01
TREND ANALYSIS OF GROUND-WATER LEVEL TO ASSESS VULNERABILITY OF
GROUNDWATER IN CHITTAR SUB BASIN OF TAMIRABHARANI RIVER BASIN IN
TAMIL NADU STATE
Meena Sakthivel Pandian1, Sridhar N2, Chinju Saju3 and Athira P4
1234
Department of Agriculture Engineering,
Hindusthan College of Engineering and Technology, Coimbatore, India
Corresponding Author: sakthivelmeena1997@gmail.com

How to cite this paper:
Pandian Meena Sakthivel,
Sridhar N., Chinju Saju, andAthira P. (2022) Trend
Analysis of Ground-Water
Level to Assess Vulnerabilityof Groundwater in Chittar
Sub Basin of Tamirabharani
River Basin in Tamil Nadu
State, Journal of Global
Resources, Vol. 08 (02)DOI:
10.46587/JGR.2022.v08i02.001

Received: 03 March 2022
Reviewed: 25 April 2022
Revised: 11 May 2022
Final Accepted: 05 June 2022

Abstract: In Chittar Sub basin, agricultural, domestic andindustrial water use is highly dependent on groundwater
extractions. For meeting the increasing demand ofgroundwater resources, it is highly important to realize about
interrelation between agriculture, climate change impacts,
groundwater levels and other water resources for
agricultural purposes. Keeping this point, the study was
under taken in Chittar sub basin of Tamirabharani river
basin, Tamil Nadu for investigating the temporal trends ingroundwater level using the Mann–Kendall test and the
Sen’s slope estimator in the area during 1986–2020. The
results of the tests indicated a mix of negative and positive
trends in the groundwater level series. The positive trends
were found to be higher than the negative trends. The
statistical tests reported a significant increasing trend in 25
percent of the wells. Moreover, the highest number of wellswith significant positive trends occurred in zone 2 and zone
3.
Key words: Chittar Sub Basin, Groundwater Level,
Groundwater Vulnerability, Mann–Kendall
Test, Sen’s Slope Estimator

Journal of Global Resources, July 2022, Volume 08 (02) Meena S. Pandian, Sridhar N., Chinju Saju, Athira P. P a g e|

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Introduction
Groundwater has made significant involvement in increasing agricultural production andproductivity in India. Groundwater is the third largest reservoir of water resources in the world
and also largest reservoir of liquid fresh water on Earth. It is also a vital natural resource forhuman endurance and it provides drinking water to more than 2 billion people around the world(Doll P, 2019 & Famiglietti, J.S., 2014). Groundwater contribution in net irrigated area is about
61 percent (CWC, 2010). Groundwater is a major element for existence of life and livelihood
in many parts of the world, particularly where the use of surface water is constrained either
due to scantiness of quantity or quality or for deficiency of infrastructure for providing supply
from surface sources. However, escalating inhabitants and alterations in the dynamics ofgroundwater recharge have underway exerting stress on the accessibility of groundwater inmany regions where complacency about constant availability of groundwater have, until
recently, masked the need for technical investigation and evaluation for sustainably managing
this valuable reserve. In India, roughly 80 percent of people are dependent on groundwater fordomestic and irrigation purposes.
G. T. Patle et al. (2015) reported that enormous development and consumption ofgroundwater in various parts of India has contributed in diminution of fresh groundwater
resources and increase of grey and dark areas in the country. This resulted in rapid depletionof water level in these states. Rising demands of water for the agricultural, domestic andindustrial requirements will further pick up the pace of the tempo of water table decline. In
Tamil Nadu, the groundwater development in 2011 is 77 percent. Over the years, the utilisationof groundwater has augmented in areas where the resource was readily obtainable. The mostof the blocks in Chittar sub basin had been designated as “semi critical to over exploited” by
the Central Ground Water Board of India in its National Compilation of Dynamic Ground Water
Resources (CGWB, 2017).Here the category “over exploited” indicates that the overall
groundwater withdrawal is between 70 percent and 90 percent in Chittar sub basin. Over thelast decade, groundwater levels in open wells and bore wells indicate that groundwater has
been depleted in most of the blocks because of climate change, over exploitation and usageof water for Industries, agriculture and domestic purposes. Since more than 60 to 70 percentof the people living in this sub basin are farmers, in this connection the utilisation ofgroundwater for irrigation is higher.
Substantial researches have been done for the sustainable utilisation of groundwater
which includes Regression analysis, Time series method, Spectrum Analysis, Artificial Neural
Network, Gray Analysis, Dynamic Neural Network Models, Kernal Function methods etc. whichanalyses the atmospheric precipitation and groundwater (Xing et. al, 2018). Zeng et al. (2017),
Uyan & Gay (2013), Chang et al. (2017) etc. analysed groundwater levels scientifically byrelating spatial temporal patterns of groundwater based on regression analysis andgeostatistical modelling which depicted an interrelation between surface water andgroundwater. Adoption of time series methods such as auto-regressive moving-average(ARMA), auto-regressive integrated moving-average (ARIMA), seasonal auto-regressive
integrated moving-average (SARIMA) etc. in the prediction of groundwater fluctuations result
in better forecasting of groundwater levels [Cuthbert (2010), Mirzavand & Ghazavi (2015) andPatle et al. (2015)]. Errikson (1970), Liu & Sato (2012) and Shih (2017) employed spectral
analysis of groundwater levels to determine hydraulic diffusivity thereby estimating dynamic
groundwater levels. Mogaddam & Nadiri (2008), Trichakis et al. (2010) and Nourani et al.
(2015) found that by adopting Artificial Neural Networking (ANN), hydraulic heads can bepredicted deprived of knowledge of exact geometry of flow route which employs hydrologicalparameters such as rainfall and temperature. Gray models utilize the moving average of dayto-day infiltration depth, chronological groundwater level, and crop water requirement from
Journal of Global Resources, July 2022, Volume 08 (02) Meena S. Pandian, Sridhar N., Chinju Saju, Athira P. P a g e|2

irrigated paddy-field districts as inputs along with the characteristics of the sites found to be
one of the accurate methods [Kang et al. (2016)]. Difficulties in regional modelling can be
replaced by using Dynamic Neural Network Modelling [Chang et al. (2013) and Rajee &Ibrahimi (2014)]. By using Kernal function, rainfall –induced quick groundwater levels can bestudied (Jan et al. (2011)).
Most of the researches employ statistical methods varying from simple linear
regression to more sophisticated parametric and non-parametric methods (Helsel and Hirsch,2002). Among the statistical methods, the classical approaches, such as the Mann-Kendall
test, its seasonal variant, Sen’s Slope estimator, etc. have been extensively used for testing
trends in climatic and hydrological time series (Hirsch et al., 1982; Aziz and Burn, 2006; Thaset al., 2007). Pathak and Dodamani (2019) applied cluster analysis on long-term monthly
groundwater levels and carried out Mann-Kendall tests to explore annual and seasonal trendsof groundwater levels in the groundwater drought-prone Ghataprabha River basin of India.
Kumar et al. (2018) used Descriptive statistics, Modified Mann-Kendall Test and Sen’s
Slope Estimator for identifying trend of groundwater levels in alluvial aquifers of Uttar Pradesh
in India. Palte et al. (2015) used Mann-Kendall test and Sen’s Slope estimator for identifying
trends in pre-monsoon and post-monsoon groundwater levels in Karnal district of Haryana in
India over the period from 1974 to 2010.Thakur and Thomas (2011) applied non-parametricKendall’s rank correlation test in order to identify trends in groundwater level data and linear
regression test in order to identify the significance of the slope in the Sagar District of Madhya
Pradesh in India. Le Brocque et al. (2018) applied a modified Mann-Kendall test and Sen’s
Slope estimator to data of 381 groundwater bores in southern Queensland in Australia from1989 to 2015 in order to investigate annual trend of groundwater as well as the trend in distinct
wet and dry climatic phases.
Moreover, the existing sustainability of groundwater use in Tamirabharani Basin islargely unknown. Only few studies have been carried out related to groundwater in this basin.
So, the groundwater level trend that exists for the past 35 years in this sub basin has to bestudied. Hence, the objective of this study is to analyse the variability of groundwater levels ofChittar Sub basin in Tamirabharani river basin. Annual trend has been investigated from 1986
to 2020 on understanding the area’s groundwater level trends and variability. Understanding
the trend associated with groundwater level will helps to provide a guideline for the better
management of agriculture, irrigation and groundwater management related activities in Chittar
Sub basin.
METHODOLOGY
Study Area Description
The study area Chittar Sub Basin of Tamirabharani River lies between geographic coordinates
N. lat. 8 º 50’00” and 9 º 10’ 00” and E. long 77 º 10’00” and 77 º 30’00”. Chittar Sub basincovers the districts of Tirunelveli and Tenkasi at an area of about 1662 sq.km as shown in
Figure 1 (National Water Mission, Tamirabarani report, 2017). It is the second largest sub basinof Tamirabharani river basin. Ground water is the primary source of water for drinking,domestic and irrigation purposes since the river has a seasonal water flow only for 2-3 months(Walton, 1970). In this sub- basin, rainfall is the main source for groundwater recharge. The
pattern of precipitation is essentially of a tropical monsoon type where effect of the wintermonsoon is dominant. The average precipitation is 722.5 mm of which 60 percent of rainfall isreceived during North East Monsoon. The actual annual evapotranspiration is found to be
636mm (Jeyavel Raja Kumar et al, 2010).

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Figure 01: Map of Chittar Sub Basin

Stratification of Chittar Sub-Basin
The sub basin has tremendous changes in the status of groundwater largely due to thevariation in the quantity and distribution of rainfall on one hand and the availability ofsupplementation from various sources of irrigation on the other. The sub basin needed to be
stratified into Zones of homogeneity based on criteria, so that a meaningful evaluation of thecurrent groundwater trend and suggestions of alternatives is possible. The sub basin is
stratified into 3 Zones based on categorization of blocks as per Groundwater Resources
Assessment Report of Tirunelveli District as shown in Table 1 (CGWB, 2017).
Table 01: Categorization of Blocks in Chittar Sub basin
Zone

Categorization

Zone 1

Safe to semi critical

Zone 2Over-exploited

Zone 3

Semi Critical toOver – exploited

Well No.
93010A
93132
93078A
93011
93019A
93073
93076A
93077
93085
93018AY
93012
93083

Assessment Unit (Blocks)
Gundar, Kadayanallur, Shencottai, Tenkasi,
Ayyikudi, Kadayam

Veerasigamani, Kurukkalpalli, Uttumalai,
Surandai, Karuvanda, Alangulam,Keezhapavoor
Nettur, Manur, Gangaikondan, Thalayuthu,
Madhuvankuruchi, Tirunelveli, Palayamkottai,
Vannikonenthal

Data Collection
Observed monthly groundwater level data from 12 groundwater monitoring wells locations in
Chittar Sub basin for a period of 35 years were acquired from State Ground and Surface Water
Data Centre, Taramani. The dataset includes monthly groundwater level for 12 stations from1986-2020 (Figure 2 shows the 12 well locations). The data were used to detect groundwater
trends in Chittar Sub basin. The groundwater well locations were chosen based on the stations
were to have continuous record over the period 1986-2020 with less missing data. The reason
for taking 35 years data for analysis is that Climate change/temperature could take minimum
20 to 30 years to stabilize, according to the IPCC Working Group I report, 2021.Moreover thehouseholds in this Chittar sub basin have reported that they observed a shift in the groundwater
status from the year 1985 onwards. So, the period 1985 to 2020 has been selected for trend
analysis in groundwater level.

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Figure 02: Location of wells in Chittar Sub Basin (Ref: Google Earth Pro)

DATA ANALYSIS
Trend Analysis
Trend is defined as the general movement of a series over an extended period of time or it is
the long-term change in the dependent variable over a long period of time (Webber andHawkins, 1980). The Groundwater well data were statistically analysed for trends. A numberof parametric and non-parametric tests have been used for trend detection. Parametric trend
test requires data to be independent and normally distributed. On the other hand, a nonparametric test does not require the data to follow any particular statistical distribution and cantolerate outliers in the data (Hamed and Rao, 1998; Kisi and Ay, 2014). Hence, non-parametrictests are more suitable for non-normally distributed data, such as groundwater levels (Yue etal., 2002; Caloiero et al., 2011). The non-parametric Mann-Kendall test and Sen’s Slope
estimator are widely used in climatic and hydrological time series analysis. These two methods
have been used in the current study for analysing the trends of groundwater levels in selectedzone.
Mann-Kendall Test
The M–K test is a statistical nonparametric test widely used for trend analysis in climatologicaland hydrological time series data (Mann, 1945; Kendall, 1975 & Gilbert, 1987). The test,
suggested by Mann in 1945, has been extensively used with environmental time series. In thistest, the null hypothesis 𝐻0 assumes that there is no trend in the series whereas the alternative
hypothesis 𝐻𝑎 assumes that there is a trend in the series. Positive values of the test statistics
indicate that there is an increasing trend while negative test statistics values show that there isa decreasing trend. Testing trends are done at the specific α significance level.
Sen’s Slope Estimator
Sen’s slope estimator is used to robustly fit a linear model as shown in Equation 1 (Sen, 1968).
𝑓𝑡 = 𝑄𝑡 + B------------- 1
Where,Q = Slope, B = constant, to n number of sample points by choosing the median of the slopesof all lines through pair of points, and hence to estimate the true slope (change per unit time)
if a linear trend is present in a time series. A positive value of Sen's slope indicates an upward
or increasing trend and a negative value gives a downward or decreasing trend in the timeseries.
In this study, Mann-Kendall (MK) test and Sen’s estimator are used to detect the natureof trends using XLSTAT 2021.1.1.1089 software. Mann-Kendall’s method is adopted to test

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5the presence of a monotonic trend and the statistical significance of the trend and Sen’s slope
estimator for estimating true change per year in the time series.
Hypothesis Testing
Hypothesis testing is a strategy for determining whether a specific treatment has an effect on
the individuals in a population (Soren Johansen, 1991) & (Stattrek, 2017). Statisticians utilize
formal processes to accept or reject statistical hypotheses. Examining the entire population isthe best technique to see if a statistical hypothesis is correct. Because this is frequently
impractical, researchers usually look at a random sample of the population. The hypothesis isrejected if the sample data do not support the statistical hypothesis.
Symbolically, these hypotheses would be expressed as
Null Hypothesis: H0: P = 1/2
Alternate Hypothesis: Ha: P ≠ 1/2
Steps in Hypothesis Testing
Based on sample data, statisticians use a formal approach to decide whether to reject
a null hypothesis. This procedure, known as hypothesis testing, consists of four steps(Stattrek, 2017).
a. State the hypotheses: The null and alternative hypotheses must be stated. The
hypotheses are expressed in a way that makes them mutually exclusive. That is to say,
if one is correct, the other must be incorrect.
b. Formulate an analysis plan: The analysis plan explains how to test the null hypothesis
with sample data. The evaluation is frequently centered on a single test statistic.
c. Analyze sample data: Determine the value of the test statistic provided in the analysis
plan (mean score, proportion, t statistic, z-score, and so on).
d. Interpret results: Using the decision rule provided in the analysis plan to make adecision. On the basis of the null hypothesis, reject the null hypothesis if the value ofthe test statistic appears implausible.
Results and Discussion
Trend Analysis of Chittar sub basin has been done with 35 years Groundwater level data from1986-2020. Mann-Kendall and Sen’s Slope Estimator has been used to determine the trend.
The following figures 3, 4 and 5shows the trend graphs of observation wells in Zone 1, Zone 2
and Zone 3 respectively. Mann- Kendall test results for the groundwater level data as shownin Table 2.
Mann- Kendall trend test and Sen’s slope test was done for groundwater level.
The test was conducted as two-tailed Mann-Kendall test. The null hypothesis (𝐻0 )and thealternate hypothesis (𝐻𝑎 ) were framed for the test as shown below.
• 𝐻0 : There is no trend in the series• 𝐻𝑎 : There is a positive trend in the series
The test was conducted at 95 percent significance level (i.e., α = 0.05)
If the computed p-value is greater than the significance level α =0.05, one should accept thenull hypothesis 𝐻0 , and reject the alternative hypothesis 𝐻𝑎 .
• Sen’s slope value 𝑄𝑖 (-ve) indicates a decreasing trend.
• Sen’s slope value 𝑄𝑖 (+ve) indicates an increasing trend.

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Figure 03 (a-c): Groundwater Level Trend Graphs of Observation Well in Zone
Figure 04 (a-c): Groundwater Level Trend Graphs of Observation Well in Zone 3

Figure 4 (a-f): Groundwater Level Trend Graphs of Observation Well in Zone 2
Figure 5 (a-f): Groundwater Level Trend Graphs of Observation Well in Zone 2

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Table 02: Mann-Kendall Test Results for the Groundwater Level Data#

Zone

Well No.

Kendall's tau

Var (S)p-value

Alpha

Sen's
Slope (Qi)

Test
Interpretation

1.

Zone 193010A-0.2874958.333

0.016

0.05

-0.044

H0 rejected2.

Zone 1

93132

0.244

4958.333

0.041

0.05

0.030

H0 rejected

3.

Zone 193078A-0.1364958.333

0.256

0.05

-0.012

Ha rejected

4.

Zone 2

93011

0.066

4958.333

0.589

0.05

0.034

Ha rejected

5.

Zone 293019A

0.207

4958.333

0.083

0.05

0.042

Ha rejected

6.

Zone 293073-0.0394958.333

0.755

0.05

-0.014

Ha rejected

7.

Zone 293076A

0.371

4958.333

0.002

0.05

0.105

H0 rejected

8.

Zone 2

93077

0.109

4958.333

0.363

0.05

0.040

Ha rejected

9.

Zone 2

93085

0.160

4958.333

0.182

0.05

0.057

Ha rejected

10.

Zone 393018AY

0.388

4958.333

0.001

0.05

0.154

H0 rejected

11.

Zone 393012-0.0184958.333

0.887

0.05

-0.005

Ha rejected

12.

Zone 3

93083

0.234

4958.333

0.051

0.05

0.065

Ha rejected

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Trend
Significant
Decreasing trend
Significant
Increasing trend
Insignificant
Decreasing trend
Insignificant
Increasing trend
Insignificant
Increasing trend
Insignificant
Decreasing trend
Significant
Increasing trend
Insignificant
Increasing trend
Insignificant
Increasing trend
Significant
Increasing trend
Insignificant
Decreasing trend
Insignificant
Increasing trendMann-Kendallof the Computed Data
From the table 2, P values of well No. 93010A, 93132, 93076A, 93018AY are lesser than 0.05.
So, 𝐻𝑎 should be accepted. It indicates that there exists a positive trend in the series. The P
values of remaining wells are greater than 0.05. So, 𝐻0 should be accepted. It indicates that
there exists no trend in the series. The value of well No. 93011, 93019A, 93077, 93085 and
93083 shows positive trend but not significant at 5 percent significant level since the computed
value (p-value (0.589)>0.05), (p-value (0.083)>0.05), (p-value (0.363)>0.05), (p-value(0.182)>0.05) and (p-value (0.051)>0.05) respectively.
The value of well No. 93078A, 93073 and 93012 shows negative trend but notsignificant at 5 percent significant level since the computed value (p-value (0.256)>0.05), (pvalue (0.755)>0.05) and (p-value (0.887)>0.05) respectively. The value of well No. 93132,
93076A and 93018AY shows positive trend and significant at 5 percent significant level since
the computed value (p-value (0.041) <0.05), (p-value (0.001) <0.05) and (p-value (0.001)<0.05) respectively. The value of well No 93010A shows negative trend and significant at 5
percent significant level since the computed value, p-value (0.016), is less than 0.05.
Sen’s Slope of the Computed Data
The 𝑄𝑖 values of well No. 93132, 93011, 93019A, 93076A, 93077, 93085, 93018AY and 93083
are positive which indicates the increasing trend at the rate of 0.030/year, 0.034/ year,
0.042/year, 0.105/ year, 0.040/year, 0.057/year, 0.154/year and 0.065/year respectively.
Whereas the 𝑄𝑖 values of remaining well No. 93010A, 93078A, 93073 and 93012 are negative
which indicates the decreasing trend at the rate of -0.044/year, -0.012/year, -0.014/year and 0.005/year respectively. For all the wells, Sen’s slope values agree with the Mann-Kendall
values.
Recommendations• The augmentation methods such as ponding over large area, check dams, modificationof village tanks as recharge structures, recharge shafts, injection wells have to be
implemented for urgent increase the groundwater status in Zone 1 since Zone 1 ishighly vulnerable to groundwater depletion.
• Overexploitation of groundwater should be minimized by enacting strict groundwater
laws in Chittar Sub basin for ensuring sustainability in groundwater resources.
• Government and non-government bodies should implement percolation tanks for filling
the groundwater in various blocks of Chittar Sub basin.
• Farmers in Chittar sub basin should be made aware of the farm ponds for recharging
the groundwater.Farmers should be encouraged to access various technical andfinancial support available to take up the farm pond construction in their farm.
• The rainwater harvesting should be made mandatory for all the households in Chittar
sub basin for improving the overall groundwater status.
Conclusion
In this study, we analysed the overall groundwater level changes through Mann- Kendall andSen’s Slope in Zone 1, Zone 2 and Zone 3 during the year 1986 to2020.There is a tendencyof groundwater depletion in Zone 1 and augmentation of groundwater level in Zone 2 and Zone
3. The augmentation of groundwater resources in Zone 2 and Zone 3 might be due to theproper management of groundwater through artificial recharging structures. Similarly, thedepletion of groundwater in Zone 1 might be due to the lack of surface flow, overexploitationof the groundwater resources, poor groundwater management practices and climate change.

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9The analysis indicates that there is a need of implementation of proper groundwater
management practices in zone 1 for improvising groundwater level to avoid water scarcity inthat area. Overall, from the analysis, it is also understood that the groundwater level over theyear showed variability in the Chittar Sub basin and hence agricultural productivity can behampered by groundwater level changes.
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Journal of Global Resources, July 2022, Volume 08 (02) Meena S. Pandian, Sridhar N., Chinju Saju, Athira P. P a g e|

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