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Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in Tampa Bay, Florida
Citation:
Beck, M. AND Jim Hagy. Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in Tampa Bay, Florida. Presented at 9th Biennial National Water Quality Monitoring Council Conference, Cincinnati, OH, April 28 - May 04, 2014.
Impact/Purpose:
Discuss weighted regression approach developed to describe trends in pollutant transport in streams and rivers to analyze a long-term water quality dataset from Tampa Bay, Florida
Description:
The increasing availability of long-term monitoring data can improve resolution of temporal and spatial changes in water quality. In many cases, the fact that changes have occurred is no longer a matter of debate. However, the relatively simple methods that have been used to evaluate trends in environmental monitoring data in estuaries are often not sufficient to disaggregate the complex effects of multiple environmental drivers, limiting the potential to relate changes to possible causes. To improve the description of long-term changes in water quality, we adapted a weighted regression approach developed to describe trends in pollutant transport in streams and rivers 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 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 4 Bay segments to evaluate spatial differences in patterns of long-term change. Observed trends reflected the known long term decrease in nitrogen loading to Tampa Bay since the 1970s;, however, more subtle changes in seasonal variability and unexplained variance were resolved in ways that have not been described previously. Significant variation in the model residuals was explained by considering additional covariates such as the El Niño-Southern Oscillation patterns and estimated nutrient loading. Results from our analyses have allowed additional insight into drivers of water quality change in Tampa Bay that has not been possible with traditional modeling approaches and could help monitor and sustain the progress of the successful nitrogen management program. The method could also be applied to water quality management in other estuarine systems where long-term monitoring data is available.