Key Factors Affecting Temporal Variability in Stream Water Quality

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.

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

“…The selection of key spatial and temporal predictors for the model was performed in our two preceding studies (Lintern et al, 2018b;Guo et al, 2019) and is briefly described in Sect. 2.1.3.…”

“…Equations (1)-(4) enable the model to separately represent the spatial and temporal variability in water quality; however, there is still a further step required to make the model fully spatiotemporal (i.e., able to predict over both time and location). Specifically, in Guo et al (2019), clear spatial variation was observed in the relationships between water quality and its key temporal predictors (i.e., in the βT N,j in Eq. 4).…”

“…The Bayesian hierarchical model was developed using 21 years of monthly stream water quality observations from 102 catchments in the state of Victoria, Australia (aggregate catchment area greater than 130 000 km 2 ). The collection and processing of the data are detailed in previous publications that worked with the same dataset (Lintern et al, 2018b andGuo et al, 2019). Briefly, stream water quality data were extracted from the Victorian Water Measurement Information System (Department of Environment, Land, Water and Planning Victoria, 2016), which contains monthly grab samples of water quality at approximately 400 sites across Victoria.…”

“…In addition, catchment-averaged monthly normalized difference vegetation index (NDVI) data were extracted from the Advanced Very High Resolution Radiometer (AVHRR) product (Eidenshink, 1992) and the Moderate Resolution Imaging Spectroradiometer MOD13A3 product (NASA LP DAAC, 2017). A summary of these datasets of temporal variables and their corresponding sources are given in Table S2, and details are provided in Guo et al (2019).…”

“…Specifically, this model was established using long-term (21 years) stream water quality observations across 102 catchments in Australia, with an aggregate catchment area of more than 130 000 km 2 . To obtain the necessary understanding of the process drivers required to develop this model, two preceding studies were conducted on the same dataset to identify the key drivers for the spatial and temporal variability of water quality, respectively (Lintern et al, 2018b;Guo et al, 2019). The aim of this study is to develop an integrated spatiotemporal model using the previously identified spatial and temporal predictors and to then assess the performance of this model.…”