  Streamflow prediction is one of the key challenges in the field of hydrology
due to the complex interplay between multiple non-linear physical mechanisms
behind streamflow generation. While physically-based models are rooted in rich
understanding of the physical processes, a significant performance gap still
remains which can be potentially addressed by leveraging the recent advances in
machine learning. The goal of this work is to incorporate our understanding of
physical processes and constraints in hydrology into machine learning
algorithms, and thus bridge the performance gap while reducing the need for
large amounts of data compared to traditional data-driven approaches. In
particular, we propose an LSTM based deep learning architecture that is coupled
with SWAT (Soil and Water Assessment Tool), an hydrology model that is in wide
use today. The key idea of the approach is to model auxiliary intermediate
processes that connect weather drivers to streamflow, rather than directly
mapping runoff from weather variables which is what a deep learning
architecture without physical insight will do. The efficacy of the approach is
being analyzed on several small catchments located in the South Branch of the
Root River Watershed in southeast Minnesota. Apart from observation data on
runoff, the approach also leverages a 200-year synthetic dataset generated by
SWAT to improve the performance while reducing convergence time. In the early
phases of this study, simpler versions of the physics guided deep learning
architectures are being used to achieve a system understanding of the coupling
of physics and machine learning. As more complexity is introduced into the
present implementation, the framework will be able to generalize to more
sophisticated cases where spatial heterogeneity is present.
