A snow-soil-vegetation-atmosphere transfer/radiobrightness model for wet snow

Snow pack models like SNTHERM predict snow behavior very well during the cold periods but do not adequately capture liquid and vapor transport between soil and snow during the snowmelt period. To get a more realistic description of the snowpack and soil dynamics, I have developed a Snow-Soil-Vegetation-Atmosphere Transfer/Radiobrightness (SSVAT) model, which couples the Land Surface Processes (LSP) model with SNTHERM. I also developed an associated Radiobrightness model based on pendular rings that describes the microwave brightness signatures.;Late winter/early spring comparisons with data show that SSVAT provided better estimates of heat fluxes, temperatures, grain sizes and densities in snow and soil than did SNTHERM. The SSVAT model predicted conditions for depth hoar in snow caused by vapor transport from soil. The SSVAT model could employ five-minute interval forcings for routine applications and two-minute interval forcings for research applications whereas SNTHERM requires a ten-minute, or longer, time resolution to avoid divergence. A sensitivity study suggests that the thatch at the snow/soil interface can be ignored for thick snowpacks.;The Radiobrightness model was simulated at lower microwave frequencies where brightness temperatures can be estimated without considering scattering. The Radiobrightness model includes liquid water in the shape of pendular rings of an appropriate size, shape and number density, as indicated by the SSVAT model. Results show that moisture in pendular rings was a more effective absorber than equivalent moisture in spheres. The brightness temperatures of wet snow are governed by loss from water absorbers and by the dielectric contrast at the snow/soil interface. The underestimation in SSVAT based brightness temperature may be caused by the underestimation of the liquid moisture content. For an open canopy like that at CLPX'03, emission from the surrounding trees will cause a brightening.;SSVAT/R provided a framework for exploring the soil-snow mechanism which is considerable interest since it plays an important role in the spring known to be influenced by water flow in snow and soil. If the melt-refreeze cycle is accurately simulated by the snow model, the darkening brightness due to increased grain size from melt-refreezing would not be mistaken for a signal of increased snow depth.