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 mai...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.展开更多
文摘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.
