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Articles
  • OpenAccess
  • Hydrological Mann-Kendal Multivariate Trends Analysis in the Upper Yangtze River Basin  [WRE 2015]
  • DOI: 10.4236/gep.2015.310006   PP.34 - 39
  • Author(s)
  • Lei Ye, Jianzhong Zhou, Xiaofan Zeng, Muhammad Tayyab
  • ABSTRACT
  • Hydrological events should be described through several correlated variables, so multivariate HFA has gained popularity and become an active research field during recent years. However, at present multivariate HFA mainly focuses directly on fitting the frequency distribution without confirming whether the assumptions are satisfied. Neglecting testing these assumptions could get severely wrong frequency distribution. This paper uses multivariate Mann-Kendal testing to detect the multivariate trends of annual flood peak and annual maximum 15 day volume for four control hydrological stations in the Upper Yangtze River Basin. Results indicate that multivariate test could detect the trends of joint variables, whereas univariate tests can only detect the univariate trends. Therefore, it is recommended to jointly apply univariate and multivariate trend tests to capture all the existing trends.

  • KEYWORDS
  • Trend Analysis, Multivariate Mann-Kendal Test, Hydrological Variable, Upper Yangtze River Basin
  • References
  • [1]
    Chebana, F., Ouarda, T.B. and Duong, T.C. (2013) Testing for Multivariate Trends in Hydrologic Frequency Analysis. Journal of Hydrology, 486, 519-530.
    http://dx.doi.org/10.1016/j.jhydrol.2013.01.007
    [2]
    Cunnane, C. (1988) Methods and Merits of Regional Flood Frequency Analysis. Journal of Hydrology, 100, 269-290.
    http://dx.doi.org/10.1016/0022-1694(88)90188-6
    [3]
    Sarhadi, A., Soltani, S. and Modarres, R. (2012) Probabilis-tic Flood Inundation Mapping of Ungauged Rivers: Linking GIS Techniques and Frequency Analysis. Journal of Hy-drology, 458, 68-86.
    http://dx.doi.org/10.1016/j.jhydrol.2012.06.039
    [4]
    Viglione, A., Merz, R., Salinas, J.L. and Bl?schl, G. (2013) Flood Frequency Hydrology: 3. A Bayesian Analysis. Water Resources Research, 49, 675-692.
    http://dx.doi.org/10.1029/2011WR010782
    [5]
    Zhang, L. and Singh, V. (2006) Bivariate Flood Frequency Analysis Using the Copula Method. Journal of Hydrologic Engineering.
    http://dx.doi.org/10.1061/(ASCE)1084-0699(2006)11:2(150)
    [6]
    Kendall, M. (1975) Rank Correlation Methods. Charles Griffin, London.
    [7]
    Mann, H.B. (1945) Nonparametric Tests against Trend. Econometrica: Journal of the Econometric Society, 245-259.
    http://dx.doi.org/10.2307/1907187
    [8]
    Mitchell, J., Dzerdzeevskii, B., Flohn, H., Hormeyr, W., Lamb, H., Rao, K. and Wallen, C. (1966) Climate Change. WMO Tech. Note, 79. WMO.
    [9]
    Lettenmaier, D.P. (1988) Multivariate Nonparametric Tests for Trend in Water Quality1. Wiley Online Library.
    [10]
    Dietz, E.J. and Killeen, T.J. (1981) A Nonparametric Multivariate Test for Monotone Trend with Pharmaceutical Applications. Journal of the American Statistical Association, 76, 169-174.
    [11]
    Hirsch, R.M. and Slack, J.R. (1984) A Nonparametric Trend Test for Seasonal Data with Serial Dependence. Water Resources Research, 20, 727-732.
    http://dx.doi.org/10.1029/WR020i006p00727

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