Journal of Hydrology 528 (2015) 238–248

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Journal of Hydrology

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Discharge and suspended sediment patterns in a small mountainouswatershed with widely distributed rock fragments

http://dx.doi.org/10.1016/j.jhydrol.2015.06.0460022-1694/� 2015 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +86 27 87288249; fax: +86 27 87671035.E-mail address: pengshi@mail.hzau.edu.cn (Z.H. Shi).

N.F. Fang a,b, Z.H. Shi c,⇑, F.X. Chen c, H.Y. Zhang a,b, Y.X. Wang b

a State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A & F University, Yangling 712100, PR Chinab Institute of Soil and Water Conservation of Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, PR Chinac College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, PR China

a r t i c l e i n f o

Article history:Received 30 April 2015Received in revised form 20 June 2015Accepted 20 June 2015Available online 27 June 2015This manuscript was handled by GeoffSyme, Editor-in-Chief

Keywords:Rock fragmentsSuspended sedimentDischargeMountainous watershedPartial least squares regression

s u m m a r y

Understanding and quantifying sediment loads is important in watersheds with highly erodible materi-als, which will eventually cause environmental and ecological problems. Within this context, suspendedsediment (SS) transport and its temporal dynamics were studied in a small mountainous watershed withsloping lands containing rock fragments in subtropical China. Soils containing rock fragments with manymacro-pores have a high permeability rate. Over a 7-year period, the mean runoff coefficient of thiswatershed was 0.65. Overall, 30 flood events were monitored and accounted for 95.5%, 27.3%, 17.1% ofthe total SS load, precipitation and total discharge, respectively, over a 5-year period. The presence of rockfragments in soils can affect soil loss. When comparing the soil loss in the studied watershed with that ofother watersheds under similar climatic conditions, rock fragments negatively affect soil loss. However,an extreme event occurred on 14 August 1990, and the sediment load exhibited a phenomenon called‘‘small deposits towards lump withdrawal’’, which resulted in a soil loss of 20,499 t (4.6 times the meanyearly soil loss). This event exhausted most of the SSs stored by the rock fragments on the slope and chan-nel. Following this event, the mean SS concentration (SSC) of the 11 events was 1.05 kg m�3, and the meanSSC of the 18 previous events was 1.75 kg m�3. Twelve variables were separated using the classical hydro-graph separation method. Partial least-squares regression (PLSR) was used to determine the highlyco-related variables of the discharge. The results indicated that PLSR could explain runoff well. The rela-tionship between discharge and SSC was highly scattered. During 24 flood events, three types of hystere-sis loops were observed: clockwise (17 events), figure-eight (3 events), and complex (4 events).

� 2015 Elsevier B.V. All rights reserved.

1. Introduction

Soil erosion is a natural geomorphic process that persistentlyoccurs on the earth’s surface. Problems associated with soil erosioninclude the loss of fertile agricultural topsoil, siltation of streamsand lakes, eutrophication of surface water bodies, and loss ofaquatic biodiversity (Onyando et al., 2005; Lu et al., 2007).Understanding and quantifying sediment load is important inwatersheds that drain highly erodible materials that eventuallycontribute to siltation in downstream reservoirs (López-Tarazónet al., 2009, 2010). In addition, SS data are essential for calibratingand validating numerical models aimed at reproducing previoussoil erosion and sediment dynamics and generating reliable datafor management purposes (Francke et al., 2008).

Rock fragments are widespread in subtropical mountainousareas. In developing countries, subtropical zones with adequaterainfall are often overexploited owing to economic and demo-graphic pressure. Cultivation on steeply sloping subtropical landscan cause serious soil erosion during intense rainfall (Fang et al.,2012). Soil rock fragments have a significant effect on hydrologicalprocess (Cousin et al., 2003; Zhu and Shao, 2008). Several previousstudies have focused on soils containing rock fragments. For exam-ple, Poesen and Ingelmo-Sanchez (1992) observed simulated rainin a laboratory and concluded that cover and the position of rockfragments can significantly affected the hydrological and erosionalresponses. Chen et al. (2011) investigated the spatial distributionof rock fragments on hillslopes in a subtropical region, and Wanget al. (2012) used a portable rainfall simulator in a subtropical areaand found that surface rock fragments can influence hydrologicalprocesses and soil loss from sloping farmland. Thus, rock fragmentscan affect soil hydrological and erosional processes (Poesen et al.,1999), influence soil water-holding capacity (Fiès et al., 2002;

N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248 239

Baetens et al., 2009), reduce evaporation from the soil surface(Meier and Hauer, 2010; Xie et al., 2010), and dominate soilhydraulic conductivity (Sauer and Logsdon, 2002). However, previ-ous studies of the effects of rock fragments on hydrology or soilerosion have mainly focused on plots or hill-slopes. Thus, SSdynamics are poorly understood for watersheds.

Several researchers have examined the temporal and spatialpatterns of sediment transfer in subtropical China at the plot andregional scales. For example, Lu and Higgitt (2001) estimated thedelivery of sediment to the Three Gorges Reservoir from its sur-rounding area. An annual soil loss of 157 million t has been esti-mated in the Three Gorges area, with 40 million t delivered tothe Yangtze River at a rate of 700 t km�2 y�1 (Lu and Higgitt,1998; Lu et al., 2003). Zhang et al. (2006) investigated the sedimentand runoff changes in the Yangtze Basin during the past 50 years.In addition, many other studies have evaluated the application ofhydrological models in subtropical China (e.g., Dawson et al.,2002; Zhu et al., 2007; Zhou et al., 2013). However, few systematicattempts have been made to examine the relationships betweendischarge and sediment yields and controlling variables at thesmall watershed scale. Knowledge of the sediment yield from smallwatersheds is critical for understanding the link between soil ero-sion processes on hillslopes and SS transport in large rivers(Rommens et al., 2006). Small watersheds provide a convenientscale for soil conservation planning because they can be easilyidentified on maps and on the ground and because they permitdetailed descriptions of ecosystem processes and capabilities(RWCSCB, 1998).

The objective of this paper is to analyze the discharge and SSdynamics in a mountainous watershed with sloping land and manyrock fragments. The first part of the analysis focuses on the dynam-ics of rainfall–runoff–sediment relationships at monthly and sea-sonal scales. Then, the runoff–sediment transport relationshipsand several hydrological variables are analyzed for each individualevent.

2. Study area and methods

2.1. Study area

This study was conducted in the Yunxigou watershed (31�520–31�550N., 111�110–111�150E.), which lies in Baokang County ofHubei Province, China, and covers 11.9 km2 (Fig. 1). Elevationswithin the watershed range from 289 to 1122 m. Areas with slopes>25� account for >52% of the total area. The soil parent materials inthis area mainly consist of sediments containing Cretaceous(145 ± 4–66 Ma) or Tertiary (66–2.58 Ma) carbonates. The mainsoil group in the study watershed is the weakly developed cinna-mon soil. In China, cinnamon soils cover 9.5 � 104 km2 (NSSB,1998). According to the USDA Soil Taxonomy, cinnamon soil isclassified as a semi-eluvial soil. The climate in this region is sub-tropical, with a mean temperature of 12 �C and a mean annual pre-cipitation of 915 mm, of which approximately 70% occurs betweenMay and September. Land use is mainly a function of elevation andtopography. Forests cover �23% of the area. The main agriculturalcrops in this watershed are maize (Zea mays L.) and wheat (Triticumaestivum L.)

2.2. Field and laboratory methods

Several instruments, including rain gauges (one manual and onetipping-bucket rain gauge), a water level stage recorder (continu-ous), and a silt sampler (metal type) were used to record rainfall,stream flow, and sediment load, respectively. The water stage datawere transformed into discharge data using a calibrated rating

curve (Franchini et al., 1999) that was obtained from periodic flowmeasurements. The SS concentrations (SSCs) were determinedusing the gravimetric method (e.g., Pavanelli and Bigi, 2005). Thewater samples were vacuum filtered, and the resulting residueswere oven-dried at 105 �C for 24 h. The weights of each of the driedresidues and the sample volumes were used to calculate the SSC(g m�3 or kg m�3). Next, the SS load was calculated from the SSCand water discharge data. Floods were identified when the increasein stream discharge exceeded 1.5 times the base flow recorded atthe beginning of the rainfall event (Lana-Renault et al., 2007).Rainfall was recorded once a day, and the flow of the gauge wasobserved twice a day at regular times. Daily rainfall and dischargedata were recorded between 1985 and 1993. The SSC samples werecollected between 1986 and 1992 during rainfall–runoff events.However, rainfall data recorded during rainfall events are onlyavailable for 1988–1992. All of these measurements were obtainedmanually. Because the rainfall records and SSs were collected sep-arately, the detailed rainfall records do not always match thedetailed discharge and SSC data. On several occasions, equipmentmalfunctions prevented complete monitoring during stormsevents. The rainfall, discharge, and SSCs were measured for 30storms between 1988 and 1992. During this period, the SSC andrainfall records are reasonably complete. These events accountedfor 95.5%, 27.3%, and 17.1% of the SS load, precipitation and totaldischarge, respectively.

2.3. Statistical analyses

Flood events were characterized using several variables(Nadal-Romero et al., 2008a) (Table 1).

R ¼ Q � BF ð1Þ

where R, Q, and BF represent the runoff, total discharge, and baseflow, respectively.

TLi ¼ SSCi � Q i ð2Þ

TL ¼Z n

1TLi ð3Þ

where TLi, SSCi, and Qi represent the total suspended sediment load,suspended sediment concentration, and discharge during time i,respectively.

Partial least-squares regression (PLSR) analyses were used inthis study. PLSR is a robust multivariate regression method thatallows users to perform a wide range of analyses (Martens andMartens, 2000) and is suitable for enhancing the selectivity of ana-lytical instruments. Details regarding the theory, principles, andapplication of PLSR can be found in the literature (Abdi, 2010;Shi et al., 2013). In this study, PLSR was performed usingSIMCA-P (Umetrics AB, Sweden).

A PLSR model was constructed to identify the main variablesthat are highly co-related to discharge. In a PLSR model, the impor-tance of a predictor for independent and dependent variables isgiven by the variable importance for the projection (VIP). Termswith large VIP values are the most relevant for explaining thedependent variable. To overcome the problem of over fitting, theappropriate number of components of each PLSR model was deter-mined using cross-validation, which achieves an optimal balancebetween the explained variation in the response (R2) and the pre-dictive ability of the model (goodness of prediction: Q2) (Shi et al.,2013).

Hysteresis patterns (Williams, 1989) are used to describe therelationship between SSC and Q. To avoid the influences of theabsolute Q and SSC quantities, we normalized both variablesaccording to the method of Aich et al. (2014), which was modified

Fig. 1. Location of the study watershed in Baokang in Hubei Province, China.

Table 1Flood variables and associated abbreviations used in the statistical analysis.

Variable Abbreviation Unit

Rainfall related variables Total precipitation P mmDuration D minMaximum 30 min rainfall depth I30 mmRunoff duration Dr mmAntecedent precipitation 1 day before A1P mmAntecedent precipitation 3 days before A3P mmAntecedent precipitation 7 days before A7P mm

Runoff related variables Base flow BF m3 s�1

Total discharge TQ m3 s�1

Flood peak discharge Qmax m3 s�1

Duration of runoff process Dr

Sediment related variables Maximum suspended sediment concentration SSCmax g m�3

Total suspended sediment load TL t

240 N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248

from the method of Lawler et al. (2006). The normalized variablescan be expressed as follows:

Q ni ¼ 1Qmax

Qi ð4Þ

SSCni ¼ 1SSCmax

SSCi ð5Þ

Fig. 2. Monthly mean rainfall, runoff, and suspended sediment transport in theYunxigou watershed (rainfall and runoff data are available for 1985–1993,suspended sediment data are available for 1986–1992) Note: the error bar is SD.

where Qni and SSCni are the normalized discharge and suspendedsediment concentration during a rainfall–runoff event, respectively,and Qi and SSCi are discharge and suspended sediment concentra-tion at time i, respectively. Descriptive statistics were obtainedusing the SPSS13.0 (2004) statistical software package with thevariables mentioned above.

Fig. 3. Daily precipitation, mean daily flow rate and sediment load for Yunxigou watershed during the monitoring period.

Fig. 4. Relationship between the SSC and Q for all samples in the study watershed.

N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248 241

3. Results

3.1. Seasonal, monthly, and daily rainfall–runoff–sediment transportrelationships

Fig. 2 shows the monthly mean rainfall and discharge for thehydrological years 1985–1993, and the SS load for 1986–1992 inthe Yunxigou watershed. During the study period, the mean totalannual rainfall was 915 mm, which was distributed between sum-mer (June, July, and August) (434 mm), spring (March, April, andMay) (284 mm), autumn (September, October, and November)(196 mm), and winter (December, January, and February)(73 mm). During the same period, the total runoff depths variedas follow: summer had the greatest runoff (233 mm), followedby spring, autumn, and winter with runoff depths of 160, 131,and 70 mm, respectively. The mean runoff coefficient was 0.65.

During the hydrological period of 1986–1992 (Fig. 3), most sed-iment yield occurred during the summer, with a mean load of4203 t, followed by spring and autumn, with total mean loads of166 and 94 t, respectively. Only a small sediment load occurredin the winter. The SS load was largely transported during summerand corresponded to high contributions from runoff and numerousfloods. Although some rainfall events occurred in winter during thestudy period, the soil conditions were generally dry and little run-off was generated, because large amounts of rainfall infiltrated intothe soil. The sediment was sparsely transported by base flow dur-ing the winter. The standard deviation of the SS load was very high,which indicates the complex and heterogeneous nature of thehydrological and sediment responses in the studied watershed.

3.2. SSC–Q relationships

In the absence of actual SSC measurements, hydrologists haveused sediment rating curves to estimate SSCs (Horowitz, 2003).The sediment rating curve approach has been widely used to dis-cuss relationships between Q and SSC (Walling, 1977; Horowitz,2003). A sediment rating curve describes the average relationshipbetween Q and SSC for a particular location (Sadeghi et al., 2008;Harrington and Harrington, 2013). Dozens of methods are availablefor developing sediment rating curves (e.g., Asselman, 2000;Horowitz, 2003; Phillips et al., 1999; Sadeghi et al., 2008;Schmidt and Morche, 2006). The most common relationship is alinear ordinary least-squares regression of the variables in loga-rithmic space to develop the following equation:

log SSC ¼ log aþ b log Q ð6Þ

where a and b are the constants of the linear regression. Someresearchers have presented other forms of the equation with thesame meaning (e.g., Ferguson, 1987; Asselman, 2000).

The non-linear model assumes constant variance (scatter) of thedependent variable (SSC), which typically does not occur becausethe scatter about the regression generally increases with increasingQ (Harrington and Harrington 2013). However, the bias of a regres-sion always exists for the curve, and a perfect fit cannot beobtained.

During the study years (1986–1992), a total of 356 SSC sampleswere collected (Fig 4). The SSC fluctuated between 4.5 and85,878 g m�3. An extreme event occurred on 14 August 1990.The SSCs of this event were considerably larger than those of theothers. Thus, the extreme SSCs of this event were excluded fromthe SSC–Q relationships analysis. This extreme event will be dis-cussed in more detail below.

The total SSC–Q relationships can be observed in Fig 4. Severalstudies have subdivided calibration data into sets of seasonal(e.g., wet/dry) or hydrological (e.g. rising limb/falling limb) group-ings to obtain improved functions (Schmidt and Morche, 2006). Inthis study, the rating curves were analyzed as rising limb and fall-ing limb rating curves (Fig. 5). The rising limb values were identi-fied as those for which the instantaneous Q value was greater thanthe previously recorded Q value, which included the starting valueand the highest value. The falling limb was generated from theremaining data. The rising limb rating curve resulted in an R2 of

Fig. 5. Relationship between the SSC and Q for (A) the rising period and (B) the falling period.

Table 2Statistical features of various rainfall events.

Date P (mm) D (min) I30 (mm) BF (m3 s�1) Qmax (m3 s�1) Dr (min) TQ (m3) SSCmax (g m�3) TL (t) P1D (mm) P3D (mm) P7D (mm)

1988/8/6 34.7 78 13.1 0.15 0.49 240 6393 3360 8.6 9.9 9.9 22.51988/8/8 41.3 1980 18.3 0.13 1.83 1920 115,516 1010 42.3 34.7 44.6 57.21988/8/12 10.0 558 8.5 0.13 0.22 960 11,228 7220 50.0 0 17.8 85.91988/8/13 24.8 192 10.0 0.79 1.84 960 73,038 24,780 142.4 10 10 86.01988/8/19 34.2 666 4.0 0.14 2.93 1440 149,699 580 52.9 0.3 0.3 39.11988/9/13 47.5 1270 4.9 0.32 7.17 2640 575,865 4960 379.8 0 0.5 45.51989/4/20 43 3060 1.9 0.28 1.84 2280 115,892 256 15.0 0 0 01989/5/9 27 1490 2.7 0.36 2.41 1410 107,984 370 19.0 0 20.1 20.11989/6/7 64 1638 7.8 0.11 6.68 1440 139,545 11,828 261.7 0 16.7 18.31989/6/26 29.2 1383 5.2 0.28 1.09 900 44,325 172 164.0 0 0 23.01989/7/10 84.4 2184 13 0.19 4.28 1530 317,730 2510 248.9 10 10.5 17.81989/7/26 45.1 2841 5.2 0.20 2.21 1920 163,436 307 766.8 0 0 7.61989/8/6 138.9 4320 11.3 0.21 3.91 4320 842,790 745 302.4 8.8 24.7 36.71989/8/17 16.8 1410 6.8 0.86 1.27 960 62,676 232 12.7 11.6 14.8 41.31990/5/21 17 933 2.3 0.36 1.49 2160 117,470 204 14.6 12.7 13.1 13.11990/6/30 55.4 960 4.5 0.17 4.80 5160 536,791 3051 312.5 36.9 37 37.01990/7/20 43.9 678 18.6 0.19 0.86 720 24,016 1209 10.9 2.6 6.4 12.91990/7/26 75.1 546 42 0.20 5.24 2220 83,841 33,902 778.7 0 0 46.31990/8/14 52.1 126 31.7 0.55 43.07 1020 690,530 85,872 20499.4 3.4 3.4 5.51991/6/1 23 696 4.4 0.32 2.94 1800 159,636 485 22.3 0 53.4 61.21991/6/12 45.1 900 8.8 0.19 1.69 2040 135,996 139 5.4 0 2.8 2.81991/6/14 18.6 715.2 6.1 0.57 1.79 2400 161,440 81 5.3 4.1 45.2 48.01991/6/29 37.8 2640 7.6 0.51 5.40 2160 393,070 3664 407.9 0 0 17.21991/7/22 58.8 165 26.9 0.15 7.89 1680 184,851 8563 492.7 0 0 3.91991/7/26 27.2 60 24.8 0.26 0.51 1920 42,726 3301 31.4 1.5 1.5 60.31991/7/28 38.8 138 26.4 0.24 1.01 582 27,679 1318 25.7 11.1 39.8 98.61991/8/2 21.5 199 2.4 0.32 1.99 1920 124,409 734 33.1 0 0 54.61991/8/4 49.4 655 10.9 0.63 8.43 1920 446,551 1104 243.2 6.9 28.4 71.91992/6/13 53.6 1120 10.0 0.08 11.93 2640 550,595 11,380 2292.5 76 76.4 76.41992/6/29 11.3 500 4.9 0.08 0.41 1080 15,312 3184 15.6 0.3 13.7 31.3

Max 138.9 4320.0 42.0 0.86 43.1 5160 842,790 85,872 20499.4 76.0 76.4 98.6Min 10.0 18.8 1.9 0.08 0.2 240 6393 81 5.3 0.0 0.0 0.0Mean 42.3 1123 11.5 0.30 4.6 1811 214,034 7217 921.9 8.0 16.4 38.1SD 25.7 1027 9.9 0.20 7.8 1014 223,832 16,674 3724.0 15.9 19.5 27.5

242 N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248

0.31 (n = 226, p < 0.001), and the falling limb rating curve resultedin an R2 = 0.44 (n = 126, p < 0.001).

3.3. Rainfall–runoff–sediment relationships for events

Sediment transport in mountainous streams is widely variableamong events (Lenzi and Marchi, 2000). Table 2 summarizes thegeneral characteristics of rainfall, discharge, and SS transport asso-ciated with the observed floods and the variables used in the sta-tistical analysis. The 40 events represented 27.3%, 17.1% and95.5% of the precipitation, runoff, and SS load, respectively,between 1988 and 1992. The maximum SS loads for a single floodeach year were 380, 767, 20,499, 493, and 2292 t, which repre-sented 32%, 34%, 94%, 20%, and 96%, respectively, of the total loadin each year.

Regarding the flood events, the maximum amount of precipita-tion for a single event was 138.9 mm (during the 6 August 1989

event). The total runoff generated by the rainfall varied from6393 to 84,2790 m3, with a mean value of 21,4034 m3. The peakdischarge fluctuated between 0.2 and 43.1 m3 s�1, and the baseflow level fluctuated from 0.1 to 0.9 m3 s�1. The mean SSC was1468 g m�3, with observed values ranging from 19 to15,144 g m�3. The maximum flood sediment concentrations variedfrom 81 to 85,872 g m�3, with a mean concentration of 7217 g m�3,and the total SS load transported by 14 floods exceeded 119 t (for aspecific SS yield of 10 t km�2).

3.4. PLSR models for discharge

Multivariate statistics are commonly used to identify the factorsthat control the dynamics of discharge or sediment yield duringhydrological processes (e.g., Estrany et al., 2010; Mayor et al.,2009; Nadal-Romero et al., 2008a; Jarvis et al., 2013;Rodríguez-Blanco et al., 2010). However, one issue with using

Fig. 6. Bivariate scatter plot matrix of selected event variables.

Table 3Summaries of the partial least-squares regression models.

Response variable Y R2 Q2 Component % of explained variability in Y (%) Cumulative explained variability in Y (%) RMSECVa (m3) Qcum2

Runoff 0.983 0.798 1 44.4 85.6 584.8 0.6212 8.9 98.3 212.6 0.7983 8.2 99.4 138.4 0.778

The bold-faced numerical values indicate the most appropriate number of components.a The RMSECV (cross-validated root mean-squared error), Qcum

2 (cross-validated goodness of prediction) per component, R2 (goodness of fit), and Q2 (cross-validatedgoodness of prediction) were calculated for the PLSR models.

Table 4VIP (variable importance for the projection) values and PLSR (partial least-squares regression) for runoffa.

P SSCmax D Qmax I30 BF Dr TL P7D P3D P1D

VIP 1.44 1.38 1.25 1.13 1.08 1.08 1.01 0.65 0.44 0.32 0.28Wã[1] 0.46 0.44 0.38 0.35 0.35 0.31 0.25 ã 0.14 ã 0.12 0.09 ã 0.08Wã[2] 0.25 0.26 0.39 ã 0.25 0.02 ã 0.38 ã 0.50 ã 0.44 ã 0.22 0.15 ã 0.14

Values > 1, which are show in bold and italics, indicate that the values are the most relevant for explaining the dependent variable.a Values > 0.3, which are shown in bold, indicate that the PLSR components are mainly weighted on the corresponding variables.

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244 N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248

conventional statistical methods to address relationships betweenvariables and runoff or sediment yield is multicollinearity. In thisstudy, the variables showed multicollinearity (Fig 6). To identifythe factors that might explain the measured hydrologicalresponses, we used PLSR.

The prediction error decreased as the number of componentsincreased, and the minimum and maximum Q2 values wereobtained with two components. An additional increase in the num-ber of components resulted in a higher prediction error, which sug-gested that the other components were not strongly correlatedwith the residuals of the predicted variable (Onderka et al.,2012). The first component explained 85.6% of the variance inthe data set in terms of the changes in runoff (Table 3). The addi-tion of the second component cumulatively explained 98.3% ofthe total variance. The results indicated that P, SSCmax, D, Qmax,I30, BL, and Dr were important for Q. Among these factors, TL andantecedent rainfall depth (P1D, P3D, and P7D) were not stronglycorrelated with Q (Table 4).

3.5. Sediment delivery process using SSC–Q hysteresis

The relationship between discharge and SS exhibits a highdegree of scatter in this study (Figs 4 and 5). The concept of thesediment delivery problem was introduced in the literature byWalling (1977). The SSC–Q hysteresis patterns result from the out-comes of the complex interactions between the processes and con-trols that determine discharge and sediment transport during anevent. According to Oeurng et al. (2010), only parts of the gross soilwithin a watershed can reach the outlet and be recorded as sedi-ment yield.

Fig. 7 shows an example of clockwise hysteresis in the water-shed. The event (13 September 1989) was generated by 47.5 mmof rainfall with a mean intensity of 4.9 mm h�1 and a maximumdischarge of 7.17 m3 s�1, which was 22 times higher than the BF.The SSCmax reached 4960 g m�3, and the total sediment load was379.73 t. Fig. 7 suggests that the decrease in the SSC was muchmore rapid than the decrease in discharge, which could indicaterapid depletion of sediment transported in the main channel.

Fig. 7. Example of hysteresis observed in the Yunxigou watershed.

Normalized Q–SSC graphs were drawn with linear axes for bothvariables. Twenty-four events provided an adequate number ofsamples to draw the SSC–Q graphs (Fig 8). In this study, mostrecorded events exhibited clockwise hysteresis loops (17), whichis the most common hysteresis class observed for small water-sheds in humid regions worldwide (Williams, 1989). Figure-eighthysteresis loops were also observed for three events (19 August1988, 7 June 1989, and 14 August 1990). These events presentopposite behaviors in which a positive loop occured for low dis-charge values, and a negative loop occured for high discharge val-ues (and vice versa). The last group of floods with complexhysteresis loops (four events) includes events with two clockwiseloops (three events) and clockwise plus figure eight loops thatoccurred on 13 June 1992. Three complex hysteresis loops can beconsidered, including two clockwise hysteresis loops. The hydro-graph shows two sediment peaks that coincide with two dischargepeaks, and the SSC decreased before Q in the falling limbs.

4. Discussion

4.1. Effects of rock fragments on discharge

The hydrological response of a watershed to a rainfall event isdetermined by several interacting factors that are highlyco-related to runoff generation (Castillo et al., 2003). The moun-tainous watershed has a high content of rock fragment (Fig 1).Rock fragments in the soil significantly affect hydrological pro-cesses, and result in increased infiltration (Zhu and Shao, 2008).The PLSR results show that most of the variables are highly corre-lated with discharge, except for the TL and the three antecedentcondition-related variables. The results of Nadal-Romero et al.(2008b) suggested that antecedent conditions (P1D and P7D) havelittle effect on the discharge or runoff coefficients obtained in asmall catchment (0.45 km2) in the central Spanish Pyrenees. A sim-ilar conclusion was reached for a large agricultural catchment(1110 km2) in southwest France (Oeurng et al., 2010). However,López-Tarazón et al. (2010) studied a mesoscale mountainouscatchment in the southern central Pyrenees and suggested thatthe total discharge is strongly correlated with P7D. A stony surfacefavors more rapid infiltration and deeper penetration of appliedwater (Poesen, 1990). Previous studies have noted reduced evapo-ration rates on soils covered by rock fragments (e.g., van Wesemaelet al., 1996; Meier and Hauer, 2010). Moreover, rock fragments sit-uated below the soil surface affect percolation rate, and thus, alsoinfiltration rate as well as runoff generation (Poesen and Lavee,1994). The mean runoff coefficient obtained for the Yunxigouwatershed was 0.65 over the 7-year period. The durations of therunoff processes in this study are limited, with an average durationof 1811 min compared with an average duration of rainfall of1123 min. Furthermore, the Qmax/BL of each event ranged from1.5 to 159 with a mean Qmax/BL of 20.4, which indicated that themean rainfall easily and quickly reached the outlet of the water-shed (Fig 9). Floods were generally flashy, with steep rising andfalling limbs of the hydrograph. The soils in the watershed aremainly weakly developed Cinnamon soils with rock fragments thatare full of macro pores. In addition, >52% of the total area slopes>25�. The results of this study confirmed that soils containing rockfragments full of macro-pores have high permeability rates. Thus,antecedent conditions have a small effect on discharge.

4.2. SSC–Q hysteresis loops

Several studies have shown that the SS in many streams aretransported during single floods and that the relationship betweenSSC and Q during floods is highly variable. The clockwise hysteresis

Fig. 8. Hysteresis loop of 24 events for the normalized SSC and Q.

Fig. 9. Dr and Qmax/BL for the 30 events.

N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248 245

pattern is the most common type of SS hysteresis (Hudson, 2003),and particularly occurs in small headwater catchments where flowpaths from the source areas of the sediments are short (de Boer andCampbell, 1989). In particular, a lag time between the peak of SSCand that of Q is often reported (Klein, 1984; Jeje et al., 1991;Slattery and Burt, 1997). The most common explanation for thisfinding is that a rapid depletion of the sediments available fortransport occurs before maximized water discharge (Williams,1989). This type of hysteresis can be related to the fast-responsecontribution from the sediments stored in the channel network(Lenzi and Marchi, 2000; Jansson, 2002). In the study watershed,flash floods, with steeply rising and falling hydrograph limbs anda similarly patterned sedigraph occurred. The shapes of the

hydrograph and sedigraph may suggest the importance ofHortonian flow in terms of runoff generation and sediment sources(Nadal-Romero et al., 2008b). Agricultural activities in the studywatershed were generally carried out in the summer season(June–August). Soil was eroded and then transported to the streamnetworks near the catchment outlet, exhibiting a clockwise pat-tern. Brasington and Richards (2000) suggested that sediment ispredominantly derived from sheet wash over hillslopes rather thanfrom riparian or channel erosion when precipitation intensity dur-ing a storm period is mirrored by the SSCs. Counterclockwiseshaped partial hysteresis loops were not observed independentlyduring our study period. However, these loops did occur duringfigure-eight and complex hysteresis events. Williams (1989)

246 N.F. Fang et al. / Journal of Hydrology 528 (2015) 238–248

concluded that counterclockwise hysteresis loops result from a dif-ference between the flood wave velocity and the mean flow veloc-ity that carries the SS or from high soil erodibility in combinationwith a prolonged erosion process. However, other studies havesuggested that counterclockwise loops are related to sedimentsources far from the main channel (Nadal-Romero et al., 2008a;López-Tarazón et al., 2009). In this study, complex hysteresis loopsare interpreted as a sequence of clockwise and counterclockwisepartial floods. Complex hysteresis loops include events with twoclockwise loops (three of four events) and clockwise plus figureeight loops. Complex hysteresis events coincide with long durationrainfall and several discharge peaks.

4.3. Effects of rock fragments on SS load

Many studies (e.g., Moustakas et al., 1995; Poesen et al., 1999)have discussed the effects of rock fragments on sediment yield.Poesen and Ingelmo-Sanchez (1992) concluded that rock frag-ments affect soil erosion as follows: (i) protecting the topsoil struc-ture and decrease the sediment detached by raindrop impact; (ii)decreasing runoff under certain conditions; and (iii) increasinghydraulic roughness and slowing down overland flow. The rolesof rock fragments in soil erosion by water at various slope scaleswere discussed by Poesen and Lavee (1994). Their results showedthe mean decrease of relative sediment yield with rock fragmentcover can be expressed using an exponential decay function forthe macroplots (101–104 m2). Cerdà (2001) concluded rock frag-ments coverage reduces the erosivity of rain and the runoff carriesrelatively little sediment. Kosmas et al. (1997) observed that cli-matic and soil conditions effected soil erosion rate more directlythen rock fragments at the watershed scale. However, because pre-vious conclusions have mainly focused on the slope scale, uncer-tainly remains at the watershed scale. In this study, onlysediment flux data from the watershed outlet were available.Thus, the within watershed hydro-erosional process remainsuncertain. However, the annual soil loss in the Yunxigou water-shed (375 t km�2 y�1) was lower than various watersheds undersimilar climatic conditions (watersheds located in thelower-and-middle section of the Yangtze River): Xu et al. (2013)calculated that the mean soil loss of the Yingwugou Watershed(1.86 km2) was 3140 t km�2 y�1. Fang et al. (2011) indicated thatthe mean soil loss of Wangjiaqiao watershed (16.7 km2) was8036 t km�2 y�1, and the annual soil loss of Pingtonghe(1067 km2) and Liushahe watersheds (1075 km2) were1674 t km�2 y�1 and 1510 t km�2 y�1, respectively (Zhang andRan, 1992). Furthermore, soil loss in the upper Du watershed(8973 km2) was 404 t km�2 y�1 (Yan et al., 2013). By comparingall of these studies, Yunxigou showed a smaller soil loss. Thus,we conclude that rock fragments have a negative effect on soil lossin the studied watershed.

Fig. 10. Hydrograph and sedigraph of the extreme event.

4.4. SS load of extreme event and the phenomenon of ‘‘small depositstowards lump withdrawal’’

The maximum load observed during a single flood occurred on14 August 1990 (20,499 t or 1722 t km�2). The TL of this eventwas 22 times the mean load for all 30 events and accounted for94% of the total TL in 1990. This flood was generated by a moderateP (P = 52.1 mm) over a very short period (126 min), resulting in apeak flood discharge of 43.1 m3 s�1, a total discharge of690,530 m3, a short Dr of 1020 min, and an SSCmax of85,872 g m�3 (Fig. 10). The results of Oeurng et al. (2010) showedthat a maximum load event contributed 40% of the TL for 17 eventsduring a 3-year study in a catchment in southwest France. The max-imum load of their study was 41,750 t with a mean SSC of1597 g m�3. The average TL and mean SSC for the 17 events were6182 t and 414 g m�3, respectively. A large proportion of soil parti-cles can be intercepted and temporarily stored by rock fragments(Poesen et al., 1994). Sediments stored on the slope or in the chan-nel and distributed within tributaries were transported after floodevents with sufficient transport capacity. During the extreme event,runoff occurred for 1020 min with a mean Q of 11.3 m3 s�1, andlarge amounts of SS were transported over a short time period.Before this event, the mean SSC of 18 events is 1.75 kg m�3, andafter the extreme event, the mean SSC of the other 11 eventsdecreased to 1.05 kg m�3. Particularly, three events just after theextreme event had the lowest mean SSC among all the events(0.14, 0.04 and 0.03 kg m�3). We concluded that the extreme eventexhausted most of the SS stored by rock fragments on the slope andin the channels. This phenomenon was called ‘‘small depositstowards lump withdrawal’’. The results clearly emphasize the vari-ability of runoff and sediment production in the studied watershed.

5. Conclusions

This paper describes the variability of SSs transported in theYunxigou watershed in subtropical China. The mean runoff coeffi-cient in this watershed was 0.65 over a 7-year period, and the SSwas mainly transported during the summer. PLSR was used toidentify the highly co-related variables of the discharge and toovercome the problem of multicollinearity. Soils containing rockfragments full of macro-pores caused a high permeability rateand the antecedent conditions show small effects on discharge.According to the peak sediment and discharge sequence, threetypes of SSC–Q relationships were obtained: clockwise (17 events),figure-eight (three events), and complex (four events). Rock frag-ments in soils have multiple effects on soil loss. Rock fragmentsprotect the topsoil structure and decrease the amount of detached,which negatively affect soil loss. However, for extreme events, theSS stored by rock fragments on the slope and in the channels couldbe exhausted over a short period. The results also show that theextreme event played a dominant role in determining the magni-tudes of soil loss in one year and influenced several years.

Acknowledgements

Financial support for this research was provided by the NationalNatural Science Foundation of China (41301294), the West LightFoundation of the Chinese Academy of Sciences, and theFundamental Research Funds for the Central Universities(2014YB053).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.jhydrol.2015.06.046.

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