Fingerprinting Meteorologic, Topographic, and Vegetation Controls on Microwave Behavior of Seasonal High-Elevation Snowpacks

Large areas of the world depend on snowmelt as a freshwater resource and for food production. Space-based remote sensing of seasonal snowpacks provides the only realistic means to monitor and quantify water availability (storage during the accumulation season, and release during the melt season) at global scales. The overarching goal of this study is to elucidate how meteorologic, topographic, and vegetation impact microwave remote-sensing measurements of high-elevation seasonal snowpacks. The working hypothesis is that changes in snowpack microwave behavior can be unambiguously attributed to snow physical processes modulated by meteorology, topography, and vegetation type. The research approach relies on the application of coupled snow hydrology and radiative transfer models to characterize the space-time evolution of snowpacks, and to support the interpretation of satellite-based microwave measurements toward enabling physically-guided estimation of Snow Water Equivalent (SWE). In the first part of this research, ensemble predictions of the seasonal snowpack over 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 SWE with the mean ensemble backscatter (R2 > 0.95) depends on weather conditions (e.g., 5-6 cm/dB in January; 2-2.5 cm/dB 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 snowpacks at Ku-band. The emissions behave as limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior, with offsets increasing with frequency, is different for deep snowpacks at winter-spring transition and shallow ones at spring-summer transition. These findings suggest potential for multi-frequency active-passive remote sensing of high-elevation SWE depending on snowpack regime, particularly suited for data-assimilation via coupled snow hydrology-radiative transfer models extended to include the snow-soil and snow-vegetation interactions. To investigate snowpack microwave behavior in complex topography, an uncalibrated distributed multiphysics snow model driven by downscaled weather forecasts (30-m, 15-min) was implemented as a Radar Observing System Simulator (ROSS) in Senator Beck Basin (SBB), Colorado to elucidate topographic controls on C-, X- and Ku-bands active microwave sensing of mountain snowpacks. Phase-space maps of time-evolving grid-scale ROSS volume backscatter show the accumulation branch of the backscatter-snow water equivalent (σ-SWE) hysteresis seasonal loop that is the physical basis for radar retrieval (direct inference) of SWE and snowpack physical properties. There is good agreement in the accumulation season (R2 ~ 0.7) between Sentinel-1 and ROSS predictions corrected using average Sentinel-1 measurements under snow free conditions to estimate snow-ground backscatter, capturing well spatial patterns tied to elevation, slope, and aspect. Root Mean Square Deviations (RMSDs) do not exceed ±3.2 dB for ripening snowpacks in early spring and ±2.4 dB for dry snowpacks in the accumulation season when the mean absolute bias is < 1 dB for all land-cover types with topographic slopes ≤ 30°. Grid-point RMSDs are attributed to the underestimation of snowfall on upwind slopes compounded with forecast errors for the weather near the ground. Like Sentinel-1, ROSS backscatter fields exhibit frequency-independent single-scaling behavior in the 60-150 m scale range for dry snowpacks in the accumulation season, while frequency-dependent scaling behavior emerges in the ablation season. This study demonstrates skillful physical modeling capabilities to emulate Sentinel-1 observations in complex terrain. Conversely, it suggests high readiness to retrieve snow mass and snowpack properties in mountainous regions from radar measurements at high-spatial resolutions enabled by SAR technology. To estimate vegetation impacts on the snowpack microwave behavior, a coupled snow physics-radiative transfer forward-inversion modeling system was applied over snow-covered terrain in Grand Mesa to estimate vegetation contributions to the total backscatter from the ground-snow-vegetation system via referring to dual-frequency SnowSAR measurements. A simplified but comprehensive first-order microwave emission model (MEMLS-V) was iteratively inverted by a global optimizer – simulated annealing to retrieve unknown parameters and backscatter components from double-bounce, snowpack volume, and snow-ground interface. The retrieved parameters offered the simulations 100% correlation with the observed SnowSAR signal dynamics tied to vegetation and snowpack heterogeneities, which highlights that the forward-inversion system accounting for complex multiple scattering within the ground-snow-vegetation system reliably regulated compensation effects of vegetation and snow-ground interface. To the best of our knowledge, this is the first time that the system, with reduced computational requirements and ancillary data demands, has been successfully operated for SnowSAR data analysis, while maintaining robustness and interpretability. The findings have practical potentials of retrieving large-scale SWE in the northern hemisphere through Earth Observation radars and satellites.