Spatiotemporal statistical modelling of long-term change in river nutrient concentrations in England &amp; Wales

Miller, Claire A.; Magdalina, A.; Willows, R.; Bowman, Adrian; Scott, E. Marian; Lee, D.; Burgess, C.; Pope, L.; Pannullo, Francesca; Haggarty, Ruth

doi:10.1016/j.scitotenv.2013.07.113

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…In contrast to sediment, our model suggests no clear shifts in the dynamics of nutrients and salts at a regional scale. Our findings are in line with a few previous studies that reported temporal changes in the concentrationdischarge relationships for sediments and nutrients, specifically when comparing high-and low-flow conditions (Zhang, 2018;Moatar et al, 2017) and drought and recovery periods (Burt et al, 2015). Our findings provide extra dimensions to what would be offered by simple trend analyses using ap-proaches such as the Mann-Kendall test or the Sen's slope (e.g., Smith et al, 1987;Chang, 2008;Hirsch et al, 1991;Bouza-Deaño et al, 2008).…”

Section: Potential Impacts Of Long-term Drought On Water Quality Dynasupporting

confidence: 90%

A data-based predictive model for spatiotemporal variability in stream water quality

Guo

Lintern

Webb

et al. 2020

Hydrol. Earth Syst. Sci.

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Section: Potential Impacts Of Long-term Drought On Water Quality Dynasupporting

confidence: 90%

A data-based predictive model for spatiotemporal variability in stream water quality

Guo

Lintern

Webb

et al. 2020

Hydrol. Earth Syst. Sci.

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“…Warmer periods are also correlated with periods with lower rainfall and streamflow. So another possible explanation of the positive temperature effects is that the lower surface flow in warmer periods reduces dilution of sediments and nutrients in streams (Houser & Richardson, ; Miller et al, ; Mulholland, ). However, this is less likely as our previous results suggest a relatively minor role of these dilution effects compared with flushing and erosion caused by surface flow (section Streamflow ).…”

Section: Discussionmentioning

confidence: 99%

Key Factors Affecting Temporal Variability in Stream Water Quality

Guo

Lintern

Webb

et al. 2019

Water Resources Research

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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 (NOx), 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 NOx 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.

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“…4 Across the EU, decreases in riverine orthophosphate-P concentrations of 2.1% per year on average in the two decades to 2012 have been ascribed to the mitigation measures implemented under the UWWTD. 5,6 The mean orthophosphate-P concentration in ca. 1000 EU rivers in 2012 was 0.06 mg P L À1 .…”

Section: Introductionmentioning

confidence: 99%

Orthophosphate-P in the nutrient impacted River Taw and its catchment (SW England) between 1990 and 2013

Tappin

Comber

Worsfold

2016

Environ. Sci.: Processes Impacts

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Excess dissolved phosphorus (as orthophosphate-P) contributes to reduced river water quality within Europe and elsewhere. This study reports results from analysis of a 23 year (1990-2013) water quality dataset for orthophosphate-P in the rural Taw catchment (SW England). Orthophosphate-P and river flow relationships and temporal variations in orthophosphate-P concentrations indicate the significant contribution of sewage (across the catchment) and industrial effluent (upper R. Taw) to orthophosphate-P concentrations (up to 96%), particularly during the low flow summer months when maximum algal growth occurs. In contrast, concentrations of orthophosphate-P from diffuse sources within the catchment were more important (>80%) at highest river flows. The results from a 3 end-member mixing model incorporating effluent, groundwater and diffuse orthophosphate-P source terms suggested that sewage and/or industrial effluent contributes ≥50% of the orthophosphate-P load for 27-48% of the time across the catchment. The Water Framework Directive (WFD) Phase 2 standards for reactive phosphorus, introduced in 2015, showed the R. Taw to be generally classified as Poor to Moderate Ecological Status, with a Good Status occurring more frequently in the tributary rivers. Failure to achieve Good Ecological Status occurred even though, since the early-2000s, riverine orthophosphate-P concentrations have decreased (although the mechanism(s) responsible for this could not be identified). For the first time it has been demonstrated that sewage and industrial effluent sources of alkalinity to the river can give erroneous boundary concentrations of orthophosphate-P for WFD Ecological Status classification, the extent of which is dependent on the proportion of effluent alkalinity present. This is likely to be a European - wide issue which should be examined in more detail.

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Spatiotemporal statistical modelling of long-term change in river nutrient concentrations in England & Wales

Cited by 12 publications

References 28 publications

A data-based predictive model for spatiotemporal variability in stream water quality

A data-based predictive model for spatiotemporal variability in stream water quality

Key Factors Affecting Temporal Variability in Stream Water Quality

Orthophosphate-P in the nutrient impacted River Taw and its catchment (SW England) between 1990 and 2013

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