Introduction

The availability of water resources is essential for the sustainability of social and economic development worldwide. The need for quality water in adequate quantity to fulfill the demands of people and ecosystems is one of the major issues facing mankind in the twenty-first century1. The increased demand for irrigation, drinking, and potable water has stressed the world’s freshwater resources including rivers, and lakes2. Thus, evaluating water quality is crucial, particularly in highly populated areas that rely on river water supply. The ongoing population growth exerts enormous pressure on the agricultural field for a large quantity of agricultural production3. However, before the development of irrigation systems, people relied on rainfall for crop production4. Following the green revolution and the development of irrigation systems in agriculture, people depended either on groundwater or surface (river, lake, and pond) water for crop production5. Hence, river water is an important source of irrigation water nowadays. However, this river’s water quality is degrading gradually due to multiple points and non-point sources of pollutants discharged into the rivers6. River water is a vital source of irrigation, and its quality influences crop health and crop production7. In Bangladesh escalating industrialization coupled with a lack of effective policy implementation increasing river water pollution threatens surface water irrigation8. Lu et al.9 studied that intensive surface water pollution leads to the degradation of grain quality in China. Thus, it is essential to assess the irrigation water quality for the sustainable development of agriculture. A lot of studies have been done previously at the global scale as well as in India. Etteieb et al.10 used hydrographical methods and the PHREEQC geochemical programme to assess the water quality of the Medjerda River, Tunisia and found that the salt concentration was high in some places and needed immediate attention. Similarly, Misaghi et al.11 showed a higher variation regarding the irrigation water quality from upstream to downstream of Ghezel Ozan River, Iram. Mandal et al.12 monitored Ganga River water from 2009 to 2014 to assess the irrigation water quality and found a high chloride concentration. Kumarasamy et al.13 revealed excellent irrigation water quality apart from some estuarine locations of the Tamiraparani River in southern India. Sarkar and Islam14 demonstrated that the lower Churni River received industrial effluents from the Darshana sugar mill situated in Bangladesh degrading the irrigation water quality.

The concentration of physicochemical parameters determines the irrigation water quality of a river. However, a higher concentration of these parameters, even a single parameter, can lead to various irrigation hazards in the agricultural field, impacting the sustainability of agricultural developments. In this regard, several hazards (sodicity, alkalinity, permeability, salinity, and magnesium) with several indices such as sodium percentage (%Na), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), permeability index (PI), potential salinity (PS), magnesium hazard (MH), and irrigation water quality index (IWQI) are used globally for evaluating the quality of irrigation water for its suitability7,15,16,17,18.

Jalangi, one of the ‘Nadia Rivers’ was navigable even in the nineteenth century and first quarter of the twentieth century, and it was in better condition than that of Bhagirathi and Mathabhanga in some years19,20, and the steamer would go through the river21. However, the river has degraded significantly after being disconnected from the Padma. Along with the natural process of closure of off-take, ploughing on banks, improper fishing practices, encroaching river bed, soil cutting from banks by brick kilns, etc., have fastened the decaying process alarmingly22. As a result, stagnant water lost its quality to serve its aquatic ecosystem, provide quality water to the villagers on the banks for their domestic uses, and irrigate agricultural lands. Recently, the pollution of the river reached an elevated level. Every year, during a few weeks of monsoon months, filthy black stinking water comes to the river Jalangi through the Suti Nadi23, a tributary of the Jalangi River.

Jalangi River has been studied by several scholars from different angles. Kumar et al.24 evaluated spatio-temporal availability and dynamics of groundwater at the Bhagirathi–Jalangi interfluve and commented that groundwater seepage contributes to the baseflow of both rivers. Chatterjee et al.25 investigated the current conditions and spatial changes and also focused on emerging issues related to riparian wetlands in Bhagirathi–Jalangi interfluve and concluded that accelerated anthropogenic intervention in wetlands requires special attention to ensure ecological sustainability. Sarkar and Das26 assessed the Jalangi River water quality and ecosystem health and found that the water quality is poor due to its lentic nature. However, the spatio-temporal and seasonal assessment of the water quality of the Jalangi River for irrigation purposes has not been studied yet. Therefore, it would be novel to address the river water chemistry, the evolution of river water and irrigation hazards from an integrated perspective in the context of surface water–groundwater interactions for an anthropogeny-controlled river.

Hence, the present study proceeds to bridge this gap in the existing literature. Thus, the present research aims to address a few objectives–(1) to characterize the water quality in terms of hydro-chemical parameters and ionic chemistry, (2) to trace out the hydro-chemical processes controlling river water, and (3) to assess the water quality for irrigation purposes using irrigation hazards indices. These issues would be explored from a spatial approach (one-time data collected from different monitoring stations from source to mouth), and a temporal and seasonal approach (variations of water quality in a hydrological station). Hence, the present study will be the first scientific attempt to analyze the suitability of Jalangi River water for irrigation from spatial and temporal (seasonal) dimensions. As this region is agro-based, the irrigational water quality analysis would be helpful for the farmers and planners for the sustainable development of agricultural production in the concerned floodplain region. This will ensure the food security of the rural communities in the Jalangi River basin. Thus, sustainable development goals (SDG 8–decent work and economic growth) and SDG 11–sustainable cities and communities) are aimed through the study findings.

Database and methodology

Study area

The river Jalangi, a distributary of the Ganga River and a tributary of the Bhagirathi River in Eastern India (Fig. 1a) maintains a course of 233 km with a catchment area of 4300 km2 27and comes from the extreme north of Nadia district to Swarupganj in West Bengal. However, only 182 km of the river i.e. downstream of the Jalangi–Bhairab confluence at Char Moktarpur village in Nadia district is maintained for only two–three weeks during the monsoon period of a year. The river exhibits a higher sinuosity of 2.6722. Several tributaries of the Jalangi River like Chota Bhairab, Sialmari, Suti, Anjana, Saraswati and Kalma collect excess rainwear during the monsoon period (Fig. 1b).

Fig. 1
figure 1

Locational attributes of the Jalangi River, (a) Jalangi River basin in eastern India, (b) Jalangi River systems and location of the water samples, (c) discharge hydrograph of Jalangi River at Swarupganj (note: spatial sample locations are placed from source to mouth i.e. the uppermost location is S1 and the lowermost point near Nabadwip is S34) (source: prepared by the authors using ArcGIS (version 10.4), Microsoft Office Excel (version 2010) and Adobe Photoshop (version 7.0).

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River Jalangi drains the moribund tract of Murshidabad and Nadia district of the deltaic West Bengal. The region is a gently south-sloping monotonous plain scattered with swamps, paleo-channels, oxbow lakes, and meander scars. The region consists of the alluvium of Jalangi formation belonging to the Paleocene to lower Paleocene28. The top of the surface is formed by recent alluvium. There is a veneer of highly fertile and productive loose silt. The Janalgi–Bhagirathi interfluves are known as ‘Kalantar’, a low-lying tract of black clay soil with fine external deposits. The soil is azonal in nature and its textural pattern (i.e., sand, loam, clay, sandy loam, loamy sand) varies through the basin, triggering the bank erosion and causing a change in the river course29. The slope of the floodplain is from northeast to southwest, influencing the river to flow in the same direction. The slope is very gentle, and the area is interspersed with beels (relict channels of rivers or other waterlogged depressions), jhils (larger beel), marshes, and old beds of rivers and human interference, that the general slope is not easily traceable. The highest elevation of the study area is 20 m at Gopalpur Police Station of Karimpur-I near the abandoned off-take of the river Jalangi. At all other places up to Krishnanagar, height above sea level ranges from 11 to 13 m which decreases gradually to 9 m at Amghata and 6 m at Mayapur ghat (location of a river used for bathing or related activities), opposite Swarupganj near Nabdwip (Fig. 1b).

Typically, the Jalangi basin comes under the tropical monsoon (MON) climate. The average annual rainfall is 1473 mm, of which 68% comes during the MON months (June to September) with a swelling of the river discharge22 (Fig. 1c). The flat terrain and fertile and productive land have attracted people long ago to settle in the Jalangi floodplain. Here a large ~ 75% of people are engaged in the agrarian economy22.

Sample collection and analytical design

A systematic sampling design was adopted for the study. A total of 34 water samples were collected from 34 cross-sections (equally spaced at 5 km distance) of the Jalangi River during 10–15 February 2022 (Fig. 1b and Table S1). From the middle portion of each cross-section, one sample was taken. A 51 km upper reach of the river was excluded from sampling because this part is completely dried up and used for agriculture. Then, water samples were tested by the Ramkrishna Ashram Krishi Vigyan Kendra, Nimpith located in South 24 Parganas district, West Bengal. Water samples were taken in prewashed high-density polypropylene (HDPP) bottles as per the American Public Health Association30. Two duplicated samples collected from each site were filtered through a 0.45 m membrane filter (MF-Millipore™, USA). Water samples in HDPP containers were refrigerated at 4 °C using cooler box31. The cations (K+, Na+, Ca2+, and Mg2+) and anions (SO42−, Cl−, HCO3−, NO3−, and F−) analysed with the help of ion chromatography dionex ICS-90. The calibration used mixed standard solutions with three concentrations (1, 5, and 20 mg/L). Analytical accuracy was measured with a recognized reference material (Fluka Analytical, Sigma–Aldrich, Germany). Moreover, temporal (February 2012 to January 2022) water quality data for the Jalangi River was taken from an available station immediately downstream of Krishnanagar as measured by the West Bengal Pollution Control Board following APHA standard methods (Table S1). Furthermore, the samples’ charge balance error (CBE), ranging from 3.32 to 8.67 (average 8.22%) indicates the preciseness of the analysis as the CBE threshold lies within 10%14. The % CBE is computed based on Eq. (1) 32.

$$\text{\%CBE }=\frac{TC-TA}{TC+TA}\times 100$$
(1)

where TA and TC depict respective total anion and cation concentrations (mg/L).

Methodology

Irrigation water quality indices

Agricultural practices of the lower Ganga delta are highly dependent on irrigation water during the non-monsoon season33. As irrigation water quality affects the soil’s response to food production, it becomes imperative to assess the irrigation water quality. Sodium and salinity play an important role in determining water quality which is widely used in assessing river water suitability for irrigation14. The following indicators are considered while quantifying irrigation water quality.

Sodium adsorption ratio (SAR)

Sodium absorption ratio (SAR) which helps quantify sodium concentration concerning calcium and magnesium, is computed using Eq. (2) 17.

$$SAR =\frac{{Na}^{+}}{\sqrt{\left({Ca}^{2+} +{Mg}^{2+}\right)/2}}$$
(2)

where Ca2+, and Mg2+, Na+ represent the concentrations of calcium, magnesium and sodium respectively in water samples.

Sodium percentage (%Na)

Percentage sodium (%Na) is computed using Eq. (3) following Richards34 and Wilcox35. Sodium’s higher concentration in irrigation water can reduce soil permeability and distress the growth of plant communities14.

$$\%Na=\frac{\left({Na}^{+}+{K}^{+}\right)}{{(Ca}^{2+}+ {Mg}^{2+} +{Na}^{+} +{K}^{+})} \times 100$$
(3)

where Na+, K+, Ca2+, and Mg2+ exhibit the sodium, potassium, calcium and magnesium concentrations respectively in water samples.

Residual sodium carbonate (RSC)

The sodium carbonate concentration in the river water is computed using Eq. (4).

$$RSC=\left({CO}_{3}^{2-}+{HCO}_{3}^{-}\right)-({Ca}^{2+}+{Mg}^{2+})$$
(4)

where Ca2+, Mg2+, CO32−, and HCO3− show the concentration of calcium, magnesium, carbonate, and bicarbonate respectively in water samples.

Permeability index (PI)

The PI is computed after Doneen15 and Raghunath16 using Eq. (5). The underlying reason for selecting the PI method is that the permeability of soil is significantly affected by the relative ionic concentrations. High sodium levels relative to calcium and magnesium can lead to soil dispersion, reducing permeability and negatively impacting soil structure14.

$$PI= \frac{\left({Na}^{+}+ \sqrt{ {{HCO}_{3}}^{-}}\right)}{\left({Ca}^{2+}+ {Mg}^{2+}+ {Na}^{+}\right)} \times 100$$
(5)

where Na⁺, Ca2⁺, Mg2⁺, and HCO₃− denote the concentrations of sodium, calcium, magnesium and bicarbonate in the water.

Salinity hazard

The salinity hazard is generally measured based on EC, TDS, chloride, and sulphate in water. The United States Salinity Laboratory (USSL) used EC and TDS for determining salinity hazard. In the present context, we have used ‘chloride plus half of sulphate concentrations’ as a measure of the potential salinity (PS)7.

Magnesium hazard (MH)

MH is another key water quality measure to assess the irrigation water suitability of the Jalangi River. The high magnesium content in irrigation water affects soil structure and reduces soil infiltration rate owing to excessive water absorption between magnesium and clay particles. Water contaminated with magnesium affects soil quality and reduces crop production changing soil pH to alkalinity18. The MH value, computed using Eq. (6), of less than 50 indicates the irrigation water is safe, but more than 50 indicates unsafe36.

$$MH= \frac{\left({Mg}^{2+}\right)}{\left({Ca}^{2+}+ {Mg}^{2+}\right)} \times 100$$
(6)

where Ca2+ and Mg2+ are calcium and magnesium concentrations in water samples.

Hydro-chemical analysis

The Gibbs plot and the saturation index are valuable tools for studying surface water hydrochemical evolution because they provide a better understanding of the geochemical processes that govern water chemistry.

Gibbs plot

The Gibbs plot was designed specifically for surface water, making it more appropriate for assessing surface water chemistry37. However, the same is also applied to groundwater for distinguishing three basic processes: precipitation, rock interactions, and evaporation by graphing Na+/(Na+ + Ca2+) vs total dissolved solids (TDS) and Cl−/(Cl− + HCO3−) versus TDS38. This distinction is crucial for understanding the genesis and evolution of solutes in the Janangi river water. Water samples falling in the precipitation dominance field are influenced primarily by atmospheric inputs37. These waters generally have low TDS values because they have not yet undergone significant interaction with geological formations. In the rock dominance field, water samples exhibit higher TDS values due to interactions with minerals in geological formations. These interactions include processes such as the weathering of rocks, dissolution of minerals, and ion exchange between water and rock materials39. The evaporation dominance signifies a hydrochemical condition characterized by elevated TDS and is influenced significantly by evaporation processes38,40.

Saturation index

Surface water and groundwater hydrochemistry are caused by various mechanisms one of which is rock-water interaction. In the present context, the evolution of Jalangi river water is examined in terms of saturation index (SI) as the Jalangi river water quality is affected by the base flow from groundwater and water coming from the Ganga River system22. Rock weathering can be estimated with the help of the SI41 using Eq. (7).

$${\text{SI}} = \frac{KIAP}{{KSP}}$$
(7)

Where KSP denotes the solubility product of that mineral and KIAP denotes the ions activity product for a mineral reaction. For determining the SI of minerals in the water, the PHREEQC programme (version 3.3.7) is beneficial42. The SI value illustrates the nature of chemical equilibrium between water and minerals in relation to water–rock interaction. SI > 0 denotes the supersaturated condition at which the minerals begin to precipitate, whereas SI < 0 denotes the unsaturated states where the minerals are continually eroded by groundwater or surface water. Additionally, SI values near 0 represent mineral phase equilibrium states.

Statistical analysis

Hierarchical cluster analysis (HCA)

HCA is applied to make clusters or groups according to the variables’ similarity for classifying variables6. Here, it is used to classify the different irrigation suitability indices for clustering. HCA was executed by Ward linkage with the Euclidean distance method using the international business machines (IBM) statistical package for social sciences (SPSS) (v.26).

$$\Vert a-b\Vert \frac{2}{2 }= \sum_{i}{({a}_{i}-{b}_{i})}^{2}$$
(8)
Principal component analysis (PCA)

The PCA is used to reduce the amount of multivariate data dimensions and help to alter these data into the principal components without missing information6. It is also a popular and operative statistical tool for analysing the irrigation suitability indices to perceive the leading, controlling factor of irrigation water quality. In this regard, PCA was performed by Quartimax with Kaiser normalization using IBM SPSS (v. 26).

$${\text{Zj}} = {\text{a}}_{{{\text{j}}1}} {\text{P}}_{1} + {\text{a}}_{{{\text{j}}2}} {\text{P}}_{2} \ldots + {\text{a}}_{{{\text{jn}}}} {\text{P}}_{{\text{n}}} \ldots \left( {{\text{j}} = 1,2, \ldots {\text{n}}} \right)$$
(9)

where i denotes 1,2, …, n (for “n” variable), j for 1,2…m (for “m” attribute), PCAr captures factor loadings of a particular stage, λr reflects eigenvalue of a stage and each of the observed variables are considered as linear with regard to the uncorrelated components like P1, P2,…, Pn.

Analysis of variance (ANOVA)

ANOVA is a statistical tool that is used to illustrate the variance between two or more variables by significance tests. This statistical analysis technique is also used in the present context to show the significant variation among the spatial and temporal (pre-monsoon as PRM, monsoon as MON, and post-monsoon as POM) irrigation suitability indices with the help of one-way ANOVA in Microsoft Office Excel (v. 2016).

$$\text{F }= \frac{MST}{MSE}$$
(10)

where F exhibits the coefficient of ANOVA, MST implies the mean sum of all the squares owing to the treatment, and MSE depicts the mean sum of squares owing to an error.

Results

Physicochemical parameters and water facies

Studied physicochemical parameters are compared based on their usual range in irrigation water, prescribed by the Food and Agriculture Organization (FAO)43. Results show that the concentration of CO32− in the samples (100%) and of K+ in almost all the samples (spatial = 94.12%, PRM = 97.44%, MON = 87.5% and POM = 100%) exceeded the usual range in the spatio-temporal framework. The higher concentration of carbonate results in soil sodicity and reduced permeability. Moreover, carbonate ions can alkalize the soil, impacting nutrient availability and microbial activity. Potassium is an essential nutrient for plant growth, but high concentrations in irrigation water can lead to soil salinity issues and affect crop yield. The elevated levels of K+ in the Jalangi River may come from potential sources such as agricultural runoff, and industrial activities. High levels of K+ in irrigation water can lead to soil salinity, which can negatively impact soil structure, reduce water infiltration, and affect nutrient availability to plants. Moreover, the concentration of HCO3- in 20.59%, 7.69%, and 5% of samples exceeded its usual range in spatial, PRM, and POM, respectively. High bicarbonate levels can elevate soil pH, resulting in alkalinity. This can reduce nutritional availability, particularly for micronutrients such as iron, manganese, zinc, and copper, which become less soluble under alkaline conditions. Elevated bicarbonate levels can cause calcium to precipitate as calcium carbonate, limiting soil permeability and altering soil structure44. This can cause poor aeration and drainage, making it difficult for roots to obtain water and nutrients14. The pH concentration in 2.94% spatial and 2.5% POM samples exceeded its threshold limit for irrigation water. High pH levels can cause nutritional imbalances by making some nutrients unavailable or precipitating them out of solution45. For example, iron deficiency is frequent in high-pH soils, causing plant chlorosis (yellowing of the leaves). A neutral pH is frequently best for soil microbial activity. High pH can inhibit microbial activity, influencing processes such as nitrogen fixation and organic matter decomposition, both of which are important for soil fertility46. However, the PO43− concentration in 35.29% of spatial samples only exceeded its usual range in irrigation water (Table 1).

Table 1 Evaluation of water quality for irrigation use.
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The spatial distribution of physicochemical parameters exhibits that chemical oxygen demand (COD) concentration is high and NO3− concentration is low. In contrast, the variability of K+ concentration is higher than the other parameters (Fig. 2a). Seasonal distribution shows that the concentration of physicochemical parameters in PRM is high in comparison to the PRM and POM seasons. Concerning all seasons, EC is high and PO43− is low among the other parameters (Fig. 2b). Turbidity exhibited higher variations in the MON season. Based on the mean values, the concentration of cationic parameters can be arranged as Ca2+ > Mg2+ > Na+ > K+ while the anionic parameters are arranged as HCO3− > CO32− > SO42−  > Cl− > PO43− > F− > NO3−. Piper trilinear diagram for spatial dynamics shows specific toxicity hazards degree and found that the dominant water facies are HCO3−–CO32−–SO42−–Cl−–Ca2+. Besides, HCO3- is dominant in the anionic triangle, while Ca2+ is in the cationic triangle. From the cationic triangle, it was also observed that 50%, 44.12%, and 5.88% of water samples were calcium, no dominant, and magnesium type, respectively. On the other hand, the anionic triangle shows 91.18%, 2.94%, and 5.88% of samples are bicarbonate, sulphate, and no dominated type, respectively. Moreover, the Piper trilinear diagram demonstrates that 91.18%, 2.94%, and 5.88% of samples fall in zones 8, 5, and 9, i.e., sodium-bicarbonate, carbonate hardness, and mixed type, respectively (Fig. 3a). These findings suggest that the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3− + CO32−) exceeds strong acids (Cl− + SO42−). Furthermore, seasonal dynamics show that all the seasons’ water is Na+–HCO3− types as Ca2+ dominates in the cationic triangle while HCO3− dominates in the anionic triangle (Fig. 3b). Besides, it also observed that a little water is a mixed type, found in the cationic triangle.

Fig. 2
figure 2

Distributional nature of the major ionic chemistry, (a) spatial variations (N = 34) and (b) seasonal variations (N = 39 for pre-monsoon, 40 each for monsoon and post-monsoon) (note: the variables are explained in Sect “Methodology”. PO for PO43−, F for F−, NO for NO3−, pH for potential of hydrogen, K for K+, BOD for biological oxygen demand, DO  for dissolved oxygen, Cl for Cl−, SO for SO42−, Na for Na+, COD for chemical oxygen demand, TB for turbidity, Mg for Mg2+, TSS for total suspended solids, Ca for Ca2+, CaCO for CaCO3, TDS for total dissolved solids, TA for total alkalinity, EC for electrical conductivity.

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Fig. 3
figure 3

Hydro-chemical classifications of water sample as per the Piper trilinear diagram, (a) spatial dynamics, (b) seasonal dynamics.

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Hydrochemical processes controlling river water

Gibbs plot for anion and cation

The Gibbs plot of spatial dynamics shows that 82% and 79% of the waters from the Jalangi River fall into the dominant rock zone for cation and anion, respectively (Fig. 4a, b). For seasonal dynamics, Gibbs plot also shows that almost all water from all the seasons falls into the dominant rock zone for both cation and anion (Fig. 4c, d). This indicates that rock weathering is dominant in controlling hydro-chemical evolution and water quality compared to other factors (evaporation and precipitation). Furthermore, MON water quality is fully controlled by rock weathering while for PRM and POM, very little evaporation dominance is also found for controlling river water quality. During the PRM and POM seasons even though there is less rainfall, the river experiences high chloride levels. Less rainfall lowers the dilution capacity of the river, which highlights the presence of existing chloride concentrations from natural and anthropogenic sources. Anthropogenic activities like industrial discharge and agricultural runoff introduce significant levels of chlorine into the river39. Groundwater interactions are especially important since the baseflow of chloride-rich groundwater in this area raises the levels of chloride in the river47.

Fig. 4
figure 4

Gibbs plot for anion and cation (a) cations (spatial dynamics), (b) anions (spatial dynamics), (c) cations (seasonal dynamics), (d) anion (seasonal dynamics) (note: 34 samples for spatial variations and seasonal variations (N = 39 for pre-monsoon, 40 each for monsoon and post-monsoon; the variables are explained in Sect “Methodology”).

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Saturation index and mineral dissolution

This index of every mineral represents the natural process of rock-water interaction, which affects surface water hydrochemistry. All SIHalite recorded negative indicates Na+ and Cl− continuously dissolve in the surface water. Similarly, regarding SISilvite, all values negatively represent sylvite minerals’ weathering. SICalcite and SIDolomite (except one sample) > 0 indicate the super-saturated status of the carbonate minerals. The SICalcite and SIDolomite ranged from 0.06 to 3.41 and − 0.14 to 6.62, with mean values of 2.71 and 5.18, respectively. About 10% of samples record SIGypsum < 0, indicating gypsum dissolution into the water. About 90% of samples record the precipitation status of the gypsum. Gypsum dissolution into groundwater results from the reaction of CaSO4·2H2O = Ca2+ + SO42− + 2H2O, which incorporates calcium and sulfate into water41. Regarding argonite and anhydrite, about 97% of samples record the SIArgonite > 0, and about 80% of samples record SIAnhydrite > 0, representing the precipitation status of the minerals in the river water. However, 35% of river water samples represent the dissolution status of fluorite (SI < 0). The concentrations of Ca2+, HCO3−, Mg2+, and HCO3− were not strongly correlated with SICalcite and SIDolomite indicating that the calcite and dolomite weathering did not continue; the mineral will precipitate and may be a negligible part of dolomite will be dissolving. SIHalite is less than 0 and positively associated with TDS (Table 2), while only 12% SIGypsum < 0, and there is no such correlation observed with TDS. The scatter plots of Na+, Cl− versus SIHalite and Ca2+, SO42− versus SIGypsum, and the correlation coefficients (R2) are 0.83, 0.20, 0.33, and 0.59, respectively (Table 2). Exponential intensifications of Na+, Cl− and Ca2+, SO42− are determined by the dissolution of halite and gypsum, suggesting the dominancy of halite and gypsum minerals along the flow direction.

Table 2 Descriptive statistics for saturation indices.
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It has been observed that seasonally the degree of precipitation and dissolution varies from region to region. From PRM to MON, the dissolution intensity decreased for the salt anhydrite and gypsum while it increased for halite and sylvite. Moreover, the precipitation rate also decreased for aragonite, calcite, dolomite, and sylvite. Similarly, regarding MON to POM, the degree of dissolution is decreased for sylvite, halite, and gypsum, while the precipitation rate increased for aragonite, calcite, dolomite and fluorite. The relationship between different ion concentrations and salts also seasonally differs. There is a strong relationship between chloride and dolomite during PRM and MON, while the relationship becomes weak during POM. A similar observation has been found for aragonite salt (Table 3). Some salts (i.e., anhydrite, aragonite, calcite, and dolomite) have a strong relationship with TDS during MON and POM, and no seasonal variation has been found in the relation with fluorite. Throughout all the seasons, the relationship remains strong for sodium and halite, chloride and sylvite, sulphate and anhydrite, and chloride and halite. Some ions and salts have a stronger relationship during MON than in other seasons (calcium and anhydrite, calcium and calcite, sodium and sylvite, aragonite and calcium) (Table 3). Gypsum and calcium have a positive relationship during PRM and POM, while a strong negative relationship is found during MON. During MON, sulphate has a comparatively strong negative relationship with aragonite, calcite and dolomite, while a strong positive relationship has been found with gypsum and anhydrite. Moreover, magnesium and anhydrite, sulphate and fluorite, and magnesium and gypsum have weak relationships throughout the season (Table 3).

Table 3 Correlation among the ion concentration and saturation indices.
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Irrigation water quality assessment

River water is an important source of irrigation water. Thus, its assessment is crucial for crop production and cropping health with sustainable irrigation development7. In the present study, sodicity (a. SAR b. %Na), alkalinity (RSC), permeability (PI), salinity (PS), and magnesium hazard (MH) are employed to analyse the river’s water suitability for irrigation.

Sodicity hazard

(a) SAR indicates the soil’s sodium hazard and the irrigation water’s suitability. It is also a significant water quality parameter for sodium-affected soil management48. Its high concentration in irrigation water distresses soil permeability and salinity. These two jointly affect crop health which reduces crop production. The spatial and seasonal dynamics of SAR are mentioned in Fig. 7a–d and Table 4. The SAR values range from 0.35 to 0.64 \((\overline{x }=0.47 )\) with a higher coefficient of variation (CV) (Table 5). Furthermore, the SAR values are classified into four categories, i.e., excellent (SAR < 10), good (10 < SAR < 18), doubtful (18 < SAR < 26), and unsuitable (SAR > 26) for agriculture (Table 5). Analysing the samples, it is found that all the samples (100%) are categorised under the excellent category (Table 5). Furthermore, these findings suggest that 100% water samples are appropriate for irrigation and do not pose any sodicity risks. Besides the spatial variation of SAR, this study has also detected temporal variation from 2012 to 2022. The SAR values were found to be 0.11 to 0.99 \((\overline{x }=0.39)\) with CV 33.07% from 2012 to 2022. While for seasonal variation, they range from 0.14 to 0.66 \((\overline{x }=0.402)\) with CV 30.8 in the PRM, 0.11 to 0.99 \((\overline{x }=0.400)\) with CV 39.38 in the MON and 0.11 to 0.59 \((\overline{x }=0.37)\) with CV 28.31 in the POM season (Table 6). Based on the SAR values, it was observed that the sodicity hazard in the PRM was highly flowed by MON and POM seasons. Moreover, through categorical classification of SAR values, it was also observed that water (100%) is excellent for agricultural use and free from sodicity hazards in all seasons. Furthermore, the United States Salinity Laboratory (USSL) diagram has been used to represent the river water suitability for irrigation. Fig demonstrates that all the water for spatial and seasonal dynamics lay under S1, i.e., low sodium (alkali) hazard (Fig. 5a, b).

Table 4 Best-fit regression line with significance level.
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Table 5 Spatial classification of water for irrigation use.
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Table 6 Temporal classification of water for irrigation use.
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Fig. 5
figure 5

Water suitability for irrigation, (a)  United States (U.S) Salinity Laboratory diagram (spatial dynamics), (b) U.S Salinity Laboratory diagram (seasonal dynamics), (c) Wilcox diagram (spatial dynamics), (d) Wilcox diagram (seasonal dynamics) (note: total samples (N = 34) for spatial variations and seasonal variations (N = 39 for pre-monsoon, 40 each for monsoon and post-monsoon).

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(b) %Na is a significant water quality parameter indicating sodicity hazard and irrigation water suitability. This study found that the %Na ranged from 10.16 to 20.81 \((\overline{x }=15.09),\) and the CV is 19.06 (Table 5). Based on the irrigation suitability (Table 5), %Na is classified into five categories, i.e., excellent (< 20), good (20–40), permissible (40–60), doubtful (60–80), and unsuitable (> 80). According to the %Na classification, it was observed that 91.18% of water samples come under the excellent and 8.82% are under the good category (Table 5). Hence, all samples are suitable for irrigation in terms of irrigation suitability. Besides the spatial variation, temporal variation of %Na has also been measured from 2012 to 2022 in this study. The %Na values range from 5.56 to 30.30 \((\overline{x }=14.75)\) with CV 29.82% from 2012 to 2022. Although they range from 5.83 to 25.26 \((\overline{x }=13.55)\) with CV 30.14 in the PRM, 5.56 to 30.30 \((\overline{x }=16.57)\) with CV 27.59 in the MON and 7.68 to 23.29 \((\overline{x }=14.11)\) with CV 28.49 in the POM season (Table 6). According to the %Na means values, it was found that the sodicity hazard was high in the MON while low in PRM seasons. Moreover, in the categorical classification of %Na values for irrigation suitability, it was also found that 92.31, 80 and 90% of water are excellent, while 7.69, 20 and 10% of water are good in PRM, MON, and POM, respectively. These results also indicate that water is free from seasonal sodicity hazards. Further, the Wilcox diagram is used to classify the suitability of Jalangi River water and found that all river water is within the excellent category throughout the spatial and temporal (seasonal) dimensions (Fig. 5c, d).

Alkalinity hazard

RSC is a crucial water quality parameter used to measure alkalinity hazards which affects crop growth by hampering water supply to its root. The RSC of the samples ranges from − 3.54 to 36.39 meq/l (\(\overline{x }=11.47 meq/l\)) with a higher CV (54.40%). A high CV and outlier of RSC values indicate that the spatial variability of RSC is high among the other indices (Fig. 6a). Based on irrigation water suitability (Table 5), the RSC values can be classified as good (RSC < 1.25), doubtful (1.25 < RSC < 2.5), and unsuitable (RSC > 2.5). Based on this classification, 94.12% of water samples were found unsuitable for irrigation use, while 2.94% of water samples were found as good and doubtful (Table 5). Besides the spatial variation of RSC, in this study, temporal variation has also been measured from 2012 to 2022. The RSC values vary from 1.45 to 14.82 meq/l \((\overline{x }=9.01\text{meq}/\text{l})\) with CV 32.07 from 2012 to 2022. While for seasonal variation, they vary from 4.62 to 14.11 meq/l \((\overline{x }=10.77\text{meq}/\text{l})\), 4.22 to 12.32 meq/l \((\overline{x }=6.88 \text{meq}/\text{l})\) and 1.45 to 14.82 meq/l \((\overline{x }=9.43 \text{meq}/\text{l})\) with CV 17.75, 28.87, and 33.26% in the PRM, MON, and POM seasons, respectively (Table 6). It was observed that the Alkalinity hazard was high in the PRM. In contrast, low in MON seasons (Fig. 6b). Moreover, according to the categorical classification of RSC values for irrigation suitability, it was also observed that the water is not suitable for irrigation use in all seasons. However, only 2.5% of water is doubtful in POM. These results also indicate that water is not free from Alkalinity hazards in all seasons.

Fig. 6
figure 6

Box plots showing the (a) spatial (b) seasonal variations in water quality indices (note: the variables such as SAR, PS, %Na, RSC, MH, and PI are explained in Sect “Methodology”.

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Permeability hazard

PI is a significant water quality parameter to evaluate irrigation water suitability. In the present study, the PI values are calculated to measure the suitability of the Jalangi River for irrigation. Results show that PI values ranged from 27.81 to 88.51% (62.12% mean) with a lower CV value. PI is classified into three classes- PI <80% (class 1) as good (>75% of maximum soil permeability); PI 80-100% (class 2) as moderate (25 – 75% of maximum soil permeability);  PI>100% (class 3) as poor (<25% of maximum soil permeability) (Table 5). Based on these classifications, it was found that 91.18% of water samples were good, and 8.82% of water samples were moderate for irrigation use (Table 5). In the temporal scale, the PI values are found to be 55.19 to 104.46 \((\overline{x }=71.46)\) with CV 15.54 from 2012 to 2022. Although they range from 55.19 to 104.46 \((\overline{x }=65.95)\) with CV 13.73 in the PRM, 59.22 to 97.67 \((\overline{x }=79.30)\) with CV 11.67 in the MON and 55.22 to 91.23 \((\overline{x }=68.98)\) with CV 15.05 in the POM season (Table 6). Based on PI mean values and box plots, it was found that the permeability hazard was high in the MON. In contrast, low in PRM seasons (Fig. 6b). Moreover, in the categorical classification of PI values for irrigation suitability, it was also found that 94.87, 47.5, and 82.5% of water was good in PRM, MON, and POM, respectively  (Table 6). In comparison, only 2.5% of water is unsuitable in PRM season. These results indicate that water is free from permeability hazards in PRM and POM seasons (Fig. 7a–d; Table 4).

Fig. 7
figure 7

Spatio-temporal variation of the irrigation hazards, (a) variation from the source to the mouth of the Jalangi River, (b) pre-monsoon variation, (c) monsoon variation, (d) post-monsoon variation (notes: 7b–d share the same legend as 7a; the trendlines are the best-fit lines; the irrigation hazard indices are explained in Sect “Methodology”).

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Salinity hazard

PS is used as a water quality parameter to indicate irrigation water’s suitability. The result shows that the PS values were 1 to 7.58, with a mean value of 2.97 (Table 5). The calculated CV value of PS (42.07) was very high, indicating more variability of the PS values. From the observed PS value, it was found that 58.82%, 38.24%, and 2.94% of studied water samples came under the excellent to good, good to injurious, and injurious to unsatisfactory categories, respectively (Table 5). Besides the spatial variation of PS, temporal variation has also been analysed from 2012 to 2022 in this study (Fig. 7a–d; Table 4). The PS values range from 0.15 to 0.73 meq/l \((\overline{x }=0.41 meq/l)\) with CV 30.12 from 2012 to 2022. While for seasonal variation, they range from 0.25 to 0.61 meq/l \((\overline{x }=0.46 \text{meq}/\text{l })\) with CV 18.09 in the PRM, 0.15 to 0.73 \((\overline{x }=0.38 \text{meq}/\text{l })\) with CV 41.10 in the MON and 0.22 to 0.60 \((\overline{x }=0.39 \text{meq}/\text{l })\) with CV 26.74 in the POM season (Table 6). According to the PS mean values, it was observed that the salinity hazard in the PRM was highly flowed by POM and MON seasons (Fig. 6b). Moreover, through categorical classification of PS values, it was also observed that water (100%) is excellent to good for agricultural use and free from salinity hazard in all seasons. Furthermore, the USSL diagram is utilized to estimate the suitability of river water for agriculture. According to this diagram, it was found that almost all water samples throughout the river are in C2, i.e., medium salinity hazard (Fig. 6a). However, seasonally, maximum water samples of PRM and POMs lay in C2. However, the water of MON comes within C1 to C2, i.e., low to medium salinity hazard (Fig. 6b). The seasonal dynamics results indicate that the MON season has a lower salinity hazard compared to the other seasons. These findings show that Jalangi River water has a medium salinity hazard in terms of irrigation suitability.

Magnesium hazard

The result shows that the MH values of samples ranged from 10.16 to 78.66 (\(\overline{x }=40.98\)) with a lower CV value (36.29%) (Table 5). Moreover, 76.47% of samples lay within safe limits, while 23.53% of water samples lay unsafe for irrigation use in the study area. Besides the spatial variation of MH in this study, temporal variation has also been measured from 2012 to 2022. The MH values vary from 6.90 to 60.39 \((\overline{x }=28.85)\) with CV 34.77 from 2012 to 2022. Although they vary from 6.90 to 47.95 \((\overline{x }=29.45)\), 16.39 to 48.70 \((\overline{x }=29.57)\) and 14.15 to 60.39 \((\overline{x }=27.54)\) with CV 39.12, 32.41, and 32.61% in the PRM, MON, and POM seasons respectively (Table 6). It was noticed that the magnesium hazard was high in the MON followed by PRM and POM seasons (Fig. 6b; Fig. 7 b-d). Moreover, according to the categorical classification of MH values for irrigation suitability, it was also observed that water is suitable for irrigation use in all seasons. However, only 5% of water is unsuitable in POM. These results indicate that water is free from magnesium hazards in all seasons.

HCA on irrigation indices

In the present work, HCA was performed on spatiotemporal irrigation suitability indices and found that the spatial indices were clustered into two groups and seven subgroups. Similarly, the temporal indices were clustered into two broad clusters and a few sub-clusters. Regarding the spatial water samples, the first broad clusters were formed with two sub-clusters and the sample Ids. 28, 31,7, 20, 6, 8, 27, 24, 25, 22, 23 and 12 formed the first sub-cluster while 19, 21, 26 and 29 formed another sub-cluster. Similarly, the second broad cluster also formed with two sub-clusters i.e. first sub-cluster with the sample Ids. 1, 34, 18, 30, 3, 5, 9 and 17 while the second sub-cluster with Ids. 10, 11, 13, 16, 32, 33, 14, 2, 4 and 15 (Fig. 8a). It is interesting to note that all the spatial samples except for sample Id. 29 located at Paschim Panditpur characterized by higher human interventions ay ghat (public bathing place) are fused at a short distance (2–5 at rescaled distance of Ward linkage). This implies a greater homogeneity in the clustering of the spatial samples. Moreover, according to the temporal study, the HCA of PRM, MON and POM shows that few samples make small clusters and the number of small clusters is high in the temporal HCA i.e. during PRM four discrete clusters formed with only one sample (Ids. 6,1,12 and 32). Similarly, six discrete clusters are formed only with two samples (Ids. 21, 22; 7, 4; 5, 37; 28, 38; 30, 31; 8, 36) (Fig. 8b). Except for sample Id. 32 of the PRM period of May 2019 with elevated pollution level, all samples are fused at a short distance (< 5) of ward linkage scale indicating intra-group homogeneity (Fig. 8b). Similar intra-group homogeneous pattern has been observed for MON and POM seasons (Fig. 8c, d). However, the inter-group pattern differs from PRM to MON and POM i.e. the smaller sub-cluster is located on the upper part of the dendrogram for PRM while the reverse is observed for the MON and POM. This is due to the elevated pollution during the PRM compared to the MON and POM.

Fig. 8
figure 8

Dendrogram using hierarchical cluster analysis of Jalangi River water quality parameters (a) spatial, (b) pre-monsoon variation, (c) monsoon variation, (d) post-monsoon variation.

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PCA for the relationship among the irrigation hazard indices

Liu et al.51 classified PCA factor loadings as strong (> 0.75), moderate (0.5–0.75), and weak (0.3–0.5). Based on this classification, the spatial data indicates that %Na, SAR, and PI are the primary contributors to the first principal component (PC1), with strong loadings of 0.967, 0.935, and 0.774, respectively. This implies that the primary determinants of the water quality are these variables, which most likely correspond to salinity and salt concentration. RSC and MH contribute significantly to the second principal component (PC2), with moderate loadings of 0.705 and 0.647, respectively, suggesting their influence on several facets of water quality. PS has a negative loading on PC2, indicating a slight but substantial impact. Besides, the seasonality-related PCA findings show that %Na, SAR, and PI are the main variables affecting water quality in the PRM, MON, and POM periods. These factors play a critical role in determining the salinity and sodium properties of water because they considerably contribute to the PC1 in all seasons. Even though RSC and MH are significant factors; their impact varies seasonally in response to shifts in the dynamics of water quality. The first two components account for a range of 65.238% to 75.9% of the total variance, which indicates a strong representation of the dataset. The post-monsoon period has the maximum cumulative variance, indicating a more stable and consistent pattern of water quality throughout this time (Table 7).

Table 7 Principal component analysis for detecting the multi-variate response of irrigation water quality indices.
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ANOVA for spatio-temporal variation in irrigation suitability

The ANOVA results show no significant difference in irrigation water quality among the spatial variation. However, some indices like SAR, %Na, MH, and PI tend to have little difference among the spatial variation of irrigation water quality. Moreover, for temporal or seasonal variation, the irrigation water quality suitability indices like PS, %Na, RSC, and PI significantly differ among the seasons (Table 8). Hence, these results indicate that there are no spatial variations in irrigation water quality due to homogenous floodplains22 with standstill water and no significant point source pollution effects27 during the pre-monsoon season when spatial water samples were collected; however seasonal variations have been found in the study area due significant differences in monsoon regime especially concentration of rainfall within three months (July to September) that influences the pollution concentrations14.

Table 8 One-way ANOVA for showing the spatio-temporal variation in irrigation water quality or suitability.
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Discussion

The physicochemical examination showed that all the water quality parameters, except for K+ and CO32− in the water, are within the acceptable range for irrigation use. Specifically, the Jalangi River is free from sodicity and permeability hazards, while the region has a relatively higher alkalinity hazard. A similar phenomenon is also observed for the neighbour rivers, i.e., the Bhagirathi and Churni Rivers14. Moreover, this study also found that the seasonal variability in Jalangi River water quality and during the PRM water quality is the worst among all the other seasons. Hence, this result indicates that the Indian MON regime is one of the main controlling aspects of Jalangi River water quality. On the other hand, Raymond et al.52 found that extensive agricultural practices (mainly due to extensive use of fertiliser and irrigation) tend to increase the ion concentration responsible for irrigation hazards. Thus, extensive agricultural practices should intensify the study area’s bicarbonate concentration. Giday Adhanom53 found that Shwarobit River water was affected by salinity hazards, and emphasis was given to crop selection and removal of excess soil leaching besides the uses of fertiliser and irrigation. In the study, area salinity hazard is relatively higher in some specific reaches where these strategies may be helpful along with the periodical monitoring of river water.

The hydrochemistry of the Jalangi River is influenced by several factors, including its link to the Ganga River, regional geological variations, and baseflow-controlled river water22. Similar results were reported by the works of Nsabimana et al.54. As the Jalangi River is connected to the Ganga River and receives a significant discharge in the MON season, the hydro-chemical properties of the Jalangi River are impacted by the rock-water interaction processes that take place in the Ganga River basin55,56. Furthermore, during the dry season, when surface water flow is decreased, the Jalangi River relies more on groundwater base flow, strengthening its link to regional geological fluctuations22. These regional geological changes might affect the mineral concentration and overall chemistry of the groundwater that feeds the Jalangi River, altering its hydrochemistry57. Understanding these complex dynamics is critical for controlling and protecting water quality in the Jalangi River and its basin. Furthermore, human activities such as agriculture, industrial effluents, and household waste disposal may release pollutants into the river, affecting its hydrochemistry58. The works of Zhao et al.59 and He and Li60 also reported similar observations from China.

The river exhibited gradual decay with time61. One of the primary factors contributing to the River Jalangi’s course deteriorating is the closure of its off-take from the river Padma, which feeds into the studied river. Moreover, Bhairab feeding the Jalangi River is also suffering low influx from the Ganga River27. This is primarily driven by the eastward titling of the Bengal basin that triggered the longitudinal disconnection of the rivers of south Bengal from the Ganga-Padma delta system39,62,63,64. The off-take of the river Jalangi has split apart and sludge-blocked due to this movement (Fig. 9a–k). Thus, the river discharge during the PRM season is very low (Fig. 1c) which is primarily controlled by base flow61. Therefore, low river flows are also a factor in natural changes in water quality during the PRM season. For instance, sustained base flows throughout the PRM season raise the water’s temperature and reduce dissolved oxygen levels65. If there is a significant amount of habitat variability, native species can endure such circumstances. Besides, the increasing anthropogenic activities such as channel narrowing, free flow blockage, and urban effluent mixing have also exacerbated the existing problems66.

Fig. 9
figure 9

Decay of Jalangi River through historical time (a) Jalangi River in 1840’s scenario in Tassin’s map (based on Das22) (b) index map of the Ganga–Bhairab offtake, Ganga–Jalangi offtake, and Jalangi–Bhairab confluence (Sentinel 2A tiles number T45QXG dated 15 Nov 2020), (c–e) represent the evolution of Ganga–Bhaibrab offtake in 1973 (Landsat 1 multi-spectral scanner (MSS), path 149, row 43 dated 17 Jan 1973), 2000 (Landsat 7 enhanced thematic mapper plus (ETM +), path 138, row 43 dated 17 Nov 2000) and 2020 (same as 9b), (f–h) show evolution of the Ganga–Jalangi offtake in 1973 (same as 9c), 2000 (same as 9d) and 2020 (same as 9b); (i–k) depict Jalangi–Bhairab confluence in 1968 (Corona Satellite KH-4A 45, Entity ID DS1045-2196DF120 dated 06 Feb 1968), 2000 (Landsat 5 thematic mapper (TM), path 138, row 43 dated 08 Oct 2000) and 2020 (same as 9b) (source: prepared by the authors based on using ArcGIS (version 10.4), and Adobe Photoshop (version 7.0).

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The man-made river decay, especially in water quality, is well demonstrated worldwide (e.g., Chin et al.67) and in the Bengal delta (e.g. Das et al.58). Therefore, the policy practice, including the structural (e.g. river offtake dredging, sewage treatment plants, etc.) and non-structural measures (e.g. lowering in the untreated discharge of effluents from urban, industrial and agricultural sectors), must be executed sustainably to restore the river’s necessary environmental flow and ecological stability. These initiatives should be participatory, involving local communities, government, and other stakeholders to revive the river and river basin. This study analyses the spatio-temporal variation of Jalangi River water quality for evaluating irrigation suitability. Thus, the findings of this study would be helpful to the stakeholders of the Jalani River, agri-irrigation engineers and regional planners.

In 2020, the Nodal Agency Municipal Engineering Directorate (NAMED) of the Department of Urban Development & Municipal Affairs, Government of West Bengal, submitted a proposal titled “Action Plan for Rejuvenation of River Jalangi, Krishnagar” to the Central Pollution Control Board (CPCB) in Delhi. This proposal was subsequently approved by the River Rejuvenation Committee (RRC) of West Bengal, established in compliance with the directives of the Hon’ble National Green Tribunal. The action plan aims to address various aspects of rejuvenating the River Jalangi and its catchment areas. This includes managing discharges from industrial sources, municipal outfalls, sewage-carrying drains, solid waste, biomedical waste, and e-waste, as well as implementing groundwater management, rainwater harvesting, environmental flow maintenance, floodplain zone protection, improved irrigation practices, riverbank plantation, and the establishment of a biodiversity park. The RRC forwarded the action plan to CPCB on February 12, 202068.

Subsequently, the Task Team suggested revisions during its 10th Meeting on February 26, 2020, which were approved by the RRC in its 7th meeting on June 9, 2020. This revised plan was sent to CPCB on June 9, 2020, reviewed once again by CPCB in its 12th Task Team meeting on June 11, 2020, and further modified based on their recommendations. The finalized action plan, incorporating CPCB’s suggestions, was approved by the RRC in its 8th meeting on July 2, 2020. The report highlighted a 5.0 km stretch of the river identified as “Polluted,” which remains so year-round, particularly around Krishnagar town. The principal pollutants in this stretch are primarily biological. Despite being perennial, water in this stretch is predominantly used for agricultural and fishing purposes. The proposed plan’s estimated cost is Indian National Rupees (INR) 47.98 crore, with an additional Operation and Maintenance Expenditure of INR 25.16 crore per year68. The scheduled completion date for the work was set for June 30, 2021. However, apart from setting some iron nets in large sewers to prevent large solid wastes (plastics) from entering the river, nothing significant was done. Additionally, a small treatment plant at Krishnagar bus stand was left incomplete and never became operational. The main limitations of this current research work are–(1) the unavailability of secondary water quality data in spatial extensions and (2) the lack of extensive monitoring sites for testing the seasonal water quality for spatial variations. These deserve further investigation.

Though river water is free from sodicity and permeability hazards, alkalinity hazards tend to affect irrigation water quality. This is due to the intensive agricultural activities and urban sewage discharge into the river before proper treatment. The study basin is almost ungauged in terms of hydrological and chemical measurements. There is no secondary data, except for one station on water quality. Thus, the study missed the temporal dimension of hydro-chemical assessment for all the river stretches. However, future attempts are required to portray the seasonal variations in water quality. Besides, eco-restoration practices are to be ushered in following a diagnostic river survey. The channel decay, and environmental flow in the context of the neotectonics movement, climate change and the ‘Anthropocene’ may be other perspectives of future endeavours. Finally, apart from irrigation water quality assessment, geochemical modelling may be extended in the future.

Conclusions

The present study focuses on the first systematic attempt to assess the Jalangi River’s water quality for irrigation purposes. The study reported that river water quality is suitable for irrigation purposes. All the water quality parameters lie within acceptable limits except K+ and CO32− for irrigation in spatio-temporal dynamics. Jalangi River water samples are Na+–HCO3− types and the alkaline earth (Ca2+ + Mg2+) exceeds alkalises (Na+ + K+) and weak acids (HCO3− + CO32−) exceeds strong acids (Cl− + SO42−). Gibbs plot shows that rock weathering has a leading role in controlling the hydro-chemical evolution of Jalangi River water. Although, very little evaporation dominance is also noticed in PRM and POM seasons. The Jalangi River water is free from all other hazards except for alkalinity in the spatio-temporal dimension. There are no spatial variations in irrigation water quality due to homogenous floodplains with an absence of point source pollution during the PRM. This study also found that MON water is more suitable for irrigation compared to PRM water.

The limited connectedness with the Ganga–Padma delta system, discharge of urban waste water and widespread agricultural practices are closely related to the deterioration of the river. While an action plan was put into place to revitalize the river, significant progress has not been made, which emphasizes the need for more focused and efficient efforts. To maintain the ecological health of the river and ensure its sustainable usage for irrigation and other functions, future research should devote the greatest attention to eco-restoration techniques, thorough spatial and temporal monitoring, and strict management of human effects. To repair and sustain the Jalangi River’s environmental flow and water quality, this research emphasizes the need for an all-encompassing and cooperative strategy including local people, government organizations, and stakeholders.