Seasonal glacier melt contribution to streamflow
Neil Schaner
Department of Civil and Environmental Engineering
Box 352700
University of Washington
Seattle, WA 98195
[email protected]
Nathalie Voisin
Pacific Northwest National Laboratories
P.O. Box 999
Richland, WA 99352
[email protected]
Dennis P. Lettenmaier*
Department of Civil and Environmental Engineering
Box 352700
University of Washington
Seattle, WA 98195
[email protected]
1
Ongoing and projected future changes in glacier extent and water storage globally
have lead to concerns about the implications for water supplies. However, the
current magnitude of glacier contributions to river runoff is not well known, nor is
the population at risk to future glacier changes. We estimate an upper bound on
glacier melt contribution to seasonal streamflow by computing the energy balance of
glaciers globally. Melt water quantities are computed as a fraction of total
streamflow simulated using a hydrology model and the melt fraction is tracked
down the stream network. In general, our estimates of the glacier melt contribution
to streamflow are lower than previously published values. Nonetheless, we find that
globally an estimated 225 (36) million people live in river basins where maximum
seasonal glacier melt contributes at least 10% (25%) of streamflow, mostly in the
High Asia region.
One sixth of the world’s population, and one quarter of its gross domestic product,
resides in areas that rely on snow or glacier melt for a majority of its water supply (1).
Glaciers contribute significantly to water resources, especially in the High Asia region,
which is the headwaters of many of that continent’s largest rivers (2). Immerziel et al. (3)
estimated that glacier melt contribution to five major Southeast Asian rivers contributes
significantly to the food supply of at least 60 million people. While the regional volume
of glacier contribution may not always be large, melt water is important locally (4).
Despite concerns about glacier extent changes and implications for water supplies (5), the
global contribution of glaciers to water supply is not well known (6).
2
!
Several previous studies have attempted to analyze the contribution of glacier melt to
water supply, mostly in India, China, Peru, and Canada (see Table 2 for a review). The
majority of those studies indirectly derive the snow and seasonal glacier melt contribution
to streamflow by subtracting other components from the water balance. Other
approaches include simulation of glacier melt directly (7) and streamflow isotope
analysis to infer glacier melt contribution (8,9). Armstrong (6) summarizes the current
state of understanding glacier contributions to water supply as, “[p]revious assessments
of the glacier melt impact on surface water supply have been primarily either highly
qualitative or local in scale, and in some cases, appear to be simply incorrect.” In this
paper, we attempt to estimate seasonal glacier melt contributions to runoff globally by
using a combination of remote sensing data and modeling.
We define glacier melt as runoff whose source is perennial snow, firn, or ice. We include
all ice caps (ice sheets covering less than 50,000 km2) and other permanent ice globally
outside of Antarctica and Greenland. Our effort is directed towards estimating the
present contribution of glaciers to water supply; we do not attempt to project future
changes. Nonetheless, our results arguably are important for understanding climate
change impacts, as they provide a basis for identifying regions that are at risk to potential
changes in glacier-derived runoff.
3
Methods
Determining the contribution of glacier melt to streamflow globally is complicated by the
absence of meteorological data for all but a very small number of the world’s glaciers.
We therefore attempted to bound the glacier contribution to streamflow using an energy
balance approach applied to known glacierized areas, estimated primarily using remote
sensing data. We assumed that all available energy over glacierized areas is converted to
melt. We then compared these estimates to model estimates of river runoff, from which
we derived a fractional contribution of glacier melt. We focused on the maximum
seasonal contribution, which generally occurs in summer when remaining seasonal snow
melt is low, glacier melt is high, and other non-glacier sources of runoff are low.
The energy available for melt is the sum of net radiation (Qnet, incoming (SWdown) minus
reflected (!SWdown) shortwave plus net longwave (LWnet)), sensible (Hsens), latent (Hlat),
and advected heat. Compared to the other components, advected heat is generally small
and is ignored (10).
We used the Variable Infiltration Capacity (VIC) hydrological model (11) applied
globally at one-quarter degree latitude by longitude to predict runoff from non-glacier
sources at each grid cell. We then computed the sum of glacier and non-glacier source
runoff accumulated downstream. The ratio of accumulated glacier source to total runoff
was then computed by month for the period 1998-2006. Stream networks were derived
using flow direction data from Wu et al. (12).
4
We estimated radiation components over glacierized areas using the Thornton and
Running (13) and Tennessee Valley Authority (14) algorithms for downward solar and
longwave radiation, respectively, as implemented by Maurer et al. (15). We estimated
emitted longwave radiation using an emissivity of 1.0 and an assumed surface
temperature of 0°C during the melt period.
Qnet is particularly sensitive to albedo, which can vary widely for glaciers. Albedo
depends on the microstructure of snow and ice, and is highest for new fallen snow, and
decreases as snow melts or is metamorphosed into glacier ice. It also has angular and
spectral dependencies, which we accounted for only indirectly using a single effective
albedo, an assumption that is common in snow models (16). Albedo estimates for
glaciers have been mostly derived from direct observations of downward and reflected
solar radiation (over small areas, which are essentially points in the context of our model
estimates) and satellite-based estimates. We compiled 100 glacier ice albedo estimates
from 15 publications, which ranged from 0.03 to 0.85 with a median of 0.35. The highest
values are for glaciers covered partially or fully with recently fallen snow; the lowest
values are from glaciers with substantial debris, black carbon, and/or biomass growth.
Given our interest in estimating an upper bound, we used the lower 25th percentile of the
100 values (0.24). We chose not to use the lowest value, which likely is associated with
debris covered glaciers to the extent that other thermal factors (notably, insulation by the
debris cover) reduce melt in ways not accounted for by our estimates. Supplementary
5
Note Glacier Albedo contains additional information regarding the glacier albedo
estimates.
We used the Global Land Ice Measurements from Space (GLIMS) (17) and the Digital
Chart of the World (DCW) (18) data sets to identify glacier area globally. The GLIMS
data have a spatial resolution of approximately 150 m, from which we computed the
glacierized fraction of each VIC quarter-degree grid cell. GLIMS is known to
underestimate the glacierized area globally (19). We therefore supplemented the GLIMS
data with DCW, essentially by creating the union of the two data sets for glacier area.
DCW data are derived from 1:1,000,000 scale maps. The merger of GLIMS with DCW
likely overestimates total glacier area because DCW is known to include some ephemeral
snowfields; which we accepted given our interest in estimating an upper bound to glacierderived river runoff. We then used the glacierized fraction of each VIC quarter-degree
grid cell to compute the maximum glacier melt as describe above, averaged for the period
1998-2006.
Streamflow for the entire domain was simulated with the VIC model, which estimates
runoff from melt of seasonal (ephemeral) snow cover and other sources (in particular,
rainfall-derived direct runoff and baseflow). A modification to VIC was implemented so
that its computation of runoff from the glacierized portions of each grid cell was
consistent with our maximized melt contributions outlined above. For snowmelt
computations, VIC uses an energy balance approach similar to the simplified approach
6
we used for glacier melt, but with an algorithm that accounts for refreezing of melt water
in the pack, the role of vegetation where present, and represents the snowpack as two
layers for purposes of thermal computations. VIC also computes albedo using a snowaging function based on elapsed time since snowfall and time of year. Details of the VIC
snow model are provided in Andreadis et al. (16). The VIC model was run from January
1998 to December 2006 at a 3-hourly time step, from which runoff was accumulated to
monthly averages. Supplementary Methods contains details of the VIC model
simulations.
Results
We first derived maps of the global area for which river runoff could be affected by
glacier melt, regardless of the amount. This mask (Figure 1) constitutes the domain for
our VIC model simulations. We then computed the ratio of accumulated glacier-derived
runoff to total runoff for each grid cell in the domain, and identified areas for which the
maximum monthly fraction of glacier-derived runoff was greater than 5, 10, 25, and 50
percent. These areas are shown in Figure 2. The largest inferred fractions are mostly for
July in the northern hemisphere and January in the southern hemisphere. The largest
areas identified are streams with headwaters in the Himalayan Mountains, coastal Alaska,
the southern Andes, Iceland, and the Alps. A number of smaller areas were also
identified.
7
To assess populations potentially at risk to changes in glacier runoff, we overlaid the
Gridded Population of the World 2010 data (20) on areas shown in Figure 2. The results
show that for the 5% threshold, the population potentially affected is about 450 million
(6.6% of global population). For 10%, 25%, and 50% thresholds the estimated
populations are 225 million (3.3%), 36 million (0.5%), and 2 million (<0.1%) people,
respectively. Table 1 shows the affected populations by region.
Discussion
Two points stand out from the results. First, while the total domain for which there is any
inferred signature of glacier melt is quite large (58,700,000 km2 or about 44% of total
global land area exclusive of Greenland and Antarctica), the area for which runoff
exceeds even the lowest threshold (5%) for the maximum melt month is much smaller,
6,630,000 km2 or about 5% of global land area. Second, many areas identified in Figure
2 have relatively small populations.
We compared our estimates of fractional contribution of glacier melt to other sources
where available (Table 2). Where referenced values are given for river basins that
constitute more than one VIC grid cell, the grid cell corresponding to the basin outlet was
used. As a result, comparisons are most relevant for point references. The references
that use water balance approaches derive glacier contribution indirectly by subtracting
8
estimated water balance components from observed flow. Some of the published values
are known to include seasonal snowmelt and are asterisked.
In addition to the comparisons in Table 2, we applied our melt estimates to published
values for two United States Geological Survey (USGS) Benchmark glaciers in Alaska,
Gulkana and Wolverine (21). Both glaciers have long-term records of mass balance,
precipitation, and streamflow discharge. Wolverine Glacier is located in a maritime
environment, whereas Gulkana Glacier is in a continental environment (22). Gulkana
glacier covers 19.6 km2 in a drainage basin of 31.6 km2 (stream gauge location about one
km downstream of the glacier terminus). Wolverine glacier is similar in size (16.8 km2)
and lies in a 24.6 km2 drainage basin, the gauge for which is about 150 m downstream of
its terminus.
From 1998 to 2006, the month of maximum potential melt, based on Qnet, for Gulkana
glacier is July, with an average recorded discharge of 10.4 m3/s. This compares well with
our predicted July average discharge of 9.7 m3/s. Maximum potential melt occurs in June
for Wolverine glacier with values of 6.3 m3/s (recorded) and 5.2 m3/s (predicted). Given
our simplifying assumptions, this agreement is encouraging.
Taken as a group our estimates of glacier-derived runoff from Table 2 are lower than
published values, and the differences are larger than for the well instrumented Gulkana
and Wolverine glaciers. Our energy balance model does not account for all of the
9
complex processes occurring within and upon glaciers and their drainage basins. We
discuss below the major simplifications and their implications.
In our model, all glaciers are uniform, flat slabs with full exposure to solar radiation and a
fixed, relatively low albedo. The assumption of a glacier surface (and implicitly,
subsurface) temperature of 0ºC during the melt period will result in an overestimation of
melt (23) because refreezing within the glacier pack is not represented (24). Also, as
glacier melt water moves downstream it is compared to total runoff that outside the
modeling environment may be diverted by water engineering works and agriculture (25).
Diversions reduce total runoff, increasing the glacier fraction contribution.
Because we do not represent storage of liquid water within the glacier, modeled melt
water enters the stream network too soon (26, 27). This could put melt water runoff out
of phase with non-glacier runoff simulated by the VIC model. The direction of this error
depends on the nature of non-glacier derived runoff. In basins where summers are
relatively dry, streamflow decreases throughout the summer, and failure to represent the
delay creates a negative bias in the glacier contribution estimate. On the other hand, for
river basins with summer monsoon runoff, late summer runoff may exceed that in early
summer, and the bias will be in the opposite direction. This is likely the case for many
Asian rivers. Complex local glacierized environments may introduce errors in turbulent
heat flux estimates. However, net radiation is the main contributor to melt in both highenergy conditions (clear and warm) and low-energy conditions (cloudy and cool) (28,
10
29), and this contribution is generally small, especially since latent and sensible heat are
usually of opposite sign. Advected energy is not included in our energy balance model
but in most cases is likely small (10). We do not consider the slope and aspect of glaciers
in our computation of net solar radiation; hence for south facing glaciers (northern
hemisphere) we likely underestimate net radiation, and hence melt. However, these
effects are likely greatest for small headwater glaciers, and should average out for larger
and/or multiple glaciers.
Biases in VIC-simulated runoff are another error source. VIC-simulated streamflow
generally agrees well with observations when the model forcings (especially
precipitation) are well known (15). To reduce precipitation biases associated with
topographic complexities, we used rescaled precipitation (30). The direction of VIC
simulation errors varies from watershed to watershed, and can be considered essentially
random on a global basis.
The contribution of non-seasonal (i.e., permanent, or at least long-term with respect to
our observation period) loss of glacier ice is another potential source of error. Our
method computes total melt, whether or not the glacier is in balance. However, we do
assume that the glacier area is fixed through the period of record. For most glaciers
however, changes in area over the 9-year analysis period are small relative to the area at
the beginning of the period. For receding glaciers, our approach will bias our estimates
upward somewhat.
11
As the climate warms, our estimates of glacier contribution fractions may increase as
mass loss accelerates, but ultimately will be more than cancelled by loss of glacier area,
and ultimately will begin to recede. It is important to note, however, that reduction in the
fraction of glacier runoff does not imply reduction of runoff and streamflow, which
depends on characteristics of precipitation and snow accumulation and ablation on nonglacier fed portions of the river basins in question. Although not explicitly examined in
our analysis, glacier-derived runoff generally is less variable on an interannual basis than
non-glacier-derived runoff, so regardless of the direction and magnitude of changes in
total runoff that might accompany glacier recession, variability is likely to increase.
Conclusions
Our analysis provides a worldwide upper-bound estimate of the glacier-melt contribution
to river flows and populations potentially at risk to glacier retreat. We find that at least
6.6% of the global population lives in river basins that depend on seasonal glacier melt
for at least 5% of runoff in the peak melt month; at least 3.3% rely on 10% of glacier melt
runoff in the peak-melt month, at least 0.5% rely on 25% and less than 0.1% rely on 50%.
In general, our estimates of the glacier melt contribution to streamflow are lower than
previously published values (7-9, 31-39). Nonetheless, most of our assumptions result in
an upward bias in our estimates, which we argue serve as plausible upper bounds on
glacier-derived streamflow globally.
12
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17
Acknowledgements
The research on which this paper is based was supported in part by Grant No.
NNX10AP90G from the National Aeronautics and Space Administration to the
University of Washington.
The data reported in this paper are archived at
ftp://ftp.hydro.washington.edu/pub/HYDRO/data/published/Schaneretal_2011_Global/
Author Contributions
D.P.L. conceived and supervised the project. N.S. and N.V. assembled input data, ran the
model, and analyzed the output data. N.S. and D.P.L. drafted the manuscript. All
authors commented on the manuscript at all stages.
Competing Financial Interests
The authors declare no competing financial interests.
Figure Legends
Figure 1: VIC model simulation domain
Figure 2: River basins for which at least 5% (green), 10% (yellow), 25% (orange), 50%
(red) of discharge is derived from glaciers in at least one month.
18
Tables
Table 1: Estimates of affected populations and land areas
Percentage of Land Area (%)
Population Affected
(millions of persons)
Threshold Contribution (%) 5
10
25
50
Total
447
225
36
2.2
Asia
424
216
34
2.1
North America
5.6
1.6
0.3
< 0.1
North America
excluding Alaska
5.4
1.4
0.2
< 0.1
South America
4.5
2.9
0.7
< 0.1
Europe
13.4
5.1
0.2
-
Total (excluding
Greenland and
Antarctica)
5.0
3.0
0.9
0.2
Asia
7.1
4.2
1.2
0.2
North America
5.7
4.1
1.7
0.6
North America
excluding Alaska
2.4
1.2
0.5
0.2
South America
5.1
2.1
0.8
0.2
Europe
5.2
0.5
0.0
-
19
Table 2: Comparison of published and calculated glacier contribution values
Source
Area/River
Kumar et al.
(31)
Singh et al.
(32)
Singh and
Jain (33)
Deoprayag, Ganga
River, India
Pandoh Dam, Beas
River, India
Akhnoor, Chenab
River, India
Bhakra Dam, Satluj
River, India
Wang (34)
Muzat, China
Jain (30)
Xu et al.
(35)
Yang (36)
Zhang et al.
(5)
Mark and
Seltzer (6)
Mark et al.
(7)
Comeau et
al. (37)
Hopkinson
and Young
(38)
Tarim Basin, China
Junggar Basin, China
Qaidam Basin, China
Hexi Corridor, China
Qinghai Lake. China
Heihe (Yingluxia
Hydro Station), China
Tuotuo River, China
Yanamarey, Cordillera
Blanca, Peru
Uruashraju, Cordillera
Blanca, Peru
Río Santa, Callejon de
Huaylas, Peru
Yanamarey, Cordillera
Blanca, Peru
Río Santa, Callejon de
Huaylas, Peru
North Saskatchewan
River at Edmonton,
Alberta, Canada
Bow River at Calgary,
Alberta, Canada
Bow River, Banff,
Alberta, Canada
Method
Referenced
glacier
contribution
(%)
Calculated upper
bound glacier
contribution (our
analysis) (%)
-
28.7*
14.7
Water balance
Water balance
Water balance
Mass and water
balance
-
37.4*
(1998-2004)
49.1*
(1982 - 1992)
59*
(1986 –1996)
21.7
32.5
5.5
82.8
7.3
40.2
13.5
12.5
13.8
0.4
24.2
6.1
8.7
1.6
9.0
Water balance
5
2.5
Modified degree
day model
32
(1961-2004)
35 ± 10
(1998-1999)
36 ± 10
(1998-1999)
12-20
(1998-1999)
58 ± 10
(2001-2004)
40
(2001-2004)
Water balance
Water balance
Hydrochemical
mixing model
Water balance
Hydrochemical
mixing model
WATFLOOD
hydrological
model
WATFLOOD
hydrological
model
Mass and water
balance
16.9
3.9
3.9
3.9
3.9
3.9
9.33
(1975-1998)
6.1
6.25
(1975-1998)
5.0
1.8-56**
31.4
* Referenced Glacier Contribution values are stated to include ephemeral snowmelt.
**Average 1952-1993 and August 1970, respectively.
20
Supplementary Note: Glacier Albedo
Glacier energy balance is particularly sensitive to albedo. Albedo is highly
variability over small areas and interannually (1-3). Our global calculations for
determining a maximum threshold of glacier melt use a universal albedo value. From 15
publications, 100 glacier ice albedo values were extracted that range from 0.03 to 0.85 (115). High values are almost certainly for cases where glacier ice is partially or fully
covered by recently fallen snow. The lowest values are from studies of glaciers that are
substantially covered by debris, black carbon, and/or biomass growth. The values fall
into three categories: those stated in the text or in a table in published articles or reports,
those derived from figures, and those averaged from a stated minimum and maximum. In
the case of continuous time series of albedo values, the average value was taken. The
statistics of the resulting 100 values are: mean = 0.39, standard deviation = 0.20, and
median = 0.35. Given our interest in estimating an upper bound, we used the lower 25th
percentile of the 100 values, 0.24. We chose not to use the lowest value, which is likely
associated with debris cover to the extent that other thermal factors (notably, insulation
by the debris cover) reduce melt in ways not accounted for by our estimates.
References
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Supplementary Methods
The Variable Infiltration Capacity (VIC) Model (1) was used to simulate global
energy and water balance components. The VIC model is a semi-distributed macro scale
hydrology model. The model was run at one-quarter degree latitude by longitude spatial
resolution for all drainage basins with any glacier runoff contribution (Figure 1).
Daily precipitation, minimum and maximum temperatures, and wind speed are the
primary forcings for the VIC model (other forcings, such as downward solar and
longwave radiation, and humidity, are derived from these variables). Precipitation data
were taken from the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite
Precipitation Analysis (TMPA) for 50ºS – 50ºN latitude band (2). Outside of this band,
precipitation was taken from Sheffield et al. (3). These data, which have a spatial
resolution of one-degree latitude by longitude, were interpolated to quarter-degree using
the inverse distance squared SYMAP algorithm (4, 5). Temperature and wind data were
taken from the National Centers for Environmental Prediction (NCEP) Reanalysis 2 data
set (6). Wind was interpolated from the Gaussian grid to the regular quarter degree grid
using the SYMAP algorithm. The daily temperature minima and maxima were
interpolated and adjusted using a pseudo-adiabatic lapse rate of 0.065º/1000m using the
SYMAP algorithm and the Global Land One-kilometer Base Elevation Project (GLOBE)
digital elevation model (DEM) (7).
Global soil parameters were derived as described in Nijssen et al. (8) using the
2009 Harmonized World Soil Database (9) and remaining structure-based parameters
from Cosby et al. (10). Soil depths and baseflow parameters were taken from Nijssen et
al. (11). Global vegetation parameters were derived as in Su et al. (12). A maximum of
five VIC elevation bands (sub grid partitioning based on elevation for more accurate
snow-related simulations) per grid cell were created with the GLOBE DEM. Radiation
components over glacierized areas were estimated using the Thornton and Running (13)
and Tennessee Valley Authority (14) algorithms for downward solar and longwave
radiation, respectively. We estimated emitted longwave radiation using an emissivity of
1.0 and the VIC surface temperature, which during the melt period was assumed to be
0°C. For purposes of representing stream networks, we used the flow direction file of
Wu et al. (15).
The VIC model was initially run at a daily time step for a total of 18 years to
initialize the model’s soil water storages, and to create a snow “reservoir” in the areas
defined as glaciers. Following the 18-year spin-up the model was run at a 3-hourly time
step for 1998-2006.
The VIC model does not represent glaciers explicitly, and therefore treats glacier
areas as seasonally ephemeral snowpack within an energy balance snow accumulation
and ablation model, details of which are included in Andreadis et al. (16). To more
accurately represent glaciers within VIC, a snow “reservoir,” or modified snow pack, was
created for each gridcell containing glaciers. The modified snow pack representing
glaciers was made permanent with an artificially high value of snow water equivalent.
Albedo of the modified snowpack follows the default VIC calculation for snow albedo,
U.S. Army Corps of Engineers empirical snow albedo decay curves (17). During the
melt season, with little snowfall, the modified snowpack surface albedo decays to values
equivalent to known glacier ice albedo values. The modified snowpack is located in the
highest elevation bands (mountain glaciers) unless the total elevation difference within
the gridcell is less than 200 meters (valley or tidewater glaciers). The modified
snowpack within each glacierized gridcell covers each elevation band either wholly or is
absent. Therefore the areal coverage of the modified snowpack is greater than or equal to
the glacier area determined with the Global Land Ice Measurements from Space
(GLIMS) and Digital Chart of the World (DCW) datasets (18, 19).
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Atmospheric Administration, National Geophysical Data Center,
http://www.ngdc.noaa.gov/mgg/topo/globe.html, 1999).
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the Terrestrial Arctic Domain , J. Geophys. Res. 110 (2005).
13. Thornton, P. E., Running, S. W. An improved algorithm for estimating incident
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networks for macroscale hydrological modeling. Water Resour. Res. 47 (2011).
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ablation processes in forested environments. Water Resour. Res. 45 (2009).
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report of the snow investigations. (1956) pp. 437.
18. Penn State University Libraries Digital Chart of the World Server. Digital Chart of
the World (DCW) [Data File]. (http://www.maproom.psu.edu/dcw/).
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Acquisition Requests for the ASTER Satellite Instrument for Monitoring a Globally
Distributed Target: Glaciers. IEEE T. Geosci. Remote 38, 1105-1112 (2000).
Figure 1: VIC model simulation domain
Figure 2: River basins for which at least 5% (green), 10% (yellow), 25% (orange), 50% (red) of discharge is
derived from glaciers in at least one month.