Volume 25, Issue 76 (3-2025)
jgs 2025, 25(76): 110-125 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
mokhayeri Z, fatahi E, Borna R. The Outlook of Precipitation Changes in the Great Karoun Using by CMIP5 series models. jgs 2025; 25 (76) :110-125
URL: http://jgs.khu.ac.ir/article-1-3714-en.html
URL: http://jgs.khu.ac.ir/article-1-3714-en.html
1- , Ebfat2002@yahoo.com
Abstract: (4448 Views)
To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs. The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts. The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.
Type of Study: Research |
Subject:
climatology
References
1. سلطانی گردفرامرزی، سعید،عارف صابری، مرتضی قیصوری(1396)، تعیین بهترین مدل سری زمانی در پیش بینی بارندگی سالانه ایستگاه های منتخب استان آذربایجان غربی ، نشریه تحقیقات کاربردی علوم جغرافیایی، سال هفدهم: شماره ۴۴، بهار .۹۶
2. شریفان حسین؛ حبیبی علی. (1392). بررسی اثر تغییر اقلیم بر روند تغییرات منابع آب سطحی در بخشی از حوضه استان گلستان،اولین همایش ملی چالشهای منابع آب و کشاورزی، انجمن آبیاری و زهکشی ایران- دانشگاه آزاد اسلامی واحد خوراسگان، اصفهان- 17 بهمن1392.
3. شکوهی مجتبی؛ ثنائی نژاد سید حسین؛ بنایان اول محمد.(1397). ارزیابی شبیه سازی دما و بارش مدل های اقلیمی CMIP5 در مطالعات منطقه ای تغییر اقلیم(مطالعه موردی: مناطق عمده تولید گندم دیم در ایران)، نشریه آب و خاک (علوم و صنایع کشاورزی(، دی 1397.
4. علیجانی، بهلول.(1388)، آب و هوای ایران، انتشارات دانشگاه پیام نور، چاپ نهم، بهمن 1388.
5. مسعودیان، ابوالفضل؛ کاویانی، محمدرضا.(1387)، اقلیم شناسی ایران، انتشارات دانشگاه اصفهان، چاپ اول، بهار1387.
6. معصوم پورسماکوش, جعفر, میری, مرتضی, پورکمر, فاطمه. (1396). ارزیابی داده‌های مدل‌های اقلیمی CMIP5 در مقابل داده‌های مشاهده‌ای ایران. مجله ژئوفیزیک ایران, 11(4), 40-53.
7. مطالعات آمایش استان خوزستان، (گزارش منابع طبیعی)، منابع آب استان، جلد 7، زمستان 1391.
8. ناصرزاده، محمدحسین؛ صیادی، فریباو طولابی نژاد، میثم،(1397). مدل‌سازی و پیش‌بینی تغییرات مکانی هسته‌های بارشی در ایران، تحقیقات کاربردی علوم جغرافیایی، مقالات آماده انتشار. https://jgs.khu.ac.ir/article-1-3101-fa.html
9. Arnell, NW., (1999), Climate change and global water resources Global Environmental Change, 9 (31): 49. [DOI:10.1016/S0959-3780(99)00017-5]
10. Frame D.J., and Stone D.A. 2013. Assessment of the first consensus prediction on climate change. Nature Clim. Change, 3(4): 357-359. [DOI:10.1038/nclimate1763]
11. Ho, CK., Stephenson, DB., Collins, M., Ferro, CAT., Brown, SJ., (2012),Calibration strategies: a source of additional uncertainty in climate change projections, Bull, Am, Meteorol, Soc, 93(1): 21-26. [DOI:10.1175/2011BAMS3110.1]
12. IPCC. (2014). Climate Change: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 2014. [Core Writing Team, R.K. Pachauri and L.A. Meyer (Eds.)]. IPCC, Geneva, Switzerland, 151 pp.
13. IPCC., (2000), SPECIAL REPORT: EMISSIONS SCENARIOS, A Special Report of IPCC Working Group III, Published for the Intergovernmental Panel on Climate Change.
14. IPCC., (2007), Climate Change 2007: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by M, Parry., Cambridge University Press, UK.
15. Jones, R., N., (2000), b: Analysing the risk of climate change using an irrigation demand model, Climate Res, 14: 89-100. [DOI:10.3354/cr014089]
16. Jones, R., N., (2000) a:Managing uncertainty in climate change projections Issues for impact assessment, Climate Change, 45: 403-419.
17. Koutroulis, A., G., Grillakis, M., G., Tsanis, I., K., and Papadimitriou, L., (2015), Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments: Climate Dynamic, 47:(5-6), 1881-1898. [DOI:10.1007/s00382-015-2938-x]
18. Lee, J-Y., Wang, B., (2014), Future change of global monsoon in the CMIP5. Climate Dynamics, 42: 101-119. [DOI:10.1007/s00382-012-1564-0]
19. Lenderink, G., Buishand, A., van, Deursen., W., (2007), Estimates. Of future discharges of the river Rhine using two scenario methodologies : direct versus . delta approach . Hydrol. Earth syst. Sci. 11:(3) : 1145- 1159. [DOI:10.5194/hess-11-1145-2007]
20. Najafi, M.R., Zwiers, F., P., and Gillett, N., P., (2015), Attribution of Arctic temperature change to greenhouse-gas and aerosol influences, Journal ofNature Climate Change, 5(3):246-249. [DOI:10.1038/nclimate2524]
21. Ramirez-Villegas, J., and Challinor A., (2012), Assessing relevant climate data for agricultural applications. Agricultural and Forest Meteorology, 161(0): 26-45. [DOI:10.1016/j.agrformet.2012.03.015]
22. Terando, A., Keller, K., and Easterling, W., E., (2012), Probabilistic projections of agro-climate indices in North America. Journal of Geophysical Research: Atmospheres, 117(D8): D08115. [DOI:10.1029/2012JD017436]
23. Sachindra, D. A., Huang, F., Barton, A. F., & Perera, B., J., C., (2014), Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables issues, challenges and possible solutions. J Water Clim Change. [DOI:10.2166/wcc.2014.056]
24. Schmidli, J., Frei, C., Vidale, P.L., (2006), Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. International Journal of Climatology 26, 679-689. [DOI:10.1002/joc.1287]
25. Scenarios [Houghton, J.T., L.G. Meira Filho, J. Bruce, H. Lee, B.A. Callander, E. Haites, N. Harris, and K. Maskell (eds.)] Cambridge University Press, Cambridge, pp. 247- 304.
26. Wan H., Zhang X., Zwiers F. and Min S.K. 2014. Attributing northern high-latitude precipitation change over the period 1966-2005 to human influence. Journal of Climate Dynamics, 45:1713-1726. [DOI:10.1007/s00382-014-2423-y]
27. Wang L., Ranasinghe R., Maskey S., van Gelder P. H. A. J. M , Vrijlinga J. K., (2015), Comparison of empirical statistical methods for downscaling daily climate projections from CMIP5 GCMs: a case study of the Huai River Basin, China, Int. J. Climatol, DOI: 10.1002/joc.4334 [DOI:10.1002/joc.4334]
28. Wilby, RL. Whitehead, PG.; Wade, AJ. Butterfield, D.; Davis, RJ. And Watts, G., (2006), Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. Hydrology, 330(1-2): 204-220. [DOI:10.1016/j.jhydrol.2006.04.033]
29. Zhang, C., & McBean, E., A., (2014), Adaptation Investigations to Respond to Climate Change Projections in Gansu Province, Northern China. Water Resources Management, 28(6), 1531-1544. [DOI:10.1007/s11269-014-0554-x]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Journal of Applied researches in Geographical Sciences are fully compliant with open access mandates, by publishing its articles under Creative Commons Attribution 4.0 International License (CC BY 4.0).
