A Regionalized Stochastic Rainfall Model for the Generation of High Resolution Data in Peninsular Malaysia

High resolution rainfall data is an important input for studies on hydrological systems. Often time synthetic data has to be generated in the absence of historical data. The stochastic Neyman Scott Rectangular Pulse (NSRP) model has been developed to produce synthetic data of high resolution. To take into account the spatial and temporal nature of rainfall, an extra domain had been added producing the Spatial Temporal NSRP (ST-NSRP) model. However these models require the repeat estimation of all the model parameters for each location. This work develops the Regionalized ST-NSRP model which produces a single model for a region. The regionalization approach is carried out by the station-year method using ten years (2001-2010) of records from sixteen stations. The statistical characteristics used in estimating the model’s parameters are hourly and daily coefficient of variation, hourly and daily lag one auto correlation, hourly cross correlation between sites and hourly skewness of rainfall series. Simulation process is conducted at two independent stations with distinctly different rainfall profile. The Regionalized ST-NSRP model produced statistical characteristics of the ungauged sites which matched those of the observed series fairly well even though there is a tendency to underestimate hourly covariance and lag one autocorrelation. Considering that spatial variability of rainfall is high within the studied region, this model is sufficiently robust in producing reasonable synthetic hourly rainfall series which captured the characteristics and pattern of observed rainfall series. Therefore the model has shown a promising potential in generating high resolution synthetic data for any ungauged site within the region.