Ecosystem health assessment based on DPSIRM framework and health distance model in Nansi Lake,China |
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| Authors: | Feng Zhang Jiquan Zhang Rina Wu Qiyun Ma Jun Yang |
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| Institution: | 1.School of Environment,Northeast Normal University,Changchun,People’s Republic of China;2.School of Urban and Environment,Liaoning Normal University,Dalian,People’s Republic of China |
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| Abstract: | As lake ecosystem assessment is the foundation to achieve lake monitoring, environmental management and ecological restoration, a new concept of lake ecosystem health and driving force-pressure-state-impact-response-management framework was proposed to find out the causal relationship of the system and health distance model was taken to represent the health level of ecosystem. An assessment indicator system comprised of water quality, ecological and socio-economic criteria was established. The evaluation models were applied for the assessment of the ecosystem health level of a typical lake, Nansi Lake, China. Depends on the values of health distance, the heath level was described as 5°: very healthy, healthy, general healthy, sub-healthy and diseased. Using field investigation data and statistic data within the theory and applied models, the results of comprehensive assessment show that: (1) the health distances of water quality indicators, ecological indicators, socio-economic indicators and comprehensive health distance were 0.3989, 0.2495, 0.4983 and 0.4362, respectively. The health level was in general healthy condition. Ecological indicators were in healthy condition, which indicate that the stability was high. The distance of water quality had shown a tendency to approach general healthy level. As the health distance of socio-economic indicators have shown a bad impact form human beings, more effective measures need to be developed. (2) The results of a case study demonstrated that the methods in this paper provide a similar result corresponding with the actual lake health condition. Therefore, this paper shows that the proposed method is efficient and worths generalization. |
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