TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-407-2017Spatial variability in mass loss of glaciers in the Everest region, central
Himalayas, between 2000 and 2015KingOwengy08ok@leeds.ac.ukQuinceyDuncan J.CarrivickJonathan L.https://orcid.org/0000-0002-9286-5348RowanAnn V.https://orcid.org/0000-0002-3715-5554School of Geography and water@leeds, University of Leeds, Leeds, LS2 9JT, UKDepartment of Geography, University of Sheffield, Sheffield, S10 2TN, UKOwen King (gy08ok@leeds.ac.uk)3February201711140742625April201623May201627December20169January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/407/2017/tc-11-407-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/407/2017/tc-11-407-2017.pdf
Region-wide averaging of Himalayan glacier mass change has masked any
catchment or glacier-scale variability in glacier recession; thus the role of
a number of glaciological processes in glacier wastage remains poorly
understood. In this study, we quantify mass loss rates over the period
2000–2015 for 32 glaciers across the Everest region and assess how future
ice loss is likely to differ depending on glacier hypsometry. The mean mass
balance of all 32 glaciers in our sample was -0.52 ± 0.22 m water
equivalent (w.e.) a-1. The mean mass balance of nine
lacustrine-terminating glaciers (-0.70 ± 0.26 m w.e. a-1) was
32 % more negative than land-terminating, debris-covered glaciers
(-0.53 ± 0.21 m w.e. a-1). The mass balance of
lacustrine-terminating glaciers is highly variable (-0.45 ± 0.13 to
-0.91 ± 0.22 m w.e. a-1), perhaps reflecting glacial lakes at
different stages of development. To assess the importance of hypsometry on
glacier response to future temperature increases, we calculated current (Dudh
Koshi – 0.41, Tama Koshi – 0.43, Pumqu – 0.37) and prospective future
glacier accumulation area Ratios (AARs). IPCC AR5 RCP 4.5 warming
(0.9–2.3 ∘C by 2100) could reduce AARs to 0.29 or 0.08 in the Tama
Koshi catchment, 0.27 or 0.17 in the Dudh Koshi catchment and 0.29 or 0.18 in
the Pumqu catchment. Our results suggest that glacial lake expansion across
the Himalayas could expedite ice mass loss and the prediction of future
contributions of glacial meltwater to river flow will be complicated by
spatially variable glacier responses to climate change.
Introduction
Estimates of Himalayan glacier ice volume range from 2300 to
7200 km3 (Frey et al., 2014 and references within) distributed
among more than 54 000 glaciers across the Hindu Kush Himalayas and the
Karakoram (Bajracharya et al., 2015). The current mass balance of Himalayan
glaciers is predominantly negative, with accelerating mass loss having been
observed over the past few decades (Bolch et al., 2012; Thakuri et al.,
2014). This mass loss is occurring because of a combination of processes.
Shrestha et al. (1999) show a rise in the mean annual air temperature of
0.057 ∘C a-1 across the Himalayas between 1971 and 1994.
Bollasina et al. (2011) show a reduction in total precipitation (-0.95 mm day-1) amounting to 9 to 11 % of total monsoon rainfall over a broad
area of northern India between 1950 and 1999. Bhutiyana et al. (2010) show
both decreasing total precipitation and a changing precipitation phase, with
a lower proportion of precipitation falling as snow across the north-western
Himalayas between 1996 and 2005. The snow cover season has been shortening as
a result (Pepin et al., 2015). Under different climate scenarios, glacier
imbalance in the region may contribute 8.7–17.6 mm of sea-level rise by
2100 (Huss and Hock, 2015). Prolonged mass loss from Himalayan glaciers may
cause diminishing discharge of the largest river systems originating in the
region (Immerzeel et al., 2010; Lutz et al., 2013), thereby impacting on
Asian water resources in the long term.
Recent studies have identified spatial heterogeneity in mass loss across the
Himalayas in the first decade of the 21st century (Kääb et al., 2012, 2015;
Gardelle et al., 2013). Glaciers in the Spiti Lahaul and
Hindu Kush are losing mass most quickly (Kääb et al., 2015). Glaciers in the
central Himalayas appear to have less negative mass balances (Gardelle et
al., 2013). The anomalous balanced, or even slightly positive, glacier mass
budget in the Karakoram is well documented (Bolch et al., 2012; Gardelle et
al., 2012). Few previous studies have assessed the variability of glacier
mass loss within catchments (Pellicciotti et al., 2015). Nuimura et al. (2012) examined the altitudinal distribution of glacier surface elevation
change in the Khumbu region, Nepal and found similar surface-lowering rates
over debris-free and debris-covered glacier surfaces. Gardelle et al. (2013)
detected enhanced thinning rates on lacustrine-terminating glaciers in
Bhutan, western Nepal and the Everest region, but did not make an explicit
comparison with land-terminating glacier recession rates. Similarly, Basnett
et al. (2013) have shown that lacustrine-terminating glaciers in Sikkim,
in the eastern Indian Himalayas, experienced greater area loss between ∼ 1990 and 2010 compared to land-terminating glaciers. Benn et al. (2012) have
considered the role of glacial lakes in the wastage of debris-covered
glaciers and proposed a conceptual model of Himalayan glacier recession that
included important thresholds between regimes of ice dynamics and mass loss
at different stages of lake development. Benn et al. (2012) suggest that an
expansive, moraine dammed and potentially hazardous glacial lake may
represent the end product of the wastage of a debris-covered glacier.
The glaciers of the Everest region. Named glaciers are the glaciers
we highlight in this study. Major catchments include the Tama Koshi and Dudh
Koshi on the southern flank of the Himalayas and the Pumqu river catchment on
the northern side of the divide, with glaciers flowing onto the Tibetan
Plateau (China). Named glacial lakes are highlighted, although many remain
unnamed. Background imagery is a Landsat OLI image from 2014 available from
http://earthexplorer.usgs.gov/.
We aim to quantify glacier mass loss rates in three major catchments of the
central Himalayas and assess the glacier-scale variability of ice loss within
and between catchments. We specifically examine the mass balance, hypsometry
and total area change of each glacier and compare those terminating in a
glacial lake with those terminating on land. We use these data together with
climatic data from the region to define the major mechanisms that may have
driven mass loss in recent decades and to assess scenarios of likely future
ice loss from our sample of glaciers.
Study area
We studied glaciers in three catchments of the Everest region (Fig. 1),
spanning both Nepal and Tibet (China). Two of the catchments, the Dudh Koshi
and the Tama Koshi, are located in north-eastern Nepal and drain the
southern flank of the Himalayas. The third catchment is located to the north
of the main orographic divide, and the glaciers drain north into Tibet
(China). Most glaciers in the studied catchments are characterised by long
(10–15 km) low-slope angled, debris-covered tongues that are flanked by
large (tens of metres high) moraine ridges (Hambrey et al., 2008). Some
glaciers have accumulation areas several kilometres wide that reach extreme
altitudes (up to 8000 m in the case of the Western Cwm of Khumbu Glacier).
Others sit beneath steep hillslopes (e.g. Lhotse and the Lhotse face), are
fed almost exclusively by avalanches and are less than 1 km in width for
their entire length.
The largest 40 of 278 glaciers in the Dudh Koshi catchment account for
70 % of the glacierised area (482 km2 – Bajracharya and Mool,
2009). These glaciers are all partially debris-covered, with debris mantles
reaching at least several decimetres in thickness (Rounce and McKinney, 2014;
Rowan et al., 2015). Here, the total area of glacier
surfaces covered by debris has
increased since the 1960s (Thakuri et al., 2014) and several previous studies
have published surface-lowering data for the catchment indicating
accelerating surface-lowering rates over recent decades (e.g. Bolch et al.,
2011; Nuimura et al., 2012). We select nine of the largest glaciers (Table S1
in the Supplement) for analysis, given that they provide the greatest
potential volume of meltwater to downstream areas.
There are a total of 80 glaciers in the Tama Koshi catchment covering a
total area of 110 km2 (Bajracharya et al., 2015). We again selected the
largest nine glaciers (Table S1) for analysis based on relative
potential contributions to river flow. The Tama Koshi is a poorly studied
catchment, perhaps best known for the existence of Tsho Rolpa glacial lake,
which underwent partial remediation during the 1990s (Reynolds, 1999).
The 14 glaciers within our sample that flow onto the Tibetan Plateau
(Table S1) all contribute meltwater to the Pumqu river catchment, which
covers an area of 545 km2 (Che et al., 2014). Debris cover is less
prevalent on glaciers of the Pumqu catchment, and glacier recession has
caused a 19 % of glacier area loss since 1970 (Jin et al., 2005; Che et
al., 2014). There is relatively little information on glacier ELAs other than
in the Dudh Koshi catchment. In the Dudh Koshi, Asahi (2001) estimated ELAs
to be at around 5600 m a.s.l. in the early 2000s. Wagnon et al. (2013)
measured annually variable ELAs of 5430–5800 m a.s.l. on the Mera and
Polkalde glaciers between 2007 and 2012, Shea et al. (2015) estimate the
current ELA to be 5500 m a.s.l., and Gardelle et al. (2013) estimated the
ELA to be around 5840 m over the period 2000–2009. In the Pumqu catchment,
those in the Rongbuk valley were estimated to be between 5800 and
6200 m a.s.l. for the period 1974–2006 (Ye et al., 2015).
A number of studies have identified an abundance of glacial water bodies in
the Everest region. Salerno et al. (2012) identified 170 unconnected glacial
lakes (4.28 km2), 17 proglacial lakes (1.76 km2) and 437 supraglacial lakes (1.39 km2) in the Dudh Koshi catchment. Gardelle et
al. (2011) identified 583 supraglacial ponds and lakes in an area comparable
in coverage to Fig. 1. Watson et al. (2016) mapped 9340 supraglacial ponds
onto eight glaciers of the Dudh Koshi catchment and Rongbuk glacier in the Pumqu
catchment. Watson et al. (2016) also show a net increase in ponded area for
six of their nine studied glaciers. Some of the largest glacial lakes in
this region have also been expanding in recent decades (Sakai et al., 2000;
Che et al., 2014; Somos-Valenzuela et al., 2014). This increased meltwater
ponding at glacier termini has the potential to affect ice dynamics and
down-valley meltwater and sediment fluxes (Carrivick and Tweed, 2013) as
well as causing a hazard to populations living downstream. Several of the
lakes have burst through their moraine dams in previous decades, causing
rapid and extensive flooding downstream; the best studied outburst floods
are those from Nare glacier in 1977 (Buchroithner et al., 1982) and from Dig
Tsho in 1985 (Vuichard and Zimmerman, 1987).
We classify nine glaciers from the sample as lacustrine terminating, where
the glacier termini and glacial lakes are actively linked. We do not
consider either Rongbuk Glacier or Gyabrag Glacier as lacustrine
terminating. Gyabrag Glacier is now separated from a large proglacial lake
by a large outwash plain, and we do not believe the lake can have an
influence on the retreat of the glacier. In the case of Rongbuk Glacier, the
lake is supraglacial and far up-glacier from its terminal region and thus
does not currently influence the recession of the terminus of the glacier.
The expanding Spillway Lake at the terminus of Ngozumpa Glacier (Thompson et
al., 2012) is currently of limited depth and is unlikely to affect glacier
dynamics in its current state so we also exclude Ngozumpa Glacier from the
lacustrine-terminating category.
Scenes used in glacier outline delineation, ASTER DEM generation,
SRTM ice facies mask generation and by the Polar Geospatial Center in the
generation of SETSM DEMs.
SensorScene IDDate ofPurposeacquisitionLandsat OLILC81400412014334LGN0030 Nov 2014Glacier outlinesLandsat ETM+LE71390412000302SGS0029 Oct 2000Glacier outlinesLandsat ETM+LE71400402002005SGS005 Jan 2002Ice facies maskLandsat ETM+LE71400412002005SGS005 Jan 2002Ice facies maskASTERL1A.003:201405054529 Nov 2014ASTER DEMWorldView 3WV03_20150121_10400100076C070021 Jan 2015SETSM DEMWorldView 1WV01_20150504_102001003C5FB9004 May 2015SETSM DEMWorldView 1WV01_20140115_102001002A289F0015 Jan 2014SETSM DEMWorldView 1WV01_20140324_102001002D26340024 Mar 2014SETSM DEMWorldView 1WV01_20150204_102001003A5B79004 Feb 2015SETSM DEMWorldView 2WV02_20150202_103001003D4C79002 Feb 2015SETSM DEMWorldView 1WV01_20140218_102001002C5FA10018 Feb 2014SETSM DEMWorldView 1WV01_20141022_102001003525D40022 Oct 2014SETSM DEMWorldView 2WV02_20141110_1030010039013C0010 Nov 2014SETSM DEMWorldView 1WV01_20141129_102001002776B50029 Nov 2014SETSM DEMWorldView 1WV01_20140514_102001003001E40014 May 2014SETSM DEMData sources and methodsData sourcesDigital elevation models
Our reference elevation dataset across all three catchments is the Shuttle
Radar Topography Mission (hereafter SRTM) version 3.0, non-void-filled,
1 arcsec digital elevation model (hereafter DEM). The main objective of the
SRTM mission was to obtain single-pass interferometric
Synthetic Aperture Radar (SAR) imagery to be used for DEM generation on a near-global scale
(56∘ S to 60∘ N – 80 % of the planet's surface) with
targeted horizontal and vertical accuracies of 16 and 20 m, respectively,
although Farr et al. (2007) report horizontal and vertical accuracies of
better than 10 m for most regions globally. This dataset was acquired in
February 2000 and was released at 30 m resolution in late 2014 (USGS, 2016).
The SRTM data we used were acquired by a 5.6 cm C-band radar system.
Our 2014/2015 elevation dataset comprises a number of high-resolution (8 m
grid) DEMs generated by Ohio State University and distributed online by the
Polar Geospatial Center at the University of Minnesota that provide coverage
of an extended area around the Everest region (Table 1). These
stereo-photogrammetric DEMs have been generated using a Surface Extraction
with TIN-based Search-space Minimization (hereafter SETSM) algorithm from
WorldView 1, 2 and 3 imagery (Noh and Howat, 2015). The SETSM algorithm is
designed to automatically extract a stereo-photogrammetric DEM from image
pairs using only the Rational Polynomial Coefficients (RPCs) as geometric
constraints. The geolocation accuracy of RPCs without ground control for
WorldView 1 and 2 data is 5 m (Noh and Howat, 2015) which may ultimately
result in matching failure. The SETSM algorithm updates RPCs to mitigate
this error and produces DEMs with an accuracy of ±4 m in X, Y and
Z directions (Noh and Howat, 2015). SETSM DEMs are gap filled using a natural
neighbour interpolation; we removed these pixels before DEM differencing and
calculating glacier mass balance.
Over two small areas of the Dudh Koshi (over the lower reaches of the Bhote
Kosi and Melung glaciers), the SETSM DEMs contained data gaps. To complete
coverage of DEMs over these glaciers we generated ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEMs and used
the surface to cover elevation bands across the glaciers for which no data
were available from the SETSM grids. We used ERDAS Imagine (2013) to generate
ASTER DEMs with ground control points (GCPs) matched between features in the
ASTER imagery and the high-resolution imagery available in Google Earth. We
used a large number of GCPs (45) and tie points (> 75) to
minimise the root mean square error of GCP
positions. All SETSM and ASTER DEMs were resampled to a 30 m resolution to
match that of the SRTM data before any differencing was carried out.
Glacier outlines
Glacier outlines were downloaded from the Global Land Ice Measurements from
Space Randolph Glacier Inventory (RGI) Version 5.0 (Arendt et al.,
2015) and modified for 2000 and 2014 glacier extents based on Landsat scenes
closely coinciding in acquisition with the DEM data. Glacier extents from
these two epochs were used to calculate area changes. The 2000 Landsat scene
was acquired by the Enhanced Thematic Mapper Plus (ETM+) sensor and thus
has a single 15 m resolution panchromatic band and six 30 m multispectral
bands. The 2014 scene was acquired by the Operational Land Imager (OLI)
sensor and has a single 15 m panchromatic band as well as eight 30 m
multispectral bands. Both scenes were pan-sharpened to match the resolution
of the multispectral bands to that of the panchromatic band before glacier
outlines were adjusted. Adjustments were limited to correcting changes in
glacier frontal position and changes along the lateral margins because of
surface lowering.
Mean differences and the standard deviation associated with
off-glacier elevation difference data between ASTER, SETSM and SRTM DEMs
before and after the DEM correction process. The uncertainty associated with
DEM difference data (sum of standard error estimates for each 100 m
elevation bin of difference data) is also listed for each SETSM and ASTER
DEM.
SensorASTER scene IDPre correction mean Post correction mean dh/dtand SD stable ground and SD stable ground uncertaintydifferences Vs SRTM (m) differences Vs SRTM (m) (±m a-1)ASTERL1A.003:2014050545-64.1225.990.4311.300.47SETSM tile WV 3WV03_20150121_10400100076C0700-6.0711.540.536.430.25WV 1WV01_20150504_102001003C5FB900-5.6815.76-0.435.890.40WV 1WV01_20140115_102001002A289F00-3.569.500.506.640.27WV 1WV01_20140324_102001002D263400-2.218.920.075.900.33WV 1WV01_20150204_102001003A5B7900-1.2617.50-0.365.650.31WV 2WV02_20150202_103001003D4C7900-3.8012.34-0.036.560.29WV 1WV01_20140218_102001002C5FA100-2.009.80-0.236.710.28WV 1WV01_20141022_102001003525D400-9.5416.500.366.890.35WV 2WV02_20141110_1030010039013C00-2.899.830.075.870.15WV 1WV01_20141129_102001002776B500-5.728.310.164.760.18WV 1WV01_20140514_102001003001E400-3.5110.12-0.265.910.26DEM correctionStereoscopic DEMs
We followed the three-step correction process of Nuth and
Kääb (2011), through which biases inherent in stereoscopic DEMs can
be corrected. We assessed and corrected where necessary for (i) a mismatch in
the geolocation of the modern DEMs versus the reference SRTM dataset (in x,
y and z direction), (ii) the existence of an elevation dependant bias and
(iii) biases related to the acquisition geometry of the data. Each step was
taken individually, so that separate error terms could be understood, rather
than bundling them together as multiple-regression-based adjustments as
previous studies have done, such as Racoviteanu et al. (2008) and Peduzzi et
al. (2010). Corrections applied to DEMs for which any one of the three biases
were present included shifting DEM corner coordinates, simple vertical
shifting through addition or subtraction, and the fitting of linear and
polynomial trends depending on the spatial variability of elevation
differences across DEMs and through their elevation ranges. Acquisition of
geometry-related biases (along or cross satellite track) were detected in two
SETSM strips (Table 3) and both ASTER scenes and were corrected for using
linear trends fitted through difference data. DEM co-registration was carried
out following the conversion of SETSM elevation data to geoid heights using
the Earth Gravitational Model (EGM) 2008 grid available from the National
Geospatial-Intelligence Agency. Table 2 shows a summary of DEM difference
data over stable, off-glacier areas before and after DEM coregistration.
SRTM DEM correction
Some studies have shown that the SRTM dataset may underestimate glacier
surface elevations because of C-band radar wave penetration into snow and
ice (Rignot et al., 2001). Kääb et al. (2012) assessed the magnitude of
C-band penetration over various test sites in the Himalayas and over
different ice facies (clean ice, snow and firn) by extrapolating ICESat vs.
SRTM glacier elevation differences back to the SRTM acquisition date,
showing penetration estimates of several metres. To account for this bias,
we have corrected the SRTM dataset using the penetration estimates of Kääb
et al. (2012), after generating a mask for clean ice, firn and snow cover
using the most suitable Landsat ETM+ scenes (Table 1) available around the
acquisition date of the SRTM dataset. We applied a correction to the SRTM
DEM of +4.8 m over areas of firn/snow and +1.2 m over areas of clean
ice (see Table S2 of Kääb et al., 2012). We do not apply any
penetration correction over debris-covered areas given the uncertainty
expressed by Kääb et al. (2012) about the influence of possibly
greater than average snowpack depth at the point of ICESat acquisition and
the properties of the snowpack at the point of SRTM data acquisition on
their penetration estimate.
Berthier et al. (2006) suggested that the extreme topography present in
mountain regions is poorly replicated in coarse-resolution DEMs such as the
SRTM DEM. Different studies have applied positive or negative corrections to
the SRTM DEM (Berthier et al., 2007; Larsen et al., 2007), depending on the
severity of the terrain at their respective study sites. Inspection of DEM
differences across the study site showed no clear relationship between
elevation differences and altitude (see Fig. S1);
thus no elevation-dependant correction was applied.
Gap filling and outlier filtering
Once DEMs had been co-registered and corrected for present biases, DEMs were
differenced to yield surface elevation change data. To remove outlying
values, we firstly excluded obviously incorrect difference values
(exceeding ±120 m) and then followed the approach of Gardelle et al. (2013) in using the standard deviation of DEM difference data to classify
probable outliers. We removed values exceeding 3 standard deviations. Such
outlier definitions are justified in areas of shallow slope and high image
contrast when DEM quality is generally high (Ragettli et al., 2016), but
could be considered lenient where featureless surfaces, for example snow-covered areas of accumulation zones, might lead to poor elevation data
derivation and limit the accuracy of stereoscopic DEMs. Noh and Howat (2015)
show how the iterative approach of the SETSM algorithm and the high spatial
and radiometric resolution of WorldView imagery preclude such an issue, and
we therefore consider a 3 standard deviation threshold appropriate.
To complete data coverage and allow for glacier mass balance estimates, the
filling of data gaps was required. Only small (< ∼ 5 × 5 grid cells) gaps were present in DEM difference data over most of the
glaciers in our sample, but some larger gaps could be found over areas of
steep surface slope, for example high in accumulation zones or where deep
shadows might have been extensive in WorldView imagery. We filled gaps in
DEM difference data using median values from the 100 m elevation band in
which the data gap was situated (Ragettli et al., 2016).
UncertaintyDEM differencing uncertainty
Our elevation change uncertainty estimates have been calculated through the
derivation of the standard error (EΔh) – the standard deviation
of the mean elevation change – of 100 m altitudinal bands of elevation
difference data (Gardelle et al., 2013; Ragettli et al., 2016):
EΔh=σstableN,
where σstable is the standard deviation of the mean elevation
change of stable, off-glacier terrain, and N is the effective number of
observations (Bolch et al., 2011). N is calculated through the following:
N=Ntot×PS2d,
where Ntot is the total number of DEM difference data points, PS is
the pixel size and d is the distance of spatial autocorrelation. We follow
Bolch et al. (2011) in estimating d to equal 20 pixels (600 m). EΔh for each DEM is the sum of standard error estimates of each altitudinal
band (Gardelle et al., 2013).
We have also considered whether the different acquisition dates of WorldView
imagery (Table 1) have led to the sampling of seasonal glacier surface
elevation variations caused by a remnant snowpack (e.g. Berthier et al.,
2016). Such a bias should be partly corrected for during vertical DEM
adjustment using off-glacier terrain assuming a similar snowpack thickness
on and off-glacier (Wang and Kääb, 2015). Two overlapping SETSM DEMs
(ending FA100 and 3C00 in Table 1) have been generated from WorldView
imagery acquired before and after the summer monsoon (when glaciers receive
most accumulation) of 2014; thus any spatially consistent vertical
differences may show a remnant snowpack that would cause an elevation bias.
The difference between these two SETSM DEMs over the Bamolelingjia and G1
glaciers is slight (mean 0.69 m, σ 3.81 m), but we cannot be sure
that these differences represent a region-wide average. We have incorporated
the mean elevation difference of these SETSM DEMs over glacier surfaces
(dZseason) into our overall uncertainty budget. We summed different
sources of error quadratically to calculate our overall uncertainty (σdh/dt) associated with DEM difference data:
σdhdt=EΔh2+dZseason2.σdh/dt is then weighted depending on the hypsometry of each
glacier, giving a glacier-specific measure of elevation change uncertainty
that considers the spatially non-uniform distribution of uncertainty
(Ragettli et al., 2016).
Glacier area change uncertainty
There are two principal sources of uncertainty in the measurement accuracy
of the position of a glacier margin: sensor resolution and the
co-registration error between the images acquired at each measurement epoch
(Ye et al., 2006; Thakuri et al., 2014). We follow the approach of Ye et al. (2006) to quantify the uncertainty associated with the total area changes
documented across our sample of glaciers. We incorporate geolocation
accuracy estimates of 10.5 m for Landsat ETM+ imagery and 6.6 m for
Landsat OLI imagery (Storey et al., 2014) into the uncertainty budget and
suggest the total measurement uncertainty in glacier area between 2000 and
2015 image sets was ±0.04 km2 a-1. Area-weighted, glacier-specific uncertainty estimates are given in Table S3.
Hypsometric analyses and elevation range normalisation
Glacier hypsometry, the distribution of a glacier area over altitude, is
governed by valley shape, relief and ice-volume distribution (Jiskoot et
al., 2009). It is important for the long-term glacier response because it
defines the distribution of mass with elevation and thus determines how the
glacier responds to changes in elevation-dependent temperature (Furbish and
Andrews, 1984). To assess glacier hypsometry, we used the aforementioned
glacier outlines and the SETSM DEMs, which offer better data coverage than
the non-void-filled SRTM dataset, to split these glacier extents into
segments covering 100 m elevation ranges, and calculated the area of each
segment. We followed the approach of Jiskoot et al. (2009) to categorise
each glacier or the population of glaciers in each catchment according to a
hypsometric index (HI), where
HI=Hmax-HmedHmed-Hmin
and Hmax and Hmin are the maximum and minimum elevations of
the glacier, and Hmed is the median elevation that divides the glacier
area in half (Jiskoot et al., 2009). Glaciers were grouped into five HI
categories: 1 is HI < -1.5, very top heavy; 2 is HI -1.2 to -1.5, top
heavy; 3 is HI -1.2 to 1.2, equidimensional; 4 is HI 1.2 to 1.5, bottom heavy;
and 5 is HI > 1.5, very bottom heavy. Top-heavy glaciers store more
ice at higher elevation, for example in broad accumulation zones, whereas
bottom heavy glaciers have small accumulation zones and long tongues.
To construct elevation change and glacier hypsometry curves for the 32 glaciers in our sample, we have normalised the elevation range of each
glacier following the method of Arendt et al. (2006):
Hnorm=H-HminHmax-Hmin,
where Hmin and Hmax are the elevations of the glacier terminus and
the elevation maximum of each glacier. This normalisation process allows for
a direct comparison of elevation changes and glacier hypsometry regardless of
termini elevation. Surface elevation change and glacier hypsometry curves
are presented in Figs. 5 and 6.
Mass loss calculations
A conversion factor of 850 kg m-3 was used to account for the density
of glacier ice for all glaciers in the sample (Huss, 2013). We assigned an
additional 7 % to mass loss uncertainty estimates to account for error in
the density conversion (Huss, 2013). The mass loss estimates generated for
lacustrine-terminating glaciers are slight underestimates because, with no
information available on bed topography, we cannot account for ice that has
been replaced by water during lake expansion. Mass balance estimates for
these glaciers therefore only incorporate aerial mass loss from the 2000
calving front, up-glacier. We also acknowledge that our surface-lowering
estimates incorporate any upward or downward flow of ice resulting from, for
example, compressional flow over a zone of transition from active to
inactive ice. We do not quantify emergence velocity as the ice thickness and
surface velocity data required to do so (Immerzeel et al., 2014) are not
available for an adequate number of glaciers in our sample.
Estimation of ELAs
We follow the approach of Braithwaite and Raper (2010) in using the median
altitude of each glacier, information available in the RGI, to estimate the
ELA of glaciers in our sample. Such an approach is most appropriate for
glaciers in a state of balanced mass budget (Braithwaite and Raper, 2010;
Braithwaite, 2015); thus the ELA estimates produced using this method could
be considered an underestimate of modern-day ELAs given the negative state of
mass balance of the majority of Himalayan glaciers. However, without measured
mass balance records of adequate length against which to compare this or
other (Braithwaite, 2015) ELA estimation methods, we take it as the best
available approach. This is a method that has previously been employed in the
Himalayas (Zhao et al., 2016), although we also note that this method cannot
account for the input of avalanched material from steep valley walls – a
substantial source of accumulation for Himalayan glaciers (Benn and Lehmkuhl,
2000). To estimate prospective future ELAs in response to temperature
increases, we used vertical temperature gradients of
-8.5 ∘C km-1 for the Pumqu catchment (Kattel et al., 2015)
and -5.4 ∘C km-1 for the Dudh Koshi and Tama Koshi
catchments (Immerzeel et al., 2014) to calculate prospective ELA shifts given
different warming scenarios. We calculated ELAs for projected minimum, mean
and maximum temperature increases under the four main RCP (Representative Concentration Pathways) scenarios
outlined in the IPCC AR5 working group report (Collins et al., 2013).
ResultsGlacier mass balance
The mean mass balance of all 32 glaciers in our sample was
-0.52 ± 0.22 m w.e. a-1 between 2000 and 2015. There is
considerable variability in the mass balance of glaciers with different
terminus type (Figs. 3 and 4) and in the rates of surface lowering through
the altitudinal range of highlighted glaciers (Figs. 5 and 6). The mean mass
balance of glaciers in catchments either side of the orographic divide are
not markedly different, however.
Glacier surface elevation change over the study area between 2000
and 2014/15. Also shown is a summary of off-glacier terrain differences.
Areas of no data show the ASTER GDEM underlay.
Mean glacier mass balance (including land and lacustrine-terminating
glaciers) was -0.51 ± 0.22 m w.e. a-1 in the Tama Koshi
catchment, -0.58 ± 0.19 m w.e. a-1 in the Dudh Koshi
catchment and -0.61 ± 0.24 m w.e. a-1 for glaciers flowing
into the Pumqu catchment over the study period. The mean mass balance of
nine lacustrine-terminating glaciers was -0.70 ± 0.26 m w.e. a-1. This was 32 % more negative than land-terminating glaciers (mean
mass balance of -0.53 ± 0.21 m w.e. a-1) we include in our
sample. The lowest mass loss rates occurred over debris-free glaciers at
high altitude (5600–6200 m a.s.l) in the Pumqu catchment. The mean mass
balance of these glaciers was -0.25 ± 0.22 m w.e. a-1
(Table S2) over the study period. Individual glacier mass
balance estimates can be found in the Supplement.
Glacier surface lowering
The altitude at which maximum surface-lowering rates occurred differed
depending only on glacier terminus type (Figs. 5 and 6). Across all three
catchments, substantial surface lowering was pervasive over the middle
portions of larger, land-terminating glaciers (Fig. 2). In the Dudh Koshi,
surface-lowering rates are at their highest (-1.06 ± 0.10 m a-1)
around 5200 m a.s.l., although similar surface-lowering rates occurred
between 5100 and 5300 m a.s.l (Fig. 5). In the Tama Koshi the highest
rates of surface lowering (-1.08 ± 0.12 m a-1) occurred at
around 5400 m a.s.l (Fig. 5). In the Pumqu catchment, the highest mean
surface-lowering rates again occurred between 5300 and 5400 m a.s.l.; the
mean surface-lowering rate at this altitude was -1.62 ± 0.14 m a-1 over the study period. Surface-lowering rates over glaciers in the
Pumqu catchment were higher than those in the Tama Koshi and Dudh Koshi
catchments (Fig. 5) up to 5700 m a.s.l. (-1.24 ± 0.21 m a-1 at this altitude). Of note is the surface lowering over clean-ice areas
high up on glaciers such as Ngozumpa, Rongbuk, Gyabrag and Bhote Kosi
(Fig. 2). Surface lowering extended into tributary branches and the
cirques of these largest glaciers. Individual glaciers showed much greater
surface lowering, particularly in the Pumqu catchment. Gyabrag glacier lost
an exceptional -3.33 ± 0.28 m a-1 between 5300 and 5400 m a.s.l (Fig. 5).
Examples of surface elevation change and total area change over the
study period on lacustrine-terminating glaciers. Semi-transparent,
off-glacier differences are also shown.
Further examples of glacier surface elevation change and total area
change over the study period on lacustrine-terminating glaciers.
Semi-transparent, off-glacier differences are also shown.
Surface elevation change and glacier hypsometry curves for all land
terminating glaciers in the three different catchments of the study area.
Surface-lowering and glacier hypsometry curves for clean ice and
lacustrine-terminating glaciers in the study area.
The maximum surface-lowering rates (-2.79 ± 0.29 m a-1)
occurred at the lowest elevations (between 4700 and 4900 m a.s.l) of
lacustrine-terminating glaciers (Fig. 6). These nine glaciers all showed a
linear surface-lowering gradient. We calculate the lowering gradient as
surface elevation change per 100 m [m a-1 (100 m)-1] vertical
elevation change below the ELA. Lacustrine-terminating glaciers showed a
lowering gradient of 0.30 m a-1 (100 m)-1 over the study
period. The lowering gradient of land-terminating glaciers was non-linear.
Surface lowering was negligible around the terminus of most land terminating
glaciers, with enhanced ice loss occurring further up-glacier where debris
cover may have been thin or patchy. Lowering gradients for the area of
land-terminating glaciers between the ELA and the altitude of maximum ice
loss were 0.59, 0.66 and 0.38 m a-1 (100 m)-1 for glaciers in
the Pumqu, the Dudh Koshi and Tama Koshi catchments, respectively. Clean-ice
glaciers also showed a linear lowering gradient –
0.77 m w.e. a-1 (100 m)-1.
Glacier area changes and hypsometryTotal area changes
Two different patterns of ice area loss occurred over the study area during
the last 15 years. Lacustrine-terminating glaciers and clean-ice glaciers
all lost ice around their termini/calving fronts (Figs. 3 and 4) as
glacial lakes expanded and termini receded. On average, lacustrine
terminating glaciers each lost 0.54 ± 0.07 km2 of ice (3.58 %
of their total area) over the 15-year study period. Drogpa Nagtsang reduced
in size by 2.37 km2 (9.12 % of its total area; Table S3) as the associated rapidly forming lake expanded. Clean-ice glaciers lost
0.09 ± 0.03 km2 of ice (1.31 % of their total area) on
average.
Land-terminating glaciers lost little area as their surfaces lowered instead of their termini retreating. In the Tama Koshi and Dudh Koshi catchments,
and in the Pumqu catchment, land-terminating glaciers lost a mean of 0.14 ± 0.12 km2 (0.50 % of their total area),
0.09 ± 0.13 km2 (0.60 % of their total area) and 0.41 ± 0.12 (1.77 % of
their total area) of ice, respectively. Over these glaciers, any ice area
loss was concentrated up-glacier, where their lateral margins dropped down
inner moraine slopes and glacier tongues narrowed slightly.
Overall, our sample of glaciers lost 0.12 ± 0.04 % of their total
area per year over the study period. This figure is identical to that of
Bolch et al. (2008), who assessed area change over a smaller number of the
same glaciers in our sample between 1962 and 2005. The annual area change
rate we calculated is lower than those estimated by Thakuri et al. (2014) and
references within. Thakuri et al. (2014) calculated a median annual surface
area change rate of -0.42 ± 0.06 % a-1 in the Dudh Koshi
catchment between 1962 and 2011. However, Thakuri et al. (2014) document area
change over a number of smaller glaciers that are free of debris cover and
therefore readily advance or retreat in response to climatic change; thus our
estimates are not directly comparable.
Glacier hypsometry and approximate ELAs
The distribution of ice with elevation varies widely among the three studied
catchments (Figs. 5 and 6). Debris-covered glaciers of the Dudh Koshi
catchment and the Pumqu catchment are typically very bottom heavy, with
average HI scores of 2.63 and 2.34 (Table S1).
Glacier hypsometry is concentrated between 4800 and 5500 m (Fig. 5) for
the Dudh Koshi catchment and between 5600 and 6500 m in the Pumqu
catchment. Notable exceptions are the Khumbu and Ngozumpa glaciers which store
ice in broad accumulations zones above 7000 m (Tables S1 and
S2). The majority of glaciers in the Tama Koshi have an equidimensional
hypsometry (mean HI of 1.14), with most ice stored between 5300 and 5800 m.
Glaciers in the Tama Koshi have broader accumulation basins than in the Dudh
Koshi catchment, and the main glacier tongues are formed of multiple, smaller
tributaries flowing from higher altitude in a number of cases (Fig. 1).
The mean hypsometry (Fig. 6) of lacustrine-terminating glaciers shows no
distinctive morphology as the sample is composed of glaciers from all three
catchments in the study area. Clean-ice glaciers have a mean HI of 1.18 and
could therefore be summarised as equidimensional, but the morphology of the
five glaciers we assess is highly variable (see Table S3). In
complete contrast to debris-covered glaciers, their ice is stored at higher
mean altitudes on average, primarily between 6000 and 6500 m (Fig. 6).
We estimate the mean ELA of debris-covered glaciers in the Dudh Koshi and
Tama Koshi catchments, and of our selection of glaciers in the Pumqu
catchment to be 5477, 5568 and 6037 m a.s.l., respectively. We estimate the
mean ELA of the five clean-ice glaciers in our sample to be 6216 m. Using those
ELAs, the accumulation area ratio (AAR) (Dyurgerov et al., 2009) can be
estimated for each glacier. We have calculated mean AARs of 0.41, 0.43 and
0.37 for debris-covered glaciers in the Dudh Koshi, Tama Koshi
and Pumqu catchments. The mean AAR of clean-ice glaciers in our sample is 0.39.
DiscussionVariability in rates of ice loss across the orographic divide
The mean mass balance estimates we have derived for glaciers situated in
catchments north and south of the main orographic divide are not markedly
different. However, the contrast in maximum surface lowering (Fig. 5)
from glaciers flowing north of the divide and the sustained surface lowering
through a broader portion of their elevation range (Fig. 5) suggest that an
additional or amplified process has driven glacier change north of the
divide over recent decades. In this section we discuss possible topographic
and climatic drivers of the difference in the rates of surface lowering
across the range divide.
The Indian summer monsoon delivers a large proportion of total annual
precipitation (up to 80 % of the total annual amount) to the Everest
region of Nepal, resulting in high glacier sensitivity to temperature
(Fujita, 2008; Sakai et al., 2015). The extreme topography in this region
and the location of the orographic divide perpendicular to the prevailing
monsoon result in rainfall peaks that are offset from the maximum
elevations, with greatest rainfall occurring to the south of the divide and
decreasing to the north across the Everest region (Bookhagen and Burbank,
2010; Wagnon et al., 2013). Around 449 mm a-1 of rainfall falls at the
Pyramid research station (5000 m a.s.l.) at Khumbu Glacier (Salerno et al.,
2015), whereas Dingri on the Tibetan Plateau (4300 m a.s.l.) to the north is
much drier with 263 ± 84.3 mm a-1 of rainfall annually (Yang et
al., 2011). Snowfall may follow a similar across-range gradient to rainfall,
although falling snow may be carried further into the range by prevailing
winds from the south. However, no reliable measurements of snowfall exist in
this region with which to compare these trends. The north–south
precipitation gradient across the orographic divide promotes differences in
the response of these glaciers to climate change, such that those to the
north are relatively starved of snow accumulation (Owen et al., 2009) and
exposed to greater incoming radiative fluxes under generally clearer skies.
Owen et al. (2009) suggest that this precipitation gradient resulted in
greater glacier sensitivity to climate change on the northern slopes of the
Himalayas during the Late Quaternary, with asymmetric patterns of ELA rise
occurring since the Last Glacial Maximum (LGM).
During the period of this study (2000–2015), mean annual air temperatures
have increased and rainfall amounts appear to have decreased in the Everest
region (Salerno et al., 2015). At the Pyramid Observatory at Khumbu Glacier
in the Dudh Koshi catchment, increases in minimum (+0.07 ∘C a-1), maximum (+0.009 ∘C a-1) and
mean (+0.044 ∘C a-1) annual air temperatures above 5000 m a.s.l. were
observed between 1994 and 2013 (Salerno et al., 2015). At Dingri on the
Tibetan Plateau, 60 km north-east of Mt Everest, increases in minimum
(+0.034 ∘C a-1), maximum (+0.041 ∘C a-1)
and mean (+0.037 ∘C a-1) annual air temperatures occurred
over the same period (Salerno et al., 2015). Yang et al. (2011) also show a
longer-term increase in the mean annual air temperature at Dingri, as do
Shrestha et al. (1999) across the southern flank of the greater Himalayas.
Between 1959 and 2007, the mean annual air temperature increased by 0.06 ∘C a-1at Dingri (Yang et al., 2011). Shestha et al. (1999)
calculated an increase in the mean annual air temperature of 0.057 ∘C a-1 between 1971 and 1994 across a number of sites in
the greater Himalayas.
The snow-line altitude also appears to have increased recently on the
southern flank of the Himalaya; Thakuri et al. (2014) showed a rapid ascent
of the snow-line altitude in the Dudh Koshi between 1962 and 2011 (albeit
through documenting transient snow lines from single scenes acquired at each
epoch), and Khadka et al. (2014) suggest declining snow cover over the
winter and spring months in the glacierised altitudinal ranges of the Tama
Koshi catchment, between 2000 and 2009; a factor that may influence
accumulation rates. Kaspari et al. (2008) showed decreasing accumulation
recorded in an ice core collected from East Rongbuk Glacier Col (6518 m a.s.l.) on the northern side of Mt Everest between the 1970s and 2001.
We suggest that the north–south orographic precipitation gradient across the
main divide may have caused greater surface-lowering rates on glaciers in the
Pumqu catchment than those glaciers to the south over the study period. We
also suggest that measured, contemporary increases in air temperature,
observations of increasing snow-line altitude and declining accumulation are
likely to enhance glacier mass loss across the range in future, but
considerable unknown factors remain in the temporal evolution of
debris cover extent and thickness (Thakuri et al., 2014), the strength of the
summer monsoon in coming decades (e.g. Boos and Storelvmo, 2016), and the
expansion or shrinkage of glacial lakes (see Sect. 5.3), all of which could
additionally influence future glacier mass balance.
Comparison of mass balance estimates with other studies
Several other studies have generated geodetic mass balance estimates for
glaciers of the Everest region over several different time periods. Bolch et
al. (2011) generated balance estimates of -0.32 ± 0.08 and -0.79 ± 0.52 m w.e. a-1 for 10 glaciers to the
south and west of Mt Everest over the periods 1970–2007 and 2002–2007,
respectively. Nuimura et al. (2012) calculated a regional mass
balance of -0.45 ± 0.25 m w.e. a-1 for 97 glaciers across the
region over the period 1992–2008. Kääb et al. (2012) estimated a mass
balance of -0.39 ± 0.11 m w.e. a-1 for a 3∘× 3∘ cell centred on the Everest region between 2003 and 2008.
Gardelle et al. (2013) calculated a slightly less negative mass balance of
-0.26 ± 0.13 m w.e. a-1 between 1999 and 2011, although the SRTM
penetration correction applied by Gardelle et al. (2013) may have caused a
bias towards a less negative mass balance (Kääb et al., 2012; Barundun et al.,
2015). The regional mass balance of -0.52 ± 0.22 m w.e. a-1 that we have calculated suggests that the mass loss rates measured by
Nuimura et al. (2012) and Kääb et al. (2012) have been sustained and
possibly increased in recent years (Table 3), as Bolch et al. (2011) also
suggest.
Mass balance estimates (from geodetic and altimetric studies) for
the broader Everest region and comparable subregions/ catchments.
Time periodMass balanceStudyand areaestimate(m w.e. a-1)Dudh Koshi 1970–2007-0.32 ± 0.08Bolch et al. (2011)1992–2008-0.45 ± 0.25Nuimura et al. (2012)2002–2007-0.79 ± 0.52Bolch et al. (2011)2000–2015-0.58 ± 0.19This studyPumqu (Tibetan Plateau) 1974–2006-0.40 ± 0.27Ye et al. (2015)2003–2009-0.66 ± 0.32Neckel et al. (2014)2000–2015-0.61 ± 0.24This studyTama Koshi 2000–2015-0.51 ± 0.22This studyEverest region 1999–2011-0.26 ± 0.13Gardelle et al. (2013)2003–2008-0.39 ± 0.11Kääb et al. (2012)2000–2015-0.52 ± 0.22This study
On the Tibetan Plateau, Neckel et al. (2014) estimated the mass balance of
glaciers on the northern side of the orographic divide in the central and
eastern Himalayas (their subregion G) to be -0.66 ± 0.36 m w.e. a-1 between 2003 and 2009. The mass balance of glaciers in our sample
within the same region was -0.59 ± 0.27 m w.e. a-1 between 2000
and 2015. Ye et al. (2015) estimated glacier mass balance to be -0.40 ± 0.27 m w.e. a-1 in the Rongbuk catchment between 1974 and 2006,
suggesting that glacier ice mass loss rates may have increased over the last
decade in this area of the Tibetan Plateau (Table 3).
The influence of glacial lakes on glacier mass balance
Only Nuimura et al. (2012) have directly compared mass loss rates of
lacustrine and land-terminating glaciers in the study area, showing faster
surface-lowering rates over Imja and Lumding glaciers in the Dudh Koshi
catchment. Our data confirm that lacustrine-terminating glaciers can indeed
lose ice at a much faster rate than land-terminating glaciers. The
variability in the mass balance of the nine lacustrine-terminating glaciers
(Fig. 6) we highlight suggests the fastest mass loss rates occur in the later
stages of lake development. Glaciers such as the Yanong and Yanong North, in
the Tama Koshi catchment, sit behind large proglacial lakes and are in a
state of heavily negative mass balance
(-0.76 ± 0.18 and -0.62 ± 0.25 m w.e. a-1,
respectively). Their surfaces lowered by 3 m a-1 or more over their
lower reaches (Fig. 6) over the study period. These glaciers are now
relatively small and steep and no longer possess a debris-covered tongue, and
so may represent the end product of debris-covered glacier wastage described
by Benn et al. (2012). In contrast, glaciers such as Duiya, in the Pumqu
catchment, currently has only a small lake at its termini, showed moderate
area losses (0.5 km2 or 4.28 % of its total area) and moderately
negative mass balance (-0.45 ± 0.13 m w.e. a-1) over the
study period. Continued thinning of the terminal regions of glaciers with
smaller glacial lakes would lead to a reduction in effective pressure, an
increase in longitudinal strain and therefore flow acceleration (Benn et al.,
2007). The retreat of the calving front up-valley into deeper bed topography
may also increase calving rates (Benn et al., 2007), and a combination of
both of these processes would lead to enhanced ice loss. Very few surface
velocity data exist for lacustrine-terminating debris-covered glaciers. Only
Quincey et al. (2009) measured high surface velocities (25 m a-1 or
more) over Yanong glacier (their Fig. 4d), suggesting it is possible for
lacustrine-terminating glaciers to become more dynamic in the later stages of
lake development in the Himalayas. Conversely, Thakuri et al. (2016) have
shown flow deceleration of glaciers that coalesce to terminate in Imja Tsho
over the period 1992–2014 and suggest that reduced accumulation caused by
decreasing precipitation is responsible for diminishing surface flow on this
glacier. Clearly, more expansive investigation into the evolving dynamics of
lacustrine-terminating glaciers in the Himalayas is required if we are to
better understand their potential future mass loss.
Glacier stagnation
A number of studies (Luckman et al., 2007; Scherler et al., 2008, 2011;
Quincey et al., 2009) have shown how many glaciers in the Everest region
appear to be predominantly stagnant, with large parts of the long,
debris-covered glacier tongues in the area showing little to no flow. Watson
et al. (2016) have documented an increasing number and total area of
supraglacial melt ponds over a number of the same glaciers studied by
Quincey et al. (2009) in the Dudh Koshi catchment (Khumbu, Ngozumpa, Lhotse,
Imja and Ama Dablam), since the early 2000s. Over these glaciers, our data
show a very distinctive surface-lowering pattern (Fig. 2), with localised,
heterogenous surface lowering appearing to mirror the distribution of large
supraglacial ponds and ponds networks. This ice loss pattern is prevalent on
the Erbu, Gyachung, Jiuda, Shalong and G1 glaciers (Fig. 2), and high-resolution imagery available on Google Earth shows that these glaciers also
have well-developed networks of supraglacial ponds. We would therefore
suggest that large parts of the biggest glaciers in the Tama Koshi catchment
and in the Pumqu catchment are also stagnant and may see increasing
supraglacial meltwater storage in the future, similar to that documented by
Watson et al. (2016).
Susceptibility of glaciers to future mass lossELA ascent in response to temperature increases
The coincidence of maximum surface-lowering rates with the altitude of
maximum hypsometry in the Dudh Koshi catchment (Fig. 5) suggests large
glacier mass losses in this catchment. Sustained and prolonged mass loss may
lead to a bimodal hypsometry here, with the physical detachment of
debris-covered glacier tongues and their high-elevation accumulation zones a
possibility (Rowan et al., 2015; Shea et al., 2015). Surface-lowering maxima
in the Tama Koshi catchment presently occur at a slightly lower elevation
range than the main hypsometric concentration, and across lower reaches of
glacier tongues in the Pumqu catchment.
Figure 7 shows projected AARs, averaged across each catchment, in response
to different levels of temperature rise. These predictions are based on
published lapse rates (Immerzeel et al., 2014; Kattel et al., 2015) that may
be spatially variable and assume no changes in precipitation type or amount
or any variability in the contribution of avalanches to accumulation.
To allow a comparison of our results with similar estimates of other
studies (Shea et al., 2015; Rowan et al., 2015), we focus specifically on
ELA rise resulting from RCP 4.5 minimum and maximum projected warming of
annual air temperatures (+0.9 to +2.3 ∘C by 2100). Such
temperature increases would cause a rise in ELA of between 165 and 425 m in
the Dudh and Tama Koshi catchments and between 107 and 270 m of ELA ascent
over glaciers in the Pumqu catchment. A rise in ELAs would most
significantly affect the Tama Koshi catchment glaciers, which currently have
the highest catchment-averaged AAR, 0.43. RCP 4.5 warming could cause AAR
decrease to 0.29 and 0.08, respectively, in the Tama Koshi catchment. The
greater altitudinal range and higher accumulation zones of glaciers in the
Dudh Koshi catchment and in the Pumqu catchment would dampen the effects of
a rise in ELA on glacier mass balance, with AAR adjustment occurring more
gradually (Fig. 7). AARs could decrease to 0.27 or 0.17 in the Dudh Koshi
and to 0.29 or 0.18 in the Pumqu catchment. ELA rise in response to this
particular warming scenario would mean a 12–30 % increase in the total
glacierised area below the ELA in the Pumqu catchment, a 24–61 %
increase in the Tama Koshi catchment and a 23–40 % increase in the Dudh
Koshi catchment. Should more substantial temperature increases occur
(> 2 ∘C warming), AARs could reduce to zero on a number of
individual glaciers, and the ELA could rise to near-maximum glacier
altitudes in the Tama Koshi catchment. Clean-ice glacier AAR adjustment may
be rapid given their limited altitudinal range (Fig. 7).
Projected AARs (averaged across each catchment) based on different
scenarios of temperature increase relative to the present day and
accompanying ELA rise. Temperature rise scenarios have been used from the
IPCC AR5 Working Group report. P is Pumqu, DK is Dudh Koshi, TK is Tama
Koshi and Clean is clean-ice glaciers. Each point represents a projected AAR
given minimum, mean or maximum temperature rise under each RCP scenario.
Glacier AAR is a parameter strongly related to long-term mass balance for
typical alpine glaciers (König et al., 2014), although the effect of a
diminishing AAR may be dampened on Himalayan glaciers given the large input
of avalanche material derived from high surrounding headwalls (Iturrizaga,
2011). Since data on the rates of avalanching in high-mountain environments
such as the Himalayas are sparse (Benn and Lehmkuhl, 2000), the impact of
predicted AAR reduction remains somewhat uncertain.
Conclusions
DEM differencing has revealed substantial mass loss from many large,
debris-covered glaciers in the central Himalayas over the last 15 years.
Geodetic mass balance estimates have been calculated for 32 glaciers across
three different catchments around the Everest region. We found similarly
negative mass budgets for glaciers flowing onto the southern flank of the
Himalayas, in the Tama Koshi (-0.51 ± 0.22 m w.e. a-1) and Dudh
Koshi (-0.58 ± 0.19 m w.e. a-1) catchments, and in the Pumqu
catchment (-0.61 ± 0.24 m w.e. a-1).
The division of our sample of glaciers depending on their terminus type
shows contrasting mass loss rates between land and lacustrine-terminating
glaciers. The mean mass balance of nine lacustrine-terminating glaciers we
assessed was -0.70 ± 0.26 m w.e. a-1, 32 % more negative than
land-terminating glaciers (mean mass balance of -0.53 ± 0.21 m w.e. a-1). The mass balance of nine lacustrine-terminating glaciers ranged
from -0.91 ± 0.22 to -0.45 ± 0.13 m w.e. a-1 and we would suggest that glacial lakes in the region are at
different stages of expansion. Accelerating mass loss is likely from several
of these lacustrine-terminating glaciers, the termini of which will retreat into
deeper lake water.
Surface-lowering curves show that the maximum-lowering rate (-1.62 ± 0.14 m a-1 between 5300 and 5400 m.a.s.l.) of glaciers in the Pumqu
catchment was well above the maximum-lowering rate of glaciers flowing south
of the orographic divide (-1.06 ± 0.10 m a-1 between 5200 and
5300 m a.s.l. in the Dudh Koshi catchment, -1.08 ± 0.12 m a-1
between 5200 and 5300 m a.s.l. in the Tama Koshi catchment), and that
glaciers in the Pumqu catchment are losing ice over a much broader
altitudinal range than their south-flowing counterparts. We suggest that the
across-range contrast in annual precipitation amount, combined with rising
mean air temperatures over recent decades may have caused greater ice loss
rates from the north-flowing glaciers.
Predicted warming in the Everest region will lead to increased ELAs and,
depending on glacier hypsometry, substantial increases in the size of
ablation areas. We show that glaciers of the Tama Koshi catchment will see
the greatest reduction in glacier AAR due to their equidimensional
hypsometry and more limited elevation range in comparison to glaciers of the
Dudh Koshi or in the Pumqu catchment. A warming of +0.9 to +2.3 ∘C by 2100 (IPCC RCP 4.5) would decrease glacier AAR to 0.29 or 0.08 in
the Tama Koshi catchment, 0.27 or 0.17 in the Dudh Koshi catchment and 0.29
or 0.18 in the Pumqu catchment.
Our findings are important for two reasons. First, they suggest that glacial
lake growth and current glacial lake expansion that have been documented
across the Himalayas could be accompanied by amplified glacier mass loss in
the near future. Second, they show that glacier AAR adjustment in response
to predicted warming across the Himalayas could be spatially very variable,
complicating the prediction of future glacier meltwater run-off contribution
from river catchments across the region.
Data availability
DEM difference data are available upon request. Please contact Owen King for
this purpose (gy08ok@leeds.ac.uk). SETSM
DEMs are available for download from http://www.pgc.umn.edu/elevation.
The SRTM dataset is available from https://lta.cr.usgs.gov/SRTM1Arc via
https://earthexplorer.usgs.gov/ (USGS, 2016).
EGM2008 gridded data are available from
http://earth-info.nga.mil/GandG/wgs84/gravitymod/egm2008/egm08_gis.html.
The Supplement related to this article is available online at doi:10.5194/tc-11-407-2017-supplement.
Owen King, Duncan J. Quincey and Jonathan L. Carrivick designed the study. Owen King carried out all data processing and
analysis. Owen King, Duncan J. Quincey, Jonathan L. Carrivick and Ann V. Rowan wrote the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
Owen King is a recipient of a NERC DTP PhD studentship. We are grateful
to Benjamin Robson for his comments on an early version of
the paper, and for guidance on the use of SETSM data from Ian Howat. We
finally thank Tobias Bolch, Joseph Shea and an anonymous reviewer for their
thorough and constructive assessments of the manuscript.
Edited by: T. Bolch
Reviewed by: J. M. Shea and one anonymous referee
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