a physically based daily hydrometeorological model for complex

A Physically Based Daily Hydrometeorological Model for Complex Mountain Terrain

RYAN J. MACDONALD, JAMES M. BYRNE, AND STEFAN W. KIENZLE

Department of Geography, University of Lethbridge, Lethbridge, Alberta, Canada

(Manuscript received 10 September 2008, in final form 29 June 2009)

ABSTRACT

This paper describes the continued development of the physically based hydrometeorological model

Generate Earth Systems Science input (GENESYS) and its application in simulating snowpack in the

St. Mary (STM) River watershed, Montana. GENESYS is designed to operate a high spatial and temporal

resolution over complex mountainous terrain. The intent of this paper is to assess the performance of the

model in simulating daily snowpack and the spatial extent of snow cover over the St. Mary River watershed. A

new precipitation estimation method that uses snowpack telemetry (SNOTEL) and snow survey data is

presented and compared with two other methods, including Parameter-elevation Regressions on In-

dependent Slopes Model (PRISM) precipitation data. A method for determining daily temperature lapse

rates from NCEP reanalysis data is also presented and the effect of temperature lapse rate on snowpack

simulations is determined. Simulated daily snowpack values compare well with observed values at the Many

Glacier SNOTEL site, with varying degrees of accuracy, dependent on the method used to estimate pre-

cipitation. The spatial snow cover extent compares well with Moderate Resolution Imaging Spectroradiometer

(MODIS) snow cover products for three dates selected to represent snow accumulation and ablation periods.

1. Introduction

Water supply in western North America is dependent

on snowpack from mountainous regions (Barnett et al.

2005; Field et al. 2007; Mote et al. 2005). The complex

interaction between snowpack and meteorological vari-

ability makes these regions extremely vulnerable to

changes in climatic processes (Beniston 2003; Leung and

Wigmosta 1999; McKenzie et al. 2003). It is expected that

mountain snow accumulations will decline with contin-

ued atmospheric warming (Hamlet and Lettenmaier

1999), resulting in a reduction of available water from

snowpack (Barnett et al. 2005; Lapp et al. 2005). Moun-

tain snowpack plays an important role in almost every

component of the hydrological balance. Snow cover has

an effect on local meteorological conditions, soil mois-

ture conditions (Groisman et al. 1994; Kane et al. 1991;

Zhang et al. 2003), the distribution and growth season of

vegetation (Stephenson 1990), and the timing and avail-

ability of runoff (R; Fontaine et al. 2002). The importance

of snow in mountainous regions has led to significant

research in snow hydrology and the development of

spatial hydrometeorological models.

Hydrometeorological measurements in mountainous

regions are sparse (Marks et al. 1992) and do not represent

the variability required for modeling entire watersheds

(Diaz 2005). Spatial estimates of hydrometeorology in

mountainous environments are, therefore, frequently

made using a low number of point measurements as input

to spatial models (Liston and Elder 2006b). Spatial models

rely on the interaction between physiographic character-

istics of the landscape and meteorological processes to

make estimates of hydrometeorological variables.

In mountainous environments, the high spatial and

temporal variability in hydrometeorological conditions

requires spatial models that are physically realistic and

computationally efficient (Liston and Elder 2006b). A

number of models have been developed to simulate

mountain hydrometeorology. The Regional Hydro-

ecological Simulation System (RHESSys; Band et al.

1991, 1993), the Precipitation–Runoff Modeling System

(PRMS; Leavesley et al. 1983), snow evolution model

(SnowModel; Liston and Elder 2006a), and Alpine3D

(Lehning et al. 2006) are four models that have been used

for hydrological and ecological modeling in mountain-

ous watersheds. RHESSys integrates GIS and a series of

Corresponding author address: James M. Byrne, Department of

Geography, University of Lethbridge, 4401 University Drive,

Lethbridge, AB T1K 3M4, Canada.

E-mail: [email protected]

1430 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 10

DOI: 10.1175/2009JHM1093.1

� 2009 American Meteorological Society

subprograms to spatially estimate ecosystem processes

at the watershed scale. PRMS is a distributed model

that was designed to evaluate the effects of precipitation

(p), climate, and land use on general basin hydrology,

whereas SnowModel is a detailed spatial snowpack

model developed for application under a range of land-

scapes where snow occurs. Alpine3D is a surface energy

balance model that has been used to simulate finescale

snow processes in mountainous regions. Although these

models are useful, they are not always available and

options may be limited for application to large water-

sheds with limited data.

This paper describes the continued development and

application of a model for simulating hydrometeoro-

logical conditions in large watersheds with relatively

little observed data. Generate Earth Systems Science

(GENESYS) is a physically based model for spatially

estimating daily hydrometeorological variables over

mountainous terrain using routinely available meteo-

rological data. GENESYS is under development at

the University of Lethbridge under the direction of

Dr. James Byrne and has been applied in several studies

(A. Sheppard 1996, personal communication; Lapp

et al. 2002, 2005; Larson et al. 2009, manuscript sub-

mitted to J. Hydrol., hereafter LBJLK), including work

described herein. The objective of this paper is to fur-

ther develop the GENESYS model to more accurately

represent mountain hydrometeorology spatially and

determine how well the model simulates snow accu-

mulation and ablation.

2. Study area and meteorological data

The headwaters of the St. Mary River watershed lie on

the eastern slopes of the Rocky Mountains, with the

majority of the upper watershed residing within Glacier

National Park, Montana. The St. Mary River flows from

the continental divide, through the upper and lower

St. Mary lakes, and ends in southern Alberta, where it

meets the Oldman River (Fig. 1).

The climatic regime is a transitional zone between

coastal and continental climates. The region is also

influenced by the orographic effect, which is most no-

ticeable during the winter months because synoptic

conditions dominate (Hanson 1982). The area receives

the majority of its precipitation in the winter, with snow

accounting for roughly 70% of the annual precipitation

at high elevations (Selkowitz et al. 2002).

The total drainage area of the study watershed is

1195 km2, with a mean elevation of 1745 m, ranging

from 1249 to 3031 m. The area is a relatively un-

disturbed, ecologically diverse region, which is largely

attributed to the fact that a large portion of the drainage

area is within Glacier National Park. Coniferous forests

account for 24% of the land cover, deciduous forests

account for 21%, and herbaceous plants cover another

29% of the area; 23% of the area is barren rock or soil,

and 3% of the area is water (USGS 2006).

The St. Mary climate station was selected to drive the

model. This station is centrally located at an elevation of

1390 m near St. Mary, Montana, in the eastern portion

FIG. 1. The STM River watershed in Montana and southern Alberta.

DECEMBER 2009 M A C D O N A L D E T A L . 1431

of the watershed. Daily temperature and precipitation

data for the period from 1960 to 2005 at the St. Mary

climate station were obtained (NCDC 2006). There

were significant data gaps in the station record from

1960 to 1982, with minor data gaps from 1982 to 2005.

Therefore, missing records from the years 1960 to 2005

were infilled using nearby climate stations and linear

regression (LBJLK).

To derive precipitation–elevation relationships, snow

water equivalent (SWE) measurements from the Preston

snow survey (Fig. 1) were used. The snow survey is op-

erated by the U.S. Geological Survey (USGS); it began

in 1994 and continues to the present. The survey has

32 sampling points located near the center of the wa-

tershed and spans an elevation range from 1438 to

2290 m. SWE data have been acquired from the in-

ception of the survey to the end of the 2006 snow year

(D. Fagre 2006, personal communication).

SWE data from the Many Glacier (MG) snowpack

telemetry (SNOTEL) site (Fig. 1) have also been used in

this study. The site is located in a small meadow sur-

rounded by trees at an elevation of 1519 m in the

western portion of the basin (NRCS 2006). This site has

been in operation since 1976 and continues to the

present; daily SWE data were obtained for the period

from 1976 to 2005.

3. Model description

The GENESYS model is designed to operate at high

spatial and temporal resolution using two individual

components to simulate the hydrological balance over

mountainous terrain. The first component, SimGrid

(A. Sheppard 1996, personal communication), applies

the Mountain Climate Simulator (MTCLIM) model

(Hungerford et al. 1989) and GIS-derived modeling units

referred to as terrain categories (TCs). The MTCLIM

model is looped in SimGrid to provide daily estimates of

temperature, precipitation, solar radiation, and relative

humidity for the subsequent modeling of hydrological

processes in each TC carried out by the SnowPack com-

ponent of the model. The SnowPack component applies

physical equations to simulate sublimation, canopy in-

terception, snowmelt, soil water storage, evapotranspi-

ration, and runoff from each TC.

a. Derivation of terrain categories

The complex nature of mountainous environments im-

plies that high-resolution spatial data are needed for hy-

drometeorological simulations to be relevant. However, it

is important to understand that increased resolution im-

plies increased complexity and not necessarily a higher

degree of accuracy (Daly 2006). Therefore, the explana-

tion of hydrometeorological variables at an appropriate

spatial scale in mountainous terrain is important.

Given the physical structure of the MTCLIM model,

we suggest that an appropriate spatial scale can be de-

termined using ecosystem responses to climatic pro-

cesses. Because vegetative cover is highly dependent on

hydrometeorological conditions (Mather and Yoshioka

1968; Stephenson 1990), it provides an ecologically sen-

sitive surrogate for the spatial variability in hydrometeo-

rological conditions. A combination of vegetative cover

and elevation is used to represent spatial variability in

hydrometeorology over the St. Mary River watershed.

A land cover grid derived using Landsat imagery

(USGS 2006) was overlaid with a 100-m digital elevation

model (DEM) classified into 100-m elevation intervals

to determine TCs for the St. Mary basin. The land cover

grid consisted of nine categories: dry herbaceous, mesic

herbaceous, deciduous trees–shrubs, coniferous trees–

shrubs, coniferous trees–open, water, snow, barren

rock–soil, and shadows. Snow and shadow classes were

eliminated from the land cover grid and assigned the

values of the nearest land cover. The combination of

elevation and land cover resulted in 82 TCs over the

St. Mary River watershed. TCs range in area from

100 m2 in the topographically heterogeneous portions of

the watershed to 88 km2 in the low elevation, relatively

homogenous portions of the watershed. For each TC

mean slope, aspect, and elevation values were derived.

The MTCLIM model was applied to all 82 TCs.

b. Application of MTCLIM

The MTCLIM model uses two types of climatological

logic: a topographic logic that determines meteoro-

logical variables by extrapolating data from a base cli-

mate station to the TC, and a diurnal climatology that

derives additional information from climate station data

(Hungerford et al. 1989). The diurnal climatology in

MTCLIM generates incident solar radiation and relative

humidity, whereas the topographic logic extrapolates

climate station data to make estimations of maximum

and minimum air temperature and precipitation (Glassy

and Running 1994). MTCLIM can be driven by any

climate station that provides maximum and minimum

temperatures and precipitation. Climate station data

used to drive the MTCLIM model can be referred to as

base data. For each TC, MTCLIM requires mean ele-

vation, mean slope, mean aspect, mean monthly precipi-

tation, and monthly-mean leaf area index (LAI) values

from the 1-km Moderate Resolution Imaging Spectro-

radiometer (MODIS)/Terra global dataset (Roy et al.

2002). Variables set as constants over all TCs are sur-

face albedo (0.2), and atmospheric transmissivity (0.65).

Here only the precipitation and temperature methods


within MTCLIM are reported. For a more detailed

description of relative humidity and solar radiation

estimates, refer to Hungerford et al. (1989) and Glassy

and Running (1994).

1) PRECIPITATION

Of the hydrological variables, precipitation is perhaps

the most difficult to quantify spatially. By accounting for

the physiographical controls on spatial and temporal

distribution of precipitation and incorporating observed

meteorological data, estimates of precipitation can be

made for mountainous environments (e.g., Daly et al.

2008). However, because of the complexities in pro-

cesses controlling precipitation in mountainous terrain,

it is difficult to estimate precipitation at the daily time

step. At a coarser temporal scale, the variability in

precipitation is reduced and, therefore, it can be better

described.

Applying monthly data to adjust daily precipitation

values enables large-scale precipitation patterns to be

maintained while accounting for daily variability. A

similar method is used by Running et al. (1987), where

they apply annual data. However, determining monthly

precipitation values over the entire watershed is difficult.

An objective of this study is to determine the most

suitable monthly spatial precipitation estimation method

by comparing how precipitation inputs affect daily SWE

simulations at the Many Glacier SNOTEL site (NRCS

2006).

Three methods are applied; two of the methods use

observed data within the St. Mary River watershed to

derive precipitation–elevation relationships. The first

method was developed by LBJLK, where precipitation

estimates are made using a precipitation–elevation

function. The second method is presented here, where

precipitation estimates are made as a function of eleva-

tion and season. The third method applies the 1971–2000

monthly precipitation averages from the Parameter-

elevation Regressions on Independent Slopes Model

(PRISM) dataset (Daly et al. 2008).

The climatological characteristics of the St. Mary and

Many Glacier sites were assessed. Figure 2 shows that the

seasonal distribution of precipitation differs significantly

between the St. Mary and Many Glacier sites (r2 5 0.09,

p 5 0.17). Three methods are presented to show the rel-

evance of accounting for differences in seasonality be-

tween low-elevation climate stations and mountainous

regions of the watershed. One method is shown that does

not account for this seasonal difference, whereas the other

two methods are shown that do account for seasonality.

(i) Precipitation method A

LBJLK derived a method that established precipitation–

elevation relationships. The method applies a linear

FIG. 2. Seasonal distribution of precipitation at MG and STM.


precipitation–elevation function to daily data [Eq. (1)].

The equation was derived using monthly changes in SWE

at the Preston snow course:

P(Daily)

5 Pstm(Daily)

1 0.232 3 elevation 3p

stm(Daily)

pstm(Monthly)

,

(1)

where P(Daily) is daily precipitation (mm) at the TC,

Pstm(Daily) is daily total precipitation (mm) at St. Mary,

Pstm(Monthly) is monthly precipitation averages (mm) for

St. Mary, and elevation (m) is the local elevation relative

to St. Mary, where the elevation at St. Mary is set to 0 m.

This method is applied in the first run of the GENESYS

model and assessed using daily SWE data from the

Many Glacier SNOTEL site.

(ii) Precipitation method B

The second method applies MTCLIM logic (Running

et al. 1987), using monthly data to adjust daily precipi-

tation values as a function of elevation over the water-

shed. This is done by calculating a ratio between mean

monthly precipitation values at each TC and the St. Mary

climate station. The monthly ratios are multiplied by the

daily precipitation value to adjust daily data over the

watershed. Data used to derive this method included

the Preston snow survey, the St. Mary climate station,

and the Many Glacier SNOTEL site.

The following series of steps was used to derive pre-

cipitation values at each TC:

1. Derive a precipitation–elevation relationship.

2. Adjust for seasonality differences between the St.

Mary climate station and the mountain portions of

the watershed.

3. Calculate ratios between monthly-mean values at the

St. Mary climate station and each TC.

4. Apply ratios to daily precipitation data.

A change in winter SWE (DSWE) was calculated for

each monthly sampling interval for 73 months (LBJLK).

These monthly values were only calculated for the cold

season (from January to March), where average temper-

atures for the sampling period were below 08C. Negative

DSWE values were omitted from the analysis, with the

assumption that these values corresponded to melt. With

these two constraints, it is assumed that monthly DSWE

values correspond with monthly increases in precipitation.

The two precipitation–elevation relationships are de-

rived relative to the St. Mary climate station and the Many

Glacier SNOTEL site. Using precipitation–elevation

relationships relative to both St. Mary and Many Glacier

enables the model to account for seasonality differences

between mountain and low land areas. Local elevations

are determined relative to both St. Mary and Many

Glacier, where St. Mary and Many Glacier were set to

have a local elevation of 0 m. The resultant relationships

between local elevation and mean winter DSWE are

presented in Figs. 3 and 4 .

An adjustment was made to account for the seasonal

differences in precipitation between the St. Mary climate

station and the Many Glacier SNOTEL site, resulting in

a better representation of precipitation over the water-

shed. Monthly relationships between St. Mary and Many

Glacier precipitation means for the years 1982–2005

were derived using linear regression (Table 1). The time

period from 1982 to 2005 was selected because of data

gaps prior to 1982 at the St. Mary climate station.

The St. Mary climate station best represents the Many

Glacier SNOTEL site during the winter months. On the

basis of the regression results listed in Table 1, it is as-

sumed that the St. Mary climate station can be reliably

used to predict monthly precipitation at Many Glacier

during the winter. The transitional months of April, May,

and September had relatively poor monthly precipitation

relationships. However, because of the limited available

FIG. 3. Linear relationship between local elevation in relation to the MG SNOTEL site and

dSWE at the Preston snow course.


data, all 12 monthly relationships were applied to predict

a mountain base station, which in this case is the Many

Glacier SNOTEL site. This was done to enable the model

to account for changes in seasonality using St. Mary as

the single low-elevation base station, where the longest

climate records are available.

To determine monthly precipitation means at each

TC, the two DSWE equations are used. For TC eleva-

tions below 1500 m, the Preston DSWE relationship

relative to St. Mary (Fig. 4) is applied with the monthly-

mean values from St. Mary climate station as the base:

SWE(,1500)

5 0.051(local elevation) 1 base. (2)

If the TC elevation is 1500 m and above, monthly-

mean precipitation values for a mountain base are cal-

culated using monthly relationships presented in Table 1.

Using the predicted mountain base, the Preston DSWE

relationship relative to Many Glacier (Fig. 3) is applied:

SWE(.1500)

5 0.063(local elevation) 1 mountain base.

(3)

This allows for a seasonal shift in precipitation to be

made for the mountainous portion of the watershed,

while accounting for the effects of elevation. At eleva-

tions greater than 2300 m (extent of the snow course

data), monthly means are assigned the same value as the

mean at 2300 m, resulting in no change in SWE with

elevation above 2300 m. Figure 5 shows how simulated

monthly-mean precipitation volumes change with sea-

son and elevation.

To apply the effect of elevation and shift in seasonality

to the daily historical precipitation record, ratios are

calculated between monthly precipitation means at the

St. Mary climate station and the monthly means at each

TC. This results in the largest ratios during the winter

and smallest ratios during the summer (Fig. 6).

These ratios are multiplied by daily precipitation vol-

umes at the St. Mary climate station, resulting in pre-

cipitation volumes that are adjusted as a function of

elevation and season over the watershed.

(iii) Precipitation method C

The third precipitation estimation method applies the

1971–2000 precipitation means from PRISM to obtain

monthly precipitation values. For each of the 12 monthly

surfaces, a mean precipitation value is calculated for each

TC. To maintain consistency at the 1400-m elevation

band, precipitation values are used from the St. Mary

climate station. The monthly values derived are used to

determine the monthly ratios between the St. Mary cli-

mate station and each TC within the watershed. Figure 7

demonstrates that PRISM accounts for the differences

in seasonality between St. Mary and the higher moun-

tainous portions of the watershed. It is important to note

that PRISM is also able to account for other topographic

influences on precipitation, such as coastal proximity

and slope orientation (Daly et al. 2002). To maintain

consistency between comparisons, the TCs shown in Fig. 7

are the same as the TCs shown in Fig. 6.

Using PRISM inputs results in slightly higher ratios

when compared to method B. However, they still reflect

FIG. 4. Same as Fig. 3, but for STM and dSWE.

TABLE 1. Linear relationships between mean monthly precipitation

at STM and mean monthly precipitation at MG (n 5 23).

Month Mountain base equation r2 p

Jan MG 5 1.526(STM) 1 58.575 0.83 ,0.0001

Feb MG 5 1.505(STM) 1 29.284 0.77 ,0.0001

Mar MG 5 1.587(STM) 1 28.825 0.82 ,0.0001

Apr MG 5 1.233(STM) 1 28.189 0.57 ,0.0001

May MG 5 0.549(STM) 1 55.335 0.29 0.006

Jun MG 5 0.863(STM) 1 33.151 0.83 ,0.0001

Jul MG 5 0.735(STM) 1 26.554 0.85 ,0.0001

Aug MG 5 0.875(STM) 1 13.842 0.72 ,0.0001

Sep MG 5 0.904(STM) 1 31.279 0.53 ,0.0001

Oct MG 5 1.875(STM) 1 17.199 0.84 ,0.0001

Nov MG 5 1.797(STM) 1 45.295 0.89 ,0.0001

Dec MG 5 1.570(STM) 1 46.428 0.87 ,0.0001


the change in seasonality between mountainous por-

tions of the watershed and the St. Mary climate station.

These ratios are used to adjust the daily precipitation

volumes at the St. Mary climate station as a function of

elevation and season over the watershed.

2) TEMPERATURE

To account for temperature changes as a function of

elevation, MTCLIM applies temperature lapse rates.

This study involves varying temperature lapse rates for

three separate model runs to determine the effect of

lapse rate on simulated snow accumulation and ablation.

The first model run applies lapse rates of 8.28C km21 for

maximum temperature and 3.88C km21 for minimum

temperature. These lapse rates were used by LBJLK and

resulted in temperature estimates that compared very

well to an alpine site on Lakeview Ridge near Waterton,

Alberta. The second run applies lapse rates derived at

Castle Mountain ski resort, approximately 100 km north-

west of the St. Mary River watershed by Pigeon and

Jiskoot (2008). They determined maximum and minimum

temperature lapse rates to be 6.18 and 5.98C km21,

respectively. The third model run applies daily lapse

rates from 1961 to 2000 derived from National Centers

for Environmental Prediction (NCEP) reanalysis data

(NCEP 2008). To derive daily lapse rates, a method is

used that calculates the differences in elevation and

temperature between the 1000- and 700-mb surfaces and

from those differences derives linear lapse rates (D. Blair

2008, personal communication). The four grid cells cov-

ering the watershed were selected and averaged for

this analysis. The 1961–2000 average of daily NCEP

lapse rates were 6.58 and 4.68C km21 with a range of

17.88 and 17.68C for maximum and minimum tempera-

tures, respectively, accounting for inverted lapse rates.

c. Snowpack

Daily spatial hydrometeorological data are used to

model changes in TC snow water equivalent (SWE).

When snowpack is present, a daily hydrological balance is

calculated according to Eq. (4):

FIG. 5. Mean monthly precipitation change as a function of season and elevation.

FIG. 6. Varying monthly-mean precipitation ratios between STM and TCs at the elevation

bands of 1400, 2000, and 2800 m.


SWE(t)

5 SWE(t�1)

1 P(t)� I

(t)� S

(t)� R

(t)� IF

(t),

(4)

where, SWE is the amount of snow water equivalent

(mm) in the snowpack, P is simulated daily precipitation

as rain or snow, I is canopy interception, S is sub-

limation, IF is infiltration, and t is the time step (days).

If the snowpack has completely melted, a hydrological

balance is calculated that accounts for evapotranspira-

tion (ET) and changes in soil moisture (SM) conditions:

SM(t)

5 SM(t�1)

1 P(t)� I

(t)� ET

(t)� R

(t), (5)

which allows for two separate hydrological balances to

be calculated.

1) PRECIPITATION PARTITIONING

The method used to partition rain and snow was de-

veloped by Kienzle (2008). This method uses an S-shaped

curve and two temperature variables:

Pr 5 5T � Tt

1.4Tr

� �2

1 6.76T � Tt

1.4Tr

� �2

1 3.19T � Tt

1.4Tr

� �1 0.5, (6)

where Pr is the proportion of precipitation that falls as

rain (range from 0 to 1), T is the mean daily temperature,

Tt is the threshold mean daily temperature, and Tr is the

range of temperatures where both rain and snow can

occur. Values for Tt and Tr of 3.78 and 168C, re-

spectively, are used, as suggested by Kienzle (2008) for

a similar climate station in southern Alberta.

2) CANOPY INTERCEPTION

The canopy snow interception model developed by

Hedstrom and Pomeroy (1998) for the southern boreal

forests of western Canada provides physically based

estimates of canopy load, interception, and unloading.

This model is adopted as a subroutine in the GENESYS

model. This adaptation assumes that the cold climatic

regime and physical properties of tree species of the

boreal forest represent the east slopes of the Rocky

Mountains well enough to provide realistic estimates of

snow interception. An empirical rainfall interception

routine based on canopy LAI was adapted for GENESYS

(Von Hoyningen-Huene 1983).

Snow interception (snowInt; mm SWE) and rain in-

terception (rainInt; mm) by the canopy are determined

for each terrain category containing coniferous forests.

Only rain interception is calculated for deciduous forests

because a subroutine was not available that accounts for

snow interception in deciduous forests.

(i) Snow interception

The snowInt subroutine uses the following formula

derived by Hedstrom and Pomeroy (1998):

SnowInt 5 I 3 0.678, (7)

where 0.678 is a determined unloading coefficient for

pine and spruce forests and I is the intercepted snow

load at the start of unloading (Hedstrom and Pomeroy

1998); I is determined using the following formula:

I 5 (L� Load)(1� 10�kP), (8)

where Load is the total snow load in the canopy on

the previous day (kg m22), L is the maximum load for

the given canopy given the boundary layer conditions

(kg m22), and P is the amount of precipitation falling as

snow. An approach is taken for the determination of k

(the proportionality factor), in which it is assumed that

there is a closed canopy and interception is completely

efficient. It is also assumed that snowflakes are falling

vertically on the canopy. The following formula repre-

sents k when the preceding assumptions are made:

FIG. 7. Same as Fig. 6, but using PRISM data.


k 51

L, (9)

where

L 5 S 3 LAI. (10)

LAI values were obtained for each TC from MODIS,

and S is the species value at a given density; S is de-

termined using the following formula:

S 5 SV 0.27 146

Ps

� �� . (11)

The value SV is a constant equal to 5.9 kg m22 derived

by Hedstrom and Pomeroy (1998) for spruce forests; Ps

(kg m23) is the density of snow calculated as a function

of mean air temperature (Hedstrom and Pomeroy 1998).

The canopy load is calculated by adding each in-

terception event to the canopy store and subtracting

snow that is sublimated. The canopy is able to store snow

until L is reached, at which time the remaining snow in

the canopy will fall to the ground and is then in-

corporated in the snowpack.

(ii) Rain interception

RainInt is estimated using the Von Hoyningen-Huene

(1983) formula that calculates interception as a function

of total rainfall and LAI. RainInt is calculated on a daily

basis using the following function:

RainInt 5 0.30 1 0.27Rain 1 0.13LAI� 0.013Rain2

1 0.0285RainLAI 3 LAI� 0.007LAI2,

(12)

where Rain is the precipitation that falls as rain. Von

Hoyningen-Huene (1983) found that this function is

unstable at precipitation values greater than 18 mm,

resulting in anomalous interception values. Therefore,

daily precipitation is set to a maximum of 18 mm for this

calculation; all other precipitation is considered as

throughfall. The intercepted rain store is determined for

each day by adding the intercepted rain to the store.

3) SUBLIMATION

The vapor transfer model developed by Thorpe and

Mason (1966) and later modified by Dery et al. (1998) is

used to estimate daily sublimation losses as a function of

snow properties and atmospheric conditions. Sub-

limation estimates in forested regions of the watershed

are made only in the canopy, because we assume the

effect of atmospheric turbulence on the ground surface

in forested areas to be minimal. The sublimation model

requires three environmental inputs calculated by the

SimGrid subroutine: total incident radiation Q*

(W m22),

daily average temperature Ta (K), and relative humidity

RH (%). Total sublimation loss is estimated by

Qsubl

5dm

dtN

(z), (13)

where Qsubl (kg m22 s21) is the sublimation rate for

a column of blowing snow over a horizontal land surface,

dm/dt is the change in mass of a blowing snow particle as

a result of sublimation per second, and N(z) is the

number of snow particles per unit volume (m23). The

number of snow particles depends on the particle shape

a and radius r. A mean a 5 5 and r 5 100 mm (Pomeroy

and Male 1988) are used. When using an a 5 5 and

a 10-m wind speed of 15 m s21, Dery et al. (1998) sug-

gest that N(z) 5 9.09 3 107 m23. Because of the lack of

wind data, we maintain the assumption that the average

winter wind speed at 10-m height is 15 m s21. Thorpe

and Mason (1966) estimate the change in mass of a

blowing snow particle by

dm

dt5

2prs � Qr

CNNu

Ta

� �Ls

Rv 3 Ta� 1

� �Ls

CNNu

Ta

� �Ls

Rv 3 Ta� 1

� �1 Rv

Ta

Nsh

Dei

� � ,

(14)

where 2pr (m) is the area function of a snow particle, s is

the water vapor deficit, where

s 5(e� ei)

ei, (15)

where e and ei are the vapor pressure, and its value at

saturation over ice is determined by

ei 5 8.55� 0.20Ta 1 0.0457Ta2 (16)

and

e 5RH

100

� �ei. (17)

Here Qr is the radiation transferred to the particle, Qr 5

pr2(1 2 ap)Q*

(Schmidt 1991), with ap the shortwave

particle albedo of 0.5 (Schmidt et al. 1998); C is the

thermal conductivity of air (2.4 3 1022 W m21 K21), Ls

is the latent heat of sublimation (2.838 3 106 J kg21), Rv

is the gas constant for water vapor (461.5 J kg21 K21),

and D is the molecular diffusivity of water vapor in air


(2.25 3 1025 m2 s21). The Nusselt number NNu and

Sherwood number Nsh are

NNu

and Nsh

5 1.79 1 (0.606N0.5rs ), (18)

where the Reynolds number Nre 5 (2rVr/V); Vr is the

terminal ventilation velocity, where horizontal particle

components are assumed equal to the horizontal wind

speed (Schmidt 1982; Dery and Taylor 1996), and V is

the kinematic viscosity of air (1.53 3 1025 m2 s21; Dery

and Yau 1999).

4) SNOWMELT

The snowmelt routine was adopted from Quick and

Pipes (1977). For snowmelt to occur, the snowpack must

ripen, which is determined by the ability of the snow-

pack to store cold. The snowpack cold storage (TREQ)

is determined by

TREQi5 (MLTF 3 TREQ

i�1) 1 T

i, (19)

where MLTF is a decay constant (set to 0.85). When

TREQ reaches 0 (enough energy is absorbed and the

snowpack is ripe), melt can occur.

Daily snowmelt (M; mm) values are calculated as

a function of air temperature:

M 5 PTM[Tmax 1 (TCEADJ 3 Tmin)], (20)

where PTM is a point melt factor (mm day21 8C21) and

TCEADJ is an energy partition multiplier [Eq. (21)],

Tmax is maximum daily temperature, and Tmin is

minimum daily temperature,

TCEADJ 5(Tmin 1 T)

(18 1 T). (21)

Wyman (1995) suggests a PTM of 1.8 for the Canadian

Rocky Mountains. While maintaining the lapse rates of

Pigeon and Jiskoot (2008), the timing and rate of melt at

the Many Glacier SNOTEL site are used to calibrate

PTM. On the basis of these results, a PTM of 1.0 appears

to be more suitable to the study area. The rate of melt

did not change significantly with changes in PTM.

However, the timing of complete melt showed that

a PTM of 1.0 provides the most suitable simulation of

the date of complete melt, especially in years when

complete melt occurred earlier (Table 2).

5) EVAPOTRANSPIRATION

Daily potential evapotranspiration (PET) estimates

are made only when the snowpack is depleted. When the

snowpack remains, sublimation is calculated. A version

of the Penman–Monteith evapotranspiration equation

developed by Valiantzas (2006) is adopted:

PET 5 0.038Q*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiT 1 9.5p

� 2.4Q

*Ra

!2

1 0.075(T 1 20) 1� RH

100

� �, (22)

where Ra is extraterrestrial radiation, determined as

a function of Julian date. This equation is used because it

does not require wind data and has been shown to pro-

vide accurate estimates of evapotranspiration when

compared with the standardized Food and Agricultural

Organization of the United Nations irrigation and

drainage paper number 56 (FAO-56; Allen et al. 1998)

Penman–Monteith scheme using a global climatic da-

taset (Valiantzas 2006). Actual evapotranspiration is

restricted by the soil moisture; potential evapotranspi-

ration is reduced when soil water declines below half of

field capacity:

XK 5 2SM

Soilmax

� �1.5

, (23)

where XK is the water supply control on ET, SM is daily

soil moisture (mm), and SoilMax is the soil field capacity

(mm) for a particular TC. When SM is less than half of

the storage capacity, ET is determined by

ET 5 ETP 3 XK. (24)

6) SOIL MOISTURE AND RUNOFF

A daily soil water budgeting routine was developed

for GENESYS. The soil water routine includes esti-

mates of PET, ET, soil water storage, and controls on the

ET/PET ratio where water supply limits ET. Estimates

of the spatial variation in soil water processes were

enabled using the Soil Survey Geographic (SSURGO)

soils database (NRCS 2008).

TABLE 2. Comparison of date of complete melt at different PTM.

Year

Day (day of year) of complete melt

Observed

Simulated

(PTM 1.0)

Simulated

(PTM 1.8)

1977 134 146 125

1978 139 145 133

1979 121 127 117

1980 117 86 86

1981 142 144 135

1982 127 120 107

1983 120 120 97

1984 127 126 117

1985 105 104 81

1986 114 110 96


To account for changes in soil moisture, soils data for

each TC are required. Soils data were only available for

the eastern portion of the St. Mary River watershed.

Therefore, land cover was used as a surrogate for soils in

the western portion of the watershed. Using the same

land cover grid as was used for the TC delineation, re-

lationships between land cover type and soil type were

used to extrapolate soil depth and water holding ca-

pacity values from the eastern portion of the watershed

to the entire basin.

Mean soil depth and field capacity values from each

soil type were used for the analysis. A GIS overlay

analysis—including land cover, mean soil depth, and

mean field capacity grids—was used to extract mean

soil depth and field capacity for each land cover type.

Soils data were sparse in the eastern portion of the

watershed above an elevation of 2700 m and a slope of

458. An analysis of land cover data showed that, al-

though present, the density of vegetative cover is also

low above this elevation and slope. It was, therefore,

assumed that elevations above 2700 m and steeper than

458 slopes do not have significant water storage ca-

pacity. Therefore, the constraints ‘‘above 2700 m,’’ and

‘‘more than 458’’ were included in the analysis and

assigned values of the barren rock–soil land cover type.

For each land cover type, mean soil depth and field

capacity values were determined. Using the land cover

type as a surrogate for soil depth and field capacity,

values from the eastern portion of the watershed were

extrapolated to the western portion of the watershed.

This is achieved by applying a soil depth and field ca-

pacity value to each land cover class, thereby represent-

ing the entire watershed.

For each TC mean soil depth and field capacity values

were determined. This enables the spatial representa-

tion of maximum soil field capacity over the entire

drainage basin. Field capacity values over the watershed

range from 0.0 to 199.4 mm, accounting for soil depth.

The values associated with each TC are then used in the

model to estimate changes to daily soil water loss

through ET and gain from snowmelt and rain infiltration

(IF). On days when either snowmelt or rainfall events

occur and soil field capacity is exceeded, R is produced.

4. Model application

The simulation was conducted from 1960 to 2001. This

time frame was selected to incorporate the PRISM

dataset and to enable application of future climate change

scenarios. To evaluate the sensitivity of snowpack simu-

lations to temperature and precipitation assumptions and

assess the suitability of the GENESYS model in simu-

lating daily snowpack, a comparison was made between

the Many Glacier SNOTEL site and simulated values at

the TC representing the SNOTEL site. These compari-

sons were made from the inception of the SNOTEL site

in 1976 to the end of the 2001 snow year.

To test how well the model simulates the spatial dis-

tribution of snow cover, a comparison was made with the

MODIS/Aqua snow cover 8-day global 500-m grid,

version 5 (Hall et al. 2007), for the dates presented in

Table 3.

The 8-day global 500-m grid provides one image for

every eight days; however, not all data could be used.

The dates shown in Table 3 were selected based on the

condition that they cover both snow accumulation and

ablation periods and that they contained less than 6%

cloud cover. The spatial resolution of MODIS is 500 m 3

500 m. Therefore, to compare GENESYS snow cover

to the MODIS data, snow cover surfaces were re-

sampled to a spatial resolution of 500 m 3 500 m. Both

MODIS and GENESYS snow surfaces were classified

using a binary classification, where pixels with snow had

a value of one and pixels without snow had a value of

zero. For MODIS, pixels containing ice during the

winter period were also included as snow-covered pixels.

To evaluate the model performance, a similar method to

that used by Garen and Marks (2005) was applied,

where the percentage of pixels that were in agreement

between MODIS and GENESYS snow surfaces for each

of the eight dates was calculated. The Hanssen–Kuipers

skill score (KSS) was also applied, which uses a contin-

gency table where A (snow–snow), B (snow–no snow),

C (no snow–snow), and D (no snow–no snow) classified

pixels are evaluated. A perfect score for the test is a

value of one. Because of the disproportionate number

of pixels that are snow covered, we applied a form

of the original KSS equation that enables equaliza-

tion of snow and no-snow classified pixels (Woodcock

1976):

KSS 5A

A 1 B1

D

C 1 D�1. (25)

TABLE 3. Dates of selected MODIS imagery and

percentage cloud cover.

Date Cloud cover (%)

16 Oct 2000 0.3

17 Nov 2000 0.3

17 Jan 2001 5.8

14 Mar 2001 1.1

15 Apr 2001 0.2

17 May 2001 1.9

12 Jul 2001 2.5

14 Sep 2001 0.0


5. Results

All components of the hydrological balance described

in Eqs. (4) and (5) are shown for three elevation bands in

Table 4. The simulation used for this demonstration of

the hydrological balance applies the precipitation esti-

mation method B, with temperature lapse rates derived

by Pigeon and Jiskoot (2008). A 40-yr average for each

of the hydrological balance components from 1961 to

2000 is presented, where elevation is in meters above sea

level, runoff is the total runoff from rain and snowmelt,

and P is both rain and snow. SnowInt is intercepted snow

lost to sublimation. This is based on the assumption that

intercepted snow that remains in the canopy is lost to

sublimation, which has been observed by Pomeroy et al.

(1998). RainInt is the amount of intercepted rain, ET is

the water loss through evapotranspiration from the soil.

Canopy rain loss from ET is accounted for in rainInt.

Table 4 demonstrates that the structure of this model

provides physically plausible estimates of hydrometeo-

rological variables. It also shows the dependency of

hydrometeorological estimates on elevation. It is shown

that as elevation increases, there is an increase in the

amount of precipitation received, a decrease in evapo-

transpiration, an increase in snow interception and

sublimation, a decrease in rain interception, and an in-

crease in runoff.

To test precipitation estimate methods, simulated

daily SWE was compared with observed daily SWE at

the Many Glacier SNOTEL site at Many Glacier from

1 October 1976 to 26 April 2001 (Figs. 8–10). These

dates correspond with the start and the end of the water

years respectively. Figure 8 demonstrates that applying

precipitation estimation method A results in relatively

poor agreement between observed and simulated SWE

values [r2 5 0.44, p , 0.0001, root-mean-square error

(RMSE) 5 158 mm]. It is also evident that the magni-

tude of maximum SWE estimates are incorrect, with low

SWE years being underestimated and some high SWE

years being drastically overestimated (Fig. 8).

Figure 9 is a comparison between observed and simu-

lated SWE at Many Glacier using precipitation method B.

There is good agreement between observed and simulated

SWE values (r2 5 0.72, p , 0.0001, RMSE 5 88 mm).

This result is a significant improvement relative to SWE

estimates using precipitation method A.

Figure 10 shows that using precipitation method C,

where PRISM data are applied, results in the best agree-

ment between observed and simulated SWE values at

Many Glacier (r2 5 0.73, p , 0.0001, RMSE 5 73 mm).

Simulated SWE values using PRISM input compare well

with simulated SWE values using precipitation method B,

showing that accounting for differences between the

precipitation climatology of mountainous and transi-

tional prairie zones is important.

The sensitivity of temperature lapse rates on daily

SWE simulations was tested for the 1982 snow year

(Fig. 11). This year was selected because it was the most

accurately simulated over the time series (r2 5 0.98, p ,

0.0001). Linear regression was applied to test the dif-

ference between observed SWE and simulated SWE

using all three lapse rates. The coefficient of determina-

tion did not differ between simulations. The RMSE did,

however, differ slightly with both of the static lapse rates

having a RMSE of 30 mm, whereas the daily lapse rates

resulted in a RMSE of 27 mm.

TABLE 4. Average water balance for three elevation bands from

1961 to 2000.

Elevation

R

(mm)

P

(mm)

ET

(mm)

SnowInt

(mm)

RainInt

(mm)

1500 690 1283 277 196 120

2000 1105 1661 232 222 102

2500 1560 1976 113 230 73

FIG. 8. Observed vs simulated SWE using precipitation method A.


The spatial snow cover extent simulated by GENESYS

compared well with MODIS snow cover for the dates

selected. The percentage of correctly classified pixels

ranged from 73% (17 January 2001) to 100% (12 July

2001 and 14 September 2001), with an average of 90%

correctly classified pixels for all eight dates. The KSSs

ranged from 0.16 (17 January 2001) to 1.0 (12 July 2001

and 14 September 2001; Table 5).

Figure 12 compares MODIS snow cover with simu-

lated snow cover for three dates. The dates were se-

lected based on their representativeness of the snow

accumulation and ablation periods.

6. Discussion

This study has demonstrated the applicability of the

GENESYS model in estimating snowpack over a moun-

tainous watershed. This physically based model provides

an alternative to spatial interpolation and can operate

at a fine spatial scale with a relatively low level of re-

quired input data. The spatial structure of the model

enables applicability to a variety of landscapes and

provides a useful tool for spatial hydrometeorological

simulations in watersheds with little observed data.

The hydrological balance simulated by GENESYS is

physically realistic. With an average of 15% over three

elevation bands, the proportion of the total annual

precipitation lost to sublimation in the canopy compares

well with Strasser et al. (2007), Hood et al. (1999), and

Zhang et al. (2004). The intercepted rain loss accounts

for an average of 6% of the annual precipitation re-

ceived over the three elevation bands. This seems rea-

sonable given the relatively large proportion of the

annual precipitation received as snow. The decrease in

evapotranspiration with elevation, despite an increase in

precipitation, demonstrates the sensitivity of the evapo-

transpiration routine to temperature estimates. This is ex-

pected given temperature is a key variable in determining

FIG. 9. Same as Fig. 8, but using precipitation method B.

FIG. 10. Same as Fig. 8, but using precipitation method C.


vapor pressure deficit (Valiantzas 2006). Table 4 shows

that runoff estimates are largely a function of the pre-

cipitation received. Table 4 also demonstrates that the

runoff contributions to streamflow from higher elevations

in the St. Mary River watershed are likely important

because a greater proportion of the annual precipitation

received is accounted for by runoff. With realistic sim-

ulations of the average annual hydrological balance, we

have confidence that the GENESYS model can account

for snow accumulation and ablation. A detailed analysis

of snowpack simulations provides further insight into

the model’s ability to simulate these processes at a finer

time step.

The method used to predict precipitation over the

watershed can provide realistic monthly precipitation

estimates, which can be used to simulate SWE at a daily

time step. It is plausible that this method can be applied

to other watersheds with low-elevation climate stations

and relatively few snowpack observations for large-scale

hydrological simulations. The PRISM 1971–2000 monthly-

mean precipitation dataset was shown to be a reliable

source for spatial precipitation inputs to snowpack sim-

ulations. The key advantage of these two methods of

precipitation estimation is that they are able to describe

the climatic characteristics of mountainous regions of the

watershed.

This study, and others (Bales et al. 2006), has shown

that mountainous regions experience very different pre-

cipitation regimes relative to low-elevation mountain–

prairie transitional zones. Figure 8 demonstrates that by

not accounting for this seasonal difference, the method

used by LBJLK fails to represent precipitation at the

Many Glacier site. Figures 9 and 10 show that accounting

for seasonal differences through the method developed

in this paper and the PRISM method enable more rep-

resentative simulations of daily SWE. The interannual

variability in the accuracy of SWE simulations demon-

strates that the meteorological conditions at the St. Mary

climate station are not always representative of the Many

Glacier SNOTEL site. This variability also suggests that

applying a mean precipitation–elevation relationship

does not account for year-to-year changes in the rela-

tionship. However, given the available data, the method

provides reasonable estimates of SWE overall.

Assumptions in estimating temperatures have little

effect on the goodness of fit of the SWE simulations at

Many Glacier (Fig. 11). However, the timing of snow

accumulation and ablation are highly sensitive to the

lapse rates used. Model runs that apply the lapse rates of

LBJLK and Pigeon and Jiskoot (2008) overestimate the

snow accumulation period and total SWE for the 1981

snow year. Figure 11 shows that there is little difference

between these lapse rates in their effects on simulated

daily SWE during the snow accumulation period. Dur-

ing the snowmelt period, however, temperature simu-

lations that use the lapse rates derived by Pigeon and

Jiskoot (2008) result in a more accurate simulation of

the date of complete melt. The lapse rates derived using

NCEP performed slightly better than the static lapse

rates. The NCEP lapse rates also resulted in the most

accurate simulation of the snow accumulation period

and total SWE. The NCEP lapse rates did not, however,

result in the most accurate simulation of the date of

complete melt.

FIG. 11. Comparison of the effect of lapse rate on SWE simulations.

TABLE 5. Percent correctly classified snow pixel and KSS test

results for comparison of simulated snow cover and MODIS snow

cover maps for eight dates.

Date KSS Correct (%)

16 Oct 2000 0.36 78

17 Nov 2000 0.96 95

17 Jan 2001 0.15 73

14 Mar 2001 0.99 98

15 Apr 2001 0.22 97

17 May 2001 0.60 80

12 Jul 2001 1.0 100

14 Sep 2001 1.0 100


The snow accumulation and ablation periods were

also assessed spatially by comparing MODIS snow cover

data with simulated snow cover for eight dates. Snow

cover simulations for 17 November 2000, 17 January

2001, and 17 May 2001 compare well with MODIS snow

cover (Fig. 12). KSS values have more variability than

the percentage correctly classified pixel calculations.

This is likely because percentage of snow pixels cor-

rectly classified does not account for those pixels that are

nonsnow covered and classified as snow or vice versa.

This result also demonstrates the sensitivity of the KSS

test. The 17 November 2000 simulation underestimates

snow accumulation in the upper watershed, with only

the highest elevations being covered by snow. This un-

derestimate is likely a function of warm temperatures at

the St. Mary climate station, which would result in less

snow accumulation. This is supported by the 17 January

simulation. Without snow cover, surface heating over

St. Mary from a reduced albedo relative to a snow-

covered surface likely results in an oversimulation of

temperature at low elevations. The 17 May simulation

has good visual agreement with MODIS, although only

80% of the pixels were correctly classified and the KSS

value is 0.60. The results of this spatial comparison

demonstrate the ability of the model to use very little

input data to reasonably represent spatial snow cover

over a mountainous watershed.

7. Limitations

Independent of the precipitation estimation method,

this study has demonstrated the difficulties in using low-

elevation climate records for driving spatially distrib-

uted hydrological models in complex terrain. Using

a single low-elevation station to determine a watershed

scale hydrological balance is subject to significant in-

fluence from local hydrometeorological conditions. To

mitigate this problem in the future, further meteoro-

logical monitoring is necessary.

The dependence of precipitation on topography is also

only partially accounted for when using elevation as the

sole predictor. The precipitation–elevation relationship

used in this study explains a maximum of 44% of the

variability in mean winter precipitation. The model would

likely explain more of the variability in precipitation

if other topographical predictors could be used. For

instance, Anderton et al. (2004), Basist et al. (1994),

Daly et al. (1994, 2002, 2007, 2008), and Marquinez et al.

(2003) have shown that using a combination of variables

including slope, aspect, coastal proximity, and orientation

can greatly increase the predictive power of regression-

based estimates of precipitation. Relationships between

precipitation and slope–aspect were not significant for the

Preston snow course (LBJLK), likely because the data do

not represent enough variability in slope and aspect.

FIG. 12. Spatial comparison of MODIS snow cover images and simulated snow cover using the

GENESYS models for three dates during the 2000 water year.


8. Summary

Limited data can be used to simulate the spatial distri-

bution of hydrometeorological characteristics of a moun-

tainous watershed. The GENESYS model can provide

physically realistic spatial estimates of snow cover and

snowpack accumulation and ablation in a mountainous

watershed. This study, however, also supports sugges-

tions by Daly et al. (2007) and Bales et al. (2006) that the

key limitation to modeling in mountainous regions is

the lack of observed data for model calibration and

validation. For simulating large watersheds, it is neces-

sary to identify the critical components of large-scale

snow hydrology and represent these processes with ap-

propriate equations that are able to resolve the com-

plexities at the relevant spatial and temporal scales (Woo

and Marsh 2005). With increased monitoring, physical

studies would be enabled, describing important processes

such as wind redistribution of snow (e.g., Anderton et al.

2004) and cold air drainage (e.g., Lundquist and Cayan

2007). Further studies using the GENESYS model will

attempt to quantify these micrometeorological processes

and their subsequent effects on modeling at the water-

shed scale, thereby determining the level at which to

monitor ecosystem processes for management applica-

tions in relatively large watersheds.

Acknowledgments. Funding support from the Alberta

Ingenuity Centre for Water Research and the Natural

Sciences and Engineering Research Council of Canada is

much appreciated. Dr. Dan Fagre, USGS, kindly pro-

vided the snow survey data used in this study. The advice

of the reviewers is very much appreciated, as they have

greatly improved the manuscript.

REFERENCES

Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop

evapotranspiration: Guidelines for computing crop water re-

quirements. Food and Agriculture Organization of the United

Nations Irrigation and Drainage Paper 56, 328 pp.

Anderton, S. P., S. M. White, and B. Alvera, 2004: Evaluation of

spatial variability in snow water equivalent for a high moun-

tain catchment. Hydrol. Processes, 18, 435–453.

Bales, R. C., N. P. Molotch, T. H. Painter, M. D. Dettinger, R. Rice,

and J. Dozier, 2006: Mountain hydrology of the western

United States. Water Resour. Res., 42, 13.

Band, L. E., D. L. Peterson, S. W. Running, J. Coughlan,

R. Lammers, J. Dungan, and R. Nemani, 1991: Forest eco-

system processes at the watershed scale: Basis for distributed

simulation. Ecol. Modell., 56, 171–196.

——, P. Patterson, R. Nemani, and S. W. Running, 1993: Forest

ecosystem processes at the watershed scale: Incorporating

hillslope hydrology. Agric. For. Meteor., 63, 93–126.

Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential

impacts of a warming climate on water availability in snow-

dominated regions. Nature, 438, 1–7.

Basist, A., G. D. Bell, and V. Meentemeyer, 1994: Statistical re-

lationships between topography and precipitation patterns.

J. Climate, 7, 1305–1315.

Beniston, M., 2003: Climatic change in mountain regions: A review

of possible impacts. Climatic Change, 59, 5–31.

Daly, C., 2006: Guidelines for assessing the suitability of spatial

climate data sets. Int. J. Climatol., 26, 707–721.

——, R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic

model for mapping climatological precipitation over moun-

tainous terrain. J. Appl. Meteor., 33, 140–158.

——, W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. P. Pasteris,

2002: A knowledge-based approach to the statistical mapping

of climate. Climate Res., 22, 99–113.

——, J. W. Smith, J. I. Smith, and R. B. McKane, 2007: High-

resolution spatial modeling of daily weather elements for

a catchment in the Oregon Cascade Mountains, United States.

J. Appl. Meteor. Climatol., 46, 1565–1586.

——, M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett,

G. H. Taylor, J. Curtis, and P. A. Pasteris, 2008: Physio-

graphically sensitive mapping of climatological temperature

and precipitation across the conterminous United States. Int.

J. Climatol., 28, 2031–2064.

Dery, S. J., and P. A. Taylor, 1996: Some aspects of the interaction

of blowing snow with the atmospheric boundary layer. Hydrol.

Processes, 10, 1345–1358.

——, ——, and J. Xiao, 1998: The thermodynamic effects of sub-

limating, blowing snow in the atmospheric boundary layer.

Bound.-Layer Meteor., 89, 251–283.

——, and M. K. Yau, 1999: A bulk blowing snow model. Bound.-

Layer Meteor., 93, 237–251.

Diaz, H. F., 2005: Monitoring climate variability and change in

the western United States. Global Change and Mountain Re-

gions, U. M. Huber, H. K. M. Bugmann, and M. A. Reasoner,

Eds., Advances in Global Change Research, Vol. 23, Springer,

636–656.

Field, C. B., and Coauthors, 2007: North America. Climate Change

2007: Impacts, Adaptation and Vulnerability, M. L. Parry et al.,

Eds., Cambridge University Press, 617–652.

Fontaine, T. A., T. S. Cruickshank, J. G. Arnold, and R. H. Hotchkiss,

2002: Development of a snowfall-snowmelt routine for moun-

tainous terrain for the soil water assessment tool (SWAT).

J. Hydrol., 262, 209–223.

Garen, D. C., and D. Marks, 2005: Spatially distributed energy

balance snowmelt modelling in a mountainous river basin:

Estimation of meteorological inputs and verification of model

results. J. Hydrol., 315, 126–153.

Glassy, J. M., and S. W. Running, 1994: Validating diurnal clima-

tology logic of the MT-CLIM model across a climatic gradient

in Oregon. Ecol. Appl., 4, 248–257.

Groisman, P., T. R. Karl, and T. W. Knight, 1994: Observed impact

of snow cover on the heat balance and the rise of continental

spring temperatures. Science, 263, 198–200.

Hall, D. K., G. A. Riggs, and V. V. Salomonson, 2007: MODIS/

Aqua snow cover 8-day L3 global 500m grid, version 5.

National Snow and Ice Data Center, Boulder, CO, digital media.

[Available online at http://nsidc.org/data/myd10a2v5.html.]

Hamlet, A. F., and D. P. Lettenmaier, 1999: Effects of climate

change on hydrology and water resources in the Columbia

basin. J. Amer. Water Resour. Assoc., 35, 1597–1623.

Hanson, C. L., 1982: Distribution and stochastic generation

of annual and monthly precipitation on a mountainous wa-

tershed in southwest Idaho. J. Water Resour. Assoc., 18,

875–883.


Hedstrom, N. R., and J. W. Pomeroy, 1998: Measurements and

modelling of snow interception in the boreal forest. Hydrol.

Processes, 12, 1611–1625.

Hood, E., M. Williams, and D. Cline, 1999: Sublimation from

a seasonal snowpack at a continental, mid-latitude alpine site.

Hydrol. Processes, 13, 1781–1797.

Hungerford, R. D., R. R. Nemani, S. W. Running, and J. C. Coughlan,

1989: MTCLIM: A mountain microclimate simulation model.

USDA Forest Service Research Paper INT-414, 52 pp.

Kane, D. L., L. D. Hinzman, C. S. Benson, and G. E. Liston, 1991:

Snow hydrology of a headwater Arctic basin 1. Physical

measurements and process studies. Water Resour. Res., 27,1099–1109.

Kienzle, S. W., 2008: A new temperature based method to separate

rain and snow. Hydrol. Processes, 22, 5067–5085.

Lapp, S., J. Byrne, S. Kienzle, and I. Townshend, 2002: Linking

global circulation model synoptics and precipitation for

western North America. Int. J. Climatol., 22, 1807–1817.

——, J. M. Byrne, I. Townshend, and S. W. Kienzle, 2005: Climate

warming impacts on snowpack accumulation in an alpine

watershed. Int. J. Climatol., 25, 521–526.

Leavesley, G. H., R. W. Lichty, B. M. Troutman, and

L. G. Saindon, 1983: Precipitation- runoff modeling system:

User’s manual. USGS Water-Resources Investigations Rep.

83-4238, 207 pp.

Lehning, M., I. Volksch, D. Gustafsson, T. A. Nguyen, M. Stahli,

and M. Zappa, 2006: ALPINE3D: A detailed model of

mountain surface processes and its application to snow hy-

drology. Hydrol. Processes, 20, 2111–2128.

Leung, L. R., and M. S. Wigmosta, 1999: Potential climate change

impacts on mountain watersheds in the Pacific northwest.

J. Amer. Water Resour. Assoc., 35, 1463–1471.

Liston, G. E., and K. Elder, 2006a: A distributed snow-evolution

modeling system (SnowModel). J. Hydrometeor., 7, 1259–1276.

——, and ——, 2006b: A meteorological distribution system for

high-resolution terrestrial modeling (MicroMet). J. Hydro-

meteor., 7, 217–234.

Lundquist, J. D., and D. R. Cayan, 2007: Surface temperature

patterns in complex terrain: Daily variations and long-term

change in the central Sierra Nevada, California. J. Geophys.

Res., 124, D11124, doi:10.1029/2006JD007561.

Marks, D., J. Dozier, and R. E. Davis, 1992: Climate and energy

exchange at the snow surface in the alpine region of the Sierra

Nevada 1. Meteorological measurements and monitoring.

Water Resour. Res., 28, 3029–3042.

Marquinez, J., J. Lastra, and P. Garcia, 2003: Estimation models for

precipitation in mountainous regions: The use of GIS and

multivariate analysis. J. Hydrol., 270, 1–11.

Mather, J. R., and G. A. Yoshioka, 1968: The role of climate in the

distribution of vegetation. Ann. Assoc. Amer. Geogr., 58, 29–41.

McKenzie, D., D. W. Peterson, D. L. Peterson, and P. E. Thornton,

2003: Climatic and biophysical controls on conifer species

distributions in mountain forests of Washington State, USA.

J. Biogeogr., 30, 1093–1108.

Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier,

2005: Declining mountain snowpack in western North America.

Bull. Amer. Meteor. Soc., 86, 39–49.

NCDC, cited 2006: Web climate service. [Available online at

www.ncdc.noaa.gov/oa/climate/stationlocator.html.]

NCEP, cited 2008: NCEP Global Ocean Data Assimilation System

(GODAS). [Available online at http://www.cdc.noaa.gov/.]

NRCS, cited 2006: SNOTEL data and products. [Available online

at http://www.wcc.nrcs.usda.gov/snow/.]

——, cited 2008: SSURGO data and products. [Available online at

http://soildatamart.nrcs.usda.gov/.]

Pigeon, K., and H. Jiskoot, 2008: Meteorological controls on

snowpack formation and dynamics in the southern Canadian

Rocky Mountains. Arct. Antarct. Alp. Res., 40, 716–730.

Pomeroy, J. W., and D. H. Male, 1988: Optical properties of snow.

J. Glaciol., 34, 3–9.

——, J. P. Parviainen, N. Hedstrom, and D. M. Gray, 1998: Cou-

pled modelling of forest snow interception and sublimation.

Hydrol. Processes, 12, 2317–2337.

Quick, M. C., and A. Pipes, 1977: UBC Watershed Model. Hydrol.

Sci. Bull., 306, 215–233.

Roy, D. P., J. S. Devadiga, R. E. Wolfe, M. Zheng, and

J. Descloitres, 2002: The MODIS land product quality as-

sessment approach. Remote Sens. Environ., 83, 62–76.

Running, S. C., R. R. Nemani, and R. D. Hungerford, 1987: Ex-

trapolation of synoptic meteorological data in mountainous

terrain and its use for simulating forest evapotranspiration and

photosynthesis. Can. J. For. Res., 17, 472–483.

Schmidt, R. A., 1982: Vertical profiles of wind speed, snow con-

centrations, and humidity in blowing snow. Bound.-Layer

Meteor., 23, 223–246.

——, 1991: The sublimation of snow intercepted by an artificial

conifer. Agric. For. Meteor., 54, 1–27.

——, C. A. Troendle, and J. R. Meiman, 1998: Sublimation of snow-

packs in subalpine conifer forests. Can. J. For. Res., 28, 501–513.

Selkowitz, D. J., D. B. Fagre, and B. A. Reardon, 2002: Interannual

variations in snowpack in the Crown of the Continent Eco-

system. Hydrol. Processes, 54, 3651–3665.

Stephenson, N. L., 1990: Climatic control of vegetation distribu-

tion: The role of the water balance. Amer. Nat., 135, 649–670.

Strasser, U., M. Bernhardt, M. Weber, G. E. Liston, and

W. Mauser, 2007: Is snow sublimation important in the alpine

water balance? Cryosphere Discuss., 1, 303–350.

Thorpe, A. D., and B. J. Mason, 1966: The evaporation of ice

spheres and ice crystals. Br. J. Appl. Phys., 17, 541–548.

USGS, cited 2006: Metadata for national land cover data for Montana.

[Available online at http://nris.mt.gov/nsdi/nris/nlcdgrid.html.]

Valiantzas, J. D., 2006: Simplified versions for the Penman evap-

oration equation using routine weather data. J. Hydrol., 331,

690–702.

Von Hoyningen-Huene, J., 1983: Die Interzeption des Niedersch-

lages in landwirtschaftlichen Pflanzenbestanden. Dtsch. Verb.

Wasserwirtsch. Kulturbau, 57, 1–66.

Woo, M. K., and P. Marsh, 2005: Snow, frozen soils and permafrost

hydrology in Canada, 1999-2002. Hydrol. Processes, 19, 215–229.

Woodcock, F., 1976: The evaluation of yes/no forecasts for scientific

and administrative purposes. Mon. Wea. Rev., 104, 1209–1214.

Wyman, R. R., 1995: Modeling snowpack accumulation and de-

pletion. Mountain Hydrology: Peaks and Valleys in Research

and Applications, B. T. Guy and J. Barnard, Eds., Canadian

Water Resources Association, 23–30.

Zhang, T., R. L. Armstrong, and J. Smith, 2003: Investigation of the

near-surface soil freeze-thaw cycle in the contiguous United

States: Algorithm development and validation. J. Geophys.

Res., 108, 8860, doi:10.1029/2003JD003530.

Zhang, Y., K. Suzuki, T. Kadota, and T. Ohata, 2004: Sublimation

from snow surface in southern mountain taiga of eastern Sibe-

ria. J. Geophys. Res., 109, D21103, doi:10.1029/2003JD003779.


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