This title slide features the presentation titled "Assessing Groundwater Resources in Central Kerala, India," using a multivariate statistical approach. It credits authors Samreena Mohammed and K. S. Arunkumar, for the ICWRER 2025 conference at the University of Žilina, with logo placeholders.

Assessing Groundwater Resources in Central Kerala, India

A Multivariate Statistical Approach Samreena Mohammed & K. S. Arunkumar ICWRER 2025 | University of Žilina [Conference logo placeholders]},

Groundwater is vital for Central Kerala's agriculture and water supply but is declining due to overexploitation and climate variability. Multivariate statistics, including PCA, correlation, and FA, analyze seasonal hydrochemical data across pre-monsoon, monsoon, and post-monsoon periods.

Introduction

• Groundwater vital for Central Kerala's agriculture and water supply.
• Declining levels due to overexploitation and climate variability.
• Multivariate statistics analyze seasonal hydrochemical data.
• PCA, correlation, and FA applied across pre-, monsoon, post-monsoon periods.

This slide, titled "Objectives," outlines four key goals for a groundwater quality study. They include assessing spatial-temporal variations, identifying dominant factors via PCA and FA, evaluating pollution source correlations, and recommending sustainable management strategies.

Objectives

• Assess spatial-temporal groundwater quality variations.
• Identify dominant factors via PCA & FA.
• Evaluate correlations for pollution sources.
• Recommend sustainable management strategies.

The slide maps the study area in central Kerala districts Palakkad, Thrissur, and Ernakulam, marking sampling sites along rivers and aquifers. It notes the geology of laterite plateaus and crystalline rocks, with sampling across pre-monsoon, monsoon, and post-monsoon periods.

Study Area

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• Central Kerala districts: Palakkad, Thrissur, Ernakulam
• Sampling sites along rivers and aquifers marked
• Geology: laterite plateaus, crystalline rocks
• Pre-monsoon, monsoon, post-monsoon sampling periods

Source: Geography of Kerala

The methodology sampled over 50 sites across three seasons: pre-monsoon, monsoon, and post-monsoon. It analyzed parameters like pH, EC, and major ions (Ca, Mg, Na, K, HCO₃, SO₄, Cl, NO₃) using multivariate statistics including PCA (factoextra R), correlation matrices, and varimax-rotated FA.

Methodology

• Over 50 sampling sites across 3 seasons (pre-, monsoon, post-monsoon).
• Parameters: pH, EC, major ions (Ca, Mg, Na, K, HCO₃, SO₄, Cl, NO₃).
• Multivariate statistics: PCA (factoextra R), correlation matrices, varimax-rotated FA.

This section header slide introduces Section 06 titled "PCA Results" in a presentation on assessing groundwater resources in Central Kerala, India. It highlights the subtitle "Principal Component Analysis: Seasonal Biplots."

Assessing Groundwater Resources in Central Kerala, India: A Multivariate Statistical Approach

06

PCA Results

Principal Component Analysis: Seasonal Biplots

Source: Samreena Mohammed & K. S. Arunkumar | ICWRER 2025 | University of Žilina

This slide features a PCA biplot of PC1 vs. PC2 for pre-monsoon data. It highlights dominant high salinity loadings from EC, Cl, and Na, with samples clearly clustering by pollution sources.

PCA: Pre-Monsoon

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• PCA biplot: PC1 vs. PC2 for pre-monsoon
• High salinity loadings: EC, Cl, Na dominant
• Samples cluster by pollution sources clearly

Source: Principal component analysis

This PCA slide for the monsoon period highlights prominent dilution effects. PC1 represents weathering (HCO3-, Ca, Mg), while PC2 indicates anthropogenic inputs.

PCA: Monsoon

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• Dilution effects prominent during monsoon
• PC1: Weathering (HCO3-, Ca, Mg)
• PC2: Anthropogenic inputs

Source: Samreena Mohammed & K. S. Arunkumar, ICWRER 2025

The PCA biplot on this post-monsoon slide shows clear recovery trends in groundwater parameters with distinct clustering. It highlights separation between geogenic and anthropogenic factors, alongside reduced human influence after the monsoon.

PCA: Post-Monsoon

!Image

• Post-monsoon PCA biplot shows clear recovery trends.
• Distinct separation of geogenic versus anthropogenic factors.
• Clustering indicates seasonal recovery in groundwater parameters.
• Reduced anthropogenic influence post-monsoon period.

Source: Recreated from study data

This section header slide introduces the "Correlation Results" section in a presentation on assessing groundwater resources in Central Kerala, India. Its subtitle highlights "Pearson Correlation Matrices: Seasonal Insights."

Assessing Groundwater Resources in Central Kerala, India: A Multivariate Statistical Approach

Correlation Results

Pearson Correlation Matrices: Seasonal Insights

Source: ICWRER 2025 | Samreena Mohammed & K. S. Arunkumar | University of Žilina

The "Correlation: Pre-Monsoon" slide features an image highlighting key chemical relationships. It shows a strong Cl⁻-Na⁺ correlation (r=0.95), strong EC-SO₄²⁻ association, and labels them as key salinization indicators.

Correlation: Pre-Monsoon

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• Strong Cl⁻-Na⁺ correlation (r=0.95)
• Strong EC-SO₄²⁻ association
• Key salinization indicators

Source: Samreena Mohammed & K. S. Arunkumar | ICWRER 2025 | University of Žilina

The "Correlation: Monsoon" slide features a heatmap showing reduced ion correlations due to rainfall dilution weakening links. Notably, HCO3-Ca displays a strong positive correlation.

Correlation: Monsoon

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• Reduced correlations observed in monsoon heatmap.
• Dilution from rainfall weakens ion links.
• HCO3-Ca shows strong positive correlation.

Source: Wikipedia - Monsoon in India

The slide "Correlation: Post-Monsoon" features a strong NO3-K correlation (r=0.82), indicating fertilizer leaching and nitrate contamination from farming. A heatmap shows dominant links to K+ and other ions, with agricultural pollution peaking due to post-monsoon runoff.

Correlation: Post-Monsoon

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• Strong NO3-K correlation (r=0.82) indicates fertilizer leaching.
• Agricultural pollution peaks post-monsoon due to runoff.
• Heatmap shows dominant links to K+ and other ions.
• Evidence of nitrate contamination from farming activities.

Source: Nitrate pollution in agriculture

This section header slide introduces Section 08 on Factor Analysis within the presentation on assessing groundwater resources in Central Kerala, India. The subtitle specifies Varimax-Rotated FA for source apportionment.

Assessing Groundwater Resources in Central Kerala, India: A Multivariate Statistical Approach

08

Factor Analysis

Varimax-Rotated FA: Source Apportionment

Source: ICWRER 2025 | University of Žilina

The slide "FA: Pre-Monsoon" highlights Factor Analysis results, with Factor 1 linked to Salinity (60% variance) and Factor 2 to Weathering (20% variance). It features a bar plot of variable loadings.

FA: Pre-Monsoon

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• Factor 1: Salinity (60% variance)
• Factor 2: Weathering (20% variance)
• Bar plot of variable loadings

Source: Image from Wikipedia article "Soil salinity"

The slide "FA: Monsoon" illustrates key factors from factor analysis in an image format. It highlights Factor 1 as leaching processes (50% variance) and Factor 2 as rain dilution of groundwater ions.

FA: Monsoon

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• Factor 1: Leaching processes (50% variance)
• Factor 2: Rain dilution of groundwater ions

Source: ICWRER 2025

This slide, titled "FA: Post-Monsoon," displays factor analysis results from an image. It highlights Factor 1 as anthropogenic sources (45% total variance) and Factor 2 as geogenic processes and minerals.

FA: Post-Monsoon

!Image

• Factor 1: Anthropogenic sources (45% total variance)
• Factor 2: Geogenic processes and minerals

Source: Assessing Groundwater Resources in Central Kerala, India: A Multivariate Statistical Approach | ICWRER 2025

PCA/FA analysis reveals seasonal shifts in groundwater quality, with salinity dominating pre-monsoon, while correlations confirm seawater intrusion and agricultural impacts. These findings highlight implications for sustainable groundwater management in Kerala.

Discussion

• PCA/FA reveal seasonal shifts: salinity dominant pre-monsoon.
• Correlations confirm seawater intrusion, agricultural impacts.
• Implications for sustainable groundwater management in Kerala.

The slide concludes that a multivariate approach identifies key hydrochemical drivers and recommends prioritizing recharge and pollution control. It proposes future isotope studies, thanks the audience, and invites questions.

Conclusion

• Multivariate approach identifies key hydrochemical drivers

• Prioritize recharge and pollution control
• Future: Isotope studies

Thank you for your attention! Questions?

Source: Assessing Groundwater Resources in Central Kerala, India: A Multivariate Statistical Approach | ICWRER 2025

The Acknowledgements slide thanks the University of Žilina and ICWRER 2025 organizers, funding sources, lab team, and field assistants. It concludes with "Thank you! Q&A".

Acknowledgements

• • University of Žilina & ICWRER 2025 organizers
• • Funding from supporting sources
• • Lab team & field assistants
• Thank you! Q&A