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ارزیابی اثرات تغییر اقلیم بر خشکسالیهای دهههای آتی در حوزه آبخیز ارس تحت مدل CMIP6 | ||
| مهندسی و مدیریت آبخیز | ||
| مقاله 4، دوره 17، شماره 1، فروردین 1404، صفحه 44-63 اصل مقاله (1.77 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwmse.2024.364619.2040 | ||
| نویسندگان | ||
| فاطمه وطنپرست قلعه جوق1؛ برومند صلاحی* 2 | ||
| 1دانشجوی دکتری آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 2استاد آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| چکیده | ||
| مقدمه حیات انسانی دائماً تحت تأثیر انواع مخاطرات طبیعی قرار دارد که برخی از آنها برآمده از پیشامدهای آب و هوایی و گرمایش جهانی است. ارزشمندی این مسئله به اندازهای است که هیئت بینالدول تغییر اقلیم تأسیسشده تا دگرگونیهای اقلیمی را در سطح جهانی مطالعه کند و نقش فعالیت بشر را در این مورد ارزیابی کند. یکی از پیامدهای تغییر اقلیم، وقوع رخدادهای حدی نظیر خشکسالی است. در واقع با رخداد تغییر اقلیم، شدت و فراوانی خشکسالی نیز پیچیدهتر میشود و بهدلیل نقش حیاتی آب در زندگی انسان، تولیدات کشاورزی و محیطزیست، ارزیابی تأثیرات سوء آن بر وقوع و شدت خشکسالیها اهمیت زیادی دارد. دلیل اصلی پدید آمدن خشکسالیها نیز نوسانات در مؤلفههای اقلیمی ازجمله کمبود بارش و افزایش دما است. مواد و روشها پژوهش حاضر با هدف پیشبینی خشکسالی در ایستگاههای اردبیل، اهر، پارسآباد، جلفا، خوی و ماکو واقع در حوزه آبخیز رودخانه ارس انجام شده است. برای این منظور، دقت مدل گردش عمومی هیئت بینالدول تغییر اقلیم (CMIP6) مورد ارزیابی قرار گرفت. مدلهای اقلیمی مورد استفاده در این پژوهش برای پیشنگری داده بارش طی دوره آینده (۲۰۴۳-۲۰۲۴) شامل مدلهای CanESM5، NorESM2-MM و MPI-ESM1-2-HR تحت سناریوهای انتشار خوشبینانه (SSP1-2.6) و بدبینانه (SSP5-8.5) هستند. آنگاه با استفاده از شاخص بارش استانداردشده (SPI)، ویژگیهای خشکسالی در مقیاس سالانه طی دوره پایه (۲۰۱۴-۱۹۸۵) با دوره آینده مورد سنجش قرار گرفت. سپس صحتسنجی مدلها با استفاده از سنجههای R2، RMSE و NSE انجام و در نهایت با دادههای تولید شده بارش، شاخص خشکسالی SPI از لحاظ شدت و فراوانی مورد تجزیه و تحلیل قرار گرفت. نتایج و بحث ارزیابی مدلها با سنجههای خطاسنجی مختلف نشان داد که در شبیهسازی بارش ایستگاههای مورد مطالعه، مدل CanESM5 بهترین عملکرد را نسبت به سایر مدلها دارد. مقایسه مدلها و سناریوهای استفاده شده در این پژوهش نشان داد که بر مبنای خروجی مدل MPI-ESM1-2-HR، شدت خشکسالیها و تحت مدل CanESM5، فراوانی خشکسالیها نسبت به سایر مدلها بیشتر شده است. همچنین نتایج نشان داد که در بدبینانهترین حالت ممکن، تعداد و شدت دورههای خشک نسبت به سناریوی خوشبینانه افزایش خواهد داشت. طبق نتایج حاصل از این پژوهش، دورههای دارای وضعیت نرمال کاهشیافته و به تعداد و فراوانی دورههای دارای وضعیت خشک افزوده شده که ناشی از پیامدهای تغییر اقلیم در محدوده مورد بررسی است. همچنین، فراوانی و شدت ترسالیها هم به مقدار اندکی نسبت به دوره مورد نظارت افزایش داشته است. بنابراین، تغییرات اقلیمی تا اندازه قابل قبولی بر وضعیت خشکسالی حوزه رود ارس در آینده اثر دارد. نتیجهگیری در مجموع میتوان بیان کرد، آگاهی از میزان تغییرات بارش و منابع آب بهدلیل مصارف گوناگون آن برای کشاورزی، شرب و صنعت از ارزشمندی فراوانی برخوردار است. در حالت کلی میتوان اظهار داشت که قدرت مدلهای اقلیمی دوره آینده بنا به آب و هوای هر گستره، ماه و متغیر اقلیمی پیشنگری شده مختلف است. حوضه رود ارس، بهعلت انجام فعالیتهای کشاورزی بیش از ظرفیت و مصرف آب در بخشهای مختلف کشاورزی در معرض کاهش منابع آبی قرارگرفته است. بنابراین، پژوهش حاضر تلاشی بهمنظور بررسی تأثیر تغییر اقلیم بر شدت و فراوانی خشکسالی حوزه رود ارس بود که میتواند سیاستهای هوشمندانه در مورد مدیریت با پابرجا منابع آب و زمین را ساده نماید. نتایج این پژوهش بر مبنای مدلهای اقلیمی مورد استفاده، وقوع تغییر اقلیم و بهخصوص تکرار خشکسالیها را در ایستگاههای مورد بررسی تأیید میکند. استفاده از مدلهای اقلیمی متعدد، بهعنوان معتبرترین ابزار تولید سناریوهای اقلیمی، پیشبینیها و مهمتر از آن عدم قطعیتها را در شرایط اقلیمی آینده بهتر ارزیابی میکند و به برنامهریزان و تصمیمگیران کمک میکند تا برای مقابله با اثرات سوء خشکسالی در بخشهای مختلف منابع آب و کشاورزی نظارت بهتری داشته باشند و نیز برنامهریزیهای قبل از وقوع بحران جهت کاهش خسارات در منطقه را آسانتر میکند. | ||
| کلیدواژهها | ||
| اقلیم؛ دره رود ارس؛ شاخص خشکسالی SPI؛ گرمایش جهانی؛ مدلهای GCM | ||
| عنوان مقاله [English] | ||
| Assessing the effects of climate change on droughts in the coming decades in aras watershed under the CMIP6 model | ||
| نویسندگان [English] | ||
| Fatemeh Vatanparast Ghaleh Juq1؛ Bromand Salahi2 | ||
| 1Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Professor of climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده [English] | ||
| Introduction Human life is constantly affected by various natural hazards, some of which result from climate events and global warming. This issue is so significant that the Intergovernmental Panel on Climate Change (IPCC) was established to study climate change on a global scale and assess the role of human activity in this matter. One of the consequences of climate change is the occurrence of extreme events such as drought. In fact, with climate change, the intensity and frequency of droughts are becoming more complex. Given the vital role of water in human life, agricultural production, and the environment, it is crucial to assess its adverse effects on the occurrence and severity of droughts. The primary cause of droughts is fluctuations in climatic components, such as decreased precipitation and increased temperature. Materials and methods This study aimed to predict drought conditions in Ardabil, Ahar, Parsabad, Jolfa, Khoi, and Maku stations, located within the Aras River watershed. To achieve this, the accuracy of the General Circulation Models (GCMs) from the IPCC’s CMIP6 dataset was evaluated. The climate models used for forecasting precipitation in the future period (2024–2043) include CanESM5, NorESM2-MM, and MPI-ESM1-2-HR under both optimistic (SSP1-2.6) and pessimistic (SSP5-8.5) emission scenarios. Then, using the Standardized Precipitation Index (SPI), drought characteristics on an annual scale during the historical period (1985–2014) were compared with those in the future period. Model validation was performed using R², RMSE, and NSE parameters. Finally, the SPI drought index was analyzed in terms of intensity and frequency based on the generated precipitation data. Results and discussion Model evaluation using different error metrics indicated that the CanESM5 model outperformed the others in simulating precipitation for the studied stations. A comparison of the models and scenarios revealed that, according to the MPI-ESM1-2-HR model, drought severity is projected to increase, while the CanESM5 model suggests a rise in drought frequency. The results also indicate that under the most pessimistic scenario, both the number and intensity of dry periods will increase compared to the optimistic scenario. Furthermore, normal conditions are expected to decrease, while the number and frequency of dry periods will rise due to the consequences of climate change in the study area. Additionally, the frequency and intensity of wet years have shown a slight increase compared to the historical period. Overall, climate change is expected to have a significant impact on the future drought conditions in the Aras River watershed. Conclusion In summary, understanding changes in precipitation and water resources is crucial due to their various applications in agriculture, drinking water supply, and industry. The accuracy of future climate models varies depending on the climate of each region, the month, and the predicted climate variable. The Aras River Basin is facing a decline in water resources due to agricultural activities exceeding capacity and high water consumption in different agricultural sectors. This study aimed to assess the impact of climate change on drought severity and frequency in the Aras River Basin, providing insights for developing intelligent policies for sustainable water and land resource management. The findings confirm the occurrence of climate change and, in particular, the recurrence of droughts in the studied stations. Using multiple climate models, as the most reliable tool for generating climate scenarios, not only allows for more accurate forecasts but also helps evaluate uncertainties in future climate conditions. These insights assist planners and decision-makers in better monitoring the adverse effects of drought on water resources and agriculture, enabling proactive planning to mitigate regional losses before a crisis occurs. | ||
| کلیدواژهها [English] | ||
| Aras River Valley, Climate, GCM Models, Global Warming, SPI Drought Index | ||
| مراجع | ||
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