GROUND WATER RESOURCES EVALUATION AND DETECTION OF WATER ...shodhganga.inflibnet.ac.in/bitstream/10603/9493/12/12_chapter 4.pdf · GROUND WATER RESOURCES EVALUATION AND DETECTION

Chapter – 4

GROUND WATER RESOURCES EVALUATION AND

DETECTION OF WATER BEARING LINEAMENTS

4.1 GENERAL

The two fundamental causes for groundwater’s active role in nature

are its ability to interact with the ambient environment and the systematized

spatial distribution of its flow (Jozsef Toth, 1999). The occurrence, movement

and control of groundwater, particularly in hard rock areas are governed by

different factors such as topography, lithology, structures like fractures, faults

and nature of weathering (Janardhanaraju and Reddy, 1998).

Groundwater is one of the important natural resources which support

the human health, economic development and ecological diversity. Over

exploitation and unabated pollution of this vital resource is threatening our

ecosystem and even the life of the future generation (Madhan Jha et al., 2007).

Groundwater is available in various permeable geologic formation called

aquifers which can store and transmit water. It is an important natural source

that has to be sustained for the future. Groundwater is not available in the same

quality and quantity everywhere. It varies depending upon the geological,

geomorphological, type of soil and the amount of water mined. The increase in

population, industrialization and the pressure for development in agriculture

has led to the over exploitation of groundwater in most of the places. Usage of

deep bore wells has lowered the groundwater level in an alarming way. The

available water resources in India are not distributed evenly. Due to variation

in climatic condition, rainfall and topographical features availability of

groundwater is not the same in all the places.

Water being the essential commodity for the survival of life on the

Earth and ground water forms the primary source of water, it is a compelling

need for the geoscientists to evaluate the water potential of the region, to

monitor its flow and control by geologic phenomena and to assess its quality

for the potability.

CChhaapptteerr -- 44 GGrroouunndd WWaatteerr RReessoouurrcceess EEvvaalluuaattiioonn…………

Geoinformatic Modelling for Certain Georesources and Geohazards of

Attur Valley, Tamil Nadu, India.

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The ground water controlling parameters like geology,

geomorphology, slope, landuse /land cover, and structure and lineament

were integrated and probable water potential zones were identified by rank

and weightage method in the present study. The highest weightage 10 was

given to the lithology, followed by geomorphology. In crystalline terrains, the

aquifer characters are mainly imparted by lineaments. So, the lineament

derived themes, viz: the lineament density, lineament frequency and

lineament intersection were given importance and equal weightage. Water

potential zone map was prepared; the output was validated with multi depth

resistivity data and water level data.

4.2 GROUND WATER RESOURCES EVALUATION

One of the best methodologies to locate and map the occurrence and

distribution of groundwater has focused on the utility of high resolution

satellite imagery to identify and delineate the surface features more

accurately. Remote sensing method provides the efficient way of mapping of

natural resources economically than those of the conventional methods and

yields better results. Krishanmurthy and Srinivas (1996); Ravindran and

Jayaram (1997); Jagadeeswara Rao et al. (2004), used the satellite remote

sensing data to define the spatial distribution of different groundwater

prospect classes on the basis of geomorphology and other associated

parameters. Sankar (2002); Basudeo Rai et al. (2005); Lokesha et al. (2005) in

their studies found that, identification of groundwater occurrence location

using remote sensing data is based on indirect analysis of some directly

observable terrain features like geological structures, geomorphology and

their hydrologic characteristics. Bahuguna et al. (2003), in their studies found

that lineaments play significant role in groundwater exploration particularly

in hard rock terrain. On the basis of hydrogeomorphology, Vijith (2007) has

delineated three categories of groundwater potential zones namely good,

moderate and poor of Kottayam district, Kerala. Various thematic maps for

delineating groundwater favorability zones were selected presuming that all




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the parameters have significant influence on the occurrence of groundwater

(e.g., Rao and Jugran 2003; Solomon and Quiel 2006; Ettazarini 2007; Sridhar

Ganapuram et al., 2009); the number of thematic layers used depends on the

availability of data in an area.

Analysis of remotely sensed data along the SOI topographical sheets

and collateral information with necessary ground truth verifications help in

generating the baseline information for groundwater targeting. Groundwater

prospecting based on mapping of landforms and lineaments is now very

common using remote sensing data. Remote sensing serves as the preliminary

inventory method to understand the groundwater prospects/conditions and

helps in delineating areas where further explorations need to be taken up

through hydrogeological and geophysical methods. In addition, the

advantage of using remote sensing techniques together GPS in a single

platform and integration of GIS techniques facilitated better data analysis and

their interpretations. Murthy et al. (2003); Khan et al. (2006), reported that,

temporal data from the remote sensing enables identification of groundwater

aquifers and assessment of their change, whereas, GIS enables user specific

management and integration of multi-thematic data. The present study

focuses on the identification of groundwater potential zones in the study

window. The availability of water (groundwater) throughout the year is a

major problem and hence, it is necessary to identify the new groundwater

potential areas and also the influence of structures in carrying the

groundwater. In the study area, no such previous studies were conducted to

find out the groundwater potential areas and also the relationship between

groundwater potential and structures. In the present study, Multi-Criteria

Evaluation (MCE) techniques were used to systematically assess the

importance of each terrain factors in the occurrence of groundwater.

4.2.1 Methodology

In the present study 9 themes have been selected based on their merits

pertaining to the study window. The themes like Geomorphology, lineament,




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landuse and structure and trendline were prepared from IRS IC LISS III data

acquired on 19th February 2004 (P100/R67). Drainage map was prepared

from Survey of India toposheets 58 I series of 1:50,000 scale. And district

resource maps published by Geological Survey of India (scale 1:2,50,000 of

year 1987) were used for lithology and slope map was produced from SRTM

data. Lineament density, lineament frequency and lineament intersection map

were derived from lineament map and drainage density map was prepared

from drainage map. The Salem province is unique by the virtue of its location

in the SGT and its topographic and structural set up. The region displays

spectacular variety of rocks and mineral wealth and well known shear zones

of SGT. Since, the water bearing qualities depends upon the porosity of the

rocks, the rocks in province like Salem which underwent multiple

deformation phases apart from the inherited porosity, higher weightage must

be given to lithology. The crystalline terrains the rock types gain more

importance in defining the geomorphology. So, higher weightages was given

to lithology prior to geomorphology followed by fractures related properties

like lineament density, lineament frequency and lineament intersection

(Table 4-1, Fig.4.1).

In this method all the vector GIS layers of the 9 terrain systems (Fig. 4.2

to 4.10) were used and for all these 9 terrains systems weightages (Wi) were

assigned on the basis of their possible influence and control to water

resources of a region. Then for the each sub variable of the 9 themes, scores

were assigned (Sij). Then these scores (Sij) of the sub variables were

multiplied with the corresponding weightage (Wi) of the terrain systems and

water potential zones weightages (Wi x Sij) were worked out for each sub

variable. For example, Wi for lithology layer was 10 and Sij for the Fissile

hornblende gneiss was 9. So the finally accrued water potential weightages

(Wi x Sij) for the Fissile hornblende gneiss was 90 (10 x 9). Similarly, for each

sub variable of the 9 terrain systems water resource weightages were worked

out and the corresponding weightages were assigned to such sub variables.




244

In this method, the total weights of the final integrated map were

derived as sum of the weights assigned to the different layers according to

their suitability. Finally, the block-wise groundwater condition has been

assessed by superimposing the block map over the groundwater potential

zone map (Fig. 4.1).

Table 4-1 Weightages assigned to 9 terrain parameters

Sl.No Themes Weightage (Wi)

1. LITHOLOGY 10

2. GEOMORPHOLOGY 9

3. LINEAMENT INTERSECTION 8

4. LINEAMENT FREQUENCY 8

5. LINEAMENT DENSITY 8

6. BUFFERED STRUCTURE AND

TREND LINE 7

7. SLOPE 6

8. DRAINAGE DENSITY 5

9. LANDUSE – LAND COVER 4

4.2.2 Assignment of Weightages (Wi) and Scores (Sij) to Terrain Systems

4.2.2.1 Lithology

As discussed in the methodology, first the weightages (Wi) were

assigned to the 9 terrain parameters as shown in Table 4-1. In the present

study, the maximum weightage of 10 was assigned to the lithology, as the

rock assemblage of Salem province is unique which includes brittle

charnockite, mafic granulite, hornblende biotite gneiss, amphibolites and so

on. These rocks differ from each other, in the water resource point, by

porosity as they were intensely of sheared and foliated.




245

Fig. 4.1 Methodology Flow Chart – Ground Water Resources Evaluation

Lineament Intersection

By Pump Test Method

Block wise Groundwater Potential Map

Neotectonics Vs Groundwater

Identification of Conduit and Discharge Lineaments

Groundwater Potential Map

Validation By Iso-Resistivity

Method

Lineament Density

Lineament

Vector Overlay

Lineament Frequency

Lithology

Geomorphology

Landuse/ Landcover

Drainage Density

Slope

District Resource Map from GSI

Structure and Trendline Map

Satellite Data

Water Resource Evaluation

Topographic Sheet from SOI (1:50000scale)

SRTM Data

Assigning Weightage (Wi) and Scores (Sij)

Drainage




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Table 4-2 Weightages and Scores – Lithology

Sl.

No Lithology Class

Weight-

age

(Wi)

Score

(Sij) (Wi x Sij)

1. Fluvial sediments (sand, silt, gravel,

clay) Laterite 10 10 100

2. Fissile Hornblende Biotite Gneiss 10 9 90

3. Charnockite, Hornblende Biotite

Gneiss, Epidote Hornblende Gneiss,

Siderite-Ankerite Gneiss

10 8 80

4.

Amphibolite, Pyroxenite, Dunite,

Peridotite, Pink Migmatite,

Carbonatite, Calc granulite and

Limestone

10 5 50

5. Garnetiferous Gabbro, Pyroxene

Granulite

10 4 40

6. Syenite, Granite, Gingee

Granite,Granite / Granolite

(Tindivanam)

10 3 30

7.

Magnetite- Quartzite, Fuchsite-

Kyanite-Magnetite-Silimanite-

Quartzite, Pegmatite, Basic dyke

10 2 20

The secondary porosity is the essential character of crystalline aquifers

where the response to shearing varies from rock to rock and hence their water

holding properties. Based on this conception higher score of 9 was assigned to

fissile hornblende gneiss next to fluvial younger sediments (Table 4-2).

Charnockite, Hornblende Biotite Gneiss, Epidote Hornblende Gneiss, Siderite-

Ankerite Gneiss are relatively low porous than fissile hornblende gneiss and

hence lesser value of 8. Similarly rocks were ranked according to their merit

of water holding properties and weighted lithology GIS map was prepared

(Fig.4.2).

4.2.2.2 Geomorphology

Authors like Edet et al. (1998), Chakraborthy and Paul (2004), Erhen Sener

et al. (2005), Ravi Shankar and Mohan (2005), Sanjeev Kumar et al. (2006),

Semere Solomon and Friedrich Quiet (2006), Trivedi et al. (2006),Prasad et

al. (2007), Srinivasarao Yamani (2007), Thakur and Raghuwanshi (2008)




247

and Hsin-Fu Yeh et al. (2009) were using hydrgeomorphology as criteria for

delineating water potential zones.

The study area has a dominant rocky terrain, which is manifested by

hills and undulating surfaces. Hence, the geomorphology was assigned

weightage (Wi) 9.

Fig. 4.2 Water Resource- Weighted Lithology Map




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Table 4-3 Weightages and Scores – Geomorphology

Sl.

No Geomorphology Class

Weigh-

tage (Wi)

Score

(Sij) (Wi x Sij)

1. Colluvial Fill Deep 9 10 90

2. Bajada 9 9 81

3. Intermontane Valley 9 8 72

4. Fracture Valley Fill 9 7 63

5. Colluvial Fill Moderate 9 6 54

6. Colluvial Fill Shallow 9 5 45

7. Hilltop Weathered / Dissected

Plateau 9 4 36

8. Undissected Plateau 9 3 27

9.

Residual Hills, Fracture Valley

Barren, Pediments / Pediment

Inselberg

9 2 18

10 Linear Ridge / Dyke, Structural

Hills, Rocky Slope / Cliffs 9 1 9

Ten distinct geomorphologic units have been identified and delineated

from the study area; include structural hills, escarpment/cliff, residual

mounds, denudational slope, pediment, fracture valley and valley fill, etc. The

distribution and extent of these geomorphic zones are varying from place to

place. Highest score was assigned to deep Colluvial fill followed by Bajada,

Intermontane valley and fracture valley fills (Table 4-3). Linear ridges, Dykes,

Inselberg were assigned low scores owing to lesser chances to hold water. The

corresponding weighted geomorphology GIS map was prepared (Fig.4.3).

4.2.2.3 Lineament

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. The extension of large lineaments

representing a shear zone or a major fault can extend subsurface from hilly




249

terrain to alluvial terrain. It may form a productive groundwater reserve.

Similarly intersection of lineaments can also be the probable sites of

groundwater accumulation. Therefore, areas with high lineament density may

have important groundwater prospects even in hilly regions which otherwise

have nil groundwater prospects.

Fig 4.3 Water Resource- Weighted Geomorphology Map




250

Ramasamy et al. (1989) have integrated the regional spatial

information of transmissivity, permeability and storage coefficient values

and they are of the opinion that the ground water flow is controlled by the

folded systems in the area south of Cauvery whereas in the northern area the

groundwater flow is controlled by the fracture systems. So, weightage (Wi)

value of 8 was assigned to lineament derived features like lineament density,

lineament frequency and lineament intersection.

4.2.2.3.1 Lineament Density

Ramasamy et al. (2001) have done fracture pattern modeling for hard

rock aquifer system of central Tamil Nadu in which they have studied the

fracture density and their influence over groundwater.

In the present study, lineament density map was prepared by

measuring the total length of the lineaments per 5x5 sq.km grid. Then the

measured values were assigned to respective centers of grids. The values

were ranging from 5000m to 30000m per grid. The values were grouped into 5

classes and scores were assigned to each class. Highest score of 10 was

assigned to highest lineament density and minimum to lowest lineament

density (Table.4.4). An isolines map was prepared for the reclassified grid

values as weighted lineament density map (Fig. 4.4).

Table 4-4 Weightages and Scores – Lineament Density

Sl.

No Lineament Density Weightage (Wi)

Score

(Sij) (Wi x Sij)

1. Very High > 30000 8 10 80

2. High 30000-20000 8 8 64

3. Moderate 20000-10000 8 6 48

4. Low 10000-5000 8 4 32

5. Very Low 0-5000 8 2 16




251

Fig. 4.4 Water Resource- Weighted Lineament Density Map

4.2.2.3.2 Lineament Frequency

Weighted lineament frequency map was prepared by counting the

number of lineaments per 5x5 sq.km grid with corresponding weighted

values (Table 4-5, Fig.4.5).

Table 4-5 Weightages and Scores – Lineament Frequency

Sl.No Lineament Frequency Weightage (Wi) Score (Sij) (Wi x Sij)

1. Very High > 20 8 10 80

2. High 20 -15 8 8 64

3. Moderate 15-10 8 6 48

4. Low 10 -5 8 4 32

5. Very Low 0-5 8 2 16




252

Fig. 4.5 Water Resource- Weighted Lineament Frequency Map

4.2.2.3.3 Lineament Intersection

Weighted lineament intersection map was prepared by counting the

number of intersections of lineaments per 5x5 sq.km grid with corresponding

weighted values (Table 4-6, Fig.4.6).

Table 4-6 Weightages and Scores – Lineament Intersection

Sl.No Lineament Intersection Weightage

(Wi)

Score

(Sij) (Wi x Sij)

1. Very High > 60 8 10 80

2. High 60-45 8 8 64

3. Moderate 45-30 8 6 48

4. Low 30-15 8 4 32

5. Very Low 0-15 8 2 16




253

Fig. 4.6 Water Resource- Weighted Lineament Intersection Map

4.2.2.4 Buffered Structure and Trendline

Palanivel and Ramasamy (2002) have traced trend line of Western

Ghats and found appreciable ground water flow control and very good water

potential in folded structures.

The compositional banding in the rocks as well as the linearity

developed during shearing, dykes, mafic granulites and banded magnetite

quartzite were traced as trendline and they are qualified enough to control

water flow and damming ground water. So, 250m buffer zone was taken as a

terrain parameter and assigned the weightage value 7 (Table 4-7) and

weighted, buffered structure and trendline map was prepared (Fig.4.7).




254

Table 4-7 Weightages and Scores – Buffered Structure and Trend line

Sl.No Structural Class Weightage (Wi) Score (Sij) (Wi x Sij)

1. Structural basin 7 10 70

2. Open fold 7 8 56

3. Trend line 7 6 42

Fig. 4.7 Water Resource- Weighted Buffered -Structure and Trendline Map

4.2.2.5 Slope

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.

Steeper the slope, greater will be the runoff and thus, lesser is the favorable

for groundwater recharge.




255

SRTM data was used for the estimation of slope in degrees. The

identified slope category varies from 1º to 56º in the study area and are

classified into four classes like, 0º to 3º (gentle), 3º0’01‖ to 20º (moderate),

20º0’01‖ to 40º (steep) and >40 (very steep). Weightage scores were assigned

and their total weighted values for each slopes have been calculated (Table 4-

8) and corresponding weighted slope map was prepared (Fig.4.8).

Table 4-8 Weightages and Scores – Slope

Sl.No Slope Name Weightage

(Wi) Score (Sij) (Wi x Sij)

1. Gentle Slope 6 10 60

2. Moderate Slope 6 8 48

3 Steep Slope 6 6 36

4. Very Steep Slope 6 4 24

4.2.2.6 Drainage Density

Drainage channels delineated using SOI topographic maps represent

channels along which surface runoff moves down slope. The surface water

infiltration is found to be more in the sheet wash than in channel flow. To

visualize the areas of sheet flow/channel flow the zones of different drainage

density, viz. very high, high, moderate, low and very low are derived based

on spatial density analysis of drainage network, comprising of 0.19%, 2.88%,

10.98%, 18.91% and 67.44% of the area respectively. The area of very high

drainage density represents more closeness of drainage lines and vice-versa.

Groundwater prospects are found to be poor in the very high drainage

density areas as major part of the water poured over them during rainfall is

lost as surface runoff with little infiltration to meet groundwater. On the

contrary low drainage density areas permit more infiltration and recharge to

the groundwater and therefore have more potential for groundwater

(Md. S. Mondal et al., 2007). Weightage scores of the total weightage for each




256

drainage density are given in Table 4-9 and weighted drainage density map

was prepared (Fig. 4.9).

Fig. 4.8 Water Resource- Weighted Slope Map

Table 4-9 Weightages and Scores – Drainage Density

Sl.No Drainage Density Weightage

(Wi)

Score

(Sij)

(Wi x

Sij)

1. Very

low < 5000 5 10 50

2. Low 10000 - 5000 5 8 40

3. Moderate 20000- 10000 5 6 30

4. High 30000- 20000 5 4 20

5. Very

high > 30000 5 2 10




257

Fig. 4.9 Water Resource- Weighted Drainage Density Map

4.2.2.7 Land use/land cover

For the identification and interpretation of land use pattern of the area,

the standard methods of visual interpretation were adopted and the various

land use classes delineated includes barren land, barren rock, built-up land,

cleared area, cropland, grassland, natural vegetation, plantations, and water

body. The weightage scores were assigned to each landuse and land cover

category (Table 4-10) and the corresponding weighted landuse and land

cover map has been prepared (Fig.4.10).




258

Table 4-10 Weightages and Scores – Landuse and Land Cover

Sl.No. Landuse and Land Cover Class Weightage

(Wi) Score (Sij)

(Wi x Sij)

1. River, River Island, River bed Vegetation, River/Sandy area,

Reservoirs, Abandoned quarries, Water Lakes/Ponds, Tanks

4 10 40

2. Crop Land in Forest, Crop land, Canal, Plantations, Grass land / degraded , Forest Plantations

4 8 32

3. Deciduous forest, Mining process 4 6 24

4. Wastelands-with/without scrub, Gullied/ Ravenous Land, Built-up, Built-up/Villages (Rural)

4 4 16

5. Barren Rocky/ Stony Waste 4 2 8

4.2.3 Vector Overlay and Identification of Groundwater Potential Zones

Finally, all these 9 weighted GIS vector layers of the different terrain

parameters were simplified by dissolving with scores of different classes of

each terrain parameters and the simplified vector layers were integrated

using ―Union‖ option in analysis tool in ARCGIS. Geographical Information

Systems are used to perform a number of fundamental spatial analysis

operations; such operations can use any number of analytical processes. An

overlay operation is much more than a simple merging of line work; all the

attributes of the features taking part in the overlay are carried through, as

shown in the example below, where polygons of layer 1 and polygons of layer

2 are overlaid (using the Union tool) to create a new polygon layer. The

parcels are split where they are crossed by the layer 2 zone boundaries, and

new polygons created




259

Fig. 4.10 Water Resource- Weighted Landuse and Land cover Map

All polygons retain their original category values in addition to

overlaid polygon value. We can use overlay analysis to combine the

characteristics of several datasets into one. Union calculates the geometric

intersection of any number of feature classes and feature layers . All inputs

must be of a common geometry type and the output will be of that same

geometry type. This means that a number of polygon feature classes and

feature layers can be unioned together. The output features will have the

attributes of all the input features that they overlap. Union does the following:




260

We can then find specific locations or areas that have a certain set of attribute

values—that is, match the criteria you specify. This approach is often used to

find locations that are suitable for a particular use or are susceptible to some

risk.

Determines the spatial reference for processing. This will also be the

output spatial reference. All the input feature classes are projected

into this spatial reference.

Cracks and clusters the features. Cracking inserts vertices at the

intersection of feature edges; clustering snaps together vertices that

are within the xy tolerance.

Discovers geometric relationships (overlap) between features from

all feature classes.

Writes the new features to the output.

The output will contain combined polygons from the unioned

maps. By using the calculator option in tables the values were

added and stored in a separate column of the table.

For example:

Layer 1 Layer2 Possible combinations (3X4=12)

1 A A1 B1 C1

2 B A2 B2 C2

3 C A3 B3* C3

4 A4 B4 C4

(*Not possible in the given layer, so actual polygons in the figure are 11)

Fig. 4.11 Vector Overlay Model




261

Fig. 4.12 Composite Map of Weighted Vector Layers

Table 4-11 Classification of Water Potential Zones

Sl.No Score Class

1. < 210 Poor

2. 211 - 270 Very low potential zone

3. 271 - 330 Low potential zone

4. 331 - 390 Moderate potential zones

5. 391 - 450 High potential zone

6. > 451 Very high potential zone




262

The scores of each class of the layers 1 and 2 can be added to the

corresponding resultant polygon so as represent the proper weightage of the

different terrain parameters are taken into account. The resultant scores are

added to scores of the next layer after ―union‖.

As explained above 9 terrain parameters with their sub classes were

unioned one after the other and the resultant polygons were 2049 and scores

were ranging from 141 to 534 (Fig. 4.12, possible maximum score was 650).

The values were classed into six categories as given in the Table 4-11. The

water potential map was prepared by dissolving all the 2049 polygons into six

polygons based on the classified scores in Table 4-11 (Fig.4.13).

Fig. 4.13 Water Potential Zones




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4.3 VALIDATION

The delineated groundwater potential zone map was validated by

choosing Salem block as a study window, with iso-resistivity map of the area

and transmissivity, permeability and specific yield (TKS) of the aquifers

derived from pump test. The resistivity valley can be taken as high water

potential zones and similarly the resistivity hills may be corresponds to low

water potential zones.TKS (transmissivity, permeability and specific yield)

signifies the characters of holding , to permit the flow and yield thus indicate

the water potential zones.

4.3.1 Iso-resistivity Method

Resistivity is one of the best geophysical methods being widely used

for groundwater exploration where the resistivity drops/changes /flats in the

curves are taken as water potential zone and it can be used as a validation tool

(Lahcen zouhri et al., 2004). Since, the study area covers nearly 9000sq.km

the identification and further correlation of low resistivity values can be an

ideal method (Fig.4.12). Using iso-resistivity values contours were drawn for

various depths (30m, 50m, 80m, 100m and 150m) and they were analyzed as

detailed below.

4.3.1.1 Resistivity at 30m depth (Fig.4.14a)

Resistivity valleys with 150 ohm contours were matching with high

potential zone in the western side of Salem block and poor zone in the

southern side. And 550m hill was observed in the eastern side which is again

matching with high potential zone. The central smaller valleys were matching

with high potential zone and moderate potential zone.

4.3.1.2 Resistivity at 50m depth (Fig.4.14b)

Resistivity valleys with 100 ohm were recorded in the western side

where it was coinciding with high potential zone and the other valleys

coinciding with moderate potential zone.




264

4.3.1.3 Resistivity at 80m depth (Fig.4.14c)

At western side the valley coincides with high potential zone and the

central and southern side it matches with moderate potential zone.

Easternside hill was matching with massive charnockite in the Uthumalai

area.

Fig. 4.14 Validation- Isoresistivity Method




265

4.3.1.4 Resistivity at 100m depth (Fig.4.14d)

At 100m depth the valley were matching with very high potential zone

in the western and central area where as southern side matches with

moderate and low potential zone. The resistivity hills in the northern side and

the southeastern side matches with massive charnockite.

4.3.1.5 Resistivity at 150m depth (Fig.4.14e)

150m depth iso-resistivity valley was found near Kandashramam and

the hill at the western side matches with massive charnockite near Uthumalai.

4.3.2 Pump Test method

Finally, the water potential map of Salem block was verified using the

available measured discharges of 30 pumping wells (Fig. 4.15a) (Table 4-12).

4.3.2.1 Specific Yield

The specific yield was high in the north western side where the

ultramafics of Chalk hill was located and the darker tone along the entire

western side matches with very high potential, high potential, and moderate

potential zones. The central part of the block was falling in low potential zone

and moderate potential zone matches with the lighter yellow tone with low

specific yield. The gradual increase in eastern side was also observed and it

was corresponding to high potential zone and also part of the MBSASZ

(Fig. 4.15 b).

4.3.2.2 Permeability

The central part of Salem block with low permeability coinciding with

the identified low potential and moderate potential zone. Higher values were

observed in the north eastern, southern and western sides and they probably

follow the footprints of the MBSASZ shear zone and also matching with the

identified very high and high potential zones in the west (Fig.4.15c).




266

4.3.2.3 Transmissivity

Higher transmissivity was observed in the northeastern side where it

was coinciding with high potential zone and similar high transmissivity was

observed near chalk hills which falls under high potential zone. The central

low transmissivity value was corresponding to low potential zone and

moderate potential zone (Fig 4.15d).

Fig. 4.15 Validation- Pump Test Method

The verification of the groundwater potential map with the well yield

data reveals that the water potential map prepared was showing good

correlation still adequate number of pump well data will provide higher

degree of validation.




267

Table 4-12 Pump Test Locations and Aquifer Parameters

Sl

No

Name of Village

De

pth

of

we

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ea

r o

f T

est

Pu

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ing

Ra

te i

n

Gp

m

Acc

ep

ted

Tra

nsm

isiv

ity

(T

in

pd

/ft)

Acc

ep

ted

P

erm

iab

ilit

y(K

in

Gp

d/s

q.f

t)

Sp

eci

fic

cap

icit

y

(S i

n g

pm

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D.D

)

1 Gajjalanakanapatty 44 Mar-79 50.25 1384.00 511.690 0.00

2 Valasiyur 40.8 May-79 59.70 2090.00 16.220 0.00

3 Kumarapalayam 54 Oct-79 27.50 500.00 0.439 0.00

4 Ammapalayam 45 Sep-79 6.00 21.28 0.172 0.00

5 Konamaduvu 80 Mar-82 12.40 158.42 0.758 0.1575

6 Governement Engg.college 70 Jan-81 6.00 126.00 0.640 5.5548

7 Gudumalai 42.5 Mar-82 12.40 0.00 0.000 0.00

8 Elampillai 78 Apr-83 96.00 1068.72 4.780 2.099

9 PWD SE'S Office compound 73 Jul-86 2.00 73.39 5.840 0.0280

10 Kannankuruchi 35 Mar-87 54.70 1623.15 22.370 0.0573

11 mettupatti 55 Mar-87 8.89 507.47 5.360 0.127

12 Pallakadi 60.5 Jan-87 2.19 27.29 0.180 0.023

13 Chettichavadi 60 Aug-87 21.70 847.86 5.180 0.225

14 Thamarainagar 60 Nov-91 2.19 - - -

15 Veerapandi Ist Pt 62 Jun-94 21.70 513.70 3.01 6.29

16 Veerapandi IInd Pt 62 Jul-94 21.70 1492.00 8.76 3.02

17 Sesanchavadi Ist Pt 75 Jun-95 34.20 0.00 0.000 0.00

18 Sesanchavadi IInd Pt 63 Jun-95 21.70 0.00 0.000 0.00

19 Chandrapillaivalasu Ist Pt 46 Jul-95 12.40 148.00 1.367 0.504

20 Chandrapillaivalasu IInd Pt 46 Aug-96 16.70 93.30 0.890 0.35

21 Muthunaicken patti Ist Pt 92 Mar-96 3.00 0.00 0.000 0.00

22 Muthunaikan patti Iind Pt 92 Mar-96

Poor yield - - -

23 Kullappanaickanoor Ist Pt 31 Oct-96 12.40 1016. 26.15 38.22

24 Kullappanaickanoor IInd Pt 36 Oct-96 70.20 2628.00 40.61 6.2

25 Kumarasamypatti 100 Sep-01 6.05 43.93 0.147 0.0414

26 Hasthampatti 79 Nov-01 2.19 - - -

27 Veeranam 44 Jan-02 16.70 188.81 1.546 0.427

28 Maniyanoor 73 Feb-04 12.40 3.09 0.015 0.279

29 Ammapet 53 Feb-01 34.20 140.38 0.92 0.378

30 Hasthampatti 42 Feb-01 8.89 48.67 0.419 0.108




268

4.4 BLOCK WISE GROUND WATER POTENTIAL MAP

After the proper validation the quantification can be done at least in

terms of areal extent which could be used for planning and execution at block

level. The blocks at the periphery of the study window were partial. Except

those blocks, total area of the water potential zones of all other blocks were

calculated and were presented in the Table 4-13. The block wise water

potential map was shown in Fig.4.16.

Fig. 4.16 Block wise Water Potential Zones




269

Table 4-13 Block wise Water Potential Zones

S.No Block name Class Area in sq.km

1 Attur

Very high potential zone 1.71

High potential zone 17.06

Moderate potential zone 132.63

Low potential zone 107.03

Very low potential zone 55.08

Poor 14.68

2 Ayothiapattinam






Poor 3.63

3 Chinnaselam






Poor 6.44

4 Dharmapuri






Poor 2.37

5 Elichipalayam






Poor 0.02

6 Erumapatti




Poor 0.00075




270

7 Gangavalli






Poor 14.69

8 Harur





Poor 24.28

9 Kadayampatti






Poor 3.95

10 Kallakurichi





Poor 2.64

11 Kalrayan hill





Poor 21.84

12 Kolli-hills





Poor 38.99

13 Kurinjipadi





Poor 9.36

14 Mallasamudram






Poor 0.056




271

15 Mangalur






Poor 0.35

16 Mcdonald-choultry






Poor 0.0118

17 Mecheri






Poor 0.00468

18 Morappur




Poor 0.46

19 Nallampalli






Poor 1.086

20 Namagiripettai






Poor 15.72

21 Namakkal





Poor 0.081

22 Omalur






Poor 0.0046




272

23 Panamarathupatty






Poor 17.43

24 Pappireddipatti






Poor 14.18

25 Paramathi





Poor 0.007

26 Pendanaickenpalayam






Poor 46.37

27 Puduchathiram





Poor 1.68

28 Rasipuram





Poor 5.75

29 Rishivandhiyam





Poor 0.23

30 Salem






Poor 0.48




273

31 Salem corporation






Poor 0.57

32 Sankarapuram






Poor 3.99

33 Sendamangalam





Poor 5.068

34 Thalaivasal





Poor 14.95

35 Thandarampet



Poor 2.05

36 Tharamangalam




Poor 0.057

37 Thuraiyur



Poor 8.12

38 Uppiliyapuram






Poor 15.104

39 Valapady






Poor 8.34




274

40 Veerapandy






Poor 0.00148

41 Vennandur






Poor 17.15

42 Veppanthattai





Poor 5.12

43 Veppur





Poor 0.52

44 Yercaud






Poor 22.20

4.5 NEOTECTONICS VS GROUNDWATER

4.5.1 General

Caponera (1989) interpreted the remote sensing image for

groundwater surveying and extracted the morphology, lineaments, structural

features, vegetation and drainage from the image. His study concluded that

the tensional fractures by tectonism are related to the groundwater storage or

movement in bedrock.

Travaglia (1989) also studied the fracture and groundwater exploration

in two areas, the Bayhan Al Qasab area (Yemen) and the Philippines. He also

revealed that the tensional fractures (N10ºW and N15º-25ºE directions) are

related with groundwater exploration in the Karst region, Philippines.




275

Sidle and Lee (1995) concluded that anisotropic hydraulic conductivity

was related to the orientation of geologic structures. Larson (1972) indicated

that wells located in fractures which were interpreted as extensional fractures

were much more productive than wells located in shear fractures.

Kafri (1970) has inferred that the active tectonic movements have

substantially altered the Cenomanian – Turonian carbonate aquifer of Galilee

region.

Usha et al. (1989), on the basis of thematic correlations, inferred that

the faults / fractures perpendicular to the fold axis are better groundwater

prospects when compared to conjugate ones.

Ramasamy and Balaji (1993) have classified the lineaments of Tamil

Nadu into Precambrian, Precambrian reactivation in Pleistocene and

exclusive Pleistocene and observed that the exclusive Pleistocene lineaments

have better groundwater prospects.

Similar modifications of groundwater flow due to recently renewed

tectonic activities were observed by Kresic (1995) in Dinaric Karst of Balkans

area.

Kissin et al. (1996), on the basis of their studies in Main Kopetdag fault

zone of Turkmenistan demonstrated that the groundwater level fluctuation

can be used as precursor for earthquakes.

Similar observations were made on the various aquifer responses and

health during both prior and after the 21st September 1999 Chi – Chi

earthquake of China (Fu-qiong Huang et al., 1999).

Gudmundsson (1999) has observed that the postglacial crustal doming

and the related formation of fractures of Norway have substantially changed

the groundwater behavior in the area.




276

Again by analyzing the various azimuthal frequencies of lineaments,

Ramasamy et al. (2001) inferred that the groundwater behaviours are

substantially modified by the Pleistocene faults of Tamil Nadu.

Fracture zones characterized by high transmissivity generally generate

the high groundwater production area but it is very problematic to identify

these highly productive fractures. Structural analysis and tectonic history

study can provide useful information for this problem in regional scale

studies (Mabee et al., 1994).

In the recent years, in Pingtung plain, Taiwan, SAR interferometry

studies were carried out to demonstrate the possible impacts of active tectonic

movements over groundwater system (Chang et al., 2004).

In India too, the studies have been done on the general tectonics and

the groundwater behaviours and in some areas, active tectonics and their

impact over groundwater systems.

Mohammad Aryamanesh et al. (2009) have showed that the location of

ground water potential zones and the artificial recharge regions have a great

relationship with seismic activities, fractured zones and the earthquake

epicenters, in the Isfahak is located in the south of the city of Tabas, eastern

Iran

Rana Chatterjee and Raja Ram Purohit (2009) have used water level

fluctuation method for groundwater resource estimation by studying long

term water level trend.

Continuous multiple tectonism and shear mechanism can change the

previous geometry of fractures, so it is difficult to define the total fracture

mechanism and to extract the extensional fractures. But, if we know the

tectonic history and define the extensional fractures, we can try to reveal the

relation between lineaments or extensional lineaments and groundwater

productivity. Though the water level fluctuation is a direct dependant on

rainfall, infiltration and usage, the fluctuation trend would be entirely




277

controlled by the general structural orientation of the aquifers, landforms,

topographic lows, structural basins and in fact the fractures.

Since we mapped the Neotectonic lineaments a behavior of water level

fluctuation linearity could reveal the water table dynamics controlled by these

lineaments.

4.5.2 Water level Fluctuations and Water Bearing Lineaments

Water level data for the entire study window was collected from Public

Works Department (PWD) and Tamilnadu water supply and drainage board

(TWAD) for 204 wells from the year 1990 for both the seasons up to 2008. The

isolines for water level were drawn with the SURFER and then exported to

ARCGIS and the trend of the fluctuation pattern was studied to establish the

relationship between Neotectonic lineaments. The valleys were correlated

with the Neotectonic lineaments and the variation could be traced with

respect to seasonal and temporal variations. The final results were tabulated

and the lineaments which bear water for the entire period of study were taken

as water bearing lineaments (Table 4-14).

A detailed analysis was made on the water level data for the study area

and they display a fluctuation trend along certain directions. The premonsoon

and post monsoon water level data showed a great variation in the water

level still the water level valleys were located along certain pockets within

the study area. Most of the water level valleys were concentrated along the

intersections of many differently oriented lineaments.

4.5.2.1 Water level Fluctuations Vs Neotectonic Lineaments (1990)

Water level valleys were observed near south western side of the the

study area and along E-W trending Swetha Nadi fault. The trends of the

valleys match with the neotectonic lineaments shown in the table. 4.14.

In the post monsoon season the valley were observed near north of

Kolli hills and along Tirumanimuthar stream and near the eastern side of the

study area.




278

Fig.4.17 Water level Fluctuations Vs Neotectonics (Year 1990)

The trend of the valley was generally aligning with NE-SW direction in

the western side. N-S trend and E-W were also seen and reflecting the nature

of fractures controlling the storage capacity of aquifer (Fig. 4.17).




279

4.5.2.2 Water level Fluctuations Vs Neotectonic Lineaments (1991- 1993)

In the year 1991, there was a marked difference in the water level. In

the post monsoon season where the water level varies between 2 to 30m but

in the pre monsoon season the water level varies between 5 to 80m. The

trends of the valleys which are concordant with the water level valleys were

taken as water bearing valleys (Table 4-14).

Max Min Fig.4.18 Water level Fluctuations Vs Neotectonics (Years 1991 - 1993)




280

In 1991 pre monsoon season, major valleys were found near Belur,

Thumbal area, South of Salem and Thalaivasal towns. The valleys were broad

and widely distributed, whereas in post monsoon season the valleys were

restricted to Tirumanimuthar stream course and north of Kalrayan hills (Fig.

4.18a&b).

In 1992 premonsoon season, the NE-SW lineaments running west and

east of Chitteri hills seems to have control over the water level of NE and SW

zones of Salem.

In post monsoon season, NE-SW trending Chitteri east lineaments have

control over the water level valleys NE and SW of Salem; NW-SE lineaments

have control over Attur- Gangavalli region and N-S lineaments in the east of

Kalrayan hills (Fig.4.18c&d).

In 1993 premonsoon season, the N-S and E-W lineaments have great

control over valley at the east and west of Salem. The NW-SE lineaments have

their role in Gangavalli and Vazhapadi region (Fig.4.18e&f).


In the year 1994 premonsoon season NE-SW, E-W and N-S lineaments

have the control over the valleys in the south of Salem sector. NW-SE

lineaments L12 and L16 have their control near Attur segment. N-S trending

L27, L28, L58 and L72 have seems to carry water from high rain fed Kalrayan

hills to Gangavalli zone and further south.

In the post monsoon season prominent valleys were observed in the

central part of Attur valley, Gangavalli, Tirumanimuthar stream course. The

NE-SW and NW-SE lineaments have good control and the valleys were

perfectly aligning with the lineaments (Fig.4.19a&b).

In the year 1995, isolated patches of valleys were found in the

Vazhapadi, and the NW-SE lineaments L97and L63 have their role of

controlling water table. The lineaments L27, L28 and L68 drain the water to

Thammampatti zone. In the post monsoon season, the NE-SW trending




281

lineaments control the flow from Belur –Vazhapadi segment to South of

Salem region. NW-SE carries water from the Chitteri and Kalrayan catchment

to Gangavalli zone (Fig.4.19c&d).

Max Min

Fig.4.19 Water level Fluctuations Vs Neotectonics (Years 1994 - 1996)




282

1996 pre monsoon season isolated patches of valleys were observed

and the post monsoon season was covered with broad valleys. The NE-SW

trending Lineaments L84 and L87 were aligning with valleys south of Salem

and NW-SE trending L74 and L75 were aligning with the Valley connecting

Rasipuram- Salem sector. NW-SE trending L68 carries water from Belur to

Attur area and N-S trending L58 and L72 carries water from Kalrayan to

Gangavalli (Fig.4.19e&f).


In 1997 premonsoon season, N-S trending L39 and L76 lineaments

seem to have control along Chitteri to Rasipuram segment. NW-SE trending

L63 and L68 have control in Gangavalli area and N-S trending L33- L58 and

L91 have control over the valleys Gangavalli and in Kallakurichi area

respectively. In post monsoon season L40, L69, L84 and L87 lineaments have

control over NE-SW direction and L11 and L63 have their control along NW-

SE direction (Fig.4.20a&b).

In 1998 premonsoon season, apart from the general controlling

lineaments the NE-SW trending L6 and L46 have their control along Metala to

Attur valley. Similar trend was observed in postmonsoon season

(Fig.4.20c&d).

In 1999 premonsoon season N-S trending L39 and L67 aligning with

valleys at Chitteri-Metala sector. L74 and L75 have their control along NW-SE

directions. Broad N-S trending valleys were observed around Attur area and

aligning with N-S lineaments L27, L58 and L72. Postmonsoon season shows

isolated valley with controlling lineaments (Fig.4.20c&d).


In 2000 premonsoon season, very broad valleys were observed around

Vazhapadi, Rasipuram, south of Salem and east of Attur. The neotectonic and

significant lineaments are equally important in the flow of water in this part

of region. Post monsoon season N-S trending L36 and L67 have their great

control in the N-S trending valleys (Fig.4.21a&b).




283

In 2001 L69, L84 and L87 lineaments have their control along NE-SW

directions and L98, L44, L75 and L74 along NW-SE directions. In post

monsoon season isolated valley were observed and E-W lineament L38 has its

contribution of controlling on the south of Kalrayan (Fig.4.21c&d).

Max Min





284

In 2002 L1, L23, and L78 have their control in post monsoon season and

NE-SW trending lineaments have higher control in both the seasons

(Fig.4.21e&f).

Max Min





285

4.5.2.6 Water level Fluctuations Vs Neotectonic Lineaments (2003-2005)

In the year 2003 premonsoon season, valleys were found along Belur –

Attur sector with NW-SE controlling lineaments NL12, L16 and NL14. N-S

lineaments were aligning valleys near Omalur and Kallakurichi sector.

Max Min





286

Similar, conditions prevails in postmonsoon season also (Fig.4.22a&b).

In the year 2004 the valleys and their spread were increased after post

monsoon and in general the NE-SW and NW-SE lineaments were dominating

the control of the water level (Fig.4.22 c&d). In the year 2005, the heavy

rainfall has completely saturated with water and the barriers were

disappeared and the almost all the lineaments aligning with them (Fig.

4.22e&f).

Max Min





287

4.5.2.7 Water level Fluctuations Vs Neotectonic Lineaments (2006-2008)

In the year 2006 the entire region received good amount of rainfall and

it was reflected in the wide and broad distributions of valleys on both the

seasons. In post monsoon season even the smallest hills were disappeared

(Fig.4.23a&b).

In the year 2007, the water level fluctuation was swinging between 0 to

28m in premonsoon and 0-25m in postmonsoon season. Almost many of the

significant and neotectonic lineaments were marked for controlling

(Fig.4.23 c&d).

In the year 2008, the dominance of the NE-SW, NW-SE and N-S

lineaments were observed along Salem-Omalur and Attur –Thalaivasal sector.

The post monsoon Chinna Salem, Gangavalli and Omalur area shows broad

valley and controlled by NE-SW and NW-SE lineaments (Fig.4.23e&f).

4.6 SYNTHESIS

This study has included all the significant lineaments as the water

conducive properties may rest upon any fractures/ lineaments irrespective of

its neotectonic characters. From the analysis of the water level valleys for 19

years from 1990 to 2008, we could observe that certain parts of the region

have permanent water level stable zones near Belur, Tirumanimuthar stream

course south of Salem, Rasipuram-Metala segment, Kallakurichi- Chinna

Salem - Thalaivasal segment, Thammampatti zone, Puthansandhai-west of

Kolli hill zone, Attur-Gangavalli zone, Thoppal Ar zone and Omalur-

Kadayampatti zone. These zones have at least small valleys throughout both

seasons of 19 years.

The E-W lineaments have control over valleys seen at the foot of

Shevaroys to Kalrayan, but seem to have a flow barrier at the middle of the

Salem-Attur segment, especially near Vazhapadi-Malliyakkarai segment. This

area is a topographic as well as a lithologic barrier. Moreover the general

foliation trend takes a northerly swerve near Godumalai but for the shear

zones and E-W trending lineaments the flow control would not have occurred




288

towards the east at heavy rainy season. This area is covered with numerous

dykes, mafic granulites and banded magnetite quartzite bands, these rocks

serve as walls in damming the flow of water despite dissected by many

lineaments. This area is also known for its carbonatites and carbonate gneiss

occurrences, and hence there is valid reason to believe the leachates from

carbonates form kankars and heals the primary and secondary porosities of

the rocks. At seasons of heavy rainfall (2002 & 2006) the barriers have been

breached and broad valleys could be seen (Fig.4.21e & 4.23b).

Fig.4.24 Filtered Water Bearing Lineaments

The NE-SW lineaments L84, L87, L69 and L32 running throw the

Chitteri hills have great control over water level stabilization. E-W trending




289

L1, L8, L38 and L40 have similar control and even they were penetrative

through the northeasterly trending foliated rocks. Similar penetrative trend

was observed along Swetha nadi fault lineament L76. The lineaments L12 and

L16 have the flow control along NW-SE directions in Thumbal-Gangavalli

sector. Apart from this N-S trending L6, L36 and L82 lineaments have the

control over the flow in Chitteri to Vazhapadi sector. Significant lineaments

do also have the control in the ground water flow especially L60, L68, L91 and

L97 have control in the Kalrayan eastern segment. N-S trending L27, L28, L58

and L72 have seems to carry water from high rain fed Kalrayan hills to

Gangavalli zone and further south. The NE-SW trending lineaments control

the flow from Belur –Vazhapadi segment to South of Salem region. NW-SE

carries water from the Chitteri and Kalrayan catchment to Gangavalli zone.

This study has given a clear idea about the conductive nature of certain

lineaments. The lineaments which have been contributing the waterlevel

maintenance of the region were filtered out after evaluating the characters for

18 years of water level data for both seasons. The significant lineaments which

have contributed for at least ten seasons were filtered out as water bearing

lineaments viz. L1,L4, L6, L12, L22, L25, L29, L33, L36, L39, L41, L44, L47,

L50, L51, L57, L58, L59, L60, L63, L64, L65, L68, L69, L72, L73,L74, L75,

L76,L79, L80, L81, L84, L86, L87, L94 and L9 (Fig.4.24, Table 4-14). Out of

these 38 significant lineaments 18 are neotectonic lineaments they are NL1,

NL4, NL10, NL20, NL21, NL24, NL26, NL29, NL32, NL36, NL39, NL40, NL41,

NL42, NL45, NL46, NL48, NL50, NL51 and NL53 (Table4-14).

These lineaments intersection zones found to be highly water potential

and the same was found in the study (Fig.4.13). The shear zone with

weathered ultramafics and fissile hornblende gneiss seems to be very good

aquifers. The region like Salem has very unique lithology and they have

higher secondary porosity owing to intense shearing. Prime weightage was

assigned to lithology and proved to be correct with resistivity and aquifer

parameters study by pump test. The water level stable zones were also found

along the shear zones and lineament intersection areas.




290

TABLE 4.14 WATER BEARING LINEAMENTS DERIVED FROM WATERLEVEL DATA FOR 19 YEARS

Sig

nif

ica

nt

Lin

eam

en

ts

Ne

ote

cto

nic

Lin

eam

en

ts

19

90 P

RE

19

90 P

M

19

91 P

RE

19

91 P

M

19

92 P

RE

19

92 P

M

19

93 P

RE

19

93 P

M

19

94 P

RE

19

94 P

M

19

95 P

RE

19

95 P

M

19

96 P

RE

19

96 P

M

19

97 P

RE

19

97 P

M

19

98 P

RE

19

98 P

M

19

99 P

RE

19

99 P

M

20

00 P

RE

20

00 P

M

20

01 P

RE

20

01 P

M

20

02 P

RE

20

02 P

M

20

03 P

RE

20

03 P

M

20

04 P

RE

20

04 P

M

20

05 P

RE

20

05 P

M

20

06 P

RE

20

06 P

M

20

07 P

RE

20

07 P

M

20

08 P

RE

20

08 P

M

L1 NL1 W W W W W W W W W W W W W W W W W W W W W W

L2 NL2 W W W

L3 NL3 W W W

L4

W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W

L5

W W W W W W W

L6 NL4 W W W W W W W W W W W W

L7 NL5 W W W

L8 NL6 W W W W W W W W

L9 NL7 W W W W W

L10 NL8 W W W W W

L11 NL9 W W W W

L12 NL10 W W W W W W W W W W W W W W W W W W W W

L13 NL11 W W W W W W

L14

L15 NL12 W W

L16 NL13 W W W W

L17 NL14 W W W W

L18

W

L19

L20

L21

L22

W W W W W W W W W W W W W W W W W W W W W W W

L23 NL15 W W W W W

L24

L25

W W W W W W W W W W

L26 NL16 W W W W W

L27 NL17 W W W W W W W


L29

W W W W W W W W W W W

L30

W W W W




291

L31

L32 NL19 W W W W

L33 NL20 W W W W W W W W W W W W W W W W

L34

W W

L35

W

L36 NL21 W W W W W W W W W W


L38 NL23 W W W W W W W W W

L39 NL24 W W W W W W W W W W W W W W W W W W W W W



L42 NL27 W W



L45 NL30 W W W


L47


L48

W W W W W W

L49


L51

W W W W W W W W W W

L52

L53 NL33 W W W W

L54

W W W

L55

L56

W W W

L57

W W W W W W W W W W W W W W

L58

W W W W W W W W W W W W W

L59

W W W W W W W W W W W W W W W W W W

L60

W W W W W W W W W W W W W W W W W W

L61 NL34 W W W W

L62

W W W

L63


L64

W W W W W W W W W W

L65

W W W W W W W W W W W W




292

L66

W

L67 NL35 W W W W

L68

W W W W W W W W W W W W

L69 NL36 W W W W W W W W W W W W W W W W W W

L70 NL37 W W W W W


L72

W W W W W W W W W


L74 NL40 W W W W W W W W W W W W W W W W W

L75 NL41 W W W W W W W W W W W W W W

L76 NL42 W W W W W W W W W W W W W W W W W W W W W W W

L77 NL43 W W W

L78 NL44 W W W W W



L81

W W W W W W W W W W W W W W W W


L83

W W W

L84 NL48 W W W W W W W W W W W W W W W W W W

L85 NL49 W W W W W


L87 NL51 W W W W W W W W W W W W W W W

L88

L89

W W W W W W W

L90

W W

L91

W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W

L92

L93

W W W W W W W

L94

W W W W W W W W W W W W W W W W W

L95

L96

W

L97

W W W W W W W


L99 NL53 W W W W W W W W W W W W W

PRE- PREMONSOON, PM-POST MONSOON, W- LINEAMENTS ALIGNING WITH WATER LEVEL VALLEYS

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