دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220250914Temporal Changes and Spatial Distribution of Suspended Particulate Matter in the Southern Alborz Mountains Based on Satellite Dataتغییرات زمانی و پراکنش مکانی شاخص ذرات معلق در دامنه جنوبی البرز مرکزی بر پایه داده‌های ماهواره‌ای11810051810.22059/jphgr.2024.383803.1007845FAمحمدناجیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، 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 Article20250426ABSTRACTAir pollution is one of the primary environmental challenges in the metropolitan areas of Tehran, Karaj, and Qazvin. Due to the concentration of industries and population in this region, high levels of particulate matter pose a significant threat to human health and the environment. This research aims to analyze the spatiotemporal distribution of particulate matter concentration and identify critical areas on a five-year average basis, with annual, seasonal, and monthly variations. For this purpose, Sentinel-5 satellite aerosol absorption index data were used to investigate the spatiotemporal variations of suspended particulate matter in the southern slopes of central Alborz. The results showed that the highest concentration of suspended particulate matter was observed in the south and eastern regions of the area, including Tehran, the political border of Qom, and Karaj. Analysis of hotspots at a 99% confidence level indicates a high concentration of pollution in these areas. Between 2018 and 2020, particulate matter concentrations exhibited a decreasing trend, reaching a minimum. However, in 2021, concentrations surged to their peak. Seasonal and monthly analyses revealed that the highest particulate matter levels occurred during summer. This pattern underscores the influence of geographic factors, population density, and industrial activities on the spatial distribution of pollutants, emphasizing the need for region-specific interventions to manage air quality.Extended AbstractIntroductionAir pollution became evident in human life when human settlements began to form as concentrated and stationary communities. Air pollution, caused by suspended particulate matter, refers to changes in the natural composition of the atmosphere due to the introduction of particles from both human and natural sources. Suspended particulate matter in the air consists of a mixture of solid and liquid particles that vary significantly in terms of shape, size, number, chemical composition, and origin. These differences cause some suspended particulate matter to have more severe health impacts than others. In megacities, the primary sources of suspended particulate matter are a combination of fossil fuel combustion from vehicles, construction equipment, furnaces, and power plants. Like many other developing countries, Iran is grappling with air pollution and is not exempt from this problem. Due to the geographical location of the study area on the southern slopes of the central Alborz Mountains and the comprehensive view of this region for spatiotemporal analysis of suspended particulate matter, the lack of air quality monitoring stations is a major challenge. This shortage is particularly noticeable in the cities of Karaj and Qazvin. Therefore, the primary objective of this study is to analyze the five-year (2018-2022) temporal and spatial patterns of suspended particulate matter in the study area. This analysis encompasses annual, seasonal, and monthly variations using the Aerosol Absorption Index (AAI) derived from Sentinel-5 satellite imagery, adopting a holistic approach to the region. MethodologyTo investigate the spatiotemporal variations of suspended particulate matter in the study area, the Aerosol Index (AAI) product from the Sentinel-5 satellite was utilized. Sentinel-5, the first Copernicus mission satellite, is a powerful tool for atmospheric monitoring. The Tropospheric Monitoring Instrument (Tropomi) was launched aboard Sentinel-5 on October 13, 2017. These analyses used JavaScript programming language through the Google Earth Engine web platform. The outputs obtained from this index include several results for evaluating these pollutants' temporal and spatial changes and understanding their distribution patterns in the environment. The products of this index include a five-year average of the suspended particulate matter index (from 2018 to 2022), an annual trend analysis over this period, and an analysis of seasonal and monthly variations. Subsequently, ArcMap software classified and produced a spatial map of the average concentration of suspended particulate matter. Finally, to more accurately discover the spatial distribution and intensity of particulate matter pollution, hot and cold spots, and Getis-Ord Gi* statistics were used. This analysis can help better understand the spatial distribution of air pollution, identify critical areas, and ultimately show the difference in the intensity of air pollution between the metropolitan areas of Tehran, Karaj, and Qazvin. Results and discussionA five-year average analysis of the spatial distribution of suspended particulate matter reveals a high concentration of particulates, including dust and smoke, in the southern and eastern regions, particularly around Tehran and the Qom border. This concentration is linked to anthropogenic factors such as population density, industries, and traffic. Additionally, wind patterns and topography play a role in particulate distribution. Hot spot analysis using the Getis-Ord-Gi statistic showed that the southern regions are more polluted, while the northern regions have better air quality. Moreover, the suspended particulate matter index decreased from 2018 to 2020, reaching its lowest point in 2020. However, it exhibited an increasing trend from 2021 onwards, peaking in 2022. This increase can be attributed to factors such as increased industrial activities and climatic changes. Pollution hotspots were concentrated in the southern regions of Tehran, Karaj, and Qazvin and remained relatively stable throughout this period. In other words, spatial variations in suspended particulate matter concentration have not been significant during this period, and the spatial distribution of suspended particulate matter has remained relatively stable. On the other hand, northern regions with lower population density and better air flow have cooler spots with less pollution. The results of the horizontal visibility field show that visibility at Mehrabad station has continuously decreased and has a significant correlation with the suspended particulate matter index. At Karaj station, visibility decreased from 2018 to 2020 and then increased until 2021, but decreased again in 2022. These changes indicate the significant impact of air pollution and suspended particulate matter on the reduction of horizontal visibility. Seasonal analysis of suspended particulate matter distribution and concentration, along with hotspot identification, reveals that the highest SPM concentrations occur during the summer months, particularly in the southern and central regions of Tehran, which are influenced by surrounding deserts and northerly winds. Similar patterns can be observed in autumn, while spring and winter exhibit different distributions. Hotspots linearly extend from southeast Tehran to west Qazvin, and the northern regions and Alborz mountains are cold spots with lower pollution. The monthly distribution of suspended particulate matter reveals a consistent increase from May to September, reaching its peak in July. The highest concentrations were observed in the Tehran, Karaj, and Qazvin regions, particularly along the Tehran-Qazvin freeway. This increase is attributed to stable atmospheric conditions, rising temperatures, reduced wind speeds, traffic congestion, industrial activities, accumulating pollutants, and deteriorating air quality. ConclusionA precise understanding of the impacts of particulate matter on human health and the environment requires continuous monitoring of particle concentration and size distribution on a global scale. The high variability of these particles in the atmosphere poses significant challenges for ground-based monitoring networks. Therefore, remote sensing technologies, especially Sentinel-5 satellite data, provide an efficient tool for examining suspended particulate matter's spatial and temporal distribution. The results indicate that the highest concentrations of suspended particulate matter are concentrated in Iran's central and southeastern regions, particularly around Tehran. Between 2018 and 2020, due to restrictions imposed by the COVID-19 pandemic, the concentration of suspended particulate matter decreased continuously and reached its lowest level in 2020. However, with the return to normal activities, pollutants increased again and reached their highest level in 2021. With its unique climatic conditions, summer experiences the highest concentrations of suspended particulate matter, particularly in Tehran. Dust storm sources influence this region in the surrounding deserts. Moreover, monthly variations reveal increased particulate concentrations from May to July. In-depth analyses can help evaluate the impact of pollution control policies and measures, and contribute to the development of predictive models for the future. Thus, increasing the number of ground-based monitoring stations and utilizing remote sensing technologies are essential steps for optimal air pollution management and implementing sustainable environmental policies. FundingThere is no funding support. Authors’ ContributionAll of the authors approved the content of the manuscript and agreed on all aspects of the work. Conflict of InterestAuthors declared no conflict of interest. AcknowledgmentsWe are grateful to all the scientific consultants of this paper.آلودگی هوا یکی از چالش‌های اصلی زیست‌محیطی در کلان‌شهرهای تهران، کرج و قزوین است که به دلیل تمرکز صنعتی و جمعیتی این منطقه، سطوح بالای ذرات معلق، سلامت انسان و محیط‌زیست را تهدید می‌کند. هدف این پژوهش، تحلیل زمانی _ مکانی غلظت ذرات معلق و شناسایی مناطق بحرانی به‌صورت میانگین پنج‌ساله، تغییرات سالانه، فصلی و ماهانه است. بدین منظور، از داده‌های شاخص جذب آئروسل (AAI) ماهواره‌ای سنتینل-5 استفاده‌شده تا تغییرات مکانی _ زمانی ذرات معلق در دامنه‌های جنوبی البرز مرکزی بررسی شوند. نتایج نشان داد بیشترین غلظت ذرات معلق در نواحی جنوبی و شرقی منطقه از جمله تهران، مرز سیاسی قم و کرج مشاهده‌شده است. تحلیل کانون‌های بحرانی در سطح اطمینان 99% حاکی از تمرکز بالای آلودگی در این نواحی است. در مقابل، مناطق شمالی قزوین و ارتفاعات البرز به دلیل جریان هوای بهتر و فعالیت‌های صنعتی کمتر دارای کیفیت هوای بهتری هستند. طی بازه زمانی 1397 تا 1399، روند تغییرات غلظت ذرات معلق نزولی بوده و به حداقل مقدار خود رسیده، اما در سال 1401 به اوج خود افزایش‌یافته است. تحلیل‌های فصلی و ماهانه نشان می‌دهند که بیشترین غلظت ذرات معلق در فصل تابستان ثبت‌شده است. این الگو تأثیر عوامل جغرافیایی، تراکم جمعیت، و فعالیت‌های صنعتی بر توزیع فضایی آلاینده‌ها را برجسته می‌کند و بر اهمیت مداخلات منطقه‌ای برای مدیریت کیفیت هوا تأکید دارد.https://jphgr.ut.ac.ir/article_100518_630085ddaec89431e7717bfe0b6bb6e4.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220250914The impact of climate change on planning military
activities on Iran's western bordersتأثیر تغییر اقلیم در برنامه‌ریزی فعالیت‌های نظامی در مرزهای غربی ایران193710400610.22059/jphgr.2025.385876.1007854FAایوبجعفریگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0002-1585-9032شاهرخرضاییگروه مدیریت بحران، دانشکده علوم اجتماعی، دانشگاه دافوس، تهران، ایرانبهروزبهرامآبادیگروه جغرافیای طبیعی، دانشکده علوم پایه، دانشگاه امام علی(ع)، تهران، ایرانJournal Article20250426ABSTRACT
Climate change, recognized as one of the most significant threats of the 21st century, profoundly impacts not only the environment and global economy but also the security and military operations of nations. Consequently, alterations in weather patterns, coupled with their unpredictability, pose a serious threat to military infrastructure. This study aims to investigate the variations in climate parameters affecting the normalized Military Climate Index (MCI) across the provinces of West Azerbaijan, Ilam, Kurdistan, Kermanshah, and Khuzestan. To achieve this, the necessary climate parameters were initially sourced from the Meteorological Organization. Subsequently, after selecting the most suitable Global Climate Model (GCM), the climate parameters for each station were downscaled using the LARS-WG model through to the year 2100. The results revealed that at all the stations examined, the MCI index decreased from late spring to the end of summer, indicating challenging conditions for military operations. Conversely, Ahvaz in late autumn and early winter, Ilam in autumn and early spring, Kermanshah and Sanandaj in early autumn and spring, and Urmia in spring, all exhibited favorable conditions due to the elevated MCI index. Ultimately, it is recommended that any military planning, including training, education, maneuvers, and strategy design, be informed by an understanding of climate change conditions and climate comfort indices, necessitating the implementation of specific measures. Thus, reviewing and adapting defense strategies to the climate change conditions of each region is crucial.
Extended Abstract
Introduction
Climate change has emerged as one of the most pressing global challenges, particularly in the environmental, political, and economic domains, due to its far-reaching impacts on various facets of human life. These changes have precipitated global warming and an increase in extreme weather events, which have had profound consequences for global security, food security, poverty, and conflict. In recent years, climate models have been utilized to predict and analyze changes in climate variables over the coming decades. Specifically, by providing projections of shifts in key climate parameters, such as temperature and precipitation, under different greenhouse gas emission scenarios, these models enable policymakers and researchers to devise plans for a range of potential future conditions. Such forecasts also inform the development of strategies aimed at mitigating negative impacts and capitalizing on potential opportunities. One established method for predicting climate change involves the use of IPCC emission scenarios and Global Climate Models (GCMs). A review of existing literature reveals that climate change can affect both the operational capabilities and military security of nations, both directly and indirectly. These effects include diminished performance of military equipment under extreme heat, rising defense expenditures required for adaptation to new climate realities, and shifts in military strategies to counter emerging climate-related threats. Consequently, it is crucial for Iranian policymakers and defense planners to better comprehend the implications of climate change in order to devise strategies that enhance the country's defense capabilities in the face of these evolving risks.
The overarching aim of this study is to examine the significance of climate change on military operations, with a particular emphasis on its effects on strategic and border regions. The research seeks to assess the potential consequences of climate change on military operational conditions and the efficiency of armed forces by analyzing climate data and employing predictive models. Ultimately, through a more nuanced understanding of regional dynamics and climate impacts, the study aims to facilitate more effective planning and the formulation of adaptive strategies. This approach will enable military forces to bolster their resilience against climate-induced challenges by implementing optimal solutions, thereby ensuring the success of military operations under fluctuating climate conditions.
Methodology
This study covers five western provinces of Iran, namely West Azerbaijan, Ilam, Kurdistan, Kermanshah, and Khuzestan. Climate data, including minimum and maximum temperatures, precipitation, and sunshine hours, were collected from five synoptic stations in Ahvaz, Ilam, Kermanshah, Urmia, and Sanandaj, spanning the period from 1985 to 2014. The data were subsequently assessed for normality and completeness, with outliers, errors, and missing values being removed. To analyze uniform trends in the meteorological time series data, the nonparametric Mann-Kendall test was applied. Additionally, the trend slope (Q) for the variables of interest was determined using the nonparametric Sen’s Slope Estimator method.
Results and Discussion
The findings indicate that the proposed Military Climate Index (MCI) effectively predicts the impact of climate change on military operations. The analysis, which utilized the Mann-Kendall test and Sen’s Slope Estimator, revealed significant trends in temperature (both minimum and maximum) and precipitation across most months of the year. These trends include a consistent rise in both minimum and maximum temperatures and a reduction in precipitation in the studied regions. Furthermore, the MCI results for the Ahvaz station show that extreme summer temperatures result in a decline in the MCI index, reflecting more challenging conditions for military operations in this area. Conversely, late autumn and early winter are characterized by significantly more favorable conditions for military activities in this region.
Conclusion
This study underscores the significant impact of climate change on military operations, particularly in strategically critical regions. The findings highlight the need for military planners and policymakers to integrate climate forecasts into defense strategies, particularly regarding temperature fluctuations and changing precipitation patterns. The proposed Military Climate Index (MCI) has proven effective in predicting the challenges posed by climate change, helping to identify regions and periods where military operations could face increased difficulty. By using climate models and analyzing long-term meteorological data, this research emphasizes the importance of adaptive strategies to enhance the resilience of military forces. In regions like Ahvaz, where extreme summer heat exacerbates operational challenges, timely adaptation measures are crucial. Ultimately, this study calls for a comprehensive approach that not only prepares military forces for climate-induced disruptions but also capitalizes on the strategic opportunities that might arise from changing climatic conditions.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.تغییر اقلیم به‌عنوان یکی از بزرگ‌ترین تهدیدات قرن بیست و یکم، نه‌تنها بر محیط‌زیست و اقتصاد جهانی، بلکه بر امنیت و فعالیت‌های نظامی کشورها نیز تأثیرات عمیقی می‌گذارد؛ لذا تغییرات الگوهای آب و هوایی و غیرقابل‌پیش‌بینی بودن آن می‌تواند تهدید جدی برای زیرساخت‌های نظامی محسوب گردد. بنابراین هدف از این تحقیق، بررسی تغییرات پارامترهای اقلیمی بر شاخص اقلیم نظامی (MCI) نرمال شده در استان‌های آذربایجان غربی، ایلام، کردستان، کرمانشاه و خوزستان است. بدین منظور، ابتدا پارامترهای اقلیمی موردنیاز از سازمان هواشناسی اخذ گردد. سپس پس از مشخص نمودن بهترین مدل اقلیم جهانی (GCM)، پارامترهای اقلیمی هر ایستگاه با کمک مدل LARS-WG تا سال 2100 میلادی ریزمقیاس-نمایی شدند. نتایج نشان داد در همه ایستگاه‌های موردمطالعه از اواخر فصل بهار تا پایان فصل تابستان، شاخص MCI کاهش‌یافته که نشان‌دهنده شرایط سخت برای انجام فعالیت‌های نظامی است. در مقابل در اهواز اواخر پاییز و اوایل زمستان، ایلام در پاییز و اوایل بهار، کرمانشاه و سنندج در اوایل پاییز و بهار و در ارومیه بهار به دلیل بالا بودن شاخص MCI از شرایط بسیار مطلوبی برخوردار بوده‌اند. در پایان، پیشنهاد می‌گردد هرگونه برنامه‌ریزی برای فعالیت‌های نظامی اعم از تمرین، آموزش، مانور و طراحی مبتنی بر شناخت شرایط تغییر اقلیم و شاخص‌های اقلیم آسایش باشد که در این صورت لازم است تدابیر ویژه‌ای اتخاذ گردد. بنابراین، هرگونه بازنگری و مطابقت راهبردهای دفاعی با شرایط تغییر اقلیم در هر منطقه ضروری است.https://jphgr.ut.ac.ir/article_104006_2d6586f430309afd7ff6729bda72d13e.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220250915Zoning of Slope Instability Using SWARA and CRITIC Decision-Making Methods in the Tabriz City Regionپهنه‌بندی ناپایداری دامنه‌ها با استفاده از روش‌های تصمیم‌گیری SWARA و CRITIC در محدودۀ شهر تبریز395510400810.22059/jphgr.2025.399814.1007899FAمحمدحسینرضائی مقدمگروه ژئومورفولوژی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران0000-0003-2626-4656نسرینسمندرگروه ژئومورفولوژی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران0009-0008-6176-9346توحیدرحیم پورگروه ژئومورفولوژی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران0000-0002-4034-8971Journal Article20250427Extended Abstract
Introduction
Landslide hazard zoning is a fundamental instrument for risk management and strategic decision-making in environmental planning, a task of particular importance in mountainous regions with complex and dynamic terrain. The metropolis of Tabriz, constrained by its geomorphological and topographical conditions, has historically pursued a sectoral development model. This growth strategy has driven the extensive construction and expansion of satellite towns around the city’s periphery. Many of these newly developed settlements—most notably Eram Town—are located in close proximity to the active fault line north of Tabriz. This precarious location exposes them to multiple natural hazards, including earthquakes, flash floods, and diverse forms of mass movements. These factors are widely acknowledged as critical indicators in geomorphological hazard assessments, a concern further compounded by the fact that many of these settlements are built on unstable slopes composed of weak and erodible lithology.
Due to the city’s mountainous topography and the prevalence of clay-rich, unstable soils, Tabriz is highly susceptible to diverse types of slope movements, from shallow landslides to deep-seated instabilities. Accordingly, this study pursued two main objectives: (1) to assess and zone slope instability within the metropolitan area of Tabriz, and (2) to develop a robust, generalizable model for systematic landslide hazard zoning. The proposed model is designed for application not only to Tabriz but also to neighboring areas with comparable geomorphological characteristics. To this end, the evaluation integrated nine key causative factors and employed two multi-criteria decision-making (MCDM) methods—Step-wise Weight Assessment Ratio Analysis (SWARA) and Criteria Importance Through Intercriteria Correlation (CRITIC)—to derive precise weightings.
Methodology
SWARA is a MCDM technique designed to determine the relative weights of criteria and sub-criteria.
Step 1: A decision model is developed based on the interrelationships among parameters. The criteria used to construct the slope instability zoning map are then ranked in descending order according to their perceived importance.
Step 2: The weights of the criteria are calculated as follows: beginning with the second criterion, the evaluator specifies the relative importance of criterion j in comparison with the preceding criterion (j-1). This procedure is repeated sequentially for all remaining criteria.
In contrast to many other approaches, the CRITIC method does not rely extensively on expert judgment. Indeed, its independence from subjective expert assessments is regarded as one of its principal strengths. The method evaluates data by measuring the degree of contrast and correlation among criteria. Each criterion is represented as a vector characterized by statistical parameters, including the standard deviation, which captures the variability in its values.
Results and Discussion
To assess the potential for slope movements, nine key factors were identified as critical triggers of instability in the region: slope gradient, aspect, lithology, land use, precipitation, proximity to faults, proximity to rivers, and proximity to roads.
The historic urban core of Tabriz was established on relatively flat terrain with gentle slopes. However, rapid urban expansion has extended development onto adjacent steep slopes, particularly in newly established satellite towns. These towns are predominantly located in the northern and southeastern sectors of the city, both of which are characterized by steep terrain. The most erosion-prone geological units include marl, shale interbedded with sandstone, and recent fluvial alluvium. By contrast, sandstone and red marl units display comparatively higher resistance to erosion, contributing to the rugged topography and elevated relief of the basin.
The eastern and northern sectors of the city are especially prone to landslides because of their distinctive geological composition. The prevalence of highly saturable marl and clay layers in these areas creates conditions that are highly conducive to slope instability. Precipitation intensifies slope instability through the combined effects of hydrological and mechanical processes. These effects are further amplified in areas characterized by steep gradients, erosion-prone lithology, and sparse vegetation cover. Zones located near fault lines are particularly vulnerable owing to concentrated tectonic stresses, intensified rock fracturing, and an elevated probability of micro-seismic activity.
The rapid physical expansion of the city has also generated severe traffic congestion. To address this issue, beltways were constructed to divert transit traffic beyond the urban limits, leading to the development of two major highways in northern and southern Tabriz. Although originally designed exclusively for transit purposes, these corridors have progressively acquired urban functions as the city expanded along their axes.
Conclusion
A careful comparison of the final hazard map with the landslide distribution map confirms the reliability of this study, as the high-risk zones correspond closely with historically recorded landslide events. Therefore, the methodology and model proposed in this study offer an effective framework for urban landslide hazard zoning. In particular, areas within the neighborhoods of Silab, Malazinal, Yousef Abad, Valiasr Alley, Baghmisheh Town, Golpark Alley, Shahid Yaghchian Town, Fajr Town, Khavaran Town, and Sayyad Shirazi Town fall within very high- and high-risk zones. Conversely, the southern and western sectors are largely classified as safe zones, whereas substantial portions of the northern and eastern sectors are designated as low-risk areas.
Considering the ongoing physical and structural expansion of Tabriz, along with its increasing population, urban development requires careful management. Specifically, uncontrolled urban expansion into areas affected by the North Tabriz Fault System has heightened the city’s susceptibility to earthquakes and related geomorphological hazards, including slope instability. To mitigate future hazards, long-term urban planning should impose restrictions on development within high-risk zones. Furthermore, policymakers are advised to establish incentive programs that encourage at-risk residents to relocate to safer locations, thereby reducing their exposure to landslides and other geological hazards.
Funding
Tabriz Metropolitan Islamic Council Research Center.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper. This article is the result of research conducted at the University of Tabriz with financial support from the Islamic Council of Tabriz Metropolitan Research Center. The authors hereby sincerely thank the efforts of the Technology Management of the University of Tabriz, especially Dr. Nadiri and Mr. Hessami, the Director of the Islamic Council of Tabriz Metropolitan Research Center, Dr. Rouhollah Rashidi, and the Deputy Director of Research, Dr. Ahad Ebrahim Pour-Lanbaran, as well as the scientific supervisors of the project, Dr. Morad Ali Shafaghi and Dr. Roghieh Salek.
ABSTRACT
Slope instability represents one of the most severe natural hazards, leading to substantial economic damage and human casualties each year, while also posing considerable challenges for infrastructure development. This study seeks to identify the key factors driving slope instability and to outline preventive strategies within the metropolitan area of Tabriz, a case of particular significance for environmental and urban planning. The research applied the SWARA and CRITIC multi-criteria decision-making methods to weight and evaluate the selected criteria, and subsequently produced a hazard zonation map using ArcGIS. The analysis considered multiple criteria, including slope, aspect, elevation, proximity to faults, rivers, and roads, as well as lithology, land use, and precipitation. Following the weighting process, the criteria were standardized and converted into thematic layers. These layers were then integrated and overlaid to generate the final zonation map identifying areas susceptible to slope movements. The results indicate that the unstable zones delineated by both models largely correspond to locations of previously recorded landslides. Specifically, neighborhoods such as Silab, Malazināl, Yusefabad, Kuy-e Valiasr, and Kuy-e Golpark, along with residential districts including Baghmesheh, Yaghchiyan, Fajr, Khavaran, and Sayyad Shirazi, are located within very high- and high-hazard zones. By contrast, the western, northwestern, and southwestern sectors of the city are classified as safe zones, whereas the central and eastern sectors are predominantly categorized as low-hazard.
Extended Abstract
Introduction
Landslide hazard zoning is a fundamental instrument for risk management and strategic decision-making in environmental planning, a task of particular importance in mountainous regions with complex and dynamic terrain. The metropolis of Tabriz, constrained by its geomorphological and topographical conditions, has historically pursued a sectoral development model. This growth strategy has driven the extensive construction and expansion of satellite towns around the city’s periphery. Many of these newly developed settlements—most notably Eram Town—are located in close proximity to the active fault line north of Tabriz. This precarious location exposes them to multiple natural hazards, including earthquakes, flash floods, and diverse forms of mass movements. These factors are widely acknowledged as critical indicators in geomorphological hazard assessments, a concern further compounded by the fact that many of these settlements are built on unstable slopes composed of weak and erodible lithology.
Due to the city’s mountainous topography and the prevalence of clay-rich, unstable soils, Tabriz is highly susceptible to diverse types of slope movements, from shallow landslides to deep-seated instabilities. Accordingly, this study pursued two main objectives: (1) to assess and zone slope instability within the metropolitan area of Tabriz, and (2) to develop a robust, generalizable model for systematic landslide hazard zoning. The proposed model is designed for application not only to Tabriz but also to neighboring areas with comparable geomorphological characteristics. To this end, the evaluation integrated nine key causative factors and employed two multi-criteria decision-making (MCDM) methods—Step-wise Weight Assessment Ratio Analysis (SWARA) and Criteria Importance Through Intercriteria Correlation (CRITIC)—to derive precise weightings.
Methodology
SWARA is a MCDM technique designed to determine the relative weights of criteria and sub-criteria.
Step 1: A decision model is developed based on the interrelationships among parameters. The criteria used to construct the slope instability zoning map are then ranked in descending order according to their perceived importance.
Step 2: The weights of the criteria are calculated as follows: beginning with the second criterion, the evaluator specifies the relative importance of criterion j in comparison with the preceding criterion (j-1). This procedure is repeated sequentially for all remaining criteria.
In contrast to many other approaches, the CRITIC method does not rely extensively on expert judgment. Indeed, its independence from subjective expert assessments is regarded as one of its principal strengths. The method evaluates data by measuring the degree of contrast and correlation among criteria. Each criterion is represented as a vector characterized by statistical parameters, including the standard deviation, which captures the variability in its values.
Results and Discussion
To assess the potential for slope movements, nine key factors were identified as critical triggers of instability in the region: slope gradient, aspect, lithology, land use, precipitation, proximity to faults, proximity to rivers, and proximity to roads.
The historic urban core of Tabriz was established on relatively flat terrain with gentle slopes. However, rapid urban expansion has extended development onto adjacent steep slopes, particularly in newly established satellite towns. These towns are predominantly located in the northern and southeastern sectors of the city, both of which are characterized by steep terrain. The most erosion-prone geological units include marl, shale interbedded with sandstone, and recent fluvial alluvium. By contrast, sandstone and red marl units display comparatively higher resistance to erosion, contributing to the rugged topography and elevated relief of the basin.
The eastern and northern sectors of the city are especially prone to landslides because of their distinctive geological composition. The prevalence of highly saturable marl and clay layers in these areas creates conditions that are highly conducive to slope instability. Precipitation intensifies slope instability through the combined effects of hydrological and mechanical processes. These effects are further amplified in areas characterized by steep gradients, erosion-prone lithology, and sparse vegetation cover. Zones located near fault lines are particularly vulnerable owing to concentrated tectonic stresses, intensified rock fracturing, and an elevated probability of micro-seismic activity.
The rapid physical expansion of the city has also generated severe traffic congestion. To address this issue, beltways were constructed to divert transit traffic beyond the urban limits, leading to the development of two major highways in northern and southern Tabriz. Although originally designed exclusively for transit purposes, these corridors have progressively acquired urban functions as the city expanded along their axes.
Conclusion
A careful comparison of the final hazard map with the landslide distribution map confirms the reliability of this study, as the high-risk zones correspond closely with historically recorded landslide events. Therefore, the methodology and model proposed in this study offer an effective framework for urban landslide hazard zoning. In particular, areas within the neighborhoods of Silab, Malazinal, Yousef Abad, Valiasr Alley, Baghmisheh Town, Golpark Alley, Shahid Yaghchian Town, Fajr Town, Khavaran Town, and Sayyad Shirazi Town fall within very high- and high-risk zones. Conversely, the southern and western sectors are largely classified as safe zones, whereas substantial portions of the northern and eastern sectors are designated as low-risk areas.
Considering the ongoing physical and structural expansion of Tabriz, along with its increasing population, urban development requires careful management. Specifically, uncontrolled urban expansion into areas affected by the North Tabriz Fault System has heightened the city’s susceptibility to earthquakes and related geomorphological hazards, including slope instability. To mitigate future hazards, long-term urban planning should impose restrictions on development within high-risk zones. Furthermore, policymakers are advised to establish incentive programs that encourage at-risk residents to relocate to safer locations, thereby reducing their exposure to landslides and other geological hazards.
Funding
Tabriz Metropolitan Islamic Council Research Center.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper. This article is the result of research conducted at the University of Tabriz with financial support from the Islamic Council of Tabriz Metropolitan Research Center. The authors hereby sincerely thank the efforts of the Technology Management of the University of Tabriz, especially Dr. Nadiri and Mr. Hessami, the Director of the Islamic Council of Tabriz Metropolitan Research Center, Dr. Rouhollah Rashidi, and the Deputy Director of Research, Dr. Ahad Ebrahim Pour-Lanbaran, as well as the scientific supervisors of the project, Dr. Morad Ali Shafaghi and Dr. Roghieh Salek.ناپایداری دامنه‌ای از جمله مهم‌ترین مخاطرات طبیعی محسوب می‌شود که سالانه خسارت‌های مالی و جانی قابل توجهی به همراه دارد و اجرای پروژه‌های عمرانی را با چالش‌های جدی مواجه می‌کند. این مطالعه با هدف شناسایی عوامل مؤثر در بروز ناپایداری دامنه‌ای و ارائه راهکارهای پیشگیرانه در کلان‌شهر تبریز انجام شده است، موضوعی که در برنامه‌ریزی‌های محیطی از اهمیت بالایی برخوردار است. در این پژوهش، از روش‌های تصمیم‌گیری SWARA و CRITIC برای وزن دهی و ارزیابی معیارها استفاده شد و نقشه پهنه‌بندی خطر با کمک نرم‌افزار ArcGIS تهیه گردید. معیارهای مورد بررسی شامل شیب، جهت شیب، ارتفاع، فاصله از گسل، فاصله از رودخانه، فاصله از جاده، لیتولوژی، کاربری اراضی و میزان بارش بودند که پس از وزن دهی، به صورت لایه‌های استانداردشده درآمدند. در نهایت، با تلفیق و رویهم‌گذاری این لایه‌ها، نقشه نهایی پهنه‌بندی مناطق مستعد حرکات دامنه‌ای تهیه شد. بررسی نتایج نشان داد که پهنه‌های ناپایدار در هر دو مدل، با مناطق وقوع لغزش‌های پیشین مطابقت دارد. به‌طور مشخص، محلاتی نظیر سیلاب، ملازینال، یوسف‌آباد، کوی ولیعصر، کوی گلپارک، شهرک‌های باغمیشه، یاغچیان، فجر، خاوران و صیاد شیرازی در محدوده‌های با خطر بسیار بالا و بالا قرار گرفته‌اند. در مقابل، مناطق غربی، شمال غربی و جنوب غربی شهر در پهنه‌های بی‌خطر جای دارند، درحالی‌که بخش‌های مرکزی و شرقی عمدتاً در محدوده‌های کم‌خطر طبقه‌بندی شده‌اند. بنابراین جهت کاهش نسبی خطرات و افزایش میزان پایداری دامنه‌ها لازم است تا حد ممکن از تغییر اکوسیستم و کاربری اراضی اجتناب کردhttps://jphgr.ut.ac.ir/article_104008_c99bb08740fa9ec29334223caccc2159.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220250915Analysis of Desertification Trends using Vegetation Idices and Albedo Coefficient over 2000-2023: A case study of Mond Basin in Southwest of Iranارزیابی روند بیابان‌زایی با استفاده از شاخص‌های پوشش گیاهی و تغییرات ضریب آلبدو در دوره زمانی 2000-2023، مطالعه موردی: حوضه آبریز مند در جنوب غرب ایران577610406110.22059/jphgr.2025.391710.1007876FAرضاذاکری نژادگروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران0000-0001-7502-5067غلامحسنجعفریگروه جغرافیا، دانشکده علوم انسانی، دانشگاه زنجان، زنجان، ایران0000-0002-8901-6855الههرضاپوریان قهفرخیگروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایرانJournal Article20250427ABSTRACT
Desertification is one of the most significant land degradation processes in arid and semi-arid regions, driven by both natural and anthropogenic factors. This study aims to assess desertification trends and environmental changes in the Mand basin using remote sensing techniques and spatiotemporal analyses over three time periods as 2000, 2013, and 2023. Satellite-derived vegetation indices, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), LAI (Leaf Area Index), VCI (Vegetation Condition Index), LST (Land Surface Temperature), and albedo, were employed to monitor ecological shifts. LAI and SAVI also demonstrated decreasing tendencies, reflecting vegetation loss and soil exposure. In contrast, the EVI slightly increased during the same period, possibly due to changes in land management practices or vegetation types. Albedo values revealed a fluctuating but meaningful trend; in 2000 (mean = 0.2378, SD = 0.0461), in 2013 (mean = 0.2343, SD = 0.0647), and in 2023 (mean = 0.2271, SD = 0.0389). Despite a temporary increase in 2013 (up to 0.4345), the overall decrease in albedo suggests progressive soil degradation and expansion of bare surfaces, reinforcing the desertification process. Regional analysis revealed that central and southern parts of the basin experienced the most severe degradation, with increasing bare soil surfaces and declining vegetation cover. Based on albedo thresholds, the basin was classified into three desertification risk zones: low risk (20%); albedo > 0.4, moderate risk (50%); albedo 0.2–0.4, and high risk (30%); albedo<0.2. Statistical correlation analysis confirmed a strong positive relationship between NDVI and LAI (r=0.85, p < 0.01), while a robust negative correlation between albedo and vegetation indices supported the hypothesis of ongoing desertification. Findings highlight the need for sustainable land management policies, vegetation restoration programs, and continuous monitoring using remote sensing technologies to mitigate further desertification and ensure long-term ecological stability.
Extended Abstract
Introduction
Desertification is one of the most important land degradation processes in arid and semi-arid regions of the world, which occurs under the influence of natural and human factors. This phenomenon means the reduction or loss of the biological capacity of soil and vegetation, which can lead to significant changes in ecosystem functioning, a decrease in biodiversity, and an increase in environmental poverty. Desertification has significant impacts on natural resources, water security, soil health, and ecosystem resilience. Driven by land mismanagement, human activities, climate change, and frequent droughts, desertification accelerates ecosystem degradation. Advanced remote sensing technologies offer powerful tools for continuous environmental monitoring, enabling a thorough understanding of desertification processes. Soil indices such as albedo, LAI, and VCI also play an important role in detecting soil surface changes and desertification trends. Zhu et al. (2020) investigated desertification in Minqing County, northeast China, using Landsat images from 1978 to 2017 and the EVI index. The results of this study showed that vegetation cover and greenness increased over 31 years. Although several studies on desertification have been conducted using remote sensing technologies, there is still a need for comprehensive and area-based assessments in sensitive areas with high land use changes. This study aims to identify the desertification trend in the period 2000-2023 in the Mand basin in southwestern Iran using vegetation indices (NDVI, EVI, SAVI), soil indices (albedo, LAI, VCI). This study, using remote sensing data, multiple environmental indicators, and spatiotemporal analyses, has a special place in the existing literature. The main goal of this research is to accurately assess the desertification process, identify the relationships between the effective variables, and provide the necessary context for sustainable management of vulnerable areas.
Methodology
This research employs a regional and temporal analysis approach based on satellite data for three key periods as 2000, 2013, and 2023. After preprocessing and geometric correction, the images were analyzed using GIS and remote sensing software. Vegetation indices such as NDVI, EVI, and SAVI were employed to evaluate vegetation health, while soil and environmental condition indicators like albedo, LAI, and VCI provided insights into soil dryness and overall ecosystem vitality. Correlation and regression analyses elucidated relationships among these variables across time. Spatial maps were generated to illustrate desertification development and land degradation patterns within the region. In this study, all spatial indices were extracted based on Landsat 5 and 8 satellite data, after loading them into Google Earth Engine (GEE). After loading the images, cloud and noise filtering, cropping based on the study area, and data purification were performed to obtain high-quality and reliable data.
Results and discussion
The results demonstrate that the area has experienced a substantial expansion of degraded and decertified zones, with approximately 35% of the region affected by desertification indicators by 2023. Key vegetation indices have declined significantly, and the correlation analysis confirms the intensification of land degradation over time. Recurring droughts, water resource depletion, and human activities such as overgrazing and improper agricultural practices primarily drive these patterns. The strong relationships between soil and vegetation degradation emphasize the urgency of adopting integrated land management strategies, soil stabilization, reforestation, and water conservation measures to halt and reverse desertification trends. This authoritative and comprehensive study, using multi-attribute analyses and intertemporal data, showed that the study area has been facing a significant trend of vegetation degradation and desertification in recent decades. The continuous decrease in NDVI, EVI, and SAVI indices and the simultaneous increase in albedo index, as desertification indices, confirm the intensification of desertification processes and the degradation of ecological structures and soil. This trend, along with the strong and significant negative relationships between vegetation indices and dryland and desert soil indices, further highlights the importance of continuous monitoring and management interventions.
Conclusion
This study confirms that satellite-based remote sensing provides critical insights into desertification dynamics, offering valuable tools for early detection and policy formulation. The findings highlight the importance of sustained environmental monitoring, community engagement, and policy interventions to combat land degradation. By integrating technological approaches with sustainable land use policies, it is possible to restore ecological balance, enhance resilience to climate variability, and secure natural resources for future generations. The insights gained here contribute meaningfully to regional efforts for desertification control and sustainable environmental stewardship.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.ارزیابی روند بیابان‌زایی با استفاده از شاخص‌های پوشش گیاهی و تغییرات ضریب آلبدو در دوره زمانی 2000-2023، مطالعه موردی: حوضه آبریز مند در جنوب غرب ایران
چکیده
بیابان‌زایی یکی از مهم‌ترین فرآیندهای تخریب اراضی در مناطق خشک و نیمه‌خشک است که تحت تأثیر عوامل طبیعی و انسانی رخ می‌دهد. این مطالعه با هدف ارزیابی روندهای بیابان‌زایی و تغییرات زیست‌محیطی در حوضه مند، با استفاده از شاخص‌های سنجش‌ازدور شامل NDVI، EVI، SAVI، LAI، VCI، LST و آلبدو در سه بازه زمانی 2000، 2013 و 2023 انجام شد. در این مطالعه مشخص شد که، منطقه دارای روند معناداری از بیابان‌زایی است. شاخص‌های پوشش گیاهی شامل NDVI، EVI و SAVI روند کاهشی معناداری را نشان دادند. همچنین شاخص‌های خاکی مانند آلبدو و LAI نیز تغییرات قابل‌توجهی را آشکار کردند. میانگین آلبدو در سال 2000 برابر با 2378/0 بود، که در سال 2013 به 2343/0 افزایش یافت و در سال 2023 به 2271/0 کاهش یافت. این روند نوسانی نشان‌دهنده گسترش خاک‌های خشک و بیابانی در منطقه است. همبستگی منفی قوی بین آلبدو و شاخص‌های پوشش گیاهی (NDVI: r ≈ -0.78، p < 0.01) نیز وجود رابطه مستقیم بین کاهش پوشش گیاهی و افزایش خاک‌های بدون پوشش را تأیید می‌کند. تحلیل‌های منطقه‌ای نشان داد که توسعه بیابان‌زایی بیشتر در مناطق مرکزی و جنوبی حوضه مورد مطالعه رخ‌داده است. طبقه‌بندی بیابان‌زایی بر اساس آلبدو نشان داد، که حدود 30٪ از حوضه در طبقه "خطر زیاد"، 50٪ در طبقه "خطر متوسط" و تنها 20٪ در طبقه "خطر کم" قرار دارد. این الگوها با کاهش منابع آب، افزایش دما و فعالیت‌های انسانی مانند چرای بی‌رویه و تغییر کاربری اراضی همراهی می‌کنند. پیشنهاد می‌شود سیاست‌گذاری‌های منطقه‌ای بر اساس داده‌های فضایی-زمانی تقویت شوند تا از گسترش بیابان‌زایی در حوضه مند جلوگیری شودhttps://jphgr.ut.ac.ir/article_104061_6bef60f452ab2b50ce2d23daa5732925.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220250916Identification of Spatiotemporal Drought Patterns in Southeastern Iran Using
a Graphical Trend Analysis Approachشناسایی الگوهای زمانی و مکانی خشکسالی در جنوب شرق ایران با رویکردی مبتنی بر روش گرافیکی تحلیل روند779810424210.22059/jphgr.2025.398145.1007893FAدانیالبراهوییگروه جغرافیای طبیعی، دانشکده جغرافیا و برنامه‌ریزی محیطی، دانشگاه سیستان و بلوچستان، زاهدان، ایران0000-0002-9282-5928محسنحمیدیان پورگروه جغرافیای طبیعی، دانشکده جغرافیا و برنامه‌ریزی محیطی، دانشگاه سیستان و بلوچستان، زاهدان، ایران0000-0001-7389-172Xفائزهشجاعگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0002-9282-5928Journal Article20250428ABSTRACT
Southeastern Iran, characterized by an arid climate, has recently experienced acute drought conditions and significant water scarcity. This study examines the spatiotemporal dynamics of drought and associated climatic parameters in the region. Minimum and maximum temperature and precipitation data from 20 meteorological stations (1995–2018) were obtained from the Iran Meteorological Organization. The Standardized Precipitation–Evapotranspiration Index (SPEI) was subsequently calculated, and its time series analyzed to quantify drought characteristics, including intensity, duration, severity, and frequency. Trends in climatic parameters were assessed using the Innovative Trend Analysis (ITA) method. Results show that the region experienced pronounced wet conditions until the end of 1997, followed by increasingly intense and prolonged drought events from the late 1990s onward. Within this period, the most extreme drought was observed in Chabahar in 2009 (SPEI = –4.4), while the wettest condition occurred in Anar in 1996 (SPEI = 2.5). Additionally, the greatest drought intensity was recorded at Bam (–1.48), the highest frequency in Khash (18.75%), and the most severe drought conditions in Baft, Qeshm Island, and Bam (–12.06, –11.65, and –11.35, respectively). These locations represent the most critically affected zones within the study region. The ITA results further revealed a significant upward trend in summer temperatures and a pronounced decline in precipitation during the cold season. The observed decline in SPEI values across wet categories underscores the direct influence of climate change on regional water resources.
Extended Abstract
Introduction
Drought represents one of the most persistent and insidious environmental hazards confronting arid and semi-arid regions worldwide. It develops gradually, often without a distinct onset or termination, yet generates profound and enduring socio-economic and environmental repercussions. Unlike abrupt extreme weather events such as floods or cyclones, droughts unfold silently, progressively undermining ecosystems, human livelihoods, and economic stability. According to the World Meteorological Organization (WMO), droughts accounted for 34% of all disaster-related fatalities worldwide between 1970 and 2019.
In southeastern Iran—a region marked by aridity, elevated temperatures, and highly variable rainfall—the frequency, intensity, and duration of drought events have intensified in recent decades. These changes are driven by climate change, atmospheric circulation anomalies, and insufficient water management infrastructure. Given these challenges, understanding the spatiotemporal dynamics of drought is crucial for effective resource planning, evidence-based policy formulation, and the development of robust disaster mitigation strategies.
Methodology
This study investigates the temporal and spatial dynamics of drought in southeastern Iran through a quantitative framework grounded in climatological and hydrometeorological analysis. The study area covers approximately one-quarter of Iran’s territory, including the provinces of Sistan and Baluchestan and Kerman, along with parts of Hormozgan, South Khorasan, Yazd, and Fars. This region is characterized by hyper-arid to semi-arid climatic zones, with mean annual precipitation ranging from 68 to 214 mm and average temperatures between 14°C and 27°C.
Climatic data—including minimum and maximum daily temperatures as well as total monthly precipitation—were obtained from 20 synoptic meteorological stations across the study area for the period 1995–2018. The Standardized Precipitation–Evapotranspiration Index (SPEI) was employed as the principal drought indicator because of its capacity to integrate precipitation and temperature anomalies. SPEI values were calculated on a 12-month timescale to represent long-term hydrological drought conditions. To examine the temporal characteristics of drought events—including severity, duration, intensity, and frequency—the study applied Run Theory, which identifies uninterrupted sequences of negative SPEI values exceeding a defined threshold.
In addition, the Innovative Trend Analysis (ITA) method was employed to detect trends in temperature, precipitation, and drought behavior. ITA offers a non-parametric graphical approach that circumvents assumptions related to data distribution, autocorrelation, and seasonality. By dividing time series into two sub-periods, independently ranking the values, and comparing their distributions against a 1:1 reference line, ITA yields nuanced insights into concealed or nonlinear trends that conventional tests (e.g., Mann–Kendall) may fail to detect.
Results and discussion
SPEI-based drought monitoring revealed that, until 1997, southeastern Iran experienced moderate to extreme wet conditions, particularly at stations in Kerman Province such as Anar, Shahr-e Babak, and Rafsanjan, where SPEI values exceeded +2.5 in 1996. From the late 1990s onward, however, the frequency and persistence of drought events increased markedly. The periods 1998–2002, 2006, and 2008–2012 emerged as the driest intervals within the study period. Stations in Sistan and Baluchestan—such as Zahedan, Zabol, Zahak, Iranshahr, and Khash—exhibited the greatest SPEI fluctuations, with prolonged drought durations of 5 to 7 years.
The most extreme drought was recorded at Chabahar in 2009, where SPEI dropped to −4.4, signifying an exceptional event. By contrast, Anar experienced the wettest year in 1996, with an SPEI of +2.5. Run Theory analysis further quantified drought characteristics: average intensity ranged from −1.15 to −1.48 across stations, while magnitude values extended from −4.5 to below −12, with the most severe conditions recorded in Bam, Baft, and Qeshm. Drought frequency varied between 10% and 19% across the region, with Khash, Saravan, and Zabol exhibiting the highest recurrence rates.
Spatial analysis revealed a clear pattern: lowland coastal areas, including southern Hormozgan and Sistan, exhibited the highest drought susceptibility due to elevated temperatures and intense evapotranspiration, whereas upland regions such as Kerman and its surroundings experienced comparatively more favorable moisture conditions. Seasonal maps of SPEI distribution corroborated this spatial contrast, particularly during winter and spring.
Trend analysis using ITA revealed significant increases in maximum temperatures, particularly during summer, consistent with broader global warming patterns. Minimum temperatures increased during spring, summer, and autumn, but declined significantly in winter. Winter precipitation exhibited a pronounced declining trend, particularly within the 80–120 mm range. These shifts in temperature and precipitation patterns culminated in a downward trend in SPEI values, particularly within categories denoting moderate to extreme wetness. These transitions reflect not only increasing aridity but also the weakening of historically wet periods, underscoring the intensification of drought across the study region.
Conclusion
This study demonstrates that southeastern Iran is experiencing pronounced climatic shifts, characterized by declining precipitation, rising temperatures, and increasingly severe and prolonged droughts. These trends pose serious risks to water availability, agricultural sustainability, and the overall resilience of the region. The findings underscore the urgent need for adaptive climate strategies and sustainable water management policies to mitigate the intensifying impacts of climate change in this highly vulnerable region.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.منطقه جنوب شرق ایران جز نواحی کم باران و خشک محسوب می‌شود که در سال‌های اخیر با خشکسالی و کم‌آبی شدید روبه‌رو شده است. در این پژوهش، به‌منظور بررسی الگوهای خشکسالی در جنوب شرق ایران و شناخت رفتار پارامترهای اقلیمی در این منطقه، مقادیر کمینه و بیشینه دما و همچنین بارش 20 ایستگاه سینوپتیک طی دوره آماری 1995-2018 از سازمان هواشناسی کشور دریافت شد. سپس با محاسبه شاخص SPEI و تحلیل سری‌های زمانی این شاخص، مشخصه‌های مختلف خشکسالی از جمله شدت، مدت، بزرگی و فراوانی رویدادهای خشکسالی تعیین گردید. در ادامه با کاربرد آزمون روند نوآورانه (ITA)، معنی‌داری روند پارامترهای اقلیمی مورد ارزیابی قرار گرفت. نتایج نشان داد که منطقه موردمطالعه تا پایان سال 1997 تحت تأثیر ترسالی‌های شدید بوده و از اواخر دهه 1990، خشک‌سالی‌ها با شدت و تداوم بیشتری رخ‌داده‌اند. در این بازه شدیدترین خشکسالی با مقدار SPEI 4/4- در سال 2009 در چابهار و مرطوب‌ترین وضعیت با شاخص 5/2 در سال 1996 در انار ثبت گردیده است. همچنین ایستگاه بم با ثبت وخیم‌ترین شدت خشکسالی (48/1-)، خاش بیشترین فراوانی (75/18 درصد) و بافت، جزیره قشم و بم با بیشترین مقادیر شاخص بزرگی خشکسالی (به ترتیب 06/12-، 65/11- و 35/11-)، بحرانی‌ترین وضعیت را در محدوده پژوهشی داشتند. نتایج آزمون ITA نیز بیانگر شیب مثبت و روند افزایشی معنی‌دار دما در تابستان و کاهشی معنی‌دار بارش در دوره سرد سال بود؛ این تغییرات به‌وضوح در روند نزولی SPEI در طبقات مرطوب شاخص مشاهده گردید که بیانگر آسیب‌پذیری وضعیت آبی منطقه در برابر تغییرات اقلیمی اخیر است.https://jphgr.ut.ac.ir/article_104242_baaedff82279097e9c941ddd93f1cfab.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57220251017Downscaling of Air Temperature Data of CMIP6 Climate Models through MODIS-Based Land Surface Temperature: A case study of Yazd Provinceمقیاس کاهی داده‌های دمایی مدل گردش کلی CMIP6 به کمک داده‌های دمای سطح زمین (LST) سنجنده MODIS مطالعه موردی: استان یزد9911710435210.22059/jphgr.2025.389196.1007871FAالهاممهدوی نژادگروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران0009-0004-1405-4787سید زین العابدینحسینیگروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران0009-0002-6750-8741حسینملکی نژادگروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران0009-0001-8480-3120محمدحسنرحیمیانمرکز ملی تحقیقات شوری، سازمان تحقیقات، آموزش و ترویج کشاورزی، یزد، ایران0000-0002-7619-7925محمد امیناسدیگروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران0009-0000-2599-5890Journal Article20250428ABSTRACT
In climate change research, the downscaling of General Circulation Model (GCM) data from coarse grid scales to finer spatial resolutions or point-based units is crucial. This study presents a method for downscaling monthly air temperature data from CMIP6 the GCMs by exploiting the empirical relationship between the near-surface air temperature of Yazd Province and the Land Surface Temperature (LST) derived from the MODIS sensor. Temperature data from three GCMs, TaiESM1, ACCESS-CM2, and CanESM5, with spatial resolutions of 105 × 118 km², 138 × 175 km², and 310 × 270 km², respectively, were downscaled to generate monthly temperature maps of Yazd Province at a spatial resolution of 1 × 1 km². The results indicated that the proposed downscaling approach outperformed conventional techniques such as the delta method. Moreover, the CanESM5 model exhibited higher accuracy than the ACCESS-CM2 and TaiESM1 models when using this approach. Subsequently, the mean monthly and annual air temperatures of Yazd Province were simulated using the TaiESM1, ACCESS-CM2, and CanESM5 models under three Shared Socioeconomic Pathway (SSP) scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, for the period 2015–2100. The findings reveal an upward trend in the mean annual temperature of Yazd Province over the coming decades, with an estimated increase of 15–35% (average 22%) relative to the baseline period. Among the examined CMIP6 models and SSP scenarios, the greatest projected temperature rise occurred in the CanESM5 model under the SSP5-8.5 scenario.
Extended Abstract
Introduction
Climate change, driven by the increasing concentration of greenhouse gases, particularly carbon dioxide, in the atmosphere, alters key climatic variables such as air temperature, precipitation, wind speed, and the amount of solar radiation reaching the Earth's surface. Accordingly, growing attention has been directed toward climate change in recent years because of its profound economic and social repercussions associated with extreme climatic events.
In this context, projections from climate models, commonly referred to as General Circulation Models (GCMs) or Atmospheric–Oceanic General Circulation Models (AOGCMs), provide a basis for assessing the future impacts of climate change on variables such as air temperature and precipitation. One of the essential steps in applying GCM data for climate parameter projection (e.g., air temperature) is the downscaling process, which transforms coarse-resolution outputs into finer spatial grids or point-scale data.
Since most downscaling techniques depend on ground-based observations (e.g., air temperature records from meteorological stations), their main limitation lies in the sparse distribution and limited number of ground reference points. For instance, when downscaling air temperature, only data from meteorological stations within the study area can be used, which restricts the validity of the results to those specific locations. In contrast, the method proposed in this study incorporates ground data across the entire study area on a pixel-by-pixel basis, as described in the following section.
In this study, air temperature data from CMIP6 climate models (TaiESM1, ACCESS-CM2, and CanESM5) were used to project air temperature maps for Yazd Province, located in the central Iranian Plateau and forming part of the Kavir Desert region. This region experiences hot summers and cold winters, where air temperature plays a critical role in environmental dynamics and the management of natural resources in arid lands.
Methodology
The study area is Yazd Province, which covers approximately 73,000 square kilometers. It is located between latitudes 29.5° and 33.5° N and longitudes 52.5° and 56.5° E. The datasets employed in this study comprise monthly air temperature records from 19 meteorological stations across Yazd Province, MODIS-derived Land Surface Temperature (LST) maps with a spatial resolution of 1 km, and air temperature outputs from several CMIP6 climate models featured in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), including TaiESM1, ACCESS-CM2, and CanESM5.
These models have spatial resolutions of 0.942° × 1.25°, 1.875° × 1.25°, and 2.813° × 2.791°, corresponding to grid cell dimensions of approximately 105 × 118 km², 175 × 138 km², and 270 × 310 km², respectively. Each grid cell of the TaiESM1, ACCESS-CM2, and CanESM5 models covers approximately 12,400 km², 23,600 km², and 83,700 km², respectively. Each model incorporates three carbon dioxide emission scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, which were used to simulate monthly air temperature under future climate conditions.
Accordingly, monthly air temperature data from CMIP6 climate models were extracted for the periods 1850–2014 (baseline) and 2015–2100 (future) under the aforementioned emission scenarios. A regression analysis was conducted to examine the relationship between monthly air temperature and LST data from the 19 meteorological stations, thereby establishing the air temperature–LST regression models for the study area. These relationships were subsequently applied to produce air temperature maps and corresponding dimensionless monthly temperature maps of the study area at a spatial resolution of 1 × 1 km². These maps represent the ratio of each pixel’s monthly temperature to the mean air temperature of the entire region. The dimensionless maps were then used to downscale the coarse-resolution temperature outputs from CMIP6 models into monthly air temperature maps with a 1 × 1 km² pixel size.
Results and Discussion
The results of this study indicate that the proposed downscaling method for CMIP6 air temperature data performs with varying efficiency across the three climate models with different spatial resolutions. Specifically, the CanESM5 model achieved higher accuracy compared with the ACCESS-CM2 and TaiESM1 models. As noted earlier, each grid cell of the CanESM5, ACCESS-CM2, and TaiESM1 models covers approximately 12,400 km², 23,600 km², and 83,700 km², respectively, with spatial resolution decreasing in the same order. Therefore, it can be concluded that the proposed downscaling method tends to yield higher accuracy for climate models with coarser spatial resolution.
Among the CMIP6 climate models examined, BCC-CSM1.1, BNU-ESM, CanESM2, FIO-ESM, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, and NorESM1-M exhibit relatively coarse grid sizes. Therefore, these models are recommended for applying the proposed method to downscale their air temperature data. The observed variations among model outputs can also be attributed to inherent uncertainties in climate modeling, arising from differences in the underlying assumptions, input data, and auxiliary variables used for simulation. Nevertheless, further research is recommended to quantify and reduce these model uncertainties.
According to the final results, the mean annual air temperature of Yazd Province is projected to exhibit an upward trend over the coming decades, with an increase of 15–35% (average 22%) relative to the baseline period. Among the three analyzed climate models, the greatest temperature increase was projected by the CanESM5 model, whereas the highest warming occurred under the SSP5-8.5 scenario.
Conclusion
This study introduced a method for downscaling large-scale air temperature data from CMIP6 climate models using region-specific monthly air temperature–LST regression relationships. The proposed approach was successfully applied to downscale data from three CMIP6 models: TaiESM1, ACCESS-CM2, and CanESM5. Each model’s grid cell covers approximately 12,400 km², 23,600 km², and 83,700 km², respectively. Using this method, the final monthly air temperature maps of the study area were produced at a spatial resolution of 1 × 1 km².
The results also indicated that the proposed approach performs more accurately for CMIP6 climate models with coarser spatial resolution compared to those with finer grids. This method is recommended for downscaling CMIP6 air temperature data in regions with similar climatic and topographic characteristics. However, additional investigations are required before applying this approach to regions with different climatic and surface conditions.
Moreover, the main limitations of the proposed method are its time-consuming nature and lack of user-friendly implementation. These challenges are expected to be addressed through the development of automated algorithms or software tools based on this approach, capable of connecting directly to satellite databases to retrieve LST values for the study area.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.در مطالعات تغییر اقلیم، مقیاس کاهی و تبدیل داده‌های مدل‌های گردش کلی (GCMs) از سلول‌های بزرگ‌مقیاس به واحدهای کوچک‌تر و یا نقطه‌ای بسیار حائز اهمیت است. در این پژوهش روشی برای مقیاس کاهی داده‌های بزرگ‌مقیاس دمای ماهانه مدل‌های گردش کلی CMIP6 ارائه گردیده است که در آن، از رابطه بین دمای هوای استان یزد با دمای سطح زمین (LST) به‌عنوان یکی از محصولات تصاویر سنجنده مودیس استفاده گردید تا داده‌های دمایی سه مدل گردش کلی TaiESM1، ACCESS-CM2 و CanESM5 با دقت‌های مکانی 105*118 کیلومترمربع، 138*175 کیلومترمربع و 310*270 کیلومترمربع را به نقشه‌های دمای ماهانه استان یزد با دقت مکانی 1*1 کیلومترمربع بهبود دهد. نتایج حاصله نشان داد که روش پیشنهادی برای مقیاس کاهی دما، کارآیی مناسب‌تری نسبت به روش‌های پیشین نظیر روش دلتا داشته و در مدل‌های مختلف گردش کلی با دقت‌های مکانی مختلف دارای کارایی‌های متفاوتی بوده است؛ به‌طوری‌که دقت این روش در مدل Can-ESM5 نسبت به مدل‌های Access-CM2 و Tai-ESM2 بیشتر بود. در ادامه، اقدام به شبیه‌سازی میانگین دمای ماهانه و سالانه استان یزد به کمک مدل‌های TaiESM1، ACCESS-CM2 و CanESM5 تحت سه سناریوی SSP1-2.6، SSP2-4.5 و SSP5-8.5 در دهه‌های آتی (2100-2015 میلادی) شد. بر اساس نتایج به‌دست‌آمده از محدوده مطالعاتی استان یزد، میانگین دمای سالانه دهه‌های آتی استان یزد دارای روند افزایشی بوده و این مدل‌ها افزایش دمای 15 تا 35 درصد (میانگین 22 درصد) نسبت به دوره پایه را پیش‌نگری می‌کند. در بین سه مدل گردش کلی مذکور، بیشترین افزایش دما توسط CanESM5 پیش‌نگری شده و در بین سه سناریوی SSP بیشترین افزایش دما توسط سناریوی SSP5-8.5 پیش‌نگری شده است.https://jphgr.ut.ac.ir/article_104352_95e65a3f8c29060f24975475d80526e0.pdf