From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s southern Murray-Darling Basin

The Murray-Darling Basin (MDB) is Australia's major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April-September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April-September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992-2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April-May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April-September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April-May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability

Analysis of a Southerly Buster Event and Associated Solitary Waves

This paper is a detailed case study of the southerly buster of October 6-7, 2015, along the New South Wales coast. It takes advantage of recently available Himawari-8 high temporal- and spatial-resolution satellite data, and other observational data. The data analyses support the widespread view that the southerly buster is a density current, coastally trapped by the Great Dividing Range. In addition, it appears that solitary waves develop in this event because the prefrontal boundary layer is shallow and stable. A simplified density current model produced speeds matching well with observational southerly buster data, at both Nowra and Sydney airports. Extending the density current theory, to include inertia-gravity effects, suggests that the solitary waves travel at speeds approximately 20% faster than the density current. This speed difference is consistent with the high-resolution satellite data, which shows the solitary waves moving increasingly ahead of the leading edge of the density current

Machine Learning Assessment of the Impact of Global Warming on the Climate Drivers of Water Supply to Australia’s Northern Murray-Darling Basin

Droughts and long dry spells, interspersed with intense rainfall events, have been characteristic of the northern Murray-Darling Basin (NMDB), a major Australian agricultural region. The NMDB precipitation results from weather systems ranging from thunderstorms to larger scale events. The larger scale events exhibit high seasonal and annual rainfall variability. To detect attributes shaping the NMDB precipitation patterns, and hence net water inflows to the vast Darling River catchment area, numerous (45) possible attributes were assessed for their influence on rainfall trends. Four periods were assessed: annual, April–May (early cool-season), June–September (remaining cool-season), and October–March (warm-season). Linear and non-linear regression machine learning (ML) methods were used to identify the dominant attributes. We show the impact of climate drivers on the increasingly dry April–May months on annual precipitation and warmer temperatures since the early 1990s. The NMDB water supply was further reduced during 1992–2018 by the lack of compensating rainfall trends for the April–May decline. The identified attributes include ENSO, the Southern Annular Mode, the Indian Ocean Dipole, and both local and global sea surface temperatures. A key finding is the prominence of global warming as an attribute, both individually and in combination with other climate drivers.</jats:p

A Case Study of a Co-Instructed Multidisciplinary Senior Capstone Project in Sustainability

As societal challenges involving sustainable development increase, the need to effectively integrate this inherently multidisciplinary topic into existing curricula becomes more pressing. Multidisciplinary, team-taught, project-based instruction has shown effectiveness in teaching teamwork, communication, and life-long learning skills, and appreciation for other disciplines. Unfortunately, this instruction mode has not been widely adopted, largely due to its resource-intensiveness. Our proposed co-instruction model of multidisciplinary senior project administration was tested to see if it could effectively teach sustainability topics and duplicate the known benefits of team-taught instruction, while overcoming its resource-intensiveness. A case study of a co-instructed senior project was undertaken with students and faculty from electrical and mechanical engineering, business, political science, and industrial design. The participating students were compared to the control group, i.e. students who chose to complete a traditional disciplinary senior project instead. Extensive assessment was performed with pre/post quizzes, online surveys, focus groups, and course deliverables. The multidisciplinary projects outperformed traditional senior projects in 4 out of the 5 participating courses. However, the students in the multidisciplinary project rated their satisfaction with the experience lower on average than the control group. A strong, positive correlation between students’ project satisfaction and rating of other instruction aspects (0.50 \u3c r \u3c 0.7, p \u3c 0.01) was discovered, which has implications for all project-based instruction. Participating faculty generally found the process illuminating and engaged in scholarship and creative endeavors as a result

Changes in frequency and location of east coast low pressure systems affecting southeast Australia

Low pressure systems off the southeast coast of Australia can generate intense rainfall and associated flooding, destructive winds, and coastal erosion, particularly during the cool season (April–September). Impacts depend on coastal proximity, strength and latitude. Therefore, it is important to investigate changes in frequency, duration, location, and intensity of these systems. First, an existing observation‐based database of these low pressure systems, for 1970–2006, is extended to 2019, focusing on April–September and using archived Australian Bureau of Meteorology MSLP charts. Second, data consistency between 1970 and 2006 and 2007 and 2019 is confirmed. Third, permutation testing is performed on differences in means and variances between the two 25‐year intervals 1970–1994 and 1995–2019. Additionally, trends in positions, durations and central pressures of the systems are investigated. p‐values from permutation tests reveal statistically significant increases in mean low pressure system frequencies. Specifically, a greater frequency of both total days and initial development days only, occurred in the latter period. Statistically significant lower variance for both latitude and longitude in systems that developed in both subtropical easterly and mid‐latitude westerly wind regimes indicate a shift south and east in the latter period. Furthermore, statistically significant differences in variance of development location of explosive low pressure systems that develop in a low level easterly wind regime indicate a shift further south and east. These changes are consistent with fewer systems projected to impact the east coast. Finally, important changes are suggested in the large scale atmospheric dynamics of the eastern Australian/Tasman Sea region

Aesthetic of prosthetic devices : from medical equipment to a work of design

Aesthetics of prosthesis design is a field of research investigating the visual aspect of the devices as a factor connected to the emotional impact in prosthetic users. In this chapter we present a revised concept of perception and use of prosthetic devices by offering a view of ‘creative product’ rather than ‘medical device’ only. Robotic-looking devices are proposed as a way of promoting a new and fresh perception of amputation and prosthetics, where ‘traditional’ uncovered or realis-tic devices are claimed not to respond with efficacy to the aesthetic requirements of a creative product. We aim to promote a vision for a change in the understand-ing of amputation - and disability in general - by transforming the concept of Dis-ability to Super-ability, and to propose the use of attractive-looking prosthetic forms for promoting this process

Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney’s water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney’s water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis was applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques, including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, Southern Annular Mode (SAM) and DMI, and measures of global warming such as the Tasman Sea sea surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data, while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insights into whether the season would be wet or dry compared to the climatology.</jats:p

Maintaining Indiana\u27s Urban Green Spaces: A Report from the Indiana Climate Change Impacts Assessment

Cities use green infrastructure, including forests, community gardens, lawns and prairies, to improve the quality of life for residents, promote sustainability and mitigate the effects of climate change. These and other kinds of green spaces can decrease energy consumption, increase carbon storage and improve water quality, among other benefits. More than 70 percent of Hoosiers reside in urban settings, and green infrastructure can provide significant economic advantages. In Indianapolis, for example, urban forests provide a $10 million annual benefit through stormwater control, carbon sequestration, energy reduction and air pollution filtration. However, just like human-built infrastructure, urban green infrastructure will be subject to the impacts of a changing climate, and its management must be considered as Indiana gets warmer and precipitation patterns change. This report from the Indiana Climate Change Impacts Assessment (IN CCIA) applies climate projections for the state to explore the potential threats to urban green infrastructure, and considers potential management implications and opportunities

Implications of climate change for managing urban green infrastructure in Indiana

Urban areas around the world are increasingly investing in networks of urban forests, gardens, and other forms of green infrastructure for its many benefits, including enhanced livability, sustainability, and climate change mitigation and adaptation. Proactive planning for climate change requires anticipating potential climate change impacts to green infrastructure and adjusting management strategies accordingly. We apply climate change projections for Indiana to assess the possible impacts of climate change on common forms of urban green infrastructure, and identify management implications. Projected changes in Indiana’s temperature and precipitation could pose numerous management challenges for managing urban green infrastructure, including water stress; pests, weeds, disease and invasive species; flooding; frost risk; and timing of maintenance. Meeting these challenges will involve managing for key characteristics of resilient systems (e.g. biodiversity, redundancy) as well as more specific strategies addressing particular climate changes (e.g. shifting species compositions, building soil water holding capacity). Climate change also presents opportunities to promote urban green infrastructure. Unlike human built infrastructure, green infrastructure is conducive to grassroots stewardship and governance, relieving climate change-related strains on municipal budgets. Many online resources for adapting urban green infrastructure to climate change are already available, and emerging research will enhance understanding of best management practices