Algal blooms in a freshwater reservoir – A network community detection analysis of potential forcing parameters

Abstract

Nutrient leakage due to modern agriculture and disposal of untreated urban wastewater results in the eutrophication of freshwater lakes and reservoirs, with elevated levels of cyanobacteria algal blooms often resulting in toxic conditions for animals and humans. A unique dataset of 22 coincident nutrient, phytoplankton, meteorological, and reservoir condition variables were recorded or calculated monthly for Hoover Reservoir near Columbus, Ohio, from February 1999 to December 2005. Network science was used in this study to visualize and differentiate selected nutrient and phytoplankton seasonality in Hoover Reservoir. Nutrient and phytoplankton concentrations in Hoover Reservoir respond independently to a number of external and internal environmental variables, but are also biochemically interdependent within the reservoir ecosystem. A number of forcing parameters can significantly alter seasonal phytoplankton growth and succession patterns. Chief among these are variability in the daily, seasonal, and/or yearly patterns and intensities of precipitation and the resultant surface runoff; duration and intensity of solar radiation; air and resultant water temperatures; and agricultural practices. The phytoplankton population in Hoover Reservoir is ecologically driven, with algal succession and nutrient levels constraining population concentrations. Complemented by selected statistical analyses, this limited network community detection study provides the hydrologist/ecologist with a dynamic and quantitative visualization of the complex hydrometeorological variable and biogeochemical process forcing parameters present in reservoirs and lakes.