Last application date Jun 15, 2020 16:48
Department LA20 – Department of Environment
Employment category Doctoral fellow
Contract Limited duration
Degree A MSc degree in a relevant field (Bio-Engineering, Physics, Geosciences, Environmental sciences, Computer Science Engineering, or equivalent degree)
Occupancy rate 100%
Vacancy Type Research staff
PhD position (vegetation modeller)
Investigating the role of aerosols and ozone on light competition and ecosystem productivity
This position is opened at CAVElab (UGent) in the framework of the FWO research project: There’s no such thing as “The Tropical Rainforest”: incorporating heterogeneity of tropical forests in a global vegetation model.
CAVElab – The Computational and Applied Vegetation Ecology is a research unit at the Faculty of Bioscience Engineering of Ghent University, Belgium. The research unit is studying vegetation dynamics, carbon and water cycling in terrestrial ecosystems. CAVElab has a broad interest in all types of terrestrial ecosystems, but currently has a strong focus on the ecology of tropical forest ecosystems. Process-based vegetation modelling is the core research tool of the group, but the questions arising from the modelling work require dedicated field work activities. These field work activities are focused on improving uncertain process descriptions within vegetation models and on data-poor regions such as the Congo Basin.
Project – Tropical forests play an essential role in the global carbon cycle and climate system. These ecosystems, however, experience raising anthropogenic pressure, including the increase of drought and wildfires. Fire is an important disturbance to the global carbon budget and is directly and indirectly responsible for important changes in ecosystem productivity. In particular, fire plumes generate short-lived climate pollutants, including ozone and fine mode aerosols which have contrasting impacts on plant productivity: ozone reduces plant photosynthesis while aerosol could promote it by increasing diffuse radiation. In this project, we would like to investigate the impact of fire air pollutions (ozone and aerosols) on ecosystem productivity while accounting for three-dimensional forest structure heterogeneity. To do so, we intend to enhance the representation of light interception in existing dynamic global vegetation models (ED2, JULES), using improved leaf and wood distributions in space and effective radiative transfer equations derived from more-detailed 3D models. The impact of ozone and aerosol loads will then be tested using long-term simulations in long-monitored sites and model predictions will be validated with three-dimensional light and photosynthesis measurements within a dense tropical forest site in central Africa in which continuous observations of ozone, diffuse light and ecosystem productivity are also tracked. Finally, unique improved simulations of regional forest biomass stocks and dynamics will be performed and the impact of tropical forest heterogeneity and air pollutants therein will be assessed.
- A contract as full-time PhD student (4 years)
- Tentative starting date: September 2020
- The successful candidate will be based in Ghent, but will have the opportunity to visit partner modelling groups in Europe (collaboration with Stephen Sitch (University of Exeter) and the JULES community), North (ED2 and PEcAn communities, e.g. at Harvard and Boston University) and South America and lead field campaigns in Central Africa
- Collaboration in a young and dynamic international scientific team
Profile of the candidate
Profile of the candidate
- A MSc degree in a relevant field (Bio-Engineering, Physics, Geosciences, Environmental sciences, Computer Science Engineering, or equivalent degree)
- Motivation to do a PhD and write scientific publications
- A keen interest in forest ecology
- Knowledge of software development, fluent in programming with FORTRAN or C++ or motivation to learn
- Knowledge of other languages such as R, MAtlab or Python
- Familiar with the linux/unix environment and handling large datasets.
- A good command of English, both written and oral
- Willingness to collaborate and be a team player with good communication skills
- Experience with vegetation modelling is a plus
- Experience with scientific writing is also a strong asset