Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea

Abstract

Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.

Keywords

Uncertainty analysis / Monte Carlo simulation / Bayesian hierarchical model / Nesting structure / Biomass estimation

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