Citation:

Myer, M., E. Urquhart, B. Schaeffer, AND JohnM Johnston. Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida. Frontiers in Environmental Science. Frontiers, Lausanne, Switzerland, 8:581091, (2020). https://doi.org/10.3389/fenvs.2020.581091

Impact/Purpose:

Water quality managers need forecasting capability to support recreational and drinking water decisions specifically related to cyanobacterial harmful algal blooms. Current satellite technology allows for the near-real time monitoring of cyanobacteria biomass but does not currently provide robust forecasting capabilities, such as the probability of a bloom occurring in a particular lake in the coming week. This study integrated the near-real time monitoring of satellite derived cyanobacteria concentrations with a model that handles both spatial and temporal measures to demonstrate a future probability of high health risk blooms. This model was successfully demonstrated for the state of Florida as a case study to illustrate its potential to provide reliable early warning.

Description:

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.

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