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

HBV model. The HBV letters were an abbreviation of the (swedish) name of the department of SMHI (Sweden's Meteorological and Hydrological Institute) where the model was developed.

2. Name and address of model developer;

Sten Bergstrom, Professor, SMHI.

3. Name and address of model user;

Answers given by Sjur Kolberg, SINTEF Civil and env. eng., Trondheim, Norway. Typical users are hydropower companies in Norway, Sweden and Finland countries. See also SnowTools project (http://emma.itek.norut.no:80/itek/jobs/projects/snowtools/) which is working on research and development of reote sensing products and algorithms for snow hydrology.

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)?

5. The first year when the model was used;

approx. 1975

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

Lumped rainfall-runoff model with a distributed snow routine. In Norwegian versions mostly 10 elevation zones, and a 5-point statistical distribution of snow in each zone. It is possible to group the zones into a lowland and mountainous group, between which also free parameters are allowed to change, but this option is seldom in use. Precipitation and temperature are the driving variables, elevation lapse rates are fixed or calibrated. A rather unique feature is the lack of an infiltration routine, all precipitation is assumed to enter the unsaturated zone. This assumption is generally valid for Scandinavian till catchments, where Hortonian overland flow hardly occurs. The soil water storage includes a non-linear term.

A range of parameters are included, a few measured, but many simply given a value by assumption. Usually between 10 and 15 are optimized by calibration.

7. Please specify any known application range or restrictions;

The model is designed for operational purposes (hydropower), and works best for daily or longer time steps, and in catchments ranging from some 100 sq. km and upwards. Attempts to use hourly steps, thus enabling small catchments with fast response, have been tried with varying results. The degree-day approach is not well suited to hourly melt calculations.

8. What are the development data needs;

Area, elevation distribution, lake percentage, glacier percentage and elevation distribution, specification of meteorological station(s), calibration (5 - 10 years generally used). If elevation zone groups are used: Timberline elevation.

9. What are the operational data needs?

Daily input values of temperature and precipitation. Preferrably a snow survey at the end of the accumulation season.

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                  :
   humidity                        :
   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?

Snow water equivalent distribution in each of 10 different elevation zones (min, low quartile, median, high quartile, max) Snow wetness (bulk snowpack volume percentage) in each of the quantiles. Snow covered area in each elevation zone.

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?

All calibrated or assumed values, restricted to the snow routine:

Rain/snow threshold temperature
Degree-day factor
Constant term in degree-day equation
Temperature lapse rate on days with precipitation
Temperature lapse rate on days without precipitation
Precipitation correction factor, rain (if more than one station, one for each)
Precipitation correction factor, snow
Precipitation lapse rate
Amount of accumulated SWE before snow redistribution starts
5 percentiles in the snow redistribution function (+5 if dividing
Maximum free water content (wetness) before water is released from snowpack
Refreezing effectiveness on days with low temperature

13. What are the output data?

Catchment discharge, usually daily values.

14. What computer language does your model use?

There are several implemented versions, most of which in Fortran, the newest in C++.

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

Depends on version, but generally there are four major parts in the model: snow routine, soil water routine, response routine and routing routine.

16. Number of lines of the snow code?

Depending of programming strategy, everything from a few lines to thousands. The core is very simple, the bulk of code goes to interfaces, data management etc.

17. What is the recommended hardware?

Some implementations works fine under DOS on a ordinary PC (for instance, a 386), newer versions demand Windows-95, NT or unix.

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

Something like: IF air temp < Tp THEN Precip :=3D snow ELSE Precip :=3D rain.=20 air temp is local air temp in the elevation zone, computed using lapse rate. Tp is calibrated, usually restricted to between -1 and 4.

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

The snowpack is considered homogeneous in depth. See specification of spatial distribution above.

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

The snowpack is considered homogeneous in depth.

21. What is your snow model time step?

In principle any, but degree-day models does not perform well on shorter than one day. In practice, one day is used.

22. Does your model snow albedo allow its

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

No albedo present in the model.

23. Is your model snow albedo a function of

      snow age
      grain size
      solar zenith angle
      snow depth?

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

No, the water storage is just intended to delay/reduce outflow compared to melting.

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?

Snow density (or depth) is not represented, only SWE is considered.

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

Neither is explicitly represented.

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?

Yes, by applying a distribution function to each snowfall. The snowpack is thus described as a 5-point distributon function, with separate wetness status in each of the quantiles.

32. How is areal snow distribution treated?

1) By elevation zones
2) In each zone: Either implicitly by the distribution function, or explicitly by a snow covered area value.

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

Within each elevation zone, no distribution. Between elevation zones, temperature are corrected for a lapse rate, usualy calibrated to negative 0.3 - 0,6 centigrade pr 100 m on days with precipitation (wet adiabatic) or 0.6 - 1.0 on days without precipitation.

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

An elevation lapse rate is often assumed. If a larger basin is divided into several subcatchments, different precip stations may be given different weight in the subcatchments.

how is solar radiation distributed?


how is wind distributed?


how are other meteorological variables distributed?


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

Only by allowing one group of elevations zones (lowland/forested) to have other calibration constant values another group of elevation zones (mountainous/alpine). The parameters are calibrated.

35. Does the snow-vegetation interaction account for

 different vegetation types     (grass vs. forest),
 different vegetation heights   (short vs. tall),      See q. 34.
 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?


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?

Yes, by the statistical distribution function. Variation in the forest is less than in open mountains.

41(a). How does your model deliver snowmelt to the soil system (e.g. affecting soil moisture)? (b). Once snowmelt is generated, how does your model relate it to runoff?

All snowmelt go directly into the soil moisture storage, except: On glaciers and natural lakes the outflow goes to the lower response storage, and on reservoirs the outflow go directly to catchment outflow. Reservoirs often, but not always, constitute the outflow end of a simulated= subcatchment.

42. How is frozen soil treated in your model?


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

    If so, what data?
    what are their temporal and spatial scales?

Snow surveys is carried out at the end of the accumulation season. These data are seldom used in model validation or verification, only as an updating procedure. In addition, some of the largest hydropower companies are using remotely sensed data to update SCA.

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

Some of the largest hydropower companies are using remotely sensed data to update SCA during the spring season. However, this is in its beginning, especially with respect to incorporate the observations in the 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? If so, what satellite data were used?


46. Please list any other previous applications.

47. Please specify verification criteria, if any?

Calibration and verification critera is Nash-Sutcliffe R2, accumulated volume error and to a varying extent some other criteria. All are applied to comparison of simulated and observed outflow.

48. What are the model fitting procedures, if any?

Usually manual trial-and-error, but some automatic procedures are under testing, e.g. the PEST program.

49. What are future plans for using/improving the model?

Using: Some institutions are requesting its use for hourly values, mainly for flood forecasting purposes, but also when the model is connected to routing models in a complex river system. There is quite some interest in using it with remote sensing data, especially by larger users. A lot of companies does not use a model, run it only weekly, or use it only in critical periods. This is likely to improve.

Improving: Several, and varying from institution to institution. Grid distribution of the whole model, a albedo/radiation term, a better redistribution function in the snow routine, limitation of calibration freedom, better incorporation of measured data (surveys, remote sensing and other). Improvements which involve additional data requirements are not popular.

50. Please provide references relevant to the model description and use.
Prof Sten Bergstrom maintains a reference list for the HBV model.

Sjur Kolberg
SINTEF Civil and Environmental Engineering / Water Resources
Klobuveien 153
N - 7034 TRONDHEIM, Norway
Direct line:		+ 47 - 73 59 60 94
Telefax::		+ 47 - 73 59 02 01
email:		Sjur.A.Kolberg@civil.sintef.no

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