1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?

SNTHERM is the modeling "kernal", which has been embedded in various schemes to spatially distribute the 1-D calculations.

2. Name and address of model developer;


Rachel Jordan
Cold Regions Research and Engineering Laboratory
72 Lyme Road
Hanover NH 03755-1290

Spatial Distribution:

Robert E. Davis
Cold Regions Research and Engineering Laboratory
72 Lyme Road
Hanover NH 03755-1290

3. Name and address of model user;

Robert E. Davis 
Cold Regions Research and Engineering Laboratory
72 Lyme Road
Hanover NH 03755-1290

plus many others

4. Please indicate whether your model is developed for application

   in understanding snow processes,     X
   in a runoff forecasting model,	X
   in a weather forecasting model,       
   in a global climate model (GCM),      
   or other (please specify)?            

5. The first year when the model was used;


6. One paragraph description of your model (e.g. abstract from report or paper);

A one-dimensional model, SNTHERM (Jordan, 1991) predicts changes of snow processes and properties: heat conduction, vapor diffusion, liquid water flow, compaction, radiation penetration, albedo change, grain growth, etc.; and profiles of temperature, snow density, grain size, liquid water content, etc. respectively. The model solves a series of partial differential equations through the use of discretized control volumes implemented as a finite difference scheme. Surface boundary conditions nominally require: incoming solar and longwave radiation; wind speed, air temperature and humidity at some reference height; and precipitation. The model will estimate solar and longwave radiation from cloud cover, if data on these variables are not available. Lower boundary conditions include soil textural properties (currently clay of sand used as defaults), wetness and temperature profile.

7. Please specify any known application range or restrictions;

Few - newer version has capillary effects, but does not have multiple flow fields.

8. What are the development data needs;

Detailed measurements of snow properties, spectral reflectance and transmittance, liquid water flow, settlement and grain size.

9. What are the operational data needs?

Incoming solar and longwave radiation or cloud cover, precipitation, wind speed, air temperature and relative humidity for upper boundary; soil texture, wetness and temperature profile at lower boundary.

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                  : X (soon)
   humidity                        : X
   downwelling shortwave radiation : X
   downwelling longwave  radiation : X
   cloud cover                     : X
   surface pressure                :  

11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?

state variables in conservation equations are: snow temperature or melt, snow water equivalent (or bulk density), effective liquid water saturation. See 6 for other variables computed by model.

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 for momentum, heat and moisture.
    height of instrument readings
    fraction of solar radiation absorbed in top node.
    viscosity coefficient (governing overburden compaction)
    upper density limit for settlement compaction.
    irreducible liquid water saturation
    optional computation of albedo by model--or--input fixed albedo
    convergence criteria

13. What are the output data?

See 6.

14. What computer language does your model use?

FORTRAN - SNTHERM; spatial distribution => UNIX shell and C

15. How many subroutines (or functions) does your snow model have?


16. Number of lines of the snow code?

SNTHERM - 10993

17. What is the recommended hardware?

spatial distribution - UNIX

18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.

An input code is required in the met file. If the code for snow is used, the model defaults to rain when the air temperature exceeds 2.5 C. Between 0 C and 2.5 C the mass fraction of liquid water in the snowfall is arbitrarily varied linearly with temperature between 0% and 40%.

19. Is your snow model one dimensional or multi-dimensional? Please specify.


20. If one dimensional, how many layers are there in your snow model? Please specify layering structure.

Unlimited and arbitrary - changes during run.

21. What is your snow model time step?

Model uses adaptive time step which adjusts to meet convergence criteria (much smaller step during water flow). User inputs max and min of range. Max is equal to or less than time step of input meteorological data.

22. Does your model snow albedo allow its

    spectral differences    (visible vs. near-IR)? Yes.
    directional differences (direct  vs. diffuse)? Soon.

23. Is your model snow albedo a function of

      snow age                 X (implicit)
      grain size               X  
      solar zenith angle       X
      pollution                X
      snow depth?              X

24. Does your snow model explicitly treat liquid water retention and percolation within the snowpack?


25. Does your snow model account for changes in the hydraulic and thermal properties of snow due to meltwater refreezing?


26. Is snow density in your snow model changing with time or fixed?

Changing with time (see 6).

27. Is heat capacity and conductivity in your snow model changing with time or fixed?

Changing with time (see 6).

28. Does your snow model simulate vapor transfer in the snowpack?


29. Does your snow model account for the heat transfer between the bottom of the snowpack and the underlying soil?


30. In snow energy balance, does your model consider heat convected by rain or falling snow?


31. Does your snow model include snow drifting and redistribution by wind (or avalanche)? If so, how?

Accumulation: 0nly if accomplished in precipitation data stream.

32. How is areal snow distribution treated?

Areal segmentation to one-dimensional solution polygons, and/or functional relationship between snow depth and water equivalent (soon).

33. Does your snow model account for sub-grid (or sub-watershed) effects of topography? If so, how is temperature distributed?

Yes, lapsed

how is precipitation (spatial, elevation and corrections) distributed?

offline mesoscale model

how is solar radiation distributed?

elevation, slope and aspect

how is wind distributed?

offline mesoscale model

how are other meteorological variables distributed?

mixture of above - ad hoc depending on land-cover and terrain data

34. Does your snow model consider snow-vegetation interaction?

Yes (in research grade).

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)?

36. Are snow interception, drip and melt on canopy surface allowed in your model?

Not explicitly.

37. How is the upper limit of the canopy interception determined?

Measurements of snow on ground - empirical relationships for vegetation type.

38. In the presence of vegetation, how is snow surface albedo altered?

Adjustment according to litter fall (research grade). Transmitted radiation estimated from hybrid Geometric-Optic, Radiative-Transfer model (research grade).

39. In the presence of vegetation, how is snow surface roughness altered?

Implicitly, from measurements.

40. In the presence of forest, does your snow model allow spatial variability of snow depth and water equivalent on forest floor?


41(a). How does your model deliver snowmelt to the soil system (e.g. affecting soil moisture)?

Ouflow from the base of the snow pack is computed from Richards equation. The public version of SNTHERM does not model water flow through soil.

(b). Once snowmelt is generated, how does your model relate it to runoff?

It does not -- yet.

42. How is frozen soil treated in your model?

Freeze/thaw in soil is modeled with an apparent heat capacity method. The freezing curve (temperature versus liquid water) is determined from input soil parameters.

43. Has your snow model been tested with the field data?

SNTHERM has been extensively tested for prediction of temperature, snow depth and snow water equivalent A site has been maintained at CRREL for this purpose and testing has been done at a range of other site as well.

If so, what data? (areas)

New England, California Sierra, Greenland, Canadia Boreal Forest Washington Cascades, Utah Wasach, Michigan, Sava River Bosnia, Arctic Circle, Southern Finland

what are their temporal and spatial scales?

Temporal: Seasonal (winter and spring), year-round in Arctic
Spatial: from 1-meter to several km.

44. Has your snow model been used together with remote sensing data as input?


If so, how?

Land cover segmentation.
Initiation, validation, update of snow model.

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?

Coupled only in post-processing sense.

If so, what satellite data were used?

soon: Landsat TM, AVIRIS, SAR

46. Please list any other previous applications.

Many - see R. Jordan response

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?

1) Fully distributed, scalable simulation of snow cover in the boreal forest (BOREAS).
2) Improved spatial distribution for operational military hydrology, Sava River Basin, Bosnia.
3) Detailed comparisons with spectral reflectance measurements (Landsat TM, AVIRIS and HYDICE and ground truth) - Mammoth Mtn. California.
4) Detailed comparisons with polarimetric SAR - Mammoth Mtn. California.
5) Scene generation (IR and MMW) for US Army Modeling and Simulation.
6) Parametric tests to improve avalanche prediction through decision trees - Alta, Utah and Mammoth Mtn. California.
7) Operational hydrology: evaluation with US Army Corps districts on Upper Mississippi Basin.
8) Spatially distributed predictions of melt and sublimation of snow and glaciers in McMurdo Dry Valleys, Antarctica.

(see also R. Jordan's response)
1) Make current research grade options publicly available (capillarity in snow, water flow and frost heave in soil, vegetation and sea ice modules)
2) Suite of snow models for different applications - fast, for hydrology; medium, for most research applications; in-house version for detailed snow physics.
3) Two-stream radiative transfer code for solar radiation.
4) Improved parameterizations of hoar growth and grain clustering.
5) Parameterization for flow fingering.
6) Solute transport

50. Please provide references relevant to the model description and use.

Search on [Jordan, R., snow], [Davis, R.E., snow], [Cline, D., snow], [Koh, G., snow], [Harrington, R., snow], [Rowe, Greenland, snow], [Melloh, Bosnia, snow], [Hardy, J.P., snow], [Glendinning, G., snow], [Nolin, A., snow], [Koivusalo, H., snow].

Thesis and dissertations (of which R. Jordan is aware): Nolin, A., University of California, Santa Barbara, 1993; Cline, D., University of Colorado, Boulder, 1995; Grundstein, A., University of Delaware, Newark, 1996; Glenndinning, G., University of Reading, Cambridge (pending), UK; Dai Yongjiu, Chinese Academy of Sciences, Bejing, 1995;

-- Last updated Fri Oct 8 12:47:54 MST 1999 by Zong-Liang Yang.
For questions and comments, please contact Zong-Liang Yang