline and water‑quality deterioration are identified as absolute critical hotspots. The spatiotemporal analyses reveal a substantial regime shift in the hydro‑dynamic and hydro‑chemical behavior of the aquifer, indicating a transition from relative stability to chronic stress conditions. An aggressive expansion of high‑risk zones was observed from the eastern and southeastern margins toward the central and western parts of the plain. A critical turning point was identified during 2010–2012, when the extent of high‑risk areas increased from approximately 30% to more than 50% of the study area. By 2022, over 65% of the aquifer was under critical stress, while approximately 42.5% was classified as high‑risk. The non‑compensatory model successfully revealed hidden critical cores in the central plain that conventional approaches had previously misclassified as moderate risk. These findings indicate that the degradation of the Qazvin aquifer follows a nonlinear and self‑reinforcing process driven by a positive feedback loop between declining saturated thickness and increasing groundwater salinity. The results demonstrate that separate evaluation of groundwater quantity and quality, or reliance on compensatory models, may lead to overly optimistic and potentially misleading assessments of aquifer conditions. The precise identification of critical hotspots in this study highlights the necessity of shifting from generalized groundwater management toward a spatially targeted management paradigm that prioritizes interventions based on cumulative risk. Accordingly, immediate and spatially differentiated management measures—such as demand management in the critical eastern sectors and artificial recharge in the western zones—are essential to prevent ecological and agricultural collapse in the region. https://jwim.ut.ac.ir/article_107602.html Research Topic: Water tourism is an opportunity that can help create employment and income for local communities and develop tourism infrastructure in the country. Considering the climatic conditions and geographical location of Khuzestan Province, as well as the abundant water resources, identifying and prioritizing water areas in this province is of great importance. Objective: The present study was conducted with the aim of analyzing and screening the diversity of water resources in Khuzestan Province to assess the capacity of tourism development in this province. Method: Water areas were identified using satellite images and then prioritized through a list of criteria for selecting water areas and prioritizing them by specialists and experts. Results: The results of scoring and screening water areas showed that among the rivers of the province, the Karun River has the highest priority with 34 points, followed by the Karkheh, Jarahi, Ab Shur, and Dez rivers. Among the internal wetlands of the province, the highest priority is Shadegan Wetland with 41 points, and other selected internal wetlands include Shimbar, Hor-al-Azim, Ab-Zalo, Bandun, and Miangaran. The results of coastal-marine wetlands showed that Karaneh Abadan with 30 points has the highest priority, followed by Karaneh Shadegan, Mosabe Bahmanshir, Khor Musa, and Mosabe Arvandroud, respectively. Regarding man-made water bodies, Gotvand Dam Lake has the highest priority with 31 points, and then other wetlands include Karkheh Dam Lake, Karun 1 Dam Lake (Shahid Abbaspour), Marun Dam Lake, Jareh Dam Lake, Dez Dam Lake, and Gotvand Tantimi-Anhrafi Dam Lake, respectively. Conclusion: According to the results obtained, Khuzestan Province has abundant water diversity, and water tourism, as a tool for balanced regional development, can help develop tourism and increase infrastructure and economic prosperity in this province. https://jwim.ut.ac.ir/article_107738.html With the expansion of urbanization and the development of industrial activities, surface water resources in western Tehran have been increasingly exposed to serious pollution challenges. The aim of this study is to evaluate the quality of surface runoff and examine its impact on the water quality of the Kan River using three water quality indices, including NSFWQI, IRWQISC, and IRWQIST. For this purpose, sampling was conducted during the 1404 water year at 15 selected stations along the Kan River during both wet (February) and dry (August) seasons, and physicochemical, microbial, and toxic parameters were measured. The results of the NSFWQI and IRWQISC indices showed that water quality in most of the stations ranged from moderate to very poor, exhibiting a considerable decline from upstream to downstream. Statistical analysis also demonstrated strong correlations among parameters associated with organic and nutrient pollution, including BOD, COD, ammonia, and phosphate, which are mainly attributed to municipal wastewater discharges and agricultural runoff. In contrast, the IRWQIST index, which focuses on toxic pollutants, assessed the water quality as good in most stations, indicating that heavy metal contamination is not widespread except in a few industrial hotspots. The simultaneous evaluation using these three indices enabled the differentiation of pollution characteristics and revealed that the primary threat to the Kan River ecosystem is the high load of organic and nutrient pollutants, whereas toxic pollution tends to be more localized. Accordingly, implementing management strategies that include controlling urban and agricultural pollution sources and monitoring industrial discharges is essential for improving the river’s water quality. https://jwim.ut.ac.ir/article_107776.html Excessive water losses due to leakage have become a major concern for national water authorities. In fact, the Tehran Water and Wastewater Company has been forced to implement complete nightly shutdowns in the city’s water distribution networks to conserve water, causing public dissatisfaction and increasing the risk of contaminant intrusion into the system.This study adopts a data-driven hybrid approach, combining numerical modeling of Reservoir 43—located in northeastern Tehran—using Water GEMS software, and training an Artificial Neural Network (ANN) with pressure and flow outputs from the hydraulic model under leak conditions to predict both location and magnitude of leaks.Model performance and accuracy were evaluated based on RMSE, MSE, and R² criteria. Numerical simulations showed that, after applying leak conditions, the average pressure decreased by approximately 0.9 bar, while flow rates in main pipelines increased by around 35%. The results indicated that the probability of detecting leaks ranged between 67% and 88%.Following full training, the integrated model estimated pressure drops and flow increases caused by leaks with very high precision, reducing the prediction error to 4% by the end of the training period. This high accuracy confirms the model’s reliability for rapid and targeted leak identification under real operational conditions.The success of this method depends on the accuracy of hydraulic model calibration and the quality of measured data. In this research, calibration was performed based on engineering knowledge, adjusting pipe roughness, local losses, and minor loss coefficients until the difference between modeled and field data was less than 5%. https://jwim.ut.ac.ir/article_107784.html Accurate modeling of groundwater quality requires a proper understanding of the spatial dependency structure of hydrochemical parameters and the application of advanced predictive approaches. In this study, six machine learning algorithms including K Nearest Neighbors (KNN), Multi Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), Gradient Boosting Regression (GBR) and Extreme Gradient Boosting (XGBoost) were employed to predict electrical conductivity (EC) and calcium (Ca²⁺) concentrations in Guilan Province, Iran. To incorporate spatial dependency into the predictive framework, the distances to neighboring wells were introduced as input features, and model performance was evaluated under different neighborhood scenarios (4 to 8 neighboring wells) using annual groundwater quality data from 2002 to 2018. The results indicate that ensemble learning models outperform the other algorithms; however, the optimal spatial configuration varies between parameters. For electrical conductivity (EC), the Random Forest model achieved the best performance when eight neighboring wells were considered, with an R‑squared value of 0.888, a root mean square error of 117.165, and a mean absolute error of 84.173. This suggests a broader spatial dependency for this parameter within the aquifer system. In contrast, for calcium ion (Ca²⁺), the XGBoost model yielded the optimal performance with four neighboring wells, achieving an R‑squared value of 0.811, a root mean square error of 1.154, and a mean absolute error of 0.820, indicating that local hydrogeochemical processes exert a stronger control on the spatial distribution of this ion. The comparison of neighborhood scenarios further demonstrates that increasing the number of neighboring wells does not necessarily improve prediction accuracy for all parameters, highlighting the importance of parameter specific spatial dependency analysis. Overall, the findings confirm that integrating machine learning with spatial feature engineering provides an effective framework for intelligent groundwater quality modeling and can support the optimization of monitoring networks and water resources management strategies. https://jwim.ut.ac.ir/article_107926.html Municipal solid waste (MSW) management is recognized as one of the most critical environmental challenges of the twenty-first century. Rapid population growth, increasing consumerism, excessive waste generation, inadequate recycling practices, and the disposal of waste in poorly designed landfills have significantly complicated sustainable waste management. Landfill leachate, in particular, poses a serious threat to soil and groundwater quality due to its complex composition and high contamination potential. In this study, previous research on landfill-related contamination was reviewed, and the effects of leachate generated from the Aradkouh landfill on the shear strength behavior of soil were experimentally investigated. In addition, the applicability of the Contamination Index Parameter (CIP) and the Leachate Pollution Index (LPI) was evaluated to assess the degree of contamination and the potential risk of groundwater pollution.To examine the influence of leachate, uncontaminated soil was used as the control specimen and compared with artificially contaminated samples containing 3%, 10%, and 15% leachate. Direct shear tests were performed on all specimens under different normal stress levels after a curing period of 72 hours.The results demonstrated that increasing the leachate content to 3%, 10%, and 15% reduced the soil shear strength by approximately 22%, 26%, and 28%, respectively. Moreover, leachate contamination adversely affected the soil's internal friction angle. These findings suggest that leachate infiltration weakens the mechanical properties of soil, reduces landfill stability, and increases the potential for environmental and groundwater contamination. https://jwim.ut.ac.ir/article_108152.html The compilation of water balance at the basin level requires processing a large amount of information based on the use of GIS tools and an appropriate conceptual model. The aim of this study is to explain the framework provided by the United Nations for water accounting in the Godar-Chai River basin. The SWAT model for hydrological simulation of the basin and the SUFI-2 algorithm for sensitivity analysis using the Nash-Sutcliffe criterion as the objective function were calibrated and validated monthly at 5 hydrometric stations. The results showed that the calibrated model for the Godar-Chai basin has a desirable performance. One of the approaches to achieve a comprehensive understanding of the conditions of the basin, which is also evaluated in this study, is the SEEA_W environmental-economic accounting for water introduced by the United Nations. The tables for each user show a water cycle and the equation of balance and exchange of water between the field of activities and the environment. In order to complete the tables of consumption and physical supply in the agricultural sector, the necessary information to complete it has been taken from the outputs of the SWAT software. Finally, in order to increase the agricultural and economic productivity of crops in the basin, the optimal crop area change scenario was applied to the model, according to which the amount of irrigation in the basin was reduced by 55 percent, and water accounting tables for the optimal agricultural development scenario were also prepared and presented. https://jwim.ut.ac.ir/article_108252.html This study aimed to evaluate the effect of irrigation method, water consumption and nitrogen fertilizer amounts on rice growth and water productivity in the 1403 crop year at the Ilam University Research Farm. The experiment was conducted in split plots in a randomized complete block design with three replications. The treatments included two irrigation methods ( tape drip irrigation whith and without plastic mulch), three levels of water supply (80, 100 and 120 percent of plant water requirement) and three levels of nitrogen fertilizer (50, 75 and 100 percent of fertilizer requirement). The results of statistical analysis showed that the main effects of irrigation method, water supply level and nitrogen fertilizer were significant on most of the studied traits. The use of mulch in the drip irrigation type system increased plant growth, yield components and water productivity by improving soil moisture conditions. The highest number of grains per spike and 1000-grain weight were obtained in the drip irrigation treatment with mulch, providing 120% of the water requirement and using 100% of nitrogen; while the highest harvest and water productivity index were observed in the drip irrigation treatment with mulch, providing 100% of the water requirement and using 100% of nitrogen. The reduction in the level of water and nitrogen supply also caused a decrease in growth traits, yield components, and water use efficiency. Based on the results, drip irrigation with mulch and full nitrogen use, especially at the level of providing 100% of the plant's water requirement, can be suggested as an effective strategy for improving yield, increasing water productivity, and sustainability of rice production in semi-arid conditions.