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,

http://www.iaeme.com/IJCIET/index.asp 140 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 7, Issue 1, Jan-Feb 2016, pp. 141-161, Article ID: IJCIET_07_01_012

Available online at

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=1

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ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication

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, N100E 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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Documents

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,