Forecasting Cyanobacteria Cell Counts in Lakes Based on Hyperspectral Sensing

We developed a forecast model for cyanobacteria bloom formation in two contrasting inland lakes in Australia by combining in situ sampling and continuous remote sensing hyperspectral reflectance (HydraSpectra) with hydrodynamic and algal growth models. Cyanobacterial distribution of a buoyant species was simulated with an algal growth model, driven by forecasted meteorological data, and modeled temperature stratification and mixing dynamics from a one-dimensional, vertical k-epsilon turbulence hydrodynamic model. The cyanobacteria model was re-initialized daily with measured cell counts derived from the hyperspectral reflectance data. Simulations of cyanobacterial concentrations (cell counts) reflected the dynamic mixing behavior in the lakes with daily phases of near-surface accumulation and subsequent daily mixing due to wind or night-time cooling. To determine the surface concentration of cyanobacteria on sub-daily time scales, it was demonstrated that the combined use of high-resolution water temperature profiles, HydraSpectra reflectance data, and a hydrodynamic model to quantify the mixing dynamics is essential. Overall, the model results demonstrated a prototype for a cyanobacteria short-term forecast model. Having these tools in place allows us to quantify the risks of cyanobacterial blooms in advance to inform options for lake management.