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واکاوی تغییرات زمانی- مکانی شاخصهای حدی بارش تحت تأثیر تغییر اقلیم در ایران | ||
| پژوهش های آبخیزداری | ||
| مقاله 4، دوره 37، شماره 4 - شماره پیاپی 145، دی 1403، صفحه 34-53 اصل مقاله (2.82 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2024.364521.1565 | ||
| نویسندگان | ||
| سپیده چوبه1؛ بهنوش فرخ زاده* 2؛ ام البنین بذرافشان3؛ هانیه حسنوند4 | ||
| 1دانشآموختة دکتری، آبخیزداری- آب، گروه مرتع و آبخیزداری، دانشکدة منابعطبیعی، دانشگاه ارومیه، ارومیه، ایران | ||
| 2استادیار گروه مهندسی طبیعت، دانشکدة منابعطبیعی و محیط زیست، دانشگاه ملایر، ملایر، ایران | ||
| 3استاد گروه مهندسی منابع طبیعی، دانشکدة کشاورزی و منابعطبیعی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| 4دانشآموختة کارشناسی ارشد آبخیزداری، گروه مهندسی طبیعت، دانشکدة منابعطبیعی و محیط زیست، دانشگاه ملایر، ملایر، ایران | ||
| چکیده | ||
| مقدمه و هدف تغییر اقلیم موجب تغییر نمایههای حدی و افزایش شدت و فراوانی رخداد رویدادهای حدی اقلیمی میشود. بارشهای سنگین در هنگام رخداد موجب آسیب گسترده به اقتصاد، محیط زیست و زندگی بشر میشود. براساس گزارش خطر جهانی سال 2019 که بهوسیلة انجمن اقتصاد جهانی (WEF) منتشرشده است، حوادث اقلیمی حدی (مانند بارشهای حدی) و سازگار نبودن با تغییرات اقلیمی بهعنوان خطرات برجسته در ارتباط با تأثیر بر محیط زیست و انسان شناخته شدهاند. از اینرو، در این پژوهش اثر تغییر اقلیم بر بارشهای سنگین در ایران بررسی شد. مواد و روشها در این پژوهش، از دادههای بارش 47 ایستگاه استفاده شد. در ابتدا، عملکرد مدل بررسی شد. سپس، دادههای بارش با استفاده از نرمافزار LARS-WG و با سناریوهای مختلف و برای دورة آینده، پیشبینی شد. برای ارزیابی شبیهسازی بارشهای حدی بهوسیلة مدل نامبرده، افزون بر مقایسة گرافیکی دادههای مشاهدهشده و تولیدشدة هر ایستگاه در فرآیند صحتسنجی، شاخصهای RB، MD و WS میان دادههای شاخصهای حدی مشاهده شده و شبیهسازی شده محاسبه شد. سرانجام روند بارشهای سنگین با آزمون من-کندال بررسی شد. نتایج و بحث نتایج شبیهسازی بارش در دورة 2050-2030 نشان داد که تغییرات سالانة ایستگاههای همدید، غیریکنواخت بود. بیشترین افزایش بارش سالانه در ایستگاه بم و با سناریوی SSP5-8.5 به اندازة 59/81% (32/32 میلیمتر) بود و بیشترین کاهش بارش سالانه در ایستگاه کیش و با سناریوی SSP5-8.5 به اندازة 11/63% (17/23 میلیمتر) بود. بر اساس نتایج پراکنش مکانی PRCPTOT بیشترین افزایش بارش سالانه در ایستگاه یاسوج و تحت سناریوی SSP1-2.6 بود. بهطوریکه در این ایستگاه بارش سالانه برای دورة آینده با سناریوهای SSP1-2.6 و SSP5-8.5 بهترتیب 933/49 و 889/3 میلیمتر برآورد شد. همچنین، اندازة این شاخص در دورة پایه در این ایستگاه 832/19 میلیمتر پیشبینی شد. از سوی دیگر، بیشترین کاهش بارش سالانه در ایستگاه تبریز با سناریوی SSP5-8.5 به اندازة 165/11 میلیمتر (68%) بود. نتایج بررسی تغییرات شاخصهای حدی بارش در دورة 2050-2030 در مقایسه با 2022-1985 نشان داد بیشترین تغییرات در مناطق شمال و غرب ایران بود که شدت این تغییرات بهویژه با سناریوی SSP5-8.5 بیشتر بود. بیشترین کاهش شاخص Rx1day، مربوط به ایستگاه مشهد با سناریوی SSP5-5.5 و 37% بود. بیشترین روند افزایشی مربوط به ایستگاه زنجان با سناریوی RCP8.5 بود. نتیجهگیری و پیشنهادها پژوهشهای پرشماری در سراسر جهان نشان دادهاند که تغییرات اقلیم منجر به تغییرات شاخصهای حدی بارش میشود. این تغییرات شامل کاهش دامنة بارش و تغییر الگوی بارش است. در این راستا، نتایج این پژوهش نشان داد که روند شاخصهای حدی، الگوی یکسانی نداشت و این روند در برخی ایستگاههای همدید افزایشی و در برخی دیگر کاهشی بود. بهطور کلی میتوان گفت که شدت بارش در دورة آینده و با سناریوهای اقلیمی در بیشتر ایستگاههای مطالعهشده افزایش خواهد یافت. بررسی دقیق رویدادهای حدی بارش و تأثیرات آنها بر برنامهریزی و سیاستگذاری آینده در بخشهای گوناگون ضروری است. ازاینرو، پیشنهاد میشود شبیه این پژوهشها در آبخیزهای اصلی کشور در مقیاس کوچکتر و در تعداد بیشتری از ایستگاهها با دیگر آزمونهای تشخیص روند انجام شود و نتایج آن با آزمون آماری من-کندال مقایسه شود. | ||
| کلیدواژهها | ||
| ایران؛ بارش سنگین؛ ریزمقیاسنمایی؛ سناریوی اقلیمی؛ مدلهای گردش عمومی جو؛ CMIP6 | ||
| عنوان مقاله [English] | ||
| Impact of Climate Change on Precipitation Extremes Indices over Iran | ||
| نویسندگان [English] | ||
| Sepideh Choobeh1؛ Behnoush Farrokhzadeh2؛ Ommolbanin Bazrafshan3؛ Haniyeh Hasanvand4 | ||
| 1Ph.D. Graduated in Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran | ||
| 2Assistant Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Malayer University, Malayer, Iran | ||
| 3Professor, Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources, University of Hormozgan, Bandar Abbas, Iran | ||
| 4M.Sc. Graduated in Watershed Management, Department of Nature Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran | ||
| چکیده [English] | ||
| Introduction and Goal Climate change leads to changes in extreme climate indices, such as an increased intensity and frequency of extreme weather events. Heavy precipitation during these events can cause widespread damage to the economy, the environment and human life. According to the World Economic Forum's (WEF) Global Risks Report 2019, extreme climate events (such as heavy precipitation) and climate inadaptability are the greatest risks in terms of their impact on the environment and people. The purpose of this study was to examine the impact of climate change on heavy precipitations in the Iran. Materials and Methods Precipitation data from 47 stations was utilized in this study. First, the performance of the model was evaluated. LARS-WG software was employed to predict precipitation data with various scenarios for the future period. To evaluate the simulation of extreme precipitation using the aforementioned model, in addition to graphically comparing the observed and generated data of each station in the validation process, the RB, MD, and WS indices were calculated between the observed and simulated extreme index data. Finally, the Mann-Kendall test was utilized to examine the pattern of heavy precipitation events. Results and Discussion The simulation results for precipitation in the period 2030-2050 show that the synoptic stations will experience uneven annual fluctuations. The largest increase in annual precipitation was 59.81% (32.32 mm) at the Bam station and SSP5-8.5 scenario, and the largest decrease in annual precipitation was 11.63% (17.23 mm) at the Kish station and SSP5-8.5 scenario. According to the spatial distribution of PRCPTOT, the Yasouj station experienced the highest increase in annual precipitation under the SSP1-2.6 scenario. At this station, the annual precipitation for the future period was estimated to be 933.49 and 889.3 mm with the SSP1-2.6 and SSP5-8.5 scenarios respectively. Also, the magnitude of this indicator during the base period at this station was predicted to be 832.19 mm. Furthermore, it was predicted that this indicator will be 832.19 mm during the base period at this station. The results of the study of changes in precipitation extreme indices in the period 2030-2050 compared to 1985-2022 showed that the greatest changes were in the northern and western regions of the Iran, and the severity of these changes was especially greater with the SSP5-8.5 scenario. The largest decrease in the Rx1day index was related to the Mashhad station with the SSP5-5.5 scenario, which was 37%. The highest increasing trend was related to the Zanjan station in the RCP8.5 scenarios. Conclusion and Suggestions Numerous studies around the world have shown that climate change leads to changes in extreme precipitation. A decrease in precipitation intensity and a change in precipitation patterns are among these changes. According to the results of this study, the trend of extreme indices was not uniform, and it was increasing in some synoptic stations and decreasing in others. In general, precipitation intensity is expected to increase in the future and with climate scenarios in most of the studied stations. The impact of extreme precipitation events on future planning and policymaking in various sectors should be carefully examined. Therefore, it is suggested that similar studies be conducted in the country's main watersheds on a smaller scale and in a larger number of stations with other trend detection tests, and the results be compared with the Mann-Kendall statistical test. | ||
| کلیدواژهها [English] | ||
| Atmospheric General Circulation Models, Climate Scenarios, CMIP6, Downscaling, Heavy Precipitation, Iran | ||
| مراجع | ||
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