Assessing the Suitability of Surface Water and Groundwater for Irrigation Based on Hydro-chemical Analysis: A Study of the Mayurakshi River Basin,India

Abstract

The present work aims to investigate the hydro-chemical properties of the surface and groundwater of the Mayurakshi River Basin, India for assessing their irrigation suitability with respect to irrigation hazards. The study involves 72 water samples classified as 48 surface water samples (pre-monsoon: 24, post-monsoon: 24) and 24 groundwater samples (pre-monsoon: 12, post-monsoon: 12). The study of ionic chemistry reveals that the cations of both the surface water and groundwater are in the order of Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ while the anions are in the order of Cl⁻ > SO₄² > PO₄³⁻ > NO₃⁻. Moreover, as per the U.S. Salinity Laboratory Staff classification of irrigation, ~ 41.67% of the groundwater samples belong to the C3S1 category indicating high salinity and low sodicity hazard while ~ 33% of samples of surface water fall into the field of C1S1 category representing low salinity and low sodicity hazard. Besides, regarding the specific irrigation hazard, percent sodium and soluble sodium percentage have also portrayed the groundwater vulnerability to sodium while the surface water is observed free from this kind of hazard. Similar findings have also been retained for magnesium hazard and potential salinity hazard. Moreover, regarding the seasonality of the hazards, the post-monsoon season has depicted a higher level of irrigation hazards compared to the pre-monsoon season. The study finds that the general evolution of groundwater hydrochemistry and the suitability of water for irrigation is principally governed by carbonate weathering. Moreover, anthropogenic activities such as sand mining, stone crushing, and the development of brick kiln industries are found to play an important role in irrigation suitability.

Keywords

Irrigation hazards hydrochemistry pre and post-monsoon variation anthropogenic interventions hot springs

Introduction

The study of river and groundwater quality from the perspective of irrigation suitability has become highly significant for achieving sustainable agricultural development through effective policy framing because the soil health and crop production are strongly influenced by the quality of irrigation water delivered (Sarkar & Islam, 2019). Therefore, for achieving the higher agricultural return with minimum environmental effect, researchers across the world are continuously addressing the issues related to the irrigation water quality of the surface and groundwater (Ayers & Westcot, 1985; Yidana et al., 2010). In this regard, many guidelines for irrigation water quality have been developed. For example, the U.S. Salinity Laboratory Staff (USSL) has established a guideline providing a classification of irrigation water quality for agricultural use based on electrical conductivity (EC) and total dissolved solids (TDS) (Sarkar, Islam, & Majumder, 2021). Food and Agriculture Organization (FAO) has also recommended a guideline based on water salinity (Jeong et al., 2016). The World Health Organization (WHO) has set a guideline for the reuse of wastewater for irrigation. Besides, numerous mythological approaches such as the irrigation water quality index (IWQI) and FUZZY-AHP have also been found for assessing the irrigation water quality (Simsek & Gunduz, 2007). In recent decades, addressing the ionic hazards in irrigation has become highly significant and popularized. The methods which can efficiently measure the ionic hazards are sodium adsorption ratio (SAR), percent Na (%Na), soluble sodium percentage (SSP), residual sodium carbonate (RSC), magnesium hazard (MH), permeability index (PI), potential salinity (PS) (Ravikumar et al., 2011; Wilcox, 1955).

In India, agriculture is the dominant sector of the economy, and about 63.25 million ha net area comes under irrigation of which 62% is from tube wells and other wells, about 26% from the canal and about 3% from tanks, and about 9% from other sources (Directorate of Economics and Statistics, 2018). Therefore, several studies on the surface and groundwater quality concerning irrigation have been carried out in different parts of India (eg, Khan et al., 2011; Majumdar & Gupta, 2000; Ravikumar et al., 2011; Shi et al., 2018; Sunitha et al., 2005; Wang et al., 2013) and others (Dişli, 2017; Fulazzaky, 2010; Rasouli et al., 2012; Yidana et al., 2010). Mayurakshi River Basin (MRB) offers huge fertile agricultural land where the cultivation of kharif (summer season crop) and rabi crops (winter season crop) require intensive irrigation water from the river and groundwater. There are seven major irrigation canals made for accessing the river water for agriculture ((i) Mayurakshi-Dwarka Main Canal, (ii) Dwarka—Brahmani Main Canal, (iii) Brahmani North Main Canal, (iv) Mayurakshi-Bakreshwar Main Canal, (v) Bakreshwar Kopai Main Canal, (vi) Kopai South Main Canal, and (vii) Bakreshwar Branch Canal). Three districts of West Bengal, that is, Birbhum (2209 km²), Murshidabad (806 km²), and Bardhhaman (897 km²) have been benefited from the seven major irrigation canals (Kandi Final Report, 2012). Moreover, agriculture in this region is heavily dependent on groundwater supply. Therefore, the quality of the river and groundwater has become a significant factor for achieving higher agricultural returns. The surface and groundwater of MRB have been studied from different perspectives For example, Sikdar et al. (2019) detected the fluoride contamination areas in the Mayurakshi river basin. Das et al. (1996) addressed the arsenic problem in groundwater in the lower Mayurakshi river basin. Pal et al. (2021) identified the water deficit areas in this river basin. However, the suitability of surface and groundwater for irrigation in the MRB has not been evaluated in the previous works. Therefore, the assessment of the river and groundwater for irrigation suitability in the MRB needs special attention for the development of agriculture and hence the following objectives have been constructed.

i. To trace out the spatial and temporal variation of hydrochemistry of river and groundwater,

ii. To find out the suitability of river and groundwater for irrigation, and

iii. To assess the factors and mechanism governing irrigation suitability of water.

Database and Methodology

Study area

The Mayurakshi River Basin comprises three main rivers—the Mayurakshi and its two tributaries-the Dwarka to the north and the Kuea to the south of the Mayurakshi. The MRB extending from the 23°37′43′′N to 24°37′36′′N latitude and from 86°50′16′′E to 88°15′52′′E longitude covers an area of 9596 km² (4260 km² in Jharkhand and 5336 km² in West Bengal) (Figure 1). The river Mayurakshi originating from Trikut Hill near Deoghar, Jharkhand (24°29′53′′N and 86°50′12′′E), flows about 250 km to make a confluence with the Bhagirathi River at Kalyanpur (Islam & Deb Barman, 2020). The upper portion of the basin is composed of basaltic trap intermixed with coarse lateritic soil with sandy and sandy loam texture (Islam et al., 2020). These soils are weakly aggregated with low water holding capacity. However, the middle portion is a part of the Rarh terrain composed of a typical transported lateritic alluvium. The soil types of the lower MRB are clay, clay loam, and loam with high water retention capacity (Chakrabarti, 1985). The deposition of Dharwanian sediments is observed at the upper part of MRB followed by Hercynian orogeny that occurred during the Cambrian to Silurian period. The vast portion of the upper part of the basin is composed of granitic gneiss while the middle part of the MRB consists of lateritic soil and hard clay. However, the lower part is characterized by the deposition of recent alluvium (Chakrabarti, 1985). In the era of Anthropocene, the hydro-geomorphological characteristics of the rivers in the MRB are drastically altered by five dams and barrages (Brahmani barrage on Brahmani River, Deucha barrage on Dwarka River, Massanjore dam and Tilpara barrage on Mayurakshi River, and Bakreshwar weir on Kuea River). Besides, the surface water quality of the basin is influenced by major anthropogenic interventions such as the effluents from a brick kiln, flying ashes from stone crushing centers, and chemicals from agricultural drainage, etc. while the groundwater quality is influenced by major geological formations.

Figure 1.

Location of the study area.

Sample design and data collection

The present study follows a systematic sampling design framed to portray the nature of surface water (river) and groundwater suitability for irrigation in lower MRB. Thus, two available monitoring stations for surface water (Suri on Mayurakshi River and Sadhak Bam Deb Ghat on Dwarka River) and four available monitoring stations (Bolpur, Bakreshwar, Nalhati, and Suri) for groundwater have been taken into consideration. The water quality data of the selected monitoring stations have been accessed from West Bengal Pollution Control Board (WBPCB, 2020). All the water quality data considered for the present study are sampled from 2017 to 2019 using a judgmental sampling technique to better reflect the hydrochemical parameters of water samples. The water samples are tested by the WBPCB. The pH of water samples is tested by pH meter. Moreover, the concentrations of cations of water samples have been measured following the flame atomic absorption spectrometer (FAAS) method while the spectrophotometric method has been followed for measuring the concentration of anions. A total of 72 water samples have been considered of which 48 are surface water (pre-monsoon: 24, post-monsoon: 24) and 24 are groundwater samples (pre-monsoon: 12, post-monsoon: 12).

Samples are collected in both the pre-monsoon and post-monsoon seasons for detecting seasonal variation of irrigation water quality. For groundwater, April is considered as pre-monsoon and October as post-monsoon while regarding surface water February, March, April, and May are considered as pre-monsoon and October, November, December, and January as post-monsoon depending upon the availability of data (Table 1). Besides, Sentinel 2B tiles no. T45QWG and T45QXG (31 March 2019), Google earth images of 2019 (9 January, 10 January, 28 March, 19 November 2019) and 30 m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) are also used in this study.

Table 1.

Dates of Surface and Groundwater Samples.

Surface water
Suri		Suri (contd.)		Sadhak Bam Deb Ghat		Sadhak Bam Deb Ghat (contd.)
Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon
08 May 2019	10 Jan 2019	20 Mar 2018	01 Nov 2018	08 May 2019	10 Jan 2019	20 Mar 2018	01 Nov 2018
04 Apr 2019	17 Oct 2019	13 Feb 2018	11 Dec 2018	04 Apr 2019	17 Oct 2019	13 Feb 2018	11 Dec 2018
19 Mar 2019	22 Nov 2019	04 May 2017	17 Jan 2017	19 Mar 2019	22 Nov 2019	04 May 2017	17 Jan 2017
07 Feb 2019	20 Dec 2019	13 Apr 2017	26 Oct 2017	07 Feb 2019	20 Dec 2019	13 Apr 2017	26 Oct 2017
18 May 2018	17 Jan 2018	22 Mar 2017	23 Nov 2017	18 May 2018	17 Jan 2018	22 Mar 2017	23 Nov 2017
10 Apr 2018	04 Oct 2018	14 Feb 2017	13 Dec 2017	10 Apr 2018	04 Oct 2018	14 Feb 2017	13 Dec 2017
Groundwater
Bolpur		Nalhati		Suri		Bakreshwar
Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon
04 Apr 2019	18 Oct 2018	04 Apr 2019	17 Oct 2019	04 Apr 2019	17 Oct 2019	04 Apr 2019	17 Oct 2019
11 Apr 2018	05 Oct 2018	10 Apr 2018	04 Oct 2018	11 Apr 2018	05 Oct 2018	10 Apr 2018	04 Oct 2018
12 Apr 2017	26 Oct 2017	13 Apr 2017	25 Oct 2017	12 Apr 2017	26 Oct 2017	12 Apr 2017	26 Oct 2017

Source. WBPCB (2020).

Methods of data analysis

Measuring the irrigation hazards

The present study intends to assess the suitability of water for irrigation based on sodium hazard, salinity hazard, and magnesium hazard. The relative concentration of sodium with respect to other ions in irrigation water is measured for sodium hazard. The higher concentration of Na⁺ when placed in soil pore space brings about sodium hazard leading to lessening the binding capacity of clay particles, swelling clay platelet, and soil dispersion responsible for reduced soil permeability. The indices most commonly used for this hazard are Na%, SAR, and SSP are expressed using equations (1)–(3) (Table 2). Besides the sodium hazard, the magnesium hazard signifies the relative concentration of Mg²⁺ to Ca²⁺ as expressed using equation (4). Though Mg²⁺ is an essential plant nutrient, exceeding concentration alters the soil quality and affects the agricultural returns. Moreover, the salinity hazard is detected through the EC, TDS, Cl⁻, and SO₄²⁻ present in water and USSL classification of water for agricultural use. Besides, the salinity hazard has been measured using the potential salinity (PS) index as mentioned in equation (5).

Table 2.

Indices for measuring the irrigation hazards.

Irrigation hazards	Acronym	Equations	Range	Water class	References
Sodium hazards	% Na	$\frac{(N a^{+} + K^{+}) * 100}{C a^{2 +} + M g^{2 +} + N a^{+} + K^{+}}$ (1)	<20	Excellent	Wilcox (1955)
			20–40	Good
			40–60	Permissible
			60–80	Doubtful
			>80	Unsuitable
	SAR	$\frac{{Na}^{+}}{\sqrt \frac{1}{2} (C a^{2 +} + M g^{2 +})}$ (2)	<10	Excellent	Richards (1954)
			10–18	Good
			18–26	Doubtful
			>26	Unsuitable
	SSP	$\frac{(N a^{+}) \times 100}{C a^{2 +} + M g^{2 +} + N a^{+}}$ (3)	<50	Suitable	Todd (1959) Szabolcs (1964)
	SSP		>50	Unsuitable
Magnesium hazards	MH	$\frac{M g^{2 +} \times 100}{C a^{2 +} + M g^{2 +}}$ (4)	<50	Suitable
Magnesium hazards	MH	$\frac{M g^{2 +} \times 100}{C a^{2 +} + M g^{2 +}}$ (4)	>50	Unsuitable
Salinity hazards	PS	$C l^{-} + \frac{1}{2} {SO}_{4}^{2^{-}}$ (5)	<3	Excellent to Good	Sarkar, Islam, and Majumder (2021)
			3–5	Good to injurious
			>5	Injurious to unsatisfactory

Processing of geo-spatial data

For assessing the role of land use and land cover on the surface and groundwater, sentinel images are processed with the help of ArcGIS 10.4 software. After land use and land cover maps have been prepared, a 2 km buffer zone has been demarcated around every groundwater station to illustrate the possible drivers controlling the hydrochemical characteristics of both the surface and groundwater.

Analysis of variance (ANOVA)

The ANOVA is a test that indicates variations among and between the groups of distribution (Fisher, 1921). It is generally shown with “F statistic” compares the difference between the groups to the difference within the group (Sarkar, Islam, & Chandra Das, 2021). For the present study, a one-way ANOVA has been run to detect the significant difference between the surface and groundwater properties to detect the difference regarding the irrigation hazards. It has been computed using equations (6)–(10).

F = \frac{M S T}{M S E}

(6)

MST = \frac{S S T}{p - 1}

(7)

S S T = \sum^{} n (x - \bar{x})^2

(8)

MSE = \frac{S S E}{N - p}

(9)

S S E = \sum^{} (n - 1) S^2

(10)

Where, Where, F = ANOVA coefficient, MST = Mean sum of squares due to treatment, MSE = mean sum of squares due to error, SST = sum of squares due to treatment, p = total number of populations, n = the total number of samples in a population, SSE = sum of squares due to error, S = standard deviation of the samples, N = total number of observations

Results

General hydrochemistry

EC and TDS

Electrical conductivity is measured to trace out the level of salinity in both drinking and irrigation water. Regarding surface water, the mean values of EC for post and pre-monsoon are recorded as 213.57 µS/cm and 321.56 µS/cm, respectively (Table 3) while for groundwater the value of EC ranges between 1168 µS/cm (Nalhati) to 434 µS/cm (Bolpur) during post-monsoon and 1233 µS/cm (Nalhati ) to 528 µS/cm (Bolpur) during pre-monsoon. The value of kurtosis for both surface water and groundwater is less than 3 and it is leptokurtic. The mean concentration of TDS is found higher for groundwater compared to surface water. Regarding surface water, the mean concentration of TDS during post-monsoon and pre-monsoon is observed as 108 mg/L and 105.05 mg/L, respectively for Suri and 167 mg/L and 271.16 mg/L, respectively for Sadhak Bam Deb Ghat. Besides, regarding groundwater water, the mean concentrations of TDS during post-monsoon are recorded as 521.33 mg/L for Bolpur, 370 mg/L for Bakreshwar, 718 mg/L for Nalhati, and 502.67 mg/L for Suri while during pre-monsoon season TDS is recorded as 434 mg/L for Bolpur, 379 mg/L for Bakreshwar, 769 mg/L for Nalhati, and 582 mg/L for Suri. Moreover, the maximum concentration of TDS is observed for Bamdeb Ghat (340 mg/L) during the pre-monsoon while for groundwater the maximum concentration is recorded for Nalhati (1088 mg/L) during the pre-monsoon in 2019.

Table 3.

Descriptive Statistics of Physicochemical Parameters of Surface Water.

Parameters	Pre-monsoon				Post-monsoon
Parameters	Mean ± SD	Range (Min.–Max)	Kurtosis	Skewness	Mean ± SD	Range Range (Min.–Max)	Kurtosis	Skewness
pH	7.75 ± 0.2	7.45–7.96	−0.75	−0.53	7.64 ± 0.17	7.45–7.88	−1.71	0.13
EC (µS/cm)	321.56 ± 215.17	160.08–639.7	−1.45	1	213.57 ± 78.54	147.83–353	1.39	1.37
Ca²⁺ (mg/L)	26.89 ± 14.24	16.8–53.2	2.32	1.67	26.63 ± 18.39	15.68–63.63	5.4	2.3
Mg²⁺ (mg/L)	12.14 ± 4.83	5.93–19.44	−0.36	0.44	9.38 ± 2.01	6.13–11.64	0.07	−0.72
Na⁺ (mg/L)	27.77 ± 20.45	11.5–65	2.05	1.5	22.29 ± 9.47	10–35.33	−1.03	0.05
K⁺ (mg/L)	5.61 ± 4.31	1.75–13.25	1.51	1.25	4.18 ± 1.67	2.25–6.37	−2.06	0.21
Cl^–(mg/L)	41.71 ± 41.51	6.95–112.31	0.36	1.25	24.31 ± 11.26	13.67–38.22	−2.35	0.4
SO₄^2–(mg/L)	9.68 ± 1.88	7.02–11.98	−1.24	−0.42	14.52 ± 6.20	7.7–23.55	−1.24	0.7
TDS(mg/L)	188.11 ± 117.67	101.5–340	−1.88	0.93	137.5 ± 40.59	94–203.5	0.19	0.71
PO₄^3–(mg/L)	0.06 ± 0.02	0.05–0.09	2.31	1.54	0.05 ± 0.01	0.04–0.07	2.77	1.57
NO₃^–(mg/L)	0.17 ± 0.05	0.11–0.23	−1.85	−0.56	0.17 ± 0.08	0.07–0.27	−1.84	−0.21

Cation chemistry

The major cations of surface and groundwater are Ca²⁺, Mg²⁺, Na⁺, and K⁺. The mean concentrations of Ca²⁺ in surface water for both the pre and post-monsoon seasons are very close to each other, that is, 26.89 mg/L and 26.63 mg/L, respectively (Table 3). The concentration of Ca²⁺ ranges from 34.68 mg/L in pre-monsoon and 36.38 mg/L in post-monsoon in Bamdeb Ghat to 19.09 mg/L in pre-monsoon and 16.89 mg/L in post-monsoon in Suri. The value of kurtosis of Ca²⁺is 5.40 for post-monsoon (indicating platykurtic distribution) and 2.32 for pre-monsoon (indicating leptokurtic distribution). Besides, for groundwater, the mean concentration of Ca²⁺ is 55.245 mg/L ranging from 2.31 mg/L (Bakreshwar) during post-monsoon to 153.84 mg/L (Bolpur) during the same season. The concentration of Na⁺ is higher in the groundwater than that of surface water. Regarding surface water Na⁺ ranges from 65 mg/L (Bamdeb Ghat in pre-monsoon) to 10 mg/L (Suri in post-monsoon).The Na⁺ concentration regarding groundwater is maximum for Bakreshwar which ranges from 140.2 mg/L (pre-monsoon) to 108.26 mg/L (post-monsoon) The excess concentration of Na⁺ increases the soil hardness and the soil becomes impervious which decreases the permeability. The maximum concentration of Mg²⁺ regarding surface water is recorded 19.44 mg/L in Bamdeb Ghat during pre-monsoon and minimum in Suri (5.93 mg/L in pre-monsoon). For the groundwater, it is found minimum in Bakreshwar (0.89 mg/L in post-monsoon to 11.52 mg/L in pre-monsoon) but the mean concentration of groundwater is 21.37 mg/L during post-monsoon and 22.53 mg/L during pre-monsoon. The concentration of K⁺ is less compared to the other cations. The K⁺ concentration is high for surface water than groundwater. The mean concentrations of K⁺ in surface water are 4.18 and 5.61 mg/L during post and pre-monsoon respectively while they are 4.48 mg/L and 2.9 mg/L for groundwater.

Anion chemistry

The major anions of surface and groundwater are Cl⁻, SO₄²⁻, PO₄³⁻, and NO₃⁻ where Cl⁻ is predominantly present for both the surface and groundwater. The concentration of Cl⁻ in surface water ranges from 6.95 mg/L in Suri (pre-monsoon) to 112.31 mg/L in Bamdeb Ghat (pre-monsoon). Regarding groundwater, the maximum concentration of Cl⁻ (274.52 mg/L) is recorded in Nalhati during post-monsoon while the minimum is recorded in Bolpur (24.95 mg/L). During pre-monsoon, the mean concentration of Cl⁻ in groundwater sample is 126.34 mg/L and ranges from 46.79 mg/L (Bolpur) to 246.44 mg/L (Nalhati). Based on the concentration of Cl⁻ the quality of irrigation water is classified into five categories, that is, very good (0–142), good (143–249), usable (250–426), usable with caution (427–710), and harmful (>710) (Amalraj & Pius, 2018). All the samples of surface water during pre and post-monsoon come under the very good category while 67% of groundwater samples belong to very good and 33% to good category during pre-monsoon. Besides, regarding post-monsoon 59%, 33%, and 8% belong to the very good, good, and usable categories respectively. The mean concentration of SO₄²⁻ in the surface water is 14.52 mg/L for post-monsoon and 9.68 mg/L for pre-monsoon. Besides, the SO₄²⁻ of groundwater ranges from 3.73 mg/L (Bolpur in post-monsoon) to 84.86 mg/L (Suri in pre-monsoon) and the mean concentration during pre-monsoon and post-monsoon are 41.04 and 33.22 mg/L, respectively (Table 4). All the samples of both the groundwater and surface water belong to very good conditions for the use of irrigation purposes. The mean concentration of PO₄³⁻ regarding groundwater is 0.07 mg/L for post-monsoon and 0.03 mg/L for pre-monsoon while for surface water it ranges from 0.04 to 0.07 mg/L for post-monsoon and 0.05–0.09 mg/L for pre-monsoon. The nitrate concentration is very less for both the surface (0.07–0.27 mg/L) and groundwater (0.15–1.18 mg/L).

Table 4.

Descriptive Statistics of Physicochemical Parameters of Groundwater.

Parameters	Pre-monsoon				Post-monsoon
Parameters	Mean ± SD	Range (Min.–Max)	Kurtosis	Skewness	Mean ± SD	Range (Min.–Max)	Kurtosis	Skewness
pH	7.63 ± 1.09	6.08–9.36	−1.15	0.08	7.60 ± 0.88	6.52–9	−1.43	0.3
EC (µS/cm)	758.73 ± 210.58	528.3–1233	0.77	0.95	767.33 ± 225.97	434–1168	−0.79	0.25
Ca²⁺ (mg/L)	44.47 ± 22.62	10.4–80	−1.01	−0.15	55.25 ± 53.13	2.31–153.84	−0.22	0.99
Mg²⁺ (mg/L)	22.53 ± 14.21	1.92–51.52	0.24	0.46	21.37 ± 19.84	0.89–55.68	−1.33	0.54
Na⁺ (mg/L)	52.83 ± 33.18	19.3–140.2	3.95	1.8	65.46 ± 38.8	28–140	−0.1	1.14
K⁺ (mg/L)	2.9 ± 2.06	1–7	0.25	1.27	4.48 ± 2.66	2–10	0.5	1.32
Cl⁻(mg/L)	126.34 ± 53.94	46.79–246.44	1.08	0.9	137.79 ± 68.61	24.95–274.52	0.05	0.43
SO₄²⁻(mg/L)	41.04 ± 21.33	17.4–84.86	−0.3	0.76	33.22 ± 15.4	3.73–55.38	0.16	−0.78
TDS(mg/L)	541.17 ± 213.79	264–1088	3.36	1.45	528.17 ± 176.06	290–814	−0.82	0.53
PO₄³⁻(mg/L)	0.03 ± 0.01	0.02–0.04	−1.52	0.07	0.07 ± 0.06	0.02–0.18	−0.27	1.14
NO₃⁻(mg/L)	0.74 ± 0.32	0.15–1.18	−0.81	−0.47	0.56 ± 0.28	0.19–1	−1.32	−0.04

Irrigation hazards

Sodium hazard

A high concentration of sodium in irrigation water harms the plant-soil system. The high concentration of Na⁺ reduces the rate of infiltration and the plant is deprived of a sufficient supply of water. In the case of clay-rich soil, the concentration of Na⁺ is more severe, that is, for Kaolinitic soil the problem is less but for montmorillonite soil it is severe. The sodicity hazard is measured with the help % Na, SAR, SSP.

Percent sodium

The index value of %Na is divided into five categories, that is, excellent (<20), good (20–40), permissible (40–60), doubtful (60–80), and unsuitable (>80) where 25% sample of groundwater during post-monsoon come under unsuitable while 25% and 42% of the samples during pre-monsoon belong to the excellent and good category respectively. During post-monsoon, the groundwater at Bakreshwar is doubtful to unsuitable but during pre-monsoon, it is under the permissible to doubtful category. In the Wilcox diagram about 42% of the pre-monsoon groundwater samples belong to the excellent to the good category, 25% sample to permissible to doubtful category and 33% to good to permissible category (Figure 2a). Besides 42% of the post-monsoon groundwater samples belong to goods to permissible post-monsoon, 25% sample to doubtful to unsuitable category and 25% to excellent to good category (Figure 2b; Table 5). Regarding surface water, all the samples during pre and post-monsoon belong to the excellent to good category (Figure 2c and d).

Figure 2.

Wilcox diagram representing the suitability of water for irrigation: (a) groundwater samples of pre-monsoon season, (b) groundwater samples of post-monsoon season, (c) surface water samples of pre-monsoon season, (d) surface water samples of post-monsoon season and groundwater in pre and post-monsoon periods for irrigation.

Table 5.

Classification of Surface Water and Groundwater Based on Irrigation Index.

Parameters	Range	Water class	Groundwater		Surface water
			Pre-monsoon	Post-monsoon	Pre-monsoon	Post- monsoon
			% of samples
% Na(Wilcox, 1955)	<20	Excellent	25	33.33	0	0
	20–40	Good	41.67	25	83.33	66.67
	40–60	Permissible	8.33	8.33	16.67	33.33
	60–80	Doubtful	8.33	8.33	0	0
	>80	Unsuitable	16.67	25	0	0
SAR (Richards, 1954)	<10	Excellent	100	100	100	100
	10–18	Good	0	0	0	0
	18–26	Doubtful	0	0	0	0
	>26	Unsuitable	0	0	0	0
SSP (Todd, 1959)	<50	Suitable	75	66.67	100	100
SSP (Todd, 1959)	>50	Unsuitable	25	33.33	0	0
MH (Szabolcs, 1964)	<50	Suitable	58.33	58.33	83.33	83.33
MH (Szabolcs, 1964)	>50	Unsuitable	41.66	41.66	16.67	16.67
PS (Sarkar, Islam, and Majumder, 2021)	<3	Excellent to Good	25	25	83.33	100
	3-5	Good to injurious	58.33	33.33	16.67	0
	>5	Injurious to unsatisfactory	16.67	41.67	0	0

Sodium adsorption ratio

To examine the suitability of irrigation water for agriculture USSL proposed a diagram considering EC as salinity hazard and SAR as sodicity hazard. The EC is classified into four categories, that is, C1 (<250) as low, C2 (250–750) as medium, C3 (750–2250) as high and C4 (>2250) as very high. The SAR values are also divided into four categories as S1 (<10) as low, S2 (10–18) as a medium, S3 (18–26) as high, and S4 (>26) as very high. During the pre-monsoon, in the USSL diagram, about 58.33% of groundwater samples belong to the C2S1 category indicating medium salinity and low sodicity hazard which may be used for irrigation if moderately leaching occurs. Besides, 41.67% belong to C3S1 which indicates high salinity but low sodicity while in the post-monsoon 50% of samples belong to C2S1 and another 50% are in the C3S1 category (Figure 3a and b). Regarding surface water, about 67% of samples during pre and post-monsoon falls into the field of the C1S1 category which indicates both low salinity and low sodicity hazard (Figure 3c and d; Table 5).

Figure 3.

USSL classification of surface and groundwater samples for irrigation: (a) groundwater samples of pre-monsoon season, (b) groundwater samples of post-monsoon season, (c) surface water samples of pre-monsoon season, and (d) surface water samples of post-monsoon season.

Soluble sodium percentage

The values of SSP of surface water samples indicate that 100% of the surface water samples are suitable for irrigation use. Besides, 75% of groundwater samples of pre-monsoon season belong to the suitable category while the rest of the samples represent unsuitable for irrigation. Similarly, in the case of the post-monsoon season, 66.67% of the groundwater sample represents suitable irrigation water while the rest of the samples are unsuitable for irrigation (Table 5). In Bakreshwar, the SSP value is >88 during post-monsoon and >75 during pre-monsoon that indicates high sodium concentration with respect to magnesium and calcium concentration. This makes the water unsuitable for agriculture as excessive sodium concentration may lead to decreasing permeability of irrigation water for agriculture and soil.

Salinity hazard

Salinity hazard can be determined with the help of the EC and TDS concentration in the surface and groundwater. The salinity of surface water ranges from low to medium while the groundwater has medium to high salinity (Table 6). Considering EC of the surface water, ~ 66% of the post and pre-monsoon samples exhibit low salinity hazard while the rest of the samples portray medium salinity hazard. Besides, 58% and 41% of the groundwater samples of post and pre-monsoon seasons respectively indicate high salinity hazard while the rest of the samples represent medium salinity hazard. Considering the TDS concentration of groundwater a similar trend has been observed as retained for EC. Moreover, TDS of surface water reveals that 66% of both the post and pre-monsoon samples belong to low salinity hazard while the rest of the samples come to the medium salinity hazard. Furthermore, the maximum EC of surface water is recorded for Bamdeb Ghat during pre-monsoon (639.7 µS/cm) while the minimum is recorded for Suri during post-monsoon (147.8 µS/cm). Besides, the highest saline groundwater is recorded for Nalhati (1233 µS/cm in pre-monsoon). The PS of surface water ranges from 0.27 (Suri in pre-monsoon) to 2.12 (Bamdeb Ghat in pre-monsoon). The maximum value of PS of groundwater is recorded for Nalhati (8.11) during post-monsoon and minimum in Bolpur (0.74) during post-monsoon. Therefore, the PS value reflects that the surface water is less saline than groundwater.

Table 6.

Irrigation Water Quality Classes for Salinity Hazard According to USDA.

Classes for salinity hazard	Degree of irrigation hazard	EC µS/cm	Groundwater		Surface water		TDS mg/L	Groundwater		Surface water		Potential injury and necessary management for use as irrigation water
			Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon		Pre-monsoon	Post-monsoon	Pre-monsoon	Post-monsoon
			(% of samples)	(%of samples)	(% of samples)	(% of samples)		(% of samples)	(% of samples)	(% of samples)	(%of samples)
C1	Low	<250	0	0	66.67	66.67	<150	0	0	66.67	66.67	Minimum salinity hazard; additional management is optional
C2	Medium	251–750	58.33	41.67	33.33	33.33	150–500	41.67	41.67	33.33	33.33	Medium salinity hazard; damage to salt-sensitive crops may occur; additional management is needed.
C3	High	751–2250	41.67	58.33	0	0	500– 1,500	58.33	58.33	0	0	Damage to crops with low tolerance to salinity may occur; quality water will be used for irrigation.
C4	Very high	2,250–5,000	0	0	0	0	>1,500	0	0	0	0	Damage to crops with low tolerance to salinity may occur; management requires salt-tolerant plants, good soil drainage, and excess irrigation for leaching.

Source. Based on Dişli (2017).

Magnesium hazard

An excess amount of Mg²⁺ over Ca²⁺ decreases the quality of irrigation water. Irrigation with a high concentration of Mg²⁺ not only alters the chemical properties of soil making it more alkaline but also damages the soil structure. It also decreases crop yields. For the present study, 58% of the groundwater samples for both the pre-monsoon and post-monsoon seasons are suitable for agricultural use while the surface water of both seasons portrays that 83% of samples are suitable for irrigation. The MH values range from 2.94 (Bolpur in post-monsoon) to 53.85 (Suri in post-monsoon).

Relative suitability of surface water and groundwater for irrigation

ANOVA has been run on five hazard indices to assess whether there is a statistically significant difference or not in irrigation suitability between the samples of surface and groundwater. Considering 1 degrees of freedom and 0.05 significance level, all the indices except the PS index portray no significant differences between surface and groundwater (p = .06 for SAR, p = .29 for %Na, p = .22 for SSP, p = .97 for MR, p = .0 for PS). Moreover, while looking at the locational differences among the six monitoring stations, the AVOVA at 5 degrees of freedom and 0.05 significance level portrays that the character of all the indices except MH statistically differ from one location to another (p = .48 for MH and p = 0 for other indices). Therefore it could be argued that though every location portrays the irrigation water quality differently, there is no statistically significant difference between the surface and groundwater.

Discussion

Factors and mechanisms driving the spatio-temporal variation of water quality

The surface and groundwater quality in the MRB exhibits the presence of significant spatial and seasonal variations. Besides, concerning the irrigation hazards, the surface water is observed as relatively suitable than the groundwater. The variations and significant differences in irrigation water quality are triggered by both the physical processes and anthropogenic activities. Therefore, this telltale pattern warrants a succinct analysis from the perspective of physical processes and anthropogenic factors influencing the evolution of the surface and groundwater chemistry.

Regarding the physical processes, the Gibbs plots of anion and cations suggest that the groundwater in this area is significantly controlled by the rock-water interaction (Figure 4).

Figure 4.

Gibbs plot representing the dominant processes involved in the hydrochemical evolution of groundwater.

For understanding the rock-water interaction in the aquifer and dominant weathering processes involved in the evolution of groundwater chemistry many researchers have discussed the molar ratio of different ions (Li et al., 2013; Wang et al., 2013). In the molar ratio of Na⁺ and Cl⁻, the value 1 indicates halite weathering while >1 represents the predominance of Na⁺ over Cl⁻, indicating the presence of silicate weathering (Sarkar et al., 2021; Zhu et al., 2017). For the present study, 29% of the groundwater samples are found to have Na⁺/Cl- values > 1 indicating that silicate weathering is responsible for increasing the Na⁺ concentration in groundwater. Moreover, the molar ratio of Ca²⁺ and SO₄²⁻ > 1 indicates the presence of carbonate weathering (Li, Zhang et al., 2016). For the present study, 83% of the groundwater samples have Ca²⁺ and SO₄²⁻ ratio greater than 1 indicating carbonate dissolution such as calcite (CaCO₃) and dolomite (MgCa(CO₃)₂). It may also be inferred that the cation exchange process is also responsible for the excess of the concentration of Ca²⁺ over SO₄²⁻ in groundwater. This fact is also supported by the observation of Mukherjee and Fryar (2008). Their investigation revealed that the presence of Cratonic sediment from the Chotanagpur plateau is responsible for carbonate weathering.

Land use and land cover (LULC) also have strong relationships with surface and groundwater quality. For example, He et al. (2020) showed the urban land agricultural land and industrial land negatively affect the groundwater while forest cover positively affects the groundwater quality. In the study, Bolpur, Suri, Nalhati, and Bakreshwar have different LULC such as hot spring and helium extraction, urban, industrial, agriculture areas which affect the groundwater quality in various ways (Figure 5a–d). For example, the physicochemical characteristics of the water are greatly influenced by the presence of hot spring at Bakreshwar. This place covers seven hot springs (Agnikund, Ksharkund, Bhairabkund, Baitarinikund, Saubhagyakund, Suryakund, Brahmakund) and the high concentration of Na⁺ has been reported from this hot spring region. The helium extracted from the main spring Agni Kund has also its potential impact on irrigation water quality (Figure 6a and b) (Chandrasekharam, 2000). Majumdar et al., (2005, 2009) observed significant differences in the physicochemical properties between the thermal spring water and the groundwater. Besides, it is also been observed that the surrounding areas of hot springs are not used for agricultural practice (Figure 5c).

Figure 5.

LULC of surrounding areas of the source of groundwater (a) Bolpur, (b) Suri, (c) Bakreshwar, and (d) Nalhati.

Figure 6.

(a) Agni kunda hot spring, (b) Helium gas holder.

The groundwater at Nalhati portrays a high concentration of Cl⁻ with a high salinity hazard. The higher salt concentration may be due to the extreme anthropogenic activities such as extensive sand mining (Haritash, 2018), stone quarrying and crushing, and also brick kiln industries (Figures 5d and 7a–e). The mining of sand, stone quarrying, and crushing in MRB induce the mixing of silicate minerals to the groundwater aquifers which is supposed to be the important factor controlling the magnitude of silicate weathering. A similar phenomenon has also been reported by the work of Peckenham et al. (2009) who observed the concentration of sodium, chloride, and sulfate near the sand and gravel pits in the United States of America (USA). Moreover, the brick kiln industries in this region have significantly affected the quality of the groundwater for irrigation through the leaching of pollutants into the groundwater reservoir (Khalid, 2019). The few groundwater samples of the post-monsoon season are observed as unsuitable for irrigation which indicates the effect of monsoonal rainfall on the mixing of anthropogenic pollutants with the groundwater through the leaching process during post-monsoon (Kumarasamy et al., 2014).

Figure 7.

Anthropogenic activities around the sample location: (a) Bolpur, (b) Suri, (c) Bakreshwar, (d) Nalhati, and(e) Bamdeb Ghat.

Regarding surface water, Suri is more suitable than Sadhak Bamdeb Ghat station for irrigation purposes. The stretches of Dwarka River between Tarapith and Sadhak Bamdeb Ghat (~1.5 km) are identified as a polluted stretch by the River Rejuvenation Committee of West Bengal (CPCB, 2020). The discharge of effluents from industrial and domestic wastewater into the river has made the surface unsuitable for irrigation. The location of sand mining centers near Bamdeb Ghat increases the salt concentration in the river water. The significant difference of river discharge between the pre-monsoon and post-monsoon seasons river is also a responsible factor inducing the seasonal variation of irrigation water quality of the surface water.

Implications of the study

The present study addressing the surface and groundwater quality for irrigation and their seasonal and spatio-temporal variation in the MRB is a pioneering effort. Therefore, this study has brought about an acquaintance with both the surface and groundwater resources of MRB from the perspective of agricultural use. Moreover, the study also provides a detailed account of the governing process of groundwater hydrochemistry. The study has also focused on the influence of anthropogenic activities (stone crushing, stone quarrying, sand mining, and brick kiln) on irrigation water. Therefore, the analytical contents of the study would help the regional planners to introduce an effective and integrated plan for the management and conservation of surface and groundwater. The study also helps select crops based on the nature of irrigation water quality. Besides, the study would also help develop an awareness of the local government about the nature and extent of anthropogenic water pollution and take actions to reduce the level of water pollution, to minimize the risk of crop production, and to achieve bumper and sustainable agricultural return for the long period.

Conclusions

Based on detailed observations of hydrogeochemical characteristics, the surface and groundwater in the study area depict that the majority of the water samples are suitable for irrigation in agriculture. However, the groundwater has been observed less suitable for irrigation compared to the surface water. Though anthropogenic activities have also been detected (such as extensive sand mining, stone quarrying, and crushing and brick kiln industries) for influencing the quality of the surface and groundwater, the geological formation (rock-water interaction) is found to play a vital role to control the groundwater hydrochemistry.

The index-based outcomes of the irrigation water quality of the study area have become useful for decision-making processes. For example, identifying the areas producing unsuitable water for irrigation, regular monitoring of water quality and making the irrigation-related policy by the government assume paramount significance. In the MRB the groundwater is relatively unsuitable due to higher salt concentration. From the present study, it is suggested that groundwater remediation can be helpful to reduce the level of salinity before using as irrigation. Government should be careful regarding human activities. The stone crushing, stone quarrying, sand mining and brick kiln affect the irrigation water extensively. The local government and decision-makers may effectively regulate the human activities regarding the types of fuels used in the brick kiln or the heavy metals mixing with river water or groundwater through the recharge of the shallow aquifer. The controlled anthropogenic interventions may reduce surface and groundwater contamination in future to sustain agricultural production.

Footnotes

Acknowledgements

The authors are grateful to the West Bengal Water Pollution Control Board (WBPCB) for making the water quality data available in the public domain. The authors also acknowledge Mr. Arnab Sen, a former student of Visva- Bharati, Shantiniketan for his help in the fieldwork. The authors are also thankful to the anonymous reviewers and Handling Editor for constructive suggestions that helped us to improve the paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been financially supported by the University Grants Commission, Govt. of India (UGC ref. no 3469/(NET-DEC 2018) awarded to the first author to carry out her PhD research work.

ORCID iD

Aznarul Islam

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