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Geomorphology 204 (2014) 532–541

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Geomorphology

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Simulated headward erosion of bank gullies in the Dry-hotValley Region of southwest China

Zhengan Su a, Donghong Xiong a,⁎, Yifan Dong a, Jiajia Li a,b, Dan Yang a, Jianhui Zhang a, Guangxiong He c

a Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, Chinab Department of Environmental Engineering, Chengdu University of Information Technology, Chengdu 610225, Chinac Institute of Tropical Eco-agricultural Sciences, Yunnan Academy of Agricultural Sciences, Yuanmou 651300, China

⁎ Corresponding author. Tel.: +86 28 8559 2865.E-mail address: [email protected] (D. Xiong).

0169-555X/$ – see front matter © 2013 Elsevier B.V. All rhttp://dx.doi.org/10.1016/j.geomorph.2013.08.033

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a r t i c l e i n f o
Article history:Received 30 January 2013Received in revised form 15 August 2013Accepted 31 August 2013Available online 8 September 2013

Keywords:Headward erosionLandform changeDEMFractal dimension

Although the development and migration of gully headcuts can increase soil loss and accelerate landscape deg-radation considerably, little attention has been paid to the spatiotemporal variations of the morphological char-acteristics of bank gully heads in the Dry-hot Valley Region of southwest China. This study explored the in-situvariations in soil loss rates and morphological characteristics in active bank gully heads, testing the overlandflowdischargewith a range of 30 to 120 L min−1. In response to thisflow, activelymigrating headcuts developedwith retreat rates ranging from2.6 to 7.9 mm h−1. All experimental runs resulted in a gradual increase in soil lossvolume, incision depth, and retreat distance over timedue to setflow rates. For the gully beds and upstreamareasof gully heads, the soil erosion rates were greatest at the beginning of each run and progressively decreased dur-ing the scouring test. Non-steady state soil erosion rates were observed in the headwall for the flow dischargelevels examined for this study. This was due to an abrupt slope collapse after long-term scouring effects. As deg-radation progressed, similar trends emerged for temporal variationswithin the fractal dimensions of topographicsurfaces, in both the gully heads and upstream areas. After the scouring was run for a period of 90 min, asymp-totic fractal dimensions of topographic surfaces were attained in the upstream areas and gully heads, suggestingthat steady state morphological characteristics had been realised. It should be noted that headwall collapses aretypically associated with a substantial increase in sediment yield where no other obvious change in morpholog-ical characteristics occurs in the headwall. Therefore, even though a significant difference in the soil erosion ratesand fractal dimensions of topographic surface values could be found between the bank gully heads and upstreamareas, the temporal variation in the morphological characteristics of bank gully heads was similar to those ob-served in upstream areas where ephemeral gullies developed.

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1. Introduction

Gully erosion has been recognised as one of the most important pro-cesses in sediment production and land degradation in a wide range ofenvironments (Chaplot et al., 2005; Valentin et al., 2005; Zhu, 2012).Soil sediment generated by gully erosion represents a minimum of 10%and up to 94% of the total sediment yield caused by water erosion(Bennett et al., 2000; Poesen et al., 2003), suggesting that soil loss on ag-ricultural lands and hillslopes as a result of gully erosion may greatly ex-ceed losses due to sheet and rill erosions. Moreover, the formation anddevelopment of gullies can substantially increase soil loss from agricul-tural lands and severely impact farm productivity (Bennett et al., 2000).Gully erosion also plays an important geomorphic and hydrologic rolein many parts of the world (Wells et al., 2009) including Europe(Nachtergaele et al., 2002; Kirkby et al., 2003; Giménez et al., 2009; DiStefano and Ferro, 2011), North America (Bennett et al., 2000; Bennett

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and Alonso, 2006; Galang et al., 2010), Asia (Zhu, 2012), Oceania (Bettset al., 2003; Hancock and Evans, 2006), and Africa (Oostwoud Wijdenesand Bryan, 2001). While ephemeral gullies (small erosion channels onagricultural fields formed by concentrated flows within topographicswales) have been intensively studied (Poesen et al., 2003; Gordonet al., 2007a), relatively few studies have dealtwith the headward erosionof bank gullies.

Some studies have suggested that bank gullies expand primarily byheadward erosion (Oostwoud Wijdenes et al., 1999, 2000). A headcutis a sudden change in bed elevation where excessive localised erosiontakes place and is due to the jet impact of overland flow from upstreamareas (Bennett and Alonso, 2006; Dey et al., 2007). As gully headsmoveupslope (retreat), headcut erosion releases sediment into channels andexposes new channel walls to erosion (Oostwoud Wijdenes and Bryan,2001). Furthermore, headcut erosion usually takes place when the sur-rounding surfaces are protected from erosion by cohesive or chemicallyhardened top layers or grass cover (Dey et al., 2007). Once headcut ero-sion has begun, it will not naturally cease until a threshold condition hasdeveloped (Kirkby et al., 2003). Hence, depending on the rates of re-treat, gully heads can be substantial sediment sources in catchments,

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Fig. 1. Location of the study area.

Fig. 2. Large-scale gully head situation at the Yuanmou Gully Erosion and Collapse Exper-imental Station operated by IMHE, CAS.

533Z. Su et al. / Geomorphology 204 (2014) 532–541

with subsequent harmful effects occurring downstream. To predict fu-ture erosion and sediment release and to implement effective measureto reduce gully expansion, it is important to understand erosion pro-cesses at gully heads.

In the Dry-hot Valley Region of the upper reaches of the YangtzeRiver, themagnitude of gully erosion results from a combination of ero-sive precipitation, steep slopes, and anthropogenic influence (Zhanget al., 2003). Erosion at bank gully heads has become a major concernfor land and river management in this region because it drastically re-duces arable land and yields abundant sediment that leads to degrada-tion in downstream reaches (Zhong, 2000). Despite the severity of thisproblem, erosion processes in bank gullies have not been well docu-mented in this region.

The impact of sediment transport is often underestimated due tocoarse elevation data taken from field measurements and the coarseresolution of the digital elevation model (DEM), from which terrain at-tributes can be derived (Ramos et al., 2008). Compared to the total sta-tion and real-time kinematic global positioning system (RTK-GPS),terrestrial laser scanning (TLS) provides the most efficient method for3-dimensional measurements and 3-dimensional image documenta-tion (Milan et al., 2007; Momm et al., 2011). Additionally, many exam-ples exist in literature describing how a coarse DEM resolution cancause an overall increase in erosion predictions and an underestimationof sediment deposition (Schoorl et al., 2000; Ramos et al., 2008; Mommet al., 2011). These studies concluded that increased soil redistributionwas found in smaller cell sizes because its landscape representationwas substantially more detailed. Therefore, determining the appropri-ate DEM cell size is important for studying erosion and deposition.

Despite the importance of gully headward erosion, there is limiteddata regarding the rates of soil loss and morphological changes at ac-tively gully heads. This study, therefore, investigates gully heads in thefield to (1) evaluate the accuracy and resolution of the DEMs used and(2) examine the temporal variations in gully head morphology andsoil erosion rates in response to a range of overland flows.

2. Material and methods

2.1. Study area

Experiments were carried out at the Yuanmou Gully Erosion andCollapse Experimental Station, a field station operated by the ChengduInstitute of Mountain Hazards and Environment (IMHE), Chinese Acad-emy of Sciences (CAS). The station is situated within Yuanmou County(lat 25°23′ N to 26°06′ N, long 101°35′ E to 102°06′ E). It is representa-tive of the Dry-hot Valley Region of southwest China, in particular theJinsha River Valley (Fig. 1). This mountainous region of southwestChina is an ecologically fragile zone, considered as one of themost diffi-cult areas to re-vegetate in the upper reaches of the Yangtze River(Zhang et al., 2003). It is characterised by a dry and hot climate, with amean annual precipitation of 634.0 mm, a mean annual temperatureof 21.8 °C, and an average annual potential evaporation of 3847.8 mm

image of Fig.�2

Table 1Distribution of data points for each gully position.ntot = total number of data points. ngen = number of data used for DEMgeneration.nval = number of data used for validation.D = datasampling density.

Landform Area(m2)

Pre-scouring operations Post-scouring operations

Number of observations D Number of observations D

ntot ngen nval (Point m−2) ntot ngen nval (Point m−2)

Gully 1 Headwall 1.03 31,847 30,255 1592 30,919 117,299 111,434 5865 113,883Gully bed 10.61 246,218 233,907 12,311 23,206 643,988 611,789 32,199 60,696Upstream area 23.13 276,872 263,028 13,844 11,970 369,869 351,376 18,493 15,991


Gully3 Headwall 1.05 48,832 46,390 2442 46,507 95,294 90,529 4765 90,756Gully bed 11.19 248,657 236,224 12,433 22,221 576,126 547,320 28,806 51,486Upstream area 22.74 294,222 279,511 14,711 12,939 456,483 433,659 22,824 20,074


534 Z. Su et al. / Geomorphology 204 (2014) 532–541

(Zhong, 2000). Dominant soils in the region are dry, red soil andvertisols, with a mean dry bulk density of 1.4 to 1.8 g cm−3. Zonal veg-etation type is tropical bushveld with a sparse distribution of trees.Dominant species are Heteropogon contortus and Dodonaea riscosa. Soilerosion is acute in the Yuanmou Dry-hot Valley, with a rate of1.64 × 104 t km−2 a−1. Gully erosion plays a dominant role in overallsoil erosion, with a gully density ranging from 3 to 5 km km−2.

2.2. Experimental procedure

To assess temporal variations in gully morphology, an active bankgully head was selected as the experimental site. A 6 m long gully bedand a 12 m long upstream area were divided into four sections andframedwith 2 mwide experimental platforms to control runoff (hereaf-ter referred to as gullies 1 to 4). In October 2011, four platforms of similardimensionswere trimmed, and a straight headwall was constructed. Theupstream area had a horizontal length of 11.89 m and amean slope gra-dient of 7.5°. The wall of the gully had a vertical height of 0.7 m with amean slope gradient of 90°, while the bed of the gully head had a hori-zontal length of 5.6 m with a mean slope gradient of 20° (Fig. 2). Soilsampling was carried out in March 2012 (dry season). Soil particle-sizefractions were analysed using the pipette method following H2O2 treat-ment to destroy organic matter and subsequent dispersion of soil sus-pensions by Na-hexametaphosphate (Nanjing Institute of Soil Science,Chinese Academy of Sciences, 1978). Soil texture in the upstream areawas sandy loam (FAO, 1988), composed of approximately 63% sand,

Table 2RMSE values determined for each DEM at each gully position for different cell sizes.

Value Landform Cell size and pre/post-scouring

0.1 m 0.05 m 0.0

Pre-scouring Post-scouring Pre-scouring Post-scouring Pre

Minimum Upstreamarea

0.006 0.006 0.006 0.005 0.0

Headwall 0.017 0.019 0.014 0.015 0.0Gully bed 0.014 0.018 0.010 0.011 0.0

Mean Upstreamarea

0.009 0.008 0.007 0.006 0.0


Maximum Upstreamarea

0.013 0.009 0.010 0.006 0.0


Standarddeviation

Upstreamarea

0.003 0.002 0.002 0.000 0.0


24% silt, and 13% clay. Soil texture in the gully heads was also sandyloam (FAO, 1988), composed of approximately 72% sand, 20% silt, and8% clay.

A series of simulated scouring tests was carried out throughout a 6-day period in August 2012, during which no precipitation fell. To main-tain a stable flow discharge, water was initially fed into a current stabi-liser that consisted of a sweep tee, equalisation pond, and flow metre.Flow discharges of 30, 60, 90, and 120 L min−1 were released ontothe soil bed in the upstream area byway of gullies 1, 2, 3, and 4, respec-tively. To prevent concentrated flows in the water pipe from directlyscouring the soil bed in the upstream area, water was transported to awater storage groove above the upstream area. The simulated scouringtest lasted 150 min. A collection groove was installed at the downslopeend of the gully bed to direct the runoff to a cylindrical sediment collec-tion tank,with a radius of 0.3 m and height of 0.4 m. A one-sixth aliquotof possible overflow from the sediment collection tank was separatedby a multi-slot divider and collected in a second 1 m3 tank. Followingthe scouring event, the gully head runoff was manually collected usinga 500 ml volumetric flask at the outlet of the collection groove at 5-minute intervals. Sediment samples were precipitated with alum,decanted with water, and oven dried at 105 °C prior to determiningthe soil sediment mass.

Riegl's TLS system LMS-Z420i was used to collect topographical datafor use in conjunction with a high-resolution DEM. The system is com-prised of a high-performance long-range 3-dimensional scanner andRiScan Pro, the accompanying operating and processing software. A

1 m 0.005 m 0.001 m

-scouring Post–scouring Pre-scouring Post-scouring Pre-scouring Post-scoring

04 0.004 0.003 0.004 0.003 0.004

10 0.009 0.008 0.008 0.007 0.00707 0.008 0.007 0.007 0.006 0.00705 0.005 0.004 0.004 0.005 0.004

11 0.010 0.009 0.010 0.008 0.00807 0.009 0.007 0.008 0.007 0.00707 0.005 0.006 0.005 0.006 0.005

11 0.011 0.010 0.012 0.009 0.00808 0.010 0.007 0.010 0.007 0.00801 0.001 0.001 0.001 0.001 0.001

00 0.001 0.001 0.002 0.001 0.00101 0.001 0.000 0.001 0.000 0.001

Table 3DEM accuracy for each DEM at each gully position for different cell sizes.

Value Landform Cell size and pre/post-scouring

0.1 m 0.05 m 0.01 m 0.005 m 0.001 m

Pre-scouring Post-scouring Pre-scouring Post-scouring Pre-scouring Post-scouring Pre-scouring Post-scouring Pre-scouring Post-scouring

Minimum Upstreamarea

0.006 0.006 0.006 0.006 0.004 0.004 0.004 0.004 0.004 0.004

Headwall 0.017 0.019 0.014 0.015 0.010 0.009 0.009 0.008 0.008 0.007Gully bed 0.014 0.019 0.010 0.011 0.007 0.008 0.007 0.007 0.006 0.007

Mean Upstreamarea

0.009 0.008 0.007 0.006 0.005 0.005 0.005 0.005 0.005 0.005


Maximum Upstreamarea

0.013 0.010 0.010 0.007 0.007 0.006 0.006 0.006 0.006 0.005


Standarddeviation

Upstreamarea

0.003 0.002 0.002 0.000 0.001 0.001 0.001 0.001 0.001 0.001



calibrated, high-resolution digital camera is also included. The instru-ment is a rugged and fully portable sensor that is specially designedfor the rapid acquisition of high-quality 3-dimensional images with awide field-of-view and a high maximum range as well as fast data pro-curement. The sensor records the time during which the near-infraredlaser is reflected from the incident surface. The resolution of angle mea-surement is 0.002° in a vertical direction and 0.0025° in a horizontaldirection.

Before scans were taken, 12 reflectors were positioned around thegully heads. They were tied into the project coordinate system using areal time kinematic GPS with a vertical accuracy of ±5 mm. They wereautomatically located by RiScan Pro and matched to the project

Table 4Multiple comparisons ofDEMaccuracy for different cell sizes using the least significant dif-ference (LSD) method.

Landform Cellsize 1

Cellsize 2

Meandifference

Standarderror

P 95% confidenceinterval

(m) (m) Lower Upper

Upstream area 0.1 0.05 0.002 0.001 0.007 0.001 0.0030.01 0.003 0.001 0.001 0.002 0.0050.005 0.003 0.001 0.001 0.002 0.0050.001 0.003 0.001 0.001 0.002 0.005

0.05 0.01 0.001 0.001 0.020 0.001 0.0030.005 0.001 0.001 0.020 0.001 0.0030.001 0.001 0.001 0.020 0.001 0.003

0.01 0.005 0.001 0.001 1.000 −0.001 0.0010.001 0.001 0.001 1.000 −0.001 0.001

0.005 0.001 0.000 0.001 1.000 −0.001 0.001Headwall 0.1 0.05 0.006 0.003 0.085 −0.001 0.012

0.01 0.014 0.003 0.002 0.008 0.0210.005 0.015 0.003 0.002 0.008 0.0220.001 0.017 0.003 0.001 0.010 0.024

0.05 0.01 0.009 0.003 0.017 0.002 0.0160.005 0.009 0.003 0.014 0.003 0.0160.001 0.011 0.003 0.007 0.005 0.018

0.01 0.005 0.000 0.003 0.853 −0.006 0.0070.001 0.003 0.003 0.375 −0.004 0.009

0.005 0.001 0.002 0.003 0.471 −0.005 0.009Gully bed 0.1 0.05 0.006 0.001 0.003 0.003 0.009

0.01 0.012 0.001 0.001 0.009 0.0150.005 0.013 0.001 0.001 0.010 0.0160.001 0.013 0.001 0.001 0.010 0.016

0.05 0.01 0.006 0.001 0.003 0.003 0.0090.005 0.007 0.001 0.002 0.004 0.0100.001 0.007 0.001 0.002 0.004 0.010

0.01 0.005 0.001 0.001 0.421 −0.002 0.0040.001 0.001 0.001 0.421 −0.002 0.004

0.005 0.001 0.000 0.001 1.000 −0.003 0.003

coordinates using a commonpoint configuration algorithm. Consequent-ly, multiple scans taken at different positions could be automaticallyplaced to create a cohesive point cloud that was based on the measuredbenchmarks. The topography of the upstream areas and gully heads wassurveyed prior to scouring and after each 30-minute scouring operationwith the TLS system. In this system, the acceptable standard deviation ofreflectors required for multiple scans is b2 mm in both the horizontaland vertical directions.

2.3. Data interpolation and DEM evaluation

Before scouring operations, the topographic data for each gully wereobtained from TLS measurements of N480,000 points, with a minimumdata sampling density of 10,496 point m−2 (Table 1). After performingfive scouring operations, the measured data were determined to begreater than onemillion points, with a minimum data sampling densityof 11,154 point m−2. All topographical data were loaded into GIS(ArcGIS 9.2). Data collected from TLSwere interpolated using a triangu-lated irregular network (TIN) from which DEMs were built (Mommet al., 2011).

To determine the most appropriate interpolation cell size to gener-ate DEMs, validation data (5% of the total data) were randomly selectedto estimate the rootmean square error (RMSE) for each generated DEM,as recommended by previous studies (Chaplot et al., 2006; Ramos et al.,2008). For this study, the TIN interpolation techniquewas used to createDEMswith a grid cell size of 0.1, 0.05, 0.01, 0.005, and 0.001 m. After theDEMs were created, RMSE for each generated DEM was obtained usingestimated altitudes (Z*) and observed altitudes (Z):

RMSE ¼ 1n

Xni¼1

Z�−Z� �2" #0:5

: ð1Þ

DEM accuracy was then evaluated using the standard deviation ofthe altitudes for each point (σz) and the RMSE:

DEM accuracy ¼ σz2 þ RMSE2� �0:5

: ð2Þ

Subsequently, the optimal cell size was selected to create DEMs.Student's t-tests for the paired difference in DEM accuracy were carriedout to evaluate differences in different cell sizes. Finally, DEM accuracyvalues were applied to eliminate DEM errors, using the reclassify algo-rithm provided by ArcGIS.


2.4. Fractal dimension of topographic surfaces

Fractal theory has been proposed for use to calculate the fractal di-mension of topographic surfaces (Mandelbrot, 1982; Cui et al., 2007).As suggested by Mandelbrot (1982), the fractal dimension (D) is de-fined as:

N x N xminð Þ ¼ αx−Dmin ð3Þ

where xmin is the linear size; N(x N xmin) is the number of objects withlinear sizes; and α is the constant. To calculate the fractal dimension,D, from topographic data, the following equation was derived fromEq. (3) by Cui et al. (2007):

A rð Þ ¼XN rð Þ

k¼1

Ak rð Þ∝r2−D ð4Þ

where r is the cell size (mm); A(r) is the surface area; N(r) is the gridnumber of the surface area; and Ak(r) is the surface area for cell size k.

This method computes the surface area of each cell size (Cui et al.,2007; Wang et al., 2008). By calculating the surface area of differentcell sizes (0.1, 0.05, 0.01, 0.005, and 0.001 m), the logarithmic correla-tion between the total area of the topographic surface and cell size isascertained and used to estimate the fractal dimension.

2.5. Landform changes of the gully head

The 3-dimensional coordinates (x, y, z) of the upstream areas,headwalls, and gully beds prior to scouring and after each 30 min ofscouring operation were used to derive DEMs, indicating soil losses or

Fig. 3. Variation of fractal dimensions under different experimental conditio

gains. The net soil loss volume can be derived from the pre- and post-scouring DEMs using ArcGIS.

3. Results

3.1. Accuracy of interpolation techniques used for altitude estimation

Table 2 provides RMSE for DEMs with different cell sizes. For thegully headwalls, RMSE ranged from 0.007 to 0.042 m, with an averageof the mean DEM accuracy being 0.015 m and an average of the stan-dard deviation being 0.003 m (Table 3). For the gully beds, RMSE rangedfrom 0.006 to 0.025 m, with an average of the mean DEM accuracybeing 0.012 m and an average of the standard deviation of 0.002 m.For the upstream areas, RMSE ranged from0.003 to 0.013 m,with an av-erage of the mean DEM accuracy being 0.006 m and an average of thestandard deviation being 0.001 m. The gully heads, including wallsand beds, exhibited higher RMSE and lower DEM accuracy values.

Grid cell size led to variations in DEMaccuracy (Table 3); the accura-cy decreased as grid cell size increased in each gully zone. Table 4 com-pares the accuracy of DEMs with different cell sizes. DEM accuracy forcell sizes 0.1 and 0.05 m was significantly less than those for cell sizes0.01, 0.005, and 0.001 m. No obvious differences in DEM accuracywere detected for cell sizes 0.01, 0.005, and 0.001 m. However, com-pared to DEM accuracy for cell size 0.05 m, a significantly lower DEMaccuracy was detected for cell size 0.1 m for both the upstream areasand gully walls. Although no significant difference in DEM accuracycould be found for cell sizes 0.01, 0.005, and 0.001 m, the 0.001 m cellsize performed the best. For this cell size, average DEM accuracy valueswere 0.008, 0.007, 0.005 m before the scouring operation in the gullywall, gully bed, and upstream area, respectively, and 0.008, 0.008,0.005 m after the five scouring operations, respectively. For these

ns. (a) 30 L min−1. (b) 60 L min−1. (c) 90 L min−1. (d) 120 L min−1.

image of Fig.�3


reasons, a cell size of 0.001 m was used to create the DEMs. Then, weeliminated the grids that had changes of altitude between pre- andpost-scouring that were less than the DEM accuracy values, whichwere determined with the reclassify algorithm provided by GIS. Inother words, we conservatively considered the grids that had changesof altitude between pre- and post- scouring that were more than theDEM accuracy values.

3.2. Variation of the fractal dimension of topographic surfaces

Even if the flow discharge increased from 30 to 120 L min−1, no sig-nificant increase in fractal dimension was observed during the experi-ments (Fig. 3). Furthermore, a similar trend in the temporal variationof the fractal dimensions of topographic surfaces was observed in theupstream areas, headwalls, and gully beds under different flow dis-charge treatments. During the first 60 min, the fractal dimensions of to-pographic surfaces gradually increased from 2.07 to 2.13, 2.04 to 2.08,2.09 to 2.15, and 2.08 to 2.12 in the upstream areas of gullies 1, 2, 3,and 4, respectively. Subsequently, the fractal dimensions of topographicsurfaces ranged from 2.06 to 2.09 in the upstream areas, from 2.26 to2.30 in the headwalls, and from 2.14 to 2.18 in the gully beds through-out the remaining 90 min. These results indicate that following an ini-tial period of morphological adjustment, steady state topography wasrealised for all of the experiments.

Significant differences occurred in the fractal dimension of topo-graphic surfaces between the bank gully heads and the upstreamareas. The fractal dimensions of the upstream areas ranged from 2.04to 2.15 (mean = 2.08); those of the gully beds ranged from 2.08 to2.23 (2.15); and those of the headwalls ranged from 2.22 to 2.33(2.26). These results revealed the highest fractal dimension in thegully walls and the lowest in the upstream areas. According to Wang

Fig. 4. Soil loss volume over time for different gully positi

et al. (2008), the fractal dimensions of hillslopes in the Chinese Loss Pla-teau range from 2.05 to 2.09, which are similar to our results for the up-stream areas.

3.3. Temporal and spatial variations in soil loss

Figs. 4, 5, and 6 show the temporal and spatial variations in the soilloss volume, incision depth, retreat distance, and soil erosion rateunder flow discharges of 30, 60, 90, and 120 L min−1. These four vari-ables gradually increasedwith an increase in flowdischarge. For the up-stream areas, the soil loss volume gradually increased from low valuesof 0.021, 0.036, 0.047, and 0.059 m3, during the initial stage for30 min, to high values of 0.035, 0.066, 0.091, and 0.136 m3, during thefinal 30 min of the experiments, under flow discharge rates of 30, 60,90, and 120 L min−1, respectively. For the gully headwalls, as the flowdischarge increased, the soil loss volume ranged from low values of0.002, 0.002, 0.003, and 0.005 m3, during the initial stage, to high valuesof 0.005, 0.011, 0.013, and 0.020 m3, during the final stage. The soil lossvolume in the gully beds also increased from low values of 0.054, 0.065,0.073, and 0.092 m3, during the initial stage, to high values of 0.085,0.159, 0.167, and 0.196 m3, during the final stage, under flow dischargerates of 30, 60, 90, and 120 L min−1, respectively. These results also in-dicate that the soil loss volumewasmuch higher in the gully heads thanin upstream areas.

As the experiment progressed, the vertical incision depth graduallyincreased in the upstream areas and gully beds (Fig. 5). After the150 minute scouring test concluded, the incision depth was muchhigher in the gully bed than in the upstream areas. For the headwall,the retreat distance also increased from low values of 2.0, 2.0, 3.3, and4.6 mm, during the initial stage (from 0 to 30 min), to high values of5.1, 11.1, 13.1, and 20.5 mm, during the final stage (from 120 to

ons. (a) Upstream area. (b) Headwall. (c) Gully bed.

image of Fig.�4


150 min), under flowdischarge rates of 30, 60, 90, and 120 L min−1, re-spectively. These results suggest that vertical incision is the dominantsoil erosion process within upstream areas, whereas vertical incisionand headcut retreat are the predominant erosion processes in thegully heads. Furthermore, these results confirm that headcut retreatplayed an important role in the morphological variation and sedimentyield in the bank gully head.

Although the incision depth increased as the experiment progressed,the soil erosion rate showed a power-function decline trend (P ≤ 0.05)in the upstream areas and gully beds (Fig. 6 and Table 5). Soil erosionrates in the upstream areas and gully beds were most dramatic duringthe initial stage of each run, just after the flow began (Fig. 6). However,following an initial period of rapid soil loss, steady state erosion condi-tions were attained in the upstream areas and gully beds. Time-averaged steady state soil erosion rates for these experiments were0.7, 1.5, 1.7, and 2.4 mm h−1 for the upstream areas and 7.6, 11.9,12.6, and 16.9 mm h−1 for the gully beds. For the headwalls, retreat dis-tances gradually increased as time progressed, whereas the soil erosionrate (retreat) did not show a power-function decline trend in theheadwall (P N 0.05). This outcome was due to the occurrence ofheadwall collapse at the final stage (from 120 to 150 min) under flowdischarges of 60, 90, and 120 L min−1. As the experiment progressedin gully 1, the retreat rate showed a power function decline trend(P b 0.05) in the headwall. Headwall time-averaged retreat rates were2.6, 3.5, 5.7, and 7.9 mm h−1 in gullies 1, 2, 3, and 4, respectively.These results also indicate that soil erosion rates were much lower inthe upstream areas than in the gully heads.

Similar to the soil erosion rate, the soil sediment concentration alsoshowed a declining trend over time (P b 0.01) in each gully head,which can be described by a power formula (Fig. 7). As the experimentprogressed, a steady state sediment concentrationwas attained for each

Fig. 5. Incision depth and retreat distance over time for different g

gully head. Additionally, the first five minutes in each flow test yieldedthe highest sediment concentration (Fig. 7). After fiveminutes of scour-ing, soil sediment remained at lower levels. These results suggest thatfollowing an initial period of morphological adjustment, gulliesdischarged sediment with a steady concentration.

4. Discussion

4.1. Temporal variation in gully head morphology

Previous experimental studies have shown systematic behaviour inheadcut erosion processes across a range of natural soils,flowdischarges,initial step heights, and bed slopes (Bennett et al., 2000; Bennett andCasali, 2001; Bennett and Alonso, 2006; Gordon et al., 2007b; Wellset al., 2009). Soil erosion rates and morphological changes were largestduring the initial stages of gully development (Bennett et al., 2000). Fol-lowing the initial period of morphological adjustment, the developedheadcuts migrated upstream at a constant rate, resulting in a nearly in-variant rate of sediment discharge (Wells et al., 2009). The presentstudy has confirmed that soil loss was fastest during the initial stage ofeach run, just after flow reached the gully bed. For all flow dischargesmeasured, steady soil erosion was also observed in the gully bed butnot in the headwall. However, the retreat rate of all gully walls graduallydecreased during the first 120 min. During the next 30 min, theheadwall collapse resulted in an abrupt increase in the retreat rateunder flowdischarge rates of 60, 90, and 120 L min−1, which is inconsis-tent with the result observed by Wells et al. (2009, 2010), who found aconstant rate, shape, and sediment discharge with headcut propagation.This difference could be ascribed to the undisturbed soil in this study butthe remoulded soil used by Wells et al. (2009, 2010).

ully positions. (a) Upstream area. (b) Headwall. (c) Gully bed.

image of Fig.�5

Table 5Erosion rates as a function of time for each position of the four gullies investigated.

Item Equation r F P N

Upstream area Gully 1 y = 17.68x−0.69 0.991 162.816 0.001 5Gully 2 y = 28.57x−0.64 0.988 127.106 0.001 5Gully 3 y = 36.39x−0.66 0.968 44.070 0.007 5Gully 4 y = 32.26x−0.56 0.935 20.968 0.020 5

Headwall Gully 1 y = 15.54x−0.41 0.967 43.015 0.007 5Gully 2 y = 3.347x0.005 0.039 0.004 0.951 5Gully 3 y = 13.47x−0.20 0.834 6.858 0.079 5Gully 4 y = 17.46x−0.18 0.638 2.055 0.247 5

Gully bed Gully 1 y = 185.5x−0.69 0.993 208.264 0.001 5Gully 2 y = 115.9x−0.49 0.973 53.333 0.005 5Gully 3 y = 141.5x−0.52 0.976 61.458 0.004 5Gully 4 y = 196.9x−0.54 0.994 258.996 0.001 5

Fig. 6. Soil erosion rates over time for different gully positions. (a) Upstream area. (b) Headwall. (c) Gully bed.


Similar temporal variations in the fractal dimension of topographicsurfaces were observed during headcut erosion. Asymptotic fractal di-mensions were observed in the gully heads after 90 min of the experi-ment, suggesting that the steady state was reached. The time taken toreach the steady state in this study (90 min) is considerably longerthan in the previous flume channel studies (120 s) reported byGordon et al. (2007b) andWells et al. (2009, 2010). This difference is as-cribed to the initial step height (i.e., 0.7 m in this study and b50 mm inthe previous flume channel study) and soil conditions (i.e., undisturbedsoil in this study and remoulded soil in previous flume channel studies).Bennett and Casali (2001) observed that steady-state headcut dimen-sions, sediment yields, and the slope of the sediment deposition in-creased as the initial step height increased. Previous studies (Bennettet al., 2000; Poesen et al., 2003) have shown that soil characteristics(i.e., soil bulk density, soil moisture) can have a significant impact onthe gully erosion process. Additionally, unlike obvious increases inheadcut retreat rates due to headwall collapse, it did not extensivelychange the morphological characteristics of gully headwalls in thefinal 30 min.

4.2. Comparison between gully heads and upstream areas

Results from this study suggest that the morphological variation inthe eroding bank gully heads was similar to that in the upstreamareas. First, ephemeral gully formation in both the upstream areas andthe gully heads were due to concentrated flow. Bennett et al. (2000)also reported that the formation of ephemeral gully headcutswas linkedto concentrated flow. Second, as headcuts migrated, incision and side-wall collapse occurred in the bank gully heads and upstream areas.The erosion of material near the bank toe widened ephemeral gulliesin the upstream areas, increasing channel bottom width. Bank collapse

also accounts for gully head development (Bennett et al., 2000; Alonsoet al., 2002; Wells et al., 2010). Third, similar topographical changeand sediment transport processes were observed in the bank gullyheads and upstream areas. Despite significant differences between theupstream areas and gully heads in terms of the fractal dimension valuesof topographic surfaces, temporal variations in the morphological char-acteristics in the upstream areas were similar to that in the headwallsand gully beds.

The experimental results presented here demonstrate that differentsoil loss volumes occur between the bank gully heads and the upstreamareas. Soil loss volume and the soil erosion rate were considerablyhigher in the bank gully heads than in the upstream areas. Vertical inci-sions were determined to be the dominant soil erosion process for theupstream areas, while both the vertical incision and headcut retreatwere dominant for the gully heads. Compared to the upstream areas,the headwalls and gully beds also had high fractal dimensions of

image of Fig.�6

Fig. 7.Variation of the soil sediment concentration of the gully head over time under different experimental conditions. (a) 30 L min−1; (b) 60 L min−1; (c) 90 L min−1; and (d) 120 L min−1.


topographic surfaces. These results indicate that soil sediment primarilyderives from active gully heads and that upstream areas play a muchsmaller role in the contribution of soil sediment andmorphological var-iation. Previous reconnaissance surveys have reported that soil erosionin gully heads produces large amounts of sediment, whichmay contrib-ute considerably to the load of local reservoirs (Oostwoud Wijdeneset al., 2000; Ghimire et al., 2006; Brooks et al., 2009).

5. Conclusions

Eroding gully heads form important links between hillslopes andchannels. They also function as zones where sediment is generated,transferred, or stored. This study sought to evaluate temporal variationsin the rates of soil loss and the morphological characteristics of in-situactively eroding gully heads. All of the experimental runs resulted in agradual increase in soil loss volume, incision depth, and retreat distanceover time. This was due to the set flow rate. However, in the upstreamareas and gully beds, the soil erosion rates declined exponentially(P ≤ 0.05). With regard to the flow discharge rates investigated bythis study, non-steady state soil erosion rates were observed in theheadwalls due to an abrupt collapse after persistent scouring. As degra-dation progressed, similar temporal variations in the fractal dimensionsof topographic surfaces were also observed in the gully heads and up-stream areas. After the scouring experiment was run for a period of90 min, asymptotic fractal dimensions of topographic surfaces wereattained in the upstream areas and gully heads, suggesting that steadystate morphological characteristics were realised. Therefore, eventhough a significant difference in the soil erosion rate and the fractal di-mensions of topographic surface values presented between the erodingbank gully heads and the upstream areas, the trend in the spatiotempo-ral variation of the morphological characteristics among actively erod-ing bank gully heads was similar to that of upstream areas whereephemeral gullies had developed.

Acknowledgements

Financial support for this study was provided by the Youth TalentTeam Programme of IMHE, CAS (Chinese Academy of Sciences)(SDSQB-2011-01), the Knowledge Innovation Program of CAS (KZCXZ-EW-QN317), the Western Light Program of Talents Cultivation of CAS(2011), the Special Foundation of President of CAS (2011), and theYouth Foundation of IMHE, CAS (2012).We are also grateful for the com-ments of Dr. Takashi Oguchi and the two anonymous reviewers.

Appendix A. Supplementary data

Supplementary data associated with this article can be found in theonline version, at http://dx.doi.org/10.1016/j.geomorph.2013.08.033.These data include Google maps of the most important areas describedin this article.

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