Quantifying Variability in Four US Streams Using a Long-Term Data Set: Patterns in Water Quality Endpoints

Abstract

Temporal and spatial patterns of variability in aquatic ecosystems can be complex and difficult to quantify or predict. However, understanding this variability is critical to making a wide range of water quality assessment and management decisions effectively. Here we report on the nature and magnitude of spatial and temporal variation observed in conductivity, total phosphorus, and total nitrogen during a 15-year study of four U.S. stream systems receiving pulp and paper mill effluent discharges. Sampling locations included mainstem sites upstream and downstream of effluent discharge, as well as tributary sites. In all four stream systems, variability in conductivity as measured by the coefficient of variation was typically in the range of 10-50%, and was as low or lower than the variability in nutrient endpoints. The effect of effluent discharge was relatively minor overall, except in some site-specific instances. Some relatively large differences between tributary and mainstem variability were also observed. Flow variation tended to have a more consistent and larger effect on conductivity variation compared to the nutrient endpoints. After removing flow effects, significant relatively complex trends over time were observed at several sites. Changes in variability during the study also were observed. This paper highlights the importance of long-term studies to accurately characterize water quality variability used in water quality management decision-making.

Development of a Water Quality Index With a Reduced Number of Parameters

“…To avoid any possible problems with statistical analysis, we have proceeded with the preparation of the database. In the pre-treatment of the censured values, the methodologies used were those used in works such as McLaughlin & Flinders (2016) and Oliveira et al (2018a). According to this methodology, the values found below the minimum limit for detection are replaced by half the minimum limit for detection.…”

Using adaptive processes and adverse outcome pathways to develop meaningful, robust, and actionable environmental monitoring programs

“…In contrast, nonparametric bootstrapping is limited to the empirical distribution, whereas parametric methods can approximate missing data using an assumed distribution. Similar to sampling in a given season, triggers can be further refined to account for the influence of environmental covariates (McLaughlin and Flinders ). Triggers can be developed for specific study sites, localities, and regions, but an optimal initial configuration focuses on change at individual study sites over time (Arciszewski and Munkittrick ).…”

Developing and applying control charts to detect changes in water chemistry parameters measured in the Athabasca River near the oil sands: A tool for surveillance monitoring

“…The control chart has value as a tool to analyze data and statistical methods better suited to the purpose of the study and the attributes of the data can also be adopted to improve sensitivity. More specifically, the sensitivity of control charts for detecting change in concentrations of chemicals in water can be increased by accounting for covariates, such as discharge (Hirsch et al 2010;McLaughlin and Flinders 2016), and the effects of analytical constraints, such as values censored below a detection limit (Helsel and Hirsch 2002;Helsel 2012). Both of these common attributes can be addressed using various and appropriate regression techniques (Helsel and Hirsch 2002;Koenker 2008;Helsel 2012).…”