Key factors affecting temporal variability in stream water quality

D. Guo, A. Lintern, J. A. Webb, D. Ryu, S. Liu, U. Bende-Michl, P. Leahy, P. Wilson, A. W. Western

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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 (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.

Original languageEnglish
Pages (from-to)112-129
Number of pages18
JournalWater Resources Research
Issue number1
Publication statusPublished - 1 Jan 2019


  • Bayesian hierarchical model
  • monitoring
  • nutrients
  • statistical modeling
  • temporal variability
  • water quality

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