LAD-32806 Soil and Landscape Variability

Vak

Studiepunten 6.00

Language of instruction:

English

Assumed knowledge on:

SOQ-21806 (Soil Quality); LAD-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 acquired in courses in statistics, dynamic modelling and GIS. Statistical measures that characterize variability are reviewed and extended with a geostatistical measure of spatial variability. Simulation models are used to gain insight in the dynamic behaviour of landscapes and soils. Theory and methods are illustrated with two real-world case studies. The first case compares three European landscapes by means of a statistical analysis. In the second case you define your own research project involving fieldwork, laboratory analysis, and use of mechanistic models and statistical analysis. This case addresses the spatial variation of soil pollution and its ecological effects for a nature area in the south of the Netherlands. This course trains students in applying and interpreting statistical theory to real-world cases.

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;
- analyze how variability propagates through simple environmental process models, using simple mechanistic models;
- design data collection strategies that account for spatial variability in soil and landscape properties;
- analyze 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:

Report, presentation and written exam.

Literature:

Reader, handouts and practical manual.