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Regional flood frequency analysis (RFFA) techniques are commonly used to estimate design floods for ungauged catchments. In Australian Rainfall and Runoff (ARR), the probabilistic rational method (PRM) was recommended for eastern New South Wales (NSW). Recent studies in Australia have shown that regression-based RFFA methods can provide more accurate design flood estimates than the PRM. This paper compares ordinary least squares (OLS) and generalised least squares (GLS) based quantile regression techniques using data from 96 smallto medium-sized catchments across NSW for average recurrence intervals of 2 to 100 years. The advantages of the GLS regression are that this accounts for the inter-station correlation and varying record lengths from site to site. An independent test based on both the split-sample and one-at-a-time validation approaches employing a wide range of statistical diagnostics indicates that the GLS regression provides more accurate flood quantile estimates than the OLS one. The developed regression equations are relatively easy to apply, which require data for only two to three predictors, catchment area, design rainfall intensity and stream density. The findings from this study together with those from other RFFA studies being examined as a part of ARR upgrade projects will inform the development of RFFA techniques for inclusion in the revised edition of ARR. [ABSTRACT FROM AUTHOR]

This paper presents a case study on streamflow data preparation for a regional flood frequency analysis (RFFA) project for the states of Victoria and NSW, in connection with the forthcoming edition of Australian Rainfall and Runoff. The study gathered annual maximum flood series data for a large number of stations from Victoria and NSW, and applied various statistical techniques to prepare the final data set. It was found that a large primary data set, even if selected using a fairly stringent set of criteria, cannot guarantee a similarly large final data set, as streamflow data are affected by many sources of uncertainty. The trade-offs between quality and quantity are discussed and illustrated. The maximum rating ratio, defined as the ratio of the largest estimated flow and the maximum measured flow at a gauging station, is used to identify stations whose quantiles may be seriously affected by rating curve errors. In a case study involving Victorian stations, the importance of maintaining a high spatial coverage of stations was demonstrated. It was shown that a 50% reduction in the number of stations used in a RFFA resulted in an increase of the standard error of prediction of flood quantiles up to 90%. [ABSTRACT FROM AUTHOR]