Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

Abstract

Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.