Exploring causal factors of coastal chlorophyll-a dynamics and their potential contributions to near future forecasting

Abstract

Harmful algal blooms have been causing significant damage worldwide, and Hong Kong is no exception. To understand the drivers of algal bloom formation and forecast the dynamics of chlorophyll-a (Chl-a), a proxy for algal abundance, in Hong Kong waters, this study utilized nonlinear time series analysis, called empirical dynamic modeling (EDM), to investigate Chl-a dynamics using in situ measurements and remote sensing data. We first conducted causality tests of EDM to identify environmental factors influencing Chl-a at different sites. As for the in situ measurement data, salinity was the strongest causal factor among environmental factors. However, inputting the causal factors into the forecasting model did not greatly improve the forecasting performance for Chl-a, suggesting that factors not included in the current dataset, such as wind direction and current speed, may play a more critical role in Chl-a dynamics. As for the remote sensing data, sea surface temperature (SST) showed significant causal effect on Chl-a at most sites and the multivariate forecasting model including Chl-a and SST outperformed the univariate model at most sites. This study is the first to employ EDM to investigate Chl-a dynamics in Hong Kong waters, showcasing its potential to identify causal factors and improve forecasting accuracy. The findings provide scientific insights into Chl-a dynamics and water quality monitoring and modeling in a coastal region.