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Atmospheric Research 138 (2014) 73–90
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Atmospheric Research
journal homepage: www.elsevier.com/locate/atmos
Spatial and temporal trends of mean and extreme rainfall and
temperature for the 33 urban centers of the arid and semi-arid
state of Rajasthan, India
Santosh M. Pingale a,⁎, Deepak Khare a, Mahesh K. Jat b, Jan Adamowski c
a
b
c
Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee 247 667 UK, India
Department of Civil Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
Department of Bioresource Engineering, McGill University, Quebec, Canada
a r t i c l e
i n f o
Article history:
Received 7 May 2013
Received in revised form 21 September 2013
Accepted 29 October 2013
Keywords:
Extreme events
Interpolation
Mann–Kendall test
Temperature
Rainfall
India
a b s t r a c t
Trend analysis of the mean (monsoon season, non-monsoon season and annual) and extreme
annual daily rainfall and temperature at the spatial and temporal scales was carried out for all the
33 urban centers of the arid and semi-arid state of Rajasthan, India. Statistical trend analysis
techniques, namely the Mann–Kendall test and Sen's slope estimator, were used to examine trends
(1971–2005) at the 10% level of significance. Both positive and negative trends were observed in
mean and extreme events of rainfall and temperature in the urban centers of Rajasthan State. The
magnitude of the significant trend of monsoon rainfall varied from (−) 6.00 mm/hydrologic year
at Nagaur to (−) 8.56 mm/hydrologic year at Tonk. However, the magnitude of the significant
negative trends of non-monsoon rainfall varied from (−) 0.66 mm/hydrologic year at Dungarpur
to (−) 1.27 mm/hydrologic year at Chittorgarh. The magnitude of positive trends of non-monsoon
rainfall varied from 0.93 mm/hydrologic year at Churu to 1.70 mm/hydrologic year at
Hanumangarh. The magnitude of the significant negative trends of annual rainfall varied from
(−) 6.47 mm/year at Nagaur to (−) 10.0 mm/year at Tonk. The minimum, average and maximum
temperature showed significant increasing warming trends on an annual and seasonal scale in
most of the urban centers in Rajasthan State. The magnitude of statistically significant annual
extreme daily rainfall varied from 2.00 mm at Jhalawar to (−) 1.64 mm at Tonk, while the
magnitude of statistically significant extreme annual daily minimum and maximum temperature
varied from 0.03 °C at Ganganagar to 0.05 °C at Jhalawar, respectively. The spatial variations of the
trends in mean (monsoon season, non-monsoon season and annual) and extreme annual daily
rainfall and temperature were also determined using the inverse-distance-weighted (IDW)
interpolation technique. IDW results are helpful to identify trends and variability in mean and
extreme rainfall and temperature in space and time for the study locations where the data is not
available and the quality of data is not good. These spatial maps of temperature and rainfall can
help local stakeholders and water managers to understand the risks and vulnerabilities related to
climate change in terms of mean and extreme events in the region.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Increasing urbanization, populations and economies are
causing adverse impacts on the natural environment. With
⁎ Corresponding author currrently at: Department of Water Resources and
Irrigation Engineering, Arba Minch University, Ethiopia.
E-mail address: [email protected] (S.M. Pingale).
0169-8095/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.atmosres.2013.10.024
increases in urbanization, problems related to climate change
are becoming worse, since additional pervious areas are
becoming impervious, leading to decreases in vegetation cover
and increases in temperature. Rapid economic development
and population growth in various parts of the world such as
India have caused concerns regarding the quantity and quality
of natural resources, and in particular water resources. As such,
information on the magnitude, duration and frequency of
74
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
extreme events (such as floods and droughts), along with
trends in various hydro-meteorological variables, is required
for the planning of optimum adaptation strategies for the
sustainable use of natural resources such as water (e.g.,
Kampata et al., 2008; Some'e et al., 2012; Huang et al., 2013).
Mall et al. (2006) have studied the potential for sustainable
development of surface water and groundwater resources
within the constraints imposed by climate change. The authors
suggested that little work has been done on hydrological
impacts of climate change for Indian regions/basins. Therefore,
assessment of climate change is required in India which
considers the causative factors of climate change (i.e., anthropogenic factors, global warming in terms of variations in
meteorological parameters) at spatial and temporal scales.
Further, it is essential in hydrological modeling to assess the
adverse impacts of climate change on water resources. Such
studies are even more significant for urbanized watersheds/
areas to better understand the relationship between climate
change and hydrological processes, which will facilitate the
sustainable utilization of water resources.
There are different methods that can be used in the
assessment of climate change. Various parametric and
non-parametric statistical tests have been used in the recent
past by many researchers to assess trends in hydrometeorological time series in India (e.g., Gadgil and
Dhorde, 2005; Singh et al., 2008a, 2008b; Basistha et al.,
2009; Kumar et al., 2010; Pal and Al-Tabbaa, 2010; Deka et
al., 2012; Duhan et al., 2013; Jeganathan and Andimuthu,
2013; Jain et al., 2013), and in other regions of the world
(e.g., Zhang et al., 2000; Zhang et al., 2001; Roy and Balling,
2004; Cheung et al., 2008; Motiee and McBean, 2009; Sahoo
and Smith, 2009; Zin et al., 2010; Tabari et al., 2011; Nalley et
al., 2012; Nemec et al., 2012; Saboohi et al., 2012; Jiang et al.,
2013). Detection of past trends, changes, and variability in the
time series of hydro-climatic variables is very important in
understanding the potential impact of future changes in the
region (Sahoo and Smith, 2009). Statistical analysis could be
extended to analyze climatic parameters and their relationships
with water resources, land use/cover changes, urbanization etc.
It has been seen that the Earth's average temperature has
increased by 0.6 °C in the later part of the 20th century; there
is also a dramatic shift in temperature change from a
minimum of 1.4 °C to a maximum of 5.4 °C as reported by
the projections made by various climate prediction models
(IPCC, 2001). The IPCC has demonstrated that climate change
assessments at sub-national (i.e., state or province) and local
scales are needed. The IPCC has also found several gaps in the
knowledge that exists in terms of observations and research
needs related to climate change and water (IPCC, 2007).
These include GCM scale resolutions and downscaling
techniques to locale scales to assess the climate change and
to quantify the contribution of anthropogenic activity to
climate change etc.
It has been found that seasonal and annual air temperatures
have been increasing at a rate of 0.57 °C per 100 years (1881 to
1997) in India (Pant and Kumar, 1997). The trends of certain
hydro-climatic variables have been studied in Indian urban
centers such as Hyderabad, Patna, Ahmedabad, Surat, Bangalore,
Mumbai, Nagpur, Pune, Jaipur, Chennai, New Delhi, Kanpur,
Lucknow and Kolkata, whose populations are more than
1 million (De and Rao, 2004). Significant increasing trends
were found in annual and monsoon rainfall over Chennai, New
Delhi, Kolkata and Mumbai. Other climatic parameters were not
considered to assess climate change impacts on timing,
distribution, pattern, frequency, variability of precipitation and
their inter-relationships in urban cities. Arora et al. (2005)
explored trends of annual average and seasonal temperatures
using the MK test at the country and regional scales in India. It
was observed that annual mean temperature, mean maximum
temperature and mean minimum temperature have increased
at the rates of 0.42, 0.92 and 0.09 °C (100 year)−1, respectively.
On a regional basis, stations in Southern and Western India
show rising trends of 1.06 and 0.36 °C (100 year)−1, respectively, while stations in the North Indian plains show a falling
trend of 0.38 °C (100 year)−1. The seasonal mean temperature
has increased by 0.94 °C (100 year)−1 for the post-monsoon
season and by 1.1 °C (100 year)−1 for the winter season. This
study deals only with trend detection of temperature; in
addition, trends in annual extreme daily events of rainfall and
temperature have not been studied.
Basistha et al. (2007) assessed the spatial trends of rainfall
over Indian subdivisions from 1872 to 2005. Their results show
decreasing trends of rainfall over North India excluding Punjab,
Haryana, West Rajasthan, and Saurashtra, and increased trends
in South India excluding Kerala and Madhya Maharashtra. The
study also concluded that the arid portion (i.e., western part)
of India has not been investigated in detail. Further, they
highlighted that more research is required to assess the spatial
patterns of trends of other climatic variables (average, minimum and maximum temperatures, relative humidity, wind
speed, evapotranspiration (ET), number of rainy days etc.) and
their inter-relationships including trends of annual and seasonal
climatic parameters to assess the impacts of climate change.
Gowda et al. (2008) studied the region of Davangere district
over a period of 32 years using statistical analysis. Climatic
parameters (i.e., rainfall, relative humidity, maximum temperature, minimum temperature, sunshine hour and wind speed)
were analyzed to assess climate change. The statistical analysis
showed that such a small data set may not represent the correct
picture of climate change and requires long term data. A
significant change in climatic variables was found in and around
the Davangere region. Ghosh et al. (2009) observed the varied
trend in Indian summer monsoon rainfall, which has not only
been affected by global warming, but may also be affected by
local changes due to rapid urbanization, industrialization and
deforestation. Patra et al. (2012) detected rainfall trends in
the twentieth century in India using parametric and nonparametric statistical trend analysis tests. The temporal variation in monthly, seasonal and annual rainfall was studied for the
Orissa State using data from 1871 to 2006. The analysis revealed
a long term, insignificant, declining trend in annual as well as
monsoon rainfall, and an increasing trend in the post-monsoon
season. Rainfall during winter and summer seasons showed an
increasing trend. The study only assessed rainfall for Orissa
State; trends in mean and extreme annual daily events were not
explored. Duhan and Pandey (2013) investigated the spatial and
temporal variabilities of precipitation in 45 districts of Madhya
Pradesh (MP), India on an annual and seasonal basis. The MK
test and Sen's slope estimator test were used for trend analysis,
and an increase and decrease in the precipitation trend were
found on annual and seasonal bases, respectively, in the districts
of MP. In this study, little attention was given to urban trends of
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
mean and extreme rainfall and temperature. A literature review
shows that in India climate related studies are restricted to
statistical analysis of some of the meteorological parameters
only. The effects of climate change on water resources were not
considered and seasonal changes are not clearly indicated with
mean and extreme events, especially in the urban centers.
As described above, the analysis of extreme trend and
variability in annual daily rainfall and temperature has been
carried out in recent years by a number of researchers in various
parts of the world, including India. However, there is a still a lack
of information on trends and variability of extreme annual daily
events, especially for the semi-arid and arid region of Rajasthan,
India. For the region of Rajasthan, there is a lack of studies
of climate change assessments that consider various region
specific aspects, and their inter-relation with the climatic
parameters at the spatial and temporal scales. Climate change
in terms of inter-seasonal and inter-annual variations in mean
and extreme annual daily rainfall and temperature, for urban
centers of arid and semi-arid regions in India, has not been
reported in much detail in the literature. In light of this, the
present study was aimed at studying climate change in terms of
mean (monsoon, non-monsoon and annual) and extreme
annual daily events in the 33 main urban centers of Rajasthan
in India via trend analysis of two important climatic parameters
(i.e., rainfall and temperature (minimum, average and
maximum)) at the spatial and temporal scales. Statistical trend
analysis techniques, namely the Mann–Kendall (MK) test
(Mann, 1945; Kendall, 1975) and Sen's slope estimator (Sen,
1968), were used in the present study. These methods have
several advantages over parametric methods (Duhan and
Pandey, 2013).
The paper is organized in five sections. Section 2 describes
the study area and data used. Section three describes the
methodology adopted in the study. Sections 4 and 5 describe the
findings of the study on climate change assessment in terms of
trends in mean (monsoon season, non-monsoon season and
annual) and extreme annual daily rainfall and temperature in
the 33 urban centers of Rajasthan State in India. Further, the
spatial variations in mean (seasonal and annual) and extreme
annual daily rainfall and temperature with their trends are
presented using the inverse-distance-weighted (IDW) interpolation method.
2. Study area and data used
Rajasthan is the largest state in India, with an area of
342,000 km2 of which about 60% falls under the arid category.
Administratively, the state is divided into 33 districts (Fig. 1)
and its climate varies from arid to semi-arid. Geographically,
Rajasthan State extends from 23° 3.50′ to 30° 14′N latitude and
69° 27′ to 78° 19′E longitude. The study region has unique
climatological characteristics (low and erratic rainfall, extremes
of diurnal and annual temperatures, low humidity and high
wind velocity). It also has unique physiographic characteristics.
For example, the Arawali Hill Range is unique because it lies
along the direction of the southwest monsoon, which brings
rains in the region. There is also the Great Indian Desert (called
the Thar Desert) in the northwestern part of Rajasthan, which is
India's largest desert, covering almost 70% of the state; in this
region there is less vegetation and the region has a significant
influence on rainfall and temperature distribution across the
75
state. The climate in the state varies from arid to semi-arid. This
makes Rajasthan State different from other regions in neighboring states. The state is bordered by Gujarat, Madhya Pradesh,
Uttar Pradesh, Haryana and Punjab, and is also bordered with
Pakistan. The climate in Rajasthan State is characterized by low
and erratic rainfall, extremes of diurnal and annual temperatures, low humidity and high wind velocity. The annual average
rainfall is highly variable, and it is most erratic in the western
region with frequent dry spells, punctuated occasionally by
heavy downpours in some years associated with passing low
pressure systems over the region. The western and southern
districts of Rajasthan State frequently experience severe
droughts. Coping with low rainfall, with a high coefficient of
variation across time and space, is the major challenge (Rathor,
2005).
Gridded data sets of rainfall and temperature have been
used in many hydrological and climatological studies worldwide, including Australia, for hydro-climatic forecasting,
climate attribution studies and climate model performance
assessments (e.g., Tozer et al., 2012; Dash et al., 2013).
Therefore, in the present study 0.5° × 0.5° and 1° × 1°
gridded data sets of daily rainfall and temperature from the
period 1971 to 2005 were used for the spatial and temporal
variability and trend analyses in the present study. These
data were obtained from the Indian Meteorological Department (IMD), Pune. From all of India, 395 stations were used
for developing the high resolution daily gridded data sets
(1° × 1°) of daily temperature (minimum, maximum and
mean) for the Indian region by the Indian Meteorological
Department (IMD) — Pune. The modified Shepard's angular
distance weighting algorithm was used for interpolating the
stations of temperature data into 1° × 1° grids. These data
sets were evaluated using the cross validation technique to
estimate the interpolation error (root mean square error),
which was found to be less than 0.5 °C. These data sets were
also compared with another (Asian Precipitation — Highly
Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE)) high resolution
monthly data set, and a correlation of more than 0.8 was
found in most parts of the country. Similarly, high resolution
daily gridded data sets of rainfall (0.5° × 0.5°) were also
developed (using Shepard's method), and validated using the
network of 6076 rain gage stations for the Indian region by
IMD, Pune. Additional detail on the methodology is given in
IMD reports (Rajeevan and Bhate, 2008; Srivastava et al.,
2008).
These data sets have been found to be useful for various
regional applications (such as studies of extreme temperatures, validation of numerical and climate model simulations
and many environmental applications including trend analysis of mean and extreme events of rainfall and temperature)
(Rajeevan and Bhate, 2008 and Srivastava et al., 2008), and
they can be used for water resource planning purposes. The
data was subjected to thorough quality checks (location of
stations, typing error, missing data etc.) before the data
analysis was performed. Hamlet et al. (2005) have reported
that existing gridded data sets are not appropriate for the
long term trend estimation of precipitation and temperature
fields for the continental United States. The gridding
procedures introduce artificial trends due to the incorporation of stations with different record lengths and locations.
76
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
Fig. 1. Location of urban centers considered in the state of Rajasthan, India.
However, the gridded set developed by IMD, Pune addressed
these issues before and after developing the gridded data sets
from the observed station data before releasing the data
to users. In addition, these data sets were validated with
other gridded data sets developed in the region, and their
application was demonstrated successfully through case
studies and by other researchers (e.g., Dash et al., 2013).
Basic data quality checks (such as rejecting values, greater
than exceeding known extreme values, minimum temperature, greater than maximum temperature, same temperature
values for many consecutive days, unusual high values,
homogeneity, location of the station etc.) were performed
for each station, and then stations were selected based on
data quality or the development of the gridded data sets of
temperature and rainfall. Further, homogeneity, persistence
and periodicity in the data were also analyzed by autocorrelation. Normalizing the time series before the analysis was
performed at each urban center. The district headquarters of
Rajasthan State, which has the largest population, greatest
developmental activities, and administrative centers of the
districts, are considered as the ‘urban centers’. The geographical extent of the urban centers is defined by their municipal
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
boundaries defined by the development authorities. The sizes
and populations of the urban centers are included in Table 1.
The climate of individual urban centers has been extracted
from the gridded surface of temperature and rainfall
corresponding to the municipal limits of the urban centers.
Therefore, it preserves the meteorological characteristics of
individual urban centers. The IDW assumes that the values of
the variables at a point to be predicted are similar to the
values of nearby observation points. The majority of the
stations are located in the urban areas. Therefore, this has a
major influence of urban climate. The grid covering the urban
center of the district headquarters of Rajasthan State is
considered to represent the climate of the urban center. First,
the grids that represent the urban center from the developed
data sets are identified, and then daily data (1971–2005) was
extracted. The daily rainfall and temperature (minimum,
average and maximum) data were extracted for the 33 urban
centers of district headquarters in Rajasthan State (Table 1).
These urban centers were selected taking into account the
length and availability of records, so that most of the urban
centers in the arid and semi-arid region of Rajasthan State were
covered by the corresponding data to assess trends in mean
(seasonal and annual) and extreme annual daily rainfall and
temperature. The grids covering the urban centers were chosen
77
for extracting the daily rainfall and temperature data, and it was
assumed that the grids covering the urban center represent the
rainfall and temperature of that urban center in Rajasthan State
over a period of time (Fig. 1 and Table 1). Further, the rainfall
and temperature (minimum, average and maximum) data were
arranged according to monsoon season, non-monsoon season
and annual scale from 1971 to 2005 in each urban center. The
monsoon season represents the months of June to September,
and the non-monsoon season represents the months of January
to May and October to December.
3. Methodology
3.1. Normalization and autocorrelation analysis of time series
Normalized rainfall and temperature time series were
used to test the outliers present in the time series data using
following relationship (Rai et al., 2010):
Xt ¼ ðxt ‐xÞ=σ
ð1Þ
where Xt is the normalized anomaly of the series, xt is the
observed time series, and x and σ are the long-term mean and
standard deviation of annual/seasonal time series, respectively.
Table 1
List of urban centers, size and population.
ID
Urban center
Longitude
Latitude
Elevation (m.a.s.l.), m
Size (km2)a
approx.
Population (2011 census)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Ajmer
Alwar
Banaswara
Baran
Barmerb
Bharatpur
Bhilwara
Bikaner
Bundi
Chittorgarh
Churu
Dausab
Dholpur
Dungarpur
Ganganagar
Hanumangarh
Jaipur
Jaisalmer
Jalore
Jhalawarb
Jodhpur
Jhunjhunu
Karauli
Kota
Nagaur
Pali
Rajsamandb
Sawai Madhopur
Sikar
Sirohib
Tonk
Udaipur
Pratapgarh
74°
76°
74°
76°
71°
77°
74°
73°
75°
74°
75°
76°
77°
73°
73°
74°
75°
70°
72°
76°
73°
75°
77°
75°
73°
73°
73°
76°
75°
72°
75°
73°
74°
26°
27°
23°
25°
25°
27°
25°
28°
25°
24°
28°
26°
26°
23°
29°
29°
26°
26°
25°
24°
26°
28°
26°
25°
27°
25°
25°
26°
27°
24°
26°
24°
24°
486
271
302
262
227
183
421
242
268
394
292
333
177
225
164
177
431
229
178
312
231
323
275
271
302
214
547
266
427
321
289
598
491
31
–
–
–
–
–
–
65
–
–
–
–
–
–
–
–
168
–
–
–
83
–
–
62
–
–
–
–
–
–
–
–
–
542,580
315,310
100,128
118,157
83,517
252,109
360,009
647,804
102,823
116,409
119,846
61,589
126,142
43,000
224,773
151,104
3,073,350
78,000
54,185
48,054
1,033,918
118,966
105,690
1,001,365
100,618
229,956
55,671
120,998
237,579
35,544
165,363
451,735
48,900
42′ E
38′ E
24′ E
33′ E
25′ E
30′ E
40′ E
22′ E
41′ E
42′ E
01′ E
20′ E
52′ E
43′ E
50′ E
21′ E
52′ E
57′ E
58′ E
10′ E
04′ E
20′ E
04′ E
52′ E
40′ E
25′ E
53′ E
21′ E
15′ E
51′ E
47′ E
42′ E
47′ E
27′
34′
30′
05′
45′
15′
21′
01′
27′
54′
19′
53′
41′
50′
49′
35′
55′
55′
22′
35′
18′
06′
30′
10′
00′
46′
04′
01′
36′
53′
10′
34′
02′
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
a
For size of urban center of district headquarter Rajasthan see e.g., Jhalawar, https://maps.google.co.in/maps?q=Jhalawar&ie=UTF8&hq=&hnear=
0x39653ede53cfaf07:0x31263af08bba421e,Jhalawar,+Rajasthan&gl=in&ei=TekFUuK8G4bLrQfk74F4&ved=0CLABELYD. More details are also available in
http://www.citypopulation.de/php/india-rajasthan.php.
b
Population census 2001.
78
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
The autocorrelation test was performed to check the
randomness and periodicity in the time series (Modarres and
da Silva, 2007). If lag-1 serial coefficients are not statistically
significant then the MK test can be applied to the original
time series (Luo et al., 2008; Karpouzos et al., 2010). The
serial correlation coefficients of the normalized climatic
series were computed for lags L = 0 to k, where k is the
maximum lag (i.e., k = n/3), and n is the length of the series.
The autocorrelation coefficient rk of a discrete time series for
lag-k was estimated as:
The Kendall test statistic S can be computed as:
S¼
n
−1
X
sgn x j −xk
ð5Þ
k¼1
where sgn(xj − xk) is the signum function. The test statistic S
is assumed to be asymptotically normal, with E(S) = 0 for
the sample size n ≥ 8 and variance as follows:
½nðn−1Þð2n þ 5Þ−
n‐k X
Xt −Xt Xtþk −Xtþk
ð2Þ
k¼1
where rk is the lag-k serial correlation coefficient. The hypothesis
of serial independence was then tested by lag-1 autocorrelation
coefficient as H0 : r1 = 0 against H1 : |r1| ≥ 0 using the test of
significance of serial correlation (Yevjevich, 1971; Rai et al.,
2010):
tðt−1Þð2t þ 5Þ
t
VðSÞ ¼
rk ¼ " k¼1
#0:5
n‐k X
2 2
Xt −Xt Xtþk −Xtþk
X
ð6Þ
18
where ti denotes number of ties up to sample i.
The standardized MK test statistic (Zmk) can be estimated
as:
Zmk
8
S−1
>
>
> pffiffiffiffiffiffiffiffiffiffi if S N 0
>
< VðSÞ
¼
0
if S ¼ 0
>
>
Sþ1
>
>
p
ffiffiffiffiffiffiffiffiffi
ffi
if S b 0
:
VðSÞ
:
ð7Þ
0:5
ðrk Þtg ¼
−1 tg ðn‐k‐Þ
n‐k
ð3Þ
where ðrk Þtg is the normally distributed value of rk, and tg is the
normally distributed statistic at ‘g’ level of significance. The
values of tg are 1.645, 1.965 and 2.326 at the significance level of
0.10, 0.05 and 0.01, respectively. The null hypothesis of serial
independence was rejected at the significance level α (here
0.05), if jrk j ≥ ðrk Þtg . For the non-normal time series data, the MK
test is an appropriate choice for trend analysis (Yue and Pilon,
2004). Therefore, the MK test was used in the present study,
where autocorrelation was found to be non-significant at the 5%
level of significance.
3.2. Mann–Kendall test
p ¼ 0:5−φðjZmk jÞ
The Mann–Kendall (MK) test is a non-parametric test that
can be used for detecting trends in a time series (Mann, 1945)
where autocorrelation is non-significant. The non-linear trend,
as well as the turning point can be derived from Kendall test
statistics (Kendall, 1975). This method searches for a trend in a
time series without specifying whether the trend is linear or
non-linear. It has been found to be an excellent tool for trend
detection and many researchers have used this test to assess
the significance of trends in hydroclimatic time series data such
as water quality, stream flow, temperature and precipitation
(e.g., Ludwig et al., 2004; Zhang et al., 2004; McBean and Motiee,
2008; Basistha et al., 2009; Rai et al., 2010; Patra et al., 2012).
The MK test can be applied to a time series xi ranked from i = 1,
2 … n − 1 and xj ranked from j = i + 1, 2 … n such that:
8
>
1
if x j −xi N0
>
>
<
0 if x j −xi ¼ 0
sgn x j −xi ¼
>
>
>
: −1 if x −x b0
j
i
The standardized MK test statistic (Zmk) follows the
standard normal distribution with a mean of zero and
variance of one. If ±Zmk ≤ Zα/2 (here α = 0.1), then the
null hypothesis for no trend is accepted in a two sided test
for trend, and the null hypothesis for the no trend is rejected
if ± Zmk ≥ Zα/2. Failing to reject H0 (i.e., null hypothesis) does
not mean that there is no trend. Rather, it is a statement that
the evidence available is not sufficient to conclude if there is a
trend (Helsel and Hirsch, 2002). A positive value of Zmk
indicates an ‘upward trend’ and a negative value indicates a
‘downward trend’. The significance levels (p-values) for each
trend test can be obtained from the relationship given as
(Coulibaly and Shi, 2005):
:
ð4Þ
ð8Þ
where φ() denotes the cumulative distribution function (CDF)
of a standard normal variate. At a significance level of 0.1, if
p ≤ 0.1 then the existing trend is considered to be statistically
significant.
3.3. Sen's estimator of slope and percentage change over a period
If a linear trend is present in a time series, then the true
slope of the trend is estimated using a simple non-parametric
procedure (Theil, 1950; Sen, 1968), which is given by:
Q i ¼ median
x j −xk
∀k ≤ j
j−k
ð9Þ
where xj and xk are data values at times j and k (j N k),
respectively. The median of N values of Qi is Sen's estimator of
slope. If N is odd, then Sen's estimator is computed by
Qmed = Q(N + 1)/2 and if N is even, then Sen's estimator is
computed by Qmed = [QN/2 + Q(N + 2)/2]/2. Finally, Qmed is
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
tested by a two-sided test at 100(1 ‐ α)% confidence interval,
and the true slope is obtained by a non-parametric test.
The percentage change over a period of time can be
estimated from Theil and Sen's median slope and mean by
assuming the linear trend (Yue and Hashino, 2003; Basistha
et al., 2009) in the time series:
% change ¼
median slope length of period
:
mean
ð10Þ
79
in rainfall and temperature were also determined. The corresponding spatial and temporal distributions of the mean
(monsoon season, non-monsoon season and annual) and
extreme events of annual daily rainfall and temperature using
the MK test statistics (Zmk) were determined by the IDW
interpolation technique. The increasing, decreasing, and no
trend at 10% level of significance are represented with the
symbols ↑, ↓ and О, respectively.
4.1. Trend analysis of mean events
3.4. Interpolation technique
In the present study, the spatial variation and trends of the
mean (monsoon, non-monsoon and annual) and extreme
annual daily rainfall and temperature were determined using
the inverse-distance-weighted (IDW) interpolation technique.
The IDW method is a simple and effective interpolation
technique that is based on the assumption that the values of
the variables at a point to be predicted are similar to the values
of nearby observation points. This method implies that each
station has a local influence, which decreases with distance by
means of the use of a power parameter (Isaaks and Srivastava,
1989; Robinson and Metternicht, 2006). The general formula of
IDW (Yavuz and Erdogan, 2012) is as follows:
^ ðS Þ ¼ m
Z
0
N
X
λi ZðSi Þ
ð11Þ
i¼1
where
^ ðS0 Þ
Z
N
λi
Z(Si)
is the value of prediction for location S0
is the number of measured sample points surrounding the prediction location
is the weight assigned to each measured point
is the observed value at the location Si.
The weighting is a function of inverse distance. The
formula determining the weighting is (Yavuz and Erdogan,
2012):
λi ¼
d−p
io
N
X −p
dio
i¼1
:
ð12Þ
The IDW method requires the choice of a power parameter
and a search radius. The power parameter (p) controls the
significance of measured values on the interpolated value based
upon their distance from the output point (d) (Erdoğan, 2009).
The choice of a relatively high power parameter ensures a high
degree of local influence and gives the output surface in more
detail (Yavuz and Erdogan, 2012).
4. Results and discussion
Detailed trend analysis of the mean (monsoon season,
non-monsoon season and annual) and extreme annual daily
rainfall and temperature was carried out for the period of
1971–2005 using the Mann–Kendall test for all 33 urban centers
of Rajasthan State. Similarly, Sen's slope and percentage change
4.1.1. Rainfall
The spatial distributions of trends in seasonal (monsoon
and non-monsoon) and annual average rainfall for the 33
urban centers of Rajasthan State are shown in Fig. 2. The
magnitude of the Sen's slope and percentage change in
average rainfall at seasonal (monsoon and non-monsoon)
and annual temporal scales for the 33 different urban centers
of Rajasthan State is also given in Tables S1 to S3 (Supporting
material), respectively. Both positive and negative trends
were observed by the MK test and Sen's slope estimator in
seasonal and annual average rainfall in the urban centers.
This is in good agreement with observations in different parts
of India (Pal and Al-Tabbaa, 2011).
All of the significant trends in monsoon and annual scale
were found to be decreasing at the 10% level of significance.
However, both positive and negative trends in non-monsoon
average rainfall were observed. As a result, the expected decrease
in rainfall will possibly cause a decrease in water availability in
the future (Gosain et al., 2006; Tabari and Talaee, 2011b). The
monsoon season rainfall pattern and trends were very similar to
the annual ones, which demonstrate the major contribution of
monsoon rainfall in annual rainfall in the urban centers of
Rajasthan State. Similar results indicating the major contribution
of seasonal precipitation in annual precipitation have also
been observed in Iran (Tabari and Talaee, 2011b). Significant
decreasing trends were observed in monsoon average rainfall
for Jodhpur (at −6.82 mm/hydrologic year and −88.16%),
Karauli (at −7.59 mm/hydrologic year and −39.03%), Nagaur
(at −6.00 mm/hydrologic year and −66.92%) and Tonk (at
−8.56 mm/hydrologic year and −50.63%). The significant
decreasing trends of average monsoon rainfall had the highest
value of decreasing slope (i.e., −8.56 mm/hydrologic year) for
Tonk, and percentage change of −88.16% in Jodhpur (Table S1).
Significant trends in non-monsoon average rainfall were found
for Dungarpur (at −0.66 mm/hydrologic year and −58.10%),
Chittorgarh (at −1.27 mm/hydrologic year and −80.29%),
Jaipur (at 1.22 mm/hydrologic year and 61.53%), Churu
(at 0.93 mm/hydrologic year and 49.61%), Ganganagar (at
1.39 mm/hydrologic year and 79.46%) and Hanumangarh (at
1.70 mm/hydrologic year and 102.85%). A significant increasing
trend was observed in non-monsoon rainfall at Hanumangarh
(1.70 mm/hydrologic year), and a decreasing trend was found at
Chittorgarh (−1.27 mm/hydrologic year) (Table S2). The percentage change of significant trend was found to be at 102.85%
and −80.29% in Hanumangarh and Chittorgarh, respectively
(Table S2).
Significant decreasing trends were observed in annual average
rainfall for Jodhpur (at −7.51 mm/year and −85.05%), Karauli
(at −7.41 mm/year and −34.65%), Nagaur (at −6.47 mm/year
and −61.93%) and Tonk (at −10 mm/year and −54.54%). In
80
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
Fig. 2. Location of urban centers with increasing, decreasing and no trends at 10% significance level for a) monsoon, b) non-monsoon and c) annual average rainfall.
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
the case of annual average rainfall, maximum values of
decreasing slopes (−10 mm/year) and percentage change
(−85.05%) were observed in Tonk and Jodhpur, respectively
(Table S3). The IPCC also reported increases in precipitation at a
global level, however, in the present study, precipitation was
found to increase in some urban centers and decrease in other
centers. This may be due to changing land use/land cover, and
several other anthropogenic activities at a local scale (Gowda et
al., 2008).
4.1.2. Minimum temperature
Significant increasing trends were observed in minimum
temperature at seasonal and annual temporal scales for the
majority of the urban centers in the northeastern part of
Rajasthan State. However, significant trends were not found in
other urban centers at a 10% level of significance by the MK test
(Fig. 3). The magnitude of the Sen's slope of significant trends in
seasonal (monsoon, non-monsoon and annual) minimum
temperature for the urban centers of Rajasthan State is shown
in Fig. 3. The maximum value of Sen's slope (0.04 °C/hydrologic
year) and percentage change (5.07%) for a significant increasing
trend of average minimum temperature in the monsoon season
were observed in Churu and Sikar, respectively. In the case of
non-monsoon average minimum temperature, maximum values
of increasing (0.05 °C/hydrologic year) slope were observed
in Bikaner, Jaipur and Sikar. Similarly, percentage change in
non-monsoon minimum temperature varied from 3.91% at
Bharatpur to 13.45% at Sikar. Also, a significant trend in annual
average minimum temperature had maximum values of increasing slopes (0.05 °C/year) at Sikar. The percentage changes
in annual average minimum temperature were found to be at a
minimum (2.61%) and maximum (9.52%) at Banaswara and
Sikar, respectively. The positive trends of seasonal and annual
minimum temperatures were also observed by other researchers
in India (e.g., Jain et al., 2013).
81
4.1.4. Maximum temperature
Significant increasing trends in monsoon average maximum
temperature were found in Barmer, Bikaner, Dausa, Jaisalmer,
Jalore and Pali (Fig. 5a). The significant increasing trends in
monsoon season average maximum temperature were observed
with a maximum value (0.03 °C/hydrologic year) of Sen's slope
in Jaisalmer and Bikaner. Percentage change in average maximum temperature during the monsoon period was at a
minimum (2.09%) and maximum (2.90%) in Jalore and Bikaner,
respectively. In the case of non-monsoon average maximum
temperature, the southwestern urban centers in Rajasthan State
had significant increasing trends. No significant trend was found
in the remaining urban centers during the non-monsoon period
at a 10% level of significance (Fig. 5b). Significant increasing
trends were observed in annual average maximum temperature
for urban centers that are located in the southeast and
southwestern parts of Rajasthan State (Fig. 5c). The significant
increasing trends in non-monsoon season average maximum
temperature were observed with a maximum value of Sen's
slopes (0.03 °C/hydrologic year) in Barmer, Jaisalmer, Jalore, Pali,
Sirohi and Udaipur. Percentage changes in non-monsoon
average maximum temperature were found to be at a minimum
(2.08%) and maximum (3.31%) in Dungarpur and Pali, respectively. No significant trends were found in the average maximum
temperature at an annual temporal scale for the urban centers
located in the northern region of the state (Fig. 5c). Similarly,
significant trends in annual average maximum temperature
were observed with maximum values of slopes (0.03 °C/year) in
Barmer, Jaisalmer, Jalore, Pali and Udaipur. The percentage
change was found to be at a minimum (1.47%) and at a
maximum (3.24%) in Pali and Baran, respectively. The detail
magnitude of the Sen's slope for average maximum temperature
for the urban centers of Rajasthan State is shown in Fig. 5.
These results are in good agreement with previous studies in
India (e.g., Singh et al., 2008a, b).
4.2. Trends in extreme events
4.1.3. Average temperature
Significant increasing trends in average temperature at
seasonal and annual temporal scales were observed for the
majority of the urban centers in Rajasthan State, accounting for
an average of 80% of the urban centers (Fig. 4). The significant
increasing trends in monsoon average temperature were
observed in Bikaner, Bundi, Churu, Dausa, Jaipur, Jaisalmer,
Kota and Sikar, which is indicated by the maximum value of
slope (i.e., 0.03 °C/hydrologic year). Also, percentage change in
monsoon average temperature was found to be at a maximum
for Jaisalmer (3.47%) and at a minimum for Alwar (2.43%).
Similarly, significant trends in non-monsoon and annual average
temperature showed the maximum value of 0.03 °C in the urban
centers. The percentage changes in significant trends in
non-monsoon average temperature were observed to be at a
minimum (2.43%) and at a maximum (4.84%) in Banaswara and
Sikar, respectively. The percentage change in annual average
temperature was found to be at a minimum (1.53%) and
maximum (4.32%) in Dungarpur and Sikar, respectively. The
detail magnitude of the Sen's slope of the significant trends in
average temperature is shown in Fig. 4. This warming trend in
average temperature is also supported by other studies in India
(e.g., Singh et al., 2008a, b).
4.2.1. Extreme annual daily rainfall
The spatial distribution of extreme annual maximum daily
rainfall trends and variation is shown in Fig. 6a, while the MK test
statistics (Zmk) with increasing, decreasing and no trends are
shown in Fig. 6b. Both positive (southeastern) and negative
(northeastern) trends were observed in extreme annual daily
rainfall in the urban centers of Rajasthan State. The results
revealed that significant decreasing trends and variability were
observed in annual extreme daily rainfall for Hanumangarh
(at −0.65 mm/year and −49.16%), Karauli (at −1.56 mm/year
and −60.31%), Nagaur (at −1.56 mm/year and −80.33%),
Sikar (at −1.33 mm/year and −63.86%) and Tonk (at
−1.64 mm/year and −65.14%). However, a significant increasing trend was found only in Jhalawar (at 1.38 mm/year and
45.97%). The maximum values of decreasing (−1.64 mm/year)
slopes and percentage change (−80.33%) in significant trends in
annual maximum daily rainfall were observed in Tonk and
Nagaur, respectively (Table S4). Significant trends were not
found for extreme events of annual daily rainfall in other urban
centers (Fig. 6). The detail magnitudes of slope and percentage
change in extreme annual maximum daily rainfall are presented
in Table S4 and Fig. 6. Similarly, only significant trends in extreme
annual maximum daily rainfall are shown in Fig. 7. Investigating
82
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
Fig. 3. Location of urban centers with increasing, decreasing and no trends for a) monsoon, b) non-monsoon and c) annual average minimum temperature.
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
83
Fig. 4. Location of urban centers with increasing, decreasing and no trends at 10% significance level for a) monsoon, b) non-monsoon and c) annual average temperature.
84
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
Fig. 5. Location of urban centers with increasing, decreasing and no trends for a) monsoon, b) non-monsoon and c) annual average maximum temperature.
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
85
Fig. 6. Location of urban centers with increasing, decreasing and no trends at 10% significance level for extreme annual daily rainfall.
trends in extreme events is necessary to assess the impact of
global climate change over regional climatic variability on
present and future water resources (Wang et al., 2013). The
assessment of trends of extreme annual daily rainfall events is
crucial for adaptation planning in arid and semi-arid regions of
India such as Rajasthan.
4.2.2. Extreme annual daily minimum temperature
The spatial distribution of extreme annual daily minimum
temperature is shown in Fig. 8a, while the MK test statistic
(Zmk) indicating trends is shown in Fig. 8b. Significant
increasing trends were observed in extreme annual daily
minimum temperature (at 0.03 to 0.07 °C/year) for the urban
centers located in the northeastern and western regions of
Rajasthan State (Fig. 8). However, significant trends were not
found in other urban centers of Rajasthan State (Fig. S1)
(Supporting material). A significant increasing trend in
annual minimum daily temperature was observed with a
maximum value of slope (0.073 °C/year) and percentage
change (102.76%) in Sikar, while significant increasing
annual daily minimum temperature was observed with a
minimum value of slope (0.040 °C/year) and percentage
change (31.01%) in Banaswara and Hanumangarh, respectively. The details of slope magnitude and variability of
extreme annual daily minimum temperature in the urban
centers of Rajasthan State are presented in Table S5 and
Fig. 8. Only the trends in significant extreme events of annual
daily minimum temperature are shown in Fig. S1. From the
results, it is clear that the magnitude of the minimum
temperature trends and variability is greater than the
maximum temperature extremes. This was also observed in
other parts of India (Duhan et al., 2013).
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
1998
2005
Rainfall
Linear (Rainfall)
350
300
250
y = 1.7368x -3347.5
R² = 0.1206
200
150
100
50
0
1970
1977
Year
y = -1.2439x + 2540.6
R² = 0.0493
200
150
100
50
0
1970 1977 1984 1991 1998 2005
Annual max. daily rainfall, mm
Annual max. daily rainfall, mm
Rainfall
Linear (Rainfall)
300
250
1991
1998
2005
400
350
Rainfall
Linear (Rainfall)
160
250
200
150
100
50
0
1970 1977 1984 1991 1998 2005
Sikar
120
100
80
60
40
y = -1.298x + 2653.4
R² = 0.1839
0
1970 1977 1984 1991 1998 2005
Year
y = -2.3587x + 4779.6
R² = 0.1561
Year
140
20
Rainfall
Linear (Rainfall)
300
Year
Nagaur
350
1984
Karauli
Year
Tonk
Annual max. daily rainfall, mm
100
90
80
70
60
50
40
30
20
y = -0.5473x + 1134.5
10
R² = 0.0907
0
1970 1977 1984 1991
Jhalawar
Annual max. daily rainfall, mm
Annual max. daily rainfall, mm
Hanumangarh
Rainfall
Linear (Rainfall)
Annual max. daily rainfall, mm
86
250
Rainfall
Linear (Rainfall)
200
150
y = -2.2845x + 4629.9
R² = 0.3097
100
50
0
1970 1977 1984 1991 1998 2005
Year
Fig. 7. Trends in statistically significant extreme annual daily rainfall in the urban centers of districts of Rajasthan State.
4.2.3. Extreme annual daily maximum temperature
The spatial distribution of extreme annual daily maximum
temperature (obtained using MK test statistic (Zmk)) was
determined using the IDW interpolation technique for the 33
urban centers of Rajasthan State (Fig. 9). A significant increasing
trend in extreme annual daily maximum temperature was
observed in the south (i.e., Banaswara, Baran, Bundi, Chittorgarh,
Jhalawar, Pali and Sirohi), east (i.e., Churu and Jaipur) and
west (i.e., Jaisalmer) urban centers of Rajasthan State (Fig. 9).
Significant increasing trend in the annual maximum daily
temperature was observed with a maximum value of Sen's
slope (0.049 °C/year) and percentage change (3.86%) in
Jhalawar, while the minimum value of slope (0.029 °C/year)
and percentage change (2.35%) was observed in Sirohi. The
minimum and maximum annual daily maximum temperatures were found to be at 44.48 °C and 47.62 °C in Sirohi and
Dholpur, respectively. The detail magnitude of the Sen's
slope and percentage change of annual daily maximum
temperature in urban centers of Rajasthan State is presented
in Table S6. The results of significant extreme events of
annual daily maximum temperature are shown in Fig. S2.
The results clearly indicate the warming trends in the
extreme annual maximum daily temperature in the urban
centers, which shows attribution of anthropogenic influence
on climate (Fig. 9a and b). These may include drastic changes
in land use/land cover, industrialization and urbanization.
In summary, the mean and extreme rainfall and temperature
trends and variability at a spatial and temporal scale were
evaluated for the 33 urban centers of Rajasthan State. The
results of mean and extreme annual daily rainfall showed
both positive and negative trends over the urban centers. An
increasing trend was found in annual and seasonal mean and
extreme minimum, and average and maximum temperatures
over the urban centers. This may be due to several natural and
anthropogenic activities such as land use/land cover, urbanization and industrialization, which contribute to the changes in
rainfall and temperature in terms of their distribution, intensity
and occurrence in space and time. The detailed evaluation of the
magnitude and variability in rainfall, and minimum and
maximum temperatures in terms of mean and extremes, is
very important for impact assessment and adaptation planning
for floods, drought and extreme events. It is suggested that in
the future, the assessment of mean and extreme events
considering climate change scenarios can be carried out on
seasonal and annual temporal scales at each urban center of
Rajasthan State, India. This would be very useful to facilitate the
proper planning and management of water resources in a
changing climate in Rajasthan State, India.
5. Conclusions
In the present study, the trends in mean (monsoon season,
non-monsoon season and annual) and extreme annual daily
rainfall and temperature were determined for the 33 urban
centers of Rajasthan State. This was carried out using the
non-parametric Mann–Kendall (MK) test. The Sen's slope and
percentage changes in rainfall and temperature were also
estimated over the study period (1971–2005). Further, the
spatial variations in mean (seasonal and annual) and extreme
annual daily rainfall and temperature with their trends were
determined using the inverse-distance-weighted (IDW) interpolation method.
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
87
Fig. 8. Location of urban centers with increasing, decreasing and no trends at 10% significance level for extreme annual daily minimum temperature.
Both positive and negative trends were observed by the MK
test and Sen's Slope estimator in seasonal and annual average
rainfall in the urban centers of Rajasthan State. All of the
significant trends in monsoon and annual rainfall were found to
be decreasing at the 10% level of significance. However, significant trends in non-monsoon average rainfall were observed
(both positive and negative trends). The significant decreasing
trends of average monsoon rainfall had the highest value of
decreasing slope (i.e., −8.56 mm/hydrologic year) for Tonk
and percentage change of −88.16% in Jodhpur. A significant
increasing trend was observed in non-monsoon rainfall at
Hanumangarh (1.70 mm/hydrologic year), and a decreasing
trend was found at Chittorgarh (−1.27 mm/hydrologic year).
In the case of annual average rainfall, maximum values
of decreasing slopes (−10 mm/year) and percentage change
(−85.05%) were observed in Tonk and Jodhpur, respectively.
Significant increasing trends were observed in minimum
temperature at seasonal (0.04 and 0.05 °C/hydrologic year) and
annual (0.05 °C/year) temporal scales for the majority of the
urban centers in the northeastern part of Rajasthan State.
Significant increasing trends (at 0.01 to 0.05 °C/year) were
observed in average temperature for most of the urban centers
(about 80% of the urban centers), in particular, all the urban
centers showed significant warming trends in annual average
temperature. Significant increasing trends in monsoon average
maximum temperature were found in the some of the urban
centers. However, the southwestern urban centers had significant increasing trends in non-monsoon average maximum
temperature. Significant increasing trends were observed in
annual average maximum temperature for urban centers that
are located in the southeast and southwestern parts of Rajasthan
State. The trends in maximum and minimum temperature may
88
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
Fig. 9. Location of urban centers with increasing, decreasing and no trends at 10% significance level for extreme annual daily maximum temperature.
be influenced in different ways due to local physical geographic
(e.g., topography, urbanization, air pollution etc.) and atmospheric circulation features (Turkes and Sumer, 2004; Dhorde et
al., 2009; Tabari and Talaee, 2011a).
In the case of extreme events for annual daily rainfall both
positive (southeastern) and negative (northeastern) trends were
observed in some of the urban centers. Other urban centers did
not show any significant trends in extreme annual daily rainfall.
Significant increasing trends were observed in extreme annual
daily minimum temperature (at 0.03 to 0.07 °C/year) for the
urban centers located in the northeastern and western regions of
Rajasthan State. However, significant trends were not found
in the other urban centers of Rajasthan State. A significant
increasing trend in extreme annual daily maximum temperature
(at 0.03 to 0.05 °C/year) was observed in the south (i.e.,
Banaswara, Baran, Bundi, Chittorgarh, Jhalawar, Pali and Sirohi),
east (i.e., Churu and Jaipur) and west (i.e., Jaisalmer) urban
centers of Rajasthan State. The results of mean (monsoon,
non-monsoon and annual) and extreme annual daily rainfall and
temperature clearly indicate prominent changes in the urban
centers, whether due to natural variability or anthropogenic
activity over the period. The trends and variability of rainfall and
temperature found in this study could be one part of a cycle with
a frequency shorter than 35 years. A similar observation was also
made for precipitation trends in Iran (Tabari and Talaee, 2011a).
There is no doubt that changes in natural factors like sea surface
temperature, the Earth's rotation and solar cycles can cause
changes in the climate. However, we do not have any control of
these factors. Anthropogenic activities like urbanization and land
use change can also affect local climates significantly. The effects
of these factors could be minimized by taking appropriate
adaptation and mitigation measures. The assessment of mean
S.M. Pingale et al. / Atmospheric Research 138 (2014) 73–90
and extreme events of rainfall and temperature is necessary to
help and prepare suitable adaptation strategies in the face of
uncertain changing climate and extreme weather events. The
outcome of this study should prove to be useful to policy makers,
hydrologists, and water resource planners dealing with climate
change in all the urban centers in Rajasthan State for the
sustainable development and planning of water resources.
Acknowledgments
We acknowledge the Indian Meteorological Department
(IMD) in Pune for providing useful meteorological gridded
datasets for this research work. The financial assistance provided
by the Ministry of Human Resource Development, Government
of India in the form of a scholarship is duly acknowledged.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
http://dx.doi.org/10.1016/j.atmosres.2013.10.024.
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