Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M
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The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.
KeywordsRoot Mean Square Error Feed Forward Neural Network Statistical Downscaling Runoff Series Statistical Downscaling Model
The authors feel responsible to thank the reviewers of Theoretical and Applied Climatology and Gul Inan (from Middle East Technical University) for their valuable comments and contributions to the revision of this study.
- Arnell NW, Liu R, Compagnucci L, da Cunha K, Hanaki C, Howe G et al (2001) Hydrology and water resources. Climate Change 2001: ımpacts, adaptation and vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, McCarthy, J.J., Canziani, O.F., Leary, N.A., Dokken, D.J. and White, K.S. (Eds.). Cambridge University Press, Cambridge, 191–234Google Scholar
- Bates BC, Kundzewicz ZW, Wu S, Palutikof JP (eds) (2008) Climate Change and water. Technical Paper of the Intergovernmental Panel on, Climate Change, 210Google Scholar
- Ham F, Kostanic I (2001) Principles of neurocomputing for science and engineering (1st ed.). Macgraw-Hill, New YorkGoogle Scholar
- IPCC (2007) Climate Change 2007: the scientific basic. Contribution of Working Group I to the fourth assessment report of the intergovernmental panel on climate change. Summary for policy makersGoogle Scholar
- Okkan U, Fistikoglu O (2012) Downscaling of precipitation to Tahtali watershed in Turkey for climate change scenarios, 10th International Congress on Advances in Civil EngineeringGoogle Scholar
- Okkan U (2013) Assessment of climate change effects on river flows. PhD Thesis, Department of Civil Engineering, Dokuz Eylul University, Izmir, TurkeyGoogle Scholar
- Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA)Google Scholar
- Wilby RL, Dawson CW (2004) Using SDSM version 3.1 A decision support tool for the assessment of regional climate change impacts, User ManualGoogle Scholar