Biophysical interactions control the progression of harmful algal blooms in Chesapeake Bay: A novel Lagrangian particle tracking model with mixotrophic growth and vertical migration

Abstract

Abstract Climate change and nutrient pollution contribute to the expanding global footprint of harmful algal blooms. To better predict their spatial distributions and disentangle biophysical controls, a novel Lagrangian particle tracking and biological (LPT‐Bio) model was developed with a high‐resolution numerical model and remote sensing. The LPT‐Bio model integrates the advantages of Lagrangian and Eulerian approaches by explicitly simulating algal bloom dynamics, algal biomass change, and diel vertical migrations along predicted trajectories. The model successfully captured the intensity and extent of the 2020 Margalefidinium polykrikoides bloom in the lower Chesapeake Bay and resolved fine‐scale structures of bloom patchiness, demonstrating a reliable prediction skill for 7–10 d. The fully coupled LPT‐Bio model initialized/calibrated by remote sensing and controlled by ambient environmental conditions appeared to be a powerful approach to predicting transport pathways, identifying bloom hotspots, resolving concentration variations at subgrid scales, and investigating responses of HABs to changing environmental conditions and human interference.