Ecosystem Modelling with LPJ-Guess
Short lectures will provide the background to the main process descriptions and input to LPJ-GUESS, and practical sessions will help the students to set-up, compile and run the model, and help with analysis of the standard model output. We will also offer specific, targeted help to students who wish to run modelling experiments relevant to their thesis topic.
The students will work with the teachers on a modelling topic of their choosing and present their results on the final afternoon of the course. Our aim with the course is to make the students aware of the importance and potential of vegetation modelling, and to provide practical skills in ecosystem modelling generally.
5 ECTS, Lund University (no grades – only Pass or Fail)
Day 1: Introductory lectures and getting started with LPJ-GUESS
A series of short lectures will cover the following topics:
Dynamic vegetation modelling – a perspective (Prof. Ben Smith)
General introduction to LPJ-GUESS & plant functional type (PFT) descriptions (Paul Miller)
Coupled carbon-nitrogen cycling (David Wårlind)
Land use and management (Mats Lindeskog)
Wetland and high-latitude soil processes (Paul Miller)
Other, specific topics will be covered on request, depending on the needs and interests of the participating students - please email Paul Miller in advance: paul [dot] miller [at] nateko [dot] lu [dot] se. Examples of topics that could be included: Detailed forest and crop management, BVOCs, Fire, other disturbances, N2O emissions, tree migration, etc.
Day 1 (afternoon): Model troubleshooting workshop – getting set up
Day 2: First simulations with LPJ-GUESS
Initial practical help to get started
Common assignment for all participants
Start of the individual project work
Day 3: Project work and hands-on guidance
Day 4: Project work and hands-on guidance
Day 5 (morning): Final project work and hands-on guidance.
Day 5 (afternoon): Project presentations in the afternoon.
Please email Paul Miller (paul [dot] miller [at] nateko [dot] lu [dot] se) to register for the course.
Deadline: 12 April 2019.
Your email should contain a short description of what you are most interested in learning on the course, preferably with reference to your own research project.