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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]

Design flood estimation for ungauged catchments is often required in hydrologic design. The most commonly adopted regional flood frequency analysis methods used for this purpose include the index flood method, regression based techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile regression technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods. Based on one-at-a-time and split sample validation tests and a range of evaluation statistics, it has been found that the GLS-based QRT performs better than the PRM. The particular advantage of the QRT is that it does not require a contour map of the runoff coefficient as with the PRM. The QRT also offers potential for improvement through inclusion of additional predictor variables. The QRT can also be integrated with the region of influence approach, where a region can be formed around an ungauged catchment by selecting an "appropriate number" of neighbouring gauged catchments. Overall, the QRT offers much greater flexibility and potential in terms of error analysis and further development as compared to the PRM. [ABSTRACT FROM AUTHOR]

Highlights: [•] Trend detection in hydrological data is important in changing climate regime. [•] We applied Mann–Kendall test to examine trend in the AM flood data in Australia. [•] We considered the impact of serial and cross correlation on the trend detection. [•] We investigated the linkage of the identified trends with large scale climatic modes. [Copyright &y& Elsevier]