A Statistical Approach to Downscaling of Daily Rainfall Process at an Ungauged Site

  • Myeong-Ho Yeo
  • Van-Thanh-Van NguyenEmail author
Part of the Springer Water book series (SPWA)


The overall objective of the present paper is to propose a statistical approach to downscale the precipitation process at an ungauged location in the context of climate change. More specifically, the proposed approach consists of a combination of three components: (i) a regionalization approach for identifying the homogeneous groups of observed daily precipitation series available at different raingauges; (ii) a stochastic model for constructing daily rainfall events at an ungauged location within a homogeneous group; and (iii) a statistical downscaling model (SDRain) for describing the linkage between the constructed daily precipitation series and the large-scale climatic predictors given by the GCM simulation outputs. The feasibility of the proposed stochastic approach has been assessed using the available daily precipitation data for the period 1973–2001 from a network of 63 raingauge stations in South Korea and the NCEP reanalysis climate predictors. Results of the numerical application have indicated that it is feasible to estimate the missing precipitation data at an ungauged site based on the data available at other sites within the same homogeneous region. Furthermore, the proposed SDRain was able to generate daily precipitation sequences for an ungauged site with comparable statistical characteristics as those given by the application of SDRain for a gauged site with available observed precipitation data.


Precipitation Regionalization Statistical downscaling Missing data Climate change Uncertainty analysis 


  1. 1.
    Bárdossy, A. (2007). Calibration of hydrological model parameters for ungauged catchments. Hydrology and Earth System Sciences, 11(2), 703–710. doi: 10.5194/hess-11-703-2007.CrossRefGoogle Scholar
  2. 2.
    Besaw, L. E., Rizzo, D. M., Bierman, P. R., & Hackett, W. R. (2010). Advances in ungauged streamflow prediction using artificial neural networks. Journal of Hydrology, 386(1–4), 27–37.CrossRefGoogle Scholar
  3. 3.
    González, J., & Valdés, J. (2008). A regional monthly precipitation simulation model based on an L-moment smoothed statistical regionalization approach. Journal of Hydrology, 348, 27–39.CrossRefGoogle Scholar
  4. 4.
    Goswami, M., O’Connor, K. M., & Bhattarai, K. P. (2007). Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment. Journal of Hydrology, 333(2–4), 517–531.CrossRefGoogle Scholar
  5. 5.
    IPCC Synthesis Report (2007). Climate Change 2007 : An Assessment of the Intergovernmental Panel on Climate Change (pp. 12–17).Google Scholar
  6. 6.
    Jöreskog, K., & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research, 36(3), 347–387.CrossRefGoogle Scholar
  7. 7.
    Li, M., Shao, Q., Zhang, L., & Chiew, F. H. S. (2010). A new regionalization approach and its application to predict flow duration curve in ungauged basins. Journal of Hydrology, 389(1–2), 137–145.CrossRefGoogle Scholar
  8. 8.
    Nguyen, V.-T.-V. (2007). On regional estimation of floods for ungaged sites. In N. Park et al. (Eds.), Advances in geosciences: Hydrological science (Vol. 6, pp. 55–66). New Jersey: World Scientific Publishing Company.Google Scholar
  9. 9.
    Nguyen, V.-T.-V., Nguyen, T-D., & Gachon, P. (2006). On the linkage of large-scale climate variability with local characteristics of daily precipitation and temperature extremes: an evaluation of statistical downscaling methods. In: N. Park et al. (Ed.), Advances in geosciences: hydrological science (Vol. 4, pp. 1–9). New Jersey: World Scientific Publishing Company.Google Scholar
  10. 10.
    Nguyen, V.-T.-V., & Yeo, M.-H. (2011). Statistical Downscaling of Daily Rainfall Processes for Climate-Related Impact Assessment Studies. World Environmental and Water Resources Congress 2011, Palm Spring, USA, American Society of Civil Engineers, pp. 4477–4482.Google Scholar
  11. 11.
    Pandey, G.R., & Nguyen, V.-T.-V. (1999). A comparative study of regression based methods in regional flood frequency analysis. Journal of Hydrology, 225(1–2), 92–101.Google Scholar
  12. 12.
    Samuel, J., Coulibaly, P., & Metcalfe, R. (2011). Estimation of continuous streamflow in Ontario ungauged basins: comparison of regionalization methods. Journal of Hydrologic Engineering, 16(5), 447–459.CrossRefGoogle Scholar
  13. 13.
    Sivapalan, M. (2003). Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrological Processes, 17(15), 3163–3170.CrossRefGoogle Scholar
  14. 14.
    Wilks, D. S. (2006). Statistical methods in the atmospheric sciences (2nd ed., p. 627). Burlington, MA ; London: Academic Press.Google Scholar
  15. 15.
    Yarnal, B., Comrie, A. C., Frakes, B., & Brown, D. P. (2001). Review developments and prospects in synoptic climatology. International Journal of Climatology, 21, 1923–1950.CrossRefGoogle Scholar
  16. 16.
    Yeo, M.-H. (2014). Statistical modeling of precipitation processes for gaged and ungaged sites in the context of climate change. PhD Thesis, Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, Quebec, Canada, 221 p.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Department of Civil Engineering and Applied MechanicsMcGill UniversityMontrealCanada

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