Volume 25, Issue 76 (4-2025)                   jgs 2025, 25(76): 0-0 | Back to browse issues page

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mokhayeri Z, fatahi E, Borna R. Investigating the Outlook of Precipitation Changes in the Great Karun Using by CMIP5 series models. jgs 2025; 25 (76)
URL: http://jgs.khu.ac.ir/article-1-3714-en.html
1- , Ebfat2002@yahoo.com
Abstract:   (2766 Views)
To conduct this research, first, the data of monthly observations of synoptic and hydrometric precipitation from the National Meteorological Organization and the Ministry of Energy during the 30-year period (2006-2005) were obtained. To examine the prospect of future rainfall changes, the historical data of the period (1976-2005) and the simulated climate data of the period (2050-2021) using two models of CM3), (CSIRO-Mk3.6 from the series) Models (CMIP5) and according to 4 scenarios RCP2.6, RCP4.5, RCP6 and RCP8.5) that are available with a spatial resolution of 0.5 x 0.5 with the BCSD method have been used.Mean-based (MB) strategy has been used to correct the bias in the output of these models. The results of the AOGCM models showed that the CSIRO-Mk3.6 error coefficient was less than the GFDL-CM3 model for simulating precipitation in the case of Large Karun.The average future rainfall (2021-2050) in the whole basin compared to the average observed rainfall during the statistical period of 1976-2005 shows, in both models and scenarios in both basins in terms of amount and area of ​​precipitation is decreasing significantly.Heavy rains in the Greater Karun Basin have been concentrated in all scenarios and models east of the basin. The highest rainfall was in the central foothills. The lowest rainfall is in the southwest and southeast. The final results of the present study are expected to be 83-116 mm. Both models are expected to have the highest rainfall in the Greater Karun Basin, with two scenarios: rcp4.5 and rcp2.6.

 
     
Type of Study: Research | Subject: climatology
Received: 2020/03/18 | Accepted: 2020/07/6

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]

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