Integrating geospatial and ground geophysical informationas guidelines for groundwater potential zones in hard rockterrains of south India

Mehnaz Rashid & Mahjoor Ahmad Lone &

Shakeel Ahmed

Received: 27 April 2011 /Accepted: 24 August 2011 /Published online: 8 September 2011# Springer Science+Business Media B.V. 2011

Abstract The increasing demand of water hasbrought tremendous pressure on groundwater resour-ces in the regions were groundwater is prime sourceof water. The objective of this study was to exploregroundwater potential zones in Maheshwaram water-shed of Andhra Pradesh, India with semi-arid climaticcondition and hard rock granitic terrain. GIS-basedmodelling was used to integrate remote sensing andgeophysical data to delineate groundwater potentialzones. In the present study, Indian Remote SensingRESOURCESAT-1, Linear Imaging Self-Scanner(LISS-4) digital data, ASTER digital elevation modeland vertical electrical sounding data along with otherdata sets were analysed to generate various thematicmaps, viz., geomorphology, land use/land cover,geology, lineament density, soil, drainage density,slope, aquifer resistivity and aquifer thickness. Basedon this integrated approach, the groundwater avail-ability in the watershed was classified into fourcategories, viz. very good, good, moderate and poor.The results reveal that the modelling assessmentmethod proposed in this study is an effective toolfor deciphering groundwater potential zones for

proper planning and management of groundwaterresources in diverse hydrogeological terrains.

Keywords Remote sensing . Geophysics . GIS . Hardrock . VES . Groundwater potential zones

Introduction

In arid and semi-arid regions across the globe, waterscarcity is a major problem, and due to deficit rainfall,tremendous pressure is brought on groundwater. Thestudy area taken for the present research is located inthe semi-arid climate zone with hard rock graniticterrain. Hard rock terrains develop complex hydro-geology over long period of geological time due toheterogeneous nature of the weathering. Groundwaterin hard rock aquifers is essentially confined tofractured and weathered horizons. The occurrenceand movement of groundwater in a watershed of ahard rock terrain are mainly controlled by secondaryporosity caused by fracturing of the underlying rocks(Srivastava and Bhattacharya 2006). In India, about65% of the country is underlain by hard rocks (Sarafand Choudhury 1998).

Satellite images are being increasingly used ingroundwater exploration because of their utility inidentifying various ground features, which may serveeither as direct or indirect indicators of groundwaterpotential. A number of attempts on delineation forgroundwater potential zones using remote sensing

Environ Monit Assess (2012) 184:4829–4839DOI 10.1007/s10661-011-2305-2

M. Rashid (*) :M. A. Lone : S. AhmedNational Geophysical Research Institute,Council of Scientific and Industrial Research,Hyderabad 500606, Indiae-mail: geo.mehnaz@gmail.com

M. A. Lonee-mail: lonemahjoor@gmail.com

data have been made by Murthy (2000), Jaiswal et al.(2003), Anbazhagan et al. (2005), Sener et al. (2005),Kumar et al. (2007), Kumar et al. (2008), Chowdhuryet al. (2009) and Dar et al.(2011).

The groundwater resources in India are facing acontinuous threat of depletion. The rate of depletionin groundwater resources of north Indian states inIndo-Gangetic plains is about 109 km2 with meanannual depletion of 4.0±1.0 cm year−1 from 2002 to2008 (Rodell et al. 2009). Remote sensing data canprovide most accurate spatial information and it canbe economically utilized over conventional methodsof hydrogeological surveys. Geographic informationsystem (GIS) techniques facilitate integration andanalysis of large volumes of data, whereas fieldstudies help to further validate results (Solomon andQuiel 2006).

Geology, geomorphology, lineaments, soil, landuse, drainage and slope all play an important role ingroundwater location (Srivastava and Bhattacharya2006). Modern technologies such as remote sensingand GIS have proved to be useful for studyinggeological, structural and geomorphological condi-tions together with conventional surveys (Solomonand Quiel 2006).

Different researchers have used different criteria fordelineating groundwater potential zones. Srivastava andBhattacharya (2006), Kumar et al. (2007) and Sreedeviet al. (2005) have integrated geophysical data withgeospatial data. Nag (2005) has used lineament andhydrogeomorphology-based approach in delineatinggroundwater potential zones. Saraf and Choudhary(1998), Jasrotia et al. (2007) and Chenini et al. (2010)have used remote sensing and GIS in delineatingartificial recharge sites. Geomorphology and linea-ments are very essential in groundwater prospecting.Many researchers like Kamal and Midorikawa (2004),Gustavsson et al. (2006) and Singh et al. (2007) haveused satellite imagery in identifying geomorphicfeatures and lineaments applying various techniquesthat are helpful in groundwater studies.

Vertical electrical sounding (VES) is the mostwidely used geophysical technique for groundwaterprospecting because it demarcates the different sub-surface layers both in terms of thickness andresistivity when compared with other geophysicalmethods. Geophysical data greatly help in locating thegroundwater potential in any hydrogeological setup.The property and thickness of various layers obtained

from geophysical survey at different location ifintegrated can yield a groundwater potential modelof higher reliability and precision (Shahid and Nath2002). VES can be used for quantitative estimates ofaquifer parameters, which reduce the additionalexpenditures of carrying out pumping tests and offeran alternate approach for estimating the hydraulicproperties (Dhakate and Singh 2005).

In order to provide effective guidelines for ground-water resource management in complex hydrologicalterrains, both surface and subsurface indicators ofgroundwater are taken into account. This methodologycan be applied effectively in the areas with similarclimate and geology like southern India which suffersfrom acute shortage of water leading to severe sufferingof farmers. Keeping this scenario into consideration, thepresent study to delineate groundwater potential zoneswas carried out in order to have proper management forsustainable use of groundwater in such a complexhydrological terrain.

Study area

Maheshwaram watershed with an area of 52.86 km2 islocated 35 km from Hyderabad city in Rangareddydistrict of Andhra Pradesh, India (Fig. 1). Thegeographic extension of the watershed is 17°06′20″N–17°11′00″ N latitudes and 78°24′30″ E–78°29′00″

Fig. 1 Location of the study area

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E longitudes. The area is underlain by hard rockgranites.The study area is located in semi-arid climaticzone. Average annual rainfall in the area is 884.1 mm,while maximum precipitation occurs during June–September with the onset of southwest monsoon. Thetemperature ranges from 22°C to 44°C. May is thehottest month with temperatures exceeding 40°C, andJanuary is the coldest month with temperature goingdown sometimes to below 15°C. The area is character-ized by the existence of almost gentle undulatingtopography with sub-dendritic drainage pattern andseasonal flow. Due to overpopulation, rapid urbaniza-tion and non-availability of any perennial river, the areais facing water scarcity problem. In the study area, themain source of irrigation is groundwater as the arearemains rain deficit most of the year.

The area is mainly composed of granites ofArchaean age. The basic lithological units presentare biotite granite, leucocratic granite, intersected bydolerite dykes and quartz veins (Dewandel et al.2006) covered by a thin layer of soil. Aquifer inMaheshwaram watershed is taken as a two-layeraquifer: the upper part consists of weathered rocksand overlays the lower weathered-fractured granitelayer. Due to overexploitation of groundwater in thearea, only fractured layer is saturated.

Material and methodology

Indian Remote Sensing Satellite (IRS) RESOURCESAT-1 (LISS-4) data was used in the present study. Themultispectral satellite image from the LISS IV sensorcontains three spectral bands, i.e. green (0.5–0.6 μm),red (0.6–0.7 μm) and near infrared (0.7–0.9 μm). Thespatial resolution is 5.8 m. The survey of India topsheet 56 K/8 with a scale of 1:50,000 was used asa source of ancillary information. ASTER digitalelevation model (DEM) with spatial resolution of30 m was used to generate slope and shaded reliefmaps. Different thematic maps generated usingsatellite data are geomorphology, soil, lineaments,drainage and land use/land cover.

The geomorphological map was prepared byprincipal component analysis (PCA; PC1, PC2 andPC3) of IRS LISS-4 image and shaded relief mapsgenerated from DEM along with ancillary data andground survey. The PCA, originally known asKarhunen–Loeve transformation, is used to compress

multispectral data sets and reduces the data redun-dancy (Jenson 1996). The transformation of the rawremote sensing data using PCA, results in newprincipal component images which are more inter-pretable than the original image (Rashid et al. 2011).In the present study, PC1, PC2 and PC3 weregenerated; PC3 was found more useful in datainterpretation as it contains more information aboutthe landforms of the study area.

The imagery was visually interpreted to prepare landuse/land cover and soil map using knowledge-basedclassification in ERDAS Imagine software package.Lineaments were identified from shaded relief maps ofthe study area. Lineament density, drainage and drainagedensity maps were prepared using ArcGIS.

A geophysical survey was conducted at 86 locationsin the study area using Schlumberger configuration,depth to bed rock and thickness, and resistivity ofdifferent zones was calculated. The thickness ofweathered layer varies from about a metre to about35.7 m, and resistivity varies from 3.5–230Ωm; thethickness of fractured layer varies from less than a metreto about 31 m, and resistivity varies from 22–560Ωm;this shows a large variation in subsurface layerparameters. The depth to bedrock varies from 4.3 to42.3 m. The results from VES data were converted toraster maps in ArcGIS 9.3.1 and were used in overlayanalysis for delineating groundwater potential zones.

All the maps were converted into raster format andgeoreferenced to common reference point in theUniversal TransverseMercator plane coordinate system.All the themes were integrated using “Spatial AnalystModule” of ArcGIS. Each theme and its individual classwere assigned weight and rank (Table 1) based onexisting literature. The resultant composite coveragewas classified into four groundwater potential zones:(1) very good, (2) good, (3) moderate and (4) poor. Theoutput map was correlated and validated with the fieldgroundwater data.

Geology

Geologically, the area is mostly dominated by granitesof Archean age. For the present study, geological mapwas prepared from the existing geological map byDewandel et al. (2006). The basic lithological unitspresent are biotite granite, leucocratic granite, quartzvein and dolerite dyke (Fig. 2). The presence of

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Theme Weight Class Rank

GWPI

Geomorphology 30 Shallow weathered pediplains 2

Moderately weathered pediplains 2

Pediment 1

Valley fill 4

Rocky outcrops 1

Geology 5 Biotite granite 2

Biotite granite with pegmatite 2

Leucocratic granite 1

Doloretic dyke 3

Quartz vein 3

Soil 10 Coarse loamy 4

Loamy skeletal 3

Fine loamy 2

Rocky outcrops 1

Lineament density 30 0–0.8 km/km2 1

0.8–1.2 km/km2 2

1.2–1.7 km/km2 3

1.7–3.4 km/km2 4

Drainage density 10 0–1 km/km2 4

1–1.4 km/km2 3

1.4–1.8 km/km2 2

1.8–3.5 km/km2 1

Slope 5 0–1% 4

1–2% 3

2–4% 2

>4% 1

Land use/land cover 10 Barren land 1

Other crops 2

Forest 4

Paddy 3

Orchid 3

Mixed plantation 3

Rocks 1

Built-up 1

Water bodies 4

GWP2

Resistivity of fractured zone 50 22–100Ω 4

100–140Ω 3

140–200Ω 2

>200Ω 1

Thickness of fractured zone 50 0–7 m 1

7–10 m 2

10–13 m 3

13–31 m 4

Table 1 Theme weightand class rank assigned todifferent thematic layersin weighed overlay analysis

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dolerite dyke and quartz vein is an indication of abarrier for groundwater movement; hence, the contactzones with the host rock act as a good source ofgroundwater potential. As biotite granite is moredeeply weathered, it was assigned higher rank thanleucocratic granite. The groundwater prospect ofgeological units is given in Table 1.

Geomorphology

Geomorphology of an area is one of the most importantfeatures in evaluating the groundwater potential andprospect (Kumar et al. 2008). The integrated use ofPCA images and field survey were used to distinguishvarious geomorphic units in the study area (Fig. 3).The area is characterized by a number of erosional anddepositional geomorphic features, viz. pediplains, pedi-ments, rocky out crops and valley fills.

Shallow weathered pediplains are described asnearly flat terrain with gentle slope. The area iscovered with relatively thick weathered material. Thisis a dominant geomorphological unit in the study areacovering an area of 27.32 km2. The groundwaterprospect of these areas is described as moderate. Themoderately weathered pediplains are found in manyplaces covering an area of 6.56 km2. The groundwaterpotential of these units is described as moderate.

Valley fills are generally unconsolidated alluvialmaterials consisting of sand, silt, gravels and pebblesdeposited along the floor of a stream valley. Valleyfills with an area of 9.32 km2 are found in centralparts along the stream network of the study area. Thearea is mainly covered by coarse sediments with goodvegetation cover, and groundwater potential of thisunit is described as very good.

Pediments consist of very low weathered zone. A flatand smooth surface of buried pediment consists ofshallow overburden of weathered derivative material.Pediments covering a total area of 7 km2 are mainlyfound at the southwestern region besides somescattered patches in the northern region of the studyarea. Groundwater prospects are moderate to poor.Rocky outcrops are found in southwestern and westernparts of the study area covering an area of 2.66 km2.Negligible to sparse vegetation is found around theunit, and the groundwater potential of this unit isdescribed as poor. Theme weight and class rankassigned to each geomorphic unit are given in Table 1.

Soil

Soil map was prepared by visual image interpretation.Climate, geology and physiography characterize soilsand play an important role in groundwater rechargeand runoff. The water-holding capacity of an areadepends upon the soil types and their permeability.The initial infiltration and transmission of surfacewater into an aquifer system is a function of soil type

Fig. 3 Geomorphology of the study area

Fig. 2 Geology of the study area

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and its texture (Anbazhagan et al. 2005). Four typesof soils are found in the study area, viz. loamyskeletal, fine loamy, coarse loamy and rocky outcrops(Fig. 4). The delineation was based on differentialmanifestations on the imagery in the form of colour,tone, texture and association. Field checks in theidentified soil units were conducted and confirmed.Weights are assigned subjectively to each soil unit aftertaking into account the type of soil, specific yield and itswater-holding capacity given in Table 1. Coarse loamysoils have been assigned highest rank, whereas fineloamy and rocky outcrops are assigned moderate andlow rank, respectively. The groundwater potential ofloamy skeletal soil is described as high.

Lineaments

Lineaments are defined as naturally occurring linearor curvilinear features. Lineaments were delineatedfrom shaded relief map generated from ASTER DEMwith sun azimuth 100 and sun elevation 30 usingERDAS 9.1 (Fig. 5). Lineaments play an importantrole in groundwater recharge in hard rock terrains;groundwater potential is high near lineament zonesSrivastava and Bhattacharya (2006). In hard rockterrains, lineaments represent areas and zones offaulting and fracturing resulting in increased second-ary porosity and permeability and are good indicatorsof groundwater (Kumar et al. 2007).

Lineament density is the total length of all thelineaments present in the basin/watershed divided by thearea of basin/watershed. Lineament density map wasprepared by dividing the study area into 1 km/1 kmgrids, and the lineament density values then obtainedwere interpolated by inverse distance weighted (IDW)interpolation method (Fig. 6). The highest value oflineament density, 1.7–3.4 km/km2, is found to bepresent in southeastern and eastern parts while as lowdensity towards the centre of the study area. On thebasis of lineament density, the area was divided intofour different zones. Since groundwater potential isdirectly proportional to lineament density, hence, highrank was assigned to high lineament density zones, andlow rank to low lineament density zones are given inTable 1.

Drainage

Drainage was derived from ASTER DEM usingArcGIS 9.3.1 (Fig. 7). Drainage density is definedas the ratio of total channel segment lengths within awatershed/basin to the watershed/basin area. Drainagedensity of the study area was prepared by dividing thearea into 1 km/1 km grids. The density of these unitcells was then interpolated by IDW method togenerate drainage density map (Fig. 8). The valuesobtained and the respective theme weight and classrank assigned to them are given in Table 1. Drainage

Fig. 5 Map showing lineaments of the study area

Fig. 4 Soil map of the study area

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density is an inverse function of permeability. The lesspermeable a rock is, the less the infiltration of rainfall,which conversely tends to be concentrated in surfacerunoff. This gives origin to a well-developed and finedrainage system. High drainage density indicates lessinfiltration and hence acts as poor groundwaterprospect compared to low drainage implying aninverse relation between the two. Low network ofdrainage course indicates presence of highly resistantand permeable rock, while a high drainage courseindicates highly weak and impermeable rocks (Kar-

anth 1999). A higher ranking was attributed to lowdrainage density zones and a lower ranking to a highdrainage density zones.

Slope

The slope map was prepared from the ASTER DEM.Most of the area is a flat terrain. Slope ranges mostlybetween 0% and 4% (Fig. 9). A high slope will causemore runoff and less infiltration, and thus have poorgroundwater prospect compared to low slope region.A higher ranking was assigned to a gentle slope and alower ranking to a higher slope. The groundwaterpotential of these classes is given in Table 1.

Land use/land cover

Land use/land cover map was prepared usingknowledge-based supervised classification techniqueand maximum likelihood classifier. Land use/landcover plays a vital role in groundwater prospecting.Different land use/land cover types affect the rate ofrecharge, runoff and evapotranspiration. The rate ofinfiltration is directly proportional to the density ofvegetation cover, i.e. if the surface is covered bydense forest, the infiltration will be more and therunoff will be less. The runoff yield is increasedgradually from forest cover, grassland, farmland,Fig. 7 Drainage map of the study area

Fig. 8 Drainage density of the study areaFig. 6 Lineament density of the study area

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barren land and urban built-up land (Anbazhagan etal. 2005). Water bodies are continuous and excellentsource of recharge to ground. Forests and waterbodies are assigned highest rank for groundwaterpotential. The orchids, mixed plantation and paddyfields with good vegetation cover promote theinfiltration rate and prevent excess runoff and there-fore are assigned high rank for groundwater prospec-ting. Rocks and built-up and barren lands are assignedlow weightage as the infiltration rate is very low.

The land use classes identified include mostlyagriculture land with paddy, orchids; other cropsinclude maize, cotton, vegetables, sunflower; forests,barren land, rocky outcrops and water bodies(Fig. 10). The agricultural land occupies 39.97 km2

area followed by built-up with 5.16 km2, forest with3.97 km2 area, barren land with 2.16 km2 and rockyoutcrops with 1.35 km2 and water bodies with an areaof 0.25 km2. The groundwater potential of theseclasses is given in Table 1.

Aquifer thickness

To delineate the aquifer thickness, vertical electricalsounding (VES) was carried out at 86 locations usingSchlumberger configuration with electrode spacing of200 m to demarcate the thickness of different layers ofaquifer. The top layer is soil with thickness of 0.1–4.1 m;

next layer is weathered granite with thickness of 1.1–35.7 m. The third layer overlying the basement is highlyfractured granite of thickness varying between 0.3 and31 m. The thickness of the weathered, fractured zoneand depth to bedrock were determined from VES data.Due to overexploitation of groundwater in the area, onlyfractured layer acts as aquifer (Fig. 11). Therefore, inthis study, the thickness of fractured zone was taken asa layer in ArcGIS. It is well known that transmissivityincreases with the thickness of the aquifer; therefore,the higher the thickness of the aquifer, the more is thetransmissivity and vice versa.

Aquifer resistivity

Due to overexploitation of groundwater resources in thearea, only fractured zone acts as aquifer. Therefore, theresistivity of fractured zone (Fig. 12) has been used as aparameter in the GIS analysis. The resistivity of the soillayer is below 3.7–1,000Ωm. The resistivity of theweathered zone is 3.5–230Ωm, and the total depth tobedrock is more than 20 m. The resistivity of fracturedzone is between 22 and 560Ωm, and the total depth tobedrock is more than 30 m. Low resistivity values, 22–100Ωm, are indicative of highly fractured and betterwater-bearing aquifers, whereas the high resistivity

Fig. 9 Slope map in percent of the study area derived fromASTER (30 m) DEM

Fig. 10 Land use/land cover of the study area

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indicates less fractured zones with negligible water-bearing aquifers.

Results and discussion

Data integration using GIS modelling

All the thematic maps, viz. aquifer thickness, aquiferresistivity, geomorphology, geology, land use/land

cover, soil, lineament density, drainage density andslope, were converted to raster format followed byassigning respective theme weight and class rank asshown in Table 1. In first step, the weighed overlayanalysis was performed on surface indicators using“Spatial Analyst Module” of ArcGIS 9.3.1.

Geomorphologyþ lineamentþ geologyþ land useþsoilþ slopeþ drainage

The output map was named as GWP1 with valuesranging from 1–4, where value 1 corresponds to poorGWP1 zone, value 2 corresponds to moderate GWP1zone, value 3 corresponds to good GWP1 zone andthe value 4 represents very good GWP1 zone. Insecond step, subsurface indicators, aquifer resistivityand thickness were overlaid using “Spatial AnalystModule” of ArcGIS 9.3.1. The output layer whichwas obtained with values ranging from 1 to 4 wasnamed as GWP2. Value 1 corresponds to poor GWP2zone, value 2corresponds to moderate GWP2 zone,value 3 corresponds to good GWP2 zone and thevalue 4 represents very good GWP2 zone.

The output of both surface indicators (GWP1) andsubsurface indicators (GWP2) were then assignedequal weight which corresponds to different ground-water potential zones classified into four categoriesfrom poor to very good.

GWP1þ GWP2

Fig. 13 Map showing groundwater potential zones of the studyarea

Fig. 12 Aquifer resistivity of the study area as derived fromVES data

Fig. 11 Aquifer thickness of the study area as derived fromVES data

Environ Monit Assess (2012) 184:4829–4839 4837

Finally, the groundwater potential map of the studyarea was prepared showing the groundwater scenarioof the study area. The areal extent of differentgroundwater potential zones is poor 1.5 km2, moder-ate 18.93 km2, good 25.49 km2 and very good6.94 km2.The groundwater potential map shows thatmost of area fall in the category of good, followed bymoderate and the least area is under the category ofvery good and poor (Fig. 13).

Conclusions

The present study demonstrates that the integrated useof geospatial and geophysical techniques is anefficient tool for assessing groundwater potential,based on which suitable locations for groundwaterwithdrawals could be identified. The methodologyhas been designed by integration of importantindicators of groundwater like geology, geomorphol-ogy, land use/land cover, lineament density, soil,drainage density, slope, aquifer resistivity and aquiferthickness for exploration of groundwater potentialzones at watershed scale. The results reveal that thearea falls in four groundwater potential zones rangingfrom poor to very good. The poor zone is indicativeof the least favourable region for groundwaterprospecting, while the good to very good zoneindicates the most favourable region. Thus, thepresent methodology can be used as a guideline forfurther research in such complex terrains all over theglobe depending upon the climate and hydrogeologyof the area. The results obtained can be used forsustainable management of groundwater resources inthe area in terms of artificial recharge. Concerneddecision makers can formulate an efficient groundwa-ter utilization plan for the study area so as to ensurelong-term sustainability.

Acknowledgements The authors are thankful to the Director,National Geophysical Research Institute, Hyderabad, India forgranting permission to publish the paper. The authors wish toacknowledge the Associate Editor and anonymous reviewersfor their valuable comments and suggestions.

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