THIS IS A WORK IN PROGRESS.
Determining the Maximum Contribution of Glacier Ice to Streamflow
Neil Schaner
A thesis
submitted in partial fulfillment of the
requirements for the degree of
Master of Science in Civil Engineering
University of Washington
2010
Program Authorized to Offer Degree:
Civil and Environmental Engineering
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Neil Schaner
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Committee Members:
Dennis P. Lettenmaier
Other
Date:
ii
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iii
University of Washington
Abstract
Determining the Maximum Contribution of Glacier Ice to Streamflow
Neil Schaner
Chair of the Supervisory Committee:
Professor Dennis P. Lettenmaier
Civil and Environmental Engineering
The importance of mountain glaciers as reservoirs of water is well known. With the receding of
many mountain glaciers over recent decades, concerns have been voiced about the implications
for water supply systems.
Previous efforts to estimate the contribution of glacier melt to
streamflow have used a variety of approaches, many of which are either for very small areas or
include the contribution of seasonal snowmelt in addition to glacier melt. Over larger areas, the
extent of glaciers remains somewhat uncertain – the primary source is satellite observations, but
estimates of glacier extent can be confounded both by the possible presence of seasonal snow
cover, and glacier debris cover. We attempt a slightly different approach, and apply a simple
energy balance model globally at one-quarter degree spatial and monthly temporal resolutions to
provide a basis for estimating an upper bound on the contribution of glacier melt to seasonal
runoff. We assume that at the time of maximum glacier melt contribution, all available energy is
converted to glacier melt. Melt water quantities are then compared to monthly total runoff
simulated using the Variable Infiltration Capacity (VIC) macroscale hydrology model on grid
cell basis. Melt water runoff and total runoff are routed downstream to track the signature of
glacier melt. Drainage basins meeting a threshold ratio of glacier melt to total runoff are used to
estimate populations at risk of lowered water resources. In general, our estimates of the
population at risk are lower than other, published values. When applied to USGS Benchmark
glaciers, we generally underestimate glacier melt as compared to drainage basin discharge
records, but we overestimate the loss of mass for the same Benchmark glaciers. Despite
uncertainties in the specific quantity of melt water, our model serves to highlight areas dependent
on glacier water resources at a time of climatic uncertainty.
iv
TABLE OF CONTENTS
List of Figures ................................................................................................................................ vi
List of Tables ................................................................................................................................ vii
Introduction ..................................................................................................................................... 1
Modeling Method............................................................................................................................ 3
Model Inputs ................................................................................................................................... 4
Radiation................................................................................................................................................... 4
Albedo ...................................................................................................................................................... 4
Total Runoff ............................................................................................................................................. 6
Elevation ................................................................................................................................................... 7
Glacier Area.............................................................................................................................................. 7
GLIMS ................................................................................................................................................................................. 7
DCW .................................................................................................................................................................................... 8
Results [OLD] ............................................................................................................................... 10
Comparison [OLD] ....................................................................................................................... 12
Published Values .................................................................................................................................... 12
Benchmark Glaciers ............................................................................................................................... 13
Discussion ..................................................................................................................................... 16
Glacier Representation ........................................................................................................................... 16
Melt Energy ............................................................................................................................................ 16
Melt Runoff Timing................................................................................................................................ 18
Water Storage ............................................................................................................................................................... 18
Surface Processes........................................................................................................................................................ 19
Intra-Glacier Flow ....................................................................................................................................................... 19
Refreezing ...................................................................................................................................................................... 20
Lakes and Reservoirs ................................................................................................................................................ 20
Timing .............................................................................................................................................................................. 20
Conclusion [OLD] ........................................................................................................................ 22
References [in progress] ............................................................................................................... 24
Data References ............................................................................................................................ 29
v
LIST OF FIGURES
Published Albedo Values ................................................................................................................ 6
Cumulative Glacier Area Distribution ............................................................................................ 8
Full Glacier Melt Signature .......................................................................................................... 10
Effected Upstream Areas, 5% [Blue], 25% [Green], 50% [Red] Glacier Melt Contribution....... 11
Mean Monthly Benchmark Glacier and Stream Gauge Discharge ............................................... 14
Mean Monthly Benchmark Glacier and Stream Gauge Discharge without Non-Glaciated Area
Contribution .......................................................................................................................... 15
Effect of Turbulent Fluxes on Gridcell Glacier Runoff ................................................................ 17
vi
LIST OF TABLES
Glacier Area Distribution ................................................................................................................ 8
Comparison of Published and Calculated Glacier Contribution Values ....................................... 13
vii
Introduction
Mountainous areas contribute a disproportionate amount of runoff per their areal size
[Viviroli et al., 2007]. The significance of mountain runoff for certain regions can be attributed
to snow and glacier melt water entering a stream network. An estimated sixth of the world’s
population relies on snow and ice melt for water [Barnett et al., 2005]. Glaciers, independent of
snow, contribute significantly to water resources [Hock, 2005]. While the regional volume of
glacier contribution may not always be large, locally the melt water is important [Weingartner et
al., 2007]. The amount of runoff from a basin is directly related to the percentage of the basin
area that is glaciated [Wang, 1989]. If glaciers are melting they are assumed to contribute to
downstream flow. Glaciers may also negatively contribute to streamflow by removing
precipitation that would otherwise discharge to a stream. With increasing concern due to
changing global climates, it is important to understand the contribution of glacier melt water to
downstream networks and ultimately populations. Quantifying and tracking the accumulated
signature of glacier melt water in streamflow is difficult given sparse data networks in glaciated
terrain. Without extensive measured data, melt must be modeled using globally available,
remotely sensed data. Melt models have been attempted before with varying success
[reference?]. Difficulties arise in determining albedo, the main driving parameter, and
separating glacier melt from snowmelt [e.g. Cottom, 2009; Schaper et al., 1999] [include
others?].
A global map of maximum glacier melt contribution to runoff has been created to identify
areas of concern for reduced glacier water resources. The lower bound of glacier contribution to
streamflow is already known – zero. An upper threshold of glacier contribution may be
calculated using an energy balance melt model tied to land surface hydrology model runoff
results. By adjusting assumptions to overestimate the potential melt water produced by glaciers
within a defined area a maximum contribution to water resources is found.
The quantification of the melt relies on remotely sensed net radiation and glacier area.
Net radiation and the closely related albedo are the main drivers of ice melt. The net radiation is
applied to glacier ice areal coverage; which has been shown to correlate well with excess melt,
melt which is beyond normal accumulation/ablation cycles [Lambrecht and Mayer, 2009].
Glacier melt water storage can be divided into long, intermediate, and short terms [Jansson et al.,
2002]. This project is concerned with intermediate to long term, i.e. months to years, of glacier
water storage and release. Without detailed mass balance information it is difficult to separate
annual glacier accumulation and ablation cycles from long term trends. In determining the
maximum potential glacier melt water contribution to streamflow, the effected upstream areas,
essentially drainage basins, are determined and used to estimate an effected population.
This project is concerned with land ice outside of the Antarctic and Greenland ice sheets.
Ice caps, defined as ice sheets covering less than 50,000 km2, are handled similarly to glaciers
and are thus included in the term glacier [NSIDC, 2010]. Glaciers are masses of snow, firn, and
ice – each component having different hydrologic properties [de Woul et al., 2006]. This study
specifically tries to separate the melt contribution of glacier ice and firn from snow. Seasonal
snow pack, snow not surviving into the next year, is not included in our glacier melt calculations.
Snow that does survive contributes to longer-term glacier mass, transitioning to firn to ice, and is
therefore included in potential glacier mass melt. Firn, with a density of about 500 kg/m3,
becomes glacier ice when density reaches from 840 to 850 kg/m3; when the interstices seal off
and become discrete air bubbles and the material becomes relatively impermeable [Meier, 1973].
The firn layer typically does not cover the whole of a glacier, but instead roughly occupies the
1
accumulation zone given steady-state conditions [Meier, 1973]. Despite less than total coverage,
firn is a long-term component of a glacier and is included in glacier melt calculations. “Glacier
ice melt” can safely be assumed to include perennial snow, firn, and ice melt.
Glacier ice melt itself must be defined. There is one common division of glacier ice melt,
between glacier wastage and glacier melt. Glacier wastage is mass loss as a long-term trend.
Wastage is ice melt volume exceeding accumulation. Glacier melt is the ice melt volume not
exceeding the accumulation in a year. Glacier melt is a storage term; the year’s precipitation is
delayed from running off until it melts [Comeau, 2008]. Glacier melt is total melt; glacier
wastage is net melt. For this paper, all use of “glacier melt” refers to the generation of runoff
sourced from perennial snow, firn, and ice – a long term loss in glacier mass – unless otherwise
stated.
2
Modeling Method
Glacier melt calculations are conducted across a quarter degree gridcell domain for all
drainage basins containing glaciers. Temporally, calculations are based on a year of average
months. The large spatial and temporal resolutions are chosen to simplify assumptions for global
determination of glacier contribution. Potential glacier melt water runoff is calculated using a
simple energy balance equation. This follows a statement by Jansson et al. [2002] that runoff
from glacierized basins is energy dominated. The energy available for melt is assumed to be the
total net radiation.
The general total net radiation equation is often a sum of net shortwave, net longwave,
sensible, latent, advected, and ground heat radiation components. Compared to the other
components, advected and ground heat fluxes are small and therefore ignored. Total net
radiation is thus taken as the incoming shortwave radiation minus the albedo dependent
reflection plus the net longwave radiation.
Qnet = [SWdown * (1 – α)] + LWnet
Total depth of melt is equal to the product of net radiation and elapsed time divided by the
product of latent heat of fusion and water density.
Melt = (Qnet * Δt ) / (HL * ρw )
This total potential glacier melt water value is compared to a total runoff value per grid cell. If
the total glacier melt amount is less than the total runoff amount, a simple fraction of glacier melt
contribution to total runoff is determined. If the total amount of calculated glacier melt is greater
than the total runoff, it is assumed glacier melt contributed all of the runoff exiting the grid cell.
From the melt information a global, quarter-degree grid for each month is produced with
values of glacier melt contribution to total runoff. The grids are used to weight the values of a
flow accumulation grid derived from a digital elevation model (DEM). The final product is a
series of maps displaying the accumulated contribution of glacier melt to runoff. Threshold
values of certain percentages are be applied to the accumulated flow fraction to delineate
drainage basins dependent on significant glacier water resources.
3
Model Inputs
Radiation
Shortwave and longwave radiation data are taken from the International Satellite Cloud
Climatology Project (ISCCP). At-the-surface down-welling shortwave and net longwave
radiation data are originally at a 2.5-degree resolution before disaggregation to quarter-degree
resolution. It is important to note the “full sky” classification of the data, meaning no
adjustments are made for cloud cover. The complete available data record of monthly mean
values extends from July 1983 to December 2004. The overlap between VIC runoff and the
ISCCP record, January 1998 to December 2004, is used to produce a year of average months for
this study. Casassa et al. [2009] states peak glacier runoff occurs in the summer, with negligible
runoff in the winter. A focus is placed on the months of maximum available energy, June for the
northern hemisphere and December for the southern. Intermediate months will produce
maximum energy in the tropics, however the tropics have few glaciers. This may cause errors in
the maximum glacier melt contribution to streamflow, as Mark and Seltzer [2003] state glacier
melt in the Cordillera Blanca, Peru is greatest in the austral spring.
Albedo
Glacier energy balance is particularly sensitive to albedo, a concern for this study. Lewis
et al. [2006] and Oerlemans et al. [2009] both discuss the importance of albedo in energy balance
studies. In relation to glacial melt, a 10% change in albedo may have a large effect [Cottom,
2009]. Changes in melt itself will lower (increase melt) or raise (decrease melt) albedo [Greuell
et al., 2007]. Albedo is largely a function of snow and ice grain size, but impurities and liquid
water alter the correlation [Brock et al., 2000]. Albedo is difficult to resolve without direct
measurement due to a list of determinants including snow cover, cloud cover, elevation and
debris.
Snow, often found on glacier surfaces prior to ice melt seasons, may increase albedo
greatly should it fall on glacier ice during the melt season [Hock, 2005]. A positive correlation
exists between winter snow accumulation and summer melt; less snow accumulation leads to
lower summer albedo, itself leading to more melt [Greuell et al., 2007]. An ice surface albedo of
about 0.3 will increase to about 0.8 with new fallen snow. However, albedo drops by as much as
0.3 within a few days of snow fall [Hock, 2005]. A thin snow cover, less than a half-centimeter
water equivalent, has an albedo correlated to the underlying surface [Brock, et al., 2000].
Further, Brock et al. [2000] observed snow albedo to almost equal ice albedo by the end of the
melt season due to the build up of impurities.
The cloudy sky producing snow or other precipitation itself affects albedo. Cloudy sky
can increase snow albedo 3-15% [Hock, 2005]. A positive feedback loop results when a
decrease in precipitation lowers albedo; the lowering is enhanced by the reduction of cloud cover
[Francou et al., 2003]. For satellite albedo data, care must be taken to account for clouds and
shadows created by complex terrain [Corripio, 2004].
Albedo has been shown to increase with elevation, a concern for glaciers in complex,
high terrain [Brock et al., 2000b; Paul et al., 2005]. Albedo patterns sometimes changed with
season and elevation in a study by Brock et al. [2000]. Brock et al. [2000] parameterizes ice
albedo with elevation, an important consideration for all mountain glaciers.
Debris on ice and snow offers the greatest effect on glacier albedo. Sediment and rock
4
debris effects on glacier albedo are outlined by Hock [2005]. Typically debris lowers albedo,
however ablation decreases after debris cover reaches a threshold of two centimeters, when
debris begins to act as insulation [Mattson, 2000]. The greatest ablation rate was observed with a
debris cover thickness of about one centimeter [Mattson, 2000]. This thickness of debris cover
rather than acting as insulation transmits more shortwave radiation to the glacier surface
[Mattson, 2000]. In contrast to Mattson’s [2000] thin cover, Pelto [2000] observed debris
thicknesses greater than 20 centimeters acting as insulation, also noting finer grained debris as a
better insulator. The resulting effect was an estimated reduction of melt on Columbia Glacier,
North Cascades, Washington, USA of 25-30% annually [Pelto, 2000].
The type of debris present will have different affects on albedo. Mineral dust on an ice
surface may stimulate the growth of algae, reducing albedo further [Oerlemans et al., 2009].
Bahadur [p.43] notes significant biomass and bacterial spread on Himalayan glacier surfaces.
Studying black carbon, Ming et al. [2009] found greater concentrations at lower elevations
(possibly transported by melt water) reducing albedo by about five percent. Oerlemans et al.
[2009] observed a similar spatial variability of albedo with glacier tongues having lower albedo,
usually from mineral and humic dust accumulation.
The accuracy of albedo measurements, even with the best methods, is rather low. Due to
instrument and model sensitivity the stated accuracy of effective albedo determination was 0.15
in a study by Weihs et al. [2001]. Remotely sensed albedo values have been used in other
studies. Greuell et al. [2007] used MODIS for albedo, finding it to be mostly accurate when
compared to in situ measurements. The disadvantages to the MODIS method are cloud cover
disruptions of measurement, no measurements above 80 degrees latitude, and a resolution that
misses small glaciers [Greuell et al., 2007]. Similarly, Paul et al. [2005] obtained albedo data
from satellite and corrected for topography and atmosphere. Visible imagery, i.e. photographs,
has a flaw in the inability to detect the transition from snowfall to firn [Greuell et al., 2007].
Other automated methods have similar difficulties in determining proper boundaries between
snow, firn, debris, and ice.
As a means to fill in missing or difficult to obtain data, albedo modeling is widely used.
Most work is focused on modeling snow albedo. Many models have been proposed to relate
albedo to grain size and atmospheric properties, but data can be cumbersome [Hock, 2005]. As
an example, Brock et al. [2000] modeled snow albedo using daily maximum temperatures since
snowfall. Deep snow followed a logarithmic function and shallow snow an exponential function
to decay albedo to that of the underlying ice [Brock et al., 2000]. Modeling ice albedo with
elevation showed only a minor improvement over a constant ice albedo [Brock et al., 2000].
Less work has been conducted on ice albedo because it is often taken as a constant; models
switch to a fixed ice albedo after snow has melted [Hock, 2005].
The variability of albedo for each glacier and on each glacier hampers the estimation of
albedo. Albedo has high variability over small areas and from year to year [Corripio, 2004; Paul
et al., 2005; Oerlemans et al., 2009]. Due to high variability and an inability to measure all
glacier albedo, an average albedo value must be determined. Greuell et al. [2007] calculated an
average albedo value over a glacier surface for computation. For global calculations and for
determining a maximum threshold of melt, a universal value is used.
5
0.9
0.8
0.7
Albedo
0.6
0.5
0.4
0.3
0.2
0.1
0
Figure 1: Published Albedo Values
All of the found, published glacier ice albedo values create a range from 0.03 to 0.85 (see
Figure 1) [e.g. Brock et al., 2000; Cottom, 2009; Paul and Haeberli, 2008] [how to cite all
albedo sources?]. High values are likely statements of glacier ice partially or fully covered in
snow. Low values originate from studies concerned with debris, black carbon, and biomass
growth on glaciers. Obtained values fall into three categories: those stated in the text or in a
table, those derived from a figure, and those averaged from a stated minimum and maximum. In
the case of continuous time series of albedo values, the average value between minimum and
maximum is taken. Using the compiled list of albedo values, basic statistics are: mean = 0.386,
standard deviation = 0.196, and median = 0.35. For the determination of the maximum glacier
melt water contribution to streamflow, a maximum reasonable amount of melt must be
calculated. As albedo is a main driver of glacier ice melt, a suitably low value must be used.
The lower 25th percentile of the compiled albedo values is used to determine a reasonable upper
bound of glacier melt. The lower 25th percentile value is 0.238.
Total Runoff
To compare the calculated glacier melt values to total runoff, global runoff values are
modeled with the Variable Infiltration Capacity (VIC) macroscale hydrology model.
Meteorological input data is sourced from NASA’s Tropical Rainfall Measurement Mission
(TRMM) and Global Precipitation Climatology Project (GPCP). The spatial domain of the
model is calculated using a DEM and glacier locations to route all potential pathways of glacier
melt water. The model domain is the extent of all areas upstream of the pathway endpoints at a
resolution of a quarter degree. Temporally the model is run from January 1998 through
December 2008. The overlap between VIC and ISCCP, January 1998 to December 2004, is used
to create the year of average months in this study. See Appendix I for more information
regarding the VIC model runs. [appendix not complete]
6
Elevation
To track the flow of glacier melt water, the Global Land One-kilometer Base Elevation
Project (GLOBE), a product of the National Oceanic and Atmospheric Administration, is used to
provide elevation data. The DEM file is disaggregated to a quarter-degree resolution and sinks
filled in order to build both flow direction and flow accumulation data.
Glacier Area
Global glacier area is estimated to be 680,000 km2 [Dyurgerov and Meier, 1997]. The
glaciers around Greenland and Antarctica are each estimated to cover about 70,000 km2 for a
total of 140,000 km2 [Dyurgerov and Meier, 1997]. Glacier area is an important factor in an
energy balance approach to melt; believed to be the largest factor in melt volume by Chen and
Ohmura [1990]. Area determines the influence of energy fluxes on a glacier for freezing and
melt. Using two remote sensing products, this study transforms glacier area into an area fraction
grid extending across the globe. Using gridcell fractional glacier cover removes detailed
information such as aspect and shape, instead creating a large, flat glacier in each gridcell.
While glacier area is important for determining melt using an energy balance, glacier
volume is important for potential total melt output. This study makes no attempt to project
future glacier contributions to streamflow. The model used in this paper does not include
changes in glacier area, one reason being unknown glacier volume. Specific glacier volume
estimates may be obtained from thickness measurements made along a grid or with profiles, e.g.
drilling and radio-echo soundings [Chen and Ohmura, 1990b]. Using a one-degree data grid,
glacier volume has been estimated at 87 ± 10 * 103 km3 [Raper and Braithwaite, 2005]. Based
on rough density estimates, water equivalence held in glaciers is about 250 ± 20 * 103 km3 [Bahr
et al., 2009].
Some authors have attempted to estimate glacier volume using known surface area, but
with obvious errors [Bahr et al., 2009]. Volume and surface area scale with the power law, but it
is unclear how accurately they relate [Bahr et al., 2009]. Chen and Ohmura [1990b] estimated
area and volume changes in Alpine glaciers using a power relationship; concluding the volumearea relationships should be improved with better estimates of glacier volume. Uncertainty in
area-volume relationships on a global scale and the transformation of glacier area data, described
below, remove estimates of glacier area and volume change from this study.
GLIMS
Glacier areas are derived from the Global Land Ice Measurements from Space (GLIMS)
database. GLIMS is a collaborative effort to monitor glaciers, primarily with optical satellite
instruments. Figure 2 is a plot of cumulative glacier area for the GLIMS data. A majority of the
glaciers are less than 200 km2 in area, representing more than half of all glacier area. Dividing
the areas into bins, in Table 1, more clearly displays the area divisions. The break down of
glacier area highlights the importance of capturing glacier surface area with an initially high
resolution. The GLIMS database holds the records of 82,721 glaciers throughout the world,
however it is stated to be complete only in Iceland, China, Nepal, Switzerland, the Caucasus
Mountains, British Columbia, and the contiguous United States.
7
Figure 2: Cumulative Glacier Area Distribution
Area Threshold (km2)
1
5
10
25
50
100
200
9999
No. of Glaciers
61032 17045 2298
1381
459
233
130
143
Area Covered (km2)
20962 35609 15739
20882
15929
16484
17536
116356
8
6.1
6.4
6.8
44.8
% of Total Area
8.1
13.7
6.1
Table 1: Glacier Area Distribution
DCW
To ensure more accurate glacier coverage data, GLIMS is supplemented with the Digital
Chart of the World (DCW). The DCW is based on aeronautical charts used as an aid to
navigation. Due to its origin, DCW does not differentiate between perennial snowfields and
glaciers. Merging GLIMS with DCW overestimates the total area of glaciers because DCW
includes some snow. Regions known to be complete in the GLIMS database are compared with
DCW. See Table 2 for a comparison of the datasets. [Create Table 2]
Downloaded GLIMS and DCW data is manipulated into a quarter-degree resolution grid
with values of fractional, areal glacier coverage using GIS software. To create the glacier
fraction grid, the polygon shapefile data from GLIMS and DCW is converted into a highresolution binary grid. To aid computation the polygons are merged; adjacent polygons are
combined into larger polygons. The created glacier extent grid matches the resolution of the
original GLOBE DEM file used for modeling, one-kilometer or roughly 0.008333 decimal
degrees squared. The creation of the 0.08333-degree glacier extent grid removes from the
glacier extent data any glaciers smaller than 0.008333 degrees squared in area that are separated
from other glaciers by at least 0.008333 degrees. These separate glaciers would not have been
8
merged with other glaciers and would not cover the area of a 0.008333 by 0.008333 degree grid
cell sufficiently to be converted to “full coverage” of the grid cell. The threshold glacier area
required for inclusion depends on latitude. Estimates of minimum required area are 0.8 km2 at
the Equator, 0.6 km2 at 30º, 0.2 km2 at 60º, and 0.015 km2 at 82º, the highest latitude in the
datasets.
Using the high-resolution glacier extent grid a fractional glacier area grid is created. The
globe is divided into quarter degree grid cell “zones” with the 0.008333-degree binary glacier
extent grid overlain on top. Using the binary glacier coverage within each zone, a fractional
glacier coverage is computed for each quarter-degree grid cell. The average glacier coverage
within each quarter degree zone is then manipulated into the fraction of each quarter degree grid
cell covered by glaciers.
9
Results [OLD]
Figure 3: Full Glacier Melt Signature
The accumulated signature of all glacier melt runoff may be seen in Figure 3 as red lines,
overlain on a DEM for reference. The image is a combination of the months of largest potential
melt for the northern hemisphere (June) and the southern hemisphere (December). Figure 4
displays the contributing drainage basins for 5, 25, and 50 percent glacier melt thresholds, in
blue, green, and red, respectively. Areas meeting the contribution thresholds are unsurprising;
the Central Asia Himalayan region, the western Canada/southern Alaska mountain ranges,
Iceland, and areas of the Alps, Eastern Greenland, Northern Europe, Caucasus Mountains, and
southern South America. The drainage basins are created using the calculated flow direction to
highlight the upstream areas of each farthest downstream 5%, 25%, and 50% flow stream. The
areas do not always follow smooth, expected watershed delineations because flow accumulation
is calculated using quarter-degree average elevation data. This disregards sharp topographic
features, slope aspects, and sometimes the delineations of known river basins.
Using the drainage basins and a population density map it is possible to estimate the
number of people relying on glacier melt water. Population estimates for the highlighted areas
are calculated using a population density grid from 2005 [reference]. For the 5% threshold the
estimated population affected is about 33.5 million people. For 25% and 50% thresholds the
estimated populations are about 6.7 million and 1 million people. [compare population to
Barnett et al. estimates below]
10
Figure 4: Effected Upstream Areas, 5% [Blue], 25% [Green], 50% [Red] Glacier Melt Contribution
11
Comparison [OLD]
Published Values
The calculated glacier melt contributions are compared to results found in other
publications (Table 2) [update, add to references]. All referenced, published values of glacier
melt water contribution to streamflow are average annual values [check this]. If a published
value is taken from a large basin, the best approximation of which modeled quarter-degree value
is used. As a result, the calculated contribution values are most accurate for point references.
The methods used by the authors to separate snowmelt from glacier melt are not always clear.
Studies known to include snow melt are marked by an asterisk.
Source
Area/River
Method
Jain, 2002
Deoprayag, Ganga
River, India
Bhakra Dam, Satluj
River, India
Akhnoor, Chenab River,
India
Yanamarey, Cordillera
Blanca, Peru
None given
Singh and
Jain, 2002
Singh, et al.,
1997
Mark and
Seltzer, 2003
Mark, et al.,
2005
Hastenrath
and Ames,
1995
Mark and
Seltzer, 2003
Hopkinson
and Young,
1998
Mark and
Seltzer, 2003
Mark, et al.,
2005
Mark and
Seltzer, 2003
Referencing
Francou2000
[cannot find
it]
Referenced Glacier
Contribution [%]
28.7*
Calculated Glacier
Contribution [%]
6
Water balance
59*
3
Water balance
49.1*
Water balance
35 [44] +- 10
[1998-1999]
Yanamarey, Cordilla
Water balance
Yanamarey, Cordillera
Blanca, Peru
Water balance
58+-10 [20012004]
50
Uruashraju, Cordillera
Blanca, Peru
Bow River, Banff,
Canada
Water balance
36 [45] +- 10
Río Santa, Callejon de
Huaylas, Peru
Río Santa, Callejon de
Huaylas, Peru
Glacier Chacaltaya,
Bolivia
Hydrochemical
mixing model
Hydrochemical
mixing model
Water
balance?
Water
balance?
12
[larger] if an
assumed 20%
annual glacier
ablation is due to
evaporation
and/or
sublimation
Discharge from
lake at terminus of
glacier
1.8 [from 19521993]
12-20,
conservative 10
~40% [20012004?]
Would reduce avg.
annual discharge
by 30%
Might be better
for a ‘Benchmark’
comparison
Yang, 1989
Wang, 1989
Xu, et al.
Zhang, et al.,
2007
Heihe [Yingluxia Hydro
Station], China
Kunes [Qiapu], China
Kuche [Langan], China
Tekes [Kapuqihai],
China
Muzat, China
Tarim Basin, China
Junggar Basin, China
Qaidam Basin, China
Hexi Corridor, China
Qinghai Lake. China
Tuotuo River, China
Water balance
5
0.5
None given
None given
None given
2.2
8.4
20
2.7
2.4
2.9
None given
Avg. Annual
Avg. Annual
Avg. Annual
Avg. Annual
Avg. Annual
Modified
degree day
model
82.8
40.2
13.5
12.5
13.8
0.4
32 [1961-2004]
[47.4% in the
1990s]
10
5
2
1
5
0.2
* Referenced Glacier Contribution values are stated to include ephemeral snowmelt.
Table 2: Comparison of Published and Calculated Glacier Contribution Values
Benchmark Glaciers [UPDATE]
As a further means of comparison, the melt model is applied to two United States
Geological Survey (USGS) Benchmark glaciers, Gulkana and Wolverine. Both glaciers are
located in Alaska and have records of mass balance, precipitation, and streamflow discharge.
Gulkana glacier is 19.6 km2 in area and rests in a drainage basin covering 31.6 km2. A stream
gauge is located one kilometer downstream of the glacier terminus. Wolverine glacier is similar
in size at 16.8 km2 and lies in a 24.6 km2 drainage basin. One hundred and fifty meters
downstream of Wolverine’s terminus is a stream gauge.
Using the discharge from the stream gauges it is possible to compare modeled glacier
melt water discharge with a physical record. Mean monthly stream discharge values are taken
from the same time period as the ISCCP radiation data. Figure 5 presents a comparison of mean
monthly melt model values versus stream gauge discharge values. The pattern of the melt model
discharge is caused by the declination of the Sun through the course of a year. At first glance the
model greatly underestimates the amount of discharge from the glacier. However, the stream
gauge is also recording discharge associated with non-glaciated land area and precipitation
falling on the glacier.
13
400
350
Discharge (cfs)
300
250
Gulkana Calculated
200
Wolverine Calculated
150
Gulkana Gauge
100
Wolverine Gauge
50
0
1
2
3
4
5
6
7
Month
8
9
10
11
12
Figure 5: Mean Monthly Benchmark Glacier and Stream Gauge Discharge
Precipitation in both basins averages about 1000 mm a year, and can be removed from
the streamflow discharge measurements. Removal reduces the recorded discharge, but raises
issues related to precipitation gauge under catchment, poor gauging of the basins [there is only
one gauge in each basin], rain versus snow, and unknown precipitation accumulation on the
glacier.
An alternate approach to adjusting the streamflow gauge discharge is proposed. By
removing the contribution of non-glaciated land, the amount of discharge contributed by the
glacier area may be determined. Figure 6 displays the result of removing non-glaciated area
contribution. The phasing of the discharge does not agree, but the total discharge is more
important for the problem of determining maximum glacier melt water contribution to
streamflow. The phasing of melt discharge may disagree due to a lack of turbulent heat fluxes in
the model and no accounting of melt water travel time, e.g. percolation through the glacier.
Fountain and Walder [1998] discuss water flow through temperate glaciers, but give no hard
estimates of travel time. The average annual total recorded discharge at Gulkana glacier is about
381,200 acre-feet. This compares well with the modeled average annual total discharge of
356,700 acre-feet; 93.5% of the recorded value. To calibrate the model to the gauge requires
reducing albedo from 0.4 to 0.379, a reasonable value for glacier ice. Wolverine glacier’s values
of 622,100 acre-feet (recorded) and 429,000 acre-feet (modeled) do not compare as well; the
model accounts for 68.9% of the recorded value. To match Wolverine’s stream discharge, the
model albedo must be reduced from 0.4 to 0.222, still a reasonable value for glacier ice.
Fountain and Tangborn [1985] offer insight into the phasing, or delay, of runoff. They
observed a peak runoff in July and August when investigating Pacific Northwest and Alaskan
glaciers [climates may be different]. A Meier, 1969 paper attributed the July and August melt
peak to clearer skies and low precipitation. This contrasted to an unglacierized basin having a
maximum runoff in May. Fountain and Tangborn [1985] hypothesized the delay to be caused by
later snow melting with increasing altitude and temporary storage of liquid water within the
14
glacier ice. Both of which are related to the percentage of a basin covered by glacier. A study of
glacierized and adjacent unglacierized basins in the Pacific Northwest revealed a relationship
between peak runoff time and percent glacierization [Fountain and Tangborn, 1985]. An
increase from 5 to 15% coverage resulted in a peak runoff delay of about a month, while a
coverage increase from 50 to 100% increased the delay by only another two weeks. Use their
curve on the benchmark glaciers to estimate delay.
The physical mechanism of liquid water storage in glacier ice is described by Tangborn et
al. 1975. Shortcomings of a purely hydrological method of determining mass balance of a
glacier, by measuring stream flow and attributing non-precipitation component to glacier melt,
are discussed. In the same respect the runoff from a glacier cannot be accurately used to
determine the heat/energy balance of a glacier due to the delayed release of liquid water storage
[Tangborn et al., 1975].
Another comparison uses the Benchmark Glacier sites’ mass balance measurements.
Gulkana and Wolverine were recorded to have lost an average 0.602 and 0.535 meters over the
period of investigation, respectively. The melt model overestimates the mass lost by both
glaciers, producing values of 1.346 and 2.240 meters for Gulkana and Wolverine.
Similarly the model is applied to Dokriani Glacier in India. Using a velocity-area method
to measure discharge in August 1992, Singh et al. [1995] calculated a maximum and minimum
mean daily discharge of 7.58 and 3.29 cms. The measurements occurred at the outlet to a 23 km2
basin with a 10 km2 glacier within. [include Dokriani?]
300
Discharge (cfs)
250
200
Gulkana Calculated
150
Wolverine Calculated
USGS Gulkana
100
USGS Wolverine
50
0
1
2
3
4
5
6
7
Month
8
9
10
11
12
Figure 6: Mean Monthly Benchmark Glacier and Stream Gauge Discharge without Non-Glaciated Area
Contribution
15
Discussion
In analyzing the results for meaning and significance the melt model must first be
investigated. The melt model is precisely that, a model, and therefore has shortcomings. The
model does not account for all of the complex processes occurring within and upon glaciers and
their basins. With each assumption or simplification the model is likely to deviate from natural
patterns and events. However, the purpose of this melt model is to estimate the maximum
contribution to down stream flow made by glacier ice.
Glacier Representation
In the model all glaciers are flat. The conversion from glacier polygons to fractional grid
cell area loses all shape, slope, and aspect data. Aspect, the general direction the glacier is
facing, may affect the amount of direct solar radiation absorbed by the glacier depending on the
glacier’s latitude and solar declination. The same is true regarding the slope of the glacier
surface. Slope additionally influences melt water runoff times, effecting the refreezing of melt
water. A simple figure [not shown] would demonstrate the parabolic pattern of incoming solar
radiation to a glacier surface dependent on aspect, slope, and solar declination.
An equilibrium line altitude is often used to divide glaciers into an accumulation and an
ablation zone. The model overlooks any distinction of processes occurring on the glacier
surface; applying melt energy equally throughout.
The model does not account for glacier volume and areal size changes with time.
Accounting for the loss of glacier mass, measured by the melt water runoff volume, would
require the determination of glacier accumulation. Glacier accumulation, driven primarily by
precipitation and temperature, is difficult to determine given the mountainous areas in which
glaciers often reside. Individual glaciers will react uniquely to a given climate, a problem
beyond the scope of this project.
Albedo is a fixed value (0.238) applied to the entire glacier surface throughout the melt
season, disregarding changes in debris cover or freshly fallen snow. However, the chosen albedo
value is assumed to be low, creating more melt. Despite creating more melt, published values
are persistently higher. [update results]
Melt Energy
High spatial resolution is often needed to determine melt in complex terrain areas [Hock,
2005]. The radiation data obtained from ISCCP is originally at a 2.5-degree resolution, far too
coarse to account for terrain. The ISCCP data used provides only the dominant components
influencing melt energy, shortwave and longwave radiation. Direct radiation is the most
important energy source for the rough terrain of the Alps [Paul et al., 2005]. The Alpine
observation may be applied to other complex terrain areas. Lesser energy fluxes, e.g. advected
energy, are not included in our model and likely would not greatly affect results. As a past
example, for a 37-day investigation over a snow surface (not an ice surface) LaChapelle [1959]
observed 6.1 percent of the total energy transfer to the surface contributed by condensation and
0.006 percent by precipitation.
Turbulent energy fluxes have proven important in determining melt water runoff.
[contradictory, section will depend on new results] However, the turbulent fluxes are not as
dominant in regards to melt as short- and longwave radiation. Upon the Haut Glacier d’Arolla in
Switzerland, the net shortwave flux was the main contributor to melt, two times that of the
turbulent fluxes under high energy conditions and three to four times that under low energy
16
conditions [Brock et al., 2000b]. The same study found an increase in surface roughness from
0.1 to >1 mm doubled turbulent heat fluxes with aerodynamic roughness decreasing up glacier
[Brock et al., 2000b]. Turbulent heat fluxes increase significantly with greater wind speed
(noted over a snow surface) [Datt et al., 2008]. Datt et al. [2008] also found turbulent fluxes to
be much smaller than short- and longwave radiation over a snow surface, almost cancelling each
other out. In the high Alpine region of Switzerland, Plüss and Mazzoni [1994] found latent and
sensible heat contributed little to the snowpack studied, likely due to low wind speeds in the
particular region and to frequent inversions. Turbulent heat flux contribution is highly variable
and depends on local meteorology, making it difficult to determine on a global scale [Plüss and
Mazzoni, 1994]. Including turbulent heat fluxes in this study will increase the uncertainty of
results for a small gain in the accuracy of the values obtained. Further, the monthly time scales
result in latent and sensible heat fluxes nearly cancelling each other out. [find reference]
Update this section: [The turbulent heat fluxes of latent and sensible heat were applied
to June’s total radiation to determine influence on glacier melt. Using ERA-40 latent and
sensible heat flux data, any change in gridcell glacier runoff with turbulent fluxes is compared to
gridcell glacier runoff without turbulent fluxes. Figure 7 displays a plot comparing the gridcell
glacier runoff. A one-to-one line in the plot shows few gridcells change total glacier runoff.
This is most likely caused by the cancelling effect of latent and sensible heat over large time
scales and their magnitude in relation to short and longwave radiation. [Check again using VIC
output] Total glacier runoff may not exceed total runoff per gridcell [total gridcell runoff is the
maximum limit for total glacier gridcell runoff], hiding some changed values. Turbulent fluxes
may also be lost in significant figures carried, typically six decimal places. The insignificance of
sensible and latent heat fluxes lead to their discount. ]
Glacier Runoff without Turbulent Heat Fluxes
Figure 7: Effect of Turbulent Fluxes on Gridcell Glacier Runoff
[Update with VIC output turbulent fluxes.]
17
The model assumes all energy results in melting surface ice. The penetration of
shortwave radiation into the glacier surface is not considered. Penetration would result in melt
occurring in the subsurface as opposed to the surface, altering the amount and timing of melt
[Oerlemans et al., 2009]. To estimate the maximum amount of melt water, no ablation occurs.
Humidity, not included in our melt model, is known to influence the energy balance of tropical
glaciers [Francou et al. 2000]. High latitude, Arctic glaciers experience negligible sublimation
due to local moisture and temperature conditions [Greuell et al., 2007]. Fohn [1973] found
condensation and evaporation nearly equal over snow.
There is no transitioning of energy components throughout the seasons, more important
in regions of high latitude [reference?]. The increase in net radiation absorbed by the glacier
area is steep in spring when snow transitions to ice. The fall transition from ice to snow plays
less of a role on net radiation because incoming radiation is declining [Oerlemans et al., 2009].
It is also important to note melt will not necessarily occur at an air temperature of zero degrees
Celsius [Hock, 2005]. The mix of energy components at any given time will determine the melt
occurring for the conditions present.
The model moves all melt water to the stream network as it is produced. Diurnal
refreezing of melt water, either across the glacier surface or percolated within, would reduce the
total melt water runoff. The process of refreezing decreases the cold content of the glacier,
creating a delay in melt water runoff until later in the season. Without refreezing the calculated
glacier melt water amount is greater earlier in the melt season. This pattern is observed in the
Benchmark Glaciers comparison.
Melt Runoff Timing
The timing of glacier melt water runoff is important in determining downstream
contribution. Melt created with applied energy fluxes must be timed with the appropriate
gridcell total runoff to determine percentage contribution. The need to analyze timing is evident
in the comparison with USGS Benchmark glacier data.
The summer melt period may be divided into three sections: a period of runoff deficit for
the amount of melting and precipitation in a basin, runoff excess with release of stored water,
and a period of balance when water input roughly equals output [Stenborg, 1970]. For an
example, Stenborg [1970] turns to a hydrograph. A hydrograph for a glacierized basin through
the summer has two shapes: potential (melt water plus precipitation discharge) and actual
(glacier delayed discharge). Comparing the two hydrographs, if the early deficit and middle
excess are equal for the actual hydrograph, they display a delay in discharge. Unequal deficit
and excess in the actual hydrograph may represent a change in glacier mass balance. The
differences between the two hydrographs display the delay of discharge from the glacierized
basin. The two Benchmark glacier hydrographs above, Figure 5 and Figure 6, display deficit and
excess. [maybe create separate figure with labeling] Excess greater than the deficit may
indicate errors in regression or data, or water is being released from previous years [Stenborg,
1970]. However, Stenborg [1970] notes almost no liquid water from the previous year enters
into discharge in the current year.
Water Storage
As a means of delaying melt water runoff, it is important to discuss the storage of water
within a glacier’s components. Long (years), intermediate (seasons), and short-term (days)
glacier storage of water has been recognized [Jansson et al., 2002]. Seasonal change in stored
18
water content in a glacier has been observed, meaning glacier hydrologic characteristics change
seasonally [Meier, 1973]. While this project is concerned with the long-term contribution of
glacier ice to streamflow, intermediate and short-term water storage affects when energyproduced melt water must align with gridcell runoff. [need to discuss the absence of glaciers
in VIC]
Firn, snow, and ice are each a different reservoir for the glacier, all with varying storage
times [Verbunt et al., 2003]. Liquid water may be stored within each component, but glaciers
may also store water temporarily as snow and ice [Hock, 2005].
Surface Processes
Glacier surface processes and conditions effect melt runoff timing. Schuster and Young
[2006] observed snow pack on a glacier stays cooler, delaying snowmelt and thus the exposure
of glacier ice [from Comeau, 2009]. Relatively low-resolution data is not likely to capture the
specific climate above a glacier to account for changes in temperature.
Capillary storage in snow on top of a glacier may store melt water [Stenborg, 1970].
Slush on the glacier tongue and under snow higher on a glacier represents the storage of water
with ice crystals [Stenborg, 1970]. A slush layer on top of an impermeable layer may uniquely
effect runoff and melt. Slush may affect discharge volume by transporting ice crystals, lowering
the amount of melt energy needed [Stenborg, 1970].
The firn layer on a glacier often has an albedo similar to glacier ice, thus removing the
firn layer does not affect runoff volumes but redistributes discharge time [de Woul et al., 2006].
Firn layers delay discharge from a glacier by increasing the amount of porous material to be
navigated by melt water [de Woul et al., 2006]. Deep firn pack in the central and upper
accumulation zones allows liquid water storage within pores [Stenborg, 1970].
Intra-Glacier Flow
Water flow within a glacier is another component of runoff timing. Fountain and Walder
[1998] offer extensive discussions of water flowing through a glacier, but no estimations of
actual time lengths of flow. The internal channel system, both englacial and subglacial, changes
throughout the year resulting in slow winter flow and faster summer flow [Flowers, 2010].
Drainage pathways are most likely closed off during times of little ablation or glacier movement
[Tangborn et al., 1975]. Tangborn et al. [1975] assumed a delay between higher water pressure
and the opening of drainage pathways in the ice.
The physical mechanisms of storage display the complex behavior of water in different
phases. Liquid water trapped in ice is denser and exerted pressures in the water-filled holes can
exceed the pressure in the solid ice nearby. Being plastic, ice will move to change storage holes
and drainage passages, rerouting or trapping liquid water [Stenborg, 1970]. The pressure
difference between water and ice is small, resulting in the ice moving plastically. Also, heat
generated by viscous dissipation or from the surface can change passage geometry and size
[Tangborn et al., 1975]. Glacier bed type (soft or hard) effects conduit formation and thus
hydrograph timing [Flowers, 2010]. Similarly, changes in frozen soil effect runoff and melt
water runoff timing by changing the environment for surface, interflow, and groundwater
movements [Yang, 1989].
The slow movement of ice could cause a delay in outflow on the scale of months
[Tangborn et al., 1975]. Fountain, et al. [2005] found copious water filled cavities in temperate
ice. Meier [1973] references one cubic meter of water can move through one cubic meter of
19
glacier ice in one year. Fountain et al. [2005] detected water movement slower than existing
conduit theory would suggest. Video analysis of Sweden’s Storglaciären showed an englacial
hydrological system dominated by fractures, not conduits.
The movement of glacier ice on a larger scale will effect crevasse development.
Crevasses not opened enough to be drained, or clogged with snow, etc., can store runoff
[Stenborg, 1970]. Naturally formed snow dams or freezing at the glacier front may delay runoff
[Stenborg, 1970].
Refreezing
In Stenborg’s [1970] analysis, about 25% of the total summer discharge was delayed
from early to middle summer. In spring, when the energy balance on a glacier becomes positive,
melting occurs; but no runoff is measured because of refreezing. However, refreezing lowers the
cold storage of the glacier. Refreezing may continue on parts of the glacier for the whole
summer, but over a decreasing area at a decreasing rate. Once the snow and ice layer near the
surface is isothermal, melt water can be held by the snow layer, delaying some runoff.
Refreezing of melt water is another means of delaying melt water runoff while potentially
reducing the total amount of melt water produced in a season [check, may not reduce amount].
Refreezing of melt water mainly occurs in the high Arctic. Antarctic glaciers, outside the scope
of this project, exhibit almost no melting and mountain glaciers outside of the Arctic are too
warm for extensive refreezing [Pfeffer et al., 1998]. The same conclusion was reached in an
earlier study; regions outside of the Arctic do not experience large amounts of refreezing because
it is too warm [Pfeffer and Meier, 1991]. It must be noted the absence of refreezing will result in
an over-estimation of runoff, a conclusion acceptable for this study [Pfeffer et al., 1998].
Incorrect conclusions are drawn when total melt is equated to total runoff because of
refreezing [Pfeffer and Meier, 1991]. Studies of snow reveal snow melt reduces the cold storage
as it refreezes until the pack is isothermal at 0 ºC [Stenborg, 1970]. Glaciers can lose mass by
the melting of snow accumulated the previous year [Pfeffer and Meier, 1991]. Refreezing poses
a problem for reliance on staked mass balance measurements. Staked measurements may not
account for the densification of the firn layer from melting and refreezing within pore spaces,
throwing off runoff calculations [Pfeffer and Meier, 1991]. Studying the Storbreen glacier in
Norway, Andreassen et al. [2008] concluded about 8% of melt water refreezes, mainly in April
and May.
Lakes and Reservoirs
A method of long term glacier melt water storage is in glacier lakes. Glacier lakes may
form at the terminus of a glacier or on top of a glacier. Frey [2010] modeled lake formation and
outbursts, but they are not included in this project. Their behavior is too erratic to be accurately
included in a global determination of glacier water resources.
Reservoirs and lakes are unaccounted for when routing all melt water immediately into
the stream network. The long time scales used in the model are assumed to allow for the release
of water temporarily dammed by snow or ice. If glacial melt water contributes to a downstream
reservoir or lake the connection to a downstream population is delayed.
Timing
Glacier ice melt occurs after last winter’s snow melts and before autumn snowfall events
[Schaper et al., 2000]. The period after snowmelt and before snowfall is labeled the ice melt
20
window. Most melt occurs in July and August when skies are clearer and precipitation is low
[Meier, 1969]. Meier [1969] measured peak runoff in July and August when studying a Pacific
Northwest glacier, not aligning with peak radiation fluxes.
The delay in peak runoff for a glacierized basin is best seen in comparison to a similar
unglacierized basin. The maximum runoff for an unglacierized basin may occur in May while
glacierized basin maximum runoff occurs later and later depending on the percentage of glacial
coverage [Fountain and Tangborn, 1985]. Fountain and Tangborn [1985] attributed the delay to
two main causes. The first cause is later snow melting with increasing altitude, the environment
of glaciers. A plot of peak flow time versus average altitude for unglacierized basins is linear.
Plotting peak flow time and percentage of glacier cover produces a parabolic plot [check]. The
second cause in delay is temporary internal storage of melt water.
More ice covered area in a basin generally leads to a greater amount of annual runoff
occurring in the summer half of the year and the occurrence of the monthly maximum runoff is
delayed [Chen and Ohmura, 1990]. This follows the idea of snow and ice covered mountains
acting as water towers. Citing Tangborn et al. [1975], about 54% of melt water was stored in the
ice to be released in the following months as compared to an unglacierized adjacent basin.
Derikx [1971] found much shorter runoff delays associated with glaciers. The time
constant was on the order of hours for a small experimental glacier basin [Derikx, 1971]. Derikx
[1971] cites studies showing the time constant, the delay in the hydrograph peak, for glacier
basins is 2 to 5 days. Derikx [1971] findings lead to the belief that melt from the ablation zone
on a glacier leaves the glacier the same day or at least within the same week; similar short time
delays were observed by Singh et al. [2003]. When looking at one glacier, time between
maximum temperature and maximum discharge was 3-6 hours. The time to peak flow ranged
from 8.5-11 hours [Singh et al., 2003]. In reference to snow, melt moves through the snowpack
to quickly affect stream discharge [Ramage and Isacks, 2003]. Ramage and Isacks [2003] did
not make observations regarding melt flow through ice or snowpack situated atop ice.
[[In the comparatively well-studied Alpine region, most ice melt occurs from July to
September [Lambrecht and Mayer, 2009], indicating turbulent fluxes may influence melt.
Melt is increased in July and August due to a greater exposure of ice and thus more areas
with lower albedo [Meier, 1973]. ]]
The above examples demonstrate the many ways melt water can be delayed in running
off. However, the large scales of the model used in this study prevent stating definitive reasons
for and length of delays. Therefore melt generated in a given month is compared to runoff in the
subsequent three months to better estimate a maximum value of glacier ice melt contribution to
streamflow. [wait for VIC]
The melt amounts are based on a recent seven-year period of data, a period during which
there is evidence that glaciers are contributing to water resources. If climate changes affect
glaciers, changes that are generally assumed to result in an increase in ablation, melt rates of
glaciers will increase. Increasing melt rates will lead to a greater contribution to total runoff
during low flow periods. This increased flow will taper off as the area and volume of glaciers
decrease [Wang, 1989]. It is not clear where in this process glaciers are currently.
21
Conclusion [OLD]
The melt model proposed for determining the maximum glacier melt water contribution
to streamflow shows mixed results. In comparison to published values and when applied to a
small spatial scale, the model underestimates discharge. However, in comparison to mass
balance measurements, the model overestimates. The mixed results highlight the difficulty of
separating snowmelt from glacier melt. Calibration of the model would rely solely on the
adjustment of albedo, the consequence of such a simple model. Subsequent versions of the
model will address turbulent heat fluxes and changing glacier size. A balance must be achieved
because adding to the model puts it in danger of increased uncertainties in determining glacier
contribution to streamflow at the global scale.
For large spatial scales the simple melt model highlights areas of interest to water
resources at a time when glaciers are thought to be at-risk. Glaciers are known to act as large
mountain reservoirs, releasing their water during dry, warm months, the time of greatest need.
Quantifying the contribution of glaciers is important for all downstream, reliant populations.
Snow and ice melt water is important not only for its volume, but also its timing. Should
the same precipitation fall as rain much of the water would pass through population centers
unused. About 500 million people rely on melt water for agriculture and economic practices,
practices that cascade to affect others [citing: Cruz et al., 2007] [Kehrwald et al., 2008]. Barnett
et al. [2005] determined areas dominated by snowmelt by using a simple ratio. If accumulated
annual snowfall divided by annual runoff is greater than one half, the basin is snowmelt
dominated. Barnett et al. [2005] continued by outlining areas receiving large amounts of
snowmelt even if local runoff is not snow dominated. These climate change at-risk areas account
for a sixth of the world's population and cover areas responsible for roughly a quarter of the
global gross domestic product.
The loss of glacier ice is a contentious debate, one showing more evidence of mass loss
than gain. Glacier retreat is well documented over most of the world, if not all [Barnett et al.,
2005]. Annual snowfall is likely to continue, even if some of it transitions to rainfall [Barnett et
al., 2005]. Global climate models generally show increases in temperature, if they don’t agree
on precipitation changes. In the interim, adverse affects on the flow of rivers have been reported
[citing: Kulkarni, 2002; 2007] [Bhambri and Bolch, 2009]. Glacier melt contribution to runoff
will increase as melting accelerates [Schaper and Seidel, 2000]. Accelerated melting, usually in
the summer, enhances river discharge in regions of substantial glacier coverage (15-20%). The
enhanced discharge of 10-15% “has become almost a continuous process during the last 20
years” [Lambrecht and Mayer, 2009].
One of the great effects of glaciers on water resources is absorption of precipitation to be
doled out later in the year. Beyond changes in glacier melt water quantity, a shift in peak river
discharge will occur. Climate models show an increase in surface temperatures, even without
precipitation intensity changes, causing the shift [Barnett et al., 2005]. There is no indication
among the climate models that precipitation will shift to summer/autumn in snow-dominated
regions [Barnett et al., 2005]. Peak runoff will occur earlier in snow-dominated regions, leaving
late summer/early autumn dry [Barnett, et al., 2005]. The effects of the shift will be enhanced if
glacier ice cannot buffer flow later in the melt season. Areas with insufficient reservoir storage
capacity will lose runoff to the oceans [Barnett et al., 2005]. As an example: Tibetan Plateau ice
fields provide dry season runoff, no ice means no runoff [citing: Cruz et al., 2007, an IPCC
book] [Kehrwald et al., 2008].
22
23
References [in progress]
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(2008). A 5 year record of surface energy and mass balance from the ablation zone of
Storbreen, Norway. Journal of Glaciology, 54, 185, 245-258.
Bahr, David B., Mark Dyurgerov, and Mark F. Meier (2009), Sea-level rise from glaciers and ice
caps: A lower bound. Geophysical Research Letters, 36, L03501,
doi:10.1029/2008GL036309.
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, 17 November
2005, doi:10.1038/nature04141, 303-309.
Barrand, N. E. and M. J. Sharp (2010). Sustained rapid shrinkage of Yukon glaciers since the
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doi:10.1029/2009GL042030.
Bhambri, Rakesh and Tobias Bolch (2009). Glacier mapping: a review with special reference to
the Indian Himalayas. Progress in Physical Geography, 33 [5], 672-704.
Brock, Ben W., Ian C. Willis, Martin J. Sharp (2000). Measurement and parameterization of
albedo variations at Haut Glacier d’Arolla, Switzerland. Journal of Glaciology, 46, 675688.
Brock, Ben W., Ian C. Willis, Martin J. Sharp, and Neil S. Arnold (2000b). Modelling seasonal
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Data References
Benchmark Glacier data were obtained from the United States Geological Survey (USGS) web
site http://ak.water.usgs.gov/glaciology/index.html maintained by Rod March, July 2009.
DCW data were obtained from the Penn State University Libraries Digital Chart of the World
Server web site http://www.maproom.psu.edu/dcw/, July 2010. Data originally
developed by Environmental Systems Research Institute, Inc. (ESRI) for the US Defense
Mapping Agency (DMA).
GLIMS data were obtained from the National Snow and Ice Data Center web site
http://glims.colorado.edu/glacierdata/ maintained by the NSIDC, Boulder, CO,
November 2009.
Global runoff data were obtained and interpolated from the Variable Infiltration Capacity [VIC]
Model forced and run by Jenny Adam. Details in the following two publications:
Adam, J.C., E.A. Clark, D.P. Lettenmaier, and E.F. Wood (2006). Correction of Global
Precipitation Products for Orographic Effects. J. Clim., 19, 1, 15-38.
Adam, J.C. and D.P. Lettenmaier (2003). Adjustment of global gridded precipitation for
systematic bias. J. Geophys. Res., 108, D9, 1-14, doi:10.1029/2002JD002499.
GLOBE Task Team and others (Hastings, David A., Paula K. Dunbar, Gerald M.
Elphingstone, Mark Bootz, Hiroshi Murakami, Hiroshi Maruyama, Hiroshi Masaharu,
Peter Holland, John Payne, Nevin A. Bryant, Thomas L. Logan, J.-P. Muller, Gunter
Schreier, and John S. MacDonald), eds., 1999. The Global Land One-kilometer Base
Elevation [GLOBE] Digital Elevation Model, Version 1.0. National Oceanic and
Atmospheric Administration, National Geophysical Data Center, 325 Broadway,
Boulder, Colorado 80305-3328, U.S.A. Digital data base on the World Wide Web (URL:
http://www.ngdc.noaa.gov/mgg/topo/globe.html) and CD-ROMs.
ISCCP FD data were obtained from the International Satellite Cloud Climatology
Project web site http://isccp.giss.nasa.gov maintained by the ISCCP research group at
the NASA Goddard Institute for Space Studies, New York, NY, November 2009.
Population data were obtained from the Gridded Population of the World on the World Wide
Web (URL: http://sedac.ciesin.columbia.edu/gpw/global.jsp) maintained by the
Socioeconomic Data and Application Center, February 2010.
29