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ORIGINAL ARTICLE
Spatio–temporal analysis of groundwater resources in Jalandhardistrict of Punjab state, India
Amanpreet Singh1 • Chandra Shekhar Sharma2 • A. T. Jeyaseelan1 •
V. M. Chowdary3
Received: 25 March 2015 / Accepted: 31 August 2015 / Published online: 20 September 2015
� Springer International Publishing 2015
Abstract Water security is widely recognised as one of
the major challenges to India’s economic and social
development. Groundwater level changes and its trends
were assessed spatially and temporally for both pre and
post-monsoon seasons in Jalandhar district, Punjab, a part
of Indus basin, using geographical information system
(GIS) for the period 1996–2010. The study area has been
experiencing severe water problems for the past few years
due to increased areas under rice cultivation lowering the
groundwater table. Long-term variations of seasonal
groundwater level were investigated using the statistical
approaches, viz., Mann–Kendall test, Sen’s slope estimator
and linear regression method. Results indicated that the
average depth to water level during pre-monsoon season
ranges from 7.60 to 18.69 m, whereas in post-monsoon
season, the average depth to water level ranges from 5.45
to 19.30 m. The mean of water level fluctuations during pre
and post-monsoon seasons was found to be -8.36 and
-8.06 m, respectively. Significant declining trends are
evident in the groundwater levels during the period from
1996 to 2010 in this region. The analysis of the results
showed mostly decreasing trends in the time series of the
study area. The percentage of wells characterized by sig-
nificant decrease of groundwater level using the Mann–
Kendall test was 89 % for pre-monsoon and 96 % for post-
monsoon at 95 % confidence level. Slopes obtained by
Sen’s estimator varied from -0.32 to -1.49 m/year,
indicating decline rates of 3.2 and 14.9 m per decade. From
the linear regression method, minimum and maximum
declining rate of -0.49 and -1.59 m/year was obtained.
Comparison of the statistical tests indicated that the sig-
nificant trends detected by the Mann–Kendall test and
Sen’s slope estimator were more or less confirmed by the
linear regression method. This declination of groundwater
level may affect most of the water-dependent activities,
especially the agriculture sector, in near future. Hence,
groundwater resources within the study area should be
managed carefully. It is necessary to set up functional
organizations and promote new rules and norms on
groundwater use in this region. The result of this research
raises concern about the sustainability of groundwater
resources in the Jalandhar district. On the other hand, the
findings of this study will assist planners and decision-
makers in developing better land use and water resource
management. Shifting from the current cropping pattern
that consumes large quantities of water to another one that
has less water requirement and also implementing
advanced irrigation techniques are suggested.
Keywords Water security � GIS � Mann–Kendall test �Sen’s slope � Linear regression � Groundwater management
Introduction
Groundwater is an important source of fresh water to meet
the domestic needs of an ever growing population and also
to meet the demands of different commercial sectors, viz.,
agriculture, fisheries, mining, manufacturing, etc.
& Amanpreet Singh
1 Regional Remote Sensing Centre-West, National Remote
Sensing Centre, ISRO, Jodhpur, Rajasthan, India
2 Agricultural and Food Engineering Department, Indian
Institute of Technology, Kharagpur, West Bengal 721302,
India
3 Regional Remote Sensing Centre-East, National Remote
Sensing Centre, ISRO, Kolkata, West Bengal, India
123
Sustain. Water Resour. Manag. (2015) 1:293–304
DOI 10.1007/s40899-015-0022-7
Sustainable management of groundwater resources is an
essential task, especially in arid and semi-arid climates that
face acute shortage of fresh water (Mende et al. 2007).
Groundwater withdrawals are increasing rapidly and con-
tinuously worldwide (Van et al. 2010) and excess use has
caused serious decline in groundwater levels (Phien-wej
et al. 2006; Shamsudduha et al. 2009; Machiwal and Jha
2014). Declining groundwater levels have adverse impacts
on the environment such as groundwater depletion and land
subsidence. The former affects aquifer sustainability (Ak-
ther et al. 2009) and the latter results due to the compaction
of aquifer materials (Konikow and Kendy 2005). Other
obvious impacts are groundwater pollution due to addi-
tional recharge from leaking sewers and other wastewater
sources (Hoque et al. 2007; Berg et al. 2007). Reduction in
the availability of surface water due to the reduced
groundwater discharges (Konikow and Kendy 2005) can
adversely affect ecosystems (Zektser et al. 2005). Given
the scarcity of available water resources in the near future
and its impending threats, it has become imperative on the
part of water scientists as well as planners to quantify the
available water resources for its judicious use (Sreekanth
et al. 2009).
The Punjab state, located in the north-western part of
India, has a geographical area of 50,362 km2. It constitutes
1.57 % of the total land area of the country (CGWB 1996)
and is experiencing declining groundwater levels due to
over-exploitation. Groundwater plays an important role in
the economic development and ecological balance of the
state and has always been considered to be a readily
available and safe source of water for domestic, agricul-
tural and industrial use. Over the past few decades,
groundwater extraction for irrigation has resulted in aquifer
overdraft in these areas (Rodell et al. 2009), disrupting the
natural equilibrium of the systems. Water levels across
much of the north-eastern Punjab state have been declining
for at least the past 20–30 years and this has attributed to
the substantial change in cropping patterns during this time.
The economy of the state is primarily agro-based. Geo-
graphically located in the Indus basin, the area is drained
by three major rivers: the Ravi, the Beas and the Sutlej,
apart from another drainage channel, Ghaggar, that drains
the southern parts. About 85 % of geographical area is
under agriculture, of which 95 % area is irrigated (Ag-
garwal et al. 2009). The cropping intensity of Punjab is
184 % (Gupta 2011).
The change in cropping pattern has increased irrigation
water requirement tremendously along with increased
irrigated areas from 71 to 95 %. As per the agricultural
statistics from the state (Aggarwal et al. 2009), the number
of tube wells for irrigation usage has increased from 0.192
to 1.165 million in the past 35 years. Thus, increased
demand of available groundwater resources has caused
decline in groundwater levels, resulting in depletion of
groundwater. All these factors have eventually led to extra
power consumption for lifting of water for irrigation con-
sumption and thus affect the economic conditions (Tiwana
et al. 2007).
Understanding the spatio–temporal behaviour of the
groundwater regime and its long-term trends are essential
for management of groundwater resources (Ferdowsian and
Pannell 2009; Hoque et al. 2007; Sreekanth et al. 2009). In
recent years, several studies were carried out on detection
of trends in water resources that mainly focused on surface
water (Marengo 1995; Douglas et al. 2000; Zhang
et al.2006; Mazvimavi and Wolski 2006; Kumar et al.
2009; Tabari and Marofi 2011). In spite of great importance
of groundwater in many parts of the world, especially in
developing countries, few studies were carried out on
temporal trend analysis of groundwater levels in this area,
perhaps due to the lack of reliable and regular data, as
groundwater level monitoring is a time- and labour- con-
suming process (Ahmadi and Sedghamiz 2007; Machiwal
and Jha 2014; Patle et al. 2015).
The supply of groundwater is not unlimited and, there-
fore, its use should be properly planned based on the
understanding of the groundwater system behaviour to
ensure its sustainable use. To understand groundwater level
behaviour in aquifer systems, the present study was carried
out (Jalandhar district of Punjab state) with the following
specific objectives: (1) to study the long-term behaviour of
groundwater levels spatially in the study area during both
pre and post-monsoon seasons; (2) to quantify the magni-
tude of changes at seasonal time scales using statistical
methods viz., Mann–Kendall test, Sen’s slope estimator
and linear regression method.
Study area
Agriculture in Punjab has a heavy requirement of water for
irrigation purposes. Paddy (a type of rice) is a major crop
which has made an impact on the agriculture of the state.
The area under paddy has increased from 2,27,000 ha in
1960–61 to 26,42,000 ha (where, ha refers to hectare) in
the year 2006. In terms of gross cropped area of the state
(total area sown once and/or more than once in a particular
year), paddy occupied around 4.8 % of the gross cropped
area in 1960–61, increased to more than 25 % in 1990–91
and then increased further to 33.37 % in 2004–05 (Tiwana
et al. 2007). The increase in area under paddy cultivation
has led to decline in area under other major kharif (summer
monsoon) crops like maize, bajra, jowar, sugarcane,
groundnut, pulses, etc. The area under maize and sugarcane
cultivation has declined from 9.77 and 2.25 % of total
gross cropped area of state in the year 1970–71 to 1.94 %
294 Sustain. Water Resour. Manag. (2015) 1:293–304
123
and 1.08 % in year 2004–05, respectively (Tiwana et al.
2007).Wheat has, however, been the dominant crop of the
state in rabi (winter) season from the very beginning. In
1960–61, 29.58 % (14,00,000 ha) of the gross cropped area
was under wheat cultivation, which increased by about
44 % in 1990–91 and has thereafter remained almost the
same (34,81,355 ha in 2004–05) (Tiwana et al. 2007). The
increase in wheat cultivation has been at the expense of
cutting down the area under cultivation of other rabi season
crops, especially gram, barley, rapeseed, mustard and
sunflower.
Jalandhar, the north-eastern district of Punjab State,
located between 30�590 and 31�370N latitudes and 75�040
and 75�570E longitudes is considered as the study area
(Fig. 1) having total geographical area around 2662 sq. km
(CGWB 2007). The mean elevation of the study area varies
between 184 and 294 m [extracted from SRTM (Shuttle
Radar Topography Mission) DEM]. The climate of the
district can be classified as tropical and dry sub-humid. The
study area receives an average annual rainfall of about
700 mm, of which 70 % of occurs during south–west
monsoon that spread over 35 rainy days. The Bist Doab
Canal System with 41 branch canals having total length of
604.40 km, of which Bist Doab canal is 43 km long, is the
major source of canal irrigation (CGWB 2007). The study
area has the network of Jalandhar branch that irrigates
northern and central parts, while the Phillaur tributary of
Nawashahar branch irrigates the southern parts. Apart from
canal irrigation, groundwater is also used through tubewells
for irrigation in most parts of the study area. The land
use/land cover map of the study area (Fig. 2) is generated
under Natural Resources Census project of NRSC/ISRO
using IRS LISS III data (India-WRIS 2014). It indicates that
the major land use in the study area is under agriculture with
paddy and wheat as the major crops in kharif and rabi
seasons. Major geological formation of the study area falls
under Quaternary age and is comprised of recent alluvial
deposits that belong to the vast Indus alluvial plains.
Groundwater exploration undertaken by CGWB has
revealed the presence of four sets of aquifer groups down to
a depth of 312 m. These zones are mainly comprised of fine
to medium-grained sand. The first granular zone forms the
water table aquifer which occurs up to 115 m below the
ground level. The second, third and fourth aquifers occur
between 130 and 175 m, 180 and 205 m and below 212 m
depth, respectively. Total thickness of the alluvium is dee-
per; bedrock has not been encountered up to 309 m depth in
the district (CGWB 2007).
Methodology
Geospatial analysis of groundwater
In the present study, pre (June) and post (October) mon-
soon season water level data pertaining to the study area
were collected from India-WRIS web Portal (www.india-
wris.nrsc.gov.in) for the study period (1996–2010). The
Fig. 1 Location map of the study area
Sustain. Water Resour. Manag. (2015) 1:293–304 295
123
period of measurements coincided with the general trend of
deepest in June (pre-monsoon) and shallowest in October
(post-monsoon) water levels. The average depth to water
level (WL) was estimated from the data of 15 years
(1996–2010) recorded at different hydrological stations
representing pre and post-monsoon periods. The data were
analysed to understand the dynamics and long-term pattern
of the groundwater table. The spatial variations of average
depth to water level and water level fluctuations from the
observed groundwater data were also analysed.
Statistical tests for trend analysis
The statistical methods like the Mann–Kendall test, Sen’s
slope estimator and linear regression method as described
in the forthcoming paragraphs were used for estimating the
trend and magnitude of change in groundwater level. The
methodology flow chart is shown in Fig. 3. The land
use/land cover map generated from satellite data was
integrated with groundwater level map for further inference
on spatial trends in observed groundwater levels in the
study area.
Mann–Kendall test
The rank-based non-parametric Mann–Kendall is highly
appropriate for trend detection in hydrological variables for
several reasons: (1) it does not require data to be normally
distributed (Tabari and Hosseinzadeh Talaee 2011), (2) it
supports multiple observations per time period, (3) it
allows missing values and censored observations in the
time series (Kundzewicz and Robson 2004) and (4) it is
low sensitive to abrupt breaks due to inhomogeneous time
series (Jaagus 2006). According to this test, the nullFig. 2 Land use/land cover of the study area (Source: India-WRIS)
Groundwater Level Data (1996-2010)
Water Level Analysis(Pre-Monsoon and Post-Monsoon)
Relating Point Data to Real World Coordinates
Spatial Interpolation
Spatial Groundwater Trend
Mann Kendall Test Sen’s Slope Estimator Linear Regression Method
Fig. 3 Methodology flowchart
296 Sustain. Water Resour. Manag. (2015) 1:293–304
123
hypothesis Ho states that the deseasonalized data
ðx1; . . .. . .xnÞ is a sample of n independent and identically
distributed random variables. The alternative hypothesis is
H1 of a two-sided test is that the distributions of xk and xjare not identical for all k; j� n with k 6¼ j. The test statistic
S, which has mean zero and a variance computed by Eq. 3,
is calculated using Eqs. 1 and 2 and is asymptotically
normal:
S ¼Xn�1
k¼1
Xn
j¼kþ1
sgnðxj � xkÞ ð1Þ
sgnðxj � xkÞ ¼þ1 if xj � xk [ 0
0 if xj � xk ¼ 0
�1 if xj � xk\0
8<
:
9=
; ð2Þ
VarðSÞ ¼nðn� 1Þð2nþ 5Þ �
Pt tðt � 1Þð2t þ 5Þ
� �
18ð3Þ
The notation t is the extent of any given tie andP
t
denotes the summation over all ties. In cases where the
sample size n[ 10, the standard normal variable Z is
computed using Eq. 4.
Z ¼
S� 1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðSÞ
p if S[ 0
0 if S ¼ 0Sþ 1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðSÞ
p if S\0
8>>>><
>>>>:
ð4Þ
Positive values of Z indicate increasing trends while
negative Z the decreasing trends. While testing either
increasing or decreasing monotonic trends at the a signifi-
cance level, the null hypothesis was rejected for an absolute
value of Z greater than Z1�a=2, obtained from the standard
normal cumulative distribution tables. In the present study,
significance levels at a = 0.05 were considered. The value
of Z for 95 % confidence level is 1.96. Therefore, when the
time series groundwater level data produce |Z|[ 1.96, there
is a significant upward or downward trend.
Sen’s slope estimator
The non-parametric Sen’s slope estimator is an unbiased
estimator of trends and has considerably higher precision
than a regression estimator, where data are highly skewed
(Hirsch et al. 1982). If a linear trend is present in a time
series, then the true slope (change per unit time) can be
estimated using a simple non-parametric procedure devel-
oped by Sen (1968). The slope estimates of N pairs of data
are first computed by
Qi ¼xj � xk
j� kfor i ¼ 1; . . .;N; ð5Þ
where xj and xk are data values at times j and kðj[ kÞ,respectively. The median of these N values of Qi is Sen’s
estimator of slope. If N is odd, then Sen’s estimator is
computed by
Qmed ¼ Q ðNþ1Þ=2½ � ð6Þ
If N is even, then Sen’s estimator is computed by
Qmed ¼ 1
2Q N=2½ � þ Q ðNþ2Þ=2½ �� �
ð7Þ
Finally, Qmed is tested with a two-sided test at the
100ð1 � aÞ% confidence interval and the true slope may be
obtained with the non-parametric test.
In the present study, the confidence interval was com-
puted at a = 0.05 confidence level and is given as follows:
Ca ¼ Z1�a=2
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðSÞ
p; ð8Þ
where VarðSÞ has been defined in Eq. 3, and Z1�a=2 is
obtained from the standard normal distribution.
Subsequently, M1 ¼ ðN � CaÞ=2 and M2 ¼ ðN þ CaÞ=2
are computed. The lower and upper limits of the confidence
interval, Qmin and Qmax, are the M1th largest and the ðM2 þ1Þth largest of the N ordered slope estimatesQi. IfM1 is not a
whole number and lies between two values, the lower limit is
obtained by interpolation between the lower and higher
values. Correspondingly, if M2 is not a whole number and
lies between two values, the upper limit is obtained by
interpolation between the lower and higher values.
Linear regression method
Linear regression analysis is also applied for detecting and
analysing trends in time series. The advantage of para-
metric linear regression method is their simplicity (Mos-
mann et al. 2004). The main statistical parameter drawn
from the regression analysis is slope which indicates the
mean temporal change of the studied variable. Positive
values of the slope show increasing trends, while negative
values of the slope indicate decreasing trends. The total
change during the period under observation is obtained
with multiplying the slope with the number of years
(Tabari and Marofi 2011).
Results and discussion
General characteristics of groundwater levels
Groundwater behaviour in pre-monsoon season
The depth to water level maps was generated for each
season, i.e., pre and post-monsoon seasons, for the study
period (1996–2010). Using these 15 years of data, average
depth to water level and water level fluctuation in pre-
monsoon were prepared. Average depth to water level map
Sustain. Water Resour. Manag. (2015) 1:293–304 297
123
for both pre and post-monsoon was prepared by taking the
average of all 15 years. The analysis of pre-monsoon depth
to water level is shown in Figs. 4, 5, 6, 7 and 8. Mean water
level depth computed for the study period during pre-
monsoon season ranges from 7.60 to 18.69 m (Fig. 4). The
average groundwater level in the study area was observed
to be 13.51 ± 2.29 m. Spatial distributions of pre-monsoon
water levels for the study period are shown as Fig. 5.
Gradual increase in groundwater depletion in the study area
is evident from Fig. 5 and nearly 80 % of the study area
showed water level beyond 11 m in the year 2010. The
annual pre-monsoon water levels (minimum, maximum
and mean) in the study area are given in Fig. 6. The depth
to groundwater level in pre-monsoon season with maxi-
mum, minimum and mean groundwater level varies from
13.22 (2001) m to 29.35 m (2009), 3.1 m (1998) to 7.98 m
(2010) and 7.90 m (1999) to 19.48 m (2010), respectively.Fig. 4 Average depth to water level in pre-monsoon (1996–2010)
Fig. 5 Pre-monsoon depth to
water level (1996–2010)
298 Sustain. Water Resour. Manag. (2015) 1:293–304
123
Well location-wise pre-monsoon average groundwater
level and water level fluctuations are shown in Fig. 7. The
depth to water level map of pre-monsoon 1996 has been
compared with water level map of pre-monsoon 2010 to
prepare a water level fluctuation map for pre-monsoon
period. Maximum and minimum water level fluctuation in
pre-monsoon season was -18.24 and -0.027 m, respec-
tively. The mean and standard deviation of water level
fluctuation was -8.36 and 2.41 m (Fig. 8).
Groundwater behaviour in post-monsoon season
Similar to the pre-monsoon analysis of groundwater
behaviour, post-monsoon analysis was also carried out
(Figs. 9, 10, 11, 12 and 13). The average depth to water
level during post-monsoon season ranges from 5.45 to
19.30 m (Fig. 9). The average groundwater level in the
study area was found to be 13.55 ± 2.49 m. Spatial dis-
tribution of post-monsoon water levels in the study area is
shown in Fig. 10. Maximum, minimum, average and
standard deviations of water level are shown as Fig. 11.
Maximum, minimum and mean groundwater level varies
from 12.61 (1997) m to 30.36 m (2009), 2.18 m (1998) to
6.50 m (2004) and 6.05 m (1998) to 19.12 m (2009),
respectively. Well location average groundwater level and
water level fluctuation are shown in Fig. 12. The depth to
water level map of post-monsoon 1996 was compared with
year 2010 to prepare a water level fluctuation map for the
post-monsoon season. Spatial distribution of groundwater
fluctuations is shown in Fig. 13. Maximum and minimum
water level fluctuation in post-monsoon season was -21.18
and 1.49 m, respectively. The mean and standard deviation
of water level fluctuation was found to be -8.06 and
3.35 m.
0
5
10
15
20
25
30
35Gr
ound
wat
er D
epth
(m b
gl)
Year
Max. Min. Mean
Fig. 6 Pre-monsoon (1996–2010) groundwater level in Jalandar
district
-25
-20
-15
-10
-5
00
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Wat
er Le
vel F
luct
ua�o
n (m
)
Grou
ndw
ater
Dep
th (m
bgl
)
Wells
Avg. Groundwater Level Water level Fluctua�on
Fig. 7 Well location wise pre-monsoon (1996–2010) average
groundwater level and fluctuation
Fig. 8 Pre-monsoon groundwater fluctuation map (1996 vs. 2010)
Fig. 9 Average depth to water level in post-monsoon (1996–2010)
Sustain. Water Resour. Manag. (2015) 1:293–304 299
123
Fig. 10 Post-monsoon depth to
water level (1996–2010)
0
5
10
15
20
25
30
35
Grou
ndw
ater
Dep
th (m
bgl
)
Year
Max. Min. Mean
Fig. 11 Post-monsoon (1996–2010) groundwater level in Jalandhar
district
-25
-20
-15
-10
-5
0
50
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Wat
er L
evel
Flu
ctua
�on
(m)
Grou
ndw
ater
Dep
th (m
bgl
)
Wells
Avg. Groundwater Level Water level Fluctua�on
Fig. 12 Well location wise post-monsoon (1996–2010) average
groundwater level and fluctuation
300 Sustain. Water Resour. Manag. (2015) 1:293–304
123
Trends in the groundwater level
Mann–Kendall (MK) test statistics, Sen’s slope and linear
regression slope helped to identify the trends in ground-
water levels of the study area at spatial and temporal scale.
Each monitoring well reflects the groundwater dynamics of
the surrounding area; therefore, its trend value provides an
idea about the water level fluctuations of that area over the
time period.
Pre-monsoon season
Results of analysis of MK test for each monitoring well
along with Z statistics are shown in Fig. 14. The Sen’s
slope and linear regression slope computed for the pre-
monsoon season are shown in Fig. 15. Descriptive statistics
is also shown in Table 1. Significant declining trend in the
groundwater levels at 95 % significance level was wit-
nessed in most of the stations across the study area. Trend
analysis through the MK test and Sen’s slope estimator
indicates drastic decline in the groundwater levels. Most of
the time series showed negative trends, indicating the
decline of groundwater levels in most areas. About 89 % of
the negative trends were statistically significant (at 95 %
significance level). Results showed that about 24 observa-
tion wells witnessed significant negative trends, while no
significant trends were observed in only three wells. Trend
of decline in groundwater levels was highest in three
observation wells, i.e., W13261, W13262 and W13275
(Table 1). These wells also showed significant negative
trend. On the other hand, three observation wells with
declining rate of more than 0.5 m/year showed no signifi-
cant trend.
Groundwater level trend line slopes in the study area
computed using Sen’s slope estimator and linear regression
method are presented in the Table 1. Slopes obtained by
Sen’s estimator varied from -0.32 to -1.49 m/year,
indicating decline of 3.2 and 14.9 m per decade. The mean
and standard deviation of declining rate by Sen’s slope was
observed as 0.75 and 0.258 m/year, respectively. From the
linear regression method, minimum and maximum
declining rates of -0.49 and -1.59 m/year were obtained.
The observed mean and standard deviation of declining
rate was 0.82 and 0.225 m/year. This implies that the water
table declined significantly during the period of analysis in
the study area. This declination of groundwater level may
affect most of the water-dependent activities, especially
agricultural water management in the study area.
Post-monsoon season
Spatial distribution of wells along with obtained ground-
water trends in post-monsoon season using MK test along
with the Z statistics are shown in Fig. 16. The statistics of
MK test are also given in Tabular form (Table 1). Com-
puted Sen’s slope and linear regression slope for analysing
post-monsoon season water level trends are shown as
Fig. 17. Declining trends in the groundwater levels were
significant at 95 % significance level for most of the sta-
tions across the study area. Trend analysis by MK test and
Sen’s slope estimator indicates drastic decline in the
groundwater levels as most of the areas showed negative
trend. Nearly 96 % of the negative trends were statistically
significant (at 95 % significance level). Results showed
that about 26 observation wells indicated significant
Fig. 14 Mann–Kendall Z statistics with trend for pre-monsoon
season (1996–2010)
Fig. 13 Post-monsoon groundwater fluctuation map (1996 vs. 2010)
Sustain. Water Resour. Manag. (2015) 1:293–304 301
123
Fig. 15 Sen’s slope and linear regression slope for pre-monsoon season (1996–2010)
Table 1 Mann–Kendall Z statistics, Sen’s slope estimate and linear regression slope for pre and post monsoon seasons (1996–2010)
Sl.
no.
Well
code
Latitude Longitude Pre-monsoon season Post-monsoon season
Mann–Kendall
statistic, Z
Sen’s slope
(m/year)
Linear regression
slope (m/year)
Mann–Kendall
statistic, Z
Sen’ slope
(m/year)
Linear regression
slope (m/year)
1 W13280 31.02 75.77 -4.35 -0.56a -0.57 -4.35 -0.59a -0.55
2 W13278 31.03 75.79 -4.16 -0.48a -0.49 -4.35 -0.48a -0.48
3 W13281 31.05 75.91 -4.26 -0.60a -0.68 -3.46 -0.75a -0.77
4 W13272 31.07 75.69 -3.96 -0.92a -0.86 -4.06 -1.00a -0.98
5 W13285 31.08 75.33 -3.07 -0.98a -0.96 -2.57 -0.56a -0.81
6 W13273 31.09 75.60 -2.87 -0.54a -0.58 -3.76 -0.82a -0.83
7 W13274 31.12 75.82 -4.06 -0.89a -0.87 -4.06 -1.01a -0.96
8 W13266 31.11 75.53 -1.88 -0.65 -0.68 -3.86 -1.14a -1.10
9 W13284 31.11 75.18 -3.27 -0.67a -0.68 -2.97 -0.61a -0.61
10 W13267 31.13 75.47 -1.88 -0.63 -0.79 -3.76 -1.25a -1.32
11 W13268 31.13 75.49 -1.88 -0.70 -0.84 -3.66 -1.19a -1.36
12 W13277 31.15 75.81 -4.06 -0.97a -0.92 -3.46 -1.08a -1.04
13 W13282 31.16 75.72 -3.04 -0.33a -0.89 -4.16 -1.01a -1.00
14 W13275 31.17 75.84 -4.16 -1.06a -1.01 -4.16 -1.11a -1.13
15 W13283 31.16 75.63 -3.96 -0.70a -0.67 -4.16 -0.90a -0.88
16 W13271 31.19 75.53 -2.97 -0.39a -0.62 -3.37 -0.35a -0.35
17 W13265 31.23 75.53 -3.37 -0.79a -1.08 -3.66 -1.63a -1.81
18 W13253 31.24 75.52 -3.37 -0.82a -1.01 -3.76 -1.50a -1.67
19 W13262 31.33 75.59 -3.27 -1.49a -1.59 -3.56 -1.67a -1.76
20 W13261 31.34 75.64 -3.56 -1.15a -1.12 -3.66 -1.16a -1.22
21 W13260 31.41 75.69 -3.56 -0.75a -0.84 -3.07 -0.80a -0.75
22 W13254 31.42 75.71 -3.66 -0.84a -0.95 -3.17 -0.97a -1.02
23 W13257 31.43 75.66 -2.08 -0.32a -0.60 -1.09 -0.08 -0.09
24 W13249 31.44 75.50 -3.56 -0.81a -0.79 -3.66 -0.88a -0.90
25 W13258 31.45 75.63 -4.16 -0.54a -0.66 -3.27 -0.32a -0.31
26 W13259 31.47 75.76 -3.07 -0.80a -0.73 -2.38 -0.58a -0.62
27 W13248 31.57 75.64 -3.76 -0.73a -0.74 -3.56 -0.76a -0.74
a Indicates significant trend (at 95 % level of significance)
302 Sustain. Water Resour. Manag. (2015) 1:293–304
123
negative trends, while only one well indicated non-signif-
icant trend.
Trend line slopes of groundwater level in the study area
using Sen’s estimator and linear regression method were
presented in Table 1. It is evident from the results that the
slopes using Sen’s estimator vary from -0.08 to -1.67 m/
year (0.8 and 16.7 m per decade) in post-monsoon season.
The mean and standard deviation of declining rate by Sen’s
slope was found to be 0.90 and 0.386 m/year, respectively.
The linear regression method showed minimum and max-
imum declining rate of -0.09 and -1.81 m/year,
respectively. The mean and standard deviation of declining
rate was observed to be -0.93 and 0.42 m/year,
respectively.
The dominance of rice and wheat monoculture cropping
pattern over the years in the study area has led to the over-
exploitation of groundwater resulting in rapid decline of
the water table not only in the study area but also in the
entire state (except the southwestern part). Perusal of his-
torical data reveals that the current paddy cultivation has
increased by about 85 times since 1950–51 against wheat
cultivation, which has increased only 1.7 times (CGWB
2007). Significant increase in the average yield of paddy
from 806 to 3588 kg/ha and wheat from 958 to 4925 kg/ha
over the period of past 50 years (CGWB 2007) could be
attributed to irrigated agriculture in the study area.
In general, results of the present study indicated that the
groundwater level declined during both cropping seasons.
During the monsoon season, in spite of monsoon rainfall
with irrigation support from surface water resources, the
decline in ground water table indicates over-exploitation of
groundwater. Thus, these regions are critical and should be
managed carefully to optimize groundwater resource
exploitation. Trends identified through statistical tests raise
concern about the sustainability of the groundwater
resources in the study area. The present analysis will be
helpful for planners and decision-makers in developing
better land use and water resource management practices.
Since current cropping patterns are posing threat to
groundwater resources of the region, adoption of advanced
irrigation techniques associated with less water intensive
crops should be promoted to curb groundwater depletion.Fig. 16 Mann–Kendall Z statistics with trend for post-monsoon
season (1996–2010)
Fig. 17 Sen’s slope and linear regression slope for post-monsoon season (1996–2010)
Sustain. Water Resour. Manag. (2015) 1:293–304 303
123
Conclusions
Long-term variations of seasonal groundwater level were
investigated in the present study using the statistical approa-
ches viz., Mann–Kendall test, Sen’s slope estimator and linear
regression method for the period 1996–2010. Significant
declining trends are witnessed in the groundwater levels.
Particularly during the monsoon season, in spite of monsoon
rainfall with irrigation support from surface water resources,
the decline in groundwater table indicates over-exploitation of
groundwater. Such decline in groundwater levels may be
attributed mainly due to the expansion in the irrigated agri-
culture in the study area. The percentage of wells character-
ized by significant decrease in the groundwater level using the
Mann–Kendall test was found to be 89 % for the pre-monsoon
and 96 % for post-monsoon season. Comparison of the sta-
tistical tests indicated that the significant trends detected by
the Mann–Kendall test and Sen’s slope estimator were mostly
confirmed by the linear regression method. It is necessary to
set up functional organizations and promote new guidelines
and norms on utilization of groundwater. Change in the cur-
rent cropping pattern and implementation of advanced irri-
gation techniques to curb groundwater depletion are needed.
Acknowledgments The authors would like to thank Director,
NRSC, for his guidance and support. India-WRIS website is
acknowledged for the groundwater data and the other water resource
data. The authours are grateful to the anonymous reviewers for their
constructive comments and suggestions which improved the quality
of the manuscript.
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