Adrienne M. Marshall

An extensive body of literature has documented declines in western U.S. snow, including increases in snow drought (extreme low snow), but the spatial extent of snow extremes may also substantially impact the consequences of snow losses and has been relatively underexamined. Here, we use a gridded snow reanalysis across the western U.S. spanning 1982–2023 to examine drivers and trends in the spatial extents of snow anomalies. The largest snow drought extents occur in warm/dry conditions, while the largest deluge extents occur in cool/wet conditions. Like other drought types, severity generally varies inversely with spatial extent. El Niño conditions generate significantly larger snow drought extents than deluge ( p < 0.05); the converse is true but not statistically significant for La Niña. Across the western U.S., the annual extent of snow drought in December has increased by 5.6% of the snow-receiving area per decade, while December deluge extent has decreased by 5.8% of area per decade. Trends in January–March are significant but smaller in magnitude. Trends also vary by extremity: the most extreme snow droughts are increasing significantly in February–May, while less extreme snow droughts are increasing significantly in December–January. Finally, significant winter declines in deluge extent are more widespread than drought increases, suggesting that declines in deluge merit further attention. These findings suggest that analyses focusing only on spring snow and drought, not deluge, may miss important trends in snow extremes. If observed trends continue, the increasingly widespread snow droughts and less widespread deluges documented here could exacerbate the impacts of snow loss for interconnected water, energy, and land management systems.

Surface hoar formation is a critical snow metamorphism process that influences the surface roughness, radar-scattering properties, albedo, and avalanche risk of snowpacks. Despite its importance, surface hoar mechanisms and climate sensitivity remain poorly constrained, creating uncertainties in remote sensing of snow properties and infrastructure hazard forecasting. To address these gaps, we use observations from the Surface Atmosphere Integrated Field Lab (SAIL) alongside the Structure for Understanding Multiple Modeling Alternatives (SUMMA) physics-based model to investigate contemporary and future surface hoar dynamics in a representative mid-latitude snow environment in the Colorado Rockies. Modeling and observations are centered around seven high-quality manual measurements of surface hoar mass during February 2023. We confirm that surface hoar is favored on clear nights with snow surfaces that are ∼ 10 °C colder than the near-surface air, leading to a favorable humidity gradient for water vapor deposition from the atmosphere onto the snowpack. Nocturnal clouds exert a 30–40 W m ⁻² radiative forcing that inhibits the snowpack from cooling, thereby limiting deposition. We evaluate stability correction parameterizations and surface roughness parameters for the SUMMA model using co-located vertical gradients and eddy-covariance observed fluxes, finding that models must use an appropriate stability correction for the highly stable surface layers characteristic of surface hoar (bulk Richardson number >0.2) to model deposition rates sufficient to explain observed surface hoar mass. After taking these factors into account, both SUMMA and observations agree that deposition fluxes are favored when overnight air temperatures are less than −8 to −10 °C and horizontal wind speed is less than 2 to 3 m s ⁻¹ . Sensitivity experiments demonstrate that surface hoar is favored for low-density snowpacks via a thermal conduction mechanism. Using SUMMA forced by an ensemble of 9 downscaled GCMs shows that, at the annual timescale, the total wintertime nocturnal water vapor flux onto the snowpack decreases in magnitude at a rate of 6.1 gm ⁻² per degree of warming, yielding an 81 % decrease by the end-of-century under the SSP3-7.0 emission scenario. This decline is driven by a 14 % decrease in nightly surface hoar events per winter and an overall increase in the size and frequency of nocturnal sublimation events. Additional work reconciling observed and modeled amounts of surface hoar mass, turbulent exchanges of water vapor during high stability, and relationships to katabatic winds in complex terrain is warranted in order to improve the understanding of this fundamental snow metamorphosis process.

Wildfires fundamentally alter snow accumulation and melt dynamics by modifying forest canopy structure, snow albedo, and snowpack physical properties. Despite the growing body of work on fire impacts on snow, few studies have used physically based models to simulate post‐fire changes, and none have examined the global parameter sensitivities that govern these dynamics. In this study, we apply the Structure for Unifying Multiple Modeling Alternatives in a high elevation Colorado watershed to identify and evaluate the model parameters that influence post‐fire snowpack evolution. We conduct a global sensitivity analysis, then sample sensitive parameters to identify behavioral parameter sets with respect to airborne LiDAR‐based snow depth in burned and unburned forests. Among behavioral parameter sets, we identify 4 parameters that differ significantly in burned versus unburned forests. These include the albedo decay rate (45% faster in burn than unburned forest), maximum canopy interception for rain (60% lower), minimum temperature for snow unloading (3.9°C warmer), and snow surface roughness (902% higher). Model simulations using behavioral parameter sets for burned and unburned conditions indicate 17% lower peak absolute cold content on average and 17% lower peak snow‐water equivalent (SWE) in burned conditions on average, with differences in depth, SWE, and cold content increasing throughout the snow season. These findings highlight the combined influence of canopy loss and accelerated albedo decay on snow energy balance and reveal additional post‐fire snow processes that are not typically represented in existing modeling frameworks. This study therefore provides a process‐level foundation for improving the simulated representation of post‐fire snow dynamics.

Droughts have been extensively studied at small to large scales, yet limited work has integrated the mountain‐range and tree‐level perspectives to explain tree drought response at the intra‐catchment scale where management decisions are made. Here, we investigated tree response to drought in terms of resistance—the ability of a forest to continue transpiring during drought—and resilience—the ability to rebound post‐drought. We estimated resistance and resilience using Landsat‐derived normalized difference vegetation index (NDVI) over a 0.5 km ² catchment of the Southern Sierra Critical Zone Observatory. At the catchment‐wide scale, we fitted generalized additive models with eight remotely sensed predictors to explain 51% of the variance in resistance and 59% in resilience. Topography and baseline greenness were the strongest predictors and exhibited opposite effects on resistance versus resilience, underscoring the need to distinguish their drivers. Aspect and snow depth were significant for resilience only, further highlighting that resistance and resilience are governed using partially distinct processes. Slope and elevation effects contradicted regional‐scale patterns, whereas canopy height effects were consistent across scales. Remote sensing revealed spatial patterns of drought response, while in situ ecohydrological, meteorological, and geophysical (electrical resistivity) data from six stations offered process‐based insights into the conditions underlying them: valley‐bottom hydrologic refugia, inferred reliance on internal sapwood water stores, and consistently low atmospheric demand were all associated with locations that experienced greater drought resistance. This work demonstrates that forest vulnerability emerges from coupled, scale‐dependent interactions among hydrology, vegetation structure, and topography.

Hydrologic models are often calibrated using streamflow alone, but increasing availability of in situ and satellite-based observations provide numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with additional data would inform or misinform streamflow prediction. Here, we carry out a multi-observational sensitivity and uncertainty analysis using the U.S. Geological Survey's National Hydrologic Model (NHM) in four headwater catchments in the Upper Colorado River Basin. We use seven different observational data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. Informative model parameters are identified using the Morris screening method across all data sets, followed by parameter estimation and streamflow performance assessment using a Latin Hypercube Sample Monte-Carlo filtering approach. Results show that an increased number of informative parameters are determined through the screening process with the use of observation data representing terms beyond streamflow, and that forcing corrections and rain-snow partitioning parameters are particularly impactful to the model fit to observations. Multi-objective Monte Carlo filtering reduces the number of behavioral parameter sets, and estimated parameter values can depend strongly on the observation data criteria. Evapotranspiration is informative for streamflow prediction across all catchments included in this study, but snow and soil moisture datasets are only informative in some. These results provide new insight into the variable value of alternative observation data for streamflow prediction and highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.

Abrupt snowmelt, triggered by rain-on-snow events or “snow-eater heat waves,” can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

In the spring of 2026, anomalously low snow conditions in the western United States threatened winter recreation and water supplies. Here, we investigate: to what extent was this snow drought attributable to the climate change that has occurred since the pre-industrial period? We find that a snow drought this severe across the western United States was approximately 4.4 [95% CI: 2.6, 9.4] times more likely in the current climate than in the preindustrial period. In the Upper Colorado River Basin, the snow drought was approximately 14 times more likely [0.09, 4,300]. Given a projected increase in the frequency of snow droughts at least this severe in a moderately high emissions warming scenario, the lived experience of this event may help scientists, resource managers, and the public consider what western US snow might look like if greenhouse gas emissions are not aggressively reduced.

In recent years, several reservoirs in the western U.S. have approached or exceeded critically low storage during drought conditions; such events could be particularly impactful to water and energy availability if they are widespread across the region or exacerbated by climate change. However, projected changes in low storage and near-minimum-power-pool (MPP) risk remain poorly quantified across large spatial domains. Here, we assess simulated low storage frequency (LSF) across 94 western U.S. reservoirs using a calibrated offline hedging release model driven by projected reservoir inflows under future climate scenarios. We evaluate near-MPP risk for 30 reservoirs for which reservoir-specific thresholds are known. Projected low storage responses are spatially and seasonally heterogeneous. Across the ensemble, annual LSF declines in many future simulations, and near-MPP exposure remains limited for most reservoirs. Thus, within the climate-driven inflow projections and hedging framework, future projections do not produce a widespread increase in critically LSF across western U.S. hydropower reservoirs. This broad pattern masks important regional and seasonal differences, with elevated late-season vulnerability emerging in California and parts of the Southwest. LSF is more strongly associated with precipitation than with temperature, with drier years producing elevated risk across nearly every reservoir and warming amplifying risk in California and the Southwest. These results indicate that estimating operational threshold risks from climate-driven inflow projections remain challenging, particularly for low storage conditions that matter most for hydropower vulnerability.

Surface hoar formation is a critical snow metamorphism process that influences surface roughness, radar-scattering properties, albedo, and avalanche risk of snowpacks. Despite its importance, surface hoar mechanisms and climate sensitivity remain poorly constrained, creating uncertainties in remote sensing of snow properties and infrastructure hazard forecasting. To address these gaps, we use observations from the Surface Atmosphere Integrated Field Lab (SAIL) alongside the Structure for Understanding Multiple Modeling Alternatives (SUMMA) physics-based model to investigate contemporary and future surface hoar dynamics in a representative mid-latitude snow environment in the Colorado Rockies. Modeling and observations are centered around seven high-quality manual measurements of surface hoar mass during February 2023. We confirm that surface hoar is favored on clear nights with snow surfaces that are 10 °C colder than the near-surface air, leading to a favorable humidity gradient for water vapor deposition from the atmosphere onto the snowpack. Nocturnal clouds exert a 30–40 W m^-2 radiative forcing that inhibits the snowpack from cooling, thereby limiting deposition. We evaluate stability correction parameterizations and surface roughness parameters for the SUMMA model using colocated vertical gradients and eddy-covariance observed fluxes, finding that models must use an appropriate stability correction for the highly stable surface layers characteristic of surface hoar (Ri > 0.2) to model deposition rates sufficient to explain observed surface hoar mass. After taking these factors into account, both SUMMA and observations agree that deposition fluxes are favored when overnight air temperatures are less than -8 to -10 °C and ω_spd is less than 3 m s^-1. Sensitivity experiments demonstrate that surface hoar is favored for low-density snowpacks via a thermal conductivity mechanism. Using SUMMA forced by an ensemble of 9 downscaled GCMs shows that, at the annual timescale, the total wintertime nocturnal water vapor flux onto the snowpack decreases at a rate of 6.1 gm² per degree of warming, yielding an 81 % decrease by the end-of-century under the SSP3-7.0 emission scenario.This decline is driven by a 14 % decrease in nightly surface hoar events per winter and an overall increase in the size and frequency of nocturnal sublimation events. Additional work reconciling observed and modeled amounts of surface hoar mass, turbulent exchanges of water vapor during high stability, and relationships to katabatic winds in complex terrain is warranted in order to improve the understanding of this fundamental snow metamorphosis process.