GEOGG124 Terrestrial Carbon: Modelling and Monitoring
OPTION GEOGG124 - TERRESTRIAL CARBON: MODELLING and MONITORING
(15 credits)
Term 2 (2014)
Staff:
Prof. Philip Lewis
Aims:
The Terrestrial Carbon: modelling and monitoring module aims:
- To outline the role of vegetation in the carbon cycle and the wider climate system
- To outline how the vegetation carbon cycle can be modelled and use the models in prediction
- To provide the linkages between the models and remote sensing observations (radiative transfer)
- To enable the students to use remote sensing (and other) data to constrain, test and criticise the models
- To expose the students to modern statistical methods in combining data and models
Content:
The module will cover:
- The role of vegetation in the climate system
- Terrestrial vegetation dynamics modelling
- Remote sensing of vegetation
- Radiation interactions with vegetation
- Model inversion in remote sensing
- Concepts and maths of data assimilation
- Using remote sensing data to constrain and test vegetation dynamics models
Assessment:
2 hour unseen exam, 100% of the assessment.
Format:
The module will be delivered through:
- Lectures (3 hour sessions providing concepts, contexts, and critiques)
- Computer laboratory work (extended practical sessions progressing technical aspects of model implementation and options hands-on experience of relevant software). Practicals will initially be based around specific vegetation models and EO radiative transfer schemes, but also include advanced concepts such as data assimilation.
- Moodle resources (hosting reading lists, lecture handouts, datasets, guides and practical support materials)
Learning outcomes:
At the end of the module, students should:
- Appreciate the role of vegetation in the carbon cycle and the climate system
- Appreciate the role, strengths and weaknesses of models of global vegetation processes
- Understand the factors affecting remote sensing measurements of vegetation (radiative transfer theory)
- Understand how to use models and observations in combination to improve estimates of carbon fluxes and pools
- Have an understanding of data assimilation