Characterizing a Snowpack’s Ability to Store Liquid Water at the Small Catchment Scale – A Comparison of Ground-Based Remote Sensing Observations and Hydrologic Modeling

Abstract:

An important consideration for water resources planning is runoff timing, which can be strongly influenced by the storage and subsequent release of liquid water within seasonal snow. We present two ground-based remote sensing techniques, terrestrial light detection and ranging (LiDAR) and ground-penetrating radar (GPR), used to estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring melt. This method was applied during the 2019 melt season at the Niwot Ridge Saddle Catchment, a 0.45 km² alpine watershed in northern Colorado, USA. Nine GPR surveys of approximately 3 km in length were conducted over a six-week period. Five of the GPR surveys occurred synchronously with terrestrial LiDAR scans of the catchment. Combining calculated snow depth from LIDAR scans with permittivity data from GPR observations, we developed estimates of bulk liquid water content stored within the snowpack across the catchment. We identify locations where snowmelt accumulates and is stored as liquid water within the snowpack. The Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic modeling framework is used to evaluate the importance of liquid water storage in snow and conduct a sensitivity analysis of model parameters. The combination of modeling and ground-based remote sensing techniques offers insights toward improving hydrologic model representation and spatially explicit understanding of liquid water storage and transport processes critical to resolving runoff timing. This work shows that the vadose zone may be conceptualized, during snowmelt, as extending above the soil-snow interface to include unsaturated flow processes within the snowpack.