https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17831 Sat 24 Mar 2018 08:03:32 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17824 2 anywhere in Australia. This paper gives an overview of the progress of the finalisation of the ARR RFFE Model. In the development of the model, a database of 877 catchments from the data-rich regions and 66 catchments from the data-poor arid regions have been selected. Australia has been divided into six regions and five fringe zones to apply the ARR RFFE Model. In the data-rich regions, a region-of-influence (ROI) approach has been adopted to form sub-regions, provided there are a good number of geographically contiguous stations. In developing the prediction equations, a Bayesian generalised least squares (GLS) regression technique has been adopted for the data-rich regions, which considers the inter-station correlation and variation in record lengths from site to site in developing regional prediction equations. A regionalised Log Pearson Type 3 (LP3) distribution is adopted to derive design flood estimates for ungauged catchments in the range of AEPs of 50% to 1%. For the data-poor arid region, a simplified index type regional flood frequency method has been adopted. For easy application by the industry, the RFFE Model software will automate the application of the model. The user will be required to provide simple input data to obtain design flood quantiles and associated uncertainty estimates with 90% confidence limits. It is expected that the new ARR RFFE Model 2014 will have a wide application in estimating design floods for small and medium sized ungauged catchments as well as to provide prior information in the at-site flood frequency analysis using ARR-FLIKE. Furthermore, the results from ARR RFFE Model will present a useful means of benchmarking other flood estimation methods in Australia.]]> Sat 24 Mar 2018 08:03:27 AEDT ]]>