Comparison between Empirical Models and the CBM-CFS3 Carbon Budget Model to Predict Carbon Stocks and Yields in Nova Scotia Forests

• Published in: Forests 2021-09, Vol.12 (9), p.1235
• Main Authors: Heffner, Jason, Steenberg, James, Leblon, Brigitte
• Format: Article
• Language: English
• Subjects: Accuracy; allometric equations; Biomass; Carbon; CBM-CFS3; empirical models; forest carbon stocks; Forestry; Forests; mechanistic models; Plot (Narrative); Predictions; scale; Trees
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f12091235

In response to the global climate crisis, the Nova Scotia Department of Lands and Forestry is using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) and associated methodologies to assess the carbon dynamics of the provincial forestry sector. The CBM-CFS3 bases simulations on a range of studies and national forest inventory plots to predict carbon dynamics using merchantable volume yield curves. Nova Scotia has also maintained thousands of permanent forest sample plots (PSPs) for decades, offering the opportunity to develop empirical, province-specific carbon models. This study used PSP tree measurements and allometric equations to compute plot-level forest carbon models from the PSP dataset and compared their output to that of the CBM-CFS3 model. The PSP-based models were stratified into five forest types and predict the carbon for seven carbon pools as a function of the plot age. Predictions with the PSP- and CBM-CFS3 models were compared to observed PSP data at the plot level and compared against each other at the stand and landscape level. At the plot level, the PSP-derived models predicted carbon closer to the observed data than the CBM-CFS3 model, the extent of over- or under-estimation depending on the carbon pool and forest type. At the stand scale, the CBM-CFS3 model predicted forest carbon to within 3.1–17.6% of the PSP method on average. Differences in predictions between the CBM-CFS3 and PSP models decreased to within 2.4% of the PSP-based models at the landscape level. Thus, the implications of using one method over the other decrease as the prediction scale increases from stand to landscape level, and the implications fluctuate as a function of the forest type and age.