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Influence of soil moisture content and soil and water conservation measures on time to runoff initiation under different rainfall intensities
Catena 182 (2019) 104172
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Influence of soil moisture content and soil and water conservation measures on time to runoff initiation under different rainfall intensities
T
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Jingling Zhang, Lili Zhou , Renming Ma, Yanfeng Jia, Fan Yang, Hongyang Zhou, Xiangying Cao College of Water Conservancy, Shenyang Agricultural University, Shenyang, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Time to runoff initiation Rainfall intensity Soil moisture content Vegetation Soil and water conservation measures
Time to runoff initiation (TRI) is an important factor related to water infiltration, runoff generation, and hillslope erosion. This study assesses TRI values for six different plots and investigates the effects of rainfall intensity, surface soil moisture content at depths of 0–5 cm, soil and water conservation measures (longitudinal ridges, cross ridges, grass, forest, and flat field under fruit trees), and vegetation on TRI for bare soil, longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees. Results showed that bare soil exhibited the shortest TRI, followed by the longitudinal ridges under maize and cross ridges under peanut crops. The remaining three plots did not generate runoff. The first principal component affecting TRI was soil moisture content, which contributed 69.87%, 64.88%, and 85.73% to the TRI of bare soil, longitudinal ridges under maize, and cross ridges under peanut crops, respectively. The second major component of these three plots were combined rainfall intensity, comprehensive rainfall and vegetation factors, and vegetation factors, which contributed 27.98%, 33.73%, and 14.27%, respectively. Maximum load factor was generated during runoff with maximum rainfall intensity (0.969), stalk diameter (0.985), and maize height (0.864). In conclusion, soil moisture content are the main factors influencing TRI, and soil and water conservation measures provided key benefits for improving the time to runoff initiation and reduction of runoff erosion in the farmlands.
1. Introduction 1.1. Background Time to runoff initiation (TRI) is defined as the time between the beginning of a rainfall event and the beginning of runoff and is used to examine runoff generation and infiltration patterns as the bases for soil and water conservation measures. TRI is affected by various factors, including rainfall intensity (Sadeghi et al., 2016), soil texture (Biddoccu et al., 2016), vegetation (Durán Zuazo and Rodríguez Pleguezuelo, 2008), soil moisture content (McDowell and Sharpley, 2002), and landuse type (Sajikumar and Remya, 2015; Zuo et al., 2016). Increasing rainfall intensity yields decreasing TRI according to a power function relationship (Wang et al., 1991; Mohamadi and Kavian, 2015). A rainfall intensity of 50 mm/h significantly affects the TRI of a plot with 30% slope (Gholami et al., 2016). At rainfall intensities of 45 mm/h, Snig tracks constructed within the last 1.5 y generated localized surface runoff within 1–3 min. At rainfall intensity of 100 mm /h or more, TRI is approximately 1 min (Croke et al., 1999). Soil properties are important factors in increasing water infiltration
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and reducing TRI. Soil crust may drastically reduce infiltration and increase TRI (Armand et al., 2009). Soil humidity and cracks help to explain the flow generation process in a semiarid tropical region (Dos Santos et al., 2016). TRI is prolonged in dry soils, but decreases with increasing antecedent soil moisture content, where the probability of surface runoff generation under dry antecedent soil moisture conditions is lower than that under moderate or wet antecedent conditions (Zhang et al., 2010; Sarkar et al., 2015). TRI is shorter when rock fragments are embedded and/or rock coverage is low, which becomes apparent with increasing surface roughness at the centimeter scale (Arnau-Rosalén et al., 2008). However, TRI of sandy slopes can be 4–11 times longer than bare slopes, depending on rainfall intensity (Tang et al., 2016). In addition, TRI increases with initial soil suction (Cuomo and Della Sala, 2013). Different vegetation and soil and water measures have varying effects on TRI. Vegetation type affects surface runoff generation, and surface runoff can be generated in a short time in farmlands and degraded pasturelands (Yaşar Korkanç, 2018). Conversely, conservation of tillage can reduce surface runoff and increase infiltration (Shipitalo et al., 2000). TRI of bare soil is the shortest compared with soil under
Corresponding author at: No. 120 Dongling Road, Shenyang Agricultural University, Shenyang, Liaoning Province 110866, China. E-mail address: [email protected] (L. Zhou).
https://doi.org/10.1016/j.catena.2019.104172 Received 22 December 2017; Received in revised form 30 June 2019; Accepted 9 July 2019 0341-8162/ © 2019 Published by Elsevier B.V.
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methods; where, five 1 × 1 m squares were selected from the lower, middle, and upper sections of the experimental plots, and each square was evenly divided into 16 (0.25 × 0.25 m) grid cells. Plant height was measured as the distance in centimeters from the soil line of the plant to the top of the plant stem. Vegetation coverage is equal to the number of grid cells covered by the aboveground vegetation divided into the total number of grid cells sampled (Jennings et al., 1999). Plant height, stalk diameter, litter thickness was measured by ruler and tape measure. Canopy density is the number of grid cells covered by the canopy divided into the total number of grid cells sampled. Data on vegatation coverage, number of plants, height, canopy density, and litter thickness were recorded for each cell, and were monitored between July 20 and September 1, 2015.
full ground mulching with jujube branches, strip tillage, jujube branch mulch + strip tillage, or jujube branch mulch + strip white clover (Trifolium repens L.) (Wang et al., 2015). In addition, the infiltration capacity of zero tillage systems amended with farmyard manure or with rice straw is higher than that of bare surfaces during stage I (the period from the start of rainfall to initiation of runoff), and stage II (the period in which infiltration and runoff occur at the same time), leading to a consequent increase in stage I duration (Rao et al., 1998). Compared to bare soil, straw cover also delays time to ponding by 242.31%, time to runoff by 104.93%, and time to runoff in outlets by 66.35% (Prosdocimi et al., 2016). Various vegetation types have different effects on TRI under different rainfall intensities, soil moisture content, and slope conditions. For example, seabuckthorn delays TRI more effectively than secondary natural grass, which is itself more effective than biological crust (Wei et al., 2014). The contribution of each factor can be ranked as rainfall intensity, number of years of grassland growth, grass type, slope, and crop type (Xiao et al., 2010).
2.3. Rainfall and runoff measurements For each runoff event, TRI was determined on the basis of the data for the first daily runoff generation and rainfall conditions observed by automatic weather station.
1.2. Objectives 2.4. Soil moisture content and soil infiltration process curve determination The soil condition, vegetation, soil and water measures, and rainfall characteristics play key roles in runoff generation, but studies that soil moisture content, vegetation, and soil and water measures to runoff generation processes under natural rainfall are still scarce. This study examines the influence of soil moisture content, vegetation at high and low levels(maize, peanut crops, grass, forest, and fruit trees), soil and water measures(longitudinal ridges, cross ridges, grass, forest, and flat field under fruit trees), and rainfall intensity on TRI, using six standard runoff plots under natural rainfall conditions.
The soil moisture content (0–1, 1–2, 2–3, 3–4, and 4–5 cm) was measured by stoving method before and after rainfall, and the ring knife method (Liu et al., 2012) was used to measure the 0–5 cm soil surface infiltration. 2.5. Soil moisture characteristic curve measurement Soil bulk density and soil particle size distribution are estimated by a RETC program simulation was carried out using Van-Genuchten model, the soil moisture characteristic curve is obtained, and the data in the map is extracted for curve fitting (Van Genuchten, 1980; Van Genuchten et al., 1991).
2. Materials and methods 2.1. Site description This study was conducted in the Soil Erosion Monitoring Site of Yao Qianhu Town, Su Jiatun District, Shenyang City, Liaoning Province, China (41° 34′ N, 123° 36′ E). The 1.5 ha study site was located in a small watershed with steep hillslopes, in a low mountainous and hilly soil-rock area, with brown soil of around 70 cm average thickness. The mean annual precipitation and humidity were 735 mm and 65%, respectively, and the mean annual temperature was 8 °C with a range from −4 l to 39.3 °C. Six uniform plots of 5 × 20 m horizontal area with 10° slope were selected. These plots included bare soil, longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees. TRI values were analyzed by monitoring all six plots between July 20 and September 1, 2015. Soil properties and key information are shown in Tables 1 and 2.
2.6. Statistical evaluation of results Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 17.0 (Nie et al., 1975). Results were evaluated using curve fitting, correlation, and principal component analyses. 3. Results Thirteen of the 34 rainfall events during the period of observation generated runoff (Table 3); of these, five occurred on different days. Runoff data for the first event of each of these days. Bare soil, longitudinal ridges under maize, and cross ridges under peanut crops generated runoffs during each rainfall event. Grass, flat field under fruit trees, and forest plots did not generate runoff. Bare soil produced the shortest TRI, followed by the longitudinal ridges under maize and cross ridges under peanut crops (Table 4). The soil infiltration coefficient of flat field under fruit trees, grass, and forest was greater than that of bare soil, cross ridge, and longitudinal ridge. The
2.2. Vegetation investigation Vegetation was investigated using five-point and grid quadrat Table 1 Soil properties at the six experimental plots. Experimental plot
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
Soil particle size distribution (%) 2–0.05 mm
0.05–0.002 mm
< 0.002 mm
8.5 25.1 12.2 16.7 33.2 29.0
49.9 33.3 58.5 50.0 29.2 37.6
41.6 41.6 29.3 33.3 37.6 33.4
2
Soil bulk density (g·cm−3)
Organic matter content (g·kg−1)
1.25 1.21 1.33 1.14 1.33 1.23
28.79 26.96 27.00 26.52 26.58 26.55
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Table 2 Planting history of the six experimental plots. Experimental plot
Crop implementation duration (year)
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
5 5 5 5 4 4
Table 3 Runoff event characteristics. Date
27-Jul 28-Jul 29-Jul 6-Aug 9-Aug
Rainfall start time
Rainfall (mm)
16:17 6:22 20:08 3:32 16:05
30.6 10.8 11.4 11.2 4.2
27-Jul 28-Jul 29-Jul 6-Aug 9-Aug
Bare soil (s) 360 240 600 660 720
Annual crop (peanut) Annual crop (maize) Annual herb Current-year seedling (willow) 3-year-old seedling (apple)
Table 5 Soil moisture content of 0–5 cm (cm3/cm3) in six experimental plots. Average rainfall intensity (mm/ min) 1.53 0.60 0.30 0.16 0.15
Table 4 Time to runoff initiation. Date
Crop implementation
Experimental plot
1
2
3
4
5
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
0.13 0.12 0.13 0.16 0.11 0.11
0.15 0.22 0.19 0.25 0.20 0.16
0.17 0.21 0.18 0.21 0.20 0.17
0.14 0.17 0.14 0.18 0.14 0.13
0.11 0.14 0.13 0.17 0.13 0.13
Table 6 The simulated curve equation of soil water suction.
Longitudinal ridges under maize (s) 480 300 780 480 1260
Cross ridges under peanut crops (s) 780 420 900 0 0
Experimental plot
Curve equation
R2
Bare soil Cross ridge under peanut Longitudinal ridge under maize Grass Forest Flat field under fruit trees
Ln(Y) = ln (120773.248) − 18.215 * X Ln(Y) = ln (133781.258) − 18.897 * X Ln(Y) = ln (44271.253) − 16.685 * X Ln(Y) = ln (57909.048) − 16.001 * X Ln(Y) = ln (134349.490) − 20.612 * X Ln(Y) = ln (64919.729) − 17.707 * X
0.858 0.860 0.847 0.855 0.863 0.856
Y = Soil water suction (cm); X = Soil moisture content (cm3/cm3).
Fig. 1. Soil infiltration curves in six different plots. 3
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layer at −0.467. According to principle component analysis, the first and the second common factors exhibited high variance and cumulatively contributed 97.85% to the total variance (Table 9), indicating that soil moisture content and rainfall intensity are well described by the extracted common factors. The eigenvalue computed from the third factor was low; therefore, only the first two components were selected as common factors. A rotating component matrix of TRI factors for bare soil was obtained through oblique rotation (Table 10). TRI for bare soil was primarily affected by two principal components. The first principal component exhibited an eigenvalue of 4.891 and contributed 69.87% of the total variance. The largest load factor of the first principal component was attributed to the moisture content of the 3–4 cm soil layer (0.989), followed by the moisture contents at 1–2 cm (0.988), 4–5 cm (0.986), 2–3 cm (0.972), and 0–1 cm (0.964). All factors exhibited high load coefficients. The average and maximum rainfall intensities negligibly contributed to the first principal component with load coefficients of −0.227 and 0.133, respectively, indicating that the effects of the average and maximum rainfall intensities on the first principal component decreased with increasing moisture content in the 0–5 cm soil layers. Therefore, the soil moisture content factor can be regarded as the first principal component. The second principal component generated an eigenvalue of 1.958 and contributed 27.98% of the total variance, with maximum rainfall intensity (0.969) as its largest load factor followed by average rainfall intensity (0.956). Therefore, the rainfall intensity factor can be regarded as the second principal component.
Fig. 2. Soil moisture characteristic curves. Table 7 Soil water suction (cm) in six different plots. Experimental plot
1
2
3
4
5
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
11,313 13,854 5060 4476 13,917 9257
7859 2094 1859 1060 2177 3819
5460 2529 2197 2011 2177 3199
9429 5386 4282 3250 7499 6496
16,285 9494 5060 3814 9215 6496
3.2. TRI of longitudinal ridges under maize and cross ridges under peanut crops Correlation coefficients between TRI of longitudinal ridges under maize and maximum rainfall intensity, soil moisture content at 0–1 cm depth, stalk diameter, maize height, and coverage were −0.750, −0.689, 0.899, 0.898, and 0.860, respectively (Table 11). In addition, TRI was significantly positively correlated with maize stalk diameter and height. The correlation coefficients between TRI of cross ridges under peanut crops and soil moisture content at 3–4 cm depth were − 0.577, and stalk diameter were 0.573. The correlation coefficient between TRI and rainfall intensity was very low, indicating that runoff was effectively reduced in the cross-ridge plot. For longitudinal ridges under maize, the cumulative contributions of the first four principal components were 100%, indicating that these four principal components already explained most variability (Table 9). The cumulative contribution of the first two principal components was 100% for cross ridges under peanut crops (Table 12), indicating that these two principal components already contained most of the information of the variables. The data for the two plots were processed in a manner similar to bare soil, where the first two principal components were retained for analysis. The rotating component matrices of the factors were obtained using the rotation matrix (Table 13). The first principal component of TRI for longitudinal ridges under maize contributed 64.88% to the total variance. The largest load factor of the first principal component was soil moisture content at 0–5 cm, followed by coverage (−0.537), maize stalk diameter (−0.526), and maize height (−0.503). The second
coefficient for cross ridge was larger than that for bare soil and longitudinal ridge (Fig. 1). Soil water suction of each plot was calculated according to initial soil moisture content (Table 5) and simulated curve. Curve simulation (Table 6) based on the data of soil moisture characteristic curves (Fig. 2), the curve simulation effect is good, and R2 is between 0.847–0.863. Under identical soil moisture content, the decreasing order of soil water suction was as follows: bare soil and cross ridges under peanut crops, forest, grass, flat field under fruit trees, and longitudinal ridges under maize (Fig. 2). Overall, the decreasing order of soil water suction was: bare soil, forest, cross ridges under peanut crops, flat field under fruit trees, longitudinal ridges under maize, and grass (Table 7).
3.1. TRI of bare soil According to correlation analysis, TRI of bare soil was primarily affected by soil moisture content and rainfall intensity (Table 8) at the 10% slope observed. TRI was negatively correlated with average rainfall intensity, maximum rainfall intensity, and soil moisture content. The correlation coefficient between TRI and maximum rainfall intensity was the highest at −0.883, followed by TRI and average rainfall intensity at −0.621, and TRI and soil moisture content at the 0–1 cm soil
Table 8 Correlation analyses for TRI with average rainfall intensity, maximum rainfall intensity, and soil moisture content at depths of 0–1, 1–2, 2–3, 3–4, and 4–5 cm. Correlation coefficient
TRI
TRI
1
Average rainfall intensity
−0.621
Maximum rainfall intensity
−0.883*
TRI = Time to runoff initiation; N = number of observations, N = 5; * = significant at p < 0.05. 4
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
−0.467
−0.179
−0.214
−0.260
−0.275
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Table 9 Common factor variance of TRI for bare soil. Principal component
1
Eigenvalue Variance contribution (%) Cumulative contribution (%)
4.891 69.87 69.87
2
3
1.958 27.98 97.85
4
0.109 1.55 99.4
5
0.042 0.60 100
6 −16
7 −17
−1.62 × 10−16 −2.31 × 10−15 100
2.71 × 10 3.86 × 10−16 100
1.66 × 10 2.37 × 10−15 100
Table 10 Rotating component matrix of TRI for bare soil. Load factor
Average rainfall intensity
PC1 PC2
−0.227 0.956
Maximum rainfall intensity
Soil moisture content
0.133 0.969
0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
0.964 0.253
0.988 −0.096
0.972 −0.201
0.989 6.75 × 10−5
0.986 −0.116
PC1 = First principal component; PC2 = second principal component.
intensity, land-use patterns, and other factors. Under the same rainfall intensity, bare soil, longitudinal ridges under maize, and cross ridges under peanut crops produced runoff, while the grass, forest, and flat field under fruit trees did not. Among all plots, bare soil exhibited the fastest flow, which can be attributed to the absence of protective vegetation and antecedent moisture content. Soil particles in bare soil are held together by low binding forces and are thus easily dispersed, causing the soil surface to crust after rainfall (Ben-Hur and Lado, 2008; García-Orenes et al., 2012; Ziadat and Taimeh, 2013; Defersha and Melesse, 2012). Bare soil has slow infiltration rates and fast runoff rates. In longitudinal ridges under maize, ridges constructed along the plot provided drainage channels for runoff, allowing rainfall runoff to flow down the slope and accelerate runoff. The cross-ridge measures under peanut changed the micro-topography of the slope and enhanced the storage capacity of the plot. These effects, coupled with the effect of vegetation interception, extended runoff time. The grass and forest plots did not generate runoff because of their extensive vegetation coverage, litter cover, and strong storage capacity. High vegetation coverage can have important effects on water filtration, sediment conservation, and slope protection (Zhang et al., 2015). Both the vegetation cover and non-sloping nature of flat field under fruit trees contributed to rainwater storage, extending the duration of rainwater infiltration and reducing surface runoff (Yang and Duan, 2002). The lack of runoff events in such sites during this study indicated that tree and grass planting measures and non-sloping fields can effectively conserve soil and water resources. In accordance with Li et al. (2011) and Xue and Gavin (2008), we found that TRI for bare soil is negatively correlated with soil moisture content and rainfall intensity. High soil moisture content translates to low infiltration capacity (Liu et al., 2011), yielding a relatively short TRI for bare soil. High rainfall intensity increases soil moisture content and decreases infiltration rate, and runoff occurs when rainfall intensity exceeds the soil infiltration rate (Harmel et al., 2006). Therefore, TRI for bare soil is negatively correlated with rainfall intensity. The primary factors affecting TRI for bare soil include comprehensive soil moisture content factors (69.87%) and rainfall intensity (27.98%). The main load
principal component contributed 33.73% of the total variance. Maximum rainfall intensity contributed the largest load factor of the second principal component (−0.978), followed by average rainfall intensity (−0.926), maize height (0.864), maize stalk diameter (0.847), and coverage (0.837). Therefore, the first and second principal components could be considered to be comprehensive soil moisture content and comprehensive rainfall and vegetation, respectively. The first principal component of the TRI of cross ridges under peanut crops contributed 85.73% to the total variance. The two large load factors of the first principal component were the soil moisture content at 0–5 cm and rainfall intensity. Stalk diameter, coverage, and maximum and average rainfall intensities were the large load factors of the second principal component. Rainfall intensity exhibited low load coefficients in both principal components. Therefore, the two principal components could be regarded as comprehensive soil moisture content and comprehensive vegetation. 3.3. Result of plots without runoff Grass, forest, and flat field under fruit trees did not develop runoff. Among the three plots, the grass plot exhibited the highest soil moisture content, followed by forest plot and fruit tree field (Fig. 3). These sites showed less soil water suction than bare soil and cross ridge under peanut, but had greater soil infiltration coefficients were greater than for bare soil, cross ridge, and longitudinal ridge. The grass plot had vegetation coverage of 99.96% and a litter layer with an average thickness of 4.31 mm. The forest plot consisted mainly of willow trees and grass and exhibited canopy density and vegetation coverage of 99.75% and 100%, respectively. The flat field was cultivated with apple trees and grass and exhibited vegetation coverage and canopy density of 98.65% and 27% (Table 14). Because of the different vegetation and soil conditions, runoff was not generated. 4. Discussion TRI is mainly affected by soil properties, vegetation indices, rainfall
Table 11 Correlation coefficients between TRI and each index for the longitudinal ridges under maize and cross ridges under peanut crops. Correlation coefficient
LRTM (TRI) CRTP (TRI)
Average rainfall intensity
−0.570 0.085
Maximum rainfall intensity
−0.750 0.059
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
−0.689 −0.482
−0.537 −0.197
−0.428 −0.500
−0.515 −0.577
−0.481 −0.355
Coverage
Stalk diameter
Height
N
0.860 0.419
0.899* 0.573
0.898* –
5 3
TRI = Time to runoff initiation; LRTM = Longitudinal ridges under maize; CRTP = Cross ridges under peanut crops; * = significant at p < 0.05; − = no correlation proven; N = number of observations. 5
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Table 12 Common factor variance of TRI for longitudinal ridges under maize and cross ridges under peanut crops. Principal component
Eigenvalue LRTM
1 2 3 4
6.49 3.37 0.13 0.01
CRTP 7.72 1.28 7.81 × 10−16 2.40 × 10−16
Variance contribution (%)
Cumulative contribution (%)
LRTM
LRTM
CRTP
64.88 33.73 1.29 0.1
85.73 14.27 8.68 × 10−15 2.67 × 10−15
CRTP
64.88 98.61 99.90 100
85.73 100 100 100
LRTM = Longitudinal ridges under maize; CRTP = Cross ridges under peanut crops.
collection. Broken ridge topography may have intercepted surface runoff and prolonged the residence time of rainfall at the bottom of the cross ridge slope. These effects likely weakened rainfall intensity. Alternatively, the observed results may reflect the fact that the correlation coefficient between rainfall intensity index and TRI is small, and the load factor of each principal component is similar. Soil moisture content and vegetation factors weaken rainfall intensity. This would indicate that TRI is not negatively correlated with rainfall intensity. This study examines the influence of soil moisture content, vegetation at high and low levels, soil and water measures(longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees), and rainfall intensity on TRI under natural rainfall conditions. However, in this research there still exist some limitations, like the natural rainfall is contingent and uncontrollable, and influencing factors (soil and vegetation conditions)still incomplete. Therefore, the relationship between TRI and soil bulk density, soil crust, soil surface roughness, leaf status and root distribution in sloping land should be studied further.
factors include soil moisture content of the 3–4 cm soil layers (0.989) and maximum rainfall intensity (0.969), which exhibited the greatest influence on runoff duration in each principal component. Under weak rainfall intensity during the early rainy season, the main process of runoff generation was the soil infiltration process, wherein the 0–4 cm soil layer gradually becomes saturated. Runoff begins when rainfall intensity exceeds the soil infiltration rate and the maximum rainfall intensity accelerates slope runoff. Similarly, Castillo and Gómez-Plaza (2003) found that the initial soil moisture content is the main factor influencing of runoff. However, the influence of rainfall intensity, surface crust, vegetation type, and weed cover on the TRI of bare soil plot mainly depends on rainfall intensity (Chen et al., 2005). The TRI of the longitudinal ridges under maize is positively correlated with vegetation indices, but negatively correlated with soil moisture content and rainfall intensity. The main influential factors of TRI include soil moisture content (64.88%) and comprehensive rainfall and vegetation factors (33.73%). The maximum load factors of the main component consist of the soil moisture content of the 2–3 cm layer (0.999) and maximum rainfall intensity (0.978), as well as maize height (0.864) and stem diameter (0.847). Maize is a tall-stalked crop with a strong capacity for rainfall redistribution by throughfall, stem flow, and canopy interception (Lamm and Manges, 2000; Zapata et al., 2018), although the effects of fall and stem flow are decreased by weak rainfall intensity during the early rainy season. In addition, the maize canopy can reduce the kinetic energy of raindrops, further decreasing throughfall and stem flow. Energy loss during throughfall weakens the capability of rainfall to reach an exposed soil surface, thus delaying runoff. Stem flow is increased by the oblique upward extension and large areas of maize leaves (Yu and D'Odorico, 2014; Ma et al., 2015). Rainfall kinetic energy is reduced by stem flow, resulting in reduced root erosion with increasing stem height and diameter. In addition, maize roots further reduce soil erosion, which can further delay runoff through stalk flow. In summary, TRI for longitudinal ridges under maize was positively correlated with vegetation indices. The TRI of cross ridge under peanut was negatively correlated with soil moisture content, but positively correlated with rainfall intensity and vegetation indices. The first and second main components of the TRI in this plot are soil moisture content (85.73%) and comprehensive vegetation factors (14.27%), respectively. In addition, the main load factors are soil moisture content in the 3–4 cm soil layer (0.985) and peanut stalk diameter (0.985). TRI was positively correlated with rainfall intensity, indicating that runoff mainly results from rainwater
5. Conclusions This study investigated TRI and corresponding impact factors in six different standard plots. TRI values of the plots decreased from a maximum in bare soil, to longitudinal ridges under maize, and cross ridges under peanut crops. Grass, forest, and flat field under fruit trees did not produce runoff. These results indicate that farmland has the poorest soil and water retention capacity among the studied plots. By contrast, forest, grassland, and flat field under fruit trees have excellent soil and water retention capacity that can be attributed to their vegetation cover. Soil moisture content at 0–5 cm depth and TRI are negatively correlated, but vegetation indices was positively correlated with TRI. In addition, rainfall intensity was negatively correlated with TRI for bare soil and longitudinal ridge under maize, but not with TRI for cross ridge under peanut. Correlation and principal component analyses revealed that soil moisture content is the main influential factor of TRI. The second principal components of the TRI of bare soil, longitudinal ridges under maize, and cross ridges under peanut crops are rainfall intensity, comprehensive rainfall and vegetation factors, and comprehensive vegetation factors, respectively.
Table 13 Rotating component matrix of TRI for longitudinal ridge under maize and cross ridge under peanut. Load factor
LRTM CRTP
PC1 PC2 PC1 PC2
Average rainfall intensity
−0.376 −0.926 −0.764 −0.645
Maximum rainfall intensity
−0.154 −0.978 −0.747 −0.665
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
0.955 −0.278 0.96 0.280
0.96 −0.191 0.832 0.555
0.999 0.029 0.966 0.260
0.982 −0.034 0.985 0.171
0.995 −0.03 0.912 0.411
LRTM = Longitudinal ridges under maize; CRTP = cross ridges under peanut crops; − = no correlation shown. 6
Coverage
Stalk diameter
Height
−0.537 0.837 0.344 0.939
−0.526 0.847 0.173 0.985
−0.503 0.864 – –
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Fig. 3. Soil moisture content of the plots without runoff initiation. 1016/j.still.2015.07.005. Castillo, V.M., Gómez-Plaza, A., Martı́nez-Mena, M., 2003. The role of antecedent soil water content in the runoff response of semiarid catchments: a simulation approach. J. Hydrol. 284, 114–130. doi:https://doi.org/10.1016/s0022-1694(03)00264-6. Chen, H.S., Shao, M.A., Zhang, X.C., Wang, K.L., 2005. Field experiment on hillslope rainfall infiltration and runoff under simulated rainfall conditions. J. Soil Water Conserv. 19, 5–8. Croke, J., Hairsine, P., Fogarty, P., 1999. Runoff generation and re-distribution in logged eucalyptus forests, south-eastern Australia. J. Hydrol. 216, 56–77. https://doi.org/ 10.1016/s0022-1694(98)00288-1. Cuomo, S., Della Sala, M., 2013. Rainfall-induced infiltration, runoff and failure in steep unsaturated shallow soil deposits. Eng. Geol. 162, 118–127. https://doi.org/10. 1016/j.enggeo.2013.05.010. Defersha, M.B., Melesse, A.M., 2012. Field-scale investigation of the effect of land use on sediment yield and runoff using runoff plot data and models in the Mara River basin, Kenya. Catena 89, 54–64. https://doi.org/10.1016/j.catena.2011.07.010. Dos Santos, J.C.N., de Andrade, E.M., Guerreiro, M.J.S., Medeiros, P.H.A., de Queiroz Palácio, H.A., de Araújo Neto, J.R., 2016. Effect of dry spells and soil cracking on runoff generation in a semiarid micro watershed under land use change. J. Hydrol. 541, 1057–1066. https://doi.org/10.1016/j.jhydrol.2016.08.016. Durán Zuazo, V.H., Rodríguez Pleguezuelo, C.R., 2008. Soil erosion and runoff prevention by plant covers: a review. Agron. Sustain. Dev. 28, 65–86. https://doi.org/10.1051/ agro:2007062. García-Orenes, F., Roldán, A., Mataix-Solera, J., Cerdà, A., Campoy, M., Arcenegui, V., Caravaca, F., 2012. Soil structural stability and erosion rates influenced by agricultural management practices in a semi-arid Mediterranean agro-ecosystem. Soil Use Manag. 28, 571–579. https://doi.org/10.1111/j.1475-2743.2012.00451.x. Gholami, L., Sadeghi, S.H.R., Homaee, M., 2016. Different effects of sheep manure conditioner on runoff and soil loss components in eroded soil. Catena 139, 99–104. https://doi.org/10.1016/j.catena.2015.12.011. Harmel, R.D., Richardson, C.W., King, K.W., Allen, P.M., 2006. Runoff and soil loss relationships for the Texas Blackland Prairies ecoregion. J. Hydrol. 331, 471–483. https://doi.org/10.1016/j.jhydrol.2006.05.033. Jennings, S.B., Brown, N.D., Sheil, D., 1999. Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry 72, 59–73.
Table 14 Vegetation indices of grass, forest, and flat field under fruit trees. Vegetation index
Value
Vegetation coverage
Canopy density
Litter layer thickness (cm) Grass
Forest
4.31
3.54
Grass
Forest
FFFT
Forest
FFFT
99.96%
100.00%
98.65%
99.75%
27.00%
FFFT = Flat field under fruit trees.
Acknowledgements The authors gratefully acknowledge the support from the National Natural Science Foundation of China [41471225]. References Armand, R., Bockstaller, C., Auzet, A.V., Van Dijk, P., 2009. Runoff generation related to intra-field soil surface characteristics variability: application to conservation tillage context. Soil. Till. Res. 102, 27–37. https://doi.org/10.1016/j.still.2008.07.009. Arnau-Rosalén, E., Calvo-Cases, A., Boix-Fayos, C., Lavee, H., Sarah, P., 2008. Analysis of soil surface component patterns affecting runoff generation: an example of methods applied to Mediterranean hillslopes in Alicante (Spain). Geomorphology 101, 595–606. https://doi.org/10.1016/j.geomorph.2008.03.001. Ben-Hur, M., Lado, M., 2008. Effect of soil wetting conditions on seal formation, runoff, and soil loss in arid and semiarid soils – a review. Aust. J. Soil Res. 46, 191–202. https://doi.org/10.1071/sr07168. Biddoccu, M., Ferraris, S., Opsi, F., Cavallo, E., 2016. Long-term monitoring of soil management effects on runoff and soil erosion in sloping vineyards in Alto Monferrato (North-West Italy). Soil. Till. Res. 155, 176–189. https://doi.org/10.
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Influence of soil moisture content and soil and water conservation measures on time to runoff initiation under different rainfall intensities
T
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Jingling Zhang, Lili Zhou , Renming Ma, Yanfeng Jia, Fan Yang, Hongyang Zhou, Xiangying Cao College of Water Conservancy, Shenyang Agricultural University, Shenyang, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Time to runoff initiation Rainfall intensity Soil moisture content Vegetation Soil and water conservation measures
Time to runoff initiation (TRI) is an important factor related to water infiltration, runoff generation, and hillslope erosion. This study assesses TRI values for six different plots and investigates the effects of rainfall intensity, surface soil moisture content at depths of 0–5 cm, soil and water conservation measures (longitudinal ridges, cross ridges, grass, forest, and flat field under fruit trees), and vegetation on TRI for bare soil, longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees. Results showed that bare soil exhibited the shortest TRI, followed by the longitudinal ridges under maize and cross ridges under peanut crops. The remaining three plots did not generate runoff. The first principal component affecting TRI was soil moisture content, which contributed 69.87%, 64.88%, and 85.73% to the TRI of bare soil, longitudinal ridges under maize, and cross ridges under peanut crops, respectively. The second major component of these three plots were combined rainfall intensity, comprehensive rainfall and vegetation factors, and vegetation factors, which contributed 27.98%, 33.73%, and 14.27%, respectively. Maximum load factor was generated during runoff with maximum rainfall intensity (0.969), stalk diameter (0.985), and maize height (0.864). In conclusion, soil moisture content are the main factors influencing TRI, and soil and water conservation measures provided key benefits for improving the time to runoff initiation and reduction of runoff erosion in the farmlands.
1. Introduction 1.1. Background Time to runoff initiation (TRI) is defined as the time between the beginning of a rainfall event and the beginning of runoff and is used to examine runoff generation and infiltration patterns as the bases for soil and water conservation measures. TRI is affected by various factors, including rainfall intensity (Sadeghi et al., 2016), soil texture (Biddoccu et al., 2016), vegetation (Durán Zuazo and Rodríguez Pleguezuelo, 2008), soil moisture content (McDowell and Sharpley, 2002), and landuse type (Sajikumar and Remya, 2015; Zuo et al., 2016). Increasing rainfall intensity yields decreasing TRI according to a power function relationship (Wang et al., 1991; Mohamadi and Kavian, 2015). A rainfall intensity of 50 mm/h significantly affects the TRI of a plot with 30% slope (Gholami et al., 2016). At rainfall intensities of 45 mm/h, Snig tracks constructed within the last 1.5 y generated localized surface runoff within 1–3 min. At rainfall intensity of 100 mm /h or more, TRI is approximately 1 min (Croke et al., 1999). Soil properties are important factors in increasing water infiltration
⁎
and reducing TRI. Soil crust may drastically reduce infiltration and increase TRI (Armand et al., 2009). Soil humidity and cracks help to explain the flow generation process in a semiarid tropical region (Dos Santos et al., 2016). TRI is prolonged in dry soils, but decreases with increasing antecedent soil moisture content, where the probability of surface runoff generation under dry antecedent soil moisture conditions is lower than that under moderate or wet antecedent conditions (Zhang et al., 2010; Sarkar et al., 2015). TRI is shorter when rock fragments are embedded and/or rock coverage is low, which becomes apparent with increasing surface roughness at the centimeter scale (Arnau-Rosalén et al., 2008). However, TRI of sandy slopes can be 4–11 times longer than bare slopes, depending on rainfall intensity (Tang et al., 2016). In addition, TRI increases with initial soil suction (Cuomo and Della Sala, 2013). Different vegetation and soil and water measures have varying effects on TRI. Vegetation type affects surface runoff generation, and surface runoff can be generated in a short time in farmlands and degraded pasturelands (Yaşar Korkanç, 2018). Conversely, conservation of tillage can reduce surface runoff and increase infiltration (Shipitalo et al., 2000). TRI of bare soil is the shortest compared with soil under
Corresponding author at: No. 120 Dongling Road, Shenyang Agricultural University, Shenyang, Liaoning Province 110866, China. E-mail address: [email protected] (L. Zhou).
https://doi.org/10.1016/j.catena.2019.104172 Received 22 December 2017; Received in revised form 30 June 2019; Accepted 9 July 2019 0341-8162/ © 2019 Published by Elsevier B.V.
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methods; where, five 1 × 1 m squares were selected from the lower, middle, and upper sections of the experimental plots, and each square was evenly divided into 16 (0.25 × 0.25 m) grid cells. Plant height was measured as the distance in centimeters from the soil line of the plant to the top of the plant stem. Vegetation coverage is equal to the number of grid cells covered by the aboveground vegetation divided into the total number of grid cells sampled (Jennings et al., 1999). Plant height, stalk diameter, litter thickness was measured by ruler and tape measure. Canopy density is the number of grid cells covered by the canopy divided into the total number of grid cells sampled. Data on vegatation coverage, number of plants, height, canopy density, and litter thickness were recorded for each cell, and were monitored between July 20 and September 1, 2015.
full ground mulching with jujube branches, strip tillage, jujube branch mulch + strip tillage, or jujube branch mulch + strip white clover (Trifolium repens L.) (Wang et al., 2015). In addition, the infiltration capacity of zero tillage systems amended with farmyard manure or with rice straw is higher than that of bare surfaces during stage I (the period from the start of rainfall to initiation of runoff), and stage II (the period in which infiltration and runoff occur at the same time), leading to a consequent increase in stage I duration (Rao et al., 1998). Compared to bare soil, straw cover also delays time to ponding by 242.31%, time to runoff by 104.93%, and time to runoff in outlets by 66.35% (Prosdocimi et al., 2016). Various vegetation types have different effects on TRI under different rainfall intensities, soil moisture content, and slope conditions. For example, seabuckthorn delays TRI more effectively than secondary natural grass, which is itself more effective than biological crust (Wei et al., 2014). The contribution of each factor can be ranked as rainfall intensity, number of years of grassland growth, grass type, slope, and crop type (Xiao et al., 2010).
2.3. Rainfall and runoff measurements For each runoff event, TRI was determined on the basis of the data for the first daily runoff generation and rainfall conditions observed by automatic weather station.
1.2. Objectives 2.4. Soil moisture content and soil infiltration process curve determination The soil condition, vegetation, soil and water measures, and rainfall characteristics play key roles in runoff generation, but studies that soil moisture content, vegetation, and soil and water measures to runoff generation processes under natural rainfall are still scarce. This study examines the influence of soil moisture content, vegetation at high and low levels(maize, peanut crops, grass, forest, and fruit trees), soil and water measures(longitudinal ridges, cross ridges, grass, forest, and flat field under fruit trees), and rainfall intensity on TRI, using six standard runoff plots under natural rainfall conditions.
The soil moisture content (0–1, 1–2, 2–3, 3–4, and 4–5 cm) was measured by stoving method before and after rainfall, and the ring knife method (Liu et al., 2012) was used to measure the 0–5 cm soil surface infiltration. 2.5. Soil moisture characteristic curve measurement Soil bulk density and soil particle size distribution are estimated by a RETC program simulation was carried out using Van-Genuchten model, the soil moisture characteristic curve is obtained, and the data in the map is extracted for curve fitting (Van Genuchten, 1980; Van Genuchten et al., 1991).
2. Materials and methods 2.1. Site description This study was conducted in the Soil Erosion Monitoring Site of Yao Qianhu Town, Su Jiatun District, Shenyang City, Liaoning Province, China (41° 34′ N, 123° 36′ E). The 1.5 ha study site was located in a small watershed with steep hillslopes, in a low mountainous and hilly soil-rock area, with brown soil of around 70 cm average thickness. The mean annual precipitation and humidity were 735 mm and 65%, respectively, and the mean annual temperature was 8 °C with a range from −4 l to 39.3 °C. Six uniform plots of 5 × 20 m horizontal area with 10° slope were selected. These plots included bare soil, longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees. TRI values were analyzed by monitoring all six plots between July 20 and September 1, 2015. Soil properties and key information are shown in Tables 1 and 2.
2.6. Statistical evaluation of results Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 17.0 (Nie et al., 1975). Results were evaluated using curve fitting, correlation, and principal component analyses. 3. Results Thirteen of the 34 rainfall events during the period of observation generated runoff (Table 3); of these, five occurred on different days. Runoff data for the first event of each of these days. Bare soil, longitudinal ridges under maize, and cross ridges under peanut crops generated runoffs during each rainfall event. Grass, flat field under fruit trees, and forest plots did not generate runoff. Bare soil produced the shortest TRI, followed by the longitudinal ridges under maize and cross ridges under peanut crops (Table 4). The soil infiltration coefficient of flat field under fruit trees, grass, and forest was greater than that of bare soil, cross ridge, and longitudinal ridge. The
2.2. Vegetation investigation Vegetation was investigated using five-point and grid quadrat Table 1 Soil properties at the six experimental plots. Experimental plot
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
Soil particle size distribution (%) 2–0.05 mm
0.05–0.002 mm
< 0.002 mm
8.5 25.1 12.2 16.7 33.2 29.0
49.9 33.3 58.5 50.0 29.2 37.6
41.6 41.6 29.3 33.3 37.6 33.4
2
Soil bulk density (g·cm−3)
Organic matter content (g·kg−1)
1.25 1.21 1.33 1.14 1.33 1.23
28.79 26.96 27.00 26.52 26.58 26.55
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Table 2 Planting history of the six experimental plots. Experimental plot
Crop implementation duration (year)
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
5 5 5 5 4 4
Table 3 Runoff event characteristics. Date
27-Jul 28-Jul 29-Jul 6-Aug 9-Aug
Rainfall start time
Rainfall (mm)
16:17 6:22 20:08 3:32 16:05
30.6 10.8 11.4 11.2 4.2
27-Jul 28-Jul 29-Jul 6-Aug 9-Aug
Bare soil (s) 360 240 600 660 720
Annual crop (peanut) Annual crop (maize) Annual herb Current-year seedling (willow) 3-year-old seedling (apple)
Table 5 Soil moisture content of 0–5 cm (cm3/cm3) in six experimental plots. Average rainfall intensity (mm/ min) 1.53 0.60 0.30 0.16 0.15
Table 4 Time to runoff initiation. Date
Crop implementation
Experimental plot
1
2
3
4
5
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
0.13 0.12 0.13 0.16 0.11 0.11
0.15 0.22 0.19 0.25 0.20 0.16
0.17 0.21 0.18 0.21 0.20 0.17
0.14 0.17 0.14 0.18 0.14 0.13
0.11 0.14 0.13 0.17 0.13 0.13
Table 6 The simulated curve equation of soil water suction.
Longitudinal ridges under maize (s) 480 300 780 480 1260
Cross ridges under peanut crops (s) 780 420 900 0 0
Experimental plot
Curve equation
R2
Bare soil Cross ridge under peanut Longitudinal ridge under maize Grass Forest Flat field under fruit trees
Ln(Y) = ln (120773.248) − 18.215 * X Ln(Y) = ln (133781.258) − 18.897 * X Ln(Y) = ln (44271.253) − 16.685 * X Ln(Y) = ln (57909.048) − 16.001 * X Ln(Y) = ln (134349.490) − 20.612 * X Ln(Y) = ln (64919.729) − 17.707 * X
0.858 0.860 0.847 0.855 0.863 0.856
Y = Soil water suction (cm); X = Soil moisture content (cm3/cm3).
Fig. 1. Soil infiltration curves in six different plots. 3
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layer at −0.467. According to principle component analysis, the first and the second common factors exhibited high variance and cumulatively contributed 97.85% to the total variance (Table 9), indicating that soil moisture content and rainfall intensity are well described by the extracted common factors. The eigenvalue computed from the third factor was low; therefore, only the first two components were selected as common factors. A rotating component matrix of TRI factors for bare soil was obtained through oblique rotation (Table 10). TRI for bare soil was primarily affected by two principal components. The first principal component exhibited an eigenvalue of 4.891 and contributed 69.87% of the total variance. The largest load factor of the first principal component was attributed to the moisture content of the 3–4 cm soil layer (0.989), followed by the moisture contents at 1–2 cm (0.988), 4–5 cm (0.986), 2–3 cm (0.972), and 0–1 cm (0.964). All factors exhibited high load coefficients. The average and maximum rainfall intensities negligibly contributed to the first principal component with load coefficients of −0.227 and 0.133, respectively, indicating that the effects of the average and maximum rainfall intensities on the first principal component decreased with increasing moisture content in the 0–5 cm soil layers. Therefore, the soil moisture content factor can be regarded as the first principal component. The second principal component generated an eigenvalue of 1.958 and contributed 27.98% of the total variance, with maximum rainfall intensity (0.969) as its largest load factor followed by average rainfall intensity (0.956). Therefore, the rainfall intensity factor can be regarded as the second principal component.
Fig. 2. Soil moisture characteristic curves. Table 7 Soil water suction (cm) in six different plots. Experimental plot
1
2
3
4
5
Bare soil Cross ridges under peanut crops Longitudinal ridges under maize Grass Forest Flat field under fruit trees
11,313 13,854 5060 4476 13,917 9257
7859 2094 1859 1060 2177 3819
5460 2529 2197 2011 2177 3199
9429 5386 4282 3250 7499 6496
16,285 9494 5060 3814 9215 6496
3.2. TRI of longitudinal ridges under maize and cross ridges under peanut crops Correlation coefficients between TRI of longitudinal ridges under maize and maximum rainfall intensity, soil moisture content at 0–1 cm depth, stalk diameter, maize height, and coverage were −0.750, −0.689, 0.899, 0.898, and 0.860, respectively (Table 11). In addition, TRI was significantly positively correlated with maize stalk diameter and height. The correlation coefficients between TRI of cross ridges under peanut crops and soil moisture content at 3–4 cm depth were − 0.577, and stalk diameter were 0.573. The correlation coefficient between TRI and rainfall intensity was very low, indicating that runoff was effectively reduced in the cross-ridge plot. For longitudinal ridges under maize, the cumulative contributions of the first four principal components were 100%, indicating that these four principal components already explained most variability (Table 9). The cumulative contribution of the first two principal components was 100% for cross ridges under peanut crops (Table 12), indicating that these two principal components already contained most of the information of the variables. The data for the two plots were processed in a manner similar to bare soil, where the first two principal components were retained for analysis. The rotating component matrices of the factors were obtained using the rotation matrix (Table 13). The first principal component of TRI for longitudinal ridges under maize contributed 64.88% to the total variance. The largest load factor of the first principal component was soil moisture content at 0–5 cm, followed by coverage (−0.537), maize stalk diameter (−0.526), and maize height (−0.503). The second
coefficient for cross ridge was larger than that for bare soil and longitudinal ridge (Fig. 1). Soil water suction of each plot was calculated according to initial soil moisture content (Table 5) and simulated curve. Curve simulation (Table 6) based on the data of soil moisture characteristic curves (Fig. 2), the curve simulation effect is good, and R2 is between 0.847–0.863. Under identical soil moisture content, the decreasing order of soil water suction was as follows: bare soil and cross ridges under peanut crops, forest, grass, flat field under fruit trees, and longitudinal ridges under maize (Fig. 2). Overall, the decreasing order of soil water suction was: bare soil, forest, cross ridges under peanut crops, flat field under fruit trees, longitudinal ridges under maize, and grass (Table 7).
3.1. TRI of bare soil According to correlation analysis, TRI of bare soil was primarily affected by soil moisture content and rainfall intensity (Table 8) at the 10% slope observed. TRI was negatively correlated with average rainfall intensity, maximum rainfall intensity, and soil moisture content. The correlation coefficient between TRI and maximum rainfall intensity was the highest at −0.883, followed by TRI and average rainfall intensity at −0.621, and TRI and soil moisture content at the 0–1 cm soil
Table 8 Correlation analyses for TRI with average rainfall intensity, maximum rainfall intensity, and soil moisture content at depths of 0–1, 1–2, 2–3, 3–4, and 4–5 cm. Correlation coefficient
TRI
TRI
1
Average rainfall intensity
−0.621
Maximum rainfall intensity
−0.883*
TRI = Time to runoff initiation; N = number of observations, N = 5; * = significant at p < 0.05. 4
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
−0.467
−0.179
−0.214
−0.260
−0.275
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Table 9 Common factor variance of TRI for bare soil. Principal component
1
Eigenvalue Variance contribution (%) Cumulative contribution (%)
4.891 69.87 69.87
2
3
1.958 27.98 97.85
4
0.109 1.55 99.4
5
0.042 0.60 100
6 −16
7 −17
−1.62 × 10−16 −2.31 × 10−15 100
2.71 × 10 3.86 × 10−16 100
1.66 × 10 2.37 × 10−15 100
Table 10 Rotating component matrix of TRI for bare soil. Load factor
Average rainfall intensity
PC1 PC2
−0.227 0.956
Maximum rainfall intensity
Soil moisture content
0.133 0.969
0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
0.964 0.253
0.988 −0.096
0.972 −0.201
0.989 6.75 × 10−5
0.986 −0.116
PC1 = First principal component; PC2 = second principal component.
intensity, land-use patterns, and other factors. Under the same rainfall intensity, bare soil, longitudinal ridges under maize, and cross ridges under peanut crops produced runoff, while the grass, forest, and flat field under fruit trees did not. Among all plots, bare soil exhibited the fastest flow, which can be attributed to the absence of protective vegetation and antecedent moisture content. Soil particles in bare soil are held together by low binding forces and are thus easily dispersed, causing the soil surface to crust after rainfall (Ben-Hur and Lado, 2008; García-Orenes et al., 2012; Ziadat and Taimeh, 2013; Defersha and Melesse, 2012). Bare soil has slow infiltration rates and fast runoff rates. In longitudinal ridges under maize, ridges constructed along the plot provided drainage channels for runoff, allowing rainfall runoff to flow down the slope and accelerate runoff. The cross-ridge measures under peanut changed the micro-topography of the slope and enhanced the storage capacity of the plot. These effects, coupled with the effect of vegetation interception, extended runoff time. The grass and forest plots did not generate runoff because of their extensive vegetation coverage, litter cover, and strong storage capacity. High vegetation coverage can have important effects on water filtration, sediment conservation, and slope protection (Zhang et al., 2015). Both the vegetation cover and non-sloping nature of flat field under fruit trees contributed to rainwater storage, extending the duration of rainwater infiltration and reducing surface runoff (Yang and Duan, 2002). The lack of runoff events in such sites during this study indicated that tree and grass planting measures and non-sloping fields can effectively conserve soil and water resources. In accordance with Li et al. (2011) and Xue and Gavin (2008), we found that TRI for bare soil is negatively correlated with soil moisture content and rainfall intensity. High soil moisture content translates to low infiltration capacity (Liu et al., 2011), yielding a relatively short TRI for bare soil. High rainfall intensity increases soil moisture content and decreases infiltration rate, and runoff occurs when rainfall intensity exceeds the soil infiltration rate (Harmel et al., 2006). Therefore, TRI for bare soil is negatively correlated with rainfall intensity. The primary factors affecting TRI for bare soil include comprehensive soil moisture content factors (69.87%) and rainfall intensity (27.98%). The main load
principal component contributed 33.73% of the total variance. Maximum rainfall intensity contributed the largest load factor of the second principal component (−0.978), followed by average rainfall intensity (−0.926), maize height (0.864), maize stalk diameter (0.847), and coverage (0.837). Therefore, the first and second principal components could be considered to be comprehensive soil moisture content and comprehensive rainfall and vegetation, respectively. The first principal component of the TRI of cross ridges under peanut crops contributed 85.73% to the total variance. The two large load factors of the first principal component were the soil moisture content at 0–5 cm and rainfall intensity. Stalk diameter, coverage, and maximum and average rainfall intensities were the large load factors of the second principal component. Rainfall intensity exhibited low load coefficients in both principal components. Therefore, the two principal components could be regarded as comprehensive soil moisture content and comprehensive vegetation. 3.3. Result of plots without runoff Grass, forest, and flat field under fruit trees did not develop runoff. Among the three plots, the grass plot exhibited the highest soil moisture content, followed by forest plot and fruit tree field (Fig. 3). These sites showed less soil water suction than bare soil and cross ridge under peanut, but had greater soil infiltration coefficients were greater than for bare soil, cross ridge, and longitudinal ridge. The grass plot had vegetation coverage of 99.96% and a litter layer with an average thickness of 4.31 mm. The forest plot consisted mainly of willow trees and grass and exhibited canopy density and vegetation coverage of 99.75% and 100%, respectively. The flat field was cultivated with apple trees and grass and exhibited vegetation coverage and canopy density of 98.65% and 27% (Table 14). Because of the different vegetation and soil conditions, runoff was not generated. 4. Discussion TRI is mainly affected by soil properties, vegetation indices, rainfall
Table 11 Correlation coefficients between TRI and each index for the longitudinal ridges under maize and cross ridges under peanut crops. Correlation coefficient
LRTM (TRI) CRTP (TRI)
Average rainfall intensity
−0.570 0.085
Maximum rainfall intensity
−0.750 0.059
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
−0.689 −0.482
−0.537 −0.197
−0.428 −0.500
−0.515 −0.577
−0.481 −0.355
Coverage
Stalk diameter
Height
N
0.860 0.419
0.899* 0.573
0.898* –
5 3
TRI = Time to runoff initiation; LRTM = Longitudinal ridges under maize; CRTP = Cross ridges under peanut crops; * = significant at p < 0.05; − = no correlation proven; N = number of observations. 5
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Table 12 Common factor variance of TRI for longitudinal ridges under maize and cross ridges under peanut crops. Principal component
Eigenvalue LRTM
1 2 3 4
6.49 3.37 0.13 0.01
CRTP 7.72 1.28 7.81 × 10−16 2.40 × 10−16
Variance contribution (%)
Cumulative contribution (%)
LRTM
LRTM
CRTP
64.88 33.73 1.29 0.1
85.73 14.27 8.68 × 10−15 2.67 × 10−15
CRTP
64.88 98.61 99.90 100
85.73 100 100 100
LRTM = Longitudinal ridges under maize; CRTP = Cross ridges under peanut crops.
collection. Broken ridge topography may have intercepted surface runoff and prolonged the residence time of rainfall at the bottom of the cross ridge slope. These effects likely weakened rainfall intensity. Alternatively, the observed results may reflect the fact that the correlation coefficient between rainfall intensity index and TRI is small, and the load factor of each principal component is similar. Soil moisture content and vegetation factors weaken rainfall intensity. This would indicate that TRI is not negatively correlated with rainfall intensity. This study examines the influence of soil moisture content, vegetation at high and low levels, soil and water measures(longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees), and rainfall intensity on TRI under natural rainfall conditions. However, in this research there still exist some limitations, like the natural rainfall is contingent and uncontrollable, and influencing factors (soil and vegetation conditions)still incomplete. Therefore, the relationship between TRI and soil bulk density, soil crust, soil surface roughness, leaf status and root distribution in sloping land should be studied further.
factors include soil moisture content of the 3–4 cm soil layers (0.989) and maximum rainfall intensity (0.969), which exhibited the greatest influence on runoff duration in each principal component. Under weak rainfall intensity during the early rainy season, the main process of runoff generation was the soil infiltration process, wherein the 0–4 cm soil layer gradually becomes saturated. Runoff begins when rainfall intensity exceeds the soil infiltration rate and the maximum rainfall intensity accelerates slope runoff. Similarly, Castillo and Gómez-Plaza (2003) found that the initial soil moisture content is the main factor influencing of runoff. However, the influence of rainfall intensity, surface crust, vegetation type, and weed cover on the TRI of bare soil plot mainly depends on rainfall intensity (Chen et al., 2005). The TRI of the longitudinal ridges under maize is positively correlated with vegetation indices, but negatively correlated with soil moisture content and rainfall intensity. The main influential factors of TRI include soil moisture content (64.88%) and comprehensive rainfall and vegetation factors (33.73%). The maximum load factors of the main component consist of the soil moisture content of the 2–3 cm layer (0.999) and maximum rainfall intensity (0.978), as well as maize height (0.864) and stem diameter (0.847). Maize is a tall-stalked crop with a strong capacity for rainfall redistribution by throughfall, stem flow, and canopy interception (Lamm and Manges, 2000; Zapata et al., 2018), although the effects of fall and stem flow are decreased by weak rainfall intensity during the early rainy season. In addition, the maize canopy can reduce the kinetic energy of raindrops, further decreasing throughfall and stem flow. Energy loss during throughfall weakens the capability of rainfall to reach an exposed soil surface, thus delaying runoff. Stem flow is increased by the oblique upward extension and large areas of maize leaves (Yu and D'Odorico, 2014; Ma et al., 2015). Rainfall kinetic energy is reduced by stem flow, resulting in reduced root erosion with increasing stem height and diameter. In addition, maize roots further reduce soil erosion, which can further delay runoff through stalk flow. In summary, TRI for longitudinal ridges under maize was positively correlated with vegetation indices. The TRI of cross ridge under peanut was negatively correlated with soil moisture content, but positively correlated with rainfall intensity and vegetation indices. The first and second main components of the TRI in this plot are soil moisture content (85.73%) and comprehensive vegetation factors (14.27%), respectively. In addition, the main load factors are soil moisture content in the 3–4 cm soil layer (0.985) and peanut stalk diameter (0.985). TRI was positively correlated with rainfall intensity, indicating that runoff mainly results from rainwater
5. Conclusions This study investigated TRI and corresponding impact factors in six different standard plots. TRI values of the plots decreased from a maximum in bare soil, to longitudinal ridges under maize, and cross ridges under peanut crops. Grass, forest, and flat field under fruit trees did not produce runoff. These results indicate that farmland has the poorest soil and water retention capacity among the studied plots. By contrast, forest, grassland, and flat field under fruit trees have excellent soil and water retention capacity that can be attributed to their vegetation cover. Soil moisture content at 0–5 cm depth and TRI are negatively correlated, but vegetation indices was positively correlated with TRI. In addition, rainfall intensity was negatively correlated with TRI for bare soil and longitudinal ridge under maize, but not with TRI for cross ridge under peanut. Correlation and principal component analyses revealed that soil moisture content is the main influential factor of TRI. The second principal components of the TRI of bare soil, longitudinal ridges under maize, and cross ridges under peanut crops are rainfall intensity, comprehensive rainfall and vegetation factors, and comprehensive vegetation factors, respectively.
Table 13 Rotating component matrix of TRI for longitudinal ridge under maize and cross ridge under peanut. Load factor
LRTM CRTP
PC1 PC2 PC1 PC2
Average rainfall intensity
−0.376 −0.926 −0.764 −0.645
Maximum rainfall intensity
−0.154 −0.978 −0.747 −0.665
Soil moisture content 0–1 cm
1–2 cm
2–3 cm
3–4 cm
4–5 cm
0.955 −0.278 0.96 0.280
0.96 −0.191 0.832 0.555
0.999 0.029 0.966 0.260
0.982 −0.034 0.985 0.171
0.995 −0.03 0.912 0.411
LRTM = Longitudinal ridges under maize; CRTP = cross ridges under peanut crops; − = no correlation shown. 6
Coverage
Stalk diameter
Height
−0.537 0.837 0.344 0.939
−0.526 0.847 0.173 0.985
−0.503 0.864 – –
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Fig. 3. Soil moisture content of the plots without runoff initiation. 1016/j.still.2015.07.005. Castillo, V.M., Gómez-Plaza, A., Martı́nez-Mena, M., 2003. The role of antecedent soil water content in the runoff response of semiarid catchments: a simulation approach. J. Hydrol. 284, 114–130. doi:https://doi.org/10.1016/s0022-1694(03)00264-6. Chen, H.S., Shao, M.A., Zhang, X.C., Wang, K.L., 2005. Field experiment on hillslope rainfall infiltration and runoff under simulated rainfall conditions. J. Soil Water Conserv. 19, 5–8. Croke, J., Hairsine, P., Fogarty, P., 1999. Runoff generation and re-distribution in logged eucalyptus forests, south-eastern Australia. J. Hydrol. 216, 56–77. https://doi.org/ 10.1016/s0022-1694(98)00288-1. Cuomo, S., Della Sala, M., 2013. Rainfall-induced infiltration, runoff and failure in steep unsaturated shallow soil deposits. Eng. Geol. 162, 118–127. https://doi.org/10. 1016/j.enggeo.2013.05.010. Defersha, M.B., Melesse, A.M., 2012. Field-scale investigation of the effect of land use on sediment yield and runoff using runoff plot data and models in the Mara River basin, Kenya. Catena 89, 54–64. https://doi.org/10.1016/j.catena.2011.07.010. Dos Santos, J.C.N., de Andrade, E.M., Guerreiro, M.J.S., Medeiros, P.H.A., de Queiroz Palácio, H.A., de Araújo Neto, J.R., 2016. Effect of dry spells and soil cracking on runoff generation in a semiarid micro watershed under land use change. J. Hydrol. 541, 1057–1066. https://doi.org/10.1016/j.jhydrol.2016.08.016. Durán Zuazo, V.H., Rodríguez Pleguezuelo, C.R., 2008. Soil erosion and runoff prevention by plant covers: a review. Agron. Sustain. Dev. 28, 65–86. https://doi.org/10.1051/ agro:2007062. García-Orenes, F., Roldán, A., Mataix-Solera, J., Cerdà, A., Campoy, M., Arcenegui, V., Caravaca, F., 2012. Soil structural stability and erosion rates influenced by agricultural management practices in a semi-arid Mediterranean agro-ecosystem. Soil Use Manag. 28, 571–579. https://doi.org/10.1111/j.1475-2743.2012.00451.x. Gholami, L., Sadeghi, S.H.R., Homaee, M., 2016. Different effects of sheep manure conditioner on runoff and soil loss components in eroded soil. Catena 139, 99–104. https://doi.org/10.1016/j.catena.2015.12.011. Harmel, R.D., Richardson, C.W., King, K.W., Allen, P.M., 2006. Runoff and soil loss relationships for the Texas Blackland Prairies ecoregion. J. Hydrol. 331, 471–483. https://doi.org/10.1016/j.jhydrol.2006.05.033. Jennings, S.B., Brown, N.D., Sheil, D., 1999. Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry 72, 59–73.
Table 14 Vegetation indices of grass, forest, and flat field under fruit trees. Vegetation index
Value
Vegetation coverage
Canopy density
Litter layer thickness (cm) Grass
Forest
4.31
3.54
Grass
Forest
FFFT
Forest
FFFT
99.96%
100.00%
98.65%
99.75%
27.00%
FFFT = Flat field under fruit trees.
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