Abstract
Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.
Highlights
Forest ecosystems are among the most important of terrestrial ecosystems, in that they store large amounts of carbon (C) and play vital roles in regulating the global C balance and mitigating climate change
The Markov Chain-Monte Carlo (MCMC) method was used to estimate the posterior distribution of the 10 parameters of the Data Assimilation Linked Ecosystem Carbon (DALEC) model (Figure 2)
Biometric data obtained from site observations and net ecosystem exchange (NEE) data derived using the eddy covariance method provide rich sources of information for the parameters and state variables of constraint models
Summary
Forest ecosystems are among the most important of terrestrial ecosystems, in that they store large amounts of carbon (C) and play vital roles in regulating the global C balance and mitigating climate change. Given the complexity of ecosystems, our current understanding of ecosystem-related key processes and control mechanisms is insufficiently comprehensive, as model parameters inevitably have associated uncertainties, and these models are still unable to accurately simulate and predict ecosystem processes and C source/sink distribution and changes [12,13,14,15] In this regard, the model–data fusion technique (MDF) provides a powerful tool for reducing the uncertainty when simulating ecosystem C cycles by combining observations and models, which can contribute to improving model simulation accuracy [16,17,18]
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