Browsing by Author "Cowpertwait, Paul S.P."

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    A continuous stochastic disaggregation model of rainfall for peak flow simulation in urban hydrologic systems
    (Massey University, 2001) Cowpertwait, Paul S.P.
    In the paper by Durrans et al. (1999), an algorithm proposed by Ormsbee (1989) is recommended for the stochastic disaggregation of hourly rainfall in continuous flow simulation studies of urban hydrologic systems. However, Durrans et al. found that the method produced a “severe negative bias” in the maximum rainfall intensity of the disaggregated series, so that peak flows in urban systems are likely to be under-estimated by the model. Here we develop a method for disaggregating hourly data to 5min series, which addresses the problem of negative bias. A regression equation is derived for the ratio of the maximum 5min depth to the total depth in the hour. Thus, for any given hourly depth this ratio can be simulated and multiplied by the hourly depth to obtain a 5min maximum. The temporal location of the maximum within the hour can be randomly placed using an appropriate distribution function, e.g. based on a geometrical construction as developed by Ormsbee (1989). The model is developed and tested using 5min rainfall data taken from Lund (1923-39) and Torsgatan (1984-93), Sweden. The results support the use of the model in urban drainage applications.
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    A renewal cluster model for the inter-arrival times of rainfall events
    (Massey University, 2000) Cowpertwait, Paul S.P.
    A statistical model, based on a renewal cluster point process, is proposed and used to infer the distributional properties of dry periods in a continuous-time record. The model incorporates a mixed probability distribution in which inter-arrival times are classified into two distinct types, representing cyclonic and anticyclonic weather. This results in rainfall events being clustered in time, and enables objective probabilistic statements to be made about storm properties, e.g. the expected number of events in a storm cluster. The model is fitted to data taken from a gauge near Wellington, New Zealand, by maximising the likelihood function with respect to the parameters. The Akaike Information Criteria is used to select the best fitting distributions from a range of candidates. The log-Normal distribution is found to provide the best fit to the times between successive storm clusters, whilst the Weibull distribution is found to provide the best fit to the times between successive events in the same storm cluster. Harmonic curves are used to provide a parsimonious parameterisation, allowing for the seasonal variation in precipitation. Under the fitted model, the interval series is transformed into a residual series, which is assessed to determine overall goodness-of-fit.