Identification of groundwater potential zones in Mandavi River basin, Andhra Pradesh, India using remote sensing, GIS and MIF techniques

Journal Pre-proof Identification of groundwater potential zones in Mandavi River basin, Andhra Pradesh, India using remote sensing, GIS and MIF techniques

R. Siddi Raju, G. Sudarsana Raju, M. Rajasekhar PII:

S2589-7578(19)30012-5

DOI:

https://doi.org/10.1016/j.hydres.2019.09.001

Reference:

HYDRES 8

To appear in: Received date:

6 May 2019

Revised date:

9 September 2019

Accepted date:

12 September 2019

Please cite this article as: R. Siddi Raju, G. Sudarsana Raju and M. Rajasekhar, Identification of groundwater potential zones in Mandavi River basin, Andhra Pradesh, India using remote sensing, GIS and MIF techniques, (2019), https://doi.org/10.1016/ j.hydres.2019.09.001

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof Identification of Groundwater Potential Zones in Mandavi River Basin, Andhra Pradesh, India Using Remote Sensing, GIS and MIF Techniques Siddi Raju R1, Sudarsana Raju G1* and Rajasekhar M1 1

Department of Geology, Yogi Vemana University, Vemanapuram, Kadapa, 516005, Andhra Pradesh, India *Corresponding author: Sudarsana Raju.G ([email protected])

Abstract Identification of groundwater potential zones (GWPZ) in a crystalline rock terrain is a crucial

oo

f

task for sustainable groundwater resource management, through scientific knowledge and modern geospatial techniques. Remote Sensing (RS) and Geographical Information System (GIS) plays a key role in evaluating, conserving and monitoring various groundwater-related

pr

development programs. The present study integrates RS, GIS and multi influence factor

e-

(MIF) techniques for evaluating GWPZ in Mandavi River basin. In this connection IRS-R2 LISS IV satellite imagery, the Survey Of India (SOI) toposheets and various auxiliary data

Pr

sets from different sources have been used in preparing thematic maps like drainage density, lineament density, geology, soil, geomorphology, slope, rainfall, soil texture, land use/land

al

cover and groundwater levels. Then the thematic layers were changed into raster format in Arc GIS 10.4 environment. Weights and ratings have been statistically calculated and

rn

assigned to raster maps based on the technique named multi-influence factor. Finally, it is

Jo u

found that the GWPZ were classified into four type’s viz. very poor, poor, good, and very good with spatial extents of 533 sq. km (36%), 510 sq. km (35%), 319 sq. km (21%) and 103 sq. km (7%) respectively. The end results were validated with the observation of well data. The overall accuracy and kappa coefficients were 72.8% and 0.63 respectively, which shows a good correlation between groundwater potential zones and the observed well data. These results will help hydrogeologists, decision-makers, planners and local authorities to formulate better groundwater resource planning in the Mandavi River Basin. Keywords Groundwater potential zones (GWPZ); Multi influence factor (MIF); Remote sensing and GIS; Mandavi River basin.

Journal Pre-proof Introduction In India, most of the seasons are dry in the semi-arid regions due to insufficient rainfall and more evaporation owing to high temperatures (CGWB, 2007). Consequently, the rainfall is scarce which affects the surface water resources and hence requirements are met from groundwater in various fields such as irrigation, domestic and industrial, etc. The hard rock formation of entire India is about 65% with low permeability (10-1 to 10-5 m/day) and porosity with <5% (Saraf and Choudary, 1998; Nagarajan and Singh, 2009). Generally, the occurrence of groundwater is very limited in a hard rock terrain and is restricted to fractured and weathered zones. Hence it is a big task to outline potential groundwater zones (Nagarajan

oo

f

and Singh, 2009; Siddiraju et al., 2016). Various factors like lack of rainfall and unplanned groundwater management practices, more runoff, evaporation, climate change, the rapid

pr

growth of urbanization and uneven distribution of water resources are liable for water scarcity (Pinto et al., 2015; Balaji et al., 2019a). Therefore proper tools in assessing

e-

groundwater potential zones are required. The geospatial technique is one of the widely used methods for quick targeting groundwater potential areas with ground truth verifications.

Pr

Geospatial techniques are reliable, simple and cost-effective in assessing groundwater reserves than the traditional methods. (Arkoprovo et al., 2012; Thapa et al., 2017).

al

In the recent years, a number of researchers have attempted both RS and GIS

in

rn

identification of GWPZ by using various techniques such as Analytical Hierarchy Process (AHP) techniques (Kaliraj et.al., 2013; Pinto

et al., 2015; Sashikkumar et al., 2017,

Jo u

Rajasekhar et al.,2018), multi influence factor analysis (Selvam et al., 2014; Das et al., 2018), fuzzy logic analysis (Tiwari et al., 2017), Multi-criteria decision analysis (MCDA) (Hussein et al., 2017; Kindie et al., 2018; Balaji et al., 2019b) etc. Amongst all, MIF technique is reliable and cost-effective in deciphering groundwater potential zones. Thus, this technique was applied in the present study. The major portion of Mandavi River basin is occupied by a hard rock environment in which the groundwater availability is limited and restricted to weathered and fractured regions. In addition to this, the study area has a semi-arid climate where the uncertainties in rainfall prevail. Hence the present investigation was executed in identifying potential groundwater zones using the latest geospatial tools.

Journal Pre-proof Study area The Mandavi River basin is to be found in between 13°51' N-14°18' N latitudes and 78°34' E -79°1' E longitudes and originates from Ellutla extension reserved forest, Gurramkonda mandal, Chittoor district, Andhra Pradesh (Figure 1) and covers an extent of 1465 sq.km. The general flow direction of the river is northeast and flows across the mandals named Chinnamandem, Rayachoti, Ramapuram, and Veeraballi and enters into palakondalu hill ranges and finally joins into Bahudariver at Rollamadugu village. Geologically most of the area is covered by the granitic rocks and the observed drainage pattern was dendritic to subdendritic. The average yearly rainfall of Mandavi River basin is about 686.62 mm. The

oo

f

minimum and maximum temperatures ranged between 20º to 45.5ºC respectively. The

pr

altitude ranges between 1080-195m.

e-

Methodology

Pr

In the present methodology (figure 2), various influential parameters viz. drainage density, soil texture, soil depth, rainfall, lineament density, geomorphology, land use land cover, geology, groundwater level data, elevation, and slope maps were derived from ample of

al

geospatial data sets, for example, SOI toposheets, IRS R2 LISS IV satellite images, mineral

rn

and soil resource maps, SRTM-30m also field surveys and the supplementary data produced from various organizations.

Jo u

Initially, all conventional maps such as toposheets, district soil resource map, and district mineral resource map were collected from SOI toposheet, Food and Agriculture Organization (FAO) and Geological Survey of India (GSI) respectively then these are mosaic and georeferenced in GIS environment. The basic thematic maps such as drainage, village locations, basin boundary, etc., were digitized from SOI toposheets. Soil texture and soil depth maps were digitized from district soil resource map, geology and lineaments were digitized from the GSI subsequently, all these thematic layers are updated to LISS IV satellite image and field verification also carried out afterward altered thematic layers within the GIS environment. Drainage density and lineament density maps were produced by using drainage and lineament layers respectively along with 20 years of rainfall data collected from district chief planning office. Groundwater level data were collected in two ways primarily, 20 years (9 stations within the basin) of GWL data collected from district groundwater department this data is used as one of the influential factors. Then GWL data of 81 stations collected through

Journal Pre-proof field surveys, for GWPZ map validation purpose. Then the spatial distribution maps of rainfall and groundwater level fluctuations were generated by using IDW tool in GIS. Subsequently RS data sets such as IRS R2 LISS IV and SRTM-30m data obtained from National remote sensing center (NRSC), and USGS website (https://earthexplorer.usgs.gov/) respectively. The geomorphology and LU&LC maps were digitized from IRS R2 LISS IV satellite image by using visual interpretation techniques and during the preparation of this map, NRSC Bhuvan LULC maps are taken as reference maps and also field verification is done with GPS. The slope and elevation maps were procured from SRTM 30m data using

f

GIS modules.

oo

After that, all the thematic layers are converted into raster and weights were assigned using MIF technique. Finally, the GWPZ map was generated from the integration of all the raster

e-

pr

layers using weighted overlay analysis and validated with 81 observation well stations.

Pr

Assigning of weight and ratings

Eleven influential input factors such as drainage density (DD), geology (G), geomorphology

al

(GM), land use and land cover (LU&LC), lineament density (LD), rainfall (R), soil depth (SD), soil texture (ST), slope (S), elevation (E) and groundwater level fluctuation (GLF) were

rn

obtained for identification of GWPZ. In all, every factor is having its own influence and interrelated to multiple factors. The relation is, one to one or one to many factors (Fig. 3),

Jo u

based on its relation strength, weights and ratings were assigned (Magesh et al., 2012, Kaliraj et al., 2015; Thapa et al., 2017) and each factor representative weight of GWPZ is the addition of all weights from every factor. Integration of all these influential factors with their weights in the GIS module called weighted overlay analysis. The major effect was assigned a weight of 1.0 and the minor effect was assigned a weight of 0.5. The major, minor effect and their cumulative sum of each influential factor were calculated with the formula given below Proposed score = [

(X + Y) ] ∗ 100 ∑(X + Y)

Where, X is a major effect. Y is a minor effect.

Journal Pre-proof The major, minor and cumulative sum of each influence factors were depicted in Table 1. Relative rates, weights of each influential factor were depicted in Table 2.

Results and discussion Groundwater potential zones were delineated by using various factors, for instance, geology, slope, geomorphology, drainage density, lineament density, land use and land cover, soil texture, soil depth, and elevation. The details about various influential factors have been

f

discussed below.

oo

Drainage Density (DD)

The drainage density denotes closeness of stream segments spatially which depends on both

pr

physical characteristics and climatic conditions of a geographic location (Krishnamurthy et

e-

al., 2000) and it is important factor prevailing the movement and saturation of water into the earth (Balaji et al., 2019a) . Drainage network has been digitized from SOI toposheets and it

Pr

is updated to LISS IV satellite image. As per Straheler (Straheler et al., 1964), this is a 7 th order basin and the drainage network is dendritic to sub dendritic patterns in nature thus

al

which indicates uniform lithology.

rn

This updated drainage network has been used for generation of DD map using following

Jo u

formula proposed by Horton (1932) in GIS environment. 𝑛

Drainage Density (DD) = ∑

D𝑙 (km) = km−1 A (sq. km. )

𝑙=0

Where, Dl = Total lengths of Channels in km; A= Area of the basin in sq. km. According to Thapa (2017), it was categorized into four classes viz. high, moderate, low and very low with spatial extents of 94 sq. km (6%) of high DD, 226 sq. km (15%) of moderate DD, 489 sq km (34%) of low DD and 656 sq km (45%) of very low DD (Figure 4a). A high priority was given to a high value of drainage density category followed by moderate, low and very low drainage density values respectively (Table 2) (Magesh et al., 2012; Kaliraj et al., 2015; Thapa et al., 2017).

Journal Pre-proof Geology Occurrence and movement of the groundwater is depending on the nature of the rocks and its parameters for example porosity and permeability which are different for each rock type (Balaji et al., 2019b; Ghasemizadeh et al., 2012). The geological features are digitized from district mineral resource maps (GSI 1990) and updated to LISS IV satellite image in a GIS environment. Further field verifications have been carried out with Germen etrex GPS. The major portion of the study area consists of crystalline rock terrain (84%) of Archean age which consists of granites, granite gneisses, granodiorites, migmatite both acidic and basic

f

intrusive rocks with negligible primary porosity hence runoff is more. The 20 years mean

oo

annual rainfall and runoff of Mandavi river basin is 699.75mm and 478.06 mm, respectively (Siddi Raju et al., 2018). Hence it is clearly noticed that the regional geology and soils are

pr

responsible for surface runoff of about 68%. A small proportion (16%) of area is made up of shales with dolomite/limestone, shales with phyllite of Cumbum formation, quartzite with

e-

shale of Bairenkonda, Nagari formations of Nallamallai group and dolomite, quartzite/arkose

Pr

with a conglomerate of Gulcheru formation of Papagni group of Cuddapah super group of Proterozoic age in the northern and north-eastern parts (Figure 4b).

al

Individual weights for each lithological unit have been determined according to their

rn

groundwater prospects. A high weight was given to dolomites followed by shales with dolomitic limestones, quartz/arkose with the conglomerate, shales with phyllite, quartzite

Geomorphology

Jo u

with shales and granite/granodiorite/migmatites respectively (Table 2).

Geomorphological features provide significant indications of groundwater resources and also it gives indirect information about groundwater occurrence, movement and evolution (Machiwal et al., 2010). The landforms are digitized using IRS-R2 LISS IV satellite image through visual interpretation technique and field verification has been carried. The basin shaped like a heart and high elevated area occupied in the northern and southern part, the remaining central part of the area is nearly plain to gentle (Figure 5e). Based on the origin, a total of fifteen geomorphologic features have been identified, such as: denudational origin (pediplain shallow weathered (205.5 sq.km), pediplain moderate (152.6 sq.km), denudational hills (20.2 sq.km), residual hills (157.5 sq.km), inselberg (53 sq.km), pediment Inselberg

Journal Pre-proof complex (449 sq.km), structural origin (structural hills (128.5 sq.km), dyke ridges (0.8 sq.km), upper plateau moderately dissected (73.8 sq.km), depositional origin (point bar (0.5 sq.km), bajada shallow (13.2 sq.km), piedmont slope (35 sq.km), pediment (10.7 sq.km), water bodies (44 sq.km) and valleys (168.3 sq.km). In the present study area, weathered and fractured rock terrain with high altitudes and steep topography have medium to low groundwater potentials hence, assigned low weight. Which includes pediment Inselberg complex, pediplain shallow weathered, pediment, structural hills, denudational, residual hills, dyke ridges, inselberg, and piedmont slope.

f

Floodplains generally show good groundwater potentials because of the high infiltration rate

oo

of the weathered material deposits (Thapa et al., 2017) hence, assigned high weight. This consists of pediplain moderate, bajada shallow, point bar, upper plateau moderately dissected,

Pr

Land use/Land Cover (LULC)

e-

pr

valleys and water bodies.

LU/LC is one of the important factors in recharging of water into subsurface (Das et al.

al

2017). In the present study area, various LULC features were identified from IRS R2 LISS IV satellite image, which was later verified by the basis of field studies. The LULC map

rn

includes the agricultural land (cropland, double crop area, fallow land and plantation), builtup land (rural and urban), forest land (forest and forest plantation), stream, river and water

Jo u

bodies and wastelands with their respective spatial extents of 834 sq km (57%), 46 sq km (1.5%), 376 sq km (25.7%), and 186 sq km (12.7%) respectively (Figure 4c). Most of the seasons are dry in condition hence cropping pattern is dependent on the rainfall and the groundwater availability. The important crops harvested in the Mandavi river basin is mango, groundnut, paddy, sunflower, and tomato, etc., among these Banganaplle mangos or also known as Benesha famous for this region. From the FCC satellite image, the agricultural land was a rectangle in shape and these were identified by its tone and texture. A light medium red tone and fine to medium texture represent agricultural cropland plantation was observed by the dark red tone and fine texture and fallow land was identified by the medium burly wood tone and medium texture (Rajaveni et al. 2015; Lone et al. 2013). Built-up land was light bluish white with the fine texture of regular shape and size (Lillesand et al. 2007). Forest plantation was displayed in light reddish light reddish-brown tone and fine medium texture with irregular shape and varying in size (Rajaveni et al. 2015; Kumar et al. 2008). Dry

Journal Pre-proof tanks/streams showed high brightness tone and tanks/streams/river with water showed in high darkness tone to light blue color depending on the depth of the water. Wastelands were observed by light to dark bluish tone with a coarse texture. A high weight was given to water bodies/streams/rivers followed by agricultural land, forest land, wastelands, and built-up lands respectively (Table 2).

Lineament density (LD) Lineaments developed by the tectonic activity and they describe the surface topography and

oo

f

subsurface structural features as well as increased secondary porosity where the fault and fracture are more (Magesh et al., 2012; Rajaveni et al. 2015). Especially hard rock terrain

pr

lineaments are pathways for groundwater movements. High lineament sectors are good indicators of high potential groundwater zones (Haridas et al., 1998). Lineaments were

e-

identified and mapped from two sources viz., the district mineral resource map from Geological Survey of India (GSI) and satellite data. Fractures were observed in cross sections of dug wells,

Pr

rock mining points and outcrops were studied in the field work time. Normally the lengths of lineaments raging from 0.14 to 14.14 km. Frequency and directions of lineaments analyzed

al

with Rose diagrams, in this diagram most of the lineaments are oriented in the E-W direction followed by NNE, SSE, NNW-SSW (Figure 5d). All the dykes were vertical in position in the

Jo u

rn

study area. The LD map was generated by the following formula 𝑛

Lineament Density (LD) = ∑

L𝑙 (km) = km−1 A (sq. km. )

𝑙=0

Where, Ll = Total lengths of lineaments in km; A= Area of the basin in sq. km. The LD map (Figure 5d) reveals that the highest lineament density is observed in southern parts of the study area. A high ranking was given to high lineament density category (7.6%) followed by moderate (26%) and low (66.4) (Table 2). Rainfall In the Mandavi River basin, rainfall is the only source for recharge of surface water into the subsurface through weathered and fractured zones. Rainfall data for 20 years was collated from district chief planning office Kadapa then based on this data spatial distribution map has been prepared using Inverse distance weighted (IDW) tool in the Arc GIS environment (Fig.

Journal Pre-proof 5f). The basin annual average rainfall is very high (836-809), high (689-736 mm), moderate (640-689 mm) and low (< 640 mm) which contributes an extent of about 167 sq km (11%), 806 sq km (55%), 440 sq km (30%) and 52 sq km (4%) respectively (Siddi Raju et al., 2018a). The weight, rankings of each factor and its sub-classes were assigned as per rainfall intensity and its recharge of groundwater.

Soils Soil plays a key role in the process of recharge of surface water into the subsurface (Das,

oo

f

2017). Primarily, soil texture and soil depth maps were digitized from the District Soil

pr

resource map in GIS environment and updated verified with field check.

e-

Soil texture

Soil texture has main controller of water percolation through pours spaces and infiltration

Pr

process to join the aquifer. The soil texture map classified based on average size of different individual soil grains such as sand, silt, and clay. The observed soils were categorized into

al

loamy soils (14 %), gravelly loam soils (2%), gravelly loam soils with stony surfaces (36 %)

rn

and gravelly clay soils (48%) see the (Fig.6h-i) (FAO 2006). Then weights assigned for each soil texture unit according to its infiltration rate. A high priority was given to gravelly clay

Jo u

soils followed by gravelly loam soils with stony surfaces, gravelly loam soils, gravelly loam soils and loamy soils. Soil depth

Soil depth is a fundamental factor in many earth science disciplines due to its critical role in many hydrogeological processes (Nicholas R et al., 2018). Based on depth, categorized into four classes namely moderately deep (75-100 cm), moderately shallow (50-75 cm), moderately shallow to shallow (25-75 cm) and shallow (25-50cm) (FAO 2006) and covering an area of 217.7 sq.km, 28.5 sq.km, 171.3 sq.km, and 1047.2 sq.km respectively. Based on soil type and capacity of infiltration the weights are assigned. High weight was assigned to moderately deep, followed by moderately shallow, moderately shallow to shallow and shallow.

Journal Pre-proof Slope In the evaluation of GPZ, the slope is one of the key parameters. Surface water intruders are directly affected by the inclination of the slope (Rajaveni et al., 2015). The slope map was generated from SRTM-30m DEM data in a GIS. In general, the ground surface of the study area was towards north-northeast direction. Based on degree of slope (NBSS&LUP 2008) the present study area have been categorized into seven slope classes viz. nearly level (00-10), very gentle (10-30), gentle (30-50), moderate (50-100), moderately steep (100-150), steep (150300), very steep (>300) and a high degree of the slope was observed in the north-eastern and south-western parts of the study area (Figure 6j). A high priority was given to nearly level

oo

f

followed by very gentle, gentle, moderate, moderately steep, steep and very steep slope

pr

classes.

e-

Elevation

Pr

The elevation factor is one of the crucial factors in GPZ delineation. Generally, plain areas have high infiltration rate than moderate and high elevated points (Thapa et al., 2017). The elevation map was generated from SRTM-30m DEM data in a GIS environment and high and

al

low elevation values range from 195 msl – 1080 msl were observed in the NNE part and

rn

northern part of the study area (Figure 6k). The based on range of altitude from msl the elevation is classified into seven subclasses viz. 110-200, 200-300, 300-410, 410-530, 530-

Jo u

650, 650-780, 780-1080 in the GIS environment. A high priority was given to plain areas followed by moderate and high elevated points.

Groundwater Levels (GWL) Data on long-term fluctuations in the groundwater levels provide information about groundwater prospects. In this connection, an attempt has been made to portray long-term GWLF are the taken as one of the influential factor for delineation of groundwater potential zones. Groundwater level data has been collected from two sources which include long term GWLF data of 9 observation well stations monitored by district groundwater department used for one of the influential factor for delineation of groundwater potential zones. The results indicated that the average depth to water level during the pre and post monsoon seasons range from be -4 and 2.3 m, below ground level respectively (Figure 5g). The high weight was assigned to

Journal Pre-proof low fluctuation and low weight has been given to the high value of fluctuation. And then, water level data of 81 observation well stations collected through field surveys for validation of groundwater potential zones map.

Groundwater Potential Zones (GWPZ) Finally, weights and ratings were assigned to all the influential factors and their relative subclass later than these factors were used for identification of GWPZ through weighted overlay analysis in the GIS environment by the following equation.

oo

f

n

GWPZ = ∑(DDw ∗ DDr ) + (Gw ∗ Gr ) + (GMw ∗ GMr ) + (LU&𝐿𝐶w ∗ LU&𝐿𝐶r ) i

pr

+ (LDw ∗ LDr ) + (R w ∗ R r ) + (SDw ∗ SDr ) + (STw ∗ STr ) + (Sw ∗ Sr ) + (Ew

e-

∗ Er ) + (GLFw ∗ GLFr )

Where GWPZ = groundwater potential zone; w and r represents factor weightage and rating

Pr

respectively. DD= drainage density; G=geology; GM=geomorphology; LU&LC= land use and land cover; LD= lineament density; R=rainfall; SD=soil depth; ST=soil texture; S=slope;

al

E=elevation; GLF=groundwater level fluctuation.

rn

A high priority has been given to lineament density and low priority has been given to soil texture. Waited overlay and multi influencing factor techniques were used in preparing

Jo u

GWPZ map in GIS software. The final map of the Mandavi River basin has been divided into very poor, poor, good, and very good potential zones (Figure 8). From the figure, it was found that the very good and good groundwater potentials were identified in North and southcentral parts while the poor and very poor potentials zones were found along the basin boundary.

Validation of results For validation of results, fieldwork has been carried out in the pre and post monsoon seasons during the 2017-18. In this field study, 81 observation well stations data has been collected randomly from the formers along with GPS locations. The observed wells water depth ranged between 3.2 and 160 m. Based on the water depth the observed wells have been divided into four classes viz. 3–25 m, 26–55 m, and 56–85 m and 86-160 m which referred to as low,

Journal Pre-proof medium, high and very high (Figure 8). Accuracy assessment was carried out in order to know the correlation between the resulted groundwater potential zones map and observed well data. The observation well data has been taken as reference points for calculating the classification accuracy. Generally, a confusion matrix or error matrix is used for accuracy assessment (Table 3).

The overall accuracy represents based on the following formula

(Jensen, 1996). Overall accuracy =

OWL= Observation Well Locations.

oo

f

Where,

No. of correct OWL 59 = = 72.83 % Total No. of OWL 81

Kappa (K) analysis represents a multivariate approach for accuracy assessment and it

pr

provides a Khat statistic which means a measure of accuracy. It is calculated from following the formula (Usman et al., 2015).

e-

Percent overall correct value − Percent correct agreement to observed values Total number of class − Percent correct agreement to observed values

Pr

𝐾=

The overall accuracy and kappa coefficients were 72.83% and 0.63 respectively and its

al

strength of agreement is substantial (Landis et al., 1977), which shows a good correlation

Conclusion

Jo u

rn

between groundwater potential zones and the observed well data.

It is essential to identify groundwater potential zone (GWPZ), where people suffered from water scarcity. The present study strongly supports remote sensing and geospatial techniques in addition to multi influence factor analysis are ingenious in the identification of groundwater prospect zones in Mandavi River basin, India. A weighted overlay model was applied with eleven different influential parameters including drainage density, lineament density, geomorphology, geology, LU/LC, rainfall, soil texture, soil depth, slope, elevation and groundwater level fluctuation. The end results reveal that only one-third of the area has good groundwater prospects. Good to very good groundwater prospect zones were occupied by small spatial extents of 103 sq. km (7%) and 319 sq. km (22%), while the poor and very poor groundwater prospect zones were occupied by the extents of 510 sq. km (35%) and 533

Journal Pre-proof sq. km (35%) respectively. The final map created will be useful in managing groundwater resources of the Mandavi River basin for sustainable water resource management.

Acknowledgments The first author Mr. R.Siddi Raju, Inspire award no [IF150036], greatly thankful to Department of Science and Technology for financial support in the form of DST Inspire fellowship. And also my sincere thanks to the Department of Geology, Yogi Vemana

oo

f

University for supporting to carry out my research work.

References

pr

Al-Abadi, A. M., Shahid, S., Ghalib, H. B., Handhal, A. M., 2017. A GIS-Based Integrated

e-

Fuzzy Logic and Analytic Hierarchy Process Model for Assessing Water-Harvesting Zones in NortheasternMaysan Governorate, Iraq. Arabian Journal for Science and Engineering,

Pr

42(6), 2487–2499. http://doi.org/10.1007/s13369-017-2487-1. Arkoprovo, B., Adarsa,J., Prakash, H.S., 2012. Delineation of Groundwater Potential Zones

al

using Satellite Remote Sensing and Geographic Information System Techniques: A Case

rn

study from Ganjam district, Orissa, India. Research Journal of Recent Sciences. 1(9), 59–66, http://203.18.51.96:8080/jspui/bitstream/123456789/817/1/19-AB.pdf. E.,

Veeraswamy,

G.,

Jo u

Balaji,

Narsimha,

A.

and Subbarao,

M.,

2019.

Factors

controlling groundwater chemistry of Renigunta area, Chittoor District, Andhra Pradesh,

South

India:

A

multivariate

statistical

approach,

HydroResearch,

https://doi.org/10.1016/j.hydres.2019.06.002. Balaji, E., Veeraswamy, G., Peiyue, L. and SiddiRaju, R., 2019b. Deciphering Groundwater Potential Zones using MIF Technique and GIS: A study from Tirupati area, Chittoor District, Andhra Pradesh, India, HydroResearch, https://doi.org/10.1016/j.hydres.2019.04.001. Burrough, P.A., 1986. Principles of Geographic Information Systems for Land Resource Assessment. Monographs on Soil and Resources Survey No. 12, Oxford Science Publications, New York. Central Ground Water Board (CGWB)., 2007. Manual of artificial recharge of groundwater. 1–198.

Journal Pre-proof Das, S., Gupta, A., Ghosh, S., 2017. Exploring groundwater potential zones using MIF technique in semi-arid region: a case study of Hingoli district, Maharashtra. Spatial Information Research, 25(6), 749–756. https://doi.org/10.1007/s41324-017-0144-0. Das, S., 2017. Delineation of groundwater potential zone in hard rock terrain in Gangajalghati block, Bankura district, India using remote sensing and GIS techniques, Modeling Earth Systems and Environment, 3(4), 1589–1599. Das, Sumit., Sudhakar, D., Pardeshi., 2018. Integration of different influencing factors in GIS to delineate groundwater potential areas using IF and FR techniques: a study of Pravara

oo

f

basin, Maharashtra, India. Appl Water Sci.8:197. https://doi.org/10.1007/s13201-018-0848-x. Food and Agriculture Organization (FAO)., 2006, Food and Agriculture Organization of The

pr

United Nations Rome, (fourth edition).

Ghasemizadeh, R., Hellweger, F., Butscher, C., Padilla, I., Vesper, D., Field, M.,

e-

lshawabkeh, A., 2012. Review: groundwater flow and transport modeling of karst aquifers,

Pr

with particular reference to the north coast limestone aquifer system of Puerto Rico. Hydrogeol. J. 20 (8), 1441–1461. https://doi.org/10.1007/s10040-012-0897-4.

al

Haridas, V.R., Aravindan, S., Girish, G., 1998. Remote sensing and its applications for

rn

groundwater favourable area identification. Quarterly Journal of GARC 6, 18–22. Horton, R.E., 1932. Drainage basin characteristics. Trans. Am. Geophys. Union 13, 350–361.

Jo u

Hussein, A.A., Govindu,V., Nigusse, A.G.M., 2017. Evaluation of groundwater potential using geospatial techniques. Appl Water Sci. 7:2447–2461. https://doi.org/10.1007/s13201016-0433-0.

Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Pers-pective. 2nd Edition, Prentice Hall, Inc., Upper Saddle River, NJ. Kaliraj, S., Chandrasekar, N., Magesh, N.S., 2013. Identification of potential groundwater recharge zones in Vaigai upper watershed, Tamil Nadu, using GIS-based analytical hierarchical

process

(AHP)

technique.

Arab

J

Geosci.

7(4):1385–1401.

https://doi.org/10.1007/s12517-013-0849-x. Kaliraj, S., Chandrasekar, N., Magesh, N. S., 2015. Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin,

Journal Pre-proof Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental Earth Sciences, 74(5), 4355–4380. doi:10.1007/s12665-015-4384-9. Kindie, A.T., Enku, T., Moges M.A., Geremew B.S., Atinkut H.B., 2018. Spatial Analysis of Groundwater Potential Using GIS Based Multi Criteria Decision Analysis Method in Lake Tana

Basin,

Ethiopia,

ICAST

2018.

Lnicst

274,

pp.

439–456,

2019.

https://doi.org/10.1007/978-3-030-15357-1_37. Krishnamurthy, J., Mani, A., Jayaraman, V. &Manivel, M., 2000. Groundwater resources development in hard rock terrain—an approach using remote sensing and GIS techniques. Int.

oo

f

J. Appl. Earth Observ. and Geoinf. 2(3/4), 204– 215. Kumar MG, Agarwal AK, Bali R., 2008. Delineation of potential sites for water harvesting

pr

structures using remote sensing and GIS. J Indian Soc Remote Sens 36(4):323–334 Landis, J.R. and Koch, G.G., 1977. A One-Way Components of Variance Model for

e-

Categorical Data. Biometrics, 33, 671– 679. https://doi.org/10.2307/2529465

Pr

Lillesand, T., Kiefer, RW., Chipman, J., 2007. Remote sensing and image interpretation. Wiley, Hoboken.

al

Lone, MS., Nagaraju, D., Mahadavesamy, G., Siddalingamurthy, S., 2013. Applications of

rn

GIS and remote sensing to delineate artificial recharge zones (DARZ) of groundwater in H.D. Kote taluk, Mysore district, Karnataka, India. Int J Remote Sens Geosci 2(3):92–97.

Jo u

Machiwal, D., Jha, M. K., & Mal, B. C., 2010. Assessment of Groundwater Potential in a Semi-Arid Region of India Using Remote Sensing, GIS and MCDM Techniques. Water Resources Management, 25(5), 1359–386.https://doi.org/10.1007/s11269-010-9749-y. Magesh, N.S., Chandrasekar, N., Soundranayagam, J.P., 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci Front. 3(2):189–196. https://doi.org/10.1016/j.gsf.2011.10.007. Moghaddam, D. D., Rezaei, M., Pourghasemi, H. R., Pourtaghie, Z. S., Pradhan, B., 2013. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan

Watershed,

Iran.

Arabian

Journal

929.https://doi.org/10.1007/s12517-013-1161-5.

of

Geosciences,

8(2),

913–

Journal Pre-proof Nagarajan, M., Singh, S., 2009. Assessment of groundwater potential zones using GIS technique.

Journal

of

the

Indian

Society

of

Remote

Sensing,

37(1),

69–

77. http://doi.org/10.1007/s12524-009-0012-z. NBSS & LUP., 2008. Soil Resources of Anantapur District, Andhra Pradesh, NBSS Report No:1017, 22p. Nicholas R, Patton., Kathleen A, Lohse., Sarah E, Godsey., Benjamin T, Crosby., Mark S, Seyfried., 2018. Predicting soil thickness on soil mantled hillslopes. Nature communications. 9:3329 | DOI: 10.1038/s41467-018-05743-y www.nature.com/naturecommunications.

f

S., 2015. Delineation of groundwater

oo

Pinto, D., Shrestha, S., Babel, M.S., Ninsawat,

potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and hierarchy process (AHP)

technique.

https://doi.org/10.1007/s1320 1-015-0270-6.

Appl

Water

Sci.

7(1),

503–519.

pr

analytic

e-

Rajasekhar, M., Sudarsana Raju, G., Bramaiah, C., Deepthi, P., Amaravathi, Y., Siddi Raju,

Pr

R., 2018. Delineation of Groundwater Potential Zones of Semi-Arid Region of YSR Kadapa District, Andhra Pradesh, India using RS,GIS and Analytic Hierarchy Process. Remote

al

Sensing of Land, 2(2), 76–86. http://dx.doi.org/10.21523/gcj1.18020201. Rajaveni, S. P., Brindha, K., Elango, L., 2015. Geological and geomorphological controls on

rn

groundwater occurrence in a hard rock region.Applied Water Science, 7(3), 1377–1389.

Jo u

https://doi:10.1007/s13201-015-0327-6. Saraf, A. K., Choudhury, P. R., 1998. Integrated remote sensing and GIS for groundwater exploration and identification of artificial recharge sites. International Journal of Remote Sensing, 19(10), 1825–1841. https://doi:10.1080/014311698215018. Sashikkumar, M.C., Selvam, S., Kalyanasundaram, V.L., Johnny, J.C., 2017. GIS based groundwater modeling study to assess the effect of artificial recharge: A case study from Kodaganar river basin, Dindigul district, Tamil Nadu. Journal of the Geological Society of India, 89(1), 57–64. https://doi:10.1007/s12594-017-0558-2. Selvam, S., Magesh , N.S., Sivasubramanian, P., Soundranayagam, J.P., Manimaran, G., Seshunarayana, T., 2014. Deciphering of Groundwater Potential Zones in Tuticorin, Tamil Nadu, using Remote Sensing and GIS Techniques. Geol. Soc. India. 84(5), pp. 597–608. https://doi.org/10.1007/s12594-014-0167-2.

Journal Pre-proof Siddi Raju, R., Sudarsana Raju, G., Ravikumar, M., Raghubabu,K., 2016. Applications of Remote Sensing and GIS, For Identification of Groudwater Prospecting Zones in and AroudNandalur, YSR District. IJEP 36(4): 293–304. Siddi Raju, R., Sudarsana Raju, Rajasekhar, M., 2018. Estimation of Rainfall-Runoff using SCS-CN Techniqies for Mandavi Basin in YSR Kadapa District of Andhra Pradesh, India. Hydrospatial Analysis, 2(1), 1–15, http://dx.doi.org/10.21523/gcj3. Siddi Raju, R., Sudarsana Raju, Rajasekhar, M., 2018a. Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi

f

River Basin, YSR Kadapa District, Andhra Pradesh, India. Journal of Remote Sensing &

oo

GIS, 9(3), 46–54.

pr

Strahler, AN., 1964. Quantitative geomorphology of drainage basins and channel networks. In: Chow ByVenTe (ed) Handbook of applied hydrology. McGraw Hill Book Company, New

e-

York, 1964.

Pr

Thapa, R., Gupta, S., Guin, S., Kaur, H., 2017. Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West

al

Bengal. Applied Water Science. 7(7), 4117–4131.https://doi.org/10.1007/s13201-017-0571-z. Tiwari, A.K., Lavy, M., Amanzio, G., De Maio, M., Singh, P.K. and Mahato, M.K., 2017.

rn

Identification of artificial groundwater recharging zone using a GIS-based fuzzy logic approach: a case study in a coal mine area of the Damodar Valley, India, Applied Water

Jo u

Science, 7(8), 4513-4524.

Usman, R Liedl, M A Shahid, Abbas, A., 2015. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. J. Geogr. Sci. 2015, 25(12): 1479-1506 DOI: 10.1007/s11442-015-1247-y.

Journal Pre-proof

e-

pr

oo

f

Figures

Jo u

rn

al

Pr

Figure 1. Location map of the Mandavi river basin.

Figure 2.Flow diagram represents methodology for Identification of Groundwater Potential Zones.

Pr

e-

pr

oo

f

Journal Pre-proof

Jo u

rn

al

Figure 3.Inter relationship between the multi influencing factors concentring the groundwater potential zone in Mandavi river basin, Andhra Pradesh.

Jo u

rn

al

Pr

e-

pr

oo

f

Journal Pre-proof

Figure 4. Drainage density, b. Geology map, c. Land use and Land cover map of the study area.

Jo u

rn

al

Pr

e-

pr

oo

f

Journal Pre-proof

Figure 5. d. Lineament density map, e. Geomorphology map, f. Rainfall map and g. Groundwater level fluctuation map of the study area.

Jo u

rn

al

Pr

e-

pr

oo

f

Journal Pre-proof

Figure 6. h. Soil depth map, i. Soil texture map, j. Slope map, k. Elevation map of the study area.

Pr

e-

pr

oo

f

Journal Pre-proof

Jo u

rn

al

Figure 8. Groundwater potential zone map of the study area.

Journal Pre-proof

Tables Table 1 Effects of influencing factor, relative rates and score for each potential factor (Thapa et al.2017; Magesh et al.2012) Major effect

Minor effect

Proposed relative rates

Proposed score of each influencing factor

(X)

(Y)

(X+Y)

[

Drainage density Geology Geomorphology Land use land covers Lineament density Rainfall Soil depth Soil texture Slope

2 2 2 1 3 1 0 0 1

0.5 1 1 1 0.5 0.5 1.5 0.5 1.5

2.5 3 3 2 3.5 1.5 1.5 0.5 2.5

Elevation Ground water level fluctuation

2

0

2

2

1

3

(𝑋+𝑌) ∑(X+Y)

] ∗ 100

10 12 12 8 14 6 6 2 10

oo

f

Factor

8

pr

12

Sub-Class High Moderate Low Very low

2. Geology

4. Land use & land covers

Rating 10

Shale with dolomite/limestone Shale with Phyllite Quartzite with slate Dolomite Quartzite/ Arkose with conglomerate Granite, Granodiorite, Granite gneiss and Migmatites

8 6 5 12 7 4

12

Bazada Shallow (BJS) Pediment (PD) Pediment Inselberg Complex Point bar (PB) Residual Hill (RH) Denudational Hill (DH) Dyke Ridge(DR) Inselberg (I) Pedi plain Shallow Weathered (PPS) Pedi plan Moderate weathered (PPM) Piedmont Slope (PS) Structural Hill (SH) Upper Plateau Moderately Dissected (UPM) Valley (V) Water Body(WB)

12 4 5 12 4 2 1 1 5 12 1 3 12 12 12

Jo u

3. Geomorphology:

Weightage 10 8 5 2

rn

1. Drainage density

al

Factor

Pr

e-

Table 2 Classification of weighted factors and their ratings influencing the potential zones in the study area.

Agricultural Crop Land Agricultural fallow land Agricultural double crop area

6 5 7

12

8

Journal Pre-proof Agricultural Plantations Built Up Land Forest plantations Forest area River/Stream Wastelands Water bodies

14

6. Rainfall

High (>736 mm) Moderate (689 – 736 mm) Low (640 – 689 mm) Very low (<640 mm)

6 4 3 2

6

7. Soil depth

Moderately deep (75-100 cm) Moderately shallow (50-75 cm) Moderately shallow to shallow (25-75 cm) Shallow (25-50 cm)

6 4 3 2

6

8. Soil texture

Gravelly clay soils Gravelly loam soil with stony surface Gravelly loam soil with very low AWC Gravelly loam soils Loamy soils

2 1 1 1 1

2

9. Slope

Level to nearly level Very gentle Gentle Moderate Moderately steep Steep Very steep

10 8 5 4 3 2 1

10

10. Elevation

<100 m 110-200 m 210-300 m 310-410 m 420-530 m 540-650 m 660-780 m 790-1080 m

8 7 6 5 4 3 2 1

8

-4 to -3.2 bgl -3.1 to -2.8bgl -2.7 to -2.4bgl -2.3 to -2bgl 1.9 to 2.3bgl

12 10 8 4 1

12

f

14 8 6

oo

High Moderate Low

Jo u

rn

al

Pr

e-

pr

5. Lineament density

5 1 4 4 8 2 8

11. 20 years avg. groundwater Level fluctuation

cm =centimetre; mm =millimetre; m = meter; bgl = below ground level

Table 3. Error matrix of Groundwater potential zones.

S.No GWPZ

Very good

Good

Very Poor poor

Correct Total samples

Journal Pre-proof 5

0

0

16

11

0 0

19 3

5 18

0 0

24 21

19 18

0

1

8

11

20

11

11

28

31

e-

pr

oo

f

11 81 59 Overall accuracy=59/81=72.8% Kappa coefficient =0.63%

Pr al rn

4

11

Jo u

1 2 3

Very good Good Poor Very poor Total

Journal Pre-proof Highlights:

f oo pr ePr al



rn

 

The study area has been divided into four groundwater prospect zones such as very poor, poor, good, very good. Majority of the study area has poor groundwater prospects (71%). Good to very good groundwater prospects confined to weathered, fractured zones and valley fills. The overall accuracy and kappa coefficients were 72.8395% and 0.63 respectively, which shows a good correlation between groundwater potential zones and the observed well data.

Jo u



Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7