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

Abstract

Abstract. A novel approach to stochastic rainfall generation that can\nreproduce various statistical characteristics of observed rainfall at hourly\nto yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly\nrainfall. Then, it downscales the generated monthly rainfall to the hourly\naggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP)\nmodel, a type of Poisson cluster rainfall model. Here, the MBLRP model is\ncarefully calibrated such that it can reproduce the sub-daily statistical\nproperties of observed rainfall. This was achieved by first generating a set\nof fine-scale rainfall statistics reflecting the complex correlation\nstructure between rainfall mean, variance, auto-covariance, and proportion of\ndry periods, and then coupling it to the generated monthly rainfall, which\nwere used as the basis of the MBLRP parameterization. The approach was tested\non 34 gauges located in the Midwest to the east coast of the continental\nUnited States with a variety of rainfall characteristics. The results of the\ntest suggest that our hybrid model accurately reproduces the first- to\nthe third-order statistics as well as the intermittency properties from the\nhourly to the annual timescales, and the statistical behaviour of monthly\nmaxima and extreme values of the observed rainfall were reproduced well.