Predicting LAI: What leaf area do plants need and how do they decide?
A modeling approach of the economy, equilibrium and dynamics of LAI.
Leaf area index (LAI) is one of the most important ecosystem properties, but also remains difficult to predict. Some current land surface models take LAI as an input, whereas others attempt to predict it based on NPP. For example, one common assumption is that a fixed proportion of net primary production (NPP) is used to grow new leaves. Very few models consider top-down constraints on LAI, which can result in unrealistic predictions under altered climate patterns.
In general, my research interest is in developing innovative methods of LAI prediction under varying environments. There are three directions I'm currently exploring:
- MAESPA simulation of EucFACE. Because the unique features and the well-documented data, EucFACE is an ideal first step with the potential of revealing LAI economy under elevated [CO2] and water stress;
- LAI equilibrium. Incorporating optimization theory, Darcy's law, and optimal stomatal behavior model, I hope to determine equilibrium LAI based on water availability at large spatial and temporal scales;
- LAI dynamics. Predict LAI through optimal leaf dropping strategy based on costs and benefits at small spatial and temporal scales.
I use R as my major modelling tool, although MAESPA is in FORTRAN. My field work includes LAI measurements in IF site and, in the near future, the leaf dropping measurements in the collaborated drought experiment. There are also possibilities of incorporating airborne and satellite-based remote-sensing data.
My research will, hopefully, benefit current climate change studies by providing both an assessment of current LAI prediction methods and evidence-based LAI models that account for environmental variations in the future.
Research Project Supervisors
Professor Belinda Medlyn, Dr Remko Duurmsa, Dr Martin De Kauwe