Search Results (15)

This paper uses a 30 m record of valley alluviation in the Lockyer Creek, a major tributary of the mid-Brisbane River in Southeast Queensland, to document the timing and nature of Quaternary fluvial response. A combination of radiocarbon and optically stimulated luminescence dating reveals a sequence of major cut and fill episodes. The earliest aggradation phase is represented by a basal gravel unit, dating to ~220 ka (marine isotope sub-stage 7d), and although little evidence supports higher fluvial discharges during MIS 5, a MIS 3 fluvial episode characterised by incision and aggradation dates to ~60 ka. A penultimate phase of incision to a depth of 30 m prior to ~14 ka saw the lower Lockyer occupy its current position within the valley floor. The Lockyer Creek shows evidence of only minor fluvial activity during MIS 2, suggesting a drier LGM climate. The appearance of alternating fine- and coarse-grained units at about 2 ka is notable and may represent higher-energy flood conditions associated with a strengthening of El Niño Southern Oscillation activity as observed in the flood of 2011. The aggradation rate for this Holocene floodplain unit is ~11 times higher than the long-term rate. Full article

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for multiple temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment, and no attention was paid to the catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.05 × 0.05° (5 × 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs were evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results revealed that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions showed comparatively better performances as opposed to the models of coarse spatial resolutions, where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Based on the performances, models were grouped into three categories, where models (MIROC4h, EC-EARTH, and MRI-CGCM3) with high performances fell in the first category, and middle (MPI-ESM-LR and MPI-ESM-MR) and comparatively low-performing models (MIROC5, CanCM4, and CMCC-CM) fell in the second and third categories, respectively. To compare the performances of multi-model ensembles’ mean (MMEMs), three MMEMs were formed. The arithmetic mean of the first category formed MMEM1, the second and third categories formed MMEM2, and all eight models formed MMEM3. The performances of MMEMs were also assessed using the same skill tests, and MMEM2 performed best, which suggests that evaluation of models’ performances is highly important before the formation of MMEM. Full article

Ross River virus (RRV) is the most common mosquito-borne disease in Australia, with Queensland recording high incidence rates (with an annual average incidence rate of 0.05% over the last 20 years). Accurate prediction of RRV incidence is critical for disease management and control. Many factors, including mosquito abundance, climate, weather, geographical factors, and socio-economic indices, can influence the RRV transmission cycle and thus have potential utility as predictors of RRV incidence. We collected mosquito data from the city councils of Brisbane, Redlands, and Mackay in Queensland, together with other meteorological and geographical data. Predictors were selected to build negative binomial generalised linear models for prediction. The models demonstrated excellent performance in Brisbane and Redlands but were less satisfactory in Mackay. Mosquito abundance was selected in the Brisbane model and can improve the predictive performance. Sufficient sample sizes of continuous mosquito data and RRV cases were essential for accurate and effective prediction, highlighting the importance of routine vector surveillance for disease management and control. Our results are consistent with variation in transmission cycles across different cities, and our study demonstrates the usefulness of mosquito surveillance data for predicting RRV incidence within small geographical areas. Full article

Flooding has become one of the most dangerous and expensive disasters due to urbanization and climate change. Tools for assessing flood impact are required to support the shift of flood mitigation management from post-disaster recovery and reconstruction to community-driven pre-disaster warning and preparation. This study aims to develop an integrated approach to spatially assess the economic and social losses and ecological gain and identify the geographical factors of locations with high impacts of floods in Brisbane using the datasets collected from both the 2011 and 2022 flood events. Water depth, inundated area, land cover, ecosystem service value, mortality, and morbidity were considered to assess flood impacts. It is found that downstream (above 23,500 m from the upper stream) riverside communities (within 800 m of the river) with low altitudes (below 15 m) are more likely to experience significant flood damage. Flood impacts have bell-shaped developments with elevation and direct distance to the upstream river source and an exponential decline with distances to the river. These findings have implications for formulating future urban land use and community-tailored mitigation strategies, particularly for flood warning and preparation. Full article

We conducted a spatial and temporal analysis of naturally occurring retirement communities (NORCs) in the Greater Brisbane region using the latest ABS Census 2021 data. Four methods of spatial analysis were employed to identify the distribution and evolution of NORCs: (i) geovisualisation, (ii) spatial autocorrelation, (iii) cluster and outlier analysis, and (iv) hotspot and cold spot analysis. The findings from this data analysis are consistent with previous research findings that NORCs are developing at a fast pace and are concentrated along the Brisbane River and coastline areas, where an increasing number of older people are relocating for better ageing in place, i.e., ageing at home in the community as long as possible. In addition, the spatial distribution of NORCs is characterized by a preference for cluster, with most of the NORC population located in coastal areas. Furthermore, older people moving out and younger people moving in are the primary reasons why the city and the south area are becoming cold spots. The findings of this study will provide practical implications for various stakeholders to assist older Australians in ageing in place as long as they desire by developing age-friendly community environments. Full article

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function. Full article

The nature of the increasingly ageing populations of developed countries places residential issues of these populations at the heart of urban policy. Retirement villages as housing options for older adults in Australia has been growing steadily in recent years; however, there have been a dearth of geographical studies looking into the distribution of existing retirement villages at the regional level. This study aims to reveal the geographical distributions and cluster patterns of retirement villages in the Greater Brisbane Region of Australia to better understand and serve the living requirements of current and potential retirement village residents. The geovisualization method was adopted to visually explore the distribution patterns of retirement villages. The Global Moran’s I and Local Moran’s I measures were employed to analyze the spatial correlation and the clusters of retirement villages in the study region. The study revealed that distribution of retirement villages was not random (z-score = 7.11; p < 0.001), but clustered in nature and included hotspot patterns, especially along the coastline and Brisbane River areas. Moreover, for-profit and not-for-profit retirement villages have different distribution patterns and adopted significantly different tenure agreements. In the study region, the spatial distribution of retirement villages aligns with the aggregation trend of older residents. The findings of this study disclosed the spatial distribution patterns of retirement villages and will provide developers and policymakers with geographically referenced data for the choice of new development sites to meet the market demand of potential customers, forming aged-friendly development strategies, and eventually leading to improved quality of life for older Australians. Full article

As most older Australians prefer to age-in-place, providing sustainable and age-friendly communities poses a significant challenge to urban policymakers. The naturally occurring retirement communities (NORCs) have organically emerged as a collaborative model of care to support older adults to age-in-place, but neither academic research nor government policies recognise this housing option for older Australians. This paper aims to analyse the distributions and temporal patterns of NORCs in the Greater Brisbane Region, Australia, to understand the formation and development of NORCs. The geovisualisation method was employed to identify the distribution changes of NORCs between 2006 and 2016. The Global Moran’s I and Local Moran’s I measures were utilised to analyse the spatial correlation and the clusters of NORCs. The results show that NORCs increased significantly from 2006 to 2016, and their distribution was mainly clustered or co-located along the coastline and Brisbane River areas. The evolvement of NORCs reflected the change of aggregation pattern of older population between 2006 and 2016. Understanding the distribution trend of NORCs informs government policy and decisions in addressing issues of service delivery and community cooperation, and eventually leads to sustainable urban development and successful ageing in place for older Australians. Full article

Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods. Full article

This study examines an innovative Coastal Reservoir (CR) technique as a feasible solution for flood adaptation and mitigation in the Brisbane River Estuary (BRE), Australia, which is vulnerable to coastal flooding. The study analysed the operation of a CR by using the MIKE 21 hydrodynamic modelling package. The 2D hydrodynamic model was calibrated and validated for the 2013 and 2011 flood events respectively, with a Nash-Sutcliffe coefficient (E_ns) between 0.87 to 0.97 at all gauges. River right branch widening and dredging produced a 0.16 m reduction in water level at the Brisbane city gauge. The results show that by suitable gate operation of CR, the 2011 flood normal observed level of 4.46 m, with reference to the Australian Height Datum (AHD) at Brisbane city, could have been reduced to 3.88 m AHD, while under the improved management operation of the Wivenhoe Dam, the flood level could be lowered to 4 m AHD at Brisbane city, which could have been reduced with CR to 2.87 m AHD with an overall water level reduction below the maximum flood level. The results demonstrated that the innovative use of a CR could considerably decrease the overall flood peak and lessen flood severity in the coastal city of Brisbane. Full article

Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually. Full article

Bottled water is one sector of the beverage industry that has recently experienced substantial growth. The littering of plastic water bottles and the carbon emissions produced from bottled water production results in harmful effects on the environment. To reduce the harm of bottled water production and litter, government and non-government organisations have implemented litter abatement and behavioural change strategies targeting bottled water consumption and subsequent loss of bottles to the environment. Our study evaluated the success of one of these strategies, which is a filtered water refill station, implemented along the Brisbane River in Queensland, Australia. We found plastic bottle litter decreased after a water refill station was put into operation. However, given the location of the refill station, we suggest the behavioural change strategy employed did not reach its full potential. We highlight factors that could be employed to achieve maximum benefits when implementing similar behavioural change strategies. Full article

A multi-omics approach was applied to an urban river system (the Brisbane River (BR), Queensland, Australia) in order to investigate surface water quality and characterize the bacterial population with respect to water contaminants. To do this, bacterial metagenomic amplicon-sequencing using Illumina next-generation sequencing (NGS) of the V5–V6 hypervariable regions of the 16S rRNA gene and untargeted community metabolomics using gas chromatography coupled with mass spectrometry (GC-MS) were utilized. The multi-omics data, in combination with fecal indicator bacteria (FIB) counts, trace metal concentrations (by inductively coupled plasma mass spectrometry (ICP-MS)) and in-situ water quality measurements collected from various locations along the BR were then used to assess the health of the river ecosystem. Sites sampled represented the transition from less affected (upstream) to polluted (downstream) environments along the BR. Chemometric analysis of the combined datasets indicated a clear separation between the sampled environments. Burkholderiales and Cyanobacteria were common key factors for differentiation of pristine waters. Increased sugar alcohol and short-chain fatty acid production was observed by Actinomycetales and Rhodospirillaceae that are known to form biofilms in urban polluted and brackish waters. Results from this study indicate that a multi-omics approach enables a deep understanding of the health of an aquatic ecosystem, providing insight into the bacterial diversity present and the metabolic output of the population when exposed to environmental contaminants. Full article

On 13th January 2011 major flooding occurred throughout most of the Brisbane River catchment, most severely in Toowoomba and the Lockyer Creek catchment (where 23 people drowned), the Bremer River catchment and in Brisbane, the state capital of Queensland. Some 56,200 claims have been received by insurers with payouts totalling $2.55 billion. This paper backgrounds weather and climatic factors implicated in the flooding and the historical flood experience of Brisbane. We examine the time history of water releases from the Wivenhoe dam, which have been accused of aggravating damage downstream. The dam was built in response to even worse flooding in 1974 and now serves as Brisbane’s main water supply. In our analysis, the dam operators made sub-optimal decisions by neglecting forecasts of further rainfall and assuming a ‘no rainfall’ scenario. Questions have also been raised about the availability of insurance cover for riverine flood, and the Queensland government’s decision not to insure its infrastructure. These and other questions have led to Federal and State government inquiries. We argue that insurance is a form of risk transfer for the residual risk following risk management efforts and cannot in itself be a solution for poor land-use planning. With this in mind, we discuss the need for risk-related insurance premiums to encourage flood risk mitigating behaviours by all actors, and for transparency in the availability of flood maps. Examples of good flood risk management to arise from this flood are described. Full article

Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. Full article