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Geochimica et Cosmochimica Acta 75 (2011) 800–824

The fluvial geochemistry, contributions of silicate, carbonate andsaline–alkaline components to chemical weathering flux and

controlling parameters: Narmada River (Deccan Traps), India

Harish Gupta a,b,⇑, Govind J. Chakrapani a, Kandasamy Selvaraj c, Shuh-Ji Kao b,c

a Department of Earth Sciences, Indian Institute of Technology, Roorkee 247667, Indiab State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China

c Research Center for Environmental Changes, Academia Sinica, Nankang, Taipei 115, Taiwan

Received 29 April 2010; accepted in revised form 2 November 2010; available online 17 November 2010

Abstract

The Narmada River in India is the largest west-flowing river into the Arabian Sea, draining through the Deccan Traps, oneof the largest flood basalt provinces in the world. The fluvial geochemical characteristics and chemical weathering rates(CWR) for the mainstream and its major tributaries were determined using a composite dataset, which includes four phasesof seasonal field (spot) samples (during 2003 and 2004) and a decade-long (1990–2000) fortnight time series (multiannual)data. Here, we demonstrate the influence of minor lithologies (carbonates and saline–alkaline soils) on basaltic signature,as reflected in sudden increases of Ca2+–Mg2+ and Na+ contents at many locations along the mainstream and in tributaries.Both spot and multiannual data corrected for non-geological contributions were used to calculate the CWR. The CWR forspot samples (CWRspot) vary between 25 and 63 ton km�2 year�1, showing a reasonable correspondence with the CWRestimated for multiannual data (CWRmulti) at most study locations. The weathering rates of silicate (SilWR), carbonate(CarbWR) and evaporite (Sal–AlkWR) have contributed �38–58, 28–45 and 8–23%, respectively to the CWRspot at differentlocations. The estimated SilWR (11–36 ton km�2 year�1) for the Narmada basin indicates that the previous studies onthe North Deccan Rivers (Narmada–Tapti–Godavari) overestimated the silicate weathering rates and associated CO2

consumption rates. The average annual CO2 drawdown via silicate weathering calculated for the Narmada basin is�0.032 � 1012 moles year�1, suggesting that chemical weathering of the entire Deccan Trap basalts consumes approximately2% (�0.24 � 1012 moles) of the annual global CO2 drawdown. The present study also evaluates the influence of meteorolog-ical parameters (runoff and temperature) and physical weathering rates (PWR) in controlling the CWR at annual scale acrossthe basin. The CWR and the SilWR show significant correlation with runoff and PWR. On the basis of observed wide tem-poral variations in the CWR and their close association with runoff, temperature and physical erosion, we propose that theCWR in the Narmada basin strongly depend on meteorological variability. At most locations, the total denudation rates(TDR) are dominated by physical erosion, whereas chemical weathering constitutes only a small part (<10%). Thus, the CWRto PWR ratio for the Narmada basin can be compared with high relief small river watersheds of Taiwan and New Zealand(1–5%) and large Himalayan Rivers such as the Brahmaputra and the Ganges (8–9%).� 2010 Elsevier Ltd. All rights reserved.

0016-7037/$ - see front matter � 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.gca.2010.11.010

⇑ Corresponding author at: State Key Laboratory of MarineEnvironmental Science, Xiamen University, Xiamen 361005,China. Tel.: +86 592 2182977; fax: +86 592 2184101.

E-mail address: [email protected] (H. Gupta).

1. INTRODUCTION

The first known effort to understand the links betweenatmospheric gases and earth surface processes was madeby Ebelmen in 1845 (Berner and Maasch, 1996). Later,Urey (1952) summarized the silicate–carbonate cycle in

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Chemical weathering of basalts in the Narmada River basin, India 801

the form of chemical reactions, popularly known asEbelmen–Urey reaction. However, most interesting partsof research on fluvial geochemistry have emerged after theseminal work by Garrels and Mackenzie (1967). Since then,the low temperature geochemical processes such as denuda-tion of continents and associated CO2 consumption havereceived wide attention due to their active role in regulatingthe global carbon cycle via silicate–carbonate sub-cycles.Walker et al. (1981) hypothesized a negative feedbackmechanism for long-term stabilization of the Earth’s sur-face temperature and argued for a direct dependency ofcontinental silicate weathering on climatic factors. Berneret al. (1983) further established that it is not the weatheringof carbonates that affect the carbon cycle, but the weather-ing of silicates in particular.

Chemical weathering of silicate rocks is thus consideredto be the principal process of removing CO2 from the atmo-sphere on geological timescale (Berner, 1992). A compara-tive study on a number of small watersheds dominated bysingle rock type has shown that basalts tend to weathermore easily than other crystalline silicate rocks (Meybeck,1987). For this reason, a number of field and laboratoryinvestigations have been conducted for understanding thedifferent aspects of basalt weathering (Dessert et al., 2009and reference therein). These studies both at the watershedand the global scales provided a quantitative picture of ba-salt weathering in different climates by determining the ma-jor controlling parameters and emphasized the importantrole of basalt weathering on the global climate (Gislasonet al., 1996, 2009; Louvat and Allegre, 1997, 1998; Dessertet al., 2001, 2003, 2009; Das et al., 2005; Pokrovsky et al.,2005; Vigier et al., 2005; Rad et al., 2006; Louvat et al.,2008). It is now well established that basalts are the silicaterocks with the highest weathering rates and therebyresponsible for disproportionate CO2 drawdown from theatmosphere (Dessert et al., 2003). Dessert et al. (2003)estimated that alteration of continental basalts accountfor �30% of total CO2 consumption. Further while therates of dissolution of silicate minerals in natural environ-ment are known to be very slow (Wollast and Chou,1988), a study in response to the recent climate change onthe Iceland Basalts (Gislason et al., 2009) shows that theweathering rates may change over much shorter timescales.Such information highlights the imperative need for a betterunderstanding the role of climatic factors on the chemicalweathering processes on shorter timescales.

The Narmada River in India flows largely through theDeccan Traps and the river basin is influenced by mon-soon-dominated tropical climate, serving as an ideal loca-tion to study the chemical weathering processes and theircontrolling factors. Deccan Traps are one of the largestbasaltic provinces on the Earth’s surface with an area of�0.5 � 105 km2. Based on the study of major elements,strontium and 87Sr/86Sr isotopic ratios of the large riversflowing through the North Deccan Traps, Dessert et al.(2001) suggested that the chemical weathering rate andassociated CO2 consumption are relatively high comparedto other basaltic regions. Their results indicate that runoffand temperature are the two main parameters that controlthe CO2 consumption during basalt weathering. Dessert

et al. (2001) also demonstrated the important control ex-erted by emplacement and weathering of large basalticprovinces on the geochemical and climatic changes onEarth. Das et al. (2005) reported a new dataset for theKrishna and the Western Ghat Rivers in India and showedthat CO2 consumption rate for the Deccan Traps in theirstudy area was two to three times lower than that reportedfor the North Deccan Rivers by Dessert et al. (2001).Although a considerable amount of published data onwater chemistry, chemical and silicate weathering ratesare available for the Deccan Rivers (Dessert et al., 2001,2003; Das et al., 2005; Sharma and Subramanian, 2008;Jha et al., 2009), the present contribution is a step forwardfor several reasons: First, the result and discussion pre-sented here are drawn from a composite dataset of surfacewater composition from field sampling done during 2003and 2004 (hereafter referred to as spot samples) as well asa decade-long (1990–2000) fortnight time-series data (here-after referred to as multiannual data). This approach pro-vides an opportunity for understanding the geochemicalprocesses at greater temporal scales. Second, availabilityof daily water discharge and annual runoff data facilitateto understand the influence of water discharge on solutetransportation, to calculate discharge weighted CWR andto access influence of runoff values on the CWR estima-tions. Given the large aerial extent of the Deccan Traps,the paucity of runoff data at spatial and temporal scalesproved to be a major gap in precise estimation of theCWR in most of the earlier studies (Dessert et al., 2001;Das et al., 2005; Jha et al., 2009). Third, among the esti-mated CWR, contributions from silicate, carbonate andsaline–alkaline components were separated. K-normalizedmolar ratios of Mg2+ derived from flood sediments wereused to obtain the Mg content of silicate component (Mgsil)from dissolved load and thus calculated Mgsil used to char-acterize Na+ and Ca2+ from river water. Fourth, CO2 con-sumption rates in silicate weathering for the Narmada basinwere estimated and used to revise annual CO2 drawdownby the entire Deccan Trap basalts. Finally, the presentstudy attempts to evaluate coupling of the CWR and itscomponents with climatic parameters and sediment erosionat annual scale. Thus, the present contribution provides abetter scope and bridges the knowledge gap in terms ofevaluating the influence of non-silicate lithologies over ba-salt signature, runoff in CWR estimation and to accessthe relationship of chemical/silicate weathering with meteo-rological parameters and sediment erosion in the DeccanTrap region.

2. STUDY AREA AND SAMPLING LOCATIONS

The Narmada is the largest west-flowing peninsularriver, ranks seventh in terms of water discharge(�38 km3 year�1) and drainage area (98,796 km2) in Indiansubcontinent. The river rises as groundwater seepage fromNarmada Kund (1057 m above mean sea level-AMSL) atAmarkantak on the eastern fringe of the Maikala Plateau(Fig. 1). Thereafter, the river flows mostly through theDeccan Traps, separating the Vindhyan and the Satpurarange of hills on both sides and after traversing 1312 km

Fig. 1. Lithological map of the Narmada River basin. Samples were collected along the Narmada mainstream and from the major tributaries.The mainstream sampling locations are indicated by black thick crosses and tributaries sampled are marked by white thick crosses. Inset mapshowing the Deccan Region in India.

802 H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824

from its source, it joins the Arabian Sea at about 10 kmnorth of Bharuch. Mean basin elevation is 760 m AMSL,whereas most of the basin is at an elevation of <500 mAMSL. In its entire course, the Narmada is fed by 41 trib-utaries, 22 are on the left bank and rest is on the right bankof the mainstream. The Burhner, Banjar, Hiran, Tawa,Chota-Tawa, Kundi and the Orsang Rivers are the majortributaries (Fig. 1), having catchment area of more than3500 km2 (CWC, 1997–1998).

Although the Deccan basalts occupy larger parts (�70%;Subramanian et al., 2006), Quaternary soils followed bythick sedimentary outcrops of Vindhyans and Gondwanasand scattered patches of other rocks also cover a consider-able portion in the basin (Fig. 1). The Vindhyan group con-sists of a thick sedimentary sequence of sandstone, shale andlimestone. The Satpura range of the Narmada basin is com-posed of Gondwana sediments of fluvial and lacustrine ori-gin (Crumansonata, 1995). The Deccan Traps represent acontinental flood basalt province that records immense accu-mulations of tholeiitic basalt magmas which erupted over arelatively short time span (�0.5 Ma) straddling the Creta-ceous–Tertiary boundary (Shrivastava and Pattanayak,2000). Based on the Ar–Ar isotopic study, Courtillot et al.(1988) argued that the bulk of Deccan eruptions occurredin a short time, between 65 and 69 Ma. On the basis of Re–Os isotopic study, Allegre et al. (1999) have established anage of 65.6 ± 0.3 Ma, which is in good agreement with theprevious estimations (Courtillot et al., 1986, 1988; Duncanand Pyle, 1988; Vandamme et al., 1991; Baksi, 1994).According to Javoy and Michard (1989), �1.6 � 1018 molesof CO2 released during the eruption of Deccan basalts, whichrepresent half of the total ocean CO2 content and that might

have played a crucial role in the mass extinctions of�65 Ma,popularly known as K–T extinction (Courtillot et al., 1988).

The Deccan Trap basalts have been studied extensivelyfor their mineralogy and geochemistry and the data havebeen summarized in a number of review articles (Mahoney,1988; Crumansonata, 1995; Subbarao et al., 2000; Sen,2001). The most prominent rock in the Deccan Traps is theoversaturated quartz-normative tholeiitic basalt and alka-line rocks associated with basalts are restricted in the lowerNarmada valley. The chemical composition of the Trap bas-alts in four different sections, Jamner–Ambadongar (J–A),Gangapur–Barwani–Pati (G–B–P), Indore–Buldana (I–B)and Jabalpur–Seoni (J–S), of Narmada and Tapti regionshas been reported by Geological Survey of India (GSI) (Cru-mansonata, 1995). The chemical data provided in this studybring out subtle but distinct spatial changes in the basaltswithin the apparently uniform tholeiitic basalt volume, sug-gesting the existence of lava types of varying compositionfrom west to east. Representative chemical compositions(Na, K, Ca and Mg) of three formations, occupying theNarmada basin are given in Table AE-4.

The soils in Narmada basin are derived from diverseparent materials, and hence are divided into four majorgroups: alluvial soils (average depth >300 cm), black soil(average depth 100–300 cm), red soil (average depth 25–50 cm) and lateritic soils (average depth 25 cm) (CPCB,2001). In terms of areal extent, the black soils derived fromthe weathering of basalts are the largest group occupyingparts of upper and most of middle-lower basin, except allu-vial dominated upper-middle Narmada valley and coastalplains. The red and lateritic soils mostly occupy the partsof upper Narmada basin.


The Narmada basin is dominated by humid tropical cli-mate. Maximum (average) temperature is observed duringMay (40–42 �C) and minimum (average) is recorded inJanuary (8–13 �C). The majority of precipitation in the basintakes place during the southwest monsoon season frommiddle June to October, accounting for approximately85–95% of the annual precipitation. Approximately 60%of the annual rainfall is received during July and August.The mean annual rainfall in the basin is approximately1178 mm, though the rainfall distribution is not uniformand varies between 600 and 1800 mm.

Approximately 60%, 35% and 5% of the basin area areunder arable land, forest cover and scrub-grassland, respec-tively. The hill slopes with thin soil cover and dissected pla-teaus are main areas under forest cover. The upper, middleand lower plains are broad, fertile and mostly cultivated(CPCB, 2001). A number of dams have been constructedon the Narmada River and its tributaries, mainly for thepurpose of electric power generation, irrigation and forcontrolling floods. The Rani Avantibai Sagar (Bargi dam)at Jamtara, Indra Sagar (IS dam) at Punasa and the SardarSarovar (SS dam) few km upstream of Garudeshwar arethree major reservoirs in the mainstream, whereas UpperBurhner Barna Kolar, Bhagwant Sagar and Tawa damsare constructed on the tributaries.

3. SAMPLING AND METHODOLOGY

In order to demonstrate the spatial variations in waterchemistry, samples were collected from 13 locations alongthe Narmada River at approximately every 100 km dis-tance. In addition to mainstream, eight of major tributarieswere also sampled (Table 1). A total of 73 river water sam-ples, 45 from the mainstream and 28 from the tributaries,were collected in four phases covering pre-monsoon (May2004), monsoon (August 2003 and September 2004) andpost-monsoon (January 2004) seasons. In addition, 36 rain-water samples, 6 during August 2003 and the remainingsamples during June–September 2004, were collected fromdifferent sampling locations during the southwest monsoonperiod (Table AE-1). Rainwater samples were collected inopen buckets/rectangular trays, filled in laboratory pre-cleaned 100 ml polypropylene bottles and stored in refriger-ator until analysis.

The samples were mostly collected from the middle ofthe river either from the bridge or with the help of boatto avoid local heterogeneity and possible human influencenear the river banks. The samples were collected in1000 ml pre-cleaned, high density polypropylene bottlesthat had been copiously pre-rinsed with river water. ThepH, temperature and carbonate alkalinity (Stumm andMorgan, 1996) were measured in the field. The sampleswere filtered through 0.45 lm cellulose nitrate membranefilters within 24 h of sample collection. Each filtered samplewas divided into two aliquots. One aliquot of 250 ml waskept un-acidified to measure anions and dissolved silicaand another aliquot was acidified to pH �2 with HNO3

(suprapure) for cation (Na+, K+, Ca2+ and Mg2+) analysis.Water samples were kept in refrigerator (4 �C) before anal-ysis and equilibrated with ambient temperature prior to

analysis. The major cations in the samples collected duringfirst two phases (August 2003 and January 2004) were ana-lyzed using AAS (Atomic Absorption Spectrophotometer;Model: GBC Avanta) with a precision of ±5%. The accu-racy of the measurement was checked by measuring freshlyprepared standards of known concentrations made fromanalytical grade reagents. For first two phases of samplingand rainwater collected during August 2003, Cl� was mea-sured by argentometric method whereas SO4

2� and NO3�

were determined by spectrophotometric method (APHA,1998). The water samples collected during the last twophases and rainwater samples of September 2004 wereanalyzed for Na+, K+, Ca2+ and Mg2+, Cl�, SO4

2� andNO3

� by Ion Chromatograph (Metrohm792 Basic IC withsuppressor module) with a precision of ±2%. The systemwas calibrated using multi-element MDML standards(Metrohm). Dissolved silica in the river water was measuredwith UV Spectrophotometer (GENESYSe 10 UV) usingammonium molybdate reagent at 410 nm wavelength(APHA, 1998) with a precision of ±2%. Most of the watersamples from the mainstream and its tributaries show spe-cific charge balance, denoted as normalized inorganiccharge balance (NICB =

P+ �P�)/

P+) within ±7%(Table 2), suggesting that the ions measured in this studyby and large account for the charge balance.

The river bed sediments were collected from theNarmada basin in August 2003 from the same samplinglocations as given in Table 1. Freshly deposited floodsediments (within 15 days of a large flood) were collectedwith a plastic spade by scooping from the upper 1 cm ofthe river bed, representing contemporary deposits. Priorto chemical analysis, the samples were oven dried andhomogenized, and the bulk sediment of each sample was fi-nely ground (<200 mesh) in an agate mortar. The contentsof major oxides were determined using X-ray fluorescence(XRF) spectrometer (Siemens SRS-3000) at Wadia Insti-tute of Himalayan Geology, Dehradun. Details of theXRF methods and standardization are described in Sainiet al. (1998).

The multiannual data for a decade (collected fortnightlybetween June 1990 and May 2000) used in this study wereobtained from the Central Water Commission (CWC), anorganization of the Ministry of Water Resources, Govern-ment of India. CWC has a large number of monitoring sta-tions along the Narmada River and its major tributaries forvarious hydrological observations. The details of the rivermonitoring stations are presented in Table 1, whereas theprocedures dealing with the sampling and analysis are de-tailed in CWC working manuals (CWC, 1990–2000). Mostof the sampling locations selected for spot sampling over-lapped with the network of CWC monitoring stations, thusproviding multiannual hydrological data of water dis-charge, sediment load and water quality parameters. Chem-ical data of most of water samples analyzed by CWC showNICB within ±10% and thus comparable with the results ofspot sampling.

The SPSS software version 11.0 for Windows was usedfor the statistical analysis. The results of spot samples fordifferent seasons were processed by one-way analysis of var-iance (ANOVA) followed by Tukey’s multiple-comparison

Table 1Location-wise hydrological characteristics of the Narmada mainstream and its major tributaries.

Sampling location Locationcodea

Lat. �N Long. �E Elevation(m)

Drainage areaup to station(km2)

Length ofriver up tostation (km)

Annualrainfall (mm)

Mean annualtemp (�C)

Water flux(109 km3 year�1)

Leanflowb (%)

Runoff(mm year�1)

fetc Sediment flux

(106 ton year�1)

Narmada mainstream

Amarkantak d N1 22�420 81�420 1057 NA 8 NA 18.9 NA NA NA NA NADindori N2 22�570 81�050 666.1 2292 95 1493 23.0 1.36 2.96g 594 0.60 NAManot N3 22�440 80�310 451.6 4467 218 1517 25.1 3.78 1.70g 846 0.44 5.9Jamtarae N4 23�050 79�570 371.5 17,157 389 1397 25.3 10.95 19.1 638 0.54 3.6Jabalpurd,f N5 23�070 79�480 352.0 18,200 404 1397 25.3 NA NA NA NA NABarmanghat N6 23�010 79�000 319.1 26,453 504 1241 25.3 14.5 12.8 550 0.56 12.0Sandia N7 22�500 78�210 308.6 33,954 594 1150 25.3 19.1 12.5 563 0.51 12.8Hoshangabad N8 22�460 77�430 292.1 44,548 676 1302 26.1 25.8 10.5 579 0.56 23.6Handia N9 22�290 77�000 270.2 54,027 747 1124 27.9 29.0 8.7 537 0.52 32.5Mortakkad N10 22�210 76�020 165.0 67,184 894 965 27.5 29.7 NA 442 0.54 NAMandleshwar N11 22�100 75�390 153.5 72,809 940 820 27.2 36.2 6.3 497 0.39 38.1Rajghat N12 22�040 74�510 128.0 77,674 1015 636 26.9 37.6 5.8 448 0.24 42.6Garudeshwar N13 21�530 73�390 31.2 87,892 1169 1123 27.0 36.2 5.9 411 0.63 28.3

Tributaries

Mohgaon T1-Burhner 22�450 80�370 447.0 4090 177 1517 25.7 2.54 0.97g 621 0.59 3.8Hirdaynagar T2-Banjar 22�320 80�230 436.0 3133 183 1528 24.1 2.09 0.10g 648 0.56 0.9Patan T3-Hiren 23�180 79�390 500.0 4795 188 1280 26.4 2.01 2.30g 419 0.67 NABelkheri T4-Sher 22�540 79�200 650.0 2903 129 1241 25.0 0.82 1.50g 545 0.56 NAGadarwara T5-Shakkar 22�540 78�540 321.0 2270 161 1268 27.8 1.55 1.25g 682 0.46 1.7Chhidgaon T6-Ganjal 22�250 77�200 700.0 1931 89 1148 29.1 1.13 0.69g 586 0.49 NAGinnoree T7-Chota Tawa 22�100 76�390 218.0 4816 169 987 27.8 2.13 0.00g 442 0.62 2.6Kogaon T8-Kundi 22�060 75�410 900.0 3973 120 820 25.9 1.19 0.00g 298 0.64 NAChandwara T9-Orsang 22�010 73�250 18.0 3846 101 631 27.0 1.55 0.00g 404 0.36 1.6

NA = Data not available.a Names following the sampling codes T1 through T10 represent the sampled tributaries.b Calculated from water discharge during March, April and May.c Evapotranspiration factor.d Multiannual data of water chemistry are not available these locations and therefore, only data of spot samples were used for calculations.e River water samples were not collected from these locations and for Ginnore (T7) multiannual data of water chemistry were used for calculations.f Multiannual data of a few km upstream located monitoring station, Jamtara (N4) were used for calculations and comparison.g Values represent the possible groundwater contribution (%) to annual water flux at corresponding location.

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Table 2Dissolved major elemental composition of water samples from the Narmada River and its tributaries at different sampling locations.

Samplinglocation

SampleCode

pH Na+

lMK+

lMCa2+

lMMg2+

lMHCO3

�

lMCl�

lMNO3

�

lMSO4

2�

lMSiO2

lMTZ+

le l�1TZ�

le l�1NICB% TDS

mg L�1CSI Na+*

lMK+*

lMCa2+*

lMMg2+*

lMSO4

2�*

lMTDSw

mg L�1

Monsoon 2003 August

Amarkanrak – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NADindori NB 1 8.2 588 12 595 234 2224 112 12 20 359 2259 2388 �5.7 208 2.4 537 7 548 229 15 74Mohgaon NB 2 7.4 174 9 202 131 808 51 11 19 201 848 909 �7.2 82 0.7 120 4 158 124 11 28Manot NB 3 7.8 445 8 416 353 1920 124 22 15 319 1991 2096 �5.3 180 1.8 383 1 369 334 8 59Bamni NB 4 7.4 428 18 351 257 1640 65 20 24 188 1661 1774 �6.8 149 1.3 376 16 307 253 15 48Jabalpur – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPatan NB 5 7.5 407 57 848 341 2760 157 29 26 208 2842 2998 �5.5 246 1.9 366 56 791 324 19 71Belkheri NB 6 8 554 9 519 280 2160 68 11 20 348 2160 2279 �5.5 199 2.1 490 7 471 275 16 69Barman NB 7 7.3 340 12 461 249 1760 45 16 17 211 1774 1855 �4.6 158 1.3 276 9 413 245 6 48Gadarwara NB 8 7.2 571 9 498 280 2176 22 17 20 379 2135 2256 �5.7 200 1.3 498 3 437 270 12 70Sandia NB 9 7.6 408 14 517 268 2016 47 10 17 212 1993 2108 �5.7 177 1.7 342 10 463 259 7 53Hosangabad NB 10 7.1 465 13 596 333 2320 45 12 19 224 2338 2414 �3.3 203 1.3 417 8 565 324 16 64Chhidgaon NB 11 7.5 418 13 692 513 2736 157 17 31 563 2843 2972 �4.6 261 1.8 379 7 638 506 23 90Handia NB 12 7.5 538 14 554 334 2320 67 11 23 216 2327 2444 �5 204 1.7 445 10 502 328 11 62Mortakka NB 13 7.2 546 15 517 317 2240 67 8 30 213 2230 2375 �6.5 197 1.3 464 10 478 299 19 61Mandleshwar NB 14 7.2 520 14 489 308 2144 22 19 24 214 2128 2233 �4.9 188 1.3 420 9 437 277 14 57Kogaon NB 15 7.4 602 16 665 449 2672 203 27 33 291 2848 2968 �4.2 246 1.7 548 13 623 441 25 80Rajghat NB 16 7.3 576 15 539 329 2336 45 19 29 213 2328 2458 �5.6 205 1.5 468 �1 433 307 12 59Garudeshwar NB 17 7.4 452 17 558 341 2280 67 5 24 222 2267 2401 �5.9 199 1.6 393 13 516 339 15 61Chandwara NB 18 7.2 532 18 465 260 1904 134 11 23 149 1999 2094 �4.8 171 1.2 426 9 391 257 6 50

Post-Monsoon 2004 January

Amarkanrak NB 19 7 238 26 160 130 752 84 18 17 166 845 888 0 77 0.2 188 18 82 121 8 26Dindori NB 20 7.5 727 41 709 435 3024 141 19 16 371 3056 3215 �5.2 271 1.9 676 36 662 429 11 90Mohgaon NB 21 7.4 638 38 609 441 2808 24 10 24 270 2776 2890 �4.1 242 1.7 584 33 565 434 16 77Manot NB 22 7.5 889 47 662 589 3456 103 15 15 313 3439 3604 �4.8 298 2 827 40 615 570 8 95Bamni NB 23 7.2 650 85 364 263 1800 122 45 52 174 1989 2071 �4.1 171 1.1 598 83 320 260 43 62Jabalpur – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPatan NB 24 7.7 743 58 1426 685 4576 427 158 58 197 5022 5277 �5.1 412 2.6 702 56 1369 669 51 119Belkheri NB 25 7.9 597 48 972 889 4320 160 59 29 461 4366 4597 �5.3 378 2.6 533 46 924 884 26 113Barman NB 26 7.6 679 51 572 484 2768 122 34 20 269 2841 2963 �4.3 245 1.9 614 48 523 479 9 77Gadarwara – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NASandia NB 27 7.6 679 55 589 700 3264 141 40 15 284 3312 3475 �4.9 282 2 613 51 534 691 5 83Hosangabad NB 28 7.7 1035 59 625 407 3056 141 62 32 293 3156 3322 �5.2 276 2.1 987 54 593 397 29 98Chhidgaon NB 29 8 854 60 463 498 2544 255 39 44 288 2835 2927 �3.2 239 2.1 815 54 408 491 36 86Handia NB 30 7.7 1354 55 575 438 3296 141 43 30 305 3434 3541 �3.1 296 2 1260 51 524 431 19 107Mortakka NB 31 8 1253 56 640 507 3456 160 34 14 417 3601 3678 �2.1 313 2.4 1171 50 600 489 3 113Mandleshwar NB 32 7.8 1051 57 628 535 3360 136 39 19 290 3434 3573 �4 295 2.2 950 52 576 505 9 96Kogaon NB 33 7.6 522 91 568 823 2896 480 53 32 291 3395 3493 �2.9 272 1.9 468 88 525 815 24 83Rajghat NB 34 7.9 1202 74 677 555 3688 141 39 21 360 3739 3909 �4.6 326 2.4 1094 57 570 533 4 107Garudeshwar NB 35 8 1277 62 564 647 3536 155 49 16 292 3761 3771 �0.3 312 2.3 1217 57 523 645 6 109Chandwara – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

(continued on next page)

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Table 2 (continued)

Samplinglocation

SampleCode

pH Na+

lMK+

lMCa2+

lMMg2+

lMHCO3

�

lMCl�

lMNO3

�

lMSO4

2�

lMSiO2

lMTZ+

le l�1TZ�

le l�1NICB% TDS

mg L�1CSI Na+*

lMK+*

lMCa2+*

lMMg2+*

lMSO4

2�*

lMTDSw

mg L�1

Pre-Monsoon 2004 May

Amarkanrak NB 36 6.9 176 28 172 86 648 62 32 13 171 720 768 �6.8 69 0.1 125 20 94 77 5 22Dindori NB 37 8.5 705 47 697 470 3056 113 7 28 427 3087 3232 �4.7 277 2.9 654 43 650 465 23 95Mohgaon NB 38 8.3 382 37 596 448 2496 86 4 30 577 2505 2646 �5.6 238 2.5 328 32 552 441 21 84Manot NB 39 8.2 803 53 752 684 3720 145 9 27 592 3726 3929 �5.4 338 2.7 741 46 705 664 21 115Bamni NB 40 8 351 37 523 270 1968 66 13 23 402 1974 2093 �6 186 2.1 299 35 479 267 14 65Jabalpur BG NB 41 7.9 228 28 589 313 2048 47 6 22 387 2060 2145 �4.1 190 2.1 187 26 532 296 15 62Jabalpur SG NB 42 7.9 259 28 678 304 2240 69 6 27 426 2252 2368 �5.2 209 2.2 208 24 607 284 19 68Patan NB 43 8.3 1237 128 475 1078 4176 324 24 29 408 4472 4582 �2.5 374 2.7 1173 127 427 1073 26 124Belkheri – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NABarman NB 44 8 388 41 746 428 2736 115 1 26 243 2777 2904 �4.6 239 2.4 315 35 685 419 17 68Gadarwara NB 45 8.3 870 44 569 912 3720 194 5 27 412 3875 3973 �2.5 328 2.7 804 40 514 903 17 105Sandia NB 46 8.2 506 46 860 497 3224 94 18 24 368 3266 3384 �3.6 285 2.7 458 41 828 487 21 90Hosangabad NB 47 7.8 749 40 775 575 3456 112 15 31 450 3489 3645 �4.5 309 2.3 710 34 720 568 23 103Chhidgaon NB 48 8.4 2627 51 461 1040 5616 66 19 26 442 5680 5752 �1.3 481 2.9 2533 47 410 1033 14 180Handia NB 49 8.4 971 52 719 696 3984 27 20 12 484 3853 4055 �5.2 345 2.9 889 47 679 679 1 112Mortakka NB 50 8.3 574 48 787 493 3120 103 23 29 261 3182 3304 �3.8 272 2.8 474 43 735 462 19 80Mandleshwar NB 51 8.4 760 35 691 541 3152 168 24 22 387 3260 3389 �4 285 2.8 706 32 649 533 14 95Kogaon NB 52 8.1 742 54 623 500 2880 221 11 12 320 3041 3135 �3.1 261 2.4 633 38 516 478 �5 79Rajghat NB 53 8.5 675 36 620 549 3024 131 3 22 450 3049 3203 �5 274 2.8 615 32 579 547 13 92Garudeshwar NB 54 8.5 338 25 594 291 2088 75 4 18 404 2133 2202 1 196 2.7 233 17 521 287 1 63Chandwara – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Monsoon 2004 September

Amarkanrak – NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 214 22 627 161 28 64Dindori NB 55 7.5 264 27 673 167 1856 74 46 33 372 1971 2041 �3.5 182 1.6 260 16 512 301 39 70Mohgaon NB 56 8.1 313 21 556 308 1960 60 11 47 442 2062 2126 �3.1 191 2.2 234 8 724 412 33 83Manot NB 57 7.9 296 15 771 431 2704 69 6 40 511 2715 2859 �5.3 251 2.3 250 20 358 212 42 51Bamni NB 58 7.7 302 22 401 216 1448 73 16 50 274 1558 1638 �5.2 142 1.6 371 31 794 588 22 86Jabalpur NB 59 8.2 422 35 865 608 3344 126 13 29 353 3402 3541 �4.1 294 2.8 290 24 749 426 45 88Patan NB 60 7.8 331 25 806 442 2808 57 5 53 512 2853 2976 �4.3 261 2.2 379 12 918 671 47 104Belkheri NB 61 8.2 444 14 966 675 3640 95 32 50 505 3740 3867 �3.4 328 2.8 350 40 627 332 20 68Barman NB 62 7.1 415 43 675 337 2416 76 35 31 275 2482 2589 �4.3 218 1.4 361 18 875 627 56 98Gadarwara NB 63 8.3 434 24 936 637 3456 75 23 65 432 3603 3684 �2.2 311 2.8 180 29 582 204 19 52Sandia NB 64 8.1 246 33 637 213 1880 73 30 29 231 1979 2042 �3.1 173 2.2 440 44 824 407 38 86Hosangabad NB 65 7 488 49 855 416 3040 87 14 41 316 3080 3223 �4.7 270 1.5 537 17 962 668 75 101Chhidgaon NB 66 7.8 576 23 1017 675 3920 114 9 83 250 3984 4209 �5.6 338 2.4 410 37 799 406 40 87Handia NB 67 7.7 504 41 851 412 3040 85 7 51 378 3071 3235 �5.3 274 2.2 236 45 630 284 22 60Mortakka NB 68 7.7 318 50 669 302 2224 80 11 34 244 2310 2382 �3.1 200 1.9 259 52 640 288 33 66Mandleshwar NB 69 7 359 57 693 319 2368 114 12 43 299 2439 2579 �5.8 217 1.3 416 54 1023 460 53 93Kogaon NB 70 8.1 470 57 1066 468 3472 135 24 61 271 3596 3753 �4.3 307 2.7 307 27 587 320 30 65Rajghat NB 71 7.7 415 44 693 342 2384 127 12 46 289 2530 2615 �3.4 220 2 241 31 552 265 19 57Garudeshwar NB 72 7.6 301 36 593 267 1960 87 4 28 261 2057 2107 �2.4 180 1.7 1154 52 1038 622 41 136Chandwara NB 73 8.1 1259 61 1112 625 4280 640 41 58 408 4793 5077 �5.9 407 2.8 214 22 627 161 28 64

NA = not analyzed; TZ+ = total cation charge; TZ� = total anion charge; NICB = normalized inorganic charge balance; TDS = total dissolved solids; CSI = carbonate saturation index; TDSw = totaldissolved solids corrected for non-geological inputs; Jabalpur BG and SG stand for two sampling stations (Bherghat and Sarswatighat), a few 100 m distance with different lithology.* Corrected for rainwater contribution.

806H

.G

up

taet

al./G

eoch

imica

etC

osm

och

imica

Acta

75(2011)

800–824

Table 3Results of analysis of variance (ANOVA) of each parameter at 95%significant level to compare seasonal and Interannual variations.

Parameter ANOVA Tukey HSD

F Calculated Significance

pH 13.649 .000 ac, bc, cdNa+ 5.983 .001 ab, bdK+ 19.625 .000 ab, ac, ad, bdCa2+ 5.676 .002 adMg2+ 6.127 .001 ab, acHCO3

� 5.018 .003 ab, acCl� 2.537 .064 abNO3

� 11.539 .000 ab, bc, bdPO4

2� 5.342 .001 ab, ac, adSO4

2� 19.848 .064 ad, bd, cdSiO2 7.537 .003 ac, ad, bcTDS 5.393 .002 ab, ac

ab: refers to comparison of parameters between monsoon 2003 andpost monsoon 2004 samples.ac: refers to comparison of parameters between monsoon 2003 andpre monsoon 2004 samples.ad: refers to comparison of parameters between monsoon 2003 andmonsoon 2004 samples.bc: refers to comparison of parameters between post monsoon 2004and pre monsoon 2004 samples.bd: refers to comparison of parameters between post monsoon2004 and monsoon 2004.cd: refers to comparison of parameters between pre monsoon 2004and monsoon 2004.


post hoc test to identify statistical differences among indi-vidual seasonal groups.

4. RESULTS AND DISCUSSION

4.1. Major ion chemistry

Table 2 presents the chemical composition of river watersamples analyzed in this study, i.e. spot sampling. Temper-ature measured only for samples collected during August2003 that ranges from 22 to 28 �C and therefore the dataare not included in Table 2. The pH varies from almost neu-tral to mild alkaline (6.9–8.5) with most samples fall withina range of 7.0–8.0. Samples collected during non-monsoonperiod (November–May) in low-flow conditions are morealkaline than those collected during monsoon (June–October) in high-flow conditions.

In the Narmada basin, total dissolved solids (TDS)range from 69 to 481 mg L�1 and is similar to the resultsof previous studies on the river flowing through the DeccanTraps (13–497 mg L�1; Dessert et al., 2001), Krishna (27–640 mg L�1; Das et al., 2005), Godavari (40–550 mg L�1;Jha et al., 2009) and Reunion Island (65–350 mg L�1; Louvatand Allegre, 1997). However, the TDS of the Narmada(average 270 mg L�1) is relatively higher than the Ganges(187 mg L�1; Dalai et al., 2002) and the Indus (164 mg L�1;Karim and Veizer, 2000) draining the Himalayas and otherrivers draining the basaltic terrains such as in Iceland (20–89 mg L�1; Louvat et al., 2008), Central Siberia (30–70 mg L�1; Pokrovsky et al., 2005), French Central Massif(40–134 mg L�1; Meybeck, 1987), Sao Miguel Island (50–140 mg L�1; Louvat and Allegre, 1998), Brazilian Paranabasin (63–166 mg L�1; Benedetti et al., 1994) and the is-lands of Martinique and Guadeloupe (27–255 mg L�1;Rad et al., 2006).

The major element composition of the Narmada and itstributaries is dominated by HCO3

�, Ca2+, Mg2+ and Na+

ions (Table 2), which together account for >80% of TDS.The HCO3

� concentration in the mainstream ranges from680 to 3984 lM, whereas the tributaries show slightly high-er HCO3

� contents (808–5616 lM). The Cl� concentrationrange between 22 and 640 lM, and constitute �1–14% oftotal anion charge (TZ�). Similar to bicarbonate, the sam-ples collected from some major tributaries (e.g., the Hiran,Kundi and the Orsang rivers) show higher Cl� values(>200 lM), suggesting a secondary source, either naturalor anthropogenic. The concentrations of NO3

� andSO4

2� are generally low, and make up a relatively smallproportion of TDS, although some tributaries show higherconcentrations of both NO3

� and SO42�.

Among the major cations, Ca2+ constitutes 16–75% of to-tal cationic charge (TZ+), followed by Mg2+ (5–48%), Na+

(11–46%) and K+ (<1–5%). Dissolved silica in the Narmadabasin ranges from 149 to 592 lM (Table 2), and the concen-trations are similar to the rivers draining volcanic rocks, suchas the Krishna (91–685 lM; Das et al., 2005), Godavari(223–761 lM; Jha et al., 2009), Reunion Island (200–800 lM; Louvat and Allegre, 1997), Sao Miguel Island(268–1250 lM; Louvat and Allegre, 1998), islands of Marti-nique and Guadeloupe (213–1000 lM; Rad et al., 2006),

Iceland (420–2700 lM; Gislason et al., 1996) and MountCameroon (276–1034 lM; Benedetti et al., 2003).

The major ion compositions of the Narmada and itstributaries measured in this study are comparable to thatreported by Dessert et al. (2001) and Das et al. (2005) inthe rivers draining the northern Deccan Traps and theKrishna River, including other west-flowing rivers in India.Compared to our values, these authors however reportedlower HCO3

�, NO3�, and SO4

2� but higher Cl� and similarrange of concentrations for all cations.

4.2. Spatial and interannual variability

The ANOVA analysis was performed for each chemicalparameter by taking values for pre-monsoon, post-monsoonand two monsoon (2003 and 2004) seasons as variables.The test of variance was determined by using F-distributionat 95% confidence level as a part of test of significance toanalyze the seasonal variability in data (Table 3). For mostparameters except Cl� and SO4

2�, variance is significant at0.05 levels, indicating significant temporal variations ofmajor ions and silica in the basin. Table 3 also presentsthe results of Tukey HSD test for inter-comparison of levelof significance between different seasons. Samples collectedin two successive monsoon seasons (2003 and 2004) alsoshow significant variations for K+, Ca2+, PO4

2�, SO42�

and SiO2 which may be attributed to the differences inwater discharge and hence dilution at the time of sampling(Table 3).

Seasonal and annual variations of water discharge, tem-perature and concentrations of major ions (Na+, K+, Ca2+,


Mg2+, HCO3�, Cl�, SO4

2�) and SiO2 are shown for tworepresentative stations, one in the mainstream at Dindori(N2; Fig. 2) and other in the Ganjal River, one of the majortributaries, at Chhidgaon (T6; Fig. 3). The major elementalconcentrations in these plots are combined with dischargeand temperature data for comparison. It is apparent thatthe concentrations of major elements vary by a factor of2–4 during the annual cycle at both locations. All majorions and silica invariably show lower concentrations during

Fig. 2. Seasonal and interannual variations in daily water discharge andcycle during three successive years (June 1996–May 1999) and daily tempTime series data of (a) water discharge, (b) temperature, (c) Na+ and K+

The solutes concentrations (c–f) are also compared with water discharge

the monsoon period and vice-versa during the non-monsoon periods. Such cyclic changes are almost consistentwith water discharge data, suggesting a dilution effectcaused by monsoon rains between June and October everyyear, even though a major part of the total dissolved load istransported during this period. Other than lack of dilution,higher concentrations during dry seasons may also beattributed to possible contributions from groundwatersources and increased mineral dissolution rates under a

fortnight solute concentrations throughout the annual hydrologicalerature (June 1998–May 1999) at Dindori (N2) in the mainstream., (d) Ca2+ and Mg2+, (e) HCO3

� and Cl�, and (f) SiO2 and SO42�.

(Q) at the time of sampling.

Fig. 3. Seasonal and interannual variations in daily water discharge and fortnight solute concentrations throughout the annual hydrologicalcycle during three successive years (June 1996–May 1999) and daily temperature (June 1998–May 1999) at Chhidgaon (T7) located in a majortributary, Ganjal. Time series data of (a) water discharge, (b) temperature, (c) Na+ and K+, (d) Ca2+ and Mg2+, (e) HCO3

� and Cl�, and (f)SiO2 and SO4

2�. The solutes concentrations (c–f) are also compared with water discharge (Q) at the time of sampling.


warm climate. Low river runoff and consequent increasedresidence time may enhance rock–water interactions, lead-ing to higher concentration of dissolved constituents inwaters during dry season. This inference is consistent withTipper et al. (2006) who suggested a higher proportion ofweathering of silicate minerals during the dry period whenthe residence time of water increases in the catchment.

It can also be inferred from the time series plots (Fig. 2)that Na-normalized molar ratios of Ca2+, Mg2+ andHCO3

� did not yield any clear seasonal pattern at Dindori

(N2). However, molar ratios at Chhidgaon (T6; Fig. 3)show distinct seasonal and identical annual patterns withgreater molar ratios for the monsoon season.

4.3. Downstream evolution and source of major ions

The distributions of anion-silica (Cl� + SO42� �

HCO3� � SiO2) and cation (Na+ + K+ � Ca2+ �Mg2+)

on ternary diagrams indicate the relative contributions ofmajor ions from diverse weathering regimes (Huh, 2003).


The major ion data of spot samples are plotted individuallyfor the mainstream and the tributaries on anion-silica andcation plots (Fig. 4). The major ion composition of multi-annual data is also included in Fig. 4 for comparison. Onanion-silica plot, both spot and multiannual data fromthe mainstream as well as tributaries show the dominanceof HCO3

� over Cl�+SO42� and SiO2 (Fig. 4a and c). On

the cation plot, spot and multiannual samples from themainstream fall mostly at the line parallel to Ca2+ andMg2+ axis, suggesting the dominance of alkaline over alkaliions (Na+ + K+), whereas the data for tributaries showmore or less equal contribution of alkaline and alkali ions(Fig. 4b and d). To know the seasonal variations of majorions in terms of monsoon and non-monsoon, the multian-nual data are plotted on the combined anion–cation ternarydiagram for the mainstream at Dindori (N2; Fig. 4e) andthe Ganjal River at Chhidgaon (T6; Fig. 4f). It is apparentthat cations in the mainstream follow a similar pattern tocations in Fig. 4b and fall on mixing line parallel toCa2+–Mg2+ both for monsoon and non-monsoon samples,whereas anions lie on HCO3

�–SiO2 axis. Although anionsfrom the tributary mimic the mainstream trend, more scat-tered cations in waters at Chhidgaon (Fig. 4f) seem to beenriched in alkali ions compared to basaltic signature evi-dent at Dindori (Fig. 4e).

The molar ratios of Ca2+/Na+, Mg2+/Na+ and HCO3�/

Na+ in spot samples range from 0.03 to 3.55 (average:1.46); 0.03 to 2.52 (average: 0.96), and 2.22 to 11.57 (aver-age: 6.06), respectively. The Na-normalized molar ratiosfrom multiannual data also show a comparable range andvary from 0.1 to 5.99 (average: 1.55); 0.1 to 6.26 (average:0.98) and 1.15 to 21.9 (average: 5.40) for Ca2+, Mg2+ andHCO3

�, respectively. Dessert et al. (2003) calculated Na-normalized molar ratios for the basaltic watersheds and ob-served that most of the HCO3

�/Na+ ratios vary between 1and 10 (average: 5.3); Ca2+/Na+ ratios range from 0.2 to3.15 (average: 1.3) and Mg2+/Na+ ratios vary between0.15 and 3.15 (average: 1). Similar to the observation ofDessert et al. (2003) for the Hawaiian Rivers, a few samplesfrom multiannual data also show high HCO3

�/Na+ ratios(>10). A significant linear correlation between Na-normal-ized molar ratios of Ca2+ vs. HCO3

� and Ca2+ vs. Mg2+

were observed for both spot and multiannual datasets(Fig. 5a and b). Plots of Na-normalized molar ratios(Fig. 5a and b) show that approximately half of theNarmada basin samples occur outside the range determinedfor average continental silicate rock (Gaillardet et al.,1999) but fall parallel to trend line between silicate and car-bonate end members. Dessert et al. (2003) explained thispattern for the rivers draining basaltic terrains as a resultof possible mixing between silicate and carbonate end mem-bers. According to Dessert et al. (2003), this pattern mayalso be due to either preferential weathering of Ca- andMg-rich silicate minerals in volcanic rocks or due to agreater dissolution of disseminated calcite in basalt. Thus,Na+-normalized molar ratios in the Narmada basin in gen-eral point to a basalt source.

It is apparent from spatial profile of cations and silica(Fig. EA-2-1) that Na+, Ca2+, Mg2+ and SiO2 showmarked variations along the mainstream with the highest

Na+ concentration observed where the saline–alkaline soilsoccurs. The lowest values of major ions at Amarkantak(N1) reflect low chemical weathering rates under the influ-ence of lower temperature and thin layer of soil in thishighly elevated and well forested part of the basin. Subse-quently, the mainstream shows the influence of lithologicalheterogeneity, as reflected by the increased Ca2+ and Na+

concentrations at Jabalpur (N5) and Handia (N9), respec-tively. The observed decline in dissolved constituents atJabalpur (N5), Mortakka (N10) and Garudeshwar (N13)may be attributed to trapping efficiency of dams on themainstream, which discussed later (in Section 4.4). Despitebeing regulated by three large dams (thus possible trappingof dissolved load and lack of major contribution from trib-utaries at immediate downstream), total dissolved solidsshow a marked increase at many locations along the main-stream. Similar to the mainstream, tributaries also showheterogeneity in surface water composition (Fig. EA-2-2),particularly the Ganjal (T6) and the Orsang (T9) are enrichedin sodium ions. The concentration of K+ remains mostlyconstant throughout the basin, suggesting conservativebehavior of K+ in river systems (Garrels and Mackenzie,1971).

In contrast to major ions having multiple sources (litho-logical, atmospheric, anthropogenic and biological), silica ismainly derived from the dissolution of primary silicate min-erals. According to Treguer et al. (1995), riverine silica ismainly controlled by natural processes which contribute�80% of annual silica input into the ocean. Silica has neg-ligible contribution from anthropogenic (Nixon, 2003) andatmospheric sources (Berner and Berner, 1996). It does notget adsorbed onto the sediments and contribution from sus-pended sediments during riverine transport is also negligi-ble (Treguer et al., 1995). With the assumption that thebiological utilization of silica is in a steady state, we plottedSiO2 normalized molar ratios of Na+, Ca2+ + Mg2+, totalcations and HCO3

� (Fig. EA-3) to delineate the relativespatial contribution of silicate and non-silicate end mem-bers to dissolved load on the basin scale. Marked increasesin silica-normalized cation and HCO3

� ratios at Jabalpur(N5; Fig. EA-3-1) indicate additional contributions fromnon-silicate sources. Contrasting trends observed betweensilica-normalized molar ratios of Na+ and Ca2+ + Mg2+

in some mainstream locations advocate different sourcesfor these cations (i.e. carbonate and saline–alkaline). Thetributaries (e.g., T3-Hiran, T4-Sher, T5-Shakkar, T6-Ganjal)draining through carbonate- and saline–alkaline mineral-bearing soils have higher concentrations of cations (Na+

and Ca2+–Mg2+) and HCO3� (Fig. EA-3-2). These elevated

TDS values accentuate the importance of weathering ofcarbonate/saline–alkaline minerals in the elemental budgetof the mainstream and most of the tributaries.

The contributions from carbonate weathering is ex-pected because the carbonate rocks of Vindhayan andGondwana groups and pedogenic carbonates in soils(Tiwari and Bhai, 1997) are exposed across the basin.Additionally, minor carbonate phases in the form ofdisseminated calcite in basalts (Dessert et al., 2003) mayalso contribute to carbonate weathering. Gaillardet et al.(1999) grouped the Narmada along with rivers having

Fig. 4. Ternary plots of major anions-silica and cations for the Narmada River basin. The data are in charge equivalent units (lEq) and arenot corrected for atmospheric input. Plots (a–d) showing the distributions of anions-silica and cations in the mainstream and the tributaries.For comparison, both multiannual and spot samples were plotted together in each panel. The combined ternary plots (e and f) of anion-silicaand cations for two representative locations in the Narmada River basin. Plot (e) showing the seasonal variation at Dindori (N2) in themainstream and plot (f) showing the seasonal variation at Chhidgaon (T7) located in a major tributary, Ganjal River. For comparison, anion-silica/cations for monsoon and non-monsoon are plotted together in each panel. In these plots, only multiannual data are used.


Fig. 5. Mixing diagrams showing Na-normalized molar ratios in the Narmada River basin. Plots (a and c) showing the relationship of Ca2+/Na+ vs. HCO3

�/Na+ for the mainstream and the tributaries and plots (b and e) showing the relationship for Ca2+/Na+ vs. Mg2+/Na+. Thedata in these plots (a–d) illustrate significant positive correlations for both multiannual and spot samples in the mainstream and thetributaries. To avoid bias among the two different sets of data, spot samples were not corrected for atmospheric input.


<10% of their dissolved loads derived from carbonatedissolution, such as Irrawady, Cauvery, Murray Darling,Parana and Tocantins. By contrast, Raymahashay (1986)suggested that up to 75% of HCO3

� in the Narmada Rivermay come from the weathering of carbonates alone. De-spite minor in occurrence, these carbonate minerals weatherin orders of magnitude faster than those of Ca-Mg silicateminerals (Gaillardet et al., 1999). In natural environment,the weathering of calcite disseminated in silicates wouldbe limited by the rate of their exposure; however, high sed-iment erosion coupled with monsoon rains (Gupta andChakrapani, 2005) in the Narmada basin may likely en-hance calcite dissolution.

The concentration of Na+ corrected for atmosphericcontribution Na* in the basin is in excess of Cl�, the Na:Clequivalent ratio varies between 2.83 and 14.9 (average:6.32), suggesting that most of the Na+ in these waters orig-inate from a source other than atmospheric precipitationand hence could be derived from weathering of silicate min-erals. The derivation of Na+ by weathering of silicate min-erals can be inferred from Si/(Na* + K) molar ratio(Stallard and Edmond, 1983) which show a variation of0.32–1.48 (average value for a given location) for the basin.Since Si/(Na* + K) molar ratio of >4 is expected for con-version of the Deccan basalts to kaolinite during weather-ing (Rengarajan et al., 2009), lower ratios as observed


here indicate either a depletion of dissolved silica duringtransportation and/or an addition of alkalis from non-sili-cate sources. The Deccan Traps are theoliitic basalts char-acterized by the dominance of Ca-rich plagioclase whichmainly occur as phenocrysts. Despite higher mobility ofNa+ relative to Ca2+ (Gaillardet et al., 1999), preferentialdissolution of Ca-minerals such as plagioclase may addconsiderable amounts of Ca2+ into the Narmada River.Being a biogenic nutrient, silica is also consumed/releasedby diatoms (Humborg et al., 2000), although there is nopublished information on this issue for the Narmada basin.For the samples collected during the monsoon (August2003 and September 2004), use of silica by diatoms is ex-pected to be minimal due to turbidity. The other possibilityis additions of Na+ from non-silicate and non-precipitationsources. Gaillardet et al. (1999) attributed the presence ofarid and semi arid zones to explain Cl� enrichment andNa+ depletion in some Indian Rivers. However, no relativeenrichment of Cl� or depletion of Na+ is noticed in case ofthe Narmada River. A large portion of the Narmada basin,particularly in middle and downstream, is overlain by salineand alkaline soils (Chhabra, 1996; CSSRI, 2007; Mondaland Sharma, 2008). These soils contain a variety of salts,such as NaCl, Na2SO4, MgCl2, MgSO4, CaCl2, Na2CO3,NaHCO3, MgCO3 and CaCO3 (Chhabra, 1996). Na-bear-ing minerals, such as NaHCO3 and NaCO3 could be asource for Na+ enrichment in some parts of the basin. Anassessment of multiannual data confirms an increased con-tribution of alkali ions (Na+ + K+) in six major tributaries(Hiran, Shakkar, Ganjal, Chota Tawa, Kundi and Orsang),suggesting for the presence of saline–alkaline soils in thecatchment of these tributaries. Mondal and Sharma(2008) reported that the poor drainage conditions coupledwith arid climate, poor groundwater quality and lack ofinfrastructure for irrigation in the black soil regions canlead to soil salinization in central India, including parts ofthe Narmada basin. According to them, the extent of alka-line soils (74%) is higher than saline soils (26%) in centralIndia. Our results suggest a large amount of dissolved loadsderived from both carbonates and saline sources in theNarmada basin. Mondal and Sharma (2008) found 37%of saline–alkaline soils in the zone of 700–800 mm year�1

rainfall and around 25% of soils in 800–900 mm year�1

rainfall zone, suggesting a greater extent of salinization inthe zone of low rainfall. Similarly, the rocks from Jurassic,Archaean and Pleistocene groups accounted for 43%, 23%and 12% of total salt-affected soils in the peninsular plainsof central India. Weathering of silicate minerals eitherre-precipitation of dissolved elements carried by the river orin-situ weathering may at least partly contribute to alkalineelements in these soils and thus can partly contribute toCO2 consumption. However, due to limited publishedinformation and experimental data, it has to be betterunderstood by further studies.

4.4. Estimation of chemical weathering rates (CWR)

To calculate CWR, it is essential to correct the riverineflux for inputs from non-weathering sources, such asanthropogenic, atmospheric, groundwater and thermal

springs. Because of the rough terrain with low populationdensity and slow industrial growth, the Narmada River re-mains in a relatively pristine state (CPCB, 2001). Time ser-ies plots (data not shown) for the major ions such as Cl�,NO3

�, PO42� and SO4

2� from multiannual data do notshow any remarkable change in their concentrations, imply-ing either negligible or constant anthropogenic inputs intothe river between 1990 and 2000.

The Narmada River flows through the Narmada-SonFault (NSF), a well-known seismotectonic feature (Biswas,1987), and is prone to inputs from groundwater and geo-thermal waters. Minissale et al. (2000) reported geochemi-cal data for a number of hydrothermal springs in theNSF zone, but none lies in the Narmada basin. Ravi Shan-ker (1995) reported the presence of three thermal springs,Anhoni, Anhoni-Samoni and Babeha, dominated by NaH-CO3 rich waters in the Narmada basin with recorded waterdischarges of 50–60 l m�1, 5–10 l m�1 and <5 l m�1, respec-tively. The Anhoni-Samoni and Anhoni both lie close toHoshangabad where the annual and non-monsoon waterdischarges (>500 and �300 m3 s�1, respectively) of theNarmada River are much higher than the contributionsfrom thermal springs. Therefore, the overall influence of thesethermal springs to the river water chemistry is consideredto be negligible in this study.

In contrast to many Himalayan Rivers fed by glacialmelt waters, the Narmada mainstream and many of itsperennial tributaries originate as groundwater seepage. Un-til now, no published data is available to estimate the con-tribution of groundwater (base-flow) to surface runoff.According to Gupta and Chakrapani (2007), the river flowis heavily dependent on monsoon rains as most of its an-nual flow is concentrated during monsoon (mainstream:70–92%; tributaries: 87–99%) between June and Novembereach year. This suggests that groundwater contribution tosurface runoff may not be large, at least in high-flow peri-ods. On the other hand, during low-flow periods, if largeamount of groundwater flows with higher solute concentra-tion, the riverine composition may be altered substantially.Water discharge during pre-monsoon season (March–May)for two upstream locations (Dindori and Manot) in themainstream and all tributaries, account for <3% of total an-nual discharge at these locations (Table 1), which is as-sumed to be the ground water contribution and thusrunoff data corrected for groundwater contributions wereused for the CWR calculation. Since the mainstream hasbeen regulated downstream of Manot (N3) where the dis-charge from Bargi Dam (at Jamtara; N4) maintains higherwater discharge (30% of annual discharge) even in non-monsoon season, the groundwater contributions forremaining locations along the mainstream (downstream ofManot) are also considered as negligible.

The atmospheric inputs for Na+, K+, Ca2+, Mg2+ andSO4

2� in the Narmada basin vary from 5% to 29% ofTDS at different sampling locations and constitute �10%of averaged TDS at basin scale (Table EA-1). Althoughlarge temporal and spatial variations are observed in therainwater composition, only average values for individuallocations have been used in this work for simplification.The data of nearby locations were used for some locations


where rainwater composition was not measured (Table EA-1). The Cl�/Na+ molar ratio varies between 0.96 and 1.14and the values are slightly lower than the typical sea salt ra-tio of 1.16, suggesting dust-derived Na+ to rains. The ob-served alkalinity of rainwater is likely due to high loadingof particulate matter in the atmosphere which is in abun-dance in India (Khare et al., 2004).

Evapotranspiration factor (fet), the ratio of runoff toprecipitation, was determined using annual mean runoffand rainfall values at each sample location (Table 1). TheCl� concentration at a given sampling location in the basinwas multiplied by the fet to evaluate the concentrations ofmajor ions in river water and the factor varies between0.29 and 0.80 (Table 1). Large-scale evapotranspiration alsoenhances the overall contribution of major ions from rain-fall. Hence, rainwater contribution of major ions to riverwater is estimated as follows (Stallard and Edmond, 1981):

X rain ¼ ðX=ClÞrain � Clrain=feÞ ð1Þ

where Xrain (X = Na+, K+, Ca2+, Mg2+ and SO42�) is the

contribution of these ions (in lM) from rain to river water;(X/Cl) is the molar abundance ratios in rain; fet is the cor-rection factor for evapotranspiration. Following the ap-proach of Garrels and Mackenzie (1967), river water iscorrected for atmospheric inputs by subtracting the compo-sition of the rainwater (Xrain) from the stream water (Xriver)and can be written as:

X � ¼ X river � X rain ð2Þ

where X* represent to major ion concentrations (Na*, K*,Ca*, Mg* and SO�4) in rivers (corrected for atmosphericcontributions) resulting from chemical weathering and aregiven in Table 2. Total dissolved solids corrected for non-geological inputs, TDSw (=

PX* + SiO2), are the amount

of geochemically derived dissolved ions in river water, rang-ing from 22 to 180 mg L�1 (Table 2). TDSw were calculatedseparately for monsoon (average of August 2003 andSeptember 2004) and non-monsoon (average of January andMay 2004) seasons and subsequently used for the calcula-tion of CWR during monsoon and non-monsoon seasons.

Table 4 presents the location-wise mean TDSw and thepercentage of water discharge for monsoon and non-mon-soon periods. The runoff at different locations varies be-tween 298 and 846 mm year�1 with a mean of411 mm year�1 for the Narmada basin (Table 1). TheCWR in the basin were calculated using two different ap-proaches (Table 4). The first estimation named as CWRspot

used the spot sampling data given in Table 2 and usingaverage seasonal runoff values, whereas the second estima-tion, CWRmulti, was based on multiannual (10 years) dis-charge weighted data. In the second case, water flux atthe time of water sample collection was used to estimatedischarge weighted values. In both cases, corrections foratmospheric inputs were made by using the data given inTable EA-1. For the calculation of CWRmulti, such correc-tion was made by converting fet-corrected rainwater com-position in terms of percentages for major ions for eachlocation. The % rainwater contribution was calculatedusing the relative contribution of rain composition to spotsamples (Table 2). This approach allows us to compare

the accuracy of CWRspot estimates with CWRmulti whichhelps to assess the temporal variations in CWR and the roleof different controlling parameters. We observed that theCWR calculated using annual runoff values are 10–50%higher than those calculated separately from monsoonand non-monsoon runoff (CWRspot

* in Table 4).The location-wise CWRspot (except Amarkantak; N1) in

the mainstream and major tributaries vary between 25 and63 ton km�2 year�1 (Table 4). The basin average of33 ton km�2 year�1 is higher than the world average chem-ical denudation rate of 24 ton km�2 year�1 (Gaillardetet al., 1999). Along the mainstream, the highest weatheringrate of 63 ton km�2 year�1 is observed at Manot (N3), fol-lowed by Jabalpur (N5) with 51 ton km�2 year�1. A fewtributaries such as the Shakkar (T5; 58 ton km�2 year�1)and the Ganjal River (T6; 57.5 ton km�2 year�1) showcomparatively higher weathering rates. It is apparent thatthe CWR for the Narmada basin are higher than the largerrivers such as Congo/Zaire, Parana, Lena, Orinoco, Yenis-sei, Indus, Mackenzie, Mississippi, and the Amazon, butsimilar to the CWR of the Ganges, Brahmaputra and theChangjiang (Gaillardet et al., 1999). The CWR estimationby Dessert et al. (2001) for the Narmada–Tapti–Godavari(range: 21–63 ton km�2 year�1; mean: 37 ton km�2 year�1)and Das et al. (2005) for the Krishna and other WesternGhat Rivers (range: 3–60 ton km�2 year�1; mean:16 ton km�2 year�1) in India also indicates variable chemi-cal denudation rates for these Deccan Rivers. Higher CWRof the Narmada and the Tapti Rivers (approximately twiceof the Krishna River) may be due to additions from non-basalt sources (Das et al., 2005).

The average CWRmulti at different river monitoring sta-tions in the Narmada basin varies between 23 and50 ton km�2 year�1 (Table 4). Although the whole rangeof CWR calculated from the multiannual and spot samplesdata span the same order of magnitude, the CWRspot arerelatively higher at most locations. To understand the ex-tent of deviations between them, we normalized theCWRspot with CWRmulti. It is interesting to note thatCWRspot was found to be either 70% higher (at Chandwara;T9) or 35% lower (at Belkheri; T4) than CWRmulti. Thesampling frequency and stage of water discharge at the timeof sample collection may lead to the observed differences inboth estimations. Therefore, given the high sampling fre-quency while covering all possible ranges of water dischargeand compositions, multiannual data seem to be more suitedfor higher resolution studies.

Dams and associated reservoirs play an important role intrapping suspended sediments (Syvitski et al., 2005; Walling,2006) and dissolved loads, including nutrients (Humborget al., 2002, 1997; Friedl et al., 2004; Teodoru and Wehrli,2005; Li et al., 2007). Presence of three large dams on themainstream and several others on the tributaries may alsoinfluence solute transport in the Narmada basin. The solutesshow an abrupt decline in concentrations at Jabalpur (N5),Mortakka (N10) and Garudeshwar (N13) after passingthrough three large dams (Fig. EA-2-1), suggesting the pos-sible influence of dams on dissolved loads. For instance, dif-ferences in multiannual (1990–2000) dissolved flux betweenRajghat (N12) and downstream location Garudeshwar

Table 4CWR estimation and comparison between different methods and contribution of different components in the Narmada basin.

Code –Samplinglocations

TDS(mg L�1)

Runoff(%)

CWR (ton km�2 year�1) TDS (mg L�1) Weathering rate(ton km�2 year�1)

% Contributionto CWR

Error% CO2 sil (106 moleskm�2 year�1)

CWRspot seasonal *CWRspot annual CWRmulti Sil. Carb. Sol–

Alk.

Sil. Carb. Sol–

Alk.

Sil. Carb. Sol–

Alk.

HF LF HF LF

HF LF Total Average Range(min–max)

Narmada mainstream

N1-Amarkanrak

NA 24 NA NA NA NA NA NA NA NA 11 5.4 4.1 NA NA NA NA NA NA NA NA

N2-Dindori 69 92 90 10 36.8 3.9 40.7 47.9 35.0 22–58 31 26.3 13.2 23.6 11.7 5.3 58.1 28.7 13.1 0.1 0.54N3-Manot 71 105 92 8 55.4 5.6 61.0 74.6 50.4 31–100 36 27.5 13.6 35.7 20.3 8.4 55.4 31.5 13.8 �5.5 0.77N4-Jabalpur 86 68 70 30 37.7 12.8 50.5 46.9 35.4 19–66 31 33.3 7.9 20.9 19.4 4.6 46.5 43.3 9.1 11.1 0.33N5-Barman 58 73 76 24 24.1 9.6 33.7 35.9 31.3 15–54 18 22.8 8.4 11.1 12.0 4.2 40.6 44.0 12.5 19.1 0.19N6-Sandia 53 87 77 23 22.9 11.2 34.1 39.3 37.1 18–69 26 31.3 13.6 15.6 17.6 6.9 39.0 43.8 20.2 �17.3 0.28N7-Hosangabad 75 101 79 21 34.1 12.2 46.4 50.7 36.6 18–65 27 31.8 16.5 18.0 17.3 8.2 41.4 39.7 17.8 6.1 0.35N8-Handia 74 110 81 19 32.3 11.2 43.5 49.5 35.0 18–71 28 31.3 18.3 17.1 16.0 8.5 41.1 38.4 19.5 4.5 0.30N9-Mortakka 61 96 82 18 21.9 7.6 29.5 34.5 NA NA 28 25.4 13.8 14.5 10.3 4.8 49.1 34.8 13.1 �0.1 0.36N10-Mandleshwar

61 95 83 17 25.3 8.1 33.4 39.0 33.9 16–63 28 25.4 14.1 15.4 12.1 5.8 46.3 36.4 17.3 0.1 0.34

N11-Rajghat 62 99 83 17 25.0 8.2 33.1 39.1 37.7 15–68 30 23.6 14.9 15.8 11.6 5.8 47.5 34.9 17.6 �0.1 0.33N12-Garudeshwar

59 86 85 15 20.6 5.3 25.9 29.8 23.0 11–49 27 23.5 12.0 12.1 9.4 4.1 47.2 36.8 15.8 1.2 0.25

Tributaries

T1-Mohgaon 49 81 93 7 28.1 3.0 31.2 40.2 30.9 16–52 26 23.7 9.3 17.1 13.7 5.2 47.6 38.0 16.7 �15.5 0.17T2-Bamni 50 63 95 5 31.4 2.1 33.5 37.7 34.9 14–68 19 19.2 11.1 14.0 12.0 6.9 42.5 36.5 20.6 1.9 0.17T3-Patan 80 122 87 13 29.1 5.5 34.5 42.2 29.7 10–52 31 41.3 17.1 14.0 16.2 6.2 38.3 44.5 18.1 �5.4 0.30T4-Belkheri 87 113 92 8 22.6 2.1 24.7 27.0 37.3 12–86 42 32.4 11.9 12.7 8.6 2.8 52.9 35.6 11.5 2.4 0.30T5-Gadarwara 84 105 93 7 53.0 4.1 57.1 61.7 45.0 19–117 31 34.4 14.8 21.2 23.1 9.4 39.5 43.0 16.5 6.0 0.25T6-Chhidgaon 95 133 93 7 52.0 4.9 56.9 66.9 43.5 7–79 35 30.9 26.9 22.2 18.7 13.3 40.9 34.5 23.5 4.9 0.46T7-Ginnore NA NA 97 3 NA NA NA NA 38.4 NA NA NA NA NA NA NA NA NA NA NA NAT8-Kogaon 86 81 97 3 25.0 0.7 25.7 25.0 29.4 5–66 33 28.1 12.8 11.0 8.0 3.1 49.7 36.4 11.9 14.0 0.31T9-Chandwara 93 NA 99 1 37.2 0.0 37.2 18.8 22.8 8–58 25 33.3 19.5 12.1 12.6 6.7 38.3 40.2 18.1 15.9 0.27

NA = data not available.HF (High-Flow) period extends from June to November and covers monsoon season (�15 June to 15 October); LF (Low-Flow) periods cover post-monsoon and pre-monsoon seasons.TDS–HF: average of August 2003 and September 2004; TDS–LF: average of January 2004 and May 2004.CWRspot seasonal calculated using TDS and corresponding runoff for HF (monsoon) and LF (non-monsoon) individually.*CWRspot calculated using annual average of TDS and runoff and was found to be 10–50% higher than CWR(spot).CWRmulti is mean of 10 years; calculated by using discharge weighted fortnight concentrations; range is also presented.Sil. refers to silicate weathering derived component of CWRspot.Carb. refers to carbonate weathering derived component of CWRspot.Sal–Alk. refers to evaporite (saline–alkaline soils) weathering derived component of CWRspot.Error% refers to possible errors in different component estimation with reference to CWRspot at different study locations.CO2 consumption rates from silicate weathering (CO2 sil) were calculated for spot samples.

Ch

emical

weath

ering

of

basalts

inth

eN

armad

aR

iverb

asin,

Ind

ia815


(N13) indicate that SS dam alone can trap more than 30% ofdissolved flux and 45% of dissolved silica. One possible rea-son for this flux reduction could be the diversion of water forirrigation. However, in case of SS dam, it is not quite thecase, because compared to <5% reduction in annual waterdischarge, decrease in dissolved flux seems to be remarkablyhigher. Furthermore, to date there is no estimation availablefor the dissolved flux contributions from the tributariesdraining into the mainstream between these two monitoringlocations. Considering the presence of a series of large damson the Narmada mainstream and numerous other mediumand small dams on the tributaries, the overall quantity oftrapped dissolved load must be considerably higher,although any specific reason for such a behavior is to beascertained. One possible explanation may be the increasedwater residence time which will enhance biological processesleading to nutrient consumption. We speculate that physio-chemical processes involving formation of precipitated min-erals (Section 4.4.2) may also lead to reduction in dissolvedload. Therefore, the CWR values estimated here may belower than that of the current erosion rates.

As mentioned earlier, a major part of the dissolved constit-uents in the Narmada basin is derived from weathering of sil-icates and carbonates and contribution from saline–alkalinesoils. The values of CWR calculated in this study representa combined outcome of these three end members. Estimatingindividual contributions of different end members is impera-tive to characterize the weathering regime, CO2 consumptionrate via silicate weathering and their controlling factors.

4.4.1. Silicate weathering rates (SilWR)

Several approaches have been proposed to characterizesilicate end member (Tipper et al., 2006 and referencestherein). However, the presence of saline–alkaline soils, car-bonate rocks and minor outcrops of dolomite in the Nar-mada basin limits the scope of these approaches. Sincethe sediments in the river bed are believed to be the repre-sentative of source rocks (Blum et al., 1998), both bed andsuspended sediments have been successfully used for calcu-lating the component derived from silicate weathering(Blum et al., 1998; Wu et al., 2008; Wolff-Boenisch et al.,2009). In this study, first of all Kr is assumed to be derivedexclusively from the dissolution of silicate minerals and nocontribution from the dissolution of carbonates and saline–alkaline soil to K+ is assumed. Following the approach ofWolff-Boenisch et al. (2009), we also used K+ normalizedratios of Mg2+ to estimate the fraction of Mg from silicateweathering (Mgsil). The Mg/K ratios were obtained fromX-ray Fluorescence (XRF) analysis of flood sediments fromthe respective sampling locations (Table EA-4). The silicatecomponent of Ca2+ and Na+ in rivers are then calculated asfollows using Mgsil as a proxy:

Ksil � Kr ð3ÞMgsil ¼ Ksil � ðMg=KÞsed ð4ÞCasil ¼Mgsil � ðCa=MgÞr ð5ÞNasil ¼Mgsil � ðNa=MgÞr ð6Þwhere the subscripts sil, r and sed are silicates, river andsediment, respectively. The Kr has already been correctedfor atmospheric contribution (Table 2).

The SilWR at different sampling locations were calcu-lated using the relation:

SilWR ¼ TDSsil � runoff ð7Þ

where TDSsil = [P

(Nasil + Ksil + Casil + Mgsil)] + SiO2 (allin mg L�1)

The SilWR for the different sampling locations vary from11.0 to 35.7 ton km�2 year�1 and the SilWR at Garudeshwar(N13), an extreme downstream location on the NarmadaRiver) were relatively low (12.1 ton km�2 year�1). To avoidbias due to trapping effect in SS dam at Garusdehwar, theSilWR at Rajghat (N12; 15.8 ton km�2 year�1) were consid-ered to be representative of entire basin and are comparableto that of the Krishna at Alamatti (14 ton km�2 year�1).However, these rates are much lower than previous combinedestimates (37 ton km�2 year�1) for the Narmada–Tapti andthe Godavari Rivers by Dessert et al. (2001) and contribute38.3–58.1% of total chemical weathering inputs.

4.4.2. Carbonate weathering rates (CarbWR)

Weathering of carbonate minerals contribute a sizeableproportion of dissolved load in the Narmada basin. It isimportant therefore to constrain carbonate weatheringbudgets in the Narmada basin. In the present study, Ca*

and Mg* are substantially higher than the Casil and Mgsil.The excess Ca* and Mg* are considered to be derived fromweathering of carbonates and carbonate minerals of saline–alkaline origin, which were estimated as:

Cacarb ¼ Car � Casil ð8ÞMgcarb ¼Mgr �Mgsil ð9Þ

where the subscript carb refers to carbonate contribution.The Car and Mgr have already been corrected for atmo-spheric contributions.

The CarbWR at different sampling locations were calcu-lated using the relation:

CarbWR ¼ TDScarb � runoff ð10Þ

where TDScarb = [Cacarb + Mgcarb] (all in mg L�1)The calculated CarbWR in the basin vary between 8.0

and 23.1 ton km�2 year�1 (Table 4) and contribute 28.7–44.5% of annual chemical weathering flux. The CarbWRare of similar range to SilWR, suggesting an equivalentcontribution of carbonate minerals to total weathering flux.However, the CarbWR estimation seems complex due tothe observed oversaturation of calcite in river water.

Calcite saturation indices (CSI; Langmuir, 1971) calcu-lated at 25 �C suggest that the majority of the surfacewaters of the Narmada basin are oversaturated with respectto calcium carbonate (average for entire basin = 2.0; Ta-ble 2). Previous studies on the Deccan Trap region (Dessertet al., 2001; Das et al., 2005; Sharma and Subramanian,2008) have also observed calcium super-saturation in riverwaters. The mainstream and the tributaries across the basinare even found to be saturated with respect to calcite in themonsoon season, similar to the Himalayan Rivers (Galyand France-Lanord, 1999). Recently, based on X-ray dif-fraction analysis of suspended sediments from the Narmadaand the Tapti Rivers, Sharma and Subramanian (2008)


suggested that 8–9% of calcite gets precipitated in both ba-sins, thus undermining CarbWR.

4.4.3. Saline–alkaline weathering rates (Sal–AlkWR)

One of the important findings of this study is inputsfrom saline–alkaline soils to dissolved load. The contribu-tions from saline–alkaline soils were estimated using therelation:

Nasal�alk ¼ Nar �Nasil ð11ÞSO4sal�alk � SO4r ð12Þ

where the subscript sal–alk refers to saline–alkaline contri-bution. The Nar and SO4r have already been corrected foratmospheric contributions.

The Sal–AlkWR at different sampling locations werecalculated using the relation:

Sal–AlkWR ¼ TDSsal�alk � runoff ð13Þ

where TDSsal–alk = [Nasal–alk + SO4sal–alk] (all in mg L�1).The Sal–AlkWR vary between 2.8 and 13.3 ton

km�2 year�1 and contributes 9.1–23.4% of annual weather-ing flux (Table 4).

It is interesting to note that the calculated weatheringrates of silicate, carbonate and saline–alkaline componentsclosely follow the catchment lithology. In general, theSilWR in the basin decreases from upstream to down-stream, whereas CarbWR and Sal–AlkWR were higher inthe middle reaches of the basin. Highest SilWR were ob-served at Manot (N3) followed by Dinodri (N2), whereasthe lowest SilWR were observed at Barman (N5) which isexpected considering the additional contribution from thewidely distributed marble rock downstream of Jabalpur.Higher Sal–AlkWR were observed at Hoshangabad (N7),Handia (N8), Gadarwara (T5) and Chhidgaon (T6) whichare coincident with the widely exposed saline–alkaline soilsin these areas.

4.4.4. Uncertainties in CWR, SilWR, CarbWR and Sal–

AlkWR estimations

Errors associated with calculation of SilWR, CarbWRand Sal–AlkWR can be marked by comparing the CWRwith the combined SilWR, CarbWR and Sal–AlkWR,which vary between ±20% (Table 4). The errors for two-thirds of study locations (12 out of 18) were approximately±6% which validate the approach used in the present work.Uncertainties associated with chemical analysis, the calciteprecipitation, the violation of assumed conservative behav-ior of rest of solutes (trapping in dams and biological utili-zation) in certain cases and the silicate componentestimation (based on Mg2+/K+ ratio derived from the floodsediments) could be possible sources for the error in SilWR,CarbWR and Sal–AlkWR calculations.

Considering the importance of K in calculating normal-ized molar ratios of Mg2+ and subsequently silicate compo-nent estimations, the following paragraph details thecomplexities associated with distribution of K+ in sedi-ments. Some factors may substantially affect the chemicalcomposition (e.g., for K) of bed sediments such as grainsize/hydraulic sorting of minerals (Pettijohn, 1975) duringtransportation of eroded material, the extent of K mobility

during weathering and exchange/adsorption of dissolved Kon clay minerals (Nesbitt et al., 1980; Banfield et al., 1991)and hence need to be taken into account. The sedimentsamples were collected immediately after a large floodand thus can be considered as representative of total sedi-ments exported by the river. Because the sediments usedfor major oxides measurement in the present study werenot characterized for their grain size and were crushed inbulk, it is not possible to identify uncertainties associatedwith these factors. To understand the extent of K-normal-ized ratio measured in bed sediments as representative ofthe erosion-averaged composition of source rocks, we com-pared our results with average major elemental compositionof the Deccan basalts and sediments (Table EA-4) previ-ously reported by Das and Krishnaswami (2007).

The Mg/K molar ratios in sediments from the WesternGhat Rivers and the Krishna basin are relatively higherthan those of the Narmada basin (Table EA-4). However,a closer look of Mg/K ratios reveals large differences withinthe Western Ghat Rivers and Krishna basin. Even the aver-age Mg/K ratio of the tributaries of Krishna and the Bhimamainstream are similar to that of the Narmada basin. Itsuggests that one or more common factors influence Mg/K ratio in these sub-catchments. Large variations observedin Mg/K molar ratios (Table EA-4) of different sectionsof the Deccan basalts (Crumansonata, 1995; Das andKrishnaswami, 2007) seems to be due to the differences inchemical composition of different lava flows within the Deccanbasalts. Additionally, sediments derived from weatheringof non-basalt rocks (30%) also influence the overall sedi-ment composition. The K content (wt.%) in Narmada sed-iments are 2–3 orders higher than that of the Krishna basin.Wilkins et al. (1994) observed that the average mobilizationof K from the Deccan basalts is lower than that of Na, Ca,and Mg which is consistent with the K distribution inweathering profiles from the Deccan Traps. According toDas and Krishnaswami (2007), smectite clays formed dur-ing chemical weathering of basalts may also trap K, leadingto the limited K mobility. The Western Ghat Rivers travelshort distances before debouching into the coastal seas andare characterized by high gradient, thus reducing the chanceof high chemical alteration during transportation. The Mg/K molar ratio of the Western Ghat Rivers are relatively clo-ser to the average major elemental abundance of the Dec-can basalts and therefore to calculate uncertaintiesassociated with using Mg/K ratios in the Narmada basin,we used an average ratio (10.5) of the Western Ghat Rivers(Table EA-5). It is interesting to note that except two trib-utaries (Banjar and Hiren) most of the locations show errorwithin ±60% of that calculated from location specific Mg/K ratios from flood sediments (Table EA-5). Uncertaintiesestimated in the present study (for Mg/K ratios) are similarto those reported by Wolff-Boenisch et al. (2009), who re-ported errors up to 60% in estimations involving Ca/Naand Mg/K ratios.

4.4.5. CO2 consumption rates

The CO2 consumption rates (UCO2: moles km�2 year�1)during weathering of silicate minerals were calculated fol-lowing the model of Wu et al. (2008).


½UCO2�sil ¼ UðTZþÞsil

¼ ð2Casil þ 2Mgsil þNasil þKsilÞ� discharge=drainage area ð14Þ

The CO2 consumption rates from silicate weathering(spot samples) in the Narmada basin vary between 0.17and 0.77 � 106 moles km�2 year�1 with an average valueof 0.33 � 106 moles km�2 year�1 at Rajghat (N12; Table 4).Though this average is slightly lower, it is of same order tothe values reported for the Krishna (0.36 � 106 moles -km�2 year�1; Das et al., 2005) and the Godavari basins(0.58 � 106 moles km�2 year�1; Jha et al., 2009). The aver-age annual CO2 drawdown via silicate weathering in theNarmada basin (area 98976 km2) based on average CO2

consumption rate is 0.032 � 1012 moles year�1.The present study on the Narmada River and previous

studies on the Krishna (Das et al., 2005) and the GodavariRivers (Jha et al., 2009) together suggest a large spatialvariation in CO2 consumption rates within the DeccanTraps. The Deccan basalts occupy �20%, 48% and 70%of catchment area of the Krishna, Godavari and theNarmada Rivers (Subramanian et al., 2006) respectivelyand all together constitute �60% of the Deccan Traps(5 � 105 km2). The combined annual CO2 drawdownduring basalt weathering by these three major DeccanRivers (Godavari–0.087 � 1012, Krishna–0.019 � 1012 andNarmada–0.023 � 1012 moles) is �0.129 � 1012 moles. Theaverage annual CO2 drawdown by the entire Deccan Trapbasalts estimated using average CO2 consumption valuesof these three rivers (�0.47 � 106 moles km�2 year�1) is�0.24 � 1012 moles, approximately 2% of the annual globalCO2 consumption (11.7 � 1012 moles year�1; Gaillardetet al., 1999) by silicate weathering. The proportion ofCO2 consumption by the Deccan basalts is �4 times higherthan its functional area of continental drainage, which ishaving an average CO2 consumption rate of0.1089 � 106 moles km�2 year�1 (Gaillardet et al., 1999).

As shown above, recently estimated values for thesethree Deccan rivers (Godavari, Krishna and Narmada)are significantly lower than the earlier reported values;0.63–2.54 � 106 moles km�2 year�1 (average: 1.26 � 106

moles km�2 year�1) by Dessert et al. (2001). In a later studyDessert et al. (2003) revised this value to 0.74 � 106 moles -km�2 year�1. In the following section we discuss some ofthe potential reasons that are responsible for this discrep-ancy. Dessert et al. (2001) used HCO3

� as a “surrogate”

for CO2 consumption with the assumption that the wholedissolved load is derived from basalt weathering, whereasthe silicate weathering and associated CO2 consumptionrates in other studies (Das et al., 2005; Jha et al., 2009and present) are calculated from cations derived from sili-cate components. We calculated the CO2 consumption ratesusing the model of Wu et al. (2008). In addition to differ-ences in abundance of catchment area occupied by basalts,variations in chemical composition of different lava flows,different proportion/type of minor rocks, different environ-mental parameters (rainfall-runoff, temperature, evapo-transpiration, soil cover, vegetation, etc.) show impactson variations in CO2 consumption across the three riversystems. Due to non-availability of water discharge data,

Dessert et al. (2001) used averaged runoff value(�463 mm year�1) for the entire Deccan, whereas Daset al. (2005) applied two different average values for theKrishna basin (�463 mm year�1) and the Western GhatRivers (�1690 mm year�1). Jha et al. (2009) showed thatrunoff values within the Godavari (�248–789 mm year�1)differ significantly. In the present study, for the first timewe used location specific runoff and evapo-transpirationvalues (Table 1) to calculate chemical/silicate weatheringrates and associated CO2 consumption. Further, in thisstudy chemical composition of 36 rainwater samples col-lected from the respective study locations were used to cor-rect atmospheric contribution. The observed variations indifferent studies from the Deccan Traps suggest diverserates of silicate weathering and associated CO2 consump-tion across the Deccan Traps as these estimations are influ-enced by a number of environmental parameters that arespecific to different river basins.

4.5. Controlling parameters

Based on the study of 60 large rivers in the world,Gaillardet et al. (1999) evaluated the role of lithology,climate (runoff and temperature), relief and physical erosionin controlling the spatial variations in chemical weatheringat global scale. Among these factors, lithology and reliefcontrol the spatial variation of CWR which remain moreor less similar on a geologic timescale. On the other hand,climate and physical erosion show considerable seasonaland annual discrepancies, resulting in temporal variationsin the CWR. Among the 60 large rivers in the world, theNarmada has been ranked 50th in terms of both catchmentarea and water discharge (Gaillardet et al., 1999). By usinga decade-long chemical data, we estimated the CWRmulti at20 locations across the basin for the first time to evaluatethe annual variations in CWR. Interestingly, these 10 years(1990–2000) cover some of the major floods in the Narmadabasin (1990, 1994 and 2000) and the globally known warmestyears (1990, 1991 1995, 1997 and 1998) on instrumentalrecords (WMO, 2002).

4.5.1. Role of climatic parameters

The climatic response of chemical weathering is consid-ered as a function of temperature and runoff (e.g., Berneret al., 1983; Velbel, 1993; White and Blum, 1995; Dessertet al., 2001). However, the relative importance of runoffand temperature remains controversial (Riebe et al., 2001)with many studies focusing on short-term climatic forcing.Here in the following sections, we examine the influence ofclimatic factors both spatial and temporal scales on theCWR and its components.

4.5.1.1. Runoff. Despite the observed higher TDS (�20%)during non-monsoon, the majority of elemental transporta-tion occurs during monsoon season. The major ion flux dur-ing monsoon accounts for 60–95% and 80–98% of the totalannual weathering flux in the mainstream and tributaries,respectively. Runoff and the CWRspot for the Narmada basinare well correlated (Fig. 6a). In contrast to the mainstreamwhich shows a good correlation (r2 = 0.86, p = 0.000,

Fig. 6. The relationship between runoff and CWR in the Narmada basin. (a) Runoff vs. CWRspot showing a significant correlation both atbasin scale (r2 = 0.49, p = 0.001, n = 19) and for the Narmada mainstream (r2 = 0.86; p = 0.000). (b) Runoff vs. CWRmulti showing asignificant correlation for both the mainstream (r2 = 0.81, p = 0.000, n = 90) and the tributaries (r2 = 0.76, p = 0.001, n = 86) at annual scale,suggesting a strong influence of monsoonal runoff in chemical weathering processes in the Deccan region.


n = 12), runoff and the CWRspot in the tributaries show noparticular correlation. The SilWR (r2 = 0.62, p = 0.000,n = 19) and CarbWR (r2 = 0.43, p = 0.002, n = 19) alsoshow significant correlations with runoff, whereas Sal–

AlkWR shows no correlation. The observed coupling be-tween runoff and CWR/SilWR/CarbWR in the Narmadabasin is consistent with a similar relationship found for theother large world rivers (Gaillardet et al., 1999), includingthe rivers from the basaltic watersheds (Dessert et al.,2003). On annual scale, the observed linear relationship be-tween SilWR and runoff reconfirms the previous findingsby Dessert et al. (2001) that atmospheric CO2 consumptionrates are a function of runoff. Das et al. (2005) also consid-ered runoff as an important parameter and observed thatthe CWR and SilWR of the Western Ghat Rivers of the Dec-can Traps are�4 times higher than that of the Krishna Riverowing to higher rainfall and runoff in the western region.

Rainfall shows large temporal and spatial variations inseasonal and annual distributions across the globe andhence in the river dissolved and suspended loads. Previousstudies mostly define the relationship between the CWRand runoff based on average runoff values at basin scale.Hence, it is imperative to examine the response of chemicalweathering rates at annual and/or seasonal scales. Being sit-uated in the monsoon climate regime, the Indian subconti-nent (including the Narmada basin) experiences a largevariation in annual rainfall and its seasonal distribution(Krishnamurthy and Shukla, 2000). This is also apparentin the seasonal flow regime of the most Indian Rivers asmany of them carry around 90% of their annul water dis-charge only during three monsoon months (July to Septem-ber). To understand the potential influence of runoff on theCWR, we plotted the data of annual CWRmulti and corre-sponding runoff in Fig. 6b. It is evident that CWRmulti issignificantly correlated with runoff both in the mainstreamand the tributaries even at annual scale, suggesting a stronginfluence of monsoon runoff.

The minimum and maximum values of the CWRmulti atany given location vary by a factor of 3–6 across the basin

(Table 4), indicating that “the monsoon strength, for in-stance abnormal, normal or deficient, seems to determinethe interannual variability of weathering rates”. Interest-ingly, despite having variations in lithology, relief andcatchment size, CWRmulti and annual runoff show similarlinear patterns across the basin.

4.5.1.2. Temperature (T). Across the Narmada basin, con-siderable diurnal T variations were observed (Figs. 2b and3b); the basin experiences hot summers with T reachingup to 50 �C (Fig. 3b). Despite having more than 5 �C vari-ations in annual mean T (Table 1), a poor correlation be-tween annual mean T and the CWRspot were observed atbasin scale. However, regression analysis of annual meanT and CWRspot for the eight tributaries illustrates a signif-icant correlation (Fig. 7). Dessert et al. (2001) showed thatat constant runoff, the observed increase of CO2 consump-tion rate reflects an increase of T. Our results also show thatthe T acts as an important controlling parameter even atsub-catchment scale. The Sal–AlkWR calculated for tribu-taries also show a significant correlation (r2 = 0.62,p = 0.021, n = 8), which may be related to the formationof saline–alkaline minerals under a warm climate. Nohet al. (2009) based on their recent study on the Three Riversregion of Eastern Tibet also showed similar correlationbetween weathering rates of halite minerals and T. In con-trast to the observed coupling between runoff and CWR atannual scale, T shows no significant correlation withCWRmulti. The possible explanation may be (1) the stronginfluence of runoff in mass transport; (2) the range of an-nual variation in runoff is significantly larger than T; and(3) the frequency and magnitude of large floods may sup-press the reflection of the role of temperature, as these ex-treme events transport huge amount of dissolved andsediment loads.

4.5.2. Physical weathering

Gupta and Chakrapani (2005, 2007) used long-termdaily sediment load data to study sediment transport and

Fig. 7. Scatter plot showing a significant positive correlation(r2 = 0.70; p = 0.009, n = 8) between the mean annual temperatureand CWRspot for the major tributaries.


physical weathering rates in the Narmada basin. A moder-ately positive but significant correlation (Fig. 8a) was ob-served between PWR and CWRspot (except Jamtara vs.Jabalpur). As discussed earlier, downstream of Manot(N3) the Narmada River has been regulated by three largedams. Trend analysis of annual sediment load between 1979and 2002 indicate that downstream sediment flux at Jamt-ara (Bargi dam) and Garudeshwar (SS dam) have reducedby >90% due to construction of dams. In comparison toa sediment flux of 44.8 � 106 tons in 1979–1980, theNarmada River discharged only 4.75 � 106 tons of sedimentto the Arabian Sea during 2002–2003. It is obvious that thesediment transport regime of the river has been alteredsignificantly, thus resulting in poor correlation betweenPWR and CWR at many locations. The SilWR (exceptJamtara vs. Jabalpur) is also well correlated with PWR(r2 = 0.75, p = 0.000, n = 12).

A significant correlation between PWR and CWRmulti atannual scale for the mainstream locations was observed(Fig. 8b), but no such correlation exists for the tributaries.An assessment of annual CWRmulti and PWR relation atdifferent locations in the mainstream reveal that: (a) a closeexamination of individual location suggests, in spite of alarge interannual variability, a positive correlation(r2 > 0.69) exists between both weathering rates; (b) the up-stream location on the Narmada, Manot (N3) does not fol-low this correlation strictly due to a dominant control ofrelief factor; (c) extreme events characterized by huge sedi-ment loads tend to deviate the general trend; and (d) thecoupling between CWR and PWR both at temporal andspatial scales implies that one or more common factors con-trol these weathering rates across the mainstream.

The coupling between CWR and PWR may be a com-bined result of two different mechanisms. First, chemicalweathering may be strongly regulated by the rate of mineralsurfaces exposed for chemical reactions by physical break-down. On the contrary, the rate of rock breakdown may de-pend on weakening caused by chemical weathering. Giventhe torrential nature of monsoon rains and heavy erosion,

Fig. 8. The relationship between PWR and CWR across the Narmada baCWRspot (r2 = 0.42, p = 0.022, n = 12). Due to high trapping of suspencorrelation. Plot (b) showing a significant correlation between the PWR acorrelation exists for tributaries at annual scale.

the former mechanism seems to be dominating in theNarmada basin. Regardless of a single climatic regime,the study locations across the Narmada basin show markedvariations in mean annual rainfall (Table 1), a key factorof sediment erosion in the Narmada basin (Gupta andChakrapani, 2007). It is interesting to note that in spite ofconsiderable variation in sediment erosion rates (more thanan order of magnitude) across the basin, PWR and CWRremain positively coupled. This suggests that either sedimenterosion process suppresses the influence of other physical(lithology, topography) and meteorological (rainfall andT) parameters or both PWR and CWR are influencedequally by these parameters.

4.6. Total denudation rate and dominance of physical

weathering

The average total denudation rate (TDR = PWR +CWR) estimated for the entire basin (at Rajghat) is

sin. Plot (a) showing a significant positive correlation of PWR withded sediment at Jamtara (N4), this location was not included innd CWRmulti (r2 = 0.40; p = 0.000) in the mainstream; however, no

Table 5Total denudation rate (TDR) and CWR–PWR ratios at sampling locations on the Narmada mainstream and the tributaries.

Code-samplinglocations

PWRa,b

(ton km�2 year�1)CWRspot

(ton km�2 year�1)TDR(ton km�2 year�1)

CWR/PWRratio

CWR% ofTWR

Average Range(min–max)

Average Average

Narmada mainstream

N3-Manot 1222 221–2104 61.0 1283 0.050 5.0N4-Jabalpur 137 34–492 50.5 188 0.368 36.8N5-Barman 549 51–2233 33.7 583 0.061 6.1N6-Sandia 502 72–1270 34.1 536 0.068 6.8N7-Hosangabad 447 43–928 46.4 493 0.104 10.4N8-Handia 644 153–1889 43.5 687 0.068 6.8N10-Mandleshwar 564 149–1429 33.4 597 0.059 5.9N11-Rajghat 526 202–1394 33.1 559 0.063 6.3N12-Garudeshwar 206 26–586 25.9 232 0.125 12.5

Tributaries

T1-Mohgaon 923 134–2815 31.2 954 0.034 3.4T2-Hirdaynagar 274 57–761 33.5 308 0.122 12.2T5-Gadarwara 859 130–2676 57.1 916 0.066 6.6T9-Chandwara 398 204–723 37.2 436 0.093 9.3

TDR = (PWR + CWR).No sediment load data is available for rest of the sampling locations (N1, N2, N9, T3, T4, T6, T7 and T8).

a Estimations based on annual loads for more than 20 years (1979–1980 to 1999–2000).b Source of data: CWC, India.


559 ton km�2 year�1 (Table 5) with the highest TDR is ob-served at Manot (1283 ton km�2 year�1). The average TDRis �2 times higher than the average TDR of the continents(252 ton km�2 year�1; Berner and Berner, 1997). The ratioof dissolved to suspended sediment loads for the Narmadabasin varies from 0.034 to 0.122, except an odd value of0.368 at Jabalpur (Table 5) and are lower than the worldaverage of 0.232 (Milliman and Meade, 1983). The CWRconstitute 3–27% of TDR at different locations (Table 5)however CWR is only 5.9% of TDR at Rajghat. At theglobal scale the average continental CWR is 20% of TDR(Berner and Berner, 1997), thus suggesting a non steady stateweathering regime for the Narmada basin. This implies thatthe PWR in the basin are 10–30 times higher (except atJabalpur and Garudeshwar) than the CWR, indicatingthe dominance of physical weathering over chemicalweathering processes. The observed deviation in ratios atJabalpur (N5) and Garudeshwar (N13) could be attributedto huge reduction in suspended sediments load due togreater trapping of suspended solid over dissolved materialsin upstream dams. Additionally, increased carbonateweathering also play an important role at Jabalpur. It isinteresting to note that our results are comparable to thesmall mountain watersheds having high relief such asTaiwan and New Zealand (1–5%; Carey et al., 2006) andlarge rivers, the Brahmaputra (8%) and the Ganges (9%),draining the Himalayas.

5. CONCLUDING REMARKS

The data presented in this study contain a composite setof spot samples (four phases) and a decade-long fortnightmultiannual data at twenty-one study locations. This com-bination provides a glimpse into variations in chemical

composition at spatial and temporal scales and allows usto delineate the sources of major ions in a basaltic river thatwere rarely covered in previous investigations. Surfacewater samples analyzed for major elements show the influ-ence of carbonates and saline–alkaline soils-derived soluteson basaltic signature. Silica-normalized molar ratios ofHCO3

� and cations (Ca2+ + Mg2+ and Na+) indicate in-creased non-silicates contribution in the middle and lowerparts of the Narmada basin. All samples invariably showhigh calcite saturation indices and suggest calcite precipita-tion in the basin, accounting up to 10% of dissolved calciumcarbonate. The influence of saline–alkaline soils can easilybe distinguished by exceptionally high Na+ content at somesampling locations (Handia-N9 and Chhidgaon-T6) and itis therefore important to carefully evaluate the sources ofNa+ before using Na-normalized molar ratios to character-ize silicate/basaltic signature. The CWRspot show large spa-tial variations and are comparable with the results ofDessert et al. (2001) for the Deccan Trap region. Theweathering rates calculated from spot samples and multian-nual data show that CWRspot can be either similar or con-siderably different than that of CWRmulti. This suggests thathigh sampling frequency is crucial to capture a wide rangeof temporal variations in CWR and to evaluate the role ofmeteorological factors, given the uncertain nature of mon-soon rains. Our results also suggest that the seasonal vari-ability of meteorological parameters must be taken intoaccount for the estimation of mean annual weathering rate.Despite a large amount of dissolved and sediment loads arebeing trapped in the dams, the CWR are still higher com-pared to some of the large world rivers. Characterizationof solute sources suggested that weathering of silicate, car-bonate, and saline–alkaline soils contribute �38–58, 29–45and 9–24% to CWR at different locations. The average CO2


consumption rate via silicate weathering in the Narmadabasin is �0.33 � 106 moles km�2 year�1, accounting for an-nual CO2 drawdown of �0.032 � 1012 moles at basin scale.The average annual CO2 drawdown by the entire DeccanTrap basalts is �0.24 � 1012 moles, approximately 2% ofthe annual global CO2 consumption by silicate weathering.The composite dataset also permitted us to figure out therole of different parameters in controlling the spatial andtemporal variations in CWR. The examination of annualCWR reveals that runoff, sediment erosion and temperaturetogether control the temporal variation in chemical weath-ering. The observed relationship of CWR with runoff andPWR at annual scale will improve our understanding fora long-term evolution of climate and to justify its influenceon chemical weathering and vice versa. The present studysuggests a strong control of runoff and PWR on SilWRand a coupling of runoff with CarbWR. Furthermore, awide range of CWR and rapid response of the Narmada ba-sin to any annual variation in climatic parameters, suggest-ing an intimate coupling among the different controllingparameters and therefore seem to be an important featureof present day weathering conditions in the Narmada basin.Due to higher sediment erosion, basin scale ratio betweenthe CWR and PWR is estimated to be 5.9. The greaterdenudation rates and the dominance of physical erosionover chemical weathering indicate that the Narmada basinis currently not operating in a steady state condition.

ACKNOWLEDGEMENTS

This work was supported by the Council of Scientific andIndustrial Research (CSIR, India) in the form of a research fellow-ship to HG. We are thankful to officials of Central Water Commis-sion in Bhopal and Surat (India) for providing the multiannualdata and their help during the collection of river and rainwatersamples. We are thankful to M. Dai for giving valuable suggestionsto develop the manuscript. We are grateful to AE J. Gaillardet, C.Dessert and four other anonymous reviewers for their thoughtfulreviews and comments on the original manuscript.

APPENDIX A. SUPPLEMENTARY DATA

Supplementary data associated with this article can befound, in the online version, at doi:10.1016/j.gca.2010.11.010.

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