Applied Water Science (2018) 8:233

https://doi.org/10.1007/s13201-018-0866-8

ORIGINAL ARTICLE

Assessment of groundwater quality for irrigation use: a peninsular

case study
Kishan S. Rawat1 · Sudhir Kumar Singh2 · Sandeep Kumar Gautam3

Received: 2 January 2017 / Accepted: 1 November 2018 / Published online: 26 November 2018
© The Author(s) 2018

Abstract
The grade of irrigation water available to irrigators has a significant impact on crops as well as yields. Therefore, it is a need
to better understand irrigation water quality. The present study mainly focuses on the assessment of the suitability of water
of forty-four fixed bore wells of Kanchipuram district, Tamil Nadu, India. The groundwater sample datasets of post-monsoon
(2005–2013) and pre-monsoon (2006–2013) season were collected for 9 years. Water quality indices, namely sodium adsorp-
tion ratio, exchangeable sodium percent (SSP or %Na), residual sodium carbonate (RSC or RA), Kelly’s ratio, permeability
index, chloroalkaline indices (CAI1 and CAI2), potential salinity (PS), magnesium hazard, total dissolved solids and total
hardness, have been calculated for separate bore wells. The r1 and r2 indices show that groundwater of the study area is
­Na+–SO42− and deep meteoric percolation type. Majority of the wells are fall under moderate to unsuitable category of water
for irrigation purposes. Further, wells water has also been classified on the base of meteoric genesis index.

Keywords Irrigation water quality · Meteoric genesis index · Sodium adsorption ratio · Magnesium adsorption ratio ·
Geochemistry

Introduction subsurface and surface water and hydro-geochemical pro-

cesses in aquifers (Keesari et al. 2016a; Das et al. 2017),
Groundwater has become the major source of water use in land-use/land-cover change (Amin et al. 2014; Srivastava
the agricultural sector in many countries where river and et al. 2013; Gajbhiye et al. 2015; Nemčić-Jurec et al. 2017;
drainage systems are not sufficient. Therefore, poor ground- Rawat et al. 2017; Rawat and Singh 2018) and mining activi-
water quality for irrigation purpose is a matter of worry in ties (Gautam et al. 2016; Keesari et al. 2016b; Gautam et al.
recent years. Under or over chemical fertilization is resulting 2018). Temporal changes in the constitution and origin of
in groundwater pollution (Ayers and Westcot 1985; Rowe the water recharged, and human factor, frequently cause
and Abdel-Magid 1995; Singh et al. 2013; Gautam et al. periodic changes in groundwater quality (Vasanthavigar
2015, 2016; Nemčić-Jurec et al. 2017). Groundwater qual- et al. 2010; Milovanovic 2007).
ity depends on the nature of recharging water, precipitation, Groundwater quality degrades in twofold, first, due to
geochemical reactions in the aquifers and soils and, second,
* Sudhir Kumar Singh time when it is supplied through improper canals/drainages
sudhirinjnu@gmail.com for irrigation. Therefore, it is necessary to perform a regular
Kishan S. Rawat assessment of irrigation and drinking water quality (Gupta
ksr.kishan@gmail.com et al. 2009; Jacintha et al. 2016; Rawat et al. 2018; Gau-
Sandeep Kumar Gautam tam et al. 2018). Irrigation demands sufficient water sup-
kgmsandeep@gmail.com ply of usable quality. The index based on composition and
concentration of dissolved elements in water can be useful
1
Centre for Remote Sensing and Geo‑Informatics, in determining its applicability for agricultural utilization
Sathyabama University, Chennai, Tamilnadu 119, India
(Gautam et al. 2013; Singh et al. 2013, 2015). The suit-
2
K. Banerjee Centre for Atmospheric and Ocean Studies, ability of groundwater for irrigation depends on the nature
IIDS, University of Allahabad, Prayagraj, U.P. 211002, India
of the mineral elements in the water and their impacts on
3
Centre of Advanced Study in Geology, University both the soil and plants (Richards 1954; Singh et al. 2009).
of Lucknow, Lucknow 226007, India

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233 Page 2 of 24 Applied Water Science (2018) 8:233

The excess of salts affects plant’s growth by redressing the and around the district. In this forest area, there are 366.675
uptake power of plant due to complex changes arouse out of ha of reserved land. Totally, 76.50 metric tonnes lands are
the osmotic processes (Todd 1980). cultivated in fuelwood and 8.039 tonnes in cashew.
Generally, water quality parameters (major cations as The pre-monsoon rainfall is almost uniform throughout
­Na+, ­Ca2+, ­Mg2+, ­K+) and anions ­Cl−, ­SO42−, ­HCO3−, the district. The coastal regions get more rains rather than
­CO32−, ­NO3−) and heavy metals are indicators of drink- the interior regions. This district is mainly depending on
ing water use, while water quality indices such as sodium the seasonal rains, and the distress conditions prevail in the
adsorption ratio (SAR), sodium percentage (SSP; %Na), event of the failure of rains. Northeast and southwest mon-
residual sodium carbonate (RSC), residual alkalinity (RA), soons are the major donors with 54% and 36% contribution
Kelly’s ratio (KR) [or Kelly’s index (KI)], permeability each to the total annual rainfall. During normal monsoon,
index (PI), chloroalkaline indices (CAI1 and CAI2), poten- the district receives a rainfall of 1213.3 mm (Table 1).
tial salinity (PS), magnesium hazard (MH) (or magnesium
adsorption ratio; MAR), total dissolved solids (TDS) and
total hardness (TH) based on primary water quality param- Materials and methods
eters are frequently used to determine quality of water for
irrigation (Singh et al. 2013, 2015; Gautam et al. 2015). The data related to groundwater quality have been acquired
In the present study, forty-four groundwater samples col- from Chennai Water Metro Board/Central Ground Water
lected from bore wells were analyzed and assessed for tempo- Board (CGWB) during pre- and post-monsoon seasons.
ral variation and change in water quality index over a period Totally, 44 samples were collected during May 2005 (pre-
of time. Most of the bore wells are from agricultural areas. monsoon) and sampling activity was repeated during Janu-
The relation between irrigation and groundwater ary 2006 (post-monsoon) of period 2005–2013. Well coor-
resources is highly interlinked. This paper describes the dinates have been collected using a handheld GPS device
groundwater quality status for irrigation purpose using water (eTrex ­Legend® HCx, having 10-m accuracy).
quality index (SAR, %Na, RSC, PI, MH, CAI1 and CAI2
CR, TDS, TH, Gibb’s 1 and 2) based on primary param- Development of rainfall datasets using satellite
eters (such as ­K+, ­Ca2+, ­Cl−, ­Na+, ­Mg2+, ­NO3−, ­SO42− and datasets
­HCO3−). Further, the classification was performed based on
Soltan method and estimation of groundwater source based The precipitation data of the entire study area have been
on meteoric genesis index. Therefore, the aim of the study derived from daily precipitation data provided by the NOAA
was to determine suitability of goundwater for irrigation. climate prediction center and were downloaded from the site
ftp://ftppr​d.ncep.noaa.gov/pub/cpc/fews/S.Asia/. The need
of satellite-estimated precipitation arises because of the non-
Study area dependable and poorly spatially distributed ground rainfall
data (Rawat and Tripathi 2016; Rawat et al. 2012a; b, 2016).
Kanchipuram district of Tamil Nadu (India) state lies between Daily precipitation satellite data have been converted into
11°00′ and 12°00′ north latitudes and 77°28′–78°50′ east lon- monthly precipitation data (Fig. 1b) by simply sum of per
gitudes (North East coast of Tamil Nadu) and on the banks of day rainfall of particular month (Rawat and Tripathi 2016;
the Vegavathi River, a tributary of the Palar River (Fig. 1a). Rawat et al. 2012c, d, 2016).
The study area has an elevation of 83.2 m above mean sea
level. The land around study area is flat and slopes toward
the south and east. It is bound by Bay of Bengal in the east. Irrigation indices
Agriculture is the main occupation of the people with
47% of the population engaged in it. Paddy is the major crop Sodicity
cultivated in this district. Industrial developments occupy
around 65 ha (160 acres), where most of the handloom Sodic soils are characterized by a disproportionately high
spinning, silk weaving, dyeing and rice production units concentration of sodium ­(Na+) in their cation exchange com-
are located. 89.06 ha (220.1 acres) are used for transport plex. Sodicity is the effect of irrigation water and can alter
and communications infrastructure, including bus stands, the chemical and physical properties of the soil due to an
roads, streets and railways lines. Groundnuts, sugarcane, accumulation of N ­ a+. Excess of N ­ a+ can affect plants in
cereals and millets and pulses are the other major crops three ways: (1) by degrading soil structure after each rainfall
(Table 1). The soil in the region is mostly clay, with some and irrigation due to crust formation which reduces water
loam, clay and sand (Table 1). The total forest area in the movement (permeability) and aeration in the soil; (2) toxic
district is 23,586 ha, and it spreads in the interior region effects when absorbed by leaves/roots; and (3) ­K+ and ­Ca2+

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Applied Water Science (2018) 8:233 Page 3 of 24 233

Fig. 1  a Study area map with 44 sampling locations. b Graphical representation of rainfall variability in the study area during 2005–2013

deficiencies may arise if the soil or irrigation water has a On the basis of SAR range, irrigation water can be classi-
high concentration of ­Na+. Therefore, evaluation of the fied into four classes as SAR < 10 (ideal or excellent), 10–18
sodicity hazard of irrigation water is important. (good), 18–26 (doubtful) and > 26 (unsuitable).
SAR also influences percolation time of water in the
soil. Therefore, the low value of SAR of irrigation water is
Sodium adsorption ratio (SAR) desirable.

The SAR is a relative ratio of ­Na+ ions to ­Ca2+ and ­Mg2+ ions Residual sodium carbonate (RSC)/residual alkalinity
present in the water sample. The SAR is used to estimate the (RA)
­ a+ to accumulate in the soil primarily (water
potential of N
movement) at the expense of ­Ca2+, ­Mg2+ and ­K+ as a result RSC represent as the amount of sodium carbonate ­(NaCO3)
of regular use of sodic water. It is formulated as Eq. (1): and sodium bicarbonate (­ NaHCO3) present in the irrigation
water if the concentration of carbonate (­ CO32−) and bicar-
Na+ bonate ­(HCO3−) ions exceeds the concentrations of C ­ a2+
SAR = √
(Ca2+ +Mg2+ ) (1) and ­Mg ions (Raghunath 1987), precipitation of ­Ca2+
2+

2 and ­Mg2+. If the carbonates are less than alkaline earths

­(Ca2+ + Mg2+), it outlined the residual ­NaCO3 which is
where ­Na+, ­Ca2+ and ­Mg2+ are in meq/l. absent. Generally, RSC is expressed as milliequivalents

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Table 1  General information

about the study area (Source: Total area 4393.37 km2
www.kanch​i.tn.nic.in) Net sown area 1364.89 km2
Net irrigation area 1236.28 km2
Forest area 426.57 km2
Poromboke area 1553.47 km2
Town area 82.57 km2
Number of panchayat villages 648
Summer temperature 21.1–36.6 °C
Winter temperature 19.8–28.7 °C
Rainfall 1133.0 mm actual, 1213.3 mm normal
Major crops Rice (145,966 ha), sugarcane (7586 ha),
groundnut (28,766 ha), gingelly (912 ha),
pulses (2966 ha), cotton (53 ha), millets and
cereals (1217 ha)
Soil types Read loam (Kanchipuram, Uthiramerur blocks),
lateritic soil (plateaus in the district), black
soil (spread in all blocks), sandy coastal
alluviam (Thiruporur and St. Thomas Mount)
and red sandy soil (Kancheepuram and urban
blocks)

per liter (meq/l) of N ­ aCO 3. An excess of ­CO 32− and (Wilcox 1948). ­Na+ reacts with ­CO32− and forms alkaline
−
­HCO3 causes precipitation of soil ­Ca2+ and ­Mg2+ impair- ­ a+ reacts with chloride and forms saline soils.
soils, while N
ing the soil structure as well as potentially activating soil Sodium-affected soil (alkaline/saline) retards crop growth
sodium. On the basis of RSC range, sodium hazard has been (Todd 1980). If concentration of N ­ a+ in irrigation water is
classified into three classes as follows: RSC < 1.25 (low), high, then the ions tend toward the clay particles, by remov-
1.25–2.5 (medium) and > 2.5 (high). RSC is expressed as ing ­Ca2+ and M­ g2+ ions through a base-exchange reaction.
Eq. (2): This exchange process in soil reduces water movement
( ) ( capacity. In this condition, air and water cannot move freely
RSC = HCO− + CO2− − Ca2+ + Mg2+ (2) or restricted during wet conditions, and such soils have
)
3 3
become hard when dry (Collins and Jenkins 1996; Saleh
A high range of RSC in irrigation water means an et al. 1999). The %Na values are calculated as Eq. (3):
increase in the adsorption of sodium on the soil. Water
Na+
having RSC > 5 has not been recommended for irrigation %Na = × 100 (3)
because of damaging effects on plant growth. Generally any Ca2+ + Mg2+ + Na+ + K+
source of water in which RSC is higher than 2.5 is not con- (all the ion concentrations are expressed in meq/l).
sidered suitable for agriculture purpose, and water < 1.25 The classification of water is based on %Na as excellent
is recommended as safe for irrigation purpose. A negative (< 20%), good (20–40%), permissible (40–60%), doubtful
value of RSC reveals that concentration of ­Ca2+ and ­Mg2+ (60–80%) and unsuitable (> 80%) (Khodapanah et al. 2009).
is in excess. A positive RSC denotes that ­Na+ existences
in the soil are possible. RSC calculation is also important
in context to calculate the required amount of gypsum or Kelly’s ratio (KR) or Kelly’s index (KI)
sulfuric acid per acre-foot in irrigation water to neutralize
residual carbonates effect. Kelly (1940) and Paliwal (1967) introduced another factor
to assess quality and classification of water for irrigation
Percent sodium (%Na) or sodium hazard ­ a+ against C
purpose based on the concentration of N ­ a2+ and
2+
­Mg . It can be calculated using Eq. (4)
The %Na is also used in classifying water for irrigation pur- Na+
pose. ­Na+ is important parameter and helps in categorization KR = (4)
Ca2+ + Mg2+
of any source of water for irrigation uses. N ­ a+ makes chemi-
cal bounding with soil to reduce water movement capacity of (all the ion concentrations are expressed in meq/l).
the soil (Ayers and Westcot 1985). Percent N ­ a+ concentra- KR/KI > 1 indicates an excess level of ­Na+ in waters.
tion is a factor to assess its suitability for irrigation purposes Therefore, water with a KI ≤ 1 has been recommended for

13
Applied Water Science (2018) 8:233 Page 5 of 24 233

irrigation, while water with KI ≥ 1 is not recommended for Potential salinity (PS)
irrigation due to alkali hazards (Ramesh and Elango 2012;
Karanth 1987). PS is another water quality parameter-based index (Doneen
1964) for categorization of water for agriculture use.
Permeability index (PI) PS < 3 meq/l is an indication of the suitability of water for
irrigation. The temporal distribution of PS of the study area is
The permeability index (PI) is an indicator to study the suit- produced for pre- and post-monsoon seasons using following
ability water for irrigation purpose. Water movement capa- Eq. (7):
bility in soil (permeability) is influenced by the long-term PS = Cl− + 0.5 × SO2− (7)
4
use of irrigation water (with a high concentration of salt)
as it is affected by ­Na+, ­Ca2+, ­Mg2+ and ­HCO3− ions of the Chloroalkaline indices (CAI1 and CAI2)
soil. PI formula has been developed by Doneen (1964), to
assess water movement capability in the soil as the suit- Information about coming changes in chemical composition of
ability of any kind of source of water for irrigation, and it is the groundwater during underground travel is also vital (Sastri
formulated as Eq. (5): 1994). The chemical reaction in which ion exchange between
the groundwater and the aquifer occurs during the movement
Na+ +
√
HCO−3 and rest condition of water. It can be analyzed through the
PI = × 100 (5) chloroalkaline indices. The CAI1 and CAI2 are evaluated
Ca2+ + Mg2+ + Na+ (Schoeller 1977) and expressed by Eqs. (8 and 9):
(all the ion concentrations are expressed in meq/l). Cl− − (Na+ + K+ )
According to Doneen (1964), PI can be categorized in three CAI1 = (8)
Cl−
classes: class I (> 75%, suitable), class II (25–75%, good) and
class III (< 25%, unsuitable). Water under class I and class II
Cl− − (Na+ + K+ )
is recommended for irrigation. CAI2 = − − (9)
(SO2−
4 + CO2−
3 + HCO3 + NO3 )

Magnesium hazard (MH) or magnesium adsorption The CAI1 and CAI2 indices may be negative or positive
ratio (MAR) depending on the exchange process of ­Na+ and ­K+ from the
rock with ­Mg2+ and ­Ca2+ present in water and vice versa. If a
Usually, alkaline earths (­ Ca2+ and M ­ g2+) are in an equilib- direct exchange process (DEP) happens between N ­ a+ and K
­ +
rium state in groundwater. Both ­Ca and ­Mg2+ ions are linked
2+
in water with M 2+
­ g and C 2+
­ a in rocks, then CAI ratio will be
with soil friability and aggregation, but both are also essen- positive. If a reverse exchange process occurs ­(Na+ and ­K+ in
tial nutrients for the crop. The high value of C ­ a2+ and M­ g2+ water with M ­ g2+ and C ­ a2+ in rocks), then CAI ratio will be
in water can increase soil pH (therefore soil converting it to negative.
saline nature of the soil; Joshi et al. 2009), resulting in decrease
in the availability of phosphorous (Al-Shammiri et al. 2005). Corrosivity ratio (CR)
Excess concentration of magnesium in groundwater affects
the soil quality by converting it into alkaline and decreases The corrosivity ratio is giving the information about water sup-
the crop yield (Gowd 2005; Singh et al. 2013; Gautam et al. ply. Any source of water with CR < 1 is recommended to the
2015). According to agriculturists, excess amount of ­Mg2+ transport of any source of water in any kind of pipes, whereas
ions in waters damage the soil quality which causes low crop CR > 1 shows corrosive nature of water, means not to be trans-
production (Ramesh and Elango 2012; Narsimha et al. 2013). ported through metal pipes (Balasubramanian 1986; Shankar
Szabolcs and Darab (1964) projected MH values for irrigation et al. 2011; Aravindan 2004). The CR can be estimated using
water, and it is calculated using Eq. (6): an Eq. (10):

Mg2+ SO2−
( ) ( )
Cl−
MH = × 100 (6) 35
+2 4
96
Ca2+ + Mg2+ CR = − (10)
CO2−
( )
3 +HCO3
100
(all the ion concentrations are expressed in meq/l).
MH > 50 is not recommended for irrigation purposes
(all ions are in ppm).
(Khodapanah et al. 2009).

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233 Page 6 of 24 Applied Water Science (2018) 8:233

The rate of corrosion depends upon some physical param-

eters like pressure, temperature and rate of flow of water. In (K+ + Na+ ) − Cl−
addition to the higher value of ­Cl− and ­SO42− also increases
r2 = (13)
SO2−
4
the corrosion rate.
If the value of r2 < 1 it means the groundwater is of deep
Total dissolved solids (TDS) meteoric percolation (DMP) type, and if the value of r2 > 1
it means the groundwater is of shallow meteoric percolation
In natural water total dissolved solids contains of miner- (SMP) type.
als, nutrients that have dissolved in water, and also includes
major ions, i.e. C ­ a2+, ­Mg2+, ­Na+, ­K+, ­CO32−, ­HCO3−, ­Cl−,
2− 3−
­SO4 and ­PO4 . Weathering or dissolution of soil and rocks Result and discussion
generates ions in water (Singh et al. 2013). After evaporation
of water, accumulation of salt at the root zone makes obstacle Irrigation indices
and plants are not capable of sucking water from soil resulting
in moisture stress (Modi 2000). For irrigation, the TDS has Figure 2a, b shows the fluctuation of SAR value during pre-
been classified as TDS < 450 mg/l and is preferred for irri- and post-monsoon season and Tables 2 and 3 show 9-year
gation and TDS > 450–2000 mg/l is slight to moderate and statistics. Tables 2 and 3 revealed that SAR value ranges
TDS > 2000 mg/l is unsuitable for agricultural purpose (FAO from 0.25 to 69.09 and 0.24 to 78.37 in pre- and post-mon-
2006). soon, respectively. The average value of SAR for 9 years was
reported as 18.74 and 17.49 which are the nearest average
Total hardness (TH) value (18.93 and 19.17 and 17.30 and 17.40) of 2010 and
2011 (pre-monsoon) and 2006 and 2008 (post-monsoon).
Water hardness is a result of existence of divalent metallic According to individual year statistics, 2007 and 2012 have
cations ­(Ca2+ and ­Mg2+), and it can be calculated as the sum lowest average value of SAR (15.92 and 15.91) during
of ­Ca2+ and ­Mg2+ concentration as meq/l equivalent to ­CaCO3 post-monsoon. It is due to good amount of rainfall (Fig. 1b,
(Todd 1980) and expressed by (Eq. 11): 7- and 8-month rainfall out of 12 during 2007 and 2012,
respectively). The same year’s range of SAR was also lowest
TH = 2.5 × Ca2+ + 4.1 × Mg2+ (11)
(1.05–46.14 and 1.94–67.93). Similarly, Table 3 shows 32
The high value of TH is due to encrustation on water supply wells (15 excellent, 31.82 percent + 17 good, 38.64 percent),
systems (pipes) for distribution. Moderate value of TH is use- and 32 wells (19 excellent, 43.18 percent + 13 good, 39.55
ful for plumbing system from corrosion. Total hardness clas- percent) fall under excellent and good category during the
sified as 100 mg/l provides good control over corrosion and years 2007 and 2012 (post-monsoon). The rainfall affects
is the usually acceptable limit. TH is usually classified (EPA SAR value of the year 2006 (pre-monsoon). The 2007 post-
1986) as soft (0–60 mg/l), moderately hard (60–120 mg/l), monsoon saw a good amount of rainfall as 70.45 percent (19
hard (120–180 mg/l) and very hard (> 180 mg/l). excellent + 12 good) wells have reported normal SAR limit,
whereas in 2011 pre-monsoon, majority of water went as
Classification of groundwater runoff (less amount of infiltration into groundwater); hence,
in 2012 pre-monsoon, only 21 wells (17 excellent + 4 good)
Soltan (1998) suggested the classification of different fell under safe limit of SAR. From Fig. 2a, during pre-mon-
sources of water facies based on the concentration (meq/l) soon well nos. 4, 12, 21, 27, 34 and 40 were cross the higher
of particular ion. Soltan (1999) suggested a new classifica- (> 26) limit of SAR, respectively, 9, 7, 6, 7, 3 and 7 time
tion method based on base-exchange indices, and it can be (year) out of 9 years, and it highlights that these wells are
calculated using Eq. (12) not appropriate for pre-monsoon irrigation. From Fig. 2b,
these same wells have higher SAR value as > 26 (limit) with
Na+ − Cl− 7, 6, 4, 3, 0 and 8 times out of 8 years during post-monsoon.
r1 = (12)
SO2−
4 Therefore, these wells (4, 12, 21, 27 and 40) were unsuitable
during the study period (2005–2013).
The groundwater of the study area can be categorized The RSC value ranges from − 0.10 to 6.87 with an aver-
as ­Na+–HCO3− type if r1 > 1 and ­Na+–SO42− type if r1 < 1. age value of 1.76 over 2005–2013 during pre-monsoon
The groundwater has also been categorized based on (Table 2), while during post-monsoon RSC value ranges
meteoric genesis index, which was calculated (Soltan 1999) from − 4.73 to 5.04 (for 8 years) with 1.48 8-year aver-
using Eq. (13). age (Table 3). From Table 4 in years 2011 and 2012, wells
(47.73% and 65.91%, respectively) fall under the good limit

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Applied Water Science (2018) 8:233 Page 7 of 24 233

Fig. 2  a Nine-year (2005–2013) temporal representation of SAR value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of SAR value during post-monsoon

(< 1.25) due to good amount of monthly rainfall during 2010 good and permissible category, but the doubtful category
and 2011 (above 200 mm), which provide higher rate of also reports the good percent of total no. of wells during
infiltration rather than runoff. Similarly, during post-mon- the pre-monsoon years 2006, 2010 and 2011, respectively,
soonof year 2013, highest no. of wells (59.09%, Table 5) 45.45, 45.45 and 59.09%. It may be due to low rainfall and
falls under the good limit ( < 1.25) of RSC. It shows during leads to slow dilution process. Table 5 shows the doubtful
2013 factor (­ HCO3−, ­CO32−, ­Ca2+ and M­ g2+) which govern category (except years 2006 and 2010) during post-mon-
RSC were less in groundwater due to slow leaching from soon and explain rainfall effect over %Na. Figure 4a, b also
rock to groundwater. Effect of rainfall over RSC can be eas- explains the effect of rainfall over groundwater quality in
ily understood from Fig. 3a, b, well nos. 2, 4, 5, 12, 21, 25, the context of %Na. Only well no. 33 showing a negative
32, 35 and 41 most of the time of year come under beyond effect of rainfall, it may be due to more leaching of ­Na+
of the unsuitable limit during pre-monsoon, but after rainfall from the rock into the water. Overall, Fig. 4b represents dilu-
or due to rainfall effect these well’s RSC range reduces. In tion in %Na with respect to Fig. 4a during the study period
Fig. 3b, some wells (6, 24, 32, 36 and 44) having nega- 2005–2013. Based on Fig. 4a, b, majority of wells come in
tive RSC values due to excess concentration of C ­ a2+ and the good and permissible category of irrigation water.
2+
­Mg . A high range of RSC in groundwater indicates an Kelly’s ratio (KR)/Kelly’s index (KI) is an indicator to
increase in the adsorption of sodium in soil (Eaton 1950) asses irrigation water suitability and it is free from the effect
during irrigation from such type of bore well. It is observed of ­K+ parameter, which purely depends on C ­ a2+, ­Mg2+ and
+
that well nos. 2, 4, 5, 12, 21, 25, 32, 35 and 41 fall under ­Na . Its classification bin (only two classes) is also easier
unsuitable category for irrigation purpose because of RSC than %Na classification bin (four classes). From Tables 2
value > 2.5 meq/l is harmful for plants growth. and 3, average value of KR/KI during the study period was
From Tables 2 and 3, 9-year average of %Na found 55.04 1.39 and 1.34 for pre- and post-monsoon, respectively, with
(with min = 3.27 and max = 87.59) and 54.38 for pre- and a range of 0.03–6.66 and 0.03–12.31. Statistical analysis
post-monsoon, respectively, and both values are under the of KR has revealed that majority of wells falls in unsuit-
permissible limit for irrigation. Individual yearwise study able category during pre-monsoon. Except for year 2007
shows the years 2006 (during pre-monsoon) and 2011 KR varies from 0.03–3.63 with average KR was found 0.92
(during post-monsoon) average value of %Na [60.05 (pre- (Table 2) during pre-monsoon. After analysis of post-mon-
monsoon) and 57.30 (post-monsoon)] was high during the soon KR value, it was found that there was very less effect
study period. Statistical analysis (Tables 2, 3) of %Na does of rainfall over KR because after rainfall average value of
not show much variation in %Na values yearwise, and also KR (Table 3) comes under the suitable range due to dilu-
Tables 2 and 3 revealed that all the year wise statistical val- tion process. Table 4 denotes that during the pre-monsoon
ues (AV, Me, Mo, Mi, Ma and SD) showing most no. of study period more than 52 percent wells in the study area
wells falls under acceptable limit of %Na. From Table 4, it is come under the suitable category (KR < 1) of irrigation
clear that majority of percent of wells come under excellent, except the years 2005 and 2007. Table 5 does not take into

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233 Page 8 of 24 Applied Water Science (2018) 8:233

Table 2  Statistics of each index 2005 2006 2007 2008 2009 2010 2011 2012 2013 N YA
during pre-monsoon
SAR
AV 19.25 21.15 13.88 21.13 17.17 18.93 19.17 20.66 17.34 18.74
Me 15.51 17.10 11.48 18.30 15.50 17.79 14.88 20.41 14.29 15.80
Mo 10.42 7.00 7.30 6.51 28.25 8.41 3.77 2.48 8.07 12.73
Mi 2.09 1.83 1.13 5.43 3.81 5.79 3.77 2.48 0.25 0.25
Ma 48.49 69.09 32.42 63.78 62.60 53.23 55.81 61.95 60.63 69.09
SD 12.70 15.61 9.01 13.22 11.39 10.86 13.81 14.95 13.67 13.01
RSC
AV 1.82 1.74 1.64 2.00 1.64 1.88 1.75 1.61 1.78 1.76
Me 1.61 1.65 1.49 1.86 1.63 1.83 1.43 0.98 1.52 1.59
Mo 1.27 0.31 0.27 1.23 1.00 0.61 2.19 0.46 0.40 1.27
Mi 0.10 − 0.10 0.27 0.15 0.02 0.01 0.04 0.01 0.32 − 0.10
Ma 6.42 5.21 3.85 4.90 3.67 5.07 5.58 6.87 5.62 6.87
SD 1.29 1.08 0.96 1.23 0.91 1.10 1.22 1.53 1.26 1.18
%Na
AV 57.42 60.05 46.98 58.50 53.11 55.75 57.66 55.64 50.22 55.04
Me 56.09 62.30 47.60 58.56 55.87 58.65 59.86 55.86 53.84 56.02
Mo 55.17 87.59 45.24 44.78 61.92 48.81 33.33 53.83 39.04 44.78
Mi 20.35 22.73 10.47 30.41 26.26 35.17 27.93 29.26 3.27 3.27
Ma 85.94 87.59 81.90 86.02 85.86 84.94 86.46 79.10 86.95 87.59
SD 15.17 16.53 16.44 13.87 14.56 12.53 14.61 12.51 18.49 15.45
KR/KI
AV 1.55 1.55 0.92 1.57 1.26 1.33 1.36 1.66 1.29 1.39
Me 1.18 1.29 0.76 1.15 1.06 1.17 1.10 1.18 0.89 1.06
Mo 1.18 1.17 0.76 0.76 1.56 0.91 0.28 0.28 0.64 1.17
Mi 0.16 0.09 0.09 0.38 0.27 0.49 0.28 0.27 0.03 0.03
Ma 5.78 5.43 3.63 5.35 6.00 5.17 4.26 5.56 6.66 6.66
SD 1.28 1.12 0.71 1.13 1.15 0.84 1.00 1.39 1.21 1.12
PI
AV 62.41 62.75 51.18 63.47 57.54 60.90 59.33 61.27 57.16 59.56
Me 60.39 65.35 51.36 60.39 58.50 61.39 61.95 58.68 55.37 59.47
Mo 63.75 71.79 53.76 56.75 65.34 60.00 33.49 40.11 47.32 63.75
Mi 25.08 18.03 18.61 36.51 29.13 39.39 33.49 30.75 12.10 12.10
Ma 99.42 88.55 87.43 89.75 90.09 90.42 86.56 91.24 94.69 99.42
SD 14.89 15.79 15.68 13.55 13.76 11.58 14.99 17.85 16.70 15.36
MH
AV 51.99 49.90 44.98 42.90 37.33 32.83 35.76 44.10 38.32 42.01
Me 46.46 50.92 47.58 42.75 35.02 32.02 33.33 39.84 37.11 39.46
Mo 33.33 22.22 39.13 50.00 33.33 20.93 33.33 37.79 37.79 33.33
Mi 21.05 14.02 4.65 16.67 11.96 13.16 5.51 18.55 17.28 4.65
Ma 82.98 92.98 72.88 75.56 69.36 59.49 79.66 74.81 68.35 92.98
SD 17.60 18.96 14.04 13.70 12.70 10.60 15.63 16.10 11.82 15.87
PS
AV 2.36 2.66 2.43 2.70 2.63 2.59 2.78 2.36 2.13 2.52
Me 1.94 2.09 1.95 1.86 1.91 1.52 2.04 1.91 1.35 1.85
Mo 0.94 0.39 0.80 0.56 4.89 2.18 3.74 0.38 1.04 1.23
Mi 0.25 0.37 0.40 0.47 0.36 0.40 0.41 0.38 0.15 0.15
Ma 7.78 8.99 7.30 11.31 9.54 10.83 12.32 9.99 6.58 12.32
SD 1.94 2.14 1.78 2.35 2.13 2.43 2.51 1.84 1.68 2.10
CIA1
AV − 0.040 − 0.107 0.233 − 0.033 0.129 − 0.122 0.120 0.122 0.092 0.04

13
Applied Water Science (2018) 8:233 Page 9 of 24 233

Table 2  (continued) 2005 2006 2007 2008 2009 2010 2011 2012 2013 N YA

Me 0.102 0.206 0.391 0.166 0.279 0.079 0.176 0.226 0.295 0.22
Mo 0.415 − 3.080 0.367 0.143 0.344 0.047 0.267 − 0.107 0.345 − 0.11
Mi − 2.438 − 3.080 − 2.256 − 2.043 − 1.258 − 2.120 − 0.930 − 1.800 − 3.339 − 3.34
Ma 0.814 0.826 0.908 0.775 0.764 0.784 0.802 0.795 0.951 0.95
SD 0.688 0.859 0.588 0.641 0.513 0.722 0.377 0.520 0.743 0.65
CIA2
AV 0.18 0.12 0.24 0.17 0.25 0.13 0.19 0.23 0.16 0.19
Me 0.04 0.09 0.16 0.08 0.14 0.03 0.05 0.16 0.10 0.09
Mo 0.31 0.00 0.00 0.00 0.00 0.00 0.06 − 0.03 0.15 0.31
Mi − 0.25 − 1.16 − 0.22 − 0.26 − 0.23 − 0.30 − 0.33 − 0.28 − 0.27 − 1.16
Ma 1.45 1.87 1.33 1.16 1.74 1.74 1.60 1.98 1.05 1.98
SD 0.37 0.48 0.33 0.35 0.45 0.42 0.37 0.42 0.29 0.39
CR
AV 2.16 2.25 1.67 1.72 1.78 1.89 1.67 1.97 1.08 1.80
Me 1.15 1.28 1.16 0.99 1.05 1.00 1.05 1.52 0.97 1.06
Mo 1.75 0.00 0.00 0.00 0.00 0.00 0.76 0.71 1.37 1.75
Mi 0.18 0.19 0.15 0.23 0.24 0.14 0.31 0.24 0.13 0.13
Ma 23.80 19.42 13.25 13.90 12.74 19.72 8.42 14.55 3.52 23.80
SD 3.77 3.36 2.26 2.31 2.20 3.23 1.71 2.40 0.69 2.59
TDS
AV 719.91 762.27 729.77 798.89 764.43 794.98 798.43 705.86 674.92 749.94
Me 622 664 587 710 636 618 678 646.5 605 648.50
Mo 253 222 269 206 1277 240 378 682 387 262.00
Mi 233 222 235 206 237 238 221 188 113 113.00
Ma 3314 2734 2955 2727 2999 2608 2481 2412 1405 3314.00
SD 516.81 463.87 463.43 491.76 493.54 499.44 511.18 431.89 349.15 468.70
TH
AV 328.09 329.60 400.67 352.33 376.13 360.33 342.38 307.56 338.62 385.21
Me 277.40 272.70 338.20 279.25 306.65 289.90 253.35 239.41 272.07 282.35
Mo 118.30 51.40 143.80 113.30 540.30 121.90 255.20 109.89 224.76 190.70
Mi 64.20 51.40 133.30 111.50 86.00 121.90 103.30 109.89 94.87 51.40
Ma 1495.60 1277.80 1337.60 1167.00 1340.80 1116.60 1419.60 1178.30 1147.04 1495.60
SD 238.18 220.09 238.06 239.84 251.92 240.26 232.99 203.95 207.48 340.52
r1
AV − 0.89 − 0.78 − 1.79 − 1.17 − 1.48 − 0.52 − 1.54 − 0.86 − 1.07 − 1.12
Me − 0.54 − 0.71 − 1.29 − 0.59 − 0.88 − 0.38 − 1.11 − 0.94 − 0.99 − 0.82
Mo − 1.50 − 0.15 − 0.63 − 0.17 − 0.88 − 0.10 − 0.61 − 0.85 − 0.52 − 0.80
Mi − 10.42 − 6.78 − 12.11 − 13.27 − 10.73 − 9.10 − 12.09 − 8.78 − 14.06 − 14.06
Ma 7.20 10.50 8.30 2.37 2.61 3.00 7.25 9.22 14.25 14.25
SD 2.38 2.32 3.04 2.93 2.53 1.77 2.79 2.57 4.63 2.87
r2
AV 0.15 − 0.12 − 1.34 − 0.95 − 1.14 − 0.28 − 0.77 − 0.40 − 0.45 − 0.59
Me − 0.20 − 0.35 − 0.99 − 0.28 − 0.53 − 0.20 − 0.30 − 0.58 − 0.82 − 0.50
Mo − 1.42 3.78 − 0.55 − 0.12 − 0.82 − 0.05 − 0.28 0.15 − 0.52 − 0.55
Mi − 10.17 − 6.26 − 10.56 − 17.14 − 10.27 − 9.00 − 17.91 − 8.70 − 13.03 − 17.91
Ma 16.50 10.50 8.80 3.26 2.83 4.50 14.00 12.00 18.88 18.88
SD 3.71 2.45 2.98 3.53 2.63 1.89 3.87 3.09 5.46 3.43

Av average, Me median, Mo mode, Mi minimum, Ma maximum, SD standard deviation, NYA nine-year

average

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233 Page 10 of 24 Applied Water Science (2018) 8:233

Table 3  Statistics of each index 2005 2006 2007 2008 2009 2010 2011 2012 2013 N YA
during post-monsoon
SAR
AV 17.30 15.92 17.40 15.74 19.24 19.56 15.91 18.87 17.49
Me 15.15 14.43 16.25 12.03 16.47 16.46 13.47 13.21 14.53
Mo 4.59 5.75 6.86 5.21 8.20 12.02 8.52 6.61 21.23
Mi 1.63 1.05 3.64 4.17 2.72 6.39 1.94 0.24 0.24
Ma 57.57 46.14 49.49 71.17 53.70 54.05 67.93 78.37 78.37
SD 11.21 9.40 10.13 12.43 11.86 11.29 13.69 18.59 12.57
RSC
AV 1.18 1.99 1.88 1.68 1.79 1.08 1.46 0.86 1.48
Me 1.38 1.81 1.93 1.56 1.89 1.23 1.43 .77 1.51
Mo 1.46 2.90 1.60 0.40 1.08 1.05 0.51 0.34 1.71
Mi − 4.73 − 1.43 − 2.42 − 2.85 − 1.54 − 3.36 − 3.31 − 3.47 − 4.73
Ma 3.79 5.04 4.56 4.89 3.92 3.74 4.28 3.81 5.04
SD 1.56 1.35 1.44 1.33 1.22 1.26 1.53 1.21 1.41
%Na
AV 55.79 51.60 54.37 52.20 56.97 57.30 53.35 53.44 54.38
Me 57.65 50.84 53.42 52.40 57.35 56.96 53.82 54.62 54.25
Mo 38.71 42.86 42.86 40.00 61.90 59.55 48.79 38.50 42.86
Mi 15.28 30.43 33.54 29.48 25.52 35.00 25.36 10.60 10.60
Ma 85.60 79.22 83.71 88.10 83.56 83.45 89.89 92.49 92.49
SD 15.40 13.00 12.61 14.11 13.06 12.51 15.29 18.84 14.48
KR/KI
AV 1.34 1.11 1.24 1.21 1.39 1.44 1.24 1.76 1.34
Me 1.11 0.94 1.01 0.88 1.18 1.20 0.89 0.79 0.98
Mo 0.53 0.72 0.67 0.59 0.82 1.42 0.91 0.62 1.00
Mi 0.15 0.08 0.29 0.35 0.16 0.53 0.12 0.03 0.03
Ma 4.83 3.56 4.38 7.34 4.49 4.56 8.58 12.31 12.31
SD 1.01 0.78 0.88 1.19 0.93 0.93 1.43 2.49 1.31
PI
AV 59.72 57.29 59.31 57.08 62.08 61.63 55.63 57.25 58.75
Me 60.64 56.09 57.88 54.12 62.73 61.23 57.22 51.88 57.96
Mo 47.27 56.02 49.21 51.43 58.33 68.03 59.08 48.72 47.27
Mi 30.26 25.48 36.50 30.56 20.85 36.64 16.95 19.69 16.95
Ma 89.38 87.11 89.42 92.00 91.82 89.01 96.08 99.19 99.19
SD 14.24 13.32 12.58 13.81 13.17 12.46 16.13 19.15 14.53
MH
AV 66.37 34.99 34.66 48.28 39.70 40.97 39.11 42.30 43.30
Me 64.31 36.14 32.63 46.78 41.46 37.99 37.79 39.61 41.03
Mo 53.85 31.25 42.03 33.33 16.00 33.33 28.83 33.29 33.33
Mi 38.64 0.85 11.69 22.87 1.64 15.29 13.78 4.82 0.85
Ma 86.44 68.75 74.63 72.17 80.00 71.76 70.85 77.74 86.44
SD 13.14 12.98 12.71 14.19 17.97 14.33 13.61 17.98 17.49
PS
AV 2.54 2.51 2.52 2.20 2.62 2.91 2.30 2.08 2.46
Me 2.27 1.77 1.54 1.63 1.63 2.04 1.50 1.32 1.67
Mo 4.03 0.72 0.51 0.50 0.49 0.99 0.70 0.67 0.66
Mi 0.21 0.21 0.35 0.38 0.34 0.74 0.32 0.00 0.00
Ma 7.98 11.80 13.26 9.33 12.68 12.29 8.65 7.26 13.26
SD 1.94 2.56 2.69 1.75 2.68 2.41 2.17 1.87 2.28
CIA1
AV 0.13 − 0.03 − 0.04 0.16 − 0.11 0.31 − 0.01 0.23 0.08

13
Applied Water Science (2018) 8:233 Page 11 of 24 233

Table 3  (continued) 2005 2006 2007 2008 2009 2010 2011 2012 2013 N YA

Me 0.25 0.22 0.15 0.20 − 0.01 0.31 0.21 0.27 0.21

Mo 0.00 0.14 − 1.00 0.19 − 0.41 0.38 0.21 0.45 0.00
Mi − 1.44 − 1.73 − 1.68 − 1.54 − 1.82 − 0.48 − 2.09 − 1.26 − 2.09
Ma 0.85 0.78 0.79 0.83 0.82 0.81 0.76 0.86 0.86
SD 0.54 0.70 0.59 0.46 0.63 0.25 0.64 0.44 0.56
CIA2
AV 0.39 0.17 0.20 0.26 0.18 0.46 0.19 0.34 0.27
Me 0.12 0.07 0.05 0.09 0.00 0.19 0.08 0.20 0.09
Mo 0.00 0.00 0.05 0.00 − 0.07 0.00 0.08 0.29 0.00
Mi − 0.28 − 0.21 − 0.28 − 0.22 − 0.26 − 0.18 − 0.47 − 0.75 − 0.75
Ma 4.36 1.88 3.07 2.73 3.25 6.34 2.54 3.21 6.34
SD 0.87 0.39 0.59 0.58 0.60 1.01 0.54 0.63 0.68
CR
AV 2.38 1.16 1.34 1.48 1.42 2.42 1.83 2.34 1.80
Me 1.27 0.91 0.69 0.99 0.82 1.49 0.95 1.17 1.07
Mo 0.00 0.00 1.24 0.00 0.50 0.00 1.15 1.09 1.24
Mi 0.20 0.09 0.14 0.21 0.15 0.43 0.20 0.00 0.00
Ma 14.82 6.25 9.50 10.82 8.01 16.74 14.78 17.97 17.97
SD 3.06 1.21 1.79 1.85 1.66 3.03 2.99 3.23 2.49
TDS
AV 738.43 761.91 766.02 670.43 781.98 782.84 714.77 616.77 729.14
Me 664.5 593 603.5 553 627 609 537 499 596.00
Mo 215 593 279 330 258 258 243 664 321.00
Mi 170 175 195 204 258 258 243 98 98.00
Ma 2436 2614 2427 1800 3087 2562 2373 2520 3087.00
SD 480.37 523.41 502.77 377.54 563.96 523.82 470.45 424.95 484.92
TH
AV 371.79 394.39 375.78 347.94 362.58 356.49 346.27 292.95 356.02
Me 289.85 295.85 274.40 282.30 265.35 255.05 272.03 241.96 269.28
Mo 130.20 96.00 155.60 118.30 137.80 109.20 199.70 254.57 254.57
Mi 114.70 96.00 106.00 118.30 132.20 109.20 99.80 69.82 69.82
Ma 1646.40 1517.00 1514.60 1298.50 1250.20 1356.40 1358.00 1397.71 1646.40
SD 289.80 311.99 291.19 231.76 271.30 271.20 260.32 223.61 269.12
r1
AV − 1.98 − 0.92 − 1.44 − 1.65 − 0.85 − 1.96 − 0.81 − 2.07 − 1.46
Me − 1.14 − 0.66 − 0.42 − 0.73 − 0.30 − 1.21 − 0.66 − 1.58 − 0.75
Mo − 1.36 − 0.15 − 0.28 − 0.25 0.27 − 1.21 − 0.39 − 4.83 − 0.25
Mi − 14.00 − 8.98 − 14.40 − 14.29 − 14.88 − 10.89 − 8.35 − 14.40 − 14.88
Ma 2.67 4.47 2.43 2.05 2.53 0.84 3.77 8.80 8.80
SD 3.36 2.40 3.83 2.91 2.74 2.12 1.70 3.73 2.95
r2
AV − 1.23 − 0.47 − 1.34 − 1.42 − 0.66 − 1.91 − 0.45 − 1.84 − 1.17
Me − 0.67 − 0.46 − 0.18 − 0.33 0.01 − 0.99 − 0.39 − 1.21 − 0.46
Mo 0.00 − 0.12 − 0.18 − 0.17 0.39 − 1.14 − 0.33 − 4.82 0.00
Mi − 8.00 − 8.98 − 19.84 − 17.86 − 19.75 − 20.74 − 9.91 − 16.00 − 20.74
Ma 3.50 9.90 3.07 2.75 2.68 1.18 4.10 8.82 9.90
SD 2.80 2.88 4.88 3.36 3.442308 3.329189 1.99 3.84 3.42

Av average, Me median, Mo mode, Mi minimum, Ma maximum, SD standard deviation, NYA nine year
average

13
233 Page 12 of 24 Applied Water Science (2018) 8:233

Table 4  Classification of fixed bore wells water during pre-monsoon within the study area for irrigation based on %Na, SAR, MH, KR RSC,
TDS (Wilcox 1948; Kelly 1940; Todd 1980; USSL 1954)
Index Range Class No. of samples (with %) under different classes per year
2005 2006 2007 2008 2009 2010 2011 2012 2013

SAR < 10 Exc. 14, 31.82 13, 29.55 19, 43.18 11, 25 13, 29.55 11, 25 11, 25 17, 38.64 17, 38.64
10–18 Go. 11, 25 11, 25 12, 27.27 10, 22.73 13, 29.55 11, 25 11, 25 04, 9.10 10, 22.73
18–26 Dou. 08, 18.18 9, 20.45 07, 15.91 10, 22.73 12, 27.27 13, 29.45 11, 25 9, 20.45 07, 15.91
> 26 UnSu. 11, 25 11, 25 6, 13.64 13, 29.55 06, 13.64 09, 20.45 11, 25 14, 31.82 10, 22.73
RSC/RA < 1.25 Go. 16, 36.36 14, 31.82 13, 29.55 16, 36.36 18, 40.91 12, 27.27 21, 47.73 29, 65.91 17, 38.62
1.25–2.5 Dou. 17, 38.64 9, 20.45 23, 52.27 16, 36.36 16, 36.36 22, 50 14, 31.82 7, 15.91 19, 43.18
> 2.5 UnSu. 11, 25 21, 47.73 08, 18.18 12, 27.27 10, 22.73 10, 22.73 09, 20.45 08, 18.18 08, 18.18
%Na > 20 Exce. 01, 2.27 01, 2.27 04, 9.09 00, 00 00, 00 00, 00 00, 00 00, 00 00, 00
20–40 Go. 06, 13.64 04, 9.09 07, 15.91 02, 4.54 09, 20.45 04, 9.09 04, 9.09 03, 6.82 13, 29.55
40–60 Perm. 21, 47.73 15, 34.09 22, 50 22, 50 23, 52.27 19, 43.18 16, 36.36 26, 59.09 17, 38.64
60–80 Dou. 13, 29.55 20, 45.45 10, 22.73 17, 38.64 10, 22.73 20, 45.45 20, 45.45 15, 34.09 10, 22.73
> 80 UnSu. 03, 6.82 04, 9.09 01, 2.27 03, 6.82 02, 4.55 01, 2.27 04, 9.09 00, 00 02, 4.55
KR/Kl <1 Su. 19, 43.18 31, 70.45 13, 29.55 25, 56.82 24, 54.55 24, 54.55 23, 52.27 23, 52.27 24, 54.55
>1 UnSu. 25, 56.82 13, 29.55 31, 70.45 19, 43.18 20, 45.45 20, 45.45 21, 47.73 21, 47.73 20, 45.45
PI > 75% Su. 09, 20.45 08, 18.18 02, 4.55 08, 18.18 03, 6.82 05, 11.36 06, 13.64 11, 25 06, 13.64
25–75% Go. 34, 77.27 35, 79.55 38, 86.36 36, 81.82 41, 93.18 39, 88.64 38, 86.36 33, 75 37, 84.09
< 25% UnSu. 01, 2.27 01, 2.27 04, 9.09 00, 00 00, 00 00, 00 0, 00 00, 00 1, 2.27
MAR/MH < 50 Su. 25, 56.82 23, 52.27 30, 68.18 34, 77.27 36, 81.82 40, 90.91 42, 95.45 26, 59.09 37, 84.09
> 50 UnSu. 19, 43.18 21, 47.73 14, 31.82 10, 22.73 8, 18.18 4, 9.09 6, 13.64 18, 40.91 7, 15.91
PS <3 Su. 33, 75 28, 63.64 30, 68.18 31, 70.45 28, 63.64 31, 70.45 31, 70.45 32, 72.73 30, 68.18
>3 UnSu. 11, 25 16, 36.36 16, 36.36 13, 29.55 16, 36.36 13, 29.55 13, 29.55 12, 27.27 14, 31.82
CAI1 − tiv REP 17, 38.64 17, 38.64 8, 18.18 17, 38.64 13, 29.55 17, 38.64 14, 31.82 14, 31.82 11, 25
+ tiv DEP 27, 61.36 27, 61.36 36, 81.82 27, 61.36 31, 70.45 27, 61.36 30, 68.18 30, 68.18 33, 75
CAI2 − tiv REP 19, 43.18 17, 38.64 8, 18.18 17, 38.64 13, 29.55 17, 38.64 14, 31.82 14, 31.82 11, 25
+ tiv DEP 25, 56.82 27, 61.36 36, 81.82 27, 61.36 31, 70.45 27, 61.36 30, 68.18 30, 68.18 33, 75
CR <1 Su. 12, 27.27 18, 40.91 20, 45.45 23, 54.27 21, 47.73 21, 47.73 19, 43.18 20, 45.45 25, 56.82
>1 UnSu. 32, 72.73 26, 59.09 24, 54.55 21, 47.73 23, 54.27 23, 54.27 25, 56.82 24, 54.55 19, 43.18
TDS < 450 Best 13, 29.55 12, 27.27 13, 29.55 14, 31.81 16, 36.36 14, 31.81 15, 34.09 16, 36.36 18, 40.91
450–2000 Mode 30, 68.18 31, 70.45 30, 68.18 29, 65.91 27, 61.36 29, 65.91 28, 63.64 27, 61.36 26, 59.09
> 2000 Hazard 01, 2.27 01, 2.27 01, 2.27 01, 2.27 01, 2.27 01, 2.27 01, 2.27 01, 2.27 00, 00
TH < 75 Soft 1, 2.27 2, 4.55 0, 0 0, 0 0, 0 0, 0 0, 0 0, 0 0, 0
75–150 Mode 3, 6.82 3, 6.82 6, 13.64 3, 6.82 2, 4.55 5, 11.36 3, 6.82 5, 11.36 3, 6.82
150–300 Hard 23, 52.27 24, 54.54 11, 25.00 23, 52.27 21, 47.73 19, 43.18 24, 6.82 25, 56.82 24, 6.82
> 300 V. Hard 17, 38.64 15, 34.09 27, 61.36 18, 40,91 21, 17.73 20, 45.45 17, 38.64 14, 31.82 17, 38.64
r1 <1 Na+–SO42− 42, 95.45 43, 97.73 41, 93.18 41, 93.18 41, 93.18 40, 90.91 43, 97.73 40, 90.91 40 90.91
>1 Na+–HCO3− 02, 4.55 01, 2.27 03, 6.82 03, 6.82 03, 6.82 04, 9.09 01, 2.27 04, 9.09 04, 9.09
r2 <1 DMP 38, 86.36 38, 86.36 40, 90.91 36, 81.82 41, 93.18 39, 88.64 37, 84.09 39, 88.64 34, 77.27
>1 SMP 6, 13.64 6, 13.64 4, 9.09 08, 18.18 3, 6.82 05, 11.36 07, 15.91 05, 11.36 10, 22.73

After “,” the values are given in %

Exc excellent, Go good, Dou doubtful, UnSu unsuitable, Perm permissible, Su suitable, REP reverse exchange process, DER direct exchange
process, Mode moderate, DMP deep meteoric percolation, SMP shallow meteoric percolation

account rainfall effect over KR because no regular patterns in comparison with ­Ca2+ and ­Mg2+ that is why after rainfall
were found for KR. The comparison of pre-monsoon and more no. of wells come under unsuitable class (Table 5).
post-monsoon shows that KR of pre-monsoon shows more a Figure 5a, b shows frequency of some wells under suitable
number of wells come under suitable category after rainfall. and unsuitable class during the study period. KR index is
­ a+ from rock to water is more
Due to rainfall, leaching of N alkali hazard indicator. If KR value is high, then the use of

13
Applied Water Science (2018) 8:233 Page 13 of 24 233

Table 5  Classification of fixed bore wells water during post-monsoon within the study area for irrigation based on %Na, SAR, MH, KR RSC,
TDS (Wilcox 1948; Kelly 1940; Todd 1980; USSL 1954)
Index Range Class No. of samples (with %) under different classes per year
2006 2007 2008 2009 2010 2011 2012 2013

SAR < 10 Exc. 10, 22.73 14, 31.82 12, 27.27 15, 34.09 09, 20.45 08, 18.18 19, 43.18 20, 45.45
10–18 Go. 17, 22.73 17, 38.64 12, 27.27 15, 34.09 15, 34.09 15, 34.09 13, 29.55 08, 18.18
18–26 Dou. 08, 18.18 04, 9.09 10, 22.73 10, 22.73 10, 22.73 11, 25.00 06, 13.64 07, 15.91
> 26 UnSu. 09, 20.45 09, 20.45 10, 22.73 04, 9.09 10, 22.73 10, 22.73 06, 13.64 09, 20.45
RSC/RA < 1.25 Go. 18, 40.91 10, 22.73 10, 22.73 14, 31.82 11, 25 20, 45.45 18, 40.91 26, 59.09
1.25–2.5 Dou. 14, 31.82 18, 40.91 18, 40.91 20, 45.45 11, 25 18, 40.91 17, 38.64 14, 31.82
> 2.5 UnSu. 09, 20.45 16, 36.36 16, 36.36 10, 22.73 22, 50 06, 13.64 09, 20.45 04, 9.09
%Na > 20 Exce. 01, 2.27 00,0 0 00, 00 00,00 00, 00 00, 00 00, 00 02, 4.55
20–40 Go. 07, 15.91 09, 20.45 07, 15.91 09, 20.45 04, 9.09 05, 11.36 12, 27.27 12, 27.27
40–60 Perm. 16, 36.64 25, 56.82 25, 56.82 23, 52.27 21, 47.73 20, 45.45 19, 43.18 15, 34.09
60–80 Dou. 17, 38.64 10, 22.73 9, 20.45 11, 25.00 16, 36.36 16, 36.36 09, 20.45 10, 22.73
> 80 UnSu. 3, 6.82 00, 00 03, 6.82 01, 2.27 03, 6.82 03, 6.82 04, 9.09 05, 11.36
KR/Kl <1 Su. 18, 40.91 28, 63.64 22, 50 18, 40.91 16, 36.36 16, 36.36 28, 63.64 25, 56.82
>1 UnSu. 26, 59.09 16, 36.36 22, 50 26, 59.09 28, 63.64 28, 63.64 16, 36.36 19, 43.18
PI > 75% Su. 05, 11.36 05, 11.36 05, 11.36 07, 15.91 05, 11.36 07, 15.91 03, 6.82 09, 20.45
25–75% Go. 39, 88.64 39, 88.64 39, 88.64 37, 84.09 38, 86.36 33, 75 40, 90.91 33, 75
< 25% UnSu. 00, 00 00, 00 00, 00 00, 00 01, 2.27 00, 00 01, 2.27 02, 4.55
MAR/MH < 50 Su. 06, 13.64 40, 90.91 06, 13.64 25, 56.82 34, 77.27 33, 75 33, 75 30, 68.18
> 50 UnSu. 38, 86.36 4, 9.09 38, 86.36 19, 43.18 10, 22.73 11, 25 11, 25 14, 31.82
PS <3 Su. 17, 38.64 13, 29.55 11, 25 10, 22.73 14, 31.82 14, 31.82 10, 22.73 34, 77.27
>3 UnSu. 27, 61.36 31, 70.45 33, 75 34, 77.27 30, 68.18 30, 68.18 34, 77.27 10, 22.73
CAI1 − tiv REP 10, 22.73 14, 31.82 15, 34.09 11, 25 22, 50 3, 6.82 17, 38.64 8, 18.18
+ tiv DEP 34, 77.27 30, 68.18 29, 65.91 33, 75 22, 50 41, 93.18 27, 61.36 36, 81.82
CAI2 − tiv REP 10, 22.73 14, 31.82 15, 34.09 11, 25 22, 50 3, 6.82 17, 38.64 8, 18.18
+ tiv DEP 34, 77.27 30, 68.18 29, 65.91 33, 75 22, 50 41, 93.18 27, 61.36 36, 81.82
CR <1 Su. 16, 36.36 29, 65.91 25, 56.82 23, 52.27 24, 54.55 8, 18.18 27, 61.36 18, 40.91
>1 UnSu. 28, 63.64 15, 43.09 19, 43.18 21, 47.73 20, 45.45 36, 81.82 17, 38.64 26, 59.09
TDS < 450 Best 12, 27.27 11, 25 14, 31.82 14, 31.82 15, 34.09 9, 20.45 17, 38.64 18, 40.91
450–2000 Mode 30, 68.18 31, 70.45 28, 63.64 30, 68.18 27, 61.36 33, 75 26, 59.09 25, 56.82
> 2000 Hazard 02, 4.55 02, 4.55 02, 4.55 00, 00 02, 4.55 02, 4.55 01, 2.27 01, 2.27
TH < 75 Soft 0, 0 0, 0 0, 0 0, 0 0, 0 0,0 0, 0 1, 2.27
75–150 Mode 6, 13.64 3, 6.82 4, 9.09 5, 11.36 4, 9.09 2, 4.55 2, 4.55 8, 18.18
150–300 Hard 17, 38.64 24, 54.55 20, 45.45 23, 52.27 23, 52.27 23, 52.27 25, 56.82 23, 52.27
> 300 V. Hard 21, 47.73 17, 38.64 20, 45.45 16, 36.36 17, 38.64 19, 43.18 17, 38.64 12, 27.27
r1 <1 Na+–SO42− 42, 95.45 38, 86.36 40, 90.91 41, 93.18 41, 93.18 44, 100 42, 95.45 39, 88.64
>1 Na+–HCO3− 02, 4.55 06, 13.64 04, 9.09 03, 6.82 03, 6.82 00, 00 02, 4.55 05, 11.36
r2 <1 DMP 40, 90.91 38, 86.36 36, 81.82 41, 93.18 39, 88.64 40, 90.91 37, 84.09 39) 88.64
>1 SMP 04, 9.09 06, 13.64 08, 18.18 03, 6.82 05, 11.36 04, 9.09 07, 15.91 05, 11.36

After “,” the values are given in %

Exc excellent, Go good, Dou doubtful, UnSu unsuitable, Perm permissible, Su suitable, REP reverse exchange process, DER direct exchange
process, Mode moderate, V very, DMP deep meteoric percolation, SMP shallow meteoric percolation

pure gypsum is recommended to reduce the effect of ­Na+ range from 12.10 (unsuitable, PI < 25%, water movement
ion. freedom is less in soil) to 99.42% (suitable, PI > 75%, water
From Tables 2 and 3, average value of PI during the study movement freedom is high in soil) and 16.95 (unsuitable,
period was 59.56% and 58.75% for pre- and post-monsoon, PI < 25%) to 99.19% (suitable, PI > 75%), respectively,
respectively, which was under good class (25–75%) with in pre- and post-monsoon. The pre-monsoon PI of 2007

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Fig. 3  a Nine-year (2005–2013) temporal representation RSC value during pre-monsoon. b Nine-year (2006–2013) temporal representation
RSC value during post-monsoon

Fig. 4  a Nine-year (2005–2013) temporal representation of %Na value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of %Na value during post-monsoon

(Table 2) shows that the average value of PI was lowest, 5 show the effect of rainfall over PI. It suggests that the
51.18% (suitable); similarly, post-monsoon PI of year no. of wells falls in unsuitable class during pre-monsoon
2007 (57.29%) and 2009 (57.08%) was also reported low (before rainfall) converted into suitable class after rainfall
(Table 3). From Tables 2 and 3, the effect of rainfall over PI (post-monsoon). From Tables 4 and 5, it is the clear that the
is not showing much significance. However, Tables 4 and effect of rainfall over PI was more during the study period

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Applied Water Science (2018) 8:233 Page 15 of 24 233

because after post-monsoon most no. of wells found under (Singh et al. 2012). Figure 7a shows magnitude of MH
the suitable and good category. Figure 6a, b is capable of for well nos. 2, 7, 12, 14, 43, 43 which were high dur-
representing a category of particular no. of wells at the par- ing post-monsoon, but from Fig. 7b, after rainfall mag-
ticular time of year like well nos. 12, 25 and 40 during pre- nitude of MH for same wells was reduced but not below
monsoon which come under the unsuitable category more the < 50% MH range. Overall, according to MH index 52%
than two times, but after post-monsoon these wells come wells fall in suitable category of water for irrigation during
under good class due to rainfall effect only. The soil perme- pre-monsoon, but due to rainfall or M ­ g2+ leaching process
ability is affected by the extensive use of irrigation water as the no. of well reduces from suitable category during post-
it is influenced by N ­ CO3− contents of
­ a+, ­Ca2+, ­Mg2+ and H monsoon. The year 2006 of post-monsoon elaborates that
the water (Gautam et al. 2015). majority of wells were not unsuitable for irrigation.
MH/MAR is ­Mg2+ and C ­ a2+ based index, and it also If MH < 50, then it is considered as safe, and if it is
represents in percent and contains only two classes greater than > 50, then it is unsafe for irrigation use. In the
MH < 50% and MH > 50%, suitable and unsuitable, respec- analyzed groundwater samples, 61.36% of the sample lies
tively. On the basis of Tables 2 and 3, average values in the unsafe (MH value > 50) and remaining 38.63% in the
of MH during the study period were 42.01 and 43.30% safe region in the pre-monsoon season. In post-monsoon sea-
for pre- and post-monsoon, respectively, with range son, 68.18% are unsafe and remaining 31.81% suitable for
4.46–92.98% (pre-monsoon) and 0.85–86.44% (post- irrigation uses. According to MH computation, majority of
monsoon). Long-term (nine-year) average of MH showing groundwater samples are not suitable for irrigation purpose.
majority of wells falls under suitable category of irrigation PS index (Doneen 1964) is ­Cl−, and ­SO42− dominant
water. The average MH shows unsuitable limit for 2005 index. From Tables 2 and 3 (in context of PS), the long-term
(pre-monsoon) and 2006 (post-monsoon) (Tables 2, 3). averages 2.52 (< 3) and 2.46 (< 3) of PS were found under
Table 4 shows that more than 52 percent (even 95.45% suitable category during pre- and post-monsoon with much
during 2011) wells in each year during pre-monsoon differences in minimum and maximum (12.17 and 13.26,
were in suitable (MH < 50%) category, while this pattern pre- and post-monsoon, respectively). Each year’s during
was not shown during post-monsoon, and each year no. pre- and post-monsoon, average PS value was under suit-
of wells (which come under < 50% category during pre- able class (PS < 3). But each year (in both the monsoon)
monsoon) fluctuate but never come equal or more than to maximum value of PS was also found which was beyond of
no. of wells during pre-monsoon. This fluctuation during suitable limit that means few no. of wells must have come
post-monsoon clearly indicates some degree of leaching under > 3 limit of PS. It is clear from analysis of Tables 4
process of M ­ g2+ (from rock to groundwater) after rainfall and 5, a clear signature of increasing and decreasing pattern

Fig. 5  a Nine-year (2005–2013) temporal representation KR/KI value during pre-monsoon. b Nine-year (2006–2013) temporal representation
KR/KI value during post-monsoon

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233 Page 16 of 24 Applied Water Science (2018) 8:233

Fig. 6  a Nine-year (2005–2013) temporal representation of PI value during pre-monsoon. b Nine-year (2006–2013) temporal representation of
PI value during post-monsoon

is observed in no. of wells during per- and post-monsoon, irrigation. Overall on the basis of PS analysis, it was found
respectively. This happens due to the effect of rainfall. that most no. of wells (more than 63.64%) in the study area
Figure 8a, b shows characteristic of PS for particular no. were suitable for irrigation during pre-monsoon, but during
of wells during the study time period. Well nos. 6, 9, 11, post-monsoon, more than 61.36% wells were found unsuit-
14, 21, 27, 28, 30, 34, 36 and 43 have a high value of PS, able for irrigation (except the year 2013).
but after rainfall well nos. 6, 11, 14, 27, 28, 34, 36 and 43 The CAI1 and CAI2 indexes indicate the direction of
remain constant with the unsuitable condition of water for reaction (DRP or ERP) between groundwater and aquifer.

Fig. 7  a Nine-year (2005–2013) temporal representation of MH value during pre-monsoon. b Nine-year (2006–2013) temporal representation of
MH value during pre-monsoon

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Applied Water Science (2018) 8:233 Page 17 of 24 233

From Tables 2 and 3 (in references of CAI1 and CAI2), The fluctuations in TDS are shown in Fig. 12a, b while its
according to long-term (nine-year) average value during pre- statistics is given in Tables 2, 3. According to Tables 2 and
and post-monsoon CAI1 (0.02 and 0.04) and CAI2 (0.19 3, average value of TDS was found 749.94 and 729.14 mg/l
and 0.27), it was found that most of the year DRP happens for pre- and post-monsoon, respectively. While during both
between groundwater and aquifer in the study area over the seasons average values fall under the moderate category
study period. From Tables 4 and 5, it is clear that during the (450–2000 mg/l). Tables 4 and 5 give the information about
study period most no. of wells (more than 61.36 percent and TDS fluctuation binwise. From Table 4, maximum no. of
max. 81.82%) come under DRP condition except the year wells fall under best (TDS < 450 mg/l) and moderate cat-
2010 during post-monsoon, 50% show DRP, and remaining egory, while each year during pre-monsoon one well comes
50% show ERP. Almost equal no. of wells were same for under hazard category (> 2000 mg/l, Table 4). Similarly,
CAI1 and CAI2 during pre- and post-monsoon; therefore, during post-monsoon most no. of wells distributed under
CAI1 and CAI2 indexes show supporting nature to each best and the moderate bin (Table 4), while each year one
other. Figures 9a, b and 10a, b reveal the supporting nature, or two wells come under hazard category. Tables 4 and 5
magnitudes may be not same, but naturewise [negative (–, clearly reveal that during pre- and post-monsoon most no.
ERP) and positive (+, DRP)], they are same. of wells come under moderate class only. From Fig. 12a,
CR is ­Cl− dominant index similar to PS, CAI1 and CAI2. b, it is clear that particular well no. 27 comes under haz-
Hence, it suggests the same nature of water. Well nos. 6, 12, ard category during both seasons and almost same time of
13, 14, 15, 24, 27, 36 and 44 show almost same increasing year (2006, 2007, 2008, 2010, 2011 and 2013), while well
nature of these indexes (Figs. 8a, d, 9a, b, 10a, b, 11a, b), but nos. 24, 34 and 36 fall under hazard category during post-
magnitude is different). CR can be used as an economical monsoon which may be increasing salt concentration after
index in point of irrigation supply system because on the rainfall due to weathering of salt from rocks. On the basis
basis of CR it can be decided that irrigation water could be of TDS analysis, we found all wells water was suitable for
transported by PVC plastic (low cost) pipes or metallic pipes irrigation (except well no. 27) during pre-monsoon, but
(cost effective). Knowledge of CR index is crucial when irri- after rainfall some salt concentration increases which influ-
gation drainage is long. When irrigation water is low in open ences TDS value of some wells; therefore, some treatment
drainage system, there is a chance of contamination. The is required in particular wells water before using water for
supply of irrigation water through the pipes is recommended irrigation.
(may be metallic or PVC plastic pipes) on the basis of CR Water hardness is represented by TH, and it is ­Ca2+ and
values of irrigation water. ­Mg2+ dominate index. From Tables 2 and 3 (in context of

Fig. 8  a Nine-year (2005–2013) temporal representation of PS value during pre-monsoon. b Nine-year (2006–2013) temporal representation of
PS value during post-monsoon

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233 Page 18 of 24 Applied Water Science (2018) 8:233

Fig. 9  a Nine-year (2005–2013) temporal representation of CIA1 value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of CAI1 value during post-monsoon

Fig. 10  a Nine-year (2005–2013) temporal representation of CAI2 value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of CAI2 value during post-monsoon

TH), we found that average value of TH for study time period (Table 2) and 69.82–1646.40 mg/l (Table 3) during pre- and
was 385.21 and 356.02 mg/l during pre- and post-monsoon, post-monsoon, respectively, and has revealed that maximum
average value of TH shows that most no. wells in the study TH limit of 1495.60 (pre-monsoon) and 1646.40 (post-mon-
area were very hard (TH > 300 mg/l) condition for long soon) mg/l were so high from TH > 300 mg/l (hazard condi-
time period. However, TH ranged from 51 to 1495.60 mg/l tion). However, Tables 4 and 5 present the number of wells

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Applied Water Science (2018) 8:233 Page 19 of 24 233

Fig. 11  a Nine-year (2005–2013) temporal representation of CR value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of CR value during post-monsoon

and their class during the study period. Table 4 shows that (TH > 300 mg/l). Few wells fall in moderate categories, and
during the study period (pre-monsoon) the study area has in soft categories, no well falls. Similarly, during post-mon-
faced a shortage of soft and moderate classes of water for soon, most no. of wells come under two classes, hard and
irrigation. On the basis of Table 4, no. of wells are classi- very hard (Table 5). Figure 13a, b, shows characteristic of
fied into two classes as hard (150–300 mg/l) and very hard each well during pre- and post-monsoon. Figure 13a reveals

Fig. 12  a Nine-year (2005–2013) temporal representation of TDS value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of TDS value during post-monsoon

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233 Page 20 of 24 Applied Water Science (2018) 8:233

Fig. 13  a Nine-year (2005–2013) temporal representation of TH value during pre-monsoon. b Nine-year (2006–2013) temporal representation
of TH value during post-monsoon

Fig. 14  a Nine-year (2005–2013) temporal representation of r1 value during pre-monsoon. b Nine-year (2006–2013) temporal representation of
r1 value during post-monsoon

that the well nos. 6, 14, 27 30, 36 and 43 show abnormal Finally, industrial fixed wells are discriminated on the
jump from 300 mg/l with respect to other wells during pre- basis of Soltan (1998) index r1. Index r1 suggests water
monsoon. Similarly, Fig. 13b shows the same thing for well type ­(Na+–HCO3− and N ­ a+–SO42−). From Tables 2 and
nos. 6, 14, 27, 36 and 43 during post-monsoon. Overall rain- 3 (r1), during study time period average water type was
fall not shows any effect over TH. ­Na+–SO42− because the average value of r1 belongs to less

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Applied Water Science (2018) 8:233 Page 21 of 24 233

Fig. 15  a Nine-year (2005–2013) temporal representation r2 value during pre-monsoon. b Nine-year (2006–2013) temporal representation r2
value during post-monsoon

Fig. 17  Wilcox diagram shows the water suitability for irrigation use

Fig. 16  Gibbs diagram shows that in both the pre-monsoon and post- than 1 (− 1.12 and − 1.46, pre- and post-monsoon, respec-
monsoon seasons water–rock/soil interaction is responsible for the tively). In yearwise average, water type was also found as
chemical composition of the groundwater in the study area
­Na+–SO42− (in both sessions); however, few r1 value was
reported greater than 1 (because maximum value of r1 during

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233 Page 22 of 24 Applied Water Science (2018) 8:233

which can be divided into five divisions (excellent to good,

good to permissible, permissible to doubtful, doubtful to
unsuitable and unsuitable). As per the Bureau of Indian
Standard (BIS), maximum sodium of 60% is recommended
for irrigation water (Gautam et al. 2015). Doneen (1964)
classified irrigation water based on the permeability index
(Fig. 18). The soil permeability is affected by the extensive
use of irrigation water as it is influenced by ­Na+, ­Ca2+, ­Mg2+
and ­HCO3− content of the water (Gautam et al. 2015).

Conclusion

The study area is under stress due to rock element reac-

tions with groundwater (and influences geochemistry
of groundwater), and some effects of land-use change
(especially where the aquifer is unconfined) are due to
urbanization, sea (saline) water interference (study area
near of sea) and aquifer refreshing, and over-exploitation.
The hydro-geochemical index-based study reveals that
the groundwater is hard to very hard. The groundwater of
the study area is more influenced by rock–water interac-
Fig. 18  Doneen (1964) classified irrigation water based on the per- tion, sea water, dominance and evaporation (due to high
meability index temperature) dominance field. The r1 and r2 indexes show
that groundwater is ­Na+–SO42− and deep meteoric perco-
lation (DMP) type. Based on the indexes [SAR, SSP (or
pre- and post-monsoon was 14.25 and 8.80, respectively), %Na), RSC (or RA), KR, PI, CAI, PS, MH, TDS and TH]
and water type is N ­ a+–HCO3−. Figure 14a, b shows that groundwater majority of the wells is falling under moder-
water type did not change. Only magnitude of r1 changed ate to the unsuitable category of water for irrigation pur-
during pre- and post-monsoon; therefore, rainfall effect came poses. Thus, the use of groundwater for irrigation (with-
over the magnitude of r1 but not the type of water. out treatment) in the study area will damage crops and
Soltan (1998) also suggested another index (r2) for clas- reduces production. However, more saline or salt-tolerable
sification of water system based on the yield of ­K+, ­Na+, crops could be grown with a good irrigation system to stop
­Cl− and ­SO42− ions in water. On the basis of r2, no. of well soil salinization [also use lime/gypsum treatment to bout
water was categorized as DMP (r2 < 1) and SMP (r2 > 1); permeability of the soils (base-exchange process)]. The
according to average value of r2 (− 0.59 and − 1.17, pre- and outcomes of this study show that index-based irrigation
post-monsoon, respectively) during the study period, the no. water quality can be useful in decision-making processes
of well’s water was categorized as DMP (Tables 2, 3, in such as identifying no. of suitable wells for irrigation sys-
context of r2). Whoever some wells (max 18%) show SMP tems and prevent the damage of crops and production. It is
nature also (Tables 4, 5). From Fig. 15a, b, only the magni- also suggested that regular monitoring of irrigation water
tude of r2 was fluctuated not nature of water (DMP/SMP). quality and pollution is essential to help farmers and the
The Gibbs diagram (Gibbs 1970; Fig. 16) is a com- concerned department for making irrigation policy to the
monly used method to establish the relationship of water state government. This would be helpful in developing the
composition and aquifer lithological characteristics. Three suitable management plan and sustainable utilization of
distinct fields of the evaporation–crystallization, weather- groundwater in irrigation. To recover the good quality of
ing/rock–water interaction and precipitation dominance are groundwater, the Government, semi-government and non-
shown in the boomerang-shaped Gibbs diagram. The dis- government organization (NGO) should make available
tribution of the sampling points elucidates that the major and support to design the artificial recharge structures and
ion chemistry of the groundwater seems to be controlled by rainwater harvesting structures in the study area.
chemical weathering of rock forming minerals and anthro-
pogenic activities (Singh et al. 2012; Gautam et al. 2015). Acknowledgements The authors are grateful to K. Santhanam [Former
Joint Director (Geology), Water Resources Division, P.W.D, Chennai],
The Wilcox (1948) diagram elucidates sodium percentage
for providing water quality datasets and thank Dr. Anil Kumar Mishra
and electrical conductivity to classify groundwater (Fig. 17),

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Applied Water Science (2018) 8:233 Page 23 of 24 233

(Principal Scientist, Water Technology Centre, IARI, New Delhi) for PK, Pandey PC, Kumar P, Raghubanshi ASHD (eds) Geospa-
his critical input and suggestions on the manuscript. SKG expresses tial technology for water resource applications. CRC Press, Boca
sincere thanks to the Science and Engineering Research Board (SERB), Raton, pp 144–169
New Delhi, India, for providing the financial support DST N-PDF (File Gautam SK, Tziritis E, Singh SK, Tripathi JK, Singh AK (2018) Envi-
No: PDF/2017/002820). ronmental monitoring of water resources with the use of PoS
index: a case study from Subarnarekha River basin, India. Envi-
Open Access This article is distributed under the terms of the Crea- ron Earth Sci 77:70. https​://doi.org/10.1007/s1266​5-018-7245-5
tive Commons Attribution 4.0 International License (http://creat​iveco​ Gibbs RJ (1970) Mechanism controlling world water chemistry. Sci-
mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribu- ence 170:795–840
tion, and reproduction in any medium, provided you give appropriate Gowd SS (2005) Assessment of groundwater quality for drinking
credit to the original author(s) and the source, provide a link to the and irrigation purposes: a case study of Peddavanka water-
Creative Commons license, and indicate if changes were made. shed, Anantapur District, Andhra Pradesh, India. Environ Geol
48(6):702–712
Gupta P, Vishwakarma M, Rawtani PM (2009) Assessment of water
quality parameters of Kerwa Dam for drinking suitability. Int J
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