Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in an Estuary

Abstract

To improve the description of long-term changes in water quality, a weighted regression approach developed to describe trends in pollutant transport in rivers was adapted to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach allows for changes in the relationships between water quality and explanatory variables by using dynamic model parameters and can more clearly resolve the effects of both natural and anthropogenic drivers of ecosystem response. The model resolved changes in chlorophyll-a (chl-a) from 1974 to 2012 at seasonal and multi-annual time scales while considering variation associated with changes in freshwater influence. Separate models were developed for each of the four Bay segments to evaluate spatial differences in patterns of long-term change. Observed trends reflected the known decrease in nitrogen loading to Tampa Bay since the 1970s. Although median chl-a has remained constant in recent decades, model predictions indicated that variation has increased for upper Bay segments and that low biomass events in the lower Bay occur less often. Dynamic relationships between chl-a and freshwater inputs were observed from the model predictions and suggested changes in drivers of primary production across the time series. Results from our analyses have allowed additional insight into water quality changes in Tampa Bay that has not been possible with traditional modeling approaches. The approach could easily be applied to other systems with long-term datasets.