Variable fresh snow albedo: how snowpack and sub-nivean properties influence fresh snow reflectance

2021 Fall.Includes bibliographical references.The understanding of albedo, or ratio of outgoing to incoming shortwave radiation, is necessary for modeling the melt characteristics of a snowpack in snow-dominated areas. The timing and supply of meltwater downstream is influenced by the energy balance, and albedo is used in those calculations. Current snow albedo models range from simple models that only reset albedo with new snowfall to complex models that are not feasible for most applications. We present a variable fresh snow model that enhances a simple albedo model, initially created by the U.S. Army Corps of Engineers, and used extensively in the Canadian LAnd Surface Scheme (CLASS). The new approach considers conditions prior to and during a snowfall event to improve fresh snow albedo estimates, instead of resetting to a static value; it also considers differences in the albedo decay rate.Hourly shortwave radiation (incoming and outgoing), snow depth, temperature, and other meteorological data from two stations at the Senator Beck Basin in the San Juan Mountains of Southwest, Colorado were used for the period from 2005 to 2014. We evaluated changes in albedo of a high-elevation seasonal snowpack during fresh snow events and apply a set of multivariate regressions to recreate values of broadband albedo. The variable fresh snow albedo model approaches the Visible and Near-Shortwave Infrared portion of the electromagnetic spectrum differently and groups values by temperature. The model needs few inputs, specifically measurements of depth and temperature, an estimation of ground albedo, and for increased accuracy, a quantification of the number of aeolian dust deposition events on the snowpack every year. This variable fresh snow model showed higher accuracy in albedo values, both of fresh and decayed snow (R2 of 0.77 and Nash Sutcliffe Efficiency, NSE of 0.75) than of CLASS (R2 of 0.67 and NSE of 0.62). When isolating fresh snow events, the variable fresh snow albedo model was much more accurate than the single-reset albedo provided by CLASS but still had a weak correlation to measured values (R2 of 0.38). The variable fresh snow albedo model especially outperformed CLASS during the melt period, with ~24% more accurate absorption values to measured values than CLASS. Since fresh snow albedo is primarily weighted by albedo from the timestep before, we suggest this model also be used to correct erroneous values of albedo given incorrect sensor measurements, such as due to snow accumulation on the upward looking shortwave radiation sensor (pyranometer)

Drivers of Dust-Enhanced Snowpack Melt-Out and Streamflow Timing

The presence of dust on the snowpack accelerates snowmelt. This has been observed through snowpack and hydrometeorological measurements at a small study watershed in southwestern Colorado. For a 13-year period, we quantified the annual dust-enhanced energy absorption (DEAE) and used this information to model the snowpack melt-out under observed (with dust present) and clean conditions (no dust). We determine the difference in snow cover duration between actual (dust present) and simulated ideal (clean) snowpack (ΔSAG) to characterize the shifts in melt timing for each year. We compute the center of mass of runoff (tQ50) as a characteristic of snowmelt. DEAE, ΔSAG and tQ50 vary from year to year, and are dictated by the quantity of snow accumulation, and to a lesser extent the number of dust events, the annual dust loading, and springtime snowfall

Drivers of Dust-Enhanced Snowpack Melt-Out and Streamflow Timing

The presence of dust on the snowpack accelerates snowmelt. This has been observed through snowpack and hydrometeorological measurements at a small study watershed in southwestern Colorado. For a 13-year period, we quantified the annual dust-enhanced energy absorption (DEAE) and used this information to model the snowpack melt-out under observed (with dust present) and clean conditions (no dust). We determine the difference in snow cover duration between actual (dust present) and simulated ideal (clean) snowpack (ΔSAG) to characterize the shifts in melt timing for each year. We compute the center of mass of runoff (tQ50) as a characteristic of snowmelt. DEAE, ΔSAG and tQ50 vary from year to year, and are dictated by the quantity of snow accumulation, and to a lesser extent the number of dust events, the annual dust loading, and springtime snowfall