应用整合模型进行西湖富营养化管理的研究
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摘要
在应用模型进行湖泊富营养化管理的过程中，不确定性是不能忽视的。本项研究运用结合了灵敏度分析和多元回归分析的Monte Carlo方法对西湖富营养化生态模型(LEEM)在实际应用过程中的不确定性进行了定量分析。在此基础上，还引入热力学概念“(火用)”和随机动态规划(SDP)模型对LEEM进行整合优化，探索开发了一个整合富营养化模拟、不确定性分析以及数学优化的管理模型框架。通过构建面向应用的富营养化管理优化模型，为管理决策者提供了有价值的信息。根据这些信息，结合数学优化模型，可以了解各种情况下的水质风险概率，客观评价并选择合适的管理策略和整治措施，在可接受的风险水平上进行决策，以实现西湖富营养化的有效控制和区域的可持续发展。研究结果表明，在当年环境条件下，西湖6月份发生“水华”的概率约为3％。通过引水1.55×10_7吨和3.1×10_7吨来使西湖浮游植物生物量PHYT降低2％以上的概率分别是44％和61％左右；减少底泥中营养物释放量的1／3和1／2_使PHYT降低12％以上的概率分别是9％和83％左右。模型的整合优化结果还表明，要想以最低的控制费用取得最大的PHYT减少量，底泥营养盐释放量应减少1／2左右。
Uncertainty should not be ignored in lake eutrophication management with models. In this study, a preliminary uncertainty analysis and quantification is performed for Eutrophication Ecosystem Model of West Lake using the Monte Carlo methods which are combined with sensitivity analysis and multivariate linear regression analysis. Based on this, thermodynamic concept Exergy and optimization model Stochastic Dynamic Programming are also introduced in the study to integrate with the Eutrophication Ecosystem Model of West Lake. Thus a management model structure integrating eutrophication modeling, uncertainty analysis and mathematic optimization is build and applied to the eutrophication management of West Lake. Building an application-oriented model of eutrophication management can provide valuable information for decision-makers. According to this information, managers can know water quality risk probabilities of different situations. Based on this, combined with optimization models, managers can evaluate the measures taken objectively then select appropriate strategies and control measures, then make decisions on the level of risk acceptable to achieve effective control of eutrophication in West Lake and the sustainable development of the region. The results of the study show that the bloom occurrence probability of West Lake in June is about 3%. The probabilities of reducing PHYT( phytoplankon biomass) more than 2% by 1.55X JO7 tons/yr and 3.1 X 107 tons/yr of drawing water are about 44% and 61% respectively, and the probabilities of reducing PHYT more than 12% by the abatement of 1/3 and 1/2 of nutrients release in sediments are about 9% and 83% respectively. The results of model integration and optimization also show that the optimal abatement of nutrient release in sediments is about 1/2 to achieve maximum PHYT reduction with minimum control cost.
Uncertainty should not be ignored in lake eutrophication management with models. In this study, a preliminary uncertainty analysis and quantification is performed for Eutrophication Ecosystem Model of West Lake using the Monte Carlo methods which are combined with sensitivity analysis and multivariate linear regression analysis. Based on this, thermodynamic concept Exergy and optimization model Stochastic Dynamic Programming are also introduced in the study to integrate with the Eutrophication Ecosystem Model of West Lake. Thus a management model structure integrating eutrophication modeling, uncertainty analysis and mathematic optimization is build and applied to the eutrophication management of West Lake. Building an application-oriented model of eutrophication management can provide valuable information for decision-makers. According to this information, managers can know water quality risk probabilities of different situations. Based on this, combined with optimization models, managers can evaluate the measures taken objectively then select appropriate strategies and control measures, then make decisions on the level of risk acceptable to achieve effective control of eutrophication in West Lake and the sustainable development of the region. The results of the study show that the bloom occurrence probability of West Lake in June is about 3%. The probabilities of reducing PHYT( phytoplankon biomass) more than 2% by 1.55X JO7 tons/yr and 3.1 X 107 tons/yr of drawing water are about 44% and 61% respectively, and the probabilities of reducing PHYT more than 12% by the abatement of 1/3 and 1/2 of nutrients release in sediments are about 9% and 83% respectively. The results of model integration and optimization also show that the optimal abatement of nutrient release in sediments is about 1/2 to achieve maximum PHYT reduction with minimum control cost.
引文
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[27] Hakman and Groth. Object-oriented biomedical system modelling-the language, Computer Methods and Programs in Biomedicine, 1999,60(3) :153-181
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[29] Hession and Storm. Watershed-level Uncertainties: Implications for Phosphorus Management and Eutrophication, Journal of Environmental Quality, 2000, 29(4) : 1172-1179
[30] Hosper. Stable states, buffers and switches: an ecosystem approach to the restoration and management of shallow lakes in the Netherlands, Water Science and
Technology,1998,37(3) :151-164
[31] Jameson and Bartlett. Selection of optimal management strategies based on stochastic dynamic ecological models, Ecological Modelling,1987,36: 3-13
[32] Janssen, M.A. An exploratory integrated model to assess management of lake eutrophication, Ecological Modelling,2001,140: 111-124
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[36] Jφrgensen, S.E. Review and comparison of goal functions in system ecology. Vie Milieu 44, 1994, 11-20
[37] Jφrgensen,S.E The growth rate of zooplankton at the edge of chaos, Journal of Theory Biology, 1995,175:13-21
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[39] Jφrgensen,S.E. State-of-the-art of ecological modeling with emphasis on development of structural dynamic models, Ecological Modelling, 1999,120: 75-96
[40] Joshi and Modak. A reliability based discrete differential dynamic programming model for river water quality management under financial constraint In: Systems analysis in water quality management, Beck,M.B (eds), Advances in Water Pollution Control, Pergamon Press, Oxford, 1987,p15-22
[41] Keesman and Van Straten. Set membership approach to identification and prediction of lake eutrophication, Water Resource Research, 1990,26: 2643-2652
[42] Kuczera, G. and Parent, E. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, Journal of Hydrology, 1998, 211: 69-85
[43] Macintosh et al. Using probabilistic exposure models in ecological risk assessment of contaminated sites, Risk Annual, 1994,14: 405-419
[44] Mariani, C. and Venini. P. On the use of stochastic models of uncertainty in active control and
structural optimization, Computers and Structures, 1998, 67:105-117
[45] Marques J.C et al. Analysis of the properties of exergy and biodiversity along an estuarine gradient of eutrophication, Ecological Modelling, 1997, 102:155-167
[46] Melching, C.S. and Yoon, C.G. Key sources of uncertainty in QUAL2E model of Passaic River, Journal of Water Resources Planning and Management, 1996, 122 (2) : 105-113
[47] Menshutkin et al. Mathematical modeling of the evolution and current conditions of the Ladoga Lake ecosystem, Ecological Modelling, 1998, 107: 1-24
[48] Nyholm N. A simulation model for phytoplankton growth and nutrient cycling in eutrophic, shallow lakes, Ecological Modelling,1978,4:279-310
[49] Odum,E.P. Important ecological viewpoints in 1990s, Bioscience, 1992,42 (7) : 542-545
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