Available online at www.sciencedirect.com
Geochimica et Cosmochimica Acta 75 (2011) 800–824
www.elsevier.com/locate/gca
The fluvial geochemistry, contributions of silicate, carbonate and
saline–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
b
a
Department of Earth Sciences, Indian Institute of Technology, Roorkee 247667, India
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, one
of 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 phases
of 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 for
spot samples (CWRspot) vary between 25 and 63 ton km2 year1, showing a reasonable correspondence with the CWR
estimated 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 different
locations. The estimated SilWR (11–36 ton km2 year1) for the Narmada basin indicates that the previous studies on
the 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 year1, suggesting that chemical weathering of the entire Deccan Trap basalts consumes approximately
2% (0.24 1012 moles) of the annual global CO2 drawdown. The present study also evaluates the influence of meteorological parameters (runoff and temperature) and physical weathering rates (PWR) in controlling the CWR at annual scale across
the basin. The CWR and the SilWR show significant correlation with runoff and PWR. On the basis of observed wide temporal variations in the CWR and their close association with runoff, temperature and physical erosion, we propose that the
CWR 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 CWR
to 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%).
Ó 2010 Elsevier Ltd. All rights reserved.
1. INTRODUCTION
⇑ Corresponding author at: State Key Laboratory of Marine
Environmental Science, Xiamen University, Xiamen 361005,
China. Tel.: +86 592 2182977; fax: +86 592 2184101.
E-mail address: [email protected] (H. Gupta).
0016-7037/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.gca.2010.11.010
The first known effort to understand the links between
atmospheric gases and earth surface processes was made
by Ebelmen in 1845 (Berner and Maasch, 1996). Later,
Urey (1952) summarized the silicate–carbonate cycle in
Chemical weathering of basalts in the Narmada River basin, India
the form of chemical reactions, popularly known as
Ebelmen–Urey reaction. However, most interesting parts
of research on fluvial geochemistry have emerged after the
seminal work by Garrels and Mackenzie (1967). Since then,
the low temperature geochemical processes such as denudation of continents and associated CO2 consumption have
received wide attention due to their active role in regulating
the global carbon cycle via silicate–carbonate sub-cycles.
Walker et al. (1981) hypothesized a negative feedback
mechanism for long-term stabilization of the Earth’s surface temperature and argued for a direct dependency of
continental silicate weathering on climatic factors. Berner
et al. (1983) further established that it is not the weathering
of carbonates that affect the carbon cycle, but the weathering of silicates in particular.
Chemical weathering of silicate rocks is thus considered
to be the principal process of removing CO2 from the atmosphere on geological timescale (Berner, 1992). A comparative study on a number of small watersheds dominated by
single rock type has shown that basalts tend to weather
more easily than other crystalline silicate rocks (Meybeck,
1987). For this reason, a number of field and laboratory
investigations have been conducted for understanding the
different aspects of basalt weathering (Dessert et al., 2009
and reference therein). These studies both at the watershed
and the global scales provided a quantitative picture of basalt weathering in different climates by determining the major controlling parameters and emphasized the important
role of basalt weathering on the global climate (Gislason
et al., 1996, 2009; Louvat and Allegre, 1997, 1998; Dessert
et 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 silicate
rocks with the highest weathering rates and thereby
responsible for disproportionate CO2 drawdown from the
atmosphere (Dessert et al., 2003). Dessert et al. (2003)
estimated that alteration of continental basalts account
for 30% of total CO2 consumption. Further while the
rates of dissolution of silicate minerals in natural environment are known to be very slow (Wollast and Chou,
1988), a study in response to the recent climate change on
the Iceland Basalts (Gislason et al., 2009) shows that the
weathering rates may change over much shorter timescales.
Such information highlights the imperative need for a better
understanding the role of climatic factors on the chemical
weathering processes on shorter timescales.
The Narmada River in India flows largely through the
Deccan Traps and the river basin is influenced by monsoon-dominated tropical climate, serving as an ideal location to study the chemical weathering processes and their
controlling factors. Deccan Traps are one of the largest
basaltic 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 rivers
flowing through the North Deccan Traps, Dessert et al.
(2001) suggested that the chemical weathering rate and
associated CO2 consumption are relatively high compared
to other basaltic regions. Their results indicate that runoff
and temperature are the two main parameters that control
the CO2 consumption during basalt weathering. Dessert
801
et al. (2001) also demonstrated the important control exerted by emplacement and weathering of large basaltic
provinces on the geochemical and climatic changes on
Earth. Das et al. (2005) reported a new dataset for the
Krishna and the Western Ghat Rivers in India and showed
that CO2 consumption rate for the Deccan Traps in their
study area was two to three times lower than that reported
for the North Deccan Rivers by Dessert et al. (2001).
Although a considerable amount of published data on
water chemistry, chemical and silicate weathering rates
are 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 forward
for several reasons: First, the result and discussion presented here are drawn from a composite dataset of surface
water composition from field sampling done during 2003
and 2004 (hereafter referred to as spot samples) as well as
a decade-long (1990–2000) fortnight time-series data (hereafter referred to as multiannual data). This approach provides an opportunity for understanding the geochemical
processes at greater temporal scales. Second, availability
of daily water discharge and annual runoff data facilitate
to understand the influence of water discharge on solute
transportation, to calculate discharge weighted CWR and
to access influence of runoff values on the CWR estimations. Given the large aerial extent of the Deccan Traps,
the paucity of runoff data at spatial and temporal scales
proved to be a major gap in precise estimation of the
CWR in most of the earlier studies (Dessert et al., 2001;
Das et al., 2005; Jha et al., 2009). Third, among the estimated CWR, contributions from silicate, carbonate and
saline–alkaline components were separated. K-normalized
molar ratios of Mg2+ derived from flood sediments were
used to obtain the Mg content of silicate component (Mgsil)
from dissolved load and thus calculated Mgsil used to characterize Na+ and Ca2+ from river water. Fourth, CO2 consumption rates in silicate weathering for the Narmada basin
were estimated and used to revise annual CO2 drawdown
by the entire Deccan Trap basalts. Finally, the present
study attempts to evaluate coupling of the CWR and its
components with climatic parameters and sediment erosion
at annual scale. Thus, the present contribution provides a
better scope and bridges the knowledge gap in terms of
evaluating the influence of non-silicate lithologies over basalt signature, runoff in CWR estimation and to access
the relationship of chemical/silicate weathering with meteorological parameters and sediment erosion in the Deccan
Trap region.
2. STUDY AREA AND SAMPLING LOCATIONS
The Narmada is the largest west-flowing peninsular
river, ranks seventh in terms of water discharge
(38 km3 year1) and drainage area (98,796 km2) in Indian
subcontinent. The river rises as groundwater seepage from
Narmada Kund (1057 m above mean sea level-AMSL) at
Amarkantak on the eastern fringe of the Maikala Plateau
(Fig. 1). Thereafter, the river flows mostly through the
Deccan Traps, separating the Vindhyan and the Satpura
range of hills on both sides and after traversing 1312 km
802
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
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 map
showing the Deccan Region in India.
from its source, it joins the Arabian Sea at about 10 km
north of Bharuch. Mean basin elevation is 760 m AMSL,
whereas most of the basin is at an elevation of <500 m
AMSL. In its entire course, the Narmada is fed by 41 tributaries, 22 are on the left bank and rest is on the right bank
of the mainstream. The Burhner, Banjar, Hiran, Tawa,
Chota-Tawa, Kundi and the Orsang Rivers are the major
tributaries (Fig. 1), having catchment area of more than
3500 km2 (CWC, 1997–1998).
Although the Deccan basalts occupy larger parts (70%;
Subramanian et al., 2006), Quaternary soils followed by
thick sedimentary outcrops of Vindhyans and Gondwanas
and scattered patches of other rocks also cover a considerable portion in the basin (Fig. 1). The Vindhyan group consists of a thick sedimentary sequence of sandstone, shale and
limestone. The Satpura range of the Narmada basin is composed of Gondwana sediments of fluvial and lacustrine origin (Crumansonata, 1995). The Deccan Traps represent a
continental flood basalt province that records immense accumulations of tholeiitic basalt magmas which erupted over a
relatively short time span (0.5 Ma) straddling the Cretaceous–Tertiary boundary (Shrivastava and Pattanayak,
2000). Based on the Ar–Ar isotopic study, Courtillot et al.
(1988) argued that the bulk of Deccan eruptions occurred
in a short time, between 65 and 69 Ma. On the basis of Re–
Os isotopic study, Allegre et al. (1999) have established an
age of 65.6 ± 0.3 Ma, which is in good agreement with the
previous estimations (Courtillot et al., 1986, 1988; Duncan
and Pyle, 1988; Vandamme et al., 1991; Baksi, 1994).
According to Javoy and Michard (1989), 1.6 1018 moles
of CO2 released during the eruption of Deccan basalts, which
represent 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 extensively
for their mineralogy and geochemistry and the data have
been 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 the
oversaturated quartz-normative tholeiitic basalt and alkaline rocks associated with basalts are restricted in the lower
Narmada valley. The chemical composition of the Trap basalts 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 regions
has been reported by Geological Survey of India (GSI) (Crumansonata, 1995). The chemical data provided in this study
bring out subtle but distinct spatial changes in the basalts
within the apparently uniform tholeiitic basalt volume, suggesting the existence of lava types of varying composition
from west to east. Representative chemical compositions
(Na, K, Ca and Mg) of three formations, occupying the
Narmada basin are given in Table AE-4.
The soils in Narmada basin are derived from diverse
parent materials, and hence are divided into four major
groups: 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 from
the weathering of basalts are the largest group occupying
parts of upper and most of middle-lower basin, except alluvial dominated upper-middle Narmada valley and coastal
plains. The red and lateritic soils mostly occupy the parts
of upper Narmada basin.
Chemical weathering of basalts in the Narmada River basin, India
The Narmada basin is dominated by humid tropical climate. Maximum (average) temperature is observed during
May (40–42 °C) and minimum (average) is recorded in
January (8–13 °C). The majority of precipitation in the basin
takes place during the southwest monsoon season from
middle June to October, accounting for approximately
85–95% of the annual precipitation. Approximately 60%
of the annual rainfall is received during July and August.
The mean annual rainfall in the basin is approximately
1178 mm, though the rainfall distribution is not uniform
and varies between 600 and 1800 mm.
Approximately 60%, 35% and 5% of the basin area are
under arable land, forest cover and scrub-grassland, respectively. The hill slopes with thin soil cover and dissected plateaus are main areas under forest cover. The upper, middle
and lower plains are broad, fertile and mostly cultivated
(CPCB, 2001). A number of dams have been constructed
on the Narmada River and its tributaries, mainly for the
purpose of electric power generation, irrigation and for
controlling floods. The Rani Avantibai Sagar (Bargi dam)
at Jamtara, Indra Sagar (IS dam) at Punasa and the Sardar
Sarovar (SS dam) few km upstream of Garudeshwar are
three major reservoirs in the mainstream, whereas Upper
Burhner Barna Kolar, Bhagwant Sagar and Tawa dams
are constructed on the tributaries.
3. SAMPLING AND METHODOLOGY
In order to demonstrate the spatial variations in water
chemistry, samples were collected from 13 locations along
the Narmada River at approximately every 100 km distance. In addition to mainstream, eight of major tributaries
were also sampled (Table 1). A total of 73 river water samples, 45 from the mainstream and 28 from the tributaries,
were collected in four phases covering pre-monsoon (May
2004), monsoon (August 2003 and September 2004) and
post-monsoon (January 2004) seasons. In addition, 36 rainwater samples, 6 during August 2003 and the remaining
samples during June–September 2004, were collected from
different sampling locations during the southwest monsoon
period (Table AE-1). Rainwater samples were collected in
open buckets/rectangular trays, filled in laboratory precleaned 100 ml polypropylene bottles and stored in refrigerator until analysis.
The samples were mostly collected from the middle of
the river either from the bridge or with the help of boat
to avoid local heterogeneity and possible human influence
near the river banks. The samples were collected in
1000 ml pre-cleaned, high density polypropylene bottles
that had been copiously pre-rinsed with river water. The
pH, temperature and carbonate alkalinity (Stumm and
Morgan, 1996) were measured in the field. The samples
were filtered through 0.45 lm cellulose nitrate membrane
filters within 24 h of sample collection. Each filtered sample
was divided into two aliquots. One aliquot of 250 ml was
kept un-acidified to measure anions and dissolved silica
and 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 analysis and equilibrated with ambient temperature prior to
803
analysis. The major cations in the samples collected during
first two phases (August 2003 and January 2004) were analyzed using AAS (Atomic Absorption Spectrophotometer;
Model: GBC Avanta) with a precision of ±5%. The accuracy of the measurement was checked by measuring freshly
prepared standards of known concentrations made from
analytical grade reagents. For first two phases of sampling
and rainwater collected during August 2003, Cl was measured by argentometric method whereas SO42 and NO3
were determined by spectrophotometric method (APHA,
1998). The water samples collected during the last two
phases and rainwater samples of September 2004 were
analyzed for Na+, K+, Ca2+ and Mg2+, Cl, SO42 and
NO3 by Ion Chromatograph (Metrohm792 Basic IC with
suppressor module) with a precision of ±2%. The system
was calibrated using multi-element MDML standards
(Metrohm). Dissolved silica in the river water was measured
with UV Spectrophotometer (GENESYSe 10 UV) using
ammonium molybdate reagent at 410 nm wavelength
(APHA, 1998) with a precision of ±2%. Most of the water
samples from the mainstream and its tributaries show specific charge balance, denoted
inorganic
P asP normalized
P
charge balance (NICB = + )/ +) within ±7%
(Table 2), suggesting that the ions measured in this study
by and large account for the charge balance.
The river bed sediments were collected from the
Narmada basin in August 2003 from the same sampling
locations as given in Table 1. Freshly deposited flood
sediments (within 15 days of a large flood) were collected
with a plastic spade by scooping from the upper 1 cm of
the river bed, representing contemporary deposits. Prior
to chemical analysis, the samples were oven dried and
homogenized, and the bulk sediment of each sample was finely ground (<200 mesh) in an agate mortar. The contents
of major oxides were determined using X-ray fluorescence
(XRF) spectrometer (Siemens SRS-3000) at Wadia Institute of Himalayan Geology, Dehradun. Details of the
XRF methods and standardization are described in Saini
et al. (1998).
The multiannual data for a decade (collected fortnightly
between June 1990 and May 2000) used in this study were
obtained from the Central Water Commission (CWC), an
organization of the Ministry of Water Resources, Government of India. CWC has a large number of monitoring stations along the Narmada River and its major tributaries for
various hydrological observations. The details of the river
monitoring stations are presented in Table 1, whereas the
procedures dealing with the sampling and analysis are detailed in CWC working manuals (CWC, 1990–2000). Most
of the sampling locations selected for spot sampling overlapped with the network of CWC monitoring stations, thus
providing multiannual hydrological data of water discharge, sediment load and water quality parameters. Chemical data of most of water samples analyzed by CWC show
NICB within ±10% and thus comparable with the results of
spot sampling.
The SPSS software version 11.0 for Windows was used
for the statistical analysis. The results of spot samples for
different seasons were processed by one-way analysis of variance (ANOVA) followed by Tukey’s multiple-comparison
804
Table 1
Location-wise hydrological characteristics of the Narmada mainstream and its major tributaries.
Sediment flux
(106 ton year1)
Narmada mainstream
N1
Amarkantak d
Dindori
N2
Manot
N3
Jamtarae
N4
N5
Jabalpurd,f
Barmanghat
N6
Sandia
N7
Hoshangabad
N8
Handia
N9
Mortakkad
N10
Mandleshwar
N11
Rajghat
N12
Garudeshwar
N13
22°420
22°570
22°440
23°050
23°070
23°010
22°500
22°460
22°290
22°210
22°100
22°040
21°530
81°420
81°050
80°310
79°570
79°480
79°000
78°210
77°430
77°000
76°020
75°390
74°510
73°390
1057
666.1
451.6
371.5
352.0
319.1
308.6
292.1
270.2
165.0
153.5
128.0
31.2
NA
2292
4467
17,157
18,200
26,453
33,954
44,548
54,027
67,184
72,809
77,674
87,892
8
95
218
389
404
504
594
676
747
894
940
1015
1169
NA
1493
1517
1397
1397
1241
1150
1302
1124
965
820
636
1123
18.9
23.0
25.1
25.3
25.3
25.3
25.3
26.1
27.9
27.5
27.2
26.9
27.0
NA
1.36
3.78
10.95
NA
14.5
19.1
25.8
29.0
29.7
36.2
37.6
36.2
NA
2.96g
1.70g
19.1
NA
12.8
12.5
10.5
8.7
NA
6.3
5.8
5.9
NA
594
846
638
NA
550
563
579
537
442
497
448
411
NA
0.60
0.44
0.54
NA
0.56
0.51
0.56
0.52
0.54
0.39
0.24
0.63
NA
NA
5.9
3.6
NA
12.0
12.8
23.6
32.5
NA
38.1
42.6
28.3
Tributaries
Mohgaon
Hirdaynagar
Patan
Belkheri
Gadarwara
Chhidgaon
Ginnoree
Kogaon
Chandwara
22°450
22°320
23°180
22°540
22°540
22°250
22°100
22°060
22°010
80°370
80°230
79°390
79°200
78°540
77°200
76°390
75°410
73°250
447.0
436.0
500.0
650.0
321.0
700.0
218.0
900.0
18.0
4090
3133
4795
2903
2270
1931
4816
3973
3846
177
183
188
129
161
89
169
120
101
1517
1528
1280
1241
1268
1148
987
820
631
25.7
24.1
26.4
25.0
27.8
29.1
27.8
25.9
27.0
2.54
2.09
2.01
0.82
1.55
1.13
2.13
1.19
1.55
0.97g
0.10g
2.30g
1.50g
1.25g
0.69g
0.00g
0.00g
0.00g
621
648
419
545
682
586
442
298
404
0.59
0.56
0.67
0.56
0.46
0.49
0.62
0.64
0.36
3.8
0.9
NA
NA
1.7
NA
2.6
NA
1.6
T1-Burhner
T2-Banjar
T3-Hiren
T4-Sher
T5-Shakkar
T6-Ganjal
T7-Chota Tawa
T8-Kundi
T9-Orsang
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.
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
Lean
Runoff
fetc
Lat. °N Long. °E Elevation Drainage area Length of
Annual
Mean annual Water flux
(m)
up to station river up to rainfall (mm) temp (°C)
(109 km3 year1) flowb (%) (mm year1)
(km2)
station (km)
Sampling location Location
codea
Table 2
Dissolved major elemental composition of water samples from the Narmada River and its tributaries at different sampling locations.
K+
lM
Ca2+
lM
Mg2+
lM
HCO3
lM
Cl
lM
NO3
lM
SO42
lM
SiO2
lM
TZ+
le l1
TZ
le l1
NICB%
TDS
mg L1
CSI
Na+*
lM
K+*
lM
Ca2+*
lM
Mg2+*
lM
SO42*
lM
TDSw
mg L1
NA
8.2
7.4
7.8
7.4
NA
7.5
8
7.3
7.2
7.6
7.1
7.5
7.5
7.2
7.2
7.4
7.3
7.4
7.2
NA
588
174
445
428
NA
407
554
340
571
408
465
418
538
546
520
602
576
452
532
NA
12
9
8
18
NA
57
9
12
9
14
13
13
14
15
14
16
15
17
18
NA
595
202
416
351
NA
848
519
461
498
517
596
692
554
517
489
665
539
558
465
NA
234
131
353
257
NA
341
280
249
280
268
333
513
334
317
308
449
329
341
260
NA
2224
808
1920
1640
NA
2760
2160
1760
2176
2016
2320
2736
2320
2240
2144
2672
2336
2280
1904
NA
112
51
124
65
NA
157
68
45
22
47
45
157
67
67
22
203
45
67
134
NA
12
11
22
20
NA
29
11
16
17
10
12
17
11
8
19
27
19
5
11
NA
20
19
15
24
NA
26
20
17
20
17
19
31
23
30
24
33
29
24
23
NA
359
201
319
188
NA
208
348
211
379
212
224
563
216
213
214
291
213
222
149
NA
2259
848
1991
1661
NA
2842
2160
1774
2135
1993
2338
2843
2327
2230
2128
2848
2328
2267
1999
NA
2388
909
2096
1774
NA
2998
2279
1855
2256
2108
2414
2972
2444
2375
2233
2968
2458
2401
2094
NA
5.7
7.2
5.3
6.8
NA
5.5
5.5
4.6
5.7
5.7
3.3
4.6
5
6.5
4.9
4.2
5.6
5.9
4.8
NA
208
82
180
149
NA
246
199
158
200
177
203
261
204
197
188
246
205
199
171
NA
2.4
0.7
1.8
1.3
NA
1.9
2.1
1.3
1.3
1.7
1.3
1.8
1.7
1.3
1.3
1.7
1.5
1.6
1.2
NA
537
120
383
376
NA
366
490
276
498
342
417
379
445
464
420
548
468
393
426
NA
7
4
1
16
NA
56
7
9
3
10
8
7
10
10
9
13
1
13
9
NA
548
158
369
307
NA
791
471
413
437
463
565
638
502
478
437
623
433
516
391
NA
229
124
334
253
NA
324
275
245
270
259
324
506
328
299
277
441
307
339
257
NA
15
11
8
15
NA
19
16
6
12
7
16
23
11
19
14
25
12
15
6
NA
74
28
59
48
NA
71
69
48
70
53
64
90
62
61
57
80
59
61
50
Post-Monsoon 2004 January
Amarkanrak
NB 19
7
Dindori
NB 20
7.5
Mohgaon
NB 21
7.4
Manot
NB 22
7.5
Bamni
NB 23
7.2
Jabalpur
–
NA
Patan
NB 24
7.7
Belkheri
NB 25
7.9
Barman
NB 26
7.6
Gadarwara
–
NA
Sandia
NB 27
7.6
Hosangabad
NB 28
7.7
Chhidgaon
NB 29
8
Handia
NB 30
7.7
Mortakka
NB 31
8
Mandleshwar NB 32
7.8
Kogaon
NB 33
7.6
Rajghat
NB 34
7.9
Garudeshwar NB 35
8
Chandwara
–
NA
238
727
638
889
650
NA
743
597
679
NA
679
1035
854
1354
1253
1051
522
1202
1277
NA
26
41
38
47
85
NA
58
48
51
NA
55
59
60
55
56
57
91
74
62
NA
160
709
609
662
364
NA
1426
972
572
NA
589
625
463
575
640
628
568
677
564
NA
130
435
441
589
263
NA
685
889
484
NA
700
407
498
438
507
535
823
555
647
NA
752
3024
2808
3456
1800
NA
4576
4320
2768
NA
3264
3056
2544
3296
3456
3360
2896
3688
3536
NA
84
141
24
103
122
NA
427
160
122
NA
141
141
255
141
160
136
480
141
155
NA
18
19
10
15
45
NA
158
59
34
NA
40
62
39
43
34
39
53
39
49
NA
17
16
24
15
52
NA
58
29
20
NA
15
32
44
30
14
19
32
21
16
NA
166
371
270
313
174
NA
197
461
269
NA
284
293
288
305
417
290
291
360
292
NA
845
3056
2776
3439
1989
NA
5022
4366
2841
NA
3312
3156
2835
3434
3601
3434
3395
3739
3761
NA
888
3215
2890
3604
2071
NA
5277
4597
2963
NA
3475
3322
2927
3541
3678
3573
3493
3909
3771
NA
0
5.2
4.1
4.8
4.1
NA
5.1
5.3
4.3
NA
4.9
5.2
3.2
3.1
2.1
4
2.9
4.6
0.3
NA
77
271
242
298
171
NA
412
378
245
NA
282
276
239
296
313
295
272
326
312
NA
0.2
1.9
1.7
2
1.1
NA
2.6
2.6
1.9
NA
2
2.1
2.1
2
2.4
2.2
1.9
2.4
2.3
NA
188
676
584
827
598
NA
702
533
614
NA
613
987
815
1260
1171
950
468
1094
1217
NA
18
36
33
40
83
NA
56
46
48
NA
51
54
54
51
50
52
88
57
57
NA
82
662
565
615
320
NA
1369
924
523
NA
534
593
408
524
600
576
525
570
523
NA
121
8
26
429
11
90
434
16
77
570
8
95
260
43
62
NA
NA
NA
669
51
119
884
26
113
479
9
77
NA
NA
NA
691
5
83
397
29
98
491
36
86
431
19
107
489
3
113
505
9
96
815
24
83
533
4
107
645
6
109
NA
NA
NA
(continued on next page)
Monsoon 2003
Amarkanrak
Dindori
Mohgaon
Manot
Bamni
Jabalpur
Patan
Belkheri
Barman
Gadarwara
Sandia
Hosangabad
Chhidgaon
Handia
Mortakka
Mandleshwar
Kogaon
Rajghat
Garudeshwar
Chandwara
August
–
NB 1
NB 2
NB 3
NB 4
–
NB 5
NB 6
NB 7
NB 8
NB 9
NB 10
NB 11
NB 12
NB 13
NB 14
NB 15
NB 16
NB 17
NB 18
805
Na+
lM
Sample
Code
Chemical weathering of basalts in the Narmada River basin, India
pH
Sampling
location
806
Table 2 (continued)
Na+
lM
K+
lM
Ca2+
lM
Mg2+
lM
HCO3
lM
Cl
lM
NO3
lM
SO42
lM
SiO2
lM
TZ+
le l1
TZ
le l1
NICB%
TDS
mg L1
CSI
Na+*
lM
K+*
lM
Ca2+*
lM
Mg2+*
lM
SO42*
lM
TDSw
mg L1
Pre-Monsoon 2004 May
Amarkanrak
NB 36
Dindori
NB 37
Mohgaon
NB 38
Manot
NB 39
Bamni
NB 40
Jabalpur BG
NB 41
Jabalpur SG
NB 42
Patan
NB 43
Belkheri
–
Barman
NB 44
Gadarwara
NB 45
Sandia
NB 46
Hosangabad
NB 47
Chhidgaon
NB 48
Handia
NB 49
Mortakka
NB 50
Mandleshwar NB 51
Kogaon
NB 52
Rajghat
NB 53
Garudeshwar NB 54
Chandwara
–
6.9
8.5
8.3
8.2
8
7.9
7.9
8.3
NA
8
8.3
8.2
7.8
8.4
8.4
8.3
8.4
8.1
8.5
8.5
NA
176
705
382
803
351
228
259
1237
NA
388
870
506
749
2627
971
574
760
742
675
338
NA
28
47
37
53
37
28
28
128
NA
41
44
46
40
51
52
48
35
54
36
25
NA
172
697
596
752
523
589
678
475
NA
746
569
860
775
461
719
787
691
623
620
594
NA
86
470
448
684
270
313
304
1078
NA
428
912
497
575
1040
696
493
541
500
549
291
NA
648
3056
2496
3720
1968
2048
2240
4176
NA
2736
3720
3224
3456
5616
3984
3120
3152
2880
3024
2088
NA
62
113
86
145
66
47
69
324
NA
115
194
94
112
66
27
103
168
221
131
75
NA
32
7
4
9
13
6
6
24
NA
1
5
18
15
19
20
23
24
11
3
4
NA
13
28
30
27
23
22
27
29
NA
26
27
24
31
26
12
29
22
12
22
18
NA
171
427
577
592
402
387
426
408
NA
243
412
368
450
442
484
261
387
320
450
404
NA
720
3087
2505
3726
1974
2060
2252
4472
NA
2777
3875
3266
3489
5680
3853
3182
3260
3041
3049
2133
NA
768
3232
2646
3929
2093
2145
2368
4582
NA
2904
3973
3384
3645
5752
4055
3304
3389
3135
3203
2202
NA
6.8
4.7
5.6
5.4
6
4.1
5.2
2.5
NA
4.6
2.5
3.6
4.5
1.3
5.2
3.8
4
3.1
5
1
NA
69
277
238
338
186
190
209
374
NA
239
328
285
309
481
345
272
285
261
274
196
NA
0.1
2.9
2.5
2.7
2.1
2.1
2.2
2.7
NA
2.4
2.7
2.7
2.3
2.9
2.9
2.8
2.8
2.4
2.8
2.7
NA
125
654
328
741
299
187
208
1173
NA
315
804
458
710
2533
889
474
706
633
615
233
NA
20
43
32
46
35
26
24
127
NA
35
40
41
34
47
47
43
32
38
32
17
NA
94
650
552
705
479
532
607
427
NA
685
514
828
720
410
679
735
649
516
579
521
NA
77
465
441
664
267
296
284
1073
NA
419
903
487
568
1033
679
462
533
478
547
287
NA
5
23
21
21
14
15
19
26
NA
17
17
21
23
14
1
19
14
5
13
1
NA
22
95
84
115
65
62
68
124
NA
68
105
90
103
180
112
80
95
79
92
63
NA
Monsoon 2004
Amarkanrak
Dindori
Mohgaon
Manot
Bamni
Jabalpur
Patan
Belkheri
Barman
Gadarwara
Sandia
Hosangabad
Chhidgaon
Handia
Mortakka
Mandleshwar
Kogaon
Rajghat
Garudeshwar
Chandwara
NA
7.5
8.1
7.9
7.7
8.2
7.8
8.2
7.1
8.3
8.1
7
7.8
7.7
7.7
7
8.1
7.7
7.6
8.1
NA
264
313
296
302
422
331
444
415
434
246
488
576
504
318
359
470
415
301
1259
NA
27
21
15
22
35
25
14
43
24
33
49
23
41
50
57
57
44
36
61
NA
673
556
771
401
865
806
966
675
936
637
855
1017
851
669
693
1066
693
593
1112
NA
167
308
431
216
608
442
675
337
637
213
416
675
412
302
319
468
342
267
625
NA
1856
1960
2704
1448
3344
2808
3640
2416
3456
1880
3040
3920
3040
2224
2368
3472
2384
1960
4280
NA
74
60
69
73
126
57
95
76
75
73
87
114
85
80
114
135
127
87
640
NA
46
11
6
16
13
5
32
35
23
30
14
9
7
11
12
24
12
4
41
NA
33
47
40
50
29
53
50
31
65
29
41
83
51
34
43
61
46
28
58
NA
372
442
511
274
353
512
505
275
432
231
316
250
378
244
299
271
289
261
408
NA
1971
2062
2715
1558
3402
2853
3740
2482
3603
1979
3080
3984
3071
2310
2439
3596
2530
2057
4793
NA
2041
2126
2859
1638
3541
2976
3867
2589
3684
2042
3223
4209
3235
2382
2579
3753
2615
2107
5077
NA
3.5
3.1
5.3
5.2
4.1
4.3
3.4
4.3
2.2
3.1
4.7
5.6
5.3
3.1
5.8
4.3
3.4
2.4
5.9
NA
182
191
251
142
294
261
328
218
311
173
270
338
274
200
217
307
220
180
407
NA
1.6
2.2
2.3
1.6
2.8
2.2
2.8
1.4
2.8
2.2
1.5
2.4
2.2
1.9
1.3
2.7
2
1.7
2.8
214
260
234
250
371
290
379
350
361
180
440
537
410
236
259
416
307
241
1154
214
22
16
8
20
31
24
12
40
18
29
44
17
37
45
52
54
27
31
52
22
627
512
724
358
794
749
918
627
875
582
824
962
799
630
640
1023
587
552
1038
627
161
301
412
212
588
426
671
332
627
204
407
668
406
284
288
460
320
265
622
161
28
39
33
42
22
45
47
20
56
19
38
75
40
22
33
53
30
19
41
28
64
70
83
51
86
88
104
68
98
52
86
101
87
60
66
93
65
57
136
64
Sample
Code
September
–
NB 55
NB 56
NB 57
NB 58
NB 59
NB 60
NB 61
NB 62
NB 63
NB 64
NB 65
NB 66
NB 67
NB 68
NB 69
NB 70
NB 71
NB 72
NB 73
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 = total
dissolved 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.
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
pH
Sampling
location
Chemical weathering of basalts in the Narmada River basin, India
post hoc test to identify statistical differences among individual seasonal groups.
4. RESULTS AND DISCUSSION
4.1. Major ion chemistry
Table 2 presents the chemical composition of river water
samples analyzed in this study, i.e. spot sampling. Temperature measured only for samples collected during August
2003 that ranges from 22 to 28 °C and therefore the data
are not included in Table 2. The pH varies from almost neutral to mild alkaline (6.9–8.5) with most samples fall within
a range of 7.0–8.0. Samples collected during non-monsoon
period (November–May) in low-flow conditions are more
alkaline 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 L1 and is similar to the results
of previous studies on the river flowing through the Deccan
Traps (13–497 mg L1; Dessert et al., 2001), Krishna (27–
640 mg L1; Das et al., 2005), Godavari (40–550 mg L1;
Jha et al., 2009) and Reunion Island (65–350 mg L1; Louvat
and Allegre, 1997). However, the TDS of the Narmada
(average 270 mg L1) is relatively higher than the Ganges
(187 mg L1; Dalai et al., 2002) and the Indus (164 mg L1;
Karim and Veizer, 2000) draining the Himalayas and other
rivers draining the basaltic terrains such as in Iceland (20–
89 mg L1; Louvat et al., 2008), Central Siberia (30–
70 mg L1; Pokrovsky et al., 2005), French Central Massif
(40–134 mg L1; Meybeck, 1987), Sao Miguel Island (50–
140 mg L1; Louvat and Allegre, 1998), Brazilian Parana
basin (63–166 mg L1; Benedetti et al., 1994) and the islands of Martinique and Guadeloupe (27–255 mg L1;
Rad et al., 2006).
The major element composition of the Narmada and its
tributaries 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 from
680 to 3984 lM, whereas the tributaries show slightly higher HCO3 contents (808–5616 lM). The Cl concentration
range between 22 and 640 lM, and constitute 1–14% of
total anion charge (TZ). Similar to bicarbonate, the samples 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 natural
or anthropogenic. The concentrations of NO3 and
SO42 are generally low, and make up a relatively small
proportion of TDS, although some tributaries show higher
concentrations of both NO3 and SO42.
Among the major cations, Ca2+ constitutes 16–75% of total cationic charge (TZ+), followed by Mg2+ (5–48%), Na+
(11–46%) and K+ (<1–5%). Dissolved silica in the Narmada
basin ranges from 149 to 592 lM (Table 2), and the concentrations are similar to the rivers draining volcanic rocks, such
as 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 Martinique and Guadeloupe (213–1000 lM; Rad et al., 2006),
807
Iceland (420–2700 lM; Gislason et al., 1996) and Mount
Cameroon (276–1034 lM; Benedetti et al., 2003).
The major ion compositions of the Narmada and its
tributaries measured in this study are comparable to that
reported by Dessert et al. (2001) and Das et al. (2005) in
the rivers draining the northern Deccan Traps and the
Krishna River, including other west-flowing rivers in India.
Compared to our values, these authors however reported
lower HCO3, NO3, and SO42 but higher Cl and similar
range of concentrations for all cations.
4.2. Spatial and interannual variability
The ANOVA analysis was performed for each chemical
parameter by taking values for pre-monsoon, post-monsoon
and two monsoon (2003 and 2004) seasons as variables.
The test of variance was determined by using F-distribution
at 95% confidence level as a part of test of significance to
analyze the seasonal variability in data (Table 3). For most
parameters except Cl and SO42, variance is significant at
0.05 levels, indicating significant temporal variations of
major ions and silica in the basin. Table 3 also presents
the results of Tukey HSD test for inter-comparison of level
of significance between different seasons. Samples collected
in two successive monsoon seasons (2003 and 2004) also
show significant variations for K+, Ca2+, PO42, SO42
and SiO2 which may be attributed to the differences in
water discharge and hence dilution at the time of sampling
(Table 3).
Seasonal and annual variations of water discharge, temperature and concentrations of major ions (Na+, K+, Ca2+,
Table 3
Results of analysis of variance (ANOVA) of each parameter at 95%
significant level to compare seasonal and Interannual variations.
Parameter
pH
Na+
K+
Ca2+
Mg2+
HCO3
Cl
NO3
PO42
SO42
SiO2
TDS
ANOVA
Tukey HSD
F Calculated
Significance
13.649
5.983
19.625
5.676
6.127
5.018
2.537
11.539
5.342
19.848
7.537
5.393
.000
.001
.000
.002
.001
.003
.064
.000
.001
.064
.003
.002
ac, bc, cd
ab, bd
ab, ac, ad, bd
ad
ab, ac
ab, ac
ab
ab, bc, bd
ab, ac, ad
ad, bd, cd
ac, ad, bc
ab, ac
ab: refers to comparison of parameters between monsoon 2003 and
post monsoon 2004 samples.
ac: refers to comparison of parameters between monsoon 2003 and
pre monsoon 2004 samples.
ad: refers to comparison of parameters between monsoon 2003 and
monsoon 2004 samples.
bc: refers to comparison of parameters between post monsoon 2004
and pre monsoon 2004 samples.
bd: refers to comparison of parameters between post monsoon
2004 and monsoon 2004.
cd: refers to comparison of parameters between pre monsoon 2004
and monsoon 2004.
808
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
Mg2+, HCO3, Cl, SO42) and SiO2 are shown for two
representative stations, one in the mainstream at Dindori
(N2; Fig. 2) and other in the Ganjal River, one of the major
tributaries, at Chhidgaon (T6; Fig. 3). The major elemental
concentrations in these plots are combined with discharge
and temperature data for comparison. It is apparent that
the concentrations of major elements vary by a factor of
2–4 during the annual cycle at both locations. All major
ions and silica invariably show lower concentrations during
the monsoon period and vice-versa during the nonmonsoon periods. Such cyclic changes are almost consistent
with water discharge data, suggesting a dilution effect
caused by monsoon rains between June and October every
year, even though a major part of the total dissolved load is
transported during this period. Other than lack of dilution,
higher concentrations during dry seasons may also be
attributed to possible contributions from groundwater
sources and increased mineral dissolution rates under a
Fig. 2. Seasonal and interannual variations in daily water discharge and fortnight solute concentrations throughout the annual hydrological
cycle during three successive years (June 1996–May 1999) and daily temperature (June 1998–May 1999) at Dindori (N2) in the mainstream.
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 SO42.
The solutes concentrations (c–f) are also compared with water discharge (Q) at the time of sampling.
Chemical weathering of basalts in the Narmada River basin, India
809
Fig. 3. Seasonal and interannual variations in daily water discharge and fortnight solute concentrations throughout the annual hydrological
cycle during three successive years (June 1996–May 1999) and daily temperature (June 1998–May 1999) at Chhidgaon (T7) located in a major
tributary, 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 SO42. The solutes concentrations (c–f) are also compared with water discharge (Q) at the time of sampling.
warm climate. Low river runoff and consequent increased
residence time may enhance rock–water interactions, leading to higher concentration of dissolved constituents in
waters during dry season. This inference is consistent with
Tipper et al. (2006) who suggested a higher proportion of
weathering of silicate minerals during the dry period when
the 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+ and
HCO3 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 with
greater 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 of
major ions from diverse weathering regimes (Huh, 2003).
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H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
The major ion data of spot samples are plotted individually
for the mainstream and the tributaries on anion-silica and
cation plots (Fig. 4). The major ion composition of multiannual data is also included in Fig. 4 for comparison. On
anion-silica plot, both spot and multiannual data from
the mainstream as well as tributaries show the dominance
of HCO3 over Cl+SO42 and SiO2 (Fig. 4a and c). On
the cation plot, spot and multiannual samples from the
mainstream fall mostly at the line parallel to Ca2+ and
Mg2+ axis, suggesting the dominance of alkaline over alkali
ions (Na+ + K+), whereas the data for tributaries show
more or less equal contribution of alkaline and alkali ions
(Fig. 4b and d). To know the seasonal variations of major
ions in terms of monsoon and non-monsoon, the multiannual data are plotted on the combined anion–cation ternary
diagram for the mainstream at Dindori (N2; Fig. 4e) and
the Ganjal River at Chhidgaon (T6; Fig. 4f). It is apparent
that cations in the mainstream follow a similar pattern to
cations in Fig. 4b and fall on mixing line parallel to
Ca2+–Mg2+ both for monsoon and non-monsoon samples,
whereas anions lie on HCO3–SiO2 axis. Although anions
from the tributary mimic the mainstream trend, more scattered cations in waters at Chhidgaon (Fig. 4f) seem to be
enriched in alkali ions compared to basaltic signature evident 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 (average: 6.06), respectively. The Na-normalized molar ratios
from multiannual data also show a comparable range and
vary 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+ and
HCO3, respectively. Dessert et al. (2003) calculated Nanormalized molar ratios for the basaltic watersheds and observed that most of the HCO3/Na+ ratios vary between 1
and 10 (average: 5.3); Ca2+/Na+ ratios range from 0.2 to
3.15 (average: 1.3) and Mg2+/Na+ ratios vary between
0.15 and 3.15 (average: 1). Similar to the observation of
Dessert et al. (2003) for the Hawaiian Rivers, a few samples
from multiannual data also show high HCO3/Na+ ratios
(>10). A significant linear correlation between Na-normalized 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 the
Narmada basin samples occur outside the range determined
for average continental silicate rock (Gaillardet et al.,
1999) but fall parallel to trend line between silicate and carbonate end members. Dessert et al. (2003) explained this
pattern for the rivers draining basaltic terrains as a result
of possible mixing between silicate and carbonate end members. According to Dessert et al. (2003), this pattern may
also be due to either preferential weathering of Ca- and
Mg-rich silicate minerals in volcanic rocks or due to a
greater dissolution of disseminated calcite in basalt. Thus,
Na+-normalized molar ratios in the Narmada basin in general 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 show
marked variations along the mainstream with the highest
Na+ concentration observed where the saline–alkaline soils
occurs. The lowest values of major ions at Amarkantak
(N1) reflect low chemical weathering rates under the influence of lower temperature and thin layer of soil in this
highly elevated and well forested part of the basin. Subsequently, the mainstream shows the influence of lithological
heterogeneity, as reflected by the increased Ca2+ and Na+
concentrations at Jabalpur (N5) and Handia (N9), respectively. The observed decline in dissolved constituents at
Jabalpur (N5), Mortakka (N10) and Garudeshwar (N13)
may be attributed to trapping efficiency of dams on the
mainstream, which discussed later (in Section 4.4). Despite
being regulated by three large dams (thus possible trapping
of dissolved load and lack of major contribution from tributaries at immediate downstream), total dissolved solids
show a marked increase at many locations along the mainstream. Similar to the mainstream, tributaries also show
heterogeneity in surface water composition (Fig. EA-2-2),
particularly the Ganjal (T6) and the Orsang (T9) are enriched
in sodium ions. The concentration of K+ remains mostly
constant throughout the basin, suggesting conservative
behavior of K+ in river systems (Garrels and Mackenzie,
1971).
In contrast to major ions having multiple sources (lithological, atmospheric, anthropogenic and biological), silica is
mainly derived from the dissolution of primary silicate minerals. According to Tréguer et al. (1995), riverine silica is
mainly controlled by natural processes which contribute
80% of annual silica input into the ocean. Silica has negligible contribution from anthropogenic (Nixon, 2003) and
atmospheric sources (Berner and Berner, 1996). It does not
get adsorbed onto the sediments and contribution from suspended sediments during riverine transport is also negligible (Tréguer et al., 1995). With the assumption that the
biological utilization of silica is in a steady state, we plotted
SiO2 normalized molar ratios of Na+, Ca2+ + Mg2+, total
cations and HCO3 (Fig. EA-3) to delineate the relative
spatial contribution of silicate and non-silicate end members to dissolved load on the basin scale. Marked increases
in silica-normalized cation and HCO3 ratios at Jabalpur
(N5; Fig. EA-3-1) indicate additional contributions from
non-silicate sources. Contrasting trends observed between
silica-normalized molar ratios of Na+ and Ca2+ + Mg2+
in some mainstream locations advocate different sources
for these cations (i.e. carbonate and saline–alkaline). The
tributaries (e.g., T3-Hiran, T4-Sher, T5-Shakkar, T6-Ganjal)
draining through carbonate- and saline–alkaline mineralbearing 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 of
carbonate/saline–alkaline minerals in the elemental budget
of the mainstream and most of the tributaries.
The contributions from carbonate weathering is expected because the carbonate rocks of Vindhayan and
Gondwana groups and pedogenic carbonates in soils
(Tiwari and Bhai, 1997) are exposed across the basin.
Additionally, minor carbonate phases in the form of
disseminated calcite in basalts (Dessert et al., 2003) may
also contribute to carbonate weathering. Gaillardet et al.
(1999) grouped the Narmada along with rivers having
Chemical weathering of basalts in the Narmada River basin, India
811
Fig. 4. Ternary plots of major anions-silica and cations for the Narmada River basin. The data are in charge equivalent units (lEq) and are
not 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-silica
and cations for two representative locations in the Narmada River basin. Plot (e) showing the seasonal variation at Dindori (N2) in the
mainstream and plot (f) showing the seasonal variation at Chhidgaon (T7) located in a major tributary, Ganjal River. For comparison, anionsilica/cations for monsoon and non-monsoon are plotted together in each panel. In these plots, only multiannual data are used.
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H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
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+. The
data in these plots (a–d) illustrate significant positive correlations for both multiannual and spot samples in the mainstream and the
tributaries. 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 carbonate
dissolution, such as Irrawady, Cauvery, Murray Darling,
Parana and Tocantins. By contrast, Raymahashay (1986)
suggested that up to 75% of HCO3 in the Narmada River
may come from the weathering of carbonates alone. Despite minor in occurrence, these carbonate minerals weather
in orders of magnitude faster than those of Ca-Mg silicate
minerals (Gaillardet et al., 1999). In natural environment,
the weathering of calcite disseminated in silicates would
be limited by the rate of their exposure; however, high sediment erosion coupled with monsoon rains (Gupta and
Chakrapani, 2005) in the Narmada basin may likely enhance calcite dissolution.
The concentration of Na+ corrected for atmospheric
contribution Na* in the basin is in excess of Cl, the Na:Cl
equivalent ratio varies between 2.83 and 14.9 (average:
6.32), suggesting that most of the Na+ in these waters originate from a source other than atmospheric precipitation
and hence could be derived from weathering of silicate minerals. The derivation of Na+ by weathering of silicate minerals can be inferred from Si/(Na* + K) molar ratio
(Stallard and Edmond, 1983) which show a variation of
0.32–1.48 (average value for a given location) for the basin.
Since Si/(Na* + K) molar ratio of >4 is expected for conversion of the Deccan basalts to kaolinite during weathering (Rengarajan et al., 2009), lower ratios as observed
Chemical weathering of basalts in the Narmada River basin, India
here indicate either a depletion of dissolved silica during
transportation and/or an addition of alkalis from non-silicate sources. The Deccan Traps are theoliitic basalts characterized by the dominance of Ca-rich plagioclase which
mainly occur as phenocrysts. Despite higher mobility of
Na+ relative to Ca2+ (Gaillardet et al., 1999), preferential
dissolution of Ca-minerals such as plagioclase may add
considerable amounts of Ca2+ into the Narmada River.
Being a biogenic nutrient, silica is also consumed/released
by diatoms (Humborg et al., 2000), although there is no
published information on this issue for the Narmada basin.
For the samples collected during the monsoon (August
2003 and September 2004), use of silica by diatoms is expected to be minimal due to turbidity. The other possibility
is additions of Na+ from non-silicate and non-precipitation
sources. Gaillardet et al. (1999) attributed the presence of
arid and semi arid zones to explain Cl enrichment and
Na+ depletion in some Indian Rivers. However, no relative
enrichment of Cl or depletion of Na+ is noticed in case of
the Narmada River. A large portion of the Narmada basin,
particularly in middle and downstream, is overlain by saline
and alkaline soils (Chhabra, 1996; CSSRI, 2007; Mondal
and Sharma, 2008). These soils contain a variety of salts,
such as NaCl, Na2SO4, MgCl2, MgSO4, CaCl2, Na2CO3,
NaHCO3, MgCO3 and CaCO3 (Chhabra, 1996). Na-bearing minerals, such as NaHCO3 and NaCO3 could be a
source for Na+ enrichment in some parts of the basin. An
assessment of multiannual data confirms an increased contribution 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 the
catchment of these tributaries. Mondal and Sharma
(2008) reported that the poor drainage conditions coupled
with arid climate, poor groundwater quality and lack of
infrastructure for irrigation in the black soil regions can
lead to soil salinization in central India, including parts of
the Narmada basin. According to them, the extent of alkaline soils (74%) is higher than saline soils (26%) in central
India. Our results suggest a large amount of dissolved loads
derived from both carbonates and saline sources in the
Narmada basin. Mondal and Sharma (2008) found 37%
of saline–alkaline soils in the zone of 700–800 mm year1
rainfall and around 25% of soils in 800–900 mm year1
rainfall zone, suggesting a greater extent of salinization in
the 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 plains
of central India. Weathering of silicate minerals either
re-precipitation of dissolved elements carried by the river or
in-situ weathering may at least partly contribute to alkaline
elements in these soils and thus can partly contribute to
CO2 consumption. However, due to limited published
information and experimental data, it has to be better
understood by further studies.
4.4. Estimation of chemical weathering rates (CWR)
To calculate CWR, it is essential to correct the riverine
flux for inputs from non-weathering sources, such as
anthropogenic, atmospheric, groundwater and thermal
813
springs. Because of the rough terrain with low population
density and slow industrial growth, the Narmada River remains in a relatively pristine state (CPCB, 2001). Time series plots (data not shown) for the major ions such as Cl,
NO3, PO42 and SO42 from multiannual data do not
show any remarkable change in their concentrations, implying either negligible or constant anthropogenic inputs into
the river between 1990 and 2000.
The Narmada River flows through the Narmada-Son
Fault (NSF), a well-known seismotectonic feature (Biswas,
1987), and is prone to inputs from groundwater and geothermal waters. Minissale et al. (2000) reported geochemical data for a number of hydrothermal springs in the
NSF zone, but none lies in the Narmada basin. Ravi Shanker (1995) reported the presence of three thermal springs,
Anhoni, Anhoni-Samoni and Babeha, dominated by NaHCO3 rich waters in the Narmada basin with recorded water
discharges of 50–60 l m1, 5–10 l m1 and <5 l m1, respectively. The Anhoni-Samoni and Anhoni both lie close to
Hoshangabad where the annual and non-monsoon water
discharges (>500 and 300 m3 s1, respectively) of the
Narmada River are much higher than the contributions
from thermal springs. Therefore, the overall influence of these
thermal springs to the river water chemistry is considered
to be negligible in this study.
In contrast to many Himalayan Rivers fed by glacial
melt waters, the Narmada mainstream and many of its
perennial tributaries originate as groundwater seepage. Until now, no published data is available to estimate the contribution of groundwater (base-flow) to surface runoff.
According to Gupta and Chakrapani (2007), the river flow
is heavily dependent on monsoon rains as most of its annual flow is concentrated during monsoon (mainstream:
70–92%; tributaries: 87–99%) between June and November
each year. This suggests that groundwater contribution to
surface runoff may not be large, at least in high-flow periods. On the other hand, during low-flow periods, if large
amount of groundwater flows with higher solute concentration, the riverine composition may be altered substantially.
Water discharge during pre-monsoon season (March–May)
for two upstream locations (Dindori and Manot) in the
mainstream and all tributaries, account for <3% of total annual discharge at these locations (Table 1), which is assumed to be the ground water contribution and thus
runoff data corrected for groundwater contributions were
used for the CWR calculation. Since the mainstream has
been regulated downstream of Manot (N3) where the discharge from Bargi Dam (at Jamtara; N4) maintains higher
water discharge (30% of annual discharge) even in nonmonsoon season, the groundwater contributions for
remaining locations along the mainstream (downstream of
Manot) are also considered as negligible.
The atmospheric inputs for Na+, K+, Ca2+, Mg2+ and
SO42 in the Narmada basin vary from 5% to 29% of
TDS at different sampling locations and constitute 10%
of averaged TDS at basin scale (Table EA-1). Although
large temporal and spatial variations are observed in the
rainwater composition, only average values for individual
locations have been used in this work for simplification.
The data of nearby locations were used for some locations
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H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
where rainwater composition was not measured (Table EA1). The Cl/Na+ molar ratio varies between 0.96 and 1.14
and the values are slightly lower than the typical sea salt ratio of 1.16, suggesting dust-derived Na+ to rains. The observed alkalinity of rainwater is likely due to high loading
of particulate matter in the atmosphere which is in abundance in India (Khare et al., 2004).
Evapotranspiration factor (fet), the ratio of runoff to
precipitation, was determined using annual mean runoff
and rainfall values at each sample location (Table 1). The
Cl concentration at a given sampling location in the basin
was multiplied by the fet to evaluate the concentrations of
major ions in river water and the factor varies between
0.29 and 0.80 (Table 1). Large-scale evapotranspiration also
enhances the overall contribution of major ions from rainfall. Hence, rainwater contribution of major ions to river
water is estimated as follows (Stallard and Edmond, 1981):
X rain ¼ ðX =ClÞrain Clrain =fe Þ
+
+
ð1Þ
2+
2+
2
where Xrain (X = Na , K , Ca , Mg and SO4 ) 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 correction factor for evapotranspiration. Following the approach of Garrels and Mackenzie (1967), river water is
corrected for atmospheric inputs by subtracting the composition 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 SO4 ) in rivers (corrected for atmospheric
contributions) resulting from chemical weathering and are
given in Table 2. Total dissolved
P solids corrected for nongeological inputs, TDSw (= X* + SiO2), are the amount
of geochemically derived dissolved ions in river water, ranging from 22 to 180 mg L1 (Table 2). TDSw were calculated
separately for monsoon (average of August 2003 and
September 2004) and non-monsoon (average of January and
May 2004) seasons and subsequently used for the calculation of CWR during monsoon and non-monsoon seasons.
Table 4 presents the location-wise mean TDSw and the
percentage of water discharge for monsoon and non-monsoon periods. The runoff at different locations varies between 298 and 846 mm year1 with a mean of
411 mm year1 for the Narmada basin (Table 1). The
CWR in the basin were calculated using two different approaches (Table 4). The first estimation named as CWRspot
used the spot sampling data given in Table 2 and using
average seasonal runoff values, whereas the second estimation, CWRmulti, was based on multiannual (10 years) discharge weighted data. In the second case, water flux at
the time of water sample collection was used to estimate
discharge weighted values. In both cases, corrections for
atmospheric inputs were made by using the data given in
Table EA-1. For the calculation of CWRmulti, such correction was made by converting fet-corrected rainwater composition in terms of percentages for major ions for each
location. The % rainwater contribution was calculated
using the relative contribution of rain composition to spot
samples (Table 2). This approach allows us to compare
the accuracy of CWRspot estimates with CWRmulti which
helps to assess the temporal variations in CWR and the role
of different controlling parameters. We observed that the
CWR calculated using annual runoff values are 10–50%
higher than those calculated separately from monsoon
and non-monsoon runoff (CWRspot* in Table 4).
The location-wise CWRspot (except Amarkantak; N1) in
the mainstream and major tributaries vary between 25 and
63 ton km2 year1 (Table 4). The basin average of
33 ton km2 year1 is higher than the world average chemical denudation rate of 24 ton km2 year1 (Gaillardet
et al., 1999). Along the mainstream, the highest weathering
rate of 63 ton km2 year1 is observed at Manot (N3), followed by Jabalpur (N5) with 51 ton km2 year1. A few
tributaries such as the Shakkar (T5; 58 ton km2 year1)
and the Ganjal River (T6; 57.5 ton km2 year1) show
comparatively higher weathering rates. It is apparent that
the CWR for the Narmada basin are higher than the larger
rivers such as Congo/Zaire, Parana, Lena, Orinoco, Yenissei, Indus, Mackenzie, Mississippi, and the Amazon, but
similar to the CWR of the Ganges, Brahmaputra and the
Changjiang (Gaillardet et al., 1999). The CWR estimation
by Dessert et al. (2001) for the Narmada–Tapti–Godavari
(range: 21–63 ton km2 year1; mean: 37 ton km2 year1)
and Das et al. (2005) for the Krishna and other Western
Ghat Rivers (range: 3–60 ton km2 year1; mean:
16 ton km2 year1) in India also indicates variable chemical denudation rates for these Deccan Rivers. Higher CWR
of the Narmada and the Tapti Rivers (approximately twice
of the Krishna River) may be due to additions from nonbasalt sources (Das et al., 2005).
The average CWRmulti at different river monitoring stations in the Narmada basin varies between 23 and
50 ton km2 year1 (Table 4). Although the whole range
of CWR calculated from the multiannual and spot samples
data span the same order of magnitude, the CWRspot are
relatively higher at most locations. To understand the extent of deviations between them, we normalized the
CWRspot with CWRmulti. It is interesting to note that
CWRspot was found to be either 70% higher (at Chandwara;
T9) or 35% lower (at Belkheri; T4) than CWRmulti. The
sampling frequency and stage of water discharge at the time
of sample collection may lead to the observed differences in
both estimations. Therefore, given the high sampling frequency while covering all possible ranges of water discharge
and compositions, multiannual data seem to be more suited
for higher resolution studies.
Dams and associated reservoirs play an important role in
trapping suspended sediments (Syvitski et al., 2005; Walling,
2006) and dissolved loads, including nutrients (Humborg
et al., 2002, 1997; Friedl et al., 2004; Teodoru and Wehrli,
2005; Li et al., 2007). Presence of three large dams on the
mainstream and several others on the tributaries may also
influence solute transport in the Narmada basin. The solutes
show an abrupt decline in concentrations at Jabalpur (N5),
Mortakka (N10) and Garudeshwar (N13) after passing
through three large dams (Fig. EA-2-1), suggesting the possible influence of dams on dissolved loads. For instance, differences in multiannual (1990–2000) dissolved flux between
Rajghat (N12) and downstream location Garudeshwar
Table 4
CWR estimation and comparison between different methods and contribution of different components in the Narmada basin.
Code –Sampling TDS
locations
(mg L1)
HF
Tributaries
T1-Mohgaon
T2-Bamni
T3-Patan
T4-Belkheri
T5-Gadarwara
T6-Chhidgaon
T7-Ginnore
T8-Kogaon
T9-Chandwara
49
50
80
87
84
95
NA
86
93
CWR (ton km2 year1)
HF LF
CWRspot seasonal
TDS (mg L1)
*CWR
% Contribution
to CWR
Error% CO2 sil (106 moles
km2 year1)
Sil. Carb. Sol– Sil.
Alk.
Carb.
Sol–
Alk.
Sil.
NA
11
5.4
4.1
NA
NA
NA
NA NA
NA
35.0
50.4
35.4
31.3
37.1
36.6
35.0
NA
33.9
22–58
31–100
19–66
15–54
18–69
18–65
18–71
NA
16–63
31
36
31
18
26
27
28
28
28
26.3
27.5
33.3
22.8
31.3
31.8
31.3
25.4
25.4
13.2
13.6
7.9
8.4
13.6
16.5
18.3
13.8
14.1
23.6
35.7
20.9
11.1
15.6
18.0
17.1
14.5
15.4
11.7
20.3
19.4
12.0
17.6
17.3
16.0
10.3
12.1
5.3
8.4
4.6
4.2
6.9
8.2
8.5
4.8
5.8
58.1
55.4
46.5
40.6
39.0
41.4
41.1
49.1
46.3
13.1
0.1
13.8 5.5
9.1
11.1
12.5
19.1
20.2 17.3
17.8
6.1
19.5
4.5
13.1 0.1
17.3
0.1
0.54
0.77
0.33
0.19
0.28
0.35
0.30
0.36
0.34
39.1
29.8
37.7
23.0
15–68
11–49
30
27
23.6
23.5
14.9 15.8
12.0 12.1
11.6
9.4
5.8
4.1
47.5 34.9
47.2 36.8
17.6
15.8
0.1
1.2
0.33
0.25
40.2
37.7
42.2
27.0
61.7
66.9
NA
25.0
18.8
30.9
34.9
29.7
37.3
45.0
43.5
38.4
29.4
22.8
16–52
14–68
10–52
12–86
19–117
7–79
NA
5–66
8–58
26
19
31
42
31
35
NA
33
25
23.7
19.2
41.3
32.4
34.4
30.9
NA
28.1
33.3
9.3
11.1
17.1
11.9
14.8
26.9
NA
12.8
19.5
13.7
12.0
16.2
8.6
23.1
18.7
NA
8.0
12.6
5.2
6.9
6.2
2.8
9.4
13.3
NA
3.1
6.7
47.6
42.5
38.3
52.9
39.5
40.9
NA
49.7
38.3
16.7 15.5
20.6
1.9
18.1 5.4
11.5
2.4
16.5
6.0
23.5
4.9
NA NA
11.9
14.0
18.1
15.9
0.17
0.17
0.30
0.30
0.25
0.46
NA
0.31
0.27
spot
annual CWRmulti
Weathering rate
(ton km2 year1)
HF
LF
Total
Average Range
(min–max)
24
NA NA NA
NA
NA
NA
NA
92
105
68
73
87
101
110
96
95
90
92
70
76
77
79
81
82
83
10
8
30
24
23
21
19
18
17
36.8
55.4
37.7
24.1
22.9
34.1
32.3
21.9
25.3
3.9
5.6
12.8
9.6
11.2
12.2
11.2
7.6
8.1
40.7
61.0
50.5
33.7
34.1
46.4
43.5
29.5
33.4
47.9
74.6
46.9
35.9
39.3
50.7
49.5
34.5
39.0
99
86
83
85
17
15
25.0
20.6
8.2
5.3
33.1
25.9
81
63
122
113
105
133
NA
81
NA
93
95
87
92
93
93
97
97
99
7
5
13
8
7
7
3
3
1
28.1
31.4
29.1
22.6
53.0
52.0
NA
25.0
37.2
3.0
2.1
5.5
2.1
4.1
4.9
NA
0.7
0.0
31.2
33.5
34.5
24.7
57.1
56.9
NA
25.7
37.2
17.1
14.0
14.0
12.7
21.2
22.2
NA
11.0
12.1
Carb. Sol–
Alk.
28.7
31.5
43.3
44.0
43.8
39.7
38.4
34.8
36.4
38.0
36.5
44.5
35.6
43.0
34.5
NA
36.4
40.2
NA
NA
Chemical weathering of basalts in the Narmada River basin, India
Narmada mainstream
N1NA
Amarkanrak
N2-Dindori
69
N3-Manot
71
N4-Jabalpur
86
N5-Barman
58
N6-Sandia
53
N7-Hosangabad 75
N8-Handia
74
N9-Mortakka
61
N1061
Mandleshwar
N11-Rajghat
62
N1259
Garudeshwar
LF
Runoff
(%)
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.
*CWR
spot 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.
815
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H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
(N13) indicate that SS dam alone can trap more than 30% of
dissolved flux and 45% of dissolved silica. One possible reason for this flux reduction could be the diversion of water for
irrigation. However, in case of SS dam, it is not quite the
case, because compared to <5% reduction in annual water
discharge, decrease in dissolved flux seems to be remarkably
higher. Furthermore, to date there is no estimation available
for the dissolved flux contributions from the tributaries
draining into the mainstream between these two monitoring
locations. Considering the presence of a series of large dams
on the Narmada mainstream and numerous other medium
and small dams on the tributaries, the overall quantity of
trapped dissolved load must be considerably higher,
although any specific reason for such a behavior is to be
ascertained. One possible explanation may be the increased
water residence time which will enhance biological processes
leading to nutrient consumption. We speculate that physiochemical processes involving formation of precipitated minerals (Section 4.4.2) may also lead to reduction in dissolved
load. Therefore, the CWR values estimated here may be
lower than that of the current erosion rates.
As mentioned earlier, a major part of the dissolved constituents in the Narmada basin is derived from weathering of silicates and carbonates and contribution from saline–alkaline
soils. The values of CWR calculated in this study represent
a combined outcome of these three end members. Estimating
individual contributions of different end members is imperative to characterize the weathering regime, CO2 consumption
rate via silicate weathering and their controlling factors.
The SilWR at different sampling locations were calculated using the relation:
Cacarb ¼ Car Casil
ð8Þ
4.4.1. Silicate weathering rates (SilWR)
Several approaches have been proposed to characterize
silicate end member (Tipper et al., 2006 and references
therein). However, the presence of saline–alkaline soils, carbonate rocks and minor outcrops of dolomite in the Narmada basin limits the scope of these approaches. Since
the sediments in the river bed are believed to be the representative of source rocks (Blum et al., 1998), both bed and
suspended sediments have been successfully used for calculating 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 derived
exclusively from the dissolution of silicate minerals and no
contribution from the dissolution of carbonates and saline–
alkaline soil to K+ is assumed. Following the approach of
Wolff-Boenisch et al. (2009), we also used K+ normalized
ratios of Mg2+ to estimate the fraction of Mg from silicate
weathering (Mgsil). The Mg/K ratios were obtained from
X-ray Fluorescence (XRF) analysis of flood sediments from
the respective sampling locations (Table EA-4). The silicate
component of Ca2+ and Na+ in rivers are then calculated as
follows using Mgsil as a proxy:
Mgcarb ¼ Mgr Mgsil
ð9Þ
Ksil Kr
Mgsil ¼ Ksil ðMg=KÞsed
Casil ¼ Mgsil ðCa=MgÞr
Nasil ¼ Mgsil ðNa=MgÞr
ð3Þ
ð4Þ
ð5Þ
ð6Þ
where the subscripts sil, r and sed are silicates, river and
sediment, respectively. The Kr has already been corrected
for atmospheric contribution (Table 2).
SilWR ¼ TDSsil runoff
ð7Þ
P
where TDSsil = [ (Nasil + Ksil + Casil + Mgsil)] + SiO2 (all
in mg L1)
The SilWR for the different sampling locations vary from
11.0 to 35.7 ton km2 year1 and the SilWR at Garudeshwar
(N13), an extreme downstream location on the Narmada
River) were relatively low (12.1 ton km2 year1). To avoid
bias due to trapping effect in SS dam at Garusdehwar, the
SilWR at Rajghat (N12; 15.8 ton km2 year1) were considered to be representative of entire basin and are comparable
to that of the Krishna at Alamatti (14 ton km2 year1).
However, these rates are much lower than previous combined
estimates (37 ton km2 year1) for the Narmada–Tapti and
the Godavari Rivers by Dessert et al. (2001) and contribute
38.3–58.1% of total chemical weathering inputs.
4.4.2. Carbonate weathering rates (CarbWR)
Weathering of carbonate minerals contribute a sizeable
proportion of dissolved load in the Narmada basin. It is
important therefore to constrain carbonate weathering
budgets 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 from
weathering of carbonates and carbonate minerals of saline–
alkaline origin, which were estimated as:
where the subscript carb refers to carbonate contribution.
The Car and Mgr have already been corrected for atmospheric contributions.
The CarbWR at different sampling locations were calculated using the relation:
CarbWR ¼ TDScarb runoff
ð10Þ
where TDScarb = [Cacarb + Mgcarb] (all in mg L1)
The calculated CarbWR in the basin vary between 8.0
and 23.1 ton km2 year1 (Table 4) and contribute 28.7–
44.5% of annual chemical weathering flux. The CarbWR
are of similar range to SilWR, suggesting an equivalent
contribution of carbonate minerals to total weathering flux.
However, the CarbWR estimation seems complex due to
the observed oversaturation of calcite in river water.
Calcite saturation indices (CSI; Langmuir, 1971) calculated at 25 °C suggest that the majority of the surface
waters of the Narmada basin are oversaturated with respect
to calcium carbonate (average for entire basin = 2.0; Table 2). Previous studies on the Deccan Trap region (Dessert
et al., 2001; Das et al., 2005; Sharma and Subramanian,
2008) have also observed calcium super-saturation in river
waters. The mainstream and the tributaries across the basin
are even found to be saturated with respect to calcite in the
monsoon season, similar to the Himalayan Rivers (Galy
and France-Lanord, 1999). Recently, based on X-ray diffraction analysis of suspended sediments from the Narmada
and the Tapti Rivers, Sharma and Subramanian (2008)
Chemical weathering of basalts in the Narmada River basin, India
suggested that 8–9% of calcite gets precipitated in both basins, thus undermining CarbWR.
4.4.3. Saline–alkaline weathering rates (Sal–AlkWR)
One of the important findings of this study is inputs
from saline–alkaline soils to dissolved load. The contributions from saline–alkaline soils were estimated using the
relation:
Nasalalk ¼ Nar Nasil
ð11Þ
SO4salalk SO4r
ð12Þ
where the subscript sal–alk refers to saline–alkaline contribution. The Nar and SO4r have already been corrected for
atmospheric contributions.
The Sal–AlkWR at different sampling locations were
calculated using the relation:
Sal–AlkWR ¼ TDSsalalk runoff
ð13Þ
1
where TDSsal–alk = [Nasal–alk + SO4sal–alk] (all in mg L ).
The Sal–AlkWR vary between 2.8 and 13.3 ton
km2 year1 and contributes 9.1–23.4% of annual weathering flux (Table 4).
It is interesting to note that the calculated weathering
rates of silicate, carbonate and saline–alkaline components
closely follow the catchment lithology. In general, the
SilWR in the basin decreases from upstream to downstream, whereas CarbWR and Sal–AlkWR were higher in
the middle reaches of the basin. Highest SilWR were observed at Manot (N3) followed by Dinodri (N2), whereas
the lowest SilWR were observed at Barman (N5) which is
expected considering the additional contribution from the
widely distributed marble rock downstream of Jabalpur.
Higher Sal–AlkWR were observed at Hoshangabad (N7),
Handia (N8), Gadarwara (T5) and Chhidgaon (T6) which
are coincident with the widely exposed saline–alkaline soils
in these areas.
4.4.4. Uncertainties in CWR, SilWR, CarbWR and Sal–
AlkWR estimations
Errors associated with calculation of SilWR, CarbWR
and Sal–AlkWR can be marked by comparing the CWR
with the combined SilWR, CarbWR and Sal–AlkWR,
which vary between ±20% (Table 4). The errors for twothirds 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 calcite
precipitation, the violation of assumed conservative behavior of rest of solutes (trapping in dams and biological utilization) in certain cases and the silicate component
estimation (based on Mg2+/K+ ratio derived from the flood
sediments) could be possible sources for the error in SilWR,
CarbWR and Sal–AlkWR calculations.
Considering the importance of K in calculating normalized molar ratios of Mg2+ and subsequently silicate component estimations, the following paragraph details the
complexities associated with distribution of K+ in sediments. Some factors may substantially affect the chemical
composition (e.g., for K) of bed sediments such as grain
size/hydraulic sorting of minerals (Pettijohn, 1975) during
transportation of eroded material, the extent of K mobility
817
during weathering and exchange/adsorption of dissolved K
on clay minerals (Nesbitt et al., 1980; Banfield et al., 1991)
and hence need to be taken into account. The sediment
samples were collected immediately after a large flood
and thus can be considered as representative of total sediments exported by the river. Because the sediments used
for major oxides measurement in the present study were
not characterized for their grain size and were crushed in
bulk, it is not possible to identify uncertainties associated
with these factors. To understand the extent of K-normalized ratio measured in bed sediments as representative of
the erosion-averaged composition of source rocks, we compared our results with average major elemental composition
of the Deccan basalts and sediments (Table EA-4) previously reported by Das and Krishnaswami (2007).
The Mg/K molar ratios in sediments from the Western
Ghat Rivers and the Krishna basin are relatively higher
than those of the Narmada basin (Table EA-4). However,
a closer look of Mg/K ratios reveals large differences within
the Western Ghat Rivers and Krishna basin. Even the average Mg/K ratio of the tributaries of Krishna and the Bhima
mainstream are similar to that of the Narmada basin. It
suggests that one or more common factors influence Mg/
K ratio in these sub-catchments. Large variations observed
in Mg/K molar ratios (Table EA-4) of different sections
of the Deccan basalts (Crumansonata, 1995; Das and
Krishnaswami, 2007) seems to be due to the differences in
chemical composition of different lava flows within the Deccan
basalts. Additionally, sediments derived from weathering
of non-basalt rocks (30%) also influence the overall sediment composition. The K content (wt.%) in Narmada sediments are 2–3 orders higher than that of the Krishna basin.
Wilkins et al. (1994) observed that the average mobilization
of K from the Deccan basalts is lower than that of Na, Ca,
and Mg which is consistent with the K distribution in
weathering profiles from the Deccan Traps. According to
Das and Krishnaswami (2007), smectite clays formed during chemical weathering of basalts may also trap K, leading
to the limited K mobility. The Western Ghat Rivers travel
short distances before debouching into the coastal seas and
are characterized by high gradient, thus reducing the chance
of high chemical alteration during transportation. The Mg/
K molar ratio of the Western Ghat Rivers are relatively closer to the average major elemental abundance of the Deccan basalts and therefore to calculate uncertainties
associated 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 tributaries (Banjar and Hiren) most of the locations show error
within ±60% of that calculated from location specific Mg/
K ratios from flood sediments (Table EA-5). Uncertainties
estimated in the present study (for Mg/K ratios) are similar
to those reported by Wolff-Boenisch et al. (2009), who reported errors up to 60% in estimations involving Ca/Na
and Mg/K ratios.
4.4.5. CO2 consumption rates
The CO2 consumption rates (UCO2: moles km2 year1)
during weathering of silicate minerals were calculated following the model of Wu et al. (2008).
818
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
½UCO2 sil ¼ UðTZþ Þsil
¼ ð2Casil þ 2Mgsil þ Nasil þ Ksil Þ
discharge=drainage area
ð14Þ
The CO2 consumption rates from silicate weathering
(spot samples) in the Narmada basin vary between 0.17
and 0.77 106 moles km2 year1 with an average value
of 0.33 106 moles km2 year1 at Rajghat (N12; Table 4).
Though this average is slightly lower, it is of same order to
the values reported for the Krishna (0.36 106 moles km2 year1; Das et al., 2005) and the Godavari basins
(0.58 106 moles km2 year1; Jha et al., 2009). The average annual CO2 drawdown via silicate weathering in the
Narmada basin (area 98976 km2) based on average CO2
consumption rate is 0.032 1012 moles year1.
The present study on the Narmada River and previous
studies on the Krishna (Das et al., 2005) and the Godavari
Rivers (Jha et al., 2009) together suggest a large spatial
variation in CO2 consumption rates within the Deccan
Traps. The Deccan basalts occupy 20%, 48% and 70%
of catchment area of the Krishna, Godavari and the
Narmada Rivers (Subramanian et al., 2006) respectively
and all together constitute 60% of the Deccan Traps
(5 105 km2). The combined annual CO2 drawdown
during basalt weathering by these three major Deccan
Rivers (Godavari–0.087 1012, Krishna–0.019 1012 and
Narmada–0.023 1012 moles) is 0.129 1012 moles. The
average annual CO2 drawdown by the entire Deccan Trap
basalts estimated using average CO2 consumption values
of these three rivers (0.47 106 moles km2 year1) is
0.24 1012 moles, approximately 2% of the annual global
CO2 consumption (11.7 1012 moles year1; Gaillardet
et al., 1999) by silicate weathering. The proportion of
CO2 consumption by the Deccan basalts is 4 times higher
than its functional area of continental drainage, which is
having an average CO2 consumption rate of
0.1089 106 moles km2 year1 (Gaillardet et al., 1999).
As shown above, recently estimated values for these
three Deccan rivers (Godavari, Krishna and Narmada)
are significantly lower than the earlier reported values;
0.63–2.54 106 moles km2 year1 (average: 1.26 106
moles km2 year1) by Dessert et al. (2001). In a later study
Dessert et al. (2003) revised this value to 0.74 106 moles km2 year1. In the following section we discuss some of
the potential reasons that are responsible for this discrepancy. Dessert et al. (2001) used HCO3 as a “surrogate”
for CO2 consumption with the assumption that the whole
dissolved load is derived from basalt weathering, whereas
the silicate weathering and associated CO2 consumption
rates in other studies (Das et al., 2005; Jha et al., 2009
and present) are calculated from cations derived from silicate components. We calculated the CO2 consumption rates
using the model of Wu et al. (2008). In addition to differences in abundance of catchment area occupied by basalts,
variations in chemical composition of different lava flows,
different proportion/type of minor rocks, different environmental parameters (rainfall-runoff, temperature, evapotranspiration, soil cover, vegetation, etc.) show impacts
on variations in CO2 consumption across the three river
systems. Due to non-availability of water discharge data,
Dessert et al. (2001) used averaged runoff value
(463 mm year1) for the entire Deccan, whereas Das
et al. (2005) applied two different average values for the
Krishna basin (463 mm year1) and the Western Ghat
Rivers (1690 mm year1). Jha et al. (2009) showed that
runoff values within the Godavari (248–789 mm year1)
differ significantly. In the present study, for the first time
we used location specific runoff and evapo-transpiration
values (Table 1) to calculate chemical/silicate weathering
rates and associated CO2 consumption. Further, in this
study chemical composition of 36 rainwater samples collected from the respective study locations were used to correct atmospheric contribution. The observed variations in
different studies from the Deccan Traps suggest diverse
rates of silicate weathering and associated CO2 consumption across the Deccan Traps as these estimations are influenced by a number of environmental parameters that are
specific 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 erosion
in controlling the spatial variations in chemical weathering
at global scale. Among these factors, lithology and relief
control the spatial variation of CWR which remain more
or less similar on a geologic timescale. On the other hand,
climate and physical erosion show considerable seasonal
and annual discrepancies, resulting in temporal variations
in the CWR. Among the 60 large rivers in the world, the
Narmada has been ranked 50th in terms of both catchment
area and water discharge (Gaillardet et al., 1999). By using
a decade-long chemical data, we estimated the CWRmulti at
20 locations across the basin for the first time to evaluate
the annual variations in CWR. Interestingly, these 10 years
(1990–2000) cover some of the major floods in the Narmada
basin (1990, 1994 and 2000) and the globally known warmest
years (1990, 1991 1995, 1997 and 1998) on instrumental
records (WMO, 2002).
4.5.1. Role of climatic parameters
The climatic response of chemical weathering is considered as a function of temperature and runoff (e.g., Berner
et al., 1983; Velbel, 1993; White and Blum, 1995; Dessert
et al., 2001). However, the relative importance of runoff
and 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 of
climatic factors both spatial and temporal scales on the
CWR and its components.
4.5.1.1. Runoff. Despite the observed higher TDS (20%)
during non-monsoon, the majority of elemental transportation occurs during monsoon season. The major ion flux during monsoon accounts for 60–95% and 80–98% of the total
annual weathering flux in the mainstream and tributaries,
respectively. Runoff and the CWRspot for the Narmada basin
are well correlated (Fig. 6a). In contrast to the mainstream
which shows a good correlation (r2 = 0.86, p = 0.000,
Chemical weathering of basalts in the Narmada River basin, India
819
Fig. 6. The relationship between runoff and CWR in the Narmada basin. (a) Runoff vs. CWRspot showing a significant correlation both at
basin 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 a
significant 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 no
particular correlation. The SilWR (r2 = 0.62, p = 0.000,
n = 19) and CarbWR (r2 = 0.43, p = 0.002, n = 19) also
show significant correlations with runoff, whereas Sal–
AlkWR shows no correlation. The observed coupling between runoff and CWR/SilWR/CarbWR in the Narmada
basin is consistent with a similar relationship found for the
other large world rivers (Gaillardet et al., 1999), including
the rivers from the basaltic watersheds (Dessert et al.,
2003). On annual scale, the observed linear relationship between SilWR and runoff reconfirms the previous findings
by Dessert et al. (2001) that atmospheric CO2 consumption
rates are a function of runoff. Das et al. (2005) also considered runoff as an important parameter and observed that
the CWR and SilWR of the Western Ghat Rivers of the Deccan Traps are 4 times higher than that of the Krishna River
owing to higher rainfall and runoff in the western region.
Rainfall shows large temporal and spatial variations in
seasonal and annual distributions across the globe and
hence in the river dissolved and suspended loads. Previous
studies mostly define the relationship between the CWR
and runoff based on average runoff values at basin scale.
Hence, it is imperative to examine the response of chemical
weathering rates at annual and/or seasonal scales. Being situated in the monsoon climate regime, the Indian subcontinent (including the Narmada basin) experiences a large
variation in annual rainfall and its seasonal distribution
(Krishnamurthy and Shukla, 2000). This is also apparent
in the seasonal flow regime of the most Indian Rivers as
many of them carry around 90% of their annul water discharge only during three monsoon months (July to September). To understand the potential influence of runoff on the
CWR, we plotted the data of annual CWRmulti and corresponding runoff in Fig. 6b. It is evident that CWRmulti is
significantly correlated with runoff both in the mainstream
and the tributaries even at annual scale, suggesting a strong
influence of monsoon runoff.
The minimum and maximum values of the CWRmulti at
any given location vary by a factor of 3–6 across the basin
(Table 4), indicating that “the monsoon strength, for instance abnormal, normal or deficient, seems to determine
the interannual variability of weathering rates”. Interestingly, despite having variations in lithology, relief and
catchment size, CWRmulti and annual runoff show similar
linear patterns across the basin.
4.5.1.2. Temperature (T). Across the Narmada basin, considerable diurnal T variations were observed (Figs. 2b and
3b); the basin experiences hot summers with T reaching
up to 50 °C (Fig. 3b). Despite having more than 5 °C variations in annual mean T (Table 1), a poor correlation between annual mean T and the CWRspot were observed at
basin scale. However, regression analysis of annual mean
T and CWRspot for the eight tributaries illustrates a significant correlation (Fig. 7). Dessert et al. (2001) showed that
at constant runoff, the observed increase of CO2 consumption rate reflects an increase of T. Our results also show that
the T acts as an important controlling parameter even at
sub-catchment scale. The Sal–AlkWR calculated for tributaries also show a significant correlation (r2 = 0.62,
p = 0.021, n = 8), which may be related to the formation
of saline–alkaline minerals under a warm climate. Noh
et al. (2009) based on their recent study on the Three Rivers
region of Eastern Tibet also showed similar correlation
between weathering rates of halite minerals and T. In contrast to the observed coupling between runoff and CWR at
annual scale, T shows no significant correlation with
CWRmulti. The possible explanation may be (1) the strong
influence of runoff in mass transport; (2) the range of annual variation in runoff is significantly larger than T; and
(3) the frequency and magnitude of large floods may suppress the reflection of the role of temperature, as these extreme events transport huge amount of dissolved and
sediment loads.
4.5.2. Physical weathering
Gupta and Chakrapani (2005, 2007) used long-term
daily sediment load data to study sediment transport and
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H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
physical weathering rates in the Narmada basin. A moderately positive but significant correlation (Fig. 8a) was observed between PWR and CWRspot (except Jamtara vs.
Jabalpur). As discussed earlier, downstream of Manot
(N3) the Narmada River has been regulated by three large
dams. Trend analysis of annual sediment load between 1979
and 2002 indicate that downstream sediment flux at Jamtara (Bargi dam) and Garudeshwar (SS dam) have reduced
by >90% due to construction of dams. In comparison to
a sediment flux of 44.8 106 tons in 1979–1980, the
Narmada River discharged only 4.75 106 tons of sediment
to the Arabian Sea during 2002–2003. It is obvious that the
sediment transport regime of the river has been altered
significantly, thus resulting in poor correlation between
PWR and CWR at many locations. The SilWR (except
Jamtara vs. Jabalpur) is also well correlated with PWR
(r2 = 0.75, p = 0.000, n = 12).
A significant correlation between PWR and CWRmulti at
annual 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 at
different locations in the mainstream reveal that: (a) a close
examination of individual location suggests, in spite of a
large interannual variability, a positive correlation
(r2 > 0.69) exists between both weathering rates; (b) the upstream location on the Narmada, Manot (N3) does not follow this correlation strictly due to a dominant control of
relief factor; (c) extreme events characterized by huge sediment loads tend to deviate the general trend; and (d) the
coupling between CWR and PWR both at temporal and
spatial scales implies that one or more common factors control these weathering rates across the mainstream.
The coupling between CWR and PWR may be a combined result of two different mechanisms. First, chemical
weathering may be strongly regulated by the rate of mineral
surfaces exposed for chemical reactions by physical breakdown. On the contrary, the rate of rock breakdown may depend on weakening caused by chemical weathering. Given
the torrential nature of monsoon rains and heavy erosion,
the former mechanism seems to be dominating in the
Narmada basin. Regardless of a single climatic regime,
the study locations across the Narmada basin show marked
variations in mean annual rainfall (Table 1), a key factor
of sediment erosion in the Narmada basin (Gupta and
Chakrapani, 2007). It is interesting to note that in spite of
considerable variation in sediment erosion rates (more than
an order of magnitude) across the basin, PWR and CWR
remain positively coupled. This suggests that either sediment
erosion process suppresses the influence of other physical
(lithology, topography) and meteorological (rainfall and
T) parameters or both PWR and CWR are influenced
equally 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
Fig. 7. Scatter plot showing a significant positive correlation
(r2 = 0.70; p = 0.009, n = 8) between the mean annual temperature
and CWRspot for the major tributaries.
Fig. 8. The relationship between PWR and CWR across the Narmada basin. Plot (a) showing a significant positive correlation of PWR with
CWRspot (r2 = 0.42, p = 0.022, n = 12). Due to high trapping of suspended sediment at Jamtara (N4), this location was not included in
correlation. Plot (b) showing a significant correlation between the PWR and CWRmulti (r2 = 0.40; p = 0.000) in the mainstream; however, no
correlation exists for tributaries at annual scale.
Chemical weathering of basalts in the Narmada River basin, India
821
Table 5
Total denudation rate (TDR) and CWR–PWR ratios at sampling locations on the Narmada mainstream and the tributaries.
Code-sampling
locations
Narmada mainstream
N3-Manot
N4-Jabalpur
N5-Barman
N6-Sandia
N7-Hosangabad
N8-Handia
N10-Mandleshwar
N11-Rajghat
N12-Garudeshwar
Tributaries
T1-Mohgaon
T2-Hirdaynagar
T5-Gadarwara
T9-Chandwara
PWRa,b
(ton km2 year1)
CWRspot
(ton km2 year1)
TDR
(ton km2 year1)
Average
Range
(min–max)
Average
Average
1222
137
549
502
447
644
564
526
206
221–2104
34–492
51–2233
72–1270
43–928
153–1889
149–1429
202–1394
26–586
61.0
50.5
33.7
34.1
46.4
43.5
33.4
33.1
25.9
923
274
859
398
134–2815
57–761
130–2676
204–723
31.2
33.5
57.1
37.2
CWR/PWR
ratio
CWR% of
TWR
1283
188
583
536
493
687
597
559
232
0.050
0.368
0.061
0.068
0.104
0.068
0.059
0.063
0.125
5.0
36.8
6.1
6.8
10.4
6.8
5.9
6.3
12.5
954
308
916
436
0.034
0.122
0.066
0.093
3.4
12.2
6.6
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 km2 year1 (Table 5) with the highest TDR is observed at Manot (1283 ton km2 year1). The average TDR
is 2 times higher than the average TDR of the continents
(252 ton km2 year1; Berner and Berner, 1997). The ratio
of dissolved to suspended sediment loads for the Narmada
basin varies from 0.034 to 0.122, except an odd value of
0.368 at Jabalpur (Table 5) and are lower than the world
average of 0.232 (Milliman and Meade, 1983). The CWR
constitute 3–27% of TDR at different locations (Table 5)
however CWR is only 5.9% of TDR at Rajghat. At the
global scale the average continental CWR is 20% of TDR
(Berner and Berner, 1997), thus suggesting a non steady state
weathering regime for the Narmada basin. This implies that
the PWR in the basin are 10–30 times higher (except at
Jabalpur and Garudeshwar) than the CWR, indicating
the dominance of physical weathering over chemical
weathering processes. The observed deviation in ratios at
Jabalpur (N5) and Garudeshwar (N13) could be attributed
to huge reduction in suspended sediments load due to
greater trapping of suspended solid over dissolved materials
in upstream dams. Additionally, increased carbonate
weathering also play an important role at Jabalpur. It is
interesting to note that our results are comparable to the
small mountain watersheds having high relief such as
Taiwan and New Zealand (1–5%; Carey et al., 2006) and
large rivers, the Brahmaputra (8%) and the Ganges (9%),
draining the Himalayas.
5. CONCLUDING REMARKS
The data presented in this study contain a composite set
of spot samples (four phases) and a decade-long fortnight
multiannual data at twenty-one study locations. This combination provides a glimpse into variations in chemical
composition at spatial and temporal scales and allows us
to delineate the sources of major ions in a basaltic river that
were rarely covered in previous investigations. Surface
water samples analyzed for major elements show the influence of carbonates and saline–alkaline soils-derived solutes
on basaltic signature. Silica-normalized molar ratios of
HCO3 and cations (Ca2+ + Mg2+ and Na+) indicate increased non-silicates contribution in the middle and lower
parts of the Narmada basin. All samples invariably show
high calcite saturation indices and suggest calcite precipitation in the basin, accounting up to 10% of dissolved calcium
carbonate. The influence of saline–alkaline soils can easily
be distinguished by exceptionally high Na+ content at some
sampling locations (Handia-N9 and Chhidgaon-T6) and it
is therefore important to carefully evaluate the sources of
Na+ before using Na-normalized molar ratios to characterize silicate/basaltic signature. The CWRspot show large spatial variations and are comparable with the results of
Dessert et al. (2001) for the Deccan Trap region. The
weathering rates calculated from spot samples and multiannual data show that CWRspot can be either similar or considerably different than that of CWRmulti. This suggests that
high sampling frequency is crucial to capture a wide range
of temporal variations in CWR and to evaluate the role of
meteorological factors, given the uncertain nature of monsoon rains. Our results also suggest that the seasonal variability of meteorological parameters must be taken into
account for the estimation of mean annual weathering rate.
Despite a large amount of dissolved and sediment loads are
being trapped in the dams, the CWR are still higher compared to some of the large world rivers. Characterization
of solute sources suggested that weathering of silicate, carbonate, and saline–alkaline soils contribute 38–58, 29–45
and 9–24% to CWR at different locations. The average CO2
822
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
consumption rate via silicate weathering in the Narmada
basin is 0.33 106 moles km2 year1, accounting for annual CO2 drawdown of 0.032 1012 moles at basin scale.
The average annual CO2 drawdown by the entire Deccan
Trap basalts is 0.24 1012 moles, approximately 2% of
the annual global CO2 consumption by silicate weathering.
The composite dataset also permitted us to figure out the
role of different parameters in controlling the spatial and
temporal variations in CWR. The examination of annual
CWR reveals that runoff, sediment erosion and temperature
together control the temporal variation in chemical weathering. The observed relationship of CWR with runoff and
PWR at annual scale will improve our understanding for
a long-term evolution of climate and to justify its influence
on chemical weathering and vice versa. The present study
suggests a strong control of runoff and PWR on SilWR
and a coupling of runoff with CarbWR. Furthermore, a
wide range of CWR and rapid response of the Narmada basin to any annual variation in climatic parameters, suggesting an intimate coupling among the different controlling
parameters and therefore seem to be an important feature
of present day weathering conditions in the Narmada basin.
Due to higher sediment erosion, basin scale ratio between
the CWR and PWR is estimated to be 5.9. The greater
denudation rates and the dominance of physical erosion
over chemical weathering indicate that the Narmada basin
is currently not operating in a steady state condition.
ACKNOWLEDGEMENTS
This work was supported by the Council of Scientific and
Industrial Research (CSIR, India) in the form of a research fellowship to HG. We are thankful to officials of Central Water Commission in Bhopal and Surat (India) for providing the multiannual
data and their help during the collection of river and rainwater
samples. We are thankful to M. Dai for giving valuable suggestions
to develop the manuscript. We are grateful to AE J. Gaillardet, C.
Dessert and four other anonymous reviewers for their thoughtful
reviews and comments on the original manuscript.
APPENDIX A. SUPPLEMENTARY DATA
Supplementary data associated with this article can be
found, in the online version, at doi:10.1016/j.gca.2010.
11.010.
REFERENCES
Allegre C. J., Birck J. L., Capmas F. and Courtillot V. (1999) Age
of the Deccan Traps using 187Re–187Os systematics. Earth
Planet. Sci. Lett. 170, 197–204.
APHA (1998) Standard Method for the Examination of Water and
Wastewater, 20th ed.. Published by APHA, AWWA WEF and
WPCE, Washington D.C.
Baksi A. K. (1994) Geochronological studies on whole-rock
basalts, Deccan Traps, India: evaluation of the timing of
volcanism relative to the K–T boundary. Earth Planet. Sci.
Lett. 121, 43–56.
Banfield J. F., Jones B. F. and Veblen D. R. (1991) An AEM–TEM
study of weathering and diagenesis, Abert Lake, Oregon: I.
Weathering reactions in the volcanics. Geochim. Cosmochim.
Acta. 55, 2781–2793.
Benedetti M. F., Dia A., Riotte J., Chabaux F., Gerard M.,
Boulegue J., Fritz B., Chauvel C., Bulourde M., Deruelle B. and
Ildefonse P. (2003) Chemical weathering of basaltic lava flows
undergoing extreme climatic conditions: the water geochemistry
record. Chem. Geol. 201, 1–17.
Benedetti M. F., Ménard O., Noack Y., Carvalho A. and Nahon
D. (1994) Water–rock interactions in tropical catchments: field
rates of weathering and biomass impact. Chem. Geol. 118, 203–
220.
Berner E. K. and Berner R. A. (1996) Global Environment: Water,
Air and Geochemical Cycles. Prentice-Hall, Upper Saddle River,
NJ.
Berner E. K. and Berner R. A. (1997) Silicate weathering and
climate. In Tectonic Uplift and Climate Change (ed. W. F.
Ruddiman). Plenum Press, New York.
Berner R. A. (1992) Weathering, plants, and the long-term carbon
cycle. Geochim. Cosmochim. Acta. 56, 3225–3231.
Berner R. A. and Maasch K. A. (1996) Chemical weathering,
controls on atmospheric O2, CO2: Fundamental principles were
enunciated by J.J. Ebelmen in 1845. Geochim. Cosmochim. Acta
60, 1633–1637.
Berner R. A., Lasaga A. C. and Garrels R. M. (1983) The
carbonate–silicate geochemical cycle and its effect on atmospheric carbon dioxide over the past 100 millions years. Am. J.
Sci. 284, 641–683.
Biswas S. K. (1987) Regional tectonic framework, structure and
evolution of the western marginal basins of India. Tectonophy
135, 307–327.
Blum J. D., Gazis C. A., Jacobson A. D. and Chamberlain C. P.
(1998) Carbonate versus silicate weathering in the Raikhot
watershed within the High Himalayan Crystalline series.
Geology 164, 411–414.
Carey A. E., Kao S.-J., Hicks D. M., Nezat C. A. and Lyons W. B.
(2006) The geochemistry of rivers in tectonically active areas of
Taiwan and New Zealand. GSA 398, 339–351, special paper.
Chhabra R. (1996) Soil salinity and Water Quality. Brookfield.
Courtillot V., Besse J., Vandamme D., Montigny R., Jaeger J. J.
and Cappetta H. (1986) Deccan flood basalt at the Cretaceous/
Tertiary boundary? Earth Planet. Sci. Lett. 80, 361–374.
Courtillot V., Feraud G., Maluski H., Vandamme D., Moreau M.
G. and Besse J. (1988) Deccan flood basalts and the Cretaceous/Tertiary boundary. Nature 333, 843–846.
CPCB (2001) Central Pollution Control Board, Environmental Atlas
of India. National Atlas & Thematic Mapping Organization,
Kolkata.
Crumansonata (1995) Geoscientific Studies of the Son–Narmada–
Tapti Lineament Zone. Geological Survey of India, Calcutta.
Special publication no. 10.
CSSRI (2007) Perspective Plan: Vision 2025. Central Soil Salinity
Research Institute, Karnal, India. Also available at
www.cssri.org..
CWC (1990–2000) Central Water Commission, Water Quality Data
Book(s). Narmada Basin Organization, Bhopal.
CWC (1997–1998) Central Water Commission, Integrated Water
Year Book. Narmada Basin Organization, Bhopal.
Dalai T. K., Krishnaswami S. and Sarin M. M. (2002) Major ion
chemistry in the headwaters of the Yamuna River system:
chemical weathering its temperature dependence and CO2
consumption in the Himalaya. Geochim. Cosmochim. Acta 66,
3397–3416.
Das A., Krishnaswami S., Sarin M. M. and Pande K. (2005)
Chemical weathering in the Krishna basin and the western
ghats of the Deccan Traps: rates of weathering and their
control. Geochim. Cosmochim. Acta 69, 2067–2084.
Chemical weathering of basalts in the Narmada River basin, India
Das A. and Krishnaswami S. (2007) Elemental geochemistry of
river sediments from the Deccan Traps, India: implications to
sources of elements and their mobility during basalt–water
interaction. Chem. Geol. 242, 232–254.
Dessert C., Dupré B., Francois L. M., Schott J., Gaillardet J.,
Chakrapani G. J. and Bajpai S. (2001) Erosion of Deccan Traps
determined by river geochemistry: impact on the global climate
and the 87Sr/86Sr ratio of seawater. Earth Planet. Sci. Lett. 188,
459–474.
Dessert C., Dupré B., Gaillardet J., Francois L. M. and Allegre C.
J. (2003) Basalt weathering laws and the impact of
basalt weathering on the global carbon cycle. Chem. Geol. 20,
1–17.
Dessert C., Gaillardet J., Dupré B., Schott J. and Pokrovsky O. S.
(2009) Fluxes of high- versus low-temperature water–rock
interactions in aerial volcanic areas: Example from the Kamchatka Peninsula, Russia. Geochim. Cosmochim. Acta 73, 148–
169.
Duncan R. A. and Pyle D. G. (1988) Rapid eruption of the Deccan
flood basalts at the Cretaceous/Tertiary boundary. Nature 333,
841–843.
Friedl G., Teodoru C. and Wehrli B. (2004) Is the iron gate I
reservoir on the Danube River a sink for dissolved silica?
Biogeochemistry 68, 21–32.
Gaillardet J., Dupré B., Louvat P. and Allegre C. J. (1999) Global
silicate weathering and CO2 consumption rates deduced from
the chemistry of large rivers. Chem. Geol. 159, 3–30.
Galy A. and France-Lanord C. (1999) Weathering processes in the
Ganges–Brahmaputra basin and the riverine alkalinity budget.
Chem. Geol. 159, 31–60.
Garrels R. M. and Mackenzie F. T. (1967). In Equilibrium Concepts
in Natural Water Systems, vol. 67 (ed. R. F. Gould). Am. Chem.
Soc. Adv. Chem. Ser., pp. 222–242.
Garrels R. M. and Mackenzie F. T. (1971) Gregor’s denudation of
the continents. Nature 231, 382–383.
Gislason S. R., Arnorsson S. and Armannsson H. (1996) Chemical
weathering of basalt in southwest Iceland: effects of runoff, age
of rocks and vegetative/glacial cover. Am. J. Sci. 296, 837–907.
Gislason S. R., Oelkers E. H., Eiriksdottir E. S., Kardjilov M. I.,
Gisladottir G., Sigfusson B., Snorrason A., Elefsen S., Hardardottir J., Torssander P. and Oskarsson N. (2009) Direct
evidence of the feedback between climate and weathering.
Earth Planet. Sci. Lett. 277, 213–222.
Gupta H. and Chakrapani G. J. (2005) Temporal and spatial
variations in water flow and sediment load in Narmada River
Basin, India: natural and man-made factors. Environ. Geol. 48,
579–589.
Gupta H. and Chakrapani G. J. (2007) Temporal and spatial
variations in water flow and sediment load in the Narmada
River. Curr. Sci. 92, 679–684.
Huh Y. (2003) Chemical weathering and climate -a global
experiment: a review. Geosci. J. 7, 277–288.
Humborg C., Blomqvist S., Avsan E., Bergensund Y., Smedberg
E., Brink J. and Mörth C.-M. (2002) Hydrological alterations
with river damming in northern Sweden: implications for
weathering and river biogeochemistry. Global Biogeochem. Cyc.
16, 1039.
Humborg C., Conley D. J., Rahm L., Wulff F., Cociasu A. and
Ittekkot V. (2000) Silicon retention in river basins: far-reaching
effects on biogeochemistry and aquatic food webs in coastal
marine environments. Ambio 29, 45–50.
Humborg C., Ittekkot V., Cociasu A. and Bodungen B. V. (1997)
Effect of Danube River dam on Black Sea biogeochemistry and
ecosystem structure. Nature 386, 385–388.
Javoy M. and Michard G. (1989). Am. Geophys. Union EOS Trans.
70, 1421.
823
Jha P. K., Tiwari J., Singh U. K., Kumar M. and Subramanian V.
(2009) Chemical weathering and associated CO2 consumption
in the Godavari river basin, India. Chem. Geol. 264, 364–374.
Karim A. and Veizer J. (2000) Weathering processes in the Indus
river basin: implications from riverine carbon, sulfur, oxygen
and strontium isotopes. Chem. Geol. 170, 153–177.
Khare P., Goel A., Patel D. and Behari J. (2004) Chemical
characterization of rainwater at a developing urban habitat of
Northern India. Atmos. Res. 69, 135–145.
Krishnamurthy V. and Shukla J. (2000) Intraseasonal and Interannual Variability of Rainfall over India. J. Climate 13, 4366–
4377.
Langmuir D. (1971) The geochemistry of some carbonate groundwaters in central Pennsylvania. Geochim. Cosmochim. Acta 35,
1023–1045.
Li M., Xu K., Watanabe M. and Chen Z. (2007) Long-term
variations in dissolved silicate, nitrogen, and phosphorus flux
from the Yangtze River into the East China Sea and impacts on
estuarine ecosystem. Estuar. Coast. Shelf Sci. 71, 3–12.
Louvat P. and Allegre C. J. (1997) Present denudation rates at
Reunion Island determined by river geochemistry: basalt
weathering and mass budget between chemical and mechanical
erosions. Geochim. Cosmochim. Acta 61, 3645–3669.
Louvat P. and Allegre C. J. (1998) Present denudatation rates on
the island of Reunion determined by river geochemistry: basalt
weathering and mass budget between chemical and mechanical
erosions. Geochim. Cosmochim. Acta 61, 3645–3669.
Louvat P., Gislason S. R. and Allègre C. J. (2008) Chemical and
mechanical erosion rates in Iceland as deduced from river
dissolved and solid material. Am. J. Sci. 308, 679–726.
Mahoney J. (1988) Deccan Traps. In Continental Flood Basalts,
Petrology and Structural Geology (ed. J. D. Macdougall).
Kluwer Academic, Dordrecht, pp. 151–194.
Meybeck M. (1987) Global chemical weathering of surficial rocks
estimated from river dissolved loads. Am. J. Sci. 287, 401–428.
Milliman J. D. and Meade R. H. (1983) Worldwide delivery of
river sediment to the oceans. J. Geol. 91, 1–21.
Minissale A., Vaselli O., Chandrasekharam D., Magro G., Casiglia
F. and Tassi A. (2000) Origin and evolution of ‘intracratonic’
thermal fluids from central-western peninsular India. Earth
Planet. Sci. Lett. 181, 377–394.
Mondal A. K. and Sharma R. C. (2008) Computerized database on
salt affected soils in western and central India using GIS.
Geocarto. Int. 23, 373–391.
Nesbitt H. W., Markovics G. and Price R. C. (1980) Chemical
processes affecting alkalis and alkaline earths during continental weathering. Geochim. Cosmochim. Acta 44, 1659–1666.
Nixon S. W. (2003) Replacing the Nile: are anthropogenic nutrients
providing the fertility once brought to the Mediterranean by
Great River? Ambio 32, 30–39.
Noh H., Huh Y., Qin J. and Ellis A. (2009) Chemical weathering in
the Three Rivers region of Eastern Tibet. Geochim. Cosmochim.
Acta 73, 1857–1877.
Pettijohn F. J. (1975) Sedimentary Rocks. Harper and Row, New
York, p. 628.
Pokrovsky O. S., Schott J., Kudryavtzev D. I. and Dupré B. (2005)
Basalt weathering in Central Siberia under permafrost conditions. Geochim. Cosmochim. Acta 69, 5659–5680.
Rad S., Louvat P., Gorge C., Gaillardet J. and Allègre C. J. (2006)
River dissolved and solid loads in the Lesser Antilles: new
insight into basalt weathering processes. J. Geochem. Exp. 88,
308–312.
Shanker Ravi. (1995) Fragmented Indian shield and recent
earthquakes. J. Geol. Surv. Ind. 27, 41–48.
Raymahashay B. C. (1986) Geochemistry of bicarbonate in river
water. J. Geol. Soc. Ind. 27, 114–118.
824
H. Gupta et al. / Geochimica et Cosmochimica Acta 75 (2011) 800–824
Rengarajan R., Singh S. K., Sarin M. M. and Krishnaswami S.
(2009) Strontium isotopes and major ion chemistry in the
Chambal River system, India: implications to silicate erosion
rates of the Ganga. Chem. Geol. 260, 87–101.
Riebe C. S., Kirchner J. W., Granger D. E. and Finkel R. C. (2001)
Strong tectonic and weak climatic control of long-term chemical weathering rates. Geology 29, 511–514.
Saini N. K., Mukherjee P. K., Rathi M. S., Khanna P. P. and
Purohit K. K. (1998) A new geochemical reference sample of
granite (DG-H) from Dalhousie, Himachal Himalaya. J. Geol.
Soc. Ind. 52, 603–606.
Sen G. (2001) Generation of Deccan Trap magmas. Proc. Ind.
Acad. Sci. (Earth Planet. Sci.) 110, 409–431.
Sharma S. K. and Subramanian V. (2008) Hydrochemistry of the
Narmada and Tapti Rivers, India. Hydrol. Process. 22, 3444–
3455.
Shrivastava J. P. and Pattanayak S. K. (2000) Basalts of the Eastern
Deccan Volcanic Province, India. Gond. Res. 5, 649–665.
Stallard R. F. and Edmond J. M. (1981) Geochemistry of the
Amazon. 1: precipitation chemistry and the marine contribution to the dissolved load at the time of peak discharge. J.
Geophys. Res. 86, 9844–9858.
Stallard R. F. and Edmond J. M. (1983) Geochemistry of the
Amazon. 2: the influence of geology and weathering environment on the dissolved load. J. Geophys. Res. 88, 9671–9688.
Stumm W. and Morgan J. J. (1996) Aquatic Chemistry: Chemical
Equilibria and Rates in Natural Waters. Wiley, New York.
Subbarao K. V., Bodas M. S., Khadri S. R. F., Bean J. E., Kale V.,
Widdowson M., Hooper P. R. and Walsh J. N. (2000) Field
Excursion Guide to the Western Deccan Basalt Province. Penrose
Deccan, p. 249.
Subramanian V., Ittekkot V., Unger D. and Madhavan N. (2006)
Silicate weathering in South Asian Tropical River Basins. In An
in Role of Silica in Land–Sea Interactions (eds. V. Ittekkot, D.
Unger, C. Humborg and N. Tac). SCOPE, Washington D.C.,
pp. 3–13.
Syvitski J. P. M., Vörösmarty C. J., Kettner A. J. and Green P.
(2005) Impact of humans on the flux of terrestrial sediment to
the global coastal ocean. Science 308, 376–380.
Teodoru C. and Wehrli B. (2005) Retention of Sediments and
Nutrients in the Iron Gate I Reservoir on the Danube River.
Biogeochemistry 76, 539–565.
Tipper E. T., Bickle M. J., Galy A., West A. J., Pomie‘s C. and
Chapman H. J. (2006) The short term climatic sensitivity of
carbonate and silicate weathering fluxes: insight from seasonal
variations in river chemistry. Geochim. Cosmochim. Acta 70,
2737–2754.
Tiwari M. P. and Bhai H. Y. (1997) Quaternary stratigraphy of the
Narmada valley. Quaternary geology of the Narmada valley.
Geol. Sur. Ind. 46, 33–63, special publication.
Tréguer P., Nelson D. M., Van Bennekom A. J., DeMaster D. J.,
Leynaert A. and Quéguiner B. (1995) The silica balance in the
world ocean: a reestimate. Science 268, 375–379.
Urey H. C. (1952) The Planets: Their Origin and Development. Yale
University Press.
Vandamme D., Courtillot V., Besse J. and Montigney R. (1991)
Palaeomagnetism and age determination of the Deccan Traps
(India): results of a Nagpur–Bombay traverse, and review of
earlier work. Rev. of Geophys. 29, 159–190.
Velbel M. A. (1993) Temperature dependence of silicate weathering
in nature: how strong of a negative feedback on long term
accumulation of atmospheric CO2 and global greenhouse
warming? Geology 21, 1059–1062.
Vigier N., Bourdon B., Lewin E., Dupré B., Turner S., Chakrapani
G. J., Calsteren P. and van, Allègre C. J. (2005) Mobility of Useries nuclides during basalt weathering: a example from the
Deccan Traps (India). Chem. Geol. 219, 69–91.
Walker J. C. G., Hays P. B. and Kasting J. F. (1981) A negative
feedback mechanism for the long-term stabilization of Earth’s
surface temperature. J. Geophys. Res. 86, 9776–9782.
Walling D. E. (2006) Human impact on land–ocean sediment
transfer by the world’s rivers. Geomorphology 79, 192–216.
White A. F. and Blum A. E. (1995) Effects of climate on chemical
weathering in watersheds. Geochim. Cosmochim. Acta 59, 1729–
1747.
Wilkins A., Subbarao K. V., Ingram G. and Walsh J. N. (1994)
Weathering regimes within the Deccan Basalts. In Volcanism
(eds. K.V.Subbarao). Wiley Eastern, New Delhi, pp. 217–
231.
WMO (2002) WMO Statement on the Status of the Global Climate
in 2001. World Meteorological Organization, WMO-No. 940,
Geneva, Switzerland.
Wolff-Boenisch D., Gabet E. J., Burbank D. W., Langner H. and
Putkonen J. (2009) Spatial variations in chemical weathering
and CO2 consumption in Nepalese High Himalayan catchments
during the monsoon season. Geochim. Cosmochim. Acta 73,
3148–3170.
Wollast R. and Chou L. (1988) Rate control of weathering of
silicate minerals at room temperature and pressure. In Physical
and Chemical Weathering in Geochemical Cycles, vol. 251 (eds.
A. Lerman and M. Meybeck). NATO ASI series, Kluwer
Academic, pp. 11–32.
Wu W., Xu S., Yang J. and Yin H. (2008) Silicate weathering and
CO2 consumption deduced from the seven Chinese rivers
originating in the Qinghai–Tibet Plateau. Chem. Geol. 249,
307–320.
Associate editor: Jérôme Gaillardet