June, 2014
Journal of Resources and Ecology
J. Resour. Ecol. 2014 5 (2) 105-114
DOI:10.5814/j.issn.1674-764x.2014.02.002
www.jorae.cn
Vol.5 No.2
Article
Sand-fixing Function under the Change of Vegetation Coverage
in a Wind Erosion Area in Northern China
GONG Guoli1,2, LIU Jiyuan1*, SHAO Quanqin1 and ZHAI Jun3
1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Satellite Environment Center, Ministry of Environmental protection, Beijing 100094, China
Abstract: Using meteorological and remote sensing data and changes in vegetation cover during the wind erosion
season in northern China, a revised wind erosion equation was applied to evaluate spatiotemporal variation in
soil erosion and conservation since the 1990s, and to reveal the effects of the change of vegetation coverage on
the wind erosion control service. The results showed that average soil erosion in northern China between 1990
and 2010 was 16.01 billion tons and was decreasing. The most seriously eroded areas were mainly distributed in
large desert areas or low cover grasslands. Most wind erosion occurred in spring, accounting for 45.93% of total
wind erosion. The average amount of sand fixation service function for northern China between 1990 and 2010
was 20.31 billion tons. Given the influence of wind erosion forces, the service function for sand fixation cannot
effectively highlight the role of sand fixation from the ecosystem itself. The retention rate of service function for sand
fixation reveals the role of the ecosystem itself. The distribution characteristics of the soil retention rate are similar
to vegetation cover, which shows a gradual decrease from southeast to northwest in the study area. Improved
spring vegetation cover was observed mainly on the Loess Plateau, Qinghai-Tibet Plateau, in northern Hebei,
eastern Inner Mongolia and northeast China after the implementation of ecosystem projects. The soil retention rate
in most areas showed a significant positive relationship with grassland vegetation in spring (r > 0.7, p < 0.01). The
increments of ecosystem service function for various ecological systems are different. Increments for the grassland
ecosystem, forest ecosystem, farmland ecosystem and desert ecosystem are 2.02%, 1.15%, 0.99% and 0.86%,
respectively.
Key words: vegetation cover change; RWEQ model; wind erosion; soil retention; northern China
1 Introduction
Northern China is significantly affected by wind erosion.
Wind erosion results in land desertification in arid, semiarid
and part of subhumid regions (Dong et al. 1987), causes
significant loss of nutrient-rich elements in topsoil fine
particles, nutrients, organic matter and other nutrient
substances, coarsens the soil, decreases soil fertility and
productivity, and significantly affects production activities
such as agriculture and animal husbandry. Surface vegetation
reduces wind erosion and the sand fixation service function
of vegetation is a major function of ecosystems in northern
China (Chepil et al. 1963; Fryrear 1985; Wolf and Nickling
1993, 1996). However, since the 1990s, climate change,
population growth, urbanization, exacerbated farmland
reclamation and severe overgrazing have resulted in
significant ecosystem degradation across northern China
(Fang et al. 2003). For this reason, a number of ecological
projects were implemented around the year 2000, such as
the Beijing-Tianjin Dust Storm Source Control Project,
Returning Farmland into Forest or Grassland Project and
Returning Grazing Land to Grassland Project. As a result of
these projects, ecosystem degeneration has been controlled
and vegetation cover improved. A decrease or increase
in vegetation cover can significantly control/intensify
windbreaks and sand fixation. After the implementation
Received: 2014-02-25 Accepted: 2014-05-12
Foundation: National Key Technology R & D Program (No.2013BAC03B04); and National Basic Research Program of China (973 Program)
(No.2009CB421105).
* Corresponding author: LIU Jiyuan. Email: [email protected].
106
Journal of Resources and Ecology Vol.5 No.2, 2014
of these ecosystem engineering projects, further study
is needed to address questions regarding the status of
vegetation restoration, the contribution of changes in
vegetation cover in wind erosion control services, and the
appropriate time and place to focus the control of wind
erosion hazards.
A number of studies have found the Revised Wind
Erosion Equation (RWEQ) yields reasonable agreement
between simulated and instrumented test plot data for
different regions and environmental situations (Fryrear
et al. 1998; Fryrear et al. 2008; Youssef et al. 2012). To
solve the issues outlined above for northern China, the
RWEQ was applied to quantitatively evaluate soil erosion
in northern China (Guo 2012). On this basis, we determined
spatiotemporal variation in soil conservation service
function since the 1990s, and reveal the effects of changes
in vegetation cover on wind erosion control service. This
study will provide a scientific basis for rational land use
planning and relevant policies, protect regional ecological
environments and promote regional ecological security and
sustainable development.
2 Materials and method
2.1 Study area
According to a 1:16 000 000 soil erosion map (Wang et
al. 2010a) combined with administrative divisions and
600 mm isohyet (Wang et al. 2010b), the textual range of
wind erosion research in northern China was demarcated.
The wind erosion area in northern China is approximately
5.53 million km2. Based on land use and land cover change
(LUCC) data in 2000, area data for each land use type in the
study area was analyzed. The area of grassland, sand land,
farmland, and woodland is approximately 2.31, 1.28, 0.55,
and 0.45 million km2, respectively; accounting for 41.83%,
23.14%, 9.97%, and 8.15% of the total area, respectively.
The wind erosion area contains different soil types. Aeolian
sandy soil, brown soil, chestnut soil, sierozem, and frigid
calcic soil, which are vulnerable to wind erosion, are mainly
distributed in the Taklamakan Desert, Qaidam Desert,
Badain Jaran Desert, Tengery Desert, northern Xinjiang,
central and eastern Inner Mongolia, north-central Ningxia,
north-central Xizang and northwest of the three-river
source area of Qinghai (Fig. 1). The Qinghai-Tibet Plateau
belongs to the Alpine climate zone, which has stronger solar
radiation and longer sunshine hours; the northeast region
and eastern Inner Mongolia bordering the northeast region
belongs to the continental monsoon climate zone; and most
of the other regions belong to continental arid and semiarid
climate zones. From the 1950s to 1990s, soil has been
significantly affected by wind erosion and heavy sandstorm
frequency in the study area has increased from 5 to 23 (Wang
et al. 2010).
2.2 Methodology
2.2.1 Wind erosion
The RWEQ model of the United States Department of
Agriculture (USDA) was used to estimate soil transported
by wind (Fryrear et al. 1998) as follows:
Qmax = 109.8(WF·EF·SCF·K′·COG)
(1)
s = 150.71(WF·EF·SCF·K′·COG)-0.3711
(2)
SL =
2x
x
Qmax e
−( )
s
2
(3)
2
s
where, Qmax is the maximum transport capacity, calculated
by climatic factor WF, soil erodibility factor EF, soil crust
Soil types
N
0
Fig. 1 Soil types in the study area.
250 500
1000
km
Frigid plateau solonchaks
Cold brown calcic soil
Castano-cinnamon soil
Mountain meadow soil
Northwest salt crust
Anthropogenic-alluvial soil
Gray-brown desert soil
Brown coniferous forest soil
Yellow cinnamon soil
Lime soil
Brown desert soil
Felty soil
Meadow soil
Cinnamon soil
Aeolian sandy soil
Yellow soil
Yellow brown soil
Loessal soil
Cold calcic soil
Irrigated desert soil
Grey desert soil
Grey cinnamon soil
Volcanic ash soil
Desert solonchaks
Black soil
Skeleton soil
Purple soil
Chislev soil
Podzolic soil
Shruby meadow soil
Moisture soil
Grey wooded soil
Frigid frozen soil
Alkali soil
Cold desert soil
Frigid calcic soil
Boggy soil
Peat soil
Alluvial soil
Dark brown soil
Red clay
Chestnut soil
Loessial soil
Dark felty soil
Chemozem soil
Brown soil
Sierozem
Takyr
Albic soil
Saline soil
Brown soil
Paddy soil
GONG Guoli, et al.: Sand-fixing Function under the Change of Vegetation Coverage in a Wind Erosion Area in Northern China
factor SCF, soil roughness K′, and combined vegetation
factor COG; s is the critical field length; x is the distance
from the upwind edge of the field; and SL is the soil loss for
the special case where Qmax and s are constant.
Climate factors include wind erosion forces (W), soil
moisture and snow coverage. The snow surface cover factor
was calculated using the long-term snow depth dataset from
the Environmental and Ecological Science Data Center for
West China, National Natural Science Foundation of China
(http://westdc.westgis.ac.cn) (Che 2011; Dai et al. 2012).
Wind and soil moisture factors were calculated using the
national station’s daily average wind speed, precipitation,
temperature, sunshine hours, latitude and other data
obtained from the China Meteorological Data Sharing
Service (http://cdc.cma.gov.cn).
W = (WS2−WSt2)×WS2
(4)
where, W is the wind erosion forces (m s-1)3; WS2 is wind
speed at 2 m (m s-1); and WSt is the threshold wind speed at
2 m (assumed 5 m s-1).
Soil erodible fraction and soil crust factor were calculated
from soil sand, silt and clay content, organic matter and
calcium carbonate (%) (Fryrear et al. 1994; Zobeck 1991).
Soil sand, silt and clay content data were provided by the
Environmental and Ecological Science Data Center for
West China (http://westdc.westgis.ac.cn) at a map scale of
1:1 000 000 and consisted of attribute data and digital
spatial data. Calcium carbonate content data were obtained
from the Data Sharing Infrastructure of Earth System
Science (http://www.geodata.cn).
29.09 + 0.31Sa + 0.17 Si + 0.33Sa / Cl
EF =
100
−
SCF =
2.59OM + 0.95CaCO3
100
1
(5)
(6)
2
1 + 0.0066(Cl ) + 0.021(OM )
The soil roughness factor was measured using a roller
chain method proposed by Ali Saleh (1993). To accurately
obtain the soil roughness factor, grassland and sand soil
roughness were measured based on the study area’s
different soil types from June 2013 to July 2013. The sand
roughness factor value is 0.96, and the grassland roughness
factor value is 0.69. The farmland soil roughness factor was
determined based on the recommended parameters of the
RWEQ model that depends on the different crop types and
implement used (Fryrear et al. 1998).
The combined vegetation factor was calculated using
vegetation cover and used to determine the effect of
withered vegetation and growing vegetation on wind
erosion. We used photos to estimate withered vegetation
cover. Remote sensing data used to calculate vegetation
cover were obtained from National Aeronautics and Space
Administration’s Earth Observing System/Moderate2
107
Resolution Imaging Spectroradiometer (MODIS) data
(http://edcimswww.cr.usgs.gov) and Advanced Very High
Resolution Radiometer (AVHRR) data (Li and Shi 1999).
As the National Oceanic and Atmospheric Administration/
AVHRR and MODIS data were obtained from different
satellite sensors, we used MODIS Normalized Difference
Vegetation Index (NDVI) data to correct the AVHRR NDVI
data using linear regression to ensure that AVHRR NDVI
and MODIS NDVI data were comparable. After format
conversion, projection conversion, resampling, image
mosaic and filter processing, each half month’s NDVI data
was obtained using the maximum value composite method
and used to calculate vegetation cover using the dimidiate
pixel model method (Gutman and Ignatov 1998).
(7)
SLRf = e−0.0438(sc)
SLRc = e
−5.614 ( cc
0.7366
)
(8)
where, SLRf is the soil loss ratio coefficient by withered
vegetation; sc is withered vegetation cover; SLRc is the
soil loss ratio coefficient by growing vegetation; and cc is
growing vegetation cover. For farmland, this factor was
determined based on the recommended parameters of the
RWEQ model that depends on crop type (Fryrear et al.
1998).
2.2.2 Soil retention
Vegetation has the function of reducing wind velocity,
weakening the wind erosion driving force, and sequentially
influencing the flux of wind erosion. The amount of wind
erosion caused by vegetation was defined as soil retention
(SLsv). Soil retention was directly represented by the
amount of potential soil erosion under bare soil conditions
(SLs) minus soil erosion under the conditions of the ground
cover vegetation (SLv), expressed as follows:
SLsv = SLs −SLv
(9)
2.2.3 Soil retention rate
Soil retention can represent the actual amount of sand
fixation by vegetation but cannot effectively highlight the
ecological system’s own contribution rate to sand fixation
because of the impact of climate factors such as wind
erosion forces. To eliminate the influence of climate factors
and further analyze the role of ecosystem sand fixation, the
ratio of the amount of soil retention (SLsv) to the amount
of potential soil erosion under bare soil conditions (SLs)
was regarded as the soil retention rate (F) and expressed as
follows:
F=
SLsv
SLs
× 100
(10)
3 Results
3.1 Distribution of the amount of soil erosion
This paper uses land use data in 2000 and digital elevation
108
70˚E
80˚E
90˚E
100˚E
110˚E
120˚E
130˚E
0 250 500
80˚E
1000
km
90˚E
Wind erosion modulus (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
30˚N
40˚N
40˚N
50˚N
N
30˚N
model data with a spatial resolution of 90 m. These data
considered glacier, water, barren land and alpine valley
regions (relief ≥ 300 m km-1) as the region with non-wind
erosion. With the use of the 600 mm equipluve, the region
where precipitation was >600 mm was set as a non-wind
erosion area.
Ground monitoring of wind erosion in China is
conducted mainly by using the field sand sampler, indoor
and field wind tunnel experiment, isotope tracer, and
other methods. Observations are different from different
observational methods. We compared the erosion modulus
estimated by using the uniform 137Cs tracing technique, with
the results simulated by the RWEQ model (Liu et al. 2007;
Qi 2008; Yan et al. 2000). We used average results for
many years in predominantly arid areas to verify the model
because the monitoring results by the uniform 137Cs tracing
technique only represent the average soil erosion modulus
for many years and cannot distinguish soil erosion types.
In addition, comparing the field sampling monitoring data
with the model simulation data results in scale matching
problems; we observed some differences in the comparison,
but the difference is not significant (Table 1).
Based on the Standard for Classification and Gradation
of Soil Erosion SL190–2007 (Ministry of Water Resources
of the People’s Republic of China, 2007), the average
amount of wind erosion from 1990–2010 is approximately
16.01 billion tons. Slight [0–2 t ha -1 y-1) and mild [2–25
t ha-1 y-1) erosion areas accounted for 71.34% of the total
wind erosion area. These erosion areas were mainly
distributed in woodland and grassland in eastern and
southern Inner Mongolia, Heilongjiang, Jilin, Hebei,
Shanxi, Shaanxi, Ningxia, Qinghai, Tibet and Xinjiang.
Regions with more intense wind erosion were mainly
distributed in large deserts, such as the Taklamakan Desert,
Gurbantunggut Desert, Kumtag Desert, Qaidam Desert,
Badain Jaran Desert, Tengery Desert, Hunshadake Sandy
Land and Khorchin Sandy Land. In addition, grassland
with lower cover is also the area with higher wind erosion
intensity (Fig. 2).
Spring is the wind erosion season in northern China.
50˚N
Journal of Resources and Ecology Vol.5 No.2, 2014
Fig. 2 Distribution of average wind erosion modulus between
1990 and 2010.
Taking the amount of wind erosion in 2010 as an example
(Fig. 3), the amount of wind erosion in spring (March–May),
summer (June–August), autumn (September–November)
and winter (December–February) accounted for 45.93%,
15.37%, 11.51%, and 27.18%, respectively, for the whole
year. These findings indicate that spring is the season that
is most significantly affected by wind erosion, followed
by winter. The wind erosion distribution had the same
tendency in different seasons, and deserts and grassland
with lower cover were the most serious wind erosion areas
in each season.
Since the 1990s, the distribution of wind erosion in
northern China has remained the same, but the intensity
and the amount of wind erosion decreased (Fig. 4). Wind
erosion modulus decreased from 35.13 t ha-1 in 1990 to
24.96 t ha-1 in 2010. Wind is the main climate driving force
of wind erosion. When the monsoon undergoes pronounced
weakening, the wind erosion modulus also decreases.
Table 1 Comparison results of the model with measurement.
Sample plots
Zhengxiangbai Qi
Taipusi Qi 1
Taipusi Qi 2
Taipusi Qi 3
Taipusi Qi 4
Xilinhot City
Golmud City
Wudaoliang 1
Wudaoliang 2
Latitude (°N)
Longitude (°E)
42.33
42.11
41.76
41.96
42.05
43.76
36.42
34.90
35.10
115.55
115.49
115.17
115.34
115.33
116.10
94.09
92.09
92.08
Wind erosion modulus (t ha-1 y-1)
Measurement results
Simulation results
3.51 (Liu et al. 2007)
11.13
4.18 (Qi 2008)
4.63
4.80 (Qi 2008)
5.10
3.10 (Qi 2008)
5.96
37.92 (Qi 2008)
10.85
3.60 (Liu et al. 2007)
4.92
84.14 (Yan et al. 2000)
68.26
20.20 (Yan et al. 2000)
20.49
22.69 (Yan et al. 2000)
26.25
109
80˚E
90˚E
100˚E
110˚E
120˚E
40˚N
80˚E
1000
km
90˚E
30˚N
30˚N
0 250 500
Wind erosion modulus in autumn (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
120˚E
130˚E
50˚N
40˚N
80˚E
50˚N
N
110˚E
N
0 250 500
130˚E
100˚E
1000
km
90˚E
70˚E
80˚E
Wind erosion modulus in summer (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
90˚E
100˚E
110˚E
120˚E
130˚E
N
40˚N
70˚E
90˚E
30˚N
40˚N
90˚E
40˚N
50˚N
80˚E
1000
km
30˚N
30˚N
0 250 500
Wind erosion modulus in spring (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
80˚E
50˚N
50˚N
N
70˚E
0 250 500
80˚E
1000
km
90˚E
Wind erosion modulus in winter (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
30˚N
130˚E
50˚N
120˚E
40˚N
110˚E
30˚N
100˚E
50˚N
90˚E
40˚N
80˚E
30˚N
70˚E
40˚N
50˚N
GONG Guoli, et al.: Sand-fixing Function under the Change of Vegetation Coverage in a Wind Erosion Area in Northern China
2010
2008
2006
2004
1000
900
800
700
600
500
400
300
200
100
0
Wind erosion forces (m s-1)3
Wind erosion forces
2002
2000
1998
1996
1994
Wind erosion modulus
1992
45
40
35
30
25
20
15
10
5
0
1990
Wind erosion modulus (t ha-1 y-1)
Fig. 3 Temporal-spatial distribution of average wind erosion modulus in 2010.
Fig. 4 Interannual variation in wind erosion forces and wind
erosion modulus 1990–2010.
However, the change in wind erosion modulus is also
related to vegetation cover condition. From 1990 to 2000,
lower vegetation cover increased wind erosion in years with
weaker wind erosion forces. For example, wind erosion
forces were lower in 1990 than in 1991, 1993, 1994, 1996
and 2001. However, wind erosion modulus was higher by
1.77, 1.51, 3.99, 2.02, and 4.57 t ha-1 respectively, compared
with that in 1990. After 2000, the vegetation situation
improved, which resulted in lower wind erosion modulus in
years with a higher wind erosion force. For example, wind
erosion forces in 2004 were 1.71 (m s-1)3 higher than in
2003. However, wind erosion modulus was 3.57 t ha-1 lower
than in 2003. Wind erosion forces in 2009 were 37.65 (m s-1)3
higher than in 2007. However, wind erosion modulus was
3.03 t ha-1 lower than in 2007.
Journal of Resources and Ecology Vol.5 No.2, 2014
60
40
30
20
0 250 500
80˚E
Wind erosion modulus (t ha-1 y-1)
>150
0-2
Non-wind erosion areas
2-25
(glacier, alpine valleys, etc)
25-50
Water
50-80
80-150
100˚E
110˚E
120˚E
1000
km
90˚E
30˚N
30˚N
2010
2008
0
2006
10
2004
40˚N
50
2002
50˚N
N
2000
130˚E
1998
120˚E
1996
110˚E
1994
100˚E
1992
90˚E
1990
80˚E
Soil retention per unit area
(t ha-1 y-1)
70˚E
40˚N
50˚N
110
Fig. 6 Soil retention per unit area between 1990 and 2010.
vegetation cover surface because of the weakened wind
erosion driving force. In addition, the amount of soil
retention was relatively decreased. Hence, considering
the climate driving force, such as wind field, the amount
of soil retention cannot represent the contribution rate of
vegetation on soil retention.
Fig. 5 Distribution of average soil retention between 1990
and 2010.
Between 1990–2010, average soil retention was
approximately 20.31 billion tons and mainly distributed in
the Gurbantunggut Desert, Badain Jaran Desert, Tengery
Desert, Wulanbuh Desert, Kubuqi Desert, Hunshadake
Sandy Land, Khorchin Sandy Land and grasslands in Inner
Mongolia and the Tibetan Plateau (Fig. 5).
Since the 1990s, a weakening trend of soil retention
was associated with decreasing annual average wind
erosion forces (Figs. 4 and 6). The amount of wind erosion
decreased significantly under the condition of bare soil or
The distribution characteristics of the soil retention rate
of terrestrial ecosystems in China are similar to those of
vegetation cover (Figs.7 and 8) in that they show a gradual
increase from the northwest to the southeast. Regions with
the lowest soil retention rate are mainly distributed in the
Taklamakan Desert, Gurbantunggut Desert, Kumtag Desert,
Qaidam Desert, Badain Jaran Desert and Tengery Desert.
Overall, forest ecosystems have the highest soil retention
rate (94.38%), followed by farmland ecosystems, grassland
ecosystems and desert ecosystems, accounting for 83.40%,
120˚E
130˚E
30˚N
30˚N
40˚N
40˚N
50˚N
N
Vegetation coverage (%)
0 250 500
80˚E
1000
km
90˚E
100˚E
60-90
<5
5-10
>90
10-30
Water
30-60
110˚E
70˚E
80˚E
Fig. 7 Distribution of average vegetation cover between
1990 and 2010.
100˚E
110˚E
120˚E
130˚E
N
0 250 500
120˚E
90˚E
50˚N
110˚E
40˚N
100˚E
80˚E
1000
km
90˚E
Rate of soil retention (%)
0-30
80-90
30-40
90-95
40-50
95-100
50-60
Non-wind erosion areas
(glacier, alpine valleys, etc)
60-70
Water
70-80
100˚E
110˚E
120˚E
Fig. 8 Distribution of average soil retention rate between
1990 and 2010.
30˚N
90˚E
40˚N
80˚E
30˚N
70˚E
50˚N
3.3 The soil retention rate of terrestrial ecosystems in
China
50˚N
3.2 Amount of soil retention of terrestrial ecosystems in
China
111
GONG Guoli, et al.: Sand-fixing Function under the Change of Vegetation Coverage in a Wind Erosion Area in Northern China
4 Discussion
4.1 Effect of changes in vegetation cover on soil
retention rate
70˚E
80˚E
90˚E
100˚E
110˚E
120˚E
130˚E
50˚N
N
0 250 500
80˚E
1000
km
90˚E
100˚E
Slope of vegetation
coverage in spring (%)
<-0.05
0.03–0.05
-0.05– -0.03
>0.05
-0.03– -0.005
Water
-0.005–0.005
0.005–0.03
110˚E
120˚E
Fig. 9 Variation in spring vegetation cover between 1990
and 2010.
30˚N
30˚N
40˚N
40˚N
50˚N
Soil erosion is larger in spring than in other seasons. The
change in vegetation cover in spring significantly affects
service function of windbreaks and sand fixation. For this
reason, we studied the effects of changes in vegetation
cover on the windbreak and sand fixation service function
of terrestrial ecosystems. Given that vegetation can
weaken the climate driving force to some extent in wind
erosion, the decrease or increase in vegetation cover is
the most sensitive factor in accelerating or restraining
wind erosion. The area of decreased vegetation cover in
spring between 1990 and 2010 was mainly distributed in
deserts, and the trend of climate change in those areas was
warming and drying (Huang 2006; Zhai and Pan 2003). The
area of increased vegetation cover in spring was mainly
concentrated on the Loess Plateau, Qinghai-Tibet Plateau,
northern Hebei, eastern Inner Mongolia and northeast
China. Precipitation also increased in parts of those areas,
70˚E
80˚E
90˚E
100˚E
110˚E
120˚E
130˚E
N
50˚N
71.57% and 42.35% respectively (Table 2).
40˚N
Variation
2.02
1.15
0.99
0.86
0.72
30˚N
2000–2010
72.57
94.95
83.90
42.78
66.09
50˚N
1990–2000
70.56
93.81
82.91
41.92
65.37
40˚N
Ecosystem types
Grassland ecosystem
Forest ecosystem
Farmland ecosystem
Desert ecosystem
All
such as northern Qinghai and eastern-central Gansu (Deng
et al. 2010). The climate change trend was warming and
drying in parts of those areas, such as the Loess Plateau.
However, the implementation of ecological projects, such
as Returning Farmland into Forest or Grassland Project
and the Returning Grazing Land to Grassland Project had
an important role in improving vegetation cover. Areas of
decreased or increased vegetation coverage in spring were
also the regions of a decreased or increased soil retention
rate. The change in vegetation cover significantly influenced
the soil retention rate in most areas (r > 0.7, p < 0.01). The
change in vegetation cover has no significant influence on
soil retention rate in a few areas. This finding is related to
the effects of changes in vegetation cover in non-erosion
seasons on the windbreak and sand fixation service function
(Figs. 9–11).
Periods were divided into two phases using 2000 as the
dividing line for the reason that many ecological projects
were implemented around 2000. The soil retention rate in
the total wind erosion area was significantly improved from
1990–2000 to 2000–2010 by 0.72%, and the rate of change
was 1.10% (Fig. 10; Table 2).
Between 2000 and 2010, the average soil retention rate
of the grassland ecosystem was 72.57%, 2.02% higher than
in 1990–2000. This improved soil retention rate may not
only be related to the increase in total precipitation, which
affected vegetation growth, but also the implementation
of ecological engineering, such as implementation of the
Returning Farmland into Grassland Project on the Loess
Plateau, the Returning Grazing Land into Grassland Project
on the Qinghai-Tibet Plateau, the Beijing-Tianjin Dust
Storm Source Control Project and converting farmland to
grassland in the Daxingan and Xiaoxingan Mountains.
30˚N
Table 2 Soil retention rate variation of the ecological system
service function (%).
Slope of soil retention rate (%)
0 250 500
80˚E
1000
km
90˚E
<-1
-0.3– -0.1
-0.1–0.1
-1– -0.5
0.1–0.3
-0.5– -0.3
Non-wind erosion areas
(glacier, alpine valleys, etc)
100˚E
110˚E
0.3–0.5
0.5–1
>1
Water
120˚E
Fig. 10 Variation in soil retention rate 1990 and 2010.
120˚E
130˚E
50˚N
N
40˚N
80˚E
1000
km
90˚E
30˚N
30˚N
0 250 500
Correlation
0.8-0.9
0-0.4
0.9-1
0.4-0.5
Non-wind erosion areas
0.5-0.6
(glacier, alpine valleys, etc)
0.6-0.7
Water
0.7-0.8
100˚E
110˚E
120˚E
70˚E
80˚E
90˚E
100˚E
110˚E
120˚E
130˚E
N
50˚N
110˚E
40˚N
100˚E
0 250 500
80˚E
1000
km
90˚E
Significance test p value
80-90
0-30
90-95
30-40
40-50
95-100
Non-wind erosion areas
50-60
(glacier, alpine valleys, etc)
60-70
Water
70-80
100˚E
110˚E
120˚E
30˚N
90˚E
40˚N
80˚E
30˚N
70˚E
50˚N
Journal of Resources and Ecology Vol.5 No.2, 2014
40˚N
50˚N
112
Fig. 11 Relationship between spring vegetation and soil retention rate and its significance.
Between 2000 and 2010, the average soil retention rate of
the forest ecosystem was 1.15% higher than in 1990–2000.
This improved soil retention rate may be related to human
activities of converting farmland to forest in the Daxingan
and Xiaoxingan Mountains and the Loess Plateau.
The average soil retention rate of the desert ecosystem
decreased mainly in the Taklamakan Desert, western Inner
Mongolia and northern Gansu. This finding may be related
to the warming and drying climate trends, which restrain
vegetation growth. However, on the whole, the average soil
retention rate between 2000–2010 is 0.86% higher than in
1990–2000.
Comparing the two decades, the average soil retention
rate of farmland ecosystems increased slightly by 0.99%.
This increase may be related to crop planting structures,
a longer period of crop cover, changes in corn farming
methods and development of high efficiency agriculture.
4.2 Application of the RWEQ model
The RWEQ model of the USDA is mainly used to evaluate
the amount of wind erosion in farmland from the surface
to a height of 2 m. To better apply the model to other land
types, withered vegetation cover and soil roughness of
different types of land cover were measured. Withered
vegetation was introduced to replace the concept of
farmland flat residues.
Given that the classification of soil content in the
United States and China is different, we need to consider
the conversion of particle size in the calculation of soil
erodibility and crust factors (Buchan et al. 1993; Crawford
et al. 1993; Guo 2012; Press et al. 1992; Skaggs et al. 2001;
Zobeck et al. 1999). In this paper, cubic spline interpolation
was used to convert soil particle content data.
Wind is the most important driving force for soil erosion.
High time resolution of wind speed is needed to estimate
wind erosion because wind speed varies from minute to
minute. The average wind speed for a certain periods of
time can affect the simulation results for different lengths
of time. Wind erosion only occurred when wind speed was
higher than the threshold wind velocity of sand movement.
Considering that instantaneous wind speed, which is higher
than the threshold wind velocity of sand movement, may
be filtered, a longer period of time results in a smaller
amount of wind erosion (Guo 2012). Although the high
time resolution of wind speed is needed, obtaining data with
high time resolution is hard. We used daily wind speed to
calculate the amount of wind erosion but to ensure a more
accurate result the downscaling method (Guo et al. 2012)
was used to improve the time resolution of wind speed.
Maldistribution of meteorological stations could
influence estimates of soil erosion. For example, only
one meteorological station is located at the center of the
Taklamakan Desert. Wind speed in that station is low,
which affected the estimation accuracy of soil erosion. At
the same time, China covers a vast geographic area, has a
complicated terrain, and has various land cover types, with
different areas having different threshold wind velocities of
sand movement (Kurosaki and Mikami 2007; Yang et al.
2000). In this paper, 1–3 m s-1 of threshold wind velocity
of sand movement was chosen to calculate wind erosion
in the Taklamakan Desert (Kurosaki and Mikami 2007). In
other regions, different threshold wind velocities of sand
movement in different regions have not been considered (5
m s-1 was used uniformly); hence, further study is needed.
Compared with results from other studies, the average
deviation between calculated data and measurement results
was 31.66%, except for data from Zhengxiangbai Qi.
Besides the above-mentioned factors in section 3.1, local
GONG Guoli, et al.: Sand-fixing Function under the Change of Vegetation Coverage in a Wind Erosion Area in Northern China
changes in vegetation type, soil moisture, soil particle
content and degree of relief can affect measurement results,
increase simulation error and affect experimental results.
Application of the RWEQ model to non-farmland areas and
issues such as soil erosion border and influences of relief on
wind erosion should be studied further. However, we can
use the model now to estimate wind erosion and study the
influence of changes in vegetation cover on windbreak and
sand fixation service functions to distinguish critical periods
and main areas in the prevention and control of wind
erosion.
5 Conclusions
Grassland with low cover and the edge of deserts are critical
areas in the control of wind erosion. The amount of wind
erosion decreased with decreasing wind erosion forces in
northern China. Vegetation cover improved because of
climate change and restoration projects. Spring is the main
wind erosion season in northern China, followed by winter.
Time and area should be distinguished in the prevention
and control of wind erosion. Vegetation cover is lower in
the wind erosion season, and improving vegetation cover
in this season is the key to preventing and controlling wind
erosion.
Average wind erosion retention is 20.31 billion tons
in northern China. The temporal-spatial distribution was
similar to the erosion modulus. Soil retention cannot
highlight the windbreak and sand fixation service function
of the ecological system itself. Hence, the soil retention rate
should be studied further to highlight the function of the
ecological system.
The distribution of soil retention rate of terrestrial
ecosystem in China was similar to that of vegetation cover,
which shows a gradual increase from northwest to southeast
across the study area. The area of improved vegetation
cover in spring after the implementation of restoration
projects was mainly distributed in the Loess Plateau,
Qinghai-Tibet Plateau, northern Hebei, eastern Inner
Mongolia, and northeast China. In most regions, changes
in vegetation cover in spring significantly affected the soil
retention rate of the ecosystem (r > 0.7, p < 0.01). Grassland
ecosystems had the highest increase in soil retention rate,
followed by forest ecosystems, farmland ecosystems and
desert ecosystems. Compared with other land types, the
protection of grassland has a multiplier role in windbreak
and sand-fixation.
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植被盖度变化背景下的中国北方风蚀区植被防风固沙功能研究
巩国丽1,2，刘纪远1，邵全琴1，翟 俊3
1 中国科学院地理科学与资源研究所，北京 100101；
2 中国科学院大学，北京 100049；
3 环境保护部卫星环境应用中心，北京 100094
摘 要：本文基于气象、遥感数据，运用RWEQ模型，结合风蚀季节的植被盖度变化对近30年的土壤风蚀量和植被的防风
固沙服务功能的时空变化趋势进行了定量评估，揭示了植被盖度变化对防风固沙服务功能的影响。研究表明：中国北方多年平
均土壤风蚀量为160.1亿t，并处于下降趋势，土壤侵蚀强度大的区域主要集中在各大沙漠区和植被盖度较低的草地，且春季为
我国土壤风蚀的多发期，占全年风蚀量的45.93%；中国北方多年平均防风固沙量为203.1亿t；防风固沙服务功能保有率的分布
特征表现为由东南到西北逐渐降低的趋势；工程实施后春季植被盖度的提升区主要集中在黄土高原、青藏高原、河北北部、
内蒙古东部以及东北地区；大部分区域的春季植被盖度减小（提高）与防风固沙的服务保有率的下降（提升）呈显著正相关
（r >0.7，p <0.01）；前后两个十年相比较草地生态系统的防风固沙服务功能提升幅度最大（2.02%），其次为林地（1.15%）、
农田（0.99%）和荒漠（0.86%）。
关键词：植被盖度变化；RWEQ模型；土壤风蚀；防风固沙；中国北方