Gains from modelling dependence of rainfall variables into a stochastic model: application of the copula approach at several sites

Abstract. Since the last decade, copulas have become more and more widespread in the construction of hydrological models. Unlike the multivariate statistics which are traditionally used, this tool enables scientists to model different dependence structures without drawbacks. The authors propose to apply copulas to improve the performance of an existing model. The hourly rainfall stochastic model SHYPRE is based on the simulation of descriptive variables. It generates long series of hourly rainfall and enables the estimation of distribution quantiles for different climates. The paper focuses on the relationship between two variables describing the rainfall signal. First, Kendall's tau is estimated on each of the 217 rain gauge stations in France, then the False Discovery Rate procedure is used to define stations for which the dependence is significant. Among three usual archimedean copulas, a unique 2-copula is chosen to model this dependence for any station. Modelling dependence leads to an obvious improvement in the reproduction of the standard and extreme statistics of maximum rainfall, especially for the sub-daily rainfall. An accuracy test for the extreme values shows the good asymptotic behaviour of the new rainfall generator version and the impacts of the copula choice on extreme quantile estimation.