Quantitative Assessment of the Ecological Vulnerability of Baiyangdian Wetlands in the North China Plain

doi:10.5814/j.issn.1674-764x.2021.06.009

Abstract

Abstract:

Quantitative assessment of vulnerability is a core aspect of wetland vulnerability research. Taking Baiyangdian (BYD) wetlands in the North China Plain as a study area and using the ‘cause-result’ model, 23 representative indicators from natural, social, sci-tech and economic elements were selected to construct an indicator system. A weight matrix was obtained by using the entropy weight method to calculate the weight value for each indicator. Based on the membership function in the fuzzy evaluation model, the membership degrees were determined to form a fuzzy relation matrix. Finally, the ecological vulnerability was quantitatively assessed based on the comprehensive evaluation index calculated by using a composite operator to combine the entropy weight matrix with the fuzzy relation matrix. The results showed that the ecological vulnerability levels of the BYD wetlands were comprehensively evaluated as Grade II, Grade Ⅲ, Grade IV, and Grade Ⅲ in 2010, 2011-2013, 2014, and 2015-2017, respectively. The ecological vulnerability of the BYD wetlands increased from low fragility in 2010 to general fragility in 2011-2013, and to high fragility in 2014, reflecting the fact that the wetland ecological condition was degenerating from 2010 to 2014. The ecological vulnerability status then turned back into general fragility during 2015-2017, indicating that the ecological situation of the BYD wetlands was starting to improve. However, the ecological status of the BYD wetlands on the whole is relatively less optimistic. The major factors affecting the ecological vulnerability of the BYD wetlands were found to be industrial smoke and dust emission, wetland water area, ammonia nitrogen, total phosphorus, rate of industrial solid wastes disposed, GDP per capita, etc. This illustrates that it is a systematic project to regulate wetland vulnerability and to protect regional ecological security, which may offer researchers and policy-makers specific clues for concrete interventions.

Key words: ecological vulnerability, fuzzy evaluation, entropy weight, Baiyangdian wetlands, the North China Plain

TIAN Jinghan, GUO Chenchen, WANG Jianhua. Quantitative Assessment of the Ecological Vulnerability of Baiyangdian Wetlands in the North China Plain[J]. Journal of Resources and Ecology, 2021, 12(6): 814-821.

Figures/Tables 5

References 24

[1]	Beroya-Eitner M A. 2016. Ecological vulnerability indicators. Ecological Indicators, 60: 329-334. DOI URL
[2]	de Lange H J, Sala S, Vighi M, et al. 2010. Ecological vulnerability in risk assessment—A review and perspectives. Science of the Total Environment, 408(18): 3871-3879. DOI URL
[3]	Ge Q S, Yang L S, Jin F J, et al. 2017. Carrying capacity of resource and environment of Xiongan New Area: Evaluation, regulation, and promotion. Bulletin of Chinese Academy of Sciences, 32(11): 1206-1215. (in Chinese)
[4]	Jiang L G, Lv P Y, Feng Z M, et al. 2018. Where should the start zone be located for Xiongan New Area? A land use perspective. Journal of Resources and Ecology, 9(4): 374-381. DOI URL
[5]	Jin Y, Meng J J. 2011. Assessment and forecast of ecological vulnerability: A review. Chinese Journal of Ecology, 30(11): 2646-2652. (in Chinese)
[6]	Li B Y, Chen H B, Tang H P. 2010. Assessing ecological fragility with AHP and fuzzy integrated evaluation in autonomous prefectures of northern Xinjiang. Journal of Beijing Normal University (Natural Science), 46(2): 197-201. (in Chinese)
[7]	Liu D, Cao C X, Dubovyk O, et al. 2017. Using fuzzy analytic hierarchy process for spatio-temporal analysis of eco-environmental vulnerability change during 1990-2010 in Sanjiangyuan region, China. Ecological Indicators, 73: 612-625. DOI URL
[8]	Liu H H, Wang N, Xie J C, et al. 2014. Assessment of ecological vulnerability based on fuzzy comprehensive evaluation in Weihe River Basin. Journal of Shenyang Agricultural University, 45(1): 73-77. (in Chinese)
[9]	Liu J G, Zhao D D, Ye B. 2019. Ecological attributes, restoration, and protection of the Baiyangdian in Xiong’an New Area. Acta Ecologica Sinica, 39(9): 3019-3025. (in Chinese)
[10]	Malekmohammadi B, Jahanishakib F. 2017. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state- impact-response (DPSIR) model. Ecological Indicators, 82: 293-303. DOI URL
[11]	Shang E P, Bai W Q. 2012. A review on the studies of wetland vulnerability assessment. Wetland Science, 10(3): 378-384. (in Chinese)
[12]	Tian Y P, Chang H. 2012. Bibliometric analysis of research progress on ecological vulnerability in China. Acta Geographica Sinica, 67(11): 1515-1525. (in Chinese)
[13]	Wang G, Shi Y H. 2019. Bibliometric analysis of international ecological vulnerability research situation. Environmental Impact Assessment, 41(2): 80-84. (in Chinese)
[14]	Wang J H, Lu X G, Jiang M, et al. 2009. Fuzzy synthetic evaluation of wetland soil quality degradation: A case study on the Sanjiang Plain, Northeast China. Pedosphere, 19(6): 756-764. DOI URL
[15]	Wang J H, Lu X G, Tian J H, et al. 2008. Fuzzy synthetic evaluation of water quality of Naoli River using parameter correlation analysis. Chinese Geographical Science, 18(4): 361-368. DOI URL
[16]	Wang Y J, Zhong L F. 2020. Research framework for ecosystem vulnerability: Measurement, prediction, and risk assessment. Journal of Resources and Ecology, 11(5): 499-507. DOI URL
[17]	Xia J, Zhang Y Y. 2017. Water resource and pollution safeguard for Xiong’an new area construction and its sustainable development. Bulletin of Chinese Academy of Sciences, 32(11): 1199-1205. (in Chinese)
[18]	Yi Y J, Yang Y F, Zhang S H, et al. 2019. Habitat simulation of benthic macroinvertebrates in a shallow lake. Water Resources and Hydropower Engineering, 50(5): 90-96. (in Chinese)
[19]	Yu K J. 2018. Three comprehensive and innovative strategies to solve the water problems in Xiongan New District. Landscape Architecture Frontiers, 6(4): 4-12. (in Chinese)
[20]	Yuan M R, Zhuge Y P, Liu R. 2011. Fuzzy evaluation of Tai’an ecological vulnerability based on AHP. Environmental Science & Technology, 34(2): 173-177. (in Chinese) DOI URL
[21]	Zhang S Z, Ma J, Li G B. 2007. The ecological problems and sustainable development countermeasures of the Baiyangdian Wetland. South-to- North Water Transfers and Water Science & Technology, (4): 53-56. (in Chinese)
[22]	Zhao J C, Ji G X, Tian Y, et al. 2018. Environmental vulnerability assessment for mainland China based on entropy method. Ecological Indicators, 91: 410-422. DOI URL
[23]	Zhuang C W, Ouyang Z Y, Xu W H, et al. 2011. Impacts of human activities on the hydrology of Baiyangdian Lake, China. Environmental Earth Sciences, 62(7): 1343-1350. DOI URL
[24]	Zou Z H, Yun Y, Sun J N. 2006. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences, 18(5): 1020-1023. DOI URL

Objective	Item	Element	Indicator	Number (*)
Ecological vulnerability assessment for the BYD wetlands	Cause	Natural	Annual average temperature	c₁ (+)
			Total precipitation in summer	c₂ (+)
			Wetland water area	c₃ (+)
			Average water level	c₄ (+)
			Shallow groundwater depth	c₅ (-)
			Overall assessment of water quality	c₆ (-)
		Social	Permanganate index	c₇ (-)
			Chemical oxygen demand	c₈ (-)
			Ammonia nitrogen	c₉ (-)
			Total phosphorus	c₁₀ (-)
			Mean composite pollution index	c₁₁ (-)
			Inland water breeding area	c₁₂ (-)
			Agricultural chemical fertilizer consumption	c₁₃ (-)
			Water consumption of industrial enterprises above a designated size	c₁₄ (-)
			Energy consumption of industrial enterprises above a designated size	c₁₅ (-)
			Total energy consumption	c₁₆ (-)
			Sulfur dioxide emission	c₁₇ (-)
			Industrial smoke and dust emission	c₁₈ (-)
			Rate of industrial solid wastes disposed	c₁₉ (+)
	Result	Sci-tech	Rate of industrial solid wastes comprehensively utilized	c₂₀ (+)
			Energy consumption per unit industrial value added	c₂₁ (-)
			Energy consumption per unit GDP	c₂₂ (-)
		Economic	GDP per capita	c₂₃ (+)

Objective	Item	Element	Indicator	Number (*)
Ecological vulnerability assessment for the BYD wetlands	Cause	Natural	Annual average temperature	c₁ (+)
			Total precipitation in summer	c₂ (+)
			Wetland water area	c₃ (+)
			Average water level	c₄ (+)
			Shallow groundwater depth	c₅ (-)
			Overall assessment of water quality	c₆ (-)
		Social	Permanganate index	c₇ (-)
			Chemical oxygen demand	c₈ (-)
			Ammonia nitrogen	c₉ (-)
			Total phosphorus	c₁₀ (-)
			Mean composite pollution index	c₁₁ (-)
			Inland water breeding area	c₁₂ (-)
			Agricultural chemical fertilizer consumption	c₁₃ (-)
			Water consumption of industrial enterprises above a designated size	c₁₄ (-)
			Energy consumption of industrial enterprises above a designated size	c₁₅ (-)
			Total energy consumption	c₁₆ (-)
			Sulfur dioxide emission	c₁₇ (-)
			Industrial smoke and dust emission	c₁₈ (-)
			Rate of industrial solid wastes disposed	c₁₉ (+)
	Result	Sci-tech	Rate of industrial solid wastes comprehensively utilized	c₂₀ (+)
			Energy consumption per unit industrial value added	c₂₁ (-)
			Energy consumption per unit GDP	c₂₂ (-)
		Economic	GDP per capita	c₂₃ (+)

Indicator	Number	Weight
Annual average temperature	c₁	0.0444
Total precipitation in summer	c₂	0.0432
Wetland water area	c₃	0.0508
Average water level	c₄	0.0441
Shallow groundwater depth	c₅	0.0429
Overall assessment of water quality	c₆	0.0442
Permanganate index	c₇	0.0465
Chemical oxygen demand	c₈	0.0433
Ammonia nitrogen	c₉	0.0489
Total phosphorus	c₁₀	0.0475
Mean composite pollution index	c₁₁	0.0397
Inland water breeding area	c₁₂	0.0429
Agricultural chemical fertilizer consumption	c₁₃	0.0430
Water consumption of industrial enterprises above a designated size	c₁₄	0.0411
Energy consumption of industrial enterprises above a designated size	c₁₅	0.0383
Total energy consumption	c₁₆	0.0440
Sulfur dioxide emission	c₁₇	0.0311
Industrial smoke and dust emission	c₁₈	0.0546
Rate of industrial solid wastes disposed	c₁₉	0.0473
Rate of industrial solid wastes comprehensively utilized	c₂₀	0.0465
Energy consumption per unit industrial value added	c₂₁	0.0331
Energy consumption per unit GDP	c₂₂	0.0354
GDP per capita	c₂₃	0.0471

Indicator	Number	Weight
Annual average temperature	c₁	0.0444
Total precipitation in summer	c₂	0.0432
Wetland water area	c₃	0.0508
Average water level	c₄	0.0441
Shallow groundwater depth	c₅	0.0429
Overall assessment of water quality	c₆	0.0442
Permanganate index	c₇	0.0465
Chemical oxygen demand	c₈	0.0433
Ammonia nitrogen	c₉	0.0489
Total phosphorus	c₁₀	0.0475
Mean composite pollution index	c₁₁	0.0397
Inland water breeding area	c₁₂	0.0429
Agricultural chemical fertilizer consumption	c₁₃	0.0430
Water consumption of industrial enterprises above a designated size	c₁₄	0.0411
Energy consumption of industrial enterprises above a designated size	c₁₅	0.0383
Total energy consumption	c₁₆	0.0440
Sulfur dioxide emission	c₁₇	0.0311
Industrial smoke and dust emission	c₁₈	0.0546
Rate of industrial solid wastes disposed	c₁₉	0.0473
Rate of industrial solid wastes comprehensively utilized	c₂₀	0.0465
Energy consumption per unit industrial value added	c₂₁	0.0331
Energy consumption per unit GDP	c₂₂	0.0354
GDP per capita	c₂₃	0.0471

Year	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
2010	0.11	0.29	0.25	0.18	0.17
2011	0.06	0.26	0.34	0.21	0.13
2012	0.09	0.15	0.42	0.25	0.08
2013	0.18	0.26	0.35	0.15	0.06
2014	0.08	0.22	0.28	0.35	0.07
2015	0.07	0.26	0.36	0.19	0.12
2016	0.14	0.29	0.36	0.13	0.08
2017	0.17	0.24	0.27	0.18	0.13