Clustering analysis of the Sargassum transport process: application to stranding prediction in the Lesser Antilles

Abstract

Abstract. The massive Sargassum algae strandings observed over the past decade are the new natural hazard that currently impacts 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 including windage effect were derived from the Mercator model and the Hybrid Coordinate Ocean Model (HYCOM). Past daily observations of Sargassum stranding on Guadeloupe coasts were also integrated. Four representative current regimes were identified for both Mercator and HYCOM data. The analysis of the backward current sequences leading to strandings showed that the recurrence of two current regimes is related to the beaching peaks observed respectively in March and in August. A decision tree classifier was built and its accuracy reaches 73.3 % with 0.04°-scale HYCOM data and 50.8 % with 0.08°-scale Mercator data. This significant accuracy difference highlights the need of very small-scale current data (i.e., lower than 5 km scale) to assess coastal Sargassum hazard in the Lesser Antilles. The present clustering analysis predictive system would help improve this risk management in the islands of this region.