Volume 10, Issue 3 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(3): 183-204 |
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:
Mohamadi N, Sari Saraf B, Rostamzadeh H. Trend investigation and spatial analysis of Warm and Cold spells duration index based on SSPs scenarios in northwest of Iran. Journal of Spatial Analysis Environmental Hazards 2023; 10 (3) :183-204
URL: http://jsaeh.khu.ac.ir/article-1-3377-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3377-en.html
1- Ph.D. in Hydrology and Meteorology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran. , n.mohamadi1974@gmail.com
2- Professor of Hydrology and Meteorology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran.
3- Assistant Professor of Hydrology and Meteorology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran.
2- Professor of Hydrology and Meteorology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran.
3- Assistant Professor of Hydrology and Meteorology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran.
Abstract: (2339 Views)
Nowadays, due to global warming, drought and the occurrence of cold periods and heat stress, the study of climatic variables is very important. Therefore, in this research, the long-term forecast of temperature changes in northwest Iran in the base period (1985-2014) and three periods of the near future (2021-2050), the medium future (2051-2080) and the distant future (2100- 2081) was paid. For this purpose, 2 extreme temperature indices including Warm spells duration index (WSDI) and cold spells duration index (CSDI) and Maan-Kendall trend test were used to check the changes. To predict the changes of the profiles in the future period after evaluating 7 general circulation models (GCMs) from the sixth report model series (CMIP6) from two optimal models under three socio-economic forcing scenarios including SSP1-2.6, SSP3-7.0 and SSP5-8.5 was used. The spatial distribution of the trend of changes in the Warm spells duration index (WSDI) in the base period showed that its maximum core is located in the south and southwest of the region, and its amount decreases by moving towards the north and northeast. Spatial changes of the Cold spells duration index (CSDI) are characterized by its maximum cores in the western regions and around Lake Urmia and minimum cores in the central and northern regions of the study area. According to the results, the average Warm spells duration index (WSDI) and of the Cold spells duration index (CSDI) are equal to 5.53 and 3.80 days per year, respectively, and the maximum and minimum Warm spells duration index (WSDI) are 1.8 and 2.7 days, respectively Piranshahr and Parsabad stations and the maximum and minimum and the Cold spells duration index (CSDI) are also 5.7 and 1.32 days corresponding to Zarineh and Marivan stations. Examining the trend of changes also showed that in most stations, the WSDI index has an increasing trend, and this trend has become significant in some stations, but the CSDI index has a decreasing trend and is not significant in any of the stations. The evaluation of different models with different error measurement indices also showed that MRI-ESM2-0 and MPI-ESM1-2-L models have the best performance in simulating temperature extreme in the studied area. The distribution of changes in the future period also showed that the WSDI will increase in most stations and based on all three scenarios, especially the SSP5-8.5 scenario, but the CSDI trend will decrease in most stations and based on the SSP3-7.0 and SSP5-8.5 scenarios will be significant.
Keywords: spatial distribution. Warm spells duration index (WSDI), cold spells duration index (CSDI), Northwest Iran, SSPs
Type of Study: Research |
Subject:
Special
Received: 2023/05/16 | Accepted: 2023/12/16 | Published: 2023/09/23
Received: 2023/05/16 | Accepted: 2023/12/16 | Published: 2023/09/23
References
1. تاجیک، اعظم و آزاده اربابی سبزواری. 1399. بررسی تغییرات فضایی دماهای حدی در سطح ایران. فصل نامه جغرافیای طبیعی، سال دوازدهم، 49: 109-124
2. جهانبخش اصل، سعید، بهروز ساری صراف، حسین عساکره و سهیلا شیر محمدی.1398. تجزیه و تحلیل تغییرات زمانی-مکانی بارندگی حدی فوقانی طی سال‌های 1965 تا 2016. تحلیل فضایی مخاطرات محیطی، سال 7. شماره 1: 106-89
3. ذرین، آذر و عباسعلی داداشی رودباری. 1399. پیش نگری چشم انداز بلند مدت دمای ایران مبتنی بر برونداد پروژه مقایسه مدل های جفت شده فاز ششمCMIP6) ). فیزیک زمین و فضا، 46: 583-602.
4. ذرین، آذر و عباسعلی داداشی رودباری.1400. پیش نگری دمای ایران در آینده نزدیک(2040-2021) بر اساس رویکردهای همادی چند مدلی CMIP6. پژوهش های جغرافیای طبیعی، 53: 75-90.
5. زهرائی، اکبر و سید اسعد حسینی. 1399. تغییر اقلیم و اثرات آن بر منابع آب، چاپ اول، انتشارات هاوار، ایلام.
6. صداقت‌کردار، علی و ابراهیم فتاحی، 1387، شاخص های پیش آگاهی خشک‌سالی در ایران، فصلنامه جغرافیا و توسعه، دانشگاه سیستان و بلوچستان، 6(76): 11-59.
7. عسکری زاده، سید محمد. 1395. آشکارسازی و پیش یابی نوسانات مکانی- زمانی نمایه های حدی دما و بارش در استان خراسان رضوی با استفاده از مدل LARS-WG. رساله دکتری مخاطرات آب و هواشناسی، استاد راهنما: غلامعلی مظفری. پردیس علوم انسانی، دانشگاه یزد.
8. علیجانی، بهلول. احمد روشنی، فاطمه پرک و روح اله حیدری.1391. روند تغییرپذیری فرین‌های دما با استفاده از شاخص‌های تغییر اقلیم در ایران. جغرافیا و مخاطرات محیطی، 2: 28-17.
9. عیسی پور، مصطفی. 1392. تحلیل و پیش‌بینی سری‌های زمانی دما در استان خراسان رضوی با استفاده از مدل باکس-جنکینز، پایان‌نامه کارشناسی ارشد اقلیم شناسی، استاد راهنما: محمود احمدی و حسن لشکری، دانشگاه شهید بهشتی.
10. لطفی سیرائی، علی. 1398. شبیه سازی و پیش بینی شاخص های حدی اقلیمی در استان تهران و البرز، رساله دکتری رشته آب و هواشناسی، استاد راهنما: بهلول علیجانی، دانشگاه خوارزمی، دانشکده جغرافیا.
11. Ayugi, B.Y; V, Dike; H, Ngoma;H, Babaousmail;R, Mumo, and Victor Ongoma. 2021. Future Changes in Precipitation Extremes over East Africa Based on CMIP6 Models. Water, 13, 2358. PP. 1-20.
12. Babaousmail, H.; B, Ayugi.;A, Rajasekar; H, Zhu;C, Oduro;R, Mumo, and Victor Ongoma. Projection of Extreme Temperature Events over the Mediterranean and Sahara Using Bias-Corrected CMIP6 Models. Atmosphere 2022, 13, 741. doi.org/10.3390/atmos13050741.
13. Chen, T; Ao,T; Zheng, X; Li, X and Kebi Yang. 2019. Climate Change Characteristics of Extreme Temperature in the Minjiang River Basin. Advances in Meteorology. Volume 2019, Article ID 1935719,15 pages. [DOI:10.1155/2019/1935719.]
14. Cheng, Q., F, Zhong, and Ping Wang. 2021. Potential linkages of extreme climate events with vegetation and large-scale circulation indices in an endorheic river basin in northwest China. Atmospheric Research, 247, PP.1-22.
15. Das, S.; M, Kamruzzaman.; R.M.T, Islam;D, Zhu, and Amit Kumar. 2022. Comparison of Future Changes in Frequency of Climate Extremes between Coastal and Inland Locations of Bengal Delta Based on CMIP6 Climate Models. Atmosphere, 13, 1747. PP. 1-19. https://doi.org/ 10.3390/atmos13111747. [DOI:10.3390/atmos13111747.]
16. Eyring,V;G, Flato; J, Meehl; C, Senior; B, Stevens; R, Stouffer and Karl Taylor. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization.2018.
17. Hu, T.S., K.C, Lam., and S.T Ng. 2001. River flow time series prediction with a range dependent neural network. Hydrological Science Journal, 46: 729-745.
18. IPCC (2013) The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change in: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (Eds.), Cambridge and New York, p. 1535.
19. Lin, J.Y., C.T, Cheng., Chau and KWOK-WING CHAU. 2006. Using support vector machines for long-term discharge prediction. Hydrological Science Journal, 51: 599-612.
20. Lin,v and Huopo CHEN. 2020. Assessment of model performance of precipitation extremes over the mid-high latitude areas of Northern Hemisphere:from CMIP5 to CMIP6, Atmospheric and Oceanic Science Letters, 13:6, 598-603, DOI:10.1080/16742834.2020.1820303.
21. Luo,M;T, Liu.;F, Meng.;Y, Duan;A.B. Frankl, and Philippe De Maeyer. 2018. Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China. Water 2018, 10, 1046. doi:10.3390/w1008104.
22. Nicholls, S.I., N. Easterling, D; Goodess, C.M; Kanae, S; Kossin, J; Luo, Y; Marengo,J; McInnes,K; Rahimi,M; Reichstein, M; Sorteberg, A; Vera, C. and Zhang,X. 2012: Changes in climate extremes and their impacts on the natural physical environment. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, PP. 109-230.
23. Nishant, N., G, Di Virgilio; F, Ji;E, Tam;K, Beyer, and Matthew L. Riley. Evaluation of Present-Day CMIP6 Model Simulations of Extreme Precipitation and Temperature over the Australian Continent. Atmosphere 2022, 13, 1478. PP. 1-28. [DOI:10.3390/atmos13091478.]
24. O’Neill B.C; E, Kriegler; K, Riahi; K.L. Ebi; S, Hallegatte; T.R. Carter; R, Mathur, and van Vuuren. 2014. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Chang 122(3):387–400.
25. Ozturk, T.; Saygili-Araci, F.S.; Kurnaz, M.L. Projected Changes in Extreme Temperature and Precipitation Indices Over CORDEX-MENA Domain. Atmosphere 2021, 12, 622. [DOI:10.3390/atmos 12050622.]
26. Riahi, K., D. P, Van Vuuren.,E, Kriegler., J, Edmonds., B.C, O’neill., S, Fujimori., N, Bauer., K, Calvin., R, Dellink., O, Fricko, and Hermann Wolfgang Lutz. 2017, The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environmental Change, 42, 153-168.
27. Shiru, M.S and Eun‑Sung Chung. 2021. Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria. Theoretical and Applied Climatology. [DOI:10.1007/s00704-021-03746-2]
28. Shiu, C. J., S. C. Liu; C, Fu, A, Dai, and Ying Sun. 2012. Howmuch do precipitation extremes change in a warming climate? Geophys. Res. Lett., 39, L17707. PP. 1-5., [DOI:10.1029/2012GL052762.]
29. Shrestha, S., M, Shrestha and Mukand. S. Babel. 2015. Modelling the potential impacts of climate change on hydrology of Indrawati River Basin in Nepal. Environmental Earth Science.
30. Shrestha, S.,M, Shrestha, and Mukand. S. Babel. (2015). Modelling the potential impacts of climate change on hydrology of Indrawati River Basin in Nepal. Environmental Earth Science.
31. Swart, N. C., , J. N, Cole., V. V, Kharin., M, Lazare., J. F, Scinocca., N. P, Gillett, and B, Winter. 2019. The canadian earth system model version 5 (CanESM5. 0.3). Geoscientific Model Development, 12(11), 4823-4873.
32. Teutschbein, C and Jan Seibert. (2012). Bias correcion of regional climate model simulation for hydrological climateHttps://Doi.Org/10.3390/W11020283.
33. Wang, S., Q, Liu, and Chang Huang. 2021. Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China; Remote Sens. 2021, 13, 1230.
34. Wu, Y., C, Miao.,Y, Sun.,A, AghaKouchak.,C, Shen, and Xuewei Fan. 2021. Global observations and CMIP6 simulations of compound extremes of monthly temperature and precipitation. GeoHealth, 5, e2021GH000390. [DOI:10.1029/2021GH000390.]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
