بهرام ملک محمدی

The issue of environment and climate change is one of the fundamental and significant challenges of human life in the current era, to the extent that this important matter has been placed on the agenda of global organizations, governments, stakeholders, and beneficiaries in the health sector, especially at the societal level. Additionally, the experiences of developed governments in implementing environmental policies at the national level indicate that the evaluation of the implementation of these policies should be further explored. This article, using a mixed (qualitative-quantitative) method and employing "Interpretive Structural Modeling," analyzes the data. In the qualitative section, a systematic literature review was conducted, and in the quantitative section, data were collected through questionnaires completed by researchers and experts with over 15 years of relevant work experience. Then, based on the mentioned method, the validity and reliability of the findings were confirmed using interpretive structural modeling software. The qualitative analysis of the literature and research background showed that content exists at two levels of strategic and operational environmental evaluation, but no integrated linkage between these two levels has been scientifically presented to date, which is one of the new findings of this research. Additionally, at the strategic level, besides the dimensions of plans, programs, and policies, a fourth dimension called prioritization emerged as another new finding from this study. Another finding of this research is the introduction of two types of indicators: "forward-looking" and "performance," which are fully introduced at each stage of the "Integrated Strategic-Operational Model" and demonstrated in a process.

In this study, the ordinary least squares (OLS) regression model was applied to estimate the impacts of  land use and land cover (LULC) changes from 1999 to 2023 derived from Landsat satellite imagery on water productivity as a key ecosystem service essential for environmental sustainability. Focusing on the Lavasanat district in Tehran province, which has undergone rapid urbanization and severe land use/cover changes, this study determined the extent to which water production performance (runoff) responded to land use/cover changes, thereby providing significant information on the environmental consequences of land use/cover changes under increasing human pressures.

This study used Landsat satellite imagery to assess the trends in land use/cover (LULC) changes over time at a spatial resolution of 30 m. Water yield modeling was performed using the annual water yield index of the InVEST software. The model inputs included land use and cover maps from three different time series along with data on precipitation, potential evapotranspiration, soil depth, water availability for plants, and local biophysical tables. The results from the InVEST model were analyzed using an ordinary least squares (OLS) regression model to estimate the impact of land use/cover changes on water yield. This method allows for a detailed examination of the relationship between land use/cover changes and their impacts on the water yield.

The results showed that between 1999 and 2023, the area of green spaces, including agricultural lands, gardens, and pastures, decreased by 161.21 km², or 31 percent. Consequently, the annual water production (runoff) increased from 105 to 130 million cubic meters. In addition, the area of the minimum error zone decreased from 445.3 to 23.5 km², indicating a decrease in the reliability of the model. Such findings indicated the high variability and complexity of hydrological interactions and indicated the severe effects of overdevelopment and increasing land use/cover pressures.

The results of this study showed that the water production capacity (runoff) of the ecosystem was vulnerable to changes in land use and land cover. These changes increased runoff, reduced water permeability in the soil, and disrupted the hydrological balance of the region. Therefore, it is necessary to use integrated modeling approaches and simultaneously pay attention to climate change, socio-economic factors, and land use/cover planning. Regional planning should also emphasize preservation and restoration of green spaces and smart management of urban development, which is an inevitable necessity to maintain ecosystem resilience and water resource sustainability.

This study employs an agent-based modeling approach using the MATSim tool to examine various behavioral scenarios and their effects on traffic flow and pollutant emissions in District 13 of Tehran. First, an artificial population of this district was developed while preserving key demographic characteristics. Subsequently, different behavioral scenarios were simulated through agent-based modeling, and the corresponding emissions were calculated. The results indicate that teleworking significantly reduces vehicle traffic and pollutants such as CO₂, NOx, SO₂, and PM₂.5, making it the most effective strategy for improving air quality and mitigating congestion. In contrast, flexible working hours exerted a limited impact on reducing air pollution—primarily due to additional trips generated—and in some cases even led to increased emissions, including a 9% rise in CO₂. Furthermore, expanding bicycle infrastructure decreased reliance on private cars, cutting CO₂ emissions by up to 26% and raising the modal share of bicycle use to 51.6%. Overall, this research demonstrates that combining travel demand management policies with sustainable infrastructure—such as cycling networks and clean public transportation—can significantly curtail pollution and traffic congestion in densely populated urban areas.

Soil is a vital component of natural capital, providing essential ecosystem services such as flood regulation, habitat provision, carbon sequestration, and agricultural production. As awareness of these services grows, soil retention has become a priority in environmental management. Key factors influencing soil retention and sediment control include changes in land use and land cover. This study aims to monitor land use and land cover changes in the Lavasanat watershed from 2000 to 2023 and assess their impact on soil-related ecosystem services. The Lavasanat watershed is crucial for managing water and soil resources due to its location upstream of the Latian Dam and its proximity to protected areas and national parks.

  Landsat satellite imagery from 2000 and 2023 was used to assess changes in land use and land cover, as well as their impacts on ecosystem services. Land-use classification maps were created using eCognition Developer 9.01 and incorporated into the InVEST-SDR model. The inputs for this analysis included the rainfall factor (R), soil erodibility (K), vegetation cover (C), protective measures (P), a Digital Elevation Model (DEM), a biophysical table, and the land-use maps. Additionally, the Geographically Weighted Regression (GWR) model was applied to examine the spatial relationships between land-use changes and ecosystem service indices, specifically focusing on Avoiding Erosion and Avoiding Export.

The study revealed that green cover—including rangelands, coniferous forests, orchards, and agricultural lands—declined by approximately 152 square kilometers (15.4 percent), leading to a conversion of these areas into built-up land. This transformation resulted in a 56.14 percent increase in potential soil loss, a 10.32 percent reduction in the Avoid Erosion index, and a 23.31 percent rise in the Avoid Export index. The Geographically Weighted Regression (GWR) model demonstrated a strong spatial correlation between land use and ecosystem services related to Avoid Erosion and Avoid Export in both 2000 and 2023. In 2000, the Adjusted R² values were 0.98 for Avoid Erosion and 0.99 for Avoid Export, with lower residual sums of squares and standard deviations compared to 2023. Rangelands exhibited the highest Avoid Erosion and the lowest Avoid Export, while built-up areas showed the highest Avoid Export. In 2023, the Adjusted R² increased to 0.99 for Avoid Erosion but decreased to 0.96 for Avoid Export, accompanied by higher residual sums of squares and standard deviations. Orchards recorded the lowest Avoid Export, whereas coniferous forests had the highest. The decrease in the Akaike Information Criterion corrected (AICc) in 2023 indicated an enhanced model fit under more homogeneous spatial conditions. However, this improvement did not reflect ecological progress, as the reduction in natural cover and the expansion of built-up areas negatively impacted ecosystem performance in soil retention and erosion prevention. A comparison between 2000 and 2023 demonstrated a diminished capacity of the ecosystem to conserve soil, evidenced by a significant decline in Avoid Erosion and an increase in Avoid Export, driven by urban expansion and reduced vegetation cover. This underscores the urgent need for improved land-use management to mitigate soil degradation and preserve ecosystem functions. 

 The findings highlight the importance of protecting vegetation cover and improving land-use management in sensitive areas. Changes in the Lavasanat watershed have led to decreased vegetation cover and an increase in built-up areas, negatively impacting ecosystem services such as soil retention and sediment control. As a result, soil erosion and excess sediment accumulation have worsened, reducing the ecosystem's resilience. Enhancing land-use management in areas with high erosion potential is crucial. Additionally, integrating the InVEST model with advanced hydrological models like SWAT and RUSLE is recommended for more accurate simulations of soil erosion and ecosystem services. Future research should explore the effects of climate and socio-economic scenarios on soil erosion and land-use changes to better understand environmental change patterns and inform effective management strategies. These approaches can help improve planning for the preservation of soil and water resources, mitigating the harmful effects of climate change and human activities on ecosystems.

The development and expansion of road transport causes a great impact on the development of a biological community. Today, rollovers and road accidents are considered as one of the most important man-made hazards in the field of transportation. Accidents that happen during the transportation of dangerous materials always have a great potential to become a disaster and a widespread crisis. This research focuses on assessing the risks caused by the transportation of dangerous substances, identifying the consequences and providing the optimal traffic route in order to reduce the effects on environmental assets in different seasons of the year. The ultimate goal of the research is to provide a comprehensive and generalizable model to be able to be used in achieving the practical purpose.

In this research, the risk of man-made hazards in the transportation of hazardous materials in the study area was calculated considering criteria such as "the number of accidents and rollovers on each road”, “the number of traffic intersections according to the number of lanes”, “the severity of the accident, the number of deaths per kilometer”, and “the travel time on each route" and a mathematical model was desinged. Then, considering the risk of each route and their ranking using the Dijkstra algorithm, the best route for transporting hazardous materials was prioritized in order to reduce the possible consequences.

Most of the studies related to the transportation of hazardous materials are usually conducted based on two general sections, including "determining and assessing transportation risk and identifying and analyzing asset vulnerability" in order to provide a solution to reduce risk. However, in this study, by utilizing the Dijkstra method to reduce possible consequences after calculating the risk of each route, the optimal traffic route was determined as the route with the lowest risk in different seasons of the year. In addition, in this research, in line with the main goal, a comprehensive conceptual model with generalizability has been presented in the form of a flowchart.

In the study process of this research the risk of man-made hazards of carriers of dangerous substances in transportation networks according to various criteria, including the adjusted number of injured on each road, the inherent risk of each route, the number of injured due to heavy vehicle accidents and the population around the route are estimated. Therefore, it is suggested that in future studies, indicators such as the inherent risk of vehicles and the inherent risk of drivers should also be considered.

Tehran is one of the cities that are continuously exposed to flooding. High density and high volume of impervious surfaces in urban runoff as well as being at the foot of the mountain are the main reasons for potential flooding in Tehran. Therefore, identifying prone areas to urban flooding is considered the most important measures in reducing losses and efficient management. 

In this study, the flood potential of region 1 in Tehran using the curve number method was evaluated in ArcMap software.

The curve number and infiltration map of the area was prepared by combining data and according to the land use. Then, using the maximum daily precipitation zoning maps and formulas, runoff for different return periods were calculated for the area.

The results show that for the precipitation with return period of 5-year and 10-year, about 90 percent of area has high and very high flooding potential. These measures in return periods of 25 and 50 years will be the top 95 percent. Also, in the return periods of 25 and 50 years, all the areas of urban flood potential are moderate to high and area with little flood potential are not found. Furthermore, in areas where the density residential and parking is more, the cumulative effect of impervious surfaces is increased flooding potential.

In today's era, intentional man-made disasters with external and internal enemy agency and the threats caused by them, which always target the infrastructures and vital assets of human societies, are one of the most important issues of crisis management and passive defense. The history of this type of disasters in all parts of the world shows; The most important reason and motivation of the enemy in actualizing his threats; Attacking the vital assets of a society is aimed at disrupting security and stability and achieving one's own interests (especially political, cultural and economic interests). In this regard, urban water and sewage  infrastructure, with the mission of providing sanitary drinking water, is one of the vital assets of the society and on the other hand, it is attractive to the enemies. One of the most important consequences of the actualization of threats to urban water and sewage assets is the formation of social crisis and populardisturbances. In this research, with the main objective of evaluating the possibility of threats to the water and sewage assets of Hamedan city in the face of intentional man-made disasters, the issue was evaluated using the combined SECA-GIS method. The results show that among the threats' evaluation criteria in this city; The intensity of the threat with a weight of 0.1844, the magnitude of the threat with a weight of 0.1780, and the occurrence history with a weight of 0.1674 have been assigned the first to third priority, respectively. Among the threats of intentional man-made disasters, in general, riots and riots rank first, air and missile attacks rank second, and vandalism ranks third. But looking at the classification of man-made disasters; Air and missile attack (by the external enemy) and riots and disturbances (by the internal enemy) are in the first place in the studied area. And according to the attractiveness of water and sewage  infrastructure assets from the enemy's point of view; The city's water treatment plants, with a score of 4.5072, are at a critical level, facing air and missile attacks, which require preventive measures and Passive defense.

Precipitation is one of the essential parameters of the water cycle, the estimation of which is effective in water and soil resources management. In this study, the SM2RAIN-NWF algorithm was used to estimate irrigation water consumption at the field scale based on satellite soil moisture data. Satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer (AMSR2) along with GLEAM products and rainfall were used for the period of 2012-2020 as model inputs. Precipitation estimation was performed for three different sites including an agricultural land in Miandoab, a vegetated surface in Malekan, and a barren land in Bonab. Using this model, the coefficient of determination (R2) of precipitation estimation was between 0.53 and 0.70. A comparison of the results revealed that the model exerted much better results in regions with no vegetation. The results of irrigation estimation in Miandoab plain showed that although the model systematically over/under-estimated irrigation data in some seasons compared to the in-situ data, the average performance of the model in irrigated regions (NS = 0.55, R2 = 0.63, and PRMSE = 2.48%) proved that the proposed approach can provide a suitable prediction of irrigation pattern.

This research aims to evaluate the vulnerability of aquifers by comparing two approaches of deep learning and machine learning in index calibration. Therefore, by analyzing the inherent vulnerability of the Amol-Babol aquifer with the DRASTIC index, the sensitive areas of the aquifer were identified. The results of the vulnerability index showed that the northwestern part of the aquifer is more sensitive than other areas. Examining the correlation value between nitrate concentration as an effective index with the DRASTIC vulnerability index indicates a value of 24%, which indicated the need for recalibration. Therefore, with two CNN-Harris Hawks and LSTM-MPA methods as deep learning approaches, weighting and index ranks were carried out as decision variables with the aim of maximizing the correlation of nitrate concentration and vulnerability index. The results showed that the CNN-HHO method with a correlation of 0.62 is superior to the LSTM-MPA method with a correlation of 0.59. Vulnerability zones in the assessment conditions showed that the western and northeastern parts have higher vulnerability. On the other hand, the recalibrated weights and ranks indicate an increase in all weights and ranks in recalibration conditions compared to the initial conditions, which was determined after analyzing the optimization approaches

Among natural disasters, flood is undoubtedly the most catastrophic hazard in the world. One of the basic strategies for reducing the damage caused by floods is to prepare a flood sensitivity map. Spatial prediction of the flooding probability using models created from spatial and historical data, which ultimately leads to the preparation of flood sensitivity maps is an appropriate solution for land management planners in different areas to prevent the occurrence of this phenomenon. In this research, in order to determine flood-prone areas, the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of imperial competition (ANFIS-ICA) and the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of particle swarm (ANFIS-PSO) are used.

The Zarine River watershed has an area of 4485 km2 and is located in the northwest of Kurdistan province between the longitude of 45°48ʹ30ʺand 46°48ʹ20ʺ east and the latitude of 35°42ʹ20ʺ and 36°23ʹ15ʺ north. The climate of the region is humid and the average annual rainfall is 480 mm. Locations of flood events were randomly divided into two groups: training (70%) and validation (30%). Various environmental factors (height, direction, slope, surface curvature, land use, lithology, rainfall, flow power index, distance from river and topographic wetness index) were selected as independent variables in the modeling and their digital layers were prepared. The ANFIS-ICA and ANFIS-PSO models were used in this research and their prediction results were evaluated based on the criterion (AUC) and the true skill statistic (TSS).

On the basis of these findings, in the validation stage, the model (ANFIS-PSO) with an AUC of 0.98 and a true skill statistic (TSS) of 0.89 had the highest accuracy. The results also showed that the factor of distance from the stream was identified as the most important environmental factor. In addition, ground slope and TWI were ranked second and third in importance, respectively.

Based on the results, the hybridization approach, which combines machine learning models and meta-exploratory optimization algorithms, improves the learning power as well as the predictive power of the model. The results of this research showed that the distance from the stream and the slope of the land are the most important factors affecting flooding. Based on the results and analysis, it can be concluded that machine learning models have a high capability for predicting flood potential. The flood potential maps prepared in this research can be very useful for managers and experts and can be used in planning flood prevention measures. Directing flood control facilities and measures in situations with a high flood potential will improve flood management from an economic and technical point of view.

The response of water balance components as indicators of hydrological performance to stimuli such as land use change is of strategic importance. In this study, the effect of land use change on water balance components was evaluated, focusing on the relationships and change of the main indicators of water yield and sediment yield. Based on this, the Markov chain model was used to predict land use in 2040. Also, the American soil and water assessment tool was developed as a base model for evaluating and estimating hydrological indicators in the Taleghan Watershed as a mountainous watershed with structural heterogeneity. The results of model showed that the increase of settlements and urban development in Taleghan watershed will result in increased runoff, increased water yield and sediment yield indicators, and more sedimentation. Land use change leads to an increase of sediment yield by 11 times until 2040. The conversion of pastures to barren lands is the most important land use change that can increase sediment yield. Also, the increase of barren lands will be the reason for the reduction of evapotranspiration in some sub-basins of this watershed. Increasing rainfall and decreasing soil permeability will increase surface runoff, and as a result, soil erosion and sediment yield will increase. In this study, exactly the sub-basins that had the highest quantitative amount of predicted water yield index also had the highest quantitative amount of sediment yield index and increased sedimentation. In this research, it was determined that land use change as a type of structural change in the land will have visible effects on the functions and hydrological responses of the watershed.

Wetlands, as one of the most important types of ecosystems in the world, are extremely threatened. In addition to being part of Iran's protected areas, the Parishan wetland is also known as an international wetland and biosphere reserve. Understanding the process of changing in this wetland, can be very helpful in improving its future status. Therefore, the purpose of the present study is to monitor the changes in the over a 30-year period.

For the purpose of the research, Landsat satellite images were prepared for four time periods of 1987, 1998, 2007 and 2016 along with other required data. By performing the required preprocessing in ENVI 4.7 software, Parishan wetland land-cover maps was extracted using Normalized Difference Vegetation Index and Normalized Difference Water Index combining with Decision Tree method in three class including water-body, vegetation and others land-cover.

The results showed that after 30 years only 13 hectares of 1963 hectares of Parishan wetland water-body remained. Monitoring of changes shows that Parishan wetland water-body has decreased by 1950 hectare in comparison to 1987, 3605 hectare in comparison to 1998 and 2272 hectare in comparison to 2007.

using satellite data and remote sensing techniques along with Decision Tree classification model indicate the capability of this method for identifying and classifying land-cover in wetland areas where vegetation and water are intertwined.

Finding the potential of groundwater resources is one of the basic principles in water resources management. The aim of this research is to determine the potential of groundwater using support vector machine learning (SVM) models as well as metaheuristic algorithms (hybrid support vector machine model and the bee metaheuristic optimization algorithm (SVM-BA) and hybrid model of the support vector machine and particle swarm optimization algorithm (SVM-PSO).

The factors of elevation, slope, aspect, topographic humidity index, distance from stream, drainage density, distance from fault, lithology, topographic position index, land roughness index, relative slope position and flow convergence index were selected in Bojnurd region. Information on the location of 359 springs was received from the regional water company. Random division algorithm was used to divide training points (70%) and validation points (30%). Based on the removal sensitivity analysis, the importance and contribution of the input variables in determining the groundwater potential were determined. The accuracy of the models was evaluated in two stages of training and validation based on the receiver operating characteristic (ROC) curve method.

The evaluation of the accuracy of the models based on the evaluation criteria of the area under curve (AUC) showed that the prediction accuracy of the hybrid model of the support vector machine and the particle swarm optimization algorithm (SVM-PSO) is 0.945 more than other models (SVM: 0.918 and SVM-BA: 0.932). Based on the results of the superior model, the high potential class and the very high potential class accounted for 7.75% and 38.66% of the area respectively. Among the factors, relative slope position with 14.5%, distance from the fault with 13.4% and lithology with 12.3% were the most important in predicting groundwater potential.

Based on the results of this research, the support vector machine model has a high performance, and two optimization algorithms, the bee metaheuristic and particle swarm optimization algorithm, strengthen the predictive power of the model. Also machine learning models can identify the relationship between the environmental factors and the water supply of the springs and determine their role by using the available data. The relative slope position factor was identified as the most important variable and the distance from the fault factor was considered as the second most important variable in the present study. The results of the research showed that the faults in the region play an important role in aquifer recharge, storage and flow of groundwater. The lithological factor was also introduced by the model as the third important variable in identifying the state of groundwater potential. In this research, by presenting the groundwater potential map, it is possible to plan and verify land use planning for the Bojnurd watershed.

In many areas, urban flood and flood control system are not yet considered as an important issue in the design of the urban fabric and the problems caused by it are clearly seen at the city level. In order to control and manage urban flood, it is necessary to extract urban flood risk zones and analyze the vulnerability of urban infrastructure assets at risk. Flood risk assessment provides valuable information for flood risk management in order to assess vulnerability and face risk. Achieving this information requires the use of hydrological and hydraulic models and the analysis of damages to various urban facilities and uses. In this research, HEC-RAS hydraulic modeling has been used for two-dimensional simulation with the design return period (25 years) and flood depth and speed zoning for this return period. In this study, with accurate two-dimensional flood modeling, using the 1.5-meter height figure model, analyze and investigate the urban flood risk. The results of the research show that most of the areas that are facing floods and inundation are among the worn-out areas that have an older drainage network design, and the areas of Shahin Vila and Gohardasht are in an unsustainable condition compared to the other two areas and in contrast to the two areas Azimieh and Jahanshahr Karaj have better resilience than the other two regions.

The lead-time and accuracy of the precipitation forecasts have a substantial influence on the flood forecast and warning systems. The application of Ensemble Precipitation Forecasts (EPFs) derived from numerical precipitation models has been developed due to their impact on increasing flood lead-time. This research aims to improve the skill of numerical precipitation models using post-processing techniques. In this regard, EPFs of three meteorological models, e.g., NCEP, UKMO, and KMA, were extracted for sex precipitation events leading to flood in the Dez river basin during 2013-2019. The statistical approaches and data-driven model were applied to post-process the EPFs. For this purpose, the raw forecast of every single model was corrected using linear and power regression models. Then, the corrected output of single models was combined using the proposed model of Group Method of Data Handling (GMDH). The results indicated that Power Regression Model (PRM) outperformed the linear models to correct raw forecasts. After correction of models' output, more accurate results were obtained by NCEP and UKMO models. Moreover, the Multi-Model Ensemble (MME) system constructed by the GMDH model (MME_GMDH) had a great effect on the skill of numerical precipitation models, so that the Nash–Sutcliffe and normalized error (NRMSE) efficiency criteria for MME_GMDH respectively were improved on average 23% and 11% in comparison with the PRM. A comparative assessment of the discrimination capability of MME with single ensemble models using ROC curve at the thresholds of 2.5 and 10 mm represented a higher discrimination ability by MME_GMDH for both thresholds. Post-processed EPFs exert as a reliable input to the hydrological models for extreme events forecast.

Water, food and energy are the main resources needed for the development of communities. The nexus view emphasizes the interconnected management of these resources and has been applied with a focus on three sources at the macro level. In this research, evaluation and prioritization of effective factors and methods of optimal resource allocation based on the nexus approach of water, energy and food production with the Analytic Hierarchy Process (AHP) in Alborz province has been considered. For this purpose, the criteria were compared in pairs in a hierarchical analysis with the opinions of experts and their importance was determined. Criteria including water, food and energy were considered. Factors affecting the optimal allocation of resources in Alborz province were determined and presented as multiple choice and compared in pairs based on questionnaires completed by experts. The results showed that the water criterion with the relative weight of 61.4% has the greatest impact on the evaluation process compared to other criteria and the energy and food criteria with the relative weight of 26.2% and 12.4% have the most impact, respectively. Among the developed strategies, education and awareness raising with a score of 35.9% was ranked first, and environmental policy with a score of 26.6% was ranked second. Therefore, it is suggested that strategies for education and promotion of awareness and environmental policy be given priority.

lack of enough attention to the public participation is the most reason for the deficiency of wetland ecosystem management. Managing wetlands based on ecological approach defined as  “taking all the key elements and indicators in long-term management of an ecosystem and also as an integrated management strategy, promotes the conservation and sustainable use of resources with the participation of all stakeholders and the active participation of people, local communities in particular”. The purpose of this study is to present a management framework in the Anzali International Wetland with an ecological approach, applied for the first time.

This study is a descriptive-analytical study based on available data. The World Conservation Union has incorporated 12 principles into five categories in this study developed for Anzali Wetland as the study area. Gephi software was used for stakeholders’ network analysis too.

The results showed the developed framework in this study is comprised of seven main sections: Pre-planning, Scoping, Analysis, Planning, Implementation, Monitoring and Evaluation. Each of thes mentioned sections has related elements. In stakeholders’network analysis individuals are represented as nodes and connections as edges of the graph that shows the linkage between stakeholders.

The main stakeholders should have participated in the ecological management of the wetland including: Governmental Organizations, Non-governmental Organizations, Local Communities, Judiciary, and Legislature. In this study, the initial stages of the developed framework were implemented including pre-planning, scoping, and some parts of the analysis. Afterwards, the identified stakeholders, at consideration of verified and modified information obtained from the previous three steps, formulate a joint implementation, monitoring and evaluation plan in the participatory workshops. It is also recommended to apply the developed framework in other wetlands and common natural resources, especially protected areas, in accordance with the legal duties of the Department of Environment (DoE).

The formulation of strategies for sustainable groundwater resources needs an integrated view of land-use, climate changes, social system, and the response of the hydrological system. Negligence in creating a balance between these variables in charging and discharging groundwater resources has led to problems such as land subsidence, saline water advancement, reduced water quality, increased pumping costs, etc. in most of the country aquifers. In this study, the Lenjanat region was selected as a case study and one of the important regions of the Zayandehrud Watershed due to its interaction with the river, the existence of land-use changes, and increasing harvest from the regional aquifer. The results of temperature and precipitation prediction in an optimistic and pessimistic scenario until 2035 by the LARS Model and the results of examining past and future land-use changes and calculating the impact factor (adjustment coefficient) using IDRISI SELVA, ENVI, and CA-Markov were included in the study model (a combination of WEAP and ANFIS) to investigate future runoff and its contribution to aquifer recharge. The results showed that both climate and land-use changes have some impacts on water resources. In the case study, the amount of runoff decreases due to the decrease in rainfall and increase in temperature, and runoff infiltration, and subsequent feeding and groundwater level decrease due to land-use changes and increase in groundwater abstraction. The average rainfall volume was estimated at 194.17 million cubic meters in the observed years, of which 99 million cubic meters rechrged the aquifer. The average volume of groundwater supply after the effect of an adjustment coefficient of 1.051 was predicted in the optimistic scenario of 10.09 and the pessimistic scenario of 93.72 million cubic meters, respectively.

The utilization of water resources models enables correct and sustainable planning in water resources management. The purpose of this study is to optimize the use of water resources in the catchment area of Varamin plain for 10 years under different management scenarios. To achieve optimal conditions in the volume of water resources, the minimization of non-supply needs and the needs of different land use in Varamin plain were studied. Accordingly, the situation of water resources in Varamin plain in three sectors of agriculture, industry, and drinking using WEAP model under two scenarios: a- reference with prioritization of the ministry of energy and b- scenario of reducing inlet volume by changing prioritization in the period 2016-2026  Solar was evaluated. The climate of the region was studied based on the model of the blue year (flow, precipitation, evapotranspiration, temperature) and entered the model. The results showed that according to Scenario A, we will have a 30% shortage of drinking water supply in the region, and this is 37% for the scenario of the reduced input volume. Based on the results, the reference scenario, which in terms of meeting different needs and reliability index of water needs and maximum volume of water not provided during the statistical period, compared to the scenario of the reduced input volume, has a better performance for the Varamin plain, It was considered as the preferred scenario.

Strategic planning and management is one of the most important factors of management systems in an organization. The purpose of this study was to plan, develop and prioritize the environmental management strategies of Anzali International Wetland for sustainable development. For this purpose, at the first listing and determining internal and external factors were done using questionnaires and Likert scale. Then SWOT analysis and quantitative strategic planning matrix (QSPM) were used. The current strategic position of the environmental management of Anzali Wetland was recognized as defensive. Defensive strategies were prioritized according to the results of the QSPM matrix. The most important defensive strategies were attention to participatory planning, communication and regional cooperation in order to protect the ecological and economic services of the wetland with a score of 5.368 followed by improving and upgrading the integrated system of comprehensive management of the wetland with a score of 5.198. Strategic planning and management in Anzali Wetland can lead to optimal resource management and adoption of appropriate environmental policies in order to protect this wetland.

 Conflicts over transboundary waters between neighboring countries is one of the most common challenges in the regional and international scale. Solving of these conflicts and getting agreements over transboundary waters has always faced many challenges that make it difficult to reach a consensus solution. Afghanistan, Iran and Turkmenistan Conflicts over Harirud river waters is among the transboundary river waters conflicts that have escalated  in recent years.

Assuming that the parties are willing to hold negotiation in order to reach an agreement over Harirud river waters, in this paper the probable options of each party were identified with respect to the prevailing international principles and theories on how to divide common border waters. After defining five options for negotiation, in order to identify social optimal situation, six common social choice rules including Condorcet Choice, Borda Scoring, Plurality Rule, Median Voting Rule, Majoritarian Compromise and Condorcet’s Practical Method were used. Finally, by using the aforementioned social choice rules, the most probable result was determined.

The results showed that water trading market option, based on all rules of social choice, exception of plurality rule, was chosen as optimal situation. Therefore, based on the social choice rules, it can be said that the water trading market option is the most likely solution which there is a possibility of an agreement between the three countries over Harirud river waters conflicts.

 Landscape connectivity plays an essential role in the conservation of protected areas. The Alborz wild sheep (Ovis orientalis × Ovis vignei) is the dominant species in Varjin Protected Area. The population of the species has been decreased during the last 20 years due to habitat loss and fragmentation. So far, many studies have been done on the environmental impact assessment of urban area developments on the protected area, but there is still a lack of habitat connectivity and quality analysis in the area. Based on the spatial location of Varjin Protected Area, connectivity analysis can help conservation planners to identify key patches and corridors that more than others, contribute to upholding species dispersal. The aim of this research is to prioritize habitat patches for conservation by analyzing the connectivity of the habitat patches and considering habitat quality as the second important factor in species distribution. Applying this method could lead to better conservation prioritization between habitat patches.

 Landsat satellite images 7 and 8 have been used as inputs for the Habitat Quality model and Connectivity analysis. The connectivity metric was analyzed by calculating the probability of connectivity (dPC) and betweenness centrality (dBC) at different spatial scales of landscape and patch. All the analyses have been done in Graphab open-source software using graph theory and applying network analysis containing nodes and edges. The threshold was pre-defined for the species, and all the calculations were based on Euclidian distance. This research used the InVEST Habitat Quality model to analyze the spatial status of habitat quality. Finally, spatial analysis was performed by ArcGIS 10.4 and the maps were classified based on natural breaks.  

 Results demonstrated that the value of dPC was between 0 to 0.796, and the value of dBC varied from 0 to 7.58E+11. Different importance values have been obtained for all 23 patches. This suggested that patches 1 and 4 showed the highest dBC and dPC metric values. The values of InVEST habitat quality decreased in the south and northeast of the area, respectively, due to proximity to urban areas and other threats. Patches with good performance in improving connectivity and higher habitat quality values were identified. By overlaying the output values of the maps, prioritized patches were recognized and suggested to be placed under protection.

 Patches with a high level of connectivity and habitat quality were located in the east and southeast of the region. This research has taken a novel step toward conservation by using connectivity analysis and habitat quality as an ecosystem service in protected areas. Landscape and patch scales as two spatial indices can be used in other regions and for other essential species. As the Varjin Protected Area is located between two important ecological areas, Lar national park and the Central Alborz Protected Area, the priority of patches would be changed by a great extent when taking those areas under consideration.

Nowadays, changes in environmentaldynamics including changes in land cover, land use, water supplies and climate are considered to be challenging issues of human communities. Thus, it is especially important to investigate different aspects of land use change and their effects on the past and future trends ofdifferent plains. Identification of previous changes and prediction of future trends help planners and managers to compensate for losses and avoid similar mistakes in future.Therefore, the present study is divided into two parts. In the first part, land usechanges of Lenjanat Plain in the 1990-2015 period are analyzed. In the second part, future land use changes (2015-2035) of the area are investigated.Adjustment coefficient is calculated to show the effect of land use changes on runoff coefficient.  

2.1 Study area and its characteristics Lenjanat Plain is a sub-basin of Gavkhuni Wetland located in the central part of the Iranian plateau with the longitude of 51˚ 8' to 51˚45' E and latitude of 32˚2' to 32˚24' N.   2.2  Land use change In the first step, preprocessing of satellite data and preparation of the information were carried out through geometric and atmospheric correction of the digital imageswith the purpose of correcting errors, removing defects in the images and omitting system errors. Landsat-4 and 5, TM sensor, Landsat-7, ETM+ sensor and Landsat-8 OLI sensor were used to evaluate and predict land use changes in the study area. Image selection was performed based on the availability criteria, and Landsat satellite images were thus obtainedfor 1990, 2005, and 2015. In the next step, unsupervised classification was used to create a general understanding of land use classes in the study area as a useful tool for determining training samples. ENVI software was used to identify suitable training samples for classification. To realize the second goal of the study, Marco integration model and a cellular automaton model were used and future land use changes in Lenjanstudy area were predicted for 2035 based on the base map and the assumption that the current trend in land use changes will continue. For this purpose, the Marco and CA-MARKOV modules were utilizedin IDRISI SELVA. CA-Markov model was used to predict land use changes with spatial contiguity and spatial transitions over time. 

3.1 Measuringland usechanges Finall and use maps represent the percentage and spatial distribution of each landuse type in the study area in the past, and at present. These maps area also used to evaluate the effects of management on the intensity of land use changes in the study area. Man-made surfaces have almost doubled in the region and reached from 3922 to 7202 hectares. In the past, 3922, 22516, 81613 and 367 hectaresof man-made areas (such as residential and industrial), agricultural lands, barren lands, and riverbeds were located in the study area which have reached 7202, 17943, 82793 and 229 hectares, respectively.   3.2 Prediction of future land usechanges Land use types in 2035 were predicted using CA-Markov chain model. Results indicate that manmade surfaces will exhibit a rising trend and increase from 7,202 to 9,122 hectaresduring 2015-2035 period.To determine the compatibility or incompatibility of actual maps and modelingresults, model validation was performed. In this regard, land usesof the study area was predicted for 2015 through the aforementionedmodel and the predicted map was compared with the actual land use map in 2015. In this method, the Kappa index of 0-1 was used to interpret the results.   3.3 Adjustment Factor Before anything else, the present study have determinedland usechangespercentage. Then, runoff coefficient of the forecast period was divided by runoff coefficient of land use changes in the pastto calculate the adjustment factor.Based on the findings of this study and the land use changesforecasted forLenjanat plain in 2035, the adjustment coefficient for the region equals 1.051.

The present study aimed to evaluate various criteria affecting the quantity of water resources. Moreover, it has evaluated and determinedadjustment factor. For this purpose, Lenjan plain was used as a representative of the plainsin the country. Five land use types, including man-made areas (such as residential and industrial), agricultural lands, Barren lands, riverbeds and rock beds were identified for 1990, 2005 and 2015. CA-Markov was applied to predict land use changes for 2035. Adjustment coefficient is also calculated to show the effect of land use changes on runoff coefficient

Water resources have always been a limiting factor in environmental planning. Understanding the status of this critical resource can have a significant impact on proper land use planning. This study aimed to estimate the water supply and demand as an ecosystem service and to identify areas with water stress in the Sirvan transboundary basin. This goal could be achieved using the concept of ecosystem services, which is new thinking in the earth sciences, and using geographical, climatic data, and satellite images. After relative corrections, Landsat satellite images for 2019 were prepared, and the LULC map was produced. Then precipitation, evapotranspiration, and root depth layers were created. The latest inputs, including the Evapotranspiration coefficients of the land cover and related tables, were prepared and modeled in the InVEST 3.8.9 software environment. The results showed that the amount of water yield in this watershed is 5,381 million m3/yr, where the sub-basin 5, 11, and 1 have the highest water yield with 1426, 906, and 621 million m3/yr. and sub-basin 2 with 100 million m3/yr have the lowest water yield. The sub-basin 5 with 110 million m3/yr has the highest consumption, and the sub-basin 2 with 7 Mm3/yr has the lowest consumption. Sub-basin 4, where the city of Sanandaj is located, has the highest water stress.

noise pollution is one of the main factors affecting the quality of human life that can affect human emotions and behavior and lead to violent behavior. The purpose of this study was to investigate the relationship between the feeling of listening noise and feelings of behavioral violence among citizens living in places) construction site (with high noise pollution. the statistical population of this study consists of residential buildings around the auxiliary tunnel project of metro line 3 of Tehran city-Abbas-Abad neighborhood up to a radius of 50 meters.‏ By making a researcher made Questionnaire, By developing researcher-made questionnaires‏ the rate of the feeling of violence with residents was measured.for this purpose, Pearson correlation and linear Regression methods were used. The occurrence of violent behaviors can be highly related to noise pollution‏. The correlation coefficient of the model (0.760) was calculated, Which represents the ability of the model to predict the purpose of the study. therefore, taking actions to control and reduce noise pollution and negative consequences in urban spaces in particular in the residential context, is essential for the desired future and in the direction of sustainable development.