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KSCE Journal of Civil Engineering

, Volume 19, Issue 4, pp 1150–1156 | Cite as

Assessing the effects of climate change on monthly precipitation: Proposing of a downscaling strategy through a case study in Turkey

  • Umut OkkanEmail author
Water Engineering

Abstract

The forecasting of future precipitation can be considered by using the outputs of the General Circulation Models (GCMs). In the study, a downscaling strategy was improved to forecast monthly precipitation over Tahtali watershed in Turkey for climate change scenarios using neural networks. First, predictor variables, which represent the monthly areal precipitation of watershed, were selected from the NCEP/NCAR reanalysis data set with the help of correlation and mutual information analyses. The study of the predictor selection showed that large scale precipitation at surface, air temperature at 850 hPa, and geopotential height at 200 hPa are the explanatory variables for downscaling. When the statistical performance measures were investigated, developed downscaling model trained with selected predictors was found satisfactory and was applied to simulate the future projections of selected two GCMs; namely, ECHAM5 and HADCM3. Finally, the simulation results were examined to assess the possibility of climate change effect on precipitation in the study area.

Keywords

precipitation climate change downscaling GCMs NCEP/NCAR reanalysis data 

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References

  1. Abrahart, R. J., Kneale, P. E., and See, L. M. (2004). Neural networks for hydrological modelling, Taylor & Francis.Google Scholar
  2. Anandhi, A., Srinivas, V. V., Nanjundiah, S. R., and Kumar, N. D. (2008). “Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine.” Int. J. Clim., Vol. 28, No. 3, pp. 401–420.CrossRefGoogle Scholar
  3. Bardossy, A., Bogardi, I., and Matyasovszky, I. (2005). “Fuzzy rule-based downscaling of precipitation.” Theo. Appl. Clim., Vol. 82, Nos. 1–2, pp. 119–129.CrossRefGoogle Scholar
  4. Cannon, A. J. (2011). “Quantile regression neural networks: Implementationin R and application to precipitation downscaling.” Comp & Geosci., Vol. 37, No. 9, pp. 1277–1284.CrossRefGoogle Scholar
  5. Cavazos, T. (1999). “Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in Northeastern Mexico and Southeastern Texas.” J. Clim., Vol. 12, No. 5, pp. 1506–1523.CrossRefGoogle Scholar
  6. Coulibaly, P., Anctil, F., and Bobee, B. (2000). “Daily reservoir inflow forecasting using temporal neural networks.” J. Hydro., Vol. 230, Nos. 3–4, pp. 244–257.CrossRefGoogle Scholar
  7. Demuth, H. and Beale, M. (1998). Neural network toolbox: For use with MATLAB user’s guide, The Math Works Inc., Natick, MA.Google Scholar
  8. Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P. (2010). “Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology — Part 1: Concepts and methodology.” Hydrol. Earth Syst. Sci., Vol. 14, No. 10, pp. 1931–1941.CrossRefGoogle Scholar
  9. Fistikoglu, O. and Okkan, U. (2011). “Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River basin in Turkey.” J. Hydro. Eng., Vol. 16, No. 2, pp. 157–164.CrossRefGoogle Scholar
  10. Ghosh, S. and Mujumdar, P. P. (2008). “Statistical downscaling of GCM simulations to streamflow using relevance vector machine.” Adv. Water Res., Vol. 31, No. 1, pp. 132–146.CrossRefGoogle Scholar
  11. Hagan, M. T. and Menhaj, M. B. (1994). “Training feed forward techniques with the Marquardt Algorithm.” IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989–993.CrossRefGoogle Scholar
  12. IPCC (2007). Climate change: The physical science basis, Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, New York.Google Scholar
  13. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D. (1996). “The NCEP/NCAR 40-year reanalysis project.” Bull. Ame. Meteo. Soc., Vol. 77, No. 3, pp. 437–471.CrossRefGoogle Scholar
  14. Maheras, P., Tolika, K., Anagnostopoulou, C., Vafiadis, M., Patrikas, I., and Flocas, H. (2004). “On the relationships between circulation types and changes in rainfall variability in Greece.” Int. J. Clim., Vol. 24, No. 13, pp. 1695–1712.CrossRefGoogle Scholar
  15. Mendes, D. and Marengo, J. A. (2010). “Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios.” Theo. Appl. Clim., Vol. 100, Nos. 3–4, pp. 413–421.CrossRefGoogle Scholar
  16. Olsson, J., Uvo, C. B., Jinno, K., Kawamura, A., Nishiyama, K., Koreeda, N., Nakashima, T., and Morita, O. (2004). “Neural Networks for Rainfall Forecasting by Atmospheric Downscaling” J. Hydro. Eng., Vol. 9, No. 1, pp. 1–12.CrossRefGoogle Scholar
  17. Schoof, J. T. and Pryor, S. C. (2001). “Downscaling Temperature and Precipitation: A comparison of regression-based methods and artificial neural networks.” Int. J. Clim., Vol. 21, No. 7, pp. 773–790.CrossRefGoogle Scholar
  18. Steuer, R., Kurths, J., Daub, C.O., Weise, J., and Selbig, J. (2002). “The mutual information: Detecting and evaluating dependencies between variables.” Bioinform., Vol. 18, Suppl. 2, pp. S231–S240.CrossRefGoogle Scholar
  19. Tatli, H., Dalfes, H. N., and Mentes, S. S. (2004). “A statistical downscaling method for monthly total precipitation over Turkey.” Int. J. Clim., Vol. 24, No. 2, pp. 161–180.CrossRefGoogle Scholar
  20. Tolika, K., Maheras, P., Flocas, H. A., and Papadimitriou, A. A. (2006). “An evaluation of a General Circulation Model (GCM) and the NCEP-NCAR reanalysis data for winter precipitation in Greece.” Int. J. Clim., Vol. 26, No. 7, pp. 933–955.CrossRefGoogle Scholar
  21. Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P., and Mearns, L. O (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods, Intergovernmental Panel on Climate Change, DDC of IPCC TGCIA.Google Scholar
  22. Wilby, R. L., Dawson, C. W., and Barrow, E. M. (2002). “SDSM — A decision support tool for the assessment of climate change impacts.” Environ. Mod. Soft., Vol. 17, No. 2, pp. 147–159.Google Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Dept. of Civil EngineeringBalikesir UniversityBalikesirTurkey

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