Estimates of lake nitrogen, phosphorus, and chlorophyll-a concentrations to characterize harmful algal bloom risk across the United States
Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human health via cyanotoxin formation. Efforts to quantify the occurrence, drivers and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll-a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms, we used spatially explicit and temporally matched climate, landscape, in-lake characteristic, and nutrient inventory datasets to predict nutrients and chlorophyll across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in TN, TP, and chlorophyll-a concentrations across US EPA’s National Lakes Assessment (n=2000 lakes). Concentrations of TN and TP were the most important predictors and, along with other variables, the RF model accounted for 68% of the variation in chlorophyll-a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll-a for ~70,000 lakes across the CONUS. Risk for high chlorophyll-a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in other nutrient pollution hot spots across the country. These catchment and lake-specific results can help managers identify potential nutrient pollution and chlorophyll-a hot spots that may fuel blooms, prioritize at-risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.