Modelling of Aquatic Ecosystems
Syllabus, Master's level, 1TV446
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Biology A1F, Earth Science A1F, Technology A1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 29 April 2015
- Responsible department
- Department of Earth Sciences
120 credits and Aquatic ecosystems and ecosystem services together with Empirical Modelling
After completion of the course, the student should be able to:
- to identify water quality problems using existing data sets, rank them according to their importance for ecosystem services and suggest management strategies
- construct conceptual and simple dynamic models
- use advanced statistical and dynamic models
- individually and in groups reflect upon and critically review the student's own and others' work
- in oral and written form present and discuss modelling results with requirements on appropriate structure, format, language and citing of scientific literature as well as awareness of ethical issues.
The course is oriented towards a better understanding of water quality issues and has its focus on identifying current and predicting future water quality problems in freshwaters. During the course a variety of different models are used (from simple mass balance models to more complex dynamic and non-parametric statistical models). The course includes the modelling of processes within a water system and its catchment area. Local, regional and global driving variables for water quality are analysed and discussed. Time series analyses are conducted to assess changes in water quality over time. An important component of the course is to be able to independently analyse water quality data through the selection of appropriate methods and to critically reflect on results. A large part of the course is a project work that deals with current water quality issues. The project work includes writing of a scientific report and peer-reviews.
Lectures, exercises with focus on data analyses, seminars and project work.
The first part of the course is evaluated by data exercises (3 credits) and a written (digital) exam (4 credits). The project work is evaluated by an oral presentation (1 credit), a written scientific report (5 credits) and written peer-reviews of others’ work (2 credits).