Spatial and temporal variability of in-stream water quality parameter influence on dissolved oxygen and nitrate within a regional stream network

ABSTRACT Cryptosporidium is a genus of waterborne protozoan parasites that causes significant gastrointestinal disease in humans. These parasites can accumulate in environmental biofilms and be subsequently released to contaminate water supplies. Natural microbial assemblages were collected each season from an eastern Pennsylvania stream and used to grow biofilms in laboratory microcosms in which influx, efflux, and biofilm retention were determined from daily oocyst counts. For each seasonal biofilm, oocysts attached to the biofilm quickly during oocyst dosing. Upon termination of oocyst dosing, the percentage of oocysts retained within the biofilm decreased to a new steady state within 5 days. Seasonal differences in biofilm retention of oocysts were observed. The spring biofilm retained the greatest percentage of oocysts, followed (in decreasing order) by the winter, summer, and fall biofilms. There was no statistically significant correlation between the percentage of oocysts attached to the biofilm and (i) any measured stream water quality parameter (including temperature, pH, conductivity, and dissolved organic carbon concentration) or (ii) experimental temperature. Seasonal differences in oocyst retention persisted when biofilms were tested with stream water from a different season. These data suggest that seasonal variation in the microbial community and resulting biofilm architecture may be more important to oocyst transport in this stream site than water quality. The biofilm attachment and detachment dynamics of C. parvum oocysts have implications for public health, and the drinking water industry should recognize that the potential exists for pathogen-free water to become contaminated during the distribution process as a result of biofilm dynamics.

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Estimation of stream water quality parameter using regime channel theory

Environmental Geology ◽

10.1007/s00254-008-1369-y ◽

2008 ◽

Vol 57 (4) ◽

Cited By ~ 3

Author(s):

K. Muthukrishnavellaisamy ◽

G. C. Mishra ◽

M. L. Kansal ◽

N. C. Ghosh

Keyword(s):

Water Quality ◽

Stream Water ◽

Quality Parameter ◽

Water Quality Parameter ◽

Stream Water Quality ◽

Channel Theory

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Key Factors Affecting Temporal Variability in Stream Water Quality

Water Resources Research ◽

10.1029/2018wr023370 ◽

2019 ◽

Vol 55 (1) ◽

pp. 112-129 ◽

Cited By ~ 15

Author(s):

D. Guo ◽

A. Lintern ◽

J. A. Webb ◽

D. Ryu ◽

S. Liu ◽

...

Keyword(s):

Water Quality ◽

Temporal Variability ◽

Stream Water ◽

Key Factors ◽

Stream Water Quality ◽

Factors Affecting

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Linking the dynamic expansion and contraction of stream networks to changes in water storage and water quality in a pre-Alpine catchment

10.5194/egusphere-egu21-10767 ◽

2021 ◽

Author(s):

Izabela Bujak ◽

Andrea Rinaldo ◽

Ilja van Meerveld ◽

Florian Käslin ◽

Jana von Freyberg

Keyword(s):

Water Quality ◽

Stream Water ◽

Network Dynamics ◽

Water Storage ◽

Stream Water Quality ◽

Stream Network ◽

Rainfall Events ◽

Headwater Catchments ◽

Research Activities ◽

Flowing Stream

<p>Many headwater catchments are characterized by temporary streams that flow only seasonally or during rainfall events. As a result, the network of flowing streams is a dynamic system that periodically expands and contracts. This dynamic is likely to affect water flow and composition: the expansion of the stream network enhances the hydrologic connectivity of hillslopes to the streams, which facilitates shorter transit times. Also, the onset of flow in previously dry streambeds can cause flushing of sediments and nutrients. However, our knowledge of the relationships between flowing stream network dynamics and water quantity and quality in headwater catchments is still limited because experimental data remain sparse.</p><p>Within the TempAqua project we investigate the processes that drive stream network dynamics by relating measurements of stream network geometry to changes in catchment water storage and stream water quality. For this, we monitored the flow state, discharge, groundwater levels, soil moisture, and precipitation in three (3-7 ha) headwater catchments in the northern Swiss pre-Alps (Alptal catchment) in summer and fall 2020 using a wireless sensor network. To obtain high-resolution data of the dynamic stream network, we did multiple mapping surveys using a self-developed mobile phone application. Moreover, we sampled streamwater and precipitation at an hourly resolution during rainfall events at multiple locations to quantify the short-term changes in water quality when the stream network expands. We will present our research activities in the Alptal catchment and discuss the initial results obtained from the combined monitoring of the flowing stream network and hydrometric and hydrochemical variables.</p>

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FILTERING OF DISSOLVED OXYGEN DATA IN STREAM WATER QUALITY ANALYSIS

JAWRA Journal of the American Water Resources Association ◽

10.1111/j.1752-1688.1982.tb04522.x ◽

1982 ◽

Vol 18 (1) ◽

pp. 15-20 ◽

Cited By ~ 2

Author(s):

Clark C. K. Liu

Keyword(s):

Water Quality ◽

Dissolved Oxygen ◽

Stream Water ◽

Quality Analysis ◽

Stream Water Quality ◽

Water Quality Analysis

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STANDING STOK IKAN PAWEH (Osteochillus hasselti) dan KONDISI LINGKUNGAN DANAU LUBUK SIAM DESA LUBUK SIAM KECAMATAN SIAK HULU PROVINSI RIAU

Asian Journal of Aquatic Sciences ◽

10.31258/ajoas.2.1.12-20 ◽

2020 ◽

Vol 2 (1) ◽

pp. 12-20

Author(s):

Anggi Widya Iswara ◽

Muhammad Fauzi ◽

Nur El Fajri

Keyword(s):

Water Quality ◽

Dissolved Oxygen ◽

Standing Stock ◽

Quality Parameter ◽

Water Quality Parameter ◽

Initial Population ◽

Gill Nets ◽

Final Population ◽

Sampling Points

Osteochillus hasselti was commonly inhabit the Lubuk Siam Lake. A research aims to understand the standing stock of O. hasselti in that lake was conducted in July to August 2018. The fish was sampled everyday for 10 days in July and 10 days in August, in 3 sampling points (in the inlet area, in the middle of the lake, and the outlet) using gill nets. The standing stock was analyzed using Leslie method. Results shown that the initial population (No) of the fish was 315 kg and the final population (Nt) was 301 kg and the exploitation level was 8%. The water quality parameter shown that temperature was 27-28oc, clarity 6395 cm, pH = 6 and dissolved oxygen was 5.32–5.40 mg/L. Data obtained indicate the water quality in the Lubuk Siam Lake is good and it is able to support the life of O. hasselti.

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A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments

10.5194/egusphere-egu2020-4725 ◽

2020 ◽

Author(s):

Danlu Guo ◽

Anna Lintern ◽

Angus Webb ◽

Dongryeol Ryu ◽

Ulrike Bende-Michl ◽

...

Keyword(s):

Water Quality ◽

Statistical Model ◽

Temporal Variability ◽

Stream Water ◽

Spatial Scales ◽

Water Quality Monitoring ◽

Catchment Management ◽

Stream Water Quality ◽

Bayesian Hierarchical ◽

Spatio Temporal

<div> <div> <div> <div>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>2</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.</div> </div> </div> </div>

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

10.5194/hess-2020-681 ◽

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.

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

10.5194/hess-2019-342 ◽

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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Nitrate sinks and sources as controls of spatio-temporal water quality dynamics in an agricultural headwater catchment

Hydrology and Earth System Sciences ◽

10.5194/hess-20-843-2016 ◽

2016 ◽

Vol 20 (2) ◽

pp. 843-857 ◽

Cited By ~ 10

Author(s):

Tobias Schuetz ◽

Chantal Gascuel-Odoux ◽

Patrick Durand ◽

Markus Weiler

Keyword(s):

Water Quality ◽

Water Resource Management ◽

Nutrient Dynamics ◽

Stream Water ◽

Nitrate Removal ◽

Stream Water Quality ◽

Spatial And Temporal Patterns ◽

Stream Network ◽

Nitrate Sources ◽

Spatio Temporal

Abstract. Several controls are known to affect water quality of stream networks during flow recession periods, such as solute leaching processes, surface water–groundwater interactions as well as biogeochemical in-stream turnover processes. Throughout the stream network, combinations of specific water and solute export rates and local in-stream conditions overlay the biogeochemical signals from upstream sections. Therefore, upstream sections can be considered functional units which could be distinguished and ordered regarding their relative contribution to nutrient dynamics at the catchment outlet. Based on snapshot sampling of flow and nitrate concentrations along the stream in an agricultural headwater during the summer flow recession period, we determined spatial and temporal patterns of water quality for the whole stream. A data-driven, in-stream-mixing-and-removal model was developed and applied for analysing the spatio-temporal in-stream retention processes and their effect on the spatio-temporal fluxes of nitrate from subcatchments. Thereby, we have been able to distinguish quantitatively between nitrate sinks, sources per stream reaches, and subcatchments, and thus we could disentangle the overlay of nitrate sink and source signals. For nitrate sources, we determined their permanent and temporal impact on stream water quality and for nitrate sinks, we found increasing nitrate removal efficiencies from upstream to downstream. Our results highlight the importance of distinct nitrate source locations within the watershed for in-stream concentrations and in-stream removal processes, respectively. Thus, our findings contribute to the development of a more dynamic perception of water quality in streams and rivers concerning ecological and sustainable water resource management.

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