EVALUATING THE PERFORMANCE OF BIOMETRIC APPROACHES FOR VERIFICATION AND MONITORING IN COMMERCIAL FOREST CARBON ACCOUNTING

Abstract

Of the strategies to increase land carbon storage and avoid GHG emissions through natural ecosystem management, Forest-based Climate Solutions (FbCS) are believed to offer the greatest mitigation potential. As more funds are allocated towards FbCS deployment, questions arise regarding their actual mitigation potential and the extent to which status-quo approaches can quantify or verify the FbCS efficiency. Carbon accounting methodologies currently attribute climate benefits to projects whose ex-ante change in aboveground biomass stocks is expected to be additional to a business-as-usual static carbon baseline derived from forest inventories and species-specific allometric models. However, stock-centric carbon baselines fail to capture all carbon sources and sinks, and dynamic meteorological conditions drive carbon uptake and forest growth. A combination of high temporal and spatial resolution monitoring of land-atmosphere carbon fluxes (flux towers) and stand-level biometric approaches (increment cores and forest inventories) provides a better understanding of the movement (or flux) and storage of CO2 between ecosystems and the atmosphere from which the net ecosystem productivity (NEP) is estimated. Thus, performing an intercomparison of NEP estimates from flux towers (NEPEC) versus biometric methods (NEPBM) provides a unique opportunity to evaluate the performance of static carbon baselines in response to interannual climate variability on tree growth. Here, we performed static carbon baseline scenarios from a forest inventory approach in three flux sites in the Eastern United States. Carbon stock-centric baselines were confronted with flux tower data from those AmeriFlux sites and tree rings data to assess the representativeness of interannual climate variability on tree growth. Differences in magnitude between NEPEC and NEPBM were sensitive to variations in meteorological conditions, tree species composition, stand age, and biases in allometric equations. Furthermore, NEPEC and NEPBM estimates show a weak correlation over time, with tower-derived estimates averaging 28% to 71% higher than those derived from biometric methods. This study highlights the representativeness of temporal variability in static carbon baselines, and the research needs to evaluate and assess the performance of allometric equations for commercial carbon accounting.