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\begin{document}

\title{Accelerating subglacial hydrology for ice sheet models with deep
learning methods}



\author[1]{Vincent Verjans}%
\author[1]{Alexander A Robel}%
\affil[1]{Georgia Institute of Technology}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Subglacial drainage networks regulate the response of ice sheet flow to
surface meltwater input to the subglacial environment. Simulating
subglacial hydrology evolution is critical to projecting ice sheet
sensitivity to climate, and contribution to sea-level change. However,
current numerical subglacial hydrology models are computationally
expensive, and, consequently, evolving subglacial hydrology is neglected
in large-scale ice sheet simulations. We present a deep learning
emulator of a state-of-the-art subglacial hydrology model, trained at
multiple Greenland glaciers. Our emulator performs strongly in both
temporal (R2\textgreater{}0.99) and spatial (R2\textgreater{}0.96)
generalization, offers high computational savings, and can be used to
force numerical ice sheet models. This will enable century- and
large-scale ice sheet model simulations, including interactions between
ice flow and increased meltwater input to the subglacial environment.
Generally, our work demonstrates that machine learning can further
improve ice sheet models, reduce computational bottlenecks, and exploit
information from high-fidelity models and novel observational platforms.%
\end{abstract}%



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