Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
  Deciduous Broadleaf Forests
Christian Reimers David Hafezi Rachti Guahua Liu Alexander J. Winkler
Understanding the future climate is crucial for informed policy decisions on
climate change prevention and mitigation. Earth system models play an important
role in predicting future climate, requiring accurate representation of complex
sub-processes that span multiple time scales and spatial scales. One such
process that links seasonal and interannual climate variability to cyclical
biological events is tree phenology in deciduous broadleaf forests.
Phenological dates, such as the start and end of the growing season, are
critical for understanding the exchange of carbon and water between the
biosphere and the atmosphere. Mechanistic prediction of these dates is
challenging. Hybrid modelling, which integrates data-driven approaches into
complex models, offers a solution. In this work, as a first step towards this
goal, train a deep neural network to predict a phenological index from
meteorological time series. We find that this approach outperforms traditional
process-based models. This highlights the potential of data-driven methods to
improve climate predictions. We also analyze which variables and aspects of the
time series influence the predicted onset of the season, in order to gain a
better understanding of the advantages and limitations of our model.