1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?
It doesn't have name. We called it the snow melt/accumulation module of the rainfall-runoff model IHACRES.
2. Name and address of model developer;
Sergei Schreider CRES , ANU, Canberra 0200, Australia email@example.com
3. Name and address of model user;
Sergei Schreider CRES, ANU, Canberra 0200, Australia firstname.lastname@example.org Michael E. Steel Dept of Geography University of Dundee DD1 4HN Scotland, UK email@example.com
4. Please indicate whether your model is developed for application
in understanding snow processes,
in a runoff forecasting model, X
in a weather forecasting model,
in a global climate model (GCM),
or other (please specify)? X
(in flow forecasting (real-time),
in estimating of possible climate change impacts.)
5. The first year when the model was used;
6. One paragraph description of your model (e.g. abstract from report or paper);
The snow melt/accumulation model is based on a modified degree-day method and provides the equivalent amount of melted/accumulated water for each gridcell of the catchments considered. The method developed allows modelling of the melt/accumulation processes directly without requiring information about observed snow cover distribution in the area. Measured daily precipitation was converted to equivalent rainfall using minimum and maximum daily temperature and modelled equivalent snow depth (in mm of water), accumulated in each particular gridcell for each day. River's runoff was modelled using as an input the equivalent rainfall estimated by the snow melt/accumulation module and the mean daily temperature in each gridcell integrated over the whole catchment. The modelled snow depth and duration of the snow season were compared with point measurements of snow depth at several stations. The modelled and measured streamflow data were compared as well. These charachteristics are used as a criterion of model quality.
7. Please specify any known application range or restrictions;
The long term climate means (p, tdaily_average, tdaily_min) are needed for each gridcell of the catchment considered. That means that the better instrumented the region is the better spatial interpolation for these means we can have.
8. What are the development data needs;
Precip-temp for implementation, Streamflow, snow depth - for verification.
9. What are the operational data needs?
Not quite clear (the diff. between operational and development data).
10. Please indicate with an "x" for those meteorological variables used to
DRIVE your snow model?
precipitation : X
air temperature : X
wind speed :
wind direction :
downwelling shortwave radiation :
downwelling longwave radiation :
cloud cover :
surface pressure :
11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?
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?
Only p-t data. Daily for some stations in the catchment and long term means for each gridcell.
13. What are the output data?
-daily snow water equivalent for each gridcell -daily equivalent rainfall for each gridcell (rainfall+melt-accumulation) -daily equvalent railfall integrated over whole catchment
14. What computer language does your model use?
Fortran 77 (UNIX)
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?
SUN and ò. Probably Pentium is OK, but with large hard disk: Output files (daily series for each gridcell) can be very large. About 60M for the catchment with area of 500 sq.km. and 20 year period of observations.
18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.
Using the fuzy degree-day factor for each gridcell. The elevation of each gridcell is the main factor affecting that.
19. Is your snow model one dimensional or multi-dimensional? Please specify.
It uses 3d (lat,lon, elev) interpolation of climatic data. Than, it uses the 2d approximation of catchments with gridcells. In our case 2.5 x 2.5 km.
20. If one dimensional, how many layers are there in your snow model? Please specify layering structure.
We considered one layer of snow.
21. What is your snow model time step?
Daily. We did some attempt to apply it using 4-hourly step.
22. Does your model snow albedo allow its
spectral differences (visible vs. near-IR)?
directional differences (direct vs. diffuse)?
23. Is your model snow albedo a function of
solar zenith angle
No for all qsts.
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?
We compared the results of modelling with measured Snow Water Equivalent, thus, the snow density is already taken into account during the measurements.
27. Is heat capacity and conductivity in your snow model changing with time or fixed?
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?
32. How is areal snow distribution treated?
By approximation of the catchments with small gridcells.
33. Does your snow model account for sub-grid (or sub-watershed) effects of topography? If so, how is temperature distributed?
See 6. Elevation correction is taken into account when the long-term climatic means are calculated for each gridcell.
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?
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?
37. How is the upper limit of the canopy interception determined?
38. In the presence of vegetation, how is snow surface albedo altered?
We considered forested headwaters catchments where the vegetation level is rather homogeneous.
39. In the presence of vegetation, how is snow surface roughness altered?
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)?
This effect is modelled by the runoff model IHACRES (the flow there is separated into two component: quick and slow. The latter one reflects the water transport in soil).
(b). Once snowmelt is generated, how does your model relate it to runoff?
The IHACRES runoff model uses its output for runoff modelling.
42. How is frozen soil treated in your model?
Through the temperature modulation factor of the IHACRES. It has nonlinear module transformating the measure rainfall (equivalent here) into rainfall excess.
43. Has your snow model been tested with the field data?
If so, what data? (areas)
what are their temporal and spatial scales?
It has been tested in 7 snow-affected catchments of the Australian Alpine region (The Kiewa nad Upper Murray Basins of the Murray-Darling Drainage Division). Total area is about 2.500 sq.km. The periods of streamflow observations are about 20 years. The relevant publications are goint to appear in Journal of Hydrology and Envirosoft.
44. Has your snow model been used together with remote sensing data as input?
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?
46. Please list any other previous applications.
47. Please specify verification criteria, if any?
Good fit (F.i. in the Nash-Sutcliffe sense) of the modelled and measured streamflow. The fit between measured and modelled snow water equivalent (for the sites where such observations are available) and modelled and real duration of the snow season.
48. What are the model fitting procedures, if any?
Nash - Sutcliffe statistics, Bias, Realtive Errors etc...
49. What are future plans for using/improving the model?
We already tested our model for some Scottish catchmewnts (area from 70 to 2,500 km.sqr.)
50. Please provide references relevant to the model description and use.
Schreider, S.Yu., Jakeman, A.J., Whetton, P.H., Pittock, A.B. Estimation of Climate Impact on Water Availability and Extreme Events for Snow-Free and Snow-Affected Catchments of the Murray-Darling Basin, 1997, Australian Journal of Water Resources, 2, No.1, 35-47.
Schreider, S.Yu., Whetton, P.H., Jakeman, A.J., Pittock, A.B. and Li, J. Runoff Modelling for Snow-Affected Catchments in the Australian Alpine Region, Eastern Victoria, 1997, Journal of Hydrology, 2, No. 1, 35-47. Also in CRES Working Paper, ANU, Canberra, 1996/5, 26 pp.
Schreider, S.Yu., Jakeman, A.J, Whetton, P.H. and Pittock, A.B. Comparative Analysis of Climate Impacts on Streamflow for Snow-Free and Snow-Affected Catchments, in: Climate Impacts Assessment Workshop Abstracts: Development and Application of Climate Change Scenarios, Hennessy, K.J. and Pittock, A.B. (eds.), 1996, CSIRO Division of Atmospheric Research / Commonwealth Department of Environment, Sport and Territories, Melbourne, pp. 84-88.
Schreider, S.Yu., Jakeman, A.J., Dyer, B.G. and Francis, R.I. A Streamflow Forecasting Algorithm Combining of Deterministic and Self-Adaptive Stochastic Approaches (the Upper Murray Basin Case Study), 1997, EnvironSoft, 12, No. 1, 93-104.