Phytoplankton dynamics in Maltese coastal waters (Central Mediterranean) using in situ, remote sensing methods, and modelling techniques

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  • Michael Saliba

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  • MPhil, School of Ocean Sciences

Abstract

This study focuses on updating current knowledge concerning the quality of coastal water of the Maltese Islands (central Mediterranean) using in-situ, remote sensing and modelling techniques. In-situ data were collected from four sampling points around the Maltese Islands from April 2015 to January 2016. A weak, but statistically significant, relationship was observed between in-situ chlorophyll and chlorophyll derived from the MODIS satellite using a recently updated Mediterranean wide algorithm MedOC4 (Santoleri et al., 2008). A ten year time series of satellite data shows that chlorophyll levels in this part of the Mediterranean are generally low (<0.5 mg m-3) and show a regular maximum in December and January, with an occasional second maximum in the spring. A two-layer bio-physical model has been constructed in order to test our understanding of this behaviour. The surface mixed layer in the model is forced by surface heating and wind-stirring. Phytoplankton growth in the surface is set to the minimum of light- and nutrient-limited growth. In the clear waters of the Mediterranean, with high levels of sunlight at all times of year, phytoplankton growth is almost always nutrient-limited. Maximum growth therefore occurs in the winter, when high wind speeds deepen the surface mixed layer and nutrients become available in the (still) sufficiently-lit surface waters. The in situ data are also used to construct a site-specific linear model for chlorophyll; this accounts for 52.6% of the variance of observed chlorophyll. The overall conclusions of this study are that phytoplankton blooms in Maltese waters occur mainly during the winter months and that these waters are nutrient limited at all times of the year. Further work needs to be done on the derivation of chlorophyll from ocean colour in these waters.

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Original languageEnglish
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Award date2017