Biomass and NPP Estimation for the Mid-Atlantic Region (Usa) Using Plot-Level Forest Inventory Data

As interest grows in quantification of global carbon cycles, process model predictions of forest biomass and net primary production (NPP) are being developed at an accelerating rate. Such models can provide useful predictions at large scales, but it has been difficult to evaluate their performance. Using the network of plots comprising the comprehensive and spatially extensive Forest Inventory and Analysis (FIA) data set collected and maintained by the USDA Forest Service, we applied methods typically used in field measurements to develop estimates of forest biomass and NPP for the mid-Atlantic region of the United States at a scale appropriate for comparison with model predictions. Plot-level and tree-level forest inventory data from a subset of plots were used together with species-specific biomass regression equations to calculate maximum current biomass and NPP values for the mid-Atlantic region. Estimates at the plot level were aggregated by forest type and to the 0.5° × 0.5° scale for analysis and comparison with process model predictions. Maximum current forest biomass averaged 248 and 200 Mg·ha−1·yr−1 in hardwood and softwood forest types, respectively; wood biomass increment averaged 559 and 460 g·m−2·yr−1 in hardwood and softwood forest types, respectively. Aggregated to the 0.5° × 0.5° scale, forest biomass ranged from 101 to 326 Mg/ha, while wood biomass increment ranged from 254 to 1050 g·m−2·yr−1. Biomass and NPP estimates for closed-canopy forests from this study were consistent with values reported in the literature but were as much as 50% lower than values reported for old-growth stands. NPP predictions from three process models were fairly consistent with the FIA-based estimates, but model predictions of biomass were higher than estimates from FIA data for the region. By describing upper and lower bounds on reasonable biomass and NPP values for closed-canopy forests, these FIA-derived estimates provide a foundation for model comparison and continued model development.