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 over North America 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 midlatitude 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 midlatitudes.

Seeds are the colonizing propagules for many plants and may therefore contribute to the filtering of species during the process of colonization and community assembly. Environmental filtering of seed traits may occur among species and influence community composition, or within species and influence the environmental breadth that a given species inhabits. To test for evidence of such filtering of seed traits, we measured morphological and germination traits of seeds of 408 angiosperm species collected across an elevational gradient in the eastern Tibetan Plateau grasslands. We tested for elevational filtering of traits at the species level, as well as within 22 of those species that occurred at different elevations, in order to test whether within‐species variation reflected among‐species patterns. Elevational patterning occurred in both seed morphology and seed germination. Seeds were smaller, more elongated and had a higher surface area:volume ratio and shorter germination times at higher elevation. Seed morphology was associated with germination such that more elongated and smaller seeds with a higher surface area:volume ratio germinated faster, leading to earlier germination in seeds from high elevation. Within species, elevational variation in seed traits was observed in several species, but species differed in how those traits were distributed across elevation. These results suggest that taxonomic differences in seed traits may contribute to elevational variation in the species composition of plant communities, but that seed traits may be variably selected by elevation within species.

Tropical Rainfall Measuring Mission (TRMM) 3B43 rainfall products during the period of 1998~2016 are evaluated via monthly and yearly precipitation data from 56 observational stations over the Yangtze River Delta (YRD). The results are as follows: (1) annual rainfall increases gradually from northwest to southwest and monthly precipitation surges sharply, owing to rainband’s abrupt pushing north caused by the westward and northward extension of Western Pacific Subtropical High, to 200 mm/month in July; (2) only seized by the TRMM, the developing process of ChunAn precipitation center synchronizes with monthly precipitation series that may recycle throughout the year; (3) 3B43 data are inclined to overestimate precipitation but performs the best (relative bias ranging within −10~10%) at most parts of the YRD zone; and its correlation coefficient with observation is 0.88 in annual scale; for monthly scale, CC peaks in March (0.96) and reaches the bottom (0.79) in August; (4) no evident connection between CC and elevation is found in the 3B43 annual rainfall products which estimate precipitation better upon urban land. This paper is of importance in understanding the impacts of complex topography and landcovers on the precipitation assessment in a river delta scale.

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.