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Abstract Colorado River streamflow has decreased 19% since 2000. Spring (March‐April‐May) weather strongly influences Upper Colorado River streamflow because it controls not only water input but also when snow melts and how much energy is available for evaporation when soils are wettest. Since 2000, spring precipitation decreased by 14% on average across 26 unregulated headwater basins, but this decrease did not fully account for the reduced streamflow. In drier springs, increases in energy from reduced cloud cover, and lowered surface albedo from earlier snow disappearance, coincided with potential evapotranspiration (PET) increases of up to 10%. Combining spring precipitation decreases with PET increases accounted for 67% of the variance in post‐2000 streamflow deficits. Streamflow deficits were most substantial in lower elevation basins (<2,950 m), where snowmelt occurred earliest, and precipitation declines were largest. Refining seasonal spring precipitation forecasts is imperative for future water availability predictions in this snow‐dominated water resource region. 

Abstract Understanding the role of snow sublimation in the alpine water balance is critical to predicting future water resource availability. During winter 2022–23, the Sublimation of Snow campaign in Colorado’s East River watershed used 12 eddy covariance (EC) instruments (2–20-m height) to measure sublimation and micrometeorology on the valley floor. ECs measured 33–42 mm of snow water equivalent sublimated (8%–10% of seasonal peak snow accumulation). Midwinter sublimation was driven by blowing snow and springtime sublimation by positive net radiation. During blowing snow, EC water vapor fluxes increased with height between 3 and 10 m, on average by 26% and by up to 200% during individual events (positive vertical turbulent flux divergence). During nonblowing snow conditions, fluxes decreased with height between 3 and 20 m, on average by 36% (negative vertical turbulent flux divergence). Estimates of transport terms in a water vapor conservation equation suggest that positive divergence arose from blowing snow sublimation and negative divergence arose from vertical water vapor advection, although horizontal advection remains unquantified, limiting our conclusions. We found that keeping one instrument functional over the entire winter is more important than having instruments at multiple heights. Seasonal uncertainty in measured total sublimation due to instrument height is estimated at ±12% due to blowing snow sublimation and water vapor advection; however, for shorter deployments, this uncertainty may be larger. The optimal instrument height for estimating total sublimation, 10 m at our site, is likely to vary by location, and further work is needed to understand the role of advection. Significance StatementMountain snowpacks act as water reservoirs for populations worldwide, and snow sublimation, which is rarely measured, removes water from those reservoirs. A recent measurement campaign offered the unique opportunity to compare sublimation measurements from 12 instruments, which reveal that sublimation estimates vary both across a winter season and with instrument height. Sublimation rates are higher during times with blowing snow compared to times without. During blowing snow, higher instruments (above 5 m) measure 26% more sublimation than lower instruments. Otherwise, higher instruments (above 10 m) measure 36% less sublimation. This work concludes that when measuring alpine sublimation, instrument height can introduce uncertainty, particularly if instruments are deployed for short periods of time. To best estimate total sublimation, instruments should be deployed over an entire winter season. 

Abstract Snow is a vital part of water resources, and sublimation may remove 10%–90% of snowfall from the system. To improve our understanding of the physics that govern sublimation rates, as well as how those rates might change with the climate, we deployed an array of four towers with over 100 instruments from NCAR’s Integrated Surface Flux System from November 2022 to June 2023 in the East River watershed, Colorado, in conjunction with the U.S. Department of Energy’s Surface Atmosphere Integrated Field Laboratory (SAIL) and the National Oceanic and Atmospheric Administration (NOAA)’s Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaigns. Mass balance observations, snow pits, particle flux sensors, and terrestrial lidar scans of the evolving snowfield demonstrated how blowing snow influences sublimation rates, which we quantified with latent heat fluxes measured by eddy-covariance systems at heights 1–20 m above the snow surface. Detailed temperature profiles at finer resolutions highlighted the role of the stable boundary layer. Four-stream radiometers indicated the important role of changing albedo in the energy balance and its relationship to water vapor losses. Collectively, these observations span scales from seconds to seasons, from boundary layer turbulence to valley circulation to mesoscale meteorology. We describe the field campaign, highlights in the observations, and outreach and education products we are creating to facilitate cross-disciplinary dialogue and convey relevant findings to those seeking to better understand Colorado River snow and streamflow. 

Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m < DCE < 27m), with a median F1 score of 0.87 across the four study sites, and shows promising accuracy for areas very close (>10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow.