SGL-32806 Soil and Landscape Variability

Course

Credits 6.00

Language of instruction:

English

Assumed knowledge on:

SOQ-21806 Soil Quality; SGL-22306 Geology, Soils and Landscapes of the Rhine-Meuse Delta; GRS-10306 Introduction Geo-information Science; MAT-15403 Statistics 2.

Contents:

The landscape is a complex system characterized by numerous properties that often vary in space and time. We can learn about the functioning of a landscape by studying its properties, their variability and their relationships. This course combines thematic knowledge gained in courses on soil science, geology, geomorphology, ecology and hydrology with skills you acquired in courses in statistics, dynamic modelling and GIS. Statistical measures that characterize variability and relationships are reviewed and extended with geo-statistical measures of spatial variability. Simulation models are used to gain insight in the dynamic behaviour of landscapes and soils. Theory and methods are illustrated with real-world examples. Real-world case studies are also extensively used in the computer exercises. The course also includes a small research project involving fieldwork, laboratory analysis, and use of mechanistic models and statistical analysis. This small project addresses the description, modelling and interpretation of the spatial variation of soil pollution and its ecological effects for a nature area in the south of the Netherlands.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- describe variability in and relationships between soil and landscape properties with statistical measures;
- infer dominant soil and landscape processes from these statistical measures;
- analyse how variability propagates through simple environmental process models, using simple mechanistic models;
- design data collection and analysis strategies that account for spatial variability in soil and landscape properties;
- analyse data collected in the field to answer a self-defined research question.

Activities:

- attend lectures;
- participate in (field) practicals:
- laboratory work, data analysis and computer modelling.

Examination:

- written test: individual, open questions (60%);
- written group assignment (15%);
- written group project proposal (10%);
- poster group presentation (15%).

Literature:

Reader, handouts and tutorials of computer practicals.