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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (11): 1604-1632    DOI: 10.1007/s40333-024-0110-3    
Research article     
A novel framework of ecological risk management for urban development in ecologically fragile regions: A case study of Turpan City, China
LI Haocheng1, LI Junfeng1,2,*(), QU Wenying1,2, WANG Wenhuai1,2, Muhammad Arsalan FARID1, CAO Zhiheng1, MA Chengxiao1, FENG Xueting1
1College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
2Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production & Construction Corps, Shihezi 832000, China
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Abstract  

Assessing and managing ecological risks in ecologically fragile areas remain challenging at present. To get to know the ecological risk situation in Turpan City, China, this study constructed an ecological risk evaluation system to obtain the ecological risk level (ERL) and ecological risk index (ERI) based on the multi-objective linear programming-patch generation land use simulation (MOP-PLUS) model, analyzed the changes in land use and ecological risk in Turpan City from 2000 to 2020, and predicted the land use and ecological risk in 2030 under four different scenarios (business as usual (BAU), rapid economic development (RED), ecological protection priority (EPP), and eco-economic equilibrium, (EEB)). The results showed that the conversion of land use from 2000 to 2030 was mainly between unused land and the other land use types. The ERL of unused land was the highest among all the land use types. The ecological risk increased sharply from 2000 to 2010 and then decreased from 2010 to 2020. According to the value of ERI, we divided the ecological risk into seven levels by natural breakpoint method; the higher the level, the higher the ecological risk. For the four scenarios in 2030, under the EPP scenario, the area at VII level was zero, while the area at VII level reached the largest under the RED scenario. Comparing with 2020, the areas at I and II levels increased under the BAU, EPP, and EEB scenarios, while decreased under the RED scenario. The spatial distributions of ecological risk of BAU and EEB scenarios were similar, but the areas at I and II levels were larger and the areas at V and VI levels were smaller under the EEB scenario than under the BAU scenario. Therefore, the EEB scenario was the optimal development route for Turpan City. In addition, the results of spatial autocorrelation showed that the large area of unused land was the main reason affecting the spatial pattern of ecological risk under different scenarios. According to Geodetector, the dominant driving factors of ecological risk were gross domestic product rating (GDPR), soil type, population, temperature, and distance from riverbed (DFRD). The interaction between driving factor pairs amplified their influence on ecological risk. This research would help explore the low ecological risk development path for urban construction in the future.

Key words： multi-scenario      ecological risk assessment      multi-objective linear programming-patch generation land use simulation (MOP-PLUS) model      Geodetector      future construction      land use change     
Received: 22 May 2024      Published: 30 November 2024
Corresponding Authors: *LI Junfeng (E-mail: ljfshz@126.com)
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LI Haocheng
LI Junfeng
QU Wenying
WANG Wenhuai
Muhammad Arsalan FARID
CAO Zhiheng
MA Chengxiao
FENG Xueting
Cite this article:

LI Haocheng, LI Junfeng, QU Wenying, WANG Wenhuai, Muhammad Arsalan FARID, CAO Zhiheng, MA Chengxiao, FENG Xueting. A novel framework of ecological risk management for urban development in ecologically fragile regions: A case study of Turpan City, China. Journal of Arid Land, 2024, 16(11): 1604-1632.

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http://jal.xjegi.com/10.1007/s40333-024-0110-3     OR     http://jal.xjegi.com/Y2024/V16/I11/1604

Fig. 1 Terrain and elevation of Turpan City
Data type Indicator Period Resolution (m) Data source
Land use Land use type 2000-2020 30 https://www.resdc.cn
Topographic factor Digital elevation model (DEM) 2000-2020 30 https://www.gscloud.cn
Slope 2000-2020 30 https://www.gscloud.cn
Soil type 2000-2020 30 https://www.resdc.cn
Climatic factor Temperature 2000-2020 30 https://www.resdc.cn
Precipitation 2000-2020 30 https://www.resdc.cn
Socio-economic factor Population 2000-2020 30 https://www.resdc.cn
Gross domestic product rating (GDPR) 2000-2020 30 https://www.resdc.cn
Distance from government (DFG) 2000-2020 30 https://www.openstreetmap.org
Distance from primary road (DFPR) 2000-2020 30 https://www.openstreetmap.org
Distance from secondary road (DFSR) 2000-2020 30 https://www.openstreetmap.org
Distance from tertiary road (DFTR) 2000-2020 30 https://www.openstreetmap.org
Distance from riverbed (DFRD) 2000-2020 30 https://www.openstreetmap.org
Distance from reservoir (DFR) 2000-2020 30 https://www.xjsedata.cn
Distance from driven well (DFDW) 2000-2020 30 https://www.xjsedata.cn
Table 1 Indicators and their data sources
Evaluation category Indicator Hierarchical analysis weight Entropy method weight Combined weight
Urban expansion pressure UEI 0.3688 0.0939 0.2841
LUCI 0.1288 0.2048 0.2066
Production pressure PCL 0.0362 0.0759 0.0226
PEL 0.1085 0.1612 0.1437
Landscape ecological risk SHDI 0.0907 0.2170 0.1617
LDI 0.1814 0.0953 0.1421
Ecological degradation pressure EC 0.0543 0.0537 0.0240
ESV 0.0232 0.0503 0.0096
PEC 0.0147 0.0478 0.0058
Table 2 Weight of each indicator used for ecological risk assessment
Fig. 2 Land use transition in Turpan City from 2000 to 2020. (a), spatial distribution of land use transition from 2000 to 2010; (b), spatial distribution of land use transition from 2010 to 2020; (c), diagram of land use transition from 2000 to 2020. In Figure 2c, the width of colored block represents the area of the corresponding land use type; the curve represents the transition between two land use types; the width of curve represents the transition area between two land use types, and the wider the curve, the larger the transition area.
Fig. 3 Proportion of the contribution of each driving factor to land use transition from 2000 to 2020. (a), cultivated land; (b), forest land; (c), grassland; (d), water body; (e), construction land; (f), unused land. DEM, digital elevation model; GDPR, gross domestic product rating; DFG, distance from government; DFRD, distance from riverbed; DFR, distance from reservoir; DFDW, distance from driven well; DFPR, distance from primary road; DFSR, distance from secondary road; DFTR, distance from tertiary road.
Fig. 4 Spatial distribution of land use types under different scenarios in 2030. (a), business as usual (BAU) scenario; (b), rapid economic development (RED) scenario; (c), ecological protection priority (EPP) scenario; (d), eco-economic equilibrium (EEB) scenario.
Land use type BAU scenario RED scenario EPP scenario EEB scenario
Area
(km2)		 Percentage (%) Area
(km2)		 Percentage (%) Area (km2) Percentage (%) Area (km2) Percentage (%)
Cultivated land 1266.12 -2.89 1303.75 0.00 1309.07 0.41 1468.54 12.64
Forest land 76.42 -5.49 76.42 -5.49 271.32 235.54 82.98 2.62
Grassland 10,559.59 0.06 10,553.03 0.00 10,553.03 0.00 10,553.03 0.00
Water body 66.43 10.39 60.18 0.00 93.99 56.18 73.43 22.02
Construction land 530.74 30.79 639.84 57.67 405.81 0.00 455.25 12.18
Unused land 56,698.28 -0.17 56,564.37 -0.40 56,564.37 -0.40 56,564.37 -0.40
Table 3 Area and change percentage of each land use type under different scenarios in 2030
Land use type ERL
2000 2010 2020 2030
BAU scenario RED scenario EPP scenario EEB scenario
Cultivated land 0.1443 0.1822 0.1802 0.1749 0.1852 0.1704 0.1779
Forest land 0.1147 0.1441 0.1423 0.1373 0.1471 0.1356 0.1380
Grassland 0.1982 0.2428 0.2421 0.2378 0.2472 0.2324 0.2364
Water body 0.1107 0.1432 0.1418 0.1369 0.1466 0.1323 0.1376
Construction land 0.1160 0.1505 0.1543 0.1538 0.1669 0.1443 0.1507
Unused land 0.3208 0.3524 0.3509 0.3461 0.3558 0.3412 0.3467
Table 4 Value of ecological risk level (ERL) of each land use type in Turpan City in 2000, 2010, 2020, and 2030 under four scenarios
Level of ERI Proportion of area (%)
2000 2010 2020 2030
BAU scenario RED scenario EPP scenario EEB scenario
I 8.46 1.28 1.50 1.73 1.39 1.95 1.78
II 4.62 8.85 8.75 9.14 8.13 9.69 9.47
III 3.56 4.62 4.79 5.02 4.62 4.96 4.73
IV 4.68 3.79 4.12 4.63 4.29 5.29 4.73
V 78.67 5.01 5.01 5.18 5.52 6.02 5.07
VI 0.00 5.01 5.52 10.14 4.46 72.09 8.74
VII 0.00 71.44 70.31 64.16 71.59 0.00 65.48
Table 5 Proportion of area at each level of ecological risk index (ERI) in Turpan City in 2000, 2010, 2020, and 2030 under four scenarios
Fig. 5 Spatial distribution of ecological risk in Turpan City. (a), 2000; (b), 2010; (c), 2020; (d), BAU scenario in 2030; (e), RED scenario in 2030; (f), EPP scenario in 2030; (g), EEB scenario in 2030. ERI, ecological risk index.
Level of ERI Proportion of area (%)
2000 2010 2020 2030
BAU scenario RED scenario EPP scenario EEB scenario
I 15.14 12.43 13.51 15.76 13.04 17.93 16.22
II 15.68 15.14 14.59 15.22 13.59 14.67 16.22
III 14.05 11.35 12.43 11.96 13.04 13.04 11.35
IV 12.43 6.49 7.57 8.15 8.70 10.87 9.73
V 42.70 13.51 12.43 14.13 14.67 11.96 11.89
VI 0.00 11.35 13.51 18.48 5.98 31.52 17.84
VII 0.00 29.73 25.95 16.30 30.98 0.00 16.76
Table 6 Proportion of area at each level of ERI in the goal region of Turpan City in 2000, 2010, 2020, and 2030 under four scenarios
Fig. 6 Spatial distribution of ecological risk in the goal region of Turpan City. (a), the location of the goal region of Turpan City; (b), 2000; (b), 2010; (c), 2020; (d), BAU scenario in 2030; (e), RED scenario in 2030; (f), EPP scenario in 2030; (g), EEB scenario in 2030.
Fig. 7 Moran's I index scatterplot of ERI in Turpan City. (a), 2000; (b), 2010; (c), 2020; (d), BAU scenario in 2030; (e), RED scenario in 2030; (f), EPP scenario in 2030; (g), EEB scenario in 2030.
Fig. 8 Spatial distribution of ecological risk cluster in Turpan City. (a), 2000; (b), 2010; (c), 2020; (d), BAU scenario in 2030; (e), RED scenario in 2030; (f), EPP scenario in 2030; (g), EEB scenario in 2030.
Driving factor 2010 2020
Goal region Whole region Goal region Whole region
q P q P q P q P
GDPR 0.543* 0.000 0.416* 0.000 0.505* 0.000 0.706* 0.000
Population 0.251* 0.000 0.426* 0.000 0.482* 0.000 0.643* 0.000
Precipitation 0.334* 0.000 0.388* 0.000 0.366* 0.000 0.380* 0.000
DFR 0.007 0.956 0.224* 0.000 0.006 0.964 0.273* 0.000
Temperature 0.322* 0.000 0.472* 0.000 0.269* 0.000 0.448* 0.000
DFSR 0.012 0.701 0.151* 0.000 0.014 0.652 0.154* 0.000
DFRD 0.020 0.324 0.218* 0.000 0.024 0.264 0.226* 0.000
Slope 0.018 0.924 0.010 0.024 0.017 0.941 0.011 0.011
DFTR 0.449* 0.000 0.047* 0.000 0.444* 0.000 0.049* 0.000
Soil type 0.388* 0.000 0.536* 0.000 0.385* 0.000 0.535* 0.000
DFPR 0.011 0.620 0.124* 0.000 0.001 0.740 0.128* 0.000
DFG 0.011 0.620 0.124* 0.000 0.014 0.548 0.131* 0.000
DFDW 0.133* 0.000 0.051* 0.000 0.145* 0.000 0.114* 0.000
Table 7 Explanatory power (q) and significance (P) of each driving factor of ecological risk for the goal region and the whole region of Turpan City in 2010 and 2020
Fig. 9 Explanatory power of each driving factor of ecological risk for the goal region and the whole region of Turpan City in 2010 and 2020
Fig. 10 Interactions between driving factor pairs of ecological risk in Turpan City in 2010 and 2020. (a), goal region in 2010; (b), whole region in 2010; (c), goal region in 2020; (d), whole region in 2020.
Fig. 11 Interaction patterns between driving factor pairs of ecological risk in Turpan City in 2010 and 2020. (a), goal region in 2010; (b), whole region in 2010; (c), goal region in 2020; (d), whole region in 2020.
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