Glacier Contribution to Runoff - Summary Report
Neil Schaner
I.
Introduction
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). 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 expansive measured data, melt must be modeled using globally available, remotely
sensed data. Melt models have been attempted before with varying success (e.g. Brock, et al.,
2000). Difficulties arise in determining albedo, the main driving parameter, and separating
glacier melt from snowmelt (Cottom, 2009 and Schaper, et al., 1999).
The creation of a global map of glacier melt runoff accumulation has been proposed to
identify areas of concern for reduced glacier water resources. A global glacier runoff
accumulation map made at a quarter-degree resolution must be made with similar resolution
remotely sensed data. The quantification of the melt will rely on net radiation and glacier area.
Net radiation and the closely related albedo are the main drivers of ice melt. Glacier ice coverage
has been shown to correlate well with excess melt, melt which is beyond normal
accumulation/ablation cycles (Lambrecht and Mayer, 2009). The melt calculation techniques and
assumptions used in this project are for determining an upper bound of glacier contribution to
downstream flows.
II.
Data Sources
1) Glacier areas are derived from Global Land Ice Measurements from Space (GLIMS) data.
Downloaded GLIMS data was manipulated into a quarter-degree resolution grid with values of
fractional, areal glacier coverage.
2) Elevation data is sourced from the Global Land One-kilometer Base Elevation Project
(GLOBE) a product of the National Oceanic and Atmospheric Administration. The DEM file
was disaggregated to a quarter-degree resolution and sinks were filled in order to build both flow
direction and flow accumulation files.
3) Solar radiation data are taken from the International Satellite Cloud Climatology Project
(ISCCP). At-the-surface net longwave and downwelling shortwave radiation data are originally
at a 2.5-degree resolution and were disaggregated to quarter-degree resolution. It is important to
note the “full sky” classification of the data, meaning that no adjustments were made for cloud
cover. Mean monthly values from the complete data record, July 1983 to December 2004, were
used in all calculations.
4) Global runoff values come from Jenny Adam’s global VIC model runs. Her half-degree runoff
output values were interpolated to a quarter-degree.
III.
Glacier Contribution to Total Runoff
Glacier melt water runoff was calculated for each quarter degree gridcell using a simple
energy balance equation. The energy available for melt is assumed to be the total net radiation.
Total net radiation is taken as the incoming shortwave radiation minus the albedo dependent
reflection plus the net longwave radiation.
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Qnet = [SWdown * (1 – α)] + LWnet
All other radiation components are disregarded. Due to the high temporal and spatial variability
of glacier albedo a fixed value of 0.4 is used (Brock, et al., 2000, Cottom, 2009, Paul and
Haeberli, 2008). Total depth of melt is equal to the product of net radiation and elapsed time
divided by the product of the latent heat and density of water.
Melt = (Qnet * Δt ) / (HL * ρw)
If the total glacier melt runoff amount is less than Adam’s total runoff amount, a simple fraction
of glacier melt runoff contribution to total runoff is determined. If the total amount of calculated
glacier melt runoff is greater than the total runoff from Adam’s VIC output, it is assumed that
glacier melt contributed all of the runoff for that gridcell.
From the melt information a global, quarter-degree grid was produced with values of
glacier melt contribution to total runoff. This grid was used to weight the values of a flow
accumulation grid derived from the DEM. The final product is a grid displaying the accumulated
contribution of glacier melt runoff to total runoff. Threshold values of 5%, 25%, and 50% were
applied to the accumulated flow fraction to display areas at risk of significant runoff losses.
IV.
Results
Figure 1: Full Glacier Melt Accumulated Signature
The accumulated signature of all glacier melt runoff may be seen in Figure 1. The month
of largest potential melt was applied to the northern hemisphere (July) and the southern
hemisphere (December). During the month of greatest melt it is assumed that all winter snowfall
has disappeared and no additional snowfall occurs. As climate changes affect glaciers, 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. No accounting for this trend or
for changes in glacier mass has been made.
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The accumulated flow routes of a five percent threshold glacier melt contribution can be
seen overlain on a DEM in Figure 2. Glacier melt runoff constitutes five percent or more of total
runoff in parts of the major river basins highlighted in Figure 3. The highlighted river basins
have a total population of 1.061 billion people. This number is much greater than the actual
population effected by glacier melt runoff due to accumulation effects and population
distribution. The major river basins are: Glama (42,000), Rhine (50 million), Ili (4 million),
Rhone (10 million), Po (17 million), Syr Darya (21 million), Amu Darya (20 million), Terek (3
million), Tarim (8 million), Indus (178 million), Yangtze (430 million), Mekong (57 million),
Salween (6 million), Ganges-Brahmaputra (200 million), Irrawaddy (31 million), and Ob (26
million) (waterwiki.net). The basin in Chile and Argentina has an unknown population and does
not follow an existing river watershed. The same is true of the highlighted area in Alaska, though
much of the estimated 650,000 living in Alaska can be found on the southern coast. While the
basins and populations mentioned may be affected by changed in glacier melt runoff, a more
accurate estimate of areas and populations effected comes from the modeled results.
Figure 2: Glacier Melt Contribution to Total Runoff , 5% Threshold
Figure 3: Major River Basins with Areas of at least 5% Glacier Melt Contribution
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Figure 4, Figure 5, and Figure 6 highlight the areas upstream of different glacier melt
contribution thresholds; 5%, 25%, and 50%. The areas upstream of 50% glacier melt runoff to
total runoff (Figure 6) are just visible in central Asia, southern South America, and along the
southern coast of Alaska. The areas do not follow smooth watershed delineations because flow
accumulation is calculated using quarter-degree average elevation data. This disregards sharp
topographic features, slope aspects, and often the delineations of river basins. Furthermore,
global runoff data from Adam’s was interpolated from half-degree to quarter-degree, smoothing
runoff values across regions. Population estimates for the highlighted areas were calculated
using a population density grid from 2005. For the 5% threshold the population estimates are:
North America (38,500), South America (59,000), Europe (4,100,000), Central Asia (63
million), and Middle East (2 million). For a 25% threshold the effected area populations are
estimated as: Central Asia (9.2 million), North America (15,000), and South America (8,000).
Figure 4: Effected Upstream Areas, 5% Glacier Melt Contribution Threshold
Figure 5: Effected Upstream Areas, 25% Glacier Melt Contribution Threshold
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Figure 6: Effected Upstream Areas, 50% Glacier Melt Contribution Threshold
V.
Comparison
The calculated glacier melt contributions are compared to results found in Xu, et al., Jain,
and Singh and Jain in the table below. The contributions given by Xu, et al. are for areal basins.
The calculated contribution values are most accurate for point references, as they vary spatially.
Source
Area/River
Time Period
Referenced Glacier
Calculated Glacier
Contribution (%)
Contribution (%)
Jain
Deoprayag, India
June
20*
6
Singh and Jain Bhakra Dam, India
Avg. Annual
28.7*
3
Tarim Basin, China
Avg. Annual
40.2
5
Junggar Basin, China Avg. Annual
13.5
2
Xu, et al.
Qaidam Basin, China Avg. Annual
12.5
1
Hexi Corridor, China Avg. Annual
13.8
5
Qinghai Lake. China Avg. Annual
0.4
0.2
* Referenced Glacier Melt Contribution values are stated to include ephemeral snow melt.
VI.
References
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.
Cottom, Brandon, 2009. Driving Forces Behind Himalayan Glacial Melt. Summer Institute in
Earth Science. Accessed January 19, 2010 from
<http://www.gest.umbc.edu/student_opp/2009_sies_reports/BCottom.pdf>.
Hock, Regine, 2005. Glacier melt: a review of processes and their modeling. Processes in
Physical Geography, 29, 362-391.
Jain, C.K., 2002. A hydro-chemical study of a mountainous watershed: the Ganga, India. Water
Research, 36, 1262-1274.
Lambrecht, Astrid and Christoph Mayer, 2009. Temporal variability of the non-steady
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contribution from glaciers to water discharge in western Austria. Journal of Hydrology,
376, 353-361.
Paul, Frank and Wilfred Haeberli, 2008. Spatial variability of glacier elevation changes in the
Swiss Alps obtained from two digital elevation models. Geophysical Research Letters,
35, doi:10.1029/2008GL034718.
Schaper, Jesko, Jaroslav Martinec, and Klaus Seidel, 1999. Distributed mapping of snow and
glaciers for improved runoff modeling. Hydrological Processes, 13, 2023-2031.
Singh, P. and S.K. Jain, 2002. Snow and glacier melt in the Satluj River at Bhakra Dam in the
western Himalayan region. Water Research, 47, 93-106.
Waterwiki.net. Various pages were accessed on February 3, 2010. <http://www.waterwiki.net>
Weingartner, Rolf, Daniel Viviroli, and Bruno Schaedler, 2007. Water resources in mountain
regions: a methodological approach to assess the water balance in a highlandlowlandsystem. Hydrological Processes, 21, 578-585.
Xu, Baiqing, Tandong Yao, and Ninglian Wang. Observed Glacier Melting and Black Soot
Deposition in the Third Pole. Presentation given at Workshop on “Ice, Snow and Water:
Impacts of Climate Change on California and Himalayan Asia,” La Jolla, CA, May 4-6,
2009.
VII. Data References
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., Vol. 108 No. D9, pp 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 web site
http://sedac.ciesin.columbia.edu/gpw/global.jsp maintained by the Socioeconomic Data
and Application Center, February 2010.
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