پیش‌آگاهی مخاطره خشکسالی در ایستگاه سینوپتیک زابل براساس برونداد مدل‌های اقلیمیCMIP6

نوع مقاله : مقاله پژوهشی


1 دانشجوی دکترا منابع آب، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران

2 دانشیار گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند

3 دانشیار، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند.

4 دانشجوی دکترای منابع آب، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند.


باتوجه به اهمیت خشکسالی و اقدام برای به حداقل رساندن خسارات ناشی از این بلیه طبیعی، در پژوهش حاضر به بررسی شاخص خشکسالی بارش استاندارد شده (SPI) در منطقه جنوب شرقی ایران در شهرستان زابل با استفاده از چهار مدل GCM، BCC-ESM2-MR، CanESM5، MIROC6 و MRI-ESM2-0 ارائه شده در CMIP6 پرداخته شده است. در این پژوهش، از دو سناریو SSP2-4.5 و SSP5-8.5 برای تخمین SPI در دو دوره 2044-2025 و 2084-2065 و برای پیش‌بینی داده‌های هواشناسی از روش ریزمقیاس‌نمایی BCSD استفاده شده است. با توجه به نتایج به‌دست آمده در هر دو سناریوی SSP2-4.5 و SSP5-8.5 و در تمامی مدل‌های مورد بررسی، میانگین 20 ساله شاخص SPI، شش ماهه نشان دهنده بیشترین مقدار منفی است. علاوه‌بر این مقایسه مدل‌ها و سناریوها در این پژوهش نشان می‌دهد که در مدل BCC-ESM2-MR ، میانگین 20 ساله‌ شاخص شش ماهه SPI و در مدل CanESM5، شاخص 12 و 48 ماهه نسبت به سایر مدل‌ها بیشتر است. همچنین شاخص SPI در مقیاس زمانی 12 و 48 ماهه شدت خشکسالی را در دوره‌های مورد مطالعه بیشتر از مقیاس زمانی شش ماهه نشان می‌دهد و در مقیاس‌های زمانی مورد مطالعه تقریباً هر دو سناریو بر هم منطبق هستند. طبق نتایج حاصل از این پژوهش، به‌طور کلی دوره‌های دارای وضعیت نرمال کاهش یافته و به تعداد دوره‌های دارای وضعیت خشک نسبت به دوره پایه افزوده شده است. همچنین نتایج نشان می‌دهد در بدبینانه‌ترین حالت، تعداد دوره‌های خشک در ایستگاه سینوپتیک زابل 14 دروه خواهد بود و می‌توان برای پیش‌بینی شاخص خشکسالی از مدل CanESM5 استفاده نمود.



عنوان مقاله [English]

Forecasting the risk of drought in Zabol synoptic station based on the output of CMIP6 climate models

نویسندگان [English]

  • Mahdieh Forouzan Mehr 1
  • Mehdi Dastourani 2
  • Mostafa Yaghoobzadeh 3
  • saeide Hoseinabadi 4
1 PhD Student, Water Resources, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
2 Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
3 Associate Professor & Research Group of Drought and Climate Change, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
4 PhD Student, Water Resources, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
چکیده [English]

Drought is one of the most complex natural hazards that affect natural and human systems (Wilhite et al., 2005, Wilhite et al., 2007). Greenhouse gas emission has contributed to climate change in the last century (Van Pelt and Swart, 2011). Climate change has a significant impact on the hydrological cycle and consequently on water resources, and the frequency and severity of droughts and floods. The most reliable tool available for future climate simulation is the output of coupled atmosphere-ocean patterns of atmospheric general circulation (Shakarami and Massahbavani, 2007). The reports of the Intergovernmental Panel on Climate Change show that if the current trend of greenhouse gas production due to the consumption of fossil fuels continues, the concentration of these gases can reach more than 600 ppm before the end of the 21st century (IPCC, 2007). The downscale model, which is a downscale statistical method, uses semi-empirical distributions for simulation and downscale and can generate future climate parameters at the station level. A major application of these data is to monitor and evaluate future droughts (Hosseinabadi et al, 2020). In the occurrence of drought, there are many factors such as changing the course of rivers and draining reservoirs, climate change and warming of the earth. Nowadays, the increasing occurrence of drought has caused the attention of many meteorologists and climatologists around the world. Drought indicators are used to diagnose and classify drought conditions. These indicators include the possibility of evaluating the standardized precipitation evaporation and transpiration index SPEI, the Palmer drought intensity index PDSI, the standard runoff index SRI and the identification drought index RDI. SPI and SPEI are the most common drought indicators.

کلیدواژه‌ها [English]

  • Drought
  • Sixth Climate Change Report
  • SPI Index
  • Climate Change
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