Direct and indirect effects
of rainfall and vegetation coverage on runoff, soil loss, and nutrient
loss
Jiayi Huob,a, Changjun Liua*,
Xinxiao Yub*, Lihua Chenb, Wenge
Zhengc, Yuanhui Yangc, Changwen
Yind
a Research Center on Flood and Drought Disaster
Reduction,
China
Institute of Water Resources and Hydropower Research, Beijing 100038,
China
b Key laboratory of State Forestry Administration on
Soil and water Conservation,
Beijing
Forestry University, Beijing 100083, China
cBeijing
Soil and Water Conservation Center, Beijing 100036, China
d Shandong
Provincial Water Diversion Project Operation and Maintenance Center,
Jinan,250100, China
Keywords: Runoff; Soil loss; Soil nutrient loss; Vegetation coverage;
Rainfall; Structural equation model;
Abstract: High nitrogen (N) and phosphorus (P) levels are the main
causes of eutrophication of water bodies, and the chemical oxygen demand
(COD) is one of the indices of relative organic matter content. Several
simulated rainfall experiments have been conducted to investigate the
effects of a single controlling factor on soil and nutrient loss.
However, the role of precipitation and vegetation coverage in
quantifying soil and nutrient loss is still unclear. We monitored
runoff, soil loss, and soil nutrient loss under natural rainfall
conditions from 2004 to 2015 in runoff plots around Beijing. Soil
erosion was significantly reduced when vegetation coverage reached 20
and 60%. At levels below 30%, nutrient loss did not differ among
different vegetation cover levels. Minimum soil N and P losses were
observed at cover levels above 60%. Irrespective of the management
measure, soil nutrient losses were higher at high-intensity rainfall
events compared to low-intensity events (p < 0.05). We applied
structural equation modelling (SEM) to systematically analyze the
relative effects of rainfall characteristics and environmental factors
on runoff, soil loss, and soil nutrient loss. At high-intensity rainfall
events, neither vegetation cover nor antecedent soil moisture content
(ASMC) affected runoff and soil loss. After log-transformation, soil
nutrient loss was significantly linearly correlated with runoff and soil
loss (p < 0.01). In addition, we identified the direct and
indirect relationships among the influencing factors of soil nutrient
loss on runoff plots and constructed a structural diagram of these
relationships. The factors positively impacting soil nutrient loss were
runoff (44-48%), maximum rainfall intensity over a 30-min period
(18-29%), rainfall depth (20-27%), and soil loss (10-14%). Studying
the effects of rainfall and vegetation coverage factors on runoff, soil
loss, and nutrient loss can improve our understanding of the underlying
mechanism of slope non-point source pollution.
Introduction
On a global level, soil erosion is a widespread phenomenon and has
become a serious environmental problem in various ecosystems (Fu et al.,
2011). Surface runoff and soil loss cause soil nutrient loss, resulting
in decreased soil fertility (Adimassu, Mekonnen, Yirga, & Kessler,
2014; Shi, Huang, & Wu, 2018) and in the eutrophication of water
bodies, which seriously threatens land productivity and water quality
(Guo, Wang, Li, & Wu, 2010; Napoli, Marta, Zanchi, & Orlandini, 2017;
Shi et al., 2018). Runoff is the main route of soil loss and nutrient
transport (Ferreira et al., 2018), and soil nutrients are partially
attached to soil particles and partially dissolved in water and lost by
runoff (Kato, Kuroda, & Nakasone, 2009; Shi et al., 2018). Runoff, soil
erosion, and soil nutrient loss are complex processes controlled by
various factors (Guo et al., 2010), and different models have been
constructed to calculate and predict soil nutrient loss (Shi et al.,
2018; Zhou et al., 2019). Various factors impact soil nutrient losses,
such as precipitation, soil physical and chemical properties, soil
moisture content, vegetation cover, and tillage measures (An et al.,
2013; Kurothe et al., 2014; Mutema, Chaplot, Jewitt, Chivenge, &
Blöschl, 2015).
Rainfall and runoff are the main factors driving soil nutrient loss
(Cantón et al., 2011; Jung et al., 2015), and 70% of rainfall are lost
in the form of runoff (Daniel, Phillips, & Northup, 2006; Liu, Yang,
Hu, Tang, & Zheng, 2016); soil erosion and soil nutrient loss caused by
runoff are significantly correlated with accumulated rainfall and
rainfall erosion rate (Jung et al., 2015; Napoli et al., 2017; M. C.
Ramos & Martínez-Casasnovas, 2006). Based on previous studies, rainfall
intensity is an important driving force for soil transport (Qin, Khu, &
Yu, 2010; Z. Wang, Li, & Yang, 2015). Hydrological changes are
generally caused by altered rainfall patterns and have a significant
impact on soil nutrient loss. Studies have shown that soil erosion
responds linearly to extreme precipitation events (Thothong et al.,
2011).
The vegetation cover is the main environmental factor affecting runoff
and soil erosion (Zhao, Mu, Wen, Wang, & Gao, 2013), and the canopy can
trap 10-20% of rainfall (Ghimire, Bruijnzeel, Lubczynski, & Bonell,
2012). Previous studies have shown that the runoff with low coverage has
the greatest correlation with soil erosion. With the increase in
coverage, soil erosion decreases (Gaal, Molnar, & Szolgay, 2014;
Marques, Bienes, Pérez-Rodríguez, Processes, & Landforms, 2008). Based
on long-term hydrological observations, vegetation restoration and
afforestation result in a decrease in runoff, thereby reducing sediment
yield and soil nutrient loss (Molina, Vanacker, Balthazar, Mora, &
Govers, 2012). The root system also has a significant effect on
hydrology. On the one hand, the macropores formed by the root system
create the conditions for the preferential flow, while on the other
hand, aboveground vegetation increases surface roughness and reduces
surface scouring by runoff (Kurothe et al., 2014). In addition,
vegetation can also improve the soil texture (Fattet et al., 2011).
An antecedent soil moisture content is related to vegetation and
hydrology, and the soil water status changes with soil nutrient loss via
alterations in soil seepage (Fattet et al., 2011). Vegetation can
improve soil conditions by adjusting evapotranspiration and hydrology;
it also absorbs soil water to reduce soil water storage. A previous
study has determined the optimal vegetation coverage by modelling the
relationship between soil water consumption and plant growth (W. Fu,
Huang, Gallichand, & Shao, 2012).
Although the effects of rainfall and
environmental factors on runoff and soil loss have been extensively
studied, research on the complex interactions between various factors
and the quantification of their influence is still scarce (Z. Wang et
al., 2015; Zhou et al., 2016). More advanced methods are needed to
determine the direct and indirect relationships between such factors and
soil nutrient loss. The structural equation model identifies the direct,
indirect, and total effects of the influence of the independent variable
on the dependent variable via statistical analysis. It has been used to
solve complex environmental problems, while few studies have applied
this method to soil nutrient loss (Chamizo, Cantón, Rodríguez-Caballero,
Domingo, & Escudero, 2012; Rodríguez-Caballero, Cantón, Chamizo,
Lázaro, & Escudero, 2013; Taka, Aalto, Virkanen, & Luoto, 2016). This
study takes slope runoff under natural rainfall events as the research
object, quantifies the impact of vegetation on runoff sediment and
nutrient loss through a large number of monitoring data, and uses the
structural equation model to quantitatively analyze the direct and
indirect impacts of various factors on slope runoff and erosion.
- Materials and Methods
- Study site
The runoff plots in this study were distributed in various districts of
Beijing (115.7°E-117.4°E,39.4°N-41.6°N), with a continental monsoon
climate. Figure 1 shows the locations of runoff plots across China.
Average annual precipitation is 600 mm, and the rainy season lasts from
June to August, accounting for 70% of the annual precipitation. The
soil type is brown soil, the parent material of loess, which is the main
soil type in Beijing. The runoff plots were 10*5 m and 20*5 m,
Vegetation coverage ranged from 5 to 90%.
Response variables and explanatory variables
Data were collected from the Beijing Soil and Water Conservation Station
and the China Academy of Water Resources and Hydropower Research. An
automatic monitoring system for runoff plots and soil and water
conservation was established in Beijing. Runoff from rainfall was
collected by runoff buckets and monitored automatically via a water
level gauge. After each rainfall event, the mixed water samples in the
runoff bucket were collected, and the sediment and nutrient contents in
the water samples were determined in the laboratory. Our dataset
contained all rainfall events recorded from 2004 to 2015 in the Beijing
plots. Missing data and faulty data, due to instrumentation problems,
were eliminated prior to analysis. A total of 997 natural rainfall
events were collected from runoff plots. The monitored variables runoff
(RO), soil loss (SL), and soil nutrient loss were classified as response
variables. Precipitation and environmental factors considerably
influence hydrological responses, and thus, the variables rainfall
duration, maximum rainfall intensity
over a 30-min period (Imax30), rainfall depth, vegetation coverage, and
antecedent soil moisture content were used to explain variation for
runoff, soil loss, and soil nutrient loss. The plot types were as
follows: Grassland (GL); Farmland (FL); Horizontal bar (HB); Terranes
(TR); Fish scale pit plot (SH). We studied the influence of vegetation
coverage on runoff, sediment, and nutrient loss. To effectively control
variables, we only selected nine runoff plots in Danli, Beijing. The
microtopography of these plots was flat, vegetation mainly consisted of
a deciduous shrub (Vitex negundo L. var. heterophylla (Franch.)
Rehd. ) and white grass (Pennisetum centrasiaticum Tzvel. ), and
vegetation coverage ranged from 5 to 90%.
Table 1 shows the mean characteristics of all groups. The K-mean
clustering divides the dataset into high-intensity events (n = 267),
with an average value (Imax30) of 22.61 mm/h, ranging from 15 to 40
mm/h, and low-intensity events (n = 730), with an average value (Imax30)
of 6.86 mm/h, ranging from 0.1 to 14.7 mm/h. The high-intensity events
produced a rainfall depth about twice as high as the low-intensity
events.
Statistical analysis
We used the structural equation
model (SEM) to quantify the direct and indirect effects of explanatory
variables on response variables. The response relationship between
hydrology and environmental factors is complex, and SEM was used to
determine, from a systematic perspective, which factors affected runoff,
soil loss, and nutrient loss. To improve data normality, data were
square root- and log-transformed. First, the correlation matrix between
variables was calculated to explore the correlation between explanatory
variables and response variables. Subsequently, an a priori hypothesis
model was established according to previous knowledge (Fig. 2), and
unsupervised K-mean classification was employed to partition rainfall
into high-intensity and low-intensity events, based on the Imax30. There
were 267 high-intensity rainfall events and 730 low-intensity rainfall
events. Individual path coefficients between variables were assessed by
the multivariate Wald test (p < 0.05), and non-significant
paths and variables were removed from the model to reduce model
complexity. Modification indices can be used to increase the path in
order to reduce the chi-square value of the model and to obtain an
acceptable model.
Six model fit indices were used to test the goodness of fit of model:
(i) p value, (ii) χ2/ df: the quotient of the Chi square and the degrees
of freedom, (iii) RMSEA: root mean square error of approximation, (iv)
CFI: comparative fit index, (vi) NFI: the non-normed fit index, (vii)
IFI: incremental fit index. The value range of indicators with good
model fitting is listed in Table 1. Standardized path coefficients were
estimated using generalized least squares analysis. The SEMs were
developed and tested with the SPSS AMOS 18 software (AMOS Development
Corp., Mount Pleasant, South Carolina, USA).
- Results
- Effects of vegetation cover on runoff, soil loss, and nutrient loss
Figure 3 shows the runoff, soil loss, and soil nutrient loss in plots
under different vegetation coverage levels. With the increase in
vegetation coverage, runoff and soil loss were significantly reduced
(Fig. 3a, b). When vegetation coverage was higher than 60%, vegetation
had no significant effect on runoff and sediment reduction. However,
soil nutrient loss did not increase significantly with increasing
vegetation coverage. At 90% vegetation coverage, soil P loss was
greater than at 60%. When
vegetation coverage was below 30%, the difference in soil nutrient loss
among different vegetation cover levels was not significant. At a
vegetation coverage level of 60%, soil N and P losses were minimal
(Fig. 3c).
3.2 Soil nutrient loss characteristics under different rainfall
intensities
Figure 4 shows the effect of soil management on soil nutrient loss under
different rainfall patterns. Significant differences were observed for
soil nutrient loss between high-intensity and low-intensity rainfall
events.
Under
all management measures, soil nutrient loss at high-intensity rainfall
events was generally higher than at low-intensity rainfall events (p
< 0.05). Regarding the different management measures, soil
nutrient loss followed the order FL > TR > GL
> HB > SH. The reduction rates of soil P loss
caused by low-intensity rainfall under different land management
measures were as follows: SH (83.82%) > HB (81.34%)
> TR (65.12%) > FL (57.79%) >
GL (23.19%). The reduction rates of soil N loss caused by low-intensity
rainfall events were as follows: TR (79.53%) > FL
(57.79%) > HB (53.18%) > SH (51.77%)
> GL (47.07%). The COD reduction rate followed the order
TR (78.67%) > SH (70.41%) > HB (68.47%)
> FL (62.25%) > GL (50.49%).
3.3 SEM of high-intensity and low-intensity rainfall events
The high-intensity and low-intensity rainfall structural equation model
is presented in Figure 5. The final models showed good fit, with CFI,
NFI, and IFI over 0.9 and p > 0.05 (Table 2). In the
low-intensity model, 29 and 54% of variance
in
runoff and soil loss, respectively, were explained (Fig. 5a). Rainfall
duration had the strongest influence on rainfall depth in both models
(path coefficient = 0.52), while rainfall depth had a strong effect on
runoff (path coefficient = 0.45). Rainfall duration had a direct
negative effect on RO (path coefficient = -0.17) and an indirect
positive effect on RO because of its positive causal effect on rainfall
depth (path coefficient = 0.52). Imax30 had a direct (0.21) and indirect
(0.45) positive effect on RO and a strong direct effect on SL (path
coefficient = 0.65), followed by Imax30 (path coefficient = 0.12).
Rainfall duration and vegetation coverage had a direct negative
influence on soil loss (path coefficient = -0.14 and -0.12).
Environmental factors, vegetation coverage, and antecedent soil moisture
content were not related with runoff and soil loss in high-intensity
rainfall events and were therefore removed from the model to improve
fit. In the high-intensity rainfall model, 23 and 46% of variance in
runoff and soil loss, respectively, were explained (Fig. 5b). At such
events, the same relationship between RO and SL was found, and the path
coefficient increased from 0.65 to 0.74. Neither rainfall duration nor
Imax30 had a significant causal effect on soil loss.
3.4 Relationship between runoff, soil loss, and soil nutrient loss.
We found low to high correlations between response variables and
explanatory variables (Table 4). The largest correlation coefficients
were observed between RO and N and COD as well as between N and COD.
Imax30 and depth had positive effects on runoff, soil loss, and soil
nutrient loss. However, ASMC and vegetation coverage were negatively
correlated with RO, SL, and nutrient loss, while Imax30 and RO and soil
nutrient loss showed moderate correlations (R = 0.27-0.35). Moderate
correlations (R = 0.31 and 0.33) were also observed between depth and
nutrient loss. The SL was only slightly significantly correlated with RO
and soil nutrient loss (R = 0.11-0.19, p <0.01), while ASMC
was negatively and significantly correlated with N and COD loss (R =
0.16 and 0.15, p < 0.01). In terms of explanatory variables,
Imax30 was significantly correlated with SL (R =0.18, p <
0.01), indicating that Imax30 considerably contributed to soil erosion.
Nutrient loss increased linearly with runoff and soil loss after
logarithmic transformation (Fig. 6). We used the stepwise multiple
linear regression model to assess the correlation between soil nutrient
loss and explanatory variables (Table 3). For soil nutrient loss, RO was
considered the largest variable; the factors RO, ASMC, and SL had the
greatest ability to predict soil N loss and explained 63.2% of the
variability.
3.5 Soil nutrient loss SEM model
The
revised SEM describes the effects of rainfall and environments factors
on soil nutrient loss (Fig. 7). Similar relationships among response
variables and explanatory variables were found for soil N, P, and COD
loss. Among all explanatory variables, rainfall factors had the
strongest direct influence on soil nutrient loss. While Imax30 had a
direct positive effect on SL, it had an indirect positive effect on soil
nutrient loss because SL had a direct positive influence on soil
nutrient loss Moreover, environmental factors had a moderate influence
on soil nutrient loss. Of these, runoff had the strongest influence on
SL and nutrient loss. The path coefficients (0.52-0.62) indicate that
runoff was the most important driving force of soil nutrient loss. In
addition to soil N loss in the SEM model, rainfall duration only
slightly negatively influenced soil P and COD loss (path coefficient =
0.10 and 0.04). The variance explained for soil P loss was 54%, while
it was slightly lower for soil N (64%) and COD (65%) loss. Vegetation
coverage directly negatively affected SL (path coefficient = -0.11).
The direct, indirect, and total effects of environmental factors,
precipitation, and hydrological factors on soil nutrient loss are shown
in Figure 8. Soil nutrient loss was directly affected by RO (78-84%),
followed by SL (19-32%). However, Imax30 and rainfall depth had the
largest indirect effects on soil nutrient loss, ranging from 38-56% and
from 17-46%, respectively. Rainfall duration had a direct negative
effect on soil nutrient loss (6-16%). Overall, the factors positively
impacting soil nutrient loss followed the order RO (44-48%), Imax30
(18-29%) and depth (20-27%), and SL (10-14%).
4. Discussion
4.1 Vegetation coverage
Based on our results, runoff and sediment losses significantly decreased
with increasing vegetation coverage (Fig. 3). Regarding soil nutrient
loss, the influence of vegetation coverage between 5 and 30% on soil
nutrient loss was not significant. There was no significant difference
in soil P loss for vegetation cover levels between 20 and 90%. When
vegetation cover was 60%, soil N and P losses were lowest (Fig. 3).
Most likely, this is because with increasing vegetation coverage, the
water retention ability of the root system increases. However, with
increasing levels of vegetation litter, soil nutrient levels with also
greatly increase (Brazier, Turnbull, Wainwright, & Bol, 2014). At the
same time, the increased root system improves the physical and chemical
properties of the surrounding soil (Gao et al., 2009). Vegetation type
can affect soil nutrient loss in the basin (Hervé‐Fernandez, Oyarzún, &
Woelfl, 2016). Turnbull et al. (2011) studied the loss and
redistribution of soil N and P caused by runoff in the process of
grassland degradation to shrub land and found that in areas dominated by
shrubs, N losses were considerably higher than in grass areas. Also,
runoff levels decreased with increasing vegetation cover. However,
previous studies have found that runoff did not change significantly
with vegetation type (Michaelides, Lister, Wainwright, & Parsons,
2009).
Vegetation is the main factor affecting runoff and erosion (Liu et al.,
2016), and a negative effect of vegetation cover on soil erosion was
found in the structural equation model. On the one hand, the canopy can
intercept rainfall and reduce the impact of raindrops on the ground,
while on the other hand, the large pores formed by the roots can also
increase infiltration and reduce runoff and soil loss (Liu et al.,
2016). Previous studies have found that soil erosion is more sensitive
to changes in vegetation than runoff (El Kateb, Zhang, Zhang, &
Mosandl, 2013; M.A.Nearing et al., 2005). When the vegetation cover is
higher than 65%, runoff is almost negligible (Descheemaeker et al.,
2006).
4.2 Rainfall intensity
Precipitation is the main factor driving runoff and soil loss. Soil
erosion is not only driven by extreme precipitation, but also by short
precipitation events with low rainfall intensity (Ramos &
Martínez‐Casasnovas, 2009). Under natural conditions, rainfall events
show a skewed distribution. Although strong storm events account for a
small percentage of all precipitation events, they exert most of the
erosion throughout the year (Ramos & Martı́Nez-Casasnovas, 2004; Ziadat
& Taimeh, 2013). In high-intensity rainfall events, the was no negative
correlation between Imax30 and rainfall duration (Fig. 5b). Natural rain
patterns are mostly short high-intensity events and long low-intensity
events. In this study, high-intensity rainfall events, classified by
K-means clustering, accounted for 26.8% of all precipitation events. In
low-intensity rainfall events, vegetation coverage negatively affected
soil loss (Fig. 5a). Nevertheless, the negative relationship between
vegetation cover and soil loss disappeared with high-intensity rainfall,
suggesting that the effect of the vegetation cover on soil loss could be
overridden by rainfall intensity. In high-intensity rainfall, Imax30 had
an indirect effect on soil loss, while in low-intensity events, this
effect was direct (Fig. 5). This phenomenon was consistent with previous
results (Rodríguez-Caballero, Cantón, Lazaro, & Solé-Benet, 2014). The
Imax30 directly affected soil loss in low-intensity rainfall events,
which was not the case in high-intensity rainfall events. One possible
reason for this interesting phenomenon is that intense rainfall can
quickly form a thin film water film, preventing the raindrops from
hitting the ground directly. In low-intensity rainfall events, raindrops
hit the surface directly and mix the disturbed soil particles with
runoff (B. Wang, Steiner, Zheng, & Gowda, 2017).
There are two mechanisms of runoff production in arid and semi-arid
areas: surface saturation and infiltration excess, which do not occur
independently. Previous studies have shown that the production of
rainfall depth and rainfall intensity is well correlated with runoff (á.
G. Mayor, Bautista, & Bellot, 2011; A. G. Mayor, Bautista, Llovet, &
Bellot, 2007), mainly because rainfall depth can well predict runoff in
arid and semi-arid areas, while rainfall intensity adequately predicts
runoff in humid areas. According to the structural equation model,
runoff was directly affected by both rainfall depth and intensity,
indicating that the runoff mechanism is the result of surface saturation
and infiltration excess. Compared with low-intensity rainfall, the path
coefficient of rainfall intensity on runoff in high-intensity rainfall
events increases, while the path coefficient of rainfall depth on runoff
decreases (Fig. 5), indicating that the dominant role of infiltration
excess was greater under high-intensity rainfall events
(Rodríguez-Caballero et al., 2014).
4.3 Relationship between runoff, soil loss, and soil nutrient loss
Based on a previous study, sediment-associated nutrient loss accounts
for 77% of the total soil nutrient loss (Cheng et al., 2018). Frequent
low-intensity rainfall poses a greater threat to soil nutrient loss
(Norton, Sandor, & White, 2007). In our study, the stepwise multiple
linear regression equation shows that runoff was the important predictor
of soil nutrient loss, explaining 35.5, 61.9, and 56.4% of soil P, N,
and COD loss, respectively (Table 3). The SEM results of direct effects
were similar to those of the stepwise multiple linear models. After
adding other factors, the predictive power of the equation was slightly
improved. A study by Girmay et al. (2009) found that the prediction
ability of the model can be improved by 16% with the addition of the
vegetation cover (Girmay, Singh, Nyssen, & Borrosen, 2009). Compared
with rainfall, plot variables account for poor hydrological variability,
and such poor explanation can be attributed to several factors: (1) The
vegetation and topographic conditions of plots are single; (2) the
influence factors are not all included, such as slope, litter, and
biological soil crusts. The multiple linear regression equation shows
that antecedent soil water content had negative effects on soil N and
COD loss (Table 3), most likely because nitrogen and phosphorus show
different forms during runoff erosion. In general, phosphorus tends to
adhere to soil particles and is lost with soil erosion, while most
nitrogen is soluble and moves with runoff (Yihe, Bojie, Liding, Guohua,
& Wei, 2016).
4.4 Direct and indirect effects of explanatory factors on response
variables
Antecedent soil water content is
closely related to runoff mechanisms. In arid and semi-arid areas, rain
reaching the surface mainly forms runoff by surface saturation. However,
in humid areas, infiltration excess is the dominant runoff mechanism. In
the structural equation model, antecedent soil water content had a
negative effect (Fig. 7). Soil erodibility is closely related to soil
type, soil structure, and soil moisture content. Cheng et al. (2018),
studying the effect of soil moisture content on erosion, found that soil
loss increased with soil moisture content in areas with a high-water
content, but decreased in areas with a low content. The higher the soil
moisture content, the more conductive it is to the formation of surface
runoff and soil nutrient loss. When soil is saturated, soil particles
are more easily separated by runoff (Chen et al., 2012; Hahn, Prasuhn,
Stamm, & Schulin, 2012; Shigaki, Sharpley, & Prochnow, 2007).
We found negative effects of rainfall depth and runoff on soil moisture
content. Higher amounts of rain are lost in the form of runoff, and only
a small amount seeps into the soil to replenish soil moisture.
Atmospheric evaporation and the time interval of the last precipitation
are the main factors determining the soil moisture content. Using the
structural equation model, we did not find a direct effect of soil
moisture on soil nutrient loss. However, based on a series of simulated
rainfall experiments, Cheng et al. (2018) observed that the soil
nutrient loss associated with runoff was highest at a soil moisture
content of 30%. Most likely, this is because soil surface nutrients
quickly dissolve and are lost with runoff when the soil moisture content
is high. However, at low soil moisture levels, a high soil infiltration
rate leads to the delay in runoff formation time, and soil nutrient loss
is caused by rainwater infiltration into the soil (Cheng et al., 2018).
5. Conclusions
We systematically analyzed the interactive effects of natural rainfall
and environmental factors on runoff, soil, and nutrient loss at the plot
scale. Soil erosion is significantly reduced when vegetation coverage
reaches 20 to 60%. At levels below 30%, the difference in soil
nutrient loss under different vegetation cover levels is not
significant. When vegetation cover is 60%, N and P losses are minimal.
Irrespective of the land use type, soil nutrient loss at high-intensity
rainfall events was higher than at low-intensity rainfall events (p
< 0.05). The structural equation model can reveal more
information on the effects of rainfall characteristics and environmental
factors on hydrological responses. Rainfall duration is still the key
factor affecting rain accumulation. In high-intensity rainfall events,
we found no causal relationship between vegetation cover, antecedent
soil moisture content, and hydrological responses. After logarithmic
transformation, soil nutrient loss was significantly linearly correlated
with runoff and soil loss, and runoff was the most important predictor
of soil nutrient loss. In the structural equation model of soil nutrient
loss, vegetation cover and soil moisture content negatively affected
soil loss. The variance explained for soil P, N, and COD was 54, 64, and
65%, respectively. We established the relationship structure of the
direct and indirect effects of rainfall characteristics and
environmental factors on soil nutrient loss in runoff plots. Our study
provides a basis for a deeper understanding of the underlying mechanisms
of soil loss and non-point source pollution.