Taking Xinjiang as the research area, this paper constructed an ecological risk model based on data of Land use/cover Quality Index (LQI), Climate Quality Index (CQI), and Soil Quality Index (SQI) to simulate the ecological risk of Xinjiang and predict it under different scenarios (Shared Socioeconomic Pathways SSPs) at 2100. There are five conclusions drawn: (1) In 2100, after reviewing different scenarios in Xinjiang, it can be seen that the LULCC risk level is dominated by the medium-quality risk level. The areas with medium-quality risk level account for about 70% of the total area of Xinjiang. The low-quality risk areas are mainly distributed in the Ili Valley in northern Xinjiang, the Keketuohai in the Altay region, and the northern Junggar Basin. Relatively high- and high-quality risk areas are mainly concentrated in residential areas on the northern and southern slopes of the Tianshan Mountains in Xinjiang. (2) In 2100, the high-quality climatic and ecological risk areas of different SSPs in Xinjiang are mainly distributed in mountains, Gobi, desert, and other regions. The medium-quality climatic and ecological risk areas are mainly distributed in the central and eastern regions of Xinjiang. (3) Under different scenarios in 2100, the spatial distribution of soil quality in Xinjiang is roughly the same, which is mainly low-quality. (4) Under different scenarios in 2100, the spatial distribution of ecological risk levels in Xinjiang is mainly dominated by areas with low and medium ecological risk levels, accounting for nearly 70% of the total area. (5) From 2020 to 2100, the ecological risk levels of different scenarios show a decreasing trend in sequence. Typically, from 2020 to SSP370 (2100), most areas change from being at a previous medium ecological risk level to being at a relatively high/high ecological risk level. Under the SSP585 scenario, a large area of areas has changed from the original low ecological risk level to the current high ecological risk level. Only a small area of areas is showing an opposite area change.
Research Article
Ecological risk analysis of future land use/cover: A case study of Xinjiang
https://doi.org/10.21203/rs.3.rs-2478462/v1
This work is licensed under a CC BY 4.0 License
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Land use/cover change (LUCC)
Scenario Simulation
Ecological Risk
Xinjiang
Land use/cover change (LULCC) has long been deemed as an important driving force affecting local, regional and even global climate (Lambin E F et al., 2001; Meyfroidt P et al., 2018; Chen M et al., 2020). LULCC is not only crucial in reflecting the aftermath of human activities on the regional ecological environment and the negative impact on natural factors but also able to affect regional ecological processes (Jin X et al., 2019; Meyfroidt P et al, 2018; Hua T et al., 2018). A fact is that changes in ecological quality in a region will be seriously affected by changes in land surface type and area. Hence, as an important input for climate, hydrological and biogeochemical models, an accurate simulation of LULCC data is essential for solving future ecological problems and simulating climate change. In order to steer future uncertainty towards sustainable development, increased efforts have been made by pioneers to forecast future land use and land use under simulated Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs) scenarios (Shared Socioeconomic Pathways SSPs) (Luo M et al., 2022; Doelman J C et al., 2018). Future LULCCs play an important role in achieving an universal goal of alleviating and decelerating global warming, which puts enormous pressure on more rational land management and effective policies related to regional land-use change (Dong N et al., 2018). Understanding the magnitude of LULCC's impact on regional climate deeply is important for framing SSP+RCP scenarios,especially assuming that climate model simulations involving specific land use scenarios are reasonable to represent climate outcomes in scenarios with the same greenhouse gas forcing but different land use scenarios. This helps to better explain the climate-land-cover interactio (Bukovsky M S et al., 2021).
Land system simulation can visualize the interaction between natural landscape and artificial landscape in the future and support regional land optimization (Liu H et al., 2021). Landscape pattern changes induced by LULCC have been a hot topic in landscape ecology research (Chen A et al., 2022; Yao Q et al., 2022; Zhang W et al., 2020; Ran P et al .,2022). Landscape pattern is the product of long-term interaction of various biological and non-biological processes within a certain area (Zhao Q et al., 2020). It is the spatial arrangement and combination of landscape elements with different patch sizes, shapes and attributes within a specific time and region (Wang Q et al., 2022; Jian Y et al., 2022). Analysis of landscape pattern change can have a knowledge of the spatial pattern of land system dynamic process and time (Wu J et al., 2019). The regional ecological and social process has important influence on landscape pattern (Darvishi A et al., 2020). Ecological risk assessment is a tool that can effectively measure and assess the negative impacts and impacts of human activities or natural disasters on the composition, structure and function of regional ecosystems. Landscape ecological risks are affected by land use/land cover, and anthropogenic impacts are most prominent in areas with high frequency of land use change (Wang H et al., 2021; Ai J et al., 2022; Xu W et al., 2021).
LULCC Ecological Risk This is an effective method to evaluate the landscape mechanism, system and ecological process needed to properly reduce the adverse impact on landscape resources. Therefore, how to correctly assess the current and future ecological risks is crucial to alleviate the contradiction between sustainable development and environmental protection. However, previous studies on ecological risk mainly focused on the assessment of the present situation and lacked attention to explore the dynamic changes of ecological risk according to the future simulation. It is more meaningful to predict future ecological risks for regional sustainable development (Zhang S et al., 2022; Kusiima S K et al., 2022), especially in arid and semi-arid areas.
2.1.Study area
Xinjiang is located in the hinterland of Eurasia (34°25'－48°10'N, 73°40 \'-96°18'E) and has 14 prefecture-level administrative regions (including 4 prefecture-level cities, 5 administrative offices and 5 autonomous prefectures) and 10 autonomous regions and municipalities directly under the Central Government. It is the largest provincial administrative region in China with an area of 166.49×106hm2 (Zhang R et al., 2022). Xinjiang is connected with the Qinghai-Tibet Plateau and is located in the hinterland of Eurasia. It is surrounded by mountains on all sides, with Altai Mountain in the north and Kunlun Mountain in the south. The Tianshan Mountains run through Xinjiang, dividing its territory into northern and southern Xinjiang. The region includes the Gurbantunggut, Taklamakan and Gobi deserts, as well as grasslands and more than 800 scattered oases, with a typical continental arid climate (Sun Y et al., 2022). Xinjiang is located in the center of Eurasia, far from the ocean, and has a typical temperate continental climate, with large intra-day temperature difference, strong and high solar radiation, mountains and basins, all of which contribute to Xinjiang's unique climatic conditions (Bin L et al., 2022).
2.2 Materials and data source
In order to evaluate the ecological risk of Xinjiang more comprehensively and effectively, this paper considers three aspects of land use/cover, climate and soil to build a model .In this paper, the LULCC data set of Globeland 30 with the resolution of 30m in 2020, SSP126, SSP245, SSP370, and SSP585 were used (Luo, M et al.,2020). The evaluation index data were mainly based on model simulation, remote sensing, soil and land use data. In order to better evaluate the ecological risk of land use/cover under the future scenarios in xinjiang, detailed information of the data selected for evaluation from LULCC, climate change and soil conditions are shown in table 1. Future climate and other indicators were selected from three GCMs(EC-Earth3, GFDL-ESM4, MRI-ESM2-0) climate data models.
Table 1 Data sources of ecological risk assessment indicators for the XinJiang.
|
|
Name |
Data |
|
Scenario data |
SSP126, SSP245, SSP370, SSP585 scenario data |
https://www.nature.com/articles/s41598-021-96958-5/tables/1#:~:text=CMIP6%20pattern%20scenario%20dataset%20(https://esgf.llnl.gov/) |
|
Climate factors |
Temperature |
China Meteorological Background Data Set (http://www.resdc.cn) |
|
Precipitation |
China Meteorological Background Data Set (http://www.resdc.cn) |
|
|
Accumulated temperature |
China Meteorological Background Data Set (http://www.resdc.cn) |
|
|
Drought index |
Global Drought Map—10 Arcs (http://www.fao.org/geonetwork/) |
|
|
Photosynthetically active radiation |
China-ASEAN 5 km resolution photosyntheticall effective radiation data set (http://www.geodoi.ac.cn/) |
|
|
Soil factors |
Soil texture |
http://data.tpdc.ac.cn/zh-hans/data/e38147a8-6bf8-4097-85c9-d20fc12df901/?q= |
|
Soil erosion(wind, hydro, freeze–thaw) |
Spatial distribution of soil erosion in China (http://www.resdc.cn) |
|
|
Soil organic matter |
China 1:1 million soil organic matter content |
|
|
Soil moisture |
http://data.tpdc.ac.cn/zh-hans/data/e38147a8-6bf8-4097-85c9-d20fc12df901/?q=%E5%9C%9F%E5%A3%A4 |
|
|
Soil depth |
http://data.tpdc.ac.cn/zh-hans/data/11573187-fd64-47b1-81a6-0c7c224112a0/ |
|
|
Land cover factor |
Globeland30_2020 |
http://globeland30.org/defaults.html?type=data&src=/Scripts/map/defaults/browse.html&head=browse&type=data |
|
SSP126, SSP245, SSP370, SSP585 scenario data |
DOI:10.1038/s41597-022-01204-w |
|
|
Terrain factor |
Elevation |
http://www.gscloud.cn/sources/accessdata/310?pid=302 |
|
Slope |
slope |
http://www.gscloud.cn/sources/accessdata/310?pid=302 |
In order to facilitate calculation and analysis, software such as cdo and ncl is used to resamble and standardize the format and accuracy of netcdf data. Arcgis10.2 converts the data into coordinates and formats, and resambs all raster data to a unit size of 1000m, with R language for data processing.
2.2 Land use/ccove Quqlity Index(LQI)
Different land types have different effects on biodiversity of ecosystem, ecosystem balance and its function, natural succession of land structure, etc. The difference of land type affects the resistance of ecosystem to outside disturbance. In this paper, based on the characteristics of landscape pattern, landscape index was selected and the landscape analysis software Fragstats3.4 was used to calculate the index values of landscape pattern in the study area (Table 2). Using landscape software Fragstats 3.4 and the statistical analysis function of R language, and according to the calculation method given in Table 2, the landscape pattern index of each landscape type of SSPs in the study area was obtained.
Xinjiang is mainly an arid and semi-arid region, so the landscape type is single. By referring to relevant literature, this paper constructs the landscape ecological risk index (ERI) of land use/cover through the landscape disturbance degree, vulnerability degree and fractal dimension. The calculation formula of the health degree of the ecological environment of the comprehensive land type based on the ecological risk value of LUCC is as follows (Shi Y et al., 2022):
Where: the loss index of R_i land type i. Aki refers to the area of land type I in sampling area K; The total area of a sample area k; Landscape risk index of land type in A_ki sampling area i..
2.3 Climate Quqlity Index(CQI)
Precipitation and temperature are the main climatic control factors affecting vegetation growth. The important factors that affect plant growth and biomass distribution and photosynthetic active radiation (PAR), it can measure the ability of synthetic organic compounds. Accumulated temperature is one of the indicators to measure the thermal conditions required for crop growth and development, which can evaluate the local climate thermal conditions. Due to Xinjiang's unique geographical location and mountainous topography, slope is also a factor that has a significant impact on climate as well as water and heat. Based on the calculated results, references score the climate quality indicators. The results are shown in Table 3.
Table3. The score of various indexes in the cliamte quality of the XinJiang
|
Index |
Classes |
Scores |
Index |
Classes |
Scores |
|
Precipitation |
0-5 |
1.7 |
Altitude |
-166-880m |
2 |
|
5-10mm |
3.4 |
880-1135m |
4 |
||
|
10-45mm |
5.1 |
1135-1530m |
6 |
||
|
45-85mm |
6.8 |
1530-3030m |
8 |
||
|
85-110mm |
8.5 |
>3230m |
10 |
||
|
>110mm |
10 |
||||
|
Temperature |
<-5℃ |
2 |
Photosynthetically active radiation |
<2800 MJ/m2 |
2 |
|
-5--3℃ |
4 |
2800-3100MJ/m2 |
4 |
||
|
-3-0℃ |
6 |
3100-3400MJ/m2 |
6 |
||
|
0-10℃ |
8 |
3400-3600MJ/m2 |
8 |
||
|
>10℃ |
10 |
>3600MJ/m2 |
10 |
||
|
Drought Index |
<0.05 |
2 |
Accumulated temperature (10 times) |
<400 |
1.25 |
|
0.05-0.2 |
4 |
400-1600 |
2.5 |
||
|
0.2-0.5 |
6 |
1600-2400 |
3.75 |
||
|
0.5-0.65 |
8 |
240-2900 |
5 |
||
|
>0.65 |
10 |
2900-3400 |
6.25 |
||
|
3400-4000 |
7.5 |
||||
|
4000-4600 |
8.75 |
||||
|
>4600 |
10 |
Therefore, based on the above indicators and with reference to the research of Wang S et al., the climate quality estimation model is as follows:(Wang S,et al,2021):
Where: Pr is the annual average precipitation, T is the annual average temperature, Di is the drought index, Al is the altitude, Par is the photosynthetic active radiation, and At is the accumulated temperature greater than 10 C in the region.
2.4 Soil Quality Index（SQI）
Soil is an important part of the ecosystem and energy cycle, and is also a necessary factor for the survival and growth of organisms. Therefore, soil quality index is one of the important indexes to evaluate ecological risk. Soil texture is composed of soil particles of different sizes. Soil texture is closely related to soil water capacity and fertility. Slope is the driving force for the horizontal flow of soil water and nutrients, which has an important impact on soil system. To sum up, soil depth, texture, moisture and other factors were finally selected to construct the soil quality model as follows:
In the equation, Sd is soil depth, Sei is soil erosion intensity, Som is soil organic matter, and Sm is soil moisture. In order to determine the relationship between other factors and soil quality, a deeper soil depth was used, which corresponds to a higher soil quality. All indicators were scored according to various soil quality indicators provided in the Salvati study (2013). The results are shown in Table 4:
Table4. The score of various indexes in the soil quality of the XinJiang
|
Index |
Classes |
Scores |
Index |
Classes |
Scores |
|
Soil depth |
0-70 |
2.5 |
Soil moisture |
0.018-0.025 |
2 |
|
70-90 |
5 |
0.025-0.50 |
4 |
||
|
90-105 |
7 |
0.050-0.12 |
6 |
||
|
>105 |
10 |
0.12-0.24 |
8 |
||
|
Soil erosion |
Severe |
1.7 |
>0.24 |
10 |
|
|
Extreme slightly |
3.4 |
Soil organic matter |
<0.3 |
2 |
|
|
Strength |
5.1 |
0.3-0.5 |
4 |
||
|
Moderate |
6.8 |
0.5-0.8 |
6 |
||
|
Mild |
8.5 |
0.8-2 |
8 |
||
|
Slightly |
10 |
>2 |
10 |
2.5 Ecological risk assessment model
Using the above three indicators, xinjiang ecological risk assessment model based on natural factors was constructed as follows:
Therefore, the weighting coefficients of each index were determined to be 0.4, 0.3 and 0.3 respectively.
3.1 LULCC risk index
This paper analyzes the spatial differentiation characteristics of LULCC ecological risk in Xinjiang by using geostatistical methods. The spatial heterogeneity of ecological risk mainly consists of random part and autocorrelation part. When the sampling distance is within the abutment scale, the ecological risk has autocorrelation, while when the sampling distance is greater than the abutment scale, the spatial distribution of ecological risk is random. Geostatistical software GS + was used to achieve the optimal fitting of the theoretical model of sample variogram, as shown in Table 5:
|
Scenario |
Model |
Nugget |
Sill |
Nuggest/Still(%) |
Range/m |
R2 |
RSS |
|---|---|---|---|---|---|---|---|
|
2020 |
Exponential |
0.00019 |
0.0013 |
14.4513 |
153000 |
0.928 |
1.51E-08 |
|
ssp126 |
Exponential |
0.00011 |
0.0006 |
17.1340 |
129000 |
0.879 |
4.51E-09 |
|
SSP245 |
Exponential |
0.00015 |
0.0008 |
19.3122 |
111000 |
0.852 |
5.26E-09 |
|
SSP370 |
Exponential |
0.00020 |
0.0010 |
19.2982 |
750000 |
0.353 |
3.44E-08 |
|
SSP585 |
Exponential |
0.00075 |
0.0045 |
16.6297 |
111000 |
0.752 |
3.76E-09 |
As shown in Table 5, the spherical model had the best fitting effect for the ecological risk of all scenarios, and the Gaussian model in 2020 was more ideal. The block value/base value [C0/(C0 + C)] of SRI for SSP126, SSP245, SSP370, and SSP585 were 17.13%, 19.31%, 19.29%, and 16.63%, respectively, showing a continuous upward trend, indicating that the random variability of SRI was large at a small scale less than 10 km. Nowadays, with the deepening of the impact of human activities on the ecological environment, the degree of random variation of the ecological risk index on a small scale is increasing.
Spatial autocorrelation is by determining whether a variable on the space correlation degree and its related to quantitatively describe the variable on the space of interdependent relationship. This paper takes LULCC quality risk as a spatial variable to explore and analyze the spatial differentiation characteristics of ecological risk in the study area, so as to better understand and analyze LULCC quality risk and its variation trend. This paper uses Geoda095i software. Moran's I scatter plot was obtained based on the LULCC quality risk values of the 2100 SSPs scenario (Fig. 1).
The calculation results of spatial global Moran's I value of LULCC quality risk in each scenario are shown in Fig. 1. The Moran's I-numbers for SSP126, SSP245, SSP370, and SSP585 were 0.5636, 0.4570, 0.2933, and 0.5436, respectively, all around 0.5. Under the scenarios of SSP126, SSP245, and SSP585, the Moran's I index indicates that there exists the phenomenon of convergent clusters in the spatial distribution of LULCC quality risk in the study area, with a strong positive correlation. However, under the scenario of SSP370, the phenomenon of convergent clusters is relatively low.
According to the analysis in Fig. 2, LULCC risk levels in Xinjiang are mainly distributed in areas with medium quality risk levels under different scenarios in 2100, accounting for about 70% of the total area. This is mainly because Xinjiang is mainly arid/semi-arid and the land is barren, and there is the largest Taklimakan desert in the central part of Xinjiang, so most of the region is desert and other areas. Under the SSP126 scenario, the LULCC ecological quality was good. The area of low quality risk area was the largest, about 9.39%, and the area of medium risk area was the largest, while the area of high risk area accounted for about 0.65% of the total area. The low-quality risk areas are mainly distributed in the Ili River Valley in northern Xinjiang and Keketohai in the northern Junggar Basin in Altay region. These areas are covered with more vegetation, mainly woodland, shrub and grassland. According to the LULCC ecological quality of the SSP245 scenario, the area of the lower quality risk area was larger than that of the SSP126 scenario, the area of the high quality area was larger than that of the SSP126 scenario, accounting for about 4.81% of the total area, and the area of the medium quality area was the largest, accounting for about 77.62% of the total area of the whole Xinjiang. In higher, higher quality risk areas are mainly residential settlements on the northern slope of Tianshan Mountains and the southern slope of Tianshan Mountains between the northern slope of Tianshan Mountains and the Gulbentonggut desert region in Xinjiang. The low-quality risk areas and the lower-quality risk areas are consistent with the SSP126 and are still distributed in the northern region of Xinjiang with more natural vegetation. SSP370, SSP585 land quality ecological risk space of the low ecological area distribution area is small, while the higher, high quality ecological risk area distribution area is larger. The medium quality risk area is the largest in the SSP370 scenario, approximately 62.55%. Relative to SSP126 and SSP245, the areas of high-quality risk area and high-quality ecological risk area were larger, accounting for 25.49% of the total area, and were mainly distributed in residential settlements and urban areas. The area of low and lower quality ecological wind area accounts for 11.97% of the total area of Xinjiang, and it is widely distributed. The area of the medium quality risk area in the SS585 scenario is approximately 68.80%. The area distribution of low-quality risk area is relatively small, accounting for about 3.70% of the total area, while that of high-quality risk area is widely distributed, accounting for 11.15% of the total area.
According to the analysis in Fig. 3, LULCC ecological risk distribution characteristics under different SSPs scenarios in Xinjiang in 2100. In xinjiang in 2100 different scenarios, the high quality ecological risk area are mainly distributed in the mountain, the gobi and desert area. The climate of Xinjiang has strong sunshine, little rain, dry, large temperature difference between day and night, and more wind sand and so on. The vegetation on the ground is relatively sparse, and there is always wind-blown sand on the Gobi desert, so evaporation is obviously greater than precipitation. Under the SSP126 scenario, the low-quality ecological risk areas were widely distributed, accounting for about 25.78% of the total area of Xinjiang, and distributed in Altay and Tacheng regions in northern Xinjiang and the area near Kunlun Mountains in southern Xinjiang. High quality ecological zones were mainly distributed in urban areas such as oases and residential settlements in Xinjiang, accounting for 26.068% of the total area. Under SSP245 scenario, the distribution area of low-quality ecological risk areas was smaller than that under SSP126 scenario, accounting for about 23.941% of the total area of Xinjiang. The quality of the ecological risk during mainly distributed in the central and eastern areas of xinjiang, accounts for about 19.70% of the total area. The higher and higher quality ecoregions were widely distributed, mainly in the Taklimakan desert, accounting for 31.649% of the total area. Under the SSP370 scenario, the low-quality ecological risk areas accounted for 22.244% of the total area. The middle quality ecological risk area is widely distributed, accounting for 28.130% of Xinjiang. The higher and high-quality ecological zones were widely distributed, mainly in the Taklimakan Desert region, Yili Valley and other areas with more oas and cities, accounting for 35.649% of the total area. Under the SSP585 scenario, the low-quality ecological risk areas were less distributed, accounting for only 16.124% of the total area of Xinjiang. The ecological risk areas of medium quality are widely distributed, accounting for 36.067% of the whole Xinjiang. The higher and high-quality ecological zone accounted for about 38.057% of the total area.
3.3 Soil Quality Index4 Discussions and conclusions
According to the analysis in Fig. 4, the spatial distribution of soil quality in Xinjiang was generally consistent under different scenarios in 2100. The spatial distribution of soil grade in Xinjiang is dominated by low-quality grade, which is mainly due to the unique geomorphology of Xinjiang (three mountains and two basins). Xinjiang's land types are mainly composed of mountainous regions, deserts and Gobi, and the land is barren. For example, the largest Taklimakan Desert and the Gurbantunggut Desert in China are both located in Xinjiang. The high-quality graded areas of soil are mainly in oases and river valleys in xinjiang and along river basins where people live. Land use/cover types in high-quality soil area are mainly cultivated land, grassland, forest land, shrub, etc., and the vegetation coverage rate in this area is high.
3.4 Future Ecological risk assessment
The analysis in Fig. 5 shows that under different scenarios in 2100, the spatial distribution of ecological risk levels in Xinjiang is mainly dominated by areas with low and medium ecological risk levels, accounting for nearly 70% of the total area. Limited by natural conditions and other factors in these areas, human intervention is rarely performed; high, extremely high ecological risk areas are mainly areas where human production and life focus on. Under the SSP126 and SSP245 scenarios, global warming can be well controlled, and the overall situation of ecological risks in Xinjiang is also ideal. The areas being at medium, low and extremely low ecological risk are large. Under the SSP370 and SSP585 scenarios, the area of medium, high and extremely high ecological risk areas expanded, accounting for almost 50% of the total area, and the area of low ecological risk areas decrease. The reason for this result is that under such scenarios, factors such as temperature, precipitation and soil have a close-knit with the environment of Xinjiang, and then the climate and distribution of land types in Xinjiang are partly affected as said factors change. A typical example is the SSP585 scenario, under which the area of medium ecological risk areas is widely distributed, accounting for about 33.513% of the total area of Xinjiang; the area of high and extremely high ecological risk areas is about 32.412%, which is somewhat larger than before. The expanded high and extremely high ecological risk areas mainly lie in the inland river basins in the Bayingoleng Mongolian Autonomous Prefecture, Kashgar, Hotan and other areas in southern Xinjiang. To find out its cause, the climate in southern Xinjiang has changed under the SSP585 scenario. The frequency of human activities in this region has increased, and because of the fragile ecological environment in Xinjiang, the influence of human activities on the environment has been amplified.
|
Transition type |
2020-SSP126 |
2020-SSP245 |
2020-SSP370 |
2020-SSP585 |
||||
|---|---|---|---|---|---|---|---|---|
|
Transition area(hm2) |
Average transition velocity (hm2·a− 1) |
Transitionarea(hm2) |
Average transition velocity (hm2·a− 1) |
Transition area(hm2) |
Average transition velocity (hm2·a− 1) |
Transition area(hm2) |
Average transition velocity (hm2·a− 1) |
|
|
12 |
74747.63 |
934.35 |
2.15 |
0.03 |
32576.29 |
407.20 |
90510.47 |
1131.38 |
|
13 |
32785.71 |
409.82 |
165782.66 |
2072.28 |
70082.39 |
876.03 |
84791.51 |
1059.89 |
|
14 |
36970.67 |
462.13 |
78797.28 |
984.97 |
44399.09 |
554.99 |
39737.93 |
496.72 |
|
15 |
3746.31 |
46.83 |
33992.56 |
424.91 |
8979.66 |
112.25 |
10877.95 |
135.97 |
|
21 |
56076.41 |
700.96 |
35236.54 |
440.46 |
19844.04 |
248.05 |
6402.07 |
80.03 |
|
23 |
20255.07 |
253.19 |
25492.80 |
318.66 |
42588.71 |
532.36 |
59542.83 |
744.29 |
|
24 |
17319.69 |
216.50 |
43546.03 |
544.33 |
24426.40 |
305.33 |
41938.56 |
524.23 |
|
25 |
4550.72 |
56.88 |
32813.29 |
410.17 |
9125.83 |
114.07 |
18437.71 |
230.47 |
|
31 |
36602.53 |
457.53 |
35903.31 |
448.79 |
15681.83 |
196.02 |
6325.95 |
79.07 |
|
32 |
20071.60 |
250.89 |
8070.14 |
100.88 |
17645.48 |
220.57 |
14044.69 |
175.56 |
|
34 |
11798.32 |
147.48 |
15501.82 |
193.77 |
45811.08 |
572.64 |
20905.28 |
261.32 |
|
35 |
2714.72 |
33.93 |
7365.64 |
92.07 |
9363.67 |
117.05 |
29369.80 |
367.12 |
|
41 |
8788.54 |
109.86 |
29954.69 |
374.43 |
5058.32 |
63.23 |
4892.87 |
61.16 |
|
42 |
7147.29 |
89.34 |
17377.62 |
217.22 |
7606.55 |
95.08 |
6112.54 |
76.41 |
|
43 |
13566.80 |
169.59 |
6696.05 |
83.70 |
5702.67 |
71.28 |
8112.39 |
101.40 |
|
45 |
922.70 |
11.53 |
4169.72 |
52.12 |
6634.66 |
82.93 |
11351.74 |
141.90 |
|
51 |
429.19 |
5.36 |
10254.81 |
128.19 |
93.60 |
1.17 |
863.40 |
10.79 |
|
52 |
852.60 |
10.66 |
8703.64 |
108.80 |
1517.10 |
18.96 |
347.89 |
4.35 |
|
53 |
2265.38 |
28.32 |
366.13 |
4.58 |
987.94 |
12.35 |
1627.28 |
20.34 |
|
54 |
2333.70 |
29.17 |
1422.91 |
17.79 |
2198.68 |
27.48 |
1467.22 |
18.34 |
| 12:Extremely low risk->Low risk; 13:Extremely low risk->Moderate risk; 14:Extremely Low risk->High risk; 15:Extremely low risk->Extremely high risk | ||||||||
| 21:Low risk->Extremely low risk; 23:Low risk->Moderate risk; 24:Low risk->High risk; 25:Low risk->Extremelyhigh risk | ||||||||
| 31:Moderate risk->Extremely low risk; 32:Moderate risk->Low risk; 34:Moderate risk->High risk; 35:Moderate risk->Extremelyhigh risk 35:Moderate risk->Extremely high risk | ||||||||
| 41:High risk->Extremely low risk; 42:High risk->Low risk; 43:High risk->Moderate risk; 45:High risk->Extremely high risk | ||||||||
| 51:Extremely high risk->Extremely low risk; 52:Extremely high risk->Low risk 53 Extremely high risk->Moderate risk 54:Extremely high risk->High risk | ||||||||
Table 6 shows an upward trend of ecological risk levels from 2020 to 2100 under the 4 different scenarios, which is SSP126, SSP245, SSP370, and SSP585 in order. In the SSP126 scenario in 2100, the area with an increased ecological risk level was 7.4×105 hm2, while the area with a decreased risk level was 1.481×105 hm2, accounting for 8.89% of the total area; the rate of changing from low ecological risk to extremely low risk is the highest, which is 9.34×102 hm2·a− 1, while the rate of changing from high ecological level to low ecological level is low, which is approximately 5.36 hm2·a−1. In 2020-SSP245 (2100) scenario, the increase in the level of risk is greater than that in the SSP126 scenario, and the increased area is larger than the area where the risk level decreases, about 4.074×105 hm2. This increase in area is about 2.593×105 hm2 larger than the area expansion in the SSP126 scenario. The area changing from low ecological risk to medium ecological risk level is the largest and the rate is with no doubt, the highest, about 2.072×102 hm2·a− 1, while the rate of changing from high ecological risk level to medium ecological risk level is the lowest, about 4.58 hm2·a− 1. In the SSP245 scenario, the main visible situation is that the ecological risk changes from low to medium, which mainly relates to human production and economic activities in the future scenario SSP245. These activities cause global warming, increase precipitation and in the end, affect Xinjiang's climate conditions, resulting in changes in Xinjiang's climate, soil moisture, and spatial distribution of LULCC quality. These changes are reflected in the data as the rate change in risk levels that shows an accelerating trend, which is consistent with the results of this study. In 2020-SSP370 (2100) scenario, the area where the ecological risk level increases is 3.120×105 hm2, and the area where the risk level decreases is 7.633×104 hm2. Although the area of increased ecological risk is smaller than that of the SSP245 scenario, the area changing from the medium ecological risk level to the relatively high and high ecological risk is larger, about 5.517×105 hm2. In the SSP585 scenario, the area changing from low ecological risk level to high ecological risk level (4.074×105 hm2) is much larger than the area changing in ecological risk level reversely (5.02×104 hm2). This is mainly because humans frequently reclaim croplands and grow crops in the piedmont area (oases) and surrounding available land in Xinjiang, resulting in changes in local land types and corresponding ecological risk indices. In the future SSP370 and SSP585 scenarios, human production and life are inseparable from the ecological environment and resources, but the intervention of the ecological environment and the demand for resources will also have a counteracting effect on the ecological environment, which will lead to an increasing degree of disturbance to the landscape pattern. With this increase, the mutual transition between various ecological quality grades becomes more and more sensitive, and eventually leads to the accelerated mutual transition rate of ecological risk grades. If the coordinated development between human production activities and ecological resources and environment can be achieved, the ecosystem will be in a virtuous circle.
Ecological risk assessment is an extremely complex process. It is not only necessary to take many uncertain factors into comprehensive consideration, but also to collect a large number of social data for reference. These factors together determine the final integrated evaluation result. This paper constructed an ecological risk model based on data of Land use/cover Quality Index (LQI), Climate Quality Index (CQI), and Soil Quality Index (SQI) to evaluate the ecological risk situation in 2020 and 2100, and to quantitatively express the ecological environment problems in Xinjiang from an ecological perspective. It has important guiding significance for understanding the spatial characteristics of Xinjiang's regional ecological environment and reasonably carrying out land use management. However, in terms of the completeness of ecological risk assessment of Xinjiang, this paper failed to adopt all related factors into consideration due to various reasons, so the assessment method and process still need further improvement.
This paper simulates and predicts the ecological risk situation in Xinjiang from 2020 to 2100. The results show that: (1) In 2100, after reviewing different scenarios in Xinjiang, it can be seen that the LULCC risk level is dominated by the medium-quality risk level. The areas with medium-quality risk level account for about 70% of the total area of Xinjiang. The low-quality risk areas are mainly distributed in the Ili Valley in northern Xinjiang, the Keketuohai in the Altay region, and the northern Junggar Basin. Relatively high- and high-quality risk areas are mainly concentrated in residential areas on the northern and southern slopes of the Tianshan Mountains in Xinjiang. (2) In 2100, the ecological risks of climate quality in Xinjiang under different SSPs (SSP126, SSP245, SSP370, SSP585) scenarios show a gradually increasing trend. The high-quality climatic and ecological risk areas of different SSPs in Xinjiang are mainly distributed in mountains, Gobi, desert, and other regions. The medium-quality climatic and ecological risk areas are mainly distributed in the central and eastern regions of Xinjiang. The relatively high- and high-quality ecological areas are widely distributed, which are mainly in the Taklimakan Desert. (3) Under different scenarios in 2100, the spatial distribution of soil quality in Xinjiang is generally consistent and mainly in a low-quality level, this is all owing to the unique landform of Xinjiang (three mountains surrounding two basins). The land types in Xinjiang are mainly composed of mountains, deserts and Gobi, and most of these land types are barren. (4) Under different scenarios in 2100, the spatial distribution of ecological risk levels in Xinjiang is mainly dominated by areas with low and medium ecological risk levels, accounting for about 70% of the total area. Limited by natural conditions and other factors, human intervention is rarely performed in these areas. (5) There is an upward trend of ecological risk levels from 2020 to 2100 under the 4 different scenarios, which is SSP126, SSP245, SSP370, and SSP585 in order. In the SSP126 scenario in 2100, the area with an increased ecological risk level was 7.4×105 hm2, while the area with a decreased risk level was 1.481×105 hm2, accounting for 8.89% of the total area; the rate of changing from low ecological risk to relatively low risk is the highest, which is 9.34×102 hm2·a− 1. In 2020-SSP245 (2100) scenario, the increase in the level of risk is greater than that in the SSP126 scenario. In the 2020-SSP245 (2100) scenario, the area changing from low ecological risk to medium ecological risk level is the largest and the rate is with no doubt, the highest, about 2.072×102 hm2·a− 1. In the 2020-SSP370 (2100) scenario, the area changing from the medium ecological risk level to the relatively high and high ecological risk is larger, about 5.517×105 hm2. In the SSP585 scenario, the area changing from low ecological risk level to high ecological risk level (4.074×105 hm2) is much larger than the area changing in ecological risk level reversely (5.02×104 hm2).
To draw a conclusion, the ecological quality of Xinjiang is undergoing a tendency of further degradation. In arid areas with extremely unstable ecosystems, more attention should be paid to the relationship between humans and ecosystems. Human beings should plan and utilize land resources reasonably and pay more attention to protecting the ecological environment while developing the economy, so as to achieve sustainable social and economic development.
Acknowledgments
We are grateful for the financial support provided by the Natural Science Foundation of Xinjiang Province, China (2016D01C029); Xinjiang Local Outstanding Young Talent Cultivation Project of National Natural Science Foundation of China (U1503302), Tianshan talent project of Xinjiang Uygur Autonomous region (400070010209); The Local People's Government of the Xinjiang Uygur Autonomous Region in China sent abroad to study abroad as a complete set of projects (L06).
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Table 2 is available in the Supplementary Files section
No competing interests reported.