JWSS - Journal of Water and Soil Science

The objective of this study is to investigate the effects of solutes and water quality on evaporation amount and rate in two sandy and clayey soils. Soil samples containing aggregates and sand particles with diameters ranging from 0.5 to 1 millimeter were collected. Six columns were prepared during the experiment; three columns were filled with sandy soil and three with aggregated soil, each measuring 60 cm in height and 15.5 cm in inner diameter. One reference column was filled with distilled water. A saturated calcium sulfate solution was added to two columns, a 0.01 molar calcium chloride solution was added to two other columns, and distilled water was added to the remaining two. The amount of water lost through evaporation was recorded every 8 to 12 hours by weighing the columns. After approximately 130 days, the columns were sectioned, allowing for the establishment of moisture and solute concentration profiles for each soil column. The results indicated that the first and second stages of evaporation were distinguishable in sandy soil, whereas in clayey soil (aggregated soil), only the first stage of evaporation occurred due to the gradual transfer of water and the continuous hydraulic connection from the surface to the water table. The presence and type of solutes affected the evaporation rate and moisture profile, reducing evaporation and increasing water retention in deeper soil layers. Hydraulic connectivity (calcium sulfate > calcium chloride > distilled water) and the resulting capillary rise of and supply of evaporated water from higher layers caused a greater evaporation rate in the calcium sulfate compared to the calcium chloride and distilled water treatments in both soil types. Additionally, the formation of a salt crust on the soil surface due to solutes disrupted the hydraulic connection with the surface, resulting in decreased evaporation rates and cumulative evaporation.

Rice is one of the most important crops and the primary food source for more than half of the world's population. The present study was conducted to compare the direct-seeded rice (DSR) of three rice varieties (Jozdan, Firuzan, and Sazandegi) using surface (DI) and subsurface (SDI) drip irrigation systems. The experiment was performed as a split–split plot arranged in a randomized complete block design with three replications in two years (2019 and 2020) in the research farm of Isfahan University of Technology in Najaf-Abad. According to the results of the variance analysis, the most suitable cultivar for DSR in the region (among the tested cultivars) is Sazandegi with a grain yield of 3400 kg/h-1. The results of this experiment showed that the amount of water consumed in DI was 20% less than in SDI. Also, DSR reduced water consumption by 40% compared to transplanted rice (TPR) in the region. However, the grain yield also decreased by about 45%.

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 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.

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.

Improvement of soil characteristics is one of the important issues in agricultural and engineering sciences. To investigate the effect of silica nanoparticles on the soil's mechanical and physical properties, a factorial experiment was conducted based on a completely randomized design with three replications. The factors included silica nanoparticles at three levels (0%, 0.5%, and 1% by weight) and two soil types with loam and clay loam textures. The results of the shear strength test showed that the addition of nanosilica increased the internal friction angle and particle adhesion in both loam and clay loam textures, but the liquid limit and plasticity index decreased in both soils. In the consolidation test, the compressibility coefficient in loam decreased from 0.38 to 0.21 and in clay loam from 0.42 to 0.23, while the swelling coefficient in loam decreased from 0.13 to 0.07 and in clay loam from 0.18 to 0.08. Overall, the results showed a significant effect of nanosilica particles on improving soil mechanical strength, especially in clay loam with higher clay content and specific surface area. Therefore, it can be concluded that the use of silica nanoparticles is an effective method for stabilizing problematic soils.
 

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.

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 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.

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.