Physical, chemical, and biological controls on algal dynamics in the Greenup Pool of the Ohio River

Large river ecosystems are complex and difficult to study. Spatial and temporal variability in managed river ecosystems is further complicated by structural alterations such as channelization, dams, and hydroelectric power stations. The increasing frequency and intensity of harmful algal blooms in lotic systems challenges the convention that productivity in these systems is low. A substantial algal bloom in the Ohio River in the fall of 2015 with a lesser bloom in the fall of 2019 demonstrated the need for research in this area. To characterize algal dynamics in managed rivers, and specifically the Ohio River, high frequency sensors were deployed on lock structures of the Robert C. Byrd and Greenup Dams bracketing the 100-km Greenup pool and providing continuous monitoring of pool conditions. Depth profiles at transect sampling locations were conducted through the pool’s entirety to profile spatial variability. Parameters measured included light, turbidity, chlorophyll-a, phycocyanin, specific conductance, temperature, pH and dissolved oxygen. These data are combined with USGS discharge data and nutrient data from multiple sources to examine temporal and spatial conditions in the pool as they pertain to algal growth and productivity. High turbidity and subsequent light limitation constrain algal growth throughout most of the year. Lower flow conditions in the late summer months demonstrated higher light penetration and limited thermal stratification and were associated with higher algal biomass as conditions approach those of a lentic system. Chlorophyll-a, indicating green algae or diatoms, dominated the late summer and fall algal communities during this investigation. Cyanobacteria, indicated by phycocyanin measurements, showed peaks in winter months with cooler temperatures and reduced pool retention, possibly indicating tributary sources. These data will support the development of models to advance harmful algal bloom (HAB) predictions and evaluation of management options. Results may be relavent to other managed river systems.