Prediction of Climate Change using General Circulation Models and SDSM and LARS-WG Downscaling Models under RCP Scenarios in Dez Watershed

Subject Areas : Climatology

Ali akbar Arab solghar ¹ ( PhD student in Water Resources Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran )
Jahangir Porhemmat ² ( Professor of Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran )
Massoud Goodarzi ³ ( Associate Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran )

Received: 2020-10-28 Accepted : 2022-04-25 Published : 2022-03-21

Keywords: LARS-WG, Climate Change, SDSM, RCP, Dez watershed,

Abstract :

Climate change is one of the greatest challenges facing humanity in the 21st century. Therefore, prediction of climate change is very important to predict the future situation and to consider the necessary measures to adjust and adapt to climate change. Therefore, in this study, climate change was predicted in the Dez watershed. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios RCP2.6, RCP4.5 and RCP8.5 and the application of two downscaling models of LARS-WG and SDSM and the climate changes in the next three periods compared to the base period (1989-2018) were examined. The results showed that both downscaling models have good accuracy in simulating climate change in the study watershed. The results of prediction the studied models also showed that in the future periods the amount of precipitation in the watershed will change between -6.3 to 15.7% compared to the base period. The most decreasing and increasing changes will be related to the eastern and southwestern areas of the watershed, respectively. Also, the maximum temperature of the watershed will fluctuate between 1.3 to 3.9 oC and the minimum temperature will fluctuate between 1.5 to 3.5 oC. The highest and lowest changes will be related to the southeastern and northwestern areas of the watershed, respectively. Therefore, due to the increase in temperature and precipitation, as well as the mountainous nature of the watershed under study, it is necessary to consider flood control and containment and management strategies.

References:

1- آقاشاهی، محسن. مجتبی اردستانی. نیک سخن، محمدحسین. طهماسبی، بهشته (1391)؛ معرفی و مقایسه مدل‌های LARS-WG و SDSM به‌منظور ریزمقیاس سازی پارامترهای زیست‌محیطی در مطالعات تغییر اقلیم، ششمین همایش ملی و نمایشگاه تخصصی مهندسی محیط‌زیست، تهران.

2- حسینی، سید اسعد. احمدی، حمزه (1395)؛ چشم‌انداز تغییرات دما با استفاده از ریزمقیاس نمایی آماری خروجی‌های مدل HadCM3، فصل­نامه هواشناسی کشاورزی، شماره یکم، صص 73-68.

3- شمسی‌پور، علی‌اکبر (1392)؛ مدل‌سازی آب و هوایی نظریه و روش، انتشارات دانشگاه تهران، چاپ اول، 294 ص.

4- صداقت‌کردار، علی. فتاحی، ابراهیم (1387)؛ شاخص‌های پیش‌آگاهی خشک‌سالی در ایران، فصل­نامه جغرافیا و توسعه، شماره یازدهم، صص 76-59.

5- طباطبایی، سید علی. حسینی، مهرداد (1382)؛ بررسی تغییر اقلیم در شهر سمنان بر اساس پارامترهای بارش ماهیانه و متوسط دمای ماهیانه، سومین کنفرانس منطقه‌ای و اولین کنفرانس ملی تغییر اقلیم اصفهان. ص 8.

6- عزیزی، قاسم. احمدی، محمد. کردوانی، پرویز (1398)؛ چشم‌انداز تغییرات دمای هوا دوره آینده بر اساس مدل‌های CMIP5 و سناریوهای واداشت تابشی (مطالعه موردی: ایستگاه‌های منتخب استان ایلام)، فصل­نامه جغرافیای طبیعی، سال دوازدهم، شماره چهل و سوم، صص 88-71.

7- کارآموز، محمد. رمضانی، فرید. رضوی، سامان (1385)؛ پیش‌بینی بلندمدت بارش با استفاده از سیگنال‌های هواشناسی: کاربرد شبکه‌های عصبی مصنوعی. هفتمین کنگره بین‌المللی مهندسی عمران، تهران، ص 11.

8- کثیری، مریم. گودرزی، مسعود. جانباز قبادی، غلامرضا. متولی، صدرالدین (1399)؛ چشم‌انداز آینده تغییرات دما و بارش در سواحل جنوبی دریای خزر. فصلنامه جغرافیای طبیعی، سال سیزدهم، شماره چهل و هفتم، صص 104-89.

9- گودرزی، مسعود. حسینی، سید اسعد. مسگری، ابراهیم (1395)؛ مدل‌های آب و هواشناسی، انتشارات آذرکلک، زنجان.

10- گودرزی، مسعود. صلاحی، برومند. حسینی، سید اسعد (1397)؛ چشم‌انداز آتی تغییرات دما و بارش با استفاده از مدل ریز گردانی آماری (مطالعه موردی: حوزه آبخیز دریاچه ارومیه). فصل­نامه فضای جغرافیایی، انتشارات دانشگاه آزاد اهر، سال هیجدهم، شماره شصت و چهارم، صص 60-41.

11- نادری، سهراب. گودرزی، مسعود. قدمی دهنو، محمد (1396)؛ اثر تغییر اقلیم بر پارامترهای اقلیمی در حوزه سیمره، علوم و مهندسی آبخیزداری، سال یازدهم، شماره سی و نهم، صص 76-69.

12- Beecham, S. Rashid, M. Chowdhury R.K. (2014): Statistical Downscaling Of Multi-Site Daily Rainfall In A South Australian Catchment Using A Generalized Linear Model. International Journal Of Climatology, 34(14): Pp. 3654–3670.

13- Carter, T. R. Parry, M. L. Harasawa, H. Nishioka, S. (1994): IPCC Technical Guidelines For Assessing Climate Change Impacts And Adaptions, IPCC Special Report To Working Group II Of IPCC, London.

14- Dibike, Y.B. Coulibaly, P. (2005): Hydrologic Impact Of Climate Change In The Saguenay Watershed: Comparison Of Own Scaling Methods And Hydrologic Models. Journal Of Hydrologic, 307: Pp. 145–163.

15- Dimri, A.P. Kumar, D. Choudhary, A. Maharana, P. (2018): Future Changes Over The Himalayas: Maximum And Minimum Temperature, Global And Planetary Change, (162): Pp. 212-234.

16- Hay, L.E. Wilby, R.L. Leavesley, G.H. (2000): A Comparison Of Delta Change And Downscaled GCM Scenarios For Three Mountainous Basins In The United States. Journal Of The American Water Resources Association, 36: Pp. 387-397.

17- Heydari, Sh. Hosseini, S.A. Heydari, A. (2020): Investigating The Effects Of Climate Change On Stream Flows Of Urmia Lake Basin In Iran, Modeling Earth Systems And Environment, 1: Pp. 329-339.

18- Hu, T.S. Lam, K.C. Ng, S.T. (2001): River Flow Time Series Prediction With A Range Dependent Neural Network. Hydrological Science Journal, 46: 729-745.

19- Hu, T.S. Lam, K.C. Ng, S.T. (2001): River Flow Time Series Prediction With A Range Dependent Neural Network. Hydrological Science Journal, 46: Pp.  729-745.

20- Hyun Cha, D. Kyou Lee, D. Chun-Sil Jin. GA Young Kim. Yonghan Choi. Myoung-Seok Suh. Joong-Bae Ahn. Song-You Hong. Seung-Ki Min. Seong-Chan Park. Hyun-Suk Kang. (2016): Future Changes In Summer Precipitation In Regional Climate Simulations Over The -Korean Peninsula Forced By Multi-RCP Scenarios Of Hadgem2-AO, Asia-Pacific Journal Of Atmospheric Sciences, 52: Pp. 139-149.

21- IPCC. (2001): In: Watson, R.T. Zinyowera, M.C. Moss, R.H. Dokken, D.J. (Eds.), Special Report On The Regional Impacts Of Climate Change, An Assessment Of Vulnerability. Cambridge University Press, UK.

22- IPCC. (2007): Solomon, S. D. Qin, M. Manning, Chen, M. Marquis, K.B. Averyt, M. Tignor And H.L. Miller (Eds.), Summary For Policymakers, In: Climate Change 2007. Climate Change 2007: The Physical Science Basis, Contribution Of Working Group I To The Fourth Assessment Report Of The Intergovernmental.

23- Kilsby, C.G. Jones, P.D. Burton, A. Ford, A.C. Fowler, H.J. Harpham, C. James, P. Smith, A. Wilby, R.L. (2007): A Daily Weather Generator For Use In Climate Change Studies. Environmental Modelling And Software, 22: Pp. 1705–1719.

24- Leong Tan, M. Latifibrahim, AB. Yusop, Z. Chua, V. And Wengchan, N. (2017): Climate Change Impacts Under CMIP5 RCP Scenarios On Water Resources Of The Kelantan River Basin, Malaysia, Atmospheric Research, 189:1-10.

25- Lin, J.Y. Cheng, C. T. Chau, K.W. (2006): Using Support Vector Machines For Long-Term Discharge Prediction. Hydrological Science Journal, 51: 599-612.

26- Nilawar, A P. Waikar, M. L. (2019): Impacts Of Climate Change On Stream Flow And Sediment Concentration Under RCP 4.5 And 8.5: A Case Study In Purna River Basin, India. Science Of The Total Environment, 2: 2685-2696.

27- Qian, B. Gameda, S. Hayhoe, H. Dejong, R. Bootsma, A. (2004): Comparison Of LARS-WG And AAFC-WG Stochastic Weather Generators For Diverse Canadian Climates. Climate Research. Pp. 26.

28- Racsko, P. L. Szeidl And M. Semenov. (1991): A Serial Approach To Local Stochastic Weather Models. Ecological Modeling, 57: Pp. 27-41.

29- Sarkar, J. Chicholikar, J. R. Rather, L. S. (2015): Predicting Future Changes In Temperature And Precipitation In Arid Climate Of Kutch, Gujarat: Analyses Based On LARS-WG Model, Research Article, Current Science, 109: Pp. 2084-2093

30- Semenov, M. Brooks, R. Barrow, E. Richardson, C. (1998): Comparison Of The WGEN And LARS-WG Stochastic Weather Generators For Diverse Climates. Climate Research, 10: Pp. 95-107.

31- Sharma, D. Gupta, A. D. Babel, M. S. (2007): Spatial Disaggregation Of Bias-Corrected GCM Precipitation For Improved Hydrologic Simulation: Ping River Basin, Thailand. Hydrol. Earth Syst. Sci. 11: Pp. 1373-1390.

32- Tatsumi, K. Oizumi, T. Yamashiki, Y. (2013): Introduction Of Daily Minimum And Maximum Temperature Change Signals In The Shikoku Region Using The Statistical Downscaling Method By Gcms. Hydrological Research Letters, 3: Pp. 48-53.

33- Wigley, T.W. L. Jones, P. D. Briffa, K. R. Smith, G. (1990): Obtaining Sub-Grid Scale Information From Coarse Resolution General Circulation Model Output, J. Geophysics. Res. 951: Pp. 1943–1953

34- Wilby, R.L. Dawson. C.W. Barrow, E.M. (2002): SDSM- A Decision Support Tool For The Assessment Of Regional Climate Change Impacts, Environmental Modeling & Software, 17: Pp. 147-159.

35- Wilby, R.L. Harris, I. (2006): A Frame Work For Assessing Uncertainties In Climate Change Impacts: Low Flow Scenarios For The River Thames, UK. Water Resour. Res. 42: 10 P.

36- Wilby, R.L. Dawson, W.C. (2007): SDSM 4.2- A Decision Support Tool For The Assessment Of Regional Climate Change Impacts, SDSM Manual Version 4.2, Environment Agency Of England And Wales, 94 P.