JWSS - Journal of Water and Soil Science

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 water crisis and climatic conditions in Iran have necessitated prioritizing low water-demand crops in agricultural investment policies. Quinoa, as a pseudo-cereal with relatively low water requirements, can be considered a suitable alternative to water-intensive crops. This study was conducted to investigate the comparative advantage of quinoa production compared to water-intensive crops under water-scarce conditions in Isfahan Province. To assess comparative advantage, two indicators—Domestic Resource Cost (DRC) and Social Cost-Benefit Ratio (SCB)—were employed. Data were collected through questionnaires and governmental sources, and shadow prices of inputs and outputs were utilized in the analyses. The statistical population consisted of quinoa producers. The results indicated that quinoa possesses a comparative advantage in Isfahan Province. The breakeven yield of this crop was estimated at 653 kg/ha, meaning that production above this level indicates the existence of a comparative advantage. Furthermore, the maximum allowable water consumption to maintain this advantage was estimated at 15,792 m³/ha; water consumption exceeding this amount eliminates the comparative advantage of quinoa cultivation. Additionally, the economic water productivity of quinoa was evaluated to be higher than that of other common crops in the region. Overall, considering comparative advantage of quinoa and its higher water productivity compared to common water-intensive crops, this product has the necessary potential to be included in the cropping pattern of Isfahan Province. However, recommendations for complete substitution require targeted and multidimensional policy interventions at technological, marketing, and policy-making levels, as well as conducting complementary research on the analysis and development of the quinoa value chain and market, investigating consumer behavior, designing targeted supportive policies for the transition period, and conducting a more comprehensive analysis of the comparative advantage of other products.

Water erosion is a great issue in Iran, and due to a lack of reliable and sufficient data for the recognition of vulnerable areas, using models is inevitable. This study was designed for the evaluation of the MPSIAC model in the subbasin of Goharbaran in Chaharmahal va Bakhtiari province. The sediments of the reservoir were surveyed and measured after exhausting the water. Landsat-8 data were applied to study land use changes on soil erosion. During intensive fieldwork, 37 surficial (0-20 cm) soil samples were collected and, after pretreatment, were used for some soil physical and chemical analyses. For the determination of 9 MPSIAC factors, we used library, field, and laboratory data. Multitemporal analysis showed that the quality of rangelands has increased, and the surface area of orchards was doubled within 20 years (1994-2014). Sensitivity analysis indicated that the MPSIAC model was highly sensitive to the river erosion factor, while the least sensitive factor was soil. The sensitivity of the model to the runoff specific peak and soil silt percentage was also very high. Application of the MPSIAC model showed that 48% of soils were in moderate and 36% of soils were in severe sediment yield rate classes. Results of sediment measurements indicated that the specific sediment yield was 11.76 t.ha-1.y-1 during 26 years (1988-2014), while the forecasted value by the MPSIAC model was 5.97 t.ha-1.y-1 Despite others' findings, a lower estimation of the model revealed the need for MPSIAC model calibration before using it in similar environments.

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.

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.

Greenhouse gas emissions and achieving acceptable water and energy efficiency are among the most important challenges facing the agricultural sector. The objective of the current research was to investigate the indicators of water physical and economic productivity and energy of wheat in Khorasan Razavi Province. To evaluate these indicators, a questionnaire was used in this research. A total of 200 questionnaires, including 50 for each city, were distributed, and the amount of input consumption and production was collected. The results showed that the physical productivity of water in the plains of Mashhad, Torbat Jam, Taybad, and Bakharz was 0.57, 0.72, 0.7, and 0.46 kg/m3, respectively. Also, the results showed that the highest Energy efficiency and energy productivity were 2.18 and 0.148 kg/MJ, respectively, for the Taybad Plain, and the highest specific energy was 10.92 MJ/kg for the Bakharz Plain. The highest (1539/68 kg/ha) and lowest (964/79 kg/ha) greenhouse gas emissions were obtained in the Mashhad and Taybad plains, respectively. The overall results showed that crop yield in relatively arid areas such as Torbat-e Jam and Taybad is higher than in semi-arid areas with higher altitudes, similar to Mashhad and Bakhrez.