2. MATERIALS AND METHODS
2.1 Description of study area
The Kuye River (Fig. 1 ) is a
first-order tributary of the middle Yellow River, which flows through
Inner Mongolia and Shaanxi and finally flows into the Yellow River. The
area of the Kuye River watershed (38°22′–39°50′ N, 109°28′–110°45′ E)
is 8651 km2, with a mainstream length of 242 km and an
average channel slope of 2.6‰. The elevation ranges from 713 m in the
southeast to 1575 m in the northwest. There are two main tributaries
(Wu-lan-mu-lun and Bei-niuchuan) in the upper reach of the watershed,
and the Wenjiachuan hydrometrical station is located at the outlet of
the Kuye River. The watershed has a typical arid to semiarid continental
climate, with a multiyear average precipitation and temperature of 415
mm and 7.9°C, respectively. The average annual evaporation is 1788 mm.
Intense storms mainly take place in July and August, accounting for more
than 52% of the annual rainfall. Therefore, local short-term floods
often occur in the watershed during this period. According to the
measured data of the Wenjiachuan hydrological station, the sediment load
in July and August accounts for 90% of the annual total (Cai et al.,
2019).
Due to the sparse vegetation cover, loose soil, terrain fragmentation
and dense gullies in this watershed, it has become one of the main
sources of sediment in the Yellow River. The average annual sediment
load measured at the Wenjiachuan hydrological station from 1954 to 2000
was 1.00×108 t (10800 t/km2/a)
(Rustomji et al., 2008). However, the observed sediment load decreased
significantly over the past six decades. Compared with the 1960–1999
period, the average annual runoff and sediment discharge from 2000–2016
decreased by 76.72% and 94.50%, respectively (Zhao et al., 2019).
2.2 Datasets
The daily precipitation records of 50 rainfall stations and the daily
sediment concentration data of the Wenjiachuan gauge in 1987 and from
2006-2016 were obtained from the “Hydrological Yearbook of the People’s
Republic of China - Hydrological Data of the Yellow River Basin”.
The digital elevation map (DEM) came from the Geospatial Data Cloud,
with a spatial resolution of 30 m (www.gscloud.cn). The DEM of the Kuye
River watershed was extracted with the hydrological analysis tool in
ArcGIS 10.2 (ESRI).
The land use scenarios for 1987 and 2006 were derived from the Landsat 5
Thematic Mapper (TM) remote sensing images downloaded from the United
States Geological Survey (USGS)
(https://glovis.usgs.gov/),
while those for 2016 were from the Geospatial Data Cloud website
(www.gscloud.cn). Then, a hybrid land use classification technique,
which involved unsupervised classification and visual interpretation,
was used. Unsupervised classifications were carried out using the
iterative self-organizing data analysis (ISODATA) clustering algorithm,
while visual interpretation was mainly employed using Google Earth’s
historical orthophoto images of corresponding years. To ensure the
quality and accuracy of the data, field investigations and in-depth
consultations with local elders were undertaken. Seven land use types
were identified: forests, grassland, shrubland, bare land, arable land,
water bodies and urban and mining areas.
2.3 Model description
The sediment delivery distributed (SEDD) model has been extensively used
in different regions of the world. The model is mainly based on the
RUSLE model and integrates GIS techniques to predict the sediment yield
within a watershed. The SEDD (Ferro and Minacapilli, 1995) model
estimates the sediment yield according to the following formula:
\(\text{SY}_{i}=\text{SE}_{i}\bullet\text{SDR}_{i}=R_{i}\bullet K_{i}\bullet\text{LS}_{i}\bullet C_{i}\bullet P_{i}\bullet\text{SDR}_{i}\)(1)
where SEi is the average of annual soil erosion
modulus (t/ha/a) on pixel i ; SDRi is the
sediment delivery ratio for pixel i ; Rirefers to the rainfall-runoff erosivity factor (MJ·mm/ha·h);Ki denotes the soil erodibility factor
(t·ha·h/MJ·ha·mm); LSi expresses the topographic
factor; Ci signifies the cover-management factor;
and Pi shows the support practice factor.
To explain the relationship between rainfall and erosion, Wischmeier and
Smith (1978) proposed the R factor to quantify the impact of rainfall
and runoff on soil erosion. Since the daily rainfall data of 50 rainfall
stations were available, the method proposed by Zhang et al. (2002) was
used to calculate the R factor, which was mainly assessed with the daily
rainfall data using a half-month rainfall erosivity model. The annual R
factor was generated by kriging interpolation using data from 50
rainfall stations, and the raster of the R factor in 2006 is shown inFig. 2 a.
The soil erodibility factor was applicable to characterize the
sensitivity of soil to water erosion (Rao et al., 2014). Based on the
soil map (1:500,000 scale) of the Loess Plateau and the revised
erosion/productivity impact calculator (EPIC) model (Williams et al.,
1984), the value of the soil erodibility factor was derived
(Fig. 2 b).
The LS factor is an index for measuring the impact of topography on soil
erosion. As the Kuye River watershed was located on the Loess Plateau,
which has complicated terrain, the terrain factor calculation tool 2.0
developed by Fu et al. (2015)
for
the Loess Plateau was imposed to calculate the LS factor
(Fig. 2 c). The DEM was adopted as the
main data source for this calculation tool.
The
cover-management factor reflects the effect of soil management on soil
erosion rates, which are mainly related to land use type, vegetation
cover, surface roughness and soil moisture (Renard et al., 1997). In
this paper, the method proposed by Cai et al. (2000) was used to
evaluate the C factor by integrating the normalized vegetation index
(NDVI), which was obtained from remote sensing images of different
periods of the Kuye River watershed (Fig.
2 d).
The P factor represents the influence of supporting measures on sediment
control, which is generally assigned according to previous research
results combined with land use types (Zhou et al., 2019).
However, compared with other soil
and water conservation activities, check dams played a dominant role in
sediment retention (Ran et al., 2008). To quantitatively analyze the
influence of the check dam on sediment load, the trapping efficiencies
of the dams were applied to evaluate the P factors. The detailed
calculation process was based on the method proposed by Zhao et al.
(2017).
According to Ferro and Porto (2000), the SDR value of each grid cell is
calculated as follows:
\(\text{SDR}_{i}=exp\left(-\beta\bullet t_{i}\right)=exp\left(-\beta\bullet\frac{l_{i}}{k_{i}\sqrt{s_{i}}}\right)\)(2)
where β is a coefficient that is affected by the roughness
distribution along the flow path and is related to time.ti is the travel time from cell i to the
nearest stream reach, and li is the flow length
(m). ki is a coefficient dependent on surface
roughness characteristics (m/s), and sirepresents the slope of the cell. The determination of the βvalue followed the method of Fu et al. (2006). To ensure the proper use
of formula (2), the minimum value of si was set
as 0.003 (Fernandez et al., 2003; Fu et al., 2006). The value ofki was extracted from previous studies (Fernandez
et al., 2003; Gashaw et al.,
2019).
To calibrate the model, in 2018, we selected two small check dams
without a sluicing gate in the Kuye River
watershed, and the sediment
produced by soil erosion in the dam-controlled watershed was completely
intercepted by the dams (Fig. 3 a).
Trench excavation was carried out on the dam land and the depth of
sediment profile of the two dams reached 4.15m and 4.05m respectively.
The flood couplets were then identified, divided and its thickness were
measured (Fig. 3 b). Finally, the
capacity curve of check dams was reconstructed by the DEM of small
watershed, and annual erosion modulus of the two dam-controlled
watersheds from 2007 to 2018 were obtained by combining the soil bulk
density of each flood couplets. The area-weighted average of the annual
erosion modulus of the two dam-controlled watersheds was used as the
annual erosion modulus for the whole Kuye River watershed from 2007 to
2018. For details of these two check dams, please refer to Zhang et al.
(2020) and Wang (2020). However, it was very difficult to calibrate a
model of such a large-scale watershed because the land use and the
number of check dams changed continuously during the study period. To
obtain a more realistic calculation, the sediment load was calculated
for 1987 without considering the effect of check dams because the number
of check dams and the total storage capacity were very limited
(Fig. 5 ). The calculation of the annual
erosion modulus and sediment load from 2006 to 2010 was based on the
land use scenario in 2006 and included the effects of the check dams.
The same method was applied from 2011-2016, except that the land use
scenario was based on 2016. The annual erosion modulus measured from
2007-2016 was used to calibrate the RUSLE model. Additionally, the SEDD
model simulation results were validated by using the observed sediment
load of Wenjiachuan station in 1987 and from 2006-2016.
2.4 Distinguish the influence of different factors on soil erosion and
sediment load
At the watershed scale of the Loess Plateau, soil erosion and sediment
yield are mainly affected by climate variation and human activities.
Human activities can be divided into land use changes affected by human
activities and related soil and water conservation measures. Climate
variation is mainly reflected in the change of precipitation, and check
dams play a major role in sediment control in the study area (mentioned
in the introduction of P factor). Therefore, we assumed that only three
main driving factors (precipitation variation, land use changes and
check dam construction) affect the soil erosion and sediment yield of
the watershed. Taking the soil erosion and sediment load in 1987 as the
reference year, the changes of soil erosion and sediment load during
1987-2006 and 1987-2016 were analyzed respectively. The calculation
formula was as follows:
\({}_{T}=\left(\text{TSE}_{i}-\text{TSE}_{1987}\right)\text{\ or\ }\left(\text{TSL}_{i}-\text{TSL}_{1987}\right)=_{P}+_{\text{LU}}+_{\text{CD}}\)(3)
where ∆T is
the total change of soil erosion or sediment load.TSEi and TSLi represent
the total soil erosion and sediment load in ith year, respectively.TSE1987 and TSL1987represent the total soil erosion and sediment load in 1987,
respectively. ∆P , ∆LU and∆CD stand for the effects of
precipitation variation (P), land
use changes (LU) and check dams (CD), respectively.
The effects of precipitation variation and land use changes on soil
erosion and sediment load can be distinguished by choosing different
scenarios. The detailed calculation formula was shown inTable 1 . Finally, the influence of
check dams can be obtained by subtracting the influences of the other
two factors from the total effects.
3. RESULTS
3.1 Land use changes
Fig. 4 shows that there were dramatic
changes in land use, with the main conversion of arable land and bare
land to grassland, shrubland, forestland and construction land. The
proportion of vegetation coverage increased from 60% in 1987 to 86% in
2016. In addition, the urban and mining areas expanded rapidly from 9.62
km2 in 1987 to 437.23 km2 in 2016
(Table 2 ). This was mainly reflected
in the urban expansion of Shenmu County, Dongsheng district and
Yijinhuoqi County, as well as in the development of coal mines, which
showed that the population, economy and urbanization had grown rapidly
in the past three decades. In contrast, after decades of implementation
and promotion of the abandonment of grazing and ‘Grain for Green’
policies, the area of arable land and bare land in the study presented a
gradually decreasing trend. The area of arable land decreased from
2182.62 km2 in 1987 to 533.71 km2 in
2016. The proportion of bare land decreased from 13.43% in 1987 to
0.75% in 2016, and the area decreased by 1097.24 km2.
3.2 Check dam construction
Fig. 5 shows the variation of the
number and storage capacity of check dams on a time scale. The number
and storage capacity of check dams showed similar change trend with
time, both increased first and then decreased. In 2005, the annual
number of dams and storage capacity reached the maximum, which were 42
and 41.61Mm3 respectively. The accumulated number and
storage capacity of check dams increased slowly before 2000, and then
increased sharply. The turning point was mainly due to the ‘Hydraulic
highlight project’ launched by the Ministry of Water Resources of China,
which further increased the construction speed of check dams. Before
1987, only 32 check dams had been built, with a total storage capacity
of 35.20 Mm3. This shows that the interception
capacity of check dams in 1987 was relatively limited. There were 306
key dams in the Kuye River watershed, with a total storage capacity of
316.64 Mm3 until 2011.
3.3 Model calibration and validation
Fig. 6 a shows the model calibration
using the annual erosion modulus, which was obtained through field
excavation in 2018. The annual erosion modulus ranged from 39.88 to
64.02 t/ha/a, with an average value of 50.83 t/ha/a. Correspondingly,
the simulated soil erosion modulus changed from 40.99 to 58.70 t/ha/a,
with an average value of 49.48 t/ha/a. The R2 reached
0.83, which indicated that the results of the model were, overall, in
good agreement with the actual measured values.
Fig. 6 b shows the relationship between
the measured and simulated annual sediment loads at Wenjiachuan station.
The annual sediment loads in 1987 and from 2006-2016 were mainly
simulated for model validation. The measured sediment load at
Wenjiachuan station varied from 0.05 Mt in 2016 to 33.97 Mt in 1987,
while the simulated annual average sediment load ranged from 8.55 Mt to
32.77 Mt. According to the fitting results of the scatter diagram
(R2 > 0.90), the simulation results of
the model were basically consistent with the measured values, but
compared with the measured values in recent years, the model tended to
overestimate the sediment load.
3.4 Characteristics of soil erosion and sediment yield
Table 3 shows the classification of
soil erosion and sediment yield under three different land use
scenarios. According to the Chinese Soil Erosion Classification and
Grading Standards (SL190–2007), the soil erosion grade changed from
micro erosion to severe erosion. In 1987, the areas of micro, mild,
moderate, intensive, extreme, and severe erosion accounted for 29.61%,
11.56%, 12.83%, 9.64%, 12.45% and 23.91%, respectively, of the area
affected by erosion. The proportion of micro, mild, moderate, intensive,
extreme, and severe erosion in 2016 was 59.68%, 11.38%, 8.80%,
5.44%, 6.08% and 8.61%, respectively. The area affected by mild or
less serious erosion accounted for more than 70% of the whole
watershed, which indicated that the soil erosion in the watershed had
been well controlled by 2016. In 1987, the area of high erosion rates
(>intensive) was 3981.29 km2, accounting
for 46% of the whole watershed area. In 2016, the area of high erosion
rates was 1739.63 km2, accounting for 20.14% of the
whole watershed area. Nevertheless, compared with in 1987, the area with
high erosion rates decreased by 2241.66 km2 in 2016,
and the area with severe erosion shrank by 1325.42
km2. The results showed that the three factors played
a remarkable role in reducing erosion.
The
sediment yield was artificially
divided into three grades: low, moderate and high sediment yield
(Table 3 ). The distribution area of
sediment yield under the three land use scenarios was low >
moderate > high. Moreover, over time, the area occupied by
the low sediment yield rate increased (from 4957.94 to 7276.03
km2), while the others decreased. This indicated that
sediment control in 2016 greatly improved under the influence of
precipitation variation, land use changes and check dams.
The average SDR values in 1987, 2006 and 2016 were 0.32, 0.28 and 0.23,
respectively, across the whole watershed, showing a decreasing trend.
The results expounded that with the implementation of the ‘Grain for
Green’ policy, land use changes had reduced the SDR.
Considering the check dams, we applied hot spot analysis (Getis-Ord Gi∗)
in ArcGIS software to analyze the soil erosion and sediment yield of the
Kuye River watershed in 1987 and 2016
(Fig. 7 ). The distribution of cold and
hot spots of soil erosion in 1987 and 2016 was consistent with that of
sediment yield, and we found that the hot spots of soil erosion and
sediment yield were mainly clustered around Shenmu County
(Fig. 1 ). This indicated that the
high-risk regions of soil erosion
and sediment yield were
mainly
concentrated in the middle reaches of the watershed. However, the hot
spot region in 2016 decreased compared with that in 1987. On the other
hand, most of the cold spots were identified in Yijinhuoqi County,
especially the sediment yield cold and hot spot maps of 2016 were the
most obvious. This signified that the erosion degree in this area was
relatively low.
3.5 The influence of different factors on soil erosion and sediment
yield
Table 4 represents the contribution of three factors
(precipitation
variation, land use changes and check dams) to soil erosion and sediment
yield in different periods. Compared with 1987, the total soil erosion
in 2006 decreased by 57.74 Mt, and the contribution rates of the three
factors were 8.61%, 20.44% and 70.95%, respectively. In 2016, the
total soil erosion decreased by 60.42 Mt, and the contribution rates of
the three factors were 34.22%, 8.01% and 57.77%, respectively. This
signified that check dams were the dominant factor, but its contribution
rate to different years was diverse. In the past three decades, the
impact of precipitation variation on the reduction of soil erosion had
increased (from 8.61% in 2006 to 34.22% in 2016), while the impact of
land use changes and check dams on soil erosion had decreased.
When compared with 1987, the total sediment load in 2006 and 2016
decreased by 21.50 Mt and 24.22 Mt, respectively, and the sediment load
reduction in 2016 was greater. From 1987 to 2006,
precipitation variation, land use
changes and check dams reduced sediment load by 11.91%, 34.32% and
53.77% respectively. From 1987 to 2016, the contribution of these three
factors to sediment reduction was 29.10%, 40.09% and 30.81%,
respectively. The check dams were still the dominant factor in 2006, but
the three factors all had obvious influence on sediment load in 2016.
The influence of precipitation variation and land use changes on
sediment load was increasing, while the influence of check dams on
sediment load was decreasing.
4. DISCUSSION
4.1 Reasons for the change of contribution of three factors to soil
erosion and sediment load reduction
By using the model to simulate different scenarios, we quantitatively
distinguish the impact of three different factors on soil erosion and
sediment load in different periods. In order to further explore the
reasons for the change of contribution rate of precipitation variation
to soil erosion and sediment load reduction, we separately analyzed the
influence of
precipitation
variation on soil erosion and sediment yield
(Fig. 8 ). The abscissa from low to high
corresponds to the average R in 2016, 2006 and 1987 (903.58, 1117.52 and
1140.64 MJ·mm/ha·h). With the increase of precipitation, soil erosion
modulus and sediment yield increased. Due to the largest reduction in
precipitation in 2016, the impact of precipitation variation on soil
erosion and sediment load also reached the maximum
(Table 4 ). According to the slope of
the graph, the impact of
precipitation
variation on erosion modulus is greater than that on sediment yield
(Fig. 8 ). Therefore, compared with the
impact on total sediment load reduction, precipitation variation has a
greater impact on total soil erosion reduction
(Table 4 ). In this study, the
contribution of precipitation variation to sediment load reduction
mainly changed from 11.91% to 29.09%, which was similar to the
previous studies (Mu et al., 2012; Yang et al., 2018).
To further ascertain the primary factors leading to soil erosion and
sediment yield reduction, we analyzed the contributions of different
land use types to soil erosion and sediment yield
(Fig. 9 ). In order to eliminate the
impact of precipitation variation, we simulated all scenarios using the
1987 rainfall conditions. Without considering the effects of check dams,
the amount of soil erosion in 1987 was mainly contributed by grassland,
arable land and bare land, accounting for 40.90%, 26.09% and 13.04%
of the total erosion, respectively. As of 2016, due to the increasing
grassland area (Table 2 ), the amount
of soil erosion caused by grassland increased slightly, and this land
use type became the main source of soil erosion. In addition, as the
area of arable land and bare land decreased rapidly, the contributions
of these two land use types to soil erosion were significantly reduced
by 21.72 Mt and 12.82 Mt, respectively. Therefore, without considering
the roles of check dams, the reduction in soil erosion was mainly due to
the change in the land use pattern, especially the conversion from
arable land and bare land to vegetation land.
Considering
the action of check dams, the erosion amounts from grassland, arable
land and bare land decreased by 52.77%, 57.12% and 42.92%,
respectively, without land use changes in 1987. Under the three land use
scenarios, the construction of check dams reduced the erosion of all
land use types to varying degrees, and grassland had the largest erosion
reduction.
In terms of sediment load, without considering the effect of check dams,
the main sources of sediment in 1987 were arable land and bare land,
accounting for 67.17% of the total sediment load. Nevertheless,
grassland was no longer a major sediment source, indicating low sediment
connectivity in the area. This
development further led to less sediment that could be transported to
the watershed outlet. Compared with 1987, the sediment contribution of
arable land and bare land decreased significantly in 2016, with a
reduction of more than 80%. Although the sediment loads of all land use
types decreased to different degrees under the condition of check dams,
the conversion of land use played a more important role in the reduction
in sediment load over time. This was due to the decrease of SDR caused
by land use changes (mentioned above), so that the contribution of land
use changes to sediment load reduction increased slightly over time
(Table 4 ).
4.2 Impact of land use changes and
check dams on sediment load
Many studies have applied various models and methods to clarify the
impact of land use changes or check dams on sediment load in different
regions of the world (Table 5 ). The
land use of the Kuye River watershed has undergone dramatic changes in
the past few decades, especially since the implementation of the Grain
for Green Project in 1999. Nevertheless, land use change will
significantly affects soil erosion and sediment transport (Fang, 2017).
In
this study, the conversion of arable land and bare land into vegetation
land reduced the erosion of the source area and further decreased the
sediment export by diminishing the SDR of the whole watershed. This was
consistent with the results of Zhou et al., (2019). In contrast, when
natural vegetation is artificially transformed into other land use
types, such as cultivated land and urban land, the opposite result is
obtained (Aneseyee et al., 2020; Sushanth and Bhardwaj, 2019).
As the main channel construction and important soil and water
conservation engineering measures, check dams play a significant role in
channeling watersheds, which can conserve water and soil, provide
fertile farmland and prevent the downstream channel from scouring,
reducing the downstream sediment load. Previous studies (Borja et al.,
2018; Polyakov et al., 2014) have confirmed that check dams can maintain
a significant proportion of the sediment load. Based on data from
previous studies, Ran et al. (2008) indicated that in the Kuye River
watershed, the reductions in sediment mass caused by check dams were
37.2% from 1970 to 1996. According to the results of this study, check
dams reduced sediment export by 53.77% in 2006 and 30.81% in 2016.
With the continued restoration of vegetation in the watershed, the
contribution rate of check dam construction to sediment control may
decrease slightly.
The SEDD model in this study overestimated the sediment load in recent
years, which may be partly because only the key dams were considered but
the small dams, terraces, concrete roads and other soil and water
conservation measures were not considered. According to the results of
the hot spot analysis, there were still some high-risk regions of soil
erosion and sediment yield in this watershed, which may be mainly due to
the rapid expansion of Shenmu County and the large-scale mining of coal
resources in the surrounding area in recent decades. Then, these hot
spot areas should become the focus for implementation of effective soil
and water conservation measures to intervene with soil erosion. In
addition, the vegetation coverage in this study area reached more than
80% in 2016 (Table 2 ), further
reforestation will be difficult with the limitation of soil moisture.
This implies that the impact of dams on sediment reduction will be very
important in the future. On the other hand, the service life of the
check dams was limited. Under extreme rainstorm conditions, check dams
are prone to collapse and damage (Bai et al., 2020), releasing more
sediment and producing serious disasters. In view of the existing
circumstances, the operation status of check dams in the Kuye River
watershed should be considered, and effective measures (such as spillway
construction and dam reinforcement) for protecting check dams must be
taken to guarantee the maximum efficiency of sediment interception.