Soil organic carbon stock changes in Swedish forest soils—A comparison of uncertainties and their sources through a national inventory and two simulation models

Swedish Forest Soil Inventory (SFSI) estimates of SOC stocks and SOC changes for forest on mineral soils under Scots pine (Pinus sylvestris)/lodgepole pine (Pinus contorta) or Norway spruce (Picea abies) were compared with estimates, including uncertainties due to parameter, input and climate variability, from two process-based models (Yasso07 and Q) for the period 1994–2000. We found that the stocks, changes, inter-annual variations and uncertainties were of the same magnitude among the different methods. The mean Swedish national stocks in 2000 were estimated to be 73 (±10) (95% CL) ton ha−1 C (SFSI); 69 (±9) (95% CL) ton ha−1 C (Yasso07); and, 67 (+10; −9) (5th and 95th percentiles) ton ha−1 C (Q). Between 1994 and 2000, the mean estimated SOC change were 6.6 (±7) Tg C yr−1 (SFSI), 1.7 (±8.8) Tg C yr−1 (Yasso07), and −3.2 (+10.5; −16.9) Tg C yr−1 (Q). Spatial variability was the main source of uncertainty for the SOC stocks and changes estimated with the SFSI. The uncertainties in the stock estimates originated from litter input for Yasso07 and from the model parameters for the Q model. In both models, litter input uncertainty was the major source of uncertainty for the estimated SOC changes, followed by climate variability and parameters. We concluded that the level of uncertainty for both methods was similar but the sources of uncertainties varied between models and measurements. Thus, comparing uncertainty between methods is difficult and further studies on SOC change estimates with related uncertainties are warranted.

► We estimated forest soil organic carbon changes at the regional scale.
► Two decomposition models were compared with inventory data.
► We assessed uncertainty contributions from parameters, litter and climate input.
► Uncertainties in data and simulations match in magnitude but their origins differ.
► Litter input was the major source of uncertainty in model estimates.