دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251109Comparative Analysis of the Spatio-Temporal Distribution of Thermal Stress Derived from ERA5-Land Reanalysis and Observational Data in Iranبررسی تطبیقی توزیع زمانی ـ مکانی تنش حرارتی به‌دست‌آمده از دادههای بازتحلیل ERA5-Land با دادههای مشاهداتی در ایران12110497910.22059/jphgr.2025.400974.1007901FAفائزهنوریگروه علوم زمین، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران0000-0003-2002-5152امیرحسینمشکوتیگروه علوم زمین، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران0000-0003-4040-8783عباسرنجبر سعادت آبادیگروه پیش آگاهی مخاطرات جوی، پژوهشگاه هواشناسی و علوم جو، تهران، ایران0000-0003-2239-0786مجتبیعظامگروه فیزیک دریا، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران0000-0002-1627-5741Journal Article20250602ABSTRACTThis study assesses the performance of ERA5-Land reanalysis data in identifying thermal stress conditions by comparison with synoptic station observations across Iran during the period 1991 to 2020. Daily temperature, humidity, and wind speed data from both datasets were employed to calculate the Effective Temperature and Baker thermal indices. Spatial and temporal variations were examined on a seasonal basis, with further comparisons conducted for cold-dry and warm-humid climatic zones. ERA5-Land showed strong agreement with observational data in reproducing the spatiotemporal distribution of thermal indices, particularly during spring and summer, with a correlation coefficient of 0.80 (p < 0.005). During winter, both datasets indicated extensive cold stress conditions; observational data showed that 79 percent of the country experienced mild to moderate cold stress, compared with approximately 95 percent in the ERA5-Land dataset. Within the cold-dry climatic zone, the number of observed very cold days declined from approximately 100 to fewer than 10, while days classified as no thermal stress increased from about 50 to more than 150. In contrast, ERA5-Land exhibited relatively stable values, indicating approximately 80 to 100 very cold days throughout the study period. During summer, observational data indicated that more than 62 percent of Iran experienced mild to moderate heat stress, whereas ERA5-Land estimated this proportion at approximately 43 percent. In the warm-humid climatic zone, the number of observed very hot days increased from fewer than 50 in 2000 to more than 350, while ERA5-Land captured only about 150 such days. Overall, ERA5-Land effectively captures broad-scale thermal stress patterns but tends to underestimate extreme local climatic variations.Extended AbstractIntroductionUnderstanding thermal conditions is essential for promoting human health, productivity, and overall well-being, as well as for informing urban and environmental planning. These conditions, shaped by temperature, humidity, wind, and solar radiation, can produce both beneficial effects, such as facilitating economic and social activities, and adverse outcomes, including heat- and cold-related illnesses and mortality. To evaluate thermal comfort and thermal stress, numerous bioclimatic indices have been developed. Their accuracy, however, depends strongly on the quality and spatial resolution of the input data.In recent years, reanalysis datasets such as ERA5-Land have been widely used in climate research because of their extensive spatial and temporal coverage. These datasets, generated through the integration of numerical modeling and meteorological observations, provide a consistent representation of atmospheric conditions. Nevertheless, their reliability at local scales and across diverse climatic zones requires careful validation. By contrast, observational data from synoptic stations, despite limitations such as incomplete spatial coverage and occasional missing values, generally provide more accurate measurements at the local scale. They therefore serve as an essential benchmark for evaluating the performance of reanalysis datasets.Previous studies conducted in Iran have largely relied on observational data, demonstrating the strong influence of thermal stress on energy demand, building design, and human thermal comfort. These findings also indicate a significant increase in heat stress during recent decades, particularly in lowland regions. Nevertheless, no comprehensive study has directly compared thermal indices derived from reanalysis data with those calculated from synoptic station observations across Iran.The present study addresses this gap by evaluating the performance of ERA5-Land data in calculating thermal indices and comparing the results with observational records across Iran’s diverse climatic conditions. This comparison is particularly relevant for applications in public health, energy management, and environmental planning. MethodologyTwo datasets were employed: ERA5-Land reanalysis data and synoptic meteorological observations from the Iran Meteorological Organization (IRIMO) covering the period 1991 to 2020. ERA5-Land, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), provides hourly data at a spatial resolution of 0.1 degrees. The observational dataset consisted of monthly mean air temperature, wind speed, and relative humidity records obtained from 130 synoptic stations with complete data coverage. Two thermal comfort indices, Net Effective Temperature (NET) and the Baker Index (CPI), were selected because both can be computed using variables available in reanalysis datasets. These indices were calculated on an hourly basis for both datasets using the Python programming language. ERA5-Land values were extracted from grid points nearest to the synoptic station coordinates and for specific observation times (00, 03, 06, 09, 12, 15, 18, and 21 UTC). Seasonal maps of the thermal indices were generated using Inverse Distance Weighting (IDW) interpolation in order to illustrate their spatial patterns. In addition, mean values of the indices were compared across two representative climatic regions, namely a cold-arid zone in northwestern Iran and a hot-humid zone along the southern coastal areas. The reliability of ERA5-Land was further evaluated by calculating correlation coefficients, coefficients of determination (R²), and corresponding statistical significance levels (p-values). Results and DiscussionBoth the observational dataset and the ERA5-Land reanalysis exhibited consistent spatial and seasonal patterns; however, ERA5-Land generally produced slightly cooler thermal values. During winter, most regions of Iran were affected by cold stress conditions, while near-comfort conditions were largely confined to the southern coastal areas. In summer, widespread heat stress was observed across central, southern, and eastern regions of the country. ERA5-Land demonstrated better performance during the warmer seasons, particularly spring and summer, showing stronger correlations with station-based observations, whereas its accuracy declined during autumn and, most notably, winter.Spatial analyses indicated that ERA5-Land tends to underestimate topographic influences in mountainous areas, frequently reporting temperatures lower than those observed at synoptic stations. Climatic comparisons revealed that observational data more effectively captured warming trends, particularly the increase in extremely hot days in the hot-humid southern regions after 2000. By contrast, ERA5-Land substantially underestimated this increasing trend.Statistical evaluations confirmed these findings; correlation coefficients were highest in spring and summer (r approximately 0.8), while weaker relationships were observed during autumn and winter (r approximately 0.5 to 0.7). Overall, ERA5-Land provides a reliable source for assessing thermal comfort during warmer periods; however, it requires calibration and integration with station observations for colder seasons and regions characterized by complex topography. ConclusionThis study demonstrates that ERA5-Land data can reliably reproduce large-scale patterns of thermal comfort across Iran during the period 1990 to 2020, with the strongest agreement observed in spring and summer. However, ERA5-Land tends to overestimate cold stress during winter and to underestimate heat stress during summer. These discrepancies were most pronounced in mountainous areas and in the hot-humid southern coastal region. Long-term analyses further revealed that observational data more accurately capture local warming signals, such as the marked increase in extremely hot days after 2013, trends that were not adequately reflected in the ERA5-Land dataset.In summary, ERA5-Land represents a valuable resource for identifying general patterns of thermal comfort and large-scale climatic conditions; however, it has limitations in representing localized variations and long-term warming trends. Integrating reanalysis datasets with ground-based observational data is therefore crucial for applications related to urban planning, energy management, and climate change adaptation. FundingNo funding was received for this study. Authors’ Contribution All authors contributed equally to the conceptualization and writing of this article. All authors approved the final manuscript and agreed on all aspects of the work. Conflict of Interest The authors declare no conflict of interest. AcknowledgmentsThe authors gratefully acknowledge the scientific advisors who contributed to this study.آسایش حرارتی از مؤلفه‌های کلیدی در ارتقاء کیفیت زندگی، بهره‌وری انسانی و برنامهریزیهای محیطی و شهری است. در این پژوهش، کارایی دادههای بازتحلیل ERA5-Land در شناسایی تنش‌های حرارتی با دادههای مشاهداتی ایستگاه‌های همدید طی دوره 1991 تا 2020 مقایسه شد. برای این منظور، دادههای روزانه دما، رطوبت و باد از هر دو منبع استخراج و شاخص‌های دمای مؤثر و بیکر محاسبه گردید. نتایج کلی نشان داد ERA5-Land در بازنمایی الگوهای فضایی و زمانی شاخص‌های حرارتی ایران، به‌ویژه در بهار و تابستان، تطابق قابل‌قبولی با مشاهدات دارد (ضریب همبستگی 0.80، p<0.005) در زمستان، هر دو منبع وجود تنش سرمایی گسترده را تأیید کردند؛ بر اساس مشاهدات، 79٪ و طبق ERA5-Land حدود 95٪ مساحت کشور در معرض سرمای خفیف تا متوسط قرار داشت. در منطقه سرد و خشک، مشاهدات کاهش روزهای «خیلی سرد» از حدود 100 به کمتر از 10 روز و افزایش روزهای «بدون تنش» از 50 به بیش از 150 روز را ثبت کردند، درحالی‌که ERA5-Land فراوانی روزهای «خیلی سرد» را تقریباً ثابت و در محدوده 80 تا 100 روز گزارش نمود. در تابستان، مشاهدات بیش از 62٪ و ERA5-Land حدود 43٪ مساحت کشور را با گرمایش خفیف تا متوسط گزارش کردند. در منطقه گرم و مرطوب، بر اساس مشاهدات، تعداد روزهای «خیلی گرم» از کمتر از 50 روز در سال 2000 به بیش از 350 روز افزایش یافت، اما ERA5-Land این تغییر را کمتر (حدود 150 روز) بازتاب داد. این یافته‌ها بیانگر توان نسبی ERA5-Land در شناسایی الگوهای کلی تنش حرارتی است، هرچند در ثبت تغییرات شدید اقلیمی محلی، دقت آن کاهش می‌یابد.https://jphgr.ut.ac.ir/article_104979_ca06b6444bbee0166ab2850a0400b8ba.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251109Analysis of 500 hPa Geopotential Anomalies and Their Impact on Dry and Wet Periods in Iran (1995-2024)تحلیل بی‌هنجاری‌های گردش‌های جوی تراز 500 هکتوپاسکال و تأثیر آن بر دوره‌های خشک و تر در ایران (1995-2024)234210481810.22059/jphgr.2025.403917.1007903FAعباسرنجبر سعادت آبادیگروه پیش‌آگاهی مخاطرات جوی، پژوهشکده هواشناسی، پژوهشگاه هواشناسی و علوم جو، تهران، ایرانJournal Article20250602ABSTRACT
This study examines anomalies in 500 hPa atmospheric circulation and their influence on wet and dry periods in Iran during 1995–2024, with particular attention to the relationship between precipitation variability and the ENSO-Modoki index. Rainfall observations from 179 stations were integrated with reanalysis-based geopotential height fields to identify dry and wet periods and to construct composite anomaly patterns. The results indicate that the highest levels of annual precipitation occur in January, February, March, April, and December. A declining precipitation trend is observed in most months, with the most pronounced reductions occurring in December, January, and March, which poses a serious challenge to national water resources. Analysis of monthly precipitation changes reveals substantial interannual variability, including increases exceeding 100 percent (March 1996: +136 percent; December 2004: +143 percent) and decreases greater than 80 percent (January 2021: −87 percent; March 2008: −89 percent). These fluctuations are primarily associated with changes in large-scale atmospheric circulation patterns, particularly the configuration of mid-tropospheric troughs. Analysis of 500 hPa geopotential height anomalies indicates that circulation patterns over the North Pacific, North Atlantic, Europe, and western Russia exhibit distinct configurations during dry and wet periods, whereby negative anomalies are associated with dry conditions and positive anomalies with wet conditions in Iran. A statistically significant positive annual correlation is identified between the ENSO-Modoki index and national precipitation (r ≈ 0.51), accompanied by strong negative correlations between 500 hPa geopotential heights over major oceanic regions and precipitation across Iran. These findings suggest that the integration of emerging teleconnection indices.
Extended Abstract
Introduction
Climate variability and climate change pose significant challenges for societies that are highly dependent on water resources, agricultural systems, and environmentally sensitive ecosystems. Iran, situated within the arid and semi-arid belt of the Middle East, is particularly vulnerable to variability in precipitation patterns. Large areas of the country receive less than 250 mm of annual precipitation, rendering these regions highly sensitive to even minor changes in atmospheric circulation patterns. Over recent decades, both the frequency and intensity of drought events have increased markedly, placing substantial pressure on agricultural productivity, groundwater resources, food security, and socio-economic stability. Accordingly, a comprehensive understanding of the atmospheric mechanisms governing precipitation variability is essential for sustainable water and resource management.
Among the principal indicators of large-scale atmospheric circulation, anomalies in 500 hPa geopotential heights serve as robust diagnostic tools for identifying synoptic-scale patterns associated with wet and dry periods. These anomalies reflect the dynamics of mid-tropospheric troughs, ridges, and jet streams, which play a central role in regulating moisture transport pathways and the spatial distribution of precipitation over Iran. Furthermore, the ENSO–Modoki index has emerged as an important teleconnection indicator influencing precipitation variability across the Middle East, underscoring the need to integrate mid-tropospheric diagnostics with large-scale teleconnection indices for more robust hydroclimatic assessments.
Methodology
This study integrates long-term precipitation observations with large-scale atmospheric reanalysis data to examine monthly precipitation variability over Iran during the period 1995–2024. Monthly precipitation data from 179 synoptic stations were compiled, and the spatial mean across all stations was calculated for each month to derive a national precipitation index. Monthly climatological means for the WMO reference period (1991–2020) were calculated and used to derive precipitation anomalies and corresponding percentage changes.
Temporal variability was assessed using simple linear regression to estimate trends, standard errors, and coefficients of determination, while the statistical significance of these trends was evaluated using the Mann–Kendall test. A ranked series of monthly percentage changes was used to identify the driest and wettest months, defined respectively as the seven lowest and seven highest values.
Atmospheric circulation patterns were analyzed using monthly 500 hPa geopotential height fields derived from the NCEP/NCAR reanalysis dataset. This pressure level was selected because it represents mid-tropospheric flow patterns that govern the major synoptic systems influencing Iran. Composite anomaly maps were constructed for dry and wet months to assess shifts in the intensity and spatial configuration of large-scale circulation features. In addition, multi-month composites for the October to May period were generated to highlight dominant dynamical signals.
To investigate large-scale teleconnections, correlation analyses were conducted between precipitation, the ENSO–Modoki Index (EMI), and geopotential height anomalies over four key regions identified from spatial patterns of anomalous circulation, namely the North Pacific, North Atlantic, Europe, and western Russia. Together, these analyses provide a coherent framework for understanding the atmospheric drivers that shape precipitation variability across Iran.
Results and Discussion
The analysis indicates a strong linkage between mid-tropospheric circulation anomalies and precipitation variability across Iran. During dry periods, subtropical high-pressure systems intensify while Mediterranean troughs weaken, leading to the formation of persistent anticyclonic ridges over the Middle East that inhibit the eastward propagation of mid-latitude cyclones. As a result, large-scale subsidence dominates, convective activity is suppressed, and widespread precipitation deficits occur. In contrast, wet periods are associated with deepened troughs and strengthened westerly flow, which facilitate the intrusion of moisture-bearing systems from the Mediterranean and Red Seas and result in more frequent and intense precipitation events.
The results further indicate that El Niño–Modoki phases, through the development of warm sea surface temperature anomalies in the central Pacific, modify subtropical pressure distributions, deepen the Mediterranean trough, and enhance the propagation of Rossby waves toward Iran, thereby creating favorable conditions for increased precipitation. Conversely, La Niña–Modoki conditions strengthen subtropical ridges and reduce moisture transport toward the region, a pattern that is closely associated with dry periods. Composite analyses reveal that 500 hPa geopotential height anomalies over the North Pacific and North Atlantic function as key centers that modulate Rossby wave patterns, thereby exerting a strong influence on precipitation variability over Iran.
Sub-monthly precipitation fluctuations are largely driven by baroclinic wave activity associated with horizontal temperature gradients, whereas intraseasonal variability is primarily controlled by the modulation of mid-latitude Rossby waves. Teleconnection patterns indicate that circulation anomalies in the North Pacific and Eurasian sectors strongly influence the intensity and eastward extension of the Mediterranean trough, thereby regulating moisture transport toward Iran. Trend analysis further demonstrates significant long-term declines in winter precipitation, particularly during December, January, and March, suggesting an increasing risk of prolonged droughts, reduced snowpack accumulation, and declining groundwater recharge.
Conclusion
This study demonstrates that precipitation variability in Iran is closely linked to mid-tropospheric circulation patterns at the 500 hPa level. The prevalence of anticyclonic ridges during dry months and intensified troughs during wet months underscores the central role of mid-tropospheric dynamics in shaping hydroclimatic extremes. ENSO–Modoki teleconnections modulate these circulation patterns by altering subtropical pressure distributions and Rossby wave propagation, with El Niño–Modoki favoring wet conditions and La Niña–Modoki reinforcing dry conditions. Integrated analyses confirm that both 500 hPa geopotential height anomalies and the ENSO–Modoki index play critical roles in explaining and predicting precipitation variability, with mid-tropospheric anomalies acting as intermediaries that transmit Modoki-related influences to the Mediterranean and Iranian regions.
These findings underscore the value of combining reanalysis-based mid-tropospheric diagnostics with large-scale teleconnection indices to improve the skill of seasonal precipitation forecasts. Future studies are encouraged to employ higher-resolution reanalysis datasets and numerical modeling approaches to deepen mechanistic understanding and further enhance predictive capability.
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.این مطالعه به بررسی بی‌هنجاری‌های گردش‌های جوی در تراز ۵۰۰ هکتوپاسکال و تأثیر آن بر دوره‌های خشک و تر ایران طی سال‌های ۱۹۹۵ تا ۲۰۲۴ و ارتباط تغییرات بارش با شاخص ENSO-Modoki می‌پردازد. با تلفیق دادههای بارش ۱۷۹ ایستگاه و میدان ژئو پتانسیل بازتحلیل، دوره‌های خشک و تر شناسایی و میانگین ترکیبی بی‌هنجاری‌ها محاسبه شد. نتایج نشان داد بیشترین سهم بارش سالانه مربوط به ماه‌های ژانویه، فوریه، مارس، آوریل و دسامبر است. روند بارش در اغلب ماه‌ها کاهشی بوده و بیشترین کاهش در دسامبر، ژانویه و مارس رخ‌داده که تهدیدی جدی برای منابع آب کشور محسوب می‌شود. تحلیل درصد تغییرات ماهانه بارش نشان داد در برخی سال‌ها نوسانات بسیار شدیدی رخ‌داده است؛ به‌گونه‌ای که افزایش‌هایی بیش از ۱۰۰ درصد (مارس ۱۹۹۶ با ۱۳۶+ درصد و دسامبر ۲۰۰۴ با ۱۴۳+ درصد) و کاهش‌هایی بیش از ۸۰ درصد (ژانویه ۲۰۲۱ با ۸۷- درصد و مارس ۲۰۰۸ با ۸۹- درصد) ثبت شد. این نوسانات عمدتاً ناشی از تغییر در الگوهای جوی، به‌ویژه ناوه‌های تراز میانی وردسپهر است. بی‌هنجاری‌های ارتفاع تراز ۵۰۰ هکتوپاسکال نشان داد در دوره‌های خشک و تر، الگوهای جوی نواحی اقیانوس آرام شمالی، اطلس شمالی، اروپا و غرب روسیه رفتار مشخصی دارند؛ به‌گونه‌ای که بی‌هنجاری‌های منفی (مثبت) در این مناطق با دوره‌های خشک (تر) ایران همراه است. همبستگی مثبت و معنادار سالانه میان انسو - مودوکی و بارش کل کشور (51/0≈ r) و همبستگی منفی قوی میان ارتفاع ۵۰۰ هکتوپاسکال اقیانوس‌ها و بارش ایران نشان داد که ترکیب شاخص‌های دور پیوندی با تحلیل‌های تراز میانی جو می‌تواند مهارت پیش‌بینی فصلی را بهبود بخشد.https://jphgr.ut.ac.ir/article_104818_a2c155c89f76d26f9bb008f833744b39.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251110Preparing a flood sensitivity map in the Eghlid urban basin using machine learning algorithmsتهیه نقشه حساسیت سیل در حوزه شهری اقلید با استفاده از الگوریتم‌های یادگیری ماشینی436210481910.22059/jphgr.2025.406510.1007909FAسعیدنگهبانگروه جغرافیا، دانشکده اقتصاد،مدیریت و علوم اجتماعی، دانشگاه شیراز، شیراز، ایران0000-0003-0701-5638مهریمرحمتگروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل. ایران0009-0001-4906-0317Journal Article20250603ABSTRACT
Flooding is among the most destructive natural hazards, causing substantial human casualties and economic losses worldwide each year. The preparation of flood risk maps and the identification of flood-prone areas play a critical role in reducing the potential damages associated with flooding. In this study, a flood sensitivity map was developed for the urban watershed of Eqlid in Fars Province using a range of flood conditioning factors, including slope, TRASP, topographic position index, stream power index, distance from the river, drainage density, surface curvature, rainfall, normalized difference vegetation index (NDVI), geology, land use, and soil type. Spatial data processing and factor preparation were conducted using ArcGIS Pro software. Subsequently, flood risk maps were generated using Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms implemented in the R programming environment, and the results of the two models were systematically compared. Model performance was evaluated using the receiver operating characteristic (ROC) curve, yielding accuracy values of 0.83 for the RF model and 0.84 for the SVM model, indicating good and nearly equivalent predictive performance. Feature importance analysis revealed that rainfall, soil type, land use, and drainage density were the most influential factors in the RF model, while rainfall, slope, soil type, and drainage density were identified as the dominant variables in the SVM model. The maps and datasets produced in this study can serve as valuable tools for urban planners and decision-makers in preventive planning, enhancing public awareness, and designing flood-resilient infrastructure.
Extended Abstract
Introduction
Flooding is one of the major natural hazards resulting from heavy rainfall, river overflows, and tidal influences. In recent years, increasing urbanization and climate change have intensified flood occurrences, particularly urban flooding, across many regions of the world. This phenomenon poses substantial social and economic risks, making accurate flood prediction essential for minimizing potential damages. Recent methodological advances, particularly in machine learning, have significantly enhanced the accuracy of flood hazard prediction. In this study, a flood hazard zoning map was developed for the urban watershed of Eqlid using advanced machine learning algorithms, namely Random Forest and Support Vector Machine. This approach integrates multi-source spatial data to support a comprehensive flood risk management strategy, with the potential to reduce damages associated with urban flooding.
Methodology
The study area covers approximately 40/623 hectares and has an elevation range from 1234 to 3213 meters above sea level, located in the southern part of Iran. Geographically, the watershed is located between 52°24'0" and 52°52'0" East longitude and between 30°43'40" and 31°4'20" North latitude. Initially, a flood point inventory map was prepared using 150 points, including both flood occurrence and non-occurrence locations, identified through field surveys, topographic maps, and data obtained from the General Directorate of Natural Resources of Fars Province. Additionally, 15 variables were selected for modeling based on methodologies commonly adopted in previous studies. These variables include elevation, slope, TRASP, drainage density, distance from the river, land use, geology, soil texture, Stream Power Index (SPI), Topographic Wetness Index (TWI), Topographic Roughness Index (TRI), Topographic Position Index (TPI), average annual rainfall, and the Normalized Difference Vegetation Index (NDVI). Multicollinearity among the selected variables was assessed using the Variance Inflation Factor (VIF) to identify potential linear correlations. Subsequently, machine learning algorithms, namely Random Forest (RF) and Support Vector Machine (SVM), were employed for flood hazard modeling. Finally, model performance was evaluated using statistical indices, including Accuracy, Sensitivity, Specificity, and the Kappa coefficient, in addition to the receiver operating characteristic (ROC) curve.
Results and discussion
Examining multicollinearity among the selected variables is essential for the reliable production of flood sensitivity maps. Among the 15 selected variables, elevation, TWI, and TRI were excluded because of high VIF values. The flood sensitivity maps indicate that areas with low to moderate susceptibility are predominantly located in the northern, northwestern, and southwestern parts of the watershed, whereas zones with high flood susceptibility are mainly concentrated in the central area.
Model performance evaluation showed that Sensitivity values for the training dataset were 0.93 for the RF model and 0.84 for the SVM model, while corresponding values for the validation dataset were 0.86 and 0.80, respectively. Specificity values reached 0.94 for RF and 0.83 for SVM in the training phase, and 0.86 and 0.73 in the validation phase. Accuracy values were 0.94 for RF and 0.89 for SVM for the training data, and 0.73 and 0.77 for the validation data, respectively. In addition, Kappa coefficients were 0.86 for RF and 0.73 for SVM in the training dataset, decreasing to 0.75 and 0.63 in the validation dataset. The AUC values for the RF model were 99% for the training data and 83% for the testing data, while the SVM model achieved AUC values of 93% and 84%, respectively, indicating high predictive accuracy for both models.
The variable importance analysis revealed that rainfall, soil type, land use, slope, and drainage density are the primary factors influencing flood occurrence, each contributing significantly to flood hazard formation. Although rainfall intensity tends to increase at higher elevations, the accumulation of runoff in lower elevation areas significantly elevates flood risk in these zones. Aridisols soils, characterized by low permeability and calcareous structure, promote increased surface runoff and accelerate flood occurrence. Land use types such as urban areas, agricultural lands, and moderately vegetated zones contribute to intensified flooding as a result of increased impervious surfaces and reduced infiltration capacity. Gentle slopes reduce flow velocity and prolong water retention time, thereby increasing the likelihood of flooding. Although high drainage density can facilitate rapid water conveyance, it also concentrates runoff in downstream areas, potentially leading to sudden and destructive flood events.
These findings are consistent with previous studies and highlight the importance of considering the combined effects of natural and anthropogenic factors in urban planning and water resource management to mitigate flood hazards. Overall, the machine learning models employed in this study produced accurate and reliable flood hazard maps that can effectively support urban management decisions and contribute to reducing flood-related damages in the urban watershed of Eqlid.
Conclusion
In this study, advanced machine learning models, including Random Forest and Support Vector Machine, were employed to generate flood hazard potential maps for the urban watershed of Eqlid with a high level of accuracy. The findings indicate that the interaction of climatic, geological, and hydrological variables, together with land use changes and urbanization expansion, plays a fundamental role in flood occurrence. The proposed models demonstrated a strong capability to accurately predict flood sensitivity and to provide valuable information for decision-makers involved in flood damage reduction planning. However, certain limitations, including the focus on a single watershed and constraints related to data availability, should be taken into account in future research. Overall, this research emphasizes that the application of data-driven methods and modern modeling approaches, alongside scientific analyses, can serve as an effective strategy for reducing the destructive impacts of floods in urban areas and supporting policymakers in informed decision-making.
Funding
There is no funding support.
Authors’ Contribution
Conceptualization and formal analysis and initial methodology and investigation by S.Negahban.; project administration and supervision and visualization and software writing review and editing by M.Marhamat.;
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.سیل از جمله بلایای طبیعی است که هر ساله خسارات مالی و جانی زیادی را در سراسر جهان به بار می‌آورد. با تهیه نقشه خطر سیل و تعیین مناطق مستعد سیل، می‌توان خسارات احتمالی این پدیده را کاهش داد. برای تهیه نقشه حساسیت سیل در حوزه شهری اقلید (استان فارس) عوامل مؤثر بر وقوع سیل شامل شیب، شاخص تابش خورشیدی، شاخص موقعیت توپوگرافی، شاخص قدرت جریان، فاصله از رودخانه، تراکم زهکشی، انحنای سطح، بارندگی، شاخص پوشش گیاهی، سنگشناسی، کاربری اراضی و نوع خاک استفاده شد. سپس، برای تهیه نقشه خطر سیل، از الگوریتم جنگل تصادفی (RF) و ماشین بردار پشتیبان (SVM) به‌عنوان دو مدل یادگیری ماشین در نرمافزار R پیاده‌سازی و نتایج آنها با هم مقایسه شده است. مدل‌های RF و SVM بر اساس منحنی ROC به ترتیب با دقت (83/0 و 84/0) اعتبارسنجی شده‌اند که هر دو عملکرد خوب و تقریباً یکسانی داشته‌اند. با تحلیل اهمیت ویژگی‌های لایه‌ها در مدل RF مشخص شد که مؤلفه‌های بارندگی، خاک، کاربری اراضی و تراکم زهکشی بیشترین تأثیر را بر خطر سیل دارند؛ همچنین در مدل SVM، بارندگی، شیب، خاک و تراکم زهکشی به ترتیب مهم‌ترین متغیرها برای پیش‌بینی خطر سیل شناخته شدند. نقشهها و دادههای حاصل از این مطالعه میتوانند به مسئولین و مدیران شهری به‌عنوان ابزارهای کلیدی برای برنامهریزیهای پیشگیرانه، آگاهسازی عمومی و طراحی زیرساختهای مقاوم در برابر سیل کمک کنند.https://jphgr.ut.ac.ir/article_104819_7f58f7069a6618e12220607122f8a81d.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251110Investigating Aeolian Sand Sources through Integrated Wind Analysis and Grain Morphometry: A Case Study of Boralan Plain, West Azerbaijan Province, Iranتحلیل منابع ماسه‌های بادی بر اساس تلفیق تحلیل باد و مورفومتری ذرات ماسه مطالعه موردی: دشت بورالان آذربایجان638510428510.22059/jphgr.2025.395829.1007885FAمهرانمقصودیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0002-4973-8327ابوالقاسمگورابیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، لیران0000-0002-2787-8687مجتبییمانیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایرانآیلاقلی زادهگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0009-0007-5868-0968Journal Article20250603ABSTRACTThis study aimed to identify the sources of aeolian sands in the Boralan Plain through an integrated approach combining wind analysis and sedimentological studies. Wind data spanning 20 years (2000–2020) from the Maku synoptic station, Iran, were analyzed. WRPLOT View 8 and a custom sand rose graphing tool were used to generate wind, storm, and sand transport roses, and to calculate wind indices, including drift potential (DP), resultant drift potential (RDP), resultant drift direction (RDD), and unidirectional index (UDI). To identify the origin of aeolian sediments, surface sand samples were collected from dunes, and grain-size and morphoscopic analyses were conducted, with distribution curves generated using GRADISTAT. Wind rose analysis showed that southeast (SE) and east (E) winds were most frequent, while storm rose analysis identified predominant erosive winds from the northwest (NW) and west (W). Sand transport potential was highest toward the east, with bidirectional winds (E-W) in autumn and winter significantly influencing aeolian sediment transport. Grain-size analysis revealed mean particle sizes ranging from 125 to 250 microns, with sorting ranging from well-sorted to poorly sorted, and distributions predominantly symmetrical. Sedimentological analyses identified the main sources of mobile sands in the Boralan Plain as alluvial deposits from the Qareh Su and Aras rivers, weathered basaltic flows, marginal alluvial fans, and eroded surfaces due to reduced vegetation from land-use changes. Overall, the aeolian sands originate from a combination of fluvial and aeolian processes driven by local geological and climatic conditions.Extended AbstractIntroductionWind erosion is a dominant geomorphic process in arid and semi-arid regions, forming landforms such as sand sheets and dunes. Iran, located within the global arid zone, has over two-thirds of its territory classified as arid or semi-arid. Low precipitation, climate change, and unsustainable land management have accelerated desertification nationwide. The Boralan Plain, in West Azerbaijan Province, northwest Iran, is highly vulnerable to sandstorms and dune migration, posing significant environmental challenges. This study investigates the impact of erosive winds, analyzes sand transport patterns, and employs granulometric and morphoscopic analyses to characterize sediment texture and provenance. The objective is to identify sediment sources and controlling factors, providing insights for sustainable environmental management in the region. MethodologyThis study examines the wind regime of the Boralan Plain using aeolian indices, including drift potential (DP), resultant drift potential (RDP), resultant drift direction (RDD), and unidirectional index (UDI). Wind data spanning 30 years (1990–2020) from the Maku synoptic station, Iran, were analyzed using WRPLOT View and a custom sand rose graphing tool to determine prevailing wind directions, assess sand transport capacity, and generate sand rose diagrams.To trace aeolian sediment provenance, sand samples were systematically collected and subjected to granulometric and morphoscopic analyses. For granulometric analysis, 100 g of each sample was processed via dry sieving with mesh sizes of 2000, 1000, 500, 250, 125, and 63 μm, including a collector for particles finer than 63 μm. Grain-size distributions were analyzed in GRADISTAT to calculate statistical parameters (mean size, sorting, skewness) following the Folk and Ward (1957) method, providing insights into transport distance and depositional environment.Morphoscopic analysis of 25 randomly selected grains per sample was conducted under a binocular microscope, documenting surface textures (e.g., matte texture, brilliance, speckling, and signs of physical or chemical weathering) to infer sedimentary processes and identify sediment sources. Results and discussionWind rose and sand rose analyses revealed dominant annual winds from the southeast (SE) and east (E). Sand transport potential, calculated using a threshold wind speed of 6.7 m/s (13 knots), exceeded 65 vector units (Fryberger’s method) annually. Sand transport is primarily directed eastward, with sediments mobilized from the southwest (SW), west (W), and northwest (NW). The strongest winds, occurring in winter and spring, originate from the SW, W, and NW sectors.Granulometric analyses showed mean grain sizes ranging from 125 to 250 μm, with sorting (σφ) ranging from 0.465 to 1.309, indicating moderately well-sorted to poorly sorted sediments. Skewness ranged from -0.541 to 0.449, with most samples showing nearly symmetrical distributions. Kurtosis values (0.9–1.11) indicated mesokurtic distributions.Morphoscopic analysis identified surface textures, including matte texture (45% of grains), brilliance (43%), speckling, and signs of physical or chemical weathering. Grain shapes were categorized into four abrasion levels: unabraded/angular (over 45%), slightly abraded, abraded, and well-rounded. The high proportion of angular grains suggests significant physical weathering and limited transport-induced rounding. ConclusionThis study indicates that alluvial deposits from the Aras and Qareh Su rivers are primary sources of aeolian sand in the Boralan Plain, alongside weathered basaltic flows and eroded surfaces. The predominantly angular grain shapes indicate physical weathering of local basaltic rocks. Overgrazing and land-use changes, leading to reduced vegetation cover, have increased the region’s susceptibility to wind erosion. Consequently, sand transport in the Boralan Plain is driven by the combined effects of fluvial and aeolian processes, highlighting the need for integrated environmental management strategies to mitigate desertification and promote sustainability in the region. FundingThere is no funding support. Authors’ ContributionAuthors 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 InterestAuthors declared no conflict of interest. AcknowledgmentsWe are grateful to all the scientific consultants of this paperاین پژوهش با هدف شناسایی منابع ماسه‌های بادی دشت بورالان از طریق رویکرد تلفیقی شامل تحلیل‌های بادی و مطالعات رسوب‌شناسی انجام شد. دادههای بادی 30 ساله (1990–2020) ایستگاه سینوپتیک ماکو، ایران، تحلیل شدند. نرمافزار WRPLOT View 8 و ابزار سفارشی ترسیم گلباد برای تولید گلباد، گل طوفان و گل حمل ماسه و همچنین محاسبه شاخص‌های بادی شامل پتانسیل جابجایی (DP)، پتانسیل جابجایی نتیجه‌گیر (RDP)، جهت جابجایی نتیجه‌گیر (RDD) و شاخص یک‌جهته (UDI) استفاده شدند. برای شناسایی منشأ رسوبات بادی، نمونه‌های ماسه‌ای سطحی از تپه‌های ماسه‌ای جمع‌آوری و آزمایش‌های گرانولومتری و مورفوسکوپی بر روی آنها انجام شدند و منحنی‌های دانه‌بندی با استفاده از نرمافزار GRADISTAT و روش Folk و Ward (1957) ترسیم شدند. تحلیل گلباد نشان داد بادهای جنوب شرق (SE) و شرق (E) بیشترین فراوانی را دارند، درحالی‌که گل طوفان، بادهای فرساینده شمال غرب (NW) و غرب (W) را مشخص کرد. پتانسیل حمل ماسه عمدتاً به سمت شرق است و بادهای دو جهته (E-W) در پاییز و زمستان نقش مهمی در جابجایی رسوبات بادی دارند. تحلیل دانه‌بندی نشان داد میانگین اندازه ذرات بین 125 تا 250 میکرون متغیر است، با جورشدی از جورشده تا ضعیف جورشده و توزیع عمدتاً متقارن. بررسی‌ها نشان داد منابع اصلی ماسه‌های روان دشت بورالان شامل آبرفت‌های رودهای قره‌سو و ارس، هوازدگی روانه‌های بازالتی، مخروط‌افکنه‌های حاشیه‌ای و سطوح فرسایش یافته ناشی از کاهش پوشش گیاهی به دلیل تغییرات کاربری اراضی هستند. بنابراین، منابع ماسه‌های بادی دشت بورالان ترکیبی از فرآیندهای آبی و بادی است که توسط ویژگی‌های زمین‌شناسی و اقلیمی منطقه هدایت می‌شوند.https://jphgr.ut.ac.ir/article_104285_4f0a1809e98b0f496cefbbf30cf06faf.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251023Identification and Analysis of Surface Interrupture Landforms in the Kopet-Dagh Region in Iran, Using Machine Learning Techniquesشناسایی و تحلیل گسیختگی اشکال سطحی زمین بر اساس روش یادگیری ماشین مطالعه موردی: کپه داغ ایران8710010552410.22059/jphgr.2026.385416.1007851FAمرتضیرضایی عارفیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0009-0005-0618-3834منصورجعفر بگلوگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0001-9745-2318ابراهیممقیمیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایرانسید موسیحسینیگروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0001-7161-8711مجیدفخریسازمان جغرافیایی نیروهای مسلح، تهران، ایرانJournal Article20250604ABSTRACTThis study investigates surface interrupture patterns in the tectonically active Kopet-Dagh region along the Iran–Turkmenistan border, utilizing advanced machine learning algorithms to enhance geomorphometric analysis for optimized water resource management and sustainable land-use planning. The methodology employed integrates high-resolution Digital Elevation Models (DEMs), Python-based analytical tools, and advanced statistical methods to extract key terrain attributes such as slope, aspect, and lineaments. Machine learning algorithms, including random forest and multivariate regression models, were applied to model surface interrupture patterns, achieving accuracies of 85% and 78%, respectively. The results reveal that surface interrupture predominantly occurs in areas with high lineament density (covering 49.51% of the study area), influenced by factors such as slope, fracture density, and surface-water flow patterns. The random forest model identified slope and fracture density as the most influential predictors, demonstrating the efficacy of combining modern machine learning methods with traditional geomorphometric analyses. This research provides an innovative framework for understanding surface water dynamics in tectonically active regions, offering valuable tools for water resource managers and land-use planners to optimize conservation and allocation strategies. The findings also contribute significantly to the expansion of knowledge of geomorphological and tectonic processes in semi-arid mountainous environments and support the development of sustainable water management strategies.Extended AbstractIntroductionSurface interrupture is a pivotal phenomenon in Earth sciences that significantly influences landscape evolution and surface water dynamics. This study examines surface interrupture patterns in the Kopet Dagh region of northeastern Iran, a tectonically active area, to support effective water resource management. It is motivated by the need to deepen understanding of tectonic and geomorphological controls on hydrology in semi-arid, mountainous regions. The primary objective is to identify and analyze surface interrupture patterns by integrating geomorphometric analysis with advanced machine learning techniques. Two research questions are addressed: (1) What are the main factors controlling surface interrupture patterns in the Kopet Dagh region? (2) How can machine learning improve the accuracy of modeling these patterns for water management? By proposing a novel analytical framework, this study aims to fill critical gaps in the understanding of surface water dynamics in this region. MethodologyTerrain attributes (slope, flow direction derived via the D8 algorithm, curvature, etc.) were derived from a 10 m Digital Elevation Model (DEM) using the open-source RichDEM Python library, which provides high-performance tools for hydrological analysis.Lineaments were extracted from the DEM using automated edge-detection algorithms, resulting in a lineament density covering about 49.5% of the study area.Fracture density was calculated by dividing mapped fracture lengths by unit area (mean ≈ 0.4355; median ≈ 0.4768). Flow accumulation was also modeled using the D8 algorithm to understand dominant drainage directions.Machine learning analyses included (a) multiple linear regression (yielding R² ≈ 0.399), (b) Random Forest analysis (to assess predictor importance), and (c) K-means clustering (to identify distinct spatial patterns of geomorphic attributes). These analyses were combined with conventional geomorphological interpretation to form a robust framework for modeling surface water flow and storage in the region.These methods were integrated into a comprehensive analytical framework for modeling and interpreting surface interrupture patterns and their implications for water resources. Results and DiscussionThe DEM analysis revealed that the Kopet Dagh study area (≈ 14,193 km²) has a rugged mountainous topography with elevations ranging from about 354 to 2,962 m. This terrain configuration, shaped by active tectonics, strongly influences surface water flow. Lineament analysis showed a high density of structural lineaments (average density ≈ 0.495), indicating pervasive tectonic deformation. The dominant flow direction is to the southeast, reflecting the regional gradient and channel network configuration. Fracture density (mean ≈ 0.4355) emerged as a key structural indicator of subsurface controls on infiltration and water storage.The Random Forest analysis confirmed that fracture density and slope are the most influential predictors of surface interrupture patterns. These findings imply that regions with high fracture and lineament densities are critical for hydrological connectivity. In practice, this suggests targeted water management strategies should focus on areas of intense tectonic fabric. More broadly, the integration of machine learning with geomorphometric analysis proved effective in identifying the factors that shape surface water dynamics, offering a powerful approach for optimizing water resource allocation. ConclusionThis study employed an integrated approach combining high-resolution DEM analysis, automated image processing, and machine learning to evaluate surface interrupture landforms in the Kopet Dagh region. The results emphasize that fracture density and slope are the primary controls on surface interrupture, while roughly half of the study area exhibits intense lineament coverage indicative of active tectonics. These insights offer valuable guidance for sustainable land-use planning and water resource management in similar semi-arid mountainous regions.However, limitations of this study include the moderate DEM resolution and limited field data for model validation. Future research should incorporate higher-resolution topographic datasets and field-based observations to refine the models and further improve prediction accuracy for water management applications. FundingThere is no funding support.Authors’ ContributionAuthors 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 InterestAuthors declared no conflict of interest. AcknowledgmentsWe are grateful to all the scientific consultants of this paper.این پژوهش به بررسی الگوهای گسیختگی سطحی در منطقه تکتونیکی فعال کپه‌داغ در امتداد مرز ایران و ترکمنستان می‌پردازد و با بهره‌گیری از الگوریتم‌های پیشرفته یادگیری ماشینی، دقت تحلیل‌های ژئومورفومتری را برای مدیریت بهینه منابع آب و برنامه‌ریزی کاربری پایدار زمین ارتقا می‌بخشد. روش مورداستفاده در این تحقیق با تلفیق مدل‌های رقومی ارتفاعی (DEM) با تفکیک مکانی بالا، ابزارهای تحلیلی پایتون و روش‌های آماری پیشرفته، ویژگی‌های کلیدی زمین‌چهر مانند شیب، جهت و خطواره‌ها را استخراج کرده است. الگوریتم‌های یادگیری ماشینی شامل جنگل تصادفی و رگرسیون چند متغیره برای مدل‌سازی الگوهای گسیختگی سطحی به کار گرفته شدند که به ترتیب دقت‌هایی معادل ۸۵٪ و ۷۸٪ را ارائه کردند. نتایج نشان داد، گسیختگی‌های سطحی عمدتاً در مناطقی با تراکم بالای خطواره‌ها رخ‌داده است که 51/49% از سطح منطقه را در برمی‌گیرند و تحت تأثیر عواملی مانند شیب، تراکم شکستگی و الگوهای جریان آب سطحی شکل‌گرفته‌اند. مدل جنگل تصادفی، شیب و تراکم شکستگی را به‌عنوان مهم‌ترین متغیرهای پیش‌بینی کننده شناسایی کرد که مؤید کارایی ترکیب روش‌های نوین یادگیری ماشینی با تحلیل‌های کلاسیک ژئومورفومتری است. این تحقیق چارچوبی نوآورانه برای شناخت پویایی‌های آب‌های سطحی در مناطق تکتونیکی فراهم می‌آورد و روش کاربردی ارزشمندی را در اختیار مدیران منابع آب و برنامه‌ریزان کاربری زمین قرار می‌دهد. یافته‌ها همچنین در گسترش مرزهای دانش در زمینه تشخیص و پایش فرایندهای ژئومورفولوژیکی و تکتونیکی در محیط‌های کوهستانی نیمه‌خشک نقش بسزایی ایفا می‌کنند و به تدوین راهبردهای پایدار در مدیریت منابع آب یاری می‌رسانند.https://jphgr.ut.ac.ir/article_105524_dc920fd02306ee53ed1169c14cbd7cf7.pdfدانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X57320251112Agricultural Role Activities Analysis in the Heritage Destruction of the Geomorphosites: A case study of Rig-e Akbar Abad, South Khorasan provinceتجزیهوتحلیل نقش فعالیت‌های زراعی در تخریب میراث ژئومورفوسایت‌ها مطالعه موردی: ریگ اکبرآباد، استان خراسان جنوبی10111710512910.22059/jphgr.2025.404490.1007904FAمهدیثقفیگروه جغرافیا، دانشگاه پیام نور، تهران، ایران0000-0001-8113-8471مریمربیعی گسکگروه آموزش جغرافیا،دانشگاه فرهنگیان،تهران.ایران0009-0002-5053-1849Journal Article20250605ABSTRACT
Among the major environmental challenges confronting the global community, desertification remains one of the most pressing. Aeolian landforms, recognized as significant geomorphosites, constitute valuable natural heritage resources that are widely used for scientific, educational, and tourism purposes. However, these landforms are highly vulnerable to human-induced degradation, which highlights the necessity of effective protection and management measures. The Rig-e Akbar Abad, located along the southern margin of the Mokhtaran Plain, represents one of the most prominent geomorphological manifestations of aeolian erosion in the region. In recent decades, intensified agricultural expansion accompanied by infrastructure development has increasingly threatened this aeolian geomorphosite. This study employed Landsat satellite imagery at ten-year intervals to assess changes in land use and vegetation cover. Following image preprocessing, land cover and land use were classified using a supervised classification approach, and the Normalized Difference Vegetation Index (NDVI) was calculated. Spatiotemporal variations within the geomorphosite were analyzed through the overlay of land-use maps from different time periods. The results indicate a substantial increase in agricultural land use accompanied by a noticeable decline in average NDVI values, suggesting intensified drought conditions. Spatial analysis further reveals that agricultural activities have gradually expanded from the dagh margins toward more distant areas, encroaching upon the sand dune system. Currently, approximately 7.87% of the Rig-e Akbar Abad area is under agricultural use, predominantly associated with irrigated farming. These findings highlight the increasing anthropogenic pressures on aeolian geomorphosites and underscore the urgent need for sustainable land management and conservation strategies in arid environments.
Extended Abstract
Introduction
Among contemporary global environmental challenges, desertification is widely regarded as one of the most significant threats. Within this context, geomorphosites, particularly those formed through aeolian processes, represent some of the most important expressions of natural heritage and geotourism in arid and semi-arid regions. In addition to their scientific and educational value, these geosites play a fundamental role in sustainable tourism development; however, they face serious threats arising from human exploitation. This research examines Rig-e Akbar Abad, one of the most significant manifestations of wind erosion in the southern part of South Khorasan Province, as a case study of a sensitive and fragile geomorphological heritage site, with the aim of analyzing the impact of expanding agricultural activities on the degradation and land-use changes of this geomorphosite.
Methodology
The research methodology is based on remote sensing techniques and spatial analysis conducted within a GIS environment. For this purpose, Landsat satellite imagery from two time points at a ten-year interval, namely 2010 and 2020, was utilized. Following the necessary preprocessing steps, including geometric and radiometric corrections, land use and land cover (LULC) maps and the Normalized Difference Vegetation Index (NDVI) were generated and compared for both periods. In addition to remote sensing data, statistical and field-based information was used, including reports from the Ministry of Agriculture, statistical yearbooks, and meteorological data such as temperature, precipitation, and wind. Data analysis was conducted using statistical tests, including the t-test, Pearson correlation, and chi-square test, implemented in ArcGIS, ERDAS IMAGINE, and Minitab software, in order to determine the relationships between changes in vegetation cover, drought conditions, and the expansion of agricultural land use in the vicinity of the rig.
Results and discussion
From a geomorphological perspective, the Rig-e Akbar Abad consists of four main sand dune units covering a total area of approximately 3,099.6 hectares. These dunes are predominantly young and active and include forms such as incomplete barchans, pyramidal dunes, and transverse dunes, which are partially stabilized by native vegetation including Calligonum, Alhagi, and Stipa agrostis. However, the conversion of these lands into farmland has disrupted their natural dynamics and poses a serious threat to their geomorphological stability. The expansion of agricultural development around the dagh, particularly in the southern parts of the plain, has disturbed the balance between natural aeolian and erosional processes, leading to the conversion of parts of the flat sand sheets into temporary farmlands.
Within the rig area, 87.7% of the total surface experienced a decrease in NDVI values, indicating a substantial decline in vegetation cover, while only 0.06% of the land remained pristine and unchanged. Concurrently, an increase of approximately 39.69% in irrigated lands and a decrease of 7.87% in active sandy areas were observed. This trend reflects the gradual conversion of sandy surfaces into agricultural land, partly driven by the overexploitation of groundwater resources. According to official statistics, the number of authorized and unauthorized wells in the region increased from 131 in 2002 to more than 250 in 2021, which has played a major role in the expansion of irrigated agriculture despite intensifying drought conditions.
Accuracy assessment of the land use classification, based on 150 field samples and an error matrix, yielded an overall accuracy of 86.89% and a Kappa coefficient of 0.799, indicating a high level of analytical reliability. These findings confirm that unbalanced agricultural development, which disregards environmental and climatic capacities, has resulted in a reduction in geomorphological diversity and the loss of some unique characteristics of the geosite.
The results indicate significant transformations in the spatial structure and ecological conditions of the Rig-e Akbar Abad geomorphosite during the study period. A significant decrease in NDVI values reflects a decline in vegetation cover and the intensification of drought conditions in the region. Spatial analysis of land use changes revealed that agricultural lands, particularly irrigated farming areas, increased at an annual rate of approximately 3.57%. In 2010, approximately 18.45% of the study area was allocated to agricultural use, whereas this proportion increased to more than 30.24% by 2020. The expansion of irrigated lands, particularly around the dagh margins and the southern parts of the rig, coincided with a 26.95% decrease in dagh areas and an 8.7% reduction in sand dune areas. The results of the chi-square test confirm that these changes are statistically significant at the α ≥ 0.05 level.
The overall findings of the study indicate that the uncontrolled expansion of agricultural activities and the unsustainable exploitation of groundwater resources are the primary factors contributing to the degradation of the Rig-e Akbar Abad geomorphosite. The expansion of irrigated farmlands, the reduction of rain-fed lands, and the significant decline in NDVI values, together with increasing drought, have transformed the environmental structure of the region. The Rig-e Akbar Abad, which could be promoted as one of the most significant geotourism attractions in eastern Iran within a sustainable development framework, now faces the risk of gradual degradation. The continuation of this trend will not only lead to the loss of valuable natural heritage but also result in the destabilization of the aeolian ecosystem and the intensification of soil erosion at the local scale.
Conclusion
It is necessary to formulate management policies aimed at the conservation and restoration of geomorphosites. These policies should include restrictions on land-use change in sandy areas, the regulation of water resource exploitation, the promotion of sustainable agriculture through low-consumption methods, and strategic planning for the geotouristic utilization of the Rig-e Akbar Abad. Furthermore, educating local communities and raising awareness of the scientific and economic values of geomorphological heritage can play a significant role in preventing further degradation.
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 بین چالش‌های زیست‌محیطی که جامعه جهانی در حال حاضر با آن روبروست، بیابان‌زایی در صدر قرار دارد از سوی دیگر سایت‌های متشکل از اشکال بادی یکی از پرطرفدارترین ژئومورفوسایت‌ها هستند. این میراث عمدتاً برای اهداف توسـعه علمی، آموزشـی و گردشـگری مورد بهره‌برداری قرار می‌گیرند. میراث‌های زمین‌شناختی و ژئومورفولوژیکی اغلب به دلیل فعالیت‌های انسانی، بسیار مستعد تأثیرات منفی و مخرب هستند و به همین دلیل ژئوسایت‌ها و ژئومورفوسایت‌های به حفاظت جدی نیاز دارند. ریگ اکبرآباد محصورشده در حاشیه جنوبی دشت مختاران ضمن اینکه از گستره قابل‌ملاحظه‌ای برخوردار است به لحاظ ژئومورفولوژیکی از مهم‌ترین مظاهر فرسایش بادی در این دشت است. تشدید فعالیت‌های اقتصادی طی دهه‌های اخیر در نتیجه توسعه فعالیت‌های زراعی و ایجاد ساختارهای زیربنایی، ژئومورفوسایت ریگ اکبرآباد را در معرض تهدید قرار داده است. در این مقاله دادههای ماهواره‌ای لندست به‌عنوان اساسی‌ترین منبع اطلاعاتی بافاصله زمانی 10 سال استفاده‌شده است. پس از انجام مراحل پیش‌پردازش در مراحل بعد پوشش طبیعی و کاربری اراضی به روش طبقه‌بندی نظارت‌شده و شاخص پوشش گیاهی نرمال شده محاسبه گردید. نهایتاً با انطباق کاربری‌های استخراج‌شده طی دوره بررسی میزان گسترش و وضعیت تغییرات انواع کاربری‌ها در محدوده این ژئومورفوسایت مورد تجزیهوتحلیل قرارگرفته است. بیلان تغییرات رخ‌داده در منطقه به نفع تغییرات افزایشی کاربری‌های زراعی است. کاهش معنی‌دار میانگین شاخص پوشش گیاهی نرمال شده در طول دوره حاکی از افزایش خشک‌سالی در منطقه است. بااین‌حال مشاهدات انجام‌شده در خصوص روند تغییرات مکانی اراضی کشاورزی آبی و نیز وضعیت مکانی کنونی آنها نشان می‌دهد که به‌تدریج این فعالیت‌ها به سمت حاشیه بیرونی دق گسترش‌یافته به‌طوری‌که در حال حاضر در بسیاری از مناطق دورتر و در قلمرو تپه‌های ماسه‌ای فعالیت‌های کشاورزی انجام می‌شود. نتایج حاصل‌شده نشان می‌دهند که 87/7 درصد از مساحت ریگ اکبرآباد تحت کاربری‌های زراعی به‌ویژه زراعت آبی قرارگرفته است. یافته‌ها، فشارهای فزاینده انسانی بر ژئومورفوسایت ماسه‌ای را برجسته کرده و بر نیاز مبرم به مدیریت پایدار زمین و استراتژی‌های حفاظتی در محیط‌های خشک تأکید می‌کند.https://jphgr.ut.ac.ir/article_105129_726a4e050db88e408d3bdab665222123.pdf