Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (12): 2114-2127.doi: 10.3864/j.issn.0578-1752.2019.12.009

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Optimal Protected Area Selection: Based on Multiple Attribute Decision Making Method and Ecosystem Service Research ——Illustrated by Guanzhong-Tianshui Economic Region Section of the Weihe River Basin

ZHANG YuMeng1,LI Jing1(),ZENG Li1,YANG XiaoNan2,LIU JingYa3,ZHOU ZiXiang4   

  1. 1 School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119
    2 Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi
    3 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101
    4 College of Geomatics, Xi’an University of Science and Technology, Xi'an 710054
  • Received:2018-12-10 Accepted:2019-04-15 Online:2019-06-16 Published:2019-06-22
  • Contact: Jing LI E-mail:lijing@snnu.edu.cn

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Abstract:

【Objective】 In present study, based on the calculation of biodiversity, carbon sequestration, water production and cultural ecosystem services, multi-attribute decision-making method is applied to select the optimal protected area of the study area. The work can provides a theoretical basis for the maintenance of ecological balance and ecological construction in the study area. 【Method】Our research is illustrated by the Guanzhong-Tianshui Economic Zone (Guantian Section) of Weihe River Basin, biodiversity and carbon sequestration services are calculated by InVEST model, SWAT model is used to measure water yield services and divide sub-basins, cultural services are quantified by SoLVES model. On this basis, 11 scenarios are established by using OWA (Ordered Weighted Averaged) multi-attribute decision making method, and the priority protected areas are screened out. 【Result】 (1) The distribution of water yield in the study area was uneven on the sub-basin scale. On the whole, the water yield in the eastern region is the highest, and that in the central region is smaller than that in the western region, and the average value is about 95 mm. The range of carbon sequestration on the sub-basin scale is between 5.041 t·hm -2 and 10.284 t·hm -2. Sub-basins with higher carbon sequestration per unit area are mostly located in Qinling area, and the sub-basins located in Guanzhong Plain are urban agglomeration area, and the regional carbon sequestration with dense construction land is very limited. Biodiversity is mainly measured by habitat quality index, the range of habitat quality in the study area is between 0.222 and 0.921. The vegetation cover is insufficient in Guanzhong Plain area, frequent human activities and urban development lead to dense distribution of threat factors. Therefore, the habitat quality is relatively low with 0.22 in Guanzhong Plain area. The region of Qinling Mountains, with high vegetation cover, is far from the habitat threat factor, its ecological suitability is relatively high and the biodiversity is maintained well. The total index of cultural value in the study area is between 1.298 and 5.667. The aesthetic and spiritual values are concentrated in forest parks and Qinling Mountains, while urban areas are rich in historical heritage and entertainment facilities , as a consequence, the urban area has the highest value of cultural services. (2) By setting up different risk values and weights to calculate OWA operator, 11 risk scenarios are obtained and their protection efficiency are calculated. Finally, risk scenario 6 is selected as the optimal protected area with highest trade-off value, its protection efficiency is about 1.499. The range of risk scenario 6 is mainly located in the southern Qinling area and the junction part of Tianshui City and Baoji City. 【Conclusion】this study analyzes and evaluates ecosystem services in the study area by combining natural and human factors. The OWA operator is used to balance the trade-offs between different ecosystem services and to select the optimal protected areas, which provides a reference for improving ecosystem services in the study area.

Key words: ecosystem services, optimal protected area, Ordered Weighted Averaged (OWA)

Fig. 1

Basic information map of study area"

Fig. 2

Maxent maximum entropy model analyze process flow"

Fig. 3

The spatial distribution of water yield, carbon sequestration, biodiversity and culture, 2015"

Fig. 4

The calibration and validation period of runoff"

Table 1

The risk and tradeoff under different scenarios"

情景Scenario 风险Risk 权衡Tradeoff w1 w2 w3 w4
1 0 0 0 0 0 1
2 0.1 0.37 0.011 0.043 0.182 0.764
3 0.2 0.57 0.045 0.106 0.252 0.596
4 0.3 0.71 0.098 0.165 0.276 0.461
5 0.4 0.86 0.167 0.213 0.272 0.347
6 0.5 1 0.25 0.25 0.25 0.25
7 0.6 0.86 0.347 0.272 0.213 0.167
8 0.7 0.71 0.461 0.276 0.165 0.098
9 0.8 0.57 0.596 0.252 0.106 0.045
10 0.9 0.37 0.764 0.182 0.043 0.011
11 1 0 1 0 0 0

Fig. 5

The relationship between tradeoff and risk"

Fig. 6

The priority area under different scenarios"

Table 2

Protection efficiency of different scenarios"

情景
Scenario		 保护区域服务值
Ecosystem services value of protected area		 保护效率
Protection efficiency
1 5.502 1.078
2 5.922 1.160
3 6.702 1.313
4 7.275 1.425
5 7.302 1.430
6 7.655 1.499
7 7.543 1.477
8 7.593 1.487
9 7.605 1.489
10 7.415 1.452
11 6.601 1.293

Table 3

The area (km2) of land use type under different scenarios"

情景
Scenario		 耕地
Cultivated land		 林地
Forest land		 草地
Grass land		 水体
Waters		 城镇
Urban areas		 未分类用地
Unclassified land
1 435.699 1976.327 3262.307 4.6035 12.0798 0.765
2 538.2909 4654.398 5696.013 6.4224 15.4278 1.7802
3 453.429 6150.767 3262.725 5.2173 13.1652 1.3986
4 323.1801 7385.671 1909.08 4.5594 9.9621 1.2528
5 1019.526 8192.438 1407.231 17.1477 15.7068 1.2717
6 561.1482 8070.34 668.6595 17.7876 11.16 1.1817
7 972.1476 8354.525 438.0714 29.2968 177.8202 1.2015
8 963.1584 8390.232 288.1458 34.443 177.5358 1.1646
9 889.7238 8446.486 295.6806 40.023 176.9463 1.1727
10 1247.008 8663.555 428.1444 65.7837 260.6175 1.9638
11 3579.965 9382.71 1822.757 215.7966 529.6032 47.9349
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