Consequences of fine-scale heterogeneity on predictions of the carbon cycle using lidar data and a height-structured ecosystem model

To more accurately predict carbon stocks and fluxes in forests, it is important to measure fine-scale heterogeneity in ecosystem structure across the landscape, and incorporate the underlying mechanisms responsible for the observed heterogeneity in ecosystem models. This study used large-footprint lidar and a height-structured ecosystem model to estimate carbon stocks and fluxes at Hubbard Brook Experimental Forest (HBEF). At HBEF elevation gradients yield a decline in aboveground carbon stock, due to changes in net growth rates and disturbance at higher elevations. Lidar and a height structured ecosystem model can accurately quantified aboveground carbon stocks. Estimates of aboveground carbon fluxes depended on the availability of lidar data, the representation of fine-scale heterogeneity in climate and soil inputs, and the simulation of spatial variation in disturbance. Predictions of forest structure depended strongly on simulating the mechanisms that drive heterogeneity in forest structure across the landscape.