Journal of Water and Soil Science

Evaluation of the Efficiency of the SewerGEMS Model in Simulating the Hydraulic Performance of the Stormwater Collection Network (Case Study: Shahrekord)

Jahangir Abedi Koupai

Urban floods and stormwater runoff are among the most significant environmental and social challenges in urban areas, caused by the accumulation of rainwater and the inadequacy of stormwater collection networks. The performance of the SewerGEMS software in analyzing rainfall events and evaluating the adequacy of the stormwater collection network in Shahrekord City under various conditions has been examined. Only two of the six observed rainfall events could be simulated. In the event of 06/11/1403 (Persian calendar), the observed peak discharge was approximately 1850 liters per second. In contrast, the simulated discharge for the two-sub-basin scenario was around 1750 liters per second, and for the eight-sub-basin scenario, about 1350 liters per second. The results of the two-sub-basin scenario are more reliable. In the event of 27/11/1403, the observed peak discharge was approximately 2000 liters per second, while the simulated discharge for the two-sub-basin scenario was around 1850 liters per second, and for the eight-sub-basin scenario, about 1400 liters per second. This demonstrates that the results for the two-sub-basin scenario are more accurate. The adequacy of the network was then evaluated for return periods of 2 years and 5 years. The results indicated that the stormwater collection network of Shahrekord is generally adequate; however, some areas, such as sections of the Bouali and 13 Aban canals, have deficiencies that lead to local flooding. Finally, recommendations such as identifying locations for artificial recharge basins and continuously monitoring and inspecting the canals, particularly before the rainy season, are proposed to improve the performance of the Shahrekord stormwater collection network and reduce flood-related risks.

Forest Cover Change Trends Analyze Using Support Vector Machine (SVM), Maximum Likelihood Classification (MLC), and Spectral Indices and Their Impact on Land Surface Temperature (Case Study: Gorab Pas Rural District)

Mehdi Feyzolahpour

By utilizing land surface temperature (LST), valuable insights can be gained regarding the impact of land use on energy balance processes. Therefore, this study aimed to investigate the trend of LST changes due to land use changes in the Gorab rural district. Four land use types, including water bodies, bare land, Agricultural area, and forest, were determined from 2013 to 2024 for the maximum likelihood classification (MLC) and support vector machine (SVM) models. The surveys showed that the area of water in the dry period decreased from 0.9 km2 in 2013 to 0.4 km2 in 2024, a decrease of 0.5 km2. In contrast, the area of forest areas increased from 136.1 km2 in the dry period of 2013 to 147.2 km2 in 2024. The Kappa coefficient values for the SVM and MLC models during the wet season of 2021 were 53.94 and 68.7, respectively. Based on this, it was found that the MLC model has higher accuracy. To match spectral indices with LST values, NDVI, NDSI, and NDWI were calculated. Land use changes during the 2013-2024 period affected land surface temperatures, causing fluctuations from 11.5°C to 21.18°C in the wet season and from 13.81°C to 31.45°C in the dry season. The highest LST values were associated with barren land, while water bodies and vegetation cover had the lowest LST values. Among the spectral indices, the highest positive correlation was observed with NDWI, with a value of 0.64 in 2024. The highest negative correlation, -0.66, was observed with NDVI in the same year. Over the 11 years, the area of forest cover increased by 8.15%, while agricultural land decreased by 33.5%. The most significant change occurred in agricultural lands, which declined in area from 35.5 km² to 23.6 km².

Numerical Investigation of the Opening of River Bridges Change and Skew on Flow Structure and Afflux: Three-Dimensional Versus One-Dimensional Approaches

Amir Mahjoob

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.

Evaporation and Wind Blowdown Losses of a Fixed Classic Sprinkler Irrigation System with Mobile Sprinklers in Isfahan Province

Mehdi Gheysari

Given the rising need for water consumption and the decrease in available water resources, improving water use efficiency appears essential. Using modern irrigation techniques and applying irrigation management based on current, accurate scientific principles will enhance irrigation efficiency. This study aimed to estimate evaporation and windfall losses using meteorological variables and measure these losses in the cities of Isfahan, Golpayegan, and Fereydounshahr under different weather conditions. Evaporation and windfall losses were examined at 3, 6, 9, 12, and 15 hours using two selected equations across three meteorological stations with seven years of weather data. Then, evaporation and windfall losses were estimated using two experimental methods (abbreviated as WD1 and WD2), a science-based method (named droplet size), and field measurements. Results showed that evaporation and windage losses calculated with the empirical equation WD1 were about 2% higher than the field measurement value, while WD2 was about 1.5% lower. The correction factors for WD1 were 0.54, 0.44, and 0.51 for Isfahan, Fereydounshahr, and Golpayegan, respectively, and for WD2, it was 1.62, 1.17, and 1.56, respectively. The differences in evaporation and windage losses at various times of day and months of the year were statistically significant at the 5% level.

Simulation of the flow of the River using the extreme learning model and the meta-heuristic optimization algorithms of Whale and Grasshopper

Maryam Hafezparast

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.

Numerical Investigation of the Effect of Side Guide Plates on Flow Pattern and Hydraulic Performance of a Converging Side Weir

Mehdi Daryaee

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.

The Effect of Nutrient Elements in the Culture Medium on the Biological Desalination of Deep Aquifer Well Water in Sistan by Live Algae Chlorella vulgaris

Narjes Sanchooli

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.

Estimation of Soil Loss Volume Caused by Gully Erosion Using Machine Learning Models in Abgendi Watershed

Iman Saleh

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.

Assessment of the Impact of Temperature- and Precipitation-Related Index Fluctuations under Climate Change on Saffron Yield (Case Study: Hamadan Province)

Sanaz Moghim

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.

Effect of Nanosilica on the Mechanical and Physical Soil Characteristics

Aliashraf Amirinejad

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.

Application of the Modified PSIAC Model for Quantitative and Qualitative Evaluation of Erosion and Sediment Yield in the Pelasjan Watershed, a Sub-Basin of the Zayandehrud River

Hamid Hosseinkhani

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.

Evaluation of Soil Quality Index in the Presence of Sugarcane Bagasse Hydrochar Using Shannon Entropy Combination and Principal Component Analysis

Laleh Divband Hafshejani

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.