تحلیل توزیع‌های آماری در برآورد اثرات تغییر اقلیم بر سیلاب‌های آینده (مطالعه ی موردی: حوضه ی آذرشهر چای)

نوع مقاله : علمی

نویسندگان

1 استادیار گروه آب، دانشکده عمران، دانشگاه یزد، یزد، ایران

2 دانشجوی ارشد آب و سازه‌های هیدرولیکی، دانشگاه آیت‌الله العظمی بروجردی(ره)، بروجرد، ایران

چکیده

افزایش گازهای گلخانه‌ای و گرمایش جهانی در اثر تغییرات اقلیمی می‌تواند باعث افزایش احتمال وقوع رخدادهای حدی اقلیمی مانند سیلاب و افزایش فراوانی و شدت آن در بعضی از مناطق کره­ی زمین شود. از این رو ضرورت بررسی مقادیر حدی شدت بارش و فراوانی رخداد این کمیت طی دوره‌های گذشته و همچنین تأثیر گرمایش جهانی بر روند آن طی دوره‌های آتی کاملاً احساس می‌شود. در پژوهش حاضر اثر تغییرات اقلیمی بر رواناب حوضه­ی آذرشهر با مدل CanESM2 تحت سناریوهای انتشار، RCP2.6، RCP4.5 و RCP8.5 با ریزمقیاس­گردانی آماری مدل SDSM از طریق مدل هیدرولوژیکی SWAT بررسی شد. نتایج ارزیابی مدل SDSM با ضریب نش-ساتکلیف 95/0 بیانگر دقت بالای این مدل در ریزمقاس نمایی داده‌های بزرگ مقیاس است. نتایج مدل اقلیمی حاکی از افزایش دما به میزان 1/0 تا 25/0 درجه­ی سانتی­گراد و افزایش 4 تا 7 درصدی بارش در دوره­ی زمانی 2005-1976 نسبت به دوره­ی 2059-2030 می‌باشد. به منظور تحلیل فرکانس و شدت سیلاب با استفاده از مدل Easy fit مناسب­ترین توزیع براساس سه آزمون نکویی برازش انتخاب شد. نتایج بررسی رژیم جریان‌های حداکثر سالانه (فراوانی و شدت) از طریق برازش توزیع‌های احتمالاتی با کمترین میزان خطا برای دوره­ی پایه توزیع ویبول، دوره­ی آینده RCP2.6 توزیع لوگ پیرسون نوع 3، RCP4.5 لوگ نرمال و RCP8.5 لوگ نرمال به عنوان بهترین توزیع انتخاب شده است. فراوانی و شدت سیلاب نیز افزایش یافته به­طوری که، در دوره­ی بازگشت 500 ساله افزایش 98 درصدی دبی حداکثر دوره­ی آتی RCP8.5 نسبت به دوره­ی پایه مشهود شده است.

تازه های تحقیق

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کلیدواژه‌ها


عنوان مقاله [English]

Statistical Distributions Analysis for Estimating of Climate Change Effects on Future Floods (Case Study: Azarshahrchay Basin)

نویسندگان [English]

  • Mohammadreza Goodarzi 1
  • Atiyeh Fatehifar 2
1 Assistant Professor, Department of Civil Engineering, Yazd University, Iran
2 M.S. Student of Water and Hydraulic Structures, Ayatollah Ozma Borujerdi University, Iran
چکیده [English]

1-Introduction
The assessment report fifth of the Intergovernmental Panel on Climate Change shows that global warming has led to a change in the water cycle due to increased greenhouse gas emissions. In the present time, with the increase of industrial activities and the neglected environmental issues, the effects of climate change have become more evident and poses this phenomenon as a global difficult. Increasing the probability of occurrence of extreme climatic events such as flood and increasing the frequency and intensity of the effects of climate change. Due to changes in climate and global warming, the probability of heavy rainfall and consequently the risk of flood due to incorrect drainage system and physical and environmental factors have increased. Therefore, the study of the region's climate is important given the new scenarios and flood frequency analysis with suitable statistical distributions for future planning.
2- Methodology
In the present study, the effects of climate changes on the runoff of Azarshahrchay Basin with CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 release scenarios assessment report fifth (AR5) of the Intergovernmental Panel on Climate Change (IPCC), with Statistical down scaling model (SDSM), for the period 1976-2005 and 2059-2030

 

by the hydrologic model SWAT have been investigated. The accuracy of the simulation was evaluated with three indicators: Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Nash–Sutcliffe Efficiency (NSE). An analysis of the frequency of maximum annual flood for both base and future periods using their probability distribution function (PDF) and the Easyfit model. In this model, 5 types of probability distribution including Normal, Normal Log, Pearson, Log Pearson Type 3 and Weibull were used. The best distribution for each basic and future time series were ranked and selected by using three Chi-square, Kolmogorov–Smirnov, Anderson–Darling tests. In order to study how the maximum flood discharge regime changes in the base and future periods were used two indices:
 1) The probability and the return period in the equal flows
 2) Intensity of flow in the equal return periods
3- Results
The obtained factors of the three RMSE, R2, and NSE indicators showed the good performance of the SDSM model in the down scaling the large-scale data. Investigating the performance of the SDSM model in the downscale of the Azarshahr station's climate data with a Coefficient of Determination and Nash–Sutcliffe of 0.99 and 0.98 for temperature for the period 1990-2001 and 0.86 and 0.83 for precipitation in the period 1976-2005. The simulation results showed a rise in temperature during the period 2030-2059 under scenarios and the highest increase was related to RCP8.5 (0.23°c). Also, rainfall at a station increased by 7.44 percent to RCP2.6 and at another station decrease by 7.57 percent to

 
RCP8.5. The performance analysis of the SWAT model indicates a good accuracy of the model in runoff simulation with R2 and Nash 0.6 on average. The results of the 2.1% increase in runoff and the maximum flood peak and the probability of flood events in March and April (late winter and early spring) have been shown by the SWAT model. Results of the study of the regime of maximum annual flows (frequency and intensity) by fitting probabilistic distributions with the lowest error rate for the base distribution period of the Weibull, future period RCP2.6 distribution Log Pearson Type 3, RCP4.5 Log Normal and RCP8.5 Log Normal as best distribution are selected. Also, the frequency and intensity of flood have increased. In the return periods of constant, the maximum discharge increased, and in maximum discharge constant, with increasing return period (1000 years), the discharge rate significantly increased. So, in the 500-year return period is expected a 98% increase in maximum discharge RCP8.5 future period than base period. The most critical scenario is RCP8.5 scenario.
4- Discussion and conclusion
The results indicate the impact of climate change on the basin in the future period. Therefore, knowing the increase in precipitation intensity, the flood risk increases. The occurrence of terrible floods due to climate change have caused many damages in different parts of the world in recent decades. The results of this study, like other previous studies, confirm that climate change is significant, especially with the increasing frequency of floods, governments, organizations, and educational centers need to take appropriate measures to eliminate or reduce the effects of climate change and adaptation to extreme events such as floods.
 

کلیدواژه‌ها [English]

  • Azarshahrchay Basin
  • Climate Change
  • Flood frequency analysis
  • Statistical Distributions
  • SWAT model
-References
Abbaspour, K.C. (2007). User Manual for SWAT-CUP. SWAT Calibration and Uncertainty Analysis Programs. Swiss Federal Institute of Aquatic Science and Technology, Eawag, Dübendorf, Switzerland, 95 p.
Abbaspour, K.C., Faramarzi, M., Ghasemi, S.S., & Yang, H. (2009). Assessing the impact of climate change on water resources in Iran. Water Resources Research, 45(10), W10434.
Ahmadzadeh, H., Saeed Abadi, R., & Nori E. (2015). A Study and Zoning of the Areas Prone to Flooding with an Emphasis on Urban Floods (Case Study: City of Maku). Journal of Hydrogeomorphology, 1(2), 1-24. (In Persian)
Alison, L.K., Richard, G.J., & Nicholas, S.R. (2004). RCM rainfall for UK flood frequency estimation, II. Climate change results, Journal of hydrology, 318(1-4), 163-172.
Arnold, J. G., Srinivasan, R., Muttiah, R.S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: model development 1. Journal of the American Water Resources Association. 34(1), 73-89.
Ashofteh, P., & Massah Bouani, A.R. (2010). Impact of Climate Change on Maximum Discharges: Case Study of Aidoghmoush Basin. East Azerbaijan. JWSS. 14(53), 28-38. (In Persian)
Haddad, K., & Rahman, A. (2011). Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia. Stochastic Environmental Research and Risk Assessment, 25(3), 415-428.
IPCC. (2014). Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K. & Meyer, L.A. (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
Iqbal, M.S., Dahri, Z.H., Querner, E.P., Khan, A., & Hofstra, N. (2018). Impact of Climate Change on Flood Frequency and Intensity in the Kabul River Basin. Geosciences. 8(4), 114-130.
Khazaei, M. R., Zahabiyoun, B., & Saghafian, B. (2011). Assessment of climate change impact on floods using weather generator and continuous rainfall‐runoff model. International Journal of Climatology. 32(13), 1997-2006.
Laio, F. Di Baldassarre, G., & Montanari, A. (2009). Model selection techniques for the frequency analysis of hydrological extremes. Water Resources Research. 45(7), 1-11. doi: 10.1029/2007 WR00666.
Mazidi, M., & Khoshravesh, M. (2016). The Effect of Climate Change on the Flood Frequency of Gorganrood Basin using Analysis of the First Order of Hydrologic Rainfall-Runoff Model. Applied Research of Water Sciences. 2(2), 35-44. (In Persian)
Meinshausen M., Smith SJ., Calvin K., Daniel J S., Kainuma M LT., Lamarque J F., Matsumoto K., Montzka S A., Raper SCB., Riahi K., Thomson A., Velders GJM., & Van Vuuren, DPP. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Journal of Climatic Change. 109(1-2), 213–241.
Naderi, M., Ildoromi, A., Nouri, H., Aghabeigi Amin, S., & Zeinivand, H. (2018). The Impact of Land Use and Climate Change on Watershed Runoff Model SWAT (Case Study: Watershed Garin). Journal of Hydrogeomorphology, 4(14), 23-42. (In Persian)
Pervez, M.S., & Henebry, G.M. (2014). Projections of the Ganges – Brahmaputra precipitation -downscaled from GCM predictors. Journal of Hydrology. 517(1), 120–134. doi:10.1016/j.jhydrol.05.016.
Qin, X.S., & Lu, Y. (2014). Study of climate change impact on flood frequencies: a combined weather generator and hydrological modeling approach. Journal of Hydrometeorology, 15(3), 1205-1219.
Vicek, O., & Huth, R. (2009). Is daily precipitation Gamma-distributed?: Adverse effects of an incorrect use of the Kolmogorov–Smirnov test. Atmospheric Research, 93(4), 759-766.
Wilby, R.L., & Dawson, C.W. (2013). The Statistical DownScaling Model: insights from one decade of application. International Journal of Climatology. 33(7), 1707-1719.
Zahabiyoun, B., Goodarzi, M. R., Bavani, A. R. & Azamathulla, H. M. (2013). Assessment of climate change impact on the Gharesou River Basin using SWAT hydrological model. CLEAN–Soil, Air, Water. 41(6), 601-609.
Zhang, A., Zhang, C., Fu, G., Wang, B., Bao, Z. & Zheng, H. (2012). Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin. Northeast China. Water Resources Management. 26(8), 2199-2217.