The Correlation Degree of Two Hydrologic Variables Used for Hydraulic Engineering

Abstract:

Most failures of hydraulic engineering caused by hydrology events are rarely a function of just one source variable (e.g.wave,tide level,river flow,rainfall),but more usually of two or more variables.So,the correlation of two variables which are partially dependent is important for hydrologic design and floodplain management.The objective of this paper is to discuss the correlation degree between two variables by kendall's rank correlation coefficient test.As a case,the observations of rainfalls, tide levels and wind speeds,collected from 1971 to 2002 in Shenzhen city of China,were used in this paper.The results show that kendall’s rank correlation coefficients obtained by the test range from 0.13 to 0.61.This means that there is a significant correlation between any two of these hydrologic variables.

• Periodical: Advanced Materials Research (Volumes 1092-1093)
• Pages: 1189-1192
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1092-1093.1189
• Online since: March 2015
• Authors: Xing Yang*, Xiao Dong Hu, Zhi Qing Li
• Keywords: Correlation Degree, HYDROLOGY, Kendall, Rainfall, Tide Level, Wind Speed

References

[1] G. Rodríguez1,A. Nistal and B. Pérez. (1999). Joint occurrence of high tide, surge and storm-waves on the northwest Spanish coast. Bol. Inst. Esp. Oceanogr, 15(1-4): 21-29.

[2] D.J. Doong, S.H. Ou and C.C. Kao. (2008). Joint Probability Analysis of Waves and Water Level during Typhoo. COPEDEC VII, Dubai, UAE.

[3] J. J. Lian,K. Xu,C. Ma. (2012). Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study for Fuzhou city, China. Hydrol. Earth Syst. Sci. Discuss., 9: 7475-7505.

DOI: 10.5194/hess-17-679-2013

[4] L. Zhang, Vijay P. Singh,F. ASCE. (2007). Gumbel-Hougaard Copula for Trivariate Rainfall Frequency Analysis. Journal of Hydrologic Engineering, 12(4): 409-419.

DOI: 10.1061/(asce)1084-0699(2007)12:4(409)

[5] Favre A C, Adlouni S E, Perrault L, et al. (2004). Multivariate hydrological frequency analysis using copulas. Water Resources Reasearch, 40(11): 1-12.

DOI: 10.1029/2003wr002456

[6] Salvatore G, Francesco S. (2006). Asymmetric copula in multivariate flood frequency analysis. Advances in Water Resources, 29: 1155-1167.

DOI: 10.1016/j.advwatres.2005.09.005

[7] Shih-Chieh Kao, Rao S. Govindaraju. (2010). A copula-based joint deficit index for droughts. Journal of Hydrology, 380: 121-134.

DOI: 10.1016/j.jhydrol.2009.10.029

[8] G. Wong M.F. Lambert,M. Leonard, et al. (2010). Drought analysis using trivariate copulas conditional on climatic states. Journal of Hydrologic Engineering, 15(2): 129-141.

DOI: 10.1061/(asce)he.1943-5584.0000169

[9] Genest,C., Ghoudi,K., Rivest,L. (1995). A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika, 82(3): 543-552.

DOI: 10.1093/biomet/82.3.543