https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 A new regionalisation model for large flood estimation in Australia: consideration of inter-site dependence in modelling https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21711 Sat 24 Mar 2018 08:06:24 AEDT ]]> Regional flood estimation in Australia: an overview of the study for the upgrade of 'Australian Rainfall and Runoff' https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21713 Sat 24 Mar 2018 08:06:24 AEDT ]]> Assessment of the impacts of rating curve uncertainty on at-site flood frequency analysis: a case study for New South Wales, Australia https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17831 Sat 24 Mar 2018 08:03:32 AEDT ]]> An overview of preparation of streamflow database for ARR Project 5 regional flood method https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17822 Sat 24 Mar 2018 08:03:27 AEDT ]]> An overview of the development of the New Regional Flood Frequency Estimation (RFFE) model for Australia 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 ]]> Development of a new regional flood frequency analysis method for semi-arid and arid regions of Australia https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17337 Sat 24 Mar 2018 08:01:43 AEDT ]]> Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:25940 -7 and 10-6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.]]> Sat 24 Mar 2018 07:41:26 AEDT ]]>