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Journal of Resources and Ecology >

2022 , Vol. 13 >Issue 5: 763 - 774

DOI: https://doi.org/10.5814/j.issn.1674-764x.2022.05.002

Evaluating Ecological Restoration

Evaluation of Inner Mongolia Wind Erosion Prevention Service based on Land Use and the RWEQ Model

  • WANG Yangyang , 1, 2 ,
  • XIAO Yu , 1, 2, * ,
  • XU Jie 3 ,
  • XIE Gaodi 1, 2 ,
  • QIN Keyu 1, 2 ,
  • LIU Jingya 1, 2 ,
  • NIU Yingnan 1, 2 ,
  • GAN Shuang 1, 2 ,
  • HUANG Mengdong 1, 2
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  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Beijing Forestry University, Beijing 100083, China
*XIAO Yu, E-mail:

WANG Yangyang, E-mail:

Received date: 2021-11-01

  Accepted date: 2022-04-15

  Online published: 2022-07-15

Supported by

The Strategic Priority Research Program of Chinese Academy of Sciences(XDA20020402)

The National Natural Science Foundation of China(41971272)

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Abstract

Inner Mongolia is the important ecological barrier zone in northern China, which plays an important role in the prevention and control of wind in the regional ecosystem. Based on the Revised Wind Erosion Equation (RWEQ) model and the cost-recovery method, this study simulated the wind erosion prevention service (WEPS) in Inner Mongolia in 2010 and 2015, investigated the spatial pattern of material and monetary value of WEPS, and analyzed the differences among various cities and various ecosystems. The results indicated that the total WEPS of Inner Mongolia was estimated to be 73.87×108 t in 2015, which was 4.61×108 t less than in 2010, while the monetary value of WEPS was calculated to be 738.66×108 yuan in 2015, which was 46.16×108 yuan less than in 2010. Among all the leagues and cities, Xilin Gol League supported the highest WEPS, reaching 18.65×108 t in 2015, while Wuhai provided the lowest. The WEPS of Hulunbeier increased the most, by 4.37×108 t from 2010 to 2015. The WEPS in the grassland ecosystem was the highest among the different ecosystems, accounting for more than 55% of the total WEPS in Inner Mongolia, but it was reduced by 1.05×108 t during the same period. The WEPS in the forest ecosystem increased the most, reaching 0.19×108 t. This study found that the implementation of projects such as returning farmland to forests and grasses and sand control effectively increased the WEPS by increasing the forest area. However, unsuitable land use increased the desertification of ecosystems which resulted in a reduction of WEPS in Inner Mongolia.

Key words： wind erosion prevention service; revised wind erosion equation; ecological technology

Cite this article

WANG Yangyang , XIAO Yu , XU Jie , XIE Gaodi , QIN Keyu , LIU Jingya , NIU Yingnan , GAN Shuang , HUANG Mengdong . Evaluation of Inner Mongolia Wind Erosion Prevention Service based on Land Use and the RWEQ Model[J]. Journal of Resources and Ecology, 2022 , 13(5) : 763 -774 . DOI: 10.5814/j.issn.1674-764x.2022.05.002

1 Introduction

Inner Mongolia is one of the most important ecological barriers in northern China, and the ecological protection and restoration in Inner Mongolia assures the ecological safety within the autonomous region, the northern parts of China, and the neighboring countries, such as North Korea, South Korea, Japan and Russia. In the case of the strong wind erosion problem, the wind erosion prevention service (WEPS) is the most important ecosystem service supported by the ecosystems in Inner Mongolia. Since the initiation of the project of returning farmland to forest and grassland, the Beijing-Tianjin sandstorm source control project and the Three-North shelterbelt project, Inner Mongolia has achieved great results in afforestation and desertification control by the enclosure of mountains, fenced grazing, grazing prohibition, grazing rotation, and rest grazing. Recently, there has been a large amount of research on various aspects of WEPS in Inner Mongolia, such as the tree species and sand-fixing modes of WEPS (Zhang et al., 2006; Meng, 2007; Ma, 2015; Liu, 2017), the benefits of the wind-sand control project (Zhang et al., 2014; Yu et al., 2016; Zhang et al., 2019; Zhang et al., 2021; Zhu et al., 2021), and the benefits of returning farmland to forests and grasslands (Hu et al., 2010; Liu et al., 2013; Wang et al., 2015; Wei, 2017; Yang, 2017).
WEPS is often estimated as the difference between the potential and actual sand erosion by ecosystems. Several models have been adopted to simulate sand erosion by ecosystems, including the sediment transport equation (Bagnold et al., 1933), the wind erosion equation (WEQ) (Woodruff and Siddoway, 1965), the Texas Erosion Analysis Model (TEAM) (Gregory et al., 2004), the description mode (Lindstrom, 1985), the Revised Wind Erosion Equation (RWEQ) (Fryrear et al., 2000), the Wind Erosion Prediction System (Hagen, 2004), the Wind Erosion Stochastic Simulator (WESS) (Van Pelt et al., 2004), and others. Among all of these models, the RWEQ model has been the most commonly used model for sand erosion simulation in Inner Mongolia. For example, Gong et al. (2014) used the RWEQ model to evaluate the temporal and spatial trends of changes in the sand erosion and WEPS of Xilin Gol League in Inner Mongolia from 1990 to 2010, and discussed the impact of grassland cover changes on the WEPS by the ecosystems. Jiang et al. (2016) used the RWEQ model to simulate the response of land use changes to sand erosion and the WEPS of Inner Mongolia from 2000 to 2010. Chi et al. (2018) investigated the soil wind erosion modulus of the Inner Mongolia Plateau from 1990 to 2015, and found that the decrease in the number of extreme weather sandstorms and the decrease in annual average wind speed were significantly related to the decline in soil wind erosion. Based on field data in Dalate Banner in Ordos City, Yin (2007) believed that the RWEQ model could become a mature method for simulating wind erosion after the parameters were localized and corrected.
As the span of Inner Mongolia is large from east to west, the spatial pattern of sand erosion and WEPS can be quite different between the eastern and the western parts. Although there have been quite a lot studies on the sand erosion and WEPS in Inner Mongolia, few have examined the wind erosion throughout Inner Mongolia or investigated the changes among different areas and ecosystems. Therefore, this study adopted the RWEQ model to simulate the WEPS in Inner Mongolia in 2010 and 2015, estimated its monetary value with the restoration cost method, and analyzed the differences of material and monetary values of WEPS among different cities and ecosystems. The results of this study will be helpful for understanding the spatial patterns of WEPS in Inner Mongolia and providing crucial knowledge to support decision-making about ecological restoration and protection in order to improve the WEPS provided by different ecosystems.

2 Methodology and data

2.1 Study area

Inner Mongolia is located along the northern border of China, stretching 2400 km from east to west and bordering Mongolia and Russia on the outside, and it plays a decisive role in maintaining the ecological safety at the local and regional levels. This area is situated in the southeastern part of the Mongolia Plateau, and has a high average elevation. The area is adjacent to the east side of Daxing’anling Mountains, the middle Yinshan Mountains, and the north side of Helan Mountains. The Alxa Plateau lies in the west. In addition, there are several deserts in Inner Mongolia, such as Badain Jaran, Tengger, Ulanbuhe, Kubu, Mu Us, and others. Due to the long span between east and west, the climate of Inner Mongolia has a gradual transition from the eastern continental monsoon climate to the western temperate continental climate, and the rainfall decreases gradually from southeast to the northwest. The average number of windy days throughout the year is 10-40 days, with 70% occurring in spring. Among the different areas, the windy days in the Xilin Gol and Ulan Qab plateaus typically last more than 50 days, while those in the northern mountains of Daxing’anling generally span less than 10 days. The number of sandstorm days is about 5-20 days in most areas, but can be more than 20 days in western Alxa and the Ordos Plateau. The number of windy days of Huluchi in Ejina Banner of Alxa League averages 108 days a year. There are various types of ecosystems in Inner Mongolia, such as forests, grasslands, wetlands, and deserts (Fig. 1).
Fig. 1 Landuse maps of Inner Mongolia in 2010 and 2015

2.2 Methods and data

2.2.1 Data sources

Meteorological datasets, including daily temperature, precipitation, wind speed, and solar radiation data from 2000 to 2015, were acquired from meteorological stations. Snow cover data with a 25-km resolution were obtained from a long-term snow depth dataset (Che et al., 2008), which was accessed from the Cold and Arid Region Science Data Center (http://westdc.westgis.ac.cn). NDVI values (spatial resolution 500 m) were derived from the international scientific data mirror website of the computer network information center, Chinese Academy of Sciences (http://www.gscloud.cn). The DEM data (spatial resolution 30 m) and land cover data were derived from the Resource and Environment Data Cloud Platform, Chinese Academy of Sciences (CAS) (http://www.resdc.cn). The 1:1000000 soil data shapefile provided by the Harmonized World Soil Database (HWSD) was developed by the International Institute for Applied System Analysis (IIASA) of FAO. In the calculations, the classification criteria of soil texture were converted from the international system in the database to the American criteria in order to meet the factor calculation requirements by using the logistic growth model proposed by Skaggs et al. (2001). The 1:4000000-scale calcium carbonate content data were accessed from the Data Sharing Infrastructure of Earth System Science (http://www.geodata.cn). All of the above data were ultimately resampled to a 1-km spatial resolution. Data for livestock numbers were accessed from the China Economic and Social Development Statistical Database (http://tongji.cnki.net).

2.2.2 Calculation of WEPS

WEPS is the reduction of the wind erosion caused by vegetation, and was calculated as the difference between the potential wind erosion under bare soil conditions and the actual wind erosion under vegetated conditions. The potential and actual wind erosion were calculated based on the RWEQ model (Fryrear et al., 2000; Du et al., 2016). The actual wind erosion was the soil erosion under the condition of vegetation coverage in a real scenario, while the potential wind erosion was the soil erosion under bare land conditions without vegetation. The WEPS values were calculated as follows:
$SL=~\frac{2z}{{{s}^{2}}}\times {{Q}_{max}}\times {{e}^{-{{\left( \frac{z}{s} \right)}^{2}}}}$
${{Q}_{max}}=109.8\times WF\times EF\times SCF\times {K}'\times C$
$s=109.8\times {{(WF\times EF\times SCF\times {K}'\times C)}^{-0.3711}}$
$SLR=\text{ }\!\!~\!\!\text{ }\frac{2z}{{{s}_{r}}^{2}}\times {{Q}_{rmax}}\times {{\text{e}}^{-{{\left( \frac{z}{{{s}_{r}}} \right)}^{2}}}}$
${{Q}_{rmax}}=109.8\times WF\times EF\times SCF\times {K}'$
${{s}_{r}}=109.8\times {{\left( WF\times EF\times SCF\times {K}' \right)}^{-0.3711}}$
$G=SLR-SL$
where, SL represents the actual wind erosion (kg m-2); z represents the calculated downwind distance (m); s represents the critical plot length (m); Qmax represents the maximum sediment transport capacity (kg m-1); SLR represents the potential wind erosion (kg m-2); Qrmax represents the maximum potential sediment transport capacity (kg m-1); sr represents the potential critical plot length (m); G represents WEPS (kg m-2); and WF, EF, SCF, K', and C correspond to the factors of weather (kg m-1), soil erodibility fraction (%), soil crust factor (dimensionless), soil roughness factor (dimensionless), and vegetation (dimensionless), respectively. WF was calculated as:
$WF=Wf\times \frac{\rho }{g}\times SW\times SD$
$Wf={{u}_{2}}\times {{({{u}_{2}}-{{u}_{1}})}^{2}}\times {{N}_{d}}$
where, Wf is the wind factor (m3 s-1); g represents the gravitational acceleration (m2 s-1); ρrepresents the air density (kg m-3); SW is the soil moisture factor (dimensionless); and SD represents the snow cover factor (dimensionless), which is the ratio of days with no snow cover to the total number of days studied. A snow cover depth of less than 25.4 mm was considered as no snow cover. u1 represents the threshold wind velocity at a height of 2 m (m s-1). The threshold wind velocity for sand lands and sandy grasslands was 5 m s-1, based on field observations of wind-sand activities (Gong et al., 2014). u2 represents the monthly average wind velocity at a height of 2 m (m s-1). Nd represents the number of days with a monthly average wind velocity that exceeds the threshold wind velocity.
In this study, EF and SCF were calculated as follows:
$\begin{align} & EF=29.09+0.31\times Sa+0.17\times Si+ \\ & \ \ \ \ \ \ \ 0.33\times \frac{Sa}{cl}-2.59\times OM-0.95\times Ca \\ \end{align}$
where, Sa, Si, cl, OM, and Ca represent the contents of sand, silt, clay, organic matter, and calcium carbonate (%), respectively.
$SCF=\frac{1}{1+0.0066\times c{{l}^{2}}+0.021\times O{{M}^{2}}}$
K' was calculated with the following equation:
${K}'=\cos \alpha $
where, α represents the slope gradient and was calculated by a digital elevation model (DEM) in ArcGIS.
C was computed as follows:
$C={{e}^{-0.0483\times SC}}$
$SC=\frac{NDVI-NDV{{I}_{min}}}{NDV{{I}_{max}}-NDV{{I}_{min}}}$
where, SC represents the vegetation coverage (%); NDVImax represents the normalized difference vegetation index (NDVI) value of a highly vegetated grid; and NDVImin represents the NDVI value of a bare land grid. Note that NDVImax and NDVImin correspond to the NDVI values at a 95% and 5% cumulative frequency, respectively.
The retention rate of WEPS more accurately quantifies the contributions of ecosystems to WEPS and avoids the impacts of climatic factors on WEPS. The retention rate of WEPS was calculated as follows:
$F=\frac{G}{SLR}$
where, F is the retention rate of WEPS; and G and SLR represent WEPS (kg m-2) and the potential wind erosion (kg m-2), respectively.

2.2.4 Calculation of the monetary value for WEPS

The monetary value of the WEPS could be measured as the cost to restore the sandy land, which was calculated with the following formula:
${{V}_{sf}}=\frac{G}{d\times h}\times c$
where, Vsf is the value of WEPS (yuan, which is the Chinese currency); d is the soil bulk density (kg m-3), which was 1.65×103 kg m-3; h is the thickness of soil, which was calculated as 0.1 m; and c is the average cost of the sand control project (yuan), based on the cost of afforestation and grass grid desertification control in Inner Mongolia, and the value is 1.65 yuan m-2.

3 Results

3.1 WEPS and its monetary value in Inner Mongolia

The total amount of SLR (potential wind erosion) in Inner Mongolia was calculated as 165.97×108 t in 2010 and 144.04×108 t in 2015. The SLR per unit area was calculated to be from 0 to 57.97 kg m-2 in 2010 and from 0 to 55.26 kg m-2 in 2015. The average SLR per unit area was estimated to be 14.59 kg m-2 in 2010, while it was 12.66 kg m-2 in 2015 (Fig. 2). The SLR per unit area was higher in the western, central, southwestern and southeastern parts, whereas it was lower in the northeastern parts. This showed that the western, central, southwestern and southeastern areas in Inner Mongolia were more threatened by wind erosion.
Fig. 2 The amounts of SLR, SL, WEPS and F per unit area in Inner Mongolia in 2010 and 2015.
The total amount of SL (actual wind erosion) in Inner Mongolia was calculated to be 87.48×108 t in 2010 and 70.17×108 t in 2015. The SL per unit area ranged from 0 to 55.65 kg m-2 in 2010 and from 0 to 55.22 kg m-2 in 2015. The average SL per unit area in 2010 and 2015 was estimated to be 7.69 kg m-2 and 6.17 kg m-2, respectively (Fig. 2). The SL per unit area in the central and western parts was higher, whereas in the eastern parts it was lower. This meant that strong wind erosion in the central and western areas in Inner Mongolia had actually occurred.
The total amount of WEPS in Inner Mongolia was calculated as 78.48×108 t in 2010 and 73.87×108 t in 2015, while the total monetary value was estimated to be 784.82×108 yuan in 2010 and 738.66×108 yuan in 2015. Therefore, the WEPS in 2015 decreased by 4.61×108 t compared with its level in 2010, while its monetary value was reduced by 46.16×108 yuan. The WEPS per unit area was calculated to range from 0 to 46.80 kg m-2 in 2010 and from 0 to 45.79 kg m-2 in 2015. The average WEPS per unit area was estimated to be 6.90 kg m-2 in 2010, while it was 6.40 kg m-2 in 2015 (Fig. 2). The WEPS per unit area was higher in the central and southeastern parts, whereas it was lower in the western and northeastern parts.
The average F (retention rate of WEPS) in Inner Mongolia was 68% and 69% in 2010 and 2015, respectively, and the F was calculated to range from 0 to 99.86% in both 2010 and 2015. The F was higher in the eastern, southeastern and southern parts, whereas it was lower in the western parts (Fig. 2). It could be concluded that the ecosystems in the eastern, southeastern and southern areas in Inner Mongolia contributed more in wind erosion prevention, where the forests were mainly located (Fig. 1).

3.2 The amount of WEPS of different cities in Inner Mongolia

Among the leagues and cities in Inner Mongolia, the SLR of Alxa League was the highest, being 67.80×108 t in 2010 and 52.08×108 t in 2015, followed by Xilin Gol League. The SLR of Wuhai City was the lowest in both 2010 and 2015. The average SLR per unit area of Alxa League also was the highest, while Xing’an City was the lowest. From 2010 to 2015, the SLR in Hulunbuir City increased the most, with a total increase of 5.54×108 t, followed by Xing’an City. The other league cities showed a decreasing trend, of which the Alxa League had the largest decrease, reaching 15.72×106 t, followed by Xilin Gol League. The SL of Alxa League was the highest, at 53.54×108 t in 2010 and 39.91×108 t in 2015. The SL of Wuhai City was the lowest in both 2010 and 2015. The average SL per unit area was the highest in Alxa League, and it was the lowest in Xing’an City. From 2010 to 2015, the SL in Hulunbuir City increased the most, with a total increase of 1.17×108 t, followed by Xing’an City. There were five leagues and cities which showed a decreasing trend, of which the Alxa League had the largest decrease, reaching 13.63×108 t, followed by Xilin Gol League (Fig. 3). These data showed that the Alxa League and Xilin Gol League were more threatened by wind erosion, and also that strong wind erosion had occurred.
Fig. 3 The SL and SLR of different cities in Inner Mongolia in 2010 and 2015
Among the leagues and cities in Inner Mongolia, the Xilin Gol League supported the highest WEPS of 20.99× 108 t in 2010 and 18.65×108 t in 2015. Their monetary values were calculated as 209.95×108 yuan in 2010 and 186.52×108 yuan in 2015. The WEPS of Wuhai City was the lowest in both 2010 and 2015. In 2010, Tongliao City provided the highest average WEPS per unit area, which reached 14.00 kg m-2, followed by Ordos City, and Hulunbuir City supported the lowest value. From 2010 to 2015, the WEPS in Hulunbuir City increased the most, with a total increase of 4.37×108 t, followed by Xing’an City. The WEPS in the other league cities showed a decreasing trend, of which the Xilin Gol League had the largest decrease, reaching 2.34× 108 t, followed by Alxa.
The average F of Xing’an City was the highest, at 95.08% in 2010 and 95.01% in 2015, while that of Alxa League was the lowest. From 2010 to 2015, the average F in Hulunbuir City decreased the most, with a total decrease of 0.62%, followed by Xing’an City. The other league cities showed an increasing trend, of which the Alxa League had the largest increase, reaching 1.79%, followed by Bayannaoer League (Table 1). These data suggested that the ecosystems in Xing’an League, Hulunbuir City and Tongliao City contributed more in wind erosion prevention than those in the other cities.
Table 1 The amount of sand-fixing function of different cities in Inner Mongolia in 2010 and 2015
City 2010 2015 Amount of change F (%)
Average (kg m-2) Total
(108 t)		 Value
(108 yuan)		 Percentage (%) Average (kg m-2) Total
(108 t)		 Value (108 yuan) Percentage (%) Total (108 t) Value
(108 yuan)		 Average 2010 Average 2015 Change
Baotou 8.38 2.31 23.13 3 7.15 1.97 19.73 3 -0.34 -3.40 63.40 64.11 0.71
Xilin Gol 10.50 20.99 209.95 27 9.33 18.65 186.52 25 -2.34 -23.43 71.46 72.35 0.89
Hohhot 4.68 0.80 7.98 1 3.44 0.59 5.87 1 -0.21 -2.11 86.93 87.36 0.43
Ulan Qab 7.47 4.05 40.48 5 5.89 3.19 31.90 4 -0.86 -8.58 71.66 72.24 0.58
Tongliao 14.00 8.25 82.45 11 13.37 7.88 78.76 11 -0.37 -3.69 91.30 91.49 0.19
Hulunbuir 1.53 3.82 38.22 5 3.28 8.19 81.88 11 4.37 43.66 94.44 93.82 -0.62
Bayannaoer 7.41 4.77 47.72 6 6.13 3.95 39.48 5 -0.82 -8.24 51.45 52.51 1.06
Chifeng 8.58 7.34 73.45 9 7.10 6.08 60.83 8 -1.26 -12.62 89.69 90.19 0.50
Ordos 12.17 10.44 104.40 13 11.16 9.57 95.73 13 -0.87 -8.67 63.64 64.47 0.83
Alxa 5.98 14.26 142.59 18 5.10 12.16 121.63 16 -2.10 -20.96 24.33 26.12 1.79
Xing’an 2.53 1.37 13.66 2 2.91 1.57 15.70 2 0.20 2.04 95.08 95.01 -0.07
Wuhai 3.72 0.08 0.80 0 2.89 0.06 0.62 0 -0.02 -0.18 56.77 57.50 0.73
Total 78.48 784.82 100 73.87 738.66 100 -4.61 -46.16

3.3 WEPS and its monetary value for different ecosystems in Inner Mongolia

Among the different types of ecosystems in Inner Mongolia, the amount of SLR in the grassland ecosystem was the highest, accounting for more than 40%, which was estimated to be 71.12×108 t in 2010 and 66.86×108 t in 2015. The settlement ecosystem had the lowest calculated SLR (Fig. 4). In 2015, the SLR per unit area was the highest for the desert ecosystem, reaching 23.10 kg m-2, while that for the forest ecosystem was the lowest. From 2010 to 2015, the SLR in grassland, farmland, desert and other ecosystems showed decreasing trends, among which the desert ecosystem decreased the most, reaching 13.70×108 t; followed by the grassland ecosystem which decreased by 4.26×108 t. The amount of SL in the desert ecosystem was the highest, accounting for more than 50%, and it was 48.92×108 t in 2010 and 37.58×108 t in 2015. The settlement ecosystem was the lowest (Fig. 4). In 2015, the SL per unit area was the highest for the desert ecosystem, while that for the forest ecosystem was the lowest. From 2010 to 2015, the SL in settlement ecosystem showed an increasing trend by 0.06×108 t, while the other ecosystems showed a decreasing trend, among which the desert ecosystem decreased the most, reaching 11.33×108 t; followed by the grassland ecosystem which decreased by 3.23×108 t. These data showed that the desert and grassland ecosystems were more threatened by wind erosion, and that strong wind erosion had occurred.
Fig. 4 The SL and SLR of different ecosystems in Inner Mongolia in 2010 and 2015
Among the different types of ecosystems in Inner Mongolia, the grassland ecosystem supported the highest amount of WEPS, accounting for more than 55%. The WEPS of the grassland ecosystem was estimated to be 43.35×108 t in 2010 and 42.31×108 t in 2015, and its monetary value was calculated as 433.53×108 yuan in 2010 and 423.14×108 yuan in 2015. The settlement ecosystem supported the lowest WEPS. In 2015, the average WEPS per unit area was the highest for the grassland ecosystem at 8.06 kg m-2, followed by the desert ecosystem, and that for the forest ecosystem was the lowest. From 2010 to 2015, the total amount of WPES in forests, water, and desert ecosystems showed an increasing trend, among which the forest ecosystem increased the most, reaching 0.19×108 t; while in farmland ecosystem and grassland ecosystem it decreased by 1.03×108 t and 1.04×108 t, respectively. Among the different types of ecosystems in Inner Mongolia, the forest ecosystem supported the highest average F of 96.84% in 2010 and 96.77% in 2015, followed by the farmland. From 2010 to 2015, the average F in farmland increased the most, with a total increase of 0.07%, followed by the water ecosystem. The other ecosystems showed a decreasing trend, of which the settlement ecosystem had the largest decrease, followed by the desert ecosystem (Table 2). Based on these results, it could be concluded that the forest ecosystem contributed the most in wind erosion prevention.
Table 2 The WEPS and F of different ecosystems in Inner Mongolia in 2010 and 2015
Type 2010 2015 Variation F (%)
Average (kg m-2) Total (108 t) Value
(108 yuan)		 Percentage (%) Average (kg m-2) Total (108 t) Value (108 yuan) Percentage (%) Total (108t) Value
(108 yuan)		 Average (kg m-2) Area (km2) Average 2010 Average 2015 Change
Farmland 6.78 7.73 77.32 10 5.89 6.70 67.03 9 -1.03 -10.28 -0.89 -147 89.56 89.63 0.07
Forest 1.20 1.97 19.74 3 1.31 2.17 21.67 3 0.19 1.93 0.12 236 96.84 96.77 -0.07
Grassland 8.25 43.35 433.53 55 8.06 42.31 423.14 57 -1.04 -10.39 -0.19 -558 73.47 73.38 -0.09
Water 6.59 2.00 19.95 3 6.91 2.09 20.88 3 0.09 0.93 0.32 -79 81.75 81.76 0.01
Settlement 7.88 0.92 9.15 1 6.64 0.95 9.54 1 0.04 0.39 -1.23 2745 84.13 81.88 -2.25
Desert 8.25 19.84 198.35 25 7.33 17.46 174.63 24 -2.37 -23.73 -0.92 -2091 33.08 32.93 -0.15
Other 5.26 2.68 26.78 3 4.28 2.18 21.76 3 -0.50 -5.01 -0.98 -105 32.61 32.58 -0.03
Total 78.48 784.82 100 73.87 738.66 100 -4.61 -46.16

3.4 Changes in the spatial pattern of WEPS in Inner Mongalia

From 2010 to 2015, the change in the SLR per unit area in Inner Mongolia ranged from -16.67 kg m-2 to 27.38 kg m-2, with an average change of -2.02 kg m-2. The increase in SLR per unit area mainly occurred in the western areas of north eastern Inner Mongolia, the eastern edge, and the central and northern parts. Both the central and western parts showed a downward trend, especially in the western part. The change in SL per unit area in Inner Mongolia ranged from -16.68 kg m-2 to 21.14 kg m-2, with an average change of -1.59 kg m-2. The increase in SL per unit area mainly occurred in northeastern Inner Mongolia, the eastern edge, and the central and northern parts. Both the central and western parts showed a downward trend (Fig. 5).
Fig. 5 The changes of SLR, SL, WEPS and F per unit area of Inner Mongolia from 2010 to 2015.
The change in the amount of WEPS per unit area in Inner Mongolia ranged from -12.39 kg m-2 to 22.12 kg m-2 from 2010 to 2015, with an average change of -0.41 kg m-2. The increase in WEPS per unit area mainly occurred in the western areas of northeastern Inner Mongolia, the eastern edge, and the central and northern parts. Both the central and western parts showed a downward trend. The WEPS per unit area of the western part of Hulunbuir increased more significantly. Those of the southeastern part of Xilin Gol League, the central and northern parts of Chifeng City, and the southeast of Ulan Qab City declined more significantly (Fig. 5).
From 2010 to 2015, the change in F per unit area in Inner Mongolia ranged from -14.89% to 11.83%, with an average change of 0.56%. The decrease in F per unit area mainly occurred in the western areas of northeastern Inner Mongolia, the eastern edge, and the central and northern parts. Both the central and western parts showed an upward trend, especially in the southeastern part of Xilin Gol League and the middle of Chifeng City (Fig. 5).

4 Discussion

The pattern of changes in WEPS was similar to that of SLR (Fig. 5) mainly because the WEPS depended more on the natural factors such as wind, while people could improve the factors such as vegetation coverage for reducing the SL. Afforestation and sand barriers were the main measures for controlling sandstorms in Inner Mongolia. The tree species used for afforestation mainly included sand willow, caragana, licorice, seabuckthorn, Haloxylon and other pioneering sand control plants which were drought-tolerant, cold- tolerant, and saline-alkali-tolerant, with high sand-fixing ability. These plants also supported considerable economic benefits. According to the statistics of the Inner Mongolia Statistical Bulletin, the total afforestation in Inner Mongolia from 2010 to 2015 was 78254 km2 (Fig. 6). Among the leagues and cities in Inner Mongolia, Chifeng City has the largest area of afforestation at 8722 km2, followed by the Xilin Gol League (Table 3).
Fig. 6 Afforestation areas of different cities in Inner Mongolia from 2010 to 2015
Table 3 Afforestation in Inner Mongolia from 2010 to 2015 (Unit: 102 km2)
Year Total
afforestation area		 Afforestation area Converting
farmland to forest and barren hills for afforestation		 Natural forest
protection resource afforestation		 Afforestation of Beijing-Tianjin sandstorm source control project The fifth phase of the shelterbelt project Total
Artificial Aerial seeding Closing hills for afforestation
2010 65.78 23.02 7.90 34.86 5.50 11.70 28.96 15.48 127.42
2011 73.20 33.50 11.40 28.30 4.00 8.70 40.80 12.50 139.20
2012 78.10 35.70 6.50 35.90 4.00 11.60 42.20 12.30 148.20
2013 80.30 34.80 7.90 37.60 4.03 8.83 46.90 8.56 148.62
2014 67.20 46.00 6.90 14.30 1.60 8.60 16.20 13.40 107.00
2015 73.40 41.60 8.30 23.50 2.50 9.50 12.60 14.10 112.10
Total 437.98 214.62 48.90 174.46 21.63 58.93 187.66 76.34 782.54
The increasing area of the forest ecosystem caused the increasing WEPS, while the increasing area of the desert ecosystem caused the decreasing average WEPS in Inner Mongolia (Table 2). Among different types of land use change, the area converted from desert ecosystem to grassland ecosystem was highest, followed by that from grassland to settlement. The increase of the forest ecosystem was mainly contributed by the grassland and the desert areas. The average WEPS per unit just increased in the area which was transformed from farmland to other land, as well as the unchanged area in forest and water ecosystems (Fig. 7, Fig. 8, Fig. 9). The increase in forest area was found to have played a certain role in improving the WEPS due to the control of desertification and the implementation of the returning farmland to forest and grassland project. However, due to the unsuitable land use, the resulting significant increase in desert area was the main reason for the decrease in the total amount of WEPS.
Fig. 7 The changes of different ecosystems in Inner Mongolia from 2010 to 2015
Fig. 8 Changes in the average of WEPS in different ecosystems in Inner Mongolia from 2010 to 2015
Fig. 9 Changes in the sum of WEPS in different ecosystems in Inner Mongolia from 2010 to 2015

5 Conclusions

In this paper, the WEPS in Inner Mongolia from 2010 to 2015 are calculated, and the spatial dynamics of the wind- breaking and sand-fixing functions are analyzed from the perspectives of various leagues and cities and ecosystems. The improvement of WEPS construction projects provides scientific support and an important basis for the management and utilization of grasslands, forests, deserts, and water bodies in Inner Mongolia.
(1) In 2015, the total WEPS in Inner Mongolia was 73.87×108 t, the total value was 738.66×108 yuan, and the average WEPS per unit area was 6.40 kg m-2. Compared with 2010, the total amount of WEPS was reduced by 4.61×108 t, and the monetary value decreased by 46.16×106 yuan.
(2) Among leagues and cities, Xilin Gol League supported the highest WEPS, providing 18.65×108 t of WEPS and 186.52×108 yuan of monetary value in 2015. Wuhai City provided the lowest amount of WEPS. The average WEPS per unit area of Tongliao City was the highest. From 2010 to 2015, the WEPS in Hulunbuir was enhanced the most, with an increase of 4.37×108 t.
(3) Among the different ecosystem types, the grassland ecosystem supported the highest WEPS, accounting for more than 55% of the total WEPS in Inner Mongolia, reaching 43.35×108 t and 42.31×108 t in 2010 and 2015, respectively. From 2010 to 2015, the total amount of WEPS for forests, water, and settlement ecosystems showed an increasing trend, whereas those for farmland ecosystems and grassland ecosystems decreased by 1.03×108 t and 1.04×108 t, respectively.
In addition, the implementation of projects such as returning farmland to forests and afforestation increased the WEPS by increasing the forest area. The increase in desert ecosystem due to unsuitable land use resulted in a significant reduction of WEPS in Inner Mongolia. However, there were still some limitations in the research. Due to the lack of field observation data, this study did not consider the seasonal variations in the wind velocity threshold, which precluded any analysis of the seasonal differences in the WEPS service. Besides, further parameter localization in the determination of the erodibility boundary of the RWEQ model should be performed in the future to improve the accuracy of the simulation results.

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