# Li isotopes in the middle Yellow River: Seasonal variability, sources and fractionation

Long-Fei Gou, Zhangdong Jin, Philip A.E. Pogge von Strandmann, Gen Li, Yuan-Xin Qu, Jun Xiao, Li Deng, Albert Galy

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Long-Fei Gou, Zhangdong Jin, Philip A.E. Pogge von Strandmann, Gen Li, Yuan-Xin Qu, et al. Li isotopes in the middle Yellow River: Seasonal variability, sources and fractionation. Geochimica et Cosmochimica Acta, 2019, 248, pp.88-108. 10.1016/j.gca.2019.01.007. hal-03401838

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Gou, L. and Jin, Z. and Pogge von Strandmann, Philip A.E. and Li, G. and Qu, Y. and Xiao, J. and Deng, L. and Galy, A. (2019) Li isotopes in the middle Yellow River: seasonal variability, sources and fractionation. Geochimica et Cosmochimica Acta 248, pp. 88-108. ISSN 0016-7037.

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Li isotopes in the middle Yellow River: Seasonal
variability, sources and fractionation

Long-Fei Gou¹, ², Zhangdong Jin¹, ³*, Philip A. E. Pogge von Strandmann⁴,
Gen Li⁵, Yuan-Xin Qu¹, ², Jun Xiao¹, Li Deng¹ & Albert Galy⁶

1. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth
Environment, Chinese Academy of Sciences, Xi'an 710061, China;

2. University of Chinese Academy of Sciences, Beijing 100049, China;

3. Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an
710049, China;

4. London Geochemistry and Isotope Centre (LOGIC), Institute of Earth and
Planetary Sciences, University College London and Birkbeck, University of
London, Gower Street, London WC1E 6BT, UK;

5. Department of Earth Sciences, University of Southern California, Los
Angeles, CA 90089, USA;

6. Centre de Recherches Pétrographiques et Géochimiques, UMR7358,
CNRS, Université de Lorraine, 54500 Vandoeuvre les Nancy, France.

*Corresponding author (Zhangdong Jin, zhdjin@ieecas.cn)

**Abstract:** To evaluate the roles of climate and hydrology in continental-scale
silicate weathering, we applied Li isotopes to the Yellow River and
systematically investigated seasonal Li flux, Li isotopic compositions and
potential sources. We collected samples from the middle reaches of the Yellow
River weekly over the full hydrological year of 2013. We find that the dissolved
Li is mainly derived from silicates and evaporites in the arid to semi-arid Yellow
27 River basin. Silicate weathering of loess during the monsoonal season
28 dominates the Li flux in the middle reaches of the Yellow River, with a positive
29 relationship between dissolved Li flux and physical erosion rate. Evaporite
30 contribution for riverine Li was relatively constant in the middle reaches of the
31 Yellow River but slightly increased after the storm event, with an average
32 proportion of ~25%, which might represent the proportion of evaporite
33 contribution to global oceans. Seasonal variations in the riverine Li isotopic
34 compositions are dominantly controlled by temperature with a fractionation
35 gradient as -0.182‰ per °C over the full year with deviations likely driven by
36 re-dissolution of suspended **particulate** matter, extreme hydrological events,
37 and groundwater **contribution**. Temperature dependent δ<sup>7</sup>Li value variation of
38 river water inputted into oceans indicates that Cenozoic climate cooling itself
39 may be able to explain ~2‰ of the 9‰ rise of Cenozoic seawater δ<sup>7</sup>Li value
40 (Misra and Forelich, 2012). The seasonal variation in riverine Li isotopes
41 highlights that erosion and weathering of loess may provide valuable clues on
42 **secular chemical** weathering and seawater δ<sup>7</sup>Li variation spanning a range of
43 time scales.

44 **Key words**: Li isotopes, temperature dependency, seasonal variation, loess
45 weathering, the Yellow River, the Chinese Loess Plateau

1. Introduction

Chemical weathering is among the most important geochemical
processes operating on Earth's surface, driving the cycling of elements from
49 continents to sedimentary basins and consequently modulating the
50 compositions of the crust (e.g. Lee, 2008; Teng et al., 2010; Liu and Rudnick,
51 2011; Liu et al., 2013). Most importantly, chemical weathering of silicate rocks,
52 i.e. silicate weathering, represents a major sink to atmospheric CO2 over
53 geological timescales, regulating the geological carbon cycle and the
54 long-term evolution of the climate system (e.g. Walker et al., 1981; Berner et
55 al., 1983; Gaillardet et al., 1999; West et al., 2005). The rate and intensity of
56 silicate weathering require accurate and precise quantification in order to
57 evaluate the efficiency of neutralizing capacity of CO2 by silicate weathering.

58 In the past decades, a number of isotopic proxies have been suggested
59 for evaluating weathering rate and intensity, such as Sr, Mg, Si, and Os
60 isotopes. However, none of them would be able to directly trace silicate
61 weathering reliably owing to the effects of other processes and/or influences
62 from unconstrained sources, such as biological processes for Mg and Si
63 isotopes (Li et al., 2014; Mavromatis et al., 2016; Pogge von Strandmann et al.,
64 2016), meta-carbonates-sourced radiogenic Sr (Edmond 1992; Galy et al.,
65 1999), and black shale-derived Os (Ravizza and Esser, 1993).

66 Due to its low concentration in carbonates and its large mass difference
67 between ⁶Li and ⁷Li, Li isotopes are considered as the most promising tracer of
68 silicate weathering processes currently available (Chan et al., 1992; Huh et al.,
69 1998, 2001; Pistiner and Henderson, 2003; Kısakürek et al., 2005; Hathorne
and James, 2006; Pogge von Strandmann et al., 2006, 2010, 2013, 2016, 2017; Vigier et al., 2008, 2009; Lemarchand et al., 2010; Millot et al., 2010b, c; Misra and Froelich, 2012; Tipper et al., 2012; Bouchez et al., 2013; Dellinger et al., 2014, 2015, 2017; Wanner et al., 2014; Clergue et al., 2015; Vigier and Goddéris, 2015; Wang et al., 2015; Henchiri et al., 2016). Dissolution experiments have proved that negligible Li isotope fractionation occurs during mineral dissolution (Pistiner and Henderson, 2003; Wimpenny et al., 2010, 2015; Verney-Carron et al., 2011; Ryu et al., 2014). Significant Li isotopic fractionation is observed when Li is incorporated into the lattices of secondary minerals and/or during adsorption onto the secondary mineral surface (Huh et al., 2001; Vigier et al., 2008; Millot et al., 2010b; Pogge von Strandmann et al., 2010; Wimpenny et al., 2015). Li isotopes have been suggested to trace both silicate weathering intensity (Huh et al., 1998, 2001; Kısakürek et al., 2005; Pogge von Strandmann et al., 2006, 2008a, b, 2010, 2012, 2013, 2016; Millot et al., 2010c; Dellinger et al., 2015, 2017; Wang et al., 2015) and silicate weathering fluxes (Vigier et al., 2009), despite some recent studies showing no universal global correlation between δ<sup>7</sup>Li and silicate weathering rates (e.g. Pogge von Strandmann et al., 2017).

Previous studies have mainly focused on the spatial variation of the riverine dissolved Li isotopes and have explored (1) which factors and what processes control riverine Li isotopic fractionation and (2) how to use Li isotopes as a tracer of silicate weathering at various scales (e.g. Huh et al.,
92 1998, 2001; Pogge von Strandmann et al., 2006, 2008a, b, 2010, 2012, 2013,
93 2016, 2017; Vigier et al., 2008, 2009; Millot et al., 2010b, c; Liu and Rudnick,
94 2011; Liu et al., 2011, 2015; Dellinger et al., 2014, 2015, 2017; Wang et al.,
95 2015). However, the dominant processes controlling riverine Li isotopic ratios
96 remains debatable since fractionation related to physicochemical would
97 involve processes during the riverine transport and/or conservative mixing
98 between isotopically different endmembers. In particular, emphasized
99 processes and sources may affect Li isotopic ratios including:

100 1. The ratio of primary mineral dissolution relative to secondary mineral
101 neoformation or expressed as the proportion of Li adsorption onto and/or
102 incorporated into secondary minerals during weathering and related
103 processes (Huh et al., 2001; Pogge von Strandmann et al., 2006, 2008a, b,
104 2010, 2013, 2016, 2017; Dellinger et al., 2015; Vigier et al., 2008; Millot et
105 al., 2010b; Pogge von Strandmann and Henderson, 2015; Wang et al.,
106 2015; Wimpenny et al., 2015). Specifically, this may be controlled by the
107 subsurface residence time or the water/rock interaction time (Wanner et al.,
108 2014; Liu et al., 2015);

109 2. The presence of evaporites (Huh et al., 1998; Liu et al., 2011; Wang et al.,
110 2015);

111 3. **Fractionation factors of Li isotopes may be mineral specific** (Wang et al.,
112 2015; Pogge von Strandmann et al., 2017).

113 Kısakürek et al. (2005) and Liu et al. (2015) found that at the same site
there was a ~10‰ difference of Li isotopic compositions in different seasons.

Some studies explored δ⁷Li signatures through collecting time-series samples

at regional and catchment scales and showed differing, location-specific,

seasonal variations of δ⁷Li (Kısakürek et al., 2005; Liu et al., 2015; Henchiri et

al., 2016; Pogge von Strandmann et al., 2016). However, most time-series

data do not have high-enough resolution to decipher the role of climate and

extreme hydrological events (e.g. storms) in Li isotopic fractionation, and to

eliminate the associated uncertainties. Therefore, further studies with samples

collected at higher temporal resolution are required to better understand the

responses of δ⁷Li to changes in climatic and hydrological conditions.

In this study, in order to elucidate the significance and utility of riverine Li

isotopic fractionation in the context of conservative mixing and related

weathering processes, we conducted a comprehensive investigation on (1)

how riverine δ⁷Li is affected by water sources and weathering processes in

semi-arid regions; and (2) how and why weathering processes vary over a

hydrological year. We collected river water samples under high temporal

resolution (weekly) from the middle reaches of the Yellow River, one of the

most turbid rivers in the world draining the highly erodible Chinese Loess

Plateau (CLP). Owing to seasonal contrast caused by the East Asian summer

monsoon, the middle reaches of the Yellow River provide a suitable setting to

evaluate the response of riverine Li isotopes to changes in climatic and

hydrological conditions.
136

137

# 2 Study areas

138

## 2.1 Geography

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150

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153

The Yellow River is the fifth longest river (~5,464 km) in the world, also is known as the most sediment-laden river with 10.8 × 10⁸ t/yr of sediment discharge ($Q_s$) (Zhang et al., 1995). Originating from the northeastern part of the Qinghai-Tibetan Plateau with an elevation of about 5,000 m, the Yellow River travels across the CLP and enters the Bohai Sea (Zhang et al., 1995; Wu et al., 2008). The upper reaches extend over a length of 3,471 km from the river source to Toudaoguai draining a catchment area of 385,996 km². The middle reaches stretch 1,206 km from Toudaoguai to Huayuankou draining an area of 343,751 km² with dozens of tributaries joining the main stream from the CLP. The lower reaches extend over a length of 786 km from Huayuankou through the flat alluvial plains to the estuary draining an area of 22,726 km² (Wang et al., 2007). Our study area is in the middle reaches of the Yellow River between Toudaoguai to Tongguan that drain the CLP (Fig. 1), characterized by highest annual $Q_s$ along the river (Saito et al., 2001; Chen et al., 2006; Sui et al., 2014).

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Though today's erosion rate of the Yellow River is significantly lower (c. 10% to what it used to be 50 to 100 years ago, Wang et al., 2007), because of the high erodibility of loess, the CLP is the largest sediment source to the Yellow River. With 21 tributaries and a total catchment area larger than 1,000 km², the
middle reaches of the Yellow River draining the CLP contributes nearly 90% of the total Qs (Saito et al., 2001; Wang et al., 2010; Yu et al., 2013), though only about 40% of the total water discharge (Qw) (Jiao et al., 2014; Zhao et al., 2014). By contrast, the upper reaches contribute ~60% of the annual Qw but merely 10% of the total Qs and 38%-47% of overall total dissolved solids (TDS, Li et al., 2018) carried by the Yellow River. Both suspended particulate matter (SPM) and TDS are sourced from rapid loess erosion into waters in the middle reaches of the Yellow River dominantly.

## 2.2 Geology

The Yellow River basin is developed on the old Sino-Korean Shield during the Archaeozoic to Proterozoic Eras (Zhang et al., 1995). It displays complex features in lithology and topography, with outcrops including rocks aging from Precambrian to Quaternary. In the upper reaches above Lanzhou (elevation 2,000-4,000 m), the bedrock is mainly composed of limestone, low-grade metamorphic rocks and clastic rocks interlaced with volcanic rocks and evaporites (Chen et al., 2005). The middle reaches draining the CLP are mainly covered by Neogene and Quaternary eolian loess and loess-like deposits, accounting for 44% of the total catchment area. Loess is deemed to be relatively homogeneous, porous, friable, pale yellow, typically non-stratified and often calcareous (Liu, 1988), which can represent the average chemical compositions of the upper continental crust (UCC, Taylor et al., 1983), and has a relatively homogeneous Li isotopic composition of 0 ± 4‰ (Teng et al., 2004;
Sauzeat et al., 2015). Mineralogically, loess is composed of quartz, feldspar, calcite, micas, and heavy minerals in minor proportion, with additional minor evaporites, such as halite, gypsum and mirabilite (Li et al., 1984; Liu, 1988; Zhang et al., 1995; Yokoo et al., 2004), though proportions of these minerals in loess and paleosol layers vary slightly (Liu, 1988). The erosion operating on the CLP shapes the numerous gullies and rolling topography and sustains the extremely high Qs of the Yellow River, particularly during the monsoon season. As an erosional product, SPM in the Yellow River is of the same mineralogical and geochemical compositions as loess (Li et al., 1984; Zhang et al., 1995).

## 2.3 Climate

The Yellow River basin is characterized by temperate climate. The upper reaches are in the region of cold-arid to semi-arid climate with mean annual temperature ranging from 1°C to 8°C, whereas the middle reaches are semi-arid to semi-humid with mean annual temperature ranging from 8°C to 14°C (both records from 1950s-2000s, Chen et al., 2005). Mean annual precipitation shows significant spatial variations across the river basin, increasing from 368 mm/yr in the upper reaches to 530 mm/yr in the middle reaches (Wang et al., 2007). Controlled by the East Asian summer monsoon, the seasonal climatic variations are more pronounced than spatial variations. In general, the climate in the Yellow River basin is featured to be cold and dry with less precipitation in winters but warm and wet following rainstorms in summers, especially in the middle reaches. The wet season is from June to
202 mid-September, accounting for 80% of the total annual precipitation (500-600 mm/yr, Zhang et al., 1995).

204

## 3 Sampling and Methodology

### 3.1 Sampling protocol

207 A total of 60 river water and SPM samples were collected weekly in 2013 at the Longmen hydrological station (35°40'06.43"N; 110°35'22.88"E). 208
209 Hereinto, four samples (Nos. LM13-31 to 34) were collected daily during a storm event that resulted in a maximum $Q_w$ (2400.53 m³/s) in 2013 (Zhang et 210
211 al., 2015; Lei et al., 2016). The Longmen station is located in the middle reaches of the Yellow River, where the tributaries mostly draining the CLP 212
213 have joined the main stream but excluded both drainages of the Fen and Wei Rivers that drain highly populated areas (Fig. 1). Therefore, anthropogenic 214
215 impact is minimized whereas weathering and erosion of the CLP is maximized on the station. To constrain atmospheric inputs, three rain water samples were 216
217 collected in July and August of 2013 at the station with a volume of 60 mL (see below), which is too little to obtain Li isotopic ratios. Thus, a 2 L rain water 218
219 sample was collected in the summer of 2017 near the station. To estimate contributions from evaporites, five typical loess samples **from different loess 220 layers** in Lingtai loess profile were collected for extracting evaporite-sourced Li 221
222 (see section 3.3.3 below). In addition, one sample of river bed sand (RBS) was 223 also collected right at the center river bed of the Longmen station.
All river water samples were collected 0.5 m below the surface in the central part of the river. For each sample, water temperature, pH, electrical conductivity (EC), and TDS were measured. All water samples were filtered on-site using a manual peristaltic pump using 0.2 µm nylon filters. Filtered water samples were stored in pre-cleaned polyethylene bottles. Filters were removed using a pre-cleaned plastic tweezers and placed into glass petri dishes on site in order to obtain SPM. For major cations, Li and Li isotope analyses, 60 mL of water were collected, acidified using ~0.25 mL of 6 M distilled HNO₃ to pH < 2 and stored at 4°C at the hydrological station before transportation to the laboratory. A separate pre-cleaned bottle was used for storing unacidified sample for anion analysis. Rainwater samples were treated in the same manner (see Zhang et al. (2015) for details). In order to characterize anthropogenic contribution, an agricultural sewage water sample (TKT1) was collected in the middle reaches of the Yellow River and was also pretreated at the same way.

## 3.2. Major cations and Li concentration analyses of water samples

Major cations were analyzed by a Leeman Labs Profile inductively coupled plasma atomic emission spectroscopy (ICP-AES), with the RSD (relative standard deviation) better than 1%. Major anions (F, Cl, and SO₄) were analyzed by ion chromatography (ICS 1200) and NO₃ was measured by a Skalar continuous flow analyzer. The RSDs were 2.0% and 2.2%, respectively. Alkalinity (expressed as HCO₃) was measured by Shimadzu
246 Corporation total organic carbon analyzer (TOC-V<sub>CPH</sub>) with the RSD less than
247 1.5% at Northwest Agriculture and Forestry University (Zhang et al., 2015).

248 Li concentrations of river water samples were conducted at the State Key
249 Laboratory of Loess and Quaternary Geology (SKLLQG) using a PerkinElmer
250 NexION 300D ICP-MS, with the RSD better than 5%.

## 3.3 Lithium isotopes analyses

252 Pretreatment and analyses of Li isotopic compositions of all samples were
253 performed at the clean room (class 100) in the SKLLQG, IEE-CAS.

### 3.3.1 Pretreatment of river water samples

255 10–20 mL river water and 600 mL rainwater, containing approximately 300 ng Li, were evaporated and then treated with a mixture of distilled HNO<sub>3</sub>: HF = 1:3 to digest organic matter. The final dried sample was picked up in 2 mL 0.5 M HNO<sub>3</sub> for column separation. The agricultural sewage water sample (TKT1) was also pretreated <span style="color: red;">with</span> the same way.

### 3.3.2 Dissolution of SPM and RBS

260 SPM samples were washed off the filter with Milli-Q water and evaporated.
261 The dried samples were weighed and then grounded using a pre-cleaned
262 agate mortar. The RBS sample was also treated <span style="color: red;">with</span> the same way. Fractions
263 containing ~300 ng Li were then dissolved using a 1:3 mixture of concentrated
264 HNO<sub>3</sub> and HF, followed by 4 mL aqua regia, and then 4 mL concentrated HNO<sub>3</sub>.
265 The final dried sample was dissolved in 2 mL 0.5 M HNO<sub>3</sub> for column
266 separation (Gou et al., 2017).
267
268 **3.3.3 Experiment of extracting evaporite-sourced Li in loess**

269 Five typical fresh loess samples were used to extract evaporites in loess,
270 following the method described in Yokoo et al. (2004) modified from Tessier et
271 al. (1979). Briefly, 5 g milled loess were leached by milli-Q water for 5 minutes
272 and then centrifuged following filtration. This leach is aimed at evaporite-
273 sourced ions because of the fast dissolution kinetic of evaporite minerals
274 (Yokoo et al., 2004). Extracted ions concentration of supernatants were
275 determined using a PerkinElmer NexION 300D ICP-MS and dried then picked
276 up in 2 mL 0.5 M HNO₃ for column separation.

277 **3.3.4 Purification and analysis for Li isotopes**

278 All of the solute samples were purified by single-step cation exchange
279 chromatography filled up with 8 mL resin (Bio-rad® AG50W X-12, 100-200
280 mesh) using a method (Gou et al., 2017) modified from James and Palmer
281 (2000) and van Hoecke et al. (2015), with 0.5 M diluted HNO3 as an eluent.
282 The purified Li fraction was picked up in 2% HNO3 and was targeted to be 100
283 µg/L of Li for Multi-Collector Inductively Coupled Plasma Mass Spectrometer
284 (MC-ICP-MS, Neptune plus) measurement. Splits collected before and after
285 the main Li elution peak were assessed for Na and Li contents, to ensure full
286 column recovery. The weight Na/Li ratio in all samples reported in this study
287 was less than 1, which we found to have insignificant effect on the Li isotopic
288 ratio measurements (Gou et al., 2017, 2018). The total procedural blank of this
289 method was less than 0.16 ng Li, which is insignificant relative to the 300 ng of
290 Li analyzed in each sample (Gou et al., 2017, 2018).

291 For each sample, triple measurements were performed to obtain the
292 average values as well as standard derivations (s.d.). Two in-house standards
293 were run repeatedly for a one-year period yielding δ<sup>7</sup>Li as 8.3 ± 0.2‰ (2 s.d., n = 43) for IEECAS-Li, and as 12.2 ± 0.2‰ (2 s.d., n = 59) for SPEX-Li,
294 respectively. In addition, another in-house standard of Isotopic Geochemical
295 laboratory of University of Science and Technology of China (USTC), namely
296 USTC-L, was also run repeatedly at IEE-CAS, yielded a δ<sup>7</sup>Li = -19.3 ± 0.1‰ (2
297 s.d., n = 38), in good agreement with that conducted at USTC (-19.3 ± 0.2‰,
298 Gou et al., 2017, 2018).
299

300 Two rock reference materials (AGV-2, BHVO-2) and a seawater reference
301 material (NASS-6) that were purified following the same procedure and
302 measured iteratively to be δ<sup>7</sup>Li = 6.7 ± 0.8‰ (2 s.d., n = 13, digestion = 13,
303 column passes = 13), δ<sup>7</sup>Li = 3.9 ± 0.9‰ (2 s.d., n = 13, digestion = 13, column
304 passes = 13), and δ<sup>7</sup>Li = 31.1 ± 0.7‰ (2 s.d., n = 15, column passes =15),
305 respectively, over a one year period, all of which are in agreement with
306 previous studies (Magna et al., 2004; Jeffcoate et al., 2007; Vigier et al., 2009;
307 Pogge von Strandmann et al., 2008a, b, 2010, 2011, 2012, 2013, 2016, 2017;
308 Huang et al., 2010; Lemarchand et al., 2010; Millot et al., 2010a, b; Liu and
309 Rudnick, 2011; Liu et al., 2013, 2015; Dellinger et al., 2014, 2015, 2017; Pogge
310 von Strandmann and Henderson, 2015; Wang et al., 2015). Overall, our
311 long-term external reproducibility is better than ± 0.9‰ (2 s.d.) for δ<sup>7</sup>Li
measurements (Gou et al., 2018). Isotopes are all reported relative to the
standard L-SVEC (Flesch et al., 1973).

314

315 **4 Results**

316 **4.1 Hydrology and SPM**

During the sampling time period, water temperature in the study area increased gradually from January to August and then decreased until the end of the year, ranging from 0°C to 28.8°C, with ice coverage in January and February and peak temperature in August (Fig. 2E). The air temperature near Longmen in 2013 varied similarly to the water temperature. From June to mid-September (monsoon season), the daily air temperature was above 20°C.

In the hydrological year of 2013, the total Qw was 24.53 km³/yr at the Longmen hydrological station (Zhang et al., 2015). The Qw was constant from January to February, then reached the first peak in March and returned quickly in the middle of April and dropped to an annual minimum period (151.82 m³/s) in May (Fig. 2E). The first Qw peak from 16th March to 13th April was caused by ice melting when the air temperature was above zero, thus we defined this time period as an "ice melting interval". During the monsoon season in 2013, the high Qw reflected the frequent, monsoon-driven rainfall within the Yellow River basin. Notably, a storm event that happens once in a century occurred in the middle reaches of the Yellow River during 22nd and 25th July (Zhang et al., 2015; Lei et al., 2016), resulting in the highest peak Q (2400.53 m³/s) of 2013.
After the rainy season, the $Q_w$ declined gradually to a relative low value with some minor fluctuations. The river waters were alkaline over the whole year with pH values ranging between 7.05 and 8.71.

SPM in the middle reaches of the Yellow River showed significant seasonal variation, accompanying the $Q_w$ changes (Fig. 2G and H). In contrast to the high concentrations and fluxes of SPM during the monsoon season, SPM was low and constant during the dry seasons with a spike during the ice melting period (Fig. 2G and H). Highest concentrations and fluxes of SPM appeared during the storm event. As a result, physical erosion rate (PER, derived from weekly gauged SPM) during the monsoon season was one to three orders of magnitude higher than that during the dry seasons (Fig. 2I). At the Toudaoguai hydrological station before major tributaries draining the CLP join the Yellow River (Fig. 1), the SPM concentrations are much lower than those of the Longmen station, with limited seasonal variations (Fig. 3). Those observations mean that erosion occurred mainly during the monsoon season, dominated by loess eroded from the CLP into the middle reaches of the Yellow River (Zhang et al., 2015).

## 4.2 The dissolved Li concentration [Li]

$[\text{Li}]$ and $\delta^7\text{Li}$ values of all the time-series river water samples are reported in Figure 2 and Table 1. The dissolved $[\text{Li}]$ of our samples from the middle reaches of the Yellow River has an average of 2.80 µmol/L, ranging from 2.22 µmol/L during the monsoon season to 4.41 µmol/L during the dry season (in
winter), with a pronounced seasonal variation as $Q_w$. Peaks in [Li] are notable during the ice melting and monsoonal intervals. These values are of one order of magnitude higher than those reported in other large rivers (0.22 µmol/L for an average, ranging from 0.05 to 0.8 µmol/L; Huh et al., 1998), but are close to those of the Changjiang (Yangtze) River that also originates from the Tibetan Plateau (Wang et al., 2015).

[Li] of the rain water samples were relatively homogeneous, ~0.13 µmol/L on average (Table 2), within the range reported by Millot et al. (2010a). Results of water-extraction fraction of the Lingtai loess were reported in Table 3, [Li] varying from 1.5 to 2.0 nmol/g of loess and paleosol samples and 1.3 nmol/g for red clay (RC). As listed in Table 4, RBS [Li] is located within the range of the UCC (Teng et al., 2004; Sauzeat et al., 2015). Similarly, [Li] in SPM, ranging from 28.9 to 68.9 µg/g (Fig. 2J), is within the range as those in SPM reported elsewhere for rivers draining continental crust (e.g. Millot et al., 2010c; Dellinger et al., 2015; Liu et al., 2015; Wang et al., 2015). [Li] of the sewage water collected in a farmland near the Longmen hydrological station has a concentration 12.3 µmol/L, one order of magnitude higher than the Yellow River water.

## 4.3 Li isotope compositions

During the sampling period, the dissolved $\delta^7\text{Li}$ values of the river water ($\delta^7\text{Li}_{\text{rw}}$) from the middle reaches of the Yellow River range between 16.9‰ in the summer to 21.5‰ in the winter. This range is smaller than the observed
seasonal changes in the Andean and Congo river systems, which were
sampled in winter and summer (Liu et al., 2015) or sampled bimestrially
(Henchiri et al., 2016), and is much smaller when compared with spatial
variations (~40‰, e.g. Vigier et al., 2009; Pogge von Strandmann et al., 2006,
2008a, b, 2010, 2012, 2013, 2016, 2017; Millot et al., 2010c; Dellinger et al.,
2015; Pogge von Strandmann and Henderson, 2015; Wang et al., 2015).
However, our observed range of variation resembles that of the Ganges-
Brahmaputra river system with samples from before and after the Indian
monsoons (Kısakürek et al., 2005).

Clearly seasonal variation was observed for the δ<sup>7</sup>Li<sub>rw</sub> of the middle reaches of the Yellow River. Similarly to [Li] behavior, δ<sup>7</sup>Li<sub>rw</sub> peaked during the ice melting interval. During the monsoon, δ<sup>7</sup>Li<sub>rw</sub> generally increased throughout, with another peak during the storm event (Fig. 2C).

The δ<sup>7</sup>Li values of SPM (δ<sup>7</sup>Li<sub>SPM</sub>) were less variable over the sampling year (Fig. 2D), ranging from 0.6‰ to 3.5‰ with an average of 1.8 ± 0.9‰ (Table 1). All δ<sup>7</sup>Li<sub>SPM</sub> were much lower than all of δ<sup>7</sup>Li<sub>rw</sub>, but within the δ<sup>7</sup>Li ranges of the CLP loess and the UCC (Teng et al., 2004; Tsai et al., 2014; Sauzeat et al., 2015).

The rain water (R1YR) has a δ<sup>7</sup>Li<sub>atmo</sub> = 9.3 ± 0.1‰ (2 s.d., Table 3). This is different with respect to the rain data from elsewhere (e.g. 31‰ in Iceland by Pogge von Strandmann et al. (2006), 33‰ in the Azores by Pogge von Strandmann et al. (2010), and 16‰ in France and 26‰ in Canada by Millot et
al. (2010a, c)), suggesting that $\delta^7\text{Li}_{\text{atmo}}$ is strongly dependent on locations.

The $\delta^7\text{Li}$ values of the evaporite-sourced Li ($\delta^7\text{Li}_{\text{evap}}$) from loess samples are averaged to be 23.6 ± 4.8‰ (2 s.d., n = 4), significantly higher than those of river water and SPM samples (Table 3).

The Li isotopic compositions are 6.9‰ for the RBS sample and 23.8 ‰ for the sewage water respectively, with the latter being higher than all the $\delta^7\text{Li}_{\text{rw}}$ (Table 4).

## 5 Discussions

### 5.1 Partitioning between dissolved and particulate Li

Fluvial export of Li from continents to basins/oceans has two major forms: 1) dissolved in river water and 2) incorporated in particulates as the SPM form. At the global scale, Li is transported mainly as the form of SPM (Misra and Froelich, 2012; Li and West, 2014; Dellinger et al., 2015). To determine how Li partitions between the two forms for the Yellow River, we calculated the proportions of Li transported in the dissolved load ($\text{Li}_{\text{rw}}$ in %) and SPM ($\text{Li}_{\text{SPM}}$ in %) using the following equations, following Bouchez et al. (2013) and Gaillardet et al. (2014):

$$ \text{Li}_{\text{rw}} (\%) = \frac{\text{Li}_{\text{rw}} \text{ flux}}{\text{Li}_{\text{rw}} \text{ flux} + \text{Li}_{\text{SPM}} \text{ flux}} \times 100 \qquad \text{Eq. (1)} $$

$$ \text{Li}_{\text{SPM}} (\%) = 100\% - \text{Li}_{\text{rw}} \qquad \text{Eq. (2)} $$

where the $\text{Li}_{\text{rw}}$ and $\text{Li}_{\text{SPM}}$ fluxes are in t/yr of dissolved and SPM loads (Fig. 2A and G), respectively. The $\text{Li}_{\text{rw}}$ flux is the product of [Li] of river water and the
corresponding one-week average Qw gauged (generally, three times/day) in the sampling site. Similarly, the LiSPM flux is calculated by multiplying [Li] of SPM and the corresponding one-week average SPM concentration gauged as same as Qw in the sampling site. The results show that 73.3%-98.5% of the total exported Li was transported as SPM form in the middle Yellow River in 2013, in excellent agreement with its dominated particulate form in global rivers as suggested by previous studies. Note that this calculation does not consider the potential variability of the Li content of SPM with water depth. For example, Dellinger et al. (2014, 2015) reported heterogeneous SPM [Li] and [δ⁷LiSPM] along vertical sections in the Amazon because of the dilution by increasing quartz proportions along vertical profile in rivers, although Pogge von Strandmann et al. (2017) reported a homogeneous SPM [δ⁷LiSPM] from surface to bottom in the Ganges. A similar [Li] of the RBS sample (36.8 µg/g) to those of the SPM as well as loess (Li et al., 1984) may indicate a homogeneous [Li] of SPM with water depth in the middle reaches of the Yellow River, and if this is the case, our estimated LiSPM flux likely represents a reasonable estimation of the LiSPM flux in the middle reaches of the Yellow River. **Nevertheless, it should be beard in mind that the depth-profile variability of SPM content as well as sediment chemistry may shed some uncertainty on this estimation.**

More than 60% of the annual dissolved Li transported by the Yellow River waters occurs during the monsoon season, due to the high Qw (Fig. 2A).
During the dry seasons when SPM fluxes are low (dozens mg/L), Li<sub>rw</sub> flux is relatively constant compared to Li<sub>SPM</sub>, with two peaks during the ice melting period and in the early January (Fig. 2A). The highest proportion (26.7%) of Li<sub>rw</sub> relative to Li<sub>SPM</sub> is observed in the early January for the sampling year when the Yellow River was frozen.

## 5.2 Sources of dissolved Li

Previous studies have suggested that dissolved Li is predominated by silicate weathering, so that carbonate weathering is negligible regardless of the covering proportions of carbonates within river basins (Kısakürek et al., 2005; Millot et al., 2010c; Dellinger et al., 2015; Wang et al., 2015), which is a premise to use Li isotopes tracing silicate weathering (e.g. Huh et al., 1998, 2001; Kısakürek et al., 2005; Vigier et al., 2009; Millot et al., 2010c; Dellinger et al., 2015; Wang et al., 2015). However, recent work revealed that in the Changjiang River evaporites likely contribute nearly half of the dissolved Li in river waters because of the wide-spread evaporites in its head watersheds and their fast dissolution kinetics (Liu et al., 2011; Wang et al., 2015). Likewise, although loess is lithologically homogeneous and represents the composition of the UCC, there is about 5-10% of evaporites in loess (Zhang et al., 1995), which contribute solutes significantly to the Yellow River waters (Zhang et al., 2015).

The evaporites in the loess of the CLP are dominated by halite, gypsum, and mirabilite (Zhang et al., 1990; Yokoo et al., 2004). Because of their faster
dissolution kinetics, dissolution of evaporites readily contributes Na, Cl, B, SO₄, and Li to the rivers (Zhang et al., 2015). The observed positive correlations between [Li] and [B], [Cl], [Na], and [SO₄] of river waters (Fig. 4) indicate that there is substantial contribution from evaporites to the dissolved Li in the Yellow River (Fig. 5), given the fact that calculated rain contribution is limited (see the following section 5.2.1). The deviation during the early monsoon (purple shadow in Fig. 5) may be attributed to the input of groundwater when rains have been penetrating, as indicated by the major ions (Zhang et al., 2015), resulting from slightly increase in contribution from silicates. Thus, it is important to discern the parts of Li from sources other than silicates.

## 5.2.1 Atmospheric input

To correct the dissolved [Li] from input of atmosphere, we refer to the three rain water samples collected at the Longmen station during the rainy season. These three rain water samples yield an average Li/Cl molar ratio of 1.18 ± 0.96 × 10⁻³. Given that the evapotranspiration correction factor (F) is 1.76 (1.61-1.90, Zhang et al., 2015) at the CLP area, we estimated the Li in river waters derived from atmosphere ([Li]atmo) to be 0.22 ± 0.28 µmol/L, which accounts 5%-10% of Li in river waters from the middle reaches of the Yellow River (Fig. 6). This contribution is in the same range as in the Changjiang but is much higher than that of the Amazon River (~2%, Dellinger et al., 2015). The high contribution of atmospheric input for the Yellow River is likely caused by high dust activities from inland Asia (Jin et al., 2011).
488 **5.2.2 Anthropogenic inputs**

489 Within the upper and middle reaches of the Yellow River basin, the
490 anthropogenic activities are dominated by agriculture (grazing, farming, etc.
491 Chen et al., 2003) with limited industry. However, it is difficult to qualify
492 agriculture input due to the highly spatiotemporal variations of Li contributions
493 and its isotopic compositions of fertilizers; this estimation can be precarious
494 unless sewage has a stable chemical composition, which is extremely unlikely
495 (Chetelat et al., 2008). Herein, because of the relatively higher NO₃⁻ than that
496 of the natural river waters, we assumed that all NO₃⁻ is from fertilizers (Chen et
497 al., 2003; Zhang et al., 2015; Fan et al., 2016). Then, using the typical fertilizer
498 compositions, (i.e. NO₃/Na = 7 ± 3, Cl/Na = 4 ± 1, and SO₄/Na = ~1, Roy et al.,
499 1999; Chetelat et al., 2008), and Li/Na ratio (1.36 × 10⁻⁵) and Li isotopic
500 composition (23.8 ± 0.2‰, defined as δ⁷Liₐnth) of the sewage water we
501 collected in a farmland in the middle Yellow River (Table 4) as end-member of
502 anthropogenic inputs ([Li]ₐnth), Li input by anthropogenic activity can be
503 approximately quantified. Our first-order estimates show that the [Li]ₐnth to the
504 middle reaches of the Yellow River range from 0.4% to 1.6%. These inputs are
505 quite limited compared to that of the Changjiang River (Chetelat et al., 2008;
506 Wang et al., 2015), and this may be due to the fact that the Changjiang River
507 drains across the highly developed industrial areas in central and southeastern
508 China.

509 The limited [Li]ₐnth is also supported by lower δ⁷Liₗw in the middle reaches
of the Yellow River in summer. Since agriculture activities generally occur in summer (Chen et al., 2003) and fertilizers are well known to have abnormally high $\delta^7$Li (Qi et al., 1997; Kısakürek et al., 2005; Millot et al., 2010b; Négrel et al., 2010), $\delta^7$Li$_{rw}$ in summer would be quite high if $[Li]_{anth}$ was significant.

### 5.2.3. Carbonate weathering

Previous studies have revealed that Li in river waters is scarcely derived from carbonates (Kısakürek et al., 2005; Millot et al., 2010c; Dellinger et al., 2015; Wang et al., 2015). Here, by assuming that all Ca in waters in the middle reaches of the Yellow River is from carbonate dissolution and using a typical carbonate Li/Ca molar ratio of $1.5 \pm 0.5 \times 10^{-5}$ (Hathorne and James, 2006; Hathorne et al., 2009; Pogge von Strandmann et al., 2013), we calculated the Li derived from carbonates ($[Li]_{carb}$). The results showed that $[Li]_{carb}$ to the middle reaches of the Yellow River is ~4.1% on average, ranging from 0.4% to 7.4%. This is the upper limit of $[Li]_{carb}$, since silicate dissolution also brings Ca to river water (Zhang et al., 2015; Li et al., 2018), with a silicate Ca/Na = 0.2 ~ 0.5, such as in the upper Yellow River (Wu et al., 2005). It is thus reasonable to conclude that carbonates contribution to Li in river waters in the middle reaches of the Yellow River is very small (Fig. 6), similar to most of large rivers in the world (Huh et al., 1998; Millot et al., 2010c; Bagard et al., 2015; Dellinger et al., 2015; Henchiri et al., 2016; Wang et al., 2015; Pogge von Strandmann et al., 2017). For such a low $[Li]_{carb}$, the carbonate effect on $\delta^7$Li$_{rw}$ is likely to be negligible.
532 **5.2.4 Evaporite dissolution**

As mentioned above, evaporite dissolution may contribute to riverine Li significantly in the Yellow River waters (Zhang et al., 2015), because of 1) rapid dissolution kinetics of evaporites, 2) high dissolved [Cl] in river waters over the full sampling period, and 3) linear relations between [Li] and [Cl], [Na], [SO₄] and [B] (Fig. 4). However, evaporite contribution to major ions in the upper reaches of the Yellow River is quite limited (i.e. from 12.4% to 15.9%, Li et al., 2018), compared to that of its middle reaches (28.4% to 66.3%, Zhang et al., 2015), indicating that riverine Li from evaporites in the Yellow River ([Li]_{evap}) is also mainly sourced from the CLP as SPM.

Based upon loess-extraction experiments, the evaporites in loess have Li/Na_{evap} = 2.52 ± 1.39 × 10⁻⁴ and δ⁷Li_{evap} = 23.6 ± 4.8‰ (2 s.d., n = 4), respectively (Table. 3). Using this Li/Na_{evap} ratio and the part of Na in river waters from evaporites ([Na]_{evap}, Zhang et al., 2015), we estimated [Li]_{evap} by the following equation:

$$ [Li]_{evap} = [Na]_{evap} \times Li/Na_{evap} \qquad \text{Eq. (3)} $$

The results show that [Li]_{evap} ranges from 16.7% to 37.6% (with an average of 25.2%) in the Yellow River water samples (Fig. 6), highlighting the importance of evaporite contribution to the riverine dissolved Li as observed in the Changjiang River (Wang et al., 2015). The contribution of [Li]_{evap} to river waters in the middle reaches of the Yellow River also shows seasonal variations in 2013. In early January, [Li]_{evap} accounted for ~30% of the riverine
Li. After the storm event, $[Li]_{evap}$ increased dramatically and generally stayed at a high level for the rest of the year, contributing by as much as 37.6% Li to river water. Generally, $[Li]_{evap}$ was positively correlated with $Q_w$ due to evaporites fast dissolution kinetics. The high Li isotopic ratios of evaporites likely affect the $\delta^7Li_{rw}$, driving it higher when evaporites contribute to a greater part. This is certainly observable immediately after the storm event (Figs. 5 and 7) and further highlights the importance of evaporite dissolution to riverine Li in such arid to semi-arid areas (Zhang et al., 2015). The reason why $[Li]_{evap}$ did not increase instantaneous following the storm event is that water chemistry generally lags rapid $Q_w$ changes in riverine systems (Godsey et al., 2009; Maher, 2011), and this is likely true in the middle reaches of the Yellow River. To the best of our knowledge, this represents the very first insight into Li isotopic compositions response to an abnormal hydrological event.

To date, a few studies have attempted to reveal the evaporite contribution to dissolved Li in river waters, using mixing equations to estimate the proportion of evaporite contribution (Huh et al., 1998; Liu et al., 2011; Wang et al., 2015; Dellinger et al., 2015). Huh et al. (1998) pointed out that evaporites would contribute significant Li to river water, but lacked quantified estimation. Liu et al. (2011) estimated that Li in the upper Changjiang tributaries is exclusively derived from evaporites. Wang et al. (2015) calculated evaporite contribution to Li in river water being from 20% to 55%. In contrast, Dellinger et al. (2015) estimated only <1% Li from evaporites in most tributaries of the
576 Amazon River. Our estimates (averaging 25%) fall within these estimates.
577 Given the representativeness of loess to chemical and Li isotopic compositions
578 of the UCC (Taylor et al., 1983; Teng et al., 2004), this proportion of [Li] evap in
579 the Yellow river may reflect an average Li contribution of evaporites to global
580 oceans, i.e. ~25%.

581 Overall, our most important observations are: (1) evaporite dissolution
582 brought more Li after the storm event and (2) this higher [Li] evap was apparently
583 sustained for at least half of one year (Figs. 6 and 7). Despite the wide
584 distribution of evaporites and their fast dissolution kinetics in arid and semi-arid
585 areas (e.g. deserts) rarely accessible to fluids, it is plausible that a large
586 hydrological event promotes their dissolution and then ultimately contributes to
587 river water (Fig. 7).

588 It is important to point out that this Na-normalized calculation for [Li] evap
589 should be considered as an upper limit of evaporite contribution to Li in the
590 Yellow River, because Li would be partially incorporated into secondary
591 minerals (clays and some oxides) due to its high affinity whereas Na is largely
592 conservative in river waters.

593 **5.2.5. Silicate weathering**

594 Apart from the above sources, the rest of Li in river waters is thought to be
595 from silicate weathering ([Li]sil), and this portion is calculated using the
596 following equation (e.g. Wang et al., 2015):

$$
597 \quad [\text{Li}]_{\text{sil}} = [\text{Li}]_{\text{rw}} - [\text{Li}]_{\text{atmo}} - [\text{Li}]_{\text{anth}} - [\text{Li}]_{\text{evap}} - [\text{Li}]_{\text{carb}} \quad \text{Eq. (4)}
$$
Among the five sources for dissolved Li in the middle reaches of the Yellow River as shown in Figure 6, silicate weathering is the dominant source of Li, accounting for ~61.8% on average, ranging from 46.1% to 65.5%. This contribution is close to that observed in the Changjiang River, ranging between 45% and 95% (Wang et al., 2015). Hence, silicates and evaporites represent two most important sources of Li to the river waters of the Yellow River.

It is noteworthy that even in such scenario for whose water chemistry is dominated by carbonate weathering and evaporite dissolution (Chen et al., 2003; Ran et al., 2015; Zhang et al., 2015; Fan et al., 2016), sources of Li in the Yellow River waters are dominated by silicate weathering (Fig. 6), further supporting the robustness of Li isotopes to be a tracer for silicate weathering (Kısağırek et al., 2005; Dellinger et al., 2015, 2017; Liu et al., 2015).

## 5.3 Fractionation of riverine Li isotopes ($\delta^7\text{Li}_{\text{rw}}$)

In order to discuss Li fractionation during silicate weathering alone, previous studies attempted to distinguish $\delta^7\text{Li}_{\text{sil}}$ from $\delta^7\text{Li}_{\text{rw}}$ based on mass balance principles (e.g. Wang et al., 2015). In fact, any sourced Li would be fractionated further once they are released into aqueous systems. Since riverine Li in the middle Yellow River is dominated by silicate-sourced, with relative constant $\delta^7\text{Li}_{\text{evap}}$, we discussed the seasonal variation of $\delta^7\text{Li}_{\text{rw}}$ to understand fractionation and silicate weathering processes in the middle Yellow River in the following section.

There are two key observations in the middle reaches of the Yellow River.
(1) [Li] in SPM range from 28.9 to 68.9 µg/g (Fig. 2J), which lies in the same range of SPMs from the Amazon and the Changjiang Rivers (e.g. Dellinger et al., 2014, 2017; Wang et al., 2015), but it is slightly higher than that in loess (Teng et al., 2004; Tsai et al., 2014; Sauzeat et al., 2015). This could be the result of the inheritance of slightly heterogeneous loess, but also correspond to the uptake into secondary minerals transported as SPM. (2) The constant δ<sup>7</sup>Li<sub>SPM</sub> values (1.9 ± 0.9‰, 2 s.d.) are much lower than those of river waters (~15 ± 5‰). Given that Li isotopic fractionation (1) is negligible during dissolution of primary minerals (Pistiner and Henderson, 2003; Wimpenny et al., 2010, 2015; Verney-Carron et al., 2011; Ryu et al., 2014) and (2) is neither affected by biological processes (Lemarchand et al., 2010; Clergue et al., 2015; Pogge von Strandmann et al., 2016) nor by redox state change (Faure and Mensing, 2005), these observations together support an enrichment of <sup>6</sup>Li to SPM, resulting from a preferential scavenging of <sup>6</sup>Li into/onto SPMs from aqueous solutions (Huh et al., 1998, 2001; Kısakürek et al., 2005; Pogge von Strandmann et al., 2006, 2008a, b, 2010, 2012, 2013, 2016, 2017; Millot et al., 2010c; Vigier et al., 2009; Dellinger et al., 2014, 2015, 2017; Wang et al., 2015). The lack of correlation between the δ<sup>7</sup>Li<sub>rw</sub> and the SPM concentration at Longmen (Fig. 8A) suggests that such preferential scavenging of <sup>6</sup>Li is likely to be occurred within each watershed rather than in main river system. This means (1) that <sup>6</sup>Li scavenging SPM may require more time than the resident time of river water at the ionic strength of the middle reaches of the Yellow
642 River or (2) that Li on loess surface is already saturated when Li reaches the
643 main stream of the river.

644 As mentioned above, the dominant source of Li for both river waters and
645 SPMs in the middle reaches of the Yellow River is loess whose δ⁷Li signature
646 of silicate phase (δ⁷LiₗₗₐₐELL) ranges from 2.5‰ to 4.7‰ (Tsai et al., 2014). The
647 systematically higher δ⁷LiₗₗₐₐELL of waters in the middle reaches of the Yellow River
648 relative to δ⁷LiₗₗₐₐELL reflects the proportion of Li adsorbed into the clay and/or
649 incorporated in the formation of secondary minerals, rather than inherited from
650 the lithology. This can be further depicted by plotting δ⁷LiₗₗₐₐELL and Li/Na molar
651 ratio sourced from silicate weathering (Li/Naₗₗ). Both as alkali metals, Li and
652 Na are soluble, but Na is less incorporated into secondary minerals compared
653 to Li, such that Li/Naₗₗ ratio sometimes behaves similarly to δ⁷Liₗₗ (Wanner et
654 al., 2014; Dellinger et al., 2015; Liu et al., 2015; Pogge von Strandmann and
655 Henderson, 2015; Wang et al., 2015; Pogge von Strandmann et al., 2016,
656 2017). The low Li/Naₗₗ but high δ⁷Liₗₗ likely result from more Li scavenged
657 into/onto SPM during silicate weathering in watersheds, such as during the
658 post-monsoon period. In contrast, δ⁷Liₗₗ would become lower if more Li
659 remains in solution (Huh et al., 2001; Wanner et al., 2014; Dellinger et al., 2015;
660 Liu et al., 2015; Pogge von Strandmann and Henderson, 2015; Wang et al.,
661 2015; Pogge von Strandmann et al., 2016, 2017), as those during the late
662 monsoon.

663 Re-dissolution of secondary minerals is suggested to affect δ⁷Liₗₗ
664 compositions, such as in the lowland Amazon Rivers (Dellinger et al., 2015).

665 Due to freezing and/or ice cover conditions that prevents loess erosion and
666 slows the solid transport during the non-monsoon seasons (Ran et al., 2015;

667 Zhang et al., 2015), the SPM concentrations appeared to be the lowest and
668 Li<sub>rw</sub> (%) were the highest within the year in the winter season (Fig. 2A).

669 Meanwhile, the low Q<sub>w</sub> resulted in a transport-limited weathering scenario in
670 the middle reaches of the Yellow River. As a result, lower δ<sup>7</sup>Li<sub>rw</sub> could be
671 caused by re-dissolution of secondary minerals (Bouchez et al., 2013;

672 Dellinger et al., 2015; Wang et al., 2015) in the middle reaches of the Yellow
673 River during the non-monsoon and our observations support this (Figs. 2B and
674 5).

5.4 Temperature controlled seasonal variations in δ<sup>7</sup>Li<sub>rw</sub> and its global
implication

677 In the middle reaches of the Yellow River, the seasonal [Li]sil in 2013 vary
678 from 1.28 to 2.94 µmol/L. The magnitude by a factor of two in [Li]sil variation is
679 broadly consistent with the contrasting seasonality of Qw in the middle reaches
680 of the Yellow River. The East Asian summer monsoon brings sufficient rainfall
681 during the summer, resulting in a dilution of the [Li]sil during the monsoonal
682 season. However, the dilution in [Li]rw and [Li]sil is subdued by comparison to
683 the rise in Qw, leading to a rise in the Li flux during the monsoon (Figs. 2 and 9).

684 Similar seasonal pattern in alpine environments was also observed elsewhere
685 and related to the Indian monsoon (Galy and France-Lanord, 1999; Tipper et
al., 2006). Considering that Li is not involved in terrestrial biological cycles (Lemarchand et al., 2010; Clergue et al., 2015; Pogge von Strandmann et al., 2016) and that vegetation on the CLP is sparse, $[Li]_{sil}$ should not be affected by biological processes in the middle reaches of the Yellow River. Rather, $[Li]_{sil}$ should be dominantly controlled by evaporation/dilution processes at the first order, and then by the balance between silicate weathering and incorporation into secondary minerals. There is a positive power-law relationship between the $Li_{sil}$ flux and PER (Fig. 9), which could support the great importance of physical erosion in dissolved Li fluxes (Gaillardet et al., 1999; Li and West, 2014), especially in such loess-dominated rapidly eroding landscapes. However, the relationship indicates that both PER and $Li_{sil}$ are closely linked with $Q_w$ in this weathering-limited region covered by easy erodable loess. Such direct response must be seen as a result of hydrological variations that mobilize different reservoirs characterized by different weathering processes (Calmels et al., 2011). As discussed above, Li isotopes should only equilibrate with the solid before into the main channel rather than during the transportation in the main river, even when SPM is particularly abundant such as in the middle reaches of the Yellow River. Together with the lack of overall relationship between SPM concentration and $\delta^7Li_{rw}$ (Fig. 8A), those facts further support the idea that seasonal variation in $\delta^7Li_{rw}$ in weathering-limited regimes such as the CLP is a strong kinetic control and temperature sensitivity (West et al., 2005). A negative correlation between river water $\delta^7Li_{rw}$ and water
708 temperature over the entire hydrological year was observed, i.e. riverine δ⁷Li<sub>rw</sub> 709 decreases with increasing temperature (Fig. 8B).

710 Similar to the kinetic of clay neoformation in soils, such fractionation of 711 riverine Li is likely to represent an isotopic equilibrium that is dominantly 712 dependent on temperature (West et al., 2005). For example, Li and West 713 (2014) suggested a gradient change of ~-0.183‰ per °C for Li fractionation. 714 For the large range (28.8°C in 2013) of seasonal temperature in this case, the 715 effect would be >5‰ for δ⁷Li, assuming temperature as a single fractionation 716 factor alone, which is exactly the entire observed δ⁷Li<sub>rw</sub> range in the Yellow 717 River. Consequently, the seasonal δ⁷Li<sub>rw</sub> variation in the middle reaches of the 718 Yellow River is likely due to neoformation of Li-bearing secondary 719 aluminosilicates within the watersheds under various temperature conditions.

720 The temperature dependency is described as the following equation:

$$
721 \quad 1000 \ln \alpha_{\text{water-clay}} = A(1000000/T^2) + B \quad \text{Eq. (5)}
$$

722 where α<sub>water-clay</sub> is the fractionation factor between δ<sup>7</sup>Li in waters and clays in 723 soils; A and B have been empirically determined to be 1.83 and -0.72, 724 respectively (Li and West, 2014). We fit our data by excluding the samples 725 (~1/8) that might have experienced SPM re-dissolution (Fig. 5, [Li] >3.15 in 726 Table 2) and assuming a δ<sup>7</sup>Li = 0‰ of the water prior to clay formation (Teng 727 et al., 2004). Using Isoplot®, the fitted A and B parameters in Eq. (5) are 1.82 728 ± 0.54 and -2.9 ± 6.5, respectively, for the observed data (Fig. 8B). The very 729 good agreement for A with literature data further supports our hypothesis.
Fitted values for B are imprecise but also encompass literature values. The fit suggests that the Yellow River $\delta^7$Li$_{rw}$ can be perfectly interpreted by temperature-dependent Li fractionation (Fig. 10), although there is a shift of around 2‰ during January and February that might have resulted from SPM re-dissolution as mentioned above (Fig. 5). Indeed, it is important to note that a temperature-dependence has been proposed in several previous studies (Marschall et al., 2007; Li and West 2014) and should exist due to standard isotopic fractionation effects. However, it has never been directly observed in rivers (i.e. Fig. 10), which probably due to the sparse seasonal sample collection (e.g. Kısakürek et al., 2005; Liu et al., 2015; Henchiri et al., 2016; Pogge von Strandmann et al., 2016) and/or limited temperature variation of the reported rivers (e.g. <14°C, Galy and France-Lanord, 1999; Kısakürek et al., 2005; Tipper et al., 2006; Liu et al., 2015; Henchiri et al., 2016). Benefiting from the **large** temperature discrepancy and relative homogeneous loess, for the first time, we directly observed such temperature dependency of Li isotopic fractionation in river waters (Fig. 10).

Given that seasonal $\delta^7$Li$_{rw}$ in the middle reaches of the Yellow River is chiefly controlled by temperature and not by SPM-water interaction, it is worth estimating $\delta^7$Li$_{rw}$ variation regulated by temperature in the world rivers. The previously reported seasonal temperature ranges from 3°C to 14°C in the world rivers (Galy and France-Lanord, 1999; Kısakürek et al., 2005; Tipper et al., 2006; Liu et al., 2015; Henchiri et al., 2016), which is much less than that in
the middle reaches of the Yellow River (28.8°C in 2013). Using Li and West (2014)'s gradient or our fitted one (see Eq. (5) and Fig. 8B), 14°C range could result in about 2‰ δ<sup>7</sup>Li variation at most. Considering current external precision for Li is around 1‰ (e.g. Magna et al., 2004; Jeffcoate et al., 2007; Vigier et al., 2009; Pogge von Strandmann et al., 2008a, b, 2010, 2011, 2012, 2013, 2016, 2017; Huang et al., 2010; Lemarchand et al., 2010; Millot et al., 2010a, b; Liu and Rudnick, 2011; Liu et al., 2013, 2015; Dellinger et al., 2014, 2015, 2017; Pogge von Strandmann and Henderson, 2015; Wang et al., 2015; Gou et al., 2018), it is unlikely to observe such a subtle temperature dependency for Li isotopic fractionation directly in these reported rivers, especially after modifications from other sources and fractionation.

Since loess in the CLP represents well the UCC, our temperature dependence of riverine δ<sup>7</sup>Li<sub>w</sub> variation in the middle reaches of the Yellow River (Fig. 10) indicates that Cenozoic climate cooling itself may be able to only explain ~2‰ of the 9‰ rise of Cenozoic seawater δ<sup>7</sup>Li assuming that overall Cenozoic temperature decreased c. 15°C (Zachos et al., 2001), besides increased tectonic uplift and accelerated continental denudation (Misra and Forelich, 2012). This is an additional temperature-dependent fractionation than that proposed during the Li sink from seawater by Coogan et al. (2017). Furthermore, a shift to lower seawater δ<sup>7</sup>Li during the middle Miocene (Misra and Froelich, 2012) might be a result of an enhanced dissolved Li flux to the oceans owing to an increased silicate weathering driven
774 by an intensified summer monsoon (Clift et al., 2008). In contrast, the more
775 extreme but shorter trends towards lighter Li isotopes reported during
776 hyperthermal Oceanic Anoxic Events (OAEs) (Pogge von Strandmann et al.,
777 2013; Lechler et al., 2015) are far less dominated by temperature changes. For
778 example during the Cenomanian-Turonian OAE2 warming was around 5°C
779 (Forster et al., 2007; Damsté et al., 2010), which would only lead to ~0.75‰ of
780 the 15‰ δ<sup>7</sup>Li excursions observed (Pogge von Strandmann et al., 2013).

781 6 Conclusions

782 This work presents a high-temporal resolution dataset of the riverine
783 dissolved Li flux and Li isotopic compositions and calculates contributions from
784 different sources for river water samples collected weekly from the middle
785 reaches of the Yellow River covering one full hydrological year. The following
786 observations and conclusions were obtained:

787 1. The dissolved load exhibited significant seasonal δ<sup>7</sup>Li variation in the middle
788 reaches of the Yellow River. The riverine dissolved δ<sup>7</sup>Li (+16.9‰ to +21.5‰) is
789 fractionated toward higher values compared to the suspended sediments (+1.8
790 ± 0.9‰) as a result of the temperature-dependent scavenging of <sup>6</sup>Li by
791 secondary minerals within watersheds. SPM re-dissolution, extreme
792 hydrological events, groundwater contribution, and presence of evaporite
793 deviate river water δ<sup>7</sup>Li to some extent.

794 2. Weathering of silicate minerals from loess dominates the dissolved Li in the
795 middle reaches of the Yellow River. Evaporites contribute ~25% approximately
796 to the total riverine dissolved Li. Because loess represents well the UCC
797 composition, it is plausible that evaporites may contribute ~25% Li to the total
798 Li flux continents to oceans.

799 3. The seasonal variation in the dissolved δ<sup>7</sup>Li<sub>rw</sub> of the middle reaches of
800 the Yellow River can be best interpreted through the temperature dependent Li
801 isotope fractionation at the first order.

802 This study provides novel insights into chemical weathering in the Yellow
803 River basin in the context of monsoonal climates and rapidly eroding CLP, as
804 well as Li dynamics in modern environments. Our observations of the
805 significant Li<sub>sil</sub> fluxes and unique Li isotopic compositions carried by the Yellow
806 River, in particular the temperature-dependence of Li chemistry, have profound
807 implications for the Cenozoic evolution of seawater Li chemistry and carbon
808 cycle.

809

810 **Acknowledgements:** This work was financially supported by the Key Research Program
811 of the CAS (QYZDJ-SSW-DQC033) and the NSFC Program (41773149). PPvS is
812 supported by ERC Consolidator grant 682760. Mathieu Dellinger and Joshua West are
813 specially thanked for their insightful comment that largely improved this manuscript. We
814 thank He Sun, Fang Huang, Hui-Min Yu, Ying-Zeng Gong and Feng-Tai Tong at University
815 of Science and Technology of China (USTC) for their help and suggestions to the sample
816 preparation and laboratory works, and for their insightful discussions during the
817 manuscript preparation. Special thanks to Jinlong Qiao at the Longmen hydrological
station for his assistance with sample collection. We are grateful to two anonymous referees for their constructive comments that greatly improved this manuscript.

820

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1140
<caption>Table 1 Li concentrations and Li isotopic ratios of dissolved load and suspended particulate matter (SPM) of river waters collected weekly from the middle Yellow River.</caption>

See Zhang et al. (2015) for major cation concentrations.

<table><thead><tr><th rowspan="2">Sample no.</th><th rowspan="2">Date of 2013 (dd-mm)</th><th rowspan="2">Water discharge<sup>a</sup> m<sup>3</sup>/s</th><th rowspan="2">T<sup>a,b</sup> (°C)</th><th rowspan="2">pH<sup>a</sup></th><th rowspan="2">TDS<sup>a,c</sup> (mg/L)</th><th colspan="3">River water</th><th colspan="3">SPM</th></tr><tr><th>Li<br>umol/L</th><th>δ<sup>7</sup>Li<br>‰</th><th>2 s.d.<sup>d</sup><br>‰</th><th>Li<br>µg/g</th><th>δ<sup>7</sup>Li<br>‰</th><th>2 s.d.<sup>d</sup><br>‰</th></tr></thead><tbody><tr><td>LM13-1</td><td>5-Jan</td><td>588.3</td><td>0.0</td><td>7.64</td><td>1063</td><td>3.95</td><td>18.9</td><td>0.1</td><td>37.6</td><td>2.3</td><td>0.3</td></tr><tr><td>LM13-2</td><td>12-Jan</td><td>456.4</td><td>1.1</td><td>7.88</td><td>1066</td><td>4.15</td><td>18.3</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-3</td><td>19-Jan</td><td>468.0</td><td>0.1</td><td>7.93</td><td>905</td><td>3.34</td><td>19.4</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-4</td><td>26-Jan</td><td>474.8</td><td>1.4</td><td>7.89</td><td>913</td><td>3.29</td><td>20.1</td><td>0.0</td><td>30.9</td><td>2.9</td><td>0.1</td></tr><tr><td>LM13-5</td><td>2-Feb</td><td>527.7</td><td>1.1</td><td>8.06</td><td>831</td><td>3.02</td><td>19.3</td><td>0.3</td><td>55.5</td><td>3.5</td><td>0.0</td></tr><tr><td>LM13-6</td><td>9-Feb</td><td>518.8</td><td>0.6</td><td>8.13</td><td>766</td><td>2.89</td><td>19.2</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-7</td><td>16-Feb</td><td>609.1</td><td>2.0</td><td>8.05</td><td>735</td><td>2.68</td><td>18.9</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>LM13-8</td><td>23-Feb</td><td>560.4</td><td>1.9</td><td>8.02</td><td>702</td><td>2.67</td><td>18.8</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>LM13-9</td><td>2-Mar</td><td>832.0</td><td>5.0</td><td>7.87</td><td>671</td><td>2.50</td><td>18.9</td><td>0.1</td><td>39.4</td><td>2.5</td><td>0.0</td></tr><tr><td>LM13-10</td><td>10-Mar</td><td>854.5</td><td>5.3</td><td>7.73</td><td>654</td><td>2.41</td><td>19.1</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-11</td><td>16-Mar</td><td>1110.1</td><td>7.1</td><td>7.89</td><td>629</td><td>2.41</td><td>18.8</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-12</td><td>23-Mar</td><td>1513.6</td><td>7.9</td><td>7.93</td><td>600</td><td>2.22</td><td>20.5</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>LM13-13</td><td>30-Mar</td><td>402.9</td><td>12.5</td><td>7.86</td><td>768</td><td>2.84</td><td>19.8</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-14</td><td>6-Apr</td><td>845.9</td><td>10.7</td><td>7.86</td><td>806</td><td>2.88</td><td>19.4</td><td>0.1</td><td>51.6</td><td>1.0</td><td>0.3</td></tr><tr><td>LM13-15</td><td>13-Apr</td><td>647.8</td><td>15.1</td><td>8.01</td><td>656</td><td>2.59</td><td>18.5</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-16</td><td>20-Apr</td><td>680.1</td><td>11.4</td><td>8.01</td><td>607</td><td>2.42</td><td>19.2</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-17</td><td>27-Apr</td><td>338.3</td><td>14.5</td><td>8.07</td><td>627</td><td>2.69</td><td>19.4</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-18</td><td>4-May</td><td>284.1</td><td>15.6</td><td>7.94</td><td>612</td><td>2.79</td><td>19.1</td><td>0.1</td><td>37.7</td><td>1.9</td><td>0.5</td></tr><tr><td>LM13-19</td><td>11-May</td><td>318.7</td><td>16.3</td><td>8.13</td><td>627</td><td>2.84</td><td>19.0</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-20</td><td>18-May</td><td>167.3</td><td>15.0</td><td>7.83</td><td>635</td><td>3.56</td><td>18.4</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>LM13-21</td><td>25-May</td><td>179.2</td><td>16.4</td><td>7.78</td><td>633</td><td>2.49</td><td>17.5</td><td>0.6</td><td></td><td></td><td></td></tr><tr><td>LM13-22</td><td>1-Jun</td><td>225.0</td><td>18.6</td><td>7.82</td><td>614</td><td>2.92</td><td>17.3</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-23</td><td>8-Jun</td><td>971.8</td><td>23.8</td><td>7.80</td><td>590</td><td>2.50</td><td>16.9</td><td>0.3</td><td></td><td></td><td></td></tr><tr><td>LM13-24</td><td>15-Jun</td><td>687.1</td><td>23.7</td><td>7.86</td><td>620</td><td>2.41</td><td>17.3</td><td>0.8</td><td></td><td></td><td></td></tr><tr><td>LM13-25</td><td>22-Jun</td><td>773.6</td><td>22.7</td><td>7.83</td><td>576</td><td>2.51</td><td>17.0</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>LM13-26</td><td>29-Jun</td><td>493.2</td><td>20.1</td><td>7.55</td><td>573</td><td>2.36</td><td>17.0</td><td>0.4</td><td>48.9</td><td>0.6</td><td>0.4</td></tr><tr><td>LM13-27</td><td>2-Jul</td><td>1276.3</td><td>27.7</td><td>7.99</td><td>553</td><td>2.73</td><td>17.9</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-28</td><td>8-Jul</td><td>589.6</td><td>27.7</td><td>7.83</td><td>587</td><td>2.84</td><td>17.4</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>LM13-29</td><td>12-Jul</td><td>1353.8</td><td>24.2</td><td>7.73</td><td>617</td><td>2.98</td><td>18.4</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>LM13-30</td><td>18-Jul</td><td>1350.9</td><td>25.0</td><td>7.83</td><td>599</td><td>2.91</td><td>18.2</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>LM13-31</td><td>22-Jul</td><td>2400.5</td><td>23.3</td><td>7.66</td><td>542</td><td>2.57</td><td>18.8</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>LM13-32</td><td>23-Jul</td><td>2207.6</td><td>22.3</td><td>8.14</td><td>527</td><td>2.72</td><td>18.5</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>LM13-33</td><td>24-Jul</td><td>1452.5</td><td>26.1</td><td>7.05</td><td>598</td><td>2.63</td><td>18.7</td><td>0.2</td><td>32.6</td><td>2.2</td><td>0.3</td></tr><tr><td>LM13-34</td><td>25-Jul</td><td>2255.6</td><td>25.4</td><td>7.22</td><td>603</td><td>3.23</td><td>20.0</td><td>0.2</td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>LM13-35</td><td>31-Jul</td><td>990.3</td><td>25.6</td><td>7.71</td><td>671</td><td>2.90</td><td>18.8</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-36</td><td>7-Aug</td><td>1370.0</td><td>24.5</td><td>8.02</td><td>623</td><td>2.78</td><td>18.4</td><td>0.4</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-37</td><td>12-Aug</td><td>1634.2</td><td>26.2</td><td>7.92</td><td>671</td><td>2.94</td><td>18.8</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-38</td><td>18-Aug</td><td>1106.8</td><td>28.8</td><td>7.89</td><td>658</td><td>2.78</td><td>18.5</td><td>0.3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-39</td><td>23-Aug</td><td>1093.3</td><td>25.6</td><td>7.85</td><td>665</td><td>2.69</td><td>17.9</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-40</td><td>30-Aug</td><td>1205.7</td><td>23.6</td><td>8.30</td><td>621</td><td>2.70</td><td>18.6</td><td>0.3</td><td>28.9</td><td>2.1</td><td>0.2</td><td></td><td></td></tr><tr><td>LM13-41</td><td>4-Sep</td><td>1392.3</td><td>23.2</td><td>8.30</td><td>622</td><td>2.69</td><td>18.1</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-42</td><td>10-Sep</td><td>1238.6</td><td>21.7</td><td>8.31</td><td>616</td><td>2.63</td><td>18.5</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-43</td><td>11-Sep</td><td>1745.0</td><td>21.1</td><td>8.30</td><td>626</td><td>2.61</td><td>18.9</td><td>0.3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-44</td><td>16-Sep</td><td>1715.6</td><td>22.0</td><td>8.30</td><td>583</td><td>2.57</td><td>18.6</td><td>0.1</td><td>31.2</td><td>1.8</td><td>0.4</td><td></td><td></td></tr><tr><td>LM13-45</td><td>18-Sep</td><td>1213.0</td><td>21.4</td><td>8.23</td><td>558</td><td>2.53</td><td>18.4</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-46</td><td>19-Sep</td><td>1723.3</td><td>21.1</td><td>7.87</td><td>548</td><td>2.26</td><td>18.9</td><td>0.3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-47</td><td>26-Sep</td><td>1211.0</td><td>17.6</td><td>8.71</td><td>585</td><td>2.41</td><td>19.8</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-48</td><td>2-Oct</td><td>901.3</td><td>19.3</td><td>8.48</td><td>652</td><td>2.53</td><td>19.4</td><td>0.2</td><td>43.4</td><td>0.6</td><td>0.3</td><td></td><td></td></tr><tr><td>LM13-49</td><td>9-Oct</td><td>797.3</td><td>20.2</td><td>8.28</td><td>693</td><td>2.66</td><td>20.1</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-50</td><td>16-Oct</td><td>789.6</td><td>15.3</td><td>8.40</td><td>649</td><td>2.49</td><td>18.9</td><td>0.3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-51</td><td>23-Oct</td><td>310.9</td><td>11.7</td><td>8.32</td><td>577</td><td>2.32</td><td>20.1</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-52</td><td>30-Oct</td><td>604.7</td><td>11.6</td><td>7.72</td><td>611</td><td>2.44</td><td>19.7</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-53</td><td>6-Nov</td><td>312.2</td><td>12.3</td><td>8.08</td><td>647</td><td>3.02</td><td>19.0</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-54</td><td>13-Nov</td><td>219.8</td><td>8.6</td><td>8.23</td><td>709</td><td>3.20</td><td>20.1</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-55</td><td>20-Nov</td><td>205.4</td><td>5.6</td><td>7.32</td><td>701</td><td>3.01</td><td>18.6</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-56</td><td>27-Nov</td><td>573.4</td><td>4.3</td><td>7.51</td><td>741</td><td>2.78</td><td>20.6</td><td>0.3</td><td>36.7</td><td>0.8</td><td>0.1</td><td></td><td></td></tr><tr><td>LM13-57</td><td>4-Dec</td><td>1113.9</td><td>3.0</td><td>7.70</td><td>697</td><td>2.54</td><td>21.0</td><td>0.3</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-58</td><td>11-Dec</td><td>712.1</td><td>1.7</td><td>7.92</td><td>807</td><td>4.41</td><td>17.5</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-59</td><td>18-Dec</td><td>626.7</td><td>0.8</td><td>7.89</td><td>847</td><td>3.06</td><td>21.5</td><td>0.1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LM13-60</td><td>25-Dec</td><td>398.7</td><td>0.3</td><td>8.30</td><td>873</td><td>3.13</td><td>21.2</td><td>0.2</td><td>68.9</td><td>1.8</td><td>0.1</td><td></td><td></td></tr></table>

a Data from Zhang et al. (2015);

b T, water temperature;

c TDS, total dissolved solids;

d 2 s.d., 2 times standard derivation.
<table><caption>Table 2 Concentrations of ions and anions of rain water samples collected at the Longmen hydrological station.</caption><thead><tr><th rowspan="2">Sample no.</th><th>Date 2013</th><th rowspan="2">Na</th><th rowspan="2">Ca</th><th rowspan="2">Mg</th><th rowspan="2">K</th><th rowspan="2">Cl</th><th rowspan="2">SO<sub>4</sub></th><th rowspan="2">NO<sub>3</sub></th><th rowspan="2">F</th><th rowspan="2">Li</th></tr><tr><th>(mm-dd)</th><th>(µmol/L)</th></tr></thead><tbody><tr><td>LM-r1</td><td>July-8</td><td>244</td><td>136</td><td>60</td><td>27</td><td>180</td><td>202</td><td>323</td><td>0.38</td><td>0.10</td></tr><tr><td>LM-r2</td><td>Aug-11</td><td>134</td><td>182</td><td>55</td><td>45</td><td>93</td><td>230</td><td>225</td><td>0.60</td><td>0.16</td></tr><tr><td>LM-r3</td><td>Aug-28</td><td>78</td><td>171</td><td>20</td><td>17</td><td>49</td><td>201</td><td>78</td><td>0.51</td><td>0.12</td></tr></tbody></table>

Major ionic concentrations are from Zhang et al. (2015).
<caption>Table 3 Li and Na concentrations and Li isotopic compositions of the evaporite fraction in</caption>

Lingtai loess profile.

<table><thead><tr><th>Stratum</th><th>Na<br>µmol/g</th><th>Li<br>nmol/g</th><th>δ<sup>7</sup>Li<br>‰</th><th>2 s.d.<br>‰</th></tr></thead><tbody><tr><td>L1</td><td>20.9</td><td>1.5</td><td>23.0</td><td>0.3</td></tr><tr><td>S1</td><td>10.5</td><td>2.0</td><td>23.6</td><td>0.1</td></tr><tr><td>S5</td><td>42.3</td><td>1.7</td><td>21.0</td><td>0.1</td></tr><tr><td>L9</td><td>7.8</td><td>1.8</td><td>26.8</td><td>0.1</td></tr><tr><td>RC</td><td>3.5</td><td>1.3</td><td>/</td><td>/</td></tr></tbody></table>

2 s.d., 2 times standard deviation from triple analyses of the same solution.
Table 4 Li and Na concentrations and lithium isotopes compositions of the river bottom sand

(RBS) and sewage samples collected at the Longmen hydrological station.

<table><thead><tr><th>Samples</th><th>Na</th><th>Li</th><th>δ<sup>7</sup>Li</th><th>2 s.d.</th></tr></thead><tbody><tr><td>River bed sand (RBS)</td><td>658 µmol/g</td><td>5.3 µmol/g</td><td>6.9‰</td><td>0.1</td></tr><tr><td>Sewage (TKT1)</td><td>902 µmol/mL</td><td>12.3 µmol/L</td><td>23.8‰</td><td>0.2</td></tr></tbody></table>

2 s.d., 2 times standard deviation from triple analyses of the same solution.
Figure captions:

Figure 1. Sketch map of the Yellow River drainage basin, with major tributaries and sampling site (Longmen hydrological station). Lithologically, loess and desert dominate within the upper and middle reaches of the Yellow River basin. Inset map shows the Yellow River drainage basin.

Figure 2 (A) Li flux, (B) concentration of Li, and (C) δ<sup>7</sup>Li of river waters collected weekly at the Longmen hydrological station over the whole year of 2013, and (D) δ<sup>7</sup>Li of monthly suspended particulate matter (SPM). (E) SPM flux, (F) concentration of SPM and (G) physical erosion rate (PER, from Zhang et al., 2015) at the Longmen hydrological station over 2013, and (J) Li concentration of monthly SPM. All showing obvious seasonal variations, along with (E, K) water and air temperatures and (F, L) water discharge in both A and B panels. The intervals of ice melting (from 16th March to 13th April), monsoon season (from June to mid-September), and a storm event (22nd to 25th of July) were shaded by green, blue, and dark blue, respectively. The significant seasonal variation in Li and δ<sup>7</sup>Li<sub>rw</sub> ratios of dissolved load is observed with highest ratios in winter (dry seasons), whereas the Li concentrations (42.4 ± 11.6 µg/g) and δ<sup>7</sup>Li values (1.85 ± 0.89‰) of SPM remains little variation over the year.

Figure 3 The suspend particulate matter (SPM) concentrations of the Toudaoguai (TDG) and Longmen (LM) hydrological stations (locations marked in Fig. 1) over 2013, showing that SPM was mainly derived from
loess between TDG and LM during the monsoon season.

Figure 4 Linear positive correlations of Li concentrations with (A) B, (B) Cl, (C) Na, and (D) SO₄, indicating significant contribution of evaporites from loess to Li in the river waters of the middle reaches of the Yellow River.

Figure 5 Mixing diagram of Li and δ⁷Li of the Yellow River waters, indicating that Li and its isotopic ratios are controlled by seasonal processes and sources. Groundwater extrusion may prevail during the early monsoon when rains have been penetrating. Decreased δ⁷Li but increased Li concentrations may be due to a SPM re-dissolution during the pre-monsoon. During the post monsoon, that evaporite dissolution triggered by a storm event contributed a bit more to river waters than other seasons. A sample (LM13-58) collected on 11th of December with abnormally high Li concentration and low isotopic ratio was excluded.

Figure 6 Partitioning of Li of the river water into five end-members, i.e. silicates, carbonates, anthropogenic, rain, and evaporites. In an annual average, silicates and evaporites contribute an average of ~60% and ~25% of the dissolved Li, respectively. It's notable that the evaporite contribution increased after the storm event.

Figure 7 Plot of Cl/Li against SO₄/Li, clearly showing the seasonal Li contribution variation sensitive to hydrological conditions. During the pre-and early monsoons, silicate contribution dominates, whereas evaporite contribution increases in the late monsoon, especially after the storm event.
Figure 8 (A) $\delta^7\text{Li}_{\text{rw}}$ of the Yellow River waters plotted against SPM concentrations, showing no overall trend of $\delta^7\text{Li}_{\text{rw}}$ values with SPM concentrations, indicating that Li isotopes in riverine system might be conservative. A sample collect on 11th of December with abnormally high Li concentration and low isotopic ratio was excluded. (B) A negative correlation of $1000 * \delta_{\text{water-clay}}^0$ (i.e. $\delta^7\text{Li}_{\text{rw}} - \delta^7\text{Li}_{\text{loess}}, \delta^7\text{Li}_{\text{loess}} = 0$, Teng et al., 2004) of the Yellow River waters against water temperature fitted using Isoplot®, the fitted A and B parameters in Eq. (6) are $1.82 \pm 0.54$ and $-2.9 \pm 6.5$, respectively, indicating a temperature dependent of $\delta^7\text{Li}_{\text{rw}}$ within the Yellow River basin. Note that the samples might affected by SPM re-dissolution (Fig. 5, [Li] >3.15 in Table 2) were excluded. See text for details.

Figure 9 Power positive correlation between $\text{Li}_{\text{sil}}$ flux (Li in river water derived from silicate dissolution) of dissolved load and physical erosion rate (PER, from Zhang et al. (2015)), further supporting that erosion rate is of great importance for chemical weathering as proposed by Gaillardet et al. (1999) and for Li isotopes by Li and West (2014), especially in such easy-erodable loess covered region.

Figure 10 Temperature dependent Li isotopic fractionation was fitted with $\delta^7\text{Li}_{\text{rw}} = 22‰$ at 20°C from Li and West (2014). Direct fitted values (blue points) are higher than the measured data systematically. Our best fit according to Eq. (6) and Fig. 8B is shown by the red points on the graph,
suggesting that temperature is the dominant player controlling the $\delta^7\text{Li}_{\text{rw}}$.

The averaged $\delta^7\text{Li}_{\text{loess}} = 0$ was set as the initial $\delta^7\text{Li}$ value for both fittings
(Teng et al., 2004).
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 7</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 8</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 9</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure 10</figcaption></figure>