ORCID Profile
0000-0002-5629-1703
Current Organisation
Queensland Government
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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.
Publisher: Wiley
Date: 22-03-2022
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.& &
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.
Publisher: MDPI AG
Date: 19-04-2022
DOI: 10.3390/RS14091961
Abstract: Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (Γ) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD/τ) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived ΔΓ is modeled as a function of both variations in SM and VOD (ΔSM and Δτ). For retrieving SM, ancillary τ data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating Δτ is suggested as an alternative. Experimental evaluation is performed for the time span from August 2019 to July 2021. Excellent agreements between the final retrievals and referenced SMAP SM products are achieved for both training (1-year period) and test (1-year duration) sets. On the whole, overall correlation coefficients (r) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm3/cm3 are obtained based on models using the SMAP and simulated Δτ, respectively. The model without τ generates an r of 0.95 and an RMSE of 0.031 cm3/cm3. The efficiency and necessity of considering τ are thus confirmed by its enhancement based on correlation and RMSE against the one without τ, and the usefulness of approximating Δτ by sinusoidal functions is also validated. Influences of SM statistics in terms of mean and variance on the retrieval accuracy are evaluated. This work unveils the interaction between CyGNSS data, SM, and τ and demonstrates the feasibility of integrating the Δτ approximation function into a bilinear regression model to obtain SM results.
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.& / & & / & & / & & / &
Publisher: Wiley
Date: 24-10-2018
DOI: 10.1002/WAT2.1260
Abstract: Globally, many rivers are experiencing declining water quality, for ex le, 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
Publisher: Elsevier BV
Date: 11-2021
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
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.& &
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.& &
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
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.
Publisher: Elsevier BV
Date: 2011
Publisher: Informa UK Limited
Date: 24-03-2015
Publisher: Copernicus GmbH
Date: 23-07-2019
Publisher: American Geophysical Union (AGU)
Date: 04-2023
DOI: 10.1029/2022JG007100
Abstract: The long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr −1 , with an upward trend of 0.21 Pg C yr −2 ( p 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP.
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.
Publisher: Wiley
Date: 10-12-2021
DOI: 10.1002/HYP.13996
Publisher: Elsevier BV
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: American Geophysical Union (AGU)
Date: 10-2018
DOI: 10.1029/2017WR022172
Abstract: This study uses water‐quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate/nitrite (NO x ), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time‐averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site‐level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time‐averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water‐quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water‐quality responses to future land use, climate, or soil and land management changes.
Publisher: IEEE
Date: 07-2011
Publisher: CRC Press
Date: 11-02-2014
DOI: 10.1201/B16606-108
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.
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.
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.
Publisher: MDPI AG
Date: 19-04-2018
DOI: 10.3390/W10040507
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.
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.
Location: China
No related grants have been discovered for Shuci Liu.