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Melbourne, Australia faced fourteen consecutive years of below average rainfall before drought breaking rains in 2010. Melbournians are also concerned about the significant increase in potable water price in the future where the cost is expected to increase by 100% in 5 years. Stormwater harvested using rainwater tanks is an alternative source of nonpotable water source where potable quality water is not required for use.

The popularity of rainwater use in Australia depends completely on the individual householder's preference. The quality of reticulated water supplies in major cities of Australia is far superior to water stored in rainwater tanks. However, due to persistent drought and the implementation of stringent water restrictions, cities such as Melbourne have encouraged the use of rainwater harvesting within the property. The benefits of trapping stormwater within a property and using it effectively also reduce polluted runoff excess reaching receiving water. The study reported herein focuses on the effectiveness of rainwater tanks as a potential water sensitive urban design element used to manage stormwater using the MUSIC model. The study shows that the installation of a 3 kL tank reduces hydraulic loading by 75%, Total Suspended Solids by 97%, Total Phosphorous by 90% and Total Nitrogen by 81% if the rainwater stored in the tank is used to meet the indoor demand (toilet flushing and laundry use) as well as the outdoor demand (garden watering).

BACKGROUND Water footprint assessment is essential for the evaluation of water scarcity that considers both direct and indirect water consumption along the supply chain. This paper presents the estimation of water footprint for locally grown fruits and vegetables in Australia. Water footprint was calculated based on the framework developed in the Water Footprint Assessment Manual for the crops which are the most practicable to grow in Australia. Nine different crops (apples, grapes, tomatoes, oranges, peaches/nectarines, cherries, potatoes, carrots/turnips and almonds) in the agricultural industry were selected and identified as the most water‐consumptive crop and least water‐consumptive crop. For each type of crop, the three main water footprint components (blue, green, and grey water) were calculated. RESULTS It was found that almond had the highest water footprint (6671.96 m3 ton−1) and tomato had the lowest water footprint (212.24 m3 ton−1) in Australia. From the global comparison, it is revealed that total water footprint for Australian crops is much higher than the corresponding international average values, except for tomatoes, potatoes and almonds. Also, almonds had the highest water footprint among the nine crops investigated. CONCLUSION The study provides an insight into future sustainable cropping patterns in Australia, which suggest that tomatoes, carrots/turnips, potatoes and apples should continue to be grown in Australia, whereas stone fruit (e.g., almonds) should no longer be grown because of its high water footprint. © 2020 Society of Chemical Industry

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Generalised extreme value (GEV) distribution is traditionally applied to model extreme event and their return period. There are three parameters (location, scale and shape) in GEV distribution, which needs to be determined before its application. Different techniques have been developed to estimate the parameters of the GEV distribution. There is no specific guidance regarding the optimal method for estimating the parameters of the GEV distribution. This paper investigated the sensitivity of different parameters estimation techniques which are being commonly used in the application of the GEV distribution. Stationary GEV was adopted for the homogeneous data sets; whereas, non-stationarity GEV was implemented for the nonhomogeneous data sets. Four methods were applied in the estimation of the GEV distribution parameters for four different timescales. The methods were applied in extreme rainfall modelling using extreme rainfall data in Tasmania, Australia as a case study. It was found that adoption of any GEV parameter estimation methods does not change the GEV type in Tasmanian extreme rainfall. The length of the data series has significant influence on the values of the GEV distribution parameters. The Fréchet type GEV distribution is suitable in most of the analysed rainfall stations in Tasmania.

Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.