Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Xomchuk, Veronica Ruiz; Duncan, Scott; McFarland, Malcolm; Beckler, Jordon · 2023 · Crossref

DOI: 10.21203/rs.3.rs-3280968/v1

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Summary

This study addresses the limitations of traditional Harmful Algal Bloom (HAB) monitoring methods, which often fail to capture the high-frequency temporal dynamics and fine-scale spatial heterogeneity of blooms in large, turbid lakes. Motivated by the increasing prevalence of cyanobacterial HABs in Lake Okeechobee, the authors deployed a long-duration Autonomous Surface Vehicle (ASV), the Nav2 Sail and Solar Drone, to provide continuous, high-resolution in situ monitoring. The goal was to detect bloom formation, track motion versus growth, and discriminate between species assemblages more effectively than fixed stations or satellite remote sensing. The Nav2, a 2-meter autonomous vessel powered by solar and wind energy, operated continuously in the northern region of Lake Okeechobee for one year (December 2020–January 2021). It was equipped with fluorometric sensors for chlorophyll-a (chl-a), phycocyanin (phyco), and colored dissolved organic matter (CDOM), alongside sensors for backscatter, temperature, conductivity, dissolved oxygen, and an Acoustic Doppler Current Profiler. The ASV navigated predefined waypoints, including a primary "HALO loop" and opportunistic grids, transmitting near real-time data every 15 minutes while logging high-frequency measurements (<1 minute intervals). Calibration involved laboratory standards for initial setup and routine solid-state field checks to monitor sensor drift and biofouling. Key findings demonstrate the ASV’s ability to detect HABs earlier than other methods, identifying *Dolichospermum* blooms in early 2021 before official reports. The high-resolution data revealed distinct seasonal trends, with *Microcystis* dominance from June to November, and captured complex spatio-temporal dynamics, such as bloom displacement speeds of approximately 1.5 km/day. Crucially, the study showed that the spatial variability of the phyco-to-chl-a ratio could serve as a "fingerprint" for bloom composition; for instance, *Microcystis*-dominated blooms exhibited significantly higher spatial patchiness and ratio variability compared to the more homogeneous *Dolichospermum* blooms. While absolute concentration validations against grab samples were poor due to high turbidity, solid-state checks confirmed sensor stability, with minimal drift (<5% for chl-a) over the deployment period. The significance of this work lies in demonstrating that long-duration ASVs can provide unique, high-fidelity data on HAB macrostructure and dynamics in challenging environments. The platform’s ability to distinguish between bloom growth and physical transport, along with its potential for species-specific fingerprinting via spatial signal analysis, offers water resource managers a powerful tool for early warning and targeted mitigation. This approach complements existing fixed and remote sensing networks by filling critical gaps in spatial and temporal resolution.

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