scholarly journals Integrating Seasonal Information on Nutrients and Benthic Algal Biomass into Stream Water Quality Monitoring

2016 ◽  
Vol 52 (5) ◽  
pp. 1223-1237
Author(s):  
Christopher P. Konrad ◽  
Mark D. Munn
Keyword(s):  
Water Quality ◽  
Algal Biomass ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Stream Water Quality

scholarly journals A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments

2021 ◽  
Author(s):  
Shuci Liu ◽  
Dongryeol Ryu ◽  
J. Anugs Webb ◽  
Anna Lintern ◽  
Danlu Guo ◽  
...  
Keyword(s):  
Water Quality ◽  
Great Barrier Reef ◽  
Temporal Variation ◽  
Temporal Variability ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Barrier Reef ◽  
Key Factors ◽  
Stream Water Quality

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.

scholarly journals A predictive model for spatio-temporal variability in stream water quality

2019 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
J. Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Model Performance ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Stream Water Quality ◽  
Spatio Temporal

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.

scholarly journals Data from selected U.S. Geological Survey National Stream Water Quality Monitoring Networks

1998 ◽  
Vol 34 (9) ◽  
pp. 2401-2405 ◽  
Author(s):  
Richard B. Alexander ◽  
James R. Slack ◽  
Amy S. Ludtke ◽  
Kathleen K. Fitzgerald ◽  
Terry L. Schertz
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Geological Survey ◽  
Monitoring Networks ◽  
Stream Water Quality

scholarly journals A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

2021 ◽  
Vol 25 (5) ◽  
pp. 2663-2683
Author(s):  
Shuci Liu ◽  
Dongryeol Ryu ◽  
J. Angus Webb ◽  
Anna Lintern ◽  
Danlu Guo ◽  
...  
Keyword(s):  
Water Quality ◽  
Great Barrier Reef ◽  
Temporal Variation ◽  
Temporal Variability ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Barrier Reef ◽  
Key Factors ◽  
Stream Water Quality

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.

scholarly journals The impact of conifer harvesting on stream water quality: the Afon Hafren, mid-Wales

2004 ◽  
Vol 8 (3) ◽  
pp. 503-520 ◽  
Author(s):  
C. Neal ◽  
B. Reynolds ◽  
M. Neal ◽  
H. Wickham ◽  
L. Hill ◽  
...  
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Strong Support ◽  
Shallow Groundwater ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Stream Water Quality ◽  
Catchment Scale ◽  
Water Quality Data ◽  
The Impact

Abstract. Results for long term water quality monitoring are described for the headwaters of the principal headwater stream of the River Severn, the Afon Hafren. The results are linked to within-catchment information to describe the influence of conifer harvesting on stream and shallow groundwater quality. A 19-year record of water quality data for the Hafren (a partially spruce forested catchment with podzolic soil) shows the classic patterns of hydrochemical change in relation to concentration and flow responses for upland forested systems. Progressive felling of almost two-thirds of the forest over the period of study resulted in little impact from harvesting and replanting in relation to stream water quality. However, at the local scale, a six years’ study of felling indicated significant release of nitrate into both surface and groundwater; this persisted for two or three years before declining. The study has shown two important features. Firstly, phased felling has led to minimal impacts on stream water. This contrasts with the results of an experimental clear fell for the adjacent catchment of the Afon Hore where a distinct water quality deterioration was observed for a few years. Secondly, there are localised zones with varying hydrology that link to groundwater sources with fracture flow properties. This variability makes extrapolation to the catchment scale difficult without very extensive monitoring. The implications of these findings are discussed in relation to strong support for the use of phased felling-based management of catchments and the complexities of within catchment processes. Keywords: deforestation, water quality, acidification, pH, nitrate, alkalinity, ANC, aluminium, dissolved organic carbon, Plynlimon, forest, spruce, Afon Hafren, podzol

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