  Snow Water-Equivalent (SWE) -- the amount of water available if snowpack is
melted -- is a key decision variable used by water management agencies to make
irrigation, flood control, power generation and drought management decisions.
SWE values vary spatiotemporally -- affected by weather, topography and other
environmental factors. While daily SWE can be measured by Snow Telemetry
(SNOTEL) stations with requisite instrumentation, such stations are spatially
sparse requiring interpolation techniques to create spatiotemporally complete
data. While recent efforts have explored machine learning (ML) for SWE
prediction, a number of recent ML advances have yet to be considered. The main
contribution of this paper is to explore one such ML advance, attention
mechanisms, for SWE prediction. Our hypothesis is that attention has a unique
ability to capture and exploit correlations that may exist across locations or
the temporal spectrum (or both). We present a generic attention-based modeling
framework for SWE prediction and adapt it to capture spatial attention and
temporal attention. Our experimental results on 323 SNOTEL stations in the
Western U.S. demonstrate that our attention-based models outperform other
machine learning approaches. We also provide key results highlighting the
differences between spatial and temporal attention in this context and a
roadmap toward deployment for generating spatially-complete SWE maps.
