1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?
Simultaneous Heat and Water (SHAW) Model
2. Name and address of model developer;
Gerald N. Flerchinger Phone: 208-422-0716
Research Hydraulic Engineer Fax : 208-334-1502
USDA - ARS gflerchi@nwrc.ars.pn.usbr.gov
800 Park Blvd., Ste 105 http://ars-boi.ars.pn.usbr.gov/
Boise, ID 83712
http://ars-boi.ars.pn.usbr.gov/Models/SHAW.html
3. Name and address of model user;
Numerous users
4. Please indicate whether your model is developed for application
in understanding snow processes, in a runoff forecasting model, in a weather forecasting model, in a global climate model (GCM), or other (please specify)? X SHAW was developed for understanding snow/frozen soil/surface energy balance processes
5. The first year when the model was used;
The model was first used in 1987
6. One paragraph description of your model (e.g. abstract from report or paper);
The Simultaneous Heat and Water (SHAW) Model is a one-dimensional model originally developed to simulate soil freezing and thawing. The SHAW model simulates a one-dimensional vertical profile extending from the top of a plant canopy or the snow, residue or soil surface to a specified depth within the soil. The system is represented by integrating detailed physics of vegetative cover, snow, residue and soil into one simultaneous solution. The model is sufficiently flexible to represent a broad range of conditions and the system may or may not include a vegetative canopy, snow, or a residue layer. Interrelated heat, water and solute fluxes are computed throughout the system and include the effects of soil freezing and thawing. Daily or hourly predictions include a surface energy balance, evaporation, transpiration, soil frost depth, snow depth, runoff and soil profiles of temperature, water, ice and solutes.
Within the model, a complete energy balance of a multi-layered snowpack is computed on a daily or hourly time step. Energy terms include solar and long-wave radiation exchange, sensible and latent heat transfer at the surface, and vapor transfer within the snowpack. Absorbed solar radiation, corrected for local slope, is based on measured incoming solar radiation, with albedo estimated from grain size, which in turn is estimated from snow density. Long-wave radiation emitted by the atmosphere is estimated from the Stefan-Boltzmann law and adjusted for cloud cover (estimated from measured solar radiation). Surface sensible and latent heat transfers are estimated using a bulk aerodynamic approach with stability corrections. The SHAW model additionally includes the effect of vegetation and a detailed energy balance of residue and soil beneath the snow cover. Liquid water is routed through the snowpack using attenuation and lag coefficients, and the influence of metamorphic changes of compaction, settling, and grain size on density and albedo are considered. Snowmelt simulation with the SHAW model was tested by applying the model to two years of data at three sites ranging from shallow (<0.1 m) snow cover on a west-facing slope to a deep (2m) snow drift on a north-facing slope. Snow depth, density, and the magnitude and timing of snow cover outflow were accurately simulated for all sites. [This paragraph is taken from Flerchinger, G.N. and K.R. Cooley, 1998: Snowmelt simulation with the simultaneous heat and water (SHAW) model, EOS, Transactions, American Geophysical Union, Fall Meeting, Supplement, Vol. 79, No. 45, page F272.]
7. Please specify any known application range or restrictions;
The current release of the model will not handle saturated soil conditions, but a beta version of the model is available that has provisions for saturated conditions.
8. What are the development data needs;
Height, LAI, etc. of plants; thickness, albedo and percent cover of plant residue on the ground; and soil albedo and hydraulic properties.
9. What are the operational data needs?
Essentially the same as developmental needs.
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 : cloud cover : surface pressure :
11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?
Snow temperature, density, and liquid water content by depth; soil temperature, and water content by depth; plant residue temperature and water content.
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?
Temperature to distinguish rain from snow. Plant height, leaf area index, and resistance parameters. Plant residue layer thicknes and percent cover. Soil albedo and hydraulic properties.
13. What are the output data?
Surface energy balance, snow depth, snow density, liquid water content of snow, evaporation, transpiration, and soil profiles of temperature, water, ice and solutes.
14. What computer language does your model use?
Fortran 77
15. How many subroutines (or functions) does your snow model have?
77 total subroutines; approximately 10 dealing specifically with snow
16. Number of lines of the snow code?
9000 total linesof code; approximately 950 directly related to snow
17. What is the recommended hardware?
None recommended; the model is very transportable between hardware
18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.
Threshold temperature specified in the input
19. Is your snow model one dimensional or multi-dimensional? Please specify.
One-dimensional profile model
20. If one dimensional, how many layers are there in your snow model? Please specify layering structure.
The model will break the snow pack into layers; the thickness of the layers will depend on its depth within the snow pack. The surface layers will be approximately 2 cm thick and the layer thickness will increase with depth. Layers at a depth of 2 m will be about 10 cm thick.
21. What is your snow model time step?
Hourly or daily
22. Does your model snow albedo allow its
spectral differences (visible vs. near-IR)? no
directional differences (direct vs. diffuse)? no
23. Is your model snow albedo a function of
snow age INDIRECTLY
grain size X
solar zenith angle
pollution
snow depth? X
24. Does your snow model explicitly treat liquid water retention and percolation within the snowpack?
Liquid water retention is explicitly treated, but not percolation.
25. Does your snow model account for changes in the hydraulic and thermal properties of snow due to meltwater refreezing?
Changes in thermal properties due to meltwater refreezing are considered, but hydraulic properties are not.
26. Is snow density in your snow model changing with time or fixed?
Snow density in the model changes with time.
27. Is heat capacity and conductivity in your snow model changing with time or fixed?
Heat capacity and conductivity are a function of density, which changes with time.
28. Does your snow model simulate vapor transfer in the snowpack?
YES.
29. Does your snow model account for the heat transfer between the bottom of the snowpack and the underlying soil?
YES, the underlying soil is considered in great detail
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?
It is not explicitly considered.
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?
how is solar radiation distributed?
how is wind distributed?
how are other meteorological variables distributed?
34. Does your snow model consider snow-vegetation interaction?
YES, the plant canopy above the snow is modeled in detail.
35. Does the snow-vegetation interaction account for
different vegetation types (grass vs. forest), X different vegetation heights (short vs. tall), X different vegetation densities (small vs. large LAI), X different vegetation coverages (sparse vs. dense vegetation)? not very well
36. Are snow interception, drip and melt on canopy surface allowed in your model?
Snow interception is allowed. However, it must evaporate from the canopy; subsequent melt and drip from the canopy is not considered.
37. How is the upper limit of the canopy interception determined?
The model will intercept a maximum depth of water equivalent per unit of leaf area index.
38. In the presence of vegetation, how is snow surface albedo altered?
The snow albedo is not altered; the model simulates radiation interception and back scattering within the vegetation canopy.
39. In the presence of vegetation, how is snow surface roughness altered?
Snow surface roughness is not altered; heat and vapor transfer are simulated through the vegetation canopy. Transfer to the ambient atmospheric conditions uses the roughness of the canopy.
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)?
Snowmelt is allowed to infiltrate into the soil (affecting soil moisture) or is assigned to runoff if the infiltration capacity of the soil is exceeded.
(b). Once snowmelt is generated, how does your model relate it to runoff?
Snowmelt is assigned to runoff if the infiltration capacity of the soil is exceeded.
42. How is frozen soil treated in your model?
The model simulates soil freezing and thawing in considerable detail.
43. Has your snow model been tested with the field data?
If so, what data? (areas)
Snow depth, snow albedo, snow density, soil temperature, and soil water content from various sites in Idaho, Washington, Alaska, and Minnesota. Surface energy balance simulations for non-snow periods have also been tested.
what are their temporal and spatial scales?
Multi-year runs using hourly and daily time steps; snowmelt modeling at points across small watersheds has been tested.
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.
Soil freezing/thawing, evaporation, transpiration, surface energy balance modeling, near-surface soil temperature and moisture modeling.
47. Please specify verification criteria, if any?
None.
48. What are the model fitting procedures, if any?
None
49. What are future plans for using/improving the model?
Incorporating provision of a saturated soil; incorporating th model into a distributed modeling framework.
50. Please provide references relevant to the model description and use.
Flerchinger, G.N. and C.L. Hanson. 1989. Modeling soil freezing and thawing on a rangeland watershed. Trans. Amer. Soc. of Agric. Engr., 32(5), 1551-1554.
Flerchinger, G.N. and K.E. Saxton. 1989. Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development. Trans. of ASAE, 32(2), 565-571.
Flerchinger, G.N. and K.E. Saxton. 1989. Simultaneous heat and water model of a freezing snow-residue-soil system II. Field verification. Trans of ASAE, 32(2), 573-578.
Flerchinger, G.N. 1991. Sensitivity of soil freezing simulated by the SHAW Model. Trans. of ASAE, 34(6), 2381-2389.
Flerchinger, G.N. and F.B. Pierson. 1991. Modeling plant canopy effects on variability of soil temperature and water. Agricultural and Forest Meteorology, 56, 227-246.
Flerchinger, G.N., R.F. Cullum, C.L. Hanson and K.E. Saxton. 1990. Soil freezing and thawing simulation with the SHAW model. pp. 77-86. In: K.R. Cooley (ed.). Frozen Soil Impacts on Agricultural, Range, and Forest Lands, Proceedings of the International Symposium. CRREL Special Report 90-1. U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH. 318p.
Flerchinger, G.N., K.R. Cooley, and Y. Deng. 1994. Impacts of spatially and temporally varying snowmelt on subsurface flow in a mountainous watershed: 1. Snowmelt simulation. Hydrologic Sci. J., 39(5), 507-520.
Flerchinger, G.N., J.M. Baker and E.J.A. Spaans. 1996. A test of the radiative energy balance of the SHAW model for snowcover. Hydrol. Proc., 10, 1359-1367.
Flerchinger, G.N., C.L. Hanson and J.R. Wight. 1996. Modeling evapotranspiration and surface energy budgets across a watershed. Water Resour. Res., 32(8), 2539-2548.
Flerchinger, G.N. and F.B. Pierson. 1997. Modeling plant canopy effects on variability of soil temperature and water: Model calibration and validation. J. Arid Environ. 35:641-653.
Flerchinger, G.N. and M.S. Seyfried. 1997. Modeling Soil Freezing and Thawing and Frozen Soil Runoff with the SHAW Model. pp. 537-543 In: I.K. Iskandar, E.A. Wright, J.K. Radke, B.S. Sharratt, P.H. Groenevelt, and L.D. Hinzman (eds.), Proceedings of the International Symposium on Physics, Chemistry, and Ecology of Seasonally Frozen Soils, Fairbanks, AK, June 10-12, 1997. CRREL Special Report 97-10. U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH.
Flerchinger, G.N., W.P. Kustas and M.A. Weltz. 1998. Simulating Surface Energy Fluxes and Radiometric Surface Temperatures for Two Arid Vegetation Communities using the SHAW Model. J. Appl. Meteor., 37(5):449-460.
Flerchinger, G.N. and K.R. Cooley, 1998: Snowmelt simulation with the simultaneous heat and water (SHAW) model, EOS, Transactions, American Geophysical Union, Fall Meeting, Supplement, Vol. 79, No. 45, page F272.
Hayhoe, H.N. 1994. Field testing of simulated soil freezing and thawing by the SHAW model. Can. Agric. Engin., 36(4), 279-285.
Kennedy, I. and B. Sharratt. 1997. A Comparison of Three Models for Predicting Frost in Soils. pp. 531-536 In: I.K. Iskandar, E.A. Wright, J.K. Radke, B.S. Sharratt, P.H. Groenevelt, and L.D. Hinzman (eds.), Proceedings of the International Symposium on Physics, Chemistry, and Ecology of Seasonally Frozen Soils, Fairbanks, AK, June 10-12, 1997. CRREL Special Report 97-10. U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH.
Pierson, F.B., G.N. Flerchinger and J.R. Wight. 1992. Simulating near-surface soil temperature and water on sagebrush rangelands: A comparison of models. Trans. of ASAE., 35(5), 1449-1455.
Xu, X., J.L. Nieber, J.M. Baker and D.E. Newcomb. 1991. Field testing of a model for water flow and heat transport in variably saturated, variably frozen soil. p 300-308 In: Transportation Research Record No. 1307, Transp. Res. Board, Nat. Res. Council, Washington D.C.