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Urban impervious areas provide a guaranteed source of runoff, especially in cities with high rainfall - this represents a source of water with low sensitivity to unfavourable climate change. Whilst the potential to reuse stormwater has long been recognised, its quality has largely limited usage to non-potable applications requiring the use of a third-pipe network, a prohibitively expensive option in established urban areas. Given recent advances in membrane filtration, this study investigates the potential of harvesting and treating stormwater to a potable standard to enable use of the potable distribution network. A case study based on the Throsby Creek catchment in Newcastle explores the issue. The high seasonally uniform rainfall provides insight into the maximum potential of such an option. Multicriterion optimisation was used to identify Pareto optimal solutions for harvesting, storing and treating stormwater. It is shown that harvesting and treating stormwater from a 13 km2 catchment can produce yields ranging from 8.5 to 14.2 ML/day at costs ranging from AU$2.60/kL to AU$2.89/kL, which may become viable as the cost of traditional supply continues to grow. However, there are significant social impacts to deal with including alienation of public land for storage and community acceptance of treated stormwater. [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]

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]

This paper investigates a Spatial Neyman-Scott Rectangular Pulse (SNSRP) model, which is one of only a few models capable of continuous simulation of rainfall in both space and time. The SNSRP is a spatial extension of the Neyman-Scott Rectangular Pulse model at a single point. The model is highly idealized having six parameters: storm arrival, cell arrival, cell radius, cell lifetime and two cell intensity parameters. A spatial interpolation of the scale parameter is used so that the model can be simulated continuously in space, rather than as a multi-site model. The parameters are calibrated using least-squares fits to statistical moments based on data aggregated to hourly and daily totals. The SNSRP model is calibrated to a very large network of 85 gauges over metropolitan Sydney and shows a good agreement to calibrated statistics. A simulation of 50 replicates over the region compares favourably to several observed temporal statistics, with an example given for one site. A qualitative discussion of the simulated spatial images demonstrates the underlying structure of non-advecting cylindrical cells. [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]

During the Millennium Drought in southeast Australia (2001-2009), dryland wetlands experienced widespread ecological deterioration, which highlighted their vulnerability to natural climate variability and the potential effects of drying climate change. Here we use 30-year observed streamflow data (1991-2020) and numerical models to assess the impacts of climate variability and climate change on the Macquarie Marshes (the Marshes), a large floodplain wetland complex in the semi-arid region of New South Wales, Australia. A fast ecohydrologic emulator based on network linear programming with side constraints was developed to simulate the spatial and temporal responses of different wetland vegetation types to water regime. The emulator represents the wetland by a series of inter-connected level-pool reservoirs with the volume-discharge relationship obtained from a calibrated quasi-2d hydrodynamic model. The emulator reproduces daily flows and volume with good accuracy (Nash-Sutcliffe statistic ranging from 0.61 to 0.96) with 1/26,000 of the computational effort. We use the emulator to simulate the potential effects of climatic variability on vegetation by running the model over 30 years of observed data and 1000 statistically representative 30-year streamflow time series, which were generated using a stochastic model calibrated to the gauged flows. The collection of results for all 1000 contemporary simulations indicates the Marshes experience severe conditions 43% (± 18%) of the time in a 30-year period. We then ran an additional 6000 simulations to assess the combined impacts of climate variability and future climate change at the end of the century. For the driest future climates (-60% and -30% reduction in runoff), the Marshes remain in severe condition 89% (± 6%) and 63% (± 16%) of the time, respectively, while no major differences with respect to the contemporary conditions were found for the wetter future. Our results highlight the importance of quantifying the extent and uncertainty in the degradation of these ecosystems due to climate variability and change for informing management decisions., (Copyright © 2022 Elsevier Ltd. All rights reserved.)