Journal of Japan Society of Civil Engineers, Ser.
B1 (Hydraulic Engineering), Vol. 68, No. 4, I_229-I_234, 2012.

Annual Journal of Hydraulic Engineering, JSCE, Vol.56, 2012, February

SIMULATION OF FOREST SNOW PROCESSES AT
FRASER WITH A ENERGY BALANCE BASEDSNOW MELT MODEL (WEB-DHM-S)
Maheswor SHRESTHA1, Lei WANG 2, Yukiko HIRABAYASHI3 and Toshio
KOIKE41Student Member of JSCE, Ph. D. Student, Dept. of Civil Eng., the University of Tokyo (Bunkyo-ku, Tokyo, Japan)
2Member of JSCE, Dr. Eng., Professor, Inst. of Tibetan Plateau Research, Chinese Academy of Sciences, (Beijing,
China)
3Member of JSCE, Dr. Eng., Assoc. Professor, Inst. of Engineering Innovation, the University of Tokyo (Bunkyo-ku,
Tokyo, Japan)
4Member of JSCE, Dr. Eng., Professor, Dept. of Civil Eng., the University of Tokyo (Bunkyo-ku, Tokyo, Japan)

A newly developed physically based distributed biosphere hydrological model with three layeredenergy balance snow melt module (WEB-DHM-S) has been implemented at point scale to evaluate theforest snow processes at Fraser Experiment Forest site (USA) for two snow seasons (2003-2005). Results
illustrate that the model is capable of representing the sub-canopy snow depth and snow water equivalent
well with average correlation coefficient of 0.9. Energy fluxes are analyzed in detail for above canopy
and below canopy snow processes. It can be concluded that the radiation energy is dominant in above
canopy where sensible heat flux is dominant in addition to the radiation energy in sub-canopy snow
processes. Furthermore, the sensitivity runs against the interception capacity shows that the interception
capacity plays a major role in canopy snow sublimation.
Key Words: WEB-DHM-S, energy balance, Fraser, forest snow processes, snow depth, snow water equivalent

1. INTRODUCTION
A large fraction of mountainous river basins isusually covered with forest where forest canopy
plays a significant role on snow processes. The
effect of forest canopy is of great importance in theevolution of seasonal snow cover over the forest
floor and in understanding the energy and waterbalance interactions between the atmosphere andland surface. Forest canopy can dramatically modify
the radiation transfer, snowfall interception, andwind regime1) and thus for a given canopy structure,
the distribution of radiant and turbulent fluxes
strongly influences the magnitude of snowmelt
runoff and sublimation rate. Despite of therecognition of its importance, an interaction of waterand energy fluxes between the atmosphere andcanopy, and the sub-canopy snowpack are poorly
understood in forested mountainous regions2).
Many studies have been conducted for better
understanding and representation of forest snow

processes in several climate and hydrologicalmodels3-7). Some snow model intercomparison
studies have been carried out to address the issues
related to the current state of snow modelingprocess8,9). The Snow model intercomparison project2 (SnowMIP2) provided the first thoroughassessment of the shortcomings of 33 forest snow
models and indicated that there was no universal‘‘best’’ model or subset of ‘‘better’’ models,
highlighting the need of improvements9).
The complexity of existing snowmelt models in
atmospheric and hydrologic research community
vary greatly that ranges from simple degree day
models3) to advanced physically based energybalance based models5,6,7). Degree day models
cannot replicate the physics of canopy snow
processes and thus physically based energy balancesnow models are commonly used9). This study
attempts to evaluate the performance of newly
developed physically based three layered energybalance snow melt model in simulating the forest

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snow processes at Fraser Experimental site (one of
the SnowMIP2 sites) in two continuous snow
seasons . The model here used is Water and Energy
Budget – based Distributed Hydrological Modelwith improved snow physics (WEB-DHM-S)10),
developed by coupling three layered snow physics
of Simplified Simple Biosphere 3 (SSiB3)11) andalbedo scheme of Biosphere Atmosphere Transfer
Scheme (BATS)12) into Water and Energy Budget –basedDistributed
Hydrological
Model(WEB-DHM)13). WEB-DHM-S has shown its
capability in capturing the snow processes
accurately in both point and basin scales10,14).
Different from these previous studies, this study
aims at investigating the forest snow physics indetail with WEB-DHM-S, to better understand theabove-canopy and below-canopy snow processes inseasonal to interannual scale. A series of simulations
are carried out to assess the sensitivity of fresh snow
albedo and canopy interception to the sub-canopy
snow depth and SWE.

3. MODEL
The model used here is the WEB-DHM-S (Water
and Energy Budget – based Distributed HydrologicalModel with improved snow physics) which inherits
the three layered energy balance based snowmelt
module of SSiB3 and albedo scheme of BATS.
WEB-DHM-S can simulate the variability of snow
density, snow depth and snow water equivalent,liquid water and ice content, snow albedo, snow layer
temperature and thermal heat due to conduction in 9
biomes as described in SiB216).
Snowpack, which is intercepted by the canopy
leaves, is treated as single layered irrespective of its
total depth where sub-canopy snowpack is divided
into three layers when total snowdepth exceeds 5cm.
The top layer thickness is kept at a fixed depth of 2
cm regardless of the total snow depth to provide
reasonable simulation of the diurnal changes in thesnow surface temperature. The maximum thicknessof the middle layer is kept at 20 cm, and the bottom
layer represents the remaining body of thesnowpack. The heat budget of top layer is controlled
by surface energy balance where that of the second
and third layers is controlled by the heat conduction.
The mass budget for each snow layer is calculated
accordingly by taking account of the precipitation,
directthroughfall,
drip
fall,
evaporation,condensation, compaction, liquid water retention,
snowmelt runoff and infiltration into the underlying
layers. The schematic view of model processes in
WEB-DHM-S is presented in Fig. 1.
The energy budget equation for the canopy is∂T(1)
C c c = Rnc − H c − λE c∂twhere Cc (Jm–2K–1) is the effective heat capacity, Tc
is the canopy temperature, Rnc, Hc and λEc (Wm–2) are
net radiation, sensible heat and latent heat flux for the
canopy respectively. WEB-DHM-S uses a
two-stream approximation scheme16) for radiationtransfer in the canopy. The equation for enthalpy ofeach snow layer below the canopy is∂H ( Z j )∂G sn ( Z j )
(2)
=−
∂t
∂Z
where H (Jm–3) is the volumetric enthalpy of water,
Zj is the snow depth of layer j and Gsn (Wm–2) is theheat flux through the snow layer. H is defined as
H ( Z j ) = C v ( Z j ) × {Tsn ( Z j ) − 273.16 } −(3)f ice ( Z j ) × hv × ρ s ( Z j )where Cv (Jm–3K–1) and Tsn (K) are mean snow
volumetric heat capacity and snow temperature
respecitively. fice is the dry-snow mass fraction in thesnow layer, hv (Jkg–1) is the latent heat of fusion for
ice and ρs (kgm–3) is the bulk density of snow.

2. STUDY AREA AND DATA
The study area is Fraser Experimental Forest,
one of the forest sites of the SnowMIP2. It is ahigh-elevation site at 2820 m above mean sea level,
located in the U.S. Forest Service at Fraser,
Colorado (39.53°N, 105.53°W). The site is coolwith long winter having mean temperature of -2oC.
The mean annual precipitation is about 750 mm,
with nearly two-thirds falling as snow from Octoberthrough May. Rainfall seldom occurs in winter. A
continuous snow cover exists from early November
to late April. The site experiences shallow snow
depth with the maximum value of about 80 cm.
Vegetation includes approximately 27m high pine,spruce and fir. For simulation, vegetation coverage
and effective leaf area index (LAI) are set to 100 %and 3.0, respectively9).
The atmospheric forcing data such as air
temperature, humidity, wind speed, shortwave andlongwave radiation were measured at 30 m hightower. Constant surface pressure was prescribed as afunction of the site elevation. This study used all theforcing data averaged at 1 hour interval though there
were made available at 30 minute interval.
Precipitation was observed in a clearing site
adjacent to the forested area. Precipitation wasclassified as snow at air temperatures 2°C and rain
above. Snow water equivalent (SWE) and snow
depth were used for evaluating the model
performance, which were given as the average of 47
stake measurements across the forest. Data from thissite have been used in the assessment of many snow
models9,15).

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Fig. 1 Energy and water balance processes in WEB-DHM-S (Rsw,Rlw are downward shortwave and longwave radiation, αc,αs are
canopy and snow albedo, ra,rb,rc,rd are aerodynamic resistances, εc is emissivity, δc is transmissivity, Tm and Ta are air temperature,e(Tm) and e(Ta) are vapor pressures at reference height and canopy air space respectively. Details can be found in Sellers et al.16)

 R nsn − H sn − λE sn + G pr , j = 3
(4)∂Tsn ( Z j )G sn ( Z j ) = 
K
(
Z
)
+
SW
(
Z
),
j
=
2
,
1
j
sn
j

∂Z

Gsn is defined in equation 4 where Rnsn (Wm–2), Hsn(Wm–2), λEsn (Wm–2), Gpr (Wm–2), K (Wm–1K–1) andSWsn (Wm–2) are net radiation, sensible heat, latent
heat flux, thermal energy from rain at the snow
surface, thermal conductivity of snow andshortwave radiation flux absorbed by the snow layer
respectively.
The mass balance equation for the canopy is
∂M cs
E
(5)
= P − Dt − Dc − ci∂tρwwhere Mcs is snow water equivalent stored on the
canopy surface (m); P is precipitation rate (ms-1); Dt
is canopy throughfall rate (ms-1); Dc is canopy
drainage rate (ms-1); Eci is evaporation rate from
canopy interception stores (kgm-2s-1); ρw is densityof liquid water (kgm-3). The mass balance equation
for sub-canopy snow is∂M snow, j
∂t

E sn

, j = 3 (6) Dt + Dc + IF0 − IF j − R j −=ρw
 IF j +1 − IF j − R j , j = 2,1
combined evaporation and sublimation rate. Threesnow compaction processes, namely destructive
metamorphism, densification due to snow
overburden and compaction due to snow melting,are parameterized following Jordan5). The bulk
density of ice for new snowfall is calculated
following the formulation used in the CROCUS
snow model17). The snow albedo for canopy iscomputed using two stream approximation modeland details can be found in Sellers et al.16). The
snow albedo over ground surface is parameterizedusing a physically based prognostic snow albedo
scheme of the BATS12). The snow albedo iscomputed for visible (VIS) and near infra red (NIR)
spectral bands with adjustments for illumination
angle and snow age.
The model is run for two snow seasons from
November to May in 2003-2004 and October to
May in 2004-2005 with initial conditions ofobserved soil temperature and volumetric soil
moisture content.

4. RESULTS AND DISCUSSIONS

where Msnow,j (m) corresponds to the SWE at snow
layer j, IFj (ms–1) is the actual liquid water
infiltration flux at the interfaces, Rj (ms–1) is runoff
from the lower interface and Esn (ms–1) is the(1) SWE, snow depth and snow density
Simulation results and observations of the SWE,
snow depth and snow density on the forest floor at
Fraser in two snow seasons (2003-2005) are shownin Fig. 2. In 2003–2004,snow gradually accumulates

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from November with abrupt increase in January
following subsequent variations and remains till the
beginning of May. The SWE is found simulated
fairly well with correct timing of the end of thesnowmelt. However the model overestimates the
SWE from mid of February to late March. Snow
depth is underestimated from the beginning ofMarch to mid of April. The correlation coefficient
between the observed and simulated SWE is 0.98
and that for snow depth is 0.95. Error statistics asshown in Table 2 demonstrates that the snow depth
is underestimated by 0.137 m whereas the SWE isoverestimated by 0.018 m. The RMSE for the SWE
and snow depth are simulated to the values of 0.02
m and 0.14 m respectively. Model parametersspecific to Fraser site are given in Table 1.

0.2

O

MBE
129
40

N

D

J

F

M

A

M

O

N

1.0

J

F

M

A

M

A

M

Obs.
Sim.

Obs.
Sim.

0.8

D

Snow depth (m)Snow depth (m)

0.6
0.4
0.2
0.0
O

N

D

J

F

M

A

M

O

N

600
500

D

J

F

M

-3

-3

Snow density (kgm )Snow density (kgm )

Obs.
Sim.

Obs.
Sim.

400
300
200
100
0
O

N

D

J

F

M

A

M

O

N

D

J

F

M

A

M

Fig. 2 Observed and simulated snow water equivalent (SWE),
snow depth and snow density in 2003 -2004 and 2004-2005

uncertainty in the density of freshly fallen snow,
since density of new snow has been parameterizedbased on air temperature and wind speed17) derived
from relations at specific site. However, it can not
be ignored that the observed values are the averageof 47 stake measurements across the study region,
which may also attribute this bias, since the model
considers the entire grid as one biomass with same
LAI and same meteorological forcing.
The results for the snow density as shown in
Fig. 2 reveal that the model is able to capture thetrend of the seasonal variation in the snow density(with correlation coefficient of 0.92 and MBE of
40 kgm-3). While in 2003-2004, the density isoverestimated from the beginning of March with
MBE = 129 kgm-3, mainly due to the bias in thesnow depth simulation. In general, WEB-DHM-S isfound to simulate the variability in the snow depth,
SWE and snow density under the forest canopy
satisfactorily.

Snowdepth (m) Snow density(kgm-3)

MBE RMSE MBE RMSE
2003-2004 0.018 0.021 -0.137 0.143
2004-2005 -0.010 0.017 -0.104 0.137

Obs.
Sim.

0.0

Table 2 Error statistics for SWE, Snowdepth & Snow density
SWE (m)

Obs.
Sim.

0.1

Table 1 The parameters used in WEB-DHM-S at Fraser.
VIS & NIR albedo of fresh snow
0.95;0.65
VIS & NIR albedo of soil
0.05;0.10
Vegetation coverage (%)
100
Canopy top height, z2 (m)27.0Canopy base height, z1 (m)5.0Roughness length of snow surface (m)0.001
Roughness length of ground surface (m)0.001
Roughness length of canopy surface (m)5.1
Sat. hydraulic conductivity for soil (ms-1)
0.00002
Sat. hydraulic conductivity for snow (ms-1)
0.01
Zero plane displacement height (m)17.10
Effective Leaf Area Index (LAI)
3.0

Year

In 2004-2005
SWE(m)In 2003-2004
SWE (m)

0.2

RMSE
139
48

In the following winter (2004-2005), SWE iswell simulated throughout the snow season. The
model remarkably underestimates the snow depth inJanuary and February with MBE = 0.104 m and
RMSE being 0.137 m. However, the model is able
to replicate the mid season ablation during mid
April and after that the snow depth is well simulated
in the late melting season. The correlation
coefficient between observed and simulated SWE
and snow depth were 0.94 and 0.81. MBE for snow
depth is found less in 2005 as compared to that in2004, primarily due to increased number ofobservations in 2005. But the time slice evaluationshows that the snow depth is underestimated byabout 0.19 m in mid January. The nature ofunderestimation of snow depth is totally different inthese two seasons. In the former year, snow depth issimulated less in melting season while in the later
season; it is underestimated in accumulation season.
The possible reason for this bias may be due to the2) Energy Fluxes
Fig. 3 presents the simulated energy fluxes (netradiation, sensible and latent heat flux) above andbelow canopy in two snow seasons. The direct
observations of these fluxes are not made available
through SnowMIP2. In 2003-2004, the canopy
energy balance is dominated by sensible and latent
heat flux till early February and after that, net
radiation flux gradually increases and attains highvalue at the end of the simulation. The net radiation

4

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-2

100

0.1

400
200

0.0

0

0

O

-100

-2

600

SWE (Sim.)
Cold content
SWE (Obs.)

5

Above Canopy (2003-2004)(10 Jm )200

SWE (m)Latent Heat Flux
Sensible Heat Flux
Net radiation Flux

Cold Content

0.2

Simulated Energy Fluxes (Wm )
300

N

D

J

F

M

A

M

20
0

300

100
0

2

0

4

-4

6

-200
80

Below Canopy (2004-2005)

40
20
0
-20
N

D

J

F

M

A

8

-8
11-Apr

21-Apr

1-May

11-May21-Maysnowpack. A summary of analysis in 2004-2005 ispresented in Fig. 4(a,b). Cold content mimics thepattern of SWE as shown in Fig. 4a. As SWE issimulated well relative to the observed one, thechange in cold content can reflect the effect ofenergy fluxes in melting season. The differential
cold content or heat energy of the snowpack per unit
time step is calculated first and compared with theheat available form the energy fluxes. Fig. 4b shows
the validation of energy fluxes with good agreement
to the differential cold content from April to May(melting season). Moreover, differential cold
content of the snowpack is simulated remarkably
high when snowfall occurs.

-100

O

0

Fig. 4 (b) Simulated differential cold content vs heat available
from radiant, sensible and latent energy fluxes in 2004-2005(April-May).

Above Canopy (2004-2005)

200

60

Precipitation

4

1-Apr

-20

Heat from energy fluxes-240

Differential Cold content

8Energy (10 Jm )Below Canopy (2003-2004)

60

5

80

Precipitation (mm)Fig. 4 (a) Simulated SWE and cold content in 2004-2005

-200

M

Fig. 3 Simulated energy fluxes above and below canopy in 2003
-2004 and 2004-2005.

flux energy is largely consumed for the sensible heat
and a very small amount of the flux is used in latentheat. In forest floor, the effect of net radiation flux
and sensible heat are noticed after early March, andboth of them are fully used in heating the snowpack.
Latent heat is used for the sublimation of interceptedsnow and evaporation of intercepted water incanopy surface. Canopy interception loss issimulated to a value of 50 mm which is about 15%of the total precipitation (320 mm). Snow
sublimation from forest floor is about 33 mm. In thissite, canopy evapotranspiration plays a significant
role in water balance which is about 40 mm.
In 2004-2005, the energy fluxes for canopy andforest floor snowpack follow the similar trend as
simulated in 2003-2004. However, the net radiationand sensible heat flux below the canopy become
dominant in early April (one month later than that in
the former season). Mid season ablation is well
captured due to remarkable increase in net radiationand sensible heat flux from 10 to 20 April (see Fig.
3). As compared to former season, the canopy
interception loss is smaller (13%) even though thesimulation period is one month longer and total
precipitation is higher (426 mm). The probablereason for less loss is because of the relatively cold
winter and spring.
We attempted to validate these energy fluxes
indirectly through analyzing the cold content of the3) Uncertainties
Despite of “so called” good models, a large
amount of uncertainties exist in the forcing dataset,model parameters, initial conditions and validating
datasets. Regarding the forcing data, precipitation
has the largest uncertainty. At Fraser, theprecipitation measured at 4m high instrument in the
open site is used as the forcing to the canopy, which
may impose some biases. Model parameters such as
snow albedo, threshold temperature for snow/rain
and morphological parameters of the canopy also
affect the simulation. A number of sensitivity runs
are carried out by assigning the VIS albedo of fresh
snow to the values of 0.7 to 0.95. It is found that thesensitivity of fresh snow albedo is insignificant insimulation of the snow depth, SWE and energy
fluxes, as expected, since the energy fluxes to theforest floor are comparatively very low as comparedto the energy available to the canopy surface.
Maximum canopy interception capacity (MCIC)
significantly controls the interception of snowfall by
the canopy and the snow loading to the forest floor.
Currently, WEB-DHM-S uses MCIC as the functionof LAI, as formulated in SiB2. This capacity is
calculated as 0.3mm for LAI = 3. The sensitivity

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Snow depth(m) in 2004-2005

Snow depth (m) in 2003-2004
1.0

REFERENCES

Obs.
MCIC=0.3mm (control)
MCIC=0.9mm

0.8
0.6

1) Niu, G.-Y. and Yang, Z.-L.: Effects of vegetation canopy
processes on snow surface energy and mass balances, J.
Geophys. Res., Vol. 109(D23111), 2004.
2) Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D.,
Rice, R., and Dozier, J.: Mountain hydrology of the western
United States, Water Resour. Res., Vol. 42(W08432), 2006.
3) Martinec, J.: Snowmelt-Runoff Model for stream flow
forecasts, Nordic Hydrol., Vol. 6(3), pp.145-154, 1975.
4) Koike, T., Takahasi, Y., and Yosino, S.: Modelling ofsnowmelt distribution for the estimation of basin-wide snow
covered area, IAHS ,Vol. 166, pp.199-212, 1986.
5) Jordan, R.: A one-dimensional temperature model for a snow
cover, U.S. Army Corps of Engineers, Cold Regions Researchand Engineering Laboratory, Special Report 91–16, pp. 49, 1991
6) Sun, S. F., Jin, J. and Xue, Y.: A simple snow–atmosphere–soiltransfer model, J. Geophys. Res., Vol. 104(D16),
pp.19587–19597, 1999.
7) Zanotti, F., Endrizzi, S., Bertoldi, G. and Rigon R.: The
GEOTOP snow module, Hydrol. Process., Vol. 18,
pp.3667–3679, 2004.
8) Etchevers, P., and coauthors: Validation of the energy budget ofan alpine snowpack simulated by several snow models(SnowMIP project), Ann. Glaciol., Vol. 38, pp.150–158, 2004.
9) Rutter, N., and coauthors: Evaluation of forest snow processmodels (SnowMip2), J. Geophys. Res., Vol. 114(D06111), 2009.
10)Shrestha, M., Wang, L., Koike, T., Xue, Y., and Hirabayashi,
Y.: Improving the snow physics of WEB-DHM and its point
evaluation at the SnowMIP sites, Hydrol. Earth Syst. Sci.,
Vol.14, pp.2577-2594, doi:10.5194/hess-14-2577-2010, 2010.
11) Xue, Y., Sun, S., Kahan, D. S. and Jiao, Y.: Impact ofparameterizations in snow physics and interface processeson the simulation of snow cover and runoff at several cold
region sites, J. Geophy. Res., Vol. 108(D22), 2003.
12) Yang, Z.–L., Dickinsion, R. E., Robock, A. and Vinnikov,
K. Y.: Validation of the snow submodel of the
Biosphere–Atmosphere Transfer Scheme with Russian
snow cover and meteorological observational data, J.
Climate., Vol. 10, pp.353–373, 1997.
13) Wang, L., Koike, T., Yang, K., Jackson, T., Bindlish, R. andYang, D.: Development of a distributed biospherehydrological model and its evaluation with the Southern
Great Plains Experiments (SGP97 and SGP99), J. Geophys.
Res.-Atmos., Vol. 114, D08107, 2009.
14) Shrestha, M., Wang, L., Koike, T., Xue, Y. andHirabayashi, Y.: Modeling the spatial distribution of snow
cover in the Dudhkoshi region of Nepal Himalaya, J.
Hydrometeor., doi: 10.1175/JHM-D-10-05027.1, 2011.
15) Dutra, E., Balsamo, G., Viterbo, P., Miranda, P., Beljaars,
A., Schär, C. and Elder, K.: An Improved Snow Scheme for
the ECMWF Land Surface Model: Description and Offline
Validation, J. Hydrometeor., Vol. 11, pp.899-916, 2010.
16) Sellers, P. J., and coauthors: A revised land surface
parameterization (SiB2) for atmospheric GCMs, Part I:
Model Formulation, J. Climate, Vol. 9, pp.676–705, 1996.
17) Brun, E., Martin, E., Simon, V., Gendre, C. and Coleou, C.: Anenergy and mass model of snow cover suitable for operational
avalanche forecasting, J. Glaciol., Vol. 35, pp.333–342, 1989.
(Received September 30, 2011)

0.4
0.2
0.0
0.2

SWE(m) in 2004-2005

SWE (m) in 2003-2004

0.1

0.0
O

N D

J

F

M A M

J

O

N

D

J

F

M

A

M

Fig. 5 Observed and simulated snow depth and SWE for control
run and run with MCIC = 0.9mm

runs are carried out to envision the impact of canopy
interception to the snow depth, SWE andinterception loss. It is found that with the increase of
the interception capacity by 3 times, the canopy
interception loss is increased up to 28% in 2004 and
22% in 2002. MBE for SWE is enhanced by 0.01 m
and that for snow depth is degraded by 0.02 m in
2003-2004 whereas MBE for SWE and snow depth
are degraded by 0.02 m and 0.06 m respectively in
2004-2005 (see Fig. 5). It shows that the canopy
storage function is very critical in simulating snow
processes beneath canopy.

5. CONCLUSIONSThe energy balance based snow melt model(WEB-DHM-S) was evaluated at Fraser site forforest snow process simulation from 2003 to 2005.
The model is able to capture the seasonal andinterannual variability of observed SWE, snow
depth and snow density on forest floor. Simulated
energy fluxes above and below canopy demonstrates
that the net radiation is dominant in above canopy
where sensible heat flux is equally important to the
net radiation in sub-canopy snow processes. In
general, it can be argued that the WEB-DHM-S cansimulate sub-canopy snow processes well, since it
provides physical basis of energy and water fluxes
above and below canopy in detail. An addition of
blowing snow module and advanced canopy
interception model to the current version will befurther research for better representation of forest
snow processes in future.
ACKNOWLEDGMENT:
This
study
was
supported by the Japanese Ministry of Education,
Science, Support and Culture (MEXT). The authors
express deep gratitude to Dr. Yongkang Xue,
University of California at Los Angeles forproviding the assistance in SSiB3 snow scheme, and
Dr. Kelly Elder from USDA forest service forproviding the Fraser dataset.

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