Validation of Groundwater Zones Using Geo-informatics

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 1, Jan-Feb 2016, pp. 141-161, Article ID: IJCIET_07_01_012
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VALIDATION OF DERIVED GROUNDWATER
POTENTIAL ZONES (GWPZ) USING GEO-
INFORMATICS AND ACTUAL YIELD FROM
WELL POINTS IN PARTS OF UPPER CAUVERY
BASIN OF MYSURU AND CHAMARAJANAGARA
DISTRICTS, KARNTAKA, INDIA
Basavarajappa H.T, Dinakar S and Manjunatha M.C
Department of Studies in Earth Science,
Centre for Advanced Studies in Precambrian Geology, University of Mysore,
Manasagangothri, Mysuru -570 006
ABSTRACT
Groundwater is a most important natural resource of the earth and its
demand is rapidly increasing with growing population, agricultural expansion
and industrialization. The present study aims to integrate the thematic layers
viz., lithology, geomorphology, soil, lineament, land use/land cover, slope,
rainfall and other related features to explore the occurrence & movement of
groundwater using geo-informatics technique. Integration of various themes is
achieved through the development of a models/ assigned weightages which
relates and delineates GWPZ and finally to generate a composite map. About
140 bore wells yield data have been collected to quantify the yield from GWPZ
map derived from geo-informatics. The final output map is reclassified into
four groundwater prospect zones by merging the polygon of same classes
using dissolve operation such as Very Good, Good, Moderate and Poor. The
final results highlight the high-tech application of Geo-informatics in
validating the GWPZ with reference to actual bore well yield data in parts of
Upper Cauvery basin in Southern tip of Karnataka State, India.
Key words: Comparison, GWPZ, Bore well yield data, Geoinformatics and
parts of Upper Cauvery basin.
Cite this Article: Basavarajappa H.T, Dinakar S and Manjunatha M.C,
Validation of Derived Groundwater Potential Zones (GWPZ) Using Geo-
Informatics and Actual Yield From Well Points In Parts of Upper Cauvery
Basin of Mysuru and Chamarajanagara Districts, Karntaka, India,
International Journal of Civil Engineering and Technology, 7(1), 2016, pp.
141-161.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=1

Validation of Derived Groundwater Potential Zones (GWPZ) Using Geo-Informatics and
Actual Yield From Well Points In Parts of Upper Cauvery Basin of Mysuru and
Chamarajanagara Districts, Karntaka, India
1. INTRODUCTION
In the present era, new approaches for the management of land and water resources
are increasing to control the land degradation, long-term sustainable utilization of
water resources. The exploration of groundwater is very much necessary for better
development of groundwater resources and improvement of techniques for its
investigation (Dinakar., 2005). Assessing the Remote Sensing (RS) satellite image
with its spatial, spectral and temporal resolution data covering large and inaccessible
areas within short period of time has become a very handy in analyzing, monitoring
and conserving the water resources (Basavarajappa and Dinakar., 2005). To handle
this information, GIS emerged as a powerful tool in analyzing spatial and non-spatial
data. The paleo-channels of the study area are also mapped using satellite data which
gives additional information regarding water bearing zones like old river course,
fractures and valley fills (Basavarajappa et al., 2014a). Hydrogeomorphic maps are
prepared and used as a tool for groundwater investigation, exploration and
exploitation (Basavarajappa et al., 2013). Attribute data can be clipped into the points/
lines/ polygons or regions with the help of GIS software’s, so that spatial and non-
spatial attribute data can be viewed at a time for better alternative scenarios in
decision making (Dinakar., 2005). The largest available source of the fresh water is
groundwater, but its targeting in hard rock terrain is very difficult due to poly phase
metamorphism, multi & repetitive deformational episodes and related variance in the
fracture pattern & their chronologies (Ramasamy, et al., 2001; Basavarajappa and
Srikantappa., 1999; 2000; Basavarajappa., 2016). Geoinformatics encompasses
Survey of India (SoI) topomaps, Remote Sensing (RS) Satellite images, Geographic
Information Systems (GIS) software’s, Global Positioning Systems (GPS), Ground
Truth Check (GTC) in validating various land and water resources exploration and
management studies (Basavarajappa et al., 2014b; 2015c). It helps in integrating
Remotely Sensed derived data with ancillary data providing the precise information
by involving various factors in groundwater resources management. In view of this,
groundwater occurrence parameters such as lithology, geomorphology, soil, land use
land pattern, lineament, slope and rainfall maps derived through remotely sensed
data/conventional methods have been analyzed using Geoinformatics to compare the
accuracy of high-tech tools capability in groundwater potential zones of the study
area.
2. STUDY AREA
The study area lies between 11°45’ to 12°15’N latitude and 76°45’ to 77°15’E
longitude with total areal extent of 3,011 Km2
(Fig.1) (Basavarajappa et al., 2015b).
The study area includes parts of 9 taluks of Karnataka state namely Yelandur,
Kollegal, Chamarajanagara, Malavalli, Mysuru, Gundlupet, T. Narsipura, Nanjungudu
and small patches of Tamil Nadu region (Sathyamangalam) in the southern and
southeastern parts. Cauvery and Kabini are the two major rivers flowing in the study
area in which Kabini is one of the tributary of River Cauvery.

Figure 1 Location map of the study area
3. METHODS & MATERIALS
The thematic layers (TL) of lithology (TL-1), geomorphological landforms (TL-2),
lineaments (TL-3), soil (TL-4) land use/land cover (TL-5), slope (TL-6), weathered
layer (TL-7), rainfall (TL-8) have been generated on 1:50,000 scale using IRS-1D
(PAN+LISS-III) merged satellite data and other collateral information. Lithological
map is derived from published geological map (GSI, 1995) on 1:250,000 scale and
updated using satellite data (Dinakar., 2005). Landforms maps were interpreted from
satellite imagery and the lineament map was generated by image process techniques.
Slope map is prepared from SoI India topographical sheets on 1:50,000 scale.
Weathered layer map is prepared from the field data (bore well casing length). The
1:50,000 scale-soil map of the study area is derived from 1:2,50,000 scale soil map of
Karnataka prepared by NBSS & LUP (2013).
a. Topomaps: 57D/16, 57H/4, 58A/13 and 58E/1.
Source: (SoI, Dehradun).
b. Satellite Data: IRS-1D LISS-III of 23.5m Resolution (March & Nov-2001) and
PAN+LISS-III of 5.8m, Date of pass 10-March-2003.
Source: (National Remote Sensing Agency (NRSA), Hyderabad.
c. Thematic layers: Lithology, Geomorphology, Soil, Lineaments, Land use/ land
cover patterns, Slope, Iso-hyetal map and GWPZ map.
d. GIS software’s: Mapinfo v7.5, Arc Info v3.2, Erdas Imagine v2011 and Arc GIS
v10.
e. GPS: Garmin 12 is used to record the exact locations of each bore well points
during Ground Truth Check (GTC).

4. RESULTS & DISCUSSIONS
4.1. Lithology (TL-1)
The study area is underlined by hard crystalline rocks mainly peninsular gneiss and
charnockites of the Precambrian age and these are intruded by dolerite, amphibolites
dyke of Proterozoic age (Basavarajappa, 1992). The hard/crystalline rocks have
limited primary porosity; whereas secondary porosity is observed due to weathering,
jointing, fracturing which shows groundwater recharge and movement. As the
peninsular gneiss and charnockites are hard rocks, they have no primary porosity and
are lack in potential for the storage & movement for groundwater (CGWB, 2008).
However, gneissic rocks may have secondary porosity such as fractures, joints, faults
and classified under good (Rank-3) category (Dinakar, 2005). Charnockites are
noticed as forming the hill ranges with less primary porosity as well as secondary
porosity. Thus, this unit is classified under the poor (Rank-1) category compared to
gneissic rocks. On the other hand, small patches of amphibolite, meta ultramafics,
basic dykes act as a barrier and classified these under poor category (Rank-1) while
banded magnetite quartzites, pyroxene granulites are classified as moderate (Rank-2).
Migmatites, kyanite-fuchsite schists, hornblende biotite schists, are classified as good
(Rank-3) groundwater potential areas. Lithology places a second highest weightage
with 40 using Geoinformatics technique in the study area (Table.1a; Fig.2) (Dinakar.,
2005).
4.2. Geomorphology (TL-2)
Geomorphology is the study of morphology, genesis, distribution and age of
landforms that help in recreating the geomorphic history of any evolved landscape
(Dinakar., 2005). Geomorphological mapping allows an improved understanding of
watershed management, groundwater exploration, land use planning, etc (Fairbridge,
1968). Hydro geomorphology based technique in groundwater exploration techniques
are widely used to decipher GWPZ (Seelan Santosh Kumar., 1982). Many of the
geomorphological features are well digitized on the high-resolution satellite data to
generate geomorphology map in conjunction with slope and drainage parameters. The
landforms that are delineated in the present study are denudational hills, residual hills,
linear ridge, pediments, inselbergs, pediplains gullied, pediplains, valley, alluvial
plains (Basavarajappa et al., 2015a). Denudational hills are formed due to differential
erosion and weathering as a more resistant formation or intrusion stand as
mountains/hills. These geomorphic units occur as continuous range of varying height
acts as runoff zones and poor in groundwater prospects. Pediments are rock floored
plains in the uplands and areas adjacent to hills into which the rain water from the
hills drains (Mukhapadhyay, 1994). A large area of pediment found in foot hill of
Biligiri-Rangan Hill ranges is covered by plantations and acting as a runoff zones as
well as recharge zone wherever the fracture & their intersections are observed and this
unit is categorized qualitatively under moderate zone. Inselbergs occurs in the form as
residual isolated barren or rocky smooth and rounded small hill (mostly conical)
standing above ground level surrounded by pediplains and mostly acts as run-off
zone. Residual hills have highly sloping topography. Vegetation is also very sparse
due to very less thickness of soil to sustain. This unit acts as surface runoff zone and
there is very less scope for infiltration except where the rocks are highly fractured,
jointed or faulted. Hydrogeomorphologically, this unit gets less importance due to its
poor water holding capacity and classified under poor category. Pediplains
moderately weathered (PPM) is the flat surface with good weathered profile covering

thick vegetation occupying the topographically low-lying areas associated with
lineaments. Groundwater zone in this unit are considered as good. On other hand,
shallow weathered pediplains (PPS) having less weathered profile with sparse
vegetation and groundwater availability is believed to be moderate in view of their
elevated ground compared to the PPM. Valley zones are the stream course with
accumulation of highly porous and permeable alluvial/ colluvial material, sand &
gravel provide more scope for infiltration and these are classified under very good
category. Alluvial plains are formed by the deposition of alluvium by major rivers
Cauvery and Kabini forming good to excellent shallow aquifers due to nature of
alluvial, its thickness and recharge condition. Overall, alluvial plain, channel island,
valley, river, streams, tanks and reservoirs are considered as good prospect zones and
are assigned as Rank-4. Pediplains moderately weathered zones are assigned as Rank-
3, Pediplains moderately shallow, pediplain gullied and linear ridge are assigned as
Rank-2, while the remaining denudational hill, residual hill, pediment, inselberg and
dyke ridge are assigned as Rank-1. Geomorphology places a highest weightage with
60 using Geoinformatics technique in the study area (Table.1b; Fig.3) (Dinakar.,
2005).
4.3. Soils (TL-3)
Hydraulic properties of soils play an important role in movement of soil moisture
from the ground surface to water table through the unsaturated zone affecting the
runoff and groundwater recharge processes (Basavarajappa et al., 2014b). Soil
properties such as depth, texture and permeability help to determine the rate of
groundwater recharge (CGWB., 2008). Land surface factors such as topography,
geology and vegetation along with soil properties determine the potential for
groundwater (Basavarajappa and Dinakar., 2005). Soil depth shows wide variation in
terms of image characteristics, nature and extent of different geomorphic unit (Reddy
et al., 2003). Based on their hydrogeological characteristics, different weightage and
ranks are assigned for clayey soils, clayey mixed, loamy skeletal and clayey skeletal
are assigned as Rank-4, Rank-3, Rank-2 and Rank-1, respectively with a weightage of
20 (Table.1c; Fig.4) (Dinakar., 2005).
4.4. Lineament (TL-4)
Lineaments are the most obvious structural feature that is important from the
groundwater point of view (Ramasamy et al., 2001). They occur as linear alignment
of structural, lithological, topographical, vegetational, drainage anomalies either as a
straight line or as curvilinear feature (Dinakar., 2005). Lineaments generally develop
due to tectonic stress, strain and provide important clue on surface feature which are
responsible for infiltration of surface runoff into subsurface and also in movement &
storage of groundwater. The observation bore wells noticed very close to lineaments
are good yielding and high prospective zones for groundwater explorations
(Basavarajappa et al., 2015b). In the present study, major rivers such as Cauvery,
Kabini and Suvarnavathi control the major lineaments which are trending towards E-
W, N100
E acting as a good groundwater zones due to neo-tectonic responses
(Basavarajappa et al., 2015b; Waldia., 1999; Satish., 2002). Density of 100 m has
been constructed around each lineament and assigned as Rank-4 with the weightage
of 50 for this layer. The intersection of lineaments automatically gets more scores
during integration (Table.1d) (Dinakar., 2005).

4.5. Land use/land cover (TL-5)
The information on land use/land cover is of utmost importance in hydrogeological
investigations as the groundwater regime is influenced by the type of land cover such
as forest cover, barren rocky cover, marsh, agriculture, urban settlements, etc
(Table.1e; Fig.5). The impact of land use in the prevailing surface and subsurface
hydrologic conditions is remarkably high (Basavarajappa et al., 2015b). The dynamics
of hydrologic processes are governed partially by the temporal and spatial
characteristics of inputs, outputs and the land use conditions (Shih, 1996). Infiltration,
runoff, erosion and evapo-transpiration are controlled by nature of surface material
and the land use pattern. Land use/land cover maps were prepared based on visual
interpretation of the multi-date satellite imagery coupled with GTC. Some of the
classes which reflect on groundwater such as, intensive agriculture are observed all
along the river bed mainly confined to low lands, alluvial plains and perennial flow of
river shows good groundwater potentials (Basavarajappa and Dinakar., 2005). Kharif
crops are scattered in almost all the part of the study area and mainly depends on
rainwater and are considered under moderate (Basavarajappa et al., 2014c). Most of
the wastelands characterized by the presence of thorny scrubs and herbs are noticed
along the ridges, steep slopes and dome-shaped hillocks. As a consequence severe
soil erosion frequently occurs during rainy season resulting in high runoff
(Basavarajappa and Dinakar., 2005). Most of the forest classes occupy the hilly
undulated terrain resulting in greater runoff and less infiltration. The maximum extent
of water bodies are observed in the central parts of the study area reflecting good
groundwater prospects (Dinakar., 2005).
4.6. Slope (TL-6)
Slope plays a significant role in infiltration versus runoff. Infiltration is inversely
related to slope, i.e. more gentle the slope, infiltration would be more and runoff
would be less and vice-versa (Dinakar., 2005). Slope is the loss or gain in altitude per
unit horizontal distance in a direction (Basavarajappa et al., 2015b). The maximum
development of slopes is noticed in the hilly terrains. Slope analysis is carried out by
employing Template method and categorized into very steep, moderate steep, strong
slope and assigned the Rank-1. Moderate slope class is assigned as Rank-2. Gentle
slope and very gentle slope class are assigned as Rank-3; while nearly level slope is
assigned as Rank-4 with a weightage of 30 (Table.1f; Fig.6) (Dinakar., 2005).
4.7. Weathered layer (TL-7)
Weathering refers to the natural process of disintegration and decomposition of the
rock depending upon topography, climate, structure, etc. The thickness of the
weathered zones varies from place to place due to variation in lithology, climate,
intensity of weathering agent, slope, etc (Dinakar., 2005). As the thickness of the
weathered layer increase, the amount of water holding capacity increases. The yield of
bore well depends on the thickness of weathered and fractured horizon (Karanth,
1987). Weathered zones mostly form shallow aquifers in the area and have been
observed generally along low-lying areas, alluvial plains and natural tanks. To
generate the weathered layer map of the study area, 60 location of bore well casing
depth information has been collected which are inserted on the hard rock or non-
collapsible rock formations. This technique indirectly provides the length of the
casing information with the thickness of weathered layer (Dinakar., 2005). The
obtained casing lengths are plotted on a base map and contours are generated showing

spatial variation of weathered layer. The thickness of the weathered zone varies from
1 to 28m. The maximum thickness of the weathered zone is observed in central part
that runs along Kollegal Shear Zone (KSZ) and all along the river courses
(Basavarajappa et al., 2015b). While the minimum weathered thickness are observed
in the southeastern parts. Gneiss and migmatite rocks are deeply weathered as
compared to the charnockites, which occurs as hill ranges. Ranks have been assigned
based on the thickness of weathering and are as follows: 1 to 10m denotes Rank-1,
10-16m denote Rank-2, 16-22m denote Rank-3 and 22-28m denote Rank-4 with a
weightage of 25 (Table.1g; Fig.7) (Dinakar., 2005).
Table.1 (a-g) Assigned Ranks, Weightages and Scores for attributes of various themes
Table. a Lithology
Lithology (Weightage - 40) Rank Score
Migmatite 3 120
Dyke 1 40
Magnetite Quartzite 2 80
Pyroxene Granulite 2 80
Hornblende Schist 3 120
Meta ultramafite 1 40
Charnockite 1 40
Amphibolite 1 40
Gneiss 3 120
Table. b Geomorphology
Geomorphology (Weightage-60) Rank Score
Alluvial Plain 4 240
Channel Island 4 240
Denudational Hill 1 60
Pediment 1 60
Pediment shallow 2 120
Pediment moderate 3 180
Residual hill 1 60
Point bar-I 4 240
Point bar-II 2 120
Point bar-III 1 60
Table. c Soil
Soil (Weightage - 20) Rank Score
Clayey 4 80
Clayey mixed 3 60
Clayey-skeletal 1 20
Loamy soil 2 40
Table. d Lineament
Lineament (Weightage - 50) Rank Score
Lineament (Buffer zone-100m) 4 200

Table. e Land use/land cover
Land use/land cover
(Weightage - 25)
Rank Score
Town/Cities 1 25
Deciduous Forest 1 25
Scrub Forest 2 50
Forest Plantation 2 50
Salt Affected Land 2 50
Villages 1 25
Land with Scrub 1 25
Land without Scrub 2 50
Sandy Area 4 100
Stony waste 1 25
Water bodies 4 100
Kharif 2 50
Double Crop 4 100
Fallow Land 3 75
Plantation 3 75
Evergreen Forest 1 25
Cauvery 4 100
Chikkahole River 4 100
Chikkahole reservoir 4 100
Gullied land 2 50
Gundal reservoir 4 100
Kabini 4 100
Streams 4 100
Suvarnavathi River 4 100
Suvarnavathi reservoir 4 100
Table. f Slope
Slope (Weightage - 30) Rank Score
Gentle Slope - 3-5 % 3 90
Moderate Slope - 5-10 % 2 60
Moderate Steep - 15-35 % 1 30
Nearly Level - 0-1 % 4 120
Strong Slope - 10-15 % 1 30
Very Gentle - 1-3 % 3 90
Very Steep - >35 % 1 30
Table. g Weathering
Weathering (Weightage - 25) Rank Score
1-10 1 25
10-16 2 50
16-22 3 75
22-28 4 100

Table. h Rainfall
Rainfall (Weightage - 35) Rank Score
560-675 1 35
675-750 1 35
750-825 2 70
825-900 2 70
900-975 2 70
975-1050 2 70
1050-1125 2 70
1125-1200 3 105
1200-1275 3 105
1275-1350 4 140
Figure 2 Assigned score of lithology (TL-1)
Figure 3 Assigned score of geomorphology (TL-2)
Figure 4 Assigned score of Soil types (TL-3)

Figure 5 Assigned score of LU/ LC (TL-5)
Figure 6 Assigned score of Slope (TL-6)
Figure 7 Assigned score of Weathered layer (TL-7)
Figure 8 Assigned score of Rainfall in mm (TL-8)

4.8. Rainfall (TL-8)
In the study area, the main source of groundwater recharge is through precipitation
and its occurrence, movement is fully controlled by hydrological, hydro-geological
and climatological factors (Dinakar., 2005). Rainfall is also considered as one of the
parameter in the present hydro-geological study. Thirty one years (1970 to 2001) of
monthly rainfall data from 19 rain gauge stations in and around the study area have
been collected; analyzed and annual normal isohyetal rainfall map has been digitized
(Basavarajappa et al., 2015a).
Fig.9. GIS Integration of all thematic layers
The normal rainfall varies from 560 to 1455 mm in the study area and a weightage
of 35 has been assigned. Rank assigned from 1 to 4 for the ranges of 560-750 mm,
750-1125 mm, 1125-1275 mm and 1275-1455 mm of rainfall respectively (Table.1h;
Fig.9). Though the maximum rainfall is received in B.R hill station, the groundwater
prospect is poor since the topography and land pattern does not favor for infiltration
(Basavarajappa and Dinakar., 2005).
5. INTEGRATION OF THEMATIC LAYERS AND MODELING
THROUGH GIS
The occurrence and movement of groundwater in the study area is controlled by
various factors and each factor is assigned a weightage depending on their influence
on the movement and storage of groundwater (Dinakar., 2005). In the present study,
higher weightage is given to topography than the lithology due to the lithological
control is comparatively less than topographical control on GWPZ (Basavarajappa et
al., 2015b).
N

Figure 10 Union of all thematic layers
(Dissolved polygons with respect to derived potential zones shown in background)
Since lineaments have greater role than lithology, this theme is also assigned
higher weightage. As a consequence, the influence of land use/land cover, slope, and
soil are comparatively less, hence lower weightages are given (Basavarajappa et al.,
2015b). The different units in each theme are assigned knowledge-based hierarchy of
ranking from 1 to 4 on the basis of their significance with reference to their
groundwater potential. In this, ranking 1 denotes poor; 2 denotes moderate; 3 denotes
good and 4 denotes very good GWPZ (Basavarajappa et al., 2013). The ranking is
done mainly based on the common logic aided by the data from inventory carried out
in bore well and open wells. The final score or each unit of a theme is equal to the
product of the rank and weightage (Fig.9 & 10) (Dinakar., 2005).

5.1. Overlay
All the themes are overlaid two at a time using UNION in ARC/INFO to generate a
final composite map helps in finding the specific union polygons (Basavarajappa et
al., 2013). By this method a new map showing the integrated feature of two thematic
maps is obtained. Over this, composite map is overlaid by a third map and so on. Each
polygon in the final composite map is associated with a particular set of information
of all thematic layers (Basavarajappa et al., 2014b). The evaluation of groundwater
prospect of each polygon in the output is based on the added values of scores of
various themes. Theoretically, the minimum total weighs of 235 and maximum
weight of 1400 should have been obtained. But practically a minimum of 270 and
maximum of 1030 have been obtained in the study area. This shows that the non-
overlap of some of higher weights polygons with one other in the integrated layer
(Dinakar., 2005).
5.2. Dissolve
The total scores obtained by integration have been classified into four categories to
facilitate the delineation of very good, good, moderate and poor GWPZ
(Basavarajappa et al., 2013). Accordingly, the poor zone ranges from 270 to 460
score, moderate zone ranges 460 to 650, good zone ranges 650 to 840 and very good
zone ranges 840 to 1030 score. All the polygons having the range of scores mentioned
earlier are merged using ’DISSOLVE’ operation (Dinakar., 2005).
6. INTEGRATION
Integration of data obtained from remote sensing and conventional methods help to
demark the groundwater potential zones effectively in the study area. GIS enables
user specific management and integration of multi-thematic data. In recent years,
extensive use of integrated approach for extracting groundwater prospect zones in
hard rock terrain using remote sensing and GIS techniques are many in recent
literature (Chi and Lee., 1994; Singh et al., 1993; Pal et al., 1997; Venkatachalam et
al., 1991: Krishnamurthy et al., 1996; Haridass et al., 1994). Groundwater potential
model has been developed based on Index overlay method using hierarchical
weightage (Jothiprakesh et al., 2003; Sarkar et al., 2001). Depending upon the
perceived importance; weightage has been assigned for individual themes by
knowledge-based hierarchy of ranking from 1 to 4 on the basis of their significance
with reference to their groundwater potential. In this ranking, 1 denotes poor, 2 –
moderate, 3 – good and 4 denotes very good groundwater potential zones.

Table.2 Comparison analysis of derived GWPZ map and actual bore well yield data
Well No Longitude Latitude Yield (gph) Class Yield Model yield Scores Remarks
1 76° 49' 37.11'' 12°14' 01.01'' 3100 Very Good Very Good 1005 Agree
2 76° 53' 21.26'' 12°14' 37.37'' 2200 Good Good 805 Agree
3 76° 54' 26.09'' 12°12' 34.59'' 2050 Good Good 775 Agree
6 76° 53' 21.17'' 12°12' 03.00'' 1700 Moderate Moderate 580 Agree
7 76° 51' 16.69'' 12°10' 25.85'' 2100 Good Moderate 550 Excess
8 76° 51' 16.69'' 12° 8' 22.46'' 700 Poor Moderate 485 Less
9 76° 50' 57.70'' 12° 7' 42.50'' 1700 Moderate Moderate 575 Agree
11 76° 53' 38.01'' 12° 6' 24.85'' 700 Poor Good 775 Less
12 76° 48' 30.38'' 12° 5' 36.35'' 850 Poor Good 770 Less
15 76° 50' 49.34'' 12° 3' 41.17'' 1800 Moderate Good 745 Less
17 76° 54' 8.65'' 12° 4' 01.10'' 1200 Moderate Poor 460 Excess
18 76° 54' 41.33'' 12° 4' 21.32'' 400 Poor Poor 460 Agree
20 76° 56' 55.74'' 12° 7' 35.47'' 3500 Very Good Very Good 945 Agree
25 76° 56' 37.88'' 12° 2' 10.92'' 700 Poor Poor 460 Agree
26 76° 56' 05.29'' 12° 1' 38.74'' 700 Poor Poor 460 Agree
27 76° 57' 53.28'' 12° 0' 49.61'' 900 Poor Poor 460 Agree
28 76° 58' 01.92'' 12° 1' 14.95'' 2200 Good Good 750 Agree
31 76° 45' 3.88'' 12° 1' 38.28'' 2200 Good Very Good 860 Less
35 76° 49' 39.62'' 11° 59' 13.41'' 2100 Good Good 835 Agree
41 76° 52' 44.47'' 11° 56' 06.25'' 2200 Good Good 810 Agree
46 76° 46' 44.73'' 11° 52' 40.24'' 2300 Good Good 860 Agree

47 76° 46' 28.03'' 11° 49' 24.44'' 2300 Good Good 710 Agree
48 76° 46' 49.87'' 11° 48' 53.87'' 2500 Good Good 710 Agree
50 76° 48' 11.91'' 11° 48' 37.58'' 2700 Good Good 805 Agree
51 76° 51' 08.78'' 11° 48' 17.54'' 2300 Good Good 660 Agree
56 76° 54' 25.62'' 11° 47' 20.06'' 2200 Good Good 720 Agree
57 76° 54' 47.28'' 11° 47' 59.00'' 2200 Good Good 690 Agree
58 76° 55' 01.74'' 11° 48' 33.53'' 2500 Good Good 690 Agree
59 76° 56' 45.11'' 11° 48' 06.75'' 200 Poor Poor 460 Agree
60 76° 58' 05.26'' 11° 48' 57.97'' 600 Poor Poor 460 Agree
62 76° 58' 44.01'' 11° 50' 37.83'' 2200 Good Good 770 Agree
63 76° 57' 35.26'' 11° 51' 30.57'' Dry Poor Good 660 Less
64 76° 57' 19.92'' 11° 52' 30.13'' 7200 Very Good Good 735 Excess
65 76° 57' 07.43'' 11° 53' 43.52'' 2300 Good Good 685 Agree
66 76° 56' 53.45'' 11° 54' 20.92'' 2000 Good Good 715 Agree
67 76° 52' 19.11'' 11° 52' 21.01'' 500 Poor Poor 430 Agree
68 76° 52' 54.35'' 11° 53' 17.70'' 700 Poor Poor 460 Agree
69 76° 53' 02.88'' 11° 54' 04.75'' 700 Poor Poor 410 Agree
73 76° 54' 52.58'' 11° 56' 33.53'' 500 Poor Good 695 Less
76 76° 58' 07.51'' 11° 56' 08.73'' 2300 Good Good 745 Agree
78 77° 00' 29.92'' 11° 48' 47.68'' 2300 Good Good 720 Agree
79 77° 00' 21.45'' 11° 49' 43.62'' 2400 Good Moderate 520 Excess
80 77° 6' 44.54'' 11° 45' 53.91'' 2100 Good Good 755 Agree
81 77° 6' 36.05'' 11° 46' 36.30'' 2100 Good Good 670 Agree
82 77° 6' 30.92'' 11° 47' 06.82'' 2200 Good Good 790 Agree
83 77° 1' 06.49'' 11° 51' 55.71'' 2300 Good Good 720 Agree
84 77° 3' 22.48'' 11° 52' 08.95'' 600 Poor Poor 330 Agree

95 77° 2' 31.91'' 11° 59' 58.88'' 2100 Good Good 695 Agree
97 77° 0' 34.25'' 12° 1' 04.93'' 2300 Good Good 745 Agree
99 77° 1' 57.05'' 12° 2' 07.05'' 2100 Good Good 720 Agree
100 77° 1' 51.86'' 12° 2' 43.42'' 2200 Good Good 695 Agree
106 77° 14' 53.87'' 12° 1' 13.69'' 720 Poor Poor 455 Agree
108 77° 12' 10.62'' 12° 6' 23.44'' 2600 Good Good 770 Agree
109 77° 11' 59.15'' 12° 6' 49.99'' 1300 Moderate Very Good 920 Less
111 77° 13' 51.11'' 12° 8' 13.66'' 800 Poor Good 715 Less
112 77° 13' 7.14'' 12° 8' 12.97'' 2100 Good Good 745 Agree
114 77° 12' 03.84'' 12° 8' 48.92'' 2200 Good Good 660 Agree
115 77° 12' 12.39'' 12° 9' 19.67'' 2300 Good Moderate 630 Excess
116 77° 11' 37.73'' 12° 9' 20.61'' 2100 Good Good 695 Agree
117 77° 10' 54.75'' 12° 10' 08.15'' Dry Poor Good 660 Less
118 77° 1' 43.74'' 12° 6' 33.33'' 2100 Good Good 745 Agree
119 77° 1' 58.47'' 12° 8' 55.54'' 2100 Good Good 720 Agree
121 77° 4' 38.34'' 12° 9' 21.76'' 2300 Good Good 755 Agree
122 77° 6' 33.37'' 12° 9' 34.77'' 2400 Good Good 700 Agree
123 77° 7' 45.52'' 12° 11' 13.56'' 2800 Good Good 660 Agree
130 77° 1' 10.83'' 12° 12' 49.39'' 2200 Good Good 680 Agree
131 77° 0' 50.48'' 12° 12' 24.48'' 2300 Good Good 830 Agree
135 77° 0' 50.97'' 12° 9' 58.08'' 2800 Good Good 805 Agree
139 77° 2' 14.64'' 12° 9' 41.25'' 2800 Good Good 830 Agree
140 77° 1' 54.02'' 12° 7' 27.38'' 2600 Good Good 745 Agree

Figure 11 Observation well points of actual groundwater yield
7. VALIDATION OF THE DERIVED MODEL/ GROUNDWATER
POTENTIAL MAP WITH ACTUAL YIELD FROM BORE WELL
The final composite map aims at providing a clear picture regarding the groundwater
condition of the study area. For such maps, the well inventory forms the main phase
of data acquisition (Basavarajappa et al., 2013). Thus information regarding the depth
of well, lithological section exposed, soil thickness, depths to bed rock and water level
data are collected during the well inventory study (Dinakar., 2005). The validity of the
model developed is checked against the bore well data which reflect the actual
groundwater yield. 140 bore wells yield data have been superimposed to validate the
model (Fig.11; Table.2). Yields of bore wells are varied from 200 gph (gallons per
hour) to 7200 gph in the study area. The same has been regrouped as very good
(>3000 gph), good (2000-3000 gph), moderate (1000-2000 gph) and poor (<1000
gph). Most part of very good potential zones falls exactly on rivers Cauvery and
Kabini (Basavarajappa et al., 2014a). Of the 37 wells in the very good prospect zone,
33 wells are in agreement, 4 wells (well no. 31, 32, 45, 109) show less yield with the
derived potential zone. Most of the very good bore well yields falling on the major
lineaments; while less yielding bore wells are away from lineaments. In the good
prospect zones, out of 55 wells; 40 wells are in agreement with derived potential zone,

3 wells (well no. 64, 74, 120) show excess yielding. 12 wells shows less yield, this
may be due to non tapping of deeper aquifers present in deeper level (Fig.13;
Table.3).
Figure 12 Derived Groundwater Prospect map of the study area
In the moderate prospect zone, out of 34 bore wells; 18 wells are in agreement
with derived potential zone, 2 wells show excess yields, 14 wells shows less yields
with the derived potential zones due to deeper aquifer availability. Hence the poor
yield wells in moderate potential zone need deeper resistively investigation. Deep
sounding apparent resistivity data can be used as a one of the layers for
Geoinformatics to find out deeper aquifers. Though the presences of poor prospect
zones are very large in aerial extent, only 14 bore wells are traced in the field due to
thick forest cover and less number of population. Out of 14 wells, 11 wells are agreed,
3 bore well (well no 17, 19 and 85) show good yield due to shallow aquifer being
tapped in this region and bore wells are close to the Suvarnavathi reservoir where
recharge is a continuous process (Fig.12).

Table.3 Validation of derived GWPZ with actual yield (Fig.6)
Sl. No Groundwater Prospect zones / Actual yield Very good Good Moderate Poor
1.
Number of bore wells modeled under
different GWPZ using Geoinformatics
37 55 34 14
2. Number of bore wells under agreement 33 40 18 11
3. Number of bore wells show excess yield - 3 2 -
4.
Number of bore wells show good to shallow
yield
- - - 3
5. Number of bore wells show less yield 4 12 14 -
Figure 13 Comparison of Derived GWPZ with Actual bore well yield
8. CONCLUSIONS
Each thematic map has been assigned grades ranking from 1 to 4, with 1 representing
the poor and 4 representing the very good groundwater prospects in validation
analysis with actual yield bore well data. The final composite map highlights very
good prospect zones falls in lineament zone; good prospect zones are noticed adjacent
to the rivers and along KSZ; moderate prospect zones occupies the pediplains;
whereas poor prospect zones occupies the Biligirirangan hills. Out of 140 bore wells,
yield validations of 102 are well with agreement, 38 well are not agreeing due to
varying in different seasonal conditions. On the whole, bore wells are well correlating
with derived potential zones using advent high-tech tools. Since the present approach
was build with logical conditions and reasoning, this approach can be successfully
used elsewhere with appropriate empirical modeling techniques. Geoinformatics tool
can be used effectively in demarcation of precise groundwater potential zones based
on the present study. By union and dissolving the final integrated map, four prospect
zones such as very good, good, moderate and poor prospect zones were delineated.
Finally, the above study has clearly demonstrated the capabilities of Geoinformatics

technique in demarcation of the precise groundwater potential zones and its validation
using actual yields from bore well data. All along the KSZ neotectonic activity affects
seepage of springs water and minor tremors of lower magnitude less than 3-3.5 are
noticed.
ACKNOWLEDGMENT
The authors are indepthly acknowledged Prof. G.S. Gopalakrishna, Chairman,
Department of Studies in Earth Science, CAS in Precambrian Geology,
Manasagangothri, University of Mysore, Mysore; Dr. M.V Satish, Rolta India Ltd,
Mumbai, Mr. Nagesh, MGD, Govt. of Karnataka for their support in GIS work and
UGC, New Delhi for financial support; CGWB., Bengaluru.
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