Infrastructure Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 23
-
ItemNo Preview AvailableShifts in stream salt loads during and after prolonged droughts(WILEY, 2023-06)Abstract It has been widely assumed that after prolonged droughts, catchment runoff recovers to pre‐drought levels. This assumption has recently been evaluated and challenged using empirical observations. However, water quality response and recovery, or otherwise, during and after prolonged droughts remains an open question. Answering this question potentially identifies any changes in catchment hydrological processes and water balance (e.g., the proportion of groundwater contribution to streamflow), thus informing the mechanisms for runoff non‐recovery after prolonged drought. Water quality responses to drought can also inform any long‐term water quality changes beyond what is observable from trend analyses. Here stream salt load changes were investigated using hidden Markov models (HMMs), where monthly rainfall was included as a predictor of stream salt loads. Monthly riverine salt fluxes at eight sites in Victoria (Australia) were examined before, during and after a prolonged drought in South‐East Australia—the Millennium Drought. Two‐state models, where salt loads varied between ‘normal’ and ‘low’ states, were found to better predict in‐stream salt loads compared to single‐state models. The results showed that catchments shifted to a low salt load state generally after the catchment changed to a low runoff state. As groundwater is understood to be the major source of salts in these catchments, this suggests that reductions in groundwater flow into rivers occur as a result of the shift to a lower runoff state. Understanding how readily water quality in catchments shift to different states during and after prolonged droughts enables appropriate catchment management based on our understanding of changes to catchment hydrology.
-
ItemNo Preview AvailableA predictive model for spatio-temporal variability in stream water quality( 2019-07-23)Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data; and (2) improving the availability of data to capture land use and management changes over time.
-
ItemA Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments( 2021-01-12)Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations; and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.
-
ItemA multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments(ELSEVIER SCI LTD, 2021-11-01)Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
-
ItemNo Preview AvailableStormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'(ELSEVIER, 2021-02-25)In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.
-
ItemThe emergence, trajectory, and impacts of emerging contaminants publications in the Journal of Environmental Quality(WILEY, 2021-11)As analytical capabilities in the early 2000s began to enable the detection of chemicals in environmental media at increasingly small concentrations, chemicals with the potential to cause adverse human and ecosystem health effects began to be found nearly ubiquitously worldwide. The types of chemicals that were targeted for analysis included natural and synthetic hormones, human and veterinary pharmaceuticals, chemicals in personal care products, novel pesticides, nanoparticles, microplastics, and other chemicals of natural and synthetic origin. The impacts of these chemicals on environmental and human health in many cases remain unknown. Collectively, these chemicals became known as "emerging contaminants" or "contaminants of emerging concern." Much progress has been made toward understanding the sources of these contaminants in the environment, the processes that control their fate and transport once they are released into the environment, and the ability of technology and/or best management practices to mitigate their occurrence. As the Journal of Environmental Quality (JEQ) celebrates its 50th anniversary, we sought to understand how publications in the journal have made impactful contributions in the research area of emerging contaminants. Here, we present the trajectory of publications in JEQ that have shaped knowledge in this field, highlight the importance of these contributions, and conclude with opportunities for JEQ to continue attracting high-quality emerging contaminants research.
-
ItemIllicit discharge detection in stormwater drains using an Arduino-based low-cost sensor network(IWA PUBLISHING, 2022-03-01)Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.
-
ItemDevelopment of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands(IWA PUBLISHING, 2021-01)Abstract Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.
-
ItemSynthesizing the impacts of baseflow contribution on concentration-discharge (C-Q) relationships across Australia using a Bayesian hierarchical model(COPERNICUS GESELLSCHAFT MBH, 2022-01-03)Abstract. Understanding concentration–discharge (C–Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C–Q relationships in some catchments. However, the current understanding on the effects of baseflow contribution in shaping the C–Q patterns is largely derived from temperate catchments. As such, we still lack quantitative understanding of these effects across a wide range of climates (e.g. arid, tropical and subtropical). The study aims to assess how baseflow contributions, as defined by the median and the range of daily baseflow indices within individual catchments (BFI_m and BFI_range, respectively), influence C–Q slopes across 157 catchments in Australia spanning five climate zones. This study focuses on six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NOx) and total nitrogen (TN). The impact of baseflow contributions is explored with a novel Bayesian hierarchical model. For sediments and nutrient species (TSS, NOx, TN and TP), we generally see largely positive C–Q slopes, which suggest a dominance of mobilization export patterns. Further, for TSS, NOx and TP we see stronger mobilization (steeper positive C–Q slopes) in catchments with higher values in both the BFI_m and BFI_range, as these two metrics are positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range is likely due to the more variable flow pathways that occur in catchments with higher baseflow contributions. These variable flow pathways can lead to higher concentration gradients between low flows and high flows, where the former is generally dominated by groundwater/slow subsurface flow while the latter by surface water sources, respectively. This result highlights the crucial role of flow pathways in determining catchment exports of solutes and particulates. Our study also demonstrates the need for further studies on how the temporal variations of flow regimes and baseflow contributions influence flow pathways and the potential impacts of these flow pathways on catchment C–Q relationships.
-
ItemKey factors influencing differences in stream water quality across space(WILEY, 2018-01-01)Globally, many rivers are experiencing declining water quality, for example, with altered levels of sediments, salts, and nutrients. Effective water quality management requires a sound understanding of how and why water quality differs across space, both within and between river catchments. Land cover, land use, land management, atmospheric deposition, geology and soil type, climate, topography, and catchment hydrology are the key features of a catchment that affect: (1) the amount of suspended sediment, nutrient, and salt concentrations in catchments (i.e., the source), (2) the mobilization ,and (3) the delivery of these constituents to receiving waters. There are, however, complexities in the relationship between landscape characteristics and stream water quality. The strength of this relationship can be influenced by the distance and spatial arrangement of constituent sources within the catchment, cross correlations between landscape characteristics, and seasonality. A knowledge gap that should be addressed in future studies is that of interactions and cross correlations between landscape characteristics. There is currently limited understanding of how the relationships between landscape characteristics and water quality responses can shift based on the other characteristics of the catchment. Understanding the many forces driving stream water quality and the complexities and interactions in these forces is necessary for the development of successful water quality management strategies. This knowledge could be used to develop predictive models, which would aid in forecasting of riverine water quality. WIREs Water 2018, 5:e1260. doi: 10.1002/wat2.1260 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality