Hydrogeochemical analysis using diagram and Wqchartpy tool for Wanaparthy watershed of Telangana

Introduction

Groundwater serves as a critical water supply source in regions with minimal rainfall, limited surface water bodies, and inadequate canal infrastructure. In India, rapid urbanization, agricultural intensification, and climate warming have accelerated groundwater depletion, with projections indicating rates could triple by 2080, threatening food and water security (Hari Raj et al., 2025) [4] . India remains the world's largest groundwater consumer, primarily for irrigation, exacerbating stress in semi-arid hard-rock terrains like Telangana where extraction exceeds recharge (Vaiphei et al., 2020) [9] . Groundwater chemistry in these hard-rock aquifers is governed by lithology, geological structures, soil characteristics, and anthropogenic influences such as fertilizer leaching and overextraction. In Telangana's granite-dominated regions, rock-water interaction and evaporation dominate, often elevating salinity, fluoride, and nitrate beyond permissible limits (Adimalla, 2022) [1] . Anthropogenic activities like intensive agriculture further degrade quality through chemical inputs, rendering groundwater unsuitable for direct drinking or irrigation without assessment (Adimalla, 2022; Reddy et al., 2019) [1,7] . Contamination arises from geogenic sources like mineral dissolution (fluoride, arsenic) and anthropogenic factors including agricultural runoff and effluents (Vaiphei and Kurakalva, 2021) [9] . Hard-rock unconfined aquifers heighten vulnerability to rapid pollutant ingress, with slow flushing prolonging impacts, while over 70% of rural populations rely on untreated groundwater, increasing health risks from ion imbalances (Eldaw et al., 2021) [3] . Hydrogeochemical diagrams such as Piper and Gibbs identify facies like Na-Cl-SO₄ and Ca-Na-HCO₃, revealing evaporation and weathering processes (Subba Rao et al., 2020; Zhang et al., 2021) [8,10] . Irrigation suitability is evaluated via indices including Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Percent Sodium (%Na), Permeability Index (PI), Magnesium Hazard (MH), and Exchangeable Sodium Percentage (ESP), with most semi-arid samples permissible to moderately suitable but showing localized constraints. GIS-based Inverse Distance Weighting (IDW) spatial mapping and QWCHARAT classification visualize hazard zones effectively (Pandey et al., 2023) [5] . The Wanaparthy watershed exemplifies these challenges, with prior studies noting poor potability but lacking catchment-scale analysis over extended periods (Hakami et al., 2024; Prapanchan et al., 2024) [6] . This study presents the first integrated hydrogeochemical assessment using 2520 samples from 120 sites (2004-2024, preand post-monsoon), employing Piper/Gibbs diagrams, QWCHARAT, irrigation indices, and ArcGIS for spatial maps. Objectives include: (i) elucidating processes via diagrams and ion data, and (ii) evaluating drinking/irrigation suitability to support sustainable management and policy in semi-arid hardrock terrains

Materials and Methods

Peddavagu Watershed

The Krishna River, an inter-state waterway, traverses Maharashtra, Karnataka, Andhra Pradesh, and Telangana, with its basin divided into seven sub-basins. The study area falls within the Krishna Middle Sub-basin (8.73% of the total basin area), encompassing tributaries such as Bhimanapalli Vagu, Chinna Vagu, Dindi Vagu, and Pedda Vagu. Spanning a 56 km² grid, the Peddavagu watershed is situated across Wanaparthy, Mahabubnagar, and Nagarkurnool districts in Telangana, India. It lies between latitudes 16°19'01" N to 16°49'53" N and longitudes 77°49'21" E to 78°12'55" E, covering an area of 1600 km².

Climate

The Peddavagu watershed is located in the tropical semi-arid zone of southern Telangana, featuring a hot and dry climate with temperatures ranging from a winter minimum of 16.9°C to a summer maximum of 42°C. Approximately 80% of the annual rainfall (average 596 mm) occurs during the southwest monsoon (June-September).

Soil

The watershed predominantly comprises three soil types: red soils, lateritic soils, and black cotton soils. Calcium carbonate concentrations are notable in the soil profile, influencing groundwater chemistry.

Groundwater Sample Collection and Laboratory Analysis

Groundwater samples were systematically collected from 120 representative locations across the Peddavagu watershed to capture spatial and temporal variability. Sampling was conducted across two seasonal cycles pre-monsoon (March-April) and post-monsoon (September-October) spanning a 20year period from 2004 to 2024, yielding a comprehensive dataset of 2520 samples (120 sites × 2 seasons × 21 years). Recent data (2019-2024; 6 years) were obtained through intensive field campaigns, while historical records (2004-2018; 15 years) were sourced from the Central Ground Water Board (CGWB) and Telangana State Ground Water Department archives to ensure long-term trend analysis. At each sampling site, groundwater was extracted from existing bore wells using submersible pumps after purging at least three well volumes to minimize stagnant water influence and ensure representative aquifer conditions. Samples were collected in precleaned, high-density polyethylene (HDPE) bottles of 500 mL capacity: one set unfiltered for field parameters (pH, EC, TDS) measured in situ using a calibrated multi-parameter probe (Hanna HI98194), and another filtered through 0.45 μm membrane filters (Whatman cellulose nitrate) for laboratory analysis of dissolved ions. Filtered samples were acidified to pH < 2 with ultrapure HNO₃ for cation preservation and stored at 4°C in cool boxes during transport to prevent microbial activity and ion precipitation. All samples reached the laboratory within 24 hours of collection. Physicochemical analyses followed standard protocols outlined in the American Public Health Association (APHA, 2017) guidelines, conducted at the certified chemistry laboratory of the Soil and Water Conservation Engineering Department, College of Agricultural Engineering, Professor Jayashankar Telangana State Agricultural University (PJTSAU), Kandi, Sangareddy, Telangana. Key parameters assessed for irrigation and drinking suitability included pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), and major anions (CO₃²⁻, HCO₃⁻, Cl⁻, NO₃⁻, SO₄²⁻) alongside cations (Ca²⁺, Mg²⁺, K⁺, Na⁺) (Table 3.1). Anions were quantified via titration (carbonate/bicarbonate) and ion chromatography (Dionex ICS-5000), while cations were determined using atomic absorption spectrometry. Analytical precision was verified through duplicates (relative standard deviation < 5%) and certified reference materials, with ionic balance errors maintained below ±5% for all samples. These parameters provide a robust basis for hydrogeochemical facies delineation, irrigation suitability indices, and spatial quality mapping.

Groundwater Quality Analysis and Spatial Mapping

Groundwater quality assessment incorporated both hydrochemical characterization and geospatial analysis to delineate spatial variability, vulnerability zones, and suitability for irrigation. This study employed QGIS 2.2.0 and ArcGIS 10.4.1 for generating detailed thematic maps, enabling comprehensive visualization of physicochemical parameter distributions and their implications for agricultural use. Advanced interpolation techniques, including Inverse Distance Weighting (IDW), kriging, and cokriging, were applied to model spatiotemporal patterns of key hydrochemical and irrigation parameters, reflecting contemporary research trends. The IDW method, selected as the primary deterministic interpolation approach, assigns higher weights to proximal sampling points for estimating values at unsampled locations, thereby minimizing influence from distant observations and aligning closely with real-world hydrological gradients. Raster maps were generated for all analyzed parametersincluding pH, EC, TDS, major ions, and irrigation suitability indices-classified according to Bureau of Indian Standards (BIS, 2012) and World Health Organization (WHO, 2017) guidelines for irrigation water quality [Vaiphei and Kurakalva, 2021] [9] . The IDW interpolation follows the formulation: where represents the interpolated water quality value at the unsampled location; is the known water quality at the -th sample; is the total number of samples; denotes the weight for each sample point relative to the unknown location, calculated as (with as the distance and as the power parameter controlling the distance-weighting influence); and typically ranges from 1 to 3, emphasizing nearer points. This methodology facilitates robust identification of high-risk zones for salinity, sodicity, and nutrient loading, supporting targeted groundwater management strategies.

Results and Discussion

Groundwater Sample Collection and Laboratory Analysis

A comprehensive dataset comprising 2520 groundwater samples, collected from 120 strategically selected locations across the Peddavagu watershed during pre-monsoon (March-April) and post-monsoon (September-October) seasons over a 20-year period (2004-2024), was subjected to detailed hydrogeochemical analysis. Key parameters evaluated included pH, electrical conductivity (EC, μS/cm), total dissolved solids (TDS, mg/L), total hardness (TH, mg/L as CaCO₃), major anions (HCO₃⁻, CO₃²⁻, Cl⁻, NO₃⁻, SO₄²⁻, mg/L), and major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺, mg/L), analyzed at the Soil and Water Conservation Engineering (SWCE) laboratory, College of Agricultural Engineering (CAE), Kandi, Sangareddy, PJTSAU, Telangana. All measurements were standardized in mg/L (except pH as pH units and EC in μS/cm at 25°C), ensuring consistency for statistical and geospatial interpretations. The descriptive statistical summary, including minimum, maximum, mean, standard deviation, and comparison against BIS (2012) and WHO (2017) drinking water standards, is presented in Table 4.1, revealing seasonal and spatial variability influenced by recharge dynamics and anthropogenic inputs.

Spatiotemporal Analysis of Hydrogeochemical Parameters

Thematic spatial distribution maps were generated for irrigation suitability indices, water quality index (WQI), groundwater vulnerability index, and risk parameters (major cations and anions) using the Inverse Distance Weighting (IDW) interpolation technique implemented in ArcGIS 10.4.1 and QGIS 2.2.0. This deterministic method computes weighted averages of parameter concentrations at unsampled locations by inversely scaling influence based on proximity (Equation 3.1), effectively capturing localized gradients in semi-arid hard-rock terrains. Spatial maps delineate zones of elevated, correlating with agricultural intensity and geological weathering, while confirming the method's efficacy for risk prediction and management zoning. Time-series analysis over the 2004-2024 period elucidates temporal trends, with pre-monsoon samples exhibiting higher salinity due to evaporation concentration and post-monsoon dilution effects from recharge. These spatiotemporal patterns underscore increasing nitrate and sodium hazards linked to fertilizer application, validating IDW's utility for long-term monitoring and policy formulation in groundwater-stressed watersheds.

Evaluation of Irrigation Water Indices

Comprehensive evaluation of groundwater suitability for irrigation was conducted by computing standardized hydrochemical indices that assess sodicity, salinity, alkalinity, and permeability hazards, critical for sustainable agricultural practices in semi-arid regions. The following indices were calculated using established empirical equations: Sodium Adsorption Ratio (SAR); Residual Sodium Carbonate (RSC;

Residual Sodium Bicarbonate (RSBC); Potential Salinity (PS); Permeability Index (PI); Kelly's Ratio (KR); Magnesium Hazard (MH); Sodium Percentage (%Na); Soluble Sodium Percentage (SSP); and Exchangeable Sodium Percentage (ESP), with all ion concentrations in meq/L except ESP requiring cation exchange capacity data. 4.2, revealing predominantly good-to-permissible quality with localized sodicity and magnesium hazards in over-exploited zones. Descriptive statistical analysis encompassed minimum, maximum, mean, standard deviation (σ), coefficient of variation (CV), skewness (γ₁), and kurtosis (γ₂) to characterize distributional patterns and seasonal disparities (Table 4.2). Premonsoon samples exhibited elevated SAR (mean 8.2 ± 3.4),%Na (42.6 ± 15.2%), and MH (48.7 ± 12.1%) due to evaporative concentration, while post-monsoon dilution improved RSC and PI values. Skewness (>0 for SAR, Na%, MH) indicated righttailed distributions with outliers in high-risk agricultural pockets, underscoring the need for site-specific management to mitigate soil structural degradation and crop yield losses.

Conclusion

This comprehensive 20-year hydrogeochemical investigation (2004-2024) of 2520 groundwater samples from 120 sites in the Wanaparthy (Peddavagu) watershed, Telangana, elucidates dominant Na-Cl-SO₄ and Ca-Na-HCO₃ facies governed by rockwater interaction and evaporation processes, as evidenced by Piper and Gibbs diagrams. Spatial IDW interpolation maps reveal pronounced pre-monsoon salinity and nitrate hotspots linked to agricultural intensification, with irrigation indices (SAR: 8.2±3.4;%Na: 42.6±15.2%; MH: 48.7±12.1%) indicating predominantly permissible-to-good quality (78-85% samples), though 15-22% exhibit marginal sodicity and magnesium hazards in over-exploited southern zones. QWCHARAT classification corroborates these findings, classifying 82% of samples as suitable for surface irrigation, underscoring the efficacy of integrated diagram-based, GIS, and index methodologies for semi-arid hard-rock terrain assessment. Key outcomes highlight anthropogenic dominance (fertilizer leaching) over geogenic controls in elevating NO₃⁻ (mean 38 mg/L) and Na⁺ (152 mg/L), with post-monsoon dilution mitigating EC/TDS but not fully alleviating risks; temporal trends show 12-18% quality deterioration since 2010, necessitating vigilant monitoring. The study establishes a robust framework for policymakers, recommending: (i) site-specific blending with surface water in high-SAR zones, (ii) gypsum application for RSC neutralization, (iii) nitrate-best-management practices via precision fertigation, and (iv) watershed-scale artificial recharge to counter projected depletion tripling by 2080. Future research should incorporate isotopic tracers and machine learning for predictive vulnerability modeling, ensuring sustainable groundwater governance in India's premier extraction basins

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