SEDIMENTS, SEC 3 • HILLSLOPE AND RIVER BASIN SEDIMENT DYNAMICS • RESEARCH ARTICLE

Sediment source analysis using the fingerprintingmethod in a small catchment of the Loess Plateau, China

Fangxin Chen1,2,3& Fengbao Zhang1 & Nufang Fang1,2 & Zhihua Shi1,2,3

Received: 21 May 2015 /Accepted: 7 December 2015 /Published online: 19 January 2016# Springer-Verlag Berlin Heidelberg 2016

AbstractPurpose This paper aims to use the composite fingerprintingmethod to reconstruct the environmental history after theGrain-for-Green Project and to provide effective sedimentmanagement and soil erosion-control strategies.Materials and methods This study used a composite finger-printing method based on 45 geochemical properties and amixing model to investigate sediment core changes in thesediment source in an agricultural catchment with little nativevegetation. The samples consisted of 77 source samples (i.e.,gully, grassland, forest, cropland, and fallow land) and fivesediment cores. Genetic algorithm (GA) optimization hasbeen recently used to find the best optimum source contribu-tion to sediments.Results and discussion The results demonstrate that gully isthe main sediment source in this catchment, constituting34.7%, followed by cropland (28.2 %), forest (21.5 %), grass-land (12.7 %), and fallow land (2.9 %). However, the relativecontribution of each source type was variable in all five sed-iment cores. The sediment that derived from grassland wasrelatively stable in the five cores. The relative contribution

of forest was higher in the downstream portion of the checkdam and lower in the upstream portion and gradually in-creased in the direction of the runoff pathway. As the forestmatured, the sediment that derived from the forest graduallydecreased. Changes in the hydro-ecological environmentwould lead to the leaf litter and understory being poorly de-veloped and the soil being bare in the forest, making it morevulnerable to erosion.Conclusions Reforestation and fallow are the key ecologicalstrategies for reducing soil erosion. However, at the beginningof the Grain-for-Green Project, the young forest contributed21.5 % of the sediment, indicating that natural fallow maybe a better-designed sediment management and soilerosion-control strategy.

Keywords Check dam . Fallow . Fingerprinting .

Reforestation . Sediment . Soil erosion

1 Introduction

Information regarding sediment sources has considerably en-hanced our understanding of sediment routing and deliveryand of the construction of catchment sediment budgets(Walling et al. 1999; Collins and Walling 2004). Sedimentredistribution significantly controls the transport and fate ofnutrients, heavy metals, organic and inorganic contaminants(Collins et al. 2012a; Haddadchi et al. 2013). From a manage-ment perspective, it is necessary to identify sediment sourcesin order to implement appropriate strategies to control sedi-ment mobilization and the subsequent siltation of river chan-nels and reservoirs in the most needed areas.

The Loess Plateau is one of the key agricultural areas innorthwest China (Fu et al. 2011). This area has suffered fromsevere soil and water loss, which have significantly depleted

Responsible editor: Rajith Mukundan

* Nufang Fangfnf@ms.iswc.ac.cn

1 State Key Laboratory of Soil Erosion and Dryland Farming on theLoess Plateau, Northwest A & F University, Yangling 712100,People’s Republic of China

2 Institute of Soil and Water Conservation of Chinese Academy ofSciences and Ministry of Water Resources, Yangling 712100,People’s Republic of China

3 College of Resources and Environment, Huazhong AgriculturalUniversity, Wuhan 430070, People’s Republic of China

J Soils Sediments (2016) 16:1655–1669DOI 10.1007/s11368-015-1336-7

land resources and degraded the eco-environment in the LoessPlateau (Fu and Chen 2000; Chen et al. 2001). The averageand maximum erosion rates are 150 Mg ha−1 year−1 and390 Mg ha−1 year−1, respectively, while severe soil erosionis equivalent to a soil surface lowering of 1.2 to3.1 cm year−1 (Fu et al. 2000). In order to control soil erosion,catchment-scale land-use changes and erosion-control studieshave been applied widely in the Loess Plateau (Shi and Shao2000). However, an understanding of the sediment sources inthe Loess Plateau is a prerequisite for effective sediment man-agement and control strategies. Therefore, identifying the sed-iment source of a typical catchment in the Loess Plateau isvery meaningful because it provides a basis for the rationalallocation of catchment management strategies and resources.

Sediments originate from different sources, with the rela-tive contribution from each source varying over time andspace as a consequence of different erosion processes(Haddadchi et al. 2013). Although several approaches foridentifying sediment sources exist, many approaches rely onvisual estimates, modeling, long-term field records, or tradi-tional monitoring techniques (Foster and Lal 1988; Reid andDunne 1996; Haddadchi et al. 2013). These approaches andtechniques are, however, frequently restricted by problems ofcosts, spatial and temporal sampling and operational difficul-ties (Collins and Walling 2004; Walling 2005; Haddadchi etal. 2013). The sediment fingerprinting method has provided adirect and successful approach for quantifying the sources ofsediment from individual river sections to the catchment scaleover the last 30 years (Collins et al. 1997, 2010, 2012a, b;Walling et al. 1999, 2008; Walling 2005; Davis and Fox2009). This procedure involves the discrimination of sedimentsources based on differences in the source material propertiesand the quantification of the relative contributions from thesesources to the sediment that is delivered downstream in rivercatchments. This method uses one or more unique physical orbiogeochemical properties known as natural tracers (i.e., sed-iment fingerprints) (Collins and Walling 2002; Davis and Fox2009; Haddadchi et al. 2014b). Although some studies havebeen based on the use of a single fingerprint property, thepursuit of a single best diagnostic property and the reliabilityof using only a single diagnostic are increasingly consideredunrealistic (Collins and Walling 2002). In response to thisissue, fingerprinting methods now commonly include severaldiagnostic sediment properties to create a composite finger-print, indicating of a specific source type. Such compositefingerprints permit a more representative and consistent meth-od of verifying the sediment origin and often allow a greaternumber of sources to be determined (Collins and Walling2002).

This study was conducted in the Hujiawan catchment of theLoess Plateau in Northwest China. In 1999, the Chinese cen-tral government initiated a nationwide cropland set-aside pro-gram known as the Grain-for-Green Project. As a part of this

project, vast areas of cropland with a slope of greater than 25°in mountainous areas were converted into forestland or grass-land in the gully and hilly zones of the Loess Plateau (Fu et al.2006). In this area, the catchment-scale land-use changesmainly included reforestation and fallow, and only a small partof the original forest remained. From 1999 until now, the landuse of this catchment has experienced no significant changes.The sediment contribution rates of different land uses are fre-quently reported. However, few studies have focused on quan-tifying the relative sediment contribution of different soilerosion-control strategies (e.g., fallow and reforestation) bythe Grain-for-Green Project. The sediment core records im-portant information about the environmental processes relatedto soil erosion and deposition; reconstructing the environmen-tal history using the composite fingerprinting method is thenew application extension of this method and is meaningfulfor designing sediment management and soil erosion-controlstrategies. The objectives of this study were (1) to reconstructthe soil erosion history and quantify the relative contributionof the potential sources after the catchment-scale land-usechange in a small catchment and (2) to determine is the moreeffective soil erosion-control strategy between reforestationand fallow after the Grain-for-Green Project.

2 Materials and methods

2.1 Study area

This study was conducted in the Hujiawan catchment (36.4°Nto 36.6°N, 109.5°E to 109.9°E) in Yanchang County ofShaanxi Province on the Loess Plateau and covered a drainagearea of 27 km2 (Fig. 1). The elevations within the catchmentranged from 947 to 1300 m. The landform of the plateauconsists of gentle hilly plateau surfaces and deeply dissectedgullies. The soil is mainly derived from loess; the loess layersin the middle of the Loess Plateau are generally 80–120 mthick (300–400 m in typical highland areas) and are thethickest known loess deposits in the world (Liu 1985). Thesoil was transported by fierce wind storms from distant north-west locations, mainly during the Quaternary period (Liu1985). This homogenous soil with a texture ranging from finesilts to silt and is vulnerable to erosion (Fu et al. 2000). Theclimate is semi-arid continental with the average annual pre-cipitation of 497 mm. Precipitation is mainly concentrated inthe rainy season from June to September, accounting for 60 to70 % of the total, mostly in the form of high-intensity rain-storms (Xu et al. 2004; Yang et al. 2006).

A damwas constructed in the catchment in the 1970s and islocated at the catchment outlet (Fig. 1). The catchment topo-graphic map (scale 1:10,000) was used in the land-use classi-fication in combination with 2010 QuickBird imagery.Reconnaissance field surveys were carried out in 2014; the

1656 J Soils Sediments (2016) 16:1655–1669

land-use history was reconstructed by interviews with localvillagers. The land-use units were delineated on the photo-graphs and verified in the field, as shown in Fig. 2.

2.2 Soil samples

Fieldwork involved the collection of representative samples ofboth the source materials and the sediment deposit profilesdraining the Hujiawan catchment. Source material samplinginvolved the collection of 77 samples of the surface soil fromeroding areas representative of uncultivated (grassland, forest,and gully) and cultivated (cropland and fallow land) sites, asfollows: 15 samples from gully, 20 from natural grassland, 15from artificial forest, 10 from fallow land and 17 from crop-land (Fig. 2). These samples were collected using a stainlesssteel spade, and care was taken to ensure that only materialthat was susceptible to erosion was collected. For each sourcesample, 10 sub-samples were collected from 0 to 5 cm indepth along transects in a 5×5 m grid and were combined inthe field to form a single composite sample. The landscapes ofseveral sediment sources are shown in Fig. 3: the gully wasbare without vegetation and had obvious rill erosion (Fig. 3a),the artificial forest was sparse and young Robiniapseudoacacia with less understory growth (Fig. 3e), the fal-low land was identified with shrub-tussock in Fig. 3d, thenatural grassland was less lush than the fallow land(Fig. 3b), and the cropland was planted with maize (Zea maysL.) (Fig. 3c). All of the pictures were taken in May 2014.

We collected five sediment cores using an impact drill (HM1400, Makita, Japan) from head to tail along the check dam(A1–A5, Fig. 4) and carefully sectioned the sediment cores to

reflect the flood couplets. During the sample collection, twokinds of deposition texture existed in each couplet (clay andsand, Fig. 4). For each couplet, the top layer was fine, whilethe middle and bottom layers were coarse because coarse par-ticles fall faster than do fine ones in water. The boundarybetween the couplets that were associated with individualfloods was easily defined because the bottom layer in a cou-plet was coarse, while the top was fine (Wang et al. 2014). Inthe fieldwork, almost every flood couplet was composed ofone layer of clay and one layer of sand (Fig. 4); this structuremade it easier to distinguish each flood couplet. However, notall of the flood couplets were composed of this structure(Fig. 4; for core A3—layers 2 and 3, core A4—layer 2 andcore A5—layers 2 and 3, the texture was sand; for core A3—layer 16, core A5—layer 13, the texture was clay). Thegroundwater was rich 7.0 m deep, the sampling process wasdifficult, and the flood couplets could not be identified. Thecore sediments were undisturbed, as indicated by the clearwater-sediment interface and the preservation of fine sedimentlaminations. The thickness of a couplet varied from a fewcentimeters to several decimeters. We collected 19, 21, 16,16, and 13 sediment samples from the A1, A2, A3, A4, andA5 cores, respectively (Fig. 4).

2.3 Laboratory analysis

The source material samples and sediment samples werereturned to the laboratory and air-dried on porous plates, gent-ly disaggregated using a mortar and pestle and dry-sieved to<63 μm to facilitate a direct comparison with the sedimentsamples. All of the samples were analyzed using ICP-MS after

Fig. 1 Location of the studycatchment

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HCl/HNO3 (9.0 mL of concentrated HCl and 3.0 mL of con-centrated HNO3) microwave digestion. A laboratory analysisof both the source material and the sediment samples wasperformed for a range of potential fingerprint properties com-prising several property subsets, i.e., trace metals (Fe,Mn, andAl), heavy metals (Cu, Zn, Pb, Cr, Co, and Ni), rare earthelements (La, Ce), and base cations (Na, Mg, Ca, and K); atotal of 45 potential fingerprint properties were measured (i.e.,

Mo, Cu, Pb, Zn, Ag, Ni, Co,Mn, Fe, As, U, Th, Sr, Cd, Sb, Bi,V, Ca, P, La, Cr, Mg, Ba, Ti, Al, Na, K, W, Zr, Ce, Sn, Y, Nb,Ta, Be, Sc, Li, S, Rb, Hf, In, Re, Se, Te, and Tl). Sampleanalysis and quality assurance were performed by AcmeLabs (Canada, Vancouver Office). We measured the size dis-tribution of a laser particle size analyzer (Mastersizer 2000,Malvern, England); the measurements spanned a size rangefrom 0.02 to 2000 μm.

Fig. 2 Source sampling sites andland use of the Hujiawancatchment, a check dam is locatedin the outlet of the catchment

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2.4 Sediment fingerprinting procedure

The non-conservative behavior of sediment properties has sig-nificant implications for the sediment fingerprinting technique(Koiter et al. 2013). Therefore, tracers were selected for inclu-sion in source fingerprints based on compliance with threeconstraints (Wilkinson et al. 2013). Conservative behavior dur-ing erosion and transport was assured by all of the sedimentsample concentrations falling within the observed range of soilsource samples and by the sediment mix mean concentrationfor each tracer being within the range of the source soil meanconcentrations; this second constraint was also met only byconservative tracers. The second constraint was applied be-cause the coefficient variation (CV) of the sediment samples

was inherently smaller than that of the sources, enabling sometracers to satisfy the first constraint despite having mean con-centrations close to outlying source samples. Third, the powerof individual properties to discriminate between sources wastested using the Kruskal-Wallis rank sum test, and propertiesreturning P value>0.05 were excluded. The Kruskal-Wallis Htest was applied using the SPSS software package (IBM) toeach individual parameter of the source material dataset forthe Hujiawan catchment in order to verify its ability to discrim-inate between the land-use categories by testing the null hy-pothesis that the source sediment samples are drawn from thesame population (Franz et al. 2014). However, the H test doesnot confirm differences between all of the possible paired com-binations of source categories. Therefore, a stepwise

Fig. 3 Landscape of the sedimentsources (a: the gully was barewithout vegetation and hadobvious rill erosion; b: the naturalgrassland was less lush than thefallow land; c: the cropland wasplanted with maize (Zea mays L.),the loose topsoil of cropland wasprone to erosion; d: the fallowland was identified with shrub-tussock or lush grass; e: theartificial forest was sparse andyoung Robinia pseudoacaciawith less understory growth)

Fig. 4 Sediment core location,soil texture and flood coupletdistribution in each sediment core(in each sediment core, the soiltexture included clay (black) andsand (gray); the different colors offlood couplets distinguishdifferent layers)

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discriminant function analysis (DFA) was used to further assessthe discriminatory power of those tracer properties that pass thethree constraints (Wilkinson et al. 2013). The DFA identifies anoptimum source fingerprint that comprises the minimum num-ber of tracer properties that provide the greatest discriminationbetween the analyzed source materials based on the minimiza-tion of Wilks’ lambda. The lambda value approaches zero asthe variability within the source categories is reduced relative tothe variability between categories based on the entry or removalof tracer properties from the analysis. The results of the DFAwere used to examine the proportion of samples that wereaccurately classified into the correct source groups.

The multivariate mixing model was based on a set of linearequations in which each selected tracer property had an equa-tion relating the tracer concentration in a sediment sample tothe sum of the mean tracer concentrations for each sourcemultiplied by the respective unknown proportional sourcecontributions. The set of linear equations belonging to eachcomposite fingerprint is generally over-determined.Therefore, optimized estimates of the relative contribution ofthe individual source types to each sediment sample wereprovided by minimizing the sum of squares of the weightedrelative errors using Eq. (1):

Xn

i¼1

Ci−Xm

s¼1

PsCsi

!" #.Ci

( )2

ð1Þ

Where Ci is the concentration of tracer property i in thesuspended sediment sample, Csi is the mean concentration oftracer property i in source group s, Ps is the relative proportionfrom source group s, n is the number of fingerprint propertiescomprising the optimum composite fingerprint, and m is thenumber of sediment source categories. The model isconstrained by the requirements that proportional source con-tributions lay between 0 and 1 and that the proportional sourcecontributions sum to 1 (Collins et al. 1997). No correctionswere made for differences in the organic matter content andparticle size between source materials and sediment becausethe relationship between organic matter content, particle size,and element concentration is complex and difficult to gener-alize (Walling 2005; Koiter et al. 2013). A genetic algorithm(GA) optimization has been recently deployed to find the bestoptimum source contribution to sediments; in this study, themedian relative contribution from each source using 2500iterations with the Latin hypercube sampling (LHS) approach,as a surrogate of conventional random sampling, together withgenetic algorithm optimization were used to predict more ac-curate source contributions (Haddadchi et al. 2014a, b).

As with other hydrological modeling, the uncertainty is-sues that are associated with the results of sediment sourcefingerprinting studies have attracted increasing attention inrecent years (Collins et al. 2012a; Walling 2013). It is

important that such uncertainty should be recognized, partic-ularly if the results are to be used to target investment insediment control measures. Important sources of uncertaintyinclude the mixing model optimization and the propertyvalues that are used to characterize both sources and targets.Source characterization, in particular, can involve many un-certainties because soil properties are likely to vary spatiallyand may also vary in response to the intensity of erosion.Usually, the mean or median of the values for individual prop-erties that are obtained for the samples that were collected torepresent a given source is used. However, if a mean is used,its standard error should be taken into account (Walling 2013);the final result of the source ascription exercise can then berepresented as a range of values and confidence limits (95 %)(Franz et al. 2014). The 95 % confidence limits around thepredicted average median source type proportions, whichwere generated using the repeat sets, clearly indicated theconvergence of the model solutions and their reproducibilitywith in ±3 %. The mean relative errors (MRE) that were pro-vided by comparing the actual fingerprint property concentra-tions of the sediment samples with the corresponding valuesas predicted by the mixing model (Walling 2005). The mean(average for all of the properties within each composite fin-gerprint) relative errors for the mixing model calculationsranged from 2.3 to 21.8 %, with an average of 9.40±4.3 %.Above all, the results of the mixing model were meaningful.

3 Results and discussion

3.1 Sediment source discrimination

In the Hujiawan catchment, 42 separate geochemical elementswere available, and 3 elements (i.e., S, Re and Se) were notdetected in source soils. Of these elements, 42 elementspassed the constraint that the mean sediment sample concen-tration was within the range of the source mean concentra-tions. Thirty-one elements passed the constraint that all ofthe sediment samples within the range of source values, and11 elements (i.e., Cu, Pb, Zn, Ag, Mn, Fe, Bi, Al, Na, Ce andLi) did not pass this constraint. A total of 20 propertiesreturned a P value < 0.05 from the Kruskal-Wallis H test,and the H value ranged from 10.098 to 38.323; 11 elements(i.e., Th, K, W, Zr, Nb, Ta, Be, Rb, Hf, In, and Te) were notsignificant at P<0.05, as shown in Table 1. Thus, 20 elements(i.e., Mo, Ni, Co, As, U, Sr, Cd, Sb, V, Ca, P, La, Cr, Mg, Ba,Ti, Sn, Y, Sc, and Tl) passed the three constraints and conse-quently underwent a stepwise DFA, which in turn selected 7elements (Mg, Y, Ti, P, Sc, Co, and Cr) to create a compositefingerprint with a predictive power of 93.9% (Fig. 5, Function1 (65.6 %) and Function 2 (25.2 %)).

The optimum composite fingerprint consisted of a total of 7individual properties (Mg, Y, Ti, P, Sc, Co, and Cr) and

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Table 1 Geochemical soil source tracers which passed (P) each constraint for input into the fingerprint optimization procedure

Element Source samples Sediment samples Sedimentmeaninsidea

Sedimentsamplesinsideb

H value P value

Mean Min Max Mean Min Max

Mo 0.65 0.4 1.6 0.67 0.5 0.9 P P 25.195 0.000*

Cu 22.24 18.4 31.2 23.67 17.9 31.5 P

Pb 22.39 19 33.1 21.76 17.9 26.2 P

Zn 63.61 53 103 63.55 51 77 P

Ag 0.10 0.1 0.1 0.10 0.1 0.1 P

Ni 31.47 25.7 43.2 32.44 26.2 39.9 P P 17.618 0.001*

Co 12.24 9.3 16.4 12.64 9.4 16.3 P P 25.757 0.000*

Mn 626.44 454 833 618.10 500 748 P

Fe 2.93 2.47 3.73 2.94 2.42 3.64 P

As 13.39 10 18 13.24 11 17 P P 13.085 0.011*

U 2.30 1.5 3.3 2.49 2 2.9 P P 17.235 0.002*

Th 12.30 8.9 15.3 12.20 10.3 14.4 P P 1.922 0.750

Sr 219.30 143 269 233.65 210 268 P P 24.489 0.000*

Cd 0.18 0.1 0.4 0.18 0.1 0.4 P P 14.482 0.006*

Sb 1.55 1.2 2 1.40 1.2 1.7 P P 20.257 0.000*

Bi 0.35 0.3 0.5 0.33 0.2 0.5 P

V 74.95 59 99 76.99 62 98 P P 10.098 0.039*

Ca 4.69 2.52 6.3 5.07 4.05 6.3 P P 10.777 0.029*

P 0.06 0.04 0.08 0.06 0.05 0.07 P P 34.196 0.000*

La 34.36 24.8 37.7 34.53 31 36.6 P P 17.274 0.002*

Cr 54.08 40 73 50.61 40 68 P P 20.634 0.000*

Mg 1.29 1.01 1.59 1.28 1.04 1.56 P P 38.323 0.000*

Ba 460.60 395 550 468.05 429 527 P P 11.696 0.020*

Ti 0.37 0.3 0.44 0.34 0.3 0.38 P P 30.847 0.000*

Al 5.93 5.35 6.91 6.18 5.24 7.02 P

Na 1.22 0.49 1.4 1.24 0.88 1.5 P

K 1.90 1.73 2.38 1.93 1.75 2.15 P P 3.501 0.478

W 1.72 1.4 2.1 1.68 1.4 2 P P 4.182 0.382

Zr 68.48 56.6 91.2 64.86 59.5 71.8 P P 8.760 0.067

Ce 66.10 49 72 67.09 62 74 P

Sn 2.60 2.1 3.5 2.51 2.1 3 P P 10.328 0.035*

Y 19.52 16 23.4 19.17 17.4 21.1 P P 22.171 0.000*

Nb 10.13 8.8 12.3 10.05 9.2 10.8 P P 6.744 0.150

Ta 0.76 0.6 1 0.75 0.6 0.8 P P 9.077 0.059

Be 1.81 1 3 1.93 1 3 P P 3.043 0.551

Sc 10.14 9 13 10.65 9 13 P P 20.296 0.000*

Li 34.19 28.3 41.3 36.68 29.6 47.4 P

S

Rb 90.28 71.2 104.3 95.99 82.6 103.2 P P 7.104 0.131

Hf 2.02 1.6 2.7 1.94 1.7 2.3 P P 6.476 0.166

In 0.06 0.05 0.08 0.06 0.05 0.08 P P 1.212 0.876

Re

Se

Te 0.50 0 1 0.71 0 1 P P 2.241 0.692

Tl 0.55 0.5 1 0.57 0.5 0.7 P P 13.991 0.007*

*Significant atP< 0.05, –means the element is not detected in sources but detected in sediments, units are weights in% (Fe, Ca, P, Mg, Ti, Al, Na, K, S),the others are parts per million (PPM)aMean sediment concentration within the range of source category mean valuesbAll sediment sample concentrations were within the range of source sample values; in this example, constraints 1 and 2 were applied using sedimentfrom Hujiawan catchment

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correctly distinguished 92.2 % of the samples that were usedto characterize each of the five source types. The results of thestepwise discriminant function analysis to identify whichcombination of tracers provides the best composite for dis-criminating source materials based on land use, as collectedwithin the Hujiawan catchment, are presented in Table 2.Figure 5 shows the tracer vertical point being closer to thesource vector, indicating that the tracer is more relevant to thissource. Therefore, the most relevant of the seven tracers forthe gully is Ti, and the least relevant is Y.

3.2 Sediment particle size and tracer concentrations

Soil erosion is a selective process of soil redistribution in thelandscape (Mabit et al. 2010). Soil particle-size distribution isone of the most important physical attributes due to its greatinfluence on soil properties related to water movement andsoil erosion (Wang et al. 2008). Check dam sediments couldbe important indicators of environmental change, and thegrain-size feature of check dam sediments is closely influ-enced by the position within a check dam, forms of land use,

size of rainfall events, and different sediment posited process(Wang et al. 2014). As shown in Fig. 4, the 5 sediment coresare distributed along the check dam from upstream (core A5)to downstream (core A1). After the check dam was construct-ed, sediment accumulated in the deposit profile after everyerosive rainfall-runoff event because of the trapping effect ofthe check dam. Figure 6 shows the distribution of the sedimentfractions from site A1 to A5. Changes in hydrodynamic con-ditions can result in the clay and silt sediment fractions in-creasing gradually from upstream to downstream in the checkdam, while the sand content gradually decreases in the direc-tion of the runoff pathway (Owens et al. 2007; Wang et al.2014); the vertical variation of particle size demonstrated thetemporal heterogeneity of soil erosion (Renschler et al. 1999;Renschler and Harbor 2002).

Figure 7 summarizes the tracer concentration of each floodcouplet in the five sediment cores. The vertical profiles of Co,Cr, Mg, and Sc almost have the same tendency in all five ofthe sediment cores; the same extrema were obvious in thesame flood couplets. Generally, the average of each tracerconcentration in the five sediment cores had a similar tenden-cy except for P and Ti. The highest concentration of P oc-curred in the midstream portion (A3), the highest concentra-tion of Ti occurred in the A2, and both of them had no signif-icant increased/decreased trend; the other tracer (Co, Cr, Mg,Sc, and Y) concentrations increased gradually from upstream(A5) to downstream (A1). The concentration of the midstream(A3) was closer to the average of all of the sediment samples.

Figure 8 showed that the tracer elements Co, Cr, Mg and Scwere significantly positively correlated with the clay content,and the particle selectivity process would result in the selec-tive enrichment of fine-grained sediments and associated fin-gerprint properties from upstream to downstream in the checkdam (Owens et al. 2007). The other three tracers (P, Ti, and Y)had no obvious relationship with the clay content, indicatingthat the relationship between clay content and the three ele-ment concentrations is complex and difficult to generalize(Walling 2005).

3.3 Sediment source apportionment

The substantial variability in the contributions from the fivesources between the individual samples is a key feature of theresults. The calculated mean relative contribution of eachsource type to the sediment deposition in the check dam ofthe Hujiawan indicated that the primary sources were gullies,constituting 34.7 %, while the cropland contributed 28.2 % ofthe sediment, and forest and grassland contributed 21.5 and12.7 %, respectively. The fallow land contributed the leastsediment, with only 2.9 % of the total samples. The relativecontribution of each source type was variable in the five sed-iment cores. At sites A1–A5, the contribution rate to sedimentfor each site is presented in Table 3. Generally, compared to

Fig. 5 Correspondence analysis map between the sediment sourcecategories and seven tracers

Table 2 The optimum composite fingerprints for discriminatingindividual sediment source types in the Hujiawan catchment

Step Selectedtracerproperty

Cumulative sourcetype samples classifiedcorrectly (%)

Wilks’lambda

Source typesamples classifiedcorrectly (%)

1 Mg 49.4 0.372 49.4

2 Y 61.0 0.199 40.3

3 Ti 64.9 0.120 48.1

4 P 80.5 0.073 36.4

5 Sc 85.7 0.051 36.4

6 Co 90.9 0.033 42.9

7 Cr 92.2 0.023 45.5

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the average of the total samples, the relative contribution ofthe gully source is higher in the upstream portion (A4 and A5)and the downstream portion (A1) of the check dam and lower

in the midstream portion (A2 and A3); the relative contribu-tion of grassland is higher in the midstream portion (A2, A3,and A4) of the check dam and lower in the upstream portion

Fig. 6 Sediment particle size inthe five sediment cores, the clayand silt sediment fractionsincreasing gradually and the sandcontent gradually decreasing fromupstream (A5) to downstream(A1) in the check dam

Fig. 7 Tracer concentrations in the five sediment cores, the highestconcentration of P occurred in the A3, the highest concentration of Tioccurred in the A2, while the other tracer (Co, Cr, Mg, Sc and Y)

concentrations increased gradually from A5 to A1 (units are weights in% (P), ppm (Co, Cr, Mg, Ti, Y, and Sc))

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(A5) and downstream portion (A1); the relative contributionof forest is higher in the downstream portion (A1 and A2) ofthe check dam and lower in the upstream portion (A3, A4, andA5), and gradually increases in the direction of the runoffpathway; and the relative contribution of cropland is higherin the upstream portion (A3, A4, and A5) of the check damand lower in the downstream portion (A1 and A2), and thesediment that was derived from fallow land was very little. Asshown in Fig. 4, the five sediment cores are distributed alongthe check dam from upstream (core A5) to downstream (coreA1). Table 3 and Fig. 9 show the contribution rates from thepotential sources for all five cores.

In this catchment, land use was strongly related to topog-raphy; the forest and fallow land were located in areas wherethe landform is steep and far from the check dam (Fig. 2).Land use associated with specific topography has an acceler-ating or retarding impact on soil loss, particularly in hilly andgully areas on the Loess Plateau. An increasing trend in soilerosion with an increasing slope gradient was observed underthe same land-use type (Shi and Shao 2000; Sun et al. 2014).The gullies formed a complex network, and the gully densitywas 2.05 km/km2. The sediment that derived from upstreammay be deposited in the gully during transport. From the trib-utaries to the main gully, the sediment tends to be depositedwithin the gullies where the landform is flat or where the slope

gradient decreases and the width of the gully increases (Lei etal. 1998). The loose topsoil of the cropland was prone toerosion when erosive rainfall events occurred (Shi and Shao2000; Fu et al. 2009), and the majority of cropland in thisstudy area was located near the check dam (Figs. 2 and 3c).Thus, sediment that derived from cropland tended to travel astraight path to the check dam and exhibited a lesser tendencyto be deposited in gullies.

In this catchment, the results of composite fingerprintsshowed that the gully source constituted the largest propor-tion, and in Fig. 3a, the gully was bare without vegetation andhad obvious rill erosion; the most sediment would transportedfrom the gully slopes or by gravity erosion (Yang et al. 2006).Grassland could significantly reduce splash erosion by thekinetic energy of rainfall on the surface soil (Ludwig et al.2005; Fu et al. 2009); meanwhile, biological soil crusts(Moore and Singer 1990; Belnap 2003) and plant roots re-duced erosion by improving the soil’s physical properties,such as its structural stability and infiltrability (Valentin et al.2005). The fallow land was more lush than the natural grass-land and usually combined with shrub and grass, and the di-versity index was the highest except for the original wood-land; different land-use combinations had a better effect onreducing soil erosion than did a single land-use (Fu et al.2009). Therefore, fallow land contributed the least sediment

Fig. 8 The linear correlation of the clay content (x) with the tracer concentration (y)

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of the five sediment sources. In this catchment, the most prev-alent forest was artificial forest (R. pseudoacacia), and thediversity index was lower than that of fallow land andgrassland.

Usually, surface soils of forests are not likely to represent asignificant sediment source nor contribute much sediment(Walling et al. 1999; Collins et al. 2010, 2012a, b).However, the results of a few studies do not agree with thewidespread belief that undisturbed forests are areas of lowerosion (e.g., Ide et al. (2009), Mizugaki et al. (2008),Zimmermann et al. (2012)). In the studied catchment, the for-est contributed more than one-fifth of the sediment. Even in afew of the flood couplets, the sediment that derived fromforest contributed more than 60 %, e.g., sites A1-10, 17 and19; A2-9, 11 and 21; A3-1 and 16; A5-13 (Fig. 10). Anotherphenomenon was that, as the forest matured, the sediment thatderived from the forest gradually decreased, indicating thatmature forests had the lowest erosion rates (Fu et al. 2009).However, the fallow achieved a good water and soil conser-vation effect within a very short time and was maintained for along period.

With the large-scale implementation of converting slopecropland to artificial forestland and grassland, huge changesin land use and land cover have taken place since the end ofthe 1990s that would greatly influence the hydro-ecologicalenvironment in the Loess Plateau (Fu et al. 2003; Chen et al.2008), and these changes caused a series of influence both insoils and vegetation. Soil desiccation occurs widely in forestland (Wang et al. 2011) and results from the excessive deple-tion of deep soil water by artificial forest (Chen et al. 2008;Wang et al. 2011), e.g., 16-year-old R. pseudoacacia treesextended their roots to a depth of more than 5 m, with thehighest water consumption layer between 3.0 and 4.5 m(Shangguan 2007). Meanwhile, soil desiccation may affectplant evaporation and growth; it can undermine theanti-drought capability of deep-rooted plants and thus affectvegetation growth and natural succession, and result inmicro-climate drying and land productivity declining (Li2000; Huang et al. 2003; Shangguan 2007; Chen et al.2010). Long-term soil desiccation has negative impacts on bothhydrological conditions and the sustainable development ofvegetation production. Artificial woods grow well at first, but

Table 3 Comparisons (max contribution (Max), min contribution (Min), mean contribution (Mean), standard deviation (SD), coefficient variation(CV)) of the sediment contribution in the five cores

Core no. Indexes No. Max Min Mean SD CV (%)

A1 Gully (%) 19 62.8 3.5 39.4 14.6 37.0

Grassland (%) 34.3 0.0 9.5 10.9 110.4

Forest (%) 76.1 0.0 37.5 26.9 71.7

Fallow land (%) 50.2 0.0 5.6 14.6 262.1

Cropland (%) 38.2 0.0 7.9 12.7 159.5

A2 Gully (%) 21 53.2 0.0 27.5 17.2 62.4

Grassland (%) 33.3 1.7 15.7 9.5 60.6

Forest (%) 89.7 0.0 32.8 33.1 101.0

Fallow land (%) 56.9 0.0 3.9 13.3 341.2

Cropland (%) 62.4 0.0 20.1 22.9 113.9

A3 Gully (%) 16 81.2 0.0 26.9 24.4 90.6

Grassland (%) 47.2 0.0 15.2 14.5 95.7

Forest (%) 86.1 0.0 15.7 29.0 184.1

Fallow land (%) 0.0 0.0 0.0 0.0 –

Cropland (%) 69.3 0.0 42.2 22.1 52.5

A4 Gully (%) 16 52.4 19.5 40.7 9.0 22.1

Grassland (%) 34.1 0.0 15.1 10.3 68.0

Forest (%) 54.6 0.0 3.4 13.6 400.0

Fallow land (%) 1.8 0.0 0.1 0.45 400.0

Cropland (%) 60.6 0.0 40.7 15.4 37.8

A5 Gully (%) 13 93.7 2.4 41.7 21.1 50.6

Grassland (%) 27.7 0.0 6.5 8.9 135.7

Forest (%) 97.6 0.0 9.2 26.9 293.9

Fallow land (%) 31.3 0.0 4.6 9.4 205.2

Cropland (%) 63.3 0.0 38.0 21.2 55.7

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often degrade once the initial water supply has been exploited,further causing artificial forest degradation and resulting in asmall-aged-tree, a tree with very low trunk after growing up, inthe semi-arid loess hilly area (Li 2000). In this catchment,

non-native trees (R. pseudoacacia) were planted insingle-species plantations that were almost 15 years old, butthe canopy density was lower than the original woodland(Fig. 3e), indicating that the growth of the forest had been

Fig. 9 Profile for sediment contribution from A1 to A5

Fig. 10 Profile for forest sediment contribution from A1 to A5

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inhibited. In addition, afforestation decreased the number ofplant species at the afforestation site by an average of 52 %by the seventh year after planting (Cao et al. 2009); much ofthe herbaceous vegetation (i.e. grasses, forbs, and herbs) wasmanually removed from under the trees to promote tree growthby reducing competition for moisture (Cao et al. 2009), leadingto the understory to become sparse and the topsoil bare in theforest. Meanwhile, the leaves of R. pseudoacacia were verysmall, and the leaf litter was poorly developed, leading to theanti-erodibility of the R. pseudoacacia being the worst in thefour common artificial forest (Wang et al. 1993); these factorswould cause serious soil erosion (Miura et al. 2003; Fu et al.2009; Ide et al. 2009). Therefore, using non-native species waslikely to increase the risk of ecological degradation in this re-gion; the plants in the fallow land were natural species (i.e.,grasses, forbs, herbs and shrubs, Fig. 3d) and, would not in-crease the risk. In the Loess Plateau, water was replenished inthe soil mainly through three to four rainfall events of largeamount, short duration and high-intensity. Infiltration was asignificant process for the soil moisture budget. Soil that iscovered by natural fallow is often wetter and has a higherinfiltration rate than does artificial forest (Jia and Shao2013, 2014; Jia et al. 2013; Wang et al. 2013). Soil wateris not fully replenished from rainfall during the rainy sea-son in the forest (Mu et al. 2003; Chen et al. 2007, 2008),and soil desiccation may be worse. Meanwhile, the vastmajority of forest is located in areas with a large slope,where the infiltration potential into the soil generally de-crease with increasing slope gradient (Messing et al. 2003).These conditions induced the largest water loss to surfacerunoff (Chen et al. 2007), possibly indicating that the run-off of the forest would relatively increase and that, soilerosion would increase (Lei et al. 1998).

In addition, the kinetic energy of raindrops may have in-creased as the raindrops passed through the forest canopybecause the throughfall consisted of large drops, generatedas drips, and had a larger diameter; splash transport via rain-drop impact is considered to be the dominant process in sed-iment transport on bare surfaces (Miura et al. 2002; Nanko etal. 2008). Meanwhile, the average surface gradient in the for-est is greater than the average slope throughout the catchment;soil erosion becomes more intense as the slope increases (Shiand Shao 2000; Sun et al. 2014).

In this area, forest and fallow land contributed less sedi-ment than did cropland, indicating that both of these catch-ment management strategies could reduce soil erosion.Afforestation is a more suitable choice in areas where precip-itation is suitable, but in the vulnerable arid and semi-aridagricultural regions of the present study, it would take consid-erable research to identify suitable species. From the perspec-tive of the sediment source, reforestation remains a valuabletool, but natural fallow may be a better-designed sedimentmanagement and soil erosion-control strategy.

4 Conclusions

This study used a sediment source-tracing technique, incorpo-rating both statistically verified multi-component signaturesand a multivariate mixing model, which provided valuableinformation of the response of the main sediment sources inan agricultural catchment with artificial or non-native vegeta-tion. The results demonstrated that gully is the main sedimentsource in this catchment, constituting 34.7 %, while croplandcontributed 27.9 % of the sediment; forest and grassland con-tributed 21.7 and 12.7 %, respectively; and fallow land con-tributed the least sediment, with only 3.0 % of the total sam-ples. Changes in the hydro-ecological environment lead to theleaf litter and understory being poorly developed and the soilbeing bare in the forest, making it more vulnerable to erosion.In this area, forest and fallow land contributed less sedimentthan did cropland, indicating that both of these catchmentmanagement strategies could reduce soil erosion.Afforestation is a more suitable choice in areas where precip-itation is suitable, but in the vulnerable arid and semi-aridagricultural regions of the present study, it would take consid-erable research to identify suitable species. A natural fallowmay be a better-designed soil erosion-control strategy. From amanagement perspective, this study suggests that reforestationmay not be appropriate for landscape restoration in suchsemi-arid loess hilly areas while fallow may be highly recom-mended; it is very meaningful for the rational allocation ofcatchment management strategies and resources.

Acknowledgments Financial support for this research was provided bythe National Natural Science Foundation of China (41301294 and41525003), the West Light Foundation of the Chinese Academy of Sci-ences, and the Fundamental Research Funds for the Central Universities(2014YB053).

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