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Model-data fusion
Data assimilation
Parameter estimation
Inverse
analysis
Carbon cycle model
terrestrial ecosystem model
soil respiration measurements
forest
carbon dynamics
eddy covariance data
land-surface models
sub-alpine
forest
parameter-estimation
data assimilation
flux
inversion
Abstract:
Model-data fusion is a powerful framework by which to combine models with various data streams (including observations at different spatial or temporal scales), and account for associated uncertainties. The approach can be used to constrain estimates of model states, rate constants, and driver sensitivities. The number of applications of model-data fusion in environmental biology and ecology has been rising steadily, offering insights into both model and data strengths and limitations. For reliable model-data fusion-based results, however, the approach taken must fully account for both model and data uncertainties in a statistically rigorous and transparent manner. Here we review and outline the cornerstones of a rigorous model-data fusion approach, highlighting the importance of properly accounting for uncertainty. We conclude by suggesting a code of best practices, which should serve to guide future efforts.
