Groundwater salinization is an ever increasing problem in coastal aquifers due to seawater intrusion resulting from excessive groundwater withdrawals, lithological conditions of the aquifer and industrial and agriculture pollutant loads. Identification of salinity sources is challenging and a prerequisite for the protection of coastal fresh water aquifers. The present aim of the study is to identify the salinity sources and to understand its dynamics in the central Godavari delta, Andhra Pradesh where groundwater is highly saline with total dissolved solids (TDS) of ∼5000 mg/L at shallow depths of <3 m bgl. Groundwater samples were collected from 42 representative observation wells in the area and analyzed for major ions and stable isotopes (δ18O). Different hydro-chemical mixing models and multivariate statistical techniques, including factor and cluster analysis, are applied to these data sets. The results revealed that very high salinity (∼25,000 mg/L) in pumping wells is due to up-coning of salt water and the salinity is palaeo in origin. The salinity in the wells along the drains and near the coast (∼10,000 mg/L) is due to the infiltration of marine waters resulting from backwaters and intrusion of seawater along the drains. The salinity (∼5000 mg/L) in the wells away from the coast is attributed to dissolution of evaporites in the groundwater and ion exchange process.

INTRODUCTION

In recent years, an increase in the salinity of groundwater has become a major problem in coastal aquifers, and this makes the use of groundwater unfit for various purposes (Surinaidu et al. 2013; Shapouri et al. 2016). On the other hand seawater intrusion into the inland aquifers is a major threat due to heavy groundwater exploration to meet the rising demands for different sectors (Todd 1982; Raghunath 2005). Groundwater and sea water are an integral part of hydrological systems in coastal areas and the balance between these fluids is very sensitive and can easily be disturbed (Mondal et al. 2010). The driving factors that influence seawater intrusion or the fresh groundwater and seawater interactions are viz., topography, sub-surface hydraulic properties, temporal variation in precipitation, temporal migration of seawater into shallow unconfined aquifers, tidal and estuarine activity, sea-level rise, and excessive groundwater withdrawals (Cruz & Silva 1999; Kim et al. 2009; Mondal et al. 2010). Groundwater salinization in the coastal aquifers is influenced by many factors, such as the lithology of the aquifer, the quality of recharge water, and the type of interaction between the liquid and mineral phases (Helena et al. 2000; Somay & Gemici 2009). In general, high salinity in the coastal aquifers could occur from several sources other than seawater intrusion that include pollution from various origins, such as industrial and agriculture wastes, and also from brines which are not directly connected to the present sea (Surinaidu et al. 2013, 2014).

In such cases, classification of wells according to their water quality and their source can be very difficult. Characterization, interpretation and understanding of groundwater chemistry are essential for not only identifying the source of the contamination, but also to understand and characterize the factors controlling the basic hydrochemistry. The classification and source identification could provide useful information for policy makers of the groundwater resource management. Composite diagrams (Back 1966; Hendry & Schwartz 1990; Howard & Mullings 1996; Marie & Vengosh 2001) and saturation indices (Nordstrom et al. 1989; Jeong 2001) are useful tools to understand the interaction between groundwater and the host rock/aquifer. On the other hand stable isotopes could be a better tool for characterizing groundwater flow, identifying potential sources of groundwater contamination and salinity source (Clark & Fritz 1997; McCarthy et al. 1998; Mancini et al. 2002; Hunkeler et al. 2004; Morrill et al. 2006; Vinson et al. 2011; Ya & Jiu 2012). Multivariate statistical techniques are very effective and are the best way to classify and distinguish very complex hydrochemical changes that control the hydrochemical dynamics in coastal aquifers. Many researchers have successfully applied multivariate methods to interpret various hydrochemical processes (Ritzi et al. 1993; Helena et al. 2000).

The present study focused on the understating of the dynamic hydrochemical process occurring in the central Godavari coastal alluvial aquifer using different hydrochemical composite models, stable isotopes and multivariate statistical methods that comprise of factor and cluster analysis. The data is based on chemical, physical parameters and isotopic signatures that are collected in the pre-monsoon (June) and post-monsoon (October) periods of 2006 and 2007.

GEOLOGIC AND HYDROLOGIC SETTINGS OF THE STUDY AREA

The central Godavari deltaic region is situated in the East Godavari District of Andhra Pradesh, India, bounded by the Bay of Bengal in the eastern side and Vainateya River to the west, and plain lands of an alluvial nature to the north (Figure 1). The area is occupied by quaternary alluvial derived from the Godavari River and has a very gentle land slope of about 0.001 (Rao 1993; Bobba 2002). A major part of the area consists of sandy loams and sandy clay loams. The quaternary sediments occupying the coastal tract and inland river valleys include thick blankets of alluvium, gravel and colluvial deposits, beach sand, kankar, and soils of various types. The rivers are intertwined in the upper floodplain and meandering in the lower floodplain. The distribution patterns of calcium and magnesium are mostly controlled by the amounts of shell fragments and clay minerals, particularly montmorillonite (Seetaramaswamy & Poornachandra Rao 1975). The coastal alluvium has varying thicknesses in different areas, and includes a number of sand beds in which groundwater is present in a confined condition. The area is represented by tidal flats, inlets which receive seawater during high tides. The thickness of granular zones in the alluvium ranges from 18 to 258 m within the explored depths (CGWB 1999; GSI 2006). The hydrogeophysical investigations in the study area indicated that loamy sandy soils are underlined by thick clay beds of about 18–25 m followed by coarse sands (Gurunadha Rao et al. 2011, 2013; Naidu et al. 2012; Surinaidu et al. 2014).

Figure 1

Location of the study area and observation wells in the central Godavari delta.

Figure 1

Location of the study area and observation wells in the central Godavari delta.

The Godavari delta area is a flat alluvial terrain and ground elevations vary from ∼2 m at the Ravva onshore terminal near the coast to a maximum elevation of 7 m from the mean sea level observed at Amalapuram on the western side. The area experiences seasonal floods in every alternate year through the Godavari River, during the study period sequential floods occurred in 2006 (Gurunadha Rao et al. 2011). The Godavari irrigation canal network is well spread out in the area and provides a perennial source of irrigation in all seasons. Irrigation canals in the area flow towards the Bay of Bengal through three important drains – Vilastippa, Kunavaram and Pikaleru. Kunavaram and Pikaleru drains pass through the Ravva onshore terminal area. The freshwater aquaculture farming is one of the major land use practices using surface water sources in the central Godavari delta. The canals have been in operation throughout the last century and contribute to groundwater recharge. This has an impact on the groundwater quality in the area. The elevation of the water table is around 3 m (amsl) in the Godavari alluvium. Fluctuations of the water level from pre-monsoon to post-monsoon is generally within 0.5 m except in some localized pockets in the delta where it is about 2.5 m (CGWB 1999).

METHODOLOGY

Major ions

For the assessment of groundwater quality, 36 groundwater samples were collected in the pre- and post-monsoon periods of 2006. The analyses results indicate the established observation wells are not sufficient to understand hydrochemical dynamics due to hydrogeological heterogeneity upstream of the area. Hence, the number of samples was increased to 42 to cover the entire area in 2007. The locations of monitoring wells are shown in Figure 1. Samples were collected and analyzed for major ions (pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), fluoride (F), bicarbonate (), chloride (Cl), sulphate (), nitrate ()) by following standard methods suggested by APHA (2005). Ca2+, Mg2+, , and Cl are analyzed by the volumetric method; Na+ and K+ are analyzed by flame photometer; F is analyzed by ion metric methods; by double beam spectrophotometer; by turbidity; pH by a pH meter; and total dissolved salt by gravimetric method and conductivity meter.

Stable oxygen isotope (δ18O)

In the study area, groundwater samples were collected in the deep wells located in and around Ravva Onshore Terminal to identify the source of salinity and to understand the mixing process of saline and fresh waters in three seasons (November 2006, November 2007 and June 2008). Oxygen isotopes in 13 samples are measured using mass spectrometry. The methodology to measure 18O/16O (δ18O) is well explained by Epstein & Mayeda (1953). Oxygen isotope compositions are commonly reported relative to an agreed sample of ocean water, referred to as the Standard Mean Ocean Water (SMOW), representing the largest and most equilibrated water body. Stable isotope ratios (18O/16O) of water are conventionally expressed as units of parts per thousand (per mil ‰) deviation from SMOW. Vienna Standard Mean Ocean Water (VSMOW) standard was used for oxygen isotope analyses of water samples (Gonfiantini 1978). 
formula

Multivariate statistical approach

Factor analysis

Factor analysis is a multivariate statistical technique that can be utilized to examine the patterns or relationships of a large number of variables and summarize information in a smaller set of factors or components to predict behavior (Davis 2002). R-mode factor analysis has proven highly effective in studies of groundwater quality and provides several positive features that allow the interoperation of data (Subbarao et al. 1995; Reghunath et al. 2002; Rao et al. 2005). The most important feature of factor techniques is their ability to reduce a large number of variables down to a smaller number of factors to produce new combinations of original variables (groups) that can then be used as new variables in some further analyses. Many researchers have successfully applied factor analysis to interpret various hydrochemical processes (Ruiz et al. 1990; Papatheodorou & Lambrakis 1997; Voudouris et al. 2000; Lambrakis et al. 2004; Panagopoulos et al. 2004; Shrestha & Kazama 2007; Zhou et al. 2007; Koklu et al. 2010). In the present study the principal component (PC) method is used for the initial factor extraction and the Varimax method is used for factor rotation using Software Package for the Social Science (SPSS) software (Landau & Everitt 2004).

Cluster analysis

Cluster analysis is a statistical tool used to classify the data according to their similarities. A small squared Euclidian distance implies a high similarity between measured objects. In clustering, the distinct groups can reveal either the interaction among the variables (R-mode) or the interaction among the samples (Q-mode) (Wu et al. 2005). The Q-mode hierarchical cluster analyses have been performed in the study area using SPSS software (Landau & Everitt 2004). Ward's clustering procedure (Ward 1963), which is commonly used in the hierarchical method of cluster analysis, is employed to identify the cluster of the samples. Various types of cluster analysis have been successfully used to view water-chemistry data for both surface water (Alther 1979; Güler et al. 2002; Templ et al. 2008; Salah et al. 2012) and groundwater (Troiano et al. 1994; Farnham et al. 2000).

RESULTS AND DISCUSSION

Hydrogeochemical process

The groundwater is apparently saline and of marine origin, as indicated by its Na+-Cl water type (Gurunadha Rao et al. 2011; Naidu et al. 2012; Surinaidu et al. 2013). Large proportions of groundwater are of the Na+-Cl water type, which generally indicates a strong seawater influence on the aquifer. In the central Godavari delta, three major hydrochemical facies are identified using the Piper geochemical classification method (Piper 1944). These are: (i) groundwater associated with evaporites (sodium chloride), (ii) groundwater of meteoric origin, and (iii) mixing of saline water and up-coning of brines/salt water (Figure 2). Water samples contain significantly higher proportions of sodium and chloride, which are most likely derived from evaporated seawater and dissolution of evaporites. Groundwater with low major ion concentrations is largely meteoric water with some influence of the mixing of sodium chloride type groundwater. In addition to physical mixing, the up-coning of brackish waters is observed and was indicated by magnesium and calcium dominated water in the Piper diagram. The Piper classification also indicates groundwater quality in the deltaic region was affected by dissolution of evaporates in the post-monsoon of 2006 (Figure 2(a)). It is due to a large number of rainy days and sequential flood events dissolution taking place in the post-monsoon 2006 (IMD 2006). However, in the post-monsoon of 2007, water samples moving towards meteoric mixing indicated that simple dilution was taking place (Figure 2(b)). This indicates the influence of the mixing of sodium chloride type groundwater by natural recharge and irrigation return flows. However, brackish water pumping wells are shifting to high saline waters/intrusion phase due to up-coning of salt water/seawater intrusion due to large scale groundwater pumping at point location (Figure 2(a) and (b)).

Figure 2

Piper classification for major ion chemistry explains major hydrochemical process in the central Godavari delta, 2006 (a) and 2007 (b).

Figure 2

Piper classification for major ion chemistry explains major hydrochemical process in the central Godavari delta, 2006 (a) and 2007 (b).

To investigate the importance of the ion exchange process in the groundwater chemistry, the groundwater quality samples are examined to identify the relationship between cations and anions and ion exchange process with X-Y diagrams for pre- and post-monsoon seasons as shown in Figure 3. Figure 3(a) and (b) shows Na+–Cl versus Ca+ + Mg+2-, indicating that almost all samples are below the 1:1 mixing line which indicates the influence of the ion exchange process (Jankowski et al. 1998), but a few of the samples indicate reverse ion exchange in the groundwater pumping wells located near the coast. In Figure 3(c) and (d) the tendency of N+-K+/Na+-K+ + Ca+2 approaches a value of 1, with increasing total dissolved solids (TDS), indicating an ion exchange process in the groundwater of the Godavari delta (Figure 3(c) and (d)). The driving force for salinity could be dissolution of evaporites or seawater mixing, when is less than 5 meq/L, which also indicates that the dissolution of calcite and dolomite is the major process influencing the water chemistry (Kalantary et al. 2007). However, almost all samples from the Godavari delta lie under the ion exchange process and is less than 5 meq/L, indicating an ion exchange and gypsum dissolution process, and it is high in the deep pumping wells (Figure 3(e) and (f)). It also indicates that seawater mixing and dissolution of evaporites are driving factors for increasing salinity in the Godavari delta.

Figure 3

X-Y diagrams illustrating relationships among the major ions and ion exchange process.

Figure 3

X-Y diagrams illustrating relationships among the major ions and ion exchange process.

Relation between stable oxygen isotopes (δ18O) to chloride and groundwater table

The isotopic compositions may vary with the type of water such as seawater, fresh water and a mixture (Ženišová et al. 2015). Therefore, groundwater affected by seawater is believed to be enriched in δ18O (18O/16O) as compared to freshwater (Izbicki 1996; Ma et al. 2007). In general the seawater will record isotopic ratios close to zero, while the meteoric waters show negative values (Craig 1961; Jager & Hunziker 1979). The analyzed data shows negative values in all the samples indicating that salinity in the wells does not belong to the recent/current seawater. The weighted mean values of the of δ18O close to positive in the Ravva Onshore Terminal wells (C29, C30, C31, C32, C33) indicate mixed waters (mixing of saline water through infiltration from back waters and entrapped sea water in deeper layers of the aquifer system, Table 1). One of the wells (C34) in the Ravva Terminal does not reflect heavier δ18O (−2.8 to −2.9) which is observed to be subjected to very low pumping and less percentage of sea water compared to other wells. The wells located near the coast on mudflats (C5 and C6) exhibit depleted values indicating the influence of accumulated rain/recent fresh water in dug wells. Hence, in the 2006 sampling period the samples are highly depleted due to heavy rains and, further, they became marginally enriched in δ18O values.

Table 1

18O/16O values in the central Godavari delta, East Godavari district, A.P.

ID November 2006 November 07 June 2008 
C2 − −1.34 −0.98 
C3 −0.98 −0.89 −0.17 
C4 −5.47 −4.94 −3.89 
C5 −4.12 −3.97 −3.12 
C6 −6.49 −6.12 −5.13 
C18 −8.67 −5.17 −3.97 
C22 −2.54 −2.12 −1.92 
C29 −0.64 −0.83 − 
C30 −0.64 −0.88 −0.96 
C31 −0.48 − −0.93 
C32 −0.36 −0.63 −0.74 
C33 −0.45 −0.59 −0.85 
C34 −2.8 −3.21 −2.96 
ID November 2006 November 07 June 2008 
C2 − −1.34 −0.98 
C3 −0.98 −0.89 −0.17 
C4 −5.47 −4.94 −3.89 
C5 −4.12 −3.97 −3.12 
C6 −6.49 −6.12 −5.13 
C18 −8.67 −5.17 −3.97 
C22 −2.54 −2.12 −1.92 
C29 −0.64 −0.83 − 
C30 −0.64 −0.88 −0.96 
C31 −0.48 − −0.93 
C32 −0.36 −0.63 −0.74 
C33 −0.45 −0.59 −0.85 
C34 −2.8 −3.21 −2.96 

The chloride concentrations and depth to water table compared with δ18O (0/00) are presented in Figure 4(a)(d). It can be seen from these figures that δ18O (0/00) enrichment increased with chloride concentrations ranging from −0.36 to −2.84 in November 2006, to −0.63 to −3.21 in November 2007 for groundwater samples inside Onshore Terminal. On the other hand δ18O (0/00) varied from −2.54 to −8.67 in November 2006 and −2.12 to −6.17 in November 2007 for wells outside Ravva Onshore Terminal. However, the isotopic ratio (−1.34) (Pikaleru drain, C2) of canal water indicated evaporation and mixing of recent saline water in November 2007, while in June 2008 the elevated isotopic ratio (−0.98) (Table 1) indicated increased mixing of salt water. Ground water possessing marginally higher isotopic ratios in the C3 well located in the downstream region of the delta near the coast is attributed to infiltration of sea water. Further, the enrichment of δ18O (0/00) in groundwater with depth is due to palaeo salinity in the deeper parts of the aquifer system. Isotopic ratios of Ravva Terminal wells approaching seawater characteristic values can be attributed to the up-coning of entrapped seawater due to heavy pumping at point location in the Ravva terminal wells. Bhishm Kumar et al. (2011) conducted hydrogeologic, hydrochemical and isotopic investigation in the Krishna and Godavari basins to find the salinity source and the results revealed that canals are contributing significant recharge that freshen saline groundwater of Palaeo origin. The high salinity of groundwater is due to dissolution of marine clays of Palaeo origin.

Figure 4

The relation between 18O/16O versus chloride in November 2006 (a) and November 2007 (b), depth to water level in m (amsl) versus 18O/16O in November 2006 (c) and November 2007 (d).

Figure 4

The relation between 18O/16O versus chloride in November 2006 (a) and November 2007 (b), depth to water level in m (amsl) versus 18O/16O in November 2006 (c) and November 2007 (d).

Factor analysis

In the central Godavari delta, groundwater elevations are less than 3 m (89% of the wells), and the elevation is more than 9 m in a few locations (11%) and they are located near the coast and are pumping the brackish water. The groundwater salinity may be due to various factors such as seawater mixing, dissolution of evaporitic minerals or evaporation from groundwater. The factor analysis is applied to identify the major hydrochemical process in the Godavari coastal region. The rotated factor loadings, communalities, eigenvalues and percentage variance associated with factors for principal component analyses (PCA) are presented in Tables 2 and 3. The factor loadings greater than 0.7 are considered as the most important parameters that participate in the major hydrochemical process in the area. From the tables, three factors with represented eigenvalues greater than unity are identified, which account for greater than 80% of the total variance in all the sampling periods of the original dataset.

Table 2

Factor scores and communalities for pre- and post-monsoon of 2006

  Pre-monsoon 2006 Post-monsoon 2006 
Parameter Factor 1 Factor 2 Factor 3 Communalities Factor 1 Factor 2 Factor 3 Communalities 
pH −0.803 0.26 0.352 0.836 −0.259 0.638 −0.501 0.724 
TDS 0.956 0.17 0.17 0.971 0.979 −0.088 −0.007 0.966 
 −0.167 0.889 −0.092 0.827 0.071 0.81 0.187 0.695 
Cl 0.893 0.004 0.283 0.877 0.95 −0.14 −0.031 0.923 
F 0.077 0.157 0.882 0.809 0.235 −0.709 −0.158 0.583 
NO3-N −0.174 0.297 −0.546 0.416 −0.066 0.259 0.858 0.807 
 0.963 −0.053 0.041 0.932 0.961 −0.111 0.099 0.946 
Na+ 0.891 0.252 0.299 0.946 0.974 −0.119 −0.036 0.965 
K+ 0.487 0.644 0.104 0.802 0.911 0.05 0.129 0.849 
Ca2+ 0.826 −0.206 0.241 0.783 0.882 −0.189 −0.068 0.818 
Mg2+ 0.876 0.138 0.048 0.789 0.807 −0.159 −0.04 0.678 
Eigenvalues 5.826 1.681 1.481   6.125 1.749 1.082   
% of variance 52.966 15.286 13.461 55.681 15.9 9.835 
Cumulative % 52.966 68.252 81.713 55.681 71.581 81.416 
  Pre-monsoon 2006 Post-monsoon 2006 
Parameter Factor 1 Factor 2 Factor 3 Communalities Factor 1 Factor 2 Factor 3 Communalities 
pH −0.803 0.26 0.352 0.836 −0.259 0.638 −0.501 0.724 
TDS 0.956 0.17 0.17 0.971 0.979 −0.088 −0.007 0.966 
 −0.167 0.889 −0.092 0.827 0.071 0.81 0.187 0.695 
Cl 0.893 0.004 0.283 0.877 0.95 −0.14 −0.031 0.923 
F 0.077 0.157 0.882 0.809 0.235 −0.709 −0.158 0.583 
NO3-N −0.174 0.297 −0.546 0.416 −0.066 0.259 0.858 0.807 
 0.963 −0.053 0.041 0.932 0.961 −0.111 0.099 0.946 
Na+ 0.891 0.252 0.299 0.946 0.974 −0.119 −0.036 0.965 
K+ 0.487 0.644 0.104 0.802 0.911 0.05 0.129 0.849 
Ca2+ 0.826 −0.206 0.241 0.783 0.882 −0.189 −0.068 0.818 
Mg2+ 0.876 0.138 0.048 0.789 0.807 −0.159 −0.04 0.678 
Eigenvalues 5.826 1.681 1.481   6.125 1.749 1.082   
% of variance 52.966 15.286 13.461 55.681 15.9 9.835 
Cumulative % 52.966 68.252 81.713 55.681 71.581 81.416 
Table 3

Factor scores and communalities for pre- and post-monsoon of 2007

  Pre-monsoon 2007 Post-monsoon 2007 
Parameter Factor 1 Factor 2 Factor 3 Communalities Factor 1 Factor 2 Factor 3 Communalities 
pH −0.719 0.349 −0.209 0.682 −0.579 0.206 0.606 0.745 
TDS 0.976 0.129 0.095 0.978 0.956 0.075 −0.267 0.992 
 0.215 0.892 0.046 0.845 0.719 0.26 −0.032 0.586 
Cl 0.979 0.066 0.081 0.969 0.954 0.053 −0.276 0.988 
F 0.024 0.26 0.768 0.659 −0.116 0.017 0.948 0.913 
NO3-N −0.146 0.189 −0.764 0.641 −0.095 0.932 0.007 0.878 
 0.967 0.115 0.028 0.949 0.954 0.151 −0.201 0.974 
Na+ 0.97 0.02 0.073 0.946 0.956 0.074 −0.26 0.988 
K+ 0.82 0.397 0.094 0.839 0.372 0.851 0.095 0.871 
Ca2+ 0.862 0.231 0.148 0.819 0.717 −0.062 0.212 0.563 
Mg2+ 0.883 0.16 0.047 0.808 0.904 0.074 −0.372 0.961 
Eigenvalues 6.565 1.293 1.275   5.993 1.749 1.717   
% of variance 59.685 11.755 11.592 54.484 15.902 15.608 
Cumulative % 59.685 71.44 83.031 54.484 70.386 85.994 
  Pre-monsoon 2007 Post-monsoon 2007 
Parameter Factor 1 Factor 2 Factor 3 Communalities Factor 1 Factor 2 Factor 3 Communalities 
pH −0.719 0.349 −0.209 0.682 −0.579 0.206 0.606 0.745 
TDS 0.976 0.129 0.095 0.978 0.956 0.075 −0.267 0.992 
 0.215 0.892 0.046 0.845 0.719 0.26 −0.032 0.586 
Cl 0.979 0.066 0.081 0.969 0.954 0.053 −0.276 0.988 
F 0.024 0.26 0.768 0.659 −0.116 0.017 0.948 0.913 
NO3-N −0.146 0.189 −0.764 0.641 −0.095 0.932 0.007 0.878 
 0.967 0.115 0.028 0.949 0.954 0.151 −0.201 0.974 
Na+ 0.97 0.02 0.073 0.946 0.956 0.074 −0.26 0.988 
K+ 0.82 0.397 0.094 0.839 0.372 0.851 0.095 0.871 
Ca2+ 0.862 0.231 0.148 0.819 0.717 −0.062 0.212 0.563 
Mg2+ 0.883 0.16 0.047 0.808 0.904 0.074 −0.372 0.961 
Eigenvalues 6.565 1.293 1.275   5.993 1.749 1.717   
% of variance 59.685 11.755 11.592 54.484 15.902 15.608 
Cumulative % 59.685 71.44 83.031 54.484 70.386 85.994 

Factor 1

The factor 1 loadings have explained >50% of variance that includes TDS, , C1, Na+, Ca+2 and Mg+2 are major parameters driving salinity in the area. These factors have factor scores greater than 0.8 (Table 2). Therefore, factor 1 associated with these paramters resulted from a strong influence of sea water mixing and dissolution of aquifer matrix in the groundwater. The mixing of saline water can control the concentrations of magnesium, calcium and sulphate. The dissolution of evaporates are the most important process affecting the ionic concentrations of water in the aquifer, as this factor accounts for greater than 50% of the variance of the concentrations of the samples in all sampling periods (Tables 2 and 3), which is a much higher percentage than that attributable to the other factors. However, in post-monsoon 2006, K+ also contributes as a major constitute in addition to TDS, , C1, Na+, Ca+2 and Mg+2 loadings which influence the groundwater quality (Table 2). It is evident that potassium is increased due to the breakdown of clay mineral resulting from the dissolution process due to heavy rainfall and flood events in this period. The factor scores indicate that the same influence has continued in the pre-monsoon 2007 period whereas in the post-monsoon 2007 period the relative decrease in these loadings and no influences of K+ loading on factors scores indicates the dilution process in the aquifer system from rainfall recharge (Table 3). The spatial and temporal distribution of factor scores for factor 1 for each of the sampling periods is shown in Figure 5. It can be seen from Figure 5(a)(d)) that the coastal groundwater pumping wells are characterized by particularly high scores. The high scores are attributed to close proximity to the sea, as a consequence of the intrusion of seawater either directly from the sea or upcoming of saline water due to heavy pumping at point source locations from Ravva terminal wells. The gradual increase in factor loadings in and around these pumping wells indicates an increase in the mixing of saline water with groundwater due to excessive withdrawals of groundwater. The negative loadings of pH, and indicate relatively no influence of these factors on the groundwater.

Figure 5

Spatial distribution of factor scores for factor 1 from 2006 to 2007, pre-monsoon 2006 (a), post-monsoon 2006 (b), pre-monsoon 2007 (c), post-monsoon 2007 (d).

Figure 5

Spatial distribution of factor scores for factor 1 from 2006 to 2007, pre-monsoon 2006 (a), post-monsoon 2006 (b), pre-monsoon 2007 (c), post-monsoon 2007 (d).

Factor 2

Factor 2 accounts for 15.2% of total variance in all sampling periods except June 2007, and it is mainly associated with very high loading of bicarbonates and pH (Tables 2 and 3). The high bicarbonate loadings may be the result of dissolution of in the water due to the influence of rainwater recharge (Lawrence & Upchurch 1982) and dissociation of the H2CO3 formed (H2CO3 + H2O = H3O + ), which increases the H3O and concentrations. The positive loadings of pH may be attributed to an increase in bicarbonates which resulted in an increase in pH.

Factor 3

This factor accounts for 11.5% of variance in the pre-monsoon period of 2006 and 9.85% of the variance in the post-monsoon period. In 2007, the percentage variance varies from 11.5 to 15.6% from the pre-monsoon to post-monsoon period. In factor 3, high positive loadings of fluoride in the pre-monsoon period and nitrate loadings in the post-monsoon period is observed (Tables 2 and 3). The presence of high fluoride concentrations may be attributed to the dissolution of phosphate fertilizers applied for agriculture at the top of the aquifer. In the post-monsoon period, the nitrate loadings are increased. This is due to leaching of agricultural inputs and aquaculture farming applied to the top of the aquifer. However, in the post-monsoon period negative loadings of fluoride indicated a dilution process.

Cluster analysis

Cluster analysis consists of a series of multivariate methods which are used to group similar objects/samples based on similarities of their chemical properties (Danielsson et al. 1999). The hierarchical method of cluster analysis, which is used in this study, has the advantage of not demanding any prior knowledge of the number of clusters, which the non-hierarchical method requires. The analysis has divided the groundwater samples into two major clusters/groups based on hydrochemical variables (pH, TDS and major ions). The grouped clusters for four different seasons are shown in Figures 6 and 7. The descriptive statistics of these clusters are presented in Tables 46 which represents ionic concentrations of these clusters and the number of samples that fall in each cluster. Cluster 1A and cluster 1B samples have groundwater depths less than 3 m whereas cluster 2 samples have depths greater than 10 m and tap the deep aquifer from depths of >67 m (Surinaidu et al. 2014). Only five samples (11.9% in total) in cluster 2 are identified with relative concentrations of Na+ > Cl > Ca+2 > Mg+2 and in the post-monsoon period of 2007 it is shifted to Na+ > Cl > Mg+2, which occurred as a result of up-coning of saline/salt water intrusion due to excessive groundwater pumping near the coast. The number of samples in each cluster varied in different seasons and the details are shown in Tables 46. Four (9.5% in total) samples in cluster 1B are identified with relative concentrations of Na+ > Mg+2 > Cl, and these wells are affected by saline water mixing/vertical infiltration during high tide through the surface water drains. These wells are located in mudflats and along the drains, and are always affected by saline water during high tides, deteriorating the groundwater quality.

Table 4

Descriptive statistics of major ions in cluster 1A

  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 Post-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean Min Max Mean 
pH 7.9 8.9 8.4 7.5 8.8 8.0 7.5 8.9 8.3 7.1 8.2 7.7 
TDS 274 5030 2426 248 6029 2585 256 5146 1598 141 6221 1994 
HCO3 49 195 108 61 390 228 61 542 259 70 850 344 
Cl 57 1334 554 64 2080 715 43 2172 566 19 3301 772 
F 0.38 0.95 0.65 0.32 1.02 0.72 0.37 0.98 0.70 0.07 0.74 0.32 
NO3-N 81 18 99 18 81 15 0.05 209 33 
SO42− 20 60 36 30 140 60 30 135 71 11 610 129 
Na+ 24 2553 1023 32 2118 564 12 880 244 1629 465 
K+ 632 169 378 83 164 33 336 57 
Ca2+ 12 80 37 24 96 50 16 264 99 30 296 78 
Mg2+ 12 114 45 83 36 180 59 243 79 
Sample numbers N = 21(C1, C9 to C17, C19 to C21, C23 to C28) N = 33 (C1,2, C5 to C28, C35, C37 To C42) N = 33 (C1 to C3, C5 to C28, C35 to C40, C42) N = 36 (C1 to C 28, C35 to C42) 
  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 Post-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean Min Max Mean 
pH 7.9 8.9 8.4 7.5 8.8 8.0 7.5 8.9 8.3 7.1 8.2 7.7 
TDS 274 5030 2426 248 6029 2585 256 5146 1598 141 6221 1994 
HCO3 49 195 108 61 390 228 61 542 259 70 850 344 
Cl 57 1334 554 64 2080 715 43 2172 566 19 3301 772 
F 0.38 0.95 0.65 0.32 1.02 0.72 0.37 0.98 0.70 0.07 0.74 0.32 
NO3-N 81 18 99 18 81 15 0.05 209 33 
SO42− 20 60 36 30 140 60 30 135 71 11 610 129 
Na+ 24 2553 1023 32 2118 564 12 880 244 1629 465 
K+ 632 169 378 83 164 33 336 57 
Ca2+ 12 80 37 24 96 50 16 264 99 30 296 78 
Mg2+ 12 114 45 83 36 180 59 243 79 
Sample numbers N = 21(C1, C9 to C17, C19 to C21, C23 to C28) N = 33 (C1,2, C5 to C28, C35, C37 To C42) N = 33 (C1 to C3, C5 to C28, C35 to C40, C42) N = 36 (C1 to C 28, C35 to C42) 
Table 5

Descriptive statistics of major ions in cluster 1B

  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean 
pH 7.40 8.70 8.08 8.00 8.10 8.05 8.00 8.60 8.23 
TDS 7827 12768 10320 8390 12301 9906 6643 14848 10028 
 73 220 142 159 1037 515 244 610 455 
Cl 299 2985 1457 973 4142 2673 2982 3990 3457 
F 0.25 0.85 0.57 0.44 0.96 0.65 0.64 1.06 0.82 
NO3-N 30 10 17 12 10 
 30 210 91 75 135 106 75 180 135 
Na+ 2829 7590 4849 798 4096 2456 1310 2526 1775 
K+ 94 803 389 52 562 273 84 275 172 
Ca2+ 24 184 97 40 360 168 164 1224 644 
Mg2+ 80 416 181 73 272 144 122 238 168 
Sample numbers N = 10 (C2,3,4,5,6,7,8,18,22,34) N = 4 (C3, 4, 34,36) N = 4 (C4,5, 34,41) 
  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean 
pH 7.40 8.70 8.08 8.00 8.10 8.05 8.00 8.60 8.23 
TDS 7827 12768 10320 8390 12301 9906 6643 14848 10028 
 73 220 142 159 1037 515 244 610 455 
Cl 299 2985 1457 973 4142 2673 2982 3990 3457 
F 0.25 0.85 0.57 0.44 0.96 0.65 0.64 1.06 0.82 
NO3-N 30 10 17 12 10 
 30 210 91 75 135 106 75 180 135 
Na+ 2829 7590 4849 798 4096 2456 1310 2526 1775 
K+ 94 803 389 52 562 273 84 275 172 
Ca2+ 24 184 97 40 360 168 164 1224 644 
Mg2+ 80 416 181 73 272 144 122 238 168 
Sample numbers N = 10 (C2,3,4,5,6,7,8,18,22,34) N = 4 (C3, 4, 34,36) N = 4 (C4,5, 34,41) 
Table 6

Descriptive statistics of major ions in cluster 2

  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 Post-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean Min Max Mean 
pH 7.30 7.70 7.56 7.60 7.90 7.73 7.56 7.90 7.71 6.90 7.20 7.05 
TDS 21307 26797 24395 21917 27771 24412 21917 27771 24997 32064 33536 32824 
 61 110 84 98 268 171 84 268 155 284 2008 1073 
Cl 4686 6308 5458 4997 6452 5839 4997 6452 5786 15467 16221 15927 
F 0.70 0.95 0.82 0.85 0.96 0.90 0.82 0.96 0.89 0.05 0.53 0.17 
NO3-N 18 23 20 
 220 285 261 270 330 304 261 330 292 1680 1870 1778 
Na+ 9143 14260 11237 8062 12008 10089 8062 14260 11140 7069 8019 7613 
K+ 312 546 412 273 789 568 273 789 522 100 150 120 
Ca2+ 140 952 638 776 1864 1265 638 1864 1142 148 1408 623 
Mg2+ 73 596 381 73 803 424 73 803 450 1394 2159 1854 
Sample numbers N = 5 (C 29 to C33) N = 5 (C 29 to C33) N = 5 (C 29 to C33) N = 6 (C 29 to C34) 
  Pre-monsoon 2006 Post-monsoon 2006 Pre-monsoon 2007 Post-monsoon 2007 
Parameter Min Max Mean Min Max Mean Min Max Mean Min Max Mean 
pH 7.30 7.70 7.56 7.60 7.90 7.73 7.56 7.90 7.71 6.90 7.20 7.05 
TDS 21307 26797 24395 21917 27771 24412 21917 27771 24997 32064 33536 32824 
 61 110 84 98 268 171 84 268 155 284 2008 1073 
Cl 4686 6308 5458 4997 6452 5839 4997 6452 5786 15467 16221 15927 
F 0.70 0.95 0.82 0.85 0.96 0.90 0.82 0.96 0.89 0.05 0.53 0.17 
NO3-N 18 23 20 
 220 285 261 270 330 304 261 330 292 1680 1870 1778 
Na+ 9143 14260 11237 8062 12008 10089 8062 14260 11140 7069 8019 7613 
K+ 312 546 412 273 789 568 273 789 522 100 150 120 
Ca2+ 140 952 638 776 1864 1265 638 1864 1142 148 1408 623 
Mg2+ 73 596 381 73 803 424 73 803 450 1394 2159 1854 
Sample numbers N = 5 (C 29 to C33) N = 5 (C 29 to C33) N = 5 (C 29 to C33) N = 6 (C 29 to C34) 
Figure 6

Dendrogram of the hierarchical cluster analysis of the groundwater quality of the central Godavari delta using complete linkage method, (a) pre-monsoon 2006 and (b) post-monsoon 2006.

Figure 6

Dendrogram of the hierarchical cluster analysis of the groundwater quality of the central Godavari delta using complete linkage method, (a) pre-monsoon 2006 and (b) post-monsoon 2006.

Figure 7

Dendrogram of the hierarchical cluster analysis of the groundwater quality of the central Godavari delta using complete linkage method, (a) pre-monsoon 2007 and (b) post-monsoon 2007.

Figure 7

Dendrogram of the hierarchical cluster analysis of the groundwater quality of the central Godavari delta using complete linkage method, (a) pre-monsoon 2007 and (b) post-monsoon 2007.

The number of samples in cluster 1A is 33 (78.5% in total samples) with relative concentrations of Na+ > Ca+2 > Cl > in the pre-monsoon period and Na+ > Mg+2 > Cl in the post-monsoon period. The groundwater quality in cluster 1A is affected by dissolution of evaporites and clay minerals, and evaporation from shallow groundwater resulted in increased salinity. Most of the wells in cluster 1A are located away from the coast.

In the post-monsoon period of 2007, the number of clusters reduced to only two groups. The samples located in cluster 1B are mixed with those in cluster 1A and formed only one cluster of samples due to the flushing and dilution effects after rainfall (Table 4). The minimum, maximum and average values of major ion concentrations are presented for each cluster group in Tables 46. This indicates that water quality of the wells of cluster 1A recorded the lowest mean concentrations of cations and anions except for TDS, sodium and chloride. However, in the post-monsoon season of 2006 these concentrations increased as a result of dissolution of aquifer material, while the highest concentration of all parameters (except for fluoride and nitrate) is recorded in wells of cluster 1A. On the other hand, wells in cluster 1B recorded intermediate mean concentrations between cluster 1A and cluster 2. The concentration of chloride, sulphate and sodium is depleted in the post-monsoon season of 2007 due to the flushing effect and dilution after rainfall. However, in the pre-monsoon season, the concentrations had increased due to the mixing of saline water during high tides, since all the wells in cluster 1B are located along the drains and mudflats.

CONCLUSIONS

The groundwater samples from 42 observation wells were collected in four different seasons of pre-monsoon (June) and post-monsoon (October) in the years 2006 and 2007 and analyzed for major ions and stable isotopes (δ18O) in the central Godavari delta, Andhra Pradesh. Different hydrochemical mixing models, stable isotope (δ18O) analysis and multivariate techniques were successfully applied for these data sets to identify the salinity source and to understand the hydrogeochemical dynamics in the area. The results of major ion chemistry and hydrochemical mixing models indicated that groundwater salinity in the area is mainly driven by seawater mixing, evaporation from groundwater and weathering of evaporites such as gypsum, ion exchange process and dissolution of marine clays. The higher proportions of sodium and chloride are derived from evaporated seawater and dissolution of evaporites. Groundwater with low major ion concentrations is largely meteoric water with some influence of the mixing of sodium chloride type groundwater. The depleted δ18O values of −2.54 to −8.67 in the wells away from the coast are due to accumulation of rainwater. The enrichment of isotopic concentrations in the groundwater near Pikaleru drain (−1.34) is due to evaporation and mixing of recent saline water. The low δ18O values close to zero is driven by up-coning of entrapped salt water of palaeo origin in the deeper part of the aquifer. Groundwater quality across the central Godavari delta region has significant spatial and temporal variations. Shallow wells are more sensitive to variations in water quality as they are closer to the ground surface.

ACKNOWLEDGEMENTS

Grateful thanks to the Director, CSIR-National Geophysical Research Institute (NGRI), Hyderabad for his kind permission to publish this paper; Mr V. V. S. Gurunadha Rao, CSIR-NGRI and Prof. P. Rajendra Prasad, Andhra University, Visakhapatnam for their kind encouragement for publication as single author; Cairn Energy India Ltd for the financial support provided to carry out this research work; Dr P. Pavelic, International Water Management Institute (IWMI), Loa office for his valuable suggestions provided when writing this paper; and Mr Mahesh for his help in Multi Variate Statistical Analysis. Thanks to Dr Mahen, Editor of International Water Management Institute (IWMI), Srilanka for English editing.

REFERENCES

REFERENCES
APHA
2005
Standard Methods for the Examination of Water and Wastewater
,
21st edn
.
American Public Health Association
,
Washington, DC
.
Back
W.
1966
Hydrochemical facies and groundwater flow patterns in northern part of Atlantic Coastal plain. US Geological Survey Professional Paper, 498-A.
United States Government Printing Office
,
Washington, DC
,
USA
.
Bhishm
Kumar
Rao
M. S.
Gupta
A. K.
Purushothaman
P.
2011
Groundwater management in a coastal aquifer in Krishna River Delta, South India using isotopic approach
.
Curr. Sci
.
100
(
7
),
1032
1043
.
CGWB
1999
Groundwater Resources and Development Prospects in East Godavari District, Andhra Pradesh
.
Ministry of Water Resources, Government of India, Hyderabad
.
Unpublished Report
, pp.
210
.
Clark
I.
Fritz
P.
1997
Environmental Isotopes in Hydrogeology
.
CRC Press
,
Boca Raton
.
Danielsson
A.
Cato
I.
Carman
R.
Rahm
L.
1999
Spatial clustering of metals in the sediments of the Skagerrak/Kattegat
.
Appl. Geochem
.
14
,
689
706
.
Davis
J. C.
2002
Statistics and Data Analysis in Geology
.
John Wiley & Sons Inc.
,
NY
.
Epstein
S.
Mayeda
T.
1953
Variation of O18 content of waters from natural sources
.
Geochim. Cosmochim. Acta
4
,
213
224
.
Geological Society of India (GSI)
2006
Miscellaneous Publication No.30 Part VII
,
2nd Revised Edition
,
Geology and Mineral Resources of Andhra Pradesh. Geological Survey of India
,
Hyderabad
, p.
91
.
Gonfiantini
R.
1978
Standard for stable isotope measurements in natural compounds.
Nature
271
,
534
534
.
Gurunadha Rao
V. V. S.
Tamma Rao
G.
Surinaidu
L.
Rajesh
R.
Mahesh
J.
2011
Geophysical and geochemical approach for seawater intrusion assessment in the Godavari Delta Basin, A.P., India
.
Water Air Soil Pollut
.
217
(
1
),
503
541
.
Gurunadha Rao
V. V. S.
Tamma Rao
G.
Surinaidu
L.
Mahesh
J.
Mallikharjuna Rao
S. T.
Mangaraja Rao
B.
2013
Assessment of geochemical processes occurring in ground waters in the coastal alluvial aquifer
.
Environ. Monit. Assess
.
185
(
10
),
8259
8272
.
Helena
B.
Pardo
B.
Vega
M.
Barrado
E.
Fernandez
J. M.
Fernandez
L.
2000
Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis
.
Water Res
.
32
,
19
30
.
Indian Meteorological Department (IMD)
2006
Available from: www.imd.gov.in/
.
Izbicki
J. A.
1996
Seawater intrusion in a coastal California aquifer
.
US Geological Survey Fact Sheet FS
4
, pp.
96
125
.
Jager
E.
Hunziker
J. C.
(eds)
1979
Lectures in Isotope Geology
.
Springer-Verlag
,
Berlin, Heidelberg, and New York
. pp.
329
.
Jankowski
J.
Shekarforosh
S.
Acworth
R. I.
1998
Reverse ion exchange in a deeply weathered prophyritic dacit fractured aquifer system, Yass, New South Wales, Australia
. In:
Proceedings of the 9th International Symposium on Water Rick Interaction, Taupo, New Zealand
(
Arehod
G. B.
Hulston
R.
, eds).
Balkema
,
Rotterdam
, pp.
243
246
.
Kalantary
N.
Rahimi
M.
Charchi
S. B.
2007
Use of composite diagram, factor analysis and saturation indices for quanitification of Zaviercherry and Kheran groundwater plain
.
J. Eng. Geol
.
2
(
1
),
339
356
.
Landau
S.
Everitt
B. S.
2004
A Handbook of Statistical Analyses Using SPSS Software
.
Champan and Hall/CRC Press
,
Boca Raton, London, New York, Washington, DC
.
Mancini
S. A.
Lacrampe-Couloume
G.
Jonker
H.
Van Breukelen
B. M.
Groen
J.
Volkering
F.
Lollar
B. S.
2002
Hydrogen isotopic enrichment: An indicator of biodegradation at a petroleum hydrocarbon field site
.
Environ. Sci. Technol
.
36
,
2464
2470
.
McCarthy
J. F.
Czerwinski
K. R.
Sanford
W. E.
Jardine
P. M.
Walsh
J. D.
1998
Mobilization of transuranic radionuclides from disposal trenches by natural organic matter
.
J. Contam. Hydrol
.
30
,
49
77
.
Naidu
L. S.
Rao
V. V. S. G.
Rao
G. T.
Mahesh
J.
Prasad
P. R.
Sarma
V. S.
Raja Rao
B. M.
2012
An integrated approach to identify the salinity source and saline water intrusion in the coastal aquifer, Andhra Pradesh, India
.
Arab. J. Geosci
.
6
(
10
),
3709
3724
.
Nordstrom
D. K.
Ball
J. W.
Donahoe
R. J.
Whittemore
D.
1989
Groundwater chemistry and water-rock interactions at Stripa
.
Geochem. Cosmochem. Acta
.
53
,
1727
1740
.
Panagopoulos
G.
Lambrakis
N.
Tsolis-Katagas
P.
Papoulis
D.
2004
Cation exchange processes and human activities in unconfined aquifers
.
Environ. Geol
.
46
,
542
552
.
Papatheodorou
G.
Lambrakis
N.
1997
Trend surface analysis of hydrochemical data. Case studies of Plio-Pleistocene aquifers from N.W. Peloponnesus and Central Crete, Greece
. In:
Proceedings of Third Annual Conference of International Association for Mathematical Geology
(
Pawlowsky
V.
, ed.).
CIMNE
,
Barcelona
, pp.
972
979
.
Piper
A. M.
1944
A graphic procedure in the geochemical interpretation of water analyses
.
Am. Geophys. Union Papers Hydrol
.
25
(
6
),
914
923
.
Raghunath
H. M.
2005
Textbook of Ground Water
,
3rd edn
.
New Age International Publishers
,
Noida, New Delhi
, pp.
1
520
.
Rao
G. N.
1993
Geology and hydrocarbon prospects sea coast sedimentary basins of India with special reference to Krishna Godavari Basin
.
J. Geo. Soc India
.
41
(
2
),
444
445
.
Rao
N. S.
Nirmala
I. S.
Suryanarayana
K.
2005
Groundwater quality in a coastal area: A case study from Andhra Pradesh, India
.
Environ. Geol
.
48
(
4–5
),
543
550
.
Salah
E. A. M.
Turki
A. M.
Al-Othman
E. M.
2012
Assessment of water quality of Euphrates River using cluster analysis
.
J. Environ. Protect
.
3
,
1629
1633
.
Seetaramaswamy
A.
Poornachandra Rao
M.
1975
Distribution of certain chemical constituents of the Krishna river
.
J. Indian Acad. Geosci
.
18
(
2
),
1
10
.
Shapouri
M.
Cancela da Fonseca
L.
Iepure
S.
Stigter
T.
Ribeiro
L.
Silva
A.
2016
The variation of stygofauna along a gradient of salinization in a coastal aquifer
.
Hydrol. Res
.
47
(
1
),
89
103
.
Subbarao
C.
Subbarao
N. V.
Chandu
S. N.
1995
Characterisation of groundwater contamination using factor analysis
.
Environ. Geol
.
28
,
175
180
.
Surinaidu
L.
Gurunadha Rao
V. V. S.
Rajendra Prasad
P.
Sarma
V. S.
2013
Use of geophysical and geochemical tools to investigate sea water intrusion in coastal alluvial aquifer, Andhra Pradesh, India. Groundwater in the Coastal Zones of Asia-Pacific
. In:
Coastal Research Library 7
(
Wetzelhuetter
C.
, ed.).
Springer Publications
,
New York
, pp.
49
65
.
Surinaidu
L.
Gurunadha Rao
V. V. S.
Rajendra Prasad
P.
Mahesh
J.
Thamma Rao
G.
Sarma
V. S.
2014
Assessment of possibility of salt water intrusion in the Central Godavari Delta, A.P., India
.
Region. Environ. Change J
.
15
(
5
),
907
918
.
Todd
D. K.
1982
Groundwater Hydrology
.
John Wiley & Sons
,
New York
, pp.
552
.
Ward
J. H.
1963
Hirachial grouping to optimize an objective function
.
J. Am. Stat. Assoc
.
58
(
301
),
236
234
.
Wu
T. N.
Huang
Y. C.
Lee
M. S.
Kao
C. M.
2005
Source identification of groundwater pollution with the aid of multivariate statistical analysis
.
Water Sci. Technol. Water Supply
5
(
6
),
281
288
.
Ženišová
Z.
Povinec
P. P.
Šivo
A.
Breier
R.
Richtáriková
M.
Ďuričková
A.
L'uptáková
A.
2015
Hydrogeochemical and isotopic characterization of groundwater at Žitný Island (SW Slovakia)
.
Hydrol. Res
.
46
(
6
),
929
942
.