1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?
SSiB
2. Name and address of model developer;
Yongkang Xue Department of Geography University of Maryland College Park, Md 20742 USA Tel: (301) 405-5880 fax: (301) 314-9299 e-mail: yxue@geog.umd.edu
3. Name and address of model user;
Adam Schlosser, GFDL Paul Dirmeyer, COLA Y. Sud, NASA S.F. Sun, IAP, China
4. Please indicate whether your model is developed for application
in understanding snow processes, X
in a runoff forecasting model,
in a weather forecasting model, X
in a global climate model (GCM), X
or other (please specify)?
5. The first year when the model was used;
1989
6. One paragraph description of your model (e.g. abstract from report or paper);
The Simplified SiB (SSiB) model (Xue et al., 1991, 1996) is used to model the land surface in the GCM and regional model. SSiB has three soil layers, one canopy layer, and eight prognostic variables: volumetric soil moisture content in three soil layers; temperature at the canopy, ground surface and deep soil layers; water stored on the canopy; and snow stored on the ground.
In the three layer soil model, water movement is described by a finite difference approximation to the diffusion equation. The force-restore method is used to predict the time variation of soil temperature. The governing equations for the interception water stores are based on water conservation equations.
7. Please specify any known application range or restrictions;
Climate study, weather forcasting study, and some process study.
8. What are the development data needs;
field measurements.
9. What are the operational data needs?
base on climatology data and will use satellite data.
10. Please indicate with an "x" for those meteorological variables used to
DRIVE your snow model?
precipitation : X
air temperature : X
wind speed : X
wind direction :
humidity : X
downwelling shortwave radiation : X
downwelling longwave radiation : X
cloud cover :
surface pressure : X
11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?
Snow depth
12. List the measurable/adjustable parameters (e.g., snow surface aerodynamic roughness, maximum albedo at visible wavelength, etc, excluding initial conditions) your snow model uses?
snow surface aerodynamic roughness snow albedo (depend on snow cover)
13. What are the output data?
Snow depth, snow melting (freezing), runoff
14. What computer language does your model use?
FORTRAN
15. How many subroutines (or functions) does your snow model have?
3
16. Number of lines of the snow code?
about 100 lines
17. What is the recommended hardware?
no
18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.
Depend on the air tempeature at reference height
19. Is your snow model one dimensional or multi-dimensional? Please specify.
one-dimensional
20. If one dimensional, how many layers are there in your snow model? Please specify layering structure.
one
21. What is your snow model time step?
from 2 min to 1 hour
22. Does your model snow albedo allow its
spectral differences (visible vs. near-IR)?
Yes.
directional differences (direct vs. diffuse)?
Yes
23. Is your model snow albedo a function of
snow age
grain size
solar zenith angle X
pollution
snow depth? X
24. Does your snow model explicitly treat liquid water retention and percolation within the snowpack?
No.
25. Does your snow model account for changes in the hydraulic and thermal properties of snow due to meltwater refreezing?
Yes.
26. Is snow density in your snow model changing with time or fixed?
Fixed.
27. Is heat capacity and conductivity in your snow model changing with time or fixed?
Fixed.
28. Does your snow model simulate vapor transfer in the snowpack?
No.
29. Does your snow model account for the heat transfer between the bottom of the snowpack and the underlying soil?
Yes.
30. In snow energy balance, does your model consider heat convected by rain or falling snow?
Yes.
31. Does your snow model include snow drifting and redistribution by wind (or avalanche)? If so, how?
No.
32. How is areal snow distribution treated?
simulated by the model
33. Does your snow model account for sub-grid (or sub-watershed) effects of topography? If so, how is temperature distributed?
No.
how is precipitation (spatial, elevation and corrections) distributed?
exponential distribution
how is solar radiation distributed?
no
how is wind distributed?
no
how are other meteorological variables distributed?
no
34. Does your snow model consider snow-vegetation interaction?
Yes.
35. Does the snow-vegetation interaction account for
different vegetation types (grass vs. forest),
YES.
different vegetation heights (short vs. tall),
YES.
different vegetation densities (small vs. large LAI),
Yes.
different vegetation coverages (sparse vs. dense vegetation)?
Yes.
36. Are snow interception, drip and melt on canopy surface allowed in your model?
Yes.
37. How is the upper limit of the canopy interception determined?
LAI*0.0001m
38. In the presence of vegetation, how is snow surface albedo altered?
albedo = snow free albedo + a1* s1 + a2 * s1*s1 + b1*s2 + b2*s2*s2 a1,a2,b1,b2 are the coefficients. s1 and s2 are the snow depth in the canopy and ground
39. In the presence of vegetation, how is snow surface roughness altered?
roughness was modified to account for the burying of vegetation by snow
40. In the presence of forest, does your snow model allow spatial variability of snow depth and water equivalent on forest floor?
no
41(a). How does your model deliver snowmelt to the soil system (e.g. affecting soil moisture)?
it will partically delever to the soil layer and partially to runoff depend on the soil layer temperatures.
(b). Once snowmelt is generated, how does your model relate it to runoff?
dpend on soil temperatures
42. How is frozen soil treated in your model?
no
43. Has your snow model been tested with the field data?
Yes.
If so, what data? (areas)
what are their temporal and spatial scales?
Russian six stations and Valdai,France Col de Porte data. 18 years for Valdai, 6 year for France data.
44. Has your snow model been used together with remote sensing data as input?
No.
If so, how?
45. If your snow model is coupled with a numerical weather forecasting model or climate model, has the model snow product been compared with satellite data? If so, what satellite data were used?
No.
46. Please list any other previous applications.
47. Please specify verification criteria, if any?
48. What are the model fitting procedures, if any?
49. What are future plans for using/improving the model?
more validation and multi-layer snow model
50. Please provide references relevant to the model description and use.
Xue, Y., P. J. Sellers, J.L. Kinter III, and J. Shukla, 1991: A Simplified Bios phere Model for Global Climate Studies. J. Climate, 4, 345-364.
Xue, Y., P.J. Sellers, F.J. Zeng, and C.A. Schlosser, 1997: Comments on "Use of midlatitude soil moisture and meteorological observations to validate soil moisture sim ulations with biosphere and bucket models". J. Climate, 10, 374-376.
Schlosser, C.A., A. Robock, K.Y. Vinnikov, N.A. Speranskaya, and Y. Xue, 1997: 18-year land surface hydrology model simulations for a midlatitude grassland catchment in Valdai, Russia. Mon. Wea. Rev., 125, 3279-3296.