Comparing statistical and process-based models for forecasting plant phenology

Yiluan Song, Ziyu Zhou, Kai Zhu
Ziyu Zhou was a 2021 cohort undergraduate student at the University of Michigan, now a Master's student at Duke University.
Manuscript in Preparation
Code

Highlights

  • We predicted the time of budburst of red oak across NEON sites with four process-based phenological models and a linear regression model.
  • Process-based models demonstrated higher in-sample and out-of-sample accuracy in short-term predictions.
  • Inferred parameters in process-based models had significant correlation, suggesting parameter identifiability issues.

Data and models

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NEON sites with phenological observations of red maple.


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Diagram of the parallel model, one of the four process-based models used in this study (Hänninen, 1990).

Predictive skills


Parameter identifiability


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