Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States

• Published in: Earth's future 2024-08, Vol.12 (8), p.n/a
• Main Authors: Brehob, Meredith M., Pennino, Michael J., Handler, Amalia M., Compton, Jana E., Lee, Sylvia S., Sabo, Robert D.
• Format: Article
• Language: English
• Subjects: Algae; Algal blooms; Animal health; Aquatic ecosystems; Chlorophyll; Cyanobacteria; Datasets; Environmental conditions; Environmental factors; Environmental management; Environmental monitoring; Environmental protection; Eutrophication; Fisheries; Hypoxia; Lakes; machine learning; Nitrogen; Nutrient concentrations; Nutrient pollution; Nutrients; Phosphorus; Pollution; Recreation areas; Remote sensing; Risk; Satellites; Spatial data; Water quality; Watershed management
• ISSN: 2328-4277; 2328-4277
• DOI: 10.1029/2024EF004493

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. 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 in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of 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 ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in 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. Plain Language Summary When lakes receive large amounts of nutrients from the surrounding landscape due to fertilizer runoff or other sources of nutrient pollution, they can develop algal blooms. Algal blooms are harmful to the lake ecosystem and sometimes produce toxins which are dangerous to humans and animals. To assess this issue, lake chlorophyll‐a, a measure of algal presence, is monitored. This monitoring is limited in reach due to the expense of in‐lake sampling and the limited resolution of satellite technology. However, there is a wealth of climate, nutrient, landscape, and in‐lake characteristic data for the conterminous US (CONUS) which explains much of what contributes to nutrient pollution and algal grow