Changing snow water storage in natural snow reservoirs

Abstract

This work introduces a novel snow metric, snow water storage (SwS), defined as the integrated area under the snow water equivalent (SWE) curve (units: length-time, e.g., m d). Unlike other widely used snow metrics that capture snow variables at a single point in time (e.g., maximum SWE) or describe temporal snow characteristics (e.g., length of snow season), SwS is applicable at numerous spatial and temporal scales. This flexibility in the SwS metric enables us to characterize the inherent reservoir function of snowpacks and quantify how this function has changed in recent decades. In this research, changes in the SwS metric are evaluated at point, gridded and aggregated scales across the conterminous United States (hereafter US), with a particular focus on 16 mountainous Environmental Protection Agency (EPA) Level III Ecoregions (ER3s). These ER3s account for 72 % of the annual SwS (SwSA) in the US, despite these ER3s only covering 16 % of the US land area. Since 1982, spatially variable changes in SwSA have been observed across the US with notable decreasing SwSA trends in the western US and in the 16 mountainous ER3s. All mountainous ER3 (except for the Northeastern Highlands in New England) exhibit decreasing trends in SwSA resulting in a 22 % overall decline in SwSA across mountainous ER3s. The peak monthly SwS (SwSM) occurs in March at all spatial scales, while the greatest percentage loss of SwSM occurs early in the snow season, particularly in November. Unsurprisingly, the highest elevations contribute most to SwSA in all mountain ranges, but the specific elevations that have experienced loss or gain in SwSA over the 39-year study period vary between mountain ranges. Comparisons of SwS with other snow metrics underscore the utility of SwS, providing insights into the natural reservoir function of snowpacks, irrespective of SWE curve variability or type (e.g., ephemeral, mountain, permanent). As we anticipate a future marked by increased climate variability and greater variability in mountain snowpacks, the spatial and temporal flexibility of snow metrics such as SwS may become increasingly valuable for monitoring and predicting snow water resources.