Browse > Article
http://dx.doi.org/10.3741/JKWRA.2010.43.8.757

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

Seo, Young-Min (Department of Civil Engineering, Yeungnam Univ.)
Yeo, Woon-Ki (Department of Civil Engineering, Yeungnam Univ)
Lee, Seung-Yoon (K-water Institute)
Jee, Hong-Kee (Department of Civil Engineering, Yeungnam Univ.)
Publication Information
Journal of Korea Water Resources Association / v.43, no.8, 2010 , pp. 757-767 More about this Journal
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.
Keywords
probability rainfall; spatial distribution; topographic factor; KED; kriging;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI
2 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.   DOI
3 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.
4 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.
5 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.   DOI
6 Hartkamp, A.D., de Beurs, K., Stein, A., and White, J.W. (1999). Interpolation techniques for climate variables. 99-01, Wageningen Agricultural University, Wageningen.
7 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.   DOI
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.   DOI
9 Matheron, G. (1969). Le Krigeage Universel. Vol. 1, Cahiers du Centre de Morpologie Mathématique, Ecole des Mines de Paris, Fontainebleau, p. NA.
10 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.   DOI
11 Webster, R., and Oliver, M.A. (2001). Geostatistics for environmental scientists, statistics in practice. Wiley, Chichester, p. 265.
12 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.   DOI   ScienceOn
13 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.
14 Wackernagel, H. (2003). Multivariate geostatistics. 3rd Edition, Springer-Verlag.
15 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.   DOI
16 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.
17 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.   DOI   ScienceOn