Forest Carbon Modeling Improved Through Hierarchical Assimilation of Pool-Based Measurements

Accurate assessment of forest carbon dynamics is a critical element of appraising forest-based Natural Climate Solutions. National forest inventory and analysis (FIA) data provide valuable pool-based estimates of carbon stocks, but have been underutilized to inform carbon cycle modeling for forest carbon dynamics with stand development. This study introduces a hierarchical data assimilation (HDA) framework to optimize modeling parameters by incrementally assimilating measured carbon pool data into the model. We found that most carbon stocks could be reproduced by constrained parameters after each HDA step. Using aboveground live biomass (AGB) alone in HDA was able to reproduce the AGB trajectories but introduced biases in estimating the downstream dead biomass and soil carbon pools. Assimilating dead biomass measurements narrowed the posterior space of parameter solutions and improved consistency between measured and modeled carbon dynamics. The HDA framework also reduced uncertainties on modeled carbon fluxes. Young stands were found to release less carbon when the model was informed by dead biomass compared to simulations guided by aboveground biomass alone. The remaining mismatches between modeled and FIA pool estimates could be attributed to wide uncertainty in some FIA estimates, differing definitions of functional carbon pools, and structural rigidity in the model. Together, this study underscores the importance of pool-based measurements in forest carbon modeling, which improves the model-observation fit and reduces process-model uncertainty.