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.

Despite the necessity of Global Climate Models (GCMs) sub-selection in the dynamical downscaling experiments, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we relatively rank 37 GCMs from the 6th phase of Coupled Models Intercomparison Project (CMIP6) over four regions representing the contiguous United States (CONUS). The ranking is based on their performance across 60 evaluation metrics in the historical period (1981–2014). To ensure that the outcome is not method-dependent, we employ two distinct approaches to remove the redundancy in the evaluation criteria. The first approach is a simple weighted averaging technique. Each GCM is ranked based on its weighted average performance across evaluation measures, after each metric is weighted between zero and one depending on its uniqueness. The second approach applies empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. While the models from the same institute tend to display comparable skills, the high-resolution model versions distinctively perform better than their lower-resolution counterparts. The results from this study should be helpful in the selection of models for dynamical downscaling efforts, such as the COordinated Regional Downscaling Experiment (CORDEX), and in understanding the strengths and deficiencies of CMIP6 GCMs in the representation of various background climate characteristics across CONUS.

Coastal cities are exposed to multiple flood drivers such as extreme coastal high tide, storm surge, and extreme river discharge. The interaction among these flood drivers may cause compound flooding events, which could exacerbate social and economic consequences. Climate change can put greater pressure on these areas by increasing the frequency and intensity of coastal and riverine flooding. In this study, a bivariate compound flooding risk assessment method is developed to incorporate sea level rise (SLR) and nonstationary river discharge conditions. Extreme sea water level (SWL) and river stage are identified using the peak-over-threshold method, and subsequently, pairs of extremes are selected when both SWL and river stage exceed their defined thresholds within ±1 day from each other. A copula-based approach is then used to estimate the joint distribution and return period of compound coastal riverine flooding by incorporating nonstationarity into the marginal distributions of extreme SWL and river stage. The future flood risk is assessed using the notation of failure probability, which here refers to (1) the probability of occurrence of at least one major coastal or riverine flooding for a given design life (i.e., total flood risk); and (2) the probability of occurrence of at least one compound major coastal riverine flooding for a given design life (i.e., compound flood risk). Compound flood risk assessment is conducted at 26 paired NOAA-USGS stations along the Contiguous United States coast with long‐term observed data and defined flood thresholds. The results indicate that in some regions the joint return period of coastal/riverine flooding are substantially lower when considering the projected future hydroclimate conditions and SLR. The importance impact of future SLR and hydroclimate conditions is discussed regionally in terms of changes in the frequency of compound major coastal riverine flooding events.