For State agencies and other water-resources managers, determining which waterbodies to allocate limited funds for protection and restoration while also maximizing cost benefit is challenging. This data release contains trophic state designations determined from secchi depth, and concentrations of chlorophyll a and microcystin at 232 lakes and reservoirs having a surface area of greater than 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Estimates of nutrient loading (nitrogen and phosphorus, Hoos and others, 2013; Moorman and others, 2014) and flushing rates were combined with waterbody morphometry (Hollister and Milstead, 2010; Hollister and others, 2011; U.S. Environmental Protection Agency, 2018) to predict summer-season Secchi depth and concentrations of chlorophyll a and microcystin. Waterbodies were categorized by type — natural lakes, headwater reservoirs, and downstream reservoirs — and were assessed independently. Recursive partitioning and the model-based boosting routine were implemented in a R script, which is provided in this data release. The script was used to create four-node regression trees that group waterbodies into five endpoints along individual low-to-high gradients of Secchi depth, chlorophyll a concentration, and microcystin concentration, according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned on the basis of the average values within each of the five endpoints. These regression trees can be used to place all waterbodies within the study area greater than or equal to 0.1 square kilometer into one of the different Secchi depth, chlorophyll a, or microcystin endpoints.
Results of this study will aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication related to nutrient loading, flushing rate, and morphometric characteristics.
References:
Hollister J.W., and Milsted W.B., 2010, Using GIS to estimate lake volume from limited data: Reservoir Management, vol. 26, pp. 194-199.
Hollister J.W., Milstead W.B., Urrutia M.A., 2011, Predicting maximum lake depth from surrounding topography: PloS ONE, vol. 6, article no. 25764, https://doi.org/10.1371/journal.pone.0025764
Hoos, A.B., Moore, R.B., Garcia, A.M., Noe, G.B., Terziotti, S.E., Johnston, C.M., and Dennis, R.L., 2013, Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000 scale hydrography: U.S. Geological Survey Scientific Investigations Report 2013-5102, 33p.
Moorman, M.C., Hoos, A.B., Bricker, S.B., Garcia, A.M., and Ator, S.W., 2014, Nutrient load summaries for major lakes and estuaries of the eastern United States, 2002: U.S. Geological Survey Data Series 820, 94p.
U.S. Environmental Protection Agency, 2018, Data from the national aquatic resource surveys: U.S. Environmental Protection Agency database, accessed February 8, 2018 at https://edg.epa.gov/clipship/. [Data downloaded for AL, FL, GA, MS, NC, SC, TN, VA, CT, DE, MA, ML, ME, NY, NH, NJ, PA, and RI]
U.S. Geological Survey, 2017, Forecasting toxic cyanobacterial blooms throughout the southeastern U.S., accessed February 13, 2017 at http://wilsonlab.com/bloom_network/. [U.S. Geological Survey project homepage on Wilsonlab at Auburn University website]
