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. A novel approach to stochastic rainfall generation that canreproduce various statistical characteristics of observed rainfall at hourlyto yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthlyrainfall. Then, it downscales the generated monthly rainfall to the hourlyaggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP)model, a type of Poisson cluster rainfall model. Here, the MBLRP model iscarefully calibrated such that it can reproduce the sub-daily statisticalproperties of observed rainfall. This was achieved by first generating a setof fine-scale rainfall statistics reflecting the complex correlationstructure between rainfall mean, variance, auto-covariance, and proportion ofdry periods, and then coupling it to the generated monthly rainfall, whichwere used as the basis of the MBLRP parameterization. The approach was testedon 34 gauges located in the Midwest to the east coast of the continentalUnited States with a variety of rainfall characteristics. The results of thetest suggest that our hybrid model accurately reproduces the first- tothe third-order statistics as well as the intermittency properties from thehourly to the annual timescales, and the statistical behaviour of monthlymaxima and extreme values of the observed rainfall were reproduced well.

A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales