دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 1 2026 04 05 Evaluation and Comparison of Different Data Mining Models for Identifying Areas at Risk of Gully Erosion: A case study of Mian Ab Watershed in Khuzestan Province ارزیابی و مقایسة الگوریتم‌های مختلف داده‌کاوی جهت تهیه نقشه خطر فرسایش آبکندی در حوضه آبخیز میان‌آب شوشتر 1 18 104353 10.22059/jphgr.2025.387445.1007862 FA یاسمین غبیشاوی گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران رضا ذاکری نژاد گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران زینب میری گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران Journal Article 2025 10 25 ABSTRACT Gully erosion refers to the formation and expansion of erosional channels in the soil as a result of concentrated water flow. Generally, when the eroded channels on the land surface become so large that they can no longer be leveled through conventional farming operations, they are referred to as gullies. The objective of this study is to compare the CART and SVM models for identifying high-risk areas of gully erosion and determining the most influential parameters contributing to gully erosion in the Mian-Ab watershed of Shushtar County in Khuzestan Province. First, the locations of existing gullies were recorded using satellite images, Google Earth software, and the Global Positioning System (GPS). Then, the independent variables influencing gully erosion were prepared, and after assigning their respective values, modeling was conducted in R software to delineate or predict gully-prone areas. In total, 3,000 data records related to gully erosion were collected, with 70% used for training and 30% for testing the models. According to the results, the SVM model, with an R² value of 0.846, demonstrated higher accuracy compared to the CART model. Moreover, based on the gully erosion risk maps, the very low risk class covered the most significant portion of the watershed area (approximately 59%), followed by the low, moderate, high, and very high-risk classes, covering 12.27%, 10.29%, 9.23%, and 8.49% of the watershed area, respectively. Based on the findings, the land-use, vegetation cover, and soil texture indices played the most significant roles in the occurrence and expansion of gully erosion. Extended Abstract Introduction Soil erosion and sediment production are significant limitations in the use of water and soil resources. Currently, gully erosion is becoming one of the most significant forms of erosion worldwide and has thus received considerable attention from researchers in recent decades. Various studies have been conducted on how gully erosion occurs and develops in different climates. In many regions, a substantial amount of sediment generated in watersheds is attributed to gully erosion. Notably, around 125 million hectares of Iran's total land area of 165 million hectares are susceptible to water erosion. Soil erosion leads to soil degradation and abandonment of farmland, resulting in irreparable damage. Developing appropriate strategies for preventing and mitigating gully erosion requires a complete understanding of its dynamics and controlling factors. Given the development of machine learning models and their successful performance in various scientific fields, many researchers have utilized machine learning models for hazard mapping and predicting erosion risk. The results indicate the successful and accurate performance of these models. This study also evaluates the effectiveness of two machine learning algorithms, SVM and CART, in mapping the risk of gully erosion. Methodology In this study, the sensitivity of gully erosion in the Mianab-Shushtar watershed has been investigated, and machine learning methods have been utilized to predict gully erosion sensitivity. In the first step, a map of gully locations has been prepared, using various methods and tools including satellite images, aerial photographs, and field visits. Subsequently, topographic indices such as elevation, slope, slope aspect, soil texture, Stream Power Index (SPI), Topographic Wetness Index (TWI), vegetation cover (NDVI), lithology, distance from rivers, Terrain Ruggedness Index (TRI), distance from roads, soil erodibility index (K), rainfall erosivity index (R), and drainage density index are examined as environmental parameters influencing gully erosion occurrence. In the next step, 70% of the gullies under study are randomly selected and used as training data, while the remaining 30% are utilized as validation data. In the following stage, the map of gully locations is entered into the SVM and CART models as the dependent variable, with the environmental layers serving as independent variables to model the occurrence of gully erosion. Results and discussion In this study, the variables of landforms, elevation, slope, slope direction and length, vegetation cover, soil texture, distance from roads, land-use, lithology, soil erodibility, topographic moisture, flow power, drainage density, erosive rainfall, and distance from rivers were selected and examined as influential factors in gully erosion. Erosion points were used as the dependent variable in this research. Field surveys and ground surveys were employed to collect these points. The exact locations of the gullies were recorded using handheld GPS and then reviewed and corrected using Google Earth software. In total, 3,000 gully erosion points were collected, representing the spatial distribution of this phenomenon in the area. Most points affected by this type of erosion are found in the southern and eastern regions of the watershed. Next, to obtain a potential gully erosion map for the watershed, layers of the studied indicators were prepared. After preparing the independent and dependent variables, a risk zoning map for gully erosion was created using the CART model in R software. The correlation coefficient between the predicted values of the CART model and the observed values was 0.889. The R² coefficient for this model was calculated to be 0.791, which is considered an appropriate level of determination for models related to gully erosion. According to the zoning map produced by the CART model, areas with very high risk are primarily concentrated in the eastern and southeastern parts of the basin, which coincide with sloped and foothill lands. Areas with high risk are distributed in a band adjacent to these regions. In contrast, areas with moderate risk are mainly located in the center of the basin and near stream networks. The results from the SVM model demonstrate its significant performance in predicting and assessing erosion. The evaluation results indicated a correlation coefficient of 0.92 between observed and predicted values, showing a robust correlation between actual and predicted data. Additionally, R² was calculated to be 0.846. Statistical indicators suggest that the SVM model successfully identified and modeled the complex patterns of gully erosion in the study area. Sensitivity analysis indicated that the most important factors affecting the SVM model included soil texture and the NDVI index. According to the zoning map from the SVM model, the "high risk" and "very high risk" classes are mainly concentrated in the eastern sections, close to tributaries and relatively steeper slopes. This geographical distribution may occur due to the high density of waterways alongside other influencing indicators of this phenomenon. Based on the results obtained from SVM and CART models, both models performed well and were able to predict gully erosion risk with reasonable accuracy; however, according to the results, the SVM model showed better performance. Conclusion The present study showed that machine learning models such as SVM and CART can play an important role in identifying and mapping the risk of gully erosion in the Miānāb watershed. A precise understanding of the factors affecting gully erosion and the implementation of appropriate management measures can significantly help reduce the damage caused by this phenomenon and preserve the region's natural resources. This research contributes to the advancement of scientific knowledge in the field of gully erosion and the application of data mining models in natural resource management, and it can provide a foundation for future studies in this area. Funding There is no funding support. Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved 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. فرسایش آبکندی در واقع تشکیل و گسترش کانال‌های فرسایشی در خاک در نتیجه جریان متمرکز آب است. به‌طورکلی وقتی آبراهه‌های فرسایش‌یافته موجود در سطح زمین به‌اندازه‌ای بزرگ باشند که نتوان آن‌ها را به‌وسیله عملیات کشت‌وزرع معمولی تسطیح کرد آبکند نامیده می‌شوند. هدف این پژوهش مقایسه مدل‌های CART و SVM جهت شناسایی مناطق پرخطر فرسایش آبکندی و همچنین تعیین مهم‌ترین پارامترهای تأثیرگذار در وقوع فرسایش آبکندی در حوضه میان آب شوشتر در استان خوزستان است. ابتدا موقعیت آبکند‌های موجود با استفاده از تصاویر ماهواره‌ای، نرم‌افزار گوگل ارث و سیستم تعیین موقعیت جهانی (GPS) ثبت شدند. سپس شاخص‌های مستقل تأثیرگذار در فرسایش آبکندی تهیه و پس از تخصیص مقادیر مربوط به شاخص‌های مستقل، مدل‌سازی جهت پهنه‌بندی یا پیش‌بینی مناطق مستعد فرسایش آبکندی در محیط نرم‌افزار R انجام شد. در مجموع 3000 داده مربوط به فرسایش آبکندی جمع‌آوری شدند که 70 درصد برای آموزش و 30 درصد جهت آزمون مدل‌ها به کار گرفته شدند. طبق نتایج مدل SVM با ثبت مقدار 846/0 برای شاخص R2 از دقت بالاتری نسبت به مدل CART برخوردار بوده است. همچنین بر اساس نقشههای پهنه‌بندی، طبقه با خطر خیلی کم، بیشترین مساحت حوضه (حدود 59 درصد) را به خود اختصاص داده و بعد از آن کلاس‌های خطر کم، خطر متوسط، خطر زیاد و خطر خیلی زیاد به ترتیب 27/12، 29/10، 23/9 و 49/8 درصد مساحت حوضه را در برگرفتهاند. طبق نتایج، شاخص‌های کاربری اراضی، پوشش گیاهی و بافت خاک بیشترین نقش را در وقوع و گسترش فرسایش آبکندی داشته‌اند.

https://jphgr.ut.ac.ir/article_104353_77c4d388b9891a46ddd2f795fae30857.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 1 2026 04 05 Synoptic–Thermodynamic Analysis and Spatial Domain of Tropical Cyclones in Iran واکاوی همدیدی-ترمودینامیک و قلمرو فضایی توفان های حاره ای در کشور ایران 19 37 104007 10.22059/jphgr.2025.397644.1007892 FA حسن لشکری گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران 0000-0002-6007-7275 محمد ناجی گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران زینب محمدی گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران ثریا اهل االله گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران Journal Article 2025 10 25 ABSTRACT Tropical cyclones, as thermal low-pressure systems, cause severe rainfall and extensive damage across the coastal and inland regions of southeastern Iran. This study aims to identify and analyze the spatiotemporal patterns of tropical cyclones affecting the region during the period 1986 to 2019. Daily precipitation data from 73 synoptic stations and associated weather phenomenon codes were analyzed across three 11-year solar cycles to detect cyclone-induced events with precipitation ≥20 mm recorded at ≥3 stations. To gain deeper insights into these cyclones, Skew-T diagrams and TRMM satellite data were also employed. During this period, 26 significant events with varying frequency patterns and spatial distributions were identified. The results reveal that cyclones are primarily active in May, June, and October, with the intensity and spatial extent of their impacts exhibiting seasonal variations. In some months, despite fewer cyclone occurrences, the associated rainfall coverage and impacts are more extensive. Synoptic pattern analysis indicates a broad field of specific humidity in the eastern sector of the cyclone, extending throughout the troposphere. In the mid-troposphere, the northward expansion of the Arabian anticyclonic ridge induces cold advection on the western flank of the cyclone, along with sharp thermal and pressure gradients. Additionally, strong negative omega values at the 700 and 500 hPa levels highlight the intense dynamical processes within the cyclone structure. Tropical cyclones in southeastern Iran are characterized by intense rainfall and variable seasonal distribution, with the interplay of moisture, atmospheric instability, and synoptic-scale structures playing a key role in their intensification. Extended Abstract Introduction Climatic hazards, particularly tropical cyclones, cause substantial human and economic losses annually across the globe and are considered among the most significant environmental threats due to their profound impacts. These systems, often accompanied by extreme rainfall, intense winds, and flash flooding, exert severe influence on both coastal and inland regions. In recent years, southeastern Iran notably the provinces of Sistan and Baluchestan, Hormozgan, Kerman, and parts of South Khorasan has repeatedly been affected by such cyclones, resulting in considerable damage to infrastructure, water resources, and the livelihoods of local communities. In this context, a comprehensive and precise analysis of the genesis, development, and interaction of tropical cyclones with synoptic patterns and thermodynamic characteristics is crucial for enhancing forecasting capabilities, reducing vulnerability, and formulating effective disaster management strategies. The identification of impactful events in southeastern Iran, along with the analysis of their spatio-temporal patterns and delineation of their zones of influence, represents a vital step toward a deeper understanding of the dynamics of this climatic hazard within the framework of forecasting systems and risk management policymaking. Methodology To assess the extent of Iran’s vulnerability to tropical cyclones, daily precipitation data from 73 synoptic stations located in cyclone-prone provinces (Sistan and Baluchestan, Kerman, Hormozgan, Fars, Bushehr, Khuzestan, and South Khorasan) were analyzed over a 33-year period (1986–2019), corresponding to three 11-year solar cycles. Stations with complete data for each cycle were examined separately. To identify tropical cyclone events, days with precipitation exceeding 20 mm at a minimum of three stations, accompanied by weather codes 91–99, were classified as storm days. In total, 26 events were identified, among which two prominent cases—June 6–8, 2007, and June 4–5, 2010—were selected for synoptic and thermodynamic analysis. Atmospheric variables including geopotential height, specific humidity, omega (vertical velocity), and wind components were retrieved from the NCEP/NCAR reanalysis dataset and analyzed using GrADS software. To examine the vertical structure of the atmosphere during these events, Skew-T diagrams for the Muscat Airport station as the closest representative profile to the storm core were utilized. Additionally, to visualize the spatial extent and intensity of precipitation, TRMM satellite data with a spatial resolution of 0.25 degrees and high correlation with ground-based observations (r > 0.8) were employed, obtained from the Giovanni data portal. Results and discussion The findings of this study indicate that over the 33-year period, tropical cyclones originating in the Arabian Sea and the northern Indian Ocean exerted a direct and variable influence on the precipitation patterns of southeastern Iran. The intensity and nature of this impact are contingent upon the timing of the event, associated synoptic conditions, and the dynamic–thermodynamic structure of the cyclone. The seasonal frequency of storms follows a discernible pattern from May to October, with a peak in May, a decline through July, and a subsequent increase in August and October, while no events were recorded in September—a trend closely linked to the gradual transition of synoptic systems during the monsoon period. Notably, a lower frequency of storms does not necessarily imply a reduced spatial impact. For instance, in July, a single cyclone may influence a broad geographical area. A case study analysis of the June 2007 and June 2010 cyclones revealed that the 2007 event, characterized by a strong cyclonic structure, abundant moisture, and extensive upper-level divergence, resulted in intense and concentrated rainfall across southeastern Iran—exceeding 300 mm at some stations. The Skew-T diagram from Muscat Airport confirmed the presence of a highly unstable troposphere. In contrast, the 2010 cyclone exhibited a different interaction with prevailing synoptic systems, particularly between a migrating anticyclone and northern cold advection versus southern warm advection, which led to the intensification of the pressure gradient, high winds, and convective rainfall, albeit with a more limited inland penetration. A comparative assessment of these two events suggests that the extent and intensity of tropical cyclone rainfall are not solely determined by internal cyclone characteristics, but are strongly influenced by the synoptic background and the degree of synergy with incoming moisture. These findings are consistent with previous studies that underscore the importance of cyclone interaction with surrounding atmospheric patterns and moisture advection in shaping the distribution and intensity of extreme precipitation events. Conclusion The findings of this study demonstrate that tropical cyclones originating in the Arabian Sea and northern Indian Ocean play a significant role in generating intense and localized precipitation in southeastern Iran. The magnitude and spatial extent of their impacts are highly dependent on the timing of occurrence, the dynamic–thermodynamic structure of the cyclone, and the surrounding synoptic atmospheric conditions. The seasonal trends and spatiotemporal variability of these storms reflect the complex interplay between large-scale systems and internal convective processes. The analysis of two major events in June 2007 and June 2010 underscores that extreme rainfall is not merely a function of a cyclone’s dynamic strength, but is also heavily influenced by the amount of moisture advection and the nature of its interaction with synoptic-scale flows. These interactions enhance thermodynamic instability, amplify convective energy, and ultimately result in very intense rainfall and sudden flash floods. Therefore, a deeper understanding of the interactive mechanisms between tropical cyclones and large-scale atmospheric patterns is crucial for improving forecasting capabilities and climate hazard management in the region. The present results, consistent with previous research, highlight the critical role of synoptic conditions and moisture transport, and may serve as a foundation for evidence-based policymaking in climate disaster risk reduction and management. 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. توفان‌های حاره‌ای به‌عنوان سامانه‌های کم‌فشار حرارتی، با بارش‌های شدید آسیب‌های گسترده‌ای به مناطق ساحلی و داخلی جنوب شرق ایران وارد می‌کنند. هدف این مطالعه، شناسایی و تحلیل الگوهای فضایی-زمانی توفان‌های حاره‌ای مؤثر در بازه زمانی ۱۹۸۶ تا ۲۰۱۹ است. داده‌های بارش روزانه ۷۳ ایستگاه سینوپتیک و کدهای پدیده‌های جوی مرتبط طی سه‌چرخه ۱۱ ساله تحلیل شد تا رخدادهای ناشی از توفان‌های حاره‌ای با بارش ≥۲۰ میلی‌متر در ≥۳ ایستگاه شناسایی شوند. همچنین جهت درک عمیق‌تر از توفان‌های حاره‌ای از نمودار اسکیوتی و داده‌های ماهواره TRMM استفاده شد. در این بازه، ۲۶ رخداد شاخص با الگوی فراوانی و پراکنش مکانی متنوع استخراج شد. نتایج نشان داد که توفان‌ها عمدتاً در ماه‌های مه، ژوئن و اکتبر فعال بوده و شدت و وسعت اثر آن‌ها به‌طور فصلی تغییر می‌کند؛ به‌گونه‌ای که در برخی ماه‌ها، اگرچه تعداد توفان‌ها کمتر است، اما گستره تأثیر و بارش‌های ناشی از آن‌ها وسیع‌تر است. نتایج الگوهای همدیدی نشان می‌دهند که میدان نم ویژه گسترده در ضلع شرقی چرخند سراسر وردسپهر را در برمی‌گیرد. در ترازهای میانی، گسترش شمال سوی پشته عربستان جریانات سرد غرب چرخند و شیو شدید دما و فشار را القا می‌کند. همچنین، امگای منفی قوی در ترازهای ۷۰۰ و ۵۰۰ هکتوپاسکال بر دینامیک قوی ساختار توفان دلالت دارد. توفان‌های حاره‌ای جنوب شرق ایران با بارش‌های شدید و پراکندگی فصلی متغیر همراه‌اند و تعامل رطوبت، ناپایداری و ساختار همدیدی، نقش اصلی در تقویت آن‌ها دارد

https://jphgr.ut.ac.ir/article_104007_df8cfd0ff1a06a312e5c43c41be37da7.pdf

دانشگاه تهران پژوهش های جغرافیای طبیعی 2008-630X 58 1 2026 03 21 Modeling the distribution of the plant species Zhumeria majdae under the influence of climatic and environmental factors in Hormozgan Province مدلسازی پراکنش گونه گیاهی مورخوش تحت تاثیر عوامل اقلیمی و زیست محیطی دراستان هرمزگان 39 63 104945 10.22059/jphgr.2025.399592.1007898 FA پریسا یوسفی گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران قاسم عزیزی گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران علی اکبر شمسی پور گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران مصطفی کریمی گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران محمد محمودی موسسه تحقیقات جنگل‌ها و مراتع، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران Journal Article 2025 08 06 ABSTRACT Climate change greatly affects the distribution and existence of the plant species. The proposed study is intended to model and assess the influence of environmental factors on defining the appropriate habitat extent of Zhumeria majdae in the Hormozgan Province, southern Iran. Presence data have been collected using the scientific resources, such as documented field observations and herbarium records (Research Institute of Forests and Rangelands).This data was combined with 19 bioclimatic and topographic variables of WorldClim database.MaxEnt model was used to evaluate the association between the surroundings and the likelihood of the presence of the species. Monthly and seasonal temperature, precipitation, and relative humidity data of synoptic and rain gauge stations of the Ministry of Energy, namely Fin, Sarchahan, and Tashkuiyeh stations, located close to the distribution ranges of the species were utilised to analyse the climatic data in more detail. The outcome showed that the species distribution was most affected by winter precipitation, slope and mean annual temperature. Zhumeria majdae displayed the best development in regions that had 250-350 mm of rain per year, winter temperatures of 8-12 C and summer temperatures of 26-32 C with a shorter growing season. Phenological adjustments like the increase of growing seasons were found in drier areas like Sarchahan and Tang-e Zagh. MaxEnt model showed high accuracy in species distribution prediction and indicate winter precipitation as the most important limiting factor. The study also indicated that Z. majdae is a climatic-sensitive species with a narrow tolerance margin, which needs pronounced seasonal rainfall and moderate temperatures to survive and reproduce successfully. Extended Abstract Introduction Climate change and global warming are among the most critical environmental challenges of the 21st century, profoundly affecting the distribution, survival, and ecological dynamics of plant species. Numerous studies have demonstrated that fluctuations in temperature, altered precipitation patterns, and recurrent droughts contribute to habitat shifts and, in some cases, the extinction of vulnerable plant species. Consequently, both current and historical climatic conditions play a central role in shaping present-day biodiversity patterns and ecosystem functioning. Over recent decades, climate change has substantially influenced plant biodiversity, altering species’ geographic ranges, reducing population sizes, and placing numerous taxa at risk of extinction, which has led to significant modifications in ecosystem structure and stability. Zhumeria majdae is an ecologically and economically valuable endemic medicinal plant species in Iran, restricted to specific mountainous regions of Hormozgan Province and characterized by a narrow distribution range. Due to its wide traditional medicinal applications—including the treatment of digestive disorders, headache relief, and wound healing—the species is also exported to Persian Gulf countries, emphasizing its ecological and commercial importance. Given its limited range, high sensitivity to climatic variability, and narrow tolerance to environmental fluctuations, understanding the key environmental factors influencing its habitat and modeling its potential distribution are essential for effective long-term conservation. The main objective of this study was to model the potential distribution of Zhumeria majdae in Hormozgan Province using climatic and environmental variables and to identify the most influential factors determining its suitable habitat using the MaxEnt model. Methodology Occurrence records of Zhumeria majdae were compiled from multiple authoritative sources, including the Flora of Iran, Flora Iranica, the GBIF database, the Herbarium of the Research Institute of Forests and Rangelands, and other verified scientific references. All occurrence points were screened for spatial accuracy using ArcGIS and Google Earth to remove duplicates and correct erroneous records. Long-term climatic data—including temperature, precipitation, and relative humidity—were obtained from synoptic and rain gauge stations located in Fin, Sarchahan, and Tashkuiyeh. To model species distribution, 19 bioclimatic variables were considered, encompassing mean annual temperature, annual precipitation, seasonal precipitation, minimum temperature of the coldest month, maximum temperature of the warmest month, and other standard BIOCLIM indices. These variables were sourced from the WorldClim global database at a spatial resolution of 1 km. To assess multicollinearity among predictors, Pearson correlation analysis was conducted in SPSS, and highly correlated variables were excluded from the modeling process. Species distribution modeling was performed using the MaxEnt algorithm, which relies exclusively on presence-only data. Model performance was evaluated using the jackknife test to determine the relative contribution of each variable and the Area Under the Curve (AUC) metric to assess predictive accuracy. Results and discussion The MaxEnt model indicated that the distribution of Zhumeria majdae is primarily influenced by winter precipitation (BIO19), which accounted for 66.5% of the model’s explanatory power. Other important predictors included slope (12.7%), precipitation of the wettest month (BIO13), and annual temperature range (BIO7). The species exhibited the highest probability of occurrence in areas with winter precipitation of 100–180 mm and mean winter temperatures of 8–12°C. Response curves demonstrated a sharp increase in habitat suitability within these climatic ranges, with further increases in precipitation or temperature having only marginal effects on predicted occurrence. Comparative analyses across three primary habitats—Mount Geno, Sarchahan, and Tang-e Zagh—revealed distinct phenological adaptations. In Mount Geno, where rainfall is higher and temperatures are milder, the growing season was relatively short (~100 days), starting in mid-February and ending with seed dispersal in late May. In contrast, Sarchahan and Tang-e Zagh exhibited longer growing seasons (~120 days), with leaf emergence in late February and seed release by mid-June, reflecting adaptation to drier and warmer conditions. The species showed a clear preference for slopes of 10–35°, where well-drained rocky substrates likely reduce interspecific competition and prevent waterlogging. Habitat suitability declined in flatter areas due to poorer drainage and increased competition. Jackknife analyses confirmed the ecological significance of both precipitation and topography in determining suitable habitats. Model performance was robust, with AUC values of 0.93 for the training dataset and 0.90 for the testing dataset, indicating excellent predictive accuracy. The MaxEnt suitability map further identified central and northern Hormozgan—including parts of Mount Geno, Hajjiabad, and the highlands near Bandar Abbas—as highly suitable habitats under current climatic conditions. These findings highlight the species’ narrow ecological niche and its dependence on specific climatic and topographic conditions, emphasizing the need for targeted conservation measures in these key areas. Conclusion Despite its medicinal properties and high economic value, Zhumeria majdae is highly vulnerable to future climate change due to its restricted distribution and strong sensitivity to climatic conditions. The results of this study indicate that climatic factors, particularly winter precipitation, play a decisive role in the species’ distribution, and any alterations in rainfall patterns could threaten its natural survival. Variations in the length of the growing season across different regions suggest the species’ capacity for phenological adaptation; however, this adaptation is only possible within a specific range of temperature and precipitation. Therefore, conservation planning, identification of new suitable habitats, and the development of targeted cultivation strategies in climatically similar areas are essential for ensuring the long-term persistence of the species. Furthermore, these findings provide a framework for habitat assessment of other endemic and climate-sensitive species in the arid regions of Iran. 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. تغییر اقلیم تأثیر قابل‌توجهی بر پراکنش و بقای گونه‌های گیاهی دارد. هدف از این مطالعه، مدل‌سازی و ارزیابی تأثیر عوامل محیطی بر تعیین محدوده مناسب رویشگاه گونه Zhumeria majdae در استان هرمزگان، جنوب ایران است. داده‌های حضور گونه با استفاده از منابع علمی، مانند مشاهدات میدانی مستند و سوابق هرباریومی (موسسه تحقیقات جنگل‌ها و مراتع) جمع‌آوری‌شده‌اند. این داده‌ها با ۱۹ متغیر زیست‌اقلیمی و توپوگرافی پایگاه داده WorldClim ترکیب شدند. از مدل MaxEnt برای ارزیابی ارتباط بین محیط اطراف و احتمال حضور این‌گونه استفاده شد. داده‌های دما، بارش و رطوبت نسبی (ماهانه و فصلی) ایستگاه‌های سینوپتیک و باران‌سنجی وزارت نیرو، یعنی ایستگاه‌های فین، سرچاهان و تشکوئیه که در نزدیکی محدوده‌های پراکنش این‌گونه قرار دارند، برای تجزیه‌وتحلیل دقیق‌تر داده‌های اقلیمی مورداستفاده قرار گرفت. نتیجه نشان داد که پراکنش گونه بیشترین تأثیر را از بارش زمستانی، شیب و میانگین دمای سالانه پذیرفته است. گونه Zhumeria majdae بهترین رشد را در مناطقی با بارندگی سالانه ۲۵۰ تا ۳۵۰ میلی‌متر، دمای زمستان ۸–۱۲ درجه سانتی‌گراد و دمای تابستان ۲۶–۳۲ درجه سانتی‌گراد و فصل رشد کوتاه‌تر نشان داد. سازگاری‌های فنولوژیکی مانند افزایش فصل رشد در مناطق خشک‌تر مانند سرچاهان و تنگ زاغ مشاهده شد. مدل MaxEnt دقت بالایی در پیش‌بینی پراکنش گونه نشان داد و بارندگی زمستانه را به‌عنوان مهم‌ترین عامل محدودکننده نشان داد. این مطالعه همچنین نشان داد که Z. majdae گونه‌ای حساس به آب‌وهوا با حاشیه تحمل محدود است که برای بقا و تولیدمثل موفق به بارندگی فصلی قابل‌توجه و دمای متوسط نیاز دارد.

https://jphgr.ut.ac.ir/article_104945_f879df7a6676e5719aa03b1d36cbb2d5.pdf