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Prediction of Harmful Algal Blooms in Lake Erie At Sub-Monthly Timescales
Citation:
Gupta, A., G. Mallaya, M. Hantush, AND R. Govindaraju. Prediction of Harmful Algal Blooms in Lake Erie At Sub-Monthly Timescales. 2021 World Environment and Water Resources Congress, N/A, N/A, June 11, 2021.
Impact/Purpose:
Develop a regression model for predicting Harmful Algal Plume (HABs) concentrations at the sub-monthly 10-day time scale in Lake Erie. The model has the potential to be a useful tool for the management of HABs outbreaks.
Description:
The growth dynamics of harmful algae Cyanobacteria (CyanoHAB) are highly variable over various temporal scales. At sub-daily timescales, CyanoHAB concentrations exhibit chaotic behavior. However, at annual timescales, these concentrations can be predicted by available nutrients and ambient air temperature. In Lake Erie, Cyanobacteria blooms occur in spring-summer months. The lake's annual maximum concentration of algae cells can be predicted by total spring phosphorus (TP) load into the lake. But the prediction of CyanoHAB concentrations at shorter timescales has rarely been attempted. This study aims to predict CyanoHAB concentrations at sub-monthly (e.g., 10 day) timescales. Prediction of CyanoHAB concentrations at short timescales is useful for watershed managers because it releases toxic compounds called cyanotoxins that may be fatal to other aquatic animals and potentially to humans also. A measure of CyanoHAB can be obtained by satellite images using Cyanobacterial index (CI) as a surrogate variable. HAB growth is affected by several environmental and lake hydrodynamics variables such as available TP, total nitrogen (TN), nitrates (NO3), ammonium (NH4), wind-speed, air temperature, solar radiation, thermal stratification status, depth of water, and residence time of water. With these predictor variables, a statistical model was developed to predict CI, and preliminary results of this modeling effort will be presented.