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As a trending mobility choice, e-scooters have become popular in many cities. A number of authorities have initiated shared e-scooter trial schemes to assess the feasibility of the vehicles prior to enacting official legalisation. This paper aims to provide an evidence review of shared e-scooters and investigate how existing evidence may inform long-term policies. This carries significant relevance for jurisdictions that are in a conflicting position with e-scooters, such as New South Wales (NSW), Australia whose context motivates this study. The evidence review focuses on three themes derived from experience with shared e-scooters within the broader micromobility landscape, namely: safety; where shared e-scooters fit into the modal landscape; and the environmental impacts. Findings confirm that ensuring the safety of shared e-scooters requires complex solutions, which may include a clear regulatory framework for e-scooters, safety education and skill training, innovative data collection and analysis methods, and an approach to safety management that is user-based, location-based, and time-based. In terms of modal fit policymakers should encourage first and last-mile combinations with public transport, with consideration of user characteristics; while climate impact is strongly correlated to the mode replaced by e-scooter trips. The paper provides insights for policymakers on the regulation and positioning of shared e-scooters. • Many jurisdictions are struggling to determine policies for e-scooters. • Existing e-scooter evidence sheds light on possible long-term policies. • Safety management should consider different users, locations, and times. • First and last-mile combinations with public transport should be encouraged. • Climate impact is strongly correlated to the mode replaced by e-scooter trips. [ABSTRACT FROM AUTHOR]

Supporters of hydrogen energy urge scaling up technology and reducing costs for competitiveness. This paper explores how hydrogen energy technologies (HET) are perceived by Australia's general population and considers the way members of the public imagine their role in the implementation of hydrogen energy now and into the future. The study combines a nationally representative survey (n = 403) and semi-structured interviews (n = 30). Results show age and gender relationships with self-reported hydrogen knowledge. Half of the participants obtained hydrogen information from televised media. Strong support was observed for renewable hydrogen, while coal (26%) and natural gas (41%) versions had less backing. Participants sought more safety-related information (41% expressed concern). Most felt uncertain about influencing hydrogen decisions and did not necessarily recognise they had agency beyond their front fence. Exploring the link between political identity and agency in energy decision-making is needed, with energy democracy a potentially productive direction. • Hydrogen's strongest appeal to Australians is its benefit for climate change mitigation. • Although widely accepted, there is limited knowledge about hydrogen in terms of how it is produced or used. • The more people know about hydrogen, the more willing they are to participate in the decision-making process. [ABSTRACT FROM AUTHOR]

Estimates of extreme sea levels have mainly been derived using atmospheric and tidal forcing only, but surface gravity waves are a key process that can cause a substantial elevation of mean sea level at the coast. In this paper a coupled wave-circulation model was used to simulate storm surge and wave setup across a broad range of coastal topographies and storm types around Australia. The main aim was a practical assessment of the benefits and limitations of using a coupled wave-surge model to determine wave effects for extreme sea level studies on the continental scale. The simulations included: tropical cyclones Yasi (2011) and George (2007) on the northeast and northwest coasts of Australia; Cyclone Alby (1978) that underwent extratropical transition in southwest Australia; and a large extratropical winter storm in the Southern Ocean (2016). Predicted wave setup was highly variable both spatially and temporally, with values up to 0.45 m, 35% of maximum storm surge height, and 9% of incident significant wave heights. In general, where storm surge heights were smaller, the relative importance of wave setup increased in the presence of large waves. The model best resolved wave setup in shallow gently sloping coastal areas, whilst unrealistically low values were predicted for steep coastlines. Predictions improved when wave setup was included for most sites and events, however, these wave effects were often secondary to other assumptions implicit in modeling extreme events. This was especially true for tropical cyclones where reliable wind forcing was not available. The results showed similar contributions from wave setup to other studies but highlighted the high temporal and spatial variability of wave setup across a wider variety of storms and coastal morphologies. The results also revealed that the maximum wave setup did not always coincide with the maximum storm surge. Overall, the coupled wave-surge model presented a useful tool to predict extreme water levels including wave setup when accurate depth and atmospheric data are available, however, significant challenges still exist to model this process at the continental scale. [Display omitted] • Continental scale coupled wave storm surge model was applied to examine extreme events around Australia. • Maximum wave setup preceded arrival of slow-moving storms and co-occurred with fast-moving storms. • Predicted wave setup contributed up to 35% of total storm surge. [ABSTRACT FROM AUTHOR]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]