Seasonal Hydrochemical Variability and Mangrove Water Quality Assessment in Vembanad Lake, Southwest India

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N. Sanjai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8228155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mangrove ecosystems are strongly influenced by estuarine hydrochemical dynamics, and their resilience depends on maintaining ionic balance and oxygen availability. This study investigates seasonal water-quality variability in the mangrove-fringed sectors of Vembanad Lake, a monsoon-regulated tropical estuary on the southwest coast of India. Ten hydrochemical variables were monitored across monsoon, pre-monsoon, and dry seasons in 2023. Salinity showed pronounced seasonality, lowest during monsoon (6.30 ± 1.87 PSU) and highest in the dry season (13.24 ± 9.71 PSU), while dissolved oxygen (DO) followed the opposite pattern, indicating saline intrusion and hypoxia outside monsoon. Nitrate and phosphate reflected flushing during monsoon and stagnation-driven accumulation during pre-monsoon. Principal Component Analysis revealed a dominant ionic stress gradient (37.6% variance), with negative association to DO, and a nutrient–thermal gradient (21.2% variance). A mangrove-specific water quality index (MWQI) indicated Good–Moderate conditions during monsoon (0.07–0.32) but Moderate–Poor quality in pre-monsoon and dry seasons (0.14–0.60). No season achieved “Good” status throughout, suggesting persistent ecological stress linked to reduced flushing, saline mixing, and anthropogenic inputs. Collectively, the results demonstrate monsoon-driven freshwater inflow as a critical recovery mechanism supporting estuarine–mangrove integrity, emphasizing the need for hydrological restoration and nutrient load control to enhance resilience under intensified salinity intrusion and climate variability. Mangrove ecosystem Hydrochemistry PCA Water quality index Monsoon estuary Vembanad Lake Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Mangrove ecosystems function as vital biogeochemical regulators within tropical coastal landscapes, modulating the exchange of freshwater and marine inputs through complex hydrological and ecological processes (Alongi, 2020 ). Their root systems act as filters that trap sediments, retain nutrients, and support microbial transformations of nitrogen and phosphorus, thereby sustaining water quality and buffering adjacent ecosystems against eutrophication (Kristensen et al., 2017 ). Hydrochemical properties such as salinity, dissolved oxygen (DO), pH, and nutrient concentrations influence not only water-column integrity but also mangrove productivity, seedling establishment, and faunal habitat suitability (Lovelock et al., 2022 ). Globally, estuarine mangrove waters have exhibited increasing vulnerability to anthropogenic stressors, including land-use change, aquaculture expansion, and altered hydrological connectivity (Herbeck et al., 2021 ). Seasonal monsoon systems further drive strong temporal oscillations in water chemistry, particularly in South and Southeast Asia, where high rainfall and riverine discharge trigger sharp salinity gradients and nutrient pulses (Gupta & Sarma, 2020 ; Islam et al., 2023 ). Recent studies in Indian coastal wetlands such as the Sundarbans (Manna et al., 2021 ), Mahanadi delta (Sahoo et al., 2020 ), and Goa estuaries (Fernandes et al., 2019 ) show that shifts in hydrochemistry often reflect intensified anthropogenic pressures superimposed on natural tidal–riverine dynamics. Despite extensive research, spatially explicit, seasonally resolved assessments linking mangrove functioning with water quality remain limited in low-gradient tropical backwaters such as the Vembanad Lake. This Ramsar-listed wetland experiences highly dynamic hydrology governed by monsoon runoff from major tributaries and tidal mixing from the Arabian Sea, creating freshwater-influenced inner lake zones and marine-influenced outer sectors (Balachandran et al., 2023 ). Mangroves that fringe these ecotones rely on optimal hydrochemical conditions to maintain microbial activity, root oxygenation, and osmotic balance. This work provides essential baseline understanding of monsoon-mediated water quality fluctuations to support conservation planning and adaptive hydrological management in the Vembanad estuarine ecosystem 2. Methodology Seasonal water sampling was undertaken during 2023 from ten geo-referenced stations along the Vembanad Lake estuary on the southwest coast of India, encompassing freshwater-influenced inner reaches, central mixing sectors, and marine-dominated outer zones representative of mangrove hydrochemical gradients (Singh & Ramasamy, 2021 ). Surface water (0–30 cm) was collected during monsoon (June–October), pre-monsoon (April–May), and dry season (November–March) (Fig. 1 ). Field measurements included temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS) using a calibrated multiparameter probe, while dissolved oxygen (DO) was determined via the Winkler titration method and salinity by argentometric titration (Grasshoff et al., 1999 ). Hardness and alkalinity were quantified using standard titrimetric methods recommended by APHA ( 2017 ). Filtered samples (0.45 µm) were analysed spectrophotometrically for nitrate (hydrazine reduction) and phosphate (molybdenum blue reaction at 880 nm). Normality was examined using the Shapiro–Wilk test; seasonal differences were compared using the non-parametric Kruskal–Wallis test, and inter-parameter relationships were evaluated using Spearman’s rank correlation. Principal Component Analysis (PCA) of standardized variables (z-scores) was used to identify dominant hydrochemical gradients, while multivariate structure among seasons and sampling sectors was assessed using PERMANOVA, hierarchical clustering, and silhouette validation to test grouping strength (Legendre & Anderson, 1999; Kassambara, 2017). To evaluate ecological water quality, a Mangrove Water Quality Index (MWQI) was developed using seven ecologically relevant parameters (pH, temperature, DO, salinity, EC, nitrate, phosphate) based on SW-III estuarine standards (CPCB, 2019 ; Alongi, 2020 ). Quality rating scores (Qi) were normalized between 0–100 as: $$\:{Q}_{i}=\mid\:\frac{{V}_{i}-{I}_{i}}{{S}_{i}-{I}_{i}}\mid\:\times\:100$$ where Vi = observed value, Ii = ideal reference, and Si = permissible limit. Parameter weights ( Wi ) were derived from PCA loadings, and the weighted penalty formulation: $$\:MWQI=100-\sum\:_{i=1}^{7}({Q}_{i}\cdot\:{W}_{i})$$ classified water quality as: Excellent (≥ 90), Good (70–89), Moderate (50–69), Poor (30–49), Very Poor (< 30). Analytical quality control included calibration curves (R² ≥ 0.995), and precision maintained within ± 5% (Rao et al., 2021 ). 3. Results and Discussion Hydrochemical conditions across the ten mangrove-influenced sites showed distinct seasonal patterns reflecting monsoon-driven freshwater inflow, tidal mixing, and evaporative concentration (Table 1 ). Table 1 Seasonal summary of physicochemical parameters in the mangrove-influenced sector of VL Parameter (unit) Dry Season (mean ± SD) Dry Season (min-max) Monsoon (mean ± SD) Monsoon (min-max) Pre-monsoon (mean ± SD) Pre-monsoon (min-max) Salinity (ppt) 13.24 ± 9.71 0.54–27.23 6.3 ± 1.87 4.00–10.00 10.5 ± 6.69 2.04–19.59 DO (mg L⁻¹) 3.28 ± 1.82 1.24–7.18 6.38 ± 2.78 0.8–10.4 2.5 ± 1.27 0.81–4.88 Nitrate (µmol L⁻¹) 0.27 ± 0.08 0.16–0.47 0.3 ± 0.13 0.06–0.49 0.5 ± 0.8 0.01–2.67 Phosphate (µmol L⁻¹) 14.39 ± 4.33 7.63–20.5 16.97 ± 6.88 6.34–27.7 24.31 ± 14.82 6.8–52 EC (mS/cm) 1580.95 ± 683.08 608-2962.5 2348.6 ± 2390.36 418–6550 1346.2 ± 1042.55 429–4072 TDS (mg/L) 5373.8 ± 3453.75 271.5–9081 1135.2 ± 1121.91 209–2895 4286.5 ± 2129.31 982–7056 Alkalinity (mg L⁻¹) 357.46 ± 148.06 97.6-530.7 72.6 ± 30.36 20–116 452.62 ± 269.84 73.2-927.2 Hardness (mg L⁻¹) 2520 ± 1745.34 250–4800 343 ± 421.98 70-1500 1790 ± 1015.93 500–3400 Temperature (°C) 30.56 ± 2.55 26.25-34 31.13 ± 1.96 26.8–33.8 33.38 ± 4.05 29.4–40 pH 6.79 ± 0.13 6.56–7.06 7.03 ± 0.28 6.73–7.64 7.09 ± 0.27 6.57–7.4 3.1 Ionic regime and hydrological drivers Salinity exhibited the strongest seasonal shift, decreasing sharply during the monsoon (6.30 ± 1.87 PSU) due to strong freshwater discharge, then increasing in the pre-monsoon (10.50 ± 6.69 PSU) and peaking in the dry season (13.24 ± 9.71 PSU) (Fig. 2 a). EC and TDS followed similar trajectories, confirming marine intrusion during reduced river flow (Gupta & Sarma 2020 ; Herbeck et al. 2021 ). Hardness and alkalinity were also elevated outside monsoon (Table 1 ), intensifying ionic stress relevant to mangrove osmoregulation (Lovelock et al. 2022 ). 3.2 Dissolved oxygen stress DO demonstrated an inverse pattern, highest during monsoon (6.38 ± 2.78 mg L⁻¹) and significantly lower in pre-monsoon (2.50 ± 1.27 mg L⁻¹) and dry seasons (3.28 ± 1.82 mg L⁻¹) (Fig. 2 b). These hypoxic conditions likely arise from elevated temperatures, reduced mixing, and enhanced microbial oxygen demand, degrading benthic and root-zone functioning (Kristensen et al. 2017 ; Alongi 2020 ). 3.3 Seasonal nutrient regime Nitrate reached its maximum during pre-monsoon (0.50 ± 0.80 µmol L⁻¹), indicating stagnation-driven accumulation, while monsoon values showed rainfall-mediated dilution and dispersion (Fig. 2 c). Phosphate increased moderately during monsoon due to catchment runoff and benthic regeneration (Fig. 2 d). 3.4 Multivariate hydrochemical structure Correlation analysis strengthened this inference. Salinity, TDS, EC, hardness, and alkalinity showed strong positive correlations whereas DO display negative correlations with ionic load, indicating antagonistic relationships between aeration and salinity stress (Figure.3). These antagonistic patterns reflect oxygen depletion under saline and stratified conditions, particularly during dry periods (Alongi, 2020 ). et al., 2023 Principal Component Analysis further revealed two dominant axes of variability, with PC1 (37.6%) representing an ionic stress gradient driven by positive loadings of salinity, TDS, EC, and hardness, and a negative association with dissolved oxygen. PC2 (21.2%) reflected a nutrient–thermal gradient, primarily influenced by nitrate and temperature(Fig. 4 ). Monsoon samples plotted toward lower PC1 scores, marking diluted and oxygen-rich conditions, while pre-monsoon and dry samples clustered toward higher solute load and stress signatures. Although PERMANOVA did not produce statistically discrete groupings, the projected continuum aligns with hydrodynamic mixing typical of monsoonal estuaries (Balachandran et al., 2023 ). 3.5 MWQI and ecological implications The Mangrove Water Quality Index (MWQI) effectively captured the seasonal transition in ecological stress across the study area. MWQI values were lowest during the monsoon (0.07–0.32), indicating Good to Moderate water quality conditions due to strong freshwater inflow and enhanced dilution capacity. Premonsoon MWQI values ranged between 0.14 and 0.53, representing Moderate conditions as warming and reduced river discharge increased ionic buildup. The highest MWQI scores were observed during the dry season (0.18–0.60), reflecting Poor to Moderate quality associated with tidal saline intrusion, restricted flushing, and anthropogenic discharge inputs. These results indicate that monsoon acts as a hydrological recovery phase, whereas premonsoon and dry seasons intensify ionic stress and oxygen limitations in mangrove creeks (Manna et al., 2021 ; Ramesh et al., 2022 ).This trend is clearly seen in the boxplot given(Fig. 5 ). Integrated interpretation Seasonal hydrology is the primary determinant of mangrove water quality in Vembanad Lake. Monsoon provides a vital recovery window, temporarily alleviating saline and oxygen stress, while dry and pre-monsoon periods compress ecological tolerance limits. Effective conservation must prioritise: Maintaining freshwater connectivity year-round Reducing nutrient and brine pollution loads Conserving mangrove buffers to stabilise hydrochemistry Such actions are critical to strengthening resilience against advancing salinity intrusion and climate variability in this Ramsar-listed estuary. 5. Conclusion This study demonstrates that seasonal hydrology governs mangrove water quality in Vembanad Lake, with monsoon inflows temporarily alleviating ionic and oxygen stress, while pre-monsoon and dry seasons impose elevated salinity, hypoxia, and nutrient accumulation. Ionic parameters exhibited strong seasonal variability, and MWQI confirmed persistent ecological pressure across the year. These results highlight the vulnerability of mangrove ecosystems to hydrological modification, saline mixing, and localized anthropogenic inputs. To sustain estuarine functioning in this Ramsar site, management strategies must prioritize maintenance of freshwater connectivity, regulation of nutrient and brine discharge from catchment and aquaculture sources, and enhancement of mangrove buffer zones. Strengthening adaptive hydrological management will be essential to preserve the resilience of mangrove ecosystems under advancing salinity intrusion and climate-driven stress. Declarations Author Contribution KN.: Conceptualization, Investigation, Laboratory analysis, Data curation, Formal analysis, Writing—original draft.V.N.: Supervision, Resources, Writing—review & editing, Validation.All authors reviewed the manuscript Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethics and Consent to Participate declarations Not applicable. Availability of Consent Not applicable. Availability of Data The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Availability of Materials and/or Code Not applicable. Use of AI Tools Large Language Model (LLM) assistance (ChatGPT, OpenAI) text editing for clarity. References Alongi DM (2020) Carbon cycling and storage in mangrove forests. Annu Rev Mar Sci 12:24–45. https://doi.org/10.1146/annurev-marine-010419-010633 APHA (2017) Standard methods for the examination of water and wastewater, 23rd edn. American Public Health Association, Washington DC Balachandran KK, Sheeba P, Nair M et al (2023) Hydrodynamic regulation of salinity gradients in the Vembanad Lake, southwest India. Mar Pollut Bull 189:114603. https://doi.org/10.1016/j.marpolbul.2023.114603 CPCB (2019) Criteria for surface water quality assessment. Central Pollution Control Board, New Delhi Fernandes ME, Naik UR, Nair SM (2019) Spatiotemporal variability of water quality in Goa estuaries with implications for coastal ecology. Estuar Coast Shelf Sci 220:33–44. https://doi.org/10.1016/j.ecss.2019.01.010 Grasshoff K, Kremling K, Ehrhardt M (1999) Methods of seawater analysis, 3rd edn. Wiley-VCH, Weinheim Gupta S, Sarma VVSS (2020) Monsoon influence on estuarine nutrient dynamics and ecosystem metabolism. Sci Total Environ 741:140326. https://doi.org/10.1016/j.scitotenv.2020.140326 Herbeck LS, Unger D, Jennerjahn TC (2021) Anthropogenic impacts on tropical estuaries: A multi-decadal perspective. Front Mar Sci 8:658412. https://doi.org/10.3389/fmars.2021.658412 Islam SM, Hasan M, Rahman MA (2023) Hydrochemical variability in mangrove-estuarine gradients of Bangladesh. Wetlands Ecol Manage 31(2):197–212. https://doi.org/10.1007/s11273-022-09893-7 Kristensen E, Bouillon S, Dittmar T, Marchand C (2017) Organic carbon cycling in mangrove ecosystems: A review. Aquat Bot 89:201–219. https://doi.org/10.1016/j.aquabot.2017.01.005 Lovelock CE, Reef R, Thuiller W et al (2022) Climate regulation by coastal wetlands across tropical regions. Glob Change Biol 28(4):1453–1468. https://doi.org/10.1111/gcb.16032 Manna R, Bhattacharyya SB, Ghosh S (2021) Eutrophication status and mangrove ecosystem health in the Sundarbans. Ocean Coast Manage 214:105915. https://doi.org/10.1016/j.ocecoaman.2021.105915 Ramesh R, Purvaja R, Senthil VP (2022) Coastal environmental change in India: Challenges for sustainable management. Curr Sci 123(10):1203–1214 Rao VR, Pillai HH, Shyni K (2021) Analytical precision in estuarine water quality monitoring. Int J Environ Anal Chem 101(10):1604–1613. https://doi.org/10.1080/03067319.2020.1751624 Sahoo B, Panda CR, Behera B (2020) Hydrochemical characteristics of Mahanadi estuary in relation to mangrove influence. Estuar Coast 43:234–246. https://doi.org/10.1007/s12237-019-00685-5 Singh A, Ramasamy P (2021) Tidal connectivity and mangrove dynamics in a southwest Indian estuary. J Coast Conserv 25(6):79. https://doi.org/10.1007/s11852-021-00879-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":1779081,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Sampling Sites in Vembanad Lake, Southwest Coast of India\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/971b59514145c6b796c8c71f.png"},{"id":97977894,"identity":"d9377568-e133-49f4-98a4-b06aef1cff4f","added_by":"auto","created_at":"2025-12-11 12:20:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":813205,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in (a) salinity, (b) dissolved oxygen (DO), (c) nitrate, and (d) phosphate across mangrove-influenced sampling stations in Vembanad Lake. Boxes represent the interquartile range with the median line shown inside, whiskers indicate the full data spread within 1.5× IQR, and outliers are plotted as points. Higher salinity and ionic enrichment during the Dry Season reflect saline intrusion and reduced river discharge, while enhanced DO and elevated nutrient levels during Monsoon indicate freshwater flushing and runoff-driven enrichment. The Pre-monsoon period shows transitional conditions influenced by shifting tidal and riverine dominance.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/e66cf47b9a0396717c710006.png"},{"id":97977895,"identity":"690e0575-0de2-4482-a948-f4cf66ce8ebd","added_by":"auto","created_at":"2025-12-11 12:20:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148734,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation heatmap showing the relationships among physicochemical parameters in the mangrove-influenced waters of Vembanad Lake. Strong positive correlations (warm colors) were observed among salinity, EC, TDS, alkalinity, and hardness, indicating a shared marine ionic source driven by tidal intrusion. Negative associations between dissolved oxygen (DO) and ionic parameters reflect reduced oxygen availability under saline and stratified conditions, particularly during low-flow periods. Weak correlations between nutrients and salinity-linked variables indicate independent sources, primarily from catchment runoff and benthic regeneration\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/e3b7d6a22ef0d0344e8e6055.png"},{"id":97977890,"identity":"ea2cb12f-d486-4567-a09d-1fcd89d5684f","added_by":"auto","created_at":"2025-12-11 12:20:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":184169,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) biplot illustrating seasonal clustering of hydrochemical characteristics in the mangrove-influenced waters of Vembanad Lake. The first two principal components explain 37.6% (Dim1) and 21.2% (Dim2) of the total variance, respectively. Vectors indicate the correlation strength and direction of physicochemical parameters with PCA axes, while 95% confidence ellipses differentiate the hydrochemical signatures of DRYSEASON, MONSOON, and PREMONSOON sampling periods.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/eba1d5aefeac22115d97b22b.png"},{"id":97977892,"identity":"9aad8439-6a85-4411-b16a-7f93fd2a941b","added_by":"auto","created_at":"2025-12-11 12:20:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":253376,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in the Mangrove Water Quality Index (MWQI) across the mangrove-influenced region of Vembanad Lake. Lower MWQI values during the Monsoon period indicate better water quality under strong freshwater discharge and dilution of ions, whereas higher MWQI scores during the Dry Season reflect degraded conditions associated with tidal saline intrusion and elevated ionic concentrations. Pre-monsoon values represent transitional water quality as hydrological control shifts from riverine to tidal dominance.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/09fbe14cf4785d4d79544a5a.png"},{"id":98775968,"identity":"57bd8d76-1f53-4a9b-bc61-7c317d228642","added_by":"auto","created_at":"2025-12-22 12:21:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3752900,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228155/v1/06820f21-07c2-4a36-824b-aab6cf9bc373.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Hydrochemical Variability and Mangrove Water Quality Assessment in Vembanad Lake, Southwest India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangrove ecosystems function as vital biogeochemical regulators within tropical coastal landscapes, modulating the exchange of freshwater and marine inputs through complex hydrological and ecological processes (Alongi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their root systems act as filters that trap sediments, retain nutrients, and support microbial transformations of nitrogen and phosphorus, thereby sustaining water quality and buffering adjacent ecosystems against eutrophication (Kristensen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hydrochemical properties such as salinity, dissolved oxygen (DO), pH, and nutrient concentrations influence not only water-column integrity but also mangrove productivity, seedling establishment, and faunal habitat suitability (Lovelock et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, estuarine mangrove waters have exhibited increasing vulnerability to anthropogenic stressors, including land-use change, aquaculture expansion, and altered hydrological connectivity (Herbeck et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Seasonal monsoon systems further drive strong temporal oscillations in water chemistry, particularly in South and Southeast Asia, where high rainfall and riverine discharge trigger sharp salinity gradients and nutrient pulses (Gupta \u0026amp; Sarma, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent studies in Indian coastal wetlands such as the Sundarbans (Manna et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Mahanadi delta (Sahoo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Goa estuaries (Fernandes et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) show that shifts in hydrochemistry often reflect intensified anthropogenic pressures superimposed on natural tidal\u0026ndash;riverine dynamics.\u003c/p\u003e\u003cp\u003eDespite extensive research, spatially explicit, seasonally resolved assessments linking mangrove functioning with water quality remain limited in low-gradient tropical backwaters such as the Vembanad Lake. This Ramsar-listed wetland experiences highly dynamic hydrology governed by monsoon runoff from major tributaries and tidal mixing from the Arabian Sea, creating freshwater-influenced inner lake zones and marine-influenced outer sectors (Balachandran et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mangroves that fringe these ecotones rely on optimal hydrochemical conditions to maintain microbial activity, root oxygenation, and osmotic balance.\u003c/p\u003e\u003cp\u003eThis work provides essential baseline understanding of monsoon-mediated water quality fluctuations to support conservation planning and adaptive hydrological management in the Vembanad estuarine ecosystem\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eSeasonal water sampling was undertaken during 2023 from ten geo-referenced stations along the Vembanad Lake estuary on the southwest coast of India, encompassing freshwater-influenced inner reaches, central mixing sectors, and marine-dominated outer zones representative of mangrove hydrochemical gradients (Singh \u0026amp; Ramasamy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Surface water (0\u0026ndash;30 cm) was collected during monsoon (June\u0026ndash;October), pre-monsoon (April\u0026ndash;May), and dry season (November\u0026ndash;March) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eField measurements included temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS) using a calibrated multiparameter probe, while dissolved oxygen (DO) was determined via the Winkler titration method and salinity by argentometric titration (Grasshoff et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Hardness and alkalinity were quantified using standard titrimetric methods recommended by APHA (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Filtered samples (0.45 \u0026micro;m) were analysed spectrophotometrically for nitrate (hydrazine reduction) and phosphate (molybdenum blue reaction at 880 nm). Normality was examined using the Shapiro\u0026ndash;Wilk test; seasonal differences were compared using the non-parametric Kruskal\u0026ndash;Wallis test, and inter-parameter relationships were evaluated using Spearman\u0026rsquo;s rank correlation. Principal Component Analysis (PCA) of standardized variables (z-scores) was used to identify dominant hydrochemical gradients, while multivariate structure among seasons and sampling sectors was assessed using PERMANOVA, hierarchical clustering, and silhouette validation to test grouping strength (Legendre \u0026amp; Anderson, 1999; Kassambara, 2017). To evaluate ecological water quality, a Mangrove Water Quality Index (MWQI) was developed using seven ecologically relevant parameters (pH, temperature, DO, salinity, EC, nitrate, phosphate) based on SW-III estuarine standards (CPCB, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Quality rating scores (Qi) were normalized between 0\u0026ndash;100 as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{i}=\\mid\\:\\frac{{V}_{i}-{I}_{i}}{{S}_{i}-{I}_{i}}\\mid\\:\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eVi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;observed value, \u003cem\u003eIi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;ideal reference, and \u003cem\u003eSi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;permissible limit. Parameter weights (\u003cem\u003eWi\u003c/em\u003e) were derived from PCA loadings, and the weighted penalty formulation:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:MWQI=100-\\sum\\:_{i=1}^{7}({Q}_{i}\\cdot\\:{W}_{i})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eclassified water quality as: Excellent (\u0026ge;\u0026thinsp;90), Good (70\u0026ndash;89), Moderate (50\u0026ndash;69), Poor (30\u0026ndash;49), Very Poor (\u0026lt;\u0026thinsp;30). Analytical quality control included calibration curves (R\u0026sup2; \u0026ge; 0.995), and precision maintained within \u0026plusmn;\u0026thinsp;5% (Rao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eHydrochemical conditions across the ten mangrove-influenced sites showed distinct seasonal patterns reflecting monsoon-driven freshwater inflow, tidal mixing, and evaporative concentration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSeasonal summary of physicochemical parameters in the mangrove-influenced sector of VL\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003cp\u003e(unit)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDry Season\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDry Season\u003c/p\u003e\u003cp\u003e(min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMonsoon\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMonsoon\u003c/p\u003e\u003cp\u003e(min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003ePre-monsoon\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003ePre-monsoon\u003c/p\u003e\u003cp\u003e(min-max)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSalinity\u003c/p\u003e\u003cp\u003e(ppt)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e13.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.54\u0026ndash;27.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.00\u0026ndash;10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e2.04\u0026ndash;19.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003cp\u003e(mg L⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.24\u0026ndash;7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8\u0026ndash;10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.81\u0026ndash;4.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate (\u0026micro;mol L⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.16\u0026ndash;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.06\u0026ndash;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.01\u0026ndash;2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate (\u0026micro;mol L⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e14.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e7.63\u0026ndash;20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e16.97\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.34\u0026ndash;27.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e6.8\u0026ndash;52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC\u003c/p\u003e\u003cp\u003e(mS/cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e1580.95\u0026thinsp;\u0026plusmn;\u0026thinsp;683.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e608-2962.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2348.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2390.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e418\u0026ndash;6550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e1346.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1042.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e429\u0026ndash;4072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS\u003c/p\u003e\u003cp\u003e(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e5373.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3453.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e271.5\u0026ndash;9081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1135.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1121.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e209\u0026ndash;2895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e4286.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2129.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e982\u0026ndash;7056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlkalinity (mg L⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e357.46\u0026thinsp;\u0026plusmn;\u0026thinsp;148.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e97.6-530.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;30.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20\u0026ndash;116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e452.62\u0026thinsp;\u0026plusmn;\u0026thinsp;269.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e73.2-927.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHardness (mg L⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e2520\u0026thinsp;\u0026plusmn;\u0026thinsp;1745.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e250\u0026ndash;4800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e343\u0026thinsp;\u0026plusmn;\u0026thinsp;421.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70-1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e1790\u0026thinsp;\u0026plusmn;\u0026thinsp;1015.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e500\u0026ndash;3400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e30.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e26.25-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26.8\u0026ndash;33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e33.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e29.4\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6.56\u0026ndash;7.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.73\u0026ndash;7.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e7.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e6.57\u0026ndash;7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Ionic regime and hydrological drivers\u003c/h2\u003e\u003cp\u003eSalinity exhibited the strongest seasonal shift, decreasing sharply during the monsoon (6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 PSU) due to strong freshwater discharge, then increasing in the pre-monsoon (10.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69 PSU) and peaking in the dry season (13.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71 PSU) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). EC and TDS followed similar trajectories, confirming marine intrusion during reduced river flow (Gupta \u0026amp; Sarma \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Herbeck et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hardness and alkalinity were also elevated outside monsoon (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), intensifying ionic stress relevant to mangrove osmoregulation (Lovelock et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Dissolved oxygen stress\u003c/h2\u003e\u003cp\u003eDO demonstrated an inverse pattern, highest during monsoon (6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78 mg L⁻\u0026sup1;) and significantly lower in pre-monsoon (2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 mg L⁻\u0026sup1;) and dry seasons (3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82 mg L⁻\u0026sup1;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). These hypoxic conditions likely arise from elevated temperatures, reduced mixing, and enhanced microbial oxygen demand, degrading benthic and root-zone functioning (Kristensen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alongi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Seasonal nutrient regime\u003c/h2\u003e\u003cp\u003eNitrate reached its maximum during pre-monsoon (0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80 \u0026micro;mol L⁻\u0026sup1;), indicating stagnation-driven accumulation, while monsoon values showed rainfall-mediated dilution and dispersion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Phosphate increased moderately during monsoon due to catchment runoff and benthic regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multivariate hydrochemical structure\u003c/h2\u003e\u003cp\u003eCorrelation analysis strengthened this inference. Salinity, TDS, EC, hardness, and alkalinity showed strong positive correlations whereas DO display negative correlations with ionic load, indicating antagonistic relationships between aeration and salinity stress (Figure.3). These antagonistic patterns reflect oxygen depletion under saline and stratified conditions, particularly during dry periods (Alongi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). et al., 2023\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis further revealed two dominant axes of variability, with PC1 (37.6%) representing an ionic stress gradient driven by positive loadings of salinity, TDS, EC, and hardness, and a negative association with dissolved oxygen. PC2 (21.2%) reflected a nutrient\u0026ndash;thermal gradient, primarily influenced by nitrate and temperature(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMonsoon samples plotted toward lower PC1 scores, marking diluted and oxygen-rich conditions, while pre-monsoon and dry samples clustered toward higher solute load and stress signatures. Although PERMANOVA did not produce statistically discrete groupings, the projected continuum aligns with hydrodynamic mixing typical of monsoonal estuaries (Balachandran et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 MWQI and ecological implications\u003c/h2\u003e\u003cp\u003eThe Mangrove Water Quality Index (MWQI) effectively captured the seasonal transition in ecological stress across the study area. MWQI values were lowest during the monsoon (0.07\u0026ndash;0.32), indicating Good to Moderate water quality conditions due to strong freshwater inflow and enhanced dilution capacity. Premonsoon MWQI values ranged between 0.14 and 0.53, representing Moderate conditions as warming and reduced river discharge increased ionic buildup. The highest MWQI scores were observed during the dry season (0.18\u0026ndash;0.60), reflecting Poor to Moderate quality associated with tidal saline intrusion, restricted flushing, and anthropogenic discharge inputs. These results indicate that monsoon acts as a hydrological recovery phase, whereas premonsoon and dry seasons intensify ionic stress and oxygen limitations in mangrove creeks (Manna et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramesh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).This trend is clearly seen in the boxplot given(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegrated interpretation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeasonal hydrology is the primary determinant of mangrove water quality in Vembanad Lake. Monsoon provides a vital recovery window, temporarily alleviating saline and oxygen stress, while dry and pre-monsoon periods compress ecological tolerance limits. Effective conservation must prioritise:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMaintaining freshwater connectivity year-round\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReducing nutrient and brine pollution loads\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConserving mangrove buffers to stabilise hydrochemistry\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSuch actions are critical to strengthening resilience against advancing salinity intrusion and climate variability in this Ramsar-listed estuary.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that seasonal hydrology governs mangrove water quality in Vembanad Lake, with monsoon inflows temporarily alleviating ionic and oxygen stress, while pre-monsoon and dry seasons impose elevated salinity, hypoxia, and nutrient accumulation. Ionic parameters exhibited strong seasonal variability, and MWQI confirmed persistent ecological pressure across the year. These results highlight the vulnerability of mangrove ecosystems to hydrological modification, saline mixing, and localized anthropogenic inputs. To sustain estuarine functioning in this Ramsar site, management strategies must prioritize maintenance of freshwater connectivity, regulation of nutrient and brine discharge from catchment and aquaculture sources, and enhancement of mangrove buffer zones. Strengthening adaptive hydrological management will be essential to preserve the resilience of mangrove ecosystems under advancing salinity intrusion and climate-driven stress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKN.: Conceptualization, Investigation, Laboratory analysis, Data curation, Formal analysis, Writing\u0026mdash;original draft.V.N.: Supervision, Resources, Writing\u0026mdash;review \u0026amp; editing, Validation.All authors reviewed the manuscript\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Consent\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Materials and/or Code\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of AI Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLarge Language Model (LLM) assistance (ChatGPT, OpenAI) text editing for clarity.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlongi DM (2020) Carbon cycling and storage in mangrove forests. 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Estuar Coast 43:234\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-019-00685-5\u003c/span\u003e\u003cspan address=\"10.1007/s12237-019-00685-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh A, Ramasamy P (2021) Tidal connectivity and mangrove dynamics in a southwest Indian estuary. J Coast Conserv 25(6):79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11852-021-00879-w\u003c/span\u003e\u003cspan address=\"10.1007/s11852-021-00879-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangrove ecosystem, Hydrochemistry, PCA, Water quality index, Monsoon estuary, Vembanad Lake","lastPublishedDoi":"10.21203/rs.3.rs-8228155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8228155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangrove ecosystems are strongly influenced by estuarine hydrochemical dynamics, and their resilience depends on maintaining ionic balance and oxygen availability. This study investigates seasonal water-quality variability in the mangrove-fringed sectors of Vembanad Lake, a monsoon-regulated tropical estuary on the southwest coast of India. Ten hydrochemical variables were monitored across monsoon, pre-monsoon, and dry seasons in 2023. Salinity showed pronounced seasonality, lowest during monsoon (6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 PSU) and highest in the dry season (13.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71 PSU), while dissolved oxygen (DO) followed the opposite pattern, indicating saline intrusion and hypoxia outside monsoon. Nitrate and phosphate reflected flushing during monsoon and stagnation-driven accumulation during pre-monsoon. Principal Component Analysis revealed a dominant ionic stress gradient (37.6% variance), with negative association to DO, and a nutrient\u0026ndash;thermal gradient (21.2% variance). A mangrove-specific water quality index (MWQI) indicated Good\u0026ndash;Moderate conditions during monsoon (0.07\u0026ndash;0.32) but Moderate\u0026ndash;Poor quality in pre-monsoon and dry seasons (0.14\u0026ndash;0.60). No season achieved \u0026ldquo;Good\u0026rdquo; status throughout, suggesting persistent ecological stress linked to reduced flushing, saline mixing, and anthropogenic inputs. Collectively, the results demonstrate monsoon-driven freshwater inflow as a critical recovery mechanism supporting estuarine\u0026ndash;mangrove integrity, emphasizing the need for hydrological restoration and nutrient load control to enhance resilience under intensified salinity intrusion and climate variability.\u003c/p\u003e","manuscriptTitle":"Seasonal Hydrochemical Variability and Mangrove Water Quality Assessment in Vembanad Lake, Southwest India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 12:20:03","doi":"10.21203/rs.3.rs-8228155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9cd1bc62-e03d-4e09-add9-0dec027f0827","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-20T16:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 12:20:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8228155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8228155","identity":"rs-8228155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}