Using physics-based machine learning to estimate unobserved quantities:
A case study for landscape-scale soil and vegetation conductances to
heat and water vapor
While machine learning (ML) techniques have proven to have exceptional
performance in prediction of variables that have long and varied
observational records, it is not clear how to use such techniques to
learn about intermediate processes which may not be readily observable.
We build on previous work that found that encoding either known, or
approximated, physical relationships into the machine learning framework
can allow the learned model to implicitly represent processes that are
not directly observed, but can be related to an observable quantity.
Zhao et al. (2019) found that encoding a Penman-Monteith-like equation
of latent heat in an artificial neural network could reliably predict
the latent heat while providing an estimate of the resistance term,
which is not readily observable at the landscape scale. Specifically, we
advance this framework in two ways. First, we expand the physics-based
layer to include the partitioning of both the latent and sensible heat
fluxes among the vegetation and soil domains, each with their own
resistance terms. Second, we couple a land-surface model (LSM), which
provides information from simulated processes to the ML model. We thus
effectively provide the ML model with both physics-informed inputs as
well as maintain constraints such as mass and energy balance on outputs
of the coupled ML-LSM simulations. Previously we found that coupling a
LSM to the ML model could provide good predictions of bulk turbulent
heat fluxes, and in this work we explore how incorporating the
additional physics-based partitioning allows the model to learn more
ecohydrologically-relevant dynamics in diverse biomes. Further, we
explore what the model learned in predicting the unobserved resistance
terms and what we can learn from the model itself. Zhao, W. L., Gentine,
P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., et al. (2019).
Physics-Constrained Machine Learning of Evapotranspiration. Geophysical
Research Letters, 46(24), 14496–14507.