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Abstract 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.

Abstract Snow in mountainous regions is a key water resource from local to continental scales. However, the link between increasingly frequent snow droughts and socio-hydrologic impacts from headwaters to lowlands is not fully understood. To untangle this relationship, here we combine snow and climate reanalyses with a range of data of terrestrial water storage, streamflow, and emergency water-use restrictions for the archetypal 2022 snow drought in the Po basin (Italy). We find that a persistent high-pressure ridge during winter 2021–2022 translated into a −88% anomaly in peak snow water equivalent, intraseasonal snowmelt, and earlier melt-out dates. Compounded by reduced summer precipitation and importantly increased temperatures, this snow deficit led to the lowest terrestrial water storage on record in summer 2022. Emergency water-use restrictions were concurrent with the peak in snowmelt deficit in early summer, rather than the peak in precipitation deficit in mid-winter. This study highlights the contribution of snowmelt deficit in driving the 2022 socio-hydrologic drought in the Po Basin.

Remote sensing observations of mountain snow depth are still lacking for the Northern Hemisphere mountains. Here authors use Sentinel-1 satellite radar measurements to assess the snow depth in mountainous areas at 1 km² resolution and show that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences.