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Quaternary International 479 (2018) 90e99

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Characteristic grain-size component - A useful process-relatedparameter for grain-size analysis of lacustrine clastics?

Yin Lu a, *, 1, Xiaomin Fang a, **, Oliver Friedrich b, Chunhui Song c

a CAS Center for Excellence in Tibetan Plateau Earth Sciences and Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan PlateauResearch, Chinese Academy of Sciences, Beijing, 100101, Chinab Sedimentology and Marine Paleoenvironmental Dynamics Group, Institute of Earth Sciences, Heidelberg University, Im Neuenheimer Feld 234-236, 69120,Heidelberg, Germanyc School of Earth Sciences & Key Laboratory of Western China's Mineral Resources of Gansu Province, Lanzhou University, Lanzhou, 730000, China

a r t i c l e i n f o

Article history:Received 16 November 2016Received in revised form10 July 2017Accepted 21 July 2017Available online 22 September 2017

Keywords:Lacustrine sedimentsGrain-size distributionGrain-size curve fittingPaleoenvironment

* Corresponding author.** Corresponding author.

E-mail addresses: yinlu@post.tau.ac.il, yinlusedimfangxm@itpcas.ac.cn (X. Fang).

1 Present address: Sedimentology and Marine PaGroup, Institute of Earth Sciences, Heidelberg Univer

http://dx.doi.org/10.1016/j.quaint.2017.07.0271040-6182/© 2017 Elsevier Ltd and INQUA. All rights

a b s t r a c t

Lacustrine sediments are important archives for paleoclimate reconstructions. The application of grain-size analysis as palaeoclimatic proxy in lacustrine clastics is valuable but also difficult because the typicalpolymodal grain-size distribution in these clastics. To better understand the grain-size distribution oflacustrine clastics and to promote the application of grain size in paleoenvironmental interpretation, thisstudy investigates lacustrine clastics from northern and southern China. The grain-size distribution ofthese sediments was decomposed by log-normal distribution function fitting method. Based on theresults, and drawing upon the concept of paleomagnetic demagnetization and “Characteristic RemnantMagnetization” from paleomagnetism, a conceptual system has been established and defined for grain-size distribution analysis. The system is composed of four components: (I) Characteristic Grain SizeComponent (ChGSC), (II) Affiliated Grain Size Component, (III) Meaningful Grain Size Component, and(IV) Combination Feature of Grain Size Components (CFGSCs). Based on the proposed system, ChGSC andCFGSCs were used to detect the grain-size distribution of clastics from the different lake zones inves-tigated. Our results show the number, modal size, and percentage of ChGSC(s) in grain-size distributionsare sensitive to changes in the lacustrine environment. The ChGSC(s) mirrors the dominant depositionalprocess and hydrodynamic conditions. The modal size of ChGSC(s) is more sensitive to hydrologicalconditions than the widely used mean grain-size approach. Thus, the ChGSC(s) provide a useful process-related parameter for paleoenvironmental reconstructions. To test this promising application, we appliedthis approach to a deep drill core from the Qaidam Basin in the northeastern Tibetan Plateau.

© 2017 Elsevier Ltd and INQUA. All rights reserved.

1. Introduction

Lakes are typically hypersensitive to climatic changes(Verschuren, 2009; Yu and Shen, 2010;Wolff et al., 2011; Herb et al.,2013; Liu et al., 2017) and therefore provide an ideal archive ofcontinental climate change because of the preservation of long (ahundred thousand years to million-year time scale), uninterruptedsedimentary records (Kashiwaya et al., 2001; Wang et al., 2012; Lu

entology@yeah.net (Y. Lu),

leoenvironmental Dynamicssity, Germany.

reserved.

et al., 2015). These conditions in a continental setting, allow forhigh-resolution palaeoclimatic studies that are crucial to betterunderstand the pattern and dynamics of the global climate system(An et al., 2011; Brauer et al., 2007; Kashiwaya et al., 2001; Litt et al.,2014; Torfstein et al., 2015; Elbert et al., 2015; Tian et al., 2017).Furthermore, clastic sediment sequences in large lakes have notbeen investigated sufficiently to examine their climatic and sedi-mentary significances.

As climate change is typically themain driver of the hydrologicalconditions within a lake, the nature and arrangement of clasticsedimentary facies in lacustrine sediments, both of which areclosely related to the hydrological conditions, can be used toreconstruct (palaeo)climatic changes. Grain size is a particularlyvaluable indicator of the hydrodynamic evolution of lakes becauseit corresponds to the hydraulic energy that is needed for clast

Y. Lu et al. / Quaternary International 479 (2018) 90e99 91

transport, sorting, and deposition. Grain-size analysis providespaleoenvironmental information at the high temporal resolutionthat is needed to reconstruct and understand the dynamics ofclimate change. However, the typical polymodal grain-size distri-bution in lacustrine sediments has hindered thewide application ofgrain-size analysis as a standard tool. This polymodal grain-sizedistribution comes from different transporting media (e.g., floods,aeolian input, lake currents, and waves) and the re-sorting ofsediment. Within a lake, however, different depositional zones arecharacterized by distinctive combination of grain size components,as was shown for Hulun Lake (Inner Mongolia) (Xiao et al., 2012).This shows that decomposition polymodal grain-size distribution isa potential tool for paleoenvironmental reconstructions based onlacustrine sediments.

Recently, two different approaches have been carried out toobtain reliable results from grain-size distribution analyses oflacustrine sediments: (1) end-member modeling analysis (EMMA)(Dietze et al., 2012, 2013, 2014; Ijmker et al., 2012; Liu et al., 2016;Parris et al., 2009; Yu et al., 2016), and (2) the log-normal distri-bution function fitting method (Xiao et al., 2009, 2012, 2013, 2015;Gammon et al., 2017). The log-normal distribution function fittingmethod is based on single sample fitting and subsequent decom-position. In contrast, the EMMA method requires an eigenspacedecomposition with different scaling procedures that extractgenetically meaningful end-member grain-size distributions andtheir percentages in each sample (Dietze et al., 2013).

The log-normal distribution function fitting method has beensuccessfully applied to obtain paleohydrological information ofancient lakes (Xiao et al., 2009, 2012, 2013, 2015; Gammon et al.,2017). Based on grain-size component fitting and decomposition,percentage of each individual component was acquired. Subse-quently, the sequence of percentage variation on the individualcomponent was compared with other proxies. However, in ourMiocene-Pleistocene deep drill core studies, we expect to get thevariation sequence of component(s) that reflects the dominantsedimentary process, not simply to do statistics of each individualcomponent. Therefore, an effective conceptual analysis system isneeded to extract the most essential elements from the largenumber of decomposed components. In this study, we establishsuch a system for grain-size distribution analysis using lacustrinesediments from southwestern, western, and northern China asrepresentative sedimentary archives. We further propose theCharacteristic Grain Size Component (ChGSC) as a useful process-related parameter for paleoenvironmental research and apply thisapproach to a deep drilling core (SG-1b) from the Qaidam Basin inthe northeastern Tibetan Plateau to test its applicability.

2. Material and methods

2.1. Lacustrine sediment sampling

For this study, surface sediments in the deep/central, theshallow/transitional and the lakeshore zones of Lugu Lake, DianLake, Yangzonghai Lake and Yilong Lake in southwestern China(Fig. 1A and B), the Qinghai Lake in western China (Fig. 1C) andAngulinao Lake in northern China (Fig. 1D) were sampled for grain-size distribution measurement and decomposition. Sedimentarysequences in these lakes both are dominated by clastics with lowlevels of organic materials. The location of these lakes and detailedinformation about the sample collection are given in Fig. 1 andTable 1. Lugu Lake, Dian Lake, Yangzonghai Lake and Yilong Lake insouthwestern China (Yunnan-Guizhou Plateau) are mainlycontrolled by the Indian monsoon and show no significant aeolianinput (Fig. 1A and B). Qinghai Lake in western China (TibetanPlateau) is impacted by the Westerlies throughout the year and

receives high-suspension dust input (An et al., 2012) (Fig. 1A, C).Angulinao Lake in northern China is controlled by the East Asianwinter monsoon (during the winter half-year) and the Westerliesand is therefore characterized by significant high-suspension (Sunet al., 2008a,b) and low-suspension (Prins et al., 2007; Sun et al.,2008a,b) dust input.

2.2. Grain-size measurement and component decomposition

Grain-size distribution was determined using a Malvern Mas-tersizer 2000 laser particle sizer after the organic matter and car-bonates were removed by H2O2 and HCl, respectively. The fractions<4 mm (84) and >63 mm (44) were regarded as clay and sand,respectively. In between, fractions 4e8 mm (74), 8e16 mm (64) and16e63 mm indicate very fine silt, fine silt and medium to coarse silt,respectively. The fractions 63e125 mm (34), 125e500 mm (14) and>500 mm indicate very fine sand, fine to medium sand and coarsesand, respectively.

Polymodal sediments are typically formed by various combi-nations of unimodal components (Ashley, 1978; Inman, 1949;Tanner, 1964; Visher, 1969) of which the grain-size distributiongenerally follows a log-normal distribution (Krumbein,1938; Passe,1997). Using the log-normal distribution function, the grain-sizedistribution can therefore be described with sufficient accuracy(Ashley,1978; Passe,1997; Qin et al., 2005). Based on these findings,the log-normal distribution function fitting method described byQin et al. (2005) was used to quantitatively fit and partition thegrain-size components within individual distributions of thesampled lake sediments.

The log-normal distribution function fitting method assumesthat a polymodal grain-size distribution is composed of severalunimodal log-normal distributions (Qin et al., 2005). The prototypeformula of the log-normal function is as follows:

FðXÞ ¼Xni¼1

264 Cisi

ffiffiffiffiffiffi2p

pZ∞�∞

exp

� ðX � aiÞ2

2s2i

!dX

375

where X ¼ lnðdÞ, d is the grain size in mm, n is the number of modes,ci is the content of the ith mode, si is the variance of the ith mode,and ai is the mean value of the ith mode's logarithm grain size, ai ¼lnðdiÞ (Xiao et al., 2009, 2012). The fitting residual is calculated asfollows:

dF ¼ 1m

Xmj¼1

�F�Xj�� G

�Xj��2

where m is the number of grain-size intervals and GðXÞ is themeasured grain-size distribution of a sample (Xiao et al., 2009,2012). The fitting process of each sample is accomplished until aminimum fitting residual is yielded. Then, the modal size (mediansize) and the relative percentage of each component are given. Thetechnical aspects of this procedure are described in detail by Qinet al. (2005) and Xiao et al. (2012).

3. Results

3.1. Grain-size component decomposition of sediments from thedeep/central lake zone

The grain-size distributions of sediments from the deep/centrallake zone are composed of three to four unimodal components,designated C1, C2, C3 and C4, from the finest to the coarsest modes,respectively (Fig. 2). All analyzed grain-size distributions are

Fig. 1. Location of the study area. (A) Map showing location of the sampled lakes in southwestern, western and northern China and the main atmospheric circulation systems. CLP:Chinese Loess Plateau; TP: Tibetan Plateau. (B) Magnification from rectangle B in Fig. 1A showing the location of four sampled lakes in southwestern China. The Indian monsoon isthe main atmospheric circulation system that controls the area. (C) Magnification from rectangle C in Fig. 1A showing the location of Qinghai Lake in western China. The area isimpacted by the Westerlies throughout the year. (D) Magnification from rectangle D in Fig. 1A showing the location of Angulinao Lake in northern China. During the winter half year,the East Asian winter monsoon is the controlling climate system in this area. Hulun Lake, Daihai Lake and Dali Lake are the places referred in this study. Detailed sampling-relatedinformation of the studied lakes are listed in Table 1.

Y. Lu et al. / Quaternary International 479 (2018) 90e9992

characterized by a unimodal component (red dashed curves inFig. 2) that not only has a non-overlapping areawith other adjacentcomponents > � 1

2 but also has a percentage value > ~10%. Themodal size of these components occurs in the very fine silt sizerange (4.4e7.5 mm, the average value is 5.9 mm), and observedpercentages vary between 75.92% and 90.52% (average value:81.96%) (Fig. 2).

3.2. Grain-size component decomposition of sediments from theshallow/transitional lake zone

The grain-size distributions of sediments from the shallow/transitional lake zone are composed of three to five unimodalcomponents, C1, C2, C3, C4 and C5 (Fig. 3). In each sample, twounimodal components (red dashed curves in Fig. 3) occur that havea non-overlapping areawith other adjacent components > � 1

2 anda percentage of > ~10%. The modal size of these components occursin (1) the clay to fine silt size range (2.3e12.4 mm, average value:5.2 mm) for the finer component and (2) the fine silt to very finesand size range (11.9e77.4 mm, average value: 50.7 mm) for thecoarser component. Percentages for the finer and coarser compo-nents varies between 34.77% and 69.06% (average value: 54.58%)

and 13.50%e46.17% (average value: 28.29%), respectively. Thecombined total percentage of the two components varies between73.13% and 96.86% (average value: 82.85%).

3.3. Grain-size component decomposition of sediments from thelakeshore

The grain-size distributions of lakeshore sediments arecomposed of four unimodal components, C1, C2, C3 and C4 (Fig. 4).Like the grain-size distributions of sediments from the shallow/transitional lake zones, the grain-size distributions of the sedi-ments from lakeshores also have two unimodal components (reddashed curves in Fig. 4) that have a non-overlapping area withother adjacent components of > � 1

2 and a percentage of > ~10%.The modal sizes of these components occur in (1) the very fine tofine silt size range (5.3e10.8 mm, average value: 7.7 mm) for the finercomponent and (2) the very fine to medium sand range(68.3e301.3 mm, average value: 189.3 mm) for the coarser compo-nent. The percentages of the fine component vary between 16.78%and 28.98% (average value: 21.63%), while percentages of thecoarser component vary between 58.83% and 77.01% (averagevalue: 68.45%). The combined total percentage of the two

Table 1Locations and sampling-related information of the studied lakes.

Type Lake Hydrological information Samplename

Sample location Water depth(m)

Plots infigure

Remark

Modern deep/central lake sediments Lugu Lake Lake area: 48.5 km2, MWD: 93.5 m,AWD: 40.3 m

LgL-1 27.691320� N,100.804959� E

38.4 2A Sag pond

Yilong Lake Lake area: 38.0 km2, MWD: 6.2 m,AWD: 2.4 m

YlL-1 23.67744� N,102.5748� E

2.0 2B

Qinghai Lake Lake area: 4340.0 km2, MWD: 27.0 m,AWD: 17.9 m

QhL-1, 2 36.811306� N,100.137083� E

2C, D Top 2 cm of coreQH-1A

AngulinaoLake

Lake area: 31.7 km2, MWD: 30.0 m,AWD: 19.5 m

AglnL-1 41.316667� N,114.35� E

2E,

Ancient deep lake sediments Qinghai Lake QhL-3 36.661861� N,100.389639� E

2F Erlangjian deepdrilling

Modern shallow lake (transitionzone) sediments

Dian Lake Lake area: 297.9 km2, MWD: 5.9 m,AWD: 2.9 m

DL-1 24.768611� N,102.664167� E

5.4 3B

Lugu Lake LgL-2 27.695944� N,100.809250� E

35.1 3C Sag pond

YangzonghaiLake

Lake area: 31.7 km2, MWD: 30.0 m,AWD: 19.5 m

YzL-1 24.90764� N,103.01031� E

22.1 3A Sag pond

AngulinaoLake

AglnL-2,3, 4

41.316667� N,114.35� E

3D, E, F

Modern lakeshore sediments Qinghai Lake QhL-4,5,6, 7,

36.811306� N,100.137083� E

4A, B, C, D Erlangjian deepdrilling

AngulinaoLake

AglnL-5, 6 41.316667� N,114.35� E

4E, F

Note: MWD: max water depth, AWD: average water depth. Hydrological information about related lakes cited from Wang and Dou (1998).

Fig. 2. Grain-size component fitting and decomposition of sediments from the deep/central lake zone of various lakes. In Fig. 2AeF, the black-coloured curves represent themeasured grain-size distribution, the green-coloured curves represent the fitted grain-size distribution (above the black-coloured curves), and the dashed curves with differentcolours represent the decomposed grain-size components. Modal size and percentage of each component and fitting residual of each sample are given. CM, component modal size.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Y. Lu et al. / Quaternary International 479 (2018) 90e99 93

components varies between 86.69% and 94.92% (average value:90.08%).

3.4. Establishing a conceptual system for grain-size distributionanalyses using the log-normal distribution function fitting method

Based on the log-normal distribution function fitting method

(Qin et al., 2005), the notion of paleomagnetic demagnetization andthe concept of “Characteristic Remnant Magnetization” (Tauxe,1998) from paleomagnetism have been used in this study toestablish a conceptual system for grain-size distribution analysis oflacustrine clastics. The reasoning behind this argument is that bothconcepts share the same aim, which is to extract the component(s)that carries the main information. The conceptual analysis system

Fig. 3. Grain-size component fitting and decomposition of sediments from the shallow/transitional lake zone of various lakes. In Fig. 3AeF, the black-coloured curves represent themeasured grain-size distribution, the green-coloured curves represent the fitted grain-size distribution (above the black-coloured curves), and the dashed curves with differentcolours represent the decomposed grain-size components. Modal size and percentage of each component and fitting residual of each sample are given. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)

Y. Lu et al. / Quaternary International 479 (2018) 90e9994

used in herein is composed of four components: (I) the Charac-teristic Grain Size Component (ChGSC), (II) the Affiliated Grain SizeComponent (AfGSC), (III) the Meaningful Grain Size Component(MeGSC), and (IV) the Combination Feature of Grain Size Compo-nents (CFGSC). Based on the statistical analysis of fitting resultsfrom lacustrine clastics (Figs. 2e4), the conceptual analysis systemwas defined and interpreted as below.

3.4.1. Characteristic Grain Size Component (ChGSC)This component is characterized by a non-overlapping areawith

other adjacent components of > � 12 and a percentage >~10% (red

dashed curves in Figs. 2e4). Each grain-size distribution of a givensediment must have at least one ChGSC. Our grain-size componentfitting and decomposition results suggest the total percentage ofChGSC(s) in one grain-size distribution is generally >70%. Sedi-mentologically, this ChGSC indicates a particular sedimentaryenvironment with one dominant sedimentary process. If more thanone ChGSC is present, this indicates a sedimentary environmentthat was dominated by different sedimentary processes. From thehighest to the lowest percentages of the ChGSCs in one sample,they can be divided into the first ChGSC, the second ChGSC, thethird ChGSC, etc.

3.4.2. Affiliated Grain Size Component (AfGSC)This component has a non-overlapping areawith other adjacent

components between 0 and � 13 (yellow dashed curves in

Figs. 2e4). Our grain-size component fitting and decompositionresults reveal the percentage of individual AfGSC varies between 1%and 15%. The total percentage of AfGSC(s) in one grain-size distri-bution is generally <20%. Each grain-size distribution of a givensediment can have one or more AfGSC(s) (e.g., Figs. 2F, 3A, 4E, 6CeFand 7D). In some case, there is no AfGSC in one grain-size distri-bution (e.g., Figs. 2D, 3F and 6B). This parameter will improve fitting

accuracy of the percentage of the ChGSC.

3.4.3. Meaningful Grain Size Component (MeGSC)This component has a non-overlapping areawith other adjacent

components between � 13 and � 1

2 or a non-overlapping area withother adjacent components > � 1

2 but a percentage that is <~10%(blue dashed curves in Figs. 2e4). The percentage of individualMeGSC varies between 1% and <20%. The total percentage ofMeGSC(s) in one grain-size distribution is generally < 20%. Like theAfGSC, each grain-size distribution of a given sediment can haveone or more MeGSC(s) (e.g., Figs. 2D, 3F, 6B and 7E), or no MeGSC(e.g., Figs. 2F, 3A, 4E, 6A and 7B). This parameter does not reflect thedominant sedimentary processes but may indicate some sedi-mentary processes that are related to either a special transportmedium or a change in the dynamic conditions of the respectivetransport medium. Its sedimentologymeaning still needs to furtherexplore.

3.4.4. Combination feature of grain size components (CFGSCs)The CFGSCs is mainly depicted by the number, modal size and

percentage of the ChGSC(s) in a grain-size distribution. Forexample, only one ChGSC in each grain-size distribution of sedi-ments from the deep/central lake zone (Fig. 2). The combinationfeature makes the grain-size distribution of sediments from thedeep/central lake zone are different with sediments from theshallow/transitional lake zone and lakeshore. Grain-size distribu-tions in the latter two types of sediments have more ChGSC(s),which with different modal size and percentage. CFGSCs indicates aparticular sedimentary environment with dominant sedimentaryprocess (es), and superimposed by some minor processes.

We choose the brackets for the components just based on ourgrain-size component fitting and decomposition of ~1100 samples.The value of the “non-overlapping area with other adjacent

Fig. 4. Grain-size component fitting and decomposition of lakeshore sediments from various lakes. In Fig. 4AeF, the black-coloured curves represent the measured grain-sizedistribution, the green-coloured curves represent the fitted grain-size distribution (above the black-coloured curves), and the dashed curves with different colours representthe decomposed grain-size components. Modal size and percentage of each component and fitting residual of each sample are given. (For interpretation of the references to colourin this figure legend, the reader is referred to the web version of this article.)

Y. Lu et al. / Quaternary International 479 (2018) 90e99 95

components” was used to define how one component stands outfrom the rest. One component that stands out from the rest (i.e.,having a non-overlapping area with other adjacent components of> � 1

2), indicates one specific sedimentary process. The percentageof one component used to evaluate the relative importance of it.The quantitative definitions of the three components, e.g., ChGSC ischaracterized by a non-overlapping area with other adjacentcomponents of > � 1

2 and a percentage >~10%, are based on oursubjective experience. Thus, it is an open system that might need aslight adjustment under certain boundary conditions.

4. Discussion

4.1. Sedimentary implications of Characteristic Grain SizeComponent (ChGSC)

The grain-size distributions of sediments from the deep/centrallake zone are characterized by one ChGSC with an average per-centage up to ~82% and a modal size in the very fine silt range(Fig. 2). This observation accords with the fact that sediments in thedeep/central lake zone aremainly composed of offshore suspensionparticles (clay to fine silt). Because of the depositional processes inthese areas, sediments are deposited under relatively still lakewater and weak hydrodynamic conditions.

In sediments from the shallow/transitional lake zone, each

grain-size distribution shows two ChGSCs. One ChGSC has a modalsize that is comparable to the ChGSC in sediments from the deep/central lake zone. The other is much coarser and characterized by amodal size in the fine silt to very fine sand range (Fig. 3). Theseresults are consistent with the fact that the shallow/transitionallake zone receives much more sediment from transporting medianear the lake margin, such as rivers.

The grain-size distribution of lakeshore sediments also showstwo ChGSCs. However, the coarser ChGSC has a much higher per-centage than the finer ChGSC and is generally much coarser thanthe ChGSCs of sediments from other lake zones (Fig. 4). These re-sults accurately reflect depositional processes at the lakeshore thatare dominated by fluvial transport and lake waves. This stronghydrodynamic regime leads to the deposition of a small amount offine silt that is combined with a larger amount of sand particles(Schieber et al., 2007).

Overall, the similar results of our conceptual analysis systemshow that the investigated lacustrine deposits have inherent andstable grain-size distribution features for a variety of modern lakeswith different sediment sources and different climatic regimes (i.e.,the westerlies, Indian monsoon, East Asian monsoon) (Fig. 1A).Moreover, the above mentioned observations suggest that theChGSC(s) of sediments from different lake zones closely mirror thedominant depositional processes and hydrodynamic conditions.Our new grain-size fitting results as well as previous studies of

Y. Lu et al. / Quaternary International 479 (2018) 90e9996

lacustrine clastics from Hulun Lake (Xiao et al., 2012), Daihai Lake(Xiao et al., 2013) and Dali Lake (Xiao et al., 2015) in northern Chinaindicate that sediments along a transect from the lakeshore to thelake centre are characterized by a decrease in the percentage oftheir coarser ChGSCs that corresponds to an increase in the per-centages of their finer ChGSCs. Therefore, the number, modal size,and percentage of ChGSC(s) in grain-size distributions of lakesediments can be used to obtain paleoenvironmental information.

4.2. Application of Characteristic Grain Size Component (ChGSC)

To test the promising application of ChGSC based on the con-ceptual grain-size distribution analysis system, we applied it to aclastics dominated sedimentary sequences in a huge lake, paleo-Qaidam Lake. The lake was located in the western Qaidam Basin,northeastern Tibetan Plateau (Fig. 1A), developed during the lateOligocene-Quaternary (Yang, 1986; Zhang et al., 1987; Wu and Xue,1993; Wang et al., 2012). A ~723 m-deep drill core SG-1b (as part ofa joint Sino-German project) was recovered in the paleo-QaidamLake with an average sediment recovery rate of ~93% (Zhanget al., 2014; Lu et al., 2015). Detailed paleomagnetic dating ofCore SG-1b constrains its age at ~7.3e1.6 Ma (Zhang et al., 2014).

Previous detailed examination of lithofacies, seismostratigraphyand grain-size records, suggest that the drilling sitewas in a deep tosemi-deep lake environment during ~7.3e3.6 Ma (~723-245 m), ina shallow lake environment during ~3.6e1.9 Ma (245-35 m), andfinally in a lakeshore-like environment during ~1.9e1.6 Ma (35-0 m) (Lu et al., 2015). The millimeter-to centimeter-scale thininterbedded marl and limestone layers are mainly deposited in thecore with depth <600 m, gypsum crystals frequently occurrencedin the corewith depth <250m (Zhang et al., 2014). Detailedmineralcomposition study (Fang et al., 2016) reveals the very low content ofhalite, gypsum, celestite, calcite and aragonite in the lower part ofthe core (with core depth >600m). Thereby, in this study, we chosethe lower 100 m of the Core SG-1b (720-620 m) to apply theconcept of ChGSC based on the conceptual analysis system. Paleo-magnetic dating constrains the age of the investigated core intervalto be between ~7.3e6.8 Ma (Zhang et al., 2014). Lithofacies of theinvestigated interval comprise fine siltstones with horizontalmillimeter-scale laminae (Fig. 5), suggesting that the sedimentswere deposited in a deep to semi-deep lake environment (Lu et al.,2015).

Within the investigated core interval, our fitting and decom-position experiment reveals fitting residuals that are in the range of~2.74e1.18% (average value: ~1.76%), suggesting a very good fittingprocess. The grain-size distribution of each sample is composed ofthree to five unimodal components, designated C1, C2, C3, C4 and C5,from the finest to the coarsest modes, respectively. In the grain-sizedistributions of 95 analyzed samples, 90 samples (94.7%) are

Fig. 5. Lithofacies of the investigated section in Core SG-1b. The core interval is characterize2015).

characterized by one ChGSC and five samples (5.3%) are charac-terized by two ChGSCs. Fig. 6 shows representative grain-size dis-tributions of the 90 samples. In these samples, observedpercentages of ChGSC vary between ~62.08% and 96.02%, with anaverage value at ~79.46%. Modal size of these ChGSCs vary between~5.01 mm and 19.0 mm, with an average value of ~9.1 mm.

One may note that the average value in modal size of ChGSC inthe Core SG-1b (~9 mm) is slightly larger than in the investigatedsediments from the deep/central lake zone in the modern lakes(very fine silt, < 8 mm). This difference should mainly result fromdifferent instruments that used for grain-size measurement. Thesediments from the deep/central lake zone in the modern lakes aremeasured by Malvern Mastersizer (2000) laser particle sizer, whilesediments from Core SG-1b are measured by Microtrac S3500 laserparticle sizer. Mudstone (late Miocene lacustrine deposits) werecollected from one drill core located in the Nanyishan anticline,western Qaidam Basin, northeastern Tibetan Plateau. Twenty par-allel samples were pre-treated in the Institute of Tibetan PlateauResearch, Chinese Academy of Sciences. Subsequently, they weremeasured at the Institute of Tibetan Plateau Research using aMicrotrac S3500 laser particle sizer and in the Key Laboratory ofWestern China's Environmental Systems, Lanzhou University witha Malvern Mastersizer 2000 laser particle sizer, respectively in2012. The parallel experiments reveal the Microtrac S3500 laserparticle sizer with lower sensitivity to clay and very fine silt par-ticles than the Malvern Mastersizer 2000 laser particle sizer. Thedifferences in mean grain-size measured by the two different in-struments are varying between 0 and 5 mm. Thus, the average valuein percentage (~79.5%) andmodal size (~9 mm) of ChGSC in the CoreSG-1b are identical to the investigated sediments from the deep/central lake zone in the modern lakes (~82.0% and ~6 mm).

The five samples which characterized by two ChGSCs are alsoshown (Fig. 7). Grain-size distribution of these samples are similarwith investigated sediments from the shallow/transitional lakezone. Among the five samples, grain-size distribution of foursamples have one coarse ChGSC (Fig. 7BeE). Percentage of thecoarse ChGSC are in the range of ~8%e10%. Thus, these sampleswere inferred to be mass movement deposits.

These fitting and decomposition results of Core SG-1b suggestthat the sediments in the investigated core interval are mainlycomposed of offshore suspension particles (clay to fine silt) andhave been deposited under relatively still lake water and weakhydrodynamic conditions. These quantitative characteristics sug-gest deposition in a deep to semi-deep lake environment, which isin agreement with the qualitative observations of the respectivelithofacies (Lu et al., 2015).

If compared to the mean grain-size, the modal size of the ChGSCin the investigated core interval shows a strikingly similar trend(Fig. 8). However, the observed variations in the modal size of

d by gray to dark-gray fine siltstones with horizontal millimeter-scale laminae (Lu et al.,

Fig. 6. Representative grain-size distributions of sediments from the lower part of Core SG-1b. The black-coloured curves represent the measured grain-size distribution, the green-coloured curves represent the fitted grain-size distribution (above the black-coloured curves), and the dashed curves with different colours represent the decomposed grain-sizecomponents. Modal size and percentage of each component and fitting residual of each sample are given. These grain-size distributions are both characterized by one ChGSC. (Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7. Grain-size distributions of sediments that inferred to be mass movement deposits in the Core SG-1b. The black-coloured curves represent the measured grain-size dis-tribution, the green-coloured curves represent the fitted grain-size distribution (above the black-coloured curves), and the dashed curves with different colours represent thedecomposed grain-size components. Modal size and percentage of each component and fitting residual of each sample are given. These grain-size distributions are both char-acterized by two ChGSCs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Y. Lu et al. / Quaternary International 479 (2018) 90e99 97

Fig. 8. Grain-size component fitting and decomposition of sediments in Core SG-1b. (A) Comparison the modal size of ChGSC(s) with the mean grain-size (from Lu et al., 2015). (B)Percentage of ChGSC in the measured samples. (C) Fitting residual of each sample.

Y. Lu et al. / Quaternary International 479 (2018) 90e9998

ChGSC show relatively high amplitude variations compared to themean grain-size (Fig. 8). This observation suggests that the modalsize of ChGSC is more sensitive to fluctuations in the hydrologicalconditions than the mean grain-size; this inference is reasonablebecause the mean grain-size represents mixed information, whilethe modal size of ChGSC reflects only information of the dominantdepositional process.

Overall, the conceptual grain-size distribution analysis systemthat is established herein will greatly improve and promote theapplication of the log-normal distribution function fitting methodin grain-size distribution analysis of lacustrine clastics by extractingsignificant sedimentological processes from a set of sedimentsdeposited in different lake environments.

5. Conclusions

A conceptual system for grain-size distribution analysis oflacustrine clastics has been established and defined based on thelog-normal distribution function fitting method. The system iscomposed of four components: (I) Characteristic Grain SizeComponent (ChGSC), (II) Affiliated Grain Size Component (AfGSC),(III) Meaningful Grain Size Component (MeGSC), and (IV) theCombination Feature of Grain Size Components (CFGSC).

Our data from modern lakes and a drill core show that theinvestigated lacustrine clastics from different lake environmentsare characterized by their inherent and stable grain-size distribu-tion features. Furthermore, the ChGSC(s) of sediments fromdifferent lake zones are suggested to mirror the dominant deposi-tional processes and hydrodynamic conditions. Therefore, themodal size of ChGSC is more sensitive to hydrological conditionsthan the widely used mean grain-size approach. The ChGSC(s)provide(s) a useful process-related parameter that can be applied inpaleoenvironmental reconstructions.

Acknowledgements

This study was co-supported by the (973) National BasicResearch Program of China (2013CB956400), the Strategic PriorityResearch Program of the Chinese Academy of Sciences (Grant No.XDB03020400) and the Priority Programme 1372 ‘Tibetan Plateau:Formation, Climate, Ecosystems (TiP)’ of the German ResearchFoundation (DFG; Grant No. AP34/34-1,2,3). We thank Yougui Song,Hucai Zhang and Zhiqiang Yin for providing some grain-size data.We thank Qiong Li (Lanzhou University) for grain-size measuring.Special thanks to Prof. Jule Xiao for providing the log-normal dis-tribution function fitting software and Prof. Erwin Appel for sup-porting Y.L.’s research at the Univ. of Tübingen, Germany, during theyear of 2013e2014. We thank Philip L. Gibbard (University ofCambridge), Ge Yu and one anonymous reviewer for theirconstructive comments, which improved the quality of the manu-script substantially.

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