Using Remote Sensing to Determine the Effects of Winter Water Level Drawdowns on Cyanobacteria Concentrations

Abstract:

Cyanobacteria harmful algal blooms (cyanoHABs) can pose major environmental, human health and economic threats. CyanoHABs deteriorate water quality and threaten aquatic ecosystem health as well as drinking water supply. Winter water level drawdowns (WDs) combined with anthropogenic climate change can stimulate cyanoHABs growth in inland lakes and reservoirs. WDs are commonly used to reduce excess shoreline biomass, protect the shoreline from high water erosion and mitigate flooding; however, climate change factors in combination with the magnitude and duration of drawdowns can negatively affect water quality and enhance the growth of harmful algal blooms, including cyanobacteria. Remote sensing has proven to be a useful tool in detecting and monitoring cyanoHABs through the use of satellite band combinations and algorithms. This study uses the Spectral Shape Algorithm to assess the presence of cyanobacteria and quantify its abundance using the Sentinel-3 OLCI dataset in various lakes and reservoirs in the Northeast United States. The chosen method measures chlorophyll-a as a proxy and calculates cyanobacteria biomass by centering the Spectral Shape Algorithm at the 681 nm for a baseline reflectance between the 665 nm and 709 nm wavelengths. The Spectral Shape Algorithm results are confirmed with in situ cyanobacteria samples and water levels. We expect the results of this study to help calibrate and validate regional models, with the overarching goal of informing the evaluation of current WD methods and providing recommendations for management techniques regarding potential impacts from climate change.