1. Damak multiple campus, Tribhuvan University, Nepal.
2. Damak multiple campus, Tribhuvan University, Nepal.

*     Correspondence: durgankarkii4@gmail.com

Keywords: irrigation water quality index (IWQI); assessment, lithology, freshwater, groundwater

1. INTRODUCTION

Not just for a state or a country, but for all of humanity, water is the most vital natural resource. Freshwater resources are increasingly scarce. Hardly 1% of the planet’s water supplies are safe for human consumption. Thus, freshwater resources must be protected and controlled well [1]. For many countries, groundwater is the primary source of water. It is a major source of domestic water, accounting for 43 percent of global irrigation water use, and is more appropriate for irrigation than surface water [2]. Water has traditionally been recognized as a limitless and abundant resource, and it now determines human, social, and economic progress. The alarming rate of human population growth, expanding industrial civilization, technological advancements, and the recent trend of groundwater depletion have all raised major environmental concerns [3]. Factors like urbanization, modernization, poor land management, and pollution added to the stress on agricultural production [4]. Valuable surface water supplies have either been degraded or contaminated to the degree that they could no longer be used for irrigation purposes along with direct human consumption. People have become more reliant on groundwater resources around the world as a result of this predicament, which has eventually resulted in greater impacts on groundwater quantity and quality. The leading cause of the erosion of soil quality and the growth of agricultural crops on such soils has been the use of poor-quality waters for watering [5]. The aggregation of different ions in the soil mass as a result of the presence of such ions in irrigation waters at high concentrations has been associated to the degradation. Water quality management has become an indispensable tool for ensuring the appropriate quantity of agricultural produce for present demands while also guaranteeing the land’s long-term viability [6]. The quality of irrigation water is primarily determined by the presence of certain quality indicators. These variables are usually linked to a specific irrigation issue or a particular threat that their presence is likely to cause. In general, the salinity hazard, infiltration or permeability hazard, specific ion toxicity, trace element toxicity, and miscellaneous impacts on sensitive crops are all linked to the quality of an irrigation water supply. In Nepal, the economy is massively depended upon the agriculture as according to census of 2011, 83% of population depend on agriculture. Due to inadequate infrastructure for the supply of surface water, many places of Nepal, especially, Terai region depends on groundwater resources for irrigation purposes. Our study area is eastern part of Terai region of Nepal, precisely, Morang district. It is situated at 26.6799⁰ N and 87.4604⁰ E. The quality of irrigation water is highly variable depending upon both the type and the quality of the salts dissolved in it. These salts originate from natural (i.e., weathering of rocks and soil) and anthropological (i.e., domestic, and industrial discharges) sources and once introduced, they follow the flow path of the water. The irrigated soils and the crops grown on such soils are ultimate sinks for these salts and minerals as a result of evaporation and crop consumption. In general, the problems associated with the soil’s salt content increase as the total salt content of the irrigation water increases. Therefore, the irrigation water quality should be considered as a valuable tool in the sustainable management of the soil resources and the agricultural [7]. It is commonly accepted that the problems originating from irrigation water quality vary in type and severity as a function of numerous factors including the type of the soil and the crop, the climate of the area as well as the farmer who utilizes the water. Nevertheless, there is now a mutual understanding that these problems can be categorized into the following major groups: (a) salinity hazard, (b) infiltration and permeability problems, (c) toxicity hazards; and(d) miscellaneous hazards can further be grouped into problems [8] associated with specific ions as well as hazards related to the presence of trace elements and heavy metals. Salinity hazard occurs when salts start to accumulate in the crop root zone reducing the amount of water available to the roots. The reduced water availability sometimes reaches to such levels that crop yield is adversely affected [9]. These salts often originate from dissolved minerals in the applied irrigation water or from a high saline water table. The reductions in the crop yield occur when the salt content of the root zone reaches to the extent that the crop is no longer able to extract sufficient water from salty soil. When this water stress is prolonged, plant slows its growth and drought-like symptoms start to develop [10]. Unless the soli are leached with low salt content water, the salinization of the soil is an irreversible process that makes agricultural lands unusable [11]. Being the most influential water quality guideline on crop productivity, the extent of salinity hazard could be measured by the ability of water to conduct an electric current. Since conductance is strong function of the total dissolved ionic solids, either an electrical conductivity (EC) measurement or a total dissolved solids (TDS) analysis could be used in measuring the salinity of water [12]. Although these terms are comparable and increase the number of salts dissolved in water, TDS is a direct measure of dissolved solids and EC is an indirect measure of ions by an electrode [13]. Although the infiltration rate of water into soil is a function of many parameters including the quality of the irrigation water and the soil factors such as structure, compaction and the organic content, the permeability and infiltration hazard typically occur when high sodium ions decrease the rate at which irrigation water enters the soil’s lower layers [14]. The reduced infiltration rate starts to show negative impacts when water cannot infiltrate to the roots of the crop to the extent that the crop requires. Hence, these salts start to accumulate at the soil surface [15]. When the crop is not able to extract the required amount of water from the soil, it is not possible to maintain an acceptable yield and the agricultural production is reduced [16]. The two most common water quality factors that influence the normal rate of infiltration of water are the salinity of water and the relative concentrations of sodium, magnesium and calcium ions in water that is also known as the sodium adsorption ratio (SAR). The SAR value of irrigation water quantifies the relative proportions of sodium (Na+) to calcium (Ca++) and magnesium (Mg++). According, a combined EC-SAR criterion is used to assess the potential infiltration hazard that might develop in a soil [17]. While a low salinity water with high SAR values has a severe infiltration hazard, a high salinity water with low SAR values does not experience any infiltration problem. As both salinity and SAR operate at the same time, the levels of sodium ions in water are the determining parameters for potential infiltration hazards. It is also important to note that these hazards typically occur in the first few centimeters of the top soil and is strongly linked to the structural stability of the surface soil and its low calcium content relative to that of sodium [18]. It has been found that when a soil is irrigated with waters of high sodium surface is reported to develop which in turn weakens the soil structure [19]. The environmental and climatologic factors strongly influence the soil and subsurface water chemistry. These factors could increase the dissolution of numerous minerals in water and could cause toxic effects [20]. The presence of some trace elements and various heavy metals in the irrigation water are known to be responsible for soil pollution and are particularly important for irrigation water quality due to some of their unique properties including their resistance to biodegradation and thermoregulation [21]. Unlike other potential pollutants that visibly build-up on soils, trace elements and heavy metals can accumulate unnoticed to extremely high toxic concentrations before affecting plant, animal, and human life [22]. When their effects on plants are considered, one could observe that they would be taken up by plant’s root system and would later be accumulated within the plant’s stems and leaves [23]. The pH value of irrigation water changes as a function of several parameters including contamination from various pollution sources and acid rains [24]. The pH value influences the carbonate equilibrium, heavy metal content and the relative ratio of nitrogen components, which in turn influences soil quality and plant growth. In acidic waters, calcium, magnesium, and aluminum are not absorbed properly by plants [25]. Alkalinity is a measure of the capacity of water to neutralize an added acid. Being the major component of alkalinity, carbonate and bicarbonate ions are responsible for high pH values (i.e., above 8.5) of water. Elevated levels of carbonates cause calcium and magnesium ions to form insoluble minerals leaving sodium as the dominant ion in solution. Hence, it is indirectly responsible from the hazards that high sodium concentrations cause on the irrigated crops and the soil [26].

3. MATERIALS AND METHODS

The study area was Morang district of Nepal which is situated at outer Terai, or plains, of Eastern Nepal which is 1855 km² in area with geographical co-ordinates 26.6799^oN and 87.4604^oE. Most of the land is taken up by rice and jute cultivation, though areas of sal forest remain along the northern part of the district where the plains meet the hills. The mean annually precipitation of Morang district is nearly 2023 mm which is quite high compared with the normal, which is 800 mm. About 80.9% of study area falls under lower tropical zone with elevation range below 300 meters. Similarly, 11.5% and 7.4% of study area is in upper tropical and subtropical climate zone with elevation ranging from 300 meters to 1000 meters and from 1000 meters to 2000 meters respectively. Only 0.2% of the Morang district falls under temperate climate zone with elevation starts from 2000 meters and goes up to 3000 meters. The average summer temperature of study area ranging from 25^o C to 35^oC whereas in winter its temperature goes from 16^oC to 8^oC.  Although, our study area has many rivers but due to poor facility of canal system, maximum number of farmers depend upon groundwater to irrigate their field. Morang district consists of 17 local administrative divisions. The sample were collected from each local administrative division. First water sample was collected in the 28^th August 2021 and analyzed on 6^th September 2021. The location of the study area is shown in figure below. The local administrative division wise separation of the study area is also shown in figure. Fourteen samples were collected randomly representing one from each local administrative division except Budiganga Gaunpalika, Katahri Gaunpalika and Biratnagar Mahanagarpalika in one liter plastic bottle for the analysis of physio- chemical parameters. Before collecting the sample, the pump set is run for 8 to 10 minutes to eliminate the metallic contamination.

Figure 01: Map of Study Area.

 Table 01: Sampling ID with respective Sampling station.

S.N.	Sampling station	Sample ID
1	Pathri- Sanichare Nagarpalika	S1
2	Kanepokhari Gaunpalika	S2
3	Belbari Nagarpalika	S3
4	Sundarharaicha Nagarpalika	S4
5	Gramthan Gaunpalika	S5
6	Jahada Gaunpalika	S6
7	Dhanpalthan Gaunpalika	S7
8	Rangeli Nagarpalika	S8
9	Sunwarsi Nagarpalika	S9
10	Ratuwamai Nagarpalika	S10
11	Urlabari Nagarpalika	S11
12	Miklajung Gaunpalika	S12
13	Kerabari Gaunpalika	S13
14	Letang Nagarpalika	S14

The fourteen samples of boring or pump set had been analyzed for thirteen parameters: pH, Turbidity, EC, TDS, Bicarbonate (HCO₃^–), Carbonate (CO₃^—), Sulphate (SO₄^—), Chloride (Cl^–), Fluoride (F^–), Calcium (Ca⁺⁺), Magnesium (Mg⁺⁺), Sodium (Na⁺), Potassium (K⁺). Among them, EC, TDS, and pH were measured instantaneously in the spot with the help of the help of the instruments and rest of the parameters had been analyzed in the Nepal Batabaraniya Sewa Kendra, Biratnagar. Before sampling, the bottles were rinsed before filling, sealed tightly, and assigned sampling code in the fixed spot. From the data after analysis, IWQ index as well as SAR, RBC, RSBC, MAR, KR, PI were calculated as the conclusive evidence for the decision that it is suitable for irrigation purpose or not.

4. RESULT & DISCUSSION

3.1 General Hydrochemistry

The ionic composition of all fourteen samples of groundwater of Morang district are presented in Table below. In comparison to global average 120 mg/L, the groundwater samples of Morang district showed relatively higher TDS value (356 92 mg/L). The pattern of cationic dominance based on mean values (mg/L) in the study area are in the following order: . The results showed that out of total cationic- budget in the study area, alone contributes 69.94%,  together with  account for 84.09%. In comparison,  and  account for 7.56% and 7.04%, respectively. The concentration of  was more than three times than global average whereas concentration of  was more than twice than the global average, which likely reflects the predominance of carbonate weathering. The average anionic concentrations (mg/L) follow a pattern of dominance as: . The dominant anion in the study area is  which contributes 84.35% followed by ,  and  with 10.91%, 4.55% and 0.20%, respectively. The relatively high concentrations of bicarbonates and sulphates are three and two folds of the global average, respectively.

 3.2 Characterization of hydrogeochemical facies

The milli-equivalent percentage (meq%) of major ions are plotted in Piper figure and further projected into central diamond field to evaluate the hydrogeochemical facies and types of water. On the cation plot, most of the samples lies in the lower left corner, indicating the calcium dominance in the groundwater. Sample from Pathri-Sanichare named S1 falls in slightly magnesium dominance region. No samples from the study area falls in the sodium and potassium region. Thus, the overall characteristics are still consistent with carbonate-dominated lithology. The anion diagram shows that most of the water samples fall on lower left corner near the  apex, again signifying the carbonate dominated lithology.

Table 02: The results of water quality analysis in Morang district, Primary parameters.

Parameters			pH	EC	TDS	Turbidity
S. N	SAMPLE ID	METHOD	UNIT			NTU
			APHA-4500 H+	APHA-2510	APHA-2540 C	APHA-2130B	APHA-2320	APHA-3500	APHA-3500	APHA-3500	APHA-3500	APHA-3111B	APHA-4500	APHA-2500B	APHA-2500D
1	S1		6.9	325	134	0	183.6	54.51	19.45	23.59	11.87	0.17	18	1	0.5
2	S2		6.3	220	92	0	103.7	28.05	7.53	2	3.01	0.06	18	1	0.28
3	S3		6.6	237	102	0.4	95.2	29.56	4.13	3	3	0.05	15	1	0.28
4	S4		6.6	702	334	11.2	278.8	69.34	24.79	3.25	2.84	3.45	38	23	0.58
5	S5		6.7	568	267	2.3	241.4	67.33	17.26	3.15	3.77	0.65	30	12	0.42
6	S6		6.9	498	231	16.7	190.4	54.91	9.24	3.04	3.12	1.78	28	11	0.19
7	S7		7	745	356	0.5	355.3	69.74	40.1	2.69	4.97	3.95	40	1	0.65
8	S8		6.6	699	306	0	146.2	52.1	3.16	19.76	25	0.05	17	42.98	0.29
9	S9		6.9	333	149	6.4	130.9	47.69	1.94	2.54	3.17	0.45	27	2	0.48
10	S10		6.8	364	164	16.1	153	49.3	6.32	2.56	3.8	2.61	32	1	0.48
11	S11		6.9	580	197	0	129.2	40.88	6.08	3.41	4.81	0.38	12	24	0.48
12	S12		7.2	678	322	1	265.2	96.19	5.1	2.94	5.65	0.22	21	10	0.55
13	S13		7.3	400	181	0	146.2	52.1	3.16	2.23	3.96	0.33	16	1	0.38
14	S14		7.1	378	177	0	139.4	46.49	5.1	2.18	2.96	0.09	19	7	0.38
MEAN			6.842857	480.5	215.143	3.9	182.8	54.16	10.954	5.4529	5.8521	1.01714	23.64	9.8557	0.42429
SD			0.268082	181.29	88.1745	6.190936	75.55	17.56	10.851	6.9262	5.9820	1.35799	8.854	12.454	0.13143
MAX.			7.3	745	356	16.7	355.3	96.19	40.1	23.59	25	3.95	40	42.98	0.65
MIN.			6.3	220	92	0	95.2	28.05	1.94	2	2.84	0.05	12	1	0.19
Global Mean			8	–	120	–	58.40	15	4.10	6.30	2.30	–	11.20	7.80
WHO Limit			6-8.5	–	1000	–	600	100	50	200	100	–	250	250

Table 03: The results of different parameters for analysis of water quality.

Parameters		SAR	RSC	RBSC	KR	MAR	PI	%Na	IWQI
1	S1	0.697	-1.317	0.284	0.237	37.002	51.581	23.515	40
2	S2	0.086	-0.322	0.298	0.043	30.648	65.937	7.505	39
3	S3	0.137	-0.257	0.083	0.072	18.694	70.814	10.205	39.5
4	S4	0.085	-0.937	1.103	0.026	37.047	40.349	3.742	39
5	S5	0.088	-0.829	0.591	0.029	29.676	43.181	4.652	40
6	S6	0.099	-0.385	0.376	0.038	21.689	52.196	5.707	40
7	S7	0.063	-0.963	2.338	0.017	48.626	36.649	3.474	39.5
8	S8	0.718	-0.468	-0.208	0.299	9.078	64.638	34.365	40
9	S9	0.098	-0.398	-0.239	0.043	6.277	59.342	7.007	40
10	S10	0.091	-0.911	0.043	0.037	17.426	54.74	6.534	40
11	S11	0.131	-0.426	0.074	0.058	19.667	59.555	9.644	40
12	S12	0.079	-0.882	-0.462	0.024	8.024	41.309	4.957	40.5
13	S13	0.057	-3.368	-0.208	0.017	54.814	28.063	3.327	40.5
14	S14	0.081	-0.459	-0.039	0.002	0.895	56.585	5.856	35.5
Min		0.057	-3.368	-0.462	0.002	0.895	28.063	3.327	35.5
Max		0.718	-0.257	2.338	0.299	54.814	70.814	34.365	40.5
Ave.		0.179286	-0.85157	0.288143	0.067286	24.2545	51.78136	9.320714	39.53571
SD		0.224864	0.790007	0.710007	0.087671	16.15325	12.352	8.827968	1.247525

 

Figure 02: A plot of different measured anions and cations of sampling stations in Piper diagram.

The results plotted in the central diamond field show the overall characteristics of groundwater chemistry: the dominance of the alkaline earth elements (  and ) over the alkaline (  and ) and the weak acids ( ) over the strong acids (  and ). Generally, six sub- fields can be identified in the diamond of Piper diagram: (1) , (2) , (3) Mixed , (4) Mixed , (5)         , and (6) . The hydrogeochemical results shown in this study are confined only one type of lithology, for example, all samples of water belong to the  type. In general, it clearly indicates that most of the major ions are of natural origin and maximum number of samples are confined to carbonate dominated lithology.

3.3 Association among the hydrogeochemical attributes Correlation matrix

Correlation matrix is widely used statistical tool to establish the relationship between two hydrogeochemical variables for predicting the degree of dependency of one variable to the other. The correlation matrix of the study is presented in Tables.

Table 04: Correlation coefficients (r) among various water quality parameters.

	1	0.197	0.210	-0.096	0.250	0.470	-0.043	-0.126	-0.151	-0.014	-0.051	-0.240	0.333	0.018
		1	0.973**	0.027	0.761**	0.734**	0.480	0.061	0.319	0.478	0.435	0.630*	0.468	0.186
			1	0.085	0.840**	0.807**	0.533*	0.008	0.255	0.543*	0.557*	0.519	0.473	0.139
				1	0.109	0.094	0.016	-0.252	-0.283	0.562*	0.570*	-0.029	-0.070	0.141
					1	0.828**	0.825**	-0.068	-0.063	0.701**	0.749**	0.041	0.676**	0.129
						1	0.397	0.003	0.056	0.349	0.477	0.148	0.587*	0.245
							1	0.0598	-0.087	0.741**	0.705**	-0.102	0.599*	-0.012
								1	0.840**	-0.265	-0.279	0.366	-0.047	0.179
									1	-0.249	-0.27431	0.662**	-0.142	0.203
										1	0.891**	-0.052	0.517	0.044
											1	-0.116	0.502	0.022
												1	-0.113	0.065
													1	0.088
														1

Table 05: Correlation coefficients (r) among SAR, RSC, RBSC, KR, MAR, PI, %Na, IWQI.

	SAR	RSC	RBSC	KR	MAR	PI	%Na	IWQI
SAR	1	0.040	-0.178	0.985**	-0.0804	0.291	0.962**	0.164
RSC		1	0.037	0.093	-0.674	0.749	0.166	-0.291
RBSC			1	-0.206	0.585*	-0.363	-0.267	-0.078
KR				1	-0.111	0.371	0.986**	0.234
MAR					1	-0.630*	-0.225	0.319
PI						1	0.458	-0.217
%Na							1	0.142
IWQI								1

The statistical analysis of the relationship among the hydrogeochemical parameters has been carried out by building a correlation matrix of fourteen parameters namely pH, EC, TDS, Turbidity, Bicarbonate, Calcium, Magnesium, Sodium, Potassium, Iron, Sulphate, Chloride, Fluoride, and IWQI. The correlation coefficient given in the matrix always measure the relationship between the dependent and independent variables. If the correlation coefficient is +1 or -1 it shows the perfect linear relationship between variables. Statistical significance of calculated correlation coefficients was evaluated based on significance p as follows:  weakly correlated,  moderately correlated (*), and  strongly correlated (**). There is strong positive and negative correlation among various hydrogeochemical parameters. It has been found that EC is strongly correlated with TDS (0.97284), bicarbonate (r=76058) and calcium (r=0.73414) while it is moderately correlated with chloride (r=0.62982) and weakly depends upon rest of the parameters. TDS exhibits significant positive correlation with bicarbonate (r=0.84027), calcium (r=0.8071), moderately positive correlation with magnesium (0.53316), iron (r=0.54268), and sulphate (r=0.557040, and weak positive correlation with rest of the parameters. Turbidity has moderately strong positive correlation with iron (0.56216), and sulphate (0.57015), and weakly correlated with remaining parameters. Bicarbonate is another important hydrogeochemical parameter for testing water quality of groundwater and it has fairly good correlation with calcium (r=0/82798), magnesium (r=0.82548), iron (r=0.70082), sulphate (r=0.7488), and fluoride (r=0.67589) and it is weakly correlated with other parameters. Calcium is moderately correlated with fluoride with r=0.58693 and weak positive correlation with rest of the parameters. Magnesium is strongly correlated with iron (0.74141), and sulphate (0.70466) and moderately with fluoride with r=0.59943 whereas it is weakly correlated with rest of hydrogeochemical parameters of our consideration. Sodium is strongly correlated with potassium with correlation coefficient value r=0.83977 and weakly correlated with rest of the parameters. Potassium is strongly correlated with chloride (r=0.66237). In the same way, iron shows strong positive correlation with sulphate with r=0.89144. The statistical analysis of the relationship among the hydrogeochemical parameters has been carried out by building a correlation matrix of eight calculated parameters for analysis namely SAR, RSC, RBSC, KR, MAR, PI, %Na and IWQI. It has been found that there is strong correlation between KR and SAR (r=0.985) and with % Na with r=0.962. MAR is moderately correlated with RBSC with r=0.585. Similarly, %Na has strong correlation with KR (r=0.986). Finally, PI has moderately negative correlation with correlation value r= -0.630.

3.4 Principal component analysis: Validity of water for agricultural irrigation.

A summarization of the PCA output based on Origin Pro 2018 software is presented in table. Generally, eleven principal components (PCs) were obtained from PCA. The PCs that had an eigenvalue greater than one were kept, and the rest were.

Figure 03: Scree Plot of Principal component vs Eigenvalues

Figure 04: Plot of parameters of PC1 vs PC2

Figure 05: Biplot of parameters of PC1 vs PC2

Table 06: Summarization of principal component analysis.

Parameters	PC1	PC2	PC3	PC4
pH	0.09921	-0.03787	-0.61567	0.27388
EC	0.34623	0.2763	-7.16638E-4	0.22591
TDS	0.37219	0.22045	8.74595E-4	0.21459
Turbidity	0.10802	-0.25078	0.45156	0.38047
	0.40843	0.00285	-0.0896	-0.10663
	0.33805	0.10948	-0.27394	0.20326
	0.32783	-0.06191	0.07591	-0.50792
	-0.04723	0.43532	0.07018	-0.41416
	-0.02062	0.54042	0.1295	-0.12666
	0.33892	-0.20762	0.28739	-0.07462
	0.34469	-0.22244	0.26573	-0.05764
	0.06443	0.46683	0.27473	0.31814
	0.30172	-0.05328	-0.27898	-0.2735
Eigenvalue	5.5781	2.90073	1.65421	1.18941
Percent of Variance	42.91%	22.31%	12.72%	9.15%
Cumulative percent	42.91%	65.22%	77.95%	87.10%

The output of the final rotated loading matrix is obtained from the present data indicates that the four principal components explain 87.10% of the total variance; principal component-I (PC1) contributes 42.91%, principal component-II (PC2) 22.31%, principal component-III (PC3) 12.72% and principal component-IV (PC4) 9.15% with 13 chemical parameters. Each principal component can be used to interpret as a specific hydrogeochemical process or processes through an examination of their loadings.

Principal Component-I (PC1): Principal component PC1 is dominated by EC, TDS, HCO₃, Fe, SO₄, and F variables; which show moderate positive loadings (0.30172 to 0.40843; The EC, TDS, HCO₃, Fe, SO₄ and F plotting close to one another in the positive region of PC1 in figure. PC1 represents an indicator for salinity and alkalinity hazards because it includes the EC, which is normally used to estimate salinity hazard. The ions Ca, Mg and HCO₃ were used to estimate the alkalinity hazard based on the calculation s of many criteria such as RSC and RBSC.

Principal Component-II (PC2): Principal component PC2 is dominated by Na, K and Cl variables, which shows moderate positive loadings (0.43532 to 0.54042), and their close plotting figure indicates that their relationship comes under the single process. PC2 is related to sodicity hazard because it includes Na. Sodicity hazard indicates the relationship of Na with the other cations (Ca, Mg, and K) that expressed various criteria such as the SSP, the SAR, and the SCAR.

Principal Component-III (PC3): Principal component PC3 is dominated by Turbidity with loading value 0.46683.

Principal Component-IV (PC4): Principal component PC4 is dominated by pH. Therefore, it may include water acidity.

 

3.5 Cluster analysis

Figure 06: A plot of Sampling stations vs Distance for cluster analysis.

 The Hierarchical clustering approach has been successfully applied in hydrogeochemical studies to group similar sampling site and/ or geochemical constituent with similar characteristics, which are affected by similar processes and sources. The cluster analysis was performed to group similar water samples in the study area based on pH, EC, TDS, Turbidity and major anion and cation concentrations of fourteen different samples collected from fourteen different local administrative division of Morang district. Represents a dendrogram which groups fourteen groundwater samples into statistically significant clustering. It indicates that sample from site 2 and site 3 (i.e., from Kanepokhari and Belbari) resembles with one another. Similarly, sample from sites 13 and 14 are closely related and their cluster has somewhat similar to sample from site 10. In the same way, cluster of samples 10, 13 and 14 again makes cluster with sample 9 indicating that they fall on same categories based on the parameters that were implemented for the analysis. On the other hand, sample from site 4 and site 12 are significantly close, and their cluster resembles with sample from site 7. Sample from site 5 and site 6 are also closely related and group of 5 and 7 further made cluster with groundwater sample from site 11 indicating that they possess same properties.

3.6 Chemical characterization of hydrogeochemical data

Chemical characterization of groundwater schemes developed are based on the concentrations of various pre- dominant cations and anions or on the interrelationship of ions and also a number of techniques and methods have been developed to interpret the chemical data. In this study, the groundwater quality with respect to irrigation/ agricultural is assessed by the following methods.

Table 07: Classification of different hazard and percentage of sample on that class.

Parameter	Classification	Range	Number of Samples	% Of samples
Salinity hazard	Excellent (C1)	Up to 250	2	14.29%
	Good (C2)	250- 750	12	85.71%
	Fail/ medium (C3)	750- 2250	Nil	Nil
	Poor/ bad (C4)	> 2250	Nil	Nil
Alkali hazard	Excellent (S1)	Up to 10	14	100%
	Good (S2)	10- 18	Nil	Nil
	Fail/ medium (S3)	18- 26	Nil	Nil
	Poor/ bad (S4 & S5)	> 26	Nil	Nil
Percent sodium (Na%)	Safe	< 60	14	100%
	Unsafe	> 60	Nil	Nil
RSC	Good	< 1.25	14	100%
	Doubtful	1.25- 2.50	Nil	Nil
	Unsuitable	> 2.50	Nil	Nil
Magnesium hazard	Suitable	< 50	13	92.86%
	Unsuitable	> 50	1	7.14%

 

3.7 Sodium adsorption ratio (SAR)

Excess sodium in water produces the undesirable effects of changing soil properties and reducing soil permeability. Hence, for considering the suitability for irrigation the assessment of sodium concentration is essential. The sodium or alkali hazard in the use of water for irrigation is determined by the absolute and relative concentration of cations. The relative activity of sodium ion in the exchange reaction with soil is expressed in terms of sodium adsorption ratio (SAR). The SAR, which indicates the effect of relative cation concentration on Na⁺ accumulation in the soil, is used for evaluating the sodicity of irrigation water. The sodicity hazard of water is described by SAR. The plot of the analytical data on the US Salinity Laboratory in which the EC is taken as salinity hazard and SAR as an alkalinity hazard shows that 85.71% of total number of samples fall into the category C2S1, indicating medium salinity and low alkalinity water which can be used for irrigating most of the soil and crops with little danger of exchangeable sodium. Remaining 14.29% of total number of samples fall into C1S1 class indicating low salinity and low alkalinity water. It can be used for irrigation for most of soil and crops with less negative impact.

Figure 07: A plot of different measured anions and cations of sampling stations in USSL- diagram

 

3.8 Electrical conductivity and percent sodium

EC and Na concentrations are important in classifying irrigation water. The EC values vary from 220 to 745 S/cm. High salt content (high EC) in irrigation water leads to formation of saline soil. Salinity, on the irrigated lands is the major cause of loss of production and is has adverse environmental impacts in irrigation. Saline conditions severely limit the choice of crops and adversely affect crop germination and yields. It is important that all evaluations regarding irrigation water quality are linked to the evaluation of the soils to be irrigated. Sodium concentration is important in classifying irrigation water because sodium reacts with soil to reduce its permeability. In all natural waters, percent sodium is a parameter to evaluate its suitability for agricultural purposes; sodium combining with carbonate forms alkaline soils, while sodium combining with chloride forms saline soils. Either type of sodium-enriched soil will support little or no plant growth. The sodium percentage (%Na) in the study area ranges from 3.327 to 34.365%. The chemical quality of groundwater samples was studied by plotting analytical data relating EC and sodium percent  that out of the fourteen groundwater samples, all samples belong to excellent to good category. It indicates that groundwater from all sampling station is suitable for irrigation.

Figure 08: A plot of different measured anions and cations of sampling stations in Wilcox diagram.

The suitability of groundwater depends upon the result of the excess of sum of carbonate and bicarbonate more than the sum of calcium magnesium content of water. The suitability of water depends on the abundance of sodium content, excess of bicarbonates and carbonate with respect to alkaline earths. Water having more than 2.5 meq/l of RSC is restricts air and water flow through soil pore space, which leads to degradation of soil, and it is not fit for irrigation. RSC ranges between -3.368 and -0.257 meq/l with an average -0.85157 meq/l. Based on RSC values, all samples fall within the category of good for irrigation type. Hence, it is found that the majority of the samples fall into the category of decent quality. MH indicates the degree of damage of the soil structure caused by magnesium in irrigation water. A high level of Mg⁺⁺ in groundwater leads to soil alkalinity; furthermore, a large amount of water is adsorbed between magnesium and clay particles, which reduces the infiltration capability of soil, which has adverse effects on crops. A value of MAR>50 indicates harmful groundwater and unsuitable for irrigation, while a value of MAR<50 indicates suitable groundwater. The MAR of the samples from study area ranged from 0.895% to 54.814% with mean 24.25%. Long- term use of minerally rich (Ca⁺⁺, Mg⁺⁺, Na⁺, and HCO₃^–) groundwater can reduce aeration in the soil and obstruct the growth of seedings. The PI is also used to reflect the applicability of groundwater for irrigation purpose. A PI of 75% or above max permeability indicates that groundwater is suitable for irrigation (class I and class II), while a PI of 25% or below max permeability is regarded as unsuitable for irrigation (class III). The PI of the samples from the study area ranged from 28.063% to 70.814% with their average is 51.78% indicting that maximum samples fall on class II and good for irrigation uses. The suitability of groundwater for irrigation can also be assessed with KR. The KR of groundwater of the study area ranged from 0.002 to 0.299. Furthermore, its average level was 0.067. If KR<1, the water is suitable for irrigation, otherwise, it is not suitable for irrigation. Thus, all samples from our study area fall within an acceptable level.

5. CONCLUSION

Groundwater plays a key role in agricultural development, its quality and suitability for irrigation is significance, in the present study, the main ions of shallow groundwater were analyzed for hydrogeochemical characterization and irrigation quality assessment of Morang district, Nepal. To evaluate suitability of groundwater for irrigation uses, fourteen groundwater samples were collected from pump sets and were analyzed for major anions and cations. The following conclusions can be drawn ,  and  are dominant in groundwater of study area. A piper plot shows that, groundwater types of study are . Irrigation water quality index was computed using equation. It shows that, 92.86% of groundwater samples have high suitability for irrigation uses and remaining 7.14% of groundwater samples have medium suitability for irrigation. The computed SAR value shows that, all groundwater samples are suitable for irrigation purposes. Assessment of groundwater suitability for irrigation purpose using SAR and Na% shows that, the groundwater of the study area suitable for irrigation use. In general, groundwater quality of study area based on various parameters that was computed is good and it is fit for irrigation. A lot of work has been done in field of water quality index for dinking purpose in this study area. However, this is first attempt of study of irrigation water quality index based on various parameters. Future researcher is suggested that to conduct continuous research on irrigation water quality index to compare the change in groundwater quality for irrigation.

REFERENCES

1. Asadi, E., Isazadeh, M., Samadianfard, S., Ramli, M. F., Mosavi, A., Nabipour, N., Shamshirband, S., Hajnal, E., & Chau, K. W. (2020). Groundwater quality assessment for sustainable drinking and irrigation. Sustainability (Switzerland), 12(1), 1–13.
2. Kawo, N. S., & Karuppannan, S. (2018). Groundwater quality assessment using water quality index and GIS technique in Modjo River Basin, central Ethiopia. Journal of African Earth Sciences, 147(June), 300–311.
3. Tanji, K.K (1990). Agricultural Salinity Assessment and Management, American Society of Civil Engineers; Manuals and Reports on Engineering Practice Number. American Society of Civil Engineers: New York, NY, USA, 71, 619p.
4. Ayers, R.S. and Westcot, D.W. (1985) Water quality for agriculture. FAO Irrigation and Drainage, Food and Agriculture Organization, Rome. Paper No. 29
5. Simsek, C.; Gunduz, O (2007). IWQ index: A GIS-integrated technique to assess irrigation water quality. Environ. Monit. Assess. 128, 277–300.
6. Hasibur Rashid, Md. Mahfuzur Rahman, Huzaifa & Golam Azam (2024). Seasonal Dynamics of Water Quality in the Turag River: A Multifaceted Analysis. Dinkum Journal of Natural & Scientific Innovations, 3(02):164-180.
7. Todd, D. K., & Mays, L. W. (2004). Groundwater hydrology (3rd ed.). New York: Wiley, 656 p.
8. Adimalla., P., Li., & S., Venkatayogi. (2018). Hydro geochemical Evaluation of Groundwater Quality for Drinking and Irrigation Purposes and Integrated Interpretation with Water Quality Index Studies. Environmental. Processes., 5(2), 363–383.
9. Asadi, E., Isazadeh, M., Samadianfard, S., Ramli, M. F., Mosavi, A., Nabipour, N., Shamshirband, S., Hajnal, E., & Chau, K. W. (2020). Groundwater quality assessment for sustainable drinking and irrigation. Sustainability (Switzerland), 12(1), 1–13.
10. Kawo, N. S., & Karuppannan, S. (2018). Groundwater quality assessment using water quality index and GIS technique in Modjo River Basin, central Ethiopia. Journal of African Earth Sciences, 147(June), 300–311.
11. Tanji, K.K (1990). Agricultural Salinity Assessment and Management, American Society of Civil Engineers; Manuals and Reports on Engineering Practice Number. American Society of Civil Engineers: New York, NY, USA, 71, 619p.
12. Ayers, R.S. and Westcot, D.W. (1985) Water quality for agriculture. FAO Irrigation and Drainage, Food and Agriculture Organization, Rome. Paper No. 29
13. Simsek, C.; Gunduz, O (2007). IWQ index: A GIS-integrated technique to assess irrigation water quality. Environ. Monit. Assess. 128, 277–300.
14. Todd, D. K., & Mays, L. W. (2004). Groundwater hydrology (3rd ed.). New York: Wiley, 656 p.
15. Adimalla., P., Li., & S., Venkatayogi. (2018). Hydro geochemical Evaluation of Groundwater Quality for Drinking and Irrigation Purposes and Integrated Interpretation with Water Quality Index Studies. Environmental. Processes., 5(2), 363–383.
16. Belic, S., Belic, A., & Vranesevic, M. (2013). Water quality as a limiting factor for irrigated agriculture. IAHS-AISH Proceedings and Reports, 361(July), 243–249.
17. Chidambaram, S., Prasanna, M. V., Venkatramanan, S., Nepolian, M., Pradeep, K., Panda, B., Thivya, C., & Thilagavathi, R. (2022). Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique. Environmental Research, 204(PA), 111729.
18. Bhattarai, A., Shrestha, S., & Regmi, R. P. (2022). Spatial Distribution of Groundwater Level over Lowlands of Morang District of Eastern-Nepal. Journal of Nepal Physical Society, 8(3), 39-44.
19. Das, B. D., & Choudhary, S. K. (2021). Application of Water Quality Index (WQI) for groundwater quality assessment of Biratnagar, Nepal. Our Nature, 19(1), 54-61.
20. Jesson Tejano Rivera (2024). Assessment of Household Hazardous Waste (HHW) in Quezon City towards a Better Management System. Dinkum Journal of Natural & Scientific Innovations, 3(01):38-57.
21. Nepal, S., Neupane, N., Koirala, S., Lautze, J., Shrestha, R. N., Bhatt, D., … & Khadka, M. (2024). Integrated assessment of irrigation and agriculture management challenges in Nepal: An interdisciplinary perspective. Heliyon, 10(9).
22. Pant, R. R., Zhang, F., Rehman, F. U., Wang, G., Ye, M., Zeng, C., & Tang, H. (2018). Spatiotemporal variations of hydro geochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal. Science of the Total Environment, 622–623, 770–782.
23. Hasibur Rashid, Hriday Ahmed, Sk.Raihana Iqbal Roshni & Abdullah (2024). The Assessment of the Effect of Industrial and Agricultural Effluent on Water Quality of Turag River, Bangladesh. Dinkum Journal of Natural & Scientific Innovations, 3(02):204-228.
24. Ghimire, R., Mandal, A., & Shrestha, A. K. (2019). Comparative Study on Water Quality Index Values of Different Rivers of Damak, Jhapa, Nepal. International Journal of Recent Research and Review, XII (1), 26–32.
25. Shrestha, A. K., & Basnet, N. (2018). An evaluation of physicochemical analysis and water quality index of Ratuwa River of Damak, Jhapa, Nepal. International Journal of Recent Research and Review, XI (2), 1–9.
26. Soleimani, H., Abbasnia, A., Yousefi, M., Mohammadi, A. A., & Khorasgani, F. C. (2018). Data on assessment of groundwater quality for drinking and irrigation in rural area Sarpol-e Zahab city, Kermanshah province, Iran. Data in Brief, 17, 148–156.