How do Future Projected Changes in Winter Snowfall Patterns Affect Our Ability to Characterize Them?

Ground snowfall measurements (e.g., from snow pillows and snow courses) are vital for water resource and hazard prediction in the Western U.S. In situ measurements of precipitation and snowpack are key predictors in streamflow forecasts, and serve as either a partial source or as validation for various data products and decision support tools. While winter precipitation information is conveniently preserved in snowpacks, our ground observation networks must be able to represent snow variability to exploit that convenience. The complex interaction of winter storms with high-relief terrain can cause even densely instrumented monitoring networks to “miss” important spatial patterns describing a catchment’s water storage. Climate change threatens to exacerbate this problem, as warming storms and an uphill-retreating rain-snow transition may amount to constrained, novel snowfall patterns. These changes might suggest that the capability of our current station networks may degrade in the future, but to what extent is unclear. Here, we use a high-resolution (3.75-km) dynamically downscaled climate projection (Gensini et al. 2023) to answer: How do the climate change-induced shifts in winter snowfall patterns affect our station networks’ ability to characterize them? We first describe the climatological snowfall changes across nine Western U.S. mountain ranges jointly with the distribution of current and idealized snow pillow station networks. We then evaluate (1) how constrained (if at all) these networks become with a rising snowline, and (2) to what extent these changes limit how effectively snowfall patterns can be approximated each year.