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Numerical and Qualitative Contrasts of Two Statistical Models for Water Quality Change in Tidal Waters
Citation:
Beck, M. AND R. Murphy. Numerical and Qualitative Contrasts of Two Statistical Models for Water Quality Change in Tidal Waters. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, 53(1):197-219, (2017).
Impact/Purpose:
This manuscript describes a quantitative comparison of two recently-developed statistical models that evaluate long-term time series of water quality in estuaries. Both methods provide similar products through different means and the relative empirical capabilities of each have not been compared.
Description:
Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and products. This study provided an empirical and qualitative comparison of both models using 29 years of data for two discrete time series of chlorophyll-a (chl-a) in the Patuxent River estuary. Empirical descriptions of each model were based on predictive performance against the observed data, ability to reproduce flow-normalized trends with simulated data, and comparisons of performance with validation datasets. Between-model differences were apparent but minor and both models had comparable abilities to remove flow effects from simulated time series. Both models similarly predicted observations for missing data with different characteristics. Trends from each model revealed distinct mainstem influences of the Chesapeake Bay with both models predicting a roughly 65% increase in chl-a over time in the lower estuary, whereas flow-normalized predictions for the upper estuary showed a more dynamic pattern, with a nearly 100% increase in chl-a in the last 10 years. Qualitative comparisons highlighted important differences in the statistical structure, available products, and characteristics of the data and desired analysis.
