Yueqian Cao1, and Ana P. Barros21. Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA* Correspondence: [email protected]: A radar observing system simulator consisting of a coupled snow hydrology (MSHM) and radiative transfer model (MEMLS) was modified to include vegetation contributions to the total backscatter from the ground-snow-vegetation system. Vegetation parameters were estimated from airborne SnowSAR (X- and Ku-band) and Sentinel-1 (C-band) measurements in Grand Mesa (flat topography) and Senator Beck Basin (steep topography) by solving the inverse problem via simulated annealing. Physics-based constraints were imposed to address indeterminacy with good results, 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, including simulating observed background backscatter under snow-free conditions. The proximal goal of this study is to quantify the integrated effect of complex multiple scattering within the ground-snow-vegetation system toward isolating volume scattering from subcanopy snowpack, and subsequent retrieval of snowpack properties such as snow water equivalent (SWE). The stretch goal is to develop a vegetation correction to expand the operational utility of radar remote sensing of snow in the boreal forests at northern latitudes. The proposed approach has high operational utility for retrieving large-scale SWE from satellite-based SAR measurements.Keywords: forward-inversion system; MEMLS; simulated annealing; SAR; vegetation heterogeneity

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. ROSS results with snow-ground scattering correction inferred from snow-free conditions capture well the seasonal march of Sentinel-1 C-band backscatter, including 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 within 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.

Ensemble predictions of the seasonal snowpack over Grand Mesa, CO were conducted for the hydrologic year 2016-2017 using a multilayer snow hydrology model. Ensembles were generated from gridded atmospheric reanalysis, model predictions were evaluated against SnowEx’17 measurements, and the signatures of the weather-dependent variability of snow physics in the behavior of multi-frequency microwave brightness temperatures and backscattering were examined through forward modeling. At sub-daily timescales , the ensemble standard deviation due to atmospheric forcing (i.e., mesoscale spatial variability of weather within the Grand Mesa) is < 3 dB for dry snow, and increases to 8-10 dB at midday when there is surficial melt that also explains the wide ensemble range (~20 dB). Further, the ensemble mean backscatter exhibits robust (R 2 > 0.95) time-varying, weather-dependent linear heuristic relationships with SWE (e.g., 5-6 cm/dB/month in January; 2-2.5 cm/dB/month in late February) as melt-refreeze cycles modify the microphysical structure in the top 50 cm of the snowpack. The nonlinear evolution of ensemble snow physics translates into seasonal hysteresis in the microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the Land C-bands and collapse for wet shallow snow at Ku-band. The ensemble mean 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 SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.