Snowpack ripening and melt-freeze cycles on Grand Mesa, Colorado from field measurements and synthetic aperture radar

Abstract:

The transition of a cold winter snowpack to one that is ripe and contributing to runoff is the result of complex processes of energy transfer at the boundaries of and within the snowpack. While gauging this transition is crucial for energy balance modeling and runoff forecasting, its onset and duration can vary substantially spatially and temporally. Before becoming fully ripe, a warming snowpack typically undergoes cycles of melt and refreeze, due to changes in weather and also with diurnal fluctuations in radiative fluxes. These differing energy fluxes can yield partial to complete refreeze of snowpack melt water. In a changing climate, we can expect increases in mid-winter rain and melt, and this transition may become persistent over increased ranges in elevations in the future.

Due to the high permittivity of liquid water, C-band synthetic aperture radar (SAR) can reliably detect melt water present in the snowpack. The European Space Agency’s Sentinel-1 C-band SAR instrument offers consistent acquisition patterns, some of which allow for a diurnal comparison of SAR indications of wet snow. Diurnally differing SAR-derived snow conditions may be used to identify snow surface melt-freeze cycles, which could provide spatially explicit information useful for energy balance and runoff forecasting models. Though previous studies have observed the impacts of melt-freeze cycles on SAR backscatter values, a lack of sensors with consistent coverage has limited explicit understanding throughout the ripening of the snowpack.

In this study, we utilize snow pit observations from 2020 on Grand Mesa, Colorado to track temperature and liquid water content changes in the snowpack with depth and time, as the snow warms and ripens. Observations were made coincident with Sentinel-1 overpasses, and snow pit observations are compared with changes in backscatter values in co- and cross-polarizations. SAR indications of melt-freeze cycles could provide useful information for validation and/or parameter calibration for energy balance and runoff forecasting models, as well as burgeoning methods of volume scattering for snow mass retrieval.