STOCHASTIC POINT PROCESS MODELING OF RAINFALL .1. SINGLE-SITE FITTINGAND VALIDATION

A Neyman-Scott clustered point process model for rainfall is developed for use in storm sewer rehabilitation studies in the UK, where predic tions are needed of the frequency of system overloading for existing a nd upgraded conditions. In the first part of this two-part paper, a fl exible model fitting procedure is presented which involves matching ap proximately a chosen set of historical rainfall statistics, which exce eds in number the set of parameters. In fitting the model to hourly da ta, it is found that wet and dry spell transition probabilities should be included in the chosen set of statistics rather than lag 1 autocor relations, as they improve the model's fit to the historical dry spell sequences. In fitting the model to daily data, estimates of the varia nces of sub-daily rainfall totals derided from regional regression rel ationships are used to ensure that sub-daily totals generated by the f itted model exhibit the desired statistical behaviour. A number of val idation checks are carried out on simulated time series, which include visual comparisons with historical series, and comparisons of crossin g properties and of the distributions of daily annual maximum rainfall s. Overall, the results support the use of the model for its intended application.