1. Introduction
Surface runoff is a multi-factor combined process that happens in a
complex underlying surface (Xu et al., 2019; Maria et al., 2018; Ochoa
et al., 2016). Vegetation and topography are the basic elements that
make up the underlying surface, as well as the main factors that affect
surface runoff (El et al., 2013). Studies have demonstrated that the
effects of vegetation and topography on surface runoff are manifested by
jointly changing the upslope inflow of a certain slope unit, and these
two factors are usually inseparable (Qin et al., 2015). Specifically,
aboveground traits of vegetation constitute the main drivers for
generating hydraulic roughness (Kervroëdan et al., 2019), while the
topography is the carrier for the occurrence and development of surface
runoff generation (Sabzevari and Talebi, 2019).
Regarding on vegetation, topography, and surface runoff, most studies
tend to analyze the impact and contribution of a single factor to the
surface runoff process (Li et al., 2008; Bal et al., 2011; Bennett and
Bridge, 2010). Related studies have shown that the surface runoff
coefficient has a negative correlation as well as logarithmic function
relationship with surface vegetation coverage, has a negative
correlation
as well as power function relationship with the thickness of the litter
layer, and has a positive correlation as well as exponential function
relationship with slope (Zhang et al., 2006; He et al., 2014; Duan et
al., 2016). Some scholars have also paid attention to the coupling
effects of vegetation and topography on surface runoff, but theoretical
research is relatively few, mostly involved in other research topics.
For example, Zhang (2018) studied the variation process of water erosion
dynamics under different vegetation spatial configuration through indoor
runoff scouring experiments and proposed that when grass strip was
planted at about 80% of the slope length, it could better exert soil
and water conservation function. Cao et al. (2017) analyzed the response
of erosion and sediment yield characteristics to the interaction of
slope and vegetation cover based on the field simulation test and
proposed that the effect of
vegetation
coverage on surface runoff gradually weakened with the increase of
slope, while the effect of slope on surface runoff gradually increased,
and finally became the dominant factor affecting soil erosion. Ren et
al. (2018) explored the response of water erosion dynamics to vegetation
cover and slope allocation based on the WEPP (Water Erosion Prediction
Project) model and proposed that the increase of vegetation canopy cover
could significantly reduce soil loss and sediment yield, and the least
soil loss occurred when vegetation was distributed in the lower slope
conditions.
The main purpose of the above research was to determine the optimal
configuration of
vegetation
under typical topography conditions. However, these studies had certain
limitations in revealing the coupling effects of vegetation and
topography on surface runoff. On the one hand, most studies had adopted
simulation methods and defaulted the slope condition to a homogeneous
slope, artificially setting or simplifying the combination of vegetation
and topography features, ignoring the fact that the formation of the
overlapping distribution of vegetation and topography was the result of
their long-term interaction and co-evolution (Saco and Mariano, 2013;
Kim and Kupfer, 2016). On the other hand, when it came to the coupling
effects of vegetation and topography on surface runoff, most of the
research objects focused on herbs or shrubs, rarely involved the spatial
distribution of tree species.
When studying the coupling effect of complex topography and the spatial
distribution of tree species on surface runoff generation, the causal
relationship between multiple factors is involved. Structural equation
modeling (SEM) is a statistical method based on confirmatory factor
analysis and path analysis to reveal the structural theory of a certain
phenomenon, which has advantages in simulating and verifying the complex
relationship between multiple factors and has been widely used in the
environmental and ecological research. For example, Xi et al. (2018)
built an SEM of three potential variables including terrain, stand
structure, and soil characteristics to study the multi-factor coupling
relationships between typical Robinia pseudoacacia L. and Pinus
tabulaeformis Carr. mixed plantations. Hou et al. (2020) used SEM to
reveal the relationships between vegetation coverage and the reduction
rate of the runoff coefficient and the reduction rate of the sediment
yield. Yang et al. (2018) used SEM to test the impacts of abiotic and
biotic driving factors on plant biomass and root/shoot ratios.
In this study, cypress forests on a steep slope in southwestern China
was taken as the research object, the SEM was used to simulate the
causal relationship between the spatial distribution of cypress,
topography, and surface runoff, and the response surface method (RSM)
was used to further analyze the response of surface runoff to the
spatial distribution of cypress and topography. Finally, the strategies
of stand structure adjustment were proposed to reduce surface runoff,
which would provide theoretical and technical support for the
improvement of water conservation capacity of cypress forests on steep
slopes in southwestern China.
2.
Materials and methods
2.1. Site description
The study was carried out on a steep slope
(slope angle>30°) in
Huaying County (30°25′21″N, 106°50′2″E), Sichuan Province, with an
annual average precipitation of 1200 mm and an annual average
temperature of 18 degrees Celsius. Most of the rainfall occurred between
May and August, accounting for 70% of the annual precipitation.
The
slope was a bedding slope and the soil was limestone yellow soil. The
vegetation conditions on the slope were composed of cypress and sparse
weeds. The cypress on the slope originated from the Grain-for-Green
Project at the beginning of the 21st century. Aerial-seeding
afforestation was carried out on the degraded slope. After two decades
of succession of vegetation communities, the slope had developed into an
open-canopied
cypress
forest, with significant differences in stand density and distribution
patterns.
2.2. Experiment design
2.2.1. Runoff plot setting
Twelve runoff plots (5 m×10 m) were built at the same slope position,
and the relative height difference of each runoff plots was similar so
that the influence of topography on surface runoff only depended on the
internal topographic characteristics. The basic characteristics
including the stand density of cypress, surface vegetation coverage, and
the
relative height difference of each runoff plot were shown in Tab.1. The
statistical results showed that the standard deviations of the surface
vegetation coverage and the relative height difference of the 12 runoff
plots were 2.25% and 0.13m, respectively, which were
not
of the same order of magnitude as the sample data. Therefore, the
interference of surface vegetation coverage and the relative height
difference to this study could be eliminated.
2.2.2. Data collection
In
each runoff plot, observation data including topography, the spatial
distribution of cypress, rainfall, and surface runoff was collected.
Specifically, Real-time kinematic GPS (RTK-GPS) was used to measure
topography, while the spatial location of cypress was also determined.
In the process of topographic measurement, spatial point data was
measured at 0.2m intervals, and when encountering areas with large
terrain variability, an intensive measurement was performed at 0.1m
intervals. After each rainfall, the surface runoff was measured by the
water-level
gauge in the
runoff
storage pond, and the rainfall data monitored by the
small
weather station was recorded. No less than 20 rainfall and runoff data
were observed during
the
rainy season.
2.3. Data processing
2.3.1. Determination and calculation of factors that characterize
the
spatial distribution of cypress
The Ripley’s K index (Ripley, 1977), the contagion index (Pommerening,
2002; Aguirre et al., 2003), and the stand density of cypress were used
to reflect the spatial distribution of cypress in each runoff plot. In
this study, Ripley’s K index described the number of individual plants
in a circle with a point as the center and r was the radius, which was
typically used to compare a given point distribution with a random
distribution: