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Coastal Freshwater HABs
Citation:
Detenbeck, N., S. Rego, T. Wolters, AND C. Connolly. Coastal Freshwater HABs. Presented at EPA Region 1 Cyanobacteria Collaborative Meeting, Chelmsford, MA, April 16, 2026.
Impact/Purpose:
Cyanobacteria have recently been detected in coastal systems in the U.S., with cyanotoxins measured in shellfish in downstream estuaries at levels of concern for human health. Goal 4 of EPA's draft Harmful Algal Blooms (HABs) Strategy highlights EPA's critical role in the Advancement of Monitoring and Forecasting Capabilities for HABs in Freshwater, Estuarine and Coastal Marine Systems. Chlorophyll (chla), cyanobacterial counts, and cyanotoxins are not routinely monitored in coastal rivers and estuaries so an efficient approach to detecting blooms through remote sensing is needed to focus more detailed monitoring and to assess the environmental conditions associated with these blooms. This presentation for the EPA Region 1 Cyanobacteria Collaborative Meeting provides an overview of this emerging issue and work EPA researchers have done to develop and apply methods for mapping algal blooms in estuaries with the aid of remote sensing. This research supports EPA Pillars 1 (Clean Air, Land, and Water for Every American) and 3 (Permitting Reform, Cooperative Federalism, and Cross-Agency Partnership).
Description:
Cyanobacteria have recently been detected in coastal systems in the U.S., with cyanotoxins measured in shellfish in downstream estuaries at levels of concern for human health. Goal 4 of EPA’s draft Harmful Algal Blooms (HABs) Strategy highlights EPA’s critical role in the Advancement of Monitoring and Forecasting Capabilities for HABs in Freshwater, Estuarine and Coastal Marine Systems. Chlorophyll (chla), cyanobacterial counts, and cyanotoxins are not routinely monitored in coastal rivers and estuaries so an efficient approach to detecting blooms through remote sensing is needed to focus more detailed monitoring and to assess the environmental conditions associated with these blooms. Challenges in applying remote sensing to map chlorophyll in estuaries include constraints for spatial and temporal resolution of various satellites, optically complex waters, and choice of appropriate cloud/cloudshadow masking methods, atmospheric corrections (AC), and chlorophyll algorithms. EPA-ORD previously published EstuarySat, a paired database of Sentinel 2 satellite and ground-truthing observations for the conterminous U.S. to facilitate evaluation of methods for coastal systems (Rego et al. 2024). We subsequently used a random stratified approach to select a subset of observations from EstuarySat for testing along gradients of salinity, turbidity, and chlorophyll. Our analyses demonstrated that the Acolite method outperformed the Polymer AC method. The best performing chlorophyll algorithm for estuaries was the multidensity network machine learning approach of Pahlevan et al. (2020) (Wolters et al. 2025). We are applying this methodology in a Region 10 ROAR project. Using EPA’s High Performing Computer (HPC) resources, we have developed workflows to standardize this approach for mapping chlorophyll time series for Puget Sound, WA, with the ultimate goal of developing a predictive model for freshwater HABs in estuaries. With the recent release of near real-time AC-corrected (Acolite) Sentinel 2 imagery by U.S. Geological Survey, we hope that these approaches can be applied to other systems in the near future.