JWSS - Journal of Water and Soil Science

Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.

The river flow prediction is a key aspect of hydrology that plays a significant role in water resources management, flood risk reduction, and agricultural planning. This study simulates the monthly flow of the Razavar River, located in western Iran, using an extreme learning machine (ELM) model enhanced by the Whale (WOA) Optimization Algorithm and Grasshopper Optimization Algorithm (GOA) metaheuristic optimization algorithms. The data used include river flow, precipitation, evaporation, and temperature, which were collected for 10 years with a monthly time step and normalized in the numerical range of zero to one. 80% of the data is used for training, and the remaining 20% for model evaluation. The performance of the models is measured with the statistical indices RMSE, NSE, and R². First, the basic ELM model is developed using the trial-and-error method to adjust the weights between the hidden and output layers. Then, the WOA and GOA algorithms are used to optimize the weights. The results show that the basic ELM model performs worse than the optimized models (Train: RMSE=0.1427, NSE=0.7795, R²=0.7911, Test: RMSE=0.1406, NSE=0.7811, R2=0.7916). While the WOA-ELM and GOA-ELM models provide similar results, the WOA-ELM model shows better performance in complex conditions (Train: RMSE=0.1215, NSE=0.7869, R2=0.7932, Test: RMSE=0.1165, NSE=0.7872, R2=0.7933). The results of this research show that meta-heuristic optimization algorithms play an important role in improving the performance of river flow prediction models due to their ability to search comprehensively and avoid getting stuck in local optima. The findings of this study emphasize the importance of applying these techniques in water resources management and sustainable planning and will pave the way for future research in this area.

The biological desalination system has lower energy consumption and environmental impacts, as well as simpler engineering technology and complexity compared to conventional desalination methods. This study aimed to investigate the effect of nutrients in the Chlorella vulgaris algae culture medium on the rate of algae growth, salinity reduction, TDS, and EC. For this purpose, an amount of algae was inoculated into culture media-containing treatments to achieve a density of 5 × 106 cells/ml. The results showed that the highest amount of dry biomass of algae was in the deep aquifer well water + BG-11 culture medium treatment, with a value of 0.76 ± 0.02 g. The highest amount of chlorophyll a and b was observed on days 4, 17, and 30 in the control treatment, which was significantly different from the other treatments (p < 0.05). The lowest value of light absorption of algae was observed in the control treatment on all days. At the end of the 30-day experimental period, the highest reduction in salinity, TDS, and EC was observed with 27.60, 26.83, and 41.60 percent reduction in the deep aquifer well water + culture medium treatment, respectively, which showed a significant difference (p < 0.05) with the deep aquifer well water treatment. The results showed that deep aquifer well water, due to its nutrient content, has a high potential for algae growth and, as a result, biological desalination and the absence of the use of commercial culture medium, which can reduce desalination costs.

The effect of climate change on agricultural productivity and efficiency is a major concern and challenge for the agricultural industry. Different hydrometeorological variables, such as extreme temperature, precipitation, and their variations, affect the growth and yield of agricultural products. Saffron is one of the most important agricultural products in Iran. Iran produces the largest amount of Saffron globally, and Hamadan Province is one of the major saffron-producing regions in Iran. This study uses different Artificial Intelligence methods not only for clustering and sensitivity analysis of the hydroclimatological variables but also for evaluating the impacts of climate change on Saffron yield in Hamadan Province. Results indicated that the Random Forest algorithm performs the best for sensitivity analysis among all algorithms. Extreme climate change indices, particularly those related to the monthly maximum and minimum temperatures, have the highest negative impact on saffron yield compared to other hydroclimatological indices. Furthermore, the minimum temperature has a more significant negative impact on saffron yield compared to the maximum temperature. Additionally, the counties of Malayer, Nahavand, and Asadabad, located in the south and west of Hamadan Province, exhibited the highest accuracy in sensitivity analysis. The findings suggest that monthly extreme temperatures can be used to assess the risk of saffron production, increase agricultural productivity, and improve decision-making for the cultivation of this product.
 

Soil erosion and sediment transport are among the key challenges in the management of water and soil resources in Iran. In this study, the Modified PSIAC (MPSIAC) empirical model was applied to estimate sediment yield and evaluate the erosion status in the Plasjan watershed. The model is based on the assessment of nine influencing factors, including geological characteristics, soil properties, climatic conditions, runoff, land slope, vegetation cover, land use, surface erosion, and channel erosion. By assigning scores to each factor and integrating the spatial layers, the sediment yield intensity of each sub-watershed was quantified both qualitatively and quantitatively. The required base data were prepared and analyzed using the Geographic Information System (GIS). Subsequently, the final erosion index for each sub-watershed was calculated, and erosion hazard classes were determined according to the model’s standard tables. The total annual sediment production in the watershed was estimated at 803,301 tons, and the Sediment Delivery Ratio (SDR) was calculated as 14.48%, indicating considerable sediment deposition along the transport paths.  The results showed that most sub-watersheds fall within the “moderate” erosion class, while insufficient vegetation cover, steep slopes, and land-use changes were identified as the main contributing factors to increased sediment yield. Based on these findings, identifying critical areas, implementing erosion control measures, and utilizing remote sensing and sediment monitoring technologies are strongly recommended. This study provides a scientific basis for improving watershed management and mitigating erosion-related risks in similar basins.

One of the most significant hydraulic issues in determining the opening of river bridges is the lack of flow choking due to a reduction in the width of the flood passage. In this paper, determining the required opening for flow passage at a bridge location has been investigated using the concept of specific energy, one-dimensional, and three-dimensional flow modeling. First, the maximum encroachment of the embankments on the sides of the bridge in the river has been determined in such a way that it does not change the flow situation upstream of the bridge, using the concept of specific energy. The dimensions obtained for the bridge opening have been simulated numerically in two one-dimensional and three-dimensional models, and the flow condition at the bridge site and upstream has been evaluated and compared. The results showed that the one-dimensional numerical model predicts, on average, 67 percent higher amount of afflux than the three-dimensional model, while the maximum shear stress obtained from the one-dimensional model is, on average, 33 percent lower than that of the three-dimensional model. The effect of the bridge skewness on the amount of afflux and other hydraulic parameters of the flow, including bed shear stress and maximum velocity, has also been investigated using a three-dimensional model. The afflux was obtained at a 19.2 percent of normal depth at a skew of 40 degrees.

One of the key challenges in the design of side weirs is enhancing discharge efficiency, which is defined as the dimensionless ratio of the flow rate over the weir to the total incoming discharge. This study investigates the hydraulic performance of a converging side weir equipped with flow-guiding side plates. A three-dimensional numerical model using FLOW-3D software was employed to simulate flow conditions in the presence of guide plates with varying angles, relative lengths (defined as the ratio of plate length to the upstream channel width), and installation positions, to identify hydraulically optimal configurations. Following validation of the model against experimental data, 28 different scenarios were evaluated. The results demonstrated that under proper conditions, the installation of side guide plates can significantly improve discharge efficiency. Among all cases, the configuration with a 60° deflecting angle and a relative length of 0.2, installed at the upstream location (X₁) of the weir, yielded the best performance, increasing efficiency from a baseline of 62% to 82%. Analysis of the velocity field further revealed that the formation of a low-velocity zone behind the plate plays a critical role in directing the flow toward the weir. Overall, the use of side guide plates presents a simple, low-cost, and effective solution for enhancing the hydraulic performance of converging side weirs without requiring structural redesign.

Soil, as one of the vital natural resources, plays a fundamental role in ecosystem sustainability and global food security; however, degradation caused by unsustainable management, intensive agriculture, and pollution threatens its capacity. The use of organic amendments such as hydrochar is considered an innovative approach to improve soil physicochemical properties and enhance the Soil Quality Index (SQI). This study aimed to investigate the effects of different levels of hydrochar on soil properties and evaluate SQI. The treatments included control and three hydrochar levels (H10, H20, and H50). Soil properties such as pH, porosity, bulk density, electrical conductivity, organic carbon, total nitrogen, and available phosphorus were measured and normalized, and parameter weighting was conducted using entropy and principal component analysis (PCA). Results showed that nitrogen and organic carbon had the greatest importance in soil quality. The H50 treatment recorded the highest SQI (0.815), significantly greater than other treatments, while H20 (0.546) and H10 (0.336) also showed positive effects compared to the control (0.159). Hydrochar application improved organic carbon, nitrogen, and phosphorus and reduced bulk density. Although an increase in electrical conductivity was observed in H50. Overall, hydrochar application had a positive and gradual effect on SQI, with H20 recommended as an optimal level to improve fertility and reduce long-term salinity risks.

Gully erosion is one of the most important types of water erosion. Since the amount of soil loss due to this erosion is directly related to environmental factors, the amount of soil loss due to each gully can be modeled based on environmental conditions. According to the high ability of machine learning models based on artificial intelligence to analyze environmental information, in addition to determining soil loss due to gully erosion, modeling has been carried out using two random forest models, and artificial neural networks and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province in this study. The dimensional parameters of 70 gullies were measured over four years (2021-2024), and the volume and weight of soil lost were calculated. 15 environmental factors were selected as predictive variables, and modeling was performed with a cross-validation approach using these two models, and the accuracy of the models was evaluated using quantitative criteria. The amount of soil loss in gullies during the study period was 15300.94 tons. The accuracy evaluation of the models showed that the random forest model had better performance based on the coefficient of determination (R2=0.66-0.73). Also, this model had the lowest value in terms of the RSR error index evaluation criterion (RSR=0.66-1.03) and the highest accuracy. In terms of the fit evaluation index (D), the random forest model also had the highest fit between the observational and forecast data and had the highest value of this index (D=0.83), and therefore, it was introduced as the superior model for predicting soil loss due to gully erosion in this watershed.

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.