Risks for cyanobacterial harmful algal blooms due to land management and climate interactions.


Department of Geography, University of Georgia, Athens, GA 30602, USA. Electronic address: [Email]


The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.


Data analytics,Google Earth Engine,Land Use Land Cover,Risk modeling,Sentinel-2A,Watershed modeling,

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