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  1. This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA (Bonner et al., 2022).

    All snow observation and meteorological forcing data are available as both .nc and .mat files.
    Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.

    This dataset will continue to be updated, via this repository, as additional years of data are collected.

     
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  2. Abstract

    We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) athttps://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation.

     
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  3. Abstract

    Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15,P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an “adaptive sampling” approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.

    Significance Statement

    Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an “adaptive sampling” application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts.

     
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  4. Abstract

    Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA model at three sites representing diverse snow climates in Colorado (USA), Oregon (USA), and Alberta (Canada) for 5 years. First, control simulations were run for open and forest sites. Comparisons to observations showed the uncalibrated model with NLDAS‐2 forcing performed reasonably. Then, experiments were completed to isolate how forest processes affected modelled snowpack density and SWE, including: (1) mass reduction due to interception loss, (2) changes in the phase and amount of water delivered from the canopy to the underlying snow, (3) varying new snow density from reduced wind speed, and (4) modification of incoming longwave and shortwave radiation. Delivery effects (2) increased forest snowpack density relative to open areas, often more than 30%. Mass effects (1) and wind effects (3) decreased forest snowpack density, but generally by less than 6%. The radiation experiment (4) yielded negligible to positive effects (i.e., 0%–10%) on snowpack density. Delivery effects on density were greatest at the warmest times in the season and at the warmest site (Oregon): higher temperatures increased interception and melted intercepted snow, which then dripped to the underlying snowpack. In contrast, mass effects and radiation effects were shown to have the greatest impact on forest‐to‐open SWE differences, yielding differences greater than 30%. The study highlights the importance of delivery effects in models and the need for new types of observations to characterize how canopies influence the flux of water to the snow surface.

     
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  5. 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.

     
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  6. Abstract

    Vertical displacements (dz) in permanent Global Positioning System (GPS) station positions enable estimation of water storage changes (ΔS), which historically have been impossible to measure directly. We use dz from 924 GPS stations in the western United States to estimate daily ΔS in California's Sierra Nevada and compare it to seasonal snow accumulation and melt over water years 2008–2017. Seasonal variations in GPS‐based ΔS are ~1,000 mm. Typically, only ~30% of ΔS is attributable to snow water equivalent (SWE). ΔS lags the snow cycle, peaking after maximum SWE and remaining positive when all snow has melted (SWE = 0). We conclude that seasonal ΔS fluctuations are not primarily driven by SWE but by rainfall and snowmelt stored in the shallow subsurface (as soil moisture and/or groundwater) and released predominantly through evapotranspiration. Seasonal peak GPS ΔS is larger than accumulated precipitation from the Parameter‐elevation Relationships on Independent Slopes Model and North American Land Data Assimilation System, indicating that these standard inputs underestimate mountain precipitation.

     
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