Developing a 21st Century framework for lake-specific eutrophication assessment using quantile regression

Xu, Y., Schroth, A., & Rizzo, D.M. (2015) Developing a 21st Century framework for lake-specific eutrophication assessment using quantile regression. Limnol. Oceanogr.: Methods, 13, 237–249.

Over the past 301 years, researchers and water resource managers have often relied on a set of regression-based equations to describe the relationships between secchi depth (SD), chlorophyll (Chl) and total phosphorous (TP) and quantitatively assess lake trophic status after Carlson (1977). Here, we develop a revised framework for eutrophication assessment that incorporates recent statistical advances in ecology and leverages the increasing availability of lake-specific datasets in the 21st Century. Long-term (1992–2012) water quality data from Lake Champlain (LC) are used to revisit and revise classic equations of tropic state indices (TSIChl/TP). The upper boundaries of SD–ln(Chl) and ln(Chl)–ln(TP) distributions within this dataset fit well with quantile regression (99th, QR) to generate LC-specific TSIChl/TP equations. Our results illustrate that Carlson (1977)’s original TSIChl/TP equations overestimate the trophic status of LC relative to LC-specific equations, and highlight the power of the QR-derived TSIChl/TP metric. We combine TSISD and TSIChl into one metric to indicate pseudoeutrophication and pseudomesotrophication of oligotrophic waters as well as pseudoeutrophication of mesotrophic waters to identify waters threatened by potential trophic shift. Additionally, TSIChl and TSITP were coupled as a complimentary dual metric to indicate potential risks of excessive phosphorus loading to oligotrophic and mesotrophic waters. With these dual metric schemes, we performed cluster analysis of 15 locations to spatially assess trophic status and phosphorous risks across LC. This study describes a relatively simple and robust approach for lake-specific status assessment, the structure of which can be broadly utilized within monitoring and research communities.