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Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics

Cite this dataset

Fang, Jing et al. (2021). Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics [Dataset]. Dryad.


Carbon sequestration is a key ecosystem service provided by forests. Inventory data based on individual trees are considered to be the most accurate method for estimating forest productivity. However, estimations of forest photosynthesis itself from inventory data remains understudied, particularly when considering the growth and development of individual trees under the background of global change. Here, we used the leaf growth process with phenology and non-structural carbohydrates (NSC) storage to revise an individual-tree based carbon model, FORCCHN. This model couples leaf development and biomass to quantify gross primary productivity (GPP) in the forests, where growth is decoupled from photosynthesis in daily step. The model was initialized with inventory-based forest data rather than the more widely used satellite-based data. We tested the model against measured aboveground woody biomass, growth of leaf biomass, daily gross ecosystem exchange (GEE), and yearly GEE at five representative forest sites in the Northern Hemisphere. We also compared the results from the original model and the revised model at five forest sites. Including leaf growth dynamics and inventory-based initialization improved the predicted performance (r2) of GPP by an average of 33%. Synthesis and applications. Our results suggest that the appropriate vegetation data sources (i.e. inventory or satellite selection) and the effective predictions of the growth process should be considered when developing future carbon cycle models and forest carbon estimation options. Applying and improving such carbon models to evaluate carbon sequestration is an important part of forest carbon sink management.


The predicted GPP data are provided by the FORCCHN2 model. The data include five representative sites (i.e. Blodgett Forest, California, USA (Blodgett); Changbai Mountain, Jilin, China (Changbai); Harvard Forest, Massachusetts, USA (Harvard); Pasoh Forest Reserve, Negeri Sembilan, Malaysia (Pasoh); and University of Michigan Biological Station, Michigan, USA (Michigan)). These five forests comprise four different forest types: deciduous broadleaf forest (DBF), evergreen needle-leaf forest (ENF), evergreen broadleaf forest (EBF), and mixed forest (MF). The data include the results simulated by leaf area and the results simulated by inventory.

Usage notes

The file name is the site number. The readme file contains a description of each site in the dataset.