2011
DOI: 10.1016/j.ecolmodel.2011.05.034
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Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

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Cited by 25 publications
(18 citation statements)
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“…2). The Q model was initialised with a steady state assumption with the parameterisations used in Ortiz et al [34]. This means that in the first year (2010), the litter input to the soil was equal to the decomposition rate.…”
Section: Soil Organic Carbon Estimates With the Q Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…2). The Q model was initialised with a steady state assumption with the parameterisations used in Ortiz et al [34]. This means that in the first year (2010), the litter input to the soil was equal to the decomposition rate.…”
Section: Soil Organic Carbon Estimates With the Q Modelmentioning
confidence: 99%
“…The Q model has been calibrated and validated for Swedish conditions, resulting in parameter uncertainty ranges [34]. The Q model simulates organic carbon and continuously tracks different fractions/qualities of organic material that decompose at a certain rate depending on the quality of the material at a given time.…”
Section: Soil Organic Carbon Estimates With the Q Modelmentioning
confidence: 99%
“…The problem is the same as for repeated measurements: the predicted changes in soil organic carbon are small compared to the soil organic carbon pool. Parameter uncertainty and uncertainty in input variables like, e.g., litter fall leads to uncertainty in the predictions which in many cases prevents conclusive results about the direction of change (Ortiz et al 2011). …”
Section: Methods For Studying Forest Carbon Accumulationmentioning
confidence: 99%
“…A refined version of the model that has functions for incorporating decomposition of old organic material and allows for variable climate [40] was used in this study. The parameterisation used in each region was based on the county-wise calibration of the model by Ortiz et al [41].…”
Section: Bioenergy Supply Chainmentioning
confidence: 99%