156 T. Venkateswara Rao, D. Ramprasad Naik, V. Venkateswara Rao & C. Janardhana Swamy
within the administrative boundaries of Greater Visakhapatnam Municipal Corporation of Visakhapatnam, Andhra Pradesh
Figure 1. The study area forms an integral part of the plains, undulating terrain and hill ranges. Geomorphologically the
area consists of structural hills, denudational and residual hills as well as buried pediment zones. The area is covered with
rocky soils, alluvium, red loamy and sandy clays. The area falls under semi-arid type of climate and the average annual
rainfall is 1184 mm and there is high variation with minimum annual rainfall of 585 mm recorded during year 2002 to a
maximum of 1680 mm recorded during the year 2006.
Figure 1: Location Map of the Study Area
METHODOLOGY
The study is based on the combination of geophysical and GIS analysis using 3-Dimensional terrain modeling.
3-Dimensional modeling is the process of developing a mathematical, wireframe representation of any three-dimensional
object via specialized software used in a computer simulation of physical phenomena (Jaligama et al., 2006). A digital
model used to represent a topographic surface should contain adequate elevation and planimetric measurements compatible
in number and distribution with the terrain being modeled, so that the elevation of any location can be interpolated
accurately for any given application (Ayeni, 1982). 3-Dimensional models of surface and subsurface layers are created and
processed in a GIS environment for estimating the aquifer volume, which are discussed below:
Surface Modelling
The observations on 3-Dimensional (3D) view will enhance the perception and accurate identification of the
objects, as compared to the interpretation of the same on a 2-Dimensional map / image. 3D models represent any object
having three dimensions, using a collection of points in 3- Dimensional space, connected by various geometric entities
such as triangles, lines, curved surfaces, etc. There are three data structures commonly used to store elevation surfaces;
Triangulated Irregular Networks (TIN), sampling at regularly spaced grids (DEM/DTM/DSM) and lines of equal elevation
(contours).
Estimation of Aquifer Volume Using Geophysical and GPS Studies for a Part of Mehadrigedda Reservoir 157 Catchment, Visakhapatnam, India - A 3-Dimensional Modelling Approach Using GIS
Figure 2: Triangulated Irregular Network of the Topographic Surface Showing the Elevations with
Respect to Mean Sea Level
Digital Elevation Model (DEM) is a generic term for digital topographic and/or bathymetric data, in all its various
forms, generally refers to a representation of the Earth's surface (or subset of this), excluding features such as vegetation,
buildings, bridges, etc. This bare earth DEM is generally synonymous with a Digital Terrain Model (DTM) (DEM User
Manual, 2001). A Digital Surface Model (DSM) on the other hand includes buildings, vegetation, and roads, as well as
natural terrain features.
A Triangulated Irregular Network (TIN) is a digital terrain model that is based on an irregular array of points
which form a sheet of non-overlapping contiguous triangle facets (Peuker et al., 1978). In general, TIN model is generated
for 3D modeling using all measuring points (Huan, 1989). It is a vector model that supports the incorporation of point, line
and area based features to capture and represent the surface morphology. An accurate, well-designed TIN maintains
consistency with the degree of variation in surface heights found in the terrain. As the terrain becomes more complex the
resolution of the TIN should increase accordingly. This occurs because more points are sampled and included in the TIN
model in areas of high complexity (Weibel and Heller, 1991). In computations, such as spot height estimation, elevation
values are interpolated for a given location based on the triangle in which it falls. Differential Global Positioning System
(DGPS) survey has been carried out in the study area, used along with the contours extracted from 1:25,000 SOI toposheet,
to construct TIN for the topographic surface, using ArcGIS – 3D Analyst module, which is shown in Figure 2.
Subsurface Modelling
Geophysical methods are surface application methods to delineate subsurface layer boundaries and physical
nature of the geological formations, using the physics of the earth. Seismic and Electrical methods are presently being
used extensively for oil and groundwater prospecting respectively. In the present study Electrical Resistivity method has
been utilized to delineate subsurface lithology and is described below.
158 T. Venkateswara Rao, D. Ramprasad Naik, V. Venkateswara Rao & C. Janardhana Swamy
Method of Resistivity Survey
In electrical resistivity survey an artificial source of current is introduced into the ground through point electrodes
and the potentials is measured at other electrodes in the vicinity of the current‟s flow. It is then possible to determine an
effective or apparent resistivity of the subsurface. The basic principle of this method is based on Ohm‟s law.
Figure 3: Schlumberger Electrode Configuration
Linear four electrode Schlumberger configuration is adopted to carry out the Vertical Electrical Soundings (VES).
The electrode configuration consists of two current electrodes (iron stakes) for sending the current into subsurface and two
non-polarisable potential electrodes that consist of porous pots filled with CuSO4 solution and copper rods placed to
measure the potential difference between the equi-potential lines and are arranged linearly, as shown in Figure 3.
Resistivity meter with D.C power pack of 180 volts is used for current input. In this linear array of electrodes C1, P1, P2 and
C2, the apparent resistivity can be computed using the following equation:
a = π *( (L2-a
2)/2a) * V /I
Where a = Apparent Resistivity in ohm-m L = Distance between centre point and current electrode
V = Potential difference in milli volts a = Distance between Potential electrode
I = Current input in milli amps O = VES location
Resistivity Data Interpretation
The apparent resistivity (a) is calculated using the above equation and the field resistivity data, shown in Table 1
(APPENDIX 1.1), was plotted on log-log graph of 62.5 mm modulus between electrode separation (L) on X-axis and
apparent resistivity (a) on Y-axis for each Vertical Electrical Sounding data. A freehand curve is drawn by joining all the
points, which is the field resistivity curve, is shown in Figure 4(a). The increase or decrease of resistivity with electrode
separation (depth) depends on the thickness and resistivity of the subsurface layers. Normally, the field curves are smooth
and any sudden change in the resistivity at one or two reading are taken as spurious values and the curve is smoothened.
Estimation of Aquifer Volume Using Geophysical and GPS Studies for a Part of Mehadrigedda Reservoir 159 Catchment, Visakhapatnam, India - A 3-Dimensional Modelling Approach Using GIS
Figure 4: (a) Field Resistivity Curve at VES Location (b) Subsurface Lithological Vertical Cross-Section
No. 19 (Chintala Agraharam Village) Along Traverse A-A'
Field curves are matched with the set of theoretical curves prepared for two layer, three layer and multi layer
cases prepared by Orellana and Moony (1966) and broadly 3 to 4 layers are demarcated, which are classified into top soil,
weathered rock, fractured rock and hard rock. Other than the curve matching, field curves are also interpreted visually as
per the hydrogeological and topographical conditions of the area. The locations of the VESs are referred with respect to
(w.r.t.) Mean Sea Level (MSL), using DGPS data, and the corresponding elevations for weathered, fractured and hard rock
surfaces are determined, which are shown in Table 2 (APPENDIX 1.2).
In order to have an illustration of subsurface lithology, vertical cross sections are prepared by selecting the VES in
a linear direction (Figure 5 (a)), choosing three traverses in west- east direction (A-A', B-B' and C-C') and three traverses in
south- north direction (D-D', E-E' and F-F '). The vertical cross section along the traverse A-A' is shown in Figure 4(b). In
total, 50 numbers (Figure 5 (a)) of VES data has been interpreted and the subsurface lithological information is also
collected from the quarrying pits and rock outcrops exposed over the hill slopes, in and around the study area, for
extrapolating the layers up to the basin boundary.
Figure 5: (a) VES Locations Map with Traverses in East-West (A,B,C) and North-South (D,E,F) Directions
(b) TIN of the Weathered Rock Surface Showing the Elevations w.r.t MSL
Generation of 3-Dimensional Models
Resistivity of a formation is influenced by the type of formation material, degree of weathering of the rock,
160 T. Venkateswara Rao, D. Ramprasad Naik, V. Venkateswara Rao & C. Janardhana Swamy
percentage of water saturation and quality of fluid filled in the pore space. 3-Dimensional models (TIN) are generated from
the interpreted resistivity data, using ArcGIS – 3D Analyst module, for weathered rock (Figure 5 (b)), fractured rock
(Figure 6 (a)) and hard rock (Figure 6 (b)) surfaces.
Figure 6: (a) TIN of the Fractured Rock Surface and (b) TIN of the Hard Rock Surface Showing the
Elevations w.r.t MSL
VOLUME ESTIMATION OF THE AQUIFER SYSTEM
The volumetric difference calculated between the topographic surface TIN and hard rock surface TIN, using TIN
Difference utility in ArcGIS - 3D Analyst module, resulted 993.44 x 106 m
3 of water holding / yielding zone.
Figure 7: (a) Raster GRID Map of the Topographic Surface and (b) Raster GRID Map of the hard rock surface
Showing the Elevations w.r.t MSL
However, being a network of triangular features with every triangle having the unique properties of slope, aspect
and constantly changing elevation, as a function of the terrain characteristics that it represents over the entire feature, a TIN
cannot be directly used for statistical operations such as subtractions, which are necessary when the differences between
two surface features are to be extracted. To overcome this problem, the TIN is converted into a GRID format, in which
each pixel represents a constant elevation.
A GRID is a plain surface represented as rows and columns of raster cells. Each cell (pixel) in such matrices has a
value, which is represented by a digital number. If the digital numbers are referring to the terrain elevations of an area, the
GRID map therefore represents the topography of that area. The GRID maps, being matrices of numbers, facilitate
Estimation of Aquifer Volume Using Geophysical and GPS Studies for a Part of Mehadrigedda Reservoir 161 Catchment, Visakhapatnam, India - A 3-Dimensional Modelling Approach Using GIS
performance of statistical functions like subtractions to extract elevation differences between two surfaces. Therefore, the
TINs of the topographic surface and the hard rock surface are converted into raster format, using „TIN-to-Raster-
conversion‟ function in 3-D Analyst module of ArcGIS, and the resultant GRID formats are shown in Figure 7(a) and
Figure 7(b) respectively.
ISOPACH GENERATION
Isopachs are the lines joining the points of equal thickness of the different units used generally to represent the
subsurface layers. The thickness of the overburden at any location in the area is the difference between the topographic
surface elevation and the hard rock surface elevation.
Figure 8: Isopach Map of the Aquifer Showing the Thickness of the Water Yielding Zone
The elevation value that a given raster cell represents in the hard rock surface of the area, if subtracted from the
value of the corresponding cell in the topographic surface, yields the thickness of the overburden at that location.
Therefore, raster analysis of subtraction was performed using these two GRID formats, by subtracting the hard rock surface
(Figure 7 (b)) from the topographic surface (Figure 7 (a)) of the study area and the thickness values were classified in
ArcGIS, by grouping all the cells into 5 classes. The output is a GRID surface with each raster cell in it representing the
thickness of the water yielding zone in the area (Figure 8).
RESULTS & CONCLUSIONS
There is a large potential for subsurface storage, notably in areas with deep groundwater tables and no risks for
collateral problems such as water logging. The thickness of the water bearing formations i.e., weathered and fractured rock
zones has increased from the foot hill regions to the low lying areas of the study area. Over the hill slopes and hill ridges,
hard rock is present immediately below the top soil. Even though there is few fractured rock zones noticed over the hill
slopes, these may not contain aquifer system but may guide the rainwater to percolate from the top soils into the aquifer
system down below. However, the hill slopes are useful to retain rainwater for some time and release it into the aquifer
system existing down below the foot hill region. Therefore this zone is considered to be suitable for constructing harvesting
162 T. Venkateswara Rao, D. Ramprasad Naik, V. Venkateswara Rao & C. Janardhana Swamy
structures, like contour trenches. Isopach map reveals that the thickness of the water yielding zone is within 20 meters
range in the hilly and pediment zones whereas the maximum thickness of more than 60 meters is in the central parts of the
area, which is supposed to be high groundwater potential zone.
APPENDICES
APPENDIX 1.1
Table 1: Vertical Electrical Sounding data at VES Location No.19
Potential Electrode
Separation (a/2 in m)
Current Electrode
Separation (L in m)
Configuration
Constant
(π (L2-a
2)/2a)
Current
(mA)
Voltage
(mV)
Apparent
Resistivity
(Ohm-m)
0.5 1 2.36 27.6 890 76.10
2 11.77 26.5 142 63.07
3 27.47 30.0 69.0 63.18
5 77.71 27.2 21.0 60.00
1.0 7 153 31.6 12.3 59.55
7 75.36 31.5 24.1 57.66
10 155.4 33.5 13.1 60.77
13 263.7 24.4 6.0 64.84
16 401.9 20.4 3.3 65.01
2.0 20 626.4 20.3 2.3 70.97
20 310.8 20.1 4.4 68.04
25 487.4 16.1 2.4 72.66
3.0 30 703.3 16.6 1.8 76.26
30 466.2 15.1 2.4 74.10
35 636.3 16.21 2.2 86.36
5.0 40 832.6 21.1 2.1 82.87
40 494.5 20.7 3.3 78.83
50 771 25.2 2.9 88.73
7.0 60 1122.5 18.5 1.5 91.01
60 796.43 18.4 2.4 103.88
70 1088.01 16.5 1.7 112.10
10.0 80 1424.43 14.4 1.5 148.38
80 989.1 14.0 1.9 134.24
90 1256 19.9 1.9 119.92
100 1554.3 18.2 1.4 119.56
APPENDIX 1.2
Table 2: Elevation Data of Surface and Subsurface Layers w.r.t. MSL (m)
VES
Location Surface
Weathered
Rock
Fractured
Rock
Hard
Rock
1 22.02 19.02 12.02 -27.98
2 23.30 21.80 17.30 -16.70
3 42.64 41.64 22.64 2.64
4 40.00 37.00 25.00 -20.00
5 24.52 21.52 14.52 -25.48
6 31.18 28.18 1.18 -58.82
7 63.85 62.85 57.85 43.85
8 53.97 51.97 45.97 23.97
9 40.74 36.74 15.74 -39.26
Estimation of Aquifer Volume Using Geophysical and GPS Studies for a Part of Mehadrigedda Reservoir 163 Catchment, Visakhapatnam, India - A 3-Dimensional Modelling Approach Using GIS
Table 2: Contd.,
10 42.12 39.12 22.12 -37.88
11 45.91 44.91 35.91 -24.09
12 68.57 62.57 48.57 28.57
13 77.06 74.06 67.06 47.06
14 53.86 51.86 33.86 -16.14
15 80.00 79.00 79.00 70.00
16 44.29 40.29 24.29 -5.71
17 32.00 30.00 22.00 -18.00
18 52.92 49.92 27.92 -27.08
19 23.21 21.21 8.21 -36.79
20 23.32 22.32 17.32 3.32
21 41.76 38.76 21.76 -28.24
22 22.02 20.02 12.02 -27.98
23 31.80 28.80 21.80 -18.20
24 80.00 78.00 78.00 70.00
25 200.00 198.00 198.00 198.00
26 70.00 68.00 68.00 60.00
27 80.00 78.00 78.00 70.00
28 230.00 228.00 228.00 228.00
29 90.00 88.00 88.00 80.00
30 290.00 287.00 287.00 287.00
31 130.00 128.00 128.00 120.00
32 170.00 168.00 168.00 160.00
33 90.00 88.00 80.00 60.00
34 290.00 288.00 288.00 288.00
35 80.00 78.00 70.00 30.00
36 280.00 277.00 277.00 277.00
37 150.00 147.00 147.00 135.00
38 150.00 148.00 148.00 140.00
39 90.00 88.00 80.00 60.00
40 280.00 277.00 277.00 277.00
41 90.00 88.00 88.00 82.00
42 240.00 238.00 238.00 238.00
43 270.00 267.00 267.00 267.00
44 80.00 78.00 78.00 74.00
45 31.00 26.00 15.00 -29.00
46 20.00 15.00 0.00 -40.00
47 22.00 19.00 -3.00 -48.00
48 15.00 11.00 -5.00 -35.00
49 15.00 12.00 5.00 -15.00
50 120.00 117.00 117.00 100.00
164 T. Venkateswara Rao, D. Ramprasad Naik, V. Venkateswara Rao & C. Janardhana Swamy
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2. Ayeni, O.O., 1982. “Optimum sampling for digital terrain models: A trend towards automation,”
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3. Chayes, F. and Y. Suzuki, 1963, Geological contours and trend surfaces, Journal of Petrology, V. 4, pp. 307-312.
