The extreme drought frequency and characteristics under SSP scenarios in Mashhad Plain

Document Type : Applied Article

Authors

1 Ph.D. Student, Department of Geography, Nour Branch, Islamic Azad University, Nour, Iran

2 Assoc. Prof., Department of Geography, Nour Branch, Islamic Azad University, Nour, Iran

3 Asst. Prof., Climate Research Institute (CRI), Research Institute of Meteorology and Atmospheric Sciences (RIMAS), Mashhad, Iran

10.22059/jhsci.2024.380699.837

Abstract

Drought has significant economic and social consequences, particularly in arid and semi-arid regions. The Mashhad Plain has recently experienced moderate to severe droughts, resulting in substantial damage to the agriculture and water sectors. This study aims to project changes in the characteristics of extreme droughts (SPI6 <-1) using outputs from the MRI-ESM2-0 model under two SSP scenarios. Additionally, the study investigated drought events through bivariate frequency analyses of drought duration and severity, based on SPI6 and the copula concept. Precipitation downscaling was performed using two methods: linear scaling (LS) and distribution mapping (DM). The LS method demonstrated superior downscaling capability based on statistical criteria. Projections for the near future (2026-2050) indicated an increase in precipitation under the SSP1-2.6 and SSP5-8.5 scenarios, with a statistically significant increase under SSP5-8.5. A decrease in drought frequency was observed under the SSP5-8.5 scenario based on SPI6-DM. Assessing future changes in characteristics of drought derived from the SPI6-LS series suggested an increase in drought frequency under the SSP5-8.5 scenario. Univariate return period analysis using the LS method indicated that, under the SSP1-2.6 scenario, drought events would remain unchanged compared to the baseline period. Conversely, under the SSP1-2.6 scenario (DM method), increased values of duration and severity were projected. Joint frequency analysis results suggested that under the SSP1-2.6 scenario, seasonal joint return periods of severity and duration would be shorter than the baseline, indicating an increased risk of drought hazards in the region under study. The application of these research results will contribute to improved future planning in the water and agriculture sectors for this area.

Keywords


  • انصاری مهابادی، ثمین؛ دهبان، حسین؛ زارعیان، محمدجواد؛ و فرخ‌نیا، اشکان (1401). بررسی روند تغییرات دما و بارش حوضه‌های آبریز ایران در افق 20 سال آینده براساس برونداد مدل‌های CMIP6. پژوهش آب ایران، 16(1)، (پیاپی 44)، 11-24.
  • بابائیان، ایمان؛ مدیریان، راهله؛ خزانه‌داری، لیلی؛ کریمیان، مریم؛ کوزه‌گران، سعیده؛ کوهی، منصوره؛ فلامرزی، یاشار؛ و ملبوسی، شراره (1402). چشم‌انداز بارش ایران در قرن 21 با به‌کارگیری مقیاس‌کاهی آماری برونداد مدل‌های منتخبCMIP6 توسط نرم‌افزار ‌CMHyd، فیزیک زمین و فضا، 49(2)، 431-449. . doi: 10.22059/jesphys.2023.332410.1007436
  • بهزادی، فرهاد؛ جوادی، سامان؛ یوسفی، حسین؛ مریدی، علی؛ و هاشمی شاهدانی، سیدمهدی (1401). تعیین تأثیر تغییر اقلیم بر خشکسالی آب زیرزمینی با استفاده از برونداد مدل‌های CMIP6 (مطالعۀ موردی: دشت شهرکرد). اکوهیدرولوژی، 9(2)، 419-436.
  • زرین، آ. صالح‌آبادی، ن. (1398). پیش‌آگاهی مخاطرۀ خشکسالی در تهران براساس برونداد مدل‌های CMIP6، ششمین کنفرانس منطقه‌ای تغییر اقلیم، تهران، آبان 1398.
  • عسگری، الهه؛ نوروزی‌نظر، محمدصادق؛ باعقیده، محمد؛ و انتظاری، علیرضا (1402). ارزیابی اثرات تغییر اقلیم بر خشکسالی‌های آینده حوضۀ آبخیز گرگانرود تحت مدل‌های ‌CMIP6. پژوهش‌های تغییرات آب‌و‌هوایی، 4(14)، 27-42.doi: 10.30488/ccr.2023.397170.1134
  • عطایی، هوشمند؛ کوهی، منصوره؛ مدیریان، راهله؛ و بذرافشان، بهاره (1400). تغییرات پیش‌نگری‌شده در دما و بارش حوضۀ کشف‌رود برمبنای روش‌های مقیاس‌کاهی دینامیکی و آماری. مخاطرات محیط طبیعی، 10(30)، 183-202., doi: 10.22111/jneh.2021.37827.1777
  • قنبرزاده، هادی؛ و بهنیافر، ابوالفضل (1388). پیامدهای اقتصادی خشکسالی‌های دورۀ 85-1375 بر نواحی روستایی دهستان شاندیز شهرستان مشهد، مطالعات برنامهریزی سکونتگاههای انسانی (چشمانداز جغرافیایی)، 4(9)، 139-164.
  • کوهی، منصوره؛ و پاکدامن، مرتضی (1401). ارزیابی عملکرد مدل‌های CMIP5 در تحلیل فراوانی دومتغیرۀ مفصل‌مبنای ویژگی‌های خشکسالی در بخش جنوبی حوضۀ آبریز کارون. فیزیک زمین و فضا، 48(1)، 153-172..doi: 10.22059/jesphys.2022.326320.1007333
  • محمدی، نیلوفر؛ و حجازی‌زاده، زهرا (1403). اثرات تغییر اقلیم بر افزایش ریسک مخاطرة خشکسالی در تهران با بهره‌گیری از سناریوهای ‌CMIP6. مدل‌سازی و مدیریت آب و خاک، 4(2)، 133-148.  doi: 10.22098/mmws.2023.12563.1252
  • مقیمی، ابراهیم (1403). رویکرد جدید به مخاطرات محیطی و توسعۀ پایدار در ایران. مدیریت مخاطرات محیطی 11(1)، 73-84.doi: 10.22059/jhsci.2024.378814.830
  • نگهبان، سعید؛ مکرم؛ مرضیه؛ و مرادی‌زاده کرمانی، ریحانه (1403). تحلیل اثرهای مخاطرۀ خشکسالی بر جوامع روستاهای پیرامون دریاچۀ مهارلو، مدیریت مخاطرات محیطی، 1(11)، 1-13. doi: 10.22059/jhsci.2024.374378.823
  • Akaike, H. (1974) A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19 (6), 716–723.
  • Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., & Rogelj, J. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group14 I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary; 2021. Available online: https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (accessed on 15 August 2022).
  • Ayantobo, O. O., Li, Y., & Song, S. (2019). Multivariate drought frequency analysis using four-variate symmetric and asymmetric Archimedean copula functions. Water Resources Management, 33, 103-127.
  • Behzadi, F., Yousefi, H., Javadi, S., Moridi, A., Shahedany, S. M. H., & Neshat, A. (2022). Meteorological drought duration–severity and climate change impact in Iran. Theoretical and Applied Climatology, 149(3), 1297-1315.
  • Bonaccorso, B., Cancelliere, A., & Rossi, G. (2003) An analytical formulation of return period of drought severity. Stochastic Environmental Research Risk, 17 (3), 157–174.
  • Chen,, Singh, V.P., Guo, S., Mishra, A.K., & Guo, J. (2013) Drought analysis using copulas. Journal of Hydrologic Engineering, 18 (7), 797–808.
  • Dai, A., Zhao, T., & Chen, J. (2018). Climate change and drought: a precipitation and evaporation perspective. Current Climate Change Reports, 4, 301-312.
  • Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958.
  • Ge, Y., Cai, X., Zhu, T., & Ringler, C. (2016) Drought frequency change:An assessment in northern India palins, Agricultural Water Management, 176, 111-121.
  • Genest,, Rémillard, B., D., & Beaudoin (2009) Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, 44, 199-213.
  • Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., ...& Lyu, K. (2020). Insights from CMIP6 for Australia's future climate. Earth's Future, 8(5), e2019EF001469.
  • Ha, K. J., Moon, S., Timmermann, A., & Kim, D. (2020). Future changes of summer monsoon characteristics and evaporative demand over Asia in CMIP6 simulations. Geophysical Research Letters, 47(8), e2020GL087492.
  • IPCC, (2001). Climate Change: The Scientific Basic. Contribution of Working Group 1 to The Third Assessment report to the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, N.Y., U.S.A., 881pp.
  • IPCC, (2007). Climate Change: The physical science basis. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller H.L. )Eds(, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,
  • IPCC, (2013). Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern-mental Panel on Climate Change Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, USA.
  • Joe, (1997) Multivariate Models and Dependence Concepts, Chapman & Hall, London.399 pp.
  • Karim, R., Tan, G., Ayugi, B., Babaousmail, H., & Liu, F. (2020). Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014. Atmosphere, 11(9), 1005.
  • Kolmogorov, N. (2018) Sulla Determinazione Empirca di una Legge diDistribuzione, Giornale dell’ Istituto Italiano degli Attuari, 4, pp.83-91.
  • Li, L., She, D., Zheng, H., Lin, P., & Yang, Z. L. (2020). Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. Journal of Hydrometeorology, 21(7), 1513-1530.
  • McKee, T.B., Doeskin, N.J. and Kleist, J. (1993) The relationship of drought frequency and duration to time scales, In: Proceedings of: the 8th Conference on Applied Climatology, January 17-22, Anaheim, California, pp. 179-184.
  • Nelsen, R.B. (2007) An introduction to copulas (3th ed.). New York: Springer.
  • Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and applied climatology, 99, 187-192.
  • Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., & Arnold, J. G. (2016). CMhyd user manual. Doc. Prep. Simulated Clim. Change Data Hydrol. Impact Study, 1413.
  • Riahi K., Rao Sh., Krey V., Cho Ch., and et al., 2011, RCP 8.5—A scenario of comparatively high greenhouse gas emissions, 109: 33-57.
  • Scholz, F. W., Stephens, M. A. (1987) K-sample Anderson-Darling tests, Journal of the American Statistical Association, 82(399), 918– 924.
  • Shiau, J.T. (2006) Fitting drought duration and severity with two-dimensional copulas. Water Resources Management, 20, 795–815.
  • Shrestha, A., Rahaman, M. M., Kalra, A., Jogineedi, R., & Maheshwari, P. (2020). Climatological drought forecasting using bias corrected CMIP6 climate data: A case study for India. Forecasting, 2(2), 59-84.
  • Sibuya, M. (1960) Bivariate extreme statistics. Annals of the Institute of Statistical Mathematics (Tokyo) 11, 195–210.
  • Sklar, A. (1959) Distribution functions of n Dimensions and Margins, Publications of the Institute of Statistics of the University of Paris 8, 229-231. (In French)
  • Su, B., Huang, J., Mondal, S. K., Zhai, J., Wang, Y., Wen, S., ... & Li, A. (2021). Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China. Atmospheric Research, 250, 105375.
  • Supharatid, S., & Nafung, J. (2021). Projected drought conditions by CMIP6 multimodel ensemble over Southeast Asia. Journal of Water and Climate Change, 12(7), 3330-3354.
  • Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G., & Pitman, A. J. (2020). Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophysical Research Letters, 47(11), e2020GL087820.
  • Wang, X. L., & Feng, Y. (2013). RHtestsV4 user manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada, 28, 780.
  • Wang, X., Yang, J., Xiong, J., Shen, G., Yong, Z., Sun, H., ... & Cui, X. (2022). Investigating the impact of the spatiotemporal bias correction of precipitation in CMIP6 Climate Models on drought assessments. Remote Sensing14(23), 6172.
  • Xu, L., Yu, W., Yang, S., & Zhang, T. (2024). Concurrent drought and heatwave events over the Asian monsoon region: insights from a statistically downscaling CMIP6 dataset. Environmental Research Letters19(3), 034044.
  • Xu, Y., Zhang, X., Hao, Z., Hao, F., & Li, C. (2021). Projections of future meteorological droughts in China under CMIP6 from a three‐dimensional perspective. Agricultural Water Management, 252, 106849.
  • Yevjevich, V. (1967) An objective approach to definitions and investigations of continental hydrologic droughts, Hydrology paper, Colorado State University.
  • Yong, Z., Xiong, J., Wang, Z., Cheng, W., Yang, J., & Pang, Q. (2021). Relationship of extreme precipitation, surface air temperature, and dew point temperature across the Tibetan Plateau. Climatic Change, 165, 1-22.
  • Yousefi, H., Ahani, A., Moridi, A., & Razavi, S. (2024). The future of droughts in Iran according to CMIP6 projections. Hydrological Sciences Journal, 69(7), 951-970.
  • Zamani, Y., Hashemi Monfared, S. A., Azhdari Moghaddam, M., & Hamidianpour, M. (2020). A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran. Theoretical and Applied Climatology, 142, 1613-1623.
  • Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144, 643-660.