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اثر تغییر اقلیم بر دما و بارش سالانه استان زنجان با بررسی عدم قطعیت‌ها | ||
| اکوهیدرولوژی | ||
| مقاله 19، دوره 4، شماره 3، مهر 1396، صفحه 847-860 اصل مقاله (975.94 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2017.62641 | ||
| نویسندگان | ||
| محمد رضا خزائی* 1؛ مطلب بایزیدی2؛ احمد شرافتی3 | ||
| 1استادیار، گروه مهندسی عمران، دانشگاه پیام نور | ||
| 2استادیار، گروه آب، دانشگاه آزاد اسلامی واحد سنندج | ||
| 3استادیار دانشکدۀ فنی و مهندسی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| چکیده | ||
| هدف از این تحقیق ارزیابی اثر تغییر اقلیم بر بارش و دمای میانگین مکانی استان زنجان در مقیاس سالانه با در‌نظر‌گرفتن عدم قطعیتهاست. بدین‌منظور از مدل سالانۀ ARMA استفاده شد. سریهای بارش و دمای سالانۀ میانگین مکانی استان محاسبه و سپس توسط مدل استوکستیک سالانه ARMA مدل‌سازی شد. 100 سری 30 سالۀ بارش و دمای سالانه برای میانگین مکانی استان تولید شد. توسط این مدلها سناریوهای آیندۀ شش مدل GCM تحت سه سناریوی انتشار ریزمقیاس شد و برای هر سناریو، 100 سری 30 سالۀ بارش و دمای سالانۀ آینده تولید شد. با مقایسۀ دامنۀ 90 درصد آمارههای دورۀ فعلی با دامنۀ 90 درصد مجموعه سناریوهای آینده، دامنۀ عدم قطعیت‌ها در نتایج بررسی شد. اعتبارسنجی مدل نشان از کفایت مدل برای شبیه‌سازی سری زمانی سالانۀ بارش و دما و کاهش مقیاس دارد. در حدود اطمینان 90 درصد، انتظار میرود دمای میانگین مکانی استان زنجان طی دورۀ 2035ـ 2064 از 6/0 تا 2/3 درجۀ سانتیگراد افزایش یابد و میانگین بارش از 25 درصد کاهش تا 15 درصد افزایش یابد. بر این مبنا عدم قطعیتهای ناشی از ساختار مدلهای GCM، نوسانات طبیعی اقلیم و سناریوهای انتشار شایان توجه است و لازم است در نظر گرفته شوند. | ||
| کلیدواژه‌ها | ||
| استوکستیک؛ تغییر اقلیم؛ عدم قطعیت؛ مدل سالانه؛ ARMA | ||
| عنوان مقاله [English] | ||
| Climate change impact on annual precipitation and temperature of Zanjan province with uncertainties investigation | ||
| نویسندگان [English] | ||
| Mohammad Reza Khazaei1؛ Motalleb Bayazidi2؛ Ahmad Sharafati3 | ||
| 1Assistant Professor, Department of Civil Engineering, Payame Noor University, Iran | ||
| 2Assistant Professor, Department of Water Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran | ||
| 3Assistant Professor, Technical and Engineering Department, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| In this paper, climate change impact on annual precipitation and temperature series for Zanjan province was assessed and uncertainties were investigated. Spatial average of annual time series of precipitation and temperature for Zanjan province were calculated and then modelled using ARMA model and 100 annual precipitation and temperature series of length 30 years were generated for spatial average of Zanjan province. Using the models, future scenarios of 6 GCMs under 3 emission scenarios were downscaled and 100 annual precipitation and temperature series of length 30 years were generated for each of the scenarios. 90% bounds of the variable statistics for the current condition were compared with the 90% bounds of the corresponding values for all of the future scenarios and the uncertainties were investigated. Model validation showed that the models are adequate for generation of the annual temperature and precipitation series. In confidence level of 90%, it is expected that average temperature of Zanjan province increase from 0.6 to 3.2 ºC and average precipitation change from -25% to +15% in 2035-64 period. So, the uncertainty due to the GCMs structure and the emission scenarios are considerable and should be taken into account. | ||
| کلیدواژه‌ها [English] | ||
| Annual model, ARMA, climate change, stochastic, Uncertainty | ||
| مراجع | ||
|
1. IPCC. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; 2013.
2. IPCC. Climate change 2001. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press; 2001.
3. Devkota L.P. and D.R. Gyawali. Impacts of climate change on hydrological regime and water resources management of the Koshi River Basin, Nepal. Journal of Hydrology: Regional Studies, 2015;4: 502–515.
4. Rana AK,FosterT,Bosshard J, Olsson and Bengtsson L. Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections. Journal of Hydrology: Regional Studies, 2014;1: 107–128.
5. Khazaei MR, Zahabiyoun B, and Saghafian B. Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. International Journal of Climatology, 2012;32: 1997-2006.
6. Ahmadvand Kahrizi M, Rouhani H. Assessing the conservation impacts of climate change based on temperature projected on 21 century (Case study: Arazkoseh and Nodeh stations). Ecohydrology, 2017;3(4): 597-609 (in Persian).
7. Khazaei MR. Climate change impact assessment on hydrological regimes of a mountainous river basin in Iran. Journal of Water and Soil Resources Conservation, 2016;5(3): 43-54 (in Persian).
8. Fowler HJ, Blenkinsop S, and Tebaldi C. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 2007;27: 1547-1578.
9. IPCC. General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment, version 2, 2007.
10. Kay AL, DaviesHN, Bell VA, and JonesRG. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, 2009;92: 41-63.
11. Semenov MA, Brooks RJ, Barrow EM,and RichardsonCW. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research, 1998;10: 95-107.
12. Chapman T. Stochastic modelling of daily rainfall: the impact of adjoining wet days on the distribution of rainfall amounts. Environmental Modelling & Software, 1998;13: 317-324.
13. Dubrovsky M, Buchtele J, and ZaludZ. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling. Climatic Change, 2004;63: 145-179.
14. Khazaei MR, Ahmadi S, Saghafian B, and Zahabiyoun B. A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, 2013;119:631–645.
15. Reaney SM, andFowler HJ. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterisation error sources, BHS 10th National Hydrology Symposium, Exeter, 2008.
16. Minville M, Brissette F, and Leconte R. Uncertainty of the impact of climate change on the hydrology of a nordic watershed. Journal of Hydrology, 2008;358:70-83.
17. Semenov MA. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology, 2007;144: 127-138.
18. Wilby RL, DawsonCW, and BarrowEM. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 2002;17: 147-159.
19. Mavromatis T. and HansenJW. Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 2001;109: 283-296.
20. Hansen JWand MavromatisT. Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology,2001;109:297-310.
21. Salahi B, Goudarzi M, HosseiniSA. Predicting the temperature and precipitation changes during the 2050s in Urmia Lake Basin. Watershed Engineering and Management, 2017;8(4): 425-438 (in Persian).
22. Rezaee M, Nahtaj M, Moghadamniya A, Abkar A, Rezaee M. Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor). Water Engineering, 2015;8(24): 25-40 (in Persian).
23. Semiromi ST, Moradi H, Khodagholi M. Predicted changes in some of climate variables using downscale model LARS-WG and output of HADCM3 model under different scenarios. Watershed Engineering and Management, 2015;7(2): 145-156 (in Persian).
24. Liu Y, Wu J,Liu Y, Hu BX,Hao Y, Huo X, et al. Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model. Quaternary International, 2015;358:137-145.
25. Salas JD, Delleur JW, Yevjevich V, Lane WL. Applied modeling of hydrologic time series. Water Resources Publications, Littleton, CO, p 484, 1980. | ||
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