Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

• Published in: Ecological modelling 2011-09, Vol.222 (17), p.3020-3032
• Main Authors: Ortiz, Carina, Karltun, Erik, Stendahl, Johan, Gärdenäs, Annemieke I., Ågren, Göran I.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Biological and medical sciences; Environmental Sciences related to Agriculture and Land-use; Forest Science; Forestry; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; General forest ecology; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology; GLUE; Kyoto; Limits of acceptability; Methods and techniques (sampling, tagging, trapping, modelling...); Miljö- och naturvårdsvetenskap; Monte Carlo; Norway spruce; Picea abies; Pinus sylvestris; Q-model; Scots pine; Skogsvetenskap; Soil carbon; Swedish Forest Soil Inventory; Uncertainties; UNFCCC
• ISSN: 0304-3800; 1872-7026; 1872-7026
• DOI: 10.1016/j.ecolmodel.2011.05.034

► We calibrate the Q model regionally with data from a national inventory using GLUE (Generalized Likelihood Uncertainty Estimation) method. ► Parameter uncertainty is decreased with updated and reshaped parameter distributions. ► The model efficiency varies strongly between the regions. ► There is high interannual variability of down-scaled inventory measured SOC estimates, compared with model estimates. ► We strike a balance between the quality of inventory data at county level vs parameter uncertainty in a process-based model due to up-scaling. Boreal forest soils such as those in Sweden contain a large active carbon stock. Hence, a relatively small change in this stock can have a major impact on the Swedish national CO 2 balance. Understanding of the uncertainties in the estimations of soil carbon pools is critical for accurately assessing changes in carbon stocks in the national reports to UNFCCC and the Kyoto Protocol. Our objective was to analyse the parameter uncertainties of simulated estimates of the soil organic carbon (SOC) development between 1994 and 2002 in Swedish coniferous forests with the Q model. Both the sensitivity of model parameters and the uncertainties in simulations were assessed. Data of forests with Norway spruce, Scots pine and Lodgepole pine, from the Swedish Forest Soil Inventory (SFSI) were used. Data of 12 Swedish counties were used to calibrate parameter settings; and data from another 11 counties to validate. The “limits of acceptability” within GLUE were set at the 95% confidence interval for the annual, mean measured SOC at county scale. The calibration procedure reduced the parameter uncertainties and reshaped the distributions of the parameters county-specific. The average measured and simulated SOC amounts varied from 60 t C ha −1 in northern to 140 t C ha −1 in the southern Sweden. The calibrated model simulated the soil carbon pool within the limits of acceptability for all calibration counties except for one county during one year. The efficiency of the calibrated model varied strongly; for five out of 12 counties the model estimates agreed well with measurements, for two counties agreement was moderate and for five counties the agreement was poor. The lack of agreement can be explained with the high inter-annual variability of the down-scaled measured SOC estimates and changes in forest areas over time. We conclude that, although we succeed in reducing the uncertainty in the model estimates, calibrating of a reg