seid omid aleyasin; bahman shamsesfandabad; Hamid Toranjzar; abas ahmadi; Shahro Mokhtari
Abstract
Abstract: Wetlands are one of the most productive ecosystems in the world. They provide a unique and rich habitat for creature .they also perform a wide range of economic and service functions such as water conservation, runoff regulation, water quality treatment and recreational services. The aim of ...
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Abstract: Wetlands are one of the most productive ecosystems in the world. They provide a unique and rich habitat for creature .they also perform a wide range of economic and service functions such as water conservation, runoff regulation, water quality treatment and recreational services. The aim of this study was to evaluate the ecosystem health of Meyghan Wetland of Arak based on different methods. To evaluate the Meyghan Wetland of Arak and also to evaluate the status of benthic organisms and other parameters, sampling of sediments of the wetland floor was performed. Sampling was performed at 10 points of the wetland and at 5 replications at each point. Several indicators were used to assess the health of Meyghan Wetland. Which included a biotic-index (BI) based on the work of Borja et al. (2000). In addition to the above, the main framework includes bio-indicators, heavy metal pollution index and water quality index, which have been considered in this study. The ecosystem health of Meyghan Wetland was evaluated based on the mentioned indicators and the map of ecosystem health of Meyghan Wetland was prepared. The results of this study showed that except for the nickel, zinc and lead as well as pH, for other elements (EC, Na, Cl, Mg, Ca, HCO3, SO4 and TDS), the lowest and highest values belong respectively To stations 3 and 6. The high amount of these elements in station 6 can be due to the activity of sodium sulfate factory in the northern part of the wetland, which causes changes in the wetland ecosystem by removing sediments from the wetland floor. In the case of copper, zinc and lead, the lowest concentration is seen in the northwestern part of the wetland and the highest concentration is seen in the western and southeastern parts of the wetland.
mohammad taghi heydari; Hosseinali Bahrami; , alireza aliyari
Abstract
Soil moisture is one of the fundamental parameters of the environment that is directly influenced by plant life, animal and activity of micro-organisms and plays a major role in energy exchanges between air and soil. Determination of the exact amount of soil moisture content in agricultural, hydrology ...
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Soil moisture is one of the fundamental parameters of the environment that is directly influenced by plant life, animal and activity of micro-organisms and plays a major role in energy exchanges between air and soil. Determination of the exact amount of soil moisture content in agricultural, hydrology and geological sciences is very important. Therefore, the use of a method that can achieve soil moisture in normal and non-corrosion conditions with high speed and accuracy is very important and fundamental. The Ground Penetrating Radar (GPR) is a non-destructive method for the subsurface investigation that is evolving and seems to be able to greatly help agriculture to identify soil and protect culture systems. Different studies have been done in the field of soil moisture determination using GPR, but in Iran, there are limited studies on the ability of this method to estimate spatial changes of soil moisture content, therefore, this research has been done with these goals. The results indicate that in the study area, the distribution of humidity at each stage of harvest shows limited changes if the time changes of humidity in the time interval between winter and spring are about 10-15% of the difference. Also, the mean square of GPR method error compared with TDR 13.2 method is also compared to the GPR and weighted 81.3 method and the correlation coefficient in these two comparisons is equal to 0.87 and 0.95, which indicates the high accuracy of the GPR method for estimating soil moisture.
mehri raoofi; Mahmoud Habibnejad Roshan; Kaka Shahedi; Fatemeh Kardel
Abstract
Rivers are the main arteries of watersheds that play an important role in providing water for agriculture, drinking and industry. On the other hand, the reduction of river water quality has been one of the biggest human concerns in the last century. In order to evaluate the quality of running water, ...
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Rivers are the main arteries of watersheds that play an important role in providing water for agriculture, drinking and industry. On the other hand, the reduction of river water quality has been one of the biggest human concerns in the last century. In order to evaluate the quality of running water, biological indicators and the study of benthic invertebrates can be used. The aim of this study was to investigate the water quality of the main rivers of Babolrood watershed using the Hilsenhof Biological Index (HFBI). For this purpose, sampling of benthic invertebrates in 5 main river stations was performed using a net frame (sorber) with a cover area of 40 cm2 and transferred to the laboratory for identification. Then, using Pennak (1953) and Mellenby (1963) identification keys, the samples were identified by family and sex and counted and weighed. Also, at the same time as sampling of benthic organisms to study the physicochemical properties of water, samples were taken from river water. Pearson correlation coefficient was used to investigate the relationship between biological samples and physicochemical properties of water. The results showed that Babolk station with the lowest FBI and Babolrood-Babol station with the highest FBI were in the category of non-organic pollution and some organic pollution, respectively. The results of correlation of biological samples with physicochemical parameters in most cases were not significant at 95% confidence level. The highest correlation coefficient between Oligochaeta species was with Diversity biodiversity.Keywords: Benthic invertebrates, water quality, HFBI, Babolrood watershed, Mazandaran province
Amin Salehpour Jam; Jamal Mosaffaie
Abstract
In this study, problem structuring and identification and prioritization of solutions to improve the health of the Kal-Aji watershed were carried out based on the DPSIR framework and non-parametric statistical tests. In the first stage, the drivers and pressures resulting in the health status of the ...
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In this study, problem structuring and identification and prioritization of solutions to improve the health of the Kal-Aji watershed were carried out based on the DPSIR framework and non-parametric statistical tests. In the first stage, the drivers and pressures resulting in the health status of the Kal-Aji watershed and the related impacts were identified through a literature review, a visit to the watershed, interviews with experts from the departments of natural resources, environment, regional water, the Agricultural Jihad, the Agricultural and Natural Resources Engineering Organization of Golestan, faculty members of academic and research centers, and interviews with local communities. Then, a working group consisting of 26 stakeholders, local knowledgeable individuals, and experts knowledgeable about the issues and problems of the watershed began to determine solutions to improve the health of the Kal-Aji watershed and eliminate or modify the related adverse impacts. In the last stage, after forming the DPSIR table and identifying the various components of this framework in the Kal-Aji watershed, the importance of each of the variables categorized under the five DPSIR components was prioritized and determined. For this purpose, a Likert-scale questionnaire was used as a measurement tool. In this study, each variable was considered as an item, and the validity of the questionnaire was finally confirmed based on the opinions of experts. Also, Cronbach's alpha method was used to calculate the reliability of the measurement tool. In this study, the questionnaire variables were based on the multiple-response coding method, qualitative ordinal variables and matched the Likert scale (very low (1), low (2), medium (3), high (4), and very high (5)), so that the opinion of the expert working group members and the determination of the priority of the items were based on the Friedman nonparametric test.
Mahmoudreza Tabatabaei; Amin Salehpour Jam; Jamal Mosaffaie
Abstract
In watershed areas, monitoring and assessing erosion and sedimentation processes are crucial, as these processes directly impact the quality and quantity of water resources. The design and construction of advanced systems, such as a specialized geographic information system for the country’s hydrometric ...
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In watershed areas, monitoring and assessing erosion and sedimentation processes are crucial, as these processes directly impact the quality and quantity of water resources. The design and construction of advanced systems, such as a specialized geographic information system for the country’s hydrometric stations, can significantly enhance watershed management research.This study developed a specialized geographic system for managing and analyzing hydrological data using the C# programming language and open-source spatial libraries. The system uses SQLite as a data storage platform and employs Entity Framework 6 (EF6) and LINQ to facilitate data management and extraction. It can perform various spatial and descriptive queries and analyses, as well as statistical analyses and summaries from sedimentation data.The results can be summarized in two sections: the design and construction of the system, and the statistical analysis of sedimentation data from the Aras basin. The statistical analysis of sedimentation data (26,156 recorded data points until 2017) indicates that at the watershed scale, the average daily suspended sediment discharge is 11,814.95 tons per day, the average suspended sediment concentration is 4,185.68 mg per liter, and the average instantaneous flow discharge is 13.16 cubic meters per second.At the study unit scale, the average maximum and minimum suspended sediment discharge correspond to the Jolfa-Duzal unit (code 1105) with 10,312.33 tons per day and the Qareh Ziyaldin unit (code 1108) with 991.96 tons per day, respectively. Additionally, at the hydrometric station scale, the average maximum and minimum daily suspended sediment discharge correspond to the Jolfa station (code 807-19) with 571,697.82 tons per day and the Naur-Exit Neur station (code 0195-19) with 2.82 tons per day.This research aims to develop a national software infrastructure for managing sedimentation data and flow discharge from the country’s hydrometric stations.
mohammad Rostami
Abstract
To evaluate the scour depth around cylindrical piles of coastal protection structures under wave impact pressure caused by wave breaking, an experimental study was designed. The study aimed to analyze how variations in wave characteristics, including wave height and period, influence scour depth. It ...
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To evaluate the scour depth around cylindrical piles of coastal protection structures under wave impact pressure caused by wave breaking, an experimental study was designed. The study aimed to analyze how variations in wave characteristics, including wave height and period, influence scour depth. It is important to note that this research focuses on breaking waves that directly impact the structure.In this study, a two-dimensional wave channel at the Coastal Engineering Laboratory of the Soil Conservation and Watershed Management Research Institute was used. To create shallow water conditions and ensure wave breaking at the pile location, as well as to assess the resulting scour depth, a sloped surface and a sediment reservoir were constructed in the middle section of the main channel. The sediment reservoir, with a depth of 0.35 meters, was installed upstream of the metal sloped surface and filled with sand sediments. A polycarbonate cylindrical pile was positioned at the center of the sediment reservoir.The wave channel was filled with water to depths ranging from 0.4 to 0.5 meters, and waves of varying heights and periods were generated using a wave paddle system. Through trial and error, the exact wave breaking location and the pile’s position relative to it were identified. A total of 34 experiments were conducted under initial water depths ranging from 0.4 to 0.5 meters. Wave heights varied between 0.05 to 0.14 meters, and wave periods ranged from 2 to 7 seconds. After each experiment, scour depth at the pile location was captured and measured using imaging techniques.The findings of this study revealed that wave breaking resulted in a 2.37-fold increase in scour depth and erosion compared to the passage of a regular wave near a cylindrical pile structure.Therefore, marine structure designers must carefully consider this issue.
Rouhangiz Akhtari; Hamidreza Hajipoor; Mojtaba Saneie; Mohammadreza Gharibreza
Abstract
This study experimentally evaluates the performance of individual check dams in mitigating flood peaks using a 1:10 scale physical model of Sijan stream, testing 90 scenarios under controlled laboratory conditions. The research systematically examines how stream characteristics (number of check dams ...
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This study experimentally evaluates the performance of individual check dams in mitigating flood peaks using a 1:10 scale physical model of Sijan stream, testing 90 scenarios under controlled laboratory conditions. The research systematically examines how stream characteristics (number of check dams and their sediment conditions) and inflow parameters (peak discharge and hydrograph time base) influence flood control effectiveness. Results demonstrate that check dams reduce peak discharge by 5-16% and increase time lag to peak by 17-21%, with performance highly dependent on flood magnitude and duration. For floods with return periods increasing from 2 to 10 years, the peak reduction efficiency decreases from 16% to 5%, revealing structural limitations against higher energy flows. The hydrograph time base emerges as a critical factor - when exceeding the watershed's time of concentration, peak mitigation drops from 17% to 5% and time lag decreases from 35% to 8%, indicating reduced effectiveness for prolonged flood events. These trends are attributed to flow dynamics: larger floods overwhelm structural resistance, while extended durations lead to control saturation and steady flow dominance. The study develops three robust empirical relationships (R² = 0.81-0.92) through dimensional analysis to quantify check dam impacts on hydrograph modification, providing practical tools for watershed management. However, the derived equations require site-specific calibration for application beyond the Sijan stream due to their dependence on local channel geometry and roughness characteristics. These findings offer valuable insights for designing check dam systems, highlighting their conditional effectiveness and the importance of considering both flood magnitude and duration in watershed management strategies. The research contributes to improved flood control planning by quantifying performance limitations under varying hydrological conditions.
Saeedreza Moazeni Noghondar; Ali Salajeghe; Shahram Khalighi Sigaroudi; Ali Golkarian
Abstract
Mountainous regions, as sensitive ecosystems, play a vital role in providing water resources, regulating climate, and preserving biodiversity. However, these areas are vulnerable to threats such as soil erosion; therefore, sustainable soil management in these regions is of particular importance. This ...
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Mountainous regions, as sensitive ecosystems, play a vital role in providing water resources, regulating climate, and preserving biodiversity. However, these areas are vulnerable to threats such as soil erosion; therefore, sustainable soil management in these regions is of particular importance. This study aimed to investigate the influence of topographic variables, including slope direction and the topographic wetness index (TWI), on the effectiveness of watershed restoration measures in improving the hydrological conditions of soil in the paired watershed of Gonbad, located in Hamadan province. Soil properties were sampled and analyzed in restored and control areas, across north-facing and south-facing slopes, and within three TWI classes. The results indicated that restoration measures generally enhanced soil quality, leading to reduced bulk density, increased porosity, aggregate stability, organic matter, saturation moisture content, infiltration rate, and vegetation cover. However, the extent of these improvements was influenced by topographic characteristics. North-facing slopes and areas with high TWI exhibited the greatest improvements in soil parameters, particularly organic matter and aggregate stability. The correlation matrix among soil parameters revealed positive correlations between organic matter and aggregate stability, porosity and infiltration rate, as well as negative correlations between bulk density and bare soil with other soil quality indicators. These findings highlight the positive impact of restoration measures on various aspects of soil quality. Additionally, the results suggest that slope direction and TWI play a decisive role in the effectiveness of restoration measures, with north-facing slopes and high TWI areas recommended as priorities for implementing such interventions. These findings can be applied in planning and implementing restoration measures in similar regions.
Shadi Jalilian; Shaban Shataee Jouibary; Mohammad Hadi Moayeri; Amir Saddodin
Abstract
AbstractIntroduction:Landslides, as one of the most destructive natural phenomena, annually cause significant human casualties and financial damage. This study aimed to evaluate the performance of two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), as well as two deep learning ...
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AbstractIntroduction:Landslides, as one of the most destructive natural phenomena, annually cause significant human casualties and financial damage. This study aimed to evaluate the performance of two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), as well as two deep learning models, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in modeling landslide risk in a part of Golestan province. Methodology:For modeling, layers of 10 influential environmental and human factors were identified and prepared, including elevation, slope, slope direction, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), land use, distance from roads, distance from rivers, distance from faults, and precipitation. From 494 landslide and non-landslide points, 70% were used for the training phase and 30% for the validation phase. To further the research objectives and prepare the information layers, ArcGIS software and the Python programming language were utilized to implement machine learning (ML) and deep learning (DL) algorithms.Results:The results demonstrated that the deep learning algorithm CNN, with an AUC score of 0.910, an overall accuracy of 87.72%, and a Kappa coefficient of 0.899 for high-risk classes (high and very high risk), was identified as the most efficient model. Variable importance analysis using the superior model (CNN) revealed that the factors Distance to Fault and Distance to River were respectively the most significant contributors to landslide occurrence in the study area.Conclusion:The results of this research can be highly effective in identifying high-risk areas and determining the factors influencing the occurrence of landslides in this area, thereby aiding in reducing potential damages and threats associated with landslides, as well as in implementing effective management strategies.
Alireza Yousefi Kebriya; Ali Khalili; hasan rezaei
Abstract
Dust storms have intensified in Ilam Province, western Iran, in recent years, posing serious environmental, health, agricultural, and infrastructural challenges. The province’s geographical location adjacent to Iraq and Syria exposes it to frequent transboundary dust events originating from arid ...
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Dust storms have intensified in Ilam Province, western Iran, in recent years, posing serious environmental, health, agricultural, and infrastructural challenges. The province’s geographical location adjacent to Iraq and Syria exposes it to frequent transboundary dust events originating from arid zones in neighboring countries. This study aimed to monitor the spatial and temporal patterns of dust activity and identify major source regions from 2020 to 2025. Ground-based particulate matter (PM) data were collected from air quality monitoring stations in Mehran and Dehloran. Additionally, satellite-derived indices including Aerosol Optical Depth (AOD) from MODIS, Absorbing Aerosol Index (AAI) from Sentinel-5P, the Normalized Difference Dust Index (NDDI), and Dust Event Count Maps (DECM) were analyzed using the Google Earth Engine platform. Dust transport pathways were investigated using 24-hour backward trajectory simulations from the HYSPLIT model.Results indicated a significant rise in dust frequency, with the highest number of events recorded in 2022 (over 200 events in border regions). Despite a slight decline in 2023 and 2024, dust activity remained concentrated near the border. AAI values exceeded 1.3 in 2022, and AOD reached critical levels above 1.85 in southern Ilam. NDDI peaked in 2021, indicating high dust deposition, then declined in subsequent years. HYSPLIT simulations for two critical dust events in 2025 traced the origins to western Iraq and eastern Syria. Dust plumes entered Ilam Province and led to maximum AQI values of 500 at Mehran and Dehloran stations.Overall, the findings confirm the intensification of dust events in Ilam and the dominant role of external sources in Iraq and Syria. The integration of satellite-based indices and trajectory modeling proved effective in determining dust origin, movement, and intensity. These results highlight the urgent need for soil stabilization, vegetation restoration, regional cooperation, and the development of satellite-based early warning systems to mitigate future risks.
Hossein Malekinezhad; Ehsan Bazrafshan; Seyed Zeynalabedin Hosseini; mehdi sepehri
Abstract
IntroductionFlood risk management is one of the most significant environmental and developmental challenges in arid and semi-arid regions of Iran. The Fakhrabad watershed in Yazd province, due to its specific climatic and topographic characteristics, is highly susceptible to flash floods, which can cause ...
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IntroductionFlood risk management is one of the most significant environmental and developmental challenges in arid and semi-arid regions of Iran. The Fakhrabad watershed in Yazd province, due to its specific climatic and topographic characteristics, is highly susceptible to flash floods, which can cause substantial economic, social, and environmental damages. Materials and MethodsThis study aimed to prioritize sub-watersheds of the Fakhrabad watershed in Yazd province in terms of flood susceptibility. Eight primary criteria were considered: Digital Elevation Model (DEM), slope, precipitation, fractal dimension, connectivity, Topographic Wetness Index (TWI), Topographic Control Index, and stream power. Relevant data were collected from hydrological records, topographic maps, and climatic datasets, and processed using a Geographic Information System (GIS).Results and DiscussionComparison with SWAT outputs indicated that both AHP and IRNAHP effectively represented flood susceptibility patterns. AHP remains a valuable tool due to its simplicityConclusionThis study compared AHP and IRNAHP for analyzing flood susceptibility of sub-watersheds in the Fakhrabad watershed, Yazd province. AHP identified sub-watersheds 4, 3, 31, 27, and 29 as having the highest, and sub-watersheds 15, 22, 14, 21, and 6 as having the lowest flood susceptibility. IRNAHP results indicated that sub-watersheds 4, 3, 31, 27, and 24 were most vulnerable, while sub-watersheds 15, 22, 14, 21, and 12 were least susceptible. Although AHP remains a reliable method for criteria prioritization due to its simplicity and interpretability, its accuracy diminishes in the presence of high uncertainty or ambiguity. IRNAHP effectively addresses these limitations, offering a more precise tool for flood risk management. Therefore, IRNAHP can be considered a complementary and more efficient approach for managing flood risks in sensitive regions.
Mahmoudreza Tabatabaei; Mohammadreza Gharib Reza
Abstract
This study examines the accurate estimation of suspended sediment in the Atrak River, particularly at the Hootan station. Suspended sediment in rivers, especially in semi-arid regions, poses significant challenges for water resource management and sediment control in dam reservoirs. In this research, ...
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This study examines the accurate estimation of suspended sediment in the Atrak River, particularly at the Hootan station. Suspended sediment in rivers, especially in semi-arid regions, poses significant challenges for water resource management and sediment control in dam reservoirs. In this research, a combination of classical and intelligent methods was used to estimate suspended sediment, including sediment rating curves, neural networks, and deep learning models. Key variables influencing sediment were identified using a random forest algorithm, and the data was divided into homogeneous groups. The ensemble learning model, XGBoost, was selected as the best model, demonstrating high accuracy in predictions. Results indicate that XGBoost outperformed other models with the lowest error and highest performance index. This model effectively manages highly skewed data and identifies complex nonlinear relationships. Additionally, the combined approach used in this study improved predictions compared to traditional methods. However, data quality and hydrological changes significantly impact model performance. This research highlights the importance of advanced machine learning techniques in analyzing hydrological data and emphasizes the need for a link between data science and water resource management. The findings of this study can serve as a reference for policymakers and water resource managers in enhancing sediment management and water quality in rivers.
Nader Jandaghi; Mehdi Alibegli
Abstract
Water resources management in arid and semi-arid regions is one of the fundamental challenges of the present century. In this context, monthly runoff prediction serves as a strategic tool for reservoir planning, flood control, and sustainable watershed management. However, the complexity of hydrological ...
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Water resources management in arid and semi-arid regions is one of the fundamental challenges of the present century. In this context, monthly runoff prediction serves as a strategic tool for reservoir planning, flood control, and sustainable watershed management. However, the complexity of hydrological processes and the nonlinear relationships among climatic variables make such predictions challenging. The present study aims to evaluate the performance of linear and deep learning models and to introduce an optimal model for enhancing water resources management.Materials and methods:In this study, monthly runoff in the Qarasu watershed was modeled and predicted using data from three hydrometric stations (Pole-Touskestan, Naharkhoran, and Siyahab) over a common 36-year period. Three single models (SARIMA, BiLSTM, and GRU) and two hybrid models (BiLSTM-GRU and SARIMA-BiLSTM-GRU) were employed to model monthly runoff and forecast values for a 12-month horizon. Model performance was evaluated using RMSE, MAD, and MSE indices. Results and discussion:The hybrid SARIMA-BiLSTM-GRU model, by integrating linear and nonlinear components, provided the most accurate monthly runoff predictions. The RMSE values of this model at the Pole-Touskestan, Naharkhoran, and Siyahab stations were estimated at 0.0295, 0.0173, and 0.1683 m³/s, respectively. The BiLSTM-GRU model ranked second, with RMSE values of 0.0326, 0.0226, and 0.3013 m³/s. Among the individual models, BiLSTM and GRU produced similar and relatively accurate results, while the linear SARIMA model, showed the lowest performance. On average, the SARIMA-BiLSTM-GRU hybrid model reduced prediction errors by 39.66% to 56.75% compared to the other models.Conclusions:This study demonstrated that hybrid models combining linear and deep learning approaches can significantly improve the accuracy and stability of monthly runoff predictions. Among them, the SARIMA-BiLSTM-GRU model provided the best performance by integrating both linear and nonlinear components.
Sina Nabizadeh; Ali asghar Naghipour; Ataollah Ebrahimi; Hamidreza Keshtkar; Elham Ghehsareh
Abstract
Land use/land cover (LULC) maps play a key role in natural resource management and sustainable land-use planning. Recent advances in remote sensing, machine learning, and cloud-based platforms such as Google Earth Engine (GEE) have enabled efficient large-scale spatiotemporal analyses. In this study, ...
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Land use/land cover (LULC) maps play a key role in natural resource management and sustainable land-use planning. Recent advances in remote sensing, machine learning, and cloud-based platforms such as Google Earth Engine (GEE) have enabled efficient large-scale spatiotemporal analyses. In this study, LULC changes in the large and heterogeneous Karun-1 watershed were assessed using Landsat 7 ETM+ (2002) and Landsat 8 OLI (2024) imagery. Cloud-free composite images derived from nine scenes during the peak growing season were generated using a median filter. A total of 1,920 training samples for seven LULC classes were extracted based on field data, aerial images, and Google Earth. Vegetation and auxiliary indices (NDVI, NDBI, NDWI, and DSM) were integrated with spectral bands. Supervised classifications were performed using CART, Random Forest (RF), and Support Vector Machine (SVM) algorithms in GEE. The results indicated that all three algorithms produced highly accurate LULC maps, with SVM achieving the highest overall accuracy and kappa coefficient (above 92% in both years). Rangelands (≈40%) and forests (≈27%) dominated the watershed area, while a declining trend was observed in rangelands, forests, and especially water bodies over time. The findings confirm the effectiveness of integrating machine-learning algorithms with GEE for large-scale environmental monitoring. However, limitations related to the spatial resolution of Landsat imagery remain a challenge. Therefore, the use of higher-resolution data such as Sentinel imagery is recommended for future studies.
