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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. 

Abstract Stormwater is a vital resource and dynamic driver of terrestrial ecosystem processes. However, processes controlling interactions during and shortly after storms are often poorly seen and poorly sensed when direct observations are substituted with technological ones. We discuss how human observations complement technological ones and the benefits of scientists spending more time in the storm. Human observation can reveal ephemeral storm-related phenomena such as biogeochemical hot moments, organismal responses, and sedimentary processes that can then be explored in greater resolution using sensors and virtual experiments. Storm-related phenomena trigger lasting, oversized impacts on hydrologic and biogeochemical processes, organismal traits or functions, and ecosystem services at all scales. We provide examples of phenomena in forests, across disciplines and scales, that have been overlooked in past research to inspire mindful, holistic observation of ecosystems during storms. We conclude that technological observations alone are insufficient to trace the process complexity and unpredictability of fleeting biogeochemical or ecological events without the shower thoughts produced by scientists’ human sensory and cognitive systems during storms. 

Abstract Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind‐induced tree sway is a viable technique for detecting snow interception and quantifying canopy snow water equivalent (SWE). Over a 6 year period in Colorado, we monitored hourly sway of two conifers, each instrumented with an accelerometer sampling at 12 Hz. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal that could not be distinguished from thermal effects. However, larger changes in tree sway could not generally be attributed to thermal effects, and canopy snow was present 93%–95% of the time, as confirmed with classified PhenoCam imagery. Using sway tests, we converted changes in sway to canopy SWE, which were correlated with total snowstorm amounts from a nearby SNOTEL site (Spearmanr = 0.72 to 0.80,p < 0.001). Greater canopy SWE was associated with storm temperatures between −7°C and 0°C and wind speeds less than 4 m s⁻¹. Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. Monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and distinguishing thermal and snow mass effects on tree sway. 

Abstract When formulating a hydrologic model, scientists rely on parameterizations of multiple processes based on field data, but literature review suggests that more frequently people select parameterizations that were included in pre‐existing models rather than re‐evaluating the underlying field experiments. Problems arise when limited field data exist, when “trusted” approaches do not get reevaluated, and when sensitivities fundamentally change in different environments. The physics and dynamics of snow interception by conifers is just such a case, and it is critical to simulation of the water budget and surface albedo. The most commonly used interception parameterization is based on data from four trees from one site, but results from this field study are not directly transferable to locations with relatively warmer winters, where the dominant processes differ dramatically. Here, we combine a literature review with model experiments to demonstrate needed improvements. Our results show that the choice of model form and parameters can vary the fraction of snow lost through interception by as much as 30%. In most simulations, the warming of mean winter temperatures from −7 to 0°C reduces the modelled fraction of snow under the canopy compared to the open, but the magnitude of simulated decrease varies from about 10% to 40%. The range of results is even larger when considering models that neglect the melting of in‐canopy snow in higher‐humidity environments where canopy sublimation plays less of a role. Thus, we recommend that all models represent canopy snowmelt and include representation of increased loading due to increased adhesion and cohesion when temperatures rise from −3 to 0°C. In addition to model improvements, field experiments across climates and forest types are needed to investigate how to best model the combination of dynamically changing forest cover and snow cover to better understand and predict changes to albedo and water supplies.