Preserving low-frequency variability in generated daily rainfall sequences

A stochastic modeling framework for multisite generation of daily rainfall is developed with an aim of representing both short and higher time scale dependence in the generated rainfall sequences. The framework simulates rainfall at individual locations using separate models for rainfall occurrences and rainfall amounts on the simulated wet days. The spatial correlations in the generated occurrences and amounts are induced using spatially correlated yet serially independent random numbers. The rainfall occurrence model is based on a modification of the transition probabilities of the traditional Markov model through an analytically derived factor that represents the influence of rainfall aggregated over long time periods in an attempt to incorporate low-frequency variability in simulations. The rainfall amounts on the wet days are generated using a nonparametric conditional simulation approach. The utility of the proposed method is illustrated by applying the model on a network of 30 raingauge stations around Sydney, Australia, and comparing a range of statistics describing daily and higher time scale distribution and dependence attributes. The analyses of the results show that the method adequately captures daily as well as aggregated higher time scale rainfall characteristics at individual locations including the spatial distribution of rainfall over the region.