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\begin{document}

\title{Exploring the Potential of Long Short-Term Memory Networks for Improving
Understanding of Continental- and Regional-Scale Snowpack Dynamics}



\author[1]{Yuan-Heng Wang}%
\author[1]{Hoshin Vijai Gupta}%
\author[1]{Xubin Zeng}%
\author[1]{Guoyue Niu}%
\affil[1]{University of Arizona}%


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  \date{June 18, 2024}


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\begin{abstract}
Accurate estimation of the spatio-temporal distribution of snow water
equivalent is essential given its global importance for understanding
climate dynamics and climate change, and as a source of fresh water.
Here, we explore the potential of using the Long Short-Term Memory
(LSTM) network for continental and regional scale modeling of daily snow
accumulation and melt dynamics at 4-km pixel resolution across the
conterminous US (CONUS). To reduce training costs (data are available
for \textasciitilde{}0.31 million snowy pixels), we combine spatial
sampling with stagewise model development, whereby the network is first
pretrained across the entire CONUS and then subjected to regional
fine-tuning. Accordingly, model evaluation is focused on out-of-sample
predictive performance across space (analogous to the prediction in
ungauged basins problem). We find that, given identical inputs
(precipitation, temperature and elevation), a single CONUS-wide LSTM
provides significantly better spatio-temporal generalization than a
regionally calibrated version of the physical-conceptual
temperature-index-based SNOW17 model. Adding more meteorological
information (dew point temperature, vapor pressure deficit, longwave
radiation and shortwave radiation) further improves model performance,
while rendering redundant the local information provided by elevation.
Overall, the LSTM exhibits better transferability than SNOW17 to
locations that were \emph{not} included in the training data set,
reinforcing the advantages of structure learning over parameter
learning. Our results suggest that an LSTM-based approach could be used
to develop continental/global-scale systems for modeling snow dynamics.%
\end{abstract}%



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