Time Trend Analysis of Rainfall and Geostatistical Modelling of Groundwater level depth in and around IIT(ISM) Dhanbad

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Contents
Certificate
Declaration
Acknowledgement
Abstract
Content
List of Table
List of Figure
Chapter 1 ........................................................................................................................................1
Introduction.................................................................................................................................1
1.1 Background........................................................................................................................1
1.2 Study Area .........................................................................................................................2
1.3 Objectives of the work .......................................................................................................2
1.4 Methodology......................................................................................................................4
Chapter 2 ......................................................................................................................................10
Geology of IIT (ISM) campus and its surrounding.....................................................................10
2.1 Background......................................................................................................................10
2.2 Physiography and Drainage of IIT (ISM) campus.............................................................11
2.3 Geomorphology ...............................................................................................................11
2.4 Hydrogeology ..................................................................................................................12
Chapter 3 ......................................................................................................................................14
Climate of the Dhanbad district and time trend analysis of rainfall.............................................14
3.1 Background......................................................................................................................14
3.2 An introduction to time trend analysis of rainfall..............................................................15
3.3 Rainfall data base organization.........................................................................................17
3.4 Time Trend Testing on rainfall data in Dhanbad city ........................................................18
3.5 Time Trend Analysis pattern for Annual mean, pre-monsoon, monsoon and post-
monsoon ................................................................................................................................23
3.6 Results and Discussion.....................................................................................................32
Chapter 4 ......................................................................................................................................33
Rainwater Harvesting ................................................................................................................33
4.1 Background......................................................................................................................33
4.2 An introduction to artificial recharge pit...........................................................................36
4.3 Need for augmentation of groundwater resource in IIT (ISM) campus..............................37

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Chapter 5 ......................................................................................................................................39
Ground water data base organization and statistical analysis......................................................39
5.1 Background......................................................................................................................39
5.2 Statistical analysis of groundwater level data....................................................................40
5.3 Results and Discussion.....................................................................................................49
Chapter 6 ......................................................................................................................................50
Geostatistical Modelling of Groundwater of pre-monsoon, post-monsoon and fluctuation .........50
6.1 Background......................................................................................................................50
6.2 Semi-variogram modelling for pre, post and fluctuation between pre and post monsoon
for the year 2015 and 2016.....................................................................................................51
6.3 Block Grids Delineation...................................................................................................66
6.4 Ordinary Kriging..............................................................................................................67
6.5 Results and Discussion for the year 2015..........................................................................92
6.6 Results and Discussion for the year 2016..........................................................................93
Chapter 7 ......................................................................................................................................94
Ground Water Resource Assessment .........................................................................................94
7.1 Estimation of Ground Water Supply for the year 2015 and 2016 in the campus................94
7.2 Groundwater Resources Estimation Methodology ............................................................96
7.3 Groundwater Recharge in monsoonal and non-monsoonal season ....................................98
7.4 Total Annual Groundwater Recharge for the year 2015 and 2016.....................................99
7.5 Results and Discussion...................................................................................................101
Chapter 8 ....................................................................................................................................103
SUMMARY AND CONCLUSION.........................................................................................103
References

1
Chapter 1
Introduction
1.1 Background
Water is one of the most important natural resource for survival of human life and the main
source of water in any area is rain. The amount or availability of water for various process is
very much dependent upon the amount of precipitation in that particular area.
Indian Institute of Technology (Indian School of Mines) campus is a part of Dhanbad city.
There is a continuous increase in demand of water in the IIT (ISM) campus, due to the increase
in number of student, faculty, staff-member, additional construction of hostels, residential
complexes and lecture hall. In the campus, consumption of groundwater has grown by many
fold within a short span of time. Presently, supply of water in the campus is only through
overhead storage tanks that are filled with water that is pumped from the subsurface using
submersible pumps. This has exerted big pressure on local aquifer(s) of the campus. There is a
connection between rainwater and groundwater, rainwater augments the groundwater by the
process of infiltration. In the campus process of infiltration is slow, because of two reasons:
first reason is created by nature i.e. subsurface of the campus constituted by pre-cambrian
metamorphic rock which has only secondary permeability like fractures and joints and second
reason is artificial, due to weak interaction between rainwater and groundwater caused by
extensive laying of concrete and cement on the surface following the construction in the
campus. When rainfall intensity is high then the above causes lead to high surface runoff and
most of rainwater drain away from the aquifers. Therefore, it is important to assess the quantity
of groundwater and rainwater and arrange a proper way to maintain the groundwater level in
the campus. It was proposed to construct various artificial recharge pits throughout the campus
and feeding them with rainwater that is collected from roof tops, subsequently passing on to the
subsurface fractures and finally collecting at the aquifer(s). Altogether a total number of 54
recharge pits were constructed in the campus. Geographic locations of the recharge pits within
the campus are shown in Figure 1.1. It is clear that rainwater is the only source to recharge the
aquifer(s) and it becomes necessary to analyse the behaviour of rainfall on different temporal
scale and estimate the volume recharge of the aquifer (s) in the campus.

2
1.2 Study Area
The study area for this thesis work is focused on Indian Institute of Technology (Indian School
of Mines) campus and located in the city of Dhanbad. The IIT (ISM) campus is a small part of
the Dhanbad city and areal extent of the study area is 250 acres. IIT (ISM) campus is bounded
between 23049’16” N, 86026’06” E and 23048’36” N, 86026’55” E with an average elevation
of 247.314m above mean sea level and is included in Survey of India Topographic map number
73I/5. There are altogether 54 artificial recharge pits constructed in the campus of IIT(ISM)
Dhanbad whose geographic location is shown in Figure 1.1.
1.3 Objectives of the work
Objectives of the present study includes two vital steps. First step includes analysis of time
trend analysis of rainfall using rainfall data for the period 1901 to 2016. Time trend analysis
provides an idea about the past rainfall and with the help of this one can forecast future rainfall.
Second step includes evaluation of spatial fluctuation in groundwater levels using geostatistical
methods based on data from 54 borewells (used as recharge wells in the recharge pits) for the
period 2015-2016 within the campus of IIT (ISM) Dhanbad. Various aims of the study:
(i) Time trend analysis of rainfall in Dhanbad district;
(ii) Geostatistical modelling of pre- and post- monsoon groundwater levels and their
fluctuations within the IIT(ISM) campus for the years 2015 and 2016;
(iii) Spatial and temporal variation of the groundwater levels;
(iv) Estimation of groundwater flow and groundwater balance;
(v) Groundwater resource assessment.

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Fig.1.1 Geographic locations of recharge pits in the campus of IIT(ISM) Dhanbad

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1.4 Methodology
1.4.1 Time Trend Analysis
There are two different methodologies adopted for present thesis work. First methodology employed
was the time trend analysis of rainfall and second methodology adopted was the geostatistical
modelling of groundwater level for pre-monsoon, post-monsoon and fluctuation between pre and post
monsoon. Mann Kendall and Sen’s slope estimator test is applied for time trend series analysis of
rainfall. Mann Kendall test is a non-parametric test for identifying trends in time series data. The test
compares the relative magnitudes of sample data rather than the data values themselves
(Gilbert,1987). One advantage of this test is that the data need not confirm to any particular
distribution. The second advantage of the test is its low sensitive to abrupt breaks due to in
homogeneous time series (Jaagus,2006). According to Mann-Kendall test, the null hypothesis Ho
states that the data (x1,,,,,,xn) is a sample of an independent and identically distributed random
variables. The alternative hypothesis, H1 states that the distributions of Xk and Xj are not identical
for all k, j ≤ n with k ≠ j. The test statistic S, which has mean zero and a variance computed by
Equation (3), is calculated using Equation. (1) and (2), and is asymptotically normal.
∑ ∑ 𝑠𝑔𝑛 (𝑥𝑗 − 𝑥𝑖)
𝑛
𝑗=𝑖+1
𝑛−1
𝑖=1 (1)
Where sgn is
𝑠𝑔𝑛 = {
1 𝑖𝑓 (𝑥𝑗 − 𝑥𝑖) > 0
0 𝑖𝑓 (𝑥𝑗 − 𝑥𝑖) = 0
−1 𝑖𝑓 (𝑥𝑗 − 𝑥𝑖) < 0
(2)
Variance 𝑉(𝑠) = 𝑛(𝑛 − 1)(2𝑛 + 5) − ∑ 𝑡𝑖(𝑡𝑖 − 1)(2𝑡𝑖 + 5)/18
𝑛
𝑖=1 (3)
where n is the number of data points, m is the number of tied groups (a tied group is a set of sample
data having the same value), and ti is the number of data points in the i th group. the standard normal
variable Z is computed by using equation (4)
Zmk=
{
𝑆−1
√𝑣𝑎𝑟(𝑠)
, 𝑤ℎ𝑒𝑛 𝑠 > 0
0, 𝑤ℎ𝑒𝑛 𝑠 = 0
𝑆−1
√𝑣𝑎𝑟(𝑠)
, 𝑤ℎ𝑒𝑛 𝑠 < 0
(4)

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Positive value of Z indicates increasing trends, while negative values of z show decreasing trends,
when testing either increasing or decreasing monotonic trends at α significance level. In research
significance level α=0.05 is applied. In Table 1.1 has been shown the decision about the hypothesis.
Table:1.1 Table of error types (Sheskin and David, 2004)
(negative) value of Zmk indicates that the data trend to increase (decrease) with time.
Suppose we want to test the null hypothesis-
1- Ho: No monotonic trend versus the alternative hypothesis.
Hα: Upward monotonic trend at the type 1 error rate α, where 0< α<0.5 (α is the tolerable
probability that Mann-Kendall test will falsely rejected the null hypothesis).
Then Ho is rejected and Hα is accepted if |Zmk|≥Z1- α|, where Z1- α is the 100(1- α)th
percentile
of the standard normal distribution. These percentiles are provided in many statistical
software package like R trend software and excel state.
2- Next to test Ho: No monotonic trend versus the alternative hypothesis.
Hα: downward monotonic trend at the type 1 error rate α, Ho is rejected and Hα is accepted
if |Zmk|≥-Z1- α|.
3- Next to test Ho: No monotonic trend versus the alternative hypothesis.
Hα: upward or downward monotonic trend at the type 1 error rate α Ho, is rejected and Hα is
accepted if |Zmk|≥-Z1- α/2| where the vertical bars denote the absolute value.
Besides, Mann-Kendall test Sen’s slope estimator (K.Drapela, I.Drapelova 2011) test is also test for
rainfall data. According to Sen’s slope estimator if a linear trend is present then the true slope (change
per unit time) can be estimated by using a simple non-parametric procedure develop by Sen’s (1968).
This can be linear model f(t) can be described as
Table of error types Null hypothesis
True False
Decision about
Null
Hypothesis
Ho
Reject Type 1 error (False
Positive)
Correct inference (True
Positive)
Fail to
Reject
Correct inference (True
Negative)
Type 2 error (False
Negative)

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F(t)= Qt+B
Where Q is the slope and B is a constant.
To desire an estimate of the slope Q, the slopes of all data pairs are calculated-
𝑄 =
𝑋𝑖 − 𝑋𝑘
𝑗 − 𝑘
i= 1,2,3…………………..N, j>k.
The sen’s slope estimator of slope is the median of the N values of Qi. The N values of Qi are ranked
from the smallest to the largest. Sen’s slope is computed on excel state software.
1.4.2 Geostatistics
Second methodology adopted is for the geostatistical modelling of the groundwater level. In present
thesis work second objective is focused on the 54 recharge pits of groundwater. These recharge pits
(borewell) are monitored from the year 2014 and on the basis of monitoring data of the year 2015
and 2016 temporal and spatial modelling of groundwater level is done by using geostatistics.
Measurement of Ground water levels were carried out for pre-monsoon, monsoon and post-monsoon
periods of the year 2015 and 2016. Statistical and geostatistical methods were applied suitably for an
understanding of population and spatial characteristics of the aquifer with reference to the
groundwater recharge from rooftop rain water harvesting structures built in the campus of IIT (ISM)
Dhanbad. Parameters such as Mean, Standard Deviation, Skewness and Kurtosis have been computed
to gain an understanding of the population characteristics. Geostatistical theory is based on a
stochastic model which allows the derivation of optimal predictions at random points in the
considered region. It allows us to take into account spatial correlation between neighbouring
observations and includes different approaches spanning from conditional estimator to simulation,
either parametric or indicator approach (Wameling 2003; Castrignano et al. 2008). Advantage of
geostatistics is the use of quantitative measures of spatial correlation, commonly expressed by
variogram (Diodato and Ceccarelli 2005). The semivariogram is a fundamental tool in geostatistics.
The empirical semivariogram ϒ(h) is defined as half the average quadratic difference between two
observations of a variable separated by a distance vector h (Journel and Huijbregts 1978). It is
calculated according to the following formula.
ϒ(h)=
1
2𝑁(ℎ)
∑ [𝑍(𝑥𝑖) − 𝑍(𝑥𝑖 + ℎ)]
𝑁(ℎ)
𝑖=1
2

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Here, ϒ(h) means the semivariogram value at the distance, h; N(h) means the total number of the
variable pairs separated this distance, and Z(x) means the value of the variable. Before the
geostatistical estimation, a semivariogram is calculated for classes of distance between sample pairs.
In present work the most widely used models are spherical model (Isaaks and Srivastava 1989).
Spherical model is one of the most frequently used models in geostatistics and good choice when the
nugget variance is important but not too large, and there is a clear range and sill. The exponential
model is a good choice when there is a clear nugget and sill, but only a gradual approach to the range.
If the variance is very smooth and the nugget variance is very small compared to the spatially
dependent random variation, then the semivariogram can often best fitted with Gaussian model
(Sunila and Kollo 2007). A pure nugget effect model is a special degenerate case of a transitive
semivariogram with an infinitesimal range the semivariogram surges directly from 0 to a constant
value (Yarus and Chambers 1994). The validation and the sufficiency of the developed model
semivariogram can be tested via a technique called cross validation. The most appropriate
semivariogram is selected on trial and error basis depending on the highest correlation coefficient
(R2
). The utmost appropriate semivariogram was selected constructed on the highest correlation
coefficient by trial and error technique. Kriging is a meticulous interpolation estimator technique
used to find the finest linear unbiased estimate. The best linear unbiased estimator essentially should
have minimum variance of estimation error. Among the different kriging methods, we used ordinary
and universal kriging for spatial and temporal analysis, respectively. Ordinary and universal kriging
methods are mainly applied for datasets without and with a trend, correspondingly. Detailed
deliberations of Kriging methods and their metaphors can be found in Goovaerts (1997). The
universal equation of linear kriging estimator is:
Z*
(xp)=∑ ƛ𝑖𝑍(𝑥𝑖)
𝑛
𝑖=1
In order to attain unbiased estimations of ordinary Kriging the following set of equations have to be
solved concurrently.
{
∑ ƛ𝑖ϒ(𝑥𝑖, 𝑥𝑗) − µ = ϒ(𝑥𝑖, 𝑥)
𝑛
𝑖=1
∑ ƛ𝑖 = 1
𝑛
𝑖=1
where Z * (Xp) is the kriged value at position xp, Z * (Xi) is the known value at location xi, λi is the
weight associated with the data, μ is the Lagrange multiplier, and ϒ(𝑥𝑖, 𝑥𝑗) is the value of semi-

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variogram corresponding to a vector with derivation in xi and extremity in Xj. The general equations
of unbiased universal kriging which must be solved concurrently are as follows.
{
∑ ƛ𝑖ϒ(𝑥𝑖, 𝑥𝑗) − ∑ µ𝑓(𝑥𝑖) = ϒ(𝑥𝑖, 𝑥)
𝑛
𝑖=1
𝑛
𝑖=1
∑ ƛ𝑖 = 1
𝑛
𝑖=1
∑ ƛ𝑖𝑓(𝑥𝑗) = 𝑓(𝑥)
𝑛
𝑖=1
Where f (x) is the type of function used to model the trend and is directly suggested by the physics
of the problem (Goovaerts, 1997). The gexsys software developed by Dr B.C.Sarkar was used for
geostatistical analysis in this study. Recorded data for each bore well consists of monthly
groundwater lavel were measured for all bore wells for the year 2015 and 2016. Here some values
are omitted as being considered completely erroneous values. Though, there exist some outliers or
extreme values which are not removed from the data set since according to Goovaerts (1997) in
environmental applications large values may indicate potentially critical points so they should be
removed only if they are clearly wrong. Work flow chart of overall study is shown in Figure 1.2.

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Fig. 1.2 Workflow of the overall study
Collection of Dhanbad Rainfall
Data (1901-2016) from IMD
website and Dhanbad Agriculture
Office
Time Trend Series Analysis of
Rainfall (on annual basis and for
Pre-, Post- and Monsoon Periods)
Groundwater level measurements in
54 Artificial Recharge Well
Statistical Analysis of
Groundwater well data from
the year 2015 to 2016
Geostatistical Analysis of
groundwater well data from the
year 2015 to 2016
Semi-variogram and Kriging of
groundwater level data for pre,
post and their fluctuation
Groundwater level structure and
flow direction map and estimation
of groundwater recharge

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Chapter 2
Geology of IIT (ISM) campus and its surrounding
2.1 Background
The geology of Indian Institute Technology (Indian School of Mines) Campus is a part of the geology
of Dhanbad urban area. The area is a part of Chotanagpur Gneissic Complex and is characterized by
a diverse assemblage of igneous and metamorphic rocks. In general, the succession of the various
rock groups are as follows.
 Soils and recent sediments.
 Coal Bearing Gondwana Group of rocks.
 Gabbro, dolerite (Intrusions).
 Pegmatite, and leucogranite (intrusions).
 Megacryst porphyritic granite (intrusions).
 Quartzo-feldspathic gneisses with mafic enclaves (Basement) equivalent to Chotanagpur
Gneissic complex.
The outcrops of these rocks are variably found scattered around IIT(ISM) within 7-8 km radius. The
Chotanagpur gneissic complex is an assemblage of quartzo-feldspathic gneisses with augen. structure
that are coarse to very coarse and occasionally inter-banded with mafic bands and lenses. These are
metamorphosed to medium grade. These rocks are well exposed in the Khudia Nala section north of
Govindpur and also along the railway cutting sections near Pradhankhanta. These are deformed into
early reclined fold that are refolded into WSW-ENE trending upright sub-vertical folds. All rocks
described above are traversed by fractures, joints and faults of different scales. The schistosity and
gneissosity of granites and basement gneisses respectively favours the directional passages of ground
water. Faults, joints are also important for infiltration and circulation.

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2.2 Physiography and Drainage of IIT (ISM) campus
Variable and undulating. Upper/lower ground, two ridges (Figure 2.1) north and south of Admin
block downing eastward are HOGBACK structures. Depression towards Housing colony from UGC
colony represents a Turtle back structure. Sudden depression 2.5-3m west of old generator room is a
TERRACE structure formed by enormous weathering of dolerites. IIT (ISM) has undulating irregular
topography. From localized central highs, precipitation diverges down slopes, accumulates in
lowlands (such as drains /nalas lying in Teacher colony and other flowing west of Ruby hostel &
Shanti Bhaawan. The former directly falls to ditches in North while the latter takes a windy turn to
conformity with strike & oblique joints exposed in nala section and moves to join same depression
as nala no. 1. Even during dry months, these nalas are estimated to drain down at least 75,000
(previous study) litres of waste water. The depression to which they pour out occupies the
downthrown side (DTS) of fault with throw of 50 cm (east of seismic observatory) to about 10m
North of N Type quarters. The westerly flows from LHC, EMM block & adjoining areas goes
abruptly down through arable lands to nearby ponds. Due to construction east of Ruby hostel a major
drainage suffered serious compression and loss of water table.
2.3 Geomorphology
Existence of plateau type topography which covers a major part of the IIT (ISM) campus indicates
that a long phase of denudation and peneplanation. Occurrence of linear ridges has resulted into a
local uneven landscape. Humid tropical climate, jointed and fractured basement rocks may be the
main controlling factors for weathering. Humid tropical climate might have led to the formation of
thick weathered basement and whitish red colour soil formation.

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Fig. 2.1 Contours showing the ridge
2.4 Hydrogeology
The campus of ISM is green and is well vegetated inside. In general, the ground surface within IIT
(ISM) campus does not show much of outcrops of rocks except for some outcrops of metamorphic
rocks in the North western part of the campus. Igneous rocks occur beneath a thin veneer of soil cover
as intrusive body. Since there are no sedimentary formations beneath campus, there is no primary
aquifer in a true sense. The secondary openings in the metamorphic rocks in the form of joints,
fractures, and faults in the hard rocks act as a media for ground water circulations and act as aquifer.
During rainy seasons these secondary openings get recharged through infiltration from open grounds.
In recent years, the per-capita consumption of groundwater has increased many folds due to increase
in intake of students and multifaceted expansion programs that include construction of new buildings,
hostels, residential complexes, beautification of the campus etc. Through such activities the open area
available for infiltration has decreased substantially. Increased use and decreased infiltration have
produced additional stress in the present aquifer leading to decline of water levels in dug wells as

13
well as bore wells in the close vicinity of the pumping bore wells. In this hydro-geological situation,
there is an urgent need of artificial recharge to rejuvenate groundwater domain.

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Chapter 3
Climate of the Dhanbad district and time trend analysis of rainfall
3.1 Background
Dhanbad has an average elevation of 227 m (745 ft). Its geographical length (extending from north
to south) is 15 miles (24 km) and the breadth (stretching across east to West) is 10 miles (16 km). It
shares its boundaries with West Bengal in the eastern and southern part, Dumka, and Giridih in the
North and Bokaro in the west. Dhanbad comes under the Chota Nagpur Plateau. The climate of
Dhanbad district is very pleasant, especially in the cold weather months November to February
during, which the temperature varies from the lowest minimum of 47° F to the highest maximum of
340 C. Dhanbad features climate that is transitional between a humid subtropical climate and a
tropical wet and dry climate. Summer starts from the last week of March and ends in mid-June. Peak
temperature in summer can reach 48 °C. Dhanbad also receives heavy rainfall. In winter, the
minimum temperature remains around 10 °C with a maximum of 22 °C. Damodar River is the main
river flowing through the district. Katri, Jamunia, Gobai, Khudia and Irji are the other rivers flowing
through the district. The average annual rainfall of the area is 1340 mm most of which is precipitated
during the rainy season – middle of June to the middle of October. The rainfall around Parasnath hills
is reported to be more than the average (source http://www.dhanbad.nic.in). State wise rainfall map
(source map http://hydro.imd.gov.in) is shown in the Figure 3.1.

15
.
Fig. 3.1 State wise rainfall map of India (source http://hydro.imd.gov.in)
3.2 An introduction to time trend analysis of rainfall
The detection, estimation and prediction of trends and associated statistical and physical significance
are important aspects of climate research. The main theme of time trend series analysis is to observe,
temporal variation in annual and monthly rainfall in any region. This analysis is essential to provide
input data for a management system and to enable the development of optimal water allocation
policies and management strategies to bridge the gap between water needs and obtainable water
supply under possible drought conditions. The Intergovernmental Panel on Climate Change (IPCC)
defines climate as “the average weather in terms of the mean and its variability over a certain time-
span and a certain area” and a statistically significant variation of the mean state of the climate or of
its variability lasting for decades or longer, is referred to as climate change. Water is one of our most

16
valuable natural resources and vital to all forms of life. Water is also used for transportation, is the
source of power, and serves many other useful purposes for domestic consumption, agriculture, and
industry. The main important source of water in any area is rain. The amount or availability of water
for various purposes is very much depending upon the amount of precipitation in that particular area.
Excess or extended absence of rainfall will cause flooding and drought, respectively. Adler et al.
Stated that precipitation information is essential for understanding the hydrologic balance on a global
scale and for understanding the complex interactions among the components within the hydrologic
cycle Rainfall is the meteorological phenomenon that has the greatest impact on human activities and
the most important environmental factor limiting the development of the semiarid regions (E. C.
Kipkorir et al). Understanding rainfall variability is essential to optimally manage the scarce water
resources that are under continuous stress due to the increasing water demands, increase in
population, and the economic development (S. Herath and U. Ratnayake). There are many aspects of
water resources management, including the optimal water allocation, quality assessment and
preservation, and prediction of future water demands to strategic water utilization, planning, and
decision making. As a preliminary step, these management aspects and others necessitate the
characterization of the water sources in the area of interest. One of the established methods to carry
out this assessment is through the time trend series analysis of the spatial and temporal variability of
rainfall. In regions that have heavy agricultural areas and undergo dense activities, water availability
and shortage challenges are further exacerbated.
The Intergovernmental Panel on Climate Change (IPCC) estimates that the global mean surface
temperature has increased 0.6 ± 0.2 0
C since 1861, and predicts an increase of 2 to 4 0
C over the next
100 years. Temperature increases also affect the hydrologic cycle by directly increasing evaporation
of available surface water and vegetation transpiration. Consequently, these changes can influence
precipitation amounts, timings, and intensity rates, and indirectly impact the flux and storage of water
in surface and subsurface reservoirs (i.e., lakes, soil moisture, groundwater). The greater variability
in rainfall could mean more frequent and prolonged periods of high or low groundwater levels, and
saline intrusion in coastal aquifers due to sea level rise and resource reduction. Groundwater
resources are related to climate change through the direct interaction with surface water resources,
such as lakes and rivers, and indirectly through the recharge process. The direct effect of climate
change on groundwater resources depends upon the change in the volume and distribution of
groundwater recharge. Therefore, quantifying the impact of climate change on groundwater resources
requires not only reliable forecasting of changes in the major climatic variables but also the accurate
estimation of groundwater recharge. The amount of water stored in the soil is fundamentally
important to agriculture and has an influence on the rate of actual evaporation, groundwater recharge,

17
and generation of runoff. The local effects of climate change on soil moisture will vary not only with
the degree of climate change but also with soil characteristics. The water-holding capacity of the soil
will affect possible changes in soil moisture deficits; the lower the capacity, the greater the sensitivity
to climate change. Climate change also may affect soil characteristics, perhaps through changes in
waterlogging or cracking, which in turn may affect soil moisture storage properties. Infiltration
capacity and water-holding capacity of many soils are influenced by the frequency and intensity of
freezing.
3.3 Rainfall data base organization
Available data consists of annual and monthly rainfall time series from the year 1901 to 2016 of
Dhanbad district and covering a region of about 2074.68 km2
. The rainfall data for the period 1901
to 2011 was obtained from Indian Meteorological Division (IMD) website and rest of the data (2001-
2016) from the Dhanbad agriculture office. The database was constructed by using the database
program Microsoft EXCEL and a .bln format file was created on ‘Notepad++’. Rainfall data was
recorded in units of millimetre of rainfall. For the purpose of time trend analysis, the 110-year period
of 1901 to 2011 was divided in to 11 equal intervals of 10 years each, while for the last period of
2011 to 2016 six-year period was considered. Rainfall data was collected from the atmosphere by an
instrument called rain gauge meter. A rain gauge (also known as a udometer, pluviometer, or an
ombrometer) is an instrument to gather measure the amount of liquid precipitation over a set period
of time. A rain gauge meter is shown in the Figure 3.2.

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Fig. 3.2 Rain gauge meter
3.4 Time Trend Testing on rainfall data in Dhanbad city
A trend analysis is an aspect of technical analysis that tries to predict the future movement of on past
data. Trend analysis is based on the idea that what has happened in the past and gives an idea of what
will happen in the future. The study of precipitation trends is critically important for a country like
India, whose food security and economy are dependent on the timely availability of water, such as
83 % water used in the agriculture sector, 12 % for the industry sector and only 5 % for the domestic
sector. The Mann-Kendall test, is done in series is significant or insignificant, and Sen’s slope
estimator was used to identify the slope of the trends. Sen’s slope is the robust estimate of the trend
magnitude. In other words, the slope estimator is the median over all possible combinations of pairs
for the whole dataset (Hirsch et al. 1982). A positive value indicates an ‘upward trend’ (increasing
values with time), while a negative value indicates a ‘downward trend’ (Xu et al. 2007; Karpouzos
et al. 2010). The Mann-Kendall and Sen’s slope estimator have been used for the determination of
the trend. The trend analysis is carried out in the Dhanbad city by using monthly, annual mean and
seasonal (monsoon, post monsoon, pre-monsoon) rainfall data. The input codes for trend-testing of
R trend software are given in the following pages.

19
INPUT CODES FOR TREND-TESTING OF R TREND SOFTWARE
library(Kendall)
dat1 <- read.table("pr.season.10.yr.txt",skip=1,nrow=116)
tt=1:116
ty=1901:2016
tm=1:10
#pre
plot(tt,dat1[,2],type="l",xlab="year",ylab="precipitation [mm]",xaxt="n",col="gray",lwd=2)
axis(1,1:116,ty,las=1,cex.axis=.8,tick=T)
idx=seq(1,110,10)
for(i in 1:length(idx)){
ndd <- idx[i]:(idx[i]+9)
fit = lm(dat1[,2][ndd]~tt[1:10])
if(MannKendall(dat1[,2][ndd])$sl[1] < 0.05){
lines(tt[ndd],(fit$coefficients[[2]] * tt[1:10] + fit$coefficients[[1]]),col=2,lwd=3)
}else{
}
cf <- round(coef(fit), 2)
## sign check to avoid having plus followed by minus for negative coefficients
eq <- paste0("y = ", cf[1],
ifelse(sign(cf[2])==1, " + ", " - "), abs(cf[2]), " x ")
## printing of the equation
#mtext(eq, 3, line=-4)
text(idx[i]+4,60,eq,cex=.5)
yrr <- paste0("(",idx[i]+1900, "-", idx[i]+1909,")")
text(idx[i]+4,57,yrr,cex=.7)
rm(fit)
#print(MannKendall(dat[,2][ndd])$sl[1])
}
#------------
ndd <- 111:116
}else{
}
eq <- paste0("y = ", cf[1],
text(111+4,60,eq,cex=.5)
yrr <- paste0("(",111+1900, "-", 111+1905,")")
text(111+4,57,yrr,cex=.7)
rm(fit,yrr,eq,cf)

20
#---------
legend("topleft",lty=1,col=2,lwd=4,"statistically significant (tested with a Mann–Kendall test at a 5% significance
level)")
text(56,120,"Pre-monsoon",cex=2,col=3)
#--
#monsoon
rm(idx,ndd,cf,eq,yrr)
idx=seq(1,110,10)
}else{
}
eq <- paste0("y = ", cf[1],
rm(fit)
}
level)")
text(56,450,"Monsoon",cex=2,col=3)
#------------
ndd <- 111:116
}else{
}
eq <- paste0("y = ", cf[1],
text(111+4,162,eq,cex=.5)

21
yrr <- paste0("(",111+1900, "-", 111+1905,")")
text(111+4,152,yrr,cex=.7)
rm(fit,yrr,eq,cf)
#---------
#post
idx=seq(1,110,10)
}else{
}
eq <- paste0("y = ", cf[1],
rm(fit)
}
level)")
text(56,120,"Post-monsoon",cex=2,col=3)
#------------
ndd <- 111:116
}else{
}
eq <- paste0("y = ", cf[1],
text(111+4,7,eq,cex=.5)

22
yrr <- paste0("(",111+1900, "-", 111+1905,")")
rm(fit,yrr,eq,cf)
#---------
#annual
dat=read.table("data.10.yr.txt",nrow=116)
tt=1:116
ty=1901:2016
tm=1:10
#---
plot(tt,dat[,2],type="l",xlab="year",ylab="precipitation [mm]",xaxt="n",col="gray",lwd=2)
idx=seq(1,110,10)
fit = lm(dat[,2][ndd]~tt[1:10])
if(MannKendall(dat[,2][ndd])$sl[1] < 0.05){
}else{
}
eq <- paste0("y = ", cf[1],
rm(fit)
}
level)")
text(56,140,"Annual mean",cex=2,col=3)
#------------
ndd <- 111:116
fit = lm(dat[,2][ndd]~tt[1:6])
if(MannKendall(dat[,2][ndd])$sl[1] < 0.05){
}else{
}
eq <- paste0("y = ", cf[1],

23
text(111+4,60,eq,cex=.5)
yrr <- paste0("(",111+1900, "-", 111+1905,")")
#rm(fit,yrr,eq,cf)
3.5 Time Trend Analysis pattern for Annual mean, pre-monsoon, monsoon and post- monsoon
To prepare a sustainable management strategy for groundwater development, it is important to
understand the fluctuation of groundwater levels with reference to natural or artificial recharge in
space and time domain. The rainfall comprises an important component of space and time domain of
the water cycle and is the prime source of groundwater recharge. Dhanbad district is leading towards
a freshwater crisis mainly due to improper management of water resources and environmental
degradation, which has to lead to a lack of access to safe water to millions of people. In recent
decades, the exploitation of groundwater has increased greatly, particularly for agricultural purpose
and urbanization, because large parts of the country have little access to rainfall due to frequent
failures of monsoon. Thus the increasing population and their dependence on groundwater for
irrigation, domestic and industrial purpose are strongly depending on the rainfall. The periodic less
rainfall and the concomitant decline in the groundwater levels over the years in parts of the Dhanbad
district constrain a detailed study to clarify the behavior of rainfall in the temporal scales. The
seasonal distribution of rain-days of different categories, namely: rain-days of light showers, rain-
days of moderate showers and rain-days of heavy rainfall. In the semi-arid region, seasonal rainfall
patterns are very important for continuous supply of water for domestic use because rainfall leads to
surface and sub-surface recharge, and for rain-fed agricultural production. The main source of
drinking water in Dhanbad district is groundwater and rainwater are the only sources to recharge the
aquifer of groundwater. For identifying the trend of the rainfall data, the statistical analysis of linear
regression was used. Each trend of the decade gives the linear equation y=mx+c where m= slope and
c is intercepted. In this equation, y is dependent variable and x is an independent variable. In this
study dependent variable y is rainfall and measured in millimeters and independent variable x is the
year. The decreasing trend line for rainfall for the period 1901 to 2016 is shown in Figure 3.3. This
is a big span of time and rainfall is decreasing with the rate of .04 mmyear over 116 years. It is clear
from the figure that average rainfall has decreased with time and this result is to indicate a critical
situation for the groundwater level of Dhanbad district.

24
Fig. 3.3 Decreasing trend line from 1901-2016.

25
Fig. 3.4 Time Trend Series of rainfall for annual mean.
Figures 3. 4, 3.5, 3.6 and 3.7 show the time trend pattern for annual mean, monsoon, pre-monsoon
and post-monsoon respectively. They describe the nature of the past climate. The Tables 3.1, 3.2, 3.3

26
and 3.4 is shown the Sen’s slope magnitude and statistically significant or insignificant variations in
annual mean, pre-monsoon, monsoon and post-monsoon respectively over the 116 years.
Fig. 3.5 Time trend series analysis of rainfall for Pre-Monsoon.

27
Fig. 3.6 Time Trend Series analysis of rainfall for Monsoon.

28
Fig. 3.7 Time trend series analysis of rainfall for Post-Monsoon

29
Table 3.1 Mann-Kandall test and Sen’s slope magnitude for Annual mean.
Serial
No.
Year Mann-Kandall
Test for annual
mean
Sen’s
slope
Linear Equation
1 1901-1910 INSIGNIFICANT 0.49 Y=95.97-0.48X
2 1911-1920 SIGNIFICANT 3.96 Y=76.27+3.81X
4 1931-1940 INSIGNIFICANT -1.08 Y=109.61-1.2X
6 1951-1960 INSIGNIFICANT 2.03 Y=85.46+1.35X
9 1981-1990 INSIGNIFICANT 0.68 Y=86.71+2X
11 2001-2010 INSIGNIFICANT -.048 Y=92.99-0.57X
Table 3.2 Mann-Kandall test and Sen’s slope magnitude for pre-monsoon
Serial
No.
Year Mann-Kandall
Test for pre-
monsoon
Sen’s
slope
Linear Equation
1 1901-1910 INSIGNIFICANT -1.91 Y=60.13-2.34x
2 1911-1920 INSIGNIFICANT 2.57 Y=47.57+0.1x
6 1951-1960 INSIGNIFICANT 0.38 Y=26.76-0.22x

30
Table 3.3 Mann-Kandall and Sen’s slope magnitude test for monsoon
Serial
No.
Year Mann-Kandall
Test for monsoon
Sen’s
slope
Linear Equation
2 1911-1920 SIGNIFICANT 13.87 Y=202.48+14.68x
3 1921-1930 SIGNIFICANT -13.69 Y=361.54-8.99x
5 1941-1950 SIGNIFICANT -18.07 Y=397.04-11.4x
Table 3.4 Mann-Kandall test and Sen’s slope magnitude for post-monsoon.
Serial
No.
Year Mann-Kandall
Test for post-
monsoon
Sen’s
slope
Linear
Equation
5 1941-1950 INSIGNIFICANT 1.57 Y=34.01+.45x
8 1971-1980 SIGNIFICANT -4.40 Y=57.99-0.71x
9 1981-1990 SIGNIFICANT 2.97 Y=9.18+3.41x
12 2011-2016 INSIGNIFICANT 1.22 Y=200+14.9x

31
3.6 Results and Discussion
Time trend series analysis of rainfall can be helpful for monitoring the volume of groundwater
recharge in rapidly developing regions like IIT (ISM) Dhanbad campus. A decreasing trend line
observed in the Figure 3.3 for the year 1901 to 2016 with the rate of 0.04mmyear and this result is a
cause of serious concern, hence there is an urgency and necessity for artificial recharge pit (bore
wells) to maintain the groundwater level to recharge the aquifer(s). Time trend series analysis of
rainfall has been done over a duration of 116 years (1901-2016). Between the period of 1901 to 2011
the time trend analysis was conducted on each decade and for period 2011 to 2016 the study was
performed on six-year window. The annual mean of rainfall data for the time trend analysis is
revealed an upward trend for the year 1911-1920, 1951-1960, 1981-1990 and the downward trend
for the year 1901-1910, 1921-1930, 1941-1950, 1961-1970, 2001-2010. An upward trend indicates
rainfall is increasing with time, and downward trend indicates rainfall is decreasing with time. Series
1911-1920 in annual mean shows a significant at 5% which implies that there is a strong correlation
between rainfall and time for that period (M. Nyatuame, V.owusu-Guimah). However, no statistically
significant trend was observed in other periods, which implies a very weak correlation between
rainfall and time for those periods. and also obtained a statistically insignificant increasing trend for
the last six years, which implies that rainfall is increasing with time and weak relation between rainfall
and time for last six years. It is evident from the results of significant and insignificant test of annual
mean that there is no significant detectable effect of climate change in the Dhanbad region from
period 1901 to 2016. Similarly, the rainfall characteristic and trend analysis are evaluated for seasonal
variabilities like Pre-Monsoon, Monsoon, and Post-Monsoon. In Pre-Monsoon, all the series of the
decade gives the statistically insignificant trend. Which implies that very weak correlation between
rainfall and year. Series 1911-1920, 1921-1930 and 1941-1950 of the trends are significant at 5%
significance level which implies that rainfall is strongly correlated with time for monsoon period. It
can be observed, there are only two-decade1971-1980 and 1981-1990 are significant at 5%
significance level for post-monsoon period. Annual mean of the series 2011-2016 shows a positive
trend and implies that rate of rainfall has increased by 4.55 mm within six years. According to the
theory of climatology, the temperature has also increased within 6 years (Berg P., Moseley C.,
Haerter J.O et all). For the same series of Pre-Monsoon (April, May, June) trend is positive and
rainfall is increased by 6.4 mm within last 6 years (2011-2016). For Monsoon (July, August,
September) period, trend is negative and decreased by 4.48 mmyear and post-Monsoon (October,
November, December) period trend is positive and increased by 14.9 mm within last six years. Post-
monsoon rainfall trend is high in comparison to pre-monsoon period for the same series (2011-2016).

32
Chapter 4
Rainwater Harvesting
4.1 Background
For many years the Indian town of Cherrapunjee has held the title of wettest place on earth but
incredible as it sounds, the world’s wettest town is now suffering from a shortage of drinking water.
The problem is that Cherrapunjee lies on top of a high limestone plateau. Rain falling on the town
drain away immediately. Similarly, in the case of IIT (ISM) campus, the subsurface lithology is
composed of Pre- Cambrian metamorphic rock type in which the infiltration of rainwater from the
surface to subsurface is low. Then a technique is applied to overcome this problem which is called
the rainwater harvesting technique. Rainwater harvesting is a technique of collection and storage of
rainwater into natural reservoirs or tanks, or the infiltration of surface water into subsurface aquifers.
Rainwater harvesting provides an independent water supply during regional water restrictions and in
developed countries is often used to supplement the main supply. It provides water when there is a
drought, and can help mitigate flooding of low-lying areas, and reduces demand on wells which may
enable groundwater levels to be sustained. It also helps in the availability of potable water as
rainwater is substantially free of salinity and other salts. Application of rainwater harvesting in urban
water system provides a substantial benefit for both water supply and wastewater subsystems by
reducing the need for clean water in water distribution system, less generated storm water in a sewer
system, as well as a reduction in storm water runoff polluting freshwater bodies (Amartya Kumar
Bhattacharya.) There has been a large body of work focused on the development of Life Cycle
Assessment and Life Cycle Costing methodologies to assess the level of environmental impacts and
money that can be saved by implementing rainwater harvesting systems. More development and
knowledge is required to understand the benefits rainwater harvesting can provide to agriculture.
Many countries especially those with an arid environment use rainwater harvesting as a cheap and
reliable source of clean water. To enhance irrigation in arid environments, ridges of soil are
constructed in order to trap and prevent rainwater from running down hills and slopes. Even in periods
of low rainfall, enough water is collected in order for crops to grow. Water can be collected from
roofs, dams, and ponds can be constructed in order to hold large quantities of rainwater so that even
on days where there is little to no rainfall, there is enough available to irrigate crops. Cycle of
rainwater in the atmosphere is shown in the Figure 4.1.

33
Fig. 4.1 Rain Water Cycle in Atmosphere (www.rainsoftottawa.wordpress.com)
Rainwater is nothing but a collection, storage and recharge of water. There are many reasons to
harvest the rainwater:
1. To arrest ground water.
2. To beneficiate water quality in aquifers.
3. To conserve surface water runoff.
4. To reduce soil erosion.
Rainwater harvesting technique can be divided in to two ways
A. Surface runoff harvesting technique
B. Roof top rainwater harvesting (RTRWH)
In IIT(ISM) campus Roof top rainwater harvesting (RTRWH) technique is adopted. The system
mainly constitutes of following sub components.
a. Catchment
The surface that receives rainfall directly is the catchment. Sloping roof or flat roof.

34
b. Transportation
Rainwater from rooftop should be carried through down take water pipes or drains to
storage/harvesting system. Water pipes should be UV resistant (ISI HDPE/ PVC pipes) of required
capacity.
c. First flush
First flush is a device used to flush off the water received in first shower. Provisions of first rain
separator should be made at outlet of each drainpipe.
d. Filter
There are many component of filtration
d.1 Sand gravel filter
These are commonly used filters, constructed by brick masonry and filled by pebbles, gravels, and
sand is shown in the figure 4.2. Each layer should be separated by wire mesh.
Fig 4.2 Sand gravel filter

35
d.2 Charcoal filter
Charcoal filter can be made in-situ or in a drum. The drum or chamber should be filled by pebbles,
gravels, sand and charcoal as shown in the figure 4.2. Each layer should be separated by wire mesh.
d.3 PVC – Pipe filter
In simple words, PVC stands for Poly Vinyl Chloride. PVC is one of the widely manufactured
synthetic plastic polymer.
d.3 Sponge filter
It is a simple filter made from PVC drum having a layer of sponge in the middle of drum. It is an
easiest & cheapest form of filter, suitable for residential units.
4.2 An introduction to artificial recharge pit
Ground water aquifers can be recharged by various kinds of structures to ensure percolation of
rainwater in the ground instead of draining away from the surface. Commonly used recharging
methods are
A. Recharging bore wells
B. Recharge pits
C. Recharge Shafts
D. Recharging dug well
E. Recharge Trench
F. Percolation Tank
Recharge pit technique is applied in the campus of IIT (ISM) to recharge the aquifer(s). Artificial
recharge pit in IIT (ISM) campus is shown in figure 4.3. Dimensions of recharge pit is 9*3*3 m3
.
Depth of the bore hole of recharge pit is 55 to 72 meter and diameter of borehole 5.90 inches.

36
Fig. 4.3 Artificial recharge pit
Inside the chamber of artificial recharge pit there is a small settling pit built with dimensions
1.8*1.2*1.2 m3
. The function of settling pit is to allow harvested rainwater to settle and filter out the
unwanted material (leaves, wooden pieces etc.) carried by the water. Artificial recharge is a process
by which excess surface water is directed into the ground either by spreading on the surface by using
recharge wells, or by altering natural conditions to increase infiltration to replenish an aquifer.
Artificial recharge (sometimes called planned recharge) is a way to store water underground in times
of water surplus to meet demand in times of shortage.
4.3 Need for augmentation of groundwater resource in IIT (ISM) campus
As discussed in chapter one, a very high demand of water is present in the campus. To meet this
demand, a project on “Rain Water Harvesting and Artificial Recharge” proposed by Indian School
Mines and was sanctioned in August-2011 under Central Sector Scheme “Ground Water
Management and Regulation” by the State of Jharkhand during XIth
plan which in turn was
sanctioned by the Central Ground Water Board (CGWB), Ministry of Water Resources for
implementation in the IIT (ISM) campus. Augmentation of Ground water has become a very crucial
matter in IIT (ISM) campus because there is a sudden increase of human population in the campus
and there is a high demand of water expected due to increase in new developmental works carried
out in the campus. It also became necessary to efficiently manage the available resources as to meet
the growing needs and demands adequately. This conservation and augmentation has to follow
appropriate means and also the effective route. It was planned to be done by conservation and storage
of surplus surface water run-off in groundwater or sub-surface reservoirs in IIT (ISM) campus and

37
enhance the sustainable yield in the campus. In the campus rainwater is the only source for aquifer
to recharge the groundwater. Figure 4.4 shows the satellite images of IIT(ISM) campus which prove
that in building construction and population of campus has increased every year.
Fig. 4.4 Satellite images of IIT (ISM) Dhanbad
Other important reasons for need of artificial recharge in the campus-
1.Increased numbers of building in the campus due to development requirements.
2. Improve the quality of existing groundwater through dilution.
3. Save energy for lifting of groundwater from depleted level
4. Decreasing area of open space or grass land which resulted in less water recharge and
increased the surface run off.
5. Decrease in infiltration due to decrease in open space area.

38
Chapter 5
Ground water data base organization and statistical analysis
5.1 Background
The first phase of a statistical analysis of a groundwater level depth consists of collecting all existing
hydrological data of the year 2015 and 2016 from the recharge well and arrange these data on the
excel sheet. There are 54 artificial recharge pits (well) are available in the campus and these recharge
pits covered the area of 218 acres. Groundwater level data were collected from the recharge well in
every month of mid with the help of water level sounder (figure 5.1). In this chapter, the groundwater
levels of pre-monsoon, monsoon and post-monsoon and fluctuation between pre and post monsoon
were organized in a database for the year 2015 and 2016, which was then analysed for the statistical
study.
Fig. 5.1 Water level sounder

39
5.2 Statistical analysis of groundwater level data
Statistical Analysis of Groundwater level for Pre-monsoon, Monsoon, Post-monsoon and fluctuation
between pre and post monsoon from the year 2014-2016 is shown in the table 5.1,5.2 and 5.3
respectively.
Table 5.1 statistics of groundwater in different periods of the year 2014 (Singh)
Sr.
No.
Periods Mean
(m)
Range SD
(m)
Skewness Kurtosis
Min Max
1 Pre-Monsoon (May, June) 240.65 225.87 246.14 3.92 -1.60 6.26
2 Monsoon (July ,Aug, Sep) 242.12 227.80 246.86 3.87 -1.69 6.36
3 Post-Monsoon (Oct, Nov,
Dec)
242.19 231.66 247.49 3.25 -1.06 4.67
4 Fluctuation ( Pre and Post) 1.86 0.04 8.17 1.40 2.70 13.22
Table 5.2 statistics of groundwater in different periods of the year 2015
Sr.
No.
Periods Mean
(m)
Range SD
(m)
Skewness Kurtosis
Min Max
1 Pre-Monsoon (April, May,
June)
240 226 244 4.03 -1.81 6.5
2 Monsoon (July ,Aug, Sep) 242.5 229.59 248.18 3.32 -1.20 7.54
Dec)
242.4 229.44 251.04 3.04 -1.20 6.36
Table 5.3 statistics of groundwater in different periods of the year 2016
Sr.
No.
Periods Mean
(m)
Range SD
(m)
Skewness Kurtosis
Min Max
1 Pre-Monsoon (April, May,
June)
239 225 248 3.76 -1 5.68
2 Monsoon (July ,Aug, Sep) 239.10 226 251 4.26 -0.27 4.03
Dec)
244 231 250.29 3.34 -1 5.46

40
Fig. 5.2 Frequency distribution diagram of the year 2015
0
2
4
6
8
10
12
14
16
18
20
225.63
0
225.63
229.13
229.13
232.63
232.63
236.13
236.13
239.63
239.63
243.13
243.13
246.63
246.63
250.13
250.13
0
Frequency
Class Interval
Pre-Monsoon 2015
0
5
10
15
20
25
30
35
229.59
0
229.59
232.41
232.41
235.23
235.23
238.05
238.05
240.87
240.87
243.69
243.69
246.51
246.51
249.33
249.33
0
Frequency
Class Interval
Monsoon 2015

41
Fig. 5.3 Frequency distribution diagram of the year 2015
0
5
10
15
20
229.44
0
229.44
232.64
232.64
235.84
235.84
239.04
239.04
242.24
242.24
245.44
245.44
248.64
248.64
251.84
251.84
0
Frequency
Class Interval
Post-Monsoon 2015
0
2
4
6
8
10
12
14
16
18
20
0.18 0 0.18
2.18
2.18
4.18
4.18
6.18
6.18
8.18
8.18
10.18
10.18
12.18
12.18
0
Frequnecy
Class Interval
Fluctuation 2015

42
Fig. 5.4 Frequency distribution diagram for the year 2016
0
5
10
15
20
25
30
225 0 225
228.5
228.5
232
232
235.5
235.5
239
239
242.5
242.5
246
246
249.5
Frequency
Class Interval
Pre-Monsoon 2016
0
5
10
15
20
225 0 225
229
229
233
233
237
237
241
241
245
245 2
Frequency
Class Interval
Monsoon 2016

43
Fig. 5.5 Frequency distribution diagram for the year 2016
In Figures from 5.2 to 5.5 have been shown the frequency diagram for the year 2015 and 2016 and
statistical analysis have been shown in the Table 5.1,5.2 and 5.3.
0
5
10
15
20
25
230
0
230
232.89
232.89
235.78
235.78
238.67
238.67
241.56
241.56
244.45
244.45
247.34
247.34
250.23
250.23
253.12
253.12
0
Frequency
Class Interval
Post-Monsoon 2016
0
2
4
6
8
10
12
14
16
0.35 1 0.35
1.66
1.66
2.97
2.97
4.28
4.28
5.59
5.59
6.9
6.9
8.21
8.21
9.52
9.52
10.83
10.83
0
Frequency
Class Interval
Fluctuation 2016

44
Fig. 5.6 Water level depth of Pre and Post monsoon of the year 2015

45
Fig. 5.7 Depth of Groundwater level of Pre- and Post- monsoon of the year 2016

46
Fig. 5.8 Fluctuation between Pre and Post monsoon of the year 2016
Fig. 5.9 Fluctuation between Pre and Post monsoon of the year 2016
0.00
2.00
4.00
6.00
8.00
Saphire
Hostel
Saphire
Hostel
Saphire
Hostel
Saphire
Hostel
Topaz
Hostel
Student
Activity
Centre
Student
Activity
Centre
Amber
Hostel
Amber
Hostel
Back
Side
of
Emerald…
Front
Side
of
Emerald…
Jasper
Hostel
Jasper
Hostel
Heritage
Building
Heritage
Building
Diamond
Hostel
Opal
Hostel
Shanti
Bhawan
Hawa
Mahal
Lecture
hall
complex
II
Teachers
colony
SBI
Bank
ISM
Fluctuation
in
Meter
Location
Fluctuation 2015
0.00
2.00
4.00
6.00
8.00
Saphire
Hostel
Saphire
Hostel
Saphire
Hostel
Saphire
Hostel
Topaz
Hostel
Student
Activity
Centre
Student
Activity
Centre
Amber
Hostel
Amber
Hostel
Back
Side
of
Emerald…
Front
Side
of
Emerald…
Jasper
Hostel
Heritage
Building
Heritage
Building
Diamond
Hostel
Opal
Hostel
Opal
Hostel
Old
Library
Petroleum
Hawa
Mahal
Work
shop
&
MME
Staff
Colony
Type
II,
Lower
ground
Lecture
hall
complex
II
Teachers
colony
SBI
Bank
ISM
Fluctuation
in
Meter
Location
Fluctuation 2016

47
Fig. 5.10 Variation in Groundwater-level depth for Pre-Monsoon from the year 2014 to 2016.
Fig. 5.11 Variation in Groundwater-level depth for Post-Monsoon from the year 2014 to 2016.
220
225
230
235
240
245
250
255
0 5 10 15 20 25 30 35 40
Groundwater
level
depth
from
mean
sea
level
(m)
Recharge Well
Pre-Monsoon Water Level Depth from the year 2014-2016
Pre-Monsoon 2014 Pre-Monsoon 2015 Pre-Monsoon 2016
225
230
235
240
245
250
255
Groundwater
level
depth
from
mean
sea
level
(m)
Recharge Well
Post-Monsoon Water Level Depth from the year 2014-2016
Post-Monsoon 2014 Post-Monsoon 2015 Post-Monsoon 2016

48
5.3 Results and Discussion
Statistical analysis of groundwater from the year 2014 to 2016 are shown in Table 5.1, 5.2 and 5.3.
It can be observed that the nature of groundwater well data during the period 2015 -2016 is same as
observed in the year 2014. The coefficient of variation (C.V.) of fluctuation between pre and post
monsoon is more than 0.5 for all the three years. It implies that groundwater well data distribution
for the three years (2014,2015,2016) is not a normal distribution. However, for the year 2016
fluctuation of coefficient of variation is 0.51 which indicate that the nature of distribution of
groundwater well data is pseudo lognormal distribution. Statistical analysis of groundwater well data
during 2014-2016 revealed that the frequency distribution of pre-monsoon and post-monsoon was
negatively skewed and their fluctuation was positively skewed. Negatively skewed distribution for
2015 implied that in 54 recharge pits most of the data are higher than their mean value and similar
observation was made for 2016. Positive skewed distribution of fluctuation between pre-monsoon
and post-monsoon of the year 2015 and 2016 implied that that most of the data are less than their
mean value. Water flow inside the aquifer is controlled by the lithology and structural geology of the
subsurface of the campus. The highest fluctuation is occurred at Jasper hostel for the year 2015 is
shown in Figure 5.8. Similarly, for the year 2016 the highest fluctuation occurred at Heritage
building, teacher colony and Lecture hall complex which is shown in Figure 5.9. Water level variation
for pre and post monsoon of the year 2015 and 2016 are shown in figure 5.6 and 5.7 respectively and
it can be observed from the figures pre-monsoon water level is higher than post-monsoon periods. It
can be observed from the figure 5.10 that water level depth of pre-monsoon periods for 2016 is
highest among the three years (2014, 2015, 2016), which indicates that the performance of artificial
recharge pit improved for every year. For post-monsoon water level depth for the year 2016 is the
lowest among the three years (2014,2015,2016) as observed from Figure 5.11.

49
Chapter 6
Geostatistical Modelling of Groundwater of pre-monsoon, post-monsoon and
fluctuation
6.1 Background
In this chapter Geostatistical modeling is carried out with semi-variography i.e. characterization of the
spatial distribution of groundwater. A semi-variogram model exhibits various characteristics that
display spatial distribution parameters i.e. Nugget effect (C0), Continuity (C), a range of influence (a)
and anisotropy. To investigate the seasonal variation in both the season i.e. pre-monsoon and post-
monsoon and its fluctuation of groundwater level in and around IIT(ISM) Dhanbad, geostatistical
method is used. The methods adopted is to study the spatial distribution of groundwater level in the
IIT (ISM) campus and to generate the ground water distribution maps. Since the area is a hard rock
terrain, the major aquifer system is in fractured zones and the basement rock with shallow fractures
generally encountered at various depths ranging from 10 to 70 meters. There are different fractured
zones and each zone has different thickness. In the year, 2014 Groundwater level of recharge-well data
were collected in 48 wells during pre-monsoon, in 34 wells during monsoon and in 54 wells during
post-monsoon periods. In present thesis, work is focused on the number of recharge-wells acting in the
year 2015 and 2016, a number of data collecting from the artificial recharge-wells have shown in table
6.1 and table 6.2.
Table 6.1 No. of recharge-wells data used for different seasonal periods in the year 2015.
Serial No. Period (2015) No. of bore-wells data used for different periods in
2015
1 Pre-Monsoon 44
2 Monsoon 48
3 Post-Monsoon 54
4 Fluctuation 44

50
Table 6.2 No. of recharge-wells data used for different seasonal periods in the year 2016.
Serial No. Period (2016) No. of bore-wells data used for different periods in
2016
1 Pre-Monsoon 51
2 Monsoon 52
3 Post-Monsoon 52
4 Fluctuation 50
All three year’s recharge-wells data collected manually and then processed for statistical and
geostatistical analysis. Entire each year is divided in to pre-monsoon, monsoon and post-monsoon in
which the pre-monsoon consists of the average months of April-May-June, monsoon consists of
average months of July-August-September and Post-monsoon consists of the average months of
October-November-December.
6.2 Semi-variogram modelling for pre, post and fluctuation between pre and post monsoon for
the year 2015 and 2016
6.2.1 Point-Kriging cross validation
According to David (1977), point kriging is a procedure for checking the validity of a semi-variogram
model that represents the underlined semi-variogram. A spherical model is fitted to an experimental
semi-variogram by adjusting C0 (Nugget effect), C (Continuity) and a (range). To understand the
anisotropy of the fluctuations and level of groundwater table during pre and post monsoon,
comparison of semi-variogram with experimental semi-variogram to cross-validate with the model
was done. During this procedure, since the sample points are randomly distributed, different lag
distance and sample interval was taken to fit the spherical model. These models were Cross-validated
with Point Kriging Cross Validation Technique and were fitted to the experimental semi-variogram
models. The Point kriging cross-validation was done by selecting the most suitable range, nugget,
continuity and keeping the sill value at the most suitable place so that maximum points can be covered
and best fit can be obtained Cross-validated models as obtained employing Point kriging cross-
validation technique for pre-monsoon, post-monsoon, and fluctuation for the year 2015 and 2016 has

51
given in Figures 6.1, 6.2 and 6.3, and their fitted model equations have shown in the Tables 6.3, 6.2
and 6.4.
Fig. 6.1 Experimental semivariogram with fitted model for Pre-Monsoon period for the year 2015 and
2016
Fig. 6.2 Experimental semi-variogram with fitted model for Post-Monsoon period for the year 2015 and
2016

52
Fig. 6.3 Experimental semi-variogram with fitted model for Fluctuation between pre and post monsoon for
the year 2015-2016
Table 6.3 Model selected for different periods for the year 2015.
Serial
No.
Different Period for the year 2015 Spherical Model Equation
1 Pre-Monsoon ϒ(h)=4.3+13.6[1.5(h/387) -0.5(h/387)3
2 Post-Monsoon ϒ(h)=5+8[1.5(h/378) -0.5(h/378)3
3 Fluctuation between pre and post
monsoon.
ϒ(h)=2.3+3.1[1.5(h/470) -0.5(h/470)3
Table 6.4 Model selected for different periods for the years 2016
Serial
No.
Different Period for the year 2016 Spherical Model Equation
1 Pre-Monsoon ϒ(h)=5+8[1.5(h/482) -0.5(h/482)3
2 Post-Monsoon ϒ(h)=3.8+8[1.5(h/475) -0.5(h/475)3
3 Fluctuation between pre and post
monsoon.
ϒ(h)=2.4+3.5[1.5(h/510) -0.5(h/510)3

53
Semi-variogram models were cross-validated by taking the various lag distances as the sample
distance were randomly distributed and to get the best-fitted model, the exercise was carried out in
Table 6.5 to 6.16 for the year 2015 and 2016 and gives the details of the exercise and various values
for fulfilling the parameters.
Table: 6.5 Semi-Variogram parameters for Pre-Monsoon of the year 2015
Serial No. Semi-Varogram
Parameters for pre-
monsoon 2015
Initial Parameters Values
For Semi-Varogramm
model
Final Model Parameters
Used For Kriging.
1. Co (%2
) 3.8 4.3 2.8 4.3
2. C (%2
) 14.5 13.9 13.8 13.6
3. Co+C (%2
) 18.3 18.2 16.6 17.9
4. (Co:C+Co)*100 20.7 23.6 16.8 24
5. Range (m) 387 387 387 387
Table: 6.6 Point Kriging cross validation parameters for pre-monsoon of the year 2015
Sr.
No.
Point Kriging Cross
Validation Parameters for
pre-monsoon 2015
Initial Parameters Used for PKCV Final Model
Parameters
Used for
Kriging
1. Radius of search (m) X Y Z. X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
360
360
360
350
350
350
320
320
320
380
380
380
387
387
387
2. Maximum No. of Samples to
kriged a point.
12 12 12 12 16
3. Minimum No. of Samples to
kriged a point.
3 3 3 3 4
4. Mean Kriging Variance (KV) 8.0844 8.7080 6.0038 8.0471 8.5912
5. Mean Estimated Variance
(EV)
9.1536 8.9770 8.8801 8.9991 8.9832
6. KV:EV 1.13 1.03 1.48 0.89 1.04

54
Table: 6.7 Semi-Variogram parameters for Post-Monsoon of the year 2015
Parameters for post-
monsoon 2015
For Semi-Varogramm
model
Used For Kriging.
1. Co (%2
) 4.8 4.5 4.1 5
2. C (%2
) 8 8 8 8
3. Co+C (%2
) 12.8 12.5 12.1 13
4. (Co:C+Co)*100 37.5 36 33.8 38.4
5. Range (m) 378 378 378 378
Table 6.8 Point Kriging cross validation parameters for post-monsoon of the year 2015
Sr.
No.
Point Kriging Cross
post-monsoon 2015
Parameters Used
for Kriging
1. Radius of search (m) X Y
Z.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
3
360
360
400
400
400
390
390
390
340
340
340
320
320
320
2. Maximum No. of Samples
to kriged a point
12 12 12 12 16
3. Minimum No. of Samples
to kriged a point
3 3 3 3 4
4. Mean Kriging Variance
(KV)
8.1629 8.7413 9.3915 8.4142 8.1128
(EV)
11.7840 15.3142 15.1487 11.7251 8.2347
6. KV:EV 1.44 1.75 1.61 1.39 1.02

55
Table 6.9 Semi-Variogram parameters for fluctuation between Pre and Post Monsoon of the year 2015
Parameters for
fluctuation 2015
For Semi-Varogramm
model
Used For Kriging.
1. Co (%2
) 2.1 2.2 2 2.3
2. C (%2
) 3.95 3.89 3.9 3.1
3. Co+C (%2
) 6.05 6.09 5.9 5.4
4. (Co:C+Co)*100 34.7 36.1 34 42.5
5. Range (m) 470 470 470 470
Table 6.10 Point Kriging cross validation parameters for fluctuation between pre and post monsoon of the
year 2015
Sr.
No.
Point Kriging Cross
fluctuation 2015
Parameters Used
for Kriging
Z.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
420
420
420
430
430
430
350
350
350
320
320
320
340
340
340
to kriged a point
12 12 12 12 16
to kriged a point.
3 3 3 3 4
(KV)
3.384 3.488 3.051 2 3.2601
(EV)
3.2022 3.1814 3.881 3.5093 3.1793
6. KV:EV 0.95 0.91 1.08 1.75 0.98

56
Table 6.11 Semi-Variogram parameters for Pre-Monsoon of the year 2016
Serial
No.
Semi-Varogram
Parameters for pre-
monsoon 2016
For Semi-Varogramm
model
Final Model
Parameters
Used For Kriging.
1. Co (%2
) 4 4.5 4.2 6.5
2. C (%2
) 16.5 17 16.5 14.7
3. Co+C (%2
) 20.5 21.5 20.7 21.2
4. (Co:C+Co)*100 19.51 20.93 20.28 31
5. Range (m) 482 482 482 482
Table 6.12 Point Kriging cross validation parameters for Pre-Monsoon of the year 2016
Sr.
No.
Point Kriging Cross
pre monsoon 2016
Parameters
Used for
Kriging
Z.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
360
360
360
400
400
400
390
390
390
340
340
340
330
330
330
to kriged a point.
12 12 12 12 16
to kriged a point.
3 3 3 3 4
(KV)
8.1629 8.7413 9.3915 8.4142 10.9335
(EV)
11.7840 15.3142 15.1487 11.7251 11.1914
6. KV:EV 1.44 1.75 1.61 1.39 1.02

57
Table 6.13 Semi-Variogram parameters for Post-Monsoon of the year 2016
Parameters for post-
monsoon 2016
For Semi-Varogramm
model
Final Model
Parameters
Used For Kriging.
1. Co (%2
) 3 3.4 3.2 3.8
2. C (%2
) 8.3 8.2 8 8
3. Co+C (%2
) 11.3 11.6 11.2 11.8
4. (Co:C+Co)*100 26.5 29.31 28.5 32.2
5. Range (m) 393 410 350 475
Table 6.14 Point Kriging cross validation parameters for post-monsoon of the year 2016
Sr.
No.
Point Kriging Cross
post-monsoon 2016
Initial Parameters Used for
PKCV
Used for Kriging
Z.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
262
262
262
300
300
300
325
325
325
400
400
400
to kriged a point.
12 12 12 16
to kriged a point.
3 3 3 4
(KV)
5.3997 6.1868 6.2014 7.0419
(EV)
7.1373 6.8269 6.6563 6.8960
6. KV:EV 1.38 1.10 1.07 0.98

58
Table 6.15 Semi-Variogram parameters for fluctuation between Pre and Post Monsoon of the year 2016
Parameters for
fluctuation 2016
For Semi-Varogramm
model
Used For Kriging.
1. Co (%2
) 2.8 3 3.2 2.4
2. C (%2
) 3.2 3.3 8 3.5
3. Co+C (%2
) 6.0 6.3 11.2 5.9
4. (Co:C+Co)*100 26.5 29.31 28.5 32.2
5. Range (m) 520 550 550 510
Table 6.16 Point Kriging cross validation parameters for fluctuation between pre and post monsoon of the
year 2016.
Sr.
No.
Point Kriging Cross
fluctuation 2016
Initial Parameters Used for
PKCV
Used for Kriging
Z.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
350
350
350
360
360
360
400
400
400
500
500
500
to kriged a point.
12 12 12 16
to kriged a point.
3 3 3 4
(KV)
3.9496 4.2018 3.7159 3.6582
(EV)
7.1373 6.8269 6.6563 3.5536
6. KV:EV 1.38 1.10 1.07 0.97

59
It is observed that nugget for pre-monsoon and post-monsoon for the year 2015 groundwater well
data is 4.3 and 5 respectively. Nugget for fluctuation on the same year well data is 2.3. Which is
almost half of the pre and post-monsoon. It is again observed on 2016 recharge well data nugget for
pre-monsoon is 6.5 and nugget for fluctuation between pre and post monsoon are 2.4 which is also
almost half of the pre-monsoon. Therefore, the accuracy of the Kriged values depends on the semi-
variogram values at most possible small lag distances (Isaaks & Srivastava, 1989) and (Ma et al.,
1999). Clearly, demonstrates that first few points associated with lag distance carry more weights of
spatial structure.
In order to verify the accuracy of the semi-variogram models fitted, that was used to estimate the
groundwater table for all the six pre, post and fluctuation for the year 2015 and 2016 of the Figures
6.4, 6.5, 6.6, 6.7,6.8 and 6.9 have shown the graph and the regression line between the measured and
the estimated values of groundwater table during the pre-monsoon and post-monsoon period and of
the fluctuation.
Fig. 6.4 Regression equation for pre-monsoon (2015)
Y = 0.9463X + 12.821 R² = 0.8204
234
236
238
240
242
244
246
234 236 238 240 242 244 246
Estimated
Value
(m)
Measured Value (m)

60
Fig 6.5 Regression equation for post-monsoon (2015)
Fig 6.6 Regression equation for fluctuation between pre and post monsoon (2015)
y = 0.9121x + 21.106 R² = 0.8181
238
239
240
241
242
243
244
245
246
238 239 240 241 242 243 244 245 246
Depth
of
Estimated
Values
(m)
Depth of Measured Value (m)
y = 0.9339x + 0.2271 R² = 0.8172
0
1
2
3
4
5
6
0 1 2 3 4 5 6
Depth
of
Estimated
Values
(m)
Depth of Measured Values (m)

61
Fig. 6.7 Regression equation for Pre-monsoon (2016)
Fig. 6.8 Regression equation for post-monsoon (2016)
y = 0.9429x + 13.468 R² = 0.8866
234
236
238
240
242
244
234 236 238 240 242 244
Depth
of
Estimated
Value
(m)
y = 0.8197x + 44.09 R² = 0.8515
238
240
242
244
246
248
250
238 240 242 244 246 248 250
Depth
of
Estimated
Value
(m)

62
Fig. 6.9 Regression equation for fluctuation between pre and post monsoon (2016)
In the above graphs has been shown the R values of Pre, post, and fluctuation for the year 2015 and
2016. For the year 2015 value of R value for pre, post and fluctuation are 0.9057, 0.9044 and 0.9039
respectively. R values for pre, post, and fluctuation of the year 2016 is 0.9415, 0.9227 and 0.9272
respectively. A little unbiasedness is seemed by the slope of the regression line. So t-test on R was
performed to determine the significance of ‘R’ for all pre, post and fluctuation for the year 2015 and
2016, the ‘t’ test was performed separately and was found that the ‘R’ is significant in all the cases.
The calculation is described below.
1. t Student test for pre, post and fluctuation of the year 2015.
t test for pre-monsoon-
no. of data used n= 44.
t (calculated on ‘R’) = R*√𝒏−𝟐//√𝟏−R2
t (calculated on ‘R’) = 13.85
t’table (α=0.05, ν=n-2, q=1-α) = 2.01
Since t cal > ttable ‘R’ is significant.
y = 0.6868x + 1.3301 R² = 0.8597
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Depth
of
Estimated
Value
(m)

63
t test for post-monsoon-
no. of data used n = 52
t (calculated on ‘R’) =16.59
t’table (α=0.05, ν=n-2, q=1-α) = 2.0
t test for fluctuation-
no. of data used n= 41
t’table (α=0.05, ν=n-2, q=1-α) = 2.02
2. t Student test for pre, post and fluctuation of the year 2016.
t test for Pre-monsoon
t’table (α=0.05, ν=n-2, q=1-α) = 2.01
t test for Post-monsoon
t’table (α=0.05, ν=n-2, q=1-α) = 2.0

64
t test for fluctuation
t’table (α=0.05, ν=n-2, q=1-α) = 2.0
6.3 Block Grids Delineation
6.3.1 Kriging
The geostatistical procedure of estimating values of a regionalized variable using the information
obtained from a semi-variogram is called kriging. Its application to groundwater hydrology has been
described by number of authors, viz. Delhomme (1976,1978,1979), Delfiner and Delhomme (1953),
Marsily et al. (1984), Marsily (1986), Aboufirassi and Marino (1983,1984),Gambolti and Volpi
(1979) to name a few. Let G* be the kriged estimate of the average value of grid G of the samples
having values g1, g2, g3……gn. Let a1, a2, a3……an be the weightage giving to each of the values
respectively such that Σai=1; and G*=Σaigi. Thus the estimation becomes unbiased; the mean error
is zero for a large number of estimated values and the estimated variance is minimum. The kriging
variance is given as
𝜎𝑘
2
= Σ (𝐠𝐢 − 𝐆∗)2
To make kriging variance minimum, a function called Lagrange multiplier (λ), is used for optimal
solution of the kriging system. Kriging carried out for a point estimate is called point kriging and that
accomplished for making estimates of a block of ground is known as block kriging. The kriging
technique is applied for analytical purpose and is discussed below. Prior to kriging the block size of
the study area was decided by taking into account the various parameters i.e. area, fluctuation of
ground water and the best fitted block which can cover the maximum extent near to the boundary of
the IIT(ISM). Since area is small and is heterogeneously extended from all direction here, 25m x 25m
x 25m dimensions of the block grid size was delineated after a number of exercises so that kriging

65
can be done for whole area. After the delineation of the block grid of the dimension 25m * 25m *
25m the centre points of each block was taken and the kriging technique was applied.
6.4 Ordinary Kriging
Kriging is a geostatistical interpolation technique which considers both distance and the degree of
variation between known and estimated values. This method is an attempt to minimize the error
variance and set the mean of the prediction error to zero, so that there is no over or underestimates,
as it is a robust interpolation technique which derives weights from surrounding measured values to
predict values at unmeasured locations. In this study, ordinary Kriging technique was applied for the
estimation of the fluctuation of groundwater level in the year 2015 and 2016 across the study area
and to delineate the groundwater level structure of pre and post monsoon for the same period. Figures
6.10, 6.12 and 6.14 show the kriged estimate map of groundwater level structure for pre-monsoon,
post-monsoon and fluctuation between them respectively. Kriged variance (error) maps were also
generated related to each of the kriged estimate maps which are shown in the Figure 6.11, 6.13 and
6.15. Similarly, for the year 2016 kriged estimate maps are represented by the 6.16, 6.18 and 6.20
and It’s related kriged variance (error) maps have been shown in the Figure 6.17, 6.19 and 6.21. To
study the flow direction of groundwater in different periods (pre-monsoon, post-monsoon and
fluctuation) in the year 2015 and 2016, different contour maps were developed on the the kriged
surfaces to visualize and simulate the groundwater scenario in the subsurface region of the IIT(ISM)
campus. These contour maps have been shown in the Figures 6.22 to 6.33.

66
Fig.6.10 kriged Estimate distribution flow map of pre-monsoon groundwater level (year 2015)

67
Fig. 6.11 Kriged Variance distribution map for pre-monsoon (year 2015)

68
Fig.6.12 Kriged Estimate distribution flow map of post-monsoon groundwater level (year 2015)

69
Fig.6.13 Kriged Variance distribution map of post-monsoon groundwater level (year 2015)

70
Fig.6.14 Kriged Estimate distribution flow map of Fluctuation of groundwater level (year 2015)

71
Fig.6.15 Kriged-Variance distribution map for fluctuation between pre and post monsoon. (year 2015

72
Fig. 6.16 Kriged-Estimate distribution flow map for pre-monsoon (year 2016)

73
Fig.6.17 Kriged Varience distribution map of Pre-Monsoon (year 2016)

74
Fig.6.18 Kriged Estimate distribution flow map of Post-Monsoon (year 2016)

75
Fig.6.19 Kriged Variance distribution map of Post-Monsoon (year 2016)

76
Fig.6.20 Kriged Estimate distribution flow map of Fluctuation (year 2016)

77
Fig.6.21 Kriged Variance distribution map of fluctuation (year 2016)

78
Fig. 6.22 Kriged Estimate contour map of Pre-monsoon (year 2015)

79
Fig. 6.23 Kriged Variance contour map of Pre-monsoon (year 2015)

80
Fig. 6.24 Kriged-Estimate contour map of post-monsoon (year 2015)

81
Fig. 6.25 Kriged-Variance contour map of post-monsoon (year 2015)

82
Fig.6.26 Kriged Estimate map of fluctuation (year 2015)

83
Fig. 6.27 Kriged-Variance map of fluctuation (year 2015)

84
Fig.6.28 Kriged Estimate contour map of Pre-monsoon (year 2016)

85
Fig.6.29 Kriged Variance contour map of Pre-monsoon (year 2016)

86
Fig.6.30 Kriged-Estimate contour map of post-monsoon (year 2016)

87
Fig. 6.31 Kriged-Variance contour map of post-monsoon (year 2016)

88
Fig. 6.32 Kriged Estimate map of fluctuation (year 2016)

89
Fig. 6.33 Kriged-Variance map of fluctuation (year 2016)

90
6.5 Results and Discussion for the year 2015
Spatial distribution of groundwater level in the year 2015 for pre-monsoon, post-monsoon and its
fluctuation have been shown in the Figures 6.10, 6.12 and 6.14. These figures are called kriged
estimate map. It can be observed from the kriged map for pre-monsoon session in the year 2015,
groundwater movement direction was from northwest side towards northeast side. Northwest side
had the highest water level (241.46 m), while the northeast had the lowest side water level (232.49
m). This also conformed to the topographic elevation. Similar, observation had been observed
from the post-monsoon period. In the post-monsoon period, highest level of groundwater was
244.58 m at the northwest side and lowest water level 237.39 m towards the northeast side. When
the kriged map of pre-monsoon was compared to that of post-monsoon, it was observed that the
groundwater level of the recharge pits shows a positive trend. It could be observed from the kriged
map of fluctuation between pre and post monsoon, the highest fluctuation (5.07 m) was observed
from the northeast side and the lowest fluctuation from northwest side (1.17 m). High fluctuation
also highlights that draft is being done by the pump house, which was pumping out the water at
the regular intervals. Kriged variance (error) maps related to the kriged estimate maps of pre-
monsoon, post-monsoon and its fluctuation have been shown in the Figures 6.11, 6.13 and 6.15.
Dark blue colour shows the maximum error while light blue colour indicates minimum error in
the kriged variance (error) maps. It can be observed from the kriged variance (error) map of pre-
monsoon, post-monsoon and fluctuation that wherever the recharge pits are present, error is less
and it gradually increases as the location moves far away from the recharge pits. To study the flow
direction of groundwater during different period in the year 2015, different contour maps were
developed on the kriged surfaces to visualize and simulate the groundwater scenario in the
subsurface region of IIT(ISM) campus. Figures 6.22, 6.24 and 6.26 show the contour map of the
pre-monsoon, post-monsoon and fluctuation on the kriged estimate map and Figures 6.23, 6.25
and 6.27 show the contour map of same periods on the kriged variance (error) map. From the
kriged estimate contour map of pre-monsoon, it is observed that the highest contour value (243
m) and the lowest contour value (234 m) occurred at northwest side and northeast side
respectively. Contour value conform the topographically controlled groundwater flow. The
spacing between the contour lines on the map was to be maximum at the northwest side of the
campus which implies that there is a higher transitivity of the fractured aquifer. It can be observed
from the kriged estimate contour map of post-monsoon the higher value of contour is 244 m at the
northwest part of the campus and the lowest contour value is 238.3 m towards the northeast part
of the campus. From the kriged estimate contour map of fluctuation the highest value of contour

91
of 5 m occurred at the northeast side of the campus and the lowest value of contour was 1.3 m
which occurred towards the northwest side of the map.
6.6 Results and Discussion for the year 2016
Spatial distribution of groundwater level in the year 2016 for pre-monsoon, post-monsoon and its
fluctuation have been shown in Figures 6.16,6.18 and 6.20 and kriged variance (error) map related
to kriged estimate map of pre-monsoon, post-monsoon and its fluctuation have been shown in
Figures 6.17,6.19 and 6.21. In kriged variance (error) map dark pink colour is implied maximum
error and light pink colour minimum error. From the kriged estimate map of pre-monsoon it can
be observed that the highest level (242.22 m) of groundwater towards the northwest and southeast
side of the campus. Similar observation is observed from the post-monsoon, in post monsoon
period higher level (246.24 m) of water level at the southeast and northwest side of the campus
and deeper level (238.53 m) of the groundwater at the northeast side. The kriged estimate map of
fluctuation for the year 2016 revealed that maximum fluctuation in the groundwater level occurred
at the central part of the campus. In contrast to this the kriged estimate map of fluctuation for the
year 2015 maximum fluctuation occurred at north-east side of the campus this contrast or change
in maximum fluctuation in groundwater level can be attributed to disturbance in topographic
ridges of the campus. This disturbance is thought to be caused by foundation laying for the
construction of a multistage building (New Library) at the central part of the campus in the year
2015, which lead to damage of the topographic ridge at the location resulting in, a shift of the
location of maximum fluctuation occurrence from north-east side to central part of the campus.

92
Chapter 7
Ground Water Resource Assessment
7.1 Estimation of Ground Water Supply for the year 2015 and 2016 in the campus
As discussed in chapter one water supply in the campus of IIT(ISM) is limited and in every year
consumption of groundwater is increasing. This study revealed that the value of consumption of
groundwater for the year 2015 and 2016 are 735840000 litres and 763920000 litres respectively. In
comparison to this, study conducted by Singh (2014) revealed that the value of groundwater
consumption in the campus of IIT (ISM) for the year 2014 was 703440000 litre. The current status
of water supply in the campus are given in tables 7.1 and 7.2, for the year 2015 and 2016 respectively.
Table 7.1 Pumping of groundwater in IIT (ISM) of year 2015 (Source work shop office of campus)
Serial
No.
Tube Well No. Location DischargeHour Pumping
Hrs
Total
Discharge
Day
1 Dug Well no. 1 Near Ruby Hostel 15000 10 150000
2 Dug Well no. 2 Workshop campus 12000 10 120000
3 Dug Well no. 3 Near Petroleum
Building
12000 10 120000
4 Dug well no. 4 Staff Colony 8000 10 80000
5 Dug well no. 5 Near UGC Colony 20000 10 200000
6 Dug well no. 6 Near CME Building 20000 10 200000
7 Dug well no. 7 Near GJLT Hall 8000 3 24000
8 Deep bore well no.
9
Staff Colony 12000 9 108000
10
Bamboo Garden 20000 12 240000
11
Beside Seismology
Observatory
20000 14 280000
12
Jasper Hostel 8000 10 80000
13
In front of Old EDC 8000 10 80000
14
SBI ISM campus
branch
8000 12 96000
15
Beside 150 Qtrs. GR
side
8000 12 96000

93
16
EDC extension
building
8000 10 80000
16 Under construction
site
Boys Hostel 2000
Project
3000 14 42000
site
New Long Wall
Building
3000 8 24000
site
CRF building 3000 8 24000
19 Total consumption
per day
2044000
Table 7.2 Pumping of groundwater in IIT (ISM) of year 2016 (Source work shop office of campus)
Serial
No.
Tube Well No. Location DischargeHour Pumping
Hrs
Total
Discharge
Day
1 Dug Well no. 1 Near Ruby Hostel 15000 10 150000
2 Dug Well no. 2 Workshop
campus
12000 10 120000
3 Dug Well no. 3 Near Petroleum
Building
12000 10 120000
4 Dug well no. 4 Staff Colony 8000 10 80000
5 Dug well no. 5 Near UGC
Colony
20000 10 200000
6 Dug well no. 6 Near CME
Building
20000 10 200000
7 Dug well no. 7 Near GJLT Hall 8000 3 24000
8 Deep bore well no. 9 Staff Colony 12000 9 108000
10
Bamboo Garden 20000 12 240000
11
Beside
Seismology
Observatory
20000 14 280000
12
Jasper Hostel 8000 10 80000
13
In front of Old
EDC
8000 10 80000
14
SBI ISM campus
branch
8000 12 96000
15
Beside 150 Qtrs.
GR side
8000 12 96000
16
EDC extension
building
8000 10 80000

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