Introduction
Soil erosion is one of the main environmental risks that restrict the
survival and development of human beings (Ongley et al., 2010), affect
regular land development, and have
been reported as the main cause of land degradation (Sun et al., 2012).
According to Miao et al. (2010), soil erosion in the Chinese Loess Plateau is
serious. The annual average soil loss in this region is about 1600 Gg, and
the annual erosion amount of surface soil in the most seriously affected
areas reaches 20 mm or more (Hessel and Jetten, 2007). Recent studies on the
Loess Plateau are mainly focused on water erosion control in the water–wind
crisscrossed erosion region, soil quality indicators in relation to land use
and topography, overland flow on abandoned slopes, effects of long-term
fertilizer applications on soil organic carbon and hydraulic properties, soil
water content, interrill erosion on unpaved roads, and temporal variations of
flow–sediment relationships (Zhao et al., 2015, 2016a; Yu et al., 2015,
2016; Shi et al., 2016; Li et al., 2016a, b; Cao et al., 2015; Gao et al.,
2016), but there is little research on the distributed-dynamic simulation of
erosion and sediment yield at watershed scales.
The Majiagou River watershed belongs to the first grade tributary of the
Yanhe River. It is located in the typically hilly and gully region of the
Loess Plateau (Li, 2009), with a particular topography and geomorphology. It
is one of the regions in the middle reaches of the Yellow River more
seriously affected by soil loss (Fu et al., 2010; Jia et al., 2014). Before
the implementation of China's returning farmland policy in 1997 (Zhao et al.,
2016b), the soil erosion area in the Majiagou River watershed reached
72.31 km2, which accounts for 98 % of the total watershed area. The
soil erosion rate was up to 8740 Mg km-2 yr-1; it belongs to the
very intensive soil erosion region (Dang et al., 2013). After the
implementation of the returning farmland to forestland project for nearly
10 years, the soil erosion rate of the Majiagou River watershed decreased to
5700 Mg km-2 yr-1 in 2008 (Wu et al., 2010). Therefore, it is
necessary to track spatiotemporal evolutions of erosion and sediment yield in
the Majiagou River watershed, and results may provide a reference for
scientific management of land resources and reasonable planning of soil and
water conservation measures.
Against the international background of serious soil loss, research on
monitoring, modelling, and other advanced technologies has developed rapidly
in the world (Chen and Cui 2006; Cui et al., 2013; Borrelli et al., 2015). In
the field of experimental study, the earliest quantitative study of soil
erosion began in 1912 (Meyer, 1984); the related scholars in the world
carried out long-term experimental studies in the runoff plot under rainfall
and natural status (Xia et al., 1998; Zhou et al., 2000; Yu et al., 2009;
Chen et al., 2010), which provides the scientific basis for the study of soil
erosion and the theoretical support for the development of a factor analysis
model. In the field of model study, soil erosion models may be divided into
factor analysis (empirical statistical models) and physical-mechanism process
models (Zhou and Shangguan, 2004; Cao et al., 2015). The factor analysis
model is simple and intuitive and can be modified according to the specific
application area. The typical representative is the USLE and its revised
version (RUSLE) (Wischmeier and Smith, 1965, 1978; Renard et al., 1997; Xie
et al., 2003; Sadeghi and Mizuyama, 2007), which have been widely used (Liu
et al., 2001, 2002; Fu et al., 2001; Yin and Chen, 1989; Wang et al., 1996;
Cheng et al., 2009; Arekhi et al., 2012; Ligonja and Shrestha 2015).
Regarding physically based models, they may be divided into four main
processes including raindrop sputtering, migration, runoff dispersing, and
sediment transport (Wang et al., 2008). Meyer (1984) established the theory
of shallow gully erosion and Foster et al. (1980) proposed a physically based
soil erosion model. The United States Department of Agriculture (USDA)
introduced the WEPP model in 1995. At the same time, in Europe and Australia,
some classic physical process-based models, such as the Holland LISEM, the
British EUROSEM and the Australian GUEST, were developed. Since 1980, Chinese
scholars have successively established soil erosion prediction models with
local characteristics (Mou and Meng, 1983; Yang et al., 2007, 2008; Tang,
1996; Cai et al., 1996; Fan, 1985). With the development of modern
information technology, the distributed and dynamic models have been
developed and applied gradually (Zhao et al., 2013). In the field of
distributed models, the typical soil erosion distributed models mainly
include the SHE, IHDM, and EUROSEM models (Wang et al., 2003). In particular,
some of the agricultural non-point source pollution evaluation models such as
SWAT and AGNPS also include soil erosion evaluation modules (Zhang et al.,
2007; Li et al., 2009). Dynamic models for soil erosion of small-scale
watershed systems also have a wide application value (Tang and Chen, 1997;
Gao and Lei, 2010; Liao et al., 2012). The most representative dynamic model
is KINEROS, which simulates storm event-based sediment processes (Singh et
al., 1999). In recent years, research on soil erosion has evolved rapidly
with new computer-based technologies, such as GIS/RS, BP neural networks,
genetic algorithms, and fruit fly algorithms (Zhao et al., 2004; Dai et al.,
2008; Ochoa-Cueva et al., 2015). These can make real-time accurate
simulations and assess quantitative spatiotemporal changes (Caro et al.,
2012). In short, with the development and popularization of information
technology, GIS/RS technology, and computing technology, research on the
watershed sediment yield has become an inevitable trend, and the dynamic
simulation has also become a necessary means to track temporal variations of
erosion and sediment yield (Yao and Xiao, 2012).
However, the existing distributed-dynamic models which focus on event-based
rainfall processes are not suitable for assessing inter-annual variability of
erosion and sediment yields, and research hardly considers the effects of
upstream–downstream interactions on soil erosion and sediment yields at
watershed scales. Therefore, the objectives of this study are (i) to
establish and modify a yearly distributed model of watershed erosion and
sediment yield and (ii) to evaluate spatiotemporal changes in erosion and
sediment yield before and after returning farmland projects in the Majiagou
River watershed. Results may provide a reliable scientific basis for the
dynamic modelling of multi-scale erosion and sediment yield, land use
planning, and watershed management.
The relative location between the Yanhe River watershed and the
Yellow River/Yellow River basin, the geographical location sketch of the
Majiagou River watershed, the Zaoyuan upstream catchment, the Ansai upstream
catchment, and the Ganguyi upstream catchment in the river system of the
Yanhe River watershed.
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Material and methods
Study area
The Majiagou River, located in western Ansai County of Yanan, northern
Shaanxi Province (China), is one of the first grade tributaries of the Yanhe
River (Fig. 1). It flows into the Yanhe River in Ansai County from north-west
to south-east. The main channel is about 17.4 km in length and the average
gully slope is about 6.5 ‰. The watershed (73.83 km2) is
situated in the typical hilly and gully region of the Loess Plateau (northern
China; 109∘9′30′′–109∘18′59′′ E and
36∘49′42′′–36∘56′42′′ N). The watershed belongs to a warm-temperature and semi-arid
continental monsoon climate. The evaporation capacity is above 1000 mm; the
annual average temperature is 6–11 ∘C. The average annual
precipitation is about 500 mm, with 80 % of rainfall concentrated
between May and October. Normally, precipitation occurs as intense and short
storms, which favours the rapid formation of runoff, which greatly increases
the risk of water erosion and flooding.
Environmental database
The parameters included in this study include a digital elevation model
(DEM), daily precipitation data, runoff, soil properties, and land use types
(Figs. 2 and 3; Table 1).
Description and source of the environmental database in the
Majiagou River watershed.
Data layer
Data format
Description
Source
DEM
Raster
30 m spatial resolution DEM data of the Majiagou watershed
Computer Network Information Center,Chinese Academy of Sciences(http://datamirror.csdb.cn/index.jsp)
Land use
Raster
30 m spatial resolution farmland,grassland, forest land, residential area, water area, sand
Data Center for Cold and Arid Region Sciences(http://westdc.westgis.ac.cn/)
Precipitation
DBF
Daily values in Ansai, Yanan, Yanchang, and other rain gauges(1957–2013)
China Meteorological Data Sharing Service Network (http://www.cdc.sciencedata.cn)
Soil
DBF
Physical and chemical properties (organic matter, soil texture, sand fraction, clay fraction, structural coefficient,permeability level)
(1) Soil Survey Office in Shaanxi Province. Dataset of the Second Soil Survey in Shaanxi Province (1979–1990).(2) Soil quality background in Loess HillyRegion (2000–2008). Data Sharing Infrastructure of Earth System Science_Data Sharing Infrastructure of Loess Plateau(http://loess.geodata.cn/)
Runoff and sediment
Excel
Time series of annual observed values in Ganguyi hydrological station(1954–2012), and Ansai and Zaoyuanhydrological stations (2006–2012)
Data Sharing Infrastructure of Earth System Science_Data Sharing Infrastructure of Loess Plateau (http://loess.geodata.cn/)
(a) Elevation map of the study area; (b) land use
types of the Majiagon River watershed.
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Soil types of the Majiagou River watershed. Type 1: tillage
erosive loessal soil (80 %) + erosive loessal soil (20 %); Type 2:
tillage erosive loessal soil (80 %) + calcareous alluvial soil (20 %);
Type 3: erosive loessal soil (80 %) + tillage erosive loessal soil
(20 %).
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Dynamic model of erosion and sediment yield
Accelerated erosion risk is the result of different natural and anthropic
factors (Fu et al., 2014; Tian et al., 2016). Climate, soil, topography, and
vegetation are the natural factors affecting soil loss (Mu et al., 2012; Zhao
et al., 2013). Inadequate land use, the destruction of forest and grass,
unsuitable reclamation and overgrazing, cultivation on steep slopes, mining,
road construction, and unreasonable waste soil and residue treatments are the
main anthropic factors affecting soil loss (Liu et al., 2014; Lieskovský
and Kenderessy 2014; Wang et al., 2016). Based on the USLE/RUSLE equations,
the Chinese soil loss equation (CSLE) model (Liu et al., 2002) was selected
and applied to quantitatively evaluate soil erosion of the Majiagou River
watershed. The basic expression is as follows:
Q=A×R×K×L×S×B×E×T,
where Q is the annual average soil erosion rate (Mg km-2 yr-1);
A is the catchment area (hm2); R is the rainfall erosivity factor
(MJ × mm hm-2 × h × yr); K is the soil
erodibility factor
(Mg × hm2 × h hm-2 × MJ × mm);
L is the slope length factor; S is the slope gradient factor; B is the
biological measure factor (equivalent to factor C of the RUSLE equation);
E is the engineering measure factor; and T is the tillage measure factor.
Because not all eroded soil is actually delivered to the basin outlet, the
sediment delivery ratio factor (λ) was introduced to estimate the
annual average sediment yield by Eq. (2):
Qs=A×R×K×L×S×B×E×T×λ.
The dynamic-continuous modelling studies are very critical and necessary for
accurately estimating annual changing trends of sediment yields (Gessesse et
al., 2015). However, Eq. (2) calculates the multi-year average sediment yield
amount; it is not a dynamic changing expression. According to the related
study results (Long et al., 2008; Miao et al., 2012), the rainfall erosivity
factor and the sediment delivery ratio factor affected by hydrological
elements are defined by the dynamic hydrological factor; the biological
measures, engineering measures, tillage measures, and the sediment delivery
ratio factor affected by human activities were designed as the dynamic land
management factor, so the dynamic equation of sediment yield suitable for the
hilly and gully region of the Loess Plateau was put forward as follows:
Qs,i=A×K×LS×(Ri×λq,i)×(Bi×Ei×Ti×λm,i),
where subscript i represents the ith year, supposing that the factor
λi can be divided approximately into the product of
λq,i related only to hydrological conditions and
λm,i related only to land management measures.
Impacts of hydrological elements on sediment transport are mainly manifested
in transport of sediments from erosion sources to river courses by surface
runoff flow (Mu et al., 2012). λq,i can be estimated by
the sediment transport capacity (Prosser and Rustomji, 2000). It can be
supposed as follows:
λq,iλq=TCiTC=k×qia×sbk×qa×sb=qiq1.45,
where TC is the average sediment transport capacity per unit width of slope
(kg m-3); q is the average runoff amount per unit width (m-3);
k, a, and b are coefficients. Those coefficients and the surface
gradient factor S are constants when there are no changes in underlying
surfaces of runoff.
From left to right and up to down: spatial distribution of the
annual average R factor, K factor, LS factor, and BET factor in the
Majiagou River watershed.
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Under the annual changing conditions of λm,i, B, E, and T in
the study area, the dynamic land management factor was introduced and defined
as
ηi=Bi×Ei×Ti×λm,iB×E×T×λm.
According to Xu et al. (2012), who studied the evolution of runoff and
sediment load of the Yanhe River basin between 1956 and 2009, the period
between 1956 and 1969 is a sporadic governance stage with little intervention
of human activities: the human intervention degree is only 0.9–3.9 % and
fluctuations of runoff and sediment are mainly caused by changes in natural
rainfall. After this stage, human land management activities gradually became
the main driving force for changes in runoff and sediment. In order to
quantitatively study impacts of human land management activities on the
sediment transport process, this study takes the year of 1956–1969 as the
base period, and the years after the 1970s have been defined as the
governance period (Wang et al., 2016). In agreement with Wang and Fan (2002),
the fitting relationship expression (R2=0.912) of runoff and sediment in
Ganguyi hydrological station in 1954–1969 was taken as the denominator, and
the fitting relationship expression (R2=0.857) of runoff and sediment in
1954–1989 as the numerator. The ratio of sediment yield amount between the
governance period and the base period was defined as the dynamic influencing
factor which reflects effects of human land management activities on yearly
changes in watershed sediment transport. The expression is
ηi=yg,iyb,i=0.449xi-5062.60.4436xi-4559.9,
where xi is the runoff amount in the ith year (104 m3), n
is the number of years, yg,i is the sediment amount in the
ith year during the governance period (104 m3), and
yb,i is the sediment amount in the ith year during the base
period (104 m3).
In summary, the dynamic model of erosion and sediment yield was determined as
follows:
Qs,i=ηi×qiq1.45×Ri×λ×A×K×LS×B×E×T,
where λ is the average sediment delivery ratio; B, E, and T
represent the multi-year average value of each measure factor.
Determination of model factors
The rainfall erosivity factor
Rainfall erosivity is the potential erosive force of rainfall. Different
authors have proposed simple algorithms of rainfall erosivity in different
forms. In this study, a half-month simple algorithm of rainfall erosivity
(Zhang et al., 2003) was applied to estimate the monthly and annual rainfall
erosivity. The half-month algorithm of rainfall erosivity estimated by daily
precipitation is calculated as
Ri=α∑j=1k(Pj)β,β=0.8363+18.144Pd12+24.455Py12,α=21.586β-7.1891,
where Ri is the rainfall erosivity value in the ith half-month period
(MJ mm hm-2 h-1), k is the number of days within the
half-month period, Pj is the rainfall in the jth day during the
half-month period (the erosive rainfall standard ≥12 mm), Pd12 is
the average daily rainfall when the daily rainfall ≥12 mm, and
Py12 is the average annual rainfall when the daily rainfall ≥12 mm.
The annual dynamic values of the R factor in the Majiagou River watershed
are estimated according to the above algorithm. Results demonstrated that
most of the rainfall erosivity values in the hilly and gully region of the
Loess Plateau are all below 2000 (MJ mm hm-2 h-1). However,
Yan'an suffered a once-in-a-century storm in July 2013, which is the key
reason for the abnormally large rainfall erosivity value (5644.205
(MJ mm hm-2 h-1)) of the Majiagou River watershed in 2013. In
addition, spatial distributions of the average annual rainfall erosivity are
spatially interpolated and shown in Fig. 4.
Soil erodibility factor
According to the related studies (Zhang et al., 2007; Lu et al., 2011), the
soil erodibility factor (K) is calculated as
K=0.74488Kn-0.03336,Kn=[2.1×10-4M1.1412-OM+3.25SSC-2+2.5PL-3]/100,
where M is calculated by the formula of particle mass fraction of
(0.002–0.1 mm) × (particle mass fraction of
(> 0.002–0.05 mm) + particle mass fraction of
(> 0.05–2 mm)); OM is the soil organic matter content,
g / kg; SSC is the structural coefficient; PL is the permeability
level.
Based on the soil quality survey results of the study area, the average K
value of soil erodibility in the watershed was calculated as
0.0542 Mg h MJ-1 mm-1, which is close to results reported by Li
and Zheng (2012) in the Yanhe River basin. The soil in the study area is
focused on loessal soil; the K values of different soil types were
calculated by the above equations. Under the GIS-aided analysis conditions,
different K values were added to the attribute table of the soil map as a
column attribute value; then, the vector map will be converted to a raster
map based on the K value field. Finally the spatial distribution map of the
soil erodibility factor in the Majiagou River watershed was presented in
Fig. 4.
Topography factor
The LS factor reflects the influencing degree of terrain factors on soil
erosion; it can be divided into slope length and slope gradient factors. Many
authors have suggested empirical formulas for quantitative analysis according
to the standard definition of the LS factor (Fu et al., 2009; Wu et al.,
2013). Through comprehensive comparison analysis, the slope length factor
(L) in this study was estimated by the equation below:
L=(λ22.13)α,
where λ is the horizontal slope length; α is the slope
length index. α is calculated as
α=ββ+1,
where β is calculated as
β=(sinθ0.0896)/3.0(sinθ)0.8+0.56
and θ is the slope gradient (∘).
The slope gradient factor (S) in this study was calculated using the
piecewise method of gentle and steep slope gradient (McCool et al., 1987; Liu
et al., 2010). Considering this mountainous terrain of the watershed, the
specific expressions are as follows:
S=10.8sinθ+0.03θ<5∘,S=16.8sinθ-0.055∘≤θ≤10∘,S=21.9sinθ-0.96θ>10∘,
where θ is the slope gradient (∘).
In this study, the multi-year average LS value from the Majiagou River
watershed is determined as 12.9 based on previous research results (Xie et
al., 2009; Zhou and Li 2015). In addition, according to the above GIS-based
extraction algorithm of slope gradient and slope length, the grid layers of
slope gradient and slope length in the study area were extracted from the
30 m resolution DEM, and then the topography factor was spatially calculated
by the optimal calculation formula of the slope gradient factor and the slope
length factor. The spatial distribution layer of the LS factor in the study
area is shown in Fig. 4.
Comprehensive measure factor (BET)
Biological measure factor (B factor) refers to the ratio of soil erosion
amount between the standard cropped plot and the abandoned plot within a
certain time under the same conditions (Wischmeier and Smith, 1965) and
varies between 0 and 1. Engineering measure factor (E factor) is defined as
the ratio of the soil erosion amount between engineering and non-engineering
measures. Tillage measure factor (T factor) is the ratio of the soil
erosion amount between the tilled farmland and the untilled land under the
same conditions and varies between 0 and 1 (Guo et al., 2013).
Considering the synchronization of human activities on underlying surface
conditions between the Majiagou River watershed and the Yanhe River basin,
based on the related research results of B, E and T factors in the
Loess hilly area (Zhang et al., 2012), 0.1562, 0.497 and 0.712 values were
assigned, respectively. The spatial distributions of the average BET factor
for nearly 10 years were spatially calculated (Fig. 4).
The sediment delivery ratio (SDR)
According to Jing et al. (2005), there are different fluctuations for annual
SDR values of the Majiagou River watershed, with an average value around
0.9. A SDR value of 0.92 for many years was determined as the average SDR
value of the Majiagou River watershed (Zhu et al., 2007).
Validation of the sediment yield modulus among Ganguyi, Ansai, and
Zaoyuan hydrological stations and the Majiagou River watershed based on the
originally established model.
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Results and discussion
Validation of erosion and sediment yield
Considering the very similar climate and underlying surface conditions, the
soil erosion rate in the study area has a certain comparability with the
Yanhe River watershed; the previous research results of the Yanhe River
watershed can be used to verify our results. According to the dynamic
simulation results of soil erosion in the Yanhe River watershed from 2001 to
2010 reported by Li and Zheng (2012), the annual average erosion rate of the
Yanhe River watershed is 5812.28 Mg km-2 yr-1 and has little
difference with the average simulated value of
6307.86 Mg km-2 yr-1 in the Majiagou River watershed from 1995
to 2012. The annual erosion rate of the Majiagou River watershed in 2008 is
2485.46 Mg km-2 yr-1 and the corresponding simulated value is
2278.2 Mg km-2 yr-1 with relative error 8.34 %. These
results demonstrate that the dynamic erosion and sediment yield model has
scientific rationality and good reliability. This study results can be used
for adsorbed NPS pollution load estimation.
Validation of the sediment yield modulus among Ganguyi, Ansai, and
Zaoyuan hydrological stations and the Majiagou River watershed based on the
modified model.
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Spatial distribution of soil erosion gradations (Mg ha-1) of the
Majiagou River watershed: (a) 1995; (b) 2010.
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In addition, previous research results of sediment variations in Ganguyi
hydrological station between 1961 and 2012 (Ren et al., 2012) and the
simulation results of sediment yield in this study confirmed that sediment
yield showed a decreasing trend; although there were fluctuations of
different degrees in individual years (Fig. 5), it indicates that the overall
changing trends of sediment yield in the study area are consistent with the
background of returning farmland policy (Zhao et al., 2013); the current
simulation accuracy basically meets the requirements of changing tendency
evaluation. However, the original established model largely fails for the
individual events, especially after 2006, when the simulated values are
distinctly different from the observed values (Fig. 5). The main reason for
this may be that sediment transport processes in the established model may
not clearly reflect spatiotemporal variations of the watershed underlying
surface, especially for the physically based complex sediment yield relations
between the upper and lower areas of the watershed after returning farmland.
Therefore, it is necessary to modify the originally established model. The
influencing factor considering relationships between the upper and lower
reaches of the watershed was introduced to further improve the accuracy of
the sediment yield model. According to the existing research results (Xie and
Li, 2012), Eq. (7) can be changed into the following formula:
Qs,i=Qw,iQi-Qb,i×ηi×qiq1.45×Ri×λ×A×K×LS×B×E×T,
where Qw,i is the annual saturated water when the saturated
sediment transport amount is the observed sediment transport amount in a
hydrological station, Qb,i is the annual baseflow, and Qi is the observed annual runoff amount.
For the Ganguyi hydrological station, the simulated value of the annual
average sediment yield rate after model modification from 1995 to 2012 has
changed from 5803.23 to 4510.66 t km-2 yr-1 between
1995 and 2012. The observed value in the Ganguyi hydrological station of the
Yanhe River watershed is 3411.53 t km-2 yr-1; the relative
error of the modified model decreases by 30–40 % (Fig. 6). For Ansai and
Zaoyuan hydrological stations, the simulation results after modification also
improved a lot.
Spatiotemporal evolutions of soil erosion gradations
Figure 7 shows the spatial distribution of soil erosion gradations of the
Majiagou River watershed in 1995 and 2010. The annual average soil erosion
rate of the Majiagou River watershed is 6307.86 Mg km-2 yr-1.
The current situation of soil and water loss in the study area is serious and
erosion control measures and adequate management plans for soil and water
resources are necessary in this hilly and gully region.
Although there are no large variations in the overall spatial distribution of
soil erosion between 1995 and 2010, small differences in the intensity of
soil erosion rates are observed (Table 2). The area with very low to
moderately low erosion rates decreased from 55.41 to
46.93 Mg km-2 yr-1 between 1995 and 2010 in the Maiagou River
watershed (approximately 8.48 % of the total area). In contrast, the area
under moderate to extreme soil erosion rates increased from 44.59 to
53.07 Mg km-2 yr-1 during the same period. The above results
indicate that spatiotemporal evolutions of soil erosion intensity in the
watershed are closely related to temporal and spatial distributions of
rainfall intensity, rainfall duration, rainfall amount, and land use
patterns. The long-duration concentrated rainfall in 2010 results in a little
higher erosion intensity than 1995 and easily eroded sloping farmland. It
also shows that current soil and water conservation measures are not suitable
for high rainfall intensity. Results potentially emphasize the necessity for
further efforts in land resource management.
Classification and gradation of soil erosion; percentage in the
Majiagou River watershed in 1995 and 2010.
Erosion
Erosion rate
1995
2010
gradation
(Mg km-2 yr-1)
Ratio (%)
Area (hm2)
Ratio (%)
Area (hm2)
Very low
< 5
11.60
856.17
9.55
704.85
Low
5–10
8.85
653.08
8.01
591.36
Moderately low
10–25
34.96
2581.46
29.37
2168.31
Moderate
25–50
37.35
2757.67
40.63
2999.59
Intense
50–80
6.12
451.98
10.34
763.59
Very intense
80–150
1.12
82.63
2.06
152.32
Extreme
> 150
ND
ND
0.04
2.99
Total
100
7383
100
7383
ND: not determined
Temporal evolutions of sediment yield
Sediment transport amount in the study area has an overall decreasing trend
from 1995 to 2012 (Fig. 8). The average sediment transport before and after
model modification in the recent 5 years (in addition to 2013) is 4574.62 and
1696.1 Mg km-2, respectively. It decreased by about 35.4 and
78.2 % of sediment transport from the early governance period
(1995–1998). Results show that the modified model is more in accordance with
practical circumstances; the main reasons for the decreasing sediment yield
mainly result from water and soil conservation measures for regular rainfall
events. Since the late 1990s, China has gradually carried out construction
projects of farmland to forestland land use changes, beautiful mountains and
rivers, warp-land dam engineering, and terracing of the Yanhe River, funded
by the World Bank loan in northern Shaanxi. Soil and water conservation
measures implemented in the Yanhe River basin have contributed to improving
underlying surface conditions and to reducing soil erosion disasters.
Especially after 2003, sediment transport in the study area not only had an
overall decreasing trend, but inter-annual fluctuations were also small and
the whole sediment transport level was low. It also fully indicates that the
effective implementation of soil and water conservation measures and the
continuous improvement of underlying surface conditions have significant
benefits of water and sediment reduction (Ran et al., 2006).
Comparative variations of sediment yield and rainfall erosivity in
the Majiagou River watershed from 1995 to 2012: (a) the established
dynamic model; (b) the modified dynamic model.
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Comparison of monthly sediment yield and rainfall erosivity in the
Majiagou River watershed in 2013.
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Spatial distribution of the sediment yield modulus
(Mg ha-1) of the Majiagou River watershed:
(a) 1995; (b) 2010.
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Soil and nutrient loss in the Loess Plateau mainly results from a few
transient rainstorms (Zhang et al., 2004; Austin et al., 2004), but only
serious soil erosion hazards in the study area due to the once-in-a-century
storm observed in 2013 can not reflect the general sediment yield evolutions.
Figure 9 shows the monthly sediment yield dynamics in 2013. It can be seen
that the monthly distribution of sediment transport in the watershed is very
uneven, and the maximum values of rainfall erosivity and sediment both
occurred in July; the sediment transport capacity in July alone accounted for
96.18 % of the whole annual sediment yield. The reason for this is that
rainfall-induced erosion in July accounted for 80.49 % of the whole
yearly erosion, and it is 3.11 times more than the corresponding average
value for many years. Thus a powerful hydraulic erosion force was formed due
to the once-in-a-century storm. According to the statistics, the
corresponding monthly runoff in the watershed also accounted for 56.22 %
of the total annual runoff, and it accounted for 76.79 % of the
multi-year average runoff amount in the Majiagou River watershed. The
corresponding monthly sediment yield reached 44.5 times more than the average
annual sediment yield. Therefore, the once-in-a-century storm in July 2013 is
the main reason for the maximum sediment yield level and shows that a
non-conventional storm plays a very critical role in the evolution process of
erosion and sediment yield.
The above analysis of sediment transport dynamics indicates that rainfall and
human activity are two main factors affecting dynamic changes in soil erosion
(Yao et al., 2011). Rainfall is the promotion factor for erosion evolution;
it can affect the formation and development of soil erosion processes by
splash effects of raindrops and erosion moving of rainfall runoff. The
positive human activities are the restraining factors for erosion evolution,
increasing vegetation cover, consolidating soil, weakened soil erosivity, and
strengthening effects of interception.
Spatial evolutions of sediment yield
Due to widely distributed sloping farmland along river banks, bank erosion
dominated sediment sources of the Majiagou River watershed and peak values of
the sediment yield also mainly appear in these areas (Fig. 10). According to
statistical analysis, the change in farmland area between 1995 and 2010 is
small, while the area of forestland in 2010 increased by about 2.2 %
more than
in 1995. The spatial distribution map of soil erosion in these 2 years also
suggests that soil erosion significantly decreased due to changes in
vegetation cover and flow path, and the increase in vegetation cover
(forestland) in the steep sloping land is stronger than the gently sloping
farmland, which results in changes in the watershed sediment distribution
pattern. Through the comparative analysis, the spatial results of this study
are basically consistent with the results of Zhu et al. (2016). In general,
spatial and temporal variations of sediment transport in the watershed are
generally related to spatial distribution of land use types; the large
spatial variations of sediment transport are also closely associated with
spatial changes in topography and soil (Gao et al., 2016).
Conclusions
A distributed-dynamic sediment yield model based on the CSLE equation was
modified and verified to investigate impacts of returning farmland on erosion
and sediment yield in the Majiagou River watershed from 1995 to 2013. Results
showed that the overall status of the watershed is at intense erosion risk.
Compared to the level before model modification, the multi-year average soil
erosion after that decreased by about 8 %. Spatiotemporal evolution of
soil erosion in the watershed is closely related to rainfall intensity,
rainfall amount, and land use pattern.
Multi-year average sediment yield decreased from 5803.23 (before model
modification) to 4510.66 Mg km-2 (after model modification) in the
Majiagou River watershed. Annual sediment yield generally decreased between
1995 and 2012. After 2003, the annual sediment transport in the study area
decreased sharply. The fluctuation trend is weak and the overall sediment
yield level is relatively low, and the average sediment yield before and
after model modification in the recent 5 years (in addition to 2013) is
4574.62 and 1696.1 Mg km-2. It has, respectively, decreased by about
35.4 and 78.2 % compared with the early governance (1995–1998).
The implementation of large-scale soil and water conservation projects in the
late 90s of the last century has continuously improved the sediment situation
of the watershed, but the changing trend of event-based rainfall urgently
needs to continuously increase the level of integrated watershed management.
In particular, extreme storms will lead to large fluctuations of sediment
yield. For example, the once-in-a-century storm of Yan'an in July 2013 is the
most important factor for the appearance of maximum sediment yield
(1983.36 × 104 Mg km-2) in the watershed. Therefore, the
current soil and water conservation measures are not suitable for high
rainfall intensity observed in July 2013, and the results potentially
emphasize the necessity for making further efforts in soil and water resource
management in sloping farmland of hilly and gully regions of the Chinese
Loess Plateau.