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The model–data fusion pitfall: assuming certainty in an uncertain world

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.

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Acknowledgments

TFK and ADR acknowledge support from the Northeastern States Research Cooperative, and from the Office of Science (BER), U.S. Department of Energy, through the Terrestrial Carbon Program under Interagency Agreement number DE-AI02-07ER64355, and through the Northeastern Regional Center of the National Institute for Climatic Change Research. MR acknowledges support from the CARBO-Extreme project of the European Commission (FP7-ENV-2008-1-226701). MSC acknowledges support from the Kearney Foundation of Soil Science.

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Correspondence to Trevor F. Keenan.

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Communicated by Russell Monson.

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Keenan, T.F., Carbone, M.S., Reichstein, M. et al. The model–data fusion pitfall: assuming certainty in an uncertain world. Oecologia 167, 587 (2011). https://doi.org/10.1007/s00442-011-2106-x

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Keywords

  • Model–data fusion
  • Data assimilation
  • Parameter estimation
  • Inverse analysis
  • Carbon cycle model