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Effects of patchy distributed Artemisia capillaris on overland flow hydrodynamic characteristics
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Original Research Article
Effects of patchy distributed Artemisia capillaris on overland flow hydrodynamic characteristics Guanhua Zhang a,b,n, Jiajun Hu c a
Soil and Water Conservation Department, Changjiang River Scientific Research Institute, China Research Center on Mountain Torrent & Geologic Disaster Prevention of Ministry of Water Resources, China c Changjiang Water Resources Commission of the Ministry of Water Resources, Wuhan 430010, China b
art ic l e i nf o
a b s t r a c t
Article history: Received 20 August 2018 Received in revised form 19 December 2018 Accepted 23 December 2018
Laboratory-simulated rainfall experiments were conducted to quantify the effects of patchy distributed Artemisia capillaris on overland flow hydrodynamics. Rainfall intensities of 60, 90, 120, and 150 mm h–1 were applied on a bare plot (CK) and four different patched patterns: a checkerboard pattern (CP), a banded pattern perpendicular to slope direction (BP), a single long strip parallel to slope direction (LP), and a pattern with small patches distributed like the letter ‘X’ (XP). Each patterned plot underwent two sets of experiments, intact plant and root (the above-ground parts were removed), respectively. Results showed that flow velocity increased with rainfall intensity, and the lower slope velocity was higher than the upper slope. The removal of grass shoots significantly increased flow velocity. The contributions of grass shoots and roots to the reductions in flow velocity under different rainfall intensities were different. The shoots made greater contribution of 53–97% at 60 and 90 mm h–1, and the roots contributed more (51–81%) at 120 and 150 mm h–1. Mean flow depth increased with rainfall intensity and it declined after the aboveground parts were cleared. Reynold numbers (Re) in this study were 25–80, indicating a laminar flow in the study. Froude numbers (Fr) were Z 1 for CK and o 1 for patterned treatments. Fr of the lower slope was higher than the upper ones. Darcy-weisbach (f ) and Manning (n) friction coefficient ranked in the order of CK o LP oBP/CP/XP with values of grass sections being higher than the bare sections and upper slope higher than the lower slope, and both decreased after removing the grass shoots. BP, CP, and XP performed more effectively than LP in retarding flow velocity and increasing hydraulic roughness. & 2019 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Overland flow Vegetation pattern Hydrodynamics Soil erosion rate Simulated rainfall
1. Introduction In arid and semi-arid areas, landscapes usually organized in a twophase mosaic of plant patches and neighboring bare soil which reflect the interplay between the species and geomorphic processes (Merino-Martín, Moreno-de las Heras, Espigares, & Nicolau, 2015). It occurs in patterns that can be either spatially periodic or random, in regular bands, spotted or fuzzy clumps, gap patterns and labyrinths (Bisigato, Villagra, Ares, & Rossi, 2009; Couteron & Lejeune, 2001; Klausmeier, 1999; Malam Issa et al., 2011). Under some circumstances, plant species in these regions also showed several different patterns of root distribution (Gill, Burke, Milchunas, & Lauenroth, 1999; Silva & Rego, 2003). Perennial root zones are wider and deeper than those of n Corresponding author at: Soil and Water Conservation Department, Changjiang River Scientific Research Institute, China. E-mail address: [email protected] (G. Zhang). Peer review under responsibility of International Research and Training Center on Erosion and Sedimentation and China Water and Power Press.
annuals, and phreatophytes are often very deep rooted (VásquezMéndez et al., 2011). The combined effects of the plant canopy, ground cover as well as plant root in vegetated patches has been proved to be important in regulating runoff processes (Archer, Quinton, & Hess, 2012; Bautista, Mayor, Bourakhouadar, & Bellot, 2007; Pueyo, Moret-Fernández, Saiz, Bueno, & Alados, 2013; Wainwright, Parsons, Schlesinger, & Abrahams, 2002) mostly because their mitigation effects on erosion forces of rainfall or overland flow and the beneficial effects on soil properties which inherently representing the soil erosion resistance (Vásquez-Méndez et al., 2010; Wang, 2018a, 2018b; Zhang, Liu, Wang, & Wang, 2012; Zhang, Liu, Zhang, & Yi, 2014a, 2014b). Influences of patchy vegetation on hydrological and erosional processes have been reported in Africa and Australia since the 1950s (Beard, 1967; Greenwood, 1957; Worrall, 1959). The more recent researches on vegetation patterns have further placed additional focus on the dynamics of the hydrological and erosional processes that generate and sustain this patchiness (Boer & Puigdefábregas, 2005; Puigdefábregas, 2005; Zhang et al., 2012, 2014a, 2014b).
https://doi.org/10.1016/j.iswcr.2018.12.003 2095-6339/& 2019 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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2
Kakembo (2009) pointed out that patchy vegetation patterns are an expression of soil surface conditions, hydrological and erosional redistribution on the soil surface and landscape function. Cammeraat and Imeson (1999) observed that at the hillslope or basin scale, the spatial patterning of vegetation determines the connectivity and concentration of overland flow. According to Holm (2000), a change in vegetation structure which entails an increase in the 'fetch' (distance between individual plants) could engender a degraded and dysfunctional landscape where water and nutrients are lost from the system. Particularly intriguing are the feedback mechanisms that result from the interaction between vegetation, soil surface conditions, runoff and soil erosion. The interaction entails the redistribution of water on slopes, inducing runoff run-on systems which are closely related to vegetation–bare surface mosaics (Bergkamp, Cerdà, & Imeson, 1999; Valentin, d'Herbès, & Poesen, 1999). The Loess Plateau of China is characterized by deep loess deposits, a unique landscape of criss-crossing ravines and gullies, and severe soil erosion (Shi & Shao, 2000). The arid and semi-arid lands in the Loess Plateau occupy about 67% of the entire region (Li, Xu, & Sun, 2003). The intense soil erosion and frequent anthropogenic activities have caused the extreme degradation of both zonal vegetation and soil quality in this region (Tang, 2004). Therefore, largescale revegetation has been carried out for the sake of preventing soil erosion and restoring degraded ecosystems (Chen, Shao, & Li, 2008). Over the last several decades, researches concerning the performance of vegetation for erosion control in this region have been widely conducted (Pan & Shangguan, 2006; Wang, Zhang, Shi, & Zhang, 2014; Wang, Zhang, Shi, Li, & Shan, 2015), and both the above-ground biomass or plant canopy (Pan, Shangguan, & Lei, 2006; Zhou & Shangguan, 2007) and below-ground biomass (Yu et al., 2014; Wang & Zhang, 2018) have been well studied and documented. However, little has been done on the vegetation spatial pattern and dynamic, especially for impacts of respective portion in vegetation community of different distribution patterns. Moreover, in most previous studies highlighting the positive effect of vegetation on soil and water conservation, this protective effect has often been related to vegetation cover without considering specific vegetation structure or distribution pattern, thus the mechanism of vegetation on erosion control might not yet be fully elucidated. Therefore, the objective of this study is to investigate the effects of patchy Artemisia capillaris on overland flow hydrodynamics and understand the relative contribution of plant shoots and roots in regulating hydrological processes. The findings can offer useful insights into the mechanism of vegetation on erosion control and provide scientific guidance for suitable land uses and the construction of soil and water conservation measures.
2. Materials and methods 2.1. Simulated rainfall set-up The study was carried out under simulated rainfall conditions and a side-sprinkle rainfall simulating set-up was used at the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Yangling, China. Rainfall height was 16 m. The measured uniformity of simulated rainfall reaches 4 85% of that of natural rainfall, which indicates the rainfall uniformity meets the experimental requirement. There was an operating system for rainfall controlling. Calibration of rainfall intensities was performed before each rainfall test. Experimental rainfall intensities can be obtained through adjusting nozzle combination and water pressure, which can generate different erosion forces and flow rate.
2.2. Plot characteristics and experimental treatments Runoff plots were manufactured with steel sheets, with a dimension of 2.0 m 1.0 m 0.5 m. The slope gradient can be adjusted in the range of 0–25°, and 15° was selected in this study since it is the critical slope gradient for ephemeral gully development which is the major factor leading to a sharp increase in soil erosion on the Loess Plateau of China (Tang, Zhang, & Lei, 1998). The soil tested was silt loam (US soil taxonomy) and collected from the top soil depth of 20 cm in an experimental field at Ansai county, Shaanxi province, northwestern China. Its sand, silt, and clay contents were 46%, 46% and 8%, respectively. The soil was air-dried, gently crushed, and then passed through a 10-mm sieve to remove gravel as well as animal and plant residues. Before packing, a 10 cm layer of fine sand was put at the bottom of each plot for better drainage. Then a 30-cm-thick soil was packed in three 10-cm layers at a bulk density of 1.2 g/cm3 in each plot. Each soil layer was raked lightly before the next layer was packed to diminish the discontinuity between the two layers. Artemisia capillaris, dominant species and pioneer plant community on abandoned farmland of Loess Plateau, was taken as the target species. The grass seeds were sown in April 2009 with a row spacing of 10 cm parallel to the plot surface, and similar sowing density was adopted to ensure uniform grass cover for all plots. After sowing, the plots were covered with straw mats and watered to guarantee germination and seedling growth. It is estimated that the threshold coverage for vegetation influencing soil erosion varied from 30% to 50% (Guo, 2000). Based on the same coverage (50%) but different distribution patterns, five treatments were implemented in the study: bare soil as control (CK), a checkerboard pattern (CP), a banded pattern perpendicular to the slope direction (BP), a single long strip parallel to slope direction (LP), and a pattern with small patches distributed like the letter ‘X’ (XP) (Fig. 1). Vegetation cover was measured using vertical and aerial photographs taken with a high-resolution digital camera, as well as empirical estimation by visual observation. Four high rainfall intensities (60, 90, 120, and 150 mm h–1) were selected in this study, since high-intense and short-duration rainstorms, which are primarily responsible for soil erosion, frequently occur on the Loess Plateau. The experiment lasted 60 min after runoff initiation. All treatments were replicated three times. 2.3. Measurements and parameter calculation For each rainfall simulation, plastic buckets were used to collect all runoff and sediment at the plot outlet at 3-min intervals. After the rainfall, runoff in each bucket was weighed on a balance. The buckets were then allowed to stand until the suspended sediment settled out. Then the supernatant was discarded, and the remaining wet sediment was transferred to iron basins to determine sediment weight after oven-drying at 105 °C to constant weight. The dry sediment weight was then used to calculate sediment concentration and soil erosion rate. Surface flow velocities (Vs) were measured by the KMnO4 racing method. The time of the
Fig. 1. Experimental treatments for different patched patterns in this study.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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acceleration (m s–2).
tracer travelling across a marked distance (1 m) was determined according to the leading edge propagation using a stopwatch at 3-min intervals. From upslope to downslope, different longitudinal cross-sections (i.e. S1, S2, S3, and S4) were designed to determine velocities for different treatments (Fig. 2). Hence, for each rainfall event, 20 measurements were performed at each section, which were then averaged to obtain the mean surface flow velocity of this section. Water temperature was measured during the experiments, which was used to calculate flow kinematical viscosity.
2.3.3. Flow resistance Darcy-Weisbach friction coefficient ( f ) and Manning coefficient (n) were calculated from Eq. (3):
f=
(1)
where h is flow depth (m), Q is runoff volume during t time (l), V is mean flow velocity (m s–1), q is unit discharge (m2 s–1), B is width of water-crossing section (m).
where
Fr = V / gh
h2/3J1/2 V
(3)
In this study, it was presumed that the reductions in flow velocity caused by intact plants were only a result of the combined effects of shoots and roots. The intact plant and root effect on flow velocity reductions can be calculated by the following equations:
RVPlant =
(2)
υ is kinematical viscosity (m s ), g is gravitational 2
n=
2.4. Data analysis
2.3.2. Flow regime Reynolds number (Re) and Froud number (Fr) were calculated from Eq. (2):
Re = Vh/υ
8ghJ V2
where J is flow energy gradient and is generally calculated as the sine value of slope gradient. To simulate vegetation structure, after the experiments with intact plants were completed, Artemisia capillaris was clipped at the soil surface and only roots remained. All these plots were subjected to the corresponding run tests again to investigate the hydrodynamic characteristics when surface condition changed. We referred to the plot with the intact plant and that with only the root as ‘plant-plot’ and ‘root-plot’, respectively.
2.3.1. Flow velocity and flow depth Measured Vs was used to estimate mean flow velocities (V) by multiplying a correction factor α according to flow regime (Re), with α being 0.67 for laminar, 0.7 for transitional flow, and 0.8 for turbulent flow, respectively (Luk & Merz, 1992). Flow depth was calculated from Eq. (1):
h = Q /VBt = q/V
3
–1
VCK − VPlant V − VRoot × 100% RVRoot = CK × 100% VCK VCK
(4)
where RVPlant and RVRoot are flow velocity reductions that resulted from the intact plant and root, respectively; Vck is the flow velocity in bare soil plot (cm s 1); VPlant and VRoot are the flow velocities in plant plots and root plots, respectively (cm s 1). Shoots effect on flow velocity retardation (RVShoot) was thus determined by subtracting roots effect (RVRoot) from total plants effect (RVPlant). Analysis of Variance (ANOVA) was used to detect treatment effects on measured variables. If significant treatment effects were observed (p o 0.05), the least significant difference (LSD) was used to test comparisons among treatment means. Paired sample t-tests were performed to analyze differences in variable
Fig. 2. Sketch map of section division for different treatments.
Table 1 Mean flow velocities of different slope positions for different patterned plots (Mean 7 Std). RI
60
90
120
150
VP
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Plant-plots
Root-plots
S1
S2
10.49 7 2.68 6.49 7 0.92 6.76 7 1.11 6.02 7 0.97 7.28 7 0.64 14.33 7 2.07 6.89 7 1.06 7.08 7 0.76 6.90 7 0.74 7.70 7 1.20 18.73 7 3.68 8.31 7 1.71 8.32 7 1.41 10.22 7 0.63 9.50 7 1.87 23.61 7 3.23 9.52 7 2.75 8.79 7 1.28 11.72 7 0.96 10.09 7 1.33
8.99 4.83 6.45 5.47 5.06 9.88 5.29 5.64 6.72 5.36 11.06 5.27 6.62 8.69 6.54 15.79 6.21 6.65 8.30 6.03
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
1.66 0.51 0.76 0.90 0.14 1.45 0.74 0.69 0.98 0.37 1.48 1.01 0.98 1.04 1.53 2.00 1.21 1.08 0.59 0.38
S3
S4
6.71 7 0.80
5.91 7 0.79
11.25 7 1.00
10.91 7 1.44
9.31 7 0.99
5.89 7 1.02
12.72 7 1.25
11.88 7 1.88
9.70 7 1.73
6.11 7 1.46
13.85 7 0.96
9.12 7 1.07
11.36 7 1.71
6.74 7 1.35
15.02 7 1.21
11.87 7 1.31
Vl
Vu
S1
10.49 6.60 6.76 8.63 7.28 14.33 8.10 7.08 9.81 7.70 18.73 9.01 8.32 12.03 9.50 23.61 10.44 8.79 13.37 10.09
8.99 5.37 6.45 8.19 5.06 9.88 5.59 5.64 9.30 5.36 11.06 5.69 6.62 8.90 6.54 15.79 6.47 6.65 10.09 6.03
10.49 7 10.20 7 8.75 7 9.11 7 10.18 7 14.33 7 11.71 7 9.61 7 10.53 7 11.43 7 18.73 7 10.62 7 11.89 7 10.34 7 12.38 7 23.61 7 13.05 7 13.43 7 12.38 7 14.84 7
S2 1.51 0.92 1.26 1.67 0.95 2.07 0.66 1.21 0.96 0.63 3.68 0.60 1.09 1.19 1.09 3.23 0.51 0.99 0.56 0.83
8.99 7 8.34 7 8.19 7 8.68 7 8.81 7 9.88 7 8.74 7 9.20 7 9.59 7 9.24 7 11.06 7 9.83 7 9.10 7 9.99 7 10.34 7 15.79 7 11.67 7 10.77 7 10.22 7 12.57 7
1.27 0.57 0.83 0.64 0.21 1.49 0.61 0.73 0.35 0.46 1.48 0.47 0.73 0.88 1.18 2.00 0.52 1.58 0.73 0.94
S3
S4
10.28 7 0.93
8.74 7 0.46
12.38 7 1.03
11.80 7 1.29
12.08 7 0.77
9.78 7 0.49
13.42 7 1.28
12.08 7 0.52
12.15 7 1.88
10.94 7 1.51
14.73 7 1.13
10.21 7 1.21
15.25 7 0.69
11.94 7 0.62
18.47 7 1.90
12.46 7 1.61
Vd
Vu
10.49 10.24 8.75 10.74 10.18 14.33 11.89 9.61 11.97 11.43 18.73 11.38 11.89 12.54 12.38 23.61 14.15 13.43 15.43 14.84
8.99 8.54 8.19 10.24 8.81 9.88 9.26 9.20 10.84 9.24 11.06 10.39 9.10 10.10 10.34 15.79 11.80 10.77 11.34 12.57
RI and VP refer to rainfall intensity and vegetation patterns; Vu and Vl refer to upper and lower slope velocity. Note that S1, S2, S3, and S4 are four cross-sections distributed on different slope positions.
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4
CK
CP
BP
LP
CK
XP -1
a
15
a
a
10
b
b cc
c
c
c
c
c
c
b
b
c
c
BP
LP
XP
Root-plots
a
c
c
5
Flow velocity/cm s
Flow velocity/cm s
-1
Plant-plots 20
CP
25
25
0
a
20 15 10
a ab ab
a b
a ab
ab
ab c
b
b b b b
bc
b b
b
5 0
60
90
120
60
150
90
-1
120
150 -1
Rainfall intensity/mm h
Rainfall intensity/mm h
Fig. 3. Mean flow velocity for different patterned plots.
means before and after clipping the above-ground parts of Artemisia capillaris. These statistical analyses were performed using the SPSS 18.0.
3. Results 3.1. Effects of patchy vegetation on flow velocity and flow depth 3.1.1. Spatial variation of flow velocity Overall, flow velocities of different sections increased with rainfall intensity increasing, being 8.99–23.61 cm s–1 for bare plots and 4.83–15.02 cm s–1 for plant-plots (Table 1). Vegetated plots had lower flow velocities than bare ones. Clipping the shoots increased flow velocity in each section. There were minor differences in flow velocities between Sections 1 and 2 (S1 and S2) for patterned plots. For treatment LP, flow velocities of Sections 3 and 4 (S3 and S4) were significantly (p o 0.05) higher than that of S1 and S2; while for CP, flow velocities in S3 and S4 were just slightly greater than S1 and S2 (not significant, p 4 0.05). Under rainfall intensity of 60 mm h–1, no statistical differences were detected between the upper slopes (Vu) and lower slopes (Vl). However, significant differences were found under other three rainfall intensities, showing that Vl was higher than Vu and the Vl/Vu ratio was in the range of 1.05–1.69. Under the same rainfall intensity, CK had the greatest mean flow velocity of the whole slope (Fig. 3), followed by LP, and no significant differences were detected among BP, CP, and XP. Flow velocity in root-plots was significantly higher than that in plantplots by paired t-tests. As rainfall intensity increased, mean flow velocity for each treatment in both plant-plots and root-plots in-
CP
BP
LP
creased, but the influence was more notable for CK. Under the experimental conditions, mean flow velocity of the whole slope varied in 9.74–19.70 cm s–1 for treatment CK, 5.99–11.73 cm s–1 for plant-plots, and 8.47–13.70 cm s–1 for root-plots. These results indicated that rainfall intensity may have a more significant effect on slope flow velocity from bare plots than from vegetated plots. One possible reason for this behavior is that the grass patches slow down runoff, which could cause backwater ahead of each grass patch, and the increased ponding would increase surface retention and runoff infiltration, especially under low rainfall intensities, but such an effect would diminish as rainfall intensity increased and the above-ground shoots were removed. 3.1.2. Flow retardation For the intact plant tests, different patterns reduced mean flow velocity by 14–60% compared with bare soil plots (Fig. 4). However, the reduction declined to o 40% for the root-plots. In addition, BP, CP, and XP performed more effectively than LP in retarding flow velocity for both intact plants and roots. This difference in flow-retarding effect could be related to the different distribution patterns of bunch grass on slopes. BP treatment in this study is similar to vegetation barriers or vegetative filter strips (VFS) which has been considered an effective means to retain runoff and sediment (Abu-Zreig, Rudra, Lalonde, Whiteley, & Kaushik, 2004). When surface runoff flows through vegetative strips, flow velocity and discharge generally decreases due to the increased hydraulic roughness and infiltration generated by vegetation elements, which leads to sediment retention layer upon layer within strips (Pan, Ma, & Shangguan, 2010). Under experimental conditions, both CP and XP exhibited a relatively higher landscape fragmentation compared with BP and LP. As a result,
XP
CP
100 Plant-plots
LP
XP
Root-plots
80
Flow retardation/%
Flow retardation/%
BP
100
60 40 20
80 60 40 20
0
0
60
90
120
Rainfall intensity/mm h
150 -1
60
90
120
Rainfall intensity/mm h
150 -1
Fig. 4. Flow velocity reductions from plant-plots and root-plots for different patterns.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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Contribution rate to flow velocity reduction/%
Root
Canopy
CP
BP
LP
5
the same rainfall intensity, flow depth in CK plot was lower than that in patterned plots and LP had lower flow depth than BP/CP/XP. Flow depth decreased slightly after removing the plant shoots.
XP
100 80
3.2. Effects of patchy vegetation on flow regime
60
Flow Reynolds number (Re) ranged in 25–80 (Table 3), thus indicating a laminar flow in this study. Re significantly increased with the increase of rainfall intensity and was higher for CK in comparison with other pattern treatments. However, clipping the plant shoots have not caused significant changes in it by paired t-tests. Froude number (Fr) for CK tended to increase with rainfall intensity, being jet or critical flow in this study. Under the same rainfall intensity, Fr in patterned plots was less than CK, being subcritical flow and suggesting the dissipation effect resulted from vegetation. Fr for LP was higher than BP, CP, and XP, further indicating the latter was more effective than the former in retarding overland flow. When removing the above-ground shoots, Fr increased in different degrees for all treatments (Table 4).
40 20 0 60
90
120
150
Rainfall intensity (mm h ) Fig. 5. Contributions of patterned roots and shoots to the reduction of flow velocity.
more circuitous flow paths, larger flow energy dissipation, as well as flow retarding and retaining take place on CP and XP covered soil surfaces, thus leading to greater reductions in flow velocity. Reduction in flow velocity caused by vegetation cover is a result of the combined effects of above-ground portion (shoots) and below-ground portion (roots). The relative contributions of Artemisia capillaris root and shoot to flow velocity reductions for different patterns are shown in Fig. 5. It can be seen shoots contributed much to the flow velocity reduction at 60 and 90 mm h–1, accounting for nearly 53–97%. Roots made a relatively greater contribution at 120 and 150 mm h–1, approximately 51–81%. To summarize, vegetation, through the joint effects of its above-ground and below-ground parts, intercepted rainfall, retarded runoff and prolonged infiltration time on the one hand; and on the other hand, increased soil surface roughness, dispersed runoff, and dissipated flow energy, which could effectively reduce flow velocity. Moreover, the patchy distribution of slope vegetation further disturbed flow paths and increased local resistance, both result in decreased flow velocity.
3.3. Effects of patchy vegetation on flow resistance 3.3.1. Darcy-Weisbach friction coefficient (f) Grass patches generally had high effectiveness in increasing flow resistance (Table 5). The patterned plots of both plant and root plots had higher Darcy-Weisbach friction coefficients ( f ) than bare plots, and grass vegetated sections higher than bare ones. The f for different slope positions decreased substantially after removing above-ground portions of Artemisia capillaris. For all treatments in plant plots, differences in f of lower slopes (S1/S3) were less than those of upper slopes (S2/S4). Mean f of the whole slope for patterned treatments was in the range of 2.8 9.1, which was 1.25 13.0 times of that for CK (Fig. 6). In addition, mean f of the whole slope was slightly different among treatments BP, CP, and XP, yet they were markedly higher than treatment LP. No statistical differences were detected between LP and CK. These results indicated that treatments BP, CP, and XP performed more effectively than LP in increasing hydraulic roughness, and thus more efficiently in flow
3.1.3. Flow depth (h) Mean flow depth increased with rainfall intensity (Table 2). Under Table 2 Flow depth for different treatments (Mean 7Std). Patterns
Plant-plots
Root-plots 1
)
Rainfall intensity (mm h 60 CK CP BP LP XP
0.47 0.62 0.52 0.49 0.62
90 7 0.05 7 0.10 7 0.02 7 0.09 7 0.09
0.53 0.82 0.78 0.65 0.84
120 7 0.14 7 0.01 7 0.16 7 0.01 7 0.14
1
Rainfall intensity (mm h 150
0.60 7 0.90 7 0.85 7 0.70 7 0.88 7
0.22 0.21 0.05 0.16 0.04
60
0.62 7 1.17 7 1.19 7 0.97 7 1.39 7
0.17 0.07 0.16 0.04 0.11
0.47 0.44 0.48 0.42 0.45
)
90 7 7 7 7 7
0.05 0.09 0.03 0.03 0.15
0.53 0.58 0.63 0.57 0.60
120 7 7 7 7 7
0.14 0.09 0.10 0.06 0.13
150
0.60 7 0.74 7 0.76 7 0.75 7 0.73 7
0.22 0.12 A 0.12 0.25 0.27
0.62 0.83 0.86 0.89 0.81
7 7 7 7 7
0.17 0.19 0.04 0.12 0.23
7 7 7 7 7
5.73 6.25 5.45 9.82 9.93
Table 3 Flow Reynolds numbers for different treatments (Mean 7Std). Patterns
Plant-plots
Root-plots
Rainfall intensity (mm h 60 CK CP BP LP XP
34.27 7 26.49 7 24.96 7 27.85 7 28.17 7
1
)
Rainfall intensity (mm h
90 1.51 2.66 3.97 6.37 7.62
43.15 38.36 35.76 40.43 36.54
120 7 7 7 7 7
2.68 3.81 9.42 2.07 3.97
55.29 43.30 42.87 47.40 46.57
150 7 7 7 7 7
7.14 12.45 2.18 8.67 2.25
79.80 62.64 64.30 72.28 71.72
60 7 5.73 7 4.97 7 7.55 7 2.06 7 2.99
34.27 25.78 26.94 27.57 27.37
1
)
90 7 1.51 7 4.82 7 2.59 7 3.77 7 4.28
43.15 37.79 37.08 41.34 38.15
120 7 7 7 7 7
2.68 1.94 3.03 3.99 3.41
55.29 47.91 49.21 51.88 49.49
150 7 7 7 7 7
7.14 8.06 5.56 6.91 8.46
79.80 58.76 61.82 66.20 61.36
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Table 4 Flow Froude numbers for different treatments (Mean 7 Std). Patterns
Plant-plots
Root-plots
Rainfall intensity (mm h 60 CK CP BP LP XP
1
)
90
0.963 0.521 0.621 0.893 0.544
7 7 7 7 7
0.209 0.178 0.093 0.155 0.133
1
Rainfall intensity (mm h 120
1.161 7 0.533 7 0.493 7 0.861 7 0.497 7
1.045 0.070 0.157 0.113 0.169
150
1.383 0.553 0.554 0.876 0.595
7 7 7 7 7
0.422 0.115 0.089 0.131 0.075
1.755 0.558 0.487 0.838 0.489
60 7 7 7 7 7
0.456 0.022 0.234 0.118 0.060
0.963 0.962 0.829 1.119 0.960
)
90 7 7 7 7 7
0.209 0.048 0.203 0.036 0.152
120
1.161 0.953 0.802 1.039 0.909
7 7 7 7 7
1.045 0.175 0.087 0.121 0.055
1.383 7 0.864 7 0.825 7 0.905 7 0.907 7
150 0.422 0.088 0.147 0.041 0.011
1.755 0.972 0.893 0.999 1.037
7 7 7 7 7
0.456 0.194 0.131 0.164 0.096
Table 5 Darcy-Weisbach friction coefficient ( f ) for different slope positions. RI
60
90
120
150
VP
Plant-plots
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Root-plots
S1
S2
S3
1.80 6.08 5.03 7.68 4.38 0.95 7.46 6.27 7.93 5.26 0.57 4.91 4.95 3.00 3.62 0.40 4.90 6.01 3.06 4.66
2.86 14.80 5.79 10.22 13.08 2.91 16.47 12.38 8.57 15.66 2.79 19.24 9.84 4.88 11.09 1.35 17.65 13.93 8.62 21.80
S4
5.51
8.06
1.18
1.29
3.02
11.94
1.26
1.55
3.09
12.38
1.20
4.22
2.88
13.83
1.46
2.95
Mean
S1
S2
2.25Ca 7.76Aa 5.39Ba 2.81Ca 7.20Aa 1.58Ba 7.60Aa 8.64Aa 2.98Ba 8.64Aa 1.14Ba 7.10Aa 6.85Aa 2.79Ba 6.02Aa 0.70 Da 6.99Ba 8.89Aa 3.06Ca 9.13Aa
1.80 1.76 2.74 2.58 1.84 1.00 1.71 3.03 2.48 1.88 0.61 3.02 2.13 3.40 1.96 0.40 2.19 1.92 2.73 1.53
2.86 3.22 3.35 2.99 2.84 3.06 4.12 3.45 3.27 3.55 2.96 3.80 4.75 3.78 3.37 1.35 3.06 3.73 4.86 2.52
S3
S4
1.72
2.81
1.03
1.19
1.56
2.93
1.20
1.64
2.01
2.76
1.18
3.54
1.37
2.85
0.82
2.68
Mean 2.25ABa 2.26ABb 3.03Ab 1.69Bb 2.27ABb 1.67Ba 2.32ABb 3.23Ab 1.95Bb 2.54ABb 1.21Ba 2.80Ab 3.09Ab 2.60ABa 2.54ABb 0.70Ba 2.22Ab 2.63Ab 2.16Aa 1.94Ab
Within a column at the same rainfall intensity means followed by the same uppercase are not significantly different at p o 0.05 level using the LSD method; within a row means followed by the same lowercase are not significantly different at p o 0.05 level using paired t-test method.
Root-plots
Plant-plots
6 60 mm/h 90 mm/h 120 mm/h 150 mm/h
10
f ratio (patterned/bare
f ratio (patterned/bare
15
5
60 mm/h 120 mm/h
90 mm/h 150 mm/h
4
2
0
0 CP
BP
LP
XP
CP
BP
LP
XP
Fig. 6. Darcy-weisbach friction coefficient ( f ) ratio of intact plant and root plots to bare soil.
retardation, which agrees with the findings of Zhang et al. (2012). Paired sample t-tests demonstrated that mean f significantly (p o 0.05) decreased after removing Artemisia capillaris shoots clinging to the ground, and no differences in mean f were detected among patterned treatments. For the patterned treatments in root plots, mean f of the whole slope was 1.69 3.23, which was just 0.75 3.78 times of that for CK (Fig. 6). Besides, there were minor differences in f among longitudinal sections, which was not the case for intact plant plots. This indicated that grass roots made
small contribution to hydraulic roughness, thus removal of grass shoots or/and leaves significantly affect overland flow resistance. This result also suggests that grass cutting or grazing is crucial for the performance of grass patches in retarding overland flow from severely eroded steep slopes. 3.3.2. Manning roughness coefficient (n) As can be seen in Table 6, the changing law of Manning roughness coefficient (n) was similar to Darcy-Weisbach friction coefficients ( f ). Manning's n was 0.020–0.040, 0.052–0.113, and
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Table 6 Manning's n for different slope positions. RI
60
90
120
150
VP
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Plant-plots
Root-plots
S1
S2
0.042 0.080 0.071 0.091 0.067 0.030 0.094 0.084 0.098 0.077 0.024 0.075 0.076 0.058 0.064 0.020 0.079 0.088 0.062 0.078
0.054 0.131 0.077 0.107 0.123 0.056 0.145 0.123 0.102 0.141 0.057 0.161 0.111 0.076 0.120 0.039 0.161 0.140 0.110 0.183
S3
S4
0.076
0.094
0.032
0.034
0.057
0.122
0.035
0.039
0.058
0.126
0.035
0.070
0.059
0.140
0.041
0.060
Mean
S1
S2
0.047Ca 0.092Aa 0.074Ba 0.052Ca 0.088Aa 0.040Ca 0.095Aa 0.101Aa 0.057Ba 0.101Aa 0.035Ca 0.093Aa 0.091Aa 0.056Ba 0.085Aa 0.027 Da 0.096Ba 0.109Aa 0.062Ca 0.113Aa
0.042 0.041 0.052 0.051 0.042 0.031 0.042 0.057 0.052 0.044 0.025 0.059 0.049 0.064 0.047 0.020 0.051 0.047 0.058 0.042
0.054 0.057 0.058 0.055 0.053 0.058 0.068 0.062 0.060 0.063 0.059 0.067 0.076 0.067 0.063 0.039 0.062 0.068 0.080 0.055
S3
S4
0.040
0.053
0.030
0.033
0.040
0.056
0.035
0.041
0.047
0.056
0.035
0.065
0.039
0.059
0.030
0.058
Mean 0.047Ba 0.047Bb 0.055Ab 0.040Bb 0.047Bb 0.042Ca 0.050ABCb 0.059Ab 0.045BCb 0.052ABb 0.036Ba 0.057Ab 0.060Ab 0.055Aa 0.054Ab 0.027Ba 0.052Ab 0.056Ab 0.051Ab 0.048Ab
Within a column at the same rainfall intensity means followed by the same uppercase are not significantly different at p o 0.05 level using the LSD method; within a row means followed by the same lowercase are not significantly different at p o 0.05 level using paired t-test method.
0.040–0.060 for CK, intact plant-plots and root-plots, respectively and decreased in the order CKoLP oBP/CP/XP. For the same treatment, n value of upper slope was higher than lower slope and the ratio of the former to the latter was 1.06–2.40. Clipping the aboveground shoots significantly reduced n by 2.1–57.4%.
4. Conclusion Simulated rainfall experiments were carried out to study the effects of patched Artemisia capillaris patterns on overland flow hydrodynamics and the relative contribution impacts of shoots and roots were analyzed. The following conclusions can be drawn. Both vegetation pattern and rainfall intensity had certain effects on overland flow hydrodynamic characteristics. Flow velocity increased with rainfall intensity increasing, and the sections covered with grass patches had lower flow velocities than bare ones. Flow velocity in the lower slope was significantly (p o 0.05) greater than that in the upper slope. Moreover, clipping the aboveground canopies increased flow velocity. Compared with the bare soil plot, the plant plots reduced mean flow velocity by 14–60%, whereas the reduction declined to o 40% for the root plots. BP, CP, and XP performed more effectively than LP in retarding flow velocity, and no significant differences in flow retardation were identified among BP, CP, and XP. The contributions of Artemisia capillaris shoots and roots to flow velocity reductions under different rainfall intensities were different. Shoots made a great contribution of 53–97% at 60 and 90mmh–1, yet roots contributed more at 120 and 150 mm h–1, approximately 51–81%. Mean flow depth increased with rainfall intensity and it declined after the aboveground parts were cleared. Reynold numbers (Re) in this study were 25–80, indicating a laminar flow. Froude numbers (Fr) were Z 1 for CK and o 1 for patterned treatments. Fr of the lower slope was higher than the upper ones. Darcyweisbach (f ) and Manning (n) friction coefficient ranked in the order of CKoLP o BP/CP/XP with values of grass sections being higher than the bare sections and upper slope higher than the lower slope, and both decreased after removing the grass shoots.
BP, CP, and XP performed more effectively than LP in retarding flow velocity and increasing hydraulic roughness.
Acknowledgments Financial supports for this paper were provided by the National Natural Science Foundation of China (No. 41877082, 41701316, 41301298) and the National Key Research and Development Program of China (No. 2017YFC050530302).
References Abu-Zreig, M., Rudra, R. P., Lalonde, M. N., Whiteley, H. R., & Kaushik, N. K. (2004). Experimental investigation of runoff reduction and sediment removal by vegetated filter strips. Hydrological Processes, 18, 2029–2037. Archer, N., Quinton, J., & Hess, T. (2012). Patch vegetation and water redistribution above and below ground in south east Spain. Ecohydrology, 120, 108–120. Bautista, S., Mayor, A. G., Bourakhouadar, J., & Bellot, J. (2007). Plant spatial pattern predicts hillslope runoff and erosion in a semiarid Mediterranean landscape. Ecosystems, 10, 987–998. Beard, J. S. (1967). A study of patterns in some Western Australian heath and mallee communities. Australian Journal of Botany, 15, 131–139. Bergkamp, G., Cerdà, A., & Imeson, A. C. (1999). Magnitude-frequency analysis of water redistribution along a climate gradient in Spain. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, 129–146. Bisigato, A. J., Villagra, P. E., Ares, J. O., & Rossi, B. E. (2009). Vegetation heterogeneity in Monte Desert ecosystems: A multi-scale approach linking patterns and processes. Journal of Arid Environments, 73, 182–191. Boer, M., & Puigdefábregas, J. (2005). Effects of spatially structured vegetation patterns on hill-slope erosion in a semiarid Mediterranean environment: A simulation study. Earth Surface Process Landforms, 30, 149–167. Cammeraat, L. H., & Imeson, A. C. (1999). The evolution and significance of soilvegetation patterns following land abandonment and fire in Spain. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, 107–127. Chen, H. S., Shao, M. A., & Li, Y. Y. (2008). Soil desiccation in the Loess Plateau of China. Geoderma, 143, 91–100. Couteron, P., & Lejeune, O. (2001). Periodic spotted patterns in semi-arid vegetation explained by a propagation-inhibition model. Journal of Ecology, 89, 616–628. Gill, R., Burke, I. C., Milchunas, D. G., & Lauenroth, W. K. (1999). Relationship between root biomass and soil organic matter pools in the shortgrass steppe of eastern Colorado. Ecosystems, 2, 226–236.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
8
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Greenwood, J. E. G. W. (1957). The development of vegetation patterns in Somaliland Protectorate. Geographical Journal, 123, 465–473. Guo, Z. S. (2000). Three coverages in vegetation construction for soil and water conservation: potential coverage, critical coverage, and effective coverage. Soil and Water Conservation in China, 4, 30–31 (in Chinese). Holm, A. M. (2000). A study of degradation processes at patch to landscape scale within the arid shrubland of Western Australia (Unpublished (Unpublished Ph.D. Thesis. Perth: University of Western Australia. Kakembo, V. (2009). Vegetation patchiness and implications for landscape function: The case of Pteronia incana invader species in Ngqushwa Rural Municipality, Eastern Cape, South Africa. Catena, 77, 180–186. Klausmeier, C. A. (1999). Regular and irregular patterns in semiarid vegetation. Science, 284, 1826–1828. Li, F. M., Xu, J. Z., & Sun, G. Z. (2003). Restoration of degraded ecosystems and development of water-harvesting ecological agriculture in the semi-arid Loess Plateau of China. Acta Ecologica Sinica, 23, 1901–1915 (In Chinese). Luk, S. H., & Merz, W. (1992). Use of the salt tracing technique to determine the velocity of overland flow. Soil Technology, 5, 289–301. Merino-Martín, L., Moreno-de las Heras, M., Espigares, T., & Nicolau, J. M. (2015). Overland flow directs soil moisture and ecosystem processes at patch scale in Mediterranean restored hillslopes. Catena, 133, 71–84. Malam Issa, O., Valentin, C., Rajot, J. L., Cerdan, O., Desprats, J. F., & Bouchet, T. (2011). Runoff generation fostered by physical and biological crusts in semi-arid sandy soils. Geoderma, 167–168, 22–29. Pan, C. Z., Ma, L., & Shangguan, Z. P. (2010). Effectiveness of grass strips in trapping suspended sediments from runoff. Earth Surface Processes and Landforms, 35, 1006–1013. Pan, C. Z., Shangguan, Z. P., & Lei, T. W. (2006). Influence of grass and moss on runoff and sediment yield on sloped loess surface under simulated rainfall. Hydrological Processes, 20, 3815–3824. Pan, C. Z., & Shangguan, Z. P. (2006). Runoff hydraulic characteristics and sediment generation in sloped grassplots under simulated rainfall conditions. Journal of Hydrology, 331, 178–185. Pueyo, Y., Moret-Fernández, D., Saiz, H., Bueno, C. G., & Alados, C. L. (2013). Relationships between plant spatial patterns, water infiltration capacity, and plant community composition in semi-arid Mediterranean ecosystems along stress gradients. Ecosystems, 16, 452–466. Puigdefábregas, J. (2005). The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms, 30, 133–147. Shi, H., & Shao, M. A. (2000). Soil and water loss from the Loess Plateau in China. Journal of Arid Environments, 45, 9–20. Silva, J. S., & Rego, F. C. (2003). Root distribution of a Mediterranean shrubland in Portugal. Plant Soil, 255, 529–540.
Tang, K. L. (2004). Soil and Water Conservation in China (p. 885) Beijing: Science Press. Tang, K. L., Zhang, K. L., & Lei, A. L. (1998). Research demonstration of the upper limit slope for returning farmland in loess hilly region. Chinese Science Bulletin, 43, 200–203. Valentin, C., d'Herbès, J. M., & Poesen, J. (1999). Soil and water components of banded vegetation. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, pp. 1–24. Vásquez-Méndez, R., Ventura-Ramos, E., Oleschko, K., Hernández-Sandoval, L., Parrot, J. F., & Nearing, M. (2010). Soil erosion and runoff in different vegetation patches from semiarid Central Mexico. Catena, 80, 162–169. Wainwright, J., Parsons, A. J., Schlesinger, W. H., & Abrahams, A. D. (2002). Hydrology–vegetation interactions in areas of discontinuous flow on a semi-arid bajada, Southern new Mexico. Journal of Arid Environments, 51, 319–338. Wang, B., Zhang, G. H., Yang, Y. F., Li, P. P., & Liu, J. X. (2018a). The effects of varied soil properties induced by natural grassland succession on the process of soil detachment. Catena, 166, 192–199. Wang, H., Zhang, G. H., Li, N. N., Zhang, B. J., & Yang, H. Y. (2018b). Soil erodibility influenced by natural restoration time of abandoned farmland on the Loess Plateau of China. Geoderma, 325, 18–27. Wang, B., & Zhang, G. H. (2018). Quantifying the binding and bonding Effects of plant roots on soil detachment by overland flow in 10 typical grasslands on the Loess Plateau. Soil Science Society of America Journal, 81, 1567–1576. Wang, B., Zhang, G. H., Shi, Y. Y., & Zhang, X. C. (2014). Soil detachment by overland flow under different vegetation restoration models in the Loess Plateau of China. Catena, 116, 51–59. Wang, B., Zhang, G. H., Shi, Y. Y., Li, Z. W., & Shan, Z. J. (2015). Effects of near soil surface characteristics on the soil detachment process in a chronological series of vegetation restoration. Soil Science Society of America Journal, 79, 1213–1222. Worrall, G. A. (1959). The Butana grass patterns. Journal of Soil Science, 10, 34–53. Zhang, G. H., Liu, G. B., Wang, G. L., & Wang, Y. X. (2012). Effects of patterned Artemisia capillaris on overland flow velocity under simulated rainfall. Hydrological Processes, 26, 3779–3787. Zhang, G. H., Liu, G. B., Zhang, P. C., & Yi, L. (2014aa). Influence of vegetation parameters on runoff and sediment characteristics in patterned Artemisia capillaris plots. Journal of Arid Land, 6, 352–360. Zhang, G. H., Liu, G. B., Zhang, P. C., & Yi, L. (2014bb). Effects of patterned Artemisia capillaris on overland flow resistance under varied rainfall intensities in the Loess Plateau of China. Journal of Hydrology and Hydromechanics, 62, 334–342. Zhou, Z. C., & Shangguan, Z. P. (2007). The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. Catena, 70, 350–355.
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Original Research Article
Effects of patchy distributed Artemisia capillaris on overland flow hydrodynamic characteristics Guanhua Zhang a,b,n, Jiajun Hu c a
Soil and Water Conservation Department, Changjiang River Scientific Research Institute, China Research Center on Mountain Torrent & Geologic Disaster Prevention of Ministry of Water Resources, China c Changjiang Water Resources Commission of the Ministry of Water Resources, Wuhan 430010, China b
art ic l e i nf o
a b s t r a c t
Article history: Received 20 August 2018 Received in revised form 19 December 2018 Accepted 23 December 2018
Laboratory-simulated rainfall experiments were conducted to quantify the effects of patchy distributed Artemisia capillaris on overland flow hydrodynamics. Rainfall intensities of 60, 90, 120, and 150 mm h–1 were applied on a bare plot (CK) and four different patched patterns: a checkerboard pattern (CP), a banded pattern perpendicular to slope direction (BP), a single long strip parallel to slope direction (LP), and a pattern with small patches distributed like the letter ‘X’ (XP). Each patterned plot underwent two sets of experiments, intact plant and root (the above-ground parts were removed), respectively. Results showed that flow velocity increased with rainfall intensity, and the lower slope velocity was higher than the upper slope. The removal of grass shoots significantly increased flow velocity. The contributions of grass shoots and roots to the reductions in flow velocity under different rainfall intensities were different. The shoots made greater contribution of 53–97% at 60 and 90 mm h–1, and the roots contributed more (51–81%) at 120 and 150 mm h–1. Mean flow depth increased with rainfall intensity and it declined after the aboveground parts were cleared. Reynold numbers (Re) in this study were 25–80, indicating a laminar flow in the study. Froude numbers (Fr) were Z 1 for CK and o 1 for patterned treatments. Fr of the lower slope was higher than the upper ones. Darcy-weisbach (f ) and Manning (n) friction coefficient ranked in the order of CK o LP oBP/CP/XP with values of grass sections being higher than the bare sections and upper slope higher than the lower slope, and both decreased after removing the grass shoots. BP, CP, and XP performed more effectively than LP in retarding flow velocity and increasing hydraulic roughness. & 2019 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Overland flow Vegetation pattern Hydrodynamics Soil erosion rate Simulated rainfall
1. Introduction In arid and semi-arid areas, landscapes usually organized in a twophase mosaic of plant patches and neighboring bare soil which reflect the interplay between the species and geomorphic processes (Merino-Martín, Moreno-de las Heras, Espigares, & Nicolau, 2015). It occurs in patterns that can be either spatially periodic or random, in regular bands, spotted or fuzzy clumps, gap patterns and labyrinths (Bisigato, Villagra, Ares, & Rossi, 2009; Couteron & Lejeune, 2001; Klausmeier, 1999; Malam Issa et al., 2011). Under some circumstances, plant species in these regions also showed several different patterns of root distribution (Gill, Burke, Milchunas, & Lauenroth, 1999; Silva & Rego, 2003). Perennial root zones are wider and deeper than those of n Corresponding author at: Soil and Water Conservation Department, Changjiang River Scientific Research Institute, China. E-mail address: [email protected] (G. Zhang). Peer review under responsibility of International Research and Training Center on Erosion and Sedimentation and China Water and Power Press.
annuals, and phreatophytes are often very deep rooted (VásquezMéndez et al., 2011). The combined effects of the plant canopy, ground cover as well as plant root in vegetated patches has been proved to be important in regulating runoff processes (Archer, Quinton, & Hess, 2012; Bautista, Mayor, Bourakhouadar, & Bellot, 2007; Pueyo, Moret-Fernández, Saiz, Bueno, & Alados, 2013; Wainwright, Parsons, Schlesinger, & Abrahams, 2002) mostly because their mitigation effects on erosion forces of rainfall or overland flow and the beneficial effects on soil properties which inherently representing the soil erosion resistance (Vásquez-Méndez et al., 2010; Wang, 2018a, 2018b; Zhang, Liu, Wang, & Wang, 2012; Zhang, Liu, Zhang, & Yi, 2014a, 2014b). Influences of patchy vegetation on hydrological and erosional processes have been reported in Africa and Australia since the 1950s (Beard, 1967; Greenwood, 1957; Worrall, 1959). The more recent researches on vegetation patterns have further placed additional focus on the dynamics of the hydrological and erosional processes that generate and sustain this patchiness (Boer & Puigdefábregas, 2005; Puigdefábregas, 2005; Zhang et al., 2012, 2014a, 2014b).
https://doi.org/10.1016/j.iswcr.2018.12.003 2095-6339/& 2019 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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2
Kakembo (2009) pointed out that patchy vegetation patterns are an expression of soil surface conditions, hydrological and erosional redistribution on the soil surface and landscape function. Cammeraat and Imeson (1999) observed that at the hillslope or basin scale, the spatial patterning of vegetation determines the connectivity and concentration of overland flow. According to Holm (2000), a change in vegetation structure which entails an increase in the 'fetch' (distance between individual plants) could engender a degraded and dysfunctional landscape where water and nutrients are lost from the system. Particularly intriguing are the feedback mechanisms that result from the interaction between vegetation, soil surface conditions, runoff and soil erosion. The interaction entails the redistribution of water on slopes, inducing runoff run-on systems which are closely related to vegetation–bare surface mosaics (Bergkamp, Cerdà, & Imeson, 1999; Valentin, d'Herbès, & Poesen, 1999). The Loess Plateau of China is characterized by deep loess deposits, a unique landscape of criss-crossing ravines and gullies, and severe soil erosion (Shi & Shao, 2000). The arid and semi-arid lands in the Loess Plateau occupy about 67% of the entire region (Li, Xu, & Sun, 2003). The intense soil erosion and frequent anthropogenic activities have caused the extreme degradation of both zonal vegetation and soil quality in this region (Tang, 2004). Therefore, largescale revegetation has been carried out for the sake of preventing soil erosion and restoring degraded ecosystems (Chen, Shao, & Li, 2008). Over the last several decades, researches concerning the performance of vegetation for erosion control in this region have been widely conducted (Pan & Shangguan, 2006; Wang, Zhang, Shi, & Zhang, 2014; Wang, Zhang, Shi, Li, & Shan, 2015), and both the above-ground biomass or plant canopy (Pan, Shangguan, & Lei, 2006; Zhou & Shangguan, 2007) and below-ground biomass (Yu et al., 2014; Wang & Zhang, 2018) have been well studied and documented. However, little has been done on the vegetation spatial pattern and dynamic, especially for impacts of respective portion in vegetation community of different distribution patterns. Moreover, in most previous studies highlighting the positive effect of vegetation on soil and water conservation, this protective effect has often been related to vegetation cover without considering specific vegetation structure or distribution pattern, thus the mechanism of vegetation on erosion control might not yet be fully elucidated. Therefore, the objective of this study is to investigate the effects of patchy Artemisia capillaris on overland flow hydrodynamics and understand the relative contribution of plant shoots and roots in regulating hydrological processes. The findings can offer useful insights into the mechanism of vegetation on erosion control and provide scientific guidance for suitable land uses and the construction of soil and water conservation measures.
2. Materials and methods 2.1. Simulated rainfall set-up The study was carried out under simulated rainfall conditions and a side-sprinkle rainfall simulating set-up was used at the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Yangling, China. Rainfall height was 16 m. The measured uniformity of simulated rainfall reaches 4 85% of that of natural rainfall, which indicates the rainfall uniformity meets the experimental requirement. There was an operating system for rainfall controlling. Calibration of rainfall intensities was performed before each rainfall test. Experimental rainfall intensities can be obtained through adjusting nozzle combination and water pressure, which can generate different erosion forces and flow rate.
2.2. Plot characteristics and experimental treatments Runoff plots were manufactured with steel sheets, with a dimension of 2.0 m 1.0 m 0.5 m. The slope gradient can be adjusted in the range of 0–25°, and 15° was selected in this study since it is the critical slope gradient for ephemeral gully development which is the major factor leading to a sharp increase in soil erosion on the Loess Plateau of China (Tang, Zhang, & Lei, 1998). The soil tested was silt loam (US soil taxonomy) and collected from the top soil depth of 20 cm in an experimental field at Ansai county, Shaanxi province, northwestern China. Its sand, silt, and clay contents were 46%, 46% and 8%, respectively. The soil was air-dried, gently crushed, and then passed through a 10-mm sieve to remove gravel as well as animal and plant residues. Before packing, a 10 cm layer of fine sand was put at the bottom of each plot for better drainage. Then a 30-cm-thick soil was packed in three 10-cm layers at a bulk density of 1.2 g/cm3 in each plot. Each soil layer was raked lightly before the next layer was packed to diminish the discontinuity between the two layers. Artemisia capillaris, dominant species and pioneer plant community on abandoned farmland of Loess Plateau, was taken as the target species. The grass seeds were sown in April 2009 with a row spacing of 10 cm parallel to the plot surface, and similar sowing density was adopted to ensure uniform grass cover for all plots. After sowing, the plots were covered with straw mats and watered to guarantee germination and seedling growth. It is estimated that the threshold coverage for vegetation influencing soil erosion varied from 30% to 50% (Guo, 2000). Based on the same coverage (50%) but different distribution patterns, five treatments were implemented in the study: bare soil as control (CK), a checkerboard pattern (CP), a banded pattern perpendicular to the slope direction (BP), a single long strip parallel to slope direction (LP), and a pattern with small patches distributed like the letter ‘X’ (XP) (Fig. 1). Vegetation cover was measured using vertical and aerial photographs taken with a high-resolution digital camera, as well as empirical estimation by visual observation. Four high rainfall intensities (60, 90, 120, and 150 mm h–1) were selected in this study, since high-intense and short-duration rainstorms, which are primarily responsible for soil erosion, frequently occur on the Loess Plateau. The experiment lasted 60 min after runoff initiation. All treatments were replicated three times. 2.3. Measurements and parameter calculation For each rainfall simulation, plastic buckets were used to collect all runoff and sediment at the plot outlet at 3-min intervals. After the rainfall, runoff in each bucket was weighed on a balance. The buckets were then allowed to stand until the suspended sediment settled out. Then the supernatant was discarded, and the remaining wet sediment was transferred to iron basins to determine sediment weight after oven-drying at 105 °C to constant weight. The dry sediment weight was then used to calculate sediment concentration and soil erosion rate. Surface flow velocities (Vs) were measured by the KMnO4 racing method. The time of the
Fig. 1. Experimental treatments for different patched patterns in this study.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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acceleration (m s–2).
tracer travelling across a marked distance (1 m) was determined according to the leading edge propagation using a stopwatch at 3-min intervals. From upslope to downslope, different longitudinal cross-sections (i.e. S1, S2, S3, and S4) were designed to determine velocities for different treatments (Fig. 2). Hence, for each rainfall event, 20 measurements were performed at each section, which were then averaged to obtain the mean surface flow velocity of this section. Water temperature was measured during the experiments, which was used to calculate flow kinematical viscosity.
2.3.3. Flow resistance Darcy-Weisbach friction coefficient ( f ) and Manning coefficient (n) were calculated from Eq. (3):
f=
(1)
where h is flow depth (m), Q is runoff volume during t time (l), V is mean flow velocity (m s–1), q is unit discharge (m2 s–1), B is width of water-crossing section (m).
where
Fr = V / gh
h2/3J1/2 V
(3)
In this study, it was presumed that the reductions in flow velocity caused by intact plants were only a result of the combined effects of shoots and roots. The intact plant and root effect on flow velocity reductions can be calculated by the following equations:
RVPlant =
(2)
υ is kinematical viscosity (m s ), g is gravitational 2
n=
2.4. Data analysis
2.3.2. Flow regime Reynolds number (Re) and Froud number (Fr) were calculated from Eq. (2):
Re = Vh/υ
8ghJ V2
where J is flow energy gradient and is generally calculated as the sine value of slope gradient. To simulate vegetation structure, after the experiments with intact plants were completed, Artemisia capillaris was clipped at the soil surface and only roots remained. All these plots were subjected to the corresponding run tests again to investigate the hydrodynamic characteristics when surface condition changed. We referred to the plot with the intact plant and that with only the root as ‘plant-plot’ and ‘root-plot’, respectively.
2.3.1. Flow velocity and flow depth Measured Vs was used to estimate mean flow velocities (V) by multiplying a correction factor α according to flow regime (Re), with α being 0.67 for laminar, 0.7 for transitional flow, and 0.8 for turbulent flow, respectively (Luk & Merz, 1992). Flow depth was calculated from Eq. (1):
h = Q /VBt = q/V
3
–1
VCK − VPlant V − VRoot × 100% RVRoot = CK × 100% VCK VCK
(4)
where RVPlant and RVRoot are flow velocity reductions that resulted from the intact plant and root, respectively; Vck is the flow velocity in bare soil plot (cm s 1); VPlant and VRoot are the flow velocities in plant plots and root plots, respectively (cm s 1). Shoots effect on flow velocity retardation (RVShoot) was thus determined by subtracting roots effect (RVRoot) from total plants effect (RVPlant). Analysis of Variance (ANOVA) was used to detect treatment effects on measured variables. If significant treatment effects were observed (p o 0.05), the least significant difference (LSD) was used to test comparisons among treatment means. Paired sample t-tests were performed to analyze differences in variable
Fig. 2. Sketch map of section division for different treatments.
Table 1 Mean flow velocities of different slope positions for different patterned plots (Mean 7 Std). RI
60
90
120
150
VP
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Plant-plots
Root-plots
S1
S2
10.49 7 2.68 6.49 7 0.92 6.76 7 1.11 6.02 7 0.97 7.28 7 0.64 14.33 7 2.07 6.89 7 1.06 7.08 7 0.76 6.90 7 0.74 7.70 7 1.20 18.73 7 3.68 8.31 7 1.71 8.32 7 1.41 10.22 7 0.63 9.50 7 1.87 23.61 7 3.23 9.52 7 2.75 8.79 7 1.28 11.72 7 0.96 10.09 7 1.33
8.99 4.83 6.45 5.47 5.06 9.88 5.29 5.64 6.72 5.36 11.06 5.27 6.62 8.69 6.54 15.79 6.21 6.65 8.30 6.03
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
1.66 0.51 0.76 0.90 0.14 1.45 0.74 0.69 0.98 0.37 1.48 1.01 0.98 1.04 1.53 2.00 1.21 1.08 0.59 0.38
S3
S4
6.71 7 0.80
5.91 7 0.79
11.25 7 1.00
10.91 7 1.44
9.31 7 0.99
5.89 7 1.02
12.72 7 1.25
11.88 7 1.88
9.70 7 1.73
6.11 7 1.46
13.85 7 0.96
9.12 7 1.07
11.36 7 1.71
6.74 7 1.35
15.02 7 1.21
11.87 7 1.31
Vl
Vu
S1
10.49 6.60 6.76 8.63 7.28 14.33 8.10 7.08 9.81 7.70 18.73 9.01 8.32 12.03 9.50 23.61 10.44 8.79 13.37 10.09
8.99 5.37 6.45 8.19 5.06 9.88 5.59 5.64 9.30 5.36 11.06 5.69 6.62 8.90 6.54 15.79 6.47 6.65 10.09 6.03
10.49 7 10.20 7 8.75 7 9.11 7 10.18 7 14.33 7 11.71 7 9.61 7 10.53 7 11.43 7 18.73 7 10.62 7 11.89 7 10.34 7 12.38 7 23.61 7 13.05 7 13.43 7 12.38 7 14.84 7
S2 1.51 0.92 1.26 1.67 0.95 2.07 0.66 1.21 0.96 0.63 3.68 0.60 1.09 1.19 1.09 3.23 0.51 0.99 0.56 0.83
8.99 7 8.34 7 8.19 7 8.68 7 8.81 7 9.88 7 8.74 7 9.20 7 9.59 7 9.24 7 11.06 7 9.83 7 9.10 7 9.99 7 10.34 7 15.79 7 11.67 7 10.77 7 10.22 7 12.57 7
1.27 0.57 0.83 0.64 0.21 1.49 0.61 0.73 0.35 0.46 1.48 0.47 0.73 0.88 1.18 2.00 0.52 1.58 0.73 0.94
S3
S4
10.28 7 0.93
8.74 7 0.46
12.38 7 1.03
11.80 7 1.29
12.08 7 0.77
9.78 7 0.49
13.42 7 1.28
12.08 7 0.52
12.15 7 1.88
10.94 7 1.51
14.73 7 1.13
10.21 7 1.21
15.25 7 0.69
11.94 7 0.62
18.47 7 1.90
12.46 7 1.61
Vd
Vu
10.49 10.24 8.75 10.74 10.18 14.33 11.89 9.61 11.97 11.43 18.73 11.38 11.89 12.54 12.38 23.61 14.15 13.43 15.43 14.84
8.99 8.54 8.19 10.24 8.81 9.88 9.26 9.20 10.84 9.24 11.06 10.39 9.10 10.10 10.34 15.79 11.80 10.77 11.34 12.57
RI and VP refer to rainfall intensity and vegetation patterns; Vu and Vl refer to upper and lower slope velocity. Note that S1, S2, S3, and S4 are four cross-sections distributed on different slope positions.
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4
CK
CP
BP
LP
CK
XP -1
a
15
a
a
10
b
b cc
c
c
c
c
c
c
b
b
c
c
BP
LP
XP
Root-plots
a
c
c
5
Flow velocity/cm s
Flow velocity/cm s
-1
Plant-plots 20
CP
25
25
0
a
20 15 10
a ab ab
a b
a ab
ab
ab c
b
b b b b
bc
b b
b
5 0
60
90
120
60
150
90
-1
120
150 -1
Rainfall intensity/mm h
Rainfall intensity/mm h
Fig. 3. Mean flow velocity for different patterned plots.
means before and after clipping the above-ground parts of Artemisia capillaris. These statistical analyses were performed using the SPSS 18.0.
3. Results 3.1. Effects of patchy vegetation on flow velocity and flow depth 3.1.1. Spatial variation of flow velocity Overall, flow velocities of different sections increased with rainfall intensity increasing, being 8.99–23.61 cm s–1 for bare plots and 4.83–15.02 cm s–1 for plant-plots (Table 1). Vegetated plots had lower flow velocities than bare ones. Clipping the shoots increased flow velocity in each section. There were minor differences in flow velocities between Sections 1 and 2 (S1 and S2) for patterned plots. For treatment LP, flow velocities of Sections 3 and 4 (S3 and S4) were significantly (p o 0.05) higher than that of S1 and S2; while for CP, flow velocities in S3 and S4 were just slightly greater than S1 and S2 (not significant, p 4 0.05). Under rainfall intensity of 60 mm h–1, no statistical differences were detected between the upper slopes (Vu) and lower slopes (Vl). However, significant differences were found under other three rainfall intensities, showing that Vl was higher than Vu and the Vl/Vu ratio was in the range of 1.05–1.69. Under the same rainfall intensity, CK had the greatest mean flow velocity of the whole slope (Fig. 3), followed by LP, and no significant differences were detected among BP, CP, and XP. Flow velocity in root-plots was significantly higher than that in plantplots by paired t-tests. As rainfall intensity increased, mean flow velocity for each treatment in both plant-plots and root-plots in-
CP
BP
LP
creased, but the influence was more notable for CK. Under the experimental conditions, mean flow velocity of the whole slope varied in 9.74–19.70 cm s–1 for treatment CK, 5.99–11.73 cm s–1 for plant-plots, and 8.47–13.70 cm s–1 for root-plots. These results indicated that rainfall intensity may have a more significant effect on slope flow velocity from bare plots than from vegetated plots. One possible reason for this behavior is that the grass patches slow down runoff, which could cause backwater ahead of each grass patch, and the increased ponding would increase surface retention and runoff infiltration, especially under low rainfall intensities, but such an effect would diminish as rainfall intensity increased and the above-ground shoots were removed. 3.1.2. Flow retardation For the intact plant tests, different patterns reduced mean flow velocity by 14–60% compared with bare soil plots (Fig. 4). However, the reduction declined to o 40% for the root-plots. In addition, BP, CP, and XP performed more effectively than LP in retarding flow velocity for both intact plants and roots. This difference in flow-retarding effect could be related to the different distribution patterns of bunch grass on slopes. BP treatment in this study is similar to vegetation barriers or vegetative filter strips (VFS) which has been considered an effective means to retain runoff and sediment (Abu-Zreig, Rudra, Lalonde, Whiteley, & Kaushik, 2004). When surface runoff flows through vegetative strips, flow velocity and discharge generally decreases due to the increased hydraulic roughness and infiltration generated by vegetation elements, which leads to sediment retention layer upon layer within strips (Pan, Ma, & Shangguan, 2010). Under experimental conditions, both CP and XP exhibited a relatively higher landscape fragmentation compared with BP and LP. As a result,
XP
CP
100 Plant-plots
LP
XP
Root-plots
80
Flow retardation/%
Flow retardation/%
BP
100
60 40 20
80 60 40 20
0
0
60
90
120
Rainfall intensity/mm h
150 -1
60
90
120
Rainfall intensity/mm h
150 -1
Fig. 4. Flow velocity reductions from plant-plots and root-plots for different patterns.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
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Contribution rate to flow velocity reduction/%
Root
Canopy
CP
BP
LP
5
the same rainfall intensity, flow depth in CK plot was lower than that in patterned plots and LP had lower flow depth than BP/CP/XP. Flow depth decreased slightly after removing the plant shoots.
XP
100 80
3.2. Effects of patchy vegetation on flow regime
60
Flow Reynolds number (Re) ranged in 25–80 (Table 3), thus indicating a laminar flow in this study. Re significantly increased with the increase of rainfall intensity and was higher for CK in comparison with other pattern treatments. However, clipping the plant shoots have not caused significant changes in it by paired t-tests. Froude number (Fr) for CK tended to increase with rainfall intensity, being jet or critical flow in this study. Under the same rainfall intensity, Fr in patterned plots was less than CK, being subcritical flow and suggesting the dissipation effect resulted from vegetation. Fr for LP was higher than BP, CP, and XP, further indicating the latter was more effective than the former in retarding overland flow. When removing the above-ground shoots, Fr increased in different degrees for all treatments (Table 4).
40 20 0 60
90
120
150
Rainfall intensity (mm h ) Fig. 5. Contributions of patterned roots and shoots to the reduction of flow velocity.
more circuitous flow paths, larger flow energy dissipation, as well as flow retarding and retaining take place on CP and XP covered soil surfaces, thus leading to greater reductions in flow velocity. Reduction in flow velocity caused by vegetation cover is a result of the combined effects of above-ground portion (shoots) and below-ground portion (roots). The relative contributions of Artemisia capillaris root and shoot to flow velocity reductions for different patterns are shown in Fig. 5. It can be seen shoots contributed much to the flow velocity reduction at 60 and 90 mm h–1, accounting for nearly 53–97%. Roots made a relatively greater contribution at 120 and 150 mm h–1, approximately 51–81%. To summarize, vegetation, through the joint effects of its above-ground and below-ground parts, intercepted rainfall, retarded runoff and prolonged infiltration time on the one hand; and on the other hand, increased soil surface roughness, dispersed runoff, and dissipated flow energy, which could effectively reduce flow velocity. Moreover, the patchy distribution of slope vegetation further disturbed flow paths and increased local resistance, both result in decreased flow velocity.
3.3. Effects of patchy vegetation on flow resistance 3.3.1. Darcy-Weisbach friction coefficient (f) Grass patches generally had high effectiveness in increasing flow resistance (Table 5). The patterned plots of both plant and root plots had higher Darcy-Weisbach friction coefficients ( f ) than bare plots, and grass vegetated sections higher than bare ones. The f for different slope positions decreased substantially after removing above-ground portions of Artemisia capillaris. For all treatments in plant plots, differences in f of lower slopes (S1/S3) were less than those of upper slopes (S2/S4). Mean f of the whole slope for patterned treatments was in the range of 2.8 9.1, which was 1.25 13.0 times of that for CK (Fig. 6). In addition, mean f of the whole slope was slightly different among treatments BP, CP, and XP, yet they were markedly higher than treatment LP. No statistical differences were detected between LP and CK. These results indicated that treatments BP, CP, and XP performed more effectively than LP in increasing hydraulic roughness, and thus more efficiently in flow
3.1.3. Flow depth (h) Mean flow depth increased with rainfall intensity (Table 2). Under Table 2 Flow depth for different treatments (Mean 7Std). Patterns
Plant-plots
Root-plots 1
)
Rainfall intensity (mm h 60 CK CP BP LP XP
0.47 0.62 0.52 0.49 0.62
90 7 0.05 7 0.10 7 0.02 7 0.09 7 0.09
0.53 0.82 0.78 0.65 0.84
120 7 0.14 7 0.01 7 0.16 7 0.01 7 0.14
1
Rainfall intensity (mm h 150
0.60 7 0.90 7 0.85 7 0.70 7 0.88 7
0.22 0.21 0.05 0.16 0.04
60
0.62 7 1.17 7 1.19 7 0.97 7 1.39 7
0.17 0.07 0.16 0.04 0.11
0.47 0.44 0.48 0.42 0.45
)
90 7 7 7 7 7
0.05 0.09 0.03 0.03 0.15
0.53 0.58 0.63 0.57 0.60
120 7 7 7 7 7
0.14 0.09 0.10 0.06 0.13
150
0.60 7 0.74 7 0.76 7 0.75 7 0.73 7
0.22 0.12 A 0.12 0.25 0.27
0.62 0.83 0.86 0.89 0.81
7 7 7 7 7
0.17 0.19 0.04 0.12 0.23
7 7 7 7 7
5.73 6.25 5.45 9.82 9.93
Table 3 Flow Reynolds numbers for different treatments (Mean 7Std). Patterns
Plant-plots
Root-plots
Rainfall intensity (mm h 60 CK CP BP LP XP
34.27 7 26.49 7 24.96 7 27.85 7 28.17 7
1
)
Rainfall intensity (mm h
90 1.51 2.66 3.97 6.37 7.62
43.15 38.36 35.76 40.43 36.54
120 7 7 7 7 7
2.68 3.81 9.42 2.07 3.97
55.29 43.30 42.87 47.40 46.57
150 7 7 7 7 7
7.14 12.45 2.18 8.67 2.25
79.80 62.64 64.30 72.28 71.72
60 7 5.73 7 4.97 7 7.55 7 2.06 7 2.99
34.27 25.78 26.94 27.57 27.37
1
)
90 7 1.51 7 4.82 7 2.59 7 3.77 7 4.28
43.15 37.79 37.08 41.34 38.15
120 7 7 7 7 7
2.68 1.94 3.03 3.99 3.41
55.29 47.91 49.21 51.88 49.49
150 7 7 7 7 7
7.14 8.06 5.56 6.91 8.46
79.80 58.76 61.82 66.20 61.36
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6
Table 4 Flow Froude numbers for different treatments (Mean 7 Std). Patterns
Plant-plots
Root-plots
Rainfall intensity (mm h 60 CK CP BP LP XP
1
)
90
0.963 0.521 0.621 0.893 0.544
7 7 7 7 7
0.209 0.178 0.093 0.155 0.133
1
Rainfall intensity (mm h 120
1.161 7 0.533 7 0.493 7 0.861 7 0.497 7
1.045 0.070 0.157 0.113 0.169
150
1.383 0.553 0.554 0.876 0.595
7 7 7 7 7
0.422 0.115 0.089 0.131 0.075
1.755 0.558 0.487 0.838 0.489
60 7 7 7 7 7
0.456 0.022 0.234 0.118 0.060
0.963 0.962 0.829 1.119 0.960
)
90 7 7 7 7 7
0.209 0.048 0.203 0.036 0.152
120
1.161 0.953 0.802 1.039 0.909
7 7 7 7 7
1.045 0.175 0.087 0.121 0.055
1.383 7 0.864 7 0.825 7 0.905 7 0.907 7
150 0.422 0.088 0.147 0.041 0.011
1.755 0.972 0.893 0.999 1.037
7 7 7 7 7
0.456 0.194 0.131 0.164 0.096
Table 5 Darcy-Weisbach friction coefficient ( f ) for different slope positions. RI
60
90
120
150
VP
Plant-plots
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Root-plots
S1
S2
S3
1.80 6.08 5.03 7.68 4.38 0.95 7.46 6.27 7.93 5.26 0.57 4.91 4.95 3.00 3.62 0.40 4.90 6.01 3.06 4.66
2.86 14.80 5.79 10.22 13.08 2.91 16.47 12.38 8.57 15.66 2.79 19.24 9.84 4.88 11.09 1.35 17.65 13.93 8.62 21.80
S4
5.51
8.06
1.18
1.29
3.02
11.94
1.26
1.55
3.09
12.38
1.20
4.22
2.88
13.83
1.46
2.95
Mean
S1
S2
2.25Ca 7.76Aa 5.39Ba 2.81Ca 7.20Aa 1.58Ba 7.60Aa 8.64Aa 2.98Ba 8.64Aa 1.14Ba 7.10Aa 6.85Aa 2.79Ba 6.02Aa 0.70 Da 6.99Ba 8.89Aa 3.06Ca 9.13Aa
1.80 1.76 2.74 2.58 1.84 1.00 1.71 3.03 2.48 1.88 0.61 3.02 2.13 3.40 1.96 0.40 2.19 1.92 2.73 1.53
2.86 3.22 3.35 2.99 2.84 3.06 4.12 3.45 3.27 3.55 2.96 3.80 4.75 3.78 3.37 1.35 3.06 3.73 4.86 2.52
S3
S4
1.72
2.81
1.03
1.19
1.56
2.93
1.20
1.64
2.01
2.76
1.18
3.54
1.37
2.85
0.82
2.68
Mean 2.25ABa 2.26ABb 3.03Ab 1.69Bb 2.27ABb 1.67Ba 2.32ABb 3.23Ab 1.95Bb 2.54ABb 1.21Ba 2.80Ab 3.09Ab 2.60ABa 2.54ABb 0.70Ba 2.22Ab 2.63Ab 2.16Aa 1.94Ab
Within a column at the same rainfall intensity means followed by the same uppercase are not significantly different at p o 0.05 level using the LSD method; within a row means followed by the same lowercase are not significantly different at p o 0.05 level using paired t-test method.
Root-plots
Plant-plots
6 60 mm/h 90 mm/h 120 mm/h 150 mm/h
10
f ratio (patterned/bare
f ratio (patterned/bare
15
5
60 mm/h 120 mm/h
90 mm/h 150 mm/h
4
2
0
0 CP
BP
LP
XP
CP
BP
LP
XP
Fig. 6. Darcy-weisbach friction coefficient ( f ) ratio of intact plant and root plots to bare soil.
retardation, which agrees with the findings of Zhang et al. (2012). Paired sample t-tests demonstrated that mean f significantly (p o 0.05) decreased after removing Artemisia capillaris shoots clinging to the ground, and no differences in mean f were detected among patterned treatments. For the patterned treatments in root plots, mean f of the whole slope was 1.69 3.23, which was just 0.75 3.78 times of that for CK (Fig. 6). Besides, there were minor differences in f among longitudinal sections, which was not the case for intact plant plots. This indicated that grass roots made
small contribution to hydraulic roughness, thus removal of grass shoots or/and leaves significantly affect overland flow resistance. This result also suggests that grass cutting or grazing is crucial for the performance of grass patches in retarding overland flow from severely eroded steep slopes. 3.3.2. Manning roughness coefficient (n) As can be seen in Table 6, the changing law of Manning roughness coefficient (n) was similar to Darcy-Weisbach friction coefficients ( f ). Manning's n was 0.020–0.040, 0.052–0.113, and
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Table 6 Manning's n for different slope positions. RI
60
90
120
150
VP
CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP CK CP BP LP XP
Plant-plots
Root-plots
S1
S2
0.042 0.080 0.071 0.091 0.067 0.030 0.094 0.084 0.098 0.077 0.024 0.075 0.076 0.058 0.064 0.020 0.079 0.088 0.062 0.078
0.054 0.131 0.077 0.107 0.123 0.056 0.145 0.123 0.102 0.141 0.057 0.161 0.111 0.076 0.120 0.039 0.161 0.140 0.110 0.183
S3
S4
0.076
0.094
0.032
0.034
0.057
0.122
0.035
0.039
0.058
0.126
0.035
0.070
0.059
0.140
0.041
0.060
Mean
S1
S2
0.047Ca 0.092Aa 0.074Ba 0.052Ca 0.088Aa 0.040Ca 0.095Aa 0.101Aa 0.057Ba 0.101Aa 0.035Ca 0.093Aa 0.091Aa 0.056Ba 0.085Aa 0.027 Da 0.096Ba 0.109Aa 0.062Ca 0.113Aa
0.042 0.041 0.052 0.051 0.042 0.031 0.042 0.057 0.052 0.044 0.025 0.059 0.049 0.064 0.047 0.020 0.051 0.047 0.058 0.042
0.054 0.057 0.058 0.055 0.053 0.058 0.068 0.062 0.060 0.063 0.059 0.067 0.076 0.067 0.063 0.039 0.062 0.068 0.080 0.055
S3
S4
0.040
0.053
0.030
0.033
0.040
0.056
0.035
0.041
0.047
0.056
0.035
0.065
0.039
0.059
0.030
0.058
Mean 0.047Ba 0.047Bb 0.055Ab 0.040Bb 0.047Bb 0.042Ca 0.050ABCb 0.059Ab 0.045BCb 0.052ABb 0.036Ba 0.057Ab 0.060Ab 0.055Aa 0.054Ab 0.027Ba 0.052Ab 0.056Ab 0.051Ab 0.048Ab
Within a column at the same rainfall intensity means followed by the same uppercase are not significantly different at p o 0.05 level using the LSD method; within a row means followed by the same lowercase are not significantly different at p o 0.05 level using paired t-test method.
0.040–0.060 for CK, intact plant-plots and root-plots, respectively and decreased in the order CKoLP oBP/CP/XP. For the same treatment, n value of upper slope was higher than lower slope and the ratio of the former to the latter was 1.06–2.40. Clipping the aboveground shoots significantly reduced n by 2.1–57.4%.
4. Conclusion Simulated rainfall experiments were carried out to study the effects of patched Artemisia capillaris patterns on overland flow hydrodynamics and the relative contribution impacts of shoots and roots were analyzed. The following conclusions can be drawn. Both vegetation pattern and rainfall intensity had certain effects on overland flow hydrodynamic characteristics. Flow velocity increased with rainfall intensity increasing, and the sections covered with grass patches had lower flow velocities than bare ones. Flow velocity in the lower slope was significantly (p o 0.05) greater than that in the upper slope. Moreover, clipping the aboveground canopies increased flow velocity. Compared with the bare soil plot, the plant plots reduced mean flow velocity by 14–60%, whereas the reduction declined to o 40% for the root plots. BP, CP, and XP performed more effectively than LP in retarding flow velocity, and no significant differences in flow retardation were identified among BP, CP, and XP. The contributions of Artemisia capillaris shoots and roots to flow velocity reductions under different rainfall intensities were different. Shoots made a great contribution of 53–97% at 60 and 90mmh–1, yet roots contributed more at 120 and 150 mm h–1, approximately 51–81%. Mean flow depth increased with rainfall intensity and it declined after the aboveground parts were cleared. Reynold numbers (Re) in this study were 25–80, indicating a laminar flow. Froude numbers (Fr) were Z 1 for CK and o 1 for patterned treatments. Fr of the lower slope was higher than the upper ones. Darcyweisbach (f ) and Manning (n) friction coefficient ranked in the order of CKoLP o BP/CP/XP with values of grass sections being higher than the bare sections and upper slope higher than the lower slope, and both decreased after removing the grass shoots.
BP, CP, and XP performed more effectively than LP in retarding flow velocity and increasing hydraulic roughness.
Acknowledgments Financial supports for this paper were provided by the National Natural Science Foundation of China (No. 41877082, 41701316, 41301298) and the National Key Research and Development Program of China (No. 2017YFC050530302).
References Abu-Zreig, M., Rudra, R. P., Lalonde, M. N., Whiteley, H. R., & Kaushik, N. K. (2004). Experimental investigation of runoff reduction and sediment removal by vegetated filter strips. Hydrological Processes, 18, 2029–2037. Archer, N., Quinton, J., & Hess, T. (2012). Patch vegetation and water redistribution above and below ground in south east Spain. Ecohydrology, 120, 108–120. Bautista, S., Mayor, A. G., Bourakhouadar, J., & Bellot, J. (2007). Plant spatial pattern predicts hillslope runoff and erosion in a semiarid Mediterranean landscape. Ecosystems, 10, 987–998. Beard, J. S. (1967). A study of patterns in some Western Australian heath and mallee communities. Australian Journal of Botany, 15, 131–139. Bergkamp, G., Cerdà, A., & Imeson, A. C. (1999). Magnitude-frequency analysis of water redistribution along a climate gradient in Spain. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, 129–146. Bisigato, A. J., Villagra, P. E., Ares, J. O., & Rossi, B. E. (2009). Vegetation heterogeneity in Monte Desert ecosystems: A multi-scale approach linking patterns and processes. Journal of Arid Environments, 73, 182–191. Boer, M., & Puigdefábregas, J. (2005). Effects of spatially structured vegetation patterns on hill-slope erosion in a semiarid Mediterranean environment: A simulation study. Earth Surface Process Landforms, 30, 149–167. Cammeraat, L. H., & Imeson, A. C. (1999). The evolution and significance of soilvegetation patterns following land abandonment and fire in Spain. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, 107–127. Chen, H. S., Shao, M. A., & Li, Y. Y. (2008). Soil desiccation in the Loess Plateau of China. Geoderma, 143, 91–100. Couteron, P., & Lejeune, O. (2001). Periodic spotted patterns in semi-arid vegetation explained by a propagation-inhibition model. Journal of Ecology, 89, 616–628. Gill, R., Burke, I. C., Milchunas, D. G., & Lauenroth, W. K. (1999). Relationship between root biomass and soil organic matter pools in the shortgrass steppe of eastern Colorado. Ecosystems, 2, 226–236.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i
8
G. Zhang, J. Hu / International Soil and Water Conservation Research ∎ (∎∎∎∎) ∎∎∎
Greenwood, J. E. G. W. (1957). The development of vegetation patterns in Somaliland Protectorate. Geographical Journal, 123, 465–473. Guo, Z. S. (2000). Three coverages in vegetation construction for soil and water conservation: potential coverage, critical coverage, and effective coverage. Soil and Water Conservation in China, 4, 30–31 (in Chinese). Holm, A. M. (2000). A study of degradation processes at patch to landscape scale within the arid shrubland of Western Australia (Unpublished (Unpublished Ph.D. Thesis. Perth: University of Western Australia. Kakembo, V. (2009). Vegetation patchiness and implications for landscape function: The case of Pteronia incana invader species in Ngqushwa Rural Municipality, Eastern Cape, South Africa. Catena, 77, 180–186. Klausmeier, C. A. (1999). Regular and irregular patterns in semiarid vegetation. Science, 284, 1826–1828. Li, F. M., Xu, J. Z., & Sun, G. Z. (2003). Restoration of degraded ecosystems and development of water-harvesting ecological agriculture in the semi-arid Loess Plateau of China. Acta Ecologica Sinica, 23, 1901–1915 (In Chinese). Luk, S. H., & Merz, W. (1992). Use of the salt tracing technique to determine the velocity of overland flow. Soil Technology, 5, 289–301. Merino-Martín, L., Moreno-de las Heras, M., Espigares, T., & Nicolau, J. M. (2015). Overland flow directs soil moisture and ecosystem processes at patch scale in Mediterranean restored hillslopes. Catena, 133, 71–84. Malam Issa, O., Valentin, C., Rajot, J. L., Cerdan, O., Desprats, J. F., & Bouchet, T. (2011). Runoff generation fostered by physical and biological crusts in semi-arid sandy soils. Geoderma, 167–168, 22–29. Pan, C. Z., Ma, L., & Shangguan, Z. P. (2010). Effectiveness of grass strips in trapping suspended sediments from runoff. Earth Surface Processes and Landforms, 35, 1006–1013. Pan, C. Z., Shangguan, Z. P., & Lei, T. W. (2006). Influence of grass and moss on runoff and sediment yield on sloped loess surface under simulated rainfall. Hydrological Processes, 20, 3815–3824. Pan, C. Z., & Shangguan, Z. P. (2006). Runoff hydraulic characteristics and sediment generation in sloped grassplots under simulated rainfall conditions. Journal of Hydrology, 331, 178–185. Pueyo, Y., Moret-Fernández, D., Saiz, H., Bueno, C. G., & Alados, C. L. (2013). Relationships between plant spatial patterns, water infiltration capacity, and plant community composition in semi-arid Mediterranean ecosystems along stress gradients. Ecosystems, 16, 452–466. Puigdefábregas, J. (2005). The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms, 30, 133–147. Shi, H., & Shao, M. A. (2000). Soil and water loss from the Loess Plateau in China. Journal of Arid Environments, 45, 9–20. Silva, J. S., & Rego, F. C. (2003). Root distribution of a Mediterranean shrubland in Portugal. Plant Soil, 255, 529–540.
Tang, K. L. (2004). Soil and Water Conservation in China (p. 885) Beijing: Science Press. Tang, K. L., Zhang, K. L., & Lei, A. L. (1998). Research demonstration of the upper limit slope for returning farmland in loess hilly region. Chinese Science Bulletin, 43, 200–203. Valentin, C., d'Herbès, J. M., & Poesen, J. (1999). Soil and water components of banded vegetation. In Valentin, C., Poesen, J. (Eds.), Special issue: The Significance of Soil, Water and Landscape Processes in Banded Vegetation Patterning. Catena, 37, pp. 1–24. Vásquez-Méndez, R., Ventura-Ramos, E., Oleschko, K., Hernández-Sandoval, L., Parrot, J. F., & Nearing, M. (2010). Soil erosion and runoff in different vegetation patches from semiarid Central Mexico. Catena, 80, 162–169. Wainwright, J., Parsons, A. J., Schlesinger, W. H., & Abrahams, A. D. (2002). Hydrology–vegetation interactions in areas of discontinuous flow on a semi-arid bajada, Southern new Mexico. Journal of Arid Environments, 51, 319–338. Wang, B., Zhang, G. H., Yang, Y. F., Li, P. P., & Liu, J. X. (2018a). The effects of varied soil properties induced by natural grassland succession on the process of soil detachment. Catena, 166, 192–199. Wang, H., Zhang, G. H., Li, N. N., Zhang, B. J., & Yang, H. Y. (2018b). Soil erodibility influenced by natural restoration time of abandoned farmland on the Loess Plateau of China. Geoderma, 325, 18–27. Wang, B., & Zhang, G. H. (2018). Quantifying the binding and bonding Effects of plant roots on soil detachment by overland flow in 10 typical grasslands on the Loess Plateau. Soil Science Society of America Journal, 81, 1567–1576. Wang, B., Zhang, G. H., Shi, Y. Y., & Zhang, X. C. (2014). Soil detachment by overland flow under different vegetation restoration models in the Loess Plateau of China. Catena, 116, 51–59. Wang, B., Zhang, G. H., Shi, Y. Y., Li, Z. W., & Shan, Z. J. (2015). Effects of near soil surface characteristics on the soil detachment process in a chronological series of vegetation restoration. Soil Science Society of America Journal, 79, 1213–1222. Worrall, G. A. (1959). The Butana grass patterns. Journal of Soil Science, 10, 34–53. Zhang, G. H., Liu, G. B., Wang, G. L., & Wang, Y. X. (2012). Effects of patterned Artemisia capillaris on overland flow velocity under simulated rainfall. Hydrological Processes, 26, 3779–3787. Zhang, G. H., Liu, G. B., Zhang, P. C., & Yi, L. (2014aa). Influence of vegetation parameters on runoff and sediment characteristics in patterned Artemisia capillaris plots. Journal of Arid Land, 6, 352–360. Zhang, G. H., Liu, G. B., Zhang, P. C., & Yi, L. (2014bb). Effects of patterned Artemisia capillaris on overland flow resistance under varied rainfall intensities in the Loess Plateau of China. Journal of Hydrology and Hydromechanics, 62, 334–342. Zhou, Z. C., & Shangguan, Z. P. (2007). The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. Catena, 70, 350–355.
Please cite this article as: Zhang, G., & Hu, J. International Soil and Water Conservation Research (2019), https://doi.org/10.1016/j. iswcr.2018.12.003i