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Soil surface roughness decay under different topographic conditions
Soil & Tillage Research 187 (2019) 92–101
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Soil surface roughness decay under different topographic conditions Guang-hui Zhang a b
a,b,⁎
, Zhe-fang Xie
T
a,b
State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Random roughness Temporal variation Slope gradient Slope position The Loess Plateau
Soil surface roughness (RR) is one of the most significant factors influencing hydrological and erosion processes. It is most likely affected by topographic conditions, but this relationship has not been quantified. This study was conducted to detect the effects of topographic conditions on soil surface roughness decay after tillage and to determine the dominant mechanism affecting RR on the Loess Plateau. From June 10 to October 3, 2017, RR was measured using a handheld 3D scanner in six plots with different slope gradients and at three different slope positions along a 60 m plot. The results showed that soil surface roughness was significantly affected by slope gradient and position. RR increased exponentially with an increase in slope gradient (R2 = 0.97). The middle slope position exhibited the maximum RR, whereas the downslope position exhibited the minimum RR. Soil surface roughness decreased exponentially over time after tillage under different topographic conditions ranging from 0.91 to 4.50 mm. For the six plots with different slope gradients, rapid decreases in RR occurred from July 24 to Sept. 10, but for the three different slope positions, rapid decreases occurred from June 10 to July 24. RR declined exponentially with an increase in cumulative rainfall (266.4 mm, R2 = 0.88) and kinetic energy (38.9 MJ ha−1, R2 = 0.92). Cumulative kinetic energy was a better predictor of RR decay than cumulative rainfall at different slope positions. However, for plots with different slope gradients, RR decay after tillage was best estimated by slope gradient and cumulative rainfall (NSE = 0.95) or kinetic energy (NSE = 0.96). After tillage, the soil bulk density, crust thickness and soil cohesion increased significantly over time under different topographic conditions. RR decreased linearly with increasing bulk density, crust thickness, and cohesion. The breakdown of aggregates or clods was the dominant process leading to soil surface roughness decay in this study.
1. Introduction Soil surface roughness (RR), defined as the spatial variation in soil surface elevation across a field at scales ranging from centimeters to millimeters or less, is one of the most significant soil surface characteristics influencing many land surface processes (Bullard et al., 2018; He et al., 2018). Römkens and Wang (1986) identified four different types of soil surface roughness. The first two describe the variability in microrelief due to the size of individual soil particles or aggregates and the surface variation as a result of the break-up of soil clods by tillage operations. These two types of soil surface roughness are quantitatively expressed and widely described as soil random roughness. RR plays an essential role in the water depression capacity, the proportion of soil surface covered by water, rainfall infiltration, runoff generation, flow velocity and resistance, hydrological connectivity and drainage network evolution, water evaporation, and soil erosion as well as gas exchange and the development of soil biota (Zobeckn and Onstad, 1987; Magunda et al., 1997; Römkens et al., 2001; Gómez and Nearing, 2005;
⁎
Lin and Richards, 2007; Moreno et al., 2010; Yang and Chu, 2015; Ding and Huang, 2017; He et al., 2018). Hence, it is vital to quantify soil random roughness under different conditions to understand the hydrology and erosion as well as their related processes on hillslopes or at watershed scales. Soil random roughness is mainly affected by climate, soil properties, vegetation, tillage operation, land use and soil erosion. It is also influenced by the less dominant factors of runoff, freeze-thaw cycles, and wind speed and direction. Raindrops impact the soil surface and can cause the dislocation, reorientation, and packing of soil particles. Macroaggregates (> 0.25 mm) are broken down to microaggregates (0.02-0.25 mm) or primary soil particles by the processes of slaking, differential swelling, raindrop impact and dispersion (Bullard et al., 2018). Soil properties, particularly clay content, organic matter content, biota, and carbonates, influence the formation and size of soil aggregates and clods, which control the rearrangement of soil particles, cementation, and flocculation (Zheng et al., 2017). The breakdown of aggregates and clods by raindrop impact can stimulate the formation of
Corresponding author at: State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (G.-h. Zhang).
https://doi.org/10.1016/j.still.2018.12.003 Received 29 May 2018; Received in revised form 28 November 2018; Accepted 1 December 2018 0167-1987/ © 2018 Elsevier B.V. All rights reserved.
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roughness decay of soil surfaces with or without ridges is quite different (Zheng et al., 2017). As indicated by the influence of these previously noted factors, soil roughness decay after tillage is complex and must be measured systematically under different conditions. In addition, soil roughness decay is also affected by soil erosion, which is spatially scale-dependent. As discussed above, the occurrence of rills will produce oriented roughness (Guzha, 2004). Even in a 2 m long test flume, distinct differences in random roughness can be detected between different slope positions under increasing rainfall intensities. The roughness of the middle and downslope positions increase gradually over time, and the roughness of the downslope position is significantly greater than that of the middle slope position. However, under decreasing rainfall intensities, no obvious changes are observed in the roughness of either the middle or downslope positions during the rainfall period (He et al., 2018). These differences are the result of complex responses by soil erosion processes to the changes in erosive force and soil resistance between smooth and rough soil surfaces (Römkens et al., 2001; Ding and Huang, 2017). In fact, the effects of soil surface roughness on erosion are ambiguous. Whether soil loss increases or decreases with roughness depends on water depression storage, runoff generation, flow friction, drainage density and soil properties (Torri et al., 1999; Römkens et al., 2001; Zheng et al., 2012; Luo et al., 2017). Furthermore, the process of sediment transport is also influenced by soil surface roughness, and the sediment selectivity of a rough soil surface is greater than that of a smooth soil surface (Ding and Huang, 2017). These results clearly imply that soil surface roughness decay after tillage is topographically dependent. Topography is one of the most significant factors controlling soil erosion, particularly slope gradient and slope length (Liu et al., 1994, 2000). Both soil detachment and the sediment transport capacity increase with slope gradient (Zhang et al., 2003, 2009a), which will probably lead to the conversion of the soil erosion mechanism from transport-limited to detachment-limited (Govers et al., 2007). As the slope length increases, the drainage area, runoff depth, velocity, connectivity, and flow power increase. Consequently, soil erosion increases with slope length as a power function (Liu et al., 2000). Sediment concentration increases exponentially with slope length due to the feedback effects of sediment transport on soil detachment (Lei et al., 2008; Zhang et al., 2009b). Therefore, the soil erosion mechanism can change from transport-limited to detachment-limited with slope length. These changes in soil erosion with topography will most likely lead to changes in soil surface roughness. However, little attention has been given to the relationship between soil surface roughness and topographic conditions. Some studies have shown that the roughness of steep cropland is markedly greater than that of terraced or gently sloping cropland on the Loess Plateau (Liu et al., 2003). The difference in soil surface roughness at different slope positions also indicates a close relationship between soil surface roughness and topographic conditions (He et al., 2018). However, to date, the potential effects of topographic conditions on soil surface roughness are not fully understood. The quantitative influences of slope gradient and slope position on soil surface roughness decay after tillage are unknown. Hence, the specific objectives of this study were to investigate the effects of slope gradient and position on soil surface roughness decay after tillage under natural rainfall conditions and to determine the dominant mechanism controlling this process on the Loess Plateau of China.
physical soil crusts, which directly reduce soil surface roughness, and indirectly influence the processes of infiltration, runoff generation, the hydraulics of overland flow, and erosion (Wuddivira et al., 2009). The cover of the vegetation canopy, litter and biological crust can effectively eliminate rainfall kinetic energy and protect the soil surface from raindrop impacts. The restoration of vegetation can enhance aggregate content and stability, organic matter content and root density. These changes are closely related to soil detachment by both raindrop impacts and flowing water and reduce the variation in soil surface roughness (Zhang et al., 2014; Wang et al., 2017, 2018). All tillage operations, including plowing, planting, hoeing, fertilizing and harvesting, disturb the soil surface to different degrees, making the soil surface rough and increasing RR. The effects of tillage operations on soil surface roughness depend on the soil type, tillage method, crop rotation, and tillage history (Taconet and Ciarletti, 2007; Bauer et al., 2015; Martinez-Agirre et al., 2016). Land use has a greater effect on soil erosion than any other single factor, i.e., a greater effect than rainfall, topography, soil or vegetation. Land use adjustments or conversions can directly influence soil properties, vegetation growth, tillage operations, runoff, and soil erosion, and hence indirectly affect soil surface roughness. Farmland is frequently disturbed by tillage operations and is more susceptible to erosion than other land uses (Zhang et al., 2008), resulting in significant changes in soil surface roughness (Liu et al., 2003). Soil erosion affects surface roughness via raindrop splash, scouring by flowing water, and sediment transport and deposition as well as the formation of physical crusts and rills (Ding and Huang, 2017; He et al., 2018). The changes in surface roughness induced by erosion depend on rainfall, topography, soil, vegetation and land use (Zhang et al., 2008; He et al., 2018). All of the factors discussed above fluctuate greatly over time, which leads to significant temporal variation in soil surface roughness. Soil surface roughness changes significantly over time, particularly in farmland. It is widely recognized that soil surface roughness decreases exponentially with cumulative rainfall after tillage or hoeing (Zobeckn and Onstad, 1987; Planchon et al., 2001; Guzha, 2004; Zheng et al., 2017). However, other studies have shown that cumulative kinetic energy or rainfall erosivity are better for quantifying the temporal variation of soil surface roughness than cumulative rainfall (Eltza and Norton, 1997; Torri et al., 1999). This is likely true because both soil consolidation and aggregate breakdown are processes that involve energy consumption. The influence of rainfall on soil surface roughness is also closely related to rainfall patterns, particularly rainfall intensity and its temporal distribution as well as rainfall duration (Haubrock et al., 2009). The response of soil roughness to an increased rainfall series is stronger than that of a decreased rainfall series (Luo et al., 2017). After rills have formed, cumulative rainfall is no longer a dominant factor because oriented roughness appears (Guzha, 2004). Soil roughness decay after tillage is strongly affected by soil properties. Soil consolidation and the breakdown of aggregates or clods are the main processes leading to temporal variation in soil surface roughness. For loam and silt loam soils, roughness decay is principally influenced by the breakdown of aggregates or clods, which is reflected by the formation of a physical crust. For very fine soils, the change in roughness over time is mainly controlled by soil consolidation (Bullard et al., 2018). The soil water content at the time that soil management is performed significantly influences soil surface roughness decay (Bauer et al., 2015). Land use also affects temporal variation in soil surface roughness. Cropland experiences the greatest temporal changes in soil surface roughness, followed by orchards and wasteland, fallow land, shrubland, and woodland (Liu et al., 2003). Moreover, tillage operations and management play significant roles in soil surface roughness decay after tillage. Generally, chisel tillage produces substantial initial roughness and exhibits high temporal variation in soil surface roughness (Eltza and Norton, 1997). The roughness decay of tilled soils is greater than that of untilled soils, and the decrease in roughness of covered soil over time is approximately half that of uncovered soil after 53.2 mm of cumulative rainfall (Guzha, 2004; Moreno et al., 2011). The
2. Materials and methods 2.1. Study region and runoff plots The study was conducted on the Dunshan runoff plots of the Ansai Comprehensive Soil and Water Conservation Station of the Institute of Soil and Water Conservation, Chinese Academy of Science, Ansai County, Shaanxi Province, China. The site is located near the center of the Loess Plateau (109°19′23〞E, 36°51′30〞N) and belongs to a typical 93
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Beijing Zhibao Agricultural Technology Co., Ltd.) was sprayed five times from June to September (Table 1). No other operations were undertaken during the entire measurement period. For each of the six selected plots with different slope gradients, three sites were randomly selected at each slope position (upslope, 0–6 m; middle slope, 7–13 m; and downslope, 14–20 m) to measure soil surface roughness. The mean value of the three positions was considered the measured soil surface roughness for that plot at any given time. For the 60 m plot, two sites were randomly selected at each slope position (upslope, 0–20 m; middle slope, 21–40 m; and downslope, 41–60 m) for soil surface roughness measurements. The mean of the two sites at the same slope position was considered the measured soil surface roughness for that slope position (upslope, middle slope or downslope) at any given time. Overall, twenty-four sites were selected in seven runoff plots. For each of the selected sites, four wood markers were placed to show the exact location of the soil surface roughness measurement (Fig. 1). Weeds that were not completely killed by the herbicide were carefully clipped.
Table 1 Basic information on runoff plots and farming operations. Number of runoff plot
Slope Length (m)
Slope gradient (%)
Date of Plowing
Date of herbicide spraying
1 2 3 4 5 6 7
20 20 20 20 20 20 60
8.7 17.4 25.9 34.2 42.3 50.0 42.3
May May May May May May May
June 10, July 1, July 24, Aug. 10, Sept. 10
20 20 21 21 22 22 23
hilly gully region where soil erosion is very serious, and there are many well-developed gullies. The elevation ranges from 1068 to 1309 m. The region has a semiarid, continental monsoon climate with a mean annual temperature of 8.8 ℃. The mean annual precipitation is 500 mm, and more than 70% of the precipitation occurs during the months of June through September as short, heavy storms. The interannual variation in precipitation is large, ranging from 300 mm to 700 mm. The soil is a typical loess soil (mixed, mesic Typic Udorthent) with a texture of silt loam (USDA). The mean clay, silt and sand contents are 9.8%, 58.4%, and 31.8%, respectively. The mean organic matter content is 0.7%. The vegetation is characteristic of a forest-steppe transitional zone. Most of the natural vegetation has been eliminated and the current vegetation is mainly shrubs and grasses. The principal land use types are cropland, wasteland, grassland, shrubland, woodland, fallow land, orchards, roads, and residential land. To investigate the potential effects of slope gradient on soil surface roughness, six runoff plots with different slope gradients (8.7%, 17.4%, 25.9%, 34.2%, 42.3% and 50.0%) were selected. The length and width of the selected runoff plots were 20 m and 5 m, respectively (Table 1; Fig. 1). To analyze the effect of slope position on soil surface roughness, one runoff plot with a length of 60 m and slope gradient of 42.3% was selected (Table 1; Fig. 1). The plots were originally constructed in the 1990s to study soil erosion. They were repaired in 2015. In 2016, they were plowed in May, left bare and then hoed in October. All seven plots were carefully plowed by hand to a depth of approximately 20 cm and flattened with a rake in late May of 2017 (Table 1). This tillage method is traditionally and widely applied on the Loess Plateau, particularly in steep slope croplands. To eliminate the effects of vegetation growth on soil surface roughness measurements, an herbicide (Glyphosate, ZHB,
2.2. Soil roughness measurement The dates of the soil surface roughness measurements were June 10, July 2, July 24, August 20, September 10, and October 3, 2017. Six measurements were taken at each of the twenty-four selected sites. The soil surface roughness was measured with a handheld noncontact 3D white light scanner (Go! SCAN 50, CREAFORM, Canada; Fig. 1B). When measuring RR, the white light was projected by an LED bulb onto the measured object, and the narrow beam laser pulse that was omitted by two quickly turning speculums scanned across the object. The distance between the scanner and the measured object was calculated via triangulation. The scanner used in this study had a self-positioning system that depended on the positioning of targets placed on the measured surface (Ameen et al., 2018). The 3D coordinates of each measured point were determined by the measured angle of the laser pulse. The scanner had a horizontal resolution of 1.5 mm and a vertical resolution of 0.5 mm. Prior to the soil surface roughness measurement, the scanner was calibrated with a calibration plate. A 50 cm × 50 cm rectangular wooden frame was placed exactly at the position of the selected site (Fig. 1C). Approximately 20 positioning targets (1.1 cm in diameter) were randomly placed on the soil surface to control for the horizontal position of the measured points. One wooden stick (2 cm × 1 cm) was vertically inserted into the soil of the slope near the frame at an exact
Fig. 1. Images of runoff plots (a), handheld scanner (b), and measuring frame (c). 94
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cumulative rainfall. Thus, the rainfall kinetic energy was also computed in this study (Brown and Foster, 1987). Based on the temporal variation in intensity, rainfall events were divided into continuously numbered periods with a relatively stable rainfall intensity, which was defined as the break-point intensity. The unit energy em was calculated as:
height of 5 cm to determine the relative vertical heights of the measured points (Fig. 1C). The distance between the scanner and the soil surface ranged from 20 to 40 cm. The site was scanned from different directions and the scanned images were automatically conjoined based on the positioning targets and image features. The quality of the scanned image was influenced by ambient light. It was better to scan on a cloudy day or on a soil surface where the measuring site was sheltered from sunlight. The scanned image was input into Geomagic Qualify software (2013 version), and the coordinates were set. The X-axis was parallel to the contour line and the Y-axis was perpendicular to the contour line. The Z-axis was perpendicular to the XOY plane, and its origin was set to the height of the bottom left corner of the wooden stick. In this way, traditional linear detrending was not necessary to remove the effect of slope (Planchon et al., 2001; Haubrock et al., 2009) because the Z-axis was perpendicular to the scanned soil surface. The image was cropped to the exact 50 cm × 50 cm dimensions. Noise was eliminated automatically, and the empty holes were filled by an interpolation method using the surrounding measured points. The point cloud data were then input into ArcGIS software (version 10.2) to build a digital elevation model (DEM) with a cell size of 2 mm (Fig. 2). For each image, 62,500 cells were generated. Soil surface roughness was defined as the root mean square of the elevational difference between any one cell and the mean of all cells:
RR =
1 MN
M
em = 0.29[1 − 0.72exp (−0.05im)]
where em is the unit energy (MJ (ha mm)−1), and im is the break point intensity (mm h−1). The kinetic energy of a rainfall event was computed as:
E=
n
∑r=1 (Emr Pr )
(3)
where E is the kinetic energy of the rainfall event (MJ ha−1), Emr is the unit energy of the rth period (MJ (ha mm)−1), Pr is the amount of rainfall in the rth period (mm), and r is the number of break points.
2.4. Soil properties measurements Three soil samples were randomly taken from the upper soil layer (0–5 cm) around each site where the roughness measurements were taken. The samples were mixed completely, air dried and used for soil texture and organic matter content measurements. The former was measured using a Mastersizer 2000 (Malvern Instruments, Malvern, England) with two replicates. The latter was measured by the potassium dichromate colorimetric method with two replicates. It was assumed that soil texture and organic matter content varied only slightly during the test period (5 months); both soil texture and organic matter content were only measured once. Soil bulk density was measured by the oven-drying method; three replicates samples were taken at each roughness measuring site using steel rings with a diameter of 5 cm and a height of 5.1 cm (100 cm3). The soil water content was measured by the oven-drying method (105 ℃ for 24 h) for three replicates at each test site using the same steel rings as the bulk density measurement. Soil cohesion was measured with a pocket torvane (Durham Geo-Enterprises, Inc., UK) on a well-wetted soil surface for ten replicates at each site. The thickness of the physical crust was measured with a caliper to the nearest 0.1 mm for three replicates at each roughness measuring site. For each measurement, the thickness of a randomly selected piece of physical crust was measured from four different directions. The mean of the twelve measurements was considered the average crust thickness for that site. For each test site, three replicate measurements of soil aggregate stability were made using Yoder’s (1936) method. All soil properties were measured six times from June to October 2017 at the same time as the soil roughness measurements.
N
∑C =0 ∑r=0 [Z (xc , yr ) − u]2
(2)
(1)
where RR is the soil random roughness (mm), M is the number of the scanned column, N is the number of the scanned line, c is the column indicator, r is the line indicator, Z(xc, yr) is the elevation of grid c and r (mm), and u is the mean elevation of the scanned image (mm). 2.3. Rainfall observation and kinetic energy calculation Raindrop impact is the dominant factor influencing soil roughness decay after tillage (Zobeckn and Onstad, 1987; Guzha, 2004; Zheng et al., 2017). The rainfall events were recorded by a HOBORG3-M tipping bucket rain gauge (Onset Co., USA), which was installed near the runoff plots with different slope gradients. The resolution of the tipping bucket was 0.2 mm and the data could be downloaded to Excel using HOBOware software. This equipment recorded the number of bucket tips per unit time, and the amount of rainfall and the intensity of each rainfall event could be calculated. Some previous studies (Eltza and Norton, 1997; Torri et al., 1999) have shown that cumulative kinetic energy is a better method of quantifying the influence of rainfall on soil roughness decay than
2.5. Data analysis The differences in soil surface roughness between different slope gradients and between different slope positions were analyzed using a one-way ANOVA. The relationships between soil surface roughness decay and rainfall characteristics or soil physical properties were analyzed using a simple regression. The relationships between soil surface roughness and rainfall or slope gradient were developed using a nonlinear stepwise regression. The regression results were evaluated based on the coefficient of determination (R2) and the coefficient of NashSutcliffe model efficiency (NSE). A paired T test was used to detect the difference in R2 between rainfall characteristics and soil surface roughness. A path analysis was used to identify the effects of aggregate or clod breakdown and soil crust development on soil surface roughness decay. All data analyses were conducted using SPSS (Statistical Product and Service Solutions) 19.0 at the 0.05 significance level.
Fig. 2. An example of a generated DEM. 95
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Table 2 Statistical properties of random roughness (mm) under different topographic conditions. Topographic condition
Min
Max
Mean
STDEV
CV
Slope gradient (%)
9.94 10.58 13.41 14.82 18.55 23.47 15.01 20.75 13.70
11.89 13.03 16.39 16.51 19.87 24.38 18.20 25.25 15.77
10.94 11.83 14.80 15.64 19.21 23.89 16.59 22.77 14.66
0.85 1.08 1.31 0.77 0.58 0.43 1.20 1.74 0.74
0.08 0.09 0.09 0.05 0.03 0.02 0.07 0.08 0.05
Slope position
8.7 17.4 25.9 34.2 42.3 50.0 Upslope Middle Downslope
3. Results 3.1. Soil surface roughness decay under different topographic conditions Soil surface roughness varied with slope gradient and slope position. The minimum RR ranged from 9.94 to 23.47 mm, whereas the maximum RR ranged from 11.89 to 25.25 mm. The mean RR ranged from 10.94 to 23.89 mm (Table 2). The standard deviation ranged from 0.43 to 1.74 mm and the coefficient of variation ranged from 0.02 to 0.09, which indicates that there was little temporal variation in soil surface roughness under different topographic conditions (Table 2). The initial soil surface roughness (measured on June 10) differed between different slope gradients and slope positions. For the different slope gradients, soil surface roughness increased with slope gradient from 11.89 to 24.38 mm. For the different slope positions, however, the pattern of RR was complex. From the upslope to the middle position, RR increased from 18.20 to 25.25 mm, and from the middle slope to the downslope position, it decreased to 15.77 mm. For the entire measuring period, the mean RR was also quite different between different slope gradients and positions. The ANOVA showed that the mean RR of the different slope gradients were significantly different. The regression results demonstrated that the mean RR increased exponentially with slope gradient (Fig. 3):
RR = 8.8815e1.8655S
R2 = 0.97
Fig. 4. Temporal variation in random roughness under different slope gradients.
m−1). The mean RR of the different slope positions were also significantly different. The middle position had the maximum mean RR, followed by the upslope and downslope positions. Soil surface roughness decreased over time under different topographic conditions. Compared with the initial roughness, RR decreased by 1.95, 2.44, 2.98, 1.69, 1.32, and 0.91 mm for the slope gradients of 8.7%, 17.4%, 25.9%, 34.2%, 42.3%, and 50.0%, respectively. The relative reductions were 16.4%, 18.8%, 18.2%, 10.2%, 6.7%, and 3.8%, respectively. The decrease in RR increased with increasing slope gradient from 8.7% to 25.9% and then declined gradually. Rapid decreases were observed between late July and early September (Fig. 4). For the different slope positions, compared with the initial stage, RR decreased by 3.19, 4.50, and 2.07 mm for upslope, middle and downslope positions, respectively. The maximum decrease was observed at the middle position, whereas the minimum decrease was observed at the downslope position. Rapid decreases were detected between early June and late July for the different slope positions (Fig. 5).
(4)
where RR is the random roughness (mm), and S is the slope gradient (m
3.2. Effects of rainfall characteristics on soil surface roughness Seventeen rainfall events occurred during the entire study period (Table 3). The cumulative rainfall was 266.4 mm and the cumulative kinetic energy was 38.88 MJ ha−1. Three heavy storms occurred on July 27, August 20 and August 26. The rainfall amounts were 55.2, 67.6, and 54.6 mm, and the kinetic energies were 9.13, 9.81, and 4.53 MJ ha−1, respectively. The rainfall intensities were relatively low (1.75, 1.29, and 0.64 mm h−1, respectively). The cumulative rainfall significantly affected soil surface roughness. RR decreased exponentially with increasing cumulative rainfall for the different slope gradients and positions (Fig. 6) according to the following equation:
RRP = αRRi e−βP
(5)
where RRP is the random roughness after rainfall (mm), RRi is the initial random roughness after tillage (mm), P is the cumulative rainfall (mm), and α and β are the fitted coefficient and the exponent, respectively. The fitted α varied with topographic conditions from 0.97 to 1.00 with a mean of 0.98. The fitted β varied with topographic conditions from −0.0001 to −0.0008 with a mean of −0.0005. The coefficients of determination ranged from 0.70 to 0.96 with a mean of 0.88.
Fig. 3. Random roughness as a function of slope gradient. 96
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Fig. 5. Temporal variation in random roughness under different slope positions.
Fig. 6. Random roughness as a function of cumulative rainfall.
Table 3 Rainfall characteristics during the random roughness measuring period. Date
Rainfall amount (mm)
Cumulative rainfall (mm)
Kinetic energy (MJ ha−1)
Cumulative kinetic energy (MJ ha−1)
June 15 July 05 July 14 July 20 July 22 July 26 July 27 August 07 August 08 August 11 August 18 August 20 August 24 August 26 September 16 September 21 October 02
7.8 7.8 5.2 4.4 13.0 3.6 55.2 3.0 5.4 2.2 9.6 67.6 2.0 54.6 8.8 2.2 14.0
7.8 15.6 20.8 25.2 38.2 41.8 97.0 100.0 105.4 107.6 117.2 184.8 186.8 241.4 250.2 252.4 266.4
0.27 1.36 0.99 0.75 3.43 0.66 9.13 0.26 1.72 0.29 1.44 9.81 0.11 4.53 1.48 0.10 1.25
1.27 2.63 3.62 4.37 7.80 8.45 17.59 17.85 19.57 19.85 21.30 31.11 31.22 35.75 37.23 37.61 38.88
The cumulative kinetic energy of rainfall also significantly influenced soil surface roughness. RR declined exponentially with increasing cumulative kinetic energy under different topographic conditions according to the following equation (Fig. 7):
RRP = αRRi e−βE
Fig. 7. Random roughness as a function of cumulative kinetic energy.
was (Fig. 8a):
(6)
RR = 9.33e1.874S − 0.0005P
where E is the cumulative kinetic energy (MJ ha−1). The fitted α ranged from 0.97 to 1.00 with a mean of 0.98. The fitted β varied from −0.001 to −0.005 with a mean of −0.004. The coefficients of determination ranged from 0.75 to 0.99 with a mean of 0.92. The paired T test showed that the coefficients of determination between soil surface roughness and cumulative rainfall, and between soil surface roughness and cumulative kinetic energy differed significantly. The mean coefficient of determination for the latter was 5% higher than the former. This result indicates that cumulative kinetic energy was a more accurate predictor of soil surface roughness decay over time after tillage under different topographic conditions than cumulative rainfall. For the six runoff plots with different slope gradients, the nonlinear stepwise regression for cumulative rainfall and slope gradient combined
R2 = 0.96
NSE = 0.95
(7)
whereas the result for cumulative kinetic energy and slope gradient was (Fig. 8b):
RR = 9.38e1.874S − 0.003P
R2 = 0.96
NSE = 0.96
(8)
Both Eqs. (7) and (8) simulated soil surface roughness satisfactorily with high coefficients of determination and NSE. This result implies that there was no difference between the ability of cumulative rainfall and cumulative kinetic energy to predict soil surface roughness decay after tillage for runoff plots with different slope gradients. Relationships between soil properties and soil surface roughness The measured soil properties showed distinct temporal variation 97
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Fig. 8. The measured and estimated random roughness (RR) using Eqs. (7) and (8).
surface roughness decreased linearly as crust thickness increased (Fig. 10). The coefficients of determination ranged from 0.74 to 0.97 with a mean of 0.92. This result indicates that the relationship between RR and crust thickness was stronger than the relationship between RR and soil bulk density. Soil cohesion varied considerably over time, ranging from 2.20 to 4.20 with a mean of 3.42 Kpa. The coefficients of variation ranged from 0.13 to 0.23 with a mean of 0.18, indicating moderate temporal variation. Soil cohesion increased continually over time after tillage until August 20. After that date, the pattern of soil cohesion was variable (Fig. 11). The changes in soil cohesion resulted from temporal variation in soil consolidation and crust development. Under different topographic conditions, soil surface roughness decreased linearly with increased soil cohesion (Fig. 11). The coefficients of determination ranged from 0.46 to 0.96 with a mean of 0.82. Compared with crust thickness, the relationship between soil surface roughness and soil cohesion was relatively weak.
after tillage, except for soil moisture and aggregate content. In the study region, soil moisture was mainly determined by the temporal variation in rainfall. Generally, soil moisture decreased from June to August and then increased until the end of the measurement period. The changes in soil aggregate content over time under different topographic conditions were complex. The soil bulk density fluctuated greatly over time with a range of 1.03 to 1.34 and a mean of 1.21 g cm−3. The coefficient of variation varied from 0.08 to 0.10 with a mean of 0.09, indicating weak temporal variation. Bulk density increased slowly from June 10 to July 24 and then increased rapidly and reached a relatively stable stage (Fig. 9). Soil consolidation caused by rainfall impacts contributed to the increase in bulk density. This process caused the loose topsoil layer to harden and smooth. Soil surface roughness decreased linearly with soil bulk density under different topographic conditions (Fig. 9). The coefficients of determination varied from 0.71 to 0.99 with a mean of 0.86. The relationship between RR and soil bulk density was stronger for slope gradient than for slope position. The crust thickness varied considerably over time from 0.61 to 2.29 mm with a mean of 1.44 mm. The coefficient of variation varied from 0.31 to 0.45 with a mean of 0.38, which implies moderate temporal variation. From June 10, the crust thickness increased rapidly until July 2 and then decreased slightly. From July 3 to July 24, the crust thickness increased rapidly and then increased slowly until the end of the measurement period (Fig. 10). The crust formation was primarily controlled by the breakdown of aggregates or clods induced by raindrop impacts. Under different topographic conditions, soil
4. Discussion 4.1. Soil surface roughness decay under different topographic conditions Soil roughness varied from 9.94 to 25.25 mm (Table 2), which falls within the documented range of random roughness (Römkens and Wang, 1986). Soil surface roughness increased exponentially with increasing slope gradient (Fig. 3). This result is consistent with the results of Liu et al. (2003), who reported that the random roughness of steep
Fig. 9. Temporal variation in bulk density and its effect on random roughness. 98
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Fig. 10. Temporal variation in crust thickness and its effect on random roughness.
be transported by shallow overland flow. The long-term effect of this flow was a decreased clay content and an increased sand content. At the middle position, the slope length varied from 21 to 40 m, and it was long enough for runoff to accumulate. All detached sediments could be transported by overland flow (Zhang et al., 2009a); there was no size selectivity. This serious erosion could increase soil surface roughness (Ding and Huang, 2017; He et al., 2018). At the downslope position, soil detachment by overland flow was limited again due to feedback between the soil detachment and sediment transport processes (Lei et al., 2008; Zhang et al., 2009b). Consequently, the middle position had the maximum RR, and the downslope position had the minimum RR. Soil surface roughness after tillage decreased over time under different topographic conditions (Figs. 4 and 5). RR decreased exponentially with time. This result is consistent with many previous studies (Eltza and Norton, 1997; Guzha, 2004; Moreno et al., 2011). The rapid decrease in soil surface roughness occurred during different periods (Figs. 4 and 5); they were earlier in plots with different slope positions than in plots with different slope gradients. The reason for this was not clear, and further studies are needed to investigate it.
cropland was much greater than that of terraced and gentle croplands on the Loess Plateau. The increase in soil surface roughness with slope gradient was most likely caused by the tillage method. In this study, the widely used hand plow was employed, which plowed the soil of the plot gradually from downslope to upslope with the farmer moving backwards step by step. Generally, the large clods are patted into small ones by simultaneous shoveling. However, the difficulty of patting soil clods increases with slope gradient. Consequently, soil surface roughness was a function of slope gradient. At the same time, both the soil detachment capacity of raindrop impacts and the sediment transport capacity of overland flow increased with slope gradient (Sharma et al., 1993; Zhang et al., 2009a), which increased the spatial heterogeneity of the soil surface. As a result, soil surface roughness increased with slope gradient. Soil surface roughness was significantly affected by slope position. The middle position had the maximum RR, followed by the upslope and downslope positions. This result is consistent with the conclusion of He et al. (2018), who found that the changes in soil surface roughness at different slope positions were quite different under different rainfall series. The difference in initial random roughness between the different slope positions was most likely caused by a difference in soil properties (Wuddivira et al., 2009; Zheng et al., 2017), although the soil texture of the different slope positions was not measured in this study. The plot was constructed in the 1990s and was subjected to soil erosion measurement for many years. At the upslope position, erosion was influenced by raindrop impact, which induced soil consolidation and the breakdown of aggregates or clods. However, only fine sediment could
4.2. Effects of rainfall characteristics on soil surface roughness Rainfall was the dominant force driving soil surface roughness decay after tillage. RR under different topographic conditions declined exponentially with cumulative rainfall and cumulative kinetic energy (Figs. 6 and 7). However, the mean fitted α (0.98) was greater and the
Fig. 11. Temporal variation in soil cohesion and its effect on random roughness. 99
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mean fitted β (−0.0005) was less than the values reported by Zobeckn and Onstad (1987) (0.89 and −0.00026, respectively). The fitted α in this study was very close to the 0.96 of Kabanyolo clay soil reported by Magunda et al. (1997). The fitted β in this study was twice the mean value (−0.00025) of the disc plow, strip catchment tillage and hand hoe reported by Guzha (2004). These differences were likely caused by differences in cumulative rainfall, soil properties and tillage methods. Under different topographic conditions, the cumulative kinetic energy was a better predictor of soil surface roughness decay than cumulative rainfall (Figs. 6 and 7). This result is confirmed by the conclusions of Eltza and Norton (1997); Torri et al. (1999); Planchon et al. (2001) and Guzha (2004). However, for the six plots with different slope gradients, no difference was found between cumulative rainfall and kinetic energy. This indicates that the soil surface roughness decay of plots with different slope positions was affected more by soil erosion than the plots with different slope gradients, because cumulative kinetic energy was more closely related to soil erosion than cumulative rainfall in the study region.
Acknowledgements Financial assistance for this work was supplied by the State Key Program of National Natural Science of China (41530858) and the Fund for Creative Research Groups of the National Natural Science Foundation of China (41621061). References Ameen, W., Al-Ahmari, A.M., Mian, S.H., 2018. Evaluation of handheld scanners for automotive applications. Appl. Sci. 8 (2), 217–232. Bauer, T., Strauss, P., Grims, M., Kamptner, E., Mansberger, R., Spiegel, H., 2015. Longterm agricultural management effects on surface roughness and consolidation of soils. Soil Tillage Res. 151, 28–38. Brown, L.C., Foster, G.R., 1987. Strom erosivity using idealized intensity distributions. Trans. ASAE 30, 379–386. Bullard, J., Ockelford, A., Strong, C., Aubault, H., 2018. Impact of multi-day rainfall events on surface roughness and physical crusting of very fine soils. Geoderma 313, 181–192. Ding, W.F., Huang, C.H., 2017. Effects of soil surface roughness on interrill erosion processes and sediment particle size distribution. Geomorphology 295, 801–810. Eltza, F.L., Norton, L.D., 1997. Surface roughness changes as affected by rainfall erosivity, tillage, and canopy cover. Soil Sci. Soc. Am. J. 61, 1746–1755. Gómez, J.A., Nearing, M.A., 2005. Runoff and sediment losses from rough and smooth soil surfaces in a laboratory experiment. Catena 59 (3), 253–266. Govers, G., Gimenez, R., Van Oost, K., 2007. Rill erosion: exploring the relationship between experiments, modelling and field observations. Earth Sci. Rev. 84 (3), 87–102. Guzha, A.C., 2004. Effects of tillage on soil microrelief, surface depression storage and soil water storage. Soil Tillage Res. 76 (2), 105–114. Haubrock, S.N., Kuhnert, M., Chabrillat, S., Guntner, A., Kaufmann, H., 2009. Spatiotemporal variation of soil surface roughness from in-situ laser scanning. Catena 79 (2), 128–139. He, S.Q., Qin, F., Zheng, Z.C., Li, T.X., 2018. Changes of soil microrelief and its effect on soil erosion under different rainfall patterns in a laboratory experiment. Catena 162 (1), 203–215. Lei, T.W., Zhang, Q.W., Yan, L.J., Zhao, J., Pan, Y.H., 2008. A rational method for estimating erodibility and critical shear stress of an eroding rill. Geoderma 144 (3), 628–633. Lin, B.B., Richards, P.L., 2007. Soil random roughness and depression storage on coffee farms of varying shade levels. Agric. Water Manage. 92 (3), 194–204. Liu, B.Y., Nearing, M.A., Risse, L.M., 1994. Slope gradient effects on erosion for high slopes. Trans. ASAE 37 (6), 1835–1840. Liu, B.Y., Nearing, M.A., Shi, P.J., Jia, Z.W., 2000. Slope length relationships for soil loss for steep slopes. Soil Sci. Soc. Am. J. 64 (5), 1759–1763. Liu, G.B., Xu, M.X., Ritsema, C., 2003. A study of soil surface characteristics in a small watershed in the hilly, gullied area on the Chinese Loess Plateau. Catena 54 (1), 31–44. Luo, J., Zheng, Z.C., Li, T.X., He, S.Q., 2017. Spatial heterogeneity of microtopography and its influence on the flow convergence of slopes under different rainfall patterns. J. Hydraul. 545 (1), 88–99. Magunda, M.K., Larson, W.E., Linden, D.R., Nater, E.A., 1997. Changes in microrelief and their effects on infiltration and erosion during simulated rainfall. Soil Technol. 10 (1), 57–67. Martinez-Agirre, A., Alvarez-Mozos, J., Gimenez, R., 2016. Evaluation of surface roughness parameters in agricultural soils with different tillage conditions using a laser profile meter. Soil Tillage Res. 161 (1), 19–30. Moreno, R.G., Alvarez, M.C., Requejo, A.S., Delfa, J.L., Tarquis, A.M., 2010. Multiscaling analysis of soil roughness variability. Geoderma 160, 22–30. Moreno, R.G., Requejo, A., Duran, J., Alvarez, M., 2011. Significance of soil erosion on soil surface roughness decay after tillage operations. Soil Tillage Res. 117 (1), 49–54. Planchon, O., Esteves, M., Silvera, N., Lapetite, J., 2001. Microrelief induced by tillage: measurement and modelling of surface storage capacity. Catena 46 (2-3), 141–157. Römkens, M.J.M., Wang, J.Y., 1986. Effect of tillage on surface roughness. Trans. ASAE 29, 429–433. Römkens, M.J.M., Helming, K., Prasad, S.N., 2001. Soil erosion under different rainfall intensities, surface roughness, and soil water regimes. Catena 46 (2), 103–123. Sharma, P.P., Gupta, S.C., Foster, G.R., 1993. Predicting soil detachment by raindrops. Soil Sci. Soc. Am. J. 57, 674–680. Taconet, O., Ciarletti, V., 2007. Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry. Soil Tillage Res. 93 (1), 64–76. Torri, D., Regues, D., Pellegrini, S., Bazzoffi, P., 1999. Within-storm soil surface dynamics and erosive effects of rainstorms. Catena 38 (2), 131–150. Wang, L.J., Zhang, G.H., Zhu, L.J., Wang, H., 2017. Biocrust wetting induced change in soil surface roughness as influenced by biocrust type, coverage and wetting patterns. Geoderma 306, 1–9. Wang, H., Zhang, G.H., Li, N.N., Zhang, B.J., Yang, H.Y., 2018. Soil erodibility influenced by natural restoration time of abandoned farmland on the Loess Plateau of China. Geoderma 325 (3), 18–27. Wuddivira, M.N., Stone, R.J., Ekwue, E.I., 2009. Clay, organic matter, and wetting effects on splash detachment and aggregate breakdown under intense rainfall. Soil Sci. Soc. Am. J. 73 (1), 226–232. Yang, J., Chu, X.F., 2015. A new modeling approach for simulating microtopographydominated, discontinuous overland flow on infiltrating surfaces. Adv. Water Res. 78
4.3. Relationships between soil properties and soil surface roughness After tillage, both soil bulk density and crust thickness increased over time via soil consolidation and the breakdown of aggregates or clods induced by raindrop impacts. Soil surface roughness decreased linearly with increasing bulk density and crust thickness (Figs. 9 and 10). The influences of soil consolidation and the breakdown of aggregates or clods on soil surface roughness most likely changes with rainfall characteristics, soil properties and tillage operations (Magunda et al., 1997). It is assumed that the change in soil bulk density of the topsoil layer (5 cm) was related to the process of soil consolidation, whereas the development of soil crust thickness was more related to the breakdown of aggregates or clods. Combining all the measured data from the six plots with different slope gradients, the path analysis showed that both soil crust thickness (p=−0.188) and bulk density (−0.004) had a negative effect on soil surface roughness decay. However, the influence of soil crust thickness was much greater than that of soil bulk density, which indicates that the breakdown of soil aggregates or clods was the dominant process driving soil roughness decay after tillage in this study. This result is consistent with the results of Magunda et al. (1997) and Bullard et al. (2018).
5. Conclusion Soil surface roughness decay after tillage under different topographic conditions was measured using a handheld 3D scanner. The results showed that both the slope gradient and slope position significantly affected soil surface roughness. Soil surface roughness increased exponentially with increasing slope gradient. The middle slope position had the maximum soil surface roughness, followed by the upslope and downslope positions. Soil surface roughness after tillage decreased exponentially over time under different slope gradients and positions. However, the rapid decreases occurred during different periods for the plots with different slope gradients and positions. Soil surface roughness declined exponentially with increasing cumulative rainfall and kinetic energy. The cumulative energy was a better predictor of RR decreases at different slope positions than cumulative rainfall. However, the temporal variation in RR was accurately estimated by slope gradient and either cumulative rainfall or kinetic energy for plots with different slope gradients. As a result of raindrop impacts, soil bulk density, crust thickness and soil cohesion increased over time after tillage under different topographic conditions. Soil surface roughness decreased linearly with increasing soil bulk density, crust thickness, and soil cohesion. The breakdown of aggregates or clods was the dominant process driving soil surface roughness decay in this study. 100
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detachment on steep slopes: II. Sediment feedback relationship. Soil Sci. Soc. Am. J. 73 (4), 1298–1304. Zhang, G.H., Tang, K.M., Sun, Z.L., Zhang, X.C., 2014. Temporal variability in rill erodibility for two types of grasslands. Soil Res. 52, 781–788. Zheng, Z.C., He, S.Q., Wu, F.Q., 2012. Relationship between soil surface roughness and hydraulic roughness coefficient on sloping farmland. Water Sci. Eng. 5 (2), 191–201. Zheng, X.M., Jiang, T., Li, X.F., Ding, Y.L., Zhao, K., 2017. The temporal variation of farmland soil surface roughness with various initial surface states under natural rainfall conditions. Soil Tillage Res. 170, 147–156. Zobeckn, T.M., Onstad, C.A., 1987. Tillage and rianfall effects on random roughness: a review. Soil Tillage Res. 9, 1–20.
(1), 80–93. Yoder, R.E., 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Agron. J. 28 (5), 337–351. Zhang, G.H., Liu, B.Y., Liu, G.B., He, X.W., Nearing, M.A., 2003. Detachment of undisturbed soil by shallow flow. Soil Sci. Soc. Am. J. 67 (3), 713–719. Zhang, G.H., Liu, G.B., Tang, M.K., Zhang, X.C., 2008. Flow detachment of soils under different land uses in the Loess Plateau of China. Trans. ASABE 51 (3), 883–890. Zhang, G.H., Liu, Y.M., Han, Y.F., Zhang, X.C., 2009a. Sediment transport and soil detachment on steep slopes: I. Transport capacity estimation. Soil Sci. Soc. Am. J. 73 (4), 1291–1297. Zhang, G.H., Liu, Y.M., Han, Y.F., Zhang, X.C., 2009b. Sediment transport and soil
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Soil & Tillage Research journal homepage: www.elsevier.com/locate/still
Soil surface roughness decay under different topographic conditions Guang-hui Zhang a b
a,b,⁎
, Zhe-fang Xie
T
a,b
State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Random roughness Temporal variation Slope gradient Slope position The Loess Plateau
Soil surface roughness (RR) is one of the most significant factors influencing hydrological and erosion processes. It is most likely affected by topographic conditions, but this relationship has not been quantified. This study was conducted to detect the effects of topographic conditions on soil surface roughness decay after tillage and to determine the dominant mechanism affecting RR on the Loess Plateau. From June 10 to October 3, 2017, RR was measured using a handheld 3D scanner in six plots with different slope gradients and at three different slope positions along a 60 m plot. The results showed that soil surface roughness was significantly affected by slope gradient and position. RR increased exponentially with an increase in slope gradient (R2 = 0.97). The middle slope position exhibited the maximum RR, whereas the downslope position exhibited the minimum RR. Soil surface roughness decreased exponentially over time after tillage under different topographic conditions ranging from 0.91 to 4.50 mm. For the six plots with different slope gradients, rapid decreases in RR occurred from July 24 to Sept. 10, but for the three different slope positions, rapid decreases occurred from June 10 to July 24. RR declined exponentially with an increase in cumulative rainfall (266.4 mm, R2 = 0.88) and kinetic energy (38.9 MJ ha−1, R2 = 0.92). Cumulative kinetic energy was a better predictor of RR decay than cumulative rainfall at different slope positions. However, for plots with different slope gradients, RR decay after tillage was best estimated by slope gradient and cumulative rainfall (NSE = 0.95) or kinetic energy (NSE = 0.96). After tillage, the soil bulk density, crust thickness and soil cohesion increased significantly over time under different topographic conditions. RR decreased linearly with increasing bulk density, crust thickness, and cohesion. The breakdown of aggregates or clods was the dominant process leading to soil surface roughness decay in this study.
1. Introduction Soil surface roughness (RR), defined as the spatial variation in soil surface elevation across a field at scales ranging from centimeters to millimeters or less, is one of the most significant soil surface characteristics influencing many land surface processes (Bullard et al., 2018; He et al., 2018). Römkens and Wang (1986) identified four different types of soil surface roughness. The first two describe the variability in microrelief due to the size of individual soil particles or aggregates and the surface variation as a result of the break-up of soil clods by tillage operations. These two types of soil surface roughness are quantitatively expressed and widely described as soil random roughness. RR plays an essential role in the water depression capacity, the proportion of soil surface covered by water, rainfall infiltration, runoff generation, flow velocity and resistance, hydrological connectivity and drainage network evolution, water evaporation, and soil erosion as well as gas exchange and the development of soil biota (Zobeckn and Onstad, 1987; Magunda et al., 1997; Römkens et al., 2001; Gómez and Nearing, 2005;
⁎
Lin and Richards, 2007; Moreno et al., 2010; Yang and Chu, 2015; Ding and Huang, 2017; He et al., 2018). Hence, it is vital to quantify soil random roughness under different conditions to understand the hydrology and erosion as well as their related processes on hillslopes or at watershed scales. Soil random roughness is mainly affected by climate, soil properties, vegetation, tillage operation, land use and soil erosion. It is also influenced by the less dominant factors of runoff, freeze-thaw cycles, and wind speed and direction. Raindrops impact the soil surface and can cause the dislocation, reorientation, and packing of soil particles. Macroaggregates (> 0.25 mm) are broken down to microaggregates (0.02-0.25 mm) or primary soil particles by the processes of slaking, differential swelling, raindrop impact and dispersion (Bullard et al., 2018). Soil properties, particularly clay content, organic matter content, biota, and carbonates, influence the formation and size of soil aggregates and clods, which control the rearrangement of soil particles, cementation, and flocculation (Zheng et al., 2017). The breakdown of aggregates and clods by raindrop impact can stimulate the formation of
Corresponding author at: State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (G.-h. Zhang).
https://doi.org/10.1016/j.still.2018.12.003 Received 29 May 2018; Received in revised form 28 November 2018; Accepted 1 December 2018 0167-1987/ © 2018 Elsevier B.V. All rights reserved.
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roughness decay of soil surfaces with or without ridges is quite different (Zheng et al., 2017). As indicated by the influence of these previously noted factors, soil roughness decay after tillage is complex and must be measured systematically under different conditions. In addition, soil roughness decay is also affected by soil erosion, which is spatially scale-dependent. As discussed above, the occurrence of rills will produce oriented roughness (Guzha, 2004). Even in a 2 m long test flume, distinct differences in random roughness can be detected between different slope positions under increasing rainfall intensities. The roughness of the middle and downslope positions increase gradually over time, and the roughness of the downslope position is significantly greater than that of the middle slope position. However, under decreasing rainfall intensities, no obvious changes are observed in the roughness of either the middle or downslope positions during the rainfall period (He et al., 2018). These differences are the result of complex responses by soil erosion processes to the changes in erosive force and soil resistance between smooth and rough soil surfaces (Römkens et al., 2001; Ding and Huang, 2017). In fact, the effects of soil surface roughness on erosion are ambiguous. Whether soil loss increases or decreases with roughness depends on water depression storage, runoff generation, flow friction, drainage density and soil properties (Torri et al., 1999; Römkens et al., 2001; Zheng et al., 2012; Luo et al., 2017). Furthermore, the process of sediment transport is also influenced by soil surface roughness, and the sediment selectivity of a rough soil surface is greater than that of a smooth soil surface (Ding and Huang, 2017). These results clearly imply that soil surface roughness decay after tillage is topographically dependent. Topography is one of the most significant factors controlling soil erosion, particularly slope gradient and slope length (Liu et al., 1994, 2000). Both soil detachment and the sediment transport capacity increase with slope gradient (Zhang et al., 2003, 2009a), which will probably lead to the conversion of the soil erosion mechanism from transport-limited to detachment-limited (Govers et al., 2007). As the slope length increases, the drainage area, runoff depth, velocity, connectivity, and flow power increase. Consequently, soil erosion increases with slope length as a power function (Liu et al., 2000). Sediment concentration increases exponentially with slope length due to the feedback effects of sediment transport on soil detachment (Lei et al., 2008; Zhang et al., 2009b). Therefore, the soil erosion mechanism can change from transport-limited to detachment-limited with slope length. These changes in soil erosion with topography will most likely lead to changes in soil surface roughness. However, little attention has been given to the relationship between soil surface roughness and topographic conditions. Some studies have shown that the roughness of steep cropland is markedly greater than that of terraced or gently sloping cropland on the Loess Plateau (Liu et al., 2003). The difference in soil surface roughness at different slope positions also indicates a close relationship between soil surface roughness and topographic conditions (He et al., 2018). However, to date, the potential effects of topographic conditions on soil surface roughness are not fully understood. The quantitative influences of slope gradient and slope position on soil surface roughness decay after tillage are unknown. Hence, the specific objectives of this study were to investigate the effects of slope gradient and position on soil surface roughness decay after tillage under natural rainfall conditions and to determine the dominant mechanism controlling this process on the Loess Plateau of China.
physical soil crusts, which directly reduce soil surface roughness, and indirectly influence the processes of infiltration, runoff generation, the hydraulics of overland flow, and erosion (Wuddivira et al., 2009). The cover of the vegetation canopy, litter and biological crust can effectively eliminate rainfall kinetic energy and protect the soil surface from raindrop impacts. The restoration of vegetation can enhance aggregate content and stability, organic matter content and root density. These changes are closely related to soil detachment by both raindrop impacts and flowing water and reduce the variation in soil surface roughness (Zhang et al., 2014; Wang et al., 2017, 2018). All tillage operations, including plowing, planting, hoeing, fertilizing and harvesting, disturb the soil surface to different degrees, making the soil surface rough and increasing RR. The effects of tillage operations on soil surface roughness depend on the soil type, tillage method, crop rotation, and tillage history (Taconet and Ciarletti, 2007; Bauer et al., 2015; Martinez-Agirre et al., 2016). Land use has a greater effect on soil erosion than any other single factor, i.e., a greater effect than rainfall, topography, soil or vegetation. Land use adjustments or conversions can directly influence soil properties, vegetation growth, tillage operations, runoff, and soil erosion, and hence indirectly affect soil surface roughness. Farmland is frequently disturbed by tillage operations and is more susceptible to erosion than other land uses (Zhang et al., 2008), resulting in significant changes in soil surface roughness (Liu et al., 2003). Soil erosion affects surface roughness via raindrop splash, scouring by flowing water, and sediment transport and deposition as well as the formation of physical crusts and rills (Ding and Huang, 2017; He et al., 2018). The changes in surface roughness induced by erosion depend on rainfall, topography, soil, vegetation and land use (Zhang et al., 2008; He et al., 2018). All of the factors discussed above fluctuate greatly over time, which leads to significant temporal variation in soil surface roughness. Soil surface roughness changes significantly over time, particularly in farmland. It is widely recognized that soil surface roughness decreases exponentially with cumulative rainfall after tillage or hoeing (Zobeckn and Onstad, 1987; Planchon et al., 2001; Guzha, 2004; Zheng et al., 2017). However, other studies have shown that cumulative kinetic energy or rainfall erosivity are better for quantifying the temporal variation of soil surface roughness than cumulative rainfall (Eltza and Norton, 1997; Torri et al., 1999). This is likely true because both soil consolidation and aggregate breakdown are processes that involve energy consumption. The influence of rainfall on soil surface roughness is also closely related to rainfall patterns, particularly rainfall intensity and its temporal distribution as well as rainfall duration (Haubrock et al., 2009). The response of soil roughness to an increased rainfall series is stronger than that of a decreased rainfall series (Luo et al., 2017). After rills have formed, cumulative rainfall is no longer a dominant factor because oriented roughness appears (Guzha, 2004). Soil roughness decay after tillage is strongly affected by soil properties. Soil consolidation and the breakdown of aggregates or clods are the main processes leading to temporal variation in soil surface roughness. For loam and silt loam soils, roughness decay is principally influenced by the breakdown of aggregates or clods, which is reflected by the formation of a physical crust. For very fine soils, the change in roughness over time is mainly controlled by soil consolidation (Bullard et al., 2018). The soil water content at the time that soil management is performed significantly influences soil surface roughness decay (Bauer et al., 2015). Land use also affects temporal variation in soil surface roughness. Cropland experiences the greatest temporal changes in soil surface roughness, followed by orchards and wasteland, fallow land, shrubland, and woodland (Liu et al., 2003). Moreover, tillage operations and management play significant roles in soil surface roughness decay after tillage. Generally, chisel tillage produces substantial initial roughness and exhibits high temporal variation in soil surface roughness (Eltza and Norton, 1997). The roughness decay of tilled soils is greater than that of untilled soils, and the decrease in roughness of covered soil over time is approximately half that of uncovered soil after 53.2 mm of cumulative rainfall (Guzha, 2004; Moreno et al., 2011). The
2. Materials and methods 2.1. Study region and runoff plots The study was conducted on the Dunshan runoff plots of the Ansai Comprehensive Soil and Water Conservation Station of the Institute of Soil and Water Conservation, Chinese Academy of Science, Ansai County, Shaanxi Province, China. The site is located near the center of the Loess Plateau (109°19′23〞E, 36°51′30〞N) and belongs to a typical 93
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Beijing Zhibao Agricultural Technology Co., Ltd.) was sprayed five times from June to September (Table 1). No other operations were undertaken during the entire measurement period. For each of the six selected plots with different slope gradients, three sites were randomly selected at each slope position (upslope, 0–6 m; middle slope, 7–13 m; and downslope, 14–20 m) to measure soil surface roughness. The mean value of the three positions was considered the measured soil surface roughness for that plot at any given time. For the 60 m plot, two sites were randomly selected at each slope position (upslope, 0–20 m; middle slope, 21–40 m; and downslope, 41–60 m) for soil surface roughness measurements. The mean of the two sites at the same slope position was considered the measured soil surface roughness for that slope position (upslope, middle slope or downslope) at any given time. Overall, twenty-four sites were selected in seven runoff plots. For each of the selected sites, four wood markers were placed to show the exact location of the soil surface roughness measurement (Fig. 1). Weeds that were not completely killed by the herbicide were carefully clipped.
Table 1 Basic information on runoff plots and farming operations. Number of runoff plot
Slope Length (m)
Slope gradient (%)
Date of Plowing
Date of herbicide spraying
1 2 3 4 5 6 7
20 20 20 20 20 20 60
8.7 17.4 25.9 34.2 42.3 50.0 42.3
May May May May May May May
June 10, July 1, July 24, Aug. 10, Sept. 10
20 20 21 21 22 22 23
hilly gully region where soil erosion is very serious, and there are many well-developed gullies. The elevation ranges from 1068 to 1309 m. The region has a semiarid, continental monsoon climate with a mean annual temperature of 8.8 ℃. The mean annual precipitation is 500 mm, and more than 70% of the precipitation occurs during the months of June through September as short, heavy storms. The interannual variation in precipitation is large, ranging from 300 mm to 700 mm. The soil is a typical loess soil (mixed, mesic Typic Udorthent) with a texture of silt loam (USDA). The mean clay, silt and sand contents are 9.8%, 58.4%, and 31.8%, respectively. The mean organic matter content is 0.7%. The vegetation is characteristic of a forest-steppe transitional zone. Most of the natural vegetation has been eliminated and the current vegetation is mainly shrubs and grasses. The principal land use types are cropland, wasteland, grassland, shrubland, woodland, fallow land, orchards, roads, and residential land. To investigate the potential effects of slope gradient on soil surface roughness, six runoff plots with different slope gradients (8.7%, 17.4%, 25.9%, 34.2%, 42.3% and 50.0%) were selected. The length and width of the selected runoff plots were 20 m and 5 m, respectively (Table 1; Fig. 1). To analyze the effect of slope position on soil surface roughness, one runoff plot with a length of 60 m and slope gradient of 42.3% was selected (Table 1; Fig. 1). The plots were originally constructed in the 1990s to study soil erosion. They were repaired in 2015. In 2016, they were plowed in May, left bare and then hoed in October. All seven plots were carefully plowed by hand to a depth of approximately 20 cm and flattened with a rake in late May of 2017 (Table 1). This tillage method is traditionally and widely applied on the Loess Plateau, particularly in steep slope croplands. To eliminate the effects of vegetation growth on soil surface roughness measurements, an herbicide (Glyphosate, ZHB,
2.2. Soil roughness measurement The dates of the soil surface roughness measurements were June 10, July 2, July 24, August 20, September 10, and October 3, 2017. Six measurements were taken at each of the twenty-four selected sites. The soil surface roughness was measured with a handheld noncontact 3D white light scanner (Go! SCAN 50, CREAFORM, Canada; Fig. 1B). When measuring RR, the white light was projected by an LED bulb onto the measured object, and the narrow beam laser pulse that was omitted by two quickly turning speculums scanned across the object. The distance between the scanner and the measured object was calculated via triangulation. The scanner used in this study had a self-positioning system that depended on the positioning of targets placed on the measured surface (Ameen et al., 2018). The 3D coordinates of each measured point were determined by the measured angle of the laser pulse. The scanner had a horizontal resolution of 1.5 mm and a vertical resolution of 0.5 mm. Prior to the soil surface roughness measurement, the scanner was calibrated with a calibration plate. A 50 cm × 50 cm rectangular wooden frame was placed exactly at the position of the selected site (Fig. 1C). Approximately 20 positioning targets (1.1 cm in diameter) were randomly placed on the soil surface to control for the horizontal position of the measured points. One wooden stick (2 cm × 1 cm) was vertically inserted into the soil of the slope near the frame at an exact
Fig. 1. Images of runoff plots (a), handheld scanner (b), and measuring frame (c). 94
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cumulative rainfall. Thus, the rainfall kinetic energy was also computed in this study (Brown and Foster, 1987). Based on the temporal variation in intensity, rainfall events were divided into continuously numbered periods with a relatively stable rainfall intensity, which was defined as the break-point intensity. The unit energy em was calculated as:
height of 5 cm to determine the relative vertical heights of the measured points (Fig. 1C). The distance between the scanner and the soil surface ranged from 20 to 40 cm. The site was scanned from different directions and the scanned images were automatically conjoined based on the positioning targets and image features. The quality of the scanned image was influenced by ambient light. It was better to scan on a cloudy day or on a soil surface where the measuring site was sheltered from sunlight. The scanned image was input into Geomagic Qualify software (2013 version), and the coordinates were set. The X-axis was parallel to the contour line and the Y-axis was perpendicular to the contour line. The Z-axis was perpendicular to the XOY plane, and its origin was set to the height of the bottom left corner of the wooden stick. In this way, traditional linear detrending was not necessary to remove the effect of slope (Planchon et al., 2001; Haubrock et al., 2009) because the Z-axis was perpendicular to the scanned soil surface. The image was cropped to the exact 50 cm × 50 cm dimensions. Noise was eliminated automatically, and the empty holes were filled by an interpolation method using the surrounding measured points. The point cloud data were then input into ArcGIS software (version 10.2) to build a digital elevation model (DEM) with a cell size of 2 mm (Fig. 2). For each image, 62,500 cells were generated. Soil surface roughness was defined as the root mean square of the elevational difference between any one cell and the mean of all cells:
RR =
1 MN
M
em = 0.29[1 − 0.72exp (−0.05im)]
where em is the unit energy (MJ (ha mm)−1), and im is the break point intensity (mm h−1). The kinetic energy of a rainfall event was computed as:
E=
n
∑r=1 (Emr Pr )
(3)
where E is the kinetic energy of the rainfall event (MJ ha−1), Emr is the unit energy of the rth period (MJ (ha mm)−1), Pr is the amount of rainfall in the rth period (mm), and r is the number of break points.
2.4. Soil properties measurements Three soil samples were randomly taken from the upper soil layer (0–5 cm) around each site where the roughness measurements were taken. The samples were mixed completely, air dried and used for soil texture and organic matter content measurements. The former was measured using a Mastersizer 2000 (Malvern Instruments, Malvern, England) with two replicates. The latter was measured by the potassium dichromate colorimetric method with two replicates. It was assumed that soil texture and organic matter content varied only slightly during the test period (5 months); both soil texture and organic matter content were only measured once. Soil bulk density was measured by the oven-drying method; three replicates samples were taken at each roughness measuring site using steel rings with a diameter of 5 cm and a height of 5.1 cm (100 cm3). The soil water content was measured by the oven-drying method (105 ℃ for 24 h) for three replicates at each test site using the same steel rings as the bulk density measurement. Soil cohesion was measured with a pocket torvane (Durham Geo-Enterprises, Inc., UK) on a well-wetted soil surface for ten replicates at each site. The thickness of the physical crust was measured with a caliper to the nearest 0.1 mm for three replicates at each roughness measuring site. For each measurement, the thickness of a randomly selected piece of physical crust was measured from four different directions. The mean of the twelve measurements was considered the average crust thickness for that site. For each test site, three replicate measurements of soil aggregate stability were made using Yoder’s (1936) method. All soil properties were measured six times from June to October 2017 at the same time as the soil roughness measurements.
N
∑C =0 ∑r=0 [Z (xc , yr ) − u]2
(2)
(1)
where RR is the soil random roughness (mm), M is the number of the scanned column, N is the number of the scanned line, c is the column indicator, r is the line indicator, Z(xc, yr) is the elevation of grid c and r (mm), and u is the mean elevation of the scanned image (mm). 2.3. Rainfall observation and kinetic energy calculation Raindrop impact is the dominant factor influencing soil roughness decay after tillage (Zobeckn and Onstad, 1987; Guzha, 2004; Zheng et al., 2017). The rainfall events were recorded by a HOBORG3-M tipping bucket rain gauge (Onset Co., USA), which was installed near the runoff plots with different slope gradients. The resolution of the tipping bucket was 0.2 mm and the data could be downloaded to Excel using HOBOware software. This equipment recorded the number of bucket tips per unit time, and the amount of rainfall and the intensity of each rainfall event could be calculated. Some previous studies (Eltza and Norton, 1997; Torri et al., 1999) have shown that cumulative kinetic energy is a better method of quantifying the influence of rainfall on soil roughness decay than
2.5. Data analysis The differences in soil surface roughness between different slope gradients and between different slope positions were analyzed using a one-way ANOVA. The relationships between soil surface roughness decay and rainfall characteristics or soil physical properties were analyzed using a simple regression. The relationships between soil surface roughness and rainfall or slope gradient were developed using a nonlinear stepwise regression. The regression results were evaluated based on the coefficient of determination (R2) and the coefficient of NashSutcliffe model efficiency (NSE). A paired T test was used to detect the difference in R2 between rainfall characteristics and soil surface roughness. A path analysis was used to identify the effects of aggregate or clod breakdown and soil crust development on soil surface roughness decay. All data analyses were conducted using SPSS (Statistical Product and Service Solutions) 19.0 at the 0.05 significance level.
Fig. 2. An example of a generated DEM. 95
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Table 2 Statistical properties of random roughness (mm) under different topographic conditions. Topographic condition
Min
Max
Mean
STDEV
CV
Slope gradient (%)
9.94 10.58 13.41 14.82 18.55 23.47 15.01 20.75 13.70
11.89 13.03 16.39 16.51 19.87 24.38 18.20 25.25 15.77
10.94 11.83 14.80 15.64 19.21 23.89 16.59 22.77 14.66
0.85 1.08 1.31 0.77 0.58 0.43 1.20 1.74 0.74
0.08 0.09 0.09 0.05 0.03 0.02 0.07 0.08 0.05
Slope position
8.7 17.4 25.9 34.2 42.3 50.0 Upslope Middle Downslope
3. Results 3.1. Soil surface roughness decay under different topographic conditions Soil surface roughness varied with slope gradient and slope position. The minimum RR ranged from 9.94 to 23.47 mm, whereas the maximum RR ranged from 11.89 to 25.25 mm. The mean RR ranged from 10.94 to 23.89 mm (Table 2). The standard deviation ranged from 0.43 to 1.74 mm and the coefficient of variation ranged from 0.02 to 0.09, which indicates that there was little temporal variation in soil surface roughness under different topographic conditions (Table 2). The initial soil surface roughness (measured on June 10) differed between different slope gradients and slope positions. For the different slope gradients, soil surface roughness increased with slope gradient from 11.89 to 24.38 mm. For the different slope positions, however, the pattern of RR was complex. From the upslope to the middle position, RR increased from 18.20 to 25.25 mm, and from the middle slope to the downslope position, it decreased to 15.77 mm. For the entire measuring period, the mean RR was also quite different between different slope gradients and positions. The ANOVA showed that the mean RR of the different slope gradients were significantly different. The regression results demonstrated that the mean RR increased exponentially with slope gradient (Fig. 3):
RR = 8.8815e1.8655S
R2 = 0.97
Fig. 4. Temporal variation in random roughness under different slope gradients.
m−1). The mean RR of the different slope positions were also significantly different. The middle position had the maximum mean RR, followed by the upslope and downslope positions. Soil surface roughness decreased over time under different topographic conditions. Compared with the initial roughness, RR decreased by 1.95, 2.44, 2.98, 1.69, 1.32, and 0.91 mm for the slope gradients of 8.7%, 17.4%, 25.9%, 34.2%, 42.3%, and 50.0%, respectively. The relative reductions were 16.4%, 18.8%, 18.2%, 10.2%, 6.7%, and 3.8%, respectively. The decrease in RR increased with increasing slope gradient from 8.7% to 25.9% and then declined gradually. Rapid decreases were observed between late July and early September (Fig. 4). For the different slope positions, compared with the initial stage, RR decreased by 3.19, 4.50, and 2.07 mm for upslope, middle and downslope positions, respectively. The maximum decrease was observed at the middle position, whereas the minimum decrease was observed at the downslope position. Rapid decreases were detected between early June and late July for the different slope positions (Fig. 5).
(4)
where RR is the random roughness (mm), and S is the slope gradient (m
3.2. Effects of rainfall characteristics on soil surface roughness Seventeen rainfall events occurred during the entire study period (Table 3). The cumulative rainfall was 266.4 mm and the cumulative kinetic energy was 38.88 MJ ha−1. Three heavy storms occurred on July 27, August 20 and August 26. The rainfall amounts were 55.2, 67.6, and 54.6 mm, and the kinetic energies were 9.13, 9.81, and 4.53 MJ ha−1, respectively. The rainfall intensities were relatively low (1.75, 1.29, and 0.64 mm h−1, respectively). The cumulative rainfall significantly affected soil surface roughness. RR decreased exponentially with increasing cumulative rainfall for the different slope gradients and positions (Fig. 6) according to the following equation:
RRP = αRRi e−βP
(5)
where RRP is the random roughness after rainfall (mm), RRi is the initial random roughness after tillage (mm), P is the cumulative rainfall (mm), and α and β are the fitted coefficient and the exponent, respectively. The fitted α varied with topographic conditions from 0.97 to 1.00 with a mean of 0.98. The fitted β varied with topographic conditions from −0.0001 to −0.0008 with a mean of −0.0005. The coefficients of determination ranged from 0.70 to 0.96 with a mean of 0.88.
Fig. 3. Random roughness as a function of slope gradient. 96
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Fig. 5. Temporal variation in random roughness under different slope positions.
Fig. 6. Random roughness as a function of cumulative rainfall.
Table 3 Rainfall characteristics during the random roughness measuring period. Date
Rainfall amount (mm)
Cumulative rainfall (mm)
Kinetic energy (MJ ha−1)
Cumulative kinetic energy (MJ ha−1)
June 15 July 05 July 14 July 20 July 22 July 26 July 27 August 07 August 08 August 11 August 18 August 20 August 24 August 26 September 16 September 21 October 02
7.8 7.8 5.2 4.4 13.0 3.6 55.2 3.0 5.4 2.2 9.6 67.6 2.0 54.6 8.8 2.2 14.0
7.8 15.6 20.8 25.2 38.2 41.8 97.0 100.0 105.4 107.6 117.2 184.8 186.8 241.4 250.2 252.4 266.4
0.27 1.36 0.99 0.75 3.43 0.66 9.13 0.26 1.72 0.29 1.44 9.81 0.11 4.53 1.48 0.10 1.25
1.27 2.63 3.62 4.37 7.80 8.45 17.59 17.85 19.57 19.85 21.30 31.11 31.22 35.75 37.23 37.61 38.88
The cumulative kinetic energy of rainfall also significantly influenced soil surface roughness. RR declined exponentially with increasing cumulative kinetic energy under different topographic conditions according to the following equation (Fig. 7):
RRP = αRRi e−βE
Fig. 7. Random roughness as a function of cumulative kinetic energy.
was (Fig. 8a):
(6)
RR = 9.33e1.874S − 0.0005P
where E is the cumulative kinetic energy (MJ ha−1). The fitted α ranged from 0.97 to 1.00 with a mean of 0.98. The fitted β varied from −0.001 to −0.005 with a mean of −0.004. The coefficients of determination ranged from 0.75 to 0.99 with a mean of 0.92. The paired T test showed that the coefficients of determination between soil surface roughness and cumulative rainfall, and between soil surface roughness and cumulative kinetic energy differed significantly. The mean coefficient of determination for the latter was 5% higher than the former. This result indicates that cumulative kinetic energy was a more accurate predictor of soil surface roughness decay over time after tillage under different topographic conditions than cumulative rainfall. For the six runoff plots with different slope gradients, the nonlinear stepwise regression for cumulative rainfall and slope gradient combined
R2 = 0.96
NSE = 0.95
(7)
whereas the result for cumulative kinetic energy and slope gradient was (Fig. 8b):
RR = 9.38e1.874S − 0.003P
R2 = 0.96
NSE = 0.96
(8)
Both Eqs. (7) and (8) simulated soil surface roughness satisfactorily with high coefficients of determination and NSE. This result implies that there was no difference between the ability of cumulative rainfall and cumulative kinetic energy to predict soil surface roughness decay after tillage for runoff plots with different slope gradients. Relationships between soil properties and soil surface roughness The measured soil properties showed distinct temporal variation 97
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Fig. 8. The measured and estimated random roughness (RR) using Eqs. (7) and (8).
surface roughness decreased linearly as crust thickness increased (Fig. 10). The coefficients of determination ranged from 0.74 to 0.97 with a mean of 0.92. This result indicates that the relationship between RR and crust thickness was stronger than the relationship between RR and soil bulk density. Soil cohesion varied considerably over time, ranging from 2.20 to 4.20 with a mean of 3.42 Kpa. The coefficients of variation ranged from 0.13 to 0.23 with a mean of 0.18, indicating moderate temporal variation. Soil cohesion increased continually over time after tillage until August 20. After that date, the pattern of soil cohesion was variable (Fig. 11). The changes in soil cohesion resulted from temporal variation in soil consolidation and crust development. Under different topographic conditions, soil surface roughness decreased linearly with increased soil cohesion (Fig. 11). The coefficients of determination ranged from 0.46 to 0.96 with a mean of 0.82. Compared with crust thickness, the relationship between soil surface roughness and soil cohesion was relatively weak.
after tillage, except for soil moisture and aggregate content. In the study region, soil moisture was mainly determined by the temporal variation in rainfall. Generally, soil moisture decreased from June to August and then increased until the end of the measurement period. The changes in soil aggregate content over time under different topographic conditions were complex. The soil bulk density fluctuated greatly over time with a range of 1.03 to 1.34 and a mean of 1.21 g cm−3. The coefficient of variation varied from 0.08 to 0.10 with a mean of 0.09, indicating weak temporal variation. Bulk density increased slowly from June 10 to July 24 and then increased rapidly and reached a relatively stable stage (Fig. 9). Soil consolidation caused by rainfall impacts contributed to the increase in bulk density. This process caused the loose topsoil layer to harden and smooth. Soil surface roughness decreased linearly with soil bulk density under different topographic conditions (Fig. 9). The coefficients of determination varied from 0.71 to 0.99 with a mean of 0.86. The relationship between RR and soil bulk density was stronger for slope gradient than for slope position. The crust thickness varied considerably over time from 0.61 to 2.29 mm with a mean of 1.44 mm. The coefficient of variation varied from 0.31 to 0.45 with a mean of 0.38, which implies moderate temporal variation. From June 10, the crust thickness increased rapidly until July 2 and then decreased slightly. From July 3 to July 24, the crust thickness increased rapidly and then increased slowly until the end of the measurement period (Fig. 10). The crust formation was primarily controlled by the breakdown of aggregates or clods induced by raindrop impacts. Under different topographic conditions, soil
4. Discussion 4.1. Soil surface roughness decay under different topographic conditions Soil roughness varied from 9.94 to 25.25 mm (Table 2), which falls within the documented range of random roughness (Römkens and Wang, 1986). Soil surface roughness increased exponentially with increasing slope gradient (Fig. 3). This result is consistent with the results of Liu et al. (2003), who reported that the random roughness of steep
Fig. 9. Temporal variation in bulk density and its effect on random roughness. 98
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Fig. 10. Temporal variation in crust thickness and its effect on random roughness.
be transported by shallow overland flow. The long-term effect of this flow was a decreased clay content and an increased sand content. At the middle position, the slope length varied from 21 to 40 m, and it was long enough for runoff to accumulate. All detached sediments could be transported by overland flow (Zhang et al., 2009a); there was no size selectivity. This serious erosion could increase soil surface roughness (Ding and Huang, 2017; He et al., 2018). At the downslope position, soil detachment by overland flow was limited again due to feedback between the soil detachment and sediment transport processes (Lei et al., 2008; Zhang et al., 2009b). Consequently, the middle position had the maximum RR, and the downslope position had the minimum RR. Soil surface roughness after tillage decreased over time under different topographic conditions (Figs. 4 and 5). RR decreased exponentially with time. This result is consistent with many previous studies (Eltza and Norton, 1997; Guzha, 2004; Moreno et al., 2011). The rapid decrease in soil surface roughness occurred during different periods (Figs. 4 and 5); they were earlier in plots with different slope positions than in plots with different slope gradients. The reason for this was not clear, and further studies are needed to investigate it.
cropland was much greater than that of terraced and gentle croplands on the Loess Plateau. The increase in soil surface roughness with slope gradient was most likely caused by the tillage method. In this study, the widely used hand plow was employed, which plowed the soil of the plot gradually from downslope to upslope with the farmer moving backwards step by step. Generally, the large clods are patted into small ones by simultaneous shoveling. However, the difficulty of patting soil clods increases with slope gradient. Consequently, soil surface roughness was a function of slope gradient. At the same time, both the soil detachment capacity of raindrop impacts and the sediment transport capacity of overland flow increased with slope gradient (Sharma et al., 1993; Zhang et al., 2009a), which increased the spatial heterogeneity of the soil surface. As a result, soil surface roughness increased with slope gradient. Soil surface roughness was significantly affected by slope position. The middle position had the maximum RR, followed by the upslope and downslope positions. This result is consistent with the conclusion of He et al. (2018), who found that the changes in soil surface roughness at different slope positions were quite different under different rainfall series. The difference in initial random roughness between the different slope positions was most likely caused by a difference in soil properties (Wuddivira et al., 2009; Zheng et al., 2017), although the soil texture of the different slope positions was not measured in this study. The plot was constructed in the 1990s and was subjected to soil erosion measurement for many years. At the upslope position, erosion was influenced by raindrop impact, which induced soil consolidation and the breakdown of aggregates or clods. However, only fine sediment could
4.2. Effects of rainfall characteristics on soil surface roughness Rainfall was the dominant force driving soil surface roughness decay after tillage. RR under different topographic conditions declined exponentially with cumulative rainfall and cumulative kinetic energy (Figs. 6 and 7). However, the mean fitted α (0.98) was greater and the
Fig. 11. Temporal variation in soil cohesion and its effect on random roughness. 99
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mean fitted β (−0.0005) was less than the values reported by Zobeckn and Onstad (1987) (0.89 and −0.00026, respectively). The fitted α in this study was very close to the 0.96 of Kabanyolo clay soil reported by Magunda et al. (1997). The fitted β in this study was twice the mean value (−0.00025) of the disc plow, strip catchment tillage and hand hoe reported by Guzha (2004). These differences were likely caused by differences in cumulative rainfall, soil properties and tillage methods. Under different topographic conditions, the cumulative kinetic energy was a better predictor of soil surface roughness decay than cumulative rainfall (Figs. 6 and 7). This result is confirmed by the conclusions of Eltza and Norton (1997); Torri et al. (1999); Planchon et al. (2001) and Guzha (2004). However, for the six plots with different slope gradients, no difference was found between cumulative rainfall and kinetic energy. This indicates that the soil surface roughness decay of plots with different slope positions was affected more by soil erosion than the plots with different slope gradients, because cumulative kinetic energy was more closely related to soil erosion than cumulative rainfall in the study region.
Acknowledgements Financial assistance for this work was supplied by the State Key Program of National Natural Science of China (41530858) and the Fund for Creative Research Groups of the National Natural Science Foundation of China (41621061). References Ameen, W., Al-Ahmari, A.M., Mian, S.H., 2018. Evaluation of handheld scanners for automotive applications. Appl. Sci. 8 (2), 217–232. Bauer, T., Strauss, P., Grims, M., Kamptner, E., Mansberger, R., Spiegel, H., 2015. Longterm agricultural management effects on surface roughness and consolidation of soils. Soil Tillage Res. 151, 28–38. Brown, L.C., Foster, G.R., 1987. Strom erosivity using idealized intensity distributions. Trans. ASAE 30, 379–386. Bullard, J., Ockelford, A., Strong, C., Aubault, H., 2018. Impact of multi-day rainfall events on surface roughness and physical crusting of very fine soils. Geoderma 313, 181–192. Ding, W.F., Huang, C.H., 2017. 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4.3. Relationships between soil properties and soil surface roughness After tillage, both soil bulk density and crust thickness increased over time via soil consolidation and the breakdown of aggregates or clods induced by raindrop impacts. Soil surface roughness decreased linearly with increasing bulk density and crust thickness (Figs. 9 and 10). The influences of soil consolidation and the breakdown of aggregates or clods on soil surface roughness most likely changes with rainfall characteristics, soil properties and tillage operations (Magunda et al., 1997). It is assumed that the change in soil bulk density of the topsoil layer (5 cm) was related to the process of soil consolidation, whereas the development of soil crust thickness was more related to the breakdown of aggregates or clods. Combining all the measured data from the six plots with different slope gradients, the path analysis showed that both soil crust thickness (p=−0.188) and bulk density (−0.004) had a negative effect on soil surface roughness decay. However, the influence of soil crust thickness was much greater than that of soil bulk density, which indicates that the breakdown of soil aggregates or clods was the dominant process driving soil roughness decay after tillage in this study. This result is consistent with the results of Magunda et al. (1997) and Bullard et al. (2018).
5. Conclusion Soil surface roughness decay after tillage under different topographic conditions was measured using a handheld 3D scanner. The results showed that both the slope gradient and slope position significantly affected soil surface roughness. Soil surface roughness increased exponentially with increasing slope gradient. The middle slope position had the maximum soil surface roughness, followed by the upslope and downslope positions. Soil surface roughness after tillage decreased exponentially over time under different slope gradients and positions. However, the rapid decreases occurred during different periods for the plots with different slope gradients and positions. Soil surface roughness declined exponentially with increasing cumulative rainfall and kinetic energy. The cumulative energy was a better predictor of RR decreases at different slope positions than cumulative rainfall. However, the temporal variation in RR was accurately estimated by slope gradient and either cumulative rainfall or kinetic energy for plots with different slope gradients. As a result of raindrop impacts, soil bulk density, crust thickness and soil cohesion increased over time after tillage under different topographic conditions. Soil surface roughness decreased linearly with increasing soil bulk density, crust thickness, and soil cohesion. The breakdown of aggregates or clods was the dominant process driving soil surface roughness decay in this study. 100
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