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Regional flood frequency analysis (RFFA) is often used in hydrology to estimate flood quantiles when there is a limitation of at-site recorded flood data. One of the commonly used RFFA methods is the index flood method, which is based on the assumptions that a region satisfies criterion of simple scaling and it can be treated homogeneous. Another RFFA method is quantile regression technique where prediction equations are developed for flood quantiles of interest as function of catchment characteristics. In this paper, the scaling property of regional floods in New South Wales (NSW) State in Australia is investigated. The results indicate that the annual maximum floods in NSW satisfy a simple scaling assumption. The application of a heterogeneity test, however, reveals that NSW flood data set does not satisfy the criteria for a homogeneous region. Finally, a set of prediction equations are developed for NSW using quantile regression technique; an independent test shows that these equations can provide reasonably accurate design flood estimates with a median relative error of about 27%.

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In recent years, the potential impacts of climate variability and change on the hydrologic regime have received a great deal of attention from researchers. Review of hydrological data recorded in different parts of the world has provided evidence of regime-like or quasiperiodic climate behaviour and of systematic trends in key climate variables due to climate change and/or climate variability. It has been established that a changing climate will have notable impacts on the rainfall runoff process, and thus hydrologic time series (e.g., flood data) can no longer be assumed to be stationary. A failure to take such change/variability into account can lead to underestimation/overestimation of the design flood estimate, which in turn will have important implications on the design and operation of water infrastructures. This paper presents preliminary results from a study aimed to identify the nature of time trends in flood data in the Australian continent with the final objective of assessing the impacts of climatic change on regional floods in Australia. This research is being carried out as a part of the on-going revision of Australian Rainfall and Runoff -the national guide of design flood estimation in Australia. For this study, 491 suitable stations with flood data in the range of 30 to 97 years have been selected across the Australian continent. Two trend tests are applied: Mann-Kendall test and Spearman's Rho test to the data set. Preliminary trend analysis results show that about 30% of the selected stations show trends in annual maxima flood series data, with downward trends in the southern part of Australia and upward trends in the northern part. Further investigation is needed before any firm conclusion can be made about the trends in Australian flood data. Future work aims to address the influence of spatial correlation and autocorrelation on the ability to detect trend in annual maximum flood series data in Australia and assess the relationship between the observed trends in annual maximum flood data and other meteorological variables.

This study performs a simultaneous evaluation of gradual and abrupt changes in Australian annual maximum (AM) flood data using a modified Mann–Kendall and Pettitt change-point detection test. The results show that AM flood data in eastern Australia is dominated by downward trends. Depending on the significance level and study period under consideration, about 8% to 33% of stations are characterised by significant trends, where over 85% of detected significant trends are downward. Furthermore, the change-point analysis shows that the percentages of stations experiencing one abrupt change in the mean or in the direction of the trend are in the range of 8% to 33%, of which over 50% occurred in 1991, with a mode in 1995. Prominent resemblance between the monotonic trend and change-point analysis results is also noticed, in which a negative shift in the mean is observed at catchments that exhibited downward trends, and a positive shift in the mean is observed in the case of upward trends. Trend analysis of the segmented AM flood series based on their corresponding date indicates an absence of a significant trend, which may be attributed to the false detection of trends when the AM flood data are characterised by a shift in its mean.