JWSS - Journal of Water and Soil Science

This research focuses on evaluating the efficiency of constructed wetlands in treating municipal wastewater using two plants: vetiver (Chrysopogon zizanioides) and common reed (Phragmites australis). Given the increasing pollution of water resources and water scarcity in Iran, the application of nature-based solutions (NBS), particularly constructed wetlands, is a crucial approach for effective wastewater management and treatment. This study concentrates on the wastewater from the treatment plant of Isfahan University of Technology and, over a period of six months, assesses the impact of four different treatments in a completely randomized design: 1) wetland planted with vetiver (V), 2) wetland planted with reed (N), 3) control wetland without plants (B), and 4) control wetland without plants but with a supporting substrate (P), on chemical parameters of wastewater and plants. The measured parameters include BOD₅, COD, nitrate, and phosphate. Results indicated the highest levels of BOD₅ and COD in the control treatments (without plants) and a significant reduction in these parameters in the treatments planted with vetiver and reed. The best removal performance for these two parameters was observed in the sixth month at a hydraulic retention time of 30 days, with reductions of 67% and 65% for BOD₅ and 85% and 84% for COD in the vetiver and reed treatments, respectively. In the sixth month, at a retention time of 15 days, nitrate levels decreased by 25% and 34% in the vetiver and reed treatments, respectively, and by 39% and 59% at 30 days retention time. These differences were statistically significant at the 5% level for both retention time and plant type. Phosphate reductions in the sixth month at 15-day retention were 65% and 81% in vetiver and reed treatments, respectively, and at 30 days, 82% and 87%, with these decreases being statistically significant for both retention times and plant types at the 5% level. Retention time results showed that the reduction of BOD₅ and COD is directly related to retention time, with longer retention times yielding higher removal percentages. Regarding nitrogen and phosphorus, the reed demonstrated the highest performance, effectively reducing these pollutants. The total nitrogen uptake in the shoots and roots of the reed after 30 days was 33.4 and 22.51 mg/kg dry plant matter, respectively, indicating the high capacity of the reed for nitrogen absorption from wastewater. This study demonstrates that planting vetiver and reed can serve as sustainable solutions for improving water quality and effective water resource management in Iran

The present study was conducted with the aim of quantitative and qualitative analysis of agricultural water consumption in Markazi Province, and calculated and examined water consumption at the level of 18 crops and 12 counties using the water footprint as a comprehensive indicator. A simultaneous study of the three components of the blue, green, and gray water footprint was conducted as an analytical tool to assess the amount and manner of water consumption. In this study, meteorological, agricultural, and input consumption data were used in the 2022-2023 crop year, and water footprint values were estimated in terms of units and totals by crop and county. The results showed that BWFU is strongly influenced by spatial factors (climate and precipitation) and plant characteristics (yield, crop type, and growth period). A difference of up to 98% in BWFU among different crops and a difference of more than 9 times in GWFU in rainfed compared to irrigated lands were observed. Also, GRWFU values exceeded BWFU for many crops, indicating a significant pollutant load from the use of chemical fertilizers. In addition to spatial factors and plant characteristics, the difference of 223 MCM between Saveh and Ashtian counties and the difference of 52.7 MCM between Shazand and Mahallat counties in BWF and GWF, respectively, indicate spatial differences in BWFU and cultivation area. Also, the difference in 1377 MCM between the GRWF of Arak and Ashtian counties is affected by the amount and type of fertilizer used, in addition to the cultivation area. In addition to improving performance, suggested management measures include reducing the cultivation area of high-consumption crops, expanding rainfed lands in high-rainfall areas, optimizing input consumption, and modifying the cultivation pattern in accordance with resources and climatic conditions in order to maintain the quantity and quality of water resources. Accordingly, the research results demonstrate the potential of the water footprint index in location-based and product-based analysis of water consumption and formulation of management responses.

Climate change significantly affects the water use efficiency (WUE) and yield of field crops. This study evaluates the impacts of climate change on biological yield, grain yield, water consumption, and WUE of two barley genotypes, Goharan and Reyhan 03, under autumn and spring planting regimes using the CERES-Barley model within the Decision Support System for Agrotechnology Transfer (DSSAT) software. Data provided for model calibration and validation were sourced from the field experiments conducted at the Isfahan University of Technology research farm located in Najafabad, Iran. Meteorological data for the period of 2003 to 2016 were obtained from the Najafabad weather station, while future climate projections for 2020–2050 were generated using the MarkSim weather generator under the Representative Concentration Pathway (RCP) 8.5 scenario. Planting dates were analyzed within a ±35-day window relative to baseline dates of October 22 for autumn and March 3 for spring. The model demonstrated high accuracy in calibrating key traits, including days to anthesis, days to maturity, leaf area index, grain yield, and biological yield. Elevated temperatures associated with climate change reduced grain and biological yields across both planting seasons, with biological yield exhibiting a more pronounced decline, particularly under spring planting. During the 2040–2050 period, water consumption peaked at 387.5 mm for Goharan in autumn planting, while spring planting recorded a minimum of 239 mm for Reyhan 03. Delaying autumn planting by 20–25 days enhanced WUE, while planting earlier in the spring )10–20 days (improved WUE by exploiting cooler temperatures. Evapotranspiration increased by 399 mm in autumn but decreased by 267 mm in spring. The earlier-maturing Reyhan 03 genotype demonstrated smaller yield losses in spring planting due to climate change. The findings of this study suggest that programmed adjustments to planting dates may mitigate the adverse impacts of climate change on barley production, thereby enhancing sustainability.

Water scarcity has made the agricultural water footprint a critical measure for sustainable resource management, particularly in water-stressed regions such as Iran. This index depends on various factors, including climate, crop yield, dietary habits, and irrigation/agricultural efficiency, which can be estimated more rapidly using modeling approaches. The SSM-iCROP2 model is a simulation model that has been parameterized and evaluated for over 30 crop species in Iran and has been widely used in studies related to crop yield. Since sugar is a key energy source in the food basket, sugarcane occupies vast cultivated areas in the country. Sugarcane is primarily grown in Khuzestan province. This study aimed to apply the aforementioned model to estimate the blue and green water footprint of this strategic crop, using upscaling methods for both potential and farmer-managed conditions from 1992 to 2022. The results showed that the total water footprint of sugarcane (sum of blue, green, and gray water footprints) was 2,251 and 3,134 cubic meters per ton for potential and actual (farmer) conditions, respectively.

This study aimed to evaluate the effectiveness of iron (Fe) aminochelate application methods on Fe chemical speciation in the soil solution, as well as Fe concentration and uptake in sunflower seeds (Helianthus annuus L. cv. Oscar). The experiment was conducted in a randomized complete block design with three replications at the research field of Shahid Chamran University of Ahvaz. Treatments included two application methods (seed priming and fertigation) and three Fe sources: Fe–glycine aminochelate [Fe(Gly)₂], Fe–methionine aminochelate [Fe(Met)₂], and ferrous sulfate (FeSO₄·7H₂O), along with an unfertilized control. Fe speciation was determined using Visual MINTEQ software. Results indicated that Fe aminochelates, [Fe(Met)₂], significantly decreased soil pH and increased DTPA-extractable Fe (by 35.7%), seed Fe concentration (by 13.5%), and seed Fe uptake (by 79.1%) compared with the control (p < 0.01). Application of Fe fertilizers also significantly enhanced the concentrations of dominant Fe species (Fe²⁺ and FeSO₄(aq)) in the soil solution, with the highest Fe²⁺ level (3.1-fold higher than the control) observed under [Fe(Met)₂] seed priming. Strong and significant positive correlations between Fe²⁺ and FeSO₄(aq) concentrations and both DTPA-extractable Fe (r = 0.88** and r = 0.89**, respectively) and seed Fe uptake (r = 0.84** and r = 0.87**, respectively) highlight the pivotal role of these species in improving Fe bioavailability and uptake by plants in calcareous soils.

Rapid urbanization and the expansion of impervious surfaces in urban areas can cause a reduction in infiltration rate, which consequently increases the flash floods and surface runoff in cities. In recent years, the use of bio-infiltration systems has been considered as one of the most effective approaches based on low-impact development (LID) for sustainable urban runoff management. In this study, the performance of six types of biological infiltration basins was investigated to reduce the volume of runoff and improve surface water management in the eastern region of Qazvin city. First, 40 years of rainfall data (1983–2023) were collected from the Qazvin meteorological station, and Intensity–Duration–Frequency (IDF) relationships were developed for various return periods. Six design scenarios were modeled: bioretention basins with and without a drainage system; tree boxes with and without a drainage system; infiltration trenches; and permeable pavements. The dimensions of all systems were kept constant to focus solely on hydrological performance without the influence of size or shape. Overall, using HEC-GeoHMS, SWMM, and MIDS models together offered a detailed and accurate framework for analyzing the hydrological behavior of bioretention systems in urban runoff management. Results showed that the runoff coefficients for the sub-basins averaged 0.79, highlighting the dominance of impervious surfaces in the area. These values were used as inputs for the MIDS model to simulate the six different bioretention scenarios. The results indicated that the permeable pavement scenario had the greatest effect on annual runoff reduction (about 728,555 m ³), while the bioretention cell with a drainage system had the lowest performance. SWMM results, based on DEM-derived sub-catchment data, showed low soil infiltration and high impervious surface coverage. These conditions highlighted the importance of bioretention systems in reducing urban flooding. Overall, the study demonstrates that well-planned bio-retention and other green infrastructure can decrease peak flows, increase time of concentration, and improve urban hydrological and environmental conditions.

Soil contamination by metals is a major problem in terrestrial ecosystems, particularly in and around industrial areas, and can pose a threat to human health in various ways. Therefore, in the present study, the bioaccumulation of heavy metals in the earthworm (Eisenia fetida) in soil around the Abik Cement Factory in Alborz Province was evaluated. Sixty soil samples were collected at depths of 0-20 cm at distances of 500, 1000, and 2000, in the directions of north, south, east, and west. Earthworm samples were also randomly collected from these distances and directions. The wet digestion method was employed to determine heavy metal concentrations in soil and earthworms, and Zn, Cu, Pb, Cr, and Ni were measured using an atomic absorption spectrometer. One- way ANOVA and Kruskal- Wallis statistical methods were used to examine the effects of distance and direction. The bioaccumulation factor (BAF) was used to evaluate the ability of earthworms as a biological indicator. According to the results, the average concentrations of heavy metals in the soil around the lands of the Abik Cement Factory for Cr, Pb, Cu, Zn, and Ni were 22. 20, 10. 19, 6. 34, 7. 63, and 135. 75 (mg/kg), respectively. The average concentrations of heavy metals in earthworms in this region for Cr, Pb, Cu, Zn, and Ni were 51.57. 57, 9. 66, 5. 18, 5. 45, and 106. 17 (mg/kg), respectively. The results indicated that the distance effect was significant only for Ni (p< 0. 05), and the direction effect was significant for all heavy metals except Zn (p> 0. 05). A decreasing trend in the concentrations of Cu, Zn, and Pb was observed with increasing distance from the cement factory, indicating the impact of the Abik Cement Factory on heavy metal pollution in the agricultural soil of the surrounding lands. The highest bioaccumulation factor (BAF) was calculated for zinc and was greater than 1, indicating bioaccumulation of Zn in earthworms. It can be concluded that earthworms can be used as a suitable biological indicator species for monitoring soil contamination with heavy metals, especially for Zn.

This study aimed to optimize the site selection of artificial groundwater recharge zones in the Yazd-Ardakan watershed (covering an area of 116,765 hectares) to address challenges such as water scarcity, severe groundwater depletion, and annual rainfall below 100 mm. The research integrated Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods. Seventeen influential criteria, including elevation, slope, land use, geology, soil type, climate, distance from faults, and isothermal lines were analyzed using ArcGIS 10.5. Criteria weighting was performed using the Best-Worst Method (BWM), and layer integration was achieved through the Weighted Linear Combination (WLC) approach. Results indicated that slope (weight: 0.203), elevation (correlation >0.75), and land use (correlation ≈0.5) had the highest impact on zone suitability, while climate and isothermal lines were less influential. The final suitability map (900×900 m resolution) revealed that central and southern plain areas with slopes <2%, permeable alluvial formations, flat topography, and optimal distance from faults were prioritized for artificial recharge. Sensitivity analysis identified eight key criteria (correlation >0.5), and by eliminating 53% of non-essential parameters, an efficient framework for sustainable water resource management was established. This study not only contributes to raising groundwater levels, improving soil fertility, and preserving local ecosystems but also offers a practical solution for water crisis management in arid regions. 
 

Proper estimation of soil water infiltration parameters and Manning roughness coefficient is one of the influential factors in the correct design and evaluation of surface irrigation systems. The EDOSIM model, as a surface irrigation simulation-optimization model, uses a combination of simulation with the Volume Balance model and meta-heuristic optimization. In the evaluation part of this model, the Elliott-Walker two-point method is used to estimate the parameters of the Kostiakov-Lewis infiltration equation. In this study, the Manning coefficient and parameters of the Kostiakov-Lewis infiltration equation were calibrated in furrow and border irrigation using observational advance data. This was done through three methods: Volume Balance model with constant shape coefficients (VB-CC), a combination of Volume Balance and Zero Inertia models (VB-ZI), and Volume Balance model with variable shape coefficients (VB-VC), using a total of 10 data series of border and furrow irrigation evaluation. Then, the ability to calibrate the infiltration parameters and roughness coefficient using a superior method was added to the EDOSIM model. The results showed that the VB-CC model had the best calibration accuracy and precision in 10 farms, according to the average statistical indices R2=0.998, NRMSE=2.4%, and MBE=-0.06. The VB-ZI and VB-VC methods underestimated the advance length even with calibrated values. The use of the VB-CC model instead of the Elliott-Walker two-point method in the EDOSIM model increased the accuracy of simulation and optimization by reducing (improving) the objective function from an average of 0.34 to 0.13 in border irrigation and from an average of 0.86 to 0.36 in furrow irrigation. Therefore, it is recommended to use the VB-CC calibration method in the EDOSIM model as a powerful tool for optimal operation of surface irrigation systems.

Precipitation use efficiency (PUE) is defined as crop yield per unit of annual precipitation in a region. Proper management of rainfed lands requires investigation of the spatial variation of PUE. In this study, two hundred ninety-eight rainfed lands were investigated in the Khodabandeh region in the south of Zanjan province. PUE was calculated as the ratio of wheat grain yield to precipitation. In addition, climatic characteristics and soil physical properties (particle size distribution and water retention) were measured, along with soil chemical properties (pH, EC, organic matter, calcium carbonate, and macro- and micronutrients) in rainfed soils. Results indicated that the mean PUE in rainfed lands is 4.43 kg mm-1. Higher values were found in the northeast, south, and west regions of the area, although these parts had lower wheat grain yields. Geostatistical analysis showed moderate spatial variation in wheat grain yield and PUE, with patterns associated with climatic variables (precipitation and temperature) and land conditions such as soil properties (particle size distribution, organic matter, and nitrogen). Rainfed lands with fine-textured soils and higher amounts of organic matter and nitrogen had higher PUE. Adjusting tillage direction and using conservation tillage are important strategies to improve organic matter content and soil productivity in rainfed lands.

One of the key factors in agricultural production is the availability of sufficient and usable nutrients for plant growth, and among these, nitrogen plays a particularly important role. The waste of nitrogen fertilizers due to the low efficiency of using fertilizers has caused environmental problems such as pollution of surface and groundwater by nitrate or ammonium. In this study, the influence of fertigation as a combination of urea, ammonium nitrate, natural zeolite of Semnan (CP), and modified zeolite by surfactant (SMZ) in the reduction of waste and manure fertilizer was evaluated. Two separate experiments with a completely randomized design with three replications were used for loam soil columns. Treatments consisted of four levels of zeolite application (0, 4, 8, and 16 g/Kg) and a fertilization level with a concentration of 60 mg per liter nitrate applied at three fertigations during six irrigations. In the first part of the study, the role of nitrogen fertilizers in fertigation as a combination of urea and ammonium nitrate (UAN) for ordinary soil was examined to reduce the concentration of nitrate and ammonium in the drainage water, and the results were compared with one of the fertigation applications. The results showed that the loss of fertilizer in the form of nitrate was reduced by 40 percent for the UAN treatment during the first period of fertigation. Since the results of the first part of the experiment showed that the concentration of ammonium nitrate does not reduce to the same level as drinking water, in the second part of the experiment, the soil amendments were used. The measured nitrate removal showed that the highest nitrate removal belonged to the soil mixed with 16 g/kg of modified zeolite in the surface layer of soil (SM16) treatment, and it was equivalent to 90 percent. The highest ammonium removal belonged to the soil mixed with 16 g/kg of natural zeolite in the surface layer of soil (CP16) treatment, and it was 85 percent. Therefore, the use of fertigation as a combination of nitrogen fertilizers with soil amendments for conditions where there is a potential for groundwater pollution by nitrate leaching is recommended.

Accurate monitoring of water surface dynamics in semi-arid regions poses challenges due to uncertainties regarding the optimal spectral index and sensor selection for effective water resource management. This study assessed the comparative performance of nine spectral water indices across the Landsat-8 and Sentinel-2 platforms to identify the best index-sensor combinations for monitoring semi-arid reservoirs. Utilizing the Google Earth Engine cloud computing platform, 181 satellite images were processed for Golestan Dam in northeastern Iran, comprising 107 Landsat-8 scenes from 2013 to 2023 and 74 Sentinel-2 scenes from 2018 to 2024. After applying atmospheric corrections using the LEDAPS and Sen2Cor algorithms, nine spectral indices (NDWI, MNDWI, ANDWI, AWEI, WI2015, WI1, WI2, LSWI, and NDTI) were calculated and evaluated against the WI2 reference through RMSE, R², and Nash-Sutcliffe efficiency metrics. MNDWI showed superior performance for Sentinel-2 (RMSE=21.42 ha, R²=0.998, NS=0.996), while ANDWI was optimal for Landsat-8 (RMSE=54.54 ha, R²=0.977, NS=0.976). Time-series analysis revealed a 35% reduction in mean annual reservoir area, decreasing from 7.28 km² in 2014 to 4.72 km² in 2021. Consistent seasonal patterns were observed, with spring maxima (9.33 km² in March) and autumn minima (3.35 km² in September) evident across both sensors. A high inter-sensor correlation (r = 0.933) supports the potential for multi-sensor integration in comprehensive monitoring efforts. The LSWI and NDTI indices displayed systematic overestimation due to interference from soil moisture and vegetation, making them unsuitable for quantifying water area. These findings highlight the sensor-dependent nature of optimal index selection, recommending MNDWI-Sentinel-2 pairing for short-term monitoring and ANDWI-Landsat-8 for long-term trend analysis in the management of semi-arid reservoirs.

The steel industry plays an important role in the release of toxic pollutants, including heavy metals, into the environment. The present descriptive-applied study was conducted in 2022 to identify the sources of heavy metal emissions in surface soils in the vicinity of a steel industry using positive matrix factor and chemical mass balance models. Soil samples (50 samples) were systematically collected from four areas within the steel plant, and a control area of 15 km was established. Five main sources, including the earth's crust (factor 1), vehicles (factor 2), steel industry (factor 3), biomass (factor 4), and other sources (factor 5), were identified as the main factors of heavy metals in the positive matrix factor model. Cobalt, nickel, and zinc had the highest mean concentrations with values of 14.5, 1.21, and 0.92mg kg-1, respectively. Cadmium and Lead showed the lowest concentrations with values of 0.02 and 0.10 mg kg-1 in samples inside and outside the Khuzestan Steel Company area, respectively. Comparing the contribution of different sources in the release of heavy metals in the identified factors showed that the steel industry, other sources, earth's crust, vehicles, and biomass accounted for 24, 23, 19, 18, and 16 percent in the positive matrix factor model and 23, 22, 20, 18, and 17 percent in the chemical mass balance model, respectively. The positive matrix factor and chemical mass balance models showed that there was a high level of soil contamination with heavy metals in the vicinity of the Khuzestan Steel Company in Ahvaz city.

Biochar, a stable, economical, and environmentally friendly carbonaceous material, has garnered significant attention in the development of advanced adsorbents for the immobilization and removal of environmental pollutants from water and soil. This interest stems from its oxygen-containing functional groups, aromatic structure, notable porosity, high specific surface area, and suitable cation exchange capacity. However, the performance limitations of pristine biochar in pollutant removal underscore the necessity for its modification and engineering. In this regard, the production of biochar composites through the combination with minerals and iron-containing compounds has emerged as an effective strategy to enhance structural and chemical properties, as well as surface reactivity. This review paper examines the primary methods for synthesizing biochar composites, including post-pyrolysis modification and direct mixing with mineral phases. Synergistic and direct loading methods are introduced as novel approaches. Furthermore, the influence of biomass type, pyrolysis conditions, modifier type, and synthesis pathway on the final physicochemical characteristics of the composites is analyzed. Additionally, the governing mechanisms underlying the performance of these materials in immobilizing and removing heavy metals are discussed, encompassing adsorption, precipitation, complexation, ligand exchange, redox reactions, electron transfer, electrostatic interactions, and ion exchange. Study results indicate that the synergy between biochar and mineral/iron-bearing phases can significantly enhance the efficiency of immobilizing pollutants, including both anionic and cationic heavy metals. This review emphasizes the importance of targeted design of biochar composites based on a precise understanding of the relationship between synthesis methods and functional properties. Future challenges and prospects concerning the application of biochar-mineral composites in environmental remediation and the promising commercialization of this technology are also addressed.

Scour is a major challenge in river engineering, as it causes bridge failures during flood events and leads to significant economic losses. This study aims to estimate the normalized scour depth (Dse/Dp) around pile groups by examining relevant hydrodynamic and geometric parameters. A dataset comprising 299 laboratory measurements collected from various sources was assembled and divided into training and testing subsets. As machine learning inputs, several models were employed, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and a meta-ensemble learning model (Stacking). Hyperparameter tuning was performed using the Grid search method to achieve optimal regression performance. Model performance evaluation indicated that the ANN and SVR models achieved coefficients of determination of R² = 0.87 and R² = 0.91, respectively. The XGBoost model outperformed these approaches, yielding R² = 0.94 with an RMSE of approximately 0.28. Ultimately, the stacking ensemble model, by integrating the outputs of the base learners, demonstrated the highest predictive accuracy with R² = 0.96 and an RMSE of 0.11, representing an improvement of approximately 15% compared to ANN and 7% compared to XGBoost. Overall, the findings highlight that ensemble machine learning models—particularly the Stacking approach—provide a robust and efficient framework for predicting scour depth around pile groups and for capturing the complex flow behaviors in hydraulic systems.