The model-data fusion pitfall: Assuming certainty in an uncertain world

Trevor F. Keenan, Mariah S. Carbone, Markus Reichstein, Andrew D. Richardson

Research output: Contribution to journalReview articlepeer-review

82 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)587-597
Number of pages11
Issue number3
Publication statusPublished - Nov 2011


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


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