Machine learning techniques have proven useful at predicting many
variables of hydrologic interest, and often out-perform traditional
models for univariate predictions. However, demonstration of
multivariate output deep learning models has not had the same success as
the univariate case in the hydrologic sciences. Multivariate prediction
is a clear area where machine learning still lags behind traditional
processed based modeling efforts. Reasons for this include the lack of
coincident data from multiple variables, which make it difficult to
train multivariate deep-learning models, as well as the need to capture
inter-variable covariances and satisfy physical constraints. For these
reasons process-based hydrologic models are still used to simulate and
make predictions for entire hydrologic systems. Therefore, we anticipate
that future state of the art hydrologic models will couple machine
learning with process based representations in a way that satisfies
physical constraints and allows for a blending of theoretical and data
driven approaches as they are most appropriate. In this presentation we
will demonstrate that it is possible to train deep learning models to
represent individual processes, forming an effective
process-parameterization, that can be directly coupled with a physically
based hydrologic model. We will develop a deep-learning representation
of latent heat and couple it to a mass and energy balance conserving
hydrologic model. We will demonstrate its performance characteristics
compared to traditional methods of predicting latent heat. We will also
compare how incorporation of this deep learning representation affects
other major states and fluxes internal to the hydrologic model.