Open AccessArticle

Hydrochemical Dynamics and Water Quality Assessment of the Ramsar-Listed Ghodaghodi Lake Complex: Unveiling the Water-Environment Nexus

Ganga Paudel

Ramesh Raj Pant

Tark Raj Joshi

²,

Ahmed M. Saqr

Bojan Đurin

⁴

Vlado Cetl

^5,*

Pramod N. Kamble

⁶ and

Kiran Bishwakarma

⁷

Central Department of Environmental Science, Tribhuvan University, Kathmandu 46000, Nepal

Faculty of Science & Technology, Far Western University, Bhimdatta 10400, Nepal

Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

⁴

Department of Civil Engineering, University North, 42000 Varaždin, Croatia

⁵

Department of Geodesy and Geoinformatics, University North, 42000 Varaždin, Croatia

⁶

Department of Environmental Science, School of Earth Sciences, Central University of Rajasthan, Ajmer 305817, India

⁷

Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

Author to whom correspondence should be addressed.

Water 2024, 16(23), 3373; https://doi.org/10.3390/w16233373

Submission received: 31 October 2024 / Revised: 14 November 2024 / Accepted: 17 November 2024 / Published: 23 November 2024

(This article belongs to the Special Issue Water Quality Assessment of River Basins)

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Graphical abstract
"> Figure 1
Study area region illustrating Ghodaghodi Lake and its adjacent lakes, including sampling sites: (i) A global map illustrating the study area, marked by a red polygon; (ii) A map of the Kailali District highlighting Ghodaghodi Municipality in yellow and the Ramsar site encompassing the Ghodaghodi Lake complex (GLC) in red; (iii) A map of the GLC-Ramsar site, depicting the locations of Ghodaghodi Lake and its associated lakes, classified into Section ‘A’ and Section ‘B’ with delineations; (iv) Locations of Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari Lakes along with their respective sampling sites BC1–BC5, BN1–BN5, R1–R5, and SP1–SP5, and (v) Locations of Ghodaghodi and Ojahuwa Lakes with their corresponding sampling sites G1–G24 and OH1–OH5. "> Figure 2
Land use/land cover map of the study area region illustrating different categories adjacent to sampling points of the lakes. "> Figure 3
Piper diagram for the classification of lake water types in Ghodaghodi and its associated lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon season, featuring three plots: anionic, cationic, and diamond plots. "> Figure 4
Piper diagram for the classification of lake water types in Ghodaghodi and its related lakes (Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari) during the post-monsoon season, featuring three plots: anionic, cationic, and diamond plots. "> Figure 5
Gibbs diagrams illustrating the fluctuation of the weight ratio of Na+/(Na+ + Ca2+) and Cl−/(Cl− + HCO3−) concerning TDS (pre-monsoon) throughout all examined lakes (Ghodaghodi, Ojahuwa, Bichka Chaita, and Sanopokhari). "> Figure 6
Gibbs diagrams illustrating the fluctuation of the weight ratio of Na+/(Na+ + Ca2+) and Cl−/(Cl− + HCO3−) concerning TDS (post-monsoon) throughout all examined lakes (Ghodaghodi, Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari). "> Figure 7
Mixing diagrams illustrating the roles of carbonate, silicate, and evaporates in the hydrochemistry of Ghodaghodi and associated lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon season. (a) represents HCO3−/Na+ vs Ca2+/Na+ and (b) represents Mg2+/Na+ vs Ca2+/Na+ of mixing diagram. "> Figure 8
Mixing diagrams illustrating the roles of carbonate, silicate, and evaporates in the hydrochemistry of Ghodaghodi and its associated lakes (Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari) during the post-monsoon season. (a) represents HCO3−/Na+ vs Ca2+/Na+ and (b) represents Mg2+/Na+ vs Ca2+/Na+ of mixing diagram. "> Figure 9
Wilcox diagram depicting the irrigation water quality based on SAR and EC for Ghodaghodi Lake and three related lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon period. "> Figure 10
Wilcox diagram depicting the irrigation water quality based on SAR and EC for Ghodaghodi Lake and five related lakes (Ojahuwa, Bichka Chaita, Sanopokhari, Budhiya Nakhrod, and Ramphal) during the post-monsoon period. "> Figure 11
Hydrochemical dynamics, sustainable development goals (SDGs) impact, and conservation strategies for Ghodaghodi Lake Complex (GLC). ">

Versions Notes

Abstract

Human activities and climate change increasingly threaten wetlands worldwide, yet their hydrochemical properties and water quality are often inadequately studied. This research focused on the Ghodaghodi Lake Complex (GLC) and associated lakes in Nepal, a Ramsar-listed site known for its biodiversity and ecological significance. The study was conducted to assess seasonal water quality, investigate the factors influencing hydrochemistry, and assess the lakes’ suitability for irrigation. Forty-nine water samples were collected from the GLC in pre-monsoon and post-monsoon periods. Nineteen physicochemical parameters, such as dissolved oxygen (DO), total dissolved solids (TDS), and major ions (calcium ‘Ca²⁺’, magnesium ‘Mg²⁺’, and bicarbonate ‘HCO₃⁻’), were analyzed using standard on-site and laboratory methods. Statistical methods, including analysis of variance (ANOVA), T-tests, and hydrochemical diagrams, e.g., Piper, were adopted to explore spatial and seasonal variations in water quality, revealing significant fluctuations in key hydrochemical indicators. Results showed marked seasonal differences, with pre-monsoon TDS levels averaging 143.1 mg/L compared to 78.9 mg/L post-monsoon, underscoring evaporation and dilution effects. The hydrochemical analysis identified Ca²⁺-HCO₃⁻ as the dominant water type, highlighting the influence of carbonate weathering on GLC’s water composition. Gibbs, mixing, and Piper diagram analysis supported these findings, confirming the predominance of HCO₃⁻, with Ca²⁺ and Mg²⁺ as the main cations. Additionally, sodium adsorption ratio (SAR) values were consistently below 1, confirming excellent irrigation quality. These findings provided critical data for policymakers and stakeholders, supporting sustainable wetland management and aligning with the United Nations’ Sustainable Development Goals relevant to environmental conservation, i.e., clean water and life on land.

Keywords:

wetland hydrochemistry; water quality; irrigation suitability; piper diagram; environmental sustainability; Ghodaghodi Lake

Graphical Abstract

1. Introduction

Wetlands are experiencing rapid degradation due to climate change, urbanization, and increased agricultural practices, undermining their ability to deliver vital environmental services globally [1]. Through carbon sequestration, these ecosystems are critically essential for preserving biodiversity, regulating water quality, and stabilizing local climates [2]. Wetlands are organically linked to sustainable development goals (SDGs), which stress the protection of ecosystems and biodiversity [3]. The degradation of wetlands significantly jeopardizes local biodiversity and compromises sustainability goals, therefore increasing the requirement of conservation actions meeting biological and socioeconomic needs [4]. Having lost over 50% in the past century, wetlands maintain over 40% of all species globally, even if they rank among the most vulnerable ecosystems [5]. This decline underlines the significance of preserving wetlands, which are essential defenses against climate change, filters of pollutants, and habitats for many species [6].

Covering nearly 7435 km², wetlands make up over 5% of Nepal’s total land area. [7]. Local populations depend on these ecosystems as they provide resources for farming, fishing, and other industries. The Ghodaghodi Lake Complex (GLC) is a well-known wetland categorized as a Ramsar site for its special biodiversity and ecological values [8]. Comprising many connected lakes, marshes, and seasonally flooded grasslands, this lake complex supports a variety of ecosystems able to sustain a great range of flora and animals [9]. The GLC’s major environmental problems are human activities altering the natural surroundings of the lake: deforestation, agricultural expansion, and urbanization [10]. Agricultural runoff and domestic trash have caused nutrient loading that has boosted phosphate levels and lowered dissolved oxygen (DO) in the lake, compromising water quality and negatively affecting aquatic and terrestrial species [11]. Furthermore, the normal hydrochemical processes of the lake have been altered by sedimentation brought on by land expansion and deforestation, disturbing the ecological equilibrium and lowering its potential to offer neighboring people necessary services [12]. The deterioration of GLC stresses the need for thorough management measures since it exposes its biodiversity and affects its capacity to ensure regional water and food security [13].

Hydrochemical analysis has proven its effectiveness in evaluating the condition of aquatic environments through several physicochemical parameters [14,15]. Fundamental indicators of water quality in hydrochemical studies are DO, total dissolved solids (TDS), potential of hydrogen (pH), and major ions: calcium (Ca²⁺) and magnesium (Mg²⁺) [16]. T-tests and Analysis of Variance (ANOVA) enable statistical methods to detect significant seasonal and spatial fluctuations in these parameters, offering information on temporal and locational changes in water quality [17]. Moreover, hydrochemical diagrams, including Piper and Gibbs diagrams, allow the classification of water types and streamline the fundamental processes regulating lake chemistry [18]. Piper diagrams enable scientists to determine the main hydrochemical facies through cation and anion concentration, classifying water samples [19]. Gibbs diagrams, on the other hand, emphasize the primary processes affecting water chemistry, which would help to elucidate both natural and artificial consequences on water quality [20]. Although hydrochemical assessments are extensively employed in river and reservoir studies across Nepal, there is a shortage of research that notably addresses lake systems such as GLC, underscoring the need for targeted studies to establish baseline data for lake preservation [21].

This study attempts to fulfill this research gap in hydrochemical dynamics and water quality evaluation of GLC and associated lakes. The major objectives of this research are to measure these hydrochemical differences and evaluate the appropriateness of GLC’s water for agriculture by directly addressing local sustainability issues that align with environmental SDGs. Utilizing statistical analysis and hydrochemical evaluation, this work provides a detailed grasp of the seasonal variations in water quality that affect both ecological health and human use. Ultimately, by providing essential baseline data for sustainable wetland management and conservation techniques, this study promotes evidence-based policy and enhances efforts to protect Nepal’s significant wetland ecosystems, including Ramsar sites. This not only benefits the local and regional levels but also contributes to global conservation initiatives, highlighting the universal importance of preserving these vital ecosystems.

2. Materials and Methods

2.1. Study Area

The GLC is a significant ecological and socio-economic resource, comprising multiple oxbow lakes. It is situated within the geographical coordinates of latitude 28°41′ 21″ N to 28°42′ 21″ N and longitude 80°56′ 23″ E to 80°56′ 27″ E, in Ghodaghodi Municipality, Kailali District, Nepal (Figure 1). This study mainly focuses on six lakes within the complex: Ghodaghodi Lake, sometimes referred to as the main lake; Ojahuwa Lake; Budhiya Nakhrod Lake; Ramphal Lake; Bichka Chaitya Lake; and Sanopokhari Lake, also known as Sunpokhari Lake. The two lakes, Ghodaghodi and Ojahuwa, encompass 75.32 and 3.29 hectares, respectively, in Ward 1. Conversely, the remaining four lakes, Budhiya Nakhrod, Ramphal, Bichka Chaitya, and Sanopokhari, with areas of 5.54, 3.68, 0.5, and 2.38 hectares, respectively, are located in ward-8 of Ghodaghodi Municipality. The Ojahuwa Lake is distinguished for facilitating boating activities, while the four lakes have been employed for aquaculture. The Ghodaghodi Lake and its adjacent lakes, located at an altitude of 205 m above sea level, encompass springs, swamps, marshes, and human-made wetlands, including ponds, irrigated fields, and canals, all situated within a tropical forest of the Siwalik hills’ lower slopes [13,22]. The lakes obtain water from multiple sources, such as precipitation, surface runoff, and flooding from adjacent rivers, enhancing their significant ecological diversity and supporting many flora and fauna [22,23,24]. The local inhabitants rely on the wetland area for their livelihoods, where the lakes’ periphery is marked by layers of conglomerates and soft shale, with soil types ranging from clay to alluvial [25].

Figure 2 illustrates the land use/land cover classes adjacent to the points of lakes where samples were taken. The Ghodaghodi Lake is surrounded by a forest-covered area; the north and south parts of the lake have been exposed to human activities, while in the middle region of the lake, there is a crocodile breeding center. Ojahuwa Lake is also surrounded by forest and settlement areas; this lake has been used for boating and recreational activities. The other four associated lakes, Budhiya Nakhrod, Bichka Chaitya, Sanopokhari, and Ramphal, are surrounded by forest land in the west direction and agricultural land along with a few settlements in the east direction.

2.2. Data Collection

Fieldwork and sampling were executed in the pre-monsoon period of March 2022 and the post-monsoon period of November 2022, adhering to standard protocols [26]. Systematic random sampling was utilized to gather 49 water samples from the coastal zones of Ghodaghodi Lake (24 samples), Ojahuwa Lake (5 samples), Budhiya Nakhrod Lake (5 samples), Ramphal Lake (5 samples), Bichka Chaitya Lake (5 samples), and Sanopokhari (5 samples), as depicted in Figure 1. At each of the 49 sites, five subsamples were collected and subsequently combined to form a single composite sample per site for analysis. Water samples from Budhiya Nakhrod Lake and Ramphal Lake were not obtained during the pre-monsoon season due to dry conditions caused by fish farmers’ water extraction.

Initially, sediment resuspension in coastal lake zones was seen as a potential factor affecting water quality. The water at the sampling locations demonstrated clean clarity, signifying negligible disturbance from sediment resuspension. To further mitigate any possible influence on sample composition, macrophytes located at specific sites were meticulously extracted using a fine mesh net.

2.3. Field and Laboratory Analysis

High-density polyethylene (HDPE) bottles, meticulously rinsed with lake water, were employed to gather water samples. The samples were carefully transferred to the Central Department of Environmental Science (CDES) laboratory at Tribhuvan University in Kirtipur, Kathmandu. They were preserved under freezing conditions to maintain their integrity for future examination.

On-site measurements were conducted for parameters such as water temperature (WT), TDS, DO, pH, chloride (Cl⁻), magnesium hardness (MgH), calcium hardness (CaH), total hardness (TH), turbidity (Tur.), electrical conductivity (EC), alkalinity (AL), and free carbon dioxide (CO₂), adhering to the APHA standard guidelines [26].

The YSI Pro Quatro Multiparameter was utilized to evaluate water temperature, pH, DO, TDS, and EC. Turbidity (Tur) was measured using the Oakton TN-100 Turbidity Meter. The Oakton TN-100 Turbidity Meter is typically more effective than the Secchi disk for assessing water clarity, especially in scientific and professional settings, owing to its higher accuracy and precision. In contrast to the Secchi disk, which depends on subjective visual assessment, the TN-100 delivers objective numerical measurements in Nephelometric Turbidity Units (NTU), hence minimizing human error. Furthermore, TH, CaH, and MgH were assessed utilizing the EDTA Titration Method. Cl⁻ was quantified using the Argentometric Titration Method, AL was evaluated via the Acid-base Titration Method, bicarbonates (HCO₃⁻) were derived from AL, and free CO₂ was measured through phenolphthalein titration.

Additional parameters such as potassium (K⁺), sodium (Na⁺), ammonium (NH₄⁺), phosphate (PO₄³⁻), nitrate (NO₃⁻), sulfate (SO₄²⁻), and iron (Fe²⁺) were examined in the laboratory. K⁺ and Na⁺ ions were measured with flame photometry. NH₄⁺ concentrations were evaluated using the phenate method, PO₄³⁻ levels were quantified via the ammonium molybdate solution method, NO₃⁻ concentrations were ascertained through the phenol disulphonic acid method, SO₄²⁻ levels were measured using the barium chloride method, and Fe²⁺ concentrations were determined by the phenanthroline method. All assessments were completed following the procedures [26]. Ca²⁺ and Mg²⁺ were determined from the assessments of CaH and MgH, respectively.

2.4. Data Analysis

The raw data obtained from fieldwork and laboratory analyses underwent thorough assessment. Descriptive statistics, the Piper diagram, Gibbs plot, mixing diagram, parametric and non-parametric tests, and irrigation appropriateness indices were employed to evaluate the comprehensive hydrochemistry and seasonal fluctuations in water quality. This research was performed utilizing MS Excel, IBM SPSS 26, Origin 2019, and R-Studio 2024.04.2 Build 764, guaranteeing a thorough evaluation and interpretation of the data [15,27,28].

2.4.1. Statistical Data Analysis

Descriptive statistics, including maximum, minimum, standard deviation, and mean values for all parameters, were calculated using the statistical package (IBM SPSS version 26). This methodology facilitated the assessment of regional and temporal fluctuations in water quality metrics and comparisons to analogous studies [29]. The Shapiro–Wilk test was adopted to evaluate normality for each parameter in each season and each lake separately. After the results, parametric and non-parametric tests were conducted in R-Studio to compare data over two seasons of the same lake or among various lakes within the same season. A t-test (for normally distributed data) or Mann-Whitney U test (for non-normally distributed data) was conducted to compare data between two seasons of the same lake [27]. A one-way ANOVA was conducted to evaluate the same variables across many analyzed lakes for the same season, contingent upon the normality of the data. The Kruskal–Wallis test (applicable for non-normally distributed data) was employed to compare identical variables across various examined lakes for the same season [29]. The parametric test was conducted when variables measured in both seasons or in all lakes were normally distributed; either in one season or one lake, there was non-normally distributed data; the comparison was carried out by a non-parametric test.

2.4.2. Evaluation of Controlling Mechanisms of Hydrochemistry

Piper diagram

A trilinear diagram utilizing the classification system established by Piper [30] was created with Origin Pro to classify the hydrochemical properties of water. This graphic consists of two triangles, one depicting cation and the other anions, accompanied by a central diamond plot, all within a triangular boundary. This diagram enables the categorization of water kinds into six unique classifications. The classes comprise (1) Ca²⁺-HCO₃⁻, (2) Na⁺-Cl⁻, (3) mixed Ca²⁺-Na⁺-HCO₃⁻, (4) mixed Ca²⁺-Mg²⁺-Cl⁻, (5) Ca²⁺-Cl⁻, and (6) Na⁺-HCO₃⁻ types, offering significant insights into the composition and features of the studied water samples [31,32].

Gibbs plot and mixing diagram

The governing mechanism of the lake’s hydrochemical dynamics was elucidated through meticulous Gibbs plot analysis, a technique that juxtaposes TDS with the ratios of Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO₃⁻) to comprehend the intricate interplay of precipitation, evaporation, and rock weathering [20,33]. Samples with low TDS (<1 mg/L) and elevated Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO₃⁻) ratios (0.5–1) were classified as end-member precipitation, congregating in the lower region of the figure. Centrally grouped samples exhibiting moderate TDS levels (70–300 mg/L) and reduced Na+/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO³⁻) ratios (<0.5) predominantly indicated the impact of rock weathering. Furthermore, samples situated in the upper right quadrant, distinguished by elevated TDS (>300 mg/L) and a high Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO₃⁻) ratio (0.5–1), exhibited the effects of evaporation. This thorough investigation offered significant insights into the fundamental mechanisms influencing lake water chemistry [34]. The mixing diagram was also constructed to demonstrate the impact of carbonate, silicate, and evaporite weathering on hydrochemistry.

2.4.3. Evaluation of Irrigation Suitability

The assessment of irrigation water and lake water quality involved evaluating several factors to ascertain their appropriateness for designated purposes. Critical parameters for assessing irrigation water quality include sodium adsorption ratio (SAR), sodium percentage (Na %), EC, Kelly’s ratio (KR), magnesium adsorption ratio (MAR), cation ratio of soil structural stability (CROSS), and permeability index (PI), as these factors significantly influence soil and plant health [33,35,36]. Furthermore, the Wilcox diagram was constructed to assess sodium salinity and the potential risks associated with lake water, providing insights into its appropriateness for diverse applications [37]. All these indexes were computed as follows:

Electrical Conductivity (EC)

Water with an EC less than 700 μS/cm, between 3000 and 700 μS/cm, and greater than 3000 μS/cm was classified as ‘Excellent’, ‘Good’, and ‘Fair’ in terms of irrigation appropriateness, respectively [38].

Sodium Percentage (Na %)

The ratio of sodium and potassium to total cationic concentrations (Equation (1)) is computed to assess sodium hazards. The Na % range for irrigation suitability was categorized as follows: ‘Unsuitable’ > 80, ‘Doubtful’ 60–80, ‘Permissible’ 40–60, ‘Good’ 20–40, ‘Excellent’ < 20 [39].

Na % = \frac{({N a}^{+} + K^{+}) * 100}{({C a}^{2 +} + {M g}^{2 +} + {N a}^{+} + K^{+})}

(1)

Sodium Adsorption Ratio (SAR)

Soil cation exchange responses were assessed using SAR, indicating that water with an elevated SAR value may adversely impact the soil’s physical structure, resulting in its degradation. The irrigation suitability following SAR was categorized as ‘Excellent’ for values less than 10 meq/L, ‘Good’ for 10–18 meq/L, ‘Fair’ for 18–26 meq/L, and ‘Poor’ for values beyond 26 meq/L [40]. SAR was presented in Equation (2).

SAR = \frac{{N a}^{+}}{\sqrt{({C a}^{2 +} + {M g}^{2 +}) / 2}}

(2)

Magnesium Adsorption Ratio (MAR)

The MAR was computed using Equation (3) to assess the alkaline danger of water, with elevated MAR values signifying increased AL and diminished agricultural productivity. The irrigation suitability was classified according to the MAR, with values greater than 50 being ‘Unsuitable’ and those less than 50 considered ‘Suitable’ [41].

MAR = \frac{{M g}^{2 +} * 100}{({C a}^{2 +} + {M g}^{2 +})}

(3)

Kelly’s Ratio (KR)

The calculation of KR, which accounted for Na⁺, about Ca²⁺, and Mg²⁺, was performed using Equation (4). The range of KR for irrigation suitability is categorized as ‘Unsuitable’ when greater than 1 and ‘Suitable’ when less than 1 [42].

KR = \frac{{N a}^{+}}{({C a}^{2 +} + {M g}^{2 +})}

(4)

Permeability Index (PI)

The irrigation suitability was categorized according to the PI range as follows: ‘Suitable Class I’ for values greater than 75, ‘Good Class II’ for values between 25 and 75, and ‘Unsuitable Class III’ for values less than 25 [40]. The formula for PI was given by Equation (5) [35].

PI = \frac{({N a}^{+} + \sqrt{{H C O}_{3}^{-}}) * 100}{{C a}^{2 +} + {M g}^{2 +} + {N a}^{+}}

(5)

Cation Ratio of Soil Structural Stability (CROSS)

The suitability of irrigation based on SAR was categorized as follows: ‘Unsuitable’ for values exceeding 26, ‘Fair’ for values between 19 and 26, ‘Good’ for values between 10 and 18, and ‘Excellent’ for values less than 10. The CROSS formula was presented in Equation (6) [43].

CROSS = \frac{({N a}^{+} + 0.56 K^{+}) * 100}{\sqrt{{(C a}^{2 +} + 0.6 {M g}^{2 +}) / 2}}

(6)

Wilcox Diagram.

The Wilcox classification was utilized to categorize the quality of irrigation water in GLC [39], according to the percentage of Na⁺, in the EC. Various levels of irrigation salinity were the basis for categorizing water quality using the Wilcox diagram [33,35].

3. Results and Discussion

3.1. General Hydrochemistry and Variation

The hydrochemical evaluation of the GLC complex indicated seasonal and spatial fluctuations influenced by climatic, biological, and anthropogenic factors. Table 1 and Table 2 presented a summary of the recorded physicochemical characteristics (i.e., ionic and non-ionic parameters), indicating a significant rise in WT throughout the post-monsoon season across the lakes, with Ghodaghodi and Ojahuwa Lakes exhibiting the highest WTs. The seasonal increase in WT was associated with elevated microbial and planktonic activity, affecting parameters such as TDS, EC, and BOD. Elevated post-monsoon WTs, observed in tropical lake systems, enhanced microbial decomposition and nutrient cycling, hence directly influencing TDS and EC [44].

WT exhibited significant variation in the pre-monsoon period, which was affected by daily changes and sample periods. These variations likely led to elevated TDS and EC since evaporative processes concentrate ions in the water [8]. Due to their reduced water volumes, the evaporation-induced ionic concentration was especially pronounced in smaller lakes, such as Sanopokhari and Bichka Chaita. The WT variations immediately influenced other factors, as elevated WT frequently coincides with aquatic species’ heightened metabolic rates, exacerbating organic matter breakdown [45].

The pH levels in the GLC lakes exhibited notable seasonal variations, fluctuating from mildly acidic to alkaline (Table 1). In the pre-monsoon period, Ghodaghodi Lake had increased acidity, presumably due to intensified biological breakdown near thick macrophyte regions, releasing dissolved CO₂. Moreover, CO₂ is combined with water to generate carbonic acid, reducing pH. This process can be controlled post-monsoon by the influx of rainwater, which neutralizes and buffers the water, stabilizing pH at slightly alkaline levels. The buffering effect of HCO₃⁻, often sourced from carbonate weathering, aids in stabilizing pH fluctuations throughout the post-monsoon season, averting sudden pH declines that could adversely affect aquatic organisms [45].

The inverse correlation between pH and free CO₂ concentrations was particularly evident in pre-monsoon samples. In GLC, elevated CO₂ concentrations were observed, especially in areas rich in macrophytes, where breakdown and biological respiration contribute substantial CO₂ emissions [46]. Elevated free CO₂ concentrations signified heightened organic decomposition activities, thus leading to water acidification and reduced pH values. CO₂ generation and pH affected the carbonate-bicarbonate buffering system, impacting lake alkalinity, especially in carbonate-rich areas [21].

EC and TDS exhibited considerable seasonal and geographical variability among the lakes (Table 1). The readings reached their zenith during the pre-monsoon period, particularly at Ghodaghodi Lake, where diminished water levels and elevated WTs resulted in increased ionic concentration. Increased EC and TDS during the pre-monsoon period signified the impacts of evapo-concentration, mineral dissolution, and potential human influences, which became more pronounced when lake water levels declined. These results corresponded with research conducted in tropical and subtropical lakes, wherein diminished water volume from elevated evapotranspiration led to the concentration of dissolved ions in lakes situated within carbonate-rich watersheds [17,44]. In contrast, post-monsoon measurements were diminished in all lakes due to dilution from precipitation, demonstrating that monsoon-induced influx significantly lowered TDS and EC by introducing water with reduced ion concentration [47].

The results obtained for the whole lakes did not yield significant new findings compared to the individual lake analyses (Table 1 and Table 2). The overall hydrochemical patterns, including TH, Ca²⁺, Mg²⁺, and HCO₃⁻, reflected similar seasonal trends influenced by carbonate weathering and rainfall-induced dilution [8]. While these parameters demonstrated the expected seasonal variations, the aggregated data for all lakes did not provide additional insights beyond the localized variations previously described [13]. This suggested that examining each lake individually remained the most informative approach for understanding the hydrochemical dynamics of the Ghodaghodi Lake Complex, aligning with findings from previous studies that have emphasized localized influences over regional patterns [11].

Since data were collected in the coastal zones of the lakes, sediment resuspension was considered a potential factor influencing the observed water quality. The role of internal recharge, involving the upward movement of nutrient-rich groundwater or mixing from sediment pore water, likely contributed to the elevated concentrations of certain ions and nutrients. This internal recharge process can amplify nutrient levels, such as PO₄³⁻ and NO₃⁻, and increase tur., especially during periods of wind-induced mixing or bioturbation by aquatic organisms. Similar findings have been noted in other wetland studies where internal nutrient cycling significantly impacted hydrochemical variations [12,13]. In many cases, this internal process may have a dominant effect compared to pollutant inflows from the catchment, impacting the hydrochemical balance [11].

3.2. Comparison of Pre- and Post-Monsoon Data for Identical Lakes and Inter-Lake Comparison Within the Same Season

A comprehensive comparative investigation of hydrochemical parameters across seasons and lakes revealed significant patterns, especially with important ions such as Ca²⁺ and HCO₃⁻ (Table 2). Most ions, particularly Ca²⁺ and HCO₃⁻, attained elevated concentrations during the pre-monsoon period due to evapo-concentration and reduced precipitation. Ghodaghodi, Ojahuwa, and Sanopokhari Lakes demonstrated the highest ion concentrations, a pattern typical with tropical lakes where pre-monsoon circumstances enhanced dissolved ion concentration [48]. In carbonate-rich lakes such as Ghodaghodi, Ca²⁺, and HCO₃⁻ concentrations were directly affected by the dissolution of adjacent carbonate rock, which was exacerbated under reduced water levels [49].

Conversely, post-monsoon data indicated reduced ion concentrations (Table 3), illustrating the dilutive impact of monsoon precipitation. TH, Ca²⁺, and Mg²⁺ concentrations were markedly diminished after the monsoon, suggesting that precipitation influx considerably decreased ionic levels [50]. Statistical results employing ANOVA and Kruskal–Wallis U tests (Table 4) validated considerable seasonal variations, especially in Ca²⁺, Mg²⁺, and TH, highlighting the influence of monsoonal dilution in reducing ionic concentrations in tropical lake systems [51].

The data indicated notable regional variations in tur. and NO₃⁻ concentrations, particularly in lakes such as Sanopokhari and Bichka Chaita, which exhibited the highest tur. levels during the pre-monsoon period (Table 1 and Table 2). Elevated tur. in Sanopokhari was likely attributable to agricultural runoff, which introduced particulate matter and NO₃⁻ that increased suspended particle concentrations [52]. The elevated tur. was associated with nutrient concentrations, as both NO₃⁻ and NH₄⁺ levels were greater in Sanopokhari, indicating nutrient enrichment from adjacent agricultural land. Nutrient concentrations in GLC lakes suggested that agricultural practices significantly impact them, particularly in the pre-monsoon period when runoff could not be mitigated by substantial rainfall [53].

Increased NO₃⁻ concentrations in lakes such as Ojahuwa further substantiated the influence of nutrient runoff from adjacent agricultural areas, especially following the monsoon season when rainfall transported fertilizers into the lakes (Table 2). The variations in nutrients underscored the seasonal susceptibility of lakes next to agricultural land to nutrient enrichment, which, if not well managed, may result in eutrophic conditions, hence compromising water quality and ecosystem health over time [53].

3.3. Comparative Assessment of Hydrochemical Facies

The hydrochemical facies of GLC lakes, depicted in Piper diagrams, revealed a predominance of Ca–HCO₃ and Mg–HCO₃ types, characteristic of carbonate weathering settings (Figure 3 and Figure 4). During the pre-monsoon season, Ghodaghodi Lake exhibited a dominating Ca–HCO₃ type, indicating the influence of HCO₃⁻ dissolution from the underlying lithology. Increased Cl⁻ concentrations in Ojahuwa and Sanopokhari, especially during the pre-monsoon period, indicated anthropogenic impacts, including agricultural and domestic runoff from adjacent regions. Increased Cl⁻ concentrations, aligned with patterns noted in other lakes, were affected by anthropogenic activities, as Cl⁻ was often derived from fertilizers and household garbage [54].

Cation analysis revealed that Ca²⁺ was the predominant ion in all lakes, succeeded by Mg²⁺, underscoring the impact of carbonate weathering as the principal ionic contributor (Table 2). The Piper diagrams illustrated seasonal variations, indicating that although Ca–HCO₃ predominated, lakes such as Sanopokhari and Bichka Chaita displayed transitions to mixed Ca–Mg–Cl facies in the post-monsoon period (Figure 3 and Figure 4). This transition indicated that human activities, such as runoff from adjacent agricultural areas, influenced water chemistry. Mixed facies patterns frequently occurred in lakes exposed to external nutrient inputs, as runoff could inject supplementary ions that modified the original water type [46].

Table 5 summarizes the classifications of water types derived from the Piper diagrams, indicating that Ghodaghodi Lake regularly exhibited a Ca–HCO₃ type in both seasons. Conversely, Sanopokhari and Bichka Chaita’s transition to mixed Ca–Mg–Cl facies after the monsoon indicated that nutrient and ion contributions from agricultural regions significantly influenced water composition. Seasonal facies variations had consequences for lake management since they signified regions where external activities might be modifying natural hydrochemical processes, potentially impacting water suitability for various applications [21].

3.4. Controlling Mechanisms of Hydrochemistry

The primary mechanisms affecting hydrochemical fluctuations in the GLC were rock weathering, evaporation, and anthropogenic contributions [22]. Gibbs plots indicated that pre-monsoon samples predominantly aggregated inside the rock-weathering dominance zone, implying that lithological sources are the principal determinants of ion concentrations in these lakes (Figure 5 and Figure 6). The elevated levels of Ca²⁺ and HCO₃⁻ in lakes, such as Ghodaghodi and Ojahuwa, indicated that HCO₃⁻ dissolution is a primary contributor, with dissolved ions mirroring the mineralogical composition of the adjacent geology [35].

Evaporation influenced hydrochemistry, especially in the pre-monsoon period, since elevated evaporation rates concentrated dissolved salts, resulting in increased TDS and EC. This phenomenon was especially evident in smaller lakes such as Sanopokhari, where reduced water volumes result in more significant evapo-concentration. Increased pre-monsoon Na⁺ and Cl⁻ concentrations further illustrated the impact of evaporation, which intensified salt concentration in the absence of dilution [55]. Monsoon-induced rainfall dilution reduced TDS and EC levels after the monsoon, demonstrating that seasonal precipitation substantially lowered ionic concentration in lakes [47].

Anthropogenic influences, particularly agricultural runoff, significantly contributed to elevated nutrient levels (e.g., NH₄⁺, NO₃⁻) in lakes such as Sanopokhari and Budhiya Nakhrod, especially following the monsoon season. Increased nutrient concentrations indicated the influence of nitrogen fertilizers and animal waste from adjacent agricultural land, which heightened the risk of eutrophication if nutrient management practices were not used. Nutrient enrichment, especially in agricultural regions, posed dangers to lake ecosystems, as excessive nitrogen and phosphorus could result in algal blooms, reduced oxygen levels, and deteriorated water quality [53].

The mixing diagrams validated the preeminent influence of carbonate weathering, with silicate weathering playing a subordinate role in determining hydrochemistry (Figure 7 and Figure 8). Pre-monsoon samples, distinguished by elevated levels of Ca²⁺ and HCO₃⁻, indicated the impact of HCO₃⁻ weathering, exacerbated during periods of reduced water levels, leading to the concentration of weathering byproducts through mineral dissolution processes. Lakes such as Sanopokhari, displaying silicate influence, demonstrated variety in lithological influence, probably attributable to the heterogeneous mineral composition of their surrounding catchments [21].

3.5. Water Suitability for Irrigation

The suitability of water for irrigation was assessed using SAR, MAR, Na%, and other indices, concentrating on salinity, permeability, and sodium risks. SAR values for all lakes remained beneath the crucial threshold (<10), categorizing the water as ‘excellent’ for irrigation during both pre- and post-monsoon seasons, according to the guidelines [40] (Table 6). Low SAR values signified a negligible sodium risk, indicating that water from these lakes was appropriate for diverse soil types without endangering soil integrity due to salt-induced dispersion.

Na% levels similarly denoted appropriateness, with values below 20% categorized as ‘good’ to ‘excellent’ according to Wilcox standards [39]. Low MAR values in the lakes indicated less alkalinity danger for sensitive crops, supporting irrigation appropriateness. Wilcox diagrams categorized the majority of samples within the C1-S1 and C2-S1 zones, indicating low sodium and moderate salinity risks appropriate for agricultural use [35] (Figure 9 and Figure 10).

The PI analysis categorized pre-monsoon water as Class I (‘Suitable’) and post-monsoon as Class II (‘Good’), indicating greater permeability in pre-monsoon conditions due to concentrated ions. These classifications underscored the necessity of ongoing monitoring and nutrient management, especially in lakes such as Sanopokhari, to maintain water quality, aligning with both ecological integrity and agricultural productivity [21].

4. Comparison of the Study Findings with the Literature

The hydrochemical dynamics of the GLC offered significant insights concerning the current literature and national water quality standards. The principal sources of pollutant influx at each lake were limited, primarily originating from agricultural runoff in proximity to agriculture, with substantial macrophyte proliferation and organic matter further exacerbating contamination at all locations [8]. Aquaculture operations in specific lakes also contributed to fertilizers, affecting water quality [11].

The pH, EC, TDS, Na⁺, Cl⁻, and Fe²⁺ indicators were typically aligned with the National Water Quality Guidelines for Irrigation Water (NWQGIW), signifying the lakes’ appropriateness for agricultural utilization [13]. Moreover, critical parameters such as WT, pH, free CO₂, DO, TH, Mg²⁺, and NO₃⁻ predominantly conformed to the National Water Quality Guidelines for Aquaculture (NWQGA), leading to fostering a conducive habitat for aquatic organisms [8]. Nonetheless, tur. levels in the majority of lakes, especially during the pre-monsoon phase, surpassed the thresholds established by the National Water Quality Guidelines for Recreation (NWQGR), indicating that these waters might not consistently be suitable for recreational activities [11].

The study results corresponded with previous studies in multiple aspects. Comparable pH values were noted in Ghodaghodi Lake relative to prior research by Bhatta et al. [11] and Pant et al. [10]. In contrast, the study data indicated a higher post-monsoon pH, diverging from earlier findings that reported more alkaline conditions during the pre-monsoon season [8,11]. EC and TDS values were significantly elevated during the pre-monsoon season, aligning with the evaporative concentration effects reported in other tropical lakes [11]. TH levels exhibited seasonal variation, with the study results showing elevated post-monsoon values, a trend that diverged from some historical accounts while corroborating others, indicative of site-specific hydrological and anthropogenic factors [8]. Table 7 illustrated these comparisons, indicating consistencies in critical parameters, such as Cl⁻ levels, with the current study [10]. The existing study emphasized the need for ongoing monitoring and efficient management to mitigate the tur. and nutrient discharge, which can represent essential elements for preserving ecological balance and sustainability.

5. Impact of the Study Finding on Environmentally Sustainable Development Goals (SDGs)

The results of this study on the water quality dynamics and hydrochemical properties of the GLC had significant consequences for environmental SDGs 6 (Clean Water and Sanitation) and 15 (Life on Land), as illustrated in Figure 11. The study indicated seasonal variations in water quality over this Ramsar-listed site, showing how factors including agricultural runoff, evapo-concentration, and natural water cycles can affect key hydrochemical parameters, including TDS, EC, and ion concentrations, notably Ca²⁺ and Mg²⁺. These differences directly supported SDG 6, which seeks to ensure the availability and sustainable management of water and sanitation, influencing local irrigation, water consumption, and community health [56]. The study’s finding that the lake water’s SAR stayed low and, hence, appropriate for irrigation was crucial for the livelihoods of the surrounding inhabitants, who depended on GLC’s resources for agricultural productivity and food security.

Beyond water quality, the high nutrient levels in GLC, especially NO₃⁻ and PO₄³⁻ from agricultural runoff, raised concerns over the likelihood of eutrophication, a process that may considerably diminish biodiversity by stimulating algal blooms and depleting oxygen in aquatic habitats [57]. Substantial runoff provided many nutrients and suspended materials; hence, this nutrient enrichment was especially evident in areas of the lake close to agricultural areas. These environmental pressures compromised the diverse flora and fauna of the lake, thereby connecting the significance of the research with SDG 15, which strived at the preservation, rehabilitation, and sustainable use of land and freshwater ecosystems [3]. Particularly, SDG Target 15.1 emphasized the preservation of inland freshwater ecosystems since it was necessary to retain biodiversity, ecosystem health, and the critical functions that wetlands and the GLC offer intact using the protection of these resources.

The study’s results underlined the need for targeted management activities, including control of nitrogen input from surrounding agricultural land and implementation of conservation measures to reduce the environmental impact on the lake system. As this work revealed, continuous hydrochemical monitoring can produce valuable data to guide these management plans and ensure sustainable use of lake resources. By advocating science-based policies and practices, this study helped to encourage the development of a thorough conservation plan for GLC, thereby harmonizing ecological sustainability with the socioeconomic needs of the local population. Employing an integrated approach that guaranteed GLC would remain a vital ecological and community resource suited for SDGs 6 and 15, and such initiatives helped to achieve more SDGs.

6. Limitations and Future Research Directions

This study of the hydrochemical dynamics and water quality of the GLC has several limitations. Data collection was confined to coastal areas due to accessibility challenges, including marshy and unstable soil conditions, and safety concerns arising from a crocodile breeding facility. Thus, the representativeness of our findings may be constrained, as central lake regions may exhibit distinct hydrochemical properties. The study’s temporal focus was limited to pre-monsoon and post-monsoon seasons, potentially overlooking hydrochemical alterations during other periods, such as the dry season or extreme weather events. The paucity of trace metal and emerging pollutant analysis, coupled with insufficient biological monitoring, constrains our comprehension of the overall ecological health and potential pollution hazards impacting the lake complex.

Future studies should rectify these constraints by broadening the spatial scope to encompass central lake regions and employing modern technology, such as drones and remote sensing, to surmount physical obstacles. Gathering data throughout all seasons would yield a more thorough comprehension of hydrochemical variability. Examining trace metals and developing contaminants in conjunction with biological evaluations of aquatic organisms would provide an enhanced understanding of the lake’s ecological integrity. Furthermore, simulating hydrochemical variations under diverse climatic and land-use scenarios may inform future management methods. Involving local populations in participatory research would facilitate translating scientific results into effective, sustainable conservation initiatives.

While sediment resuspension appeared minimal, we acknowledge that internal recharge and nutrient release from sediments could still influence hydrochemical parameters. Future studies should investigate these processes more comprehensively, incorporating direct measurements of sediment-water interactions and internal nutrient cycling to understand their impact on water quality better.

7. Conclusions

The hydrochemical evaluation of the GLC in this work demonstrated notable seasonal fluctuations in water quality driven by both human activity and natural processes. TDS averaged 143.1 mg/L in the pre-monsoon period, compared with a post-monsoon drop to 78.9 mg/L, stressing the significant influence of evaporation and consequent dilution. Driven mainly through carbonate weathering processes, the GLC’s hydrochemistry was defined by a Ca²⁺-HCO₃⁻ water type. Leading anions were HCO₃⁻; principal cations were Mg²⁺ (4.13 mg/L to 7.94 mg/L) and Ca²⁺ (range from 17.07 mg/L in pre-monsoon to 26.62 mg/L in Ghodaghodi Lake). Piper diagram studies validated these profiles, therefore highlighting the inherent lithological contributions to the water composition of the lake. DO levels also showed seasonal fluctuations, averaging 5.12 mg/L pre-monsoon and somewhat raised post-monsoon, presumably under the influence of decreased organic breakdown with rainfall. Statistical studies revealed significant seasonal variations across pH, TDS, and DO levels, emphasizing the lake’s dynamic response to climatic events. SAR across all sites was consistently low, below 1, and confirmed great irrigation appropriateness with zero salinity hazards. Although the irrigation quality was good, fertilizer was loaded from nearby farms. Moreover, NO₃⁻ levels up to 5.23 mg/L pre-monsoon posed a possible eutrophication danger, compromising water quality and biodiversity. Future studies should track nutrient influx throughout time, evaluate seasonal agricultural runoff effects, and combine biological evaluations to offer a clear ecological picture. These actions are essential for guiding SDG development plans, ensuring GLC stays a strong environmental, ecological, and community resource. This study was limited by access constraints to central lake areas and the exclusion of certain contaminants. This suggests that future research should incorporate broader spatial coverage and a more comprehensive analysis of pollutants.

Author Contributions

The authors confirm their contribution to the paper: G.P. and R.R.P.: Data collection, experiments, analysis and interpretation of results, draft manuscript preparation. P.N.K., T.R.J., A.M.S., B.Đ., V.C. and K.B.: Writing—review and co-wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

The research was partially funded by University North, Croatia, Scientific Project UNIN—TEH-24-1-16. The role of geodesy and geomatics in the development of smart spaces (2024).

Data Availability Statement

The datasets generated and analyzed during the current study are available from the authors upon reasonable request.

Acknowledgments

The first author expresses deep gratitude to the Ministry of Industry, Tourism, Forest and Environment, Sudurpaschim Province, Nepal, for their invaluable support. Special thanks to the Department of Forests and Soil Conservation, Ministry of Forests and Environment, Government of Nepal, for their field support. Additionally, heartfelt appreciation goes to the Central Department of Environmental Science (CDES) for the opportunity to conduct this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area region illustrating Ghodaghodi Lake and its adjacent lakes, including sampling sites: (i) A global map illustrating the study area, marked by a red polygon; (ii) A map of the Kailali District highlighting Ghodaghodi Municipality in yellow and the Ramsar site encompassing the Ghodaghodi Lake complex (GLC) in red; (iii) A map of the GLC-Ramsar site, depicting the locations of Ghodaghodi Lake and its associated lakes, classified into Section ‘A’ and Section ‘B’ with delineations; (iv) Locations of Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari Lakes along with their respective sampling sites BC1–BC5, BN1–BN5, R1–R5, and SP1–SP5, and (v) Locations of Ghodaghodi and Ojahuwa Lakes with their corresponding sampling sites G1–G24 and OH1–OH5.

Figure 2. Land use/land cover map of the study area region illustrating different categories adjacent to sampling points of the lakes.

Figure 3. Piper diagram for the classification of lake water types in Ghodaghodi and its associated lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon season, featuring three plots: anionic, cationic, and diamond plots.

Figure 4. Piper diagram for the classification of lake water types in Ghodaghodi and its related lakes (Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari) during the post-monsoon season, featuring three plots: anionic, cationic, and diamond plots.

Figure 5. Gibbs diagrams illustrating the fluctuation of the weight ratio of Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO³⁻) concerning TDS (pre-monsoon) throughout all examined lakes (Ghodaghodi, Ojahuwa, Bichka Chaita, and Sanopokhari).

Figure 6. Gibbs diagrams illustrating the fluctuation of the weight ratio of Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO³⁻) concerning TDS (post-monsoon) throughout all examined lakes (Ghodaghodi, Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari).

Figure 7. Mixing diagrams illustrating the roles of carbonate, silicate, and evaporates in the hydrochemistry of Ghodaghodi and associated lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon season. (a) represents HCO₃⁻/Na⁺ vs Ca²⁺/Na⁺ and (b) represents Mg²⁺/Na⁺ vs Ca²⁺/Na⁺ of mixing diagram.

Figure 8. Mixing diagrams illustrating the roles of carbonate, silicate, and evaporates in the hydrochemistry of Ghodaghodi and its associated lakes (Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari) during the post-monsoon season. (a) represents HCO₃⁻/Na⁺ vs Ca²⁺/Na⁺ and (b) represents Mg²⁺/Na⁺ vs Ca²⁺/Na⁺ of mixing diagram.

Figure 9. Wilcox diagram depicting the irrigation water quality based on SAR and EC for Ghodaghodi Lake and three related lakes (Ojahuwa, Bichka Chaita, and Sanopokhari) during the pre-monsoon period.

Figure 10. Wilcox diagram depicting the irrigation water quality based on SAR and EC for Ghodaghodi Lake and five related lakes (Ojahuwa, Bichka Chaita, Sanopokhari, Budhiya Nakhrod, and Ramphal) during the post-monsoon period.

Figure 11. Hydrochemical dynamics, sustainable development goals (SDGs) impact, and conservation strategies for Ghodaghodi Lake Complex (GLC).

Table 1. Descriptive statistical investigation of ionic parameters during the pre-monsoon and post-monsoon seasons in the lakes: Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari.

Lake	Season	Statistics	WT	pH	Free CO₂	EC	TDS	Tur	DO
Ghodaghodi	PRM	M	28.89	6.98	26.86	251.17	143.14	10.58	5.12
	PRM	SD	5.05	1.35	16.68	156.34	105.91	7.93	1.05
	POM	M	19.96	6.98	14.32	156.11	78.9	6.33	5.1
	POM	SD	1.41	1.35	7.60	78.05	39.84	2.61	1.49
Ojahuwa	PRM	M	31.48	7.36	15.40	344.04	193.98	22.68	5.22
	PRM	SD	0.69	0.31	6.41	9.13	5.42	2.48	0.38
	POM	M	19.80	7.48	12.76	344.04	196.37	20.80	7.10
	POM	SD	3.25	0.26	10.25	10.51	119.20	6.78	2.04
Bichka Chaita	PRM	M	31.32	8.31	12.76	264.46	156.35	15.05	6.28
	PRM	SD	1.78	0.57	4.77	8.90	6.68	9.46	1.37
	POM	M	16.26	7.65	8.36	248.96	137.06	12.35	8.34
	POM	SD	0.18	0.05	1.84	11.43	15.32	0.67	0.48
Sanopokhari	PRM	M	27.66	8.20	13.64	173.00	100.71	43.58	5.60
	PRM	SD	0.49	0.42	3.94	7.97	6.65	16.40	0.52
	POM	M	15.90	7.74	6.16	279.40	149.23	6.30	8.60
	POM	SD	0.30	0.05	0.98	13.96	8.67	0.76	0.43
Budhiya Nakhrod	POM	M	16.98	7.72	17.60	323.80	167.27	10.39	8.01
Budhiya Nakhrod	POM	SD	0.13	0.22	3.48	16.63	11.32	6.19	0.57
Ramphal	POM	M	16.60	7.60	12.76	331.60	176.23	6.59	7.26
Ramphal	POM	SD	0.10	0.07	5.48	107.16	49.91	0.52	0.61
Whole lakes	PRM	M	29.38	7.35	21.89	250.30	145.91	16.93	5.34
Whole lakes	PRM	SD	4.18	1.21	14.76	130.62	85.99	14.15	1.03
Whole lakes	POM	M	18.50	7.64	12.90	224.07	117.24	8.86	6.51
Whole lakes	POM	SD	2.22	0.25	7.10	95.35	51.06	5.58	1.91

Note: Tur is in NTU, WT is in °C, EC is in μS/cm, while other parameters are in mg/L, PRM = pre-monsoon, POM = post-monsoon, SD = standard deviation, and M = mean.

Table 2. Descriptive statistical investigation of non-ionic parameters during the pre-monsoon and post-monsoon seasons in the lakes: Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Ramphal, and Sanopokhari.

Lake	Season	Statistics	TH	Ca²⁺	Mg²⁺	K⁺	Na⁺	NH₄⁺	Cl⁻	Fe²⁺	NO₃⁻	SO₄²⁻	HCO₃⁻	PO₄³⁻
Ghodaghodi	PRM	M	59.58	17.07	4.13	4.16	8.4	0.1	9.93	0.58	5.23	0.91	85.04	0.43
	PRM	SD	10.63	3.32	1.73	2.06	2.14	0.07	5.60	0.37	5.17	0.49	23.01	0.23
	POM	M	99.07	26.62	7.94	1.77	5.19	0.1	12.7	0.31	4.78	0.64	99.27	0.2
	POM	SD	24.11	7.93	1.93	0.93	0.89	0.07	7.17	0.11	3.26	0.27	54.37	0.07
Ojahuwa	PRM	M	97.20	25.28	8.30	0.75	5.52	0.02	14.48	0.48	2.13	0.52	145.80	0.33
	PRM	SD	5.40	2.23	0.49	0.37	2.69	0.02	8.72	0.11	0.12	0.07	37.80	0.16
	POM	M	177.60	46.72	14.84	2.40	9.58	0.08	17.32	0.71	3.37	1.84	164.40	0.07
	POM	SD	37.40	10.18	3.06	0.31	7.03	0.01	15.01	0.47	0.44	0.79	131.66	0.00
Bichka Chaita	PRM	M	76.00	20.96	5.76	0.45	5.86	0.07	4.83	0.30	1.88	0.57	147.00	0.19
	PRM	SD	5.10	1.91	1.11	0.01	0.05	0.00	1.62	0.07	0.19	0.02	22.93	0.06
	POM	M	136.40	35.52	11.61	2.52	7.82	0.06	4.83	0.26	2.86	0.30	115.60	0.13
	POM	SD	12.84	4.85	0.53	0.33	0.62	0.00	1.62	0.05	0.35	0.07	15.69	0.04
Sanopokhari	PRM	M	80.00	22.24	5.95	0.60	2.89	0.20	5.40	1.93	1.94	2.38	93.00	0.23
	PRM	SD	6.78	2.49	0.80	0.52	1.78	0.01	1.19	0.22	0.24	0.41	26.38	0.02
	POM	M	174.00	53.12	10.05	1.82	7.66	0.08	14.20	0.31	3.26	0.33	124.00	0.19
	POM	SD	7.87	1.34	1.12	1.15	1.95	0.00	4.49	0.06	0.40	0.02	4.12	0.08
Budhiya Nakhrod	POM	M	118.00	32.00	9.27	2.04	9.22	0.06	16.76	0.47	3.01	0.63	34.00	0.10
Budhiya Nakhrod	POM	SD	5.10	1.26	1.14	0.15	1.62	0.01	2.73	0.06	0.16	0.04	4.18	0.01
Ramphal	POM	M	172.00	46.40	13.66	0.91	4.68	0.05	8.24	0.23	3.05	0.40	151.00	0.07
Ramphal	POM	SD	55.28	12.29	6.03	0.15	1.97	0.00	2.11	0.04	0.67	0.11	57.76	0.01
Whole lakes	PRM	M	69.13	19.28	5.11	2.79	7.00	0.10	9.28	0.70	3.98	1.00	101.74	0.36
Whole lakes	PRM	SD	16.19	4.19	2.03	2.39	2.79	0.07	5.98	0.57	4.33	0.69	36.26	0.21
Whole lakes	POM	M	127.91	34.85	9.95	1.86	6.52	0.08	12.48	0.35	3.93	0.67	106.67	0.15
Whole lakes	POM	SD	42.26	12.32	3.48	0.85	2.94	0.03	7.68	0.21	2.43	0.52	57.64	0.08

Note: All parameters are in mg/L; PRM = pre-monsoon, POM = post-monsoon, SD = standard deviation, and M = mean.

Table 3. Comparison of pre-monsoon and post-monsoon data of four lakes: Ghodaghodi, Ojahuwa, Sanopokhari, and Bichka Chaita.

Ghodaghodi			Ojahuwa			Sanopokhari			Bichka Chaita
t-test	md	p-Value	t-test	md	p-Value	t-test	md	p-Value	t-test	md	p-Value
DO	0.017	0.960	WT	11.680	0.002	WT	11.76	<0.001	pH	0.666	0.049
M-WU test	W	p-Value	pH	−0.122	0.236	TDS	−48.52	0.001	WT	15.060	<0.001
pH	237.5	0.302	EC	81.240	0.007	Tur	37.28	0.006	Free CO₂	4.400	0.034
WT	568	<0.001	Tur	1.884	0.608	DO	−2.99	0.001	EC	15.500	0.038
Free CO₂	477.5	<0.001	Mg²⁺	−6.539	0.008	TH	−94.00	<0.001	TDS	19.283	0.019
EC	397	0.025	K⁺	−1.650	0.001	Ca²⁺	−30.88	<0.001	Tur	2.700	0.554
TDS	431.5	0.003	NH₄⁺	−0.056	0.008	K⁺	−1.22	0.065	DO	−2.058	0.015
Tur	365.5	0.112	NO₃⁻	−1.243	0.001	Na⁺	−4.77	0.002	TH	−60.400	0.001
TH	0	<0.001	PO₄³⁻	0.261	0.023	NH₄⁺	0.12	<0.001	Ca²⁺	−14.560	0.004
Ca²⁺	14	<0.001	M-W U test	W	p-Value	Cl⁻	−8.80	0.008	Mg²⁺	−5.856	0.001
Mg²⁺	39.5	<0.001	Free CO₂	19	0.203	Fe²⁺	1.61	<0.001	K⁺	−2.072	<0.001
K⁺	481.5	<0.001	TDS	25	0.012	NO₃⁻	−1.32	0.006	Cl⁻	0.000	1.000
Na⁺	524	<0.001	DO	5	0.144	SO₄²⁻	2.05	<0.001	Fe²⁺	0.044	0.401
NH₄⁺	263	<0.001	TH	0	0.012	HCO₃⁻	−31.00	0.060	NO₃⁻	−0.982	0.006
Cl⁻	200.5	<0.001	Ca²⁺	0	0.012	PO₄³⁻	0.04	0.253	SO₄²⁻	0.269	0.001
Fe²⁺	441.5	0.002	Na⁺	5.5	0.173	M-W U test	W	p-Value	PO₄³⁻	0.064	0.014
NO₃⁻	286	0.975	Cl⁻	12	1.000	pH	20	0.138	M-W U test	W	p-Value
SO₄²⁻	422	0.006	Fe²⁺	8	0.403	Free CO₂	25	0.009	Na⁺	0	0.011
HCO₃⁻	240.5	0.330	SO₄²⁻	0	0.012	EC	0	0.012	NH₄⁺	25	0.011
PO₄³⁻	506.5	<0.001	HCO₃⁻	16	0.531	Mg²⁺	0	0.010	HCO₃⁻	23	0.037

Note: md = mean difference, p-value < 0.05 = there is a significant difference in variable measured during two different seasons in the same lake, W = the sum of the ranks of the first sample, and M-W = Mann-Whitney.

Table 4. Comparison of pre-monsoon and post-monsoon data among the lakes: Ghodaghodi, Ojahuwa, Bichka Chaita, Sanopokhari, Budhiya Nakhrod, and Ramphal.

Pre-Monsoon				Post-Monsoon
ANOVA Test	df	f-Value	p-Value	ANOVA Test	df	f-Value	p-Value
DO	3	2.032	0.127	WT	5	14.34	<0.001
TH		27	<0.001	Tur		17.2	<0.001
Ca²⁺		13.28	<0.001	K⁺		2.808	<0.001
K-W test	df	chi-squared	p-value	K-W test	df	chi-squared	p-value
pH	3	11.66	0.009	pH	5	7.73	0.172
WT		3.23	0.358	Free CO₂		23.85	<0.001
Free CO₂		11.19	0.011	EC		30.27	<0.001
EC		8.39	0.039	TDS		29.14	<0.001
TDS		8.16	0.043	DO		32.95	<0.001
Tur		17.03	0.001	TH		34.00	<0.001
Mg²⁺		18.32	<0.001	Ca²⁺		30.75	<0.001
K⁺		15.55	0.001	Mg²⁺		29.89	<0.001
Na⁺		18.29	<0.001	Na⁺		25.38	<0.001
NH₄⁺		19.65	<0.001	NH₄⁺		33.62	<0.001
Cl⁻		11.30	0.010	Cl⁻		25.48	<0.001
Fe²⁺		16.09	0.001	Fe²⁺		22.03	0.001
NO₃⁻		10.64	0.014	NO₃⁻		3.66	0.599
SO₄²⁻		21.54	<0.001	SO₄²⁻		29.07	<0.001
HCO₃⁻		17.34	0.001	HCO₃⁻		28.14	<0.001
PO₄³⁻		12.34	0.006	PO₄³⁻		33.83	<0.001

Note: df = degree of freedom, p-value < 0.05 = a significant difference in variable measured in lakes during the same season, K-W = Kruskal–Wallis, and ANOVA = analysis of variance.

Table 5. Classification of lake water types (Ghodaghodi, Ojahuwa, Bichka Chaita, Budhiya Nakhrod, Sanopokhari, and Ramphal) utilizing the diamond plot derived from the Piper trilinear diagram.

Box	Types [32]	Pre-Monsoon (%)	Post-Monsoon (%)
1	Ca²⁺-HCO₃⁻	95.83 (All Lakes)	95.83 (All Lakes)
2	Ca²⁺-Cl⁻	Nill	Nill
3	Mixed type Ca²⁺-Mg²⁺-Cl⁻	4.17 (Ghodaghodi)	4.17 (Bichka Chaita)
4	Mixed type Ca²⁺-Na⁺-HCO₃⁻	Nill	Nill
5	Na⁺-Cl⁻	Nill	Nill
6	Na⁺-HCO₃⁻	Nill	Nill

Table 6. Results of various indices for irrigation water quality (SAR, Na %, MAR, KR, PI, and CROSS) calculated for six studied lakes for both seasons.

Lake	Season	SAR: M ± SD (WQ)	Na%: M ± SD (WQ)	MAR: M ± SD (WQ)	KR: M ± SD (WQ)	PI: M ± SD (WQ)	Cross: M ± SD (WQ)
Ghodaghodi	PRM	0.68 ± 0.16 (E)	28.47 ± 6.53 (G)	28.46 ± 9.4 (S)	0.32 ± 0.08 (S)	101.48 ± 18.05 (S)	0.44 ± 0.11 (E)
Ghodaghodi	POM	0.33 ± 0.07 (E)	12.66 ± 3.89 (E)	33.65 ± 5.21 (S)	0.12 ± 0.04 (S)	68.12 ± 8.15 (G)	0.21 ± 0.05 (E)
Ojahuwa	PRM	0.35 ± 0.17 (E)	11.68 ± 4.21 (E)	35.47 ± 2.68 (S)	0.12 ± 0.06 (S)	81.96 ± 12.26 (S)	0.21 ± 0.10 (E)
Ojahuwa	POM	0.43 ± 0.25 (E)	11.23 ± 3.62 (E)	34.69 ± 1.90 (S)	0.11 ± 0.05 (S)	47.66 ± 4.13 (G)	0.27 ± 0.14 (E)
Bichka Chaita	PRM	0.42 ± 0.05 (E)	15.05 ± 0.76 (E)	31.38 ± 5.34 (S)	0.17 ± 0.01 (S)	102.35 ± 3.64 (S)	0.24 ± 0.01 (E)
Bichka Chaita	POM	0.42 ± 0.04 (E)	13.08 ± 1.55 (E)	35.52 ± 3.3 (S)	0.13 ± 0.02 (S)	56.79 ± 8.13 (G)	0.26 ± 0.03 (E)
Sanopokhari	PRM	0.2 ± 0.12 (E)	8.05 ± 3.99 (E)	30.95 ± 4.00 (S)	0.08 ± 0.05 (S)	79.49 ± 12.07 (S)	0.12 ± 0.07 (E)
Sanopokhari	POM	0.36 ± 0.09 (E)	9.86 ± 2.25 (E)	23.95 ± 1.56 (S)	0.10 ± 0.02 (S)	46.6 ± 2.35 (G)	0.21 ± 0.05 (E)
Budhiya Nakhrod	PRM	-	-	-	-	-	-
Budhiya Nakhrod	POM	0.53 ± 0.10 (E)	16.24 ± 2.61 (E)	32.54 ± 3.12 (S)	0.17 ± 0.04 (S)	41.84 ± 4.05 (G)	0.32 ± 0.05 (E)
Ramphal	PRM	-	-	-	-	-	-
Ramphal	POM	0.22 ± 0.10 (E)	6.48 ± 2.90 (E)	32.22 ± 3.08 (S)	0.06 ± 0.03 (S)	50.11 ± 7.06 (G)	0.14 ± 0.06 (E)

Note: WQ = water quality, E = excellent, G = good, S = suitable, PRM = pre-monsoon, POM = post-monsoon, SD = standard deviation, and M = mean.

Table 7. Comparative Analysis of Hydrochemical Findings of the Ghodaghodi Lake Complex with Previous Studies.

Study Area	Major Findings	Comparison to Our Study	Recommendations	Reference
Ghodaghodi Lake	Water was slightly alkaline, low in ionic strength, and primarily influenced by rock weathering and anthropogenic activities.	Similar TH, Cl⁻ values.	Nitrate concentrations required careful monitoring due to rising human activities.	[10]
Ghodaghodi Lake	Water was suitable for irrigation and aquaculture.	Similar pH values were observed in Ghodaghodi Lake.	Continuous monitoring was required to ensure the sustainability of using the lake’s water for irrigation and aquaculture.	[11]
Ghodaghodi Lake and Rara Lake	Both lakes were dominated by carbonate weathering with higher Na⁺ and Cl⁻ post-monsoon, suggesting marine transport.	Matches in seasonal variation in Tur of Ghodaghodi Lake water.	There is a need for continuous assessment of hydrochemical dynamics to ensure the sustainability of these Ramsar wetlands.	[49]

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Paudel, G.; Pant, R.R.; Joshi, T.R.; Saqr, A.M.; Đurin, B.; Cetl, V.; Kamble, P.N.; Bishwakarma, K. Hydrochemical Dynamics and Water Quality Assessment of the Ramsar-Listed Ghodaghodi Lake Complex: Unveiling the Water-Environment Nexus. Water 2024, 16, 3373. https://doi.org/10.3390/w16233373

AMA Style

Paudel G, Pant RR, Joshi TR, Saqr AM, Đurin B, Cetl V, Kamble PN, Bishwakarma K. Hydrochemical Dynamics and Water Quality Assessment of the Ramsar-Listed Ghodaghodi Lake Complex: Unveiling the Water-Environment Nexus. Water. 2024; 16(23):3373. https://doi.org/10.3390/w16233373

Chicago/Turabian Style

Paudel, Ganga, Ramesh Raj Pant, Tark Raj Joshi, Ahmed M. Saqr, Bojan Đurin, Vlado Cetl, Pramod N. Kamble, and Kiran Bishwakarma. 2024. "Hydrochemical Dynamics and Water Quality Assessment of the Ramsar-Listed Ghodaghodi Lake Complex: Unveiling the Water-Environment Nexus" Water 16, no. 23: 3373. https://doi.org/10.3390/w16233373

APA Style

Paudel, G., Pant, R. R., Joshi, T. R., Saqr, A. M., Đurin, B., Cetl, V., Kamble, P. N., & Bishwakarma, K. (2024). Hydrochemical Dynamics and Water Quality Assessment of the Ramsar-Listed Ghodaghodi Lake Complex: Unveiling the Water-Environment Nexus. Water, 16(23), 3373. https://doi.org/10.3390/w16233373

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