Mountain environments are heterogeneous and dynamic geomorphic environments sensitive to land use and cli-mate change. Heterogenic environmental conditions result in a large variability of mountain soil properties, and thus in large uncertainties of inventories of soil organic carbon (SOC). In this study we analyzed the variability of soil properties associated with the calculation of a SOC inventory in a mountain environment in the Canadian Rocky Mountains (Alberta). Therefore, we calculated the analytical uncertainty and spatial variability of SOC stocks using Gaussian error propagation and Taylor series expansion along seventeen 36 m long transects to identify major sources of uncertainty. SOC stocks in the upper 10 cm and 30 cm are 2.4 ± 0.7 kg C m −2 and 6.4 ± 5.6 kg C m −2 , respectively. The bulk densities generated the largest uncertainty associated with the analyt-ical precision (10.0%). However, analytical uncertainties (ranging between 2.3 and 24.2%) are much smaller than the uncertainty introduced by the spatial variability, for instance of the coarse fraction (63.8%) and SOC concen-tration (40.1%). This study contributes to insufficiently considered analysis of uncertainties in SOC stocks and demonstrate the high potential of nested sampling approaches to identify sources of uncertainties of SOC stocks. To reduce the uncertainties associated with heterogeneous mountain environments, we propose to apply more sophisticated statistics (e.g. regression analysis considering frequency distributions of measured coarse fractions in different geomorphic environments) rather than simple mean per unit approaches, as frequently applied in re-gionalization studies of soil properties.