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

Jason Heffner,James Steenberg,Brigitte Leblon

doi:10.3390/f12091235

Abstract

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.

Highlights

Forests are excellent for carbon sequestration, but this requires having suitable monitoring and modelling approaches to predict current and future carbon stocks in forests and their response to management [1]
Non-linear least squares regressionspools wereand usedseparated to fit the relationship were aggregated into CBM-CFS3-recognized into the fivebetween forthese biomasses and the age (X)
Our results indicate that the national-scale CBM-CFS3 has utility even at fine spatial scales and suggest that the localized permanent forest sample plots (PSPs)-derived models behave reasonably when scaled up to the landscape level

Summary

Introduction

Forests are excellent for carbon sequestration, but this requires having suitable monitoring and modelling approaches to predict current and future carbon stocks in forests and their response to management [1]. Carbon models use sampling data, and their accuracy and precision, depend on the availability and quality of representative data. The United Nations Intergovernmental Panel on Climate Change (IPCC). Guidelines define three levels of data, i.e., coarse (Tier 1) and higher resolution (Tiers 2 and 3) for national greenhouse gas inventories [2]. Tier 1 data are often based on global or continental averages and can be a starting point for regions with little existing data or scientific resources [3]. The IPCC protocol recommends using national and regional data wherever possible to increase reporting accuracy. Carbon enters the forest ecosystem through photosynthesis and comprises approximately half the dry weight of biomass [4].

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