An Assessment of the Intensity of the Climate Change on the Rainfall-Runoff Relationships of the Sufi-chai Watershed
Subject Areas : Article frome a thesisMasoud gharibdoust 1 , Mohamad Ali i Ghorban 2 , Iman Forozandeh Shahraki 3
1 - مسعود غریب دوست ، کارشناس ارشد مهندسی منابع آب ، گروه مهندسی آب ، دانشکده کشاورزی ، دانشگاه تبریز
2 - محمد علی قربانی، دانشیار گروه مهندسی آب ، دانشکده کشاورزی ، دانشگاه تبریز،
3 - ایمان فروزنده شهرکی ، کارشناس ارشد مهندسی منابع آب ، گروه مهندسی آب ، دانشکده کشاورزی ، دانشگاه تبریز
Keywords: Downscaling, Intelligent models, LARS-WG, Rainfall-runoff, Sufi-Chai basin,
Abstract :
The impact of global warming on climate change due to an increase in the greenhouse gases in the atmosphere has been proven in many natural systems. All of the general circulation models (GCM)of the atmosphere predict a warmer future for the planet Earth. Hydrological processes such as rainfall and river flows as main sources of water supply will be affected under such circumstances. Due to the low spatial resolution or simplification of some micro-scale phenomena in atmospheric GCMs, they cannot be employed for an accurate approximation of the climate of a certain area; therefore, their output must be downscaledto the specifiedmeteorological station’s domain. Therefore, the data generated by the Had CM3-GCM were downscaled applying theLARS-WG model under two scenarios A2 and A1B, and the parametersof daily rainfall, and theminimum and maximum temperature of the Sufi-Chi basin generated for three periods (2011-203, 2046-2065-, 2080-2099). The artificial neural networks and genetic programming of intelligent model were used to assess the climate change’s effects on runoff generation of the Sufi-chai Basin.The results indicate that rainfall will increase in the 2011-2030 period and will decrease thereafter. Furthermore, the maximum and minimum temperatures will generallyincrease in the three mentionedperiods; however, the runoff volumewill decrease in the future relative to the presenttime.
1- اشرف ب، موسوی بایگی م، کمالی غ، داوری ک. 1390. پیش بینی تغییرات فصلی پارامترهای اقلیمی در 20 سال آتی با استفاده از ریز مقیاس نمایی آماری داده های مدل HadCM3 (مطالعه موردی: استان خراسان رضوی). نشریه آب و خاک (علوم و صنایع کشاورزی). جلد 25، شماره 4، صفحه 952-940.
2- عباسی ف، ملبوسی ش، بابائیان ا، اثمری م، برهانی ر. 1389. پیش بینی تغییرات اقلیمی خراسان جنوبی در دوره 2039-2010 میلادی با استفاده از ریز مقیاس نمایی آماری خروجی مدل ECHO-G. نشریه آب و خاک (علوم و صنایع کشاورزی). جلد 2، شماره 24، صفحه 233-218.
3- Abdo, KS., Fiseha, B.M., Rientjes, THM., Gieske, A.S.M., Haile, A.T. 2009. Assessment of climate change impacts on the hydrology of Gilgel Abay catchment in lake Tana Basin, Ethiopia. Hydrological Processes 23: 3661-3669.
4- Ferreira, C. 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems 13: 87-129.
5- Guven, A. 2009. Linear genetic programming for time-series modeling of daily flow rate. Journal of Earth System Science, 118: 157-173.
6- Hashmi M.Z., Shamseldin, A.Y., and Melville, B W. 2010. Comparison of SDSM and LARS-WG for simulation and down scaling of extreme precipitation events in a watershed. Stochastic Enviromental Research an Risk Assessment, 25: 475-484.
7- Johnson, G.L., Hanson, C.L., Hardegree, SP., and Ballard, E.B. 1996. Stochastic weather simulation: over view and analysis of two Commonly used model. Journal of Applied meteorology 35: 1878-1896.
8- Kisi, O., Shiri, J., and Tombul. M. 2012. Modeling rainfall – runoff process using soft computing techniques. Computers & Geosciences. 23:412-422.
9- Koza, J.R. 1992. Genetic programming on the programming of computers by means of natural selection. MIT Press, Cambridge.
10- Mitchell, T.D. 2003. Pattern scaling : An examination of accuracy of the technique
for describing future climates. Climate Change, 60:217-242.
11- Semenov, M.A., and Barrow, E.M. 2002. LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual, Version 3.0.
12- Souvignet, M., Gaese, H., Ribbe, L., Kretschmer, N., and Oyarzun, R. 2010. Statistical downscaling of precipitation and temperature in north-central Chile: an assessment of possible climate change impacts in an arid Andean watershed. Hydrological Sciences Journal, 55: 41-57.
13- Wilby, L.R., Hay, L.E., and Leavesley, G.H. 1998. A comparison of downscaled and raw GCM output: Implications for climate change scenarios in the San Juan River Basin. Colorado. Journal of Hydrology 225: 67-91.
14- Wilby, R.L., and Harris, I. 2006. A frame work for assessing uncertainties in climate impact : Low flow scenarios for the river thames, UK. Water resource research. Doi: 10.1029/2005 WR004065.
15- Yu, H.H., and Jenq, N.H. 2002. Handbook of Neural Network Signal Processing. CRC Press.
16- Zarghami, M., Hassanzadeh, Y., Babaeian, I., and Kanani, R. 2009. climate Change and water resources vulnerability: case study of Tabriz City. SENSE Symposium on Climate Proofing