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The steady increase of urban population and the possible effects of climate change that may adversely affect the amount of water available in current water supply systems, makes the study of stormwater and rainwater harvesting and wastewater recycling a high priority. The basic proposition is that any system of water supply that can reduce the amount of water drawn from main reservoirs will be of benefit to the whole supply region especially in terms of drought security. This paper describes a versatile modelling framework which can simulate a wide variety of combinations of centralised and decentralised Integrated Urban Water Management schemes from the allotment to the whole suburb scale. The framework combines two modelling approaches. The first, called urbanCycle, simulates water supply and demand, stormwater and wastewater using allotments as the basic building block. Although urbanCycle can simulate processes in great detail, it assumes that the network forms a directed acyclic graph. This simplifies the connectivity logic but precludes investigation of systems with decentralised storage, feedbacks and multiple supply paths. To overcome this, a second model, called urbanNet, based on network linear programming, is embedded in the urbanCycle framework to enable the modelling of recycling and harvesting options, as well as on-the-fly supply and demand decision making, based on objectives rather than pre-set operating rules. [ABSTRACT FROM AUTHOR]

Integrated urban water cycle management presents a new framework in which solutions to the provision of urban water services can be sought. It enables new and innovative solutions currently constrained by the existing urban water paradigm to be implemented. This paper introduces the UrbanCycle model. The model is being developed in response to the growing and changing needs of the water management sector and in light of the need for tools to evaluate integrated watercycle management approaches. The key concepts underpinning the UrbanCycle model are the adoption of continuous simulation, hierarchical network modelling, and the careful management of computational complexity. The paper reports on the integration of modelling capabilities across the allotment, and subdivision scales, enabling the interactions between these scales to be explored. A case study illustrates the impacts of various mitigation measures possible under an integrated water management framework. The temporal distribution of runoff into ephemeral streams from a residential allotment in Western Sydney is evaluated and linked to the geomorphic and ecological regimes in receiving waters. [ABSTRACT FROM AUTHOR]

• Captured UAS LiDAR across 39 sites with 2790 field-measured trees. • Novel ITCD and canopy segmentation algorithm for structurally complex eucalypt forest. • ITCD algorithm generalised with all parameters fixed across broad forest types. • Stratified vegetation profile to differentiate understorey from overstorey. Estimates of forest stocking density per hectare (N Ha) are important in characterising ecological conditions and assessing changes in forest dynamics after disturbances due to pyrogenic, anthropogenic and biotic factors. We use Unmanned Aircraft Systems (UAS) LiDAR with mean point density of 1485 points m−2 across 39 flight sites to develop a bottom-up approach for individual tree and crown delineation (ITCD). The ITCD algorithm was evaluated across mixed species eucalypt forests (MSEF) using 2790 field measured stem locations across a broad range of dominant eucalypt species with randomly leaning trunks and highly irregular intertwined canopy structure. Two top performing ITCD algorithms in benchmarking studies resulted in poor performance when optimised to our plot data (mean F score : 0.61 and 0.62), which emphasises the challenge posed for ITCD in the structurally complex conditions of MSEF. To address this, our novel bottom-up ITCD algorithm uses kernel densities to stratify the vegetation profile and differentiate understorey from the rest of the vegetation. For vegetation above understorey, the ITCD algorithm adopted a novel watershed clustering procedure on point density measures within horizontal slices. A Principal Component Analysis (PCA) procedure was then applied to merge the slice-specific clusters into trunks, branches, and canopy clumps, before a voxel connectivity procedure clustered these biomass segments into overstorey trees. The segmentation process only requires two parameters to be calibrated to site-specific conditions across 39 MSEF sites using a Shuffled Complex Evolution (SCE) optimiser. Across the 39 field sites, the ITCD algorithm had mean F score of 0.91, True Positive (TP) trees represented 85% of measured trees and predicted plot-level stocking (N P) averaged 94% of actual stocking (N Ob). As a representation of plot-level basal area (BA), TP trees represented 87% of BA, omitted trees represented slightly smaller trees and made up 8% of BA, and a further 5% of BA had commission error. Spatial maps of N Ha using 0.5 m grid-cells showed that omitted trees were more prevalent in high density forest stands, and that 63% of grid-cells had a perfect estimate of N Ha , whereas a further 31% of the grid-cells overestimate or underestimate one tree within the search window. The parsimonious modelling framework allows for the two calibrated site-specific parameters to be predicted (R2: 0.87 and 0.66) using structural characteristics of vegetation clusters within sites. Using predictions of these two site-specific parameters across all sites results in mean F Score of 0.86 and mean TP of 0.77, under circumstances where no ground observations were required for calibration. This approach generalises the algorithm across new UAS LiDAR data without undertaking time-consuming ground measurements within tall eucalypt forests with complex vegetation structure. [ABSTRACT FROM AUTHOR]

Optimisation can assist in the management of riverine ecosystems through the exploration of multiple alternative management strategies, and the evaluation of trade-offs between conflicting objectives. In addition, it can facilitate communication and learning about the system. However, the effectiveness of optimisation in aiding decision making for ecological management is currently limited by four major challenges: identification and quantification of ecosystem objectives; representation of ecosystems in predictive simulation models; specification of objectives and management alternatives in an optimisation framework; and evaluation of model results against actual ecological outcomes. This study evaluates previous literature in ecology, optimisation and decision science, and provides a strategy for addressing the challenges identified. It highlights the need for better recognition and analysis of assumptions in optimisation modelling as part of a process that generates and shares knowledge. [ABSTRACT FROM AUTHOR]

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]

Inverse methods used in assessing landfill liner design have not yet taken advantage of current developments in inverse procedures. Here, a method for inverting contaminant transport models is presented including a general error model and procedures for differentially weighted multiple response regression. General error models are employed in cases where the residuals are heteroscedastic and correlated, and lead to valid inference on model parameter and predictive uncertainty. The Shuffled Complex Evolution algorithm is used to optimise model parameters. Model parameter uncertainty is assessed by exploring the posterior probability distribution with the Metropolis algorithm, a Markov chain Monte Carlo sampling method. The inverse method is applied to simultaneously determine the sorption and diffusion parameters from laboratory diffusion cell experiments. In these experiments, fluoride migration through kaolin clays was measured by sampling the source and collector cells over time. To uniquely determine the transport model parameters, it was necessary to simultaneously fit the observed data from two independent diffusion cell experiments with different initial concentrations. The jointly fitted transport model parameters compared well with those fitted to independent batch experiments. [Copyright &y& Elsevier]

The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts. [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]

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]

Abstract: The use of harvested rainwater in domestic hot water systems can result in optimised environmental and economic benefits to urban water cycle management, however, the water quality and health risks of such a scenario have not been adequately investigated. Thermal inactivation analyses were carried out on eight species of non-spore-forming bacteria in a water medium at temperatures relevant to domestic hot water systems (55–65°C), and susceptibilities to heat stress were compared using D-values. The D-value was defined as the time required to reduce a bacterial population by 90% or 1log reduction. The results found that both tested strains of Enterococcus faecalis were the most heat resistant of the bacteria studied, followed by the pathogens Shigella sonnei biotype A and Escherichia coli O157:H7, and the non-pathogenic E. coli O3:H6. Pseudomonas aeruginosa was found to be less resistant to heat, while Salmonella typhimurium, Serratia marcescens, Klebsiella pneumoniae and Aeromonas hydrophila displayed minimal heat resistance capacities. At 65°C, little thermal resistance was demonstrated by any species, with log reductions in concentration occurring within seconds. The results of this study suggested that the temperature range from 55 to 65°C was critical for effective elimination of enteric/pathogenic bacterial components and supported the thesis that hot water systems should operate at a minimum of 60°C. [Copyright &y& Elsevier]