Do snow models represent differences in snow density between forests and open areas for remote sensing of SWE?

The amount of water stored in seasonal snowpack, snow water equivalent (SWE), depends on its depth and bulk density, both of which are controlled by interacting physical processes that vary across gradients in topography, forests, and climate. Several remote sensing techniques (e.g., altimetry mapping with lidar or radar) can measure snow depth at a high spatial resolution (sub-meter to tens of meters), but there is no parallel advance in density measurement technology and thus robust density models are essential. The hydrologic utility of remotely sensed snow depth depends critically on the robustness of snow density models and their ability to represent physical processes across diverse landscapes, such as forests. Relative to open areas, forests alter snow accumulation and the energy balance, which in turn can reduce or enhance snow densification processes. Prior snow model intercomparison studies (i.e., SnowMIP2) show a wide range of density estimates in both open and forested areas, with no clear consensus across models on whether snow density tends to be higher, lower, or similar between forests and open areas. This highlights an important source of uncertainty when using modeled snow density to map SWE with remote sensing across forested regions in the cryosphere.

Here, we synthesize field observations from past campaigns and apply modeling experiments to assess how bulk snow density and snow depth vary between forests and open areas. Drawing upon community field campaigns (e.g., SnowEx and CLPX) and individual studies at research areas, we review snow depth and density observations collected with a variety of measurement approaches, and attempt to explain these variations in bulk properties with detailed stratigraphic observations (e.g., snow pit profiles) when available. Using modular model experiments, we assess whether specific physical processes (e.g., canopy interception, shading) can account for observed relationships between snow density and depth in forests and open areas of SnowEx, which will identify strengths and limitations of current modeling approaches. Finally, we assess the intra- and inter-annual variability of these bulk snow properties (from observations and models), which has implications for the timing of field operations and duration of snow measurement campaigns.