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Application of KED Method for Estimation of Spatial Distribution of Probability Rainfall

확률강우량의 공간분포 추정을 위한 KED 기법의 적용

  • 서영민 (영남대학교 건설시스템공학과) ;
  • 여운기 (영남대학교 건설시스템공학과) ;
  • 이승윤 (한국수자원공사, K-water 수자원연구원) ;
  • 지홍기 (영남대학교 건설시스템공학과)
  • Received : 2010.06.30
  • Accepted : 2010.08.12
  • Published : 2010.08.31

Abstract

This study employs the KED method using the correlations between probability rainfall and topographical factors as single auxiliary variable for assessing the effectiveness of external variables to improve the reliability in the estimation of spatial distribution of probability rainfall. As a result, the KED method gives similar results compared with deterministic spatial interpolation methods and kriging methods in the estimation of rainfall spatial distribution and mean areal rainfall, and as a result of the cross-validations of KED and kriging methods, the KED method using terrain elevation as auxiliary variable gives the best results, which are not significantly different in comparisons with other methods.

본 연구는 확률강우량에 대한 공간분포 추정시 신뢰도를 향상시키는데 있어서 외부변수 사용의 유효성을 평가하기 위하여 확률강우량과 단일 보조변수로서 지형특성인자들과의 상관관계를 고려한 KED 기법을 적용하였으며, 그 결과 강우공간분포 및 유역평균강우량의 추정에 있어서 확정론적 공간보간기법 및 크리징 기법과 대체로 비슷한 결과를 나타내는 것으로 분석되었으며, KED 및 크리징 기법에 대한 교차검증 결과 보조변수로서 표고를 사용한 KED 기법이 가장 좋은 결과를 나타내고는 있으나 다른 기법들과 비교했을 때 큰 차이를 보이지 않는 것으로 분석되었다.

Keywords

References

  1. Ahmed, S., and de Marsily, G. (1987). “Comparision of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity.” Water Resources Research, Vol. 23, No. 9, pp. 1717-1737. https://doi.org/10.1029/WR023i009p01717
  2. Bostan, P.A., and Akyürek, Z. (2007). “Exploring the mean annual precipitation and temperature values over Turkey by using environmental variables.” ISPRS Joint Workshop of Visualization and Exploration of Geospatial Data, University of Applied Sciences, Stuttgart, Germany.
  3. Bourennane, H., King, D., and Couturier, A. (2000). “Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities.” Geoderma, Vol. 97, No. 3-4, pp. 255-271. https://doi.org/10.1016/S0016-7061(00)00042-2
  4. Buytaert, W., Celleri, R., Willems, P., Bievre, B.D., and Wyseure, G. (2006). “Spatial and temporal rainfall variability in mountainous areas: A case study from the South Ecuadorian Andes.” Journal of Hydrology, Vol. 329, No. 3-4, pp. 413-421. https://doi.org/10.1016/j.jhydrol.2006.02.031
  5. Cole, S.J., and Moore, R.J. (2008). “Hydrological modelling using raingauge- and radar-based estimators of areal rainfall.” Journal of Hydrology, Vol. 358, No. 3, pp. 159-181. https://doi.org/10.1016/j.jhydrol.2008.05.025
  6. Daly, C. (2002). Variable influence of terrain on precipitation patterns: Delineation and use of effective terrain height in PRISM. Oregon State University, Corvallis, available at: http://www.prism.oregonstate.edu/pub/prism/docs/effectiveterrain-daly.pdf.
  7. Hartkamp, A.D., de Beurs, K., Stein, A., and White, J.W. (1999). Interpolation techniques for climate variables. 99-01, Wageningen Agricultural University, Wageningen.
  8. Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P.D., and New, M. (2008), “A European daily high-resolution gridded dataset of surface temperature and precipitation.” Journal of Geophysical Research, Vol. 113, D20119. https://doi.org/10.1029/2008JD010201
  9. Kieffer, W.A., and Bois, P. (2000). “Topographic effects on statistical characteristics of heavy rainfall and mapping in the French Alps.” Journal of Applied Meteorology, Vol. 40, pp. 720-740. https://doi.org/10.1175/1520-0450(2001)040<0720:TEOSCO>2.0.CO;2
  10. Krähenmann, S., and Ahrens, B. (2010). “On daily interpolation of precipitation backed with secondary information.” Advances in Science and Research, Vol. 4, pp. 29-35. https://doi.org/10.5194/asr-4-29-2010
  11. Matheron, G. (1969). Le Krigeage Universel. Vol. 1, Cahiers du Centre de Morpologie Mathématique, Ecole des Mines de Paris, Fontainebleau, p. NA.
  12. Moulin, L., Gaume, E., and Obled, C. (2009). “Uncertainties on mean areal precipitation: Assess- ment and impact on streamflow simulations.” Hydrology and Earth System Sciences, Vol. 13, pp. 99-114. https://doi.org/10.5194/hess-13-99-2009
  13. Odeh, I.O.A., McBratney, A.B., and Chittleborough, D.J. (1995). “Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging.” Geoderma, Vol. 67, No. 3-4, pp. 215-226. https://doi.org/10.1016/0016-7061(95)00007-B
  14. Tibshirani, R. (1996). “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society, Series B (Methodological), Vol. 58, No. 1, pp. 267-288.
  15. Wackernagel, H. (2003). Multivariate geostatistics. 3rd Edition, Springer-Verlag.
  16. Webster, R., and Oliver, M.A. (2001). Geostatistics for environmental scientists, statistics in practice. Wiley, Chichester, p. 265.
  17. Yatagai, A., Arakawa, O., Kamaguchi, K., Kawamato, H., Nodzu, M.I., and Hamada, A. (2009). “A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges.” SOLA: Scienfiic Online Letters of the Atmosphere, Vol. 5, pp. 137-140.

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