Improved process understanding of snow density and SWE across forested mountain landscapes from coordinated field observations and model analyses

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

Seasonal snow in mountains is a critical water resource for billions of people worldwide. The amount of water stored in snow, or snow water equivalent (SWE), depends on its depth and density, both of which vary in space and time. New technologies permit mapping of snow depth across large watersheds, but there is no similar advance for measuring snow density. Instead, snow density models are used, but different models can yield divergent results across mountain landscapes. Therefore, understanding why snow density models differ is essential for reducing uncertainty in SWE. This project will improve knowledge of the physical processes that affect snowpack density, focusing on how forests yield predictable variations in snowpack density across watersheds. The project will advance snow density modeling and thus predictions of SWE, snowmelt, and runoff. Snow density models will be used to transform snow depth datasets into SWE datasets, benefiting hydrologic research. The project will train one graduate student, develop two field classes, and support multiple undergraduate interns at the university and through programs that strives to increase diversity in geoscience students. Spatial variations in snowpack density are driven primarily by differential compaction due to the mass of overlying snow - density tends to increase with greater snow depth. In contrast, secondary processes lead to increased density as depth decreases (e.g., wind compaction) or decreased density as depth increases (e.g., new snowfall). The guiding hypothesis is that landscape properties and climate govern the relative importance of primary and secondary densification processes. Snow in open areas is typically deeper than in forests, so snowpack density is predicted to be greater in open areas. However, secondary processes can enhance or counteract this effect, depending on environmental factors. Hypotheses that assess the roles of primary and secondary densification effects will be tested using coordinated field investigations and modeling experiments. Field investigations in distinct snow climates with landscape controls (e.g., forest vs. open) will measure differences in snow density, depth, water content, and layer characteristics. Snowpack will be simulated with process-based modular snow models and reference models. Field data will be used to quantify model errors, evaluate model representation of primary and secondary effects, and test model sensitivity to meteorological uncertainty. Models will be further evaluated using data from the NASA SnowEx campaign and the second Snow Model Intercomparison Project. Robust models identified will be applied to produce datasets of density, SWE, and uncertainty for community usage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.