Groundwater-mediated chemical erosion: Evidence from a humid tropical experimental catchment, southern Western Ghats, India

Groundwater-mediated chemical erosion: Evidence from a humid tropical experimental catchment, southern Western Ghats, India | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 November 2025 V1 Latest version Share on Groundwater-mediated chemical erosion: Evidence from a humid tropical experimental catchment, southern Western Ghats, India Authors : Rajat Kumar Sharma 0000-0002-6744-930X [email protected] , Vipin Raj , Prasenjit Das , Sreelash K , Padmalal D , and Muddu Sekhar 0000-0001-9326-1813 Authors Info & Affiliations https://doi.org/10.22541/au.176305374.47696460/v1 291 views 125 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Tropical rivers are undergoing significant changes in hydrochemical composition and solute fluxes due to alterations in monsoon patterns and increasing anthropogenic pressures. To better understand solute dynamics and hydrochemical interactions within a tropical river system, monthly water samples were collected from nested catchments representing upstream (Kallar, KRGS) and downstream (Chettachal, CRGS) segments. Concentration–discharge (C–Q) analysis revealed that the Kallar watershed functions as a chemostatic system with stable solute delivery, whereas the Chettachal watershed exhibits a more hydrologically responsive, chemodynamic behavior. Groundwater contributions, quantified using isotopic tracers, indicate that subsurface pathways play a major role in catchment-scale chemical erosion, accounting for approximately 55% and 70% of total solute fluxes at KRGS and CRGS, respectively. Silicate weathering emerged as the dominant geochemical process controlling solute dynamics, modulated by both seasonal hydrological variability and local anthropogenic influences. Strong seasonality was observed in silicate weathering rate (SWR) and CO 2 consumption rate (CCR), with annual mean SWR values of 21.93 and 26.67 t km -2 yr -1 , and CCR values of 2.68 × 10 5 and 3.86 × 10 5 mol km -2 yr -1 at KRGS and CRGS, respectively. Correlation between groundwater hydrochemistry and water table depth further indicates distinct geochemical responses of shallow and deep aquifers to rainfall inputs. Insights from this nested catchment study provide a clearer understanding of tropical river hydrochemistry and highlight the importance of integrating hydrological and geochemical observations to elucidate runoff generation and surface water–groundwater interactions across monsoonal cycles. Groundwater-mediated chemical erosion: Evidence from a humid tropical experimental catchment, southern Western Ghats, India Rajat Kr Sharma a,b *, Vipin T Raj a , Prasenjit Das a , Sreelash K a , Padmalal D a , M Sekhar b a- Environmental Hydrology Group, National Centre for Earth Science Studies, Trivandrum-695011, INDIA b- Dept. of Civil Engineering., Indian Institute of Science, Bangalore-560012, INDIA * - Communicating author, Email - [email protected] Con- +91-8317028380 Abstract- Tropical rivers are undergoing significant changes in hydrochemical composition and solute fluxes due to alterations in monsoon patterns and increasing anthropogenic pressures. To better understand solute dynamics and hydrochemical interactions within a tropical river system, monthly water samples were collected from nested catchments representing upstream (Kallar, KRGS) and downstream (Chettachal, CRGS) segments. Concentration–discharge (C–Q) analysis revealed that the Kallar watershed functions as a chemostatic system with stable solute delivery, whereas the Chettachal watershed exhibits a more hydrologically responsive, chemodynamic behavior. Groundwater contributions, quantified using isotopic tracers, indicate that subsurface pathways play a major role in catchment-scale chemical erosion, accounting for approximately 55% and 70% of total solute fluxes at KRGS and CRGS, respectively. Silicate weathering emerged as the dominant geochemical process controlling solute dynamics, modulated by both seasonal hydrological variability and local anthropogenic influences. Strong seasonality was observed in silicate weathering rate (SWR) and CO₂ consumption rate (CCR), with annual mean SWR values of 21.93 and 26.67 t km⁻² yr⁻¹, and CCR values of 2.68 × 10⁵ and 3.86 × 10⁵ mol km⁻² yr⁻¹ at KRGS and CRGS, respectively. Correlation between groundwater hydrochemistry and water table depth further indicates distinct geochemical responses of shallow and deep aquifers to rainfall inputs. Insights from this nested catchment study provide a clearer understanding of tropical river hydrochemistry and highlight the importance of integrating hydrological and geochemical observations to elucidate runoff generation and surface water–groundwater interactions across monsoonal cycles. Keywords- Tropical experimental catchment, C-Q analysis, Silicate weathering rate, CO 2 consumption rate, chemical erosion, hydro-chemical response 1.0 Introduction Rivers are integral components of both the global water cycle and Earth’s Critical Zone, acting as dynamic pathways that transport water, sediments, and dissolved materials from terrestrial landscapes to the ocean realm (Meybeck, 2003; Padmalal et al., 2018). As critical connectors between land and sea, they influence global element cycles, regulate carbon fluxes, and sustain aquatic and terrestrial ecosystems (Meybeck, 1987; Zhang et al., 2021). The chemical composition of river water reflects an integration of natural processes, such as rock weathering, sea-salt aerosol deposition, and atmospheric inputs, and anthropogenic influences arising from agriculture, industry, and urbanisation within the drainage basin (Gaillardet et al., 1999; Dalai et al., 2002; Pattanaik et al., 2013; Raj et al., 2021; Raj et al., 2023a). Among these processes, weathering and solute transport play pivotal roles in controlling river water chemistry and mediating the transfer of materials from land to sea. Weathering, together with erosion, redistributes dissolved and particulate matter across Earth’s surface, thereby shaping the exogenic geochemical cycle (Goudie, & Viles, 2012; Zaharescu et al., 2020). In addition to regulating the composition of riverine solutes, these processes deliver essential nutrients to soils, aquatic systems, and marine environments, supporting ecosystem productivity and linking geochemical transformations to ecological function (Padmalal et al., 2012; Raj et al., 2025). Chemical silicate weathering is particularly significant in the context of long-term climate regulation. Through mineral dissolution reactions, silicate weathering mobilises chemical elements in ways that depend on mineralogy, climatic conditions, and hydrological regimes. It represents a major sink for atmospheric CO 2 over geological timescales because dissolved bicarbonate produced during weathering is ultimately transported to oceans, where part of it is fixed in marine sediments through biogenic pathways, effectively removing CO 2 from the atmosphere for millions of years (Berner & Berner, 1997; Bach et al., 2019). In contrast, carbonate weathering, although often more rapid, tends to have a neutral effect on long-term CO 2 budget, as the carbon released during carbonate precipitation in the ocean is roughly balanced by the initial atmospheric drawdown (Gaillardet et al., 1999; Kump et al., 2000). Understanding the balance between silicate and carbonate weathering in river systems is therefore essential for quantifying their role in global carbon cycling. The magnitude and mechanisms of weathering are strongly modulated by climatic and hydrological conditions. Tropical environments, in particular, are globally significant due to their high rates of biological productivity, biodiversity, and critical role in climate regulation through energy and water fluxes (Bazzaz, 1998; Malhi, 2012; Artaxo, 2022). These environments are characterised by intense and often seasonal rainfall, elevated soil moisture, fluctuating water tables, and dynamic redox conditions. In such settings, rapid chemical weathering is facilitated by high temperatures and abundant water availability, while physical erosion is enhanced by steep topography and intense precipitation events. From dense evergreen rainforests to seasonally flooded wetlands and riverine floodplains, tropical landscapes exhibit intricate couplings between hydrology, climate, and biogeochemistry (Hamilton, 2010; Cusack et al., 2016; Tanaka, 2021). The Western Ghats of India, a UNESCO World Heritage site, is one such region where climate, topography, and geology converge to produce distinctive hydrological and biogeochemical regimes. This mountain chain, which runs parallel to the west coast of the Indian peninsula and plays a central role in shaping the hydrological and hydroclimatic patterns of the region (Dey et al., 2024). It intercepts the moisture-laden southwest monsoon, resulting in extremely high rainfall on the western slopes and creating a marked west–east climatic gradient. The west-flowing rivers originating from the Western Ghats are typically short, steep, and fast-flowing. Draining humid catchments, these rivers rapidly transfer water and solutes to the Arabian Sea, with minimal residence time on the land surface. Such hydrological behaviour has important implications for weathering dynamics, solute transport, and nutrient delivery to coastal and marine ecosystems. The rapid flushing of solutes during monsoonal events may limit in-stream processing while promoting the export of freshly weathered material (Thomas et al., 2015; Padmalal et al., 2018). Consequently, these rivers represent important but understudied contributors to the regional and global biogeochemical budgets. Alongside rivers, groundwater systems play an equally critical role in regulating the generation and export of solutes from catchments. Groundwater interacts with bedrock and soils over longer residence times compared to surface waters, leading to enhanced mineral dissolution and storage of weathering products (Gayathri et al., 2021). The chemistry of groundwater often reflects the depth to the water table, with shallow aquifers more directly influenced by rainfall inputs, organic matter decomposition, and rapid flushing of solutes, while deeper aquifers show signatures of prolonged rock–water interaction, ion exchange, and redox-driven transformations. In tropical mountainous settings, groundwater contributes substantially to baseflow, thereby sustaining stream chemistry during dry periods (Raj et al., 2023b). Thus, integrating groundwater hydrochemistry with riverine fluxes provides a more complete understanding of catchment-scale weathering, hydrological connectivity, and solute dynamics. Moreover, the Western Ghats’ rivers are embedded within a mosaic of land uses ranging from relatively undisturbed forested headwaters to intensively cultivated or urbanised lowlands. These varying land-use patterns, combined with differences in lithology and hydrological connectivity, can produce spatially and temporally heterogeneous chemical signatures in river water. Seasonal hydrological extremes, ranging from intense monsoon floods to dry-season baseflows, further modulate the mobilisation, dilution, and transformation of dissolved and particulate matter. Understanding how these multiple drivers interact is essential for predicting how such systems may respond to future climatic and anthropogenic pressures. In this study, we apply the mass balance approach to the Vamanapuram experimental catchment, in the Southern Western Ghats, Kerala, focusing on the Chettachal catchment (CRGS) and Kallar catchment (KRGS), to examine solute dynamics and weathering fluxes. Quantifying solute export from watersheds provides the most robust means of assessing weathering reactions in natural environments, as it integrates geochemical, biological, and ecological processes across the landscape while linking catchment dynamics to global biogeochemical cycles (Velbel & Price, 2007; Laudon & Sponseller, 2018). Riverine solute fluxes, derived from the measurement of dissolved constituents in conjunction with discharge, offer direct estimates of chemical weathering rates and solute generation. This watershed-scale approach inherently incorporates spatial heterogeneity, hydrological variability, and seasonal to interannual controls, thereby yielding a realistic assessment of natural weathering compared to laboratory or plot-scale studies (Gao et al., 2018). Such data are fundamental for constraining global chemical weathering budgets, quantifying atmospheric CO 2 consumption through silicate weathering, and evaluating the functioning of the critical zone. One approach to elucidating these controls is through concentration-discharge (C-Q) relationships, which examine how solute concentrations vary with river discharge across different flow regimes (Godsey et al., 2009; Thompson et al., 2011; Koger et al., 2018). In tropical mountainous catchments such as those in the Western Ghats, C-Q relationships may reveal the relative importance of subsurface versus surface flow paths, the timing and intensity of solute mobilisation, and the interplay between physical and chemical weathering processes. Coupling C–Q analysis with geochemical mass balance approaches enables quantification of the contributions of silicate weathering to riverine solute loads. Such analyses not only improve our understanding of catchment-scale processes but also help constrain the role of tropical rivers in the global carbon cycle. The role of groundwater contributions in sustaining streamflow in the tropical peninsular river basins has been highlighted in many stuides (Sharma et al., 2024). In the Vamanapuram experimental catchment, it is found that pre-event water dominates the runoff generation processes at storm event scale (Sharma et al., 2025 a ). At the seasonal and annual scale also baseflow found to be dominant contributor to streamflow (Sharma et al., 2026 b ), therefore it would be interesting to study the groundwater contributions to chemical erosion in humid tropical river system. The study area falls in the entry zone of Indian Summer Monsoon (ISM) on land (i.e., the Gateway of Indian Summer Monsoon) which in recent decades is frequently impacted by the adversities of cloud bursts/extreme rainfall events, landslides and flash floods. Given the above context, the present study focuses on a tropical mountainous watershed to address the following key objectives: (i) To examine C-Q dynamics and identify the dominant hydrological and geochemical controls on solute mobilisation and fluxes (ii) To quantify the contributions of major weathering processes, particularly silicate weathering, to the observed solute loads, and to assess their implications for CO 2 consumption and long-term carbon sequestration (iii) Hydro-chemical variation in groundwater (shallow vs deep wells) with changes in the depth to water table depth, iv) To evaluate hydrochemical variations in groundwater (shallow vs. deep wells) in relation to water table depth and their linkages with solute mobilisation. 2. Materials and methods 2.1 Study Area The present study has been carried out in the Vamanapuram Experimental Catchment (VAMEC), the headwater segment (Watershed) of the Vamanapuram River Basin. The catchment is situated on the western flank of the Southern Western Ghats in the Trivandrum district of Kerala State, India (Fig.1(a)). The Vamanpuram River is 88 km long and originates from Chemmunji Motai in the Western Ghats and drains through highly varied geologic and geomorphological terrains before emptying into the Arabian Sea through the Anjengo Lake at the land-sea interface (Fig.1(b)). VAMEC is a nested forested headwater catchment spread in the foothills of the Southern Western Ghats. As it is selected as an experimental catchment of the National Centre for Earth Science Studies for long term hydrological investigations, the watershed is instrumented with rain gauges, river water level loggers, electrical conductivity sensors, an Automatic Weather Station (AWS), and groundwater level loggers (Fig. 1(c)) to systematically collect various hydrometeorological parameters. It has been found that baseflow sustains (around the year) the river in the upstream 19 km stretch only, and the river dries out afterwards. Elevation ranges between 1621 meters and 84 meters in VAMEC; the dominant soil type is Sandy Clayey loam (Fig. 1(d), and the dominant LULC (Fig. 1(c) is forest (>70%), followed by plantations (mostly rubber). The major rock type in the catchment is the Khandolite group of rocks (>85% area) which is followed by the Migmatite complex (14% area). The VAMEC receives rainfall from the South-West Monsoon (SWM) from June to September, North-east Monsoon (NEM) from October to December, and few rain events during tropical cyclones in the dry season (NonMon) from Jan to May. Thus, it generally has a bimodal rainfall regime, with the first prominent peak in July and the second in October. The catchment receives an annual rainfall between 3000-4000 mm. The upstream part of the catchment has a high infiltration rate, and the downstream segment has a moderate infiltration rate (Ajin et al., 2013). The geology, geomorphology, LULC, drainage density, slope, soil, and rainfall distribution map of the Vamanapuram river basin has been discussed in detail in a previous study by Arulbalaji et al. 2019. 2.2 Dataset used VAMEC encompasses river gauging stations, river water electrical conductivity sensors (temperature compensated), rain gauges, Automatic Weather Stations (AWS), groundwater level loggers, river and groundwater electrical conductivity, and temperature loggers. All sensors provide data at 30-minute intervals. For more details of catchment instrumentation, and VAMEC readers are encouraged to refer Sharma et al., 2025 (under review HP). Monthly river water, and groundwater samples have been collected from May 2022 to Feb 2024. River water has been collected at four locations on the main river (designated as VRW-1, VRW-2, VRW-3, and VRW-4) and at six locations on the tributaries at all possible locations. A total of 12 groundwater samples were collected near and far from the river and distributed in the catchment (Fig.1(d)). Groundwater samples were collected from dug wells, and the depth of shallow aquifers ranged between 1.5 to 12.5 meters. River water samples were collected from the middle of the river, where the flow was maximum, and stagnant water was absent. Similarly, well-mixed groundwater samples were collected by bucket sampling. Utmost effort has been taken to prevent contamination during sampling and analysis. We have used narrow-mouth low-density polyethylene (LDPE) Trason make for the collection of river water and groundwater. Hanna-98194 was a measurement of in-situ field parameters, which includes temperature, dissolved oxygen, conductivity, PH, etc. Major ions (Ca 2+ , Mg 2+ , Na + , K + , Sulphate, Chloride) were analysed using an Ion Chromatograph (IC), nutrients were measured using a Continuous Flow Analyser (CFA). 2.3 Silicate Weathering Rates (SWR) and CO 2 Consumption Rates (CCR) In this study, chemical weathering rates (CWR) for the selected watersheds were quantified using a mass balance forward approach, with rainwater correction applied by considering chloride as a conservative tracer of atmospheric inputs. Rainwater samples collected during the study period were used to account for atmospheric contributions following established methodologies (Galy and France-Lanord, 1999; Moon et al., 2007; Ryu et al., 2008; Wu et al., 2008; Gurumurthy et al., 2012). The total concentration of a solute in river water can be expressed as the sum of contributions from atmospheric deposition, anthropogenic inputs, silicate weathering, carbonate weathering, and evaporite dissolution: [X] river water = [X] atmospheric +[X] anthropogenic +[X] silicate weathering +[X] carbonate weathering +[X] evaporites (1) The mass balance forward approach, widely employed in weathering studies (Gaillardet et al., 1999; Galy and France-Lanord, 1999; Gurumurthy et al., 2012; Amrish et al., 2022; Raj et al., 2023), was used to isolate the silicate-derived fraction of major cations and silica. These corrected concentrations, together with river discharge and drainage area, were used to estimate the silicate weathering rate (SWR) and associated cation consumption rate (CCR). The calculations follow the standardised formulations (Roy et al., 1999; Moon et al. 2007; Gurumurthy et al. 2012; Raj et al., 2023 ). SWR = [Ca silicate + Mg silicate +Na silicate + K silicate + SiO 2 ] x (discharge/drainage area) (2) CCR = [Ca silicate + Mg silicate +Na silicate +K silicate ] x (discharge/drainage area) (3) where Ca silicate ,Mg silicate ,Na silicate and K silicate represent cations supplied by silicate mineral weathering [mg/l for SWR and mol/l for CCR]. 2.4 Elemental flux, and chemical erosion River discharge measured at the river gauging station is used to estimate elemental flux, and chemical erosion using following formulae- E I = C I * Q (4) Where E I (t) is elemental flux with respect to ion I, C I (mg/l) is the mean ionic concentration (seasonal/annual), and Q is flow volume in (in million cubic meters, MM 3 ) CE = E I /A (5) Where CE is chemical erosion (t.km -2 ), and A is catchment area in Km 2 . Elemental flux, total flux, and chemical erosion have been calculated for the river. The groundwater contributions to elemental flux, total flux, and chemical erosion have been quantified. The volume of seasonal groundwater contributions has been quantified using mass balance approach using stable isotopic and chemical tracers at the river gauging stations. The groundwater contributions estimated from Sharma et al., 2025 (under review) has been used here for flux estimations. The groundwater samples near the main river channels are assumed to have representative groundwater chemical compositions contributing to river flow, and mean concentration of these groundwater wells have been used to estimate groundwater contributions to elemental fluxes, and chemical erosion. 3. Results 3.1 Hydro-geochemistry of river water and groundwater 3.1.1 Hydro-chemical facies and Groundwater mineralization- The Piper diagram is used to define geo-chemical water types of river water, and groundwater and its seasonal changes. It is noted that in the PreMon season, most of the river water samples are Na-HCO 3 type, followed by Ca-HCO 3 , and mixed Ca-Mg-Cl type, whereas GW showed Na-HCO 3 , Na-Cl, followed by mixed Ca-Mg-Cl type water (Fig. 2. (a)). In SWM, river water belongs to Na-HCO 3 , and Ca-HCO 3 type, and NEM all river water samples are Na-HCO 3 type (Fig. 2. (b). As expected, GW samples are found to have higher solute load than river water in all seasons (Fig. 2). Primarily river water is of Na-HCO 3 , Ca-HCO 3 type, and groundwater is Na-K-HCO 3 and Na-Cl type. The underlying geology of the study area is dominated by the Khadolite group of rocks. Ca, Na and Mg enrichment in water samples may be attributed to weathering of plagioclase mineral, while K enrichment to K feldspar and biotite mineral in the host rocks. It is noted that, during the PreMon season, most of the RW and GW samples are Alkaline-type, followed by no dominant type, and bicarbonate-type, followed by chloride-type and no dominant type. SWM water resembles PreMon waters mostly. However, in the NEM season, except for the 3 GW samples, all are found to be alkaline or bicarbonate type, unlike PreMon and SWM, where a few samples are found to be of no dominant type. A Gibbs diagram is used to understand the dominant geochemical processes controlling the river water chemistry. For anions, it is noted that river water chemistry is governed by weathering of host rocks, and rainfall characteristics irrespective of season (Fig. 3). For cations, river water chemistry is found to be controlled by rainfall, indicating that surface water is recent recharged water. Lower Na/Na+Ca values indicate dominance of carbonate minerals, whereas higher values indicate the dominance of silicate. 3.1.2 Weathering, Dissolution and cation exchange processes - Hydro-chemistry of river water, and groundwater is found to be largely governed by silicate weathering (Fig Ca/Na vs HCO 3 /Na) (Fig 4.) and precipitation. In plots of Ca+Mg vs TC (Total Cations), and Na+K vs TC, indicates RW and GW samples falls below 1:1 line indicate that silicate weathering is responsible for Na, K, Ca, and Mg in river water, and groundwater (TC vs Ca+Mg, and TC vs Na+K) (Senthilkumar and Elango, 2013; Kanagaraj and Elango, 2019; Brindha et al., 2020). All river water and groundwater samples exhibit indications of silicate weathering, and direct ion exchange processes (Fig. 3(d) Ca+Mg vs HCO 3 +SO 4 ). Silicate weathering largely governs the geochemical processes. A few groundwater well water samples are affected by anthropogenic activities irrespective of the season (Fig. 3(c) Na vs Cl). The direct ion exchange and reverse ion exchange can be differentiated through a plot Na–Cl vs (Ca+Mg) – (HCO 3 +SO 2- 4). It is noted that direct ion exchange, and reverse are dominant processes in the PreMon and SWM, whereas in the NEM direct ion exchange is found to be dominant over reverse ion exchange in river water and groundwater samples. 3.2 Seasonal and spatial variability in river water and groundwater chemistry The river water EC is found to be 29.4±6.48, 29.5±6.58 and 40.2±10.85, Na + is 2.4±0.99, 2.9±0.36 and 3.0±1.33, Ca 2+ is 1.6±0.53, 1.5±0.69 and 1.9±0.85,\(\text{HCO}_{3}^{-}\) is 10.5±3.13, 11.2±3.02 a 12.4±5.22, and Cl - is 2.9±0.57, 3.0±0.51 and 3.6±0.84 for SWM, NEM and PreMon respectively (Fig. 5(a) and Table 2.). The variability (standard deviation) in river water chemistry is found to be larger in the PreMon than in the SWM or NEM, and the variability in the SWM and NEM is almost similar. The river water EC is found to be 24.5±1.22, 26.8±1.51 and 27.9±2.32, Na + is 2.6±1.00, 2.9±1.13 and 2.8±1.09, Ca 2+ is 1.3±0.39, 2.1±0.84 and 2.0±0.78,\(\text{CO}_{3}^{-}\) is 9.9±3.05, 13.2±4.76 and 12.4±4.15, and Cl - is 3.0±0.72, 3.3±0.70 and 3.6±0.82 for upstream, middle and downstream, respectively. The variability among river water samples increases from upstream to downstream for most of the chemical parameters. It is noted that as we go from upstream to downstream, solute concentration increases, and is largest for the downstream stretch; however, solute concentrations are not much different for the middle and downstream sections. The averaged values of chemical parameters at river water sampling location indicates that (Fig. 6(a) and Table 7) Temp varies between 24-28 °C, EC between 25-45 µS/cm, Na + between 2-3.5 mg/l, K + between 0.8-1.7 mg/l, Mg 2+ between 0.5-0.9 mg/l, Ca 2+ between 1-2.8 mg/l, Cl - between 2.5–3.9, \(\text{SO}_{4}^{4-}\) between 1.1–1.8 mg/l, \(\text{HCO}_{3}^{-}\) between 8-16 mg/l, and\(\text{SiO}_{4}^{4-}\) between 4.7 to 6.3 mg/l. It is noted that tributary river water VTR-3, and VTR-5 are found to have higher concentrations than other river water, or tributary river water. It is noted that concentration increased from VRW-1, VRW-2, and VRW-3, the increase (or decrease) from VRW-3 to VRW4 was very marginal probably due to the tributary river water contributions (VTR-3, and VTR-5) that occurs before VRW-3 (Fig. 7.). Similarly for groundwater EC is found to be 77.8±61.46, 76.8±59.58 and 90.8±68.30, Na + is, 5.8±5.34, 6.6±5.06 and 6.7±4.86, Ca 2+ is 4.3±6.95, 4.5±8.15 and 5.3±9.24,\(\text{HCO}_{3}^{-}\) is 19.4±24.25,20.6±26.12 and 23.2±31.11, and Cl - is 7.8±7.13, 7.9±7.01 and 8.7±7.50 for SWM, NEM and PreMon, respectively (Fig. 5(b) and Table 1.). The variability (standard deviation) among groundwater chemistry is found to be similar in all seasons. The groundwater EC is found to be 31.0±10.26, 65.1±47.51 and 128.5±69.22, Na + is 2.0±1.25, 5.5±4.37 and 9.4±5.41, Ca 2+ is 1.4±1.05, 3.5±3.92 and 7.8±12.51,\(\text{HCO}_{3}^{-}\) is 6.2±3.30, 20.7±19.35 and 27.5±38.66, and Cl - is 3.6±1.15,4.8±2.96 and 15.2±8.07 for upstream, middle and downstream, respectively. It is noted that as we go from upstream to downstream, solute concentration increases, and is largest for the downstream stretch, except for silicate, which is found to be largest in the middle section, which could be more silicate coming from adjoining tributaries from the left in the middle stretch. It is noted that EC increases twice from upstream to middle (from 31 to 65) and again from middle to downstream (from 65 to 128.5). Season-wise averaged groundwater and spatially averaged groundwater chemistry are presented in Table 3. The averaged values of chemical parameters at groundwater sampling location indicates that (Fig. 6(b)) temp varies between 25.5-28 °C, EC between 55-210 µS/cm, Na + between 4-15 mg/l, K + between 0.5-5 mg/l, Mg 2+ between 0.5-2.25 mg/l, Ca 2+ between 2-35 mg/l, Cl - between 2 – 23, \(\text{SO}_{4}^{4-}\) between 1 – 10 mg/l, \(\text{HCO}_{3}^{-}\) between 10-100 mg/l,\(\text{SiO}_{4}^{4-}\) between 2 to 20 mg/l, and DWT between -8.5 - 1.5 meters . It is noted that VGW-2, VGW-4, VGW-18, VGW-20, VGW-21 are found to have higher EC (>100 µS/cm) than other groundwater we VGW-1, VGW-3, VGW-13, VGW-14, VGW-15, VGW-17, and VGW-19 which have relatively lesser EC (~50 µS/cm). Usually, wells in the downstream are found to have higher EC than in the upstream (Fig. 8). 3.3 ANOVA on seasonal and spatial variability of river water, and groundwater- Seasonal and spatial variability in the river water and groundwater has been studied using one-way Analysis of variance (ANOVA). ANOVA is used to identify whether there are any significant differences in seasons (SWM, NEM, and PreMon) and segments (upstream, middle, and downstream) in the hydrochemistry of the river water and groundwater. We have carried out ANOVA analysis with respect to ten parameters temperature, EC, Na + , K + , Mg 2+ , Ca 2+ , Cl - ,\(\text{HCO}_{3}^{-}\), \(\text{SO}_{4}^{4-},\ \) and\(\text{SiO}_{4}^{4-}\). As expected, it is noted that river water is significantly different in SWM, NEM, and PreMon (Table 5) with p-value less than 0.001 for all parameters except \(\text{HCO}_{3}^{-}.\) River water chemistry is found to be significantly different in the upstream, middle, and downstream except Na + , and\(\text{SO}_{4}^{4-}\) (Table 5). Unlike river water, groundwater has shown no significant difference among seasons (except for silicate, p-value = 0.042), however, spatially significant differences have been observed with respect to all parameters similar to river water with p-value less than 0.05. As expected PreMon river water and groundwater ionic concentration are found to be higher than SWM, and NEM due to the effect of rainfall dilution during the monsoon period, and increased enrichment during prolonged dry period, facilitating rock-water interaction, and higher evaporation. For river water, no significant difference in hydrochemistry is noticed between the SWM and NEM, except for Na + , and Mg 2+ . 3.4 PCA on river water, and groundwater hydro-chemistry During the SWM season, river water chemistry is largely governed by geogenic inputs, as indicated by the high loadings of Mg²⁺, Ca²⁺, and K⁺ in PC1 (which explains 27.6% variance), suggesting enhanced mineral weathering due to increased flow and catchment flushing (can be related to the chemostatic behaviour if it exists) (Table 6 and Fig 9). PC2 (21.8%) exhibits the influence of SO₄²⁻, Cl⁻, EC, and SiO 2 , indicating contributions from atmospheric deposition and anthropogenic inputs from diffuse sources, such as agriculture and domestic runoff. PC3 (17.6%) has high positive loadings of Na⁺ and HCO₃⁻, reflecting silicate weathering and possible cation exchange, while PC4 (14.9%) reflects temperature-driven solute mobilisation, likely enhanced by the warm, humid tropical climate, which promotes intense biological activity and hydrological fluxes during the monsoon. During the NEM season, the PC1 (43.6%) exhibits high positive loadings for HCO₃⁻, Ca²⁺, Mg²⁺, Na⁺, K⁺, EC, and temperature, indicating intense water-rock interaction under humid tropical conditions. PC2 (24.2%) is associated with SO 4 ²⁻, Cl⁻, Na⁺, and EC, signifying evaporative concentration and anthropogenic loading during low-flow conditions. PC3 (10.5%) is characterised by high positive loading of SiO 2 , consistent with the continued breakdown of silicate minerals under longer residence times. During the PRM season, the PC1 (36.5%) is defined by high contributions from Mg²⁺, SO₄²⁻, K⁺, and Ca²⁺, reflecting solute accumulation under dry conditions and intensified mineral weathering, particularly from mafic lithologies. PC2 (24.6%) highlights the role of temperature, EC, and Cl⁻, emphasising solute concentration through evaporation and anthropogenic inputs. PC3 (19.9%) is dominated by Na⁺, SiO₂, and Cl⁻, further reinforcing the influence of silicate weathering and ion mobilisation during periods of minimal discharge. PCA of groundwater chemistry across the three seasons highlights the dynamic interplay between geogenic processes, anthropogenic inputs, and hydrological variability in shaping groundwater composition. During the SWM, the first principal component (33.1% variance) is dominated by Ca²⁺, SO₄²⁻, HCO₃⁻, and EC, reflecting intense mineral weathering under saturated conditions. The PC2 (25.6%) exhibits strong positive loadings of Na⁺, Cl⁻, and EC, indicating anthropogenic influences such as domestic effluents and agricultural runoff. The PC3 and PC4, characterised by SiO 2 , Mg²⁺, and K⁺, suggest silicate weathering and ion exchange processes. During the NEM season, a similar pattern is observed, with PC1 (32.9%) reflecting mineral dissolution through strong associations with Ca²⁺, SO₄²⁻, and HCO₃⁻. PC2 (26.2%) emphasises anthropogenic contributions via Na⁺, Cl⁻, and EC, while PC3 and PC4 highlight the roles of silicate weathering (SiO 4- 2 , Mg²⁺) and ion exchange (K⁺, Mg²⁺), respectively. During the PRM, PC1 accounts for 35.2% of the variance and is defined by Ca²⁺, HCO₃⁻, SO₄²⁻, and EC, suggesting dominant geogenic inputs. PC2 (17.9%) is characterised by Cl⁻ and Na⁺, indicating increased anthropogenic influence and evaporative concentration resulting from reduced recharge. Subsequent components PC3 and PC4 further capture the effects of silicate weathering and prolonged water-rock interaction (SiO 4- 2 , Mg²⁺, K⁺) (Fig. 10). 3.5 Silicate Weathering Rates (SWR) and CO 2 Consumption Rates (CCR) At the annual scale, the KRGS exhibited SWR values of 22.34 and 21.51 t km⁻ 2 yr⁻ 1 , and CCR values of 2.76 × 10 5 and 2.60 × 10 5 mol km⁻ 2 yr⁻ 1 for 2022–2023 and 2023–2024, respectively. In comparison, the CRGS recorded higher absolute fluxes, with annual SWR values of 27.74 and 25.64 t km⁻ 2 yr⁻ 1 , and CCR values of 3.77 × 10 5 and 3.95 × 10 5 mol km⁻ 2 yr⁻ 1 over the same periods (Fig. 11(a)). While the parent catchment understandably integrates larger weathering fluxes, KRGS attained nearly 70–80% of CRGS’s areal rates despite contributing only 22–28% of the total discharge and about 20% of the total fluxes. This disproportion highlights the efficiency of smaller headwater basins in generating solute fluxes per unit area, likely driven by steeper slopes, shorter flow paths, and intensified rock–water interactions. Seasonal dynamics illustrate further contrasts between the two systems. In both years, the SWM was the dominant driver of weathering fluxes. During SWM 2022, KRGS recorded CCR and SWR values of 1.99 × 10 5 mol km⁻² and 16.0 t km⁻² respectively, whereas CRGS showed higher values of CCR (2.66 × 10 5 mol km⁻²) and SWR (20.5 t km⁻²) (Fig. 11(b)). Both catchments experienced slightly reduced fluxes in SWM 2023 (KRGS: CCR and SWR values of 1.54 × 10 5 mol km⁻² and 13.11 t km⁻ 2 , CRGS: CCR and SWR values of 1.86 × 10 5 mol km⁻² and 11.80 t km⁻ 2 ), reflecting interannual variability in rainfall and runoff. However, a divergence emerged during the NEM: while KRGS displayed moderate values in NEM 2023 (CCR: 1.01 × 10 5 mol km⁻ 2 ; SWR: 5.81 t km⁻ 2 ), the CRGS sustained fluxes nearly equal to its SWM output (CCR: 1.90 × 10 5 mol km⁻ 2 ; SWR: 11.66 t km⁻ 2 ). This indicates that the broader CRGS system integrates a greater proportion of NEM precipitation, whereas the smaller sub-catchment is less responsive to this rainfall regime. In both catchments, pre-monsoon seasons were marked by suppressed weathering fluxes, reflecting the tight coupling between water availability and solute export. KRGS registered CCR values as low as 1.57 × 10 5 mol km⁻² and SWR of 0.80 t km⁻ 2 during PRM 2023, while CRGS exhibited similar minima, despite retaining slightly higher residual discharge. Such observations highlight the hydrological sensitivity of silicate weathering in tropical mountain basins, where low-flow conditions sharply constrain mineral dissolution and CO 2 consumption. 3.6 Concentration-Discharge relationship Examining how solute concentrations vary with stream discharge is a well-established approach for understanding the interplay between hydrological processes and geochemical fluxes within a catchment. This relationship serves as a diagnostic tool for identifying the mechanisms of solute generation, the nature of water sources, and the pathways through which dissolved constituents are transported (Godsey et al., 2009; Thompson et al., 2011). Variations in concentration-discharge (C-Q) behaviour provide insight into the coupling between chemical weathering and riverine transport, highlighting the processes that govern the mobilisation of both natural geochemical signatures and anthropogenic inputs. In doing so, C-Q analysis captures the catchment’s role as a regulator of solute export, bridging the link between landscape weathering dynamics and downstream water chemistry (Godsey et al., 2009; Thompson et al., 2011; Koger et al., 2018; Raj et al., 2023). The C-Q relationship is mathematically expressed as \(C=aQ^{b}\). In the C-Q relationship, C refers to solute concentration, Q represents stream discharge, and b denotes the slope of the log C versus log Q relationship (Godsey et al., 2009; Clow & Mast, 2010; Koger et al., 2018; Cartwright et al., 2020). The value of b serves as an indicator of distinct hydrological behaviours, reflecting differences in solute sourcing, flow pathways, and catchment storage dynamics. By examining the slope of log-log C-Q plots, it is possible to infer whether a solute exhibits dilution (negative slope), enrichment (positive slope), or chemostatic behaviour (slope near zero). These trends reflect the degree of connectivity between hydrological flow paths and solute sources, as well as the extent to which chemical signals are buffered by subsurface storage. In this study, C-Q relationships were evaluated for two sub-catchments within the Vamanapuram river basin, at Kallar River Gauging Station (KRGS) (VRW-1) and Chettachal River Gauging Station (CRGS) using data for major cations (Na, K, Ca, Mg) and anions (Cl, HCO₃, SO₄) along with dissolved silica (SiO 2 ). Fig. 12, illustrates the variability in solute concentrations across a range of flow conditions, while Table 8 presents the corresponding C-Q slopes derived from regression analysis. In KRGS (VRW-1), the slopes for most solutes are close to zero, indicating relatively weak dilution with increasing discharge. Among the cations, sodium shows the steepest decline (-0.13), followed by silica (-0.10), suggesting that these constituents are more susceptible to flushing and mixing with lower-concentration waters during high-flow events. Potassium, calcium, magnesium, and sulphate display slopes between -0.01 and -0.02, consistent with chemostatic behaviour and pointing to sustained contributions from groundwater and deeper subsurface flow paths. For the anions, chloride (-0.06) and bicarbonate (-0.05) also show minimal change across the observed discharge range, reinforcing the interpretation that KRGS maintains a stable chemical signal even under variable hydrological conditions. The relatively gentle slopes observed for Ca, Mg, and HCO₃-common products of mineral weathering, indicate that silicate weathering processes continue to supply solutes during both baseflow and stormflow periods. In contrast, CRGS (VRW-4) exhibits systematically steeper negative slopes for nearly all solutes, signifying stronger dilution responses under high-flow conditions. Sodium (-0.18) and potassium (-0.11) show the largest decreases among the cations, followed by calcium (-0.09) and magnesium (-0.06). These patterns suggest a greater reliance on near-surface flow paths, where solute concentrations are more readily diluted by precipitation and rapid runoff. The anion patterns further support this interpretation. The bicarbonate (-0.15) and chloride (-0.12) exhibit significant dilution, indicating that even relatively conservative solutes are impacted by increased flow in CRGS. In contrast, SO₄ (-0.04) and SiO 2 (-0.04) show more stable trends, possibly reflecting slower weathering kinetics or a greater proportion of their load derived from deeper groundwater sources. The C-Q analysis underscores how KRGS operates as a chemostatic system with consistent solute delivery, whereas CRGS represents a more hydrologically responsive, chemodynamic system. These differences have significant implications for predicting solute export under altered flow regimes and for upscaling event-based measurements to catchment-scale weathering budgets. 3.7 Relationship of conductivity, Ca, Mg, K, Cl, and Silicate with DWT Correlation analysis between DWT and conductivity, Major Ions, and silicate has been carried out (Fig. & Table). In the Deeper wells (mean DWT > 6.5 meters) VGW-3, and VGW-4 it is noted that conductivity increased with lowering of the water table, whereas in the shallower wells VGW-14 and VGW-17 (mean DWT < 5 meters), it is noted that conductivity increased with the rise in the water table, indicating recharge has higher mineral content. The Opposite behaviour in shallower and deeper wells may be due to a decrease in the influence of recharge with depth, and increasing distance from infiltration zones (Leduc et al., 1997; Elbaz-Poulichet et al., 2002). The rise in water table is associated with an increase in Mg, Ca, and K in groundwater (VGW-13, VGW-14, VGW-15, VGW-17), with no significant correlation with Na, possibly due to the leaching process transferring dissolved or suspended minerals/Ions/nutrients through soil into the groundwater. The decrease in water table depth is accompanied by an increase in chloride concentrations at VGW-4, whereas there is a decrease in chloride concentrations at VGW-15 and VGW-17. The rise in the water table was associated with an increase in sulphate in VGW-14, VGW-15, and VGW-20. VGW-4, VGW-14, VGW-15, and VGW-17 indicated a decrease in silicate concentrations with the rise in water table depth. Peng et al., 2025 noted negative correlation between water table depth and major ions. 3.8 Elemental flux and chemical erosion The elemental fluxes are found to be largest in the SWM, followed by NEM and PreMon season (Fig. 14 (a) and (b)). The order of elemental flux contributions follows Na + > Ca 2+ > K + > Mg 2+ at KRGS and CRGS, with total fluxes of 526 and 3989 kilo tones/year, and chemical erosion of 21.5 and 39.1 Kilo tones/km 2 /year respectively. The groundwater contributes 59%, 40 %, 44%, 49%, 65%, 64%, 55% of total Ca 2+ , Mg 2+ , Na + , Cl - , \(\text{SO}_{4}^{2-}\), total flux respectively at KRGS, and 49%, 61%, 73%, 67%, 65%, 74%, 53% and 70% at CRGS respectively. Overall, 55% and 70% of total fluxes are moving out via groundwater pathways. 4.0 Discussion 4.1 Seasonal and spatial variability in river water and groundwater chemistry Major ionic concentrations are found to be higher in the PreMon period than SWM and NEM periods, and almost similar concentrations in the SWM and NEM periods. Similar seasonal variability in river water chemistry has also been reported in other studies conducted in Peninsular (Mehto and Chakrapani, 2013; Thomas et al., 2014; Aditya et al., 2024; Gayathri et al., 2025) and Himalayan River systems (Mir et al., 2016; Samanta et al., 2019) with higher concentration during the pre-monsoon period than the monsoon season. The ionic concentrations of river water were generally found to increase from upstream to middle, but were found to be almost identical for the middle and downstream segments. The increase from upstream to the middle stretch of the river was substantial due to higher contributions from the tributaries compared to the concentration along the main river; this could be one reason for the similar concentration in the middle and downstream segments. River water EC increased with a decrease in elevation (Mir et al., 2016). Higher values in tributary water VTR-3, and VTR-5 could be either due to contributions of more concentrated groundwater and/or due to human influence, since VTR-5 is surrounded by built-up area (Fig. 14) . ANOVA results indicated that no significant difference in groundwater chemistry (all wells averaged over seasons) has been observed across SWM, NEM, and PreMon. However, it is noted that relatively PreMon has higher mean constituents’ concentrations and variability among groundwater samples in Na + , K + , Ca 2+ , Cl - , \(\text{HCO}_{3}^{-}\), \(\text{SiO}_{4}^{4-}\), and EC than NEM, and SWM. In line with the present work, other studies also highlighted the effect of seasonal variations in the groundwater chemistry with higher concentrations during dry periods (Adnani et al., 2020; Maroubo et al., 2021), whereas others have reported similar concentrations during dry and wet period (Zhai et al., 2015; Guo et al., 2018; Tanwar et al., 2024). Higher concentrations in river water, and groundwater during dry period than monsoon period maybe attributed to higher mineralization due to increased residence time, and absence of diluted rainfall inputs. Spatial variability in hydro-chemical attributes is controlled by tributary flows (Meyer et al., 1988), land cover changes (Bucker et al., 2010), soil characteristics, and/or geology (Welsh et al., 2001; Harmon et al., 2009). ANOVA results indicated spatial variability in the groundwater catchment with least concentration at upstream, and highest at the downstream stretch. Major ions in the water originate from natural (weathering, geochemical reactions, atmospheric deposition, volcanic activity, and seawater intrusion) and anthropogenic sources (agricultural activities, industrial discharge, and urban runoff). VAMEC exhibits similar geology (Khondalites) and soil (Sandy-Clayey-Loam) throughout the catchment; however, land cover is dominated by evergreen dense forest in the upstream area, mixed forest and plantations in the middle, and plantations and built-up areas in the downstream area. The geographical location of the VAMEC indicates that major ions in the water may originate mostly from weathering, atmospheric deposition, and geochemical reactions, and partly from agricultural activities and sewage discharge from built-up areas. The observed spatial variations in the groundwater constituents may be attributed to the LULC changes, and increased water-rock interaction. Similar spatial variation in groundwater concentration has been observed in previous studies carried out in different parts of the world (Jeong, 2001; Fan et al., 2014; Gayathri et al., 2021). It is observed that in the downstream wells (VGW-4, VGW-14, VGW-18, VGW-20, VCGW-21) nitrate concentration is greater than 2 mg/ltr, whereas it is less than 0.3 mg/ltr for other wells in the PreMon. Higher nitrate values in the downstream indicate the anthropogenic influence on groundwater chemistry primarily arising from changes in the land use and land cover (Jeong, 2001; Akshitha et al., 2023). 4.2 PCA on river water, and groundwater The PCA of river water chemistry across seasons reveals that geogenic processes, particularly silicate weathering, are the dominant drivers of solute dynamics, modulated by seasonal hydrological variations and anthropogenic inputs. During the SWM, enhanced flow and catchment flushing promote mineral weathering and the mobilisation of solutes, with additional contributions from atmospheric deposition and diffuse anthropogenic sources. In the NEM, sustained baseflow conditions support prolonged water-rock interactions, intensifying weathering signatures, while anthropogenic loading becomes prominent during low-flow phases. The PRM season is characterised by evaporative enrichment and solute accumulation, with elevated contributions from silicate weathering and anthropogenic influences under reduced discharge conditions. Across all seasons, river water chemistry is shaped by the interplay between hydrological regimes, underlying lithology, and land-use patterns, driving both spatial and temporal variability. Whereas groundwater PCA reveal seasonally modulated geochemical processes, with stronger anthropogenic signatures emerging during recharge periods (SWM and NEM), driven by surface runoff and infiltration, while geogenic controls dominate during the drier Pre-Monsoon season due to evaporative concentration and prolonged water-rock interaction. 4.3 SWR and CCR of the study area Being small tropical mountainous catchments, the SWR of the CRGS–KRGS system is comparable to other larger basins of the Western Ghats, Peninsular, as well as the World’s largest rivers, signifying the role of these tropical mountainous rivers in controlling dissolved flux to the sea. The SWR estimated for the CRGS (26.69 t km⁻² yr⁻¹) and its nested Kallar sub-watershed (KRGS; 21.92 t km⁻² yr⁻¹) underscore the significance of small tropical mountainous catchments in regulating dissolved fluxes to the receiving basin. These values are broadly comparable to those reported for other short, steep, west-flowing rivers of the Western Ghats such as the Muvattupuzha (27 t km⁻² yr⁻¹; Trivikramji & Joseph, 2001), Sharavathi (27 t km⁻² yr⁻¹; Amrish et al., 2022), and Karamana (30.96 t km⁻² yr⁻¹; Upendra et al., 2025), highlighting the combined influence of intense monsoonal precipitation, steep topography, and rapid runoff in sustaining elevated weathering intensities across tropical mountainous terrains. In contrast, lower SWR values are reported for the Bhavani (8.86 t km⁻² yr⁻¹; Raj et al., 2023), Thuthapuzha (17.9 t km⁻² yr⁻¹; Raj et al., 2023), and Krishna (8.11 t km⁻² yr⁻¹; Gaillardet et al., 1999), where larger basin size and lower specific discharge tend to moderate weathering rates. When compared with other Peninsular Indian rivers, the CRGS–KRGS system records higher SWR than the Cauvery headwaters (7.9–9.4 t km⁻² yr⁻¹; Pattanaik et al., 2013), but approaches the values reported for the Narmada (33.9 t km⁻² yr⁻¹; Sharma & Subramanian, 2008) and Tapti (33.6 t km⁻² yr⁻¹; Sharma & Subramanian, 2008). These rates, however, remain distinctly lower than hyperactive systems such as the Nethravati (42 t km⁻² yr⁻¹; Gurumurthy et al., 2012), Chandragiri–Payaswini (39 t km⁻² yr⁻¹; Nisha et al., 2021), and Swarnamukhi (30.6 t km⁻² yr⁻¹; Patel et al., 2020). At the global scale, the SWR of CRGS and KRGS is higher than or comparable to those of major rivers such as the Amazon (13–23 t km⁻² yr⁻¹; Gaillardet et al., 1999, Yangtze (5.5 t km⁻² yr⁻¹; Gaillardet et al., 1999), and Mississippi (3.8 t km⁻² yr⁻¹; Gaillardet et al., 1999). Yet, these values fall below those of hyperactive systems, such as the Red River (27.5 t km⁻² yr⁻¹; Moon et al., 2007) and the small tropical Kajli catchment (48 t km⁻² yr⁻¹; Das et al., 2005). Compared with the Himalaya, the CRGS–KRGS system displays SWR values broadly similar to the Ganga at Rishikesh (12.9 t km⁻² yr⁻¹; Krishnaswami & Singh, 1998) and Brahmaputra (10.3 t km⁻² yr⁻¹; Gaillardet et al., 1999), and lower than the Yamuna (28 t km⁻² yr⁻¹; Dalai et al., 2002), Alakananda (18.9 t km⁻² yr⁻¹), and Bhagirathi (9.4 t km⁻² yr⁻¹; Shaifullah & Sen, 2025). The CCR of CRGS (3.86 × 10⁵ mol km⁻² yr⁻¹) and KRGS (2.68 × 10⁵ mol km⁻² yr⁻¹) are consistent with values from other medium-sized Western Ghats catchments such as the Sharavathi (3.9 × 10⁵ mol km⁻² yr⁻¹; Amrish et al., 2022) and Cauvery headwaters (3.83 × 10⁵ mol km⁻² yr⁻¹; Pattanaik et al., 2013). They are, however, lower than the Chandragiri–Payaswini system (8.95 × 10⁵ mol km⁻² yr⁻¹; Nisha et al., 2021) and the Muvattupuzha River (5.5 × 10⁵ mol km⁻² yr⁻¹; Trivikramji & Joseph, 2001), but higher than the Bhavani (1.39 × 10⁵ mol km⁻² yr⁻¹), Thuthapuzha (2.18 × 10⁵ mol km⁻² yr⁻¹; Raj et al., 2023), and Karamana (1.16 × 10⁵ mol km⁻² yr⁻¹; Upendra et al., 2025). Globally, the CCR values of CRGS and KRGS, which is higher than the global average of 0.9×10⁵ mol/km²/y (Gaillardet et al. 1999) and surpass those of several large river systems including the Amazon (0.52 × 10⁵ mol km⁻² yr⁻¹), Congo (0.5 × 10⁵ mol km⁻² yr⁻¹), Paraná (0.9 × 10⁵ mol km⁻² yr⁻¹), and Orinoco (0.6 × 10⁵ mol km⁻² yr⁻¹; Gaillardet et al., 1999), underscoring the disproportionate carbon sequestration potential of small, steep tropical catchments. Within India, however, they remain lower than the Ganges (4.5 × 10⁵ mol km⁻² yr⁻¹; Gaillardet et al., 1999), Yamuna (5 × 10⁵ mol km⁻² yr⁻¹; Dalai et al., 2002), Narmada (9.3 × 10⁵ mol km⁻² yr⁻¹), Tapti (18.1× 10⁵ mol km⁻² yr⁻¹; Sharma & Subramanian, 2008) and Godavari (5.8 × 10⁵ mol km⁻² yr⁻¹; Jha et al., 2009). Nevertheless, they are comparable to the Koshi (3.40 × 10⁵ mol km⁻² yr⁻¹; Bishwakarma et al., 2024), Bhima (3.3 × 10⁵ mol km⁻² yr⁻¹; Das et al., 2005), Kali (2.9 × 10⁵ mol km⁻² yr⁻¹; Arun et al., 2022), Nethravati (3 × 10⁵ mol km⁻² yr⁻¹; Gurumurthy et al., 2012), and Bhagirathi (2.7 × 10⁵ mol km⁻² yr⁻¹; Shaifullah & Sen, 2025) and exceed the Krishna (1.6 × 10⁵ mol km⁻² yr⁻¹; Gaillardet et al., 1999), Damodar (1.38 × 10⁵ mol km⁻² yr⁻¹; Samanta et al., 2021), Alakananda (1.8 × 10⁵ mol km⁻² yr⁻¹; Shaifullah & Sen, 2025)), and Subarnarekha (0.42 × 10⁵ mol km⁻² yr⁻¹; Samanta et al., 2021). Taken together, these results emphasize two important points. First, runoff is the primary control on seasonal variations in weathering fluxes, but solute export efficiency differs with scale. The higher relative weathering intensity in KRGS compared to CRGS suggests that headwater basins play a disproportionately large role in regulating silicate weathering and atmospheric CO 2 consumption. Second, monsoon asymmetry exerts distinct signatures across scales: while SWM dominates fluxes universally, NEM contributions become more evident at the parent catchment scale, reflecting spatial heterogeneity in precipitation and hydrological routing. Thus, the CRGS–KRGS system demonstrates that weathering fluxes do not scale linearly with discharge or catchment area. Instead, solute generation reflects a complex interplay of hydrology, geomorphology, and scale-dependent dilution effects. For robust estimates of chemical denudation and CO 2 drawdown in tropical mountainous rivers, nested monitoring of both headwater sub-catchments and larger parent catchments is essential. Overall, the CRGS–KRGS system demonstrates that even small, humid tropical mountainous watersheds can sustain weathering and carbon consumption rates comparable to or exceeding those of much larger river systems. This highlights the disproportionate role of short, steep tropical rivers in global chemical weathering and CO 2 drawdown, driven by their orographic setting, intense rainfall, and rapid hydrological flushing. 4.4 Dependency of hydrochemical parameters on DWT in shallow vs deep wells Correlation analysis between DWT and physicochemical parameters (pH, EC, and major ions) indicates contrasting trends between shallow and deep wells in the study area. The VGW-3 and VGW-4 wells are deep (mean DWT > 6.5 m) and exhibit a strong increase in conductivity with decreasing water table, implying solute accumulation during dry seasons (Appelo and Postma, 2005). The enrichment of Na + and Cl – at low water levels, particularly in VGW-4, reflects the process of evaporative concentration and longer residence times. High dissolved SiO 2 concentrations during dry periods further suggest prolonged rock-water interaction, typical of deeper Khondalite weathering profiles (Meybeck, 1987; Hiscock and Bense, 2014). The negative correlation between DWT and conductivity also aligns with minimal recharge input at depth. Their location within plantation and mixed-use LULC zones, which often have exposed or modified soils and higher evapotranspiration, supports the interpretation of solute build up and possibly anthropogenic contribution such as fertilizers or irrigation return flow (Elbaz-Poulichet et al., 1994). A group of four shallow wells (VGW-13, VGW-14, VGW-15, VGW-17) located in the evergreen forest and transitional zones exhibits an increase in conductivity with rising water table, indicating solute-rich recharge, rather than evaporative effects (Leduc et al., 1997). This is reinforced by the concurrent rise in Mg 2+ , Ca 2+ , K + , and SO 4 2– concentrations during recharge periods. These trends suggest active leaching of minerals and soil-bound nutrients into the shallow aquifer during monsoon infiltration. The negative correlation between rising DWT and SiO 2 content further indicates dilution and reduced water-rock interaction due to shorter residence times. Located within or adjacent to evergreen broadleaf forests, these wells benefit from high infiltration rates and organic-rich topsoils, which enhance the leaching of mobile ions into shallow groundwater. VGW-15 and VGW-17, situated in transition zones with partial plantation influence, also show evidence of SO 4 2– input from surface sources, possibly related to land management practices. The VGW-2 and VGW-13 wells, located near main stream courses, likely experience significant surface water-groundwater interactions, especially during monsoon periods (Akshitha et al., 2023). Though not deeply observed in the correlation matrix, their position implies possible dilution during high flows, followed by solute mixing during low-flow conditions. The relatively moderate ionic trends here may represent buffered chemical signature, affected by both local recharge and lateral flow contributions. The trends in Cl – and Na + contents may reflect both rainfall input and minor bank infiltration. The southern plantation-dominated catchment margin hosts two wells, viz., VGW-20 and VGW-21, among which VGW-20 shows increased SO 4 2– with recharge and moderate changes in conductivity, while VGW-21 (not explicitly covered in the matrix) is likely influenced by agricultural return flow at the southern boundary. These areas, marked by extensive plantation cover and bare land, may experience variable recharge rates, soil erosion, and enhanced solute migration during early monsoon infiltration (Fohrer et al., 2005). Such patterns can lead to transient enrichment in SO 4 2– and cations, followed by dilution phases. 4.5 Role of groundwater in exporting chemical outputs- The total chemical erosion rates estimated for the KRGS and CRGS catchments (39.2 and 55.9 t km⁻² yr⁻¹, respectively) are notably higher than the global mean value of 24.4 t km⁻² yr⁻¹ (Galy and France-Lanord, 1999), underscoring the efficiency of solute export in these humid tropical mountain systems. These proportions correspond to chemical erosion rates of 21.5 t km⁻² yr⁻¹ and 39.1 t km⁻² yr⁻¹, respectively, highlighting the critical role of groundwater in sustaining weathering-derived solute export, even under varying hydrological regimes. Researchers have compared the chemical erosion rates of different river systems of world in the previous studies (Galy and France-Lanord, 1999; Raj et al., 2023). Seasonally, the elemental fluxes are highest during the SWM, followed by the NEM and PreMon periods, reflecting the coupling between discharge magnitude and solute mobilization. However, during the NEM and PreMon seasons, when surface runoff declines, groundwater contributions become proportionally more significant, maintaining baseflow and continuing mineral dissolution through prolonged water–rock interaction.The higher share of groundwater-mediated solute export suggests that a large part of chemical denudation takes place in the subsurface, within the deep weathering mantle and fractured crystalline rocks of the charnockite–gneiss terrain. Longer residence time of groundwater allows greater water–rock interaction, promoting the dissolution and exchange of ions such as Na⁺ and Ca²⁺, and the formation of secondary minerals. The sequence of elemental fluxes (Na⁺ > Ca²⁺ > K⁺ > Mg²⁺) in both groundwater and surface water pathways points to silicate weathering as the main geochemical process, with only limited input from carbonate or anthropogenic sources. The relatively higher groundwater flux of Na⁺ and Cl⁻ also indicates that baseflow plays a key role in transporting solutes from deeper weathering zones. Similar observations have been reported from other tropical headwater basins, such as the Mule Hole watershed in the southern Western Ghats (Maréchal et al., 2011), where baseflow was found to dominate solute export, and in the work of Peralta-Tapia et al. (2015), which demonstrated the influence of baseflow chemistry on stream composition. The importance of groundwater inputs to weathering processes and broader geochemical cycles has also been recognized in several studies (Shand et al., 2007; Andarani et al., 2021), and recent studies stress the importance of quantifying the contributions from different hydrological compartments to river chemistry (Steefel et al., 2022). The present results confirm that subsurface flow paths strongly control solute generation, linking rapid surface runoff during monsoon periods with slow but continuous dissolution during dry phases. The significant groundwater contribution (55–70%) to total elemental fluxes highlights its key role in catchment-scale chemical erosion. This partitioning between surface and groundwater pathways provides a clearer understanding of solute transport and offers a robust approach for evaluating chemical erosion in tropical mountain basins. 5.0 Conclusions- (1) The PCA of river water chemistry across seasons reveals that geogenic processes, particularly silicate weathering, are the dominant drivers of solute dynamics, modulated by seasonal hydrological variations and anthropogenic inputs. PCA of groundwater chemistry across the three seasons highlights the dynamic interplay between geogenic processes, anthropogenic inputs, and hydrological variability in shaping groundwater composition. (2) The C-Q analysis underscores how KRGS operates as a chemostatic system with consistent solute delivery, whereas CRGS represents a more hydrologically responsive, chemodynamic system. These differences have significant implications for predicting solute export under altered flow regimes and for upscaling event-based measurements to catchment-scale weathering budgets. (3) The CCR of CRGS (3.86 × 10⁵ mol km⁻² yr⁻¹) and KRGS (2.68 × 10⁵ mol km⁻² yr⁻¹) are consistent with values from other medium-sized tropical Western Ghats catchments. The CRGS–KRGS system demonstrates that weathering fluxes do not scale linearly with discharge or catchment area. Instead, solute generation reflects a complex interplay of hydrology, geomorphology, and scale-dependent dilution effects. Results indicate that even small, humid tropical mountainous watersheds can sustain weathering and carbon consumption rates comparable to or exceeding those of much larger river systems. This highlights the disproportionate role of short, steep tropical rivers in global chemical weathering and CO₂ drawdown, driven by their orographic setting, intense rainfall, and rapid hydrological flushing (4) Different hydro-chemical variations with water table depth have been observed in shallow and deep wells. (5) The groundwater plays an important role in chemical erosion at catchment scale, with 55% and 70% of chemical outputs being transported via groundwater pathways in the upstream and downstream catchment respectively. Authorship contribution statement Rajat Kr Sharma: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. M Sekhar: Conceptualization, Resources, Formal analysis, Methodology, Supervision, Writing – review and editing. Sreelash K: Conceptualization, Resources, Formal analysis, Visualization, Writing – review and editing. Padmalal D: Conceptualization, Resources, Supervision, Project administration, Writing – review and editing Funding Sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Authors would like to thank dataset-providing agencies NASA, ISRO, and FAO. The authors thank the Director, National Centre for Earth Science Studies (NCESS), Thiruvananthapuram and Head of Environmental Hydrology Group, NCESS for supporting this experimental study. The authors would like to thank field assistants Arjun BM, Maneesh TJ, and Ratheesh MK for their support in routine maintenance and data collection. 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Keywords c-q analysis chemical erosion co2 consumption rate hydro-chemical response silicate weathering rate tropical experimental catchment Authors Affiliations Rajat Kumar Sharma 0000-0002-6744-930X [email protected] National Centre for Earth Science Studies View all articles by this author Vipin Raj National Centre for Earth Science Studies View all articles by this author Prasenjit Das National Centre for Earth Science Studies View all articles by this author Sreelash K National Centre for Earth Science Studies View all articles by this author Padmalal D National Centre for Earth Science Studies View all articles by this author Muddu Sekhar 0000-0001-9326-1813 Indian Institute of Science Department of Civil Engineering View all articles by this author Metrics & Citations Metrics Article Usage 291 views 125 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rajat Kumar Sharma, Vipin Raj, Prasenjit Das, et al. 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