Statistical Downscaling of Monthly Precipitation Using NCEP/NCAR Reanalysis Data for Tahtali River Basin in Turkey
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 2
Abstract
Statistical downscaling methods describe a statistical relationship between large-scale atmospheric variables such as temperature, humidity, precipitation, etc., and local-scale meteorological variables like precipitation. This study examines the potential predictor variables selected from the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set for downscaling monthly precipitation in Tahtali watershed in Turkey. An approach based on the assessment of all possible regression types was used to select the predictors among the NCEP reanalysis data set, and artificial neural network (ANN)–based downscaling models were designed separately for each station in the basin. The results of the study showed that precipitation, surface and sea level pressures, air temperatures at surface, 850-, 500-, and 200-hPa pressure levels, and geopotential heights at 850- and 200-hPa pressure levels are the most explanatory NCEP/NCAR parameters for the study area. It was concluded that ANN-based downscaling models can be implemented to downscale coarse-scale atmospheric parameters to monthly precipitation at station scale by using the above parameters as inputs in the study area.
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Acknowledgments
The research leading to this paper is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. UNSPECIFIED108Y301.
References
Bardossy, A., Bogardi, I., and Matyasovszky, I. (2005). “Fuzzy rule-based downscaling of precipitation.” Theor. Appl. Climatol., 82, 119–129.
Benestad, R. E., Hanssen-Bauer, I., and Førland, E. J. (2007). “An evaluation of statistical models for downscaling precipitation and their ability to capture long-term trends.” Int. J. Climatol., 27(5), 649–665.
Bürger, G. (1996). “Expanded downscaling for generating local weather scenarios.” Clim. Res., 7, 111–128.
Cavazos, T., and Hewitson, B. C. (2005). “Performance of NCEP variables in statistical downscaling of daily precipitation.” Clim. Res., 28, 95–107.
Coulibaly, P., Anctil, F., and Bobee, B. (2000). “Daily reservoir inflow forecasting using temporal neural networks.” J. Hydrol., 230, 244–257.
Crane, R. G., and Hewitson, B. C. (1998). “Doubled CO2 precipitation changes for the Susquehanna basin: Downscaling from the GENESIS general circulation model.” Int. J. Climatol., 18, 65–76.
Cubasch, U., von Storch, H., Waszkewitz, J., and Zorita, E. (1996). “Estimates of climate change in southern Europe using different downscaling techniques.” Clim. Res., 7, 129–149.
Dehn, M., Burger, G., Buma, J., and Gasparetto, P. (2000). “Impact of climate change on slope stability using expanded downscaling.” Eng. Geol. (Amsterdam), 55, 193–204.
Demuth, H., and Beale, M. (1998). Neural network toolbox: For use with MATLAB user’s guide, The Math Works Inc., Natick, MA.
Dibike, Y. B., and Coulibaly, P. (2005). “Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models.” J. Hydrol., 307, 145–163.
Dibike, Y. B., and Coulibaly, P. (2006). “Temporal neural networks for downscaling climate variability and extremes.” Neural Networks, 19(2), 135–144.
Efroymson, M. A. (1960). “Multiple regression analysis.” Mathematical methods for digital computers, A. Ralston and H. S. Wilf, eds., Wiley, New York.
Fowler, H. J., Blenkinsop, S., and Tebaldi, C. (2007). “Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modeling.” Int. J. Climatol., 27, 1547–1578.
Fowler, H. J., Ekstrom, M., Kilsby, C. G., and Jones, P. D. (2005). “New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 1. Assessment of control climate.” J. Hydrol., 300, 212–233.
Frei, C., Christensen, J. H., Deque, M., Jacob, D., Jones, R. G., and Vidale, P. L. (2003). “Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps.” J. Geophys. Res., 108(D3), 4124.
Frei, C., Scholl, R., Fukutome, S., Schmidli, J., and Vidale, P. L. (2006). “Future change of precipitation extremes in Europe: An intercomparison of scenarios from regional climate models.” J. Geophys. Res., [Atmos.], 111, D06105.
Gardner, M. W., and Dorling, S. R. (1998). “Artificial neural networks (the multi layer perceptron)—A review of applications in the atmospheric sciences.” Atmos. Environ., 32, 2627–2636.
Giorgi, F., et al. (2001). “Regional climate information evaluation and projections.” Climate change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J. T. Houghton, et al., eds., Cambridge University Press, Cambridge, U.K.
Govindaraju, R. S., and Rao, A. R. (2000). Artificial neural networks in hydrology, Kluwer, Dordrecht, The Netherlands.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feed forward network with the Marquardt algorithm.” IEEE Trans. Neural Netw., 5(6), 989–993.
Hanssen-Bauer, I., Førland, E. J., Haugen, J. E., and Tveito, O. E. (2003). “Temperature and precipitation scenarios for Norway: Comparison of results from dynamical and empirical downscaling.” Clim. Res., 25, 15–27.
Haykin, S. (1994). Neural networks: A comprehensive foundation, MacMillan, New York.
Haylock, M. R., Cawley, G. C., Harpham, C., Wilby, R. L., and Goodess, C. (2006). “Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios.” Int. J. Climatol., 26, 1397–1415.
Hellström, C., Chen, D., Achberger, C., and Raisanen, J. (2001). “Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation.” Clim. Res., 19, 45–55.
Hewitson, B. C., and Crane, R. G. (1996). “Climate downscaling: Techniques and application.” Clim. Res., 7, 85–95.
Hinnes, W. W., and Montgomery, D. C. (1990). Probability and statistics in engineering and management science, Wiley, Singapore.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-rainoff process.” Water Resour. Res., 31(10), 2517–2530.
Huth, R. (1999). “Statistical downscaling in central Europe: Evaluation of methods and potential predictors.” Clim. Res., 13, 91–101.
Kalnay, E., et al. (1996). “The NCEP/NCAR 40-year reanalysis project.” Bull. Am. Meteorol. Soc., 77, 437–471.
Karl, T. R., Wang, W. C., Schlesinger, M. E., Knight, R. W., and Portman, D. (1990). “A method of relating general circulation model simulated climate to the observed local climate. Part I: Seasonal statistics.” J. Climate, 3, 1053–1079.
Katz, R. W. (1996). “Use of conditional stochastic models to generate climate change scenarios.” Clim. Change, 32, 237–255.
Katz, R. W., and Parlange, M. B. (1998). “Overdispersion phenomenon in stochastic modeling of precipitation.” J. Climate, 11, 591–601.
Kavvas, M. L., et al. (1998). “A regional-scale land surface parameterization based on areally-averaged hydrological conservation equations.” Hydrol. Sci. J., 43(4), 611–631.
Khan, M. S., Coulibaly, P., and Dibike, Y. (2006). “Uncertainty analysis of statistical downscaling methods.” J. Hydrol., 319, 357–382.
Kidson, J. W., and Thompson, C. S. (1998). “A comparison of statistical and model-based downscaling techniques for estimating local climate variations.” J. Climate, 11, 735–753.
Kilsby, C. G., et al. (2007). “A daily weather generator for use in climate change studies.” Environ. Modell. Software, 22, 1705–1719.
Leung, R. L., Mearns, L. O., Giorgi, F., and Wilby, R. L. (2003). “Regional climate research: Needs and opportunities.” Bull. Am. Meteorol. Soc., 84, 89–95.
Lorenz, E. N. (1969). “Atmospheric predictability as revealed by naturally occurring analogues.” J. Atmos. Sci., 26, 636–646.
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. Climatol., 24, 1695–1712.
Mallows, C. L. (1973). “Some comments on Cp.” Technometrics, 15(4), 661–675.
McQuarrie, A. D., and Tsai, C. (1998). Regression and time series model selection, World Scientific, River Edge, N.J.
Mearns, L. O., Bogardi, I., Giorgi, F., Matyasovszky, I., and Palecki, M. (1999). “Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling.” J. Geophys. Res., 104, 6603–6621.
Müller-Wohlfeil, D. I., Bürger, G., and Lahmer, W. (2000). “Response of a river catchment to climate change: application of expanded downscaling to northern Germany.” Clim. Change, 47, 61–89.
Murphy, J. M. (1999). “An evaluation of statistical and dynamical techniques for downscaling local climate.” J. Climate, 12, 2256–2284.
Neter, J., Kutner, M., Nachtsheim, C., and Wasserman, W. (1996). Applied linear statistical models, McGraw-Hill, New York.
Palutikof, J. P., Goodess, C. M., Watkins, S. J., and Holt, T. (2002). “Generating rainfall and temperature scenarios at multiple sites: examples from the mediterranean.” J. Climate, 15, 3529–3548.
Salathé, E. P. (2005). “Downscaling simulations of future global climate with application to hydrologic modeling.” Int. J. Climatol., 25, 419–436.
Schmidli, J., Frei, C., and Vidale, P. L. (2006). “Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods.” Int. J. Climatol., 26, 679–689.
Schoof, J. T., Pryor, S. C., and Robeson, S. M. (2007). “Downscaling daily maximum and minimum temperatures in the midwestern USA: A hybrid empirical approach.” Int. J. Climatol., 27(4), 439–454.
Sharma, S. (1996). Applied multivariate techniques, Wiley, Toronto, Canada.
Singh, A., and Harrison, A. (1985). “Standardized principal components.” Int. J. Remote Sens., 6, 883–896.
Tatli, H., Dalfes, H. N., and Mentes, S. S. (2004). “A statistical downscaling method for monthly total precipitation over Turkey.” Int. J. Climatol., 24, 161–180.
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. Climatol., 26, 935–955.
von Storch, H., Zorita, E., and Cubasch, U. (1993). “Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime.” J. Climate, 6, 1161–1171.
von Storch, H., and Zwiers, F. (1999). Statistical analysis in climate research, Cambridge University Press, Cambridge, U.K.
Wang, Y. Q., et al. (2004). “Regional climate modeling: Progress, challenges, and prospects.” J. Meteorol. Soc. Jpn., 82, 1599–1628.
Wetterhall, F., Halldin, S., and Xu, C. Y. (2005). “Statistical precipitation downscaling in central Sweden with the analogue method.” J. Hydrol., 306, 174–190.
Wilby, R. L. (1994). “Stochastic weather type simulations for regional climate change impact assessment.” Water Resour. Res., 30, 3395–3403.
Wilby, R. L., et al. (1998). “Statistical downscaling of general circulation model output: A comparison of methods.” Water Resour. Res., 34(11), 2995–3008.
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,” Supporting material of the Intergovernmental Panel on Climate Change, DDC of IPCC TGCIA, 27.
Wilby, R. L., Dawson, C. W., and Barrow, E. M. (2002). “SDSM—A decision support tool for the assessment of climate change impacts.” Environ. Modell. Software, 17, 145–157.
Wilby, R. L., Tomlinson, O. J., and Dawson, C. W. (2003). “Multi-site simulation of precipitation by conditional resampling.” Clim. Res., 23, 183–194.
Wilks, D. S., and Wilby, R. L. (1999). “The weather generation name: A review of stochastic weather models.” Prog. Phys. Geogr., 23, 329–357.
Xu, C. Y. (1999). “From GCMs to river flow: A review of downscaling methods and hydrologic modelling approaches.” Prog. Phys. Geogr., 23(2), 229–249.
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Published In
Journal of Hydrologic Engineering
Volume 16 • Issue 2 • February 2011
Pages: 157 - 164
Copyright
© 2011 ASCE.
History
Received: Dec 21, 2009
Accepted: Jul 21, 2010
Published online: Jul 22, 2010
Published in print: Feb 2011
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