A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability

A semi-parametric stochastic modeling framework for generation of daily rainfall at multiple locations is presented. The proposed framework represents longer-term variability and low-frequency features such as drought, while still simulating other daily key distributional and dependence attributes present in the observed rainfall record with sufficient spatial coherency. The rainfall occurrences at individual sites are simulated using a two-state, first-order Markov model. The transition probabilities of the Markov model are modified by using “aggregate” predictor variables that are indicative of how wet it has been over a period of time. The rainfall amounts on the simulated wet days are generated using a nonparametric kernel density estimation approach. Multisite spatial correlations in the rainfall occurrences and amounts series are represented by driving the single-site models with spatially correlated random numbers. The model is applied on a network of 30 raingauge stations around Sydney in eastern Australia. The analyses of results show that the model is capable of reproducing daily and higher time-scale key spatial and temporal characteristics of rainfall desired in most hydrologic applications.