Key Factors Affecting Temporal Variability in Stream Water Quality. Issue 1 (8th January 2019)

• Record Type: Journal Article
• Authors: Guo, D.; Lintern, A.; Webb, J. A.; Ryu, D.; Liu, S.; Bende‐Michl, U.; Leahy, P.; Wilson, P.; Western, A. W.
• Is Part Of: Water resources research. Volume 55:Issue 1(2019)
• Page Start: 112
• Page End: 129
• Publication Date: 2019-01-08
• Subjects: water quality -- temporal variability -- nutrients -- statistical modeling -- Bayesian hierarchical model -- monitoring; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR023370
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Plain Language Summary: Water quality in rivers can change greatly over time. Understanding the causes of these changes is important for managing water quality. In this study, we used a statistical modeling approach to identify the influences of these temporal changes across 102 catchments in Victoria, Australia. The models were based on monthly measurements of water quality indicators (sediments, nutrients, and salts) obtained over 20 years. We find that the streamflow is the most important influence on temporal changes in water quality. Additional important drivers include soil moisture, recent streamflow, vegetation cover, and water temperature. The effects of these influences on the temporal patterns of water quality vary between catchments. Catchment managers could use the results to identify catchments and periods with poor water quality and thus to develop localized management strategies. Key Points: Streamflow is the most important factor that drives water quality temporal variability in most catchments Antecedent flows, soil moisture, vegetation cover, and water temperature also help to explain temporal variability Relationships between temporal variability of water quality and its driving factors vary between catchments