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L‐moment homogeneity test in trivariate regional frequency analysis of extreme precipitation events

Identification of homogeneous regions is a key task in regional frequency analysis to obtain accurate estimates of a given event. Homogeneity testing based on multivariate L‐moments has recently been proposed and all studies dealing with application of the methodology using real‐world data still focus on the bivariate case. The practical aspects of the trivariate homogeneity tests for maximum annual 1, 3 and 7 day precipitation totals measured at stations in the Czech Republic are investigated. A trivariate model of the precipitation amounts for simulating synthetic homogeneous regions in parametric testing was determined by goodness‐of‐fit tests based on the Cramér–von Mises and Vuong statistics and Akaike and Bayesian information criteria while considering selected trivariate exchangeable Archimedean, fully nested Archimedean and mixed C‐vine copulas in the copula model test space. Each of the regions into which the area of the Czech Republic is divided meets the homogeneity condition when using both the parametric and non‐parametric L‐moment homogeneity tests and considering three mixed C‐vine copula models given by the bivariate Gumbel copula for two unconditional pair‐copulas and normal, Frank or Plackett copula families for the conditional pair‐copula for simulating synthetic homogeneous regions.