In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall1–3, with impacts on the timing, amount, and spatial patterns of snowpack4–7. The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE)8,9 in strategic but fixed locations10,11. However, this network may not be impacted by climate change in the same way as the surrounding area12 and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.
