A multiple constraints approach to gross primary productivity of a Pinus sylvestris stand

Background

The gross primary productivity (GPP) is the proximal driver of the current land carbon sink which removes around one third of the anthropogenic emissions annually. Ecosystem-scale GPP, however, cannot be quantified directly but must be inferred from related measurements through some sort of model. The resulting uncertainty severely limits our ability to project how GPP will respond to future climatic conditions, especially more frequent and severe periods of drought.

Research questions and hypothesis

This project aims at reducing the uncertainty of GPP and its response to drought by separately quantifying the diffusional and biochemical limitations to GPP at ecosystem scale using three complementary experimental approaches, which will be synthesized within the frame of a process-based model. We hypothesize that (i) drought stress will induce diffusional and, at a later stage, biochemical limitations to GPP which can be separately picked up thanks to the complementary nature of the planned measurements and (ii) that the assimilation of these complementary experimental constraints into the model will reduce the uncertainty of the resulting GPP estimates.

Approach and methods

GPP will be quantified in situ at a newly established forest flux tower facility in a Pinus sylvestris stand which is regularly exposed to periods of drought using a novel combination of three complementary approaches: eddy covariance COS and CO2 flux measurements and active/passive chlorophyll fluorescence measurements. The resulting data will be used to inversely calibrate a process-based canopy radiative transfer and gas exchange model using a Bayesian framework, which will then be used to simulate GPP and its uncertainty.

Time frame