دانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Assessment and Comparative Evaluation of Machine Learning Algorithms (SVM, ANN, and MaxEnt) for Landslide Susceptibility Zonation (LSZ) in the Ziarat Watershed, Golestan Province, Iranارزیابی و مقایسه کارایی مدل‌های یادگیری ماشین SVM، ANN و MaxEnt در تهیه نقشه حساسیت زمین‌لغزش آبخیز زیارت، استان گلستان1224830110.22067/geoeh.2026.97226.1635FAحبیبنظرنژادگروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران0000-0001-7672-5794آیدینگکرنژادیمرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، گرگان، ایران0000-0002-4143-2518چوقی بایرامکمکیگروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران0000-0002-7778-817Xفرخندهآقاجان لیافوگروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران0009-0005-7225-5242Journal Article20260303Landslide is one of the most destructive slope hazards in mountainous regions. The performance of three machine learning models — Artificial Neural Network (ANN), Support Vector Machine (SVM), and Maximum Entropy (MaxEnt) — was evaluated and compared for landslide susceptibility mapping in the Ziarat watershed (Golestan Province). A total of 116 landslide points were recorded through GPS field surveys. Stratified random sampling was applied to preserve the spatial distribution of points across geological units, elevation classes, and slope categories. Of these, 81 points (70%) were allocated for training and 35 points (30%) for validation. A total of 5 main factor groups (topography, hydrology, geology, morphometry, and anthropogenic factors) comprising 23 sub-factors were selected: slope, aspect, elevation, valley depth, profile and plan curvature, topographic position index, relative slope position, convergence index, positive and negative openness, rainfall, distance from drainage network, topographic wetness index, stream power index, flow accumulation, geological formation, distance from faults, soil texture, mass balance index, Melton ruggedness index, and vector ruggedness measure. Modeling was performed using ModEco software, and accuracy assessment was carried out using the ROC curve and the Area Under the Curve (AUC) index. Results showed that the ANN model (AUC = 0.936) and the SVM model (AUC = 0.934) performed comparably well, while the MaxEnt model (AUC = 0.904) ranked lower. The most influential factors controlling landslide occurrence were, in order: elevation (corresponding to zones with annual rainfall exceeding 550 mm), the Shemshak and Gorgan Schist formations, and distance from residential areas. Given the ongoing land use changes and increasing construction activity in the Ziarat watershed, restrictions on land use conversion and a ban on construction on steep slopes are recommended.Introduction Landslides represent a prevalent and destructive geohazard globally, imposing substantial socioeconomic costs through loss of life, infrastructure damage, and environmental degradation. Their recurrent nature and profound destructive potential underscore the imperative for advanced research in watershed management and geotechnical engineering to devise robust predictive and mitigation strategies. Landslide susceptibility zonation (LSZ) stands as a critical spatial planning and management tool, enabling the precise identification of areas inherently prone to slope instability. This proactive approach facilitates informed land-use planning and targeted risk reduction interventions. The intrinsic complexity and non-linear interactions among diverse geomorphological, hydrological, and anthropogenic factors governing landslide initiation frequently render conventional modeling techniques insufficient. Consequently, the application of sophisticated machine learning algorithms has gained considerable traction in LSZ studies, attributed to their enhanced capacity to model these intricate spatial relationships. Selecting an accurate and computationally efficient model is crucial for reliable landslide prediction and effective risk management in watershed planning. This research endeavors to rigorously assess and comparatively analyze the predictive performance of three prominent machine learning models—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Maximum Entropy (MaxEnt)—in generating accurate landslide susceptibility maps for the Ziarat watershed, located in Golestan Province, Iran. The primary objective is to ascertain the optimal predictive model for this geomorphologically dynamic region, thereby contributing to more effective landslide hazard management.Material and Methods The study area, the Ziarat watershed in Golestan Province, Iran, is characterized by diverse topography and geological formations, rendering it susceptible to frequent landslide events. The initial step in landslide susceptibility zoning involves generating a spatial distribution map of historical landslide events, which serves as a critical foundation for subsequent susceptibility and hazard assessment studies. A comprehensive landslide inventory was compiled through detailed field investigations, identifying 116 distinct landslide locations. To ensure model impartiality and robust validation, these locations were stochastically partitioned: 81 points (%70) were designated for model training, while the remaining 35 points (%30) were reserved for independent model validation. A total of 23 conditioning factors, recognized for their influence on landslide occurrence, were meticulously prepared as input variables. These factors encompassed a broad spectrum of environmental parameters, including eleven topographic indices (aspect, slope degree, elevation, valley depth, profile curvature, plan curvature, LS factor, relative slope position (RSP), convergence, positive openness, negative openness), five hydrological variables (rainfall, distance to drainage network, topographic wetness index (TWI), stream power index (SPI), flow accumulation), three geological attributes (Lithology, distance to faults, soil texture), and three morphometric indices (mass balance index (MBI), Melton roughness index (MRI), vector ruggedness measure (VRM)), along with one anthropogenic factor (distance to residential areas). To evaluate multicollinearity among predictive factors, tolerance and Variance Inflation Factor (VIF) indices were computed using SPSS version 26. The machine learning models (ANN, SVM, and MaxEnt) were implemented within the ModEco software environment, configured to elucidate the complex, non-linear relationships between these environmental factors and landslide susceptibility. Model performance was quantitatively evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric.Results and Discussion The comparative assessment of the three machine learning models for landslide susceptibility mapping in the Ziarat watershed yielded distinct performance outcomes during the validation phase. The Artificial Neural Network (ANN) model, achieving an Area Under the Curve (AUC) value of 0.936 and the Support Vector Machine (SVM) model with an AUC value of 0.934 exhibited a highly comparable and robust performance. These results signify the exceptional accuracy and reliability of both ANN and SVM models in delineating landslide-prone areas within the study region. In contrast, the Maximum Entropy (MaxEnt) model, while demonstrating good performance, recorded a comparatively lower AUC of 0.904. The marginal difference in AUC between ANN and SVM suggests their analogous effectiveness in capturing the intricate spatial dependencies between the 23 conditioning factors and landslide incidence. Further analysis focused on quantifying the relative importance of these conditioning factors. This assessment revealed that elevation, geological characteristics, and distance from residential areas were the most influential predictors of landslide susceptibility. This finding is highly consistent with the local geomorphological context, where steep slopes at higher elevations and the presence of unstable geological formations are primary drivers of instability. The significant influence of distance from residential areas highlights the critical role of anthropogenic pressures in exacerbating landslide risk in the watershed.ConclusionThis research successfully evaluated the efficacy of Artificial Neural Network (ANN), Support Vector Machine (SVM), and Maximum Entropy (MaxEnt) models for landslide susceptibility mapping in the Ziarat watershed. The generated susceptibility maps represent invaluable assets for local authorities and land-use planners, offering a scientific basis for targeted risk management and mitigation strategies. Specifically, these maps can inform the prioritization of slope stabilization measures and guide sustainable development in high-risk zones. This study significantly advances the understanding of landslide susceptibility modeling in complex terrains and offers a transferable methodology for similar investigations in other susceptible regions. For future research, it is recommended to employ hybrid modeling approaches that integrate machine learning algorithms with deep learning architectures to enhance the accuracy and robustness of landslide prediction. Furthermore, given the observed land-use transformations including the expansion of residential zones and increased villa construction in the Ziarat watershed implementing regulatory measures to constrain unplanned land-use changes in this area is strongly advised to mitigate landslide risk.زمین‌لغزش یکی از خسارت‌بارترین مخاطرات دامنه‌ای در مناطق کوهستانی است. کارایی سه مدل یادگیری ماشین شامل شبکه عصبی مصنوعی (ANN)، ماشین بردار پشتیبان (SVM) و بیشینه آنتروپی (MaxEnt) در تهیه نقشه حساسیت زمین‌لغزش آبخیز زیارت (استان گلستان) ارزیابی و مقایسه شد. با برداشت میدانی GPS، ۱۱۶ نقطه لغزشی ثبت شد. به منظور حفظ پراکنش فضایی نقاط در واحدهای زمین‌شناسی، طبقات ارتفاعی و شیب، از روش نمونه‌گیری تصادفی طبقه‌ای استفاده شد. ۸۱ نقطه (۷۰درصد) برای آموزش و ۳۵ نقطه (۳۰درصد) برای اعتبارسنجی تخصیص یافت. در مجموع ۵ گروه عامل اصلی (توپوگرافی، هیدرولوژی، زمین‌شناسی، مورفومتری و انسانی) با ۲۳ زیرعامل (شیب، جهت، ارتفاع، عمق دره، انحنای پروفیل و سطح، شاخص توپوگرافی، موقعیت نسبی شیب، همگرایی، بازشدگی مثبت و منفی، بارندگی، فاصله از آبراهه، شاخص رطوبت توپوگرافی، شاخص توان آبراهه، جریان تجمعی، سازند زمین‌شناسی، فاصله از گسل، بافت خاک، شاخص تعادل جرم، شاخص زبری ملتون، شاخص قدرت بردار) انتخاب شدند. مدل‌سازی با نرم‌افزار ModEco و ارزیابی دقت با منحنی ROC و شاخص سطح زیر منحنی (AUC) انجام شد. یافته‌ها نشان داد که مدل ANN با AUC معادل 0.936 و مدل SVM با AUC معادل 0.934 عملکردی خوب و بسیار نزدیک دارند و مدل MaxEnt با AUC معادل 0.904 در رتبه پایین‌تری قرار می‌گیرد. مؤثرترین عوامل وقوع زمین‌لغزش به ترتیب ارتفاع (منطبق بر پهنه بارش بالای ۵۵۰ میلی‌متر)، سازندهای شمشک و شیست گرگان، و فاصله از مناطق مسکونی بودند. با توجه به تغییرات کاربری اراضی و افزایش ساخت‌وساز در آبخیز زیارت، اعمال محدودیت در تغییر کاربری و ممنوعیت ساخت‌وساز در دامنه‌های شیب تند پیشنهاد می‌شود.https://geoeh.um.ac.ir/article_48301_98274a39ea76330620f284fd74771b86.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Knowledge-Driven Integration for Landslide Susceptibility Zonation Using a Standardized Weighting Approach Based on Previous Studies: A Case Study of Mazandaran Province, Iranتلفیق دانش مبنا در پهنه‌بندی حساسیت زمین‌لغزش با رویکرد استانداردسازی اوزان مطالعات پیشین (مطالعه موردی: استان مازندران)23474857710.22067/geoeh.2026.99156.1674FAعلیاشرفیگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه بیرجند، بیرجند، ایران0000-0002-7282-4776داوداکبریگروه مهندسی ژئوماتیک، دانشکده مهندسی، دانشگاه زابل، زابل، ایران0000-0001-8617-8093محدثهکچمی ملاییگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه بیرجند، بیرجند، ایرانJournal Article20260605Knowledge-based landslide hazard zonation, particularly in areas with limited field data, is an important approach to hazard management. However, the lack of a scientific mechanism for standardizing weights derived from heterogeneous studies represents a major challenge to such approaches. To address this gap, the present study implemented a relative-importance normalization framework in Mazandaran Province, Iran, and compared its performance with that of a median-weighting method. Based on their frequency of occurrence in seven reliable previous studies, nine criteria—slope, aspect, elevation, precipitation, distance to faults, roads, streams, and population centers, and land use—were selected and weighted. The results showed that slope, with a weight of 0.239, had the greatest influence, followed by distance to roads (0.133) and precipitation (0.121). The use of the median preserved the overall ranking of the criteria while reducing the weights assigned to elevation and land use. The weight of distance to population centers was also associated with uncertainty because this criterion appeared in only two studies. Compared with the median-based method, the relative-importance normalization method performed better, concentrating 10.5% of landslide points in the highest-hazard class and increasing the Frequency Ratio from 0.72 in the low-hazard class to 1.04 in the high-hazard class. In contrast, the median-based method concentrated 0.15% of landslide points in the highest-hazard class. The Area Under the Curve (AUC) values for the two methods were 0.477 and 0.592, respectively, indicating that this index alone is insufficient for performance evaluation. Validation using 674 landslide points confirmed the increasing trend of the Frequency Ratio. In addition, 34.2% of the provincial area was classified within the high- and very-high-hazard zones. This framework is suitable for hazard zonation in data-scarce mountainous regions.Introduction Landslides represent one of the most destructive natural hazards in mountainous and foothill regions worldwide, causing substantial annual economic losses and threatening critical infrastructure and human settlements. Mazandaran Province in northern Iran, situated within the active tectonic framework of the Alborz mountain range, is particularly susceptible due to its steep slopes, humid climate, and intense orographic precipitation. While knowledge-driven methods offer a practical approach for landslide hazard zonation especially in regions lacking comprehensive field data they face a fundamental challenge: the absence of a scientific mechanism for integrating and standardizing criterion weights derived from heterogeneous previous studies. Traditional expert-based methods like AHP are prone to subjective bias, whereas purely statistical approaches suffer from data quality issues. A critical review of both national and international literature highlights a major flaw in the common practice of simply averaging criterion weights. This approach introduces systematic error because previous studies differ considerably in their total number of criteria and employ heterogeneous scoring schemes. To address this methodological gap, the present study introduces and implements a systematic framework called "Relative Importance Normalization" (RIN) for landslide hazard zonation in Mazandaran Province. The RIN framework normalizes weights internally within each source study prior to final aggregation, thereby eliminating bias caused by varying numbers of criteria across studies. In parallel, a median‑based weighting approach commonly used in previous research was also applied to serve as a benchmark for comparative evaluation. Both weighting approaches were validated using 674 recorded landslide points and assessed through Frequency Ratio (FR) and Receiver Operating Characteristic (ROC) analysis. The comparative assessment of these two approaches forms the core contribution of this study, providing insights into the effectiveness of standardized weighting frameworks in knowledge‑based landslide susceptibility mapping.Material and Methods This study adopted a knowledge‑driven approach comprising three main steps. In the first step, nine conditioning factors slope, aspect, elevation, rainfall, distance to faults, distance to roads, distance to streams, land use, and distance to population centers were selected based on their highest frequency of occurrence in seven authoritative previous studies that employed pairwise comparison for landslide hazard assessment. The raw global weights for these criteria and their sub‑criteria were extracted from the literature. In the second step, two parallel weighting approaches were pursued. The first approach involved extracting the median weights for each criterion directly from the literature and applying them to the corresponding spatial layers. The second approach applied the Relative Importance Normalization (RIN) algorithm to eliminate bias caused by varying numbers of criteria across studies. For each study, the sum of weights for the nine target criteria was calculated. The relative importance of each criterion within a study was then obtained by dividing its global weight by this sum. Subsequently, for each criterion, the mean of the relative importance values was computed across all studies containing that criterion. Finally, these mean values were normalized to sum to unity, producing the final standardized weights. In the third step, for sub‑criterion standardization, a reference classification system was defined for each criterion based on frequent class intervals identified in the literature and local environmental characteristics. Mapped weights from different studies were normalized within each criterion, averaged across studies, and then multiplied by the corresponding final criterion weight to obtain absolute sub‑criterion weights. For model validation, 674 recorded landslide points were used. The Frequency Ratio (FR) index was calculated as the ratio of the percentage of landslide points in each hazard class to the percentage of area occupied by that class. Additionally, the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) were computed to quantitatively assess the performance of both weighting approaches. The AUC values for the median‑based method and the RIN method were compared to evaluate their respective capabilities in distinguishing high‑risk from low‑risk zones.Results and DiscussionThe findings revealed that slope, with a weight of 0.239, exerts the greatest influence on landslide occurrence in the region, followed by distance from roads (0.133) and precipitation (0.121). Distance from faults (0.105), land use (0.098), distance from streams (0.085), elevation (0.076), aspect (0.075), and distance from population centers (0.068) ranked subsequently. Sub‑criteria analysis revealed that among the evaluated factors, the most susceptible conditions were associated with the 30–45% slope class (absolute weight: 0.080), the 0–100 m road buffer (0.047), and the 500–650 mm precipitation range (0.039). Sensitivity analysis substituting the median for the mean showed a stable rank order, though elevation and land use weights decreased, indicating some influence from outlier data. Regional analysis for the Alborz cluster revealed an increased elevation weight (0.103), highlighting its local significance. To evaluate model performance, two parallel weighting approaches were compared: (1) a median‑based weighting method, and (2) the Relative Importance Normalization (RIN) framework. Validation using 674 recorded landslide points demonstrated that the RIN method concentrated 10.5% of the points in the very high hazard class (class 5), whereas the median‑based method placed only 0.15% of points in this class. The Frequency Ratio (FR) for the RIN method showed a consistent upward trend from the very low hazard zone (FR = 0.72) to the very high hazard zone (FR = 1.04), confirming the model's spatial correspondence with field reality. The highest FR value (1.15) belonged to the moderate hazard class (class 3), which can be explained by its extensive areal coverage (30.2%) and the concentration of instability factors such as 15–30% slopes and transitional elevations. Classes 4 and 5 together contained 34.1% of recorded landslide points within 34.2% of the total area, indicating a density ratio of approximately 1.5‑fold compared to the provincial average. Quantitative assessment using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) yielded an AUC of 0.592 for the median‑based method and 0.477 for the RIN method. While the AUC of the median‑based method was higher, this value is misleading, as the majority of landslide points (77.6%) were concentrated in the low and moderate hazard classes (classes 2 and 3), with only 18% falling into high‑risk zones (classes 4 and 5). In contrast, the RIN method, despite its lower AUC, distributed 68.7% of the points across moderate to very high hazard classes (classes 3–5) and maintained a logical increasing FR trend. These findings indicate that AUC alone is insufficient for evaluating landslide susceptibility models and that the distribution of points within high‑risk classes serves as a more critical performance indicator. The final hazard zonation map, generated in a GIS environment through weighted overlay summation and classified using Natural Breaks into five classes, showed that high and very high hazard zones (classes 4 and 5) collectively cover 34.2% of the study area (7,469 km²), while moderate hazard covers 30.2% (6,573 km²).Conclusion This study introduces and implements the Relative Importance Normalization (RIN) framework as a systematic computational solution to the fundamental challenge of objective criterion weighting in knowledge‑based landslide hazard assessments. Unlike conventional statistical averaging—which introduces systematic bias when synthesizing heterogeneous studies with varying numbers of input criteria—the RIN framework normalizes weights internally within each source study prior to final aggregation, thereby enabling a more consistent and replicable weighting scheme. The framework was applied and validated in Mazandaran Province using 674 recorded landslide points. The validation results demonstrated a clear, monotonic increase in Frequency Ratio (FR) values from the very low hazard zone (FR = 0.72) to the very high hazard zone (FR = 1.04), confirming the model's spatial accuracy and reliability. Comparative analysis with a median‑based weighting approach further revealed that the RIN method significantly outperformed the conventional method in identifying high‑risk zones, concentrating 10.5% of landslide points in the very high hazard class, compared to only 0.15% for the median‑based approach. While the RIN framework offers strong theoretical transparency, high reproducibility, and flexibility for dynamic updates as new studies become available, certain limitations remain. These include the low representation of specific parameters (e.g., distance to population centers, which appeared in only two studies) and the coarse spatial resolution of some input layers. The quantitative assessment using the Area Under the Curve (AUC) yielded values of 0.592 for the median‑based method and 0.477 for the RIN method. Although the AUC of the median‑based method was higher, this value was misleading, as 77.6% of landslide points were concentrated in low and moderate hazard classes, whereas the RIN method, despite its lower AUC, distributed 68.7% of points across moderate to very high hazard classes and maintained a logical increasing FR trend. These findings indicate that AUC alone is insufficient for evaluating landslide susceptibility models and that the distribution of points within high‑risk classes serves as a more critical performance indicator. Future investigations should explore refining RIN‑derived weights using local expert knowledge and integrating them into hybrid models alongside data‑driven algorithms such as logistic regression or support vector machines. Finally, the resulting hazard maps serve as a dependable decision‑support tool for emergency management, infrastructure routing, and prioritizing mitigation measures in the landslide‑prone terrains of Mazandaran and analogous mountainous regions.Acknowledgements The authors sincerely thank the management and expert staff of the General Department of Natural Resources and Watershed Management of Mazandaran Province for providing the foundational spatial data layers, which played a fundamental role in advancing this research. It is also worth noting that the initial idea of this study originated from a master's thesis in Environmental Hazards, supervised by the corresponding author at the University of Birjand. Following extensive revisions in content, methodology, and results, the thesis was developed into the present article.پهنه‌بندی خطر زمین‌لغزش با روش‌های دانش مبنا، به‌ویژه در مناطق دارای کمبود داده‌های میدانی، از راهکارهای مهم مدیریت مخاطرات است؛ بااین‌حال، نبود سازوکاری علمی برای استانداردسازی اوزان حاصل از مطالعات ناهمگون، چالش اصلی این رویکردهاست. پژوهش حاضر باهدف رفع این شکاف، چارچوب نرمال‌سازی اهمیت نسبی را در استان مازندران اجرا و با روش وزن‌های میانه مقایسه کرده است. بر پایه فراوانی حضور در هفت مطالعه معتبر، ۹ معیار شامل شیب، جهت شیب، ارتفاع، بارش، فاصله از گسل، جاده، آبراهه و مراکز جمعیتی و کاربری اراضی انتخاب و وزن‌دهی شدند. نتایج نشان داد شیب با وزن 0.239 بیشترین تأثیر را دارد و فاصله از جاده (0.133) و بارش (0.121) در رتبه‌های بعدی قرار می‌گیرند. استفاده از میانه، ضمن حفظ ترتیب کلی معیارها، وزن ارتفاع و کاربری اراضی را کاهش داد. وزن فاصله از مراکز جمعیتی نیز به دلیل حضور در تنها دو مطالعه، با عدم قطعیت همراه بود. روش نرمال‌سازی اهمیت نسبی با تمرکز 10.5 درصد نقاط لغزشی در پرخطرترین رده و افزایش نسبت فراوانی از 0.72 در رده کم‌خطر به 1.04 در رده پرخطر، نسبت به روش میانه با تمرکز 0.15 درصد نقاط، عملکرد بهتری داشت. مقادیر سطح زیر منحنی برای این دو روش به ترتیب 0.477 و 0.592 بود که ناکافی‌بودن این شاخص به‌تنهایی را نشان داد. اعتبارسنجی با 674 نقطه لغزشی، روند صعودی نسبت فراوانی را تأیید کرد. همچنین، 34.2 درصد مساحت استان در پهنه‌های خطر بالا و بسیار بالا قرار گرفت. این چارچوب برای پهنه‌بندی خطر در مناطق کوهستانی دارای داده کم مناسب است.https://geoeh.um.ac.ir/article_48577_64d0072584e8593e682126fa42306f19.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Morphometric Asymmetry of the Dena Mountains along the Main Zagros Thrust: The Controlling Role of Subsurface Structures on Surface Topographyنا‌تقارنی مورفومتریک کوه‌های دنا در امتداد گسل اصلی زاگرس: نقش کنترل‌کننده ساختارهای زیرسطحی بر ناهمواری‌های سطحی48674810210.22067/geoeh.2026.97282.1637FAغلام حسنجعفریگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه یاسوج، یاسوج، ایران0000-0002-8901-6855Journal Article20251231The Zagros Mountains represent one of the most tectonically active orogenic belts worldwide, exhibiting pronounced morphological asymmetry between their southwestern and northeastern flanks. This study presents a quantitative analysis of topographic relief across both flanks using a high-resolution (12.5 m) digital elevation model (DEM). Over 200 longitudinal profiles and transverse cross-sections were extracted and fitted with linear and polynomial regression models. Key morphometric indices—including effective slope, coefficient of determination (R²), and curvature trend (convex vs. concave)—were systematically analyzed. Results show the southwestern flank has a steeper effective slope (0.46–0.51), deeply incised concave upper profiles shaped by glacial erosion amplified by structural steepness, and morphological instability in lower segments due to intense fluvial and mass-wasting processes. In contrast, the northeastern flank displays gentler slopes (0.27–0.46), predominantly linear or convex profiles, and greater morphological stability. Although Quaternary glaciation influenced elevations above 3700 m on both flanks, the final morphology of cirques and glacial benches is structurally controlled: gentle convex platforms dominate the structurally concordant northeastern flank, whereas steep, U-shaped valleys characterize the discordant southwestern flank. These findings confirm that subsurface structures—particularly the Main Zagros Thrust and bedrock dip orientation—primarily govern surface topography, with surface processes operating within a tectonically defined framework. This study provides a robust quantitative methodology for analyzing tectonics–erosion interactions in young orogens, transferable to other active mountain belts globally.IntroductionThe Zagros Mountains, formed by the ongoing collision between the Arabian and Eurasian plates, constitute one of the most tectonically active orogenic belts on Earth. A striking morphological asymmetry characterizes this range: the southwestern flank—adjacent to the Main Zagros Thrust—exhibits steep, dynamic topography, whereas the northeastern flank—dipping in harmony with sedimentary strata—displays gentler, more extended slopes. Although numerous studies have addressed the tectonics and geomorphology of the Zagros, a critical research gap persists in the systematic, quantitative analysis of morphometric differences between these two flanks at elevations above 3,000 m, where Quaternary glacial processes and active tectonics interact simultaneously. This study aims to address this gap by testing the hypothesis that the observed topographic asymmetry directly reflects the geometry and activity of subsurface structures.Material and MethodsThe study area is located in the central-southern part of the Zagros heights and spans the provinces of Isfahan, Chaharmahal and Bakhtiari, and Kohgiluyeh and Boyer-Ahmad. This region includes part of the Dena Mountains, one of the most significant and prominent morphological structures in the southern fold belt of the Zagros. The geographical coordinates of the area are precisely: latitude from 30° 40' to 31° 10' north and longitude from 51° 20' to 51° 40' east.We employed a high-resolution (12.5 m) digital elevation model (DEM), processed in Global Mapper, to extract and analyze topographic profiles across the Dena Mountains in the Central Zagros. Over 200 longitudinal profiles were categorized into three groups: (1) northeastern flank profiles (facing the Iranian Plateau), (2) southwestern flank profiles (adjacent to the Main Zagros Thrust), and (3) valley-aligned profiles on both flanks. Additionally, transverse cross-sections perpendicular to the mountain trend were generated. For each profile, elevation data were exported as numerical series and statistically analyzed in Microsoft Excel. Linear regressions were first fitted to each profile, and the coefficient of determination (R²) was calculated. Where R² fell below 0.95, polynomial models (degrees 2–6) were applied to improve fit quality. Key morphometric parameters—including effective slope, horizontal length, relative relief, and curvature behavior (convex vs. concave)—were systematically compared between flanks.Results and DiscussionAll profiles consistently reveal a pronounced morphological asymmetry: the southwestern flank displays steeper effective slopes (0.46–0.51), while the northeastern flank exhibits gentler gradients (0.27–0.46) and longer horizontal extents. Above 3,700 m, glacial landforms—such as cirques and glacial benches are present on both flanks. However, their final morphology is structurally controlled: on the structurally concordant northeastern flank, cirques manifest as broad, convex platforms; in contrast, the structurally discordant southwestern flank hosts deep, U-shaped valleys with steep, glacially sculpted walls. The coefficient of determination (R²) is consistently higher on the northeastern flank (0.970–0.998) than on the southwestern flank (0.900–0.990), indicating greater morphological stability and homogeneity. Conversely, the southwestern flank shows greater R² dispersion and curvature oscillations, reflecting high geomorphic dynamism and rapid response to tectonic forcing.These findings align with established structural models of the Zagros. According to the thick-skinned wedge model (Mouthereau et al., 2006) and the blind thrust framework (Berberian, 1995), focused crustal shortening along the Main Zagros Thrust generates steep, unstable topography on the southwestern flank, while the northeastern flank—acting as the upper surface of the tectonic wedge—retains relatively stable, gently sloping forms. Thus, even under similar surface processes (e.g., glacial erosion), structural geometry dictates the expression of topography.ConclusionThis study provides robust quantitative evidence for deep‑seated structural control over surface topography in the Zagros Mountains. The morphometric asymmetry between the two flanks is not merely a surface geomorphological feature but a direct expression of contrasting tectonic settings: the active collisional front along the southwestern side versus the relatively stable hinterland domain in the northeast. These findings reinforce the theoretical framework of structural control on surface morphology in young orogenic belts and demonstrate that geomorphic analyses cannot fully explain landscape evolution without considering underlying structural geology. Methodologically, the quantitative approach employed in this study—based on fitting mathematical relationships to longitudinal profiles and analyzing morphometric indices such as effective slope, coefficient of determination (R²), and curvature trends—provides a transferable framework for systematic comparison of tectonically active mountain belts worldwide. In addition, the results have practical implications for geomorphological hazard assessment and environmental planning in the Zagros Mountains, particularly with respect to rockfall susceptibility and slope instability along the steep southwestern slopes. Overall, the contrasting morphometric characteristics of the two flanks illustrate two distinct states of tectonic–surface interaction in a young mountain system, offering insight into the long‑term evolution of collisional mountain belts.رشته‌کوه زاگرس به‌عنوان یکی از پویاترین کمربندهای کوهستانی جهان، نا‌تقارن مورفولوژیکی آشکاری بین دامنه‌های جنوب‌غربی و شمال‌شرقی خود نشان میدهد. این پژوهش با استفاده از مدل رقومی ارتفاعی (DEM) با قدرت تفکیک 5/12 متر، تحلیل کمّی ناهمواری‌های دو دامنه را انجام داده است. بیش از 200 نیمرخ طولی و نیمرخ‌های عرضی عمود بر روند کوهستان ترسیم و با روابط خطی و چندجمله‌ای برازش شدند. شاخص‌های مورفومتریک شامل شیب مؤثر، ضریب تعیین (R²) و روند انحنا (کوژ در مقابل کاو) تحلیل شدند. یافته‌ها نشان می‌دهند که دامنه جنوب‌غربی دارای شیب مؤثر تندتر (46/0–51/0)، نیمرخ‌های کاو عمیق در بالادست (ناشی از فرسایش یخچالی تقویت‌شده توسط شیب ساختاری) و ناپایداری مورفولوژیکی در پایین‌دست است. در مقابل، دامنه شمال‌شرقی شیب ملایم‌تر (27/0–46/0)، نیمرخ‌های عمدتاً خطی یا کوژ و پایداری بیشتری دارد. هرچند فعالیت‌های یخبندانی کواترنری در ارتفاعات بالاتر از 3700 متر در هر دو دامنه مؤثر بوده‌اند، شکل نهایی سیرک‌ها و لندفرم‌ها تحت کنترل هندسه ساختاری است: لندفرم‌های محدب در دامنه شمال‌شرقی (هم‌شیب با لایه‌ها) و دره‌های U شکل تند در دامنه جنوب‌غربی. این نتایج تأیید می‌کنند که ساختارهای زیرسطحی، به‌ویژه گسل اصلی زاگرس و جهت لایه‌ها، نقش اولیه را در شکل‌دهی به ناهمواری‌های سطحی ایفا می‌کنند و فرآیندهای سطحی در چارچوبی که توسط تکتونیک تعیین شده است، عمل می‌کنند. این مطالعه چارچوبی کمّی برای درک تعامل تکتونیک–فرسایش در کوه‌های جوان فراهم می‌آورد و قابلیت تعمیم به سایر کمربندهای فعال جهان را دارد.https://geoeh.um.ac.ir/article_48102_d43601e8a1b7a88689e57107367b0d58.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Assessment of Landslide Susceptibility Using the CRITIC Model in the Lanbaran Chay Watershed, Varzeqan County, Iranارزیابی حساسیت زمین لغزش با استفاده از مدل CRITIC در حوضه آبریز لنبران چای شهرستان ورزقان68914822710.22067/geoeh.2026.97294.1638FAشهرامروستاییگروه ژئومورفولوژی، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران0000-0003-0664-1688سید اسداللهحجازیگروه ژئومورفولوژی، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران0000-0002-9289-5635سیدحسینفقیه خلجانیگروه ژئومورفولوژی، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایرانJournal Article20251231Landslide hazard mapping is one of the most essential tools for crisis management and environmental planning in watershed areas. The Lenbran Chay watershed, located on the eastern flank of the Ahar Chay River and covering an area of 8,226 hectares, represents one of its most important headwater basins. Owing to its topographic and geological characteristics, the watershed is highly susceptible to landslide occurrence. The aim of the present study is to identify the factors influencing landslide occurrence and to delineate susceptibility zones within this watershed. For this purpose, a set of natural, anthropogenic, and morphologic variables—including distance from drainage networks, lithology, soil type, slope, aspect, elevation classes, precipitation, land use, and distance from transportation routes—was selected as the conditioning factors. The CRITIC multi criteria decision making model was then employed to determine the relative weight of each factor, based on which the final landslide hazard map was produced. The results indicated that slope is the most influential factor in landslide occurrence, followed by distance from drainage networks and lithology. Model performance assessment using training and validation datasets showed that CRITIC, with coefficients of 89.13 and 83.3 respectively, provides an acceptable level of accuracy and reliability in landslide hazard zonation. Overall, the findings suggest that the CRITIC model can be effectively utilized for risk assessment and optimal land use planning in landslide prone areas.Introduction Landslides are major geomorphological processes that play a critical role in shaping landscape evolution in mountainous regions and have been responsible for numerous catastrophic events worldwide. Although landslides are natural phenomena that contribute to terrain formation and erosion, they remain among the most destructive natural hazards after earthquakes and floods, causing extensive damage to infrastructure, human settlements, and natural resources. In recent decades, rapid population growth and the expansion of human activities such as deforestation, overgrazing, agricultural land degradation, and unregulated construction have significantly increased both the frequency and severity of landslide occurrences.These developments highlight the urgent need for effective natural hazard management strategies aimed at reducing local vulnerability and mitigating broader environmental and socio‑economic impacts. Such strategies must be embedded within long‑term planning frameworks through preventive measures, preparedness initiatives, and integrated risk management approaches. Addressing landslide hazards therefore requires the application of robust analytical frameworks capable of capturing the complex interactions among geomorphological, climatic, hydrological, and anthropogenic factors that govern slope instability.In this context, the present study applies the CRITIC multi‑criteria decision‑making model to assess and map landslide hazard in the Lanbran‑Chai Watershed. This approach, which has not previously been implemented in this watershed, provides a transparent and objective mechanism for weighting and integrating diverse conditioning factors within a spatial modeling environment. By enhancing the precision of landslide susceptibility assessment, the proposed framework offers reliable, evidence‑based support for identifying high‑risk areas and guiding targeted mitigation measures and sustainable land‑use planning.Material and Methods The Lenbran‑Chai sub‑Watershed is located on the eastern side of the Ahar‑Chai River and covers an area of 8226 hectares within the defined study boundary. It represents one of the main headwater branches contributing to the Ahar‑Chai River system. Geographically, the watershed lies between 46°20′07″ to 46°30′00″ E longitude and 38°28′17″ to 38°33′52″ N latitude.To analyze landslide hazard in the watershed, available spatial and environmental datasets were first collected and examined. Based on theoretical principles, previous research, and expert knowledge, nine conditioning factors representing geological, geomorphological, climatic, environmental, and anthropogenic characteristics were selected for analysis. These factors include:distance from drainage networks, lithology, soil type, slope gradient, slope aspect, elevation classes, precipitation, land use, and distance from roads.Because these datasets originally had different units and measurement scales, all layers were standardized prior to integration. Continuous variables were converted into discrete classes using GIS‑based reclassification rules. Each layer was categorized into five susceptibility classes very high, high, moderate, low, and very low based on its contribution to landslide occurrence. Standardization was performed using the Reclassify tool in ArcGIS.After preparing the layers, the CRITIC model was used to calculate the objective weight of each conditioning factor. These weights were subsequently applied to the standardized layers within ArcMap to generate the final landslide hazard map for the Lenbran‑Chay Watershed.Results and Discussion Standardization and integration of the conditioning factors made it possible to conduct a consistent multi‑criteria assessment of landslide susceptibility. Each layer slope, aspect, lithology, precipitation, elevation, distance to roads, distance to drainage networks, and landuse was classified into five hazard levels, ensuring comparability across all criteria.The landslide hazard zonation map produced using the CRITIC model indicates that 5.4, 14.7, 25.8, 22.16, and 13.6 square kilometers of the watershed fall within the very high, high, moderate, low, and very low hazard classes, respectively. These spatial patterns show that the most vulnerable areas are typically located on steep slopes, in lithologically weak zones, and in areas close to drainage channels.Weighting results reveal that slope (26%), distance from drainage networks (13%), and lithology (11%) are the most influential factors in landslide occurrence. This is consistent with the geomorphological characteristics of the region, where steep gradients and active runoff processes, combined with susceptible rock formations, significantly increase the likelihood of slope failure.Model validation using the Area Under the ROC Curve (AUC) demonstrates strong predictive performance. The CRITIC‑based model achieved AUC values of 0.83 (training dataset) and 0.89 (validation dataset), confirming its reliability and accuracy in identifying landslide‑prone areas within the watershed.ConclusionThe results of this study indicate that the CRITIC multi‑criteria decision‑making model is an effective tool for landslide hazard assessment in the Lenbran‑Chai Watershed. The analysis confirms that slope, distance from drainage networks, and lithology are the most influential factors controlling landslide occurrence in the region. The final hazard map shows that 6.6%, 18.04%, 31.6%, 27.09%, and 16.64% of the watershed fall within the very high, high, moderate, low, and very low hazard categories, respectively.The high predictive accuracy of the CRITIC model, demonstrated by robust AUC values, highlights its capability for objective weighting and reliable spatial modeling. The resulting hazard zonation map can support preventive policymaking, optimized land‑use planning, and efficient resource allocation in disaster management.Given these promising results, future research is recommended to integrate the CRITIC model with advanced analytical approaches such as machine learning techniques, ensemble methods, or hybrid decision‑making frameworks to further increase the precision and reliability of landslide susceptibility mapping.تهیه نقشه وقوع خطر زمین‌لغزش، ابزاری اساسی برای فعالیت‌های مدیریت بحران در حوضه های آبریز است. حوضه آبریز لنبران چای در سمت ضلع شرقی رودخانه اهرچای واقع شده است. مساحت این حوضه 8226 هکتار می‌باشد و سرشاخه اصلی رودخانه اهرچای محسوب می‌شود. هدف اصلی پژوهش حاضر شناسایی عوامل مؤثر در وقوع این پدیده و پهنه‌بندی نواحی مستعد زمین لغزش در حوضه آبریز لنبران‌چای می‌باشد. بدین منظور، با بهره‌گیری از ویژگی‌های طبیعی، انسانی و مورفولوژیکی، ۹ عامل کلیدی شامل فاصله از آبراهه، لیتولوژی، نوع خاک، شیب، جهت شیب، طبقات ارتفاعی، بارش، کاربری اراضی و فاصله از راه‌های ارتباطی مورد استفاده قرار گرفت. سپس، با بهره‌گیری از مدل تصمیم‌گیری چندمعیاره CRITIC، ارزیابی و پهنه‌بندی خطر وقوع زمین‌لغزش در حوضه آبریز لنبران‌چای انجام گردید. نتایج نشان می دهد که عامل شیب بیشترین وزن را داشته و فاصله از آبراهه و لیتولوژی در رتبه های بعدی قرار دارند. همچنین نتیجه بررسی میزان دقت و کارایی مدل CRITIC نشان داد که این مدل به ترتیب با ضرایب 89.13 و 83.3 برای داده‌های آموزشی و اعتبارسنجی، عملکرد خوبی در تهیه نقشه پهنه بندی زمین لغزش داشته است.https://geoeh.um.ac.ir/article_48227_7de9a4ece8e24c7f614caa04ab1e5e1d.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Analyzing the Drivers of Lake Urmia’s Desiccation through the Lens of Water Governanceواکاوی پیشران‌های خشک‌شدن دریاچه ارومیه از منظر حکمرانی آب921174849310.22067/geoeh.2026.98216.1659FAمحمدعلیکیانیگروه جغرافیای سیاسی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0009-0009-5143-8778افسانهدهقان پور فراشاهگروه حکمرانی فرهنگی و اجتماعی، دانشکدۀ حکمرانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران0000-0002-4248-6884Journal Article20260404Over the past decades, Lake Urmia, the most important water body in northwestern Iran, has played a role far beyond that of a local ecosystem, becoming deeply intertwined with regional livelihoods, habitability, environmental equilibrium, and social stability. The severe decline in its water level has generated multifaceted ecological, socio-economic, and governance-related consequences, turning the lake into one of the central challenges of water governance in Iran. This qualitative study aims to analyze the drivers of Lake Urmia’s desiccation. Using purposive sampling, semi-structured interviews were conducted with 31 experts in water governance, hydropolitics, hydrology, public policy, and natural resource management. The collected data were analyzed through thematic analysis. The findings led to the identification of 153 basic themes, 17 organizing themes, and 5 global themes, reflecting a wide range of interrelated drivers contributing to the lake’s desiccation. The identified global themes are: Hydropolitical Challenges and Infrastructure-Oriented Developmental Paradigm; Water Propaganda and Politicization of Allocation; Governance Fragmentation; Legal and Regulatory Gaps; and Hydro-Climatic Challenges and Physical Transformations. Overall, the findings indicate that the desiccation of Lake Urmia is not merely the outcome of climatic variability, but rather the result of the synergistic interaction of human, institutional, political, legal, and hydro-climatic factors within a fragmented water governance system.IntroductionLake Urmia, the most significant water body in northwestern Iran, has long played a role far beyond that of a local ecosystem. Over recent decades, it has been deeply intertwined with regional livelihoods, habitability, environmental balance, and socio-economic stability. The severe decline in its water level and its gradual desiccation have not only generated ecological degradation but have also produced far-reaching social, economic, health-related, and security implications. As a result, Lake Urmia has become one of the central arenas of water governance challenges in Iran. Thus, the Lake Urmia crisis offers an analytical lens for understanding the interconnections among environmental resilience, development trajectories, territorial governance, and public policy in Iran.The central issue addressed in this study is how diverse natural, institutional, socio-economic, and human-induced drivers have interacted synergistically to reproduce the crisis at the sub-national scale. Identifying these drivers deepens understanding of the structural and functional constraints of water governance and provides an evidence-based foundation for policy interventions aimed at strengthening institutional coherence, improving cross-sectoral coordination, protecting wetlands, mitigating human-induced desertification, and reducing the risks of climate-induced migration.Material and MethodsThis applied qualitative study adopts an inductive approach to investigate the drivers of Lake Urmia’s desiccation within the broader socio-ecological context of Iran. Data were collected through purposive sampling and semi-structured interviews with experts and specialists in the fields of water governance, hydropolitics, hydrology, public policy, and natural resource management.Interviews continued until theoretical saturation was achieved. Although data adequacy was reached in the twenty-sixth interview, data collection continued up to the thirty-first participant to enhance credibility and capture the hidden dimensions of this complex issue. Ultimately, 31 experts participated in the study.The qualitative data obtained from the interviews were analyzed using thematic analysis, with the assistance of MAXQDA software. Following Attride-Stirling’s thematic network approach, the analysis began with open coding to identify basic themes. These initial codes were then interpreted and grouped into organizing themes through continuous semantic comparison. Finally, through higher levels of conceptual abstraction and secondary interpretive analysis, 5 global themes were extracted. To enhance the trustworthiness of the findings, Guba and Lincoln’s criteria, namely credibility, confirmability, dependability, and transferability, were considered.Results and DiscussionThe thematic analysis led to the identification of 153 basic themes, 17 organizing themes, and 5 global themes, reflecting a wide range of drivers contributing to the desiccation of Lake Urmia. The identified global themes are as follows: Hydropolitical Challenges and Infrastructure-Oriented Developmental ParadigmThe crisis is deeply rooted in an infrastructure-oriented developmental paradigm characterized by excessive reliance on engineering-centered interventions, such as extensive dam construction and inter-basin water transfers. These supply-side interventions, often implemented without considering the basin’s ecological capacity, marginalized environmental water rights in favor of technical manipulation of nature. This unsustainable trajectory was further reinforced by the political economy of water, rent-seeking in transfer projects, and institutional conflicts of interest. Water Propaganda and Politicization of AllocationDecisions regarding water allocation have frequently been shaped by political pressures, electoral promises, and symbolic development narratives rather than scientific and hydrological principles. Using water projects as tools for political legitimacy has weakened technical decision-making, created unrealistic public expectations, and intensified stakeholder competition for limited resources. Governance FragmentationFragmented institutional responsibilities, sectoral approaches, and conflicting mandates have systematically prevented integrated water resources management. This fragmentation produces a significant gap between policy formulation and implementation. Restoration plans are consistently hindered by bureaucratic rigidity, weak monitoring capacity, and the notable absence of a unified, authoritative basin-level governance structure. Legal and Regulatory GapsDeficiencies in legal frameworks governing water ownership, allocation, and exploitation have exacerbated the crisis. The absence of enforceable guarantees for Lake Urmia’s environmental water rights leaves it vulnerable to agricultural and industrial demands. Ineffective monitoring of withdrawals, unchecked proliferation of illegal wells, and unauthorized land-use changes highlight the urgent need for robust legal reform and regulatory enforcement. Hydro-Climatic Challenges and Physical TransformationsWhile climate variability and drought episodes have contributed to environmental stress, hydro-climatic factors alone cannot explain the crisis’s scale. Extensive physical transformations—including agricultural expansion, groundwater depletion, disruption of hydrological connectivity, and encroachment into ecological buffer zones—have severely weakened the socio-ecological resilience of the basin.Overall, the findings indicate that the Lake Urmia crisis emerges from the continuous and synergistic interaction of these natural, institutional, socio-economic, and political drivers, which collectively accelerate desiccation and undermine the basin’s socio-ecological resilience.ConclusionThis study demonstrates that the desiccation of Lake Urmia is a direct consequence of structural deficiencies in water governance, rather than merely a climatic phenomenon. The crisis reflects the failure of fragmented, engineering-centered, and politically driven approaches to address the complexity of a socio-ecological system. Protecting Iran’s wetlands, preventing irreversible soil salinization and land degradation, mitigating human-induced desertification, reducing climate-induced migration, and safeguarding regional habitability require a fundamental reconsideration of prevailing governance paradigms.Crucially, overcoming the crisis necessitates transitioning from structural and supply-oriented water management toward participatory, adaptive, and network-based water governance. Addressing legal gaps, ensuring transparency in resource allocation based on scientific indicators, strengthening cross-sectoral coordination, and recognizing the environmental water right of Lake Urmia as a binding priority are essential pillars of effective territorial governance. Policymakers should acknowledge the complexity of socio-ecological systems and move beyond temporary, symbolic, or purely engineering-based solutions.Policy-wise, restoring Lake Urmia requires simultaneous action across the five identified dimensions. This entails shifting from supply-side water management to demand management, halting the expansion of new hydraulic infrastructures, revising dam operation policies, ensuring the release of environmental water rights, establishing transparent water allocation systems, sealing illegal wells, regulating licensed withdrawals, and disclosing allocation data to the public.Institutionally and legally, establishing a single authoritative basin-level governance body, supported by local water management councils and stakeholder participation, is essential for overcoming fragmentation and strengthening social accountability. Revising legal frameworks governing water ownership, allocation, exploitation, and land-use change, together with GIS-based and smart monitoring systems, can enhance regulatory enforcement. Climate adaptation measures, including crop pattern reform, reduction of water-intensive cultivation, restoration of hydrological connectivity, protection of ecological buffer zones, wastewater reuse, and rehabilitation of conveyance canals, are also necessary for rebuilding socio-ecological resilience.Sustainable restoration of Lake Urmia therefore requires a decisive shift from fragmented, short-term, and infrastructure-centered approaches toward integrated, knowledge-based, participatory, and adaptive water governance. Only within such a framework can crisis reproduction be prevented and the conditions for ecological resilience, sustainable development, and territorial stability be restored.دریاچه ارومیه به‌مثابه مهم‌ترین پهنه آبی شمال‌غرب ایران در طول دهه‌های گذشته، نقشی فراتر از یک اکوسیستم محلی داشته و در پیوند با معیشت، زیست‌پذیری، تعادل محیطی و ثبات اجتماعی منطقه، معنا یافته است. کاهش شدید تراز آب این دریاچه، پیامدهای گوناگونی بر جای گذاشته و آن را به یکی از کانون‌های اصلی چالش‌های حکمرانی آب ایران، بدل کرده است. هدف از پژوهش کیفی حاضر، واکاوی پیشران‌های خشک شدن دریاچه ارومیه است. با روش نمونه‌گیری هدفمند، مصاحبه‌های نیمه‌ساختاریافته با 31 نفر از خبرگان حوزه حکمرانی آب، هیدروپلیتیک، هیدرولوژی، خط‌مشی گذاری عمومی و مدیریت منابع طبیعی انجام شد و از روش تحلیل مضمون، برای تحلیل داده‌ها استفاده شد. یافته‌های پژوهش منجر به شناسایی 153 مضمون پایه، 17 مضمون سازمان‌دهنده و 5 مضمون فراگیر (اصلی) شد که بیانگر طیف گسترده‌ای از پیشران‌های خشک شدن دریاچه ارومیه است. مضامین اصلی شناسایی شده عبارتند از چالش‌های هیدروپلیتیک و پارادایم توسعه‌گرای سازه‌محور؛ پروپاگاندای آبی و سیاست‌زدگی در تخصیص؛ گسست در حکمرانی؛ خلأهای حقوقی و نظارتی و نیز چالش‌های هیدروکلیماتیک و تغییرات کالبدی.https://geoeh.um.ac.ir/article_48493_4e13cab9991e5cfbfcdc71acc908495b.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Improving Spatial Prediction of Soil Salinity Using an Ensemble Machine Learning Model in Arid Regions of Eastern Iranبهبود پیش‌بینی مکانی شوری خاک با مدل ترکیبی یادگیری ماشین در مناطق خشک شرق ایران1181364861910.22067/geoeh.2026.99019.1669FAمرتضیاکبریگروه مدیریت مناطق خشک و بیابانی، دانشکده منابع طبیعی و محیط زیست دانشگاه فردوسی مشهد، مشهد، ایران0000-0002-8637-266Xصدیقهملکیگروه علوم خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران0000-0001-7052-1918ملیحهپورعلیگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه فردوسی مشهد، مشهد، ایران0000-0003-4444-9013عبدالحسینبوعلیگروه مدیریت مناطق خشک و بیابانی، دانشکده منابع طبیعی و محیط زیست دانشگاه فردوسی مشهد، مشهد، ایران0009-0002-8014-1067Journal Article20260530Soil salinity is a major environmental hazard in arid regions, directly affecting soil and water resource productivity and ecosystem sustainability. This study aimed to develop and optimize an ensemble machine learning model for improving the spatial prediction of soil salinity and identifying the key environmental factors controlling its distribution in the Mahvelat Plain, Khorasan Razavi Province, Iran. A total of 137 soil samples were collected, and electrical conductivity of the saturated soil extract (ECe) was measured. In addition, 32 auxiliary variables, including remote sensing spectral indices, topographic features, geological information, land use data, and groundwater quality parameters, were used in the modeling process. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Generalized Linear Model (GLM), along with a combined model, were developed and evaluated. The results showed that the combined model outperformed the individual models, achieving the highest prediction accuracy (R² = 0.81, RMSE = 5.14, and MAE = 4.11). Among the individual models, SVM showed the best performance (R² = 0.76). Highly saline areas were mainly concentrated in the central, western, and northwestern parts of the study area, particularly around Playa Bajestan, with an overall increase in salinity from east to west. Groundwater quality parameters, especially HCO₃⁻, SAR, and TDS, were the most influential predictors. The combined model also showed a greater ability to estimate extreme salinity values, reaching 21 dS/m. Overall, the proposed framework provides an accurate and reliable approach for soil salinity mapping and sustainable soil resource management.
Introduction
Nowadays, soil salinity has become an escalating environmental crisis that directly threatens global food security. In arid regions, the gradual accumulation of salts degrades both the physical and chemical properties of soil, posing a serious obstacle to agricultural production. The Mahvelat Plain in eastern Iran is one such area. It is an important agricultural zone well known for its intensive pistachio cultivation. However, the region’s hot and dry climate, together with its proximity to the Bajestan playa, creates conditions that strongly favor salinization. The present study attempts to bridge the gap between traditional soil sampling and predictive digital soil mapping using an advanced ensemble machine learning approach. According to official reports, 424 million hectares of topsoil and 833 million hectares of cultivated soils in the world are affected by salt. Despite the increasing availability of remote sensing and environmental covariates, accurately characterizing the spatial heterogeneity of soil salinity in arid agricultural landscapes remains challenging because salinity is controlled by complex interactions among soil, groundwater, topography, climate, and land-use conditions. Therefore, integrating multiple machine learning algorithms within an ensemble framework may provide a more robust approach for improving prediction accuracy and reducing the limitations associated with relying on a single predictive model.
Material and Methods
Soil salinity values have previously been assessed through laboratory analyses, which have often been time-consuming and costly. Due to the limitations of past scientific methods, alternative techniques such as geostationary and remote sensing are currently being used to predict soil salinity in unsampled areas. Also, the integration of digital soil mapping techniques and machine learning to estimate soil properties has also expanded the application of these methods in soil salinity prediction. The study was conducted in the Mahvelat Plain. A total of 137 soil samples were collected to determine the electrical conductivity of the saturated paste extract (ECe). To account for spatial complexity, 32 environmental covariates were selected. These included spectral remote sensing data, terrain attributes derived from a digital elevation model (DEM), geological maps, and groundwater chemical analyses. Four different models were trained initially: random forest (RF), support vector machine (SVM), boosted regression trees (BRT), and generalized linear model (GLM). Afterwards, a stacking ensemble model was developed using a five‑fold cross‑validation framework to generate the final salinity prediction map.
Results and Discussion
Model evaluation of model performance clearly showed that the stacking ensemble approach was superior. This model achieved high statistical accuracy, with an R² of 0.81 and an RMSe of 5.14, thereby outperforming each of the individual RF, SVM, BRT, and GLM models. The spatial distribution map revealed a clear salinity gradient, with values increasing progressively from the eastern part toward the western and northwestern parts of the plain. According to the variable importance analysis, soil salinity in the study area is mainly governed by subsurface hydro geochemical conditions. In particular, the strong correlations observed for HCO₃‑SAR and TDS indicate that groundwater quality is the primary factor driving soil degradation in this plain. Unlike single models, the ensemble framework proved notably more capable of capturing extreme salinity, which is of particular concern for agricultural management. The ensemble approach outperformed all individual models, achieving an R² of 0.81, RMSe of 5.14 dS m⁻¹, and MAE of 4.11 dS m⁻¹. The final salinity map revealed high values in the western and northwestern parts of the plain near the Bajestan playa. Variable importance analysis identified groundwater hydro geochemical parameters, particularly bicarbonate (HCO₃⁻), sodium adsorption ratio (SAR), and total dissolved solids (TDS), as the primary predictors. Notably, the ensemble model substantially improved the estimation of extreme salinity values exceeding 21 dS m⁻¹ compared to any single model.
Conclusion
The results confirmed that ensemble machine learning techniques can markedly improve the accuracy of soil salinity mapping in arid regions. By capturing the nonlinear interactions between groundwater quality and soil surface properties, the proposed framework offers a practical and reliable tool for site-specific soil management. For pistachio farmers and regional policymakers in Iran, this tool can help identify priority areas for drainage and desalination interventions, thereby supporting more sustainable agricultural development in salt-affected environments. Overall, the proposed approach can serve as an efficient tool for the intelligent management of water and soil resources in saline lands, particularly in pistachio‑growing regions of the country. On the other hand, the results of this research showed that despite the extreme spatial and temporal variability of salinity, the use of remote sensing data and auxiliary variables in the framework of machine learning can predict soil salinity patterns with optimal accuracy. The resulting spatial information can also provide a scientific basis for prioritizing field investigations, optimizing irrigation and drainage practices, and allocating soil reclamation resources to areas with the greatest salinity risk. Furthermore, the proposed framework is transferable to other salt-affected arid and semi-arid agricultural regions where sufficient soil observations and environmental covariates are available, providing a scalable approach for supporting sustainable land and water management.شوری خاک یکی از مخاطرات عمده محیطی در مناطق خشک است که مستقیماً بر بهره‌وری منابع خاک و آب و پایداری اکوسیستم تأثیر می‌گذارد. این مطالعه با هدف توسعه و بهینه‌سازی یک مدل یادگیری ماشین گروهی برای بهبود پیش‌بینی مکانی شوری خاک و شناسایی عوامل محیطی کلیدی کنترل‌کننده توزیع آن در دشت مه ولات، استان خراسان رضوی، ایران انجام شد. هدایت الکتریکی عصاره گل اشباع (ECe) در ۱۳۷ نمونه خاک اندازه گیری شد. 32 متغیر کمکی، شامل؛ شاخص‌های طیفی سنجش از دور، توپوگرافی، زمین‌شناسی، کاربری اراضی و پارامترهای کیفیت آب‌های زیرزمینی، در فرآیند مدل‌سازی استفاده شد. چهار مدل یادگیری ماشین، شامل جنگل تصادفی ( RF)، ماشین بردار پشتیبان (SVM )، درخت رگرسیون تقویت‌شده ( BRT) و مدل خطی تعمیم‌یافته ( GLM)، به همراه یک مدل ترکیبی، توسعه داده و ارزیابی شدند. نتایج نشان داد که مدل ترکیبی نسبت به مدل‌های منفرد عملکرد بهتری داشته است (R² = 0.81, RMSE = 5.14, MAE = 4.11) به ویژه نسبت به مدل (SVM) با (R² = 0.76) . نقشه نهایی شوری نشان داد که مناطق بسیار شور عمدتاً در بخش‌های مرکزی، غربی و شمال غربی منطقه، به ویژه در اطراف پلایا بجستان، متمرکز بوده و افزایش شوری از شرق به غرب مشاهده شده است .پارامترهای کیفی آب زیرزمینی، به ویژه HCO₃⁻، SAR و TDS، بیشترین سهم را در پیش‌بینی شوری خاک داشته‌اند. مدل ترکیبی همچنین توانایی بیشتری در تخمین مقادیر شدید شوری نشان داد (تا 21 دسی‌زیمنس بر متر). در مجموع، چارچوب پیشنهادی یک رویکرد دقیق و قابل اعتماد برای نقشه‌برداری شوری خاک و مدیریت پایدار منابع خاک ارائه میدهد.https://geoeh.um.ac.ir/article_48619_2ae8ad4afeeb8dc42b877d6d5bc83b82.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Spatiotemporal Variations and Identification of Critical SO₂ Pollution Hotspots on the Southern Slopes of the Central Alborz Using Satellite Dataتغییرات فضایی _ زمانی و شناسایی مناطق بحرانی آلاینده دی‌اکسید گوگرد در دامنه جنوبی البرز مرکزی با استفاده از داده‌های ماهواره‌ای1371594860510.22067/geoeh.2026.98532.1664FAمحمدناجیگروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران0009-0003-2451-1841قاسمعزیزیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0002-9797-4834مصطفیکریمیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0001-7820-6728Journal ArticleSulfur dioxide is an atmospheric pollutant emitted primarily through the combustion of fossil fuels in power plants and industrial facilities. This pollutant is present across the industrial Tehran–Karaj–Qazvin corridor. This study aimed to analyze the spatiotemporal pattern of SO₂ and identify areas, with emphasis on power plants and industrial centers, during 2018–2022. Total vertical column SO₂ was retrieved from TROPOMI observations aboard Sentinel-5P. Data processing was conducted in Google Earth Engine, while spatial analyses were performed in ArcGIS. Annual, seasonal, and monthly variations, as well as the five-year mean, were examined, and Hot Spot Analysis was applied. The results showed that the highest SO₂ concentrations were concentrated in industrial areas of Qazvin Province, southern Tehran, and eastern Alborz. Hot Spot Analysis revealed spatial clustering along the Tehran–Qazvin industrial corridor, where industrial zones, thermal power plants, and heavy freight traffic contribute to emissions. Areas surrounding power plants such as Shahid Rajaei were identified as SO₂ hotspots. In contrast, elevated and northern areas exhibited lower concentrations due to better atmospheric ventilation and limited emission sources. Annual SO₂ concentrations showed an increasing trend, reaching a maximum in 2021. Global Moran’s I confirmed strong and significant spatial autocorrelation, indicating clustered pollution patterns and the influence of emission sources. The highest concentrations occurred during the cold season, particularly in January and February, whereas July and August showed lower values. The findings suggest that industrial decentralization, fuel-use reform, and emission control can contribute to SO₂ management.IntroductionAir pollution is one of the most pressing environmental challenges in urban and industrial areas and has become a global concern alongside rapid urbanization and the expansion of industrial activities. Among atmospheric pollutants, sulfur dioxide (SO₂) is of particular importance due to its widespread occurrence and substantial environmental and health impacts. It is primarily emitted through the combustion of fossil fuels in power plants, industrial facilities, and oil refineries. Exposure to elevated concentrations of SO₂ can adversely affect human health, particularly by contributing to respiratory disorders, cardiovascular complications, and overall deterioration of air quality. Consequently, understanding the spatiotemporal distribution of SO₂ has become an important priority for environmental researchers and policymakers worldwide. Iran, like many developing countries, faces persistent challenges associated with SO₂ pollution, particularly in densely populated and industrialized regions. The southern slopes of the Central Alborz, encompassing the metropolitan areas of Tehran, Karaj, and Qazvin, constitute one of the country’s major population and industrial centers and are therefore highly susceptible to air pollution. However, comprehensive assessment of SO₂ pollution in this region is constrained by the limited spatial coverage and temporal continuity of ground-based air quality monitoring stations, particularly in Karaj and Qazvin. Station-based observations are often characterized by spatial gaps and temporal discontinuities, limiting their ability to provide a consistent basis for long-term and spatially comprehensive assessments. In this context, satellite remote sensing provides an effective complementary approach for monitoring atmospheric pollutants over extensive areas and overcoming some of the limitations associated with conventional ground-based observations. In particular, Sentinel-5 satellite observations offer valuable capabilities for investigating the spatial and temporal variability of atmospheric SO₂ at regional scales. Accordingly, the present study employs Sentinel-5 observations to investigate the spatiotemporal distribution of SO₂ across the metropolitan areas of Tehran, Karaj, and Qazvin. The analysis focuses on the five-year mean concentration during 2018–2022, together with annual, seasonal, and monthly variations, to characterize the temporal behavior of the pollutant and identify areas with elevated SO₂ concentrations. Particular attention is given to the spatial correspondence between SO₂ hotspots and major emission sources, including power plants and industrial centers.Material and MethodsSentinel-5P/TROPOMI satellite observations were used to investigate the spatiotemporal variability of sulfur dioxide (SO₂) over the metropolitan areas of Tehran, Karaj, and Qazvin during 2018–2022. The analysis employed the Copernicus Sentinel-5P OFFL Level-3 SO₂ product (COPERNICUS/S5P/OFFL/L3_SO2), which provides total vertical column SO₂ measurements derived from TROPOMI observations. Unlike conventional ground-based monitoring, which is constrained by the limited spatial coverage and temporal continuity of monitoring stations, satellite observations provide spatially continuous measurements over broad areas, making them particularly suitable for assessing regional-scale patterns and temporal variability of atmospheric SO₂. The TROPOMI instrument, aboard Sentinel-5P since its launch in October 2017, provides high-resolution observations of atmospheric trace gases and enables consistent monitoring of SO₂ over the study area. The satellite data were processed using JavaScript within the Google Earth Engine (GEE) platform. Monthly, seasonal, annual, and five-year mean SO₂ fields were derived from the TROPOMI observations for 2018–2022, and annual means were subsequently used to assess temporal changes in SO₂ concentrations. The processed raster outputs were exported from GEE and analyzed in ArcMap to generate spatial distribution and classification maps. To identify statistically significant spatial concentrations of elevated and low SO₂ values, the Getis-Ord Gi* Hot Spot Analysis was applied after converting the raster observations into point features. In addition, Global Moran’s I was calculated to evaluate the overall degree of spatial autocorrelation and determine whether the observed SO₂ distribution exhibited clustered, random, or dispersed spatial patterns. The satellite product reports SO₂ as total vertical column density (mol m⁻²); therefore, the retrieved values were interpreted as column amounts rather than near-surface concentrations and were not directly compared with ambient air-quality standards expressed in units such as µg m⁻³ or ppb.Results and DiscussionThe spatial distribution of SO₂ during 2018–2022 revealed pronounced heterogeneity across the Tehran–Karaj–Qazvin metropolitan corridor, with the highest concentrations concentrated in industrial areas of Qazvin, southern Tehran, and eastern Alborz. These areas largely coincide with major industrial zones, thermal power plants, cement industries, and high-traffic corridors, indicating the dominant contribution of combustion-related and stationary sources to regional SO₂ loading. In contrast, the Alborz highlands, northern Tehran, and southern Qazvin generally exhibited lower SO₂ values, which can be associated with lower population and industrial densities and more favorable atmospheric ventilation. The Getis-Ord Gi* analysis further confirmed this spatial differentiation, identifying statistically significant hot spots at the 99% confidence level in northeastern Qazvin and central Tehran. Several of these hot spots spatially corresponded with major emission sources, including the Shahid Rajaei, Montazeri Qaim, Ba’ath, Parand, Rey, and Pakdasht power plants. Conversely, significant cold spots were predominantly detected over the Alborz mountain range, northern Tehran, and southern Qazvin, reinforcing the role of topography, emission-source density, and atmospheric ventilation in shaping the spatial distribution of SO₂. The Global Moran’s I results likewise indicated a non-random spatial structure, demonstrating that SO₂ concentrations tend to form spatially clustered patterns rather than being randomly distributed across the study area.Temporal analysis revealed substantial variability in SO₂ concentrations throughout the study period. Annual concentrations increased from 2019 and reached their highest levels in 2020, followed by a relative decline in 2021. This temporal variability likely reflects the combined influence of emission intensity, fossil-fuel consumption, atmospheric conditions, and changes in energy use and emission-control practices. In particular, periods characterized by atmospheric stability and temperature inversions can suppress vertical dispersion and promote the accumulation of SO₂ within the lower troposphere. The seasonal analysis showed that winter consistently recorded the highest SO₂ concentrations, whereas spring and summer generally exhibited lower values. The winter maximum can be attributed to increased fuel consumption for heating, greater reliance on fossil fuels, and the frequent occurrence of stable atmospheric conditions and temperature inversions. The use of higher-sulfur fuels, including fuel oil (mazut) during periods of natural-gas shortages, may further intensify SO₂ emissions from thermal power plants. Despite the generally lower concentrations during summer, elevated values persisted around major industrial sources, particularly the Shahid Rajaei power plant, highlighting the sustained influence of stationary emission sources independent of seasonal meteorological variability. The monthly analysis revealed a clear seasonal cycle, with SO₂ concentrations beginning to increase from September and reaching their maximum in January, followed by a gradual decline toward spring and summer. Concentrations remained comparatively low from approximately April to August, coinciding with stronger atmospheric mixing, more effective ventilation, and reduced heating-related fuel demand. The spatial distribution of hot spots remained relatively stable across seasons, with a persistent concentration of significant hot spots along the industrial corridor between Tehran and Qazvin. However, the number and extent of 99% confidence-level hot spots were comparatively lower during winter, which may reflect the broader regional accumulation of SO₂ under stagnant atmospheric conditions and consequently a reduction in the spatial contrast between highly polluted and less polluted areas. Overall, the consistent spatial correspondence between SO₂ hot spots and major industrial and power-generation facilities, together with the observed seasonal cycle, indicates that both emission sources and atmospheric dispersion conditions play complementary roles in controlling the regional distribution of SO₂. These findings demonstrate the value of Sentinel-5P/TROPOMI observations combined with spatial statistical techniques for identifying persistent pollution hotspots and characterizing regional-scale patterns that may not be adequately captured by the limited spatial coverage of ground-based monitoring networks.ConclusionThe findings demonstrate that SO₂ distribution across the Tehran–Karaj–Qazvin metropolitan corridor is strongly associated with industrial activities, thermal power plants, and other combustion-related emission sources. Significant hot spots were consistently concentrated around major industrial and power-generation centers, particularly in southern Tehran and Qazvin, whereas mountainous and less industrialized areas generally exhibited lower concentrations. The pronounced seasonal cycle, characterized by maximum SO₂ levels in winter and January and lower concentrations during spring and summer, reflects the combined effects of increased fuel consumption, temperature inversions, and reduced atmospheric ventilation during the cold season. Overall, the integration of Sentinel-5P/TROPOMI observations with Getis-Ord Gi* and spatial autocorrelation analysis provides an effective framework for identifying persistent SO₂ hotspots and improving understanding of its regional spatiotemporal variability. The results can support more targeted air-quality monitoring and emission-control strategies, particularly around major industrial and power-generation sources and during periods of unfavorable atmospheric conditions.Author ContributionsAll authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.Data Availability StatementThe satellite-based sulfur dioxide (SO₂) data used in this study were obtained from the Sentinel-5 Precursor (Sentinel-5P) mission, specifically the TROPOMI instrument, via the Google Earth Engine platform (https://earthengine.google.com/). These data are publicly available and can be accessed through the Earth Engine Data Catalog under the product ID COPERNICUS/S5P/NRTI/L3_SO2. All data processing and analysis were performed using the Earth Engine Code Editor environment, and the procedures are reproducible based on the descriptions provided in the Methods section.AcknowledgementsWe are grateful to all the scientific consultants of this paper.Ethical considerationsThe authors avoided data fabrication, falsification, plagiarism, and misconduct.دی‌اکسید گوگرد از آلاینده‌های جوی ناشی از احتراق سوخت‌های فسیلی در نیروگاه‌ها و صنایع است. این آلاینده در ناحیه صنعتی تهران–کرج–قزوین به‌طور قابل‌توجهی مشاهده می‌شود. این پژوهش با هدف تحلیل الگوی فضایی–زمانی SO₂ و شناسایی نواحی بحرانی، با تأکید بر نیروگاه‌ها و مراکز صنعتی، در دوره ۱۳۹۷–۱۴۰۱ انجام شد. برای استخراج ستون عمودی کل SO₂ از داده‌های سنجنده TROPOMI ماهواره Sentinel-5P استفاده شد. پردازش در Google Earth Engine و تحلیل‌های فضایی در ArcGIS انجام گرفت. تغییرات سالانه، فصلی و ماهانه و میانگین پنج‌ساله بررسی و تحلیل لکه‌های داغ اجرا شد. نتایج نشان داد بیشترین غلظت SO₂ در مناطق صنعتی استان قزوین، نواحی جنوبی تهران و بخش‌هایی از شرق البرز متمرکز است. تحلیل لکه‌های داغ، خوشه‌بندی معنادار آلودگی را در محور صنعتی تهران–قزوین نشان داد که استقرار شهرک‌های صنعتی، نیروگاه‌های حرارتی و تردد وسایل نقلیه باری مؤثر هستند. نواحی پیرامون نیروگاه‌هایی مانند شهید رجایی از کانون‌های آلودگی شناسایی شدند. در مقابل، نواحی مرتفع و شمالی به دلیل تهویه طبیعی مناسب‌تر و محدودیت منابع انتشار، غلظت کمتری داشتند. روند سالانه SO₂ طی دوره مطالعه افزایشی بود و در سال ۱۴۰۰ به بیشینه رسید. شاخص موران جهانی خودهمبستگی فضایی قوی و معناداری را تأیید کرد که بیانگر خوشه‌ای بودن توزیع آلاینده و نقش منابع انتشار ثابت است. بیشترین غلظت در فصل سرد و به‌ویژه دی و بهمن مشاهده شد، درحالی‌که تیر و مرداد مقادیر پایین‌تری داشتند. نتایج نشان میدهد تمرکززدایی صنعتی، اصلاح الگوی سوخت و کنترل انتشار در مدیریت آلودگی SO₂ مؤثر است.https://geoeh.um.ac.ir/article_48605_7067338bcbd7695a74e4950d00e5b482.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Spatiotemporal Analysis of Drought in the Lake Urmia Basin Using the Crossing Empirical Trend Analysis (CETA)بررسی تغییرات زمانی- مکانی خشکسالی در حوضه آبریز دریاچه ارومیه با بهره‌گیری از روش تحلیل روند تجربی متقاطع (CETA)1601804870910.22067/geoeh.2026.100096.1689FAفرزانهسعیدپورگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه ارومیه، ارومیه، ایران0009-0002-8141-4565خدیجهجوانگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه ارومیه، ارومیه، ایران0000-0002-1005-5051Journal Article20260727Drought is one of the most important climatic phenomena affecting water resources and environmental conditions in the Lake Urmia Basin, making the assessment of its spatiotemporal variability particularly important. This study aimed to investigate the spatiotemporal variations in drought using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Crossing Empirical Trend Analysis (CETA) method over the period 1987–2024. Precipitation and temperature data from eight selected stations across the basin were used. The results showed substantial temporal fluctuations in SPEI, with a non-uniform spatial pattern of variability across the basin. CETA analysis indicated that the upper slope (Us) and pivot slope (Ps) were decreasing at most stations, whereas the behavior of the lower slope (Ls) varied among stations. At Mahabad, Saqez, Takab, and Urmia, us showed a decreasing trend while Ls showed an increasing trend, indicating opposing changes in the two tails of the SPEI distribution. In contrast, both slopes showed decreasing trends at Maragheh, Sahand, and Sarab. Sen’s slope (Ss) was also decreasing at most stations, while differences in its magnitude indicated spatial heterogeneity in the rate of temporal changes in SPEI. Overall, the findings indicate a predominant decreasing tendency in SPEI across most parts of the basin, along with spatial differences in the behavior of different segments of the index distributionIntroduction Drought is one of the most important climatic phenomena affecting water resources, agricultural systems, and environmental conditions, particularly in arid and semi-arid regions. Its temporal evolution and spatial distribution are influenced by variations in precipitation and atmospheric water demand, making the identification of changes in drought conditions important for sustainable water resources management. The Lake Urmia Basin in northwestern Iran has experienced substantial fluctuations in moisture conditions during recent decades, and changes in drought characteristics have important implications for the management of water resources in the basin. Previous studies in the Lake Urmia Basin have mainly focused on the temporal behavior of drought indices and their overall trends. However, an overall trend may not fully represent changes occurring across different portions of the distribution of a climatic index. In particular, the upper and lower parts of a drought-index distribution may exhibit different temporal behaviors. The Crossing Empirical Trend Analysis (CETA), introduced by Şen (2022), provides an approach for examining trend behavior at different portions of a time-series distribution through the upper slope (Us), lower slope (Ls), and pivot slope (Ps). In addition, Sen’s slope (Ss) can be used to characterize the overall direction and magnitude of temporal change. Therefore, this study aimed to investigate the spatiotemporal variations in drought in the Lake Urmia Basin using the 12-month Standardized Precipitation Evapotranspiration Index (SPEI-12) and the CETA method during 1987–2024.Material and Methods The Lake Urmia Basin, with an area of approximately 51,876 km², is located in northwestern Iran. Daily precipitation and air temperature data from eight synoptic stations, including Urmia, Tabriz, Sahand, Sarab, Maragheh, Mahabad, Saqez, and Takab, were used for the period 1987–2024. The selected stations represent different geographical and climatic conditions within the basin. Missing observations in the original meteorological records were reconstructed and completed before the analysis. The SPEI was calculated at a 12-month timescale based on the climatic water balance, defined as the difference between precipitation and potential evapotranspiration. Potential evapotranspiration was estimated using the Hargreaves–Samani method based on the available daily temperature data. The resulting daily potential evapotranspiration values were aggregated to monthly values and used together with monthly precipitation to calculate SPEI-12. The temporal behavior of SPEI was subsequently examined using the Crossing Empirical Trend Analysis (CETA). In this method, the time series is divided into two temporal sections and the crossing behavior of the series is used to derive trend lines representing different portions of the distribution. The upper slope (Us), lower slope (Ls), and pivot slope (Ps) were calculated to characterize changes in the upper, lower, and central portions of the SPEI distribution, respectively. Sen’s slope (Ss) was also calculated to provide an overall measure of the direction and magnitude of temporal change. Because CETA does not provide a statistical significance test for the estimated slopes in the present implementation, the direction of change was determined according to the sign of each slope. Positive values were therefore interpreted as increasing and negative values as decreasing. The results were summarized in tables and spatial distribution maps to identify differences among the stations.Results and Discussion The temporal variations in SPEI-12 showed considerable fluctuations between dry and wet conditions across the eight stations, with noticeable spatial differences in the magnitude and temporal behavior of these variations. The CETA results indicated that the upper slope (Us) was decreasing at all stations, with values ranging from −0.00640 at Maragheh to −0.00013 at Tabriz, indicating differences in the rate of change across the basin. The pivot slope (Ps) was also decreasing at seven stations, whereas Tabriz showed a positive value. In contrast, the lower slope (Ls) exhibited a heterogeneous spatial pattern. Ls was positive at Mahabad, Saqez, Tabriz, Takab, and Urmia, and negative at Maragheh, Sahand, and Sarab. The highest positive Ls occurred at Urmia (0.010571), highlighting the marked spatial contrast in the lower portion of the SPEI distribution. Accordingly, Us and Ls showed opposite directions at Mahabad, Saqez, Tabriz, Tekab, and Urmia, while both were decreasing at Maragheh, Sahand, and Sarab. Sen’s slope (Ss) was negative at seven stations, with the strongest overall decrease occurring at Maragheh (−0.00320), whereas Tabriz showed a positive slope (0.000863). The differences between Ss and the CETA parameters indicate that the overall trend did not always represent the same behavior across different portions of the SPEI distribution. The Pivot Point and pivot trend line further helped distinguish the behavior of the central portion from the upper and lower segments. Overall, the spatial distribution maps confirmed considerable heterogeneity in drought trends across the basin. These findings are consistent with previous studies reporting spatial and temporal heterogeneity in drought conditions in Iran and with applications of CETA to climatic time series. The results highlight the value of considering different portions of the SPEI distribution alongside the overall trend when examining drought variability.Conclusion This study investigated the spatiotemporal variations in drought across the Lake Urmia Basin using SPEI-12 and the Crossing Empirical Trend Analysis (CETA) method during 1987–2024. The results demonstrated substantial spatial and temporal heterogeneity in SPEI behavior across the eight selected stations. The upper slope (Us) was decreasing at all stations, while the lower slope (Ls) showed a heterogeneous spatial pattern, with increasing values at Mahabad, Saqez, Tabriz, Takab, and Urmia and decreasing values at Maragheh, Sahand, and Sarab. This resulted in opposing Us and Ls directions at five stations, whereas both slopes were decreasing at the remaining three stations. The pivot slope (Ps) was decreasing at seven stations and increasing at Tabriz. Sen’s slope was also decreasing at seven stations, with the largest overall decrease occurring at Maragheh, while Tabriz showed a positive overall slope. These results indicate that the predominant decreasing direction of SPEI across the basin was accompanied by substantial spatial differences in the magnitude and distributional behavior of the changes. The findings also demonstrate that the overall trend of SPEI does not necessarily describe the behavior of all portions of its distribution. Considering Us, Ls, Ps, and Ss together therefore provides a more detailed description of the spatial and temporal characteristics of drought conditions. The identified spatial differences can contribute to drought monitoring, the identification of areas with greater changes, and improved water resources management in the Lake Urmia Basin.خشکسالی یکی از مهم‌ترین پدیده‌های اقلیمی مؤثر بر منابع آب و شرایط محیطی در حوضه دریاچه ارومیه است و بررسی تغییرات زمانی و مکانی آن اهمیت زیادی دارد. این پژوهش با هدف بررسی تغییرات زمانی- مکانی خشکسالی با استفاده از شاخص SPEI و روش تحلیل روند تجربی متقاطع (CETA) طی دوره 1987-2024 انجام شد. داده‌های بارش و دما در هشت ایستگاه منتخب حوضه مورد استفاده قرار گرفت. نتایج نشان داد که SPEI از نوسانات زمانی قابل‌توجهی برخوردار بوده و الگوی تغییرات آن در سطح حوضه یکنواخت نیست. تحلیل CETA نشان داد که در بیشتر ایستگاه‌ها شیب بخش بالایی (Us) و شیب محوری (Ps) کاهشی است، در حالی که رفتار بخش پایینی (Ls) در برخی ایستگاه‌ها متفاوت است. در مهاباد، سقز، تکاب و ارومیه، Us کاهشی و Ls افزایشی بود که بیانگر ناهم‌جهتی تغییرات در دو بخش انتهایی توزیع SPEI است؛ در مقابل، در مراغه، سهند و سراب، هر دو شیب جهت کاهشی داشتند. همچنین، شیب سن (Ss) در بیشتر ایستگاه‌ها کاهشی بود و تفاوت مقدار آن، ناهمگنی شدت تغییرات زمانی SPEI را نشان داد. در مجموع، نتایج بیانگر غلبه جهت کاهشی SPEI در بیشتر بخش‌های حوضه و وجود تفاوت مکانی در رفتار بخش‌های مختلف توزیع شاخص است.https://geoeh.um.ac.ir/article_48709_8476d966ed57bf67ad4ce07d59ca80ac.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923The Role of Temperature and 500-hPa Geopotential Height Anomalies in Heavy Precipitation Events over Iranنقش ناهنجاری‌های دما و ارتفاع ژئوپتانسیل 500 هکتوپاسکال در رخداد بارش‌های سنگین ایران1812034865910.22067/geoeh.2026.99097.1672FAفرحنازنخجیریگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه رازی، کرمانشاه، ایران0000-0001-9914-2145جعفرمعصوم پور سماکوشگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه رازی، کرمانشاه، ایران0000-0001-9914-2145نبیمیرزاییگروه آب و هواشناسی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایرانJournal Article20260602In this study, atmospheric circulation anomalies associated with heavy precipitation events in Iran during the 1990–2022 period were investigated through the analysis of 500-hPa geopotential height and temperature fields, using both observational daily precipitation records and gridded datasets. Heavy precipitation days were first identified based on the 95th percentile threshold, after which associated atmospheric patterns were extracted using Principal Component Analysis (PCA). The results indicated that in autumn and winter, heavy precipitation events predominantly occurred over western Iran and the Caspian Sea coasts, while in spring their maximum occurrence was observed over the western and northern regions of the country. Cluster analysis revealed five patterns of negative geopotential height anomalies, including the expansion of the polar vortex, a mid-tropospheric cut-off low accompanied by strong geopotential height and temperature anomalies, a geopotential height anomaly linked to a Mediterranean trough, and the expansion of a European low-pressure system together with its interaction with the Mediterranean trough over the region. From a temporal perspective, the first and second patterns mainly occur in January and February, concurrent with cold-air outbreaks from higher northern latitudes. The mid-tropospheric cut-off low pattern, characterized by a −180 geopotential meter anomaly, represents the most dominant atmospheric pattern associated with heavy precipitation events in Iran, accounting for 157 occurrences and 34% of the total frequency. Accordingly, the most influential thermal pattern governing heavy precipitation in Iran is the polar cold-air outbreak surrounding the mid-tropospheric cut-off low.IntroductionClimate hazards, particularly heavy precipitation, have increased in frequency and intensity under ongoing climate change and pose significant risks to human societies. In arid and semi-arid regions such as Iran, heavy rainfall frequently triggers flooding and substantial socio-economic damage. These extreme events are largely controlled by large-scale atmospheric circulation anomalies, especially at mid-tropospheric levels. Negative anomalies in geopotential height and temperature at 500 hPa strengthen cyclonic circulation, enhance dynamic ascent, and favour the development of deep troughs or cut-off lows. Identifying the dominant mid-level circulation patterns associated with heavy precipitation is therefore essential for improved understanding, prediction, and water-resource management in Iran. The present study examines the role of 500 hPa geopotential-height and temperature anomalies in the occurrence of heavy precipitation across the country.Material and Methods Daily precipitation data from 100 synoptic stations for the period 1990–2022 were obtained from the Iran Meteorological Organization. Geopotential height and temperature fields at 500 hPa were extracted from the ECMWF archive. Heavy-precipitation days were identified, and the corresponding mid-level anomaly fields were calculated relative to the long-term climatology. Principal component analysis (PCA) followed by cluster analysis was applied to the anomaly fields to extract the dominant circulation patterns. The contribution of each pattern to the total variance and its temporal frequency were quantified. Selected representative cases were subjected to detailed synoptic analysis. Statistical relationships between the magnitude of the geopotential-height anomaly and precipitation intensity were also examined. Data processing was performed in MATLAB and Excel; maps were produced with GrADS and Surfer.Results and Discussion Spatially, the highest frequencies and intensities of heavy precipitation (>100 mm day⁻¹) occur over the western Caspian Sea basin, the central and northern Zagros, and parts of north-eastern Iran. Temporally, events are concentrated in the cold season, with a clear maximum in March and a minimum in October.Cluster analysis of the 500 hPa anomaly fields identified five distinct synoptic patterns that together account for the majority of heavy-precipitation events:Pattern 1 is characterised by pronounced meridional flow and the deepening of a mid-level trough accompanied by a cut-off low west of Iran. It develops under a blocking high over the northern Caucasus–Russia region. The resulting negative geopotential-height and temperature anomalies intensify the thermal gradient and promote strong ascent, producing heavy rainfall mainly over western and south-western Iran. This pattern is essentially confined to winter.Pattern 2 (116 events, ≈25 % of cases) features a strong European blocking ridge that generates a cut-off low (inverted-omega type) over the eastern Mediterranean. The associated intense negative height and temperature anomalies enhance dynamic lifting and moisture transport from southern water bodies. Precipitation is widespread, frequently exceeding 30–50 mm over eastern, southern and north-western Iran. The pattern peaks in December–March and occasionally appears in April.Pattern 3 (60 events, ≈16 %) consists of a coupled European–Mediterranean trough system. Deepening of the Atlantic–European low and simultaneous amplification of the Mediterranean trough place most of Iran beneath the trough axis. Broad negative height and temperature anomalies favour extensive moderate-to-heavy rainfall, especially over western Iran. Frequency is highest in April, May and November.Pattern 4 is linked to an omega-type blocking over the North Atlantic–Europe. Meridional amplification on the eastern flank of the block extends a deep trough southward to approximately 25° N. Strong cold-air advection over the eastern Mediterranean produces marked negative temperature anomalies and frontogenesis, resulting in widespread heavy precipitation (often >15 mm, locally up to 90 mm) across more than half of the country. The pattern is most frequent in February and April.Pattern 5 (157 events, ≈34 % of total variance) is the dominant mode. It is a hybrid blocking configuration that isolates a cut-off low (cold pool) over the eastern Mediterranean. Geopotential-height anomalies reach −180 to −250 gpm and temperature anomalies reach −6 to −12 °C at the centre of the low. Intense cyclonic ascent draws moist air from the Arabian Sea, Oman Sea, Persian Gulf and Red Sea, generating heavy precipitation along the trough axis across large parts of Iran. This pattern occurs throughout the cold season, with a maximum in March, and is observed in a greater number of months than the other patterns.In all five patterns, heavy precipitation is accompanied by a pronounced negative anomaly of both geopotential height and temperature at 500 hPa over the eastern Mediterranean and western Iran. The common dynamical ingredient is enhanced mid-level ascent associated with the trough or cut-off low.Conclusion Heavy precipitation in Iran is primarily driven by large-scale mid-tropospheric circulation anomalies that appear as deep troughs or cut-off lows over the eastern Mediterranean–western Iran sector. The five identified patterns share a common signature of strong negative geopotential-height and temperature anomalies that promote dynamical ascent and moisture convergence. The most frequent and impactful configuration is the mid-level cut-off low (Pattern 5), which alone accounts for approximately one-third of all heavy-precipitation events. Recognition of these recurring anomaly patterns provides a useful synoptic framework for forecasting and risk management of extreme rainfall in Iran.در این پژوهش، ناهنجاری‌های گردش جوی مرتبط با رخدادهای بارش سنگین ایران طی دوره 2022-1990 با تحلیل میدان‌های دما و ارتفاع ژئوپتانسیل تراز 500 هکتوپاسکال و استفاده از داده‌های بارش روزانه مشاهداتی و شبکه‌ای بررسی شد. ابتدا روزهای با بارش سنگین (صدک 95ام) مشخص و سپس با تحلیل مؤلفه اصلی، الگوهای جوی مرتبط شناسایی گردید. نتایج نشان داد در فصل پاییز و زمستان، بارش‌های سنگین در نیمه غربی ایران و سواحل دریای خزر و در فصل بهار نیز بیشینه آن در غرب و شمال کشور رخ داده است. تحلیل خوشه‌ای 5 الگوی ناهنجاری منفی ارتفاع ژئوپتانسیل شامل گسترش تاوه قطبی، کم‌ارتفاع بریدۀ تراز میانی با ناهنجاری شدید ارتفاع ژئوپتانسیل و دما، ناهنجاری ارتفاع ژئوپتانسیل با فرود مدیترانه، و گسترش کم‌فشار اروپایی و ادغام آن با فرود مدیترانه بر منطقه را نشان داد. به لحاظ زمانی، الگوی اول و دوم عموماً در ژانویه و فوریه همزمان با ریزش هوای سرد شمال‌سو رخ میدهد. الگوی کم‌ارتفاع بریدۀ تراز میانی با ناهنجاری 180- ژئوپتانسیل متر، مهم‌ترین الگوی غالب جوی رخداد بارش‌های سنگین ایران با 157 رخداد و 34% فراوانی است. بر این اساس، مهم‌ترین الگوی دمایی مؤثر بر رخداد بارش سنگین ایران، الگوی ریزش سرد قطبی حول کم‌ارتفاع بریدۀ تراز میانی جو است.https://geoeh.um.ac.ir/article_48659_2ee4162f4a89db5fa43b3b08900ee370.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Spatial–Temporal Analysis of Climate Hazards Associated with Cold-Season Extreme Precipitation in East and Northeastern Iranتحلیل مکانی - زمانی مخاطرات اقلیمی ناشی از بارش‌های فرین فصل سرد در شرق و شمال‌شرق ایران2042224849210.22067/geoeh.2026.97860.1649FAسعیدنگهبانگروه جغرافیا، دانشکده اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز، شیراز، ایران0000-0003-0701-5638پریسایوسفیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایراناباذراسمعیلی محمودآبادیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0009-0007-4575-1739Journal Article20260215Heavy and extreme rainfall events are among the most critical climatic hazards in the arid and semi-arid regions of Iran. This study investigates the spatiotemporal patterns of intensity, frequency, and trends of extreme precipitation at 18 selected stations in eastern and northeastern Iran over the period 1995–2024. The 95th and 99th percentile indices were employed to identify extreme events, representing relatively frequent high intensity rainfall and very rare but exceptionally intense events, respectively. The results indicated that the highest intensities of heavy rainfall occurred at Quchan, Kashmar, and Torbat-Heydarieh stations, while the greatest frequencies were recorded at Neyshabur, Torbat-Heydarieh, and Golmakan, highlighting the spatial concentration of extreme events in the northern highlands and confirming the role of topography in intensifying such phenomena. Conversely, the 99th percentile analysis revealed that in southern areas such as Nehbandan, although event frequency was lower, the intensity of extreme rainfall reached remarkably high levels, indicating the occurrence of rare but hydrologically powerful events. This points to a strong spatial heterogeneity in extreme rainfall behavior across the study area. Trend analysis using the Mann–Kendall test and Sen’s slope estimator showed a statistically significant decrease in daily rainfall at some stations, whereas changes in the intensity of extreme events were mostly limited and exhibited no consistent pattern. Overall, the findings emphasize the necessity of simultaneously considering intensity, frequency, and spatial distribution as key components for accurate assessment of meteorological hazards associated with extreme rainfall in eastern and northeastern Iran.IntroductionIn recent years, extreme precipitation events have received increasing attention as a major manifestation of climate change. Characterized by intense rainfall over short periods, these events can generate substantial hydrological, urban, and environmental impacts. Recent studies have shown that the intensity of extreme precipitation in the Northern Hemisphere has increased, in many cases more rapidly than snowfall. Iran, located within the arid and semi-arid belt of the Iranian Plateau, has experienced rising temperatures and a relative decline in precipitation. Previous research indicates that climate change has increased the number of extremely dry days while also altering the probability of heavy rainfall events. Even in areas where mean precipitation has declined, the intensity and frequency of extreme events, particularly those exceeding the 95th and 99th percentiles, remain highly important because warmer atmospheric conditions can increase moisture availability and enhance convective activity.At broader spatial scales, studies across Asia have documented substantial spatiotemporal changes in extreme precipitation. Increasing trends in indices such as Rx1day and R95p have been reported in East and Southeast Asia, elevating the risk of flash floods. In the Indian subcontinent, heavy daily precipitation has increased in some coastal areas but declined in parts of the interior. In West Asia and Iran, despite reductions in total annual precipitation, the relative contribution of extreme rainfall has increased in many regions. Europe and North America have generally experienced increases in the intensity and frequency of heavy rainfall, whereas trends in Africa and South America remain spatially heterogeneous and strongly dependent on local and seasonal conditions.Against this background, the present study investigates the intensity, frequency, and long-term trends of heavy and very heavy cold-season precipitation in northeastern Iran over the 30-year period 1995–2024. The main objective is to identify the spatial and temporal patterns of extreme precipitation and assess their long-term behavior using percentile-based indices and non-parametric trend tests, thereby providing information relevant to hydrological risk management and water-resources planning.Material and MethodsDaily precipitation data for the five cold months of the year (November to March) during 1995–2024 were obtained from the Iran Meteorological Organization. Eighteen synoptic stations in northeastern Iran were selected: Torbat-e Jam, Bojnord, Boshruyeh, Birjand, Torbat-e Heydariyeh, Zabol, Zahedan, Sabzevar, Sarakhs, Quchan, Kashmar, Gonabad, Mashhad, Nehbandan, Nishapur, Ferdows, Golmakan, and Qaen. The data were processed in Excel, and days with precipitation totals below 1 mm were excluded from the analysis. Extreme precipitation events were identified using the 95th and 99th percentiles of wet-day precipitation. Trend analysis was performed using the non-parametric Mann–Kendall test and Sen’s slope estimator. The Mann–Kendall test is widely used to detect monotonic trends in hydroclimatic time series and is relatively robust to outliers, whereas Sen’s slope estimator is used to quantify the magnitude and direction of trends.Results and DiscussionBased on the 95th percentile, the highest heavy-precipitation threshold values were observed at Quchan (15.25 mm), Kashmar (14.85 mm), and Torbat-e Heydariyeh (14.69 mm), whereas Zabol (9.17 mm), Zahedan (9.59 mm), and Bojnord (9.90 mm) recorded the lowest values. In terms of frequency, the highest numbers of heavy-rainfall days were recorded in Nishapur (71 days), Torbat-e Heydariyeh (70 days), and Golmakan (62 days), while Zabol (16 days) and Nehbandan (17 days) had the fewest. These patterns suggest that topography and elevation may play an important role in shaping the spatial distribution of heavy precipitation.For the 99th percentile, the highest very heavy precipitation values were observed in Nehbandan (35.24 mm), Torbat-e Heydariyeh (28.51 mm), and Kashmar (27.45 mm). The highest frequencies were recorded in Bojnord (27 days), Quchan (25 days), and Mashhad (24 days). Despite its high intensity, Nehbandan experienced only five days of very heavy precipitation, indicating the occurrence of rare but potentially hazardous events. By contrast, northern stations such as Quchan and Torbat-e Heydariyeh exhibited both relatively high intensity and high frequency, implying greater cumulative hazard.More than 70% of heavy precipitation events during the cold season occurred between December and March. Golmakan recorded 30 events in March, representing the highest monthly frequency, whereas Nishapur and Torbat-e Heydariyeh showed a more even monthly distribution. Zabol and Nehbandan experienced notable events mainly in only one or two months, reflecting the influence of the arid, low-precipitation climate of southeastern Iran.Trend analysis using the Mann–Kendall test and Sen’s slope estimator indicated decreasing tendencies in daily precipitation totals at most stations. Statistically significant declines were identified in Nehbandan (p=0.02p = 0.02p=0.02), Boshruyeh (p=0.01p = 0.01p=0.01), and Mashhad (p=0.04p = 0.04p=0.04). The estimated slope was −0.04-0.04−0.04 mm/day in Boshruyeh, while it was close to zero in Torbat-e Jam. No station showed a statistically significant increasing trend. These findings suggest that declines in mean precipitation do not necessarily imply a reduced risk of extreme rainfall events. Annual variations also revealed marked interannual variability in heavy and very heavy precipitation, with peaks in 1999, 2007, 2017, and 2019, and minima in 2001, 2010, 2016, and 2021. Northern and high-elevation areas such as Nishapur, Quchan, and Bojnord experienced more frequent and persistent heavy precipitation, whereas the drier southeastern areas were characterized by infrequent but highly intense events.ConclusionAnalysis of the 95th and 99th percentiles at 18 stations in northeastern Iran revealed substantial spatial and temporal heterogeneity in heavy and very heavy cold-season precipitation. Northern and high-elevation areas, characterized by both high intensity and high frequency, are more exposed to recurrent flooding, whereas the arid southeastern regions, despite lower event frequency, face an elevated risk of sudden and localized floods due to the occurrence of rare but intense rainfall. The seasonal concentration of events is broadly consistent with the influence of Mediterranean systems and western atmospheric disturbances. Long-term trends indicate a general decline in daily precipitation, although reductions in mean rainfall do not necessarily correspond to a lower risk of extreme events. These findings highlight the need for resilient infrastructure, improved runoff management, and long-term monitoring systems to support more accurate climate-hazard assessment and adaptation planning.در این پژوهش، الگوی مکانی - زمانی شدت، فراوانی و روند بارش‌های سنگین و فوق سنگین در ۱۸ ایستگاه منتخب طی دوره ۱۹۹۵–۲۰۲۴ تحلیل شد. داده‌های روزانه بارش با استفاده از شاخص‌های صدک ۹۵ و ۹۹ بررسی گردید؛ به‌طوری‌که صدک ۹۵ بیانگر بارش‌های سنگین نسبتاً مکرر و صدک ۹۹ نمایانگر رخدادهای بسیار نادر با شدت بالا و ماهیت بالقوه مخاطره‌آمیز است. نتایج نشان داد که بیشترین شدت بارش‌های سنگین در ایستگاه‌های قوچان (۱۵٫۲۵ میلی‌متر)، کاشمر (۱۴٫۸۵ میلی‌متر) و تربت‌حیدریه (۱۴٫۶۹ میلی‌متر) رخ داده است، در حالی که بیشترین فراوانی این رخدادها به نیشابور (۷۱ روز)، تربت‌حیدریه (۷۰ روز) و گلمکان (۶۲ روز) تعلق دارد؛ الگویی که بیانگر تمرکز مکانی مخاطرات هواشناسی ناشی از بارش‌های فرین در نواحی مرتفع شمالی منطقه است. در مقابل، بررسی صدک ۹۹ نشان داد که در ایستگاه‌های خشک‌تر جنوبی نظیر نهبندان، با وجود فراوانی اندک رخدادها، شدت بارش‌های فوق سنگین به مقادیری تا ۳۵٫۲۴ میلی‌متر می‌رسد که از دیدگاه مخاطرات هواشناسی، نشان‌دهنده وقوع رخدادهای فرین و نادر با پتانسیل اثرگذاری بالا است. تحلیل روند با آزمون من کندال و شیب سن حاکی از کاهش آماری معنادار بارش‌های روزانه در برخی ایستگاه‌ها بود، در حالی که تغییرات شدت رخدادهای فوق سنگین محدود باقی مانده است.https://geoeh.um.ac.ir/article_48492_62bd738b34b531c688288d719e54d5f8.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923An Analysis of the Environmental Risks of Microplastic Pollutants and Their Role in Human Healthتحلیلی بر مخاطرات زیست‌محیطی آلاینده میکروپلاستیک و نقش آن بر سلامت انسان2232534852610.22067/geoeh.2026.97629.1645FAحسینمحمدزادهگروه پژوهشی تحقیقات آب‌های زیرزمینی و ژئوترمال (متاب)، پژوهشکده آب و محیط زیست، دانشگاه فردوسی مشهد، مشهد، ایرانگروه زمین‌شناسی، دانشکده علوم، دانشگاه فردوسی مشهد، مشهد، ایران0000-0002-3387-595Xعاطفهرضیگروه زمین‌شناسی، دانشکده علوم، دانشگاه فردوسی مشهد، مشهد، ایران0009-0005-6678-0526Journal Article20260131Microplastics, as one of the emerging environmental pollutants, have raised increasing concerns about the health of ecosystems and humans. This study aimed to investigate the sources, distribution, characteristics, and environmental and health risks of microplastics with an emphasis on Iran. The present study was conducted as a systematic review based on the PRISMA 2020 guidelines. A search for articles was conducted in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar databases for the period 2015 to 2026. After applying the inclusion and exclusion criteria for articles, eligible studies were included in the final analysis. Data (including: study characteristics, environments studied, concentrations and characteristics of microplastics, sampling methods) were extracted and analyzed. The results show that microplastics are widely present in all aquatic, terrestrial, aerial and biological environments of Iran, and the dominant polymers are PE, PP, PS and PET, and are often in the form of fibers and pieces. The main sources of microplastics include urban and industrial wastewater, fishing, road traffic and poor waste management. The concentration of microplastics in the sediments of the Persian Gulf (up to 1346 particles.kg-1) and in the air of Tehran (up to 876 ng.m-³) is among the highest in the world. Microplastics can affect the health of living organisms by causing oxidative stress, inflammation and biological disorders, and pose serious risks to human health. Given the significant research gaps regarding the chronic effects of microplastics and the standardization of measurement methods - factors that have contributed to the ever-increasing spread of this emerging pollutant throughout the life cycle - it is crucial to formulate strategies for integrated monitoring, waste management, and the reduction of microplastic pollution in the country.Introduction Plastics have become an integral part of modern life due to their light weight, high durability, low cost, and malleability. Global plastic production has increased from 2 million tons in 1950 to 400 million tons in 2022 and is projected to exceed 33 billion tons by 2050. These characteristics have made plastics an environmental problem. Plastic particles are degraded by physical, chemical, and biological factors into smaller pieces less than 5 mm in size, which are called microplastics. Iran is one of the countries with poor waste management in the world, with an annual production of 4 million tons of plastic waste and a recycling rate of only 10–12%. According to the Plastic Overshoot Day (2024) report, the rate of plastic release into nature in Iran is about 78.4%, which is much higher than the global average (43%). This rate is more than 80% in the Persian Gulf and neighboring countries. Iran’s geographical location with access to the Persian Gulf, the Sea of Oman, and the Caspian Sea has exposed the country to widespread microplastic pollution.Material and Methods The present study was conducted as a systematic review based on the PRISMA 2020 guidelines. Articles were searched in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar databases for the period 2015 to 2026. The inclusion criteria included original studies, English and Persian articles, studies investigating the concentration and characteristics of microplastics in different environments in Iran, and studies related to human health. Unrelated articles, non-systematic reviews, and studies lacking quantitative data were excluded from the analysis. After initial screening and removal of duplicate and irrelevant articles, eligible studies were included in the final analysis. Data including study characteristics, study settings, microplastic concentrations and properties (polymer type, shape, size, and color), sampling methods, and main results were extracted and analyzed.Results and Discussion Microplastics are divided into two groups: primary (produced at microscale, such as industrial granules and cosmetic microbeads) and secondary (derived from the degradation of larger plastics). Polyethylene with 36% and polypropylene with 21% have the largest share of plastic demand. Common shapes of microplastics include fibers, fragments, spheres, and polygons.According to data from scientific databases, microplastic research has started in the world since 2007 and in Iran since 2017. The number of Iranian articles increased from 100% in 2017–2020 to 250% in 2023–2024 and then continued with greater diversity. Studies conducted in Iran indicate the widespread presence of microplastics in all environmental matrices. For example, the concentration of microplastics in Tehran air (876 ng.m-3) is the highest reported globally, mainly due to heavy traffic and vehicle tire wear. This finding is consistent with other studies in large cities such as London, Paris, and Shanghai (with concentrations of 0.3 to 1.5 particles) and demonstrates the important role of urban transportation in the emission of atmospheric microplastics. In sediments of the Persian Gulf and Anzali Wetland, the microplastics' concentrations are in the upper range of global reports (0–18,000 ppm), indicating significant input from untreated urban and industrial wastewater, fishing and tourism activities, and poor management of plastic waste. Despite 80% of the country’s dependence on groundwater, limited studies have been conducted on microplastic pollution in Iran aquifers. The concentrations of 0.1–1.3 ppm and 9–54 ppm have been reported for Shiraz and Kerman aquifers, respectively. In agricultural soils of Shiraz, concentrations of 0–93 particles/kg and in compost of Isfahan, very high concentrations of 34,267–44,267 particles/kg have been recorded, indicating the role of organic fertilizers and sewage sludge in the entry of microplastics into the soil and agricultural cycle. Numerous studies have confirmed the presence of microplastics in fish from the Persian Gulf, the Caspian Sea, rivers and wetlands. For example, 49.6% of fibers with the dominant polymer PE were observed in commercial species from the Persian Gulf. Hyrcanian forest amphibians showed 72% contamination, with fibers less than 0.5 mm in size and polyurethane and polystyrene polymers being the most abundant. Studies show that amphibians absorb about 9% more microplastics than fish, indicating a greater vulnerability of this group due to their permeable skin and dual life cycle.Microplastics enter the body through ingestion, inhalation, and skin contact. In Iran, the presence of microplastics has been confirmed in human hair, skin, saliva, lung lavage fluid, sputum, pleural fluid, and placenta. Microplastics have also been found in food products such as salt (up to 5470 particles.kg-1), sugar, mineral water, carbonated drinks, sausages, spices, hookah tobacco, and infant formula. Microplastics pose serious risks to human health by causing oxidative stress, inflammation, reduced intestinal mucosa, liver metabolic disorders, and changes in absorption and excretion.Conclusion Microplastics, as pervasive and emerging pollutants, have affected all aquatic, terrestrial, air, and biological environments. The concentrations reported in many regions of Iran, especially in the Persian Gulf, Anzali Wetland, and Tehran air, are at or above the global average. The entry of microplastics into the food chain and human body through food, drinking water, and breathing air has raised serious concerns for public health. Given the poor management of plastic waste in Iran (release rate of 78.4%), it is necessary to develop integrated monitoring strategies, waste management based on a circular economy, reduce the consumption of single-use plastics, efficiently treat municipal and industrial wastewaters, and conduct further research on the identified gaps (especially on groundwater nanoplastics, and chronic effects on human health). Also, developing national standards for sampling, identification, and measurement methods for microplastics in Iran will be a crucial step for comparability of results and making effective management decisions.میکروپلاستیک‌ها، به‌عنوان یکی از آلاینده‌های نوظهور، نگرانی‌های فزاینده‌ای را در زمینه سلامت اکوسیستم‌ها و انسان ایجاد کرده‌اند. این مطالعه با هدف بررسی منابع، پراکندگی، ویژگی‌ها و مخاطرات زیست‌محیطی و بهداشتی میکروپلاستیک‌ها با تاکید بر ایران انجام شده است. پژوهش حاضر به‌صورت مرور نظام‌مند و بر اساس دستورالعمل 2020PRISMA انجام‌گرفته است. جستجوی مقالات در پایگاه‌های PubMed، Scopus، Web of Science، ScienceDirect و Google Scholar برای بازه زمانی ۲۰۱۵ تا ۲۰۲۶ انجام گردید. پس از اعمال معیارهای ورود و خروج مقالات، مطالعات واجد شرایط وارد تحلیل نهایی شدند. داده‌ها (شامل: ویژگی‌های مطالعات، محیط‌های مورد بررسی، غلظت و ویژگی‌های میکروپلاستیک‌ها، روش‌های نمونه‌برداری) استخراج و مورد تحلیل قرار گرفت. نتایج نشان میدهد که میکروپلاستیک‌ها در تمام محیط‌های آبی، خاکی، هوایی و زیستی ایران به طور گسترده و غالبا بصورت الیاف و قطعات حضور دارند. غلظت میکروپلاستیک‌ها در رسوبات خلیج‌فارس (تا ۱۳۴۶ /kgذره) و در هوای تهران (تا ۸۷۶ng/m³) از بالاترین مقادیر جهانی است. میکروپلاستیک‌ها می‌توانند از طریق ایجاد تنش اکسیداتیو، التهاب و اختلالات زیستی بر سلامت موجودات زنده و انسان اثر بگذارند. با توجه به وجود شکاف‌های پژوهشی در زمینه اثرات مزمن و استانداردسازی روش‌های اندازه گیری میکروپلاستیک‌ها، تدوین راهبردهای پایش یکپارچه، مدیریت پسماند و کاهش آلودگی میکروپلاستیکی در کشور بسیار ضروری می باشد.https://geoeh.um.ac.ir/article_48526_03efea10c0a58b377c1e875505982652.pdfدانشگاه فردوسی مشهدجغرافیا و مخاطرات محیطی2322-168215320260923Comparison of the Efficiency of VHI and VTCI Indices for Agricultural Drought Monitoring in Markazi Province, Iran, Using MODIS Productsمقایسه کارایی شاخص‌های VHI و VTCI در پایش خشکسالی کشاورزی استان مرکزی با استفاده از محصولات MODIS2542764845110.22067/geoeh.2026.97561.1646FAعلی اکبردماوندیگروه منابع طبیعی، مؤسسه آموزش و ترویج کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران0000-0002-3952-9492Journal Article20260131Drought can lead to substantial crop losses and may exacerbate food insecurity and socio-economic tensions. Agricultural production and rangelands in Markazi Province, Iran, are also adversely affected by drought events. In this study, the performance of remote sensing–based indices under drought and wet conditions was evaluated using 15-year MODIS-derived time series of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) for August. August was selected because it coincides with peak vegetation cover and maximum thermal stress, enabling a clearer assessment of index performance. Accordingly, the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were computed from the 15-year NDVI and LST time series, respectively, and the Vegetation Health Index (VHI) was then derived by assigning equal weights to VCI and TCI. The Vegetation Temperature Condition Index (VTCI) was also calculated by integrating NDVI and LST using an alternative algorithm that accounts for minimum and maximum vegetation thermal conditions. Drought severity maps based on VHI and VTCI were generated for August, and temporal variations in drought severity classes were analyzed over the 15-year period. The results indicated good agreement between VHI and VTCI in representing drought severity across the study years. For validation, the 3-month Standardized Precipitation Index (SPI) ending in August was calculated using data from 36 meteorological stations. Comparison with SPI showed that VHI better captured agricultural drought severity (R2=0.67), likely due to the balanced contribution of vegetation condition and land surface temperature in the VHI formulation. Overall, the findings suggest that remote sensing–based agricultural drought indices can support decision-makers and planners by mitigating the spatial limitations of point-based measurements.Extended Abstract Introduction Agricultural drought disrupts plant growth by reducing soil moisture. It occurs when the water demand for evapotranspiration exceeds the available water in the soil. As a natural and recurring phenomenon driven by rainfall variability, agricultural drought leads to delayed planting, reduced growth, and decreased crop yield, ultimately affecting farmers' livelihoods. Monitoring agricultural drought using satellite imagery is an effective method for mitigating its impacts at local and regional scales. In this context, remote sensing has been widely employed for agricultural drought monitoring, leading to the development of indices such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). NDVI indicates vegetation health and density, but alone it is insufficient for drought detection as it does not reveal initial water stress. LST serves as a complementary measure, reflecting the surface energy balance and plant water stress. The relationship between these two indices—often visualized as a triangular scatter plot—can be negative (when water is the limiting factor) or positive (when sunlight is the limiting factor), depending on the season and regional moisture conditions. To effectively monitor agricultural drought, various composite indices have been developed. Two important examples are the Vegetation Health Index (VHI) and the Vegetation Temperature Condition Index (VTCI). VHI is derived from a simplified, equally weighted combination of NDVI and LST, while VTCI employs a more complex algorithm that targets thermal conditions within specific NDVI ranges. Field studies (e.g., in Bangladesh and Markazi Province, Iran) have confirmed the efficacy of these indices, particularly VHI, in zoning and classifying drought severity from mild to very severe. This research compares the effectiveness of these two indices in monitoring agricultural drought.Material and Methods This study was conducted in Markazi Province, Iran. The province covers an area of approximately 29,127 km², has a semi-arid climate, and receives an average annual rainfall of about 290 mm, ranging from 520 mm in Shazand to 177 mm in Delijan. About 75% of the province is mountainous. The data used in this study include meteorological data extracted from monthly rainfall statistics of 36 synoptic and rain gauge stations over a 15‑year period (1999–2014). The primary meteorological index used was the Standardized Precipitation Index (SPI) on a 3‑month scale. Remote sensing data were obtained from the MODIS sensor onboard the Terra satellite. NDVI was derived from the MOD13Q1 product with a spatial resolution of 250 m and a temporal resolution of 16 days for the months April to September. LST was extracted from the MOD11A2 product with a resolution of 1 km and an 8‑day interval for the same period. The remote sensing time‑series indices studied included two composite indices based on NDVI and LST for agricultural drought monitoring: (1) the Vegetation Health Index (VHI), obtained from a simple combination of NDVI and LST, which itself comprises the Vegetation Condition Index (VCI)—measuring long‑term NDVI changes—and the Temperature Condition Index (TCI)—evaluating long‑term LST changes—with VHI calculated as the equally weighted average of VCI and TCI; and (2) the Vegetation Temperature Condition Index (VTCI), which has a more complex algorithm defined based on the relationship between LST and NDVI on a specific date within the study area. In this method, for each NDVI value, a maximum (warm/dry edge) and minimum (cold/wet edge) range of LST is determined, and the temperature position of each pixel within this range is measured. Lower VTCI values indicate more severe drought. The aim of this research is to compare the efficiency of VHI and VTCI in monitoring agricultural drought in Markazi Province.Results and discussion This research aimed to monitor agricultural drought in Markazi Province from 2000 to 2014 and compared two major remote sensing indices—the Vegetation Health Index (VHI) and the Vegetation Temperature Condition Index (VTCI)—with the meteorological Standardized Precipitation Index (SPI). The characteristics of the indices are as follows:SPI was calculated as a ground‑based meteorological drought indicator for August at a 3‑month scale, using precipitation data from 36 synoptic and rain gauge stations. The extracted SPI values indicated mild to moderate drought conditions in most stations during August. VHI is a composite index derived from VCI and TCI, obtained by normalizing NDVI and LST time series, respectively. This index was calculated using equal weighting of the two sub‑indices and classified into five drought intensity classes. The final maps revealed that 2000, 2001, and 2010 were the driest years, while 2003, 2004, 2006, and 2007 had the wettest conditions. VTCI, with a more complex algorithm, operates based on the relationship between LST and NDVI on specific dates. By plotting a scatter diagram of these two variables, the "warm edge" (dry conditions) and "cold edge" (wet conditions) were determined, and the position of each pixel within this spectrum was assessed. This index was also divided into five drought classes. The results indicated that 2000, 2004, 2011, and 2013 experienced the most severe droughts, while 2006, 2010, and 2014 had the least stressful conditions.Comparison of the indices revealed that VTCI is more sensitive to drought and wetness variations than VHI. This difference stems from their mathematical logic: VHI is derived from a global averaging of NDVI and LST across the entire region, whereas VTCI analyzes temperature variations among pixels with similar NDVI values, making it more detail‑oriented. Regression analysis between the satellite‑based indices and SPI showed that in August, TCI and VHI had a significant correlation with SPI. In contrast, VTCI did not show a significant relationship with SPI during this month. This finding indicates that, in this specific study, VHI aligns better with the standard meteorological drought index (SPI). For validation, a linear regression model based on TCI and NDVI was developed to predict SPI. Evaluating this model for the year 2015 (outside the study period) using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) confirmed the model’s acceptable accuracy (low error values). This research demonstrated the effectiveness of MODIS satellite imagery and its derived indices (e.g., NDVI and LST) in the spatiotemporal monitoring of agricultural drought in Markazi Province. Although VTCI exhibited higher analytical sensitivity, in this particular case, the composite VHI index was identified as a more suitable tool for monitoring agricultural drought and estimating conditions consistent with precipitation data, due to its significant correlation with the ground‑based SPI and its relatively simpler calculation. This approach can address the gap caused by the scarcity of ground stations and enable extensive, timely drought monitoring.Conclusion Satellite imagery, particularly products from the MODIS sensor such as NDVI and LST, has high capability for monitoring dynamic phenomena like agricultural drought due to its temporal and spatial continuity. These products require no complex preprocessing and enable the creation of long‑term time series, which form the basis for developing composite indices such as VHI and VTCI. This study compared two key composite indices for monitoring agricultural drought in Markazi Province. VHI operates based on the simplified average of vegetation density (NDVI) and land surface temperature (LST). VTCI uses a more complex algorithm and determines drought intensity by identifying the warm edge (dry conditions) and cold edge (wet conditions) in the LST–NDVI relationship. The research findings indicated that in the study area, the performance of VHI in monitoring agricultural drought was superior to that of VTCI. This superiority was demonstrated by a more significant correlation of VHI results with the ground‑based SPI. Specifically, VHI maps identified 2000, 2001, and 2010 as the driest years, which aligned better with SPI results that identified 2000 and 2008 as the driest. Furthermore, VHI classified over 56% of the province’s area as "no drought," while VTCI placed about 44% of the area in the "moderate drought" category. In conclusion, composite remote sensing indices such as VHI can serve as suitable alternatives to meteorological indices for regional drought monitoring, especially in areas with sparse weather stations, and can assist planners in drought risk management. To improve accuracy, future studies are recommended to incorporate more ground data, consider physiographic factors, and utilize other sensors (e.g., Sentinel and Landsat).محصولات کشاورزی و مراتع در استان مرکزی تحت تأثیر خشک‌سالی‌ها قرار می‌گیرند . بنابراین پایش خشکسالی کشاورزی با شاخص‌های مناسب از ضروریات است. در این تحقیق برای مقایسه کارایی شاخص‌های سنجش‌ازدوری در ارزیابی خشکسالی کشاورزی، سری‌های زمانی شاخص‌های NDVI و LST 15 ساله سنجنده MODIS ماه مرداد در استان مرکزی موردبررسی قرار گرفت. انتخاب ماه مرداد به دلیل اوج پوشش گیاهی و تنش حداکثر درجه حرارت بوده تا تفاوت کارایی شاخص‌های مورد مقایسه آشکارتر گردند. برای این منظور، شاخص VCI و TCI به ترتیب بر اساس سری‌های زمانی 15ساله NDVI و LST برای ماه مرداد ، تولید و درنهایت شاخص VHI و VTCI با دو الگوریتم متفاوت ایجاد گردید. در ادامه نقشه‌های درجات شدت خشکسالی این دو شاخص، استخراج و تغییرات شدت طبقات در سری‌های زمانی 15 ساله موردبررسی قرار گرفت. نتایج نقشه‌های حاصل نشان داد که شاخص‌های VHI و VTCI در نمایش شدت‌های خشکسالی در سال‌های موردمطالعه، تطابق خوبی داشتند. برای راستی آزمایی نتایج این دو شاخص، از شاخص SPI سه‌ماهه منتهی به ماه مرداد، استخراج‌شده از 36 ایستگاه هواشناسی استفاده شد. مقایسه نتایج حاصل از شاخص‌های مذکور با شاخص خشکسالی هواشناسی SPI، نشان داد که شاخص VHI در ارزیابی شدت خشکسالی کشاورزی مؤثرتر بوده است(0.67=R2). دلیل اصلی این امر، دخالت برابر پوشش گیاهی و درجه حرارت سطح زمین در شاخص VHI است. اندازه‌گیری‌های زمینی تغییرات کمّی پوشش گیاهی و درجه حرارت سطح زمین و نیز تأثیر متغیرهای توپوگرافی منطقه مطالعاتی و نوع کاربری اراضی، به‌منظور مطالعات تکمیلی برای پایش زمانی-مکانی خشکسالی، پیشنهاد می‌گردد.https://geoeh.um.ac.ir/article_48451_c30a870d21a9d939e49a93972b7b8c71.pdf