عنوان مقاله [English]
Climate change affects runoff flow of the basin by changing in hydrological cycle parameters. Knowing the possible changes in the amount of precipitation and runoff of the basin will help to better planning and management of water resources. Precipitation changes due to climate change can be simulated using atmospheric general circulation models under different scenarios. Assessment of runoff needs using precipitation- runoff models. The aim of this research is flow modelling in some parts of the Great Karun Basin as a result of possible changes in future climate. For this purpose, temperature and precipitation changes of the Great Karun Basin are simulated for years 2011-2030 and 2046-2065 using two general circulation models and downscaling process under B1 and A2 scenarios. Then, the output flow of Andimeshk, Ahwaz and Yasouj sub-basins was predicted by IHACRES rainfall- runoff model and using precipitation and temperature data predicted under B1 and A2 scenarios. Compare revealed that, the amount of precipitation, maximum temperature and minimum temperature will increase in future periods under both scenarios. The results of flow simulation also show that the runoff of future periods under both scenarios will decrease in spring and summer and increase in autumn and winter in study area.
1. Wilby R. L, Harris I. A frame work for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames. UK. Water Resources Research. 2006; 42: 7.
2. Mitchell T.D. Pattern scaling: An examination of accuracy of the technique for describing future climates. Climate Change. 2003; 60:217-242.
3. Barrow E, Hulme M, Semenov MA. Effect of using different methods in the construction of climate change scenarios: examples from Europe. Clim Res 1996; 7:195–211.
4. Bardossy A. Downscaling from GCMs to local climate through stochastic linkages. J Environ Manage. 1997; 49:7–17.
5. Wilby RL, Wigley TML, Conway D, Jones PD, Hewiston BC, Main J, Wilks DS. Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res. 1998; 34:2995–3008.
6. Mearns LO, Bogardi I, Giorgi F, Matyasovskey I, Paleski M. Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. J Geophys Res. 1999; 104:6603–6621.
7. Murphy J. An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim. 1999; 12:2256–2284.
8. Salon S, Cossarini G, Libralato S, Gao X, Solidoro S, Giorgi F. Downscaling experiment for the Venice lagoon. I. Validation of the present-day precipitation climatology. Clim Res. 2008;38:31–41.
9. Carcano, E.C., P. Bartolini, M. Muselli and L. Piroddi. Jordan recurrent neural vetwork versus IHACRES in modeling daily stream flows. Hydrology. 2008; 362: 291-307.
10. Ye, W., A.J. Jakeman and P.C. Young. Identification of improved rainfallrunoff models for an ephemeral low-yielding Australian catchment. Environmental Modelling and Software. 1998; 13: 59-74.
11. Bazrafshan J., A. Khalili., Horfar A., Torabi, S. And Hajjam S. Evaluation and comparison of the two models (ClimGen and LARS-WG) of the simulated meteorological variables in different climatic conditions. Iran Water Resources Research. Fifth year. 2010; 1:44-57. [Persian].
12. Hajarizadeh, Z., Fattahi. A, Masahbavani, A. And Naserzade, M. Effects of climate change on flood hydrograph in future periods Case Study: Watershed Bakhtiari. Journal of geography. New period. Tenth year. 2012; 34:5-23. [Persian].
13. Kheyrfam H, Mostafazadeh, H. Sadeghi. Estimation of daily discharge using catchment areas of Golestan province. Journal of Watershed Management. forth year. 2013; 7: 114-127. [Persian].
14. Yaqubi, M, Masahbavani, A. Sensitivity analysis and comparison of three conceptual model of HBV, IHACRES and HEC-HMS rainfall-runoff Simulation joined in the semi-arid basins (case study of a large basin Herat- Yazd). Journal of Earth and Space Physics. 2014; 40(2):153-172. [Persian].
15. Tramblay Y, Badi W, Driouech F, Adlouni, S. El, Neppel, L. and Servat, E. Climate change impacts on extreme precipitation in Morocco, Global and Planetary Change. 2012;83: 104-114.
16. Samadi S.Z, Gregory J. Carbone, Mahdavi M, Sharifi F, Bihamta M. Statistical downscaling of river runoff in a semi arid catchment. Journal of Water Resourse Manage. 2012; 10.1007/s11269-012-0170-6.
17. Sato, Y, T. Kojiri, Y. Michihiro, Y. Suzuki, and E. Nakakita. Estimates of climate change impact on river discharge in Japan based on a super-high-resolution climate model. Terr. Atmos. Ocean. Sci. 2012;23: 527-540. doi: 10.3319/TAO.2012.05.03.02(WMH).
18. Chang, J, Y. Wang, E. Istanbulluoglu, T. Bai, Q. Huang, D. Yang and S. Huang. Impact of Climate Change and Human Activities on Runoff in the Weihe River Basin, China. Quaternary International, 2014; 169-179.
19. Parracho AC, Melo-Gonçalves P, Rocha A. Regionalization of precipitation for the Iberian Peninsula and climate change. Physics and Chemistry of the Earth. 2015; 94: 146-154.
20. Almazroui M, Saeed F, Nazrul Islam Md, Alkhalaf AK. Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR4 Global Climate Models over the Arabian Peninsula. Atmospheric Research. 2016; 182 (15): 163–175.
21. Almasi, P. & Soltani, S. Assessment of the climate change impacts on flood frequency (case study: Bazoft Basin, Iran). Stochastic Environmental Research and Risk Assessment. pp 1–12. (2016). doi:10.1007/s00477-016-1263-1.
22. Ghorbani kh, sohrabian e, salarijazi m, abdolhosseini m. Prediction of climate change impact on monthly river discharge trend using ihacres hydrological model (case study: galikesh watershed). Journal of soil and water resources conservation summer 2016; (5) 4: 19-34.
24. Babaeian., A. Najafinik, Z. Zabulabbasi F., Habibinokhandan, M. Adab H., Malbusi, S., Evaluation of climate change in the period from 2010 to 2039 AD, using downscaling data general circulation models ECHO-G. Geography and Development. 2009;16: 135-152. [Persian].
25. Kuchaki A, Nasirimahallati. M, A. Soltani., Sharifi H., Kamali, Gh., Rezvanimoghaddam, P., Simulation of climate change in a doubling of CO2 to VsylhY general circulation models. desert. 2003; 2: 178-191. [Persian].
26. Tabatabaei M., Shahed K., Soleimani. Artificial neural network model to estimate the suspended sediment concentration of river using MODIS data (Case study Molasani station - Karoon River). Journal of Soil and Water. 2013; 27 (1): 193-204. [Persian].
27. Abbasi F, Babaeian I, Malbusi S, Asmari M, Golimokhtari L. Assessment of climate change in the coming decades (2025 to 2100) using data from the downscaling of general circulation models, Geographical Research Quarterly. 2012;27(1):190-205. [Persian].
28. Jakeman A. J. and Hornberger G. M. How much complexity Is warranted in a rain fall runoff model? Water resources research. 1993;29(8): 26 37- 2 649.
29. Croke B.F.W., Letcher R.A., and Jakeman A.J. 2006. Development of a distributed flow model for underpinning assessment of water allocation options in the Naomi River Basin, Australia. Journal of Hydrology. 319:51–71.
30. Littlewood I.G., Down K., Parker J.R., and Post D.A. 1997. IHACRES Catchment-scale rainfall-streamflow modelling (PC version). Center for Ecology and Hydrology, The Australian National University. 95p
31. Croke B.F.W., and Jakeman A.J. Use of the IHACRES rainfall-runoff model in arid and semi arid regions. In: Wheatear, H.S. Sorooshian, S. Sharma, K.D.(Eds.): Hydrological Modeling in Arid and Semi-arid Areas. Cambridge University Press, Cambridge. 2008; 41-48.
32. Croke, B.F.W., F. Andrews, J. Spate and S.M. Cuddy. IHACRES user guide. Technical Report 2005/19. Second Edition. iCAM, School of Resources, Environment and Society, The Australian National University, Canberra. 2005. http://www.toolkit.net.au/ihacres.
33. McIntyre, N. and A. Al-Qurashi. Performance of ten rainfall-runoff models applied to an aarid catchment in Oman. Environmental Modelling and Software. 2009; 24: 726-738.
34. Taesombat, W. and N. Sriwongsitanon. Flood Investigation in the Upper Ping river basin using mathematical models. Kasetsart Natural Science. 2010; 44: 152- 166.
35. Blaker, R.S. and J.P. Norton. Efficient investigation of the feasible parameter set for large models. Proceedings of the International Congress on Modelling and Simulation, MODSIM: Modelling and Simulation Society of Australia and New Zealand. 2007; 1526-1532.
36. Motovilov, Y. G., L. Gottschalk, K. England, and A. Rodhe. Validation of distributed hydrological model against spatial observations. Agric. Forest Meteorology. 1999;98-99: 257-277.
37. Meshkaty, A., Kordjazy M., Babaeian, A. Evaluation of meteorological data simulated LARS in Golestan province in the period 1993-2007. Research Applied Geographical Sciences. 2010;16 (19) :81-96. [Persian].
38. Khaliliaghdam, N. Mosaedi, H. Soltani, A. Kamkar, B. Evaluation of LARS-WG model ability in forcasting some of Sanandaj atmospheric parameter. Journal of Soil and Water Conservation researches. 2012; 19(4):85-103. [Persian].
39. Babaian B., Mirzaei F., T. Sohrabi. LARS-WG model performance evaluation in 12 coastal stations of Iran. Technical Note. Journal of Water Research. 2011;9:222-217. [Persian].
40. Abbasi, F., Babaeian. A, Habibinokhandan M., Golimokhtari, L., Malbusi,s S. Evaluation of the impact of climate change on temperature and precipitation in Iran in the coming decades with the help of models MAGICC-SCENGEN. Physical Geography Research. 2008;72: 91-110. [Persian].
41. Roshan, GH. Khoshakhlagh, F. Azizi, GH. Test for Suitable general circulation model to detecting of temperature and precipitation amounts, under conditions of global warming. Geography and Development. 2012; 27:19-36. [Persian].
42. Bahri, M. Zahedi, E. Effects of climate change on the hydrological regime of surface flow Arazkuseh catchment. Applied Research of Geographic Sciences. 2016;16 (4): 109-132. [Persian].