Hydrological Impact of Climate change on the GavehRud Watershed as Affected by the Parameter Uncertainty

Document Type : Research Paper

Authors

• Behrouz Yaghoubi
• Seyed Abbas Hosseini
• Sara Nazif

Ashrafi Esfehani

Abstract

The latest investigations have demonstrated the climate change is definitely happening. As the climate change strongly impacts the global temperature and rainfall, and their variabilities, it highly affects water resources, thus the environment, as well as agricultural and industrial activities. In this study, the climate change impacts on the Gavehrud basin runoff during the 2016-2099 periods was investigated using the outputs of the HadCM3 GCM model under the A2 scenario. The SDSM (statistical downscaling model) was used for the statistical downscaling of temperature and rainfall. For simulation of the future runoff using the downscaled data, HYMOD model was used. The HYMOD was calibrated using the DREAM model for considering its parameters uncertainty. The SDSM results indicated a considerable change in temperature and rainfall, in the future as compared with the historical time horizon of 1989-2000. The mean temperature increase in the future would be about 5% to 8.5%, while the mean rainfall decrease would be about 3% to 5%. The results of this study indicated that a 5% to 23% decrease in the future average runoff in comparison to the historical values.

Keywords

• Climate Change
• temperature
• Rainfall
• Runoff
• HadCM3 GCM
• Gavehrud

20.1001.1.20086377.1396.10.32.4.0

---

References

 

1)       آذری م، ح. مرادی، ب.ثقفیان ،م. فرامرزی، 1392). ارزیابی اثرات هیدرولوژیکی تغییراقلیم در حوضه آبخیز گرگانرود. نشریه آب و خاک 3(27):547-537

2)       بابائیان ا، م. ضرغامی، م. کوهی، ا. بابائیان،م. کریمیان، ر. مدیریان، 1392.بررسی رفتار منابع آب حوضه قره‏قوم تحت شرایط تغییراقلیم (مطالعه موردی: زیر حوضه درگز). نشریه آب و خاک ، 5(27):918-907

3)       رضائی م، م. نهتانی،ع. مقدم‏نیا، ع. آبکار، م. رضائی 1394. مقایسه روش‏های شبکه عصبی مصنوعی و SDSM در ریزمقیاس کردن اندازه بارش سالانه شبیه‏سازی شده با HadCM3 (مطالعه موردی: کرمان، راور و رابر). مجله مهندسی منابع آب ، 24(8): 40-25

4)       سهرابیان ا، م. مفتاح‏هلقی، خ. قربانی، س. گلیان، م. ذاکری‏نیا.، 1394. بررسی تأثیر تغییراقلیم بر آبدهی حوضه با دخالت مدل هیدرولوژی (مطالعه موردی: حوضه گالیکش در استان گلستان). نشریه پژوهش‏های حفاظت آب و خاک، شماره 2(22): 125-111.

5)       سعیدی ر، ب. زهرایی. مدلسازی فرآیند بارش - رواناب در حوضه آبریز سد پیشین برای شبیه سازی اثرات تغییر اقلیم.ششمین کنگره ملی مهندسین عمران.

6)         صادقی‏طبس ص، م. پوررضا بیلندی، 1394. مقایسه روشهای بهینه‏سازی فراکاوشی در تحلیل عدم قطعیت پارامترهای مدل مفهومی بارش- رواناب. نشریه مرتع و آبخیزداری شماره 3(68): 552-533.

7)        عجم‏زاده ع، م. ملائی‏نیا، 1395.ارزیابی اثرات تغییراقلیم بر رواناب رودخانه فیروزآباد استان فارس، با ریزمقیاس‏نمایی خروجی مدلهای گردش جوی به وسیله نرم‏افزارهای LARS-WG و SDSM. مجله تحقیقات منابع آب ایران 1(35): 109-95

8)        Ahmadi, M., O. B. Haddad, and H. A. Loáiciga 2015. Adaptive reservoir operation rules under climatic change. Water Resour. Manage. 29: 1247-1266.

9)        Bates, B. C,. and E. P. Campbell. 2001. A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling. Water Resour. Res. 37: 937-947.

10)    Boyle, D. P. 2001. Multicriteria calibration of hydrologic models.

11)    Chen, H., C.-Y. Xu, and S. Guo, 2012. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Hydrol. 434: 36-45.

12)    De Vos, N., T. Rientjes, and H. V. Gupta, 2010. Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering. Hydrol.  Proc. 24: 2840-2850.

13)    Harpham, C. and R. L. Wilby, 2005. Multi-site downscaling of heavy daily precipitation occurrence and amounts. Hydrol 312: 235-255.

14)    Huang, J., J. Zhang, Z. Zhang, C. Xu, B. Wang and J. Yao, 2011. Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch.  Environ. Res. Risk Assess. 25: 781-792.

15)    Huang, S., J. Chang, Q. Huang, Y. Chen and G. Leng, 2016. Quantifying the relative rontribution of climate and human impacts on runoff change based on the Budyko Hypothesis and SVM model. Water Resour. Manage. 30: 2377-2390.

16)    Karamouz, M., M. Fallahi, S. Nazif, and M. Rahimi Farahani, 2009. Long lead rainfall prediction using statistical downscaling and artificial neural network modeling. Scintia Iranica 16: 165-172.

17)    Khan, M. S., P. Coulibaly, and Y. Dibike (2006). Uncertainty analysis of statistical downscaling methods. Hydrol. 319: 357-382.

18)    Ma, Z., S. Kang, L. Zhang, L. Tong, and X. Su, 2008. Analysis of impacts of climate variability and human activity on streamflow for a river basin in arid region of northwest China. Hydrol. 352: 239-249.

19)    Moore, R., 1985. The probability-distributed principle and runoff production at point and basin scales. Hydrol. Sci. J. 30: 273-297.

20)    Moradkhani, H., S. Sorooshian, H. V. Gupta, and P. R. Houser, 2005. Dual state–parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour. 28: 135-147.

21)    Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50: 885-900.

22)    Nohara, D., A. Kitoh, M. Hosaka, and T. Oki, 2006. Impact of climate change on river discharge projected by multimodel ensemble. Hydromet. 7: 1076-1089.

23)    Parry, M. L., 2007. Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC, Cambridge University Press.

24)    Pereira, A. R., and Â. P. De Camargo. 1989. An analysis of the criticism of Thornthwaite's equation for estimating potential evapotranspiration. Agric. For. Meteor. 46: 149-157.

25)    Shamir, E., B. Imam, E. Morin, H. V. Gupta, and S. Sorooshian, 2005. The role of hydrograph indices in parameter estimation of rainfall–runoff models. Hydrological Processes 19(11): 2187-2207.

26)    Sun, W., H. Ishidaira, and S. Bastola. 2010. Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet. Hydrol. and Earth Sys. Sci. 14.: 2011-2022.

27)    Van Delft, G., G. El Serafy, and A. Heemink. 2009. The ensemble particle filter (EnPF) in rainfall-runoff models. Stoch. Environ. Res. Risk Assess. 23: 1203-1211.

28)    Varanou, E., E. Gkouvatsou, E. Baltas, and M. Mimikou. 2002. Quantity and quality integrated catchment modeling under climate change with use of soil and water assessment tool model. Hydrol. Eng. 7: 228-244.

29)    Vrugt, J. A., C. J. Ter Braak, M. P. Clark, J. M. Hyman, and B. A. Robinson 2008. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour. Res. 44: .

30)    Wilby, R., and C. Dawson. 2007. Using SDSM Version 4.1 SDSM 4.2. 2—a decision support tool for the assessment of regional climate change impacts. User Manual, Leicestershire, UK.

31)    Wilby, R. L., C. W. Dawson, and E. M. Barrow. 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environ. Model. & Soft. 17: 145-157.

32)    Yang, G., S. Guo, L. Li, X. Hong, and L. Wang. 2016. Multi-objective operating rules for danjiangkou reservoir under climate change." Water Resour. Manag. 30: 1183-1202.