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Storm Peak Laboratory, located on the Steamboat Springs Ski Resort in Colorado on the west summit of Mount Werner at 10 532 ft (3220 m) MSL, is an internationally recognized high-elevation atmospheric research station that has been in use for over 40 years. This article provides a brief history of the Storm Peak Laboratory and the major research themes it has supported and discusses opportunities to leverage mountain observatory measurements to advance our understanding of the atmospheric processes. This facility provides long-term measurements of meteorology, clouds, aerosols, snow hydrology, and atmospheric gases, and it serves as a “proving ground” for instrument development and testing. Storm Peak Laboratory is part of multiple national and international observational networks. Due to the unique capabilities of Storm Peak Laboratory, there is a long history of targeted field campaigns primarily within the following research areas: mixed-phase cloud microphysics; atmospheric chemistry pertaining to the formation, characterization, and hygroscopicity of aerosols; and the transport and transformation of atmospheric mercury. Research training has been central to the mission of Storm Peak Laboratory (SPL) over the last 40 years. Currently, SPL hosts both undergraduate- and graduate-level courses in atmospheric science and snow hydrology organized by numerous institutions. Examples of these unique research training opportunities are provided. 

Abstract. Alpine ecosystems are experiencing rapid change as a result of warming temperatures and changes in the quantity, timing and phase of precipitation. This in turn impacts patterns and processes of ecohydrologic connectivity,vegetation productivity and water provision to downstream regions. The fine-scale heterogeneous nature of these environments makes them challengingareas to measure with traditional instrumentation and spatiotemporally coarse satellite imagery. This paper describes the data collection,processing, accuracy assessment and availability of a series of approximately weekly-interval uncrewed-aerial-system (UAS) surveys flown over the Niwot Ridge Long Term Ecological Research site during the 2017 summer-snowmelt season. Visible, near-infrared and thermal-infrared imagery was collected. This unique series of 5–25 cm resolution multi-spectral and thermal orthomosaics provides a unique snapshot of seasonal transitions in a high alpine catchment. Weekly radiometrically calibrated normaliseddifference vegetation index maps can be used to track vegetation health at the pixel scale through time. Thermal imagery can be used to map themovement of snowmelt across and within the near sub-surface as well as identify locations where groundwater is discharging to the surface. A 10 cm resolution digital surface model and dense point cloud (146 points m−2) are also providedfor topographic analysis of the snow-free surface. These datasets augment ongoing data collection within this heavily studied and importantalpine site; they are made publicly available to facilitate wider use by the research community. Datasets and related metadata can be accessed through the Environmental Data Initiative Data Portal, https://doi.org/10.6073/pasta/dadd5c2e4a65c781c2371643f7ff9dc4 (Wigmore, 2022a), https://doi.org/10.6073/pasta/073a5a67ddba08ba3a24fe85c5154da7 (Wigmore, 2022c), https://doi.org/10.6073/pasta/a4f57c82ad274aa2640e0a79649290ca(Wigmore and Niwot Ridge LTER, 2021a), https://doi.org/10.6073/pasta/444a7923deebc4b660436e76ffa3130c (Wigmore and Niwot Ridge LTER, 2021b), https://doi.org/10.6073/pasta/1289b3b41a46284d2a1c42f1b08b3807 (Wigmore and Niwot Ridge LTER, 2022a), https://doi.org/10.6073/pasta/70518d55a8d6ec95f04f2d8a0920b7b8 (Wigmore and Niwot Ridge LTER, 2022b). A summary of the available datasets can be found in the data availability section below. 

An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this presentation is to introduce a novel method that combines light detection and ranging (LiDAR) with ground-penetrating radar (GPR) to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring melt. This method was developed at multiple plots in Colorado in 2017 and applied at the small catchment scale in 2019. We developed this method in a manner to observe rapid changes that occur at subdaily timescales. Observed volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters during method development. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for studies to estimate the complex spatio-temporal dynamics of liquid water in snow. During the spring snowmelt season of 2019, we applied this method to a small headwater catchment in the Colorado Front Range. A total of 9 GPR surveys of approximately 3 km in length were conducted over a six-week period. Additionally, five LiDAR scans occurred over the same area. Using this technique, we identify locations that melting snow accumulates and is stored as liquid water within the snowpack. This work shows that the vadose zone may be conceptualized, during snowmelt, as extending above the soil-snow interface to include variably saturated flow processes within the snowpack. 

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km 2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (10 2 ->10 3 m) and microtopography (<10 0 -10 2 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems. 

Abstract. A critical component of hydrologic modeling in cold andtemperate regions is partitioning precipitation into snow and rain, yetlittle is known about how uncertainty in precipitation phase propagates intovariability in simulated snow accumulation and melt. Given the wide varietyof methods for distinguishing between snow and rain, it is imperative toevaluate the sensitivity of snowpack model output to precipitation phasedetermination methods, especially considering the potential of snow-to-rainshifts associated with climate warming to fundamentally change the hydrologyof snow-dominated areas. To address these needs we quantified thesensitivity of simulated snow accumulation and melt to rain–snowpartitioning methods at sites in the western United States using theSNOWPACK model without the canopy module activated. The methods in thisstudy included different permutations of air, wet bulb and dew pointtemperature thresholds, air temperature ranges, and binary logisticregression models. Compared to observations of snow depth and snow water equivalent (SWE), thebinary logistic regression models produced the lowest mean biases, whilehigh and low air temperature thresholds tended to overpredict andunderpredict snow accumulation, respectively. Relative differences betweenthe minimum and maximum annual snowfall fractions predicted by the differentmethods sometimes exceeded 100 % at elevations less than 2000 m in theOregon Cascades and California's Sierra Nevada. This led to rangesin annual peak SWE typically greater than 200 mm,exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelttiming predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater.Conversely, the three coldest sites in this work were relatively insensitiveto the choice of a precipitation phase method, with average ranges in annualsnowfall fraction, peak SWE, snowmelt timing, and snow cover duration of lessthan 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmeltrate were typically less than 4 mm d−1 and exhibited a smallrelationship to seasonal climate. Overall, sites with a greater proportionof precipitation falling at air temperatures between 0 and4 ∘C exhibited the greatest sensitivity to method selection,suggesting that the identification and use of an optimal precipitation phasemethod is most important at the warmer fringes of the seasonal snow zone. 

Abstract. Cold content is a measure of a snowpack's energy deficit and is a linear function of snowpack mass and temperature. Positive energy fluxes into a snowpack must first satisfy the remaining energy deficit before snowmelt runoff begins, making cold content a key component of the snowpack energy budget. Nevertheless, uncertainty surrounds cold content development and its relationship to snowmelt, likely because of a lack of direct observations. This work clarifies the controls exerted by air temperature, precipitation, and negative energy fluxes on cold content development and quantifies the relationship between cold content and snowmelt timing and rate at daily to seasonal timescales. The analysis presented herein leverages a unique long-term snow pit record along with validated output from the SNOWPACK model forced with 23 water years (1991–2013) of quality controlled, infilled hourly meteorological data from an alpine and subalpine site in the Colorado Rocky Mountains. The results indicated that precipitation exerted the primary control on cold content development at our two sites with snowfall responsible for 84.4 and 73.0% of simulated daily gains in the alpine and subalpine, respectively. A negative surface energy balance – primarily driven by sublimation and longwave radiation emission from the snowpack – during days without snowfall provided a secondary pathway for cold content development, and was responsible for the remaining 15.6 and 27.0% of cold content additions. Non-zero cold content values were associated with reduced snowmelt rates and delayed snowmelt onset at daily to sub-seasonal timescales, while peak cold content magnitude had no significant relationship to seasonal snowmelt timing. These results suggest that the information provided by cold content observations and/or simulations is most relevant to snowmelt processes at shorter timescales, and may help water resource managers to better predict melt onset and rate.