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Urbanization, changes in land use and land cover (LULC), and an increase in population collectively have significant impacts on urban catchments. However, a vast majority of LULC studies have been conducted using readily available satellite imagery, which often presents limitations due to its coarse spatial resolution. Such imagery fails to accurately depict the surface characteristics and diverse spectrum of LULC classifications contained within a single pixel. This study focused on the highly urbanized Dry Creek catchment in Adelaide, South Australia and aimed to determine the impact of urbanization on spatiotemporal changes in LULC and its implications for the land surface condition of the catchment. Very high spatial resolution imagery was utilized to examine changes in LULC over the past four decades. Support Vector Machine-learning-based image classification was utilized to classify and identify the changes in LULC over the study area. The classification accuracy showed strong agreement, with a kappa value greater than 0.8. The findings of this analysis showed that extensive urban development, which expanded the built-up area by 34 km2, were responsible for the decline in grass cover by 43.1 km2 over the last 40 years (1979–2019). Moreover, built-up areas, plantation, and water features, in contrast to grass cover, have demonstrated an increasing trend during the study period. The overall urban expansion over the study period was 136.6%. Urbanization intensified impervious area coverage, increasing the runoff coefficient, equivalent impervious area, and curve number by 60.6%, 60.6%, and 7.9%, respectively, while decreasing the retention capacity by 38.6%. These modifications suggest a potential variability in catchment surface runoff, prompting the need for further research to understand the surface runoff changes brought by the changes in LULC resulting from urbanization. The findings of this study can be used for land use planning and flood management. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort is influenced by people's climate expectations, perceptions and adaptation capacity. Varied individual response to comfortable or stressful thermal environments results in a deviation between actual outdoor thermal activity choices and those predicted by thermal comfort indices. This paper presents a passive activity observation (PAO) method for estimating contextual limits of outdoor thermal adaptation. The PAO method determines which thermal environment result in statistically meaningful changes may occur in outdoor activity patterns, and it estimates thresholds of outdoor thermal neutrality and limits of thermal adaptation in public space based on activity observation and microclimate field measurement. Applications of the PAO method have been demonstrated in Adelaide, Melbourne and Sydney, where outdoor activities were analysed against outdoor thermal comfort indices between 2013 and 2014. Adjusted apparent temperature (aAT), adaptive predicted mean vote (aPMV), outdoor standard effective temperature (OUT_SET), physiological equivalent temperature (PET) and universal thermal comfort index (UTCI) are calculated from the PAO data. Using the PAO method, the high threshold of outdoor thermal neutrality was observed between 24 °C for optional activities and 34 °C for necessary activities (UTCI scale). Meanwhile, the ultimate limit of thermal adaptation in uncontrolled public spaces is estimated to be between 28 °C for social activities and 48 °C for necessary activities. Normalised results indicate that city-wide high thresholds for outdoor thermal neutrality vary from 25 °C in Melbourne to 26 °C in Sydney and 30 °C in Adelaide. The PAO method is a relatively fast and localised method for measuring limits of outdoor thermal adaptation and effectively informs urban design and policy making in the context of climate change. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, ). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, ), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. [ABSTRACT FROM AUTHOR]

Precise prediction of wave energy is indispensable and holds immense promise as ocean waves have a power capacity of 30–40 kW/m along the coast. Utilising this energy source does not generate harmful emissions, making it a superior substitute for fossil fuel-based energy. The computational expense associated with simulating and computing intricate hydrodynamic interactions in wave farms restricts optimisation methods to a few thousand evaluations and makes a challenging situation for training in deep neural prediction models. To address this issue, we propose a new solution: a Meta-learner gradient boosting method that employs four multi-layer convolutional dense neural network surrogate models combined with an optimised extreme gradient boosting. In order to train and validate the predictive model, we used four wave farm datasets, including the absorbed power outputs and 2D coordinates of wave energy converters (WECs) located along the southern coast of Australia, Adelaide, Sydney, Perth and Tasmania. Furthermore, the capability of the transfer learning strategy is evaluated. The WECs used in this study are of the fully submerged three-tether converter type, similar to the CETO prototype. The effectiveness of the proposed approach is assessed by comparing it with 15 well-established and effective machine learning (ML) methods. The experimental findings indicate that the proposed model is competitive with other ML and deep learning approaches, exhibiting considerable accuracy of 88.8%, 90.0%, 90.3%, and 84.4% in Adelaide, Perth, Sydney and Tasmania and improved robustness in predicting wave farm power output. [ABSTRACT FROM AUTHOR]

Heatwaves are the most dangerous natural hazard to health in Australia. The frequency and intensity of heatwaves will increase due to climate change and urban heat island effects in cities, aggravating the negative impacts of heatwaves. Two approaches exist to develop population heat stress resilience. Firstly, the most vulnerable social groups can be identified and public health services can prepare for the increased morbidity. Secondly, the population level of adaptation and the heat stress resistance of the built environment can be increased. The evaluation of these measures and their efficiencies has been fragmented across research disciplines. This study explored the relationships between the elements of heat stress resilience and their potential demographic and housing drivers and barriers. The responses of a representative online survey ( N = 393) about heat stress resilience at home and work from Adelaide, South Australia were analysed. The empirical findings demonstrate that heat stress resistant buildings increased adaptation capacity and decreased the number of health problems. Air-conditioning increased dependence upon it, limited passive adaptation and only people living in homes with whole-house air-conditioning had less health problems during heatwaves. Tenants and respondents with pre-existing health conditions were the most vulnerable, particularly as those with health conditions were not aware of their vulnerability. The introduction of an Energy Performance Certificate is proposed and discussed as an effective incentive to increase the heat stress resistance of and the general knowledge about the built environment. [ABSTRACT FROM AUTHOR]

A significant shift towards consolidating residential neighbourhoods has dramatically influenced the Australian national urban tree canopy benchmark. Recurrent tree planting, in densely settled residential suburbs, is an insufficient, emerging environmental and long-term energy conservation strategy. A global and Australian original research review, spanning the past two decades, reveals a better understanding of the link between trees and the built environment. This review defines potential tree allocation parameters, in urban energy conservation, within residential landscape constraints. This assessment focuses on regions similar to various Australian temperate to sub–tropical climate zones, defined as Mediterranean climate type by Köppen climate classification. Based on this review, the paper then identifies the importance of residential tree requirements and energy demand credibility projections as an amendment to existing metropolitan guidelines, using Sydney, Adelaide, and Perth in Australia as an example. Currently, Australian residential planning and design codes, and landscaping software action-codes, like iTree canopy software or Tree Planting Predictor Tool, do not consider residential tree arrangement. These parameters unify the building-energy assessment scheme with an optimal residential tree arrangement concept, leading to implementable residential development plans. This optimisation, primarily optimal residential tree arrangement, provides housing designers with ideal tree allocation data to yield the greatest effect. In addition, this optimal residential tree arrangement model will transform how researchers measure future urban canopy cover performance. • A comprehensive review of urban tree canopy requirements to promote energy efficiency in residential neighbourhood. • Identification of residential tree distribution in reducing cooling and heating energy usage to cut urban carbon emissions. • Understanding the asset of residential trees strengthening Australian suburban redevelopment policies. • Summarised the effectiveness of tree arrangement parameters in medium-density residential projects. • Development of a framework in arranging residential trees to revaluate the dwelling energy efficiency. [ABSTRACT FROM AUTHOR]

Concentrations of building mass and insufficient greenery in cities are identified to contribute significantly to extended heat stress in the built environment, commonly known as the urban heat island (UHI) effect. This paper presents a scenario-based modelling built on climate change projections and potential alterations of urban greenery in 2030 and 2090 in Adelaide, South Australia. The model is based on regional climate change scenarios, potential urban surface cover alternatives and resulted urban microclimate variations in Adelaide. Projected energy demand variations and corresponding carbon emission are calculated in each scenario. Results indicate that an ideal urban landscape transformation scenario with 30% tree canopy can effectively decrease the surface temperature by 1 °C in winter and 3 °C in summer by 2090. It is estimated that having greener and more heat resilient public spaces could save a total carbon emission of 140,000 tone CO2e in Adelaide annually compared to a business-as-usual scenario. Urban greenery may be used as a mean of increased urban life resilience to climate change by reducing surface temperature, increasing urban resilience and decreasing the energy demand during summer. [ABSTRACT FROM AUTHOR]