Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.

Hydropower is a low-carbon emission renewable energy source that provides competitive and flexible electricity generation and is essential to the evolving power grid in the context of decarbonization. Assessing hydropower availability in a changing climate is technically challenging because there is a lack of consensus in the modeling representation of key dynamics across scales and processes. The SECURE Water Act requires a periodic assessment of the impact of climate change on the United States federal hydropower. The uncertainties associated with the structure of the tools in the previous assessment was limited to an ensemble of climate models. We leverage the second assessment to evaluate the compounded impact of climate and reservoir-hydropower models’ structural uncertainties on monthly hydropower projections. While the second assessment relies on a mostly-statistical regression-based hydropower model, we introduce a mostly-conceptual reservoir operations-hydropower model. Using two different types of hydropower model allows us to provide the first hydropower assessment with uncertainty partitioning associated with both climate and hydropower models. We also update the second assessment, performed initially at an annual time scale, to a seasonal time scale. Results suggest that at least 50% of the uncertainties, both at annual and seasonal scales, are attributed to the climate models. The annual predictions are consistent between hydropower models which marginally contribute to the variability in annual projections. However, up to 50% of seasonal variability can be attributed to the choice of the hydropower model in regions over the western US where the reservoir storage is substantial. The analysis identifies regions where multi-model assessments are needed and presents a novel approach to partition uncertainties in hydropower projections. Another outcome includes an updated evaluation of CMIP5-based federal hydropower projection, at the monthly scale and with a larger ensemble, which can provide a baseline for understanding the upcoming 3rd assessment based on CMIP6 projections.