سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 "Interaction Between Surface Phenomena (ENSO) and Upper-Level Atmospheric Circulation (Jet Stream) in the Occurrence of Monthly Wet/Dry Periods Across Iran's Rainfall Regions" اندرکنش پدیده‌های سطحی (ال‌نینو) و جریان لایه‌های فوقانی (جت استریم) در رخداد دوره‌های خشک و تر ماهانه نواحی بارشی ایران 1 22 235219 10.22034/jcr.2025.534094.1707 FA بهرام آصفی دانشجوی دکترای آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران. تهران 0009-0006-4288-0781 قاسم عزیزی استاد اقلیم شناسی، گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران. تهران 0000-0002-9797-4834 مصطفی کریمی دانشیار اقلیم شناسی، گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران. تهران 0000-0001-7820-6728 معصومه مقبل دانشیار اقلیم شناسی، گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران. تهران 0000-0001-9393-9954 فرامرز خوش اخلاق دانشیار اقلیم شناسی، گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران. تهران 0000-0003-4602-7084 Journal Article 2025 07 12 Introduction Numerous studies have shown that El Niño induces large-scale Rossby waves by altering the temperature and pressure distribution over the Pacific Ocean. These waves influence the position, intensity, and trajectory of the subtropical and polar jet streams. As the subtropical jet stream serves as a key pathway for Mediterranean precipitation systems, El Niño events may cause it to shift, intensify, weaken, or change its latitude. These alterations can impact the flow of moisture-laden low-pressure systems into Iran, sometimes diverting them northward or southward, or altering their intensity. Understanding these interactions is crucial for seasonal forecasting and assessing climate impacts, as jet stream behavior during ENSO events plays significant role in shaping regional and global weather patterns. In domestic studies, the relationship between El Niño and the jet stream has been relatively understudied. This research identifies precipitation zones in Iran and examines the monthly correlation between each zone and ENSO teleconnections. High- and low-precipitation El Niño events were then selected for further synoptic analysis. Materials and methods SOI data, calculated and archived monthly by NOAA, were used to identify El Niño phases (negative SOI values). The monthly SOI data from 1987 to 2018 were paired with daily precipitation data from 98 synoptic stations across Iran. The precipitation data were aggregated monthly and averaged over 31 years. Given the significance of altitude and latitude in precipitation patterns, data were weighted accordingly. Using SPSS software, the stations were spatially clustered into 10 zones via Ward’s hierarchical clustering method and squared Euclidean distance. Next, the correlation between monthly SOI values and precipitation for each cluster was calculated, excluding the dry summer months (June, July, August). As some El Niño years showed increased precipitation and others showed decreases, the highest and lowest precipitation events per month were selected based on spatial extent and amount. Synoptic maps were plotted for each case, and key atmospheric variables including 500 hPa geopotential height, omega, temperature, specific humidity, sea-level pressure, and jet stream patterns were analyzed for the Middle East and Pacific regions. Results and Discussion In Iran, in terms of time, October, November and December, and in terms of space, the Alborz and Zagros mountain ranges and the central plateau in the areas adjacent to these mountain ranges have the highest rainfall correlation with the El Niño activity period. This correlation is lower in the southern and northern coasts of the Iranian plateau, northern Azerbaijan, Khuzestan, the Lut plain and the southeastern regions. In conditions of strong El Niño in Iran, the Pacific low pressure is stronger than in the month prior for the rainy months of October and December, and the high pressure adjacent to Chile is weaker, especially in December. In high-rainfall rainfall, the jet stream at 250 hpa is stronger over North Africa and Eastern Northt America and weaker in East Asia. Also, the 500 hp trough is located over the eastern Mediterranean instead of Central Asia and Central Europe, and the subtropical high has moved south. At the 850 to 500 hpa, moisture supply from the Red Sea and Africa is evident, and sea level pressure in Iran is lower, the Siberian high pressure is weaker, and its influence extends towards Europe and more northern latitudes instead of Central Asia and Iran. Conclusion The relationship between El Niño, the jet stream, and precipitation in Iran is complex, inconsistent, and often weak. Unlike other regions such as coastal South America or the southern U.S. where El Niño has a clear and predictable impact, Iran shows limited spatial and temporal consistency. This complexity is partly attributed to: Iran’s geographic distance from the ENSO source region, The strong influence of other teleconnection patterns like the North Atlantic Oscillation (NAO) and the Indian Ocean Dipole (IOD), Local factors such as mountainous topography and proximity to the Caspian Sea. These regional and global influences often override or modify the ENSO signal, making its impact on Iran more nuanced and less predictable. The main objective of this research was to identify and comprehensively analyze synoptic-dynamic atmospheric patterns affecting autumn precipitation (October, November, and December) in Iran and to distinguish the atmospheric mechanisms governing high and low precipitation conditions. This research, using a multi-scale approach, examined atmospheric systems from sea level to the upper atmosphere (250 hpa) and analyzed the interaction between different systems, including the Siberian high pressure, the zonal high pressure, the jet stream, the Mediterranean troughs, the Sudanese low pressure, and moisture sources. Finally, the development of an applicable seasonal forecasting system based on the identified patterns that can be practically used practically in water resources and agricultural management is one of the key suggestions for future studies. پژوهش حاضر باهدف تحلیل ارتباط بین ال‌نینو و دوره‌های خشک و تر ماهانه ایران انجام‌شده است. برای دست‌یابی به هدف پژوهش، بارش روزانه 98 ایستگاه همدید (1987- 2018) از سازمان هواشناسی کشور و داده‌های شاخص SOI از پایگاه داده NOAA اخذ شد. ابتدا ایستگاه‌ها با وزن دهی ارتفاع و عرض جغرافیایی به 10 خوشه مکانی (نواحی بارشی) ناحیه‌بندی گردید. نتایج حاکی از همبستگی بالای داده‌های ماهانه SOI و میانگین ماهانه بارش در ماه‌های اکتبر، نوامبر و دسامبر در نواحی مجاور رشته‌کوه البرز و زاگرس است. به دلیل تفاوت بارش در سال‌های ال‌نینو، برای حالت ال‌نینو پربارش و کم بارش ماه نماینده انتخاب شد. در اکتبر و دسامبر پربارش، کم‌فشار اقیانوس آرام از یک ماه قبل قوی‌تر و درمقابل پرفشار مجاور کشور شیلی به‌ویژه برای ماه دسامبر ضعیف‌تر بوده است، جت تراز 250 ه.پ بر روی شمال آفریقا و شرق امریکا قوی‌تر و در شرق آسیا ضعیف‌تر بوده و پرارتفاع جنب‌حاره به سمت جنوب جابجا شده است. دراین ماه ها، انتقال رطوبت از دریاهای حاشیه‌ای شمال‌غربی اقیانوس هند و شرق آفریقا مشهود بوده و فشار سطح دریا در ایران پایین‌تر می‌باشد، پرفشار سیبری ضعیف‌تر بوده و نفوذ آن به‌جای آسیای مرکزی و ایران به سمت اروپا و عرض‌های شمالی‌تر می‌باشد. همچنین در ماه‌های پربارش ناوه تراز 500 ه.پ برروی شرق مدیترانه مستقر می باشد در حالی که در ماه‌های کم بارش ناوه بر روی آسیای مرکزی و مرکز مدیترانه قرار گرفته است. شرایط همدید و الگوی گردش جو در ماه‌های کم بارش عکس حالت پربارش بوده‌اند.

https://clima.irimo.ir/article_235219_bfd36861e8e2f1b9776695112b4ec434.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 The impact of El Niño-Southern Oscillation (ENSO) on Southwest Asia in future climate تاثیر ال‌نینو-نوسان جنوبی (انسو) بر جنوب‌غرب آسیا در اقلیم آینده 23 38 178892 FA مریم جمشیدی دانشجوی دکتری هواشناسی، دانشکده علوم و فنون دریایی و جوی، دانشگاه هرمزگان، بندرعباس. مریم رضازاده دکتری فیزیک هواشناسی، دانشیار گروه علوم غیر زیستی و جوی، دانشکده علوم و فنون دریایی، دانشگاه هرمزگان، بندرعباس. امید علیزاده دکتری فیزیک هواشناسی، دانشیار گروه علوم غیر زیستی و جوی، دانشکده علوم و فنون دریایی، دانشگاه هرمزگان، بندرعباس. Journal Article 2022 10 25 The climate is an important factor in the life of humans and other living species. This is particularly the case when a region is influenced by extreme weather and climate events. The atmospheric and oceanic phenomena are the main cause of a large part of climate variability. Teleconnections are among the main factors contributing to interannual climate variability in different regions of the globe, the most important of which in the interannual timescale is the El Niño-Southern Oscillation (ENSO). The first studies about the contribution of teleconnection to climate variability of different regions were conducted by Walker in the 1920s. His studies indicated that atmospheric circulation in the tropical Pacific largely influences the South Asian monsoon. Later studies also confirmed that ENSO has global teleconnections. Indeed, ENSO largely influences agriculture, forestry, the hydrological cycle, the carbon cycle, land and ocean ecosystems, and fisheries. Thus, any substantial changes in the frequency and intensity of ENSO have large socioeconomic impacts. In recent decades, there has been growing interest in investigating changes in the frequency and intensity of El Niño and La Niña events in response to global warming. There is growing evidence that the climate of different regions of our planet has undergone substantial changes in recent decades in response to anthropogenic global warming. For example, ENSO characteristics have changed since the 1990s, which could be caused by changes in natural or anthropogenic forcings or a combination of both. Accordingly, ENSO teleconnections have also changed in recent decades, making the prediction of weather and climate extremes a challenging task. Most regions of Southwest Asia are characterized by a semi-arid to arid climate, implying that a substantial decrease in its water resources under future global warming would be a major threat and may hamper sustainable development plans. Indeed, among the vulnerable regions to global warming is Southwest Asia because the shortage of water supply has already been a major challenge in this region. Despite that, there has been little investigation to understand climate change impacts in Southwest Asia. In particular, the impact of changes in the characteristics of ENSO on the climate of Southwest Asia has received little attention. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an opportunity for a better understanding of ENSO impacts on the climate of Southwest Asia under future global warming. In this study, we investigate the impact of El Niño and La Niña on the climate of Southwest Asia in the future period (2050 to 2099) compared to the historical period (1950 to 1999). Atmospheric variables that are analyzed in Southwest Asia include sea surface temperature (SST), near-surface temperature, geopotential height at 500 hPa, and precipitation. First, based on the empirical orthogonal function (EOF) analysis, we evaluated the performance of 12 CMIP6 models in terms of their ability to diagnose El Niño and La Niña events in recent decades. The EOF analysis of the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) data and its comparison with the ensemble of the applied 12 CMIP6 models indicate the weak performance of the ensemble of the selected models in diagnosing El Niño and La Niña events. Among the examined models, our results indicate that the CNRM-CM6-1 model shows better performance in terms of the diagnosis of El Niño and La Niña events, while the worst performance belongs to CIESM. As such, we used the outputs of the CNRM-CM6-1 model under three scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 and investigated the impact of the two extreme phases of ENSO (El Niño and La Niña) on the climate of Southwest Asia. Our results show that the frequency of El Niño and La Niña will decrease under all three scenarios in the future climate. The comparison of the CNRM-CM6-1 model with the ERSSTv5 data in the historical period indicates that the frequency of both El Niño and La Niña events is about 31% less than that diagnosed based on the ERSSTv5. The analysis of the outputs of the CNRM-CM6-1 model indicates that the frequency of El Niño events in the future period under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios compared to the historical period decreases by 5, 10, and 10 percent, while the frequency of La Niña events under SSP1-2.6 and SSP2-4.5 scenarios decreases by 18 and 6 percent. The analysis of geopotential height at 500 hPa over Southwest Asia indicates the development of different patterns in El Niño and La Niña years under all three scenarios in the future period, while in the historical period, the pattern of geopotential height at 500 hPa does not significantly change from El Niño to La Niña years. We also found that the intensity of the subtropical jet stream in Southwest Asia in the historical period was higher during La Niña events compared to El Niño and neutral events. The intensified jet stream in La Niña years implies the development of a less wavy jet stream, which may have implications for changes in extreme weather events. Under the SSP2-4.5 scenario, precipitation is relatively high in the winter of El Niño years in most parts of Southwest Asia except northeastern Iran. Relatively high precipitation is also simulated in the summer of El Niño years in most parts of Southwest Asia except southeastern Iran. Our analysis indicates different patterns of near-surface temperature in Southwest Asia in El Niño and La Niña years under SSP1-2.6 and SSP5-8.5 scenarios in the future period, particularly for the latter scenario. We argue that the response of ENSO and its teleconnections to global warming is nonlinear. چکیده : دورپیوندها مهم‌ترین عوامل اثرگذار بر نوسان‌ها و تغییرات اقلیمی بین‌سالانه در یک منطقه می‌باشند، که ال‌نینو-نوسان جنوبی (اﻧﺴﻮ) ﺑﺮﺟﺴﺘﻪ-ﺗﺮﯾﻦ آنها است. در دهه‌های اخیر تغییراتی در فراوانی و شدت رخدادهای ال‌نینو و لانینا در پاسخ به گرمایش زمین مورد توجه محققان قرار گرفته است. هرگونه تغییر در الگوی چرخه انسو در مناطق حاره‌ای اقیانوس آرام می‌تواند منجر به تغییر در الگوی واداشت‌های دورپیوندی آن شود. در مطالعه پیش‌رو الگوهای دورپیوندی دو فاز اصلی انسو، یعنی ال‌نینو و لانینا روی برخی از متغیرهای جوی منطقه جنوب غرب آسیا (شامل دمای سطح دریا، دمای هوای نزدیک سطح زمین، ارتفاع ژئوپتانسیلی- تراز 500 هکتوپاسکال و بارش) در اقلیم آینده (2050 تا 2099) نسبت به اقلیم گذشته (1950 تا 1999) بررسی شده است. ابتدا با استفاده از روش تحلیل تابع متعامد تجربی، عملکرد 12 مدل CMIP6 در شناسایی فازهای مختلف انسو بررسی شد سپس، با استفاده از داده‌های مدل CNRM-CM6-1 به‌عنوان مدلی با بهترین عملکرد، تحت سه سناریویSSP1-2.6، SSP2-4.5 و SSP5-8.5، تاثیر فازهای انسو بر اقلیم جنوب‌غرب آسیا بررسی شد که نتایج نشان داد تحت هرسه سناریو فراوانی وقوع ال‌نینو و لانینا در اقلیم آینده کاهش می‌یابد. در بررسی تغییر پارامترهای اقلیمی، شدت جت جنب حاره نیز در جنوب‌غرب آسیا در اقلیم گذشته در طی سال‌های وقوع لانینا بیشتر از سال‌های وقوع ال‌نینو و سال‌های خنثی است در حالی که تحت سناریوهای تغییر اقلیم آینده دمای هوا و بارش تغییر الگوی مشخصی را نشان نمی‌دهد.

https://clima.irimo.ir/article_178892_057fd54ce544234825e7afe36202abc8.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Projection of climatic variables of temperature and precipitation using CMIP6 models outputs under SSP scenarios (Case Study: Shoor river catchment area) پیش نگری متغیرهای اقلیمی دما و بارش با استفاده از برونداد مدل CMIP6 تحت سناریوهای SSP (مطالعه موردی: حوضه آبریز رودخانه شور) 39 60 235660 10.22034/jcr.2025.536389.1708 FA اسماء اصغری پور دشت بزرگ گروه جغرافیا، واحد اهواز، دانشگاه آزاد اسلامی، خوزستان، اهواز،ایران 0009-0003-3667-4349 جعفر مرشدی استادیار گروه شهرسازی، واحد شوشتر، دانشگاه آزاد اسلامی، شوشتر، ایران. 0000-0002-6115-5749 رضا برنا عضو هیأت علمی گروه جغرافیا، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. جبرائیل قربانیان استادیار گروه جغرافیا، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. منوچهر جوانمردی استادیار گروه جغرافیا، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. Journal Article 2025 07 23 Projection of climatic variables of temperature and precipitation using CMIP6 models outputs under SSP scenarios (Case Study: Shoor river catchment area) Abstract Projection long-term changes in meteorological variables is important in climate change studies. accordingly, the objective of this study is to project long term changes in key climatic variables namely temperature and precipitation in the Shoor river catchment area located in Khuzestan Province using CMIP6 model outputs under extreme (SSP) scenarios. findings from the Mann–Kendall trend analysis revealed a statistically significant upward trend in maximum, minimum, and annual temperatures, whereas precipitation showed a downward trend. furthermore, the outputs of the CanESM5 model suggest that maximum temperature, minimum temperature, and daily precipitation are projected to increase during the 2015–2050 period under extreme SSP scenarios, relative to the baseline period. Introduction Climate fluctuations are considered sinusoidal variations in climatic parameters on global or regional scales and are regarded as specific climate patterns. these fluctuations occur in temperature, precipitation, or other climatic parameters and unfold over specific time intervals. climate projections offer fundamental information for identifying the extent, rate, and scope of future climate change, which is essential for policy-making and planning adaptation strategies. the hydrological unit of Masjedsoleyman (Shoor river catchment area or Dasht Bozorg Basin) holds particular importance especially in the agricultural sector due to the presence of the Shoor River and groundwater resources. although various studies have been conducted in this basin, no research has yet addressed the projection of climatic variables under the extreme SSP scenarios of the IPCC sixth assessment report, which account for socio-economic activities. therefore, the objective of this study is to forecast changes in climatic variables, specifically temperature and precipitation, in the Shoor river catchment area based on the output of the CMIP6 model, utilizing SSP119, SSP126, SSP245, SSP370, and SSP585 scenarios for the future period of 2015–2050. Materials and methods The first part involves analyzing the trend of changes in climatic variables temperature and precipitation at the Masjedsoleyman synoptic station under current conditions. to assess the statistical significance of trends in temperature and precipitation time series, the Mann-Kendall test statistic was employed. for this purpose, annual average data of precipitation, minimum temperature, and maximum temperature from the Masjedsoleyman synoptic station were used over a 40 year statistical period (1985–2024), in order to identify the annual trend of the mentioned variables during the baseline period at the selected station. the second part of this study involves forecasting changes in climatic variables temperature and precipitation in the Shoor river catchment area using the outputs of the CMIP6 model under extreme SSP scenarios. for this purpose, general circulation Models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) were selected. this project employs a new set of scenarios known as SSPs, which are combined with the representative concentration pathways (RCPs) used in the fifth IPCC assessment report. the new scenarios introduced in CMIP6 include SSP1-1.9, SSP4-3.4, and SSP3-7.0, while four other scenarios SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5 are updated versions of RCP2.6, RCP4.5, and RCP8.5 from CMIP5. Results and discussion The results of examining changes in maximum temperature, minimum temperature, annual temperature, and precipitation during the baseline study period (1985–2024) indicate that at the Masjedsoleyman synoptic station, maximum temperature, minimum temperature, and annual temperature have shown an increasing trend, while precipitation has exhibited a decreasing trend. these variations in climate elements reflect the manifestation of climate change. according to the Mann-Kendall trend analysis, the maximum temperature at Masjed Soleyman station shows a rising trend, the minimum temperature also displays an upward trend, and the mean temperature follows the same increasing pattern. additionally, the Mann-Kendall test indicates a decreasing trend in precipitation over the 40-year statistical period. the combination of increasing maximum and minimum temperatures along with declining precipitation reflects a growing diurnal temperature range and reduced humidity, leading to the emergence of drier conditions in the region. the annual warming trend at the study station confirms that the region is progressing toward a warmer climate. in this study, future changes (2015–2050) in climatic variables temperature and precipitation were projected for the Shoor river catchment area based on the CanESM5 model and using the statistical downscaling model (SDSM). the accuracy and performance of the model were evaluated using statistical indicators including mean absolute error (MAE), root mean square error (RMSE), and the correlation coefficient (R). according to the results from the model performance evaluation, CanESM5 demonstrated acceptable accuracy in simulating observed values during the baseline period (1985–2014). subsequently, the CanESM5 outputs under different SSP scenarios were downscaled using the SDSM model. Conclusion The aim of this study is to investigate future changes (2015–2050) in climatic variables, specifically temperature and precipitation, in the Shoor river catchment area under SSP scenarios of the Sixth Assessment Report (CMIP6). according to the results obtained, the capability of the SDSM model was confirmed in this study. these findings are consistent with the results of studies by Naderi etal. (2017), Shagga etal. (2020), Wang et al. (2021), Li etal. (2021), Mesgari etal. (2022), Halim etal. (2023), Fissa etal. (2024), Azad and Ahmadi (2023), Abdolalizadeh etal. (2023), Mohammadi etal. (2024), and Maleki Marshet etal (2025). based on the conducted analyses, an increase in both maximum and minimum temperatures, as well as an increase in daily precipitation, was observed for the future period. the results of this study are in line with the findings of most researchers regarding the upward trend in maximum and minimum temperatures, as well as increased precipitation. Keywords Projection, climatic variables, CMIP6 Model, SSP scenarios, shoor river catchment area پیش نگری تغییرات متغیرهای هواشناسی در درازمدت، از اهمیت زیادی در مطالعات تغییرات اقلیمی برخوردار است. بنابراین هدف از پژوهش حاضر، پیش نگری تغییرات متغیرهای اقلیمی دما و بارش در حوضه آبریز رودخانه شور واقع در استان خوزستان با استفاده از برونداد مدل CMIP6 تحت سناریوهای حدی SSP بوده است. بدین منظور داده‌ های متغیر اقلیمی دمای حداکثر، دمای حداقل و بارش روزانه ایستگاه سینوپتیک مسجدسلیمان طی دوره‌‌ آماری 2024-1985 از سازمان هواشناسی کشور اخذ گردید و پس از روندیابی با بهره‌گیری از آزمون من-کندال با استفاده از افزونه XLSTAT و ماکرواکسل؛ تغییرات متغیرهای اقلیمی دما و بارش تحت سناریوهای SSP مدل‌ CanEsm5 در نرم‌افزار SDSM6.1 شبیه‌ سازی و برای سال های آینده 2050-2015 پیش نگری گردید. کالیبراسیون و واسنجی مدل SDSM با استفاده از داده‌های مشاهداتی ایستگاه سینوپتیک مسجدسلیمان و داده های NCEP انجام شد، همچنین جهت ارزیابی میزان کارایی و عملکرد مدل SDSM و مقایسه‌ مقادیر مشاهداتی و شبیه سازی شده در دوره پایه 2014-1985، از 3 سنجه‌ آماری شامل: میانگین خطای مطلق (MAE) و مجذور میانگین مربعات خطا (RSME) و ضریب همبستگی (R) بهره گرفته شد. پس از اطمینان از کارایی مدل، خروجی‌های مدل CanESM5 در دوره زمانی 2050-2015 در حوضه آبریز رودخانه شور، تحت سناریوهای SSP119، SSP126، SSP245، SSP370 و SSP585 توسط مدل آماری SDSM ریزمقیاس انجام گرفت. نتایج حاصل از روندیابی به روش من-کندال حاکی از روند صعودی معنی دار دمای حداکثر، دمای حداقل و دمای سالانه و تغییرات بارش روند کاهشی را دارا می باشد. همچنین نتایج به دست آمده از مدل CanESM5 نشان می دهد که متغیرهای دمای حداکثر، دمای حداقل و بارش روزانه طی دوره آماری 2050-2015 تحت سناریوهای حدی SSP روند افزایشی را نسبت به دوره پایه تجربه خواهند کرد.

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Analysis of Climate Capacity Building in Wind Exploitation in the Southern Strip of Iran تحلیل ظرفیت سازی اقلیمی در بهره برداری از باد در نوار جنوبی ایران 61 75 232505 10.22034/jcr.2025.513510.1694 FA مصطفی قویدل دانشجوی دکتری اقلیم شناسی، دانشگاه تهران، تهران قاسم عزیزی دکترای تخصصی، استاد اقلیم شناسی، دانشگاه تهران 0000-0002-9797-4834 معصومه مقبل دکترای تخصصی، دانشیار اقلیم شناسی، دانشگاه تهران، تهران 0000-0001-9393-9954 سعید بازگیر دکترای تخصصی، دانشیار جغرافیای طبیعی، دانشگاه تهران، تهران علی اکبر شمسی پور دکترای تخصصی، دانشیار جغرافیای طبیعی، دانشگاه تهران، تهران Journal Article 2025 03 22 Analysis of Climate Capacity Building in Wind Exploitation in the Southern Strip of Iran Introduction: In an era marked by environmental challenges and unsustainable energy consumption, the need for renewable energy sources is greater than ever. Fossil fuel reliance has not only accelerated climate change but also endangered ecological balances. Wind energy emerges as a sustainable and clean solution to meet rising energy demands while reducing greenhouse gas emissions. Iran's southern belt, covering the provinces of Khuzestan, Fars, Bushehr, Hormozgan, and parts of Sistan and Baluchestan, represents a region of untapped potential for wind energy. This study examines the climatic suitability of 98 counties in these provinces to identify favorable and unfavorable zones for wind energy development. Using advanced clustering techniques, we aim to provide a detailed understanding of the region’s wind energy potential and its role in transitioning towards renewable energy sources. Materials and Methods The area assessed in this study is located in the southern strip of Iran and has significant geographical and climatic diversity. This area includes the provinces of Khuzestan, Fars, Bushehr, Hormozgan, and parts of the south of the Baluchestan watershed within the geographical limits of Sistan and Baluchestan province. This area is located along the northern coast of the Persian Gulf and the Strait of Hormuz, which includes the Zagros Mountains and coastal plains. In this study, daily data on wind speed, pressure, and average temperature from the Meteorological Organization were used. The period of use of meteorological data was 2015-2024. To prepare the daily average of the aforementioned data, the average was obtained for each of the 366 days of the year from the period 2015-2024. This study was conducted based on the Analytic Hierarchy Process (AHP) method. First, the data of various factors required for the analysis were prepared. Apart from the average temperature, pressure, and wind speed, other factors were extracted by calculation. After extracting the data of the factors in question, in the first stage, the data of the factors obtained were scored using the Analytic Hierarchy Process. The purpose of scoring was to determine what rank the stations in the study area could achieve in terms of wind energy utilization capacity building based on the 8 factors described and their weights. In weighting the factors used in this analysis, the total weights are 1, meaning the weights are based on hundredths or percentages. Annual wind power density has been assigned the highest possible weight, 0.25. This is because this factor is more influential than other factors in wind capacity building. The next factor that has received more weight is wind speed. This factor also has a higher score because of its direct impact on wind power density. A weight of 0.1 has been considered for other factors. However, a weight of -0.1 (negative one-tenth) has been assigned to the average temperature factor. The negative weight given to this factor is because the higher the temperature value, the lower the wind power density. Therefore, when running the hierarchical analysis, in order to make the weight of this factor positive, all the data in the temperature column is inverted during execution so that the weight assigned to the temperature factor becomes positive. Results and Discussion. This study has investigated the climatic capacity building of wind exploitation in the southern strip of Iran. In this study, the analytic hierarchy process method has been used to separate the desirable and undesirable areas in terms of climatic capacity building in wind exploitation. The analytic hierarchy process method is one of the powerful methods for separating and extracting specific points in studies with large amounts of data. In this method, the author can obtain results close to reality from his study by selecting the desired factors and giving appropriate weight to each factor. After extracting the results, it was found that the northwest regions of Fars Province and the southeast of Khuzestan and Hormozgan provinces have obtained the highest possible scores compared to other regions and are introduced as desirable areas in the capacity building of wind exploitation in the southern strip of Iran. Also, the southern regions of Fars Province, the northwest of Hormozgan Province, the center of Khuzestan Province, and the east of the south of Baluchestan watershed have obtained the lowest scores compared to other regions and are introduced as undesirable areas in the capacity building of wind exploitation in the southern strip of Iran. By station analysis, Jask station with a score of 35.42 percent from Hormozgan was the most favorable, and Qir and Karzin with a score of 8.80 percent from Fars were the most unfavorable stations in this study in terms of climate capacity building in wind utilization. The most important factors affecting climate capacity building in wind utilization were also identified as wind speed, wind power density, and stability in wind speed above the energy production threshold. در این مطالعه ظرفیت سازی اقلیمی بهرمندی از باد در نوار جنوبی ایران مورد ارزیابی قرار گرفته است. مصرف انرژی با توجه به افزایش جمعیت، تغییر سبک زندگی و گرمایش جهانی روز به روز بیشتر می شود. با توجه به حجم عظیم مصرف انرژی بهره مندی از انرژی های تجدید پذیر امری ضروری است. در جنوب ایران نیز با توجه به حجم زیاد جمعیت ساکن و گستردگی فعالیت های اقتصادی انرژی بسیاری زیادی مصرف می شود و استفاده از سوخت های فسیلی نیز ضمن فشار به منابع فسیلی سبب انتشار گازهای گلخانه می شود. لذا ضروری است ظرفیت سازی اقلیمی در بحث بهره مندی از انرژی باد در این منطقه مورد بررسی قرار گیرد. هدف این مطالعه شناسایی مناطق مطلوب و غیر مطلوب در ظرفیت سازی بهره مندی از باد است تا در نهایت مناطق مناسب و نامناسب جهت بهره برداری از باد در نوار جنوبی ایران مشخص شوند. برای اینکار از روش تحلیل سلسله مراتبی استفاده شده است. داده های هواشناسی مورد استفاده مطالعه فشار، سرعت باد و دما میانگین در مقیاس روزانه هستند که برای 61 ایستگاه نوار جنوبی در دوره زمانی 2024-2015 از آنها استفاده شده است. در تحلیل سلسله مراتبی از 8 عامل بهره برداری شد. جهت انجام تحلیل سلسله مراتبی از زبان برنامه نویسی متلب و جهت تولید نقشه ها از زبان برنامه نویسی آر استفاده شد. نتایج نشان داد که مناطق شمال غرب استان فارس، جنوب خوزستان و جنوب شرق استان هرمزگان مطلوب ترین مناطق و شرق نیمه جنوبی استان سیستان و بلوچستان، نیمه جنوبی فارس و مرکز خوزستان نامطلوب ترین مناطق در ظرفیت سازی بهرمندی از باد در نوار جنوبی ایران هستند. همچنین مهم ترین عوامل در ظرفیت سازی بهره مندی از باد سه عامل چگالی توان باد، سرعت باد و پایداری باد تشخیص داده شد.

https://clima.irimo.ir/article_232505_6e968b219c5764500455d67346f01257.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Using Machine Learning for Daily Precipitation Forecasting Using Surface and Upper Air Data: A Case Study in Mashhad بکارگیری الگوریتم‌های یادگیری ماشین در پیش‌بینی بارش روزانه با استفاده از داده‌های مشاهداتی سطح زمین و جو بالا، مطالعه موردی: شهر مشهد 77 91 232995 10.22034/jcr.2025.541424.1710 FA امیرحسین بابائیان کارشناسی ارشد، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، خراسان رضوی 0009-0006-4042-9890 مهدی رستم زاده کارشناسی ارشد، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، خراسان رضوی 0009-0004-6038-2968 مصطفی فاضلی کارشناسی ارشد، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، خراسان رضوی Journal Article 2025 08 15 Introduction Accurate 1-day (next-day) rainfall forecasts underpin water resources operations, smart agriculture, and early warning of hydro-meteorological hazards. Yet the short lead prediction of daily precipitation remains difficult because rainfall emerges from multiscale, nonlinear interactions that are only partly captured by single-source datasets. Machine learning (ML) can learn such relationships directly from data, but the relative value of surface observations versus upper air information—and their combination—has not been systematically assessed for Mashhad, Iran. This study addresses that gap by benchmarking several ensemble ML algorithms across three data scenarios and by applying feature selection to balance predictive skill and model simplicity. Data and Study Area We used two data sources for the Mashhad synoptic station during 2000–2023: (i) surface observations (maximum, minimum, and mean temperature; maximum, minimum, and mean relative humidity; wind speed; mean sea level pressure; sunshine hours; and daily rainfall), and (ii) ERA5 upper air reanalysis at pressure levels of 700, 500, and 300 hPa, including geopotential height, temperature, specific humidity, relative humidity, horizontal wind components (u, v) and vorticity. All predictors were used with a one-day lag to forecast next-day precipitation. The dataset was split into training (2000–2017) and testing (2018–2023) periods to enable out-of-sample evaluation. Methodology We designed three scenarios of S (surface only), U (upper air only), and S&U (combined). In scenarios S and U, each dataset was independently provided to five ensemble learning algorithms — Random Forest, AdaBoost, XGBoost, CatBoost, and LightGBM. Before model fitting, the Variance Inflation Factor (VIF) was computed to diagnose multicollinearity among predictors, and variables with VIF values above the acceptable threshold were excluded to ensure statistical independence and model stability. In the combined scenario, surface and upper air variables were merged into a unified feature matrix. To curb dimensionality, remove redundancy, and avoid overfitting, we applied Sequential Forward Floating Selection (SFFS), using five-fold cross-validated R² as the selection criterion. The six features retained by SFFS were v700 and v500 (meridional wind at 700/500 hPa), Spe_Hum500 (specific humidity at 500 hPa), Rel_Hum300 (relative humidity at 300 hPa), and two surface indicators (Umax and nm, representing near-surface wind and sunshine hours). Models were evaluated on the test set using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R², and Adjusted R². Results and Discussion In the surface-only scenario (S), the CatBoost model demonstrated the best performance with an R² of 0.171, Adjusted R² of 0.168, and the lowest RMSE of 2.309 in the test data. However, AdaBoost achieved the lowest MAE of 0.767, making it the best model in terms of minimizing mean absolute error, even though its R², Adjusted R², and RMSE were lower than those of CatBoost. In the upper-air-only scenario (U), similarly, CatBoost emerged as the top-performing model, achieving an R² of 0.182, Adjusted R² of 0.175, and the lowest RMSE of 2.294. Notably, the results in the upper-air scenario (U) showed better performance across all metrics compared to the surface-only scenario (S) for all algorithms. These results highlight the importance of upper air dynamics in improving model performance, particularly in terms of reducing error and enhancing explanatory power. In the combined scenario (S&U), which integrates both surface and upper-air data, CatBoost achieved the highest R² of 0.190, Adjusted R² of 0.188, and the lowest RMSE of 2.283 on the test data. Moreover, CatBoost achieved the second-lowest MAE of 0.795, just after AdaBoost, making it the best-performing model overall across multiple metrics. This suggests that the combination of surface and upper-air data with CatBoost provides the best balance of accuracy and model simplicity, making it the most effective model for predicting next-day rainfall. Time series comparisons demonstrate that the selected CatBoost model accurately reproduces the sequence and magnitude of many light to moderate rainfall days, closely tracking day-to-day fluctuations. However, like most data-driven approaches trained on imbalanced samples dominated by zero/low rainfall, the model tends to under-estimate peaks during very heavy events. Two factors likely contribute to this: (i) class imbalance that downweights extremes in the loss landscape, and (ii) the one-day lag design that limits access to multi-day precursors (e.g., moisture build-up and synoptic persistence). Despite these limitations, the combined data approach delivers stable performance with a favorable accuracy–complexity trade-off and demonstrates the utility of integrating thermodynamic and dynamic information from different atmospheric layers. Conclusion and Implications The experiments confirm three key takeaways. First, upper air reanalysis fields provide distinct, complementary information to surface observations for next-day rainfall forecasting in Mashhad. Second, fusing surface and upper air predictors and then pruning with SFFS yields a compact feature set that preserves—or even improves—skill while enhancing parsimony, as reflected in Adjusted R². Third, tree-based gradient boosting methods, particularly CatBoost in the combined scenario, offer a practical balance between performance and simplicity for operational use. Future work should target heavy rainfall underestimation by (a) enriching temporal context (multi day lags, moving averages, recent sum rainfall, dry/wet spell counters), (b) incorporating spatial context from neighboring stations and regional reanalysis tiles, (c) adopting two stage pipelines (occurrence classification followed by conditional amount regression) to mitigate zero inflation, and (d) testing sequence models (e.g., LSTM/GRU) or hybrid ML–NWP ensembles. Such extensions could elevate extreme event fidelity without sacrificing interpretability or operational feasibility. در این پژوهش، عملکرد مدل‌های یادگیری ماشین در پیش‌بینی بارش یک روزه ایستگاه مشهد با استفاده از داده‌های روزانه دوره ۲۰۰۰ تا ۲۰۲۳ مورد بررسی قرار گرفت. برای این منظور، سه گروه (سناریو) از داده‌ها در نظر گرفته شد: (۱) داده‌های سطح زمین، (۲) داده‌های جو بالا و (۳) ترکیب هر دو مجموعه داده. پیش از مدل‌سازی، متغیرهای دارای هم‌خطی بالا با استفاده از شاخص VIF شناسایی و حذف شدند تا از استقلال آماری ویژگی‌های ورودی اطمینان حاصل شود. در سناریوی ترکیبی، پس از ادغام داده‌ها، از روش انتخاب ویژگی حذف شناور (SFFS) برای گزینش شش شاخص مؤثر استفاده گردید که شامل چهار متغیر جو بالا (v700، Rel_Hum300، Spe_Hum500 و v500) و دو متغیر سطح زمین (nm وUmax ) بودند. نتایج ارزیابی مدل‌ها نشان داد که در تمامی سناریوهای مطالعه‌شده، الگوریتم CatBoost به ‌عنوان بهترین مدل شناسایی شد. در سناریوی اول (داده‌های سطح زمین)، CatBoost با MAE برابر 0.841، RMSE معادل 2.309، R² برابر 0.171 و Adjusted R² برابر 0.168، عملکردی برتر از سایر الگوریتم‌ها داشت. در سناریوی دوم (داده‌های جو بالا)، CatBoost دوباره پیشتاز ظاهر شد و با MAE برابر 0.772، RMSE معادل 2.294، R² برابر 0.182 و Adjusted R² برابر 0.175، کارایی بالاتری نسبت به رقبا از خود نشان داد. در نهایت، در سناریوی سوم (ترکیب داده‌های سطح زمین و جو بالا)، CatBoost با بهره‌گیری از شش ویژگی منتخب، بهترین نتایج را کسب کرد: R² = 0.190، Adjusted R² برابر 0.188 و RMSE = 2.283. یافته‌ها نشان می‌دهد که تلفیق داده‌های سطح زمین و جو بالا، همراه با انتخاب بهینه ویژگی‌ها، نه‌تنها دقت پیش‌بینی را افزایش می‌دهد، بلکه تعادل مناسبی میان پیچیدگی مدل و توان پیش‌بینی را ایجاد می‌کند. مدل CatBoost در این سناریو توانسته است بارش‌های خفیف و متوسط را با خطای کم پیش‌بینی کند و روند کلی نوسانات بارش را به‌خوبی دنبال نماید.

https://clima.irimo.ir/article_232995_e1b7d3c5d269dc0eefdb02f7229513a6.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 The impact of Caspian Sea water level fluctuations on port management (case study of Amirabad port area) تاثیر نوسانات سطح آب دریای خزر بر مدیریت بنادر (مطالعه موردی پهنه بندر امیرآباد) 93 107 235608 10.22034/jcr.2025.532009.1706 FA عباس آبکار دانشجوی دوره دکترای رشته آب و هواشناسی دانشگاه آزاد اسلامی علوم وتحقیقات، تهران ، ایران. آزاده اربابی سبزواری استاد گروه جغرافیا، دانشگاه آزاد اسلامی. واحد اسلامشهر. تهران. ایران. فریده اسدیان استادیار گروه جغرافیای دانشگاه آزاد اسلامی علوم وتحقیقات، تهران ، ایران. Journal Article 2025 07 01 The impact of Caspian Sea water level fluctuations on port management (case study of Amirabad port area) Abstract: The Caspian Sea has experienced very severe level fluctuations in the last century, such that in 1977 it dropped to a level of -28.30 meters, and then over the course of 18 years it increased to its highest level in 1995, which was -25.5 meters compared to open waters. Since 1995, it has been on a downward trend, and this downward trend has accelerated in the last decade. It has now reached its lowest level, which is -28.30 meters, and the downward trend will continue. With this downward trend, we can predict a decrease in the level of more than half a meter per decade, and therefore, in less than 2 decades, a decrease in the level of more than one meter will occur, which requires the management of the country's ports and coasts to make the necessary arrangements in the multi-year plan. Considering the climatic patterns of precipitation and evaporation and the forecast of the decreasing trend of the level in various articles and references, it is necessary for the countries bordering the Caspian Sea to plan management and engineering measures regarding port engineering and coastal management in the Caspian Sea. In this study, Amirabad Port has been examined as one of the most important ports of Iran in the southeast of the Caspian Sea. The Caspian Sea water level is still decreasing and technical and management solutions are necessary in the port. The process of developing and equipping ports is much slower than the decreasing process of the Caspian Sea level, and therefore short-term planning is not useful in this regard. The only possible and short-term solution in less than a decade is to change the floating fleet in the port and transfer large vessels to larger ports. Keywords: Sea level fluctuations, Caspian Sea, Amirabad, sustainable management The impact of Caspian Sea water level fluctuations on port management (case study of Amirabad port area) Abstract: The Caspian Sea has experienced very severe level fluctuations in the last century, such that in 1977 it dropped to a level of -28.30 meters, and then over the course of 18 years it increased to its highest level in 1995, which was -25.5 meters compared to open waters. Since 1995, it has been on a downward trend, and this downward trend has accelerated in the last decade. It has now reached its lowest level, which is -28.30 meters, and the downward trend will continue. With this downward trend, we can predict a decrease in the level of more than half a meter per decade, and therefore, in less than 2 decades, a decrease in the level of more than one meter will occur, which requires the management of the country's ports and coasts to make the necessary arrangements in the multi-year plan. Considering the climatic patterns of precipitation and evaporation and the forecast of the decreasing trend of the level in various articles and references, it is necessary for the countries bordering the Caspian Sea to plan management and engineering measures regarding port engineering and coastal management in the Caspian Sea. In this study, Amirabad Port has been examined as one of the most important ports of Iran in the southeast of the Caspian Sea. The Caspian Sea water level is still decreasing and technical and management solutions are necessary in the port. The process of developing and equipping ports is much slower than the decreasing process of the Caspian Sea level, and therefore short-term planning is not useful in this regard. The only possible and short-term solution in less than a decade is to change the floating fleet in the port and transfer large vessels to larger ports. Keywords: Sea level fluctuations, Caspian Sea, Amirabad, sustainable management The impact of Caspian Sea water level fluctuations on port management (case study of Amirabad port area) Abstract: The Caspian Sea has experienced very severe level fluctuations in the last century, such that in 1977 it dropped to a level of -28.30 meters, and then over the course of 18 years it increased to its highest level in 1995, which was -25.5 meters compared to open waters. Since 1995, it has been on a downward trend, and this downward trend has accelerated in the last decade. It has now reached its lowest level, which is -28.30 meters, and the downward trend will continue. With this downward trend, we can predict a decrease in the level of more than half a meter per decade, and therefore, in less than 2 decades, a decrease in the level of more than one meter will occur, which requires the management of the country's ports and coasts to make the necessary arrangements in the multi-year plan. Considering the climatic patterns of precipitation and evaporation and the forecast of the decreasing trend of the level in various articles and references, it is necessary for the countries bordering the Caspian Sea to plan management and engineering measures regarding port engineering and coastal management in the Caspian Sea. In this study, Amirabad Port has been examined as one of the most important ports of Iran in the southeast of the Caspian Sea. The Caspian Sea water level is still decreasing and technical and management solutions are necessary in the port. The process of developing and equipping ports is much slower than the decreasing process of the Caspian Sea level, and therefore short-term planning is not useful in this regard. The only possible and short-term solution in less than a decade is to change the floating fleet in the port and transfer large vessels to larger ports. Keywords: Sea level fluctuations, Caspian Sea, Amirabad, sustainable management چکیده: دریای خزر در یک قرن اخیر دارای نوسانات تراز بسیار شدیدی بوده است به طوریکه در سال 1356 تا تراز 28.30- متر پایین روی داشته و سپس طی 18 سال روند افزایشی در سال 1374 به بالاترین تراز خود یعنی 25.5- متر نسبت به آبهای آزاد رسیده است. از سال 1374 تا کنون روند کاهشی داشته و این فرایند کاهشی در یک دهه اخیر نیز سرعت بیشتری یافته است. هماکنون به پایین تراز خود یعنی تراز 28.30- متر رسیده است و روند کاهشی همچنان ادامه دار خواهد بود. با این روند کاهشی می توان در هر دهه بیش از نیم متر کاهش تراز را پیش بینی کنیم و لذا در کمتر از 2 دهه بیش از یک متر کاهش تراز اتفاق می افتد که لازم است در این خصوص مدیریت بنادر و سواحل کشور تمهیدات لازم را در برنامه چندساله داشته باشد. با توجه به الگوهای اقلیمی بارش و تبحیر و پیش بینی روند کاهشی تراز در مقالات و مراجع مختلف، لازم است در خصوص مهندسی بنادر و مدیریت سواحل در دریای خزر کشورهای حاشبه دریای خزر تمهیدات مدیریتی و مهندسی طرح ریزی نمایند. در این تحقیق بندر امیرآباد بعنوان یکی از مهمترین بنادر ایران در جنوب شرقی دریای خزر مورد بررسی قرار گرفته است. تراز آب دریای خزر همچنان کاهشی بوده و چاره اندیشی فنی و مدیریتی در بندر ضروری می باشد. روند توسعه و تجهیز بنادر به مراتب کندتر از روند کاهشی تراز خزر می باشد و لذا در این خصوص برنامه ریزی کوتاه مدت مفید نمی باشد. تنها چاره میسر و کوتاه مدت در کمتر از یک دهه، تغییر ناوگان شناور در بندر و انتقال شناور های بزرگ به بنادر بزرگ تر است. کلمات کلیدی: نوسانات سطح دریا، خزر، امیرآباد، مدیریت پایدار

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 09 23 Evaluation of Meta-Heuristic Hybrid Models in Estimating Flood Discharge Due to Effective Precipitation (Case Study: Kakareza River, Lorestan Province) ارزیابی مدلهای هیبریدی فراابتکاری در براورد دبی سیلابی ناشی از بارش موثر (مطالعه موردی:رودخانه کاکارضا استان لرستان) 109 119 212715 FA حمیدرضا باباعلی دانشیار، گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد خرم آباد، لرستان ابراهیم نوحانی استادیار گروه عمران، مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران. امین پورحقی کارشناسی ارشد زمین شناسی، دانشکده علوم پایه، دانشگاه لرستان Journal Article 2024 12 16 Abstract Introduction: Flooding is a natural phenomenon that can have devastating effects on communities and ecosystems, making it a significant concern for disaster preparedness and management. It can cause significant damage to the environment and human life, resulting in damage to property and infrastructure, and can occur gradually or suddenly, resulting in flash floods. Various factors such as global warming, land use and land cover change, and urbanization can exacerbate the impact and frequency of flood events. One of the important aspects of understanding and managing floods is capturing the dynamics of runoff, which is one of the main factors in flood events. Accurate flood risk assessment relies on accurate estimates of peak runoff, which are determined through rainfall-runoff simulations. Accurate discharge prediction is a critical factor in flood control and reducing damage to the environment and infrastructure. In recent years, due to the nonlinear and complex nature of hydrological problems, models based on artificial intelligence approaches have been used. These models are inspired by the nature of living organisms and are capable of solving problems of great complexity and scope.Therefore, in this study, optimization algorithms were used with the aim of combining with the support vector regression model to estimate flood discharge. Methodology: The study area is the Kakarezha station located in Lorestan province. This station is located in a river called Kakarezha in Lorestan province, which is one of the permanent rivers of Lorestan province and originates from the southeastern mountains of Al-Ashtar city and Chaghlondi district (Herud) and is known as Kakarezha within the Al-Ashtar city. This river is located between 15°48″ to 49°48″ east longitude and 22°32″ to 52°33″ north latitude and is located in Lorestan province and east of Khorramabad city and forms part of the headwaters of the Karkheh River in the Zagros. In this study, a support vector regression model with wavelet, bat and firefly algorithms was used to model the flood discharge of the Kakarezha River located in Lorestan province. The precipitation parameter corresponding to the flood discharge was used as the input of the model and the flood discharge parameter was used as the output of the model in the daily time period, 2012-2022. Results and Discussion: In order to model the flood discharge of the Kakarreza River located in Lorestan Province, a support vector regression model with wavelet, bat and firefly algorithms was used. Also, in the support vector regression model, driving functions called kernels were used. These functions include radial, polygonal and linear basis functions, which were investigated in this study. For this purpose, the precipitation parameter values of the Kakarreza hydrometric station are normalized and then entered into the support vector regression model. In recent years, because the values of the kernel function adjustment parameters are randomly selected in the support vector regression model, optimization algorithms have been used to increase the accuracy and reduce the model error. As is clear in Table 1, all models have better accuracy in the radial basis kernel function. The results of the models under study are shown in Table 1. As is clear from the table, the support vector-wavelet regression model with the highest correlation coefficient of 0.980, the lowest root mean square (m3/s) of 0.168, the lowest mean absolute error (m3/s) of 0.088, and the highest Nash-Sutcliffe coefficient of 0.985 has shown better performance in the validation stage. Therefore, the support vector-wavelet regression model has better performance than the other models under study. The superiority of this model is due to the wavelet transform, which divides the received signals into two high-pass and low-pass categories, and in the high-pass category, the resolution is increased, which causes the maximum signal values to be analyzed with desired accuracy. Conclusions: Flood discharge estimation using hybrid models based on support vector regression is an efficient tool in designing hydrological systems. In the present study, a case study was conducted to evaluate the performance of the hybrid meta-heuristic model of support vector regression to estimate flood discharge in the Kakarreza watershed located in Lorestan province. The results of the evaluation criteria showed that the wavelet-support vector regression model has high accuracy and negligible error. Also, according to the graphs examined, the wavelet-support vector regression model has estimated flood discharge values close to their actual values. In summary, the results of this study show that the use of artificial intelligence models based on the support vector regression model approach can be used in the field of flood discharge estimation for other regions of the country and a step towards making appropriate management decisions. کشور ایران تقریباً هر ساله با خسارات جانی و مالی قابل توجهی از سیل روبرو می شود. بنابراین، هدف این مطالعه ارائه اطلاعات به موقع و بسیار دقیق پیش‌بینی سیل با استفاده از یک مدل ترکیبی توسعه‌یافته با ترکیب مدل بارش-رواناب و مدل مبتنی بر هوش مصنوعی است.در این پژوهش، بمنظور برآورد دبی سیلابی رودخانه کاکارضا واقع در استان لرستان از مدل‌ هیبریدی رگرسیون بردار پشتیبان با الگوریتم های موجک، کرم شب تاب و خفاش در طی دوره زمانی 1402-1392 استفاده شد. پارامترهای بارش متناظر با هر دبی سیلابی در مقیاس زمانی روزانه بعنوان ورودی مدل بکار برده شد. به منظور ارزیابی عملکرد مدلها از معیارهای ارزیابی ضریب همبستگی، ریشه میانگین مربعات خطا، میانگین قدر مطلق خطا و ضریب نش ساتکلیف استفاده شد. همچنین جهت تحلیل نتایج مدلها از نمودار سری زمانی، باکس پلات و تیلور استفاده شد. نتایج نشان داد سناریو های ترکیبی در مدلهای مورد بررسی باعث بهبود عملکرد مدل می شود. مقایسه نتایج نشان داد مدل رگرسیون بردار پشتیبان – کرم شب تاب عملکرد بهتری نسبت به مدل رگرسیون بردار پشتیبان-خفاش در مدل‌سازی دارد، بگونه ای که مدل رگرسیون بردار پشتیبان – موجک با ضریب همبستگی 980/0 ، کمترین ریشه میانگین مربعات (m3/s) 168/0 ، کمترین میانگین قدر مطلق خطا (m3/s) 088/0 و بیشترین ضریب نش ساتکلیف 985/0 در مرحله صحت سنجی در اولویت قرار گرفت. درمجموع نتایج نشان داد استفاده از مدلهای هوشمند مبتنی بر رویکرد رگرسیون بردار پشتیبان می تواند رویکردی موثر در مدیریت سیلابها باشد.

https://clima.irimo.ir/article_212715_1eeb5f7be38d18978bb45eae40669bbc.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Forecasting 24-hour pollution and air pollutant emissions in Hamedan city using a hybrid recurrent neural network model پیش‌بینی آلودگی 24 ساعته و میزان انتشار آلاینده‌های هوا در شهر همدان با استفاده از مدل ترکیبی شبکه عصبی بازگشتی 121 142 233125 10.22034/jcr.2025.531399.1704 FA صابر قاسمیان مظاهر دانشجوی دکترای تخصصی مدیریت فناوری اطلاعات – مدیریت خدمات و توسعه فناوری اطلاعات، واحد همدان، دانشگاه آزاد اسلامی، همدان، ایران. 0009-0002-8399-7573 علیرضا اسفندیاری مقدم گروه علم اطلاعات و دانش شناسی، واحد همدان، دانشگاه آزاد اسلامی، همدان، ایران. منصور اسماعیل پور گروه کامپیوتر، واحد همدان، دانشگاه آزاد اسلامی، همدان، ایران. Journal Article 2025 06 26 Abstract: Air pollution is among the most critical challenges faced by metropolitan areas, leaving serious impacts on human health and environmental sustainability. Employing accurate and intelligent methods for air quality prediction can play a vital role in managerial decision-making and mitigating the adverse effects of pollutants. In this study, aiming to analyze the factors contributing to the increase of air pollutants in Hamedan city, a hybrid model based on Recurrent Neural Networks (RNN) was developed. Data spanning two and a half consecutive years were collected, preprocessed, and used for model training. The proposed model integrates air pollutant concentrations including PM2.5, PM10, O₃, NO, NOx, NO₂, SO₂, and CO, along with meteorological parameters such as temperature, humidity, wind speed and direction, as well as data related to vehicle traffic and fleet deterioration, to predict pollutant levels for the next 24 hours. The results showed that the proposed model achieved an accuracy of over 97%, demonstrating its high reliability and potential as an effective tool for air quality management and public health protection. Rapid urbanization, expansion of industrial activities, and increased dependence on motor vehicles in large and industrial cities have led to a significant decline in air quality and an increase in environmental pollution. Air pollution not only threatens human health, but also has devastating effects on ecosystems and the process of climate change. This complex and multifaceted phenomenon encompasses diverse fields, including physics, chemistry, engineering, medicine, and economics, and requires interdisciplinary studies. The lived experience of people in polluted cities, including contracting respiratory diseases or observing human damage caused by pollution, has provided a strong incentive to address this issue. Given the harmful effects of air pollution on human health and other living organisms, this issue has become one of the priorities of public health [1,2]. Numerous studies have shown that there is a direct relationship between air pollution and the prevalence of cardiovascular and respiratory diseases [3]. In response to these challenges, researchers have attempted to provide reliable information to urban managers, planners, and the general public by developing air pollution prediction systems. Given the complex and variable nature of air pollution in both temporal and spatial dimensions, its modeling and prediction are associated with particular difficulties [4]. Therefore, the use of modern methods such as artificial intelligence and neural-fuzzy models has been considered; because these methods have a high ability to analyze complex and nonlinear systems. One of the effective tools in reducing data dimensions and selecting key inputs is principal component analysis (PCA).Rapid urbanization, expansion of industrial activities, and increased dependence on motor vehicles in large and industrial cities have led to a significant decline in air quality and an increase in environmental pollution. Air pollution not only threatens human health, but also has devastating effects on ecosystems and the process of climate change. This complex and multifaceted phenomenon encompasses diverse fields, including physics, chemistry, engineering, medicine, and economics, and requires interdisciplinary studies. The lived experience of people in polluted cities, including contracting respiratory diseases or observing human damage caused by pollution, has provided a strong incentive to address this issue. Given the harmful effects of air pollution on human health and other living organisms, this issue has become one of the priorities of public health [1,2]. Numerous studies have shown that there is a direct relationship between air pollution and the prevalence of cardiovascular and respiratory diseases [3]. In response to these challenges, researchers have attempted to provide reliable information to urban managers, planners, and the general public by developing air pollution prediction systems. Given the complex and variable nature of air pollution in both temporal and spatial dimensions, its modeling and prediction are associated with particular difficulties [4]. Therefore, the use of modern methods such as artificial intelligence and neural-fuzzy models has been considered; because these methods have a high ability to analyze complex and nonlinear systems. One of the effective tools in reducing data dimensions and selecting key inputs is principal component analysis (PCA). This method helps to simplify the modeling process by creating new independent variables [5]. Also, due to the nonlinear and complex behavior of temperature and pollutant concentration changes, traditional modeling methods do not meet the needs of accurate prediction [6]. The use of intelligent models in predicting complex environmental phenomena Given the inherent complexities of phenomena such as air pollution, the use of intelligent methods based on neural networks and neural-fuzzy systems has been considered as efficient tools in modeling and predicting these processes [7]. One of the prominent models in this field is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which, by combining fuzzy logic and the learning ability of neural networks, has a high capability in analyzing and predicting nonlinear and complex behaviors [8]. Numerous studies have shown that evolutionary learning algorithms such as the Particle Swarm Optimization (PSO) algorithm can improve the performance of neural networks and prevent them from getting stuck in local optimum points [5,6]. For example, Cheng et al. (2012) successfully predicted the inflow of the Hongqidi Dam in China by combining an artificial neural network (ANN) and a hybrid PSO algorithm [8]. آلودگی هوا از مهم‌ترین چالش‌های کلان‌شهرها به شمار می‌رود و پیامدهای جدی بر سلامت انسان و پایداری محیط زیست بر جای می‌گذارد. به‌کارگیری روش‌های دقیق و هوشمند در پیش‌بینی وضعیت آلودگی هوا می‌تواند نقش مؤثری در تصمیم‌گیری‌های مدیریتی و کاهش اثرات آلاینده‌ها ایفا کند. در این پژوهش با هدف تحلیل عوامل مؤثر بر افزایش آلاینده‌های هوا در شهر همدان، یک مدل ترکیبی مبتنی بر شبکه عصبی بازگشتی (RNN) توسعه داده شد. داده‌های مربوط به دو سال و نیم متوالی گردآوری و پس از پیش‌پردازش، برای آموزش مدل به کار گرفته شدند. این مدل با استفاده از داده‌های مربوط به آلاینده‌های PM2.5، PM10، O₃، NO، NOx، NO₂، SO₂ و CO، در کنار پارامترهای جوی شامل دما، رطوبت، سرعت و جهت باد و همچنین داده‌های مرتبط با تردد خودروها و فرسودگی ناوگان حمل‌ونقل، توانست غلظت آلاینده‌ها را برای بازه زمانی ۲۴ ساعته آینده پیش‌بینی کند. نتایج نشان داد مدل پیشنهادی با دقتی بیش از ۹۷ درصد عملکردی قابل اعتماد داشته و می‌تواند به عنوان ابزاری کارآمد در مدیریت کیفیت هوا و حفاظت از سلامت عمومی مورد استفاده قرار گیرد.رشد سریع شهرنشینی، گسترش فعالیت‌های صنعتی، و افزایش وابستگی به وسایل نقلیه موتوری در شهرهای بزرگ و صنعتی، منجر به افت محسوس کیفیت هوا و تشدید آلودگی‌های زیست‌محیطی شده است. آلودگی هوا نه‌تنها سلامت انسان‌ها را تهدید می‌کند، بلکه آثار مخربی بر اکوسیستم‌ها و روند تغییرات اقلیمی دارد.

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Students' viewpoint and level of awareness of climate literacy (case study: students of Shahid Chamran University of Ahvaz دیدگاه و میزان آگاهی دانشجویان از سواد اقلیمی (مطالعه موردی: دانشجویان دانشگاه شهید چمران اهواز) 143 151 229381 10.22034/jcr.2025.505357.1684 FA شهناز خادمی زاده دانشیار گروه علم اطلاعات و دانش‌شناسی، دانشکده علوم تربیتی و روان‌شناسی، دانشگاه شهید چمران اهواز، خوزستان،ایران . 0000-0003-4494-7709 زینب محمدی دکتری علم اطلاعات و دانش‌شناسی‌، گروه علم اطلاعات و دانش‌شناسی، دانشکده علوم تربیتی و روان‌شناسی، دانشگاه شهید چمران اهواز، خوزستان، ایران. 0000-0003-3082-7693 فاظمه بهلول دانشجوی دکتری علم اطلاعات و دانش‌شناسی‌، گروه علم اطلاعات و دانش‌شناسی، دانشکده علوم تربیتی و روان‌شناسی، دانشگاه شهید چمران اهواز، خوزستان، ایران. علی طالبی دانشجوی کارشناسی ارشد مدیریت اطلاعات، گروه علم اطلاعات و دانش‌شناسی، دانشکده علوم تربیتی و روان‌شناسی، دانشگاه شهید چمران اهواز، خوزستان، ایران. Journal Article 2025 02 08 This study was conducted to evaluate the level of climate literacy and awareness among students at Shahid Chamran University. It employed an applied research design with a descriptive–survey methodology. The target population consisted of all undergraduate, master’s, and doctoral students enrolled at Shahid Chamran University of Ahvaz during the 2024–2025 academic year. According to official data from the university portal, the total student population was approximately 14,000. The sample size was determined using the Krejcie and Morgan table, which indicated that a minimum of 375 participants would provide adequate representation. A simple random sampling method was applied: students’ names in each faculty were numbered, and the sample was drawn using a random number table. Questionnaires were distributed through both face-to-face delivery and electronic submission via Gmail. In total, 356 valid questionnaires were returned and included in the final dataset. The inclusion criteria ensured that participants were (1) actively enrolled in the current semester, (2) willing to participate with informed consent, and (3) had completed or were currently enrolled in at least one basic or general course relevant to environmental sciences. The exclusion criteria were defined as (1) more than 20% missing responses in the questionnaire and (2) withdrawal from the study at any stage of data collection. Data were collected using the standardized Climate Literacy Questionnaire developed by Clary and Wandersee (2013). The original version contained 18 items, combining a five-point Likert scale with short-answer questions. The Persian translation underwent a face validity review by three faculty members from the Department of Environmental Engineering and two from the Department of Library and Information Science. Based on their feedback, four items that were inconsistent with the cultural and academic context of the study population were removed, resulting in a final instrument containing 12 items. To assess reliability, a pilot test was conducted with 30 students outside the main sample. The Cronbach’s alpha coefficient for the revised version was 0.82, confirming satisfactory internal consistency. Data analysis included descriptive statistics—frequency, percentage, mean, and standard deviation—as well as inferential analyses, including one-sample t-tests and analysis of variance (ANOVA), all performed using SPSS version 20. The findings revealed that over half of the participants held relatively homogeneous attitudes toward climate change. Many respondents demonstrated awareness of the role of fossil fuels, particularly emissions from vehicles and industrial processes, in producing nitrogen oxides and greenhouse gases. This indicated a foundational understanding of the link between anthropogenic activities and climate change. Students also recognized the contribution of traditional agricultural practices and the excessive use of chemical fertilizers to increased atmospheric carbon dioxide levels. While this reflects awareness of certain major sources of environmental pollution, it also underscores the need for a more comprehensive understanding of other sources and the broader impacts of greenhouse gas emissions. Respondents displayed clear recognition of the consequences of global warming, including thermal instability, glacier melting, and rising sea levels. Overall, the level of climate literacy among the surveyed students was found to be satisfactory. No statistically significant differences in climate literacy were observed when analyzed by demographic variables such as age and academic level. The study also examined the primary channels through which students acquired climate-related knowledge. Results indicated that a substantial portion of this knowledge was obtained from both formal e ducational sources—such as in-person and online classes—and informal digital platforms, including social media and online news websites. This finding highlights the necessity of integrating formal instruction with credible scientific resources to strengthen climate literacy. Improving climate literacy is essential for equipping younger generations with the knowledge and skills required to address environmental challenges and contribute meaningfully to sustainable development. To prevent superficial or fragmented understanding, students should be encouraged to consult peer-reviewed scientific literature and other reliable resources alongside easily accessible online content. University libraries can play a pivotal role in advancing climate literacy. As central academic hubs, they can host training programs, workshops, and webinars on climate change, specifically tailored to meet the needs of Shahid Chamran University students. Such initiatives can help mitigate the long-term adverse effects of climate change and safeguard the well-being of future generations. Collaborative engagement with subject-matter experts and faculty members in environmental sciences and agriculture would ensure the scientific rigor and accuracy of these programs. Beyond transferring knowledge, these activities have the potential to foster positive and responsible environmental attitudes, as well as to encourage sustainable behaviors aimed at reducing the impacts of climate change. In conclusion, enhancing climate literacy empowers students to act as informed, proactive, and responsible citizens, committed to environmental protection and the pursuit of sustainable development. پژوهش حاضر با هدف بررسی میزان آگاهی و سواد اقلیمی دانشجویان دانشگاه شهید چمران اهواز انجام شد. روش این تحقیق از نوع کاربردی و با رویکرد توصیفی–پیمایشی انجام شد. جهت گردآوری داده‌ها از پرسش‌نامه استاندارد سنجش سواد اقلیمی کلاری و واندرسی (2013) استفاده شد. جامعه آماری شامل ۳۵۶ دانشجو بود که به‌صورت تصادفی انتخاب شدند. یافته‌ها بیانگر آن بود بیش از نیمی از پاسخ‌دهندگان معتقدند استفاده از سوخت‌های فسیلی علت افزایش سطح اکسید نیتروژن است. اکثر شرکت‌کنندگان، تشکیل کلاس‌های حضوری و آنلاین را مؤثرترین روش برای آموزش مسائل اقلیمی دانستند. علاوه‌براین، یافته‌ها نشان می‌دهد سطح سواد اقلیمی دانشجویان دانشگاه شهید چمران اهواز در وضعیت مطلوبی قرار دارد. . نتایج آزمون تحلیل واریانس نشان داد تفاوت معناداری در میانگین نمرات سواد اقلیمی بر اساس سن و مقطع تحصیلی وجود ندارد ( .(p>0.همچنین، مشخص شد که بخش قابل توجهی از آگاهی اقلیمی دانشجویان از طریق کلاس‌های حضوری و مجازی، بحث‌های گروهی و منابع غیررسمی مانند شبکه‌های اجتماعی و وب‌سایت‌های خبری شکل گرفته است. این امر اهمیت ترکیب آموزش رسمی و منابع علمی معتبر را برای ارتقای کیفیت دانش اقلیمی نشان می‌دهد. ارتقای سواد اقلیمی می‌تواند نقش مؤثری در آماده‌سازی نسل جوان برای مقابله با چالش‌های زیست‌محیطی و تحقق توسعه پایدار ایفا کند.

https://clima.irimo.ir/article_229381_e8de30f398043fa486ef9f0483778997.pdf

سازمان هواشناسی کشور- پژوهشکده اقلیم شناسی پژوهش های اقلیم شناسی 2228-5040 1404 63 2026 04 21 Estimation of emission of greenhouse gases with changes in rainfall in citrus orchards and groves (Case study: Dezful and Abadan) برآورد تصاعد گازهای گلخانه‌ای با تغییرات بارش در باغات مرکبات و نخلستان (مطالعه موردی: دزفول و آبادان) 153 163 232941 10.22034/jcr.2025.259081.1393 FA نسرین مرادی مجد گروه اقلیم شناسی دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران غلامعباس فلاح قالهری گروه جغرافیا و گردشگری، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران. منصور چترنور گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران Journal Article 2020 11 25 Introduction: Climate change is one of most important environmental issues that can affect agriculture and water resources in an area. Due to importance of climate change on structure of planet and its inhabitants, especially in arid and semi-arid regions, in recent years, as one of most common topics, has been considered by scientific societies and many studies to study effects it is done. Global warming and climate change due to human activities is one of major environmental problems that has attracted the attention of many scientific and political circles in world in last two decades. Climate is considered as most important factor of agricultural production and climate change affects agricultural and livestock production, hydrological balance and other components of agricultural systems. Agricultural sector is affected by both climate change and climate change. Materials and methods: In present paper, using DAYCENT model, rate of methane, nitrous oxide and nitric oxide emissions is estimated by considering the annual average and precipitation changes of -30, -20, -10, 10 and 20%, and finally amount of global warming potential of citrus orchards and groves of Khuzestan province in these conditions, rainfall changes were calculated. This model was first widely used in 1970s to simulate changes in soil organic matter (SOM), plant productivity, nutrient availability, and other ecosystem parameters in response to changes in land and climate management. Which can be used to simulate plant growth and changes in soil organic matter for most terrestrial ecosystems around world. Increased attention to greenhouse gas analysis led to development of DAYCENT in 1994. DAYCENT model includes a sub-model in plant production phase and a sub-model for step-by-step dynamics of daily scarce gas flow, nutrient circulation, water flow and soil organic matter (SOM). DAYCENT model program is written in FORTRAN and C programming languages and can use a DOS window or a Linux platform. DAYCENT model inputs include observed daily rainfall and maximum and minimum daily temperature; Soil variables include texture, density, thickness, field capacity, wilting point, pH, saturated hydraulics, and EC. This model has been validated using observed data related to crop production, soil organic matter, nutrient circulation and trace gases. Results and discussion: Based on estimation results of DAYCENT model, highest amount of methane emission modeled at Dezful station, highest rate of oxidation of nitrous and oxidized nitrogen at Abadan station and highest rate of oxidation of nitrous and oxidantric modeled at Dezful station were determined. Also, according to results obtained from the average calculations of global warming potential coefficient of methane, nitrous oxide and nitric oxide in citrus orchards of Dezful 59.150 and in Abadan groves 47.200 tons equivalent to carbon dioxide was obtained. Rainfall changes also showed greatest effect in Dezful station for citrus cultivation and least effect in Abadan station for date cultivation. For gas methane in Dezful station, maximum value of 0.769 tons per hectare per year in precipitation changes was 20% and minimum value of 0.421 tons per year in precipitation changes was -30%. For nitrous oxide in Dezful station, maximum value was 0.201 tons per hectare per year in precipitation changes of 20% and minimum value was 0.004 tons per hectare per year in precipitation changes of -30%. Also for nitric oxide in Dezful station, maximum value of 0.342 tons per hectare per year in precipitation changes was 20% and minimum value of 0.132 tons per hectare per year in precipitation changes was -30%. In general, with decreasing precipitation, progression of all three gases decreases and with increasing precipitation by 10 and 20%, gas flux has an increasing trend. For gas methane at Abadan station, maximum value of 0.638 tons per hectare per year in precipitation changes was 20% and minimum value was 0.304 tons per hectare per year in precipitation changes of -30%. For nitrous oxide in Abadan station, maximum value was 0.278 tons per hectare per year in 20% precipitation changes and minimum value was 0.006 tons per hectare per year in precipitation changes of -30%. Also for nitric oxide gas in Abadan station, maximum value of 0.342 tons per hectare per year in precipitation changes was 20% and minimum value of 0.169 tons per hectare per year in precipitation changes was -30%. In general, with decreasing rainfall, we have a decrease in methane flux. This trend increases with increasing rainfall by 10 and 20%. The flux of nitrous oxide and nitric oxide also decreases with decreasing rainfall and increases very much with increasing it. Conclusion: The high growth of methane gas in Dezful station is due to type of cultivation. While high flux of nitrous oxide in Abadan station and nitric oxide in Dezful station due to lack of principled use of chemical fertilizers and non-compliance with expert principles in farms has caused high flux of these gases. In two stations, with decreasing precipitation, progress of all three gases decreases, and with increasing precipitation by 10 and 20%, gas flux has an increasing trend. The potential for global warming in Dezful region was greater because more dense cultivation and more fertilizer use was expected. Also in citrus orchards, changes in global warming potential are very high as a result of rainfall changes. In summary, largest share of global warming potential is in evolution of nitric oxide gas. Calculating emission of greenhouse gases and potential for global warming due to agricultural activities can provide necessary warnings to planners and policy makers in agricultural sector and protection of country's environment to take necessary measures for more attention and financial support. تغییر اقلیم یکی از مسایل مهم زیست محیطی است که می تواند بر کشاورزی و منابع آب یک منطقه اثر بگذارد. باتوجه به اهمیتی که تغییر اقلیم بر ساختار کره ی زمین و ساکنین آن به ویژه در مناطق خشک و نیمه خشک داشته است، در سال های اخیر، به عنوان یکی از شایع ترین موضوعات، مورد توجه مجامع علمی بوده و مطالعات زیادی در زمینه ی صورت گرفته است. پژوهش حاضر با هدف استفاده از مدل DAYCENT در برآورد نرخ تصاعد گازهای متان، اکسیدنیتروس و اکسید نیتریک، با درنظر گرفتن میانگین سالانه و تغییرات بارش 30 -، 20-، 10-، 10 و 20 درصد بوده و در نهایت میزان پتانسیل گرمایش جهانی باغات مرکبات و نخلستان های دزفول و آبادان را در این شرایط تغییرات بارش محاسبه گردید. بر اساس نتایج، مدل DAYCENT، بیش‌ترین میزان انتشار گاز متان را در ایستگاه دزفول و بیش‌ترین میزان تصاعد اکسیدنیتروس و اکسیدنیتریک را در ایستگاه آبادان و بیش‌ترین میزان تصاعد اکسیدنیتروس و اکسیدنیتریک مدل شده در ایستگاه دزفول برآورد کرده است. همچنین مطابق نتایج بدست آمده از میانگین محاسبات ضریب پتانسیل گرمایش جهانی متان، اکسید نیتروس و اکسید نیتریک در باغات مرکبات دزفول 150/59 و در نخلستان های آبادان 200/47تن معادل دی اکسید کربن بدست آمد. تغییرات بارش نیز بیشترین تاثیر را در ایستگاه دزفول برای کشت مرکبات و کم ترین تاثیر را در ایستگاه آبادان جهت کشت خرما نشان داده است.

https://clima.irimo.ir/article_232941_75ae4b5060c731b12cca0309376c0e6c.pdf