Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget

Abstract

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.

Hydrologic connectivity at the hillslope scale through intra‐snowpack flow paths during snowmelt

“…For this study, the combination of snow, soil, and meteorological variables resulted in the largest effective intra‐snowpack contributing area of meltwater to snow LWC at the highest elevation site, AT (Figure ). Higher elevation snowpacks will generally have higher cold content magnitudes (Jennings, Kittel, et al ()), which can refreeze surface melt produced prior to the snowpack reaching isothermal conditions. Refrozen meltwater often takes the form of ice lenses that act as permeability barriers (Webb, Fassnacht, Gooseff, et al, ) for later formation of meltwater intra‐snowpack flow paths as observed in this study (Table and Figure ).…”

“…We used the physics‐based SNOWPACK model (Bartelt & Lehning, ; Lehning, Bartelt, Brown, & Fierz, ; Lehning, Bartelt, Brown, Fierz, & Satyawali, ) to simulate snowmelt rates at the study sites. Model set‐up was the same as described in Webb, Jennings, Fend, and Molotch () and Jennings, Kittel, & Molotch () with the addition of the BTN site. We ran the model at an hourly time step using quality controlled air temperature, relative humidity, wind speed, incoming shortwave radiation, incoming longwave radiation, and snow depth data (Table ).…”

“…We estimated LW as a function of T a , RH, and SW based on the findings of Flerchinger, Xaio, Marks, Sauer, and Yu () and Jennings, Kittel, et al ().…”

“…Superscripted letters correspond to the forcing data notes as follows: a NT forcing data were derived from the nearby AT meteorological tower and corrected using a−6. (2009) and Jennings, Kittel, et al (2018). d Changes in snow depth were converted to precipitation using the approach of Hedstrom and Pomeroy (1998).…”

“…In order to obtain physically realistic results from these models, it is necessary to understand the nature of intra-snowpack flow paths and the resulting meltwater outflow variability (Webb, Williams, et al, 2018). Observed melt patterns have shown variogram ranges increase with elevation (Rikkers et al, 1996;Sommerfeld, Bales, & Mast, 1994;Webb, 2017;Jennings, Kittel, & Molotch, 2018;Williams, Sommerfeld, Massman, & Rikkers, 1999) suggesting that vegetation and other physiographic parameters influence the scale of intra-snowpack flow processes, though these studies were conducted in different locations and years making the connections to elevation and the resulting climate gradients less certain.…”