Using the Monte Carlo Method to Quantify Uncertainty in Predictions of a Soil Carbon Cycle Model in Balsam Fir (Abies balsamea (L.) Mill.) and Black Spruce (Picea mariana (Mill.) B.S.P.) Forest Ecosystems in the Boreal Forest

Using the Monte Carlo Method to Quantify Uncertainty in Predictions of a Soil Carbon Cycle Model in Balsam Fir (Abies balsamea (L.) Mill.) and Black Spruce (Picea mariana (Mill.) B.S.P.) Forest Ecosystems in the Boreal Forest

The majority of process-based models of thecarbon cycle in forest ecosystems aredeterministic. Very few components have beenimplemented in these models to represent theuncertainty that may result from natural variation,model structure and parameter estimates. Thereare many sources of natural variation in thecarbon cycle of forest ecosystems. The mainsources of variation occur in the soil organicmatter (SOM) in terms of quantity and quality,both of which vary according to vegetation type,climatic conditions, soil characteristics (textureand structure) and carbon fluxes. For instance, thelitterfall rate and periodicity influencesignificantly the carbon input in the soil organicand mineral horizons. The litter carbon andnutrient contents affect both the SOM turnoverand nutrient cycling rates. While a proportion ofthe natural variation observed may be explainedby the differences in species composition,climatic conditions or soil characteristics, theamplitude of natural variation can nevertheless beimportant within a forest ecosystem due to theimportance of extreme small-scale naturalvariations in soil characteristics and microclimaticconditions. Models can theoretically capture thelast type of variation by using many variables inthe description of the processes. However, the useof many variables may be impractical. The morevariables and parameters a model contains, themore likely its capacity of application to simulatethe carbon cycle for different forest ecosystemtypes will decrease. Thus, there has to be acompromise between the number of variables thatmust be included in a model and its intended use.On the other hand, the interactions among sitevariables also contribute to creating thestochasticity observed in forest ecosystems.