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Chapter 5 : Application of GIS and Remote Sensing in Groundwater Prospecting and Analysis of Observation
Well Data
5.1 Introduction
Exploitation of groundwater has increased greatly in the last two to three
decades in India, particularly for agricultural purposes, because large parts of the
country have little access to surface water sources (rivers, lakes and artificial basins).
The total area cultivated in India using groundwater has increased from 6.5 million
ha (Mha) in 1951 to 35.38 Mha in 1993 (GWREC, 1997). The development of
agriculture is a key factor in rural environments.
Groundwater is an important source of water supply in the study area; water
supply comes mainly from dug wells and from boreholes that are found along major
streams and valleys. Selection of well sites for groundwater supply relies heavily on
traditional field methods using known water yielding sites as guidelines. In general a
systematic approach to groundwater exploration is lacking. A large portion of the
country is underlain by hard rock. Groundwater in hard rock aquifers is essentially
confined to fractured and/or weathered horizons. Therefore, extensive
hydrogeological investigations are required to thoroughly understand groundwater
conditions, and improve the agrarian economy of the country, which contributes
46% to the gross national product (Singh 1983).
5.2 Modelling Remote Sensing Data by Use of GIS
Remotely sensed data should not be analysed in a vacuum without benefit of
other collateral information, such as soils, hydrology, and topography (Price et al.,
1994; Ramsey et al., 1995). Unfortunately, many scientist promoting integration of
remote sensing and GIS assume that flow of data should be unidirectional‐ that is,
from the remote sensing system to the GIS. Actually, the backward flow of ancillary
data from the GIS to the remotely sensed data is very valuable (Stow, 1993). For
example, land cover mapping using remotely sensed data has been significantly
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improved by incorporating topographic information from digital terrain models and
other GIS data (Fraklin ad Wilson, 1992). Remote sensing can benefit from access to
accurate ancillary information to improve classification accuracy and other types of
modelling (Jensen et al., 1994).
5.3 Application of Remote Sensing and GIS in Groundwater Studies
Modern technologies such as remote sensing and geographic information
systems (GIS) have proved to be useful for studying geological, structural and
geomorphological conditions together with conventional surveys. Integration of the
two technologies has proven to be an efficient tool in groundwater studies
(Krishnamurthy et al., 1996; Sander 1997; Saraf and Choudhury, 1998). Satellite
images are increasingly used in ground water exploration because of their utility in
identifying various ground features, which may serve as either direct or indirect
indicators of presence of groundwater (Bahuguna et al., 2003 and Das et al., 1997).
The Geographic Information System (GIS) has emerged as a powerful tool in
analysing and quantifying such multivariate aspects of groundwater occurrence. It is
very helpful in delineation of groundwater prospect and deficit zones (Carver, 1991).
Lithology, lineament, landform, slope, vegetation, groundwater recharge and
discharge are common features used for many groundwater resource assessments in
hard rock areas. Remote sensing data provide accurate spatial information and are
cost‐effective compared with conventional methods of hydrogeological surveys.
Digital enhancement of satellite data improves maximum extraction of information
useful for groundwater studies. GIS techniques facilitate integration and analysis of
large volumes of data, whereas field studies help to further validate results.
Integrating all these approaches offers a better understanding of features controlling
groundwater occurrence in hard rock aquifers.
Groundwater is by definition subterranean. Though aerial photographs and
satellite imagery contain information about the uppermost layer of the earth’s crust
only, various studies have shown how remotely sensed data can contribute to
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hydrogeological investigations (Waters 1989; Krishnamurthy et al., 1996; Lloyd 1999
and Jackson, 2002). A few studies have attempted to establish relationships between
remotely sensed data and data related to groundwater in hard rocks. In certain
cases, the imagery proved to contain features which have a direct link to
groundwater discharges (Kresic, 1994). In hard rock terrain where water is restricted
to secondary porosity and thus to weathered zones, fractures and solution openings,
the evaluation of the hydrogeological significance of remotely sensed lineaments
(linear features identified as long, narrow, and relatively straight tonal alignments on
aerial photographs or on satellites images) attracted immediate attention and has
continued to do so (Knapp et al., 1994; Sander et al., 1996, 1997; Edet et al., 1998
and Tam et al., 2004). An interesting method of statistical evaluation of lineaments
significance in groundwater exploration has been described by Waters (1989).
Remote Sensing and Geographic Information System has become one of the
leading tools in the field of hydrogeological science, which helps in assessing,
monitoring and conserving groundwater resources. It allows manipulation and
analysis of individual layer of spatial data. It is used for analysing and modelling the
interrelationship between the layers. Remote sensing technique provides an
advantage of having access to large coverage, even in inaccessible areas. It is a rapid
and cost‐effective tool in producing valuable data on geology, geomorphology,
lineaments, slope, etc., that helps in deciphering groundwater potential zone. A
systematic integration of these data with follow up of hydrogeological investigation
provides rapid and cost‐effective delineation of groundwater potential zones.
Although it has been possible to integrate these data visually and delineate
groundwater potential zones, it becomes time consuming, difficult and introduces
manual error. In the recent years digital technique is used to integrate various data
to delineate not only groundwater potential zones but also solve other problems
related to groundwater. These various data are prepared in the form of thematic
maps using geographical information system (GIS) software tool. These thematic
maps are then integrated using ‘‘Spatial Analyst’’ tool. The ‘‘Spatial Analyst’’ tool
with mathematical and Boolyan operators is then used to develop models depending
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on the objectives of the problem at hand, such as delineation of groundwater
potential zones. In the recent years many workers such as Teeuw (1995), Shahid and
Nath (1999), Goyal et al., (1999), Saraf and Choudhary (1998) have used approach of
remote sensing and GIS for ground water exploration and identification of artificial
recharge sites. Jaiswal et al., (2003) have used GIS technique for generation of
groundwater prospect zones towards rural development. Krishnamurthy et al.,
(1996); Murthy (2000); Obi Reddy et al., (2000); Pratap et al., (2000); Singh, Prakash
(2002) and Lokesh and G.S. Gopalakrishna (2005), have used GIS to delineate
groundwater potential zone. Srinivasa Rao and Jugran (2003) have applied GIS for
processing and interpretation of groundwater quality data.
5.4 Data used and methodology
Following data products were used in the study area:
1) Cartosat ‐1 (PAN data) and Resourcesat (LISS III) Multispectral data.
2) Survey of India toposheets on 1:50,000 scale.
3) Field observations
4) Field observations, Preparing and integrating different thematic layers
viz., hydrogeomorphlogy, slope, drainage density, lineament density,
DEM, lithology, soil, land use/land cover
In the present study evaluation of groundwater potential in the area has been
attempted by using the satellite imagery (Plate 5.1) and preparing the different
thematic layers based on the image and integrating the various thematic maps in GIS
domain. Thematic maps pertaining to hydrogeomorphology, geology, drainage,
lineament, slope and DEM were prepared by using LISS III plus PAN merged data
coupled with Survey of India topographical sheets on 1: 50,000 scale and Geological
Survey of India geological map of the study area.
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5.5 Hydrogeomorphology of the study area
Automatic classification of geomorphological land units mainly focuses on
morphometric parameters (Giles and Franklin, 1998; Miliaresis, 2001; Bue and
Stepinski, 2006), which can describe the form of a land surface in relation to
landform formation processes (Jamieson et al., 2004).
Based on the satellite imageries of merged IRS of 1C and ID of LISS III (2001)
plus PAN data (2001) and topographic maps, different hydrogeomorphic units of the
study area have been mapped and are shown in the Fig. 5.1 The different
hydrogeomorphic units have been classified as Linear Ridge (LR), Residual hills (RH),
Inselberg (I), Pediment inselberg complex (PIC), Pediments (P), Shallow weathered
pediplains (PPS), Moderately weathered pediplains (PPM) and Valley fills (V). Based
on Lillesand and Kiefer (2002), the standard visual interpretation methods have been
adopted for this classification. The basic interpretation keys like specific tone,
texture, size, shape and association have been used. In the False Colour Composite
(FCC) of bands 2 3 4, denudational hills and residual hills exhibit dark green colour,
inselberg and pediment inselberg complex show dark green to grey, pediments
exhibit grey to medium grey, shallow weathered pediplains show light green and
moderately weathered pediplains and valley fills show light red to dark red colours.
5.5.1 Residual hills
These hills are formed as a result of complex erosional processes
predominantly by erosion, circum dedundation, weathering and mass wasting (Plate
5.2). The dip of strata controls the rate of denudation process in these structural hills
(Sreedevi et al., 2004).
Residual hills are the end products of the process of pediplanation, which
reduces the original mountain masses into a series of scattered knolls standing on
the pediplains (Thornbury, 1990). Residual hills (Fig. 5.1) as isolated hillocks with
moderate steep to very steep slopes forming low relief formed due to differential
erosion. The groundwater potential is very poor to poor and acts as runoff zone.
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Plate 5.1: Satellite Image of Hunsur Taluk
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Plate 5.2: Residual hill near Omkareshwara betta
Plate 5.3: Ayyapa Swami temple in Hunsur town on a linear ridge
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Plate 5.44: Linear ridge adjacent to the Uddur Canal
5.5.2 Linear Ridge
Linear ridges are generally, long intrusive features and are emplaces within
the pre‐existing fractures or where the fluid pressure is greater enough for them to
form their own fracture during emplacements. Geologically the linear ridges/dykes
are made up of pyroxene Granulite, Amphibolite, Dolerites and Charnokites. Linear
ridges mainly the runoff zones and prospects are very poor. General trend of linear
ridges are seen in NW‐SE direction. A curvy –linear ridge, (where Ohmkareshwar
temple is seen) is at the western side of Ramanahalli village and its 851 m height and
4 km long (Plate 5.3). Other linear ridges are located on south eastern side of the
study area are of less height but more elongated (Plate 5.4). Linear ridges are mainly
run‐off zones and the prospects are very low.
5.5.3 Pediment Inselberg Complex
This complex consists of small isolated island like hills standing out
prominently in a domal form because of their resistance to weathering. The
pediments dotted with a number of inselbergs which cannot be separated and
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mapped as individual units are referred to as Pediment Inselberg Complex having
moderate to strong slope On FCC, these features look as dark green to green in
colour with course to medium texture. These are seen mainly in the North eastern
part of the study area and also as small patches in the North western part. From the
groundwater point of view, these units are poor to moderate and contribute for
limited to moderate recharge.
5.5.4 Pediment
Pediment is a broad and gently sloping rock erosion surface of low relief
extending from the periphery of the debris slope of the hill, until it meets the next
geomorphic unit (Plate 5.5). It is a clear cut rock surface with or without soil cover,
which normally encircles a hill. The low moisture content of this unit gives a bright
signature on the imagery, especially around the hill. Pediments are found in the
study area mainly in the northern and western part. Usually these pediments do not
favour much infiltration and they form run‐off and recharge zones with poor to
moderate groundwater prospects along favourable structural features like fractures
and lineaments.
5.5.5 Shallow Weathered Pediplains
These are areas of gentle sloping, and are characterized by high porosity,
permeability and infiltration. They are seen in the eastern, western and southern
part of the study area.
5.5.6 Moderately Weathered Pediplains
These weathered zones are covered with more vegetation (Plate 5.6). The
thickness of the weathered zones ranges from 20–25 m as observed in the casing
provided to borewells. On the FCC, they exhibit light red to dark red and fine texture.
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Plate 5.5: Pediment near Kalakunike and Naganaham
Plate 5.6: Pediplain near Madapura village
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5.5.7 Valley fills
These units occupy the lowest reaches in topography with nearly level slope
(Plate 5.7). These landforms are almost linear forms reflecting influence of
fractures/joints. The valley fills are present along the stream courses varying in
thickness and comprising of both alluvial and colluvial materials ranging in size from
pebbles, sand, fine silt and other detrital materials resulting in high infiltration rate.
The valley fills have been identified in the study area and are developed in gneisses.
Plate 5.7: Valley Fills near Hanagodu
Geomorphology map of the study area has been prepared by combining the
different geomorphologic units described above and is shown in Fig. 5.1.
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Figure 5.1: Geomorphology map of the study area
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5.6 Slope Analysis
Slope analysis is an important parameter in geomorphic studies. The slope
elements in turn, are controlled by the climatomorphogenic process in the study
area. An understanding of slope distribution is essential, as slope map provides data
for planning, settlement, mechaniazation of agriculture, reforestation,
deforestration, planning of engineering structures, morphoconservation practices,
etc.
Slope is one of the factors controlling the infiltration of groundwater into
subsurface; hence an indicator for the suitability for groundwater prospect. In the
gentle slope area the surface runoff is slow allowing more time for rainwater to
percolate, whereas high slope area facilitate high runoff allowing less residence time
for rainwater hence comparatively less infiltration. Slope plays a key role in the
groundwater occurrence as infiltration is inversely related to slope. A break in the
slope (i.e. steep slope followed by gentler slope) generally promotes an appreciable
groundwater infiltration (Saraf et al., 1998).
In the present study, the slope analysis has been carried out for the study
area and the topographic information has been collected from Survey of India
topographic maps on 1:50,000 scales in which ground contours of 20 m interval have
been used for the analysis. From the TIN model generated in the Dem model, the
slope map has been prepared using the surface analysis in the 3D analysis of Arcmap
(9.1 v). The guidelines of All India Soil and Land Use Survey (AIS and LUS) on slope
categories (Vide Soil Survey Manual, IARI, 1971) have been adopted for classification
of different category of slope (Table 5.1). The maximum and minimum elevations are
960 m and 740 m respectively. Slope map for the study area has been prepared and
presented (Fig. 5.2).
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Sl.No. Slope Category Slope %
1 Nearly level 0‐1
2 Very gently sloping 1‐3
3 Gently sloping 3‐5
4 Moderately sloping 5‐10
5 Strong sloping 10‐15
6 Moderately steep to 15‐35
7 Very steep slope > 35
Table 5.1: Classification of different slope category according Guidelines of All India Soil and Land Use Survey (AIS&LUS)
5.7 Drainage Density
Drainage pattern reflects the characteristic of surface as well as subsurface
formation. Drainage density (in terms of km/km2) indicates closeness of spacing of
channels as well as the nature of surface material. The more the drainage density,
the higher would be runoff. Thus, the drainage density characterizes the runoff in the
area or in other words, the quantum of relative rainwater that could have infiltrated.
Hence lesser the drainage density, higher is the probability of recharge or potential
groundwater zone. The drainage density in the area has been calculated after
digitization of the entire drainage basin pattern which was discussed in detail in
chapter 4. Here the drainage density of the study area is shown in Fig. 5.3. It varies
from 0.91 to 2.45 km/km2. The high drainage density area indicates low‐infiltration
rate whereas the low‐density areas are favourable with high infiltration rate.
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Figure 5.2: Slope map of the study area
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Figure 5.3: Drainage map of the study area
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5.8 Lineament analysis
Lineaments like joints, fractures and faults are hydrogeologically very
important and may provide the pathways for groundwater movement (Sankar,
2002). Presence of lineaments may act as a conduit for ground water movement
which results in increased secondary porosity and therefore, can serve as
groundwater prospective zone (Obi Reddy et al., 2000). Lineaments give a clue to
movement and storage of groundwater (Subba et al., 2001) and therefore are
important guides for groundwater exploration. Recently, many groundwater
exploration projects made in many different countries have obtained higher success
rates when sites for drilling were guided by lineament mapping (Teeuw, 1995).
Lineaments are large scale linear features which expresses itself in terms of
topography which is in itself an expression of the underlying structural features.
From the ground water point of view such features includes valleys controlled by
folding, faulting and jointing, hill ranges and ridge lines, abrupt truncation of rocks,
straight segments of streams and right angled offsetting of stream courses
(Ravindran et al., 1995) as these linear features are commonly associated with
dislocation and deformation they provide the pathways for groundwater movements
(Small, 1970).
Lineaments are important in rocks where secondary permeability and
porosity dominate the intergranular characteristics combine in secondary openings
influencing weathering, soil water and ground water movements. The fracture zones
forms an interlaced network of high transmissivity and acts as ground water conduits
in massive rocks in inter fractured areas. The lineament intersection areas are
considered to be good ground water potential zones. The areas with higher
lineament density and topographically low elevated grounds are considered to be
the best aquifer zones. All the linear features in the study area are marked on the
lineament map. These lineaments range between a few kilometres to several
kilometres in length (Fig. 5.4). The remote sensing techniques have given further
boost to lineament studies as on a satellite image/aerial photograph. Identification of
lineaments/linear features becomes quite easy because of the synoptic view,
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availability of data in different spectral bands and receptivity. Even lineaments of
inaccessible terrains can be mapped and analyzed using remotely sensed data. Dykes
and ridges also appear as linear features on image but can be segregated from other
linear features because of the positive relief (Ganesh Raj, 1994). On a satellite image,
the lineaments can be easily identified by visual interpretation using tone, texture,
pattern and association (Gupta, 2003). It has been suggested that south India has
been subjected to certain epeirogenic uplifts since the Jurassic (Vaidyanadhan,
1962). Lineaments are the main features that control the occurrence of groundwater
in the study area. Secondary porosity is imparted by joints and fractures in the areas
of higher lineament density. The lineaments of the study area have been traced from
the satellite data of IRS 1C and ID of ‐LISS III imagery plus PAN. A number of mega‐
and micro‐lineaments are identified from the satellite imagery, further checked by
field studies, and demarcated at a 1:50,000 map scale (Fig. 5.4)
River Lakshmantirtha is all along following a fracture zone in the study area.
Lakshmantirtha River flows in NE‐SW direction and the nature of the river is this
sector clearly indicates the presence of NE‐SW lineaments. River takes a sharp turn
near kirijaji farm (Plate 5.8). The sharp bend of the river is evidence of these
fractures.
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Plate 5.5: Lakshmantirtha River taking a sharp westerly turn near Kirijai village
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Figure 5.4: Lineament map of the study area
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5.8.1 Rose Diagram
In earth sciences, circular diagrams and circular statistics are mostly used for
orientation distributions (Graham Borradaile, 2003). For representing the orientation
distribution of the lineaments a rose diagram has been constructed with the help of
Rozeta software(V.2), (Fig. 5.5 ). A Rose Diagram is used to display the linear features
for angles ranging from 1 through 180 degrees simultaneously (Davis, 1986).
The total lineament length of the study area is around 354 km. Lineaments
lengths varied from around 0.14 to 3.78 km, with an average of 1.0 km. Lineaments
were grouped according to their orientation in 18 classes, each one 10° wide. A
frequency rose diagram of lineaments is plotted, from which major lineament
orientations are revealed. The frequency of orientation of the lineaments is shown in
Table 5.2.
Sl. No. Angle Frequency
1 0‐10 25
2 10‐20 13
3 20‐30 18
4 30‐40 13
5 40‐50 17
6 50‐60 17
7 60‐70 15
8 70‐80 14
9 80‐90 28
10 90‐100 10
11 100‐110 16
12 110‐120 31
13 120‐130 20
14 130‐140 31
15 140‐150 44
16 150‐160 30
17 160‐170 21
18 170‐180 29
Table 5.2: Frequency distribution of lineament with respect to their angle
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The three lineament sets (NE–SW, NW–SE and latitudinal) exist all over the
Precambrian region in India although the actual orientation with respect to the
azimuth might differ from place to place by a few degrees (Vaidyanadhan et al.,
1971). As seen in the rose diagram (Fig. 5.5) majority of the lineaments of the study
area are trending towards NW–SE direction, which is parallel to the faulting of west
cost of India indicating these lineaments are syngenetic and sympathetic (Ganesh
Raj, 1994)
Figure 5.5: Rose Diagram of Lineament of the study area
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Figure 5.6: Screen shot of frequency distribution of lineaments in Rozeta software (v.2)
5.8.2 Lineament density map
The lineaments present in the study area have varying dimensions. Based on
the concentration and length of lineaments, a lineament density map was prepared.
Lineament delineated using satellite images were converted into zones of different
lineament densities, viz. high, moderate, low to nil using spatial density analysis in
GIS domain (Fig. 5.7).
The lineament‐density map reveals the variations in the potential for
obtaining groundwater in the basin. According to Stephen Mabee et al., (1994), from
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a study of regional‐scale lineament analysis for fractured bedrock aquifers,
concluded that wells located on or near fracture‐correlated lineaments are generally
more transmissive. High porosity and hydraulic conductivity zones are associated
with lineaments (Kukillaya et al., 1999; Subba Rao and Prathap Reddy 1999;
Harinarayana et al., 2000; Subba Rao et al., 2001). Mabee et al., (1994) have found
that the normalised transmissivity near the lineaments is high. A good relationship
exists between higher fracture densities and higher well yields (Magowe and Carr,
1999). Generally, it is expected that the thickness of weathered/fractured rocks is
greater along the lineaments hence, the lineaments are assumed to have a control
on the availability of groundwater.
Although lineaments have been identified throughout the area, from the
groundwater prospecting point of view the lineaments in the pediplain or valley fill
are of importance. Those across the denudational hills (DH), residual hills (RH), in the
high‐drainage density and high‐slope area or in the area occupied by clay zones are
of less significance as there could be high runoff along them and these may act only
as conduit to transmit infiltrated rain water.
5.9 DIGITAL ELEVATION MODELING (DEM)
The availability of digital elevation models (DEMs) is critical for performing
geometric and radiometric corrections for terrain on remotely sensed imagery, and
allows the generation of contour lines and terrain models, thus providing another
source of information for analysis. A digital elevation model (DEM) is a well known
means of representing any internal or superficial relief of the Earth at any scale
where elevation differences yield relevant geological information. Application of
DEM is very useful in deciphering geomorphic and structural features, especially
those of large‐scale edifices and deposits which cannot be readily studied or
identified in the field (e.g., Cappadoccia, Turkey: Froger et al., 1998; Socompa, Chile:
Wadge et al., 1995; Etna, Italy: Favalli et al., 1999). In the present study, an attempt
has been made to create DEM for the study area by incorporating the following input
data.
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Figure 5.7: Lineament density map of the study area
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5.9.1 Spot Height
Spot height values are the height values of points on the earth’s surface. They
normally represent heights above mean sea level. Spot height values of the study
area are portrayed on maps with point symbols and annotation of the numerical
value spot heights or soundings (Fig. 5.8).
5.9.2 Contours: Lines
Contour lines connect a series of points of equal elevation and are used to
illustrate topography, or relief, on a map. They show the height of ground above
Mean Sea Level (M.S.L.) in either feet or metres and can be drawn at any desired
interval Imhof (1982).
5.9.3 Generating DEM for the study area
For this purpose the contours lines of the topographic maps of the survey of
India (57D/3, 57D/4, 57D/7 and 57D/8) are digitized keeping a 20 m interval between
the contour lines. The digitized contours represent elevation between 760 m to 960
m. In the first step, by activating the 3D analysis in Arc map (9.1 v) the input data,
contour lines and spot heights are converted into TIN models (Fig. 5.9). The TIN
model is converted into raster model using the tools in Arcmap (9.1 v), (Fig. 5.10). In
the next step the raster model is exported in to ArcScene (9.1 v) in which the original
raster data set of elevation can be multiplied by integers to get different height‐
exaggeration. A DEM is created and the exaggerated view of it is shown in different
angles (Fig. 5.11 and Fig. 5.12). Varying sun azimuths and angles are input
parameters for the illumination process in order that output images can display
enhancement on different features. By observing the 3D view of the DEM it can be
observed that the in Fig. 5.11 and Fig. 5.12 blue zones have maximum topographic
gradient (denudational hill, residual hills and inserberg), green zones have medium to
gentle gradient (pediment inserberg complex and pediment zone) and the yellow to
brown have very low topographic gradient (shallow and moderately weathered
pediplains and valley fills.
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Figure 5.8: Contour and spot height if the study area
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Figure 5.9: TIN model of the terrain of the study area
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Figure 5.10: Raster model of the terrain of the study area
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Figure 5.11: Exaggerated perspective view of DEM of the study area
Figure 5.12: Exaggerated perspective view of DEM of the study area
136
5.10 Land use/land cover
Land use can be defined as the use of lands by humans, usually with emphasis
on the functional role of land in economic activities. Land use in an abstraction not
always directly observable by even the closest inspection. One cannot see the actual
use of a parcel of land, only the physical artifacts of that use (Campbell, 2002). In
contrast, land cover, in its narrowest sense, often designates only the vegetations,
either natural or manmade, on the earth’s surface at a specific time of observation.
In a much broader sense, land cover designates the visible evidence of land use, to
include both vegetative and non vegetative features. In this meaning, sense forest,
powered land, urban structures all constitute land cover. Whereas land use in
concrete, and therefore is subject to direct observation
Discrimination of different land use/land cover classes is feasible using multi‐
spectral data from satellites with its synoptic coverages near real time, base line
information and its relative economy over other methods of survey. The technique of
remote sensing has been widely applied by various workers in India and abroad for
land use/ land cover studies (Gautam and Narayanan; 1983; Sharma et al., 1984; Jain
1992; Shreedhara et al., 1992 and Surendra Singh et al., 1993). Using remotely
sensed data, the accurate assessment of existing land use/land cover patterns and
their spatial extent in the study area is essential for conservation and management
of water and natural resources.
5.10.1 Details of Land use/Land Cover classes and spatial distribution of the area
In the present study, the land use / land cover maps were prepared using
satellite images on 1:50,000 scale in conjunction with collateral data like topographic
maps on the same scale (Fig. 5.13). The four main land use / land cover classes like
built‐up land (settlements), agricultural land, forest , wastelands and water bodies
were delineated based on the image characteristics like tone, texture, shape, colour,
association, background, etc., based on standard visual interpretation techniques
suggested by Lillesand and Kiefer (2002).
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5.10.1.1 Build up land (Settlements)
Built‐up Land is comprised of areas of intensive use with much of the land
covered by structures. Included in this category are cities, towns, villages, strip
developments along highways, transportation, power, and communications facilities,
etc.
5.10.1.2 Agricultural land
It is defined as the land primarily used for farming and for production of food,
fiber, and other commercial and horticultural crops. It includes crop land, fallow and
agricultural plantations.The crops which are grown in the region include crops grown
either in kharif or rabi or double crop (Kharif and Rabi) seasons. Most of the double
cropped areas are found in command areas like major tanks, either side of stream
where deep clay loamy to clayed soil patches found. The double crop area consists
mainly of paddy, ragi and groundnut. The agricultural plantations prominently seen
in the study area during ground truth verification was mostly of coconut plantation.
Fallow land too consists of a small portion of the agricultural land, which is taken up
for cultivation but is temporarily allowed to rest, un‐cropped for one or more
seasons.
5.10.1.3 Forest:
It is an area (within the notified forest boundary) bearing an association
predominantly of trees and other vegetation types capable of producing timber and
other forest produce. This class is distributed in the south, south‐west, east and
central parts of the study area (Fig. 5.13). The sub‐classes under this class have been
identified and described.
5.10.1.4 Scrub forest
It is described as a forest where the vegetative density is less than 20 % of the
canopy cover. It is the result of both biotic and abiotic influences. Scrub is a stunted
tree or bush/ shrub.
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5.10.1.5 Forest plantation
It is described as an area of trees of species of forestry importance and raised
on notified forestlands. This sub‐class consists mainly of Eucalyptus plantations as
observed during field visit.
5.10.1.6 Wasteland
It is described as degraded land, which is deteriorating due to lack of
appropriate water and soil management or an account of natural causes, which can
be brought under vegetative cover with reasonable effort. Stony wastes are classified
under the waste land and are defined as the rock exposures of varying lithology often
barren and devoid of soil cover and vegetation. They occur amidst forest hills as
openings or scattered as isolated exposures or loose fragments of boulders or as
sheet rocks on plateau and plains, in the study area.
5.10.1.7 Water body
Water body is an area of impounded water, aerial in extent and often with a
regulated flow of water. It includes man‐made lakes / tanks besides natural lakes,
rivers, streams and canals.
5.10.1.8 Streams
These are the natural course of flowing water on the land along definite
channels. They include a small stream to a big river and its branches. These may be
perennial or non‐perennial. The small streams are observed in the study area which
is finally joining the Lakshmantirtha River.
5.10.1.9 Tanks
Tanks are the natural or man‐made enclosed water body with a regulated
flow of water. These features are medium/smaller in aerial extent when compared to
reservoirs with limited use. Based on the observations on the satellite image in all
the three seasons, tanks may be differentiated into tank dry and tank water spread.
139
Figure 5.13: Land use/Land cover of the study area
140
5.11 Lithology and Soil map
Lithology and soil thematic layers have been prepared and discussed in
chapter 2. These two thematic maps have been considered for preparing the
groundwater prospecting map.
5.12 Groundwater prospecting
The integration of various thematic maps describing favourable groundwater
zones, into a single groundwater potential map has been carried out through the
application of GIS. It requires mainly three steps.
1. Spatial database building
2. Spatial data analysis and
3. Data integration.
5.12.1 Spatial data base building
The tools provided in Arc Catalogue have been used to create the scheme for
feature data sets, tables, geometric networks and other items inside the database.
5.12.2 Spatial Data analysis
It is an analytical technique associated with the study of locations of
geographic phenomena together with their spatial dimension and their associated
attributes (like table analysis, classification, polygon classification and weight
classification). The various thematic maps as described above have been converted
into raster form considering cell width as 100 m to achieve considerable accuracy.
These were then reclassified and assigned suitable weight following the patterns as
used by Srinivasa Rao and Jugran (2003); Musa K.A (2000); Krishnamurthy et al.,
(1996) and Saraf and Choudhary (1998). These are given in Table 5.3.
141
Hydrogeomorphology
Categories Wheightage
assigned
Residual Hills 1
Linear Ridge 1
Dyke Ridge 1
Pediment Iselberg Complex 1
Dissected Pediment 2
Pediplain shallow weathered 3
Pediplain moderately weathered 4
Valley fills 5
Lithology
Massive Gneiss 1
Charnokites 1
Amphibolites 1
Dolerite Dykes 1
Weathered Gneiss 3
Soil
Clay 1
Fine sandy clay 2
Gravel clay‐silty, clay‐clay 2
Coarse sandy clay‐clay 3
Fine sandy clay loam 3
Sandy loam‐sandy clay 3
Coarse sandy clay 4
Slope
0‐1 % 4
1‐3 % 3
3‐5 % 2
5‐10 % 1
142
10‐15 % 1
15‐35 % 1
>35 % 1
Drainage Density
Low density/Coarse texture (0‐1 km/km²) 4
Medium density/Medium texture(1‐2 km/km²) 2
High density/High texture (2‐4 km/km²) 1
Very high density/Superfine texture(4‐6 Km/km²) 1
Lineament Density
High 3
Medium 2
Low 1
Relief
740‐780 5
780‐820 4
820‐860 3
860‐900 2
900‐960 1
Land use/land Cover
Settlement 1
Waste land 2
Forest 3
Scrub 4
Agriculture 5
Water Body 6
Table 5.3: Different parameters considered for groundwater prospects evaluation and their class weights
143
5.12.3 Data integration
Each thematic map such as geology, geomorphology, lithology, soil, drainage
density, lineament, slope, DEM and land use/land cover provides certain clue for the
occurrence of groundwater. In order to get all this information unified, it is essential
to integrate these data with appropriate factors. Although, it is possible to
superimpose this information manually, it is time consuming and error may occur.
Therefore, this information is integrated through the application of GIS. Various
thematic maps are reclassified on the basis of weight assigned and brought into the
‘‘Raster Calculator’’ function of Spatial Analysis tool for integration. A simple
arithmetical model has been adopted to integrate various thematic maps by
averaging the weight. The overlay analysis allows a linear combination of weights of
each thematic map with the individual capability value with respect to groundwater
potential.
The formula of the groundwater potential model (GP) is shown as below:
GP = Hg + Lt +Sl + S+ Dd+ Ld + Te + Lu
Where; Hg = Hydrogeomorphology
Lt= lithology (geology),
Sl= Soil
S=Slope
Dd drainage density
Ld= lineament density,
Te= topography elevation (relief) and
Lu = land use
The final map has been categorized into five zones, from groundwater potential
point of view; Excellent, Good, Good‐Moderate, Poor and Poor‐Nil (Run‐off zone).
The groundwater potential map thus derived is shown in Fig. 5.14. The extent of
various zones in terms of percentage of area is shown in Table 5.4.
144
Figure 5.14: Groundwater prospect map of the study area
145
Sl. No. Prospective zone Area in Km² Percentage
1 Excellent 50.72 8.09
2 Good 150.63 24.03
3 Good‐Moderate 300.18 47.89
4 Poor 52.42 8.33
5 Poor‐Nil(Run‐off zone) 72.82 11.61
Table 5.4: Result showing potential zone without integrating yield layer
Good‐moderate groundwater prospect dominates the area and occupies
47.89% of the total area. The next zone to good‐moderate is the good zone which
occupies about 24.03% of the area. The excellent prospect zone is only marked by
8.09 % of the total area and only a small part of the studied area is occupied by these
landforms. Areas with poor prospects constitute 8.33% of the total study area; and
the poor‐Nil is about 11.61 %. In this area the groundwater prospects is very nil since
this area does not favour much infiltration and it is basically a run‐off zone. It is
imperative that ground resources are deficient in the hilly regions and in the runoff‐
zones which together occupy almost 20% of the total area. Hence these areas should
be taken up for water resource management with development of water harvesting
structures.
5.13 Water Level Analysis
Any anomaly in the atmosphere will have impacts on every component of the
whole hydrologic cycle (Loaiciga et al., 1996). The groundwater is the invisible and
ultimate indicator of the atmospheric anomalies in the hydrologic cycle. The
occurrence of drought and heavy precipitation are the most important climatic
extremes having both short and long‐term impacts on the groundwater availability.
These impacts include changes in groundwater recharge resulting from the erratic
behaviour of the annual and seasonal distribution of precipitation and temperature;
changes in evapotranspiration resulting from changes in vegetation; and possible
increased demands for groundwater as a backup source of water supply (Alley,
2001). However, the link between climate variability and the groundwater response
146
is more complicated than that with the surface water regime. Its dynamics is rather a
stable system, and responds slowly with a time lag to climate variability. Further, the
diverse aquifer characteristics respond differently to the surface stresses (Chen et al.,
2004; Environment Canada, 2004). During the past few years, India has experienced
extreme weather events such as droughts, floods and cyclones more frequently.
However, the effect of drought is more pronounced given the quantum of economic
and environmental losses. Drought in the year 2002 was one of the severest in the
history of India which affected 56% of the geographical area and the livelihoods of
300 million people in 18 states (Samra, 2004). The groundwater level declined
significantly, which will take years to recharge and recover in many parts of the
country. The groundwater in all the rock formations occurs in unconfined and semi‐
confined aquifers (Raju et. al., 1994). The permeabilities of all the formations depend
on secondary porosity, except for alluvium where the porous material (gravel and
sand) is highly permeable.
Identifying the rainfall‐water level relationship is very important for the
efficient management of the water resources and for undertaking precautionary
measures to prevent potential natural disasters (Dimitriou and Zacharias Ierotheos,
2006). In fact interpretation of water level data of borewell also helps to determine
the relationship of the productivity of the borewells and their proximity to the
lineament. So if there is a positive correlation between the lineament density and
yield of the borewell, then the locations of lineaments could be useful in locating
zones of high productivity in the aquifer, and areas of high recharge potential in the
unsaturated zone of the aquifer.
In this study an attempt has been made to identify the groundwater level
trend to know the type and amount of thrust of extreme climate events in
conjunction with anthropogenic pressure on the groundwater resources of the study
area, using non‐parametric statistical methods. Before this the spatial distribution of
pre‐monsoon and post‐monsoon of water level and grid deviation of water table
have been discussed. The groundwater level data of the national hydrograph
network stations during the period 1990–2007 collected from Central Ground Water
147
Board and Department of Mines and Geology, Bangalore, are used for the analysis.
The water level data of the observation wells are shown in Table 5.5.The descriptive
statistics for the both pre and post‐monsoon seasons have been shown in Table 5.6
and Table 5.7. Based on these data the box plot for both the seasons is presented in
Fig. 5.15 and Fig. 5.16. In descriptive statistics, a box plot is a convenient way of
graphically depicting groups of numerical data through their five‐number summaries
(the smallest observation (sample minimum), lower quartile (Q1), median (Q2),
upper quartile (Q3), and largest observation (sample maximum). As clearly indicated
in both the figures (Fig. 5.15 and Fig. 5.16) ground water level decreases in pre‐
monsoon where there is no occurrence of rainfall and just after monsoon the water
level depth decreases and the recharge begins with the onset of monsoon. The
Udavepu, Somanahalli and Comibatore colony (post monsoon) observation wells
show a larger variation of the water level when compared to the other stations.
Another important significant variation is in the year 2002. Almost in all the
observation wells of both the seasons the groundwater level has reached its
minimum level during this year where in the graph it has been represented as an
outlier data.
5.13.1 Spatial distribution of water level data of the study area
Inverse Distance Weight method (IDW), which was described in detail in
chapter 3, is used to show the spatial distribution of water level for both the seasons
(Fig. 517 and Fig. 5.18). By observing both the figures it is understood the eastern
part of the study area in both the pre‐monsoon and post‐monsoon seasons shows a
more drawdown of water level compare to its western side. Another significant
observation made was the seasonal fluctuation of water level. The groundwater
levels of the network observation wells are very sensitive to the monsoon rainfall,
and any irregularity in rainfall directly influences the groundwater levels. The south‐
west monsoon contributes the major portion of the annual rainfall which in turn
recharges the groundwater. This is the reason why during post‐monsoon the water
levels of the observation wells have increased compare to the pre‐monsoon where
decline of water level in the observation wells were noticed.
148
Name Years
Considered
Pre‐monsoon
Post‐monsoon
pre –monsoon
AMSL
post –monsoon
AMSL AMSL
avg. of seasons (amsl)=xi
x grid
deviation
Kamagowdanahalli
(dug well) 1990‐2007 12.36 9.03 787.64 790.97 800.00 795.48 800.61 ‐5.13
Hunsur ( borewel) 1990‐2007 7.59 6.65 773.49 774.43 781.08 777.75 800.61 ‐22.86
Goudegere (dug well)
1990‐2007 9.42 6.82 799.37 801.97 808.79 805.38 800.61 4.77
Kattemalavadi (dug well)
1990‐2007 10.79 8.52 771.15 773.42 781.94 777.68 800.61 ‐22.93
Somanahalli (bore well)
1990‐2007 17.82 16.93 774.01 774.92 791.18 783.05 800.61 ‐17.56
Coimbatore
Colony(bore well) 1990‐2007 12.25 9.49 787.75 790.51 800.00 795.26 800.61 ‐5.35
Karnakuppe 1990‐2007 4.93 1.68 835.07 838.32 840.00 839.16 800.61 38.55
Chikka Hunsur 1990‐2007 11.53 7.46 790.45 794.52 801.97 798.25 800.61 ‐2.36
Udavepura 1990‐2007 13.22 6.74 826.78 833.26 840.00 836.63 800.61 36.02
Hanagodu 1990‐2007 7.50 4.65 794.39 797.24 801.89 799.57 800.61 ‐1.04
Gowdegere 1990‐2007 5.55 3.00 794.45 797.00 800.00 798.50 800.61 ‐2.11
Table 5.5: Water Level data of pre and post monsoon of observation wells of the study area
149
N Minimum Maximum Mean Std. Deviation Variance
Kamagowdanahalli 18 10.58 15.20 12.3650 1.08870 1.185
Karnakuppe 18 2.70 8.30 4.9289 1.42899 2.042
Chikka Hunsur 18 6.13 15.50 11.5283 2.51246 6.312
Udavepura 18 8.25 19.38 13.2228 3.08126 9.494
Hunsur 18 6.82 9.06 7.5906 .63036 .397
Gaudegere 18 3.69 9.22 5.5489 1.54576 2.389
Hanagodu 18 4.89 11.26 7.5033 1.72264 2.967
Gaudeger Dug well 18 6.80 13.50 9.4222 1.70396 2.903
Kattemalavadi 18 8.91 13.35 10.7911 1.19074 1.418
Somanahalli 18 14.33 22.80 17.4778 2.80580 7.872
Coimbatore colony 18 9.90 15.15 12.2522 1.40220 1.966
Valid N (listwise) 18
Table 5.6: Descriptive statistics of water level data of pre‐monsoon
Figure 5.15: Box plot for pre‐monsoon water level data
150
N Minimum Maximum Mean Std. Deviation Variance
Kamagowdanahalli 18 7.10 12.40 9.0333 1.30879 1.713
Karnakuppe 18 .83 3.22 1.6761 .67865 .461
Chikka Hunsur 18 4.26 11.10 7.4556 1.68062 2.824
Udavepura 18 2.20 19.13 6.7394 5.26057 27.674
Hunsur 18 4.60 8.18 6.6539 1.10605 1.223
Gaudegere 18 .63 7.90 3.0000 1.81594 3.298
Hanagodu 18 2.64 8.34 4.6456 1.62364 2.636
Gaudegere Dug well 18 4.45 9.38 6.8161 1.41057 1.990
Kattemalavadi 18 5.70 11.60 8.5150 1.66090 2.759
Somanahalli 18 12.31 22.41 16.3967 3.48162 12.122
Coimbatore colony 18 4.65 14.00 9.4856 2.81780 7.940
Valid N (listwise) 18
Table 5.7: Descriptive statistics of water level data of post‐monsoon
Figure 5.16: Box plot for post‐monsoon water level data
151
Figure 5.17:Spatial distribution of water level during pre‐monsoon
152
Figure 5.18: Spatial distribution of water level during post‐monsoon
153
5.13.2 Grid Deviation Water Table
Grid deviation method applied in other quantitative studies appears to
provide a more convenient form of representation of hydrogeological variables (Saha
and Chakravarthy, 1963). To evaluate the recharge‐discharge zones this method is
widely adopted (Narasimha Prasad, 1984; Balasubramanian, 1986; Subramanian,
1994; Sakthimurugan, 1967 and Harinarayanan, 2000). It is objective, more
informative and brings out more sharply the regional trend by eliminating the local
interference (Biswas and Chaterjee, 1967). Grid deviation water tables for the study
area have been prepared by using the following methodology.
1. Bimonthly water levels, measured below ground level have been recalculated
to water level altitude Above Mean Sea Level (AMSL).
2. An average elevation of water table for each observation well has been
computed for the months from January to December
3. Annual average water level of each well has been computed. This is called the
well average.
4. Using the well average of wells, a zonal average has been computed for
watershed and it is called the grid average.
5. The deviation of values between well averages and the grid average for all
wells have been computed.
6. The deviation can be used to prepare a thematic contour map called grid
deviation groundwater table map.
The grid deviation water level and well average of the area is presented in
Table 5.5. The grid deviation water table map of the study area is given in Fig. 5.19.
The positive zones are recharge zones and negative zones are discharge zones, and
are lying nearer to confluence point. The wide spacing of contours and the
disposition in discharge is suggestive of flat to gentle hydraulic gradient of water
table and moderate permeability of the formation. It is found that the area under the
discharge is more that the recharge zone. The normal groundwater potentiality is
expected to be higher in the discharge zones than the recharge zones
(Balasubramanian, 1986).
154
Figure 5.19: Grid deviation map of the study area
155
5.14 Statistical analysis of waterlevel data of borewell/openwell of the study area
It has been observed during the last five decades that, percentage
groundwater utilizations have almost doubled. There are arguments that extensive
rice and wheat growth has encouraged the people to extract more and more
groundwater causing decline in the water table. The declining water table reduces
runoff due to base flow and hence the inflow to a wetland (Sanjay k. Jain et. al.,
2008,). For detecting the trend in changes of the water level data statistical trend
analysis is performed.
5.14.1 Nonparametric test for trend detection
Recently, the Mann–Kendall non‐parametric statistical procedure given by
Mann (1945) and Kendall (1975) has been extensively used to assess the significance
of monotone trends in hydro‐meteorological time series such as precipitation,
temperature and stream flow (Gan, 1998; Zhang et al., 2001; Burn and Elnur, 2002;
Xu et al., 2003 and Yang et al., 2004). The non‐parametric statistical tests are flexible,
and can handle the idiosyncrasies of data like presence of missing values, censored
data, seasonality and highly skewed data. This test was later on modified by Helsel
and Frans (2006) to form the Regional Kendall (RK) test for trend. In this form, trends
at numerous locations within a region are tested to determine whether the direction
of trend is consistent across the entire region. Like the Seasonal Kendall test, the
Regional Kendall test is an “intrablock” test (Van Belle, G. and Hughes, J. P., 1984).
Test statistics are computed on each block of data separately, and the overall test
combines the individual test statistics so that no cross‐block comparisons are made.
For the Regional Kendall test, the blocking factor is location. If some locations exhibit
an upward trend while others exhibit a downward trend, their S statistics will cancel
out, and no consistent trend in the same direction across the locations will be found.
The Regional Kendall test looks for consistency in the direction of trend at each
location, and tests whether there is evidence for a general trend in a consistent
direction throughout the region. The Regional Kendall test substitutes location for
season and computes the equivalent of the Seasonal Kendall test. For computing the
156
Regional Kendal test, the water level data of the study area was processed on the
computer coded program developed by USGS (2005). The performance of this
program was explained in detail when Mann‐Kendal and Seasonal Kendal test was
explained. In this program the third format (itype = 3) produces the Regional Kendall
(RK) test.
5.14.2 Output of Regional Kendal Test of Water Level Data of Pre and Postmonsoon (19902007)
The regional Kendal test was performed on pre and post‐monsoon water
table data. Normally, the groundwater levels are recorded four times in a year such
as the pre‐monsoon, monsoon), post‐monsoon and irrigation periods. The unit of the
groundwater level records is meter below ground level (m.b.g.l). The pre‐ and post‐
monsoon monitoring occasions are more important as they reflect the influence of
both natural and anthropogenic intervention more accurately. The out puts of the
regional Kendal test are presented in the following Tables 5.8 and 5.9.
Regional Kendall Test for Trend US Geological Survey, 2005
Data set: Pre‐monsoon‐ Regional Kendal test, input type 3
The record is 18 years at 11 locations beginning in year 1990.
The tau correlation coefficient is 0.136
S = 224. z = 2.585 p = 0.0097
The estimated median trend throughout the region during years 1990
through 2007 is:
Change in Y = 0.5000E‐01 per year.
Table 5.8: Regional Kendal test output of pre‐monsoon
157
Regional Kendall Test for Trend US Geological Survey, 2005
Data set: Post‐monsoon‐ Regional Kendal test, input type 3
The record is 18 years at 11 locations beginning in year 1990.
The tau correlation coefficient is 0.128
S = 211. z = 2.432 p = 0.0150
The estimated median trend throughout the region during years 1990
through 2007 is:
Change in Y = 0.5359E‐01 per year.
Table 5.9: Regional Kendal test output of post‐monsoon
5.14.3 Interpretation of Trend Analysis of Water Level Data
In both the tests, the level of significance was tested at 0.05 or 5%.
Comparing this value to the p values obtained by the software in both the tests it can
be said indicates that in both the output files the p value is smaller than 0.05. By this
the Null hypothesis which states that there is no trend gets rejected. The application
of this has resulted in the identification of trend direction of the groundwater levels
in the study area. As the groundwater levels are recorded in m.b.g.l. (i.e., meters
below ground level), the positive p value indicate a drop in the water table. Hence, a
positive trend indicates the decline of water level. As each monitoring well reflects
the groundwater dynamics of the surrounding area, each trend value gives an idea
about the water table fluctuation of that area over years.
Scatter diagrams plotted for all the 11 stations of both the pre‐monsoon and
post‐monsoon seasons (Fig. 5.21 and Fig. 5.22) indicate an upward positive trend for
majority of the wells and reveals decline of water levels for these observation wells.
158
0.00
5.00
10.00
15.00
20.00
1985 1990 1995 2000 2005 2010
mbgl
year
Kamagowdanahalli
0246810
1985 1990 1995 2000 2005 2010
mbgl
year
Karnakuppe
0
5
10
15
20
1985 1990 1995 2000 2005 2010
mbgl
year
Chikka Hunsur
0510152025
1985 1990 1995 2000 2005 2010mbgl
year
Udavepur
02468
10
1985 1990 1995 2000 2005 2010
mbgl
year
Hunsur
02468
10
1985 1990 1995 2000 2005 2010
mbgl
year
Gawdegere
0
5
10
15
1985 1990 1995 2000 2005 2010
mbgl
year
Hanagodu
0.00
5.00
10.00
15.00
1985 1990 1995 2000 2005 2010
mbgl
year
Gawdegere(Dug‐well)
159
Figure 5.20: Scatter plot of water level data of pre‐ monsoon water level data
0.00
5.00
10.00
15.00
1985 1990 1995 2000 2005 2010mbgl
year
Kattemalavadi
0510152025
1985 1990 1995 2000 2005 2010
mbgl
year
Somanahalli
0
5
10
15
20
1985 1990 1995 2000 2005 2010
mbgl
year
Coimbatore colony
0.00
5.00
10.00
15.00
1985 1990 1995 2000 2005 2010
mbgl
year
Kamagowdanahalli
0
1
2
3
4
1985 1990 1995 2000 2005 2010
mbgl
year
Karnakuppe
0
5
10
15
1985 1990 1995 2000 2005 2010
mbgl
year
Chikka Hunsur
0510152025
1985 1990 1995 2000 2005 2010
mbgl
year
Udavepura
160
Figure 5.21: Scatter plot of water level data of post‐ monsoon water level data
0246810
1985 1990 1995 2000 2005 2010mbgl
year
Hunsur
0246810
1985 1990 1995 2000 2005 2010
mbgl
year
Gawdegere
0246810
1985 1990 1995 2000 2005 2010
mbgl
year
Hanagodu
0.002.004.006.008.00
10.00
1985 1990 1995 2000 2005 2010
mbgl
year
Gawdegere(Dug‐well)
0.00
5.00
10.00
15.00
1985 1990 1995 2000 2005 2010
mbgl
year
Kattemalavadi
0510152025
1985 1990 1995 2000 2005 2010
mbgl
year
Somanahalli
0
5
10
15
1985 1990 1995 2000 2005 2010
mbgl
year
Coimbatore colony
161
The monitoring stations showing groundwater level decline in terms of
positive trends were more in number than the stations showing negative trends. The
advantage of adopting the Regional Kendall test is that it looks for consistency in the
direction of trend at each location, and tests whether there is evidence for a general
trend in a consistent direction throughout the region. Patterns at an individual
location occurring in the same direction as the regional trend provide some evidence
toward a significant regional trend, even if there is insufficient evidence of trend for
that one location. So it can be said that the overall trend of the region shows a
decline in the water level. The decline of the water level of the observation wells can
be attributed to the variation of the rainfall. In chapter 3 it was discussed that there
was a slight downward trend in the amount of rainfall received in the study area.
To link climate variables with groundwater levels, the weather station should
exist in the recharge zone of the observation well (Van der Kamp and Maathuis,
1991; Chen et al., 2002). But, for a large‐scale groundwater‐monitoring network it
may not be possible. However, the groundwater level data itself provides a direct
means of measuring the overall impacts of both natural and anthropogenic changes
to groundwater resources (Taylor and Alley, 2001). Such kind of a condition was seen
in the study area where all the gauge stations were not close to the mentoring
stations. For example in 2002 due to drought condition a deficit amount of rainfall
was observed when compared to the normal rainfall, due to which the water level
dropped significantly. This study shows that the groundwater levels of the network
observation wells are very sensitive to the monsoon rainfall, and any irregularity in
rainfall influences the groundwater levels. Another important reason which has
contributed to dipping of the groundwater levels is the increased anthropogenic
activities and increase in demand which puts a stress on the water level and revealed
that the recharge is not significant enough to balance the groundwater discharge due
to the anthropogenic and natural processes.
