Comment on egusphere-2025-3133

Abstract. Accurate and unbiased estimation of tree aboveground biomass (AGB) is essential for large-scale monitoring of forest carbon stocks. But estimating AGB typically requires several data imputation steps that can introduce substantial errors that are hard to quantify and correct for. Two sources of uncertainty that are thought to be particularly important but remain poorly understood are tree height – which is generally estimated using allometric models – and wood density – which is most commonly assigned from databases based on taxonomic matching. Here, we used data from 154 destructively harvested trees in Zambia’s miombo woodlands that span a large range of sizes to develop a framework to partition errors in AGB arising from uncertainty in tree height and wood density. We found that when locally-calibrated allometries are used to estimate missing tree height information and when wood density is imputed from species-specific values derived from public databases, AGB can be estimated with high precision and little or no bias. However, when tree height and wood density are imputed more coarsely using generic information, errors in AGB can be substantial. In particular, estimating tree height using a regional allometric model developed for tropical dry forests led to 35 % underestimation of AGB. Our study provides an intuitive approach for quantifying and partitioning errors in AGB arising from uncertainty in tree height and wood density, paving the way for more robust mapping of forest carbon stocks and fluxes.