Wetlands deliver many ecosystem services but are under continuous threat due to various anthropogenic activities. The present study has been carried out to examine the suitability of Kusheshwar-Asthan wetland's water for agriculture. A total of 57 water samples were analyzed for various water quality parameters like electrical conductivity, pH, temperature, dissolved oxygen (DO), major cations (Ca2+, Mg2+, Na+, K+), and major anions (PO43–, SO42−, N-NO3, Cl, HCO3). Overall, the water of the wetland was found to be alkaline. The pre-monsoon samples had a relatively higher concentration in most of analyzed parameters except for pH, DO, NO3, PO43− and Cl. The concentration of cations follows the order of Na+ > Ca2+ > Mg2+ > K+ in both seasons and for anions it is HCO3 > SO42− > Cl > NO3 > PO43− for pre-monsoon and HCO3 > Cl > SO42− > NO3 > PO43− for post-monsoon. According to Piper diagram and Durov plot, Na-K-HCO3 was the major hydro chemical facies of the surface water. The various irrigation quality parameters showed that wetland water can be categorized as good to excellent quality. As a result, this finding can aid in the long-term sustainable use of the wetland water with regulated anthropogenic interventions. The study will be beneficial in designing long-term extensive management plans for the conservation of the wetland.

  • Major cation was Na+ and major anion was HCO3 in Kusheshwar Asthan Wetland water.

  • The surface water was alkaline in nature.

  • Hydrochemistry of water is influenced by silicate weathering.

  • Salinity hazard, SAR, RSC, Mg/Ca and chloride hazard indicates the agriculture suitability of the water.

  • Novel work for wetlands protection and conservation.

Graphical Abstract

Graphical Abstract

Freshwater resources are essential for the survival of various life forms. The freshwater resources contribute only 3% of the total water resources, out of which only 1% is available for human usage (Deep et al. 2020). Due to the population explosion and higher human dependency on freshwater sources, these resources are under tremendous stress and are deteriorating at a faster rate (Bassi et al. 2014). The area of fresh water sources is shrinking and is also being polluted by anthropogenic influences, thus both quantity and quality are under threat. Today, the world is struggling with basic consumptive water usages like drinking, manufacturing, livestock rearing, and agriculture due to the dilapidated condition of freshwater resources and their distribution (Singh et al. 2017). Wetlands as an important freshwater resource are transition areas between land and water ecosystems and have unique features in terms of soil, vegetation, flora and fauna. Wetlands deliver many ecosystem services including flood control, food and water supply, water for irrigation, groundwater recharge, biodiversity support, nutrient cycling, pollution abatement, and livelihood support (Verma & Negandhi 2011). Ramsar Convention on wetlands (2021) points out that more than 35% of natural wetlands have been lost globally between 1970 and 2015 and the loss of inland wetlands is higher than the coastal ones. The wetlands are under continuous threat from various anthropogenic activities including encroachment for urbanization, industrialization, agricultural land expansion and alteration of hydrological regimes (Gopal 2013). The discharge of untreated or partially treated wastewaters emanating from sewage treatment plants (STPs), industries, municipalities, agricultural and urban runoff into wetlands are also reasons for the poor condition of the wetlands (Bassi et al. 2014; Shan et al. 2021). The inflow and outflow of the water from wetlands is an important factor that governs various wetland processes and their hydrological regimes (Mitsch & Gossilink 2000). The hydrological condition of the wetlands causes variations in the hydro-geochemistry of the water of the wetlands; at times these variations are influenced by human–wetlands relations. Hydro-geochemical assessment of wetlands water is thus conducive to the understanding of its nature, source of ions, pollution status and its suitability for water usage, mostly for irrigation.

North Bihar (northern side of the River Ganges) is blessed with plenty of wetlands locally known as chaurs (natural depressions), moins (oxbow lakes), and pokhars (natural or artificial ponds) that are lifelines by serving the community for irrigation, fishing, foxnut (makhana) and water chestnut (singhara) cultivation. Kusheshwar Asthan (KA) wetland is one of the important wetlands of North Bihar and is situated in the Darbhanga district of Bihar (India). The wetland has been considered a wetland of international importance (MoEF&CC 2020). It is a bird sanctuary and habitat for a variety of endemic birds and migratory birds of other countries. Besides that, the KA wetlands are a religious place. Still, KA wetlands are struggling with agriculture encroachment, railway-track construction and lesser inflow of water due to the construction of flood control embankments (bunds) on the Koshi and Kamla rivers (WII 2017). The KA wetlands have an unconsolidated boundary without clear-cut demarcations, which results in conflict in land usage. Approximately 78% of the land is government-owned and the rest is private land. The land settlement rights are still pending since it was designated as a bird sanctuary in 1994. Such conflicts have resulted in encroachment of land for agriculture and are responsible for the shrinkage of wetlands and change in land use. Studies on fish diversity (Das et al. 2015), prospects for fisheries development (CIFRI) and biodiversity assessment and its management plan (WII 2017) are the significant studies conducted on KA wetlands. Almost two decades ago one hydro-geochemistry-related study was conducted on KA wetlands with single-season (January 2005) data (Ranjan et al. 2017). Thus, the present study was carried out (i) to explore the hydro-geochemistry of the KA wetlands and to assess the seasonal (pre -monsoon and post-monsoon) influence and spatial variation on it; (ii) to identify and elaborate the sources of major ions in the KA water; and (iii) to assess the suitability of wetlands water for irrigation purposes by using irrigation water quality parameters (sodium absorption ratio, sodium% and Kelly's ratio, etc.). This study will provide scientific baseline data for proper conservation and management of the KA wetlands meeting the requirements of the local population.

Study area

KA wetlands are one of the important wetlands in North Bihar, a group of seasonally flooded wetlands (comprising 4–5 chaurs), situated in Darbhanga district of Bihar (Figure 1).
Figure 1

Study area map of Kusheshwar Asthan (KA) wetland.

Figure 1

Study area map of Kusheshwar Asthan (KA) wetland.

Close modal

Geological and hydrogeological setting

Kusheshwar Asthan Wetlands lies between 26°27′N and 25°53′N to 86°40′E and 86°25′E in Koshi-Gandak Basin and has an altitude of 49 m above the mean sea level. KA wetland is spread over 2,921.43 ha including 14 villages within its boundary. The area lies in a hot sub-humid zone with a mean rainfall of 1,025.1 mm per year (Guhathakurta et al. 2020). KA wetlands experience summer from March to mid-June, monsoon from mid-June to September and winter from November to February. These wetlands are fed by the several channels of the perennial rivers of Kamla Balan and Jibish Dhar and by the flood waters of Kamla and Koshi rivers. The slope of the wetland is from north to the southeast which defines the flow of water in the wetland. According to the Ramsar classification system for ‘wetland type’, the wetland is classified as ‘TS-Seasonal’ or intermittent freshwater marshes (WII 2017). During the monsoon season, the wetland is inundated with rain water and flood from adjoining rivers, and more than 80% of the wetland is flooded, while during summer, the wetland shrinks, and clear patches of individual chaurs appear. The KA wetland is a Gangetic alluvial floodplain wetland. Rivers and floods bring large amounts of sand, silt and clay every year. The soil has a high amount of sand followed by clay and silt. The soil is generally rich in organic matter making it suitable for agriculture (Mandal 2010).

Environmental and cultural significance

The KA wetlands are home to plenty of winter migratory birds besides the resident water birds. Due to its importance for water birds, the wetland was declared a bird sanctuary in 1994. It was classified as an Important Bird Area (A1 category) by Bird Life International and the Royal Society for Protection of Birds, UK (Ranjan et al. 2017). IUCN red-listed species like avian fauna (Anhinga melanogaster, Ciconia episcopus, Mycteria leucocephala, Aythya nyroca, Numenius arquata) and reptiles (Python molurus) were reported in the wetland area (WII 2017). The major aquatic vegetation communities include Alternanthera, Eichhornia, Ottelia, Nymphoides, Ipomea and Vallisneria (WII 2017). Having a rich repository of flora, fauna, and fishes, the wetland is recognized as a wetland of national importance under the National Wetland Conservation and Management Programme by the Government of India (https://indianwetlands.in/). The wetland absorbs the excessive flood water from adjoining rivers and protects the area from complete inundation. Furthermore, wetland supports the livelihood of the local people. These people depend on the wetland for agriculture, fisheries, and fodder mainly. Besides this, the KA temple of Lord Shiva popularly known as ‘Kush Mahadev’ adds religious and cultural importance to the wetland area. The temple is visited by thousands of pilgrims every year.

Sample collection

The water samples were collected from 25 sites (5 sites per wetland) in pre-monsoon (March 2021) and from 32 sites in post-monsoon (October 2021). During the post-monsoon the inundated area of wetland increases due to rainfall and floods from adjoining rivers. The sampling was done according to the availability and accessibility of water across the wetland. Thus, the number of sampling sites are higher in post-monsoon (32) as compared to pre-monsoon (25). Water samples were collected in pre-washed clean 1 litre HDPE bottles. The samples were stored in the ice box and carefully transported to the laboratory for further chemical analysis. The parameters such as electrical conductivity (EC), pH, temperature, dissolved oxygen (DO), and bicarbonates were measured at the site while other parameters were further analyzed in the laboratory. Sample collection, preservation, transportation and analyses were done according to Standard Methods suggested in APHA (2017). Details of the analytical method used in the study are shown in Table 1.

Table 1

Water quality parameters with their analytical methods

Water parameterUnit of measurementInstrument/Method usedMake and model
Electrical conductivity μS/cm Digital conductivity meter Eutech eco Testr EC 
pH pH units Digital pH meter Eutech eco Testr pH 
Temperature °C Thermometer  
Total dissolved solids mg/L Oven drying at 105 °C  
Dissolved oxygen mg/L Winkler's titrimetric method  
N-NO3 mg/L Spectrophotometer PerkinElmer Lamba25 
Ortho-phosphate mg/L Spectrophotometer PerkinElmer Lamba25 
Sulfate mg/L Spectrophotometer PerkinElmer Lamba25 
Sodium mg/L Flame photometer Systronics 
Potassium mg/L Flame photometer Systronics 
Chloride mg/L Mohr method  
Total hardness mg/L Titration method  
Calcium mg/L Titration method  
Magnesium mg/L Titration method  
Bicarbonate mg/L Acid titration method  
Water parameterUnit of measurementInstrument/Method usedMake and model
Electrical conductivity μS/cm Digital conductivity meter Eutech eco Testr EC 
pH pH units Digital pH meter Eutech eco Testr pH 
Temperature °C Thermometer  
Total dissolved solids mg/L Oven drying at 105 °C  
Dissolved oxygen mg/L Winkler's titrimetric method  
N-NO3 mg/L Spectrophotometer PerkinElmer Lamba25 
Ortho-phosphate mg/L Spectrophotometer PerkinElmer Lamba25 
Sulfate mg/L Spectrophotometer PerkinElmer Lamba25 
Sodium mg/L Flame photometer Systronics 
Potassium mg/L Flame photometer Systronics 
Chloride mg/L Mohr method  
Total hardness mg/L Titration method  
Calcium mg/L Titration method  
Magnesium mg/L Titration method  
Bicarbonate mg/L Acid titration method  
Table 2

Comparison of the average concentration of major ions (in mg/L) of KA wetlands with selected freshwater wetlands of India

WetlandNa+Ca2+Mg2+K+HCO3-SO42-Cl-NO3-PO43-Reference
KA wetlands (pre-monsoon) 37.28 21.46 10.28 3.45 110.72 18.22 14.51 0.0472 0.021 Present study 
KA wetlands (post-monsoon) 25.43 17.87 7.70 3.58 93.28 2.65 27.93 0.072 0.029 Present study 
Saman wetland, Uttar Pradesh 91.5 23 21.5 16.5 119 108.5 49 54.5 NA (Khan et al. 2022
Loktak Lake, India (pre-monsoon) 7.28 13.05 8.11 2.66 55.8 0.01 29.36 0.171 0.092 (Mayanglambam & Neelam 2022
Loktak Lake, India (post-monsoon) 6.53 9.06 7.31 2.85 58.5 0.21 16.62 0.339 0.407 (Mayanglambam & Neelam 2022)  
Bhindawas Bird Sanctuary, Haryana, India 31.3 18.8 17.3 2.4 100 85.3 58.2 14.2 4.9 (Shan et al. 2021
Kabar Tal, Bihar, India (pre-monsoon) 3.2 13.7 5.7 0.5 11.2 18.40 86.5 1.10 0.047 (Gupta et al. 2021
Kabar Tal, Bihar, India (post-monsoon) 4.1 22.7 12.9 4.8 17.4 47.7 37.8 0.80 0.028 (Gupta et al. 2021
Kabar Tal, Bihar, India (July to November) NA 28.34 6.34 NA 219 30.92 25.74 2.583 0.06 (Singh et al. 2020
Gogabil, Bihar, India NA NA NA NA NA NA 4.77 0.037 0.034 (Adhishwar & Chaudhary 2020
Deepor Beel, Assam 24.43 94.10 3.09 17.08 101.03 56.61 63.57 15.05 2.49 (Dash et al. 2020
Ropar wetland, Punjab NA 27.71 114.77 NA 99.225 2.71 24.89 0.11 0.12 (Akhter & Brraich 2020
Renuka Lake (pre-monsoon) 24.78 54.81 44.12 3.032 201.4 159.0 -- 02.60 NA (Kumar et al. 2019
Renuka Lake (post-monsoon) 24.85 94.44 47.32 3.105 353.6 121.8 -- 6.911 NA (Kumar et al. 2019
Kanjli wetland, Punjab (pre-monsoon) NA 30.53 7.031 NA NA NA 22.49 4.77 0.57 (Singh et al. 2017
Kanjli wetland, Punjab (post-monsoon) NA 40.39 11.46 NA NA NA 30.64 1.65 0.28 (Singh et al., 2017
Nainital Lake, India 13.1 32.7 59.3 3.7 351 NA 15.3 NA NA (Das et al., 2005
WetlandNa+Ca2+Mg2+K+HCO3-SO42-Cl-NO3-PO43-Reference
KA wetlands (pre-monsoon) 37.28 21.46 10.28 3.45 110.72 18.22 14.51 0.0472 0.021 Present study 
KA wetlands (post-monsoon) 25.43 17.87 7.70 3.58 93.28 2.65 27.93 0.072 0.029 Present study 
Saman wetland, Uttar Pradesh 91.5 23 21.5 16.5 119 108.5 49 54.5 NA (Khan et al. 2022
Loktak Lake, India (pre-monsoon) 7.28 13.05 8.11 2.66 55.8 0.01 29.36 0.171 0.092 (Mayanglambam & Neelam 2022
Loktak Lake, India (post-monsoon) 6.53 9.06 7.31 2.85 58.5 0.21 16.62 0.339 0.407 (Mayanglambam & Neelam 2022)  
Bhindawas Bird Sanctuary, Haryana, India 31.3 18.8 17.3 2.4 100 85.3 58.2 14.2 4.9 (Shan et al. 2021
Kabar Tal, Bihar, India (pre-monsoon) 3.2 13.7 5.7 0.5 11.2 18.40 86.5 1.10 0.047 (Gupta et al. 2021
Kabar Tal, Bihar, India (post-monsoon) 4.1 22.7 12.9 4.8 17.4 47.7 37.8 0.80 0.028 (Gupta et al. 2021
Kabar Tal, Bihar, India (July to November) NA 28.34 6.34 NA 219 30.92 25.74 2.583 0.06 (Singh et al. 2020
Gogabil, Bihar, India NA NA NA NA NA NA 4.77 0.037 0.034 (Adhishwar & Chaudhary 2020
Deepor Beel, Assam 24.43 94.10 3.09 17.08 101.03 56.61 63.57 15.05 2.49 (Dash et al. 2020
Ropar wetland, Punjab NA 27.71 114.77 NA 99.225 2.71 24.89 0.11 0.12 (Akhter & Brraich 2020
Renuka Lake (pre-monsoon) 24.78 54.81 44.12 3.032 201.4 159.0 -- 02.60 NA (Kumar et al. 2019
Renuka Lake (post-monsoon) 24.85 94.44 47.32 3.105 353.6 121.8 -- 6.911 NA (Kumar et al. 2019
Kanjli wetland, Punjab (pre-monsoon) NA 30.53 7.031 NA NA NA 22.49 4.77 0.57 (Singh et al. 2017
Kanjli wetland, Punjab (post-monsoon) NA 40.39 11.46 NA NA NA 30.64 1.65 0.28 (Singh et al., 2017
Nainital Lake, India 13.1 32.7 59.3 3.7 351 NA 15.3 NA NA (Das et al., 2005

Statistical analysis

Statistical analysis of the water samples (maximum, minimum, mean, and standard deviation) and irrigation parameters, Gibbs diagram, and plots related to water samples were prepared by Microsoft Excel 2019. Pearson's correlation was analyzed by using SPSS 20. Piper diagram and Durov plot were prepared by Grapher software. Wilcox diagram and USSL diagram were processed by Diagrammes software. Map of the study area and interpolation was done by Arc GIS.

Hydro-geochemistry of KA wetland

The water quality of wetland is a reliable indicator of the environmental changes that depend upon various factors including topography, seasons (Kumari & Sharma 2019), soil type, precipitation, surface runoff, evapotranspiration, flooding frequency, nutrient and sediment loading (Ajorlo et al. 2013).

Temperature regulates chemical reactions in water, influences the macrophytes composition and productivity (Kumar et al. 2018), and affects the solubility of gases in water (Saha et al. 2021; Singh et al. 2022). The temperature of the water of the KA wetland ranged from 26 °C to 28.2 °C during pre-monsoon and 25° to 26.5 °C during post-monsoon reflecting a minute variation (Figure 2(a)). During the pre-monsoon, electrical conductivity (EC), which is a measure of soluble salts in water, was in a range of 250 μs/cm to 540 μs/cm. The average concentration was 380 μs/cm. A relatively higher EC during pre-monsoon may be due to low water availability, higher evaporation rate during this period (Gupta et al. 2021; Mayanglambam & Neelam 2022) or dissolution of ions due to weathering of rocks (Alam et al. 2020). Gupta et al. (2021) also reported higher EC during pre-monsoon period at Kabar Taal, wetland, Bihar that has been linked to higher temperature and evaporation. The southern part of KA wetlands had a relatively higher EC than the other parts (Figure 2(c)). This can be ascertained due to the relatively low water column supporting the soil-water interaction and weathering processes. During the post-monsoon, the range of EC was 101.50 μs/cm to 222.39 μs/cm, which is less than the pre-monsoon range. The dilution due to precipitation and runoff is a describing factor behind the lesser EC during the post-monsoon period (Aboyeji & Ogunkoya 2017). The trend corroborates the study of Gupta et al. (2021) in Kabar Taal and Mayanglambam & Neelam (2022) in Loktak Lake.
Figure 2

(a) Spatial distribution of temperature in pre-monsoon and post-monsoon in KA wetland. (b) Spatial distribution of pH in pre-monsoon and post-monsoon in KA wetland. (c) Spatial distribution of electrical conductivity in pre-monsoon and post-monsoon in KA wetland. (d) Spatial distribution of dissolved oxygen in pre-monsoon and post-monsoon in KA wetland. (e) Spatial distribution of Ca in pre-monsoon and post-monsoon in KA wetland. (f) Spatial distribution of Mg in pre-monsoon and post-monsoon in KA wetland. (g) Spatial distribution of Na in pre-monsoon and post-monsoon in KA wetland. (h) Spatial distribution of potassium in pre-monsoon and post-monsoon in KA wetland. (i) Spatial distribution of HCO3 in pre-monsoon and post-monsoon in KA wetland. (j) Spatial distribution of chloride in pre-monsoon and post-monsoon in KA wetland. (k) Spatial distribution of nitrate in pre-monsoon and post-monsoon in KA wetland. (l) Spatial distribution of sulfate in pre-monsoon and post-monsoon in KA wetland. (m) Spatial distribution of PO4 in pre-monsoon and post-monsoon in KA wetland.

Figure 2

(a) Spatial distribution of temperature in pre-monsoon and post-monsoon in KA wetland. (b) Spatial distribution of pH in pre-monsoon and post-monsoon in KA wetland. (c) Spatial distribution of electrical conductivity in pre-monsoon and post-monsoon in KA wetland. (d) Spatial distribution of dissolved oxygen in pre-monsoon and post-monsoon in KA wetland. (e) Spatial distribution of Ca in pre-monsoon and post-monsoon in KA wetland. (f) Spatial distribution of Mg in pre-monsoon and post-monsoon in KA wetland. (g) Spatial distribution of Na in pre-monsoon and post-monsoon in KA wetland. (h) Spatial distribution of potassium in pre-monsoon and post-monsoon in KA wetland. (i) Spatial distribution of HCO3 in pre-monsoon and post-monsoon in KA wetland. (j) Spatial distribution of chloride in pre-monsoon and post-monsoon in KA wetland. (k) Spatial distribution of nitrate in pre-monsoon and post-monsoon in KA wetland. (l) Spatial distribution of sulfate in pre-monsoon and post-monsoon in KA wetland. (m) Spatial distribution of PO4 in pre-monsoon and post-monsoon in KA wetland.

Close modal

pH regulates the biochemical processes within the aquatic environment (Jalal & Kumar 2013; Palit et al. 2018; Deep et al. 2020). The pH in the pre-monsoon ranged from 7.5 to 8.2 and 7.6 to 8.8 in the post-monsoon. Lesser values of pH were observed in the northern part of the wetland. Still there was no major change in the pH of water all over the wetland (Figure 2(b)). The sites having lower pH values were proximate to the human settlements releasing organic waste and their further degradation is linked to formation of decomposition originated acids; however, the higher pH sites were densely populated by wetlands macrophytes. The vigorous growth of phytoplankton cause increase in the pH value (William et al. 1970; Olsen & Summerfield 1977). Decay of organic matter (Langmuir 1997) and the use of agrochemicals for agricultural purposes (Chegbeleh et al. 2020) can contribute to lowering the pH of the wetland in pre-monsoon; at the same time high photosynthesis of macrophytes in wetlands can contribute to higher pH (Gupta et al. 2021). The overall pH of both seasons reflects the alkaline nature of wetland water. The presence of bicarbonates in wetland water may be a reason for its alkaline nature (Tank & Chippa 2013; Kumar et al. 2018). Dissolved oxygen (DO) ranged from 5.6 mg/L to 10.5 mg/L during pre-monsoon. It was 5.8 mg/L to 12 mg/L in post-monsoon. A good spatial variation in the DO was observed at KA wetland. The northern part had relatively lesser DO as grey water was being discharged from the community. Higher DO was recorded in the western part of the wetland, especially in post-monsoon, where abundance of macrophytes was also observed (Figure 2(d)). A similar pattern of DO in pre-monsoon and post-monsoon was observed by Gupta et al. (2021), at Kabar Taal wetland, Bihar and by Singh et al. (2022) at selected Ramsar wetlands of Punjab. Total hardness in water is present due to carbonates, bicarbonates, sulfates and chlorides of magnesium and calcium. Total hardness was observed in the range of 72 mg/L to 128 mg/L in pre-monsoon and 50 mg/L to 100 mg/L in post-monsoon. The minimum and maximum concentrations of calcium in pre-monsoon and post-monsoon were 13.27 mg/L and 28.56 mg/L and 8.41 mg/L to 25.23 mg/L respectively (Figure 2(e)). Calcium is one of the dominant cations in surface water and its major source can be geogenic or it may have entered the wetland from the adjoining streams flowing over rocks composed of gypsum and limestone (Bhateria & Jain 2016). The presence of calcium ions can be due to weathering of silicate rocks containing plagioclase minerals (Kadam et al. 2021). Magnesium, which is naturally present in surface water, is derived from weathering of rocks containing magnesium minerals like biotite, olivine, and augite (Kadam et al. 2021). The concentration of magnesium remains lower than the calcium (Tulsankar et al. 2020). It is an important micronutrient for the algae and macrophytes and for phytoplankton it is a limiting factor for their growth (White & Brown 2010; Dijkstra et al. 2019). The concentration of magnesium ranged from 6.32 to 17.84 mg/L and 2.92 to 14.22 mg/L in pre- and post-monsoon respectively (Figure 2(f)). The average concentration of magnesium was 10.28 mg/L in pre-monsoon and 7.70 mg/L in post-monsoon. The range of calcium and magnesium is similar to the findings of Ranjan et al. (2017) and Singh et al. (2017). Sodium ranged from 17.54 mg/L to 59.28 mg/L in pre-monsoon and 20.12 mg/L to 34.07 mg/L in post-monsoon (Figure 2(g)). A relatively high concentration of sodium was also reported in Bhindawas wetland (Shan et al. 2021) and Saman wetland (Khan et al. 2022). Chemical dissolution of minerals and cation exchange can be ascribed to the increased concentration of sodium. The concentration of potassium was found to be 1.84 mg/L to 6.11 mg/L in pre-monsoon and 2.90 mg/L to 4.73 mg/L in post-monsoon (Figure 2(h)). The concentration of potassium in KA wetlands was similar to most other freshwater wetlands (Table 2) except Deepor Beel, which receives landfill leachate and industrial waste (Dash et al. 2020). The major source of sodium and potassium in natural water may be attributed to the weathering and dissolution of silicate minerals like feldspars (Arulbalaji & Gurugnanam 2017), agricultural activities (Kumar et al. 2018), cation-anion exchange (Khan et al. 2022) and sewage discharge from localities (Dixit et al. 2021). A relatively lower value in post-monsoon can be due to dilution of ions through monsoon showers.

The cationic trend follows the order of Na+ > Ca2+ >Mg2+ >K+ in both seasons at KA wetlands. Bhindawas bird sanctuary, Haryana and Saman Wetland, Uttar Pradesh, have a similar order of cation concentrations to that of KA wetland (Table 2).

The concentration of chloride in KA wetland varied from 10.78 mg/L to 19.96 mg/L in pre-monsoon and 21.99 mg/L to 33.99 mg/L in post-monsoon (Figure 2(j)). Chloride in surface water comes from rocks, runoff from agricultural fields, or the discharge of sewage from surrounding areas (Singh et al. 2022). The increase in concentration of chloride in post-monsoon can be due to runoff from adjoining areas in monsoon. A similar trend for chloride ions (pre-monsoon and post-monsoon) was found in Kanjli Wetland (Singh et al. 2017) (Table 2). Bicarbonates were observed to be higher in pre-monsoon than post-monsoon. It was reported to be 88.0 mg/L to 146.0 mg/L and 60 mg/L to 105 mg/L in pre-monsoon and post-monsoon respectively (Figure 2(i)). The data hints that there is intense weathering of silicate and carbonate minerals and decomposition of organic matter in pre-monsoon (Mayanglambam & Neelam 2022). Its source can be also linked to the dissolution of carbonates (Kumar et al. 2021). Similar values of bicarbonates were reported in Saman Wetland, Uttar Pradesh and Ropar wetland, Punjab (Table 2).

The phosphate content at KA wetlands was observed in the range of 0.01 mg/L to 0.09 mg/L in pre-monsoon and 0.01 mg/L to 0.06 mg/L in post-monsoon. The spatial variation of the phosphate is presented in Figure 2(m). Weathering of rocks containing phosphorus and decomposition of organic matter are some of the natural sources of phosphate in surface water (Dixit et al. 2021), whereas intensive use of phosphate fertilizers is one of the most common anthropogenic sources of phosphate in surface water (Khan et al. 2012). The phosphate concentration in the KA wetland water is similar to the other wetlands of Bihar like Kabar Tal and Gogabil (Table 2). The concentration of nitrate varied from 0.01 mg/L to 0.09 mg/L in pre-monsoon and 0.04 mg/L to 0.10 mg/L in post-monsoon (Figure 2(k)). The natural sources of nitrate include nitrification processes and weathering of igneous rocks (Stadler et al. 2012; Dixit et al. 2021), whereas anthropogenic sources include agricultural runoff or animal wastes (Arulbalaji & Gurugnanam 2017). Similar nitrate content has been reported in the Gogabil wetland of Bihar (Table 2). Nitrate and phosphate are important parameters that indicate the pollution status of wetland water. Higher concentration of nitrate and phosphate can lead to eutrophication (Prashant et al. 2022). KA wetlands are surrounded by 14 nearby villages. The discharge of untreated municipal waste directly into wetland area and the use of urea, diammonium phosphate (DAP) and other fertilizers for increasing crop productivity are some of the major sources of nitrate and phosphate. Sulfate is found naturally in almost all kinds of water resources. The key sources of sulfate are weathering of rocks like gypsum and pyrite (Ranjan et al. 2017), atmospheric deposition, and the use of sulfate-rich fertilizers (Arulbalaji & Gurugnanam 2017). Pre-monsoon data showed an elevated concentration of sulfates compared with post-monsoon. It ranged from 2.21 mg/L to 34.12 mg/L in pre-monsoon and 0.56 mg/L to 5.5 mg/L in post-monsoon (Figure 2(l)). The sulfate content of KA wetland and Kabar Taal wetland has similar values during pre-monsoon (Table 2). The KA wetlands remain inundated for nearly six months annually. A single season crop (mostly maize) is cultivated within the wetland area during dry periods. The central and southern parts of the wetland are agricultural infested areas. The higher values of phosphate and sulfate in the central and southern parts is ascribed to fertilizer usages to enhance crop productivity (Figure 2(m) and 2(l)). The dominance of anions in the wetland follows the trend of HCO3 > SO42− > Cl > NO3 > PO43− in pre-monsoon and HCO3 > Cl > SO42− > NO3 > PO43− in post-monsoon. The central part and southern part of the wetland are fed by more than one river stream (Kamla Balan and Jibish dhar). Sometimes, flood water of Koshi river also joins the area, whereas the western part is being fed by flooded Kamla alone. The site has relatively less water than the other sites. This can be one of the reasons for the relatively different behavior of the site.

Table 3 depicts physicochemical composition of KA wetland water during pre-monsoon and post-monsoon.

Table 3

Physicochemical composition of KA wetland water during pre-monsoon and post-monsoon

ParametersPre-monsoon
Post-monsoon
MinimumMaximumMeanStandard deviationMinimumMaximumMeanStandard deviation
Temperature 26 28.2 27.40 0.55 25 26.50 25.83 0.52 
EC 250 540 380.4 74.08 101.5 222.39 165.85 31.83 
pH 7.5 8.2 7.87 0.186 7.60 8.80 8.09 0.28 
DO (mg/L) 5.6 10.5 7.968 1.044 5.80 12 8.38 1.58 
TH (mg/L) 72 128 98.96 13.07 50 100 76.28 15.87 
Ca2+ (mg/L) 13.27 28.56 21.46 4.50 8.41 25.23 17.87 3.87 
Mg2+ (mg/L) 6.32 17.84 10.28 2.35 2.92 14.22 7.70 2.62 
Na + (mg/L) 17.54 59.28 37.28 12.43 20.12 34.07 25.43 2.80 
K + (mg/L) 1.84 6.11 3.45 1.34 2.90 4.73 3.58 0.371 
HCO3 (mg/L) 88 146 110.72 17.40 60 105 93.28 2.072 
SO42− (mg/L) 2.21 34.23 18.22 11 0.56 5.5 2.65 1.14 
PO43− (mg/L) 0.01 0.03 0.021 0.007 0.01 0.061 0.029 0.015 
NO3 (mg/L) 0.01 0.09 0.0472 0.02 0.045 0.108 0.072 0.019 
Cl (mg/L) 10.78 19.96 14.51 2.58 21.99 33.99 27.93 3.57 
ParametersPre-monsoon
Post-monsoon
MinimumMaximumMeanStandard deviationMinimumMaximumMeanStandard deviation
Temperature 26 28.2 27.40 0.55 25 26.50 25.83 0.52 
EC 250 540 380.4 74.08 101.5 222.39 165.85 31.83 
pH 7.5 8.2 7.87 0.186 7.60 8.80 8.09 0.28 
DO (mg/L) 5.6 10.5 7.968 1.044 5.80 12 8.38 1.58 
TH (mg/L) 72 128 98.96 13.07 50 100 76.28 15.87 
Ca2+ (mg/L) 13.27 28.56 21.46 4.50 8.41 25.23 17.87 3.87 
Mg2+ (mg/L) 6.32 17.84 10.28 2.35 2.92 14.22 7.70 2.62 
Na + (mg/L) 17.54 59.28 37.28 12.43 20.12 34.07 25.43 2.80 
K + (mg/L) 1.84 6.11 3.45 1.34 2.90 4.73 3.58 0.371 
HCO3 (mg/L) 88 146 110.72 17.40 60 105 93.28 2.072 
SO42− (mg/L) 2.21 34.23 18.22 11 0.56 5.5 2.65 1.14 
PO43− (mg/L) 0.01 0.03 0.021 0.007 0.01 0.061 0.029 0.015 
NO3 (mg/L) 0.01 0.09 0.0472 0.02 0.045 0.108 0.072 0.019 
Cl (mg/L) 10.78 19.96 14.51 2.58 21.99 33.99 27.93 3.57 

Note: In pre-monsoon (n = 25) and in post-monsoon (n = 32).

Hydrochemical facies

Piper diagram

A Piper diagram is useful to interpret the ion chemistry of water. It helps to identify the water type and dominant ionic constituents (Chegbeleh et al. 2020). Major cations (Ca2+, Mg2+, Na+, and K+) and major anions (SO42−, Cl and HCO3) are plotted. The two triangles (trilinear) represent cations and anions. The central diamond shows the overall classification of water (Piper 1944). The Piper diagram for pre-monsoon (Figure 3) reveals that the majority of water samples are of the Na + K category. Minor representations are found in the no dominant cation type. All the anions are found to be under HCO3 type. The central diamond indicates that there are two different types of water, Ca-HCO3 type and mixed Ca-Na-HCO3 type. The majority of samples fall within the Ca-Na-HCO3 type. The Piper diagram (Figure 3) indicates that during the post-monsoon, some samples are Na + K type and some are under no cation dominance type. There is a little shift of samples to no dominance type. The anionic portion has decreased HCO3 content. Furthermore, it has shifted towards the Cl type category. The central diamond reveals that many samples shift to the Ca-HCO3 type. So, from the plot it seems that alkali (Na+ and K+) significantly exceeds the alkaline earth Ca2+ and Mg2+ and weak acid (HCO3 and CO32−) exceeds Cl and SO42−.
Figure 3

Piper diagram showing different hydrochemical classifications for pre-monsoon and post-monsoon of KA wetland.

Figure 3

Piper diagram showing different hydrochemical classifications for pre-monsoon and post-monsoon of KA wetland.

Close modal

Durov plot

The Durov plot is more advanced than the Piper diagram. It comprises ternary diagrams plotted together including the concentration of total dissolved solids (TDS) (mg/L) and pH (Durov 1948). It not only provides information regarding the whole water classification but also indicates the hydrochemical processes involved in ionic chemistry. During the present investigation, the durov plot (Figure 4) shows similarity to the Piper diagram with two water types prevailing in pre-monsoon i.e. Na + K type and mixed no cation dominant (mixed) type for cations and HCO3 dominancy in anions. Furthermore, the plot suggests a reverse ion exchange process in wetland water. Durov plot for post-monsoon also has similarity to the Piper diagram for cations and anions. The plot, in addition, suggests mixing or simple dissolution to be the major hydrochemical process.
Figure 4

Durov plot for pre-monsoon and post-monsoon of KA wetland.

Figure 4

Durov plot for pre-monsoon and post-monsoon of KA wetland.

Close modal

Gibbs diagram

Apart from anthropogenic disturbances, water chemistry of wetland is generally influenced by three major natural processes such as weathering of rocks, atmospheric precipitation and evaporation. These major processes occurring in the aquatic environment can be determined by Gibbs diagram (Gibbs 1970). It helps to understand water chemistry and indicates major source of ions in the water system. It is plotted by calculating ratio of (Na+ + K+) and (Na+ + K+ + Ca2+) as a function of TDS for cations and Cl/(Cl + HCO3) for anions. Gibbs diagram illustrates rock dominance in pre-monsoon in KA wetland, suggesting chemical weathering (Figure 5(a)) whereas the diagram for post-monsoon (Figure 5(b)) illustrates rock -dominance along with precipitation, suggesting the role of precipitation too in wetland water in post-monsoon. High concentrations of Na+ and K+ may be due to the weathering of the parent rock. The results indicate that the source of Ca2+and HCO3 can be the decomposition of organic matter by microbes in the wetland as the Ca2+/HCO3 is less than one for both seasons (Ranjan et al. 2017).
Figure 5

(a) log TDS vs Cl/(Cl + HCO3), (b) log (TDS) vs (Na + K)/(Na + K + Ca) for pre-monsoon and post-monsoon.

Figure 5

(a) log TDS vs Cl/(Cl + HCO3), (b) log (TDS) vs (Na + K)/(Na + K + Ca) for pre-monsoon and post-monsoon.

Close modal
As the Gibbs diagram suggested weathering as a major source of ions in KA wetland, the plot of Mg2+/Na+ vs Ca2+/Na+ (Figure 6) further elaborates the weathering of related end-members whether it is of carbonates or silicates or evaporates. The Mg2+/Na+ vs Ca2+/Na+ plot suggests that there is silicate weathering in both seasons. Furthermore, the HCO3 vs Ca2+/Na+ plot indicates that weathering is a little shifted towards silicate-carbonate end members. Therefore, the plot suggests that KA wetland is governed by both silicate and carbonate weathering.
Figure 6

Plots of (a) Mg2+/Na+ vs Ca2+/Na+ and (b) HCO3 vs Ca2+/Na+ representing the end-member diagram for identification of type of weathering prevailing in KA wetland.

Figure 6

Plots of (a) Mg2+/Na+ vs Ca2+/Na+ and (b) HCO3 vs Ca2+/Na+ representing the end-member diagram for identification of type of weathering prevailing in KA wetland.

Close modal

Correlation matrix

To understand the interrelationship between different physicochemical parameters, a Pearson-correlation analysis was conducted in the study area (Table 4).

Table 4

Correlation matrix for (a) pre-monsoon and (b) post-monsoon

Correlations
TemppHECDOTHCaMgNaKClNO3PO4SO4HCO3
(a) Pre-monsoon 
Temp              
pH −0.092             
EC −0.195 −0.334            
DO −0.446* 0.502* −0.312           
TH −0.348 0.372 0.642** 0.099          
Ca −0.07 0.463* 0.109 0.171 0.500*         
Mg −0.24 −0.2 0.670** −0.197 0.525** −0.349        
Na −0.124 −0.322 0.961** −0.282 0.597** 0.03 0.732**       
−0.385 −0.199 0.874** −0.097 0.647** 0.075 0.750** 0.905**      
Cl −0.408* −0.164 0.763** 0.003 0.648** 0.283 0.526** 0.762** 0.848**     
NO3 0.038 0.332 0.408* 0.025 0.471* 0.511** 0.09 0.441* 0.442* 0.421*    
PO4 0.05 0.319 0.348 0.064 0.611** 0.765** −0.094 0.259 0.242 0.407* 0.516**   
SO4 −0.297 −0.06 0.775** −0.133 0.523** 0.113 0.535** 0.807** 0.811** 0.665** 0.599** 0.229   
HC03 − 0.317 − 0.038 0.877** − 0.024 0.817** 0.289 0.699** 0.869** 0.850** 0.784** 0.481* 0.463* 0.747** 
 Temp pH EC DO TH Ca Mg Na Cl PO4 NO3 SO4 HCO3 
(b) Post-monsoon 
Temp              
pH −0.292             
EC −0.271 −0.307            
DO −0.133 0.624** −0.225           
TH −0.596** 0.246 0.478** 0.106          
Ca −0.406* 0.208 0.488** −0.027 0.747**         
Mg −0.511** 0.174 0.263 0.181 0.798** 0.195        
Na −0.059 −0.251 0.475** −0.24 0.28 0.124 0.301       
−0.123 −0.088 0.023 0.181 0.174 −0.148 0.392* 0.116      
Cl −0.414* 0.298 0.348 0.18 0.654** 0.511** 0.501** 0.380* −0.093     
PO4 −0.413* 0.348 0.207 0.116 0.661** 0.505** 0.517** 0.081 0.009 0.473**    
NO3 0.18 0.035 −0.106 −0.044 −0.144 0.04 −0.25 −0.177 −0.589** −0.177 0.231   
SO4 0.003 0.157 −0.011 0.347 0.135 0.158 0.056 0.248 0.172 0.203 0.23 0.077  
HCO3 −0.373* −0.086 0.509** −0.382* 0.504** 0.462** 0.324 0.268 −0.204 0.378* 0.243 0.093 −0.123 
Correlations
TemppHECDOTHCaMgNaKClNO3PO4SO4HCO3
(a) Pre-monsoon 
Temp              
pH −0.092             
EC −0.195 −0.334            
DO −0.446* 0.502* −0.312           
TH −0.348 0.372 0.642** 0.099          
Ca −0.07 0.463* 0.109 0.171 0.500*         
Mg −0.24 −0.2 0.670** −0.197 0.525** −0.349        
Na −0.124 −0.322 0.961** −0.282 0.597** 0.03 0.732**       
−0.385 −0.199 0.874** −0.097 0.647** 0.075 0.750** 0.905**      
Cl −0.408* −0.164 0.763** 0.003 0.648** 0.283 0.526** 0.762** 0.848**     
NO3 0.038 0.332 0.408* 0.025 0.471* 0.511** 0.09 0.441* 0.442* 0.421*    
PO4 0.05 0.319 0.348 0.064 0.611** 0.765** −0.094 0.259 0.242 0.407* 0.516**   
SO4 −0.297 −0.06 0.775** −0.133 0.523** 0.113 0.535** 0.807** 0.811** 0.665** 0.599** 0.229   
HC03 − 0.317 − 0.038 0.877** − 0.024 0.817** 0.289 0.699** 0.869** 0.850** 0.784** 0.481* 0.463* 0.747** 
 Temp pH EC DO TH Ca Mg Na Cl PO4 NO3 SO4 HCO3 
(b) Post-monsoon 
Temp              
pH −0.292             
EC −0.271 −0.307            
DO −0.133 0.624** −0.225           
TH −0.596** 0.246 0.478** 0.106          
Ca −0.406* 0.208 0.488** −0.027 0.747**         
Mg −0.511** 0.174 0.263 0.181 0.798** 0.195        
Na −0.059 −0.251 0.475** −0.24 0.28 0.124 0.301       
−0.123 −0.088 0.023 0.181 0.174 −0.148 0.392* 0.116      
Cl −0.414* 0.298 0.348 0.18 0.654** 0.511** 0.501** 0.380* −0.093     
PO4 −0.413* 0.348 0.207 0.116 0.661** 0.505** 0.517** 0.081 0.009 0.473**    
NO3 0.18 0.035 −0.106 −0.044 −0.144 0.04 −0.25 −0.177 −0.589** −0.177 0.231   
SO4 0.003 0.157 −0.011 0.347 0.135 0.158 0.056 0.248 0.172 0.203 0.23 0.077  
HCO3 −0.373* −0.086 0.509** −0.382* 0.504** 0.462** 0.324 0.268 −0.204 0.378* 0.243 0.093 −0.123 

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

The results showed that pH has no significant relation with other parameters except DO (r > 0.5) in both seasons. Total hardness (TH) and Mg2+ has a moderate (r = 0.5 to 0.6) correlation with EC whereas Na+, Mg2+, K+, Cl, SO42− and HCO3(r > 0.7) have a very strong correlation with EC. The result indicates weathering process in pre-monsoon. Na+ had a strong relationship with almost all parameters except pH, EC, DO, and Ca2+ in pre-monsoon, and it was 0.96 for EC and 0.905 for K+. Na did not show any significant correlation with other parameters in post-monsoon except a weak correlation with EC (0.47). HCO3 had strong to moderate positive correlations with EC, Mg2+, Na+, K+, Cl and SO42− invariably in pre-monsoon. Cl had very strong correlations with K+ (0.848), HCO3 (0.784) and Na+ (0.76), while it had moderate correlation with SO42− (0.66) and Mg2+ (0.53) in the pre-monsoon, and in post-monsoon it had moderate correlation with total hardness (0.65) and good correlation with Ca2+ (0.51) and Mg2+ (0.50) only with PO43− (0.59) in the post-monsoon. The correlation between Cl, Na+, K+, and Mg2+ hints at a derivation from a common source. PO43− showed positive correlations with TH, Ca2+, and NO3 in pre-monsoon and with TH, Ca2+, and Mg2+ in post-monsoon. It was observed that nitrate had a good correlation with SO42−, Ca2+, and PO43− (r = 0.5 to 0.6). SO42− was strongly correlated with Na+, K+, and EC in pre-monsoon and moderately correlated with TH, SO42−, Mg2+, and NO3. NO3 did not show any significant correlation with other parameters in post-monsoon. The correlation between nitrate and sulfate can be ascribed to use of fertilizers in wetland area and disposal of municipal waste (Khan et al. 2022).

The concentration of different ions determines the quality of water and governs its suitability for various purposes, such as agriculture, the sector which is the largest consumer of fresh water. Thus, it is of utmost importance that irrigation water should be potent for crop growth and crop yield. Otherwise, a poor irrigation water quality can harm crop and soil characteristics due to either nutrient deficiency or excessive toxicity. Hence, in the present study different irrigation water quality parameters have been accounted for determining the suitability of wetland water for irrigation. The irrigation water quality of the KA wetlands was assessed on the basis of irrigation water quality parameters (Table 5).

Table 5

Different irrigation parameters used in the study

Sl. No.Irrigation water quality parametersReferences
01 Salinity hazard Bryan et al. (2007)  
02 Sodium adsorption ratio (SAR) Todd & Mays (1980); Richards (1954)  
03 Sodium percentage (Na %) Wilcox (1955)  
04 Magnesium adsorption ratio (MAR) Raghunath (1987)  
05 Residual sodium carbonate (RSC) Richards (1954); Eaton (1950)  
06 Kelly's ratio (KR) Kelley (1963)  
07 Chloride hazard Ayers & Wescot (1985)  
Sl. No.Irrigation water quality parametersReferences
01 Salinity hazard Bryan et al. (2007)  
02 Sodium adsorption ratio (SAR) Todd & Mays (1980); Richards (1954)  
03 Sodium percentage (Na %) Wilcox (1955)  
04 Magnesium adsorption ratio (MAR) Raghunath (1987)  
05 Residual sodium carbonate (RSC) Richards (1954); Eaton (1950)  
06 Kelly's ratio (KR) Kelley (1963)  
07 Chloride hazard Ayers & Wescot (1985)  

Salinity hazard

The salinity hazard is a reliable index for determining the potency of irrigation water in terms of electrical conductivity. Electrical conductivity is due to the presence of ions in water. High salinity leads to the accumulation of salts in the root zones of the crop, makes soil compact, disturbs osmoregulation, damages plant cells and reduces crop yield (Bauder et al. 2011; Hedjal et al. 2018; Sreedevi et al. 2019; El Bilali & Taleb 2020).
Figure 7

Salinity hazard in pre-monsoon and post-monsoon.

Figure 7

Salinity hazard in pre-monsoon and post-monsoon.

Close modal
Table 6 shows that all water samples of pre-monsoon fall under good quality whereas all the water samples of post-monsoon are of excellent quality posing much less hazard. A spatial map of salinity hazard of pre-monsoon and post-monsoon is illustrated in Figure 7.
Table 6

Classification of irrigation water based on salinity hazard

RangeWater Class% of samples
Pre-monsoonPost-monsoon
Salinity hazard (EC)(μs/cm) <250 Excellent Nil 100% 
250–750 Good 100% Nil 
750–2,000 Permissible Nil Nil 
2,000–3,000 Poor Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
Salinity hazard (EC)(μs/cm) <250 Excellent Nil 100% 
250–750 Good 100% Nil 
750–2,000 Permissible Nil Nil 
2,000–3,000 Poor Nil Nil 
Figure 8

Spatial distribution of SAR in pre-monsoon and post-monsoon.

Figure 8

Spatial distribution of SAR in pre-monsoon and post-monsoon.

Close modal

Sodium adsorption ratio

Sodium adsorption ratio (SAR) (Equation (1)) is the proportion of sodium with respect to that of calcium and magnesium in water (Todd & Mays 1980). It is expressed in meq/L as
(1)
It is an important parameter to determine the sodium hazard in irrigation water. Excess sodium affects the cation-exchange capacity of the soil. High sodium content causes the replacement of calcium and magnesium ions with sodium ions in the soil, causes de-flocculation of soil, reduces infiltration rate and permeability, and restricts the supply of essential nutrients and water, leading to lower crop yield (Chaudhary & Satheeshkumar 2018; Gaikwad et al. 2020). The classification of water samples based on SAR values is shown in Table 7. Both the samples of pre-monsoon and post-monsoon were found to be within the excellent category, thus making them fit for irrigation purposes. Spatial map of SAR for pre- and post-monsoon is illustrated in Figure 8.
Table 7

Irrigation water quality of wetland water based on SAR

RangeWater Class% of samples
Pre-monsoonPost -monsoon
SAR <10 Excellent 100 100 
10–18 Good Nil Nil 
18–26 Medium Nil Nil 
>26 Bad Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost -monsoon
SAR <10 Excellent 100 100 
10–18 Good Nil Nil 
18–26 Medium Nil Nil 
>26 Bad Nil Nil 
Figure 9

(a) USSL diagram for pre-monsoon; (b) USSL diagram for post-monsoon.

Figure 9

(a) USSL diagram for pre-monsoon; (b) USSL diagram for post-monsoon.

Close modal

The USSL diagram plot SAR against EC C1 to C4 represents low salinity to high salinity range whereas S1 to S4 represents low to high SAR value (USSL 1954). The USSL diagram (Figure 9(a) and 9(b)) for the present study illustrates that most of the water samples of pre-monsoon and post-monsoon fall in C1S1 and C2S2 categories, indicating lower salinity and low SAR hazard and medium salinity and low SAR hazard, respectively, and are suitable for irrigation purpose.

Sodium percentage

Sodium percentage (%Na) (Equation (2)) is widely used in assessing the suitability of water for irrigation purposes. It is expressed as

(2)

Na% is a representation of sodium content with respect to other cations in water (Wilcox 1955). Sodium is limited concentration and is beneficial for various biological activities. Higher sodium content clogs the soil pores and reduces the permeability of soil (Mukiza et al. 2021). It disturbs the osmotic pressure and thus reduces the absorption of essential nutrients by crops (Singaraja et al. 2014). It may stun the growth of plants. The classification of water samples based on Na% values is shown in Table 8.

Table 8

Irrigation water quality of wetland water based on Na%

RangeWater Class% of samples
Pre-monsoonPost-monsoon
Na% <20% Excellent Nil Nil 
20–40% Good 12% 16% 
40–60% Permissible 76 84 
60–80% Doubtful 12% Nil 
>80% Unsuitable Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
Na% <20% Excellent Nil Nil 
20–40% Good 12% 16% 
40–60% Permissible 76 84 
60–80% Doubtful 12% Nil 
>80% Unsuitable Nil Nil 

The majority of the samples of pre-monsoon and post-monsoon fall under the permissible range. The Na% ranged from 36% to 62.7% and 35% to 58% in pre-monsoon and post-monsoon respectively.

Wilcox diagram (Figure 10) represents the plot of electrical conductivity against Na% illustrating that the samples of pre- and post-monsoon are of excellent quality. Spatial map of Na% of pre- and post-monsoon is illustrated in Figure 11.
Figure 10

Wilcox diagram for pre-monsoon and post-monsoon.

Figure 10

Wilcox diagram for pre-monsoon and post-monsoon.

Close modal
Figure 11

Spatial distribution of Na% in pre- and post-monsoon.

Figure 11

Spatial distribution of Na% in pre- and post-monsoon.

Close modal

Residual sodium carbonate

Residual sodium carbonate (RSC) is another tool to assess the suitability of irrigation water. It represents the toxicity of carbonates and bicarbonates over calcium and magnesium. Carbonates and bicarbonates are important constituents of irrigation water and affect soil properties. High carbonates and bicarbonates cause precipitation of Ca2+ and Mg2+ (Rawat et al. 2018) and intensify the sodium hazard which is generally not reflected by SAR (Chaudhary & Satheeshkumar 2018). Higher RSC leads to an increase in adsorption of sodium in soil (Hedjal et al. 2018).

RSC (Equation (3)) is calculated as
(3)
In the study area, RSC ranged from 0.05 meq/l to 0.9 meq/l for pre-monsoon and from −0.06 to 0.64 meq/l for post-monsoon. Considering RSC values, all the samples in both seasons were in the good range and water can be deemed fit for irrigation (Table 9). The spatial map of RSC of pre-monsoon and post-monsoon is illustrated in Figure 12.
Table 9

Irrigation water quality of wetland water based on residual sodium carbonate (RSC)

RangeWater Class% of samples
Pre-monsoonPost-monsoon
RSC <1.25 Good 100% 100% 
1.25–2.5 Doubtful Nil Nil 
>2.5 Unsuitable Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
RSC <1.25 Good 100% 100% 
1.25–2.5 Doubtful Nil Nil 
>2.5 Unsuitable Nil Nil 
Table 10

Irrigation water quality of wetland water based on magnesium adsorption ratio (MAR)

RangeWater Class% of samples
Pre-monsoonPost-monsoon
MAR ≤50 Unsuitable 4% Nil 
≥50 Suitable 96% 100% 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
MAR ≤50 Unsuitable 4% Nil 
≥50 Suitable 96% 100% 
Figure 12

Spatial distribution of RSC in pre-monsoon and post-monsoon.

Figure 12

Spatial distribution of RSC in pre-monsoon and post-monsoon.

Close modal

Magnesium adsorption ratio (MAR)

Magnesium present in excess increases soil alkalinity, reduces the infiltration and thus disturbs the growth of crops (Raghunath 1987). MAR (Equation (4)) measures the toxicity of magnesium ions over calcium ions.

It is expressed as:
(4)
The present study found that the average value of MAR was 19.6 to 52.5 meq/l for pre-monsoon. Out of all samples, only one sample was found unsuitable as per MAR standards (Table 10). For post-monsoon, it ranged from 12.49 to 48.11 meq/l. Spatial map of MAR of pre-monsoon and post-monsoon is illustrated in Figure 13.
Table 11

Irrigation water quality of wetland water based on Mg/Ca

RangeWater Class% of samples
Pre-monsoonPost-monsoon
Mg/Ca <1.5 Safe 100% 100% 
1.5–3.0 Moderate Nil Nil 
>3.0 Unsafe Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
Mg/Ca <1.5 Safe 100% 100% 
1.5–3.0 Moderate Nil Nil 
>3.0 Unsafe Nil Nil 
Figure 13

Spatial distribution of MAR in pre-monsoon and post-monsoon.

Figure 13

Spatial distribution of MAR in pre-monsoon and post-monsoon.

Close modal
Figure 14

Spatial distribution of Mg/Ca in pre- and post-monsoon.

Figure 14

Spatial distribution of Mg/Ca in pre- and post-monsoon.

Close modal
Figure 15

Spatial distribution of Kelly's ratio in pre-monsoon and post-monsoon.

Figure 15

Spatial distribution of Kelly's ratio in pre-monsoon and post-monsoon.

Close modal
Figure 16

Spatial distribution of chloride hazard in pre-monsoon and post-monsoon.

Figure 16

Spatial distribution of chloride hazard in pre-monsoon and post-monsoon.

Close modal

The irrigation water quality ratings based on the Mg/Ca ratio shows that all the samples of pre-monsoon and post-monsoon belonged to the safe category (Table 11). Figure 14 shows the spatial distribution of Mg/Ca in pre- and post-monsoon.

Table 12

Irrigation water quality of wetland water based on Kelly's ratio

RangeWater Class% of samples
Pre-monsoonPost-monsoon
Kelly's ratio <1 Suitable 60% 75% 
>1 Unsuitable 40% 25% 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
Kelly's ratio <1 Suitable 60% 75% 
>1 Unsuitable 40% 25% 

Kelly's ratio

Kelly's ratio (KR) (Equation (5)) is the ratio of the concentration of sodium ions against calcium and magnesium ions.
(5)

A value greater than one is not considered safe for irrigation as it denotes a higher sodium concentration leading to poor growth of crops.

Table 13

Irrigation water quality of wetland water based on chloride hazard

RangeWater Class% of samples
Pre-monsoonPost-monsoon
Chloride hazard <2 Low hazard 100% 100% 
2–4 Medium hazard Nil Nil 
4–10 High hazard Nil Nil 
>10 Very high hazard Nil Nil 
RangeWater Class% of samples
Pre-monsoonPost-monsoon
Chloride hazard <2 Low hazard 100% 100% 
2–4 Medium hazard Nil Nil 
4–10 High hazard Nil Nil 
>10 Very high hazard Nil Nil 

Kelly's ratio ranged from 0.58 to 1.8 for pre-monsoon and 0.68 to 1.50 for post-monsoon, respectively; 60% of the samples were detected suitable for pre-monsoon while 75% of the samples were detected suitable for post-monsoon (Table 12). Spatial map of Kelly's ratio for pre-monsoon and post-monsoon is illustrated in Figure 15.

Chloride hazard

Chloride is a micronutrient and plays an imperative role in the growth and development of plants. Adsorption of chloride by soil is less and it is absorbed by plants. Excessive chloride accumulates in leaves and causes leaf burn or necrosis (www.fao.org). In irrigation water, the concentration of chloride should not be more than 2 meq/l (Sreedevi et al. 2019). The observed range of chloride ions was 0.3 meq/l to 0.56 meq/l and 0.61 meq/l to 0.96 meq/l for pre-monsoon and post-monsoon respectively, suggesting its suitability for agriculture (Table 13). The spatial map of chloride hazard for pre- and post-monsoon is illustrated in Figure 16.

A comprehensive summary of different irrigation water quality parameters in pre-monsoon and post-monsoon is presented in Table 14.

Table 14

Comprehensive summary of different irrigation water quality parameters in pre-monsoon and post-monsoon

Sl. No.Irrigation water quality parametersPre-monsoonPost-monsoon
01 Salinity hazard 100% good 100% excellent 
02 SAR 100% suitable 100% suitable 
03 Sodium % 76% permissible 84% permissible 
04 RSC 100% suitable 100% suitable 
05 MAR 96% suitable 100% suitable 
06 KR 60% suitable 75% suitable 
07 Mg/Ca 100% suitable 100% suitable 
08 Chloride hazard 100% suitable 100% suitable 
Sl. No.Irrigation water quality parametersPre-monsoonPost-monsoon
01 Salinity hazard 100% good 100% excellent 
02 SAR 100% suitable 100% suitable 
03 Sodium % 76% permissible 84% permissible 
04 RSC 100% suitable 100% suitable 
05 MAR 96% suitable 100% suitable 
06 KR 60% suitable 75% suitable 
07 Mg/Ca 100% suitable 100% suitable 
08 Chloride hazard 100% suitable 100% suitable 

This study focused on comprehensive analysis of hydro geochemistry of KA wetland water and its suitability for agricultural purposes. The wetlands water is alkaline in nature as reflected by the pH values. EC, TH, Na+, Ca2+, SO42− and HCO3 were relatively high, and temperature, DO, Mg2+ and Cl were lower in pre-monsoon than post-monsoon. There was no major change in the concentration of nitrate and phosphate over both the seasons. Gibb's diagram reveals that the weathering of rocks in pre-monsoon and rock weathering along with precipitation in post-monsoon are the major sources of ions in wetland. Silicate weathering is the dominant contributor for ions in the wetland. The Piper diagram represents Na + K type and mixed type for cations and HCO3 for anions. The concentration of major cations was in order of Na+ > Ca2+ > Mg2+ > K+ and concentration of anions was in order of HCO3 > SO42− > Cl > NO3 > PO43− in pre-monsoon and HCO3 > Cl > SO42− > NO3 > PO43− in post-monsoon. Irrigation parameters like salinity hazard, SAR, Na%, KR, MAR, RSC, Mg/Ca and chloride hazard were applied to assess the wetland water suitability for irrigation. As per SAR standards, all samples were found to be suitable for both seasons. The majority of samples were with in the permissible limit for Na%. RSC was found to be good for both seasons. MAR was found to be suitable for both seasons (60% of the samples were found suitable in pre-monsoon and 75% in post-monsoon). There was less chloride hazard in both seasons. The wetlands water recedes in the summer season and during this period the dry land is used for agriculture purposes. Use of fertilizer during this period has been observed. In monsoon the dry land is inundated, thus causing mixing of the fertilizer residues. The agriculture activity in the wetlands catchments and human settlements are sources of nutrient enhancement to the wetlands. Overall, it is stated that the water quality of KA wetlands is suitable for agriculture purposes where there is abundance of water. The seasonal variation and anthropogenic activities are major contributors to the hydrochemistry of the wetlands.

The authors are grateful to Union Grant Mission for financial assistance in terms of a fellowship for one for the authors. The authors are grateful to the Principal Chief Conservator of Forests, Government of Bihar and Divisional Forest Officer, Darbhanga, Bihar for granting permission to carry out research work in KA wetlands. The authors wish to thank the anonymous reviewers for their valuable suggestions and comments which improved the quality of the paper.

All relevant data are included in the paper or its Supplementary Information.

Aboyeji
O. S.
&
Ogunkoya
O. O.
2017
Assessment of surface water quality of inland valleys for cropping in SW Nigeria
.
Applied Water Science
7
(
2
),
987
996
.
https://doi.org/10.1007/S13201-015-0309-8
.
Adhishwar
A. K.
&
Choudhary
S. K.
2020
Physico-chemical characteristics of Gogabil Lake – a wetland of national importance – in Katihar district of Bihar (India)
.
Pollution Research
39
,
196
201
.
Ajorlo
M.
,
Abdullah
R. B.
,
Yusoff
M. K.
,
Halim
R. A.
,
Hanif
A. H. M.
,
Willms
W. D.
&
Ebrahimian
M.
2013
Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems
.
Environmental Monitoring and Assessment
185
(
10
),
8649
8658
.
https://doi.org/10.1007/S10661-013-3201-8
.
Akhter
S.
&
Brraich
O. S.
2020
Physico-chemical analysis of fresh water of Ropar wetland (Ramsar site), India
.
Current World Environment
15
(
1
),
117
.
Alam
W.
,
Singh
K. S.
,
Gyanendra
Y.
,
Laishram
R. J.
&
Nesa
N.
2020
Hydrogeochemical assessment of groundwater quality for few habitations of Chandel District, Manipur (India)
.
Applied Water Science
10
(
5
).
https://doi.org/10.1007/S13201-020-01208-0
.
APHA
2017
Standard Methods for the Examination of Water and Wastewater
.
American Public Health Association, Washington, DC
.
Arulbalaji
P.
&
Gurugnanam
B.
2017
Groundwater quality assessment using geospatial and statistical tools in Salem District, Tamil Nadu, India
.
Applied Water Science
7
(
6
),
2737
2751
.
https://doi.org/10.1007/S13201-016-0501-5
.
Ayers, R. S. & Westcot, D. W. 1985 Water Quality for Agriculture. Food and Agriculture Organization of the United Nations, Rome.
Bassi
N.
,
Kumar
M. D.
,
Sharma
A.
&
Pardha-Saradhi
P.
2014
Status of wetlands in India: a review of extent, ecosystem benefits, threats and management strategies
.
Journal of Hydrology: Regional Studies
2
,
1
19
.
https://doi.org/10.1016/j.ejrh.2014.07.001
.
Bauder
T.
,
Waskom
R.
,
Sutherland
P.
&
Davis
J. G.
2011
Irrigation Water Quality Criteria
.
Available from: www.ext.colostate.edu
Bhateria
R.
&
Jain
D.
2016
Water quality assessment of lake water: a review
.
Sustainable Water Resources Management
2
(
2
),
161
173
.
https://doi.org/10.1007/S40899-015-0014-7
.
Bryan
G. H.
,
Donald
A. H.
,
Robert
G. S.
,
Jason
W. E.
&
Dan
M. S.
2007
Managing irrigation water quality for crop production in the pacific northwest. A pacific northwest extension publication 597-E. https://catalog.extension.oregonstate.edu/sites/catalog/files/project/pdf/pnw597.pdf
Chaudhary
V.
&
Satheeshkumar
S.
2018
Assessment of groundwater quality for drinking and irrigation purposes in arid areas of Rajasthan, India
.
Applied Water Science
8
(
8
).
https://doi.org/10.1007/S13201-018-0865-9
.
Chegbeleh
L. P.
,
Akurugu
B. A.
&
Yidana
S. M.
2020
Assessment of groundwater quality in the Talensi District, Northern Ghana
.
Scientific World Journal
2020
,
24
.
https://doi.org/10.1155/2020/8450860
.
Das
J. P. L.
,
Kolay
S. R.
&
Rahmatullah
M.
2015
Status of ornamental fish diversity in Jhang – A wet land of Kusheshwar sthan chaur
.
International Journal of Fisheries and Aquatic Studies
2
(
4
),
142
146
.
Dijkstra
Y. M.
,
Chant
R. J.
&
Reinfelder
J. R.
2019
Factors controlling seasonal phytoplankton dynamics in the Delaware River estuary: an idealized model study
.
Estuaries and Coasts
42
(
7
),
1839
1857
.
https://doi.org/10.1007/S12237-019-00612-3/FIGURES/11
Deep
A.
,
Gupta
V.
,
Bisht
L.
&
Kumar
R.
2020
Application of WQI for water quality assessment of high-altitude snow-fed sacred Lake Hemkund, Garhwal Himalaya
.
Sustainable Water Resources Management
6
(
5
).
https://doi.org/10.1007/S40899-020-00449-W
.
Dixit
A.
,
Siddaiah
N. S.
&
Joshi
P.
2021
Hydrogeochemical assessment of wetlands of Gurugram, Haryana, India: implications for natural processes and anthropogenic changes
.
Arabian Journal of Geosciences
14
(
3
),
1
23
.
https://doi.org/10.1007/S12517-020-06423-2/TABLES/5
.
Durov
S. A.
1948
Natural waters and graphic representation of their composition
.
Dokl Akad Nauk SSSR
59
(
3
),
87
90
.
Eaton
F. M.
1950
Significance of carbonates in irrigated water
.
Soil Science
69
(
2
),
127
128
.
El Bilali
A.
&
Taleb
A.
2020
Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment
.
Journal of the Saudi Society of Agricultural Sciences
19
(
7
),
439
451
.
https://doi.org/10.1016/J.JSSAS.2020.08.001
.
Gaikwad
S. K.
,
Kadam
A. K.
,
Ramgir
R. R.
,
Kashikar
A. S.
,
Wagh
V. M.
,
Kandekar
A. M.
,
Gaikwad
S. P.
,
Madale
R. B.
,
Pawar
N. J.
&
Kamble
K. D.
2020
Assessment of the groundwater geochemistry from a part of west coast of India using statistical methods and water quality index
.
Hydro Research
3
,
48
60
.
https://doi.org/10.1016/J.HYDRES.2020.04.001
.
Gibbs
R. J.
1970
Mechanisms controlling world water chemistry
.
Science
170
(
3962
),
1088
1090
.
https://doi.org/10.1126/SCIENCE.170.3962.1088
.
Gopal
B.
2013
Future of wetlands in tropical and subtropical Asia, especially in the face of climate change
.
Aquatic Sciences
75
(
1
),
39
61
.
https://doi.org/10.1007/S00027-011-0247-Y/FIGURES/4
.
Guhathakurta
P.
,
Kumar
S.
&
Prasad
A. K.
2020
Observed Rainfall Variability and Changes Over Bihar State. Drought Monitoring View Project Neural Network Application to Forecasting View Project
.
https://doi.org/10.13140/RG.2.2.33589.70885
.
Gupta
D.
,
Kumar Ranjan
R.
,
Parthasarathy
P.
&
Ansari
A.
2021
Spatial and seasonal variability in the water chemistry of Kabar Tal wetland (Ramsar site), Bihar, India: multivariate statistical techniques and GIS approach
.
Water Science and Technology
83
(
9
),
2100
2117
.
https://doi.org/10.2166/wst.2021.115
.
Hedjal
S.
,
Zouini
D.
&
Benamara
A.
2018
Hydrochemical assessment of water quality for irrigation: a case study of the wetland complex of Guerbes-Sanhadja, North-East of Algeria
.
Journal of Water and Land Development
38
(
1
),
43
52
.
https://doi.org/10.2478/jwld-2018-0041
.
Jalal
F. N.
&
Kumar
M. S.
2013
Water quality assessment of Pamba River of Kerala, India in relation to pilgrimage season
.
International Journal of Research in Chemistry and Environment (IJRCE)
3
(
1
),
341
347
.
Kadam
A.
,
Wagh
V.
,
Patil
S.
,
Umrikar
B.
&
Sankhua
R.
2021
Seasonal assessment of groundwater contamination, health risk and chemometric investigation for a hard rock terrain of western India
.
Environmental Earth Sciences
80
(
5
).
https://doi.org/10.1007/S12665-021-09414-Y
.
Kelley
W.
1963
Use of saline irrigation water
.
Soil Science
95
,
385
391
Khan
J. A.
,
Gavali
R. S.
&
Shouche
Y. S.
2012
Exploring present status of hydrochemistry and sediment chemistry of Dal Lake, Kashmir and effect of anthropogenic disturbances on it
.
Indian Journal of Innovations and Developments
1
(
7
),
554
571
.
Kumar
V.
,
Kumar
M.
&
Prasad
R.
2018
Phytobiont and Ecosystem Restitution
.
Springer Singapore
,
Singapore
.
https://doi.org/10.1007/978-981-13-1187-1
.
Kumar
P.
,
Meena
N. K.
&
Mahajan
A. K.
2019
Major ion chemistry, catchment weathering and water quality of Renuka Lake, north-west Himalaya, India
.
Environmental Earth Sciences
78
(
10
),
1
16
.
Kumar
R.
,
McInnes
R.
,
Finlayson
C. M.
,
Davidson
N.
,
Rissik
D.
,
Paul
S.
,
Cui
L.
,
Lei
Y.
,
Capon
S.
&
Fennessy
S.
2021
Wetland ecological character and wise use: towards a new framing
.
Marine and Freshwater Research
72
(
5
),
633
637
.
https://doi.org/10.1071/MF20244
.
Kumari
R.
&
Sharma
R. C.
2019
Assessment of water quality index and multivariate analysis of high-altitude sacred Lake Prashar, Himachal Pradesh, India
.
International Journal of Environmental Science and Technology
16
(
10
),
6125
6134
.
https://doi.org/10.1007/S13762-018-2007-1/FIGURES/3
.
Langmuir
D.
,
1997
Aqueous Environmental Geochemistry
(
McConnin
R.
ed.).
Prentice-Hall, Inc
,
Upper Saddle River, NJ
.
Available from: http://www.prenhall.com.
Mandal
R. B.
2010
Wetlands Management in North Bihar
.
Concept Publishing Company Pvt. Ltd
,
New Delhi
.
Mayanglambam
B.
&
Neelam
S. S.
2022
Physicochemistry and water quality of Loktak Lake water, Manipur, India
.
International Journal of Environmental Analytical Chemistry
102
(
7
),
1638
1661
.
https://doi.org/10.1080/03067319.2020.1742888
.
Mitsch
W. J.
&
Gossilink
J. G.
2000
The value of wetlands: importance of scale and landscape setting
.
Ecological Economics
35
(
1
),
25
33
.
https://doi.org/10.1016/S0921-8009(00)00165-8
.
MoEF&CC
2020
Wetlands of India Portal
.
India's Wetlands of International Importance
.
Mukiza
P.
,
Bazimenyera
J. D. D.
,
Nkundabose
J. P.
,
Niyonkuru
R.
&
Bapfakurera
N. E.
2021
Assessment of irrigation water quality parameters of Nyandungu wetlands
.
Journal of Geoscience and Environment Protection
09
(
10
),
151
160
.
https://doi.org/10.4236/gep.2021.910011
.
Olsen
R. D.
&
Summerfield
M. R.
1977
The physical, chemical limnology of a desert reservoir
.
Hydrobiologia
53
(
2
),
117
129
.
Palit
D.
,
Mondal
S.
&
Chattopadhyay
P.
2018
Analyzing water quality index of selected Pit-Lakes of Raniganj Coal Field Area, India
.
Environment and Ecology
36
(
4A
),
1167
1175
.
Piper
A. M.
1944
A graphic procedure in the geochemical interpretation of water-analyses
.
Eos, Transactions American Geophysical Union
25
(
6
),
914
928
.
https://doi.org/10.1029/TR025I006P00914
.
Prashant
,
Jyoti
A.
,
Kumar
S.
,
Siddiqui
F. A.
,
Singh
R.
&
Kumar
S.
2022
Phosphorus removal in vertical flow reed beds using baked clay balls as an alternative media
.
Current World Environment
17
(
1
),
236
244
.
http://dx.doi.org/10.12944/CWE.17.1.21
.
Raghunath
H. M.
1987
Groundwater, 2nd edn
.
Wiley Eastern Ltd
,
New Delhi
.
Ranjan
R.
,
Srivastava
S. K.
&
Ramanathan
A. L.
2017
An assessment of the hydrogeochemistry of two wetlands located in Bihar State in the subtropical climatic zone of India
.
Environmental Earth Sciences
76
(
1
),
1
19
.
https://doi.org/10.1007/S12665-016-6330-X/FIGURES/9
.
Rawat
K. S.
,
Singh
S. K.
&
Gautam
S. K.
2018
Assessment of groundwater quality for irrigation use: a peninsular case study
.
Applied Water Science
8
(
8
).
https://doi.org/10.1007/S13201-018-0866-8
.
Richards
L. A.
1954
Diagnosis and Improvement of Saline and Alkali Soils. USDA Hand Book No 60: 160
.
Saha
A.
,
Ramya
V. L.
,
Jesna
P. K.
,
Mol
S. S.
,
Panikkar
P.
,
Vijaykumar
M. E.
,
Sarkar
U. K.
&
Das
B. K.
2021
Evaluation of spatio-temporal changes in surface water quality and their suitability for designated uses, Mettur Reservoir, India
.
Natural Resources Research
30
(
2
),
1367
1394
.
https://doi.org/10.1007/S11053-020-09790-5
.
Shan
V.
,
Singh
S. K.
&
Haritash
A. K.
2021
Evaluation of water quality and potential metal contamination in ecologically important Bhindawas bird sanctuary, India
.
Applied Water Science
11
(
1
),
1
9
.
https://doi.org/10.1007/S13201-020-01334-9
.
Singaraja
C.
,
Chidambaram
S.
,
Anandhan
P.
,
Prasanna
M. v.
,
Thivya
C.
,
Thilagavathi
R.
&
Sarathidasan
J.
2014
Hydrochemistry of groundwater in a coastal region and its repercussion on quality, a case study – Thoothukudi district, Tamil Nadu, India
.
Arabian Journal of Geosciences
7
(
3
),
939
950
.
https://doi.org/10.1007/S12517-012-0794-0
.
Singh
N.
,
Kaur
M.
&
Katnoria
J. K.
2017
Spatial and temporal heavy metal distribution and surface water characterization of Kanjli wetland (a Ramsar site), India using different indices
.
Bulletin of Environmental Contamination and Toxicology
99
(
6
),
735
742
.
https://doi.org/10.1007/S00128-017-2194-3/FIGURES/2
.
Singh
A. K.
,
Sathya
M.
,
Verma
S.
&
Jayakumar
S.
2020
Spatiotemporal variation of water quality index in Kanwar wetland, Begusarai, India
.
Sustainable Water Resources Management
6
(
3
),
1
8
.
Singh
Y.
,
Singh
G.
,
Khattar
J. S.
,
Barinova
S.
,
Kaur
J.
,
Kumar
S.
&
Singh
D. P.
2022
Assessment of water quality condition and spatiotemporal patterns in selected wetlands of Punjab, India
.
Environmental Science and Pollution Research
29
(
2
),
2493
2509
.
https://doi.org/10.1007/S11356-021-15590-Y
.
Sreedevi
P. D.
,
Sreekanth
P. D.
,
Ahmed
S.
&
Reddy
D. v.
2019
Evaluation of groundwater quality for irrigation in a semi-arid region of South India
.
Sustainable Water Resources Management
5
(
3
),
1043
1056
.
https://doi.org/10.1007/S40899-018-0279-8
.
Tank
S. K.
&
Chippa
R. C.
2013
Analysis of water quality of halena block in Bharatpur area
.
International Journal of Scientific and Research Publications
3
(
3
),
57
62
.
Todd
D. K.
&
Mays
L. W.
1980
Groundwater Hydrology
.
John Wiley & Sons, New York
.
USSL
1954
Diagnosis and Improvement of Saline and Alkali Soils. Handbook no. 60
.
US Department of Agriculture
,
Washington, DC
.
Verma
M.
&
Negandhi
D.
2011
Valuing ecosystem services of wetlands – a tool for effective policy formulation and poverty alleviation
.
56
(
8
),
1622
1639
.
https://doi.org/10.1080/02626667.2011.631494
.
White
P. J.
&
Brown
P. H.
2010
Plant nutrition for sustainable development and global health
.
Annals of Botany
105
(
7
),
1073
1080
.
https://doi.org/10.1093/AOB/MCQ085
.
WII
2017
Management Plan for Kusheshwar Asthan Chaur, Bihar
. Technical Report. Wildlife Institute of India, Dehradun and Department of Environment and Forests, Government of Bihar.
Wilcox
L. V.
1955
Classification and Use of Irrigation Waters
.
United States Department of Agriculture, Washington, DC
.
William
J. D. H.
,
Syers
S. K.
,
Harris
R. F.
&
Armstrong
D. E.
1970
Fractionation of inorganic phosphate in calcareous lake sediments
.
Soil Science Society of America Journal
35
(
2
),
250
255
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).