Project properties

Title MSc thesis: It's getting hot in here! - Does the sensitivity of permafrost to changing weather conditions differ across the Arctic?
Group Plant Ecology and Nature Conservation Group
Project type thesis
Credits 36 (??)
Supervisor(s) Rúna Magnússon & Monique Heijmans
Examiner(s) Dr. Monique Heijmans
Contact info Rúna Magnússon: runa.magnusson@wur.nl
Begin date 2021/11/01
End date 2024/11/01
Description Across the Arctic, researchers monitor the thawing depth of permafrost as a critical environmental variable. You will work with a database with end-of-season thaw depths, also known as "Active Layer Thicknesses". This is the top layer of permafrost soil that thaws in summer and largely controls the extent to which exchange of greenhouse gases can take place. In general, researchers find that - logically - warmer temperatures cause deeper thaw, which can result in emissions of additional greenhouse gases. But is this relation between thaw depth and temperature equally strong across different Arctic sites? And what about other climate factors such as snow- and rainfall? Perhaps local conditions related to soil types, permafrost characteristics and vegetation also regulate the climate response of permafrost?

You will make use of an annual thaw depth database, weather station databases and potentially gridded datasets of vegetation and soil characteristics to determine the extent to which thaw depth can be explained by various climate factors across the Arctic. You will work closely with your daily supervisor (Rúna Magnússon) who will work on a publication on this topic. If your contributions are valuable, co-authorship can be discussed.

We're looking for someone who is not scared of a big pile of data, someone who likes a statistical challenge and someone who is handy with extracting information from online databases (e.g. weather time series and extraction of point information from spatial data layers). Sadly, due to the remoteness of the study areas, logistical challenges and the pandemic, we cannot offer fieldwork for this project.
Used skills Time series and trend analysis, advanced regression techniques (e.g. partial-least squares regression), and, depending on your interests, additional skills such as GIS (e.g. extracting point information from maps).
Requirements Experience with more advanced statistics for environmental research (e.g. Ecological Methods II or an equivalent). You will most likely work in R or another coding environment of your choice (e.g. Python or Matlab). Experience with temporal and spatial data is an advantage. Please also get in touch if you're interested but not entirely confident about all the requirements, we can discuss this!