Infrastructure Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 9 of 9
-
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 Comprehensive Framework to Evaluate Hydraulic and Water Quality Impacts of Pipe Breaks on Water Distribution Systems(AMER GEOPHYSICAL UNION, 2018-10)Abstract Pipe breaks have significant impacts on the hydraulic and water quality performance of water distribution systems (WDSs). Therefore, it is important to evaluate these impacts for developing effective strategies to ultimately minimize the consequences of these events. However, there has been surprisingly limited research focusing on impact evaluation for pipe breaks so far. To address this gap, this paper proposes a framework to comprehensively evaluate hydraulic and water quality impacts of pipe breaks on a WDS using six quantitative metrics. These metrics primarily focus on identifying (i) break outflow volume, (ii) water shortage, (iii) nodes with reduced service quality, (iv) pipes with affected pressures, (v) pipes with reversed flow directions, and (vi) pipes with significantly increased velocities, for each breaking event within a WDS. Statistical behaviors, spatial properties, and pipe rankings of metric results are analyzed to reveal the underlying characteristics of impacts induced by pipe breaks. We illustrate the proposed framework using three WDSs with different properties. Results show that impacts of pipe breaks not only vary with pipe diameters but are also significantly influenced by pipe locations, when the break occurs, and the specific metric considered. The proposed framework greatly enhances the fundamental understanding of the underlying properties of breaking impacts on the hydraulic and water quality of WDSs, as well as the ranking of pipes based on the consequences of breaks. Such understanding offers important guidance to develop effective pipe management, resource planning, and break restoration strategies to minimize the impacts of breaking events on WDSs.
-
ItemAssessing the Potential Robustness of Conceptual Rainfall-Runoff Models Under a Changing Climate(AMER GEOPHYSICAL UNION, 2018-07)Abstract Conceptual rainfall‐runoff (CRR) models are commonly used to assess the potential impact of climate change on water resources systems. However, they are often characterized by poorer performance when used to simulate a different climate compared to that of the calibration period. This is generally referred to as low model robustness, and these issues have been thoroughly explored using historical data. However, the implications of robustness are unknown for a changing climate where models may have to operate under conditions that lie beyond existing observations. This study extends these ideas to evaluate the “potential robustness” of different CRR models in the context of a changing climate. To achieve this aim, we combine a generalized split‐sample test framework with a stochastic weather generator. This allows us to assess the variabilities in runoff predictions obtained from using different calibration periods within each CRR model. We tested the potential robustness on three catchments with contrasting hydroclimatic conditions. We observed a consistent higher potential robustness in all models under drier conditions at all catchments. The three catchments illustrate contrasting patterns in the relative potential robustness of the three CRR models, which are related to both the structures of the CRR models and the unique catchment characteristics, highlighting the need of case‐specific assessment. This study illustrates a transferable empirical testing strategy to understanding variabilities in CRR model predictions. This approach can improve our knowledge of model behavior and thus informs the suitability of alternative models to simulate catchments hydrology under a changing climate.
-
ItemCommonwealth Environmental Water Office Long Term Intervention Monitoring Project Goulburn River Selected Area Scientific Report 2018–19(University of Melbourne Commercial, 2019)This Scientific Report is a companion volume to the Summary Report for the Goulburn River Long Term Intervention Monitoring (LTIM) Project Webb et al. 2020). The two documents complement each other and overlap very little.
-
ItemUnderstanding the Impacts of Short-Term Climate Variability on Drinking Water Source Quality: Observations From Three Distinct Climatic Regions in Tanzania(American Geophysical Union, 2019-04-01)Climate change is expected to increase waterborne diseases especially in developing countries. However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20‐month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short‐term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short‐term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge.
-
ItemA web-based interface to visualize and model spatio-temporal variability of stream water quality(River Basement Management Society, 2018)Understanding the spatio-temporal variability in stream water quality is critical for designing effective water quality management strategies. To facilitate this, we developed a web-based interface to visualize and model the spatio-temporal variability of stream water quality in Victoria. We used a dataset of long-term monthly water quality measurements from 102 monitoring sites in Victoria, focusing on six water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjedahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). The interface models spatio-temporal variability in water quality via a Bayesian hierarchical modelling framework, and produces summaries of (1) the key driving factors of spatio-temporal variability and (2) model performance assessed by multiple metrics. Additional features include predicting the time-averaged mean concentration at an un-sampled site, and testing the impact of land-use changes on the mean concentration at existing sites. This tool can be very useful in supporting the decision-making processes of catchment managers in (1) understanding the key drivers of changes in water quality and (2) designing water quality mitigation and restoration strategies.
-
ItemCharacterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis(PERGAMON-ELSEVIER SCIENCE LTD, 2018-12)Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.
-
ItemIntegrated modelling of spatio-temporal variability in stream water quality across victorian catchments(Engineers Australia, 2018-01-01)Degraded water quality in rivers and streams can have large economical, societal and ecological impacts. Stream water quality can be highly variable both over space and time, so understanding and modelling these spatio-temporal variabilities is critical to developing management and mitigation strategies to improve riverine water quality. However, there is currently limited capacity to model stream water quality due to the lack of understanding of the key factors driving spatio-temporal variability in water quality. To address this, a Bayesian hierarchical statistical model has been developed to describe the spatio-temporal variability in stream water quality across multiple catchments in the state of Victoria, Australia. We used monthly water quality monitoring data collected at 102 sites over 20 years. The modelling focused on three key water quality indicators: total suspended solids (TSS), nitrate-nitrite (NOx) and salinity (EC). It was found that both human-influenced catchment characteristics (land use) and other natural characteristics such as climate or topography are important drivers of spatial variabilities. The key drivers of temporal variability are changes in streamflow, climate and vegetation cover. These key drivers have been integrated into a spatio-temporal modelling framwork. These models can be applied at different spatial and temporal scales, and explain a reasonable proportion of spatio-temporal variation in the different water quality constituents. The extension and adaption of these models is currently underway to create an operational tool to forecast stream water quality responses to potential land use and climatic changes.
-
ItemKey Factors Affecting Temporal Variability in Stream Water Quality(AMER GEOPHYSICAL UNION, 2019-01)Abstract Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.