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Using national datasets surveys to identify lakes at-risk for toxic cyanobacteria blooms in the U.S.
Citation:
Handler, A., J. Compton, Ryan A Hill, S. Leibowitz, B. Schaeffer, AND M. Dumelle. Using national datasets surveys to identify lakes at-risk for toxic cyanobacteria blooms in the U.S. Region 10 Quarterly State HABs Meeting, Corvallis, OR, November 07, 2022.
Impact/Purpose:
Region 10 States Quarterly HABs Meeting (Nov 7): CPHEA’s Amalia Handler was invited to present at the Region 10 quarterly meeting about harmful algal blooms with state partners. Amalia will present her work with CPHEA and CEMM researchers to build models for lake HABs risk across the country. These periodic meetings are an excellent forum for receiving feedback on analyses in order to better meet states’ needs.
Description:
Algal blooms caused by toxin-producing cyanobacteria are a threat to global water resources and human health. Water resource managers need tools that identify which lakes are at risk of toxic cyanobacteria blooms. We address this need by using Cyanobacteria Assessment Network satellite imagery and US EPA National Lakes Assessment field surveys to model the probability of large lakes exceeding lower and higher demonstration thresholds of microcystin toxin, cyanobacteria, and chlorophyll a. For every satellite-derived Cyanobacteria Index (CI_cyano) increase of 0.01 (CI_cyano/km2) the odds of exceeding the six thresholds increased by 23–54%. When applied to all 2,192 satellite monitored lakes, the models identified lakes with ≥75% probability of exceeding the lower or higher thresholds as less than 336 and less than 70 lakes, respectively. Our approach identified lakes that are at higher risk of experiencing toxic algal blooms and may require additional monitoring. We compiled lake water chemistry, lake morphology, watershed characteristics, and basin climate data to evaluate potential predictors of blooms. In addition, we compared models with and without lake physical and chemical data to assess the need for in-lake information to determine lakes at higher risk for experiencing blooms. These approaches are a critical advancement in using national datasets to determine which lakes may require additional field monitoring and management priorities.