Abstract
Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016-June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.
Highlights
Harmful algal blooms are environmental events that occur when algal populations achieve sufficiently high density resulting in possible adverse ecological and public health effects (Smayda, 1997)
We present a hierarchical Bayesian spatio-temporal modeling approach in R-Integrated Nested Laplace Approximation (INLA) to estimate the likelihood of highrisk cyanoHABs in Florida inland waterbodies
Using Deviance Information Criterion (DIC) to evaluate model performance, the full spatio-temporal model (M4, Table 2) was selected as the best model and used to forecast the likelihood of bloom occurrence across Florida lakes for a week outside of the dataset, with Area Under Curve (AUC) 0.93 (Table 5)
Summary
Harmful algal blooms are environmental events that occur when algal populations achieve sufficiently high density resulting in possible adverse ecological and public health effects (Smayda, 1997). Several of Florida’s largest aquatic systems including Lake Okeechobee (Havens et al, 1998; Havens and Steinman, 2015); the Harris Chain of Lakes (Williams et al, 2001, 2007); and the St. Johns, St. Lucie and Caloosahatchee (Glibert et al, 2006; Boyer and FitzPatrick, 2016; United States Army Corps of Engineers, 2016) rivers have experienced the increasing adverse impacts of cyanoHABs. In 2005, the St. Johns County Department of Health released Florida’s first official health alert for a toxigenic harmful algal bloom. Strong storms resulted in reservoir operators increasing the outflow from Lake Okeechobee causing the incursion of a toxic M. aeruginosa bloom into the St. Lucie Estuary (Boyer and FitzPatrick, 2016; United States Army Corps of Engineers, 2016)
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