A multisite daily rainfall generator driven by bivariate copula-based mixed distributions

This work describes a simple and parsimonious multisite Markov model deduced from bivariate copula-based mixed distributions useful for modeling and generating daily rainfall series. The structure of the model (involving only joint probabilities of wet/dry condition, a copula, the distribution of positive rainfall values, and a spatial cross-correlation matrix) allows preserving spatial and temporal intermittency and several other properties of hydrologic interest (e. g., wet spell length, daily rainfall amount, rainfall accumulated on wet spells, first steps of the autocorrelation function, and spatial Kendall's correlation coefficient). The model does not require marginal transformations of the observed values, and non-Gaussian temporal structures of dependence can be introduced by copulas. The spatial dependence is accounted for by simulating temporally independent but spatially correlated standard uniform random sequences. Application to observed rainfall time series and Monte Carlo simulations are carried out to assess the model performances. The approach appears to be a viable way to simulate daily rainfall sequences in a fairly easy way.