University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Mapping of Flood Inundation Area in Tehran City with HEC-Geo-RAS (Case Study: Darakeh, Farahzad, and Kan Rivers) Mapping of Flood Inundation Area in Tehran City with HEC-Geo-RAS (Case Study: Darakeh, Farahzad, and Kan Rivers) 1 34 29769 10.22108/gep.2025.145506.1729 FA Seiyed Mossa Hosseini Associate Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Amir Reza Sadeghi PhD Student of Geomorphology, Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Journal Article 2025 06 10 Abstract The objective of this study was to create flood inundation maps for the Darekeh, Farahzad, Western Flood Diversion Canal, and Kan River, focusing on return periods of 10, 25, 50, and 100 years. The HEC-Geo-RAS software was employed to simulate the hydraulic properties of flow in these canals. The results revealed that the largest flood extents occurred during a 100-year return period across all studied rivers. Hydraulic flow analysis indicated that the floodplain area for the Kan River reached 292 ha during the 100-year return period with predominant land uses in the flood risk zone comprising green spaces (203.8 ha) and a mix of service, administrative, and commercial areas (80.8 ha). The Western Flood Diversion Canal inundated 99.6 ha, primarily consisting of residential areas (57.2 ha) alongside industrial, workshop, and service-administrative uses (26.1 ha). The Darekeh River floodplain covered 74.8 ha, mainly featuring green spaces (55.8 ha) and residential areas (14.5 ha), while the Farahzad River floodplain extended to 34 ha predominantly made up of green spaces (32.6 ha) and limited residential areas (1.2 ha). The findings highlighted that the Kan River (downstream of the Western Flood Diversion Canal) and the Western Flood Diversion Canal possessed the most extensive floodplains. Notably, residential, service, administrative, commercial, and workshop land uses were situated within high-risk flood zones that carried significant economic value. The results of this study can be utilized to identify vulnerable areas in Tehran at risk of flooding, guide urban development toward safer locations, inform urban infrastructure design, and optimize surface runoff collection systems. Keywords: Urban Flooding, Flood Hazard Map, Land use, Flow Hydraulic Modeling. Introduction Urban flooding has become an increasingly significant natural hazard exacerbated by climate change, urbanization, and population growth. In recent decades, identification of flood-prone areas has garnered considerable attention from managers and planners. Studies indicate that the risk of flooding to human health and property is projected to rise, primarily due to population expansion in vulnerable regions. By 2050, it is estimated that approximately 1.3 billion people will reside in flood-prone areas. Tehran, covering around 700 km2and home to over 9 million residents, is particularly susceptible to fluvial flooding. This vulnerability arises from its foothill location, steep slopes, sparse vegetation, heavy rainfall, uncontrolled development in riverine areas, and inadequate construction practices. Flood management in this densely populated metropolis is highly complex. To mitigate potential damages, it is essential to accurately identify flood inundation areas associated with various return periods. The HEC-RAS model serves as an effective hydraulic tool for this purpose, providing critical information for emergency planning and guiding urban development towards safer areas by accurately modeling the hydraulic properties of floods. This research aimed to create flood inundation maps for the Kan, Farahzad, and Darekeh rivers, and the Western Flood Diversion Canal in northern and northwestern Tehran using HEC-Geo-RAS software. The findings of this study will assist in identifying high-risk areas and preparing comprehensive flood hazard maps for Tehran. Materials & Methods Data Analysis Initially, the annual peak discharge data recorded from 1977 to 2017 at 3 hydrometric stations (Haft Hoz, Soulaghan, and Lashkar) were collected from Iran’s Water Resources Management Company. Using Easyfit software, the best-fit distribution for these data was determined based on 3 statistical goodness-of-fit criteria: Anderson-Darling, Kolmogorov-Smirnov, and Chi-Square, all evaluated at a 95% confidence level. Hydraulic Model Execution (HEC-RAS) The HEC-RAS hydraulic tool was employed to simulate flood characteristics along the studied rivers for the return periods of 10, 25, 50, and 100 years. Being developed by the U.S. Army, this model could simulate both steady and unsteady flows while modeling floodplains. The main inputs for the model included: Manning's Roughness Coefficient: Based on field visits and Cowan's formula, this coefficient was assigned values of 0.015 to 0.017 for main river sections and 0.020 to 0.040 for natural beds and floodplains. Digital Elevation Model (DEM): A DEM with a resolution of 10 m was utilized to create a Triangulated Irregular Network (TIN) in ArcGIS software. River Geometric Information: Using the HEC-Geo-RAS extension, river centerlines, banks, and cross-sections (at 50-meter intervals) were extracted. Flow Information: This included details on flow regimes, discharges, and hydraulic boundary conditions. Hydraulic and Topographic Information: This encompassed roughness coefficients, river course conditions, bridges, and both longitudinal and cross-sectional profiles. After executing the model, flood inundation maps and hydraulic properties of the flood (such as flow velocity and width) were generated. Subsequently, the flood extents were validated using Google Earth and previous studies and the affected land uses were identified. Research Findings The study produced detailed flood inundation maps for the Darekeh, Farahzad, Western Flood Diversion Canal, and Kan River, assessing flood extents for return periods of 10, 25, 50, and 100 years. The key findings were as follows: Flood Extents by River: Kan River: The largest floodplain was observed during the 100-year return period, measuring 292 ha. This area comprised predominantly green spaces (203.8 ha) and a mix of service, administrative, and commercial land uses (80.8 ha). Western Flood Diversion Canal: The floodplain reached 99.6 ha, primarily involving residential land (57.2 ha) and service-administrative uses (26.1 ha). Darekeh River: The floodplain extended to 74.8 ha with green spaces (55.8 ha) and residential areas (14.5 ha) dominating the landscape. Farahzad River: The smallest floodplain at 34 ha was characterized mainly by green spaces (32.6 ha) and limited residential areas (1.2 ha). Impact of Slope and Geometric Characteristics: The steep slopes of the Darekeh and Farahzad rivers limited flood extents upstream, while reduced slopes downstream significantly increased flooding risk. The Western Flood Diversion Canal also exhibited increased flood extents due to similar topographical changes. Hydraulic Structures: Many bridges along the rivers lacked adequate capacity to manage floodwaters, particularly during higher return periods. The blockage of culverts due to sediment accumulation exacerbated flooding risks in urban areas. Affected Land Uses: Floodplains primarily encompassed green spaces, residential, and commercial-service areas, indicating a high risk of flood damage. The presence of sensitive land uses within these zones necessitated improved management and expansion of green spaces. Flood Management Implications: The findings underscored the importance of effective flood management and urban planning. Identifying vulnerable sites allowed for targeted interventions, such as improving water infrastructure, clearing culverts, and preserving green spaces. In conclusion, the study highlighted the significant flood risks in northern and northwestern Tehran, which were driven by slope reduction, insufficient culvert capacity, sediment buildup, and human interventions. Urgent measures were required to enhance flood management strategies and protect vulnerable urban areas from flooding. Discussion of Results & Conclusion Impact of Slope and Geometric Characteristics on Flood Zoning In the Darakeh and Farhzad rivers, steep upstream slopes limited flood extents; however, downstream areas experienced a significant increase in flooding due to reduced slopes. In the Western Flood Diversion Canal, the gradual slope decreased in the middle and lower sections promoted runoff accumulation, resulting in expanded flood extents. Similarly, the reduced longitudinal slope in the Kan River contributed to a notable increase in flood areas downstream. Effect of Hydraulic Structures (Bridges and Culverts) on Flood Dynamics Many bridges along the rivers and canals are inadequate in capacity to handle floodwaters, particularly during higher return periods (25, 50, and 100 years). The blockage of culvert inlets due to sediment accumulation and high runoff volumes has led to street flooding and further expansion of flood extents. This situation heightens the risk of urban flooding and necessitates a re-evaluation and modification of existing structures. Impact on Urban Land Use Floodplains in the studied areas primarily consisted of green spaces, residential properties, and commercial/service land uses. The presence of residential areas and sensitive land uses within these floodplains indicated a high risk of flood damage. Enhancing and expanding green spaces and water storage in vulnerable areas can help mitigate flooding impacts. Factors like reduced natural slopes in downstream sections, river section contractions, blockages caused by bridge piers, sediment accumulation, unauthorized constructions, and unsustainable land use practices contribute to increased flood volumes. Importance of Flood Management and Urban Planning The results of flood-prone zoning through hydraulic modeling clearly identified both safe areas and vulnerable sites. This information is essential for effective landscape design, urban ecology, and flood management planning. Key actions to consider included strengthening water infrastructure, rebuilding bridges, clearing culverts, and preserving green spaces. The results of hydraulic simulations using the HEC-RAS model for the Darakeh and Farhzad rivers, the Western Flood Diversion Canal, and the Kan River in the northern and northwestern Tehran Basin illustrated flood zoning conditions for return periods of 10 to 100 years. The model outputs included inundation maps, tables, and images, which were valuable for defining safe river boundaries, landscape and ecological design, and urban management studies. The main findings of this study could be summarized as follows: Darakeh and Farhzad Rivers: In the upstream sections, the floodplain extension was limited due to steep slopes. However, as the slope decreased downstream, the width of the floodplain increased significantly. In the Darakeh River, restaurants near the river were at risk of flooding. The maximum floodplain area for the 100-year return period was 8.74 ha, primarily consisting of green spaces and residential areas. Factors contributing to flooding included low slopes and the blockage and contraction of bridge culverts. In the Farhzad River, the maximum floodplain occurred at the intersection with the Behrud and Moradabad tributaries, primarily affecting private gardens and highlighting the need for effective green space management. The floodplain had expanded from Hemmat Highway to Marzdaran Boulevard, with Khoshmaram and Asbar bridges experiencing flooding during high return periods. The maximum floodplain area for the 100-year return period was 34 ha, mainly comprising green spaces and residential areas. Causes of flooding included narrowing and clogging of bridge culverts, reduced slopes, and high runoff volumes. Western Flood Diversion Canal: This canal played a crucial role in transporting floodwaters to the Kan River. The expansion of the floodplain in the downstream areas of the canal was significant due to reduced slopes. Several bridges, including the Ariafer Street Bridge, those near Yadegar Imam, Khosravi Bridge, bridges after the Second Sadeghieh Square, Vali Asr Street Bridge, Salimi Jahromi Street Bridges, and Sattari Highway, lacked sufficient capacity to manage high floodwaters. The largest floodplain area for the 100-year return period was 99.6 ha, encompassing residential areas along with commercial, service, and workshop uses. Key factors contributing to flooding in this area included high runoff volumes, reduced slopes, and insufficient capacity of both the canal and its bridges. Kan River (Upstream of the Western Flood Diversion Canal): The floodplain in this area was less mountainous and steep, but it expanded towards the area of Azadi in Zibadasht. The largest floodplain for the 100-year return period was measured 66.1 ha, comprising green spaces and residential areas. Contributing factors to flooding included narrowing and clogging of bridge culverts, construction along the riverbanks, and reduced slopes. Kan River (Downstream of the Western Flood Diversion Canal): At the junction with the Western Flood Diversion Canal, the accumulation of flows had led to significant floodplain expansion. This floodplain extended from the west of Tehransar to the east of Mehrabad Airport and continued towards Azadegan and Saveh Highway. The largest floodplain for the 100-year return period was 231 ha, making it the largest in the region. Major land uses in this area included green spaces (146.2 ha), commercial and shopping centers (80.1 ha), and residential areas (4.7 ha). The extensive flooding in this section with a maximum width of 1,037 m was primarily due to the accumulation of runoff from upstream catchments and slope reductions. These findings provide critical information for effective urban planning and flood prevention measures in the northern and northwestern regions of Tehran. The northern and northwestern basins of Tehran are particularly vulnerable to floods. Key factors exacerbating this risk include reduced slopes, insufficient capacity of culverts and bridges to handle floodwaters, sediment accumulation, and human alterations to riverbeds. There is a significant risk of damage to life and property in residential, commercial, and green areas within the floodplains. Urgent measures are needed to enhance the water transfer capacity of critical sections, reconstruct bridges, and manage land use effectively in the river basins. Abstract The objective of this study was to create flood inundation maps for the Darekeh, Farahzad, Western Flood Diversion Canal, and Kan River, focusing on return periods of 10, 25, 50, and 100 years. The HEC-Geo-RAS software was employed to simulate the hydraulic properties of flow in these canals. The results revealed that the largest flood extents occurred during a 100-year return period across all studied rivers. Hydraulic flow analysis indicated that the floodplain area for the Kan River reached 292 ha during the 100-year return period with predominant land uses in the flood risk zone comprising green spaces (203.8 ha) and a mix of service, administrative, and commercial areas (80.8 ha). The Western Flood Diversion Canal inundated 99.6 ha, primarily consisting of residential areas (57.2 ha) alongside industrial, workshop, and service-administrative uses (26.1 ha). The Darekeh River floodplain covered 74.8 ha, mainly featuring green spaces (55.8 ha) and residential areas (14.5 ha), while the Farahzad River floodplain extended to 34 ha predominantly made up of green spaces (32.6 ha) and limited residential areas (1.2 ha). The findings highlighted that the Kan River (downstream of the Western Flood Diversion Canal) and the Western Flood Diversion Canal possessed the most extensive floodplains. Notably, residential, service, administrative, commercial, and workshop land uses were situated within high-risk flood zones that carried significant economic value. The results of this study can be utilized to identify vulnerable areas in Tehran at risk of flooding, guide urban development toward safer locations, inform urban infrastructure design, and optimize surface runoff collection systems. Keywords: Urban Flooding, Flood Hazard Map, Land use, Flow Hydraulic Modeling. Introduction Urban flooding has become an increasingly significant natural hazard exacerbated by climate change, urbanization, and population growth. In recent decades, identification of flood-prone areas has garnered considerable attention from managers and planners. Studies indicate that the risk of flooding to human health and property is projected to rise, primarily due to population expansion in vulnerable regions. By 2050, it is estimated that approximately 1.3 billion people will reside in flood-prone areas. Tehran, covering around 700 km2and home to over 9 million residents, is particularly susceptible to fluvial flooding. This vulnerability arises from its foothill location, steep slopes, sparse vegetation, heavy rainfall, uncontrolled development in riverine areas, and inadequate construction practices. Flood management in this densely populated metropolis is highly complex. To mitigate potential damages, it is essential to accurately identify flood inundation areas associated with various return periods. The HEC-RAS model serves as an effective hydraulic tool for this purpose, providing critical information for emergency planning and guiding urban development towards safer areas by accurately modeling the hydraulic properties of floods. This research aimed to create flood inundation maps for the Kan, Farahzad, and Darekeh rivers, and the Western Flood Diversion Canal in northern and northwestern Tehran using HEC-Geo-RAS software. The findings of this study will assist in identifying high-risk areas and preparing comprehensive flood hazard maps for Tehran. Materials & Methods Data Analysis Initially, the annual peak discharge data recorded from 1977 to 2017 at 3 hydrometric stations (Haft Hoz, Soulaghan, and Lashkar) were collected from Iran’s Water Resources Management Company. Using Easyfit software, the best-fit distribution for these data was determined based on 3 statistical goodness-of-fit criteria: Anderson-Darling, Kolmogorov-Smirnov, and Chi-Square, all evaluated at a 95% confidence level. Hydraulic Model Execution (HEC-RAS) The HEC-RAS hydraulic tool was employed to simulate flood characteristics along the studied rivers for the return periods of 10, 25, 50, and 100 years. Being developed by the U.S. Army, this model could simulate both steady and unsteady flows while modeling floodplains. The main inputs for the model included: Manning's Roughness Coefficient: Based on field visits and Cowan's formula, this coefficient was assigned values of 0.015 to 0.017 for main river sections and 0.020 to 0.040 for natural beds and floodplains. Digital Elevation Model (DEM): A DEM with a resolution of 10 m was utilized to create a Triangulated Irregular Network (TIN) in ArcGIS software. River Geometric Information: Using the HEC-Geo-RAS extension, river centerlines, banks, and cross-sections (at 50-meter intervals) were extracted. Flow Information: This included details on flow regimes, discharges, and hydraulic boundary conditions. Hydraulic and Topographic Information: This encompassed roughness coefficients, river course conditions, bridges, and both longitudinal and cross-sectional profiles. After executing the model, flood inundation maps and hydraulic properties of the flood (such as flow velocity and width) were generated. Subsequently, the flood extents were validated using Google Earth and previous studies and the affected land uses were identified. Research Findings The study produced detailed flood inundation maps for the Darekeh, Farahzad, Western Flood Diversion Canal, and Kan River, assessing flood extents for return periods of 10, 25, 50, and 100 years. The key findings were as follows: Flood Extents by River: Kan River: The largest floodplain was observed during the 100-year return period, measuring 292 ha. This area comprised predominantly green spaces (203.8 ha) and a mix of service, administrative, and commercial land uses (80.8 ha). Western Flood Diversion Canal: The floodplain reached 99.6 ha, primarily involving residential land (57.2 ha) and service-administrative uses (26.1 ha). Darekeh River: The floodplain extended to 74.8 ha with green spaces (55.8 ha) and residential areas (14.5 ha) dominating the landscape. Farahzad River: The smallest floodplain at 34 ha was characterized mainly by green spaces (32.6 ha) and limited residential areas (1.2 ha). Impact of Slope and Geometric Characteristics: The steep slopes of the Darekeh and Farahzad rivers limited flood extents upstream, while reduced slopes downstream significantly increased flooding risk. The Western Flood Diversion Canal also exhibited increased flood extents due to similar topographical changes. Hydraulic Structures: Many bridges along the rivers lacked adequate capacity to manage floodwaters, particularly during higher return periods. The blockage of culverts due to sediment accumulation exacerbated flooding risks in urban areas. Affected Land Uses: Floodplains primarily encompassed green spaces, residential, and commercial-service areas, indicating a high risk of flood damage. The presence of sensitive land uses within these zones necessitated improved management and expansion of green spaces. Flood Management Implications: The findings underscored the importance of effective flood management and urban planning. Identifying vulnerable sites allowed for targeted interventions, such as improving water infrastructure, clearing culverts, and preserving green spaces. In conclusion, the study highlighted the significant flood risks in northern and northwestern Tehran, which were driven by slope reduction, insufficient culvert capacity, sediment buildup, and human interventions. Urgent measures were required to enhance flood management strategies and protect vulnerable urban areas from flooding. Discussion of Results & Conclusion Impact of Slope and Geometric Characteristics on Flood Zoning In the Darakeh and Farhzad rivers, steep upstream slopes limited flood extents; however, downstream areas experienced a significant increase in flooding due to reduced slopes. In the Western Flood Diversion Canal, the gradual slope decreased in the middle and lower sections promoted runoff accumulation, resulting in expanded flood extents. Similarly, the reduced longitudinal slope in the Kan River contributed to a notable increase in flood areas downstream. Effect of Hydraulic Structures (Bridges and Culverts) on Flood Dynamics Many bridges along the rivers and canals are inadequate in capacity to handle floodwaters, particularly during higher return periods (25, 50, and 100 years). The blockage of culvert inlets due to sediment accumulation and high runoff volumes has led to street flooding and further expansion of flood extents. This situation heightens the risk of urban flooding and necessitates a re-evaluation and modification of existing structures. Impact on Urban Land Use Floodplains in the studied areas primarily consisted of green spaces, residential properties, and commercial/service land uses. The presence of residential areas and sensitive land uses within these floodplains indicated a high risk of flood damage. Enhancing and expanding green spaces and water storage in vulnerable areas can help mitigate flooding impacts. Factors like reduced natural slopes in downstream sections, river section contractions, blockages caused by bridge piers, sediment accumulation, unauthorized constructions, and unsustainable land use practices contribute to increased flood volumes. Importance of Flood Management and Urban Planning The results of flood-prone zoning through hydraulic modeling clearly identified both safe areas and vulnerable sites. This information is essential for effective landscape design, urban ecology, and flood management planning. Key actions to consider included strengthening water infrastructure, rebuilding bridges, clearing culverts, and preserving green spaces. The results of hydraulic simulations using the HEC-RAS model for the Darakeh and Farhzad rivers, the Western Flood Diversion Canal, and the Kan River in the northern and northwestern Tehran Basin illustrated flood zoning conditions for return periods of 10 to 100 years. The model outputs included inundation maps, tables, and images, which were valuable for defining safe river boundaries, landscape and ecological design, and urban management studies. The main findings of this study could be summarized as follows: Darakeh and Farhzad Rivers: In the upstream sections, the floodplain extension was limited due to steep slopes. However, as the slope decreased downstream, the width of the floodplain increased significantly. In the Darakeh River, restaurants near the river were at risk of flooding. The maximum floodplain area for the 100-year return period was 8.74 ha, primarily consisting of green spaces and residential areas. Factors contributing to flooding included low slopes and the blockage and contraction of bridge culverts. In the Farhzad River, the maximum floodplain occurred at the intersection with the Behrud and Moradabad tributaries, primarily affecting private gardens and highlighting the need for effective green space management. The floodplain had expanded from Hemmat Highway to Marzdaran Boulevard, with Khoshmaram and Asbar bridges experiencing flooding during high return periods. The maximum floodplain area for the 100-year return period was 34 ha, mainly comprising green spaces and residential areas. Causes of flooding included narrowing and clogging of bridge culverts, reduced slopes, and high runoff volumes. Western Flood Diversion Canal: This canal played a crucial role in transporting floodwaters to the Kan River. The expansion of the floodplain in the downstream areas of the canal was significant due to reduced slopes. Several bridges, including the Ariafer Street Bridge, those near Yadegar Imam, Khosravi Bridge, bridges after the Second Sadeghieh Square, Vali Asr Street Bridge, Salimi Jahromi Street Bridges, and Sattari Highway, lacked sufficient capacity to manage high floodwaters. The largest floodplain area for the 100-year return period was 99.6 ha, encompassing residential areas along with commercial, service, and workshop uses. Key factors contributing to flooding in this area included high runoff volumes, reduced slopes, and insufficient capacity of both the canal and its bridges. Kan River (Upstream of the Western Flood Diversion Canal): The floodplain in this area was less mountainous and steep, but it expanded towards the area of Azadi in Zibadasht. The largest floodplain for the 100-year return period was measured 66.1 ha, comprising green spaces and residential areas. Contributing factors to flooding included narrowing and clogging of bridge culverts, construction along the riverbanks, and reduced slopes. Kan River (Downstream of the Western Flood Diversion Canal): At the junction with the Western Flood Diversion Canal, the accumulation of flows had led to significant floodplain expansion. This floodplain extended from the west of Tehransar to the east of Mehrabad Airport and continued towards Azadegan and Saveh Highway. The largest floodplain for the 100-year return period was 231 ha, making it the largest in the region. Major land uses in this area included green spaces (146.2 ha), commercial and shopping centers (80.1 ha), and residential areas (4.7 ha). The extensive flooding in this section with a maximum width of 1,037 m was primarily due to the accumulation of runoff from upstream catchments and slope reductions. These findings provide critical information for effective urban planning and flood prevention measures in the northern and northwestern regions of Tehran. The northern and northwestern basins of Tehran are particularly vulnerable to floods. Key factors exacerbating this risk include reduced slopes, insufficient capacity of culverts and bridges to handle floodwaters, sediment accumulation, and human alterations to riverbeds. There is a significant risk of damage to life and property in residential, commercial, and green areas within the floodplains. Urgent measures are needed to enhance the water transfer capacity of critical sections, reconstruct bridges, and manage land use effectively in the river basins.

https://gep.ui.ac.ir/article_29769_b4b2a22ea37314e86837cc887ae3274b.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Evaluation and Prediction of Seasonal Precipitation in Different Climatic Regions of Iran Using a Hybrid Deep Learning Model (Case Study: Rasht and Yazd) Evaluation and Prediction of Seasonal Precipitation in Different Climatic Regions of Iran Using a Hybrid Deep Learning Model (Case Study: Rasht and Yazd) 35 64 29932 10.22108/gep.2025.145951.1735 FA Aynaz Vafaei M.Sc. student, Department of Water Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Mohammad Taghi Sattari Ph.D., Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran 0000-0002-5139-2118 Journal Article 2025 07 16 Abstract The primary objective of this study was to evaluate the stability and generalizability of machine learning models under varying climatic conditions and identify the optimal approach for seasonal precipitation prediction. This research is novel in its first-time application of the hybrid CNN-BiLSTM model in the arid climate of Yazd, alongside a comparison with the humid climate of Rasht. The findings provided valuable insights for enhancing early warning systems and improving water resource management across different climates. To achieve this, we assessed the performance of 4 models: Support Vector Machine (SVM), Deep Neural Network (DNN), Bidirectional Long Short-Term Memory (BiLSTM), and the hybrid Convolutional Neural Network BiLSTM (CNN-BiLSTM). This evaluation utilized 28 years of climatic and precipitation data (1995–2022) from Rasht and Yazd stations. Two input scenarios were designed: the first included climatic parameters (minimum temperature, maximum relative humidity, average wind speed, and sunshine hours), while the second combined these parameters with 3 rainfall time lags. The dataset was divided into training and testing subsets using a ratio of 70:30. The results indicated that the second scenario significantly outperformed the first due to the inclusion of rainfall lags. In Yazd, the CNN-BiLSTM model achieved the best performance with a correlation coefficient of 0.89, a Root Mean Square Error (RMSE) of 7.48 mm, and a Nash–Sutcliffe efficiency of 0.76. In Rasht, the same model produced even better results, with a correlation coefficient of 0.94, an RMSE of 61.89 mm, and a Nash–Sutcliffe efficiency of 0.90. These findings underscore the effectiveness of hybrid deep learning architectures in capturing complex spatio-temporal precipitation patterns across diverse climatic conditions. Keywords: Arid and Humid Climate, Precipitation Prediction, Seasonal Modeling, Deep Learning, CNN-BiLSTM. Introduction Rainfall is a fundamental component of the hydrological cycle, playing a crucial role in water supply, agricultural planning, flood control, drought mitigation, and environmental management. Accurate seasonal rainfall forecasting remains a significant challenge in water and climate sciences, directly influencing management decisions, sustainable development, and food security. With the intensification of climate change and increasing irregularities in rainfall patterns, the demand for timely and reliable forecasts of this complex climatic variable has become increasingly critical. The nonlinear, complex, and stochastic nature of rainfall resulting from the interaction of multiple climatic and atmospheric factors makes its prediction a challenging scientific and technical task. Factors like temperature, humidity, wind speed, solar radiation, rainfall intensity and duration, evaporation, and large-scale teleconnection phenomena, such as the Madden–Julian Oscillation (MJO), significantly affect rainfall variability, creating highly intricate interdependencies. Recent advances in machine learning and deep learning have enabled more effective modeling of these nonlinear and complex relationships. Traditional methods, such as linear regression and time series models, are limited by their linear assumptions and their inability to capture dynamic dependencies. In contrast, modern models—such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and particularly advanced hybrid architectures like CNN-BiLSTM—have shown promising accuracy in modeling and forecasting climatic variables. However, most existing research has focused on humid and semi-humid regions, with relatively few studies addressing arid and semi-arid zones, particularly in Iran. These regions characterized by low and irregular rainfall require more sophisticated modeling and detailed analysis. Additionally, comparative evaluations of models across contrasting climatic zones can provide valuable insights into model robustness and generalizability, which are essential for enhancing forecasting systems. Thus, the main objective of this study was to assess the performance and generalizability of 4 machine learning and deep learning models—SVM, DNN, BiLSTM, and CNN-BiLSTM—in predicting seasonal rainfall in two climatically distinct regions: Rasht (humid) and Yazd (arid). This research pioneered the application of the CNN-BiLSTM model in the arid climate of Yazd and compares its performance with that in the humid climate of Rasht, offering practical recommendations for flood early-warning systems and water resource management. Materials & Methods The dataset utilized in this research comprised seasonal climate and rainfall data from the meteorological stations of Rasht and Yazd over a 28-year period (1995–2022). The climatic variables included minimum temperature, maximum relative humidity, average wind speed, and sunshine duration, along with seasonal rainfall data. Two input scenarios were designed to assess the impact of past rainfall data on model performance: Scenario 1 included only the climatic parameters, while Scenario 2 combined these with 3 rainfall time lags. These scenarios aimed to evaluate how different input configurations influenced the predictive capabilities of the models. The dataset was divided into 70% for training and 30% for testing, following standard practices in machine learning research. The four main models employed in the study were: SVM (Support Vector Machine): A classic machine learning model known for its effectiveness in modeling nonlinear relationships DNN (Deep Neural Network): Capable of learning complex patterns and features from high-dimensional data BiLSTM (Bidirectional Long Short-Term Memory): Effective at capturing long- and short-term dependencies from both forward and backward temporal directions CNN-BiLSTM (Convolutional Neural Network – BiLSTM): A hybrid architecture where the CNN extracts spatial and short-term features, while the BiLSTM models temporal dependencies The CNN component employs convolutional filters to reduce noise and extract local patterns, while the BiLSTM component captures bidirectional temporal relationships. This hybrid design enabled the model to effectively reconstruct complex rainfall patterns across diverse climatic conditions. The performances of the models were evaluated using 4 criteria: Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). Each of these metrics reflected different aspects of the model's accuracy and reliability. Research Findings The results clearly demonstrated the effectiveness of Scenario 2, which incorporated past rainfall data, in enhancing model performance. The inclusion of lagged rainfall significantly improved prediction accuracy across both regions. In the humid and complex climate of Rasht, the CNN-BiLSTM model achieved impressive results with a correlation coefficient (R) of 0.94, a Root Mean Square Error (RMSE) of 61.89 mm, and a Nash-Sutcliffe Efficiency (NSE) of 0.90. The model effectively captured intricate and variable rainfall patterns, providing accurate seasonal forecasts. In Yazd characterized by sporadic and irregular rainfall, the CNN-BiLSTM model still demonstrated high accuracy, achieving an R of 0.89, an RMSE of 7.48 mm, and an NSE of 0.76. These results highlighted the model's robustness, even under challenging climatic conditions. The BiLSTM model ranked second due to its bidirectional temporal memory followed by the DNN and SVM models, which were less effective in capturing complex temporal dependencies. The differences in performance were particularly evident during extreme rainfall events and periods of seasonal variability. These findings confirmed the importance of incorporating past rainfall data as model input, as well as the superiority of deep and hybrid architectures in addressing temporal and nonlinear relationships. The results aligned with similar studies and underscored the value of memory-based and hierarchical feature extraction models. Discussion of Results & Conclusion Incorporation of rainfall time lags significantly improved the predictive accuracy of all models, especially in irregular climates like that in Yazd. Notably, the CNN-BiLSTM model surpassed all other models in both regions, highlighting the benefits of hybrid deep learning architectures for managing complex spatiotemporal data. This study enhanced the broader understanding of intelligent forecasting systems for climate-related applications. It illustrated the adaptability and robustness of the CNN-BiLSTM model across various climatic conditions, providing reliable support for decision-making in water management, agricultural planning, and disaster preparedness. To further enrich this research, it is recommended to integrate satellite and radar datasets, expand temporal data coverage, and assess model performance across wider and more diverse geographical regions. These initiatives will strengthen the accuracy and generalizability of seasonal rainfall forecasts. Abstract The primary objective of this study was to evaluate the stability and generalizability of machine learning models under varying climatic conditions and identify the optimal approach for seasonal precipitation prediction. This research is novel in its first-time application of the hybrid CNN-BiLSTM model in the arid climate of Yazd, alongside a comparison with the humid climate of Rasht. The findings provided valuable insights for enhancing early warning systems and improving water resource management across different climates. To achieve this, we assessed the performance of 4 models: Support Vector Machine (SVM), Deep Neural Network (DNN), Bidirectional Long Short-Term Memory (BiLSTM), and the hybrid Convolutional Neural Network BiLSTM (CNN-BiLSTM). This evaluation utilized 28 years of climatic and precipitation data (1995–2022) from Rasht and Yazd stations. Two input scenarios were designed: the first included climatic parameters (minimum temperature, maximum relative humidity, average wind speed, and sunshine hours), while the second combined these parameters with 3 rainfall time lags. The dataset was divided into training and testing subsets using a ratio of 70:30. The results indicated that the second scenario significantly outperformed the first due to the inclusion of rainfall lags. In Yazd, the CNN-BiLSTM model achieved the best performance with a correlation coefficient of 0.89, a Root Mean Square Error (RMSE) of 7.48 mm, and a Nash–Sutcliffe efficiency of 0.76. In Rasht, the same model produced even better results, with a correlation coefficient of 0.94, an RMSE of 61.89 mm, and a Nash–Sutcliffe efficiency of 0.90. These findings underscore the effectiveness of hybrid deep learning architectures in capturing complex spatio-temporal precipitation patterns across diverse climatic conditions. Keywords: Arid and Humid Climate, Precipitation Prediction, Seasonal Modeling, Deep Learning, CNN-BiLSTM. Introduction Rainfall is a fundamental component of the hydrological cycle, playing a crucial role in water supply, agricultural planning, flood control, drought mitigation, and environmental management. Accurate seasonal rainfall forecasting remains a significant challenge in water and climate sciences, directly influencing management decisions, sustainable development, and food security. With the intensification of climate change and increasing irregularities in rainfall patterns, the demand for timely and reliable forecasts of this complex climatic variable has become increasingly critical. The nonlinear, complex, and stochastic nature of rainfall resulting from the interaction of multiple climatic and atmospheric factors makes its prediction a challenging scientific and technical task. Factors like temperature, humidity, wind speed, solar radiation, rainfall intensity and duration, evaporation, and large-scale teleconnection phenomena, such as the Madden–Julian Oscillation (MJO), significantly affect rainfall variability, creating highly intricate interdependencies. Recent advances in machine learning and deep learning have enabled more effective modeling of these nonlinear and complex relationships. Traditional methods, such as linear regression and time series models, are limited by their linear assumptions and their inability to capture dynamic dependencies. In contrast, modern models—such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and particularly advanced hybrid architectures like CNN-BiLSTM—have shown promising accuracy in modeling and forecasting climatic variables. However, most existing research has focused on humid and semi-humid regions, with relatively few studies addressing arid and semi-arid zones, particularly in Iran. These regions characterized by low and irregular rainfall require more sophisticated modeling and detailed analysis. Additionally, comparative evaluations of models across contrasting climatic zones can provide valuable insights into model robustness and generalizability, which are essential for enhancing forecasting systems. Thus, the main objective of this study was to assess the performance and generalizability of 4 machine learning and deep learning models—SVM, DNN, BiLSTM, and CNN-BiLSTM—in predicting seasonal rainfall in two climatically distinct regions: Rasht (humid) and Yazd (arid). This research pioneered the application of the CNN-BiLSTM model in the arid climate of Yazd and compares its performance with that in the humid climate of Rasht, offering practical recommendations for flood early-warning systems and water resource management. Materials & Methods The dataset utilized in this research comprised seasonal climate and rainfall data from the meteorological stations of Rasht and Yazd over a 28-year period (1995–2022). The climatic variables included minimum temperature, maximum relative humidity, average wind speed, and sunshine duration, along with seasonal rainfall data. Two input scenarios were designed to assess the impact of past rainfall data on model performance: Scenario 1 included only the climatic parameters, while Scenario 2 combined these with 3 rainfall time lags. These scenarios aimed to evaluate how different input configurations influenced the predictive capabilities of the models. The dataset was divided into 70% for training and 30% for testing, following standard practices in machine learning research. The four main models employed in the study were: SVM (Support Vector Machine): A classic machine learning model known for its effectiveness in modeling nonlinear relationships DNN (Deep Neural Network): Capable of learning complex patterns and features from high-dimensional data BiLSTM (Bidirectional Long Short-Term Memory): Effective at capturing long- and short-term dependencies from both forward and backward temporal directions CNN-BiLSTM (Convolutional Neural Network – BiLSTM): A hybrid architecture where the CNN extracts spatial and short-term features, while the BiLSTM models temporal dependencies The CNN component employs convolutional filters to reduce noise and extract local patterns, while the BiLSTM component captures bidirectional temporal relationships. This hybrid design enabled the model to effectively reconstruct complex rainfall patterns across diverse climatic conditions. The performances of the models were evaluated using 4 criteria: Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). Each of these metrics reflected different aspects of the model's accuracy and reliability. Research Findings The results clearly demonstrated the effectiveness of Scenario 2, which incorporated past rainfall data, in enhancing model performance. The inclusion of lagged rainfall significantly improved prediction accuracy across both regions. In the humid and complex climate of Rasht, the CNN-BiLSTM model achieved impressive results with a correlation coefficient (R) of 0.94, a Root Mean Square Error (RMSE) of 61.89 mm, and a Nash-Sutcliffe Efficiency (NSE) of 0.90. The model effectively captured intricate and variable rainfall patterns, providing accurate seasonal forecasts. In Yazd characterized by sporadic and irregular rainfall, the CNN-BiLSTM model still demonstrated high accuracy, achieving an R of 0.89, an RMSE of 7.48 mm, and an NSE of 0.76. These results highlighted the model's robustness, even under challenging climatic conditions. The BiLSTM model ranked second due to its bidirectional temporal memory followed by the DNN and SVM models, which were less effective in capturing complex temporal dependencies. The differences in performance were particularly evident during extreme rainfall events and periods of seasonal variability. These findings confirmed the importance of incorporating past rainfall data as model input, as well as the superiority of deep and hybrid architectures in addressing temporal and nonlinear relationships. The results aligned with similar studies and underscored the value of memory-based and hierarchical feature extraction models. Discussion of Results & Conclusion Incorporation of rainfall time lags significantly improved the predictive accuracy of all models, especially in irregular climates like that in Yazd. Notably, the CNN-BiLSTM model surpassed all other models in both regions, highlighting the benefits of hybrid deep learning architectures for managing complex spatiotemporal data. This study enhanced the broader understanding of intelligent forecasting systems for climate-related applications. It illustrated the adaptability and robustness of the CNN-BiLSTM model across various climatic conditions, providing reliable support for decision-making in water management, agricultural planning, and disaster preparedness. To further enrich this research, it is recommended to integrate satellite and radar datasets, expand temporal data coverage, and assess model performance across wider and more diverse geographical regions. These initiatives will strengthen the accuracy and generalizability of seasonal rainfall forecasts.

https://gep.ui.ac.ir/article_29932_c43fa040c2ea0243dcb1ca980e7016b7.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Optimal Site Selection for Cultural Ecosystem Services in Kerman Province Using GIS and Multi-Criteria Evaluation (MCE) Optimal Site Selection for Cultural Ecosystem Services in Kerman Province Using GIS and Multi-Criteria Evaluation (MCE) 65 92 29993 10.22108/gep.2025.145212.1723 FA Malihe Erfani Associate Professor, Department of Environment, Faculty of Natural Resourses, University of Zabol, Zabol, Iran Tahereh Ardakani Assistant Professor, Department of Environmental Sciences & Engineering, Faculty of Agriculture and Natural Resources, ‎Ardakan University, P.O. Box 184, Ardakan, Iran Journal Article 2025 05 11 Abstract This study explored the cultural ecosystem services of Kerman Province, categorizing them into 4 distinct groups: aesthetic, recreational, educational-scientific, and spiritual-religious. A Multi-Criteria Evaluation (MCE) approach was employed within a Geographic Information System (GIS) framework. To achieve this, we identified 34 aesthetic, 28 recreational, 8 educational, and 4 spiritual criteria, which were further organized into 3 categories: ecological, socio-economic, and visual. All criteria were standardized and weighted using the Analytic Hierarchy Process (AHP) and then integrated through the Weighted Linear Combination (WLC) method. The service layers generated by WLC were aggregated to create an overall cultural service value layer, subsequently classified into 4 levels of suitability. The classification map revealed that areas classified as having high and very high suitability spanned 9,501,028 and 1,306,668 hectares, respectively, accounting for over 58% of the total area of the province. The spatial distribution of the 4 types of cultural services varied significantly, with spiritual services exhibiting the least coverage. The central and western regions of the province were identified as the richest in overall cultural services, while the northeastern, eastern, and southern areas demonstrated the lowest suitability. The findings of this study provide a valuable foundation for policy-making, spatial planning, and sustainable land management aimed at enhancing the delivery and accessibility of cultural ecosystem services in Kerman Province. Keywords: Spatial Analysis, Cultural Ecosystem Services, Weighted Liners Combination (WLC), Multi-Criteria Evaluation (MCE). Introduction In recent years, human activities have significantly transformed the structure and functioning of natural ecosystems, leading to both direct and indirect impacts on human well-being and quality of life. The pursuit of sustainable social development is closely linked to the ecological health of our environment. Therefore, preserving the integrity of ecosystems is essential for ensuring long-term economic and social progress. A crucial way to highlight the importance of ecosystems is by quantifying their services. Ecosystem services are the benefits that humans derive from these natural systems. In spatial planning, effectively leveraging ecosystem services requires accurate spatial data on the current conditions and future trends of ecosystems. Consequently, a major challenge facing contemporary governments is the urgent need to address environmental crises while also dispelling prevalent misconceptions about the essential services provided by ecosystems. Cultural services represent a vital aspect of the non-material benefits of ecosystems, playing a significant role in enhancing quality of life, fostering human well-being, shaping the cultural identity of societies, and enriching the intangible and aesthetic value of spaces. These services, which encompass intangibles, such as scenic beauty, recreational opportunities, environmental education, and spiritual connections with nature, have often been overlooked in many regions, particularly in arid and semi-arid areas like Kerman Province. As unsustainable development and the overexploitation of natural resources threaten these services, identifying, assessing, and protecting areas that provide cultural benefits have become a priority in environmental policy-making. Thus, this study aimed to investigate the cultural services of Kerman Province. Materials & Methods This research investigated cultural ecosystem services in Kerman Province, categorizing them into 4 types: aesthetic, recreational, scientific-educational, and spiritual-religious services. A combination of Geographic Information Systems (GIS) and Multi-Criteria Evaluation (MCE) methods was utilized for the spatial analysis of these services. The research process was conducted in 4 key steps: Step 1: Creating the Spatial Database In the first step, we established decision-making criteria based on regional conditions and expert insights. These criteria encompassed 34 factors for aesthetic services, 28 for recreational services, 8 for educational services, and 4 for spiritual-religious services grouped into 3 broad categories: ecological, socio-economic, and visual. The spatial database was developed using TerrSet IDRISI software with all spatial data formatted as raster layers in the GCS_WGS_1984 coordinate system at a spatial resolution of 30 m. Step 2: Standardizing Factor Criteria Fuzzy logic was employed to standardize the factor criteria, scaling suitability values between 0 and 255. A linear fuzzy function was applied to continuous data, while user-defined functions were utilized for discrete data. Step 3: Weighting Factor Criteria and Assessing Their Influence A pairwise comparison method based on the Analytic Hierarchy Process (AHP) was used to assign weights calculated via the eigenvector method. A questionnaire was developed for experts to compare the relative importance of the factor criteria across different hierarchical levels. Experts rated the priority of each criterion on a scale of 1 to 9 and the statistical mode was applied to determine the final weights in the AHP matrix. Step 4: Integrating Factor Layers and Multi-Criteria Evaluation of Cultural Services The quality of cultural services was assessed using Multi-Criteria Evaluation (MCE) through Weighted Linear Combination (WLC) for each service. This approach is based on the concept of weighted averages, allowing decision-makers to assign weights that reflect the relative importance of each factor. The final suitability score was calculated by summing the products of each factor weight and its normalized value. After generating WLC maps for aesthetic, recreational, scientific-educational, and spiritual-religious services, these maps were merged to create the final cultural services map of Kerman Province. This map was classified into 4 equal intervals to illustrate varying levels of suitability for cultural services. Additionally, we analyzed the statistics of cultural services across different land-use types to compare the contributions of various land uses in providing these services. Research Findings The results indicated that natural waterfalls held the highest weight within the category of recreational services, while their visibility was also the most significant factor in aesthetic services. In the category of spiritual services, locations like cemeteries demonstrated greater spatial values. Universities and scientific centers were identified as more important in the realm of educational services. Analysis of the maps of combined criteria revealed that areas classified as having high and very high suitability for cultural services spanned 9,501,028 and 1,306,668 hectares, respectively, accounting for over 58% of the entire province. Additionally, we found that the spatial distribution of cultural services across the province was uneven. Spiritual services exhibited the least coverage, whereas recreational and aesthetic services showed greater dispersion. The study further indicated that among various land uses and land covers, dense rangelands, dense forests, agricultural areas, and settlements provided the highest scores for cultural services in Kerman Province. Discussion of Results & Conclusion The findings of this study revealed that over half of the area of the province possessed high to very high potential for providing cultural services. Among the evaluated services, aesthetic and recreational services exhibited the greatest spatial extent. Waterfalls and their visibility emerged as the most significant criteria for these two services, underscoring the importance of water resources in arid and semi-arid regions. The multi-criteria assessment indicated that areas extending from the northwest to the southeast of the province had a higher potential for delivering aesthetic services, while the central region, northern edges, and southwestern parts showed comparatively less potential. An analysis of the characteristics of the province suggested that the northwest to southeast corridor, particularly the southeastern regions, was prioritized for its ecological features, such as vegetation cover, topographical diversity, and natural attractions like waterfalls. In contrast, human-made elements were less prevalent in these areas, contributing to increased aesthetic values and services. A similar trend was observed in recreational services although the regions with low desirability had expanded. Scientific and educational services, however, were confined to specific areas of the province, particularly in the central and western regions. Given that dense rangelands and dense forests yielded the highest scores for cultural services across the province, strengthening and expanding these land cover types while protecting them could significantly enhance the value of cultural services in the region. Additionally, spiritual and religious services were dispersed and existed at a limited level throughout the province. Agricultural areas also demonstrated a high average value in cultural services, emphasizing the role of land use in providing aesthetic and recreational benefits. The increased availability of spiritual and religious services in residential areas had further contributed to the rise in average spirituality values within this land use category. This study demonstrated that integrating spatial analysis tools with MCE could effectively identify key areas that provided cultural services. The findings can serve as a scientific and practical reference for planners, land managers, and environmental decision-makers seeking to optimize the utilization of cultural capacities of the ecosystems in Kerman Province, thereby contributing to sustainable development and integrated natural resource management. Furthermore, this methodology can be applied to other regions of the country with similar conditions and may serve as a framework for future research. Abstract This study explored the cultural ecosystem services of Kerman Province, categorizing them into 4 distinct groups: aesthetic, recreational, educational-scientific, and spiritual-religious. A Multi-Criteria Evaluation (MCE) approach was employed within a Geographic Information System (GIS) framework. To achieve this, we identified 34 aesthetic, 28 recreational, 8 educational, and 4 spiritual criteria, which were further organized into 3 categories: ecological, socio-economic, and visual. All criteria were standardized and weighted using the Analytic Hierarchy Process (AHP) and then integrated through the Weighted Linear Combination (WLC) method. The service layers generated by WLC were aggregated to create an overall cultural service value layer, subsequently classified into 4 levels of suitability. The classification map revealed that areas classified as having high and very high suitability spanned 9,501,028 and 1,306,668 hectares, respectively, accounting for over 58% of the total area of the province. The spatial distribution of the 4 types of cultural services varied significantly, with spiritual services exhibiting the least coverage. The central and western regions of the province were identified as the richest in overall cultural services, while the northeastern, eastern, and southern areas demonstrated the lowest suitability. The findings of this study provide a valuable foundation for policy-making, spatial planning, and sustainable land management aimed at enhancing the delivery and accessibility of cultural ecosystem services in Kerman Province. Keywords: Spatial Analysis, Cultural Ecosystem Services, Weighted Liners Combination (WLC), Multi-Criteria Evaluation (MCE). Introduction In recent years, human activities have significantly transformed the structure and functioning of natural ecosystems, leading to both direct and indirect impacts on human well-being and quality of life. The pursuit of sustainable social development is closely linked to the ecological health of our environment. Therefore, preserving the integrity of ecosystems is essential for ensuring long-term economic and social progress. A crucial way to highlight the importance of ecosystems is by quantifying their services. Ecosystem services are the benefits that humans derive from these natural systems. In spatial planning, effectively leveraging ecosystem services requires accurate spatial data on the current conditions and future trends of ecosystems. Consequently, a major challenge facing contemporary governments is the urgent need to address environmental crises while also dispelling prevalent misconceptions about the essential services provided by ecosystems. Cultural services represent a vital aspect of the non-material benefits of ecosystems, playing a significant role in enhancing quality of life, fostering human well-being, shaping the cultural identity of societies, and enriching the intangible and aesthetic value of spaces. These services, which encompass intangibles, such as scenic beauty, recreational opportunities, environmental education, and spiritual connections with nature, have often been overlooked in many regions, particularly in arid and semi-arid areas like Kerman Province. As unsustainable development and the overexploitation of natural resources threaten these services, identifying, assessing, and protecting areas that provide cultural benefits have become a priority in environmental policy-making. Thus, this study aimed to investigate the cultural services of Kerman Province. Materials & Methods This research investigated cultural ecosystem services in Kerman Province, categorizing them into 4 types: aesthetic, recreational, scientific-educational, and spiritual-religious services. A combination of Geographic Information Systems (GIS) and Multi-Criteria Evaluation (MCE) methods was utilized for the spatial analysis of these services. The research process was conducted in 4 key steps: Step 1: Creating the Spatial Database In the first step, we established decision-making criteria based on regional conditions and expert insights. These criteria encompassed 34 factors for aesthetic services, 28 for recreational services, 8 for educational services, and 4 for spiritual-religious services grouped into 3 broad categories: ecological, socio-economic, and visual. The spatial database was developed using TerrSet IDRISI software with all spatial data formatted as raster layers in the GCS_WGS_1984 coordinate system at a spatial resolution of 30 m. Step 2: Standardizing Factor Criteria Fuzzy logic was employed to standardize the factor criteria, scaling suitability values between 0 and 255. A linear fuzzy function was applied to continuous data, while user-defined functions were utilized for discrete data. Step 3: Weighting Factor Criteria and Assessing Their Influence A pairwise comparison method based on the Analytic Hierarchy Process (AHP) was used to assign weights calculated via the eigenvector method. A questionnaire was developed for experts to compare the relative importance of the factor criteria across different hierarchical levels. Experts rated the priority of each criterion on a scale of 1 to 9 and the statistical mode was applied to determine the final weights in the AHP matrix. Step 4: Integrating Factor Layers and Multi-Criteria Evaluation of Cultural Services The quality of cultural services was assessed using Multi-Criteria Evaluation (MCE) through Weighted Linear Combination (WLC) for each service. This approach is based on the concept of weighted averages, allowing decision-makers to assign weights that reflect the relative importance of each factor. The final suitability score was calculated by summing the products of each factor weight and its normalized value. After generating WLC maps for aesthetic, recreational, scientific-educational, and spiritual-religious services, these maps were merged to create the final cultural services map of Kerman Province. This map was classified into 4 equal intervals to illustrate varying levels of suitability for cultural services. Additionally, we analyzed the statistics of cultural services across different land-use types to compare the contributions of various land uses in providing these services. Research Findings The results indicated that natural waterfalls held the highest weight within the category of recreational services, while their visibility was also the most significant factor in aesthetic services. In the category of spiritual services, locations like cemeteries demonstrated greater spatial values. Universities and scientific centers were identified as more important in the realm of educational services. Analysis of the maps of combined criteria revealed that areas classified as having high and very high suitability for cultural services spanned 9,501,028 and 1,306,668 hectares, respectively, accounting for over 58% of the entire province. Additionally, we found that the spatial distribution of cultural services across the province was uneven. Spiritual services exhibited the least coverage, whereas recreational and aesthetic services showed greater dispersion. The study further indicated that among various land uses and land covers, dense rangelands, dense forests, agricultural areas, and settlements provided the highest scores for cultural services in Kerman Province. Discussion of Results & Conclusion The findings of this study revealed that over half of the area of the province possessed high to very high potential for providing cultural services. Among the evaluated services, aesthetic and recreational services exhibited the greatest spatial extent. Waterfalls and their visibility emerged as the most significant criteria for these two services, underscoring the importance of water resources in arid and semi-arid regions. The multi-criteria assessment indicated that areas extending from the northwest to the southeast of the province had a higher potential for delivering aesthetic services, while the central region, northern edges, and southwestern parts showed comparatively less potential. An analysis of the characteristics of the province suggested that the northwest to southeast corridor, particularly the southeastern regions, was prioritized for its ecological features, such as vegetation cover, topographical diversity, and natural attractions like waterfalls. In contrast, human-made elements were less prevalent in these areas, contributing to increased aesthetic values and services. A similar trend was observed in recreational services although the regions with low desirability had expanded. Scientific and educational services, however, were confined to specific areas of the province, particularly in the central and western regions. Given that dense rangelands and dense forests yielded the highest scores for cultural services across the province, strengthening and expanding these land cover types while protecting them could significantly enhance the value of cultural services in the region. Additionally, spiritual and religious services were dispersed and existed at a limited level throughout the province. Agricultural areas also demonstrated a high average value in cultural services, emphasizing the role of land use in providing aesthetic and recreational benefits. The increased availability of spiritual and religious services in residential areas had further contributed to the rise in average spirituality values within this land use category. This study demonstrated that integrating spatial analysis tools with MCE could effectively identify key areas that provided cultural services. The findings can serve as a scientific and practical reference for planners, land managers, and environmental decision-makers seeking to optimize the utilization of cultural capacities of the ecosystems in Kerman Province, thereby contributing to sustainable development and integrated natural resource management. Furthermore, this methodology can be applied to other regions of the country with similar conditions and may serve as a framework for future research.

https://gep.ui.ac.ir/article_29993_d493963b68a642b9b60c3e948857f8aa.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Impacts of Solar Irradiance and Geopotential Height on Snow Cover in the Karun and Marun River Basins Impacts of Solar Irradiance and Geopotential Height on Snow Cover in the Karun and Marun River Basins 93 126 29671 10.22108/gep.2025.142875.1668 FA Nezam Tani Ph.D. student, Department of Meteorology, University of Yazd, Yazd, Iran Kamal Omidvar Ph.D., Department of Meteorology, University of Yazd, Yazd, Iran Golamali Mozafari Mozafari Ph.D., Department of Meteorology, University of Yazd, Yazd, Iran Ahmad Mazidi Associate Professor, Department of Meteorology, Yazd University, Yazd, Iran Journal Article 2024 09 23 Abstract Solar irradiance and synoptic patterns are critical factors influencing the distribution of snow cover in a region. Variations in these elements can significantly impact regional snow cover. This study explored the effects of downward and upward solar radiation, as well as geopotential height, on the snow cover extent of the Karun and Marun river basins. Daily snow cover data were extracted from MODIS Terra satellite observations and compiled on a monthly basis. Data on geopotential height and solar radiation (both downward and upward) were sourced from the NOAA National Centers for Environmental Prediction over a 22-year period (2001–2022) and subsequently processed. The synoptic analysis confirmed that atmospheric patterns, geopotential height, and solar radiation significantly influenced the snow cover in the study area, particularly during colder months. Specifically, a negative correlation was observed between geopotential height and downward solar radiation at the 0.05 and 0.01 significance levels. Conversely, during the cold months, an increase in upward solar radiation was directly associated with an expansion of snow cover extent. Notably, synoptic maps indicated higher upward solar radiation values during periods of substantial snow cover compared to those with diminished snow cover. Overall, from November to March, a decrease in geopotential height and solar radiation correlated with an increase in snow cover. This study highlighted a robust relationship between fluctuations in snow cover and variations in geopotential height and solar radiation within the examined watershed. The ability to predict these variables offered valuable insights for forecasting snow cover changes, which is crucial for effective water resource management, especially in tackling challenges, such as droughts and floods. Keywords: Snow Cover, Karun and Marun River Basins, Geopotential Height, Solar Radiation Flux. Introduction Solar radiation, or solar irradiance, refers to the radiant energy emitted by the sun. This energy is a fundamental component of the Earth's climate system and plays a crucial role in various biological and physical processes on our planet. Both solar irradiance and synoptic patterns are essential for determining the distribution of snow cover in a region as changes in either factor can significantly affect regional snow cover. The primary objective of this research was to evaluate the impact of shifting atmospheric circulation patterns on fluctuations in snow cover within the Karun and Marun river basins with a particular focus on key parameters, such as geopotential height and solar irradiance. The findings of this study will provide valuable insights for both short-term and long-term planning, addressing challenges posed by climate change, including droughts and floods. Materials & Methods This study examined the relationship between atmospheric patterns—including geopotential height, downward and upward solar irradiance, and snow cover extent—in the Karun and Marun river basins located in the southern Zagros region. Data were collected as outlined in Table 1. Utilizing MODIS satellite data with a spatial resolution of 500 m and the powerful Google Earth Engine (GEE) platform, we analyzed the river basins over a time series from 2001 to 2022. After applying necessary corrections to the data, satellite images were processed on a monthly, seasonal, and annual basis. These images were then converted into binary formats to distinguish between snow-covered and snow-free areas. The Normalized Difference Snow Index (NDSI) was calculated for each pixel to assess snow cover extent. Following this, snow cover images were converted from pixel values to binary values (0 and 1) and the NDSI snow cover was categorized into 6 distinct classifications. To further investigate the atmospheric structure at upper levels and its relationship with snow cover in the study area, we obtained data on geopotential height, downward solar irradiance, and upward solar irradiance with a spatial resolution of 2.5° x 2.5° from the NCEP/NCAR database. The quantitative relationship between snow cover across various categories (low, medium, high, dense, and total snow cover) and the synoptic patterns corresponding to the Karun and Marun river basins was analyzed using Pearson correlation methods. Research Findings In this study, data related to snow cover in the Karun and Marun river basins were collected from the MODIS satellite, along with information on geopotential height and upward and downward solar irradiance at various atmospheric levels. The data were organized and analyzed accordingly. The results revealed a significant relationship at the 0.05 and 0.01 confidence levels between geopotential height, downward solar irradiance, and snow cover extent, particularly during the winter months, especially in January and February. This correlation persisted throughout most of the cold season. In contrast to the previous two variables, upward solar irradiance—which reflected energy from the snow surface—exhibited a direct relationship with increasing snow cover. For instance, in January 2014, snow cover extent was above the normal average, while in January 2019, it was below average. During this same period, the average geopotential height in January 2019 was notably higher than in January 2014, indicating differing atmospheric patterns for that month. This trend also applied to February. A comparative analysis of maps and atmospheric diagrams confirmed an inverse correlation between fluctuations in geopotential height and downward solar irradiance with snow cover extent in April. Specifically, an increase in geopotential height at the 700-hPa level in April, similar to the patterns observed in colder months, was associated with greater atmospheric stability and a reduction in snow cover extent. Conversely, an increase in downward solar irradiance resulted in higher temperatures and accelerated snowmelt, leading to a decrease in snow cover. From May to October, a significant reduction in snow cover occurred as temperatures rose and the rainy season receded, resulting in no notable correlation between the studied variables. However, starting in November, as precipitation increased and snow cover expanded, the correlation between the variables strengthened. In November, a negative correlation was observed between snow cover extent and both geopotential height and upward solar irradiance. Specifically, as geopotential height increased, there was a confirmed decrease in snow cover extent, particularly in the medium category. Additionally, during this month, an increase in downward solar irradiance corresponded to a decline in snow cover across all classifications. Conversely, an increase in low-class snow cover showed a significant correlation with rising upward solar irradiance. Thus, during the cold period from November to March, a decrease in geopotential height and solar irradiance was associated with an increase in snow cover. From December onwards, the correlation between these variables and snow cover reached its peak. In essence, varying atmospheric patterns during periods of high and low snow cover significantly influenced the overall snow cover extent. Discussion of Results & Conclusion Overall, the strength of the correlation between the variables fluctuated across different seasons. This correlation was notably stronger in autumn and winter. The analysis indicated that geopotential height and solar radiation were two primary factors influencing snow cover. A decrease in geopotential height, along with a reduction in downward solar irradiance, contributed to an increase in snow cover. During the cold months, snow acted as a reflective surface, reflecting a significant portion of solar radiation back into space. This reflection resulted in higher upward solar irradiance, which helped maintain lower temperatures and supported the persistence of snow. The findings from the synoptic analysis reinforced the impact of geopotential height and atmospheric patterns of solar irradiance on snow cover in the study basin, particularly during colder periods. In essence, geopotential height and downward solar irradiance demonstrated a negative correlation with snow cover extent at the 0.05 and 0.01 confidence levels. Conversely, during the cold months, an increase in upward solar irradiance was directly associated with an increase in snow cover extent. This relationship was evident as the rise in upward solar irradiance values observed in synoptic maps during periods of high snow cover was markedly distinct compared to periods of low snow cover in the study basin. Abstract Solar irradiance and synoptic patterns are critical factors influencing the distribution of snow cover in a region. Variations in these elements can significantly impact regional snow cover. This study explored the effects of downward and upward solar radiation, as well as geopotential height, on the snow cover extent of the Karun and Marun river basins. Daily snow cover data were extracted from MODIS Terra satellite observations and compiled on a monthly basis. Data on geopotential height and solar radiation (both downward and upward) were sourced from the NOAA National Centers for Environmental Prediction over a 22-year period (2001–2022) and subsequently processed. The synoptic analysis confirmed that atmospheric patterns, geopotential height, and solar radiation significantly influenced the snow cover in the study area, particularly during colder months. Specifically, a negative correlation was observed between geopotential height and downward solar radiation at the 0.05 and 0.01 significance levels. Conversely, during the cold months, an increase in upward solar radiation was directly associated with an expansion of snow cover extent. Notably, synoptic maps indicated higher upward solar radiation values during periods of substantial snow cover compared to those with diminished snow cover. Overall, from November to March, a decrease in geopotential height and solar radiation correlated with an increase in snow cover. This study highlighted a robust relationship between fluctuations in snow cover and variations in geopotential height and solar radiation within the examined watershed. The ability to predict these variables offered valuable insights for forecasting snow cover changes, which is crucial for effective water resource management, especially in tackling challenges, such as droughts and floods. Keywords: Snow Cover, Karun and Marun River Basins, Geopotential Height, Solar Radiation Flux. Introduction Solar radiation, or solar irradiance, refers to the radiant energy emitted by the sun. This energy is a fundamental component of the Earth's climate system and plays a crucial role in various biological and physical processes on our planet. Both solar irradiance and synoptic patterns are essential for determining the distribution of snow cover in a region as changes in either factor can significantly affect regional snow cover. The primary objective of this research was to evaluate the impact of shifting atmospheric circulation patterns on fluctuations in snow cover within the Karun and Marun river basins with a particular focus on key parameters, such as geopotential height and solar irradiance. The findings of this study will provide valuable insights for both short-term and long-term planning, addressing challenges posed by climate change, including droughts and floods. Materials & Methods This study examined the relationship between atmospheric patterns—including geopotential height, downward and upward solar irradiance, and snow cover extent—in the Karun and Marun river basins located in the southern Zagros region. Data were collected as outlined in Table 1. Utilizing MODIS satellite data with a spatial resolution of 500 m and the powerful Google Earth Engine (GEE) platform, we analyzed the river basins over a time series from 2001 to 2022. After applying necessary corrections to the data, satellite images were processed on a monthly, seasonal, and annual basis. These images were then converted into binary formats to distinguish between snow-covered and snow-free areas. The Normalized Difference Snow Index (NDSI) was calculated for each pixel to assess snow cover extent. Following this, snow cover images were converted from pixel values to binary values (0 and 1) and the NDSI snow cover was categorized into 6 distinct classifications. To further investigate the atmospheric structure at upper levels and its relationship with snow cover in the study area, we obtained data on geopotential height, downward solar irradiance, and upward solar irradiance with a spatial resolution of 2.5° x 2.5° from the NCEP/NCAR database. The quantitative relationship between snow cover across various categories (low, medium, high, dense, and total snow cover) and the synoptic patterns corresponding to the Karun and Marun river basins was analyzed using Pearson correlation methods. Research Findings In this study, data related to snow cover in the Karun and Marun river basins were collected from the MODIS satellite, along with information on geopotential height and upward and downward solar irradiance at various atmospheric levels. The data were organized and analyzed accordingly. The results revealed a significant relationship at the 0.05 and 0.01 confidence levels between geopotential height, downward solar irradiance, and snow cover extent, particularly during the winter months, especially in January and February. This correlation persisted throughout most of the cold season. In contrast to the previous two variables, upward solar irradiance—which reflected energy from the snow surface—exhibited a direct relationship with increasing snow cover. For instance, in January 2014, snow cover extent was above the normal average, while in January 2019, it was below average. During this same period, the average geopotential height in January 2019 was notably higher than in January 2014, indicating differing atmospheric patterns for that month. This trend also applied to February. A comparative analysis of maps and atmospheric diagrams confirmed an inverse correlation between fluctuations in geopotential height and downward solar irradiance with snow cover extent in April. Specifically, an increase in geopotential height at the 700-hPa level in April, similar to the patterns observed in colder months, was associated with greater atmospheric stability and a reduction in snow cover extent. Conversely, an increase in downward solar irradiance resulted in higher temperatures and accelerated snowmelt, leading to a decrease in snow cover. From May to October, a significant reduction in snow cover occurred as temperatures rose and the rainy season receded, resulting in no notable correlation between the studied variables. However, starting in November, as precipitation increased and snow cover expanded, the correlation between the variables strengthened. In November, a negative correlation was observed between snow cover extent and both geopotential height and upward solar irradiance. Specifically, as geopotential height increased, there was a confirmed decrease in snow cover extent, particularly in the medium category. Additionally, during this month, an increase in downward solar irradiance corresponded to a decline in snow cover across all classifications. Conversely, an increase in low-class snow cover showed a significant correlation with rising upward solar irradiance. Thus, during the cold period from November to March, a decrease in geopotential height and solar irradiance was associated with an increase in snow cover. From December onwards, the correlation between these variables and snow cover reached its peak. In essence, varying atmospheric patterns during periods of high and low snow cover significantly influenced the overall snow cover extent. Discussion of Results & Conclusion Overall, the strength of the correlation between the variables fluctuated across different seasons. This correlation was notably stronger in autumn and winter. The analysis indicated that geopotential height and solar radiation were two primary factors influencing snow cover. A decrease in geopotential height, along with a reduction in downward solar irradiance, contributed to an increase in snow cover. During the cold months, snow acted as a reflective surface, reflecting a significant portion of solar radiation back into space. This reflection resulted in higher upward solar irradiance, which helped maintain lower temperatures and supported the persistence of snow. The findings from the synoptic analysis reinforced the impact of geopotential height and atmospheric patterns of solar irradiance on snow cover in the study basin, particularly during colder periods. In essence, geopotential height and downward solar irradiance demonstrated a negative correlation with snow cover extent at the 0.05 and 0.01 confidence levels. Conversely, during the cold months, an increase in upward solar irradiance was directly associated with an increase in snow cover extent. This relationship was evident as the rise in upward solar irradiance values observed in synoptic maps during periods of high snow cover was markedly distinct compared to periods of low snow cover in the study basin.

https://gep.ui.ac.ir/article_29671_9211426a73bdebb17d64eaf106c15070.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Assessment of the Integration of Main Axes in the Quality of Public Spaces within Historical Context (Case Study: Main Axes of the Historical Context of Khorramabad City) Assessment of the Integration of Main Axes in the Quality of Public Spaces within Historical Context (Case Study: Main Axes of the Historical Context of Khorramabad City) 127 156 29994 10.22108/gep.2025.144295.1704 FA Arefeh Esmaeilvand Master's student in Urban Design, Department of Urbanism, Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran Hassan Sajadzadeh Professor, Department of Urbanism, Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran 0000-0002-3989-9389 Narges Houresfand Ph.D. student, Department of Urbanism, Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran Journal Article 2025 02 13 Abstract Interconnection within the spatial structure of urban environments is vital for ensuring coherence and enhancing the quality of urban spaces. Disruptions to the spatial layout and original character of urban neighborhoods can result in fragmentation and a diminished sense of identity. Consequently, the spatial interconnections along the primary axes of urban fabrics significantly influence the functional quality and vibrancy of these areas. This study employed a descriptive-analytical research method, utilizing both library and field studies for data collection. Specifically, 8 main axes within the historical fabric of Khorramabad City were analyzed using spatial layout techniques. The data were compiled into a block map using AutoCAD software and analyzed through Depthmap software, focusing on key indices like interconnection, connectivity, depth, and selection as essential components of spatial arrangement along these axes. The results indicated that the historical neighborhoods of Khorramabad City exhibited weak continuity and connection with their surrounding elements and streets, primarily due to recent developments. Furthermore, the research findings revealed that physical interventions over the past few decades had diminished interconnections within the historical fabric and its main axes, resulting in only a few axes displaying a high selection index. Overall, the analysis of graphs and maps generated by the software demonstrated a higher degree of connectivity at the local scale compared to the global scale along the main axes. The more interconnected axes showed greater activity quality and functional diversity. Additionally, the findings underscored the importance of connectivity in preserving the coherence of the spatial structure and fostering diverse activities within the historical context of Khorramabad City. Notably, Hafez Street (4.92), Imam Khomeini Street (4.29), Bastan Street (4.81), and Hakim Street (4.81) exhibited the highest values in local connectivity. At the macro scale, Imam Khomeini, Hafez, Ferdowsi, and Mojahedin Islam streets also demonstrated superior connectivity. Therefore, it is recommended that efforts to revitalize the historical textures of Khorramabad City pay special attention to the primary structure and the relationships among the components of these textures. Such consideration can enhance spatial quality and improve the efficiency of activities in these areas. Keywords: Spatial Integration, Public Spaces, Connectivity, Spatial Arrangement, Historical Context of Khorramabad. Introduction Urban growth and expansion are essential processes that transform urban systems fostering continuity and integration. This phenomenon initiates both physical and functional changes within cities. A significant outcome of urban development is the alteration of spatial structures, often resulting in the transformation of historical areas. When disruptions occur in the spatial framework, the fabric of neighborhoods can collapse, leading to deterioration and decline. Neglecting the preservation and revitalization of interconnectivity and integrity in these areas can result in decreased environmental quality and fragmentation. Furthermore, uncoordinated urban expansion has created uneven distributions of services and infrastructure across regions, reflecting a loss of coherence in the spatial development of newly urbanized areas. This fragmentation gives rise to isolated components known as urban blocks. The rise of individualism in urban planning, along with a disregard for historical spatial structures, has led to the neglect of many historical buildings and contexts, significantly eroding the identity and essential character of cities. The historical fabric of each city plays a crucial role in maintaining its identity and culture, as well as attracting tourists and investments. These fabrics shape the city's character and enhance its vibrancy and dynamism. Unfortunately, many of these areas have fallen into disrepair and abandonment, resulting in diminished quality of public spaces. For an integrated system of historical fabrics, it is vital that components are interconnected and hierarchically arranged across all scales. The theory of spatial arrangement provides a framework for analyzing urban spatial structure and configuration, emphasizing the relationship between spatial forms and social dimensions. Within this framework, 4 key indicators—connectivity, accessibility, depth, and choice—are considered essential for effective spatial arrangement. Like many other historical centers, the historical fabric of Khorramabad has experienced damage to its intrinsic values, resulting in significant disintegration today. The quality of public spaces in these areas is often overlooked, despite their potential for revitalization. This research aimed to identify the key factors influencing the spatial structure of the city within these historical fabrics and explore ways to enhance the connectivity and integration of main areas. Additionally, it sought to clarify the role of spatial structure in linking public spaces within the historical neighborhoods of Khorramabad. The study addressed two primary questions: "What factors contribute to the coherence of the spatial structure in the historical neighborhoods of Khorramabad?" and "What is the relationship between the elements that reinforce the spatial structure of those neighborhoods?" Materials & Methods This study employed a descriptive-analytical research method. The theoretical principles and foundations related to spatial connections were gathered from relevant reference books and scholarly articles. Following this, field observations and engagements were conducted in the selected areas to adapt previous studies and analyze the current state of Khorramabad. Ultimately, the concepts and characteristics of coherence patterns in the spatial structures of the historical neighborhoods of Khorramabad were examined. For data analysis, the Depthmap spatial arrangement technique was utilized to investigate the role of spatial structure in promoting connectivity and coherence among those neighborhoods. Additionally, the validity of the data was ensured by using reliable sources and verifying the accuracy of the collected information. Research Findings The results indicated that Hafez Street with a score of 4.92 was followed closely by Bastan Street and Hakim Street, both scoring 4.81, and Ferdowsi Street at 3.93, all demonstrating the highest levels of integration within the local radius (R3). Notably, streets with greater local connectivity tended to exhibit better accessibility, increased permeability, and more vibrant land uses. Further analysis at the main radius (Rn) revealed that the correlation index for Imam, Mojahedin-e-Islam, Ferdowsi, and Ardeshir Karami streets was increasing due to their interregional functionality, while it decreased at the intersection of Hafez and Bastan streets. Additionally, a significant relationship existed between the connectivity of the radii of Rn and R3. This relationship was influenced by the placement of population-attracting uses along axes that demonstrated a greater connectivity potential compared to others. The quality index and the degree of spatial coherence were directly related to connectivity, spatial depth, and types of land use. According to the spatial depth analysis, it was evident that as one moved away from the main routes into the neighborhood fabric, the spatial depth of the neighborhoods increased. The analysis of the choice index illustrated the degree of selection for each connected space, reflecting a significant level of separation within the studied area. The results established a direct correlation between the choice index of the axes in Rn and R3 influenced by various factors. Examining spatial depth in R3 alongside the choice component revealed a clear relationship: as spatial depth decreased in R3, the choice index increased. This suggested that reducing spatial depth was essential for enhancing user selection along urban axes; thus, improved access to these axes correlated with a higher likelihood of user choices. Based on the findings and analyses conducted across all neighborhoods, Pasangar, Baba Taher, Sabzeh Meydan, and Zayd ibn Ali neighborhoods demonstrated a greater potential for attracting pedestrian movement compared to Qaleh, Darb Delakan, Posht Bazaar, and Bajgiran neighborhoods. This potential signified the ability to effectively direct foot traffic within the historical fabric. In Pasangar and Baba Taher, the elevated connectivity index suggested that residents benefited from multiple routes and options for navigating their paths, indicating a higher level of street permeability. Consequently, the specific layout of Pasangar and Baba Taher neighborhoods might enhance overall coherence within the essential framework of the area. Similarly, Sabzeh Meydan and Zayd ibn Ali neighborhoods exhibited this characteristics, possessing a cohesive main structure that supported and maintained spatial coherence. In contrast, Bajgiran and Posht Bazaar neighborhoods with their less pronounced structural depth exhibited a comparatively lower potential for coherence and connectivity in their passageways. Overall, the core historical neighborhoods of Khorramabad with the exception of Baba Taher and Zayd ibn Ali primarily reflected minimal depth in their spatial configurations. Discussion of Results & Conclusion The results of this study revealed a significant correlation between spatial connectivity indices and elements of spatial integration, consistent with the findings of Abbaszadegan (2002), Farahnaki et al. (2022), Mokhtarzadeh et al. (2018), and Roshani et al. (2017). Additionally, the findings indicated that indices of spatial configuration were effectively related to components of spatial structure, including the main framework and types of fabric. A lack of connection between the spatial structures of neighborhood fabrics and main streets in certain areas resulted in diminished spatial continuity. Generally, connectivity was higher along main axes, while it was lower in secondary axes and residential neighborhoods characterized by greater spatial depth. The connectivity map illustrated that streets with higher degrees of connectivity provided users with better options. The analysis of spatial depth graphs and field observations varied significantly across different neighborhoods. For example, while increasing spatial depth led to reduced traffic and functional diversity in peripheral neighborhoods like Bajgiran and Pasangar, central neighborhoods, such as Baba Taher and Zayd ibn Ali, showed no significant impact on traffic flow and functional diversity with increased depth. This research underscored the critical role of connectivity in maintaining the coherence of spatial structures and fostering a diversity of activities within the historical fabric of Khorramabad. Therefore, it is recommended that efforts to revitalize historical urban fabrics prioritize attention to the framework and interconnections among various components. Axes characterized by high connectivity and visibility, particularly those adjacent to historical elements, should be prioritized by organizations as they play a crucial role in unifying the overall structure. A significant challenge for these axes is the lack of land uses that reflect identity of the area. This can be addressed by creating spatial sequences and incorporating diverse facade and flooring elements to avoid uniformity along pathways while enhancing physical and visual permeability. In instances where distinctive landmarks are absent, a new prominent feature should be strategically introduced in relation to notable buildings, ensuring it stands out within the designated area. The secondary priority axes, which offer high accessibility but lack engaging landmarks, serve both connective and recreational-touristic functions, facilitating links between historical spaces and valued areas. Based on the research conducted, the answers to the two questions posed were as follows: The analysis highlighted several key factors that significantly impacted the spatial coherence of the historical neighborhoods of Khorramabad. These factors contribute to the formation of cultural identity, foster social interactions, and enhance the overall quality of life within the neighborhoods. A vital aspect of these neighborhoods was the presence of a central area, which served as an appropriate venue for gatherings, social interactions, and communication among residents. Another important element of the spatial structure was the hierarchical arrangement of streets and alleys, which delineated public and private spaces within the neighborhood, determining accessibility for citizens. Connectivity emerged as a key component from the perspective of spatial arrangement in relation to the objectives of this study. It was recognized as a primary indicator of spatial arrangement and was closely linked to the concept of spatial coherence. In any area, higher connectivity signifies greater cohesion and integration between the designated space and the overall unit, facilitating social interactions and enhancing the quality of life in the neighborhoods. The fundamental elements of spatial structure comprise land use zones, road networks, axes, centers, landmarks, and edges, all of which are interconnected, forming the urban spatial framework and creating a cohesive whole. As one of the most critical components of spatial structure and urban configuration, the road network plays a vital role in shaping and organizing various parts of a city, including its historical fabric. Therefore, the first step in understanding the complexities of spatial form is to analyze the spatial values of pathways. Abstract Interconnection within the spatial structure of urban environments is vital for ensuring coherence and enhancing the quality of urban spaces. Disruptions to the spatial layout and original character of urban neighborhoods can result in fragmentation and a diminished sense of identity. Consequently, the spatial interconnections along the primary axes of urban fabrics significantly influence the functional quality and vibrancy of these areas. This study employed a descriptive-analytical research method, utilizing both library and field studies for data collection. Specifically, 8 main axes within the historical fabric of Khorramabad City were analyzed using spatial layout techniques. The data were compiled into a block map using AutoCAD software and analyzed through Depthmap software, focusing on key indices like interconnection, connectivity, depth, and selection as essential components of spatial arrangement along these axes. The results indicated that the historical neighborhoods of Khorramabad City exhibited weak continuity and connection with their surrounding elements and streets, primarily due to recent developments. Furthermore, the research findings revealed that physical interventions over the past few decades had diminished interconnections within the historical fabric and its main axes, resulting in only a few axes displaying a high selection index. Overall, the analysis of graphs and maps generated by the software demonstrated a higher degree of connectivity at the local scale compared to the global scale along the main axes. The more interconnected axes showed greater activity quality and functional diversity. Additionally, the findings underscored the importance of connectivity in preserving the coherence of the spatial structure and fostering diverse activities within the historical context of Khorramabad City. Notably, Hafez Street (4.92), Imam Khomeini Street (4.29), Bastan Street (4.81), and Hakim Street (4.81) exhibited the highest values in local connectivity. At the macro scale, Imam Khomeini, Hafez, Ferdowsi, and Mojahedin Islam streets also demonstrated superior connectivity. Therefore, it is recommended that efforts to revitalize the historical textures of Khorramabad City pay special attention to the primary structure and the relationships among the components of these textures. Such consideration can enhance spatial quality and improve the efficiency of activities in these areas. Keywords: Spatial Integration, Public Spaces, Connectivity, Spatial Arrangement, Historical Context of Khorramabad. Introduction Urban growth and expansion are essential processes that transform urban systems fostering continuity and integration. This phenomenon initiates both physical and functional changes within cities. A significant outcome of urban development is the alteration of spatial structures, often resulting in the transformation of historical areas. When disruptions occur in the spatial framework, the fabric of neighborhoods can collapse, leading to deterioration and decline. Neglecting the preservation and revitalization of interconnectivity and integrity in these areas can result in decreased environmental quality and fragmentation. Furthermore, uncoordinated urban expansion has created uneven distributions of services and infrastructure across regions, reflecting a loss of coherence in the spatial development of newly urbanized areas. This fragmentation gives rise to isolated components known as urban blocks. The rise of individualism in urban planning, along with a disregard for historical spatial structures, has led to the neglect of many historical buildings and contexts, significantly eroding the identity and essential character of cities. The historical fabric of each city plays a crucial role in maintaining its identity and culture, as well as attracting tourists and investments. These fabrics shape the city's character and enhance its vibrancy and dynamism. Unfortunately, many of these areas have fallen into disrepair and abandonment, resulting in diminished quality of public spaces. For an integrated system of historical fabrics, it is vital that components are interconnected and hierarchically arranged across all scales. The theory of spatial arrangement provides a framework for analyzing urban spatial structure and configuration, emphasizing the relationship between spatial forms and social dimensions. Within this framework, 4 key indicators—connectivity, accessibility, depth, and choice—are considered essential for effective spatial arrangement. Like many other historical centers, the historical fabric of Khorramabad has experienced damage to its intrinsic values, resulting in significant disintegration today. The quality of public spaces in these areas is often overlooked, despite their potential for revitalization. This research aimed to identify the key factors influencing the spatial structure of the city within these historical fabrics and explore ways to enhance the connectivity and integration of main areas. Additionally, it sought to clarify the role of spatial structure in linking public spaces within the historical neighborhoods of Khorramabad. The study addressed two primary questions: "What factors contribute to the coherence of the spatial structure in the historical neighborhoods of Khorramabad?" and "What is the relationship between the elements that reinforce the spatial structure of those neighborhoods?" Materials & Methods This study employed a descriptive-analytical research method. The theoretical principles and foundations related to spatial connections were gathered from relevant reference books and scholarly articles. Following this, field observations and engagements were conducted in the selected areas to adapt previous studies and analyze the current state of Khorramabad. Ultimately, the concepts and characteristics of coherence patterns in the spatial structures of the historical neighborhoods of Khorramabad were examined. For data analysis, the Depthmap spatial arrangement technique was utilized to investigate the role of spatial structure in promoting connectivity and coherence among those neighborhoods. Additionally, the validity of the data was ensured by using reliable sources and verifying the accuracy of the collected information. Research Findings The results indicated that Hafez Street with a score of 4.92 was followed closely by Bastan Street and Hakim Street, both scoring 4.81, and Ferdowsi Street at 3.93, all demonstrating the highest levels of integration within the local radius (R3). Notably, streets with greater local connectivity tended to exhibit better accessibility, increased permeability, and more vibrant land uses. Further analysis at the main radius (Rn) revealed that the correlation index for Imam, Mojahedin-e-Islam, Ferdowsi, and Ardeshir Karami streets was increasing due to their interregional functionality, while it decreased at the intersection of Hafez and Bastan streets. Additionally, a significant relationship existed between the connectivity of the radii of Rn and R3. This relationship was influenced by the placement of population-attracting uses along axes that demonstrated a greater connectivity potential compared to others. The quality index and the degree of spatial coherence were directly related to connectivity, spatial depth, and types of land use. According to the spatial depth analysis, it was evident that as one moved away from the main routes into the neighborhood fabric, the spatial depth of the neighborhoods increased. The analysis of the choice index illustrated the degree of selection for each connected space, reflecting a significant level of separation within the studied area. The results established a direct correlation between the choice index of the axes in Rn and R3 influenced by various factors. Examining spatial depth in R3 alongside the choice component revealed a clear relationship: as spatial depth decreased in R3, the choice index increased. This suggested that reducing spatial depth was essential for enhancing user selection along urban axes; thus, improved access to these axes correlated with a higher likelihood of user choices. Based on the findings and analyses conducted across all neighborhoods, Pasangar, Baba Taher, Sabzeh Meydan, and Zayd ibn Ali neighborhoods demonstrated a greater potential for attracting pedestrian movement compared to Qaleh, Darb Delakan, Posht Bazaar, and Bajgiran neighborhoods. This potential signified the ability to effectively direct foot traffic within the historical fabric. In Pasangar and Baba Taher, the elevated connectivity index suggested that residents benefited from multiple routes and options for navigating their paths, indicating a higher level of street permeability. Consequently, the specific layout of Pasangar and Baba Taher neighborhoods might enhance overall coherence within the essential framework of the area. Similarly, Sabzeh Meydan and Zayd ibn Ali neighborhoods exhibited this characteristics, possessing a cohesive main structure that supported and maintained spatial coherence. In contrast, Bajgiran and Posht Bazaar neighborhoods with their less pronounced structural depth exhibited a comparatively lower potential for coherence and connectivity in their passageways. Overall, the core historical neighborhoods of Khorramabad with the exception of Baba Taher and Zayd ibn Ali primarily reflected minimal depth in their spatial configurations. Discussion of Results & Conclusion The results of this study revealed a significant correlation between spatial connectivity indices and elements of spatial integration, consistent with the findings of Abbaszadegan (2002), Farahnaki et al. (2022), Mokhtarzadeh et al. (2018), and Roshani et al. (2017). Additionally, the findings indicated that indices of spatial configuration were effectively related to components of spatial structure, including the main framework and types of fabric. A lack of connection between the spatial structures of neighborhood fabrics and main streets in certain areas resulted in diminished spatial continuity. Generally, connectivity was higher along main axes, while it was lower in secondary axes and residential neighborhoods characterized by greater spatial depth. The connectivity map illustrated that streets with higher degrees of connectivity provided users with better options. The analysis of spatial depth graphs and field observations varied significantly across different neighborhoods. For example, while increasing spatial depth led to reduced traffic and functional diversity in peripheral neighborhoods like Bajgiran and Pasangar, central neighborhoods, such as Baba Taher and Zayd ibn Ali, showed no significant impact on traffic flow and functional diversity with increased depth. This research underscored the critical role of connectivity in maintaining the coherence of spatial structures and fostering a diversity of activities within the historical fabric of Khorramabad. Therefore, it is recommended that efforts to revitalize historical urban fabrics prioritize attention to the framework and interconnections among various components. Axes characterized by high connectivity and visibility, particularly those adjacent to historical elements, should be prioritized by organizations as they play a crucial role in unifying the overall structure. A significant challenge for these axes is the lack of land uses that reflect identity of the area. This can be addressed by creating spatial sequences and incorporating diverse facade and flooring elements to avoid uniformity along pathways while enhancing physical and visual permeability. In instances where distinctive landmarks are absent, a new prominent feature should be strategically introduced in relation to notable buildings, ensuring it stands out within the designated area. The secondary priority axes, which offer high accessibility but lack engaging landmarks, serve both connective and recreational-touristic functions, facilitating links between historical spaces and valued areas. Based on the research conducted, the answers to the two questions posed were as follows: The analysis highlighted several key factors that significantly impacted the spatial coherence of the historical neighborhoods of Khorramabad. These factors contribute to the formation of cultural identity, foster social interactions, and enhance the overall quality of life within the neighborhoods. A vital aspect of these neighborhoods was the presence of a central area, which served as an appropriate venue for gatherings, social interactions, and communication among residents. Another important element of the spatial structure was the hierarchical arrangement of streets and alleys, which delineated public and private spaces within the neighborhood, determining accessibility for citizens. Connectivity emerged as a key component from the perspective of spatial arrangement in relation to the objectives of this study. It was recognized as a primary indicator of spatial arrangement and was closely linked to the concept of spatial coherence. In any area, higher connectivity signifies greater cohesion and integration between the designated space and the overall unit, facilitating social interactions and enhancing the quality of life in the neighborhoods. The fundamental elements of spatial structure comprise land use zones, road networks, axes, centers, landmarks, and edges, all of which are interconnected, forming the urban spatial framework and creating a cohesive whole. As one of the most critical components of spatial structure and urban configuration, the road network plays a vital role in shaping and organizing various parts of a city, including its historical fabric. Therefore, the first step in understanding the complexities of spatial form is to analyze the spatial values of pathways.

https://gep.ui.ac.ir/article_29994_11279cab2dde22faa559e7d30ab76e8f.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 Identifying the Synoptic Patterns Generating Dust Storms in the Ardabil Plain and Their Local Origin Identifying the Synoptic Patterns Generating Dust Storms in the Ardabil Plain and Their Local Origin 157 184 29976 10.22108/gep.2025.143518.1691 FA Bromand Salahi Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Mahmoud Behrouzi Ph.D. in Climatology, Marine Science Institute, Kish International Campus, University of Tehran, Tehran, Iran. Mahnaz Saber Postdoctoral Researcher of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2024 11 27 Abstract This study analyzed wind direction and speed data from 2000 to 2018 to identify the high winds responsible for dust events in the Ardabil Plain. To uncover the atmospheric patterns leading to dust formation, sea level pressure maps were constructed for dusty days and dust trajectories were tracked using the HYSPLIT model. The atmospheric Dust Column Mass Density (DCMD) and Aerosol Optical Depth (AOD) were employed to determine dust column concentration in the Ardabil Plain. The findings highlighted the significant influence of surface pressure systems, which contributed to dust storms by inducing instability in the region. The analysis indicated that local sources heavily influenced the frequency of summer dust storms. Tracking dust particles and analyzing synoptic patterns demonstrated that dust predominantly entered the Ardabil Plain from Iraq and Syria, as well as border areas between Iran and Iraq. The substantial pressure gradient generated by the arrival of unstable systems exacerbated local gusts, resulting in the formation of extensive dust masses in the Ardabil Plain. Notably, the maximum dust column density and optical depth recorded in July reached 0.43 kg/m² and 0.5, respectively. Keywords: AOD Index, Ardabil Plain, Dust, HYSPLIT model, Sea Level Pressure. Introduction Dust storms are a significant environmental hazard in arid and semi-arid regions, particularly during the warmer months of late spring and early summer. These storms have far-reaching consequences for public health, natural resources, economy, and agriculture. Various factors contribute to dust generation, including changes in wind intensity driven by pressure gradients, Coriolis force, poor vegetation cover, drought, conflicts, soil texture and composition, channelized winds, and synoptic patterns associated with strong winds. The interplay between climate change and human activities, including settlement patterns, has exacerbated soil degradation, wind erosion, desertification, and deterioration of soil properties, thereby intensifying dust storms. In Ardabil, industrial and mining activities have adversely affected the photosynthesis cycle, disrupted road traffic, and negatively impacted human and livestock health, placing additional strain on the healthcare system and incurring significant costs. Consequently, this study aimed to identify and analyze the factors contributing to dust formation and the atmospheric patterns responsible for dust events during warmer months in the Ardabil Plain. Materials & Methods This research utilized a combination of synoptic, statistical, satellite, and modeling approaches. To analyze dust occurrence patterns during warm months over a 19-year period (2000-2018), data were extracted and examined through an environmental lens focused on circulation. The HYSPLIT model was employed to track dust pathways in the region. The principal component reduction method was applied to identify the dust generation patterns in the Ardabil Plain for each warm month. Factor analysis was then utilized to determine the key variables influencing atmospheric circulation patterns and dust storms. Additionally, to assess the role of local sources in the occurrence of dust storms, land use changes were analyzed using the Support Vector Machine (SVM) algorithm, along with atmospheric Dust Column Mass Density (DCMD) and Aerosol Optical Depth (AOD) derived from reanalyzed MERRA data. Finally, a stepwise multivariate linear regression model was employed to estimate atmospheric dust concentration and develop a dust model for the Ardabil Plain. Research Findings Monthly analysis of dust-generating patterns in the Ardabil Plain revealed that local sources significantly contributed to the occurrence of dust storms in the study area. While dust particle tracking and synoptic pattern investigations indicated that dust primarily entered the Ardabil Plain from Iraq, Syria, and areas along the Iran-Iraq border, the large pressure gradients and shear forces created by the intrusion of unstable systems intensified local winds in the region. In the agricultural lands east of Ardabil City—specifically, approximately 3.6 kilometers from the city center and near the southern edge of Ardabil Airport and Ardabil Industrial Park No. 2—there were areas devoid of vegetation. During the dry season, the loose surface soils in these regions became destabilized due to moisture deficiency. As strong winds intensified, soil particles were lifted from the ground and transported westward towards Ardabil City, resulting in local dust storms that adversely impacted the environmental ecosystem and human health. Additionally, Ardabil Industrial Park No. 2 situated 11 kilometers east of the city near the airport and a prominent dust source contributed to increased air pollution. The activities of factories in this industrial zone released dust particles into the atmosphere, which further elevated the concentration of air pollutants when combined with mineral dust from the dust center. Discussion of Results & Conclusion The results of monitoring land use changes in the Ardabil Plain indicated that local sources significantly contributed to the occurrence of dust storms in the region. Over the past 20 years (2000-2019), the percentage of dry surface soils—identified as the primary sources of dust—had increased, while vegetation cover had diminished. This decline in vegetation had notably contributed to the rising frequency of dust storms. Vegetation played a critical role in absorbing surface moisture and retaining it on the soil, which in turn enhanced soil stability and raised the wind shear threshold speed required to mobilize surface soil particles. Consequently, stable soil conditions helped mitigate dust storms. The findings of this study revealed a reduction in vegetation cover, which had led to decreased surface soil moisture and disrupted soil balance. This instability ultimately lowered the wind shear threshold speed, making the surface soil more susceptible to being lifted by localized gusts, resulting in airborne dust that manifested as dust storms. Moreover, the regression analysis identified the role of relative humidity in estimating atmospheric dust column concentration in the Ardabil Plain. It was demonstrated that the concentration model could effectively predict dust levels based on AOD and relative humidity variables. Abstract This study analyzed wind direction and speed data from 2000 to 2018 to identify the high winds responsible for dust events in the Ardabil Plain. To uncover the atmospheric patterns leading to dust formation, sea level pressure maps were constructed for dusty days and dust trajectories were tracked using the HYSPLIT model. The atmospheric Dust Column Mass Density (DCMD) and Aerosol Optical Depth (AOD) were employed to determine dust column concentration in the Ardabil Plain. The findings highlighted the significant influence of surface pressure systems, which contributed to dust storms by inducing instability in the region. The analysis indicated that local sources heavily influenced the frequency of summer dust storms. Tracking dust particles and analyzing synoptic patterns demonstrated that dust predominantly entered the Ardabil Plain from Iraq and Syria, as well as border areas between Iran and Iraq. The substantial pressure gradient generated by the arrival of unstable systems exacerbated local gusts, resulting in the formation of extensive dust masses in the Ardabil Plain. Notably, the maximum dust column density and optical depth recorded in July reached 0.43 kg/m² and 0.5, respectively. Keywords: AOD Index, Ardabil Plain, Dust, HYSPLIT model, Sea Level Pressure. Introduction Dust storms are a significant environmental hazard in arid and semi-arid regions, particularly during the warmer months of late spring and early summer. These storms have far-reaching consequences for public health, natural resources, economy, and agriculture. Various factors contribute to dust generation, including changes in wind intensity driven by pressure gradients, Coriolis force, poor vegetation cover, drought, conflicts, soil texture and composition, channelized winds, and synoptic patterns associated with strong winds. The interplay between climate change and human activities, including settlement patterns, has exacerbated soil degradation, wind erosion, desertification, and deterioration of soil properties, thereby intensifying dust storms. In Ardabil, industrial and mining activities have adversely affected the photosynthesis cycle, disrupted road traffic, and negatively impacted human and livestock health, placing additional strain on the healthcare system and incurring significant costs. Consequently, this study aimed to identify and analyze the factors contributing to dust formation and the atmospheric patterns responsible for dust events during warmer months in the Ardabil Plain. Materials & Methods This research utilized a combination of synoptic, statistical, satellite, and modeling approaches. To analyze dust occurrence patterns during warm months over a 19-year period (2000-2018), data were extracted and examined through an environmental lens focused on circulation. The HYSPLIT model was employed to track dust pathways in the region. The principal component reduction method was applied to identify the dust generation patterns in the Ardabil Plain for each warm month. Factor analysis was then utilized to determine the key variables influencing atmospheric circulation patterns and dust storms. Additionally, to assess the role of local sources in the occurrence of dust storms, land use changes were analyzed using the Support Vector Machine (SVM) algorithm, along with atmospheric Dust Column Mass Density (DCMD) and Aerosol Optical Depth (AOD) derived from reanalyzed MERRA data. Finally, a stepwise multivariate linear regression model was employed to estimate atmospheric dust concentration and develop a dust model for the Ardabil Plain. Research Findings Monthly analysis of dust-generating patterns in the Ardabil Plain revealed that local sources significantly contributed to the occurrence of dust storms in the study area. While dust particle tracking and synoptic pattern investigations indicated that dust primarily entered the Ardabil Plain from Iraq, Syria, and areas along the Iran-Iraq border, the large pressure gradients and shear forces created by the intrusion of unstable systems intensified local winds in the region. In the agricultural lands east of Ardabil City—specifically, approximately 3.6 kilometers from the city center and near the southern edge of Ardabil Airport and Ardabil Industrial Park No. 2—there were areas devoid of vegetation. During the dry season, the loose surface soils in these regions became destabilized due to moisture deficiency. As strong winds intensified, soil particles were lifted from the ground and transported westward towards Ardabil City, resulting in local dust storms that adversely impacted the environmental ecosystem and human health. Additionally, Ardabil Industrial Park No. 2 situated 11 kilometers east of the city near the airport and a prominent dust source contributed to increased air pollution. The activities of factories in this industrial zone released dust particles into the atmosphere, which further elevated the concentration of air pollutants when combined with mineral dust from the dust center. Discussion of Results & Conclusion The results of monitoring land use changes in the Ardabil Plain indicated that local sources significantly contributed to the occurrence of dust storms in the region. Over the past 20 years (2000-2019), the percentage of dry surface soils—identified as the primary sources of dust—had increased, while vegetation cover had diminished. This decline in vegetation had notably contributed to the rising frequency of dust storms. Vegetation played a critical role in absorbing surface moisture and retaining it on the soil, which in turn enhanced soil stability and raised the wind shear threshold speed required to mobilize surface soil particles. Consequently, stable soil conditions helped mitigate dust storms. The findings of this study revealed a reduction in vegetation cover, which had led to decreased surface soil moisture and disrupted soil balance. This instability ultimately lowered the wind shear threshold speed, making the surface soil more susceptible to being lifted by localized gusts, resulting in airborne dust that manifested as dust storms. Moreover, the regression analysis identified the role of relative humidity in estimating atmospheric dust column concentration in the Ardabil Plain. It was demonstrated that the concentration model could effectively predict dust levels based on AOD and relative humidity variables.

https://gep.ui.ac.ir/article_29976_eb559bbe516b2f4d9307f61c1c161de4.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 36 3 2025 09 23 A Multidimensional Assessment of Vulnerability of Rural Areas to Natural Hazard Risks (Case Study: Rural Regions of Marivan County) A Multidimensional Assessment of Vulnerability of Rural Areas to Natural Hazard Risks (Case Study: Rural Regions of Marivan County) 185 214 30087 10.22108/gep.2025.146080.1739 FA Abdolmajid Ahmadi Assistant professor, Department of Geography, Faculty of Literature and Humanities, Razi University, Kermanshah, Iran Soran Manouchehri Ph.D. in Geography and Rural Planning, University of Isfahan, Isfahan, Iran Journal Article 2025 07 27 Abstract Reducing vulnerability is a fundamental principle in the risk-management approach to enhancing the resilience of human settlements. Achieving this requires a comprehensive assessment of the factors influencing vulnerability followed by targeted planning based on the identified vulnerability levels within communities. This applied research was conducted in the rural areas of Marivan County, Kurdistan Province, by using a descriptive-analytical methodology and an inductive approach. The study evaluated exposure, sensitivity, and adaptive capacity—key components of vulnerability in the selected villages—and generalized the vulnerability levels across the entire county using GIS spatial-statistics tools, such as Inverse-Distance Weighting (IDW) interpolation, kriging density, and Moran’s spatial autocorrelation, focusing on two primary hazards: earthquakes and land subsidence resulting from prolonged drought and excessive water extraction. The status of settlements was assessed across economic, social, natural, and physical variables defined through consensus in a Delphi group and corroborated with input from villagers in the sample communities. The findings revealed that vulnerability levels in the eastern rural settlements significantly differed from those in the central and western regions of the county. This disparity reflected greater distance from the city center, smaller population sizes, a center-periphery development pattern, low adaptive capacity, and limited economic diversity. Inequalities and spatial variations in vulnerability exhibited a clustered pattern, indicating a spatial bipolarity between areas of high and low vulnerability. Keywords: Spatial Analysis, Exposure, Sensitivity, Adaptation, Natural Hazard Risks, Rural Villages, Marivan County. Introduction Vulnerability to natural hazards is a persistent and multifaceted challenge impacting rural development, especially in areas where ecological fragility intersects with infrastructural deprivation. In the rural districts of Marivan County located in Iran’s Kurdistan Province, a convergence of seismic instability, prolonged droughts, excessive groundwater extraction, and land subsidence has created a landscape of chronic risk. Here, vulnerability exceeds mere exposure to hazards; it is amplified by systemic deficiencies in social, economic, environmental, and institutional domains. This paper conceptualized vulnerability through a triad of exposure, sensitivity, and adaptive capacity, drawing on global frameworks, such as the United Nations’ Hyogo and Sendai strategies, and informed by theoretical insights from the Intergovernmental Panel on Climate Change (IPCC). The research posited that risk transforms into disaster only when it intersects with the susceptibility of a population, which is contingent upon how communities adapt, cope, and recover. In Marivan, these vulnerabilities exhibited spatial layering, revealing clusters of rural settlements that were either increasingly resilient or perilously exposed. The central and western districts of the county benefiting from richer ecological resources and proximity to trade and tourism possessed comparatively greater adaptive capacity. In contrast, the eastern regions—particularly Sarshiv, Golchidar, and Komasi—remained developmentally marginalized, structurally fragile, and highly susceptible to cascading environmental threats. By employing a spatially differentiated and theoretically grounded approach, this research aimed to elucidate how localized vulnerabilities corresponded with broader patterns of underdevelopment and policy neglect, ultimately providing an evidence-based foundation for targeted interventions. Materials & Methods Given the complexity and sensitivity of the border region under study, traditional data collection methods presented significant challenges. Many official datasets were either unavailable or intentionally withheld due to the strategic nature of the county’s location. To address these limitations, the research employed a hybrid methodology that combined participatory evaluation with geospatial modeling. A key component of this methodology was the establishment of a Delphi panel consisting of 18 local experts, including geographers, sociologists, agricultural specialists, and rural development practitioners. This panel conducted a 3-round consensus-based evaluation of selected sample villages across all rural districts. Village headmen also contributed data through structured questionnaires addressing the realities of exposure, sensitivity, and resilience on the ground. Villages were scored on a 5-point scale across multiple indicators relevant to each component of vulnerability. For exposure, indicators included proximity to active fault lines, susceptibility to drought, and signs of subsidence due to groundwater depletion. Sensitivity was assessed through demographic profiles, economic dependence, land use patterns, and ecological degradation. Adaptive capacity was evaluated based on infrastructure access, presence of functional cooperatives, involvement of women in economic activities, and institutional responsiveness. Negative indicators, such as unemployment or excessive water extraction, were inverted in scoring to reflect an increase in vulnerability. In instances where quantitative data were absent, qualitative proxies and triangulated responses from local administrators were utilized. Scores were further validated and refined through group deliberations and feedback loops within the Delphi panel. To extrapolate findings to non-sampled villages, the study applied Inverse Distance Weighting (IDW) interpolation and employed Moran’s I statistic to assess spatial autocorrelation in vulnerability distribution. Kernel density maps were generated to visualize areas of concern. The final vulnerability index was calculated using the formula of Vulnerability = (Exposure + Sensitivity) – Adaptive Capacity, highlighting how resilience could mitigate the impacts of hazards even in areas with significant exposure. Research Findings The results revealed a layered and spatially uneven picture of vulnerability in Marivan County. Regarding exposure to earthquakes, 38% of villages—primarily in the eastern zone—were located within one kilometer of active fault lines. These villages exhibited the weakest infrastructure and the least access to emergency services. A separate yet equally alarming finding pertained to groundwater depletion and subsidence risks, which were concentrated in the western and central regions due to excessive agricultural water usage, illegal wells, and poorly regulated tourism developments. Nearly 40% of villages were classified as vulnerable to these hydro-ecological threats. Sensitivity displayed a similarly troubling pattern. Approximately 42% of villages representing 44% of the rural population exhibited high sensitivity. These communities were characterized by fragile livelihoods, fragmented landholdings, aging populations, and degraded natural resource bases. Many villages lacked diversified income sources and relied heavily on traditional agriculture or grazing, which had long surpassed ecological thresholds. Adaptive capacity, the third and perhaps most critical pillar, revealed the deepest disparities. Nearly half of all villages were poorly scored with limited access to schools, clinics, piped water, and paved roads. Women’s economic participation was minimal in most settlements with only a single handicrafts workshop identified throughout the county. However, some western zones, such as Zaribar and Khaw-Mirabad, stood out as exceptions. These areas demonstrated greater resilience bolstered by border trade, tourism growth, and better integration into urban supply chains. Residents reported more diversified livelihoods and actively participated in local decision-making processes. When synthesizing all three dimensions of vulnerability, significant spatial clustering emerged. Approximately 43.2% of villages, primarily in the central and western zones, exhibited relatively low vulnerability, while 41.7% fell into high-risk categories, particularly in eastern districts. Villages with populations under 200 residents showed disproportionately high vulnerability, reflecting both their isolation and lack of service provision. Moran’s Index confirmed a strong spatial autocorrelation, with vulnerability clustering around administrative and ecological fault lines. Kernel density estimations reinforced this finding, illustrating the concentration of low-resilience areas in the east and pockets of relative resilience in the west. This analysis revealed a distinct east-west divide—a dichotomy in development and risk exposure that threatened long-term spatial equity. Discussion of Results & Conclusion The analysis revealed significant spatial and structural disparities between the eastern rural districts (Komasi, Sarshiv, Golchidar) and the western and central districts (Khavmirabad, Sarkal, Zaribar). Continuation of this trend threatened to further disrupt development and livelihoods in the region. To mitigate vulnerability, it is crucial to enhance infrastructure and diversify the economy—particularly by promoting tourism, medicinal plant cultivation, and a semi-industrial livestock sector. Supporting educated youth and developing growth hubs in medium-sized villages will contribute to a more balanced spatial development. Furthermore, the regional crisis management system should transition from a reactive approach to one that emphasizes risk management, focusing on reducing vulnerability, prevention, and resilience building. Forward-looking planning that incorporates multi-risk assessments with clearly defined roles for government and local institutions will be a significant step toward ensuring the long-term sustainability of rural communities. The findings of this study highlighted the structural asymmetries and developmental contradictions inherent in the rural landscape of Marivan County. Vulnerability is not merely a reaction to external threats; it is a product of entrenched inequalities regarding access to resources, infrastructure, and institutional support. The stark contrast between the fragility of the eastern districts and the resilience of the western districts exemplified a broader spatial injustice rooted in decades of uneven policy attention and investment. The eastern districts were trapped in a debilitating cycle of poverty, ecological degradation, and socio-economic exclusion. Despite their proximity to strategic water reservoirs, such as the Garan and Azad dams, these communities lacked formal water rights and the necessary infrastructure to benefit from this closeness. Their economies remained narrowly focused on low-yield livestock farming and manual labor, resulting in high rates of youth outmigration and land abandonment. In contrast, the western districts showcasing how diversified economic activities—such as trade, tourism, and agriculture—could help buffer communities against vulnerabilities, even in the face of similar environmental threats. Importantly, the middle-tier villages characterized by moderate vulnerability emerged as potential pivot points. With targeted support, these settlements could transform into resilience hubs, facilitating skill transfer, technological diffusion, and community mobilization. Such a polycentric development approach could provide a strategic alternative to the long-standing central-periphery model that had failed rural Kurdistan. This study made a compelling case for adopting a multidimensional, multi-hazard approach to regional planning. Moving from crisis-reactive models to proactive risk management required not only technical solutions, but also institutional transformation. The governance paradigm had to shift from siloed hazard responses to integrated vulnerability reduction rooted in local contexts. Practical recommendations include enhancing adaptive infrastructure in the eastern districts, investing in vocational training and gender-inclusive enterprises, enforcing water regulations to mitigate subsidence risks, and promoting stakeholder co-management of natural resources. Furthermore, there is a pressing need to redefine regional development logic—shifting from an urban-centric growth model towards a more spatially balanced and socially just framework that prioritizes resilience and sustainability. Ultimately, this research emphasizes that vulnerability is not inevitable; it can be mapped, understood, and addressed if there is the political will and community agency to do so. In the rural districts of Marivan, the path forward lies in recognizing that risk is not solely a technical challenge, but also a moral one. Only by emphasizing spatial justice and ecological stewardship can the region escape its vulnerability trap and build a more resilient future. Abstract Reducing vulnerability is a fundamental principle in the risk-management approach to enhancing the resilience of human settlements. Achieving this requires a comprehensive assessment of the factors influencing vulnerability followed by targeted planning based on the identified vulnerability levels within communities. This applied research was conducted in the rural areas of Marivan County, Kurdistan Province, by using a descriptive-analytical methodology and an inductive approach. The study evaluated exposure, sensitivity, and adaptive capacity—key components of vulnerability in the selected villages—and generalized the vulnerability levels across the entire county using GIS spatial-statistics tools, such as Inverse-Distance Weighting (IDW) interpolation, kriging density, and Moran’s spatial autocorrelation, focusing on two primary hazards: earthquakes and land subsidence resulting from prolonged drought and excessive water extraction. The status of settlements was assessed across economic, social, natural, and physical variables defined through consensus in a Delphi group and corroborated with input from villagers in the sample communities. The findings revealed that vulnerability levels in the eastern rural settlements significantly differed from those in the central and western regions of the county. This disparity reflected greater distance from the city center, smaller population sizes, a center-periphery development pattern, low adaptive capacity, and limited economic diversity. Inequalities and spatial variations in vulnerability exhibited a clustered pattern, indicating a spatial bipolarity between areas of high and low vulnerability. Keywords: Spatial Analysis, Exposure, Sensitivity, Adaptation, Natural Hazard Risks, Rural Villages, Marivan County. Introduction Vulnerability to natural hazards is a persistent and multifaceted challenge impacting rural development, especially in areas where ecological fragility intersects with infrastructural deprivation. In the rural districts of Marivan County located in Iran’s Kurdistan Province, a convergence of seismic instability, prolonged droughts, excessive groundwater extraction, and land subsidence has created a landscape of chronic risk. Here, vulnerability exceeds mere exposure to hazards; it is amplified by systemic deficiencies in social, economic, environmental, and institutional domains. This paper conceptualized vulnerability through a triad of exposure, sensitivity, and adaptive capacity, drawing on global frameworks, such as the United Nations’ Hyogo and Sendai strategies, and informed by theoretical insights from the Intergovernmental Panel on Climate Change (IPCC). The research posited that risk transforms into disaster only when it intersects with the susceptibility of a population, which is contingent upon how communities adapt, cope, and recover. In Marivan, these vulnerabilities exhibited spatial layering, revealing clusters of rural settlements that were either increasingly resilient or perilously exposed. The central and western districts of the county benefiting from richer ecological resources and proximity to trade and tourism possessed comparatively greater adaptive capacity. In contrast, the eastern regions—particularly Sarshiv, Golchidar, and Komasi—remained developmentally marginalized, structurally fragile, and highly susceptible to cascading environmental threats. By employing a spatially differentiated and theoretically grounded approach, this research aimed to elucidate how localized vulnerabilities corresponded with broader patterns of underdevelopment and policy neglect, ultimately providing an evidence-based foundation for targeted interventions. Materials & Methods Given the complexity and sensitivity of the border region under study, traditional data collection methods presented significant challenges. Many official datasets were either unavailable or intentionally withheld due to the strategic nature of the county’s location. To address these limitations, the research employed a hybrid methodology that combined participatory evaluation with geospatial modeling. A key component of this methodology was the establishment of a Delphi panel consisting of 18 local experts, including geographers, sociologists, agricultural specialists, and rural development practitioners. This panel conducted a 3-round consensus-based evaluation of selected sample villages across all rural districts. Village headmen also contributed data through structured questionnaires addressing the realities of exposure, sensitivity, and resilience on the ground. Villages were scored on a 5-point scale across multiple indicators relevant to each component of vulnerability. For exposure, indicators included proximity to active fault lines, susceptibility to drought, and signs of subsidence due to groundwater depletion. Sensitivity was assessed through demographic profiles, economic dependence, land use patterns, and ecological degradation. Adaptive capacity was evaluated based on infrastructure access, presence of functional cooperatives, involvement of women in economic activities, and institutional responsiveness. Negative indicators, such as unemployment or excessive water extraction, were inverted in scoring to reflect an increase in vulnerability. In instances where quantitative data were absent, qualitative proxies and triangulated responses from local administrators were utilized. Scores were further validated and refined through group deliberations and feedback loops within the Delphi panel. To extrapolate findings to non-sampled villages, the study applied Inverse Distance Weighting (IDW) interpolation and employed Moran’s I statistic to assess spatial autocorrelation in vulnerability distribution. Kernel density maps were generated to visualize areas of concern. The final vulnerability index was calculated using the formula of Vulnerability = (Exposure + Sensitivity) – Adaptive Capacity, highlighting how resilience could mitigate the impacts of hazards even in areas with significant exposure. Research Findings The results revealed a layered and spatially uneven picture of vulnerability in Marivan County. Regarding exposure to earthquakes, 38% of villages—primarily in the eastern zone—were located within one kilometer of active fault lines. These villages exhibited the weakest infrastructure and the least access to emergency services. A separate yet equally alarming finding pertained to groundwater depletion and subsidence risks, which were concentrated in the western and central regions due to excessive agricultural water usage, illegal wells, and poorly regulated tourism developments. Nearly 40% of villages were classified as vulnerable to these hydro-ecological threats. Sensitivity displayed a similarly troubling pattern. Approximately 42% of villages representing 44% of the rural population exhibited high sensitivity. These communities were characterized by fragile livelihoods, fragmented landholdings, aging populations, and degraded natural resource bases. Many villages lacked diversified income sources and relied heavily on traditional agriculture or grazing, which had long surpassed ecological thresholds. Adaptive capacity, the third and perhaps most critical pillar, revealed the deepest disparities. Nearly half of all villages were poorly scored with limited access to schools, clinics, piped water, and paved roads. Women’s economic participation was minimal in most settlements with only a single handicrafts workshop identified throughout the county. However, some western zones, such as Zaribar and Khaw-Mirabad, stood out as exceptions. These areas demonstrated greater resilience bolstered by border trade, tourism growth, and better integration into urban supply chains. Residents reported more diversified livelihoods and actively participated in local decision-making processes. When synthesizing all three dimensions of vulnerability, significant spatial clustering emerged. Approximately 43.2% of villages, primarily in the central and western zones, exhibited relatively low vulnerability, while 41.7% fell into high-risk categories, particularly in eastern districts. Villages with populations under 200 residents showed disproportionately high vulnerability, reflecting both their isolation and lack of service provision. Moran’s Index confirmed a strong spatial autocorrelation, with vulnerability clustering around administrative and ecological fault lines. Kernel density estimations reinforced this finding, illustrating the concentration of low-resilience areas in the east and pockets of relative resilience in the west. This analysis revealed a distinct east-west divide—a dichotomy in development and risk exposure that threatened long-term spatial equity. Discussion of Results & Conclusion The analysis revealed significant spatial and structural disparities between the eastern rural districts (Komasi, Sarshiv, Golchidar) and the western and central districts (Khavmirabad, Sarkal, Zaribar). Continuation of this trend threatened to further disrupt development and livelihoods in the region. To mitigate vulnerability, it is crucial to enhance infrastructure and diversify the economy—particularly by promoting tourism, medicinal plant cultivation, and a semi-industrial livestock sector. Supporting educated youth and developing growth hubs in medium-sized villages will contribute to a more balanced spatial development. Furthermore, the regional crisis management system should transition from a reactive approach to one that emphasizes risk management, focusing on reducing vulnerability, prevention, and resilience building. Forward-looking planning that incorporates multi-risk assessments with clearly defined roles for government and local institutions will be a significant step toward ensuring the long-term sustainability of rural communities. The findings of this study highlighted the structural asymmetries and developmental contradictions inherent in the rural landscape of Marivan County. Vulnerability is not merely a reaction to external threats; it is a product of entrenched inequalities regarding access to resources, infrastructure, and institutional support. The stark contrast between the fragility of the eastern districts and the resilience of the western districts exemplified a broader spatial injustice rooted in decades of uneven policy attention and investment. The eastern districts were trapped in a debilitating cycle of poverty, ecological degradation, and socio-economic exclusion. Despite their proximity to strategic water reservoirs, such as the Garan and Azad dams, these communities lacked formal water rights and the necessary infrastructure to benefit from this closeness. Their economies remained narrowly focused on low-yield livestock farming and manual labor, resulting in high rates of youth outmigration and land abandonment. In contrast, the western districts showcasing how diversified economic activities—such as trade, tourism, and agriculture—could help buffer communities against vulnerabilities, even in the face of similar environmental threats. Importantly, the middle-tier villages characterized by moderate vulnerability emerged as potential pivot points. With targeted support, these settlements could transform into resilience hubs, facilitating skill transfer, technological diffusion, and community mobilization. Such a polycentric development approach could provide a strategic alternative to the long-standing central-periphery model that had failed rural Kurdistan. This study made a compelling case for adopting a multidimensional, multi-hazard approach to regional planning. Moving from crisis-reactive models to proactive risk management required not only technical solutions, but also institutional transformation. The governance paradigm had to shift from siloed hazard responses to integrated vulnerability reduction rooted in local contexts. Practical recommendations include enhancing adaptive infrastructure in the eastern districts, investing in vocational training and gender-inclusive enterprises, enforcing water regulations to mitigate subsidence risks, and promoting stakeholder co-management of natural resources. Furthermore, there is a pressing need to redefine regional development logic—shifting from an urban-centric growth model towards a more spatially balanced and socially just framework that prioritizes resilience and sustainability. Ultimately, this research emphasizes that vulnerability is not inevitable; it can be mapped, understood, and addressed if there is the political will and community agency to do so. In the rural districts of Marivan, the path forward lies in recognizing that risk is not solely a technical challenge, but also a moral one. Only by emphasizing spatial justice and ecological stewardship can the region escape its vulnerability trap and build a more resilient future.

https://gep.ui.ac.ir/article_30087_c2ec4d4905e45eb4cd5b2eeee06cae4b.pdf