Novel data-driven method to model the nonlinear dynamics of leafing phenology

Yiluan Song, Department of Environmental Studies, University of California Santa Cruz, Santa Cruz, CA, Stephan B. Munch, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Santa Cruz, CA and Kai Zhu, University of California, Santa Cruz

Background/Question/Methods
Changes in plant phenology caused by climate change have important implications in conservation, agriculture, and public health. There are urgent calls to improve the predictability of phenology. Previous models that assume linear relationships between the timing of events and “critical environmental cues” often fail to accurately predict phenology, as mechanisms of phenology may be highly nonlinear.
We forecast the nonlinear dynamics of leafing phenology with a state-of-the-art data-driven model, Gaussian process empirical dynamic modeling (GP-EDM). Specifically, leafing phenology at each location was modeled as a function of past leafing phenology and environmental conditions. We adopt a hierarchical model structure that allows this nonlinear function to be correlated across locations and time. Here, we demonstrate our prediction of phenology characterized by Enhanced Vegetation Index from satellite remote sensing and that characterized by Green Chromatic Coordinate from near-surface digital imagery (PhenoCam).
With inference, model comparison, and simulation, this model helps us answer the questions: 1) are the driving mechanisms of plant phenology nonlinear, 2) what environmental factors drive phenology, and 3) how climate change may influence plant phenology. This analysis not only helps to decipher the mechanism of phenology but may also better inform decision-making in a changing world.
Results/Conclusions
The non-parametric GP-EDM outperforms traditional methods (e.g., using growing degree-days) in predicting leaf onset, by reducing RMSE from 8 to 3 days. It also outperforms other linear data-driven models in forecasting the continuous dynamics of greenness, by reducing normalized RMSE from 7% to 5%. These results provide empirical evidence that mechanisms of phenology are highly nonlinear, questioning the linear representation of phenology in many ecological studies.
The model allows us to detect key environmental drivers of phenology. Preliminary results show temperature and precipitation to be relevant, but their relative importance and critical time period depend on the vegetation type, locations, and time.
We demonstrate that by increasing daily temperature in a hypothetical year, the increase in spring greenness leads to a significant advancement of green-up date, similar to the field observations and predictions with traditional models. Nevertheless, our model gives additional information on the change in summer and fall greenness.
Overall, the use of GP-EDM improves the forecasting accuracy of leafing phenology. It can therefore be used to infer and simulate the nonlinear responses of leafing phenology to climate change. This project demonstrates a novel and promising approach to improve mechanistic understanding and the predictability of the Earth system.