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
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.
Mehri Dinarvand; saba peyrov; seyed hossein Arami; behzad tajari; kohzad heidari
Abstract
Iran is located in the arid and semi-arid belt of the world and is very far from moisture sources. Arid and desert areas, due to a lack of moisture, high temperatures, strong winds, soil erosion, and land degradation caused by human activity, have created tough conditions for plant growth and development, ...
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Iran is located in the arid and semi-arid belt of the world and is very far from moisture sources. Arid and desert areas, due to a lack of moisture, high temperatures, strong winds, soil erosion, and land degradation caused by human activity, have created tough conditions for plant growth and development, such that only a relatively limited number of plant species can survive. Native plants of such areas are considered highly valuable species due to their ability to adapt to harsh environmental conditions and play a crucial role in the region's climate, soil formation, and hydrology; therefore, their identification is of great importance. With the aim of monitoring and recording spatial data statistics of meteorological, hydrometric, erosion and sedimentation, vegetation cover, soil and groundwater climatic parameters, the Shush representative basin station was established in 2007 by the General Directorate of Natural Resources and Watershed Management of Khuzestan Province. In this area, in addition to the Moorlands, shallow depressions and old gullies are observed in this basin, some of which have been stabilized due to enclosure and reduction of livestock pressure. These stabilized depressions themselves act as natural micro-reservoirs and have provided suitable conditions for the establishment of permanent species by increasing infiltration, reducing surface runoff, and trapping plant seeds. In this study, the floristic composition, richness, and species diversity were compared in plots located in stabilized micro-watersheds (treatment) and hills (control).Materials and methods:In this study, vegetation cover analysis was conducted in the Shoosh representative basin area using biodiversity indices. During the appropriate growing season (early February to late March), during field visits, a list of plant species in the area was taken, and typification was performed based on the presence of shrub and perennial species.
Seyed Morteza Seydian; Hossein Emami
Abstract
Any modeling and decision-making process in watershed management fundamentally depends on accurate discharge data at the basin outlet. However, direct measurement of discharge is both costly and time-consuming. Therefore, at hydrometric stations, water stage is routinely recorded, and discharge is estimated ...
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Any modeling and decision-making process in watershed management fundamentally depends on accurate discharge data at the basin outlet. However, direct measurement of discharge is both costly and time-consuming. Therefore, at hydrometric stations, water stage is routinely recorded, and discharge is estimated using the rating-curve equation corresponding to the observed stage. Nevertheless, several sources of uncertainty, including errors in measuring flow velocity, cross-sectional area, and stage height, as well as model limitations in estimating extreme flows and temporal variations in channel morphology caused by erosion, sediment deposition, and vegetation growth, result in inaccuracies in rating-curve-based discharge estimations. Such uncertainties propagate through hydrological model outputs and management decisions, potentially leading to considerable economic and environmental losses. Consequently, it is essential to quantify the uncertainty bounds of rating-curve-derived discharge estimates in order to mitigate risks associated with measurement and estimation errors. To date, both classical statistical and Bayesian approaches have been employed for uncertainty estimation in rating curves. The Bayesian framework, in particular, offers significant advantages: in addition to incorporating observational data through the likelihood function, it allows prior hydraulic knowledge of the station to be embedded in the model through prior distributions. With recent advances in computational power and the widespread application of Markov Chain Monte Carlo (MCMC) sampling techniques, Bayesian methods have become a powerful and flexible tool for rating-curve uncertainty estimation, and various model structures have been introduced in recent years. Accordingly, the present study estimates rating-curve uncertainty using a Bayesian approach for three hydrometric stations located in Golestan Province.
Ramtin Tavoosi Rad; Mohamad ansarighojghar; Arash Malekian
Abstract
Accurate runoff prediction plays a crucial role in water resource management, flood control, and climate change adaptation planning. Given the nonlinear, complex, and multifactorial nature of hydrological processes, the use of data-driven methods and machine learning algorithms has become an efficient ...
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Accurate runoff prediction plays a crucial role in water resource management, flood control, and climate change adaptation planning. Given the nonlinear, complex, and multifactorial nature of hydrological processes, the use of data-driven methods and machine learning algorithms has become an efficient approach for runoff analysis and modeling in recent years. Two individual models, XGBoost, CatBoost, and a hybrid model Boost(Cat-XG) were evaluated to predict runoff in the Karaj watershed. The models were measured with 4 evaluation criteria NS, R, RMSE and MAE. The prediction results showed that the hybrid model (Cat-XG)Boost with a significant difference provides the best performance in predicting monthly runoff of the Karaj watershed compared to the two individual models evaluated. This model recorded NS above 0.957 and correlation above 0.939 in all stations studied. In addition, it recorded significantly fewer errors than the other two models. While the individual models XGBoost and CatBoost, especially in stations with more extensive data, faced increased errors. The two individual models studied provided average performance in predicting values related to extreme climate events, but by combining the two individual models and introducing the hybrid model Boost(Cat-XG), the defects in the individual models were covered and also by eliminating existing errors, much more accurate predictions were recorded.
Bagher Ghermez-cheshme
Abstract
Knowledge of temporal variation of base flow and its change trends in arid and semi-arid regions such as Iran is essential for developing basin water resources management plans. In this study, the Barun-chay River basin was selected with daily stream flow discharge for the period of 1976-1997. Using ...
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Knowledge of temporal variation of base flow and its change trends in arid and semi-arid regions such as Iran is essential for developing basin water resources management plans. In this study, the Barun-chay River basin was selected with daily stream flow discharge for the period of 1976-1997. Using a topographic map with a scale of 1:50,000, the location of the station and the area under study were determined, and the initial parameters of the basin were extracted. Then, the base flow index (BFI) was extracted using the time series of daily stream flow data and the one-parameter Recursive digital filter method. Monthly, seasonal, and annual time series of BFI were prepared, and trend analysis was conducted before and after the dam was constructed upstream of the hydrometric station using the Mann-Kendall method. The results showed that the average long-term annual BFI was 0.552. On a seasonal scale, the highest BFI was related to winter and summer, and the minimum was related to spring. The maximum long-term monthly BFI was related to January at 0.651 and the minimum was related to May at 0.470. The distribution of BFI data indicates that 50 percent of BFI in spring was between 0.47 and 0.53, in summer between 0.55 and 0.62, in autumn between 0.49 and 0.55, in winter between 0.52 and 0.64, and annually between 0.52 and 0.57. The trend of BFI in all time steps of the month, season, and year, except for June and October, was negative. The long-term average BFI before the construction of the dam, 1976 to 1995, was 0.542, and after the dam construction, 1995 to 1997, it was 0.562. It is noteworthy that the impact of human interventions resulting from the construction of the dam on the long-term annual BFI was 0.02.
Amin Salehpour Jam; Noredin Rostami; Shokoufeh Abdali
Abstract
In this study, aimed at assessing managerial-institutional resilience across different sub-watersheds of the Sang Sefid watershed, the indicators for each of the aforementioned components were first identified based on a review of the literature, library studies, interviews with experts, and field observations. ...
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In this study, aimed at assessing managerial-institutional resilience across different sub-watersheds of the Sang Sefid watershed, the indicators for each of the aforementioned components were first identified based on a review of the literature, library studies, interviews with experts, and field observations. In this research, using a multi-response coding method, the questionnaire variables were qualitative ordinal variables, consistent with the Likert scale (Very Low = 1, Low = 2, Moderate = 3, High = 4, and Very High = 5). Following the assessment of the questionnaire's validity and reliability, a survey was conducted among the watershed residents. In this regard, the validity of the measurement instrument was confirmed by a panel of experts, and the sample size was calculated using Cochran's formula. It should be noted that 14 expert specialists were consulted to assess the validity of the questionnaire and the indicators for measuring resilience, and finally, the validity of the measurement instrument was confirmed by them. Regarding reliability, Cronbach's alpha coefficient was employed to calculate the reliability or dependability of the measurement instrument. Furthermore, the managerial-institutional resilience of local communities exposed to flood risk within the hydrological units of the Sang Sefid watershed was assessed using one-way analysis of variance (ANOVA) and the K-means cluster analysis method. The comparison of the two methods—one-way ANOVA and K-means cluster analysis—demonstrated a significant convergence in assessing the managerial-institutional flood resilience potential of the hydrological units. The identification of Units S-int3 and S9 as the least and most resilient units, respectively, in both methods reinforces the internal validity of the findings. Moreover, the similar grouping of the other units into three distinct classes (with the exception of Unit S-int5) reveals a clear pattern of spatial distribution of resilience across the region, which can serve as a basis for prioritizing management interventions.
Negin Rashidi; Vahid moosavi
Abstract
Introduction:Modeling groundwater level is challenging due to its complex, nonlinear, spatiotemporal nature. This study aims to present and evaluate a distributed deep learning approach to predict groundwater levels in the Koshk-Fahan aquifer (Sefidrud basin). By combining graph neural networks with ...
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Introduction:Modeling groundwater level is challenging due to its complex, nonlinear, spatiotemporal nature. This study aims to present and evaluate a distributed deep learning approach to predict groundwater levels in the Koshk-Fahan aquifer (Sefidrud basin). By combining graph neural networks with convolutional and LSTM structures, we seek to capture spatial and temporal dependencies and improve prediction accuracy over conventional methods.Materials and Methods:Four graph-based deep learning structures were used: Graph Neural Network (GNN), Graph Attention Network (GAT), Graph Convolutional Network (GCN), and the hybrid GCN-LSTM. Each piezometric well was a node; the adjacency matrix used inverse distance weighting. Input data included hydro-climatic variables, geology, pumping characteristics, and spatial distances. Evaluation indices: R², RMSE, MAE, NSE, KGE.Results and discussion:The results indicated that while the GNN model could partially reconstruct the general trends of groundwater level changes, it lacked accuracy in representing extreme behaviors and temporal dependencies. The GAT model showed a slight improvement over GNN but remained limited in extracting deep temporal patterns. In contrast, the GCN model demonstrated better performance in identifying spatial dependencies, leading to a significant improvement in evaluation metrics. The best performance was achieved by the GCN-LSTM model, which effectively represented both spatial and temporal features simultaneously, showing the highest overlap with observed data. This model reached an R2 of 0.896, RMSE of 0.336, and MAE of 0.269, indicating its high accuracy in predicting groundwater levels.Conclusion:Hybrid graph-sequential architectures, especially GCN-LSTM, are highly effective for modeling complex aquifer hydrodynamics. This model predicted groundwater levels with high accuracy and outperformed purely graph-based models. Therefore, it is recommended for operational groundwater prediction and sustainable water management, serving as a novel framework to support better decision-making in water resources.
Seyed Ahmad Hosseini; mohamad rostami; masoud akbari; roohangiz akhtari; mehrnaz baniamam; mohammadreza gharibreza; nezam asgharipour
Abstract
The Fair Water Distribution Act, enacted in 1982 with the aim of establishing public ownership over water resources, preventing excessive abstraction, and ensuring reasonable consumption, introduced instruments such as exploitation permits, water-meter installation, designation of forbidden zones, and ...
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The Fair Water Distribution Act, enacted in 1982 with the aim of establishing public ownership over water resources, preventing excessive abstraction, and ensuring reasonable consumption, introduced instruments such as exploitation permits, water-meter installation, designation of forbidden zones, and local commissions. At the time, the Act provided a relatively comprehensive and progressive framework with a shared legal language for integrated management of both surface and groundwater resources. Four decades later, despite its enduring strengths, the Act faces profound structural and implementation challenges and lacks a holistic perspective. Severe weaknesses in enforcement, excessive centralization, exclusion of stakeholders from decision-making, disregard for environmental water rights, incompatibility with climate change and land subsidence, insufficient digitalization, ambiguities in the concepts of justice and reasonable use, and the absence of participatory basin-level governance have all emerged. Iran’s current water crisis stems less from the absence of legislation and more from the inadequacy of this Act in confronting twenty-first-century realities and from its ineffective implementation. Addressing this crisis requires, in the short term, rigorous and intelligent enforcement of the existing law, followed by, in the medium term, a complete re-drafting of the Act with an integrated, participatory, environmentally oriented, data-driven, and climate-adaptive approach to basin management.
Golaleh Ghaffari
Abstract
Accurate modeling of suspended sediment is a key challenge in water resources management, requiring methods capable of capturing the complex and nonlinear behavior of sediment transport processes. This study investigates a hybrid modeling approach that integrates sediment rating curves (SRC) and artificial ...
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Accurate modeling of suspended sediment is a key challenge in water resources management, requiring methods capable of capturing the complex and nonlinear behavior of sediment transport processes. This study investigates a hybrid modeling approach that integrates sediment rating curves (SRC) and artificial neural networks (ANNs) to predict suspended sediment load at the Glink hydrometric station in the Taleghan watershed. Historical streamflow and suspended sediment data from 1971 to 2023 were preprocessed and analyzed using three modeling frameworks: (i) empirical models (six types of SRCs), (ii) data-driven models (MLP, GFF, RBF, SVM, SOFM, and CANFIS), and (iii) two hybrid structures. Among the empirical models, the "class-mean method" outperformed others with the highest coefficient of determination (R² = 0.75) and the lowest relative mean error (RME), making it the most reliable rating curve approach.The results unequivocally demonstrate that the synergistic integration of empirical hydrological reasoning and artificial intelligence provides a resilient and efficient modeling framework for watersheds characterized by pronounced skewness and a high prevalence of anomalous observations. In this context, the CANFIS architecture, leveraging fuzzy logic to resolve complex nonlinear interactions, substantially improves the representation of hysteretic sediment dynamics and offers clear advantages over conventional computational structures, including multilayer perceptron (MLP), general feed-forward (GFF), radial basis function (RBF), support vector machine (SVM), and self-organizing feature map (SOFM) networks.Given the strategic significance of flood events in water resources engineering, the rigorous interrogation of outlier behavior is not optional but imperative. The proposed hybrid framework effectively captures and interprets these statistical anomalies with acceptable precision, thereby reinforcing its applicability in suspended sediment transport management under extreme hydrological conditions.To advance methodological robustness and scientific depth in future investigations, the establishment of comprehensive, high-resolution databases—particularly those documenting peak flood discharges—is strongly recommended.
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