Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks

Yueqian Cao,Ana Barros

doi:10.3390/rs12203422

Abstract

Ensemble predictions of the seasonal snowpack over the Grand Mesa, CO (~300 km2) for the hydrologic year 2016–2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time-scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8–10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of the ensemble mean backscatter with SWE (R2 > 0.95) depends on weather conditions (e.g., 5–6 cm/dB/month in January; 2–2.5 cm/dB/month in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snow at Ku-band. The emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer), and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of high-elevation SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models extended to include snow-soil and snow-vegetation interactions.

Highlights

Snow plays an essential role in governing the surface energy and water budgets at high elevations, over large regions of the world at high-latitudes, and at mid-latitudes depending on the time of the year
This study aims to elucidate the propagation of spatial uncertainty in atmospheric forcing from snowpack physics to radiometric and scattering behavior for high-elevation snowpacks
A key challenge is that the snow hydrology model must represent the governing physical processes and capture the fundamental drivers of radiative properties change in the snowpack

Summary

Introduction

Snow plays an essential role in governing the surface energy and water budgets at high elevations, over large regions of the world at high-latitudes, and at mid-latitudes depending on the time of the year. Mean snow cover varies from 7% to 40% over the Northern Hemisphere [1], and changes in snow cover due to interannual variability and increasing surface air temperatures affect regional atmospheric conditions and large-scale circulation systems, including the global monsoons [2,3,4,5]. The large spatial variability of precipitation, clouds, winds, land-cover, and topography translates into large spatial variability in snow accumulation patterns (snow depth and SWE) and snowpack microphysical properties. Temperature, grain size, and material composition (ice, liquid water content, and particles such as dust or pollution) determine local snowpack surface radiative properties, including emissivity and backscattering behavior, that are spatially organized by topography and land-cover at the meso- and regional scales [8,9]

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