Wu, Jian1; Jansson, P.E.3; van der Linden, Leon1; Pilegaard, Kim4; Beier, Claus2; Ibrom, Andreas4
1 Department of Chemical and Biochemical Engineering, Technical University of Denmark2 Ecosystems Programme, Department of Chemical and Biochemical Engineering, Technical University of Denmark3 KTH - Royal Institute of Technology4 Department of Environmental Engineering, Technical University of Denmark
Temperate forests are globally important carbon sinks and stocks. Trends in net ecosystem exchange have been observed in a Danish beech forest and this trend cannot be entirely attributed to changing climatic drivers. This study sought to clarify the mechanisms responsible for the observed trend, using a dynamic ecosystem model (CoupModel) and model data fusion with multiple constraints and model experiments. Experiments with different validation datasets showed that a multiple constraints model data fusion approach that included the annual tree growth, the seasonal canopy development, the latent and sensible heat fluxes and the CO2 fluxes decreased the parameter uncertainty considerably compared to using CO2 fluxes as validation data alone. The fitted model was able to simulate the observed carbon fluxes well (R2=0.8, mean error=0.1gCm−2d−1) but did not reproduce the decadal (1997–2009) trend in carbon uptake when global parameter estimates were used. Annual parameter estimates were able to reproduce the decadal scale trend; the yearly fitted posterior parameters (e.g. the light use efficiency) indicated a role for changes in the ecosystem functional properties. A possible role for nitrogen demand during mast years is supported by the inter-annual variability in the estimated parameters. The inter-annual variability of photosynthesis parameters was fundamental to the simulation of the trend in carbon fluxes in the investigated beech forest and this demonstrates the importance of functional change in carbon balance.
Ecological Modelling, 2013, Vol 260, p. 50-61
Net ecosystem exchange; CoupModel; Functional change; Model data fusion; Multiple constraints approach