بررسی تأثیر تغییر اقلیم بر شدت، مدت و دوره بازگشت خشکسالی در محدوده مطالعاتی اردبیل

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب، فاضلاب و محیط زیست، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.

10.22059/jwim.2024.379156.1173

چکیده

نگرانی در مورد تأثیر گرمایش زمین ناشی از فعالیت‌های انسانی، در سراسر جهان به‌ویژه در مناطقی با پتانسیل بالا برای رویدادهای حاد، به‌طور فزاینده‌ای در حال افزایش است. از سوی دیگر، انتخاب مدل گردش عمومی جو GCM مناسب یکی از اصلی‌ترین دغدغه‌های هیدرولوژیست‌ها و اقلیم‌شناسان برای بررسی تأثیرات تغییرات آب‌وهوایی است. تغییر اقلیم ناشی از فعالیت‌های انسانی فشار زیادی را بر منابع آب ایران وارد کرده است. این مطالعه بارش آینده و خشک‌سالی‌های هواشناسی را در محدوده مطالعاتی اردبیل با درنظرگرفتن عملکرد هفت مدل گردش عمومی جو GCM ازنظر آماری موردبررسی قرار داد و بهترینGCM  با بیش‌ترین همبستگی با بارش تاریخی (MIROC6) توسط R2 انتخاب شد. بارش و تنوع خشک‌سالی در آینده توسط یک مدل GCM تحت دو سناریو خوش‌بینانه و بدبینانه موردبررسی قرار گرفت. نتایج نشان می‌دهد که میانگین دما 3-5/1 درجه سانتی‌گراد افزایش می‌یابد، میانگین بارش سالانه برای ایستگاه سینوپتیک اردبیل از 279 میلی‌متر براساس سناریویSSP1-2.6  به 292 میلی‌متر افزایش و براساس سناریویSSP5-8.5  به 228 میلی‌متر کاهش می‌یابد. تغییرات میانگین بارندگی سالانه تا پایان قرن بیست‌ویکم تحت سناریوها بین 3/18- تا 8/4 درصد تغییر می‌کند که این تغییرات بارندگی مستعد رویدادهای شدیدتری هستند. بااین‌حال، تمرکز صرف بر متوسط بارندگی سالیانه گمراه‌کننده است و عوامل دیگری مانند تغییرات الگوی زمانی بارندگی نیز باید در نظر گرفته شود. طبق نتایج خشک‌سالی‌های متوسط با شدت دو برابر و مدت 2/2 برابر افزایش و خشک‌سالی‌های بلندمدت با شدت دو برابر و مدت 5/2 برابر افزایش نسبت به داده‌های مشاهداتی خواهند داشت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating the impact of climate change on drought intensity, duration, and recurrence period in the Ardabil study area.

نویسندگان [English]

  • Farhad Rostami
  • Ali Moridi
Department of Water, Wastewater and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
چکیده [English]

Concerns about the effects of global warming due to anthropogenic activities, all over the world especially in high-potential regions for extreme events, are increasingly growing. On the other hand, choosing the proper Global Circulation Model (GCM) is one of the main concerns of hydrologists and climatologists to investigate climate change impacts. Human-induced climate change has exerted immense pressure on Iran's water resources. This study statistically downscaled future precipitation and meteorological droughts over the Ardabil study area using the performance of seven General Circulation Models (GCMs). The best-performing GCM, MIROC6, was selected based on R2. A GCM was used to examine future precipitation and drought variability under optimistic and pessimistic scenarios.  The results show that the average temperature increases 1.5-3 °C. The average annual precipitation for Ardabil synoptic station is projected to increase from 279 mm to 292 mm under the SSP1-2.6 scenario and decrease to 228 mm under the SSP5-8.5 scenario.  Annual average precipitation changes by -18.3% to 4.8% by the end of the 21st century under various scenarios that this is precipitation changes are prone to more extreme events. However, focusing solely on average annual precipitation can be misleading. Other factors, such as changes in the timing of precipitation, should also be considered. According to the results, moderate droughts will increase in severity by a factor of 2 and in duration by a factor of 2.2, and long-term droughts will increase in severity by a factor of 2 and in duration by a factor of 2.5 compared to the observational data.

کلیدواژه‌ها [English]

  • Extreme events
  • CMIP6
  • GCM
  • Standardizad Precipitation Index (SPI)

مراجع

  1. Abbasian, M., Moghim, S., & Abrishamchi, A. (2019). Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135, 1465-1483.
  2. Ahmed, S. M. (2020). Impacts of drought, food security policy and climate change on performance of irrigation schemes in Sub-saharan Africa: The case of Sudan. Agricultural Water Management, 232, 106064.
  3. Babel, M., Sirisena, T., & Singhrattna, N. (2017). Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand. Hydrology Research, 48(3), 867-882.
  4. Ballarin, A. S., Barros, G. L., Cabrera, M. C., & Wendland, E. C. (2021). A copula-based drought assessment framework considering global simulation models. Journal of Hydrology: Regional Studies, 38, 100970.
  5. Bezak, N., Zabret, K., & Šraj, M. (2018). Application of copula functions for rainfall interception modelling. Water, 10(8), 995.
  6. Cammalleri, C., Spinoni, J., Barbosa, P., Toreti, A., & Vogt, J. V. (2022). The effects of non‐stationarity on SPI for operational drought monitoring in Europe. International Journal of Climatology, 42(6), 3418-3430.
  7. Cong, R.-G., & Brady, M. (2012). The interdependence between rainfall and temperature: copula analyses. The Scientific World Journal, 2012(1), 405675.
  8. Favre, A. C., El Adlouni, S., Perreault, L., Thiémonge, N., & Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water resources research, 40(1).
  9. Fawzy, S., Osman, A. I., Doran, J., & Rooney, D. W. (2020). Strategies for mitigation of climate change: a review. Environmental Chemistry Letters, 18, 2069-2094.
  10. Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Downscaling RCM precipitation to the station scale using statistical transformations–a comparison of methods. Hydrology and Earth System Sciences, 16(9), 3383-3390.
  11. Haile, G. G., Tang, Q., Hosseini‐Moghari, S. M., Liu, X., Gebremicael, T., Leng, G., Kebede, A., Xu, X., & Yun, X. (2020). Projected impacts of climate change on drought patterns over East Africa. Earth's Future, 8(7), e2020EF001502.
  12. Hasebe, T. (2013). Copula-based maximum-likelihood estimation of sample-selection models. The Stata Journal, 13(3), 547-573.
  13. Hayes, M. J., Svoboda, M. D., Wiihite, D. A., & Vanyarkho, O. V. (1999). Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American meteorological society, 80(3), 429-438.
  14. Kavwenje, S., Zhao, L., Chen, L., & Chaima, E. (2022). Projected temperature and precipitation changes using the LARS‐WG statistical downscaling model in the Shire River Basin, Malawi. International Journal of Climatology, 42(1), 400-415.
  15. McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology,
  16. Mesbahzadeh, T., Miglietta, M., Mirakbari, M., Soleimani Sardoo, F., & Abdolhoseini, M. (2019). Joint modeling of precipitation and temperature using copula theory for current and future prediction under climate change scenarios in arid lands (Case Study, Kerman Province, Iran). Advances in Meteorology, 2019(1), 6848049.
  17. Nelsen, R. B. (2006). An introduction to copulas. Springer.
  18. O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., Van Ruijven, B. J., Van Vuuren, D. P., Birkmann, J., & Kok, K. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global environmental change, 42, 169-180.
  19. Peres, D. J., Bonaccorso, B., Palazzolo, N., Cancelliere, A., Mendicino, G., & Senatore, A. (2023). A dynamic approach for assessing climate change impacts on drought: an analysis in Southern Italy. Hydrological Sciences Journal, 68(9), 1213-1228.
  20. Raymond, C., Horton, R. M., Zscheischler, J., Martius, O., AghaKouchak, A., Balch, J., Bowen, S. G., Camargo, S. J., Hess, J., & Kornhuber, K. (2020). Understanding and managing connected extreme events. Nature climate change, 10(7), 611-621.
  21. Raziei, T. (2021). Performance evaluation of different probability distribution functions for computing Standardized Precipitation Index over diverse climates of Iran. International Journal of Climatology, 41(5), 3352-3373.
  22. Raziei, T. (2022). Climate of Iran according to Köppen-Geiger, Feddema, and UNEP climate classifications. Theoretical and Applied Climatology, 148(3), 1395-1416.
  23. Reboita, M. S., Kuki, C. A. C., Marrafon, V. H., de Souza, C. A., Ferreira, G. W. S., Teodoro, T., & Lima, J. W. M. (2022). South America climate change revealed through climate indices projected by GCMs and Eta-RCM ensembles. Climate Dynamics, 58(1), 459-485.
  24. Reddy, M. J., & Ganguli, P. (2012). Bivariate flood frequency analysis of upper Godavari River flows using Archimedean copulas. Water resources management, 26(14), 3995-4018.
  25. Sangelantoni, L., Russo, A., & Gennaretti, F. (2019). Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy. Theoretical and Applied Climatology, 135, 725-740.
  26. Shiau, J. (2006). Fitting drought duration and severity with two-dimensional copulas. Water resources management, 20, 795-815.
  27. Srinivas, S., Menon, D., & Meher Prasad, A. (2006). Multivariate simulation and multimodal dependence modeling of vehicle axle weights with copulas. Journal of transportation engineering, 132(12), 945-955.
  28. 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.
  29. Van Noije, T., Bergman, T., Le Sager, P., O'Donnell, D., Makkonen, R., Gonçalves-Ageitos, M., Döscher, R., Fladrich, U., Von Hardenberg, J., & Keskinen, J.-P. (2020). EC-Earth3-AerChem, a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6. Geoscientific Model Development Discussions, 2020, 1-46.
  30. Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the drought phenomenon: the role of definitions. Water international, 10(3), 111-120.
  31. Woli, P., Jones, J. W., Ingram, K. T., & Fraisse, C. W. (2012). Agricultural reference index for drought (ARID). Agronomy Journal, 104(2), 287-300.
  32. Yang, X., Wood, E. F., Sheffield, J., Ren, L., Zhang, M., & Wang, Y. (2018). Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models. Journal of Hydrometeorology, 19(3), 609-623.
  33. Yousefi, H., & Moridi, A. (2022). Multiobjective optimization of agricultural planning considering climate change impacts: Minab reservoir upstream watershed in Iran. Journal of Irrigation and Drainage Engineering, 148(4), 04022007.
  34. 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. https://doi.org/10.1080/02626667.2024.2348720
  35. 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.
مدیریت آب و آبیاری
دوره 14، شماره 4
اسفند 1403
صفحه 877-895

فایل ها

هم رسانی

ارجاع به این مقاله

آمار