A Bayesian spatiotemporal model evaluation of forecasting cyanobacterial harmful algal bloom events.

The U.S. Harmful Algal Bloom and Hypoxia Research Control Act calls for robust approaches to forecasting cyanobacterial harmful algal blooms (cyanoHABs). Accurate forecasting technology could save local communities healthcare costs through the early detection of cyanoHABs and therefore faster advisory warnings. However, most existing forecasting models require time-consuming parametrization and/or are limited to well-sampled individual lake systems. An Integrated Nested Laplace Approximation hierarchical Bayesian spatiotemporal model forecasted weekly lake exceedance of 12 μg/L chlorophyll-a, the World Health Organization’s recreation Alert Level 1 threshold, for 2192 satellite resolved lakes.  Model deficiencies were evaluated to improve the functionality of the forecast. We investigate if temporally short events may commonly occur as false negatives, and if reoccurring annual events are prematurely forecasted, resulting in a false positive. We also consider the impacts of lake type and geographic location on these predictions. This evaluation identifies key targets for model improvement with the goal of making the forecast operational in the future.

Primary Presenter: Kate Meyers, Oak Ridge Institute for Science and Education (Meyers.Kate@EPA.gov)

Authors:

Kate Meyers, Oak Ridge Institute for Science and Education  (meyers.kate@epa.gov)

Blake Schaeffer, Environmental Protection Agency  (Schaeffer.Blake@epa.gov)

Olivia Cronin-Golomb, Oak Ridge Institute for Science and Education  (CroninGolomb.Olivia@epa.gov)

Wilson Salls, Environmental Protection Agency  (salls.wilson@epa.gov)