Computational marine ecological modelling

• Confidently formulate new ecological models or sub-models.
• Solve partial differential equations representing the processes of advection and diffusion using basic finite-difference numerical algorithms.
• Describe and model basic processes relevant for constructing box-type Nutrients-Plankton-Zooplankton-Detritus (NPZD) models.
• Design, parameterize, implement, operate, and interpret results of a NPZD model of a water column.
• Be confident in converting between different units in equations and parameters.
• Discuss strengths and weaknesses of NPZD types of models for predicting production in the water column, e.g. spring and autumn blooms and sub-surface production maxima.
• Have knowledge of the Mike-Ecolab modelling system.
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Computational Ecology focuses on advanced ecological models solved by numerical solutions of partial differential models.

The students develop a 1D hydrographic model for a water column driven by the seasonal cycle of wind and temperature, including the nutrient, plankton, zooplankton, and detritus cycle (NPZD model). This involves acquiring the basic theory for solving partial differential equations numerically. The model will be used to simulate the seasonal cycle of the hydrography and primary/secondary production.

The second part of the course involves application of existing advanced model system: the Mike EcoLab system from DHI. Finally, the students develop their own project, which could either involve extending one of the developed models, e.g. applying it to a specific situation, developing a new ecological model platform, or apply existing professional ecological modelling tools, e.g. to use DHIs EcoLab for a practical problem.

• 6 – 20 (Tues 8-12)

25303/25328, Ability to program in Matlab or an equivalent programming language.

Lectures, computational exercises and a project.

This course provides students with competences relevant to UN SDGs, particularly #14 (Life below water)