Internationa l Journal of Geosciences, 2014, 5, 27-37
Published Online January 2014 (http://www.scirp.org/journal/ijg)
Remote Sensing Data Application to Monitor Snow Cover
Variation and Hydrological Regime in a Poorly Gauged
River Catchment—No rthern P ak istan*
Samreen Abdul Hakeem, Muhammad Bilal, Arshid Pervez, Adnan Ahmad Tahir#
Department of Environmental Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan
Received November 16, 2013; revise d December 13, 2013; accepted January 7, 2014
Copyright © 2014 Samreen Abdul Hakeem et al. This i s an open access article di stribu ted under the Creat ive Commons Attribu tion
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snow- and glacier-nourished river ba sins located in the Himalaya-Karakoram-Hindukush ( HKH) ranges s upply
a significant amount of discharge in River Indus up stream Tarbela Dam. It is, hence, importa nt to comprehend
the cryosphere variation and its relationship to the stream flow in these high-altitude river catchments. The
MODIS re motely sensed da tabase of snow products wa s chosen to exa mine the a verage annua l snow and gla cier
cover (cryosphere) variations in the Shigar River basin (poorly gauged mountainous sub-catchment of the Indus
River). Hydrological regime in the area was investigated through monthly database of observed stream fluxes
and climate variables (precipitation and mean temperature) for the Shigar River catchment. Analysis indicated
the usefulness of remote sensing techniques for estimation of the snow cover variation in the poorly or un-gauged
high-elevation catchments of the HKH zone. Results also showed that Shigar River discharge was influenced
mainly by the seasonal and annual snow cover area (SCA) variation and the temperature seasonality. Moreover,
it is i mportant to uncover s uch inter-relationship of stream flow, climate variables and snow cover in the poo rly
gauged high-altitude catchments of Karakoram region for better water resource manage ment and accurate flood
hazards predictions at Tarbela.
Shigar River Ba sin; Satellite Remote Sensing; MODIS Snow Products; Water Resources Management;
1. Introduction and Background
Pakistan, the world’s sixth largest populated country, is
an agro-based economy where the lives of ~60% people
(mostly rural inhabitants) are either directly or indirectly
affiliated to agriculture. Pakistan is an arid to semi-arid
country with rainfall contributing only ~10% to agricul-
ture . Agriculture of Pakistan thus depends on irriga-
tion system constituted by a gigantic chain of hydraulic
structures. The Indus River and its two eastern contribu-
tories—Jhelum and Chenab, commit a main share of the
water supply to this irrigation system. Reference  has
shown that the livelihood of the people of Pakistan is
strongly dependent on the agriculture, which in turn is
relying on the vast irrigation system of the country i.e.
Indus Basin Irrigation system. Originating in Tibetan
Plateau, the Indus River passes through the towering
mountainous ranges of Hindukush-Karakoram-Himalaya
(HKH). The catchment area upstream Tarbela reservoir
is named as Upper Indus River Basin (UIB) (Figure 1).
The catchment area is located in four countries of South
Asia namely Pakistan, India, China and Afghanistan,
with the largest part in Pakistan. Permanent ice caps in-
cluding many of the world’s largest glaciers cover
~11.5 % of the total UIB a rea that make s it largest glacie-
rized area out of Polar and Greenland regions [3,4].
Mount ains bring t he main wate r reso urce fo r the Ind us
Basin Irrigation System as a consequence of snow- and
Remote sensing of sn ow cover va r iation in poorly gauged catchment.
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S. A. HAKEEM ET AL.
Figure 1 . Location of the study area and its Global digital elevation model (GDEM).
glacier-melt water . The I ndus river channel i n mounta ins
of northern-Pakistan contributes more than half the total
flow upstream Tarbela Dam (key storage at Indus)
(Figure 1). Indus River flow is dispensed by coalition of
dir ect runof f from rain fall bot h durin g the winte r and the
summer monsoon seasons, and melted outflow from
seasonal and permanent snow and ice fields . Different
studies (e.g. [6-11]) propose that the snow and glacier
melt in the HKH range contributes more than 50% of
annua l flow in the Upp er Indus River.
Deterioration of snow cover area (SCA) is a globally
prevalent phenomenon since past few decades, which is
accredited to global warming. The degradation of SCA
has an intense influence on stream water availability in
the river basins where snow and glacial melt constitute
the main component of stream flow in summer. The sta-
tus a nd conditio n of cryo sphere is crucial in future water
planning; providing more melt water initially on shrink-
ing and then reducing it gradually till the glaciers disap-
pearance. On the contrary, advancing glaciers may store
solid-precipitation, reducing summer stream flow and
can cause local hazards . In the preceding century a
clear effect of climate change has been observed on snow
and ice globally . Climatic factors such as precipita-
tion and cloud cover also have an effect on the glacial
recede but air temperature is aforethought to be the
greatest influential. Refer e nce  has stated that
changes in temperature are likely to govern the glacial
contraction even in the regions where precipitation is
expected to raise.
While accelerated ice cover melting has been proc-
lai med fro m the wor ld incl uding t he nei ghbo uring Gr eat-
er Himalaya , the Karakoram Range shows a subtle
situation as highlighted by the IPCC “Intergovernmental
Panel on Climate Change” . Central Karakoram is
the biggest of those regions where augmentation of
glaciers in length and mass is noticed by various re-
searchers (e.g. [15-18]). Since mid-1990s, few of the
substantia l gla ci er s i n the Ka r a ko ram Ran ge have surged ;
13 glaciers of intermediate size (10 - 20 km in length)
and 16 high-altitude tributaries are perceived to be
thickening [4,19]. These variations have been investi-
gated only in the high alti tude ce ntral Kar akora m regio n.
This discrepancy in glacier development indicates a dif-
ferent climate pattern in Karakoram fro m in the Greater
Himalaya . Different climatic regimes influence the
UIB: the monsoon rainfall dominates the Southern Hi-
malayan sub-catchments of UIBand snow and ice abla-
tion i s the ma in sour ce of r unoff fr o m Kar a kor a m re gions
of UIB . Therefore, the Indus River can be very de-
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S. A. HAKEEM ET AL.
licate to the climate change in HKH region which, if
comes true, will significantly increase or decrease the
Indus River discharge in future.
Snow cover has a momentous part in the system of
Earth’s climate. But the actua l amount of precipitatio n in
the form of snow received at high-altitudes is poorly
gauged because of well known catching errors (e.g.
stro ng wi nds at highe r ele vati ons) or even un -gauge d d ue
to the lack of observation stations in these remote areas.
Shiga r Rive r basi n (Figure 1) in HKH r egion i san exa m-
ple of inadequately gauged catchments, thus posing key
concerns in flow forecast from snow and glacier melt.
Reference  has reported that few measures from ac-
cumulation pits above 4000-meters above sea level
(m.a.s.l.) in UIB varied from 1000-mm to more than
3000-mm annual precipitation, conditional upon the lo-
cation. Mapping snow-cover area and snow volume as
explained by  is another way to estimate the solid
precipitation. Satellite remote sensing can be a very
helpful tool to map and examine snow and glacier cover
variation in such remote and inaccessible areas  and
predict the seasonal flow from snowmelt. The use of re-
motely sensed satellite data for the mapping of snow-
cover extent has a long history reaching back until the
1960s . Reference  shows that the satellite re-
mote sensing data can efficiently map snow-cover cha-
racteristics and snow mass in both high temporal and
spatial resolutions. Reference  coupled the satellite
remote sensing technique with ground observations to
analyse successfully the influence of global warming on
the Himalayan cryosphere.
The management of Tarbela reservoir (Figure 1) cal-
culates largely on the summer influx conferred by the
snow- and glacier-nourished tributaries of Indus River
located in the high-altitudes of HKH range. It is, for this
reason, crucial to estimate the seasonal snow cover in
high-altitude sno w-fed catchments using remotely sensed
satellite data for various ai ms such as hydro meteorology,
flood predictions and water resource management.
Aforementioned study is one of the few to look into the
annual cryosphere (snow and ice cover) change using the
remotely sensed MODIS snow cover data (MOD10A2)
in a snow- and glacier-fed River basin (Karakoram re-
gion). The main research contents of this paper are to
inves tigat e the:
1) Average annual snow and glacier cover variation in
a high-altitude river catchment area situated in the HKH
region using MODIS remote sensing snow pr oduct ;
2) Dependence of River flow on annual cryosphere
varia tio n in t he s tud y are a usi ng satel lit e da ta and gr ound
Correlation of River flow, climatic variables (mean
temperature and precipitation) and snow cover variation
in the study area.
2. Study Area
The geographical area taken into account for this study is
Shigar River basin (Figure 1) (one of the UIB sub-cat-
chments) situated in the northern areas (Karakoram re-
gion) of Pakistan. Some key characteristics of this river
basin are presented in Ta b l e 1.
Shigar River basin ranges fro m 74.5˚ - 76.5˚E in Lon-
gitude, and 35.2˚ - 36.38˚N in Latitude. The catchment
area of Shigar River is ~7000-km2 at S higa r Bri dge (flow
gauge point of Shigar River) (Figure 1), as calculated
from the Advanced Spaceborne Thermal Emission and
Reflection Radiometer-Global Digital Elevation Model
(ASTER-GDEM) i n this study.
Its entire catchment is situated within Pakistan and is
signi fica nt for eva luati ng the influe nce o f climate change
within the Karakoram Range of Pakistan. The peak ele-
vation in this catchment area is reached by K2-Mountain
(Figure 1) with 8611-m.a.s.l. and the lowest point is at
Shigar Bridge at 2187-m.a.s.l (Figure 1). The mean ele-
vation of the catchment is ~4705-m.a.s.l. (calculated
from the hypsometry using ASTER–GDEM) (Figure 2)
and ~35% of the area is over 5000-m.a.s.l. (calculated-
from the hypsometry using ASTER–GDEM) (Figure 2).
The major proportion (~80%, surface area of ~13,000-
km2) of total Karakoram glaciers is located within Pakis-
tan . The most important are the Biafo (~63-km long)
and Baltoro (~60-km long) (Figure 3(a)) situated in the
Shigar Ri ver basin.
The hydrological regime in the Shigar River basin is,
to some extent, influenced by monsoon while the snow
and glacier meltin g contrib ute s the maj or pr opor tion. The
Shigar River has a mean yearly flow of ~206 m3/s (i. e .
930-mm of water depth equivalent) gauged at Shigar
Bridge (Figure 1), as calculated from the 15-years
(1985-2001) flow record of the SWHP-WAPDA in this
study. The governing sustenance for the glacier systems
of the Karakoram Range is provided by the Westerlies
precipitation system during winter . There is one
AWS (Automatic Weather Station) (Figure 1) installed
in the Shigar basin and the other climate station (CS) is
installed at Skardu (Figure 1) outside the catchment
boundary of Shigar near the catchment outlet. The cli-
mate data used in this study is the averaged data of both
AWS and CS. The mean total annual precipitation is
~216-mm at Skardu CS and ~405-mm at Shigar AWS
according to the available data in the catchment. Being at
higher-altitude (~3500-m.a.s.l. as estimated by super-
posing the AWS on GDEM—exact value of altitude for
AWS should be verified), Shigar AWS receives more
precipitation than Skardu CS (~2200-m.a.s.l.). However,
the present total annual precipitation records at both cli-
mate stations are not illustrative of the runoff at the Shi-
gar River outlet because of the climate data scarcity at
altitudes above 4000-m and under-estimation of winter
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S. A. HAKEEM ET AL.
Table 1. Key characterist ics of the study area (Shigar River basin).
Catchment name Shigar
River flow gaugin g station Shigar Bridge
Elevation of river gauging station. ~2 187-m
Drainage area (com puted from ASTER GDEM in this study). ~7000-km2
Glacier-cover ed area. ~2120-km2
(Calculated using, “World Glacier Inventory” data in this study)
Glacier c o v er perc entage. 30% - 39% (Estimated from di f f er ent s ources mentioned a bove e. g. W G I ,
Mean eleva tion (com puted from ASTER GDEM in this study). ~4705-m
Area above 5000-m (computed from ASTER GDEM in thi s study). ~2460-km2, ~35 %
Meteorol ogical station data used in this study.
1 (managed by PMD) – Skardu (~2210-m)
1 (managed by WAPDA) – Shiga r AWS (~3500-m, estimated by
supe r-posing the AW S on GDEM) – exact value of altitude for AWS
shoul d be verified
aCampbell JG (2004) Inventory of Glaciers, Glacial Lakes and the Identification of Potential Glacial Lake Outburst Floods (GLOFs) Affected by Global
Warmin g in the Mount ains of Indi a, Paki stan and Chin a/Tib et Auton om ous Region, Fina l rep ort for APN pr oject 2004-03-CMY-Campbe ll. Kath mandu , Nepal:
International Centre for Integrated Mountain Development; Kobe, Japan: Asia-Pacific Network for Globa l Change Research.
Figure 2 . Hypsometric cu rve and the area u nder 500 -m elevation b and of the Shigar R iver basin.
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S. A. HAKEEM ET AL.
Figure 3. (a) GDEM showing glacier cover area using WGI (World Glacier Inventory) data and (b) An example of MODIS
snow cover image for the S higar River basin.
precipitation (snow) in this basin and may also be asso-
ciated to the well-known gauging errors (wind induced,
wetting loss, trace precipitation, blowing and drifting
snow, and systematic mechanical errors etc.) in high
mountain environment described by many authors, e.g.
3. Data Sets and Treat m ent
In order to accomplish the study objectives, two types of
data sets were obtained:
1) Satellite Remote Sensing Data (downloaded from
the NASA web site http://reverb.echo.nasa.gov/reverb).
● ASTER–GDEM .
● MODIS MOD10A2 snow product.
2) Hydro-meteorological Data (Stream flow, mean
temperature and precipitation).
3.1. Satellite Remote Sensing Data
The satellite remote sensing data were obtained from two
sources to be used for two different purposes. Advanced
Spaceborne Thermal Emission and Reflection Radiome-
ter (ASTER)-Global Digital Elevation Model (GDEM)
data was employed for sketching out the catchment area
(Shigar Basin) (Figure 1) and calculating the hypso-
metric curve (area-elevation) (Figure 2). The ASTER–
GDEM based on ASTER images is handy for high-
latitude and steep mountainous areas not attained by
SRTM3 . It is set up on ASTER images and has a
resolution of 30-m × 30-m. It was referenced to WGS84/
EGM96 coordinate system. The downloaded ASTER
tiles were mosaicked and treated through a variety of
steps using standard ARCMAP techniques (©1995-2012
ESRI) to draw the Shigar River catchment region. AS-
TER-GDEM was also treated to estimate the hypsomet-
ric-curve and the catchment area in every 500-m eleva-
tion band (Figure 2). World Glacier Inventory (WGI)
data  was used along with ASTER–GDEM to extract
the glacier cover (Figure 3(a)) from the catchment area.
Moderate Resolution Imaging Spectroradiometer MO-
DIS/Terra Snow Cover 8-Day L3 Global 500-m Grid
(MOD10A2) snow products for 10-years (2000- 2009)
were utilized to estimate the snow cover variation in the
study area. The monthly average values of SCA were
estimated to analyse its relationship with hydro-meteo-
rological variables. MODIS Snow products (MOD10A2)
contain data fields for maximum snow cover extent over
an 8-day composite time, projected with the WGS 1984
UTM ZONE 43N projection system. MOD10A2 is con-
stituted of 1200-km × 1200-km tiles of 500-m resolution
data gridded in a sinusoidal map projection. MODIS
cryosphere data is established on a snow-mapping algo-
rithm that procures a Normalized Difference Snow Index
(NDSI) and other criterion tests .
MODIS snow cover products have been extensively
exploited to reckon the snow cover area (to couple it with
hydrological models for flow simulation and forecasting)
by various researchers (e.g. [33-37]) in numerous geo-
graphical areas of the world. Reference  correlated
the accuracy of MODIS, NOAA and IRS Data and illu-
strated that MODIS data could be more effective for
SCA evaluation, which is an important factor for snow-
melt runoff modelling. Many researchers (e.g. [39-41])
evaluated MODIS/Terra daily snow-cover and meteoro-
logical product through comparability with ground inves-
tigations and found that the misclassified fractions of
MOD10A1 were in general lesser at high elevations than
at low elevations. Reference  observed the snow-
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S. A. HAKEEM ET AL.
mapping correlation coefficient value of ~95% between
seasonal MODIS daily snow maps and in-situ observa-
tions under clear-sky conditions. Reference  ex-
plained that the snow products derived from EOS-
MODIS, AMSR-E and QSCAT data have beenharmo-
nised into a solo, worldwide, diurnal, user-fri endly p rod-
uct via a latest developed Air Force Weather Agency
(AFWA)/NASA Snow Algorithm (ANSA). A higher ef-
ficiency is also suggested for outlining snow-cover area
with the MODIS product than with the AMSR product
upon cloud-free MODIS images availability. Reference
 used MODIS snow product as the default for esti-
mati ng snow co ver for thi s previ ously me ntioned reason.
The passive microwave product AMSR was preferred
barely in those domains where MODIS data were not
suitable due to clouds and darkness .
The snow-mapping algorithm differentiates pixels as
snow, ice lakes, cloud, water, land or other. Snow extent
is the variable of prime significance in this data set. The
temporal search of snow cover through MOD10A2 was
carried out through images downloaded from the above-
mentionedNASA website. The study area (Shigar basin)
was then extracted by mask function from the treated
MODIS snow cover images (Figure 3(b)) using ARC-
MAP (©1995-2012 ESRI). Snow cover area was esti-
mated from the cloud free images. If the cloud cover ex-
ceeded 15% on a specific date then the image was ex-
cluded from the time serie s. The sno w cover o n this dat e
was then estimated by linearly interpolating between the
previous and next cloud free image.
3.2. Hydro-Meteorological Data
The hydrological and meteorological data sets were ob-
tained from WAPDA (Water and Power Development
Authority) and Pakistan Meteorological Department
(PMD), respectively. Surface Water Hydrology Project
of Water and Power Development Authority (SWHP-
WAPDA) mostly do stream flow assessment in Pakistan.
PMD-CS has quite a long data series for climate va-
riables while WAPDA–AWS are installed in 1995 and
thus have shorter data series.
The monthly average stream flow was estimated from
the available data of ~15-yrs (1985-2001) in order to
compr e he nd t he c o rr el at io n a mo n g str eam flo w a nd snow
cover area of Shigar Basin. Theavailable data on climatic
variables including temperature (min. and max) and pre-
cipitation was averaged for the entire period of ~55-yrs
(1952-2007) for PMD–CS and ~11-yrs (1996-2006) for
WAPD A–AWS to get the monthly average values. The
Pearson Product Moment correlation , Spearman
Rank Order correlation  and Kendall’s rank correla-
tion [45,46] tests were performed (with a significance
level of p = 5%) to evaluate the relationship between
monthly stream flow, monthly snow cover variation and
climate variables ( monthly mean temperat ure and monthly
total precipitation) to scrutinize the core regulating co n-
stituents of runoff from t he Sh igar River basin.
4. Results and Discussion
4.1. Snow Cover Variation in the Shigar River
Ba si n
In the present study, the Snow Cover Area (SCA) for
Shiga r B asin was esti mate d th ro ugh MODI S i mage s over
the 10-years period (2000-2009). The images were
treated through ArcGIS software (©1995-2012 ESRI)
and SCA percentage was calculated for each 8-days im-
age. This S CA was t he n ave r age d t o mont hl y ti me ste p to
get the mean monthly values of SCA variation. MODIS
images showing intra-annual SCA variation and its val-
ues in the Shigar River basin are presented in Figure 4.
The average SCA varies from maximum in winter (De-
cember-January) to minimum in summer (July-August)
(Figure 4). The snow accumulation begins in the months
of September-October and the snow-covered area
reached to maximum (~88%) in January (Figure 4). T he
maximum snowmelt (minimum s now-covered area ~10%)
was observed in the month of August (Figure 4). This
huge variation of SCA from ~88% to 10% can be re-
flected by high discharges in the Shigar River during
snowmelt season because the large snow surface area
may contain the higher depths too. This is supported by
the study of  conducted on the neighbouringHunza
basin. This study  explains that the Central Karako-
ram region undergoes a snow cover change of about 40%
- 70% where SCA is 70% - 80% in the winter (snow ac-
cumulation period), whereas, SCA is 30% - 40% in the
summer (snowmelt period).
Thus these snow- and glacier-fed sub-catchments of
Upper Indus River Basin (UIB) make this huge catch-
ment dependent on the snow and glacier melt water. This
result also proves the suitability of remote sensing snow
products for the snow cover mapping in the un-gauged
snow- and glacier-fed high-altitude river basins. Even if
the ground observation stations are installed in these riv-
er basins; these are not able to catch the exact amounts of
winter solid precipitations due to previously mentioned
gauging errors (like strong winds) at high-altitude s. It is
not possible to regularly monitor these high-altitude re-
mote areas, due to severe climate conditions to cope-with,
for the researchers. Therefore, the remote-sensing satel-
lite snow images can be the best solution in these in-ac-
cessible regions (during certain seasons) to monitor the
SCA on regular basis even in the severe climatic condi-
The quality of these satellite snow cover datasets is al-
ready affirmed by many studies so these may be used for
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S. A. HAKEEM ET AL.
Figure 4. MODIS snow cover images showing intra-annual variation of snow cover for the period of 2000-2009 in Shigar
the snow mapping of larger areas like Upper Indus River
4.2. Dependence of Shigar River Discharge on
the Snow Cover Variation
The correlation coefficient values showing the relation-
ship of mean monthly Shigar river discharge and mean
monthly SCA are presented in Figure 5 and Table 2.
High stream flow was observed from July to September
when the snow cover area was at its minimum (i.e. in
summer during the snow melt period) (Figure 5). A si g-
nificant (p = 0.05) inverse correlation was observed be-
tween SCA and river discharge with r = −0.87 for Pear-
son product moment correlation test and r = −0.95 for
Spearman rank order correlation test (Figure 5 and Ta-
ble 2). Kendall’s rank correlation test indicated a coeffi-
cient value of r = −0.82 (p = 0.05) (Table 2). All these
values were significant with a significance level of p =
5%. Data reflect that January to April is the low flow
period when the snow accumulates, and it begins to in-
crease gradually when the snowmelt starts in the spring
season. The peak river discharge is witnessed at mini-
mum S CA in the months of J uly to Sep tember where the
glacial melt and monsoon rainfall over laps the sno w melt.
This peak discharge is also the result of ~65% drop in the
SCA from April (~78.3%) to July (~13.6%) that gradu-
ally increased the river discharge. Assessing the winter
snow accumulatio n may be helpful to pred ict the follow-
ing spring and summer season inflows in the Shigar Riv-
Inter-relationship of river discharge, SCA, mean tem-
perature and precipitation for the Shigar catchment area
is presented in the next section.
4.3. Inter-Relationship o f SCA, River Discharge
and Climate Variables (Mean Temperature
All the available data of SCA, river discharge and cli-
mate variables (mean temperature and monthly precipita-
tion) was analysed to determine the inter-relationship
between all these variables in the study area. Three dif-
ferent correlation tests (Pearson, Spearman and Kendall)
were applied with a significance level of p = 0.05 (Table
Correlation of SCA and mean Temperature: A strong
inver se co rrela tion wa s found bet ween aver aged monthl y
SCA and averaged monthly mean temperature. The cor-
relation coefficient values of r = −0.82, −0.78 and −0.64
were found for Pearson, Spearman and Kendall’s corre-
lation tests (Ta ble 2), respectively. All these values were
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S. A. HAKEEM ET AL.
Figure 5. Relationship of mean snow cover area, mean River discharge, mean tempe rature and total precipitation ( monthly
time step) in the Shigar River basin.
Table 2. Correlation coefficient values between monthly
values of Snow cover area (SC A), River disc harge (Q ), Pre-
cipit atio n (P) an d Mean t emper ature ( Tmean) in the S higar
Variables Kendall’s rank
Q vs SCA −0.82 −0.87 −0.95
Q vs P 0.54 0.63 0.66
Q vs Tmean 0.70 0.65 0.85
SCA vs Tme an −0.64 −0.78 −0.82
All the correlation coefficient values in this table are significant at p = 0.05.
significant with a significance level of p = 0 .05. The co r-
relation graph indicated an inverse relationship between
the mean temperature and SCA as shown in Figure 5,
where an increase in monthly temperature leads to a re-
duced SCA and more snow melt and vice versa.
The sno w cover is a t maximu m in t he l ow te mper at ure
months from December until March, when the accumula-
tion of snow is dominant over ablation that becomes
prominent in the following warmer months June to Sep-
tember causing a marked decrease in the SCA. Minimum
SCA is observed at higher mean temperatures in the
months of July and August where the snow melt is at
peak as discussed earlier.
Correlation of Shigar River discharge with Climatic
Variables (mean temperature and precipitation): Figure
5 and Table 2 showed the correlation coefficient values
between Shigar River discharge and mean temperature of
the catchment area observed at Skardu and Shigar cli-
mate stations (Figure 1).
The significant (p = 0.05) correlation coefficient val-
ues of r = 0.85, 0.65 and 0.70 were found for Pearson,
Spearman and Kendall’s correlation tests (Table 2), re-
spectively. The river discharge and mean temperature
correlation curve reflected a direct correlation in the
temperature and river discharge in the study area (Figure
5). The river flow starts increasing as the spring season
starts and mean temperature tends to increase. The flow
gets its peak in July when the mean temperature is at
maximum of the year. The low flow periods are observed
during low mean temperature periods and the flow is at
minimum in the months of December and January when
the mean temperature is below freezing point (Figure 5).
The increase in mean temperature resulted in a coinci-
dent increase in the snow and glacial melt, which re-
flected in an increased river flow where the peak was
observed at the maximum temperature in the month of
J uly (Figure 5) in the study area.
The hydrograph of the Shigar River basin was pre-
sented in Figure 5 with the precipitation in the form of
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S. A. HAKEEM ET AL.
inverted bars and river discharge shown in black solid-
line curve. The peak discharge event occurred in the
month of July, whereas, the maximum precipitation was
rec eived in the mon th o f Aug ust ( Figure 5). This may be
due to the accumulation of precipitation at high-altitudes
in the form of snowfall in August, which melts later in
Table 2 presented the correlation coefficient values
between Shigar River discharge and monthly precipita-
tion of the catchment area observed at Skardu CS and
Shigar AWS (Figure 1). The correlation coefficient val-
ues of r = 0.66, 0.63 and 0.54 were found for Pearson,
Spearman and Kendall’s correlation tests, respectively.
All these values were significant with a significance level
of p = 0.05. The peak river discharge and the maximum
average rainfall do not coincide which indicates some
other controlling factor for Shigar River discharge rather
than precipitatio n. The co mparative amount of rainfall i n
the months of July and August (peak monsoon) was
higher than the other months of the year but yet it was
much lower than the mean monthly discharge during
these months. Reference  stated that as a component
of the entire yearly discharge, the catchments of western
Himalaya, Karakoram and Hindukush range (e.g. Indus
River catchment) are sustained up to 50% by the snow-
melt. The correlation values between SCA, stream flow
and mean temperature are higher than those between the
stream flo w and p r e c ipitation.
The above correlation results indicate that the Shigar
River flow is affected by the snow cover variation and
temperature seasonality rather than by the monthly pre-
Located in the high-altitudes of Karakoram, the Shigar
river discharge confides primarily on the amount of snow
stored at high altitudes and energy influx, indirectly al-
lied to mean temperature. This is proposing that the data
estimation methods for climate variables should be up-
graded and the remote sensing techniques should be
adopted in un-gauged high-altitude catchments to under-
stand t he snow co ve r var ia tion a nd r uno f f co nt ro l fa cto r s,
further efficaciously. Analyzing the results obtained in
this s tudy, can concl ude the follo wing statements:
● Remotely sensed MODIS snow products are useful
to estimate the snow cover variation in the poorly-
gauged/un-gauged high-altitude catchments of the
Karakoram region. These are also useful at times of
high snowfall during winter season that subsequently
limits the accessibility in such high altitude remote
● Shigar river discharge is largely controlled by the
seasonal and annual snow cover area (SCA) variation
that is directl y linked to the temperature seasonality.
● Temperature variation strongly affects the snow cover
change in the study area that subsequently affects the
river discharge. Precipitation does not put forth pro-
found effect in the river discharge in comparison of
SCA and mean temperature.
A continuous monitoring of the snow cover dynamics
is required on the high-altitude catchments of the Upper
Indus r egio n e .g. Gi lgit , H unza , Shiga r a nd Sh yok. The se
are chiefly snow- and glacier- nourished catchments that
inject a hefty amount of discharge to the Indus River.
The composite of satellite snow cover data and ground
data (e.g. snow-pits and AWS) or information generated
by s no wme lt -runoff models may be a pertinentscheme to
overwhelm the poor gauging of precipitation records at
these high-altitudes. Eventually, presentinvestigation
may aid to better understand and, enhance the integrated
water resource management at the Tarbela reservoir.
Thanking to WAPDA “Water and Power Development
Authority” and the PMD “Pakistan Meteorological De-
partment” for providing the hydro-meteorological data.
Authors wish also to thank Muhammad Yasir for his
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