Improved Harmful Algal Blooms Prediction with Hybrid Models

Methods to quantify and predict vulnerability to harmful algal blooms (HABs) has not been developed for most lakes and reservoirs in the U.S. (and the world). This limits the ability for water quality stakeholders to 1) avoid costly emergency events, 2) efficiently design source water monitoring, 3) evaluate the impact of interventions, and 4) maintain trust with the public. Current approaches for detection and prediction of harmful algal blooms rely on infrequent satellite imagery and/or water samples, and provide predictions only at specific sites (i.e., where samples are taken). Furthermore, the models used to make predictions do not typically provide an ability to diagnose the specific drivers of HABs, beyond weather variables. The challenges confronting water quality stakeholders like the USACE are to 1) improve the spatial and temporal resolution of HAB predictions; 2) have the ability to diagnose the causes of HABs in managed reservoirs.

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