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Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future inter-annual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past (1970–2010) as well as future (2011–2100) spatial and temporal variability in temperature (maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The non-parametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen’s slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past (1970–2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020s and 2050s and in TMin during 2020s, 2050s and 2080s under A1B and A2 scenarios. Similar results of warming are also predicted at Sunni for annual TMin in future under both scenarios which witnessed cooling during 1970–2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.
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J. Mt. Sci. (
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Abstract:
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J. Mt. Sci. (2015) 12
higher in economically deprived and agricultural
countries of the world due to their low adaptive
capacity. India, mainly an agricultural country
receives more than 50% of its water resources from
three major river systems; Indus, Ganga and
Brahmaputra which originate from the Himalayan
glaciers (Singh et al. 2006). CWC (2001) estimated
that India will reach a state of water stress before
2025 if availability falls below 1000 m3 per capita.
The study of Gupta and Deshpande (2004)
predicted a decline in gross per capita water
availability in India from about 1820 m3/year in
2001 to as low as 1140 m3/year in 2050s.
The change in patterns of precipitation and
temperature caused by global warming has been
studied and discussed in several regions of the world
(Fowler and Archer 2006). India has witnessed a
rise in mean surface annual temperature of about
0.42°C over the last 100 years (MoWR 2008). The
Earth surface warming is widespread and relatively
more pronounced over Northern parts of India. The
warming rate over Himalayan region is higher than
global average of 0.74°C±0.18/100 year (IPCC 2007;
Shrestha et al. 2012). However, warming rate is not
uniform either spatially or temporally over the
Himalayan region. Besides, positive relationship
between altitude and warming rate was observed
over the Greater Himalayan region (New et al.
2002). This is supported by Shrestha et al. (1999)
who analyzed maximum temperature (TMax) data
from 49 stations in Nepal Himalaya for the period
1971-1994. Middle mountain region revealed higher
rate of increase (0.06°C/year to 0.12°C/year) in TMax
as compared to Siwalik and Terai regions (less than
0.03°C/year). Similar patterns in warming were also
reported over Indian Himalayan region (Borgaonkar
et al. 1996; Sharma et al. 2000). Recent studies
performed over North-Western (N-W) Himalayan
region using observed instrumental records by
Bhutiyani et al. (2007) observed a rise in mean
surface annual temperature (1.6°C/ 100 year).
The future patterns in surface temperature
over this region were studied by Rupa Kumar et al.
(2006) and Moor et al. (2011) using high resolution
Regional Climate Model (RCM) and rise in
temperature was predicted. Recently, Kulkarni et al.
(2013) used PRECIS (Providing Regional Climates
for Impact Studies) RCM and predicted increase in
temperature for entire HKH (Hindu Kush
Himalayan) region under A1B emission scenario.
The projected increase in temperature was in the
range of 0.5°C to 1°C for 2020s (2011-2040), 1°C to
3°C for 2050s (2041-2070), 4°C to 5°C for 2080s
(2071-2099) respectively. The future climate
change will alter flows (time and duration) in
major rivers originating from the Himalayan
mountain systems. This will put constraint on
regional water resources and raise concern for
sustainable usage of water in the river basins
(Moors et al. 2011). This necessitates for a proper
assessment of likely future temperature and its
variability in order to make sustainable usage of
water resources in the river basins.
The future time series of temperature data can
be generated from Global Climate Models (GCMs).
GCMs are used for accounting the effect of GHGs
in the atmosphere and to investigate the
anticipated behaviour of complex atmosphere-
land-ocean systems under changing climatic
conditions (MacCracken et al. 2003; Anandhi et al.
2008). The GCM simulations offer information at
coarse spatial resolution. Due to this, direct
applications of their outputs in climate change
impact studies at regional and local scales have
shown poor results. Therefore, global or
continental scale projections of future climate are
less useful for local or regional scale planning
(Martinez et al. 2012). An alternative option is to
downscale GCM’s climate output. In downscaling,
large-scale climate information are applied to
predict local meteorological conditions (Maurer
and Hidalgo 2008; Willby and Dawson 2013). The
techniques of downscaling are grouped into two
categories, namely, dynamical and statistical
(Ghosh 2010).
Dynamic downscaling techniques use RCMs or
Limited Area Models (LAMs) that involves nesting
of GCMs. A horizontal resolution of the order of
tens of kilometers is obtained from RCMs over
selected area of interests. RCMs use initial
boundary conditions and time dependent lateral
meteorological conditions derived from GCMs to
provide information at high spatial and temporal
scales (Giorgi 1990; Jones et al. 1995). The complex
design and computationally expensive nature of
RCMs has limited their applications in climate
change impact studies (Hewitson and Crane 1996;
Ghosh and Mishra 2010). However, the statistical
downscaling approach is less computationally
demanding. In this approach, regional climate is
J. Mt. Sci. (2015) 12
conditioned by large scale climatic state and
regional/local physiographic features, e.g.
topography, land-sea distribution and land use/
land cover (Kim et al. 1984). In statistical
downscaling methods, large scale atmospheric
variables (predictors) of GCMs are related to
station-scale climate variables (predictands) based
on empirical relationship (von Storch et al. 2000;
Raje and Mujumdar 2011).
Sutlej basin, a mountainous river basin, is
located in N-W Himalayan region. Limited
numbers of studies on temperature trends are
performed over the basin. This may be attributed
to inaccessibility and scarcity of well distributed
meteorological stations, non-availability of past
records of climate data and complex physiographic
and climatic conditions prevailing in the basin. The
basin is sensitive to climate change and increase in
mean annual temperature has been observed in
lower and middle elevation ranges of the basin
(Jain et al. 2009). This variability in temperature
has altered the flow of Sutlej river, resulting in the
decrease of mean annual and summer discharge
(Bhutiyani et al. 2008). However, studies on recent
temperature trends over the region are still lacking.
The main objective of the present study is to
analyze trend in TMax and TMin temperature from
1970-2010 (past period) and 2011-2100 (future
period) at three stations, namely Kasol, Sunni and
Rampur in the Sutlej basin. Modified Mann-
Kendall test and Sen’s slope estimator test are used
to detect monotonic trend direction and magnitude
of change over time on annual and seasonal basis.
The future time series of temperature (TMax and
TMin) data is generated at
different stations using
statistical downscaling method.
The Cumulative Sum chart
method is used to quantify
shift in thermal regime and
feasible causes of variation in
maximum and minimum
temperature.
1 Geographical Settings
The Sutlej basin drained
by the Sutlej river (1450 km) is
the largest tributary of the
Indus river systems. Sutlej river originates from
Mansarovar-Rakastal lakes (Western Tibet) and
flows in N-W direction through rugged and
dissected terrain of the Greater and Middle
Himalayan ranges before joining the Indus river a
few kilometers above Mithankot in Pakistan. The
major part of this river lies in Indian territory
(1050 km). It is a perennial river which receives
water from glaciers and precipitation during
summer and from ground flow during winter. It
contributes to the economy of India by supplying
water and electricity to agriculture and various
industrial sectors in the States of Himachal
Pradesh, Punjab, Haryana and Rajasthan.
The study has been carried out in middle part
of the Sutlej basin. It is confined in the hilly State
of Himachal Pradesh, India. The State shares its
boundary with four Indian States, namely Jammu
and Kashmir from North, Punjab from West,
Haryana from South, Uttarakhand from South-
East. The study area covers parts of Simala, Kullu,
Mandi, Bilaspur and Solan districts of Himachal
Pradesh. It covers an area of 2457 km2 and lies
between 31°05'00"N and 31°39'26"N latitudes and
76°51'11"E and 77°45'17"E longitudes (Figure 1).
The altitude in the basin ranges between 502m to
5128m. The elevation and slope aspects control
temperature and also have strong impact on spatial
and temporal distribution of precipitation in the
region.
The mean annual temperature and
precipitation has been recorded as 21.23°C and 103
cm respectively. The mean monthly TMax varies
between 18.59°C to 35.77°C with mean annual TMax
Figure 1 Location map of the study area.
J. Mt. Sci. (2015) 12
being 28.26°C. The mean monthly TMin ranges from
4.26°C to 22.81°C. Sometimes temperature below
1°C is recorded in the region. The hottest months
are May and June with mean TMax being 35.5°C.
December and January are the coldest months with
mean TMin being 4.5°C and 5.5°C respectively. The
diurnal difference (on annual basis) between the
TMax and TMax is in the range of 4.5°C–16°C.
2 Data Sets Description
The meteorological data used in the present
study are:
observed time series of TMax and TMin
temperature
the observed large scale predictors of
National Centre for Environmental Prediction/
National Centre for Atmospheric Research
(NCEP/NCAR) reanalysis data sets
the modelled predictors of third
generation Canadian Coupled Global Climate
Model (CGCM3) under A1B and A2 scenarios
The observed records of daily TMax and TMin
were collected for three hydro-meteorological
stations, namely Kasol, Sunni and Rampur from
Bhakra Beas Management Board (BBMB), India.
The location of these three stations is shown in
Figure 1 earlier while the details of TMax and TMin are
given in Table 1. For the analysis, daily values of
TMax and TMin were summed to obtain annual and
seasonal values at each station.
The predictor variables for NCEP/NCAR and
CGCM3 were downloaded from the Earth System
Research Laboratory (2012) and Data Access
Integration (DAI) (2012). The NCEP/NCAR
reanalysis data is available from 1961 to 2010. This
data was interpolated to CGCM3 grid resolution
(3.75° latitude × 3.75° longitude). The modelled
predictors are simulated under historical GHGs
and aerosol concentration experiment for 20th
Century run (20C3M) as well as Special Report on
Emission Scenarios (SRES) for future run. The
future scenarios considered in this study are A1B
and A2. The predictor variables of CGCM3 are
available for period 1961-2000 under 20C3M
experiment and 2001-2100 under A1B and A2
scenario respectively. All the predictor variables
are available on daily time step, normalized over
1961-1990 period.
3 Methodology
3.1 Method for trend analysis
In this study, modified Mann-Kendall (MK)
test and Sen’s method (Sen 1968) are used for
analyzing temporal trend in TMax and TMin. MK test
originally devised by Mann (1945) and Kendall
(1975) is a non-parametric test method. MK test is
least affected by presence of outliers in data and
assume that the time series under research are
stable, independent and random with equal
probability distribution (Zhang et al. 2005).
However, presence of serial correlation in time
series may increase or decrease probability of
detecting significant trends (Yue and Pilon 2003).
Cunderlik and Burn (2002) suggested removing
serial correlation from time series using Pre-
whitening method and then applying MK test over
uncorrelated time series. But it was noticed that
pre-whitening had reduced the rate of detection of
significant trend in the MK test (Yue et al. 2002).
To take this into account, Hamed and Rao (1998)
proposed a modified MK test for serially correlated
data based on a correction of the variance (S) for
the effective number of observations. In MK test,
test statistics Z is used to check monotonic trend in
time series whereas magnitude of change is
determined from Sen’ slope estimator (Q) (Sonali
and Kumar 2013).
Table 1 Location details of the stations (1970-2010) considered for the study in Sutlej basin
Station Latitude Longitude Elevation
(m)
Average Annual
Temperature (°C)
Standard
deviation(°C)
Coefficient of
variance(°C)
TMax TMin TMax TMin TMax TMin
Kasol 31°21'25" 76°52'42" 662 28.80 16.70 0.85 0.66 0.02 0.03
Sunni 31°14'15" 77°06'30" 655 27.07 12.29 1.19 0.96 0.04 0.07
Rampur 31°27'15" 77°38'40" 976 27.10 13.70 1.19 0.53 0.04 0.03
J. Mt. Sci. (2015) 12
3.2 Method for abrupt change analysis
The abrupt temporal change in time series of
TMax and TMin is investigated using Cumulative Sum
(CUSUM) chart. CUSUM chart, introduced by Page
(1961), is a nonparametric test based technique
used to study sequential changes in one or more
variables. It is a cumulative sum of the deviations
of a time series about a target value (mean of time
series) (Mansell 1997). The CUSUM is widely used
because of its ability to detect unusual patterns,
simplicity and better graphical representation of
results (Sonali and Kumar 2013). Kettel and Yao
(2013) described procedures for constructing
CUSUM charts. Let’s consider x samples, each of n
size with mean µ0 and standard deviation σ. Then,
cumulative sum of deviation (Si) from target value
(mean) is calculated as:
)(
1
0
=
= i
j
ji xS
μ
(1)
where, xj is the mean of jth sample. The Upper
Control Limit (UCL) and Lower Control Limit
(LUC) of the CUSUM chart are defined by a
statistical parameter H (decision interval), and
should not exceed five times the sample standard
deviation. The shift in mean is detected in CUSUM
chart by reference or allowable value (k). It is
selected as halfway between target µ0 and shift of
mean (xj- µ0). In this study, limits of UCL and LCL
are estimated between ±2σ and k = 0.5 respectively.
3.3 Method for statistical downscaling
The statistical downscaling of TMax and TMin is
done using Statistical Downscaling Model (SDSM).
SDSM, invented by R.L. Wilby and C.W. Dawson, is
an accepted statistical downscaling technique
(Wilby et al. 2002). Wilby and Dawson (2013)
described the development of SDSM tool and its
characteristics in details. In SDSM, the generation
of station scale weather parameters is linearly
conditioned by observed large scale predictors of
atmosphere (j = 1, 2, …, n). In case of unconditional
process like temperature, a direct linear
relationship is established between the predictand
Ui and selected NCEP/NCAR predictors Xij on
individual stations, as given below:
ijji Xi
n
j
U
εγγ
+
=
+=
0
1
(2)
where, Ui is temperature on day i and Xij is selected
NCEP/NCAR predictors on day i. γj are regression
coefficients estimated for each month using least-
squares regression and εi is the model error. It is
generated stochastically using a series of serially
independent Gaussian numbers and is added to the
deterministic components on daily basis.
The relevant predictors are selected based on
explained variance, correlation analysis, partial
correlation analysis and scatter plots. The physical
sensitivity between selected predictors and
predictands is also taken into account for the site
(Khan et al. 2006). Monthly percentages of
explained variance demonstrate the capability of a
given predictor to give details of local climate
variability (Gagnon et al. 2005). In the present
work, the model is structured as monthly model for
both TMax and TMin downscaling. This resulted in 12
regression equations between predictors and
observed TMax and TMin for each station, one for
each month. After establishing the model, the daily
data of NCEP/ NCAR and GCM is used to generate
current and future daily weather series (Wilby et al.
2002).
4 Results
In this section, past (1970-2010) as well as
future (2011-2100) spatial and temporal variability
in TMax and TMin over selected stations is discussed
in details under subsequent heads.
4.1 Preliminary analysis and generation of
future time series for TMax and TMin
The monthly data series of observed TMax and
TMin is inspected at each station for all the years.
The Grubbs method is employed for detecting
outliers (Grubbs 1969). The suspicious values were
removed and replaced by normal ratio methods.
Regression through origin was performed to check
the recorded values for outliers. The annual and
seasonal anomalies in TMax and TMin at each station
have been computed for the past as well as for
future time series by subtracting mean of TMax and
TMin averaged over period 1970-2010 (for past) and
2011-2040, 2041-2070 and 2071-2100 (for future)
from the time series.
J. Mt. Sci. (2015) 12
The future time series for TMax and TMin data at
each station was generated using SDSM. The model
was calibrated from the predictors of NCEP/NCAR
reanalysis datasets, selected based on their
statistical properties and physical sensitivity. The
observed daily time series of temperature (TMax and
TMin) data (predictands) was also inputted in SDSM
model during calibration. The model was
calibrated using 31 years (1970-2000) data. The
statistical indicators such as monthly average
percentage of explained variance (E) and the
monthly average standard error (SE) are used to
reflect downscaling results of TMax and TMin at each
station in the basin. The values of explained
variance E (%) and standard error (SE) determine
credibility of the results. The values of E (%) are
used to explain the extent of daily variations in
predictands due to regional forcing (Wilby et al.
2002). The variability has been observed in the
values of E (%) for TMax and TMin at different
stations. The monthly average value of E (%) for
TMax lies between 45.8% to 54.2% and SE (°C)
between 2.05°C to 2.60°C. For TMin, these values
are in the range of 56.6% to 58.7% and 1.42°C to
1.70°C respectively. Further, coefficient of
determination (R2) and Root Mean Square Error
(RMSE) are used to evaluate the performance of
SDSM model during calibration and the observed
are compiled in Table 2. The maximum correlation
between observed and downscaled TMax and TMin is
observed at Rampur (R2 = 0.92 and 0.97) followed
by Kasol (R2 = 0.90 and 0.94) and Sunni (R2 = 0.81
and 0.94). The model was also validated using
independent time series data of 10 years i.e. 2001
to 2010 of T
Max and TMin at each station. During
validation, the maximum value of R2 for TMax was
found at Sunni (0.84) followed by Kasol (0.80) and
Rampur (0.78). Conversely, the maximum
correlation between observed and downscaled TMin
during validation is observed at Rampur (R2 = 0.95)
and minimum at Kasol (R2
= 0.91) respectively.
The calibrated model is
further employed to
generate future scenarios of
TMax and TMin data using
scenario generator function.
The future scenarios of
daily TMax and TMin data are
generated from predictors
of CGCM3 model under A1B and A2 scenarios.
These data are generated on daily time steps for
100 years from 2001 to 2100. For this study, the
future period is grouped into three time slices of 30
years period, namely 2020s (2011–2040), 2050s
(2041–2070) and 2080s (2071–2100).
4.2 Bias correction and comparison
between observed and simulated TMax
and TMin under A1B and A2 scenarios
Due to uncertainties involved in
parameterizations of GCMs and empirical
relationships established among predictors and
predictands during statistical downscaling, the
model is unable to downscale and simulate TMax
and TMin accurately. Difference between observed
and simulated TMax and TMin, known as bias, is
computed. Salzmann et al. (2007) advised to
eliminate biases from the daily time series of
downscaled data. In this study, method discussed
by Mahmood and Babel (2013) is applied for
removing biases in the time series. First of all,
biases are computed by subtracting the long term
monthly mean (30 years from 1971-2000) of
observed data from the mean monthly simulated
control data (downscaled data by SDSM for the
period of 1971-2000 under 20C3M experiment).
Then, biases are adjusted with the future
downscaled daily time series according to their
respective months. Equation 3 is used to de-bias
daily temperature (TMax and TMin) data.
)( OBSCONTSCENdeb TTTT = (3)
where, Tdeb and TSCEN are the de-biased (corrected)
and biased (uncorrected) future daily time series of
temperature data downscaled by SDSM. CONTT
and OBST are long term mean monthly values of
temperature during control simulation (20C3M)
and observed period (1971-2000) respectively.
Table 2 Performance assessment of the SDSM model during calibration
period (1970-2000)
Station TMax TMin
E (%) SE (°C) R2 RMSE (°C) E (%) SE(°C) R2 RMSE(°C)
Kasol 54.2 2.05 0.90 1.84 57.7 1.42 0.94 1.46
Sunni 52.5 2.60 0.81 2.56 58.7 1.70 0.94 1.93
Rampur 45.8 2.60 0.92 1.69 56.6 1.43 0.97 1.00
Notes: E (%)=Percentage of explained variance; SE = Standard Error; R2=
Correlation coefficient; RMSE= Root Mean Square Error
Furth
e
monthly t
i
under A1
B
observed
m
2001-2010
of the tw
o
plausible.
T
Figure 2.
A
between o
b
for TMin a
t
However f
o
simulated
Figure 2
A
1B and A
2
e
r, the dow
n
i
me series
B
and A2 s
c
m
onthly ti
m
. This is do
n
o
scenarios
u
T
he results
o
A
high corre
l
b
served and
t
all station
s
o
r TMax, it is
more accu
r
Mean month
l
2
scenario du
r
n
scaled and
d
of TMax an
d
c
enarios are
m
e series fo
r
n
e in order t
u
sed in thi
s
o
f comparis
o
l
ation (R2 >
0
simulated f
u
s
under bot
h
below 0.82
r
ately as co
m
l
y correlation
r
ing 2001-20
1
d
e-biased fu
d
T
Min obta
i
compared
w
r
the perio
d
o observe w
h
s
study is
m
o
n are sho
w
0
.90) was f
o
u
ture time s
e
h
the scena
r
. Thus, TMin
m
pared to
T
between obs
e
1
0.
ture
i
ned
w
ith
d
of
h
ich
m
ore
w
n in
o
und
e
ries
r
ios.
was
T
Max.
Be
s
si
m
sli
g
4.
3
are
Ra
m
are
lev
e
e
rved and sim
s
ides, corr
e
m
ulated TMa
x
g
htly higher
t
3
Annual a
n
period (1
9
Trends in
examined
m
pur for th
e
analyzed us
e
l of signifi
c
ulated T
Max
(
2
e
lation be
t
x
and TMin
u
t
han A2 sce
n
n
d season
a
9
70-2010)
annual and
for station
s
e
period of
1
ing MK test
c
ance. A su
m
2
a and 2b) a
n
J. Mt.
S
t
ween obs
e
u
nder A1B
n
ario except
a
a
l trend fo
r
seasonal T
M
s
at Kasol,
1
970-2010.
T
and Sen’s m
m
mary of t
h
n
d T
Min
(2c an
d
S
ci. (2015) 12
e
rved and
scenario is
a
t Kasol.
r
past
M
ax and TMin
Sunni and
T
he records
e
thod at 5%
h
e results of
d
2d) under
J. Mt. Sci. (2015) 12
the analysis of annual and seasonal trend in TMax
and TMin is presented in Tables 3 and 4 respectively
to illustrate the common patterns and regional
differences. Statistically insignificant decreasing
trends (cooling) in annual TMax are observed at all
stations (Kasol and Sunni) except at Rampur which
revealed no trend in TMax (Table 3). However,
statistically significant decreasing trend in annual
TMin are observed at Kasol (0.03°C/year) and Sunni
(0.04°C/year) along with statistically insignificant
increasing trend at Rampur (0.02°C/year).
The analysis of seasonal trend reveals cooling
in TMax during winter (December, January,
February) and autumn (September, October,
November) at Kasol (0.04°C/year and 0.05°C/year)
and Sunni (0.06°C/year and 0.04°C/year) followed
by warming (during winter) at Rampur
(0.03°C/year) respectively. However, the trends
are statistically significant at Kasol only. No
particular statistically significant trends in TMax are
detected during summer (June, July, August) and
spring (March, April, May) seasons at all the three
stations (Table 4). Similarly, during winter and
summer, cooling in TMin is observed at Kasol
(0.05°C/year and 0.01°C/year) and Sunni
(0.09°C/year and 0.02°C/year) respectively. This is
only significant at Kasol (winter) and Sunni (winter
and summer). Conversely, warming is experienced
over Rampur with rates of 0.01°C/year and
0.02°C/year during winter and summer
respectively, which is statistically significant during
summer only. During spring season, no trends
could be detected in TMin at Kasol and Rampur
while Sunni revealed cooling (statistically
significant) with high rate of 0.07°C/year. Similar
conditions are observed during autumn season
where Kasol revealed cooling in TMin with rate of
0.02°C/year and no trends could be detected over
remaining two stations.
4.4 Annual and seasonal trend analysis of
downscaled and projected temperature
(TMax and TMin) data
Detection of annual and seasonal trends in de-
biased future time series of T
Max and TMin,
generated for A1B and A2 scenarios using SDSM,
has been carried out for the future periods (2020s,
2050s and 2080s). The patterns observed in
annual trends of TMax and TMin for future periods
(A1B and A2 scenarios) are shown in Figure 3 and
4 respectively. The warming in annual TMax is
observed at Kasol under A1B and A2 scenarios
during 2020s and 2050s respectively. The warming
rates are 0.03°C/year under A1B scenario and
0.02°C/year under A2 scenario. However, no
Table 3 Annual trend analysis of TMax and TMin for historical period performed at all stations
(* indicates that values are statistically significant at 5% level of significance)
Station Year MK Test (Z) Sen’s Slope (Q) estimate 0C/yea
r
Remarks
TMax TMin TMax TMin TMax TMin
Kasol 1970-2010 -1.81 -3.70* -0.02 -0.03 Decreasing Decreasing
Sunni 1970-2010 -1.00 -2.84* 0.01 -0.04 Decreasing Decreasing
Rampur 1970-2010 0.78 1.71 0.00 0.02 No trend Increasing
Table 4 Seasonal trend analysis of TMax and TMin for historical period performed at all stations
(* indicates that values are statistically significant at 5% level of significance)
Station Season MK Test (Zs) Sen’s Slope (Q) estimate 0C/yea
r
Remarks
TMax TMin TMax TMin TMax TMin
Kasol
W
inter -2.24* -3.94* -0.04 -0.05 Decreasing Decreasing
Spring 1.07 -0.08 0.02 -0.00 Increasing No trend
Summer 0.57 -1.53 0.00 0.01 No trend Decreasing
A
utumn -3.96* -2.03* -0.05 -0.02 Decreasing Decreasing
Sunni
W
inter -1.76 -2.86* -0.06 -0.09 Decreasing Decreasing
Spring -0.60 -2.46* -0.05 -0.07 No trend Decreasing
Summer 1.36 -2.41* 0.03 -0.02 Increasing Decreasing
A
utumn -1.92 -0.98 -0.04 -0.01 Decreasing No trend
Rampur
W
inter 1.54 1.09 0.03 0.01 Increasing Increasing
Spring 0.75 0.86 0.02 0.01 No trend No trend
Summer 0.84 2.33* 0.01 0.02 No trend Increasing
A
utumn -0.78 0.78 0.01 0.00 No trend No trend
J. Mt. Sci. (2015) 12
trends could be detected in annual T
Max at Kasol
during 2080 under both the scenarios. No clear
trend in TMax is recognized at Sunni for future
periods except in 2050s where statistically
insignificant increasing trend is observed under
A1B scenario. The study of annual trend analysis
Figure 3 Annual trend analysis for TMax under A1B and A2 scenario for future period (2020s, 2050s, 2080s).
J. Mt. Sci. (2015) 12
performed at Rampur initially revealed no trend in
TMax in 2020s whereas statistically significant
increasing trends are observed in 2050s (A1B and
A2 scenarios) and 2080s (A2 scenario) followed by
Figure 4 Annual trend analysis for TMin under A1B and A2 scenario for future period (2020s, 2050s, 2080s).
J. Mt. Sci. (2015) 12
no trend in 2080s under A1B scenario. The
warming rates of 0.03°C/year under A1B scenario
and 0.05°C/year under A2 scenario during 2050s
and 0.03°C/year under A2 scenario during 2080s
are observed.
The trends observed in annual TMin for future
periods (2020s, 2050s and 2080s) under both the
scenarios are illustrated in Figure 4. The study
reveals warming with varying rates in TMin in future
as all the stations showed increasing trends under
both the scenarios. In future, warming would be
more prominent at Rampur which showed
statistically significant increasing trends in annual
TMin for 2020s, 2050s and 2080s under both the
scenarios. The rate of warming observed at
Rampur is 0.02°C/year, 0.03°C/year and
0.02°C/year under A1B and 0.02°C/year,
0.04°C/year and 0.04°C/year under A2 scenario
for 2020s, 2050s and 2080s respectively.
Statistically significant increasing trends in annual
TMin are also observed at Sunni (0.02°C/year,
0.04°C/year and 0.02°C/year) during 2020s,
2050s and 2080s under A1B scenario and at Kasol
(0.02°C/year and 0.03°C/year) during 2050s and
2080s under A2 scenario. However, statistically
insignificant increasing trends in annual TMin are
observed at Kasol and Sunni for these future
periods under A1B and A2 scenarios respectively.
Significant spatial and temporal variations are
observed in seasonal trend analysis of TMax and TMin
under A1B and A2 scenarios for the future periods
(Table 5). In winter, increasing trends in TMax are
observed at Kasol and Rampur followed by no
trend or statistically insignificant trend at Sunni
under both the scenarios during 2020s, 2050s and
2080s. During spring, increasing trends in TMax are
detected at Kasol during 2020s, 2050s and 2080s
respectively. However, Sunni and Rampur, which
showed no trend in TMax till 2020s, reveals rising
trends during 2050s and 2080s respectively. The
trends in TMax during summer are more complex in
nature as no trends could be detected at Kasol
under A1B (2020s, 2050s and 2080s) and A2
(2020s and 2050s) scenarios while statistically
significant decreasing trend is detected under A2
scenario for 2080s. At Sunni, no trend is observed
during 2020s while decreasing trends in TMax is
observed during 2050s and 2080s under both the
scenarios. But the results are statistically
significant under A2 scenario only. During 2020s,
statistically insignificant decreasing trend in TMax
under A1B scenario is detected at Rampur and
statistically significant increasing trend during
2050s under A2 scenario whereas no trends are
detected during 2080s under both the scenarios
respectively. In autumn, increasing trends in TMax
are observed under A1B scenario at Kasol (2020s
and 2050s), Sunni (2050s and 2080s) and Rampur
(2020s and 2050s) followed by no trends at Kasol
and Rampur during 2080s and Sunni during
2020s respectively. Most of the times, these trends
are statistically insignificant in nature at given level
of significance. No trends are detected under A2
scenario at Kasol during 2020s, 2050 and 2080s
followed by Sunni and Rampur during 2020s and
2050s respectively.
The seasonal trend analysis reveals rising
trend in TMin during winter under A1B and A2
scenario at Kasol (2050s and 2080s) Sunni and
Rampur (2020s, 2050s and 2080s). No trends in
TMin could be detected at Kasol during 2020s. In
spring, increasing trends are detected at Kasol
during 2020s and 2050s under A1B and during
2050s and 2080s under A2 scenario respectively.
The increasing as well as no trends in TMin is
observed at Sunni followed by increasing trends at
Rampur during spring under both the scenarios
(Table 5). During summer, no trends in TMin are
observed at Kasol during 2020s, 2050s and 2080s
under both the scenarios except 2080s under A2
scenario. At Sunni, no trends in summer for T
Min
during 2020s is observed while increasing trends
are observed during 2050s (A1B scenario) and
2080s (A1B and A2 scenarios) respectively.
However, increasing trends in TMin are observed at
Rampur for both the summer and the autumn for
all the future periods under both the scenarios.
4.5 Analysis of abrupt change in annual TMax
and TMin during 1970-210
The abrupt change in annual time series
(observed) of TMax and TMin is investigated using
CUSUM chart at all the stations for the period of
1970-2010. The slope of CUSUM chart as discussed
by Mansell (1997) and Shapiro et al. (2010) are
used for detecting change in the regime shift. A
non-random pattern of temperature variability is
estimated from the chart if it is beyond ± 2σ
(Upper CUSUM and Lower CUSUM). CUSUM
J. Mt. Sci. (2015) 12
Table 5 Seasonal trend analysis of TMax and TMin for future periods performed at all stations
(* indicates that values are statistically significant at 5% level of significance) (-To be Continued-)
Station Season Periods Scenario
MK Test (Zs)
Sen’s Slope (Q) estimate
(0C/year)
Remarks
Tmax Tmin Tmax Tmin Tmax Tmin
Kasol
W
inter 2020s
A
1B 1.78 0.39 0.04 0.00 Increasing No trend
A
2 1.96* 0.93 0.04 0.01 Increasing No trend
2050s
A
1B 1.32 1.00 0.02 0.01 Increasing Increasing
A
2 1.71 2.21* 0.03 0.03 Increasing Increasing
2080s
A
1B 0.11 1.86 0.00 0.02 No trend Increasing
A
2 2.89* 1.93 0.05 0.03 Increasing Increasing
Spring 2020s
A
1B 1.64 2.43* 0.05 0.04 Increasing Increasing
A
2 1.64 0.14 0. 04 0.00 Increasing No trend
2050s
A
1B 2.25* 2.43* 0.07 0.04 Increasing Increasing
A
2 2.43* 2.00* 0.07 0.04 Increasing Increasing
2080s
A
1B 1.43 0.04 0.01 0.00 Increasing No trend
A
2 1.53 2.18* 0.03 0.03 Increasing Increasing
Summer 2020s
A
1B 0.11 -0.86 0.00 -0.01 No trend No trend
A
2 -0.57 0.92 -0.01 0.00 No trend No trend
2050s
A
1B 0.07 -0.07 0.00 0.00 No trend No trend
A
2 -0.46 0.93 -0.00 0.01 No trend No trend
2080s
A
1B -0.89 1.57 -0.02 0.01 No trend Increasing
A
2 -2.93* 0.00 -0.06 0.00 Decreasing No trend
A
utumn 2020s
A
1B 1.86 -1.36 0.04 -0.02 Increasing Decreasing
A
2 -0.36 -0.71 0.00 -0.01 No trend Decreasing
2050s
A
1B 1.96* 1.75 0.03 0.05 Increasing Increasing
A
2 -0.36 2.39 0.00 0.03 No trend Increasing
2080s
A
1B 0.50 0.96 0.00 0.2 No trend No trend
A
2 0.29 0.64 0.00 0.1 No trend No trend
Sunni
W
inter 2020s
A
1B 0.29 1.68 0.00 0.05 No trend Increasing
A
2 -0.04 1.21 0.00 0.02 No trend Increasing
2050s
A
1B 0.39 1.36 0.00 0.04 No trend Increasing
A
2 0.86 1.00 0.01 0.01 No trend Increasing
2080s
A
1B 0.96 1.21 0.01 0.02 No trend Increasing
A
2 1.39 1.86 0.02 0.02 Increasing Increasing
Spring 2020s
A
1B 0.29 0.18 0.00 0.00 No trend No trend
A
2 0.18 2.43* 0.00 0.04 No trend Increasing
2050s
A
1B 0.89 2.68* 0.03 0.04 No trend Increasing
A
2 2.68* 2.03* 0.04 0.04 Increasing Increasing
2080s
A
1B 1.00 1.21 0.01 0.01 Increasing Increasing
A
2 1.21 -0.04 0.01 0.00 Increasing No trend
Summer 2020s
A
1B 0.36 0.57 0.00 0.00 No trend No trend
A
2 0.57 -0.97 0.00 -0.01 No trend No trend
2050s
A
1B -1.00 1.68 -0.01 0.01 Decreasing Increasing
A
2 -2.71* -0.07 -0.06 0.00 Decreasing No trend
2080s
A
1B -1.64 1.53 -0.01 0.01 Decreasing Increasing
A
2 -2.39* 1.57 -0.05 0.01 Decreasing Increasing
A
utumn 2020s
A
1B 0.61 0.61 0.01 0.01 No trend No trend
A
2 -0.29 -1.36 -0.00 -0.02 No trend Decreasing
2050s
A
1B 1.65 2.36* 0.05 0.05 Increasing Increasing
A
2 -0.21 1.75 0.00 0.03 No trend Increasing
2080s
A
1B 1.57 1.21 0.01 0.03 Increasing Increasing
A
2 1.11 0.96 0.03 0.02 Increasing No trend
Note: A1B = First Emission scenario of CGCM3 model; A2 = Second Emission scenario of CGCM3 model.
J. Mt. Sci. (2015) 12
charts are plotted for TMax and TMin and
interpretations are made in order to study feasible
causes in annual trend of TMax and TMin at different
stations (Figure 5). A negative slope beyond the
limit of Lower CUSUM ((-2σ) is observed for TMax
on two occasions at Kasol (1982-1983 and 1997-
2000) and Sunni (1997-2000 and 2007-2010)
followed by a positive shift beyond the limit of
Upper CUSUM (+2σ) during 2001-2007 and 1988-
1997 respectively. However, no such negative
regime shift is noticed at Rampur during this
period rather a positive regime shift is detected
from 2001 to 2005. The positive shift (C+) signifies
a period when a value of TMax is above the climatic
average and for negative shift (C-), it is below the
average. The cooling observed at Kasol during
(1982-1983 and 1997-2000) is more prominent
compared to recent warming (2001-2007). This
might be responsible for statistically insignificant
decreasing trend in annual TMax observed at Kasol
during 1970-2010. Similarly, the effect of
prolonged warming (1988-1997) observed at Sunni
is suppressed by cooling (1997-2000 and 2007-
2010) and resulted into statistically insignificant
decreasing trend in annual TMax over Sunni.
However, recent warming (2001-2005) observed at
Rampur shows a signal of increase in annual TMax
but no significant trend in annual TMax could be
observed in long term (1970-2010).
For TMin, a positive regime shift is observed at
Kasol from 1979 to 1983 followed by the negative
shift from 2003 to 2010. This prolonged cooling
(2003-2010) observed in annual TMin at Kasol
might be responsible for statistically significant
decreasing trend detected in annual TMin over the
period of 1970-2010. A positive regime shift
between 1975 and 1985 followed by sharp negative
shift from 1994 to 2008 is noticed in annual TMin at
Sunni. The prolonged period of cooling suppressed
the influence of warming and has resulted into
(-Continued-)
Table 5 Seasonal trend analysis of TMax and TMin for future periods performed at all stations
(* indicates that values are statistically significant at 5% level of significance)
Station Season Periods Scenario
MK Test (Zs)
Sen’s Slope (Q) estimate
(0C/year)
Remarks
Tmax Tmin Tmax Tmin Tmax Tmin
Rampur
W
inter 2020s
A
1B 1.39 0.96 0.03 0.01 Increasing No trend
A
2 0.96 1.50 0.02 0.01 No trend Increasing
2050s
A
1B 2.36* 1.28 0.04 0.01 Increasing Increasing
A
2 3.68* 2.03* 0.07 0.02 Increasing Increasing
2080s
A
1B 2.03* 0.64 0.03 0.01 Increasing No trend
A
2 2.46* 3.18* 0.05 0.04 Increasing Increasing
Spring 2020s
A
1B 0.36 2.07* 0.01 0.02 No trend Increasing
A
2 -0.46 1.21 0.00 0.01 No trend Increasing
2050s
A
1B 2.93* 3.03* 0.05 0.04 Increasing Increasing
A
2 2.75* 3.32* 0.07 0.05 Increasing Increasing
2080s
A
1B 0.29 1.57 0.01 0.02 No trend Increasing
A
2 1.46 1.89 0.03 0.04 Increasing Increasing
Summer 2020s
A
1B -1.25 1.43 -0.03 0.01 Decreasing Increasing
A
2 -0.93 1.07 0.00 0.01 No trend Increasing
2050s
A
1B -0.89 0.57 -0.02 0.00 No trend No trend
A
2 2.01* 4.25* 0.04 0.05 Increasing Increasing
2080s
A
1B 0.46 0.82 0.01 0.01 No trend No trend
A
2 0.54 2.75* 0.01 0.04 No trend Increasing
A
utumn 2020s
A
1B 1.00 1.11 0.01 0.02 Increasing Decreasing
A
2 -0.14 2.11* -0.01 0.04 No trend Increasing
2050s
A
1B 3.10* 2.18* 0.05 0.04 Increasing Increasing
A
2 0.82 2.25* 0.02 0.03 No trend Increasing
2080s
A
1B -0.14 1.50 0.00 0.02 No trend Increasing
A
2 1.57 1.43 0.03 0.02 Increasing Increasing
Notes: A1B = First Emission scenario of CGCM3 model; A2 = Second Emission scenario of CGCM3 model.
J. Mt. Sci. (2015) 12
statistically significant decreasing trend in annual
TMin at Sunni. However, continuous positive regime
shift detected at Rampur for TMin after 2005 is
expected to be responsible for positive (statistically
insignificant) trend in annual TMin.
5 Discussion and Conclusion
Spatial and temporal variations in terms of
amount and direction of change in TMax and TMin
are observed at three stations in Sutlej basin. This
may be attributed to the dissimilarity in
physiographic characteristics and local climatic
conditions of these three stations, and the
Monsoon circulation. The analysis of past records
(1970-2010) has revealed decreasing trends in
annual, summer and winter TMin at Kasol and
Sunni stations. The patterns observed in this study
are similar in nature from the patterns observed in
previous studies conducted in Western Himalayan
region and Upper Indus basin (Yadav et al. 2004;
Fowler and Archer 2006). Yadav et al. (2004)
analyzed a Western Himalaya data series and
found a marked decrease in annual TMin in the later
part of the 20th century. Fowler and Archer (2006)
studied long term trend in mean temperature, TMax
and TMin over Upper Indus Basin (UIB) and
observed cooling in annual and summer TMin for all
seven stations. Similarly, the studies done by
various authors revealed decline in annual and
summer TMin over Nepal (Sharma et al. 2000; Cook
et al. 2003; Kattel and Yao 2013). Cook et al. (2003)
attributed this summer cooling to a coincident
increase in monsoonal rainfall in Nepal after 1950,
which would tend to suppress temperatures
through increased daytime cloud cover. The
decrease in summer TMin can be explained by
decreasing trends in cloud cover (day-time, night-
time and daily mean) and influence of regional
atmospheric circulation in terms of the relative
latitudinal positions of the (westerly) jet stream as
Figure 5 Representative charts of the cumulative sum of deviations of TMax and TMin for historical periods (1971-
2005).
J. Mt. Sci. (2015) 12
it interacts with the northward progression of the
Indian Summer Monsoon (Archer and Fowler
2004). However, some studies considered aerosol
induced cooling over south Asia and Indian
subcontinent responsible for decrease in TMin.
Generally, stations show large variability in TMax
with increasing, decreasing or no trend. Most of the
times, trends in TMax are statistically insignificant
in nature. The recent warming (after 2001)
observed in annual, summer and winter TMax and
TMin at Rampur is mainly result of developmental
activities started after 2001 in the region. The
installation of 1500 MW Naptha-Jhakari Hydro-
Electric Project, the largest hydro-electric power
plant of India, during 2003-05 and 412 MW
Rampur Hydro-Electric Project during 2007 on
Sutlej river may have attributed in warming of
temperature at Rampur.
In order to examine whether the patterns of
change observed in the past would remain same or
change in the future, the station based plausible
scenario of temperature data for future periods
(2011-2100) is generated using SDSM from large
scale predictors of CGCM3 model under A1B and
A2 scenarios. The suitable sets of predictors
influencing predictands (TMax and TMin) were
identified from suite of NCEP/NCAR reanalysis
data sets at each individual site. Further, the model
was calibrated and validated using 31 years (1970-
2000) and 10 years (2001-2010) daily data.
Statistical parameters like E (%) and SE have been
used to reflect downscaling results of TMax and TMin
at each station. Besides, statistical measure such as
R2 and RMSE are also applied to examine the
performance of downscaled data for calibration
and validation periods. However, comparatively
higher values of E (%) and R2 followed by lower
values of SE and RMSE are observed for TMin than
TMax .This caused better simulation of T
Min for the
future under A1B and A2 scenarios. The simulated
future time series of TMax and TMin are de-biased
(corrected) using bias correction method before
trend analysis. Bias correction method was applied
to minimize biases in simulated time series of
temperature data. A comparison has been made
between de-biased future monthly time series of
TMax and TMin and the observed monthly time series
under A1B and A2 scenarios for the period of 2001-
2010. Further, the downscaled and de-biased
future monthly time series of TMax and TMin are
compared with observed monthly time series for
the period of 2001-2010. Again higher correlation
(R2 >0.90) is found for TMin whereas for TMax, it is
below 0.82. The study shows that generally,
simulation made under A1B is more plausible as
compared to A2 because slightly higher correlation
between observed and simulated TMax and TMin was
found for A1B scenario instead of A2 scenario.
MK test and Sen’s methods are applied for
detecting trend in de-biased time series of
temperature data for the future periods (2020s,
2050s and 2080s). Trends in TMin are more evident
and clear, but variability has been observed for TMax.
The cooling experienced in annual TMax and TMin at
Kasol in past (1970-2010) would be replaced by
warming in future as increasing trends have been
detected in TMax during 2020s and 2050s and in
TMin during 2020s, 2050s and 2080s under A1B
and A2 scenarios. However, no change in annual
TMax would occur at Kasol during 2080 under both
the scenarios. Similar results of warming have also
been predicted at Sunni for annual TMin in future
under both scenarios which witnessed cooling
during 1970-2010. The rise in TMin at Rampur is
expected to be continued in future as increasing
trends have been detected under both the scenarios.
Similarly, seasonal trend analysis also reveals large
variability in trends of TMax and TMin over these
stations for the future periods. These kinds of
annual and seasonal variations in TMax and TMin
may cause decline of snowlines and alter the
behaviour of the river discharge. Therefore, the
present study will provide useful insight to devise
better strategy for the management of water
resources in the Sutlej basin.
Acknowledgement
Authors acknowledge the financial support in
the form of fellowship provided by University
Grant Commission (UGC), Government of India to
Mr. Dharmaveer Singh as Research Fellow for
carrying out the research. Authors are also thankful
to Bhakara Beas Management Board (BBMB),
India for providing the meteorological data used in
the present work. Authors also acknowledge
anonymous reviewers for constructive comments
and suggestions to improve the quality of the
manuscript.
J. Mt. Sci. (2015) 12
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Himalaya is an ecologically fragile region. Around 1.3 billion people are dependent on Himalaya for their water, energy, and economic needs. Air quality in the Himalayan region has deteriorated significantly in past decades. Air pollution is on the rise in the Himalayan region, and pollutant levels have crossed World Health Organization (WHO) annual prescribed standards at many places. Seasonal and diurnal cycles of air pollutants are observed in the Himalayan region, with both emission sources and meteorology significantly affecting air pollutant levels. The causes of the rapid rise in air pollutant levels are population growth, rapid and unplanned urbanization, increase in the number of vehicles, diesel pump sets along with traditional sources such as cookstoves, brick kilns, forest fire, mining industries, and garbage and stubble burning. The pollution in Himalayan not only is in situ but it also receives a significant amount of air pollution from surrounding regions. Air pollution has significant impacts on the Himalayan region, affecting its ecosystem, health of inhabitants, the cryosphere, water availability, agroforestry, precipitation patterns, seasons, income, and nutrition status. Despite recent improvements in understanding of air quality and its impact, major challenges in air quality monitoring, mitigation effort, and regional coordination networks persist. Promotion and venturing in clean energy, infrastructure, and technology is needed to mitigate air pollution and its effects. Dedicated institutional arrangements with skilled workforce actively engaging multiple stakeholders and enabling inter-agency collaboration and cooperation at both national and regional levels in the Himalayan countries are needed to tackle trans-boundary air pollution and scale and implement policies for mitigationefforts. Knowledge generation and dissemination and public awareness are required to bring behavioral change and build public support for the promulgation and implementation of eco-friendly policies.
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There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.
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