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Journal of Hydrology 409 (2011) 104–117
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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Modeling snowmelt-runoff under climate scenarios in the Hunza River basin,
Karakoram Range, Northern Pakistan
Adnan Ahmad Tahir a,⇑, Pierre Chevallier a, Yves Arnaud b, Luc Neppel a, Bashir Ahmad c
a
Laboratoire Hydrosciences, UMR 5569 – CNRS, IRD, Montpellier Universities 1 & 2, CC57, Université Montpellier 2, 34095 Montpellier Cedex 5, France
Laboratoire d’étude des Transferts en Hydrologie et Environnement, UMR 5564 – CNRS, IRD, Grenoble University 1, Grenoble INP, LGGE, 54 rue Molière, Domaine
Universitaire, BP 96, 38402 Saint Martin d’Hères Cedex, France
c
Director (Environment), Natural Resources Division, Pakistan Agricultural Research Council, G1/5, Islamabad, Pakistan
b
a r t i c l e
i n f o
Article history:
Received 24 January 2011
Received in revised form 25 July 2011
Accepted 1 August 2011
Available online 23 August 2011
This manuscript was handled by
Konstantine P. Georgakakos, Editor-in-Chief,
with the assistance of Ana P. Barros,
Associate Editor
Keywords:
Upper Indus River Basin (UIB)
Hunza River basin
MODIS
Water resources management
Snowmelt-Runoff Model
Climate change
s u m m a r y
A major proportion of flow in the Indus River is contributed by its snow and glacier-fed river catchments
situated in the Karakoram Range. It is therefore essential to estimate the snowmelt runoff from these
catchments (with no or scarce precipitation records) for water resources management. The snowmelt
runoff model (SRM) integrated with MODIS remote-sensing snow cover products was selected to simulate the daily discharges and to study the climate change impact on these discharges in the Hunza River
basin (the snow- and glacier-fed sub-catchment of the Indus River). The results obtained suggest that the
SRM can be used efficiently in the snow- and glacier-fed sub-catchments of the Upper Indus River Basin
(UIB). The application of the SRM under future climate (mean temperature, precipitation and snow cover)
change scenarios indicates a doubling of summer runoff until the middle of this century. This analysis
suggests that new reservoirs will be necessary for summer flow storage to meet with the needs of irrigation supply, increasing power generation demand, flood control and water supply.
Ó 2011 Elsevier B.V. All rights reserved.
1. Introduction
The economy of Pakistan, an agriculture-based country, is
highly dependent on the Indus irrigation system. The Indus River
is one of the major water carriers of South Asia emerging from
the Tibetan Plateau and the Himalayas. It has a controlling storage
at Tarbela (Fig. 1) as the river leaves the mountains. Tarbela is the
first major structure on the Indus River and supplies the flow to the
Indus Irrigation System to irrigate the agricultural lands of Punjab
and Sindh (provinces of Pakistan), the dominant producer of agricultural products in the country. Inflow to Tarbela is measured at
Besham Qila (Indus basin area at Besham = 201,388 km2), situated
approximately 80 km upstream of Tarbela (Fig. 1), with a mean annual flow of 2410 m3/s according to the SWHP (Surface Water
Hydrology Project) flow records from 1969 to 2008. The catchment
⇑ Corresponding author. Tel.: +33 467 14 90 64; fax: +33 467 14 47 74.
E-mail addresses: [email protected], [email protected] (A.A.
Tahir), [email protected] (P. Chevallier), [email protected] (Y. Arnaud),
[email protected] (L. Neppel), [email protected] (B. Ahmad).
0022-1694/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2011.08.035
area upstream of Tarbela reservoir is called the Upper Indus Basin
(UIB) (Fig. 1), which contributes the major part of inflow into the
Indus River from high elevations as a result of snow and glacier
melt.
The active hydrological zone of the Upper Indus Basin lies in the
high-altitude Himalaya and Karakoram Ranges. Several authors
(Archer and Fowler, 2004; Hewitt et al., 1989; Wake, 1989; Young
and Hewitt, 1990; Bookhagen and Burbank, 2010) reported that
more than 65% of the annual flow of the Upper Indus River is contributed by the seasonal and permanent snowfields and glacierized
areas above 3500 m in elevation. Liniger et al. (1998) stated that
some 90% of the lowland flow of the Indus River System originates
from the Hindukush, Karakoram and western Himalaya mountain
areas.
The management of Tarbela reservoir depends to a large extent
on the summer inflow contributed by the snow- and glacier-fed
tributaries situated in the Karakoram Range. The presence of snow
in a basin strongly affects the moisture that is stored at the surface
and is available for future runoff (Maurer et al., 2003). The summer
runoff is highly correlated with the summer mean temperature in
these high-altitude sub-catchments of the UIB, mostly covered
with permanent snow pack and glaciers (Archer, 2003). The Hunza
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
105
Fig. 1. Location map of study area (Hunza River basin).
River at Dainyor Bridge (Fig. 1) represents the moderately high
runoff catchments in the center of the Karakoram where a significant proportion of the flow is derived from cryosphere melt. It
nearly doubles the runoff rate along with the Gilgit River in the
overall Indus catchment at Partab Bridge station (Fig. 1), i.e., at
its confluence point with the Indus River. Climate change in these
sub-catchments will directly influence the inflow to the Indus irrigation system. Linear regression analysis by Archer (2003) indicates that a 1 °C rise in mean summer temperature would result
in a 16% increase in summer runoff into the Hunza and Shyok River
due to accelerated glacier melt. Analytical studies representing
temperature increases of 1–3 °C in the western Himalayan region
suggest an increase in glacial melt runoff by 16–50% (Singh and
Kumar, 1997). It is therefore important to use reliable hydrological
tools to simulate runoff, resulting from the snow and glacier melt,
under future climate change scenarios in the high-altitude catchments to assess the water supply available for future needs. Regional climate investigations by IPCC (2007) indicate that the median
temperature will rise up to 3.7 °C in Central Asia by the end of 21st
century. According to the IPCC (2007), the temperature increase in
the Himalayan region has been greater than the global average of
0.74 °C over the last 100 years. This temperature increase may be
associated specifically to the Greater Himalaya but not to the Hindukush and Karakoram region. The Karakoram region shows a different climate trend from that of the Greater Himalaya (Fowler and
Archer, 2005). The Karakoram region is influenced by the westerly
circulations which bring the maximum of precipitation in winter in
the form of snow whereas the Greater Himalayan region is under
the influence of monsoon regime. This climate variability in the region is explained in detail by Fowler and Archer (2005) and Tahir
et al. (2011). The future climate variables simulated by Akhtar
et al. (2008) using a regional climate model indicate that the annual mean temperature will rise up to 4.8 °C and annual mean precipitation up to 16% until the end of the 21st century (2071–2100).
1.1. Snowmelt Runoff Model
Different hydrological models with a snow component (e.g.,
Sorman et al., 2009; Verdhen and Prasad, 1993; WMO, 1986) are
used to simulate the daily stream flows in snow- and glacier-fed
catchments.
Nevertheless, most of the hydrological models are not satisfactory for daily stream flow simulation and forecasting in the highaltitude catchments where the snowmelt is a major factor in the
water cycle (Martinec et al., 2007). One of the widely applied models to simulate and forecast the daily stream flows in these types of
catchment is the Snowmelt-Runoff Model (SRM) developed by
Martinec (1975), which uses remotely sensed cryosphere data as
basic input (Georgievsky, 2009). Most recently, it has also been applied to evaluate the effect of climate change on seasonal snow
cover and runoff (Martinec et al., 2007).
The SRM had successfully undergone tests by the World Meteorological Organization with regard to runoff simulations (WMO,
1986) and to partially simulate conditions of real-time runoff forecasts (WMO, 1992). Due to the progress of satellite remote-sensing
of the cryosphere, the SRM has been applied to larger and larger
basins. To date the model has been applied by various agencies,
institutes and universities to over 100 basins, situated in 29 countries (Martinec et al., 2007). It is currently based on a simple degree-day method. The daily precipitation, air temperature and
snow cover area are the input data. In addition to the input variables, a number of basin characteristics such as basin area, zone
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A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
area (in zone-wise application) and the hypsometric (areaelevation) curve are also needed. The daily water produced from
the snowmelt and rainfall is superimposed on the calculated recession flow and transformed into daily discharge from the catchment
according to the following equation:
Q nþ1¼½C sn an ðT n þ DT n ÞSn þ C rn Pn þ Q n knþ1
A:10; 000
ð1 knþ1 Þ
86; 400
ð1Þ
where Q is the average daily discharge (m3/s), C, the runoff coefficient, with Cs referring to snowmelt and Cr to rain, a, the degreeday factor, DDF (cm °C1 d1), T, the number of degree-days (°C d),
S, the ratio of the snow covered area (SCA) to the total area, n, the sequence of days, P, the precipitation contributing to runoff, A, the area
of the basin or zone, k is the recession coefficient (Xc and Yc).
The detailed description of the variables and parameters used in
this equation is given by Martinec et al. (2007).
In the Himalayas and its surroundings, Immerzeel et al. (2009,
2010) investigated the effects of snow cover dynamics on the discharge of the Upper Indus River and concluded that stream flows
can be predicted with a high degree of accuracy using MODIS snow
cover data in the SRM. Bookhagen and Burbank (2010) found that
the SRM combined with MODIS snow cover and TRMM rainfall
data significantly improves regional runoff modeling for the Himalayas. Prasad and Roy (2005) applied the SRM to the Beas River basin and found that the observed and computed discharges were
well correlated. Hong and Guodong (2003) found a Nash–Sutcliffe
coefficient value of 0.87 when simulating the stream flow in the
Gongnaisi River basin in the western Tianshan Mountains by
applying the SRM.
In other regions of the world where the impact of the snow cover is significant, Lee et al. (2005) reported that the SRM has sufficient accuracy for stream flow prediction using the MODIS snow
cover products in the snowmelt-dominated basin of the Upper
Rio Grande basin. Georgievsky (2009) tested the SRM as a shortterm forecasting model and concluded that the model can be used
for short-term runoff forecasts in the mountain and foothill areas
of the Krasnodar reservoir basin. Sirguey et al. (2009) applied the
SRM to the Waitaki catchment, New Zealand, and found that the
simulation of daily stream flows is reliable and that the SRM has
the potential to provide short- to medium-term forecasts of water
availability in these catchments.
The previous studies of applying the SRM to mountainous river
catchments of Himalaya and other regions of the world suggest
that the model can efficiently be applied in the snow- and glacier-fed Hunza River basin to simulate and forecast the daily
stream flows as well as to study the effect of future climate change
on river runoff. The other factor that suggests the use of SRM is its
weak sensibility towards the precipitation forcing. Since, the precipitation data available from the high altitude catchments is not
of very good quality so we preferred to use the SRM which is more
sensitive to the daily snow cover and temperature input. In this
study, the SRM (WinSRM version 1.12) was applied to the Hunza
River basin using observed and APHRODITE (Yatagai et al., 2009)
precipitation data (described in Section 3.3), which will show the
reliability of APHRODITE precipitation products for hydrological
modeling in the remote regions of the UIB. This study will also help
manage the water reservoirs (existing Tarbela and proposed
Diamer Bhasha) (Fig. 1) of the UIB for irrigation, hydropower
generation, flood control and water supply by providing a reliable
estimation of the future snowmelt runoff in one of the main
Fig. 2. Global Digital Elevation Model (GDEM) of the Hunza River basin indicating the six altitudinal zones extracted for this study.
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A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
snow- and glacier-fed tributaries of the Indus River under different
future climate change scenarios.
The main objectives of this study were to:
(a) Evaluate the SRM’s ability to simulate the daily stream flows
in the snow- and glacier-fed Hunza River basin.
(b) Investigate the reliability of APHRODITE precipitation data
for modeling snowmelt runoff in the high-altitude river
catchment.
(c) Simulate stream flow in the Hunza River basin under different future climate change scenarios for water resources
management in the UIB.
The study area is described in Section 2 of this manuscript followed by the description of the data sets and treatment of data in
Section 3. The principle and method of the SRM is described in Section 4. The results obtained from this study and discussion on these
results are described in Section 5 following by the conclusions
drawn in Section 6.
tions with precipitation gauges are installed by WAPDA at different
altitudes within the catchment, as shown in Fig. 2. The mean total
annual precipitation is 170 mm at Khunjerab (4730 m), 225 mm at
Ziarat (3669 m) and 680 mm at Naltar (2858 m) according to the
10-year record (1999–2008) of the three climate stations in the
catchment. The Hunza River has a mean annual flow of 323 m3/s
(i.e., 742 mm of water depth equivalent) (standard deviation for
mean annual flow = 69 m3/s), gauged at Dainyor Bridge (Fig. 1),
according to the SWHP-WAPDA 40-year (1966–2008) flow record.
Obviously, present precipitation records are not representative of
the runoff at the outlet because of the well-known gauging errors
at high altitudes, as explained in detail by Sevruk (1985, 1989) and
Førland et al. (1996). The other factor participating in this contrast
is the absence of precipitation gauging stations at high altitudes
above 4730 m where maximum snowfall and accumulation occurs,
as confirmed by Young and Hewitt (1990).
3. Data sets
3.1. Topography
2. Study area: Hunza River basin
The present evaluation of the SRM is undertaken in the Hunza
River basin (drainage area, 13,733 km2) (Fig. 1), situated in the
high-altitude central Karakoram region, with a mean catchment
elevation of 4631 m. Approximately 4463 km2 of catchment area
is at an elevation above 5000 m and almost the same area (33%)
is glaciated (Akhtar et al., 2008), as shown in Fig. 2. Area distribution for different altitudinal zones in the catchment is shown by
the hypsometric curve in Fig. 3 and given in Table 1.
A previous paper described the hydrological behaviors and regime of the Hunza River basin at Dainyor Bridge (Tahir et al.,
2011). The main elements are summarized here.
The snow cover area in the Hunza River basin varies from
approximately 80% in winter to 30% in summer. Three climate sta-
The Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER), Global Digital Elevation Model (GDEM) is
used to delineate the catchment boundary studied. The ASTER
GDEM is available for high-latitude and steep mountainous areas
not covered by SRTM3 (METI and NASA, 2009). Six different altitudinal zones were extracted from the GDEM of the study area for
detailed analysis of snow cover area (SCA) estimation. Some characteristics of these zones are given in Table 1.
3.2. Hydrometeorological data
Stream flow measurement in Pakistan is mostly carried out by
the Surface Water Hydrology Project of the Water and Power
Development Authority (SWHP-WAPDA) with the earliest records
8000
7500
Zone F
7000
6500
Zone E
6000
5500
4500
Zone D
4000
Zone C
3500
3000
2500
Zone B
2000
1000
500
Zone A
1500
Hypsometric curve
Cumulated area (km²)
Fig. 3. Hypsometric curve of the Hunza River basin showing the area distribution in six different elevation zones.
14000
13000
12000
11000
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0
Elevation (m)
5000
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A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
Table 1
Characteristics of the six elevation zones extracted from the DEM of the Hunza River
basin.
Zone
Elevation range
(m)
Mean elevation
(m)
Area
(%)
A
B
C
D
E
F
1432–2500
2500–3500
3500–4500
4500–5500
5500–6500
6500–7849
1966
3000
4000
5000
6000
7166
3.1
11.5
29.4
44.6
10.1
1.3
425
1574
4038
6123
1388
173
100
13,733
Total
Area
(km2)
Climate
station
–
Yasin
Ziarat
Khunjerab
–
–
Table 2
Snowmelt-runoff model (SRM) parameters to be calibrated.
Runoff coefficient (snow), Cs
Critical temperature, Tcrit. (°C)
Rainfall contributing area, RCA
Temperature lapse rate, c
Runoff coefficient (rain), Cr
Degree day factor (DDF), a (cm °C1 d1)
Recession coefficient (Xc and Yc), k
Time lag, L
beginning in 1960. The database of daily discharges for the Hunza
River gauged at Dainyor Bridge was made available for this analysis from January 2000 to December 2004. Meteorological data
(daily mean temperatures and precipitation) available at three
high-altitude climate stations (Fig. 2 and Table 1) was provided
by WAPDA for the same period.
3.3. APHRODITE precipitation data
A database of gridded daily precipitation products, constructed
by the Asian Precipitation – Highly-Resolved Observational Data
Integration Towards Evaluation of the Water Resources (APHRODITE Water Resources) project, was used in this study to assess
the reliability of this product in high-altitude catchments of the
UIB. The gridded fields of daily precipitation are defined by inter-
polating rain-gauge observations obtained from meteorological
and hydrological stations over the region (Yatagai et al., 2009).
The data were downloaded from the APHRODITE water resources
project web page (http://www.chikyu.ac.jp/precip/). The product
used in this study comprises the 0.25 0.25° gridded data over
Monsoon Asia (APHRO_MA_V0902) from 2000 to 2004. The APHRODITE data grid has a coarse resolution (30 30 km2) and the
precipitation is assumed to be constant at any elevation in this grid
area.
3.4. Satellite data
3.4.1. MODIS snow cover
The Moderate Resolution Imaging Spectroradiometer (MODIS)
snow products were selected to calculate the percentage of snow
cover area in the study area. MODIS snow cover products are used
by several researchers to use as input for the snowmelt runoff
model (e.g., Bookhagen and Burbank, 2010; Immerzeel et al.,
2009; Prasad and Roy, 2005). The MODIS/Terra Snow Cover
8-Day L3 Global 500 m Grid (MOD10A2), used for this study, contains data fields for maximum snow cover extent over an 8-day repeated period (Hall et al., 2006, updated weekly.) and has a
resolution of approximately 500 m completely covering the Hunza
River basin. A data set of 228 processed MOD10A2 (V005) images
available from March 2000 to December 2004 was downloaded
from http://nsidc.org/cgi-bin/snowi/search.pl. The available
MODIS images were mosaicked and projected with the WGS
1984 UTM ZONE 43N projection system. The Hunza River basin
area was then extracted from this mosaicked scene to assess the
percentage of snow and ice cover (cryosphere) in the study area.
When the percentage of cloud cover exceeded 20% on a specific
date, the record was removed and then the average snow cover
was estimated on this date by interpolating linearly between the
previous and the next available cloud-free images. The snow cover
area was also calculated for the different altitudinal zones (Table 1)
to use further for snowmelt-runoff modeling.
Fig. 4. Snow cover distribution (basin-wide) in the Hunza River basin over a period of 2000–2004. Snow cover area is estimated from the remotely sensed MODIS (MOD10A2)
snow cover data.
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
4. Principle and method
The snowmelt-runoff model (SRM) was used to simulate the daily
discharges in the Hunza River at Dainyor Bridge. It uses the remote
sensing snow cover data as basic input. The basic parameters to be
calibrated for the SRM are given in Table 2. The model was run for
four hydrological years (April 2000 to March 2004) to simulate the
daily discharges in the Hunza River basin. The SRM parametric values were estimated during the calibration of the model and extracted from the studies (e.g., Dey et al., 1989; Hock, 2003;
Immerzeel et al., 2010; Prasad and Roy, 2005; Zhang et al., 2006) conducted previously on other Himalayan basins. It was applied by
using both proposed options: ‘‘basin-wide’’ and ‘‘zone-wise’’.
4.1. Basin-wide application
The average daily precipitation and daily mean temperatures
observed at the three climate stations in the Hunza River basin,
as well as the daily snow cover area percentage were used as input
variables. The total catchment area and the hypsometric (areaelevation) curve (Fig. 3) for the basin were calculated using the
GDEM. The daily basin-wide snow cover area (Fig. 4) for the Hunza
River basin was estimated by the linear interpolation of the snow
cover area percentage calculated from 8-day composite MOD10A2
satellite images. The SRM was also run by replacing the daily
observed precipitation with the APHRODITE precipitation data
(described in Section 3.3) to determine the reliability of
APHRODITE precipitation products for runoff modeling in the
high-altitude catchments. The values for different parameters used
in the basin-wide simulations are given in Table 3.
109
rate is not necessary because the SRM simulated discharge is
strongly influenced by the change in snow cover area and less by
the precipitation input. Therefore, the average of daily precipitation values observed at the three climate stations was used for
each altitudinal zone. The daily zonal snow cover area (Fig. 6)
was estimated by the linear interpolation of the snow cover area
percentage calculated from 8-day composite MOD10A2 satellite
images. The total zonal area and the area distribution (hypsometry) curve for each zone were calculated from the GDEM. The degree-day factor (DDF) values were extracted from the previous
studies e.g. Hock (2003) and Zhang et al. (2006) for the Himalayan
region. These studies present a DDF value of 5.7–7.4 mm °C1 d1
for ice and about 4–5 mm °C1 d1) for snow. We assumed that
the altitudinal zones higher than 5000 m are glacier covered and
used the higher value of DDF (7 mm °C1 d1) for these zones.
The values for different parameters used in the zone-wise simulations are given in Table 4.
The main equation of the model computes the water produced
from snowmelt and from rainfall, superimposed on the calculated
recession flow and transforms this into daily discharge from the
basin. The data series available for 2000–2001 was used for the calibration of SRM parameters. Both classical criteria in hydrological
modeling were used to evaluate the SRM: the Nash–Sutcliffe coefficient (NS) and the difference of volume (Dv).
In addition, the Pearson correlation coefficient (Rodgers and
Nicewander, 1988) and the Kendall rank correlation (Kendall,
1975; Kendall and Gibbons, 1990) tests were used (significance level, 5%) to evaluate the relationship between the measured and
simulated daily discharges from 2000 to 2004, in the basin-wide
and zone-wise applications of the SRM.
4.3. Climate change scenarios
4.2. Zone-wise application
The Hunza River basin was divided into six altitudinal zones
with a nearly 1000-m difference in mean hypsometric elevation
between two zones. The daily zonal mean temperatures (Fig. 5)
for zones A, E and F (zones with no climate stations) were determined by extrapolating the mean temperature records available
in zones B and D (Naltar and Khunjerab) by using the lapse rate value of 0.48 °C/100 m for zone A and 0.76 °C/100 m for each of zones
E and F. These lapse rate values were calculated by using the available observed data on three climate stations of the Hunza River basin. The estimation of precipitation data for each zone using a lapse
Table 3
SRM parameter values for basin-wide application using observed and spatial data in
the Hunza River basin.
Parameter
Parameter values (using
observed precipitation data)
Parameter values (using
APHRODITE precipitation
data)
Lapse rate
(°C/100 m)
Tcric (°C)
DDF (cm °C1 d1)
Lag time (h)
0.64
0.64
0
0.5
18
0
0.5
18
Cs
0.20 (June–August)
0.15 (September–May)
0.30 (June–August)
0.15 (September–May)
Cr
0.15 (July–August)
0.10 (September–June)
0.20 (June–August)
0.10 (September–May)
RCA
1 (June–August)
0 (September–May)
1 (June–August)
0 (September–May)
Xc
0.80 (June–September)
1.06 (October–May)
0.80 (June–September)
1.06 (October–May)
Yc
0.02
0.02
After calibration and validation on the present time (year of reference: 2000), the SRM was used to study the impact of climate
variability on the Hunza River runoff. The different future climate
scenarios for mean temperature, precipitation and snow cover area
were used to estimate the relative stream flow in the future for
water resources management. These scenarios are described as
follows:
(a) A 20% increase in cryosphere area until 2075 and a 10%
increase until 2050. The cryosphere area is assumed to
increase under the increasing precipitation scenario, as
explained by Akhtar et al. (2008), if the mean temperature remains constant. Some of the previous studies indicate a trend toward increasing cryosphere area in the
Karakoram region, for example (Hewitt, 2005; Immerzeel
et al., 2010).
(b) A 4 °C increase in mean temperature until 2075 (Akhtar
et al., 2008; IPCC, 2007), a 3 °C increase until 2050 and a
2 °C increase by 2025 (considering other variables as constant, particularly precipitation).
(c) An increase of 2–4 °C in the mean temperature (4 °C rise in
zones A, B and C; 3 °C in zone D; 2 °C in zones E and F)
and a 20% increase in snow cover area until 2075 (this scenario is assumed by taking into account the two previous
scenarios, a and b).
5. Results and discussion
5.1. Basin-wide application of the SRM (using observed precipitation
data)
The results obtained for the basin-wide application of the SRM
in the Hunza River basin over 4 hydrological years (2000–2004)
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A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
Fig. 5. Temperature variations in different elevation zones of the study area, presented with a mobile average of 15 days period line. The data for zones B, C and D is the actual
observed data from the climate stations (WAPDA) installed in these zones. The data for zones A, E and F is estimated from the actual data by using the mean temperature lapse
rate of 0.48–0.76 °C/100 m (lapse rate is calculated from the actual observed data of the catchment).
Fig. 6. Snow cover distribution in six different altitudinal zones of the Hunza River basin. Snow cover area is estimated from the remotely sensed MODIS (MOD10A2) snow
cover data.
(annual, snowmelt and extreme discharge periods) are presented
in Table 5. These results suggest that the model has efficiently simulated the daily discharges in the Hunza River during the annual
(April–March), snow melt (April–September) and most of the extreme discharge (July–August) periods. The simulation of daily discharge in the snow accumulation period is not substantial because
the discharge is very low during this period in the Hunza River basin. The management of water resources is more closely associated
with the discharge contributed by the Hunza River during the
snowmelt period. The NS coefficient value was never less than
0.86. The best performance of the model was found over the validation year 2001–2002 with the NS coefficient value of 0.97 and
a 2% difference in volume (Fig. 7). A NS coefficient value of 0.92
was found over the entire simulation period from 2000 to 2004.
The correlation coefficient values were never less than 0.94 and
0.82 for the Pearson and Kendall rank correlation tests, respectively. The extreme events are not modeled as efficiently as the entire snowmelt period. A maximum NS coefficient value of 0.76 was
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A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
Table 4
SRM parameter values for zone-wise SRM application to the Hunza River basin.
Parameter
Parametric values for each altitudinal zone
A (1450–2500 m)
B (2500–3500 m)
C (3500–4500 m)
D (4500–5500 m)
E (5500–6500 m)
F (6500–7800 m)
Lapse rate (°C/
100 m)
Tcric (°C)
DDF (cm °C1 d1)
Lag time (h)
0.48
0.48
0.64
0.76
0.76
0.76
0
0.5
6
0
0.5
6
0
0.5
12
0
0.6
12
0
0.7
18
0
0.7
18
Cs
0
June–August = 0.25
September–
May = 0.20
June–August = 0.35
Sep–May = 0.3
June–August = 0.35
September–May = 0.3
June–August = 0.50
September–
May = 0.4
June–August = 0.50
September–
May = 0.4
Cr
June–August = 0.50
September–
May = 0.40
June–August = 0.35
September–
May = 0.30
June–August = 0.35
September–
May = 0.3
June–August = 0.35
September–May = 0.3
0
0
RCA
1
1
1
Jun–Sep = 1 Oct–
May = 0
0
0
Xc
June–
September = 0.80
October–May = 1.06
June–
September = 0.80
October–May = 1.06
June–
September = 0.80
October–May = 1.06
June–September = 0.80
October–May = 1.06
June–
September = 0.8
October–May = 1.06
June–
September = 0.80
October–May = 1.06
Yc
0.2
0.2
0.2
0.2
0.2
0.2
June–August
September/October–May
Zone E and F
Zone D
Cs and Cr
RCA
DDF
Snow melt and monsoon period
Snow accumulation and dry period
Zones with almost 100% glacierized area
Almost 50% glacierized area
Zone A has a small snowmelt runoff contribution and Zones E and F have a small rainfall runoff contribution
Rainfall contributing area is small in Zone E and F because at this altitude the precipitation is in solid form
Degree day factor for snow is less and for ice/glacier is greater in Karakoram Range (Hock, 2003; Zhang et al., 2006)
found over the year 2001 (Table 5). This may be associated to the
glacial contribution during this period from the catchment which
is not captured efficiently by the model.
5.2. Zone-wise application of SRM
The results obtained for the zone-wise application of the SRM in
the Hunza River basin over 4 hydrological years (2000–2004) (annual, snowmelt and extreme discharge periods) are presented in
Table 6. The NS coefficient value was never less than 0.83 and
0.72 over the annual and snowmelt simulation periods, respectively. The best performance of the model was found over the validation year 2001–2002 with the NS coefficient value of 0.94 and a
3% difference in volume (Fig. 7). A NS coefficient value of 0.90 was
found for the entire simulation period from 2000 to 2004. The correlation coefficient values were never less than 0.94 and 0.74 for
the Pearson and Kendall rank correlation tests over the annual simulation periods, respectively. The extreme events are also modeled
more efficiently than for basin-wide application. A maximum NS
coefficient value of 0.85 was found over the year 2003 (Table 6).
Table 5
Evaluation of basin-wide SRM application to the Hunza River basin during the hydrological year, snowmelt period (April–September) and extreme discharge period (July–August),
using observed and spatial precipitation data.
Model efficiency (with
APHRODITE precipitation data)
Correlation between observed and
simulated discharge (with
APHRODITE precipitation data)
Kendall rank
correlation
coefficient
Difference of
volume (Dv%)
Nash–
Sutcliffe
coefficient
Pearson
correlation
coefficient
Kendall rank
correlation
coefficient
0.97
0.90
7.4
0.89
0.95
0.89
0.97
0.90
0.86
0.99
0.95
0.94
0.92
0.89
0.82
9
21
4.5
0.93
0.80
0.94
0.98
0.98
0.98
0.91
0.91
0.85
Snowmelt period (April–September)
Calibration
2000
2.5
0.88
0.94
0.82
6.7
0.79
0.91
0.80
Validation
2001
2002
2003
0.95
0.82
0.78
0.98
0.92
0.90
0.91
0.77
0.76
11.7
25
9.6
0.87
0.64
0.90
0.96
0.96
0.96
0.87
0.87
0.85
0.50
0.76
0.19
0.74
0.70
0.87
0.44
0.86
–
–
–
–
11.4
13.2
30.8
9.0
0.25
0.62
0.40
0.83
0.51
0.79
0.64
0.91
–
–
–
–
Model efficiency (with
observed precipitation data)
Correlation between observed and
simulated discharge (with observed
precipitation data)
Difference of
volume (Dv%)
Nash–
Sutcliffe
coefficient
Pearson
correlation
coefficient
Calibration
2000–2001
0.7
0.93
Validation
2001–2002
2002–2003
2003–2004
2.0
4.8
12
Hydrological
year (April–
March)
0.3
0.7
9.0
Extreme events (July–August)
2000
4.5
2001
5.3
2002
3.9
2003
22.2
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
The basin-wide and zone-wise simulated discharge data were plotted against each other to analyse the reaction of both datasets
against the high discharges (Fig. 8). The zone-wise simulation
seems to capture high discharges more efficiently than the basinwide simulation. This may be associated with the fact that the
model parameters estimated for each zone are more reliable than
an average value estimated basin-wide.
5.3. Basin-wide application of the SRM (using APHRODITE
precipitation products)
2500
0
2250
5
2000
10
1750
15
1500
Nash-Sutcliffe coefficient, NS = 0.97 (BW), 0.94 (ZW)
Difference of volume, Dv = 2% (BW), 3.1% (ZW)
1250
Measured discharge
Simulated discharge-BW
Simulated discharge-ZW
Gauged precipitation
1000
20
25
30
Feb-02
Dec-01
Aug-01
Mar-02
50
Jan-02
0
Nov-01
45
Oct-01
250
Sep-01
40
Jul-01
500
Jun-01
35
May-01
750
Apr-01
3
Discharge (m /s)
The results obtained for the basin-wide application of the SRM
using APHRODITE precipitation data over the annual, snowmelt
and extreme discharge periods are presented in Table 5. The NS
coefficient value over the annual simulation period was never less
than 0.80. The best performance of the model was found over the
validation year 2003–2004 with the NS coefficient value of 0.94
and a 4.5% difference in volume (Dv). The SRM performance over
the hydrological year 2001–2002, using APHRODITE precipitation
data, is shown in Fig. 9. The NS coefficient value of 0.90 was found
over the entire simulation period from 2000 to 2004 (Fig. 10). The
correlation coefficient values were never less than 0.95 and 0.85
for the Pearson and Kendall rank correlation tests, respectively.
The efficiency of the SRM using APHRODITE precipitation products
was slightly lower than that using observed precipitation (except
for the year 2003–2004), mainly for the high discharge (snowmelt)
Precipitation (mm)
112
Time period (Daily)
Fig. 7. Evaluation of basin-wide (BW) and zone-wise (ZW) SRM application over the hydrological year 2001–2002 in the Hunza River basin using observed precipitation data.
Table 6
Evaluation of zone-wise SRM application to the Hunza River basin during the hydrological year, snowmelt period (April–September) and extreme discharge period (July–August),
using observed precipitation data.
Hydrological year (April–March)
Model efficiency (with observed precipitation data)
Correlation between observed and simulated discharge
Difference of volume (Dv%)
Nash–Sutcliffe coefficient
Pearson correlation coefficient
Kendall rank correlation coefficient
Calibration
2000–01
2.6
0.90
0.95
0.89
Validation
2001–2002
2002–2003
2003–2004
3.0
1.2
8.1
0.94
0.92
0.83
0.97
0.96
0.94
0.86
0.75
0.74
Snowmelt period (April–September)
Calibration
2000–2001
0.46
0.82
0.92
0.77
Validation
2001–2002
2002–2003
2003–2004
6.43
0.01
13.20
0.89
0.85
0.72
0.95
0.92
0.92
0.86
0.78
0.79
Extreme events (July–August)
2000
2001
2002
2003
0.4
0.5
10.0
29.8
0.72
0.72
0.46
0.85
0.85
0.85
0.68
0.92
–
–
–
–
113
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
1800
Linear regression line
y = 0.95 x + 15.05
95% confidence interval line
95% Prediction interval line
1600
Nash-Sutcliff coefficient, NS = 0.92
Difference of volume, Dv = 0.2%
Number of data points, n = 1461
3
ZW Simulated discharge (m /s)
1400
1200
1000
800
600
400
200
0
0
200
400
600
800
1000
1200
1400
1600
1800
3
BW simulated discharge (m /s)
Fig. 8. Comparison of basin-wide (BW) and zone-wise (ZW) simulated runoff by applying the SRM from April 2000 to March 2004 (using observed precipitation data).
simulation periods where the discharges are underestimated
(Fig. 10), but these performances remained significantly efficient
and acceptable. The underestimation of the high discharges is
due to the fact that the total annual APHRODITE precipitation is
lower than the observed precipitation in the Hunza River basin,
as shown in Figs. 7 and 9.
The results obtained suggest that the SRM is a good choice to
simulate the daily discharges in the snow- and glacier-fed Hunza
River basin. The simulation results obtained using APHRODITE precipitation data suggest that this product may effectively be used to
simulate and forecast the discharges using the SRM in the high-altitude catchments of the Himalayan region with no precipitation
gauges. This result may be associated with the fact that the discharge in the Hunza River is influenced by the snow cover dynamics
and mean temperatures of the catchment. The results obtained by
SRM application on the extreme discharges (July–August) suggest
that the overall efficiency of model simulation is satisfactory. The
underestimation of some high discharges events may be associated
to the glacier melt contribution during this period which may not
be taken into account by the SRM. The daily precipitation of the
catchment does not greatly influence the discharge. Moreover, the
SRM model was developed to simulate the daily discharges in high
mountain regions, keeping in mind the well-known deficit of precipitation gauges and the scarcity of measurements in high-altitude
regions. Consequently, the SRM uses the snow-covered area whenever possible to compute the runoff input, thus compensating the
lack of precipitation data measurements.
5.4. Impact of climate variability on Hunza River runoff
The impact of climate variability on the Hunza River runoff was
studied using the SRM under the different future climate scenarios
described in Section 4. The simulated discharge in 2000 was used
as the reference for the present climate.
(a) The discharges simulated under different snow cover area
scenarios are presented in Fig. 11. The snow cover area
was assumed to increase by 10% by 2050 and 20% by 2075
as a result of increasing precipitation while the mean temperatures of the catchment remained constant. The impact
of a 10% decrease in the snow cover area of the catchment
on the Hunza River discharge was also studied. The results
obtained indicate that the summer stream flow is likely to
increase with increased snow cover area. A 10% increase in
snow cover area results in a nearly 7% (36 m3/s) increase
in mean summer discharge and a 20% snow cover increase
forces the mean summer discharge to increase by 14%
(72 m3/s) of present mean summer discharge (498 m3/s).
The mean summer discharge decreases by 7% if the snow
cover area is assumed to decrease by 10%. This means that
a 1% increase in snow cover area in the Hunza catchment
can increase the mean summer discharge up to 3.6 m3/s
(0.7%) in the Hunza River.
(b) The mean temperature of the catchment was assumed to
rise up to 2 °C by 2025, 3 °C by 2050 and 4 °C by 2075 (at
the end of 21st century), keeping the other variables constant, particularly precipitation and the snow cover area.
The monthly discharges for the Hunza River simulated under
these scenarios are presented in Fig. 12. The results obtained
indicate that a 2 °C rise in temperature will result in an
increase of almost 64% (816 m3/s) in the mean summer discharge (April–September) and a 3 °C rise of temperature will
force the mean summer discharge to increase almost 100%
(1010 m3/s) from the present mean summer discharge
(498 m3/s). The relationship between the simulated discharge and the input variables used in the scenarios ‘‘a’’
and ‘‘b’’ appears as linear and we adopt it in order to simplify
the interpretation, without more detailed and reliable information. This result (a nearly 33% increase in summer discharge results in a 1 °C rise in mean temperatures) is
2000
0
1800
5
1600
10
1400
15
20
1200
Nash-Sutcliffe coefficient, NS = 0.93
Difference of volume, Dv = 8.9%
1000
25
Measured discharge
Simulated discharge
APHRODITE Precipitation data
800
30
Mar-02
Oct-01
Sep-01
Apr-01
Feb-02
50
Jan-02
0
Dec-01
45
Nov-01
200
Aug-01
40
Jul-01
400
Jun-01
35
May-01
600
Precipitation (mm)
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
Discharge (m3/s)
114
Time period (Daily)
Fig. 9. Evaluation of the basin-wide SRM application over the hydrological year 2001–2002 in the Hunza River basin using APHRODITE precipitation data.
1800
Linear regression line
y = 0.998 x + 16.01
95% confidence interval
95% prediction interval
1600
Nash-Sutcliffe coefficient, NS = 0.90
Difference of volume, Dv = −5%
Number of data points, n = 1461
3
Simulated discharge (m /s)
1400
1200
1000
800
600
400
200
0
0
200
400
600
800
1000
1200
1400
1600
1800
Measured discharge (m3/s)
Fig. 10. Relationship between daily measured (Qm) and simulated (Qs) runoff applying the SRM basin-wide, April 2000 to March 2004 (using APHRODITE precipitation data).
different from that estimated by Akhtar et al. (2008) and
Archer (2003), who explained a 16% increase in summer discharge resulting from a 1 °C rise in temperature in the Hunza
and Shyok rivers. The discrepancy between the results
obtained by different studies may possibly be due to the
methods, hypothesis and limitations under which the results
are obtained (e.g. Archer (2003) used statistical analysis and
Akhtar et al. (2008) used a different model). These results
115
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
1000
Discharge, Q (current SCA)
Discharge, Q (−10% SCA)
Discharge, Q (+10% SCA)
Discharge, Q (+20% SCA)
900
800
Discharge (m3/s)
700
600
500
400
300
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Time period (Monthly)
Fig. 11. Monthly discharge simulations in the Hunza River for different climate change (snow cover area) scenarios. Discharge simulated for 2000 with the SRM is used as
current discharge.
ing snow- and glacier-fed catchments. However, these
results may be specific to this particular region because
the catchment response to the climate warming may not
be the same in other catchments as explained by Null
et al. (2010) and Young et al. (2009) that the river runoff
indicate that if the temperature continues to rise in the
Himalayan region with a rate estimated by the IPCC
(2007), i.e., 3.7 °C until the end of the 21st century, then larger-capacity reservoirs will be needed to manage the river
flow in the UIB, contributed by the Hunza and its neighbor-
2000
Year 2000 (current climate)
Year 2025 (T−2°C)
Year 2025 (T+2°C)
Year 2050 (T+3°C)
Year 2075 (T+4°C)
Year 2075 (T+2 to +4°C,+20% SCA)
1800
1600
3
Discharge (m /s)
1400
1200
1000
800
600
400
200
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Time period (Monthly)
Fig. 12. Monthly discharge simulations in the Hunza River for different climate change (mean temperature) scenarios. Discharge simulated for 2000 with the SRM is used as
current discharge.
116
A.A. Tahir et al. / Journal of Hydrology 409 (2011) 104–117
decreases with increasing temperature scenarios. This scenario will also result in a shift of runoff period as presented
in Fig. 12, where the discharge increase is shifted by 1 month
(from April to March). This fact is also explained by Null
et al. (2010) and Young et al. (2009) that the high elevation
watersheds in the southern-central region of Sierra Nevada
are most susceptible to earlier runoff timing. On the other
hand, if this scenario plays itself out, then the glaciers may
retreat extensively or even disappear after a certain period
of producing a high discharge, as a result of rapid melting.
The rapid melting of glaciers is also reported by Cannone
et al. (2008) who explained that many of the small glaciers
(80% of total glacial coverage and an important contribution
to water resources) in the Alps could disappear in the next
decades under current climate warming trend. These shrinking glaciers, surface-albedo change, and modifications to the
soil–water storage capacities may result in a different longterm hydrograph in the high elevation catchments.
(c) The monthly discharge simulating a 20% increase in snow
cover and a 2–4 °C rise in mean temperature is presented
in Fig. 12. The simulation indicates a 100% increase in the
future stream flow. This result suggests that the Hunza River
discharges are highly influenced by the snow cover melt
caused by the mean temperature input in summer and not
by the catchment precipitation, as explained by Archer
(2003). However, the Hunza River discharge has a positive
correlation with winter precipitation that falls in the form
of snow and starts melting as the mean temperature
increases in the catchment (Tahir et al., 2011). Moreover,
33% of the area of the Hunza River basin is glacier-covered
and contributes actively to the snowmelt-produced runoff.
This is the area that is considered to receive an increasing
amount of precipitation in the future and no significant
increase in the mean temperatures (Tahir et al., 2011),
resulting in an increased snow cover area. This increased
snow cover area will produce a large amount of runoff on
melting if the mean temperature tends to rise in the future
as presented in Fig. 12. Moreover, if the mean temperature
and winter precipitation tend to increase until the end of
century, this may result in a balance of cryosphere area in
the Karakoram region.
overcome by using the SRM on the snow- and glacier-fed
catchments.
The analysis of the climate change impact indicated that the
basin’s hydrology will alter under different climate change scenarios. This change apparently results from the increasing temperature, which will increase the volume of summer stream flow in
the region. The availability of the snowmelt runoff seems to shift
accordingly with the mean temperature rise in the spring. This
analysis suggests that new reservoirs (such as the projected Diamer
Bhasha Dam that is planned on the Indus River about 315 km upstream from Tarbela Dam) (Fig. 1) are required for the summer flow
storage to meet the needs for supplementary irrigation supplies
during the low-flow period in the downstream regions, increasing
power-generation demand, flood control and water supply. Though
the climate scenarios used in this study were assumed or derived
from previous studies, they provide useful information to manage
water resources for the country’s future needs.
The application of the SRM to the other snowmelt runoffdominated sub-catchments of the UIB may help manage the integrated water resources of the Indus River irrigation system. The
next step of the study is to apply the SRM to the neighboring snowand glacier-fed catchments – Shigar, Shyok, Astore, Gilgit and
Kharmong (Fig. 1) – to simulate and forecast the summer stream
flow as well as to evaluate the impact of future climate scenarios
in these Himalayan regions.
Acknowledgements
Adnan Ahmad Tahir was financially supported by the Higher
Education Commission of Pakistan within the framework of a
France-Pakistan collaboration program for overseas studies. This
financial support is gratefully acknowledged and appreciated.
The authors extend their thanks to the Water and Power Development Authority (WAPDA) for contributing their hydrological and
meteorological data. The authors also wish to thank NASA and
Japan’s Ministry of the Economy, Trade and Industry (METI) for
providing ASTER GDEM.
Ralph Roberts, Computer Specialist at the USDA-ARS Hydrology
and Remote-sensing Lab, provided valuable information to solve
the SRM installation problems.
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