Soil Surface Roughness Effects on Infiltration Processof a Cultivated Slopes on the Loess Plateau of China

Longshan Zhao & Linhua Wang & Xinlan Liang &

Jian Wang & Faqi Wu

Received: 2 June 2012 /Accepted: 14 August 2013 /Published online: 10 September 2013# Springer Science+Business Media Dordrecht 2013

Abstract Infiltration is the only way water enters soil on the cultivated slopes of the China’sLoess Plateau, so infiltration plays an important role in conserving soil moisture. The objective ofthis study was to investigate how a soil wetting front created by simulated rainfall migrated in soilwith different types of surface roughness. The three types of soil surface treatments studiedincluded surfaces of smooth, medium rough and rough soil. The results showed that, 1) comparedwith a smooth surface texture, medium rough and rough surface textures have a higher infiltrationcapacity; 2) the infiltration rate gradually decreases as the wetting front deepens and the rate tendsstabilize over time. This change could be described by a logarithmic function; 3) at the early stageof rainfall, the wetting front of medium rough and rough surface textures varied greatly, while thevariability of the wetting front decreases markedly after the infiltration rate stabilizes; 4) withincreasing depth of the wetting front, the similarity between the wetting front and soil surfaceprofile decreased significantly for the medium rough and rough surface textures. These resultsindicate that the process of infiltration on cultivated slopes on the Loess Plateau changed from anon-uniform pattern to a uniform pattern as time passed during a rainfall event. Overall, soils withrougher soil surfaces experienced a larger effect of roughness on the process of infiltration.

Keywords Geographic information system . Loess Plateau . Non-uniform infiltration .

Soil surface roughness

1 Introduction

Infiltration is a major process which transports precipitation, surface water, soil moisture andunderground water in the natural environment. From the perspective of hydraulics, infiltra-tion is the downward movement of soil moisture in unsaturated soil (Liu 1997; Yang et al.2004). Generally, infiltration proceeds based on Darcy’s Law (Philip 1991a, b).

Water Resour Manage (2013) 27:4759–4771DOI 10.1007/s11269-013-0428-7

L. Zhao : L. Wang : X. Liang : J. Wang : F. Wu (*)College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100,Chinae-mail: wufaqi@263.net

L. Zhaoe-mail: zls@nwsuaf.edu.cn

Many factors influence the infiltration capacity of soil, such as soil moisture content, soilcrust, enclosure, organic matter content, temperature, and water storage capacity of the landsurface (Hasegawa 1997; Fox et al. 1998a). More importantly, tillage also has conspicuousinfluence on the infiltration capacity of land-based soils (Góvers et al. 2000). Infiltration canbe divided into no-ponded infiltration and ponded infiltration based on the presence or absenceof standing water (Swartzendruber and Hogarth 1991). Rainfall infiltration is generally referredto as “non-ponded infiltration” if the land surface is smooth with no depressions. Conversely, ifthe land surface is rough and irregular, both types of infiltration co-exist after a rainfall event(Huang and Lee 2009; Gómez et al. 2009); that is, the infiltration mechanism of a rough surfaceis more complicated than that of a smooth surface.

Much work has been done to understand the influence of surface roughness on infiltrationduring recent decades. Ahuja et al. (1998) considered how soil management practices cansignificantly improve the infiltration rate of soil because they can increase soil porosity and changethe pore-size distribution. Likewise, several experiments have been done designed to understandwhy roughness can affect infiltration (Magunda et al. 1997; Gómez and Nearing 2005). Most ofthese studies proved that the storage of water in depressions is a key factor influencing infiltrationbecause depressions can retain a considerable amount of water and so increase infiltration ratesduring a rainfall event. Furthermore, Moldenhauer (1970) pointed out that surface sealing occurredless frequently on a rough surface during rainfall. Valentin (1991) suggested that the spatialvariability of crust morphology was another factor influencing infiltration capacity of tilled soilbecause he found that crust morphology was related to microtopography (i.e., depressions andmounds) in his experiments, a finding qualitatively confirmed by Fox et al. (1998a, b). Moreover,the effective hydraulic conductivity of soil is also an important effect factor explaining the effect ofroughness on infiltration rates of tilled soil (Falayi and Bouma 1975; Govindaraju et al. 2011).

The above discussion shows that soil roughness has a significant effect on infiltration andthat the mechanisms driving infiltration in rough surfaces is more complicated than that ofsmooth surfaces. Nevertheless, the information related to infiltration discussed above doesnot explain the process of infiltration in soil with a heterogeneous surface, because thesestudies did not consider characteristics of surface roughness, even though roughness is thefactor which most directly influences the infiltration of water into soil.

In addition, various researchers have tried to investigate the infiltration process throughstudying the dynamic changes of a wetting front during rainfall. Jackson (1992) showed thatthe migration of an infiltration wetting front is nearly vertical during a rainfall event for most soilsexcept those with high roughness. Polmann et al. (1991) showed that if we consider the effect ofsoil heterogeneity on a wetting front, then we can accurately predict water movement inunsaturated soils. By adopting the section-digging method, Zhang et al. (1996) observed thecharacteristics of an infiltration wetting front using loess soil during a rainfall event. Theyconcluded that the migration depth of a wetting front is positively related to the duration andintensity of rainfall. Moreover, the types of land use in an area influence the depth of a wettingfront (Warrick et al. 2004; Chang and Yeh 2010). Some studies also pointed out that the depth ofinfiltration differs greatly in different surface texture patterns (Youngs and Poulovassilis 1976).Philip (1991a) showed that the effect of surface texture pattern on infiltration should also beconsidered until the surface curvature is ten times greater than the infiltration depth. Thus thewetting front of infiltration plays an important role in studying the process of infiltration.However, past studies have rarely considered the effects of surface spatial heterogeneity on themigration of a wetting front. For example, the Green-Ampt (Loáiciga and Huang 2007), Philip(Philip 1991b) and Horton (Yang et al. 2004) models do not consider surface heterogeneity.Conversely, these models assume water infiltrates into soil uniformly (Beven 1984). Althoughsome scholars now recognize this issue and proposed some new and improved methods

4760 L. Zhao et al.

(Kerkides et al. 1997; Parhi et al. 2007; Kargas and Kerkides 2011; Ali et al. 2013), researchwhich specifically addresses the effect of surface roughness on a wetting front is rare.

The objective of this study was to investigate the migration characteristics of a wettingfront using a varied soil surface roughness during simulated rainfall.

2 Materials and Methods

2.1 Soil Properties

Top soil (0–20 cm depth) was collected from farm fields at Yangling, Shaanxi Province,China (34°17′56″N, 108°04′07″E) in May 2009. The fields had been continuously cultivatedfor more than 10 years. The soil was a Lou soil based on the Chinese classification system andan Udic Haplustalf soil based on the United States Department of Agriculture system. The soilwas analyzed using ISRIC/FAO methods (van Reeuwijl 2002) (Table 1).

2.2 Experimental Design

Air-dried soil was crushed and passed through a 5 mm sieve to ensure homogeneity. The soilwas then packed into a soil bin to a mean bulk density of 1.3 g cm−3, which is similar to thebulk density of this soil under field conditions. The soil surfaces were then prepared byadopting the most popular tillage practices in the agricultural production on cultivated slopesin the Loess Plateau to form a heterogeneous surface with different experimental soil surfaceroughness (SSR). The smooth surface texture was considered to be the control (Fig. 1).

The 2.0 m long, 1.0 m wide and 0.5 deep soil bin was used which had a long side oforganic glass (0.5 cm thickness) as a partition plate with measure marks at 0.5 cm intervals.Test gradients of 10° and 15° were used. Simulated rainfall was created in the rainfall hall ofthe State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Institute of Soil and Water Conservation, China. A rainfall intensity of 60 mm h–1 andduration of 90 min was tested. Each treatment was repeated three times. For each run, thesoil bin was prepared with fresh soil.

2.3 Methods

2.3.1 Measurement of Soil Moisture Content

Before and after rainfall, soil samples were collected using a small 2 cm diameter soil augerand with a vertical interval of 5 cm. The samples were then dried at 110 °C for about 24 h toeliminate water weight. The samples were cooled to room temperature and then weighed.These data were used for calculating the soil water content.

Table 1 Selected properties of the soil (0–20 cm depth)

Soiltype

Organicmatter

Water holdingcapacity

pH Textural analysis Soil texture

Sand Silt Clay

% %

Lou soil 1.176±0.13 21–23 8.62±0.06 57±0.85 18±1.68 25±2.51 Sandy clay loam

Soil Surface Roughness Effects on Infiltration Process 4761

2.3.2 Measurement of Wetting Fronts

Before rainfall, the initial soil surface profile (i.e. the projection of soil surface on the organicglass) was first drawn on the glass with a pencil. During the rainfall, the wetting front ofinfiltration was drawn on the glass surface every 10 min until the rain ended. After rainfall,these records were transcribed to a 1:1 coordinate paper and then scanned into a computerwith a scanner. Furthermore, these data were geometrically corrected and digitized usingArcGIS 9.3 software (ESRI Corp., Redlands, CA, USA) to obtain a set of vector graphs witheach vector graph consisting of nine curves (named as ‘wetting front curve’ in this study),which corresponded with the different depths of the wetting front during a rainfall event.These data were used to determine the spatial characteristics of the wetting front duringinfiltration.

2.3.3 Similarity Measurement of Soil Surface Profile and Each Wetting Front Curves

The Shape Context method, which has been widely used in model recognition and wasproposed by Belongie et al. (2002), was used to measure the similarity of each pair of curves.This paper adopted this method to calculate the similarity of the soil surface profile and thewetting front curve of differently sloped surfaces to study the characteristics and spatialvariation of the wetting front in soils with different soil surface roughness.

First, suppose the soil surface profile is expressed by a set of P={p1,p2,⋯,pn} and pi∈R2,and the wetting front curve is expressed by a set of G={g1,g2,⋯,gn} and gj∈R2; then thecalculation of the similarity between a point on the soil surface profile pi and a point gj on thewetting front curve is,

C pi; g j

� �¼ 1

2

Xk¼1

K hi kð Þ−g j kð Þh i2

hi kð Þ þ g j kð Þ ð1Þ

where C(pi,dj) refers to the similarity of the pi on the soil surface profile and dj on the wettingfront curve. The value of C(pi,gj) varies within the scope of 0–1. The smaller the value ofC(pi,gj) was, the greater the similarity of the two curves. hi(k) and gj(k) denote the K-binnormalized histogram at pi and dj after the χ2 test statistic for points of two curves,respectively.

Smooth Medium rough Rough

Fig. 1 Treatments of different slopes used in the experiments; the roughness differs with different soilmanagement practices

4762 L. Zhao et al.

Then, to determine the similarity of the soil surface profile and the wetting frontcurve, we computed the shape contexts of all points on the curve using the followingexpression (Bai et al. 2008):

SPg ¼ C P;Gð Þ ¼ 1

n

Xp∈P

argming∈G

C p; gð Þ þ 1

n

Xg∈G

argminp∈P

C p; gð Þ ð2Þ

where SPg refers to the similarity measurement of the soil surface profile and the wetting frontcurve.

3 Results and Discussion

3.1 Soil Moisture Content

The variability of soil moisture content has been studied extensively in the past. Table 2 showssoil moisture content of soils with different surface textures before and after simulated rainfall inthis study; this shows that soil moisture content increased markedly after rainfall for alltreatments. The maximum soil moisture content of smooth, median rough and rough surfacetextures were 36.88 %, 49.26 %, 38.69 %, respectively, which were 9–14 times higher than thesoil moisture content prior to simulated rainfall. However, after rainfall, the soil moisturecontent decreased with increasing depth of infiltration. Furthermore, the soil moisture contentof both medium rough and rough surface texture soils in the subsurface layer (15–20 cm and20–25 cm) were higher than that of the smooth surface texture. This suggests that morerainwater infiltrated into soil for both rough surface texture than the smooth surface textureover time. Our experiments clearly confirmed the conclusion that soil surface roughness canincrease infiltration during rainfall (Steichen 1984; Freebairn et al. 1989; Góvers et al. 2000;Guzha 2004). This occurs because the rough surface texture can retain much more rainwaterthroughout the rainfall than the smooth surface as a result of the depressions created in the roughsoil’s surface texture. As a result, the runoff and erosion were both significantly less in thesurfaces with roughness (Johnson et al. 1979; Steichen 1984).

3.2 Depth of the Wetting Front Under Different SSR

The migration of the wetting front reflects the dynamic changes of rainwater in soilthroughout the rainfall event. Table 3 provides statistics on the characteristics of the wettingfronts of slopes with smooth, medium rough and rough surfaces, including average, mini-mum, median, maximum, standard variance and coefficient of variation of the depth of thewetting fronts. The average depth of the wetting front on the smooth surface texture soil wasconspicuously smaller than that on the medium rough and rough surface texture soils.

On the 10° slope, the averagewetting front depths of smooth, medium rough and rough surfacetexture soils were 9.10, 9.64 and 19.06 cm, respectively. The rough surface texture wetting frontdepthwas at least 2 times greater than that on the smooth surface texture soil. On the 15° slope, theaverage wetting front depths of smooth and rough surface textures decreased somewhat, while themedium rough surface texture increased by 3.63 cm relative to those on the 10° slope. Moreover,the maximum depths of the wetting fronts of smooth, medium rough and rough surface textureswere 10.40, 14.36 and 21.05 cm, respectively. The minimum depths of the wetting fronts ofsmooth, medium rough and rough surface textures were 7.26, 7.85 and 12.34 cm, respectively.Clearly, the depth of the wetting front was rough >medium rough > smooth surface texture for the

Soil Surface Roughness Effects on Infiltration Process 4763

10° and 15° slopes. Theminimum value of the wetting front of the rough surface texture was evenlarger than the maximum value of the smooth surface texture soil.

The results of our experiments demonstrate that increased SSR had a marked effect onincreasing the infiltration depth of rainwater. Rainfall on a rougher sloped surface infiltratedto a greater depth. The authors conclude that the infiltration capacity of soil increases as theroughness of the surface increases. This is consistent with a previous review report by

Table 2 The changes of soil moisture content of sloping surfaces with different levels of roughness during arainfall event

SSR Depth(cm)

Slope 10° Slope 15°

Before rainfall After rainfall Before rainfall After rainfall

Soilwatercontent

Standarddeviation

Soilwatercontent

Standarddeviation

Soilwatercontent

Standarddeviation

Soilwatercontent

Standarddeviation

Smooth 0–5 4.29 % 0.001 36.88 % 0.031 3.86 % 0.001* 31.53 % 0.023

5–10 4.19 % 0.001* 28.07 % 0.032 3.86 % 0.001* 30.42 % 0.025

10–15 4.23 % 0.001* 20.03 % 0.027 3.81 % 0.001 23.85 % 0.034

15–20 4.29 % 0.001 5.64 % 0.001 3.79 % 0.001* 6.80 % 0.001

20–25 4.25 % 0.001* 4.25 % 0.001 3.82 % 0.001* 3.82 % 0.001

Mediumrough

0–5 3.51 % 0.001* 49.26 % 0.023 4.89 % 0.001 30.99 % 0.032

5–10 3.52 % 0.001* 48.27 % 0.010 4.89 % 0.001 31.01 % 0.037

10–15 3.51 % 0.001 44.13 % 0.029 4.91 % 0.001* 22.47 % 0.026

15–20 3.50 % 0.001* 41.74 % 0.017 4.78 % 0.001* 7.78 % 0.001

20–25 3.52 % 0.001* 15.05 % 0.009 4.85 % 0.001* 4.84 % 0.001

Rough 0–5 4.01 % 0.001 32.90 % 0.100 5.21 % 0.001 38.69 % 0.037

5–10 4.03 % 0.001 20.52 % 0.124 5.20 % 0.001* 32.37 % 0.016

10–15 4.01 % 0.001* 20.11 % 0.121 5.21 % 0.001* 31.43 % 0.013

15–20 3.98 % 0.001* 19.54 % 0.112 4.97 % 0.001 27.12 % 0.053

20–25 4.00 % 0.001* 20.25 % 0.004 5.12 % 0.001* 21.24 % 0.008

*indicates that is the actual data was <0.001

Table 3 Statistics related to wetting front depth for different combinations of soil roughness and slope

SSR Slope Wetting front depth

Average Maximum Minimum Median Standardvariance

Coefficientof variation

Cm

Smooth 10° 9.10 10.40 8.48 8.95 0.598 0.066

15° 8.41 9.88 7.26 8.39 0.917 0.109

Medium rough 10° 9.64 11.93 7.85 9.52 1.175 0.122

15° 13.00 14.36 11.19 13.10 0.989 0.076

Rough 10° 19.06 21.05 15.30 19.26 1.965 0.103

15° 13.74 15.37 12.34 13.82 0.811 0.059

4764 L. Zhao et al.

Góvers et al. (2000). In addition, in viewing the statistical results, the gradient of slope hadlittle influence on the depth of the wetting front, especially for medium rough and roughsurface textures. The increase in the gradient of the slope would result in a decrease in theeffectiveness of depression storage for the rough surface texture (Kamphorst et al. 2000;Borselli and Torri 2010). As a result, the amount of water available for infiltration declines,while runoff increases (Góvers et al. 2000; Darboux et al. 2002).

3.3 Characteristics of a Wetting Front Under Different SSR

Figure 2 shows the soil surface profile and how several wetting fronts (No. 1–9) migrated insoil at different times during the simulated rainfall event. The time interval between adjacentwetting fronts was 10 min. The arrowhead indicates the spatial tendency of the wetting front.

The differences in the distance which the wetting fronts had migrated in soils during thesame time period decreased gradually with increasing infiltration depth for all treatments. Bycomparing the nine wetting fronts, one can see that the characteristics of the wetting front ofboth the medium rough and rough surface textures was quite different from that of thesmooth surface texture soil. Also, the main differences in these wetting fronts are themigration distance of the wetting front in soil within the same rainfall time and the spatialtendency of the wetting front. The migration distance of the wetting front of the roughsurface texture was generally larger than the smooth surface texture soil. When the

No.9

No.1

Soil surface profile

a

b

c

No.1

No.1

No.9

No.9

Fig. 2 The variation tendencies of the wetting front curves (No. 1–9) of slopes under different amounts of soilsurface roughness (a. Smooth; b. Medium rough; c. Rough); the gradient is 10°

Soil Surface Roughness Effects on Infiltration Process 4765

intersection angle between the soil surface profile and the surface normal vectors (see thearrowhead in Fig. 2) was roughly equivalent to 90°, the tendency of the wetting front wasless influenced by the soil surface profile (Fig. 2a). If it was not 90°, then the spatialtendency of the wetting front was more influenced by the soil surface profile(Fig. 2b and c). This result suggests that SSR has an effect on how quickly and how deeplythe wetting front migrates.

Furthermore, the variability of the wetting fronts of medium rough and rough surfacetextures was more conspicuous than that of the smooth surface texture (Table 4). On the 10°slope, the difference in the wetting fronts of the soils with smooth surface texture exhibitedsome regularity with infiltration depth. For example, there was no significant difference inthe wetting fronts between No. 1 and 2, No. 3 and 4, No. 5 and 6, No. 7, 8 and 9. This resultsuggests that the wetting fronts of the smooth surface texture strongly tend to vary in asimilar manner in the direction perpendicular to the general slope. However, on the 15°slope, the variability between the wetting fronts appeared extremely irregular with increasinginfiltration depth. Moreover, for medium rough and rough surface textures, the complexityof variability among wetting fronts was greater than that of smooth surface texture.Therefore, the wetting fronts of the medium rough and rough surface textures have a highlevel of heterogeneity. This may be related to the rainwater stored in the surface depressions.Swartzendruber and Hogarth (1991) verified that the pressure head of water ponded on thesoil surface can increase water infiltration into soil. In our experiment, the depressions in thesoil surface retained water during the early stage of rainfall, while the mounds did not. As aresult, the actual infiltration rate in the areas of depressions was greater than that of themounds on the surface. However, over time sediment eroded from the mounds and wasdeposited in depressions forming depositional crusts in depressions. This behavior reducedthe infiltration capacity in depressions (Fox et al. 1998a). Moreover, the condition of themound surface was different from that of the depressions. The sediment detached byraindrops was transported immediately by overland flow. Therefore, no depositional crustformed but the surface did tend to seal with a structural crust. Based on studies by Dunneet al. (1991) and Fox et al. (1998b) the effect of this surface sealing on soil hydraulicconductivity was much lower than the effect of depositional crust. In addition, the depres-sion storage capacity rapidly decreased as a flow network was generated so that the entiresurface was soon contributing to runoff, which may be another reason for the loss ofdepressions (Darboux and Huang 2005).

Table 4 Correlation analysis ofwetting front curves (No. 1–9)with different soil surfaceroughness

Based on the Fisher’s least-significant-difference (LSD)method, 0.05 level

No. Smooth Medium rough Rough

10° 15° 10° 15° 10° 15°

1 a abcd a a a b

2 a A a ab bc bc

3 b abc ab abc bc a

4 b ab cd def c b

5 c cde bc cde b ab

6 c bcd cd ef c b

7 d ef bc f c b

8 d F d ef c c

9 d De cd bcd c c

4766 L. Zhao et al.

3.4 SSR Effects on Infiltration Rate

Figure 3 refers to the dynamic change of the infiltration rate with increasing infiltration depthunder different SSR conditions. The infiltration rate decreased with increasing infiltrationdepth and gradually stabilized when the three treatments were compared. With a 10° slope,the infiltration rate of smooth, medium rough and rough surface textures stabilized at 0.45,

Fig. 3 Features of changes in the infiltration rate with the depth of the wetting front under different soilsurface roughness conditions

Soil Surface Roughness Effects on Infiltration Process 4767

0.78 and 1.04 mm min−1, respectively. On the 15° slope, the infiltration rates of smooth,medium rough and rough surface textures stabilized at 0.45, 0.84 and 1.62 mm min−1,respectively (Table 5). This result showed that the rougher surface had a higher finalinfiltration rate than the smooth surface (Freebairn et al. 1989; Góvers et al. 2000).However, this contradicts the findings of Magunda et al. (1997) who observed the highinfiltration rate of the rough surface was maintained until runoff was initiated. That is, thefinal infiltration rate of the rough surface was at least no higher than that of the smoothsurface after runoff was generated; soil texture most likely caused this difference. TheKachwekano clay (51 % clay, 16 % silt and 33 % sand) and Renova silt loam (15 % clay,61 % silt and 14 % sand) used in their experiments were completely different from the soilused in our experiments (Table 1).

Least squares regression analysis showed that the relationship between the infiltrationrate and the depth of the wetting front could be described by a logarithmic function(Table 5). The general equation is: I=a ln (H)+b where a and b are coefficient regressions,I is infiltration rate (mm min−1) and H is the depth of wetting front (m).

3.5 Relationship of the Wetting Front to the Soil Surface Profile

The similarity of each wetting front and soil surface profile (hereafter similarity) wascomputed using a Shape Context method (Table 6). For the 10° slope, the average similar-ities of smooth, medium rough and rough surface textures were 0.19, 0.58 and 0.72,respectively. On the 15° slope, the average similarities of smooth, medium rough and roughsurface textures were 0.22, 0.57, and 0.65, respectively. These results indicate that thesimilarity on the rough surface textures (i.e., medium rough and rough surface textures) wereconspicuously lower than that of the smooth surface texture. For the smooth surface texture, thewetting front No. 9 had the highest similarity and No. 1 had the least. However, for the mediumrough and rough surface textures were similar even though wetting front No. 1 had the highestsimilarity, the wetting front with the least similarity was not at a constant depth.

Although many fluctuations were seen in the measure of similarity, the results showed anoverall decrease in similarity with increasing infiltration depth for the medium rough andrough surface textures. The morphology of the wetting front tended to flatten out eventually(see red box in Fig. 2). However, the similarity increased with increasing infiltration depthfor the smooth surface texture. This suggests that the variability of the wetting frontdecreased with increasing infiltration depth for the medium rough and rough surface

Table 5 Relationships between infiltration rate and infiltration depth under different soil surface roughnessconditions

SSR Slope Regression relation R2 Stable infiltration ratemm min−1

Smooth 10° I= −0.79 ln (H)+1.29 0.87 0.45

15° I= −0.28 ln (H)+0.06 0.41 0.45

Medium rough 10° I= −0.38 ln (H)−0.07 0.72 0.78

15° I= −0.40 ln (H)+0.36 0.58 0.84

Rough 10° I= −0.73 ln (H)+0.30 0.83 1.04

15° I= −0.85 ln (H)+0.05 0.80 1.62

I refers to the infiltration rate, L hm−2 s−1 ; H refers to the depth of wetting front, m; Stable infiltration raterefers to the infiltration rate which approached a constant value

4768 L. Zhao et al.

textures. In other words, the migration of the wetting front was mainly affected by soilproperties after it reaches a certain depth, rather than surface texture. Furthermore, thefluctuations in the similarity appear related to the change in surface roughness caused byraindrop impact and runoff transportation, because the changing in roughness, in turn, willaffect the surface hydrology such as flowpath, temporary depression storage and hydraulicresistance (Góvers et al. 2000; Darboux and Huang 2005).

4 Conclusions

Infiltration is a key step in the water cycle as water moves from precipitation through surfacewater and soil to ground water. Most soils are naturally environment porous media, but withthe long-term influence of natural and human activities, the various properties of soil are in aconstant state of change. Within a series of plots in an area, the soil moisture content,porosity, and particle positioning can differ greatly. Since soil is a borderless continuum,delineating the dividing lines between different categories of soil layers is difficult; there-fore, soil has certain degree of variability in space. Infiltration is a process by which watermoves within soil as the medium, so infiltration naturally has conspicuous spatial variability.

This study was designed to investigate the migration characteristics of a wetting front insoils with different surface roughness during a simulated rainfall event; the followingconclusion can be drawn from the results of this study. The migration process of a wettingfront can be observed during a rainfall event. Soil with a rough surface experienced muchgreater infiltration than soils with a smooth surface and the wetting front in rough soil alsovaried more during infiltration. The infiltration rate decreased with increasing depth of thewetting front and eventually tended to stabilize with increased depth. Regression analysisshowed that the relationship between the infiltration rate and the depth of the wetting frontcould be described by a logarithmic function.

Analysis of the similarity between the soil surface profile and a wetting front indicatesthat this relationship is very similar for smooth, medium rough and rough surface textures.The similarity of the rougher surface texture can be divided into three levels 1) During theinitial stage of a rainfall event, the similarity of the wetting front and the soil surface profile

Table 6 A comparison of characteristics comparing the wetting front curve and the soil surface profile

No. Smooth Medium rough Rough

Slope 10° Slope 15° Slope 10° Slope 15° Slope 10° Slope 15°

1 0.21 0.23 0.28 0.23 0.32 0.29

2 0.26 0.22 0.46 0.77 0.66 0.62

3 0.42 0.18 0.66 0.66 0.76 0.74

4 0.26 0.16 0.59 0.76 0.68 0.47

5 0.17 0.17 0.37 0.47 0.75 0.65

6 0.12 0.12 0.52 0.51 0.72 0.77

7 0.11 0.8 0.61 0.58 0.91 0.64

8 0.1 0.1 0.83 0.47 0.83 0.76

9 0.06 0.04 0.88 0.64 0.82 0.92

Average 0.19 0.22 0.58 0.57 0.72 0.65

Smaller numbers indicate higher similarity and vice versa

Soil Surface Roughness Effects on Infiltration Process 4769

is relatively high; 2) As rainfall continues, the variability of wetting front increases betweenthe different surface textures and the similarity between soils with different textures lessensand appears to fluctuate considerable; 3) As rainfall continued and the infiltration ratereached a final steady-state, the wetting front varied little with surface texture.

The above analysis indicates that infiltration process of cultivated slopes on the LoessPlateau changed from non-uniform infiltration to uniform during a rainfall event. SSR has asignificant effect on the formation of a wetting front, while the significance decreases withincreasing infiltration depth.

Acknowledgments This study was supported by the National Natural Science Foundation of China(41271288). The authors are thankful to experimenters in the State Key Laboratory of Dryland Agricultureand Soil Erosion on the Loess Plateau for their valuable assistance. We also thank two anonymous reviewersand the Editor for helpful comments.

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