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Soil Environ. 43(1): 94-111, 2024
DOI:10.25252/SE/2024/243346
Online ISSN: 2075-1141
Print ISSN: 2074-9546
*Email: engrbilal000@gmail.com
Cite This Paper: Bilal, M., M. Arshad , M.A. Shahid and M. Tahir. 2024. Calibration of soil and water assessment model for its potential impact on climate
change. Soil Environ. 43(1): 94-111.
© 2024, Soil Science Society of Pakistan (http://www.sss-pakistan.org)
Calibration of soil and water assessment model for its potential impact on climate
change
Muhammad Bilal1*, Muhammad Arshad1, Muhammad Adnan Shahid1 and Muhammad Tahir2
1Department of Irrigation and Drainage, University of Agriculture, Faisalabad, 38000 Pakistan.
2Department of Agronomy, University of Agriculture, Faisalabad, 38000 Pakistan.
[Received: Februay 03, 2024 Accepted: April 18, 2024 Published Online: May 31, 2024]
Abstract
The present study is to understand how climatic variables such as precipitation and temperature vary over
time and how those changes affect stream flow in the Jhelum River basin in Pakistan under different emission
scenarios A2 and B2. The simulation results of HadCM3 were employed to create potential climate change
scenarios with the Statistically Downscale Model (SDSM). The calibrated model Soil and Water Assessment
Tool (SWAT) was used to forecast imminent stream flow to develop a proposed future climate change scenario.
Results indicated that cooling patterns were identified in the north portion of the study area whereas warming
patterns were detected in the south portion. The projected mean annual maximum temperature (T max) of 2020’s
2050’s and 2080’s would be 0.3 oC, 0.8 oC, and 0.99 oC, respectively, under the A2 scenario. The changes in
mean annual minimum temperature (Tmin) were also observed as it would be 0.4 oC, 0.7 o C, and 1.4 oC during
2020’s (2021-2040), 2050’s (2041-2070) and 2080’s (2071-2100), respectively. Similarly, it was observed that
average annual rainfall would rise by 14%, 10%, and 20% during the 2020s, 2050s, and 2080s, respectively, in
the Mangla basin. The results showed an increase in annual stream flows of 100% (1545 m3/sec), with increases
in the winter and autumn seasons of up to 409% and 211%, respectively, and a drop in the spring and summer
seasons of up to 29% and 25%, respectively, in the 2080’s compared to baseline. Water managers should
consider the current trends and variability brought on by climate change to improve water management where
water is scarce.
Keywords: Climate change, GCMs, statistically downscaled model, SWAT, temperature
Introduction
Global warming and energy imbalances are caused by a
fast increment in the concentration of
greenhouse gases (ozone, nitrous oxide, methane, water
vapors, CO2, and CFCs) in the
environment due to urbanization, for example, the intense
practice of fossil fuels, and land use changes (Huang et al.,
2011). Heat is absorbed by greenhouse gases (GHG) from the
environment and slowly releases it, which results in changes
in the climate variables such as precipitation and temperature
(Gebremeskel et al., 2004). Many countries in South Asia are
facing water tension because of the earth’s atmospheric
variations. The Intergovernmental Panel on Climate Change
(IPCC) reported a 0.73 oC rise in worldwide temperature
during 1950 - 2012. Depending on the different global
warming scenarios, an increase of 1°C to 3°C is expected till
the 2050s, and a rise of 2°C - 5°C is possible until 2100 (IPCC
2014). The abrupt changes in temperature have an impact on
land use and on the streamflow pattern (Stocker et al., 2014).
Over half of the water needed for human consumption is
provided by rivers (Bolch, 2017). However, in places where
snowpack is the main source of total runoff, river streams are
connected to long-term variations in temperature and rainfall
(Lutz et al., 2014, Deng et al., 2015).
Such warming has an impact on global atmospheric
patterns, which are generally simulated and projected by using
general circulation models (GCM). Many approaches for
downscaling to local and large scale of 0-50 km and 50x50 km
have been explored (Bates et al., 2008). Based on expected
greenhouse gas emission rates, these models represent climatic
fluctuations. Almost all modern general circulation models
(GCM) have a geographical resolution of 150 km to 300 km,
and the spatial resolution of each GCM varies, when compared
to other GCMs (Huang et al., 2012). Many climatic factors are
anticipated on a broad scale by GCMs; however, the coarser
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
resolution makes it difficult to determine many climatic
variables effectively. To solve this problem, several
downscaling methods are frequently used to reduce the coarse
resolution data of GCMs to fine resolution, although these
methods also introduce organized variations (Fowler et al.,
2007: Di Luca et al., 2013; Kotlarski et al., 2014). There are
several downscaling methods that have been developed to get
rid of these systematic variations (Gellens and Roulin, 1998;
Chen et al., 2013). Regional-scale and local-scale climatic
components are connected via certain downscaling techniques.
It is possible to forecast localized temperature and precipitation
by studying regional moisture transfer and air flow rate ( Wilby
et al., 2002; Mahmood and Babel, 2013; Pervez and Henebry,
2015; Zhang et al., 2016). Because the geographical resolution
of the GCMs is not considered in statistical downscaling
procedures, it is essential to accurately calculate the bias
correction coefficient using long-term observed and historical
climate data (Maraun et al., 2010; Ozbuldu and Irvem, 2021).
Several statistical downscaling approaches have been used in
various studies to examine the changes of hydro-climatic
elements from different basins in the upper part of the Indus
basin, and a persistent increase in temperature has been
predicted( Archer and Fowler, 2008; Tahir et al., 2016; Garee
et al., 2017; ul Hasson et al., 2019). However, the rate of
temperature increase varies among the sub-basins. Past study
explains many precipitation drifts in various upper Indus
subbasins (Amin et al., 2018).
Previous research made considerable use of three distinct
kinds of statistical downscaling techniques: weather
generators, weather organization systems and statistical
regression techniques(Maurer et al., 2014; Abbas et al., 2020).
As a result, a downscaling approach is frequently used in
estimating the expected impact under distinct research settings
(Cheung et al., 2016). Despite its general use in analyzing the
implications of climate change, the Statistical Downscaled
Model (SDSM) has been utilized in numerous investigations
throughout the world (Ahmed et al., 2013; Mahmood and
Babel, 2013b; Mahmood and Babel, 2014; Campozano et al.,
2016; Anuchaivong et al., 2017; Le Roux et al., 2018; Abbas
and Mayo, 2021). As a result, the effectiveness of the statistical
downscaling approach is also tested while modeling daily
precipitation and temperature time sequences data. The Soil
and Water Assessment Tool (SWAT) model is employed in the
research to comprehend the hydrologic processes and
interactions between the different elements of the water
balance in various scenarios of climate change of the Mangla
Basin. The streams network, catchment point at outflow, the
division of sub-catchments, and HRUs are all defined by the
QGIS-built interface model QSWAT(Olsson et al., 2015;
Zaman et al., 2015; Mbaye et al., 2016; Babur et al., 2016;
Figure (1): Location map of Mangla Basin
Calibration of Soil and water assessment tool
96
Soil Environ. 43(1): 94-111, 2024
Tschöke et al., 2017; Zaman et al., 2018; Adnan et al., 2019).
SWAT determines the time of absorption by combining the
channel flow and overland flow time (Neitsch et al., 2011). The
current work aims to use SWAT by using existing gauged data
poised from five main sub-basins, namely Neelum, Jhelum,
Kunhar, Ponch, and Kansi as fundamental employed to
SWAT. Furthermore, this study intends to evaluate the
possibility of a link between Tmin, Tmax and precipitation at
local and large-scale climatic variables using a statistically
downscaled approach. The present paper directs the effect of
climate change scenarios from GCM on potential water
sources using the SWAT hydrologic model. The main
objectives of this study are (i) to develop a hydrological model
by using daily temperature, streamflow, and precipitation data
for the Mangla watershed. (ii) to have future prediction of
temperature and precipitation of general circulation model
(GCM) data by utilizing statistical downscaling techniques.
(iii) to assess the impact of climate change on future stream
flow of Mangla watershed. The outcomes of the current study
may help water resource management to better manage their
water reserves by altering their management policies to lessen
the consequences of potential climate change.
Materials and Methods
Study area
The Mangla basin is situated between 33° 00 to 35°12 N
latitudes and 73° to 75°45 E longitudes, as shown in Figure
(1). The watershed is situated on the Himalayas' southerly
slope that covers an area of 33424 km2 in Mangla watershed.
The Dam generates electricity and manages the water flow of
Mangla basin, which is located at 300 meters above sea level.
Around 55% of the territory is in Indian-controlled Kashmir,
while the remaining 45% is in Pakistan, which includes Azad
Kashmir. There are Five subbasins that drain water to Mangla
reservoir, Jhelum, Kanshi, Neelum/Kishan Ganga, Poonch,
and Kunhar. The Jhelum river rises from Verinag Spring,
which is in Jammu and Kashmir at the foot of the Pir Panjal
Ranges on the south part of the Himalayan Mountain range.
The Kunhar river and Neelum river, which enter the
mainstream in Domel, Kohala Bridge, and Muzaffarabad,
respectively, are significant tributaries of the Jhelum river. In
Mirpur district, the Jhelum River flows into Mangla
Reservoir. Mangla reservoir receives water from the Poonch
and Kanshi rivers as well. India and Pakistan share the
Mangla basin in the southeast. Even though monsoon
precipitation impacts the lower portion of the basin while
summer runoff due to glacier melt and snowfall in winter also
contribute drastically to the river stream.
Data description and analysis
The datasets utilized for runoff modeling to investigate
the current and potential impacts of climate change on
streamflow in the Mangla watershed are displayed in table
(1).
Climate scenario data
The future scenarios were created by using the outcomes
of GCM model (HadCM3) for emission scenario of A2 and
B2. The SDSM model was applied to downscale HadCM3
model results from global scale to the local scale watershed.
Predictor variables give daily data on the status of atmosphere
at a broad scale. Predictor variables are HadCM3 model
outputs accessible on grid 2.5o latitude 3.75o longitude from
Canadian climate scenario website (https://climate-
scenarios.canada.ca/?page=pred-hadcm3).
Table (1): Data set
Data Type
Sourcing
Resolution / scale
Explanation
Topography
Earth env
(https://www.earthenv.org/DEM)
90 x 90m
DEM
Soil data
FAO
1 Km
Soil texture (Sand, Silt and
Clay)
Landuse Data
https://swat.tamu.edu/data/
300 x 300m
Landuse classification
(forest, agriculture, crop and
water)
Climate Data
Pakistan Metrological Department (PMD)
Daily
Solar Radiation, Rainfall,
Temp and Wind Speed
(1981-2010)
Hydrological Data
Water And Power Development
Authority
Daily
Streams flow data (1981-
2010)
Climate Scenario
data (HADCM3)
https://climate-
scenarios.canada.ca/?page=pred-hadcm3
2.5 latitude x 3.75
longitude
1961-2099
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
Metrological data
WAPDA-SWHP and Pakistan Meteorological
Department provided daily meteorological time data for 12
stations from 1981 to 2010 for this study (PMD). Table (2)
shows site-specific information as well as mean monthly
values of Tmin, Tmax, and precipitation for different stations.
Tmax and Tmin mean temperatures vary from -3°C to 39.3°C
and -10.3°C to 26.2°C, respectively.
Hydrological data
The WAPDA-SWHP project has made daily streamflow
data (Qst) for 9 gauging stations public, with the first records
dating back to 1981. In all five subbasins, streamflow gauges
have been installed. Streamflow flows from the Ponch,
Neelum, and Kanshi subbasins are monitored at Kotli,
Muzaffarabad, and Palote, respectively. Table (3) lists the
characteristics of several gauging locations. In August, the
stations located at low elevations have the greatest mean
flow, whereas the stations located at upper elevations have
the maximum mean flow during June. With an average
inflow of 967 m3/sec from 1960 to 2010, three subbasins
(Poonch, Jhelem, and Kanshi) drain into the Mangla basin
(Mahmood and Babel, 2013).
Soil and land use data
A land use map was created through analyzing global
land use data to determine primary land use groups in
watersheds. Table (4) and Figure 2(b) show the land
use/cover map for this research and the proportion of sub
basin area. The map depicts eight different lands cover
types: Irrigated and rainfed farmland covers 27.41% of the
basin, mosaic grassland covers 19.31%, ‘and Water,
Snow, and Ice covers roughly 4.40 %. The worldwide
IPCC (Intergovernmental Panel on Climate Change) of
soil classes from the FAO regional and global scales soil
database were obtained to create a user soil file table (5)
for SWAT modeling in specified watershed. Different
elements such as geological bedrock, temperature,
overlying vegetation, and topographic circumstances
Table (2): Different climate stations in mangla basin
Stations
Lat
Long
Elevation
basin
Data period
Max temp
(OC)
Min temp
(OC)
Precp
(mm)
Bagh
34.0
73.80
1067
Jhelum
1981-2010
25.14
12.19
1416
Balakot
34.60
73.40
995
Kunhar
1981-2010
26.21
12.29
1552
Garidupatta
34.20
73.60
813
Jhelum
1981-2010
23.09
11.76
1442
Gujjar khan
33.33
73.33
457
Kanshi
1981-2010
26.21
12.29
831
Kotli
33.50
73.90
610
Poonch
1981-2010
28.48
16.29
1244
Muzaffarabad
34.40
73.50
702
Neelum
1981-2010
27.62
13.50
1523
Kallar
33.40
73.40
518
Kanshi
1981-2010
28.51
14.46
932
Mangla
33.10
73.60
282
Mangla
1981-2010
29.75
17.03
845
Murree
33.90
73.40
2206
Jhelum
1981-2010
17.66
8.46
1770
Naran
34.90
73.70
2363
Kunhar
1981-2010
11.95
2.60
1766
Rawalakot
34.0
74.0
1677
Jhelum
1981-2010
20.50
9.51
1269
Palandri
33.70
73.70
1402
Jhelum
1981-2010
23.43
12.10
1306
Table (3): Stream flow measurements at Mangla basin
Stations
Lat
(dd)
Long
(dd)
Elevation
(meter)
Basin
area (km2)
River
Data period
Average yearly flow
(m3/sec)
Naran
34.90
73.70
2400
1108
Kunhar
1981-2010
45.07
Garihabibullah
34.40
73.40
820
2434
Kunhar
1981-2010
104.27
Palote
33.20
73.40
400
867
Kansi
1981-2010
5.59
Muzaffrabad
34.40
73.50
670
7412
Neelum
1981-2010
326.80
Chinari
34.20
73.80
1070
13652
Jhelum
1981-2010
297.57
Domel
34.40
73.50
701
14396
Jhelum
1981-2010
326.91
Kohala
34.10
73.50
560
24464
Jhelum
1981-2010
784.61
Kotli
33.50
73.90
530
3210
Ponch
1981-2010
129.20
Azadpattan
33.70
73.60
485
25967
Jhelum
1981-2010
840.60
Calibration of Soil and water assessment tool
98
Soil Environ. 43(1): 94-111, 2024
influence the kind of soil in a watershed (Neill et al.,
2013). The distribution of soil types in the Mangla
watershed is depicted in the accompanying map Figure
(2)(a). This watershed is mountainous, with heavy
precipitation in the form of snow in the winter, resulting
in weathering soil types.
Statistical downscaling model (SDSM)
SDSM, a model based on this statistical method, was
utilized in this study. It's a decision-making tool that uses
a statistical downscaling method to evaluate local climate
change implications. The Statistical Downscaling Method
is the amalgamation of stochastic downscaling and
Multiple Linear Regression (Taye et al., 2015, Mullan et
al., 2016).
The initial phase in SDSM downscaling technique was
to determine empirical correlations between local scale
predictand variables (actual precipitation and air
temperature) gathered from climate stations and large-scale
predictor variables received from NCEP re-analysis data for
the present climate. Most significant large-scale variables
(predictors) in SDSM were chosen by using analysis of
partial correlation, linear correlation, and scatter plots of both
variables (predictand and predictors). Some essential SDSM
model parameters, such as transformation functions, bias
correction, and variance inflation were evaluated through the
screening of predictor variables (Fiseha et al., 2012; Behulu
et al., 2013). The selected predictors and predictands (local
scale temperature and precipitation) was used in this research
for different stations enlisted in Table (6). From 30 years of
Table 4: Land use classification of mangla watershed
Land Use Type
Symbol
Area (km2)
% in Catchment
Rainfed cropland
ARGL
9327
27.41
Post flooding or Irrigated Cropland
WETL
6340
18.63
Grassland (40-70%) / shrubland (30-50%)
RNGE
6571
19.31
Vegetation (50-70%) / cropland (25-50%)
RNGB
6045
17.77
Closed (>40%) needle-leaved evergreen forest (>5m)
FRSE
4001
11.76
Water bodies, snow, and ice
WATR
1496
4.40
Mixed Forest (deciduous forest >5m)
FRSD
227
0.67
Artificial surface and associated Areas (Urban area >50%)
URMD
14
0.04
(a) (b)
Figure (2): Soil map (a) and land use map (b) in Mangla watershed
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
Table (5): Soil classification of Mangla basin
Soil class
Soil texture
Associated soil
Area (km2)
% in catchmment
Be72-3c-3672
Clay
Orthic Luvisols
7849.10
23.07
Be73-2c-3673
Loam
Lithosols
376.74
1.11
Be71-2-3a-3668
Clay-Loam
CambisolGleyic
390.62
1.15
I-B-U-3712
Loam
Cambisols
16187.60
47.57
I-X-c-3512
Loam
Xerosols
360.07
1.06
Lo44-1b-3799
Sandy_Loam
Chromic luvisol
980.98
2.88
Rc40-2b-3843
Loam
Calcic Xerosols
529.22
1.56
GLACIER-6998
UWB
Glacier
231.01
0.68
Be78-2c-3679
Loam
Haplic Phaeozems
230.70
0.68
WATER-6997
Water
Water
113.26
0.33
Be79-2a-3680
Loam
Eutric gleysol
6776.74
19.920
Table (6): Screening predictors variables with their relevant predictand
Stations
Precipitation
Tmax
Tmin
Bagh
p5_zas, shumas
p5_zast, p500as, tempas
p5_zas, p500as, p8zhas, tempas
Balakot
p_zhas, p5_fas, p5thas, shumas
p5_zas, p500as, tempas
p5_zas, r500as, shumas, tempas
Garhidupatta
p__uas, p_zhas, p5thas, p8_vas, p8zhas,
shumas
p5_zas, p500as, tempas
p5_zas, p500as, p8zhas, tempas
Gujjar khan
p__fas, p__uas, p_thas, p_zhas, p500as,
p8_uas, shumas
p5_zas, p500as, tempas
p5_zas, p500as, p8_vas, tempas
Kallar
p__fas, p__uas, p_zhas, p500as, p8_uas,
shumas
p5_zas, p500as, tempas
p5_zas, p500as, tempas
Kotli
p__fas, p__uas, p5thas, p8_vas
p5_zas, p500as, tempas
p5_zas, p500as, p8_vas, tempas
Mangla
mslpas, p5zhas, p850as, shumas
p5_zas, p500as, tempas
p5_vas, p5_zas,
p500as, tempas
Murree
p8_fas, p850as, shumas
p5_zas, p500as, tempas
p5_zas, p500as, p8zhas, tempas
Muzaffarabad
p_zhas, p8_vas, p8zhas, shumas
p5_zas, p500as, tempas
p5_zas, p500as, p8zhas, tempas
Naran
p_zhas, p8_zas, shumas
p5_vas, p5_zas, p500as,
tempas
p5_zas, p500as, p8_vas, tempas
Pallandri
p__fas, p__uas, p__zas, p8_uas, p8_zas
p5_zas, p500as, tempas
p5_vas, p5_zas, p500as, tempas
Rawalakot
mslpas, p5_fas, p850as, r850as
p5_zas, p500as, tempas
p_zhas, p5_zas, p500as, p8zhas,
tempas
Table (7). Comparison of R2, correlation, MAE and RMSE between real and model results of Precipitation,
Tmax and Tmin for all stations in validation period (2001–2010)
Variables
Correlation %
R2 %
RMSE (mm)
µ (mm)
Range
Mean
Range
Mean
Range
Mean
Range
Mean
Precipitation
Obs
64-157
102.10
NCEP
15-89
61.40
23-79
41.82
31-87
62.16
66-174
128.04
H3A2
29-89
57.76
20-80
35.36
88-124
109.18
81-205
134.80
H3B2
26-74
54.95
45-54
31.72
89-163
115.25
80-205
137.76
Tmax
Obs
2.5-17.6
12.32
NCEP
88-98
96.65
0.86-0.96
93.49
0.83-2.89
1.93
2.1-17.7
12.18
H3A2
86-99
89.87
0.90-0.95
83.71
0.92-3.30
3.03
2.8-18.6
12.47
H3B2
85-99
89.78
0.85-0.99
83.39
0.93-3.36
3.04
3.03-16.9
12.52
Tmin
Obs
11.8-30.8
24.56
NCEP
90-99
96.86
87-97
93.89
1.27-5.68
2.23
12.1-30.1
24.50
H3A2
91-97
91.72
88-94
85.54
1.66-5.53
3.37
13.9-30.7
25.45
H3B2
91-99
92.55
87-95
86.44
1.61-5.56
3.29
13.4-29.8
25.38
Calibration of Soil and water assessment tool
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Soil Environ. 43(1): 94-111, 2024
actual data (1981-2010), For calibration, first 20 years data
(1981-2000) were used and for validation remaining 10 years
of data (2001-2010) were used.
In this study, most of the sites were calibrated during a
20-year period (1981-2000). The model on monthly-based
was employed to study the monthly temporal deviation. The
validation procedure was simulated for ten years, from 2001
to 2010.
For validation of Statistically Downscaled Model, three
modes of climate data set were used in HadCM3 under
scenario A2 and B2. Four primary valuation goal functions—
average, correlation, R2 mean absolute error (MAE), and root
mean square error—were employed to simulate the results of
temperature and precipitation successions at every station, as
indicated in Table (7). It was observed that R2 value for
precipitation is higher than 0.31 on a monthly basis. In
comparison to earlier investigations, these levels are high.
(Maurer et al., 2014; Van Vliet et al., 2016; Mascaro, 2018).
Due to stochastic factors, such downscaling is extremely
challenging for daily and monthly precipitation. The results
show that for analyzed weather stations R2 values between
actual and modeled temperature climb beyond 90%, but
downscaled rainfall is less than 42%.
SWAT model description
The Soil and Water Assessment Tool (SWAT) is the
physical-based, partially allocated, hydrology model created
by USDA-ARS (Saleh et al., 2000). Crop yield, surface
runoff, pesticide, nutrients, and sediment yields are all
(a)
(b)
Figure (3): Comparison between observed and simulated flow of Mangla watershed, (a) calibration (1981-1995), (b)
validation (1996-2010)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
Jan-81
Aug-81
Mar-82
Oct-82
May-83
Dec-83
Jul-84
Feb-85
Sep-85
Apr-86
Nov-86
Jun-87
Jan-88
Aug-88
Mar-89
Oct-89
May-90
Dec-90
Jul-91
Feb-92
Sep-92
Apr-93
Nov-93
Jun-94
Jan-95
Aug-95
Monthly Stream Flow (cusec)
Months
L95PPU observed Simulation
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
Jan-96
Aug-96
Mar-97
Oct-97
May-98
Dec-98
Jul-99
Feb-00
Sep-00
Apr-01
Nov-01
Jun-02
Jan-03
Aug-03
Mar-04
Oct-04
May-05
Dec-05
Jul-06
Feb-07
Sep-07
Apr-08
Nov-08
Jun-09
Jan-10
Aug-10
Monthly Stream Flow (cumec)
Time (Months)
95% Prediction uncertainity observed Simulation
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
calculated by SWAT. The SWAT model is incorporated into
a geographic information system (GIS) that combines
multiple data of spatial environment such as land cover,
topography, soil, and climate characteristics. SWAT is now
included in the QGIS interface named QSWAT. The model
is built around five linear reservoirs: vegetation cover
reservoir, snow melting and buildup reservoir, surface
reservoir, subsurface reservoir, and reservoir for runoff.
SWAT model has been used effectively across the globe to
solve numerous environmental concerns related to an
investigation of water quantity and quality, such as water
surface of diffuse contamination. The model has been
classified partial distributed as it separates watershed into
smaller units known as hydrological response units (HRUs).
HRU’s are made up of soil features, uniform land use and
managing approaches. The additional segmentation of sub-
basins into HRUs allows for accounting the influence of
diverse types of land cover, managing strategies, and soil
features on basin's hydrologic system. A weather generator in
the model provides daily data of precipitation, temperature,
relative humidity, wind speed and solar radiation depending
on statistical factors obtained from mean monthly values. The
DEM, soil map, land-use map, soil characteristics, river flow
data and climatic data were utilized to create SWAT model
for the research region.
Performance evaluation of model
A common hydrological model performance criterion
has been proposed in several research. Model assessment
statistics employed in this work were determination
coefficient (R2), Nash-Sutcliffe efficiency (NSE), and Bias
Percentage (PBIAS) (Moriasi et al., 2007). NSE>0.5 and
PBIAS 0±25 percent were deemed good model performance.
The strong link between observed and modelled values is
measured by NSE. NSE values range from -1 to +1. (Nash
and Sutcliffe, 1970). Model performance is greater when the
value is near to +1.
Nash-Sutcliffe efficiency = 1−∑(𝑆𝑖 −𝑂𝑖)2
𝑁
𝑖=1
∑(𝑂𝑖
𝑁
𝑖=1 −𝑂
)2 (1)
Percent Bias = ∑(𝑆𝑖−𝑂𝑖)
𝑁
𝑖=1
𝑂
(2)
Where
N = observations,
Oi = observe value
Si = simulate value at ith time
O = average observed value.
Time series
Three-time frames for future eras, each encompassing
non-overlapping 30 years, were used to analyse the simulated
scenarios of climate change. The 2020’s (2021–2040),
2050’s (2041–2070), and 2080’s (2071–2100) are time
periods in question. On a yearly and seasonal basis future
scenarios were created. The annual mean is the average of the
monthly averages from January to December. Seasonal
means is the average of the monthly averages for Winter
(DJF), Spring (MAM), Summer (JJA) and Autum (SON)
seasons.
Results
Calibration and validation of model
The findings of calibration and validation revealed that
a model performed well and could be employed to examine
the effects of changing climate on stream discharges. The
basis of the SWAT model for the estimate of future flow
was demonstrated by a significant connection between
observed and modeled flows. The R2, NSE, and PBIAS
values are 0.80, 0.79, and 1.2, respectively. These outcomes
demonstrate a strong foundation of the model for upcoming
projections. The estimated value of R2 is 0.79, indicating a
significant connection between modeled and real flow. The
value of percentage bias (Pbias) must be range between -20
and +20% it should be close to zero for excellent model
fitting. The value of percentage Bias is 1.2 which shows the
good performance of the model for upcoming scenarios.
Similarly, the model gave a good value of NSE that
represents good performance of the model. Table (8)
displays the calibration and validation findings for R2,
NSE, R2, and Pbias. These findings show that the SWAT
model performs exceptionally well in the Mangla
watershed.
Some parameters, including curve number 2 (CN2),
alpha factor pro initial flow (ALPHA_BF), and
groundwater delay. (GW_DELAY), minimum depth of
water at shallow aquifer basic for backflow (GWQMN),
and revap coefficient of groundwater (GW_REVAP), were
taken from the literature(Adnan et al., 2019). Table 9
shows these parameters utilized for the calibration of the
model.
Future scenario of climate change
After calibration and validation minimum temperature,
maximum temperature, and rainfall are determined for the
future era of 2020’s (2011-2040), 2050’s (2041-2070), and
2080’s (2071-2099) under HadCM3A2 and HadCM3B2
scenario. Then simulated seasonal and annual Tmax, Tmin
and precipitation data are compared with the baseline to
examine future changes.
Calibration of Soil and water assessment tool
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Soil Environ. 43(1): 94-111, 2024
Downscaling tmax and tmin under potential
scenarios A2 and B2
Figure (4) shows the mean seasonal and annual percentage
change of maximum temperature (Tmax) compared to the historic
period (1981-2010) in A2 and B2 scenario. It is examined that for
A2 scenario, the annual change for Tmax in the whole basin
during a future period (2020s, 2050s, 2080s) would be increased
by 0.22, 0.70, 0.99 oC, respectively. Decreasing trends are also
observed for the Kunhar basin in 2020 under the B2 scenario
because of the highly elevated area but in low-elevation basins
have shown an increasing trend.
Seasonal Tmax would be increased by different degrees in
the whole basin. All subbasins have an increasing trend of winter
and spring under scenarios A2 and B2. Highest positive change
was seen in spring which shows the shifting trend of climate
change from summer to spring. It is also observed that summer
and autumn show a decreasing trend. While Neelum basin will
increase a little bit increasing in 2020 and 2050 during the
summer season for A2 and B2.
Figure (5) shows the mean annual and seasonal percentage
change (2020s , 2050s, 2080s) of minimum temperature
compared to the baseline period (1981-2010) during scenarios
of A2 and B2. Annual change under A2 scenarios, the minimum
temperature (Tmin) in Mangla basin for future periods 2020,
2050, and 2080 would be 0.3 oC, 0.72 oC, 1.35 oC respectively.
As in Kunhar basin it would be -0.25 oC, 2.47 oC, 0.82 oC
respectively. Increasing trends were also seen in Kansi basin and
Neelum basin. For Kansi basin 2.0 oC (2020s), 1.72 oC (2050s),
and 3.12oC in 2080s and Neelum basin 0.66(2020s),
0.73(2050s), and 1.72oC in 2080s.
For the seasonal, an increasing trend of Tmin was seen in
spring and winter while a decreasing trend in fall and summer in
Kunhar, Neelum, and Ponch basin. Tmin of the spring season
for Mangla basin in 2020s, 2050s and 2080s would be 2.2 oC,
Table( 8): Statistical results for calibration (1981-1995) and validation (1996-2010)
Objective function
Initial (Calibrate)
Final (Calibrate)
Validate
COD (R2)
0.30
0.79
0.77
Percentage Bias (Pbias)
30.45
1.2
-8.1
Nash–Sutcliffe Efficiency (E)
0.60
0.79
0.67
Table (9): Calibrated parameters and their value
Rank
Parameter
Detail
Initial
Rang
Calibrate
Rang
Sensitivity
Assessment
min
max
P Values
Tstat
1
CN2
Curve Number Runoff
-0.5
0.3
0.082
1.73x10-7
-5.70
2
ALPHA_BF
Alpha factor of base flow
0
0.7
0.243
0.660
-0.45
3
GW_DELAY
Groundwater delay
95
200
187.049
0.125
-1.55
4
GWQMN
Min depth of water in shallow aquifer
0
500
0.828
0.806
-0.26
5
GW_REVAP
Ground Water Coefficient
0
0.4
0.078
0.940
0.09
6
RCHRG_DP
Deep Percolation
0
1
-0.367
0.244
-1.18
7
CH-N2
Manning n cooffient
0
0.2
0.009
0.085
1.73
8
CH_K2
Hydraulic conductivity of channel
5
100
47.531
0.955
-0.07
9
ALPHA_BNK
Alpha factor of Base flow
0
1
0.977
0.285
1.09
10
SOL_AWC
Soil availability capacity of water
-0.3
0.5
-0.014
0.485
-0.8
11
SOL_K
Soil Hydraulic Conductivity
-0.5
0.5
-0.603
0.112
-1.7
12
SOL_BD
Bulk Density of Soil
0
1
0.676
0.420
-0.82
13
SMFMX
Max Snow melt/year
0
20
1.608
1.85x10-7
8.89
14
SMFMN
Min Snow melt/ year
0
20
-0.194
0.05
-1.90
15
SMTMP
Temp of Snow melt
-5
5
1.488
0.490
0.70
16
SFTMP
Temp of snow fall
-5
5
14.153
2.49x10-8
8.55
17
TIMP
Temp lag factor for Snow pack
0
1
0.969
0.850
-0.3
18
TLAPS
Temp laps rate
-20
20
-11.045
2.50x10-6
-5.20
19
PLASP
Precp laps rate
-300
300
-117.857
0.017
-2.45
20
ESCO
Soil evaporation Factor
0
1
-0.202
0.440
-0.80
21
SNOCOVMX
Min Snow water amount
0
400
17.764
0.39
-0.88
22
SNO50COV
Vol of snow that cover 50%
0.1
0.6
0.476
0.94
-0.08
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
4.7 oC, and 2.66 oC respectively. The spring season has a great
warming trend in all seasons.
Downscaled precipitation under potential
scenarios A2 and B2
Figure (6) describes the change in the percentage of mean
precipitation for seasonal and annual in Mangla watershed
and its sub-basins in 2020’s, 2050’s, and 2080’s with respect
to baseline period under HadCM3A2 and HadCM3B2
scenarios. The mean annual rainfall of the Mangla watershed
under scenario A2 would be 14.22%, 10.78%, and 19.18% in
periods of (2021-2040), (2041-2070) and (2071-2100)
individually. The mean annual precipitation under A2 in
2080s of sub-basin
Figure (4). percentage change of annual and seasonal Tmax in 2020, 2050, 2080 of
mangla sub-basin
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Soil Environ. 43(1): 94-111, 2024
Figure 5. percentage change of annual and seasonal Tmin in 2020, 2050, 2080 of mangla sub-basin
-10
-5
0
5
10
winter spring summer autum annual
Minimum Temperature (C)
Mangla Basin-A2 2020 2050 2080
-10
-5
0
5
10
winter spring summer autum annual
Minimum Temperature (C)
Mangla Basin-B2 2020 2050 2080
-10
-5
0
5
10
winter spring summer autum annual
Minimum Temperature (C)
Kunhar Basin-A2 2020 2050 2080
-10
-5
0
5
10
winter spring summer autum Annual
Minimum Temperature (C)
Kunhar Basin-B2 2020 2050 2080
-10
-5
0
5
winter spring summer autum annual
Minimum Temperature (C)
Ponch Basin-A2 2020 2050 2080
-10
-5
0
5
winter spring summer autum annual
Minimum Temperature (C)
Ponch Basin-B2 2020 2050
-5
0
5
winter spring summer autum annual
Minimum Temperature (C)
Neelum Basin-A2 2020 2050 2080
-5
0
5
winter spring summer autum Annual
Minimum Temperature (C)
Neelum Basin-B2 2020 2050 2080
-5
0
5
10
winter spring summer autum annual
Minimum Temperature (C)
Kansi Basin-A2 2020 2050 2080
-5
0
5
10
winter spring summer autum annual
Minimum Temperature (C)
Kansi Basin-B2 2020 2050 2080
Bilal, Arshad, Adnan and Tahir
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Soil Environ. 43(1): 94-111, 2024
Figure (6): Percentage change of annual and seasonal Prep in 2020, 2050, 2080 of mangla sub-basin
-60
-40
-20
0
20
40
winter spring summer autum annual
Precipitation (%)
Mangla Basin-A2 2020 2050 2080
-60
-40
-20
0
20
40
60
winter spring summer autum annual
Precipitation (%)
Mangla Basin-B2 2020 2050
-50
0
50
100
150
winter spring summer autum annual
Precipitation (%)
Kunhar Basin-A2 2020 2050 2080
-50
0
50
100
winter spring summer autum annual
Precipitation (%)
Kunhar Basin-B2 2020 2050 2080
-50
0
50
100
150
200
winter spring summer autum annual
Precipitation (%)
Neelum Basin-A2 2020 2050 2080
-50
0
50
100
150
winter spring summer autum annual
Precipitation (%)
Neelum Basin-B2 2020 2050 2080
0
20
40
60
winter spring summer autum annal
Precipitation (%)
Ponch Basin-A2 2020 2050 2080
-20
0
20
40
60
winter spring summer autum annual
Precipitation (%)
Ponch Basin-B2 2020 2050
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Soil Environ. 43(1): 94-111, 2024
Figure 7: Projected mean seasonal and annual stream flow w.r.t baseline period (1981-2010)
during A2 & B2 scenario. (1, 2 Jehlum basin, 3, 4 Ponch basin, 5, 6 Kanshi
0
500
1000
1500
2000
2500
Winter(DJF)
Spring(MAM)
Summer(JJA)
Autumn(SON
)
Annual(J-D)
(1) A2
obs 2020 2050 2080
0
1000
2000
3000
Winter(DJF)
Spring(MA
M)
Summer(JJA
)
Autumn(SO
N)
Annual(J-D)
(2) B2
obs 2020 2050 2080
0
50
100
150
200
250
300
Winter(DJF)
Spring(MAM)
Summer(JJA)Autum(SON)
Annual(J-D)
(3) A2
obs 2020 2050 2080
0
50
100
150
200
250
300
Winter(DJF)
Spring(MAM)
Summer(JJA)Autum(SON)
Annual(J-D)
(4) B2
obs 2020 2050 2080
0
5
10
15
20
Winter(DJF)
Spring(MAM)
Summer(JJA)Autum(SON)
Annual(J-D)
(5) A2
obs 2020 2050 2080
0
5
10
15
20
25
Winter(DJF)
Spring(MAM
)
Summer(JJA)Autum(SON)
Annual(J-D)
(6) B2
obs 2020 2050 2080
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Soil Environ. 43(1): 94-111, 2024
Neelum, Kunhar and Kansi basin would be increased by 52%,
45%, and 37% respectively. Under B2 scenario, the average
annual precipitation of the Mangla basin in 2020s, 2050s, and
2080s will be 19%, 7% and 13% respectively. As to Neelum
basin they would be changed by 33%, 20%, and 47%.
respectively. When comes to low elevation basin (Kansi
basin), changes would be 25%, 67%, and 21% in 2020s,
2050s, and 2080s under B2.
For seasonal change mean precipitation of Mangla and
its sub-basin shows a decreasing trend in spring and summer
under both scenarios A2 and B2 except Ponch basin which
shows an increasing change of mean seasonal precipitation of
all seasons in 2020s, 2050s, and 2080s. The highest increase
was seen for autumn 152% and 121% in 2080s under A2 and
B2. It is observed that autumn and winter would be wetter
than summer and spring under both scenarios HadCM3A2
and HadCM3B2.
Impact of climate change on future stream flow
The quantity of rain that occurs in watershed areas and
the amount of actual evapotranspiration discharged in the
atmosphere determine the availability of soil water. As a
result, fluctuations in precipitation and temperature can have
a major impact on yearly soil moisture patterns. Climate
change impacts on the hydrological system were analyzed by
using the scenarios of GCMs which were downscaled
through the SDSM model. Even though future predicted
precipitation and temperatures for the three future climatic
periods were downscaled using two alternative climate
change scenarios A2 and B2 in the 2020s, 2050s, and 2080s.
The climatic effect was investigated using the SWAT
simulation for the period 1981 to 2010. With the help of
weather generator parameters for both scenarios, daily Tmax,
Tmin, and Prep from the regional climate change model were
directly utilized as input for SWAT to make three future
periods of 2020s (2021-2040), 2050s (2041-2070), and 2080s
(2071-2100). The downscaled climatic variables were then
used to re-run the SAWT model for future periods. Relative
humidity, Solar radiation, and Wind speed were considered
to remain constant throughout the simulated periods.
Figure (7) shows mean annual and seasonal stream flows
in possible periods of 2020’s, 2050’s and 2080’s during A2
and B2 scenarios at different river basins. It is also observed
that projected flow will rise in the summer season and future
stream flow will be reduced in autumn. The main reason for
the flow rise in summer is due to snow melting and monsoon
events in this season. The large volume of stream flows is
also noticed in the spring season under 2020s-2080s due to
earlier glacier melting. Nevertheless, stream flow results
depend on Statistically Downscaled Data (SDSM) results. An
average annual flow would be 1522 m3/sec to 1680 m3/sec,
163 m3/sec to 170 m3/sec, and 2 m3/sec to 21 m3/sec for the
Jehlum river, Ponch river and Kanshi river respectively,
during all future period of 2020’s, 2050’s and 2080’s. The
seasonal flow follows same kind of trend but in different
magnitudes. Extreme flow during the summer season would
be 1941m3/sec, 280 m3/sec, and 5 m3/sec for Jehlum Ponch
and Kansi river, respectively, while autumn season flow will
be 1366 m3/sec, 163 m3/sec, and 21m3/sec in 2080’s.
Discussion
In this study, the impact of climate change was
examined under A2 and B2 emission scenarios. A wide range
of topics were investigated about the potential impacts of
changing precipitation and temperature on the water
resources of the Mangla Watershed. Four of the eight
GCMs—CSISRO, CCCma, HadCM3, and CCSRIES—were
chosen for statistical analysis. GCMs daily data for the period
(1981-2010) were acquired from IPCC-DDC under A2 and
B2 scenarios. In comparison to other GCMs, HadCM3 has
the lowest value of root mean square error. On the other hand,
the standard deviation of HadCM3 for precipitation and
temperature is closer to the observed standard deviation. As
a result, the HadCM3 model is chosen for precipitation and
temperature downscaling for future investigation, since the
findings are consistent with past research (Mendez et al.,
2020). Soil and Water Assessment Tool (SWAT), a semi-
distributed hydrological model, was used to produce stream
flows from downscaled data. Due to data paucity and
instability, evapotranspiration was overlooked in this study.
The limited number of metrological sites within the Mangla
Watershed may contribute to poor calibration performance.
The land use categorization and soil type were maintained
constant during the simulations. This sort of assumption may
have an impact on streamflow prediction. During calibration
and validation process of SDSM, it was discovered that
SDSM has a greater capacity to predict temperature in
(monthly and seasonally) along with variation of R2 ranges
(0.85-0.94) for monthly to seasonally. When it comes to
precipitation, SDSM downscales well-performing findings
for monthly and seasonally data, with mean R2 values ranging
from (0.31 - 0.51) for monthly to seasonally. Meteorological
parameters, particularly rainfall, can be difficult to forecast,
leading to uncertainty in streamflow (Ouyang et al., 2015;
Zaman et al., 2018).According to stream flow variations at
Azad Pattan under two scenarios (A2andB2), high flows (Q5)
in Mangla basin would be expected by 3680 m3/sec to 3960
m3/sec in 2020s, For 2050’s flow would be 3980 m3/sec to
4630 m3/sec and in 2080’s projected flow may be 3790
Calibration of Soil and water assessment tool
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Soil Environ. 43(1): 94-111, 2024
m3/sec to 4370 m3/sec. The median flows (Q50) would be
1525 m3/sec, 1540 m3/sec and 1690 m3/sec for 2020’s, 2050’s
and 2080’s, respectively, with respect to baseline. Low flows,
on the other hand, might be projected 415 m3/sec, 595 m3/sec
and 454 m3/sec in the 2020s, 2050s and 2080s, respectively,
during both A2 and B2 scenarios. It is highly advised to take
a number of preventative measures, including
the construction of additional dams, as this increase in
streamflow in the rivers would likely result in floods (Khan
et al., 2020). The findings of this study agree with those of
previous studies on temperature and precipitation (Anjum et
al., 2019). To properly manage water reserves, planners and
resource managers may find value in these forecasted climate
factors and stream flows. For the management of water
resources, the construction of new water retention structures,
and modernizing the designs and storage capacities of
existing dams, this study can be helpful for water resource
planners. Some of the predicted streamflow showed a large
increase that may cause floods in the future.
Conclusion
According to the findings from current study's, the
statistical downscaling approach and hydrological model
known as (SWAT) may be implemented at the sub-basin size
to the transboundary Mangla River basin. The SWAT
simulations for the present and future were conducted using
the SDSM methodology. All time intervals showed
outstanding model performance using our downscaled
climatological data. The following are the study's precise
findings:
• The lower Mangla basin is seeing the most dramatic
effects of climate change, with a warming trend, whilst
the higher basin exhibits a cooling trend, having a
significant impact on stream flows.
• Rainfall has reduced in low-elevation basins (Poonch
and Kanshi). whereas in high-elevation basins (such as
Kunhar and Neelum), the considerable rising patterns of
winter and spring have seen a rise in streamflow,
whereas summer and fall have seen a drop.
• Future changes for annual Tmax would be raised by 0.2,
0.7, and 0.9 oC during a projected period of 2020’s
(2021-2040), 2050’s (2041-2070) and 2080’s (2071-
2100), respectively.
• The annual change for Tmin in the Mangla watershed
maybe 0.3, 0.7 and 1.3 oC in 2020s, 2050s and 2080s,
respectively.
• It was also observed that precipitation during future
periods would be different in magnitude for different sub
basins. The change in annual precipitation may be
increased in Neelum, Kunhar, Kansi and ponch by 47%,
45%, 37%, and 29%, respectively in 2080s.
Therefore, using these results, a more definite GCMs
ensemble scenario might be utilized to forecast climate
variables in the future. The watershed will have a significant
danger of flooding due to an increase in flow. Pakistan might
be capable to provide more food for its people and more
electricity by building reservoirs and dams, as well as by
implementing sound management methods. Future trends
indicate that the highest temperature, lowest temperature, and
peak precipitation will all trend to rise The findings of this
study will assist scholars studying climate change, planners
for development, and decision-makers in creating and
implementing effective water management systems that will
lessen the effects of climate change.
Acknowledgements
We would like to thank the Water and Power
Development Authority (WAPDA) and Pakistan
Metrological Department (PMD) for providing valuable and
essential data for this research. W are also grateful to Higher
Education Commission (HEC) for Indigenous fellowship.
We also acknowledge the Lyes School of Civil Engineering,
Purdue University, USA for proving ideal learning
atmosphere and its lab.
Data availability
All climate data used for this study were collected from
Pakistan Metrological Department (PMD) and Water and
Power Development Authority (WAPDA). However, general
circulation model (GCMs) data were downloaded from IPCC
Data Distribution Center.
Conflict of interest
Author declare that there is no conflict of interest.
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