Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

Dreano, Denis

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

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Type

Dissertation

Tandeo, Pierre
Coutu, Sylvain

Program

Applied Mathematics and Computational Science

KAUST Department

Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

Date

2017-05-31

Abstract

Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

Citation

Dreano, D. (2017). Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll. KAUST Research Repository. https://doi.org/10.25781/KAUST-2K727