Ecological vulnerability assessment based on multi-sources data and SD model in Yinma River Basin, China

Feng, Zhang; Liu, Xingpeng; Zhang, Jiquan; Wu, Rina; Ma, Qiyun; Chen, Yanan

doi:10.1016/j.ecolmodel.2017.01.016

Cited by 68 publications

(33 citation statements)

References 29 publications

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“…The hazard focused on disaster-inducing factor, reflecting the threat of geographical and geological conditions and human activities. Vulnerability, a function of interactions among susceptibility factors to exposure, sensitivity to the stressor, and adaptive capacity, was established as a link between risk stressor and risk receptor, and characterized by the landscape pattern [36][37][38]. From the perspective of protection and management, heritage values can be viewed as the risk receptor, and meanwhile, these values can be used as a tool for weighting the potential damage in NWHS protection [16,35].…”

Section: Ecological Risk Assessment Modelmentioning

confidence: 99%

Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan

et al. 2019

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Ecological risk assessment plays an important role in avoiding disasters and reducing losses. Natural world heritage site is the most precious natural assets on earth, yet few studies have assessed ecological risks from the perspective of world heritage conservation and management. A methodology for considering ecological threats and vulnerabilities and focusing on heritage value was introduced and discussed for the Bogda component of the Xinjiang Tianshan Natural World Heritage Site. Three important results are presented. (1) Criteria layers and ecological risk showed obvious spatial heterogeneity. Extremely high-risk and high-risk areas, accounting for 13.60% and 32.56%, respectively, were mainly gathered at Tianchi Lake and Bogda Glacier, whereas the extremely low-risk and low-risk areas, covering 1.33% and 17.51% of the site,were mainly distributed to the north and scattered around in the southwest montane region. (2) The level of risk was positively correlated with the type of risk, and as the level of risk increases, the types of risk increase. Only two risk types were observed in the extremely low-risk areas, whereas six risk types were observed in the high-risk areas and eight risk types were observed in the extremely high-risk areas. (3) From the perspective of risk probability and ecological damage, four risk management categories were proposed, and correlative strategies were proposed to reduce the possibility of ecological risk and to sustain or enhance heritage value.

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Section: Ecological Risk Assessment Modelmentioning

confidence: 99%

Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan

et al. 2019

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“…The main reservoirs include Shitou Koumen, Xinlicheng, and Xingxingshao. The total water resource is 2.472 billion m 3 , and the per capita water resource of 378 m 3 accounts for 18% of the total national value for China [15].…”

Section: Study Sites and Water Samplingmentioning

confidence: 99%

Evaluating CDOM Sources Using Excitation-Emission Matrix Fluorescence and Parallel Factor Analysis, and their Links to Water Quality in Highly Polluted Rivers in China

Ji¹,

Zhang²,

Li³

et al. 2019

Pol. J. Environ. Stud.

Self Cite

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Chromophoric dissolved organic matter (CDOM) is a valuable optically active substance. This study examined the seasonal characteristics of fluorescent CDOM components in the Yinma and Songhua rivers, highly polluted watershed tributaries of the Songhua River, and a drinking water source for the city of Changchun, China. Parallel factor analysis (PARAFAC) and excitation-emission matrix (EEM) spectra were used to assess CDOM fluorescence. Four components attributed to CDOM in 51 water samples were identified: three were humic-like (C1, C3, and C4) and one was protein-like (C2). The average fluorescence intensities of the four components showed seasonal variation from May to November 2016. A positive linear correlation was found between components C3 and C4 (R = 0.86, p<0.001). The results from this investigation demonstrated that the frequency of rainfall and human activities may be the main factors influencing the quantity and quality of CDOM in samples collected from the watershed, and that the Yinma and Yitong rivers were polluted by terrestrial and agricultural pollution. Furthermore, the utility of combining EEM fluorescence and PARAFAC to study CDOM dynamics for different seasons and to quantify CDOM components for similar environmental (water quality) conditions was established.

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“…[14]. When external stresses from nature and humans exceed the carrying capacity of an ecosystem, the system loses its stable state and begins to degenerate, and then, to endanger the development of ecosystems and socio-economic sustainability [15]. The concept of ecological vulnerability is closely related to the concerns and operations of sustainability and environmental justice [16].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have proposed a variety of frameworks to estimate ecological vulnerability, such as the island ecological vulnerability index (IEVI) [20], social-ecological vulnerability (SEV) [21], revised vulnerability (RV) [15], vulnerability of landscapes (VL) [22], and the coastal vulnerability index (CVI) [23]. In general, the comprehensive index method and scenario analysis method were widely applied at diverse spatial and temporal ranges to assess ecological vulnerability [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…However, subjective factors tend to be susceptible to indicator selection and weight determination in the comprehensive index method. Quantitative evaluation models focus on changes in various aspects, such as the pressure-support-state-response model (PSSR) [28], the driving force-pressure-state-impact-response model (DPSIR) [15], and the ecological index-ecological sensitivity index-landscape structure composite index-exposure indicators-climate sensitivity indicators-adaptive indicators model (EI-ESI-LSCI-EI-CSI-AI) [29], which were used in ecological vulnerability assessment. Although these models diminish the impact of subjective judgements to a certain extent, their application in a specific area and scale is still limited because of a shortage of appropriate data.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

Liu

Shi

2019

Sustainability

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Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

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Ecological vulnerability assessment based on multi-sources data and SD model in Yinma River Basin, China

Cited by 68 publications

References 29 publications

Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan

Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan

Evaluating CDOM Sources Using Excitation-Emission Matrix Fluorescence and Parallel Factor Analysis, and their Links to Water Quality in Highly Polluted Rivers in China

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

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