TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-1433-2016Glacier melting and precipitation trends detected by surface area
changes in Himalayan pondsSalernoFranco https://orcid.org/0000-0002-3419-6780ThakuriSudeepGuyennonNicolashttps://orcid.org/0000-0002-0306-0610VivianoGaetanoTartariGianniNational Research Council, Water Research Institute (IRSA-CNR), Brugherio, ItalyNational Research Council, Water Research Institute (IRSA-CNR), Rome, ItalyEv-K2-CNR Committee, Via San Bernardino, 145, Bergamo 24126, ItalyFranco Salerno (salerno@irsa.cnr.it)11July2016104143314488February201622March201614June201615June2016This 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/10/1433/2016/tc-10-1433-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/1433/2016/tc-10-1433-2016.pdf
Climatic time series for high-elevation Himalayan regions are decidedly
scarce. Although glacier shrinkage is now sufficiently well described, the
changes in precipitation and temperature at these elevations are less clear.
This contribution shows that the surface area variations of unconnected
glacial ponds, i.e. ponds not directly connected to glacier ice, but that may
have a glacier located in their hydrological basin, can be considered as
suitable proxies for detecting past changes in the main hydrological
components of the water balance. On the south side of Mt Everest, glacier
melt and precipitation have been found to be the main drivers of unconnected
pond surface area changes (detected mainly with Landsat imagery). In general,
unconnected ponds have decreased significantly by approximately 10±5 % in terms of surface area over the last 50 years (1963–2013 period)
in the study region. Here, an increase in precipitation occurred until the
mid-1990s followed by a decrease until recent years. Until the 1990s, glacier
melt was constant. An increase occurred in the early 2000s, while a declining
trend in maximum temperature has caused a reduction in the glacier melt
during recent years.
Introduction
Meteorological measurements in high-elevation Himalayan regions
are scarce due to the harsh conditions of these environments and their
remoteness, which limit the suitable maintenance of weather stations (e.g.
Vuille, 2011; Salerno et al., 2015). Consequently, the availability of long
series is even more rare (Barry, 2012; Rangwala and Miller, 2012; Pepin et
al., 2015). Generally, gridded and reanalysis meteorological data are used to
overcome this lack of data and can be considered as an alternative (e.g. Yao et
al., 2012). However, in these remote environments, their use in climate
change impact studies at the synoptic scale must be done with caution
due to the absence of weather stations across the overall region, which
limits the ability to perform land-based evaluations of these products (e.g.
Xie et al., 2007). Consequently, the meager knowledge on how the climate has
changed in recent decades in high-elevation Himalayan regions presents a
serious challenge to the interpretation of the relationships between causes and
recently observed effects on the cryosphere. Although glacier reduction in
the Himalayas is now sufficiently well described (Bolch et al., 2012; Yao et
al., 2012; Kääb et al., 2012), the manner in which changes in climate
drivers (precipitation and temperature) have influenced the shrinkage and
melting processes is less clear (e.g. Bolch et al., 2012; Salerno et
al., 2015), and this lack of understanding is amplified when forecasts are
conducted.
In this context, the recent literature has already demonstrated the high
sensitivity of lakes and ponds to climate (e.g. Pham et al., 2008; Williamson
et al., 2008; Adrian et al., 2009; Lami et al., 2010). Some climate-related
signals are highly visible and easily measurable in lakes. For example,
climate-driven fluctuations in lake surface areas have been observed in many
remote sites. Smol and Douglas (2007a) reported decadal-scale drying of high
Arctic ponds due to changes in the evaporation/precipitation ratio. Smith et
al. (2005), among other authors, found that lakes in areas of discontinuous
permafrost in Alaska and Siberia have disappeared in recent decades. In the
Italian Alps, Salerno et al. (2014a) found that since the 1980s, lower
elevation ponds have experienced surface area reductions due to increased
evaporation / precipitation ratio for the effect of higher temperature,
while higher elevation ponds have increased in size and new ponds have
appeared as a consequence of glacial retreat.
In high mountain Asia and in particular in the interior of the Tibetan
Plateau, the observed lake growth since the late 1990s is mainly attributed
to increased precipitation and decreased evaporation (Lei et al., 2014; Song
et al., 2015). In contrast, Zhang et al. (2015) attribute the observed
increases in lake surface areas since the 1990s across the entire Pamir–Hindu
Kush–Karakoram–Himalayas region and the Tibetan Plateau region to enhanced
glacier melting. Wang et al. (2015) reached similar conclusions in a basin
located in the south-central Himalaya. In our opinion, the divergences in the
causes leading to the lake surface area variations in central Asia are due to
the fact that different types of glacial lakes (described below) have been
considered in these studies.
In general, in high mountain Asia, three types of glacial lakes can be
distinguished according to Ageta et al. (2000) and Salerno et al. (2012):
(i) lakes that are not directly connected with glacier ice but that may have
a glacier located in their hydrological basin (unconnected glacial lakes);
(ii) supraglacial lakes, which develop on the surface of a downstream portion
of a glacier; and (iii) proglacial lakes, which are moraine-dammed lakes that
are in contact with the glacier front. Some of these lakes store large
quantities of water and are susceptible to glacial lake outburst floods
(GLOFs). Factors controlling the growth of supraglacial lakes depend on the
glacier features from which they develop (surface gradient, mass balance,
cumulative surface lowering, and surface velocity) (Reynolds, 2000; Quincey
et al., 2007; Sakai and Fujita, 2010; Salerno et al., 2012; Sakai, 2012;
Thakuri et al., 2016). The causes of proglacial lake development are
decidedly similar to those described for supraglacial lakes (e.g. Bolch et
al., 2008; Salerno et al., 2012; Thakuri et al., 2016). Their filling and
drainage are linked to the supply of meltwater from snow or glacial sources
(Benn et al., 2001; Liu et al., 2015). In contrast, unconnected glacial lakes do not have a close
dependence on glacier dynamics, and this aspect makes them potential
indicators of the water balance components in high-elevation lake basins i.e.
precipitation, glacier melting, and evaporation. These main contributions
would best explain the causes of lake changes (e.g. Song et al., 2014; Wang
et al., 2015; Salerno et al., 2015). A valuable opportunity for a fine-scale
investigation on climate-driven fluctuations in lake surface area is
particularly evident on the south slopes of Mt Everest (Nepal), which is one
of the most heavily glacierized parts of the Himalayas (Scherler et
al., 2011). Additionally, this region has the largest number of lakes in the
overall Hindu-Kush-Himalayas range (Gardelle et al., 2011), and a 20-year
series of temperature and precipitation data has recently been reconstructed
for these high elevations (5000 m a.s.l.) (Salerno et al., 2015). Moreover,
the relative small size of the water bodies in this region, which we can be
defined as ponds according to Hamerlík et al. (2013) (a threshold of
2×104m2 exists between ponds and lakes), make them
especially susceptible to the effects of climatic changes because of their
relatively high surface area to depth ratios (Smol and Douglas, 2007b). This
contribution examines the surface area changes of unconnected glacial ponds
on the south side of Mt Everest (an example is shown in Fig. 1) during the
last 50 years (1963–2013). This study aims to evaluate whether they might be
used as a proxy to infer past spatial and temporal trends of the main
components of the hydrological cycle (precipitation, glacier melting, and
evaporation) at high elevations. Possible drivers of change are investigated
through land climatic data, available in the area, and correlation analysis.
Furthermore, morphological boundary conditions (glacier cover, pond size,
pond location, basin aspect, basin elevation) are analysed as possible
factors controlling the pond surface area changes. The study is concluded
comparing gridded and reanalysis time series (evaluated vs. land climatic
data) with observed pond surface area changes in the last 50 years.
Example of an unconnected glacial pond (LCN5) with a glacier within
the basin. Pictures were taken in September 1992 (Gabriele Tartari):
(a) view looking north showing the distance between the glacier and
the pond surface; (b) from east showing the frontal moraine.
(c) LCN5 basin tracked on ALOS 2008 imagery.
(a) Location of the
study area in the Himalayas and a detailed map of the spatial distribution of
all 64 unconnected ponds considered in this study. (b) Hypsometric
curve of SNP. Along this curve, the locations of 10 selected ponds are shown.
The 0 ∘C isotherms corresponding to the mean and maximum temperature
in 2013 are plotted for the pre-, post-, and monsoon period according to the
lapse rates reported in Salerno et al. (2015). The mean glacier elevation
distribution (mean ± 1 standard deviation) of 10 selected ponds and the
location of the Pyramid meteorological station are also reported.
Region of investigation
The current study is focused on the southern Koshi (KO) Basin, which is
located in the eastern part of central Himalayas (Guzzella et al., 2016),
(Fig. 2). In particular, the region of investigation is the southern slopes
of Mt Everest in Sagarmatha (Mt Everest) National Park (SNP)
(27∘45′ to 28∘7′ N; 85∘59′ to
86∘31′ E) (Fig. 2a) (Amatya et al., 2010; Salerno et al., 2010).
The SNP (1148 km2) is the highest protected area in the world,
extending from an elevation of 2845 to 8848 m a.s.l. (Salerno et
al., 2013). Land cover classification shows that almost one-third of the
territory contains temperate glaciers and less than 10 % is forested
(Bajracharya et al., 2010), mainly with Abies spectabilis and
Betula utilis (Bhuju et al., 2010).
The climate is characterized by monsoons, with a prevailing S–N direction
(Ichiyanagi et al., 2007). For the 1994–2013 period at the Pyramid
meteorological station (5050 m a.s.l.) (Fig. 2a), the total annual
accumulated precipitation is 446 mm, with a mean annual temperature
of -2.45 ∘C. In total, 90 % of the precipitation falls between
June and September. The probability of snowfall during these months is very
low (4 %) but reaches 20 % at the annual level. Precipitation
linearly increases to an elevation of 2500 m and exponentially
decreases at higher elevations (Salerno et al., 2015; Derin et al., 2016).
Most of the large glaciers in the SNP are debris-covered; i.e. the ablation
zone is partially covered with supraglacial debris (e.g. Scherler et
al., 2011; Bolch et al., 2011; Thakuri et al., 2014). However, the glaciers
located within the considered pond basins are very small, steep, and cling to
the mountain peaks; and thus they did not develop a debris-covered ablation
area. The glacier surfaces are distributed from approximately 4300 to above
8000 m a.s.l., with more than 75 % of the glacier surfaces lying
between 5000 and 6500 m a.s.l. The area-weighted mean elevation of the
glaciers is 5720 m a.s.l. in 2011 (Thakuri et al., 2014). Glaciers in this
region are identified as summer-accumulation glaciers that are fed mainly by
summer precipitation from the South Asian monsoon system (Ageta and Fujita,
1996; Soncini et al., 2016). Salerno et al. (2012) performed the complete
inventory of lakes and ponds in the SNP by digitizing ALOS-08 imagery and
assigning each body of water a numerical code (LCN, lake cadaster number)
according to Tartari et al. (1998). They reported a total of 624 water bodies
in the park, including 17 proglacial ponds, 437 supraglacial ponds, and 170
unconnected ponds. Previous studies revealed that the areas of proglacial
ponds increased on the south slopes of Mt Everest after the early 1960s
(Bolch et al., 2008; Tartari et al., 2008; Gardelle et al., 2011; Thakuri et
al., 2016). Many studies have indicated that the current moraine-dammed or
ice-dammed ponds are the result of coalescence and growth of supraglacial
ponds (e.g. Fujita et al., 2009; Salerno et al., 2012). Such ponds pose a
potential threat due to GLOFs. Imja Tsho (lake) is one of the proglacial
lakes in the Everest region that developed in the early 1960s as a small pond
and subsequently expanded continuously (Bolch et al., 2008; Somos-Valenzuela
et al., 2014; Fujita et al., 2009; Thakuri et al., 2016).
Data and methodsOverall methodological approach
This section provides a brief description of the overall methodological
approach applied in this study, whereas in the following sections, data and
methods are described in detail.
An intra-annual analysis was carried out for throughout the year 2001 on a
limited set of unconnected ponds for detecting the months characterized by
the lowest surface area intra-annual variability and consequently the best
period of the year to select the satellite images necessary for the
inter-annual analysis.
An inter-annual analysis was carried out for the 2000–2013 period
(hereafter we refer to this analysis as short-term inter-annual analysis), considering the wide availability of satellite imagery in this
period, on some selected unconnected ponds (hereafter we refer to these ponds
as selected ponds) to continuously track the inter-annual variations
in surface area. This analysis aims to investigate the possible drivers of
change (precipitation, evaporation, and glacier melt) considering the
availability of continuous series of annual pond surface areas and climatic
data from a land station located in the area. The study has been carried out
through correlation analysis and principal component analysis (PCA).
An inter-annual analysis was carried out for the 1963–2013 period (hereafter
we refer to this analysis as long-term inter-annual analysis) on a
wider unconnected pond population (hereafter we refer to this population as
all considered ponds) and on glaciers located within their
hydrological basin. Two kinds of analyses have been carried out on this set
of data: (1) pond surface area changes have been related to certain
morphological boundary conditions. This analysis allows the factors
controlling the pond surface area changes to be investigated. The
significance of the observed differences has been evaluated with specific
statistical tests. (2) Pond surface area changes have been related to
climatic data. This analysis aims to point out the capability of unconnected
ponds to infer information on the detected drivers of change also in the past
when land climatic data did not exist. This study needed a preliminary
analysis to reconstruct the climatic trends before the year 1994. Selected
regional gridded and reanalysis datasets have been compared with land weather
data available for the 1994–2013 period.
Climatic data
The monthly mean of daily maximum, minimum, and mean temperature and monthly
cumulated precipitation time series used in this study have been
reconstructed for the elevation of the Pyramid Laboratory (5050 m a.s.l.)
(Fig. 2) for the 1994–2013 period (Salerno et al., 2015). The potential
evaporation for the period (2003–2013) has been calculated by applying the
Jensen–Haise model (Jensen and Haise, 1963) using the mean daily air
temperature and daily solar radiation recorded continuously during the
2003–2013 period at Pyramid Laboratory. The Jensen–Haise model is
considered to be one of the most suitable evaporation estimation methods for
high elevations (e.g. Gardelle et al., 2011; Salerno et al., 2012).
To obtain information on climatic trends in the antecedent period (before
1994), we used some regional gridded and reanalysis datasets. We selected the
closest grid point to the location of the Pyramid Laboratory, and all data
were aggregated monthly to allow for a comparison at the relevant timescale.
With respect to precipitation, we test the monthly correlation between the
Pyramid data and the GPCC (Global Precipitation Climatology Centre),
APHRODITE (Asian Precipitation-Highly Resolved Observational Data Integration
Towards Evaluation of Water Resources), ERA-Interim reanalysis of the
European Centre for Medium-Range Weather Forecasts (ECMWF), and CRU (Climate
Research Unit Time Series) datasets. For mean air temperature, we considered
the ERA-Interim, CRU, GHCN (Global Historical Climatology Centre), and
NCEP-CFS (National Centers for Environmental Prediction Climate Forecast
System) datasets, whereas for maximum and minimum temperatures, we used the
ERA-Interim and NCEP-CFS datasets (details on the gridded and reanalysis
products are reported in Table S1 in the Supplement).
Pond digitizationLong-term inter-annual analysis
Pond surface areas were manually identified and digitized using a topographic
map from 1963 and more recent satellite imagery from 1992 to 2013. The
topographic map of the Indian survey of the year 1963 (hereafter TISmap-63,
scale 1:50 000) was used to complement the results obtained from the
declassified Corona KH-4 (15 December 1962, spatial resolution 8 m).
Thakuri et al. (2014) described the co-registration and rectification
procedures applied to the Corona KH-4 imagery. Unfortunately, on these
satellite images many ponds are snow-covered. Therefore here we considered
the ponds' surface area digitalized on TISmap-63. The accuracy of this map
has been tested comparing the surface areas of 13 ponds digitalized on both
data sources (favouring the cloud- and shadow-free ponds). Figure S1 in the
Supplement shows the proper correspondence of these comparisons. Furthermore,
in order to estimate the mean bias associated with TISmap-63, we calculated
the mean absolute error (MAE) (Willmott and Matsuura,
2005) between data, of which the result was sufficiently low (3.6 %), in
this way assuring the accuracy of ponds surface area digitalized on
TISmap-63.
In total, five scenes were considered according to the availability of
satellite imagery. Landsat images have been mainly used, except in 2008, when
in the region the ALOS image, presenting a better resolution, was available
(details on data sources are provided in Table S2).
We only tracked those ponds present continuously in all these five periods to
exclude possible ephemeral water bodies. As described below, 64 ponds have
been tracked from 1963 to 2013 (Fig. 2a).
Short-term inter-annual analysis
From the 2000 to 2013 period, due to a wider availability of satellite imagery
(and in particular the Landsat imagery), 10 ponds were selected among the
pond population (64 ponds) considered in the long-term analysis (1963–2013)
to continuously track the inter-annual variations in surface area in the
recent years. The largest ponds, free from cloud cover, and with diverse
glacier coverages (from 1 to 32 %) within their hydrological basin, were
favoured in the selection (details on data sources used for these ponds are
provided in Table S3).
Intra-annual analysis
The intra-annual variability in pond surface area was investigated for
throughout the year 2001 through the availability of five cloud-free
satellite images from June to December (details on data sources used for
these ponds are provided in Table S4). The first months of the year were
excluded from the analysis because many ponds were frozen until April/May.
Even in this case, the main criterion driving the ponds selection was the
absence of cloud cover from the satellite images over the pixels representing
the pond surface area. Only ponds for which a continuous series of data was
retrieved from June to December were selected. Moreover the largest ponds
were favoured in order to reduce the uncertainty in the shoreline
delineation. Thus, four ponds were selected, and their intra-annual
variability is tracked in Fig. 3. We observe a common significant increase in
pond surface area during the summer months, likely due to monsoon
precipitation and high glacier melting rates. This increase in surface area
disappears on average during the fall. Some single ponds present a dispersion
of around 5 % between October and December (LCN4 and LCN77). However, the
same figure points out that just by averaging this information on a
population only slightly larger, the dispersion between October and December
becomes almost zero (1 %). Therefore these months are the best period to
select the satellite images necessary for the inter-annual analysis of pond
surface area. In fact, during these months, the ponds are not yet frozen, the
sky is almost free from cloud cover, and, as observed in Fig. 3, the
inter-annual analysis on average is not affected by intra-annual seasonality.
Consequently all images for the inter-annual analysis have been selected from
these months (Tables S1, S2). Generally, climatic inferences coming from the
analysis of surface area of ponds surely need to consider a wider number of
ponds in order to reduce the intra-annual variability due to the local
conditions of each lake.
Glacier surface areas and melt
Glacier surface areas within the basins containing the ponds were derived
from the Landsat 8 remote imagery (10 October 2013) taken by the Operational
Land Imager (OLI) with a resolution of 15 m. The satellite imagery
used to track the inter-annual variations in glaciers since the early 1960s
is reported in Table S2. Detailed information on digitization methods are
described in Thakuri et al. (2014).
Intra-annual analysis (June–December) of selected pond surface
areas. Percent dispersions are computed dividing the anomalies by the mean.
To simulate the daily melting of the glaciers associated with the 10 selected
ponds, we used a simple T-index model (Hock, 2003). This model is able to
generate daily melting discharges as a function of daily air temperature
above zero, the glacier elevation bands (using the digital elevation model –
DEM – described below), and a melt factor
(0.0087 md-1∘C-1) provided by Kayastha et
al. (2000) from a field study (Glacier AX010) located close to the SNP
(southwest). The Glacier AX010 glacier is a small debris-free glacier,
located in the Dudh Koshi valley in the same climatic and geographic setting
of glaciers considered here. The choice of using a simple model of melting is
due to the fact that this paper does not have the specific objective to
provide an accurate evaluation of the magnitude of the meltwater released
from glaciers located in the pond basins, but rather to estimate its trend,
as a function of the temperature, in order to evaluate whether the glacier
melt is a possible driver of changes of the pond surface areas. Being
interested in the melt trend and not in its absolute magnitude and
considering that these small glaciers are ungauged, we do not need more
sophisticated melt models, which consider specific geometries and
differentiated melt factors.
The T-index model has been applied here considering the daily temperature
of the Pyramid Laboratory corrected using the monthly lapse rates reported in
Salerno et al. (2015) for each 50 m glacier elevation band. The melt
estimated for each band has been then summed to calculate the total melt for
each glacier.
Morphometric parameters
The parameters related to the ponds' basin as the area, slope, aspect, and
elevation were calculated through the DEM derived from the ASTER GDEM
(Tachikawa et al., 2011). The vertical and horizontal accuracy of the GDEM
are ∼ 20 and ∼ 30 m, respectively (Tachikawa et
al., 2011; Hengl and Reuter, 2011). We decided to use the ASTER GDEM instead
of the Shuttle Radar Topography Mission (SRTM) DEM considering the higher
resolution (30 and 90 m, respectively) and the large data gaps of the
SRTM DEM in this study area (Bolch et al., 2011). Furthermore, the ASTER GDEM
shows better performance in mountainous terrains (Frey et al., 2012).
Hydrological basins have been digitalized using
ArcGIS® hydrology tools as carried out by
other authors (e.g. Pathak and Whalen, 2013). The circular statistic has been
used for computing the (vector) mean and median values of glaciers and basins
aspect (Fisher, 1993).
Uncertainty of measurements
All of the imagery and map were co-registered in the same coordinate system
of WGS 1984 UTM Zone 45N. The Landsat scenes were provided in standard
terrain-corrected level (Level 1T) with the use of ground control points
(GCPs) and necessary elevation data (LANDSAT SPPA Team, 2015). The ALOS-08
image used here was orthorectified and corrected for atmospheric effects as
described in Salerno et al. (2012).
Concerning the accuracy of the measurements, we refer mainly to the work of
Tartari et al. (2008) and Salerno et al. (2012, 2014a) which address the
problem of uncertainty in the morphometric measurements related to ponds and
glaciers obtained from remote sensing imagery, maps, and photos in detail.
The uncertainty in the measurement of a shape's dimension is dependent both
upon the linear error (LE) and its perimeter. In particular for ponds, as
discussed by many authors, only the linear resolution error (LRE) needs to be
considered (e.g. Fujita et al., 2009; Gardelle et al., 2011). Therefore we
did not consider the co-registration error because the comparison was not
performed pixel by pixel, at the entity level (pond) (Salerno et al., 2012,
2015; Thakuri et al., 2016; Wang et al., 2015). The LRE is limited by the
resolution of the source data. In the specific study of temporal variations
of ponds, Fujita et al. (2009) and Salerno et al. (2012) assumed an error of
±0.5 pixels, assuming that on average the lake margin passes through the
centres of pixels along its perimeter. The uncertainties in the changes in
pond surface area were derived using a standard error propagation rule, i.e.
the root sum of the squares (uncertainty =e12+e22),
where e1 and e2 are uncertainties from the first and second scene of
the mapping uncertainty in two scenes (Salerno et al., 2012; Thakuri et
al., 2016).
Statistical analysis
In the short-term inter-annual analysis, the degree of correlation among the
data was verified through the Pearson correlation coefficient (r) after
testing that the quantile–quantile plot of model residuals follows a normal
distribution (not shown here) (e.g. Venables and Ripley, 2002). All tests
are implemented in the R software (R Development Core Team, 2008) with the
significance level at p< 0.05. The normality of the data is tested using
the Shapiro–Wilk test (Shapiro and Wilk, 1965; Hervé, 2015). Razali and
Waph (2011) demonstrate that the Shapiro–Wilk test presents the highest
power for small sample size. The data were also tested for homogeneity of
variance with the Levene's test (Fox and Weisberg, 2011). All comparisons
conducted in this study are homoscedastic.
To evaluate the significance of differences in surface area changes of ponds'
population, both in time and in respect to certain morphological boundary
conditions, some parametric and non-parametric tests have been used. We
applied the paired t-test to compare the means of two normally distributed
series. If the series were not normal, as a non-parametric analysis of
variance (ANOVA), we used the Friedman test for paired comparisons and the
post-hoc test according to Nemenyi (Pohlert, 2014), while for non-paired
comparisons we applied the Kruskal–Wallis test and the post-hoc test
according to Nemenyi–Damico–Wolfe–Dunn (Hothorn et al., 2015). The
significance of the temporal trends has been tested using the Mann–Kendall
test (p< 0.10) (Mann, 1945; Kendall, 1975; Guyennon et al., 2013). When a
time series is not very long, the associated significance level should be
considered with caution.
We conducted principal component analysis (PCA) as described in Wold et
al. (1987) between pond surface area variations and climatic variables to
obtain information on relationships among the data and to look for reasons
that could justify the observed changes in the pond size (e.g. Settle et
al., 2007; Salerno et al., 2014a, b; Viviano et al., 2014).
Trend analysis of climate and glaciers' and ponds' surface area for the
last 50 years in the SNP: (a) ERA-Interim mean annual temperature
compared with Pyramid's land-based data; (b) GPCC annual
precipitation and Pyramid's land-based data; (c) glacier surface
area variations for the overall SNP (Thakuri et al., 2014) and for glaciers
located in basins of 64 considered ponds. (d) Surface area
variations of all 64 considered ponds. Y axis units: (a) and
(b) trends are expressed in terms of standardized anomalies divided
by the standard deviation (dimensionless); (c) and (d)
relative variations with respect to 1963. Errors bars represent the
uncertainty of measurements.
ResultsPond and glacier surface area variations
Among the 170 unconnected ponds inventoried in the 2008 satellite imagery
(Salerno et al., 2012) in the SNP, we tracked, according to the criteria
described above, a total of 64 ponds (approximately 1/3) (Fig. 2a). Table 2
provides a general summary of their morphological features. We use the median
values to describe these water bodies because, in general, we observed that
these morphological data do not follow a normal distribution. The population
consists of ponds larger than approximately 1 ha (1.1×104m2), located on relatively steep slopes (27∘), and
mainly oriented towards south–southeast (159∘). These ponds are
located at a median elevation of 5181 m a.s.l. and within an elevation zone
ranging from 4460 to 5484 m a.s.l. The observed changes in the surface area
of all the considered ponds are listed in Table 3. In general, all
unconnected ponds decreased by approximately 10±5 % in surface area
in the last 50 years (1963–2013), with a significant difference based on the
Friedman test (p< 0.01). Figure 4d and Table 3 show that, until the 2000s,
the ponds had a slight but not significant increasing trend (+7±4 %,
p> 0.05). Since 2000, they have decreased significantly (-1.7±0.6%yr-1, p< 0.001 corresponding to -22±18 %).
As for glaciers, Fig. 4c reports the glacier surface area changes observed
across the SNP (approximately 400 km2) observed by Thakuri et
al. (2014). They reported a decrease of -13±3 % from 1963 to 2011.
We updated this series to 2013 and found loss of surface area of -18±3 %. For the glaciers located in the basins containing the considered
ponds, we tracked changes that were slightly larger. Their overall surface was
32.2 km2 in 1963 and 25.0 km2 in 2013, with a decrease of
-26±20 % (Fig. 4c; Table 3). According to many authors (e.g. Loibl
et al., 2014), as we observe here, the main losses in area over the last
decades in the Himalayas have been observed in smaller glaciers.
DiscussionShort-term inter-annual analysis: investigation on potential
drivers of change
Considering the wide availability of satellite imagery during the 2000–2013
period, an inter-annual analysis has been carried out on 10 selected ponds in
order to investigate the possible drivers of change. This was made possible
exploiting the continuous series of annual pond surface areas on the one
side, and climatic data from Pyramid station on the other.
Trends in pond surface areas
Table 4 provides the morphometric characteristics of 10 selected ponds. We
observe that the median features of these ponds are comparable with the
entire pond population (Table 2), highlighting the good representativeness of
the selected case studies. Figure S2 shows, for each pond, the annual surface
area variations that occurred during the 2000–2013 period. All the selected
ponds show a significant (p< 0.05) decreasing trend according to what has
been observed for the whole pond population during the same period.
Trends in possible drivers of change
The selected possible drivers of change are temperature (daily maximum,
minimum and mean), precipitation, potential evaporation, and glacier melt of
the pre-monsoon, monsoon (Fig. 5), and post-monsoon seasons. Pyramid data
have been used for computing or aggregating these variables. The assumption
behind this analysis is that these series can be considered representative
both along the altitudinal gradient and in the different valleys of the SNP.
The scarcity of land weather data at these elevations makes this
assumption licit; although, at this regard, the detected drivers of change will be
analysed in this respect in the last paragraph.
Principal component analyses (PCAs) between pond surface area from
2000 to 2013 and potential drivers of change (precipitation, glacier melt,
and potential evaporation) related to the monsoon season. Coefficients of
correlation are reported in Table S5. All trends related to ponds and
variables are provided in Figs. S5 and S6.
Annual trends from 2000 to 2013 related to pond surface area grouped
according to the relevant main drivers of change (monsoon season):
(a) precipitation, (b) glacier melt. Coefficients of
correlation are reported in Table S5. All trends related to ponds and
variables are provided in Figs. S5 and S6. Standardized anomalies
(dimensionless) are computed dividing the anomalies by the standard
deviation. Percent dispersions are computed dividing the anomalies by the
mean.
All these trends are noted in Fig. S3, and a correlation table comparing pond
surface area variations and potential drivers of change is presented in
Table S5. In general, we observe from this table that the highest
correlations are found for the monsoon period. The reason is because 90 %
of the precipitation and the highest temperatures are recorded during this
period (Salerno et al., 2015). Consequently, the main hydrological processes
in the Himalayas occur during the monsoon season. Focusing on this season, we
first observe a large and significant precipitation decrease
(-11 mmyr-1; p< 0.1) from Fig. S3. Even the mean temperature decreases,
but slightly and not significantly. This is a result of a significant
decrease in maximum temperature (-0.08 ∘Cyr-1;
p< 0.05) balanced by an increase in minimum temperature. The potential
evaporation, calculated on the basis of the mean temperature and global
radiation, is constant during the summer period. These trends have been more
broadly discussed in Salerno et al. (2015). They observed, for a longer
period (since 1994), that the mean air temperature has increased by
0.9 ∘C (p< 0.05) at the annual level. However, the warming has
occurred mainly outside the monsoon period and mainly in the minimum
temperatures. Moreover, as we observed here for the 2000–2013 period, a
decrease in maximum temperature from June to August
(-0.05 ∘Cyr-1, p< 0.1) has been observed. In terms
of precipitation, a substantial reduction during the monsoon season
(47 %, p< 0.05) has been observed.
The glacier melt related to each glacier within the pond basins has been
calculated considering both maximum and mean daily temperature. The averages
for all selected cases are analysed for each season in Fig. S3, which reveals
that the only period producing a sensible contribution is the monsoon period
if the maximum daily temperatures are considered the main driver of the
process. The reason can be easily observed in Fig. 2b, which shows the
0 ∘C isotherms corresponding to the mean and maximum temperatures.
Only the 0 ∘C isotherm related to the daily maximum temperature
during the monsoon period is located higher than the mean elevation of the
analysed glaciers. The T-index model only calculates the melting associated
with temperatures above 0 ∘C, thereby explaining this pattern. In
other words, the diurnal temperatures influence the melting processes much
more than the nocturnal ones, which are considered in the mean daily
temperature. Figure 6b shows that the trend is significantly decreasing
(3 %yr-1, p< 0.05), according to the decrease observed in
maximum temperature.
Detection of drivers of change
As anticipated, the highest correlations pond surface areas are found for the
monsoon period. Based on Table S5, we observe that precipitation, maximum
temperature, and glacier melt (calculated from temperature) are the more
correlated variables. The PCA shown in Fig. 5 attempts to provide an overall
overview of the relationships, during the monsoon period, among the trends
related to the potential drivers of change and the pond surface areas. This
representation helps to further summarize the main components of the water
balance system that influence the pond surface areas, i.e. glacier melt and
precipitation. We observe that evaporation is not an important factor at
these elevations, and that the evaporation / precipitation ratio is
approximately 0.41. Therefore, a hypothetical variation in the precipitation
regime affects the pond water balance 2.5 times more than the same variation
in the evaporation rate. Moreover, from Fig. 5, we observe that there are
some ponds that are more correlated with the monsoon precipitation (i.e.
LCN76, LCN141, LCN77, LCN11, and LCN93) and others that are more correlated
with the glacier melt (i.e. LCN68, LCN3, and LCN9). A few ponds seem
influenced by both drivers (i.e. LCN24 and LCN139). The coefficients of
correlation are reported in Table 4. According to the grouping observed with
the PCA.
Figure 6 shows good fits between the pond surface area trends and the main
drivers of change. Based on Table 4, ponds with higher glacier coverage
within the basin show higher correlations with the glacier melt, and, in
contrast, ponds with lower glacier coverage show higher correlations with
precipitation; i.e. the glacier coverage is the discriminant variable. In
our case study, the threshold between the two groups appears to be a glacier
coverage of 10 %.
Long-term inter-annual analysis
An inter-annual analysis has been carried out from 1963 to 2013 for all 64
considered ponds in order to investigate (1) which morphological boundary
conditions control the pond surface area changes and (2) the capability of
unconnected ponds to infer information on the detected drivers of change also
in the past when land climatic data did not exist.
Morphological boundary conditions controlling the pond surface
area changes
We analysed whether all 64 considered ponds experienced changes in surface
area in relation to certain morphological boundary conditions, such as the
mean elevation of the basin, the pond surface area, the main three valleys of
SNP (Fig. 2a), and the glacier cover. In this case, evaluating the normality
of data, we apply the ANOVA test as well as the relevant post-hoc test
described above. Figure S4 shows the surface area changes observed during the
1992–2013 period vs. morphological factors. The same analysis has also been
carried out for the 1963–1992 period, reporting decidedly similar results
(not shown here). We observe that the pond surface area changes are
independent from elevation, valley, and pond size, whereas significant
differences can be observed between ponds with and without glacier cover. In
particular, ponds with glaciers experienced a lower surface area reduction.
This analysis reconfirms that the glacier cover at these altitudes is the
main discriminant parameter in the hydrological cycle of unconnected ponds.
We now analysed whether ponds with and without glacier cover within their
hydrological basin experienced changes in surface area in relation to the
aspect and the elevation of the basin. The two classes have been defined
according to the observed threshold of 10 %. Hereafter, we define these
ponds as ponds without glaciers in the basin, neglecting in this way
relatively small glacier bodies, which could possibly be confused with snow
fields. The opposite class is defined as ponds with glaciers in the basin.
Among ponds with glaciers, Table 2 shows that they are characterized by a
median glacier coverage of 19 %, oriented toward the east–southeast and
relatively steep (31∘). The observed changes according to this new
classification are reported in Table 3.
Pond surface area changes observed during the 1992–2013 period in
relation to certain morphological boundary conditions in the basin: elevation
(upper graphs) and aspect (lower graphs). On the left, ponds without glaciers
are shown, and on the right, ponds with glaciers. The white points in the box
plots indicate the mean, whereas the red lines indicate the median.
In this analysis, we apply the Kruskal–Wallis test as the relevant post-hoc
test described above. Figure 7 shows the surface area changes observed during
the 1992–2013 period. The changes were independent of both elevation and
aspect for ponds without glaciers (Fig. 7a, c), whereas significant
differences can be observed for ponds with glaciers. Ponds located at higher
elevations experienced greater decreases (Fig. 7b). In particular, ponds over
5400 ma.s.l. decreased significantly (p< 0.01) more than ponds
located below 5100 ma.s.l. In terms of aspect, the south-oriented
ponds (Fig. 7d) experienced greater decreases, which was significantly
different from southeast (p< 0.01) and southwest (p< 0.01)
orientations.
Coefficients of correlation between precipitation and temperature
time series recorded at Pyramid station for the 1994–2013 period and gridded
and reanalysis datasets (pre-monsoon, monsoon, and post-monsoon seasons as
the months of February–May, June–September, and October–January,
respectively). Bold values are significant with p< 0.01.
The tracking of pond surface area provides important information on
precipitation and glacier melt trends in space. Ponds without glaciers allow
us to understand that precipitation in the SNP generally occurs homogeneously
at all elevations and in all valleys independent of the orientation
(Fig. 7a, c). Based on the greater loss of surface area for
ponds with glaciers at lower elevations, we can infer that glacier melt is
actually higher at these elevations, surely due to the effect of higher
temperatures (Fig. 7b). Even in valleys oriented in directions other than
south, we observe greater losses in surface area for ponds with glaciers
(Fig. 7d). Small glaciers lying in perpendicular valleys, which are much
steeper than the north–south-oriented valleys (following the monsoon
direction), likely melt more due to their small size and higher
gravitational stresses (e.g. Bolch et al., 2008; Quincey et al., 2009).
Pond surface areas as proxy of past changes of the
hydrological cycleClimate reconstruction
To reconstruct the climatic trends before 1994, we compared the annual and
seasonal precipitation and temperature time series which have been recorded at Pyramid
station since 1994 (Salerno et al., 2015) with selected regional gridded and
reanalysis datasets (Table S1). Table 1 shows the coefficient of correlation
found for these comparisons. ERA-Interim (r=0.92, p< 0.001) for mean
temperature (Fig. 4a) and GPCC (r=0.92, p< 0.001) for precipitation
(Fig. 4b) provide the best performance at the annual level. Figure S5 shows
the location of ERA-Interim and GPCC nodes close to the region of
investigation and in particular in relation to the Pyramid station. The
comparisons between gridded/reanalysis and land data are visualized in
Fig. S6. We observe that precipitation increased significantly until the
middle 1990s (+25.6 %, p< 0.05, 1970–1995 period), then it started
to decrease significantly (-23.9 %, p< 0.01, 1996–2010 period), as
observed by the Pyramid station and described by Salerno et al. (2015). The
mean temperature reveals a continuous increasing trend
(+0.039 ∘Cyr-1, p< 0.001, 1979–2013 period) that
has accelerated since the early of 1990s.
Changes in pond surface area in the Mt Everest region. The left
box plots represent the annual rates of change of ponds in the analysed
periods: (a) ponds without glaciers within the basin,
(c) ponds with glaciers within the basin. The red points in the
box plots indicate the mean, whereas the red lines indicate the median. Data are
expressed in %yr-1. On the right side, the maps
(b, d) visualize the variations that occurred in the pond population
during the same three periods considered in the relevant box plots on the
left. Reference data are reported in Table 3. All percentages refer to the
initial year of the analysis (1963).
Comparison for the last 50 years between the annual precipitation
and the glacier melt with the surface areas for (a) ponds without
glaciers and (b) ponds with glaciers. Standardized anomalies
(dimensionless) are computed by dividing the anomalies by the standard
deviation. Error bars represent the uncertainty of measurements.
Furthermore, Table 1 shows the low capability of all the products to
correctly simulate monsoon temperatures and in particular the daily maximum
ones. Figure S7a reports the correlations at monthly level for
maximum temperature visually, while Fig. S7b highlights the misfit in the time
between the maximum, mean, and minimum temperature trends during the monsoon
period.
General summary of the morphological features of all 64 considered
ponds (data from 2013). Ponds are grouped according to the glacier cover
present into each pond basin.
TopographyGlacier cover < 10 % median (range)Glacier cover > 10 % median (range)All lakes median (range)Pond elevation (m a.s.l.)5181 (4460–5484)5159 (4505–5477)5170 (4460–5484)Pond area (104m2)0.8 (0.1–6.2)1.3 (0.3–56.3)1.1 (0.1–56.3)Basin area (104m2)30 (2–430)130 (30–2300)70 (2–2300)Basin slope (∘)25 (10–39)29 (23–41)27 (10–41)Basin aspect (∘)163 (68–256)141 (94–280)159 (68–280)Basin mean elevation (m a.s.l.)5293 (4760–5531)5400 (5119–5945)5315 (4760–5945)Basin / pond area ratio (m2/m2)60 (3–485)67 (10–523)64 (3–523)Glacier area (%)0 (0–9)19 (10–61)0.5 (0–61)Glacier slope (∘)–31 (21–38)–Glacier aspect (∘)–166 (150–250)–Glacier mean elevation (m a.s.l.)–5680 (5470–7500)–
General summary of surface area changes related to all 64 considered
ponds from 1963 to 2013. The surface area changes of the glaciers located
within the basins are also reported. For each comparison the uncertainty of
measurement is also shown. On the right the cumulative loss in respect to
1963 is reported for each intermediate period (these data are used for
Fig. 8). On the left the relative annual rates are calculated (these data are
used for Fig. 7). Bold values represent the main periods of
analysis.
PeriodPond surface area change PeriodPond surface area change Cumulative loss (%) Relative annual rate (%yr-1) Glacier coverage< 10%> 10%All pondsAll basinsGlacier coverage< 10 %> 10 %All ponds1963–1992+13±12a0±3+3±78±81963–19920.9±0.5a0.0±0.1+0.5±0.31963–2000-1± 6+9±2*+7±4-2±81992–2000-1.1±1.9+0.7±0.5*-0.4±0.11963–2008-4± 5+3±2+1±4-13±9**2000–20080.3±1.0-1.6±0.6-0.7±0.71963–2011-7± 60±2-2±5-14±14**2008–20110.0±2.80.0±1.60.0±2.21963–2013-25±6***-6±2*-10±5**-26±20**2011–201312.9±4.4***-5.8±2.5*-11±3.5**1992–2013-38±6***-6±2*-13±5**-34±15***2000–2013-2.3±0.7***-1.5±0.4***-1.7±0.6***
Morphometric features of 10 selected ponds considered in the
2000–2013 analysis. Data are from 2013. Coefficients of correlation are for
the monsoon season. The relationships with the other seasons are reported in
Table S5.
PondGlacierPondBasinBasinBasinPondBasinCoefficient codecoverelevationaspectslopeareaareaelevationof correlation (%)(m a.s.l.)(∘)(∘)(km2)(104m2)(m a.s.l.)(ponds surface area vs. precipitation)glacier melt)LCN1391474975300.64.655960.500.35LCN9325244116230.70.655020.70**0.39LCN14135316152271.42.657010.72**0.37LCN1135029229241.21.853720.76**0.49LCN7774920142268.618.355070.55*0.29LCN76948001402513.659.254570.65**0.23LCN241044661622823.054.054770.440.65**LCN9135202117360.70.65792-0.270.61**LCN3305261154352.011.759810.170.87***LCN68325006232351.23.256860.120.65**Median85018147281.33.95551––
Analysis of ponds surface area in the last 50 years
The maps in Fig. 8 show the spatial differences between the two pond classes
and compare the relative annual rate of change. Generally, no difference can
be observed at valley level, as confirmed by the test applied above
(Fig. S4). It is interesting to visually observe that most of the
ponds without glaciers increased in the 1963–1992 period, while
ponds with glaciers increased in the 1992–2000 period. Almost all the
considered ponds decreased during the 2000–2013 period.
Figure 9 tracks their trends over time. We have already discussed (Fig. 4d)
that, in general, all unconnected ponds over the last 50 years have
decreased by approximately 10 %. Additionally, the presence of glaciers
within the pond basins results in divergent trends. The surface area of
ponds without glaciers strongly decreased (-25±6 %, p< 0.001),
from 1963 to 2013 (Fig. 9a). In contrast, the surface area of
ponds with glaciers decreased much less (-6±2 %, p< 0.05) for the
same period (Fig. 9b). Differences in behaviour are also noticeable among the
periods pointed out in Table 3. In this case, we compare the median values of
the relative annual rates of change. From 1963 to 1992,
ponds without glaciers increased slightly (0.9±0.5%yr-1, p< 0.1), whereas the other ones remained
constant (0.0±0.1%yr-1). From 1992 to 2000,
ponds without glaciers decreased slightly (-1.1±1.9%yr-1, p> 0.1), whereas the other ones increased
slightly but significantly (+0.7±0.5%yr-1, p< 0.05).
In the most recent period (2000–2013), both categories decreased, but
ponds without glaciers decreased more (-2.3±0.7%yr-1,
p< 0.001; -1.5±0.4%yr-1, p< 0.001).
The significance of the divergent trend observed between the two groups has
been tested for two periods (1963–1992 and 1992–2013). Based on a
Kruskal–Wallis test, in the first period, ponds without glaciers presented
significantly (p< 0.01) higher increases than ponds with glaciers (+13±12 %; 0±3 %, respectively). In contrast, in the second period
ponds without glaciers showed higher and significantly (p< 0.01) decreases
(-38±6 %; -6±2 %, respectively).
Focusing our attention on Fig. 9, this analysis concludes by assessing what
we have learned from pond surface areas for the last 50 years. An increase in
precipitation occurred until the mid-1990s followed by a decrease until
recent years. This is shown, observing the GPCC precipitation series, but it
is also confirmed by the behaviour of ponds without glaciers (Fig. 9a). With
regard to the glacier melt, until the 1990s, it was constant. Then, an
increase occurred in the early 2000s, while in the recent years a decline was
observed (Fig. 9b). This is the trend shown by ponds with glaciers.
Furthermore, since 1994 the glacier melt, calculated directly from the
maximum temperature, which has been recorded by the Pyramid Laboratory, have
been fully in agreement with the behaviour of ponds with glaciers. For before
1994, suitable maximum temperature cannot be derived from gridded and
reanalysis products (Table 1, Fig. S7), but the ponds demonstrate that the
glacier melt in those years has been constant. Simply tracking the glacier
surface areas did not yield information on the temporal behaviour of glacier
melt. A decrease in glacier surface area has been identified over the last
50 years (Fig. 4c), but this reduction does not correspond to an increase in
glacier melt, as normally expected. As discussed by other authors (Thakuri et
al., 2014; Salerno et al., 2015; Wagnon et al., 2013), on the south slopes of
Mt Everest, the weaker precipitation could be the main cause of glacier
shrinkage. In recent years, glaciers are accumulating less than they were
decades ago; thus, their size is declining. In contrast, the tracking of pond
surface areas demonstrates that glacier melt did not have a trend congruent
to the glacier shrinkage, being influence more to the maximum temperature
trend.
Conclusion
The main contribution provided by this study is to
have demonstrated for our case study that surface areas of unconnected ponds
could be tracked to detect the behaviour of precipitation and glacier melt in
remote and barely accessible regions where, even for recent decades, few or
no time series exist. Local end peculiar morphological conditions of each
pond (possibly enhanced or reduced sediment supply, landslides, groundwater,
etc.) could influence the pond surface area. However, the significant
relationships found here on a wide pond population demonstrate that these
factors are secondary in respect to the main components of the hydrological
cycle.
In high-elevation Himalayan areas, unconnected glacial ponds have
demonstrated a high sensitivity to climate change. In general, over the last
50 years (1963–2013), unconnected ponds have decreased significantly by
approximately 10±5 %. We attribute this change to both a drop in
precipitation and a decrease in glacier melt caused by a decline in the
maximum temperature in the recent years. Evaporation has little effect at
these elevations and has remained constant over the last decade, during which
the main decline in ponds surface area has been observed.
An increase in precipitation occurred until the middle 1990s followed by a
decrease until recently. With regard to the glacier melt, until the 1990s it
was constant. Then, an increase occurred in the early 2000s, while in recent
years a decline has occurred. Simply tracking the glacier surface areas did
not yield information on the temporal behaviour of glacier melt. A decrease
in glacier surface area has been identified over the last 50 years,
attributed by other authors mainly to the observed weaker precipitation. In
contrast, the tracking of pond surface areas demonstrates that glacier melt
did not have a trend congruent to the glacier shrinkage, being chiefly
influenced by the maximum temperature trend.
In conclusion, a question arises in regard to the portability of this method.
Here, portability refers to the degree to which the proposed method is
replicable in other remote environments. In the Himalaya, other land-based
climatic series at high elevations are decidedly scarce (Barry, 2012;
Rangwala and Miller, 2012; Pepin et al., 2015; Salerno et al., 2015). The
inferences developed here could be simply applied and trends in precipitation
and glacier melt were inferred for the overall mountain range. Observing
differences in the magnitude of changes between the two classes that differ
in glacier coverage (threshold of 10 %) across different periods, along
an elevation gradient, or according to the basin aspect, as carried out here,
could improve the confidence of the inferred findings. In contrast, in other
mountain ranges with other climatic conditions, the inferences developed here
might not be valid, and station-observed climatic data would be required to
test the ability of glacier ponds to detect the main water balance
components.
The Supplement related to this article is available online at doi:10.5194/tc-10-1433-2016-supplement.
Franco Salerno and Gianni Tartari designed research; Franco
Salerno, Nicolas Guyennon, and Sudeep Thakuri analysed data; Franco Salerno
wrote the paper. Franco Salerno, Nicolas Guyennon, Sudeep Thakuri, Gaetano
Viviano, and Gianni Tartari check the data quality.
Acknowledgements
This work was supported by the MIUR through Ev-K2-CNR/SHARE and
CNR-DTA/NEXTDATA project within the framework of the Ev-K2-CNR and Nepal
Academy of Science and Technology (NAST).
Edited by: G. H. Gudmundsson
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