Event Details

MEM/TRANSFOR-M Report Presentation of Jason Heffner will be held:

Thursday, December 17th,  2020 at 9am
Via Microsoft Teams

Report Presentation Chaired By: Dr. T. Beckley, FOREM

Advisory Committee:  

Dr. B. Leblon, FOREM

Dr. J. Steenberg, HRA-FOREM/Nova Scotia Lands and Forests

Dr. J. Bauhus, Albert-Ludwigs-Universität Freiburg

Abstract: In response to the global climate crisis, the Nova Scotia Department of Lands and Forestry is assessing the carbon dynamics of the provincial forest sector using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) (Kurz et al., 2009) and its internal methodologies. The CBM-CFS3 is the federal carbon model, basing simulations on a range of national forest inventory plots and studies to predict carbon dynamics over time using merchantable volume yield curves. The province has also maintained thousands of permanent forest sample plots (PSPs) for decades, measuring forest growth every five years. This vast dataset offers the opportunity to develop empirical, province-specific carbon models. We used chronological plot 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. The PSP-based models are stratified by five forest types and predict the carbon in seven pools as a function of plot age. Predictions of the PSP- and CBM-CFS3 methods were compared to observed PSP data at the plot level and compared against each other at the stand and landscape level. The goal was not to prove one method as more accurate at predicting carbon, but to assess the implications of using one method over the other.

At the plot level, analysis showed that the PSP-derived models tend to predict carbon closer to observed data (lower RMSE) than the CBM-CFS3 and that the extent of over- or under-estimation (bias) changes depending on the pool and forest type being predicted. Our analyses suggest that on average, the CBM-CFS3 predicts forest carbon to within 3.1-17.6% of the PSP method at the stand scale, depending on the forest type and age range being predicted. Differences in prediction between methods decreased at larger scales, with the CBM-CFS3 predicting forest carbon 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 implications fluctuate with changing forest types and age ranges in the stands being assessed. Researchers can consider these results when choosing a method to predict forest carbon, as method suitability can change depending on the purpose of the estimations.

Building: MSTeams

Contact: Faith Sharpe
1 506 453 4901
faith.sharpe@unb.ca
https://www.unb.ca/fredericton/forestry/events/index.html