بررسی اثرات تغییر اقلیم بر منابع آب حوضه زرینه رود با استفاده از مدل SWAT

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه تهران

2 دانشگاه تبریز

3 دانشگاه تربیت مدرس

4 دفتر برنامه ریزی کلان آب و آبفا وزارت نیرو

چکیده

تحقیق حاضر آثار ناشی از تغییر اقلیم را بر دما، بارندگی و رواناب در دوره های آتی با کمک مدل آماری LARS-WG و مدل مفهومی هیدرولوژیکی SWAT مورد ارزیابی قرار می دهد. برای مطالعه موردی، حوضه زرینه رود، بزرگترین زیرحوضه دریاچه ارومیه، انتخاب شد. در گام اول، به منظور تولید داده‌های هواشناسی دوره آتی در حوضه مدل LARS-WG مورد واسنجی قرار گرفت و سپس از 14 مدل از مدل های AOGCM استفاده گردیده و خروجی این مدل ها برای دوره 2030-2015 در 6 ایستگاه سینوپتیک با استفاده از مدل LARS-WG کوچک مقیاس شدند. از مدل SWAT جهت ارزیابی تأثیرات تغییر اقلیم بر میزان رواناب حوضه استفاده گردید. بدین منظور ابتدا این مدل با استفاده از 6 ایستگاه هیدرومتری برای دوره 2007-1987 واسنجی و اعتبارسنجی شد که مقادیر ضریب تعیین (R2 ) به ترتیب بین 49/0 تا 71/0 و 54/0 تا 77/0 بدست آمد. در ادامه با معرفی میانگین نتایج ریزمقیاس شده مدل های AOGCM به مدل SWAT تغییرات رواناب خروجی از حوضه در طی دوره 2030-2015 شبیه سازی گردید. میانگین نتایج مدل LARS-WG نشان داد که متوسط ماهانه درجه حرارت حداقل و حداکثر در دوره 2030-2015 افزایش خواهد یافت. همچنین متوسط ماهانه بارندگی در فصل بهار کاهش یافته در حالی که به مقدار آن در فصل های تابستان و پاییز افزوده خواهد شد. نتایج نشان داد که در دوره‌ آتی نه تنها در مقدار بارش بلکه در الگوی بارش نیز تغییراتی رخ خواهد داد. نهایتا نتایج نشان از کاهش 28 درصدی رواناب ورودی به سد زرینه رود در دوره‌ی آتی نسبت به دوره‌ی پایه دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of Climate Change Impacts on Water Resources in Zarrinehrud Basin Using SWAT Model

نویسندگان [English]

  • B. Mansouri 1
  • H. Ahmadzadeh 2
  • A. Massah Bavani 1
  • S. Morid 3
  • M. Delavar 3
  • S. Lotfi 4
1 University of Tehran
2 University of Tabriz
3 Tarbiat Modares University
4 Water Management Office, Ministry of Energy
چکیده [English]

This paper evaluate impacts of climate change on temperature, rainfall and runoff in the future Using statistical model, LARS-WG, and conceptual hydrological model, SWAT. In order to the Zarrinehrud river basin, as the biggest catchment of the Lake Urmia basin was selected as a case study. At first, for the generation of future weather data in the basin, LARS-WG model was calibrated using meteorological data and then 14 models of AOGCM were applied and results of these models were downscaled using LARS-WG model in 6 synoptic stations for period of 2015 to 2030. SWAT model was used for evaluation of climate change impacts on runoff in the basin. In order to, the model was calibrated and validated using 6 gauging stations for period of 1987-2007 and the value of R2 was between 0.49 and 0.71 for calibration and between 0.54 and 0.77 for validation. Then by introducing average of downscaled results of AOGCM models to the SWAT, runoff changes of the basin were simulated during 2015-2030. Average of results of LARS-WG model indicated that the monthly mean of minimum and maximum temperatures will increase compared to the baseline period. Also monthly average of precipitation will decrease in spring season but will increase in summer and autumn. The results showed that in addition to the amount of precipitation, its pattern will change in the future period, too. The results of runoff simulation showed that the amount of inflow to the Zarrinehrud reservoir will reduce 28.4 percent compared to the baseline period.

کلیدواژه‌ها [English]

  • Climate change
  • Zarrinehrud Basin
  • LARS-WG
  • AOGCM
  • runoff
  • SWAT
1- مرکز آمار و اطلاعات وزارت جهاد کشاورزی، تهران، ایران .1390.
2- Abbaspour K.C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J. and Srinivasan R. 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333: 413-430.
3- Arnold J.G., Srinivasan P., Muttiah R.S. and Williams J.R. 1998. Large area hydrologic modelling and assessment. Part I. Model development. Journal of the American Water Resources Association, 34: 73–89.
4- Arnold J.G., and Fohrer N. 2005. SWAT2000: Current capabilities and research opportunities in applied watershed modeling. Hydrol. Process. 19(3): 563-572.
5- Barrow E., Hulme M. and Semenov M.A. 1996. Effect of using different methods in the construction of climate change scenarios: examples from Europe. Clim Res 7:195–211.
6- Bolin B.R. 1986. The greenhouse effect, climate change and ecosystems. SCOPE Rep., 29, 541pp.
7- Cao L., Zhang Y. and Shi Y. 2011. Climate change effect on hydrological processes over the Yangtze River basin. Quaternary International, 244: 202-210.
8- Doblas-Reyes F.J., Hagedorn R. and Palmer T.N. 2006. Developments in dynamical seasonal forecasting relevant to agricultural management. Clim Res 33:19–2.
9- Gosling S.N., Taylor R.G., Arnell N.W. and Todd M.C. 2011. A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol. Earth Syst. Sci., 15, 279–294.
10- Hooghoudt S.B. 1940. Bijdrage tot de kennis van enige natuurkundige grootheden van de grond. Versl. Landbouwkd. Onderz. 46:515-707 .
11- IPCC. 2007. Climate Change 2007. The Physical Science Basis. Summary for Policymakers.
12- IPCC. 2010. Stocker Th, Dahe Q, Plattner G.K, Tigner M, Midgley P. IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections. National Center for atmospheric Research, Boulder, Colorado, USA.
13- Jones R.N. and Page C.M. 2001. Assessing the risk of climate change on the water resources of the Macquarie River Catchment, In: Integrating Models for Natural Resources Management across Disciplines, issues and scales (part 2), eds. Ghassemi, F., Whetton, P., Little, R. and Littleboy, M., pp. 673-678. Modsim 2001 International Congress on Modeling and Simulation, Modeling and Simulation Society of Australia and New Zealand, Canberra.
14- Jung W., Moradkhani H. and Chang H. 2012. Uncertainty assessment of climate change impacts for hydrologically distinct river basins. Journal of Hydrology, 466–467: 73–87.
15- Kundzewicz Z.W., Mata L.J., Arnell N.W., D¨oll P., Kabat P., Jim´enez B., Miller K.A., Oki T., Sen Z. and Shiklomanov I.A. 2007. Freshwater resources and theirmanagement. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Parry, M. L. Canziani O. F. Palutikof J. P. van der Linden P. J. and Hanson C. E. Cambridge University Press, Cambridge, UK, 173–210, 2007.
16- Merritt W.S., Alila Y., Barton M., Taylor B., Cohen S. and Neilsen D. 2006. Hydrologic response to scenarios of climate change in subwatersheds of the Okanagan basin, British Columbia. Journal of Hydrology 326, 79-108.
17- Najafi M.R., Moradkhani H. and Jung I.W. 2011. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol. Process. 25 (18), 2814–2826.
18- Nakicenovic N. and Swart R. 2000. Emissions scenarios. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
19- Neitsch S.L., Arnold J.G., Kiniry J.R. and Williams J.R. 2011. Soil and water assessment tool theoretical document (version 2009), Texas water resource institute technical report.
20- Palmer T.N., Doblas-Reyes F.J., Hagedorn R. and Weisheimer A. 2005. Probabilistic prediction of climate using multimodel ensembles: from basics to applications. Philos Trans R Soc B 360:1991–1998.
21- Racsko P., Szeidl L. and Semenov M. 1991. A serial approach to local stochastic weather models. Ecol Model 57:27–41.
22- Richardson C.W. and Wright D.A. 1984. WGEN: a model for generating daily weather variables. Report No. ARS-8, US Department of Agriculture, Agricultural Research Service.
23- Semenov M.A. 2007. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric For Meteorol 144:127–138.
24- Semenov M.A. and Stratonovitch P. 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research, 41: 1-14.
25- Solomon S., Qin D., Manning M., Marquis M. and others (eds). 2007. Climate Change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovermental Panel on Climate Change. Cambridge University Press, Cambridge.
26- Wilks D.S. 1992. Adapting stochastic weather generation algorithms for climate changes studies. Clim Change 22: 67–84.
27- Wilks D.S. and Wilby R.L. 1999. The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23: 329–357.
28- Wilby R.L. and Harris I. 2006. A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour. Res. 42, W02419.