Cyanobacterial harmful algal bloom forecasting with Bayesian modelling of large U.S. lakes and reservoirs
Citation:
Johnston, J., B. Schaeffer, W. Salls, D. Smith, AND M. Myer. Cyanobacterial harmful algal bloom forecasting with Bayesian modelling of large U.S. lakes and reservoirs. 2024 International Congress on Environmental Modelling and Software, East Lansing, MI, June 23 - 27, 2024.
Impact/Purpose:
No methodology exists to forecast cyanobacterial harmful algal blooms (cyanoHABs) nationally at weekly (or finer) timeframes. Freshwater cyanoHABs can pose human, animal, and environmental health risks in lakes and reservoirs. We developed a one-week-out forecasting approach predicting the probability of high-risk cyanobacterial harmful algal blooms (cyanoHABs) occurrence. Forecasts are analogous to weather forecasting, an early warning of possible blooms to be verified with field sampling and advisories posted as necessary. The R package INLA (Integrated Nested Laplace Approximation), a hierarchical, Bayesian, spatiotemporal model, was used to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance of >12 μg/L chlorophyll-a with cyanobacteria dominance, from 2192 satellite-resolved lakes in the United States. Modeling methodology developed from cyanoHAB bloom study in Florida and mosquito-borne disease and environmental modeling in Long Island, NY and Brownsville, TX
Description:
This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L−1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.
