Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Chemical weathering and carbon dioxide consumption in a small tropical river catchment, southwestern India

Chemical weathering and carbon dioxide consumption in a small tropical river catchment,... Studies done on small tropical west-flowing river catchments located in the Western Ghats in southwestern India have suggested very intense chemical weathering rates and associated CO consumption. Very less studies are reported from these catchments notwithstanding their importance as potential sinks of atmospheric C O at the global scale. A total of 156 samples were collected from a small river catchment in the southwestern India, the Payas- wini–Chandragiri river Basin, during pre-monsoon, monsoon and post-monsoon seasons in 2016 and 2017, respectively. This river system comprises two small rivers originating at an elevation of 1350 m in the Western Ghats in peninsular India. The catchment area is domi- nated by biotite sillimanite gneiss. Sodium is the dominant cation, contributing ~ 50% of the total cations, whereas HC O contributes ~ 75% of total anions. The average anion con- − − 2− − − centration in the samples varied in the range HCO > Cl > SO > NO > F , whereas 3 4 3 + 2+ 2+ + major cation concentration varied in the range Na > Ca > Mg > K . The average sili- −2 −1 −2 −1 cate weathering rate (SWR) was 42 t km   y in the year 2016 and 36 t km   y in 2017. The average annual carbon dioxide consumption rate (CCR) due to silicate rock weathering 5 −2 −1 5 −2 −1 was 9.6 × 10 mol  km y and 8.3 × 10 mol  km  y for 2016 and 2017, respectively. The CCR in the study area is higher than other large tropical river catchments like Ama- zon, Congo-Zaire, Orinoco, Parana and Indus because of its unique topography, hot and humid climate and intense rainfall. Keywords Tropical river system · Water geochemistry · Silicate weathering rate · Atmospheric CO consumption · Southwest coast of India * Keshava Balakrishna k.balakrishna@manipal.edu Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India Department of Marine Geology, Mangalore University, Mangalagangothri 574199, India 1 3 Vol.:(0123456789) 174 Aquatic Geochemistry (2021) 27:173–206 1 Introduction The chemical composition of rivers is derived from diverse sources like weathering of catch- ment rocks and soils, atmospheric deposition and anthropogenic discharges. In unpolluted river waters, lithological characteristics (source rock abundance) dominantly affect the con- centration of major ions and trace elements (Gaillardet et al. 1999). In a hot and humid tropi- cal setup, chemical weathering predominates over physical weathering, which has a bearing on the long-term global climate change. Chemical weathering of a terrain plays a key role in the atmospheric C O consumption. The chemical weathering of a silicate rock converts the atmospheric CO into dissolved inor- ganic carbon and deposits in the form of carbonic sediments in the ocean (Berner 1991) as noted in 2+ − CaSiO + 2CO + H O → Ca + 2HCO + SiO 3 2 2 2 (1) 2+ Ca + 2HCO → CaCO ↓ + H O + CO ↑ 3 3 2 2 There are several factors affecting the rate of chemical weathering, such as the geology of the terrain (rock type), topography (relief), soil cover, discharge, temperature and precipitation (Gaillardet et al. 1999; Huh 2003; Millot et al. 2002, 2003; Oliva et al. 2003; Guo and Wang 2005; Andersson et al. 2006; Moon et al. 2007). Studies were carried out across the world to estimate the chemical weathering rate and CCR of the major world rivers in the past decades, notably Amazon (Stallard and Edmond 1983), Ganges–Brahmaputra (Sarin et al. 1989), Yel- low (Zhang et  al. 1995), Nile (Dekov et  al. 1997), Indus (Ahmad et  al. 1998), Mississippi (Sharif et al. 2008), Mekong (Huang et al. 2009), Tigris (Varol et al. 2013), Yangtze (Huang et  al. 2009; Jiang et al. 2015), Netravathi (Gurumurthy et al. 2012), Kavery (Pattanaik et al. 2013) and Brahmaputra (Das et al. 2016). Tropical rivers are the largest carriers of dissolved and sediment load to the world’s oceans and significantly influence the biogeochemical cycles of elements. Limited studies are done on the tropical systems worldwide, because of their location in the developing and underdevel- oped countries. Meybeck (1987) emphasized the importance of tropical ecosystems and the paucity of data on these systems, though they are responsible for contributing 50% of water, 38% of dissolved ions and 68% of dissolved silica into the global oceans. The objectives of the present study are to partially fill the paucity in data from the tropical systems. Samples were collected from the tropical Payaswini–Chandragiri river basin, southwest coast of India dur- ing pre-monsoon, monsoon and post-monsoon in 2016 and 2017. Currently, no studies have been reported on the major ion chemistry of this river system. This river system is the biggest in northern Kerala state among the 13 river systems draining silicate-rich rock terrains. This study investigated the presence, distribution and source of major ions in the southwest-flowing river and estimated the SWR and associated CCR using the forward model (Wu et al. 2008). These data will add to the database of global silicate weathering rates of world rivers and fill- ing the gaps existing on the silicate weathering and CCR in Indian tropical rivers. 1 3 Aquatic Geochemistry (2021) 27:173–206 175 2 Materials and methods 2.1 Study area The Chandragiri–Payaswini river system (area 1406 km and length 105  km) is located along the southwest coast of peninsular India originating in the Western Ghats. Geographi- cally the study area lies between 74° 48′ E and 75° 45′ E and 12° 18′ N and 12° 32′ N longitudes and latitudes, respectively. These rivers originate at an altitude of about 1350 m above mean sea level (MSL) and join the Arabian sea near Kasaragod town (Fig. 1). Geologically, the basement of the study area belongs to the Archean metamorphics (Fig. 1). The main rock types are granite biotite sillimanite gneiss, charnockite, schist and dolerite. Charnockites, hornblende-biotite gneiss and high-grade schistose rocks are exten- sively lateritized in the lower reaches and dominate the drainage basin of Chandragiri river. There are no reports of the presence of carbonates/evaporates. The study area experiences typical tropical climate with hot (20°–38 °C) and humid conditions (4,000 mm annual rain- fall), high surface runoff (2715 mm for entire west-flowing river catchment) with an annual water discharge of 4.40 km / year (Reddy et al. 2019) (Fig. S1). Anthropogenic activities are minimal in the area, though two plywood industries located near Sullia town, on the banks of Payaswini River, and hospital discharge outlet near Aleti are discharging their effluents into the river (Fig.  1). Kasaragod municipal wastewater is discharged directly into the river estuary. 2.2 Sampling and analysis Twenty-six river water samples were collected in each season, from the mainstream and tributaries (Fig.  1) during pre-monsoon (April), monsoon (August) and post-monsoon (December) seasons for a period of 2 years (2016, 2017). A total of 156 samples were col- lected in six seasons. Water samples were collected from road bridges such that samples are received from the center of the river and in the well-mixed condition. A polypropylene (PP) bucket tied with Nylon rope was dropped to the river for the sample collection. pH, temperature, electric conductivity (EC) and dissolved oxygen (DO) were measured on-site using HACH-make portable multiparameter, calibrated with the standard solution. Locations 1. Jodupala 2. Abhikolli 3. Koyanadu 4. Sampaje 5. Peraje 6. Adikehitilu 7. Doddathotta 8. Paichar 9. Adkar 10. Uddanthadka 11. Aletti 12. Parappa 13. Pandi 14. Panjikkal 15. Adoor 16. Erinjipuzha 17. Pandikandam 18. Karike 19. Karike 20. Panathur 21. Balamthod 22. Kolichal 23. Kallar 24. Kottody 25. Moonnamkadav 26. Periya Fig. 1 Geological map of Payaswini–Chandragiri river basin with sampling locations ( source of the data; 1:20 K geological map of India) 1 3 176 Aquatic Geochemistry (2021) 27:173–206 The samples were stored in pre-cleaned PP-grade bottles (1000  ml). Water samples were filtered through 0.22-µm pore size, 47-mm-diameter Nuclepore polycarbonate filters using a Sartorius-make membrane filtration apparatus in a laminar flow bench and stored at 4 °C for further analysis. The filtered water samples were analyzed for major ions using DIONEX-1100 ion chromatography with autosampler having separate cation–anion sup- pressor and column system. Accuracy of the results was checked with a known concen- tration of the standard solutions, which were within ± _5%. Precision of the results was checked with duplicate samples, which were within ± 3% (Table  S1). Alkalinity of the samples was measured using the standardized HCl titration method using an autotitrator (METROHM TIAMO). Since the pH of all the samples is less than 8.3, the carbonate alka- linity was nonexistent. The end point of bicarbonate alkalinity ranged from 4.8 to 5.5 (Fig. S2), with ± 2% accuracy and precision. Dissolved silica (SiO ) in the river water samples was measured through the UV–Vis spectrometer, HACH DR 5000 by silicon molybdate method at 452 nm wavelengths with a precision of ± 2%. Normalized inorganic charge bal- ance (NICB) was calculated between total dissolved cations (TZ ) and total dissolved ani- ons (TZ ), and the charge balance was within ± 15% (Fig. 2). Above 10% of NICB values of the samples could be due to the presence of organic anions and cations. 3 Result and discussion 3.1 Hydrogeochemistry The physiochemical composition of the Payaswini–Chandragiri river water is tabulated in Table  1. The pH of the river was slightly alkaline in nature and showed a small variation seasonally (6.1–8.3) and spatially. In monsoon season, the average pH was lower than the rest of the seasons, because of the mixing of rain water, which has typical pH of 5.5. The electric conductivity of the river samples varied from 31 to 176 µS/cm in pre-mon- soon, 49–93 µS/cm in monsoon and 31–83 µS/cm in post-monsoons. Total dissolved solid of the samples was calculated from the concentration of obtained major ions and silica. The concentration of TDS in the pre-monsoon varied from 29 to 112  mg/l, 36–80  mg/l in monsoon and 29–84  mg/l in post-monsoon, respectively. The average TDS of Payas- wini–Chandragiri river (60  mg/l) is less than the world’s major rivers (Gaillardet et  al. Fig. 2 Correlation between total anion and total cation 1 3 Aquatic Geochemistry (2021) 27:173–206 177 1 3 Table 1 Physicochemical composition of the Payaswini–Chandragiri river basin + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 1. Jodupala N 12° 26′ 25.99" E 75° 39′ 36.23" Pre-mon- 27.1 7.6 81.0 7.2 217.9 78.5 127.5 158.4 60.0 17.6 11.6 835.2 433.3 868.2 936.5 98.8 − 7.6 soon Monsoon 23.9 6.7 69.4 7.6 132.8 12.7 71.9 109.8 75.7 24.7 9.7 433.1 216.7 508.9 553.6 54.3 − 8.4 Post- 22.0 7.5 59.5 8.2 199.7 20.5 114.8 146.5 63.6 18.0 10.2 590.2 150.0 742.9 692.3 63.4 7.1 mon- soon Pre-mon- 24.0 7.2 64.1 7.4 230.2 17.8 142.0 187.1 70.8 13.4 12.7 780.2 466.7 906.1 890.9 97.1 1.7 soon Monsoon 23.6 6.9 43.6 7.9 127.3 13.5 73.5 88.7 69.8 25.2 8.0 410.5 33.3 465.2 521.6 40.6 − 11.4 Post- 27.1 7.7 61.2 7.4 206.7 17.9 145.3 171.9 67.4 22.7 7.6 659.3 133.3 859.0 764.6 68.6 11.6 mon- soon 2. Abhikolli N 12° 26′ 26.15" E 75° 39′ 02.15" Pre-mon- Dry soon Monsoon 23.1 6.9 70.5 7.8 148.0 35.4 79.8 81.6 61.9 13.7 9.7 368.4 333.3 506.0 463.3 56.4 8.8 2016 178 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 22.0 7.5 58.5 8.2 160.8 36.6 91.0 119.7 66.6 5.9 9.3 456.7 216.7 618.8 548.4 56.6 12.1 mon- soon Pre-mon- 23.6 7.8 64.3 7.3 217.0 48.7 64.3 182.3 63.4 3.2 10.8 482.3 450.0 630.2 571.5 74.1 9.8 soon Monsoon 23.5 7.3 44.6 7.9 148.0 66.9 58.4 64.8 56.5 15.6 7.8 411.0 250.0 461.4 498.6 53.8 − 7.8 Post- 29.8 7.6 66.4 7.1 157.8 11.9 84.1 77.9 61.8 7.2 10.2 456.2 233.3 493.7 545.6 54.7 − 10.0 mon- soon 3. Koyanadu N 12° 28′ 50.55" E75° 34′ 47.70" Pre-mon- 31.2 7.9 95.5 7.5 177.6 22.6 78.8 139.3 58.0 1.0 11.7 647.4 150.0 636.4 730.7 64.2 − 13.8 soon Monsoon 25.2 6.9 81.8 8.1 148.9 12.9 94.5 119.2 79.0 15.8 12.7 526.9 300.0 589.2 647.8 66.1 − 9.5 Post- 23.9 7.7 68.6 8.5 202.9 16.7 261.2 170.2 82.0 6.5 13.2 866.9 133.3 1082 982.5 83.9 9.7 mon- soon Pre-mon- 27.2 7.8 73.6 7.6 242.7 19.8 163.8 218.5 85.2 1.8 14.2 890.2 250.0 1027 1006 92.8 2.0 soon Monsoon 24.9 7.5 53.7 8.0 147.3 14.3 88.5 99.8 74.3 15.7 15.5 471.0 150.0 538.3 592.0 52.9 − 9.5 2017 Aquatic Geochemistry (2021) 27:173–206 179 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 32.5 7.8 82.5 8.3 166.3 14.8 84.9 101.7 78.1 6.3 12.4 503.4 166.7 554.3 634.1 56.0 − 13.4 mon- soon 4. Sampaje N 12° 29′ 37.67" E 75° 33′ 57.45" Pre-mon- 30.9 7.1 65.3 4.1 267.4 21.1 160.6 201.6 66.5 0.2 10.7 832.1 166.7 1012. 921.3 83.1 9.5 soon Monsoon 27.6 6.6 62.2 7.4 165.8 12.0 65.5 91.3 76.7 8.1 10.5 443.3 100.0 491.5 549.5 46.8 − 11.2 Post- 25.1 7.3 47.5 8.0 169.7 12.4 81.9 105.5 81.0 4.7 12.7 430.2 100.0 556.8 541.7 47.2 2.8 mon- soon Pre-mon- 27.7 6.7 49.2 4.4 220.1 23.1 87.3 138.8 103.3 5.1 12.5 520.2 233.3 695.3 654.6 64.5 6.0 soon Monsoon 26.3 6.9 43.2 7.5 146.0 12.7 66.9 76.0 77.0 6.7 9.3 390.2 166.7 444.5 495.6 46.4 − 10.9 Post- 31.8 7.8 56.6 7.8 159.3 18.0 93.1 95.6 83.2 4.0 11.8 450.2 266.7 554.6 561.8 58.2 − 1.3 mon- soon 5. Peraje N 12° 31′ 06.70" E 75° 26′ 13.68" Pre-mon- 36.1 7.2 78.5 6.4 247.1 17.8 143.2 166.4 45.1 4.8 15.3 796.4 116.7 884.1 877.8 75.4 0.7 soon 2016 180 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 26.0 7.0 60.6 7.6 171.9 11.6 63.6 73.8 80.4 11.9 9.6 377.9 66.7 458.3 489.6 40.5 -6.6 Post- 27.2 7.6 48.8 8.1 169.7 12.4 81.9 105.5 81.0 4.7 12.7 430.2 100.0 556.8 541.7 47.2 2.8 mon- soon Pre-mon- 30.6 7.6 61.0 6.8 216.9 21.7 128.9 169.2 92.3 BLD 11.0 700.2 250.0 834.7 815.6 77.8 2.3 soon Monsoon 26.2 7.2 40.4 7.6 162.3 11.7 71.8 53.3 78.5 11.3 7.5 341.8 216.7 424.1 446.6 46.1 -5.2 Post- 37.9 7.4 54.0 7.7 170.8 13.2 60.0 57.6 77.7 2.7 10.0 335.9 66.7 419.2 436.7 36.6 -4.1 mon- soon 6. Adikehitilu N 12° 34′ 59.77" E 75° 30′ 11.93" Pre-mon- 31.2 6.9 53.1 5.0 88.9 0.5 42.2 60.7 38.0 BLD 14.1 262.8 83.3 295.1 329.9 29.3 − 11.1 soon Monsoon 27.6 6.8 49.4 7.5 121.4 10.5 45.8 58.6 81.7 7.6 7.9 290.7 166.7 340.7 395.9 38.5 − 15.0 Post- 25.3 6.5 33.4 8.0 58.7 5.8 81.4 37.2 34.8 BLD 3.4 280.0 116.7 301.6 321.7 30.7 − 6.4 mon- soon Pre-mon- Dry soon 2017 Aquatic Geochemistry (2021) 27:173–206 181 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 25.7 6.1 33.6 7.7 136.3 16.4 49.2 59.9 72.1 5.8 6.5 299.0 166.7 370.8 389.9 39.1 − 5.0 Post- 33.2 6.2 39.9 7.9 136.4 15.5 41.9 55.5 69.8 2.6 7.0 284.3 166.7 346.8 370.7 37.6 − 6.7 mon- soon 7. Doddathotta N 12° 35′ 34.19" E 75° 26′ 15.66" Pre-mon- 28.3 6.4 70.5 2.7 496.3 79.5 184.5 133.1 40.0 4.4 17.0 975.2 216.7 1210 1054 100.1 13.8 soon Monsoon 26.3 6.9 56.7 7.4 133.3 14.8 49.0 72.7 105.3 20.8 11.3 216.7 216.7 391.6 365.7 40.1 6.8 Post- 24.0 7.5 30.8 8.0 136.6 9.2 32.0 45.2 65.0 6.3 6.9 230.0 100.0 300.2 315.1 29.5 − 4.8 mon- soon Pre-mon- 24.2 7.1 39.5 6.8 160.1 25.9 65.3 98.7 96.4 8.6 10.7 380.2 116.7 514.0 507.1 45.4 1.4 soon Monsoon 26.1 7.0 37.8 7.4 140.2 12.4 45.9 58.8 99.3 20.1 9.3 280.0 150.0 362.0 418.0 38.9 − 14.4 Post- 29.3 7.2 38.9 8.0 151.3 39.7 50.3 72.8 91.2 11.4 9.1 378.4 133.3 437.2 499.6 45.1 − 13.3 mon- soon 2017 182 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 8. Paichar N 12° 34′ 06.96" E 75° 23′ 19.60" Pre-mon- 30.4 8.0 175.5 2.3 210.1 27.0 107.2 184.0 235.2 53.6 19.5 589.8 266.7 819.5 920.2 81.4 − 11.6 soon Monsoon 25.9 7.2 60.3 7.6 172.3 14.1 57.5 75.3 95.4 21.6 10.3 354.1 16.7 452.1 492.4 37.2 − 8.5 Post- 25.0 7.1 35.1 7.0 141.9 12.6 71.5 73.0 108.1 6.1 16.0 332.0 133.3 443.4 478.4 42.4 − 7.6 mon- soon Pre-mon- 28.0 7.3 61.2 6.3 256.2 30.5 114.5 150.4 151.9 7.2 12.1 600.2 166.7 816.6 784.6 69.5 4.0 soon Monsoon 26.2 6.8 37.2 7.6 129.3 16.7 64.1 70.5 103.0 20.3 10.0 250.0 33.3 415.1 393.4 31.1 5.4 Post- 29.4 7.3 44.2 7.1 159.3 17.2 77.8 81.1 101.9 8.2 17.0 398.2 100.0 494.3 542.8 45.5 − 9.4 mon- soon 9. Adkar N 12° 34′ 06.96" E 75° 21′ 06.84" Pre-mon- 32.9 7.6 74.6 7.4 341.0 42.4 175.0 237.8 118.6 19.8 16.9 1046.9 66.7 1209 1046 91.1 − 0.4 soon Monsoon 26.5 7.6 61.0 7.8 156.2 13.3 59.4 100.2 91.4 16.4 10.4 436.2 183.3 488.8 565.3 52.4 − 14.5 2016 Aquatic Geochemistry (2021) 27:173–206 183 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 28.1 8.1 47.6 7.6 161.4 19.5 86.5 101.8 101.1 13.9 15.8 380.2 266.7 557.6 527.5 41.8 5.5 mon- soon Pre-mon- 28.6 6.9 58.2 5.8 228.3 39.7 115.4 153.8 136.4 16.6 11.0 580.2 183.3 806.4 739.3 68.0 8.7 soon Monsoon 26.1 6.9 41.4 7.7 157.7 14.8 61.4 88.3 87.4 16.2 9.3 375.4 150.0 421.8 497.5 45.1 − 5.3 Post- 34.9 8.0 51.2 7.0 150.0 21.4 83.8 94.4 92.4 10.0 12.8 430.2 183.3 527.6 558.7 42.4 − 5.7 mon- soon 10. Uddanthadka N 12° 34′ 44.37" E 75° 22′ 04.11" Pre-mon- Dry soon Monsoon 26.5 6.5 67.3 7.9 92.1 6.3 51.3 132.5 76.0 16.2 7.8 430.7 150.0 461.7 539.3 48.5 -14.6 Post- 25.0 6.6 44.3 8.0 183.6 10.2 92.3 105.6 84.2 7.0 15.6 410.2 183.3 589.4 532.6 52.0 10.1 mon- soon Pre-mon- 29.0 7.0 69.8 7.2 174.3 25.7 102.1 122.5 65.2 1.6 15.5 460.5 200.0 649.2 554.0 56.2 14.8 soon Monsoon 26.6 7.0 65.9 7.4 87.7 8.0 58.5 117.5 75.3 16.6 8.4 407.0 116.7 447.5 516.4 44.8 -14.3 2017 184 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 34.7 7.1 45.7 8.1 159.9 15.5 116.3 90.0 69.7 4.0 14.0 564.6 133.3 588.0 675.2 57.3 -13.8 mon- soon 11. Aletti N 12° 32.851′ E 75° 23.575′ Pre-mon- 35.1 7.5 69.5 9.1 253.6 39.5 165.1 203.3 68.9 BLD 8.3 810.2 116.7 1029 897.0 79.2 13.8 soon Monsoon 26.5 7.2 62.9 7.5 136.4 13.0 63.9 84.8 98.2 14.9 10.2 379.7 50.0 446.8 513.2 40.1 − 13.8 Post- 27.7 7.1 46.6 7.6 161.4 16.3 45.7 103.2 86.8 7.2 10.5 410.2 116.7 475.5 525.8 46.1 − 10.1 mon- soon Pre-mon- 30.1 7.2 56.2 7.0 209.3 21.4 115.7 149.5 105.2 BLD 8.6 620.2 166.7 761.0 743.6 66.8 2.3 soon Monsoon 25.6 7.0 40.5 7.7 131.0 13.2 60.3 70.2 82.4 13.2 8.4 340.2 33.3 405.0 452.6 35.1 − 11.1 Post- 37.2 7.1 52.4 7.6 158.1 21.5 85.3 91.3 83.6 6.4 9.6 508.5 83.3 532.8 618.1 50.5 − 14.8 mon- soon 12. Parappa N 12° 34′ 57.96" E 75° 14′ 47.84" Pre-mon- Dry soon 2016 Aquatic Geochemistry (2021) 27:173–206 185 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 28.6 6.5 56.2 7.7 166.6 16.7 106.0 74.2 92.7 15.6 10.5 472.6 183.3 543.7 602.3 55.1 − 10.2 Post- 29.2 6.4 37.8 8.4 165.7 13.3 62.0 68.0 17.3 1.7 3.4 391.1 183.3 438.9 417.0 44.4 5.1 mon- soon Pre-mon- 26.9 7.1 58.0 7.0 151.7 22.9 37.4 147.5 66.4 2.5 10.8 427.7 150.0 544.4 518.6 49.8 4.9 soon Monsoon 23.8 6.9 55.9 7.9 149.2 14.1 85.2 65.5 84.5 4.3 10.9 377.9 216.7 464.7 488.9 49.0 − 5.1 Post- 33.9 7.0 40.0 7.7 168.7 14.0 101.3 97.3 87.3 3.8 11.6 471.3 133.3 579.9 590.2 52.0 − 1.8 mon- soon 13. Pandi N 12° 32′ 54.51" E 75° 14′ 19.47" Pre-mon- Dry soon Monsoon 27.0 6.7 59.0 7.7 145.9 12.6 62.5 68.1 66.6 9.8 6.8 376.2 166.7 419.7 466.8 44.7 − 10.6 Post- 27.9 7.0 36.8 7.6 169.3 10.3 52.4 68.6 10.6 6.1 12.4 387.4 166.7 421.5 429.5 43.9 − 1.9 mon- soon Pre-mon- 24.8 6.8 38.0 174.3 34.1 43.1 72.6 29.4 3.8 2.4 410.8 116.7 439.8 449.5 42.9 − 2.2 soon 2017 186 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 27.5 6.9 40.3 7.5 143.6 10.0 68.0 65.2 108.0 15.6 9.2 290.0 216.7 419.8 440.5 44.6 − 4.8 Post- 31.0 7.0 45.1 7.3 177.1 18.3 60.2 76.9 106.0 5.0 12.1 400.2 166.7 469.6 535.4 49.0 − 13.1 mon- soon 14. Panjikkal N 12° 34′ 12.71" E 75° 15′ 58.89" Pre-mon- 30.6 7.0 58.3 8.2 507.9 33.4 165.0 220.6 67.7 BLD 9.0 1260.3 66.7 1312 1348 110.0 − 2.7 soon Monsoon 25.6 7.0 61.4 7.4 139.3 5.3 50.7 182.1 97.7 18.8 11.1 491.8 116.7 610.0 631.5 54.6 − 3.5 Post- 29.9 7.9 46.3 8.4 184.7 18.7 81.3 99.7 100.2 12.3 14.8 370.2 183.3 565.3 513.0 50.2 9.7 mon- soon Pre-mon- 33.7 8.1 56.1 8.4 243.7 24.7 110.5 138.3 134.9 5.9 17.4 560.2 66.7 765.9 735.7 59.7 4.0 soon Monsoon 26.8 7.2 40.5 7.6 136.0 15.1 74.0 73.7 87.8 16.0 9.0 390.2 66.7 446.6 511.9 41.2 − 13.6 Post- 34.3 7.6 51.5 7.8 132.9 12.8 62.7 173.4 93.0 10.0 14.3 456.2 216.7 618.0 587.8 58.1 5.0 mon- soon 2017 Aquatic Geochemistry (2021) 27:173–206 187 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 15. Adoor N 12° 33′ 51.79" E 75° 14′ 45.85" Pre-mon- 32.6 8.1 113.6 6.6 307.2 28.0 128.7 128.5 104.4 28.6 14.8 704.6 200.0 849.5 869.6 78.3 − 2.3 soon Monsoon 26.5 7.1 61.0 7.5 152.8 13.6 81.3 46.2 89.5 15.4 9.2 330.2 50.0 421.3 453.4 36.0 − 7.3 Post- 28.9 7.7 46.2 7.9 160.0 8.3 79.8 52.3 47.1 5.4 7.1 361.5 233.3 432.6 428.3 46.8 1.0 mon- soon Pre-mon- 34.4 7.7 60.3 7.9 248.9 26.5 113.5 153.1 134.5 7.5 21.5 590.2 33.3 808.6 775.0 60.9 4.2 soon Monsoon 27.0 7.5 41.2 7.6 139.8 13.6 60.4 71.2 89.5 15.4 9.2 330.2 33.3 416.6 453.4 35.2 − 8.5 Post- 33.7 7.5 51.9 7.5 154.3 18.2 83.5 91.3 91.7 4.6 12.2 420.2 233.3 522.0 540.8 54.3 − 3.5 mon- soon 16. Erinjipuzha N 12° 29′ 40.54" E 75° 09′ 24.93" Pre-mon- 35.1 8.3 84.5 8.1 223.3 19.9 131.4 130.4 113.6 BLD 13.3 657.0 66.7 766.7 799.1 63.7 − 4.1 soon Monsoon 26.4 7.4 60.9 7.9 152.3 7.0 128.7 124.4 100.5 16.0 11.1 580.2 200.0 665.6 719.5 64.9 − 7.8 Post- 29.8 7.5 46.0 8.0 165.7 3.0 59.5 70.3 19.0 1.7 3.1 472.1 33.3 428.2 499.6 40.1 − 15.4 mon- soon 2016 188 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Pre-mon- 33.8 7.7 59.3 7.6 212.7 19.6 103.3 124.1 141.7 BLD 22.1 560.2 50.0 687.1 746.7 57.4 − 8.3 soon Monsoon 27.3 7.2 42.1 7.7 136.7 11.7 73.8 68.5 92.1 16.7 10.0 330.2 200.0 433.0 459.0 45.5 − 5.8 Post- 35.4 7.6 52.2 7.7 168.7 12.4 101.1 86.9 92.8 4.1 11.0 500.2 66.7 557.0 619.1 49.4 − 10.0 mon- soon 17. Pandikandam N 12° 28′ 20.60" E 75° 07′ 02.76" Pre-mon- 36.3 7.6 128.2 7.6 507.9 33.4 165.0 220.6 296.8 BLD 23.0 1082.5 116.7 1312 1425 111.5 − 8.2 soon Monsoon 26.6 7.3 59.8 7.8 180.9 12.9 58.3 76.2 86.5 19.8 11.5 394.9 83.3 462.6 524.1 43.6 − 12.5 Post- 30.0 8.0 45.4 8.1 119.7 11.4 68.3 49.4 40.4 10.0 6.4 331.0 133.3 366.5 394.4 37.7 − 7.4 mon- soon Pre-mon- 32.0 7.4 54.6 6.7 207.1 26.1 106.3 129.7 131.2 BLD 14.3 530.2 100.0 705.1 691.5 57.9 1.9 soon Monsoon 27.3 7.6 40.9 7.7 573.3 13.6 61.5 73.1 291.8 16.0 10.0 573.3 250.0 856.0 901.1 80.4 − 5.1 2017 Aquatic Geochemistry (2021) 27:173–206 189 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 35.5 7.7 51.5 7.6 165.0 17.8 82.2 92.9 90.8 5.3 12.3 497.4 133.3 519.4 618.2 53.0 − 14.8 mon- soon 18. Karike N 12° 26′ 19.85" E 75° 27′ 56.59" Pre-mon- Dry soon Monsoon 27.6 7.2 93.2 7.6 128.2 9.9 79.9 77.2 91.7 6.7 12.5 398.2 233.3 452.4 522.4 51.5 − 14.3 Post- 23.9 7.5 52.9 8.4 135.7 13.5 76.3 77.5 60.1 1.2 8.2 440.2 66.7 456.8 517.9 42.4 − 12.6 mon- soon Pre-mon- Dry soon Monsoon 24.5 7.6 42.1 7.5 127.9 9.0 91.8 72.8 81.1 5.6 8.2 350.2 116.7 466.1 453.3 40.8 2.8 Post- 30.5 7.6 54.7 7.9 151.9 10.6 115.7 103.0 81.9 2.5 9.6 551.0 66.7 599.9 654.9 52.4 − 8.8 mon- soon 19. Karike N 12° 26′ 47.83" E 75° 25′ 06.56" Pre-mon- 33.2 8.0 74.2 7.4 133.3 13.1 85.8 120.7 31.5 32.0 27.6 500.5 150.0 559.4 619.7 55.8 − 10.2 soon 2016 190 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 27.8 7.3 66.4 7.7 157.3 7.6 125.3 74.6 86.7 10.0 10.3 545.4 216.7 564.7 662.7 60.9 − 13.0 Post- 21.7 7.8 48.4 8.4 140.1 10.2 100.8 106.1 70.1 5.9 15.9 490.2 66.7 563.9 605.6 48.7 − 7.1 mon- soon Pre-mon- 27.4 7.6 44.8 7.6 165.2 10.2 107.6 109.5 66.0 BLD 16.1 510.2 300.0 609.5 609.5 64.2 0.1 soon Monsoon 24.4 7.8 43.7 9.1 122.6 8.5 80.6 69.7 69.4 7.4 7.8 400.2 100.0 431.6 492.6 42.0 − 13.2 Post- 30.2 7.7 54.4 8.3 158.1 12.9 114.6 92.9 69.2 5.5 14.1 500.2 100.0 586.0 603.7 51.3 − 3.0 mon- soon 20. Panathur N 12° 27′ 26.17" E 75° 21′ 36.85" Pre-mon- 35.2 7.3 64.7 6.9 202.7 24.2 125.5 122.6 81.1 BLD BLD 780.2 266.7 722.9 781.3 77.1 − 7.8 soon Monsoon 26.1 7.4 60.8 7.6 130.3 24.1 76.3 89.9 75.2 15.9 9.6 440.7 116.7 470.3 551.1 47.6 − 12.4 Post- 25.3 7.6 51.2 7.7 178.1 16.7 114.5 109.2 80.7 8.0 12.1 470.2 216.7 642.2 583.6 58.1 9.6 mon- soon Pre-mon- 29.9 7.7 50.5 6.3 183.1 14.5 118.2 116.5 83.5 BLD 15.5 550.2 216.7 666.9 665.4 63.3 0.2 soon 2017 Aquatic Geochemistry (2021) 27:173–206 191 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 25.4 7.5 42.6 8.0 171.7 8.5 77.5 67.2 71.2 11.2 8.0 420.2 50.0 426.0 518.6 40.4 − 9.9 Post- 35.9 7.4 57.0 7.9 142.8 12.4 104.8 81.3 78.6 5.2 11.4 500.2 166.7 527.3 607.2 54.3 − 14.1 mon- soon 21. Balamthod N 12° 27′ 38.34" E 75° 19′ 17.58" Pre-mon- 32.7 7.8 69.3 6.6 214.0 20.0 107.8 99.4 84.8 BLD 13.6 627.0 283.3 648.2 740.5 71.9 − 13.3 soon Monsoon 26.4 6.5 58.8 7.7 214.0 20.0 107.8 99.4 56.6 BLD BLD 620.2 233.3 648.2 678.1 66.1 − 4.5 Post- 28.0 7.0 46.7 7.6 105.3 8.7 119.9 82.1 61.2 4.7 9.4 450.2 166.7 518.0 535.5 49.8 − 3.3 mon- soon Pre-mon- 30.0 7.1 51.4 6.0 195.0 19.4 118.3 121.9 102.3 BLD 13.4 540.2 183.3 694.8 669.9 61.8 3.7 soon Monsoon 26.6 6.4 41.8 7.8 154.3 7.4 65.9 65.9 77.8 14.1 8.3 370.2 116.7 407.8 478.7 41.7 − 11.8 Post- 34.7 7.1 49.8 7.6 156.4 14.1 96.0 82.2 81.5 7.3 11.4 501.8 166.7 513.8 613.2 54.5 − 15.2 mon- soon 2017 192 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 22. Kolichal N 12° 26′ 45.29" E 75° 17′ 27.99" Pre-mon- Dry soon Monsoon 26.5 6.6 62.4 7.6 168.6 9.0 70.0 73.8 99.3 37.3 10.6 344.6 183.3 421.5 502.5 46.7 − 7.8 Post- 25.0 6.6 36.0 8.2 157.2 10.2 62.5 69.6 117.2 22.7 12.9 260.0 183.3 431.6 425.8 42.0 1.3 mon- soon Pre-mon- Dry soon Monsoon 26.6 6.6 41.2 7.8 157.8 8.6 65.1 65.3 99.7 29.8 10.3 347.4 133.3 427.1 497.5 43.7 − 15.2 Post- Dry mon- soon 23. Kallar N 12° 25′ 50.53" E 75° 16′ 33.51" Pre-mon- 36.7 6.4 43.8 7.5 52.9 79.5 59.5 135.6 40.0 4.4 17.0 483.4 66.7 522.5 562.5 48.0 − 7.4 soon Monsoon 26.7 6.7 60.6 9.6 139.3 31.0 9.0 182.1 97.7 18.8 11.1 491.8 33.3 552.3 631.5 49.6 − 13.4 2016 Aquatic Geochemistry (2021) 27:173–206 193 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 24.5 6.7 42.4 7.5 90.5 8.2 116.6 38.4 52.3 10.2 7.8 280.0 133.3 408.8 358.7 35.1 13.1 mon- soon Pre-mon- 36.7 7.0 57.0 7.4 130.8 12.7 108.5 76.0 48.8 6.7 9.3 390.2 100.0 512.7 467.5 42.1 9.2 soon Monsoon 26.7 7.1 44.7 7.4 137.0 9.7 115.8 68.0 107.0 34.6 12.8 360.2 50.0 514.2 527.5 41.2 − 2.5 Post- Dry mon- soon 24. Kottody N 12° 26′ 06.47" E 75° 13′ 28.42" Pre-mon- 29.0 7.1 62.2 6.0 175.9 46.5 72.1 51.4 68.4 14.2 10.5 417.9 150.0 469.4 522.6 48.5 − 10.7 soon Monsoon 25.8 7.3 60.3 7.8 225.6 33.5 104.2 68.4 88.8 20.5 9.8 538.2 250.0 434.0 667.1 64.9 − 9.9 Post- 27.5 7.6 44.5 8.2 166.6 17.4 90.7 89.7 99.4 10.3 12.7 380.2 116.7 544.9 516.1 39.9 5.4 mon- soon Pre-mon- 28.7 7.0 50.8 5.8 197.8 18.4 113.3 117.1 110.9 BLD 11.5 510.2 166.7 677.0 644.9 52.8 4.9 soon Monsoon 25.7 7.1 42.4 7.9 157.1 9.0 65.9 75.9 90.6 20.4 8.3 320.2 166.7 449.7 447.9 43.4 0.4 2017 194 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 35.2 6.4 52.0 7.2 349.0 13.9 104.4 76.5 86.3 11.3 12.0 630.2 116.7 724.8 751.7 58.5 − 3.6 mon- soon 25. Moonnamkadav N 12° 25′ 41.44" E 75° 07′ 42.05" Pre-mon- Dry soon Monsoon 27.2 7.4 57.1 7.7 137.0 39.8 59.5 77.2 96.9 24.3 9.6 386.2 266.7 446.0 527.1 54.6 − 15.7 Post- 27.7 7.3 42.5 8.1 121.3 9.9 79.9 44.5 6.5 3.7 5.2 404.6 66.7 380.0 426.0 36.5 − 11.4 mon- soon Pre-mon- 28.5 7.2 49.2 6.6 173.0 48.8 111.3 110.6 123.1 BLD 4.8 510.2 50.0 665.4 643.0 51.9 3.4 soon Monsoon 26.0 7.2 44.3 8.0 167.3 1.4 91.9 57.8 103.6 23.6 8.4 384.6 166.7 468.0 528.7 47.8 − 12.2 Post- 31.8 7.4 48.5 7.5 131.1 25.7 83.8 48.0 96.8 7.8 11.1 331.0 100.0 420.2 458.6 39.1 − 8.7 mon- soon 26. Periya N 12° 25′ 40.57" E 75° 07′ 20.99" Pre-mon- Dry soon 2016 Aquatic Geochemistry (2021) 27:173–206 195 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 29.0 6.8 56.8 7.7 169.4 16.0 63.8 70.0 75.5 19.7 9.9 374.6 116.7 452.9 489.6 43.6 − 7.8 Post- 28.3 6.4 50.1 7.9 140.0 17.4 79.0 79.7 31.9 2.9 1.4 400.2 33.3 474.7 437.6 36.8 8.1 mon- soon Pre-mon- 24.5 7.7 58.9 6.8 164.7 17.7 124.6 24.5 81.4 BLD 16.3 425.7 83.3 480.6 539.6 43.9 − 11.6 soon Monsoon 26.7 7.3 57.9 8.9 130.7 21.6 54.3 94.0 79.8 21.7 10.4 377.9 166.7 449.0 500.2 47.1 − 10.8 Post- 35.8 7.5 52.0 7.9 212.9 10.6 119.9 130.9 73.5 2.5 10.0 570.2 66.7 725.0 666.2 55.9 8.5 mon- soon + − where dry: no flow, BLD: below detection limit, TDS = (Na + K + Ca + Mg + Cl + F + SO + NO + HCO + SiO), TZ = total dissolved cation, TZ = total dissolved anion, 4 3 3 2 − + NICB = ((TZ − TZ )/ Mean) × 100% (Gurumurthy et al. 2012). All the samples are in the range of less than 15% 196 Aquatic Geochemistry (2021) 27:173–206 1999) and adjacent river Nethravati (Gurumurthy et al. 2012) but higher than Amazon river (44 mg/l) (Stallard and Edmond, 1983). The TDS (and EC) increases toward the outlet spa- tially, because of the increasing sea-salt influence. The mean concentration of major ions in the study area was plotted in bar dia- gram (Fig.  3). The abundance of the major cations in the samples varied in the order of + 2+ 2+ + + Na > Ca > Mg > K in all the seasons. N a is the major cation dominating in the study area. The concentration of Na was ranging from 1.2 to 13  mg/l during the study period. The main source of the sodium ion is Na- plagioclase (albite) weathering. The con- centration of K temporally varied from 0.02 to 3.1  mg/l throughout the study area. The + 2+ major source is from the K feldspar of basement rock. Ca was the second dominant 2+ ion in the study area. The Ca concentration was ranging from 1 to 10 mg/l in the study area. The source of calcium is from the basement rock biotite–sillimanite gneiss. Major 2+ cation Mg was showing temporal variation from 0.2 to 6.3  mg/l in their concentration. The main source of magnesium is from the weathering of ferromagnetic minerals. The average anion concentration in the samples varied in the range − − 2− − − HCO > Cl > SO > NO > F . Bicarbonate concentration ranged from 13 to 77  mg/l 3 4 3 in the river system. Average value of HC O of the river Payaswini–Chandragiri is higher than the global average (Gaillardet et al. 1999), Yamuna (Dalai et al. 2002), Kaveri (Pat- tanaik et al. 2013), Brahmaputra (Galy and France-Lanord, 1999) and river Congo (Dupre et  al. 1996). The major source of HC O ion is from the weathering of silicate rocks in the catchment. Rainwater reacts with atmospheric C O and soil CO and leaches the sili- 2 2 cate rocks, leading to the release of HCO . The major contribution of chloride is from the atmospheric deposition. The concentration was showing high temporal variation, with maximum concentration of 11 mg/l recorded during the pre-monsoon season. The concen- tration was ranging from 0.2 to 11 mg/l in the study area. The higher concentration of chlo- ride in the pre-monsoon season could be because of the low discharge in this season couple with pre-concentration due to evapotranspiration. The silica concentration in the study area ranged from 2 to 28  mg/l in pre-monsoon; 1–20 mg/l in monsoon; and 1–16 mg/l during the post-monsoon, respectively. The source of SiO in the study area is from the weathering of catchment silicate rocks (Fig. 1). Minimal concentration of major ions was observed during the monsoon season in the study area, which suggested a high level of dilution due to mixing of rainwater. The Fig. 3 Bar diagram of major ions concentration (μmol/l) of the Payaswini–Chandragiri river in different seasons 1 3 Aquatic Geochemistry (2021) 27:173–206 197 pre-monsoon sample showed a high level of concentration in all major ions compared to the other two seasons. The yearly mean TDS of this river system (55 mg/l) is less than half the concentration of the world average rivers (120 mg/l- Gaillardet et al. 1999). During the post-monsoon, major contribution to the river water is from the groundwater discharge. The leaching of terrain rock into the river water through groundwater was the reason for the high ionic concentration in the post-monsoon season in the study area (Thomas et al. 2015). The spatial variable can be noticed in water geochemistry in all the seasons, which is mainly due to the influence of runoff from the different regions. 3.2 Major ion chemistry in Payaswini–Chandragiri river basin The percentage concentration of major ions is plotted in ternary plot (Fig. 4) to evaluate the dominance of ions and percentage concentration of major ions in Payaswini–Chandragiri river water. 2+ On the cation plot (Fig.  4a), most of the samples lie in between the Na + K and Ca + 2+ region. The dominance of N a and Ca in Payaswini–Chandragiri river water indicates silicate dominant lithology. Forty percentage of the total cations was contributed by N a , 2+ 2+ + whereas Ca, Mg and K contributed 35%, 19% and 6%, respectively. − − In the anion plot (Fig.  4b), the samples plot in between the HC O and Cl region. − − 2− HCO, Cl and SO contributed 85%, 12% and 3%, respectively. Percentage of anion 3 4 concentration indicated that the carbonic acid weathering was contributing to high concen- tration of ions into the river basin, whereas sulfuric acid weathering was negligible. 3.3 Source of major ions in the Payaswini–Chandragiri river basin The dissolved major ion concentration in Payaswini–Chandragiri river water was mainly derived from weathering of basin rocks, atmospheric deposition, anthropogenic activities and biomass deposit (2). X river = X + X + X + X weathering atmospheric precipitation anthropogenic deposition biomass contribution (2) Fig. 4 Ternary plot of a major cations and b major anions in the river water 1 3 198 Aquatic Geochemistry (2021) 27:173–206 where X = X + X weathering carbonate weathering silicate weathering. According to Krishnaswami and Singh (2005), at steady state, plant uptake and its decay may not change the ionic budget of the river water, thus indicating negligible contribution from the biomass. 3.3.1 Atmospheric deposition to the river basin The scatter plot of Na/Cl vs Cl (Fig. 5) explains the major ion contribution by rainwater to the river water. The cyclic salt input correction (Stallard and Edmond 1981, 1983) deducts atmospheric deposition of Cl ions from the river water. This is given in Eq:3. Sea salt corrected ion =(X −Cl )∗ (X∕Cl) (3) river river rain where X = major ion concentration measured in the river water. river The atmospheric contribution from the rain water is corrected using weighted mean − + 2+ + 2+ value of published rain water data (Cl = 47; Na = 45; Ca = 20; K = 5; Mg = 7 and 2− SO = 9 μmol/l) of Western Ghats (Gurumurthy et al. 2012). In the Payaswini–Chandra- giri river system, approximately 50% of N a was contributed from the atmosphere to the downstream region (S1) because of its proximity to the Arabian sea. The river water Na/Cl molar ratio was higher than 1, which indicated that Na was sourced from the catchment bedrock (Hem 1985; Meybeck 1987). 3.3.2 Major ions from the anthropogenic deposition Anthropogenic sources like domestic and industrial sewage and agricultural effluents can modify the ion concentrations of river water (Sun et  al. 2010; Han et  al. 2010; Liu et  al. − 2− − 2018). Cl, SO and NO are the major ions associated with the anthropogenic activ- 4 3 ity, and they are used as proxies to identify anthropogenic activities in various watersheds (Shin et al. 2011). The influence of anthropogenic activities on the river in the environment was calculated based on the percentage of pollution as stated by Pacheco and Van der Weijden (1996). Fig. 5 Scatter plot Na/Cl versus Cl in Payaswini-Chandragiri river basin 1 3 Aquatic Geochemistry (2021) 27:173–206 199 Fig. 6 Variation of pollution percentage versus Na/Cl (Faso et al. 2018) % pollution = Cl + SO + NO ∕ Cl + SO + NO + HCO ∗ 100 (4) 4 3 4 3 3 The percentage of pollution was calculated in every sample and plotted against the Na/Cl ratio (Fig.  6). The areas having ratio ≥ 40% were dominated by pollution from anthropo- genic activities, while those with ≤ 40% were dominated by the rock weathering process. The figure shows all the samples lie within the limits, indicating that the river basin is not affected by anthropogenic activities. So the contribution of major ions, especially chloride, to the Payaswini–Chandragiri river water from the anthropogenic inputs is negligible. 3.3.3 Major ions from the rock weathering Major ion chemistry of Payaswini–Chandragiri river system is dependent on various 2+ 2+ − natural processes. The ionic ratio of (Ca + Mg )/HCO in the river water varied from 0.20 to 0.25 suggesting the significance of chemical weathering of silicate rock in − 2+ the Payaswini–Chandragiri river hydrochemistry. The ionic ratio of HC O /Ca was higher than 7 in all the seasons, also indicating the dominance of silicate weathering 2+ 2+ + in the study area (Holland 1978). The ratios of Ca and Mg versus N a were used to calculate the relative concentration from the bedrock (Thomas et  al. 2014). The ratios 2+ 2+ of these indicated that the Ca and Mg were dominated by silicate rock weathering. The mixing plot of the atmospheric input corrected Na/Ca versus Mg/Na (Fig.  7) molar ratios suggested that the Payaswini–Chandragiri river water was influenced by the 2+ 2+ + + water–silicate rock interaction. The low mean ionic ratios of (Ca + Mg )/(Na + K ) − + + and HCO /(Na + K ) also confirmed that the basin was dominated by the silicate rock weathering. The degree of rock–water interaction varied seasonally depending upon the climatic condition (temperature, humidity and rainfall), leading to temporal variation in the concentration of silicate-derived ions. Samples collected in all seasons were plotting in the silicate weathering region. 1 3 200 Aquatic Geochemistry (2021) 27:173–206 Fig. 7 Mixing diagram of + 2+ 2+ normalized Na, Ca and Mg , the molar ratio (Gaillardet et al. 1999) 3.4 Silicate weathering rate and carbon dioxide consumption rate 3.4.1 Silicate weathering rate Silicate weathering rates of the study area for pre-monsoon, monsoon and post-monsoon in 2016 and 2017 were calculated by using the forward model (Wu et al. 2008), the product of discharge per unit area and concentrations of major elements (Eq. 5). SWR = Q (Na + K + Mg + Ca) + SiO (5) sil 2 where (Na + K + Mg + Ca) = dissolved cations derived from silicate weathering, Q = water sil discharge per unit area. + + During chemical weathering, it is assumed that N a and K are derived from the feld- 2+ 2+ spar minerals and Ca and Mg from the pyroxene minerals. After the atmospheric and anthropogenic corrections, remaining cations are from the chemical weathering of major 2+ 2+ rocks in the river basin. So, silicate-derived Ca and Mg were calculated from the gneissic and charnockite rock, based on the following equations. Ca from silicate rock = Na ∗∕(Ca∕Na) (6) bedrock Mg from silicate rock = Na ∗∕(Mg∕Na) (7) bedrock where Na* is corrected Na mean concentration from atmospheric inputs. In this study, the basement rock was completely of gneissic and charnockite origin, and thus, the value of Ca/Na was 0.41 and Mg/Na was 0.325, respectively. These ratios for the bedrock were obtained through a compilation of previous literature mentioned in Gurumurthy et  al. (2012). The silicate weathering rate of the Payaswini–Chandragiri river system was calculated based on the major ion composition at Adoor and Kottody. Estimated seasonal and annual val- ues are given in Table 2. The average annual silicate weathering rate of Payaswini–Chandra- −2 −1 −2 −1 giri river basin was 42 t km y and 36 t km y in 2016 and 2017, respectively. The river −2 −2 system shows higher SWR in monsoon season (37 t km in 2016 and 26 t km in 2017) 1 3 Aquatic Geochemistry (2021) 27:173–206 201 Table 2 Silicate weathering rate Seasons 2016 2017 and CO drawdown rate of the study area in various seasons −2 SWR (t km ) Pre-monsoon 0.63 0.39 Monsoon 37.30 25.97 Post-monsoon 8.24 9.92 Annual 42.17 36.28 5 −2 CCR (×  10 mol  km ) Pre-monsoon 0.13 0.10 Monsoon 7.97 5.83 Post-monsoon 1.45 2.34 Annual 9.55 8.28 −2 −1 with 82% of total discharge. The estimated annual SWR (39 t km y ) of this study was 0.9 −2 −1 times lower than the adjacent southwest-flowing river Netravathi (42 t km y ; Gurumurthy et al. 2012), mainly due to the runoff and variations in the drainage area. Comparing this study with the other Indian rivers, the silicate weathering flux of the Payaswini–Chandragiri river was higher than the other Indian rivers. The silicate weathering rates of the Himalayan river −2 −1 systems such as Ganga [10.2–15.2 t km y , (Krishnaswami et  al. 1999; Gaillardet et  al. −2 −1 1999; Dalai et al. 2002)], Indus [3.8 t km y , (Gaillardet et al. 1999)]; Bramhmputra [6.47 −2 −1 −2 −1 t km y , (Das et al. 2016)], Narmada [12.67 t km y ; (Gupta et al. 2011)], Tapti (7.32 −2 −1 −2 −1 t km y ), Kavery [9.44 t km y , (Pattanaik et  al. 2013)] are all lower than the Payas- wini–Chandragiri river system (Table 3). And the annual SWR of the study area was higher −2 −1 than the river Mahanadi [32 t km y , (Bastia and Equeenuddin 2019)] and Swarnamukhi −2 −1 river [30.57 t km y (Patel et al. 2020)]. Also, SWR of the study area was higher than the −2 −1 −2 −1 −2 global watersheds like Amazon (13 t km y ), Mackenzie (1.8 t km y ), Parana (5 t km −1 −2 −1 −2 −1 −2 −1 y ), Congo-Zaire (4.2 t km y ), Orinoco (9.5 t km y ), Mekong (14.3 t  km  y ) and −2 −1 Rio Icacos (40 t km   y ) (Gaillardet et al. 1999). The southwest-flowing rivers show less SWR as compared to the rivers draining basaltic rock rivers, though are higher than the other Himalayan rivers. The resultant weathering vari- ability could be due to variable lithologies in the Indian river basin and the climatic difference in the tropical region. Higher runoff, temperature and basin formation are the main control- ling factors of high SWR in the southwest-flowing rivers. The southwest-flowing rivers show higher SWR comparing to the east-flowing rivers in monsoon season, Krishna Basin and the −2 −1 Western Ghats of the Deccan Traps [53 t km y (Pattanaik et al. 2013; Gurumurthy et al. 2012)], due to intense rainfall and resulting higher runoff (Pattanaik et al. 2013). The large silicate weathering rate was mostly resulting from the high physical erosion rate (thus exposing the rock surface) in the basin due to high-intensity rainfall (4000 mm/y) (Vinu- tha 2014). Runoff, granitic terrain, morphology of the study area were the prime factors con- trolling the silicate weathering rate. Tropical climate speeds up the weathering of silicate min- erals and CO sequestration in the Western Ghats, while high runoff contributes to excessive bicarbonates into the Arabian Sea (Reddy et al. 2019). 3.4.2 Carbon dioxide consumption rate (CCR) during silicate rock weathering The intense silicate weathering leads to the drawdown of atmospheric CO . The actual rate of carbon dioxide consumption during silicate rock weathering is calculated using the fol- lowing equation: 1 3 202 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 3 Comparison of silicate weathering rate and associated CO drawdown rate of the Payaswini–Chandragiri river catchment with other rivers River basin Discharge Area SWR CO sil References 3 3 2 −2 −1 5 −2 −1 (km /year) (10 km )(t km year ) (10 mol  km year ) Ganga, Rishikesh 22.4 20 12.9 3.80 Krishnaswami and Singh (1998) Amazon 6590 6112 13 0.52 Gaillardet et al. (1999) Brahmaputra 510 580 10.3 1.50 Ganges 493 1050 14 4.50 Mississippi 580 2980 3.8 0.70 Indus 90 916 3.8 0.60 Yangtze 928 1808 5.5 0.60 Mackenzie 308 1787 1.8 0.34 Yamuna 10.8 10 28 7.00 Bhima 34 12 3.30 Dalai et al. (2002) Gad 1 40 5.70 Krishna 36 14 4.20 Das et al. (2005) Red 123 120 27.5 6.83 Moon et al. (2007) Tapti 61 33.6 18.10 Sharma and Subramanian (2008) Narmada 89 33.9 21.20 Godavari 110 310 34.69 5.80 Jha et al. (2009) Upper Han 41.1 95 5.6 1.01 Li et al. (2009) Nethravati 12 4 42 2.8–2.9 Gurumurthy et al. (2012) Kaveri, Musiri 8.5 66 7.9 2.6–3 Pattanaik et al. (2013) Huanghe 28.3 752 3.23 0.35 Fan et al. (2014) Mekong 470 795 10.2 1.91 Li et al. (2014) Swarnamukhi 5.39 3 30.57 8.77 Patel et al. (2020) Payaswini-Chandragiri river 4.40 1.4 42.17 (2016) 9.55 (2016) This study 36.28 (2017) 8.28 (2017) Aquatic Geochemistry (2021) 27:173–206 203 Fig. 8 Comparison of annual silicate weathering rate (SWR) and associated CCR in the Payaswini–Chan- dragiri river in the year 2017 with major Indian rivers and selected world rivers (Gaillardet et  al. 1999; Dalai et al. 2002; Krishnaswami and Singh (1998;Das et al. 2005; Jha et al. 2009; Gupta et al. 2011; Guru- murthy et al. 2012; Pattanaik et al. 2013) + + 2+ 2+ CCR = Q ∕A ⋅ Na + K + Mg + Ca (8) sil 3 2 where Q is the discharge in m /s, A is the surface area of the watershed in km and + + 2+ 2+ (Na + K + Mg + Ca ) is the silicate-derived cations. sil The average annual CCR due to silicate rock weathering of Payaswini–Chandragiri river 5 −2 −1 5 −2 −1 catchment was found to be 9.6 × 10 mol  km y and 8.3 × 10 mol  km y for 2016 5 −2 −1 and 2017, respectively. The Netravathi showed lower CCR (2.9 × 10 mol  km y , Guru- murthy et al. 2012) than this study. The CCR of Payaswini–Chandragiri river catchment was higher than other tropical 5 −2 −1 river systems (Fig.  8), Godavari (5.8 × 10 mol  km y ) (Jha et  al. 2009) and Yamuna 5 −2 −1 (5.5 × 10 mol  km y ) (Krishnaswami and Singh 2005), Bhagirathi–Alaknanda 5 −2 −1 (4 × 10 mol  km y ) (Krishnaswami and Singh 1998), Narmada and Tapti Riv- 5 −2 −1) ers (12.6 × 10 mol  km y , Krishna Basin and Western Ghats of the Deccan Traps 5 −2 −1) 5 −2 −1 (7.4 × 10 mol  km y (Dessert et al. 2003) and Brahmaputra (5.2 × 10 mol  km y ) 5 −2 −1) (Das et al. 2016), Mahanadi ( 4.78 × 10 mol  km y (Bastia and Equeenuddin 2019) and 5 −2 −1) Kaveri basin (3.83 × 10 mol  km y (Pattanaik et al. 2013). The calculated CCR during silicate weathering in the study area was, however, 5 −2 higher than the world river watersheds (Table  3) such as Amazon (0.5 × 10 mol  km −1 5 −2 −1 5 −2 −1 y ), Congo-Zaire (0.5 × 10 mol  km y ), Orinoco (0.6 × 10 mol  km y ), Parana 5 −2 −1 5 −2 −1 (0.9 × 10 mol  km y ) (Gaillardet et al. 1999) and Indus basin (0.6 × 10 mol  km y ) (Fig.  8). Intense monsoonal rainfall and the dominant silicate minerals in the catchment area could be the reasons for the higher consumption of CO during the silicate weath- 5 −2 ering of Payaswini–Chandragiri river in the monsoonal season (8 × 10 mol  km and 5 −2 6 × 10 mol  km in 2016 and 2017) compared to other seasons (Table 2). 4 Conclusions This study analyzed the geochemical characteristics and tabulated the atmospheric C O drawdown rate during silicate weathering of a tropical river catchment, Payaswini–Chan- dragiri river basin, southwestern coast of India. The analyzed results indicate that the 1 3 204 Aquatic Geochemistry (2021) 27:173–206 hydrochemical characteristics of river water gradually change with seasons compared to spatial variations due to runoff, climate and temperature. The dominance of major ions in − − 2− − the Payaswini–Chandragiri river system follows the order of HCO > Cl > SO > NO 3 4 3 + 2+ 2+ + for anions, whereas major cation concentration followed the order N a > Ca > Mg > K + 2+ 2+ − in all the seasons. The Na normalized Ca versus Mg and HCO plots suggested the contribution of major ions through silicate minerals. The estimated silicate weathering rate in Payaswini–Chandragiri river catchment was −2 −1 −2 −1 42 t km y in the year 2016 and 36 t km y in 2017. This value was 0.9 times that of the adjacent west-flowing river Nethravati (Gurumurthy et  al. 2012). The average annual CCR due to silicate rock weathering of Payaswini–Chandragiri river catchment was 5 −2 −1 5 −2 −1 9.6 × 10 mol  km y and 8.3 × 10 mol  km y for 2016 and 2017, respectively. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s10498- 021- 09394-2. Acknowledgements The first author is thankful to DST-INSPIRE fellowship (IF150682) provided by the Department of Science and Technology. Central Instrumentation Facility (CIF), MIT, is thanked for provid- ing the analytical facilities. The insightful and painstaking reviews from the referees are greatly appreciated. Funding Open access funding provided by Manipal Academy of Higher Education, Manipal. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Ahmad T, Khanna PP, Chakrapani GJ, Balakrishnan S (1998) Geochemical characteristics of water & sedi- ment of the Indus River, Trans-Himalaya, India: constraints on weathering & erosion. J Asian Earth Sci 16(2–3):333–346. https:// doi. org/ 10. 1016/ S0743- 9547(98) 00016-6 Andersson L, Wilk J, Todd MC, Hughes DA, Earle A, Kniveton D, Savenije HHG (2006) Impact of climate change and development scenarios on flow patterns in the Okavango River. J Hydrol 331(1–2):43–57. https:// doi. org/ 10. 1016/j. jhydr ol. 2006. 04. 039 Bastia F, Equeenuddin SM (2019) Chemical weathering and associated CO consumption in the Mahanadi River Basin, India. J Asian Earth Sci 174:218–231. https:// doi. org/ 10. 1016/j. jseaes. 2018. 12. 010 Berner RA (1991) A model for atmospheric CO over Phanerozoic time. Am J Sci 291:339–376. https:// doi. org/ 10. 2475/ ajs. 291.4. 339 Dalai TK, Krishnaswami S, Sarin MM (2002) Major ion chemistry in the headwaters of the Yamuna river system: chemical weathering, its temperature dependence and CO consumption in the Himalaya. Geo- chem Cosmochim Acta 66:3397–3416. https:// doi. org/ 10. 1016/ S0016- 7037(02) 00937-7 Das A, Krishnaswami S, Sarin MM, Pande K (2005) Chemical weathering in the Krishna basin and western Ghats of the Deccan traps, India: rates of basalt weathering and their controls. Geochem Cosmochim Acta 69:2067–2084. https:// doi. org/ 10. 1016/j. gca. 2004. 10. 014 Das P, Sarma KP, Jha PK, Ranjan R, Herbert R, Kumar M (2016) Understanding the cyclicity of chemi- cal weathering and associated CO consumption in the Brahmaputra River Basin (India): the role of major rivers in climate change mitigation perspective. Aquat Geochem 22(3):225–251. https:// doi. org/ 10. 1007/ s10498- 016- 9290-6 Dekov VM, Komy Z, Araújo F, Van Put A, Van Grieken R (1997) Chemical composition of sediments, suspended matter, river water and ground water of the Nile (Aswan-Sohag traverse). Sci Total Environ 201(3):195–210 1 3 Aquatic Geochemistry (2021) 27:173–206 205 Dessert CD, Gaillardet B, François J, Louis M, Allègre CJ (2003) Basalt weathering laws and the impact of basalt weathering on the global carbon cycle. Chem Geol 202(3–4):257–273. https:// doi. org/ 10. 1016/j. chemg eo. 2002. 10. 001 Dupre B, Gaillardet J, Rousseau D, Allegre CJ (1996) Major and trace elements of river borne material: the Congo Basin. Geochem Cosmochim Acta 60:1301–1321. https:// doi. org/ 10. 1016/ 0016- 7037(96) 00043-9 Faso BS, Sawadogo A, Yoni S, Nimi M, Zongo M, Bamba O, Ousmane B (2018) Hydrogeochemical char- acterization of dug well water and its suitability for domestic water supply in the village of Passa- kongo, Dédougou Municipality, hydrogeochemical characterization of dug well water & its suitability for domestic water supply in T. Environ Nat Resour Res 8(3):126–137. https:// doi. org/ 10. 5539/ enrr. v8n3p 126 Gaillardet J, Dupre B, Louvat P, Allegre CJ (1999) Global silicate weathering and CO consumption rates deduced from the chemistry of large rivers. Chem Geol 159:3–30. https:// doi. org/ 10. 1016/ S0009- 2541(99) 00031-5 Galy A, France-Lanord C (1999) Weathering processes in the Ganges-Brahmaputra basin and the river- ine alkalinity budget. Chem Geol 159:31–60. https:// doi. org/ 10. 1016/ S0009- 2541(99) 00033-9 Guo H, Wang Y (2005) Geochemical characteristics of shallow groundwater in Datong Basin, North- western China. J Geochem Explor 87(3):109–120. https:// doi. org/ 10. 1016/j. gexplo. 2005. 08. 002 Gupta H, Chakrapani GJ, Selvaraj K, Kao SJ (2011) The fluvial geochemistry, contributions of silicate, carbonate and saline-alkaline components to chemical weathering flux and controlling parameters: Narmada River (Deccan Traps), India. Geochim Cosmochim Acta 75(3):800–824. https:// doi. org/ 10. 1016/j. gca. 2010. 11. 010 Gurumurthy GP, Balakrishna KR, Braun J, Audry JJ, Shankar S, Udaya HN, Manjunatha BR (2012) Controls on intense silicate weathering in a Tropical River, Southwestern India. Chem Geol 300– 301:61–69. https:// doi. org/ 10. 1016/j. chemg eo. 2012. 01. 016 Han G, Tang Y, Zhifang Xu (2010) Fluvial geochemistry of rivers draining Karst Terrain in Southwest China. J Asian Earth Sci 38(1–2):65–75. https:// doi. org/ 10. 1016/j. jseaes. 2009. 12. 016 Hem JD (1985) Study and interpretation of the chemical characteristics of natural water, vol 2254. Department of the Interior, US Geological Survey. Huang X, Sillanpää M, Gjessing ET, Vogt RD (2009) Water quality in the Tibetan Plateau: major ions and trace elements in the headwaters of four major Asian rivers. Sci Total Environ 407(24):6242– 6254. https:// doi. org/ 10. 1016/j. scito tenv. 2009. 09. 001 Huh Y (2003) Chemical weathering and climate—a global experiment: a review. Geosci J 7(3):277–288. https:// doi. org/ 10. 1007/ BF029 10294 Jha PK et al (2009) Chemical weathering and associated CO consumption in the Godavari River Basin, India. Chem Geol 264(1–4):364–374. https:// doi. org/ 10. 1016/j. chemg eo. 2009. 03. 025 Jiang LY, Wang Z, Liu R, Wang Z, Lei Wu, Shanshan. (2015) Hydrochemistry of the middle and upper reaches of the Yarlung Tsangpo River system: weathering processes and CO consumption. Environ Earth Sci 74(3):2369–2379. https:// doi. org/ 10. 1007/ s12665- 015- 4237-6 Krishnaswami S, Singh SK (1998) Silicate and carbonate weathering in the drainage basins of the Ganga-Ghaghara-Indus head waters: contributions to major ion and Sr Isotope geochemistry. Proc Indian Acad Sci Earth Planet Sci 107(4):283–291. https:// doi. org/ 10. 1007/ BF028 41595 Krishnaswami S, Singh SK (2005) Chemical weathering in the River Basins of the Himalaya, India. Curr Sci 89(5):841–849 Krishnaswami S, Singh SK, Dalai T (1999) Silicate weathering in the Himalaya: role in contributing to major ions and radiogenic Sr to the Bay of Bengal. In: Somayajulu BLK (ed) Ocean science, trends & future directions. Indian National Science Academy and Academia International, New Delhi, pp 23–51 Liu WX, Sun Z, Zhao H, Shi T, Liu C, Taoze. (2018) Geochemistry of the dissolved loads during high- flow season of rivers in the Southeastern Coastal Region of China: anthropogenic impact on chemi- cal weathering and carbon sequestration. Biogeosciences 15(16):4955–4971. https:// doi. org/ 10. 5194/ bg- 2018- 109 Meybeck M (1987) Global chemical weathering of surficial rocks estimated from river dissolved loads. Am J Sci 287(5):401–428. https:// doi. org/ 10. 2475/ ajs. 287.5. 401 Millot R, Gaillardet J, Dupré B, Allègre CJ (2002) The global control of silicate weathering rates and the coupling with physical erosion: new insights from rivers of the Canadian Shield. Earth Planet Sci Lett 196(1–2):83–98. https:// doi. org/ 10. 1016/ S0012- 821X(01) 00599-4 Millot R, Gaillardet J, Dupré B, Allégre CJ (2003) Northern latitude chemical weathering rates: clues from the Mackenzie River Basin, Canada. Geochim Cosmochim Acta 67(7):1305–1329. https:// doi. org/ 10. 1016/ S0016- 7037(02) 01207-3 1 3 206 Aquatic Geochemistry (2021) 27:173–206 Moon S, Huh Y, Qin J, van Pho N (2007) Chemical weathering in the Hong (Red) River Basin: rates of silicate weathering and their controlling factors. Geochim Cosmochim Acta 71(6):1411–1430. https:// doi. org/ 10. 1016/j. gca. 2006. 12. 004 Oliva P, Viers J, Dupré B (2003) Chemical weathering in granitic environments. Chem Geol 202(3– 4):225–256. https:// doi. org/ 10. 1016/j. chemg eo. 2002. 08. 001 Pacheco F, Van der Weijden CH (1996) Contributions of water–rock interactions to the composition of groundwaters in areas with a sizeable anthropogenic input: a case study of the water of the Fundao area, central Portugal. Water Resour Res 32:3553–3570. https:// doi. org/ 10. 1029/ 96WR0 1683 Patel P, Raju NJ, Subramanian V, Gossel W, Wycisk P (2020) Chemical weathering and atmospheric CO consumption in the Semi-Arid Swarnamukhi Basin (Peninsular India) estimated from river water geochemistry. Appl Geochem 113:104520. https:// doi. org/ 10. 1016/j. apgeo chem. 2020. 104520 Pattanaik JK, Balakrishnan S, Bhutani R, Singh P (2013) Estimation of weathering rates and CO drawdown based on solute load: significance of granulites and gneisses dominated weathering in the Kaveri River Basin, Southern India. Geochim Cosmochim Acta 121:611–636. https:// doi. org/ 10. 1016/j. gca. 2013. 08. 002 Reddy SKK, Gupta H, Reddy DV (2019) Dissolved inorganic carbon export by mountainous tropical rivers of the Western Ghats, India. Chem Geol 530:119316. https:// doi. org/ 10. 1016/j. chemg eo. 2019. 119316 Sarin MM, Krishnaswami S, Dilli K, Somayajulu BLK, Moore WS (1989) Major ion chemistry of the Ganga-Brahmaputra river system: Weathering processes and fluxes to the Bay of Bengal. Geochimica et Cosmochimica Acta 53(5):997–1009 Sharif MU, Davis RK, Steele KF, Kim B, Kresse TM, Fazio JA (2008) Inverse geochemical modeling of groundwater evolution with emphasis on arsenic in the Mississippi River Valley alluvial aquifer, Arkansas (USA). J Hydrol 350(1–2):41–55 Sharma SK, Subramanian V (2008) Hydrochemistry of the Narmada and Tapti Rivers, India. Hydrol Pro- cess 22(17):3444–3455 Shin WJ, Ryu JS, Park Y, Lee KS (2011) Chemical weathering and associated CO consumption in six major river basins, South Korea. Geomorphology 129(3–4):334–341. https:// doi. org/ 10. 1016/j. geomo rph. 2011. 02. 028 Stallard RF, Edmond JM (1981) Geochemistry of the Amazon precipitation chemistry and the marine con- tribution to the dissolved load at the time of peak discharge. J Geophys Res 86(10):9844–9858. https:// doi. org/ 10. 1029/ JC086 iC10p 09844 Stallard RF, Edmond JM (1983) Geochemistry of the Amazon: 2. The influence of geology and weathering environment on the dissolved load. J Geophys Res 88:9671–9688. https:// doi. org/ 10. 1029/ JC088 iC14p Sun HH, Li J, Zhang D, Shurong Lu, Xixi (2010) Science of the total environment chemical weathering inferred from riverine water chemistry in the lower Xijiang. Sci Total Environ 408(20):4749–4760. https:// doi. org/ 10. 1016/j. scito tenv. 2010. 06. 007 Thomas J, Joseph S, Thrivikramji KP, Manjusree TM, Arunkumar KS (2014) Seasonal variation in major ion chemistry of a Tropical Mountain River, the Southern Western Ghats, Kerala, India. Environ Earth Sci 71(5):2333–2351. https:// doi. org/ 10. 1007/ s12665- 013- 2634-2 Thomas J, Joseph S, Thrivikramji KP (2015) Hydrochemical variations of a Tropical Mountain River sys- tem in a rain shadow region of the Southern Western Ghats, Kerala, India. Appl Geochem 63:456–471. https:// doi. org/ 10. 1016/j. apgeo chem. 2015. 03. 018 Varol M, Gökot B, Bekleyen A, Şen B (2013) Geochemistry of the Tigris River Basin, Turkey: spatial & seasonal variations of major ion compositions and their controlling factors. Quatern Int 304:22–32. https:// doi. org/ 10. 1016/j. quaint. 2012. 12. 043 Wu W, Yang J, Shijin Xu, Yin H (2008) Geochemistry of the headwaters of the Yangtze River, Tongtian He and Jinsha Jiang: silicate weathering and CO consumption. Appl Geochem 23(12):3712–3727. https:// doi. org/ 10. 1016/j. apgeo chem. 2008. 09. 005 Zhang J, Huang WW, Létolle R, Jusserand C (1995) Major element chemistry of the Huanghe (Yellow River), China—weathering processes and chemical fluxes. J Hydrol 168(1–4):173–203. https:// doi. org/ 10. 1016/ 0022- 1694(94) 02635-O Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1 3 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aquatic Geochemistry Springer Journals

Chemical weathering and carbon dioxide consumption in a small tropical river catchment, southwestern India

Download PDF
34 pages

Loading next page...
 
/lp/springer-journals/chemical-weathering-and-carbon-dioxide-consumption-in-a-small-tropical-g1aCtyzfk6

References (46)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2021
ISSN
1380-6165
eISSN
1573-1421
DOI
10.1007/s10498-021-09394-2
Publisher site
See Article on Publisher Site

Abstract

Studies done on small tropical west-flowing river catchments located in the Western Ghats in southwestern India have suggested very intense chemical weathering rates and associated CO consumption. Very less studies are reported from these catchments notwithstanding their importance as potential sinks of atmospheric C O at the global scale. A total of 156 samples were collected from a small river catchment in the southwestern India, the Payas- wini–Chandragiri river Basin, during pre-monsoon, monsoon and post-monsoon seasons in 2016 and 2017, respectively. This river system comprises two small rivers originating at an elevation of 1350 m in the Western Ghats in peninsular India. The catchment area is domi- nated by biotite sillimanite gneiss. Sodium is the dominant cation, contributing ~ 50% of the total cations, whereas HC O contributes ~ 75% of total anions. The average anion con- − − 2− − − centration in the samples varied in the range HCO > Cl > SO > NO > F , whereas 3 4 3 + 2+ 2+ + major cation concentration varied in the range Na > Ca > Mg > K . The average sili- −2 −1 −2 −1 cate weathering rate (SWR) was 42 t km   y in the year 2016 and 36 t km   y in 2017. The average annual carbon dioxide consumption rate (CCR) due to silicate rock weathering 5 −2 −1 5 −2 −1 was 9.6 × 10 mol  km y and 8.3 × 10 mol  km  y for 2016 and 2017, respectively. The CCR in the study area is higher than other large tropical river catchments like Ama- zon, Congo-Zaire, Orinoco, Parana and Indus because of its unique topography, hot and humid climate and intense rainfall. Keywords Tropical river system · Water geochemistry · Silicate weathering rate · Atmospheric CO consumption · Southwest coast of India * Keshava Balakrishna k.balakrishna@manipal.edu Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India Department of Marine Geology, Mangalore University, Mangalagangothri 574199, India 1 3 Vol.:(0123456789) 174 Aquatic Geochemistry (2021) 27:173–206 1 Introduction The chemical composition of rivers is derived from diverse sources like weathering of catch- ment rocks and soils, atmospheric deposition and anthropogenic discharges. In unpolluted river waters, lithological characteristics (source rock abundance) dominantly affect the con- centration of major ions and trace elements (Gaillardet et al. 1999). In a hot and humid tropi- cal setup, chemical weathering predominates over physical weathering, which has a bearing on the long-term global climate change. Chemical weathering of a terrain plays a key role in the atmospheric C O consumption. The chemical weathering of a silicate rock converts the atmospheric CO into dissolved inor- ganic carbon and deposits in the form of carbonic sediments in the ocean (Berner 1991) as noted in 2+ − CaSiO + 2CO + H O → Ca + 2HCO + SiO 3 2 2 2 (1) 2+ Ca + 2HCO → CaCO ↓ + H O + CO ↑ 3 3 2 2 There are several factors affecting the rate of chemical weathering, such as the geology of the terrain (rock type), topography (relief), soil cover, discharge, temperature and precipitation (Gaillardet et al. 1999; Huh 2003; Millot et al. 2002, 2003; Oliva et al. 2003; Guo and Wang 2005; Andersson et al. 2006; Moon et al. 2007). Studies were carried out across the world to estimate the chemical weathering rate and CCR of the major world rivers in the past decades, notably Amazon (Stallard and Edmond 1983), Ganges–Brahmaputra (Sarin et al. 1989), Yel- low (Zhang et  al. 1995), Nile (Dekov et  al. 1997), Indus (Ahmad et  al. 1998), Mississippi (Sharif et al. 2008), Mekong (Huang et al. 2009), Tigris (Varol et al. 2013), Yangtze (Huang et  al. 2009; Jiang et al. 2015), Netravathi (Gurumurthy et al. 2012), Kavery (Pattanaik et al. 2013) and Brahmaputra (Das et al. 2016). Tropical rivers are the largest carriers of dissolved and sediment load to the world’s oceans and significantly influence the biogeochemical cycles of elements. Limited studies are done on the tropical systems worldwide, because of their location in the developing and underdevel- oped countries. Meybeck (1987) emphasized the importance of tropical ecosystems and the paucity of data on these systems, though they are responsible for contributing 50% of water, 38% of dissolved ions and 68% of dissolved silica into the global oceans. The objectives of the present study are to partially fill the paucity in data from the tropical systems. Samples were collected from the tropical Payaswini–Chandragiri river basin, southwest coast of India dur- ing pre-monsoon, monsoon and post-monsoon in 2016 and 2017. Currently, no studies have been reported on the major ion chemistry of this river system. This river system is the biggest in northern Kerala state among the 13 river systems draining silicate-rich rock terrains. This study investigated the presence, distribution and source of major ions in the southwest-flowing river and estimated the SWR and associated CCR using the forward model (Wu et al. 2008). These data will add to the database of global silicate weathering rates of world rivers and fill- ing the gaps existing on the silicate weathering and CCR in Indian tropical rivers. 1 3 Aquatic Geochemistry (2021) 27:173–206 175 2 Materials and methods 2.1 Study area The Chandragiri–Payaswini river system (area 1406 km and length 105  km) is located along the southwest coast of peninsular India originating in the Western Ghats. Geographi- cally the study area lies between 74° 48′ E and 75° 45′ E and 12° 18′ N and 12° 32′ N longitudes and latitudes, respectively. These rivers originate at an altitude of about 1350 m above mean sea level (MSL) and join the Arabian sea near Kasaragod town (Fig. 1). Geologically, the basement of the study area belongs to the Archean metamorphics (Fig. 1). The main rock types are granite biotite sillimanite gneiss, charnockite, schist and dolerite. Charnockites, hornblende-biotite gneiss and high-grade schistose rocks are exten- sively lateritized in the lower reaches and dominate the drainage basin of Chandragiri river. There are no reports of the presence of carbonates/evaporates. The study area experiences typical tropical climate with hot (20°–38 °C) and humid conditions (4,000 mm annual rain- fall), high surface runoff (2715 mm for entire west-flowing river catchment) with an annual water discharge of 4.40 km / year (Reddy et al. 2019) (Fig. S1). Anthropogenic activities are minimal in the area, though two plywood industries located near Sullia town, on the banks of Payaswini River, and hospital discharge outlet near Aleti are discharging their effluents into the river (Fig.  1). Kasaragod municipal wastewater is discharged directly into the river estuary. 2.2 Sampling and analysis Twenty-six river water samples were collected in each season, from the mainstream and tributaries (Fig.  1) during pre-monsoon (April), monsoon (August) and post-monsoon (December) seasons for a period of 2 years (2016, 2017). A total of 156 samples were col- lected in six seasons. Water samples were collected from road bridges such that samples are received from the center of the river and in the well-mixed condition. A polypropylene (PP) bucket tied with Nylon rope was dropped to the river for the sample collection. pH, temperature, electric conductivity (EC) and dissolved oxygen (DO) were measured on-site using HACH-make portable multiparameter, calibrated with the standard solution. Locations 1. Jodupala 2. Abhikolli 3. Koyanadu 4. Sampaje 5. Peraje 6. Adikehitilu 7. Doddathotta 8. Paichar 9. Adkar 10. Uddanthadka 11. Aletti 12. Parappa 13. Pandi 14. Panjikkal 15. Adoor 16. Erinjipuzha 17. Pandikandam 18. Karike 19. Karike 20. Panathur 21. Balamthod 22. Kolichal 23. Kallar 24. Kottody 25. Moonnamkadav 26. Periya Fig. 1 Geological map of Payaswini–Chandragiri river basin with sampling locations ( source of the data; 1:20 K geological map of India) 1 3 176 Aquatic Geochemistry (2021) 27:173–206 The samples were stored in pre-cleaned PP-grade bottles (1000  ml). Water samples were filtered through 0.22-µm pore size, 47-mm-diameter Nuclepore polycarbonate filters using a Sartorius-make membrane filtration apparatus in a laminar flow bench and stored at 4 °C for further analysis. The filtered water samples were analyzed for major ions using DIONEX-1100 ion chromatography with autosampler having separate cation–anion sup- pressor and column system. Accuracy of the results was checked with a known concen- tration of the standard solutions, which were within ± _5%. Precision of the results was checked with duplicate samples, which were within ± 3% (Table  S1). Alkalinity of the samples was measured using the standardized HCl titration method using an autotitrator (METROHM TIAMO). Since the pH of all the samples is less than 8.3, the carbonate alka- linity was nonexistent. The end point of bicarbonate alkalinity ranged from 4.8 to 5.5 (Fig. S2), with ± 2% accuracy and precision. Dissolved silica (SiO ) in the river water samples was measured through the UV–Vis spectrometer, HACH DR 5000 by silicon molybdate method at 452 nm wavelengths with a precision of ± 2%. Normalized inorganic charge bal- ance (NICB) was calculated between total dissolved cations (TZ ) and total dissolved ani- ons (TZ ), and the charge balance was within ± 15% (Fig. 2). Above 10% of NICB values of the samples could be due to the presence of organic anions and cations. 3 Result and discussion 3.1 Hydrogeochemistry The physiochemical composition of the Payaswini–Chandragiri river water is tabulated in Table  1. The pH of the river was slightly alkaline in nature and showed a small variation seasonally (6.1–8.3) and spatially. In monsoon season, the average pH was lower than the rest of the seasons, because of the mixing of rain water, which has typical pH of 5.5. The electric conductivity of the river samples varied from 31 to 176 µS/cm in pre-mon- soon, 49–93 µS/cm in monsoon and 31–83 µS/cm in post-monsoons. Total dissolved solid of the samples was calculated from the concentration of obtained major ions and silica. The concentration of TDS in the pre-monsoon varied from 29 to 112  mg/l, 36–80  mg/l in monsoon and 29–84  mg/l in post-monsoon, respectively. The average TDS of Payas- wini–Chandragiri river (60  mg/l) is less than the world’s major rivers (Gaillardet et  al. Fig. 2 Correlation between total anion and total cation 1 3 Aquatic Geochemistry (2021) 27:173–206 177 1 3 Table 1 Physicochemical composition of the Payaswini–Chandragiri river basin + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 1. Jodupala N 12° 26′ 25.99" E 75° 39′ 36.23" Pre-mon- 27.1 7.6 81.0 7.2 217.9 78.5 127.5 158.4 60.0 17.6 11.6 835.2 433.3 868.2 936.5 98.8 − 7.6 soon Monsoon 23.9 6.7 69.4 7.6 132.8 12.7 71.9 109.8 75.7 24.7 9.7 433.1 216.7 508.9 553.6 54.3 − 8.4 Post- 22.0 7.5 59.5 8.2 199.7 20.5 114.8 146.5 63.6 18.0 10.2 590.2 150.0 742.9 692.3 63.4 7.1 mon- soon Pre-mon- 24.0 7.2 64.1 7.4 230.2 17.8 142.0 187.1 70.8 13.4 12.7 780.2 466.7 906.1 890.9 97.1 1.7 soon Monsoon 23.6 6.9 43.6 7.9 127.3 13.5 73.5 88.7 69.8 25.2 8.0 410.5 33.3 465.2 521.6 40.6 − 11.4 Post- 27.1 7.7 61.2 7.4 206.7 17.9 145.3 171.9 67.4 22.7 7.6 659.3 133.3 859.0 764.6 68.6 11.6 mon- soon 2. Abhikolli N 12° 26′ 26.15" E 75° 39′ 02.15" Pre-mon- Dry soon Monsoon 23.1 6.9 70.5 7.8 148.0 35.4 79.8 81.6 61.9 13.7 9.7 368.4 333.3 506.0 463.3 56.4 8.8 2016 178 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 22.0 7.5 58.5 8.2 160.8 36.6 91.0 119.7 66.6 5.9 9.3 456.7 216.7 618.8 548.4 56.6 12.1 mon- soon Pre-mon- 23.6 7.8 64.3 7.3 217.0 48.7 64.3 182.3 63.4 3.2 10.8 482.3 450.0 630.2 571.5 74.1 9.8 soon Monsoon 23.5 7.3 44.6 7.9 148.0 66.9 58.4 64.8 56.5 15.6 7.8 411.0 250.0 461.4 498.6 53.8 − 7.8 Post- 29.8 7.6 66.4 7.1 157.8 11.9 84.1 77.9 61.8 7.2 10.2 456.2 233.3 493.7 545.6 54.7 − 10.0 mon- soon 3. Koyanadu N 12° 28′ 50.55" E75° 34′ 47.70" Pre-mon- 31.2 7.9 95.5 7.5 177.6 22.6 78.8 139.3 58.0 1.0 11.7 647.4 150.0 636.4 730.7 64.2 − 13.8 soon Monsoon 25.2 6.9 81.8 8.1 148.9 12.9 94.5 119.2 79.0 15.8 12.7 526.9 300.0 589.2 647.8 66.1 − 9.5 Post- 23.9 7.7 68.6 8.5 202.9 16.7 261.2 170.2 82.0 6.5 13.2 866.9 133.3 1082 982.5 83.9 9.7 mon- soon Pre-mon- 27.2 7.8 73.6 7.6 242.7 19.8 163.8 218.5 85.2 1.8 14.2 890.2 250.0 1027 1006 92.8 2.0 soon Monsoon 24.9 7.5 53.7 8.0 147.3 14.3 88.5 99.8 74.3 15.7 15.5 471.0 150.0 538.3 592.0 52.9 − 9.5 2017 Aquatic Geochemistry (2021) 27:173–206 179 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 32.5 7.8 82.5 8.3 166.3 14.8 84.9 101.7 78.1 6.3 12.4 503.4 166.7 554.3 634.1 56.0 − 13.4 mon- soon 4. Sampaje N 12° 29′ 37.67" E 75° 33′ 57.45" Pre-mon- 30.9 7.1 65.3 4.1 267.4 21.1 160.6 201.6 66.5 0.2 10.7 832.1 166.7 1012. 921.3 83.1 9.5 soon Monsoon 27.6 6.6 62.2 7.4 165.8 12.0 65.5 91.3 76.7 8.1 10.5 443.3 100.0 491.5 549.5 46.8 − 11.2 Post- 25.1 7.3 47.5 8.0 169.7 12.4 81.9 105.5 81.0 4.7 12.7 430.2 100.0 556.8 541.7 47.2 2.8 mon- soon Pre-mon- 27.7 6.7 49.2 4.4 220.1 23.1 87.3 138.8 103.3 5.1 12.5 520.2 233.3 695.3 654.6 64.5 6.0 soon Monsoon 26.3 6.9 43.2 7.5 146.0 12.7 66.9 76.0 77.0 6.7 9.3 390.2 166.7 444.5 495.6 46.4 − 10.9 Post- 31.8 7.8 56.6 7.8 159.3 18.0 93.1 95.6 83.2 4.0 11.8 450.2 266.7 554.6 561.8 58.2 − 1.3 mon- soon 5. Peraje N 12° 31′ 06.70" E 75° 26′ 13.68" Pre-mon- 36.1 7.2 78.5 6.4 247.1 17.8 143.2 166.4 45.1 4.8 15.3 796.4 116.7 884.1 877.8 75.4 0.7 soon 2016 180 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 26.0 7.0 60.6 7.6 171.9 11.6 63.6 73.8 80.4 11.9 9.6 377.9 66.7 458.3 489.6 40.5 -6.6 Post- 27.2 7.6 48.8 8.1 169.7 12.4 81.9 105.5 81.0 4.7 12.7 430.2 100.0 556.8 541.7 47.2 2.8 mon- soon Pre-mon- 30.6 7.6 61.0 6.8 216.9 21.7 128.9 169.2 92.3 BLD 11.0 700.2 250.0 834.7 815.6 77.8 2.3 soon Monsoon 26.2 7.2 40.4 7.6 162.3 11.7 71.8 53.3 78.5 11.3 7.5 341.8 216.7 424.1 446.6 46.1 -5.2 Post- 37.9 7.4 54.0 7.7 170.8 13.2 60.0 57.6 77.7 2.7 10.0 335.9 66.7 419.2 436.7 36.6 -4.1 mon- soon 6. Adikehitilu N 12° 34′ 59.77" E 75° 30′ 11.93" Pre-mon- 31.2 6.9 53.1 5.0 88.9 0.5 42.2 60.7 38.0 BLD 14.1 262.8 83.3 295.1 329.9 29.3 − 11.1 soon Monsoon 27.6 6.8 49.4 7.5 121.4 10.5 45.8 58.6 81.7 7.6 7.9 290.7 166.7 340.7 395.9 38.5 − 15.0 Post- 25.3 6.5 33.4 8.0 58.7 5.8 81.4 37.2 34.8 BLD 3.4 280.0 116.7 301.6 321.7 30.7 − 6.4 mon- soon Pre-mon- Dry soon 2017 Aquatic Geochemistry (2021) 27:173–206 181 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 25.7 6.1 33.6 7.7 136.3 16.4 49.2 59.9 72.1 5.8 6.5 299.0 166.7 370.8 389.9 39.1 − 5.0 Post- 33.2 6.2 39.9 7.9 136.4 15.5 41.9 55.5 69.8 2.6 7.0 284.3 166.7 346.8 370.7 37.6 − 6.7 mon- soon 7. Doddathotta N 12° 35′ 34.19" E 75° 26′ 15.66" Pre-mon- 28.3 6.4 70.5 2.7 496.3 79.5 184.5 133.1 40.0 4.4 17.0 975.2 216.7 1210 1054 100.1 13.8 soon Monsoon 26.3 6.9 56.7 7.4 133.3 14.8 49.0 72.7 105.3 20.8 11.3 216.7 216.7 391.6 365.7 40.1 6.8 Post- 24.0 7.5 30.8 8.0 136.6 9.2 32.0 45.2 65.0 6.3 6.9 230.0 100.0 300.2 315.1 29.5 − 4.8 mon- soon Pre-mon- 24.2 7.1 39.5 6.8 160.1 25.9 65.3 98.7 96.4 8.6 10.7 380.2 116.7 514.0 507.1 45.4 1.4 soon Monsoon 26.1 7.0 37.8 7.4 140.2 12.4 45.9 58.8 99.3 20.1 9.3 280.0 150.0 362.0 418.0 38.9 − 14.4 Post- 29.3 7.2 38.9 8.0 151.3 39.7 50.3 72.8 91.2 11.4 9.1 378.4 133.3 437.2 499.6 45.1 − 13.3 mon- soon 2017 182 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 8. Paichar N 12° 34′ 06.96" E 75° 23′ 19.60" Pre-mon- 30.4 8.0 175.5 2.3 210.1 27.0 107.2 184.0 235.2 53.6 19.5 589.8 266.7 819.5 920.2 81.4 − 11.6 soon Monsoon 25.9 7.2 60.3 7.6 172.3 14.1 57.5 75.3 95.4 21.6 10.3 354.1 16.7 452.1 492.4 37.2 − 8.5 Post- 25.0 7.1 35.1 7.0 141.9 12.6 71.5 73.0 108.1 6.1 16.0 332.0 133.3 443.4 478.4 42.4 − 7.6 mon- soon Pre-mon- 28.0 7.3 61.2 6.3 256.2 30.5 114.5 150.4 151.9 7.2 12.1 600.2 166.7 816.6 784.6 69.5 4.0 soon Monsoon 26.2 6.8 37.2 7.6 129.3 16.7 64.1 70.5 103.0 20.3 10.0 250.0 33.3 415.1 393.4 31.1 5.4 Post- 29.4 7.3 44.2 7.1 159.3 17.2 77.8 81.1 101.9 8.2 17.0 398.2 100.0 494.3 542.8 45.5 − 9.4 mon- soon 9. Adkar N 12° 34′ 06.96" E 75° 21′ 06.84" Pre-mon- 32.9 7.6 74.6 7.4 341.0 42.4 175.0 237.8 118.6 19.8 16.9 1046.9 66.7 1209 1046 91.1 − 0.4 soon Monsoon 26.5 7.6 61.0 7.8 156.2 13.3 59.4 100.2 91.4 16.4 10.4 436.2 183.3 488.8 565.3 52.4 − 14.5 2016 Aquatic Geochemistry (2021) 27:173–206 183 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 28.1 8.1 47.6 7.6 161.4 19.5 86.5 101.8 101.1 13.9 15.8 380.2 266.7 557.6 527.5 41.8 5.5 mon- soon Pre-mon- 28.6 6.9 58.2 5.8 228.3 39.7 115.4 153.8 136.4 16.6 11.0 580.2 183.3 806.4 739.3 68.0 8.7 soon Monsoon 26.1 6.9 41.4 7.7 157.7 14.8 61.4 88.3 87.4 16.2 9.3 375.4 150.0 421.8 497.5 45.1 − 5.3 Post- 34.9 8.0 51.2 7.0 150.0 21.4 83.8 94.4 92.4 10.0 12.8 430.2 183.3 527.6 558.7 42.4 − 5.7 mon- soon 10. Uddanthadka N 12° 34′ 44.37" E 75° 22′ 04.11" Pre-mon- Dry soon Monsoon 26.5 6.5 67.3 7.9 92.1 6.3 51.3 132.5 76.0 16.2 7.8 430.7 150.0 461.7 539.3 48.5 -14.6 Post- 25.0 6.6 44.3 8.0 183.6 10.2 92.3 105.6 84.2 7.0 15.6 410.2 183.3 589.4 532.6 52.0 10.1 mon- soon Pre-mon- 29.0 7.0 69.8 7.2 174.3 25.7 102.1 122.5 65.2 1.6 15.5 460.5 200.0 649.2 554.0 56.2 14.8 soon Monsoon 26.6 7.0 65.9 7.4 87.7 8.0 58.5 117.5 75.3 16.6 8.4 407.0 116.7 447.5 516.4 44.8 -14.3 2017 184 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 34.7 7.1 45.7 8.1 159.9 15.5 116.3 90.0 69.7 4.0 14.0 564.6 133.3 588.0 675.2 57.3 -13.8 mon- soon 11. Aletti N 12° 32.851′ E 75° 23.575′ Pre-mon- 35.1 7.5 69.5 9.1 253.6 39.5 165.1 203.3 68.9 BLD 8.3 810.2 116.7 1029 897.0 79.2 13.8 soon Monsoon 26.5 7.2 62.9 7.5 136.4 13.0 63.9 84.8 98.2 14.9 10.2 379.7 50.0 446.8 513.2 40.1 − 13.8 Post- 27.7 7.1 46.6 7.6 161.4 16.3 45.7 103.2 86.8 7.2 10.5 410.2 116.7 475.5 525.8 46.1 − 10.1 mon- soon Pre-mon- 30.1 7.2 56.2 7.0 209.3 21.4 115.7 149.5 105.2 BLD 8.6 620.2 166.7 761.0 743.6 66.8 2.3 soon Monsoon 25.6 7.0 40.5 7.7 131.0 13.2 60.3 70.2 82.4 13.2 8.4 340.2 33.3 405.0 452.6 35.1 − 11.1 Post- 37.2 7.1 52.4 7.6 158.1 21.5 85.3 91.3 83.6 6.4 9.6 508.5 83.3 532.8 618.1 50.5 − 14.8 mon- soon 12. Parappa N 12° 34′ 57.96" E 75° 14′ 47.84" Pre-mon- Dry soon 2016 Aquatic Geochemistry (2021) 27:173–206 185 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 28.6 6.5 56.2 7.7 166.6 16.7 106.0 74.2 92.7 15.6 10.5 472.6 183.3 543.7 602.3 55.1 − 10.2 Post- 29.2 6.4 37.8 8.4 165.7 13.3 62.0 68.0 17.3 1.7 3.4 391.1 183.3 438.9 417.0 44.4 5.1 mon- soon Pre-mon- 26.9 7.1 58.0 7.0 151.7 22.9 37.4 147.5 66.4 2.5 10.8 427.7 150.0 544.4 518.6 49.8 4.9 soon Monsoon 23.8 6.9 55.9 7.9 149.2 14.1 85.2 65.5 84.5 4.3 10.9 377.9 216.7 464.7 488.9 49.0 − 5.1 Post- 33.9 7.0 40.0 7.7 168.7 14.0 101.3 97.3 87.3 3.8 11.6 471.3 133.3 579.9 590.2 52.0 − 1.8 mon- soon 13. Pandi N 12° 32′ 54.51" E 75° 14′ 19.47" Pre-mon- Dry soon Monsoon 27.0 6.7 59.0 7.7 145.9 12.6 62.5 68.1 66.6 9.8 6.8 376.2 166.7 419.7 466.8 44.7 − 10.6 Post- 27.9 7.0 36.8 7.6 169.3 10.3 52.4 68.6 10.6 6.1 12.4 387.4 166.7 421.5 429.5 43.9 − 1.9 mon- soon Pre-mon- 24.8 6.8 38.0 174.3 34.1 43.1 72.6 29.4 3.8 2.4 410.8 116.7 439.8 449.5 42.9 − 2.2 soon 2017 186 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 27.5 6.9 40.3 7.5 143.6 10.0 68.0 65.2 108.0 15.6 9.2 290.0 216.7 419.8 440.5 44.6 − 4.8 Post- 31.0 7.0 45.1 7.3 177.1 18.3 60.2 76.9 106.0 5.0 12.1 400.2 166.7 469.6 535.4 49.0 − 13.1 mon- soon 14. Panjikkal N 12° 34′ 12.71" E 75° 15′ 58.89" Pre-mon- 30.6 7.0 58.3 8.2 507.9 33.4 165.0 220.6 67.7 BLD 9.0 1260.3 66.7 1312 1348 110.0 − 2.7 soon Monsoon 25.6 7.0 61.4 7.4 139.3 5.3 50.7 182.1 97.7 18.8 11.1 491.8 116.7 610.0 631.5 54.6 − 3.5 Post- 29.9 7.9 46.3 8.4 184.7 18.7 81.3 99.7 100.2 12.3 14.8 370.2 183.3 565.3 513.0 50.2 9.7 mon- soon Pre-mon- 33.7 8.1 56.1 8.4 243.7 24.7 110.5 138.3 134.9 5.9 17.4 560.2 66.7 765.9 735.7 59.7 4.0 soon Monsoon 26.8 7.2 40.5 7.6 136.0 15.1 74.0 73.7 87.8 16.0 9.0 390.2 66.7 446.6 511.9 41.2 − 13.6 Post- 34.3 7.6 51.5 7.8 132.9 12.8 62.7 173.4 93.0 10.0 14.3 456.2 216.7 618.0 587.8 58.1 5.0 mon- soon 2017 Aquatic Geochemistry (2021) 27:173–206 187 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 15. Adoor N 12° 33′ 51.79" E 75° 14′ 45.85" Pre-mon- 32.6 8.1 113.6 6.6 307.2 28.0 128.7 128.5 104.4 28.6 14.8 704.6 200.0 849.5 869.6 78.3 − 2.3 soon Monsoon 26.5 7.1 61.0 7.5 152.8 13.6 81.3 46.2 89.5 15.4 9.2 330.2 50.0 421.3 453.4 36.0 − 7.3 Post- 28.9 7.7 46.2 7.9 160.0 8.3 79.8 52.3 47.1 5.4 7.1 361.5 233.3 432.6 428.3 46.8 1.0 mon- soon Pre-mon- 34.4 7.7 60.3 7.9 248.9 26.5 113.5 153.1 134.5 7.5 21.5 590.2 33.3 808.6 775.0 60.9 4.2 soon Monsoon 27.0 7.5 41.2 7.6 139.8 13.6 60.4 71.2 89.5 15.4 9.2 330.2 33.3 416.6 453.4 35.2 − 8.5 Post- 33.7 7.5 51.9 7.5 154.3 18.2 83.5 91.3 91.7 4.6 12.2 420.2 233.3 522.0 540.8 54.3 − 3.5 mon- soon 16. Erinjipuzha N 12° 29′ 40.54" E 75° 09′ 24.93" Pre-mon- 35.1 8.3 84.5 8.1 223.3 19.9 131.4 130.4 113.6 BLD 13.3 657.0 66.7 766.7 799.1 63.7 − 4.1 soon Monsoon 26.4 7.4 60.9 7.9 152.3 7.0 128.7 124.4 100.5 16.0 11.1 580.2 200.0 665.6 719.5 64.9 − 7.8 Post- 29.8 7.5 46.0 8.0 165.7 3.0 59.5 70.3 19.0 1.7 3.1 472.1 33.3 428.2 499.6 40.1 − 15.4 mon- soon 2016 188 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Pre-mon- 33.8 7.7 59.3 7.6 212.7 19.6 103.3 124.1 141.7 BLD 22.1 560.2 50.0 687.1 746.7 57.4 − 8.3 soon Monsoon 27.3 7.2 42.1 7.7 136.7 11.7 73.8 68.5 92.1 16.7 10.0 330.2 200.0 433.0 459.0 45.5 − 5.8 Post- 35.4 7.6 52.2 7.7 168.7 12.4 101.1 86.9 92.8 4.1 11.0 500.2 66.7 557.0 619.1 49.4 − 10.0 mon- soon 17. Pandikandam N 12° 28′ 20.60" E 75° 07′ 02.76" Pre-mon- 36.3 7.6 128.2 7.6 507.9 33.4 165.0 220.6 296.8 BLD 23.0 1082.5 116.7 1312 1425 111.5 − 8.2 soon Monsoon 26.6 7.3 59.8 7.8 180.9 12.9 58.3 76.2 86.5 19.8 11.5 394.9 83.3 462.6 524.1 43.6 − 12.5 Post- 30.0 8.0 45.4 8.1 119.7 11.4 68.3 49.4 40.4 10.0 6.4 331.0 133.3 366.5 394.4 37.7 − 7.4 mon- soon Pre-mon- 32.0 7.4 54.6 6.7 207.1 26.1 106.3 129.7 131.2 BLD 14.3 530.2 100.0 705.1 691.5 57.9 1.9 soon Monsoon 27.3 7.6 40.9 7.7 573.3 13.6 61.5 73.1 291.8 16.0 10.0 573.3 250.0 856.0 901.1 80.4 − 5.1 2017 Aquatic Geochemistry (2021) 27:173–206 189 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 35.5 7.7 51.5 7.6 165.0 17.8 82.2 92.9 90.8 5.3 12.3 497.4 133.3 519.4 618.2 53.0 − 14.8 mon- soon 18. Karike N 12° 26′ 19.85" E 75° 27′ 56.59" Pre-mon- Dry soon Monsoon 27.6 7.2 93.2 7.6 128.2 9.9 79.9 77.2 91.7 6.7 12.5 398.2 233.3 452.4 522.4 51.5 − 14.3 Post- 23.9 7.5 52.9 8.4 135.7 13.5 76.3 77.5 60.1 1.2 8.2 440.2 66.7 456.8 517.9 42.4 − 12.6 mon- soon Pre-mon- Dry soon Monsoon 24.5 7.6 42.1 7.5 127.9 9.0 91.8 72.8 81.1 5.6 8.2 350.2 116.7 466.1 453.3 40.8 2.8 Post- 30.5 7.6 54.7 7.9 151.9 10.6 115.7 103.0 81.9 2.5 9.6 551.0 66.7 599.9 654.9 52.4 − 8.8 mon- soon 19. Karike N 12° 26′ 47.83" E 75° 25′ 06.56" Pre-mon- 33.2 8.0 74.2 7.4 133.3 13.1 85.8 120.7 31.5 32.0 27.6 500.5 150.0 559.4 619.7 55.8 − 10.2 soon 2016 190 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 27.8 7.3 66.4 7.7 157.3 7.6 125.3 74.6 86.7 10.0 10.3 545.4 216.7 564.7 662.7 60.9 − 13.0 Post- 21.7 7.8 48.4 8.4 140.1 10.2 100.8 106.1 70.1 5.9 15.9 490.2 66.7 563.9 605.6 48.7 − 7.1 mon- soon Pre-mon- 27.4 7.6 44.8 7.6 165.2 10.2 107.6 109.5 66.0 BLD 16.1 510.2 300.0 609.5 609.5 64.2 0.1 soon Monsoon 24.4 7.8 43.7 9.1 122.6 8.5 80.6 69.7 69.4 7.4 7.8 400.2 100.0 431.6 492.6 42.0 − 13.2 Post- 30.2 7.7 54.4 8.3 158.1 12.9 114.6 92.9 69.2 5.5 14.1 500.2 100.0 586.0 603.7 51.3 − 3.0 mon- soon 20. Panathur N 12° 27′ 26.17" E 75° 21′ 36.85" Pre-mon- 35.2 7.3 64.7 6.9 202.7 24.2 125.5 122.6 81.1 BLD BLD 780.2 266.7 722.9 781.3 77.1 − 7.8 soon Monsoon 26.1 7.4 60.8 7.6 130.3 24.1 76.3 89.9 75.2 15.9 9.6 440.7 116.7 470.3 551.1 47.6 − 12.4 Post- 25.3 7.6 51.2 7.7 178.1 16.7 114.5 109.2 80.7 8.0 12.1 470.2 216.7 642.2 583.6 58.1 9.6 mon- soon Pre-mon- 29.9 7.7 50.5 6.3 183.1 14.5 118.2 116.5 83.5 BLD 15.5 550.2 216.7 666.9 665.4 63.3 0.2 soon 2017 Aquatic Geochemistry (2021) 27:173–206 191 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 25.4 7.5 42.6 8.0 171.7 8.5 77.5 67.2 71.2 11.2 8.0 420.2 50.0 426.0 518.6 40.4 − 9.9 Post- 35.9 7.4 57.0 7.9 142.8 12.4 104.8 81.3 78.6 5.2 11.4 500.2 166.7 527.3 607.2 54.3 − 14.1 mon- soon 21. Balamthod N 12° 27′ 38.34" E 75° 19′ 17.58" Pre-mon- 32.7 7.8 69.3 6.6 214.0 20.0 107.8 99.4 84.8 BLD 13.6 627.0 283.3 648.2 740.5 71.9 − 13.3 soon Monsoon 26.4 6.5 58.8 7.7 214.0 20.0 107.8 99.4 56.6 BLD BLD 620.2 233.3 648.2 678.1 66.1 − 4.5 Post- 28.0 7.0 46.7 7.6 105.3 8.7 119.9 82.1 61.2 4.7 9.4 450.2 166.7 518.0 535.5 49.8 − 3.3 mon- soon Pre-mon- 30.0 7.1 51.4 6.0 195.0 19.4 118.3 121.9 102.3 BLD 13.4 540.2 183.3 694.8 669.9 61.8 3.7 soon Monsoon 26.6 6.4 41.8 7.8 154.3 7.4 65.9 65.9 77.8 14.1 8.3 370.2 116.7 407.8 478.7 41.7 − 11.8 Post- 34.7 7.1 49.8 7.6 156.4 14.1 96.0 82.2 81.5 7.3 11.4 501.8 166.7 513.8 613.2 54.5 − 15.2 mon- soon 2017 192 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) 22. Kolichal N 12° 26′ 45.29" E 75° 17′ 27.99" Pre-mon- Dry soon Monsoon 26.5 6.6 62.4 7.6 168.6 9.0 70.0 73.8 99.3 37.3 10.6 344.6 183.3 421.5 502.5 46.7 − 7.8 Post- 25.0 6.6 36.0 8.2 157.2 10.2 62.5 69.6 117.2 22.7 12.9 260.0 183.3 431.6 425.8 42.0 1.3 mon- soon Pre-mon- Dry soon Monsoon 26.6 6.6 41.2 7.8 157.8 8.6 65.1 65.3 99.7 29.8 10.3 347.4 133.3 427.1 497.5 43.7 − 15.2 Post- Dry mon- soon 23. Kallar N 12° 25′ 50.53" E 75° 16′ 33.51" Pre-mon- 36.7 6.4 43.8 7.5 52.9 79.5 59.5 135.6 40.0 4.4 17.0 483.4 66.7 522.5 562.5 48.0 − 7.4 soon Monsoon 26.7 6.7 60.6 9.6 139.3 31.0 9.0 182.1 97.7 18.8 11.1 491.8 33.3 552.3 631.5 49.6 − 13.4 2016 Aquatic Geochemistry (2021) 27:173–206 193 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 24.5 6.7 42.4 7.5 90.5 8.2 116.6 38.4 52.3 10.2 7.8 280.0 133.3 408.8 358.7 35.1 13.1 mon- soon Pre-mon- 36.7 7.0 57.0 7.4 130.8 12.7 108.5 76.0 48.8 6.7 9.3 390.2 100.0 512.7 467.5 42.1 9.2 soon Monsoon 26.7 7.1 44.7 7.4 137.0 9.7 115.8 68.0 107.0 34.6 12.8 360.2 50.0 514.2 527.5 41.2 − 2.5 Post- Dry mon- soon 24. Kottody N 12° 26′ 06.47" E 75° 13′ 28.42" Pre-mon- 29.0 7.1 62.2 6.0 175.9 46.5 72.1 51.4 68.4 14.2 10.5 417.9 150.0 469.4 522.6 48.5 − 10.7 soon Monsoon 25.8 7.3 60.3 7.8 225.6 33.5 104.2 68.4 88.8 20.5 9.8 538.2 250.0 434.0 667.1 64.9 − 9.9 Post- 27.5 7.6 44.5 8.2 166.6 17.4 90.7 89.7 99.4 10.3 12.7 380.2 116.7 544.9 516.1 39.9 5.4 mon- soon Pre-mon- 28.7 7.0 50.8 5.8 197.8 18.4 113.3 117.1 110.9 BLD 11.5 510.2 166.7 677.0 644.9 52.8 4.9 soon Monsoon 25.7 7.1 42.4 7.9 157.1 9.0 65.9 75.9 90.6 20.4 8.3 320.2 166.7 449.7 447.9 43.4 0.4 2017 194 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Post- 35.2 6.4 52.0 7.2 349.0 13.9 104.4 76.5 86.3 11.3 12.0 630.2 116.7 724.8 751.7 58.5 − 3.6 mon- soon 25. Moonnamkadav N 12° 25′ 41.44" E 75° 07′ 42.05" Pre-mon- Dry soon Monsoon 27.2 7.4 57.1 7.7 137.0 39.8 59.5 77.2 96.9 24.3 9.6 386.2 266.7 446.0 527.1 54.6 − 15.7 Post- 27.7 7.3 42.5 8.1 121.3 9.9 79.9 44.5 6.5 3.7 5.2 404.6 66.7 380.0 426.0 36.5 − 11.4 mon- soon Pre-mon- 28.5 7.2 49.2 6.6 173.0 48.8 111.3 110.6 123.1 BLD 4.8 510.2 50.0 665.4 643.0 51.9 3.4 soon Monsoon 26.0 7.2 44.3 8.0 167.3 1.4 91.9 57.8 103.6 23.6 8.4 384.6 166.7 468.0 528.7 47.8 − 12.2 Post- 31.8 7.4 48.5 7.5 131.1 25.7 83.8 48.0 96.8 7.8 11.1 331.0 100.0 420.2 458.6 39.1 − 8.7 mon- soon 26. Periya N 12° 25′ 40.57" E 75° 07′ 20.99" Pre-mon- Dry soon 2016 Aquatic Geochemistry (2021) 27:173–206 195 1 3 Table 1 (continued) + + 2+ 2+ - - 2- - + - Loca- T pH EC DO Na K Mg Ca Cl NO SO HCO SiO TZ TZ TDS NICB 3 4 3 2 tions −1 -1 (°C)   (μS/cm) (mg/l)(μmol.  L ) (µequ.L ) (mg/l) (%) Monsoon 29.0 6.8 56.8 7.7 169.4 16.0 63.8 70.0 75.5 19.7 9.9 374.6 116.7 452.9 489.6 43.6 − 7.8 Post- 28.3 6.4 50.1 7.9 140.0 17.4 79.0 79.7 31.9 2.9 1.4 400.2 33.3 474.7 437.6 36.8 8.1 mon- soon Pre-mon- 24.5 7.7 58.9 6.8 164.7 17.7 124.6 24.5 81.4 BLD 16.3 425.7 83.3 480.6 539.6 43.9 − 11.6 soon Monsoon 26.7 7.3 57.9 8.9 130.7 21.6 54.3 94.0 79.8 21.7 10.4 377.9 166.7 449.0 500.2 47.1 − 10.8 Post- 35.8 7.5 52.0 7.9 212.9 10.6 119.9 130.9 73.5 2.5 10.0 570.2 66.7 725.0 666.2 55.9 8.5 mon- soon + − where dry: no flow, BLD: below detection limit, TDS = (Na + K + Ca + Mg + Cl + F + SO + NO + HCO + SiO), TZ = total dissolved cation, TZ = total dissolved anion, 4 3 3 2 − + NICB = ((TZ − TZ )/ Mean) × 100% (Gurumurthy et al. 2012). All the samples are in the range of less than 15% 196 Aquatic Geochemistry (2021) 27:173–206 1999) and adjacent river Nethravati (Gurumurthy et al. 2012) but higher than Amazon river (44 mg/l) (Stallard and Edmond, 1983). The TDS (and EC) increases toward the outlet spa- tially, because of the increasing sea-salt influence. The mean concentration of major ions in the study area was plotted in bar dia- gram (Fig.  3). The abundance of the major cations in the samples varied in the order of + 2+ 2+ + + Na > Ca > Mg > K in all the seasons. N a is the major cation dominating in the study area. The concentration of Na was ranging from 1.2 to 13  mg/l during the study period. The main source of the sodium ion is Na- plagioclase (albite) weathering. The con- centration of K temporally varied from 0.02 to 3.1  mg/l throughout the study area. The + 2+ major source is from the K feldspar of basement rock. Ca was the second dominant 2+ ion in the study area. The Ca concentration was ranging from 1 to 10 mg/l in the study area. The source of calcium is from the basement rock biotite–sillimanite gneiss. Major 2+ cation Mg was showing temporal variation from 0.2 to 6.3  mg/l in their concentration. The main source of magnesium is from the weathering of ferromagnetic minerals. The average anion concentration in the samples varied in the range − − 2− − − HCO > Cl > SO > NO > F . Bicarbonate concentration ranged from 13 to 77  mg/l 3 4 3 in the river system. Average value of HC O of the river Payaswini–Chandragiri is higher than the global average (Gaillardet et al. 1999), Yamuna (Dalai et al. 2002), Kaveri (Pat- tanaik et al. 2013), Brahmaputra (Galy and France-Lanord, 1999) and river Congo (Dupre et  al. 1996). The major source of HC O ion is from the weathering of silicate rocks in the catchment. Rainwater reacts with atmospheric C O and soil CO and leaches the sili- 2 2 cate rocks, leading to the release of HCO . The major contribution of chloride is from the atmospheric deposition. The concentration was showing high temporal variation, with maximum concentration of 11 mg/l recorded during the pre-monsoon season. The concen- tration was ranging from 0.2 to 11 mg/l in the study area. The higher concentration of chlo- ride in the pre-monsoon season could be because of the low discharge in this season couple with pre-concentration due to evapotranspiration. The silica concentration in the study area ranged from 2 to 28  mg/l in pre-monsoon; 1–20 mg/l in monsoon; and 1–16 mg/l during the post-monsoon, respectively. The source of SiO in the study area is from the weathering of catchment silicate rocks (Fig. 1). Minimal concentration of major ions was observed during the monsoon season in the study area, which suggested a high level of dilution due to mixing of rainwater. The Fig. 3 Bar diagram of major ions concentration (μmol/l) of the Payaswini–Chandragiri river in different seasons 1 3 Aquatic Geochemistry (2021) 27:173–206 197 pre-monsoon sample showed a high level of concentration in all major ions compared to the other two seasons. The yearly mean TDS of this river system (55 mg/l) is less than half the concentration of the world average rivers (120 mg/l- Gaillardet et al. 1999). During the post-monsoon, major contribution to the river water is from the groundwater discharge. The leaching of terrain rock into the river water through groundwater was the reason for the high ionic concentration in the post-monsoon season in the study area (Thomas et al. 2015). The spatial variable can be noticed in water geochemistry in all the seasons, which is mainly due to the influence of runoff from the different regions. 3.2 Major ion chemistry in Payaswini–Chandragiri river basin The percentage concentration of major ions is plotted in ternary plot (Fig. 4) to evaluate the dominance of ions and percentage concentration of major ions in Payaswini–Chandragiri river water. 2+ On the cation plot (Fig.  4a), most of the samples lie in between the Na + K and Ca + 2+ region. The dominance of N a and Ca in Payaswini–Chandragiri river water indicates silicate dominant lithology. Forty percentage of the total cations was contributed by N a , 2+ 2+ + whereas Ca, Mg and K contributed 35%, 19% and 6%, respectively. − − In the anion plot (Fig.  4b), the samples plot in between the HC O and Cl region. − − 2− HCO, Cl and SO contributed 85%, 12% and 3%, respectively. Percentage of anion 3 4 concentration indicated that the carbonic acid weathering was contributing to high concen- tration of ions into the river basin, whereas sulfuric acid weathering was negligible. 3.3 Source of major ions in the Payaswini–Chandragiri river basin The dissolved major ion concentration in Payaswini–Chandragiri river water was mainly derived from weathering of basin rocks, atmospheric deposition, anthropogenic activities and biomass deposit (2). X river = X + X + X + X weathering atmospheric precipitation anthropogenic deposition biomass contribution (2) Fig. 4 Ternary plot of a major cations and b major anions in the river water 1 3 198 Aquatic Geochemistry (2021) 27:173–206 where X = X + X weathering carbonate weathering silicate weathering. According to Krishnaswami and Singh (2005), at steady state, plant uptake and its decay may not change the ionic budget of the river water, thus indicating negligible contribution from the biomass. 3.3.1 Atmospheric deposition to the river basin The scatter plot of Na/Cl vs Cl (Fig. 5) explains the major ion contribution by rainwater to the river water. The cyclic salt input correction (Stallard and Edmond 1981, 1983) deducts atmospheric deposition of Cl ions from the river water. This is given in Eq:3. Sea salt corrected ion =(X −Cl )∗ (X∕Cl) (3) river river rain where X = major ion concentration measured in the river water. river The atmospheric contribution from the rain water is corrected using weighted mean − + 2+ + 2+ value of published rain water data (Cl = 47; Na = 45; Ca = 20; K = 5; Mg = 7 and 2− SO = 9 μmol/l) of Western Ghats (Gurumurthy et al. 2012). In the Payaswini–Chandra- giri river system, approximately 50% of N a was contributed from the atmosphere to the downstream region (S1) because of its proximity to the Arabian sea. The river water Na/Cl molar ratio was higher than 1, which indicated that Na was sourced from the catchment bedrock (Hem 1985; Meybeck 1987). 3.3.2 Major ions from the anthropogenic deposition Anthropogenic sources like domestic and industrial sewage and agricultural effluents can modify the ion concentrations of river water (Sun et  al. 2010; Han et  al. 2010; Liu et  al. − 2− − 2018). Cl, SO and NO are the major ions associated with the anthropogenic activ- 4 3 ity, and they are used as proxies to identify anthropogenic activities in various watersheds (Shin et al. 2011). The influence of anthropogenic activities on the river in the environment was calculated based on the percentage of pollution as stated by Pacheco and Van der Weijden (1996). Fig. 5 Scatter plot Na/Cl versus Cl in Payaswini-Chandragiri river basin 1 3 Aquatic Geochemistry (2021) 27:173–206 199 Fig. 6 Variation of pollution percentage versus Na/Cl (Faso et al. 2018) % pollution = Cl + SO + NO ∕ Cl + SO + NO + HCO ∗ 100 (4) 4 3 4 3 3 The percentage of pollution was calculated in every sample and plotted against the Na/Cl ratio (Fig.  6). The areas having ratio ≥ 40% were dominated by pollution from anthropo- genic activities, while those with ≤ 40% were dominated by the rock weathering process. The figure shows all the samples lie within the limits, indicating that the river basin is not affected by anthropogenic activities. So the contribution of major ions, especially chloride, to the Payaswini–Chandragiri river water from the anthropogenic inputs is negligible. 3.3.3 Major ions from the rock weathering Major ion chemistry of Payaswini–Chandragiri river system is dependent on various 2+ 2+ − natural processes. The ionic ratio of (Ca + Mg )/HCO in the river water varied from 0.20 to 0.25 suggesting the significance of chemical weathering of silicate rock in − 2+ the Payaswini–Chandragiri river hydrochemistry. The ionic ratio of HC O /Ca was higher than 7 in all the seasons, also indicating the dominance of silicate weathering 2+ 2+ + in the study area (Holland 1978). The ratios of Ca and Mg versus N a were used to calculate the relative concentration from the bedrock (Thomas et  al. 2014). The ratios 2+ 2+ of these indicated that the Ca and Mg were dominated by silicate rock weathering. The mixing plot of the atmospheric input corrected Na/Ca versus Mg/Na (Fig.  7) molar ratios suggested that the Payaswini–Chandragiri river water was influenced by the 2+ 2+ + + water–silicate rock interaction. The low mean ionic ratios of (Ca + Mg )/(Na + K ) − + + and HCO /(Na + K ) also confirmed that the basin was dominated by the silicate rock weathering. The degree of rock–water interaction varied seasonally depending upon the climatic condition (temperature, humidity and rainfall), leading to temporal variation in the concentration of silicate-derived ions. Samples collected in all seasons were plotting in the silicate weathering region. 1 3 200 Aquatic Geochemistry (2021) 27:173–206 Fig. 7 Mixing diagram of + 2+ 2+ normalized Na, Ca and Mg , the molar ratio (Gaillardet et al. 1999) 3.4 Silicate weathering rate and carbon dioxide consumption rate 3.4.1 Silicate weathering rate Silicate weathering rates of the study area for pre-monsoon, monsoon and post-monsoon in 2016 and 2017 were calculated by using the forward model (Wu et al. 2008), the product of discharge per unit area and concentrations of major elements (Eq. 5). SWR = Q (Na + K + Mg + Ca) + SiO (5) sil 2 where (Na + K + Mg + Ca) = dissolved cations derived from silicate weathering, Q = water sil discharge per unit area. + + During chemical weathering, it is assumed that N a and K are derived from the feld- 2+ 2+ spar minerals and Ca and Mg from the pyroxene minerals. After the atmospheric and anthropogenic corrections, remaining cations are from the chemical weathering of major 2+ 2+ rocks in the river basin. So, silicate-derived Ca and Mg were calculated from the gneissic and charnockite rock, based on the following equations. Ca from silicate rock = Na ∗∕(Ca∕Na) (6) bedrock Mg from silicate rock = Na ∗∕(Mg∕Na) (7) bedrock where Na* is corrected Na mean concentration from atmospheric inputs. In this study, the basement rock was completely of gneissic and charnockite origin, and thus, the value of Ca/Na was 0.41 and Mg/Na was 0.325, respectively. These ratios for the bedrock were obtained through a compilation of previous literature mentioned in Gurumurthy et  al. (2012). The silicate weathering rate of the Payaswini–Chandragiri river system was calculated based on the major ion composition at Adoor and Kottody. Estimated seasonal and annual val- ues are given in Table 2. The average annual silicate weathering rate of Payaswini–Chandra- −2 −1 −2 −1 giri river basin was 42 t km y and 36 t km y in 2016 and 2017, respectively. The river −2 −2 system shows higher SWR in monsoon season (37 t km in 2016 and 26 t km in 2017) 1 3 Aquatic Geochemistry (2021) 27:173–206 201 Table 2 Silicate weathering rate Seasons 2016 2017 and CO drawdown rate of the study area in various seasons −2 SWR (t km ) Pre-monsoon 0.63 0.39 Monsoon 37.30 25.97 Post-monsoon 8.24 9.92 Annual 42.17 36.28 5 −2 CCR (×  10 mol  km ) Pre-monsoon 0.13 0.10 Monsoon 7.97 5.83 Post-monsoon 1.45 2.34 Annual 9.55 8.28 −2 −1 with 82% of total discharge. The estimated annual SWR (39 t km y ) of this study was 0.9 −2 −1 times lower than the adjacent southwest-flowing river Netravathi (42 t km y ; Gurumurthy et al. 2012), mainly due to the runoff and variations in the drainage area. Comparing this study with the other Indian rivers, the silicate weathering flux of the Payaswini–Chandragiri river was higher than the other Indian rivers. The silicate weathering rates of the Himalayan river −2 −1 systems such as Ganga [10.2–15.2 t km y , (Krishnaswami et  al. 1999; Gaillardet et  al. −2 −1 1999; Dalai et al. 2002)], Indus [3.8 t km y , (Gaillardet et al. 1999)]; Bramhmputra [6.47 −2 −1 −2 −1 t km y , (Das et al. 2016)], Narmada [12.67 t km y ; (Gupta et al. 2011)], Tapti (7.32 −2 −1 −2 −1 t km y ), Kavery [9.44 t km y , (Pattanaik et  al. 2013)] are all lower than the Payas- wini–Chandragiri river system (Table 3). And the annual SWR of the study area was higher −2 −1 than the river Mahanadi [32 t km y , (Bastia and Equeenuddin 2019)] and Swarnamukhi −2 −1 river [30.57 t km y (Patel et al. 2020)]. Also, SWR of the study area was higher than the −2 −1 −2 −1 −2 global watersheds like Amazon (13 t km y ), Mackenzie (1.8 t km y ), Parana (5 t km −1 −2 −1 −2 −1 −2 −1 y ), Congo-Zaire (4.2 t km y ), Orinoco (9.5 t km y ), Mekong (14.3 t  km  y ) and −2 −1 Rio Icacos (40 t km   y ) (Gaillardet et al. 1999). The southwest-flowing rivers show less SWR as compared to the rivers draining basaltic rock rivers, though are higher than the other Himalayan rivers. The resultant weathering vari- ability could be due to variable lithologies in the Indian river basin and the climatic difference in the tropical region. Higher runoff, temperature and basin formation are the main control- ling factors of high SWR in the southwest-flowing rivers. The southwest-flowing rivers show higher SWR comparing to the east-flowing rivers in monsoon season, Krishna Basin and the −2 −1 Western Ghats of the Deccan Traps [53 t km y (Pattanaik et al. 2013; Gurumurthy et al. 2012)], due to intense rainfall and resulting higher runoff (Pattanaik et al. 2013). The large silicate weathering rate was mostly resulting from the high physical erosion rate (thus exposing the rock surface) in the basin due to high-intensity rainfall (4000 mm/y) (Vinu- tha 2014). Runoff, granitic terrain, morphology of the study area were the prime factors con- trolling the silicate weathering rate. Tropical climate speeds up the weathering of silicate min- erals and CO sequestration in the Western Ghats, while high runoff contributes to excessive bicarbonates into the Arabian Sea (Reddy et al. 2019). 3.4.2 Carbon dioxide consumption rate (CCR) during silicate rock weathering The intense silicate weathering leads to the drawdown of atmospheric CO . The actual rate of carbon dioxide consumption during silicate rock weathering is calculated using the fol- lowing equation: 1 3 202 Aquatic Geochemistry (2021) 27:173–206 1 3 Table 3 Comparison of silicate weathering rate and associated CO drawdown rate of the Payaswini–Chandragiri river catchment with other rivers River basin Discharge Area SWR CO sil References 3 3 2 −2 −1 5 −2 −1 (km /year) (10 km )(t km year ) (10 mol  km year ) Ganga, Rishikesh 22.4 20 12.9 3.80 Krishnaswami and Singh (1998) Amazon 6590 6112 13 0.52 Gaillardet et al. (1999) Brahmaputra 510 580 10.3 1.50 Ganges 493 1050 14 4.50 Mississippi 580 2980 3.8 0.70 Indus 90 916 3.8 0.60 Yangtze 928 1808 5.5 0.60 Mackenzie 308 1787 1.8 0.34 Yamuna 10.8 10 28 7.00 Bhima 34 12 3.30 Dalai et al. (2002) Gad 1 40 5.70 Krishna 36 14 4.20 Das et al. (2005) Red 123 120 27.5 6.83 Moon et al. (2007) Tapti 61 33.6 18.10 Sharma and Subramanian (2008) Narmada 89 33.9 21.20 Godavari 110 310 34.69 5.80 Jha et al. (2009) Upper Han 41.1 95 5.6 1.01 Li et al. (2009) Nethravati 12 4 42 2.8–2.9 Gurumurthy et al. (2012) Kaveri, Musiri 8.5 66 7.9 2.6–3 Pattanaik et al. (2013) Huanghe 28.3 752 3.23 0.35 Fan et al. (2014) Mekong 470 795 10.2 1.91 Li et al. (2014) Swarnamukhi 5.39 3 30.57 8.77 Patel et al. (2020) Payaswini-Chandragiri river 4.40 1.4 42.17 (2016) 9.55 (2016) This study 36.28 (2017) 8.28 (2017) Aquatic Geochemistry (2021) 27:173–206 203 Fig. 8 Comparison of annual silicate weathering rate (SWR) and associated CCR in the Payaswini–Chan- dragiri river in the year 2017 with major Indian rivers and selected world rivers (Gaillardet et  al. 1999; Dalai et al. 2002; Krishnaswami and Singh (1998;Das et al. 2005; Jha et al. 2009; Gupta et al. 2011; Guru- murthy et al. 2012; Pattanaik et al. 2013) + + 2+ 2+ CCR = Q ∕A ⋅ Na + K + Mg + Ca (8) sil 3 2 where Q is the discharge in m /s, A is the surface area of the watershed in km and + + 2+ 2+ (Na + K + Mg + Ca ) is the silicate-derived cations. sil The average annual CCR due to silicate rock weathering of Payaswini–Chandragiri river 5 −2 −1 5 −2 −1 catchment was found to be 9.6 × 10 mol  km y and 8.3 × 10 mol  km y for 2016 5 −2 −1 and 2017, respectively. The Netravathi showed lower CCR (2.9 × 10 mol  km y , Guru- murthy et al. 2012) than this study. The CCR of Payaswini–Chandragiri river catchment was higher than other tropical 5 −2 −1 river systems (Fig.  8), Godavari (5.8 × 10 mol  km y ) (Jha et  al. 2009) and Yamuna 5 −2 −1 (5.5 × 10 mol  km y ) (Krishnaswami and Singh 2005), Bhagirathi–Alaknanda 5 −2 −1 (4 × 10 mol  km y ) (Krishnaswami and Singh 1998), Narmada and Tapti Riv- 5 −2 −1) ers (12.6 × 10 mol  km y , Krishna Basin and Western Ghats of the Deccan Traps 5 −2 −1) 5 −2 −1 (7.4 × 10 mol  km y (Dessert et al. 2003) and Brahmaputra (5.2 × 10 mol  km y ) 5 −2 −1) (Das et al. 2016), Mahanadi ( 4.78 × 10 mol  km y (Bastia and Equeenuddin 2019) and 5 −2 −1) Kaveri basin (3.83 × 10 mol  km y (Pattanaik et al. 2013). The calculated CCR during silicate weathering in the study area was, however, 5 −2 higher than the world river watersheds (Table  3) such as Amazon (0.5 × 10 mol  km −1 5 −2 −1 5 −2 −1 y ), Congo-Zaire (0.5 × 10 mol  km y ), Orinoco (0.6 × 10 mol  km y ), Parana 5 −2 −1 5 −2 −1 (0.9 × 10 mol  km y ) (Gaillardet et al. 1999) and Indus basin (0.6 × 10 mol  km y ) (Fig.  8). Intense monsoonal rainfall and the dominant silicate minerals in the catchment area could be the reasons for the higher consumption of CO during the silicate weath- 5 −2 ering of Payaswini–Chandragiri river in the monsoonal season (8 × 10 mol  km and 5 −2 6 × 10 mol  km in 2016 and 2017) compared to other seasons (Table 2). 4 Conclusions This study analyzed the geochemical characteristics and tabulated the atmospheric C O drawdown rate during silicate weathering of a tropical river catchment, Payaswini–Chan- dragiri river basin, southwestern coast of India. The analyzed results indicate that the 1 3 204 Aquatic Geochemistry (2021) 27:173–206 hydrochemical characteristics of river water gradually change with seasons compared to spatial variations due to runoff, climate and temperature. The dominance of major ions in − − 2− − the Payaswini–Chandragiri river system follows the order of HCO > Cl > SO > NO 3 4 3 + 2+ 2+ + for anions, whereas major cation concentration followed the order N a > Ca > Mg > K + 2+ 2+ − in all the seasons. The Na normalized Ca versus Mg and HCO plots suggested the contribution of major ions through silicate minerals. The estimated silicate weathering rate in Payaswini–Chandragiri river catchment was −2 −1 −2 −1 42 t km y in the year 2016 and 36 t km y in 2017. This value was 0.9 times that of the adjacent west-flowing river Nethravati (Gurumurthy et  al. 2012). The average annual CCR due to silicate rock weathering of Payaswini–Chandragiri river catchment was 5 −2 −1 5 −2 −1 9.6 × 10 mol  km y and 8.3 × 10 mol  km y for 2016 and 2017, respectively. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s10498- 021- 09394-2. Acknowledgements The first author is thankful to DST-INSPIRE fellowship (IF150682) provided by the Department of Science and Technology. Central Instrumentation Facility (CIF), MIT, is thanked for provid- ing the analytical facilities. The insightful and painstaking reviews from the referees are greatly appreciated. Funding Open access funding provided by Manipal Academy of Higher Education, Manipal. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Ahmad T, Khanna PP, Chakrapani GJ, Balakrishnan S (1998) Geochemical characteristics of water & sedi- ment of the Indus River, Trans-Himalaya, India: constraints on weathering & erosion. J Asian Earth Sci 16(2–3):333–346. https:// doi. org/ 10. 1016/ S0743- 9547(98) 00016-6 Andersson L, Wilk J, Todd MC, Hughes DA, Earle A, Kniveton D, Savenije HHG (2006) Impact of climate change and development scenarios on flow patterns in the Okavango River. J Hydrol 331(1–2):43–57. https:// doi. org/ 10. 1016/j. jhydr ol. 2006. 04. 039 Bastia F, Equeenuddin SM (2019) Chemical weathering and associated CO consumption in the Mahanadi River Basin, India. J Asian Earth Sci 174:218–231. https:// doi. org/ 10. 1016/j. jseaes. 2018. 12. 010 Berner RA (1991) A model for atmospheric CO over Phanerozoic time. Am J Sci 291:339–376. https:// doi. org/ 10. 2475/ ajs. 291.4. 339 Dalai TK, Krishnaswami S, Sarin MM (2002) Major ion chemistry in the headwaters of the Yamuna river system: chemical weathering, its temperature dependence and CO consumption in the Himalaya. Geo- chem Cosmochim Acta 66:3397–3416. https:// doi. org/ 10. 1016/ S0016- 7037(02) 00937-7 Das A, Krishnaswami S, Sarin MM, Pande K (2005) Chemical weathering in the Krishna basin and western Ghats of the Deccan traps, India: rates of basalt weathering and their controls. Geochem Cosmochim Acta 69:2067–2084. https:// doi. org/ 10. 1016/j. gca. 2004. 10. 014 Das P, Sarma KP, Jha PK, Ranjan R, Herbert R, Kumar M (2016) Understanding the cyclicity of chemi- cal weathering and associated CO consumption in the Brahmaputra River Basin (India): the role of major rivers in climate change mitigation perspective. Aquat Geochem 22(3):225–251. https:// doi. org/ 10. 1007/ s10498- 016- 9290-6 Dekov VM, Komy Z, Araújo F, Van Put A, Van Grieken R (1997) Chemical composition of sediments, suspended matter, river water and ground water of the Nile (Aswan-Sohag traverse). Sci Total Environ 201(3):195–210 1 3 Aquatic Geochemistry (2021) 27:173–206 205 Dessert CD, Gaillardet B, François J, Louis M, Allègre CJ (2003) Basalt weathering laws and the impact of basalt weathering on the global carbon cycle. Chem Geol 202(3–4):257–273. https:// doi. org/ 10. 1016/j. chemg eo. 2002. 10. 001 Dupre B, Gaillardet J, Rousseau D, Allegre CJ (1996) Major and trace elements of river borne material: the Congo Basin. Geochem Cosmochim Acta 60:1301–1321. https:// doi. org/ 10. 1016/ 0016- 7037(96) 00043-9 Faso BS, Sawadogo A, Yoni S, Nimi M, Zongo M, Bamba O, Ousmane B (2018) Hydrogeochemical char- acterization of dug well water and its suitability for domestic water supply in the village of Passa- kongo, Dédougou Municipality, hydrogeochemical characterization of dug well water & its suitability for domestic water supply in T. Environ Nat Resour Res 8(3):126–137. https:// doi. org/ 10. 5539/ enrr. v8n3p 126 Gaillardet J, Dupre B, Louvat P, Allegre CJ (1999) Global silicate weathering and CO consumption rates deduced from the chemistry of large rivers. Chem Geol 159:3–30. https:// doi. org/ 10. 1016/ S0009- 2541(99) 00031-5 Galy A, France-Lanord C (1999) Weathering processes in the Ganges-Brahmaputra basin and the river- ine alkalinity budget. Chem Geol 159:31–60. https:// doi. org/ 10. 1016/ S0009- 2541(99) 00033-9 Guo H, Wang Y (2005) Geochemical characteristics of shallow groundwater in Datong Basin, North- western China. J Geochem Explor 87(3):109–120. https:// doi. org/ 10. 1016/j. gexplo. 2005. 08. 002 Gupta H, Chakrapani GJ, Selvaraj K, Kao SJ (2011) The fluvial geochemistry, contributions of silicate, carbonate and saline-alkaline components to chemical weathering flux and controlling parameters: Narmada River (Deccan Traps), India. Geochim Cosmochim Acta 75(3):800–824. https:// doi. org/ 10. 1016/j. gca. 2010. 11. 010 Gurumurthy GP, Balakrishna KR, Braun J, Audry JJ, Shankar S, Udaya HN, Manjunatha BR (2012) Controls on intense silicate weathering in a Tropical River, Southwestern India. Chem Geol 300– 301:61–69. https:// doi. org/ 10. 1016/j. chemg eo. 2012. 01. 016 Han G, Tang Y, Zhifang Xu (2010) Fluvial geochemistry of rivers draining Karst Terrain in Southwest China. J Asian Earth Sci 38(1–2):65–75. https:// doi. org/ 10. 1016/j. jseaes. 2009. 12. 016 Hem JD (1985) Study and interpretation of the chemical characteristics of natural water, vol 2254. Department of the Interior, US Geological Survey. Huang X, Sillanpää M, Gjessing ET, Vogt RD (2009) Water quality in the Tibetan Plateau: major ions and trace elements in the headwaters of four major Asian rivers. Sci Total Environ 407(24):6242– 6254. https:// doi. org/ 10. 1016/j. scito tenv. 2009. 09. 001 Huh Y (2003) Chemical weathering and climate—a global experiment: a review. Geosci J 7(3):277–288. https:// doi. org/ 10. 1007/ BF029 10294 Jha PK et al (2009) Chemical weathering and associated CO consumption in the Godavari River Basin, India. Chem Geol 264(1–4):364–374. https:// doi. org/ 10. 1016/j. chemg eo. 2009. 03. 025 Jiang LY, Wang Z, Liu R, Wang Z, Lei Wu, Shanshan. (2015) Hydrochemistry of the middle and upper reaches of the Yarlung Tsangpo River system: weathering processes and CO consumption. Environ Earth Sci 74(3):2369–2379. https:// doi. org/ 10. 1007/ s12665- 015- 4237-6 Krishnaswami S, Singh SK (1998) Silicate and carbonate weathering in the drainage basins of the Ganga-Ghaghara-Indus head waters: contributions to major ion and Sr Isotope geochemistry. Proc Indian Acad Sci Earth Planet Sci 107(4):283–291. https:// doi. org/ 10. 1007/ BF028 41595 Krishnaswami S, Singh SK (2005) Chemical weathering in the River Basins of the Himalaya, India. Curr Sci 89(5):841–849 Krishnaswami S, Singh SK, Dalai T (1999) Silicate weathering in the Himalaya: role in contributing to major ions and radiogenic Sr to the Bay of Bengal. In: Somayajulu BLK (ed) Ocean science, trends & future directions. Indian National Science Academy and Academia International, New Delhi, pp 23–51 Liu WX, Sun Z, Zhao H, Shi T, Liu C, Taoze. (2018) Geochemistry of the dissolved loads during high- flow season of rivers in the Southeastern Coastal Region of China: anthropogenic impact on chemi- cal weathering and carbon sequestration. Biogeosciences 15(16):4955–4971. https:// doi. org/ 10. 5194/ bg- 2018- 109 Meybeck M (1987) Global chemical weathering of surficial rocks estimated from river dissolved loads. Am J Sci 287(5):401–428. https:// doi. org/ 10. 2475/ ajs. 287.5. 401 Millot R, Gaillardet J, Dupré B, Allègre CJ (2002) The global control of silicate weathering rates and the coupling with physical erosion: new insights from rivers of the Canadian Shield. Earth Planet Sci Lett 196(1–2):83–98. https:// doi. org/ 10. 1016/ S0012- 821X(01) 00599-4 Millot R, Gaillardet J, Dupré B, Allégre CJ (2003) Northern latitude chemical weathering rates: clues from the Mackenzie River Basin, Canada. Geochim Cosmochim Acta 67(7):1305–1329. https:// doi. org/ 10. 1016/ S0016- 7037(02) 01207-3 1 3 206 Aquatic Geochemistry (2021) 27:173–206 Moon S, Huh Y, Qin J, van Pho N (2007) Chemical weathering in the Hong (Red) River Basin: rates of silicate weathering and their controlling factors. Geochim Cosmochim Acta 71(6):1411–1430. https:// doi. org/ 10. 1016/j. gca. 2006. 12. 004 Oliva P, Viers J, Dupré B (2003) Chemical weathering in granitic environments. Chem Geol 202(3– 4):225–256. https:// doi. org/ 10. 1016/j. chemg eo. 2002. 08. 001 Pacheco F, Van der Weijden CH (1996) Contributions of water–rock interactions to the composition of groundwaters in areas with a sizeable anthropogenic input: a case study of the water of the Fundao area, central Portugal. Water Resour Res 32:3553–3570. https:// doi. org/ 10. 1029/ 96WR0 1683 Patel P, Raju NJ, Subramanian V, Gossel W, Wycisk P (2020) Chemical weathering and atmospheric CO consumption in the Semi-Arid Swarnamukhi Basin (Peninsular India) estimated from river water geochemistry. Appl Geochem 113:104520. https:// doi. org/ 10. 1016/j. apgeo chem. 2020. 104520 Pattanaik JK, Balakrishnan S, Bhutani R, Singh P (2013) Estimation of weathering rates and CO drawdown based on solute load: significance of granulites and gneisses dominated weathering in the Kaveri River Basin, Southern India. Geochim Cosmochim Acta 121:611–636. https:// doi. org/ 10. 1016/j. gca. 2013. 08. 002 Reddy SKK, Gupta H, Reddy DV (2019) Dissolved inorganic carbon export by mountainous tropical rivers of the Western Ghats, India. Chem Geol 530:119316. https:// doi. org/ 10. 1016/j. chemg eo. 2019. 119316 Sarin MM, Krishnaswami S, Dilli K, Somayajulu BLK, Moore WS (1989) Major ion chemistry of the Ganga-Brahmaputra river system: Weathering processes and fluxes to the Bay of Bengal. Geochimica et Cosmochimica Acta 53(5):997–1009 Sharif MU, Davis RK, Steele KF, Kim B, Kresse TM, Fazio JA (2008) Inverse geochemical modeling of groundwater evolution with emphasis on arsenic in the Mississippi River Valley alluvial aquifer, Arkansas (USA). J Hydrol 350(1–2):41–55 Sharma SK, Subramanian V (2008) Hydrochemistry of the Narmada and Tapti Rivers, India. Hydrol Pro- cess 22(17):3444–3455 Shin WJ, Ryu JS, Park Y, Lee KS (2011) Chemical weathering and associated CO consumption in six major river basins, South Korea. Geomorphology 129(3–4):334–341. https:// doi. org/ 10. 1016/j. geomo rph. 2011. 02. 028 Stallard RF, Edmond JM (1981) Geochemistry of the Amazon precipitation chemistry and the marine con- tribution to the dissolved load at the time of peak discharge. J Geophys Res 86(10):9844–9858. https:// doi. org/ 10. 1029/ JC086 iC10p 09844 Stallard RF, Edmond JM (1983) Geochemistry of the Amazon: 2. The influence of geology and weathering environment on the dissolved load. J Geophys Res 88:9671–9688. https:// doi. org/ 10. 1029/ JC088 iC14p Sun HH, Li J, Zhang D, Shurong Lu, Xixi (2010) Science of the total environment chemical weathering inferred from riverine water chemistry in the lower Xijiang. Sci Total Environ 408(20):4749–4760. https:// doi. org/ 10. 1016/j. scito tenv. 2010. 06. 007 Thomas J, Joseph S, Thrivikramji KP, Manjusree TM, Arunkumar KS (2014) Seasonal variation in major ion chemistry of a Tropical Mountain River, the Southern Western Ghats, Kerala, India. Environ Earth Sci 71(5):2333–2351. https:// doi. org/ 10. 1007/ s12665- 013- 2634-2 Thomas J, Joseph S, Thrivikramji KP (2015) Hydrochemical variations of a Tropical Mountain River sys- tem in a rain shadow region of the Southern Western Ghats, Kerala, India. Appl Geochem 63:456–471. https:// doi. org/ 10. 1016/j. apgeo chem. 2015. 03. 018 Varol M, Gökot B, Bekleyen A, Şen B (2013) Geochemistry of the Tigris River Basin, Turkey: spatial & seasonal variations of major ion compositions and their controlling factors. Quatern Int 304:22–32. https:// doi. org/ 10. 1016/j. quaint. 2012. 12. 043 Wu W, Yang J, Shijin Xu, Yin H (2008) Geochemistry of the headwaters of the Yangtze River, Tongtian He and Jinsha Jiang: silicate weathering and CO consumption. Appl Geochem 23(12):3712–3727. https:// doi. org/ 10. 1016/j. apgeo chem. 2008. 09. 005 Zhang J, Huang WW, Létolle R, Jusserand C (1995) Major element chemistry of the Huanghe (Yellow River), China—weathering processes and chemical fluxes. J Hydrol 168(1–4):173–203. https:// doi. org/ 10. 1016/ 0022- 1694(94) 02635-O Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1 3

Journal

Aquatic Geochemistry – Springer Journals

Published: Apr 28, 2021

Keywords: Tropical river system; Water geochemistry; Silicate weathering rate; Atmospheric CO2 consumption; Southwest coast of India

There are no references for this article.