دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 26 The Role of Summer and Winter Shamal Winds in the Occurrence of Dust Storms in Western Iran نقش باد شمال تابستانه و زمستانه در رخداد توفان‌های گردوغبار در غرب ایران 1 19 100535 10.22059/jphgr.2025.372219.1007809 FA آذر بیرانوند نویسنده مسئول، گروه جغرافیای طبیعی، دانشگاه تهران، تهران، ایران قاسم عزیزی گروه جغرافیای طبیعی، دانشگاه تهران، تهران ، ایران امید علیزاده گروه فیزیک فضا، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران. Journal Article 2024 08 03 ABSTRACT We analyzed meteorological data from 33 synoptic stations spanning 1987-2022 to investigate dust storms in western Iran. Dusty days were defined as instances where suspended dust coincided with horizontal visibility below 1 km at a minimum of three nearby synoptic stations. We employed ERA-Interim data to identify synoptic patterns associated with dust storms. By analyzing surface pressure and geopotential height maps at lower atmospheric levels, we distinguished the summer and winter Shamal Wind patterns, both of which play a crucial role in dust transport to western Iran. Of the 229 recorded dust events, 70 were linked to summer Shamal Winds and 25 to winter Shamal Winds. Summer Shamal Wind storms predominantly originated from dust sources in northern Iraq, particularly around Lake Tharthar, Nineveh Province, and Kirkuk Province. In contrast, winter Shamal Wind storms showed a reduced contribution from central Iraqi lakes (15%), with dust primarily sourced from eastern and southeastern Iraq, northern Saudi Arabia, Kuwait, and southwestern Iran. Dust storms are among the most significant climatic phenomena affecting arid and semi-arid regions worldwide. The interannual variability of atmospheric general circulation patterns (Kaskaoutis et al., 2012; Jin et al., 2018) plays a crucial role in determining the frequency and intensity of dust events, making this relationship a subject of interest for researchers (Yu et al., 2015; Alizadeh-Choobari et al., 2016; Mashat et al., 2017; Beyranvand et al., 2019, 2023). One of the primary meteorological drivers of dust activity in the Middle East is the Shamal Wind. Walters (1990) categorizes Shamal Wind events into two types: short-term events lasting 24 to 36 hours with wind speeds of approximately 15.5 m s-1, and long-term events persisting for 3 to 5 days with wind speeds reaching 25.7 m s-1. Winds between 9 and 11.2 m s-1 can lift dust into the atmosphere. The impact of summer Shamal Winds on dust events has been emphasized in prior research (e.g., Yu et al., 2015; Francis et al., 2017; Ranjbar Saadat Abadi et al., 2022). Meanwhile, the winter Shamal Wind, a northwesterly wind lasting between 3 and 7 days from December to early March (Pakhirehzan et al., 2018), is categorized based on duration as one type lasting 1 to 3 days and another lasting 3 to 5 days. During winter Shamal events, strong frontal winds develop due to interactions between the polar frontal jet stream and the subtropical jet stream, with surface wind speeds in the central Persian Gulf reaching 15 to 20 m s-1. Methodology We analyzed horizontal visibility and present weather from 33 synoptic stations across Iran to identify dust storms in western Iran from 1987-2022. Dust events are where suspended dust coincides with horizontal visibility below 1 km in at least three synoptic stations. To assess the contribution of Shamal Winds, we utilized the hourly European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis Interim (ERA-Interim) data. The variables analyzed included sea-level pressure, geopotential height, temperature, horizontal (u) and meridional (v) wind components, vertical wind speed, divergence, convergence, and relative humidity across multiple atmospheric levels. Results and discussion Our analysis indicates that the highest frequency of dust events in Iran occurred between 2008 and 2012. Seasonal distribution analysis revealed that 37.2% of dust events occurred in spring, 36.3% in summer, 18.8% in winter, and 7.7% in autumn. The Shamal Wind is a dominant meteorological pattern responsible for dust storms in Southwest Asia. Over half of the summer Shamal Wind events occurred in 2000, 2005, 2008, 2009, 2010, and 2012. In fewer than 15% of cases, low-level jet streams (winds exceeding 15 m s-1) were observed at 850 hPa. However, in more than 80% of cases, near-surface wind speeds at latitudes 35°N or 31°N exceeded 10 m s-1. Approximately 65% of Shamal Wind cases were associated with frontal systems, low-pressure tongues, and low-pressure centers with pressures below 1000 hPa extending from central to northwestern Iraq. Analysis of geopotential heights at upper levels suggests that the development of subtropical high pressure plays a key role in the occurrence of summer Shamal Winds. Convergence between this ridge and the monsoon trough, particularly at 700 hPa, contributes to summer Shamal wind formation. Of the 20 dust storms linked to winter Shamal Winds, 75% lasted one day, while 25% persisted for two to three days. These storms occurred between October and May. In over 70% of cases, the development of the Siberian High extending into Turkey generated a strong pressure gradient, facilitating Shamal Wind formation. Dust events associated with the winter Shamal Wind were most frequent in 2008, 2011, and 2012. Conclusion Our analysis of the Pakistan-Afghanistan low-pressure system during dust events caused by summer Shamal Winds revealed that in 35.7% of cases, pressures in both locations were nearly similar, while in 37.5% of cases, pressures were lower in southwestern Afghanistan, and in 26.8% of cases, pressures were lower in Pakistan. Khosravi et al. (2015) found that the low pressure in Pakistan has increased over time, though this trend has weakened between 1980 and 2015. Our findings suggest that during dust events triggered by summer Shamal Winds, the low-pressure system over Afghanistan was slightly stronger and more influential than that over Pakistan. Comparing dust events driven by summer and winter Shamal Winds, we found that tilting vorticity was stronger in winter, and wind speeds during winter Shamal events exceeded those of summer Shamal events. In dust storms caused by the summer Shamal Wind, the primary dust sources were in the northern half of Iraq, particularly around Sarsar Lake, Nineveh Province, and Kirkuk Province. In contrast, dust storms driven by winter Shamal Winds exhibited a reduced role of central Iraqi lakes as dust sources (contributing only 15% of emitted dust), with eastern and southeastern Iraq, northern Saudi Arabia, Kuwait, and southwestern Iran playing a more prominent role. به‌منظور شناسایی توفان‌های گردوغبار در غرب ایران، داده‌های هواشناسی 33 ایستگاه همدیدی طی دوره زمانی 2022-1987 استفاده شد. روزهایی که پدیده تعلیق با دید افقی کمتر یا مساوی یک کیلومتر حداقل در سه ایستگاه مشاهده‌شده باشد به‌عنوان رویداد گردوغبار در نظر گرفته شد. به‌منظور استخراج الگوهای همدیدی از داده‌های ERA-Interim استفاده شد. با بهره‌گیری از نقشه فشار سطح و نقشه ارتفاع ژئوپتانسیل در سطوح پایین جو الگوی باد شَمال تابستانه و زمستانه که از مهم‌ترین الگوهای جوی انتقال و انتشار گردوغبار به غرب ایران هستند، جداسازی شدند. از 229 رخداد بررسی‌شده، 70 مورد توفان ناشی از باد شَمال تابستانه و 25 مورد ناشی از باد شَمال زمستانه بودند. در توفان‌های ناشی از وزش باد شَمال تابستانه، بیشترین میزان خیزش گردوغبار از کانون‌های گردوغبار نیمه شمالی کشور عراق بوده است. به عبارتی نقش کانون‌های اطراف دریاچه ثرثار، استان نینوا و استان کرکوک در توفان‌های ناشی از باد شَمال تابستانه قابل‌توجه است. در توفان‌های ناشی از باد شَمال زمستانه نقش دریاچه‌های مرکزی عراق در خیزش گردوغبار کمتر و در حدود 15 درصد و نقش کانون‌های واقع در شرق و جنوب شرق عراق، شمال عربستان، کویت و جنوب غرب ایران نسبت به توفان‌های ناشی از باد شمال تابستانه بیشتر است.

https://jphgr.ut.ac.ir/article_100535_ebb5899c3bf5b0ae0f4777dff7acf77e.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 26 Assessing the groundwater vulnerability with DRASTIC and Fuzzy-AHP models and validating the results based on the nitrate concentration (Case study: Fumanat area, Guilan province) ارزیابی آسیب‌پذیری آب‌های زیرزمینی با مدل‌های DRASTIC و Fuzzy-AHP و صحت‌سنجی نتایج بر اساس میزان غلظت نیترات، مطالعه موردی: محدوده فومنات استان گیلان 21 38 101211 10.22059/jphgr.2024.365677.1007789 FA معصومه اقبالی مردخه سنجش از دور و سیستم اطلاعات جغرافیایی GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران سعید حمزه نویسنده مسئول، گروه سنجش‌ازدور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران نجمه نیسانی سامانی گروه سنجش‌ازدور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران. میثم ارگانی دانشیار گروه سنجش از دور و سیستم های اطلاعات مکانی - دانشکده جغرافیا - دانشگاه تهران - تهران - ایران Journal Article 2024 08 03 ABSTRACT Groundwater is one of the most important sources of water supply in agriculture and drinking water, nowadays. These sources are very vulnerable to surface pollutant sources such as chemical and animal fertilizers, then identifying areas with high vulnerability is one of the great importance. The aim of this research is to findout vulnerable groundwater areas in the Anzali watershed (Fumanet sub-basin) using DRASTIC and Fuzzy-AHP models. The DRASTIC model uses seven data layers or parameters for modeling, including water depth (D), net recharge (R), aquifer saturation environment (A), soil environment (S), topography (T), impact of vadozone (I), and hydraulic conductivity (C), then it uses fixed weights for input parameters and fixed rankings for sub-parameters. The Fuzzy-AHP model was used to improve the weighting in the DRASTIC model. By validating 20 nitrate wells located in the Fomanat region, using multivariate and univariate linear regression, the output of the Fuzzy-AHP model improved the results compared to DRASTIC. In the vulnerability map produced, the DRASTIC method showed that 0.18% of the area had low vulnerability, 11.22% medium, 58.33% high and 30.42% very high vulnerability. Then, in Fuzzy-AHP, 99.6% low vulnerability, 13.11% medium, 56.45% high and 23.43% very high vulnerability were identified. Both models were successful in identifying areas with medium and high vulnerability risk, and the correlation of the Fuzzy-AHP model with the nitrate map of the region was positive. Waters are divided into two categories, surface and underground. surface waters are more exposed to pollution, but with a general view, it can be understood that underground waters are exposed to human settlements so the possibility of sewage entering them is higher. Groundwater supplies more than 60% of irrigated agriculture and 85% of drinking water resources. underground aquifers in areas that are more populated and economically rich is decreasing. Groundwater as the most important source of water supply plays an important role in agricultural, drinking and industrial uses. Water scarcity occurs in all populated continents. The increase in population also causes the demand for more food resources and the use of chemical fertilizers and pesticides. Throughout history, groundwater has been inseparable from human life and sustainable agricultural production, but it is not evenly distributed around the world. In Iran, the best source of drinking water supply is underground water. One of the main human inputs for the physical and chemical pollution of underground water is urban and industrial wastewater, which is increasing with population growth, urbanization and lifestyle changes. Groundwater pollutants include organic and inorganic pollutants such as arsenic, mercury, aluminum, lead, fluoride, nitrate, iron, pesticides, chlorinated solvents, where nitrate from fertilizers and animal waste is the most common pollutant. Vulnerability assessment is an essential part of land use planning and zoning protection approaches for groundwater protection. Identifying high risk areas of contamination is essential for healthy management of groundwater resources. Generally the environment is a phenomenon that does not exist in all parts of the world in the same way and any model in any area may not have the same output according to the altitude of the area (flat, hilly or mountainous) and the type of aquifer. Of course it is clear that each model has its advantages and disadvantages so the better results of one model compared to another model in the vulnerable area do not mean that model is rejected. The aim of this research is to find the vulnerable areas of underground water in the Anzali watershed (Fumanat sub-basin) using DRASTIC and Fuzzy-AHP models in the geographic information system. The land area of Fumanat includes rice fields, tea gardens and fish breeding ponds. The DRASTIC model uses seven data layers for modeling, including water depth (D), net recharge (R), aquifer saturation environment (A), soil environment (S), topography (T), unsaturated zone influence (I), conductivity and hydraulic (C). This model has a fixed weight for the input parameters and a fixed ranking for the sub-parameters that are below the criterion of the input parameters. Methodology First, we rank and weight the input layers of the model. The data includes the boundary of the study area, pumping test data, the location of nitrate wells and their concentration, piezometric wells and the water depth of the wells, the layers related to the type of underground soil, the amount of rainfall in the area along with the location of the stations. The elevations of the area and the slope were obtained from the ASTER satellite data with an accuracy of 30 meters and the soil layer of the area was also obtained from the Google Earth Engine system. The map layers of water depth, aquifer feeding, soil environment, aquifer environment, influence of unsaturated zone, hydraulic conductivity and permeability were prepared using IDW and Kriging methods, depending on which interpolation model had the best adaptation to the area. Results and discussion Fuzzy-AHP model was used to improve weighting in DRASTIC model. By validating 20 nitrate wells in Fumanat region, using multivariate and univariate linear regression, Fuzzy-AHP model had a better output than DRASTIC. For the Fuzzy-AHP method, the same layers classified in the DRASTIC method were used as input, but first, the layers were standardized using the Fuzzy membership command, and then they were divided into four regions with the Reclassify command, and finally, with AHP model and ranking, A vulnerability map was generated. The results of the DRASTIC method showed that 0.18% of the area had low vulnerability, 11.22% had medium vulnerability, 58.33% had high vulnerability and 30.42% had very high vulnerability, while in the Fuzzy-AHP model, 6.99% was identified as low vulnerability, 13.11% moderate, 56.45% high and 23.43% very high. By improving the weighting and using fuzzy functions to standardize the inputs and eliminate the uncertainty in the collected data, the vulnerability map had a better match with the nitrate map of the area. Nitrate does not occur naturally in the ground and enters the ground through surface contaminants, so it is used as a reliable indicator of groundwater vulnerability. Nitrate ions are usually found and measured in wells located in high-risk areas for groundwater pollution. The average nitrate measurement (seasonally in Fumanat area) was used. The nitrate concentration map of the region was also prepared by interpolation method from 20 wells in Fumanat, and then the value of those wells was obtained in all three vulnerability maps produced. Finally the relationship between them was discussed with regression model. Conclusion The validation and comparison of the two methods were done. The distribution of nitrate concentration is higher in the west and southwest of Fumanat region, and the concentration of nitrate is higher in the higher altitudes. The results showed that both models were successful in identifying medium and high vulnerable areas, but the correlation of the Fuzzy-AHP model with the nitrate map of the area was positive so this model had better output due to reality of vulnerability. The high vulnerability was due to the shallow depth of the water table, the unsaturated zone and the high amount of net nutrition under the Fumanat basin. Based on the results of the concentration of nitrate wells, the areas with higher altitude had more pollution, which was somewhat consistent with the DRASTIC model and Fuzzy-AHP. The cause of high pollution in high areas can be high rainfall and low water depth of piezometric wells, which causes a large amount of surface pollution to enter the underground water table. آب‌های زیرزمینی از مهم‌ترین منابع تأمین آب در کشاورزی و آب شرب آشامیدنی هستند. این منابع نسبت به منابع آلاینده سطحی از قبیل کودهای شیمایی و حیوانی بسیار آسیب‌پذیر هستند و تشخیص مناطق با آسیب‌پذیری بالا از اهمیت زیادی برخوردار است. هدف این پژوهش، یافتن ناحیه‌های آسیب‌پذیر آب زیرزمینی در حوضه آبریز انزلی (زیر حوضه فومنات) با استفاده از مدل‌های DRASTIC و Fuzzy-AHP است. مدل DRASTIC از هفت لایه داده برای مدل‌سازی استفاده می‌کند که شامل عمق آب (D)، تغذیه خالص (R)، محیط اشباع آبخوان (A)، محیط خاک (S)، توپوگرافی (T)، تأثیر ناحیه غیراشباع (I) و هدایت هیدرولیکی (C) می‌باشد و دارای وزن ثابت برای پارامترهای ورودی و رتبه‌بندی ثابت برای پارامترهای فرعی است. مدل Fuzzy-AHP برای بهبود وزن دهی در مدل DRASTIC، استفاده شد. با صحت سنجی 20 چاه نیترات واقع در منطقه فومنات، به‌وسیله رگرسیون خطی چند متغیره و تک متغیره، خروجی مدل Fuzzy-AHP نسبت به DRASTIC نتایج را بهبود داد. در نقشه آسیب‌پذیری تولیدشده، روش DRASTIC نشان داد که 18/0 درصد از مساحت منطقه دارای آسیب‌پذیری کم، 22/11 درصد متوسط، 33/58 درصد زیاد و 42/30 درصد آسیب‌پذیری خیلی زیاد بودند. سپس در Fuzzy-AHP مقدار 99/6 درصد آسیب‌پذیری کم، 11/13 درصد متوسط، 45/56 درصد زیاد و 43/23 درصد خیلی زیاد شناسایی شد. هر دو مدل در شناسایی ناحیه‌های با خطر آسیب‌پذیری متوسط و زیاد، موفق عمل کردند و همبستگی مدل Fuzzy-AHP با نقشه نیترات منطقه مثبت شد.

https://jphgr.ut.ac.ir/article_101211_3076debb44e6f7f8869cc2f8fc6245ca.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 26 Assessment of Land Use Changes Based on the Integration of Machine Learning Method and Spectral Angle Mapper Algorithm Using Training Samples Migration: A Case Study of Anzali Wetland Basin ارزیابی تغییرات کاربری اراضی بر مبنای تلفیق روش یادگیری ماشین و الگوریتم نقشه‌بردار زاویه طیفی با استفاده از نمونه‌های آموزشی متغیر، مطالعه موردی: حوضه آبریز تالاب انزلی 39 56 100823 10.22059/jphgr.2025.384462.1007848 FA علی حاجی الیاسی گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکدگان فنی، دانشگاه تهران، تهران، ایران محسن ناصری نویسنده مسئول، گروه مهندسی و مدیریت منابع آب، دانشکدگان فنی، دانشگاه تهران، تهران، ایران. سید پیمان بدیعی گروه مهندسی و مدیریت منابع آب، دانشکدگان فنی، دانشگاه تهران، تهران، ایران Journal Article 2024 08 03 ABSTRACT Given the significance of land use changes in spatial planning and the conservation of critical ecosystems such as wetlands, this study aims to analyze land use changes in the Anzali Wetland basin by integrating the Spectral Angle Mapper (SAM) algorithm with the Random Forest (RF) classifier, utilizing dynamic training samples within the Google Earth Engine (GEE). For this purpose, harmonized Sentinel-2 imagery from 2019–2023 and six spectral indices were employed to enhance classification accuracy. By collecting 500 ground points in the base year and using spectral angle difference analysis, new training samples were generated for 2021 and 2023, and classification maps were produced using the RF algorithm. The results show that over these five years, the most significant land use changes were a decrease in water bodies and an increase in wetlands and built-up areas. The modeling outcomes demonstrated an overall accuracy and kappa exceeding 87% for the study period. Additionally, the water body class exhibited the highest user and producer accuracy, exceeding 90%. The results of the relative importance of bands and indices also highlight their role in enhancing the accuracy of the generated maps. It was found that the green, blue, and red bands, along with the MNDWI, had the greatest effect on land use discrimination and the transfer of training samples. Based on the research findings, the hybrid method, incorporating dynamic sampling and automated sample generation, can effectively improve the accuracy of land use classification in wetlands. Therefore, it is a reliable and applicable method for future studies in other wetland basins. Wetlands are among the most significant aquatic bodies that interact with both natural and human ecosystems, providing diverse ecosystem services. Over the past century, more than half of the world's wetlands have disappeared, despite their ecological significance. Anzali International Wetland, which is listed under the Ramsar Convention, is one of the wetlands experiencing degradation due to stress factors such as climate change and human activities. These pressures have resulted in a decline in both the quantity and quality of its water body, leading to habitat loss and environmental deterioration. Understanding and analyzing land use changes in the watershed draining into the wetland, combined with spatial planning and environmental management, can help to mitigate wetland degradation. Thanks to the progress in satellite sensor technology, the assessment of land use changes has become increasingly feasible, offering significant time and cost savings compared to traditional methods. However, selecting an efficient classification method and ensuring its accuracy remain critical challenges. A review of previous studies indicates that although supervised classification techniques generally outperform other methods, no universally optimal approach has yet been identified for accurately classifying land use in wetland watersheds. Furthermore, while the Google Earth Engine (GEE) cloud platform offers distinct advantages over software like ENVI, it has been underutilized in wetland studies. Additionally, newer integrated approaches, such as combining machine learning algorithms with the Spectral Angle Mapper (SAM) method—designed to detect spectral differences between land cover types—have not been specifically applied to monitoring wetland changes. Another limitation of previous studies is the uniform approach to training sample collection, which is typically conducted manually through multiple ground-truth surveys. The classification models in these studies rely on predefined land use labels, potentially limiting adaptability. To address these gaps, this study, for the first time, utilizes a time series of harmonized Sentinel-2 imagery with a 10-meter resolution to assess land use changes in the Anzali Wetland watershed. Moreover, it is the first study to implement a new hybrid methodology on this platform by integrating the SAM algorithm with the Random Forest machine learning classifier, incorporating dynamic training samples to automatically generate land use classification maps for target years based on a base-year map. Additionally, the study evaluates the relative importance of spectral bands and indices to determine their contribution to land use classification within the study area. Methodology Summer season data (2019–2023) for the Fumanat sub-watershed were collected using the GEE platform and harmonized Sentinel-2 imagery. To enhance land use class separability, various spectral indices and bands were incorporated into the dataset. A total of 500 reference data points for the base-year map (2019) were sampled via Google Earth, corresponding to different land use classes. Subsequently, the SAM algorithm was applied alongside reference data, preprocessed base-year and target-year (2021 and 2023) images, and spectral angle calculations for land use classes to generate new training samples for the target yearsOf these samples, 70% were utilized for training the Random Forest model, while the remaining 30% were used for accuracy assessment based on overall accuracy, kappa coefficient, and other validation metrics. Finally, the relative importance of spectral bands and indices was evaluated based on their impact on classification performance. Results and discussion The results indicate that the new hybrid approach enhances the accuracy of land use classification maps in complex environments compared to previous methods. The mean values of overall accuracy and kappa coefficient demonstrate a high classification accuracy exceeding 85%. Furthermore, since the user’s and producer’s accuracy for all land use classes remained above 70%, confirming reliable training sample transfer and effective class separability. Additionally, the transfer of the training sample and the automated model training helped minimize human-induced errors in sample collection. The estimated land use area and percentage of changes over the study period revealed a 43% increase in built-up areas, while the Anzali Wetland water body experienced a decline of more than 28%. Analyzing the importance of spectral bands showed that in 2019, the red band had the highest effect on classification accuracy and sample transfer, while in 2021, the blue band played a more significant role. In 2023, the Modified Normalized Difference Water Index (MNDWI) proved to be the most influential factor in distinguishing land use classes and optimizing sample transfer accuracy. Conclusion The application of the SAM algorithm and spectral angle analysis between the base image and target images facilitates the automated generation of dynamic training samples. This approach significantly enhances the separability of land cover features in classification mapping, particularly in environments with high land use complexity and diversity, such as wetlands, compared to static training sample selection. Additionally, integrating this method with the Random Forest classifier improves model accuracy in land use classification. The observed land use change trends in the study area highlight the urgent need for conservation as well as sustainable restoration initiatives for Anzali Wetland, one of Iran’s and the world's most critical ecosystems. Therefore, it is recommended to conduct ecological capacity assessments and implement governance-based policies with the participation of stakeholders to ensure informed decision-making. Furthermore, revising spatial planning strategies and shifting economic policies that promote urban and industrial expansion should be prioritized to enhance the resilience of vulnerable resources such as Anzali Wetland. These measures are essential for halting its degradation and initiating long-term restoration initiatives. Funding This work is based upon research funded by Iran National Science Foundation (INFS) under project No. 4033513 با توجه به اهمیت تغییرات کاربری اراضی در آمایش سرزمین و حفاظت اکوسیستم‌های حیاتی نظیر تالاب‌ها، هدف از این مطالعه بررسی تغییر کاربری اراضی حوضه آبریز تالاب انزلی مبتنی بر تلفیق الگوریتم نقشه‌بردار زاویه طیفی (SAM) و جنگل تصادفی (RF) با به‌کارگیری نمونه‌های آموزشی متغیر در پلتفرم گوگل ارث انجین (GEE) است. لذا، از تصاویر سنتینل-2 هارمونایز شده در طی سال‌های 2019-2023 و شش شاخص طیفی به‌منظور افزایش دقت الگوریتم در طبقه‌بندی کاربری اراضی استفاده شد. با جمع‌آوری 500 نقطه زمینی در سال پایه و بر اساس اختلاف زاویه طیفی تصاویر، نمونه‌های آموزشی جدید در سال‌های 2021 و 2023 تولید و نقشه‌های طبقه‌بندی با استفاده از الگوریتم RF ایجاد شدند. نتایج حاکی از بیشترین تغییرات در طی 5 سال در کاربری‌های پهنه آب (کاهشی)، تالاب و انسان‌ساخت (افزایشی) است. نتایج مدل‌سازی نیز بیانگر دقت بالای 87 درصد برای صحت کلی و کاپا در بازه زمانی در نظر گرفته‌شده است. از طرفی کاربری پهنه آب با دقت بالای 90 درصد، بیشترین مقدار را برای شاخص‌های صحت کاربر و تولیدکننده دارد. نتایج اهمیت نسبی نیز بیانگر آن بود که باندهای سبز، آبی، قرمز و شاخص اصلاح‌شده اختلاف آب نرمال بیشترین تأثیر را در تفکیک کاربری‌ها و انتقال نمونه‌های آموزشی داشته‌اند. ازاین‌رو مشخص می‌شود که روش نوین اتخاذشده با توجه به در نظر گرفتن پویایی و خودکارسازی تولید نمونه‌های آموزشی جدید، قابلیت بالایی در تفکیک‌پذیری و بهبود دقت طبقه‌بندی در مناطق تالابی دارد؛ بنابراین این روش برای مطالعات آینده و سایر حوضه‌های تالابی نیز قابل‌اتکا است.

https://jphgr.ut.ac.ir/article_100823_f052ba1f0a553001ef61c12a2d88512c.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 27 Valuation of Nature Tourism Services for Tourism Development Planning: case study of Zarrin Gol Region Aliabad Katoul County ارزش‌گذاری خدمات طبیعت‌گردی به‌منظور برنامه‌ریزی توسعه گردشگری مطالعه موردی: منطقه زرین‌گل شهرستان علی‌آباد کتول 57 72 100826 10.22059/jphgr.2025.387369.1007861 FA حسن یگانه نویسنده مسئول، گروه مدیریت مرتع، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران راضیه شاهی مریدی گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران، Journal Article 2024 08 04 ABSTRACT Today, researchers emphasize the economic valuation of environmental resources to underscore the significance of the environment, its role in the national economy, and the need to protect these resources. In this study, the ecotourism value of the Zarrin Gol region in Azadshahr County, Golestan Province, was estimated using the conditional valuation method (CVM), and the factors affecting the value of this region were determined using the logit model. Moreover, a two-dimensional selection questionnaire was employed to gauge visitors' willingness to pay (WTP). The questionnaires were filled out by 181 visitors over a one-year period (2017–2018). The findings revealed that the average WTP for ecotourism in the Zarrin Gol area is 13,725 IRR per person and visit, amounting to an annual average of 82,350 IRR. Due to a lack of statistics on the annual number of tourists in the study area, the population data of Golestan province —the main tourist destination in the region—was used to estimate the annual economic value of this area. According to the 2016 Sensus data for Golestan Province, which stood at 1,868,819 people, the annual economic value of the region was estimated under two different visitation rate scenarios: 5% and 10%. These scenarios projected economic values of 1,282.5 million IRR and 2,565 million IRR, respectively. This indicates the significant appeal of the area to tourists. The findings also revealed that factors such as gender, visiting fee, monthly income, and overnight stay determined tourists' WTP. Therefore, it seems that by allocating a small part of the area to the private sector, it is possible to enhance welfare and health facilities for tourists, increase the efficiency of the region, and increase economic growth through job creation by collecting entry fees. Ecotourism involves traveling to natural areas while minimizing environmental harm and preserving local culture. This approach not only fosters economic growth but also enhances the quality of life for local communities. Today, ecotourism is recognized as a significant industry, serving as a source of income in many developed and developing countries. However, the value of the natural environment far exceeds the short-term benefits gained from exploiting it. Therefore, sustainable tourism must focus on preserving natural resources and respecting the needs of future generations (Godin, 1996). The appeal and rapid expansion of tourism in the twentieth century have led some observers to call it the "century of tourism." Global economic activities through ecotourism make substantial contributions to the economies of tourist destination countries (Barzegar et al., 2022). The economic valuation of environmental changes is grounded in people's preferences regarding alterations to their environment. Environmental gains and losses are assessed based on factors such as improvements or declines in human health, which can be measured by individuals' willingness to pay (WTP) or accept. By calculating these amounts, economic valuation enables the comparison of environmental impacts using consistent financial costs and benefits associated with any project or policy (Wit, 2006). The economic valuation of ecosystem services is a challenging yet crucial step toward conserving these natural resources amid the increasing demand for natural recreational areas. Methodology To estimate the ecotourism value of the Zarrin Gol region in Azadshahr County, Golestan Province, the conditional valuation method (CVM) was used, and the factors affecting the value of this region were determined using the logit model. Moreover, a two-dimensional selection questionnaire was used to gauge visitors' WTP. The questionnaires were filled out by 181 visitors over a one-year period from 2017 to 2018. Initially, 30 pre-test questionnaires were administered to determine the sample size needed for an accurate estimation of ecotourism value, identify proposed amounts, and address potential issues. The sample size was determined based on the formula provided by Mitchell and Carson (1989) with a 95% confidence level and a 5% margin of error for estimating recreational value. Ultimately, 181 questionnaires were collected, 5 of which were omitted due to incomplete responses and misunderstanding of WTP questions. Therefore, the analysis of the recreational value of the area was conducted using 176 questionnaires. Results and Discussion The results of this research indicated that gender, visiting fee, monthly income, and overnight stay are influential factors in tourists’ WTP. Notably, the amount offered in this study has a negative coefficient, suggesting that as the proposed payment for the tourism value of the area increases, the likelihood of tourists accepting the payment decreases. Considering the weight elasticity of the proposed amount variable (-0.4), with all other factors remaining constant, a 1% increase in the proposed price will decrease the likelihood of WTP by 0.4%. Additionally, considering the final effect of this variable (-0.00015), an increase of 10 IRR in the proposed amount will reduce the likelihood of WTP by 0.00015%. This finding aligns with the results reported by Raheli et al. (2013) and Ahmad Yousefi and Yeganeh (2016). In this study, the variable of individuals' monthly income directly affected their acceptance of the proposed fee. The estimated coefficient sign for the income variable was found to be positive, indicating that the likelihood of accepting this amount increases with an increase in income. According to the weight elasticity of income, a 1% increase in respondents' income leads to a 0.35% increase in the likelihood of WTP. According to the findings, around 60% of individuals are willing to pay for visiting and supporting the region. This data provides a strong justification for planners and officials to protect the area's natural resources and prevent their undervaluation. The likelihood ratio statistic was obtained at 38.05, indicating that the variations explained by the model are significant at a level greater than 1%. Additionally, the model's accuracy in predicting outcomes is over 70%. Conclusion The findings revealed that the average WTP for eco-tourism in the Zarrin Gol area is 13,725 IRR per person per visit, with an annual average of 82,350 IRR. This highlights the special value these ecosystems hold for tourists. The results of the logit model estimation indicated that the coefficients for proposed variables and gender are significant at the 1% level, while those for the overnight stay variable in camps and monthly income are significant at the 5% level. Overall, 58.5% of visitors were willing to pay an amount for recreational use of the region. Funding There is no funding support. Authors’ Contribution Conceptualization: H. Yeganeh; Data curation: R. Shahimoridi; Formal analysis: H. Yeganeh; Methodology: H. Yeganeh; Project administration: H. Yeganeh., R. Shahimoridi; Roles/Writing - original draft: H. Yeganeh Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. امروزه، ارزش‌گذاری اقتصادی منابع زیست‌محیطی به‌منظور نمایان ساختن اهمیت محیط‌زیست، نقش آن در اقتصاد ملی و حفاظت از منابع، به یکی از موضوعات موردتوجه پژوهشگران تبدیل‌شده است. در این راستا پژوهش حاضر به برآورد ارزش بوم‌گردی منطقه زرین گل در شهرستان آزادشهر، استان گلستان با استفاده از روش تمایل به پرداخت افراد پرداخته است. برای این منظور، از روش ارزش‌گذاری مشروط، پرسش‌نامه انتخاب دوگانه و الگوی کیفی لاجیت بهره‌گیری شده است. پارامترهای مدل با استفاده از روش حداکثر درست‌نمایی برآورد گردیدند. تعداد پرسش‌نامه‌های جمع‌آوری‌شده برای این تحقیق 181 عدد بوده که در بازه زمانی یک‌ساله (97-96) تکمیل گردیدند. برای محاسبه ارزش اقتصادی سالانه این منطقه، از آمار جمعیت استان گلستان به‌عنوان نماینده‌ای از گردشگران غالب منطقه استفاده شد. با توجه به جمعیت استان گلستان در سال 1395 معادل 1868819 نفر، تحت دو سناریوی مختلف بازدید 5 و 10 درصدی از منطقه، ارزش اقتصادی سالانه منطقه موردمطالعه به ترتیب برابر با 5/1282 و 2565 میلیون ریال برآورد گردید، که بیانگر ارزش بالای این منطقه از دیدگاه گردشگران می‌باشد. همچنین نتایج نشان داد، متغیرهای جنسیت افراد، مبلغ ارائه‌شده برای پرداخت، درآمد ماهیانه افراد و اقامت شبانه تأثیر معنی‌داری بر روی میزان تمایل به پرداخت گردشگران داشته‌اند. لذا به نظر می‌رسد با واگذاری بخش کوچکی از عرصه به بخش خصوصی می‌توان با دریافت ورودیه، امکانات رفاهی و بهداشتی مناسبی را جهت رفاه حال گردشگران، افزایش کارایی منطقه و افزایش رشد اقتصادی از طریق اشتغال‌زایی فراهم ساخت.

https://jphgr.ut.ac.ir/article_100826_82aceca62178a6b41de9d3d83aa7946f.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 27 Application of Entropy Theory and Principal Component Analysis to Determine Input Variables for Estimating Solar Radiation using Machine Learning Algorithms کاربرد تئوری آنتروپی و تحلیل مؤلفه اصلی جهت تعیین متغیرهای ورودی تخمین تابش خورشیدی با الگوریتم‌های یادگیری ماشین 73 87 101919 10.22059/jphgr.2025.386916.1007858 FA سمیه سلطانی گردفرامرزی گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران مژگان عسکری زاده گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه اردکان، اردکان، ایران Journal Article 2024 08 04 ABSTRACT Solar radiation is crucial in energy balance models and plant growth simulations. This research investigates the performance of Principal Component Analysis (PCA) and Shannon Entropy Theory (ENT) in determining the input for machine learning models – Random Forest (RF), Linear Regression (LR), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Decision Tree (DT), and XGBoost (XGB) – for estimating solar radiation at the Yazd synoptic station between 2006 and 2023. Daily data for average temperature, minimum temperature, maximum temperature, sunshine hours, relative humidity, and solar radiation were obtained from the Meteorological Organization. Extraterrestrial radiation, the relative Earth-Sun distance, solar declination angle, and maximum sunshine hours were calculated using existing formulas and selected as inputs for the pre-processing methods. The results of machine learning algorithms indicated their acceptable accuracy in estimating solar radiation. By reducing the dimensionality of the input data to the machine learning algorithms, the results showed that the Principal Component Analysis (PCA) method increased the model's accuracy. Among the models used, the PCA-SVR model showed the best result at the Yazd station with a coefficient of determination of 0.923 and an accuracy of 92.84%. It is worth mentioning that the Shannon entropy theory method failed to improve the modeling results compared to the method without initial pre-processing. This analysis shows that using dimensionality reduction techniques and selecting appropriate models can lead to greater accuracy and less computational complexity in prediction problems. However, sufficient care should be taken when selecting a pre-processing model for the initial data. Extended Abstract Introduction In terms of selecting all influential parameters and the lack of statistical information, the complexity of meteorological and hydrological systems makes complete modeling of these systems impossible. Using system modeling based on mathematical relationships is of interest in such conditions. Solar radiation is one of the important and effective meteorological variables in estimating evapotranspiration and the water needs of plants, and it is the energy source for all atmospheric and surface processes. Although the measurement of this variable has a relatively long history in Iran, due to the high costs of measuring instruments, many existing stations in the country lack a radiometer or pyranometer, or face issues such as calibration problems and the accumulation of water and dust on the sensor. Even at weather stations that measure radiation, there are days when radiation data is not recorded, or unrealistic values outside the expected range are observed due to equipment malfunctions or other issues. On the other hand, due to the many factors affecting solar radiation studies, it is impossible to include all elements in the relevant equations. As a result, only a limited number of these variables are applicable for estimating solar radiation using empirical and semi-empirical equations. In recent years, many researchers have focused their studies on using data mining methods and mathematical modeling to estimate solar radiation. Methodology The data used in this research are daily climatic variables measured at the Yazd synoptic station from 2006 to 2023. The Yazd station is located at 31.8974° North latitude and 54.3569° East longitude, at an altitude of 1216 meters above sea level. The average solar and extraterrestrial radiation at the Yazd synoptic station are 19.35 and 32 megajoules per square meter per day. The ratio of sunshine hours to maximum possible sunshine hours is 0.75, the average relative humidity is 27%, and the average temperature is 28°C. Data from 2006 to 2014 were used for calibrating the equations, and data from 2015 to 2023 were used for evaluating the results. Extraterrestrial radiation and maximum daily sunshine hours, which depend on the geographical latitude and day number based on the Gregorian calendar, were calculated using the relationships presented by Duffie and Beckman (1991). This research investigates the performance of Principal Component Analysis (PCA) and Shannon Entropy (ENT) for determining the input variables of Random Forest (RF), Linear Regression (LR), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Decision Tree (DT), and XGBoost (XGB) machine learning models in estimating solar radiation. Daily data for mean temperature, minimum temperature, maximum temperature, sunshine hours, relative humidity, and solar radiation were obtained from the Meteorological Organization. Extraterrestrial radiation, relative earth-sun distance, solar declination angle, and maximum sunshine hours were calculated using existing relationships and selected as inputs for the preprocessing methods. Results and discussion Results showed that, in the training phase, the employed models were well-trained and exhibited acceptable results. In the testing phase, the modeling results for the raw input data (without pre-processing) also yielded satisfactory results for all models. The coefficient of determination varied between 0.790 for the KNN model and 0.893 for the SVR model, depending on the algorithms used. In other words, regarding R-squared values, all the algorithms used showed good results for solar radiation prediction. Considering all evaluation metrics, the Support Vector Regression (SVR) algorithm performed better than other models to predict solar radiation with RMSE = 1.732, MSE = 0.003, MAE = 0.826, R² = 0.893, and an accuracy of 90.75%. Results showed that using Principal Component Analysis (PCA) for dimensionality reduction, the first principal component accounted for approximately 49% of the variance, and the second principal component accounted for approximately 36%. The first two principal components comprised over 85% of the original data's variability; therefore, these two components were considered as inputs for the predictive models to estimate solar radiation. Based on the training results, the PCA-DT and ENT-DT models exhibited the best performance in solar radiation estimation and model training at the Yazd station, achieving zero mean squared error and mean absolute percentage error, and a coefficient of determination of 1.00 compared to other models. The results of the model testing section indicate that the PCA-SVR model outperforms other methods. As can be seen, the PCA-SVR model, with a coefficient of determination of 0.923 and an accuracy of 92.84%, achieved the best results among the mentioned models at Yazd station, exhibiting the lowest error metrics. The ENT-DT model, with a coefficient of determination of 0.535 and an accuracy of 79.34%, showed weaker results among the models used at Yazd station. Conclusion Given the importance of accurate solar radiation estimation in hydrological phenomena and the need for advanced methods in its estimation, this research utilized Principal Component Analysis (PCA) and entropy theory for data pre-processing. Model inputs for the estimation models were identified using these two methods. Modeling was performed using Random Forest (RF), Linear Regression (LR), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Decision Tree (DT), and XGBoost (XGB) models. Entropy theory results indicated that at the Yazd station, solar declination angle, minimum temperature, minimum relative humidity, and average relative humidity were effective variables in estimating solar radiation. Furthermore, PCA reduced the number of input variables to two principal components, and modeling was performed using these two derived input variables. Overall, the modeling results showed that the PCA-SVR model outperformed other models in estimating solar radiation. In general, PCA pre-processing demonstrated that this method determines better inputs for the estimation models. It is worth noting that Shannon's theoretical method did not improve the modeling results compared to the method without pre-processing. This analysis shows that using dimensionality reduction techniques and selecting appropriate models can lead to higher accuracy and lower computational complexity in prediction problems. However, care must be taken when selecting the pre-processing model for the initial data. Similar research using new data or in different geographical conditions could also help further validate the results. Funding There is no funding support. Authors’ Contribution In this study, the authors' contributions are as follows: Somayeh Soltani-Gardfaramarzi was responsible for the study design, data collection, analysis, writing the initial draft, and final editing of the article, and Mojgan Askarizadeh was responsible for modeling and results. Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. تابش خورشیدی به‌عنوان یکی از متغیرهای مهم در مدل‌های بیلان انرژی و شبیه‌سازی رشد گیاهان اهمیت زیادی دارد. در این پژوهش عملکرد روش تحلیل مؤلفه اصلی (PCA) و تئوری آنتروپی شانون (ENT) برای تعیین ورودی مدل‌های یادگیری ماشین جنگل تصادفی (RF)، رگرسیون خطی (LR)، ماشین بردار پشتیبان (SVR)، نزدیک‌ترین همسایه (KNN)، درخت تصمیم (DT) و (XGB) XGBoost در برآورد تابش خورشیدی در ایستگاه سینوپتیک یزد در حد فاصل سال‌های 2006 تا 2023 موردبررسی قرار گرفت. متغیرهای میانگین دما، دمای کمینه، دمای بیشینه، ساعات آفتابی، رطوبت نسبی و تابش خورشیدی به‌صورت روزانه از سازمان هواشناسی دریافت و متغیرهای تابش فرازمینی، فاصله نسبی زمین تا خورشید، زاویه میل خورشیدی و حداکثر ساعات آفتابی با روابط موجود محاسبه و به‌عنوان ورودی روش‌های پیش‌پردازش انتخاب شدند. نتایج الگوریتم‌های یادگیری ماشین حاکی از دقت قابل‌قبول آن‌ها در تخمین تابش خورشیدی بود. با کاهش بعد داده‌های ورودی به الگوریتم‌های یادگیری ماشین، نتایج نشان داد که روش تحلیل مؤلفه اصلی دقت مدل را افزایش داد و در بین مدل‌های به‌کاررفته، مدل PCA-SVR با ضریب تبیین 923/0 و دقت 84/92% بهترین نتیجه را در ایستگاه یزد نشان داد. لازم به ذکر است که روش تئوری آنتروپی شانون نتوانست نتایج مدل‌سازی را نسبت به روش بدون پیش‌پردازش اولیه بهبود بخشد. این تحلیل نشان می‌دهد که استفاده از تکنیک‌های کاهش ابعاد و انتخاب مدل‌های مناسب می‌تواند منجر به‌دقت بیشتر و پیچیدگی محاسباتی کمتر در مسائل پیش‌بینی شود، هرچند در انتخاب مدل پیش‌پردازش داده‌های اولیه باید دقت کافی داشت.

https://jphgr.ut.ac.ir/article_101919_687725b080c408d1c89d5958b6cc6d61.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 56 4 2024 12 28 Analysis of the spatial and temporal distribution and changes of thunderstorms in southern Iran over the last three solar cycles واکاوی پراکنش و تغییرات زمانی –مکانی توفان‌های تندری در جنوب ایران در سه چرخه خورشیدی اخیر 89 106 101918 10.22059/jphgr.2025.387701.1007864 FA حسن لشکری گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران 0000-0002-6007-7275 زینب محمدی گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران محمد ناجی گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران علی رضا فدایی باش گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران Journal Article 2024 08 05 ABSTRACT Thunderstorms, one of the most common and characteristic phenomena of convective systems, can cause severe damage and psychological impacts on individuals. In this study, thunderstorm-related codes from synoptic meteorological stations with complete records during each solar cycle were extracted from the dataset of the Iranian Meteorological Organization over a 33-year statistical period, corresponding to solar cycles 22, 23, and 24 (1986–2018). The Inverse Distance Weighting (IDW) interpolation method was applied to illustrate the spatiotemporal variations of thunderstorms. The findings indicate that the frequency of thunderstorm events does not follow a regular pattern across solar cycles 22 to 24, with the highest occurrence observed during cycle 23. Overall, during the 33 years, 1997 recorded the highest number of thunderstorms, while the lowest occurred in 1990. In terms of spatial distribution, during the cold seasons, when Sudanese low-pressure systems are the dominant atmospheric pattern, the highest concentration of thunderstorm events was recorded at Bushehr station, with a decreasing trend toward the east and north. In contrast, the lowest frequencies were observed at Siri Island and Bandar Lengeh stations. The peak occurrences were reported in December and January, particularly at Bushehr during the cold months, whereas during summer, due to the influence of monsoon systems, a notable eastward shift in thunderstorm hotspots was evident, especially at Bandar Abbas, Lar, and Hajjiabad stations. Extended Abstract Introduction Natural hazards claim thousands of lives worldwide each year, with a significant portion of these fatalities attributed to weather-related hazards. Thunderstorms, regional floods, and other severe weather events are examples of such hazards. Thunderstorms are localized, mesoscale weather systems that develop within a limited area of 20 to 50 kilometers and depend on the height of convective clouds. Rainfall associated with thunderstorms and accompanying weather systems, coupled with lightning, are complex and composite atmospheric phenomena. Due to their unique dynamic and structural characteristics, these events typically negatively impact the natural environment, infrastructure, civil structures, transportation systems, and social activities. With its topographic features, large-scale climate systems influencing the region, and access to moisture sources from the southern warm seas, Southern Iran is susceptible to thunderstorm formation. This study uses station data to analyze the spatial variations in thunderstorm frequency over three solar cycles (22, 23, and 24). The relationship between thunderstorm frequency and solar cycles is of particular interest, as variations in solar radiation can influence atmospheric and climatic processes. Analyzing these changes contributes to a better understanding of the temporal and spatial patterns of thunderstorm occurrences and identifying high-risk areas. Moreover, these studies can provide a solid foundation for disaster risk management planning and more accurate weather forecasts in affected regions. Methodology For this study, data on thunderstorm occurrences was collected from 24-hour observational reports provided by the Iranian Meteorological Organization, covering a long-term period from 1986 to 2018. The study period was specifically selected to align with three 11-year solar cycles (Cycles 22, 23, and 24). This long-term and comprehensive dataset allows for examining the potential impacts of solar cycle variations on thunderstorm occurrence and intensity. Solar Cycle 22 spans from 1986 to 1996, Cycle 23 from 1997 to 2007, and Cycle 24 from 2008 to 2018. Therefore, in addition to analyzing the temporal and spatial variations of thunderstorms within each solar cycle, this research also compares these variations across different cycles. In the next step, the data obtained from the Iranian Meteorological Organization's database was organized and sorted in Excel based on stations and corresponding regions. For more accurate analysis, days with recorded thunderstorm occurrences in 6-hourly observational reports were extracted for each station and month of the year. Subsequently, the frequency of thunderstorm occurrences was calculated for each year within the 33-year study period (1986-2018). The Inverse Distance Weighting (IDW) method was employed to analyze and visualize the spatiotemporal variations of thunderstorms. This analysis was conducted on a monthly average basis for each solar cycle. In this phase, thunderstorm data and the stations' geographical coordinates were entered into ArcMap software. Subsequently, using the IDW method, spatial distribution maps of the data were generated, and the resulting outputs were prepared for spatial analysis and the examination of spatiotemporal patterns. Results and discussion The primary findings of this research can be summarized as follows. In terms of temporal distribution, the frequency of thunderstorm occurrences did not follow a consistent pattern from Solar Cycle 22 to 24. Solar Cycle 23 exhibited the highest frequency of thunderstorms compared to the preceding and succeeding cycles. The analysis of temporal distribution revealed that the highest number of occurrences was reported during the years of Solar Cycle 24, indicating a significant influence of solar activity on this phenomenon. For instance, within this cycle, the peak frequency was recorded in 2009 (the beginning of the cycle), while the lowest was in 2016 (towards the end of the cycle). Overall, during the 33-year study period, the highest number of thunderstorms occurred in 1997 and the lowest in 1990. In terms of monthly distribution, the highest frequency of thunderstorms was observed in December and January, while the lowest occurred in September. Regarding spatial distribution, the thunderstorm hotspot was located over the Bushehr station during colder months when Sudanese systems dominate. This hotspot exhibited a decreasing trend towards the east and north. Another hotspot was observed over Bandar Abbas and Shiraz stations in February and April. The spatial distribution pattern in the early spring months resembled that of the cold months. However, in the final month of spring and throughout summer, the spatial pattern of thunderstorm occurrences changed significantly, with hotspots shifting eastward to stations such as Lar and Bandar Abbas. This phenomenon indicates the undeniable impact of topographic conditions on the intensification of incoming systems over the region. Conclusion Due to their unique dynamic and structural characteristics, thunderstorms cause widespread devastation to the environment, infrastructure, transportation systems, and social activities. Beyond inducing psychological distress among affected populations, these phenomena pose significant challenges to aviation and maritime operations. Neglecting the impacts of thunderstorms can lead to fatal accidents for passengers, pilots, and crew members of both air and sea vessels. With its distinctive topography, influence of large-scale climate systems, and proximity to the warm southern seas, Southern Iran provides a conducive environment for thunderstorm formation. The findings of this study reveal that November experiences the highest frequency of reported thunderstorms in Southern Iran. Bushher Airport meteorological station records the most frequent occurrences during this month and is identified as one of the primary entry points for storm systems from the southwest into the country's coastline. Furthermore, an increase in thunderstorm activity is observed around Bandar Abbas in March. Results indicate that the peak thunderstorm activity in Southern Iran occurs during the autumn season, although significant activity is also recorded in spring. Nevertheless, thunderstorms can occur throughout the year. A detailed analysis of monthly and seasonal frequencies reveals that May, April, June, and October exhibit the highest occurrence rates. Funding There is no funding support. Authors’ Contribution All of the authors approved the content of the manuscript and agreed on all aspects of the work. Conflict of Interest Authors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper. چکیده توفان‌های تندری، به‌عنوان یکی از پدیده‌های متداول و شاخص سامانه‌های همرفتی، موجب خسارت‌های شدید و آثار روانی بر افراد می‌شوند. بدین منظور، ابتدا کدهای مربوط به پدیده توفان تندری در دوره آماری ۳۳ ساله که متناظر با سیکل‌های ۲۲، ۲۳ و ۲۴ خورشیدی (۲۰۱۸-۱۹۸۶) بودند از داده‌های سازمان هواشناسی کشور برای ایستگاه‌های سینوپتیک که در هر چرخه اقلیمی دارای آماره کامل بوده‌اند، استخراج گردید. در ادامه به‌منظور نمایش تغییرات زمانی-مکانی توفان‌های تندری، از روش IDW استفاده شد. یافته‌های این پژوهش نشان می‌دهد که از چرخه خورشیدی ۲۲ الی ۲۴، فراوانی رخداد توفان‌های تندری از الگوی منظمی پیروی نمی‌کند و چرخه ۲۳ بالاترین رخدادها را داشته است. در مجموع در دوره آماری 33 ساله بالاترین رخداد مربوط به سال 1997 و کمترین رخداد توفان تندری در سال 1990 گزارش‌شده است. از لحاظ پراکنش مکانی توفان‌های تندری در فصول سرد سال که سامانه‌های سودانی پدیده غالب این دوره از سال می‌باشد هسته پر رخداد توفان‌های تندری بر روی ایستگاه بوشهر قرار دارد. این هسته پر رخداد به سمت شرق و شمال روندی کاهشی دارد. درحالی‌که ایستگاه‌های جزیره سیری و بندرلنگه کمترین میزان را داشته‌اند. بیشترین رخداد در ماه‌های دسامبر و ژانویه و از نظر مکانی در ماه‌های سرد بر ایستگاه بوشهر متمرکز است درحالی‌که در تابستان جابجایی هسته‌ها به سمت شرق، به‌ویژه در ایستگاه‌های بندرعباس، لار و حاجی‌آباد، به دلیل فعالیت سامانه‌های مونسونی، قابل‌توجه بود.

https://jphgr.ut.ac.ir/article_101918_b4548d3c11aa7116b6a91a2d871b1e33.pdf