AUTHOR=Yu Xin , Tomlinson Michelle C. , Shen Jian , Li Yizhen , Hounshell Alexandria G. , Scott Gail P. , Reece Kimberly S. 

TITLE=Using a coupled satellite image-numerical model framework to simulate Margalefidinum polykrikoides in the York River estuary

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1561340

DOI=10.3389/fmars.2025.1561340

ISSN=2296-7745

ABSTRACT=Recent advances in satellite remote sensing technology for detecting harmful algal blooms (HABs) make it possible to combine numerical modeling approaches and satellite imagery to track and predict HABs in estuarine and coastal waters. We employed a particle-tracking model using a high-resolution hydrodynamic model capable of simulating algal mixotrophic growth, respiration, and vertical diurnal migration to predict the spatial distribution and temporal evolution of a Margalefidinium polykrikoides (M. polykrikoides) bloom in the lower York River, VA USA, where HABs have occurred nearly annually over the past decade. Particle release location and density were determined by chlorophyll-a concentrations obtained from Ocean Land Colour Imager (OLCI) satellite imagery collected during August-September 2022. Numerous high-quality satellite images (n=34) available in the two-month bloom period allow for a comprehensive examination of the model framework. Here, we demonstrate the potential of the coupled satellite-model framework to predict short-term bloom movement by comparing model predictions and satellite observations 1-5 days after the particle release date. We also carried out sensitivity tests and found that setting a maximum swimming depth and including sub-surface aggregation depth for phytoplankton vertical migration substantially improved and advanced the model performance. True positive prediction (TPP; an index used to quantify model performance) for bloom 3 days after particle release increases from 50% in base setup to ~70% when including sub-surface aggregation at 2 m and maximum swimming depth of 5 m. Overall, model evaluation results show that a combined numerical modeling and satellite remote sensing approach is an effective way to track HABs in the York River estuary and provides a framework to forecast HAB location and intensity for coastal managers in the lower Chesapeake Bay and other coastal and estuarine waters.