Clustering analysis of the <i>Sargassum</i> transport process: application to beaching prediction in the Lesser Antilles

Abstract

The massive Sargassum algae beachings observed over the past decade are a new natural hazard currently impacting the island states of the Caribbean region (human health, environmental damages, and economic losses). This study aims to improve the prediction of the surface current dynamic leading to beachings in the Lesser Antilles using clustering analysis methods. The input surface currents were derived from the Mercator model and the Hybrid Coordinate Ocean Model (HYCOM) outputs in which we integrated the windage effect. Past daily observations of Sargassum beaching on Guadeloupe coasts and satellite-based Sargassum offshore abundance were also integrated. Four representative current regimes were identified for both Mercator and HYCOM data. The analysis of the current sequences leading to beachings showed that the recurrence of two current regimes is related to the beaching peaks respectively observed in March and August. The performance score of the predictive model showed that the HYCOM data seem more suitable to assess coastal Sargassum hazard in the Lesser Antilles. For 1 year of tests (i.e., 2021), the decision tree accuracy respectively reached 70.1 % and 58.2 % for HYCOM and Mercator with a temporal uncertainty range ±3 d around the forecast date. The present clustering analysis predictive system, requiring lower computational resources compared to conventional forecast models, would help improve this risk management in the islands of the region.