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Satellites predict lakes at risk from cyanobacteria and microcystin toxins
Citation:
Handler, A., J. Compton, Ryan A Hill, S. Leibowitz, AND B. Schaeffer. Satellites predict lakes at risk from cyanobacteria and microcystin toxins. Oregon Lakes Association Seminar Series, NA, Oregon, November 03 - December 01, 2021.
Impact/Purpose:
Cyanobacterial harmful algal blooms (HABs) can produce toxins which impair freshwater ecosystems used for drinking water, recreation, and habitat for aquatic biota. With reports of CHABs are increasing around the U.S., water managers need information about how to prioritize lake monitoring. This invited talk to the Oregon Lakes Association Seminar Series highlights an analysis from Pacific Ecological System Division scientists that combines data from the remote sensing Cyanobacteria Assessment Network (CyAN) and the National Lakes Assessments completed in 2007 and 2012 to predict the probability of CHABs, including toxins. By developing models based on the subset of lakes that are captured in the CyAN data and those that are sampled in the NLA, the probability of exceeding demonstration thresholds associated with HABs can be extrapolated to all lakes monitored via CyAN. The Seminar Series will attended by state officials, researchers, and stakeholders interested in Oregon lakes.
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 lakes at risk of toxic cyanobacteria blooms. We address this need by using satellite imagery and US-wide field surveys to model the probability of large lakes (>1.25 km2) exceeding lower and higher demonstration thresholds of microcystin toxin, cyanobacteria, and chlorophyll a. For every increase of 0.01 satellite cyanobacteria magnitude the odds of exceeding the threshold increase by 23–54%. Among the 2,192 satellite monitored lakes, those identified with high probability of exceeding the lower and higher thresholds included ≤335 and ≤70 lakes, respectively. Our approach represents a critical advancement in using satellite imagery to aid field bloom monitoring and identify management priorities.