Ecological and Environmental Modelling (U01816)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : GEO-4-EEM

Along with observation and experimentation, modelling is a critical tool for scientific investigations and it is becoming increasingly important in environmental management, from local to global scales. A model is a mathematical description of a system, necessarily simplified and only an approximation to the truth, but designed to describe and predict the behaviour of that system. Comparisons between model predictions and independent data provide a key test of scientific understanding and a basis for refinement of hypotheses. In this course, we will explore the philosophy of modelling and examine strengths and weaknesses of various modelling approaches. Then, using ecological and environmental models based on physical, chemical and biological principles, we will examine issues related to non-linear systems, dynamic simulation, feedback and control and the problem of scaling. The course will emphasise problem-solving and will provide training in mathematical formalisation of a problem, model construction and parameterisation, prediction and testing and sensitivity and error analyses.

Entry Requirements

none

Subject Areas

Home subject area

Ecological Science, (School of GeoSciences, Schedule N)

Other subject areas

Environmental Courses, (School of GeoSciences, Schedule N)

Delivery Information

? Normal year taken : 4th year

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 3 hour(s) per week for 11 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
09/01/2007	14:00	17:00	Room 3, Crew Building Annexe	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	14:00	17:00	KB
Laboratory	Friday	14:00	17:00	KB

Summary of Intended Learning Outcomes

The overall outcome is to develop the conceptual and technical skills necessary for the design, implementation and evaluation of ecological/environmental simulation models. Students will learn the principles of model construction, process calibration, sensitivity analysis, and model corroboration.