Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning

Harmful algal blooms of cyanobacteria (CyanoHAB) have emerged as a serious environmental concern in large and small water bodies including many inland lakes. The growth dynamics of CyanoHAB can be chaotic at very short timescales but predictable at coarser timescales. In Lake Erie, cyanobacteria blooms occur in the springsummer months, which, at annual timescale, are controlled by the total spring phosphorus (TP) load into the lake. This study aimed to forecast CyanoHAB cell count at sub-monthly (e.g., 10-day) timescales. Satellitederived cyanobacterial index (CI) was used as a surrogate measure of CyanoHAB cell count. CI was related to the in-situ measured chlorophyll-a and phycocyanin concentrations and Microcystis biovolume in the lake. Using available data on environmental and lake hydrodynamics as predictor variables, four statistical models including LASSO (Least Absolute Shrinkage and Selection Operator), artificial neural network (ANN), random forest (RF), and an ensemble average of the three models (EA) were developed to forecast CI at 10-, 20- and 30-day lead times. The best predictions were obtained by using the RF and EA algorithms. It was found that CyanoHAB growth dynamics, even at sub-monthly timescales, are determined by coarser timescale variables. Meteorological, hydrological, and water quality variations at sub-monthly timescales exert lesser control over CyanoHAB growth dynamics. Nutrients discharged into the lake from rivers other than the Maumee River were also important in explaining the variations in CI. Surprisingly, to forecast CyanoHAB cell count, average solar radiation at 30 to 60 days lags were found to be more important than the average solar radiation at 0 to 30 days lag. Other important variables were TP discharged into the lake during the previous 10 years, TP and TKN discharged into the lake during the previous 120 days, the average water level at 10-day lag and 60-day lag.

Automated summary

Harmful algal blooms of cyanobacteria (CyanoHAB) have become a serious environmental concern in various water bodies. The growth dynamics of CyanoHAB are predictable at coarser timescales but chaotic at very short timescales. This study aimed to forecast CyanoHAB cell count at sub-monthly timescales using satellite-derived cyanobacterial index (CI) as a surrogate measure. Four statistical models were developed and the best predictions were obtained using random forest (RF) and ensemble average (EA) algorithms. The results showed that coarser timescale variables and nutrients from rivers other than the Maumee River were important in explaining the variations in CI.