Search

• Qualitative outcomes of energy poverty across Australian climate zones are compared. • Outcomes influenced by energy costs and local energy, housing and welfare policies. • Measures of energy poverty and policy responses need to reflect local contexts. Academic interests into energy poverty have been growing since the 1990s. These have ranged from quantitative measures and qualitative reflections, as well as ones that aimed to provide more nuanced definitions. These studies have, however, typically focused on single jurisdictions or across national borders, with very few examining the issue across different climatic and policy contexts within the same nation. This paper aims to address this gap by focussing on differences in climate conditions, local policies and quality of the housing stock across Australia and reflects on how these factors each played a role, to varying degrees, in the prevalence of energy poverty amongst lower-income households across eight metropolitan and regional settings conducted as part of a recent study in Australia. Through a review of focus group, interview and workshop data, it especially highlights the more qualitative impacts of energy poverty, and how each of these factors come to influence the extent to which lower-income households experience energy poverty and the support they were able to access. Additional to the need for more contextualised measures of energy poverty, this paper calls for localised responses to alleviate the negative outcomes reflected by our participants in regard to both energy and housing policies. [ABSTRACT FROM AUTHOR]

• Machine learning algorithms have been applied to estimate wheat yield for Australia. • Combining climate and satellite data achieves high performance for yield prediction. • The unique and overlapping information of climate and satellite data is quantified. • Optimal prediction performance can be achieved two-month lead time before maturity. Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction. [ABSTRACT FROM AUTHOR]

Abstract: Building adaptive reuse plays a critical role in emissions reduction and supports global climate protection. Thus, the designing of future buildings with embedded adaptive reuse potential is a useful criterion for sustainability. This paper describes the development of a new rating tool known as adaptSTAR, which offers holistic and unified design criteria suitable for assessing the adaptive reuse potential of future buildings. The findings show that criteria can be identified and weighted according to physical, economic, functional, technological, social, legal and political categories to calculate an adaptive reuse star rating. In addition, this paper reports on the first stage of the research methodology used in the initial development of the rating tool and concludes with some preliminary observations from 12 selected successful case studies in New South Wales (NSW) and Melbourne, Australia. The use of adaptSTAR in designing future buildings will lead and help promote low carbon built environments. [Copyright &y& Elsevier]

In the last decade, multiple studies focusing on national-scale assessments of the ocean wave energy resource in Australia identified the Southern Margin to be one of the most energetic areas worldwide suitable for the extraction of wave energy for electricity production. While several companies have deployed single unit devices, the next phase of development will most likely be the deployment of parks with dozens of units, introducing the risk of conflicts within the marine space. This paper presents a geo-spatial multi-criteria evaluation approach to identify optimal locations to deploy a wave energy farm while minimizing potential conflicts with other coastal and offshore users. The methodology presented is based around five major criteria: ocean wave climatology, nature of the seabed, distance to key infrastructure, environmental factors and potential conflict with other users such as shipping and fisheries. A case study is presented for an area off the south-east Australian coast using a total of 18 physical, environmental and socio-economic parameters. The spatial restrictions associated with environmental factors, wave climate, as well as conflict of use, resulted in an overall exclusion of 20% of the study area. Highly suitable areas identified ranged between 11 and 34% of the study area based on scenarios with varying criteria weighting. By spatially comparing different scenarios we identified persistence of a highly suitable area of 700 km 2 off the coast of Portland across all model domains investigated. We demonstrate the value of incorporation spatial information at the scale relevant to resource exploitation when examining multiple criteria for optimal site selection of Wave Energy Converters over broad geographic regions. [ABSTRACT FROM AUTHOR]

Summary One of the fundamental steps in regional rainfall frequency analysis is deciding the method by which rainfall stations are to be grouped together to form regions. This paper compares three methods of forming regions for use in estimating design rainfalls: a fixed region approach where all the available sites are included in a single region, a Region of Influence (ROI) approach based on geographical proximity and a hybrid approach where sites with similar topographic orientations are grouped together. The three region types were implemented in a Bayesian Generalized Least Squares Regression (BGLSR) framework which leads to regionalized regression equations that can be used to predict rainfall L-moments at ungauged sites. A leave-one-out cross validation approach was used to compare the relative accuracy, reliability and uncertainty of the derived rainfall statistics and resulting estimates of the rainfall quantiles. The study used data from two areas of Australia chosen for their highly varied topography and different climatic influences. It was found that all three methods provided good estimates of the L-moment statistics and the rainfall quantiles. The hybrid approach produced the smallest errors in the South-East Queensland region whilst for the Tasmanian region the fixed region approach was best. The results from this study show that although there is a slight benefit in using the proposed hybrid approach for BGLSR, these benefits were minor compared to maximizing the number of stations used to calibrate the BGLSR equations. This conclusion regarding the number of stations could be tested in future work by repeating the analyses in areas with sparser station density. Another test could be to simulate reduced station coverage in the current study areas by leaving stations out of the analyses. Finally it would be interesting to see if similar results are obtained by expanding the study area so that different climatological regimes are assessed. [ABSTRACT FROM AUTHOR]

Estimates from the FAO Penman–Monteith (FAO-PM) and the standardized ASCE Penman–Monteith (ASCE-PM) hourly and daily reference evapotranspiration ( ET 0 ) equations were compared at daily scale, based on the hourly climate data collected from forty (40) geographically and climatologically diverse Automatic Weather Stations (AWS) across the Australian continent. These locations represent 23 agricultural irrigation areas in tropical, arid and temperate climates. The aims of this paper are to: compare the effects of different methods of estimating Clear-sky-radiation—( R so ); compare sum-of-hourly and daily ET 0 ; compare the results of aggregation of hourly ET 0 over 24 h compared with daylight hours; and examine the impact of seasonality and climate type. At selected AWS locations, the hourly ET 0 was calculated using the hourly FAO-PM and the ASCE-PM equations and then summed to derive daily ET 0 (reported as ET 0,soh ). This was compared against the daily ET 0 values, calculated using the corresponding daily equation (reported as ET 0,daily ). Using R so calculated following the “complex” approach improves the agreement between ET 0,soh and ET 0,daily of both hourly equations, compared with the “simple” approach. Better agreement between ET 0,soh and ET 0,daily estimates for the FAO-PM and ASCE-PM were found, when the hourly values are aggregated over 24 h rather than over daylight hours. The average ratio between ET 0,soh and ET 0,daily for the FAO-PM and ASCE-PM equations is 0.95 and 1.00, respectively. The range of the former is 0.90–0.98 and that of the latter is 0.96–1.04. There was very strong correlation between the two hourly equations at the daily time step: on average 0.997, with a range of 0.993–0.998. The results imply that the ASCE-PM hourly equation's daily ET 0 values are higher than those of FAO-PM, which can be explained by the difference in the treatment of surface resistances. Better agreements between ET 0,soh and ET 0,daily values for winter, spring and autumn were found for the FAO-PM version, while during summer, the ASCE-PM version showed better agreement. The best agreement between the hourly and daily results for the FAO-PM version was found in temperate climates and the ASCE-PM version showed best agreement in the tropical and arid climates. [ABSTRACT FROM AUTHOR]

Climate models use broad definitions of vegetation-based biophysical parameters that may not represent the continentally-specific nature of Australian vegetation well. This paper explores the impact on the January simulation of climate over Australia of this common simplification. First, we map the Australian distribution of vegetation types onto the default classification used in one high-resolution climate model. Second, through a search of the literature we chose replacement values for the biophysical parameters to better reflect the properties of eucalypts. Third, we choose several sets of biophysical parameters and allow these to vary regionally. We assess the impact of these changes on simulated rainfall, temperature and latent heat flux over an ensemble of six different Januaries. We find that the model simulates rainfall and temperature reasonably well over Australia in January and that replacing the default parameter set with a single set of more appropriate values degrades model performance slightly. Allowing the biophysical parameters to vary regionally leads to some small improvements in the simulation of temperature and precipitation. We find large impacts on the simulated latent heat flux. Overall, the model is not substantially improved by careful selection of eucalypt parameters. We comment that given the shortage of observed data on eucalypts and the almost total absence of biophysical parameters for other Australian vegetation types, this lack of sensitivity to the biophysical parameters is reassuring since it implies that the lack of data is not presently seriously limiting to our particular modelling capability. [Copyright &y& Elsevier]

Across much of the world, wind gust data are continuously measured by Automatic Weather Stations (AWS). However, the meteorological origin of individual extreme gust events within these datasets are seldom automatically assigned. To overcome this, Self-Organizing Maps (SOM) are proposed here as an automated tool to classify gust events within 1-min AWS data. In this paper, this method is specifically used to distinguish between wind gust events of convective (often broadly termed non-synoptic or thunderstorm) and non-convective origin, with the latter events further sub-classified as either, wind only, transition, or other. The efficacy of a range of different SOMs and input variables were assessed, and it was found that those that utilised gust wind speed, temperature, and pressure generally outperformed other models when classifying wind gusts. Applying this approach in the Australian context, all wind gusts of convective origin greater than 70 ​km ​h−1 (19.4 ​m ​s−1) and 90 ​km ​h−1 (25 ​m ​s−1) were identified at 306 AWS across Australia and a climatology of seasonal and annual convective wind gust occurrence developed and discussed. • Gusts from 1-min weather station data are categorised using machine-learning. • Self-Organizing Maps are shown to categorise storm modes accurately and efficiently. • Convective gusts are best classified using wind speed, pressure and temperature data. • Convective gust climatology developed for Australia. [ABSTRACT FROM AUTHOR]