تحلیل حساسیت و بررسی تغییرات شاخص خشکی (AI) در چند نمونه اقلیمی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه شهید باهنر کرمان

2 بخش مهندسی آب - دانشکده کشاورزی - دانشگاه شهید باهنر کرمان - کرمان - ایران .

3 بخش مهندسی آب. دانشکده کشاورزی. دانشگاه شهید باهنر کرمان.کرمان. ایران،

چکیده

شاخص خشکی (AI) به عنوان نسبت تبخیر-تعرق به بارش برای کمی‌سازی مقدار خشکی به کار می‌رود. در این مطالعه، تحلیل حساسیت AI براساس 7 متغیر هواشناسی شامل بارش، دمای حداکثر، دمای حداقل، فشار بخار هوا، سرعت باد و تابش ورودی در 5 نمونه اقلیمی متفاوت بر اساس طبقه‌بندی دومارتن طی دوره 2019-1990 در مقیاس زمانی سالانه با استفاده از روش مشتقات جزئی محاسبه شد. برای روندیابی تغییرات AI، از آزمون من-کندال و روش تخمین‌گر شیب خط سن استفاده شد. به منظور پیش‌نگری تغییرات AI در دوره آینده 2050-2020، از مدل گردش عمومی جو (canESM2) تحت دو سناریوی RCP4.5 و RCP8.5 و مدل ریز‌مقیاس‌نمایی (SDSM) استفاده شد. نتایج تحلیل روند حاکی از افزایش خشکی در همه ایستگاه‌ها به‌جز مشهد بود. نتایج ضریب حساسیت نشان داد، در بین متغیرهای اقلیمی، بارش بیش‌ترین سهم را در تغییرات AI داشته است. به طوری‌که %۱۰ افزایش در بارش، باعث کاهش AI در ایستگاه‌های رشت، ایلام، یاسوج، مشهد و کرمان به ترتیب %10/72، %11/81، %12/48، %11/14 و %12/82 می‌شود. بعد از بارش، بیشترین حساسیت AI به متغیرهای اقلیمی در ایستگاه‌های ایلام و رشت به ترتیب دمای حداکثر و حداقل، در ایستگاه‌های کرمان و مشهد، فشار بخار هوا، و در ایستگاه یاسوج مقدار تابش ورودی است. طی دوره 30 سال پیش‌رو، انتظار می‌رود مقادیر AI در همه‌ی ایستگاه‌ها‌ی مطالعاتی به جز یاسوج افزایش یافته و بیشترین و کمترین نرخ افزایش AI سالانه به ترتیب در ایستگاه‌های رشت تحت سناریوی RCP8.5 و مشهد تحت سناریوی RCP4.5، رخ دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Variations and Sensitivity Analysis on Aridity Index (AI) in Some Climate Samples in Iran

نویسندگان [English]

  • Bahram Bakhtiari 1
  • Nakisa Mahdavi 2
  • Nasrin Sayari 3
1 Water Engineering Dep., Faculty of Agriculture, Shahid Bahonar University of Kerman
2 Water Resource Engineering, Shahid Bahonar University, Kerman, Iran
3 Water Resource Engineering, Shahid Bahonar University, Kerman, Iran
چکیده [English]

As the ratio of evapotranspiration to precipitation, Aridity Index (AI) is used to quantify the value of aridity. In this study, sensitivity analysis was performed on AI using the partial derivative method based on 7 meteorological variables including Pre, TMax, TMin, ea, U2 and RS in 5 different climates based on De Martonne’s classification method during the period 1990-2019 on annual time scale. Mann-Kendall test and Sen’s slope estimation method were employed for the purpose of trend detection of AI variations. CanESM2 atmospheric circulation method under two scenarios including RCP4.5 and RCP8.5 and Statistical Downscaling Model (SDSM) were used to predict AI variations in the future period of 2020-2050. The Results of AI trend analysis indicated increased aridity in all stations excluding Mashhad. The Results of sensitivity coefficient showed that precipitation, among all other climate variables, contributed the most to AI variations, as 10% increase in precipitation resulted in a decrease in AI in Rasht, Ilam, Yasouj, Mashhad and Kerman by 10.72%, 11.81%, 12.48%, 11.14% and 12.82%, respectively. AI had then the highest sensitivity to TMax and TMin in Ilam and Rasht stations, to ea in Kerman and Mashhad stations, and to Rs in Yasouj station. Throughout the next 30-year period, it is expected that AI values increase in all observed stations except Yasouj. The highest and lowest annual AI increase rates throughout this period would take place in Rasht station under scenario RCP8.5, and in Mashhad station under scenario RCP4.5.

کلیدواژه‌ها [English]

  • Aridity Index (AI)
  • sensitivity coefficients
  • Climate Change Scenarios
  • Trend test
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