scholarly journals A framework for estimating pollutant export coefficients from long-term in-stream water quality monitoring data

2008 ◽  
Vol 23 (2) ◽  
pp. 182-194 ◽  
Author(s):  
S. Shrestha ◽  
F. Kazama ◽  
L.T.H. Newham
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
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.

A Nonparametric Long-Term Trend Analysis Using Water Quality Monitoring Data in Nam-River

2018 ◽  
Vol 27 (11) ◽  
pp. 1029-1048
Author(s):  
Kang-Young Jung ◽  
Myojeong Kim ◽  
Kwang Duck Song ◽  
Kwon Ok Seo ◽  
Seong Jo Hong ◽  
...  
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Term Trend ◽  
Long Term Trend

Combining Ecohydrological Catchment Modelling and Water Quality Monitoring to Assess Surface Water Pollution in the Swist River Basin

2020 ◽  
Author(s):  
Alexander Ahring ◽  
Marvin Kothe ◽  
Christian Gattke ◽  
Ekkehard Christoffels ◽  
Bernd Diekkrüger
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Surface Waters ◽  
Water Quality Monitoring ◽  
Pollutant Emissions ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Quality Data

<p>Inland surface waters like rivers, streams, lakes and reservoirs are subject to anthropogenic pollutant emissions from various sources. These emissions can have severe negative impacts on surface water ecology, as well as human health when surface waters are used for recreational activities, irrigation of cropland or drinking water production. In order to protect aquatic ecosystems and freshwater resources, the European Water Framework Directive (WFD) sets specific quality requirements which the EU member states must meet until 2027 for every water body.</p><p>Implementing effective measures and emission control strategies requires knowledge about the important emission pathways in a given river basin. However, due to the abundance of pollution sources and the heterogeneity of emission pathways in time and space, it is not feasible to gain this knowledge via water quality monitoring alone. In our study, we aim to combine SWAT ecohydrological modelling and long term water quality monitoring data to establish a spatially differentiated nitrogen emission inventory on the sub-catchment scale. SWAT (short for Soil and Water Assessment Tool) is a semi-distributed, dynamic and process-driven watershed model capable of simulating long term hydrology as well as nutrient fluxes on a daily time step.</p><p>The study area is the Swist river basin in North Rhine-Westphalia (Germany). Belonging to the Rhine river system, the Swist is the largest tributary of the Erft River and drains a basin area of approximately 290 km². As part of its legal obligations and research activities, the Erftverband local waterboard collects a large variety of long term monitoring data in the Swist river catchment, which is available for this study. This includes operational data from the wastewater treatment plants in the watershed, discharge data from four stream gauging stations, river water quality data from continuous and discontinuous monitoring, groundwater quality data as well as quality data from surface, sub-surface and tile drainage runoff from various land uses.</p><p>Our contribution will be made up of two equal parts: First, we will present our water quality monitoring activities in the catchment and the related data pool outlined above, with special emphasis on recent monitoring results from agricultural tile drainages. Apart from nutrients and other pollutants, the data suggests considerable inputs of herbicide transformation products like Chloridazon-Desphenyl (maximum concentration measured: 15 µg/l) via this pathway. Second, we will explain how we integrate the monitoring data into the SWAT simulations and how we tackle related challenges like parameter equifinality (meaning that multiple parameter sets can yield similar or identical model outputs). The overall goal is to take all possible emission pathways into consideration, including those often neglected in past SWAT studies, like tile drainages and combined sewer overflows (CSO). As the Swist catchment is affected by groundwater extraction due to lignite mining in the Lower Rhine Bay area, we will discuss how this is considered during SWAT model setup and calibration, and will present first simulation results concerning catchment hydrology.</p>

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.

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