http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10035 Australia is typical of many countries in that it has an extensive network of rainfall recording stations which record rainfall data in various forms ranging from a daily time step down to 6-min resolution. However, while the length of historical daily records is often large, there are very few 6-min (pluviograph) records available of significant length. Not only does this lack of significant short time scale data impose a major obstacle in the application of a Monte Carlo approach to risk estimation, it also inhibits the application of rainfall simulation models that use this data for direct calibration. While the advent of numerous stochastic rainfall models provides methods for extending historical rainfall records, without adequate historical rainfall data available for calibration their accuracy is questionable. This paper describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique uses a new ‘master–target’ scaling relationship. A model calibration is undertaken at a ‘master’ site with a long pluviograph record, which is then scaled to the ‘target’ site using the information from the target site in form of either a short pluviograph or a daily rainfall record. This approach removes the need for significant pluviograph data at the ‘target’ site and enables the stochastic rainfall model to be applied at sites with either short pluviograph or daily rainfall records. The master–target scaling technique is demonstrated using an existing high-resolution point rainfall model based on wet-dry alternating storm events. Extensive testing using numerous pairs of Australian sites demonstrates its validity. 2012-02-10T05:30:03.780Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:1309 The point rainfall model presented extends previous work on event-based rainfall models and overcomes some of their shortcomings. The model uses event-based data and can be calibrated using rainfall data substantially affected by missing or corrupted values. Particular attention was given to adequately simulating extreme storm rainfall events for use in hydrological risk assessment. The model is capable of simulating the inter-event time, storm duration, average event intensity and intra-event temporal characteristics. Conditioning of the average event rainfall intensity on rainfall duration and time of year is a feature of the model. Rainfall events are disaggregated using a conditional random walk on a dimensionless mass curve. Pluviograph data in 6 min increments from three Australian capital cities (Sydney, Brisbane and Melbourne) was used to calibrate the model parameters. It was found that the constrained random walk parameters were almost identical for the three cities. The model was tested using statistics not used in its calibration and the simulated intensity–frequency–duration extreme rainfall statistics compared very favorably with observed values. In addition, simulated aggregated statistics compared favorably with observed statistics from 30 min to monthly durations. The simulated annual rainfalls significantly underestimated the observed variability for Brisbane and Sydney, whereas satisfactorily reproduced the Melbourne variability. An explanation is offered for these differences. 2010-04-27T06:56:33.135Z ]]>