1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?
Soil-Plant-Snow (SPS) model. This model was formerly introduced to PILPS with a name CAPS-LLNL (LLNL version of the CAPS).
2. Name and address of model developer;
Dr. Jinwon Kim
L-256
Lawrence Livermore National Lab.
Jinwon Kim
MS90-1116
Earth Sciences Division
Lawrence Berkeley National Laboratory
1 Cyclotron Road
Berkeley, CA 94720, USA
(E-mail) Jinwon_Kim@LBL.gov
(Phone) 510-495-2375
(FAX) 510-486 7070
3. Name and address of model user;
Jinwon Kim
MS90-1116
Earth Sciences Division
Lawrence Berkeley National Laboratory
1 Cyclotron Road
Berkeley, CA 94720, USA
(E-mail) Jinwon_Kim@LBL.gov
(Phone) 510-495-2375
(FAX) 510-486 7070
4. Please indicate whether your model is developed for application in understanding snow processes, X (mostly for surface snow budget)
in a runoff forecasting model,
in a weather forecasting model, X
in a global climate model (GCM),
or other (please specify)? X
(Regional Climate System Model (RCSM
surface energy and water budget study
as a column version)
5. The first year when the model was used;
1992 (for the SPS: Original CAPS dates back to 1984 and earlier)
6. One paragraph description of your model (e.g. abstract from report or
paper);
A physically-based land surface model that include
multi-layer soil, vegetation effects, and snow cover
prediction. Ref: Kim and Ek, 1995, JGR, v. 100, No. D10,
20845-20854.
7. Please specify any known application range or restrictions;
Application range:
(1) PILPS Phase 2 experiments as a column model driven by the observed
atmospheric forcing to evaluate the performance of the scheme.
(2) SPS model is coupled to the Mesoscale Atmospheric Simulation (MAS) model
to simulated interaction between the atmosphere and land surface processes. I
also use the coupled model to evaluate hydroclimate of California and East
Asia and for assessments of water resources, surface energy and water budget,
variation of snow cover, and effects on agriculture and ecosystems.
8. What are the development data needs;
Accurate atmospheric forcing data including rainfall and snowfall,
low-level temperature and mixing ratio, and solar and terrestrial radiation.
To evaluate the model results, accurate flux and surface observations are
needed (net radiation, sensible and latent heat fluxes, variation of snow
cover (if possible, separate snowmelt data), soil water variation, soil and
snow temperature).
9. What are the operational data needs?
For short term, initial soil water content and snow cover, soil texture,
vegetation type and coverage, large-scale atmospheric forcing (coupled model)
or low-leve observation (column model).
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 : (sometimes if surface momentum flux is needed)
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 water equivalent
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?
roughness length, shortwave albedo
13. What are the output data?
predicted water-equivalent snow depth, snowmelt in addition to skin
temperature and mixing ratio, soil water content, soil temperature, canopy
water content.
14. What computer language does your model use?
Fortran
15. How many subroutines (or functions) does your snow model have?
16. Number of lines of the snow code?
17. What is the recommended hardware?
workstations - CRAY
18. How does your model determine the form of precipitation
(i.e., snowfall and rainfall)?
Please give the formulation.
rainfall and snowfall are input variables (from mesoscale model or
observations)
19. Is your snow model one dimensional or multi-dimensional?
Please specify.
single snow layer on top of multiple soil layer
20. If one dimensional, how many layers are there in your snow model?
Please specify layering structure.
one layer
21. What is your snow model time step?
5 min - hours (usually 5 min for
coupled model, column version was run up to 3-hr steps).
22. Does your model snow albedo allow its spectral differences (visible vs. near-IR)?
directional differences (direct vs. diffuse)?
No.
23. Is your model snow albedo a function of snow age
grain size
solar zenith angle
pollution
snow depth?
No.
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?
No.
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?
changes as a function of snowdepth
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?
No.
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?
uniform over a grid box
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?
-- uniform over a model grid
how is solar radiation distributed?
-- uniform over a model grid
how is wind distributed?
-- uniform over a model grid
how are other meteorological variables distributed?
-- uniform over a model grid
34. Does your snow model consider snow-vegetation interaction?
No.
35. Does the snow-vegetation interaction account for different vegetation types (grass vs. forest),
different vegetation heights (short vs. tall),
different vegetation densities (small vs. large LAI),
different vegetation coverages (sparse vs. dense vegetation)?
N/A.
36. Are snow interception, drip and melt on canopy surface allowed
in your model?
No.
37. How is the upper limit of the canopy interception determined?
38. In the presence of vegetation, how is snow surface albedo altered?
Fixed.
39. In the presence of vegetation, how is snow surface roughness altered?
Fixed.
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)?
Yes.
(b). Once snowmelt is generated, how does your model relate it to
runoff?
Yes.
42. How is frozen soil treated in your model?
reduced soil water conductivity
43. Has your snow model been tested with the field data?
underway
If so, what data? (areas)
Valdai, Russia (PILPS-Phase 2)
what are their temporal and spatial scales?
(single station, more than 10 years)
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?
50. Please provide references relevant to the model description and use.
Kim and Ek, 1995, JGR, 100 (D10), 20845-20854.
-- Last updated Fri Oct 8 12:47:54 MST 1999
by Zong-Liang Yang.
For questions and comments, please
contact Zong-Liang Yang