Observations of high-elevation meteorological conditions, glacier mass balance, and glacier run-off are sparse in western Canada and the Canadian Rocky Mountains, leading to uncertainty about the importance of glaciers to regional water resources. This needs to be quantified so that the impacts of ongoing glacier recession can be evaluated with respect to alpine ecology, hydroelectric operations, and water resource management. In this manuscript the seasonal evolution of glacier run-off is assessed for an alpine watershed on the continental divide in the Canadian Rocky Mountains. The study area is a headwaters catchment of the Bow River, which flows eastward to provide an important supply of water to the Canadian prairies. Meteorological, snowpack, and surface energy balance data collected at Haig Glacier from 2002 to 2013 were analysed to evaluate glacier mass balance and run-off. Annual specific discharge from snow- and ice-melt on Haig Glacier averaged 2350 mm water equivalent from 2002 to 2013, with 42 % of the run-off derived from melting of glacier ice and firn, i.e. water stored in the glacier reservoir. This is an order of magnitude greater than the annual specific discharge from non-glacierized parts of the Bow River basin. From 2002 to 2013, meltwater derived from the glacier storage was equivalent to 5–6 % of the flow of the Bow River in Calgary in late summer and 2–3 % of annual discharge. The basin is typical of most glacier-fed mountain rivers, where the modest and declining extent of glacierized area in the catchment limits the glacier contribution to annual run-off.
Meltwater run-off from glacierized catchments is an interesting and poorly understood water resource. Glaciers provide a source of interannual stability in streamflow, supplementing snow melt, and rainfall (e.g. Fountain and Tangborn, 1985). This is particularly significant in warm, dry years (i.e. drought conditions) when ice melt from glaciers provides the main source of surface run-off once seasonal snow is depleted (e.g. Hopkinson and Young, 1998). At the same time, glacier run-off presents an unreliable future due to glacier recession in most of the world's mountain regions (Meier et al., 2007; Radić and Hock, 2011).
There is considerable uncertainty concerning the importance of glacier run-off in different mountain regions of the world. As an example, recent literature reports glacier inputs of 2 % (Jeelani et al., 2012) to 32 % (Immerzeel et al., 2009) within the upper Indus River basin in the western Himalaya. In the Rio Santo watershed of the Cordillera Blanca, Peru, Mark and Seltzer (2003) estimate glacier contributions of up to 20 % of the annual discharge, exceeding 40 % during the dry season. Based on historical streamflow analyses and hydrological modelling in the Cordillera Blanca, Baraer et al. (2012) report even larger glacier contributions in highly glacierized watersheds: up to 30 and 60 % of annual and dry-season flows respectively. In the Canadian Rocky Mountains, hydrological modelling indicates glacier meltwater contributions of up to 80 % of July to September (JAS) flows, depending on the extent of glacier cover in a basin (Comeau et al., 2009).
Different studies cannot be compared, as the extent of glacier run-off depends on the time of year and the proportion of upstream glacier cover. Close to the glacier source (i.e. for low-order alpine streams draining glacierized valleys), glacial inputs approach 100 % in late summer or in the dry season. Further downstream, distributed rainfall and snowmelt inputs accrue, often filtered through the groundwater system such that glacier inputs diminish in importance. Glacier run-off also varies over the course of the year, interannually, and over longer periods (i.e. decades) as a result of changing glacier area, further limiting comparison between studies.
Confusion also arises from ambiguous terminology: glacier run-off sometimes refers to meltwater derived from glacier ice and sometimes to all water that drains off a glacier, including both rainfall and meltwater derived from the seasonal snowpack (e.g. Comeau et al., 2009; Nolin et al., 2010). The distinction is important because the seasonal snowpack on glaciers is “renewable” – it will persist (although in altered form) in the absence of glacier cover. In contrast, glacier ice and firn serve as water reservoirs that are available as a result of accumulation of snowfall over decades to centuries. This storage is being depleted in recent decades, which eventually leads to declines in streamflow (Moore et al., 2009; Baraer et al., 2012). Glaciers are also intrinsically renewable, but sustained multi-decadal cooling is needed to build up the glacier reservoir, i.e. something akin to the Little Ice Age. In that sense, glaciers are similar to groundwater aquifers; depleted aquifers can recover, but not necessarily on time scales of relevance to societal water resource demands (Radic and Hock, 2014).
The importance of glaciers to surface run-off derived from the Canadian Rocky Mountains is also unclear. Various estimates of glacial run-off are available for the region, based largely on modelling studies and glacier mass balance measurements at Peyto Glacier (Hopkinson and Young, 1998; Comeau et al., 2009; Marshall et al., 2011), but there are little direct data concerning glacier inputs to streamflow for the many significant rivers that drain east, west, and north from the continental divide. This manuscript presents observations and modelling of glacier run-off from a 12-year study on Haig Glacier in the Canadian Rocky Mountains with the following objectives: (i) quantification of daily and seasonal meltwater discharge from the glacier, (ii) separation of run-off derived from the seasonal snowpack and that derived from the glacier ice reservoir, and (iii) evaluation of glaciers as landscape elements or hydrological “response units” within the broader scale of watersheds in the Canadian Rocky Mountains.
Haig Glacier is one of several glacierized headwaters catchments that feed
the Bow River, which drains eastward into the Canadian prairies. The Bow
River is a modest but important drainage system that serves several
population centres in southern Alberta, with a mean annual naturalised flow
of 88 m
Source waters in the Rocky Mountains need to be better understood and quantified for water resource management in the basin, particularly in light of increasing population stress combined with the risk of declining summer flows in a warmer climate (Schindler and Donahue, 2006). Based on relatively simple models, glacier storage inputs (ice and firn melt) for the period 2000–2009 have been estimated to constitute about 2 and 6 % of annual and JAS flow of the Bow River in Calgary (Comeau et al., 2009; Marshall et al., 2011; Bash and Marshall, 2014).
Glacial inputs are therefore relatively unimportant in the downstream water budget for the basin relative to contributions from rainfall and the seasonal mountain snowpack. They are likely to be in decline, however, given persistently negative glacier mass balance in the region over the last four decades and associated reductions in glacier area (Demuth et al., 2008; Bolch et al., 2010). This may impact on the available water supply in late summer of drought years, when flows may not be adequate to meet high municipal, agricultural, and in-stream ecological water demands. Moreover, glacier run-off during warm, dry summers can be significant in the Bow River (Hopkinson and Young, 1998), when demand is high and inputs from rainfall and seasonal snow are scarce. Glacier run-off has also been reported to be important in glacier-fed basins with limited glacier extent in the European Alps – e.g. more than 20 % of August flow of the lower Rhone and Po Rivers (Huss et al., 2011).
The analysis presented here contributes observationally based estimates of glacial run-off, which can be used to improve modelling efforts, to understand long-term discharge trends in glacially fed rivers (Rood et al., 2005; Schindler and Donahue, 2006), and to inform regional water resource management strategies. Sections 2 and 3 provide further details on the field site and glaciometeorological observations for the period 2002–2013, which are used to force a distributed energy balance and melt model for Haig Glacier. Section 4 summarises the meteorological regime and provides estimates of glacier mass balance and meltwater run-off from the site, and Sects. 5 and 6 discuss the main hydrological results and implications.
Glaciological and meteorological studies were established at Haig Glacier in
the Canadian Rocky Mountains in August 2000. Haig Glacier (50
Haig Glacier, Canadian Rocky Mountains, indicating the
location of the automatic weather stations (GAWS, FFAWS), additional snow pit
sites (mb02, mb10, and French Pass), the mass balance transect (red/blue
circles), the Veriteq
The eastern slopes of the Canadian Rocky Mountains are in a continental climate with mild summers and cold winters. However, snow accumulation along the continental divide is heavily influenced by moist Pacific air masses. Persistent westerly flow combines with orographic uplift on the western flanks of the Rocky Mountains, giving frequent winter precipitation events associated with storm tracks along the polar front (Sinclair and Marshall, 2009). This combination of mixed continental and maritime influences gives extensive glaciation along the continental divide in the Canadian Rockies, with glaciers at elevations from 2200 to 3500 m on the eastern slopes.
The snow accumulation season in the Canadian Rockies extends from October to May, though snowfall occurs in all months. The summer melt season runs from May through September. Winter snow accumulation totals from 2002 to 2013 averaged 1700 mm water equivalent (w.e.) at the continental divide location at the head of Haig Glacier (results presented below). For comparison, October to May precipitation in Calgary, situated about 100 km east of the field site, averaged 176 mm from 2002 to 2013 (Environment Canada, 2014), roughly 10 % of the precipitation received at the continental divide.
This study focuses on summer melt modelling at Haig Glacier, with the winter snowpack taken as an “input” or initial condition. Winter snowpack data used to initiate the model are based on annual snow surveys typically carried out in the second week of May. Snow depth and density measurements are available from a transect of 33 sites along the glacier centre line (Fig. 1), with the sites revisited each spring. Snow pits were dug to the glacier surface at four sites, with density measurements at 10 cm intervals, and snow depths were attained by probing. Sites along the transect have an average horizontal spacing of 80 m, with finer sampling on the lower glacier where observed spatial variability is higher.
Snow survey data are available for the centre-line transect for 9 years
from 2002 to 2013. For years without data, the mean snow distribution for the
study period was assumed. Snowpack variability with elevation,
A Campbell Scientific automatic weather station (AWS) was set up on the glacier in the summer of 2001 (GAWS) and an additional AWS was installed in the glacier forefield in 2002 (FFAWS). The weather stations are located at elevations of 2665 and 2340 m respectively and are 2.1 km apart (Fig. 1). AWS instrumentation is detailed in Table 1. Station locations were stable over the study, but instruments were swapped out on occasion for replacement or calibration. From 2001 to 2008, the glacier AWS was drilled into the glacier and was raised or lowered through additional main-mast poles during routine maintenance every few months to keep pace with snow accumulation and melt. After 2008 the glacier AWS was installed on a tripod. The station blew over in winter 2012–2013 and was damaged beyond recovery due to snow burial and subsequent drowning during snowmelt in summer 2013; the last data download from the site was September 2012.
Instrumentation at the glacier (G) and forefield (FF) AWS sites. Meteorological fields are measured each 10 s, with 30 min averages archived to the data loggers. Campbell Scientific data loggers are used at each site with a transition from CR10X to CR1000 loggers in summer 2007. Radiometers are both upward and downward looking.
There are 2520 complete days (6.9 years) of observations from the GAWS from 2002 to 2012, of which 909 days are from June to August (JJA). This represents 90 % coverage for the summer months (9.9 summers). Data are more complete from the FFAWS, with 3937 complete days of data (10.8 years) and 1004 days in JJA (10.9 summers) from 2002 to 2013. The glacier was visited year-round to service the weather stations with a total of 67 visits from 2000 to 2013. The weather stations nevertheless failed on occasion due to power loss, snow burial, storm damage, excessive leaning, and, on two occasions, blow-down. Snow burial was problematic on the glacier in late winter, and in some years observations at the glacier site were restricted to the summer. This gives numerous data gaps at the GAWS, but there are sufficient data to examine year-round meteorological conditions.
Additional temperature–humidity (
Meltwater from Haig Glacier drains through a combination of supraglacial streams and subglacial channels. The latter transport the bulk of the run-off due to interception of surface drainage channels by moulins and crevasses. Meltwater is funnelled into a waterfall in front of the glacier, and within about 500 m of the glacier terminus run-off is collected into a single, confined bedrock channel. This proglacial stream flows into the Upper Kananaskis River and goes on to feed the Kananaskis and Bow rivers in the Rocky Mountain foothills. Glacier run-off was measured in Haig Stream in 2002, 2003, 2013, and 2014 at a site about 900 m from the glacier terminus (Fig. 1).
The stream-gauging site and general hydrometeorological relationships are described in Shea et al. (2005). In summer 2013, continuous pressure measurements in Haig Stream were conducted from late July until late September using a LevelTroll 2000. To establish a stream rating curve, discharge measurements were made using the velocity profile method on three different visits from July through September, including bihourly measurements over a diurnal cycle to capture high and low flows.
The run-off data are limited but provide insights into the nature and time scale of meltwater drainage from Haig Glacier. Shea et al. (2005) report delays in run-off of approximately 3 h from peak glacier melt rates to peak discharge at Haig Stream during the late summer (July through September). Delays are longer in May and June when the glacier is still snow covered, probably due to a combination of mechanisms (Willis et al., 2002): (i) the supraglacial snow cover acts effectively as an aquifer to store meltwater and retard its drainage, (ii) access to the main englacial drainage pathways, crevasses, and moulins is limited, and (iii) the subglacial drainage system (tunnel network) is not established. Some early summer meltwater runs off as the proglacial waterfall awakens and Haig Stream becomes established during May or June each year, initially as a sub-nival drainage channel. A portion of early summer meltwater on the glacier may experience delays of weeks to months.
Haig Glacier meltwater estimates in this paper are reported for 2002–2013, for which winter snowpack and meteorological data are available from the site. Meteorological and surface energy balance regimes are characterized at the GAWS site, and distributed energy balance and melt models are developed and forced using these data. This is common practice in glacier melt modelling (e.g. Arnold et al., 1996; Klok and Oerlemans, 2002; Hock and Holmgren, 2005), although simplified temperature-index melt models are still widely used where insufficient meteorological input data are available (e.g. Huss et al., 2008; Nolin et al., 2010; Immerzeel et al., 2013; Bash and Marshall, 2014).
Temperature-index melt models are more easily distributed than surface energy balance models and can perform better in the absence of local data (Hock, 2005). However, there are numerous reasons to develop and explore more detailed, physically based energy balance models and to resolve daily energy balance cycles, particularly as interest grows in modelling of glacial run-off. Diurnal processes that affect seasonal run-off include: overnight refreezing, which delays meltwater production the following day; systematic differences in cloud cover through the day (e.g. cloudy conditions developing in the afternoon in summer months); diurnal development of the glacier boundary layer due to daytime heating; storage/delay of meltwater run-off, which can be evaluated from diurnal hydrographs and their seasonal evolution. These processes are not the focus of this manuscript, but the model is being developed with such questions in mind, and the energy balance treatment described below will serve as a building block for future studies.
Meteorological data from the GAWS are used to calculate surface energy
balance at this site for the May through September (MJJAS) melt season, and
year-round daily mean conditions from 2002 to 2012 at the forefield and glacier
AWS sites are compiled to characterise the general meteorological regime.
Relations between the two sites are used to fill in missing data from the
GAWS, following either
Temperature,
GAWS air pressure,
Where both GAWS and FFAWS data are unavailable, missing meteorological data
are filled using mean values for that day. For energy balance and melt
modelling, diurnal cycles of temperature and incoming solar radiation are
important. Where GAWS data are available (90 % of days for June–August and
86 % of days for May–September), 30 min temperature and radiation data
resolve the daily cycle directly. Otherwise a sinusoidal temperature cycle
for temperature is adopted, using
Net surface energy,
To evaluate the surface energy budget, the radiation terms are taken from
direct measurements at the GAWS and
Turbulent fluxes (W m
Surface values are assumed to be representative of the near-surface layer –
The point energy balance model is calibrated and evaluated at the GAWS site
based on ultrasonic depth gauge melt estimates in combination with
snow-pit-based snow density measurements. Local albedo measurements also
assist with this by indicating the date of transition from seasonal snow to
exposed glacier ice. Surface roughness values are tuned to achieve closure
in the energy balance (e.g. Braun and Hock, 2004), adopting
Glacier-wide run-off estimates require distributed meteorological and energy
balance fields (e.g. Arnold et al., 1995; Klok and Oerlemans, 2002) along
with characterisation of glacier surface albedo and roughness.
Meteorological forcing across the glacier is based on 30 min GAWS data
for the period 1 May to 30 September, which spans the melt season. Following
the methods described in Sect. 3.1, FFAWS data are used where GAWS data are
unavailable. If FFAWS data are also missing for a particular field, average
GAWS values for that day are used as a default, based on the available
observations from 2002 to 2012. The glacier surface is represented using a
digital elevation model (DEM) derived from 2005 Aster imagery with a
resolution of 1 arcsec, giving grid cells of 22.5 m
Distributed meteorological forcing requires a number of approximations
regarding either homogeneity or spatial variation in meteorological and
energy-balance fields. For incoming short wave radiation, slope, aspect, and
elevation are taken into account through the calculation of local potential
direct solar radiation,
For each grid cell, total daily potential direct short wave radiation is
calculated through integration of Eq. (5) from sunrise to sunset. This is
done at 10 min intervals, including the effects of local topographic
shading based on a regional DEM, i.e. examining whether a terrain obstacle is
blocking the direct solar beam (e.g. Arnold et al., 1996; Hock and
Holmgren, 2005). This spatial field
Temporal variations in incoming short wave radiation due to variable cloud
cover or aerosol depth are characterized by a mean daily sky clearness
index,
Incoming long wave radiation is also taken to be uniform over the glacier
using the measured GAWS value. Where this is unavailable, an empirical
relation developed at Haig Glacier is used:
Parameters in the distributed energy balance and melt model.
Outgoing short wave and long wave radiation are locally calculated as a
function of albedo,
Turbulent fluxes are estimated at each site from Eq. (3). Wind speed is
assumed to be spatially uniform while temperature and specific humidity are
assumed to vary linearly with elevation on the glacier, with lapse rates
In contrast, specific humidity variations in the atmosphere are driven by
larger-scale air mass, rainout, and thermodynamic constraints, which are
affected by elevation but not necessarily the surface environment. Estimates
of
Local albedo modelling is necessary to estimate absorbed solar radiation, the
largest term in the surface energy balance for midlatitude glaciers (e.g.
Greuell and Smeets, 2001). This in turn requires an estimate of the initial
snowpack based on May snowpack measurements from each year. As the snowpack
melts, albedo declines as a result of liquid water content, increasing
concentration of impurities, and grain growth (Cuffey and Patterson, 2010).
Brock et al. (2000) showed that these effects can be empirically approximated
as a function of cumulative melt or maximum daily temperatures. This approach
is adapted here to represent snow albedo decline through the summer melt
season as a function of cumulative positive degree days,
Fresh snowfall in summer is assigned an initial albedo of
Parameter values in the distributed meteorological and energy balance models are summarised in Table 2. The energy balance equations are solved to compute 30 min melt, and meltwater that does not refreeze is assumed to run off within the day. Half-hour melt totals are aggregated for each day and for all grid cells to give modelled daily run-off.
Winter mass balance on the glacier averaged 1360 mm w.e. from 2002 to 2013 with a standard deviation of 230 mm w.e. (Table 3). The spatial pattern of winter snow loading recurs from year to year in association with snow redistribution from down-glacier winds interacting with the glacier topography (e.g. snow scouring on convexities, snow deposition on the lee side of the concavity at the toe of the glacier). Lateral snow-probing transects reveal some systematic cross-glacier variation in the winter snowpack, but snow depths on the lateral transects are typically within 10 % of the centre line value. More uncertain are steep, high-elevation sections of Haig Glacier along the north-facing valley wall (Fig. 1). These sites cannot be sampled, so all elevations above French Pass (2750 m) are assumed to have constant winter SWE based on the value at French Pass.
Mean value
In most years the snowpack is still dry and is below 0
The winter snowpack as measured is an approximation of the true winter
accumulation on the glacier, sometimes missing late-winter snow and
sometimes missing some early summer run-off. Assuming an uncertainty of
10 % associated with this, combined with the independent 10 %
uncertainty arising from spatial variability, the overall uncertainty in
winter mass balance estimates can be assessed at
Table 4 presents mean monthly, summer, and annual meteorological conditions measured at the GAWS. Monthly values are based on the mean of all available days with data for each month from 2002 to 2012. Figure 2 depicts the annual cycle of temperature, humidity, and wind at the two AWS sites, as well as average daily radiation fluxes at the glacier AWS. Values in the figure are mean daily values for the multi-year data set.
Mean monthly weather conditions at Haig Glacier, Canadian
Rocky Mountains, 2002–2012, as recorded at an automatic weather station at
2665 m.
Mean daily weather at Haig Glacier, 2002–2012. Black and red
lines are GAWS and FFAWS data respectively.
On average, the GAWS site is cooler, drier, and windier than the glacier
forefield. Mean annual wind speeds at the glacier and forefield AWS sites
are 3.2 m s
Mean annual and mean summer temperatures derived from the GAWS data are
Monthly temperature differences are plotted in Fig. 3b, expressed as both
monthly offsets and as lapse rates. Temperature gradients are stronger in
the summer months at Haig Glacier, with a mean of
Mean monthly temperatures at Haig Glacier, 2002–2012.
Figure 4 plots the short wave radiation budget and albedo evolution at the two AWS sites, illustrating this summer divergence. Net short wave radiation is similar at the two sites through the winter until about the second week of May, after which time the GAWS maintains a higher albedo until mid-October, when the next winter sets in. Bare rock is typically exposed at the FFAWS site for about a 3-month period from mid-June until mid-September, with intermittent snow cover in September and early October. In wet years, snow persists into early July, with the FFAWS snow-free by 10 July in all years of the study. These dates provide a sense of the high-elevation seasonal snow cover on non-glacierized sites in the region. Meltwater run-off from the Canadian Rocky Mountains is primarily glacier-derived (a mix of snow and ice) from mid-July through September.
Mean daily
The albedo data also provide good constraint on the summer albedo evolution
and the bare-ice albedo at this site. The mean annual GAWS albedo value is
0.75 with a summer value of 0.55 and a minimum of 0.41 in August. The GAWS
was established near the median glacier elevation in the vicinity of the
equilibrium line altitude for equilibrium mass balance: ELA
Table 5 summarises the average monthly surface energy balance fluxes at the
GAWS. Peak temperatures and positive degree days are in July, but maximum
net energy,
Mean monthly surface energy balance at the Haig Glacier AWS,
2002–2012. Radiation fluxes are measured. Turbulent and conductive heat
fluxes are modelled. All fluxes are in W m
Mean monthly surface energy fluxes (W m
The distributed energy balance model is run from May through September of
each year based on May snowpack initialisations and 30 min AWS data from
2002 to 2013. This provides estimates of surface mass balance and glacier
run-off for each summer (Table 6). Glacier-wide winter snow accumulation,
Modelled surface mass balance and summer (JJA) surface energy
balance at Haig Glacier, 2002–2013.
An example of the modelled summer melt and net mass balance as a function of
elevation for all glacier grid cells is plotted in Fig. 6 for the summer
of 2012. This year is representative of mean 2002–2013 conditions at the
site with
Modelled
Model results are in accord with observations of extensive mass loss at the
site over the study period. The snow line retreated above the glacier by the end
of summer (i.e. with no seasonal snow remaining in the accumulation area) in
2003, 2006, 2009, and 2011. Surface mass balance was measured on the glacier
from 2002 to 2005:
Figure 7a plots measured vs. modelled melt for all available periods with
direct data (snow pits or ablation stakes) at the GAWS. Data shown are for
different time periods from 2002 to 2012, ranging from 2 weeks to 3
months. The fit to the data is good (
Measured vs. modelled
There are also departures associated with actual vs. modelled summer snow
events. On average, the stochastic precipitation model predicts 9.2
Glacier summer (JJA) temperature ranged from 4.1 to 6.5
Summer 2010 offers a contrast, having the lowest number of melt days (103),
the lowest temperature, and the highest albedo. This gave limited mass loss
in 2010 despite an unusually thin spring snowpack. Summer temperatures and
melt extent are generally more influential on net mass balance than winter
snowpack at this site. Winter mass balance is only weakly correlated with
net balance (
With the assumption that no surface melt is stored in the glacier, modelled
specific run-off from the glacier from 2002 to 2013 was 2350
Mean (
Figure 8 plots the average daily melt and the cumulative summer melt derived from seasonal snow and from the ice/firn reservoir. The average snowpack depletion curve is also plotted in Fig. 8b. The first appreciable glacier melt begins in mid-July and run-off typically switches from snow- to ice-dominated around the second week of August. Snowmelt run-off continues through the month of August, declining steadily as the snow line advances up the glacier.
Daily and cumulative run-off from Haig Glacier, 1 May–30 September,
based on average daily values from 2002 to 2013.
Direct stream run-off measurements from the glacier illustrate the nature of the melt–discharge relationship on Haig Glacier. Figure 9 plots measured discharge from 24 July to 22 September 2013, a period when the glacier drainage system was well established. Insolation-driven daily melt cycles produce a strong diurnal discharge cycle typical of alpine glacier outlet streams (Fountain and Tangborn, 1985). Periods of high overnight flows reflect either rain events or warm nights when melting did not shut down on the glacier (e.g. the third week of August). The end of summer is evident in the discharge record with low flows commencing after 20 September. New snow cover was beginning to accumulate on the glacier at this time, and the baseflow recorded through this period probably reflects residual summer meltwater that is still being evacuated through the subglacial drainage system.
Measured discharge in Haig Stream, 24 July–22 September
2013 (m
The diurnal cycle and lags between melt and stream discharge are shown more clearly in Fig. 10, which plots modelled glacier melt and the observed stream discharge over an 8-day period in late summer. Peak run-off lags maximum snow/ice melt by an average of 3.5 h over the summer, based on the time lag of peak correlation between the two time series. The run-off curve is more diffuse with a broader daily peak. Meltwater generation shuts down rapidly on most nights in late summer, while the discharge hydrograph has a broader recession limb. This is a consequence of different meltwater pathways and travel distances through the glacier drainage system.
Discharge in Haig Stream (blue, m
The period of measurements of glacier run-off is limited and is biased to the
late summer when the glacier surface is mostly exposed ice, so it is
difficult to use these data to test or constrain the melt model. Lags in
run-off relative to meltwater generation are likely to evolve through the
summer melt season with the value of 3.5 h noted above specific to the
second half of the ablation season, when meltwater drainage pathways are
well developed. Nevertheless, some comparison of measured stream discharge
vs. modelled meltwater run-off is possible. For the periods where stream data
are available, the maximum lagged correlation between daily totals of
discharge and meltwater run-off is
Total modelled meltwater over the 60-day record in Fig. 9 is equal to 4.73
Meteorological and mass balance data collected at Haig Glacier provide
insights into the hydrometeorological regime of glaciers in the Canadian
Rocky Mountains. From 2002 to 2013, the mean annual and summer (JJA)
temperatures at 2670 m altitude at the Haig Glacier AWS were
The corresponding values at the forefield AWS, at 2340 m altitude, are
The differences in climatology over a distance of 2.1 km between the AWS sites illustrate some of the difficulty in modelling glacier energy and mass balance without in situ data. It can be even more difficult to estimate glacier conditions based on distal (e.g. valley bottom) data, as is often necessary. Long-term meteorological data from Banff, Alberta (Environment Canada, 2014) are probably the best available data to assess the historical glacier evolution in the Canadian Rocky Mountains, but the site is at an elevation of 1397 m and in a snow shadow relative to locations along the continental divide (Shea and Marshall, 2007). October to May precipitation in Banff averaged 225 mm w.e. from 2002 to 2013, 17 % of that on Haig Glacier. Conditions become drier as one moves east from the continental divide, as discussed above with respect to Calgary, Alberta. It is difficult to apply a realistic precipitation–elevation gradient in mountain regions, as is often necessary in glacier mass balance modelling (e.g. Nolin et al., 2010; Jeelani et al., 2012). This challenge may be exacerbated when one is not on the windward side of the mountain range within the classical orographic precipitation belt.
Temperatures are also difficult to map. Relative to Banff, the Haig Glacier
AWS site is 6.9
The choice of temperature lapse rates is critical in glacier melt modelling,
but the most appropriate values to use are generally unknown. Daily or
monthly temperature offsets
Temperature and precipitation conditions discussed above, along with wind,
radiation, and humidity data from the site, offer insights into the
climatology of glacierized regions in the Canadian Rocky Mountains, although
Haig Glacier is in disequilibrium with these conditions. The relation
between net mass balance and summer temperature is
As has been demonstrated at other midlatitude glacier sites (e.g. Greuell and Smeets, 2001; Klok and Oerlemans, 2002), net radiation provides about 75 % of the available melt energy at Haig Glacier over the summer melt season with sensible heat flux contributing the rest. Latent heat flux and net long wave radiation act as energy loss terms in the summer. Modelled glacier-wide values are similar to those at the GAWS site with about 10 % less incoming solar radiation and similar annual melt totals. The differences are likely because much of the glacier experiences more topographic shading than the GAWS site, but lies at lower (i.e. warmer) altitudes.
The annual time series is limited (
The relation between net mass balance and mean summer radiation budget is
stronger than the
The distributed energy balance model predicts melt estimates in good accord with available observations, although these are limited to point measurements at the GAWS site and four years of surface mass balance data. Direct observations of the annual snow line retreat (end of summer ELA and accumulation–area ratio, AAR) are consistent with the modelled end-of-summer snow line and the finding that the glacier has experienced a consistently negative annual mass balance over the period of study.
Estimates of glacier mass loss and thinning over the study period also
reflect net mass balance measurements from Peyto Glacier, Alberta, which are
available from 1966 to 2012 (Demuth et al., 2008; WGMS, 2014). Peyto Glacier
is situated 140 km northwest of Haig Glacier (Fig. 1) and it is an outlet of
the Wapta Icefield, flowing eastward from the continental divide in the
Canadian Rocky Mountains (Moore and Demuth, 2001). Surface mass balance data from Peyto Glacier
indicate (Moore and Demuth, 2001) a
cumulative thinning of about 29 m (ice equivalent) from 1966 to 2012 and
9.9 m for the period 2002–2012. This compares with 10.6 m of thinning at
Haig Glacier for the period of overlap of the observations from 2002 to 2012.
Net specific mass balance averaged
Snowpack depth and specific run-off at glaciers in the Canadian Rockies are
exceptional within the context of the Bow River basin, which spans a steep
climatic gradient from the semi-arid southern Canadian prairies to the Rocky
Mountains. Average naturalised flows in the Bow River basin are estimated at
3.95
Nevertheless, 320 mm compares with 2350 mm of glacier-derived specific
run-off from 2002 to 2013. As landscape elements, glaciers contribute
disproportionately to streamflow by a ratio of more than
Over the months of July to September, when glacier ice and firn dominate the
run-off, naturalised Bow River flows in Calgary were 1.01
These numbers are based on the assumption that glacier run-off enters the river system within the months of July to September, without significant losses to evaporation or delays due to groundwater infiltration. Glacial streams are channelised, draining down steep gradients in the mountains, so initial losses and delays in transit are likely to be minimal, but some of the glacier meltwater will enter the groundwater drainage system and will also be delayed through storage in downstream lakes and reservoirs. Summer run-off contributions to the Bow River presented here should therefore be taken as maximum estimates.
These simulations also neglect changes in run-off associated with glacier geometric changes over the study period. The DEM used to drive the model is from 2005, so is reasonably representative of conditions over the study period (2002–2013), but the glacier retreated by about 40 m over this time, with an associated loss in area of about 2 %. A sensitivity study carried out with the melt model indicates that a 2 % decrease in glacier extent, introduced at the terminus, reduces summer run-off by 2.6 %. For a glacier area loss of 5 %, modelled run-off declines by 6.6 %. The relation is nonlinear because melt rates at the glacier terminus exceed average values over the glacier. There is also a small effect from glacier thinning over the study period, which acts in the other direction (i.e. increased discharge as the glacier thins), but this is weaker than the effect of glacier area changes. Overall, glacier retreat from 2002 to 2013 gives summer run-off estimates in Table 7 that are a bit too low for the early years of the study and slightly overestimated post-2005, but the errors associated with neglecting glacier geometric changes are assessed to be less than 2 %. Longer-term glacier hydrological studies would need to accommodate glacier geometric adjustments, however.
Results provide observationally based support for previous estimates of
glacier contributions to the Bow River based on basin-scale modelling (Comeau
et al., 2009; Marshall et al., 2011; Bash and Marshall, 2014). Prior modelling
studies use relatively simple treatments of the glacier geometry and surface
energy balance/melt processes and do not clearly capture the separate
contributions of snow and ice melt. Similarly, run-off data from hydrometric
gauging stations include combined contributions from both seasonal snow and
glacier ice/firn. Observations and modelling presented here provide insight
into the provenance and timing of run-off. The results indicate a large range
of interannual variability in run-off derived from the ice/firn reservoir.
From 2002 to 2013, Haig Glacier specific run-off from ice/firn melt ranged from
420 to 2290 mm, averaging 980
It is important to separate these components because the seasonal snowpack is intrinsically renewable from year to year, while run-off derived from the long-term glacier storage reservoir is declining as glaciers retreat (Moore et al., 2009). As in most midlatitude mountain regions, this reservoir dates to the Little Ice Age in the Canadian Rocky Mountains (17th to 19th century), and is being steadily depleted in recent decades (e.g. Demuth et al., 2008; Moore et al., 2009). This will compromise the ability of glaciers to buffer streamflow in warm, dry summers, as they have historically done.
Glaciers remain third behind seasonal snowpack and spring/summer rainfall in the overall contributions to streamflow in the Bow Basin. Moreover, much of the flow in the Bow River and in other critical rivers that issue from the Rocky Mountains is filtered through the groundwater drainage system (Grasby et al., 1999), delaying downstream discharge of seasonal snow melt and spring rains. This is responsible for most of the river discharge at low-elevation sites in the Canadian prairies in late summer and fall, with the glaciers serving to top this up. The largest concern with respect to future water supply is the spectre of declining mountain snowpack in western North America (Mote et al., 2005; Barnett et al., 2005). It is likely that this is also contributing to the widespread glacier decline, with positive feedbacks. Glaciers serve as highly effective “snow traps”, accumulating snow in the early autumn through to early summer; the loss of glaciers in the Rocky Mountains will contribute to declines in the spring snowpack at high elevations and associated run-off from seasonal snow melt.
The methodological approach developed here – a fully distributed energy balance model forced by 30 min data – is probably excessive for estimation of monthly and annual run-off from the glacier, which is the main objective of this contribution. Daily mean meteorological variables and a simpler methodology, like temperature-index melt modelling, might give similar values for the monthly melt and run-off. Follow-up investigations are recommended to explore and quantitatively assess the level of sophistication and resolution that is warranted if one is only interested in monthly run-off or seasonal glacier mass balance.
Meteorological and surface energy balance data collected at Haig Glacier
provide the first available decade-long measurements of year-round
conditions from a glacier in the Canadian Rocky Mountains. These data give
new insights into alpine meteorological and hydrological conditions and
controls of glacier mass balance in the region. The glacier, which flows
eastward from the North American continental divide, experiences relatively
wet, mild conditions, with a climatology that has more in common with
neighbouring British Columbia than the eastern slopes of the Canadian Rocky
Mountains. Pacific moisture nourishes the glacier, while summer temperatures
are typical of continental climate conditions, with a mean JJA temperature
of 5
A distributed energy balance and melt model developed for Haig Glacier effectively captures interannual mass balance variations. Modelled mass balances are in good accord with data from Peyto Glacier, Alberta and are likely representative of regional conditions. The energy balance model reveals the importance and inseparability of absorbed short wave radiation, albedo and temperature in determining summer melt extent. The summer melt season is more important than winter snow accumulation for interannual mass balance variability at Haig Glacier.
Haig Glacier is well out of equilibrium with the climate conditions over the
study period 2002–2013, with a succession of years of negative mass balance
driving a cumulative glacier-wide thinning of about 12.5 m over this period.
A summer cooling of about 2.3
The model allows separation of glacier run-off derived from seasonal snow vs.
the firn/ice storage reservoir. Melting of the seasonal snowpack accounted
for 58
On an annual basis, total glacier run-off (combined snow, firn, and ice melt) made up 5–6 % of the Bow River in Calgary from 2002 to 2013, with 2–3 % coming from firn and ice. Run-off from glacier storage is concentrated in the period July through September and exceeds 10 % of the late-summer discharge of the Bow River in Calgary in hot, dry summers. Under drought conditions, when water demand is highest, run-off from glacier storage therefore provides an important late-summer supplement to the rivers on the eastern slopes of the Canadian Rocky Mountains. Glacier decline will reduce the efficacy of the natural reservoir function that has been historically provided by glaciers, and this should be accounted for in long-range water resource management planning in this region (Schindler and Donahue, 2006).
Caution is needed in extrapolating from observations at just one site, but the glaciological and hydroclimatic conditions at Haig Glacier are typical of continental, midlatitude mountain regions. This study offers insight into the hydrological role of glaciers as landscape elements in such regions. Glaciers provide unusually high rates of specific discharge, concentrated late-summer release of meltwater, and an important supplement to streamflow under drought conditions. They also serve an interesting, largely unexplored role as “snow traps”, augmenting the mountain snowpack. Reductions in summer snowmelt run-off due to glacier retreat would exacerbate the loss of meltwater derived from glacier storage in alpine regions.
Glacier run-off is the dominant component of mountain streams in glacierized catchments, but glacier contributions to streamflow will be limited at downstream sites for most mountain rivers as a result of the small fraction of the landscape covered by glaciers. Simple calculations based on the results presented here illustrate this well. Assuming that glaciers provide 10 times more specific discharge than other landscape elements in a basin, a catchment that is 1 % glacierized has 9 % of its run-off originating from the glaciers. About 40 % of this is derived from glacier storage during a period of strong glacier recession like the 2000s, giving 4 % of the annual river discharge. This is well below the interannual variability in precipitation and discharge. It may also be negligible in the hydrological budget of major mountain rivers relative to uncertainties and possible increases in precipitation under future climate change (e.g. Immerzeel et al., 2013). Glaciers do matter for rivers draining from highly glacierized catchments (e.g. more than 5 % glacier cover) and for dry-season discharge in basins with limited upstream storage capacity.
I am indebted to the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Canada Research Chairs program for support of long-term field studies at Haig Glacier. Tom Holland, Mike Norton, and the Canadian Olympic Development Association kindly tolerate us at their summer training facility on Haig Glacier. Steve Donelon, Melanie Percy, and Alberta Sustainable Resources Development have supported this research since its inception. Rick Smith at the University of Calgary Weather Research Station is instrumental in keeping the Haig Glacier weather stations ticking. It is odd to write a sole-authored article on Haig Glacier findings. This research has only been possible through the help of a host of students and friends too numerous to name who have contributed to the Haig Glacier effort since 2000.Edited by: J. Seibert