Projection of drought indices in Iran based on CMIP5 multi-model ensemble

Document Type : Original Article


1 Assistant Professor of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran

2 2. Postdoctoral Researcher of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran.


Increasing the intensity and frequency of drought indices due to global warming can severely affect the natural environment. Therefore, it is necessary to project drought indices using General Circulation Models (GCMs) in order to provide projection of drought conditions as well as climate risk management. For this purpose, nine models were selected from a set of CMIP5 models with horizontal resolution of 0.5 ° and bias corrected by Quantile Delta-Mapping (QDM) method. Then, using a Bayesian mean model (BMA), an ensemble model was generated and its performance was evaluated using Taylor diagram. The results showed that the CMIP5-MME model generated by BMA method performed better than the existing nine individual models. The generated ensemble model simulates the dry spells and dry days more accurately than the intensity of drought in Iran. It shows that CMIP5 models simulate the precipitation event better than the amount of precipitation. The results showed that the frequency of dry days, drought period and also the severity of drought in Iran will increase in the future. The aridity index (AI), which shows the balance between water supply and demand in the atmospheric-Earth interaction, will increase by a maximum of 3.15% in the average area of Iran. Also, dry days and dry spells will increase by 7.50% and 28.84%, respectively, in the upcoming decades. The results show that under climate change conditions, the length of the drought period will increase more than the aridity index (intensity of the drought). This result is considered a serious threat to water resources and ecosystems and requires special attention to drought management programs (DMP) in the country.


  1. تاج بخش، سحر. عیسی خانی، نسرین و فضل کاظمی، امین. (1394). ارزیابی خشک‌سالی هواشناسی در ایران با استفاده از شاخص «استانداردشدة بارش و تبخیر-تعرق (SPEI)». فیزیک زمین و فضا، 41(2)، 313-321.
  2. جوان، خدیجه. (1400). بررسی خشکسالی هواشناسی در ایستگاه ارومیه با استفاده از شاخص SPI تحت سناریوهای تغییر اقلیم (RCP). پژوهش­های تغییرات آب و هوایی, 2(5)، 81-94.
  3. خزانه‌داری، لیلی. زابل عباسی، فاطمه. قندهاری، شهرزاد. کوهی، منصوره و ملبوسی، شراره. (1388). دورنمایی از وضعیت خشکسالی ایران طی سی سال آینده. جغرافیاوتوسعه ناحیه­ای، 7(12)، 83-99.
  4. خوش اخلاق، فرامرز. کریمی احمدآباد، مصطفی. جاسمی، سید میثم و کاکی، سیف‌اله. (1399). واکاوی آماری - همدید تغییرپذیری آب ‌و‌ هواشناختی رژیم بارش غرب میانی ایران با تاکید بر رخداد خشکسالی‌های شدید. پژوهش­های تغییرات آب و هوایی، 1(1)، 63-82.
  5. دارند، محمد. (1393). پایش خشکسالی ایران به کمک شاخص شدت خشکسالی پالمر و ارتباط آن با الگوهای پیوند از دور جوی- اقیانوسی. فصل‌نامه تحقیقات جغرافیایی. ۲۹ (۴)، ۸۲-۶۷.
  6. زرین, آذر و داداشی رودباری، عباسعلی. (1400الف). مدیریت ریسک خشکسالی در شرایط تغییر اقلیم: نقش سیاست‌های ملی و برنامه مدیریت خشکسالی (DMP). آب و توسعه پایدار، 8(1)، 107-112.
  7. زرین, آذر و داداشی رودباری، عباسعلی. (1400ب). پیش نگری شدت بارش در ایران با بکارگیری رویکرد همادی چند مدلی با استفاده از داده‌های مقیاس کاهی شده NEX-GDDP. مجله ژئوفیزیک ایران، پذیرفته شده برای چاپ.
  8. زرین، آذر و داداشی رودباری، عباسعلی. (1400ج). پیش‌نگری دوره‌های خشک و مرطوب متوالی در ایران مبتنی‌ بر برونداد همادی مدل‌های تصحیح شده اریبی CMIP6. فیزیک زمین و فضا، 47(3)، 561-578.
  9. Ahmadalipour, A., Moradkhani, H., Castelletti, A., and Magliocca, N. (2019). Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment, 662, 672-686.
  10. Bowell, A., Salakpi, E.E., Guigma, K., Muthoka, J.M., Mwangi, J., and Rowhani, P. (2021). Validating commonly used drought indicators in Kenya. Environmental Research Letters, 16(8), 084066.
  11. Calow, R.C., MacDonald, A.M., Nicol, A.L., and Robins, N.S. (2010). Ground water security and drought in Africa: linking availability, access, and demand. Groundwater, 48(2), 246-256.
  12. Cannon, A.J., Sobie, S.R., and Murdock, T.Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959.
  13. Cook, B.I., Mankin, J.S., Williams, A.P., Marvel, K.D., Smerdon, J.E., and Liu, H. (2021). Uncertainties, limits, and benefits of climate change mitigation for soil moisture drought in Southwestern North America. Earth's Future, 9(9), e2021EF002014.
  14. Crausbay, S.D., Ramirez, A.R., Carter, S.L., Cross, M.S., Hall, K.R., Bathke, D.J., ... and Sanford, T. (2017). Defining ecological drought for the twenty-first century. Bulletin of the American Meteorological Society, 98(12), 2543-2550.
  15. CRED, U. (2020). Human Cost of Disasters. An Overview of the last 20 years: 2000–2019. CRED, UNDRR, Geneva.
  16. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... and Thépaut, J.N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.
  17. Hessl, A.E., Anchukaitis, K.J., Jelsema, C., Cook, B., Byambasuren, O., Leland, C., ... and Hayles, L.A. (2018). Past and future drought in Mongolia. Science Advances, 4(3), e1701832.
  18. Lesk, C., Rowhani, P., and Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84-87.
  19. Li, M., Chu, R., Islam, A.R.M., Jiang, Y., and Shen, S. (2020). Attribution analysis of long-term trends of aridity index in the Huai River basin, eastern China. Sustainability, 12(5), 1743.
  20. Linke, A.M., Witmer, F.D., O’Loughlin, J., McCabe, J.T., and Tir, J. (2018). Drought, local institutional contexts, and support for violence in Kenya. Journal of Conflict Resolution, 62(7), 1544-1578.
  21. Massoud, E.C., Lee, H., Gibson, P.B., Loikith, P., and Waliser, D.E. (2020). Bayesian model averaging of climate model projections constrained by precipitation observations over the contiguous United States: Journal of Hydrometeorology, 21(10), 2401-2418.
  22. Miao, L., Li, S., Zhang, F., Chen, T., Shan, Y., and Zhang, Y. (2020). Future drought in the dry lands of Asia under the 1.5 and 2.0 C warming scenarios. Earth's Future, 8(6), e2019EF001337.
  23. Mondal, S.K., Huang, J., Wang, Y., Su, B., Zhai, J., Tao, H., ... and Jiang, T. (2021). Doubling of the population exposed to drought over South Asia: CMIP6 multi-model-based analysis. Science of The Total Environment, 771, 145186.
  24. Monjo, R., Royé, D., and Martin-Vide, J. (2020). Meteorological drought lacunarity around the world and its classification. Earth System Science Data, 12(1), 741-752.
  25. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren, D.P.,.. and Wilbanks, T.J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756.
  26. Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R.A., Carrao, H., ... and Feyen, L. (2018). Global changes in drought conditions under different levels of warming. Geophysical Research Letters, 45(7), 3285-3296.
  27. Otkin, J.A., Svoboda, M., Hunt, E. D., Ford, T.W., Anderson, M.C., Hain, C., and Basara, J.B. (2018). Flash droughts: A review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bulletin of the American Meteorological Society, 99(5), 911-919.
  28. Parker, W.S. (2016). Reanalyses and observations: What’s the difference?. Bulletin of the American Meteorological Society, 97(9), 1565-1572.
  29. Reichler, T., and Kim, J. (2008). How well do coupled models simulate today's climate?. Bulletin of the American Meteorological Society, 89(3), 303-312.
  30. Scheff, J., and Frierson, D.M. (2015). Terrestrial aridity and its response to greenhouse warming across CMIP5 climate models. Journal of Climate, 28(14), 5583-5600.
  31. Sun, C.X., Huang, G.H., Fan, Y., Zhou, X., Lu, C., and Wang, X.Q. (2019). Drought occurring with hot extremes: Changes under future climate change on Loess Plateau, China. Earth's Future, 7(6), 587-604.
  32. Switanek, M.B., Troch, P.A., Castro, C.L., Leuprecht, A., Chang, H.I., Mukherjee, R., and Demaria, E. (2017). Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrology and Earth System Sciences, 21(6), 2649-2666.
  33. Taylor, K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.
  34. Taylor, K.E., Stouffer, R.J., and Meehl, G.A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American meteorological Society, 93(4), 485-498.
  35. Tegegne, G., Melesse, A.M., and Worqlul, A.W. (2020). Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Science of the Total Environment, 704, 135357.
  36. Wallemacq, P. (2018). Economic losses, poverty & disasters: 1998-2017. Centre for Research on the Epidemiology of Disasters, CRED.