https://jwim.ut.ac.ir/ Water and Irrigation Management en daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://jwim.ut.ac.ir/article_101699.html Lake Urmia has experienced a severe decline in water levels in recent years due to climate change, reduced precipitation, and excessive water withdrawal for agricultural purposes. One proposed strategy for managing this crisis is modifying the cropping pattern and replacing high-water-consuming crops with drought-resistant alternatives. This study assesses the feasibility of saffron (Crocus sativus) cultivation as a low-water-demand and high-value crop in the southern regions of Lake Urmia. Monthly temperature and precipitation data for the region were obtained from the Meteorological Organization and analyzed using ombrothermic diagrams. These data were then compared with those of Iran’s traditional saffron-growing regions. The results indicate that the southern areas of Lake Urmia, particularly Bonab and Malekan, exhibit high climatic similarity to the saffron-producing regions of Razavi Khorasan Province. The recorded mean monthly temperature ranges between -5 to 30°C, and the annual precipitation is 300 to 400 mm. Field observations confirm that experimental saffron cultivation has been successful in some areas. However, expanding saffron cultivation in this region faces challenges such as severe winter frosts, the need for soil and irrigation improvements, and a lack of supportive policies. Data analysis shows that saffron requires 60% less water compared to commonly grown crops in the region. The findings of this study suggest that, with proper water resource management and optimized cultivation techniques, saffron could contribute to reducing water consumption, increasing farmers' income, and assisting in the restoration of Lake Urmia. https://jwim.ut.ac.ir/article_103131.html This research aims to develop a scientific and practical method for urban flood prediction using citizen reports and satellite rainfall data. The city area is divided into a grid with 3,000-meter intervals, and the intersection points of the grid lines are selected as rainfall measurement stations. Days are classified as wet, dry, and normal based on citizen reports and observed rainfall, and machine learning models including Support Vector Machine, Logistic Regression, and Random Forest are trained on these data. The results show that the Random Forest model has the highest performance, with 98percent precision and an F1 score of 66 percent in correctly identifying flood events. Considering Qom’s semi-arid climate and the non-lethal damages caused by floods, emphasis was placed on the precision of models in correctly detecting flood cases to enhance confidence and prevent community anxiety due to false alarms. Stations 1 and 23 are identified as critical locations for rainfall equipment installation based on feature importance analysis. Subsequently, a critical rainfall threshold curve at a 90 percent probability level was plotted for flood prediction, and validated with real data. Rainfall above the threshold generally results in floods, while values below are considered safe. This approach is an effective tool for rapid warning systems and can aid urban disaster management to prevent financial and human losses. https://jwim.ut.ac.ir/article_102609.html Water, food, and energy, as intertwined and interconnected systems, have significant mutual impacts on each other, and quantitative and qualitative changes in each system can adversely affect the others. The Harirud River located on the border between Iran and Afghanistan, as a prime example of an intertwined system, is a major supplier of drinking water for the metropolis of Mashhad and supports the agricultural needs of the Sarakhs Plain in Iran and also this river plays a vital role in Herat’s agriculture and hydropower energy supply in Afghanistan. However, in recent years, water supply as a key factor for the development of agriculture and energy in this basin, has faced serious challenges. Reasons such as political, social, and hydrological issues, along with inefficiencies in resources management are factors for such challenges at the basin level. This study provides an analytical framework that examines and compares the performance and processes of water, food, and energy systems at the national and basin levels using the concept of metabolism. The quantification of this approach is carried out based on reliable data and resources available at the watershed level. The results of this study reveal the significant dependence on water, food, and energy systems at the basin level and within the riparian countries, in such a way that Iran consumed 1,914 million cubic meters of water and 10,701 terajoules of energy annually to produce approximately 2.7 million tons of selected agricultural products. Afghanistan, meanwhile, produced 740,000 tons of agricultural products by consuming 1400 million cubic meters of water and 3,153 terajoules of energy. Additionally, the Salma powerplant has generated 197,000 megawatt-hours of electricity to meet the region’s energy demands by utilizing a flow volume of 755 million cubic meters through its turbines. However, they remain highly dependent on food and energy imports. The results emphasize the necessity of strengthening cooperation between Iran and Afghanistan and establishing joint mechanisms to ensure the optimal management of the Harirud water resources. https://jwim.ut.ac.ir/article_102875.html Drought stress and the increasing need for food production have raised serious concerns about the sustainability of water resources and agricultural systems in the arid regions of the Middle East, particularly Iran. Field experiments were conducted in four replicates at four irrigation levels including 120%, 100%, 80%, and 60% of the full irrigation requirement for sweet corn. The Decision Support System for Agricultural Technology Transfer (DSSAT) model was calibrated and validated to evaluate various irrigation management strategies and their effects on yield, leaf area index, and biomass of sweet corn. The study revealed that the model displayed moderate accuracy (NRMSE between 20-30%) in simulating the impact of severe irrigation stress on the growth and development of sweet corn. The evaluation of the Ceres-Maize model in capturing dynamic growth trends of leaf area index, height, and aerial biomass showed that this model successfully simulated the biomass growth process. During calibration and validation under the 20% deficit treatment, the model reduced the maximum leaf area index by 0.52 and 0.71, respectively. However, the model exhibited significant errors in simulating initial plant growth stages under intense drought stress. The study also indicated that in deficit treatments, measured values reached their final levels more rapidly a trend evident for the plant height index as well. In the 40% deficit regime, the model showed substantial errors in grain yield simulation, with NRMSE values of 27.03 during the validation stage. Overall, the findings demonstrated that the Ceres-Maize model performs adequately in simulating yield and biomass of sweet corn. https://jwim.ut.ac.ir/article_103130.html One of the main important and influential factors on the performance of water conveyance and distribution systems is the type of structure and its performance. The LOPAC gate is one of these structures, whose discharge coefficient is a key factor in estimating the flow rate. In this research, after dimensional analysis and identifying the effective parameters, including the opening angle, submergence ratio (the ratio of downstream depth to upstream depth), and opening ratio (the ratio of LOPAC gate opening to canal width), the discharge coefficient of a LOPAC gate under semi opened submerged operating conditions with and without contraction was predicted using a support vector machine (SVM). To this end, a support vector machine-based prediction model was developed using experimental and numerical data to estimate the discharge coefficient of a Sluice gate under submerged flow conditions. Thus, 70% of the data was used for training and 30% for testing. After optimizing the support vector machine parameters using systematic methods, different models with varying input parameter combinations were evaluated. The results showed that the optimal model using the submergence ratio and opening ratio as the main input factors provided the highest accuracy with a correlation coefficient of 0.984 and a root mean square error of 0.006. https://jwim.ut.ac.ir/article_103474.html The aim of this study was to analyze the effects of conventional irrigation (DI) and partial root-zone drying (PRD) treatments using drip-tape irrigation on the yield of grain maize (hybrid single cross 704), through simulation with the SALTMED model. The experiment was conducted with six irrigation treatments (three levels of 100%, 75%, and 55% of crop water requirement), each replicated three times, at the research farm of the Faculty of Agricultural Technology, University of Tehran. The field experiment followed a split-plot design based on a randomized complete block design and was conducted in Pakdasht City. Results under conventional irrigation showed measured yields of 11.86, 9.80, and 6.12 t/ha for the 100%, 75%, and 55% water requirement levels, respectively. The corresponding model-simulated yields were 11.71, 10.08, and 6.11 t/ha. The statistical indices NRSME were 8.75%, 11.2%, and 12.34%, RMSE were 0.55, 0.52, and 0.34 t/ha, and R² was 0.99. Under PRD treatments, measured yields were 10.10, 8.34, and 5.73 t/ha, while the simulated yields were 10.65, 8.56, and 5.65 t/ha, respectively. The NRSME values were 14.04%, 10.63%, and 9.48%, RMSE were 0.74, 0.39, and 0.27 t/ha, and R² was 0.98. No significant differences were observed between the measured and model-estimated values. The SALTMED model demonstrated good accuracy in predicting maize grain yield, optimizing water consumption, and enhancing irrigation management, particularly for deficit irrigation practices in arid climates. https://jwim.ut.ac.ir/article_103501.html Precipitation forecasting is of great importance due to its impact on agriculture, natural disaster management, and water supply. Therefore, in this study, the monthly precipitation of the Telezang station from 1966 to 2020 was modeled using the Variable Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Input data were defined for the Support Vector Machine (SVM) model based on four scenarios. In the first scenario, monthly precipitation values with up to four lags were considered as model inputs. In the second scenario, in addition to the lagged precipitation data, a periodic term was added to the input patterns of the model. In the third and fourth scenarios, the monthly precipitation data were decomposed using CEEMD and VMD, respectively, and provided to the model. The findings of this research indicated that adding the periodic term slightly improved the model’s performance. Additionally, a comparison of the results from the data preprocessing methods using VMD and CEEMD showed that the VMD-SVM model outperformed the CEEMD-SVM model significantly, reducing the MAE index by an average of approximately 35.25 mm compared to the standalone model and 13.77 mm compared to the CEEMD-SVM model, while also achieving greater accuracy. https://jwim.ut.ac.ir/article_103380.html The increase in water demand and the uncontrolled harvesting of groundwater resources in the Qazvin Plain has led to a sharp drop in groundwater levels and adverse environmental consequences. Since 2023, the right market has been established, including productivity markets (selling water from agriculture to industry) and secondary markets (selling water from agriculture to agriculture) in order to increase productivity and protect groundwater resources in the province. The study was conducted to identify factors affecting supply, demand and supply function estimation, demand function and water equilibrium price estimation. Using data on traded water volume, selling price, production costs, well specifications, irrigation technologies, annual water share, cultivated area, storage coefficient, farmer income, and temperature from July 2023 to December 2024, supply and demand functions were estimated using the ordinary least squares regression method. The price coefficient in the estimated demand function is equivalent to -0.663 and in the supply function it is equivalent to -2.091. The equilibrium price of water in the secondary market was estimated at 4682 tomans and the results of the sensitivity analysis showed that the equilibrium price is highly sensitive to temperature , as well as the estimated equilibrium price in the water market is higher than the penalty for excessive water withdrawal in this plain. This can lead to a decrease in farmers' willingness to purchase water from the secondary market and an increase in unauthorized groundwater extraction. Therefore, it is recommended to revise the regulations regarding penalties for excessive water withdrawal from authorized wells. https://jwim.ut.ac.ir/article_104310.html In this study, the crop evapotranspiration (ETc) of greenhouse cucumber was investigated under controlled greenhouse conditions during two growing periods: autumn – winter 2022 and spring – summer 2023, at the College of Agriculture and Natural Resources, University of Tehran. The objective was to model and accurately predict ETc. Daily soil moisture was measured using TDR sensors installed at a depth of 0–30 cm and the soil water balance equation was applied. Reference evapotranspiration (ETo) was measured using a grass micro-lysimeter with an accuracy of 2 grams. The input data for modeling included 10 meteorological variables recorded during the growing period: days after transplanting, minimum, maximum, and average temperature, minimum, maximum, and average relative humidity, solar radiation, and reference evapotranspiration. Pearson correlation analysis revealed that days after transplanting, average temperature, and solar radiation had the strongest positive correlations with ETc. To predict ETc, six machine learning algorithms were implemented in Python: Principal Component Regression (PCR), Partial Least Squares Regression (PLS), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB). Hyperparameter optimization was conducted using the Tree-structured Parzen Estimator (TPE) algorithm from the Optuna library with 200 iterations. Model performance was evaluated based on R², RMSE, MAE, and NSE metrics using five-fold cross-validation with three repetitions. The modeling results indicated that the GB algorithm achieved the highest accuracy and performance, with average R², RMSE, MAE, and NSE values of 0.90, 0.59 mm/day, 0.41 mm/day, and 0.89, respectively. Following GB, the XGB, RF, and SVM models also performed well, with no statistically significant differences compared to GB. SHAP analysis, used as a model interpretability tool, revealed that days after transplanting, reference evapotranspiration, and solar radiation were the most influential features in predicting ETc. Overall, the results demonstrated that tree-based machine learning algorithms can serve as accurate tools for forecasting the water requirements of greenhouse cucumber and can play a key role in optimizing irrigation management and improving water use efficiency. https://jwim.ut.ac.ir/article_104269.html Groundwater recharge in the Marvdasht aquifer was estimated using two approaches: the water table fluctuation (WTF) method and unsaturated zone flow simulation with the HYDRUS-1D model. In the first approach, recharge was calculated based on variations in groundwater level and specific yield, whereas in the second approach, it was estimated using soil column hydraulic properties within the unsaturated zone. The average recharge for the entire study area was estimated at 291.33 million cubic meters using HYDRUS-1D and 204.68 million cubic meters using the WTF method, indicating a difference of 29.7%. This discrepancy can be attributed to the fundamental differences in the assumptions underlying the two approaches. HYDRUS-1D simulates infiltration and recharge dynamically by solving Richards’ equation and considering the soil hydraulic parameters and boundary conditions, while the WTF method indirectly estimates recharge based on groundwater level fluctuations and specific yield, and is therefore sensitive to the accuracy of these observations. Zonation maps revealed that both methods produced similar spatial recharge patterns. Moreover, analysis of water infiltration delay through the unsaturated zone indicated that the lag time varied between 30 and 730 days, depending on soil texture and the thickness of the unsaturated zone. It is recommended that, where data availability permits, multiple methods be applied to assess groundwater recharge, thereby obtaining more reliable estimates of this critical parameter and supporting sustainable management and optimal utilization of groundwater resources. https://jwim.ut.ac.ir/article_104270.html Rivers, as one of the main sources of water supply, play a vital role in meeting environmental, agricultural, and  human needs. Assessing the water quality of these resources is essential for sustainable management and reducing environmental risks. Selecting an appropriate index for evaluating surface water quality is one of the  key challenges in water resources management. In this study, in order to compare three widely used water  quality indices, the Iranian National Water Quality Index (IRWQI), the National Sanitation Foundation Water  Quality Index (NSFWQI), and the Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI), data collected from the Haraz River (nine stations during two seasons: winter and summer)  were used as a case study. The results showed that all three indices were able to classify water quality  relatively well; however, differences were observed in their sensitivity and discriminatory capability, with the NSFWQI generally providing more optimistic results. Furthermore, Response Surface Methodology (RSM)  was employed to analyze the influence of different parameters on each index and to optimize water quality  modeling. The findings indicated that in summer, nitrate, phosphate, dissolved oxygen, and ammonium were  the most influential parameters, accounting for a total of 83%, while in winter, biochemical oxygen demand,  chemical oxygen demand, total hardness, and pH were the most influential, accounting for 82%. Water quality  based on the NSFWQI was lower in summer than in winter; during winter, all stations fell within the medium  to good categories, whereas only about 34% of the stations were in this range in summer. A decreasing trend in  water quality along the river was also observed in both seasons. This study demonstrates that the choice of  index can significantly affect the interpretation of water quality status, and that integrating indices with  statistical methods such as RSM can provide an effective tool for surface water quality management in other rivers. https://jwim.ut.ac.ir/article_103275.html The unstable condition of water resources in Iran has made it essential to fundamentally revise the policies and governing regulations of water resource management. This article examines the approach of the Seventh Five-Year Development Plan of the Islamic Republic of Iran toward water data and information, analyzing the role of such data in effective water governance. The objective of this study is to evaluate the contents of Chapter Eight of the law, with a focus on data-driven water resource management, improving efficiency, protecting aquatic ecosystems, and enhancing transparency. The plan emphasizes the establishment of a national water monitoring and accounting system, the use of smart tools, the development of advanced remote sensing technologies, and the generation of accurate water resource and consumption balance sheets. Moreover, the role of data in policymaking, tariff design, water diplomacy, and local participation through water users’ associations is specifically highlighted. The findings indicate that the successful implementation of this law requires technical infrastructure, inter-agency coordination, skilled human resources, and continuous oversight. Ultimately, while the law represents a significant step toward smart and sustainable water resource management, achieving its goals depends on overcoming operational challenges, securing financial resources, and adopting modern technologies for data collection and analysis. https://jwim.ut.ac.ir/article_104254.html Due to the increasing limitations of water resources, knowing the amount and distribution of moisture in subsurface irrigation is very important in irrigation management. This research was carried out with the aim of investigating sub-irrigation planter (SIP) as one of the suitable methods of irrigation. In this study, two different cultivation media (soil-cocopeat-perlite and cocopeat-perlite) and two depths of the media in the pot (30 and 50 cm) and three water salinity levels of 1.2, 3.5, and 5 dS.m-1 were used. The results showed that there is a significant difference in the wet and dry performance of basil plants between the two irrigation methods at the level of 1 percent and 5 percent. However, the yield values decreased significantly with increasing water salinity. Also, the results showed that the productivity of SIP and surface irrigation were significantly different from each other at the level of 1 percent. The results showed that in the SIP system, the highest salinity is in the surface layers of the soil, while in surface irrigation, the highest salinity is in the bottom layer. The results of investigating the soil moisture in different layers of the substrate showed that the soil moisture in both irrigation methods increases with the increase in the depth of the substrate and at the depth of the substrate, the amount of moisture in SIP is much higher than surface irrigation, and the highest soil moistures were obtained in the deepest layers of both cultivation depths 30 and 50 cm in SIP. 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. https://jwim.ut.ac.ir/article_103210.html Flood Routing is one of the fundamental topics in water resources system management and flood control engineering. The Muskingum model is among the most well-known and widely used hydrological routing methods. In the nonlinear Muskingum method, three parameters must be estimated: the storage coefficient (K), the weighting factor for inflow and outflow (χ), and the exponent of the storage term (m). In contrast, the linear Muskingum method only involves the first two parameters (K and χ), making it simpler with one less variable. This research focuses on presenting a simple graphical method for estimating the parameters of the nonlinear Muskingum model. To assess the accuracy and reliability of the proposed graphical method, results were compared with those obtained from Excel's SOLVER tool, which is considered a more precise technique. The graphical method was applied to three flood events. The results showed that the parameters derived using the proposed method were very close to those estimated using SOLVER. Notably, across all flood events, both the performance criteria and hydrographs indicated that the graphical method for the nonlinear Muskingum model outperformed its linear counterpart in terms of accuracy. Furthermore, comparisons between observed peak discharge and the estimated peak discharge revealed that, in all cases, the nonlinear Muskingum model provided values closer to the actual recorded data than the linear model. For instance, the Nash-Sutcliffe Efficiency (NSE) values obtained for the Wilson, Wye, and Karun flood events were as follows: Wilson Flood: 0.19 (linear graphical), 0.35 (nonlinear graphical), 0.96 (SOLVER); Wye Flood: 0.86 (linear graphical), 0.95 (nonlinear Graphical), 0.97 (Solver); Karun Flood: 0.62 (linear graphical), 0.98 (nonlinear graphical), 0.99 (SOLVER). https://jwim.ut.ac.ir/article_103370.html Water resources management in arid and semi-arid regions is confronted with challenges such as limited availability, environmental demands, and socio-economic pressures. Achieving a balance between environmental needs, social equity, and economic efficiency is of critical importance. This study simulates and evaluates the performance of various scenarios for supplying environmental water demands and cropping patterns downstream of the Karkheh Dam, using economic, social, and water resource indicators. The 22-year operational period of the Karkheh reservoir (2001–2023) was selected as the study timeframe. In this research, various water supply scenarios downstream of the Karkheh Dam were examined through simulation of agricultural and environmental water demands. To assess the effect of cropping patterns on water resource evaluation criteria, three hydrological years—dry (2017–2018), normal (2020–2021), and wet (2019–2020)—were considered as minimum, normal, and maximum conditions, respectively. Environmental demand satisfaction was analyzed under three scenarios: optimal, favorable, and moderate. The results indicated that in the minimum cropping pattern, due to the smaller cultivated area, the supply reliability and social equity indicators showed more favorable outcomes. In the optimal environmental scenario, supply reliability decreased from 100 percent to 76.33 percent, and water deficit increased from 0 to 20.32 percent. However, the economic value was approximately 2,560 trillion IRR lower than in the maximum cropping pattern. Under the same scenario with maximum cropping, supply reliability dropped to 53.91 percent and the deficit rose to 42.04 percent, with increased social inequality. Despite greater stress, the economic value reached 7,441.94 trillion IRR. The findings highlight the necessity of balancing environmental requirements, economic productivity, and social justice. https://jwim.ut.ac.ir/article_103424.html Environmental considerations should be a priority for project implementers. Environmental impacts are one of the key pillars of sustainable development. This study examines the environmental assessment of the irrigation and drainage network in Varamin, located in Tehran Province. To achieve the research objective, significant positive and negative impacts resulting from the project implementation were identified and predicted using the ICOLD matrix. The environmental impact assessment matrix for the implementation option of the Varamin drainage network, along with the cumulative effects, is presented. The implementation option was evaluated in three phases: construction, operation, and post-impact mitigation measures. Of all the impacts, 32.88% pertained to the physical environment, 27.76% to the natural environment, and 39.36% to the socio-economic environment. The most significant positive impacts resulting from the implementation of the Varamin drainage network include 14 effects. After mitigation measures were taken, 2 effects were observed in the physical environment, 2 effects in the natural environment, and 2 effects in the socio-economic environment. https://jwim.ut.ac.ir/article_104479.html Given the increasing and uncontrolled depletion of groundwater resources, rethinking the design of necessary management measures to maximize the potential of collective action among users has become an inevitable necessity. This study, addressing the absence of foresight-based and systematic analytical approaches, utilizes the Social–Ecological Systems Framework and the assumptions underlying self-organization of users to analyze how policy interventions impact ten key variables influencing the capacity of farmers for collective action in the Qaresou Basin. To assess these effects, causal relationships between the policy interventions outlined in Iran’s national document on water scarcity adaptation and the key variables were examined. The findings indicate that during the 18-year study period, in the absence of collective action and purposeful alignment between water and agricultural policies, social sustainability consistently posed a threat to ecological sustainability. In the tightly interwoven social–ecological system of the Qaresou Basin, this requires an analysis of the cumulative impact of interventions on policy-responsive variables including “user knowledge”, “collective-choice rules”," “local leadership”, “institutional trust”, and “system productivity” and contextual variables such as “resource importance”, “groundwater mobility”, and “the number of farming households”. This study provides a practical basis for assessing the implementation of participatory water management, in accordance with clause “T”, article 40 of the law of the seventh five-year development plan of the Islamic Republic of Iran. https://jwim.ut.ac.ir/article_104480.html Food insecurity is a major global development challenge, requiring investment in agriculture, poverty reduction, and conservation of natural resources to ensure universal access to food. This study aimed to identify and analyze the linkages between key food and water security components. The target population included experts in food and water security, particularly faculty members and practitioners, with 15 participants selected through purposive sampling. Data were collected using a questionnaire, validated by agricultural management and development experts at the University of Tehran, and its reliability was confirmed through theoretical saturation. The DEMATEL technique was used for data analysis. Results revealed that the sustainability of water access had the greatest impact on other water and food security components, while food consumption had the least influence. Food availability was identified as the most influenced component, whereas economic capacity for water supply was the least influenced. The sustainability of food access was highlighted as the most critical factor for improving food security. The study emphasizes sustainable natural resource management, investment in innovative technologies, and increased resilience to climate change to enhance water and food security. Additionally, economic policies should be designed to improve the economic capacity for water and food access, particularly for low-income groups. These measures can foster more sustainable and equitable systems in water and food security. https://jwim.ut.ac.ir/article_104571.html This study aims to identify and analyze the key factors influencing water productivity in agriculture through the application of the Fuzzy Delphi Method.Through a systematic literature review, 194 factors were extracted and categorized into three domains: climate change (43 factors, 22.1 Percent), policy (77 factors, 39.6 Percent), and technology (74 factors, 38.2 Percent). These factors were incorporated into a semi-structured questionnaire distributed to 18 experts. Applying a consensus threshold of 0.8 in the fuzzy Delphi model, the data were fuzzified, aggregated, and defuzzified. Findings revealed that 32 factors (16.5 Percent) were selected as consensus-based key determinants. Among them, 18 factors were from the policy domain (23.4 Percent), 10 from the technology domain (13.5 Percent), and 4 from the climate change domain (9.3 Percent). The highest defuzzified value corresponded to "hybrid seed production" (0.92), highlighting the significance of biotechnological innovations in enhancing water productivity. Other consensus-driven factors included "promotion of genetic improvement" (0.90), "establishment of national Based on the research findings, two controllable pillars—data-driven infrastructure and technological innovations—are recommended as the most effective factors for enhancing agricultural water productivity. https://jwim.ut.ac.ir/article_104613.html This research investigates the variations in the distribution and quantity of winter precipitation at 18 stations located in the Dez River Basin, western Iran, during the period 1975-2024. The results from the modified Mann-Kendall test indicate that 50% of the stations (9 stations, including Takht Dareh, Keshvar, and Tang Pang Bakhtiari) have experienced a significant decreasing trend at the 5% level. According to the Sen's slope estimator, the most severe decreases (more than 3 mm per year) occurred at the Tang Pang Bakhtiari, Telezang, and Keshvar stations, which collectively led to a reduction of more than 150 mm over the study period. Temporal changes were primarily concentrated around two points: 1990 and 2006, indicating the influence of large-scale climatic factors. Furthermore, the analysis of the statistical distribution of data before and after the change points revealed a change in the distribution type (Changing from Weibull to Log-Normal based on Kolmogorov-Smirnov statistics) and significant changes in statistical indices such as variance and skewness at many stations. These findings are consistent with the predictions of IPCC reports and other regional studies and demonstrate that the decrease in winter precipitation is a large-scale challenge with serious implications for the region's water resources. The results also indicate changes in the form of the distribution function and severe changes in skewness in the sub-interval after the change point. https://jwim.ut.ac.ir/article_104616.html The inherent complexity of tidal flows poses significant challenges for accurate analysis and prediction, necessitating the use of advanced analytical and numerical models. In this study, the capability of the two-dimensional hydraulic model HEC-RAS 2D was evaluated for simulating the hydrodynamics of tidal currents in the Musa Estuary, located in the northern Persian Gulf. The main objective was to assess the model’s accuracy in reproducing the temporal and spatial patterns of flow velocity and water surface elevation in a highly complex estuarine system. For the simulation, water level and flow velocity data were collected from four hydrodynamic stations over a 44-day period, covering two complete tidal cycles. The model geometry was generated by integrating local hydrographic survey data with the 30 m Copernicus digital elevation model, and further processed in a GIS environment with a horizontal resolution of 20 m. To enhance numerical stability and ensure better conformity of the mesh, the topological mesh-generation system of the latest HEC-RAS 2025 version was used. After calibration, the model demonstrated high accuracy in reproducing tidal behavior, with performance indices within an acceptable range (NSE = 0.94, RMSE = 0.079, MAE = 0.61). The depth and velocity distribution maps also showed good agreement with the actual dynamics of the system. Overall, the findings indicate that HEC-RAS 2D, particularly when using the advanced features of its latest release, can serve as a reliable and efficient tool for simulating hydrodynamic processes in tidal environments such as the Musa Estuary. However, some limitations were identified in simulating flow velocity near the estuary entrance, which may be due to the incomplete representation of external forcing effects. https://jwim.ut.ac.ir/article_104785.html In the arid and semi-arid climatic conditions of Iran, optimal water resource management is of paramount importance. Accurate prediction of river flow discharge serves as an effective strategy in this regard, playing a key role in dam operation planning. This study aimed to simulate flow discharge downstream of the Maijaran Dam in Mazandaran Province, using monthly discharge data from the Maijaran Dam hydrometric station spanning the period 2007 to 2022. Following preliminary analyses—including normality and stationarity tests—and decomposition of the data into deterministic and stochastic components, the stochastic part of the time series was selected for modeling. Various time series model structures were evaluated using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). Among the candidate models, the ARMA model was identified as the most suitable. Model orders were determined using ACF and PACF analyses, and model performance was assessed using the Akaike Information Criterion (AIC) and the coefficient of determination (R²). Results indicated that the ARMA(3,2) model, with an AIC value of 144.06 and R² of 0.79, outperformed other models and provided acceptable accuracy in flow discharge simulation. The findings demonstrate the high efficacy of stochastic ARMA models in simulation of hydrological time series in data-scarce regions, offering a reliable tool for supporting water resource management decisions. https://jwim.ut.ac.ir/article_104812.html The background and objective of this study relate to the significant hydrological and ecological role of Shadegan Wetland in maintaining the natural functions of the Persian Gulf. The wetland hosts a wide variety of aquatic plants and animals and is recognized as the most important breeding site of the marbled duck in the world. The aim of this research is to identify an appropriate balance in water allocation to the wetland for the conservation of its ecological functions, as well as to upstream agricultural lands to improve agricultural productivity. Accordingly, the main objective is to determine and calculate the water requirement of Shadegan Wetland based on vegetation monitoring variables. The methods employed include the use of the Google Earth Engine platform and Landsat 7 and 8 satellite imagery. With these tools, NDVI and NDWI indices for the study area were calculated. The results showed that the annual water requirement of Shadegan Wetland was estimated at three levels, 821, 1652, and 2887 million cubic meters per year. In addition to receiving water from the Jarahi River, the wetland also obtains part of its water from the Ramhormoz River, which accounts for 62 percent of the total supply. Therefore, the three levels of water requirement were adjusted to 509, 1024, and 1789 million cubic meters per year, respectively. These correspond to an economic value of 1741, 2273, and 2568 billion Tomans at levels one, two, and three, respectively. Vegetation monitoring further revealed that the average annual green surface area of the study region decreased from 484 km² in 2002 to 216 km² in 2018. 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_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_104875.html Water, as a vital resource in sustainable agriculture, plays a key role, especially in arid regions such as the central district of Mahneshan County. This descriptive-analytical study with a correlational approach examined farmers' perception of the economic value of water and its impact on the selection of irrigation methods. The statistical population consisted of 1,451 farmers from 14 selected villages out of 56, with 306 farmers chosen using random cluster sampling and Cochran’s formula. Data were collected through a questionnaire with content validity and reliability (Cronbach’s alpha 0.79) and analyzed using Pearson/Spearman correlation coefficients, multiple regression, and analysis of variance. The findings revealed that 64.4% of farmers use traditional methods, 13.7% use modern methods, and 21.9% use a combination of both. Modern methods, with lower water consumption (4,200 m³/ha) and higher yield (3,900 kg/ha), demonstrated greater efficiency. The perception of the economic value of water (mean score of 4.10 for understanding water’s importance) and education (β=0.35) significantly influenced the adoption of modern methods, while financial barriers (72.3%) and infrastructural constraints (58.7%) hindered their adoption. The overall conclusion suggests that enhancing awareness, education, and supportive policies can contribute to improved water management and agricultural sustainability. https://jwim.ut.ac.ir/article_104876.html Optimizing water management in irrigation networks, especially in arid and semi-arid regions, is of critical importance. This research aimed to design an intelligent control system for surface water conveyance canals. To this end, an integrator-delay linear model was first developed, capable of simulating the canal's hydraulic behavior. Subsequently, an innovative hybrid control system based on the integration of a classic PID controller and a continuous reinforcement learning agent was designed and implemented. The controller gains were set to 0.209, 0.243, and 0.086 via manual tuning, and to 1.69, 0.055, and 0.086 respectively when tuned by reinforcement learning. Performance evaluation under scenarios with 10%, 20%, and 30% flow changes demonstrated that the RL-tuned controller has significantly superior stability and accuracy compared to the manually tuned controller. The most important indicator of this superiority was a five-fold reduction in the maximum depth error for the 10% input change. The proposed system represents an effective step towards intelligent water management in irrigation canals; however, its application under critical conditions requires integration with more complex non-linear models, which is suggested for future research. 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_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_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, vertically, and water resources development plans in the upstream of the basins, horizontally, change the balance of water resources distribution and increase the water-shortage and conflicts between stakeholders. The interaction of water resources systems with socio-economic and environmental systems means that management to water resources has effects on the aforementioned systems. Therefore, this study aims to study the impact of climate change on sources and uses of surface water in the Sefidroud Dam and Anzali Wetland basins then study changes of inflow to Anzali Wetland by using system dynamics. Consequently, precipitation data of CMIP6 project was selected in climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 from 2017 to 2040. The subsystems of the Sefidroud Dam & Anzali Wetland basins and a part of the Sefidroud irrigation-drainage network were simulated by a system dynamics approach by using VensimPLE software. According to the findings, in the future period within the RCP scenarios a significant increase will occur in the annual average of the precipitation, between 1.2 to 4.1 percent in the Sefidroud Dam basin and between 0.8 to 15.6 percent in the Anzali Wetland basin. As well, in regard to the role of the significant second six-months of water year on the irrigation network, the average discharge of rivers entering the wetland basin in the second six-months of water year will decrease 16.3 to 32.9 percent. Lastly, the annual average inflow discharge to the Wetland will decrease between 3.1 to 16.3 percent. 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_105799.html رودخانه‌ها به‌عنوان یک حلقه اساسی در چرخه هیدرولوژیکی عمل می‌کنند و جریان آن‌ها همواره پایدار و مداوم است. مدل‌سازی جریان‌های رودخانه‌ای به دلیل وجود عدم‌قطعیت‌های ذاتی در داده‌های ورودی و پارامترهای هندسی، به چالشی پیچیده در تحلیل حساسیت و ارزیابی منابع خطا تبدیل شده است. یکی از مهم‌ترین منابع عدم‌قطعیت، خطاهای اندازه‌گیری و تعریف مقاطع عرضی رودخانه است که می‌تواند اثر مستقیمی بر نتایج مدل‌های هیدرودینامیکی داشته باشد. در این مطالعه، به منظور بررسی نحوه انتشار عدم‌قطعیت در شبیه‌سازی جریان هیدرولیکی، از چارچوب شبیه‌سازی مونت‌کارلو استفاده شد. عدم‌قطعیت‌ها در سه مثال کاربردی شامل دو رودخانه‌ی واقعی و یک رودخانه فرضی تحلیل شدند. سناریوهای شبیه‌سازی بر اساس توابع توزیع احتمال نرمال و یکنواخت با خطاهای تصادفی ۱۰درصد و ۲۰ درصد و همچنین خطاهای سیستماتیک صفر و ± سه درصد در داده‌های مقاطع تعریف شدند. نتایج نشان دادند که خطاهای تصادفی با توزیع یکنواخت بیشترین پراکندگی را در نتایج جریان ایجاد می‌کنند و افزایش دامنه خطا در داده‌های هندسی مستقیماً منجر به افزایش واریانس خروجی‌های مدل می‌شود. در مقابل، تأثیر خطاهای سیستماتیک بر خروجی‌ها نسبت به خطاهای تصادفی کمتر است، که نشان‌دهنده‌ی حساسیت بالای مدل به تغییرات تصادفی داده‌های هندسی می‌باشد. یافته‌های این پژوهش می‌تواند در ارتقاء دقت مدل‌های هیدرودینامیک و تفسیر نتایج آن‌ها مورد استفاده قرار گیرد. همچنین این نتایج برای مدیریت منابع آب، طراحی سازه‌های هیدرولیکی و اتخاذ تصمیم‌های مبتنی بر ریسک در حوزه مهندسی رودخانه کاربرد عملی خواهد داشت. https://jwim.ut.ac.ir/article_105846.html In order 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_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 AdjR² 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_105980.html Optimal resource management in greenhouses requires quantitative and integrated approaches. This study presents a modeling-optimization framework to identify optimal operational configurations within the Water-Energy-Food-Ecosystem (WEFE) nexus. Field data from ten greenhouses in the Pakdasht region (40 annual observations from 2021-2024) were collected. First, a semi-empirical parametric model was developed and calibrated using this data to establish quantitative relationships between key decision variables (the share of water treated by Reverse Osmosis (RO) and the share of renewable energy) and five WEFE objective functions: minimizing water consumption, energy use, CO₂ emissions, and total cost, while maximizing a water quality index. Subsequently, a multi-objective genetic algorithm (NSGA-II) was executed on this calibrated model to extract the full Pareto fronts of optimal solutions. Finally, the TOPSIS multi-criteria decision-making method was employed to select the ultimate operational point from among the Pareto solutions. The optimization results identified a specific operational configuration utilizing RO for approximately 65 percent of the water flow and supplying 25 percent of energy from renewable sources as achieving the best trade-off (TOPSIS score: 0.7854) among the conflicting objectives. Compared to a conventional baseline, This optimum point, compared to a conventional baseline, resulted in a 24 percent reduction in water consumption, 20 percent in energy consumption, 33 percent in operating costs, and a 30 percent improvement in water quality. These findings demonstrate the strategic priority of increasing resource efficiency through technologies such as RO over simply expanding the clean energy portfolio and justify the “efficiency first, renewables then” strategy in similar regional conditions. This study provides a powerful tool for evidence-based, quantitative decision-making in sustainable agricultural management.