National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 A novel synthesis Index-Based approach for drought monitoring Northwestern Iran A novel synthesis Index-Based approach for drought monitoring Northwestern Iran 7 25 732503 10.22131/sepehr.2025.2073394.3154 FA Misag Sepehri Amin Master of science, Surveying engineering department, Faculty of civil engineering, University of Tabriz, Tabriz-Iran. 0009-0003-4672-7096 Mansoureh Sadrykia Assistant professor, Surveying engineering department, Faculty of civil engineering, University of Tabriz, Tabriz-Iran, 0000-0001-7391-8056 Hassan Emami Associate professor, Surveying engineering department, Faculty of Marand engineering, University of Tabriz, Tabriz-Iran 0000-0002-0171-6487 Journal Article 2025 10 06 Extended Abstract Introduction Drought, as one of the most important climate hazards with widespread impacts on environmental sustainability and human livelihoods, requires a multidimensional approach for monitoring and assessment. In this study, with the aim of providing a comprehensive model for assessing drought in northwest Iran, a new composite index was developed based on the integration of four perspectives: meteorology, agriculture, hydrology, and remote sensing. The data used included synoptic observations of the Iran Meteorological Organization, Landsat image time series, and MODIS data for the period 2000 to 2024. All processing and calculations of the indices were performed in the Google Earth Engine environment. The resulting indices were normalized using the Analytic Hierarchy Process (AHP) method and optimized weights, and six-month drought maps were produced. The combined results showed that the severity of drought has increased significantly after 2015, especially in the Lake Urmia basin, while higher mountainous areas show a more stable pattern of moisture. So that the area of severe drought areas in the first half of 2015 reached an area of 49.68 km2 and in the second half it reached 34.50 km2, which is almost more than half of the area of the study area. This amount reached 58.70 km2 in the first half of 2019 and 35.20 km2 in the second half, which reached the peak of drought in 2021, so that the first half of this number reached 42.36 km2 and in the second half it reached 41 km2. Also, the combined results of drought maps emphasize that the most droughts occurred in the areas around Lake Urmia and these areas are under serious threat. Finally, the combined method presented in this study provides an efficient method for more accurate spatial and temporal identification of critical areas and provides an effective decision-making tool for managing water resources and agriculture in arid and semi-arid regions. Materials & Methods This study introduces a novel hybrid methodology for drought monitoring by integrating four distinct perspectives—meteorological, agricultural, hydrological, and remote sensing—to achieve a more comprehensive and accurate assessment. Focusing on the drought-stricken region of Lake Urmia in the provinces of East and West Azerbaijan, the research combined multi-source data within a Geographic Information System (GIS) environment. To manage the computationally intensive workload, key indices from each perspective were calculated over a 25-year period, segmented into six-month intervals, using the Google Earth Engine platform. The process first synthesized indices within each perspective using the Analytic Hierarchy Process (AHP) algorithm to generate individual drought severity maps. These four distinct maps were then integrated into a single, comprehensive six-monthly drought map through an overlay analysis with equal weighting, effectively transforming qualitative, multi-domain assessments into a quantifiable, spatially explicit drought severity index. The accuracy of this integrated index was validated against meteorological maps derived from reliable in-situ data, offering a refined tool for precise drought monitoring. Results and Discussion Spatio-temporal analysis of composite drought maps reveals a critical trend of intensifying drought severity in the Lake Urmia basin, particularly from 2000 onwards. The quantitative evidence demonstrates a dramatic expansion of areas classified under "severe dryness," which escalated from approximately 73 km² in the first half of 2015 to 154 km² in the second half, and further soared to 300 km² and 615 km² in the respective halves of 2020. By 2024, the affected area remained persistently high at 310 km² and 200 km² for the first and second halves, marking increases of approximately 24% and 25% compared to the corresponding periods in 2015. Spatially, the results confirm that the most severe drought conditions are concentrated in the lands immediately surrounding Lake Urmia. This spatial pattern suggests a vicious cycle whereby the lake's desiccation exacerbates local agricultural and ecological drought through feedback mechanisms such as salt-dust storms, thereby rendering these areas acutely vulnerable and threatening their long-term habitability if the current trend persists. Conclusions This study conclusively demonstrates that the proposed multi-perspective, index-based methodology is a powerful and efficient tool for the comprehensive spatio-temporal monitoring and assessment of drought. Empirical findings confirm a significant intensification of drought within the Lake Urmia basin following 2015, successfully identifying the lake's periphery as the critical epicenter of this environmental crisis. By providing a robust model for monitoring agricultural and ecological drought, this integrated approach equips policymakers and environmental managers with a precise mechanism to pinpoint critical areas at risk. Consequently, the identified regions on the resulting maps offer essential information for planners to implement timely, targeted mitigation and adaptation strategies, thereby enabling proactive measures to combat the devastating effects of drought in vulnerable ecosystems globally. Extended Abstract Introduction Drought, as one of the most important climate hazards with widespread impacts on environmental sustainability and human livelihoods, requires a multidimensional approach for monitoring and assessment. In this study, with the aim of providing a comprehensive model for assessing drought in northwest Iran, a new composite index was developed based on the integration of four perspectives: meteorology, agriculture, hydrology, and remote sensing. The data used included synoptic observations of the Iran Meteorological Organization, Landsat image time series, and MODIS data for the period 2000 to 2024. All processing and calculations of the indices were performed in the Google Earth Engine environment. The resulting indices were normalized using the Analytic Hierarchy Process (AHP) method and optimized weights, and six-month drought maps were produced. The combined results showed that the severity of drought has increased significantly after 2015, especially in the Lake Urmia basin, while higher mountainous areas show a more stable pattern of moisture. So that the area of severe drought areas in the first half of 2015 reached an area of 49.68 km2 and in the second half it reached 34.50 km2, which is almost more than half of the area of the study area. This amount reached 58.70 km2 in the first half of 2019 and 35.20 km2 in the second half, which reached the peak of drought in 2021, so that the first half of this number reached 42.36 km2 and in the second half it reached 41 km2. Also, the combined results of drought maps emphasize that the most droughts occurred in the areas around Lake Urmia and these areas are under serious threat. Finally, the combined method presented in this study provides an efficient method for more accurate spatial and temporal identification of critical areas and provides an effective decision-making tool for managing water resources and agriculture in arid and semi-arid regions. Materials & Methods This study introduces a novel hybrid methodology for drought monitoring by integrating four distinct perspectives—meteorological, agricultural, hydrological, and remote sensing—to achieve a more comprehensive and accurate assessment. Focusing on the drought-stricken region of Lake Urmia in the provinces of East and West Azerbaijan, the research combined multi-source data within a Geographic Information System (GIS) environment. To manage the computationally intensive workload, key indices from each perspective were calculated over a 25-year period, segmented into six-month intervals, using the Google Earth Engine platform. The process first synthesized indices within each perspective using the Analytic Hierarchy Process (AHP) algorithm to generate individual drought severity maps. These four distinct maps were then integrated into a single, comprehensive six-monthly drought map through an overlay analysis with equal weighting, effectively transforming qualitative, multi-domain assessments into a quantifiable, spatially explicit drought severity index. The accuracy of this integrated index was validated against meteorological maps derived from reliable in-situ data, offering a refined tool for precise drought monitoring. Results and Discussion Spatio-temporal analysis of composite drought maps reveals a critical trend of intensifying drought severity in the Lake Urmia basin, particularly from 2000 onwards. The quantitative evidence demonstrates a dramatic expansion of areas classified under "severe dryness," which escalated from approximately 73 km² in the first half of 2015 to 154 km² in the second half, and further soared to 300 km² and 615 km² in the respective halves of 2020. By 2024, the affected area remained persistently high at 310 km² and 200 km² for the first and second halves, marking increases of approximately 24% and 25% compared to the corresponding periods in 2015. Spatially, the results confirm that the most severe drought conditions are concentrated in the lands immediately surrounding Lake Urmia. This spatial pattern suggests a vicious cycle whereby the lake's desiccation exacerbates local agricultural and ecological drought through feedback mechanisms such as salt-dust storms, thereby rendering these areas acutely vulnerable and threatening their long-term habitability if the current trend persists. Conclusions This study conclusively demonstrates that the proposed multi-perspective, index-based methodology is a powerful and efficient tool for the comprehensive spatio-temporal monitoring and assessment of drought. Empirical findings confirm a significant intensification of drought within the Lake Urmia basin following 2015, successfully identifying the lake's periphery as the critical epicenter of this environmental crisis. By providing a robust model for monitoring agricultural and ecological drought, this integrated approach equips policymakers and environmental managers with a precise mechanism to pinpoint critical areas at risk. Consequently, the identified regions on the resulting maps offer essential information for planners to implement timely, targeted mitigation and adaptation strategies, thereby enabling proactive measures to combat the devastating effects of drought in vulnerable ecosystems globally.

https://www.sepehr.org/article_732503_e271d691fdb93958d6af9a7c35267e4a.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Prediction of flood occurrence in the Torogh Dam watershed, located in Khorasan Razavi Province Prediction of flood occurrence in the Torogh Dam watershed, located in Khorasan Razavi Province 27 44 733439 10.22131/sepehr.2026.2052338.3120 FA Majid Goodarzi Associate professor of geography and urban planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 0000-0001-7982-8027 Zahra Soltani Associate professor of geography and rural planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0001-5876-0473 Farkhondeh Hashemi Ghandali Ph.D. Student of geography and urban planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0001-7982-8027 Journal Article 2025 02 01 Extended Abstract Introduction Watersheds, as fundamental units of water resources management, possess unique characteristics that influence the occurrence and intensity of floods. In Iran, where the climate is predominantly arid and semi-arid, flood events, particularly in small to medium-sized watersheds like the Torogh Dam watershed, pose significant challenges for water resource management. The Torogh Dam, located in Razavi Khorasan Province near the city of Mashhad, plays a vital role in supplying drinking and agricultural water to the region. Sudden flood events in this watershed can negatively impact the dam's water storage, the safety of downstream areas, and local infrastructure. Therefore, the development of precise and efficient forecasting tools is essential for managing this watershed effectively. Materials and Methods To predict floods in the Torogh Dam watershed, Artificial Neural Networks (ANNs) were utilized as a powerful computational tool. This approach involved various stages, including data collection, data processing, and modeling using deep learning algorithms. The methodology for this study is outlined as follows: Selection of the Study Area: The Torogh Dam watershed, located in Khorasan Razavi Province, was selected due to its unique physiographic and hydrological characteristics and its economic significance. With a concentration time of less than three hours, this watershed provides suitable conditions for evaluating the performance of ANN models in flood forecasting. Data Collection The data utilized in this study comprised meteorological data (such as precipitation and temperature), hydrological data (streamflow), and physiographic data of the watershed (e.g., area, slope, and river length). Precipitation data were collected daily from reliable meteorological stations and subjected to quality assessments. Statistical methods were applied to correct and fill missing data, minimizing uncertainties in the dataset. Data Preprocessing To enhance the accuracy of the model, raw data were normalized during preprocessing to ensure all inputs fell within a specified numerical range. Additionally, only precipitation data from one or two days prior to flood events were included in the model to more accurately account for temporal dependencies. Design of the Artificial Neural Network Model The ANN used in this study consisted of two primary structures: Two-Layer Network: This structure included an input layer, a single hidden layer, and an output layer. The number of neurons in the hidden layer was determined through trial and error to achieve optimal results. Three-Layer Network: This structure featured two hidden layers and an output layer, designed to improve accuracy and increase the model's regression performance. In both structures, modeling parameters, such as the number of neurons and learning rate, were optimized. The backpropagation algorithm was employed as the learning method. Model Training and Evaluation The data were divided into two sets: a training set (70% of the data) and a test set (30% of the data). The models were trained using the training set, and their performance was evaluated with the test set. Evaluation metrics included the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Output Analysis The results indicated that using only precipitation data from the day of the flood and the previous day was sufficient due to the short concentration time of the watershed. Increasing the number of neurons in both the two-layer and three-layer networks improved the prediction accuracy, particularly when only precipitation data from the two most recent days were used as inputs to the network. Results and discussion In two-layer networks, it is observed that when the transfer function of the first layer is Tansig and that of the second layer is Purelin, and precipitation intensity is considered only for the day of the flood and the previous day, the network's output demonstrates a direct relationship with the target and aligns more closely with reality. Increasing the number of neurons under this configuration further improves the results, particularly in both the training and generalization phases. In three-layer networks with a T-T-T transfer function arrangement, increasing the number of neurons enhances regression performance, resulting in outputs that more accurately match reality. This is especially true when precipitation intensity is limited to the day of the flood and the preceding day. In the same three-layer networks, when the number of neurons in the first layer is set to 10 and in the second layer to 15, and the transfer function arrangement is P-T-P, results become more realistic when using only single-day precipitation intensity as input. When the number of layers increases to four, it is observed that if the transfer function for all four layers is Tansig, the outputs of the network matrix and the target exhibit an inverse relationship. Therefore, it is recommended to adopt a configuration in which the last two layers utilize the Purelin transfer function. By transitioning to a cascade-forward network structure, it is observed that a four-layer network with a P-T-P-P transfer function arrangement maintains a consistent direct relationship in most cases except for validation. However, this relationship deteriorates when considering precipitation intensity from the past two days. Conclusion In conclusion, given the short concentration time of watersheds, accounting only for the precipitation intensity on the day of the flood and the preceding day is sufficient. This has been confirmed in practice in most cases. Additionally, networks perform better when using a Purelin transfer function in the initial layers and a Tansig transfer function in the final layers. Therefore, it is recommended to configure the transfer functions accordingly. For cascade-forward networks, it is preferable to use the Tansig transfer function in the middle layers, while for backpropagation networks, using the Tansig transfer function in the initial layers yields better results. Furthermore, for flood calculations, it is advisable to utilize three-layer or four-layer backpropagation networks, or four-layer cascade-forward networks, as their outputs are closer to the expected real-world values. Extended Abstract Introduction Watersheds, as fundamental units of water resources management, possess unique characteristics that influence the occurrence and intensity of floods. In Iran, where the climate is predominantly arid and semi-arid, flood events, particularly in small to medium-sized watersheds like the Torogh Dam watershed, pose significant challenges for water resource management. The Torogh Dam, located in Razavi Khorasan Province near the city of Mashhad, plays a vital role in supplying drinking and agricultural water to the region. Sudden flood events in this watershed can negatively impact the dam's water storage, the safety of downstream areas, and local infrastructure. Therefore, the development of precise and efficient forecasting tools is essential for managing this watershed effectively. Materials and Methods To predict floods in the Torogh Dam watershed, Artificial Neural Networks (ANNs) were utilized as a powerful computational tool. This approach involved various stages, including data collection, data processing, and modeling using deep learning algorithms. The methodology for this study is outlined as follows: Selection of the Study Area: The Torogh Dam watershed, located in Khorasan Razavi Province, was selected due to its unique physiographic and hydrological characteristics and its economic significance. With a concentration time of less than three hours, this watershed provides suitable conditions for evaluating the performance of ANN models in flood forecasting. Data Collection The data utilized in this study comprised meteorological data (such as precipitation and temperature), hydrological data (streamflow), and physiographic data of the watershed (e.g., area, slope, and river length). Precipitation data were collected daily from reliable meteorological stations and subjected to quality assessments. Statistical methods were applied to correct and fill missing data, minimizing uncertainties in the dataset. Data Preprocessing To enhance the accuracy of the model, raw data were normalized during preprocessing to ensure all inputs fell within a specified numerical range. Additionally, only precipitation data from one or two days prior to flood events were included in the model to more accurately account for temporal dependencies. Design of the Artificial Neural Network Model The ANN used in this study consisted of two primary structures: Two-Layer Network: This structure included an input layer, a single hidden layer, and an output layer. The number of neurons in the hidden layer was determined through trial and error to achieve optimal results. Three-Layer Network: This structure featured two hidden layers and an output layer, designed to improve accuracy and increase the model's regression performance. In both structures, modeling parameters, such as the number of neurons and learning rate, were optimized. The backpropagation algorithm was employed as the learning method. Model Training and Evaluation The data were divided into two sets: a training set (70% of the data) and a test set (30% of the data). The models were trained using the training set, and their performance was evaluated with the test set. Evaluation metrics included the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Output Analysis The results indicated that using only precipitation data from the day of the flood and the previous day was sufficient due to the short concentration time of the watershed. Increasing the number of neurons in both the two-layer and three-layer networks improved the prediction accuracy, particularly when only precipitation data from the two most recent days were used as inputs to the network. Results and discussion In two-layer networks, it is observed that when the transfer function of the first layer is Tansig and that of the second layer is Purelin, and precipitation intensity is considered only for the day of the flood and the previous day, the network's output demonstrates a direct relationship with the target and aligns more closely with reality. Increasing the number of neurons under this configuration further improves the results, particularly in both the training and generalization phases. In three-layer networks with a T-T-T transfer function arrangement, increasing the number of neurons enhances regression performance, resulting in outputs that more accurately match reality. This is especially true when precipitation intensity is limited to the day of the flood and the preceding day. In the same three-layer networks, when the number of neurons in the first layer is set to 10 and in the second layer to 15, and the transfer function arrangement is P-T-P, results become more realistic when using only single-day precipitation intensity as input. When the number of layers increases to four, it is observed that if the transfer function for all four layers is Tansig, the outputs of the network matrix and the target exhibit an inverse relationship. Therefore, it is recommended to adopt a configuration in which the last two layers utilize the Purelin transfer function. By transitioning to a cascade-forward network structure, it is observed that a four-layer network with a P-T-P-P transfer function arrangement maintains a consistent direct relationship in most cases except for validation. However, this relationship deteriorates when considering precipitation intensity from the past two days. Conclusion In conclusion, given the short concentration time of watersheds, accounting only for the precipitation intensity on the day of the flood and the preceding day is sufficient. This has been confirmed in practice in most cases. Additionally, networks perform better when using a Purelin transfer function in the initial layers and a Tansig transfer function in the final layers. Therefore, it is recommended to configure the transfer functions accordingly. For cascade-forward networks, it is preferable to use the Tansig transfer function in the middle layers, while for backpropagation networks, using the Tansig transfer function in the initial layers yields better results. Furthermore, for flood calculations, it is advisable to utilize three-layer or four-layer backpropagation networks, or four-layer cascade-forward networks, as their outputs are closer to the expected real-world values.

https://www.sepehr.org/article_733439_7d378e34d155f1be165a51c2f5d95d08.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Surface interrupture assessment using spatial mapping and integrated spatial-temporal approach with Monte Carlo simulation - A case study of the Kopeh Dagh tectonic region Surface interrupture assessment using spatial mapping and integrated spatial-temporal approach with Monte Carlo simulation - A case study of the Kopeh Dagh tectonic region 45 61 734053 10.22131/sepehr.2026.2072943.3152 FA Morteza Rezaei Arefi Ph.D. Candidate, Department of physical geography, Faculty of geography, University of Tehran, Tehran, Iran 0009-0005-0618-3834 Mansour Jafarbaglou Associate professor, Department of physical geography, Faculty of geography, University of Tehran, Tehran, Iran 0000-0001-9745-2318 Ebrahim Moghimi Professor, Department of physical geography, Faculty of geography, University of Tehran, Tehran, Iran 0009-0005-0618-3834 Seyyed Moosa Hosseini Associate professor, Department of physical geography, Faculty of geography, University of Tehran, Tehran, Iran 0000-0001-7161-8711 Majid Fakhri Ph.D in strategic management in the passive defense, National Defense University , Tehran, Iran 0009-0002-5935-0892 Journal Article 2025 10 04 Extended Abstract Introduction Part of the Alpine–Himalayan orogenic belt, shaped by active deformation, folding, and the presence of major strike-slip and thrust fault systems. These processes contribute significantly to the development of surface ruptures, which serve as key geomorphic indicators of crustal stress release and active tectonics. Despite the importance of this region from a neotectonic and seismic-hazard perspective, quantitative and spatially explicit assessments of surface rupture potential remain scarce. Most previous studies have focused either on fault geometry, morphotectonic indices, or seismicity patterns in isolation, while the integrated and probabilistic modelling of rupture susceptibility has received limited attention. Recognizing this gap, the present study develops a spatial–probabilistic framework that integrates multi-source geological, seismological, and geomorphological datasets to predict potential zones of surface rupture within the Kopeh Dagh structural domain. The proposed framework employs a Weighted Linear Combination (WLC) model complemented by Monte Carlo simulation to quantify uncertainty and assess the stability of the model under varying input conditions. By combining these methods, the study aims to provide a robust, reproducible, and spatially coherent evaluation of rupture potential across diverse lithological units and structural environments. Ultimately, this work contributes to a more comprehensive understanding of the tectonic behavior of Kopeh Dagh and enhances regional hazard assessment. Materials and Methods The methodological framework is based on a systematic integration of spatial datasets representing seismic, structural, lithological, and geomorphological variables. Earthquake data—including magnitude, depth, and epicentral coordinates—were collected from reliable seismic catalogs and used to model the radius of influence based on empirical magnitude–rupture relationships. Fault density was computed through kernel density estimation, capturing the spatial clustering of active fault traces. Lithological sensitivity was classified according to the mechanical properties of rock units, distinguishing brittle formations from weaker, more deformable sediments. Geomorphological indices such as slope, curvature, and landform type were extracted from high-resolution DEMs to represent surface instability and morphological predisposition to rupture. All datasets were standardized to a common scale and projected into a uniform coordinate system. A Weighted Linear Combination (WLC) model was then applied, incorporating expert-defined weights (0.40 for earthquake influence, 0.30 for fault density, 0.20 for lithology, and 0.10 for geomorphology). This produced an initial rupture-potential index ranging from 0 (very low potential) to 1 (very high potential). To address uncertainty—an inherent component of tectonic and geomorphic processes—Monte Carlo simulation with 1000 iterations was implemented. In each iteration, the weights assigned to input variables were perturbed according to a normal distribution (σ = 0.05), enabling the evaluation of model sensitivity and probabilistic variation. This approach allowed the identification of zones where minor changes in input parameters resulted in significant shifts in potential rupture values, thereby highlighting structurally complex or poorly constrained areas. Model performance and stability were evaluated through the coefficient of variation (CV) and cross-validation metrics, including R² and RMSE. Results and Discussion The integrated model demonstrates that tectonic factors overwhelmingly dominate the spatial distribution of surface rupture potential in the Kopeh Dagh region. Among the variables, earthquake magnitude exhibits the strongest correlation with rupture potential (r = 0.85), followed by fault density (r = 0.73). This confirms that areas exposed to higher seismic energy release and greater structural segmentation are inherently more susceptible to rupture propagation. Lithological properties and geomorphological characteristics, while influential, play a secondary reinforcing role rather than acting as primary controls. Spatial analysis reveals that the highest rupture-potential zones are concentrated in the central and western parts of Kopeh Dagh, where active tectonic deformation, dense fault networks, and moderate-to-large seismic events coincide. These areas correspond closely with previously documented neotectonic activity and align with regional patterns of distributed deformation. Monte Carlo simulation results further validate the robustness of the model. The mean potential value across simulations is 0.51, with an average standard deviation of 0.18 and a low coefficient of variation (CV = 0.11). This indicates that the model is relatively insensitive to moderate fluctuations in weighting schemes, and the resulting spatial patterns remain stable across iterations. Areas exhibiting elevated CV values correspond to structurally intricate fault intersections, reflecting known complexities in fault kinematics and stress interactions. The strong agreement between modeled rupture potential and observed seismic–structural patterns is further supported by the high R² value (0.89) obtained during cross-validation. This suggests that the model not only captures the statistical relationships among variables but also succeeds in reproducing the spatial behavior of rupture-prone zones. Overall, the findings underscore the necessity of incorporating probabilistic methods when assessing tectonic hazards in regions where geological heterogeneity and data quality may introduce uncertainty. Conclusion This study provides a novel and reliable framework for modeling and mapping surface rupture potential in tectonically active regions. The findings highlight the dominant role of tectonic factors, particularly earthquake magnitude and fault density, in determining surface rupture risk. The model’s ability to integrate uncertainty through Monte Carlo simulation enhances its predictive power, making it a valuable tool for future studies in tectonically active regions. The results can inform risk management strategies and contribute to the development of disaster mitigation plans in high-risk areas. It is recommended that future research focus on incorporating higher-resolution data and more accurate field measurements to further improve the model's accuracy and reliability. Extended Abstract Introduction Part of the Alpine–Himalayan orogenic belt, shaped by active deformation, folding, and the presence of major strike-slip and thrust fault systems. These processes contribute significantly to the development of surface ruptures, which serve as key geomorphic indicators of crustal stress release and active tectonics. Despite the importance of this region from a neotectonic and seismic-hazard perspective, quantitative and spatially explicit assessments of surface rupture potential remain scarce. Most previous studies have focused either on fault geometry, morphotectonic indices, or seismicity patterns in isolation, while the integrated and probabilistic modelling of rupture susceptibility has received limited attention. Recognizing this gap, the present study develops a spatial–probabilistic framework that integrates multi-source geological, seismological, and geomorphological datasets to predict potential zones of surface rupture within the Kopeh Dagh structural domain. The proposed framework employs a Weighted Linear Combination (WLC) model complemented by Monte Carlo simulation to quantify uncertainty and assess the stability of the model under varying input conditions. By combining these methods, the study aims to provide a robust, reproducible, and spatially coherent evaluation of rupture potential across diverse lithological units and structural environments. Ultimately, this work contributes to a more comprehensive understanding of the tectonic behavior of Kopeh Dagh and enhances regional hazard assessment. Materials and Methods The methodological framework is based on a systematic integration of spatial datasets representing seismic, structural, lithological, and geomorphological variables. Earthquake data—including magnitude, depth, and epicentral coordinates—were collected from reliable seismic catalogs and used to model the radius of influence based on empirical magnitude–rupture relationships. Fault density was computed through kernel density estimation, capturing the spatial clustering of active fault traces. Lithological sensitivity was classified according to the mechanical properties of rock units, distinguishing brittle formations from weaker, more deformable sediments. Geomorphological indices such as slope, curvature, and landform type were extracted from high-resolution DEMs to represent surface instability and morphological predisposition to rupture. All datasets were standardized to a common scale and projected into a uniform coordinate system. A Weighted Linear Combination (WLC) model was then applied, incorporating expert-defined weights (0.40 for earthquake influence, 0.30 for fault density, 0.20 for lithology, and 0.10 for geomorphology). This produced an initial rupture-potential index ranging from 0 (very low potential) to 1 (very high potential). To address uncertainty—an inherent component of tectonic and geomorphic processes—Monte Carlo simulation with 1000 iterations was implemented. In each iteration, the weights assigned to input variables were perturbed according to a normal distribution (σ = 0.05), enabling the evaluation of model sensitivity and probabilistic variation. This approach allowed the identification of zones where minor changes in input parameters resulted in significant shifts in potential rupture values, thereby highlighting structurally complex or poorly constrained areas. Model performance and stability were evaluated through the coefficient of variation (CV) and cross-validation metrics, including R² and RMSE. Results and Discussion The integrated model demonstrates that tectonic factors overwhelmingly dominate the spatial distribution of surface rupture potential in the Kopeh Dagh region. Among the variables, earthquake magnitude exhibits the strongest correlation with rupture potential (r = 0.85), followed by fault density (r = 0.73). This confirms that areas exposed to higher seismic energy release and greater structural segmentation are inherently more susceptible to rupture propagation. Lithological properties and geomorphological characteristics, while influential, play a secondary reinforcing role rather than acting as primary controls. Spatial analysis reveals that the highest rupture-potential zones are concentrated in the central and western parts of Kopeh Dagh, where active tectonic deformation, dense fault networks, and moderate-to-large seismic events coincide. These areas correspond closely with previously documented neotectonic activity and align with regional patterns of distributed deformation. Monte Carlo simulation results further validate the robustness of the model. The mean potential value across simulations is 0.51, with an average standard deviation of 0.18 and a low coefficient of variation (CV = 0.11). This indicates that the model is relatively insensitive to moderate fluctuations in weighting schemes, and the resulting spatial patterns remain stable across iterations. Areas exhibiting elevated CV values correspond to structurally intricate fault intersections, reflecting known complexities in fault kinematics and stress interactions. The strong agreement between modeled rupture potential and observed seismic–structural patterns is further supported by the high R² value (0.89) obtained during cross-validation. This suggests that the model not only captures the statistical relationships among variables but also succeeds in reproducing the spatial behavior of rupture-prone zones. Overall, the findings underscore the necessity of incorporating probabilistic methods when assessing tectonic hazards in regions where geological heterogeneity and data quality may introduce uncertainty. Conclusion This study provides a novel and reliable framework for modeling and mapping surface rupture potential in tectonically active regions. The findings highlight the dominant role of tectonic factors, particularly earthquake magnitude and fault density, in determining surface rupture risk. The model’s ability to integrate uncertainty through Monte Carlo simulation enhances its predictive power, making it a valuable tool for future studies in tectonically active regions. The results can inform risk management strategies and contribute to the development of disaster mitigation plans in high-risk areas. It is recommended that future research focus on incorporating higher-resolution data and more accurate field measurements to further improve the model's accuracy and reliability.

https://www.sepehr.org/article_734053_d3ea14f4298f003e6a6aeb1e656838ee.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Analysis of spatiotemporal changes in winter vegetation index in the Maroon Basin with emphasis on the effects of teleconnection patterns Analysis of spatiotemporal changes in winter vegetation index in the Maroon Basin with emphasis on the effects of teleconnection patterns 63 77 734690 10.22131/sepehr.2026.2074423.3157 FA Leila Mokaram Ph.D. Student in climatology, Department of geography, Yazd University, Yazd, Iran 0009-0009-0947-9162 Ahmad Mazidi Associate professor, Department of geography and climatology, Yazd University, Yazd, Iran 0000-0003-4558-9907 Kamal Omidvar Professor, Department of geography and climatology, Yazd University, Yazd, Iran 0000-0002-4956-3002 Hamidreza Ghafarian Malmiri Associate professor, Department of geography, Yazd University, Yazd, Iran 0000-0001-6083-1517 Journal Article 2025 10 13 Extended Abstract Introduction Vegetation is one of the most sensitive environmental components to changes in climatic variables, and any fluctuation in precipitation, temperature, and humidity can result in rapid and observable responses in its growth and dynamics. Teleconnection patterns, particularly ENSO (El Niño–Southern Oscillation), are recognized as major drivers of climatic conditions at regional and global scales and can substantially influence vegetation cover by altering precipitation and temperature patterns. Monitoring and modeling vegetation cover can therefore help in tracking regional climate-change trends (Jiao et al., 2018; Ding et al., 2020; Jien et al., 2023). Identifying climate-induced fluctuations in vegetation conditions is particularly important, especially given recent climate change and the role of vegetation in mitigating its impacts. The most widely used parameter for evaluating vegetation responses to climate variability is the Normalized Difference Vegetation Index (NDVI), derived from satellite remote-sensing data (Adole et al., 2016; Huang et al., 2021; Suberi et al., 2021; Buras et al., 2020; Barbosa et al., 2019). Teleconnection patterns represent persistent, large-scale atmospheric circulation regimes that influence distant regions by altering temperature, precipitation, and pressure patterns. These patterns are statistically defined and explain variations in local and regional climatic variables in response to different phases of large-scale climate modes. Among these, ENSO (El Niño–Southern Oscillation) and NAO (North Atlantic Oscillation) are the most influential in climate–biosphere studies. Jien et al. (2025), in a study titled Impact of the El Niño–Southern Oscillation on Global Vegetation, demonstrated that ENSO, through its influence on precipitation and temperature patterns, is one of the most important drivers of interannual vegetation variability worldwide. Their findings suggest that ENSO impacts differ across regions and that the type of ENSO event—whether the Eastern Pacific (EP) or Central Pacific (CP) pattern—can induce distinct vegetation responses. Nevertheless, the precise interactions between vegetation and ENSO require further investigation. A review of previous research shows that only a limited number of studies in Iran have examined the influence of teleconnection patterns on vegetation dynamics. By focusing on the Maroon Basin in the southern Zagros region and employing key teleconnection indices, the present study addresses a major research gap in this field. Materials and Methods In this study, to investigate the relationship between teleconnection indices (NINO4, NINO3, NINO3.4, NINO1+2, and SOI) and winter vegetation changes in the Maroon watershed, the Normalized Difference Vegetation Index (NDVI) from the MODIS sensor was used for the period 2001–2023. MODIS data, with a spatial resolution of 500 m and a 16-day temporal interval, were obtained after atmospheric correction and processed on the Google Earth Engine platform. NDVI was calculated based on the ratio (NIR − Red)/(NIR + Red). To assess the relationship, Pearson correlation coefficients were calculated between the teleconnection indices during winter and winter NDVI over the 23-year period. Following this, the locations with the highest and lowest correlation coefficients were identified. The coefficient indicates both the strength and direction of the relationship, with values close to ±1 representing a strong dependency. Teleconnection indices reflect the synchronization of climate fluctuations in a given region with changes in sea-level pressure and temperature in other regions, and their data were obtained from the NCEP/NCAR database. Additionally, the mean NDVI for each winter season was calculated in a GIS environment, as winter represents the main rainy season in the watershed. The low vegetation density during this season facilitates the detection of changes induced by climate variability and human activities. To examine the relationships, Pearson correlation coefficients were calculated between the winter teleconnection indices and the winter NDVI values over a 23-year period. After computing the coefficients, the locations with the highest and lowest correlation values were identified. Results and Discussion The winter NDVI time-series analysis for the Maroon Basin from 2001 to 2023 showed that the southern and southwestern parts of the basin consistently exhibited the highest vegetation density, while the northern and central areas had the lowest. NDVI values displayed considerable interannual variability, with years such as 2023 recording the highest and years like 2008 and 2012 the lowest vegetation levels. Correlation analysis with ENSO indices revealed that areas with very dense vegetation were most sensitive to the warm phase of ENSO, showing a strong negative correlation with NINO3.4. In contrast, moderate vegetation classes showed a positive correlation with NINO4. Sparse vegetation exhibited weaker responses to ENSO fluctuations. The positive correlation with SOI further indicated that the cold phase of ENSO is generally associated with slight improvements in vegetation conditions. Overall, the findings demonstrate that vegetation in the Maroon Basin is highly responsive to ENSO variability, and the magnitude of this response depends on vegetation type and density. These results are consistent with previous studies conducted in semi-arid regions both within Iran and internationally. Conclusion Winter vegetation in the Maroon watershed was analyzed over a 23-year period (2001–2023) to assess the influence of ENSO indices. The results showed that the southern and southwestern parts of the basin had the highest vegetation density, while the northern and northeastern areas exhibited the lowest density. The strongest negative correlation with ENSO was observed between the NINO3.4 index and the very dense vegetation class (r = -0.68), whereas sparse vegetation classes showed weak responses. The SOI index exhibited a weak positive correlation with dense vegetation. Overall, ENSO had a moderate to weak impact on winter NDVI, while local and ecological factors played a more decisive role in vegetation changes. These findings highlight the importance of long-term monitoring and the concurrent consideration of both local and teleconnection factors for effective natural resource management in semi-arid regions. Extended Abstract Introduction Vegetation is one of the most sensitive environmental components to changes in climatic variables, and any fluctuation in precipitation, temperature, and humidity can result in rapid and observable responses in its growth and dynamics. Teleconnection patterns, particularly ENSO (El Niño–Southern Oscillation), are recognized as major drivers of climatic conditions at regional and global scales and can substantially influence vegetation cover by altering precipitation and temperature patterns. Monitoring and modeling vegetation cover can therefore help in tracking regional climate-change trends (Jiao et al., 2018; Ding et al., 2020; Jien et al., 2023). Identifying climate-induced fluctuations in vegetation conditions is particularly important, especially given recent climate change and the role of vegetation in mitigating its impacts. The most widely used parameter for evaluating vegetation responses to climate variability is the Normalized Difference Vegetation Index (NDVI), derived from satellite remote-sensing data (Adole et al., 2016; Huang et al., 2021; Suberi et al., 2021; Buras et al., 2020; Barbosa et al., 2019). Teleconnection patterns represent persistent, large-scale atmospheric circulation regimes that influence distant regions by altering temperature, precipitation, and pressure patterns. These patterns are statistically defined and explain variations in local and regional climatic variables in response to different phases of large-scale climate modes. Among these, ENSO (El Niño–Southern Oscillation) and NAO (North Atlantic Oscillation) are the most influential in climate–biosphere studies. Jien et al. (2025), in a study titled Impact of the El Niño–Southern Oscillation on Global Vegetation, demonstrated that ENSO, through its influence on precipitation and temperature patterns, is one of the most important drivers of interannual vegetation variability worldwide. Their findings suggest that ENSO impacts differ across regions and that the type of ENSO event—whether the Eastern Pacific (EP) or Central Pacific (CP) pattern—can induce distinct vegetation responses. Nevertheless, the precise interactions between vegetation and ENSO require further investigation. A review of previous research shows that only a limited number of studies in Iran have examined the influence of teleconnection patterns on vegetation dynamics. By focusing on the Maroon Basin in the southern Zagros region and employing key teleconnection indices, the present study addresses a major research gap in this field. Materials and Methods In this study, to investigate the relationship between teleconnection indices (NINO4, NINO3, NINO3.4, NINO1+2, and SOI) and winter vegetation changes in the Maroon watershed, the Normalized Difference Vegetation Index (NDVI) from the MODIS sensor was used for the period 2001–2023. MODIS data, with a spatial resolution of 500 m and a 16-day temporal interval, were obtained after atmospheric correction and processed on the Google Earth Engine platform. NDVI was calculated based on the ratio (NIR − Red)/(NIR + Red). To assess the relationship, Pearson correlation coefficients were calculated between the teleconnection indices during winter and winter NDVI over the 23-year period. Following this, the locations with the highest and lowest correlation coefficients were identified. The coefficient indicates both the strength and direction of the relationship, with values close to ±1 representing a strong dependency. Teleconnection indices reflect the synchronization of climate fluctuations in a given region with changes in sea-level pressure and temperature in other regions, and their data were obtained from the NCEP/NCAR database. Additionally, the mean NDVI for each winter season was calculated in a GIS environment, as winter represents the main rainy season in the watershed. The low vegetation density during this season facilitates the detection of changes induced by climate variability and human activities. To examine the relationships, Pearson correlation coefficients were calculated between the winter teleconnection indices and the winter NDVI values over a 23-year period. After computing the coefficients, the locations with the highest and lowest correlation values were identified. Results and Discussion The winter NDVI time-series analysis for the Maroon Basin from 2001 to 2023 showed that the southern and southwestern parts of the basin consistently exhibited the highest vegetation density, while the northern and central areas had the lowest. NDVI values displayed considerable interannual variability, with years such as 2023 recording the highest and years like 2008 and 2012 the lowest vegetation levels. Correlation analysis with ENSO indices revealed that areas with very dense vegetation were most sensitive to the warm phase of ENSO, showing a strong negative correlation with NINO3.4. In contrast, moderate vegetation classes showed a positive correlation with NINO4. Sparse vegetation exhibited weaker responses to ENSO fluctuations. The positive correlation with SOI further indicated that the cold phase of ENSO is generally associated with slight improvements in vegetation conditions. Overall, the findings demonstrate that vegetation in the Maroon Basin is highly responsive to ENSO variability, and the magnitude of this response depends on vegetation type and density. These results are consistent with previous studies conducted in semi-arid regions both within Iran and internationally. Conclusion Winter vegetation in the Maroon watershed was analyzed over a 23-year period (2001–2023) to assess the influence of ENSO indices. The results showed that the southern and southwestern parts of the basin had the highest vegetation density, while the northern and northeastern areas exhibited the lowest density. The strongest negative correlation with ENSO was observed between the NINO3.4 index and the very dense vegetation class (r = -0.68), whereas sparse vegetation classes showed weak responses. The SOI index exhibited a weak positive correlation with dense vegetation. Overall, ENSO had a moderate to weak impact on winter NDVI, while local and ecological factors played a more decisive role in vegetation changes. These findings highlight the importance of long-term monitoring and the concurrent consideration of both local and teleconnection factors for effective natural resource management in semi-arid regions.

https://www.sepehr.org/article_734690_a5b8de1860a7a931f6d8cf921f668263.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Flood susceptibility assessment of Flood-Prone areas in the urban region of Nourabad using geostatistical methods and the Google Earth Engine platform Flood susceptibility assessment of Flood-Prone areas in the urban region of Nourabad using geostatistical methods and the Google Earth Engine platform 79 95 734117 10.22131/sepehr.2026.2069744.3151 FA Shirin Mohammadkhan Assistant Prof. Geomorphology, University of Tehran. Tehran, Iran 0000-0002-0529-6678 Farkhondeh Moradi Ph.D. Studend of geomorphologhy, University of Tehran. Tehran, Iran 0009-0004-0660-6211 Journal Article 2025 10 16 Extended Abstract Introduction One of the hazards that is always associated with great human and financial losses is flood risk. Flood is one of the natural hazards in which human activities play an important role. In recent years, the increasing population trend has led to the development of residential areas towards the river banks, and this issue, along with land use changes and destruction of vegetation, has increased the probability and intensity of floods and the resulting damages. The increasing population trend and industrial development have led to the advancement of human societies towards the river banks and the concentration of economic activities in flood plains, and this factor has caused many urban areas to be exposed to flood risk and face billions in financial losses and human losses annually. Considering that flood risk is considered one of the challenges facing societies, it is very important to implement management measures, monitor and control land use changes, manage river courses, and identify areas prone to flooding. Different regions have different potentials for flooding, depending on the hydrogeomorphology and human factors. One of the areas at risk of flooding is the city of Nourabad in Lorestan province. The location of Nourabad city on the Badavar River and its topographic condition have made this city vulnerable to flooding, and for this reason, in recent years, including in April 2019, it has faced the risk of flooding. Given the importance of the issue, this study aims to identify areas prone to flooding and also areas flooded in Nourabad city during the April 2019 flood. Materials and Methods In this study, a 30-meter digital elevation model, Landsat 7 and 8 satellite images, and Sentinel 1 radar images were used as the most important research data. The most important research tools were ArcGIS (to prepare the desired maps and standardize the information layers), SuperDecisions (to implement the ANP model), ENVI (to prepare land use maps), IDRISI (to implement the WLC model), and Google Earth Engine (to identify flooded areas). Considering the subject and objectives, this study was conducted in several stages. In the first stage, six parameters of elevation, distance from the river, slope, slope direction, lithology, and land use of the region were used to identify flood-prone areas, as well as WLC and ANP models. In the second stage, Google Earth Engine and Sentinel 1 images were used to identify flooded areas. In the third stage, the results obtained from zoning methods and radar images were compared. In the fourth stage, in order to evaluate the trend of development of residential areas towards flood-prone areas, Landsat 7 and 8 satellite images from 2010 and 2020 were used. Discussion and Results The location of Nourabad city has caused this city to have a high flood potential. In this study, in order to identify areas vulnerable to flood risk, 6 parameters of height, distance from the river, slope, slope direction, land use and lithology were used. Based on the final map, the southern and central areas of Nourabad urban area have a high flood potential due to their low height and slope, as well as proximity to the river. Also, in this study, a map of the flooded areas in April 2019 was prepared using radar images and Google Earth Engine. Based on the prepared map, a large part of the Nourabad urban area, including its southern and central areas, is facing the risk of flooding. Also, the urban periphery areas, including the areas along the main river of this city, are facing flooding. According to the results obtained, there is a correspondence between the results obtained from zoning methods and radar images. Accordingly, using the parameters and methods used in this research, areas vulnerable to flood risk can be identified with high accuracy. Conclusion The results obtained from the WLC-ANP zoning method have shown that a large part of the urban area of Nourabad, including its central and southern areas, has a high flood potential due to its low elevation and slope, as well as proximity to the main river. Also, the peripheral areas of Nourabad, which are located in the vicinity of the main river, also have a high flood potential. In this study, the status of the flooded areas during the flood of April 2019 was also evaluated using radar images. Based on the results obtained, a large part of the urban area of Nourabad, including the central and southern areas of Nourabad, has faced flood risk. Comparing the results obtained from the zoning methods and radar images has shown the consistency of the results obtained; accordingly, it can be concluded that using the parameters and methods used in this study, areas vulnerable to flood risk can be identified with high accuracy. Also, in this study, the process of physical development of residential areas in Nourabad city towards flood-prone areas was evaluated, and based on the results, the area of residential areas in the category with very high flood potential in the years 2000 and 2020 was about 1.3 and 1.9 square kilometers, respectively. According to the results, it can be concluded that in the process of physical development of residential areas in Nourabad city, the flood potential of this city has not been taken into account, and this has led to the development of residential areas towards vulnerable areas. Extended Abstract Introduction One of the hazards that is always associated with great human and financial losses is flood risk. Flood is one of the natural hazards in which human activities play an important role. In recent years, the increasing population trend has led to the development of residential areas towards the river banks, and this issue, along with land use changes and destruction of vegetation, has increased the probability and intensity of floods and the resulting damages. The increasing population trend and industrial development have led to the advancement of human societies towards the river banks and the concentration of economic activities in flood plains, and this factor has caused many urban areas to be exposed to flood risk and face billions in financial losses and human losses annually. Considering that flood risk is considered one of the challenges facing societies, it is very important to implement management measures, monitor and control land use changes, manage river courses, and identify areas prone to flooding. Different regions have different potentials for flooding, depending on the hydrogeomorphology and human factors. One of the areas at risk of flooding is the city of Nourabad in Lorestan province. The location of Nourabad city on the Badavar River and its topographic condition have made this city vulnerable to flooding, and for this reason, in recent years, including in April 2019, it has faced the risk of flooding. Given the importance of the issue, this study aims to identify areas prone to flooding and also areas flooded in Nourabad city during the April 2019 flood. Materials and Methods In this study, a 30-meter digital elevation model, Landsat 7 and 8 satellite images, and Sentinel 1 radar images were used as the most important research data. The most important research tools were ArcGIS (to prepare the desired maps and standardize the information layers), SuperDecisions (to implement the ANP model), ENVI (to prepare land use maps), IDRISI (to implement the WLC model), and Google Earth Engine (to identify flooded areas). Considering the subject and objectives, this study was conducted in several stages. In the first stage, six parameters of elevation, distance from the river, slope, slope direction, lithology, and land use of the region were used to identify flood-prone areas, as well as WLC and ANP models. In the second stage, Google Earth Engine and Sentinel 1 images were used to identify flooded areas. In the third stage, the results obtained from zoning methods and radar images were compared. In the fourth stage, in order to evaluate the trend of development of residential areas towards flood-prone areas, Landsat 7 and 8 satellite images from 2010 and 2020 were used. Discussion and Results The location of Nourabad city has caused this city to have a high flood potential. In this study, in order to identify areas vulnerable to flood risk, 6 parameters of height, distance from the river, slope, slope direction, land use and lithology were used. Based on the final map, the southern and central areas of Nourabad urban area have a high flood potential due to their low height and slope, as well as proximity to the river. Also, in this study, a map of the flooded areas in April 2019 was prepared using radar images and Google Earth Engine. Based on the prepared map, a large part of the Nourabad urban area, including its southern and central areas, is facing the risk of flooding. Also, the urban periphery areas, including the areas along the main river of this city, are facing flooding. According to the results obtained, there is a correspondence between the results obtained from zoning methods and radar images. Accordingly, using the parameters and methods used in this research, areas vulnerable to flood risk can be identified with high accuracy. Conclusion The results obtained from the WLC-ANP zoning method have shown that a large part of the urban area of Nourabad, including its central and southern areas, has a high flood potential due to its low elevation and slope, as well as proximity to the main river. Also, the peripheral areas of Nourabad, which are located in the vicinity of the main river, also have a high flood potential. In this study, the status of the flooded areas during the flood of April 2019 was also evaluated using radar images. Based on the results obtained, a large part of the urban area of Nourabad, including the central and southern areas of Nourabad, has faced flood risk. Comparing the results obtained from the zoning methods and radar images has shown the consistency of the results obtained; accordingly, it can be concluded that using the parameters and methods used in this study, areas vulnerable to flood risk can be identified with high accuracy. Also, in this study, the process of physical development of residential areas in Nourabad city towards flood-prone areas was evaluated, and based on the results, the area of residential areas in the category with very high flood potential in the years 2000 and 2020 was about 1.3 and 1.9 square kilometers, respectively. According to the results, it can be concluded that in the process of physical development of residential areas in Nourabad city, the flood potential of this city has not been taken into account, and this has led to the development of residential areas towards vulnerable areas.

https://www.sepehr.org/article_734117_6668a33a623a87ed06a61495b12c64c5.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Spatial analysis of ecological vulnerability in the Kalybarchay watershed using multi-criteria decision making models Spatial analysis of ecological vulnerability in the Kalybarchay watershed using multi-criteria decision making models 97 117 735947 10.22131/sepehr.2026.2074119.3156 FA Fariba Karami Professor, Department of geomorphology, University of Tabriz, Tabriz, Iran 0000-0002-0021-5680 Masoumeh Rajabi Professor, Department of Geomorphology, University of Tabriz, Tabriz, Iran 0009-0003-3073-6297 Isan Rostampur Master's degree graduate in spatial planning, Department of geomorphology, University of Tabriz, Tabriz, Iran 0000-0002-0021-5680 Journal Article 2025 10 09 Extended Abstract Introduction With the continuous development of society, human disturbance to the ecosystem is also growing, and the ecological environment is gradually deteriorating. This will seriously affect the sustainable development of human society and the ecological environment on which it depends. Generally, ecological vulnerability is the variability of ecosystem under natural or human factors, and this variability is not conducive to the development of ecosystem and human society. Ecological vulnerability is characterized by weak resistance and low resilience of ecosystems in response to external disturbances, including both natural and anthropogenic drivers, within a specific spatial scale. Spatial assessments of ecological vulnerability help identify areas that are exposed to environmental disturbances or pressures, thereby providing a scientific basis for controlling environmental degradation and promoting regional ecological development. In the assessment of ecological vulnerability, multiple variables—such as climate, topography, land resources, and human activities—are influential. The present study aims to conduct a spatial analysis and zoning of ecological vulnerability in the Kaleybarchay watershed. This watershed, located in East Azerbaijan Province, is one of the key regions for nature tourism and ecotourism. Therefore, assessing its ecological vulnerability is essential for sustainable management and conservation. The watershed covers an area of approximately 1,201 km² on the northern slopes of the Qaradagh (Arasbaran) mountain range. Over 27% of the watershed area is covered by dense, semi-dense, or sparse forests. Due to the sensitivity and fragility of this ecosystem, evaluating the ecological vulnerability of the Kaleybarchay watershed is considered necessary. Materials and Methods To achieve the research objectives, the AHP-Fuzzy model and Critic model, the Digital Elevation Model (DEM) of the Kaleybarchay watershed, and Landsat 8 OLI satellite imagery were utilized. The criteria (natural and human) and sub-criteria—including elevation, slope, slope aspect, precipitation, temperature, distance from rivers, lithology, soil erosion, vegetation cover, land use, distance from roads, distance from mines and industries, and distance from residential areas—were determined based on theoretical foundations and previous studies using the Delphi technique. Weighting of the layers was performed using the Analytic Hierarchy Process (AHP) model, while the standardization of the layers was conducted through the Fuzzy logic model. The Critic model was used for validation. After integrating the weighted and standardized layers, a zoning map of ecological vulnerability for the Kaleybarchay watershed was produced. Results and Discussion According to the pairwise comparisons in the questionnaire, the average comparative weights of the criteria and sub-criteria related to ecological vulnerability zoning were obtained. Using the results from the Expert Choice software, weights for each criterion were determined. These weights were then applied to the shape file layers of the criteria in ArcGIS, and through map overlay, the final ecological vulnerability map was generated. The results showed that the criterion “distance from industries and mines” had the highest importance with a weight of 0.216, whereas “slope aspect” had the lowest importance with a weight of 0.009. Land-use change was identified as the second most influential factor affecting ecological vulnerability in the Kaleybarchay watershed. Zoning results indicated that approximately 40% of the watershed area exhibited high to very high ecological vulnerability, mainly concentrated in the central parts of the watershed. Meanwhile, about 40% of the area displayed low to very low vulnerability, predominantly located in the northern and southern regions of the basin. Conclusion The findings of this study indicate that human factors play a more significant role in the ecological vulnerability of the Kaleybarchay watershed. Human activities such as the expansion of industries and mines conflict with the ecological capacity of the region and lead to the degradation of environmental quality. Continued expansion of industrial and mining activities would likely increase the level of ecological vulnerability. Moreover, land-use and land-cover changes are among the major contributing factors. Overlaying the vulnerability map with land-use and vegetation cover layers revealed that areas with high and very high vulnerability mostly overlap with sparsely to moderately dense forests, medium rangelands, and agricultural lands. Among the natural factors, lithological units and soil erosion were identified as the most influential variables affecting ecological vulnerability within the watershed. Comparing the results of the two methods confirmed the accuracy of the zoning. Also, the correlation coefficient between the results of the two methods was 0.89%. Extended Abstract Introduction With the continuous development of society, human disturbance to the ecosystem is also growing, and the ecological environment is gradually deteriorating. This will seriously affect the sustainable development of human society and the ecological environment on which it depends. Generally, ecological vulnerability is the variability of ecosystem under natural or human factors, and this variability is not conducive to the development of ecosystem and human society. Ecological vulnerability is characterized by weak resistance and low resilience of ecosystems in response to external disturbances, including both natural and anthropogenic drivers, within a specific spatial scale. Spatial assessments of ecological vulnerability help identify areas that are exposed to environmental disturbances or pressures, thereby providing a scientific basis for controlling environmental degradation and promoting regional ecological development. In the assessment of ecological vulnerability, multiple variables—such as climate, topography, land resources, and human activities—are influential. The present study aims to conduct a spatial analysis and zoning of ecological vulnerability in the Kaleybarchay watershed. This watershed, located in East Azerbaijan Province, is one of the key regions for nature tourism and ecotourism. Therefore, assessing its ecological vulnerability is essential for sustainable management and conservation. The watershed covers an area of approximately 1,201 km² on the northern slopes of the Qaradagh (Arasbaran) mountain range. Over 27% of the watershed area is covered by dense, semi-dense, or sparse forests. Due to the sensitivity and fragility of this ecosystem, evaluating the ecological vulnerability of the Kaleybarchay watershed is considered necessary. Materials and Methods To achieve the research objectives, the AHP-Fuzzy model and Critic model, the Digital Elevation Model (DEM) of the Kaleybarchay watershed, and Landsat 8 OLI satellite imagery were utilized. The criteria (natural and human) and sub-criteria—including elevation, slope, slope aspect, precipitation, temperature, distance from rivers, lithology, soil erosion, vegetation cover, land use, distance from roads, distance from mines and industries, and distance from residential areas—were determined based on theoretical foundations and previous studies using the Delphi technique. Weighting of the layers was performed using the Analytic Hierarchy Process (AHP) model, while the standardization of the layers was conducted through the Fuzzy logic model. The Critic model was used for validation. After integrating the weighted and standardized layers, a zoning map of ecological vulnerability for the Kaleybarchay watershed was produced. Results and Discussion According to the pairwise comparisons in the questionnaire, the average comparative weights of the criteria and sub-criteria related to ecological vulnerability zoning were obtained. Using the results from the Expert Choice software, weights for each criterion were determined. These weights were then applied to the shape file layers of the criteria in ArcGIS, and through map overlay, the final ecological vulnerability map was generated. The results showed that the criterion “distance from industries and mines” had the highest importance with a weight of 0.216, whereas “slope aspect” had the lowest importance with a weight of 0.009. Land-use change was identified as the second most influential factor affecting ecological vulnerability in the Kaleybarchay watershed. Zoning results indicated that approximately 40% of the watershed area exhibited high to very high ecological vulnerability, mainly concentrated in the central parts of the watershed. Meanwhile, about 40% of the area displayed low to very low vulnerability, predominantly located in the northern and southern regions of the basin. Conclusion The findings of this study indicate that human factors play a more significant role in the ecological vulnerability of the Kaleybarchay watershed. Human activities such as the expansion of industries and mines conflict with the ecological capacity of the region and lead to the degradation of environmental quality. Continued expansion of industrial and mining activities would likely increase the level of ecological vulnerability. Moreover, land-use and land-cover changes are among the major contributing factors. Overlaying the vulnerability map with land-use and vegetation cover layers revealed that areas with high and very high vulnerability mostly overlap with sparsely to moderately dense forests, medium rangelands, and agricultural lands. Among the natural factors, lithological units and soil erosion were identified as the most influential variables affecting ecological vulnerability within the watershed. Comparing the results of the two methods confirmed the accuracy of the zoning. Also, the correlation coefficient between the results of the two methods was 0.89%.

https://www.sepehr.org/article_735947_5b38202e0a4631ce21c81e2ef86ad9b9.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 35 137 2026 04 21 Spatial zoning analysis of safety and physical insecurity levels in the historical fabric of Kashan with a passive defense approach Spatial zoning analysis of safety and physical insecurity levels in the historical fabric of Kashan with a passive defense approach 119 150 733920 10.22131/sepehr.2026.2073210.3153 FA Faezeh Mohammadisheshkal Ph.D student in urban planning, Allameh Tabataba'i University, Tehran, Iran 0009-0004-8398-1154 Hassan Sajadzadeh Professor, Department of urbanism ,Faculty of art and architecture, Bu-Ali Sina University, Hamedan,, Iran 0000-0002-3989-9389 Mostafa Mohammadi Deh Cheshmeh Associate professor of geography and urban planning at Chamran University, Ahvaz, Iran 0000-0003-3928-6299 Mozhdeh Bahari Ph.D student in urban planning, Allameh Tabataba'i University, Tehran, Iran 0009-0001-4336-3827 Journal Article 2025 10 09 Extended Abstract Introduction: Historic urban fabrics represent valuable cultural heritage assets, yet they are often among the most physically vulnerable areas within cities. In seismic regions such as Iran, features including fine-grained urban parcels, narrow and irregular alleyways, aging structures, and traditional low-resistance materials intensify vulnerability, restrict emergency access, and complicate evacuation. The historic fabric of Kashan exemplifies these challenges, making the assessment of safety levels both necessary and urgent. While preserving cultural identity is a key priority, achieving resilience in historic areas requires a careful balance between heritage conservation and disaster risk reduction. The passive defense approach—emphasizing preventive, non-intrusive, and context-compatible strategies—aligns with this objective by enhancing safety without compromising historical authenticity. Previous research on Kashan and similar cities has often focused on regional seismic risk, yet few studies have undertaken a detailed and localized assessment tailored to the specific morphological and structural characteristics of historical fabrics. Moreover, earlier models typically rely on hierarchical approaches that do not consider interdependencies between vulnerability parameters. This study addresses these gaps by applying a multi-criteria framework that incorporates 15 measurable sub-criteria related to accessibility, land-use adjacency, and physical building attributes. By integrating expert-derived ANP weights into GIS and employing Fuzzy Membership functions and spatial overlayering, the research provides a precise, local-scale analysis of physical safety in Kashan’s historical core. The resulting zoning maps serve as a practical tool for planners, heritage managers, and crisis-response authorities seeking to identify priority intervention zones and develop targeted passive-defense strategies. Overall, this research contributes to a comprehensive understanding of physical vulnerability in historic fabrics and underscores the potential of ANP–GIS integration as a robust methodology for enhancing urban safety while protecting cultural heritage. Materials and Methods: This study adopts a descriptive-analytical methodology with an applied purpose. A total of 14 sub-criteria were identified, categorized into three major criteria: (1) physical accessibility, (2) land use and adjacency patterns, and (3) physical characteristics of buildings. These criteria were selected based on their direct influence on the vulnerability of historic urban fabrics. The required data were collected from multiple sources, including base maps, spatial datasets, field observations, and expert surveys. The Analytic Network Process (ANP) was employed to weigh and prioritize the criteria, capturing interdependencies between them. Expert opinions from 15 specialists were used to perform pairwise comparisons, ensuring robust weighting. Subsequently, Geographic Information Systems (GIS) tools were utilized for spatial analysis and mapping. Data layers were standardized, weighted, and integrated through fuzzy overlay and weighted sum techniques in ArcGIS, producing zoning maps that classify safety levels from very high to very low. Results and Discussion: The results show a stark spatial disparity in safety across Kashan’s historic fabric. Out of the total 370.86 hectares: 41% of the area falls within low and very low safety zones. Only 12.34% achieves a very high level of safety. The remaining areas are distributed across medium to high safety levels. The most vulnerable neighborhoods include Soltan Mir Ahmad, Darb-e Esfahan, Mohtasham, and especially Taher and Mansour. These areas exhibit high population density, deteriorated buildings, narrow alleys, and reliance on weak traditional materials, all of which heighten vulnerability. In contrast, neighborhoods like Bazaar and Posht-e Mashhad (upper and lower) display higher safety due to partial renovations, better accessibility, and the use of stronger construction materials. The findings highlight that vulnerability is not uniformly distributed; instead, it reflects variations in urban morphology, structural quality, and accessibility. For example, neighborhoods with relatively wider streets and more durable materials, despite being part of the historic fabric, perform better in terms of safety. Conversely, compact areas with aging structures and limited open space show the highest risks. From a passive defense perspective, the results emphasize several strategic needs: 1. Structural reinforcement of historic buildings using context-sensitive retrofitting methods. 2. Improvement of street networks to facilitate emergency access and evacuation. 3. Expansion of open and safe spaces to serve as emergency gathering points. 4. Consolidation of fine-grained parcels to reduce fragmentation and improve resilience. 5. Enhancement of social participation, mobilizing local communities in safety planning and resilience initiatives. These findings also underscore the importance of integrating disaster risk reduction with heritage conservation. Without intervention, vulnerable neighborhoods such as Taher and Mansour remain highly exposed to catastrophic risks, representing not only a threat to human lives but also to the continuity of cultural heritage. Conclusion: This research demonstrates that Kashan’s historic fabric, despite its cultural significance, suffers from considerable physical vulnerability that requires immediate attention. Systematic zoning of safety and unsafety levels through GIS and ANP provides a clear framework for identifying priority areas. The study concludes that enhancing resilience in historic urban fabrics necessitates an integrated strategy that balances two key goals: (1) preserving cultural and architectural heritage, and (2) reducing disaster risk through passive defense measures. Ultimately, the approach and methodology applied in this study—combining expert-driven multi-criteria decision-making with GIS-based spatial analysis—offer a replicable model for other historic cities. This model supports policymakers, urban planners, and heritage managers in designing targeted, evidence-based interventions that foster both cultural continuity and urban safety. Extended Abstract Introduction: Historic urban fabrics represent valuable cultural heritage assets, yet they are often among the most physically vulnerable areas within cities. In seismic regions such as Iran, features including fine-grained urban parcels, narrow and irregular alleyways, aging structures, and traditional low-resistance materials intensify vulnerability, restrict emergency access, and complicate evacuation. The historic fabric of Kashan exemplifies these challenges, making the assessment of safety levels both necessary and urgent. While preserving cultural identity is a key priority, achieving resilience in historic areas requires a careful balance between heritage conservation and disaster risk reduction. The passive defense approach—emphasizing preventive, non-intrusive, and context-compatible strategies—aligns with this objective by enhancing safety without compromising historical authenticity. Previous research on Kashan and similar cities has often focused on regional seismic risk, yet few studies have undertaken a detailed and localized assessment tailored to the specific morphological and structural characteristics of historical fabrics. Moreover, earlier models typically rely on hierarchical approaches that do not consider interdependencies between vulnerability parameters. This study addresses these gaps by applying a multi-criteria framework that incorporates 15 measurable sub-criteria related to accessibility, land-use adjacency, and physical building attributes. By integrating expert-derived ANP weights into GIS and employing Fuzzy Membership functions and spatial overlayering, the research provides a precise, local-scale analysis of physical safety in Kashan’s historical core. The resulting zoning maps serve as a practical tool for planners, heritage managers, and crisis-response authorities seeking to identify priority intervention zones and develop targeted passive-defense strategies. Overall, this research contributes to a comprehensive understanding of physical vulnerability in historic fabrics and underscores the potential of ANP–GIS integration as a robust methodology for enhancing urban safety while protecting cultural heritage. Materials and Methods: This study adopts a descriptive-analytical methodology with an applied purpose. A total of 14 sub-criteria were identified, categorized into three major criteria: (1) physical accessibility, (2) land use and adjacency patterns, and (3) physical characteristics of buildings. These criteria were selected based on their direct influence on the vulnerability of historic urban fabrics. The required data were collected from multiple sources, including base maps, spatial datasets, field observations, and expert surveys. The Analytic Network Process (ANP) was employed to weigh and prioritize the criteria, capturing interdependencies between them. Expert opinions from 15 specialists were used to perform pairwise comparisons, ensuring robust weighting. Subsequently, Geographic Information Systems (GIS) tools were utilized for spatial analysis and mapping. Data layers were standardized, weighted, and integrated through fuzzy overlay and weighted sum techniques in ArcGIS, producing zoning maps that classify safety levels from very high to very low. Results and Discussion: The results show a stark spatial disparity in safety across Kashan’s historic fabric. Out of the total 370.86 hectares: 41% of the area falls within low and very low safety zones. Only 12.34% achieves a very high level of safety. The remaining areas are distributed across medium to high safety levels. The most vulnerable neighborhoods include Soltan Mir Ahmad, Darb-e Esfahan, Mohtasham, and especially Taher and Mansour. These areas exhibit high population density, deteriorated buildings, narrow alleys, and reliance on weak traditional materials, all of which heighten vulnerability. In contrast, neighborhoods like Bazaar and Posht-e Mashhad (upper and lower) display higher safety due to partial renovations, better accessibility, and the use of stronger construction materials. The findings highlight that vulnerability is not uniformly distributed; instead, it reflects variations in urban morphology, structural quality, and accessibility. For example, neighborhoods with relatively wider streets and more durable materials, despite being part of the historic fabric, perform better in terms of safety. Conversely, compact areas with aging structures and limited open space show the highest risks. From a passive defense perspective, the results emphasize several strategic needs: 1. Structural reinforcement of historic buildings using context-sensitive retrofitting methods. 2. Improvement of street networks to facilitate emergency access and evacuation. 3. Expansion of open and safe spaces to serve as emergency gathering points. 4. Consolidation of fine-grained parcels to reduce fragmentation and improve resilience. 5. Enhancement of social participation, mobilizing local communities in safety planning and resilience initiatives. These findings also underscore the importance of integrating disaster risk reduction with heritage conservation. Without intervention, vulnerable neighborhoods such as Taher and Mansour remain highly exposed to catastrophic risks, representing not only a threat to human lives but also to the continuity of cultural heritage. Conclusion: This research demonstrates that Kashan’s historic fabric, despite its cultural significance, suffers from considerable physical vulnerability that requires immediate attention. Systematic zoning of safety and unsafety levels through GIS and ANP provides a clear framework for identifying priority areas. The study concludes that enhancing resilience in historic urban fabrics necessitates an integrated strategy that balances two key goals: (1) preserving cultural and architectural heritage, and (2) reducing disaster risk through passive defense measures. Ultimately, the approach and methodology applied in this study—combining expert-driven multi-criteria decision-making with GIS-based spatial analysis—offer a replicable model for other historic cities. This model supports policymakers, urban planners, and heritage managers in designing targeted, evidence-based interventions that foster both cultural continuity and urban safety.

https://www.sepehr.org/article_733920_427855ba93d33897a60d09736b8196f5.pdf