A nonparametric approach for daily rainfall simulation Original Research Article

A nonparametric model for daily rainfall simulation is presented. Nearest neighbour methods are used to conditionally simulate rainfall spells and amounts. A “local” subset of the observed record is used to formulate the conditional densities needed for simulation. This provides an effective yet simple way to model local and seasonal features in the observed rainfall time series. The model is applied in two stages. First dry and wet spell lengths are conditionally simulated. Next rainfall amounts for each day of the wet spell are simulated assuming an order one Markov dependence structure. Higher order dependence is modelled by considering the number of days from start of the spell as an additional variable. Rainfall distributional characteristics are observed to have distinctly different characteristics depending on the length of the wet spell. A procedure is developed to reproduce such differences in the simulations. The model is applied to 123 years of daily rainfall from Sydney, Australia.