ORCID Profile
0000-0003-1083-1214
Current Organisations
University of Adelaide
,
University of New South Wales
,
Australian National University School of Engineering
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Surface water hydrology | Hydrology | Contaminant hydrology | Groundwater hydrology
ANZSRC Socio-Economic Objective (SEO)
Related Links
Publications
A data-based predictive model for spatiotemporal variability in stream water quality
Publisher: Copernicus GmbH
Date: 24-02-2020
Abstract: Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling 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). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit) and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.
Controls on spatial variability in mean concentrations and export patterns of river chemistry across the Australian continent
Publisher: Wiley
Date: 22-03-2022
Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems
Publisher: MDPI AG
Date: 05-03-2022
DOI: 10.3390/RS14051286
Abstract: Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
Effects of streamflow regime on the concentration-flow (CQ) relationships across climate zones: a study across the Australian continent
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4394
Abstract: & & Understanding the spatial and temporal variation of concentration-flow (CQ) relationships is valuable to enhance understanding of the key processes that drive changes in catchment water quality. This study used a data-driven approach to understand how the CQ relationship is influenced by catchment flow regimes (baseflow versus runoff dominated) throughout the Australian continent. To summarize the CQ relationship, we focus on the b exponent in a power-law relationship (C=aQ& sup& b& /sup& ). We considered six commonly monitored constituents, namely, electrical conductivity (EC), total phosphorus (TP), filterable reactive phosphorus (FRP), total suspended solids (TSS), nitrate& #8211 nitrite (NO& sub& x& /sub& ) and total nitrogen (TN), at a total of 251 catchments in Australia. A novel Bayesian hierarchical model was developed to assess a) the impacts of flow regime on CQ relationships, both across catchments (spatial variation) and within in idual catchments (temporal variation) and b) how these impacts vary across five typical Australian climate zones & #8211 arid, Mediterranean, temperate, sub-tropical and tropical.& & & & We found that for in idual constituents: 1) spatial variations in CQ relationships are clearly influenced by the catchment-level baseflow contribution, and these influences differ with climate regions 2) across climate zones, runoff-dominated catchments (i.e. with low baseflow contribution) have relatively stable CQ relationships, while groundwater-dominated catchments (i.e. with high baseflow contribution) have highly variable CQ patterns across climate zones 3) within in idual catchments, the variations in instantaneous baseflow contribution have no systematic and consistent effect on the CQ relationships. The influence of catchment baseflow contribution on CQ relationships has potential to be used to predict catchment water quality across Australia, with over half the total variability in concentration of sediment, salt and phosphorus species explained by variations in catchment-level baseflow contribution.& &
Assessing the Potential Robustness of Conceptual Rainfall‐Runoff Models Under a Changing Climate
Publisher: American Geophysical Union (AGU)
Date: 07-2018
DOI: 10.1029/2018WR022636
Sensitivity of potential evapotranspiration to changes in climate variables for different climatic zones
Publisher: Copernicus GmbH
Date: 19-09-2016
Abstract: Abstract. Understanding the factors that impact on the sensitivity of potential evapotranspiration (PET) to changes in different climate variables is critical to assessing the possible implications of anthropogenic climate change on the catchment water balance. Using global sensitivity analysis, this study assessed the implications of baseline climate conditions on the sensitivity of PET to a large range of plausible changes in temperature (T), relative humidity (RH), solar radiation (Rs) and wind speed (uz). The analysis was conducted at 30 Australian locations representing different climatic zones, using the Penman–Monteith and Priestley–Taylor PET models. Results from both models suggest that the baseline climate can have a substantial impact on overall PET sensitivity. In particular, approximately 2-fold greater changes in PET were observed in cool-climate energy-limited locations compared to other locations in Australia, indicating the potential for elevated water loss as a result of increasing actual evapotranspiration (AET) in these locations. The two PET models consistently indicated temperature to be the most important variable for PET, but showed large differences in the relative importance of the remaining climate variables. In particular, for the Penman–Monteith model wind and relative humidity were the second-most important variable for dry and humid catchments, respectively, whereas for the Priestley–Taylor model solar radiation was the second-most important variable, particularly for warmer catchments. This information can be useful to inform the selection of suitable PET models to estimate future PET for different climate conditions, providing evidence on both the structural plausibility and input uncertainty for the alternative models.
Using historical data to identify future water quality trends at a regional scale
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4308
Abstract: & & Using historical data to identify future water quality trends& & & ol& & li& Lintern& /li& & li& Kho& /li& & li& Guo& /li& & li& Liu& /li& & li& Duvert& /li& & /ol& & & & & & & & Climate change is expected to have a severe impact on water resources management in Australia. This is expected to lead to increasing frequency in extreme hydrological events such as droughts and floods, which will in turn contribute to higher risks of bushfires, fish kills, and water shortage for both humans and the environment. The potential impacts of these climate-change-induced extreme events on the quantity of water available to humans and the environment are relatively well understood. However, we have little understanding of the effect on the water quality of Australian rivers. This project aims to start filling this gap in our understanding.& & & & Our key objectives are:& & & & (1) to identify how extreme hydrological events such as droughts and floods have affected river water quality over the last two decades, and explore how spatially variable these impacts have been across the Australian continent.& & & & (2) to use these past observations as a basis to predict how river water quality will be affected by climate change across the continent, and identify the locations within Australia that will be most vulnerable to water quality deterioration in the near future.& & & & There is a wealth of historical water quality data for each state in Australia, but these datasets have not yet been investigated systematically to develop a nation-wide understanding of water quality patterns. We believe that only a continental-scale understanding of the response of river water quality to extreme hydrological events will allow for the development of robust predictive models of climate change impacts on water quality. Knowing the potential hotspots for future water quality deterioration will be a key step towards identifying priorities for catchment planning and management.& & & & In this poster, we will present the preliminary findings of this project by detailing the spatial variability in the impact of hydrological events on water quality across the state of Victoria in South-East Australia.& &
Impact of evapotranspiration process representation on runoff projections from conceptual rainfall‐runoff models
Publisher: American Geophysical Union (AGU)
Date: 2017
DOI: 10.1002/2016WR019627
A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
Publisher: Elsevier BV
Date: 11-2021
Diverging projections for flood and rainfall frequency curves
Publisher: Elsevier BV
Date: 05-2023
Key controls of catchment attribute on spatial differences and export regimes in riverine water quality: a study across the Australian continent using a Bayesian approach
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-11003
Abstract: & & Investigations of concentration (& em& C& /em& ) and discharge (& em& Q& /em& ) relationships (& em& C& #8211 Q& /em& relationships) at the catchment scale are commonly used to characterize export regimes of instream particulates and solutes. & em& C& #8211 Q& /em& relationships also provide insights on spatial and temporal variability in pollutant export, allowing identification of the sources and transfer pathways of pollutants. Previous studies have shown that several key catchment attributes control the export of sediment and dissolved nutrients within catchments. These catchment attributes include land use, topography, geology and soils. However, only few studies have investigated the relative importance of multiple catchment attributes over large spatial scales (e.g., at the continental scale) and between different climate zones. This is mostly due to either a limited number of catchments that have been monitored or a strong focus on temperate catchments. Therefore, our current understanding of key controls on spatial variability and export regimes across different climates is still limited. In this study, we investigated spatial differences and the & em& C& #8211 Q& /em& relationships of six commonly monitored constituents (i.e., total suspended solid & #8211 TSS, total nitrogen & #8211 TN, sum of nitrate and nitrite & #8211 NO& sub& x& /sub& , total phosphorus & #8211 TP, soluble reactive phosphorus & #8211 SRP and electrical conductivity & #8211 EC) from 507 catchments across the Australian continent. These catchments represent five main climate zones in Australia (i.e., arid, Mediterranean, temperate, subtropical and tropical). We used a hierarchical Bayesian multi-model averaging approach to 1) identify key catchment attributes (e.g., land use, topography, geology and hydrology) driving the spatial variability of mean concentration and export regimes (& em& C& /em& & em& & #8211 & /em& & em& Q& /em& relationship) for in idual constituents 2) understand the role of climatic gradients in determining the magnitude and direction of the key controls, and 3) use the key controls identified to predict the mean concentration and & em& C& /em& & em& & #8211 & /em& & em& Q& /em& relationship in multiple catchments across Australia.& & & & The proposed Bayesian modelling framework provided a higher predictive capability for mean concentrations (Nash-Sutcliffe efficiency (NSE) ranging from 0.58 for SRP to 0.86 for EC), compared to & em& log& /em& (& em& C) & #8211 log(Q)& /em& slopes (NSE ranging from 0.25 for NO& sub& x& /sub& to 0.39 for TP). For mean concentrations, land use (e.g., agriculture and urban) has a significantly positive effect on nutrients (i.e., TN, NO& sub& x& /sub& , TP and SRP), particularly in the Mediterranean, subtropical and tropical regions, indicating that land use is a key driver for these constituents. For & em& log& /em& (& em& C) & #8211 log(Q)& /em& slopes, catchment topographical characteristics (e.g., slope and maximum flow pathway) have relatively high impacts on TSS, TP and EC, indicating export of sediments and solutes in catchments largely controlled by mobilization (sediment) and surface-subsurface flow interaction (solutes). Findings from our study provide a data-driven understanding of key controls on riverine water quality across multiple climate types and can inform future water quality management strategies.& &
Impact of salmon farming on Atlantic cod spatio-temporal reproductive dynamics
Publisher: Inter-Research Science Center
Date: 07-10-2021
DOI: 10.3354/AEI00415
Abstract: Salmon farming in marine net pens is a major activity in many temperate regions. This industry may affect coastal ecosystems in several ways, such as with waste pollution and parasite spillover. Less is known about the extent to which salmon farming disrupts the use of inshore spawning grounds by wild fish, such as the Atlantic cod Gadus morhua . Acoustic telemetry was therefore used to explore cod space use during the spawning season in a coastal region in mid-Norway with multiple salmon farms. Acoustic receivers were placed in clusters at 5 known cod spawning grounds and 6 nearby salmon farms. Data from 481 adult cod caught at the spawning grounds during 2017-2019 and equipped with acoustic telemetry transmitters were analysed. Overall, fewer fish were detected at farms than spawning grounds, even when accounting for distance from release point. In idual cod residency (days detected / duration of spawning period) was generally higher at the spawning grounds close to farms but low at the farms themselves, with little apparent spawning at the farm localities. In contrast, spawning was clearly occurring at the nearby spawning grounds, with cod spending weeks (n = 316) or months (n = 158) there during the spawning period. Males had longer residence times at spawning grounds than females, likely linked to the cod mating system. Overall, we found little support for the assertion that salmon farms disrupt inshore spawning dynamics of cod using nearby spawning grounds presently, either by attracting spawners to farms or causing fish to leave these grounds.
Key Factors Affecting Temporal Variability in Stream Water Quality
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2018WR023370
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 (NO x ), 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 NO x 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.
Shifts in stream salt loads during and after prolonged droughts
Publisher: Wiley
Date: 06-2023
DOI: 10.1002/HYP.14901
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.
An R package for modelling actual, potential and reference evapotranspiration
Publisher: Elsevier BV
Date: 04-2016
Can hydraulic measures of river conditions improve our ability to predict ecological responses to changing flows? Flow velocity and spawning of an iconic native Australian fish
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1927
Abstract: Ecological responses are key indicators of river water quality. Ecological responses to changing riverine flows are often evaluated by describing the relationship between river discharge and response. However, aquatic organisms experience the hydraulics (i.e. velocity, shear stress, depth) of a river, not its discharge. Hydraulic characterizations of riverine habitats may improve our ability to predict ecological responses. We used two-dimensional hydraulic models to translate river discharge into reach-averaged velocity. Combining these flow data with water temperature and 8 years of field observations of fish spawning, we developed a Bayesian hierarchical model to predict the spawning of golden perch (Macquaria ambigua) in the lower Goulburn River, south-east Australia. The model suggested that probability of spawning was positively related to both discharge and reach-averaged velocity. The model also identified the critical water temperature above which both discharge and velocity start to affect spawning. Antecedent flows prior to spawning had a weak positive effect on spawning.& Against expectations, there was little difference in predictive uncertainty for the effect of flows when reach-averaged velocity was used as the main predictor rather than discharge. The lower Goulburn River has a relatively simple channel and so discharge and velocity are monotonically related over most flows. We expect that in a more geomorphically complex environment, improvement in predictive ability would be substantial. This research only explores one ex le of a hydraulic parameter being used as a predictor of ecological response many others are possible. The extra effort and expense involved in hydraulic characterization of river flows (e.g., velocity) is only justified if our understanding of flow-ecology relationships is substantially improved. Further research to understand which environmental responses might be best understood through different hydraulic parameters, and how to better characterize hydraulic characteristics relevant to riverine biota, would help inform decisions regarding investment in hydraulic models.&
Better understanding of the capacity of pressure sensor systems to detect pipe burst within water distribution networks
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2018
The influence of climate on water chemistry states and dynamics in rivers across Australia
Publisher: Wiley
Date: 12-2021
DOI: 10.1002/HYP.14423
Abstract: For effective water quality management and policy development, spatial variability in the mean concentrations and dynamics of riverine water quality needs to be understood. Using water chemistry (calcium, electrical conductivity, nitrate‐nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus and total suspended solids) data for up to 578 locations across the Australian continent, we assessed the impact of climate zones (arid, Mediterranean, temperate, subtropical, tropical) on (i) inter‐annual mean concentration and (ii) water chemistry dynamics as represented by constituent export regimes (ratio of the coefficients of variation of concentration and discharge) and export patterns (slope of the concentration‐discharge relationship). We found that inter‐annual mean concentrations vary significantly by climate zones and that spatial variability in water chemistry generally exceeds temporal variability. However, export regimes and patterns are generally consistent across climate zones. This suggests that intrinsic properties of in idual constituents rather than catchment properties determine export regimes and patterns. The spatially consistent water chemistry dynamics highlights the potential to predict riverine water quality across the Australian continent, which can support national riverine water quality management and policy development.
An inverse approach to perturb historical rainfall data for scenario-neutral climate impact studies
Publisher: Elsevier BV
Date: 2018
A modelling framework and R-package for evaluating system performance under hydroclimate variability and change
Publisher: Elsevier BV
Date: 05-2021
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Publisher: Copernicus GmbH
Date: 19-04-2017
DOI: 10.5194/HESS-21-2107-2017
Abstract: Abstract. Assessing the factors that have an impact on potential evapotranspiration (PET) sensitivity to changes in different climate variables is critical to understanding the possible implications of climatic changes on the catchment water balance. Using a global sensitivity analysis, this study assessed the implications of baseline climate conditions on the sensitivity of PET to a large range of plausible changes in temperature (T), relative humidity (RH), solar radiation (Rs) and wind speed (uz). The analysis was conducted at 30 Australian locations representing different climatic zones, using the Penman–Monteith and Priestley–Taylor PET models. Results from both models suggest that the baseline climate can have a substantial impact on overall PET sensitivity. In particular, approximately 2-fold greater changes in PET were observed in cool-climate energy-limited locations compared to other locations in Australia, indicating the potential for elevated water loss as a result of increasing actual evapotranspiration (AET) in these locations. The two PET models consistently indicated temperature to be the most important variable for PET, but showed large differences in the relative importance of the remaining climate variables. In particular for the Penman–Monteith model, wind and relative humidity were the second-most important variables for dry and humid catchments, respectively, whereas for the Priestley–Taylor model solar radiation was the second-most important variable, with the greatest influence in warmer catchments. This information can be useful to inform the selection of suitable PET models to estimate future PET for different climate conditions, providing evidence on both the structural plausibility and input uncertainty for the alternative models.
Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment
Publisher: Elsevier BV
Date: 11-2017
Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.MARPOLBUL.2018.10.019
Abstract: 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.
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments
Publisher: Copernicus GmbH
Date: 20-05-2021
DOI: 10.5194/HESS-25-2663-2021
Abstract: Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical 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 and 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.
Supplementary material to "Synthesizing the impacts of baseflow contribution on C-Q relationships across Australia using a Bayesian Hierarchical Model"
Publisher: Copernicus GmbH
Date: 08-07-2021
Synthesizing the impacts of baseflow contribution on concentration–discharge (<i>C</i>–<i>Q</i>) relationships across Australia using a Bayesian
Publisher: Copernicus GmbH
Date: 03-01-2022
Abstract: 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 in idual 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.
Controls on Spatial Variability in Mean Concentrations and Export Patterns of River Chemistry Across the Australian Continent
Publisher: American Geophysical Union (AGU)
Date: 12-2022
DOI: 10.1029/2022WR032365
Abstract: The state and dynamics of river chemistry are influenced by both anthropogenic and natural catchment characteristics. However, understanding key controls on catchment mean concentrations and export patterns comprehensively across a wide range of climate zones is still lacking, as most of this research is focused on temperate regions. In this study, we investigate the catchment controls on mean concentrations and export patterns (concentration – discharge relationship, C–Q slope) of river chemistry, using a long‐term data set of up to 507 sites spanning five climate zones (i.e., arid, Mediterranean, temperate, subtropical, tropical) across the Australian continent. We use Bayesian model averaging (BMA) and hierarchical modeling (BHM) approaches to predict the mean concentrations and export patterns and compare the relative importance of 26 catchment characteristics (e.g., topography, climate, land use, land cover, soil properties and hydrology). Our results demonstrate that mean concentrations result from the interaction of catchment indicators and anthropogenic factors (i.e., land use, topography and soil), while export patterns are influenced by topography. We also found that incorporating the effects of climate zones in a BHM framework improved the predictability of both mean concentrations and C–Q slopes, suggesting the importance of climatic controls on hydrological and biogeochemical processes. Our study provides insights into the contrasting effects of catchment controls across different climate zones. Investigating those controls can inform sustainable water quality management strategies that consider the potential changes in river chemistry state and export behavior.
Using short-term ensemble weather forecast to evaluate outcomes of irrigation
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4210
Abstract: & & Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.& & & & This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.& &
Understanding the Impacts of Short‐Term Climate Variability on Drinking Water Source Quality: Observations From Three Distinct Climatic Regions in Tanzania
Publisher: American Geophysical Union (AGU)
Date: 04-2019
DOI: 10.1029/2018GH000180
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments
Publisher: Copernicus GmbH
Date: 12-01-2021
Abstract: 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.
An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts
Publisher: Springer Science and Business Media LLC
Date: 14-07-2023
DOI: 10.1007/S00271-022-00807-W
Abstract: Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.
A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4725
Abstract: & & & & & & & & Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km& sup& & /sup& . 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 (NO& sub& x& /sub& ), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO& sub& x& /sub& ) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.& / & & / & & / & & / &
A Comprehensive Framework to Evaluate Hydraulic and Water Quality Impacts of Pipe Breaks on Water Distribution Systems
Publisher: American Geophysical Union (AGU)
Date: 10-2018
DOI: 10.1029/2018WR022736
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.
How does baseflow contribution affect catchment C-Q relationships? A continental synthesis using a Bayesian Hierarchical Model
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-6847
Abstract: & & Understanding concentration-discharge (C-Q) relationships is critical to inform catchment export processes for solute and particulates. The contribution of baseflow to streamflow has been found to affect C-Q relationships in some catchments in previous studies. Current understanding on the effects of baseflow contribution in shaping the C-Q patterns is largely limited to temperate catchments, but 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 within in idual catchments influence C-Q slopes across Australia. The wide range of hydro-climatic regimes and land use/land cover conditions in Australian catchments make this continent the ideal experimental field to gain such an understanding. We analyzed 157 catchments in Australia spanning five climate zones, for six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NO& sub& x& /sub& ) and total nitrogen (TN). The impact of baseflow contributions was defined by the median and the range of daily baseflow indices (& em& BFI_m& /em& and & em& BFI_range& /em& , respectively) for each catchment. A novel Bayesian hierarchical model was developed to synthesize these effects for in idual catchments across the continent. & & & & & Sediments and nutrient species (TSS, NO& sub& x& /sub& , TN and TP) generally show positive C-Q slopes for most catchments, suggesting a dominance of mobilization export patterns. Further, TSS, NO& sub& x& /sub& and TP show stronger mobilization (i.e., steeper positive C-Q slopes) in catchments with higher values in both the & em& BFI_m& /em& and & em& BFI_range& /em& , while these two metrics are also positively correlated for most catchments. The enhanced mobilization in catchments with higher & em& BFI_m& /em& or & em& BFI_range& /em& might be explained by more variable flow pathways in catchments with higher baseflow contributions. In such catchments, the more variable flow pathways can lead to higher concentration gradients between low flows and high flows. These gradients are due to & different dominant flow pathways and contributions of groundwater/slow subsurface flow and surface water sources. Our results highlight the need for further studies focusing on identifying and quantifying: a) the influences of temporal variations of baseflow contributions on flow pathways, and b) the impacts of variable flow pathways on catchment C-Q relationships.& &
A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
Supplementary material to "A predictive model for spatio-temporal variability in stream water quality"
Publisher: Copernicus GmbH
Date: 23-07-2019
Understanding event runoff coefficient variability across Australia using the hydroEvents R package
Publisher: Wiley
Date: 04-2022
DOI: 10.1002/HYP.14563
Abstract: Identification and pairing of hydrologic events form the basis of various analyses, from identifying events for the calibration of hydrologic models, to calculation of event runoff coefficients for catchment characterization. Despite this, there is no unified approach for identifying hydrologic events. Here, using the R package, hydroEvents ( CRAN.Rackage=hydroEvents ), we compare multiple methods of extracting and pairing hydrologic events focussing on the relationship between rainfall and runoff. We find the four common analytical approaches used to identify runoff events—based on either event threshold, local maxima/minima, or proportion of baseflow contribution, give similar results. However, when rainfall events are paired to runoff, the type of algorithm and the direction of pairing (either from rainfall to runoff, or runoff to rainfall) make a considerable difference to the final event pairs identified and resulting analyses. Here, we demonstrate the value of automated event extraction and pairing algorithms for large‐s le hydrology analysis by calculating event runoff coefficients across Australia. Our results show that climatology is a key driver of catchment rainfall‐runoff response with much of Australia dominated by excess rainfall runoff generation. However, our results also show that the variability due to pairing method can introduce a variability equal to that of the climatology due to biasing the runoff mechanism within the s le. With this analysis we demonstrate the importance of systematic and consistent approaches to hydrologic characterization when identifying and pairing hydrological events.
On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation
Publisher: American Geophysical Union (AGU)
Date: 03-2020
DOI: 10.1029/2019WR026752
Predicting quantiles of water quality from catchment characteristics
Publisher: Wiley
Date: 10-12-2021
DOI: 10.1002/HYP.13996
On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-3205
Abstract: & & Conceptual Rainfall-Runoff (CRR) models are widely used for runoff simulation, and for prediction under a changing climate. The models are often calibrated with only a portion of all available data at a location, and then evaluated independently with another part of the data for reliability assessment. Previous studies report a persistent decrease in CRR model performance when applying the calibrated model to the evaluation data. However, there remains a lack of comprehensive understanding about the nature of this & #8216 & em& low transferability& /em& & #8217 problem and why it occurs. In this study we employ a large s le approach to investigate the robustness of CRR models across calibration/validation data splits. Specially, we investigate: 1) how robust is CRR model performance across calibration/evaluation data splits, at catchments with a wide range of hydro-climatic conditions and 2) is the robustness of model performance somehow related to the hydro-geo-climatic characteristics of a catchment? We apply three widely used CRR models, GR4J, AWBM and IHACRE_CMD, to 163 Australian catchments having long-term historical data. Each model was calibrated and evaluated at each catchment, using a large number of data splits, resulting in a total of 929,160 calibrated models. Results show that: 1) model performance generally exhibits poor robustness across calibration/evaluation data splits 2) lower model robustness is correlated with specific catchment characteristics, such as a higher runoff skewness, lower aridity and runoff coefficient. These results provide a valuable benchmark for future model robustness assessments, and useful guidance for model calibration and evaluation.& &
Response to Comments from Reviewer #3
Publisher: Copernicus GmbH
Date: 16-10-2019
Response to Comments from Reviewer #2
Publisher: Copernicus GmbH
Date: 16-10-2019
Response to Comments from Reviewer #1
Publisher: Copernicus GmbH
Date: 16-10-2019
Response to Comments from Reviewer #4
Publisher: Copernicus GmbH
Date: 16-10-2019
In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4987
Abstract: & & Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User& #8217 s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.& & & & Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest& &
Synthesizing the impacts of baseflow contribution on C-Q relationships across Australia using a Bayesian Hierarchical Model
Publisher: Copernicus GmbH
Date: 08-07-2021
Abstract: Abstract. The spatial and temporal variation of concentration-discharge (C-Q) relationships inform 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, these patterns have not yet been investigated across large spatial scales. To address this, the study aims to assess how baseflow contributions, as defined by the median catchment baseflow index (BFI_m), 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), nitrate–nitrite (NOx) and total nitrogen (TN). The impact of baseflow contribution is explored with a novel Bayesian hierarchical model. We found that BFI_m has a strong impact on C-Q slopes. C-Q slopes are largely positive for nutrient species (NOx, TN, SRP and TP) and are steeper in catchments with higher BFI_m across all climate zones (for TN, SRP and TP). On the other hand, we also found a generally higher variation in instantaneous BFI for catchments with high BFI_m. Thus, the steeper C-Q slopes found in catchments with high BFI_m may be a result of a larger variation in water sources and flow pathways between low (baseflow-dominated) and high (quickflow-dominated) flow conditions. In contrast, catchments with low BFI_m may have more homogeneous flow pathways at both low and high flows, resulting in less variable concentrations and thus a flatter C-Q slope. Our model can explain over half of the observed variability in concentration of TSS, EC and P species across all catchments (93 % for EC, 63 % for TP, 63 % for SRP, and 60 % for TSS), while being able to predict C-Q slopes across space by BFI_m. This indicates that our parsimonious model has potential for predicting the C-Q slopes for catchments in different climate zones, and thus improving the predictive capacity for water quality across Australia.
Improving scheduling, benchmarking and forecasting to boost irrigation productivity
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-6275
Abstract: & & Irrigation water is an expensive and limited resource, and optimized water use is beneficial to saving water while boosting productivity. This project aims to develop integrated irrigation scheduling, benchmarking and forecasting capabilities to inform optimal irrigation practices and the suitable tools and information required for this. To achieve this, we designed a three-year project which combines simulations and field-scale monitoring. One aspect of this project is to develop a comprehensive uncertainty framework to better understand the uncertainty in scheduling, which is informed by soil water models, along with multiple sources of information such as soil, crop, weather and field management. Besides, we are also conducting large-scale benchmarking study to identify better irrigation practices across multiple farms, fields, crop types and seasons. The project outcomes will be integrated with our partner, Rubicon& #8217 s water ordering portal and adopted by most Australian irrigation farmers, with significant long-term benefits expected in agricultural production and water conservation.& & &
How does wildfire and climate variability affect streamflow in forested catchments? A regional study in eastern Australia
Publisher: Elsevier BV
Date: 10-2023
A predictive model for spatio-temporal variability in stream water quality
Publisher: Copernicus GmbH
Date: 23-07-2019
Abstract: 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.
Related Organisations
Australian National University School Of Engineering
Location: Australia
Related Funding Activities
Discovery Projects - Grant ID: DP230100618
Start Date: 2023
End Date: 12-2025
Amount: $570,000.00
Funder: Australian Research Council
View Funded Activity