Estimation of the vertical phytoplankton distribution in the Philippine Sea: Influence of turbulence following passage of typhoons

Abstract

The subsurface chlorophyll-a maximum (SCM) is a phenomenon that contributes significantly to the total primary production of the open ocean but it is not observable from remote sensing, thus primary production based on satellite information is highly underestimated. In a highly dynamic region such as the Philippine Sea, turbulence caused by tropical storms may exert significant impact on the SCM feature. In this study, we attempt to estimate the vertical phytoplankton profile in the Philippine Sea from remote sensing images by applying a generic quantitative approach. Generalized Additive Models (GAM) followed by Generalized Linear Models (GLMs) were used create predictive equations between response variables (Chl-a profile parameters) and predictor variables (RS parameters). GAM was able to predict integrated Chl-a biomass using photosynthetic active radiation (PAR), wind speed and wind stress, the depth of the Chl-a peak using surface Chl-a, wind speed and wind stress, and the baseline Chl-a concentration using sea surface temperature, sea surface salinity and PAR. GLM found wind stress and wind speed as significant predictors for integrated Chl-a biomass, while surface Chl-a, wind speed and wind stress were significant predictors for depth of the Chl-a peak. When the predictive equations were applied to 2020 monthly satellite images, they were seen to adequately estimate the offshore spatial distribution of the two Chl-a parameters. Increased turbulence due to high wind speed and wind stress during passage of tropical storms was seen to result in shallowing of the SCM and subsequent increase in Chl-a within the water column. These equations could be refined if long-term observational data was available. The capacity to estimate vertical distribution of primary productivity in the Philippines provides a means to better understand fisheries productivity and biogeochemical cycling in the region.