Exogenous drivers of lake water quality in the continental US
Lakes are known to respond to surrounding drivers of water quality, including geomorphology, landscape position, land cover, land use, and physical characteristics of the lake. In this study, we are using regression tree analysis and general linear models to quantify the extent to which these exogenous drivers predict key in-lake water quality variables across the continental scale, including total nitrogen and phosphorus, chlorophyll-a, dissolved organic carbon, and conductivity using the EPA’s National Lakes Assessment (NLA) data. This EPA NLA dataset is one of the largest and most consistent water quality datasets available, and includes measures of hundreds of variables for >1,000 lakes across the continental US, and description of land use at the watershed and lake buffer scales. In addition, we have collected and derived several additional explanatory variables including road density and proximity, soil type, and lake residence time. We aim to identify the most important drivers of water quality at the regional and continental scales, and the processes that underlie them.