Increasing wildfire frequency and severity in high-elevation seasonal snow zones presents a considerable water resource management challenge across the western U.S. Wildfires can affect snowpack accumulation and melt patterns, altering the quantity and timing of runoff. While prior research has shown that wildfire generally increases snow melt rates and advances snow disappearance dates, uncertainties remain regarding variations across complex terrain and the energy balance between burned and unburned areas. Utilizing multiple paired in-situ data sources within the 2020 Cameron Peak burn area during the 2021–2022 winter, we found no significant difference in peak snow water equivalent (SWE) magnitude between burned and unburned areas. However, the burned south aspect reached peak SWE 22 days earlier than burned north. During the ablation period, burned south melt rates were 71% greater than unburned south melt rates, whereas burned north melt rates were 94% greater than unburned north aspects. Snow disappeared 7 to 11 days earlier in burned areas than unburned areas. Net energy differences at the burned and unburned AWS sites were seasonally variable, with the burned area losing more energy during the winter but gaining significantly more energy during the spring. Net shortwave radiation was 56% greater at the burned area during the winter and 137% greater during the spring driving a ~60% greater cumulative net energy at the burned site during May. These findings emphasize the need for post-wildfire water resource planning that accounts for aspect-dependent differences in energy and mass balance to accurately predict snowpack storage and runoff timing.

Extensive efforts are made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at the global scale remains elusive. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately understood. The dielectric relative permittivity (k) determines the velocity of the radar wave through snow. Equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work is to further understand the dielectric permittivity of seasonal snow under dry and wet conditions. We utilize extensive in situ observations of k with snow density and liquid water content (LWC) observations to: (1) Test current permittivity equations for dry snow conditions, (2) Test current permittivity equations for wet snow conditions, and (3) Determine if any improvements to current permittivity equations are necessary. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We will present empirical relationships based on 146 snow pits for dry snow conditions and 92 LWC observations in naturally melting snowpacks. Regression results have r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, though further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ testing. Many previous equations assume a background (dry) k that we found to be inaccurate, as previously stated, that is the primary driver of resulting uncertainty. Our results suggest large errors in SWE or LWC estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future remote sensing opportunities such as NISAR and ROSE-L.