1 Supplementary Information for

2

3 K isotopes as a tracer for continental weathering and geological K cycling

4

5 Shilei Lia.b, Weiqiang Li*c, Brian L. Beardd,e, Maureen E. Raymob, Xiaomin Wangc, Yang 6 Chena and Jun Chena

7

8 a MOE Key Laboratory of Surficial Geochemistry, School of Earth Sciences and 9 Engineering, Nanjing University, 163 Xianlindadao, Nanjing 210023, China.

10 b Lamont-Doherty Earth Observatory, Palisades, NY, United States

11 c State Key Laboratory for Mineral Deposits Research, School of Earth Sciences and 12 Engineering, Nanjing University, Nanjing 210093, China

13 d Department of Geoscience, University of Wisconsin-Madison, 1215W Dayton Street, 14 Madison, WI 53706, United States

15 e NASA Astrobiology Institute, University of Wisconsin-Madison, Madison, WI, United 16 States

17 * Corresponding author: [email protected]

18

19

20 This PDF file includes:

21 Supplementary text

22 Figs. S1 to S7

23 Tables S1 to S4

24 References for SI reference citations

25

S1

www.pnas.org/cgi/doi/10.1073/pnas.1811282116 26 Appendix 1. Sample information

27

28 The sampling locations for the river waters and sediments are shown in Figure S1 and Figure S2.

29 The Shangrao (SR) watershed is located in Shangyao County, Jiangxi Province, China. The annual 30 precipitation is 1600-1800 mm/yr. The watershed is mostly composed of Jurassic monzonite granite with 31 Cretaceous granite porphyry exposed in the minor part of the basin (data source: 32 http://geocloud.cgs.gov.cn). The elevation of the river basin ranges from about 200m to 1000m (Figure 33 S2) and is covered by forests, which grows on thin soils.

34 The Qichun (QC) watershed is located in Qichun County, Jiangxi Province, China. The annual 35 precipitation is 1342 mm/yr. The watershed is underlain by Cretaceous monzonite granite (data source: 36 http://geocloud.cgs.gov.cn). The elevation of the river basin ranges from about 300m to 1200m with steep 37 relief (Figure S2). The watershed is highly vegetated and is colonized by forests.

38

39

40 Figure S1. Location of major river samples (Red circles), the SR watershed (the red cross) and the QC 41 watershed (the blue cross).

42

S2

43

44 Figure S2. Location of river samples (red circles) and bedrock samples (the orange triangle) in the SR 45 watershed (a) and the QC watershed (b) . River sediment and river water samples are both sampled at 46 each location. The two bedrock samples analyzed were collected as gravel sized pieces from the river 47 bed.

48

49

S3

50 Appendix 2. Correction for atmospheric input

51 The riverine dissolved K load is derived from chemical weathering as well as any K that is added 52 from atmospheric inputs such as dust and sea salt spray dissolved in rainwater. In order to get the δ41K 53 composition of K from chemical weathering, we made corrections for atmosphere input. As discussed 54 below, these corrections are very small (0.01 per mil in 41K/39K), implying that variations in dissolved 55 riverine δ41K values are a result of chemical weathering processes.

56 Potassium in rainwater comes from two sources: marine inputs (sea salt spray) and continental 57 inputs (dust), the latter comes from dissolution from soil salts suspended in air, which ultimately comes 58 from silicate weathering in the river basin. Dissolution of long-transported dust by rain would contribute 59 K into riverine dissolved K. Among the regions investigated in this study, North China is the region that 60 is most affected by dust activity. The desert sands on average contain ~15ppm of water-soluble K (Zhu 61 et al., 2012), and the average dust deposition rate is ~200 t/km2/yr (Maher et al., 2010). Therefore the K 62 flux contributed from dust sources via rainwater is ~0.003 t/km2/yr in North China. This is negligible 63 compared to the riverine dissolved K flux that ranges from 0.33 to 2.32 t/km2/yr in the catchments 64 investigated in this study, as calculated from data in Table S1 and Table S4. Because the dissolved K 65 flux from dust is so small we only consider marine atmospheric inputs (sea salt spray) and this correction 66 is applied using two mass balance equations:

67 [K]corrected=[K]riverine-[K/Cl]seawater*[Cl]SS (1)

41 41 41 68 [K]corrected*δ K corrected=[K]riverine*δ K riverine-[K/Cl]seawater*[Cl]SS*δ Kseawater（2）

69 where [Cl]ss denotes the concentration of Cl in river water contributed from sea salt spray.

70 A number of studies have investigated the concentrations of Cl in river water ([Cl]rain) by assuming 71 the lowest observed Cl concentration in river water exclusively comes from rain input. Although Cl in 72 rainwater may be affected by pollution (e.g.,Lü et al., 2017), we suggest that our approach approximates

73 [Cl]ss, because the lowest observed riverine Cl concentrations occur in river basins that do not contain

74 evaporites and where air pollution is low. Overall, there are significant geographic variations in [Cl]rain 75 in China (Ding et al., 2017; Hren et al., 2007; Li and Zhang, 2009; Liu et al., 2016; Noh et al., 2009; Xu 76 and Liu, 2007; Zhang et al., 2009). Due to the remarkable geographic difference in the river catchments

77 investigated in this study, it is problematic to apply the same [Cl]SS in all our river samples since the 78 seasalt spray contribution in rainwater could vary from region to region (Berner and Berner, 2012). In 79 order to improve the accuracy of atmospheric K input correction, we divide the river catchments in China 80 into 6 regions: the southeast China region (SEC), the northeast China region(NEC), the northwest China 81 region(NWC), the central part of China (CC), the east Tibet region (ET) and south Tibet region (ST),

82 then we applied [Cl]SS for K isotope composition correction. [Cl]SS in each of the 6 regions is estimated 83 based on the lowest observed Cl concentration in river water from the rivers in the region. Using this

84 approach, we compiled the river water chemistry data and estimate the [Cl]SS in SEC, NWC, ET, ST, 85 SWC to be around 19 μmol/l (Liu et al., 2016), 106 μmol/l (Zhang et al., 2009), 1.8 μmol/l (Noh et al.,

S4

86 2009), 10 μmol/l (Hren et al., 2007) and 26 μmol/l (Xu and Liu, 2007), respectively. The [Cl]SS in CC 87 and NEC were estimated to be around 108 μmol/l (Ding et al., 2017) and 34.7 μmol/l (Li and Zhang, 88 2009). These are obtained by multiplying Cl concentration in local rainwater with the evapo-transpiration 89 factor, which is the ratio of precipitation (mm) to runoff (mm).

90 For the main stream river waters sampled in the middle and lower reaches of Changjiang River Basin

91 and Yellow River Basin, we assume [Cl]SS values are the same with those in their major regions. For 92 example, YB13W is sampled at Yibin from the main stream of Changjiang, because its drainage area

93 include ET and SWC region but mostly consist of ET, we assume [Cl]SS is the same with that in ET 94 region.

95 For water samples from rivers that drain the small-scale granite watersheds, the riverine dissolved 96 chlorine is considered to be purely from sea salt spray transported through the atmosphere, therefore

97 [Cl]SS are set as the measured Cl values from each small rivers. We note that human activities are a source 98 of Cl in riverine dissolved chlorine, however Cl in rain water comes mostly from seawater even in the 99 high populated large cities in Southern China (Huang et al., 2008; Xiao et al., 2013). This assumption 100 would result in a slight overestimation on marine K input. Even with this overestimated marine K input, 41 41 101 the difference between δ K riv and δ K corrected are still negligible (mostly below 0.01‰; Table S1). As - 41 102 discussed in the main text of the manuscript, the lack of correlation between NO3 and δ K riv (Figure S3) 103 is supportive of the insignificance of anthropogenic influence on riverine K isotope signature.

104

41 105 Figure S3. Cross plot of δ K versus NO3/K molar ratios in riverine dissolved load

106

107 Rivers with Cl concentrations higher than the [Cl]SS are considered to have been affected by Cl from 108 other sources, such as the dissolution of evaporites. For the rivers considered in this study only the 109 Tongtian, Jinsha river sampled at Shigu, Huangshui river and Huanghe rivers sampled at river mouth

110 have Cl concentrations that are thousands of μmol/l higher than the derived [Cl]SS concentrations and we 111 consider these rivers have significant impact from evaporite sources. Indeed, these river drainages 112 contain areas with evaporite-bearing Quaternary deposits (Wu et al., 2008a; Zhang et al., 2015).

113 Attempts have been made to correct for the addition of K from evaporite so that the true amount of 114 silicate weathering can be estimated. For example, we can assume that all Cl in river waters come from S5

115 modern seawater and apply correction using the modern seawater Cl/K ratio to obtain the concentration 116 and isotopic composition of K from silicate weathering, and the results are shown in Figure S4. Plot A 117 demonstrates that the majority of samples are not sensitive to such correction, except for samples with 118 high Cl concentrations (i.e., Cl>1000 ppm). Plot B shows that the 41K values of the majority of samples 119 are not affected by seawater correction, except for a few samples with high Cl concentrations, particularly, 120 when δ41K of river water sample is close to 0‰, the corrected δ41K is insensitive to Cl concentration. It 121 should be noted that such correction is difficult to make because the high Cl content in river waters are 122 believe to come from evaporites, which record ancient seawater compositions, but the δ41K of ancient 123 seawater is unknown and likely to be variable in geological history. Consequently, the δ41K signatures 124 from different evaporite sources cannot be corrected. Nonetheless, the plots in Figure S4 and the 125 associated analyses as made above strongly suggest that δ41K values of the majority of the riverine 126 samples represent signatures of silicate weathering. No correction for evaporite contribution is needed 127 for the discussions made in the manuscript, except for samples with very high Cl (i.e., >1000 ppm) 128 concentrations.

129

130

131 Figure S4. Cross plots showing corrected K concentration (A) and δ41K (B) of river water by 132 assuming that all Cl are originated from modern seawater, the contributions from seawater K on 133 the river water K concentration and δ41K values are thus corrected based on seawater K/Cl and 134 seawater δ41K values.

135

136

137

138

S6

139 Appendix 3. The mechanism behind the negative correlation between dissolved δ41K and 140 weathering intensity

141 To order to explain the negative relationship between dissolved δ41K and weathering 142 intensity (Figure 3a), we suggest this is due to more light K are incorporated into secondary 143 clay mineral under lower weathering intensity. However, Secondary clay formation is often 144 limited under low weathering intensity (Dellinger et al., 2015), therefore, to meet this criterion, 145 under the lower weathering intensity, the secondary clay mineral must be K-rich (i.e., illite) 146 compared to those formed under higher weathering intensity. This phenomenon that illite 147 formation is favored under early-stage weathering (i.e., low weathering intensity) have been 148 observed for decades (e.g., Glikes et al., 1973; Mavris et al., 2010). In order to support this 149 argument, we have compiled the mineralogical composition data of some of our studied sites 150 from many studies (Feng et al., 2014; He et al., 2013; Liu et al., 2007; Mao, 2009; Yang et al., 151 2002). Figure S5 shows that illite content in the clay fraction in river bed sediment decreases 152 from about 70% to around 20% when weathering intensity increases from 0.002 to 0.154. This 153 negative correlation between illite content in the clay fraction in river bed sediment and 154 weathering intensity is also consistent with and supports our interpretation of the negative 155 correlation between dissolved δ41K and weathering intensity.

156 157 Figure S5. Cross plot of illite content (%) in the clay fraction of river bed sediment versus 158 weathering intensity

159

S7

160 Appendix 4．Calculating silicate weathering flux in the Minjiang River basin.

161 Although the silicate weathering rates for most river basins in China are available from the literature, 162 the silicate weathering rates for the Minjiang River Basin is lacking. Therefore we used a forward 163 model (Wu et al., 2008b) to calculate silicate weathering rates for the Minjiang River basin sampled at

164 Minqing. Firstly, rain input was corrected by using an atmospheric Cl concentration ([Cl]SS) of 19 μmol/l

165 (Liu et al., 2016) and the rain input of Na, Ca, Mg and K are calculated by multiplying [Cl]SS by the X/Cl 166 (X= Na, Ca, Mg and K) molar ratios in seawater (see appendix 1). Subsequently, Na input from evaporite

167 dissolution was assumed to equal the [Cl]SS corrected Cl concentration. Subtracting Na contributed by

168 rain and evaporates from riverine Na gives the Na concentrations from silicate weathering ([Na]sil).

169 Then Ca and Mg concentration from silicate weathering is calculated by multiplying [Na]sil with a 170 silicate endmember composition: Ca/Na=0.54, Mg/Na=0.20 (Liu et al., 2016), and K concentration

171 from silicate weathering was the same as the [Cl]SS corrected riverine K concentration. Then cation 172 silicate weathering rates and silicate weathering rates (W) is calculated as:

173 Cation silicate weathering rates=([Na]sil +[Mg] sil +[Ca] sil +[K] sil)*discharge/drainage area

174 W=([Na] sil +[Mg] sil +[Ca] sil +[K] sil +[SiO2] )*discharge/drainage area

175

176

S8

177 Appendix 5. A Monte Carlo approach to constrain the modern K cycle.

178 The modern K cycle remains poorly constrained with large estimated ranges for different K fluxes 179 (e.g., Bloch and Bischoff, 1979; Holland, 2005; Jarrard, 2003). For example, the K fluxes incorporated 180 into marine sediment during marine diagenesis is reported to range from 25 to 52 Tg/yr (Bloch and 181 Bischoff, 1979; Holland, 2005; Jarrard, 2003). Although K fluxes released by hydrothermal fluids 182 reported by Jarrard (2003) and Holland (2005) are close to each other, Staudigel (2014) suggests this flux 183 is much larger at 36 Tg/yr. Elderfield and Schultz (1996) reported a large range of K fluxes released by 184 hydrothermal fluids from 9 to 27 Tg/yr. The riverine K flux and K flux incorporated by low-temperature 185 altered basalt have slightly smaller uncertainties with a range from 52 to 60 Tg/yr (Berner and Berner, 186 2012; Holland, 2005; Jarrard, 2003) and from 12 to 15.6 Tg/yr (Holland, 2005; Jarrard, 2003), 187 respectively. 188 In this study, we provide important information on the δ41K composition of the continental river 189 runoff. With K isotope fractionation factors constrained by recent studies (Parendo et al., 2017; Ramos 190 et al., 2018), we can start to constrain the K cycle from an isotopic mass balance perspective. With the 191 Eq.2 and 3 in the main text, we use a simple Monte Carlo approach to better evaluate K fluxes that are 192 consistent with the K isotope data. For this Monte Carlo calculation, we take random values within 41 193 previously reported ranges of each parameter including Friv, FMOR, Falt, Fsed , δ KMOR and Δsed.; which 194 range from 52 to 62 Tg/yr (Berner and Berner, 2012; Holland, 2005; Jarrard, 2003), 5 to 36 Tg/yr (Bloch 195 and Bischoff, 1979; Elderfield and Schultz, 1996; Holland, 2005; Jarrard, 2003; Staudigel, 2014), 12 196 to 15.6 Tg/yr (Holland, 2005; Jarrard, 2003), 25 to 52 Tg/yr (Bloch and Bischoff, 1979; Holland, 2005; 197 Jarrard, 2003), -1 to 0 ‰, and -2 to 0 ‰ (Ramos et al., 2018), respectively. Since no information is 41 198 available for δ KMOR, here it is assumed to be within a very large range from -1 to 0 ‰. Then we test if 199 these random values within these ranges can satisfy both Eq.2 and 3. If these random values meet this 200 criterion, we take it as a valid solution otherwise they are discarded (Figure S6). These calculations are 201 repeated until all possible solutions within the previously reported ranges for the six model parameters 202 are obtained. 203 In order to constrain the minimum number of calculations needed to get all possible solutions that 204 satisfy Eq.2 and Eq3, we performed the Monte Carlo simulations for differing numbers of calculations 205 (n= 2.4×106，4.8×106 , 4.8×107, 9.2×107, 2.4×108, 4.8×108, 1.2×109 and 2.4×109 times). There 206 is no significant difference in the range of acceptable fluxes and isotope compositions when n is larger 207 than 4.8×108 (Figure S7). Thus we believe that the 2.4×109 Monte Carlo simulations that we 208 performed capture the range of possible fluxes and isotope compositions that satisfy our new K isotope 209 data

210

211 Figure S6. The algorithm of our simple Monte Carlo calculation.

212 S9

213

214

41 215 Figure S7. The range (dark blue lines) of Friv (a), FMOR (b), Falt (c) , Fsed (d) , δ KMOR(e) and Δsed 216 (f) obtained from the Monte Carlo calculations plotted versus the number of times we run the calculation. 217 The black arrows represent the previously reported ranges for the six parameters. The light blue areas 218 approximately represent the envelope of the ranges of the parameters we obtained when we run the 219 calculation for different times.

220

S10

221

222 Table S1. Major ion concentrations (in μmol/L) and δ41K values (in ‰) of river waters sampled from different rivers.

No F Cl NO3 SO4 DIC Ca K Mg Na Si Sr NICB % δ41K 2sd Corrected δ41K

ACJ2 9 3368 133 811 2840 1371 93 814 3750 95 nd 0.11 0.07 0.16 0.07

ACJ5 8 1688 25 490 2271 923 55 517 1832 97 nd -2.12 -0.25 0.04 -0.25

NC01W 14 278 76 101 450 291 60 99 202 176 0.23 1.04 -0.30 0.06 -0.30

CS02W 15 301 103 234 1400 744 69 166 324 136 0.40 -1.65 -0.31 0.03 -0.32

DRJ04W 11 317 3 325 1850 877 57 282 328 117 0.97 -2.30 -0.25 0.05 -0.25

WH05W 14 303 4 369 2400 1076 63 378 364 100 0.94 -1.84 0.01 0.09 0.01

MQ08W 11 75 80 89 380 176 54 58 155 206 0.18 -3.33 -0.44 0.00 -0.45

JN10W 43 4378 119 2194 3173 1369 180 1202 5130 37 4.38 -7.31 0.12 0.08 0.12

BT11W 7 89 123 407 2363 1299 45 346 138 101 1.86 1.10 -0.10 0.14 -0.11

YB13W 7 270 65 285 1938 851 45 335 341 124 1.01 -1.65 -0.35 0.04 -0.35

YB14W 8 162 83 283 2366 899 43 337 246 119 1.02 -7.15 -0.31 0.15 -0.31

PZH15W 5 24 26 116 1783 667 31 301 102 117 0.69 -0.02 -0.17 0.10 -0.17

ZQ19W 5 112 104 162 2205 1129 41 204 104 132 0.48 1.11 -0.33 0.02 -0.34

YRLZ1501 9 693 43 641 3250 1477 54 697 973 109 2.90 0.91 -0.03 0.08 -0.04

Hs15-01 15 2602 330 1908 3450 2025 125 910 3078 144 4.01 -5.91 -0.08 0.12 -0.08

S11

LN-55W 38 873 300 531 2480 1377 89 374 798 108 3.50 -3.57 0.06 0.11 0.06

LXY071 12 162 125 360 2402 1243 53 322 226 134 1.44 -0.15 -0.08 0.08 -0.08

LXY074 9 313 77 302 1845 894 50 327 367 117 1.04 0.19 -0.20 0.02 -0.20

SR02W 6 7 7 33 120 47 10 13 56 173 0.05 -4.38 -0.10 0.17 -0.10

SR03W 13 10 7 13 210 54 11 17 86 233 0.06 -5.32 -0.03 0.03 -0.03

SR04W 13 16 4 13 250 58 16 16 86 226 0.06 -10.43 -0.18 0.05 -0.18

SR05W 14 28 19 25 260 81 24 21 110 257 0.08 -4.68 -0.24 0.08 -0.24

SR06W 15 36 2 23 320 84 28 26 121 249 0.07 -6.40 0.03 0.09 0.03

SR07W 18 15 11 19 150 46 12 11 65 172 0.04 -10.19 -0.28 0.14 -0.28

QC11W 10 32 53 62 60 58 7 17 90 214 0.12 -6.44 0.03 0.12 0.03

wql001 9 176 39 279 1300 578 31 165 241 105 1.24 -8.48 -0.21 0.02 -0.21

wql011 19 910 145 1284 3400 1431 42 912 1240 106 7.68 -8.25 -0.12 0.03 -0.13

223

224

225

S12

226

227 Table S2. δ41K (in ‰) in river sediments and bedrocks sampled from QC and MY granitic 228 watersheds.

229

location δ41K 2sd

Coarse fraction

SR02w -0.68 0.05

SR03w -0.56 0.13

SR04w -0.62 0.05

SR05w -0.56 0.08

SR06w -0.60 0.12

SR07w -0.48 0.19

QC11w -0.56 0.14

Clay fraction

SR02w -0.62 0.09

SR03w -0.72 0.08

SR04w -0.60 0.00

SR05w -0.71 0.15

SR06w -0.74 0.18

SR07w -0.65 0.00

QC11w -0.58 0.10

Bedrock

SR09W -0.51 0.21

-0.43 0.16

230

S13

231 Table S3 Seasonal variations in δ41K (in ‰) in Changjiang river waters sampled at Nanjing

riverine K flux No. sampling date mol/s δ41K 2sd

CJNJ01 05/07/2010 1982 -0.57 0.05

CJNJ 03 06/04/2010 2473 -0.54 0.12

CJNJ 04 07/02/2010 3360 -0.34 0.08

CJNJ 06 07/28/2010 3135 -0.37 0.11

CJNJ 08 08/26/2010 2203 -0.37 0.03

CJNJ 10 09/30/2010 1417 -0.40 0.00

CJNJ 14 11/30/2010 768 -0.39 0.02

CJNJ 16 01/01/2011 1129 -0.34 0.01

CJNJ 18 01/30/2011 940 -0.35 0.06

CJNJ 20 03/03/2011 742 -0.39 0.18

CJNJ 22 04/09/2011 832 -0.38 0.05

average -0.41 0.07

232

233 S14

234 Table S4. Drainage area, chemical and physical weathering rates at each sampling site.

basin area Silicate weathering rates Cationic silicate weathering physical erosion flux Sample No (106km2) (t/km2/yr) rates (t/km2/yr) (108t/yr) WI CIA Runoff (mm)

Tongtian River @Zhimenda ACJ2 0.138 3.17 a 1.67 0.0942 b 0.044 56.89 c 243

Jinshang River @Shigu ACJ5 0.233 4.36 a 2.84 0.252 d 0.039 63.1 t 182

Yalong River@Panzhihua PZH15W 0.129 8.99 a 5.67 0.314 e 0.036 57.55 c 458

Xiangjiang River @Changsha CS02W 0.0947 11.31 f 1.7 0.0952 d 0.101 77.61 g 863

Ganjiang River@Nanchang NC01W 0.0809 19.32 f 3.2 0.0861 d 0.154 - 872

Hanshui River@ Wuhan WH05W 0.159 4.46 f 2.4 0.9543 h 0.007 - 259

Wujiang River@Fuling BT11W 0.0879 2.27 f 0.69 0.3055 h 0.006 - 192

Min River@Yibin YB14W 0.133 11.61 f 7.1 0.5154 h 0.029 - 616

Jialingjiang@Beibei Summer LXY 071 0.158 2.53 f 1.2 1.547 h 0.003 59.29 i 163

Xijiang River @Gaoyao ZQ19W 0.352 16.6 s 5.2 0.638 d 0.084 76.68 j 620

Changjiang@Nanjing multi-samples 1.7 8.55 f 2.4 4.818 h 0.029 72.8 r 509

Brahmaputra River@Pai wql01W 0.205 0.65 k 0.5 0.149 l 0.009

Mekong River@Rumei wql11W 0.0751 3.73 m 2.61 0.185 n 0.015 241

Huanghe river@Lanzhou YRLZ1501 0.146 3.71 o 2.69 0.663 d 0.008 55.12i 159

Minjiang@ Minqing MQ08W 0.0545 18.25 p 6.10 0.0546 d 0.152 74.82 u 985

S15

Changjiang@Jiangjin LXY074 0.6974 5.15 f 2.69 d 0.013 67.4 r 381

Huanghe river mouth JN10W 0.752 2.74 p 2.32 11.15 j 0.002 55.60 i 55

changjiang@Yibin YB13W 74.50 i

Liao River@ Liaozhong LN55W 45.09 i

Changjiang Yueyang DRJ04W 69.8 r

Huangshui River@Ledou HS1501 55.0 q

235 Data source: a. Wu et al. (2008b); b. Wu and Yu (2002); c. Wu et al. (2012);d. http://www.mwr.gov.cn/sj/tjgb/zghlnsgb/; e. Deng (1998); f. Chetelat et al. (2008) ;g. Bao et 236 al. (2012) ; h. Fu et al. (2003) ; i. Li (2003) ; j. Chen (1985); k. Hren et al. (2007); l. Liu (1999); m. (Noh et al., 2009); n. He (1995); o. Wu et al. (2011b) p. This 237 study ;q.Chen and Huang (2013) ;r. Mao (2009);s. Sun et al. (2010) ; t.Wu et al. (2011a) u. You (2008)

S16

238 References

239 Bao, Z., Peng, B., Xu, J., 2012. Geochemical study on the relation of chemical compositions to heavy 240 metal contamination of sediments from the lowermost Xiangjiang River, Hunan Province GEOCHIMICA 241 ( in Chinese with English abstract) 41, 545-558. 242 Berner, E.K., Berner, R.A., 2012. Global environment: water, air, and geochemical cycles. Princeton 243 University Press. 244 Bloch, S., Bischoff, J.L., 1979. The effect of low-temperature alteration of basalt on the oceanic budget 245 of potassium. Geology 7, 193-196. 246 Chen, T.a.Z., Chunian, 1985. The runoff and suspended discharge of major Chinese Rivers and their 247 influence on coastal zone. Acta Oceanologica Sinica (in Chinese ) 7. 248 Chen, Z., Huang, Y., 2013. Chemical alternation index and element geochemistry of riverbed sediments 249 from Huangshui river. Journal of Arid Land Resources and Environment (in Chinese with English abstract) 250 5, 030. 251 Chetelat, B., Liu, C.-Q., Zhao, Z., Wang, Q., Li, S., Li, J., Wang, B., 2008. Geochemistry of the dissolved 252 load of the Changjiang Basin rivers: anthropogenic impacts and chemical weathering. Geochimica et 253 Cosmochimica Acta 72, 4254-4277. 254 Deng, X., 1998. Analysis of Sedimentation Formation Area in Yalong River Basin and Influences of Human 255 Activites Journal of Sichuan University(Engineering Science Edition) ( in Chinese with English abstract), 256 02. 257 Ding, H., Liu, C.-Q., Zhao, Z.-Q., Li, S.-L., Lang, Y.-C., Li, X.-D., Hu, J., Liu, B.-J., 2017. Geochemistry of the 258 dissolved loads of the Liao River basin in northeast China under anthropogenic pressure: Chemical 259 weathering and controlling factors. Journal of Asian Earth Sciences 138, 657-671. 260 Elderfield, H., Schultz, A., 1996. Mid-ocean ridge hydrothermal fluxes and the chemical composition of 261 the ocean. Annual Review of Earth and Planetary Sciences 24, 191-224. 262 Fu, R., Yu, Z., Jin, L., Fang, H., 2003. Variation trend of runoff and sediment load in Yangtze River Journal 263 of Hydraulic Engineering ( in Chinese with English abstract) 11, 23-29. 264 He, D., 1995. Analysis of hydrological characteristics in lancang-mekong river [J]. Yunnan Geographic 265 Environment Research ( in Chinese with English abstract) 1. 266 Holland, H.D., 2005. Sea level, sediments and the composition of seawater. American Journal of Science 267 305, 220-239. 268 Hren, M.T., Chamberlain, C.P., Hilley, G.E., Blisniuk, P.M., Bookhagen, B., 2007. Major ion chemistry of 269 the Yarlung Tsangpo–Brahmaputra river: chemical weathering, erosion, and CO 2 consumption in the 270 southern Tibetan plateau and eastern syntaxis of the Himalaya. Geochimica et Cosmochimica Acta 71, 271 2907-2935. 272 Huang, K., Zhuang, G., Xu, C., Wang, Y., Tang, A., 2008. The chemistry of the severe acidic precipitation 273 in Shanghai, China. Atmospheric Research 89, 149-160. 274 Jarrard, R.D., 2003. Subduction fluxes of water, carbon dioxide, chlorine, and potassium. Geochemistry, 275 Geophysics, Geosystems 4. 276 Lü, P., Han, G., Wu, Q., 2017. Chemical characteristics of rainwater in karst rural areas, Guizhou Province, 277 Southwest China. Acta Geochimica 36, 572-576. 278 Li, J., 2003. A study on the chemical weathering, mechanical denudation correlative with river water 279 and sediment geochemistry and CO2 consumption budget and controlling factors in the major drainage 280 basins of China. Ph.D. Thesis, Ocean University of China. China, Qingdao. (In Chinese with English 281 abstract). 282 Li, S., Zhang, Q., 2009. Geochemistry of the upper Han River basin, China: 2: Seasonal variations in major 283 ion compositions and contribution of precipitation chemistry to the dissolved load. Journal of hazardous 284 materials 170, 605-611. 285 Liu, T., 1999. hydrological characteristics of Yarlung Zangbo River. Acta Geographica Sinica ( in Chinese 286 with English abstract) 54, 157-164. 287 Liu, W., Shi, C., Xu, Z., Zhao, T., Jiang, H., Liang, C., Zhang, X., Zhou, L., Yu, C., 2016. Water geochemistry 288 of the Qiantangjiang River, East China: Chemical weathering and CO 2 consumption in a basin affected 289 by severe acid deposition. Journal of Asian Earth Sciences 127, 246-256. 290 Maher, B., Prospero, J., Mackie, D., Gaiero, D., Hesse, P.P., Balkanski, Y., 2010. Global connections 291 between aeolian dust, climate and ocean biogeochemistry at the present day and at the last glacial S17

292 maximum. Earth-Science Reviews 99, 61-97. 293 Mao, C., 2009. Geochemical and mineralogical studies of the sediment (suspended sediment) in 294 Changjiang River Drainage Basin. Ph.D. Thesis, Nanjing University. China, Nanjing. (In Chinese with 295 English abstract). 296 Noh, H., Huh, Y., Qin, J., Ellis, A., 2009. Chemical weathering in the Three Rivers region of Eastern Tibet. 297 Geochimica et Cosmochimica Acta 73, 1857-1877. 298 Parendo, C.A., Jacobsen, S.B., Wang, K., 2017. K isotopes as a tracer of seafloor hydrothermal alteration. 299 Proceedings of the National Academy of Sciences, 201609228. 300 Ramos, D.P.S., Morgan, L.E., Lloyd, N.S., Higgins, J.A., 2018. Reverse weathering in marine sediments 301 and the geochemical cycle of potassium in seawater: Insights from the K isotopic composition (41K/39K) 302 of deep-sea pore-fluids. Geochimica et Cosmochimica Acta 236, 99-120. 303 Staudigel, H., 2014. Chemical fluxes from hydrothermal alteration of the oceanic crust. Treatise on 304 Geochemistry (Second Edition) 4, 583-606. 305 Sun, H., Han, J., Li, D., Zhang, S., Lu, X., 2010. Chemical weathering inferred from riverine water 306 chemistry in the lower Xijiang basin, South China. Science of the Total Environment 408, 4749-4760. 307 Wu, H., Yu, X., 2002. Changes of runoff and sediment in the source region of Yangtze River during the 308 last four decades. Resources and Environment in the Yangtze Basin ( in Chinese with English abstract) 309 11, 175-178. 310 Wu, W., Xu, S., Lu, H., Yang, J., Yin, H., Liu, W., 2011a. Mineralogy, major and trace element geochemistry 311 of riverbed sediments in the headwaters of the Yangtze, Tongtian River and Jinsha River. Journal of Asian 312 Earth Sciences 40, 611-621. 313 Wu, W., Xu, S., Yang, J., Yin, H., 2008a. Silicate weathering and CO2 consumption deduced from the seven 314 Chinese rivers originating in the Qinghai-Tibet Plateau. Chemical Geology 249, 307-320. 315 Wu, W., Yang, J., Xu, S., Yin, H., 2008b. Geochemistry of the headwaters of the Yangtze River, Tongtian 316 He and Jinsha Jiang: Silicate weathering and CO2 consumption. Applied Geochemistry 23, 3712-3727. 317 Wu, W., Zheng, H., Xu, S., Yang, J., Yin, H., 2012. Geochemistry and provenance of bed sediments of the 318 large rivers in the Tibetan Plateau and Himalayan region. International Journal of Earth Sciences 101, 319 1357-1370. 320 Wu, W., Zheng, H., Yang, J., Luo, C., Zhou, B., 2011b. Chemical weathering of large river catchments in 321 China and global carbon cycle. Quaternary Sciences (in Chinese with English abstract) 31, 397-407. 322 Xiao, H.-W., Xiao, H.-Y., Long, A.-M., Wang, Y.-L., Liu, C.-Q., 2013. Chemical composition and source 323 apportionment of rainwater at Guiyang, SW China. Journal of Atmospheric Chemistry 70, 269-281. 324 Xu, Z., Liu, C.-Q., 2007. Chemical weathering in the upper reaches of Xijiang River draining the Yunnan– 325 Guizhou Plateau, Southwest China. Chemical Geology 239, 83-95. 326 You, A., 2008. A comparative study of chemical composition of sediments from of Fujian Province. 327 Geophysical＆Geochemical Exloration ( in Chinese with English abstract) 32. 328 Zhang, F., Jin, Z., Hu, G., Li, F., Shi, Y., 2009. Seasonally chemical weathering and CO2 consumption flux 329 of Lake Qinghai river system in the northeastern Tibetan Plateau. Environ Earth Sci 59, 297. 330 Zhang, Q., Jin, Z., Zhang, F., Xiao, J., 2015. Seasonal variation in river water chemistry of the middle 331 reaches of the Yellow River and its controlling factors. Journal of Geochemical Exploration 156, 101-113. 332 Zhu, B., Yang, X., Liu, Z., Rioual, P., Li, C., Xiong, H., 2012. Geochemical compositions of soluble salts in 333 aeolian sands from the Taklamakan and Badanjilin deserts in northern China, and their influencing 334 factors and environmental implications. Environ Earth Sci 66, 337-353. 335 336

S18