Application Of Integrated Models To Eutrophication Management Of West Lake

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.