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Temporal variability in rill erodibility for two types of grasslands.

Introduction

Accurate prediction of the seasonal patterns of rill erosion requires accurate estimation of temporal variability in rill erodibility (Knapen et al. 2007b). It is useful to relate rill erodibility to some easily measurable soil and vegetation properties so it can be independently estimated rather than determined through calibration in erosion models. Rill erodibility of concentrated flow erosion is influenced mainly by soil and vegetation properties, i.e. soil texture, moisture, consolidation, and root growth. Temporal variations in these factors probably lead to significant variability in rill erodibility, which is not yet fully quantified, especially for non-cultivated lands (King et al. 1995; Mamo and Bubenzer 2001; Knapen et al. 2007a).

In the Water Erosion Prediction Project (WEPP) model, rill erodibility is estimated as (Nearing et al. 1989):

[D.sub.c] = [K.sub.r] ([tau] - [[tau].sub.c]) (1)

where [D.sub.c] is the flow detachment capacity (kg [m.sup.-2] [s.sup.-1]), [K.sub.r] is the rill erodibility (s [m.sup.-1]), [tau] is the flow shear stress (Pa), and [[tau].sub.c] is the critical shear stress (Pa). When the measured soil detachment capacity by concentrated flow is plotted against flow shear stress, the slope of fitted linear line is the rill erodibility and the intercept on the x-axis is the critical shear stress (Flanagan et al. 2007).

Rill erodibility of concentrated flow is greatly influenced by soil properties. It decreases with increasing clay content, rock fragment cover, bulk density, stability of soil aggregates, and organic matter content (Norton and Brown 1992; Rapp 1998; Poesen et al. 1999; Bennett et al. 2000; Lei et al. 2002, 2006; Zhang et al. 2009). It increases as silt content increases (Knapen et al. 20076). The effect of soil moisture on rill erodibility is complex (Bryan 2000). Optimum soil-water content is assumed when maximum soil cohesion can develop and soils will have the lowest erodibility (Hanson and Cook 1999). The influence of soil moisture on rill erodibility is probably via the processes of slaking and the formation of micro-cracks, and is closely related to soil aggregate and structure stability (Kemper et al. 1985; Govers and Loch 1993).

Rill erodibility of concentrated flow is strongly affected by farming activities and by plant type and root systems. It varies widely with crop type and increases greatly with tillage, depending on the intensity or degree of disturbance on land surface (West et al. 1992; King et al. 1995; Zhang et al. 2009). Soil detachment capacity and thus rill erodibility reduce greatly with soil consolidation. Rill erodibility declines exponentially as the mass of buried residue increases (Flanagan et al. 2007). Root growth and its distribution also influence soil erodibility. The Kr value decreases significantly as an exponential function with increasing root density of crop or grass, though the exponents vary widely from -0.046 to -0.593 (Li et al. 1992; Mamo and Bubenzer 2001; De Baets et al. 2006, 2007; Gyssels et al. 2006; De Baets and Poesen 2010; Zhang et al. 2013). The impact of root system on soil detachment is related to root architecture. Grasses with fibrous root systems are more effective in reducing soil erosion than those with taproot systems. The function of root systems also varies with plant growth stage (Gyssels et al. 2006; De Baets and Poesen 2010).

Land use influences both soil and vegetation properties, and hence affects rill erodibility. Zhang et al. (2008) found that rill erodibility was affected greatly by land use. For five typical land uses in the Loess Plateau of China, [K.sub.r] varied from 0.0021 to 0.164 s [m.sup.-1]. Cropland had the highest rill erodibility, and followed by grassland, shrubland, wasteland, and woodland, respectively. After cropland was abandoned, rill erodibility decreased significantly because of biological crust formation, natural vegetation restoration, and soil consolidation. Values of Kr decreased gradually with restoration time and reached a steady stage after 28 years (Wang et al. 2013). Rill erodibility was also greatly influenced by vegetation restoration models. For five typical restoration models in the Loess Plateau, [K.sub.r] varied from 0.0013 to 0.0039 s [m.sup.-1], because of changes in soil cohesion, total porosity, and root mass density (Wang et al. 2014a).

Many soil and vegetation properties exhibit significant temporal or seasonal variations, which probably lead to temporal variation in soil detachment by concentrated flow and, hence rill erodibility. Nevertheless, the potential effects of temporal variation in soil properties and root systems on rill erodibility are not fully understood. Limited studies demonstrate that only a few soil properties are related to seasonal variation in rill erodibility. Nachtergaele and Poesen (2002) found that for a given loamy soil horizon, the detachment rate by concentrated flow during the year could be estimated reasonably well by flow shear stress and soil moisture. Seasonal variations in rill erodibility for a loess soil under two contrasting tillage practices were investigated during one growing season by Knapen et al. (2007a). The results showed that the temporal variation in rill erodibility could be mainly explained by variations in soil moisture, but consolidation effects, root growth, residue decomposition, and soil biological crust also had some effects. Zhang et al. (2009) demonstrated that soil detachment capacity of different land uses fluctuated considerably over time. Distinctive temporal variation in detachment capacity was detected throughout the growing season, and variations in soil detachment capacity differed significantly between land uses in most cases. The major factors affecting temporal variation in soil detachment were tillage operations, soil consolidation, and root growth. Nevertheless, the variation in rill erodibility was not tested because only one constant-flow shear stress was applied in their study (Zhang et al. 2009).

Planting grass or converting cropland for natural succession is widely used in China for soil and water conservation. Compared with cropland, the influencing factors and their mechanism on soil detachment by concentrated flow are likely changed in grassland. The effects of disturbance due to tillage practice on detachment processes vanish and the influences of near land surface characteristics (i.e. stem cover, litter, biological crust, and root system) increase (Zhang et al. 2009; Jiao et al. 2011; Wang et al. 2013, 2014b). Those changes likely cause differences in soil detachment and, thus, rill erodibility between cropland and grassland. However, few studies have quantified the processes of soil detachment by concentrated flow in grassland. Meanwhile, in grassland, large temporal variations in shoot cover, soil moisture, biological crust, and root biomass have been found in many studies (Garwood 1967; Makkonen and Helmisaari 1998; Bolinder et al. 2002; Fu et al. 2003; Neave and Rayburg 2007; Jimenez Aguilar et al. 2009; Nandintsetseg and Shinoda 2011), which probably lead to seasonal variation in rill erodibility of grassland. Nevertheless, the temporal variations in rill erodibility of grassland are not fully quantified. It is crucial to quantify the temporal variability in rill erodibility of grassland in order to better calibrate and validate process-based erosion models (i.e. WEPP).

This study was conducted to investigate temporal variations in rill erodibility of concentrated flow erosion, and to quantify the potential effects of root density on seasonal changes in rill erodibility using undisturbed soil samples taken from switchgrass (Panicum virgatum L.), smooth bromegrass (Bromus inermis Leyss.), and bare soil under a wide range of hydraulic conditions. The two grasses were selected for the study because, in China, switchgrass is a new biomass grass and smooth bromegrass is a typical grass used for soil and water conservation.

Materials and methods

The experiment was performed at the Fangshan Field Station (115[degrees]25'E, 39[degrees]35'N) of the Key Laboratory of Earth Surface Processes and Resources Ecology of Beijing Normal University. The climate is temperate monsoon. Mean annual precipitation is 600 mm, most of which occurs in summer. The soil is a Typic Haplustalf (Soil Survey Staff 2010) and contains (g [kg.sup.-1]): 163.0 clay, 470.0 silt, and 367.0 sand. The organic matter content was 0.8% and mean soil bulk density 1210 kg [m.sup.-3].

One-year-old switchgrass and smooth bromegrass growing in two plots (5 m long and 4 m wide) (Zhang et al. 2013) were selected to investigate temporal variation in rill erodibility. The grasses were planted in June 2010 at seed densities of 1150 and 600 [m.sup.-2], which are the recommended seed densities in the Beijing region for switchgrass and smooth bromegrass, respectively. To ensure that the root systems were well developed, especially in top 5 cm soil layer and to measure the whole growing season, the experiments were started in 2011. Precipitation during the testing period (April-October) was 514.4 mm and was typical for the Fangshan area (Fig. 1). Both grasses grew well during the measurement period. Collection of soil samples was at ~20-day intervals from 12 April to 28 October. Soil samples were also collected from one bare soil plot near the grasslands. During the experiment, soil samples were collected 10 times, with 35 soil samples from each plot each time. Thirty samples were used for measurement of soil detachment capacity; the remaining five samples were for determination of the mean water content of soil samples and were utilised further for calculation of soil detachment capacity. Altogether, 1050 soil samples were collected.

A detailed description of soil sample collection and preparation can be found in Zhang et al. (2003, 2008, 2009). Before collection of soil samples, the aboveground biomass was clipped at the surface. The undisturbed soil samples were collected from the top 5-cm layer with steel rings (9.8 cm diameter and 5.0 cm depth). The ring was gently pressed into the ground by hand, and the soil and roots surrounding the ring were sliced with a knife if necessary to ensure that soil core was not disturbed. When the ring rim was flush with the soil surface, it was excavated, cushioned with cotton cloth, and capped. Then the sample was turned over and the bottom was trimmed flush with the rim, cushioned, and capped (Zhang et al. 2003). To ensure the same soil moisture, the collected samples were wetted for 8h in a container. The water depth was raised in five incremental steps and the final water depth was 1 cm below the sample surface. Samples were drained for 12 h and weighed. Five samples were oven-dried, and the mean soil water content was used to calculate the initial dry soil masses of the other 30 samples for scouring tests, assuming soil moisture was homogeneous among all samples.

The flume used in this study has been described previously (Zhang et al. 2013) therefore, only the main features are outlined here. The flume was 5.0 m long and 0.4 m wide and the slope could be adjusted from 0% to 60%. Soil collected from the bare plot was glued on the flume-bed; hence, the roughness of the flume-bed was similar to that of soil samples and kept constant during the experiment. Flow rate was controlled by a series of valves and was measured with plastic buckets and a volumetric cylinder at the flume outlet. After the flow became stable, flow depth was measured with a digital level probe (SX40-A; Chongqing Hydrological Equipment Factory, Chongqing, China) 0.6 m from the flume outlet. For each combination of flow rate and slope gradient, 12 depths were measured. The maximum and minimum readings were eliminated to reduce the random error. The average of the remaining 10 depths was considered as the mean flow depth (Table 1). The ratios of water width to depth ranged from 72.5 to 112.1 and the sidewall effect was negligible. Consequently, the mean flow depth was used directly to compute flow shear stress (Zhang et al. 2003):

[tau] = [rho]gHS (2)

where [tau] is the shear stress (Pa), [rho] is the water mass density (kg [m.sup.-3]), g is the gravity constant (m [s.sup.-2]), H is the measured mean flow depth (m), and S is the sine of the flume-bed slope. Six combinations of slope gradients and flow rates were used to get six different shear stresses (Table 1).

Immediately before the test, the drained soil sample was placed in a 10-cm hole in the flume-bed, located 0.5 m above the flume outlet, keeping the sample surface even with the flume-bed. The test period was adjusted to keep a similar scouring depth (2 cm) of the soil samples to minimise the influence of uneven detachment within the sample ring (Zhang et al. 2003). Soil detachment capacity ([D.sub.c], kg [m.sup.-2] [s.sup.-1]) was defined as the mass per unit area per unit time and was computed as the total soil loss (initial weight of wetted soil sample minus the weight of water within the sample, and then minus the final oven-dried mass) divided by the sectional area of the soil sample and the test duration (Zhang et al. 2003). Five soil samples were tested under each shear stress for each plot, and the mean was considered as the soil detachment capacity under that flow shear stress.

One 1-mm sieve was installed below the flume outlet to collect eroded roots during the test, which, in general, were very few. After the test, the soil sample was oven-dried at 105[degrees]C for 12 h and weighed to determine the final oven-dry mass. The roots within the soil sample were washed over a sieve (1 mm) and oven-dried at 65[degrees]C for 12 h along with the roots collected during the test. The dry roots were weighed and the root mass density (RD, kg [m.sup.-3]) was calculated. The average root density of five soil samples tested under the same flow shear stress was considered as the mean root density and used for further analysis.

A paired-sample t-test was used to detect the differences in rill erodibility between land uses. Simple regression was used to analyse the relationship between rill erodibility and root density. Nonlinear regression was used to quantify the relationship between grassland rill credibility and bare soil rill credibility and root density. The regression results were evaluated by the coefficient of determination.

Results and discussion

The measured soil detachment capacities were plotted against flow shear stress as described in the WEPP model (Nearing et al. 1989; Flanagan et al. 2007) to estimate the rill credibility (Fig. 2). Taking the data of 1 October as an example, the fitted rill credibilities of switchgrass, smooth bromegrass, and bare soil were 0.0018, 0.0006 and 0.0829 s [m.sup.-1] respectively. The temporal variation in rill credibilities of different land uses is shown in Fig. 3. It is clear from Fig. 3 that rill credibilities of each land use fluctuated greatly over time. The statistical properties of Kr are given in Table 2. The rill credibility of bare soil was greatest among three land uses. The [K.sub.r] of the bare soil was 13.2 times greater than that of switchgrass and 19.6 times greater than smooth bromegrass, implying that the bare soil was much more erodible than grasslands. Smooth bromegrass was more resistant to flow scouring than switchgrass, and its mean [K.sub.r], 0.0035 s [m.sup.-1], was only 67.3% that of switchgrass. The greatest fluctuation in rill credibility was found in smooth bromegrass. The ratio of maximum to minimum rill credibility was 26 and the coefficient of variation (CV) was 1.17. The lowest fluctuation was detected in bare soil, and the ratio of maximum to minimum [K.sub.r] and CV were 7.5 and 0.43, respectively. This result revealed that the rill credibility of bare soil was less variable than that of grassland during the measured period 12 April-20 October in the study region.

Distinctive temporal variation patterns in rill credibility were found throughout the measurement period between land uses (Fig. 3). Rill credibility in switchgrass was relatively low in mid-April, increased rapidly in May, and reached a maximum in early June. It then declined quickly with grass growth and gradually approached a minimum in mid-September. It increased slightly in October. The rill credibility of smooth bromegrass was at maximum in mid-April and decreased sharply in May. It increased greatly in mid-June and then declined until the end of the measurement period. For the bare soil, rill credibility was relatively high in the mid-April and declined gradually in May, June, and July. It reached a minimum at early August, then increased again and continued to the end of the measurement period. Temporal variation in rill credibility was influenced greatly by land use. Paired-samples t-test showed that rill credibility of grasslands differed significantly (P<0.()5) from bare soil. However, no significant difference was found between the two grasslands (Table 3). Temporal variation in rill credibility was probably influenced by root growth, dynamic of soil water content, crust development, and consolidation (Makkonen and Helmisaari 1998; Nachtergaele and Pocsen 2002; Knapen et al. 2007a; Zhang et al. 2009; Nandintsetseg and Shinoda 2011). The differences in temporal variation of rill credibility between grassland and bare soil were probably caused by root growth and soil crust development. A 3-5-mm crust was developed on land surface of bare soil in early August produced by raindrop impact. However, because of the protection of grass cover, no obvious crust was found on the land surface of grassland. Further studies are needed to quantify the potential effects of crust development on temporal variation in rill credibility of bare soil.

Soil detachment by concentrated flow was significantly influenced by root density and declined exponentially with root density. Soil resistance increased as root density increased, especially for the fibrous root system. For the present study, rill credibility declined with increasing root density for both grasslands (Fig. 4). For switchgrass:

[K.sub.rsw] = 0.020[e.sup.-255RD], [R.sup.2] = 0.820 (3)

where [K.sub.rsw] is the rill credibility of switchgrass (s [m.sup.-1]), and RD is the root mass density (kg [m.sup.-3]). For rill credibility of smooth bromegrass ([K.sub.rsm]):

[K.sub.rsm] = 0.057[e.sup.-0.998RD] [R.sup.2] = 0.833 (4)

This result is consistent with the conclusions of previous studies (De Baets et al. 2007; Knapen et al. 2007a). Comparison of the two curves in Fig. 4 shows that the role of the root system in increasing soil resistance was greater for smooth bromegrass than switchgrass. This difference was probably related to the architecture of root system, as discussed in some other studies (Gysscls et al. 2006; De Baets et al. 2007; Knapen et al. 20076). Generally, the effect of root system on soil detachment declined with increasing root diameter (De Baets and Poesen 2010; Burylo et al. 2012). The mean root diameter of switchgrass (0.422 mm) was four times that of smooth bromegrass (0.103 mm). For a given root mass density, the contact area between root and soil was less for switchgrass than for smooth bromegrass. Thus, the role of roots of smooth bromegrass was greater than that of switchgrass. This result implied that the root length density was probably a better predictor than the root mass density to simulate the effects of root system on rill credibility. However, it was very difficult to measure the whole length of grass, particularly for smooth bromegrass, which was very fine. Therefore, the effect of root length density on rill credibility was not quantified in this study.

The temporal variation in rill credibility ratios between grasslands and bare soil also fluctuated greatly over the study period (Fig. 5) due to the combined effects of temporal variation in root density, dry and wet cycle (Fig. 1), and soil crust. The mean values were 0.083 and 0.048 for switchgrass and smooth bromegrass. The rill credibility ratios between grasslands and bare soil varied significantly for all cases for both switchgrass and smooth bromegrass. To develop process-based soil erosion models (i.e. WEPP), an adjustment coefficient or equation for grassland was necessary to simulate concentrated flow erosion based on the observed rill erodibility of bare soil (Flanagan el al. 2007). Then, the relationship in rill erodibility between grassland and bare soil was plotted and analysed. As shown in Fig. 6, the ratio between grassland and bare soil rill erodibility decreased with root density for both grasses. The best fit for switchgrass was:

[K.sub.rsw] = [K.sub.rbs] [e.sup.-0671RD], [R.sup.2] = 0.919 (5)

and for smooth bromegrass:

[K.sub.rsm] = [K.sub.rbs][e.sup.-1.030RD], [R.sup.2] = 0.992 (6)

Compared with switchgrass, the benefit of smooth bromegrass to control soil erosion was great. This result also indicated that the adjustment coefficient for rill erodibility of grassland was closely related to grass species and was probably a function of mass, length, diameter, and horizontal and vertical distribution of root system. In the WEPP model (Flanagan et al. 2007), the adjustment equations for rill erodibility are proposed for living root system and thus compared with the results of this study.

For rangeland:

[K.sub.rr] = 0.0017 + 0.0024CL - 0.0088OM -0.00088[[rho].sub.b]/l000 - 0.00048ROOT (7)

where [K.sub.rr] is the rill erodibility of rangeland in WEPP model (s [m.sup.-1]), CL is the clay content (0-1), OM is the organic matter content (0-1), [[rho].sub.b] is the dry soil bulk density (kg [m.sup.-3]), and ROOT is total mass in top 10 cm of soil surface (kgm 2). In this study, CL = 0.163, OM = 0.01, and [[rho].sub.p] = 1210 kg [m.sup.-3]; the calculated [K.sub.rr] was divided by the observed rill erodibility of bare soil and then plotted in Fig. 7. For cropland:

[K.sub.rc]/[K.sub.rbs] = exp(-3.5LRM) (8)

where [K.sub.rc] is the rill erodibility of cropland in the WEPP model (s [m.sup.-1]), and LRM is the living root mass (kg [m.sup.-2]) in 15cm of topsoil. The computed results were also plotted in Fig. 7. The ratio between estimated rill erodibility of grassland ([K.sup.rg]) to bare soil (Krbs) by the WEPP rangeland equation was much less than that of observed switchgrass and smooth bromegrass; however, root mass of only 5 cm of topsoil was considered in this study (Fig. 7). The predicted [K.sub.rg]/[K.sub.rbs] by the WEPP cropland equation was much greater than the observed value (Fig. 7). The results indicated that the WEPP linear model for rangeland overestimated the effects of living root on soil resistance to overland flow, whereas the exponential model for cropland under-predicted the role of the living root system on soil detachment by concentrated flow. This result is different from the finding of De Baets et al. (2006), probably due to the differences in soil, vegetation and experimental conditions.

Conclusion

Temporal variations in rill erodibility under concentrated flow erosion and its influencing factors in two grassland soils and one bare soil were investigated for one growing season under a wide range of hydraulic conditions. The results showed that bare soil was much more erodible than grassland. The mean [K.sub.r] of bare soil was 13.2 and 19.6 times greater than of switchgrass and smooth bromegrass soils. Rill erodibility of each land use fluctuated significantly over time in most cases. The temporal variation in rill erodibility of grasslands differed significantly (P < 0.05) from that of the bare soil. Distinctive patterns of temporal variation in rill erodibility were found throughout the period of measurement between land uses. Compared with grassland, the temporal variations in rill erodibility were relatively small in bare soil. Rill erodibility decreased with increasing root density. The rill erodibility of grassland could be well estimated from the measured seasonal change in rill erodibility of bare soil and root density ([R.sup.2] [greater than or equal to] 0.919). Further studies are needed to develop accurate models to simulate the role of root systems in increasing soil resistance to concentrated flow erosion and to quantify the effects of soil crust on temporal variation in rill erodibility under different conditions.

http://dx.doi.org/10.1071/SR14076

Received 3 April 2014, accepted 7 September 2014, published online 20 November 2014

Acknowledgements

Financial assistance for this work was provided by the Hundred Talents Project of the Chinese Academy of Sciences and National Natural Science Foundation of China (41271287).

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Zhang GH, Liu GB, Tang MK, Zhang XC (2008) Flow detachment of soils under different land uses in the Loess Plateau of China. Transactions of the ASABE 51, 883-890. doi: 10.13031/2013.24527

Zhang GH, Tang KM, Zhang XC (2009) Temporal variation in soil detachment under different land uses in the Loess Plateau of China. Earth Surface Processes and Landforms 34, 1302-1309. doi: 10.1002/ esp.1827

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Guang-hui Zhang (A,B,E), Ke-ming Tang (B,C), Zhen-ling Sun (B), and X.

C. Zhang (D)

(A) State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling, Shaanxi 712100, China.

(B) School of Geography, Beijing Normal University, Beijing 100875, China.

(C) College of Information and Engineering Technology, Sichuan Agricultural University, Yaan, Sichuang 625014, China.

(D) USDA-ARS Grazinglands Research Laboratory, EL Reno, OK 73036, USA.

Corresponding author. Email: ghzhang@bnu.edu.cn

Table 1. Flow depth and shear stress used in this study

Flow rate ([10.sup.-3]          2.50                 5.00
[m.sup.2] [s.sup.-1]):          17.4                 17.4
Slope gradient (%):

Flow depth (mm)          3.78 [+ or -] 0.36   5.52 [+ or -] 0.32
Flow shear stress (Pa)          6.53                 9.55

Flow rate ([10.sup.-3]          5.00                 2.50
[m.sup.2] [s.sup.-1]):          25.9                 42.3
Slope gradient (%):

Flow depth (mm)          5.47 [+ or -] 0.40   3.57 [+ or -] 0.61
Flow shear stress (Pa)          14.4                 16.3

Flow rate ([10.sup.-3]          3.75                 6.25
[m.sup.2] [s.sup.-1]):          42.3                 42.3
Slope gradient (%):

Flow depth (mm)          4.23 [+ or -] 0.40   5.12 [+ or -] 0.24
Flow shear stress (Pa)          19.3                 23.4

Table 2. Statistical properties of fitted rill credibility,
[K.sub.r] (s [m.sup.-1])

Land use              Min.        Max.        Mean       s.d.
                    [K.sub.r]   [K.sub.r]   [K.sub.r]

Switchgrass          0.0014      0.0118      0.0052     0.0039
Smooth bromegrass    0.0005      0.0130      0.0035     0.0041
Bare soil            0.0156      0.1172      0.0685     0.0296

Table 3. Significance of the paired-sample (-tests of rill
credibility under different land uses (n = 10)

([dagger]) P>0.05; **P<0.01

Land use              Switchgrass        Smooth     Bare
                                       bromegrass   soil
Switchgrass                --
Smooth bromegrass   0.157 ([dagger])       --
Bare soil               0.000 **        0.000 **     --
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Author:Zhang, Guang-hui; Tang, Ke-ming; Sun, Zhen-ling; Zhang, X.C.
Publication:Soil Research
Article Type:Report
Date:Nov 1, 2014
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