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

Handle URI:
http://hdl.handle.net/10754/623753
Title:
Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll
Authors:
Dreano, Denis ( 0000-0001-7956-5538 )
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.
Advisors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Committee Member:
Knio, Omar Mohamad; Jones, Burton ( 0000-0002-9599-1593 ) ; Tandeo, Pierre; Coutu, Sylvain
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Applied Mathematics and Computational Science
Issue Date:
31-May-2017
Type:
Dissertation
Appears in Collections:
Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoteit, Ibrahimen
dc.contributor.authorDreano, Denisen
dc.date.accessioned2017-05-31T09:48:50Z-
dc.date.available2017-05-31T09:48:50Z-
dc.date.issued2017-05-31-
dc.identifier.urihttp://hdl.handle.net/10754/623753-
dc.description.abstractPhytoplankton 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.en
dc.language.isoenen
dc.subjectdata assimilationen
dc.subjectred seaen
dc.subjectStatisticsen
dc.subjectChlorophyllen
dc.titleData and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyllen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberKnio, Omar Mohamaden
dc.contributor.committeememberJones, Burtonen
dc.contributor.committeememberTandeo, Pierreen
dc.contributor.committeememberCoutu, Sylvainen
thesis.degree.disciplineApplied Mathematics and Computational Scienceen
thesis.degree.nameDoctor of Philosophyen
dc.person.id120819en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.